WorldWideScience

Sample records for modeling snow accumulation

  1. Modeling snow accumulation and ablation processes in forested environments

    Science.gov (United States)

    Andreadis, Konstantinos M.; Storck, Pascal; Lettenmaier, Dennis P.

    2009-05-01

    The effects of forest canopies on snow accumulation and ablation processes can be very important for the hydrology of midlatitude and high-latitude areas. A mass and energy balance model for snow accumulation and ablation processes in forested environments was developed utilizing extensive measurements of snow interception and release in a maritime mountainous site in Oregon. The model was evaluated using 2 years of weighing lysimeter data and was able to reproduce the snow water equivalent (SWE) evolution throughout winters both beneath the canopy and in the nearby clearing, with correlations to observations ranging from 0.81 to 0.99. Additionally, the model was evaluated using measurements from a Boreal Ecosystem-Atmosphere Study (BOREAS) field site in Canada to test the robustness of the canopy snow interception algorithm in a much different climate. Simulated SWE was relatively close to the observations for the forested sites, with discrepancies evident in some cases. Although the model formulation appeared robust for both types of climates, sensitivity to parameters such as snow roughness length and maximum interception capacity suggested the magnitude of improvements of SWE simulations that might be achieved by calibration.

  2. Modelling snow accumulation and snow melt in a continuous hydrological model for real-time flood forecasting

    International Nuclear Information System (INIS)

    Stanzel, Ph; Haberl, U; Nachtnebel, H P

    2008-01-01

    Hydrological models for flood forecasting in Alpine basins need accurate representation of snow accumulation and snow melt processes. A continuous, semi-distributed rainfall-runoff model with snow modelling procedures using only precipitation and temperature as input is presented. Simulation results from an application in an Alpine Danube tributary watershed are shown and evaluated with snow depth measurements and MODIS remote sensing snow cover information. Seasonal variations of runoff due to snow melt were simulated accurately. Evaluation of simulated snow depth and snow covered area showed strengths and limitations of the model and allowed an assessment of input data quality. MODIS snow cover images were found to be valuable sources of information for hydrological modelling in alpine areas, where ground observations are scarce.

  3. Modelling snow accumulation and snow melt in a continuous hydrological model for real-time flood forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Stanzel, Ph; Haberl, U; Nachtnebel, H P [Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Applied Life Sciences, Muthgasse 18, 1190 Vienna (Austria)], E-mail: philipp.stanzel@boku.ac.at

    2008-11-01

    Hydrological models for flood forecasting in Alpine basins need accurate representation of snow accumulation and snow melt processes. A continuous, semi-distributed rainfall-runoff model with snow modelling procedures using only precipitation and temperature as input is presented. Simulation results from an application in an Alpine Danube tributary watershed are shown and evaluated with snow depth measurements and MODIS remote sensing snow cover information. Seasonal variations of runoff due to snow melt were simulated accurately. Evaluation of simulated snow depth and snow covered area showed strengths and limitations of the model and allowed an assessment of input data quality. MODIS snow cover images were found to be valuable sources of information for hydrological modelling in alpine areas, where ground observations are scarce.

  4. Experimental measurement and modeling of snow accumulation and snowmelt in a mountain microcatchment

    Science.gov (United States)

    Danko, Michal; Krajčí, Pavel; Hlavčo, Jozef; Kostka, Zdeněk; Holko, Ladislav

    2016-04-01

    Fieldwork is a very useful source of data in all geosciences. This naturally applies also to the snow hydrology. Snow accumulation and snowmelt are spatially very heterogeneous especially in non-forested, mountain environments. Direct field measurements provide the most accurate information about it. Quantification and understanding of processes, that cause these spatial differences are crucial in prediction and modelling of runoff volumes in spring snowmelt period. This study presents possibilities of detailed measurement and modeling of snow cover characteristics in a mountain experimental microcatchment located in northern part of Slovakia in Western Tatra mountains. Catchment area is 0.059 km2 and mean altitude is 1500 m a.s.l. Measurement network consists of 27 snow poles, 3 small snow lysimeters, discharge measurement device and standard automatic weather station. Snow depth and snow water equivalent (SWE) were measured twice a month near the snow poles. These measurements were used to estimate spatial differences in accumulation of SWE. Snowmelt outflow was measured by small snow lysimeters. Measurements were performed in winter 2014/2015. Snow water equivalent variability was very high in such a small area. Differences between particular measuring points reached 600 mm in time of maximum SWE. The results indicated good performance of a snow lysimeter in case of snowmelt timing identification. Increase of snowmelt measured by the snow lysimeter had the same timing as increase in discharge at catchment's outlet and the same timing as the increase in air temperature above the freezing point. Measured data were afterwards used in distributed rainfall-runoff model MIKE-SHE. Several methods were used for spatial distribution of precipitation and snow water equivalent. The model was able to simulate snow water equivalent and snowmelt timing in daily step reasonably well. Simulated discharges were slightly overestimated in later spring.

  5. Modeling blowing snow accumulation downwind of an obstruction: The Ohara Eulerian particle distribution equation

    Science.gov (United States)

    Kinar, N. J.

    2017-05-01

    An equation was proposed to model the height of blowing snow accumulation downwind of an obstacle such as vegetation, a snow fence, a building, or a topographic feature. The equation does not require aerodynamic flow condition parameters such as wind speed, allowing for the spatial distribution of snow to be determined at locations where meteorological data is not available. However, snow particle diffusion, drift, and erosion coefficients must be estimated for application of the equation. These coefficients can be used to provide insight into the relative magnitude of blowing snow processes at a field location. Further research is required to determine efficient methods for coefficient estimation. The equation could be used with other models of wind-transported snow to predict snow accumulation downwind of an obstacle without the need for wind speed adjustments or correction equations. Applications for this equation include the design of snow fences, and the use of this equation with other hydrological models to predict snow distribution, climate change, drought, flooding, and avalanches.

  6. Performances of the snow accumulation melting model SAMM: results in the Northern Apennines test area

    Science.gov (United States)

    Lagomarsino, Daniela; Martelloni, Gianluca; Segoni, Samuele; Catani, Filippo; Fanti, Riccardo

    2013-04-01

    In this work we propose a snow accumulation-melting model (SAMM) to forecast the snowpack height and we compare the results with a simple temperature index model and an improved version of the latter.For this purpose we used rainfall, temperature and snowpack thickness 5-years data series from 7 weather stations in the Northern Apennines (Emilia Romagna Region, Italy). SAMM is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a mass conservation equation is solved to model snowpack thickness and an empirical equation is used for the snow density. The processes linked to the accumulation/depletion of the snowpack (e.g. compression of the snowpack due to newly fallen snow and effects of rainfall) are modelled identifying limiting and inhibitory factors according to a kinetic approach. The model depends on 13 empirical parameters, whose optimal values were defined with an optimization algorithm (simplex flexible) using calibration measures of snowpack thickness. From an operational point of view, SAMM uses as input data only temperature and rainfall measurements, bringing the additional advantage of a relatively easy implementation. In order to verify the improvement of SAMM with respect to a temperature-index model, the latter was applied considering, for the amount of snow melt, the following equation: M = fm(T-T0), where M is hourly melt, fm is the melting factor and T0 is a threshold temperature. In this case the calculation of the depth of the snowpack requires the use of 3 parameters: fm, T0 and ?0 (the mean density of the snowpack). We also performed a simulation by replacing the SAMM melting module with the above equation and leaving unchanged the accumulation module: in this way we obtained a model with 9 parameters. The simulations results suggest that any further extension of the simple temperature index model brings some improvements with a consequent decrease of the mean error

  7. The distribution of snow accumulation across the Austfonna ice cap, Svalbard: direct measurements and modelling

    OpenAIRE

    Taurisano, Andrea; Schuler, Thomas V.; Hagen, Jon Ove; Eiken, Trond; Loe, Even; Melvold, Kjetil; Kohler, Jack

    2007-01-01

    We present an analysis of the spatial variability in the snow accumulation on the Austfonna ice cap in Svalbard, Norway, based on the results of field investigations conducted in the spring of 1999, 2004 and 2005. During the campaigns ground penetrating radar measurements at 500 and 800 MHz were collected along profiles, along with additional manual snow sounding and pit stratigraphy work. The analysis of the data reveals a consistent pattern in the spatial distribution of the snow accumulati...

  8. Modelling snow accumulation on Greenland in Eemian, glacial inception, and modern climates in a GCM

    Directory of Open Access Journals (Sweden)

    H. J. Punge

    2012-11-01

    Full Text Available Changing climate conditions on Greenland influence the snow accumulation rate and surface mass balance (SMB on the ice sheet and, ultimately, its shape. This can in turn affect local climate via orography and albedo variations and, potentially, remote areas via changes in ocean circulation triggered by melt water or calving from the ice sheet. Examining these interactions in the IPSL global model requires improving the representation of snow at the ice sheet surface. In this paper, we present a new snow scheme implemented in LMDZ, the atmospheric component of the IPSL coupled model. We analyse surface climate and SMB on the Greenland ice sheet under insolation and oceanic boundary conditions for modern, but also for two different past climates, the last glacial inception (115 kyr BP and the Eemian (126 kyr BP. While being limited by the low resolution of the general circulation model (GCM, present-day SMB is on the same order of magnitude as recent regional model findings. It is affected by a moist bias of the GCM in Western Greenland and a dry bias in the north-east. Under Eemian conditions, the SMB decreases largely, and melting affects areas in which the ice sheet surface is today at high altitude, including recent ice core drilling sites as NEEM. In contrast, glacial inception conditions lead to a higher mass balance overall due to the reduced melting in the colder summer climate. Compared to the widely applied positive degree-day (PDD parameterization of SMB, our direct modelling results suggest a weaker sensitivity of SMB to changing climatic forcing. For the Eemian climate, our model simulations using interannually varying monthly mean forcings for the ocean surface temperature and sea ice cover lead to significantly higher SMB in southern Greenland compared to simulations forced with climatological monthly means.

  9. From the clouds to the ground - snow precipitation patterns vs. snow accumulation patterns

    Science.gov (United States)

    Gerber, Franziska; Besic, Nikola; Mott, Rebecca; Gabella, Marco; Germann, Urs; Bühler, Yves; Marty, Mauro; Berne, Alexis; Lehning, Michael

    2017-04-01

    Knowledge about snow distribution and snow accumulation patterns is important and valuable for different applications such as the prediction of seasonal water resources or avalanche forecasting. Furthermore, accumulated snow on the ground is an important ground truth for validating meteorological and climatological model predictions of precipitation in high mountains and polar regions. Snow accumulation patterns are determined by many different processes from ice crystal nucleation in clouds to snow redistribution by wind and avalanches. In between, snow precipitation undergoes different dynamical and microphysical processes, such as ice crystal growth, aggregation and riming, which determine the growth of individual particles and thereby influence the intensity and structure of the snowfall event. In alpine terrain the interaction of different processes and the topography (e.g. lifting condensation and low level cloud formation, which may result in a seeder-feeder effect) may lead to orographic enhancement of precipitation. Furthermore, the redistribution of snow particles in the air by wind results in preferential deposition of precipitation. Even though orographic enhancement is addressed in numerous studies, the relative importance of micro-physical and dynamically induced mechanisms on local snowfall amounts and especially snow accumulation patterns is hardly known. To better understand the relative importance of different processes on snow precipitation and accumulation we analyze snowfall and snow accumulation between January and March 2016 in Davos (Switzerland). We compare MeteoSwiss operational weather radar measurements on Weissfluhgipfel to a spatially continuous snow accumulation map derived from airborne digital sensing (ADS) snow height for the area of Dischma valley in the vicinity of the weather radar. Additionally, we include snow height measurements from automatic snow stations close to the weather radar. Large-scale radar snow accumulation

  10. A Distributed Snow Evolution Modeling System (SnowModel)

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    Liston, G. E.; Elder, K.

    2004-12-01

    A spatially distributed snow-evolution modeling system (SnowModel) has been specifically designed to be applicable over a wide range of snow landscapes, climates, and conditions. To reach this goal, SnowModel is composed of four sub-models: MicroMet defines the meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowMass simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. While other distributed snow models exist, SnowModel is unique in that it includes a well-tested blowing-snow sub-model (SnowTran-3D) for application in windy arctic, alpine, and prairie environments where snowdrifts are common. These environments comprise 68% of the seasonally snow-covered Northern Hemisphere land surface. SnowModel also accounts for snow processes occurring in forested environments (e.g., canopy interception related processes). SnowModel is designed to simulate snow-related physical processes occurring at spatial scales of 5-m and greater, and temporal scales of 1-hour and greater. These include: accumulation from precipitation; wind redistribution and sublimation; loading, unloading, and sublimation within forest canopies; snow-density evolution; and snowpack ripening and melt. To enhance its wide applicability, SnowModel includes the physical calculations required to simulate snow evolution within each of the global snow classes defined by Sturm et al. (1995), e.g., tundra, taiga, alpine, prairie, maritime, and ephemeral snow covers. The three, 25-km by 25-km, Cold Land Processes Experiment (CLPX) mesoscale study areas (MSAs: Fraser, North Park, and Rabbit Ears) are used as SnowModel simulation examples to highlight model strengths, weaknesses, and features in forested, semi-forested, alpine, and shrubland environments.

  11. A New Estimate of North American Mountain Snow Accumulation From Regional Climate Model Simulations

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    Wrzesien, Melissa L.; Durand, Michael T.; Pavelsky, Tamlin M.; Kapnick, Sarah B.; Zhang, Yu; Guo, Junyi; Shum, C. K.

    2018-02-01

    Despite the importance of mountain snowpack to understanding the water and energy cycles in North America's montane regions, no reliable mountain snow climatology exists for the entire continent. We present a new estimate of mountain snow water equivalent (SWE) for North America from regional climate model simulations. Climatological peak SWE in North America mountains is 1,006 km3, 2.94 times larger than previous estimates from reanalyses. By combining this mountain SWE value with the best available global product in nonmountain areas, we estimate peak North America SWE of 1,684 km3, 55% greater than previous estimates. In our simulations, the date of maximum SWE varies widely by mountain range, from early March to mid-April. Though mountains comprise 24% of the continent's land area, we estimate that they contain 60% of North American SWE. This new estimate is a suitable benchmark for continental- and global-scale water and energy budget studies.

  12. Snow as an accumulator of air pollutants

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    Robert T. Brown

    1976-01-01

    Using simple analytical techniques, the amounts of air pollutants accumulated in winter snow were determined and the results correlated with lichen survival on trees. Pollutants measured were particulate matter, sulfate, and chloride. An inverse relationship was found between amounts of each of these pollutants and the abundance of various lichens.

  13. Modelling of snow exceedances

    Science.gov (United States)

    Jordanova, Pavlina K.; Sadovský, Zoltán; Stehlík, Milan

    2017-07-01

    Modelling of snow exceedances is of great importance and interest for ecology, civil engineering and general public. We suggest the favorable fit for exceedances related to the exceptional snow loads from Slovakia, assuming that the data is driven by Generalised Pareto Distribution or Generalized Extreme Value Distribution. Further, the statistical dependence between the maximal snow loads and the corresponding altitudes is studied.

  14. A distributed snow-evolution modeling system (SnowModel)

    Science.gov (United States)

    Glen E. Liston; Kelly. Elder

    2006-01-01

    SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D...

  15. Regional Antarctic snow accumulation over the past 1000 years

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    E. R. Thomas

    2017-11-01

    Full Text Available Here we present Antarctic snow accumulation variability at the regional scale over the past 1000 years. A total of 79 ice core snow accumulation records were gathered and assigned to seven geographical regions, separating the high-accumulation coastal zones below 2000 m of elevation from the dry central Antarctic Plateau. The regional composites of annual snow accumulation were evaluated against modelled surface mass balance (SMB from RACMO2.3p2 and precipitation from ERA-Interim reanalysis. With the exception of the Weddell Sea coast, the low-elevation composites capture the regional precipitation and SMB variability as defined by the models. The central Antarctic sites lack coherency and either do not represent regional precipitation or indicate the model inability to capture relevant precipitation processes in the cold, dry central plateau. Our results show that SMB for the total Antarctic Ice Sheet (including ice shelves has increased at a rate of 7 ± 0.13 Gt decade−1 since 1800 AD, representing a net reduction in sea level of ∼ 0.02 mm decade−1 since 1800 and ∼ 0.04 mm decade−1 since 1900 AD. The largest contribution is from the Antarctic Peninsula (∼ 75 % where the annual average SMB during the most recent decade (2001–2010 is 123 ± 44 Gt yr−1 higher than the annual average during the first decade of the 19th century. Only four ice core records cover the full 1000 years, and they suggest a decrease in snow accumulation during this period. However, our study emphasizes the importance of low-elevation coastal zones, which have been under-represented in previous investigations of temporal snow accumulation.

  16. Snow model analysis.

    Science.gov (United States)

    2014-01-01

    This study developed a new snow model and a database which warehouses geometric, weather and traffic : data on New Jersey highways. The complexity of the model development lies in considering variable road : width, different spreading/plowing pattern...

  17. Analysis of the spatial and temporal variation of seasonal snow accumulation in alpine catchments using airborne laser scanning : basic research for the adaptation of spatially distributed hydrological models to mountain regions

    International Nuclear Information System (INIS)

    Helfricht, K.

    2014-01-01

    Information about the spatial distribution of snow accumulation is a prerequisitefor adaptating hydro-meteorological models to achieve realistic simulations of therunoff from mountain catchments. Therefore, the spatial snow depthdistribution in complex topography of ice-free terrain and glaciers was investigatedusing airborne laser scanning (ALS) data. This thesis presents for the first time an analysis of the persistence and the variability of the snow patterns at the end of five accumulation seasons in a comparatively large catchment. ALS derived seasonal surface elevation changes on glaciers were compared to the actual snow depths calculated from ground penetrating radar (GPR) measurements. Areas of increased deviations. In the investigated region, the ALS-derived snow depths on most of the glacier surface do not deviate markedly from actual snow depths. 75% of a the total area showed low inter-annual variability of standardized snow depths at the end of the five accumulation seasons. The high inter-annual variability of snow depths could be attributed to changes in the ice cover within the investigated 10-yearperiod for much of the remaining area. Avalanches and snow sloughs continuously contribute to the accumulation on glaciers, but their share of the total snow covervolume is small. The assimilation of SWE maps calculated from ALS data in the adaptation of snow-hydrological models to mountain catchments improved the results not only for the but also for the simulated snow cover distribution and for the mass balance of the glaciers. The results demonstrate that ALS data are a beneficial source for extensive analysis of snow patterns and for modeling the runoff from high Alpine catchments.(author) [de

  18. Measurement of snow interception and canopy effects on snow accumulation and melt in a mountainous maritime climate, Oregon, United States

    Science.gov (United States)

    Storck, Pascal; Lettenmaier, Dennis P.; Bolton, Susan M.

    2002-11-01

    The results of a 3 year field study to observe the processes controlling snow interception by forest canopies and under canopy snow accumulation and ablation in mountain maritime climates are reported. The field study was further intended to provide data to develop and test models of forest canopy effects on beneath-canopy snowpack accumulation and melt and the plot and stand scales. Weighing lysimeters, cut-tree experiments, and manual snow surveys were deployed at a site in the Umpqua National Forest, Oregon (elevation 1200 m). A unique design for a weighing lysimeter was employed that allowed continuous measurements of snowpack evolution beneath a forest canopy to be taken at a scale unaffected by variability in canopy throughfall. Continuous observations of snowpack evolution in large clearings were made coincidentally with the canopy measurements. Large differences in snow accumulation and ablation were observed at sites beneath the forest canopy and in large clearings. These differences were not well described by simple relationships between the sites. Over the study period, approximately 60% of snowfall was intercepted by the canopy (up to a maximum of about 40 mm water equivalent). Instantaneous sublimation rates exceeded 0.5 mm per hour for short periods. However, apparent average sublimation from the intercepted snow was less than 1 mm per day and totaled approximately 100 mm per winter season. Approximately 72 and 28% of the remaining intercepted snow was removed as meltwater drip and large snow masses, respectively. Observed differences in snow interception rate and maximum snow interception capacity between Douglas fir (Pseudotsuga menziesii), white fir (Abies concolor), ponderosa pine (Pinus ponderosa), and lodgepole pine (Pinus contorta) were minimal.

  19. Annual Greenland Accumulation Rates (2009-2012) from Airborne Snow Radar

    Science.gov (United States)

    Koenig, Lora S.; Ivanoff, Alvaro; Alexander, Patrick M.; MacGregor, Joseph A.; Fettweis, Xavier; Panzer, Ben; Paden, John D.; Forster, Richard R.; Das, Indrani; McConnell, Joseph R.; hide

    2016-01-01

    Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet through increasing surface melt, emphasizing the need to closely monitor its surface mass balance in order to improve sea-level rise predictions. Snow accumulation is the largest component of the ice sheet's surface mass balance, but in situ observations thereof are inherently sparse and models are difficult to evaluate at large scales. Here, we quantify recent Greenland accumulation rates using ultra-wideband (2-6.5 gigahertz) airborne snow radar data collected as part of NASA's Operation IceBridge between 2009 and 2012. We use a semi-automated method to trace the observed radiostratigraphy and then derive annual net accumulation rates for 2009-2012. The uncertainty in these radar-derived accumulation rates is on average 14 percent. A comparison of the radarderived accumulation rates and contemporaneous ice cores shows that snow radar captures both the annual and longterm mean accumulation rate accurately. A comparison with outputs from a regional climate model (MAR - Modele Atmospherique Regional for Greenland and vicinity) shows that this model matches radar-derived accumulation rates in the ice sheet interior but produces higher values over southeastern Greenland. Our results demonstrate that snow radar can efficiently and accurately map patterns of snow accumulation across an ice sheet and that it is valuable for evaluating the accuracy of surface mass balance models.

  20. Hydrological Implications of Covering Wind-Blown Snow Accumulations with Geotextiles on Mount Aragats, Armenia

    Directory of Open Access Journals (Sweden)

    Alexander Nestler

    2014-07-01

    Full Text Available Snow is an excellent water reservoir, naturally storing large quantities of water at time scales from a few days to several months. In summer-dry countries, like Armenia, runoff due to snow melt from mountain regions is highly important for a sustained water supply (irrigation, hydropower. Snow fields on Mount Aragats, Armenia’s highest peak, often persist until July, providing vital amounts of melt water. Artificially managing these wind-driven snow accumulations as a natural water reservoir might have considerable potential. In the context of the Swiss-Armenian joint venture, Freezwater, snow fields are covered with geotextiles in order to delay snow melt long enough to provide additional melt water in the dry season of the year. In this study, we analyze the hydrological effectiveness of the artificial management of the natural snow cover on Mount Aragats based on various field measurements acquired over a three-year period and numerical modeling. Over the winter season, partly more than five meter-thick snow deposits are formed supported by snow redistribution by strong wind. Repeated mappings of snow fields indicate that snow cover patterns remain highly consistent over time. Measurements of ablation below manually applied geotextiles show a considerable reduction of melt rates by more than 50%. Simulations with an energy-balance model and a distributed temperature-index model allow assessing the hydrological effect of artificial snow management for different initial snow depths and elevations and suggest that coverage is needed at a large scale in order to generate a significant impact on discharge.

  1. A simple algorithm for identifying periods of snow accumulation on a radiometer

    Science.gov (United States)

    Lapo, Karl E.; Hinkelman, Laura M.; Landry, Christopher C.; Massmann, Adam K.; Lundquist, Jessica D.

    2015-09-01

    Downwelling solar, Qsi, and longwave, Qli, irradiances at the earth's surface are the primary energy inputs for many hydrologic processes, and uncertainties in measurements of these two terms confound evaluations of estimated irradiances and negatively impact hydrologic modeling. Observations of Qsi and Qli in cold environments are subject to conditions that create additional uncertainties not encountered in other climates, specifically the accumulation of snow on uplooking radiometers. To address this issue, we present an automated method for estimating these periods of snow accumulation. Our method is based on forest interception of snow and uses common meteorological observations. In this algorithm, snow accumulation must exceed a threshold to obscure the sensor and is only removed through scouring by wind or melting. The algorithm is evaluated at two sites representing different mountain climates: (1) Snoqualmie Pass, Washington (maritime) and (2) the Senator Beck Basin Study Area, Colorado (continental). The algorithm agrees well with time-lapse camera observations at the Washington site and with multiple measurements at the Colorado site, with 70-80% of observed snow accumulation events correctly identified. We suggest using the method for quality controlling irradiance observations in snow-dominated climates where regular, daily maintenance is not possible.

  2. Modeling the influence of snow cover temperature and water content on wet-snow avalanche runout

    Directory of Open Access Journals (Sweden)

    C. Vera Valero

    2018-03-01

    Full Text Available Snow avalanche motion is strongly dependent on the temperature and water content of the snow cover. In this paper we use a snow cover model, driven by measured meteorological data, to set the initial and boundary conditions for wet-snow avalanche calculations. The snow cover model provides estimates of snow height, density, temperature and liquid water content. This information is used to prescribe fracture heights and erosion heights for an avalanche dynamics model. We compare simulated runout distances with observed avalanche deposition fields using a contingency table analysis. Our analysis of the simulations reveals a large variability in predicted runout for tracks with flat terraces and gradual slope transitions to the runout zone. Reliable estimates of avalanche mass (height and density in the release and erosion zones are identified to be more important than an exact specification of temperature and water content. For wet-snow avalanches, this implies that the layers where meltwater accumulates in the release zone must be identified accurately as this defines the height of the fracture slab and therefore the release mass. Advanced thermomechanical models appear to be better suited to simulate wet-snow avalanche inundation areas than existing guideline procedures if and only if accurate snow cover information is available.

  3. Forest impacts on snow accumulation and ablation across an elevation gradient in a temperate montane environment

    Science.gov (United States)

    Roth, Travis R.; Nolin, Anne W.

    2017-11-01

    Forest cover modifies snow accumulation and ablation rates via canopy interception and changes in sub-canopy energy balance processes. However, the ways in which snowpacks are affected by forest canopy processes vary depending on climatic, topographic and forest characteristics. Here we present results from a 4-year study of snow-forest interactions in the Oregon Cascades. We continuously monitored snow and meteorological variables at paired forested and open sites at three elevations representing the Low, Mid, and High seasonal snow zones in the study region. On a monthly to bi-weekly basis, we surveyed snow depth and snow water equivalent across 900 m transects connecting the forested and open pairs of sites. Our results show that relative to nearby open areas, the dense, relatively warm forests at Low and Mid sites impede snow accumulation via canopy snow interception and increase sub-canopy snowpack energy inputs via longwave radiation. Compared with the Forest sites, snowpacks are deeper and last longer in the Open site at the Low and Mid sites (4-26 and 11-33 days, respectively). However, we see the opposite relationship at the relatively colder High sites, with the Forest site maintaining snow longer into the spring by 15-29 days relative to the nearby Open site. Canopy interception efficiency (CIE) values at the Low and Mid Forest sites averaged 79 and 76 % of the total event snowfall, whereas CIE was 31 % at the lower density High Forest site. At all elevations, longwave radiation in forested environments appears to be the primary energy component due to the maritime climate and forest presence, accounting for 93, 92, and 47 % of total energy inputs to the snowpack at the Low, Mid, and High Forest sites, respectively. Higher wind speeds in the High Open site significantly increase turbulent energy exchanges and snow sublimation. Lower wind speeds in the High Forest site create preferential snowfall deposition. These results show the importance of

  4. The Impacts of Pine Tree Die-Off on Snow Accumulation and Distribution at Plot to Catchment Scales

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    Biederman, J. A.; Harpold, A. A.; Gutmann, E. D.; Reed, D. E.; Gochis, D. J.; Brooks, P. D.

    2011-12-01

    stands to small catchments, maximal snow water equivalent (SWE) as a fraction of winter precipitation (P) ranged from 62 to 74%. Despite an expectation of decreased interception and increased snow accumulation with advanced mortality, surveys at stand and catchment scales found no significant increases in net snow water input between healthy and impacted forests. These observations suggest that the spatial scale of processes affecting net snow accumulation and ablation increase following die-off. Using data from our sites and other studies, this presentation will develop a predictive model of how interception, shading, and wind redistribution interact to control net snow water input following large-scale forest mortality.

  5. Snow in a very steep rock face: accumulation and redistribution during and after a snowfall event

    Directory of Open Access Journals (Sweden)

    Christian Gabriel Sommer

    2015-12-01

    Full Text Available Terrestrial laser scanning was used to measure snow thickness changes (perpendicular to the surface in a rock face. The aim was to investigate the accumulation and redistribution of snow in extremely steep terrain (>60°. The north-east face of the Chlein Schiahorn in the region of Davos in eastern Switzerland was scanned before and several times after a snowfall event. A summer scan without snow was acquired to calculate the total snow thickness. An improved postprocessing procedure is introduced. The data quality could be increased by using snow thickness instead of snow depth (measured vertically and by consistently applying Multi Station Adjustment to improve the registration.More snow was deposited in the flatter, smoother areas of the rock face. The spatial variability of the snow thickness change was high. The spatial patterns of the total snow thickness were similar to those of the snow thickness change. The correlation coefficient between them was 0.86. The fresh snow was partly redistributed from extremely steep to flatter terrain, presumably mostly through avalanching. The redistribution started during the snowfall and ended several days later. Snow was able to accumulate permanently at every slope angle. The amount of snow in extremely steep terrain was limited but not negligible. Areas steeper than 60° received 15% of the snowfall and contained 10% of the total amount of snow.

  6. Continuous Estimates of Surface Density and Annual Snow Accumulation with Multi-Channel Snow/Firn Penetrating Radar in the Percolation Zone, Western Greenland Ice Sheet

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    Meehan, T.; Marshall, H. P.; Bradford, J.; Hawley, R. L.; Osterberg, E. C.; McCarthy, F.; Lewis, G.; Graeter, K.

    2017-12-01

    A priority of ice sheet surface mass balance (SMB) prediction is ascertaining the surface density and annual snow accumulation. These forcing data can be supplied into firn compaction models and used to tune Regional Climate Models (RCM). RCMs do not accurately capture subtle changes in the snow accumulation gradient. Additionally, leading RCMs disagree among each other and with accumulation studies in regions of the Greenland Ice Sheet (GrIS) over large distances and temporal scales. RCMs tend to yield inconsistencies over GrIS because of sparse and outdated validation data in the reanalysis pool. Greenland Traverse for Accumulation and Climate Studies (GreenTrACS) implemented multi-channel 500 MHz Radar in multi-offset configuration throughout two traverse campaigns totaling greater than 3500 km along the western percolation zone of GrIS. The multi-channel radar has the capability of continuously estimating snow depth, average density, and annual snow accumulation, expressed at 95% confidence (+-) 0.15 m, (+-) 17 kgm-3, (+-) 0.04 m w.e. respectively, by examination of the primary reflection return from the previous year's summer surface.

  7. Forest canopy effects on snow accumulation and ablation: an integrative review of empirical results

    Science.gov (United States)

    Andres Varhola; Nicholas C. Coops; Markus Weiler; R. Dan Moore

    2010-01-01

    The past century has seen significant research comparing snow accumulation and ablation in forested and open sites. In this review we compile and standardize the results of previous empirical studies to generate statistical relations between changes in forest cover and the associated changes in snow accumulation and ablation rate. The analysis drew upon 33 articles...

  8. Impact of the snow cover scheme on snow distribution and energy budget modeling over the Tibetan Plateau

    Science.gov (United States)

    Xie, Zhipeng; Hu, Zeyong; Xie, Zhenghui; Jia, Binghao; Sun, Genhou; Du, Yizhen; Song, Haiqing

    2018-02-01

    This paper presents the impact of two snow cover schemes (NY07 and SL12) in the Community Land Model version 4.5 (CLM4.5) on the snow distribution and surface energy budget over the Tibetan Plateau. The simulated snow cover fraction (SCF), snow depth, and snow cover days were evaluated against in situ snow depth observations and a satellite-based snow cover product and snow depth dataset. The results show that the SL12 scheme, which considers snow accumulation and snowmelt processes separately, has a higher overall accuracy (81.8%) than the NY07 (75.8%). The newer scheme performs better in the prediction of overall accuracy compared with the NY07; however, SL12 yields a 15.1% underestimation rate while NY07 overestimated the SCF with a 15.2% overestimation rate. Both two schemes capture the distribution of the maximum snow depth well but show large positive biases in the average value through all periods (3.37, 3.15, and 1.48 cm for NY07; 3.91, 3.52, and 1.17 cm for SL12) and overestimate snow cover days compared with the satellite-based product and in situ observations. Higher altitudes show larger root-mean-square errors (RMSEs) in the simulations of snow depth and snow cover days during the snow-free period. Moreover, the surface energy flux estimations from the SL12 scheme are generally superior to the simulation from NY07 when evaluated against ground-based observations, in particular for net radiation and sensible heat flux. This study has great implications for further improvement of the subgrid-scale snow variations over the Tibetan Plateau.

  9. The Snow Must Go On: Ground Ice Encasement, Snow Compaction and Absence of Snow Differently Cause Soil Hypoxia, CO2 Accumulation and Tree Seedling Damage in Boreal Forest.

    Science.gov (United States)

    Martz, Françoise; Vuosku, Jaana; Ovaskainen, Anu; Stark, Sari; Rautio, Pasi

    2016-01-01

    At high latitudes, the climate has warmed at twice the rate of the global average with most changes observed in autumn, winter and spring. Increasing winter temperatures and wide temperature fluctuations are leading to more frequent rain-on-snow events and freeze-thaw cycles causing snow compaction and formation of ice layers in the snowpack, thus creating ice encasement (IE). By decreasing the snowpack insulation capacity and restricting soil-atmosphere gas exchange, modification of the snow properties may lead to colder soil but also to hypoxia and accumulation of trace gases in the subnivean environment. To test the effects of these overwintering conditions changes on plant winter survival and growth, we established a snow manipulation experiment in a coniferous forest in Northern Finland with Norway spruce and Scots pine seedlings. In addition to ambient conditions and prevention of IE, we applied three snow manipulation levels: IE created by artificial rain-on-snow events, snow compaction and complete snow removal. Snow removal led to deeper soil frost during winter, but no clear effect of IE or snow compaction done in early winter was observed on soil temperature. Hypoxia and accumulation of CO2 were highest in the IE plots but, more importantly, the duration of CO2 concentration above 5% was 17 days in IE plots compared to 0 days in ambient plots. IE was the most damaging winter condition for both species, decreasing the proportion of healthy seedlings by 47% for spruce and 76% for pine compared to ambient conditions. Seedlings in all three treatments tended to grow less than seedlings in ambient conditions but only IE had a significant effect on spruce growth. Our results demonstrate a negative impact of winter climate change on boreal forest regeneration and productivity. Changing snow conditions may thus partially mitigate the positive effect of increasing growing season temperatures on boreal forest productivity.

  10. Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

    Science.gov (United States)

    Förster, Kristian; Hanzer, Florian; Stoll, Elena; Scaife, Adam A.; MacLachlan, Craig; Schöber, Johannes; Huttenlau, Matthias; Achleitner, Stefan; Strasser, Ulrich

    2018-02-01

    This article presents analyses of retrospective seasonal forecasts of snow accumulation. Re-forecasts with 4 months' lead time from two coupled atmosphere-ocean general circulation models (NCEP CFSv2 and MetOffice GloSea5) drive the Alpine Water balance and Runoff Estimation model (AWARE) in order to predict mid-winter snow accumulation in the Inn headwaters. As snowpack is hydrological storage that evolves during the winter season, it is strongly dependent on precipitation totals of the previous months. Climate model (CM) predictions of precipitation totals integrated from November to February (NDJF) compare reasonably well with observations. Even though predictions for precipitation may not be significantly more skilful than for temperature, the predictive skill achieved for precipitation is retained in subsequent water balance simulations when snow water equivalent (SWE) in February is considered. Given the AWARE simulations driven by observed meteorological fields as a benchmark for SWE analyses, the correlation achieved using GloSea5-AWARE SWE predictions is r = 0.57. The tendency of SWE anomalies (i.e. the sign of anomalies) is correctly predicted in 11 of 13 years. For CFSv2-AWARE, the corresponding values are r = 0.28 and 7 of 13 years. The results suggest that some seasonal prediction of hydrological model storage tendencies in parts of Europe is possible.

  11. Use of machine learning techniques for modeling of snow depth

    Directory of Open Access Journals (Sweden)

    G. V. Ayzel

    2017-01-01

    Full Text Available Snow exerts significant regulating effect on the land hydrological cycle since it controls intensity of heat and water exchange between the soil-vegetative cover and the atmosphere. Estimating of a spring flood runoff or a rain-flood on mountainous rivers requires understanding of the snow cover dynamics on a watershed. In our work, solving a problem of the snow cover depth modeling is based on both available databases of hydro-meteorological observations and easily accessible scientific software that allows complete reproduction of investigation results and further development of this theme by scientific community. In this research we used the daily observational data on the snow cover and surface meteorological parameters, obtained at three stations situated in different geographical regions: Col de Porte (France, Sodankyla (Finland, and Snoquamie Pass (USA.Statistical modeling of the snow cover depth is based on a complex of freely distributed the present-day machine learning models: Decision Trees, Adaptive Boosting, Gradient Boosting. It is demonstrated that use of combination of modern machine learning methods with available meteorological data provides the good accuracy of the snow cover modeling. The best results of snow cover depth modeling for every investigated site were obtained by the ensemble method of gradient boosting above decision trees – this model reproduces well both, the periods of snow cover accumulation and its melting. The purposeful character of learning process for models of the gradient boosting type, their ensemble character, and use of combined redundancy of a test sample in learning procedure makes this type of models a good and sustainable research tool. The results obtained can be used for estimating the snow cover characteristics for river basins where hydro-meteorological information is absent or insufficient.

  12. Mass balance re-analysis of Findelengletscher, Switzerland; benefits of extensive snow accumulation measurements

    Directory of Open Access Journals (Sweden)

    Leo eSold

    2016-02-01

    Full Text Available A re-analysis is presented here of a 10-year mass balance series at Findelengletscher, a temperate mountain glacier in Switzerland. Calculating glacier-wide mass balance from the set of glaciological point balance observations using conventional approaches, such as the profile or contour method, resulted in significant deviations from the reference value given by the geodetic mass change over a five-year period. This is attributed to the sparsity of observations at high elevations and to the inability of the evaluation schemes to adequately estimate accumulation in unmeasured areas. However, measurements of winter mass balance were available for large parts of the study period from snow probings and density pits. Complementary surveys by helicopter-borne ground-penetrating radar (GPR were conducted in three consecutive years. The complete set of seasonal observations was assimilated using a distributed mass balance model. This model-based extrapolation revealed a substantial mass loss at Findelengletscher of -0.43m w.e. a^-1 between 2004 and 2014, while the loss was less pronounced for its former tributary, Adlergletscher (-0.30m w.e. a^-1. For both glaciers, the resulting time series were within the uncertainty bounds of the geodetic mass change. We show that the model benefited strongly from the ability to integrate seasonal observations. If no winter mass balance measurements were available and snow cover was represented by a linear precipitation gradient, the geodetic mass balance was not matched. If winter balance measurements by snow probings and snow density pits were taken into account, the model performance was substantially improved but still showed a significant bias relative to the geodetic mass change. Thus the excellent agreement of the model-based extrapolation with the geodetic mass change was owed to an adequate representation of winter accumulation distribution by means of extensive GPR measurements.

  13. Seasonal snow accumulation in the mid-latitude forested catchment

    Czech Academy of Sciences Publication Activity Database

    Šípek, Václav; Tesař, Miroslav

    2014-01-01

    Roč. 69, č. 11 (2014), s. 1562-1569 ISSN 0006-3088 R&D Projects: GA TA ČR TA02021451 Institutional support: RVO:67985874 Keywords : snow depth * snow water equivalent * forested catchment Subject RIV: DA - Hydrology ; Limnology Impact factor: 0.827, year: 2014

  14. Modelling the snowmelt and the snow water equivalent by creating a simplified energy balance conceptual snow model

    Science.gov (United States)

    Riboust, Philippe; Thirel, Guillaume; Le Moine, Nicolas; Ribstein, Pierre

    2016-04-01

    A better knowledge of the accumulated snow on the watersheds will help flood forecasting centres and hydro-power companies to predict the amount of water released during spring snowmelt. Since precipitations gauges are sparse at high elevations and integrative measurements of the snow accumulated on watershed surface are hard to obtain, using snow models is an adequate way to estimate snow water equivalent (SWE) on watersheds. In addition to short term prediction, simulating accurately SWE with snow models should have many advantages. Validating the snow module on both SWE and snowmelt should give a more reliable model for climate change studies or regionalization for ungauged watersheds. The aim of this study is to create a new snow module, which has a structure that allows the use of measured snow data for calibration or assimilation. Energy balance modelling seems to be the logical choice for designing a model in which internal variables, such as SWE, could be compared to observations. Physical models are complex, needing high computational resources and many different types of inputs that are not widely measured at meteorological stations. At the opposite, simple conceptual degree-day models offer to simulate snowmelt using only temperature and precipitation as inputs with fast computing. Its major drawback is to be empirical, i.e. not taking into account all of the processes of the energy balance, which makes this kind of model more difficult to use when willing to compare SWE to observed measurements. In order to reach our objectives, we created a snow model structured by a simplified energy balance where each of the processes is empirically parameterized in order to be calculated using only temperature, precipitation and cloud cover variables. This model's structure is similar to the one created by M.T. Walter (2005), where parameterizations from the literature were used to compute all of the processes of the energy balance. The conductive fluxes into the

  15. Forest influences on snow accumulation and snowmelt at the Hubbard Brook Experimental Forest, New Hampshire, USA

    Science.gov (United States)

    Colin A. Penn; Beverley C. Wemple; John L. Campbell

    2012-01-01

    Many factors influence snow depth, water content and duration in forest ecosystems. The effects of forest cover and canopy gap geometry on snow accumulation has been well documented in coniferous forests of western North America and other regions; however, few studies have evaluated these effects on snowpack dynamics in mixed deciduous forests of the northeastern USA....

  16. Modelling stable atmospheric boundary layers over snow

    NARCIS (Netherlands)

    Sterk, H.A.M.

    2015-01-01

    Thesis entitled:

    Modelling Stable Atmospheric Boundary Layers over Snow

    H.A.M. Sterk

    Wageningen, 29th of April, 2015

    Summary

    The emphasis of this thesis is on the understanding and forecasting of the Stable Boundary Layer (SBL) over snow-covered surfaces. SBLs

  17. Effects of Planting of Calluna Vulgaris for Stable Snow Accumulation in Winter

    Science.gov (United States)

    Ibuki, R.; Harada, K.

    2017-12-01

    Recent year climate of the winter season is changing and the period of snow accumulation is reduced compared with before. It affects the management of the ski resort. Snowfall had occurred in December 2016, but the snow accumulated after January 2017 at the ski resort located in the Pacific Ocean side of the Northeast region of Japan. This situation is thought to be originated from two reasons, one is snow thawing, another is to be blown away by the strong monsoon wind. We are considering utilizing planting to stabilize snow accumulation. Currently building rock gardens with shrubs, mainly Calluna Vulgaris in the ski resort for attracting customers in the summer. These are difficult to raise in the lowlands of Japan because they are too hot, but because of their good growth in relatively low-temperature highlands, it is rare for local residents to appreciate the value of these. In addition, it is excellent in low temperature resistance, and it will not die even under the snow. We investigated the pressure resistance performance due to snowfall and the appropriateness of growth under the weather conditions of the area. Regarding Calluna Vulgaris, Firefly, the plants were not damaged even under snow more than 1 m. In addition, three years have passed since planting, relatively good growth is shown, and the stock has been growing every year. Based on these results, we plan to stabilize the snow accumulation by carrying out planting of Calluna vulgaris inside the slope. The growth of the Calluna species is gentle and the tree height grows only about 50 cm even if 15 years have passed since planting. Therefore, it is considered that the plant body is hard to put out their head on the snow surface during the ski season. Next season will monitor the snow accumulation around the planting area through the snow season.

  18. Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

    Directory of Open Access Journals (Sweden)

    E. E. Stigter

    2017-07-01

    Full Text Available Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE. Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF. Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May and decreases during the late melt season (June to September as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

  19. Snow model design for operational purposes

    Science.gov (United States)

    Kolberg, Sjur

    2017-04-01

    A parsimonious distributed energy balance snow model intended for operational use is evaluated using discharge, snow covered area and grain size; the latter two as observed from the MODIS sensor. The snow model is an improvement of the existing GamSnow model, which is a part of the Enki modelling framework. Core requirements for the new version have been: 1. Reduction of calibration freedom, motivated by previous experience of non-identifiable parameters in the existing version 2. Improvement of process representation based on recent advances in physically based snow modelling 3. Limiting the sensitivity to forcing data which are poorly known over the spatial domain of interest (often in mountainous areas) 4. Preference for observable states, and the ability to improve from updates. The albedo calculation is completely revised, now based on grain size through an emulation of the SNICAR model (Flanner and Zender, 2006; Gardener and Sharp, 2010). The number of calibration parameters in the albedo model is reduced from 6 to 2. The wind function governing turbulent energy fluxes has been reduced from 2 to 1 parameter. Following Raleigh et al (2011), snow surface radiant temperature is split from the top layer thermodynamic temperature, using bias-corrected wet-bulb temperature to model the former. Analyses are ongoing, and the poster will bring evaluation results from 16 years of MODIS observations and more than 25 catchments in southern Norway.

  20. Distributed snow modeling suitable for use with operational data for the American River watershed.

    Science.gov (United States)

    Shamir, E.; Georgakakos, K. P.

    2004-12-01

    The mountainous terrain of the American River watershed (~4300 km2) at the Western slope of the Northern Sierra Nevada is subject to significant variability in the atmospheric forcing that controls the snow accumulation and ablations processes (i.e., precipitation, surface temperature, and radiation). For a hydrologic model that attempts to predict both short- and long-term streamflow discharges, a plausible description of the seasonal and intermittent winter snow pack accumulation and ablation is crucial. At present the NWS-CNRFC operational snow model is implemented in a semi distributed manner (modeling unit of about 100-1000 km2) and therefore lump distinct spatial variability of snow processes. In this study we attempt to account for the precipitation, temperature, and radiation spatial variability by constructing a distributed snow accumulation and melting model suitable for use with commonly available sparse data. An adaptation of the NWS-Snow17 energy and mass balance that is used operationally at the NWS River Forecast Centers is implemented at 1 km2 grid cells with distributed input and model parameters. The input to the model (i.e., precipitation and surface temperature) is interpolated from observed point data. The surface temperature was interpolated over the basin based on adiabatic lapse rates using topographic information whereas the precipitation was interpolated based on maps of climatic mean annual rainfall distribution acquired from PRISM. The model parameters that control the melting rate due to radiation were interpolated based on aspect. The study was conducted for the entire American basin for the snow seasons of 1999-2000. Validation of the Snow Water Equivalent (SWE) prediction is done by comparing to observation from 12 snow Sensors. The Snow Cover Area (SCA) prediction was evaluated by comparing to remotely sensed 500m daily snow cover derived from MODIS. The results that the distribution of snow over the area is well captured and the

  1. Winter Insulation By Snow Accumulation in a Subarctic Treeline Ecosystem Increases Summer Carbon Cycling Rates

    Science.gov (United States)

    Parker, T.; Subke, J. A.; Wookey, P. A.

    2014-12-01

    The effect of snow accumulation on soil carbon and nutrient cycling is attracting substantial attention from researchers. We know that deeper snow accumulation caused by high stature vegetation increases winter microbial activity and therefore carbon and nitrogen flux rates. However, until now the effect of snow accumulation, by buffering winter soil temperature, on subsequent summer soil processes, has scarcely been considered. We carried out an experiment at an alpine treeline in subarctic Sweden in which soil monoliths, contained within PVC collars, were transplanted between forest (deep winter snow) and tundra heath (shallow winter snow). We measured soil CO2efflux over two growing seasons and quantified soil microbial biomass after the second winter. We showed that respiration rates of transplanted forest soil were significantly reduced compared with control collars (remaining in the forest) as a consequence of colder, but more variable, winter temperatures. We hypothesised that microbial biomass would be reduced in transplanted forests soils but found there was no difference compared to control. We therefore further hypothesised that the similarly sized microbial pool in the control is assembled differently to the transplant. We believe that the warmer winters in forests foster more active consortia of decomposer microbes as a result of different abiotic selection pressures. Using an ecosystem scale experimental approach, we have identified a mechanism that influences summer carbon cycling rates based solely on the amount of snow that accumulates the previous winter. We conclude that modification of snow depth as a consequence of changes in vegetation structure is an important mechanism influencing soil C stocks in ecosystems where snow persists for a major fraction of the year.

  2. Experimental and model based investigation of the links between snow bidirectional reflectance and snow microstructure

    Science.gov (United States)

    Dumont, M.; Flin, F.; Malinka, A.; Brissaud, O.; Hagenmuller, P.; Dufour, A.; Lapalus, P.; Lesaffre, B.; Calonne, N.; Rolland du Roscoat, S.; Ando, E.

    2017-12-01

    Snow optical properties are unique among Earth surface and crucial for a wide range of applications. The bi-directional reflectance, hereafter BRDF, of snow is sensible to snow microstructure. However the complex interplays between different parameters of snow microstructure namely size parameters and shape parameters on reflectance are challenging to disentangle both theoretically and experimentally. An accurate understanding and modelling of snow BRDF is required to correctly process satellite data. BRDF measurements might also provide means of characterizing snow morphology. This study presents one of the very few dataset that combined bi-directional reflectance measurements over 500-2500 nm and X-ray tomography of the snow microstructure for three different snow samples and two snow types. The dataset is used to evaluate the approach from Malinka, 2014 that relates snow optical properties to the chord length distribution in the snow microstructure. For low and medium absorption, the model accurately reproduces the measurements but tends to slightly overestimate the anisotropy of the reflectance. The model indicates that the deviation of the ice chord length distribution from an exponential distribution, that can be understood as a characterization of snow types, does not impact the reflectance for such absorptions. The simulations are also impacted by the uncertainties in the ice refractive index values. At high absorption and high viewing/incident zenith angle, the simulations and the measurements disagree indicating that some of the assumptions made in the model are not met anymore. The study also indicates that crystal habits might play a significant role for the reflectance under such geometries and wavelengths. However quantitative relationship between crystal habits and reflectance alongside with potential optical methodologies to classify snow morphology would require an extended dataset over more snow types. This extended dataset can likely be obtained

  3. Data Fusion of Gridded Snow Products Enhanced with Terrain Covariates and a Simple Snow Model

    Science.gov (United States)

    Snauffer, A. M.; Hsieh, W. W.; Cannon, A. J.

    2017-12-01

    Hydrologic planning requires accurate estimates of regional snow water equivalent (SWE), particularly areas with hydrologic regimes dominated by spring melt. While numerous gridded data products provide such estimates, accurate representations are particularly challenging under conditions of mountainous terrain, heavy forest cover and large snow accumulations, contexts which in many ways define the province of British Columbia (BC), Canada. One promising avenue of improving SWE estimates is a data fusion approach which combines field observations with gridded SWE products and relevant covariates. A base artificial neural network (ANN) was constructed using three of the best performing gridded SWE products over BC (ERA-Interim/Land, MERRA and GLDAS-2) and simple location and time covariates. This base ANN was then enhanced to include terrain covariates (slope, aspect and Terrain Roughness Index, TRI) as well as a simple 1-layer energy balance snow model driven by gridded bias-corrected ANUSPLIN temperature and precipitation values. The ANN enhanced with all aforementioned covariates performed better than the base ANN, but most of the skill improvement was attributable to the snow model with very little contribution from the terrain covariates. The enhanced ANN improved station mean absolute error (MAE) by an average of 53% relative to the composing gridded products over the province. Interannual peak SWE correlation coefficient was found to be 0.78, an improvement of 0.05 to 0.18 over the composing products. This nonlinear approach outperformed a comparable multiple linear regression (MLR) model by 22% in MAE and 0.04 in interannual correlation. The enhanced ANN has also been shown to estimate better than the Variable Infiltration Capacity (VIC) hydrologic model calibrated and run for four BC watersheds, improving MAE by 22% and correlation by 0.05. The performance improvements of the enhanced ANN are statistically significant at the 5% level across the province and

  4. Forest impacts on snow accumulation and ablation across an elevation gradient in a temperate montane environment

    Directory of Open Access Journals (Sweden)

    T. R. Roth

    2017-11-01

    Full Text Available Forest cover modifies snow accumulation and ablation rates via canopy interception and changes in sub-canopy energy balance processes. However, the ways in which snowpacks are affected by forest canopy processes vary depending on climatic, topographic and forest characteristics. Here we present results from a 4-year study of snow–forest interactions in the Oregon Cascades. We continuously monitored snow and meteorological variables at paired forested and open sites at three elevations representing the Low, Mid, and High seasonal snow zones in the study region. On a monthly to bi-weekly basis, we surveyed snow depth and snow water equivalent across 900 m transects connecting the forested and open pairs of sites. Our results show that relative to nearby open areas, the dense, relatively warm forests at Low and Mid sites impede snow accumulation via canopy snow interception and increase sub-canopy snowpack energy inputs via longwave radiation. Compared with the Forest sites, snowpacks are deeper and last longer in the Open site at the Low and Mid sites (4–26 and 11–33 days, respectively. However, we see the opposite relationship at the relatively colder High sites, with the Forest site maintaining snow longer into the spring by 15–29 days relative to the nearby Open site. Canopy interception efficiency (CIE values at the Low and Mid Forest sites averaged 79 and 76 % of the total event snowfall, whereas CIE was 31 % at the lower density High Forest site. At all elevations, longwave radiation in forested environments appears to be the primary energy component due to the maritime climate and forest presence, accounting for 93, 92, and 47 % of total energy inputs to the snowpack at the Low, Mid, and High Forest sites, respectively. Higher wind speeds in the High Open site significantly increase turbulent energy exchanges and snow sublimation. Lower wind speeds in the High Forest site create preferential snowfall deposition. These

  5. Snow accumulation and ablation in different canopy structures at a plot scale: using degree-day approach and measured shortwave radiation

    Directory of Open Access Journals (Sweden)

    Michal Jeníček

    2017-02-01

    The measured SWE in the plot with healthy forest was on average by 41% lower than in open area during snow accumulation period. The disturbed forest caused the SWE reduction by 22% compared to open area indicating increasing snow storage after forest defoliation. The snow ablation in healthy forest was by 32% slower compared to open area. On the contrary, the snow ablation in disturbed forest (due to the bark beetle was on average only by 7% slower than in open area. The relative decrease in incoming solar radiation in the forest compared to open area was much bigger compared to the relative decrease in snowmelt rates. This indicated that the decrease in snowmelt rates cannot be explained only by the decrease in incoming solar radiation. The model simulated best in open area and slightly worse in healthy forest. The model showed faster snowmelt after forest defoliation which also resulted in earlier snow melt-out in the disturbed forest.

  6. Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

    Directory of Open Access Journals (Sweden)

    T. M. Saloranta

    2012-11-01

    Full Text Available Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE, snow depth (SD, and the snow bulk density (ρ. In this paper the set of equations contained in the seNorge model code is described and a thorough spatiotemporal statistical evaluation of the model performance from 1957–2011 is made using the two major sets of extensive in situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the overestimation of SWE increases with elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet nonetheless process-based method to construct snow maps of high spatiotemporal resolution. It is an especially well suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway.

  7. Satellite gravimetry observation of Antarctic snow accumulation related to ENSO

    OpenAIRE

    Ingo Sasgen; Henryk Dobslaw; Z. Martinec; Maik Thomas

    2010-01-01

    Interannual ice-mass variations along the Antarctic Peninsula (AP) and in the Amundsen Sea Sector (AS) are obtained for the years 2002 until 2009 using satellite data of the Gravity Recovery and Climate Experiment, that correlate well (r ≈ 0.7) with accumulation variations based on the net precipitation from the European Centre for Medium Range Weather Forecasts. Moreover, mass signals for AP and AS are anti-correlated in time (r ≈ − 0.4) and contain El Niño Southern Oscillation signatures re...

  8. A reassessment of North American river basin water balances in light of new estimates of mountain snow accumulation

    Science.gov (United States)

    Wrzesien, M.; Durand, M. T.; Pavelsky, T.

    2017-12-01

    The hydrologic cycle is a key component of many aspects of daily life, yet not all water cycle processes are fully understood. In particular, water storage in mountain snowpacks remains largely unknown. Previous work with a high resolution regional climate model suggests that global and continental models underestimate mountain snow accumulation, perhaps by as much as 50%. Therefore, we hypothesize that since snow water equivalent (one aspect of the water balance) is underestimated, accepted water balances for major river basins are likely wrong, particularly for mountainous river basins. Here we examine water balances for four major high latitude North American watersheds - the Columbia, Mackenzie, Nelson, and Yukon. The mountainous percentage of each basin ranges, which allows us to consider whether a bias in the water balance is affected by mountain area percentage within the watershed. For our water balance evaluation, we especially consider precipitation estimates from a variety of datasets, including models, such as WRF and MERRA, and observation-based, such as CRU and GPCP. We ask whether the precipitation datasets provide enough moisture for seasonal snow to accumulate within the basin and whether we see differences in the variability of annual and seasonal precipitation from each dataset. From our reassessment of high-latitude water balances, we aim to determine whether the current understanding is sufficient to describe all processes within the hydrologic cycle or whether datasets appear to be biased, particularly in high-elevation precipitation. Should currently-available datasets appear to be similarly biased in precipitation, as we have seen in mountain snow accumulation, we discuss the implications for the continental water budget.

  9. Cartographic modelling of aerotechnogenic pollution in snow cover in the landscapes of the Kola Peninsula.

    Science.gov (United States)

    Ratkin, N E; Asming, V E; Koshkin, V V

    2001-01-01

    The goal of this work was to develop computational techniques for sulphates, nickel and copper accumulation in the snow in the local pollution zone. The main task was to reveal the peculiarities of formation and pollution of snow cover on the region with complex cross-relief. A digital cartographic model of aerotechnogenic pollution of snow cover in the landscapes of the local zone has been developed, based on five-year experimental data. Data regarding annual emissions from the industrial complex, information about distribution of wind and the sum of precipitation from meteostation "Nikel" for the winter period, allowed the model to ensure: * material presentation in the form of maps of water capacity and accumulation of sulphates, nickel and copper in the snow over any winter period in retrospective; * calculation of water capacity and accumulation of pollutants for watersheds and other natural-territorial complexes; * solution of the opposite problem about the determination of the emissions of sulphates, nickel and copper from the enterprise by measuring snow pollution in datum points. The model can be used in other northern regions of the Russian Federation with similar physical-geographical and climatic conditions. The relationships between the sum of precipitation and water capacity in the landscapes of the same type and also the relationships between pollution content in snow and relief, pollution content in snow and distance from the source of emissions, were used as the basis for the model.

  10. Improvement of a snow albedo parameterization in the Snow-Atmosphere-Soil Transfer model: evaluation of impacts of aerosol on seasonal snow cover

    Science.gov (United States)

    Zhong, Efang; Li, Qian; Sun, Shufen; Chen, Wen; Chen, Shangfeng; Nath, Debashis

    2017-11-01

    The presence of light-absorbing aerosols (LAA) in snow profoundly influence the surface energy balance and water budget. However, most snow-process schemes in land-surface and climate models currently do not take this into consideration. To better represent the snow process and to evaluate the impacts of LAA on snow, this study presents an improved snow albedo parameterization in the Snow-Atmosphere-Soil Transfer (SAST) model, which includes the impacts of LAA on snow. Specifically, the Snow, Ice and Aerosol Radiation (SNICAR) model is incorporated into the SAST model with an LAA mass stratigraphy scheme. The new coupled model is validated against in-situ measurements at the Swamp Angel Study Plot (SASP), Colorado, USA. Results show that the snow albedo and snow depth are better reproduced than those in the original SAST, particularly during the period of snow ablation. Furthermore, the impacts of LAA on snow are estimated in the coupled model through case comparisons of the snowpack, with or without LAA. The LAA particles directly absorb extra solar radiation, which accelerates the growth rate of the snow grain size. Meanwhile, these larger snow particles favor more radiative absorption. The average total radiative forcing of the LAA at the SASP is 47.5 W m-2. This extra radiative absorption enhances the snowmelt rate. As a result, the peak runoff time and "snow all gone" day have shifted 18 and 19.5 days earlier, respectively, which could further impose substantial impacts on the hydrologic cycle and atmospheric processes.

  11. A probabilistic model for snow avalanche occurrence

    Science.gov (United States)

    Perona, P.; Miescher, A.; Porporato, A.

    2009-04-01

    Avalanche hazard forecasting is an important issue in relation to the protection of urbanized environments, ski resorts and of ski-touring alpinists. A critical point is to predict the conditions that trigger the snow mass instability determining the onset and the size of avalanches. On steep terrains the risk of avalanches is known to be related to preceding consistent snowfall events and to subsequent changes in the local climatic conditions. Regression analysis has shown that avalanche occurrence indeed correlates to the amount of snow fallen in consecutive three snowing days and to the state of the settled snow at the ground. Moreover, since different type of avalanches may occur as a result of the interactions of different factors, the process of snow avalanche formation is inherently complex and with some degree of unpredictability. For this reason, although several models assess the risk of avalanche by accounting for all the involved processes with a great detail, a high margin of uncertainty invariably remains. In this work, we explicitly describe such an unpredictable behaviour with an intrinsic noise affecting the processes leading snow instability. Eventually, this sets the basis for a minimalist stochastic model, which allows us to investigate the avalanche dynamics and its statistical properties. We employ a continuous time process with stochastic jumps (snowfalls), deterministic decay (snowmelt and compaction) and state dependent avalanche occurrence (renewals) as a minimalist model for the determination of avalanche size and related intertime occurrence. The physics leading to avalanches is simplified to the extent where only meteorological data and terrain data are necessary to estimate avalanche danger. We explore the analytical formulation of the process and the properties of the probability density function of the avalanche process variables. We also discuss what is the probabilistic link between avalanche size and preceding snowfall event and

  12. Improving snow density estimation for mapping SWE with Lidar snow depth: assessment of uncertainty in modeled density and field sampling strategies in NASA SnowEx

    Science.gov (United States)

    Raleigh, M. S.; Smyth, E.; Small, E. E.

    2017-12-01

    The spatial distribution of snow water equivalent (SWE) is not sufficiently monitored with either remotely sensed or ground-based observations for water resources management. Recent applications of airborne Lidar have yielded basin-wide mapping of SWE when combined with a snow density model. However, in the absence of snow density observations, the uncertainty in these SWE maps is dominated by uncertainty in modeled snow density rather than in Lidar measurement of snow depth. Available observations tend to have a bias in physiographic regime (e.g., flat open areas) and are often insufficient in number to support testing of models across a range of conditions. Thus, there is a need for targeted sampling strategies and controlled model experiments to understand where and why different snow density models diverge. This will enable identification of robust model structures that represent dominant processes controlling snow densification, in support of basin-scale estimation of SWE with remotely-sensed snow depth datasets. The NASA SnowEx mission is a unique opportunity to evaluate sampling strategies of snow density and to quantify and reduce uncertainty in modeled snow density. In this presentation, we present initial field data analyses and modeling results over the Colorado SnowEx domain in the 2016-2017 winter campaign. We detail a framework for spatially mapping the uncertainty in snowpack density, as represented across multiple models. Leveraging the modular SUMMA model, we construct a series of physically-based models to assess systematically the importance of specific process representations to snow density estimates. We will show how models and snow pit observations characterize snow density variations with forest cover in the SnowEx domains. Finally, we will use the spatial maps of density uncertainty to evaluate the selected locations of snow pits, thereby assessing the adequacy of the sampling strategy for targeting uncertainty in modeled snow density.

  13. Collaborative Research: Snow Accumulation and Snow Melt in a Mixed Northern Hardwood-Conifer Forest, Version 1

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains snow depth, Snow Water Equivalent (SWE), and forest cover characteristics for sites at the Hubbard Brook Experimental Forest in northern New...

  14. Evaluation of probable maximum snow accumulation: Development of a methodology for climate change studies

    Science.gov (United States)

    Klein, Iris M.; Rousseau, Alain N.; Frigon, Anne; Freudiger, Daphné; Gagnon, Patrick

    2016-06-01

    Probable maximum snow accumulation (PMSA) is one of the key variables used to estimate the spring probable maximum flood (PMF). A robust methodology for evaluating the PMSA is imperative so the ensuing spring PMF is a reasonable estimation. This is of particular importance in times of climate change (CC) since it is known that solid precipitation in Nordic landscapes will in all likelihood change over the next century. In this paper, a PMSA methodology based on simulated data from regional climate models is developed. Moisture maximization represents the core concept of the proposed methodology; precipitable water being the key variable. Results of stationarity tests indicate that CC will affect the monthly maximum precipitable water and, thus, the ensuing ratio to maximize important snowfall events. Therefore, a non-stationary approach is used to describe the monthly maximum precipitable water. Outputs from three simulations produced by the Canadian Regional Climate Model were used to give first estimates of potential PMSA changes for southern Quebec, Canada. A sensitivity analysis of the computed PMSA was performed with respect to the number of time-steps used (so-called snowstorm duration) and the threshold for a snowstorm to be maximized or not. The developed methodology is robust and a powerful tool to estimate the relative change of the PMSA. Absolute results are in the same order of magnitude as those obtained with the traditional method and observed data; but are also found to depend strongly on the climate projection used and show spatial variability.

  15. Testing a blowing snow model against distributed snow measurements at Upper Sheep Creek, Idaho, United States of America

    Science.gov (United States)

    Rajiv Prasad; David G. Tarboton; Glen E. Liston; Charles H. Luce; Mark S. Seyfried

    2001-01-01

    In this paper a physically based snow transport model (SnowTran-3D) was used to simulate snow drifting over a 30 m grid and was compared to detailed snow water equivalence (SWE) surveys on three dates within a small 0.25 km2 subwatershed, Upper Sheep Creek. Two precipitation scenarios and two vegetation scenarios were used to carry out four snow transport model runs in...

  16. The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

    Science.gov (United States)

    Kim, E.; Tedesco, M.; Reichle, R.; Choudhury, B.; Peters-Lidard C.; Foster, J.; Hall, D.; Riggs, G.

    2006-01-01

    Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

  17. Variations of snow accumulation rate in Central Antarctica over the last 250 years

    Directory of Open Access Journals (Sweden)

    A. A. Ekaykin

    2017-01-01

    Full Text Available The present-day global climate changes, very likely caused by anthropogenic activity, may potentially present a serious threat to the whole human civilization in a near future. In order to develop a plan of measures aimed at elimination of these threats and adaptation to these undesirable changes, one should deeply understand the mechanism of past and present (and thus, future climatic changes of our planet. In this study we compare the present-day data of instrumental observations of the air temperature and snow accumulation rate performed in Central Antarctica (the Vostok station with the reconstructed paleogeographic data on a variability of these parameters in the past. First of all, the Vostok station is shown to be differing from other East Antarctic stations due to relatively higher rate of warming (1.6 °C per 100 years since 1958. At the same time, according to paleogeographic data, from the late eighteenth century to early twenty-first one the total warming amounted to about 1 °C, which is consistent with data from other Antarctic regions. So, we can make a conclusion with high probability that the 30-year period of 1985–2015 was the warmest over the last 2.5 centuries. As for the snow accumulation rate, the paleogeographic data on this contain a certain part of noise that does not allow reliable concluding. However, we found a statistically significant relationship between the rate of snow accumulation and air temperature. This means that with further rise of temperature in Central Antarctica, the rate of solid precipitation accumulation will increase there, thus partially compensating increasing of the sea level.

  18. Integration of snow management practices into a detailed snow pack model

    Science.gov (United States)

    Spandre, Pierre; Morin, Samuel; Lafaysse, Matthieu; Lejeune, Yves; François, Hugues; George-Marcelpoil, Emmanuelle

    2016-04-01

    The management of snow on ski slopes is a key socio-economic and environmental issue in mountain regions. Indeed the winter sports industry has become a very competitive global market although this economy remains particularly sensitive to weather and snow conditions. The understanding and implementation of snow management in detailed snowpack models is a major step towards a more realistic assessment of the evolution of snow conditions in ski resorts concerning past, present and future climate conditions. Here we describe in a detailed manner the integration of snow management processes (grooming, snowmaking) into the snowpack model Crocus (Spandre et al., Cold Reg. Sci. Technol., in press). The effect of the tiller is explicitly taken into account and its effects on snow properties (density, snow microstructure) are simulated in addition to the compaction induced by the weight of the grooming machine. The production of snow in Crocus is carried out with respect to specific rules and current meteorological conditions. Model configurations and results are described in detail through sensitivity tests of the model of all parameters related to snow management processes. In-situ observations were carried out in four resorts in the French Alps during the 2014-2015 winter season considering for each resort natural, groomed only and groomed plus snowmaking conditions. The model provides realistic simulations of the snowpack properties with respect to these observations. The main uncertainty pertains to the efficiency of the snowmaking process. The observed ratio between the mass of machine-made snow on ski slopes and the water mass used for production was found to be lower than was expected from the literature, in every resort. The model now referred to as "Crocus-Resort" has been proven to provide realistic simulations of snow conditions on ski slopes and may be used for further investigations. Spandre, P., S. Morin, M. Lafaysse, Y. Lejeune, H. François and E. George

  19. Objective Characterization of Snow Microstructure for Microwave Emission Modeling

    Science.gov (United States)

    Durand, Michael; Kim, Edward J.; Molotch, Noah P.; Margulis, Steven A.; Courville, Zoe; Malzler, Christian

    2012-01-01

    Passive microwave (PM) measurements are sensitive to the presence and quantity of snow, a fact that has long been used to monitor snowcover from space. In order to estimate total snow water equivalent (SWE) within PM footprints (on the order of approx 100 sq km), it is prerequisite to understand snow microwave emission at the point scale and how microwave radiation integrates spatially; the former is the topic of this paper. Snow microstructure is one of the fundamental controls on the propagation of microwave radiation through snow. Our goal in this study is to evaluate the prospects for driving the Microwave Emission Model of Layered Snowpacks with objective measurements of snow specific surface area to reproduce measured brightness temperatures when forced with objective measurements of snow specific surface area (S). This eliminates the need to treat the grain size as a free-fit parameter.

  20. Snow Climatology of Arctic Sea Ice: Comparison of Reanalysis and Climate Model Data with In Situ Measurements

    Science.gov (United States)

    Chevooruvalappil Chandran, B.; Pittana, M.; Haas, C.

    2015-12-01

    Snow on sea ice is a critical and complex factor influencing sea ice processes. Deep snow with a high albedo and low thermal conductivity inhibits ice growth in winter and minimizes ice loss in summer. Very shallow or absent snow promotes ice growth in winter and ice loss in summer. The timing of snow ablation critically impacts summer sea ice mass balance. Here we assess the accuracy of various snow on sea ice data products from reanalysis and modeling comparing them with in situ measurements. The latter are based on the Warren et al. (1999) monthly climatology derived from snow ruler measurements between 1954-1991, and on daily snow depth retrievals from few drifting ice mass balance buoys (IMB) with sufficiently long observations spanning the summer season. These were compared with snow depth data from the National Center for Environmental Prediction Department of Energy Reanalysis 2 (NCEP), the Community Climate System Model 4 (CCSM4), and the Canadian Earth System Model 2 (CanESM2). Results are quite variable in different years and regions. However, there is often good agreement between CanESM2 and IMB snow depth during the winter accumulation and spring melt periods. Regional analyses show that over the western Arctic covered primarily with multiyear ice NCEP snow depths are in good agreement with the Warren climatology while CCSM4 overestimates snow depth. However, in the Eastern Arctic which is dominated by first-year ice the opposite behavior is observed. Compared to the Warren climatology CanESM2 underestimates snow depth in all regions. Differences between different snow depth products are as large as 10 to 20 cm, with large consequences for the sea ice mass balance. However, it is also very difficult to evaluate the accuracy of reanalysis and model snow depths due to a lack of extensive, continuous in situ measurements.

  1. Modelling technical snow production for skiing areas in the Austrian Alps with the physically based snow model AMUNDSEN

    Science.gov (United States)

    Hanzer, F.; Marke, T.; Steiger, R.; Strasser, U.

    2012-04-01

    Tourism and particularly winter tourism is a key factor for the Austrian economy. Judging from currently available climate simulations, the Austrian Alps show a particularly high vulnerability to climatic changes. To reduce the exposure of ski areas towards changes in natural snow conditions as well as to generally enhance snow conditions at skiing sites, technical snowmaking is widely utilized across Austrian ski areas. While such measures result in better snow conditions at the skiing sites and are important for the local skiing industry, its economic efficiency has also to be taken into account. The current work emerges from the project CC-Snow II, where improved future climate scenario simulations are used to determine future natural and artificial snow conditions and their effects on tourism and economy in the Austrian Alps. In a first step, a simple technical snowmaking approach is incorporated into the process based snow model AMUNDSEN, which operates at a spatial resolution of 10-50 m and a temporal resolution of 1-3 hours. Locations of skiing slopes within a ski area in Styria, Austria, were digitized and imported into the model environment. During a predefined time frame in the beginning of the ski season, the model produces a maximum possible amount of technical snow and distributes the associated snow on the slopes, whereas afterwards, until to the end of the ski season, the model tries to maintain a certain snow depth threshold value on the slopes. Due to only few required input parameters, this approach is easily transferable to other ski areas. In our poster contribution, we present first results of this snowmaking approach and give an overview of the data and methodology applied. In a further step in CC-Snow, this simple bulk approach will be extended to consider actual snow cannon locations and technical specifications, which will allow a more detailed description of technical snow production as well as cannon-based recordings of water and energy

  2. Modeling Snow Regime in Cores of Small Planetary Bodies

    Science.gov (United States)

    Boukaré, C. E.; Ricard, Y. R.; Parmentier, E.; Parman, S. W.

    2017-12-01

    Observations of present day magnetic field on small planetary bodies such as Ganymede or Mercury challenge our understanding of planetary dynamo. Several mechanisms have been proposed to explain the origin of magnetic fields. Among the proposed scenarios, one family of models relies on snow regime. Snow regime is supported by experimental studies showing that melting curves can first intersect adiabats in regions where the solidifying phase is not gravitationaly stable. First solids should thus remelt during their ascent or descent. The effect of the snow zone on magnetic field generation remains an open question. Could magnetic field be generated in the snow zone? If not, what is the depth extent of the snow zone? How remelting in the snow zone drive compositional convection in the liquid layer? Several authors have tackled this question with 1D-spherical models. Zhang and Schubert, 2012 model sinking of the dense phase as internally heated convection. However, to our knowledge, there is no study on the convection structure associated with sedimentation and phase change at planetary scale. We extend the numerical model developped in [Boukare et al., 2017] to model snow dynamics in 2D Cartesian geometry. We build a general approach for modeling double diffusive convection coupled with solid-liquid phase change and phase separation. We identify several aspects that may govern the convection structure of the solidifying system: viscosity contrast between the snow zone and the liquid layer, crystal size, rate of melting/solidification and partitioning of light components during phase change.

  3. Dynamic-stochastic modeling of snow cover formation on the European territory of Russia

    OpenAIRE

    A. N. Gelfan; V. M. Moreido

    2014-01-01

    A dynamic-stochastic model, which combines a deterministic model of snow cover formation with a stochastic weather generator, has been developed. The deterministic snow model describes temporal change of the snow depth, content of ice and liquid water, snow density, snowmelt, sublimation, re-freezing of melt water, and snow metamorphism. The model has been calibrated and validated against the long-term data of snow measurements over the territory of the European Russia. The model showed good ...

  4. Estimation of Snow Particle Model Suitable for a Complex and Forested Terrain: Lessons from SnowEx

    Science.gov (United States)

    Gatebe, C. K.; Li, W.; Stamnes, K. H.; Poudyal, R.; Fan, Y.; Chen, N.

    2017-12-01

    SnowEx 2017 obtained consistent and coordinated ground and airborne remote sensing measurements over Grand Mesa in Colorado, which feature sufficient forested stands to have a range of density and height (and other forest conditions); a range of snow depth/snow water equivalent (SWE) conditions; sufficiently flat snow-covered terrain of a size comparable to airborne instrument swath widths. The Cloud Absorption Radiometer (CAR) data from SnowEx are unique and can be used to assess the accuracy of Bidirectional Reflectance-Distribution Functions (BRDFs) calculated by different snow models. These measurements provide multiple angle and multiple wavelength data needed for accurate surface BRDF characterization. Such data cannot easily be obtained by current satellite remote sensors. Compared to ground-based snow field measurements, CAR measurements minimize the effect of self-shading, and are adaptable to a wide variety of field conditions. We plan to use the CAR measurements as the validation data source for our snow modeling effort. By comparing calculated BRDF results from different snow models to CAR measurements, we can determine which model best explains the snow BRDFs, and is therefore most suitable for application to satellite remote sensing of snow parameters and surface energy budget calculations.

  5. A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model

    Directory of Open Access Journals (Sweden)

    S. Kolberg

    2006-01-01

    Full Text Available A method for assimilating remotely sensed snow covered area (SCA into the snow subroutine of a grid distributed precipitation-runoff model (PRM is presented. The PRM is assumed to simulate the snow state in each grid cell by a snow depletion curve (SDC, which relates that cell's SCA to its snow cover mass balance. The assimilation is based on Bayes' theorem, which requires a joint prior distribution of the SDC variables in all the grid cells. In this paper we propose a spatial model for this prior distribution, and include similarities and dependencies among the grid cells. Used to represent the PRM simulated snow cover state, our joint prior model regards two elevation gradients and a degree-day factor as global variables, rather than describing their effect separately for each cell. This transformation results in smooth normalised surfaces for the two related mass balance variables, supporting a strong inter-cell dependency in their joint prior model. The global features and spatial interdependency in the prior model cause each SCA observation to provide information for many grid cells. The spatial approach similarly facilitates the utilisation of observed discharge. Assimilation of SCA data using the proposed spatial model is evaluated in a 2400 km2 mountainous region in central Norway (61° N, 9° E, based on two Landsat 7 ETM+ images generalized to 1 km2 resolution. An image acquired on 11 May, a week before the peak flood, removes 78% of the variance in the remaining snow storage. Even an image from 4 May, less than a week after the melt onset, reduces this variance by 53%. These results are largely improved compared to a cell-by-cell independent assimilation routine previously reported. Including observed discharge in the updating information improves the 4 May results, but has weak effect on 11 May. Estimated elevation gradients are shown to be sensitive to informational deficits occurring at high altitude, where snowmelt has not started

  6. Altitudinal gradients, midwinter melt, and wind effects on snow accumulation in semiarid midlatitude Andes under La Niña conditions

    Science.gov (United States)

    Ayala, A.; McPhee, J.; Vargas, X.

    2014-04-01

    The Andes Cordillera remains a sparsely monitored and studied snow hydrology environment in comparison to similar mountain ranges in the Northern Hemisphere. In order to uncover some of the key processes driving snow water equivalent (SWE) spatial variability, we present and analyze a distributed SWE data set, sampled at the end of accumulation season 2011. Three representative catchments across the region were monitored, obtaining measurements in an elevation range spanning 2000 to 3900 m asl and from 32.4° to 34.0°S in latitude. Climatic conditions during this season corresponded to a moderate La Niña phenomenon, which is generally correlated with lower-than normal accumulation. Collected measurements can be described at the regional and watershed extents by altitudinal gradients that imply an increase by a factor of two in snow depth between 2200 and 3000 m asl, though with significant variability at the upper sites. In these upper sites, we found north-facing, wind-sheltered slopes showing 25% less average SWE values than south-facing, wind-exposed ones. This suggests that under these conditions, solar radiation dominated wind transport effects in controlling end-of-winter variability. Nevertheless, we found clusters of snow depth measurements above 3000 m asl that can be explained by wind exposure differences. This is the first documented snow depth data set of this spatial extent for this region, and it is framed within an ongoing research effort aimed at improving understanding and modeling of snow hydrology in the extratropical Andes Cordillera.

  7. Application of Snowfall and Wind Statistics to Snow Transport Modeling for Snowdrift Control in Minnesota.

    Science.gov (United States)

    Shulski, Martha D.; Seeley, Mark W.

    2004-11-01

    Models were utilized to determine the snow accumulation season (SAS) and to quantify windblown snow for the purpose of snowdrift control for locations in Minnesota. The models require mean monthly temperature, snowfall, density of snow, and wind frequency distribution statistics. Temperature and precipitation data were obtained from local cooperative observing sites, and wind data came from Automated Surface Observing System (ASOS)/Automated Weather Observing System (AWOS) sites in the region. The temperature-based algorithm used to define the SAS reveals a geographic variability in the starting and ending dates of the season, which is determined by latitude and elevation. Mean seasonal snowfall shows a geographic distribution that is affected by topography and proximity to Lake Superior. Mean snowfall density also exhibits variability, with lower-density snow events displaced to higher-latitude positions. Seasonal wind frequencies show a strong bimodal distribution with peaks from the northwest and southeast vector direction, with an exception for locations in close proximity to the Lake Superior shoreline. In addition, for western and south-central Minnesota there is a considerably higher frequency of wind speeds above the mean snow transport threshold of 7 m s-1. As such, this area is more conducive to higher potential snow transport totals. Snow relocation coefficients in this area are in the range of 0.4 0.9, and, according to the empirical models used in this analysis, this range implies that actual snow transport is 40% 90% of the total potential in south-central and western areas of the state.

  8. Modeling of multi-phase interactions of reactive nitrogen between snow and air in Antarctica

    Science.gov (United States)

    McCrystall, M.; Chan, H. G. V.; Frey, M. M.; King, M. D.

    2016-12-01

    In polar and snow-covered regions, the snowpack is an important link between atmospheric, terrestrial and oceanic systems. Trace gases, including nitrogen oxides, produced via photochemical reactions in snow are partially released to the lower atmosphere with considerable impact on its composition. However, the post-depositional processes that change the chemical composition and physical properties of the snowpack are still poorly understood. Most current snow chemistry models oversimplify as they assume air-liquid interactions and aqueous phase chemistry taking place at the interface between the snow grain and air. Here, we develop a novel temperature dependent multi-phase (gas-liquid-ice) physical exchange model for reactive nitrogen. The model is validated with existing year-round observations of nitrate in the top 0.5-2 cm of snow and the overlying atmosphere at two very different Antarctic locations: Dome C on the East Antarctic Plateau with very low annual mean temperature (-54ºC) and accumulation rate (rate and high background level of sea salt aerosol. We find that below the eutectic temperature of the H2O/dominant ion mixture the surface snow nitrate is controlled by kinetic adsorption onto the surface of snow grains followed by grain diffusion. Above the eutectic temperature, in addition to the former two processes, thermodynamic equilibrium of HNO3 between interstitial air and liquid water pockets, possibly present at triple junctions or grooves at grain boundaries, greatly enhances the nitrate uptake by snow in agreement with the concentration peak observed in summer.

  9. Assessing the ability of operational snow models to predict snowmelt runoff extremes (Invited)

    Science.gov (United States)

    Wood, A. W.; Restrepo, P. J.; Clark, M. P.

    2013-12-01

    In the western US, the snow accumulation and melt cycle of winter and spring plays a critical role in the region's water management strategies. Consequently, the ability to predict snowmelt runoff at time scales from days to seasons is a key input for decisions in reservoir management, whether for avoiding flood hazards or supporting environmental flows through the scheduling of releases in spring, or for allocating releases for multi-state water distribution in dry seasons of year (using reservoir systems to provide an invaluable buffer for many sectors against drought). Runoff forecasts thus have important benefits at both wet and dry extremes of the climatological spectrum. The importance of the prediction of the snow cycle motivates an assessment of the strengths and weaknesses of the US's central operational snow model, SNOW17, in contrast to process-modeling alternatives, as they relate to simulating observed snowmelt variability and extremes. To this end, we use a flexible modeling approach that enables an investigation of different choices in model structure, including model physics, parameterization and degree of spatiotemporal discretization. We draw from examples of recent extreme events in western US watersheds and an overall assessment of retrospective model performance to identify fruitful avenues for advancing the modeling basis for the operational prediction of snow-related runoff extremes.

  10. A novel linear physical model for remote sensing of snow wetness and snow density using the visible and infrared bands

    Science.gov (United States)

    Varade, D. M.; Dikshit, O.

    2017-12-01

    Modeling and forecasting of snowmelt runoff are significant for understanding the hydrological processes in the cryosphere which requires timely information regarding snow physical properties such as liquid water content and density of snow in the topmost layer of the snowpack. Both the seasonal runoffs and avalanche forecasting are vastly dependent on the inherent physical characteristics of the snowpack which are conventionally measured by field surveys in difficult terrains at larger impending costs and manpower. With advances in remote sensing technology and the increase in the availability of satellite data, the frequency and extent of these surveys could see a declining trend in future. In this study, we present a novel approach for estimating snow wetness and snow density using visible and infrared bands that are available with most multi-spectral sensors. We define a trapezoidal feature space based on the spectral reflectance in the near infrared band and the Normalized Differenced Snow Index (NDSI), referred to as NIR-NDSI space, where dry snow and wet snow are observed in the left diagonal upper and lower right corners, respectively. The corresponding pixels are extracted by approximating the dry and wet edges which are used to develop a linear physical model to estimate snow wetness. Snow density is then estimated using the modeled snow wetness. Although the proposed approach has used Sentinel-2 data, it can be extended to incorporate data from other multi-spectral sensors. The estimated values for snow wetness and snow density show a high correlation with respect to in-situ measurements. The proposed model opens a new avenue for remote sensing of snow physical properties using multi-spectral data, which were limited in the literature.

  11. Simulation of Wind-Driven Snow Redistribution at a High-Elevation Alpine Site Using a Meso-Scale Atmospheric Model

    Science.gov (United States)

    Vionnet, V.; Martin, E.; Masson, V.; Guyomarc'h, G.; Naaim-Bouvet, F.; Prokop, A.; Durand, Y.; Lac, C.

    2012-12-01

    In alpine regions, blowing snow events strongly influence the temporal and spatial evolution of the snow depth distribution throughout the winter season. We recently developed a new simulation system to gain understanding on the complex processes that drive the redistribution of snow by the wind in complex terrain. This new system couples directly the detailed snow-pack model Crocus with the meso-scale atmospheric model Meso-NH. A blowing snow scheme allows Meso-NH to simulate the transport of snow particles in the atmosphere. We used the coupled system to study a blowing snow event with snowfall that occurred in February 2011 in the Grandes Rousses range (French Alps). Three nested domains at an horizontal resolution of 450, 150 and 50 m allow the model to simulate the complex 3D precipitation and wind fields around our experimental site (2720 m a.s.l.) during this 22-hour event. Wind-induced snow transport is activated over the domains of higher resolution (150 and 50 m). We firstly assessed the ability of the model to reproduce atmospheric flows at high resolution in alpine terrain using a large dataset of observations (meteorological data, vertical profile of wind speed). Simulated blowing snow fluxes are then compared with measurements from SPC and mechanical snow traps. Finally a map of snow erosion and accumulation produced by Terrestrial Laser measurements allows to evaluate the quality of the simulated snow depth redistribution.

  12. Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

    NARCIS (Netherlands)

    Stigter, Emmy E.; Wanders, Niko; Saloranta, Tuomo M.; Shea, Joseph M.; Bierkens, M.F.P.; Immerzeel, W.W.

    2017-01-01

    Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water

  13. SWANN: The Snow Water Artificial Neural Network Modelling System

    Science.gov (United States)

    Broxton, P. D.; van Leeuwen, W.; Biederman, J. A.

    2017-12-01

    Snowmelt from mountain forests is important for water supply and ecosystem health. Along Arizona's Mogollon Rim, snowmelt contributes to rivers and streams that provide a significant water supply for hydro-electric power generation, agriculture, and human consumption in central Arizona. In this project, we are building a snow monitoring system for the Salt River Project (SRP), which supplies water and power to millions of customers in the Phoenix metropolitan area. We are using process-based hydrological models and artificial neural networks (ANNs) to generate information about both snow water equivalent (SWE) and snow cover. The snow-cover data is generated with ANNs that are applied to Landsat and MODIS satellite reflectance data. The SWE data is generated using a combination of gridded SWE estimates generated by process-based snow models and ANNs that account for variations in topography, forest cover, and solar radiation. The models are trained and evaluated with snow data from SNOTEL stations as well as from aerial LiDAR and field data that we collected this past winter in northern Arizona, as well as with similar data from other sites in the Southwest US. These snow data are produced in near-real time, and we have built a prototype decision support tool to deliver them to SRP. This tool is designed to provide daily-to annual operational monitoring of spatial and temporal changes in SWE and snow cover conditions over the entire Salt River Watershed (covering 17,000 km2), and features advanced web mapping capabilities and watershed analytics displayed as graphical data.

  14. SMRT: A new, modular snow microwave radiative transfer model

    Science.gov (United States)

    Picard, Ghislain; Sandells, Melody; Löwe, Henning; Dumont, Marie; Essery, Richard; Floury, Nicolas; Kontu, Anna; Lemmetyinen, Juha; Maslanka, William; Mätzler, Christian; Morin, Samuel; Wiesmann, Andreas

    2017-04-01

    Forward models of radiative transfer processes are needed to interpret remote sensing data and derive measurements of snow properties such as snow mass. A key requirement and challenge for microwave emission and scattering models is an accurate description of the snow microstructure. The snow microwave radiative transfer model (SMRT) was designed to cater for potential future active and/or passive satellite missions and developed to improve understanding of how to parameterize snow microstructure. SMRT is implemented in Python and is modular to allow easy intercomparison of different theoretical approaches. Separate modules are included for the snow microstructure model, electromagnetic module, radiative transfer solver, substrate, interface reflectivities, atmosphere and permittivities. An object-oriented approach is used with carefully specified exchanges between modules to allow future extensibility i.e. without constraining the parameter list requirements. This presentation illustrates the capabilities of SMRT. At present, five different snow microstructure models have been implemented, and direct insertion of the autocorrelation function from microtomography data is also foreseen with SMRT. Three electromagnetic modules are currently available. While DMRT-QCA and Rayleigh models need specific microstructure models, the Improved Born Approximation may be used with any microstructure representation. A discrete ordinates approach with stream connection is used to solve the radiative transfer equations, although future inclusion of 6-flux and 2-flux solvers are envisioned. Wrappers have been included to allow existing microwave emission models (MEMLS, HUT, DMRT-QMS) to be run with the same inputs and minimal extra code (2 lines). Comparisons between theoretical approaches will be shown, and evaluation against field experiments in the frequency range 5-150 GHz. SMRT is simple and elegant to use whilst providing a framework for future development within the

  15. Snow cover and End of Summer Snowline statistics from a simple stochastic model

    Science.gov (United States)

    Petrelli, A.; Crouzy, B.; Perona, P.

    2012-04-01

    One essential parameter characterizing snow cover statistics is the End Of Summer Snowline (EOSS), which is also a good indicator of actual climatic trends in mountain regions. EOSS is usually modelled by means of spatially distributed physically based models, and typically require heavy parameterization. In this paper we validate the simple stochastic model proposed by Perona et al. (2007), by showing that the snow cover statistics and the position of EOSS can in principle be explained by only four essential (meteorological) parameters. Perona et al. (2007) proposed a model accounting for stochastic snow accumulation in the cold period, and deterministic melting dynamics in the warm period, and studied the statistical distribution of the snowdepth on the long term. By reworking the ensemble average of the steady state evolution equation we single out a relationship between the snowdepth statistics (including the position of EOSS) and the involved parameters. The validation of the established relationship is done using 50 years of field data from 73 Swiss stations located above 2000 m a.s.l. First an estimation of the meteorological parameters is made. Snow height data are used as a precipitation proxy, using temperature data to estimate SWE during the precipitation event. Thresholds are used both to separate accumulation from actual precipitation and wind transport phenomena, and to better assess summer melting rate, considered to be constant over the melting period according to the simplified model. First results show that data for most of the weather stations actually scales with the proposed relationship. This indicates that, on the long term, the effect of spatial and temporal noise masks most of the process detail so that minimalist models suffice to obtain reliable statistics. Future works will test the validity of this approach at different spatial scales, e.g., regional, continental and planetary. Reference: P. Perona, A. Porporato, and L. Ridolfi, "A

  16. Performance of complex snow cover descriptions in a distributed hydrological model system: A case study for the high Alpine terrain of the Berchtesgaden Alps.

    Science.gov (United States)

    Warscher, M; Strasser, U; Kraller, G; Marke, T; Franz, H; Kunstmann, H

    2013-05-01

    [1] Runoff generation in Alpine regions is typically affected by snow processes. Snow accumulation, storage, redistribution, and ablation control the availability of water. In this study, several robust parameterizations describing snow processes in Alpine environments were implemented in a fully distributed, physically based hydrological model. Snow cover development is simulated using different methods from a simple temperature index approach, followed by an energy balance scheme, to additionally accounting for gravitational and wind-driven lateral snow redistribution. Test site for the study is the Berchtesgaden National Park (Bavarian Alps, Germany) which is characterized by extreme topography and climate conditions. The performance of the model system in reproducing snow cover dynamics and resulting discharge generation is analyzed and validated via measurements of snow water equivalent and snow depth, satellite-based remote sensing data, and runoff gauge data. Model efficiency (the Nash-Sutcliffe coefficient) for simulated runoff increases from 0.57 to 0.68 in a high Alpine headwater catchment and from 0.62 to 0.64 in total with increasing snow model complexity. In particular, the results show that the introduction of the energy balance scheme reproduces daily fluctuations in the snowmelt rates that trace down to the channel stream. These daily cycles measured in snowmelt and resulting runoff rates could not be reproduced by using the temperature index approach. In addition, accounting for lateral snow transport changes the seasonal distribution of modeled snowmelt amounts, which leads to a higher accuracy in modeling runoff characteristics.

  17. Spectral reflectance of solar light from dirty snow: a simple theoretical model and its validation

    OpenAIRE

    A. Kokhanovsky

    2013-01-01

    A simple analytical equation for the snow albedo as the function of snow grain size, soot concentration, and soot mass absorption coefficient is presented. This simple equation can be used in climate models to assess the influence of snow pollution on snow albedo. It is shown that the squared logarithm of the albedo (in the visible) is directly proportional to the soot concentration. A new method of the determination of the soot mass absorption coefficient in snow is proposed. The equations d...

  18. Demonstrating the Uneven Importance of Fine-Scale Forest Structure on Snow Distributions using High Resolution Modeling

    Science.gov (United States)

    Broxton, P. D.; Harpold, A. A.; van Leeuwen, W.; Biederman, J. A.

    2016-12-01

    Quantifying the amount of snow in forested mountainous environments, as well as how it may change due to warming and forest disturbance, is critical given its importance for water supply and ecosystem health. Forest canopies affect snow accumulation and ablation in ways that are difficult to observe and model. Furthermore, fine-scale forest structure can accentuate or diminish the effects of forest-snow interactions. Despite decades of research demonstrating the importance of fine-scale forest structure (e.g. canopy edges and gaps) on snow, we still lack a comprehensive understanding of where and when forest structure has the largest impact on snowpack mass and energy budgets. Here, we use a hyper-resolution (1 meter spatial resolution) mass and energy balance snow model called the Snow Physics and Laser Mapping (SnowPALM) model along with LIDAR-derived forest structure to determine where spatial variability of fine-scale forest structure has the largest influence on large scale mass and energy budgets. SnowPALM was set up and calibrated at sites representing diverse climates in New Mexico, Arizona, and California. Then, we compared simulations at different model resolutions (i.e. 1, 10, and 100 m) to elucidate the effects of including versus not including information about fine scale canopy structure. These experiments were repeated for different prescribed topographies (i.e. flat, 30% slope north, and south-facing) at each site. Higher resolution simulations had more snow at lower canopy cover, with the opposite being true at high canopy cover. Furthermore, there is considerable scatter, indicating that different canopy arrangements can lead to different amounts of snow, even when the overall canopy coverage is the same. This modeling is contributing to the development of a high resolution machine learning algorithm called the Snow Water Artificial Network (SWANN) model to generate predictions of snow distributions over much larger domains, which has implications

  19. Snow Accumulation Variability Over the West Antarctic Ice Sheet Since 1900: A Comparison of Ice Core Records With ERA-20C Reanalysis

    Science.gov (United States)

    Wang, Yetang; Thomas, Elizabeth R.; Hou, Shugui; Huai, Baojuan; Wu, Shuangye; Sun, Weijun; Qi, Shanzhong; Ding, Minghu; Zhang, Yulun

    2017-11-01

    This study uses a set of 37 firn core records over the West Antarctic Ice Sheet (WAIS) to test the performance of the twentieth century from the European Centre for Medium-Range Weather Forecasts (ERA-20C) reanalysis for snow accumulation and quantify temporal variability in snow accumulation since 1900. The firn cores are allocated to four geographical areas demarcated by drainage divides (i.e., Antarctic Peninsula (AP), western WAIS, central WAIS, and eastern WAIS) to calculate stacked records of regional snow accumulation. Our results show that the interannual variability in ERA-20C precipitation minus evaporation (P - E) agrees well with the corresponding ice core snow accumulation composites in each of the four geographical regions, suggesting its skill for simulating snow accumulation changes before the modern satellite era (pre-1979). Snow accumulation experiences significantly positive trends for the AP and eastern WAIS, a negative trend for the western WAIS, and no significant trend for the central WAIS from 1900 to 2010. The contrasting trends are associated with changes in the large-scale moisture transport driven by a deepening of the low-pressure systems and anomalies of sea ice in the Amundsen Sea Low region.

  20. Improving snow cover mapping in forests through the use of a canopy reflectance model

    International Nuclear Information System (INIS)

    Klein, A.G.; Hall, D.K.; Riggs, G.A.

    1998-01-01

    MODIS, the moderate resolution imaging spectro radiometer, will be launched in 1998 as part of the first earth observing system (EOS) platform. Global maps of land surface properties, including snow cover, will be created from MODIS imagery. The MODIS snow-cover mapping algorithm that will be used to produce daily maps of global snow cover extent at 500 m resolution is currently under development. With the exception of cloud cover, the largest limitation to producing a global daily snow cover product using MODIS is the presence of a forest canopy. A Landsat Thematic Mapper (TM) time-series of the southern Boreal Ecosystem–Atmosphere Study (BOREAS) study area in Prince Albert National Park, Saskatchewan, was used to evaluate the performance of the current MODIS snow-cover mapping algorithm in varying forest types. A snow reflectance model was used in conjunction with a canopy reflectance model (GeoSAIL) to model the reflectance of a snow-covered forest stand. Using these coupled models, the effects of varying forest type, canopy density, snow grain size and solar illumination geometry on the performance of the MODIS snow-cover mapping algorithm were investigated. Using both the TM images and the reflectance models, two changes to the current MODIS snow-cover mapping algorithm are proposed that will improve the algorithm's classification accuracy in forested areas. The improvements include using the normalized difference snow index and normalized difference vegetation index in combination to discriminate better between snow-covered and snow-free forests. A minimum albedo threshold of 10% in the visible wavelengths is also proposed. This will prevent dense forests with very low visible albedos from being classified incorrectly as snow. These two changes increase the amount of snow mapped in forests on snow-covered TM scenes, and decrease the area incorrectly identified as snow on non-snow-covered TM scenes. (author)

  1. Geochemistry Model Validation Report: External Accumulation Model

    International Nuclear Information System (INIS)

    Zarrabi, K.

    2001-01-01

    The purpose of this Analysis and Modeling Report (AMR) is to validate the External Accumulation Model that predicts accumulation of fissile materials in fractures and lithophysae in the rock beneath a degrading waste package (WP) in the potential monitored geologic repository at Yucca Mountain. (Lithophysae are voids in the rock having concentric shells of finely crystalline alkali feldspar, quartz, and other materials that were formed due to entrapped gas that later escaped, DOE 1998, p. A-25.) The intended use of this model is to estimate the quantities of external accumulation of fissile material for use in external criticality risk assessments for different types of degrading WPs: U.S. Department of Energy (DOE) Spent Nuclear Fuel (SNF) codisposed with High Level Waste (HLW) glass, commercial SNF, and Immobilized Plutonium Ceramic (Pu-ceramic) codisposed with HLW glass. The scope of the model validation is to (1) describe the model and the parameters used to develop the model, (2) provide rationale for selection of the parameters by comparisons with measured values, and (3) demonstrate that the parameters chosen are the most conservative selection for external criticality risk calculations. To demonstrate the applicability of the model, a Pu-ceramic WP is used as an example. The model begins with a source term from separately documented EQ6 calculations; where the source term is defined as the composition versus time of the water flowing out of a breached waste package (WP). Next, PHREEQC, is used to simulate the transport and interaction of the source term with the resident water and fractured tuff below the repository. In these simulations the primary mechanism for accumulation is mixing of the high pH, actinide-laden source term with resident water; thus lowering the pH values sufficiently for fissile minerals to become insoluble and precipitate. In the final section of the model, the outputs from PHREEQC, are processed to produce mass of accumulation

  2. Application of snow models to snow removal operations on the Going-to-the-Sun Road, Glacier National Park

    Science.gov (United States)

    Fagre, Daniel B.; Klasner, Frederick L.

    2000-01-01

    Snow removal, and the attendant avalanche risk for road crews, is a major issue on mountain highways worldwide. The Going-to-the-Sun Road is the only road that crosses Glacier National Park, Montana. This 80-km highway ascends over 1200m along the wall of a glaciated basin and crosses the continental divide. The annual opening of the road is critical to the regional economy and there is public pressure to open the road as early as possible. Despite the 67-year history of snow removal activities, few stat on snow conditions at upper elevations were available to guide annual planning for the raod opening. We examined statistical relationships between the opening date and nearby SNOTEL data on snow water equivalence (WE) for 30 years. Early spring SWE (first Monday in April) accounted for only 33% of the variance in road opening dates. Because avalanche spotters, used to warn heavy equipment operators of danger, are ineffective during spring storms or low-visibility conditions, we incorporated the percentage of days with precipitation during plowing as a proxy for visibility. This improved the model's predictive power to 69%/ A mountain snow simulator (MTSNOW) was used to calculate the depth and density of snow at various points along the road and field data were collected for comparison. MTSNOW underestimated the observed snow conditions, in part because it does not yet account for wind redistribution of snow. The severe topography of the upper reaches of the road are subjected to extensive wind redistribution of snow as evidence by the formation of "The Big Drift" on the lee side of Logan Pass.

  3. Improving Snow Modeling by Assimilating Observational Data Collected by Citizen Scientists

    Science.gov (United States)

    Crumley, R. L.; Hill, D. F.; Arendt, A. A.; Wikstrom Jones, K.; Wolken, G. J.; Setiawan, L.

    2017-12-01

    Modeling seasonal snow pack in alpine environments includes a multiplicity of challenges caused by a lack of spatially extensive and temporally continuous observational datasets. This is partially due to the difficulty of collecting measurements in harsh, remote environments where extreme gradients in topography exist, accompanied by large model domains and inclement weather. Engaging snow enthusiasts, snow professionals, and community members to participate in the process of data collection may address some of these challenges. In this study, we use SnowModel to estimate seasonal snow water equivalence (SWE) in the Thompson Pass region of Alaska while incorporating snow depth measurements collected by citizen scientists. We develop a modeling approach to assimilate hundreds of snow depth measurements from participants in the Community Snow Observations (CSO) project (www.communitysnowobs.org). The CSO project includes a mobile application where participants record and submit geo-located snow depth measurements while working and recreating in the study area. These snow depth measurements are randomly located within the model grid at irregular time intervals over the span of four months in the 2017 water year. This snow depth observation dataset is converted into a SWE dataset by employing an empirically-based, bulk density and SWE estimation method. We then assimilate this data using SnowAssim, a sub-model within SnowModel, to constrain the SWE output by the observed data. Multiple model runs are designed to represent an array of output scenarios during the assimilation process. An effort to present model output uncertainties is included, as well as quantification of the pre- and post-assimilation divergence in modeled SWE. Early results reveal pre-assimilation SWE estimations are consistently greater than the post-assimilation estimations, and the magnitude of divergence increases throughout the snow pack evolution period. This research has implications beyond the

  4. Coupling the snow thermodynamic model SNOWPACK with the microwave emission model of layered snowpacks for subarctic and arctic snow water equivalent retrievals

    Science.gov (United States)

    Langlois, A.; Royer, A.; Derksen, C.; Montpetit, B.; Dupont, F.; GoïTa, K.

    2012-12-01

    Satellite-passive microwave remote sensing has been extensively used to estimate snow water equivalent (SWE) in northern regions. Although passive microwave sensors operate independent of solar illumination and the lower frequencies are independent of atmospheric conditions, the coarse spatial resolution introduces uncertainties to SWE retrievals due to the surface heterogeneity within individual pixels. In this article, we investigate the coupling of a thermodynamic multilayered snow model with a passive microwave emission model. Results show that the snow model itself provides poor SWE simulations when compared to field measurements from two major field campaigns. Coupling the snow and microwave emission models with successive iterations to correct the influence of snow grain size and density significantly improves SWE simulations. This method was further validated using an additional independent data set, which also showed significant improvement using the two-step iteration method compared to standalone simulations with the snow model.

  5. Spectral reflectance of solar light from dirty snow: a simple theoretical model and its validation

    Directory of Open Access Journals (Sweden)

    A. Kokhanovsky

    2013-08-01

    Full Text Available A simple analytical equation for the snow albedo as the function of snow grain size, soot concentration, and soot mass absorption coefficient is presented. This simple equation can be used in climate models to assess the influence of snow pollution on snow albedo. It is shown that the squared logarithm of the albedo (in the visible is directly proportional to the soot concentration. A new method of the determination of the soot mass absorption coefficient in snow is proposed. The equations derived are applied to a dusty snow layer as well.

  6. NOAA's National Snow Analyses

    Science.gov (United States)

    Carroll, T. R.; Cline, D. W.; Olheiser, C. M.; Rost, A. A.; Nilsson, A. O.; Fall, G. M.; Li, L.; Bovitz, C. T.

    2005-12-01

    NOAA's National Operational Hydrologic Remote Sensing Center (NOHRSC) routinely ingests all of the electronically available, real-time, ground-based, snow data; airborne snow water equivalent data; satellite areal extent of snow cover information; and numerical weather prediction (NWP) model forcings for the coterminous U.S. The NWP model forcings are physically downscaled from their native 13 km2 spatial resolution to a 1 km2 resolution for the CONUS. The downscaled NWP forcings drive an energy-and-mass-balance snow accumulation and ablation model at a 1 km2 spatial resolution and at a 1 hour temporal resolution for the country. The ground-based, airborne, and satellite snow observations are assimilated into the snow model's simulated state variables using a Newtonian nudging technique. The principle advantages of the assimilation technique are: (1) approximate balance is maintained in the snow model, (2) physical processes are easily accommodated in the model, and (3) asynoptic data are incorporated at the appropriate times. The snow model is reinitialized with the assimilated snow observations to generate a variety of snow products that combine to form NOAA's NOHRSC National Snow Analyses (NSA). The NOHRSC NSA incorporate all of the available information necessary and available to produce a "best estimate" of real-time snow cover conditions at 1 km2 spatial resolution and 1 hour temporal resolution for the country. The NOHRSC NSA consist of a variety of daily, operational, products that characterize real-time snowpack conditions including: snow water equivalent, snow depth, surface and internal snowpack temperatures, surface and blowing snow sublimation, and snowmelt for the CONUS. The products are generated and distributed in a variety of formats including: interactive maps, time-series, alphanumeric products (e.g., mean areal snow water equivalent on a hydrologic basin-by-basin basis), text and map discussions, map animations, and quantitative gridded products

  7. Advances in snow cover distributed modelling via ensemble simulations and assimilation of satellite data

    Science.gov (United States)

    Revuelto, J.; Dumont, M.; Tuzet, F.; Vionnet, V.; Lafaysse, M.; Lecourt, G.; Vernay, M.; Morin, S.; Cosme, E.; Six, D.; Rabatel, A.

    2017-12-01

    Nowadays snowpack models show a good capability in simulating the evolution of snow in mountain areas. However singular deviations of meteorological forcing and shortcomings in the modelling of snow physical processes, when accumulated on time along a snow season, could produce large deviations from real snowpack state. The evaluation of these deviations is usually assessed with on-site observations from automatic weather stations. Nevertheless the location of these stations could strongly influence the results of these evaluations since local topography may have a marked influence on snowpack evolution. Despite the evaluation of snowpack models with automatic weather stations usually reveal good results, there exist a lack of large scale evaluations of simulations results on heterogeneous alpine terrain subjected to local topographic effects.This work firstly presents a complete evaluation of the detailed snowpack model Crocus over an extended mountain area, the Arve upper catchment (western European Alps). This catchment has a wide elevation range with a large area above 2000m a.s.l. and/or glaciated. The evaluation compares results obtained with distributed and semi-distributed simulations (the latter nowadays used on the operational forecasting). Daily observations of the snow covered area from MODIS satellite sensor, seasonal glacier surface mass balance evolution measured in more than 65 locations and the galciers annual equilibrium line altitude from Landsat/Spot/Aster satellites, have been used for model evaluation. Additionally the latest advances in producing ensemble snowpack simulations for assimilating satellite reflectance data over extended areas will be presented. These advances comprises the generation of an ensemble of downscaled high-resolution meteorological forcing from meso-scale meteorological models and the application of a particle filter scheme for assimilating satellite observations. Despite the results are prefatory, they show a good

  8. A Snow Density Dataset for Improving Surface Boundary Conditions in Greenland Ice Sheet Firn Modeling

    DEFF Research Database (Denmark)

    S. Fausto, Robert; E. Box, Jason; Vandecrux, Baptiste Robert Marcel

    2018-01-01

    The surface snow density of glaciers and ice sheets is of fundamental importance in converting volume to mass in both altimetry and surface mass balance studies, yet it is often poorly constrained. Site-specific surface snow densities are typically derived from empirical relations based...... on temperature and wind speed. These parameterizations commonly calculate the average density of the top meter of snow, thereby systematically overestimating snow density at the actual surface. Therefore, constraining surface snow density to the top 0.1 m can improve boundary conditions in high-resolution firn......-evolution modeling. We have compiled an extensive dataset of 200 point measurements of surface snow density from firn cores and snow pits on the Greenland ice sheet. We find that surface snow density within 0.1 m of the surface has an average value of 315 kg m−3 with a standard deviation of 44 kg m−3, and has...

  9. Conceptualisation of Snowpack Isotope Dynamics in Spatially Distributed Tracer-Aided Runoff Models in Snow Influenced Northern Cathments

    Science.gov (United States)

    Ala-aho, P. O. A.; Tetzlaff, D.; Laudon, H.; McNamara, J. P.; Soulsby, C.

    2016-12-01

    We use the Spatially distributed Tracer-Aided Rainfall-Runoff (STARR) modelling framework to explore non-stationary flow and isotope response in three northern headwater catchments. The model simulates dynamic, spatially variable tracer concentration in different water stores and fluxes within a catchment, which can constrain internal catchment mixing processes, flow paths and associated water ages. To date, a major limitation in using such models in snow-dominated catchments has been the difficulties in paramaterising the isotopic transformations in snowpack accumulation and melt. We use high quality long term datasets for hydrometrics and stable water isotopes collected in three northern study catchments for model calibration and testing. The three catchments exhibit different hydroclimatic conditions, soil and vegetation types, and topographic relief, which brings about variable degree of snow dominance across the catchments. To account for the snow influence we develop novel formulations to estimate the isotope evolution in the snowpack and melt. Algorithms for the isotopic evolution parameterize an isotopic offset between snow evaporation and melt fluxes and the remaining snow storage. The model for each catchment is calibrated to match both streamflow and tracer concentration at the stream outlet to ensure internal consistency of the system behaviour. The model is able to reproduce the streamflow along with the spatio-temporal differences in tracer concentrations across the three studies catchments reasonably well. Incorporating the spatially distributed snowmelt processes and associated isotope transformations proved essential in capturing the stream tracer reponse for strongly snow-influenced cathments. This provides a transferrable tool which can be used to understand spatio-temporal variability of mixing and water ages for different storages and flow paths in other snow influenced, environments.

  10. When Models and Observations Collide: Journeying towards an Integrated Snow Depth Product

    Science.gov (United States)

    Webster, M.; Petty, A.; Boisvert, L.; Markus, T.; Kurtz, N. T.; Kwok, R.; Perovich, D. K.

    2017-12-01

    Knowledge of snow depth is essential for assessing changes in sea ice mass balance due to snow's insulating and reflective properties. In remote sensing applications, the accuracy of sea ice thickness retrievals from altimetry crucially depends on snow depth. Despite the need for snow depth data, we currently lack continuous observations that capture the basin-scale snow depth distribution and its seasonal evolution. Recent in situ and remote sensing observations are sparse in space and time, and contain uncertainties, caveats, and/or biases that often require careful interpretation. Likewise, using model output for remote sensing applications is limited due to uncertainties in atmospheric forcing and different treatments of snow processes. Here, we summarize our efforts in bringing observational and model data together to develop an approach for an integrated snow depth product. We start with a snow budget model and incrementally incorporate snow processes to determine the effects on snow depth and to assess model sensitivity. We discuss lessons learned in model-observation integration and ideas for potential improvements to the treatment of snow in models.

  11. The Alpine snow-albedo feedback in regional climate models

    Science.gov (United States)

    Winter, Kevin J.-P. M.; Kotlarski, Sven; Scherrer, Simon C.; Schär, Christoph

    2017-02-01

    The effect of the snow-albedo feedback (SAF) on 2m temperatures and their future changes in the European Alps is investigated in the ENSEMBLES regional climate models (RCMs) with a focus on the spring season. A total of 14 re-analysis-driven RCM experiments covering the period 1961-2000 and 10 GCM-driven transient climate change projections for 1950-2099 are analysed. A positive springtime SAF is found in all RCMs, but the range of the diagnosed SAF is large. Results are compared against an observation-based SAF estimate. For some RCMs, values very close to this estimate are found; other models show a considerable overestimation of the SAF. Net shortwave radiation has the largest influence of all components of the energy balance on the diagnosed SAF and can partly explain its spatial variability. Model deficiencies in reproducing 2m temperatures above snow and ice and associated cold temperature biases at high elevations seem to contribute to a SAF overestimation in several RCMs. The diagnosed SAF in the observational period strongly influences the estimated SAF contribution to twenty first century temperature changes in the European Alps. This contribution is subject to a clear elevation dependency that is governed by the elevation-dependent change in the number of snow days. Elevations of maximum SAF contribution range from 1500 to 2000 m in spring and are found above 2000 m in summer. Here, a SAF contribution to the total simulated temperature change between 0 and 0.5 °C until 2099 (multi-model mean in spring: 0.26 °C) or 0 and 14 % (multi-model mean in spring: 8 %) is obtained for models showing a realistic SAF. These numbers represent a well-funded but only approximate estimate of the SAF contribution to future warming, and a remaining contribution of model-specific SAF misrepresentations cannot be ruled out.

  12. Constraining snowmelt in a temperature-index model using simulated snow densities

    KAUST Repository

    Bormann, Kathryn J.; Evans, Jason P.; McCabe, Matthew

    2014-01-01

    Current snowmelt parameterisation schemes are largely untested in warmer maritime snowfields, where physical snow properties can differ substantially from the more common colder snow environments. Physical properties such as snow density influence the thermal properties of snow layers and are likely to be important for snowmelt rates. Existing methods for incorporating physical snow properties into temperature-index models (TIMs) require frequent snow density observations. These observations are often unavailable in less monitored snow environments. In this study, previous techniques for end-of-season snow density estimation (Bormann et al., 2013) were enhanced and used as a basis for generating daily snow density data from climate inputs. When evaluated against 2970 observations, the snow density model outperforms a regionalised density-time curve reducing biases from -0.027gcm-3 to -0.004gcm-3 (7%). The simulated daily densities were used at 13 sites in the warmer maritime snowfields of Australia to parameterise snowmelt estimation. With absolute snow water equivalent (SWE) errors between 100 and 136mm, the snow model performance was generally lower in the study region than that reported for colder snow environments, which may be attributed to high annual variability. Model performance was strongly dependent on both calibration and the adjustment for precipitation undercatch errors, which influenced model calibration parameters by 150-200%. Comparison of the density-based snowmelt algorithm against a typical temperature-index model revealed only minor differences between the two snowmelt schemes for estimation of SWE. However, when the model was evaluated against snow depths, the new scheme reduced errors by up to 50%, largely due to improved SWE to depth conversions. While this study demonstrates the use of simulated snow density in snowmelt parameterisation, the snow density model may also be of broad interest for snow depth to SWE conversion. Overall, the

  13. Constraining snowmelt in a temperature-index model using simulated snow densities

    KAUST Repository

    Bormann, Kathryn J.

    2014-09-01

    Current snowmelt parameterisation schemes are largely untested in warmer maritime snowfields, where physical snow properties can differ substantially from the more common colder snow environments. Physical properties such as snow density influence the thermal properties of snow layers and are likely to be important for snowmelt rates. Existing methods for incorporating physical snow properties into temperature-index models (TIMs) require frequent snow density observations. These observations are often unavailable in less monitored snow environments. In this study, previous techniques for end-of-season snow density estimation (Bormann et al., 2013) were enhanced and used as a basis for generating daily snow density data from climate inputs. When evaluated against 2970 observations, the snow density model outperforms a regionalised density-time curve reducing biases from -0.027gcm-3 to -0.004gcm-3 (7%). The simulated daily densities were used at 13 sites in the warmer maritime snowfields of Australia to parameterise snowmelt estimation. With absolute snow water equivalent (SWE) errors between 100 and 136mm, the snow model performance was generally lower in the study region than that reported for colder snow environments, which may be attributed to high annual variability. Model performance was strongly dependent on both calibration and the adjustment for precipitation undercatch errors, which influenced model calibration parameters by 150-200%. Comparison of the density-based snowmelt algorithm against a typical temperature-index model revealed only minor differences between the two snowmelt schemes for estimation of SWE. However, when the model was evaluated against snow depths, the new scheme reduced errors by up to 50%, largely due to improved SWE to depth conversions. While this study demonstrates the use of simulated snow density in snowmelt parameterisation, the snow density model may also be of broad interest for snow depth to SWE conversion. Overall, the

  14. A Creep Model for High-Density Snow

    Science.gov (United States)

    2017-04-01

    proportionality, Q = activation energy (Cal/mol), R = the ideal gas constant (1.985 Cal/mol K), and T = absolute temperature in Kelvin. Applying this, I...modifies Mellor and Smith’s creep model for dense snow to conform to the more general creep power law form (Glen’s creep law for ice is a special case of...this power law ). The present study used this general form as the basis for developing two creep models: one to describe the pri- mary creep and

  15. Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment

    Science.gov (United States)

    Koskela, J. J.; Croke, B. W. F.; Koivusalo, H.; Jakeman, A. J.; Kokkonen, T.

    2012-11-01

    Bayesian inference is used to study the effect of precipitation and model structural uncertainty on estimates of model parameters and confidence limits of predictive variables in a conceptual rainfall-runoff model in the snow-fed Rudbäck catchment (142 ha) in southern Finland. The IHACRES model is coupled with a simple degree day model to account for snow accumulation and melt. The posterior probability distribution of the model parameters is sampled by using the Differential Evolution Adaptive Metropolis (DREAM(ZS)) algorithm and the generalized likelihood function. Precipitation uncertainty is taken into account by introducing additional latent variables that were used as multipliers for individual storm events. Results suggest that occasional snow water equivalent (SWE) observations together with daily streamflow observations do not contain enough information to simultaneously identify model parameters, precipitation uncertainty and model structural uncertainty in the Rudbäck catchment. The addition of an autoregressive component to account for model structure error and latent variables having uniform priors to account for input uncertainty lead to dubious posterior distributions of model parameters. Thus our hypothesis that informative priors for latent variables could be replaced by additional SWE data could not be confirmed. The model was found to work adequately in 1-day-ahead simulation mode, but the results were poor in the simulation batch mode. This was caused by the interaction of parameters that were used to describe different sources of uncertainty. The findings may have lessons for other cases where parameterizations are similarly high in relation to available prior information.

  16. Effects of snow grain shape on climate simulations: sensitivity tests with the Norwegian Earth System Model

    Directory of Open Access Journals (Sweden)

    P. Räisänen

    2017-12-01

    Full Text Available Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR model and in the Los Alamos sea ice model, version 4 (CICE4, both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM. In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH is compared with another (NONSPH in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77–0.78 in the visible region than in the spherical case ( ≈  0.89. Therefore, for the same effective snow grain size (or equivalently, the same specific projected area, the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02–0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. −0.22 W m−2. Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2 m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain

  17. Effects of snow grain shape on climate simulations: sensitivity tests with the Norwegian Earth System Model

    Science.gov (United States)

    Räisänen, Petri; Makkonen, Risto; Kirkevåg, Alf; Debernard, Jens B.

    2017-12-01

    Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77-0.78 in the visible region) than in the spherical case ( ≈ 0.89). Therefore, for the same effective snow grain size (or equivalently, the same specific projected area), the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02-0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. -0.22 W m-2. Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2 m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain size increased by ca. 70 %, the

  18. Spatial analysis and statistical modelling of snow cover dynamics in the Central Himalayas, Nepal

    Science.gov (United States)

    Weidinger, Johannes; Gerlitz, Lars; Böhner, Jürgen

    2017-04-01

    General circulation models are able to predict large scale climate variations in global dimensions, however small scale dynamic characteristics, such as snow cover and its temporal variations in high mountain regions, are not represented sufficiently. Detailed knowledge about shifts in seasonal ablation times and spatial distribution of snow cover are crucial for various research interests. Since high mountain areas, for instance the Central Himalayas in Nepal, are generally remote, it is difficult to obtain data in high spatio-temporal resolutions. Regional climate models and downscaling techniques are implemented to compensate coarse resolution. Furthermore earth observation systems, such as MODIS, also permit bridging this gap to a certain extent. They offer snow (cover) data in daily temporal and medium spatial resolution of around 500 m, which can be applied as evaluation and training data for dynamical hydrological and statistical analyses. Within this approach two snow distribution models (binary snow cover and fractional snow cover) as well as one snow recession model were implemented for a research domain in the Rolwaling Himal in Nepal, employing the random forest technique, which represents a state of the art machine learning algorithm. Both bottom-up strategies provide inductive reasoning to derive rules for snow related processes out of climate (temperature, precipitation and irradiance) and climate-related topographic data sets (elevation, aspect and convergence index) obtained by meteorological network stations, remote sensing products (snow cover - MOD10-A1 and land surface temperatures - MOD11-A1) along with GIS. Snow distribution is predicted reliably on a daily basis in the research area, whereas further effort is necessary for predicting daily snow cover recession processes adequately. Swift changes induced by clear sky conditions with high insolation rates are well represented, whereas steady snow loss still needs continuing effort. All

  19. Use of In-Situ and Remotely Sensed Snow Observations for the National Water Model in Both an Analysis and Calibration Framework.

    Science.gov (United States)

    Karsten, L. R.; Gochis, D.; Dugger, A. L.; McCreight, J. L.; Barlage, M. J.; Fall, G. M.; Olheiser, C.

    2017-12-01

    Since version 1.0 of the National Water Model (NWM) has gone operational in Summer 2016, several upgrades to the model have occurred to improve hydrologic prediction for the continental United States. Version 1.1 of the NWM (Spring 2017) includes upgrades to parameter datasets impacting land surface hydrologic processes. These parameter datasets were upgraded using an automated calibration workflow that utilizes the Dynamic Data Search (DDS) algorithm to adjust parameter values using observed streamflow. As such, these upgrades to parameter values took advantage of various observations collected for snow analysis. In particular, in-situ SNOTEL observations in the Western US, volunteer in-situ observations across the entire US, gamma-derived snow water equivalent (SWE) observations courtesy of the NWS NOAA Corps program, gridded snow depth and SWE products from the Jet Propulsion Laboratory (JPL) Airborne Snow Observatory (ASO), gridded remotely sensed satellite-based snow products (MODIS,AMSR2,VIIRS,ATMS), and gridded SWE from the NWS Snow Data Assimilation System (SNODAS). This study explores the use of these observations to quantify NWM error and improvements from version 1.0 to version 1.1, along with subsequent work since then. In addition, this study explores the use of snow observations for use within the automated calibration workflow. Gridded parameter fields impacting the accumulation and ablation of snow states in the NWM were adjusted and calibrated using gridded remotely sensed snow states, SNODAS products, and in-situ snow observations. This calibration adjustment took place over various ecological regions in snow-dominated parts of the US for a retrospective period of time to capture a variety of climatological conditions. Specifically, the latest calibrated parameters impacting streamflow were held constant and only parameters impacting snow physics were tuned using snow observations and analysis. The adjusted parameter datasets were then used to

  20. Remote sensing, hydrological modeling and in situ observations in snow cover research: A review

    Science.gov (United States)

    Dong, Chunyu

    2018-06-01

    Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

  1. Was the extreme and widespread marine oil-snow sedimentation and flocculent accumulation (MOSSFA) event during the Deepwater Horizon blow-out unique?

    NARCIS (Netherlands)

    Vonk, S.M.; Hollander, D.J.; Murk, A.J.

    2015-01-01

    During the Deepwater Horizon blowout, thick layers of oiled material were deposited on the deep seafloor. This large scale benthic concentration of oil is suggested to have occurred via the process of Marine Oil Snow Sedimentation and Flocculent Accumulation (MOSSFA). This meta-analysis investigates

  2. Dynamic-stochastic modeling of snow cover formation on the European territory of Russia

    Directory of Open Access Journals (Sweden)

    A. N. Gelfan

    2014-01-01

    Full Text Available A dynamic-stochastic model, which combines a deterministic model of snow cover formation with a stochastic weather generator, has been developed. The deterministic snow model describes temporal change of the snow depth, content of ice and liquid water, snow density, snowmelt, sublimation, re-freezing of melt water, and snow metamorphism. The model has been calibrated and validated against the long-term data of snow measurements over the territory of the European Russia. The model showed good performance in simulating time series of the snow water equivalent and snow depth. The developed weather generator (NEsted Weather Generator, NewGen includes nested generators of annual, monthly and daily time series of weather variables (namely, precipitation, air temperature, and air humidity. The parameters of the NewGen have been adjusted through calibration against the long-term meteorological data in the European Russia. A disaggregation procedure has been proposed for transforming parameters of the annual weather generator into the parameters of the monthly one and, subsequently, into the parameters of the daily generator. Multi-year time series of the simulated daily weather variables have been used as an input to the snow model. Probability properties of the snow cover, such as snow water equivalent and snow depth for return periods of 25 and 100 years, have been estimated against the observed data, showing good correlation coefficients. The described model has been applied to different landscapes of European Russia, from steppe to taiga regions, to show the robustness of the proposed technique.

  3. Modeling the isotopic composition of Antarctic snow using backward trajectories: simulation of snow pit records

    NARCIS (Netherlands)

    Helsen, M.M.; van de Wal, R.S.W.; van den Broeke, M.R.; Masson-Delmotte, V.; Meijer, H.A.J.; Scheele, M.P.; Werner, M.

    2006-01-01

    The quantitative interpretation of isotope records (d18O, dD, and d excess) in ice cores can benefit from a comparison of observed meteorology with associated isotope variability. For this reason we studied four isotope records from snow pits in western Dronning Maud Land (DML), Antarctica, covering

  4. A Snow Density Dataset for Improving Surface Boundary Conditions in Greenland Ice Sheet Firn Modeling

    Directory of Open Access Journals (Sweden)

    Robert S. Fausto

    2018-05-01

    Full Text Available The surface snow density of glaciers and ice sheets is of fundamental importance in converting volume to mass in both altimetry and surface mass balance studies, yet it is often poorly constrained. Site-specific surface snow densities are typically derived from empirical relations based on temperature and wind speed. These parameterizations commonly calculate the average density of the top meter of snow, thereby systematically overestimating snow density at the actual surface. Therefore, constraining surface snow density to the top 0.1 m can improve boundary conditions in high-resolution firn-evolution modeling. We have compiled an extensive dataset of 200 point measurements of surface snow density from firn cores and snow pits on the Greenland ice sheet. We find that surface snow density within 0.1 m of the surface has an average value of 315 kg m−3 with a standard deviation of 44 kg m−3, and has an insignificant annual air temperature dependency. We demonstrate that two widely-used surface snow density parameterizations dependent on temperature systematically overestimate surface snow density over the Greenland ice sheet by 17–19%, and that using a constant density of 315 kg m−3 may give superior results when applied in surface mass budget modeling.

  5. Modelling snow ice and superimposed ice on landfast sea ice in Kongsfjorden, Svalbard

    Directory of Open Access Journals (Sweden)

    Caixin Wang

    2015-08-01

    Full Text Available Snow ice and superimposed ice formation on landfast sea ice in a Svalbard fjord, Kongsfjorden, was investigated with a high-resolution thermodynamic snow and sea-ice model, applying meteorological weather station data as external forcing. The model shows that sea-ice formation occurs both at the ice bottom and at the snow/ice interface. Modelling results indicated that the total snow ice and superimposed ice, which formed at the snow/ice interface, was about 14 cm during the simulation period, accounting for about 15% of the total ice mass and 35% of the total ice growth. Introducing a time-dependent snow density improved the modelled results, and a time-dependent oceanic heat flux parameterization yielded reasonable ice growth at the ice bottom. Model results suggest that weather conditions, in particular air temperature and precipitation, as well as snow thermal properties and surface albedo are the most critical factors for the development of snow ice and superimposed ice in Kongsfjorden. While both warming air and higher precipitation led to increased snow ice and superimposed ice forming in Kongsfjorden in the model runs, the processes were more sensitive to precipitation than to air temperature.

  6. Realism versus simplicity in the snow routine of the HBV model

    Science.gov (United States)

    Girons Lopez, Marc; Vis, Marc; Seibert, Jan

    2017-04-01

    The HBV model still enjoys great popularity, in part because of its simplicity. Depicting the hydrological processes in a catchment in a simple way reduces data requirements and minimises parameter uncertainty. However, the representation of some processes might benefit from an increased model complexity. This is, for instance, the case of snow routine in the HBV-light version of the HBV model. HBV-light uses a degree-day method with a single threshold parameter to distinguish between rain and snow and simulate snow melt. Recent research has shown that hydrological models with a more realistic representation of snow processes might be more successful in estimating runoff. In this study we explore and test different improvements to the HBV-light snow routine design by considering different threshold temperature values for rain and snow distinction as well as for the beginning of snow melt, and introducing gradual transitions instead of the current sharp threshold. The use of radiation data, which recently became available as gridded data product in Switzerland, is an additional possibility. These modifications would allow for a more realistic depiction of important hydrological processes in alpine and other snow-covered areas while preserving the characteristic simplicity of HBV. Furthermore, in this contribution we evaluate the balance between introducing more realism into the snow routine of the HBV model and keeping the number of parameters as low as possible.

  7. Modeling of light absorbing particles in atmosphere, snow and ice in the Arctic

    Science.gov (United States)

    Sobhani, N.; Kulkarni, S.; Carmichael, G. R.

    2015-12-01

    Long-range transport of atmospheric particles from mid-latitude sources to the Arctic is the main contributor to the Arctic aerosol loadings and deposition. Black Carbon (BC), Brown Carbon (BrC) and dust are considered of great climatic importance and are the main absorbers of sunlight in the atmosphere. Furthermore, wet and dry deposition of light absorbing particles (LAPs) on snow and ice cause reduction of snow and ice albedo. LAPs have significant radiative forcing and effect on snow albedo. There are high uncertainties in estimating radiative forcing of LAPs. We studied the potential effect of LAPs from different emission source regions and sectors on snow albedo in the Arctic. The transport pathway of LAPs to the Arctic is studies for different high pollution episodes. In this study a modeling framework including Weather Research and Forecasting Model (WRF) and the University of Iowa's Sulfur Transport and dEpostion model(STEM) is used to predict the transport of LAPs from different geographical sources and sectors (i.e. transportation, residential, industry, biomass burning and power) to the Arctic. For assessing the effect of LAP deposition on snow single-layer simulator of the SNow, Ice, and Aerosol Radiation (SNICAR-Online) model was used to derive snow albedo values for snow albedo reduction causes by BC deposition. To evaluate the simulated values we compared the BC concentration in snow with observed values from previous studies including Doherty et al. 2010.

  8. Coupling of snow and permafrost processes using the Basic Modeling Interface (BMI)

    Science.gov (United States)

    Wang, K.; Overeem, I.; Jafarov, E. E.; Piper, M.; Stewart, S.; Clow, G. D.; Schaefer, K. M.

    2017-12-01

    We developed a permafrost modeling tool based by implementing the Kudryavtsev empirical permafrost active layer depth model (the so-called "Ku" component). The model is specifically set up to have a basic model interface (BMI), which enhances the potential coupling to other earth surface processes model components. This model is accessible through the Web Modeling Tool in Community Surface Dynamics Modeling System (CSDMS). The Kudryavtsev model has been applied for entire Alaska to model permafrost distribution at high spatial resolution and model predictions have been verified by Circumpolar Active Layer Monitoring (CALM) in-situ observations. The Ku component uses monthly meteorological forcing, including air temperature, snow depth, and snow density, and predicts active layer thickness (ALT) and temperature on the top of permafrost (TTOP), which are important factors in snow-hydrological processes. BMI provides an easy approach to couple the models with each other. Here, we provide a case of coupling the Ku component to snow process components, including the Snow-Degree-Day (SDD) method and Snow-Energy-Balance (SEB) method, which are existing components in the hydrological model TOPOFLOW. The work flow is (1) get variables from meteorology component, set the values to snow process component, and advance the snow process component, (2) get variables from meteorology and snow component, provide these to the Ku component and advance, (3) get variables from snow process component, set the values to meteorology component, and advance the meteorology component. The next phase is to couple the permafrost component with fully BMI-compliant TOPOFLOW hydrological model, which could provide a useful tool to investigate the permafrost hydrological effect.

  9. Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

    Science.gov (United States)

    Griessinger, Nena; Seibert, Jan; Magnusson, Jan; Jonas, Tobias

    2016-09-01

    In Alpine catchments, snowmelt is often a major contribution to runoff. Therefore, modeling snow processes is important when concerned with flood or drought forecasting, reservoir operation and inland waterway management. In this study, we address the question of how sensitive hydrological models are to the representation of snow cover dynamics and whether the performance of a hydrological model can be enhanced by integrating data from a dedicated external snow monitoring system. As a framework for our tests we have used the hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) in the version HBV-light, which has been applied in many hydrological studies and is also in use for operational purposes. While HBV originally follows a temperature-index approach with time-invariant calibrated degree-day factors to represent snowmelt, in this study the HBV model was modified to use snowmelt time series from an external and spatially distributed snow model as model input. The external snow model integrates three-dimensional sequential assimilation of snow monitoring data with a snowmelt model, which is also based on the temperature-index approach but uses a time-variant degree-day factor. The following three variations of this external snow model were applied: (a) the full model with assimilation of observational snow data from a dense monitoring network, (b) the same snow model but with data assimilation switched off and (c) a downgraded version of the same snow model representing snowmelt with a time-invariant degree-day factor. Model runs were conducted for 20 catchments at different elevations within Switzerland for 15 years. Our results show that at low and mid-elevations the performance of the runoff simulations did not vary considerably with the snow model version chosen. At higher elevations, however, best performance in terms of simulated runoff was obtained when using the snowmelt time series from the snow model, which utilized data assimilation

  10. Intercomparison and validation of snow albedo parameterization schemes in climate models

    Energy Technology Data Exchange (ETDEWEB)

    Pedersen, Christina A.; Winther, Jan-Gunnar [Norwegian Polar Institute, Tromsoe (Norway)

    2005-09-01

    Snow albedo is known to be crucial for heat exchange at high latitudes and high altitudes, and is also an important parameter in General Circulation Models (GCMs) because of its strong positive feedback properties. In this study, seven GCM snow albedo schemes and a multiple linear regression model were intercompared and validated against 59 years of in situ data from Svalbard, the French Alps and six stations in the former Soviet Union. For each site, the significant meteorological parameters for modeling the snow albedo were identified by constructing the 95% confidence intervals. The significant parameters were found to be: temperature, snow depth, positive degree day and a dummy of snow depth, and the multiple linear regression model was constructed to include these. Overall, the intercomparison showed that the modeled snow albedo varied more than the observed albedo for all models, and that the albedo was often underestimated. In addition, for several of the models, the snow albedo decreased at a faster rate or by a greater magnitude during the winter snow metamorphosis than the observed albedo. Both the temperature dependent schemes and the prognostic schemes showed shortcomings. (orig.)

  11. Remote Sensing-based Methodologies for Snow Model Adjustments in Operational Streamflow Prediction

    Science.gov (United States)

    Bender, S.; Miller, W. P.; Bernard, B.; Stokes, M.; Oaida, C. M.; Painter, T. H.

    2015-12-01

    Water management agencies rely on hydrologic forecasts issued by operational agencies such as NOAA's Colorado Basin River Forecast Center (CBRFC). The CBRFC has partnered with the Jet Propulsion Laboratory (JPL) under funding from NASA to incorporate research-oriented, remotely-sensed snow data into CBRFC operations and to improve the accuracy of CBRFC forecasts. The partnership has yielded valuable analysis of snow surface albedo as represented in JPL's MODIS Dust Radiative Forcing in Snow (MODDRFS) data, across the CBRFC's area of responsibility. When dust layers within a snowpack emerge, reducing the snow surface albedo, the snowmelt rate may accelerate. The CBRFC operational snow model (SNOW17) is a temperature-index model that lacks explicit representation of snowpack surface albedo. CBRFC forecasters monitor MODDRFS data for emerging dust layers and may manually adjust SNOW17 melt rates. A technique was needed for efficient and objective incorporation of the MODDRFS data into SNOW17. Initial development focused in Colorado, where dust-on-snow events frequently occur. CBRFC forecasters used retrospective JPL-CBRFC analysis and developed a quantitative relationship between MODDRFS data and mean areal temperature (MAT) data. The relationship was used to generate adjusted, MODDRFS-informed input for SNOW17. Impacts of the MODDRFS-SNOW17 MAT adjustment method on snowmelt-driven streamflow prediction varied spatially and with characteristics of the dust deposition events. The largest improvements occurred in southwestern Colorado, in years with intense dust deposition events. Application of the method in other regions of Colorado and in "low dust" years resulted in minimal impact. The MODDRFS-SNOW17 MAT technique will be implemented in CBRFC operations in late 2015, prior to spring 2016 runoff. Collaborative investigation of remote sensing-based adjustment methods for the CBRFC operational hydrologic forecasting environment will continue over the next several years.

  12. Effect of clear cutting on snow accumulation and water outflow at Fraser, Colorado

    Directory of Open Access Journals (Sweden)

    C. A. Troendle

    1997-01-01

    Full Text Available This paper compares of snowpack accumulation and ablation, evapotranspiration, and water outflow from clearcut and forested plots within a high elevation (2900 m mixed conifer forest at the Fraser Experimental Forest near Fraser, Colorado, USA. Also presented is a method for defining contributing area where outflow is measured from unbounded plots. Plots were monitored from 1980 to 1990 and again in 1993. The clearcut plot was harvested in late 1984. Evapotranspiration (ET of the forested plot at zero discharge (ETo was estimated at 426 mm while the ET was 500 mm at the mean precipitation of 596 mm. ET was dependent on precipitation with about 28% of precipitation input in excess of 426 mm contributing to increased ET, while the remainder contributed to increased outflow. During the six monitored post-harvest years, Peak Water Equivalent of the snowpack averaged 36% higher on the cut plot than on the control, and the mean discharge increased from 85 mm to 356 mm. Area estimates were obtained from the slopes of the regression of outflow on precipitation inputs. Hydrologic parameters corresponded closely to those previously determined at Fraser Experimental Forest using other methods, lending credence to the validity of the area estimates.

  13. Impacts of Satellite-Based Snow Albedo Assimilation on Offline and Coupled Land Surface Model Simulations.

    Directory of Open Access Journals (Sweden)

    Tao Wang

    Full Text Available Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique improve the simulation accuracy of mean seasonal (October throughout May snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the

  14. Hydrological Modelling and data assimilation of Satellite Snow Cover Area using a Land Surface Model, VIC

    Directory of Open Access Journals (Sweden)

    S. Naha

    2016-06-01

    Full Text Available The snow cover plays an important role in Himalayan region as it contributes a useful amount to the river discharge. So, besides estimating rainfall runoff, proper assessment of snowmelt runoff for efficient management and water resources planning is also required. A Land Surface Model, VIC (Variable Infiltration Capacity is used at a high resolution grid size of 1 km. Beas river basin up to Thalot in North West Himalayas (NWH have been selected as the study area. At first model setup is done and VIC has been run in its energy balance mode. The fluxes obtained from VIC has been routed to simulate the discharge for the time period of (2003-2006. Data Assimilation is done for the year 2006 and the techniques of Data Assimilation considered in this study are Direct Insertion (D.I and Ensemble Kalman Filter (EnKF that uses observations of snow covered area (SCA to update hydrologic model states. The meteorological forcings were taken from 0.5 deg. resolution VIC global forcing data from 1979-2006 with daily maximum temperature, minimum temperature from Climate Research unit (CRU, rainfall from daily variability of NCEP and wind speed from NCEP-NCAR analysis as main inputs and Indian Meteorological Department (IMD data of 0.25 °. NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated

  15. Land Surface Model Biases and their Impacts on the Assimilation of Snow-related Observations

    Science.gov (United States)

    Arsenault, K. R.; Kumar, S.; Hunter, S. M.; Aman, R.; Houser, P. R.; Toll, D.; Engman, T.; Nigro, J.

    2007-12-01

    Some recent snow modeling studies have employed a wide range of assimilation methods to incorporate snow cover or other snow-related observations into different hydrological or land surface models. These methods often include taking both model and observation biases into account throughout the model integration. This study focuses more on diagnosing the model biases and presenting their subsequent impacts on assimilating snow observations and modeled snowmelt processes. In this study, the land surface model, the Community Land Model (CLM), is used within the Land Information System (LIS) modeling framework to show how such biases impact the assimilation of MODIS snow cover observations. Alternative in-situ and satellite-based observations are used to help guide the CLM LSM in better predicting snowpack conditions and more realistic timing of snowmelt for a western US mountainous region. Also, MODIS snow cover observation biases will be discussed, and validation results will be provided. The issues faced with inserting or assimilating MODIS snow cover at moderate spatial resolutions (like 1km or less) will be addressed, and the impacts on CLM will be presented.

  16. Application of SNODAS and hydrologic models to enhance entropy-based snow monitoring network design

    Science.gov (United States)

    Keum, Jongho; Coulibaly, Paulin; Razavi, Tara; Tapsoba, Dominique; Gobena, Adam; Weber, Frank; Pietroniro, Alain

    2018-06-01

    Snow has a unique characteristic in the water cycle, that is, snow falls during the entire winter season, but the discharge from snowmelt is typically delayed until the melting period and occurs in a relatively short period. Therefore, reliable observations from an optimal snow monitoring network are necessary for an efficient management of snowmelt water for flood prevention and hydropower generation. The Dual Entropy and Multiobjective Optimization is applied to design snow monitoring networks in La Grande River Basin in Québec and Columbia River Basin in British Columbia. While the networks are optimized to have the maximum amount of information with minimum redundancy based on entropy concepts, this study extends the traditional entropy applications to the hydrometric network design by introducing several improvements. First, several data quantization cases and their effects on the snow network design problems were explored. Second, the applicability the Snow Data Assimilation System (SNODAS) products as synthetic datasets of potential stations was demonstrated in the design of the snow monitoring network of the Columbia River Basin. Third, beyond finding the Pareto-optimal networks from the entropy with multi-objective optimization, the networks obtained for La Grande River Basin were further evaluated by applying three hydrologic models. The calibrated hydrologic models simulated discharges using the updated snow water equivalent data from the Pareto-optimal networks. Then, the model performances for high flows were compared to determine the best optimal network for enhanced spring runoff forecasting.

  17. Acoustic Wave Propagation in Snow Based on a Biot-Type Porous Model

    Science.gov (United States)

    Sidler, R.

    2014-12-01

    Despite the fact that acoustic methods are inexpensive, robust and simple, the application of seismic waves to snow has been sparse. This might be due to the strong attenuation inherent to snow that prevents large scale seismic applications or due to the somewhat counterintuitive acoustic behavior of snow as a porous material. Such materials support a second kind of compressional wave that can be measured in fresh snow and which has a decreasing wave velocity with increasing density of snow. To investigate wave propagation in snow we construct a Biot-type porous model of snow as a function of porosity based on the assumptions that the solid frame is build of ice, the pore space is filled with a mix of air, or air and water, and empirical relationships for the tortuosity, the permeability, the bulk, and the shear modulus.We use this reduced model to investigate compressional and shear wave velocities of snow as a function of porosity and to asses the consequences of liquid water in the snowpack on acoustic wave propagation by solving Biot's differential equations with plain wave solutions. We find that the fast compressional wave velocity increases significantly with increasing density, but also that the fast compressional wave velocity might be even lower than the slow compressional wave velocity for very light snow. By using compressional and shear strength criteria and solving Biot's differential equations with a pseudo-spectral approach we evaluate snow failure due to acoustic waves in a heterogeneous snowpack, which we think is an important mechanism in triggering avalanches by explosives as well as by skiers. Finally, we developed a low cost seismic acquisition device to assess the theoretically obtained wave velocities in the field and to explore the possibility of an inexpensive tool to remotely gather snow water equivalent.

  18. Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Model

    Science.gov (United States)

    Zaitchik, Benjamin F.; Rodell, Matthew

    2008-01-01

    Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation SCA indicates only the presence or absence of snow, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes both during the snow season and, in some regions, on into the following spring.

  19. Scaling precipitation input to spatially distributed hydrological models by measured snow distribution

    Directory of Open Access Journals (Sweden)

    Christian Vögeli

    2016-12-01

    Full Text Available Accurate knowledge on snow distribution in alpine terrain is crucial for various applicationssuch as flood risk assessment, avalanche warning or managing water supply and hydro-power.To simulate the seasonal snow cover development in alpine terrain, the spatially distributed,physics-based model Alpine3D is suitable. The model is typically driven by spatial interpolationsof observations from automatic weather stations (AWS, leading to errors in the spatial distributionof atmospheric forcing. With recent advances in remote sensing techniques, maps of snowdepth can be acquired with high spatial resolution and accuracy. In this work, maps of the snowdepth distribution, calculated from summer and winter digital surface models based on AirborneDigital Sensors (ADS, are used to scale precipitation input data, with the aim to improve theaccuracy of simulation of the spatial distribution of snow with Alpine3D. A simple method toscale and redistribute precipitation is presented and the performance is analysed. The scalingmethod is only applied if it is snowing. For rainfall the precipitation is distributed by interpolation,with a simple air temperature threshold used for the determination of the precipitation phase.It was found that the accuracy of spatial snow distribution could be improved significantly forthe simulated domain. The standard deviation of absolute snow depth error is reduced up toa factor 3.4 to less than 20 cm. The mean absolute error in snow distribution was reducedwhen using representative input sources for the simulation domain. For inter-annual scaling, themodel performance could also be improved, even when using a remote sensing dataset from adifferent winter. In conclusion, using remote sensing data to process precipitation input, complexprocesses such as preferential snow deposition and snow relocation due to wind or avalanches,can be substituted and modelling performance of spatial snow distribution is improved.

  20. Assessing the Climate Change Impact on Snow-Glacier Melting Dominated Basins in the Greater Himalaya Region Using a Distributed Glacio-Hydrologic Model

    Science.gov (United States)

    Wi, S.; Yang, Y. C. E.; Khalil, A.

    2014-12-01

    Glacier and snow melting is main source of water supply making a large contribution to streamflow of major river basins in the Greater Himalaya region including the Syr Darya, the Amu Darya, the Indus, the Ganges and the Brahmaputra basins. Due to the critical role of glacier and snow melting as water supply for both food production and hydropower generation in the region (especially during the low flow season), it is important to evaluate the vulnerability of snow and glacier melting streamflow to different climate conditions. In this study, a distributed glacio-hydrologic model with high resolution climate input is developed and calibrated that explicitly simulates all major hydrological processes and the glacier and snow dynamics for area further discretized by elevation bands. The distributed modeling structure and the glacier and snow modules provide a better understanding about how temperature and precipitation alterations are likely to affect current glacier ice reserves. Climate stress test is used to explore changes in the total streamflow change, snow/glacier melting contribution and glacier accumulation and ablation under a variety of different temperature and precipitation conditions. The latest future climate projections provided from the World Climate Research Programme's Coupled Model Intercomparison Project Phase 5 (CMIP5) is used to inform the possibility of different climate conditions.

  1. Occurrence of blowing snow events at an alpine site over a 10-year period: Observations and modelling

    Science.gov (United States)

    Vionnet, V.; Guyomarc'h, G.; Naaim Bouvet, F.; Martin, E.; Durand, Y.; Bellot, H.; Bel, C.; Puglièse, P.

    2013-05-01

    Blowing snow events control the evolution of the snow pack in mountainous areas and cause inhomogeneous snow distribution. The goal of this study is to identify the main features of blowing snow events at an alpine site and assess the ability of the detailed snowpack model Crocus to reproduce the occurrence of these events in a 1D configuration. We created a database of blowing snow events observed over 10 years at our experimental site. Occurrences of blowing snow events were divided into cases with and without concurrent falling snow. Overall, snow transport is observed during 10.5% of the time in winter and occurs with concurrent falling snow 37.3% of the time. Wind speed and snow age control the frequency of occurrence. Model results illustrate the necessity of taking the wind-dependence of falling snow grain characteristics into account to simulate periods of snow transport and mass fluxes satisfactorily during those periods. The high rate of false alarms produced by the model is investigated in detail for winter 2010/2011 using measurements from snow particle counters.

  2. Air–snow exchange of nitrate: a modelling approach to investigate physicochemical processes in surface snow at Dome C, Antarctica

    Directory of Open Access Journals (Sweden)

    J. Bock

    2016-10-01

    Full Text Available Snowpack is a multiphase (photochemical reactor that strongly influences the air composition in polar and snow-covered regions. Snowpack plays a special role in the nitrogen cycle, as it has been shown that nitrate undergoes numerous recycling stages (including photolysis in the snow before being permanently buried in the ice. However, the current understanding of these physicochemical processes remains very poor. Several modelling studies have attempted to reproduce (photochemical reactions inside snow grains, but these have relied on strong assumptions to characterise snow reactive properties, which are not well defined. Air–snow exchange processes such as adsorption, solid-state diffusion, or co-condensation also affect snow chemical composition. Here, we present a physically based model of these processes for nitrate. Using as input a 1-year-long time series of atmospheric nitrate concentration measured at Dome C, Antarctica, our model reproduces with good agreement the nitrate measurements in the surface snow. By investigating the relative importance of the main exchange processes, this study shows that, on the one hand, the combination of bulk diffusion and co-condensation allows a good reproduction of the measurements (correlation coefficient r = 0.95, with a correct amplitude and timing of summer peak concentration of nitrate in snow. During winter, nitrate concentration in surface snow is mainly driven by thermodynamic equilibrium, whilst the peak observed in summer is explained by the kinetic process of co-condensation. On the other hand, the adsorption of nitric acid on the surface of the snow grains, constrained by an already existing parameterisation for the isotherm, fails to fit the observed variations. During winter and spring, the modelled concentration of adsorbed nitrate is respectively 2.5 and 8.3-fold higher than the measured one. A strong diurnal variation driven by the temperature cycle and a peak occurring in early

  3. Simulation of wind-induced snow transport and sublimation in alpine terrain using a fully coupled snowpack/atmosphere model

    Science.gov (United States)

    Vionnet, V.; Martin, E.; Masson, V.; Guyomarc'h, G.; Naaim-Bouvet, F.; Prokop, A.; Durand, Y.; Lac, C.

    2014-03-01

    In alpine regions, wind-induced snow transport strongly influences the spatio-temporal evolution of the snow cover throughout the winter season. To gain understanding on the complex processes that drive the redistribution of snow, a new numerical model is developed. It directly couples the detailed snowpack model Crocus with the atmospheric model Meso-NH. Meso-NH/Crocus simulates snow transport in saltation and in turbulent suspension and includes the sublimation of suspended snow particles. The coupled model is evaluated against data collected around the experimental site of Col du Lac Blanc (2720 m a.s.l., French Alps). First, 1-D simulations show that a detailed representation of the first metres of the atmosphere is required to reproduce strong gradients of blowing snow concentration and compute mass exchange between the snowpack and the atmosphere. Secondly, 3-D simulations of a blowing snow event without concurrent snowfall have been carried out. Results show that the model captures the main structures of atmospheric flow in alpine terrain. However, at 50 m grid spacing, the model reproduces only the patterns of snow erosion and deposition at the ridge scale and misses smaller scale patterns observed by terrestrial laser scanning. When activated, the sublimation of suspended snow particles causes a reduction of deposited snow mass of 5.3% over the calculation domain. Total sublimation (surface + blowing snow) is three times higher than surface sublimation in a simulation neglecting blowing snow sublimation.

  4. Numerical modeling of a snow cover on Hooker Island (Franz Josef Land archipelago

    Directory of Open Access Journals (Sweden)

    V. S. Sokratov

    2013-01-01

    Full Text Available Results obtained by simulating snow characteristics with a numerical model of surface heat and moisture exchange SPONSOR are presented. The numerical experiments are carried out for Franz Josef Land with typical Arctic climate conditions. The blizzard evaporation parameter is shown to have great influence on snow depth on territories with high wind speed. This parameter significantly improves the simulation quality of the numerical model. Some discrepancies between evaluated and observed snow depth values can be explained by inaccuracies in precipitation measurements (at least in certain cases and errors in calculations of incoming radiation, mostly due to low accuracy in the cloudiness observations.

  5. Estimation of snow albedo reduction by light absorbing impurities using Monte Carlo radiative transfer model

    Science.gov (United States)

    Sengupta, D.; Gao, L.; Wilcox, E. M.; Beres, N. D.; Moosmüller, H.; Khlystov, A.

    2017-12-01

    wavelength range (300 nm - 2000 nm). Results will be compared with the SNICAR model to better understand the differences in snow albedo computation between plane-parallel methods and the statistical Monte Carlo methods.

  6. Identification of magnetic particulates in road dust accumulated on roadside snow using magnetic, geochemical and micro-morphological analyses

    International Nuclear Information System (INIS)

    Bucko, Michal S.; Magiera, Tadeusz; Johanson, Bo; Petrovsky, Eduard; Pesonen, Lauri J.

    2011-01-01

    The aim of this study is to test the applicability of snow surveying in the collection and detailed characterization of vehicle-derived magnetic particles. Road dust extracted from snow, collected near a busy urban highway and a low traffic road in a rural environment (southern Finland), was studied using magnetic, geochemical and micro-morphological analyses. Significant differences in horizontal distribution of mass specific magnetic susceptibility (χ) were noticed for both roads. Multi-domain (MD) magnetite was identified as the primary magnetic mineral. Scanning electron microscope (SEM) analyses of road dust from both roads revealed: (1) angular-shaped particles (diameter ∼1-300 μm) mostly composed of Fe, Cr and Ni, derived from circulation of motor vehicles and (2) iron-rich spherules (d ∼ 2-70 μm). Tungsten-rich particles (d < 2 μm), derived from tyre stud abrasion were also identified. Additionally, a decreasing trend in χ and selected trace elements was observed with increasing distance from the road edge. - Highlights: → Snow surveying is an effective method in studies of vehicle-derived particles. → Multi-domain (MD) magnetite was identified as the primary magnetic mineral. → Particles mostly composed of Fe, Cr and Ni were identified in the roadside snow. → Snow located near the road is contaminated by heavy metals. - Snow surveying is an effective method in detailed studies of vehicle-derived magnetic particles.

  7. Integration, Validation, and Application of a PV Snow Coverage Model in SAM

    Energy Technology Data Exchange (ETDEWEB)

    Freeman, Janine M. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ryberg, David Severin [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-08-01

    Due to the increasing deployment of PV systems in snowy climates, there is significant interest in a method capable of estimating PV losses resulting from snow coverage that has been verified for a variety of system designs and locations. Many independent snow coverage models have been developed over the last 15 years; however, there has been very little effort verifying these models beyond the system designs and locations on which they were based. Moreover, major PV modeling software products have not yet incorporated any of these models into their workflows. In response to this deficiency, we have integrated the methodology of the snow model developed in the paper by Marion et al. (2013) into the National Renewable Energy Laboratory's (NREL) System Advisor Model (SAM). In this work, we describe how the snow model is implemented in SAM and we discuss our demonstration of the model's effectiveness at reducing error in annual estimations for three PV arrays. Next, we use this new functionality in conjunction with a long term historical data set to estimate average snow losses across the United States for two typical PV system designs. The open availability of the snow loss estimation capability in SAM to the PV modeling community, coupled with our results of the nationwide study, will better equip the industry to accurately estimate PV energy production in areas affected by snowfall.

  8. Application of a Snow Growth Model to Radar Remote Sensing

    Science.gov (United States)

    Erfani, E.; Mitchell, D. L.

    2014-12-01

    Microphysical growth processes of diffusion, aggregation and riming are incorporated analytically in a steady-state snow growth model (SGM) to solve the zeroth- and second- moment conservation equations with respect to mass. The SGM is initiated by radar reflectivity (Zw), supersaturation, temperature, and a vertical profile of the liquid water content (LWC), and it uses a gamma size distribution (SD) to predict the vertical evolution of size spectra. Aggregation seems to play an important role in the evolution of snowfall rates and the snowfall rates produced by aggregation, diffusion and riming are considerably greater than those produced by diffusion and riming alone, demonstrating the strong interaction between aggregation and riming. The impact of ice particle shape on particle growth rates and fall speeds is represented in the SGM in terms of ice particle mass-dimension (m-D) power laws (m = αDβ). These growth rates are qualitatively consistent with empirical growth rates, with slower (faster) growth rates predicted for higher (lower) β values. In most models, β is treated constant for a given ice particle habit, but it is well known that β is larger for the smaller crystals. Our recent work quantitatively calculates β and α for cirrus clouds as a function of D where the m-D expression is a second-order polynomial in log-log space. By adapting this method to the SGM, the ice particle growth rates and fall speeds are predicted more accurately. Moreover, the size spectra predicted by the SGM are in good agreement with those from aircraft measurements during Lagrangian spiral descents through frontal clouds, indicating the successful modeling of microphysical processes. Since the lowest Zw over complex topography is often significantly above cloud base, the precipitation is often underestimated by radar quantitative precipitation estimates (QPE). Our SGM is capable of being initialized with Zw at the lowest reliable radar echo and consequently improves

  9. Modeling of radiation transport in coupled atmosphere-snow-ice-ocean systems

    International Nuclear Information System (INIS)

    Stamnes, K.; Hamre, B.; Stamnes, J. J.; Ryzhikov, G.; Biryulina, M.

    2009-01-01

    A radiative transfer model for coupled atmosphere-snow-ice-ocean systems is used to develop accurate and efficient tools for computing the BRDF of sea ice for a wide range of situations occurring in nature. (authors)

  10. Evaluating MODIS snow products for modelling snowmelt runoff: Case study of the Rio Grande headwaters

    Science.gov (United States)

    Steele, Caitriana; Dialesandro, John; James, Darren; Elias, Emile; Rango, Albert; Bleiweiss, Max

    2017-12-01

    Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM) and in other models for simulating discharge from snowmelt. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM +) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and may not always provide sufficient cloud-free dates. The coarser spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) offers better temporal resolution and in cloudy years, MODIS data offer the best alternative for mapping snow cover when finer spatial resolution data are unavailable. However, MODIS' coarse spatial resolution (500 m) can obscure fine spatial patterning in snow cover and some MODIS products are not sensitive to end-of-season snow cover. In this study, we aimed to test MODIS snow products for use in simulating snowmelt runoff from smaller headwater basins by a) comparing maps of TM and MODIS-based SCA and b) determining how SRM streamflow simulations are changed by the different estimates of seasonal snow depletion. We compared gridded MODIS snow products (Collection 5 MOD10A1 fractional and binary SCA; SCA derived from Collection 6 MOD10A1 Normalised Difference Snow Index (NDSI) Snow Cover), and the MODIS Snow Covered-Area and Grain size retrieval (MODSCAG) canopy-corrected fractional SCA (SCAMG), with reference SCA maps (SCAREF) generated from binary classification of TM imagery. SCAMG showed strong agreement with SCAREF; excluding true negatives (where both methods agreed no snow was present) the median percent difference between SCAREF and SCAMG ranged between -2.4% and 4.7%. We simulated runoff for each of the four study years using SRM populated with and calibrated for snow depletion curves derived from SCAREF. We then substituted in each of the MODIS-derived depletion curves. With efficiency coefficients ranging between 0.73 and 0.93, SRM

  11. Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S.

    OpenAIRE

    Oaida, CM; Xue, Y; Flanner, MG; Skiles, SMK; De Sales, F; Painter, TH

    2015-01-01

    © 2015. American Geophysical Union. All Rights Reserved. Two important factors that control snow albedo are snow grain growth and presence of light-absorbing impurities (aerosols) in snow. However, current regional climate models do not include such processes in a physically based manner in their land surface models. We improve snow albedo calculations in the Simplified Simple Biosphere (SSiB) land surface model coupled with the Weather Research and Forecasting (WRF) regional climate model (R...

  12. The Airborne Snow Observatory: fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo

    Science.gov (United States)

    Snow cover and its melt dominate regional climate and water resources in many of the world’s mountainous regions. Snowmelt timing and magnitude in mountains tend to be controlled by absorption of solar radiation and snow water equivalent, respectively, and yet both of these are very poorly known ev...

  13. Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models

    Science.gov (United States)

    Snauffer, Andrew M.; Hsieh, William W.; Cannon, Alex J.; Schnorbus, Markus A.

    2018-03-01

    Estimates of surface snow water equivalent (SWE) in mixed alpine environments with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief, and snow accumulations. These three confounding factors dominate much of the province of British Columbia (BC), Canada. An artificial neural network (ANN) was created using as predictors six gridded SWE products previously evaluated for BC. Relevant spatiotemporal covariates were also included as predictors, and observations from manual snow surveys at stations located throughout BC were used as target data. Mean absolute errors (MAEs) and interannual correlations for April surveys were found using cross-validation. The ANN using the three best-performing SWE products (ANN3) had the lowest mean station MAE across the province. ANN3 outperformed each product as well as product means and multiple linear regression (MLR) models in all of BC's five physiographic regions except for the BC Plains. Subsequent comparisons with predictions generated by the Variable Infiltration Capacity (VIC) hydrologic model found ANN3 to better estimate SWE over the VIC domain and within most regions. The superior performance of ANN3 over the individual products, product means, MLR, and VIC was found to be statistically significant across the province.

  14. Simulation of wind-induced snow transport in alpine terrain using a fully coupled snowpack/atmosphere model

    Science.gov (United States)

    Vionnet, V.; Martin, E.; Masson, V.; Guyomarc'h, G.; Naaim-Bouvet, F.; Prokop, A.; Durand, Y.; Lac, C.

    2013-06-01

    In alpine regions, wind-induced snow transport strongly influences the spatio-temporal evolution of the snow cover throughout the winter season. To gain understanding on the complex processes that drive the redistribution of snow, a new numerical model is developed. It couples directly the detailed snowpack model Crocus with the atmospheric model Meso-NH. Meso-NH/Crocus simulates snow transport in saltation and in turbulent suspension and includes the sublimation of suspended snow particles. A detailed representation of the first meters of the atmosphere allows a fine reproduction of the erosion and deposition process. The coupled model is evaluated against data collected around the experimental site of Col du Lac Blanc (2720 m a.s.l., French Alps). For this purpose, a blowing snow event without concurrent snowfall has been selected and simulated. Results show that the model captures the main structures of atmospheric flow in alpine terrain, the vertical profile of wind speed and the snow particles fluxes near the surface. However, the horizontal resolution of 50 m is found to be insufficient to simulate the location of areas of snow erosion and deposition observed by terrestrial laser scanning. When activated, the sublimation of suspended snow particles causes a reduction in deposition of 5.3%. Total sublimation (surface + blowing snow) is three times higher than surface sublimation in a simulation neglecting blowing snow sublimation.

  15. A Comparison of the SNICAR Radiative Transfer Model to In Situ Snow Characterization Measurements at Sites in New England, USA

    Science.gov (United States)

    Adolph, A. C.; Albert, M. R.; Dibb, J. E.; Lazarcik, J.; Amante, J.

    2016-12-01

    As a highly reflective material, snow serves as an important control on surface energy balance. Given the current changes in climate and the sensitivity of snow cover to rising temperatures, it is critical that we understand the role of snow and its associated feedbacks in the climate system. Much of snow albedo research has focused on polar or high altitude snow packs, but rapid changes are also occurring in temperate regions; in the northeastern United States of America, changing climate has resulted in shallower snow packs and fewer days of snow cover. As these changes occur and we seek to understand the associated implications for snow albedo within climate dynamics, it is imperative that we are able to accurately represent snow in models. The SNow, ICe, and Aerosol Radiation model (SNICAR), developed by Flanner and Zender (2005) and used in the IPCC assessments, provides upward and downward radiative fluxes of one or many snow layers based on the following inputs: snow depth, density, grain size, and impurity content; solar zenith angle; lighting conditions; and albedo of the surface beneath the snowpack. To our knowledge, the SNICAR model has not been validated with data from a mid-latitude temperate region. Through a measurement campaign that occurred from winter 2013-2016, we have collected over 400 independent observations of a suite of snow characterization measurements and spectral snow albedo from three different sites in New Hampshire, USA. Comparison of our spectral albedo measurements to the SNICAR albedo derived from measured snow properties and illumination conditions will allow for validation of the model or recommendations for improvement based on the sensitivities found in the data.

  16. Physics-based distributed snow models in the operational arena: Current and future challenges

    Science.gov (United States)

    Winstral, A. H.; Jonas, T.; Schirmer, M.; Helbig, N.

    2017-12-01

    The demand for modeling tools robust to climate change and weather extremes along with coincident increases in computational capabilities have led to an increase in the use of physics-based snow models in operational applications. Current operational applications include the WSL-SLF's across Switzerland, ASO's in California, and USDA-ARS's in Idaho. While the physics-based approaches offer many advantages there remain limitations and modeling challenges. The most evident limitation remains computation times that often limit forecasters to a single, deterministic model run. Other limitations however remain less conspicuous amidst the assumptions that these models require little to no calibration based on their foundation on physical principles. Yet all energy balance snow models seemingly contain parameterizations or simplifications of processes where validation data are scarce or present understanding is limited. At the research-basin scale where many of these models were developed these modeling elements may prove adequate. However when applied over large areas, spatially invariable parameterizations of snow albedo, roughness lengths and atmospheric exchange coefficients - all vital to determining the snowcover energy balance - become problematic. Moreover as we apply models over larger grid cells, the representation of sub-grid variability such as the snow-covered fraction adds to the challenges. Here, we will demonstrate some of the major sensitivities of distributed energy balance snow models to particular model constructs, the need for advanced and spatially flexible methods and parameterizations, and prompt the community for open dialogue and future collaborations to further modeling capabilities.

  17. Macroscopic modeling for heat and water vapor transfer in dry snow by homogenization.

    Science.gov (United States)

    Calonne, Neige; Geindreau, Christian; Flin, Frédéric

    2014-11-26

    Dry snow metamorphism, involved in several topics related to cryospheric sciences, is mainly linked to heat and water vapor transfers through snow including sublimation and deposition at the ice-pore interface. In this paper, the macroscopic equivalent modeling of heat and water vapor transfers through a snow layer was derived from the physics at the pore scale using the homogenization of multiple scale expansions. The microscopic phenomena under consideration are heat conduction, vapor diffusion, sublimation, and deposition. The obtained macroscopic equivalent model is described by two coupled transient diffusion equations including a source term arising from phase change at the pore scale. By dimensional analysis, it was shown that the influence of such source terms on the overall transfers can generally not be neglected, except typically under small temperature gradients. The precision and the robustness of the proposed macroscopic modeling were illustrated through 2D numerical simulations. Finally, the effective vapor diffusion tensor arising in the macroscopic modeling was computed on 3D images of snow. The self-consistent formula offers a good estimate of the effective diffusion coefficient with respect to the snow density, within an average relative error of 10%. Our results confirm recent work that the effective vapor diffusion is not enhanced in snow.

  18. Process-level model evaluation: a snow and heat transfer metric

    Science.gov (United States)

    Slater, Andrew G.; Lawrence, David M.; Koven, Charles D.

    2017-04-01

    Land models require evaluation in order to understand results and guide future development. Examining functional relationships between model variables can provide insight into the ability of models to capture fundamental processes and aid in minimizing uncertainties or deficiencies in model forcing. This study quantifies the proficiency of land models to appropriately transfer heat from the soil through a snowpack to the atmosphere during the cooling season (Northern Hemisphere: October-March). Using the basic physics of heat diffusion, we investigate the relationship between seasonal amplitudes of soil versus air temperatures due to insulation from seasonal snow. Observations demonstrate the anticipated exponential relationship of attenuated soil temperature amplitude with increasing snow depth and indicate that the marginal influence of snow insulation diminishes beyond an effective snow depth of about 50 cm. A snow and heat transfer metric (SHTM) is developed to quantify model skill compared to observations. Land models within the CMIP5 experiment vary widely in SHTM scores, and deficiencies can often be traced to model structural weaknesses. The SHTM value for individual models is stable over 150 years of climate, 1850-2005, indicating that the metric is insensitive to climate forcing and can be used to evaluate each model's representation of the insulation process.

  19. Study on the snow drifting modelling criteria in boundary layer wind tunnels

    Directory of Open Access Journals (Sweden)

    Georgeta BĂETU

    2014-07-01

    Full Text Available The paper presents a study on modelling the wind drifting of the snow deposited on the flat roofs of buildings in wind tunnel. The physical model of snow drifting in wind tunnel simulating the urban exposure to wind action is not frequently reported in literature, but is justified by the serious damages under accidental important snow falls combined with strong wind actions on the roofs of various buildings. A uniform layer of snow deposited on the flat roof was exposed to wind action in order to obtain the drifting. The parameters involved in the modelling at reduced scale, with particles of glass beads, of the phenomenon of transportation of the snow from the roof were analysed, particularly the roughness length and the friction wind speed. A numerical simulation in ANSYS CFX program was developed in parallel, by which a more accurate visualization of the particularities of the wind flow over the roof was possible, in the specific areas where the phenomenon of snow transportation was more susceptible to occur. Modified roughness length and friction wind speed were determined through methods used in the literature, an attempt being made in this work to analyse the factors that influence their values.

  20. Identification of magnetic particulates in road dust accumulated on roadside snow using magnetic, geochemical and micro-morphological analyses

    Czech Academy of Sciences Publication Activity Database

    Bućko, M. S.; Magiera, T.; Johanson, B.; Petrovský, Eduard; Pesonen, L. J.

    2011-01-01

    Roč. 159, č. 5 (2011), s. 1266-1276 ISSN 0269-7491 Institutional research plan: CEZ:AV0Z30120515 Keywords : road dust * vehicle emissions * magnetic properties * snow * geochemical analyses Subject RIV: DE - Earth Magnetism, Geodesy, Geography Impact factor: 3.746, year: 2011

  1. Technical snow production in skiing areas: conditions, practice, monitoring and modelling. A case study in Mayrhofen/Austria

    Science.gov (United States)

    Strasser, Ulrich; Hanzer, Florian; Marke, Thomas; Rothleitner, Michael

    2017-04-01

    The production of technical snow today is a self-evident feature of modern alpine skiing resort management. Millions of Euros are invested every year for the technical infrastructure and its operation to produce a homogeneous and continuing snow cover on the skiing slopes for the winter season in almost every larger destination in the Alps. In Austria, skiing tourism is a significant factor of the national economic structure. We present the framing conditions of technical snow production in the mid-size skiing resort of Mayrhofen (Zillertal Alps/Austria, 136 km slopes, elevation range 630 - 2.500 m a.s.l.). Production conditions are defined by the availability of water, the planned date for the season opening, and the climatic conditions in the weeks before. By means of an adapted snow production strategy an attempt is made to ecologically and economically optimize the use of water and energy resources. Monitoring of the snow cover is supported by a network of low-cost sensors and mobile snow depth recordings. Finally, technical snow production is simulated with the spatially distributed, physically based hydroclimatological model AMUNDSEN. The model explicitly considers individual snow guns and distributes the produced snow along the slopes. The amount of simulated snow produced by each device is a function of its type, of actual wet-bulb temperature at the location, of ski area infrastructure (in terms of water supply and pumping capacity), and of snow demand.

  2. Alpine snow cover in a changing climate: a regional climate model perspective

    Science.gov (United States)

    Steger, Christian; Kotlarski, Sven; Jonas, Tobias; Schär, Christoph

    2013-08-01

    An analysis is presented of an ensemble of regional climate model (RCM) experiments from the ENSEMBLES project in terms of mean winter snow water equivalent (SWE), the seasonal evolution of snow cover, and the duration of the continuous snow cover season in the European Alps. Two sets of simulations are considered, one driven by GCMs assuming the SRES A1B greenhouse gas scenario for the period 1951-2099, and the other by the ERA-40 reanalysis for the recent past. The simulated SWE for Switzerland for the winters 1971-2000 is validated against an observational data set derived from daily snow depth measurements. Model validation shows that the RCMs are capable of simulating the general spatial and seasonal variability of Alpine snow cover, but generally underestimate snow at elevations below 1,000 m and overestimate snow above 1,500 m. Model biases in snow cover can partly be related to biases in the atmospheric forcing. The analysis of climate projections for the twenty first century reveals high inter-model agreement on the following points: The strongest relative reduction in winter mean SWE is found below 1,500 m, amounting to 40-80 % by mid century relative to 1971-2000 and depending upon the model considered. At these elevations, mean winter temperatures are close to the melting point. At higher elevations the decrease of mean winter SWE is less pronounced but still a robust feature. For instance, at elevations of 2,000-2,500 m, SWE reductions amount to 10-60 % by mid century and to 30-80 % by the end of the century. The duration of the continuous snow cover season shows an asymmetric reduction with strongest shortening in springtime when ablation is the dominant factor for changes in SWE. We also find a substantial ensemble-mean reduction of snow reliability relevant to winter tourism at elevations below about 1,800 m by mid century, and at elevations below about 2,000 m by the end of the century.

  3. Comparison of Commonly-Used Microwave Radiative Transfer Models for Snow Remote Sensing

    Science.gov (United States)

    Royer, Alain; Roy, Alexandre; Montpetit, Benoit; Saint-Jean-Rondeau, Olivier; Picard, Ghislain; Brucker, Ludovic; Langlois, Alexandre

    2017-01-01

    This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (T(sub B)): the Dense Media Radiative Transfer - Multi-Layer model (DMRT-ML), the Dense Media Radiative Transfer - Quasi-Crystalline Approximation Mie scattering of Sticky spheres (DMRT-QMS), the Helsinki University of Technology n-Layers model (HUT-nlayers) and the Microwave Emission Model of Layered Snowpacks (MEMLS). Using the same extensively measured physical snowpack properties, we compared the simulated T(sub B) at 11, 19 and 37 GHz from these four models. The analysis focuses on the impact of using different types of measured snow microstructure metrics in the simulations. In addition to density, snow microstructure is defined for each snow layer by grain optical diameter (Do) and stickiness for DMRT-ML and DMRT-QMS, mean grain geometrical maximum extent (D(sub max)) for HUT n-layers and the exponential correlation length for MEMLS. These metrics were derived from either in-situ measurements of snow specific surface area (SSA) or macrophotos of grain sizes (D(sub max)), assuming non-sticky spheres for the DMRT models. Simulated T(sub B) sensitivity analysis using the same inputs shows relatively consistent T(sub B) behavior as a function of Do and density variations for the vertical polarization (maximum deviation of 18 K and 27 K, respectively), while some divergences appear in simulated variations for the polarization ratio (PR). Comparisons with ground based radiometric measurements show that the simulations based on snow SSA measurements have to be scaled with a model-specific factor of Do in order to minimize the root mean square error (RMSE) between measured and simulated T(sub B). Results using in-situ grain size measurements (SSA or D(sub max), depending on the model) give a mean T(sub B) RMSE (19 and 37 GHz) of the order of 16-26 K, which is similar for all models when the snow

  4. Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system

    Science.gov (United States)

    Kushner, Paul J.; Mudryk, Lawrence R.; Merryfield, William; Ambadan, Jaison T.; Berg, Aaron; Bichet, Adéline; Brown, Ross; Derksen, Chris; Déry, Stephen J.; Dirkson, Arlan; Flato, Greg; Fletcher, Christopher G.; Fyfe, John C.; Gillett, Nathan; Haas, Christian; Howell, Stephen; Laliberté, Frédéric; McCusker, Kelly; Sigmond, Michael; Sospedra-Alfonso, Reinel; Tandon, Neil F.; Thackeray, Chad; Tremblay, Bruno; Zwiers, Francis W.

    2018-04-01

    The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their

  5. Challenges in land model representation of heat transfer in snow and frozen soils

    Science.gov (United States)

    Musselman, K. N.; Clark, M. P.; Nijssen, B.; Arnold, J.

    2017-12-01

    Accurate model simulations of soil thermal and moisture states are critical for realistic estimates of exchanges of energy, water, and biogeochemical fluxes at the land-atmosphere interface. In cold regions, seasonal snow-cover and organic soils form insulating barriers, modifying the heat and moisture exchange that would otherwise occur between mineral soils and the atmosphere. The thermal properties of these media are highly dynamic functions of mass, water and ice content. Land surface models vary in their representation of snow and soil processes, and thus in the treatment of insulation and heat exchange. For some models, recent development efforts have improved representation of heat transfer in cold regions, such as with multi-layer snow treatment, inclusion of soil freezing and organic soil properties, yet model deficiencies remain prevalent. We evaluate models that participated in the Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) experiment for proficiency in simulating heat transfer between the soil through the snowpack to the atmosphere. Using soil observations from cold region sites and a controlled experiment with Structure for Unifying Multiple Modeling Alternatives (SUMMA), we explore the impact of snow and soil model decisions and parameter values on heat transfer model skill. Specifically, we use SUMMA to mimic the spread of behaviors exhibited by the models that participated in PLUMBER. The experiment allows us to isolate relationships between model skill and process representation. The results are aimed to better understand existing model challenges and identify potential advances for cold region models.

  6. The Ross Sea Dipole - temperature, snow accumulation and sea ice variability in the Ross Sea region, Antarctica, over the past 2700 years

    Science.gov (United States)

    Bertler, Nancy A. N.; Conway, Howard; Dahl-Jensen, Dorthe; Emanuelsson, Daniel B.; Winstrup, Mai; Vallelonga, Paul T.; Lee, James E.; Brook, Ed J.; Severinghaus, Jeffrey P.; Fudge, Taylor J.; Keller, Elizabeth D.; Baisden, W. Troy; Hindmarsh, Richard C. A.; Neff, Peter D.; Blunier, Thomas; Edwards, Ross; Mayewski, Paul A.; Kipfstuhl, Sepp; Buizert, Christo; Canessa, Silvia; Dadic, Ruzica; Kjær, Helle A.; Kurbatov, Andrei; Zhang, Dongqi; Waddington, Edwin D.; Baccolo, Giovanni; Beers, Thomas; Brightley, Hannah J.; Carter, Lionel; Clemens-Sewall, David; Ciobanu, Viorela G.; Delmonte, Barbara; Eling, Lukas; Ellis, Aja; Ganesh, Shruthi; Golledge, Nicholas R.; Haines, Skylar; Handley, Michael; Hawley, Robert L.; Hogan, Chad M.; Johnson, Katelyn M.; Korotkikh, Elena; Lowry, Daniel P.; Mandeno, Darcy; McKay, Robert M.; Menking, James A.; Naish, Timothy R.; Noerling, Caroline; Ollive, Agathe; Orsi, Anaïs; Proemse, Bernadette C.; Pyne, Alexander R.; Pyne, Rebecca L.; Renwick, James; Scherer, Reed P.; Semper, Stefanie; Simonsen, Marius; Sneed, Sharon B.; Steig, Eric J.; Tuohy, Andrea; Ulayottil Venugopal, Abhijith; Valero-Delgado, Fernando; Venkatesh, Janani; Wang, Feitang; Wang, Shimeng; Winski, Dominic A.; Winton, V. Holly L.; Whiteford, Arran; Xiao, Cunde; Yang, Jiao; Zhang, Xin

    2018-02-01

    High-resolution, well-dated climate archives provide an opportunity to investigate the dynamic interactions of climate patterns relevant for future projections. Here, we present data from a new, annually dated ice core record from the eastern Ross Sea, named the Roosevelt Island Climate Evolution (RICE) ice core. Comparison of this record with climate reanalysis data for the 1979-2012 interval shows that RICE reliably captures temperature and snow precipitation variability in the region. Trends over the past 2700 years in RICE are shown to be distinct from those in West Antarctica and the western Ross Sea captured by other ice cores. For most of this interval, the eastern Ross Sea was warming (or showing isotopic enrichment for other reasons), with increased snow accumulation and perhaps decreased sea ice concentration. However, West Antarctica cooled and the western Ross Sea showed no significant isotope temperature trend. This pattern here is referred to as the Ross Sea Dipole. Notably, during the Little Ice Age, West Antarctica and the western Ross Sea experienced colder than average temperatures, while the eastern Ross Sea underwent a period of warming or increased isotopic enrichment. From the 17th century onwards, this dipole relationship changed. All three regions show current warming, with snow accumulation declining in West Antarctica and the eastern Ross Sea but increasing in the western Ross Sea. We interpret this pattern as reflecting an increase in sea ice in the eastern Ross Sea with perhaps the establishment of a modern Roosevelt Island polynya as a local moisture source for RICE.

  7. The Ross Sea Dipole – temperature, snow accumulation and sea ice variability in the Ross Sea region, Antarctica, over the past 2700 years

    Directory of Open Access Journals (Sweden)

    N. A. N. Bertler

    2018-02-01

    Full Text Available High-resolution, well-dated climate archives provide an opportunity to investigate the dynamic interactions of climate patterns relevant for future projections. Here, we present data from a new, annually dated ice core record from the eastern Ross Sea, named the Roosevelt Island Climate Evolution (RICE ice core. Comparison of this record with climate reanalysis data for the 1979–2012 interval shows that RICE reliably captures temperature and snow precipitation variability in the region. Trends over the past 2700 years in RICE are shown to be distinct from those in West Antarctica and the western Ross Sea captured by other ice cores. For most of this interval, the eastern Ross Sea was warming (or showing isotopic enrichment for other reasons, with increased snow accumulation and perhaps decreased sea ice concentration. However, West Antarctica cooled and the western Ross Sea showed no significant isotope temperature trend. This pattern here is referred to as the Ross Sea Dipole. Notably, during the Little Ice Age, West Antarctica and the western Ross Sea experienced colder than average temperatures, while the eastern Ross Sea underwent a period of warming or increased isotopic enrichment. From the 17th century onwards, this dipole relationship changed. All three regions show current warming, with snow accumulation declining in West Antarctica and the eastern Ross Sea but increasing in the western Ross Sea. We interpret this pattern as reflecting an increase in sea ice in the eastern Ross Sea with perhaps the establishment of a modern Roosevelt Island polynya as a local moisture source for RICE.

  8. Constraining the Spatial Extent of Marine Oil Snow Sedimentation and Flocculent Accumulation Following the Deepwater Horizon Event Using an Excess 210Pb Flux Approach.

    Science.gov (United States)

    Schwing, P T; Brooks, G R; Larson, R A; Holmes, C W; O'Malley, B J; Hollander, D J

    2017-06-06

    Following the Deepwater Horizon (DWH) event in 2010, there were several lines of evidence indicating the presence of marine oil snow sedimentation and flocculent accumulation (MOSSFA). A significant amount of marine oil snow formed in the water column of the northern Gulf of Mexico (nGoM), settled rapidly, and ultimately accumulated in the sediments of the nGoM. This study utilized a commonly used radioisotope tracer (excess 210 Pb, 210 Pb xs ) from 32 sediment cores collected from 2010 to 2013 to characterize the spatial extent of MOSSFA on the seafloor. Relative to pre-DWH conditions, an increase in 210 Pb xs flux occurred in two distinct regions: (1) in the western portion of the study area on an east-northeast to west-southwest axis, stretching 230 km southwest and 140 km northeast of the DWH wellhead, and (2) in the eastern portion of the study area on a 70 km northeast to southwest axis near the DeSoto Canyon. The total sedimentary spatial extent of MOSSFA, as calculated by increased 210 Pb xs flux after 2010, ranged from 12 805 to 35 425 km 2 . 210 Pb xs flux provides a valuable tool for documenting the spatial extent of MOSSFA following DWH and will continue to aid in the determination of advective transport and ultimate depocenters of MOSSFA material.

  9. Numerical modeling of coupled water flow and heat transport in soil and snow

    Science.gov (United States)

    Thijs J. Kelleners; Jeremy Koonce; Rose Shillito; Jelle Dijkema; Markus Berli; Michael H. Young; John M. Frank; William Massman

    2016-01-01

    A one-dimensional vertical numerical model for coupled water flow and heat transport in soil and snow was modified to include all three phases of water: vapor, liquid, and ice. The top boundary condition in the model is driven by incoming precipitation and the surface energy balance. The model was applied to three different terrestrial systems: A warm desert bare...

  10. Modeling the Observed Microwave Emission from Shallow Multi-Layer Tundra Snow Using DMRT-ML

    Directory of Open Access Journals (Sweden)

    Nastaran Saberi

    2017-12-01

    Full Text Available The observed brightness temperatures (Tb at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE due to volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases, the emission from the snowpack offsets the scattering of the sub-nivean emission. In tundra snow, the Tb slope reversal occurs at shallower snow thicknesses. While it has been postulated that the inflection point in the seasonal time series of observed Tb V 37 GHz of tundra snow is controlled by the formation of a thick wind slab layer, the simulation of this effect has yet to be confirmed. Therefore, the Dense Media Radiative Transfer Theory for Multi Layered (DMRT-ML snowpack is used to predict the passive microwave response from airborne observations over shallow, dense, slab-layered tundra snow. Airborne radiometer observations coordinated with ground-based in situ snow measurements were acquired in the Canadian high Arctic near Eureka, NT, in April 2011. The DMRT-ML was parameterized with the in situ snow measurements using a two-layer snowpack and run in two configurations: a depth hoar and a wind slab dominated pack. With these two configurations, the calibrated DMRT-ML successfully predicted the Tb V 37 GHz response (R correlation of 0.83 when compared with the observed airborne Tb footprints containing snow pits measurements. Using this calibrated model, the DMRT-ML was applied to the whole study region. At the satellite observation scale, observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E over the study area reflected seasonal differences between Tb V 37 GHz and Tb V 19 GHz that supports the hypothesis of the development of an early season volume scattering depth hoar layer, followed by the growth of the late season emission-dominated wind slab layer. This research highlights the necessity to consider the two

  11. A multilayer physically based snowpack model simulating direct and indirect radiative impacts of light-absorbing impurities in snow

    Science.gov (United States)

    Tuzet, Francois; Dumont, Marie; Lafaysse, Matthieu; Picard, Ghislain; Arnaud, Laurent; Voisin, Didier; Lejeune, Yves; Charrois, Luc; Nabat, Pierre; Morin, Samuel

    2017-11-01

    Light-absorbing impurities (LAIs) decrease snow albedo, increasing the amount of solar energy absorbed by the snowpack. Its most intuitive and direct impact is to accelerate snowmelt. Enhanced energy absorption in snow also modifies snow metamorphism, which can indirectly drive further variations of snow albedo in the near-infrared part of the solar spectrum because of the evolution of the near-surface snow microstructure. New capabilities have been implemented in the detailed snowpack model SURFEX/ISBA-Crocus (referred to as Crocus) to account for impurities' deposition and evolution within the snowpack and their direct and indirect impacts. Once deposited, the model computes impurities' mass evolution until snow melts out, accounting for scavenging by meltwater. Taking advantage of the recent inclusion of the spectral radiative transfer model TARTES (Two-stream Analytical Radiative TransfEr in Snow model) in Crocus, the model explicitly represents the radiative impacts of light-absorbing impurities in snow. The model was evaluated at the Col de Porte experimental site (French Alps) during the 2013-2014 snow season against in situ standard snow measurements and spectral albedo measurements. In situ meteorological measurements were used to drive the snowpack model, except for aerosol deposition fluxes. Black carbon (BC) and dust deposition fluxes used to drive the model were extracted from simulations of the atmospheric model ALADIN-Climate. The model simulates snowpack evolution reasonably, providing similar performances to our reference Crocus version in terms of snow depth, snow water equivalent (SWE), near-surface specific surface area (SSA) and shortwave albedo. Since the reference empirical albedo scheme was calibrated at the Col de Porte, improvements were not expected to be significant in this study. We show that the deposition fluxes from the ALADIN-Climate model provide a reasonable estimate of the amount of light-absorbing impurities deposited on the

  12. Scaling and Numerical Model Evaluation of Snow-Cover Effects on the Generation and Modification of Daytime Mesoscale Circulations.

    Science.gov (United States)

    Segal, M.; Garratt, J. R.; Pielke, R. A.; Ye, Z.

    1991-04-01

    Consideration of the sensible heat flux characteristics over a snow surface suggests a significant diminution in the magnitude of the flux, compared to that over a snow-free surface under the same environmental conditions. Consequently, the existence of snow-covered mesoscale areas adjacent to snow-free areas produces horizontal thermal gradients in the lower atmosphere during the daytime, possibly resulting in a `snow breeze.' In addition, suppression of the daytime thermally induced upslope flow over snow-covered slopes is likely to occur. The present paper provides scaling and modeling evaluations of these situations, with quantification of the generated and modified circulations. These evaluations suggest that under ideal situations involved with uniform snow cover over large areas, particularly in late winter and early spring, a noticeable `snow breeze' is likely to develop. Additionally: suppression of the daytime thermally induced upslope flow is significant and may even result in a daytime drainage flow. The effects of bare ground patchiness in the snow cover on these circulations are also explored, both for flat terrain and slope-flow situations. A patchiness fraction greater than 0.5 is found to result in a noticeably reduced snow-breeze circulation, while a patchiness fraction of only 0.1 caused the simulated daytime drainage flow over slopes to he reversed.

  13. Brittle Fracture Mechanics of Snow : In Situ Testing and Distinct Element Modeling

    Science.gov (United States)

    Faillettaz, J.; Daudon, D.; Louchet, F.

    A snow slab avalanche release usually results from the rupture of the snow cover at the interface between an upper layer (slab) and an underlying substrate. Amazingly, the models proposed so far to predict this kind of rupture were only based on continuum mechanics, as they did not take into account the existing cracks or cohesion defects at the interface between the two layers, and their possible unstable propagation that eventually triggers the avalanche. This is why the present work, essentially devoted to human triggered avalanches, is based instead on Griffith's fracture approach, widely used in modelling brittle fracture of materials. The possible rupture scenario involves a propagation in a shear mode of a "basal crack" nucleated and gradually grown at the interface by the skier's weight, followed by a mode I opening and propagation of a "crown crack" at the top of the sheared zone. Different avalanche sizes are predicted according whether the basal crack propagation reaches or not the Griffith's instabil- ity size before crown crack opening (Louchet 2000). Accurate predictions therefore require a precise knowledge of snow toughness values in both modes. A theoretical estimation of toughness considering snow as an ice foam was proposed by Kirchner and Michot (2000), but the question of whether these results may be extended to an assembly of sintered grains is still open. A mode I toughness measurement of snow was also published for the first time by Kirchner and Michot on samples gathered in the Vosges range. In the present work, we developed an experimental set similar to Michot's, in order to measure mode I toughness: a vertical crack of increasing size is gradually machined from the top surface in an horizontal snow beam until failure takes place under its own weight. The toughness value is computed from the snow weight and the crack length at the onset of rapid crack propagation. A similar device was designed for mode II testing, but is still under

  14. Understanding snow-transport processes shaping the mountain snow-cover

    Directory of Open Access Journals (Sweden)

    R. Mott

    2010-12-01

    Full Text Available Mountain snow-cover is normally heterogeneously distributed due to wind and precipitation interacting with the snow cover on various scales. The aim of this study was to investigate snow deposition and wind-induced snow-transport processes on different scales and to analyze some major drift events caused by north-west storms during two consecutive accumulation periods. In particular, we distinguish between the individual processes that cause specific drifts using a physically based model approach. Very high resolution wind fields (5 m were computed with the atmospheric model Advanced Regional Prediction System (ARPS and used as input for a model of snow-surface processes (Alpine3D to calculate saltation, suspension and preferential deposition of precipitation. Several flow features during north-west storms were identified with input from a high-density network of permanent and mobile weather stations and indirect estimations of wind directions from snow-surface structures, such as snow dunes and sastrugis. We also used Terrestrial and Airborne Laser Scanning measurements to investigate snow-deposition patterns and to validate the model. The model results suggest that the in-slope deposition patterns, particularly two huge cross-slope cornice-like drifts, developed only when the prevailing wind direction was northwesterly and were formed mainly due to snow redistribution processes (saltation-driven. In contrast, more homogeneous deposition patterns on a ridge scale were formed during the same periods mainly due to preferential deposition of precipitation. The numerical analysis showed that snow-transport processes were sensitive to the changing topography due to the smoothing effect of the snow cover.

  15. Changes in snow cover over China in the 21st century as simulated by a high resolution regional climate model

    International Nuclear Information System (INIS)

    Shi Ying; Gao Xuejie; Wu Jia; Giorgi, Filippo

    2011-01-01

    On the basis of the climate change simulations conducted using a high resolution regional climate model, the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model, RegCM3, at 25 km grid spacing, future changes in snow cover over China are analyzed. The simulations are carried out for the period of 1951–2100 following the IPCC SRES A1B emission scenario. The results suggest good performances of the model in simulating the number of snow cover days and the snow cover depth, as well as the starting and ending dates of snow cover to the present day (1981–2000). Their spatial distributions and amounts show fair consistency between the simulation and observation, although with some discrepancies. In general, decreases in the number of snow cover days and the snow cover depth, together with postponed snow starting dates and advanced snow ending dates, are simulated for the future, except in some places where the opposite appears. The most dramatic changes are found over the Tibetan Plateau among the three major snow cover areas of Northeast, Northwest and the Tibetan Plateau in China.

  16. Retrospective Snow Analysis Across the Continental United States for the National Water Model

    Science.gov (United States)

    Karsten, L. R.; Gochis, D.; Dugger, A. L.; McCreight, J. L.; Barlage, M. J.; Fall, G. M.; Olheiser, C.

    2016-12-01

    For large portions of the United States, snow plays a vital role in hydrologic prediction. This is particularly true in the mountain west where snowmelt contributes up to 80% of total streamflow runoff. The Office of Water Prediction (OWP) will begin running the National Water Model (NWM) during the second half of 2016, which is a continental-scale implementation of the WRF-Hydro community hydrologic modeling framework. Assessing and benchmarking the performance of the snow component of the NWM is important for future research-to-operations activities and for forecasters to better understand NWM output. For this study, WRF-Hydro was ran using the same domain and physics options as the NWM (1 km LSM, 250m overland routing, and NHDPlus Version 2.1 channel network). The land surface component chosen is Noah-MP land surface model. Forcing from the National Land Data Assimilation System (NLDAS-2) was downscaled from the native 0.125 degree resolution to the 1 km modeling domain to drive the model. The model was ran over a 5-year retrospective period to gauge multi-year performance of the snow states. Output was analyzed against both in-situ observations, such as SNOTEL, and the Snow Data Assimilation System (SNODAS). In addition, gridded snow states and SNODAS grids were aggregated to Omernik-derived ecological regions. This was done in order to break up snow analysis by regions that share similar ecological and physiographic characteristics. Results show WRF-Hydro is able to capture peak timing across most of the mountain west fairly well. In terms of magnitudes, the model struggles across portions of the west with a low bias. This is especially true in the Cascades, which could be traced back to precipitation partitioning issues in the model. Across the central Rockies, the model exhibits a lower dry bias showing improved performance there. Previous literature suggests a dry bias in the precipitation out west may be contributing to model performance. East of the

  17. Retrieval of Dry Snow Parameters from Radiometric Data Using a Dense Medium Model and Genetic Algorithms

    Science.gov (United States)

    Tedesco, Marco; Kim, Edward J.

    2005-01-01

    In this paper, GA-based techniques are used to invert the equations of an electromagnetic model based on Dense Medium Radiative Transfer Theory (DMRT) under the Quasi Crystalline Approximation with Coherent Potential to retrieve snow depth, mean grain size and fractional volume from microwave brightness temperatures. The technique is initially tested on both noisy and not-noisy simulated data. During this phase, different configurations of genetic algorithm parameters are considered to quantify how their change can affect the algorithm performance. A configuration of GA parameters is then selected and the algorithm is applied to experimental data acquired during the NASA Cold Land Process Experiment. Snow parameters retrieved with the GA-DMRT technique are then compared with snow parameters measured on field.

  18. Extraordinary blowing snow transport events in East Antarctica

    Energy Technology Data Exchange (ETDEWEB)

    Scarchilli, Claudio; Agnoletto, Lucia [ENEA, Rome (Italy); Universita di Siena, Dipartimento di Scienze della Terra, Siena (Italy); Frezzotti, Massimo; Grigioni, Paolo; Silvestri, Lorenzo de [ENEA, Rome (Italy); Dolci, Stefano [CNR, Rome (Italy); Consorzio P.N.R.A. S.C.r.l., Rome (Italy)

    2010-06-15

    In the convergence slope/coastal areas of Antarctica, a large fraction of snow is continuously eroded and exported by wind to the atmosphere and into the ocean. Snow transport observations from instruments and satellite images were acquired at the wind convergence zone of Terra Nova Bay (East Antarctica) throughout 2006 and 2007. Snow transport features are well-distinguished in satellite images and can extend vertically up to 200 m as first-order quantitatively estimated by driftometer sensor FlowCapt trademark. Maximum snow transportation occurs in the fall and winter seasons. Snow transportation (drift/blowing) was recorded for {proportional_to}80% of the time, and 20% of time recorded, the flux is >10{sup -2} kg m{sup -2} s{sup -1} with particle density increasing with height. Cumulative snow transportation is {proportional_to}4 orders of magnitude higher than snow precipitation at the site. An increase in wind speed and transportation ({proportional_to}30%) was observed in 2007, which is in agreement with a reduction in observed snow accumulation. Extensive presence of ablation surface (blue ice and wind crust) upwind and downwind of the measurement site suggest that the combine processes of blowing snow sublimation and snow transport remove up to 50% of the precipitation in the coastal and slope convergence area. These phenomena represent a major negative effect on the snow accumulation, and they are not sufficiently taken into account in studies of surface mass balance. The observed wind-driven ablation explains the inconsistency between atmospheric model precipitation and measured snow accumulation value. (orig.)

  19. Evaluation of two models for predicting elemental accumulation by arthropods

    International Nuclear Information System (INIS)

    Webster, J.R.; Crossley, D.A. Jr.

    1978-01-01

    Two different models have been proposed for predicting elemental accumulation by arthropods. Parameters of both models can be quantified from radioisotope elimination experiments. Our analysis of the 2 models shows that both predict identical elemental accumulation for a whole organism, though differing in the accumulation in body and gut. We quantified both models with experimental data from 134 Cs and 85 Sr elimination by crickets. Computer simulations of radioisotope accumulation were then compared with actual accumulation experiments. Neither model showed exact fit to the experimental data, though both showed the general pattern of elemental accumulation

  20. NHM-SMAP: spatially and temporally high-resolution nonhydrostatic atmospheric model coupled with detailed snow process model for Greenland Ice Sheet

    Science.gov (United States)

    Niwano, Masashi; Aoki, Teruo; Hashimoto, Akihiro; Matoba, Sumito; Yamaguchi, Satoru; Tanikawa, Tomonori; Fujita, Koji; Tsushima, Akane; Iizuka, Yoshinori; Shimada, Rigen; Hori, Masahiro

    2018-02-01

    To improve surface mass balance (SMB) estimates for the Greenland Ice Sheet (GrIS), we developed a 5 km resolution regional climate model combining the Japan Meteorological Agency Non-Hydrostatic atmospheric Model and the Snow Metamorphism and Albedo Process model (NHM-SMAP) with an output interval of 1 h, forced by the Japanese 55-year reanalysis (JRA-55). We used in situ data to evaluate NHM-SMAP in the GrIS during the 2011-2014 mass balance years. We investigated two options for the lower boundary conditions of the atmosphere: an offline configuration using snow, firn, and ice albedo, surface temperature data from JRA-55, and an online configuration using values from SMAP. The online configuration improved model performance in simulating 2 m air temperature, suggesting that the surface analysis provided by JRA-55 is inadequate for the GrIS and that SMAP results can better simulate physical conditions of snow/firn/ice. It also reproduced the measured features of the GrIS climate, diurnal variations, and even a strong mesoscale wind event. In particular, it successfully reproduced the temporal evolution of the GrIS surface melt area extent as well as the record melt event around 12 July 2012, at which time the simulated melt area extent reached 92.4 %. Sensitivity tests showed that the choice of calculation schemes for vertical water movement in snow and firn has an effect as great as 200 Gt year-1 in the GrIS-wide accumulated SMB estimates; a scheme based on the Richards equation provided the best performance.

  1. Effects of snow grain non-sphericity on climate simulations: Sensitivity tests with the NorESM model

    Science.gov (United States)

    Räisänen, Petri; Makkonen, Risto; Kirkevåg, Alf

    2017-04-01

    Snow grains are non-spherical and generally irregular in shape. Still, in radiative transfer calculations, they are often treated as spheres. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this work, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (≈ 0.78 in the visible region) than in the spherical case (≈ 0.89). Therefore, for a given snow grain size, the use of non-spherical snow grains yields a higher snow broadband albedo, typically by ≈0.03. Consequently, considering the spherical case as the baseline, the use of non-spherical snow grains results in a negative radiative forcing (RF), with a global-mean top-of-the-model value of ≈ -0.22 W m-2. Although this global-mean RF is modest, it has a rather substantial impact on the climate simulated by NoRESM. In particular, the global annual-mean 2-m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further found that the difference between NONSPH and SPH could be largely "tuned away" by adjusting the snow grain size in the NONSPH experiment by ≈ 70%. The impact of snow grain shape on the radiative effect (RE) of absorbing aerosols in snow (black carbon and mineral dust) is also discussed. For an

  2. Was the extreme and wide-spread marine oil-snow sedimentation and flocculent accumulation (MOSSFA) event during the Deepwater Horizon blow-out unique?

    Science.gov (United States)

    Vonk, Sophie M; Hollander, David J; Murk, AlberTinka J

    2015-11-15

    During the Deepwater Horizon blowout, thick layers of oiled material were deposited on the deep seafloor. This large scale benthic concentration of oil is suggested to have occurred via the process of Marine Oil Snow Sedimentation and Flocculent Accumulation (MOSSFA). This meta-analysis investigates whether MOSSFA occurred in other large oil spills and identifies the main drivers of oil sedimentation. MOSSFA was found to have occurred during the IXTOC I blowout and possibly during the Santa Barbara blowout. Unfortunately, benthic effects were not sufficiently studied for the 52 spills we reviewed. However, based on the current understanding of drivers involved, we conclude that MOSSFA and related benthic contamination may be widespread. We suggest to collect and analyze sediment cores at specific spill locations, as improved understanding of the MOSSFA process will allow better informed spill responses in the future, taking into account possible massive oil sedimentation and smothering of (deep) benthic ecosystems. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Evaluation of North Eurasian snow-off dates in the ECHAM5.4 atmospheric general circulation model

    Directory of Open Access Journals (Sweden)

    P. Räisänen

    2014-12-01

    Full Text Available The timing of springtime end of snowmelt (snow-off date in northern Eurasia in version 5.4 of the ECHAM5 atmospheric general circulation model (GCM is evaluated through comparison with a snow-off date data set based on space-borne microwave radiometer measurements and with Russian snow course data. ECHAM5 reproduces well the observed gross geographical pattern of snow-off dates, with earliest snow-off (in March in the Baltic region and latest snow-off (in June in the Taymyr Peninsula and in northeastern parts of the Russian Far East. The primary biases are (1 a delayed snow-off in southeastern Siberia (associated with too low springtime temperature and too high surface albedo, in part due to insufficient shielding by canopy; and (2 an early bias in the western and northern parts of northern Eurasia. Several sensitivity experiments were conducted, where biases in simulated atmospheric circulation were corrected through nudging and/or the treatment of surface albedo was modified. While this alleviated some of the model biases in snow-off dates, 2 m temperature and surface albedo, especially the early bias in snow-off in the western parts of northern Eurasia proved very robust and was actually larger in the nudged runs. A key issue underlying the snow-off biases in ECHAM5 is that snowmelt occurs at too low temperatures. Very likely, this is related to the treatment of the surface energy budget. On one hand, the surface temperature Ts is not computed separately for the snow-covered and snow-free parts of the grid cells, which prevents Ts from rising above 0 °C before all snow has vanished. Consequently, too much of the surface net radiation is consumed in melting snow and too little in heating the air. On the other hand, ECHAM5 does not include a canopy layer. Thus, while the albedo reduction due to canopy is accounted for, the shielding of snow on ground by the overlying canopy is not considered, which leaves too much solar radiation available for

  4. Water losses during technical snow production

    Science.gov (United States)

    Grünewald, Thomas; Wolfsperger, Fabian

    2017-04-01

    These days, the production of technical snow can be seen as a prerequisite for winter tourism. Huge amounts of water are used for technical snow production by ski resorts, especially in the beginning of the winter season. The aim is to guarantee an appropriate amount of snow to reliably provide optimal ski runs until the date of season opening in early December. Technical snow is generated by pumping pressurized water through the nozzles of a snow machine and dispersing the resulting spray of small water droplets which freeze during their travel to the ground. Cooling and freezing of the droplets can only happen if energy is emitted to the air mass surrounding the droplets. This heat transfer is happening through convective cooling and though evaporation and sublimation of water droplets and ice particles. This means that also mass is lost from the droplets and added in form of vapor to the air. It is important to note that not all water that is pumped through the snow machine is converted to snow distributed on the ground. Significant amounts of water are lost due to wind drift, sublimation and evaporation while droplets are traveling through the air or to draining of water which is not fully frozen when arriving at the ground. Studies addressing this question are sparse and the quantity of the water losses is still unclear. In order to assess this question in more detail, we obtained several systematic field observations at a test site near Davos, Switzerland. About a dozen of snow making tests had been performed during the last winter seasons. We compare the amount of water measured at the intake of the snow machine with the amount of snow accumulating at the ground during a night of snow production. The snow mass was calculated from highly detailed repeated terrestrial laser scanning measurements in combination with manually gathered snow densities. In addition a meteorological station had been set up in the vicinity observing all relevant meteorological

  5. Accuracy assessment of a net radiation and temperature index snowmelt model using ground observations of snow water equivalent in an alpine basin

    Science.gov (United States)

    Molotch, N. P.; Painter, T. H.; Bales, R. C.; Dozier, J.

    2003-04-01

    In this study, an accumulated net radiation / accumulated degree-day index snowmelt model was coupled with remotely sensed snow covered area (SCA) data to simulate snow cover depletion and reconstruct maximum snow water equivalent (SWE) in the 19.1-km2 Tokopah Basin of the Sierra Nevada, California. Simple net radiation snowmelt models are attractive for operational snowmelt runoff forecasts as they are computationally inexpensive and have low input requirements relative to physically based energy balance models. The objective of this research was to assess the accuracy of a simple net radiation snowmelt model in a topographically heterogeneous alpine environment. Previous applications of net radiation / temperature index snowmelt models have not been evaluated in alpine terrain with intensive field observations of SWE. Solar radiation data from two meteorological stations were distributed using the topographic radiation model TOPORAD. Relative humidity and temperature data were distributed based on the lapse rate calculated between three meteorological stations within the basin. Fractional SCA data from the Landsat Enhanced Thematic Mapper (5 acquisitions) and the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) (2 acquisitions) were used to derive daily SCA using a linear regression between acquisition dates. Grain size data from AVIRIS (4 acquisitions) were used to infer snow surface albedo and interpolated linearly with time to derive daily albedo values. Modeled daily snowmelt rates for each 30-m pixel were scaled by the SCA and integrated over the snowmelt season to obtain estimates of maximum SWE accumulation. Snow surveys consisting of an average of 335 depth measurements and 53 density measurements during April, May and June, 1997 were interpolated using a regression tree / co-krig model, with independent variables of average incoming solar radiation, elevation, slope and maximum upwind slope. The basin was clustered into 7 elevation / average

  6. A New, Two-layer Canopy Module For The Detailed Snow Model SNOWPACK

    Science.gov (United States)

    Gouttevin, I.; Lehning, M.; Jonas, T.; Gustafsson, D.; Mölder, M.

    2014-12-01

    A new, two-layer canopy module with thermal inertia for the detailed snow model SNOWPACK is presented. Compared to the old, one-layered canopy formulation with no heat mass, this module now offers a level of physical detail consistent with the detailed snow and soil representation in SNOWPACK. The new canopy model is designed to reproduce the difference in thermal regimes between leafy and woody canopy elements and their impact on the underlying snowpack energy balance. The new model is validated against data from an Alpine and a boreal site. Comparisons of modelled sub-canopy thermal radiations to stand-scale observations at Alptal, Switzerland, demonstrate the improvements induced by our new parameterizations. The main effect is a more realistic simulation of the canopy night-time drop in temperatures. The lower drop is induced by both thermal inertia and the two-layer representation. A specific result is that such a performance cannot be achieved by a single-layered canopy model. The impact of the new parameterizations on the modelled dynamics of the sub-canopy snowpack is analysed and yields consistent results, but the frequent occurrence of mixed-precipitation events at Alptal prevents a conclusive assessment of model performances against snow data.Without specific tuning, the model is also able to reproduce the measured summertime tree trunk temperatures and biomass heat storage at the boreal site of Norunda, Sweden, with an increased accuracy in amplitude and phase. Overall, the SNOWPACK model with its enhanced canopy module constitutes a unique (in its physical process representation) atmosphere-to-soil-through-canopy-and-snow modelling chain.

  7. Chemical Atmosphere-Snow-Sea Ice Interactions: defining future research in the field, lab and modeling

    Science.gov (United States)

    Frey, Markus

    2015-04-01

    The air-snow-sea ice system plays an important role in the global cycling of nitrogen, halogens, trace metals or carbon, including greenhouse gases (e.g. CO2 air-sea flux), and therefore influences also climate. Its impact on atmospheric composition is illustrated for example by dramatic ozone and mercury depletion events which occur within or close to the sea ice zone (SIZ) mostly during polar spring and are catalysed by halogens released from SIZ ice, snow or aerosol. Recent field campaigns in the high Arctic (e.g. BROMEX, OASIS) and Antarctic (Weddell sea cruises) highlight the importance of snow on sea ice as a chemical reservoir and reactor, even during polar night. However, many processes, participating chemical species and their interactions are still poorly understood and/or lack any representation in current models. Furthermore, recent lab studies provide a lot of detail on the chemical environment and processes but need to be integrated much better to improve our understanding of a rapidly changing natural environment. During a 3-day workshop held in Cambridge/UK in October 2013 more than 60 scientists from 15 countries who work on the physics, chemistry or biology of the atmosphere-snow-sea ice system discussed research status and challenges, which need to be addressed in the near future. In this presentation I will give a summary of the main research questions identified during this workshop as well as ways forward to answer them through a community-based interdisciplinary approach.

  8. Light-absorbing Particles in Snow and Ice: Measurement and Modeling of Climatic and Hydrological Impact

    Energy Technology Data Exchange (ETDEWEB)

    Qian, Yun; Yasunari, Teppei J.; Doherty, Sarah J.; Flanner, M. G.; Lau, William K.; Ming, J.; Wang, Hailong; Wang, Mo; Warren, Stephen G.; Zhang, Rudong

    2015-01-01

    Light absorbing particles (LAP, e.g., black carbon, brown carbon, and dust) influence water and energy budgets of the atmosphere and snowpack in multiple ways. In addition to their effects associated with atmospheric heating by absorption of solar radiation and interactions with clouds, LAP in snow on land and ice can reduce the surface reflectance (a.k.a., surface darkening), which is likely to accelerate the snow aging process and further reduces snow albedo and increases the speed of snowpack melt. LAP in snow and ice (LAPSI) has been identified as one of major forcings affecting climate change, e.g. in the fourth and fifth assessment reports of IPCC. However, the uncertainty level in quantifying this effect remains very high. In this review paper, we document various technical methods of measuring LAPSI and review the progress made in measuring the LAPSI in Arctic, Tibetan Plateau and other mid-latitude regions. We also report the progress in modeling the mass concentrations, albedo reduction, radiative forcing, andclimatic and hydrological impact of LAPSI at global and regional scales. Finally we identify some research needs for reducing the uncertainties in the impact of LAPSI on global and regional climate and the hydrological cycle.

  9. Snow cover distribution over elevation zones in a mountainous catchment

    Science.gov (United States)

    Panagoulia, D.; Panagopoulos, Y.

    2009-04-01

    A good understanding of the elevetional distribution of snow cover is necessary to predict the timing and volume of runoff. In a complex mountainous terrain the snow cover distribution within a watershed is highly variable in time and space and is dependent on elevation, slope, aspect, vegetation type, surface roughness, radiation load, and energy exchange at the snow-air interface. Decreases in snowpack due to climate change could disrupt the downstream urban and agricultural water supplies, while increases could lead to seasonal flooding. Solar and longwave radiation are dominant energy inputs driving the ablation process. Turbulent energy exchange at the snow cover surface is important during the snow season. The evaporation of blowing and drifting snow is strongly dependent upon wind speed. Much of the spatial heterogeneity of snow cover is the result of snow redistribution by wind. Elevation is important in determining temperature and precipitation gradients along hillslopes, while the temperature gradients determine where precipitation falls as rain and snow and contribute to variable melt rates within the hillslope. Under these premises, the snow accumulation and ablation (SAA) model of the US National Weather Service (US NWS) was applied to implement the snow cover extent over elevation zones of a mountainous catchment (the Mesochora catchment in Western-Central Greece), taking also into account the indirectly included processes of sublimation, interception, and snow redistribution. The catchment hydrology is controlled by snowfall and snowmelt and the simulated discharge was computed from the soil moisture accounting (SMA) model of the US NWS and compared to the measured discharge. The elevationally distributed snow cover extent presented different patterns with different time of maximization, extinction and return during the year, producing different timing of discharge that is a crucial factor for the control and management of water resources systems.

  10. iTree-Hydro: Snow hydrology update for the urban forest hydrology model

    Science.gov (United States)

    Yang Yang; Theodore A. Endreny; David J. Nowak

    2011-01-01

    This article presents snow hydrology updates made to iTree-Hydro, previously called the Urban Forest Effects—Hydrology model. iTree-Hydro Version 1 was a warm climate model developed by the USDA Forest Service to provide a process-based planning tool with robust water quantity and quality predictions given data limitations common to most urban areas. Cold climate...

  11. Quantifying the performance of two conceptual models for snow dominated catchments in Austria and Turkey

    Science.gov (United States)

    Sensoy, Aynur; Parajka, Juraj; Coskun, Cihan; Sorman, Arda; Ertas, Cansaran

    2014-05-01

    In many mountainous regions, snowmelt makes significant contribution to streamflow, particularly during spring and summer months. Understanding the magnitude and timing of this contribution and hydrological forecasts are essential for a range of purposes concerning the implications with water resources management. Conceptual hydrological models have been widely applied for mountain catchments both for operational and scientific applications. Hydrologiska Byran Vattenbalansavdelning (HBV) and Snowmelt Runoff Model (SRM) are selected in this study as the commonly used conceptual models in hydrological modeling forecasting for a number of basins in several countries. Moreover, this selection is also supported by the experiences on the improvement and application in remote sensing techniques in snow dominated regions. The greatest similarity between the two models is that each uses a temperature index method to predict melt rate whereas the greatest difference lies in the way snow cover is handled. In mountainous regions, data limitations prevent detailed understanding of the variability of snow cover and melt. In situ snowpack measurements are sparsely distributed relative to snowpack heterogeneity therefore, to supplement ground measurements; remotely sensed images of snow covered area (SCA) provide useful information for runoff prediction during the snowmelt season. SCA has been used as a direct input to SRM and as a means of checking the internal validity for HBV model. Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products with 500 m spatial resolution are used to derive SCA data in this study. A number of studies have been reported in the literature indicated that the model performance can vary depending on several factors, including the scale and characteristics of the catchment, availability of the data required and runoff producing mechanism. Therefore, five different catchments including data scare and rich basins, areas and reliefs

  12. Snow reliability in ski resorts considering artificial snowmaking

    Science.gov (United States)

    Hofstätter, M.; Formayer, H.; Haas, P.

    2009-04-01

    Snow reliability is the key factor to make skiing on slopes possible and to ensure added value in winter tourism. In this context snow reliability is defined by the duration of a snowpack on the ski runs of at least 50 mm snow water equivalent (SWE), within the main season (Dec-Mar). Furthermore the snowpack should form every winter and be existent early enough in season. In our work we investigate the snow reliability of six Austrian ski resorts. Because nearly all Austrian resorts rely on artificial snowmaking it is of big importance to consider man made snow in the snowpack accumulation and ablation in addition to natural snow. For each study region observed weather data including temperature, precipitation and snow height are used. In addition we differentiate up to three elevations on each site (valley, intermediate, mountain top), being aware of the typical local winter inversion height. Time periods suitable for artificial snow production, for several temperature threshold (-6,-4 or -1 degree Celsius) are calculated on an hourly base. Depending on the actual snowpack height, man made snow can be added in the model with different defined capacities, considering different technologies or the usage of additives. To simulate natural snowpack accumulation and ablation we a simple snow model, based on daily precipitation and temperature. This snow model is optimized at each site separately through certain parameterization factors. Based on the local observations and the monthly climate change signals from the climate model REMO-UBA, we generate long term time series of temperature and precipitation, using the weather generator LARS. Thereby we are not only able to simulate the snow reliability under current, but also under future climate conditions. Our results show significant changes in snow reliability, like an increase of days with insufficient snow heights, especially at mid and low altitudes under natural snow conditions. Artificial snowmaking can partly

  13. Snow cover monitoring model and change over both time and space in pastoral area of northern China

    Science.gov (United States)

    Cui, Yan; Li, Suju; Wang, Ping; Zhang, Wei; Nie, Juan; Wen, Qi

    2014-11-01

    Snow disaster is a natural phenomenon owning to widespread snowfall for a long time and usually affect people's life, property and economic. During the whole disaster management circle, snow disaster in pastoral area of northern china which including Xinjiang, Inner Mongolia, Qinghai, Tibet has been paid more attention. Thus do a good job in snow cover monitoring then found snow disaster in time can help the people in disaster area to take effective rescue measures, which always been the central and local government great important work. Remote sensing has been used widely in snow cover monitoring for its wide range, high efficiency, less conditions, more methods and large information. NOAA/AVHRR data has been used for wide range, plenty bands information and timely acquired and act as an import data of Snow Cover Monitoring Model (SCMM). SCMM including functions list below: First after NOAA/AVHRR data has been acquired, geometric calibration, radiometric calibration and other pre-processing work has been operated. Second after band operation, four threshold conditions are used to extract snow spectrum information among water, cloud and other features in NOAA/AVHRR image. Third snow cover information has been analyzed one by one and the maximum snow cover from about twenty images in a week has been selected. Then selected image has been mosaic which covered the pastoral area of China. At last both time and space analysis has been carried out through this operational model ,such as analysis on the difference between this week and the same period of last year , this week and last week in three level regional. SCMM have been run successfully for three years, and the results have been take into account as one of the three factors which led to risk warning of snow disaster and analysis results from it always play an important role in disaster reduction and relief.

  14. Improvement of Mars surface snow albedo modeling in LMD Mars GCM with SNICAR

    Science.gov (United States)

    Singh, D.; Flanner, M.; Millour, E.

    2017-12-01

    The current version of Laboratoire de Météorologie Dynamique (LMD) Mars GCM (original-MGCM) uses annually repeating (prescribed) albedo values from the Thermal Emission Spectrometer observations. We integrate the Snow, Ice, and Aerosol Radiation (SNICAR) model with MGCM (SNICAR-MGCM) to prognostically determine H2O and CO2 ice cap albedos interactively in the model. Over snow-covered regions mean SNICAR-MGCM albedo is higher by about 0.034 than original-MGCM. Changes in albedo and surface dust content also impact the shortwave energy flux at the surface. SNICAR-MGCM model simulates a change of -1.26 W/m2 shortwave flux on a global scale. Globally, net CO2 ice deposition increases by about 4% over one Martian annual cycle as compared to original-MGCM simulations. SNICAR integration reduces the net mean global surface temperature, and the global surface pressure of Mars by about 0.87% and 2.5% respectively. Changes in albedo also show a similar distribution as dust deposition over the globe. The SNICAR-MGCM model generates albedos with higher sensitivity to surface dust content as compared to original-MGCM. For snow-covered regions, we improve the correlation between albedo and optical depth of dust from -0.91 to -0.97 with SNICAR-MGCM as compared to original-MGCM. Using new diagnostic capabilities with this model, we find that cryospheric surfaces (with dust) increase the global surface albedo of Mars by 0.022. The cryospheric effect is severely muted by dust in snow, however, which acts to decrease the planet-mean surface albedo by 0.06.

  15. Simulation of snow distribution and melt under cloudy conditions in an Alpine watershed

    Directory of Open Access Journals (Sweden)

    H.-Y. Li

    2011-07-01

    Full Text Available An energy balance method and remote-sensing data were used to simulate snow distribution and melt in an alpine watershed in northwestern China within a complete snow accumulation-melt period. The spatial energy budgets were simulated using meteorological observations and a digital elevation model of the watershed. A linear interpolation method was used to estimate the daily snow cover area under cloudy conditions, using Moderate Resolution Imaging Spectroradiometer (MODIS data. Hourly snow distribution and melt, snow cover extent and daily discharge were included in the simulated results. The root mean square error between the measured snow-water equivalent samplings and the simulated results is 3.2 cm. The Nash and Sutcliffe efficiency statistic (NSE between the measured and simulated discharges is 0.673, and the volume difference (Dv is 3.9 %. Using the method introduced in this article, modelling spatial snow distribution and melt runoff will become relatively convenient.

  16. Snowpack modeling in the context of radiance assimilation for snow water equivalent mapping

    Science.gov (United States)

    Durand, M. T.; Kim, R. S.; Li, D.; Dumont, M.; Margulis, S. A.

    2017-12-01

    Data assimilation is often touted as a means of overcoming deficiences in both snowpack modeling and snowpack remote sensing. Direct assimilation of microwave radiances, rather than assimilating microwave retrievals, has shown promise, in this context. This is especially the case for deep mountain snow, which is often assumed to be infeasible to measure with microwave measurements, due to saturation issues. We first demonstrate that the typical way of understanding saturation has often been misunderstood. We show that deep snow leads to a complex microwave signature, but not to saturation per se, because of snowpack stratigraphy. This explains why radiance assimilation requires detailed snowpack models that adequatley stratgigraphy to function accurately. We examine this with two case studies. First, we show how the CROCUS predictions of snowpack stratigraphy allows for assimilation of airborne passive microwave measurements over three 1km2 CLPX Intensive Study Areas. Snowpack modeling and particle filter analysis is performed at 120 m spatial resolution. When run without the benefit of radiance assimilation, CROCUS does not fully capture spatial patterns in the data (R2=0.44; RMSE=26 cm). Assimlilation of microwave radiances for a single flight recovers the spatial pattern of snow depth (R2=0.85; RMSE = 13 cm). This is despite the presence of deep snow; measured depths range from 150 to 325 cm. Adequate results are obtained even for partial forest cover, and bias in precipitation forcing. The results are severely degraded if a three-layer snow model is used, however. The importance of modeling snowpack stratigraphy is highlighted. Second, we compare this study to a recent analysis assimilating spaceborne radiances for a 511 km2 sub-watershed of the Kern River, in the Sierra Nevada. Here, the daily Level 2A AMSR-E footprints (88 km2) are assimilated into a model running at 90 m spatial resolution. The three-layer model is specially adapted to predict "effective

  17. Spatio-Temporal Variability of Recent Snow Accumulation Across the West Antarctic Ice Sheet Divide Using Ultra-High Frequency Radar and Shallow Firn Cores

    Science.gov (United States)

    Keeler, D. G.; Rupper, S.; Forster, R. R.; Miège, C.; Brewer, S.; Koenig, L.

    2017-12-01

    The West Antarctic Ice Sheet (WAIS) could be a substantial source of future sea level rise, with 3+ meters of potential increase stored in the ice sheet. Adequate predictions of WAIS contributions, however, depend on well-constrained surface mass balance estimates for the region. Given the sparsity of available data, such estimates are tenuous. Although new data are periodically added, further research (both to collect more data and better utilize existing data) is critical to addressing these issues. Here we present accumulation data from 9 shallow firn cores and 600 km of Ku band radar traces collected as part of the Satellite Era Antarctic Traverse (SEAT) 2011/2012 field season. Using these data, combined with similar data collected during the SEAT 2010/2011 field season, we investigate the spatial variability in accumulation across the WAIS Divide and surrounding regions. We utilize seismic interpretation and 3D visualization tools to investigate the extent and variations of laterally continuous internal horizons in the radar profiles, and compare the results to nearby firn cores. Previous results show that clearly visible, laterally continuous horizons in radar returns in this area do not always represent annual accumulation isochrones, but can instead represent multi-year or sub-annual events. The automated application of Bayesian inference techniques to averaged estimates of multiple adjacent radar traces, however, can estimate annually-resolved independent age-depth scales for these radar data. We use these same automated techniques on firn core isotopic records to infer past snow accumulation rates, allowing a direct comparison with the radar-derived results. Age-depth scales based on manual annual-layer counting of geochemical and isotopic species from these same cores provide validation for the automated approaches. Such techniques could theoretically be applied to additional radar/core data sets in polar regions (e.g. Operation IceBridge), thereby

  18. Next Generation Snow Cover Mapping: Can Future Hyperspectral Satellite Spectrometer Systems Improve Subpixel Snow-covered Area and Grain Size in the Sierra Nevada?

    Science.gov (United States)

    Hill, R.; Calvin, W. M.; Harpold, A.

    2017-12-01

    Mountain snow storage is the dominant source of water for humans and ecosystems in western North America. Consequently, the spatial distribution of snow-covered area is fundamental to both hydrological, ecological, and climate models. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were collected along the entire Sierra Nevada mountain range extending from north of Lake Tahoe to south of Mt. Whitney during the 2015 and 2016 snow-covered season. The AVIRIS dataset used in this experiment consists of 224 contiguous spectral channels with wavelengths ranging 400-2500 nanometers at a 15-meter spatial pixel size. Data from the Sierras were acquired on four days: 2/24/15 during a very low snow year, 3/24/16 near maximum snow accumulation, and 5/12/16 and 5/18/16 during snow ablation and snow loss. Building on previous retrieval of subpixel snow-covered area algorithms that take into account varying grain size we present a model that analyzes multiple endmembers of varying snow grain size, vegetation, rock, and soil in segmented regions along the Sierra Nevada to determine snow-cover spatial extent, snow sub-pixel fraction, and approximate grain size. In addition, varying simulated models of the data will compare and contrast the retrieval of current snow products such as MODIS Snow-Covered Area and Grain Size (MODSCAG) and the Airborne Space Observatory (ASO). Specifically, does lower spatial resolution (MODIS), broader resolution bandwidth (MODIS), and limited spectral resolution (ASO) affect snow-cover area and grain size approximations? The implications of our findings will help refine snow mapping products for planned hyperspectral satellite spectrometer systems such as EnMAP (slated to launch in 2019), HISUI (planned for inclusion on the International Space Station in 2018), and HyspIRI (currently under consideration).

  19. Modeling the influence of snow cover on low Arctic net ecosystem exchange

    International Nuclear Information System (INIS)

    Luus, K A; Kelly, R E J; Lin, J C; Humphreys, E R; Lafleur, P M; Oechel, W C

    2013-01-01

    The Arctic net ecosystem exchange (NEE) of CO 2 between the land surface and the atmosphere is influenced by the timing of snow onset and melt. The objective of this study was to examine whether uncertainty in model estimates of NEE could be reduced by representing the influence of snow on NEE using remote sensing observations of snow cover area (SCA). Observations of NEE and time-lapse images of SCA were collected over four locations at a low Arctic site (Daring Lake, NWT) in May–June 2010. Analysis of these observations indicated that SCA influences NEE, and that good agreement exists between SCA derived from time-lapse images, Landsat and MODIS. MODIS SCA was therefore incorporated into the vegetation photosynthesis respiration model (VPRM). VPRM was calibrated using observations collected in 2005 at Daring Lake. Estimates of NEE were then generated over Daring Lake and Ivotuk, Alaska (2004–2007) using VPRM formulations with and without explicit representations of the influence of SCA on respiration and/or photosynthesis. Model performance was assessed by comparing VPRM output against unfilled eddy covariance observations from Daring Lake and Ivotuk (2004–2007). The uncertainty in VPRM estimates of NEE was reduced when respiration was estimated as a function of air temperature when SCA ≤ 50% and as a function of soil temperature when SCA > 50%. (letter)

  20. Crossing physical simulations of snow conditions and a geographic model of ski area to assess ski resorts vulnerability

    Science.gov (United States)

    François, Hugues; Spandre, Pierre; Morin, Samuel; George-Marcelpoil, Emmanuelle; Lafaysse, Matthieu; Lejeune, Yves

    2016-04-01

    In order to face climate change, meteorological variability and the recurrent lack of natural snow on the ground, ski resorts adaptation often rely on technical responses. Indeed, since the occurrence of episodes with insufficient snowfalls in the early 1990's, snowmaking has become an ordinary practice of snow management, comparable to grooming, and contributes to optimise the operation of ski resorts. It also participates to the growth of investments and is associated with significant operating costs, and thus represents a new source of vulnerability. The assessment of the actual effects of snowmaking and of snow management practices in general is a real concern for the future of the ski industry. The principal model use to simulate snow conditions in resorts, Ski Sim, has also been moving this way. Its developers introduced an artificial input of snow on ski area to complete natural snowfalls and considered different organisations of ski lifts (lower and upper zones). However the use of a degree-day model prevents them to consider the specific properties of artificial snow and the impact of grooming on the snowpack. A first proof of concept in the French Alps has shown the feasibility and the interest to cross the geographic model of ski areas and the output of the physically-based reanalysis of snow conditions SAFRAN - Crocus (François et al., CRST 2014). Since these initial developments, several ways have been explored to refine our model. A new model of ski areas has been developed. Our representation is now based on gravity derived from a DEM and ski lift localisation. A survey about snow management practices also allowed us to define criteria in order to model snowmaking areas given ski areas properties and tourism infrastructures localisation. We also suggest to revisit the assessment of ski resort viability based on the "one hundred days rule" based on natural snow depth only. Indeed, the impact of snow management must be considered so as to propose

  1. Spatially explicit modeling of conflict zones between wildlife and snow sports: prioritizing areas for winter refuges.

    Science.gov (United States)

    Braunisch, Veronika; Patthey, Patrick; Arlettaz, Raphaël

    2011-04-01

    Outdoor winter recreation exerts an increasing pressure upon mountain ecosystems, with unpredictable, free-ranging activities (e.g., ski mountaineering, snowboarding, and snowshoeing) representing a major source of stress for wildlife. Mitigating anthropogenic disturbance requires the spatially explicit prediction of the interference between the activities of humans and wildlife. We applied spatial modeling to localize conflict zones between wintering Black Grouse (Tetrao tetrix), a declining species of Alpine timberline ecosystems, and two free-ranging winter sports (off-piste skiing [including snow-boarding] and snowshoeing). Track data (snow-sports and birds' traces) obtained from aerial photographs taken over a 585-km transect running along the timberline, implemented within a maximum entropy model, were used to predict the occurrence of snow sports and Black Grouse as a function of landscape characteristics. By modeling Black Grouse presence in the theoretical absence of free-ranging activities and ski infrastructure, we first estimated the amount of habitat reduction caused by these two factors. The models were then extrapolated to the altitudinal range occupied by Black Grouse, while the spatial extent and intensity of potential conflict were assessed by calculating the probability of human-wildlife co-occurrence. The two snow-sports showed different distribution patterns. Skiers' occurrence was mainly determined by ski-lift presence and a smooth terrain, while snowshoers' occurrence was linked to hiking or skiing routes and moderate slopes. Wintering Black Grouse avoided ski lifts and areas frequented by free-ranging snow sports. According to the models, Black Grouse have faced a substantial reduction of suitable wintering habitat along the timberline transect: 12% due to ski infrastructure and another 16% when adding free-ranging activities. Extrapolating the models over the whole study area results in an overall habitat loss due to ski infrastructure of

  2. Merging a Terrain-Based Parameter and Snow Particle Counter Data for the Assessment of Snow Redistribution in the Col du Lac Blanc Area

    Science.gov (United States)

    Schön, Peter; Prokop, Alexander; Naaim-Bouvet, Florence; Vionnet, Vincent; Guyomarc'h, Gilbert; Heiser, Micha; Nishimura, Kouichi

    2015-04-01

    Wind and the associated snow drift are dominating factors determining the snow distribution and accumulation in alpine areas, resulting in a high spatial variability of snow depth that is difficult to evaluate and quantify. The terrain-based parameter Sx characterizes the degree of shelter or exposure of a grid point provided by the upwind terrain, without the computational complexity of numerical wind field models. The parameter has shown to qualitatively predict snow redistribution with good reproduction of spatial patterns. It does not, however, provide a quantitative estimate of changes in snow depths. The objective of our research was to introduce a new parameter to quantify changes in snow depths in our research area, the Col du Lac Blanc in the French Alps. The area is at an elevation of 2700 m and particularly suited for our study due to its consistently bi-modal wind directions. Our work focused on two pronounced, approximately 10 m high terrain breaks, and we worked with 1 m resolution digital snow surface models (DSM). The DSM and measured changes in snow depths were obtained with high-accuracy terrestrial laser scan (TLS) measurements. First we calculated the terrain-based parameter Sx on a digital snow surface model and correlated Sx with measured changes in snow-depths (Δ SH). Results showed that Δ SH can be approximated by Δ SHestimated = α * Sx, where α is a newly introduced parameter. The parameter α has shown to be linked to the amount of snow deposited influenced by blowing snow flux. At the Col du Lac Blanc test side, blowing snow flux is recorded with snow particle counters (SPC). Snow flux is the number of drifting snow particles per time and area. Hence, the SPC provide data about the duration and intensity of drifting snow events, two important factors not accounted for by the terrain parameter Sx. We analyse how the SPC snow flux data can be used to estimate the magnitude of the new variable parameter α . To simulate the development

  3. Modeling of radiation transport in coupled atmosphere-snow-ice-ocean systems

    International Nuclear Information System (INIS)

    Stamnes, K.; Hamre, B.; Stamnes, J.J.; Ryzhikov, G.; Biryulina, M.; Mahoney, R.; Hauss, B.; Sei, A.

    2011-01-01

    A radiative transfer model for coupled atmosphere-snow-ice-ocean systems (CASIO-DISORT) is used to develop accurate and efficient tools for computing the bidirectional reflectance distribution function (BRDF) of sea ice for a wide range of situations occurring in nature. These tools include a method to generate sea ice inherent optical properties (IOPs: single-scattering albedo, extinction optical depth, and scattering asymmetry parameter) for any wavelength between 300 and 4000 nm as a function of sea ice physical parameters including real and imaginary parts of the sea ice refractive index, brine pocket concentration and effective brine pocket size, air bubble concentration and effective air bubble size, volume fraction of ice impurities and impurity absorption coefficient, and sea ice thickness. The CASIO-DISORT code was used to compute look-up tables (LUTs) of the Fourier expansion coefficients of the BRDF as a function of angles of illumination and observation, sea ice IOPs, and ocean albedo. By interpolation in the LUTs one efficiently obtains accurate BRDF values. To include snow on the ice we modified DISORT2 to accept Fourier expansion coefficients for the BDRF as input instead of the BRDF itself, thereby reducing the computation time by a factor of about 60. The BRDF computed by CASIO-DISORT or retrieved from the LUTs applies to diffuse light only. To remedy this shortcoming we added a specular Gaussian beam component to the new BRDF tool and verified that it works well for BRDFs for bare and snow-covered sea ice.

  4. Algorithm for modelling the removal of snow from streches of the manoeuvring aera of an airport

    Energy Technology Data Exchange (ETDEWEB)

    Soriguera Marti, F.; Martinez-Diaz, M.; Perez Perez, I.

    2016-07-01

    This article presents an algorithm and a structured methodology to address the issue of the optimisation of resources when clearing snow from stretches of the manoeuvring area of an airport. This overall issue is how to best utilise limited resources to remove snow from taxiways and runways so as to leave surfaces in an acceptable state for aircraft operations. To achieve this the airfield is divided into subsets of significant stretches for the purpose of operations and target times are set at which these are to be open to aircraft traffic. The manoeuvring area is also divided into zones, with the condition that the subsets of significant stretches lie within just one of these zones. The mathematical model contains operating restrictions with regard to the fulfilment of partial operational targets applied to the subsets of significant stretches, and also concerning the snow-clearing machines. The problem is solved by an iterative optimisation process based on linear programming applied successively to the zones that make up the manoeuvring area during each iteration. The method is particularised for the case of the manoeuvring area of Adolfo Suarez Madrid - Barajas Airport. (Author)

  5. A Coupled Snow Operations-Skier Demand Model for the Ontario (Canada) Ski Region

    Science.gov (United States)

    Pons, Marc; Scott, Daniel; Steiger, Robert; Rutty, Michelle; Johnson, Peter; Vilella, Marc

    2016-04-01

    The multi-billion dollar global ski industry is one of the tourism subsectors most directly impacted by climate variability and change. In the decades ahead, the scholarly literature consistently projects decreased reliability of natural snow cover, shortened and more variable ski seasons, as well as increased reliance on snowmaking with associated increases in operational costs. In order to develop the coupled snow, ski operations and demand model for the Ontario ski region (which represents approximately 18% of Canada's ski market), the research utilized multiple methods, including: a in situ survey of over 2400 skiers, daily operations data from ski resorts over the last 10 years, climate station data (1981-2013), climate change scenario ensemble (AR5 - RCP 8.5), an updated SkiSim model (building on Scott et al. 2003; Steiger 2010), and an agent-based model (building on Pons et al. 2014). Daily snow and ski operations for all ski areas in southern Ontario were modeled with the updated SkiSim model, which utilized current differential snowmaking capacity of individual resorts, as determined from daily ski area operations data. Snowmaking capacities and decision rules were informed by interviews with ski area managers and daily operations data. Model outputs were validated with local climate station and ski operations data. The coupled SkiSim-ABM model was run with historical weather data for seasons representative of an average winter for the 1981-2010 period, as well as an anomalously cold winter (2012-13) and the record warm winter in the region (2011-12). The impact on total skier visits and revenues, and the geographic and temporal distribution of skier visits were compared. The implications of further climate adaptation (i.e., improving the snowmaking capacity of all ski areas to the level of leading resorts in the region) were also explored. This research advances system modelling, especially improving the integration of snow and ski operations models with

  6. Modeling of surface dust concentration in snow cover at industrial area using neural networks and kriging

    Science.gov (United States)

    Sergeev, A. P.; Tarasov, D. A.; Buevich, A. G.; Shichkin, A. V.; Tyagunov, A. G.; Medvedev, A. N.

    2017-06-01

    Modeling of spatial distribution of pollutants in the urbanized territories is difficult, especially if there are multiple emission sources. When monitoring such territories, it is often impossible to arrange the necessary detailed sampling. Because of this, the usual methods of analysis and forecasting based on geostatistics are often less effective. Approaches based on artificial neural networks (ANNs) demonstrate the best results under these circumstances. This study compares two models based on ANNs, which are multilayer perceptron (MLP) and generalized regression neural networks (GRNNs) with the base geostatistical method - kriging. Models of the spatial dust distribution in the snow cover around the existing copper quarry and in the area of emissions of a nickel factory were created. To assess the effectiveness of the models three indices were used: the mean absolute error (MAE), the root-mean-square error (RMSE), and the relative root-mean-square error (RRMSE). Taking into account all indices the model of GRNN proved to be the most accurate which included coordinates of the sampling points and the distance to the likely emission source as input parameters for the modeling. Maps of spatial dust distribution in the snow cover were created in the study area. It has been shown that the models based on ANNs were more accurate than the kriging, particularly in the context of a limited data set.

  7. A simple model for predicting soil temperature in snow-covered and seasonally frozen soil: model description and testing

    Directory of Open Access Journals (Sweden)

    K. Rankinen

    2004-01-01

    Full Text Available Microbial processes in soil are moisture, nutrient and temperature dependent and, consequently, accurate calculation of soil temperature is important for modelling nitrogen processes. Microbial activity in soil occurs even at sub-zero temperatures so that, in northern latitudes, a method to calculate soil temperature under snow cover and in frozen soils is required. This paper describes a new and simple model to calculate daily values for soil temperature at various depths in both frozen and unfrozen soils. The model requires four parameters: average soil thermal conductivity, specific heat capacity of soil, specific heat capacity due to freezing and thawing and an empirical snow parameter. Precipitation, air temperature and snow depth (measured or calculated are needed as input variables. The proposed model was applied to five sites in different parts of Finland representing different climates and soil types. Observed soil temperatures at depths of 20 and 50 cm (September 1981–August 1990 were used for model calibration. The calibrated model was then tested using observed soil temperatures from September 1990 to August 2001. R2-values of the calibration period varied between 0.87 and 0.96 at a depth of 20 cm and between 0.78 and 0.97 at 50 cm. R2-values of the testing period were between 0.87 and 0.94 at a depth of 20cm, and between 0.80 and 0.98 at 50cm. Thus, despite the simplifications made, the model was able to simulate soil temperature at these study sites. This simple model simulates soil temperature well in the uppermost soil layers where most of the nitrogen processes occur. The small number of parameters required means that the model is suitable for addition to catchment scale models. Keywords: soil temperature, snow model

  8. Modeling and analysis of Off-beam lidar returns from thick clouds, snow, and sea ice

    International Nuclear Information System (INIS)

    Varnai, T.; Cahalan, R. F.

    2009-01-01

    A group of recently developed lidar (laser ranging and detection) systems can detect signals returning from several wide field-of-views, allowing them to observe the way laser pulses spread in thick media. The new capability enabled accurate measurements of cloud geometrical thickness and promises improved measurements of internal cloud structure as well as snow and sea ice thickness. This paper presents a brief overview of radiation transport simulation techniques and data analysis methods that were developed for multi-view lidar applications and for and considering multiple scattering effects in single-view lidar data. In discussing methods for simulating the three-dimensional spread of lidar pulses, we present initial results from Phase 3 of the Intercomparison of 3-D Radiation Codes (I3RC) project. The results reveal some differences in the capabilities of participating models, while good agreement among several models provides consensus results suitable for testing future models. Detailed numerical results are available at the I3RC web site at http://i3rc.gsfc.nasa. gov. In considering data analysis methods, we focus on the Thickness from Off-beam Returns (THOR) lidar. THOR proved successful in measuring the geometrical thickness of optically thick clouds; here we focus on its potential for retrieving the vertical profile of scattering coefficient in clouds and for measuring snow thickness. Initial observations suggest considerable promise but also reveal some limitations, for example that the maximum retrievable snow thickness drops from about 0.5 m in pristine areas to about 0.15 m in polluted regions. (authors)

  9. An accumulator model of semantic interference

    NARCIS (Netherlands)

    van Maanen, Leendert; van Rijn, Hedderik

    To explain latency effects in picture-word interference tasks, cognitive models need to account for both interference and stimulus onset asynchrony (SOA) effects. As opposed to most models of picture-word interference, which model the time course during the task in a ballistic manner, the RACE model

  10. Using High Resolution Simulations with WRF/SSiB Regional Climate Model Constrained by In Situ Observations to Assess the Impacts of Dust in Snow in the Upper Colorado River Basin

    Science.gov (United States)

    Oaida, C. M.; Skiles, M.; Painter, T. H.; Xue, Y.

    2015-12-01

    The mountain snowpack is an essential resource for both the environment as well as society. Observational and energy balance modeling work have shown that dust on snow (DOS) in western U.S. (WUS) is a major contributor to snow processes, including snowmelt timing and runoff amount in regions like the Upper Colorado River Basin (UCRB). In order to accurately estimate the impact of DOS to the hydrologic cycle and water resources, now and under a changing climate, we need to be able to (1) adequately simulate the snowpack (accumulation), and (2) realistically represent DOS processes in models. Energy balance models do not capture the impact on a broader local or regional scale, nor the land-atmosphere feedbacks, while GCM studies cannot resolve orographic-related precipitation processes, and therefore snowpack accumulation, owing to coarse spatial resolution and smoother terrain. All this implies the impacts of dust on snow on the mountain snowpack and other hydrologic processes are likely not well captured in current modeling studies. Recent increase in computing power allows for RCMs to be used at higher spatial resolutions, while recent in situ observations of dust in snow properties can help constrain modeling simulations. Therefore, in the work presented here, we take advantage of these latest resources to address the some of the challenges outlined above. We employ the newly enhanced WRF/SSiB regional climate model at 4 km horizontal resolution. This scale has been shown by others to be adequate in capturing orographic processes over WUS. We also constrain the magnitude of dust deposition provided by a global chemistry and transport model, with in situ measurements taken at sites in the UCRB. Furthermore, we adjust the dust absorptive properties based on observed values at these sites, as opposed to generic global ones. This study aims to improve simulation of the impact of dust in snow on the hydrologic cycle and related water resources.

  11. Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

    Science.gov (United States)

    Terzago, Silvia; von Hardenberg, Jost; Palazzi, Elisa; Provenzale, Antonello

    2017-07-01

    The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow-climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes - near-surface air temperature and precipitation - against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We

  12. Assessing a Top-Down Modeling Approach for Seasonal Scale Snow Sensitivity

    Science.gov (United States)

    Luce, C. H.; Lute, A.

    2017-12-01

    Mechanistic snow models are commonly applied to assess changes to snowpacks in a warming climate. Such assessments involve a number of assumptions about details of weather at daily to sub-seasonal time scales. Models of season-scale behavior can provide contrast for evaluating behavior at time scales more in concordance with climate warming projections. Such top-down models, however, involve a degree of empiricism, with attendant caveats about the potential of a changing climate to affect calibrated relationships. We estimated the sensitivity of snowpacks from 497 Snowpack Telemetry (SNOTEL) stations in the western U.S. based on differences in climate between stations (spatial analog). We examined the sensitivity of April 1 snow water equivalent (SWE) and mean snow residence time (SRT) to variations in Nov-Mar precipitation and average Nov-Mar temperature using multivariate local-fit regressions. We tested the modeling approach using a leave-one-out cross-validation as well as targeted two-fold non-random cross-validations contrasting, for example, warm vs. cold years, dry vs. wet years, and north vs. south stations. Nash-Sutcliffe Efficiency (NSE) values for the validations were strong for April 1 SWE, ranging from 0.71 to 0.90, and still reasonable, but weaker, for SRT, in the range of 0.64 to 0.81. From these ranges, we exclude validations where the training data do not represent the range of target data. A likely reason for differences in validation between the two metrics is that the SWE model reflects the influence of conservation of mass while using temperature as an indicator of the season-scale energy balance; in contrast, SRT depends more strongly on the energy balance aspects of the problem. Model forms with lower numbers of parameters generally validated better than more complex model forms, with the caveat that pseudoreplication could encourage selection of more complex models when validation contrasts were weak. Overall, the split sample validations

  13. Application of statistical and dynamics models for snow avalanche hazard assessment in mountain regions of Russia

    Science.gov (United States)

    Turchaninova, A.

    2012-04-01

    The estimation of extreme avalanche runout distances, flow velocities, impact pressures and volumes is an essential part of snow engineering in mountain regions of Russia. It implies the avalanche hazard assessment and mapping. Russian guidelines accept the application of different avalanche models as well as approaches for the estimation of model input parameters. Consequently different teams of engineers in Russia apply various dynamics and statistical models for engineering practice. However it gives more freedom to avalanche practitioners and experts but causes lots of uncertainties in case of serious limitations of avalanche models. We discuss these problems by presenting the application results of different well known and widely used statistical (developed in Russia) and avalanche dynamics models for several avalanche test sites in the Khibini Mountains (The Kola Peninsula) and the Caucasus. The most accurate and well-documented data from different powder and wet, big rare and small frequent snow avalanche events is collected from 1960th till today in the Khibini Mountains by the Avalanche Safety Center of "Apatit". This data was digitized and is available for use and analysis. Then the detailed digital avalanche database (GIS) was created for the first time. It contains contours of observed avalanches (ESRI shapes, more than 50 years of observations), DEMs, remote sensing data, description of snow pits, photos etc. Thus, the Russian avalanche data is a unique source of information for understanding of an avalanche flow rheology and the future development and calibration of the avalanche dynamics models. GIS database was used to analyze model input parameters and to calibrate and verify avalanche models. Regarding extreme dynamic parameters the outputs using different models can differ significantly. This is unacceptable for the engineering purposes in case of the absence of the well-defined guidelines in Russia. The frequency curves for the runout distance

  14. Monitoring of the Liquid Water Content During Snowmelt Using C-Band SAR Data and the Snow Model CROCUS

    Science.gov (United States)

    Rondeau-Genesse, G.; Trudel, M.; Leconte, R.

    2014-12-01

    Coupling C-Band synthetic aperture radar (SAR) data to a multilayer snow model is a step in better understanding the temporal evolution of the radar backscattering coefficient during snowmelt. The watershed used for this study is the Nechako River Basin, located in the Rocky Mountains of British-Columbia (Canada). This basin has a snowpack of several meters in depth and part of its water is diverted to the Kemano hydropower system, managed by Rio-Tinto Alcan. Eighteen RADARSAT-2 ScanSAR Wide archive images were acquired in VV/VH polarization for the winter of 2011-2012, under different snow conditions. They are interpreted along with CROCUS, a multilayer physically-based snow model developed by Météo-France. This model discretizes the snowpack into 50 layers, which makes it possible to monitor various characteristics, such as liquid water content (LWC), throughout the season. CROCUS is used to model three specific locations of the Nechako River Basin. Results vary from one site to another, but in general there is a good agreement between the modeled LWC of the first layer of the snowpack and the backscattering coefficient of the RADARSAT-2 images, with a coefficient of determination (R²) of 0.80 and more. The radar images themselves were processed using an updated version of Nagler's methodology, which consists of subtracting an image in wet snow conditions to one in dry snow conditions, as wet snow can then be identified using a soft threshold centered around -3 dB. A second filter was used in order to differentiate dry snow and bare soil. That filter combines a VH/VV ratio threshold and an altitude criterion. The ensuing maps show a good agreement with the MODIS snow-covered area, which is already obtained daily over the Nechako River Basin, but with additional information on the location of wet snow and without sensibility to cloud cover. As a next step, the outputs of CROCUS will be used in Mätzler's Microwave Emission Model of Layered Snowpacks (MEMLS) to

  15. Unexpected Patterns in Snow and Dirt

    Science.gov (United States)

    Ackerson, Bruce J.

    2018-01-01

    For more than 30 years, Albert A. Bartlett published "Thermal patterns in the snow" in this journal. These are patterns produced by heat sources underneath the snow. Bartlett's articles encouraged me to pay attention to patterns in snow and to understanding them. At winter's end the last snow becomes dirty and is heaped into piles. This snow comes from the final clearing of sidewalks and driveways. The patterns observed in these piles defied my intuition. This melting snow develops edges where dirt accumulates, in contrast to ice cubes, which lose sharp edges and become more spherical upon melting. Furthermore, dirt absorbs more radiation than snow and yet doesn't melt and round the sharp edges of snow, where dirt accumulates.

  16. Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

    Directory of Open Access Journals (Sweden)

    S. Terzago

    2017-07-01

    Full Text Available The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow–climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR, and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs, participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX and in the Fifth Coupled Model Intercomparison Project (CMIP5 respectively. We evaluate their reliability in reproducing the main drivers of snow processes – near-surface air temperature and precipitation – against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around

  17. Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

    Science.gov (United States)

    Alonso-González, Esteban; López-Moreno, J. Ignacio; Gascoin, Simon; García-Valdecasas Ojeda, Matilde; Sanmiguel-Vallelado, Alba; Navarro-Serrano, Francisco; Revuelto, Jesús; Ceballos, Antonio; Jesús Esteban-Parra, María; Essery, Richard

    2018-02-01

    We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

  18. Snow Matters

    DEFF Research Database (Denmark)

    Gyimóthy, Szilvia; Jensen, Martin Trandberg

    2018-01-01

    attribute of high altitude mountain destinations. Hitherto, researchers mostly engaged with snowclad landscapes as a backstage; trying to deconstruct the complex symbolism and representational qualities of this elusive substance. Despite snow being a strategically crucial condition for tourism in the Alps......This chapter explores the performative potential of snow for Alpine tourism, by drawing attention to its material and nonrepresentational significance for tourism practices. European imagination has been preoccupied with snow since medieval times and even today, snow features as the sine que non...

  19. Improvement of distributed snowmelt energy balance modeling with MODIS-based NDSI-derived fractional snow-covered area data

    Science.gov (United States)

    Joel W. Homan; Charles H. Luce; James P. McNamara; Nancy F. Glenn

    2011-01-01

    Describing the spatial variability of heterogeneous snowpacks at a watershed or mountain-front scale is important for improvements in large-scale snowmelt modelling. Snowmelt depletion curves, which relate fractional decreases in snowcovered area (SCA) against normalized decreases in snow water equivalent (SWE), are a common approach to scale-up snowmelt models....

  20. Dynamic stochastic accumulation model with application to pension savings management

    Directory of Open Access Journals (Sweden)

    Melicherčik Igor

    2010-01-01

    Full Text Available We propose a dynamic stochastic accumulation model for determining optimal decision between stock and bond investments during accumulation of pension savings. Stock prices are assumed to be driven by the geometric Brownian motion. Interest rates are modeled by means of the Cox-Ingersoll-Ross model. The optimal decision as a solution to the corresponding dynamic stochastic program is a function of the duration of saving, the level of savings and the short rate. Qualitative and quantitative properties of the optimal solution are analyzed. The model is tested on the funded pillar of the Slovak pension system. The results are calculated for various risk preferences of a saver.

  1. Anoxia in the snow

    Science.gov (United States)

    Bristow, Laura A.

    2018-04-01

    Substantial amounts of denitrification and other anaerobic metabolisms can occur in anoxic microenvironments within marine snow particles, according to model simulations. This microbial activity may have a global impact on nitrogen cycling.

  2. High-accuracy measurements of snow Bidirectional Reflectance Distribution Function at visible and NIR wavelengths – comparison with modelling results

    Directory of Open Access Journals (Sweden)

    M. Dumont

    2010-03-01

    Full Text Available High-accuracy measurements of snow Bidirectional Reflectance Distribution Function (BRDF were performed for four natural snow samples with a spectrogonio-radiometer in the 500–2600 nm wavelength range. These measurements are one of the first sets of direct snow BRDF values over a wide range of lighting and viewing geometry. They were compared to BRDF calculated with two optical models. Variations of the snow anisotropy factor with lighting geometry, wavelength and snow physical properties were investigated. Results show that at wavelengths with small penetration depth, scattering mainly occurs in the very top layers and the anisotropy factor is controlled by the phase function. In this condition, forward scattering peak or double scattering peak is observed. In contrast at shorter wavelengths, the penetration of the radiation is much deeper and the number of scattering events increases. The anisotropy factor is thus nearly constant and decreases at grazing observation angles. The whole dataset is available on demand from the corresponding author.

  3. Long-term analyses of snow dynamics within the french Alps on the 1900-2100 period. Analyses of historical snow water equivalent observations, modelisations and projections of a hundred of snow courses.

    Science.gov (United States)

    Mathevet, T.; Joel, G.; Gottardi, F.; Nemoz, B.

    2017-12-01

    The aim of this communication is to present analyses of climate variability and change on snow water equivalent (SWE) observations, reconstructions (1900-2016) and scenarii (2020-2100) of a hundred of snow courses dissiminated within the french Alps. This issue became particularly important since a decade, in regions where snow variability had a large impact on water resources availability, poor snow conditions in ski resorts and artificial snow production. As a water resources manager in french mountainuous regions, EDF (french hydropower company) has developed and managed a hydrometeorological network since 1950. A recent data rescue research allowed to digitize long term SWE manual measurments of a hundred of snow courses within the french Alps. EDF have been operating an automatic SWE sensors network, complementary to the snow course network. Based on numerous SWE observations time-series and snow accumulation and melt model (Garavaglia et al., 2017), continuous daily historical SWE time-series have been reconstructed within the 1950-2016 period. These reconstructions have been extented to 1900 using 20 CR reanalyses (ANATEM method, Kuentz et al., 2015) and up to 2100 using GIEC Climate Change scenarii. Considering various mountainous areas within the french Alps, this communication focuses on : (1) long term (1900-2016) analyses of variability and trend of total precipitation, air temperature, snow water equivalent, snow line altitude, snow season length , (2) long term variability of hydrological regime of snow dominated watersheds and (3) future trends (2020 -2100) using GIEC Climate Change scenarii. Comparing historical period (1950-1984) to recent period (1984-2016), quantitative results within a region in the north Alps (Maurienne) shows an increase of air temperature by 1.2 °C, an increase of snow line height by 200m, a reduction of SWE by 200 mm/year and a reduction of snow season length by 15 days. These analyses will be extended from north to south

  4. High Resolution Insights into Snow Distribution Provided by Drone Photogrammetry

    Science.gov (United States)

    Redpath, T.; Sirguey, P. J.; Cullen, N. J.; Fitzsimons, S.

    2017-12-01

    Dynamic in time and space, New Zealand's seasonal snow is largely confined to remote alpine areas, complicating ongoing in situ measurement and characterisation. Improved understanding and modeling of the seasonal snowpack requires fine scale resolution of snow distribution and spatial variability. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial and temporal variability of snow depth and water equivalent in a New Zealand alpine catchment is assessed in the Pisa Range, Central Otago. This approach yielded orthophotomosaics and digital surface models (DSM) at 0.05 and 0.15 m spatial resolution, respectively. An autumn reference DSM allowed mapping of winter (02/08/2016) and spring (10/09/2016) snow depth at 0.15 m spatial resolution, via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by comparison of snow-free regions of the spring and autumn DSMs, while accuracy of RPAS retrieved snow depth was assessed with 86 in situ snow probe measurements. Results show a mean vertical residual of 0.024 m between DSMs acquired in autumn and spring. This residual approximated a Laplace distribution, reflecting the influence of large outliers on the small overall bias. Propagation of errors associated with successive DSMs saw snow depth mapped with an accuracy of ± 0.09 m (95% c.l.). Comparing RPAS and in situ snow depth measurements revealed the influence of geo-location uncertainty and interactions between vegetation and the snowpack on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine scale spatial variability. Despite limitations accompanying RPAS photogrammetry, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological basin ( 0.5 km2), at high resolution. Resolving snowpack features associated with re-distribution and preferential

  5. Subpixel Snow-covered Area Including Differentiated Grain Size from AVIRIS Data Over the Sierra Nevada Mountain Range

    Science.gov (United States)

    Hill, R.; Calvin, W. M.; Harpold, A. A.

    2016-12-01

    Mountain snow storage is the dominant source of water for humans and ecosystems in western North America. Consequently, the spatial distribution of snow-covered area is fundamental to both hydrological, ecological, and climate models. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were collected along the entire Sierra Nevada mountain range extending from north of Lake Tahoe to south of Mt. Whitney during the 2015 and 2016 snow-covered season. The AVIRIS dataset used in this experiment consists of 224 contiguous spectral channels with wavelengths ranging 400-2500 nanometers at a 15-meter spatial pixel size. Data from the Sierras were acquired on four days: 2/24/15 during a very low snow year, 3/24/16 near maximum snow accumulation, and 5/12/16 and 5/18/16 during snow ablation and snow loss. Previous retrieval of subpixel snow-covered area in alpine regions used multiple snow endmembers due to the sensitivity of snow spectral reflectance to grain size. We will present a model that analyzes multiple endmembers of varying snow grain size, vegetation, rock, and soil in segmented regions along the Sierra Nevada to determine snow-cover spatial extent, snow sub-pixel fraction and approximate grain size or melt state. The root mean squared error will provide a spectrum-wide assessment of the mixture model's goodness-of-fit. Analysis will compare snow-covered area and snow-cover depletion in the 2016 year, and annual variation from the 2015 year. Field data were also acquired on three days concurrent with the 2016 flights in the Sagehen Experimental Forest and will support ground validation of the airborne data set.

  6. The DMRT-ML Model: Numerical Simulations of the Microwave Emission of Snowpacks Based on the Dense Media Radiative Transfer Theory

    Science.gov (United States)

    Brucker, Ludovic; Picard, Ghislain; Roy, Alexandre; Dupont, Florent; Fily, Michel; Royer, Alain

    2014-01-01

    Microwave radiometer observations have been used to retrieve snow depth and snow water equivalent on both land and sea ice, snow accumulation on ice sheets, melt events, snow temperature, and snow grain size. Modeling the microwave emission from snow and ice physical properties is crucial to improve the quality of these retrievals. It also is crucial to improve our understanding of the radiative transfer processes within the snow cover, and the snow properties most relevant in microwave remote sensing. Our objective is to present a recent microwave emission model and its validation. The model is named DMRT-ML (DMRT Multi-Layer), and is available at http:lgge.osug.frpicarddmrtml.

  7. MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

    Science.gov (United States)

    Riggs, George A.; Hall, Dorothy K.

    2010-01-01

    Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes.

  8. A coupled melt-freeze temperature index approach in a one-layer model to predict bulk volumetric liquid water content dynamics in snow

    Science.gov (United States)

    Avanzi, Francesco; Yamaguchi, Satoru; Hirashima, Hiroyuki; De Michele, Carlo

    2016-04-01

    Liquid water in snow rules runoff dynamics and wet snow avalanches release. Moreover, it affects snow viscosity and snow albedo. As a result, measuring and modeling liquid water dynamics in snow have important implications for many scientific applications. However, measurements are usually challenging, while modeling is difficult due to an overlap of mechanical, thermal and hydraulic processes. Here, we evaluate the use of a simple one-layer one-dimensional model to predict hourly time-series of bulk volumetric liquid water content in seasonal snow. The model considers both a simple temperature-index approach (melt only) and a coupled melt-freeze temperature-index approach that is able to reconstruct melt-freeze dynamics. Performance of this approach is evaluated at three sites in Japan. These sites (Nagaoka, Shinjo and Sapporo) present multi-year time-series of snow and meteorological data, vertical profiles of snow physical properties and snow melt lysimeters data. These data-sets are an interesting opportunity to test this application in different climatic conditions, as sites span a wide latitudinal range and are subjected to different snow conditions during the season. When melt-freeze dynamics are included in the model, results show that median absolute differences between observations and predictions of bulk volumetric liquid water content are consistently lower than 1 vol%. Moreover, the model is able to predict an observed dry condition of the snowpack in 80% of observed cases at a non-calibration site, where parameters from calibration sites are transferred. Overall, the analysis show that a coupled melt-freeze temperature-index approach may be a valid solution to predict average wetness conditions of a snow cover at local scale.

  9. COMPARISON OF DIGITAL SURFACE MODELS FOR SNOW DEPTH MAPPING WITH UAV AND AERIAL CAMERAS

    Directory of Open Access Journals (Sweden)

    R. Boesch

    2016-06-01

    Full Text Available Photogrammetric workflows for aerial images have improved over the last years in a typically black-box fashion. Most parameters for building dense point cloud are either excessive or not explained and often the progress between software releases is poorly documented. On the other hand, development of better camera sensors and positional accuracy of image acquisition is significant by comparing product specifications. This study shows, that hardware evolutions over the last years have a much stronger impact on height measurements than photogrammetric software releases. Snow height measurements with airborne sensors like the ADS100 and UAV-based DSLR cameras can achieve accuracies close to GSD * 2 in comparison with ground-based GNSS reference measurements. Using a custom notch filter on the UAV camera sensor during image acquisition does not yield better height accuracies. UAV based digital surface models are very robust. Different workflow parameter variations for ADS100 and UAV camera workflows seem to have only random effects.

  10. Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts

    DEFF Research Database (Denmark)

    Bokhorst, Stef; Pedersen, Stine Højlund; Brucker, Ludovic

    2016-01-01

    on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict...

  11. Improving the Terrain-Based Parameter for the Assessment of Snow Redistribution in the Col du Lac Blanc Area and Comparisons with TLS Snow Depth Data

    Science.gov (United States)

    Schön, Peter; Prokop, Alexander; Naaim-Bouvet, Florence; Nishimura, Kouichi; Vionnet, Vincent; Guyomarc'h, Gilbert

    2014-05-01

    Wind and the associated snow drift are dominating factors determining the snow distribution and accumulation in alpine areas, resulting in a high spatial variability of snow depth that is difficult to evaluate and quantify. The terrain-based parameter Sx characterizes the degree of shelter or exposure of a grid point provided by the upwind terrain, without the computational complexity of numerical wind field models. The parameter has shown to qualitatively predict snow redistribution with good reproduction of spatial patterns, but has failed to quantitatively describe the snow redistribution, and correlations with measured snow heights were poor. The objective of our research was to a) identify the sources of poor correlations between predicted and measured snow re-distribution and b) improve the parameters ability to qualitatively and quantitatively describe snow redistribution in our research area, the Col du Lac Blanc in the French Alps. The area is at an elevation of 2700 m and particularly suited for our study due to its constant wind direction and the availability of data from a meteorological station. Our work focused on areas with terrain edges of approximately 10 m height, and we worked with 1-2 m resolution digital terrain and snow surface data. We first compared the results of the terrain-based parameter calculations to measured snow-depths, obtained by high-accuracy terrestrial laser scan measurements. The results were similar to previous studies: The parameter was able to reproduce observed patterns in snow distribution, but regression analyses showed poor correlations between terrain-based parameter and measured snow-depths. We demonstrate how the correlations between measured and calculated snow heights improve if the parameter is calculated based on a snow surface model instead of a digital terrain model. We show how changing the parameter's search distance and how raster re-sampling and raster smoothing improve the results. To improve the parameter

  12. Evaluating the performance of coupled snow-soil models in SURFEXv8 to simulate the permafrost thermal regime at a high Arctic site

    Science.gov (United States)

    Barrere, Mathieu; Domine, Florent; Decharme, Bertrand; Morin, Samuel; Vionnet, Vincent; Lafaysse, Matthieu

    2017-09-01

    Climate change projections still suffer from a limited representation of the permafrost-carbon feedback. Predicting the response of permafrost temperature to climate change requires accurate simulations of Arctic snow and soil properties. This study assesses the capacity of the coupled land surface and snow models ISBA-Crocus and ISBA-ES to simulate snow and soil properties at Bylot Island, a high Arctic site. Field measurements complemented with ERA-Interim reanalyses were used to drive the models and to evaluate simulation outputs. Snow height, density, temperature, thermal conductivity and thermal insulance are examined to determine the critical variables involved in the soil and snow thermal regime. Simulated soil properties are compared to measurements of thermal conductivity, temperature and water content. The simulated snow density profiles are unrealistic, which is most likely caused by the lack of representation in snow models of the upward water vapor fluxes generated by the strong temperature gradients within the snowpack. The resulting vertical profiles of thermal conductivity are inverted compared to observations, with high simulated values at the bottom of the snowpack. Still, ISBA-Crocus manages to successfully simulate the soil temperature in winter. Results are satisfactory in summer, but the temperature of the top soil could be better reproduced by adequately representing surface organic layers, i.e., mosses and litter, and in particular their water retention capacity. Transition periods (soil freezing and thawing) are the least well reproduced because the high basal snow thermal conductivity induces an excessively rapid heat transfer between the soil and the snow in simulations. Hence, global climate models should carefully consider Arctic snow thermal properties, and especially the thermal conductivity of the basal snow layer, to perform accurate predictions of the permafrost evolution under climate change.

  13. Bayesian inference method for stochastic damage accumulation modeling

    International Nuclear Information System (INIS)

    Jiang, Xiaomo; Yuan, Yong; Liu, Xian

    2013-01-01

    Damage accumulation based reliability model plays an increasingly important role in successful realization of condition based maintenance for complicated engineering systems. This paper developed a Bayesian framework to establish stochastic damage accumulation model from historical inspection data, considering data uncertainty. Proportional hazards modeling technique is developed to model the nonlinear effect of multiple influencing factors on system reliability. Different from other hazard modeling techniques such as normal linear regression model, the approach does not require any distribution assumption for the hazard model, and can be applied for a wide variety of distribution models. A Bayesian network is created to represent the nonlinear proportional hazards models and to estimate model parameters by Bayesian inference with Markov Chain Monte Carlo simulation. Both qualitative and quantitative approaches are developed to assess the validity of the established damage accumulation model. Anderson–Darling goodness-of-fit test is employed to perform the normality test, and Box–Cox transformation approach is utilized to convert the non-normality data into normal distribution for hypothesis testing in quantitative model validation. The methodology is illustrated with the seepage data collected from real-world subway tunnels.

  14. Automated identification of potential snow avalanche release areas based on digital elevation models

    Science.gov (United States)

    Bühler, Y.; Kumar, S.; Veitinger, J.; Christen, M.; Stoffel, A.; Snehmani

    2013-05-01

    The identification of snow avalanche release areas is a very difficult task. The release mechanism of snow avalanches depends on many different terrain, meteorological, snowpack and triggering parameters and their interactions, which are very difficult to assess. In many alpine regions such as the Indian Himalaya, nearly no information on avalanche release areas exists mainly due to the very rough and poorly accessible terrain, the vast size of the region and the lack of avalanche records. However avalanche release information is urgently required for numerical simulation of avalanche events to plan mitigation measures, for hazard mapping and to secure important roads. The Rohtang tunnel access road near Manali, Himachal Pradesh, India, is such an example. By far the most reliable way to identify avalanche release areas is using historic avalanche records and field investigations accomplished by avalanche experts in the formation zones. But both methods are not feasible for this area due to the rough terrain, its vast extent and lack of time. Therefore, we develop an operational, easy-to-use automated potential release area (PRA) detection tool in Python/ArcGIS which uses high spatial resolution digital elevation models (DEMs) and forest cover information derived from airborne remote sensing instruments as input. Such instruments can acquire spatially continuous data even over inaccessible terrain and cover large areas. We validate our tool using a database of historic avalanches acquired over 56 yr in the neighborhood of Davos, Switzerland, and apply this method for the avalanche tracks along the Rohtang tunnel access road. This tool, used by avalanche experts, delivers valuable input to identify focus areas for more-detailed investigations on avalanche release areas in remote regions such as the Indian Himalaya and is a precondition for large-scale avalanche hazard mapping.

  15. Automated identification of potential snow avalanche release areas based on digital elevation models

    Directory of Open Access Journals (Sweden)

    Y. Bühler

    2013-05-01

    Full Text Available The identification of snow avalanche release areas is a very difficult task. The release mechanism of snow avalanches depends on many different terrain, meteorological, snowpack and triggering parameters and their interactions, which are very difficult to assess. In many alpine regions such as the Indian Himalaya, nearly no information on avalanche release areas exists mainly due to the very rough and poorly accessible terrain, the vast size of the region and the lack of avalanche records. However avalanche release information is urgently required for numerical simulation of avalanche events to plan mitigation measures, for hazard mapping and to secure important roads. The Rohtang tunnel access road near Manali, Himachal Pradesh, India, is such an example. By far the most reliable way to identify avalanche release areas is using historic avalanche records and field investigations accomplished by avalanche experts in the formation zones. But both methods are not feasible for this area due to the rough terrain, its vast extent and lack of time. Therefore, we develop an operational, easy-to-use automated potential release area (PRA detection tool in Python/ArcGIS which uses high spatial resolution digital elevation models (DEMs and forest cover information derived from airborne remote sensing instruments as input. Such instruments can acquire spatially continuous data even over inaccessible terrain and cover large areas. We validate our tool using a database of historic avalanches acquired over 56 yr in the neighborhood of Davos, Switzerland, and apply this method for the avalanche tracks along the Rohtang tunnel access road. This tool, used by avalanche experts, delivers valuable input to identify focus areas for more-detailed investigations on avalanche release areas in remote regions such as the Indian Himalaya and is a precondition for large-scale avalanche hazard mapping.

  16. MODELING OF TEMPERATURE FIELDS IN A SOLID HEAT ACCUMULLATORS

    Directory of Open Access Journals (Sweden)

    S. S. Belimenko

    2016-10-01

    Full Text Available Purpose. Currently, one of the priorities of energy conservation is a cost savings for heating in commercial and residential buildings by the stored thermal energy during the night and its return in the daytime. Economic effect is achieved due to the difference in tariffs for the cost of electricity in the daytime and at night. One of the most common types of devices that allow accumulating and giving the resulting heat are solid heat accumulators. The main purpose of the work: 1 software development for the calculation of the temperature field of a flat solid heat accumulator, working due to the heat energy accumulation in the volume of thermal storage material without phase transition; 2 determination the temperature distribution in its volumes at convective heat transfer. Methodology. To achieve the study objectives a heat transfer theory and Laplace integral transform were used. On its base the problems of determining the temperature fields in the channels of heat accumulators, having different cross-sectional shapes were solved. Findings. Authors have developed the method of calculation and obtained solutions for the determination of temperature fields in channels of the solid heat accumulator in conditions of convective heat transfer. Temperature fields over length and thickness of channels were investigated. Experimental studies on physical models and industrial equipment were conducted. Originality. For the first time the technique of calculating the temperature field in the channels of different cross-section for the solid heat accumulator in the charging and discharging modes was proposed. The calculation results are confirmed by experimental research. Practical value. The proposed technique is used in the design of solid heat accumulators of different power as well as full-scale production of them was organized.

  17. Stream Flow Simulation of a Snow-Fed Mountainous Basin Using the SWAT Model

    Science.gov (United States)

    Shukla, S.; Kansal, M. L.; Jain, S. K.

    2017-12-01

    Hydrological budget of the Satluj River (a major tributary of Indus river system) in Western Himalaya, is dominated by monsoonal rainfall and snowmelt during the non-monsoon months. The river watershed experiences extensive snowfall in the winters and snowmelt runoff substantially contributes to the streamflow of the river in the spring and summer months. In order to understand the hydrologic response of Satluj basin, hydrological modeling study is carried out using a semi distributed hydrological model Soil and Water Assessment Tool (SWAT), for the period of thirty years (1985-2014). The basic intent of this study is to derive the parameters required for runoff modeling using the geospatial database. The Sequential Uncertainty Fitting (SUFI-2) algorithm is used to calibrate and validate the model and incorporate uncertainties in the analysis. The results are validated with the observed daily streamflow data at Rampur, in terms of Nash-Sutcliffe Coefficient (NSC), R2 and Root Mean Square Error (RMSE). Further, the snowmelt-runoff mechanism is modelled by relating the temperature changes to the elevation band in the basin. The northern part of the basin and the south part of the basin on the high elevation zones have the coldest maximum temperatures that is about 7°C. It is found that the average contribution of snow and glacier runoff in the annual flow of the Satluj River at Rampur is about 66% and remaining 34% is from rainfall.

  18. Evidence accumulation as a model for lexical selection.

    Science.gov (United States)

    Anders, R; Riès, S; van Maanen, L; Alario, F X

    2015-11-01

    We propose and demonstrate evidence accumulation as a plausible theoretical and/or empirical model for the lexical selection process of lexical retrieval. A number of current psycholinguistic theories consider lexical selection as a process related to selecting a lexical target from a number of alternatives, which each have varying activations (or signal supports), that are largely resultant of an initial stimulus recognition. We thoroughly present a case for how such a process may be theoretically explained by the evidence accumulation paradigm, and we demonstrate how this paradigm can be directly related or combined with conventional psycholinguistic theory and their simulatory instantiations (generally, neural network models). Then with a demonstrative application on a large new real data set, we establish how the empirical evidence accumulation approach is able to provide parameter results that are informative to leading psycholinguistic theory, and that motivate future theoretical development. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Development and testing of a snow interceptometer to quantify canopy water storage and interception processes in the rain/snow transition zone of the North Cascades, Washington, USA

    Science.gov (United States)

    Martin, Kael A.; Van Stan, John T.; Dickerson-Lange, Susan E.; Lutz, James A.; Berman, Jeffrey W.; Gersonde, Rolf; Lundquist, Jessica D.

    2013-06-01

    Tree canopy snow interception is a significant hydrological process, capable of removing up to 60% of snow from the ground snowpack. Our understanding of canopy interception has been limited by our ability to measure whole canopy water storage in an undisturbed forest setting. This study presents a relatively inexpensive technique for directly measuring snow canopy water storage using an interceptometer, adapted from Friesen et al. (2008). The interceptometer is composed of four linear motion position sensors distributed evenly around the tree trunk. We incorporate a trunk laser-mapping installation method for precise sensor placement to reduce signal error due to sensor misalignment. Through calibration techniques, the amount of canopy snow required to produce the measured displacements can be calculated. We demonstrate instrument performance on a western hemlock (Tsuga heterophylla) for a snow interception event in November 2011. We find a snow capture efficiency of 83 ± 15% of accumulated ground snowfall with a maximum storage capacity of 50 ± 8 mm snow water equivalent (SWE). The observed interception event is compared to simulated interception, represented by the variable infiltration capacity (VIC) hydrologic model. The model generally underreported interception magnitude by 33% using a leaf area index (LAI) of 5 and 16% using an LAI of 10. The interceptometer captured intrastorm accumulation and melt rates up to 3 and 0.75 mm SWE h-1, respectively, which the model failed to represent. While further implementation and validation is necessary, our preliminary results indicate that forest interception magnitude may be underestimated in maritime areas.

  20. Performance tests of snow-related variables over the Tibetan Plateau and Himalayas using a new version of NASA GEOS-5 land surface model that includes the snow darkening effect

    Science.gov (United States)

    Yasunari, T. J.; Lau, W. K.; Koster, R. D.; Suarez, M.; Mahanama, S. P.; da Silva, A.; Colarco, P. R.

    2011-12-01

    The snow darkening effect, i.e. the reduction of snow albedo, is caused by absorption of solar radiation by absorbing aerosols (dust, black carbon, and organic carbon) deposited on the snow surface. This process is probably important over Himalayan and Tibetan glaciers due to the transport of highly polluted Atmospheric Brown Cloud (ABC) from the Indo-Gangetic Plain (IGP). This effect has been incorporated into the NASA Goddard Earth Observing System model, version 5 (GEOS-5) atmospheric transport model. The Catchment land surface model (LSM) used in GEOS-5 considers 3 snow layers. Code was developed to track the mass concentration of aerosols in the three layers, taking into account such processes as the flushing of the compounds as liquid water percolates through the snowpack. In GEOS-5, aerosol emissions, transports, and depositions are well simulated in the Goddard Chemistry Aerosol Radiation and Transport (GOCART) module; we recently made the connection between GOCART and the GEOS-5 system fitted with the revised LSM. Preliminary simulations were performed with this new system in "replay" mode (i.e., with atmospheric dynamics guided by reanalysis) at 2x2.5 degree horizontal resolution, covering the period 1 November 2005 - 31 December 2009; we consider the final three years of simulation here. The three simulations used the following variants of the LSM: (1) the original Catchment LSM with a fixed fresh snowfall density of 150 kg m-3; (2) the LSM fitted with the new snow albedo code, used here without aerosol deposition but with changes in density formulation and melting water effect on snow specific surface area, (3) the LSM fitted with the new snow albedo code as same as (2) but with fixed aerosol deposition rates (computed from GOCART values averaged over the Tibetan Plateau domain [lon.: 60-120E; lat.: 20-50N] during March-May 2008) applied to all grid points at every time step. For (2) and (3), the same setting on the fresh snowfall density as in (1) was

  1. Snow Leopard

    Indian Academy of Sciences (India)

    adult females (dimorphic); a male on average weighing between. 45–55 kg, while a .... performance of wild prey, eventually leading to a decline in their population. Research .... working towards enhancing knowledge on snow leopard ecology.

  2. A New Integrated Weighted Model in SNOW-V10: Verification of Categorical Variables

    Science.gov (United States)

    Huang, Laura X.; Isaac, George A.; Sheng, Grant

    2014-01-01

    This paper presents the verification results for nowcasts of seven categorical variables from an integrated weighted model (INTW) and the underlying numerical weather prediction (NWP) models. Nowcasting, or short range forecasting (0-6 h), over complex terrain with sufficient accuracy is highly desirable but a very challenging task. A weighting, evaluation, bias correction and integration system (WEBIS) for generating nowcasts by integrating NWP forecasts and high frequency observations was used during the Vancouver 2010 Olympic and Paralympic Winter Games as part of the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) project. Forecast data from Canadian high-resolution deterministic NWP system with three nested grids (at 15-, 2.5- and 1-km horizontal grid-spacing) were selected as background gridded data for generating the integrated nowcasts. Seven forecast variables of temperature, relative humidity, wind speed, wind gust, visibility, ceiling and precipitation rate are treated as categorical variables for verifying the integrated weighted forecasts. By analyzing the verification of forecasts from INTW and the NWP models among 15 sites, the integrated weighted model was found to produce more accurate forecasts for the 7 selected forecast variables, regardless of location. This is based on the multi-categorical Heidke skill scores for the test period 12 February to 21 March 2010.

  3. Diurnal variations in the UV albedo of arctic snow

    Directory of Open Access Journals (Sweden)

    O. Meinander

    2008-11-01

    Full Text Available The relevance of snow for climate studies is based on its physical properties, such as high surface reflectivity. Surface ultraviolet (UV albedo is an essential parameter for various applications based on radiative transfer modeling. Here, new continuous measurements of the local UV albedo of natural Arctic snow were made at Sodankylä (67°22'N, 26°39'E, 179 m a.s.l. during the spring of 2007. The data were logged at 1-min intervals. The accumulation of snow was up to 68 cm. The surface layer thickness varied from 0.5 to 35 cm with the snow grain size between 0.2 and 2.5 mm. The midday erythemally weighted UV albedo ranged from 0.6 to 0.8 in the accumulation period, and from 0.5 to 0.7 during melting. During the snow melt period, under cases of an almost clear sky and variable cloudiness, an unexpected diurnal decrease of 0.05 in albedo soon after midday, and recovery thereafter, was detected. This diurnal decrease in albedo was found to be asymmetric with respect to solar midday, thus indicating a change in the properties of the snow. Independent UV albedo results with two different types of instruments confirm these findings. The measured temperature of the snow surface was below 0°C on the following mornings. Hence, the reversible diurnal change, evident for ~1–2 h, could be explained by the daily metamorphosis of the surface of the snowpack, in which the temperature of the surface increases, melting some of the snow to liquid water, after which the surface freezes again.

  4. Modeling sludge accumulation rates in lined pit latrines in slum ...

    African Journals Online (AJOL)

    Yvonne

    should include geo-physical characterization of soil and drainage of pit latrine sites so as ... Key words: Faecal, sludge accumulation rates, slum areas, lined pit latrines. .... Value and its unit Source .... overall quality of the models had to be assessed by validation on ..... Sanitation partnership series: Bringing pit emptying out.

  5. Response of snow-dependent hydrologic extremes to continued global warming

    Energy Technology Data Exchange (ETDEWEB)

    Diffenbaugh, Noah [Stanford University; Scherer, Martin [Stanford University; Ashfaq, Moetasim [ORNL

    2012-01-01

    Snow accumulation is critical for water availability in the Northern Hemisphere1,2, raising concern that global warming could have important impacts on natural and human systems in snow-dependent regions1,3. Although regional hydrologic changes have been observed (for example, refs 1,3 5), the time of emergence of extreme changes in snow accumulation and melt remains a key unknown for assessing climate- change impacts3,6,7. We find that the CMIP5 global climate model ensemble exhibits an imminent shift towards low snow years in the Northern Hemisphere, with areas of western North America, northeastern Europe and the Greater Himalaya showing the strongest emergence during the near- termdecadesandat2 Cglobalwarming.Theoccurrenceof extremely low snow years becomes widespread by the late twenty-first century, as do the occurrences of extremely high early-season snowmelt and runoff (implying increasing flood risk), and extremely low late-season snowmelt and runoff (implying increasing water stress). Our results suggest that many snow-dependent regions of the Northern Hemisphere are likely to experience increasing stress from low snow years within the next three decades, and from extreme changes in snow-dominated water resources if global warming exceeds 2 C above the pre-industrial baseline.

  6. The effectiveness of snow cube throwing learning model based on exploration

    Science.gov (United States)

    Sari, Nenden Mutiara

    2017-08-01

    This study aimed to know the effectiveness of Snow Cube Throwing (SCT) and Cooperative Model in Exploration-Based Math Learning in terms of the time required to complete the teaching materials and student engagement. This study was quasi-experimental research was conducted at SMPN 5 Cimahi, Indonesia. All student in grade VIII SMPN 5 Cimahi which consists of 382 students is used as population. The sample consists of two classes which had been chosen randomly with purposive sampling. First experiment class consists of 38 students and the second experiment class consists of 38 students. Observation sheet was used to observe the time required to complete the teaching materials and record the number of students involved in each meeting. The data obtained was analyzed by independent sample-t test and used the chart. The results of this study: SCT learning model based on exploration are more effective than cooperative learning models based on exploration in terms of the time required to complete teaching materials based on exploration and student engagement.

  7. J-SEx : The Jollie Snow Experiment, New Zealand

    Science.gov (United States)

    Kerr, T.; Singh, S.; Kees, L.; Webster, C.; Clark, M.; Hendrikx, J.; Woods, R.

    2012-04-01

    Intensive snow observations have been collected in a steep alpine catchment (the Jollie Valley) in the Southern Alps of New Zealand for four years at the time of maximum snow storage. The campaign, called the Jollie Snow Experiment (J-SEx), was undertaken to improve understanding of snow variability in a steep alpine landscape. Observation methods included manual depth-probing at hundreds of locations with associated snowpit-digging, and surface and air-borne ground penetrating radar. In addition, repeat (daily) oblique photography was carried out on a subcatchment for two of the years. Analysis of the observations, in conjunction with similar observations from around the world has enabled direction to be given for selecting optimal modelling scales, and an indication of what processes need to be resolved at the different scales. For instance, if models are to operate at sub-100 m horizontal scales, they need to resolve drifting, sloughing and avalanching processes. Binary regression tree methods have been applied to identify terrain variables which explain the observed snow mass variability. This has enabled an assessment of total catchment snow storage to be established for each year. This assessment showed that the controlling variables change from year to year, so that no single terrain-based interpolation method may be generally applied. The change in the terrain relationships each year has been taken as an indication that the different frequencies of snowfall-related climate types from one year to the next affects which terrain characteristics have the greatest impact on snow variability. Assessment of terrain effects at the slope scale indicates that slope angle has the potential to be a strong influence on snow variability in that steep slopes do not build up large accumulations, and that low-angled regions below steep areas become areas of large snow build-up. This "slope" effect is clearly evident from the repeat photography, with the last areas to melt

  8. Snow and ice blocking of tunnels

    Energy Technology Data Exchange (ETDEWEB)

    Lia, Leif

    1998-12-31

    Hydroelectric power development in cold regions causes much concern about operational reliability and dam safety. This thesis studies the temperature distribution in tunnels by means of air temperature measurements in six tunnel spillways and five diversion tunnels. The measurements lasted for two consecutive winters. The air through flow tunnel is used as it causes cooling of both rock and water. In open spillway tunnels, frost reaches the entire tunnel. In spillway tunnels with walls, the frost zones reach about 100 m from the downstream end. In mildly-inclined diversion tunnels, a frost free zone is located in the middle of the tunnel and snow and ice problems were only observed in the inlet and outlet. Severe aufeis is accumulation is observed in the frost zones. The heat transfer from rock to air, water and ice is calculated and used in a prediction model for the calculation of aufeis build-up together with local field observation data. The water penetration of snow plugs is also calculated, based on the heat balance. It takes 20 to 50 days for water to enter the blocked tunnel. The empirical values are 30 to 60 days, but only 1 day if the temperature of the snow pack is 0{sup o}C. Sensitivity analyses are carried out for temperature variations in rock, snow, water and ice. Systematic field observation shows that it is important for hydropower companies to know about the effects of snow and ice blocking in an area. A risk analysis of dam safety is presented for a real case. Finally, the thesis proposes solutions which can reduce the snow and ice problems. 79 refs., 63 figs., 11 tabs.

  9. Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data for snow modeling in mountain basins

    Science.gov (United States)

    Havens, Scott; Marks, Danny; Kormos, Patrick; Hedrick, Andrew

    2017-12-01

    In the Western US and many mountainous regions of the world, critical water resources and climate conditions are difficult to monitor because the observation network is generally very sparse. The critical resource from the mountain snowpack is water flowing into streams and reservoirs that will provide for irrigation, flood control, power generation, and ecosystem services. Water supply forecasting in a rapidly changing climate has become increasingly difficult because of non-stationary conditions. In response, operational water supply managers have begun to move from statistical techniques towards the use of physically based models. As we begin to transition physically based models from research to operational use, we must address the most difficult and time-consuming aspect of model initiation: the need for robust methods to develop and distribute the input forcing data. In this paper, we present a new open source framework, the Spatial Modeling for Resources Framework (SMRF), which automates and simplifies the common forcing data distribution methods. It is computationally efficient and can be implemented for both research and operational applications. We present an example of how SMRF is able to generate all of the forcing data required to a run physically based snow model at 50-100 m resolution over regions of 1000-7000 km2. The approach has been successfully applied in real time and historical applications for both the Boise River Basin in Idaho, USA and the Tuolumne River Basin in California, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of a physically based snow model possible.

  10. Performance and Uncertainty Evaluation of Snow Models on Snowmelt Flow Simulations over a Nordic Catchment (Mistassibi, Canada

    Directory of Open Access Journals (Sweden)

    Magali Troin

    2015-11-01

    Full Text Available An analysis of hydrological response to a multi-model approach based on an ensemble of seven snow models (SM; degree-day and mixed degree-day/energy balance models coupled with three hydrological models (HM is presented for a snowmelt-dominated basin in Canada. The present study aims to compare the performance and the reliability of different types of SM-HM combinations at simulating snowmelt flows over the 1961–2000 historical period. The multi-model approach also allows evaluating the uncertainties associated with the structure of the SM-HM ensemble to better predict river flows in Nordic environments. The 20-year calibration shows a satisfactory performance of the ensemble of 21 SM-HM combinations at simulating daily discharges and snow water equivalents (SWEs, with low streamflow volume biases. The validation of the ensemble of 21 SM-HM combinations is conducted over a 20-year period. Performances are similar to the calibration in simulating the daily discharges and SWEs, again with low model biases for streamflow. The spring-snowmelt-generated peak flow is captured only in timing by the ensemble of 21 SM-HM combinations. The results of specific hydrologic indicators show that the uncertainty related to the choice of the given HM in the SM-HM combinations cannot be neglected in a more quantitative manner in simulating snowmelt flows. The selection of the SM plays a larger role than the choice of the SM approach (degree-day versus mixed degree-day/energy balance in simulating spring flows. Overall, the snow models provide a low degree of uncertainty to the total uncertainty in hydrological modeling for snow hydrology studies.

  11. Sentinels for snow science

    Science.gov (United States)

    Gascoin, S.; Grizonnet, M.; Baba, W. M.; Hagolle, O.; Fayad, A.; Mermoz, S.; Kinnard, C.; Fatima, K.; Jarlan, L.; Hanich, L.

    2017-12-01

    Current spaceborne sensors do not allow retrieving the snow water equivalent in mountain regions, "the most important unsolved problem in snow hydrology" (Dozier, 2016). While the NASA is operating an airborne mission to survey the SWE in the western USA, elsewhere, however, snow scientists and water managers do not have access to routine SWE measurements at the scale of a mountain range. In this presentation we suggest that the advent of the Copernicus Earth Observation programme opens new perspectives to address this issue in mountain regions worldwide. The Sentinel-2 mission will provide global-scale multispectral observations at 20 m resolution every 5-days (cloud permitting). The Sentinel-1 mission is already imaging the global land surface with a C-band radar at 10 m resolution every 6 days. These observations are unprecedented in terms of spatial and temporal resolution. However, the nature of the observation (radiometry, wavelength) is in the continuity of previous and ongoing missions. As a result, it is relatively straightforward to re-use algorithms that were developed by the remote sensing community over the last decades. For instance, Sentinel-2 data can be used to derive maps of the snow cover extent from the normalized difference snow index, which was initially proposed for Landsat. In addition, the 5-days repeat cycle allows the application of gap-filling algorithms, which were developed for MODIS based on the temporal dimension. The Sentinel-1 data can be used to detect the wet snow cover and track melting areas as proposed for ERS in the early 1990's. Eventually, we show an example where Sentinel-2-like data improved the simulation of the SWE in the data-scarce region of the High Atlas in Morocco through assimilation in a distributed snowpack model. We encourage snow scientists to embrace Sentinel-1 and Sentinel-2 data to enhance our knowledge on the snow cover dynamics in mountain regions.

  12. The stepwise discriminant algorithm for snow cover mapping based on FY-3/MERSI data

    Science.gov (United States)

    Han, Tao; Wang, Dawei; Jiang, Youyan; Wang, Xiaowei

    2013-10-01

    supervise-classified and expert-verified snow cover maps derived from integrated MERSI and MODIS images, we found FY-3A/MERSI has higher accuracy and stability not only for nearly cloud-free scenes but also the cloud scenes, namely, FY-3A/MERSI data can objectively reflect finer spatial distribution of snow and its dynamic development process, and the snow identification model perform better in snow/cloud discrimination. However, the ability of the FY-3A/MERSI model to discriminate thin snow and thin cloud need to be refined. And the limitation, error sources of FY-3A/MERSI snow products would be assessed based on the accumulation of large amounts of data in the future.

  13. Distributed modelling of climate change impacts on snow sublimation in Northern Mongolia

    Directory of Open Access Journals (Sweden)

    L. Menzel

    2009-08-01

    Full Text Available Sublimation of snow is an important factor of the hydrological cycle in Mongolia and is likely to increase according to future climate projections. In this study the hydrological model TRAIN was used to assess spatially distributed current and future sublimation rates based on interpolated daily data of precipitation, air temperature, air humidity, wind speed and solar radiation. An automated procedure for the interpolation of the input data is provided. Depending on the meteorological parameter and the data availability for the individual days, the most appropriate interpolation method is chosen automatically from inverse distance weighting, Ordinary Least Squares interpolation, Ordinary or Universal Kriging. Depending on elevation simulated annual sublimation in the period 1986–2006 was 23 to 35 mm, i.e. approximately 80% of total snowfall. Moreover, future climate projections for 2071–2100 of ECHAM5 and HadCM3, based on the A1B emission scenario of the Intergovernmental Panel on Climate Change, were analysed with TRAIN. In the case of ECHAM5 simulated sublimation increases by up to 17% (26...41 mm while it remains at the same level for HadCM3 (24...34 mm. The differences are mainly due to a distinct increase in winter precipitation for ECHAM5. Simulated changes of the all-season hydrological conditions, e.g. the sublimation-to-precipitation ratio, were ambiguous due to diverse precipitation patterns derived by the global circulation models.

  14. Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models

    Science.gov (United States)

    Suzuki, Kazuyoshi; Zupanski, Milija

    2018-01-01

    In this study, we investigate the uncertainties associated with land surface processes in an ensemble predication context. Specifically, we compare the uncertainties produced by a coupled atmosphere-land modeling system with two different land surface models, the Noah- MP land surface model (LSM) and the Noah LSM, by using the Maximum Likelihood Ensemble Filter (MLEF) data assimilation system as a platform for ensemble prediction. We carried out 24-hour prediction simulations in Siberia with 32 ensemble members beginning at 00:00 UTC on 5 March 2013. We then compared the model prediction uncertainty of snow depth and solid precipitation with observation-based research products and evaluated the standard deviation of the ensemble spread. The prediction skill and ensemble spread exhibited high positive correlation for both LSMs, indicating a realistic uncertainty estimation. The inclusion of a multiple snowlayer model in the Noah-MP LSM was beneficial for reducing the uncertainties of snow depth and snow depth change compared to the Noah LSM, but the uncertainty in daily solid precipitation showed minimal difference between the two LSMs. The impact of LSM choice in reducing temperature uncertainty was limited to surface layers of the atmosphere. In summary, we found that the more sophisticated Noah-MP LSM reduces uncertainties associated with land surface processes compared to the Noah LSM. Thus, using prediction models with improved skill implies improved predictability and greater certainty of prediction.

  15. Using isotopes to constrain water flux and age estimates in snow-influenced catchments using the STARR (Spatially distributed Tracer-Aided Rainfall–Runoff model

    Directory of Open Access Journals (Sweden)

    P. Ala-aho

    2017-10-01

    Full Text Available Tracer-aided hydrological models are increasingly used to reveal fundamentals of runoff generation processes and water travel times in catchments. Modelling studies integrating stable water isotopes as tracers are mostly based in temperate and warm climates, leaving catchments with strong snow influences underrepresented in the literature. Such catchments are challenging, as the isotopic tracer signals in water entering the catchments as snowmelt are typically distorted from incoming precipitation due to fractionation processes in seasonal snowpack. We used the Spatially distributed Tracer-Aided Rainfall–Runoff (STARR model to simulate fluxes, storage, and mixing of water and tracers, as well as estimating water ages in three long-term experimental catchments with varying degrees of snow influence and contrasting landscape characteristics. In the context of northern catchments the sites have exceptionally long and rich data sets of hydrometric data and – most importantly – stable water isotopes for both rain and snow conditions. To adapt the STARR model for sites with strong snow influence, we used a novel parsimonious calculation scheme that takes into account the isotopic fractionation through snow sublimation and snowmelt. The modified STARR setup simulated the streamflows, isotope ratios, and snow pack dynamics quite well in all three catchments. From this, our simulations indicated contrasting median water ages and water age distributions between catchments brought about mainly by differences in topography and soil characteristics. However, the variable degree of snow influence in catchments also had a major influence on the stream hydrograph, storage dynamics, and water age distributions, which was captured by the model. Our study suggested that snow sublimation fractionation processes can be important to include in tracer-aided modelling for catchments with seasonal snowpack, while the influence of fractionation during snowmelt

  16. [Job crisis and transformations in the new model of accumulation].

    Science.gov (United States)

    Zerda-Sarmiento, Alvaro

    2012-06-01

    The general and structural crisis capitalism is going through is the token of the difficulties accumulation model has been dealing with since 70's in developed countries. This model has been trying to settle down again on the basis of neoliberal principle and a new technical-economical paradigm. The new accumulation pattern has had a effect in employment sphere which have been made evident at all the elements that constitute work relationships. In Colombia, this model implementation has been partial and segmented. However, its consequences (and the long-term current crisis) have been evident in unemployment, precarious work, segmentation, informal work and restricted and private health insurance. Besides, financial accumulation makes labour profits flow at different levels. The economic model current government has aimed to implement leads to strengthening exports, so making population life conditions more difficult. In order to overcome the current state of affairs, the work sphere needs to become more creative. This creative approach should look for new schemes for expression and mobilization of work sphere's claims. This is supposed to be done by establishing a different economic model aimed to build a more inclusive future, with social justice.

  17. Snow accretion on overhead wires

    Energy Technology Data Exchange (ETDEWEB)

    Sakamoto, Y. [Meteorological Research Inst. for Technology Co. Ltd., Tokyo (Japan); Tachizaki, S.; Sudo, N. [Tohoku Electric Power Co. Ltd., Miyagi (Japan)

    2005-07-01

    Wet snow accretion can cause extensive damage to transmission systems. This paper reviewed some of the difficulties faced by researchers in the study of wet snow accretion on overhead lines in Japan. The study of snow accretion phenomena is complicated by the range of phase changes in water. Snowflakes produced in an upper atmospheric layer with a temperature below freezing do not melt when they go through a lower atmospheric layer with a temperature above freezing, but are in a mixed state of solid and liquid due to the latent heat of melting. The complicated properties of water make studies of snow accretion difficult, as well as the fact that snow changes its physical properties rapidly, due to the effects of ambient temperature, rainfall, and solar radiation. The adhesive forces that cause snow accretion include freezing; bonding through freezing; sintering; condensation and freezing of vapor in the air; mechanical intertwining of snowflakes; capillary action due to liquids; coherent forces between ice particles and water formed through the metamorphosis of snowflakes. In addition to these complexities, differences in laboratory room environments and natural snow environments can also pose difficulties for researchers. Equations describing the relationship between the density of accreted snow and the meteorological parameters involved were presented, as well as empirical equations which suggested that snow accretion efficiency has a dependency on air temperature. An empirical model for estimating snow loads in Japan was outlined, as well as various experiments observing show shedding. Correlations for wet snow accretion included precipitation intensity; duration of precipitation; air temperature; wind speed and wind direction in relation to the overhead line. Issues concerning topography and wet snow accretion were reviewed. It was concluded that studies of snow accretion will benefit by the collection of data in each matrix of the relevant parameters. 12 refs

  18. Towards Improved Snow Water Equivalent Estimation via GRACE Assimilation

    Science.gov (United States)

    Forman, Bart; Reichle, Rofl; Rodell, Matt

    2011-01-01

    Passive microwave (e.g. AMSR-E) and visible spectrum (e.g. MODIS) measurements of snow states have been used in conjunction with land surface models to better characterize snow pack states, most notably snow water equivalent (SWE). However, both types of measurements have limitations. AMSR-E, for example, suffers a loss of information in deep/wet snow packs. Similarly, MODIS suffers a loss of temporal correlation information beyond the initial accumulation and final ablation phases of the snow season. Gravimetric measurements, on the other hand, do not suffer from these limitations. In this study, gravimetric measurements from the Gravity Recovery and Climate Experiment (GRACE) mission are used in a land surface model data assimilation (DA) framework to better characterize SWE in the Mackenzie River basin located in northern Canada. Comparisons are made against independent, ground-based SWE observations, state-of-the-art modeled SWE estimates, and independent, ground-based river discharge observations. Preliminary results suggest improved SWE estimates, including improved timing of the subsequent ablation and runoff of the snow pack. Additionally, use of the DA procedure can add vertical and horizontal resolution to the coarse-scale GRACE measurements as well as effectively downscale the measurements in time. Such findings offer the potential for better understanding of the hydrologic cycle in snow-dominated basins located in remote regions of the globe where ground-based observation collection if difficult, if not impossible. This information could ultimately lead to improved freshwater resource management in communities dependent on snow melt as well as a reduction in the uncertainty of river discharge into the Arctic Ocean.

  19. Ash accumulation effects using bench marked 0-D model

    International Nuclear Information System (INIS)

    Hu, S.C.; Guo, J.P.; Miley, G.H.

    1990-01-01

    Ash accumulation is a key issue relative to our ability to achieve D- 3 He ARIES III burn conditions. 1-1/2-d transport simulations using the BALDUR code have been used to examine the correlation between the global ash particle confinement time and the edge exhaust (or recycling) efficiency. This provides a way to benchmark the widely used 0-D model. The burn conditions for an ARIES-III plasma with various ash edge recycling coefficients are examined

  20. Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts

    NARCIS (Netherlands)

    Bokhorst, Stef; Pedersen, Stine Højlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W.; Brown, Ross D.; Ehrich, Dorothee; Essery, Richard L. H.; Heilig, Achim; Ingvander, Susanne; Johansson, Cecilia; Johansson, Margareta; Jónsdóttir, Ingibjörg Svala; Inga, Niila; Luojus, Kari; Macelloni, Giovanni; Mariash, Heather|info:eu-repo/dai/nl/41327697X; Mclennan, Donald; Rosqvist, Gunhild Ninis; Sato, Atsushi; Savela, Hannele; Schneebeli, Martin; Sokolov, Aleksandr; Sokratov, Sergey A.; Terzago, Silvia; Vikhamar-schuler, Dagrun; Williamson, Scott; Qiu, Yubao; Callaghan, Terry V.

    2016-01-01

    Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for

  1. A Sensitivity Study on Modeling Black Carbon in Snow and its Radiative Forcing over the Arctic and Northern China

    Energy Technology Data Exchange (ETDEWEB)

    Qian, Yun; Wang, Hailong; Zhang, Rudong; Flanner, M. G.; Rasch, Philip J.

    2014-06-02

    Black carbon in snow (BCS) simulated in the Community Atmosphere Model (CAM5) is evaluated against measurements over Northern China and the Arctic, and its sensitivity to atmospheric deposition and two parameters that affect post-depositional enrichment is explored. The BCS concentration is overestimated (underestimated) by a factor of two in Northern China (Arctic) in the default model, but agreement with observations is good over both regions in the simulation with improvements in BC transport and deposition. Sensitivity studies indicate that uncertainty in the melt-water scavenging efficiency (MSE) parameter substantially affects BCS and its radiative forcing (by a factor of 2-7) in the Arctic through post-depositional enrichment. The MSE parameter has a relatively small effect on the magnitude of BCS seasonal cycle but can alter its phase in Northern China. The impact of the snow aging scaling factor (SAF) on BCS, partly through the post-depositional enrichment effect, shows more complex latitudinal and seasonal dependence. Similar to MSE, SAF affects more significantly the magnitude (phase) of BCS season cycle over the Arctic (Northern China). While uncertainty associated with the representation of BC transport and deposition processes in CAM5 is more important than that associated with the two snow model parameters in Northern China, the two uncertainties have comparable effect in the Arctic.

  2. Modelling the physical multiphase interactions of HNO3 between snow and air on the Antarctic Plateau (Dome C) and coast (Halley)

    Science.gov (United States)

    Chan, Hoi Ga; Frey, Markus M.; King, Martin D.

    2018-02-01

    Emissions of nitrogen oxide (NOx = NO + NO2) from the photolysis of nitrate (NO3-) in snow affect the oxidising capacity of the lower troposphere especially in remote regions of high latitudes with little pollution. Current air-snow exchange models are limited by poor understanding of processes and often require unphysical tuning parameters. Here, two multiphase models were developed from physically based parameterisations to describe the interaction of nitrate between the surface layer of the snowpack and the overlying atmosphere. The first model is similar to previous approaches and assumes that below a threshold temperature, To, the air-snow grain interface is pure ice and above To a disordered interface (DI) emerges covering the entire grain surface. The second model assumes that air-ice interactions dominate over all temperatures below melting of ice and that any liquid present above the eutectic temperature is concentrated in micropockets. The models are used to predict the nitrate in surface snow constrained by year-round observations of mixing ratios of nitric acid in air at a cold site on the Antarctic Plateau (Dome C; 75°06' S, 123°33' E; 3233 m a.s.l.) and at a relatively warm site on the Antarctic coast (Halley; 75°35' S, 26°39' E; 35 m a.s.l). The first model agrees reasonably well with observations at Dome C (Cv(RMSE) = 1.34) but performs poorly at Halley (Cv(RMSE) = 89.28) while the second model reproduces with good agreement observations at both sites (Cv(RMSE) = 0.84 at both sites). It is therefore suggested that in winter air-snow interactions of nitrate are determined by non-equilibrium surface adsorption and co-condensation on ice coupled with solid-state diffusion inside the grain, similar to Bock et al. (2016). In summer, however, the air-snow exchange of nitrate is mainly driven by solvation into liquid micropockets following Henry's law with contributions to total surface snow NO3- concentrations of 75 and 80 % at Dome C and Halley

  3. Changes in Snow Albedo Resulting from Snow Darkening Caused by Black Carbon

    Science.gov (United States)

    Engels, J.; Kloster, S.; Bourgeois, Q.

    2014-12-01

    We investigate the potential impact of snow darkening caused by pre-industrial and present-day black carbon (BC) emissions on snow albedo and subsequently climate. To assess this impact, we implemented the effect of snow darkening caused by BC emitted from natural as well as anthropogenic sources into the Max Planck Institute for Meteorology Earth System Model (MPI-M ESM). Considerable amounts of BC are emitted e.g. from fires and are transported through the atmosphere for several days before being removed by rain or snow precipitation in snow covered regions. Already very small quantities of BC reduce the snow reflectance significantly, with consequences for snow melting and snow spatial coverage. We implemented the snow albedo reduction caused by BC contamination and snow aging in the one layer land surface component (JSBACH) of the atmospheric general circulation model ECHAM6, developed at MPI-M. For this we used the single-layer simulator of the SNow, Ice, and Aerosol Radiation (SNICAR-Online (Flanner et al., 2007); http://snow.engin.umich.edu) model to derive snow albedo values for BC in snow concentrations ranging between 0 and 1500 ng(BC)/g(snow) for different snow grain sizes for the visible (0.3 - 0.7 μm) and near infrared range (0.7 - 1.5 μm). As snow grains grow over time, we assign different snow ages to different snow grain sizes (50, 150, 500, and 1000 μm). Here, a radius of 50 μm corresponds to new snow, whereas a radius of 1000 μm corresponds to old snow. The deposition rates of BC on snow are prescribed from previous ECHAM6-HAM simulations for two time periods, pre-industrial (1880-1889) and present-day (2000-2009), respectively. We perform a sensitivity study regarding the scavenging of BC by snow melt. To evaluate the newly implemented albedo scheme we will compare the modeled black carbon in snow concentrations to observed ones. Moreover, we will show the impact of the BC contamination and snow aging on the simulated snow albedo. The

  4. Observation and modeling of snow melt and superimposed ice formation on sea ice

    OpenAIRE

    Nicolaus, Marcel; Haas, Christian

    2004-01-01

    Sea ice plays a key role within the global climate system. It covers some 7% of earths surface and processes a strong seasonal cycle. Snow on sea ice even amplifies the importance of sea ice in the coupled atmosphere-ice-ocean system, because it dominates surface properties and energy balance (incl. albedo).Several quantitative observations of summer sea ice and its snow cover show the formation of superimposed ice and a gap layer underneath, which was found to be associated to high standing ...

  5. Snow clearance

    CERN Multimedia

    Mauro Nonis

    2005-01-01

    In reply to the numerous questions received, we should like to inform you of the actions and measures taken in an effort to maintain the movements of vehicles and pedestrians since the heavy snow fall on Sunday 23 January. Our contractor's employees began clearing the snow during the morning of Sunday 23 January on the main CERN sites (Meyrin, Prévessin), but an accident prevented them from continuing. The vehicle in question was repaired by Monday morning when two other vehicles joined it to resume snow clearing; priority was given to access points to the main sites and the LHC sites, as well as to the main roads inside the sites. The salt sprinklers were also brought into action that same day; the very low temperature during the night from Monday to Tuesday prevented the snow from melting and compacted the ice; the continuing cold during the day on Tuesday (-6°C at 10:00 on the Meyrin site) meant that all efforts to remove the ice were doomed to failure. In order to ensure more efficie...

  6. A sensitivity study on modeling black carbon in snow and its radiative forcing over the Arctic and Northern China

    International Nuclear Information System (INIS)

    Qian, Yun; Wang, Hailong; Rasch, Philip J; Zhang, Rudong; Flanner, Mark G

    2014-01-01

    Black carbon in snow (BCS) simulated in the Community Atmosphere Model (CAM5) is evaluated against measurements over Northern China and the Arctic, and its sensitivity to atmospheric deposition and two parameters that affect post-depositional enrichment is explored. Improvements in atmospheric BC transport and deposition significantly reduce the biases (by a factor of two) in the estimation of BCS concentration over both Northern China and the Arctic. Further sensitivity simulations using the improved CAM5 indicate that the melt-water scavenging efficiency (MSE) parameter plays an important role in regulating BC concentrations in the Arctic through the post-depositional enrichment, which not only drastically changes the amplitude but also shifts the seasonal cycle of the BCS concentration and its radiative forcing in the Arctic. The impact of the snow aging scaling factor (SAF) on BCS shows more complex latitudinal and seasonal dependence, and overall impact of SAF is much smaller than that of MSE. The improvements of BC transport and deposition in CAM5 have a stronger influence on BCS than perturbations of the two snow model parameters in Northern China. (letters)

  7. Antarctic snow and global climate

    International Nuclear Information System (INIS)

    Granberg, H.B.

    2001-01-01

    Global circulation models (GCM) indicate that global warming will be most pronounced at polar regions and high latitudes, causing concern about the stability of the Antarctic ice cap. A project entitled the Seasonal Snow in Antarctica examined the properties of the near surface snow to determine the current conditions that influence snow cover development. The goal was to assess the response of the snow cover in Queen Maud Land (QML) to an increased atmospheric carbon dioxide content. The Antarctic snow cover in QML was examined as part of the FINNARP expeditions in 1999 and 2000 which examined the processes that influence the snow cover. Its energy and mass balance were also assessed by examining the near surface snow strata in shallow (1-2 m) pits and by taking measurements of environmental variables. This made it possible to determine if the glacier is in danger of melting at this northerly location in the Antarctic. The study also made it possible to determine which variables need to change and by how much, for significant melting to occur. It was shown that the Antarctic anticyclone creates particular conditions that protect the snow cover from melting. The anticyclone brings dry air from the stratosphere during most of the year and is exempt from the water vapour feedback. It was concluded that even a doubling of atmospheric carbon dioxide will not produce major snow melt runoff. 8 refs

  8. Monitoring snow cover and its effect on runoff regime in the Jizera Mountains

    Science.gov (United States)

    Kulasova, Alena

    2015-04-01

    The Jizera Mountains in the northern Bohemia are known by its rich snow cover. Winter precipitation represents usually a half of the precipitation in the hydrological year. Gradual snow accumulation and melt depends on the course of the particular winter period, the topography of the catchments and the type of vegetation. During winter the snow depth, and especially the snow water equivalent, are affected by the changing character of the falling precipitation, air and soil temperatures and the wind. More rapid snowmelt occurs more on the slopes without forest oriented to the South, while a gradual snowmelt occurs on the locations turned to the North and in forest. Melting snow recharges groundwater and affects water quality in an important way. In case of extreme situation the snowmelt monitoring is important from the point of view of flood protection of communities and property. Therefore the immediate information on the amount of water in snow is necessary. The way to get this information is the continuous monitoring of the snow depth and snow water equivalent. In the Jizera Mountains a regular monitoring of snow cover has been going on since the end of the 19th century. In the 80s of the last century the Jizera Mountains were affected by the increased fallout of pollutants in the air. There followed a gradual dieback of the forest cover and cutting down the upper part of the ridges. In order to get data for the quantification of runoff regime changes in the changing natural environment, the Czech Hydrometeorological Institute (CHMI) founded in the upper part of the Mountains several experimental catchments. One of the activities of the employees of the experimental basis is the regular measurement of snow cover at selected sites from 1982 up to now. At the same time snow cover is being observed using snow pillows, where its mass is monitored with the help of pressure sensors. In order to improve the reliability of the continuous measurement of the snow water

  9. Performance Tests of Snow-Related Variables Over the Tibetan Plateau and Himalayas Using a New Version of NASA GEOS-5 Land Surface Model that Includes the Snow Darkening Effect

    Science.gov (United States)

    Yasunari, Tppei J.; Lau, K.-U.; Koster, Randal D.; Suarez, Max; Mahanama, Sarith; Dasilva, Arlindo M.; Colarco, Peter R.

    2011-01-01

    The snow darkening effect, i.e. the reduction of snow albedo, is caused by absorption of solar radiation by absorbing aerosols (dust, black carbon, and organic carbon) deposited on the snow surface. This process is probably important over Himalayan and Tibetan glaciers due to the transport of highly polluted Atmospheric Brown Cloud (ABC) from the Indo-Gangetic Plain (IGP). This effect has been incorporated into the NASA Goddard Earth Observing System model, version 5 (GEOS-5) atmospheric transport model. The Catchment land surface model (LSM) used in GEOS-5 considers 3 snow layers. Code was developed to track the mass concentration of aerosols in the three layers, taking into account such processes as the flushing of the compounds as liquid water percolates through the snowpack. In GEOS-5, aerosol emissions, transports, and depositions are well simulated in the Goddard Chemistry Aerosol Radiation and Transport (GO CART) module; we recently made the connection between GOCART and the GEOS-5 system fitted with the revised LSM. Preliminary simulations were performed with this new system in "replay" mode (i.e., with atmospheric dynamics guided by reanalysis) at 2x2.5 degree horizontal resolution, covering the period 1 November 2005 - 31 December 2009; we consider the final three years of simulation here. The three simulations used the following variants of the LSM: (1) the original Catchment LSM with a fixed fresh snowfall density of 150 kg m-3 ; (2) the LSM fitted with the new snow albedo code, used here without aerosol deposition but with changes in density formulation and melting water effect on snow specific surface area, (3) the LSM fitted with the new snow albedo code as same as (2) but with fixed aerosol deposition rates (computed from GOCART values averaged over the Tibetan Plateau domain [Ion.: 60-120E; lat.: 20-50N] during March-May 2008) applied to all grid points at every time step. For (2) and (3), the same setting on the fresh snowfall density as in (1

  10. Assimilation of ground and satellite snow observations in a distributed hydrologic model to improve water supply forecasts in the Upper Colorado River Basin

    Science.gov (United States)

    Micheletty, P. D.; Day, G. N.; Quebbeman, J.; Carney, S.; Park, G. H.

    2016-12-01

    The Upper Colorado River Basin above Lake Powell is a major source of water supply for 25 million people and provides irrigation water for 3.5 million acres. Approximately 85% of the annual runoff is produced from snowmelt. Water supply forecasts of the April-July runoff produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), are critical to basin water management. This project leverages advanced distributed models, datasets, and snow data assimilation techniques to improve operational water supply forecasts made by CBRFC in the Upper Colorado River Basin. The current work will specifically focus on improving water supply forecasts through the implementation of a snow data assimilation process coupled with the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM). Three types of observations will be used in the snow data assimilation system: satellite Snow Covered Area (MODSCAG), satellite Dust Radiative Forcing in Snow (MODDRFS), and SNOTEL Snow Water Equivalent (SWE). SNOTEL SWE provides the main source of high elevation snowpack information during the snow season, however, these point measurement sites are carefully selected to provide consistent indices of snowpack, and may not be representative of the surrounding watershed. We address this problem by transforming the SWE observations to standardized deviates and interpolating the standardized deviates using a spatial regression model. The interpolation process will also take advantage of the MODIS Snow Covered Area and Grainsize (MODSCAG) product to inform the model on the spatial distribution of snow. The interpolated standardized deviates are back-transformed and used in an Ensemble Kalman Filter (EnKF) to update the model simulated SWE. The MODIS Dust Radiative Forcing in Snow (MODDRFS) product will be used more directly through temporary adjustments to model snowmelt parameters, which should improve melt estimates in areas affected by dust on snow. In

  11. Evaluating a Local Ensemble Transform Kalman Filter snow cover data assimilation method to estimate SWE within a high-resolution hydrologic modeling framework across Western US mountainous regions

    Science.gov (United States)

    Oaida, C. M.; Andreadis, K.; Reager, J. T., II; Famiglietti, J. S.; Levoe, S.

    2017-12-01

    Accurately estimating how much snow water equivalent (SWE) is stored in mountainous regions characterized by complex terrain and snowmelt-driven hydrologic cycles is not only greatly desirable, but also a big challenge. Mountain snowpack exhibits high spatial variability across a broad range of spatial and temporal scales due to a multitude of physical and climatic factors, making it difficult to observe or estimate in its entirety. Combing remotely sensed data and high resolution hydrologic modeling through data assimilation (DA) has the potential to provide a spatially and temporally continuous SWE dataset at horizontal scales that capture sub-grid snow spatial variability and are also relevant to stakeholders such as water resource managers. Here, we present the evaluation of a new snow DA approach that uses a Local Ensemble Transform Kalman Filter (LETKF) in tandem with the Variable Infiltration Capacity macro-scale hydrologic model across the Western United States, at a daily temporal resolution, and a horizontal resolution of 1.75 km x 1.75 km. The LETKF is chosen for its relative simplicity, ease of implementation, and computational efficiency and scalability. The modeling/DA system assimilates daily MODIS Snow Covered Area and Grain Size (MODSCAG) fractional snow cover over, and has been developed to efficiently calculate SWE estimates over extended periods of time and covering large regional-scale areas at relatively high spatial resolution, ultimately producing a snow reanalysis-type dataset. Here we focus on the assessment of SWE produced by the DA scheme over several basins in California's Sierra Nevada Mountain range where Airborne Snow Observatory data is available, during the last five water years (2013-2017), which include both one of the driest and one of the wettest years. Comparison against such a spatially distributed SWE observational product provides a greater understanding of the model's ability to estimate SWE and SWE spatial variability

  12. Statistical 3D damage accumulation model for ion implant simulators

    CERN Document Server

    Hernandez-Mangas, J M; Enriquez, L E; Bailon, L; Barbolla, J; Jaraiz, M

    2003-01-01

    A statistical 3D damage accumulation model, based on the modified Kinchin-Pease formula, for ion implant simulation has been included in our physically based ion implantation code. It has only one fitting parameter for electronic stopping and uses 3D electron density distributions for different types of targets including compound semiconductors. Also, a statistical noise reduction mechanism based on the dose division is used. The model has been adapted to be run under parallel execution in order to speed up the calculation in 3D structures. Sequential ion implantation has been modelled including previous damage profiles. It can also simulate the implantation of molecular and cluster projectiles. Comparisons of simulated doping profiles with experimental SIMS profiles are presented. Also comparisons between simulated amorphization and experimental RBS profiles are shown. An analysis of sequential versus parallel processing is provided.

  13. Statistical 3D damage accumulation model for ion implant simulators

    International Nuclear Information System (INIS)

    Hernandez-Mangas, J.M.; Lazaro, J.; Enriquez, L.; Bailon, L.; Barbolla, J.; Jaraiz, M.

    2003-01-01

    A statistical 3D damage accumulation model, based on the modified Kinchin-Pease formula, for ion implant simulation has been included in our physically based ion implantation code. It has only one fitting parameter for electronic stopping and uses 3D electron density distributions for different types of targets including compound semiconductors. Also, a statistical noise reduction mechanism based on the dose division is used. The model has been adapted to be run under parallel execution in order to speed up the calculation in 3D structures. Sequential ion implantation has been modelled including previous damage profiles. It can also simulate the implantation of molecular and cluster projectiles. Comparisons of simulated doping profiles with experimental SIMS profiles are presented. Also comparisons between simulated amorphization and experimental RBS profiles are shown. An analysis of sequential versus parallel processing is provided

  14. Evaluation and operationalization of a novel forest detrainment modeling approach for computational snow avalanche simulation

    Science.gov (United States)

    Teich, M.; Feistl, T.; Fischer, J.; Bartelt, P.; Bebi, P.; Christen, M.; Grêt-Regamey, A.

    2013-12-01

    Two-dimensional avalanche simulation software operating in three-dimensional terrain are widely used for hazard zoning and engineering to predict runout distances and impact pressures of snow avalanche events. Mountain forests are an effective biological protection measure; however, the protective capacity of forests to decelerate or even to stop avalanches that start within forested areas or directly above the treeline is seldom considered in this context. In particular, runout distances of small- to medium-scale avalanches are strongly influenced by the structural conditions of forests in the avalanche path. This varying decelerating effect has rarely been addressed or implemented in avalanche simulation. We present an evaluation and operationalization of a novel forest detrainment modeling approach implemented in the avalanche simulation software RAMMS. The new approach accounts for the effect of forests in the avalanche path by detraining mass, which leads to a deceleration and runout shortening of avalanches. The extracted avalanche mass caught behind trees stops immediately and, therefore, is instantly subtracted from the flow and the momentum of the stopped mass is removed from the total momentum of the avalanche flow. This relationship is parameterized by the empirical detrainment coefficient K [Pa] which accounts for the braking power of different forest types per unit area. To define K dependent on specific forest characteristics, we simulated 40 well-documented small- to medium-scale avalanches which released in and ran through forests with varying K-values. Comparing two-dimensional simulation results with one-dimensional field observations for a high number of avalanche events and simulations manually is however time consuming and rather subjective. In order to process simulation results in a comprehensive and standardized way, we used a recently developed automatic evaluation and comparison method defining runout distances based on a pressure

  15. Evapotranspiration sensitivity to air temperature across a snow-influenced watershed: Space-for-time substitution versus integrated watershed modeling

    Science.gov (United States)

    Jepsen, S. M.; Harmon, T. C.; Ficklin, D. L.; Molotch, N. P.; Guan, B.

    2018-01-01

    Changes in long-term, montane actual evapotranspiration (ET) in response to climate change could impact future water supplies and forest species composition. For scenarios of atmospheric warming, predicted changes in long-term ET tend to differ between studies using space-for-time substitution (STS) models and integrated watershed models, and the influence of spatially varying factors on these differences is unclear. To examine this, we compared warming-induced (+2 to +6 °C) changes in ET simulated by an STS model and an integrated watershed model across zones of elevation, substrate available water capacity, and slope in the snow-influenced upper San Joaquin River watershed, Sierra Nevada, USA. We used the Soil Water and Assessment Tool (SWAT) for the watershed modeling and a Budyko-type relationship for the STS modeling. Spatially averaged increases in ET from the STS model increasingly surpassed those from the SWAT model in the higher elevation zones of the watershed, resulting in 2.3-2.6 times greater values from the STS model at the watershed scale. In sparse, deep colluvium or glacial soils on gentle slopes, the SWAT model produced ET increases exceeding those from the STS model. However, watershed areas associated with these conditions were too localized for SWAT to produce spatially averaged ET-gains comparable to the STS model. The SWAT model results nevertheless demonstrate that such soils on high-elevation, gentle slopes will form ET "hot spots" exhibiting disproportionately large increases in ET, and concomitant reductions in runoff yield, in response to warming. Predicted ET responses to warming from STS models and integrated watershed models may, in general, substantially differ (e.g., factor of 2-3) for snow-influenced watersheds exhibiting an elevational gradient in substrate water holding capacity and slope. Long-term water supplies in these settings may therefore be more resilient to warming than STS model predictions would suggest.

  16. Phenomenological marine snow model for optical underwater image simulation: Applications to color restoration

    OpenAIRE

    Boffety , Matthieu; Galland , Frédéric

    2012-01-01

    International audience; Optical imaging plays an important role in oceanic science and engineering. However, the design of optical systems and image processing techniques for subsea environment are challenging tasks due to water turbidity. Marine snow is notably a major source of image degradation as it creates white bright spots that may strongly impact the performance of image processing methods. In this context, it is necessary to have a tool to foresee the behavior of these methods in mar...

  17. Groundwater dynamics mediate low-flow response to global warming in snow-dominated alpine regions

    Science.gov (United States)

    Christina Tague; Gordon E. Grant

    2009-01-01

    In mountain environments, spatial and temporal patterns of snow accumulation and melt are dominant controls on hydrologic responses to climate change. In this paper, we develop a simple conceptual model that links the timing of peak snowmelt with geologically mediated differences in rate of streamflow recession. This model demonstrates that within the western United...

  18. Spatial and temporal variability in seasonal snow density

    KAUST Repository

    Bormann, Kathryn J.

    2013-03-01

    Snow density is a fundamental physical property of snowpacks used in many aspects of snow research. As an integral component in the remote sensing of snow water equivalent and parameterisation of snow models, snow density may be used to describe many important features of snowpack behaviour. The present study draws on a significant dataset of snow density and climate observations from the United States, Australia and the former Soviet Union and uses regression-based techniques to identify the dominant climatological drivers for snow densification rates, characterise densification rate variability and estimate spring snow densities from more readily available climate data. Total winter precipitation was shown to be the most prominent driver of snow densification rates, with mean air temperature and melt-refreeze events also found to be locally significant. Densification rate variance is very high at Australian sites, very low throughout the former Soviet Union and between these extremes throughout much of the US. Spring snow densities were estimated using a statistical model with climate variable inputs and best results were achieved when snow types were treated differently. Given the importance of snow density information in many snow-related research disciplines, this work has implications for current methods of converting snow depths to snow water equivalent, the representation of snow dynamics in snow models and remote sensing applications globally. © 2013 Elsevier B.V.

  19. Spatial and temporal variability in seasonal snow density

    KAUST Repository

    Bormann, Kathryn J.; Westra, Seth; Evans, Jason P.; McCabe, Matthew

    2013-01-01

    Snow density is a fundamental physical property of snowpacks used in many aspects of snow research. As an integral component in the remote sensing of snow water equivalent and parameterisation of snow models, snow density may be used to describe many important features of snowpack behaviour. The present study draws on a significant dataset of snow density and climate observations from the United States, Australia and the former Soviet Union and uses regression-based techniques to identify the dominant climatological drivers for snow densification rates, characterise densification rate variability and estimate spring snow densities from more readily available climate data. Total winter precipitation was shown to be the most prominent driver of snow densification rates, with mean air temperature and melt-refreeze events also found to be locally significant. Densification rate variance is very high at Australian sites, very low throughout the former Soviet Union and between these extremes throughout much of the US. Spring snow densities were estimated using a statistical model with climate variable inputs and best results were achieved when snow types were treated differently. Given the importance of snow density information in many snow-related research disciplines, this work has implications for current methods of converting snow depths to snow water equivalent, the representation of snow dynamics in snow models and remote sensing applications globally. © 2013 Elsevier B.V.

  20. ACCUMULATED DEFORMATION MODELING OF PERMANENT WAY BASED ON ENTROPY SYSTEM

    Directory of Open Access Journals (Sweden)

    D. M. Kurhan

    2015-07-01

    Full Text Available Purpose. The work provides a theoretical research about the possibility of using methods that determine the lifetime of a railway track not only in terms of total stresses, and accounting its structure and dynamic characteristics. The aim of these studies is creation the model of deformations accumulation for assessment of service life of a railway track taking into account these features. Methodology. To simulate a gradual change state during the operation (accumulation of deformations the railway track is presented as a system that consists of many particles of different materials collected in a coherent design. It is appropriate to speak not about the appearance of deformations of a certain size in a certain section of the track, and the probability of such event on the site. If to operate the probability of occurrence of deviations, comfortable state of the system is characterized by the number of breaks of the conditional internal connections. The same state of the system may correspond to different combinations of breaks. The more breaks, the more the number of options changes in the structure of the system appropriate to its current state. Such a process can be represented as a gradual transition from an ordered state to a chaotic one. To describe the characteristics of the system used the numerical value of the entropy. Findings. Its entropy is constantly increasing at system aging. The growth of entropy is expressed by changes in the internal energy of the system, which can be determined using mechanical work forces, which leads to deformation. This gives the opportunity to show quantitative indication of breaking the bonds in the system as a consequence of performing mechanical work. According to the results of theoretical research methods for estimation of the timing of life cycles of railway operation considering such factors as the structure of the flow of trains, construction of the permanent way, the movement of trains at high

  1. Temporal trend of the snow-related variables in Sierra Nevada in the last years: An analysis combining Earth Observation and hydrological modelling

    Science.gov (United States)

    Pérez-Luque, Antonio J.; Herrero, Javier; Bonet, Francisco J.; Pérez-Pérez, Ramón

    2016-04-01

    Climate change is causing declines in snow-cover extent and duration in European mountain ranges. This is especially important in Mediterranean mountain ranges where the observed trends towards precipitation and higher temperatures can provoke problems of water scarcity. In this work, we analyzed temporal trends (2000 to 2014) of snow-related variables obtained from satellite and modelling data in Sierra Nevada, a Mediterranean high-mountain range located in Southern Spain, at 37°N. Snow cover indicators (snow-cover duration, snow-cover onset dates and snow-cover melting dates) were obtained by processing images of MOD10A2 MODIS product using an automated workflow. Precipitation data were obtained using WiMMed, a complete and fully distributed hydrological model that is used to map the annual rainfall and snowfall with a resolution of 30x30 m over the whole study area. It uses expert algorithms to interpolate precipitation and temperature at an hourly scale, and simulates partition of precipitation into snowfall with several methods. For each snow-related indicator (snow-covers and snowfall), a trend analysis was applied at the MODIS pixel scale during the study period (2000-2014). We applied Mann-Kendall test and Theil-Sen slope estimation in each of the pixels comprising Sierra Nevada. The trend analysis assesses the intensity, magnitude and degree of statistical significance during the period analysed. The spatial pattern of these trends was explored according to elevation ranges. Finally, we explored the relationship between trends of snow-cover related indicators and precipitation trends. Our results show that snow-cover has undergone significant changes in the last 14 years. 80 % of the pixels covering Sierra Nevada showed a negative trend in the duration of snow-cover. We also observed a delay in the snow-cover onset date (68.03 % pixels showing a positive trend in the snow-cover onset date) and an advance in the melt date (80.72 % of pixels followed a

  2. Integrated Modeling and Decision-Support System for Water Management in the Puget Sound Basin: Snow Caps to White Caps

    Energy Technology Data Exchange (ETDEWEB)

    Copping, Andrea E. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Yang, Zhaoqing [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Voisin, Nathalie [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Richey, Jeff [Univ. of Washington, Seattle, WA (United States); Wang, Taiping [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Taira, Randal Y. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Constans, Michael [Univ. of Washington, Seattle, WA (United States); Wigmosta, Mark S. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Van Cleve, Frances B. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Tesfa, Teklu K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2013-12-01

    Final Report for the EPA-sponsored project Snow Caps to White Caps that provides data products and insight for water resource managers to support their predictions and management actions to address future changes in water resources (fresh and marine) in the Puget Sound basin. This report details the efforts of a team of scientists and engineers from Pacific Northwest National Laboratory (PNNL) and the University of Washington (UW) to examine the movement of water in the Snohomish Basin, within the watershed and the estuary, under present and future conditions, using a set of linked numerical models.

  3. Evaluation of an assimilation scheme to estimate snow water equivalent in the High Atlas of Morocco.

    Science.gov (United States)

    Baba, W. M.; Baldo, E.; Gascoin, S.; Margulis, S. A.; Cortés, G.; Hanich, L.

    2017-12-01

    The snow melt from the Atlas mountains represents a crucial water resource for crop irrigation in Morocco. Due to the paucity of in situ measurements, and the high spatial variability of the snow cover in this semi-arid region, assimilation of snow cover area (SCA) from high resolution optical remote sensing into a snowpack energy-balance model is considered as a promising method to estimate the snow water equivalent (SWE) and snow melt at catchment scales. Here we present a preliminary evaluation of an uncalibrated particle batch smoother data assimilation scheme (Margulis et al., 2015, J. Hydrometeor., 16, 1752-1772) in the High-Atlas Rheraya pilot catchment (225 km2) over a snow season. This approach does not require in situ data since it is based on MERRA-2 reanalyses data and satellite fractional snow cover area data. We compared the output of this prior/posterior ensemble data assimilation system to output from the distributed snowpack evolution model SnowModel (Liston and Elder, 2006, J. Hydrometeor. 7, 1259-1276). SnowModel was forced with in situ meteorological data from five automatic weather stations (AWS) and some key parameters (precipitation correction factor and rain-snow phase transition parameters) were calibrated using a time series of 8-m resolution SCA maps from Formosat-2. The SnowModel simulation was validated using a continuous snow height record at one high elevation AWS. The results indicate that the open loop simulation was reasonably accurate (compared to SnowModel results) in spite of the coarse resolution of the MERRA-2 forcing. The assimilation of Formosat-2 SCA further improved the simulation in terms of the peak SWE and SWE evolution over the melt season. During the accumulation season, the differences between the modeled and estimated (posterior) SWE were more substantial. The differences appear to be due to some observed precipitation events not being captured in MERRA-2. Further investigation will determine whether additional

  4. Modeling the Physical Multi-Phase Interactions of HNO3 Between Snow and Air on the Antarctic Plateau (Dome C) and coast (Halley)

    Science.gov (United States)

    Chan, Hoi Ga; Frey, Markus M.; King, Martin D.

    2017-04-01

    Nitrogen oxides (NOx = NO + NO2) emissions from nitrate (NO3-) photolysis in snow affect the oxidising capacity of the lower troposphere especially in remote regions of the high latitudes with low pollution levels. The porous structure of snowpack allows the exchange of gases with the atmosphere driven by physicochemical processes, and hence, snow can act as both source and sink of atmospheric chemical trace gases. Current models are limited by poor process understanding and often require tuning parameters. Here, two multi-phase physical models were developed from first principles constrained by observed atmospheric nitrate, HNO3, to describe the air-snow interaction of nitrate. Similar to most of the previous approaches, the first model assumes that below a threshold temperature, To, the air-snow grain interface is pure ice and above To, a disordered interface (DI) emerges assumed to be covering the entire grain surface. The second model assumes that Air-Ice interactions dominate over the entire temperature range below melting and that only above the eutectic temperature, liquid is present in the form of micropockets in grooves. The models are validated with available year-round observations of nitrate in snow and air at a cold site on the Antarctica Plateau (Dome C, 75°06'S, 123°33'E, 3233 m a.s.l.) and at a relatively warm site on the Antarctica coast (Halley, 75°35'S, 26°39'E, 35 m a.s.l). The first model agrees reasonably well with observations at Dome C (Cv(RMSE) = 1.34), but performs poorly at Halley (Cv(RMSE) = 89.28) while the second model reproduces with good agreement observations at both sites without any tuning (Cv(RMSE) = 0.84 at both sites). It is therefore suggested that air-snow interactions of nitrate in the winter are determined by non-equilibrium surface adsorption and co-condensation on ice coupled with solid-state diffusion inside the grain. In summer, however, the air-snow exchange of nitrate is mainly driven by solvation into liquid

  5. The Microwave Radiative Properties of Falling Snow Derived from Nonspherical Ice Particle Models. Part II: Initial Testing Using Radar, Radiometer and In Situ Observations

    Science.gov (United States)

    Olson, William S.; Tian, Lin; Grecu, Mircea; Kuo, Kwo-Sen; Johnson, Benjamin; Heymsfield, Andrew J.; Bansemer, Aaron; Heymsfield, Gerald M.; Wang, James R.; Meneghini, Robert

    2016-01-01

    In this study, two different particle models describing the structure and electromagnetic properties of snow are developed and evaluated for potential use in satellite combined radar-radiometer precipitation estimation algorithms. In the first model, snow particles are assumed to be homogeneous ice-air spheres with single-scattering properties derived from Mie theory. In the second model, snow particles are created by simulating the self-collection of pristine ice crystals into aggregate particles of different sizes, using different numbers and habits of the collected component crystals. Single-scattering properties of the resulting nonspherical snow particles are determined using the discrete dipole approximation. The size-distribution-integrated scattering properties of the spherical and nonspherical snow particles are incorporated into a dual-wavelength radar profiling algorithm that is applied to 14- and 34-GHz observations of stratiform precipitation from the ER-2 aircraft-borne High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) radar. The retrieved ice precipitation profiles are then input to a forward radiative transfer calculation in an attempt to simulate coincident radiance observations from the Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR). Much greater consistency between the simulated and observed CoSMIR radiances is obtained using estimated profiles that are based upon the nonspherical crystal/aggregate snow particle model. Despite this greater consistency, there remain some discrepancies between the higher moments of the HIWRAP-retrieved precipitation size distributions and in situ distributions derived from microphysics probe observations obtained from Citation aircraft underflights of the ER-2. These discrepancies can only be eliminated if a subset of lower-density crystal/aggregate snow particles is assumed in the radar algorithm and in the interpretation of the in situ data.

  6. Food, energy, and water in an era of disappearing snow

    Science.gov (United States)

    Mote, P.; Lettenmaier, D. P.; Li, S.; Xiao, M.

    2017-12-01

    Mountain snowpack stores a significant quantity of water in the western US, accumulating during the wet season and melting during the dry summers and supplying more than 65% of the water used for irrigated agriculture, energy production (both hydropower and thermal), and municipal and industrial uses. The importance of snow to western agriculture is demonstrated by the fact that most snow monitoring is performed by the US Department of Agriculture. In a paper published in 2005, we showed that roughly 70% of monitoring sites showed decreasing trends through 2002. Now, with 14 additional years of data, over 90% of snow monitoring sites with long records across the western US show declines through 2016, of which 33% are significant (vs 5% expected by chance) and 2% are significant and positive (vs 5% expected by chance). Declining trends are observed across all months, states, and climates, but are largest in spring, in the Pacific states, and in locations with mild winter climate. We corroborate and extend these observations using a gridded hydrology model, which also allows a robust estimate of total western snowpack and its decline. Averaged across the western US, the decline in total April 1 snow water equivalent since mid-century is roughly 15-30% or 25-50 km3, comparable in volume to the West's largest man-made reservoir, Lake Mead. In the absence of rapid reductions in emissions of greenhouse gases, these losses will accelerate; snow losses on this scale demonstrate the necessity of rethinking water storage, policy, and usage.

  7. Snow cover volumes dynamic monitoring during melting season using high topographic accuracy approach for a Lebanese high plateau witness sinkhole

    Science.gov (United States)

    Abou Chakra, Charbel; Somma, Janine; Elali, Taha; Drapeau, Laurent

    2017-04-01

    Climate change and its negative impact on water resource is well described. For countries like Lebanon, undergoing major population's rise and already decreasing precipitations issues, effective water resources management is crucial. Their continuous and systematic monitoring overs long period of time is therefore an important activity to investigate drought risk scenarios for the Lebanese territory. Snow cover on Lebanese mountains is the most important water resources reserve. Consequently, systematic observation of snow cover dynamic plays a major role in order to support hydrologic research with accurate data on snow cover volumes over the melting season. For the last 20 years few studies have been conducted for Lebanese snow cover. They were focusing on estimating the snow cover surface using remote sensing and terrestrial measurement without obtaining accurate maps for the sampled locations. Indeed, estimations of both snow cover area and volumes are difficult due to snow accumulation very high variability and Lebanese mountains chains slopes topographic heterogeneity. Therefore, the snow cover relief measurement in its three-dimensional aspect and its Digital Elevation Model computation is essential to estimate snow cover volume. Despite the need to cover the all lebanese territory, we favored experimental terrestrial topographic site approaches due to high resolution satellite imagery cost, its limited accessibility and its acquisition restrictions. It is also most challenging to modelise snow cover at national scale. We therefore, selected a representative witness sinkhole located at Ouyoun el Siman to undertake systematic and continuous observations based on topographic approach using a total station. After four years of continuous observations, we acknowledged the relation between snow melt rate, date of total melting and neighboring springs discharges. Consequently, we are able to forecast, early in the season, dates of total snowmelt and springs low

  8. The distribution of snow black carbon observed in the Arctic and compared to the GISS-PUCCINI model

    Directory of Open Access Journals (Sweden)

    T. Dou

    2012-09-01

    Full Text Available In this study, we evaluate the ability of the latest NASA GISS composition-climate model, GISS-E2-PUCCINI, to simulate the spatial distribution of snow BC (sBC in the Arctic relative to present-day observations. Radiative forcing due to BC deposition onto Arctic snow and sea ice is also estimated. Two sets of model simulations are analyzed, where meteorology is linearly relaxed towards National Centers for Environmental Prediction (NCEP and towards NASA Modern Era Reanalysis for Research and Applications (MERRA reanalyses. Results indicate that the modeled concentrations of sBC are comparable with present-day observations in and around the Arctic Ocean, except for apparent underestimation at a few sites in the Russian Arctic. That said, the model has some biases in its simulated spatial distribution of BC deposition to the Arctic. The simulations from the two model runs are roughly equal, indicating that discrepancies between model and observations come from other sources. Underestimation of biomass burning emissions in Northern Eurasia may be the main cause of the low biases in the Russian Arctic. Comparisons of modeled aerosol BC (aBC with long-term surface observations at Barrow, Alert, Zeppelin and Nord stations show significant underestimation in winter and spring concentrations in the Arctic (most significant in Alaska, although the simulated seasonality of aBC has been greatly improved relative to earlier model versions. This is consistent with simulated biases in vertical profiles of aBC, with underestimation in the lower and middle troposphere but overestimation in the upper troposphere and lower stratosphere, suggesting that the wet removal processes in the current model may be too weak or that vertical transport is too rapid, although the simulated BC lifetime seems reasonable. The combination of observations and modeling provides a comprehensive distribution of sBC over the Arctic. On the basis of this distribution, we estimate the

  9. Ensemble mean climatology of snow darkening effect due to deposition of dust, black carbon, and organic carbon as simulated with the NASA GEOS-5 Earth System Model

    Science.gov (United States)

    Yasunari, T. J.; Lau, W. K.; Mahanama, S. P.; Colarco, P. R.; Koster, R. D.; Kim, K.; da Silva, A.

    2013-12-01

    The importance of the snow darkening effect (SDE) caused by solar absorbing aerosols such as dust and black carbon (BC) on climate has been discussed in previous studies. We have developed a snow darkening package for the catchment land surface model coupled to the NASA Goddard Earth Observing System, version 5 (GEOS-5), Earth System Model. Our snow darkening package includes the schemes for snow albedo and mass concentration calculations in polluted snow by dust, BC, and organic carbon (OC) depositions. The snow darkening package is currently available for seasonal snowpack over the model-defined land areas, excluding sea ice and inland of the ice sheets. The depositions of the solar absorbing aerosols are obtained from the GOCART aerosol module in the GEOS-5. Here we show the preliminary results of ensemble mean climatology (EMC) of the full SDE (i.e., dust+BC+OC). Ensemble simulations covering 10-year of 2002-2011 were carried out with the GEOS-5 including and excluding the full SDE for which each has 10 ensemble members. Shortwave radiative forcing (RF) at the top of atmosphere under all-sky condition for the 10-member EMC of the full SDE was relatively larger over Europe, Central Asia (CA), the Himalayas, the Tibetan Plateau (TP), East Asia (EA), Eastern Siberia (ES), the US, and Canadian Arctic. The RF was the strongest over the Himalayas and the TP in the northern hemisphere. The increases of surface air temperature also well correspond to the RF pattern. Larger reductions of snow water equivalent in seasonal snowpack were seen over the Himalayas, the TP, Alaska, Western Canada, and Arctic regions. We will discuss more on the day of the presentation.

  10. Storing snow for the next winter: Two case studies on the application of snow farming.

    Science.gov (United States)

    Grünewald, Thomas; Wolfsperger, Fabian

    2016-04-01

    Snow farming is the conservation of snow during the warm half-year. This means that large piles of snow are formed in spring in order to be conserved over the summer season. Well-insulating materials such as chipped wood are added as surface cover to reduce melting. The aim of snow farming is to provide a "snow guaranty" for autumn or early winter - this means that a specific amount of snow will definitively be available, independent of the weather conditions. The conserved snow can then be used as basis for the preparation of winter sports grounds such as cross-country tracks or ski runs. This helps in the organization of early winter season sport events such as World Cup races or to provide appropriate training conditions for athletes. We present a study on two snow farming projects, one in Davos (Switzerland) and one in the Martell valley of South Tyrol. At both places snow farming has been used for several years. For the summer season 2015, we monitored both snow piles in order to assess the amount of snow conserved. High resolution terrestrial laser scanning was performed to measure snow volumes of the piles at the beginning and at the end of the summer period. Results showed that only 20% to 30 % of the snow mass was lost due to ablation. This mass loss was surprisingly low considering the extremely warm and dry summer. In order to identify the most relevant drivers of snow melt we also present simulations with the sophisticated snow cover models SNOWPACK and Alpine3D. The simulations are driven by meteorological input data recorded in the vicinity of the piles and enable a detailed analysis of the relevant processes controlling the energy balance. The models can be applied to optimize settings for snow farming and to examine the suitability of new locations, configurations or cover material for future snow farming projects.

  11. Modeling sensitivity study of the possible impact of snow and glaciers developing over Tibetan Plateau on Holocene African-Asian summer monsoon climate

    Directory of Open Access Journals (Sweden)

    L. Jin

    2009-08-01

    Full Text Available The impacts of various scenarios of a gradual snow and glaciers developing over the Tibetan Plateau on climate change in Afro-Asian monsoon region and other regions during the Holocene (9 kyr BP–0 kyr BP are studied by using the Earth system model of intermediate complexity, CLIMBER-2. The simulations show that the imposed snow and glaciers over the Tibetan Plateau in the mid-Holocene induce global summer temperature decreases over most of Eurasia but in the Southern Asia temperature response is opposite. With the imposed snow and glaciers, summer precipitation decreases strongly in North Africa and South Asia as well as northeastern China, while it increases in Southeast Asia and the Mediterranean. For the whole period of Holocene (9 kyr BP–0 kyr BP, the response of vegetation cover to the imposed snow and glaciers cover over the Tibetan Plateau is not synchronous in South Asia and in North Africa, showing an earlier and a more rapid decrease in vegetation cover in North Africa from 9 kyr BP to 6 kyr BP while it has only minor influence on that in South Asia until 5 kyr BP. The precipitation decreases rapidly in North Africa and South Asia while it decreases slowly or unchanged during 6 kyr BP to 0 kyr BP with imposed snow and glacier cover over the Tibetan Plateau. The different scenarios of snow and glacier developing over the Tibetan Plateau would result in differences in variation of temperature, precipitation and vegetation cover in North Africa, South Asia and Southeast Asia. The model results suggest that the development of snow and ice cover over Tibetan Plateau represents an additional important climate feedback, which amplify orbital forcing and produces a significant synergy with the positive vegetation feedback.

  12. A model to accumulate fractionated dose in a deforming organ

    International Nuclear Information System (INIS)

    Yan Di; Jaffray, D.A.; Wong, J.W.

    1999-01-01

    Purpose: Measurements of internal organ motion have demonstrated that daily organ deformation exists throughout the course of radiation treatment. However, a method of constructing the resultant dose delivered to the organ volume remains a difficult challenge. In this study, a model to quantify internal organ motion and a method to construct a cumulative dose in a deforming organ are introduced. Methods and Materials: A biomechanical model of an elastic body is used to quantify patient organ motion in the process of radiation therapy. Intertreatment displacements of volume elements in an organ of interest is calculated by applying an finite element method with boundary conditions, obtained from multiple daily computed tomography (CT) measurements. Therefore, by incorporating also the measurements of daily setup error, daily dose delivered to a deforming organ can be accumulated by tracking the position of volume elements in the organ. Furthermore, distribution of patient-specific organ motion is also predicted during the early phase of treatment delivery using the daily measurements, and the cumulative dose distribution in the organ can then be estimated. This dose distribution will be updated whenever a new measurement becomes available, and used to reoptimize the ongoing treatment. Results: An integrated process to accumulate dosage in a daily deforming organ was implemented. In this process, intertreatment organ motion and setup error were systematically quantified, and incorporated in the calculation of the cumulative dose. An example of the rectal wall motion in a prostate treatment was applied to test the model. The displacements of volume elements in the rectal wall, as well as the resultant doses, were calculated. Conclusion: This study is intended to provide a systematic framework to incorporate daily patient-specific organ motion and setup error in the reconstruction of the cumulative dose distribution in an organ of interest. The realistic dose

  13. Snow farming: conserving snow over the summer season

    Science.gov (United States)

    Grünewald, Thomas; Wolfsperger, Fabian; Lehning, Michael

    2018-01-01

    Summer storage of snow for tourism has seen an increasing interest in the last years. Covering large snow piles with materials such as sawdust enables more than two-thirds of the initial snow volume to be conserved. We present detailed mass balance measurements of two sawdust-covered snow piles obtained by terrestrial laser scanning during summer 2015. Results indicate that 74 and 63 % of the snow volume remained over the summer for piles in Davos, Switzerland and Martell, Italy. If snow mass is considered instead of volume, the values increase to 83 and 72 %. The difference is attributed to settling and densification of the snow. Additionally, we adapted the one-dimensional, physically based snow cover model SNOWPACK to perform simulations of the sawdust-covered snow piles. Model results and measurements agreed extremely well at the point scale. Moreover, we analysed the contribution of the different terms of the surface energy balance to snow ablation for a pile covered with a 40 cm thick sawdust layer and a pile without insulation. Short-wave radiation was the dominant source of energy for both scenarios, but the moist sawdust caused strong cooling by long-wave emission and negative sensible and latent heat fluxes. This cooling effect reduces the energy available for melt by up to a factor of 12. As a result only 9 % of the net short-wave energy remained available for melt. Finally, sensitivity studies of the parameters thickness of the sawdust layer, air temperature, precipitation and wind speed were performed. We show that sawdust thickness has a tremendous effect on snow loss. Higher air temperatures and wind speeds increase snow ablation but less significantly. No significant effect of additional precipitation could be found as the sawdust remained wet during the entire summer with the measured quantity of rain. Setting precipitation amounts to zero, however, strongly increased melt. Overall, the 40 cm sawdust provides sufficient protection for mid

  14. Method for modeling driving cycles, fuel use, and emissions for over snow vehicles.

    Science.gov (United States)

    Hu, Jiangchuan; Frey, H Christopher; Sandhu, Gurdas S; Graver, Brandon M; Bishop, Gary A; Schuchmann, Brent G; Ray, John D

    2014-07-15

    As input to a winter use plan, activity, fuel use, and tailpipe exhaust emissions of over snow vehicles (OSV), including five snow coaches and one snowmobile, were measured on a designated route in Yellowstone National Park (YNP). Engine load was quantified in terms of vehicle specific power (VSP), which is a function of speed, acceleration, and road grade. Compared to highway vehicles, VSP for OSVs is more sensitive to rolling resistance and less sensitive to aerodynamic drag. Fuel use rates increased linearly (R2>0.96) with VSP. For gasoline-fueled OSVs, fuel-based emission rates of carbon monoxide (CO) and nitrogen oxides (NOx) typically increased with increasing fuel use rate, with some cases of very high CO emissions. For the diesel OSVs, which had selective catalytic reduction and diesel particulate filters, fuel-based NOx and particulate matter (PM) emission rates were not sensitive to fuel flow rate, and the emission controls were effective. Inter vehicle variability in cycle average fuel use and emissions rates for CO and NOx was substantial. However, there was relatively little inter-cycle variation in cycle average fuel use and emission rates when comparing driving cycles. Recommendations are made regarding how real-world OSV activity, fuel use, and emissions data can be improved.

  15. MODIS Snow and Sea Ice Products

    Science.gov (United States)

    Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.

    2004-01-01

    In this chapter, we describe the suite of Earth Observing System (EOS) Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow and sea ice products. Global, daily products, developed at Goddard Space Flight Center, are archived and distributed through the National Snow and Ice Data Center at various resolutions and on different grids useful for different communities Snow products include binary snow cover, snow albedo, and in the near future, fraction of snow in a 5OO-m pixel. Sea ice products include ice extent determined with two different algorithms, and sea ice surface temperature. The algorithms used to develop these products are described. Both the snow and sea ice products, available since February 24,2000, are useful for modelers. Validation of the products is also discussed.

  16. Snow darkening caused by black carbon emitted from fires

    Science.gov (United States)

    Engels, Jessica; Kloster, Silvia; Bourgeois, Quentin

    2014-05-01

    We implemented the effect of snow darkening caused by black carbon (BC) emitted from forest fires into the Max Planck Institute for Meteorology Earth System Model (MPI-M ESM) to estimate its potential climate impact of present day fire occurrence. Considerable amounts of black carbon emitted from fires are transported into snow covered regions. Already very small quantities of black carbon reduce the snow reflectance, with consequences for snow melting and snow spatial coverage. Therefore, the SNICAR (SNow And Ice Radiation) model (Flanner and Zender (2005)) is implemented in the land surface component (JSBACH) of the atmospheric general circulation model ECHAM6, developed at the MPI-M. The SNICAR model includes amongst other processes a complex calculation of the snow albedo depending on black carbon in snow and snow grain growth depending on water vapor fluxes for a five layer snow scheme. For the implementation of the SNICAR model into the one layer scheme of ECHAM6-JSBACH, we used the SNICAR-online version (http://snow.engin.umich.edu). This single-layer simulator provides the albedo of snow for selectable combinations of impurity content (e.g. black carbon), snow grain size, and incident solar flux characteristics. From this scheme we derived snow albedo values for black carbon in snow concentrations ranging between 0 and 1500 ng(BC)/g(snow) and for different snow grain sizes for the visible (0.3 - 0.7 µm) and near infrared range (0.7 - 1.5 µm). As snow grains grow over time, we assign different snow ages to different snow grain sizes (50, 150, 500, and 1000 µm). Here, a radius of 50 µm corresponds to new snow, whereas a radius of 1000 µm corresponds to old snow. The required snow age is taken from the BATS (Biosphere Atmosphere Transfer Scheme, Dickinson et al. (1986)) snow albedo implementation in ECHAM6-JSBACH. Here, we will present an extended evaluation of the model including a comparison of modeled black carbon in snow concentrations to observed

  17. Snow multivariable data assimilation for hydrological predictions in mountain areas

    Science.gov (United States)

    Piazzi, Gaia; Campo, Lorenzo; Gabellani, Simone; Rudari, Roberto; Castelli, Fabio; Cremonese, Edoardo; Morra di Cella, Umberto; Stevenin, Hervé; Ratto, Sara Maria

    2016-04-01

    The seasonal presence of snow on alpine catchments strongly impacts both surface energy balance and water resource. Thus, the knowledge of the snowpack dynamics is of critical importance for several applications, such as water resource management, floods prediction and hydroelectric power production. Several independent data sources provide information about snowpack state: ground-based measurements, satellite data and physical models. Although all these data types are reliable, each of them is affected by specific flaws and errors (respectively dependency on local conditions, sensor biases and limitations, initialization and poor quality forcing data). Moreover, there are physical factors that make an exhaustive reconstruction of snow dynamics complicated: snow intermittence in space and time, stratification and slow phenomena like metamorphism processes, uncertainty in snowfall evaluation, wind transportation, etc. Data Assimilation (DA) techniques provide an objective methodology to combine observational and modeled information to obtain the most likely estimate of snowpack state. Indeed, by combining all the available sources of information, the implementation of DA schemes can quantify and reduce the uncertainties of the estimations. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model, strengthened by a robust multivariable data assimilation algorithm. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state. The implementation of an Ensemble Kalman Filter (EnKF) scheme enables to assimilate simultaneously ground

  18. Review of ice and snow runway pavements

    Directory of Open Access Journals (Sweden)

    Greg White

    2018-05-01

    Full Text Available Antarctica is the highest, driest, coldest, windiest, most remote and most pristine place on Earth. Polar operations depend heavily on air transportation and support for personnel and equipment. It follows that improvement in snow and ice runway design, construction and maintenance will directly benefit polar exploration and research. Current technologies and design methods for snow and ice runways remain largely reliant on work performed in the 1950s and 1960s. This paper reviews the design and construction of polar runways using snow and ice as geomaterials. The inability to change existing snow and ice thickness or temperature creates a challenge for polar runway design and construction, as does the highly complex mechanical behaviour of snow, including the phenomena known as sintering. It is recommended that a modern approach be developed for ice and snow runway design, based on conventional rigid and flexible pavement design principles. This requires the development on an analytical model for the prediction of snow strength, based on snow age, temperature history and density. It is also recommended that the feasibility of constructing a snow runway at the South Pole be revisited, in light of contemporary snow sintering methods. Such a runway would represent a revolutionary advance for the logistical support of Antarctic research efforts. Keywords: Runway, Pavement, Snow, Ice, Antarctic

  19. Impact of model resolution on simulated wind, drifting snow and surface mass balance in Terre Adélie, East Antarctica

    NARCIS (Netherlands)

    Lenaerts, J.T.M.|info:eu-repo/dai/nl/314850163; van den Broeke, M.R.|info:eu-repo/dai/nl/073765643; Scarchilli, C.; Agosta, C.

    2012-01-01

    This paper presents the impact of model resolution on the simulated wind speed, drifting snow climate and surface mass balance (SMB) of Terre Ad´elie and its surroundings, East Antarctica. We compare regional climate model simulations at 27 and 5.5 km resolution for the year 2009. The wind speed

  20. How autumn Eurasian snow anomalies affect east asian winter monsoon: a numerical study

    Science.gov (United States)

    Luo, Xiao; Wang, Bin

    2018-03-01

    Previous studies have found that snow Eurasian anomalies in autumn can affect East Asian winter monsoon (EAWM), but the mechanisms remain controversial and not well understood. The possible mechanisms by which Eurasian autumn snow anomalies affect EAWM are investigated by numerical experiments with a coupled general circulation model and its atmospheric general circulation model component. The leading empirical orthogonal function mode of the October-November mean Eurasian snow cover is characterized by a uniform anomaly over a broad region of central Eurasia (40°N-65°N, 60°E-140°E). However, the results from a 150-ensemble mean simulation with snow depth anomaly specified in October and November reveal that the Mongolian Plateau and Vicinity (MPV, 40°-55°N, 80°-120°E) is the key region for autumn snow anomalies to affect EAWM. The excessive snow forcing can significantly enhance EAWM and the snowfall over the northwestern China and along the EAWM front zone stretching from the southeast China to Japan. The physical process involves a snow-monsoon feedback mechanism. The excessive autumn snow anomalies over the MPV region can persist into the following winter, and significantly enhance winter snow anomalies, which increase surface albedo, reduce incoming solar radiation and cool the boundary layer air, leading to an enhanced Mongolian High and a deepened East Asian trough. The latter, in turn, strengthen surface northwesterly winds, cooling East Asia and increasing snow accumulation over the MPV region and the southeastern China. The increased snow covers feedback to EAWM system through changing albedo, extending its influence southeastward. It is also found that the atmosphere-ocean coupling process can amplify the delayed influence of Eurasian snow mass anomaly on EAWM. The autumn surface albedo anomalies, however, do not have a lasting "memory" effect. Only if the albedo anomalies are artificially extended into December and January, will the EAWM be

  1. Modelling the physical multiphase interactions of HNO3 between snow and air on the Antarctic Plateau (Dome C and coast (Halley

    Directory of Open Access Journals (Sweden)

    H. G. Chan

    2018-02-01

    Full Text Available Emissions of nitrogen oxide (NOx  =  NO + NO2 from the photolysis of nitrate (NO3− in snow affect the oxidising capacity of the lower troposphere especially in remote regions of high latitudes with little pollution. Current air–snow exchange models are limited by poor understanding of processes and often require unphysical tuning parameters. Here, two multiphase models were developed from physically based parameterisations to describe the interaction of nitrate between the surface layer of the snowpack and the overlying atmosphere. The first model is similar to previous approaches and assumes that below a threshold temperature, To, the air–snow grain interface is pure ice and above To a disordered interface (DI emerges covering the entire grain surface. The second model assumes that air–ice interactions dominate over all temperatures below melting of ice and that any liquid present above the eutectic temperature is concentrated in micropockets. The models are used to predict the nitrate in surface snow constrained by year-round observations of mixing ratios of nitric acid in air at a cold site on the Antarctic Plateau (Dome C; 75°06′ S, 123°33′ E; 3233 m a.s.l. and at a relatively warm site on the Antarctic coast (Halley; 75°35′ S, 26°39′ E; 35 m a.s.l. The first model agrees reasonably well with observations at Dome C (Cv(RMSE  =  1.34 but performs poorly at Halley (Cv(RMSE  =  89.28 while the second model reproduces with good agreement observations at both sites (Cv(RMSE  =  0.84 at both sites. It is therefore suggested that in winter air–snow interactions of nitrate are determined by non-equilibrium surface adsorption and co-condensation on ice coupled with solid-state diffusion inside the grain, similar to Bock et al. (2016. In summer, however, the air–snow exchange of nitrate is mainly driven by solvation into liquid micropockets following Henry's law with

  2. Investigations on socio economic indicators of French Alps ski industry from an explicit spatial modelling of managed snow on ski slopes

    Science.gov (United States)

    Spandre, Pierre; François, Hugues; Morin, Samuel; George-Marcelpoil, Emmanuelle; Lafaysse, Matthieu

    2017-04-01

    Investigations of the capacity of ski resorts to anticipate, cope with and recover from the impact of natural snow scarcity through snow management (grooming, snowmaking) have been realized in most of the major regions in terms of international ski offer although not in the French Alps hitherto. The present work therefore introduces an innovative approach for the investigation of socio economic implications of changes in snow conditions for the French Alps ski resorts based on a panel of 129 resorts representing 96% of the total French Alps ski lifts infrastructures. We integrated detailed spatial representations of ski resorts (including priority areas for snowmaking equipment) along with physically based snowpack modelling (including the physical impact of grooming and snowmaking). The viability of ski resorts was further adressed thanks to a commonly used rule based on the snow season duration at the village and ski lifts average elevations along with the development of original viability indicators of snow conditions in the French Alps ski resorts based on the specific periods for the economic success of winter sports: Christmas and February school holidays. Such indicators were correlated to the number of ski lifts tickets sales over the 2001 - 2014 period and proved to be relevant to investigate and predict the evolutions of ski lifts tickets sales under the current ski market conditions in the French Alps. Our results outlined the contrast of snow conditions between French Alps ski resorts, even when accounting for snow management, particularly regarding the geographical location of resorts (Southern versus Northern Alps), the size and related elevation range of ski resorts. Our physically based approach also allowed to compute the water and energy requirements for the production of Machine Made snow since the start of the development of snowguns in the French Alps. Our computations proved to be strongly correlated to the observed amounts of water from the

  3. Modelling Holocene carbon accumulation and methane emissions of boreal wetlands – an Earth system model approach

    Directory of Open Access Journals (Sweden)

    R. J. Schuldt

    2013-03-01

    Full Text Available Since the Last Glacial Maximum, boreal wetlands have accumulated substantial amounts of peat, estimated at 180–621 Pg of carbon. Wetlands have significantly affected the atmospheric greenhouse gas composition in the past and will play a significant role in future changes of atmospheric CO2 and CH4 concentrations. In order to investigate those changes with an Earth system model, biogeochemical processes in boreal wetlands need to be accounted for. Thus, a model of peat accumulation and decay was developed and included in the land surface model JSBACH of the Max Planck Institute Earth System Model (MPI-ESM. Here we present the evaluation of model results from 6000 yr BP to the pre-industrial period. Over this period of time, 240 Pg of peat carbon accumulated in the model in the areas north of 40° N. Simulated peat accumulation rates agree well with those reported for boreal wetlands. The model simulates CH4 emissions of 49.3 Tg CH4 yr−1 for 6000 yr BP and 51.5 Tg CH4 yr−1 for pre-industrial times. This is within the range of estimates in the literature, which range from 32 to 112 Tg CH4 yr−1 for boreal wetlands. The modelled methane emission for the West Siberian Lowlands and Hudson Bay Lowlands agree well with observations. The rising trend of methane emissions over the last 6000 yr is in agreement with measurements of Antarctic and Greenland ice cores.

  4. Implementation and evaluation of prognostic representations of the optical diameter of snow in the SURFEX/ISBA-Crocus detailed snowpack model

    Science.gov (United States)

    Carmagnola, C. M.; Morin, S.; Lafaysse, M.; Domine, F.; Lesaffre, B.; Lejeune, Y.; Picard, G.; Arnaud, L.

    2014-03-01

    In the SURFEX/ISBA-Crocus multi-layer snowpack model, the snow microstructure has up to now been characterised by the grain size and by semi-empirical shape variables which cannot be measured easily in the field or linked to other relevant snow properties. In this work we introduce a new formulation of snow metamorphism directly based on equations describing the rate of change of the optical diameter (dopt). This variable is considered here to be equal to the equivalent sphere optical diameter, which is inversely proportional to the specific surface area (SSA). dopt thus represents quantitatively some of the geometric characteristics of a porous medium. Different prognostic rate equations of dopt, including a re-formulation of the original Crocus scheme and the parameterisations from Taillandier et al. (2007) and Flanner and Zender (2006), were evaluated by comparing their predictions to field measurements carried out at Summit Camp (Greenland) in May and June 2011 and at Col de Porte (French Alps) during the 2009/10 and 2011/12 winter seasons. We focused especially on results in terms of SSA. In addition, we tested the impact of the different formulations on the simulated density profile, the total snow height, the snow water equivalent (SWE) and the surface albedo. Results indicate that all formulations perform well, with median values of the RMSD between measured and simulated SSA lower than 10 m2 kg-1. Incorporating the optical diameter as a fully fledged prognostic variable is an important step forward in the quantitative description of the snow microstructure within snowpack models, because it opens the way to data assimilation of various electromagnetic observations.

  5. Implementation and evaluation of prognostic representations of the optical diameter of snow in the detailed snowpack model SURFEX/ISBA-Crocus

    Science.gov (United States)

    Carmagnola, C. M.; Morin, S.; Lafaysse, M.; Domine, F.; Lesaffre, B.; Lejeune, Y.; Picard, G.; Arnaud, L.

    2013-09-01

    In the SURFEX/ISBA-Crocus multi-layer snowpack model, the snow microstructure was up to now characterized by the grain size and by semi-empirical shape variables which cannot be measured easily in the field or linked to other relevant snow properties. In this work we introduce a new formulation of snow metamorphism directly based on equations describing the rate of change of the optical diameter (dopt). This variable is considered here to be equal to the equivalent sphere optical diameter, which is inversely proportional to the specific surface area (SSA). dopt thus represents quantitatively some of the geometric characteristics of a porous medium. Different prognostic rate equations of dopt, including a re-formulation of the original Crocus scheme and the parametrizations from Taillandier et al. (2007) and Flanner and Zender (2006), were evaluated by comparing their predictions to field measurements carried out at Summit Camp (Greenland) in May and June 2011 and at Col de Porte (French Alps) during the 2009/10 and 2011/12 winter seasons. We focused especially on results in terms of SSA. In addition, we tested the impact of the different formulations on the simulated density profile, the total snow height, the snow water equivalent (SWE) and the surface albedo. Results indicate that all formulations perform well, with median values of the RMSD between measured and simulated SSA lower than 10 m2 kg-1. Incorporating the optical diameter as a fully-fledged prognostic variable is an important step forward in the quantitative description of the snow microstructure within snowpack models, because it opens the way to data assimilation of various electromagnetic observations.

  6. Mesoscale spiral vortex embedded within a Lake Michigan snow squall band - High resolution satellite observations and numerical model simulations

    Science.gov (United States)

    Lyons, Walter A.; Keen, Cecil S.; Hjelmfelt, Mark; Pease, Steven R.

    1988-01-01

    It is known that Great Lakes snow squall convection occurs in a variety of different modes depending on various factors such as air-water temperature contrast, boundary-layer wind shear, and geostrophic wind direction. An exceptional and often neglected source of data for mesoscale cloud studies is the ultrahigh resolution multispectral data produced by Landsat satellites. On October 19, 1972, a clearly defined spiral vortex was noted in a Landsat-1 image near the southern end of Lake Michigan during an exceptionally early cold air outbreak over a still very warm lake. In a numerical simulation using a three-dimensional Eulerian hydrostatic primitive equation mesoscale model with an initially uniform wind field, a definite analog to the observed vortex was generated. This suggests that intense surface heating can be a principal cause in the development of a low-level mesoscale vortex.

  7. Impacts of Recent Climatic Wetting on Distributed Snow and Streamflow Responses in a Terminal Lake Basin.

    Science.gov (United States)

    Van Hoy, D.; Mahmood, T. H.; Jeannotte, T.; Todhunter, P. E.

    2017-12-01

    The recent shift in hydroclimatic conditions in the Northern Great Plains (NGP) has led to an increase in precipitation, rainfall rate, and wetland connectivity over the last few decades. These changes yield an integrated response resulting in high mean annual streamflow and subsequent flooding in many NGP basins such as the terminal Devils Lake Basin (DLB). In this study, we investigate the impacts of recent climatic wetting on distributed hydrologic responses such as snow processes and streamflow using a field-tested and physically-based cold region hydrologic model (CRHM). CHRM is designed for cold prairie regions and has modules to simulate major processes such as blowing snow transport, sublimation, interception, frozen soil infiltration, snowmelt and subsequent streamflow generation. Our modeling focuses on a tributary basin of the DLB known as the Mauvais Coulee Basin (MCB). Since there were no snow observations in the MCB, we conducted a detailed snow survey at distributed locations estimating snow depth, density, and snow water equivalent (SWE) using a prairie snow tube four times during winter of 2016-17. The MCB model was evaluated against distributed snow observations and streamflow measured at the basin outlet (USGS) for the year 2016-2017. Preliminary results indicate that the simulated SWEs at distributed locations and streamflow (NSE ≈ 0.70) are in good agreement with observations. The simulated SWE maps exhibit large spatiotemporal variation during 2016-17 winter due to spatial variability in precipitation, snow redistribution from stubble field to wooded areas, and snow accumulations in small depressions across the subbasins. The main source of snow appears to be the hills and ridges of the eastern and western edges of the basin, while the main sink is the large flat central valleys. The model will be used to examine the effect of recent changes to precipitation and temperature on snow processes and subsequent streamflow for 2004-2017 season. We

  8. A stochastic model for density-dependent microwave Snow- and Graupel scattering coefficients of the NOAA JCSDA community radiative transfer model

    Science.gov (United States)

    Stegmann, Patrick G.; Tang, Guanglin; Yang, Ping; Johnson, Benjamin T.

    2018-05-01

    A structural model is developed for the single-scattering properties of snow and graupel particles with a strongly heterogeneous morphology and an arbitrary variable mass density. This effort is aimed to provide a mechanism to consider particle mass density variation in the microwave scattering coefficients implemented in the Community Radiative Transfer Model (CRTM). The stochastic model applies a bicontinuous random medium algorithm to a simple base shape and uses the Finite-Difference-Time-Domain (FDTD) method to compute the single-scattering properties of the resulting complex morphology.

  9. Collaborative Project. 3D Radiative Transfer Parameterization Over Mountains/Snow for High-Resolution Climate Models. Fast physics and Applications

    Energy Technology Data Exchange (ETDEWEB)

    Liou, Kuo-Nan [Univ. of California, Los Angeles, CA (United States)

    2016-02-09

    Under the support of the aforementioned DOE Grant, we have made two fundamental contributions to atmospheric and climate sciences: (1) Develop an efficient 3-D radiative transfer parameterization for application to intense and intricate inhomogeneous mountain/snow regions. (2) Innovate a stochastic parameterization for light absorption by internally mixed black carbon and dust particles in snow grains for understanding and physical insight into snow albedo reduction in climate models. With reference to item (1), we divided solar fluxes reaching mountain surfaces into five components: direct and diffuse fluxes, direct- and diffuse-reflected fluxes, and coupled mountain-mountain flux. “Exact” 3D Monte Carlo photon tracing computations can then be performed for these solar flux components to compare with those calculated from the conventional plane-parallel (PP) radiative transfer program readily available in climate models. Subsequently, Parameterizations of the deviations of 3D from PP results for five flux components are carried out by means of the multiple linear regression analysis associated with topographic information, including elevation, solar incident angle, sky view factor, and terrain configuration factor. We derived five regression equations with high statistical correlations for flux deviations and successfully incorporated this efficient parameterization into WRF model, which was used as the testbed in connection with the Fu-Liou-Gu PP radiation scheme that has been included in the WRF physics package. Incorporating this 3D parameterization program, we conducted simulations of WRF and CCSM4 to understand and evaluate the mountain/snow effect on snow albedo reduction during seasonal transition and the interannual variability for snowmelt, cloud cover, and precipitation over the Western United States presented in the final report. With reference to item (2), we developed in our previous research a geometric-optics surface-wave approach (GOS) for the

  10. Past and future of the Austrian snow cover - results from the CC-Snow project

    Science.gov (United States)

    Strasser, Ulrich; Marke, Thomas; Hanzer, Florian; Ragg, Hansjörg; Kleindienst, Hannes; Wilcke, Renate; Gobiet, Andreas

    2013-04-01

    This study has the goal to simulate the evolution of the Austrian snow cover from 1971 to 2050 by means of a coupled modelling scheme, and to estimate the effect of climate change on the evolution of the natural snow cover. The model outcomes are interepreted with focus on both the future natural snow conditions, and the effects on winter skiing tourism. Therefore the regional temperature-index snow model SNOWREG is applied, providing snow maps with a spatial resolution of 250 m. The model is trained by means of assimilating local measurements and observed natural snow cover patterns. Meteorological forcing consists of the output of four realizations of the ENSEMBLES project for the A1B emission scenario. The meteorological variables are downscaled and error corrected with a quantile based empirical-statistical method on a daily time basis. The control simulation is 1971-2000, and the scenario simulation 2021-2050. Spatial interpolation is performed on the basis of parameter-elevation relations. We compare the four different global/regional climate model combinations and their effect on the snow modelling, and we explain the patterns of the resulting snow cover by means of regional climatological characteristics. The provinces Tirol and Styria serve as test regions, being typical examples for the two climatic subregions of Austria. To support the interpretation of the simulation results we apply indicators which enable to define meaningful measures for the comparison of the different periods and regions. Results show that the mean duration of the snow cover will decrease by 15 to 30 days per winter season, mostly in elevations between 2000 and 2500 m. Above 3000 m the higher winter precipitation can compensate this effect, and mean snow cover duration may even slightly increase. We also investigate the local scale by application of the physically based mountain snow model AMUNDSEN. This model is capable of producing 50 m resolution output maps for indicators

  11. Through the Looking Glass: Droughtorama to Snowpocalypse in the Sierra Nevada as studied with the NASA Airborne Snow Observatory

    Science.gov (United States)

    Painter, T. H.; Bormann, K.; Deems, J. S.; Hedrick, A. R.; Marks, D. G.; Skiles, M.; Stock, G. M.

    2017-12-01

    Across the last five years, the Sierra Nevada has seen increasing drought and then an abrupt return to a top five snowpack. Fortunately, the NASA Airborne Snow Observatory has been flying the Central Sierra Nevada since the spring of 2013, quantifying critical mountain basins' snow water equivalent and snow albedo. The huge variation of snowpack years captured by the NASA ASO is of enormous benefit to water cycle science, ecosystem science, and water management utilization of ASO data and its modeling. It allows a much broader understanding of mountain basin snow season cases for understanding snowmelt runoff, snow/rain mixes, snowfall distribution, evapotranspiration, soil moisture, and glacier mass balance. For water management, trust in empirical and physically-based modeling from the ASO data for application anywhere in the range of snow years is greatly improved by having consistency in that modeling with the span of years ASO has characterized. The NASA ASO was designed to characterize mountain snowpack and fill this void in water cycle science. Our original conversations with partner California Department of Water Resources in 2011 focused on the utility of ASO for flood risk mitigation, given the large snowfall of that year. However, from 2012 through 2016, California snowpacks expressed horrible drought, reaching the nadir in 2015 with the lowest snowpack on record. The 2016 snowpack was nearly normal according to snow pillows and snow courses (ASO's record is too short to define a `normal' year). However, 2017 had enormous snowfall in January and February, keeping snow pillows on track with the largest year on record, 1982-83. However, March backed off and the record year was lost. Still, accumulation was enormous. In parts of the San Joaquin basin, snow depths were > 30 m. The sum of near April 1 ASO total basin SWE for 2013 through 2016 in the Tuolumne Basin was only 92% of the near April 1, 2017 acquisition. In addition to the large accumulation of

  12. Deriving Snow Cover Metrics for Alaska from MODIS

    Directory of Open Access Journals (Sweden)

    Chuck Lindsay

    2015-09-01

    Full Text Available Moderate Resolution Imaging Spectroradiometer (MODIS daily snow cover products provide an opportunity for determining snow onset and melt dates across broad geographic regions; however, cloud cover and polar darkness are limiting factors at higher latitudes. This study presents snow onset and melt dates for Alaska, portions of western Canada and the Russian Far East derived from Terra MODIS snow cover daily 500 m grid data (MOD10A1 and evaluates our method for filling data gaps caused by clouds or polar darkness. Pixels classified as cloud or no data were reclassified by: spatial filtering using neighboring pixel values; temporal filtering using pixel values for days before/after cloud cover; and snow-cycle filtering based on a time series assessment of a pixel’s position within snow accumulation, cover or melt periods. During the 2012 snow year, these gap-filling methods reduced cloud pixels from 27.7% to 3.1%. A total of 12 metrics (e.g., date of first and last snow, date of persistent snow cover and periods of intermittence for each pixel were calculated by snow year. A comparison of MODIS-derived snow onset and melt dates with in situ observations from 244 weather stations generally showed an early bias in MODIS-derived dates and an effect of increasing cloudiness exacerbating bias. Our results show that mean regional duration of seasonal snow cover is 179–311 days/year and that snow cover is often intermittent, with 41% of the area experiencing ≥2 snow-covered periods during a snow season. Other regional-scale patterns in the timing of snow onset and melt are evident in the yearly 500 m gridded products publically available at http://static.gina.alaska.edu/NPS_products/MODIS_snow/.

  13. Simulating Snow in Canadian Boreal Environments with CLASS for ESM-SnowMIP

    Science.gov (United States)

    Wang, L.; Bartlett, P. A.; Derksen, C.; Ireson, A. M.; Essery, R.

    2017-12-01

    The ability of land surface schemes to provide realistic simulations of snow cover is necessary for accurate representation of energy and water balances in climate models. Historically, this has been particularly challenging in boreal forests, where poor treatment of both snow masking by forests and vegetation-snow interaction has resulted in biases in simulated albedo and snowpack properties, with subsequent effects on both regional temperatures and the snow albedo feedback in coupled simulations. The SnowMIP (Snow Model Intercomparison Project) series of experiments or `MIPs' was initiated in order to provide assessments of the performance of various snow- and land-surface-models at selected locations, in order to understand the primary factors affecting model performance. Here we present preliminary results of simulations conducted for the third such MIP, ESM-SnowMIP (Earth System Model - Snow Model Intercomparison Project), using the Canadian Land Surface Scheme (CLASS) at boreal forest sites in central Saskatchewan. We assess the ability of our latest model version (CLASS 3.6.2) to simulate observed snowpack properties (snow water equivalent, density and depth) and above-canopy albedo over 13 winters. We also examine the sensitivity of these simulations to climate forcing at local and regional scales.

  14. Everywhere and nowhere: snow and its linkages

    Science.gov (United States)

    Hiemstra, C. A.

    2017-12-01

    Interest has grown in quantifying higher latitude precipitation change and snow-related ecosystem and economic impacts. There is a high demand for creating and using snow-related datasets, yet available datasets contain limitations, aren't scale appropriate, or lack thorough validation. Much of the uncertainty in snow estimates relates to ongoing snow measurement problems that are chronic and pervasive in windy, Arctic environments. This, coupled with diminishing support for long-term snow field observations, creates formidable hydrologic gaps in snow dominated landscapes. Snow touches most aspects of high latitude landscapes and spans albedo, ecosystems, soils, permafrost, and sea ice. In turn, snow can be impacted by disturbances, landscape change, ecosystem, structure, and later arrival of sea or lake ice. Snow, and its changes touch infrastructure, housing, and transportation. Advances in snow measurements, modeling, and data assimilation are under way, but more attention and a concerted effort is needed in a time of dwindling resources to make required advances during a time of rapid change.

  15. The Potential for Snow to Supply Human Water Demand in the Present and Future

    Science.gov (United States)

    Mankin, Justin S.; Viviroli, Daniel; Singh, Deepti; Hoekstra, Arjen Y.; Diffenbaugh, Noah S.

    2015-01-01

    Runoff from snowmelt is regarded as a vital water source for people and ecosystems throughout the Northern Hemisphere (NH). Numerous studies point to the threat global warming poses to the timing and magnitude of snow accumulation and melt. But analyses focused on snow supply do not show where changes to snowmelt runoff are likely to present the most pressing adaptation challenges, given sub-annual patterns of human water consumption and water availability from rainfall. We identify the NH basins where present spring and summer snowmelt has the greatest potential to supply the human water demand that would otherwise be unmet by instantaneous rainfall runoff. Using a multi-model ensemble of climate change projections, we find that these basins - which together have a present population of approx. 2 billion people - are exposed to a 67% risk of decreased snow supply this coming century. Further, in the multi-model mean, 68 basins (with a present population of more than 300 million people) transition from having sufficient rainfall runoff to meet all present human water demand to having insufficient rainfall runoff. However, internal climate variability creates irreducible uncertainty in the projected future trends in snow resource potential, with about 90% of snow-sensitive basins showing potential for either increases or decreases over the near-term decades. Our results emphasize the importance of snow for fulfilling human water demand in many NH basins, and highlight the need to account for the full range of internal climate variability in developing robust climate risk management decisions.

  16. Hydrologic response across a snow persistence gradient on the west and east slopes of the Rocky Mountains in Colorado

    Science.gov (United States)

    Richard, G. A.; Hammond, J. C.; Kampf, S. K.; Moore, C. D.; Eurich, A.

    2017-12-01

    Snowpack trend analyses and modeling studies suggest that lower elevation snowpacks in mountain regions are most sensitive to drought and warming temperatures, however, in Colorado, most snow monitoring occurs in the high elevations where snow lasts throughout the winter and most streamflow monitoring occurs at lower elevations. The lack of combined snow and streamflow monitoring in watersheds along the transition from intermittent to persistent snow creates a gap in our understanding of snowmelt and runoff within the intermittent-persistent snow transition. Expanded hydrologic monitoring that spans the gradient of snow conditions in Colorado can help improve streamflow prediction and inform land and water managers. This study established hydrologic monitoring watersheds in intermittent, transitional, and persistent snow zones on the east slope and west slope of the Rocky Mountains in Colorado, and uses this monitoring network to improve understanding of how snow accumulation and melt affect soil moisture and streamflow generation under different snow conditions. We monitored six small watersheds (three west slope, three east slope) (0.8 to 3.9 km2) that drain intermittent, transitional, and persistent snow zones. At each site, we measured: streamflow, snow depth, soil moisture, precipitation, air temperature, and snow water equivalent (SWE). In our first season of monitoring, the west slope persistent and transitional sites had more mid-winter melt and infiltration, shorter snowpack duration, and lower peak SWE than the east slope sites. Snow cover remained at the east slope persistent site into June, whereas much of the snow at the persistent site on the west slope had already melted by early June. The difference in soil water input likely has consequences for streamflow response that we will continue to examine in future years. At the west slope intermittent site, the stream did not flow during the entire first year of monitoring, while at the east slope

  17. A statistical adjustment approach for climate projections of snow conditions in mountain regions using energy balance land surface models

    Science.gov (United States)

    Verfaillie, Deborah; Déqué, Michel; Morin, Samuel; Lafaysse, Matthieu

    2017-04-01

    Projections of future climate change have been increasingly called for lately, as the reality of climate change has been gradually accepted and societies and governments have started to plan upcoming mitigation and adaptation policies. In mountain regions such as the Alps or the Pyrenees, where winter tourism and hydropower production are large contributors to the regional revenue, particular attention is brought to current and future snow availability. The question of the vulnerability of mountain ecosystems as well as the occurrence of climate-related hazards such as avalanches and debris-flows is also under consideration. In order to generate projections of snow conditions, however, downscaling global climate models (GCMs) by using regional climate models (RCMs) is not sufficient to capture the fine-scale processes and thresholds at play. In particular, the altitudinal resolution matters, since the phase of precipitation is mainly controlled by the temperature which is altitude-dependent. Simulations from GCMs and RCMs moreover suffer from biases compared to local observations, due to their rather coarse spatial and altitudinal resolution, and often provide outputs at too coarse time resolution to drive impact models. RCM simulations must therefore be adjusted using empirical-statistical downscaling and error correction methods, before they can be used to drive specific models such as energy balance land surface models. In this study, time series of hourly temperature, precipitation, wind speed, humidity, and short- and longwave radiation were generated over the Pyrenees and the French Alps for the period 1950-2100, by using a new approach (named ADAMONT for ADjustment of RCM outputs to MOuNTain regions) based on quantile mapping applied to daily data, followed by time disaggregation accounting for weather patterns selection. We first introduce a thorough evaluation of the method using using model runs from the ALADIN RCM driven by a global reanalysis over the

  18. ESA GlobSnow Snow Water Equivalent (SWE)

    Data.gov (United States)

    National Aeronautics and Space Administration — The European Space Agency (ESA) Global Snow Monitoring for Climate Research (GlobSnow) snow water equivalent (SWE) v2.0 data record contains snow information derived...

  19. An electrostatic charge measurement of blowing snow particles focusing on collision frequency to the snow surface

    Science.gov (United States)

    Omiya, S.; Sato, A.

    2010-12-01

    Blowing snow particles are known to have an electrostatic charge. This charge may be a contributing factor in the formation of snow drifts and snow cornices and changing of the trajectory of blowing snow particles. These formations and phenomena can cause natural disaster such as an avalanche and a visibility deterioration, and obstruct transportation during winter season. Therefore, charging phenomenon of the blowing snow particles is an important issue in terms of not only precise understanding of the particle motion but disaster prevention. The primary factor of charge accumulation to the blowing snow particles is thought to be due to “saltation” of them. The “saltation” is one of movement forms of blowing snow: when the snow particles are transported by the wind, they repeat frictional collisions with the snow surface. In previous studies, charge-to-mass ratios measured in the field were approximately -50 to -10 μC/kg, and in the wind tunnel were approximately -0.8 to -0.1 μC/kg. While there were qualitatively consistent in sign, negative, there were huge gaps quantitatively between them. One reason of those gaps is speculated to be due to differences in fetch. In other words, the difference of the collision frequency of snow particles to the snow surface has caused the gaps. But it is merely a suggestion and that has not been confirmed. The purpose of this experiment is to measure the charge of blowing snow particles focusing on the collision frequency and clarify the relationship between them. Experiments were carried out in the cryogenic wind tunnel of Snow and Ice Research Center (NIED, JAPAN). A Faraday cage and an electrometer were used to measure the charge of snow particles. These experiments were conducted over the hard snow surface condition to prevent the erosion of the snow surface and the generation of new snow particles from the surface. The collision frequency of particle was controlled by changing the wind velocity (4.5 to 7 m/s) under

  20. Ultra-Wideband Radiometry Remote Sensing of Polar Ice Sheet Temperature Profile, Sea Ice and Terrestrial Snow Thickness: Forward Modeling and Data Analysis

    Science.gov (United States)

    Tsang, L.; Tan, S.; Sanamzadeh, M.; Johnson, J. T.; Jezek, K. C.; Durand, M. T.

    2017-12-01

    The recent development of an ultra-wideband software defined radiometer (UWBRAD) operating over the unprotected spectrum of 0.5 2.0 GHz using radio-frequency interference suppression techniques offers new methodologies for remote sensing of the polar ice sheets, sea ice, and terrestrial snow. The instrument was initially designed for remote sensing of the intragalcial temperature profile of the ice sheet, where a frequency dependent penetration depth yields a frequency dependent brightness temperature (Tb) spectrum that can be linked back to the temperature profile of the ice sheet. The instrument was tested during a short flight over Northwest Greenland in September, 2016. Measurements were successfully made over the different snow facies characteristic of Greenland including the ablation, wet snow and percolation facies, and ended just west of Camp Century during the approach to the dry snow zone. Wide-band emission spectra collected during the flight have been processed and analyzed. Results show that the spectra are highly sensitive to the facies type with scattering from ice lenses being the dominant reason for low Tbs in the percolation zone. Inversion of Tb to physical temperature at depth was conducted on the measurements near Camp Century, achieving a -1.7K ten-meter error compared to borehole measurements. However, there is a relatively large uncertainty in the lower part possibly due to the large scattering near the surface. Wideband radiometry may also be applicable to sea ice and terrestrial snow thickness retrieval. Modeling studies suggest that the UWBRAD spectra reduce ambiguities inherent in other sea ice thickness retrievals by utilizing coherent wave interferences that appear in the Tb spectrum. When applied to a lossless medium such as terrestrial snow, this coherent oscillation turns out to be the single key signature that can be used to link back to snow thickness. In this paper, we report our forward modeling findings in support of instrument

  1. Long-term variability in Northern Hemisphere snow cover and associations with warmer winters

    Science.gov (United States)

    McCabe, Gregory J.; Wolock, David M.

    2010-01-01

    A monthly snow accumulation and melt model is used with gridded monthly temperature and precipitation data for the Northern Hemisphere to generate time series of March snow-covered area (SCA) for the period 1905 through 2002. The time series of estimated SCA for March is verified by comparison with previously published time series of SCA for the Northern Hemisphere. The time series of estimated Northern Hemisphere March SCA shows a substantial decrease since about 1970, and this decrease corresponds to an increase in mean winter Northern Hemisphere temperature. The increase in winter temperature has caused a decrease in the fraction of precipitation that occurs as snow and an increase in snowmelt for some parts of the Northern Hemisphere, particularly the mid-latitudes, thus reducing snow packs and March SCA. In addition, the increase in winter temperature and the decreases in SCA appear to be associated with a contraction of the circumpolar vortex and a poleward movement of storm tracks, resulting in decreased precipitation (and snow) in the low- to mid-latitudes and an increase in precipitation (and snow) in high latitudes. If Northern Hemisphere winter temperatures continue to warm as they have since the 1970s, then March SCA will likely continue to decrease.

  2. An integrated modeling system for estimating glacier and snow melt driven streamflow from remote sensing and earth system data products in the Himalayas

    Science.gov (United States)

    Brown, M. E.; Racoviteanu, A. E.; Tarboton, D. G.; Gupta, A. Sen; Nigro, J.; Policelli, F.; Habib, S.; Tokay, M.; Shrestha, M. S.; Bajracharya, S.; Hummel, P.; Gray, M.; Duda, P.; Zaitchik, B.; Mahat, V.; Artan, G.; Tokar, S.

    2014-11-01

    Quantification of the contribution of the hydrologic components (snow, ice and rain) to river discharge in the Hindu Kush Himalayan (HKH) region is important for decision-making in water sensitive sectors, and for water resources management and flood risk reduction. In this area, access to and monitoring of the glaciers and their melt outflow is challenging due to difficult access, thus modeling based on remote sensing offers the potential for providing information to improve water resources management and decision making. This paper describes an integrated modeling system developed using downscaled NASA satellite based and earth system data products coupled with in-situ hydrologic data to assess the contribution of snow and glaciers to the flows of the rivers in the HKH region. Snow and glacier melt was estimated using the Utah Energy Balance (UEB) model, further enhanced to accommodate glacier ice melt over clean and debris-covered tongues, then meltwater was input into the USGS Geospatial Stream Flow Model (GeoSFM). The two model components were integrated into Better Assessment Science Integrating point and Nonpoint Sources modeling framework (BASINS) as a user-friendly open source system and was made available to countries in high Asia. Here we present a case study from the Langtang Khola watershed in the monsoon-influenced Nepal Himalaya, used to validate our energy balance approach and to test the applicability of our modeling system. The snow and glacier melt model predicts that for the eight years used for model evaluation (October 2003-September 2010), the total surface water input over the basin was 9.43 m, originating as 62% from glacier melt, 30% from snowmelt and 8% from rainfall. Measured streamflow for those years were 5.02 m, reflecting a runoff coefficient of 0.53. GeoSFM simulated streamflow was 5.31 m indicating reasonable correspondence between measured and model confirming the capability of the integrated system to provide a quantification of

  3. An Integrated Modeling System for Estimating Glacier and Snow Melt Driven Streamflow from Remote Sensing and Earth System Data Products in the Himalayas

    Science.gov (United States)

    Brown, M. E.; Racoviteanu, A. E.; Tarboton, D. G.; Sen Gupta, A.; Nigro, J.; Policelli, F.; Habib, S.; Tokay, M.; Shrestha, M. S.; Bajracharya, S.

    2014-01-01

    Quantification of the contribution of the hydrologic components (snow, ice and rain) to river discharge in the Hindu Kush Himalayan (HKH) region is important for decision-making in water sensitive sectors, and for water resources management and flood risk reduction. In this area, access to and monitoring of the glaciers and their melt outflow is challenging due to difficult access, thus modeling based on remote sensing offers the potential for providing information to improve water resources management and decision making. This paper describes an integrated modeling system developed using downscaled NASA satellite based and earth system data products coupled with in-situ hydrologic data to assess the contribution of snow and glaciers to the flows of the rivers in the HKH region. Snow and glacier melt was estimated using the Utah Energy Balance (UEB) model, further enhanced to accommodate glacier ice melt over clean and debris-covered tongues, then meltwater was input into the USGS Geospatial Stream Flow Model (Geo- SFM). The two model components were integrated into Better Assessment Science Integrating point and Nonpoint Sources modeling framework (BASINS) as a user-friendly open source system and was made available to countries in high Asia. Here we present a case study from the Langtang Khola watershed in the monsoon-influenced Nepal Himalaya, used to validate our energy balance approach and to test the applicability of our modeling system. The snow and glacier melt model predicts that for the eight years used for model evaluation (October 2003-September 2010), the total surface water input over the basin was 9.43 m, originating as 62% from glacier melt, 30% from snowmelt and 8% from rainfall. Measured streamflow for those years were 5.02 m, reflecting a runoff coefficient of 0.53. GeoSFM simulated streamflow was 5.31 m indicating reasonable correspondence between measured and model confirming the capability of the integrated system to provide a quantification

  4. Influence of beech and spruce sub-montane forests on snow cover in Poľana Biosphere Reserve

    Czech Academy of Sciences Publication Activity Database

    Šatala, T.; Tesař, Miroslav; Hanzelová, M.; Bartík, M.; Šípek, Václav; Škvarenina, J.; Minďáš, J.; Waldhauserová, P.D.

    2017-01-01

    Roč. 72, č. 8 (2017), s. 854-861 ISSN 0006-3088 R&D Projects: GA ČR GA16-05665S Institutional support: RVO:67985874 Keywords : snow water equivalent * snow depth * snow accumulation * snow melting Subject RIV: DA - Hydrology ; Limnology OBOR OECD: Hydrology Impact factor: 0.759, year: 2016

  5. A webgis supported snow information system with long time satellite data for Turkey

    Science.gov (United States)

    Surer, S.; Bolat, K.; Akyurek, Z.

    2012-04-01

    KARBILSIS is an online platform which is developed in order to provide end-users with daily remote sensing snow products for Turkey (www.karbilsis.com). The project has been started as a research activity after an award by Ministry of Science and Technology has been granted to our company. At the first stage of our project MODIS atmospherically corrected reflectance data has been downloaded covering the period of 2000-2011 which makes more than ten years of satellite imagery for Turkey. The archived MODIS data that have been obtained from National Snow and Ice Data Center (NSIDC) is mainly MOD09GA product that includes seven spectral bands. Only the tiles which are covering Turkey have been archived namely 19&20 horizontal and 4&5 vertical ones. In order to provide scientists with a website giving the availability of analysis of snow covered area for long terms based on their area of interests, a fractional snow extent (FSE) product has been generated. For FSE product a normalized difference snow index (NDSI) based algorithm has been developed using daily land surface reflectance values (MOD09GA). In addition to MODIS data, four different Landsat images belonging to different days of snowy period (January, March, and May) have been used during algorithm development taking into account a better representation of different reflectance values of snow which highly varies depending on the accumulation and melting periods. Landsat images were used as reference images. First the Landsat images were orthorectified and mapped to a cartographic projection. Then image segmentation was applied to obtain homogeneous tiles, where the homogeneity is defined as similarity in pixel values. The mean-shift segmentation approach, where each pixel was associated with a significant mode of the joint domain density located in its neighborhood, was applied. After segmentation, the image was classified into snow and no-snow classes with Maximum Likelihood Classification Method. FSE

  6. Comparison of different snow model formulations and their responses to input uncertainties in the Upper Indus Basin

    Science.gov (United States)

    Pritchard, David; Fowler, Hayley; Forsythe, Nathan; O'Donnell, Greg; Rutter, Nick; Bardossy, Andras

    2017-04-01

    Snow and glacier melt in the mountainous Upper Indus Basin (UIB) sustain water supplies, irrigation networks, hydropower production and ecosystems in extensive downstream lowlands. Understanding hydrological and cryospheric sensitivities to climatic variability and change in the basin is therefore critical for local, national and regional water resources management. Assessing these sensitivities using numerical modelling is challenging, due to limitations in the quality and quantity of input and evaluation data, as well as uncertainties in model structures and parameters. This study explores how these uncertainties in inputs and process parameterisations affect distributed simulations of ablation in the complex climatic setting of the UIB. The role of model forcing uncertainties is explored using combinations of local observations, remote sensing and reanalysis - including the high resolution High Asia Refined Analysis - to generate multiple realisations of spatiotemporal model input fields. Forcing a range of model structures with these input fields then provides an indication of how different ablation parameterisations respond to uncertainties and perturbations in climatic drivers. Model structures considered include simple, empirical representations of melt processes through to physically based, full energy balance models with multi-physics options for simulating snowpack evolution (including an adapted version of FSM). Analysing model input and structural uncertainties in this way provides insights for methodological choices in climate sensitivity assessments of data-sparse, high mountain catchments. Such assessments are key for supporting water resource management in these catchments, particularly given the potential complications of enhanced warming through elevation effects or, in the case of the UIB, limited understanding of how and why local climate change signals differ from broader patterns.

  7. Predictive modeling of crystal accumulation in high-level waste glass melters processing radioactive waste

    Energy Technology Data Exchange (ETDEWEB)

    Matyáš, Josef; Gervasio, Vivianaluxa; Sannoh, Sulaiman E.; Kruger, Albert A.

    2017-11-01

    The effectiveness of HLW vitrification is limited by precipitation/accumulation of spinel crystals [(Fe, Ni, Mn, Zn)(Fe, Cr)2O4] in the glass discharge riser of Joule-heated ceramic melters during idling. These crystals do not affect glass durability; however, if accumulated in thick layer, they can clog the melter and prevent discharge of molten glass into canisters. To address this problem, an empirical model was developed that can predict thicknesses of accumulated layers as a function of glass composition. This model predicts well the accumulation of single crystals and/or small-scale agglomerates, but, excessive agglomeration observed in high-Ni-Fe glass resulted in an under-prediction of accumulated layers, which gradually worsen over time as an increased number of agglomerates formed. Accumulation rate of ~53.8 ± 3.7 µm/h determined for this glass will result in ~26 mm thick layer in 20 days of melter idling.

  8. Snow snake performance monitoring.

    Science.gov (United States)

    2008-12-01

    A recent study, Three-Dimensional Roughness Elements for Snow Retention (FHWA-WY-06/04F) (Tabler 2006), demonstrated : positive evidence for the effectiveness of Snow Snakes, a new type of snow fence suitable for use within the highway right-of...

  9. Population models for Greater Snow Geese: a comparison of different approaches to assess potential impacts of harvest

    Directory of Open Access Journals (Sweden)

    Gauthier, G.

    2004-06-01

    Full Text Available Demographic models, which are a natural extension of capture-recapture (CR methodology, are a powerful tool to guide decisions when managing wildlife populations. We compare three different modelling approaches to evaluate the effect of increased harvest on the population growth of Greater Snow Geese (Chen caerulescens atlantica. Our first approach is a traditional matrix model where survival was reduced to simulate increased harvest. We included environmental stochasticity in the matrix projection model by simulating good, average, and bad years to account for the large inter-annual variation in fecundity and first-year survival, a common feature of birds nesting in the Arctic. Our second approach is based on the elasticity (or relative sensitivity of population growth rate (lambda to changes in survival as simple functions of generation time. Generation time was obtained from the mean transition matrix based on the observed proportion of good, average and bad years between 1985 and 1998. If we assume that hunting mortality is additive to natural mortality, then a simple formula predicts changes in lambda as a function of changes in harvest rate. This second approach can be viewed as a simplification of the matrix model because it uses formal sensitivity results derived from population projection. Our third, and potentially more powerful approach, uses the Kalman Filter to combine information on demographic parameters, i.e. the population mechanisms summarized in a transition matrix model, and the census information (i.e. annual survey within an overall Gaussian likelihood. The advantage of this approach is that it minimizes process and measured uncertainties associated with both the census and demographic parameters based on the variance of each estimate. This third approach, in contrast to the second, can be viewed as an extension of the matrix model, by combining its results with the independent census information.

  10. Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

    Science.gov (United States)

    Cook, Joseph M.; Hodson, Andrew J.; Gardner, Alex S.; Flanner, Mark; Tedstone, Andrew J.; Williamson, Christopher; Irvine-Fynn, Tristram D. L.; Nilsson, Johan; Bryant, Robert; Tranter, Martyn

    2017-11-01

    The darkening effects of biological impurities on ice and snow have been recognised as a control on the surface energy balance of terrestrial snow, sea ice, glaciers and ice sheets. With a heightened interest in understanding the impacts of a changing climate on snow and ice processes, quantifying the impact of biological impurities on ice and snow albedo (bioalbedo) and its evolution through time is a rapidly growing field of research. However, rigorous quantification of bioalbedo has remained elusive because of difficulties in isolating the biological contribution to ice albedo from that of inorganic impurities and the variable optical properties of the ice itself. For this reason, isolation of the biological signature in reflectance data obtained from aerial/orbital platforms has not been achieved, even when ground-based biological measurements have been available. This paper provides the cell-specific optical properties that are required to model the spectral signatures and broadband darkening of ice. Applying radiative transfer theory, these properties provide the physical basis needed to link biological and glaciological ground measurements with remotely sensed reflectance data. Using these new capabilities we confirm that biological impurities can influence ice albedo, then we identify 10 challenges to the measurement of bioalbedo in the field with the aim of improving future experimental designs to better quantify bioalbedo feedbacks. These challenges are (1) ambiguity in terminology, (2) characterising snow or ice optical properties, (3) characterising solar irradiance, (4) determining optical properties of cells, (5) measuring biomass, (6) characterising vertical distribution of cells, (7) characterising abiotic impurities, (8) surface anisotropy, (9) measuring indirect albedo feedbacks, and (10) measurement and instrument configurations. This paper aims to provide a broad audience of glaciologists and biologists with an overview of radiative transfer and

  11. Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

    Directory of Open Access Journals (Sweden)

    J. M. Cook

    2017-11-01

    Full Text Available The darkening effects of biological impurities on ice and snow have been recognised as a control on the surface energy balance of terrestrial snow, sea ice, glaciers and ice sheets. With a heightened interest in understanding the impacts of a changing climate on snow and ice processes, quantifying the impact of biological impurities on ice and snow albedo (bioalbedo and its evolution through time is a rapidly growing field of research. However, rigorous quantification of bioalbedo has remained elusive because of difficulties in isolating the biological contribution to ice albedo from that of inorganic impurities and the variable optical properties of the ice itself. For this reason, isolation of the biological signature in reflectance data obtained from aerial/orbital platforms has not been achieved, even when ground-based biological measurements have been available. This paper provides the cell-specific optical properties that are required to model the spectral signatures and broadband darkening of ice. Applying radiative transfer theory, these properties provide the physical basis needed to link biological and glaciological ground measurements with remotely sensed reflectance data. Using these new capabilities we confirm that biological impurities can influence ice albedo, then we identify 10 challenges to the measurement of bioalbedo in the field with the aim of improving future experimental designs to better quantify bioalbedo feedbacks. These challenges are (1 ambiguity in terminology, (2 characterising snow or ice optical properties, (3 characterising solar irradiance, (4 determining optical properties of cells, (5 measuring biomass, (6 characterising vertical distribution of cells, (7 characterising abiotic impurities, (8 surface anisotropy, (9 measuring indirect albedo feedbacks, and (10 measurement and instrument configurations. This paper aims to provide a broad audience of glaciologists and biologists with an overview of

  12. Estimating Snow Water Storage in North America Using CLM4, DART, and Snow Radiance Data Assimilation

    Science.gov (United States)

    Kwon, Yonghwan; Yang, Zong-Liang; Zhao, Long; Hoar, Timothy J.; Toure, Ally M.; Rodell, Matthew

    2016-01-01

    This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature T(sub B) at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting T(sub B) based on their correlations with the prior T(sub B) (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171m RMSE), the overall snow depth estimates are improved by 1.6% (0.168m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177mRMSE). Significant improvement of the snow depth estimates in the rule-based RA as observed for tundra snow class (11.5%, p < 0.05) and bare soil land-cover type (13.5%, p < 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.

  13. Are Model Transferability And Complexity Antithetical? Insights From Validation of a Variable-Complexity Empirical Snow Model in Space and Time

    Science.gov (United States)

    Lute, A. C.; Luce, Charles H.

    2017-11-01

    The related challenges of predictions in ungauged basins and predictions in ungauged climates point to the need to develop environmental models that are transferable across both space and time. Hydrologic modeling has historically focused on modelling one or only a few basins using highly parameterized conceptual or physically based models. However, model parameters and structures have been shown to change significantly when calibrated to new basins or time periods, suggesting that model complexity and model transferability may be antithetical. Empirical space-for-time models provide a framework within which to assess model transferability and any tradeoff with model complexity. Using 497 SNOTEL sites in the western U.S., we develop space-for-time models of April 1 SWE and Snow Residence Time based on mean winter temperature and cumulative winter precipitation. The transferability of the models to new conditions (in both space and time) is assessed using non-random cross-validation tests with consideration of the influence of model complexity on transferability. As others have noted, the algorithmic empirical models transfer best when minimal extrapolation in input variables is required. Temporal split-sample validations use pseudoreplicated samples, resulting in the selection of overly complex models, which has implications for the design of hydrologic model validation tests. Finally, we show that low to moderate complexity models transfer most successfully to new conditions in space and time, providing empirical confirmation of the parsimony principal.

  14. ON THE ISSUE OF "MEMORY" MARKOV MODEL OF DAMAGE ACCUMULATION

    Directory of Open Access Journals (Sweden)

    A. I. Lantuh-Lyaschenko

    2010-04-01

    Full Text Available This paper presents the application of a probabilistic approach for the modeling of service life of highway bridge elements. The focus of this paper is on the Markov stochastic deterioration models. These models can be used as effective tool for technical state assessments and prediction of residual resource of a structure. For the bridge maintenance purpose these models can give quantitative criteria of a reliability level, risk and prediction algorithms of the residual resource.

  15. Go With the Flow, on Jupiter and Snow. Coherence from Model-Free Video Data Without Trajectories

    Science.gov (United States)

    AlMomani, Abd AlRahman R.; Bollt, Erik

    2018-06-01

    Viewing a data set such as the clouds of Jupiter, coherence is readily apparent to human observers, especially the Great Red Spot, but also other great storms and persistent structures. There are now many different definitions and perspectives mathematically describing coherent structures, but we will take an image processing perspective here. We describe an image processing perspective inference of coherent sets from a fluidic system directly from image data, without attempting to first model underlying flow fields, related to a concept in image processing called motion tracking. In contrast to standard spectral methods for image processing which are generally related to a symmetric affinity matrix, leading to standard spectral graph theory, we need a not symmetric affinity which arises naturally from the underlying arrow of time. We develop an anisotropic, directed diffusion operator corresponding to flow on a directed graph, from a directed affinity matrix developed with coherence in mind, and corresponding spectral graph theory from the graph Laplacian. Our methodology is not offered as more accurate than other traditional methods of finding coherent sets, but rather our approach works with alternative kinds of data sets, in the absence of vector field. Our examples will include partitioning the weather and cloud structures of Jupiter, and a local to Potsdam, NY, lake effect snow event on Earth, as well as the benchmark test double-gyre system.

  16. Calculation of new snow densities from sub-daily automated snow measurements

    Science.gov (United States)

    Helfricht, Kay; Hartl, Lea; Koch, Roland; Marty, Christoph; Lehning, Michael; Olefs, Marc

    2017-04-01

    In mountain regions there is an increasing demand for high-quality analysis, nowcasting and short-range forecasts of the spatial distribution of snowfall. Operational services, such as for avalanche warning, road maintenance and hydrology, as well as hydropower companies and ski resorts need reliable information on the depth of new snow (HN) and the corresponding water equivalent (HNW). However, the ratio of HNW to HN can vary from 1:3 to 1:30 because of the high variability of new snow density with respect to meteorological conditions. In the past, attempts were made to calculate new snow densities from meteorological parameters mainly using daily values of temperature and wind. Further complex statistical relationships have been used to calculate new snow densities on hourly to sub-hourly time intervals to drive multi-layer snow cover models. However, only a few long-term in-situ measurements of new snow density exist for sub-daily time intervals. Settling processes within the new snow due to loading and metamorphism need to be considered when computing new snow density. As the effect of these processes is more pronounced for long time intervals, a high temporal resolution of measurements is desirable. Within the pluSnow project data of several automatic weather stations with simultaneous measurements of precipitation (pluviometers), snow water equivalent (SWE) using snow pillows and snow depth (HS) measurements using ultrasonic rangers were analysed. New snow densities were calculated for a set of data filtered on the basis of meteorological thresholds. The calculated new snow densities were compared to results from existing new snow density parameterizations. To account for effects of settling of the snow cover, a case study based on a multi-year data set using the snow cover model SNOWPACK at Weissfluhjoch was performed. Measured median values of hourly new snow densities at the different stations range from 54 to 83 kgm-3. This is considerably lower than a 1

  17. Modelling Accumulator Stripper Foil Heating for ESSNUSB Facility

    CERN Document Server

    Martini, Michel

    2015-01-01

    It is proposed to use the 2.0 GeV, 5 MW proton linac, 2.86 ms long pulses at 14 Hz of the European Spallation Source [1], [2] being built in Lund, Sweden to deliver, alternately with the spallation neutron production a very intense neutrino beam to enable the discovery of leptonic CP violation. To this end the linac would be upgraded to supply, in addition to the 2.86 ms long proton pulses at 14 Hz, four 0.72 ms H short pulses at 70 Hz for neutrino production. Because of the high current required in the pulsed neutrino horn, the length of the pulses used for neutrino production will need to be compressed to a few s with the aid of an accumulator ring. Charge exchange injection of an H- beam from the linac will be used, the linac delivering 1.1E15 H- per pulse. This paper is about stripping foil heating considerations, emphasizing the detailed evaluation of the foil temperature over the multiple ring re-fills

  18. A 40-year accumulation dataset for Adelie Land, Antarctica and its application for model validation

    Energy Technology Data Exchange (ETDEWEB)

    Agosta, Cecile; Favier, Vincent [UJF-Grenoble 1 / CNRS, Laboratoire de Glaciologie et de Geophysique de l' Environnement UMR 5183, Saint Martin d' Heres (France); Genthon, Christophe; Gallee, Hubert; Krinner, Gerhard [CNRS / UJF-Grenoble 1, Laboratoire de Glaciologie et de Geophysique de l' Environnement UMR 5183, Saint Martin d' Heres (France); Lenaerts, Jan T.M.; Broeke, Michiel R. van den [Utrecht University, Institute for Marine and Atmospheric Research Utrecht (Netherlands)

    2012-01-15

    The GLACIOCLIM-SAMBA (GS) Antarctic accumulation monitoring network, which extends from the coast of Adelie Land to the Antarctic plateau, has been surveyed annually since 2004. The network includes a 156-km stake-line from the coast inland, along which accumulation shows high spatial and interannual variability with a mean value of 362 mm water equivalent a{sup -1}. In this paper, this accumulation is compared with older accumulation reports from between 1971 and 1991. The mean and annual standard deviation and the km-scale spatial pattern of accumulation were seen to be very similar in the older and more recent data. The data did not reveal any significant accumulation trend over the last 40 years. The ECMWF analysis-based forecasts (ERA-40 and ERA-Interim), a stretched-grid global general circulation model (LMDZ4) and three regional circulation models (PMM5, MAR and RACMO2), all with high resolution over Antarctica (27-125 km), were tested against the GS reports. They qualitatively reproduced the meso-scale spatial pattern of the annual-mean accumulation except MAR. MAR significantly underestimated mean accumulation, while LMDZ4 and RACMO2 overestimated it. ERA-40 and the regional models that use ERA-40 as lateral boundary condition qualitatively reproduced the chronology of interannual variability but underestimated the magnitude of interannual variations. Two widely used climatologies for Antarctic accumulation agreed well with the mean GS data. The model-based climatology was also able to reproduce the observed spatial pattern. These data thus provide new stringent constraints on models and other large-scale evaluations of the Antarctic accumulation. (orig.)

  19. A field evaluation of soil moisture modelling with the Soil, Vegetation, and Snow (SVS) land surface model using evapotranspiration observations as forcing data

    Science.gov (United States)

    Maheu, Audrey; Anctil, François; Gaborit, Étienne; Fortin, Vincent; Nadeau, Daniel F.; Therrien, René

    2018-03-01

    To address certain limitations with their current operational model, Environment and Climate Change Canada recently developed the Soil, Vegetation, and Snow (SVS) land surface model and the representation of subsurface hydrological processes was targeted as an area for improvement. The objective of this study is to evaluate the ability of HydroSVS, the component of SVS responsible for the vertical redistribution of water, to simulate soil moisture under snow-free conditions when using flux-tower observations of evapotranspiration as forcing data. We assessed (1) model fidelity by comparing soil moisture modelled with HydroSVS to point-scale measurements of volumetric soil water content and (2) model complexity by comparing the performance of HydroSVS to that of HydroGeoSphere, a state-of-the-art integrated surface and subsurface hydrologic model. To do this, we performed one-dimensional soil column simulations at four sites of the AmeriFlux network. Results indicate that under Mediterranean and temperate climates, HydroSVS satisfactorily simulated soil moisture (Nash-Sutcliffe efficiency between 0.26 and 0.70; R2 ≥ 0.80), with a performance comparable to HydroGeoSphere (Nash-Sutcliffe efficiency ≥0.60; R2 ≥ 0.80). However, HydroSVS performed weakly under a semiarid climate while HydroGeoSphere performed relatively well. By decoupling the magnitude and sourcing of evapotranspiration, this study proposes a powerful diagnostic tool to evaluate the representation of subsurface hydrological processes in land surface models. Overall, this study highlights the potential of SVS for hydrological applications.

  20. Assessing the spatial variability of mountain precipitation in California's Sierra Nevada using the Airborne Snow Observatory

    Science.gov (United States)

    Brandt, T.; Deems, J. S.; Painter, T. H.; Dozier, J.

    2016-12-01

    In California's Sierra Nevada, 10 or fewer winter storms are responsible for most of the annual precipitation, which falls mostly as snow. Presently, surface stations are used to measure the dynamics of mountain precipitation. However, even in places like the Sierra Nevada—one of the most gauged regions in the world—the paucity of surface stations can lead to large errors in precipitation thereby biasing both total water year and short-term streamflow forecasts. Remotely sensed snow depth and water equivalent, at a time scale that resolves storms, might provide a novel solution to the problems of: (1) quantifying the spatial variability of mountain precipitation; and (2) assessing gridded precipitation products that are mostly based on surface station interpolation. NASA's Airborne Snow Observatory (ASO), an imaging spectrometer and LiDAR system, has measured snow in the Tuolumne River Basin in California's Sierra Nevada for the past four years, 2013-2016; and, measurements will continue. Principally, ASO monitors the progression of melt for water supply forecasting, nonetheless, a number of flights bracketed storms allowing for estimates of snow accumulation. In this study we examine a few of the ASO recorded storms to determine both the basin and subbasin orographic effect as well as the spatial patterns in total precipitation. We then compare these results to a number of gridded climate products and weather models including: Daymet, the Parameter-elevation Regressions on Independent Slopes Model (PRISM), the North American Land Data Assimilation System (NLDAS-2), and the Weather Research and Forecasting (WRF) model. Finally, to put each ASO recorded storm into context, we use a climatology produced from snow pillows and the North American Regional Reanalysis (NARR) for 2014-2016 to examine key accumulation events, and classify storms based on their integrated water vapor flux.

  1. A Model for Evidence Accumulation in the Lexical Decision Task

    Science.gov (United States)

    Wagenmakers, Eric-Jan; Steyvers, Mark; Raaijmakers, Jeroen G. W.; Shiffrin, Richard M.; van Rijn, Hedderik; Zeelenberg, Rene

    2004-01-01

    We present a new model for lexical decision, REM-LD, that is based on REM theory (e.g., Shiffrin & Steyvers, 1997). REM-LD uses a principled (i.e., Bayes' rule) decision process that simultaneously considers the diagnosticity of the evidence for the 'WORD' response and the 'NONWORD' response. The model calculates the odds ratio that the presented…

  2. The hydrological role of snow and glaciers in alpine river basins and their distributed modeling

    NARCIS (Netherlands)

    Verbunt, M.; Gurtz, J.; Jasper, K.; Lang, H.; Warmerdam, P.M.M.; Zappa, M.

    2003-01-01

    A temperature index approach including incoming solar radiation was used as a sub-model in the gridded hydrological catchment model WaSiM-ETH to simulate the melt rate of glacierized areas. Melt water and rainfall are transformed into glacier discharge by using linear reservoir approaches. The

  3. Explicit validation of a surface shortwave radiation balance model over snow-covered complex terrain

    Science.gov (United States)

    Helbig, N.; Löwe, H.; Mayer, B.; Lehning, M.

    2010-09-01

    A model that computes the surface radiation balance for all sky conditions in complex terrain is presented. The spatial distribution of direct and diffuse sky radiation is determined from observations of incident global radiation, air temperature, and relative humidity at a single measurement location. Incident radiation under cloudless sky is spatially derived from a parameterization of the atmospheric transmittance. Direct and diffuse sky radiation for all sky conditions are obtained by decomposing the measured global radiation value. Spatial incident radiation values under all atmospheric conditions are computed by adjusting the spatial radiation values obtained from the parametric model with the radiation components obtained from the decomposition model at the measurement site. Topographic influences such as shading are accounted for. The radiosity approach is used to compute anisotropic terrain reflected radiation. Validations of the shortwave radiation balance model are presented in detail for a day with cloudless sky. For a day with overcast sky a first validation is presented. Validation of a section of the horizon line as well as of individual radiation components is performed with high-quality measurements. A new measurement setup was designed to determine terrain reflected radiation. There is good agreement between the measurements and the modeled terrain reflected radiation values as well as with incident radiation values. A comparison of the model with a fully three-dimensional radiative transfer Monte Carlo model is presented. That validation reveals a good agreement between modeled radiation values.

  4. Chemical elements contamination of snow cover in region of coal production 'Karazhyra'

    International Nuclear Information System (INIS)

    Evlampieva, E.P.; Panin, M.S.

    2008-01-01

    Peculiarities of space distribution of chemical elements in hardphase and water-soluble falls of snow cover in region of coal deposit 'Karazhyra' are investigated. The maximal, minimal and background areas of elements accumulation in the snow of this region and distribution of their cumulative rates are determined. The main pollutants of snow cover unto background level are revealed.

  5. Investigating the Relationships between Canopy Characteristics and Snow Depth Distribution at Fine Scales: Preliminary Results from the SnowEX TLS Campaign

    Science.gov (United States)

    Glenn, N. F.; Uhlmann, Z.; Spaete, L.; Tennant, C.; Hiemstra, C. A.; McNamara, J.

    2017-12-01

    Predicting changes in forested seasonal snowpacks under altered climate scenarios is one of the most pressing hydrologic challenges facing today's society. Airborne- and satellite-based remote sensing methods hold the potential to transform measurements of terrestrial water stores in snowpack, improve process representations of snowpack accumulation and ablation, and to generate high quality predictions that inform potential strategies to better manage water resources. While the effects of forest on snowpack are well documented, many of the fine-scale processes influenced by the forest-canopy are not directly accounted for because most snow models don't explicitly represent canopy structure and canopy heterogeneity. This study investigates the influence of forest canopy on snowpack distribution at fine scales and quantifies the influence of canopy heterogeneity on snowpack accumulation and ablation processes. We use terrestrial laser scanning (TLS) data collected during the SnowEX campaign to discover how the relationships between canopy and snow distributions change across scales. Our sample scales range from individual trees to patches of trees across the Grand Mesa, CO, SnowEx site.

  6. Operational warnings issued by the SMC in the 8th March snow event in Catalonia

    Science.gov (United States)

    Vilaclara, E.; Segalà, S.; Andrés, A.; Aran, M.

    2010-09-01

    The snowfall event of 8th March 2010 was one of the most important of the last years in Catalonia, with high societal impact in the communication and in the power distribution. Since 2002, after an agreement between Meteorological Service of Catalonia (SMC) and Civil Protection authority, the SMC is the agency responsible to carry out meteorological warnings in Catalonia. These warnings are issued depending on the thresholds which are expected to be exceeded (three different probability degrees are defined), and are characterized by a high spatial and temporal resolution. In the snow event of 8th March, forecasting team of the SMC did the first meteorological warning three days before, on 5th March. During the two following days broadcasted four different warnings, taking into account the high probability of exceeding 2 cm of snow above 200 meters, 15 above 400 m and 30 above 800 m. Furthermore, the SMC disseminated also two special press releases with the aim to extend the forecast to the public. In the afternoon of Sunday, 7th March, the snow precipitation started in some areas of Catalonia. From this moment, the SMC followed the surveillance of situation with the remote sensing tools and the Meteorological Observers Network data. This network is formed by a hundred of spotters with mobile phones able to transmit observations in real time to our web site. This surveillance and the conceptual model of snow events in Catalonia allowed the forecasters to improve the estimation of the snow level forecasted by mesoscale meteorological models. In this event, the snow level obtained from these models was higher than the real one. In spite of the accuracy of the forecast, the impact was very important in Catalonia. On one hand, it was due to the exceptionality of the event, with 3 cm of snow in Barcelona in the afternoon of a working day. On the other hand, the large amount of wet snow precipitation (until 100 mm), and the wind, contributed to an important snow

  7. Oxygen isotope variability in snow from western Dronning Maud Land, Antarctica and its relation to temperature

    International Nuclear Information System (INIS)

    Helsen, M.M.; Wal, R.S.W. van de; Broeke, M.R. van den; As, D. van; Reijmer, C.H.; Meijer, H.A.J.

    2005-01-01

    This paper presents (delta) 18 O records from snow pits from four locations in Dronning Maud Land, Antarctica that contain at least four annual cycles. The aim of the study was to analyse in detail these records as well as the prevailing temperatures during accumulation in order to infer to what extent isotopic composition in this area can be interpreted as temperature information. The original seasonal amplitudes of the isotope records were reconstructed by use of a simple back-diffusion model. Automatic weather station data were used to describe the accumulation history and the near-surface temperatures; the temperatures at the atmospheric level of snow formation were inferred from a regional climate model. The results show that the strongly intermittent nature of the accumulation in this area can result in the exclusion of entire seasons from the isotope records. The temperature records also reveal that the oxygen isotope records in these snow pits are biased towards higher temperatures, since snowfall conditions are associated with higher temperatures. This effect is greatest at low temperatures. A comparison between the seasonal extreme isotopic and temperature values points out that on timescales of seasons to several years, isotopic variability cannot be interpreted with confidence as temperature changes at the accumulation sites

  8. Snow water equivalent monitoring retrieved by assimilating passive microwave observations in a coupled snowpack evolution and microwave emission models over North-Eastern Canada

    Science.gov (United States)

    Royer, A.; Larue, F.; De Sève, D.; Roy, A.; Vionnet, V.; Picard, G.; Cosme, E.

    2017-12-01

    Over northern snow-dominated basins, the snow water equivalent (SWE) is of primary interest for spring streamflow forecasting. SWE retrievals from satellite data are still not well resolved, in particular from microwave (MW) measurements, the only type of data sensible to snow mass. Also, the use of snowpack models is challenging due to the large uncertainties in meteorological input forcings. This project aims to improve SWE prediction by assimilation of satellite brightness temperature (TB), without any ground-based observations. The proposed approach is the coupling of a detailed multilayer snowpack model (Crocus) with a MW snow emission model (DMRT-ML). The assimilation scheme is a Sequential Importance Resampling Particle filter, through ensembles of perturbed meteorological forcings according to their respective uncertainties. Crocus simulations driven by operational meteorological forecasts from the Canadian Global Environmental Multiscale model at 10 km spatial resolution were compared to continuous daily SWE measurements over Québec, North-Eastern Canada (56° - 45°N). The results show a mean bias of the maximum SWE overestimated by 16% with variations up to +32%. This observed large variability could lead to dramatic consequences on spring flood forecasts. Results of Crocus-DMRT-ML coupling compared to surface-based TB measurements (at 11, 19 and 37 GHz) show that the Crocus snowpack microstructure described by sticky hard spheres within DMRT has to be scaled by a snow stickiness of 0.18, significantly reducing the overall RMSE of simulated TBs. The ability of assimilation of daily TBs to correct the simulated SWE is first presented through twin experiments with synthetic data, and then with AMSR-2 satellite time series of TBs along the winter taking into account atmospheric and forest canopy interferences (absorption and emission). The differences between TBs at 19-37 GHz and at 11-19 GHz, in vertical polarization, were assimilated. This assimilation

  9. Snow chemistry of high altitude glaciers in the French Alps

    OpenAIRE

    MAUPETIT, FRANÇOIS; DELMAS, ROBERT J.

    2011-01-01

    Snow samples were collected as snowcores in the accumulation zone of four high altitude glaciers (2980–3540 m.a.s.l.) from each of the 4 highest mountain areas of the French Alps, during 3 consecutive years: 1989, 1990 and 1991. Sampling was performed in spring (∼ May), before the onset of late spring–summer percolation. The accumulated snow therefore reflects winter and spring conditions. A complementary sampling of fresh-snow was performed on an event basis, on one of the studied glaciers, ...

  10. Evaluation of SNODAS snow depth and snow water equivalent estimates for the Colorado Rocky Mountains, USA

    Science.gov (United States)

    Clow, David W.; Nanus, Leora; Verdin, Kristine L.; Schmidt, Jeffrey

    2012-01-01

    The National Weather Service's Snow Data Assimilation (SNODAS) program provides daily, gridded estimates of snow depth, snow water equivalent (SWE), and related snow parameters at a 1-km2 resolution for the conterminous USA. In this study, SNODAS snow depth and SWE estimates were compared with independent, ground-based snow survey data in the Colorado Rocky Mountains to assess SNODAS accuracy at the 1-km2 scale. Accuracy also was evaluated at the basin scale by comparing SNODAS model output to snowmelt runoff in 31 headwater basins with US Geological Survey stream gauges. Results from the snow surveys indicated that SNODAS performed well in forested areas, explaining 72% of the variance in snow depths and 77% of the variance in SWE. However, SNODAS showed poor agreement with measurements in alpine areas, explaining 16% of the variance in snow depth and 30% of the variance in SWE. At the basin scale, snowmelt runoff was moderately correlated (R2 = 0.52) with SNODAS model estimates. A simple method for adjusting SNODAS SWE estimates in alpine areas was developed that uses relations between prevailing wind direction, terrain, and vegetation to account for wind redistribution of snow in alpine terrain. The adjustments substantially improved agreement between measurements and SNODAS estimates, with the R2 of measured SWE values against SNODAS SWE estimates increasing from 0.42 to 0.63 and the root mean square error decreasing from 12 to 6 cm. Results from this study indicate that SNODAS can provide reliable data for input to moderate-scale to large-scale hydrologic models, which are essential for creating accurate runoff forecasts. Refinement of SNODAS SWE estimates for alpine areas to account for wind redistribution of snow could further improve model performance. Published 2011. This article is a US Government work and is in the public domain in the USA.

  11. Improving representation of canopy temperatures for modeling subcanopy incoming longwave radiation to the snow surface

    Science.gov (United States)

    Webster, Clare; Rutter, Nick; Jonas, Tobias

    2017-09-01

    A comprehensive analysis of canopy surface temperatures was conducted around a small and large gap at a forested alpine site in the Swiss Alps during the 2015 and 2016 snowmelt seasons (March-April). Canopy surface temperatures within the small gap were within 2-3°C of measured reference air temperature. Vertical and horizontal variations in canopy surface temperatures were greatest around the large gap, varying up to 18°C above measured reference air temperature during clear-sky days. Nighttime canopy surface temperatures around the study site were up to 3°C cooler than reference air temperature. These measurements were used to develop a simple parameterization for correcting reference air temperature for elevated canopy surface temperatures during (1) nighttime conditions (subcanopy shortwave radiation is 0 W m-2) and (2) periods of increased subcanopy shortwave radiation >400 W m-2 representing penetration of shortwave radiation through the canopy. Subcanopy shortwave and longwave radiation collected at a single point in the subcanopy over a 24 h clear-sky period was used to calculate a nighttime bulk offset of 3°C for scenario 1 and develop a multiple linear regression model for scenario 2 using reference air temperature and subcanopy shortwave radiation to predict canopy surface temperature with a root-mean-square error (RMSE) of 0.7°C. Outside of these two scenarios, reference air temperature was used to predict subcanopy incoming longwave radiation. Modeling at 20 radiometer locations throughout two snowmelt seasons using these parameterizations reduced the mean bias and RMSE to below 10 W m s-2 at all locations.

  12. Measured Black Carbon Deposition on the Sierra Nevada Snow Pack and Implication for Snow Pack Retreat

    Energy Technology Data Exchange (ETDEWEB)

    Hadley, O.L.; Corrigan, C.E.; Kirchstetter, T.W.; Cliff, S.S.; Ramanathan, V.

    2010-01-12

    Modeling studies show that the darkening of snow and ice by black carbon deposition is a major factor for the rapid disappearance of arctic sea ice, mountain glaciers and snow packs. This study provides one of the first direct measurements for the efficient removal of black carbon from the atmosphere by snow and its subsequent deposition to the snow packs of California. The early melting of the snow packs in the Sierras is one of the contributing factors to the severe water problems in California. BC concentrations in falling snow were measured at two mountain locations and in rain at a coastal site. All three stations reveal large BC concentrations in precipitation, ranging from 1.7 ng/g to 12.9 ng/g. The BC concentrations in the air after the snow fall were negligible suggesting an extremely efficient removal of BC by snow. The data suggest that below cloud scavenging, rather than ice nuclei, was the dominant source of BC in the snow. A five-year comparison of BC, dust, and total fine aerosol mass concentrations at multiple sites reveals that the measurements made at the sampling sites were representative of large scale deposition in the Sierra Nevada. The relative concentration of iron and calcium in the mountain aerosol indicates that one-quarter to one-third of the BC may have been transported from Asia.

  13. A new model for fatigue damage accumulation of austenitic stainless steel under variable amplitude loading

    International Nuclear Information System (INIS)

    Taheri, S.; Vincent, L.; Le-Roux, J.C.

    2013-01-01

    The application of Miner's rule using a loading issued from a mock-up of a RHR system (removal heat system) of PWR plant, made of 304 steel gives a very important non-conservative fatigue life in strain control when strain fatigue curve is used. This result is due to the absence of sequence effect in Miner's rule. Many non linear damage accumulation models have been proposed to get a sequence effect. Shortcomings of some non linear damage accumulation models are discussed. So Smith-Watson-Topper and Fatemi-Socie criterions with a linear damage accumulation rule are then applied to experimental data. A major issue is the need for an elastic-plastic constitutive law which is difficult to propose in the presence of high cycle secondary hardening observed in austenitic stainless steels. A conservative model for fatigue damage accumulation under variable amplitude loading is then proposed for austenitic stainless steels in strain control, which does not need a constitutive law, but takes into account plasticity through cyclic strain stress curve. The model uses a linear damage accumulation rule. This model is based on the fact that for stainless steels, pre-hardening is detrimental for fatigue life in strain control, while it is beneficial in stress control. In the presence of low mean stress, the model is approved based on a large number of tests. Moreover the model allows to explain the larger detrimental effect of a tension mean stress in strain control tests than in stress control tests. (authors)

  14. Comparison of Two Models for Damage Accumulation in Simulations of System Performance

    Energy Technology Data Exchange (ETDEWEB)

    Youngblood, R. [Idaho National Laboratory, Idaho Falls, ID (United States); Mandelli, D. [Idaho National Laboratory, Idaho Falls, ID (United States)

    2015-11-01

    A comprehensive simulation study of system performance needs to address variations in component behavior, variations in phenomenology, and the coupling between phenomenology and component failure. This paper discusses two models of this: 1. damage accumulation is modeled as a random walk process in each time history, with component failure occurring when damage accumulation reaches a specified threshold; or 2. damage accumulation is modeled mechanistically within each time history, but failure occurs when damage reaches a time-history-specific threshold, sampled at time zero from each component’s distribution of damage tolerance. A limiting case of the latter is classical discrete-event simulation, with component failure times sampled a priori from failure time distributions; but in such models, the failure times are not typically adjusted for operating conditions varying within a time history. Nowadays, as discussed below, it is practical to account for this. The paper compares the interpretations and computational aspects of the two models mentioned above.

  15. California's Snow Gun and its implications for mass balance predictions under greenhouse warming

    Science.gov (United States)

    Howat, I.; Snyder, M.; Tulaczyk, S.; Sloan, L.

    2003-12-01

    Precipitation has received limited treatment in glacier and snowpack mass balance models, largely due to the poor resolution and confidence of precipitation predictions relative to temperature predictions derived from atmospheric models. Most snow and glacier mass balance models rely on statistical or lapse rate-based downscaling of general or regional circulation models (GCM's and RCM's), essentially decoupling sub-grid scale, orographically-driven evolution of atmospheric heat and moisture. Such models invariably predict large losses in the snow and ice volume under greenhouse warming. However, positive trends in the mass balance of glaciers in some warming maritime climates, as well as at high elevations of the Greenland Ice Sheet, suggest that increased precipitation may play an important role in snow- and glacier-climate interactions. Here, we present a half century of April snowpack data from the Sierra Nevada and Cascade mountains of California, USA. This high-density network of snow-course data indicates that a gain in winter snow accumulation at higher elevations has compensated loss in snow volume at lower elevations by over 50% and has led to glacier expansion on Mt. Shasta. These trends are concurrent with a region-wide increase in winter temperatures up to 2° C. They result from the orographic lifting and saturation of warmer, more humid air leading to increased precipitation at higher elevations. Previous studies have invoked such a "Snow Gun" effect to explain contemporaneous records of Tertiary ocean warming and rapid glacial expansion. A climatological context of the California's "snow gun" effect is elucidated by correlation between the elevation distribution of April SWE observations and the phase of the Pacific Decadal Oscillation and the El Nino Southern Oscillation, both controlling the heat and moisture delivered to the U.S. Pacific coast. The existence of a significant "Snow Gun" effect presents two challenges to snow and glacier mass

  16. Photopolarimetric Retrievals of Snow Properties

    Science.gov (United States)

    Ottaviani, M.; van Diedenhoven, B.; Cairns, B.

    2015-01-01

    Polarimetric observations of snow surfaces, obtained in the 410-2264 nm range with the Research Scanning Polarimeter onboard the NASA ER-2 high-altitude aircraft, are analyzed and presented. These novel measurements are of interest to the remote sensing community because the overwhelming brightness of snow plagues aerosol and cloud retrievals based on airborne and spaceborne total reflection measurements. The spectral signatures of the polarized reflectance of snow are therefore worthwhile investigating in order to provide guidance for the adaptation of algorithms currently employed for the retrieval of aerosol properties over soil and vegetated surfaces. At the same time, the increased information content of polarimetric measurements allows for a meaningful characterization of the snow medium. In our case, the grains are modeled as hexagonal prisms of variable aspect ratios and microscale roughness, yielding retrievals of the grains' scattering asymmetry parameter, shape and size. The results agree with our previous findings based on a more limited data set, with the majority of retrievals leading to moderately rough crystals of extreme aspect ratios, for each scene corresponding to a single value of the asymmetry parameter.

  17. Blowing snow detection from ground-based ceilometers : Application to East Antarctica

    NARCIS (Netherlands)

    Gossart, Alexandra; Souverijns, Niels; Gorodetskaya, Irina V.; Lhermitte, S.L.M.; Lenaerts, Jan T M; Schween, Jan H.; Mangold, Alexander; Laffineur, Quentin; van Lipzig, Nicole P. M.

    2017-01-01

    Blowing snow impacts Antarctic ice sheet surface mass balance by snow redistribution and sublimation. However, numerical models poorly represent blowing snow processes, while direct observations are limited in space and time. Satellite retrieval of blowing snow is hindered by clouds and only the

  18. Small scale variability of snow properties on Antarctic sea ice

    Science.gov (United States)

    Wever, Nander; Leonard, Katherine; Paul, Stephan; Jacobi, Hans-Werner; Proksch, Martin; Lehning, Michael

    2016-04-01

    Snow on sea ice plays an important role in air-ice-sea interactions, as snow accumulation may for example increase the albedo. Snow is also able to smooth the ice surface, thereby reducing the surface roughness, while at the same time it may generate new roughness elements by interactions with the wind. Snow density is a key property in many processes, for example by influencing the thermal conductivity of the snow layer, radiative transfer inside the snow as well as the effects of aerodynamic forcing on the snowpack. By comparing snow density and grain size from snow pits and snow micro penetrometer (SMP) measurements, highly resolved density and grain size profiles were acquired during two subsequent cruises of the RV Polarstern in the Weddell Sea, Antarctica, between June and October 2013. During the first cruise, SMP measurements were done along two approximately 40 m transects with a horizontal resolution of approximately 30 cm. During the second cruise, one transect was made with approximately 7.5 m resolution over a distance of 500 m. Average snow densities are about 300 kg/m3, but the analysis also reveals a high spatial variability in snow density on sea ice in both horizontal and vertical direction, ranging from roughly 180 to 360 kg/m3. This variability is expressed by coherent snow structures over several meters. On the first cruise, the measurements were accompanied by terrestrial laser scanning (TLS) on an area of 50x50 m2. The comparison with the TLS data indicates that the spatial variability is exhibiting similar spatial patterns as deviations in surface topology. This suggests a strong influence from surface processes, for example wind, on the temporal development of density or grain size profiles. The fundamental relationship between variations in snow properties, surface roughness and changes therein as investigated in this study is interpreted with respect to large-scale ice movement and the mass balance.

  19. Snowscape Ecology: Linking Snow Properties to Wildlife Movements and Demography

    Science.gov (United States)

    Prugh, L.; Verbyla, D.; van de Kerk, M.; Mahoney, P.; Sivy, K. J.; Liston, G. E.; Nolin, A. W.

    2017-12-01

    Snow enshrouds up to one third of the global land mass annually and exerts a major influence on animals that reside in these "snowscapes," (landscapes covered in snow). Dynamic snowscapes may have especially strong effects in arctic and boreal regions where dry snow persists for much of the year. Changes in temperature and hydrology are transforming northern regions, with profound implications for wildlife that are not well understood. We report initial findings from a NASA ABoVE project examining effects of snow properties on Dall sheep (Ovis dalli dalli). We used the MODSCAG snow fraction product to map spring snowline elevations and snow-off dates from 2000-2015 throughout the global range of Dall sheep in Alaska and northwestern Canada. We found a negative effect of spring snow cover on Dall sheep recruitment that increased with latitude. Using meteorological data and a daily freeze/thaw status product derived from passive microwave remote sensing from 1983-2012, we found that sheep survival rates increased in years with higher temperatures, less winter precipitation, fewer spring freeze-thaw events, and more winter freeze-thaw events. To examine the effects of snow depth and density on sheep movements, we used location data from GPS-collared sheep and a snowpack evolution model (SnowModel). We found that sheep selected for shallow, fluffy snow at high elevations, but they selected for denser snow as depth increased. Our field measurements identified a critical snow density threshold of 329 (± 18 SE) kg/m3 to support the weight of Dall sheep. Thus, sheep may require areas of shallow, fluffy snow for foraging, while relying on hard-packed snow for winter travel. These findings highlight the importance of multiple snowscape properties on wildlife movements and demography. The integrated study of snow properties and ecological processes, which we call snowscape ecology, will greatly improve global change forecasting.

  20. Loropetalum chinense 'Snow Panda'

    Science.gov (United States)

    A new Loropetalum chinense, ‘Snow Panda’, developed at the U.S. National Arboretum is described. ‘Snow Panda’ (NA75507, PI660659) originated from seeds collected near Yan Chi He, Hubei, China in 1994 by the North America-China Plant Exploration Consortium (NACPEC). Several seedlings from this trip w...

  1. An 18-yr long (1993–2011 snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt. for driving and evaluating snowpack models

    Directory of Open Access Journals (Sweden)

    S. Morin

    2012-07-01

    Full Text Available A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France. Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September, when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN, in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo and hourly (snow depth, albedo, runoff, surface temperature, soil temperature time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://dx.doi.org/10.1594/PANGAEA.774249 as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

  2. Laboratory study of nitrate photolysis in Antarctic snow. I. Observed quantum yield, domain of photolysis, and secondary chemistry

    Energy Technology Data Exchange (ETDEWEB)

    Meusinger, Carl; Johnson, Matthew S. [Department of Chemistry, University of Copenhagen, Copenhagen (Denmark); Berhanu, Tesfaye A.; Erbland, Joseph; Savarino, Joel, E-mail: jsavarino@lgge.obs.ujf-grenoble.fr [Univ. Grenoble Alpes, LGGE, F-38000 Grenoble (France); CNRS, LGGE, F-38000 Grenoble (France)

    2014-06-28

    Post-depositional processes alter nitrate concentration and nitrate isotopic composition in the top layers of snow at sites with low snow accumulation rates, such as Dome C, Antarctica. Available nitrate ice core records can provide input for studying past atmospheres and climate if such processes are understood. It has been shown that photolysis of nitrate in the snowpack plays a major role in nitrate loss and that the photolysis products have a significant influence on the local troposphere as well as on other species in the snow. Reported quantum yields for the main reaction spans orders of magnitude – apparently a result of whether nitrate is located at the air-ice interface or in the ice matrix – constituting the largest uncertainty in models of snowpack NO{sub x} emissions. Here, a laboratory study is presented that uses snow from Dome C and minimizes effects of desorption and recombination by flushing the snow during irradiation with UV light. A selection of UV filters allowed examination of the effects of the 200 and 305 nm absorption bands of nitrate. Nitrate concentration and photon flux were measured in the snow. The quantum yield for loss of nitrate was observed to decrease from 0.44 to 0.003 within what corresponds to days of UV exposure in Antarctica. The superposition of photolysis in two photochemical domains of nitrate in snow is proposed: one of photolabile nitrate, and one of buried nitrate. The difference lies in the ability of reaction products to escape the snow crystal, versus undergoing secondary (recombination) chemistry. Modeled NO{sub x} emissions may increase significantly above measured values due to the observed quantum yield in this study. The apparent quantum yield in the 200 nm band was found to be ∼1%, much lower than reported for aqueous chemistry. A companion paper presents an analysis of the change in isotopic composition of snowpack nitrate based on the same samples as in this study.

  3. Metal accumulation in the earthworm Lumbricus rubellus. Model predictions compared to field data

    Science.gov (United States)

    Veltman, K.; Huijbregts, M.A.J.; Vijver, M.G.; Peijnenburg, W.J.G.M.; Hobbelen, P.H.F.; Koolhaas, J.E.; van Gestel, C.A.M.; van Vliet, P.C.J.; Jan, Hendriks A.

    2007-01-01

    The mechanistic bioaccumulation model OMEGA (Optimal Modeling for Ecotoxicological Applications) is used to estimate accumulation of zinc (Zn), copper (Cu), cadmium (Cd) and lead (Pb) in the earthworm Lumbricus rubellus. Our validation to field accumulation data shows that the model accurately predicts internal cadmium concentrations. In addition, our results show that internal metal concentrations in the earthworm are less than linearly (slope < 1) related to the total concentration in soil, while risk assessment procedures often assume the biota-soil accumulation factor (BSAF) to be constant. Although predicted internal concentrations of all metals are generally within a factor 5 compared to field data, incorporation of regulation in the model is necessary to improve predictability of the essential metals such as zinc and copper. ?? 2006 Elsevier Ltd. All rights reserved.

  4. Analysis of snow-glacial historical and projected flows in Olivares river basin. Comparison between DHSVM and WEAP models.

    Science.gov (United States)

    Cepeda, Javier; Vargas, Ximena

    2017-04-01

    In the Andes Mountains, in central Chile, glaciers are a key element to both environment and economy, since they contribute highly to streamflow during the summer season. Many studies have been performed in order to understand the actual contribution of glacial-based streamflow and the expected response of glaciers to climatological alterations such as climate change. This work studies and analyses the historical and future streamflow on the Olivares river basin, located close to Chile's capital city, Santiago, under climatic change scenario RCP8.5. For this, we use two hydrological models with different topology, to have more consistency in the results, and analysing the differences because of the conceptualization of the processes and its spatial scale. DHSVM is a distributed, physically based model, while WEAP is a semi-distributed model that represents some processes conceptually and others physically based. Both models are calibrated considering streamflow and snow cover data from the period 2001-2012 at a daily scale. Additionally, comparisons between the modelled glacier area variations and LANDSAT images are performed to strengthen the calibration process. Climate change projections are obtained from five Global Circulation Models (GCM) under RCP8.5 scenario. Changes in glacier area, volume and glacial streamflow contribution to basin discharge are analysed, comparing two future time lapses, near-future period (2015-2044) and far-future (2045-2074), to a baseline period (1985-2004). The basin has an area of 543 km2, with elevations ranging from 1,528 to 6,024 m.a.s.l. and an important glacier presence. According to the National Glacier Cadastre developed by Chile Water Authority (DGA) in 2012, there are 80 uncovered glaciers within the basin, the most important being Juncal Sur, Olivares Alfa, Beta and Gamma. Glacier area represented 17% of the basin in 1985, while they made up only to 11% in 2015.The glaciers are located at altitudes ranging from 3,500 to

  5. On the changing contribution of snow to the hydrology of the Fraser River Basin, Canada

    Science.gov (United States)

    Dery, S. J.; Kang, D.; Shi, X.; Gao, H.

    2013-12-01

    This talk will present an application of the Variable Infiltration Capacity (VIC) model to the Fraser River Basin (FRB) of British Columbia (BC), Canada over the latter half of the 20th century. The Fraser River is the longest waterway in BC and supports the world's most abundant Pacific Ocean salmon populations. Previous modeling and observational studies have demonstrated that the FRB is a snow-dominated system but with climate change it may evolve to a pluvial regime. Thus the goal of this study is to evaluate the changing contribution of snow to the hydrology of the watershed over the latter half of the 20th century. To this end, a 0.25° atmospheric forcing dataset is used to drive the VIC model from 1948 to 2006 at a daily time step over a domain covering the entire FRB. A model evaluation is first conducted over 11 major sub-watersheds of the FRB to quantitatively assess the spatial variations of snow water equivalent (SWE) and runoff. The ratio of the spatially averaged maximum SWE to runoff (RSR) is used to quantify the contribution of snow to the runoff in the 11 sub-watersheds of interest. From 1948 to 2006, RSR exhibits a significant decreasing trend in 9 of the 11 sub-watersheds (at a 0.05 of p-value according to the Mann-Kendall Test statistics). Changes in snow accumulation and melt lead to significant advances of the spring freshet throughout the basin. As the climate continues to warm, ecological processes and human usage of natural resources in the FRB may be substantially affected by its transition from a snow to a hybrid (nival/pluvial) and even a rain-dominated watershed.

  6. Predictive modeling of crystal accumulation in high-level waste glass melters processing radioactive waste

    Science.gov (United States)

    Matyáš, Josef; Gervasio, Vivianaluxa; Sannoh, Sulaiman E.; Kruger, Albert A.

    2017-11-01

    The effectiveness of high-level waste vitrification at Hanford's Waste Treatment and Immobilization Plant may be limited by precipitation/accumulation of spinel crystals [(Fe, Ni, Mn, Zn)(Fe, Cr)2O4] in the glass discharge riser of Joule-heated ceramic melters during idling. These crystals do not affect glass durability; however, if accumulated in thick layers, they can clog the melter and prevent discharge of molten glass into canisters. To address this problem, an empirical model was developed that can predict thicknesses of accumulated layers as a function of glass composition. This model predicts well the accumulation of single crystals and/or small-scale agglomerates, but excessive agglomeration observed in high-Ni-Fe glass resulted in an underprediction of accumulated layers, which gradually worsened over time as an increased number of agglomerates formed. The accumulation rate of ∼53.8 ± 3.7 μm/h determined for this glass will result in a ∼26 mm-thick layer after 20 days of melter idling.

  7. Modelling anaerobic digestion of concentrated black water and faecal matter in accumulation system

    NARCIS (Netherlands)

    Elmitwalli, T.; Zeeman, G.; Otterpohl, R.

    2011-01-01

    A dynamic mathematical model based on anaerobic digestion model no. 1 (ADM1) was developed for accumulation (AC) system treating concentrated black water and faecal matter at different temperatures. The AC system was investigated for the treatment of waste(water) produced from the following systems:

  8. Snow observations in Mount Lebanon (2011-2016)

    Science.gov (United States)

    Fayad, Abbas; Gascoin, Simon; Faour, Ghaleb; Fanise, Pascal; Drapeau, Laurent; Somma, Janine; Fadel, Ali; Bitar, Ahmad Al; Escadafal, Richard

    2017-08-01

    We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km2 and an elevation up to 3088 m). The dataset consists of (1) continuous meteorological and snow height observations, (2) snowpack field measurements, and (3) medium-resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA, 2296 m; LAQ, 1840 m; and CED, 2834 m a.s.l.) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 min intervals for the snow seasons (November-June) between 2011 and 2016 for MZA and between 2014 and 2016 for CED and LAQ. Precipitation data were filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a.s.l. during the two snow seasons of 2014-2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011-2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m-3. We find that the relationship between snow depth and snow density is specific to the Mediterranean climate. The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a.s.l.) and 62 % for high mountain regions (elevation 2200-2900 m a.s.l.). The dataset is suitable for the investigation of snow dynamics and for the forcing

  9. Catchment-scale evaluation of pollution potential of urban snow at two residential catchments in southern Finland.

    Science.gov (United States)

    Sillanpää, Nora; Koivusalo, Harri

    2013-01-01

    Despite the crucial role of snow in the hydrological cycle in cold climate conditions, monitoring studies of urban snow quality often lack discussions about the relevance of snow in the catchment-scale runoff management. In this study, measurements of snow quality were conducted at two residential catchments in Espoo, Finland, simultaneously with continuous runoff measurements. The results of the snow quality were used to produce catchment-scale estimates of areal snow mass loads (SML). Based on the results, urbanization reduced areal snow water equivalent but increased pollutant accumulation in snow: SMLs in a medium-density residential catchment were two- to four-fold higher in comparison with a low-density residential catchment. The main sources of pollutants were related to vehicular traffic and road maintenance, but also pet excrement increased concentrations to a high level. Ploughed snow can contain 50% of the areal pollutant mass stored in snow despite its small surface area within a catchment.

  10. Seasonal monitoring of melt and accumulation within the deep percolation zone of the Greenland Ice Sheet and comparison with simulations of regional climate modeling

    Science.gov (United States)

    Heilig, Achim; Eisen, Olaf; MacFerrin, Michael; Tedesco, Marco; Fettweis, Xavier

    2018-06-01

    Increasing melt over the Greenland Ice Sheet (GrIS) recorded over the past several years has resulted in significant changes of the percolation regime of the ice sheet. It remains unclear whether Greenland's percolation zone will act as a meltwater buffer in the near future through gradually filling all pore space or if near-surface refreezing causes the formation of impermeable layers, which provoke lateral runoff. Homogeneous ice layers within perennial firn, as well as near-surface ice layers of several meter thickness have been observed in firn cores. Because firn coring is a destructive method, deriving stratigraphic changes in firn and allocation of summer melt events is challenging. To overcome this deficit and provide continuous data for model evaluations on snow and firn density, temporal changes in liquid water content and depths of water infiltration, we installed an upward-looking radar system (upGPR) 3.4 m below the snow surface in May 2016 close to Camp Raven (66.4779° N, 46.2856° W) at 2120 m a.s.l. The radar is capable of quasi-continuously monitoring changes in snow and firn stratigraphy, which occur above the antennas. For summer 2016, we observed four major melt events, which routed liquid water into various depths beneath the surface. The last event in mid-August resulted in the deepest percolation down to about 2.3 m beneath the surface. Comparisons with simulations from the regional climate model MAR are in very good agreement in terms of seasonal changes in accumulation and timing of onset of melt. However, neither bulk density of near-surface layers nor the amounts of liquid water and percolation depths predicted by MAR correspond with upGPR data. Radar data and records of a nearby thermistor string, in contrast, matched very well for both timing and depth of temperature changes and observed water percolations. All four melt events transferred a cumulative mass of 56 kg m-2 into firn beneath the summer surface of 2015. We find that

  11. Modelling of space-charge accumulation process in dielectrics of MDS structures under irradiation

    International Nuclear Information System (INIS)

    Gurtov, V.A.; Nazarov, A.I.; Travkov, I.V.

    1990-01-01

    Results of numerical modelling of radiation-induced space charge (RISC) accumulation in MOS structure silicon dioxide are given. Diffusion-drift model which takes account of trap heterogeneous distribution within dielectric volume and channeling of carriers captured at traps represents basis for calculations. Main physical processes affecting RISC accumulation are picked out and character of capture filling in dielectric volume under stress in MOS structure shutter during irradiation on the basis of comparison of experimental results for different thickness oxides with calculation data are predicted

  12. Magnetic resonance imaging (MRI to study striatal iron accumulation in a rat model of Parkinson's disease.

    Directory of Open Access Journals (Sweden)

    Ana Virel

    Full Text Available Abnormal accumulation of iron is observed in neurodegenerative disorders. In Parkinson's disease, an excess of iron has been demonstrated in different structures of the basal ganglia and is suggested to be involved in the pathogenesis of the disease. Using the 6-hydroxydopamine (6-OHDA rat model of Parkinson's disease, the edematous effect of 6-OHDA and its relation with striatal iron accumulation was examined utilizing in vivo magnetic resonance imaging (MRI. The results revealed that in comparison with control animals, injection of 6-OHDA into the rat striatum provoked an edematous process, visible in T2-weighted images that was accompanied by an accumulation of iron clearly detectable in T2*-weighted images. Furthermore, Prussian blue staining to detect iron in sectioned brains confirmed the existence of accumulated iron in the areas of T2* hypointensities. The presence of ED1-positive microglia in the lesioned striatum overlapped with this accumulation of iron, indicating areas of toxicity and loss of dopamine nerve fibers. Correlation analyses demonstrated a direct relation between the hyperintensities caused by the edema and the hypointensities caused by the accumulation of iron.

  13. Biodynamic modelling and the prediction of accumulated trace metal concentrations in the polychaete Arenicola marina

    International Nuclear Information System (INIS)

    Casado-Martinez, M. Carmen; Smith, Brian D.; DelValls, T. Angel; Luoma, Samuel N.; Rainbow, Philip S.

    2009-01-01

    The use of biodynamic models to understand metal uptake directly from sediments by deposit-feeding organisms still represents a special challenge. In this study, accumulated concentrations of Cd, Zn and Ag predicted by biodynamic modelling in the lugworm Arenicola marina have been compared to measured concentrations in field populations in several UK estuaries. The biodynamic model predicted accumulated field Cd concentrations remarkably accurately, and predicted bioaccumulated Ag concentrations were in the range of those measured in lugworms collected from the field. For Zn the model showed less but still good comparability, accurately predicting Zn bioaccumulation in A. marina at high sediment concentrations but underestimating accumulated Zn in the worms from sites with low and intermediate levels of Zn sediment contamination. Therefore, it appears that the physiological parameters experimentally derived for A. marina are applicable to the conditions encountered in these environments and that the assumptions made in the model are plausible. - Biodynamic modelling predicts accumulated field concentrations of Ag, Cd and Zn in the deposit-feeding polychaete Arenicola marina.

  14. Modelling accumulation of marine plastics in the coastal zone; what are the dominant physical processes?

    Science.gov (United States)

    Critchell, Kay; Lambrechts, Jonathan

    2016-03-01

    Anthropogenic marine debris, mainly of plastic origin, is accumulating in estuarine and coastal environments around the world causing damage to fauna, flora and habitats. Plastics also have the potential to accumulate in the food web, as well as causing economic losses to tourism and sea-going industries. If we are to manage this increasing threat, we must first understand where debris is accumulating and why these locations are different to others that do not accumulate large amounts of marine debris. This paper demonstrates an advection-diffusion model that includes beaching, settling, resuspension/re-floating, degradation and topographic effects on the wind in nearshore waters to quantify the relative importance of these physical processes governing plastic debris accumulation. The aim of this paper is to prioritise research that will improve modelling outputs in the future. We have found that the physical characteristic of the source location has by far the largest effect on the fate of the debris. The diffusivity, used to parameterise the sub-grid scale movements, and the relationship between debris resuspension/re-floating from beaches and the wind shadow created by high islands also has a dramatic impact on the modelling results. The rate of degradation of macroplastics into microplastics also have a large influence in the result of the modelling. The other processes presented (settling, wind drift velocity) also help determine the fate of debris, but to a lesser degree. These findings may help prioritise research on physical processes that affect plastic accumulation, leading to more accurate modelling, and subsequently management in the future.

  15. Role of Tibetan Buddhist monasteries in snow leopard conservation.

    Science.gov (United States)

    Li, Juan; Wang, Dajun; Yin, Hang; Zhaxi, Duojie; Jiagong, Zhala; Schaller, George B; Mishra, Charudutt; McCarthy, Thomas M; Wang, Hao; Wu, Lan; Xiao, Lingyun; Basang, Lamao; Zhang, Yuguang; Zhou, Yunyun; Lu, Zhi

    2014-02-01

    The snow leopard (Panthera uncia) inhabits the rugged mountains in 12 countries of Central Asia, including the Tibetan Plateau. Due to poaching, decreased abundance of prey, and habitat degradation, it was listed as endangered by the International Union for Conservation of Nature in 1972. Current conservation strategies, including nature reserves and incentive programs, have limited capacities to protect snow leopards. We investigated the role of Tibetan Buddhist monasteries in snow leopard conservation in the Sanjiangyuan region in China's Qinghai Province on the Tibetan Plateau. From 2009 to 2011, we systematically surveyed snow leopards in the Sanjiangyuan region. We used the MaxEnt model to determine the relation of their presence to environmental variables (e.g., elevation, ruggedness) and to predict snow leopard distribution. Model results showed 89,602 km(2) of snow leopard habitat in the Sanjiangyuan region, of which 7674 km(2) lay within Sanjiangyuan Nature Reserve's core zones. We analyzed the spatial relation between snow leopard habitat and Buddhist monasteries and found that 46% of monasteries were located in snow leopard habitat and 90% were within 5 km of snow leopard habitat. The 336 monasteries in the Sanjiangyuan region could protect more snow leopard habitat (8342 km(2) ) through social norms and active patrols than the nature reserve's core zones. We conducted 144 household interviews to identify local herders' attitudes and behavior toward snow leopards and other wildlife. Most local herders claimed that they did not kill wildlife, and 42% said they did not kill wildlife because it was a sin in Buddhism. Our results indicate monasteries play an important role in snow leopard conservation. Monastery-based snow leopard conservation could be extended to other Tibetan Buddhist regions that in total would encompass about 80% of the global range of snow leopards. © 2013 Society for Conservation Biology.

  16. Modeling and control of a hybrid wind-tidal turbine with hydraulic accumulator

    International Nuclear Information System (INIS)

    Fan, YaJun; Mu, AnLe; Ma, Tao

    2016-01-01

    This paper presents the modeling and control of a hybrid wind-tidal turbine with hydraulic accumulator. The hybrid turbine captures the offshore wind energy and tidal current energy simultaneously and stores the excess energy in hydraulic accumulator prior to electricity generation. Two hydraulic pumps installed respectively in wind and tidal turbine nacelles are used to transform the captured mechanical energy into hydraulic energy. To extract the maximal power from wind and tidal current, standard torque controls are achieved by regulating the displacements of the hydraulic pumps. To meet the output power demand, a Proportion Integration Differentiation (PID) controller is designed to distribute the hydraulic energy between the accumulator and the Pelton turbine. A simulation case study based on combining a 5 MW offshore wind turbine and a 1 MW tidal current turbine is undertaken. Case study demonstrates that the hybrid generation system not only captures all the available wind and tidal energy and also delivers the desired generator power precisely through the accumulator damping out all the power fluctuations from the wind and tidal speed disturbances. Energy and exergy analyses show that the energy efficiency can exceed 100% as the small input speeds are considered, and the exergy efficiency has the consistent change trends with demand power. Further more parametric sensitivity study on hydraulic accumulator shows that there is an inversely proportional relationship between accumulator and hydraulic equipments including the pump and nozzle in terms of dimensions. - Highlights: • A hybrid wind-tidal turbine is presented. • Hydraulic accumulator stores/releases the surplus energy. • Standard torque controls extract the maximal power from wind and tidal. • Generator outputs meet the electricity demand precisely. • Parametric sensitivity study on accumulator is implemented.

  17. Evaluating Multispectral Snowpack Reflectivity With Changing Snow Correlation Lengths

    Science.gov (United States)

    Kang, Do Hyuk; Barros, Ana P.; Kim, Edward J.

    2016-01-01

    This study investigates the sensitivity of multispectral reflectivity to changing snow correlation lengths. Matzler's ice-lamellae radiative transfer model was implemented and tested to evaluate the reflectivity of snow correlation lengths at multiple frequencies from the ultraviolet (UV) to the microwave bands. The model reveals that, in the UV to infrared (IR) frequency range, the reflectivity and correlation length are inversely related, whereas reflectivity increases with snow correlation length in the microwave frequency range. The model further shows that the reflectivity behavior can be mainly attributed to scattering rather than absorption for shallow snowpacks. The largest scattering coefficients and reflectivity occur at very small correlation lengths (approximately 10(exp -5 m) for frequencies higher than the IR band. In the microwave range, the largest scattering coefficients are found at millimeter wavelengths. For validation purposes, the ice-lamella model is coupled with a multilayer snow physics model to characterize the reflectivity response of realistic snow hydrological processes. The evolution of the coupled model simulated reflectivities in both the visible and the microwave bands is consistent with satellite-based reflectivity observations in the same frequencies. The model results are also compared with colocated in situ snow correlation length measurements (Cold Land Processes Field Experiment 2002-2003). The analysis and evaluation of model results indicate that the coupled multifrequency radiative transfer and snow hydrology modeling system can be used as a forward operator in a data-assimilation framework to predict the status of snow physical properties, including snow correlation length.

  18. Incorporating cold-air pooling into downscaled climate models increases potential refugia for snow-dependent species within the Sierra Nevada Ecoregion, CA.

    Directory of Open Access Journals (Sweden)

    Jennifer A Curtis

    Full Text Available We present a unique water-balance approach for modeling snowpack under historic, current and future climates throughout the Sierra Nevada Ecoregion. Our methodology uses a finer scale (270 m than previous regional studies and incorporates cold-air pooling, an atmospheric process that sustains cooler temperatures in topographic depressions thereby mitigating snowmelt. Our results are intended to support management and conservation of snow-dependent species, which requires characterization of suitable habitat under current and future climates. We use the wolverine (Gulo gulo as an example species and investigate potential habitat based on the depth and extent of spring snowpack within four National Park units with proposed wolverine reintroduction programs. Our estimates of change in spring snowpack conditions under current and future climates are consistent with recent studies that generally predict declining snowpack. However, model development at a finer scale and incorporation of cold-air pooling increased the persistence of April 1st snowpack. More specifically, incorporation of cold-air pooling into future climate projections increased April 1st snowpack by 6.5% when spatially averaged over the study region and the trajectory of declining April 1st snowpack reverses at mid-elevations where snow pack losses are mitigated by topographic shading and cold-air pooling. Under future climates with sustained or increased precipitation, our results indicate a high likelihood for the persistence of late spring snowpack at elevations above approximately 2,800 m and identify potential climate refugia sites for snow-dependent species at mid-elevations, where significant topographic shading and cold-air pooling potential exist.

  19. Monitoring Areal Snow Cover Using NASA Satellite Imagery

    Science.gov (United States)

    Harshburger, Brian J.; Blandford, Troy; Moore, Brandon

    2011-01-01

    The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data

  20. Effects of multilayer snow scheme on the simulation of snow: Offline Noah and coupled with NCEP CFSv2

    Science.gov (United States)

    Saha, Subodh Kumar; Sujith, K.; Pokhrel, Samir; Chaudhari, Hemantkumar S.; Hazra, Anupam

    2017-03-01

    The Noah version 2.7.1 is a moderately complex land surface model (LSM), with a single layer snowpack, combined with vegetation and underlying soil layer. Many previous studies have pointed out biases in the simulation of snow, which may hinder the skill of a forecasting system coupled with the Noah. In order to improve the simulation of snow by the Noah, a multilayer snow scheme (up to a maximum of six layers) is introduced. As Noah is the land surface component of the Climate Forecast System version 2 (CFSv2) of the National Centers for Environmental Prediction (NCEP), the modified Noah is also coupled with the CFSv2. The offline LSM shows large improvements in the simulation of snow depth, snow water equivalent (SWE), and snow cover area during snow season (October to June). CFSv2 with the modified Noah reveals a dramatic improvements in the simulation of snow depth and 2 m air temperature and moderate improvements in SWE. As suggested in the previous diagnostic and sensitivity study, improvements in the simulation of snow by CFSv2 have lead to the reduction in dry bias over the Indian subcontinent (by a maximum of 2 mm d-1). The multilayer snow scheme shows promising results in the simulation of snow as well as Indian summer monsoon rainfall and hence this development may be the part of the future version of the CFS.

  1. Polar boundary layer bromine explosion and ozone depletion events in the chemistry-climate model EMAC v2.52: implementation and evaluation of AirSnow algorithm

    Science.gov (United States)

    Falk, Stefanie; Sinnhuber, Björn-Martin

    2018-03-01

    Ozone depletion events (ODEs) in the polar boundary layer have been observed frequently during springtime. They are related to events of boundary layer enhancement of bromine. Consequently, increased amounts of boundary layer volume mixing ratio (VMR) and vertical column densities (VCDs) of BrO have been observed by in situ observation, ground-based as well as airborne remote sensing, and from satellites. These so-called bromine explosion (BE) events have been discussed serving as a source of tropospheric BrO at high latitudes, which has been underestimated in global models so far. We have implemented a treatment of bromine release and recycling on sea-ice- and snow-covered surfaces in the global chemistry-climate model EMAC (ECHAM/MESSy Atmospheric Chemistry) based on the scheme of Toyota et al. (2011). In this scheme, dry deposition fluxes of HBr, HOBr, and BrNO3 over ice- and snow-covered surfaces are recycled into Br2 fluxes. In addition, dry deposition of O3, dependent on temperature and sunlight, triggers a Br2 release from surfaces associated with first-year sea ice. Many aspects of observed bromine enhancements and associated episodes of near-complete depletion of boundary layer ozone, both in the Arctic and in the Antarctic, are reproduced by this relatively simple approach. We present first results from our global model studies extending over a full annual cycle, including comparisons with Global Ozone Monitoring Experiment (GOME) satellite BrO VCDs and surface ozone observations.

  2. Towards Year-round Estimation of Terrestrial Water Storage over Snow-Covered Terrain via Multi-sensor Assimilation of GRACE/GRACE-FO and AMSR-E/AMSR-2.

    Science.gov (United States)

    Wang, J.; Xue, Y.; Forman, B. A.; Girotto, M.; Reichle, R. H.

    2017-12-01

    The Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth's terrestrial hydrologic cycle and has provided an unprecedented observational constraint for global land surface models. However, the coarse-scale (in space and time), vertically-integrated measure of terrestrial water storage (TWS) limits GRACE's applicability to smaller scale hydrologic applications. In order to enhance model-based estimates of TWS while effectively adding resolution (in space and time) to the coarse-scale TWS retrievals, a multi-variate, multi-sensor data assimilation framework is presented here that simultaneously assimilates gravimetric retrievals of TWS in conjunction with passive microwave (PMW) brightness temperature (Tb) observations over snow-covered terrain. The framework uses the NASA Catchment Land Surface Model (Catchment) and an ensemble Kalman filter (EnKF). A synthetic assimilation experiment is presented for the Volga river basin in Russia. The skill of the output from the assimilation of synthetic observations is compared with that of model estimates generated without the benefit of assimilating the synthetic observations. It is shown that the EnKF framework improves modeled estimates of TWS, snow depth, and snow mass (a.k.a. snow water equivalent). The data assimilation routine produces a conditioned (updated) estimate that is more accurate and contains less uncertainty during both the snow accumulation phase of the snow season as well as during the snow ablation season.

  3. Relating C-band Microwave and Optical Satellite Observations as A Function of Snow Thickness on First-Year Sea Ice during the Winter to Summer Transition

    Science.gov (United States)

    Zheng, J.; Yackel, J.

    2015-12-01

    The Arctic sea ice and its snow cover have a direct impact on both the Arctic and global climate system through their ability to moderate heat exchange across the ocean-sea ice-atmosphere (OSA) interface. Snow cover plays a key role in the OSA interface radiation and energy exchange, as it controls the growth and decay of first-year sea ice (FYI). However, meteoric accumulation and redistribution of snow on FYI is highly stochastic over space and time, which makes it poorly understood. Previous studies have estimated local-scale snow thickness distributions using in-situ technique and modelling but it is spatially limited and challenging due to logistic difficulties. Moreover, snow albedo is also critical for determining the surface energy balance of the OSA during the critical summer ablation season. Even then, due to persistent and widespread cloud cover in the Arctic at various spatio-temporal scales, it is difficult and unreliable to remotely measure albedo of snow cover on FYI in the optical spectrum. Previous studies demonstrate that only large-scale sea ice albedo was successfully estimated using optical-satellite sensors. However, space-borne microwave sensors, with their capability of all-weather and 24-hour imaging, can provide enhanced information about snow cover on FYI. Daily spaceborne C-band scatterometer data (ASCAT) and MODIS data are used to investigate the the seasonal co-evolution of the microwave backscatter coefficient and optical albedo as a function of snow thickness on smooth FYI. The research focuses on snow-covered FYI near Cambridge Bay, Nunavut (Fig.1) during the winter to advanced-melt period (April-June, 2014). The ACSAT time series (Fig.2) show distinct increase in scattering at melt onset indicating the first occurrence of melt water in the snow cover. The corresponding albedo exhibits no decrease at this stage. We show how the standard deviation of ASCAT backscatter on FYI during winter can be used as a proxy for surface roughness

  4. A dynamic IS-LM business cycle model with two time delays in capital accumulation equation

    Science.gov (United States)

    Zhou, Lujun; Li, Yaqiong

    2009-06-01

    In this paper, we analyze a augmented IS-LM business cycle model with the capital accumulation equation that two time delays are considered in investment processes according to Kalecki's idea. Applying stability switch criteria and Hopf bifurcation theory, we prove that time delays cause the equilibrium to lose or gain stability and Hopf bifurcation occurs.

  5. Mass balance-based regression modeling of PAHs accumulation in urban soils, role of urban development

    International Nuclear Information System (INIS)

    Peng, Chi; Wang, Meie; Chen, Weiping; Chang, Andrew C.

    2015-01-01

    We investigated the polycyclic aromatic hydrocarbons (PAHs) contents in 68 soils samples collected at housing developments that represent different length of development periods across Beijing. Based on the data, we derived a mass balanced mathematical model to simulate the dynamics of PAH accumulations in urban soils as affected by the urban developments. The key parameters were estimated by fitting the modified mass balance model to the data of PAH concentrations vs. building age of the sampling green area. The total PAH concentrations would increase from the baseline of 267 ng g −1 to 3631 ng g −1 during the period of 1978–2048. It showed that the dynamic changes in the rates of accumulations of light and heavy PAH species were related to the shifting of sources of fuels, combustion efficiencies, and amounts of energy consumed during the course of development. - Highlights: • Introduced a mass balance model for soil PAHs accumulation with urbanization. • Reconstructed the historical data of PAH accumulation in soil of Beijing, China. • The soil PAH concentrations would be doubled in the following 40 years. • The composition of PAH emissions were shifting to light PAH species. - Introduced a regression modeling approach to predict the changes of PAH concentrations in urban soil

  6. Determination of Micronutrient Accumulation in Greenhouse Cucumber Crop Using a Modeling Approach

    Directory of Open Access Journals (Sweden)

    Lino J. Ramírez-Pérez

    2017-11-01

    Full Text Available The control of micronutrient application in cucumber cultivation has great importance as they participate in many functions of metabolism. In addition, micronutrient application efficiency is fundamental to avoid periods of overconsumption or deficits in the crop. To determine micronutrient accumulation using a dynamic model, two cycles of Vitaly and Luxell cucumber crops were grown. During the development of the crop, micronutrient content (Fe, B, Mn, Cu, and Zn in the different organs of the cucumber plant was quantified. The model dynamically simulated the accumulation of biomass and micronutrients using climatic variables recorded inside the greenhouse as inputs. It was found that a decrease in photosynthetically active radiation and temperature significantly diminished the accumulation of biomass by the cucumber plants. On the other hand, the results demonstrated that the model efficiently simulated both the accumulation of biomass and micronutrients in a cucumber crop. The efficiency evaluation showed values higher than R2 > 0.95. This dynamic model can be useful to define adequate strategies for the management of cucumber cultivation in greenhouses as well as the application of micronutrients.

  7. Merging datasets from NASA SnowEx to better understand how snow falls and moves.

    Science.gov (United States)

    Gongora, J. A.; Marshall, H. P.; Glenn, N. F.

    2017-12-01

    A modelers ability to estimated snow depth and snow water equivalent (SWE) for mountain snow depends strongly on their understanding of snowpack spatial distribution and the soundness of the models initial conditions. This work focuses on the application of data science and efficient algorithms to find optimal locations in mountainous terrain to act as initital conditions for machine driven interpolation methods. By using graphs, collections of pairwise relationships between objects, to describe our data we were able to efficiently search, partition, and understand snowpack from the persepetive of this data structure.

  8. Estimating snow water equivalent (SWE) using interferometric synthetic aperture radar (InSAR)

    Science.gov (United States)

    Deeb, Elias J.

    Since the early 1990s, radar interferometry and interferometric synthetic aperture radar (InSAR) have been used extensively to measure changes in the Earth's surface. Previous research has presented theory for estimating snow properties, including potential for snow water equivalent (SWE) retrieval, using InSAR. The motivation behind using remote sensing to estimate SWE is to provide a more complete, continuous set of "observations" to assist in water management operations, climate change studies, and flood hazard forecasting. The research presented here primarily investigates the feasibility of using the InSAR technique at two different wavelengths (C-Band and L-Band) for SWE retrieval of dry snow within the Kuparuk watershed, North Slope, Alaska. Estimating snow distribution around meteorological towers on the coastal plain using a three-day repeat orbit of C-Band InSAR data was successful (Chapter 2). A longer wavelength L-band SAR is evaluated for SWE retrievals (Chapter 3) showing the ability to resolve larger snow accumulation events over a longer period of time. Comparisons of InSAR estimates and late spring manual sampling of SWE show a R2 = 0.61 when a coherence threshold is used to eliminate noisy SAR data. Qualitative comparisons with a high resolution digital elevation model (DEM) highlight areas of scour on windward slopes and areas of deposition on leeward slopes. When compared to a mid-winter transect of manually sampled snow depths, the InSAR SWE estimates yield a RMSE of 2.21cm when a bulk snow density is used and corrections for bracketing the satellite acquisition timing is performed. In an effort to validate the interaction of radar waves with a snowpack, the importance of the "dry snow" assumption for the estimation of SWE using InSAR is tested with an experiment in Little Cottonwood Canyon, Alta, Utah (Chapter 5). Snow wetness is shown to have a significant effect on the velocity of propagation within the snowpack. Despite the radar

  9. McSnow: A Monte-Carlo Particle Model for Riming and Aggregation of Ice Particles in a Multidimensional Microphysical Phase Space

    Science.gov (United States)

    Brdar, S.; Seifert, A.

    2018-01-01

    We present a novel Monte-Carlo ice microphysics model, McSnow, to simulate the evolution of ice particles due to deposition, aggregation, riming, and sedimentation. The model is an application and extension of the super-droplet method of Shima et al. (2009) to the more complex problem of rimed ice particles and aggregates. For each individual super-particle, the ice mass, rime mass, rime volume, and the number of monomers are predicted establishing a four-dimensional particle-size distribution. The sensitivity of the model to various assumptions is discussed based on box model and one-dimensional simulations. We show that the Monte-Carlo method provides a feasible approach to tackle this high-dimensional problem. The largest uncertainty seems to be related to the treatment of the riming processes. This calls for additional field and laboratory measurements of partially rimed snowflakes.

  10. DEM code-based modeling of energy accumulation and release in structurally heterogeneous rock masses

    Science.gov (United States)

    Lavrikov, S. V.; Revuzhenko, A. F.

    2015-10-01

    Based on discrete element method, the authors model loading of a physical specimen to describe its capacity to accumulate and release elastic energy. The specimen is modeled as a packing of particles with viscoelastic coupling and friction. The external elastic boundary of the packing is represented by particles connected by elastic springs. The latter means introduction of an additional special potential of interaction between the boundary particles, that exercises effect even when there is no direct contact between the particles. On the whole, the model specimen represents an element of a medium capable of accumulation of deformation energy in the form of internal stresses. The data of the numerical modeling of the physical specimen compression and the laboratory testing results show good qualitative consistency.

  11. SNOW THICKNESS ON AUSTRE GRØNFJORDBREEN, SVALBARD, FROM RADAR MEASUREMENTS AND STANDARD SNOW SURVEYS

    Directory of Open Access Journals (Sweden)

    I. I. Lavrentiev

    2018-01-01

    Full Text Available Summary Comparison of two methods of measurements of snow cover thickness on the glacier Austre Grønfjordbreen, Svalbard was performed in the spring of 2014. These methods were the radar (500 MHz observations and standard snow surveys. Measurements were conducted in 77 different points on the surface of the glacier. A good correlation (R2 = 0.98 was revealed. In comparison with the data of snow surveys, the radar measurements show a similar but more detailed pattern of the distribution of the snow cover depth. The discrepancy between the depths of snow cover on maps plotted from data of both methods did not exceed 30 cm in most parts of the glacier. The standard error of interpolation of the radar data onto the entire glacier surface amounts, on average, to 18 cm. This corresponds to the error of radar measurements of 18.8% when an average snow depth is about 160 cm and 9.4% at its maximum thickness of 320 cm. The distance between the measurement points at which the spatial covariance of the snow depth disappears falls between 236 and 283 m along the glacier, and between 117 and 165 m across its position. We compared the results of radar measurements of the pulse-delay time of reflections from the base of the snow cover with the data of manual probe measurements at 10 points and direct measurements of snow depth and average density in 12 snow pits. The average speed of radio waves propagation in the snow was determined as Vcr = 23.4±0.2 cm ns−1. This magnitude and the Looyenga and Kovacs formulas allowed estimating the average density of snow cover ρL = 353.1±13.1 kg m−3 and ρK = 337.4±12.9 kg m−3. The difference from average density measured in 12 pits ρav.meas = 387.4±12.9 kg m−3 amounts to −10.8% and −14.8%. In 2014, according to snow and radar measurements, altitudinal gradient of snow accumulation on the glacier Austre Grønfjordbreen was equal to 0.21 m/100 m, which is smaller than the

  12. Prediction of daily spring hydrographs for future climatic scenarios based on an integrated numerical modelling approach: Application on a snow-governed semi- arid karst catchment area.

    Science.gov (United States)

    Doummar, J.; Kassem, A.; Gurdak, J. J.

    2017-12-01

    In the framework of a three-year USAID/NSF- funded PEER Science project, flow in a karst system in Lebanon (Assal Spring; discharge 0.2-2.5 m3/s yearly volume of 22-30 Mm3) dominated by snow and semi arid conditions was simulated using an integrated numerical model (Mike She 2016). The calibrated model (Nash-Sutcliffe coefficient of 0.77) is based on high resolution input data (2014-2017) and detailed catchment characterization. The approach is to assess the influence of various model parameters on recharge signals in the different hydrological karst compartments (Atmosphere, unsaturated zone, and saturated zone) based on an integrated numerical model. These parameters include precipitation intensity and magnitude, temperature, snow-melt parameters, in addition to karst specific spatially distributed features such as fast infiltration points, soil properties and thickness, topographical slopes, Epikarst and thickness of unsaturated zone, and hydraulic conductivity among others. Moreover, the model is currently simulated forward using various scenarios for future climate (Global Climate Models GCM; daily downscaled temperature and precipitation time series for Lebanon 2020-2045) in order to depict the flow rates expected in the future and the effect of climate change on hydrographs recession coefficients, discharge maxima and minima, and total spring discharge volume . Additionally, a sensitivity analysis of individual or coupled major parameters allows quantifying their impact on recharge or indirectly on the vulnerability of the system (soil thickness, soil and rock hydraulic conductivity appear to be amongst the highly sensitive parameters). This study particularly unravels the normalized single effect of rain magnitude and intensity, snow, and temperature change on the flow rate (e.g., a change of temperature of 3° on the catchment yields a Residual Mean Square Error RMSE of 0.15 m3/s in the spring discharge and a 16% error in the total annual volume with

  13. Observations of distributed snow depth and snow duration within diverse forest structures in a maritime mountain watershed

    Science.gov (United States)

    Dickerson-Lange, Susan E.; Lutz, James A.; Gersonde, Rolf; Martin, Kael A.; Forsyth, Jenna E.; Lundquist, Jessica D.

    2015-11-01

    Spatially distributed snow depth and snow duration data were collected over two to four snow seasons during water years 2011-2014 in experimental forest plots within the Cedar River Municipal Watershed, 50 km east of Seattle, Washington, USA. These 40 × 40 m forest plots, situated on the western slope of the Cascade Range, include unthinned second-growth coniferous forests, variable density thinned forests, forest gaps in which a 20 m diameter (approximately equivalent to one tree height) gap was cut in the middle of each plot, and old-growth forest. Together, this publicly available data set includes snow depth and density observations from manual snow surveys, distributed snow duration observations from ground temperature sensors and time-lapse cameras, meteorological data collected at two open locations and three forested locations, and forest canopy data from airborne light detection and ranging (LiDAR) data and hemispherical photographs. These colocated snow, meteorological, and forest data have the potential to improve understanding of forest influences on snow processes, and provide a unique model-testing data set for hydrological analyses in a forested, maritime watershed. We present empirical snow depletion curves within forests to illustrate an application of these data to improve subgrid representation of snow cover in distributed modeling.

  14. The AMSR2 Satellite-based Microwave Snow Algorithm (SMSA) to estimate regional to global snow depth and snow water equivalent

    Science.gov (United States)

    Kelly, R. E. J.; Saberi, N.; Li, Q.

    2017-12-01

    With moderate to high spatial resolution (observation approaches yet to be fully scoped and developed, the long-term satellite passive microwave record remains an important tool for cryosphere-climate diagnostics. A new satellite microwave remote sensing approach is described for estimating snow depth (SD) and snow water equivalent (SWE). The algorithm, called the Satellite-based Microwave Snow Algorithm (SMSA), uses Advanced Microwave Scanning Radiometer - 2 (AMSR2) observations aboard the Global Change Observation Mission - Water mission launched by the Japan Aerospace Exploration Agency in 2012. The approach is unique since it leverages observed brightness temperatures (Tb) with static ancillary data to parameterize a physically-based retrieval without requiring parameter constraints from in situ snow depth observations or historical snow depth climatology. After screening snow from non-snow surface targets (water bodies [including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]), moderate and shallow snow depths are estimated by minimizing the difference between Dense Media Radiative Transfer model estimates (Tsang et al., 2000; Picard et al., 2011) and AMSR2 Tb observations to retrieve SWE and SD. Parameterization of the model combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Evaluation of the SMSA performance is achieved using in situ snow depth data from a variety of standard and experiment data sources. Results presented from winter seasons 2012-13 to 2016-17 illustrate the improved performance of the new approach in comparison with the baseline AMSR2 algorithm estimates and approach the performance of the model assimilation-based approach of GlobSnow. Given the variation in estimation power of SWE by different land surface/climate models and selected satellite-derived passive microwave approaches, SMSA provides SWE estimates that are independent of real or near real

  15. Snow and ice: Chapter 3

    Science.gov (United States)

    Littell, Jeremy; McAfee, Stephanie A.; O'Neel, Shad; Sass, Louis; Burgess, Evan; Colt, Steve; Clark, Paul; Hayward, Gregory D.; Colt, Steve; McTeague, Monica L.; Hollingsworth, Teresa N.

    2017-01-01

    Temperature and precipitation are key determinants of snowpack levels. Therefore, climate change is likely to affect the role of snow and ice in the landscapes and hydrology of the Chugach National Forest region.Downscaled climate projections developed by Scenarios Network for Alaska and Arctic Planning (SNAP) are useful for examining projected changes in snow at relatively fine resolution using a variable called “snowday fraction (SDF),” the percentage of days with precipitation falling as snow.We summarized SNAP monthly SDF from five different global climate models for the Chugach region by 500 m elevation bands, and compared historical (1971–2000) and future (2030–2059) SDF. We found that:Snow-day fraction and snow-water equivalent (SWE) are projected to decline most in late autumn (October to November) and at lower elevations.Snow-day fraction is projected to decrease 23 percent (averaged across five climate models) from October to March, between sea level and 500 m. Between sea level and 1000 m, SDF is projected to decrease by 17 percent between October and March.Snow-water equivalent is projected to decrease most in autumn (October and November) and at lower elevations (below 1500 m), an average of -26 percent for the 2030–2059 period compared to 1971– 2000. Averaged across the cool season and the entire domain, SWE is projected to decrease at elevations below 1000 m because of increased temperature, but increase at higher elevations because of increased precipitation.Compared to 1971–2000, the percentage of the landscape that is snowdominant in 2030–2059 is projected to decrease, and the percentage in which rain and snow are co-dominant (transient hydrology) is projected to increase from 27 to 37 percent. Most of this change is at lower elevations.Glaciers on the Chugach National Forest are currently losing about 6 km3 of ice per year; half of this loss comes from Columbia Glacier (Berthier et al. 2010).Over the past decade, almost all

  16. Accumulator and random-walk models of psychophysical discrimination: a counter-evaluation.

    Science.gov (United States)

    Vickers, D; Smith, P

    1985-01-01

    In a recent assessment of models of psychophysical discrimination, Heath criticises the accumulator model for its reliance on computer simulation and qualitative evidence, and contrasts it unfavourably with a modified random-walk model, which yields exact predictions, is susceptible to critical test, and is provided with simple parameter-estimation techniques. A counter-evaluation is presented, in which the approximations employed in the modified random-walk analysis are demonstrated to be seriously inaccurate, the resulting parameter estimates to be artefactually determined, and the proposed test not critical. It is pointed out that Heath's specific application of the model is not legitimate, his data treatment inappropriate, and his hypothesis concerning confidence inconsistent with experimental results. Evidence from adaptive performance changes is presented which shows that the necessary assumptions for quantitative analysis in terms of the modified random-walk model are not satisfied, and that the model can be reconciled with data at the qualitative level only by making it virtually indistinguishable from an accumulator process. A procedure for deriving exact predictions for an accumulator process is outlined.

  17. Photochemical degradation of PCBs in snow.

    Science.gov (United States)

    Matykiewiczová, Nina; Klánová, Jana; Klán, Petr

    2007-12-15

    This work represents the first laboratory study known to the authors describing photochemical behavior of persistent organic pollutants in snow at environmentally relevant concentrations. The snow samples were prepared by shock freezing of the corresponding aqueous solutions in liquid nitrogen and were UV-irradiated in a photochemical cold chamber reactor at -25 degrees C, in which simultaneous monitoring of snow-air exchange processeswas also possible. The main photodegradation pathway of two model snow contaminants, PCB-7 and PCB-153 (c approximately 100 ng kg(-1)), was found to be reductive dehalogenation. Possible involvement of the water molecules of snow in this reaction has been excluded by performing the photolyses in D2O snow. Instead, trace amounts of volatile organic compounds have been proposed to be the major source of hydrogen atom in the reduction, and this hypothesis was confirmed by the experiments with deuterated organic cocontaminants, such as d6-ethanol or d8-tetrahydrofuran. It is argued that bimolecular photoreduction of PCBs was more efficient or feasible than any other phototransformations under the experimental conditions used, including the coupling reactions. The photodegradation of PCBs, however, competed with a desorption process responsible for the pollutant loss from the snow samples, especially in case of lower molecular-mass congeners. Organic compounds, apparently largely located or photoproduced on the surface of snow crystals, had a predisposition to be released to the air but, at the same time, to react with other species in the gas phase. It is concluded that physicochemical properties of the contaminants and trace co-contaminants, their location and local concentrations in the matrix, and the wavelength and intensity of radiation are the most important factors in the evaluation of organic contaminants' lifetime in snow. Based on the results, it has been estimated that the average lifetime of PCBs in surface snow, connected

  18. Laboratory study of nitrate photolysis in Antarctic snow. II. Isotopic effects and wavelength dependence

    Energy Technology Data Exchange (ETDEWEB)

    Berhanu, Tesfaye A.; Erbland, Joseph; Savarino, Joël [Laboratoire de Glaciologie et Géophysique de l’Environnement, CNRS, F-38041 Grenoble (France); Univ. Grenoble Alpes, LGGE, F-38041 Grenoble (France); Meusinger, Carl; Johnson, Matthew S. [Copenhagen Center for Atmospheric Research (CCAR), Department of Chemistry, University of Copenhagen, Copenhagen (Denmark); Jost, Rémy [Laboratoire de Interdisciplinaire de Physique (LIPHY) Univ. de Grenoble, Grenoble (France); Bhattacharya, S. K. [Research Center for Environmental Changes, Academia Sinica, Nangang, Taipei 115, Taiwan (China)

    2014-06-28

    Atmospheric nitrate is preserved in Antarctic snow firn and ice. However, at low snow accumulation sites, post-depositional processes induced by sunlight obscure its interpretation. The goal of these studies (see also Paper I by Meusinger et al. [“Laboratory study of nitrate photolysis in Antarctic snow. I. Observed quantum yield, domain of photolysis, and secondary chemistry,” J. Chem. Phys. 140, 244305 (2014)]) is to characterize nitrate photochemistry and improve the interpretation of the nitrate ice core record. Naturally occurring stable isotopes in nitrate ({sup 15}N, {sup 17}O, and {sup 18}O) provide additional information concerning post-depositional processes. Here, we present results from studies of the wavelength-dependent isotope effects from photolysis of nitrate in a matrix of natural snow. Snow from Dome C, Antarctica was irradiated in selected wavelength regions using a Xe UV lamp and filters. The irradiated snow was sampled and analyzed for nitrate concentration and isotopic composition (δ{sup 15}N, δ{sup 18}O, and Δ{sup 17}O). From these measurements an average photolytic isotopic fractionation of {sup 15}ε = (−15 ± 1.2)‰ was found for broadband Xe lamp photolysis. These results are due in part to excitation of the intense absorption band of nitrate around 200 nm in addition to the weaker band centered at 305 nm followed by photodissociation. An experiment with a filter blocking wavelengths shorter than 320 nm, approximating the actinic flux spectrum at Dome C, yielded a photolytic isotopic fractionation of {sup 15}ε = (−47.9 ± 6.8)‰, in good agreement with fractionations determined by previous studies for the East Antarctic Plateau which range from −40 to −74.3‰. We describe a new semi-empirical zero point energy shift model used to derive the absorption cross sections of {sup 14}NO{sub 3}{sup −} and {sup 15}NO{sub 3}{sup −} in snow at a chosen temperature. The nitrogen isotopic fractionations obtained by applying

  19. Effects of dirty snow in nuclear winter simulations

    International Nuclear Information System (INIS)

    Vogelmann, A.M.; Robock, A.; Ellingson, R.G.

    1988-01-01

    A large-scale nuclear war would inject smoke into the atmosphere from burning forests, cities, and industries in targeted areas. This smoke could fall out onto snow and ice and would lower cryospheric albedos by as much as 50%. A global energy balance climate model is used to investigate the maximum effect these ''dirty snow'' albedos have on the surface temperature in nuclear winter simulations which span several years. These effects are investigated for different nuclear winter scenarios, snow precipitation rates, latitudinal distributions of smoke, and seasonal timings. We find that dirty snow, in general, would have a small temperature effect at mid- and low latitudes but could have a large temperature effect at polar latitudes, particularly if the soot is able to reappear significantly in later summers. Factors which limit the climatic importance of the dirty snow are (1) the dirty snow albedo is lowest when the atmosphere still contains a large amount of light-absorbing smoke; (2) even with dirty snow, sea ice areas can still increase, which helps maintain colder temperatures through the sea ice thermal inertial feedback; (3) the snow and ice areas affected by the dirty snow albedos are largest when there is little seasonal solar insolation; and (4) the area affected by the dirty snow is relatively small under all circumstances. copyright American Geophysical Union 1988

  20. Irreversible thermodynamics models and constitutive equations of the irradiation induced deformation and damage accumulating processes

    International Nuclear Information System (INIS)

    Wassilew, C.

    1989-11-01

    This report gives an overall evaluation of several in-reactor deformation and creep-rupture experiments performed in BR-2, FFTF, and Rapsodie on pressurised tubes of the stabilized austenitic stainless steels 1.4970, 1.4981, 1.4988, and the nickel base alloy Hastelloy-X. The irradiation induced deformation processes observed in the components operating in a neutron environment can be divided into two main groups: 1. volume conserving creep and 2. volumetric swelling. Since the observed deformation as well as damage accumulating phenomena are caused by the same constrained generated and free disposable point defects and helium atoms, it is obvious and advisable to analyze, and to model simultaneously the ensemble of the elementary mechanisms and processes effective at the same time. Phenomenological models based on the thermodynamics of irreversible processes have been developed, with the aim of: 1. grasping the partial relationships between the external variables and the response functions (creep, swelling, creep driven swelling, and time to rupture), 2. fathoming the rate-controlling mechanisms, 3. providing insight into the structural details and changes occurring during the deformation and the damage accumulating processes, 4. integrating the damage accumulating processes comprehensively, and 5. formulating the constitutive equations required to describe the elementary processes that generate plastic deformations as well as damage accumulation. (orig./MM)

  1. Sands subjected to repetitive vertical loading under zero lateral strain: accumulation models, terminal densities, and settlement

    KAUST Repository

    Chong, Song Hun

    2016-08-09

    Geosystems often experience numerous loading cycles. Plastic strain accumulation during repetitive mechanical loads can lead to shear shakedown or continued shear ratcheting; in all cases, volumetric strains diminish as the specimen evolves towards terminal density. Previously suggested models and new functions are identified to fit plastic strain accumulation data. All accumulation models are formulated to capture terminal density (volumetric strain) and either shakedown or ratcheting (shear strain). Repetitive vertical loading tests under zero lateral strain conditions are conducted using three different sands packed at initially low and high densities. Test results show that plastic strain accumulation for all sands and density conditions can be captured in the same dimensionless plot defined in terms of the initial relative density, terminal density, and ratio between the amplitude of the repetitive load and the initial static load. This observation allows us to advance a simple but robust procedure to estimate the maximum one-dimensional settlement that a foundation could experience if subjected to repetitive loads. © 2016, Canadian Science Publishing. All rights reserved.

  2. An evidence accumulation model for conflict detection performance in a simulated air traffic control task.

    Science.gov (United States)

    Neal, Andrew; Kwantes, Peter J

    2009-04-01

    The aim of this article is to develop a formal model of conflict detection performance. Our model assumes that participants iteratively sample evidence regarding the state of the world and accumulate it over time. A decision is made when the evidence reaches a threshold that changes over time in response to the increasing urgency of the task. Two experiments were conducted to examine the effects of conflict geometry and timing on response proportions and response time. The model is able to predict the observed pattern of response times, including a nonmonotonic relationship between distance at point of closest approach and response time, as well as effects of angle of approach and relative velocity. The results demonstrate that evidence accumulation models provide a good account of performance on a conflict detection task. Evidence accumulation models are a form of dynamic signal detection theory, allowing for the analysis of response times as well as response proportions, and can be used for simulating human performance on dynamic decision tasks.

  3. Experimental analysis, modeling and simulation of a solar energy accumulator with paraffin wax as PCM

    International Nuclear Information System (INIS)

    Reyes, A.; Henríquez-Vargas, L.; Aravena, R.; Sepúlveda, F.

    2015-01-01

    Highlights: • Enhancement of paraffin wax thermal conductivity using soft drink can stripes. • Thermal analysis and simulations results agree well with experimental data. • Increase in accumulator thermal efficiencies through addition of external aluminum stripes. • Proposed accumulator allows up to 13,000 kJ of energy storage. - Abstract: Soft drink cans filled with paraffin wax mixed with 7.5% aluminum stripes, obtained from disposable cans, doubled the thermal conductivity of cans filled only with paraffin wax. Promising results obtained in a prototype heat exchanger encouraged the construction of this unit 6 times bigger. We experimentally evaluated and model a heat exchanger for solar energy accumulation, composed by 300 disposable soft drink cans filled with a total of 59.25 kg of paraffin wax mixed with 7.5% aluminum stripes. The effect of adding 2.75 kg of aluminum fins for enhancing heat transfer from the outer surface of the cans to the circulant air was experimentally analyzed. In sunny days, the wax melted completely in about 4 h. The accumulated energy in form of latent heat (about 13,000 kJ) allowed to increase the temperature of 0.040 kg/s of circulant air in at least 20 °C during a period of 2.5 h. For an air mass rate of 0.018 kg/s the period was extended practically to 5 h. The accumulator thermal analysis was presented and a subsequent numerical simulation with Matlab was performed to compare with the experimental results obtaining good agreement specially for higher air mass flow rates. The low cost accumulator presented is of simple construction and will allow extended use of solar energy for applications such as drying processes or household calefaction system.

  4. Evaluation, Calibration and Comparison of the Precipitation-Runoff Modeling System (PRMS) National Hydrologic Model (NHM) Using Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) Gridded Datasets

    Science.gov (United States)

    Norton, P. A., II; Haj, A. E., Jr.

    2014-12-01

    The United States Geological Survey is currently developing a National Hydrologic Model (NHM) to support and facilitate coordinated and consistent hydrologic modeling efforts at the scale of the continental United States. As part of this effort, the Geospatial Fabric (GF) for the NHM was created. The GF is a database that contains parameters derived from datasets that characterize the physical features of watersheds. The GF was used to aggregate catchments and flowlines defined in the National Hydrography Dataset Plus dataset for more than 100,000 hydrologic response units (HRUs), and to establish initial parameter values for input to the Precipitation-Runoff Modeling System (PRMS). Many parameter values are adjusted in PRMS using an automated calibration process. Using these adjusted parameter values, the PRMS model estimated variables such as evapotranspiration (ET), potential evapotranspiration (PET), snow-covered area (SCA), and snow water equivalent (SWE). In order to evaluate the effectiveness of parameter calibration, and model performance in general, several satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) gridded datasets including ET, PET, SCA, and SWE were compared to PRMS-simulated values. The MODIS and SNODAS data were spatially averaged for each HRU, and compared to PRMS-simulated ET, PET, SCA, and SWE values for each HRU in the Upper Missouri River watershed. Default initial GF parameter values and PRMS calibration ranges were evaluated. Evaluation results, and the use of MODIS and SNODAS datasets to update GF parameter values and PRMS calibration ranges, are presented and discussed.

  5. Deriving Snow-Cover Depletion Curves for Different Spatial Scales from Remote Sensing and Snow Telemetry Data

    Science.gov (United States)

    Fassnacht, Steven R.; Sexstone, Graham A.; Kashipazha, Amir H.; Lopez-Moreno, Juan Ignacio; Jasinski, Michael F.; Kampf, Stephanie K.; Von Thaden, Benjamin C.

    2015-01-01

    During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow-covered area (SCA) once snow-free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry stations were related to SCA derived from moderate-resolution imaging spectro radiometer images to produce snow-cover depletion curves. The snow depletion curves were created for an 80,000 sq km domain across southern Wyoming and northern Colorado encompassing 54 snow telemetry stations. Eight yearly snow depletion curves were compared, and it is shown that the slope of each is a function of the amount of snow received. Snow-cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A stations peak SWE was much more important than the main topographic variables that included location, elevation, slope, and modelled clear sky solar radiation. The threshold SWE mostly illustrated inter-annual consistency.

  6. Modelling accumulation of radionuclides in terrestrial ecosystems originating from a long-term groundwater contamination

    Energy Technology Data Exchange (ETDEWEB)

    Gaerdenaes, Annemieke I. [Dept. of Soil and Environment, Swedish University of Agricultural Sciences (SLU), P.O. Box 7001, 750 07 Uppsala (Sweden); Eckersten, Henrik [Dept. of Ecology and Crop Production, SLU, P.O. Box 7042, 750 07 Uppsala (Sweden); Reinlert, Andre [Dept. of Physical Geography and Ecosystems Analysis, Lund University, 223 62 Lund (Sweden); MMT, Sven Kaellfelts Gata 11 SE 426 71 Vaestra Froelunda (Sweden); Gustafsson, David; Jansson, Per-Erik [Dept. Land and Water Resources, KTH, SE 100 44, Stockholm (Sweden); Ekstroem, Per-Anders [Facilia AB, Gustavlundsvaegen 151A, 167 51 Bromma (Sweden); Greger, Maria [Dept. of Ecology, Environment and Plant Sciences, Stockholm University, 106 91 Stockholm (Sweden)

    2014-07-01

    This study was conducted as part of the risk assessment of final deposits of nuclear fuel waste. The overall objective is to assess the possible accumulation of radionuclides in terrestrial ecosystems after an eventual long-term groundwater contamination. The specific objectives are to assess: i) What proportion of the contamination will accumulate in the soil-plant-system? ii) Where in the soil-plant- system will it accumulate? iii) Which ecosystem characteristics and radionuclides properties are important for the accumulation? and iv) Under which circumstances do losses from the ecosystems occur? We developed the dynamic model Tracey (Gaerdenaes et al. 2009) describing cycling of radionuclides in terrestrial ecosystems with high temporal resolution (1 day). The model is a multi-compartmental model in which fluxes and storage of radionuclides are described for different plant parts and soil pools in each of the 10 soil layers. The radionuclide fluxes are driven either by water or carbon fluxes. The water and the carbon fluxes are simulated with the dynamic, bio-geophysical Coup Model (Jansson and Karlberg, 2004). Tracey includes two root uptake approaches of radionuclides; (i) passive uptake driven by root water uptake and (ii) active uptake driven by plant growth. A linear approach describes the adsorption of radionuclides to soil particles and organic matter. Tracey was applied on two ecosystems with contrasting hydrology, the mixed Pinus-Picea forests found in the dry, elevated areas and the Alnus forests found in the wet, low-land areas of Uppland in central east Sweden. Different varieties of the two forest types were created by varying the root depth and radiation use efficiency. The climate was cold-temperate and based on 30-year daily weather data from Uppsala. The assumed groundwater contamination was close to 1 mg of an unspecified radionuclide per m2 and year. This load corresponds to 1 Bq per m{sup 2} and year of {sup 238}U, a common long

  7. Modulation of snow reflectance and snowmelt from Central Asian glaciers by anthropogenic black carbon.

    Science.gov (United States)

    Schmale, Julia; Flanner, Mark; Kang, Shichang; Sprenger, Michael; Zhang, Qianggong; Guo, Junming; Li, Yang; Schwikowski, Margit; Farinotti, Daniel

    2017-01-12

    Deposited mineral dust and black carbon are known to reduce the albedo of snow and enhance melt. Here we estimate the contribution of anthropogenic black carbon (BC) to snowmelt in glacier accumulation zones of Central Asia based on in-situ measurements and modelling. Source apportionment suggests that more than 94% of the BC is emitted from mostly regional anthropogenic sources while the remaining contribution comes from natural biomass burning. Even though the annual deposition flux of mineral dust can be up to 20 times higher than that of BC, we find that anthropogenic BC causes the majority (60% on average) of snow darkening. This leads to summer snowmelt rate increases of up to 6.3% (7 cm a -1 ) on glaciers in three different mountain environments in Kyrgyzstan, based on albedo reduction and snowmelt models.

  8. Estimating the surface layer refractive index structure constant over snow and sea ice using Monin-Obukhov similarity theory with a mesoscale atmospheric model.

    Science.gov (United States)

    Qing, Chun; Wu, Xiaoqing; Huang, Honghua; Tian, Qiguo; Zhu, Wenyue; Rao, Ruizhong; Li, Xuebin

    2016-09-05

    Since systematic direct measurements of refractive index structure constant ( Cn2) for many climates and seasons are not available, an indirect approach is developed in which Cn2 is estimated from the mesoscale atmospheric model outputs. In previous work, we have presented an approach that a state-of-the-art mesoscale atmospheric model called Weather Research and Forecasting (WRF) model coupled with Monin-Obukhov Similarity (MOS) theory which can be used to estimate surface layer Cn2 over the ocean. Here this paper is focused on surface layer Cn2 over snow and sea ice, which is the extending of estimating surface layer Cn2 utilizing WRF model for ground-based optical application requirements. This powerful approach is validated against the corresponding 9-day Cn2 data from a field campaign of the 30th Chinese National Antarctic Research Expedition (CHINARE). We employ several statistical operators to assess how this approach performs. Besides, we present an independent analysis of this approach performance using the contingency tables. Such a method permits us to provide supplementary key information with respect to statistical operators. These methods make our analysis more robust and permit us to confirm the excellent performances of this approach. The reasonably good agreement in trend and magnitude is found between estimated values and measurements overall, and the estimated Cn2 values are even better than the ones obtained by this approach over the ocean surface layer. The encouraging performance of this approach has a concrete practical implementation of ground-based optical applications over snow and sea ice.

  9. Measurement of the Spectral Absorption of Liquid Water in Melting Snow With an Imaging Spectrometer

    Science.gov (United States)

    Green, Robert O.; Dozier, Jeff

    1995-01-01

    Melting of the snowpack is a critical parameter that drives aspects of the hydrology in regions of the Earth where snow accumulates seasonally. New techniques for measurement of snow melt over regional scales offer the potential to improve monitoring and modeling of snow-driven hydrological processes. In this paper we present the results of measuring the spectral absorption of liquid water in a melting snowpack with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data were acquired over Mammoth Mountain, in east central California on 21 May 1994 at 18:35 UTC. The air temperature at 2926 m on Mammoth Mountain at site A was measured at 15-minute intervals during the day preceding the AVIRIS data acquisition. At this elevation. the air temperature did not drop below freezing the night of the May 20 and had risen to 6 degrees Celsius by the time of the overflight on May 21. These temperature conditions support the presence of melting snow at the surface as the AVIRIS data were acquired.

  10. Nutrient accumulation models in the banana (Musa AAA Simmonds cv Williams plant under nitrogen doses

    Directory of Open Access Journals (Sweden)

    Jaime Torres Bazurto

    2017-07-01

    Full Text Available This research determined the effect of four nitrogen (N doses on the nutritional behavior of (N, potassium (K, phosphorus (P, calcium (Ca and magnesium (Mg, respectively, in banana Williams, during five plant development stages and two productive cycles. The treatments were as follows: 1 absolute control, 2 0 N, 3 161 kg N ha-1, 4 321.8 kg N ha-1 and 5 483 kg N ha-1, respectively. A multivariate approach of the differences among cycles was used to adjust the models and eliminate their individual effect, with a randomized complete block design with repeated measurements over time. There were significant differences among plant development stages, with an increase in nutrient accumulation in the banana plant, there were no differences among treatments or blocks, nor in the interaction block by treatment, but the dose of 321.8 kg of N, exhibited a fructification increase in terms of N accumulation, harvest was exceeded by the dose of 483 kg of nitrogen, Ca and Mg, were the other nutrients, which showed effect at the dose of 483 kg of N but increasing only to harvest. It was concluded that high doses of nitrogen showed a trend to increase nutrient accumulation during the development of the banana plant, but especially until fructification, with the exception of Ca and Mg, which achieved the greatest accumulation in harvest.

  11. Don Quixote Pond: A Small Scale Model of Weathering and Salt Accumulation

    Science.gov (United States)

    Englert, P.; Bishop, J. L.; Patel, S. N.; Gibson, E. K.; Koeberl, C.

    2015-01-01

    The formation of Don Quixote Pond in the North Fork of Wright Valley, Antarctica, is a model for unique terrestrial calcium, chlorine, and sulfate weathering, accumulation, and distribution processes. The formation of Don Quixote Pond by simple shallow and deep groundwater contrasts more complex models for Don Juan Pond in the South Fork of Wright Valley. Our study intends to understand the formation of Don Quixote Pond as unique terrestrial processes and as a model for Ca, C1, and S weathering and distribution on Mars.

  12. Modeled Sources, Transport, and Accumulation of Dissolved Solids in Water Resources of the Southwestern United States.

    Science.gov (United States)

    Anning, David W

    2011-10-01

    Information on important source areas for dissolved solids in streams of the southwestern United States, the relative share of deliveries of dissolved solids to streams from natural and human sources, and the potential for salt accumulation in soil or groundwater was developed using a SPAtially Referenced Regressions On Watershed attributes model. Predicted area-normalized reach-catchment delivery rates of dissolved solids to streams ranged from Salton Sea accounting unit.

  13. Pavement Snow Melting

    Energy Technology Data Exchange (ETDEWEB)

    Lund, John W.

    2005-01-01

    The design of pavement snow melting systems is presented based on criteria established by ASHRAE. The heating requirements depends on rate of snow fall, air temperature, relative humidity and wind velocity. Piping materials are either metal or plastic, however, due to corrosion problems, cross-linked polyethylene pipe is now generally used instead of iron. Geothermal energy is supplied to systems through the use of heat pipes, directly from circulating pipes, through a heat exchanger or by allowing water to flow directly over the pavement, by using solar thermal storage. Examples of systems in New Jersey, Wyoming, Virginia, Japan, Argentina, Switzerland and Oregon are presented. Key words: pavement snow melting, geothermal heating, heat pipes, solar storage, Wyoming, Virginia, Japan, Argentina, Klamath Falls.

  14. Role of brain iron accumulation in cognitive dysfunction: evidence from animal models and human studies.

    Science.gov (United States)

    Schröder, Nadja; Figueiredo, Luciana Silva; de Lima, Maria Noêmia Martins

    2013-01-01

    Over the last decades, studies from our laboratory and other groups using animal models have shown that iron overload, resulting in iron accumulation in the brain, produces significant cognitive deficits. Iron accumulation in the hippocampus and the basal ganglia has been related to impairments in spatial memory, aversive memory, and recognition memory in rodents. These results are corroborated by studies showing that the administration of iron chelators attenuates cognitive deficits in a variety of animal models of cognitive dysfunction, including aging and Alzheimer's disease models. Remarkably, recent human studies using magnetic resonance image techniques have also shown a consistent correlation between cognitive dysfunction and iron deposition, mostly in the hippocampus, cortical areas, and basal ganglia. These findings may have relevant implications in the light of the knowledge that iron accumulates in brain regions of patients suffering from neurodegenerative diseases. A better understanding of the functional consequences of iron dysregulation in aging and neurological diseases may help to identify novel targets for treating memory problems that afflict a growing aging population.

  15. Iron snow in the Martian core?

    Science.gov (United States)

    Davies, Christopher J.; Pommier, Anne

    2018-01-01

    The decline of Mars' global magnetic field some 3.8-4.1 billion years ago is thought to reflect the demise of the dynamo that operated in its liquid core. The dynamo was probably powered by planetary cooling and so its termination is intimately tied to the thermochemical evolution and present-day physical state of the Martian core. Bottom-up growth of a solid inner core, the crystallization regime for Earth's core, has been found to produce a long-lived dynamo leading to the suggestion that the Martian core remains entirely liquid to this day. Motivated by the experimentally-determined increase in the Fe-S liquidus temperature with decreasing pressure at Martian core conditions, we investigate whether Mars' core could crystallize from the top down. We focus on the "iron snow" regime, where newly-formed solid consists of pure Fe and is therefore heavier than the liquid. We derive global energy and entropy equations that describe the long-timescale thermal and magnetic history of the core from a general theory for two-phase, two-component liquid mixtures, assuming that the snow zone is in phase equilibrium and that all solid falls out of the layer and remelts at each timestep. Formation of snow zones occurs for a wide range of interior and thermal properties and depends critically on the initial sulfur concentration, ξ0. Release of gravitational energy and latent heat during growth of the snow zone do not generate sufficient entropy to restart the dynamo unless the snow zone occupies at least 400 km of the core. Snow zones can be 1.5-2 Gyrs old, though thermal stratification of the uppermost core, not included in our model, likely delays onset. Models that match the available magnetic and geodetic constraints have ξ0 ≈ 10% and snow zones that occupy approximately the top 100 km of the present-day Martian core.

  16. Polar boundary layer bromine explosion and ozone depletion events in the chemistry–climate model EMAC v2.52: implementation and evaluation of AirSnow algorithm

    Directory of Open Access Journals (Sweden)

    S. Falk

    2018-03-01

    Full Text Available Ozone depletion events (ODEs in the polar boundary layer have been observed frequently during springtime. They are related to events of boundary layer enhancement of bromine. Consequently, increased amounts of boundary layer volume mixing ratio (VMR and vertical column densities (VCDs of BrO have been observed by in situ observation, ground-based as well as airborne remote sensing, and from satellites. These so-called bromine explosion (BE events have been discussed serving as a source of tropospheric BrO at high latitudes, which has been underestimated in global models so far. We have implemented a treatment of bromine release and recycling on sea-ice- and snow-covered surfaces in the global chemistry–climate model EMAC (ECHAM/MESSy Atmospheric Chemistry based on the scheme of Toyota et al. (2011. In this scheme, dry deposition fluxes of HBr, HOBr, and BrNO3 over ice- and snow-covered surfaces are recycled into Br2 fluxes. In addition, dry deposition of O3, dependent on temperature and sunlight, triggers a Br2 release from surfaces associated with first-year sea ice. Many aspects of observed bromine enhancements and associated episodes of near-complete depletion of boundary layer ozone, both in the Arctic and in the Antarctic, are reproduced by this relatively simple approach. We present first results from our global model studies extending over a full annual cycle, including comparisons with Global Ozone Monitoring Experiment (GOME satellite BrO VCDs and surface ozone observations.

  17. Endolithic Microbial Life in Extreme Cold Climate: Snow Is Required, but Perhaps Less Is More

    Directory of Open Access Journals (Sweden)

    Henry J. Sun

    2013-04-01

    Full Text Available Cyanobacteria and lichens living under sandstone surfaces in the McMurdo Dry Valleys require snow for moisture. Snow accumulated beyond a thin layer, however, is counterproductive, interfering with rock insolation, snow melting, and photosynthetic access to light. With this in mind, the facts that rock slope and direction control colonization, and that climate change results in regional extinctions, can be explained. Vertical cliffs, which lack snow cover and are perpetually dry, are devoid of organisms. Boulder tops and edges can trap snow, but gravity and wind prevent excessive buildup. There, the organisms flourish. In places where snow-thinning cannot occur and snow drifts collect, rocks may contain living or dead communities. In light of these observations, the possibility of finding extraterrestrial endolithic communities on Mars cannot be eliminated.

  18. Predictive models for deposit accumulation and corrosion on secondary side of steam generators

    International Nuclear Information System (INIS)

    Choi, Samuel; Moroney, Velvet; Marks, Chuck; Kreider, Marc

    2012-09-01

    Experience demonstrates that deposit accumulation in steam generators (SGs) can lead to corrosion of tubes. To minimize the probability of this corrosion, utilities employ a variety of deposit control strategies. However, these processes can involve significant costs and potentially affect outage critical paths. Since there has been no model that quantifies tube degradation as a function of deposit accumulation, utilities have had to make decisions regarding deposit control strategies without a reliable quantitative basis. The objective of this study is to develop methods that utilities can use to quantify benefits of SG deposit control strategies with regard to rates of secondary-side tube corrosion. Two different methodologies are employed in this work. The first methodology is empirical and is involved an attempt to correlate degradation rates with deposit accumulation as indicated by sludge pile height. Because there has been relatively little tube degradation in currently operating steam generators, this correlation is developed using data for Alloy 600MA SG tubes. To increase the number of units that could be used for defect/deposit correlations, a method to relate the sludge pile deposit mass and the number of tubes with non-zero sludge height is developed. The second methodology is theoretical and is based on the use of calculated differences in temperature and chemistry to predict the effect of deposits on corrosion rates. Computational fluid dynamics (CFD) models are developed to simulate thermal-hydraulic conditions representative of conditions that are present within porous deposits formed at the top of tube sheet. This paper will discuss the development and application of the predictive models for deposit accumulation and corrosion on the secondary side of steam generators. (authors)

  19. Dose-rate and temperature dependent statistical damage accumulation model for ion implantation into silicon

    Energy Technology Data Exchange (ETDEWEB)

    Hernandez-Mangas, J.M. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain)]. E-mail: jesus.hernandez.mangas@tel.uva.es; Arias, J. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain); Marques, L.A. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain); Ruiz-Bueno, A. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain); Bailon, L. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain)

    2005-01-01

    Currently there are extensive atomistic studies that model some characteristics of the damage buildup due to ion irradiation (e.g. L. Pelaz et al., Appl. Phys. Lett. 82 (2003) 2038-2040). Our interest is to develop a novel statistical damage buildup model for our BCA ion implant simulator (IIS) code in order to extend its ranges of applicability. The model takes into account the abrupt regime of the crystal-amorphous transition. It works with different temperatures and dose-rates and also models the transition temperature. We have tested it with some projectiles (Ge, P) implanted into silicon. In this work we describe the new statistical damage accumulation model based on the modified Kinchin-Pease model. The results obtained have been compared with existing experimental results.

  20. Dose-rate and temperature dependent statistical damage accumulation model for ion implantation into silicon

    International Nuclear Information System (INIS)

    Hernandez-Mangas, J.M.; Arias, J.; Marques, L.A.; Ruiz-Bueno, A.; Bailon, L.

    2005-01-01

    Currently there are extensive atomistic studies that model some characteristics of the damage buildup due to ion irradiation (e.g. L. Pelaz et al., Appl. Phys. Lett. 82 (2003) 2038-2040). Our interest is to develop a novel statistical damage buildup model for our BCA ion implant simulator (IIS) code in order to extend its ranges of applicability. The model takes into account the abrupt regime of the crystal-amorphous transition. It works with different temperatures and dose-rates and also models the transition temperature. We have tested it with some projectiles (Ge, P) implanted into silicon. In this work we describe the new statistical damage accumulation model based on the modified Kinchin-Pease model. The results obtained have been compared with existing experimental results

  1. A model of accumulation of radionuclides in biosphere originating from groundwater contamination

    Energy Technology Data Exchange (ETDEWEB)

    Gaerdenaes, Annemieke [Swedish Univ. of Agricultural Sciences, Uppsala (Sweden). Dept. of Soil Sciences; Jansson, Per-Erik; Karlberg, Louise [Royal Inst. of Technology, Stockholm (Sweden). Dept. Land and Water Resources

    2006-03-15

    The objective of this study is to introduce a module in CoupModel describing the transport and accumulation in the biosphere of a radionuclide originating from a ground water contamination. Two model approaches describing the plant uptake of a radionuclide were included, namely passive and active uptake. Passive uptake means in this study that the root uptake rate of a radionuclide is governed by water uptake. Normal mechanism for the passive water uptake is the convective flux of water from the soil to the plant. An example of element taken up passively is Ca. Active plant uptake is in this model defined as the root uptake rate of a radionuclide that is governed by carbon assimilation i.e. photosynthesis and plant growth. The actively taken up element can for example be an element essential to plant, but not available in high enough concentration by passive uptake alone, like the major nutrients N and P or an element that very well resembles a plant nutrient, like Cs resembles K. Active uptake of trace element may occur alone or in addition to passive uptake. Normal mechanism for the active uptake is molecular diffusion from the soil solution to the roots or via any other organism living in symbiosis with the roots like the mycorrhiza. Also a model approach describing adsorption was introduced. CoupModel dynamically couples and simulates the flows of water, heat, carbon and nitrogen in the soil/plant/atmosphere system. Any number of plants may be defined and are divided into roots, leaves, stem and grain. The soil is considered in one vertical profile that may be represented into a maximum of 100 layers. The model is the windows-successor and integrated version of the DOS-models SOIL and SOILN, which have been widely used on different ecosystems and climate regions during 25 years time period. To this soil/plant/atmosphere model were introduced a module describing accumulation of a radionuclide in the biosphere originating from groundwater contamination. The

  2. A model of accumulation of radionuclides in biosphere originating from groundwater contamination

    International Nuclear Information System (INIS)

    Gaerdenaes, Annemieke; Jansson, Per-Erik; Karlberg, Louise

    2006-03-01

    The objective of this study is to introduce a module in CoupModel describing the transport and accumulation in the biosphere of a radionuclide originating from a ground water contamination. Two model approaches describing the plant uptake of a radionuclide were included, namely passive and active uptake. Passive uptake means in this study that the root uptake rate of a radionuclide is governed by water uptake. Normal mechanism for the passive water uptake is the convective flux of water from the soil to the plant. An example of element taken up passively is Ca. Active plant uptake is in this model defined as the root uptake rate of a radionuclide that is governed by carbon assimilation i.e. photosynthesis and plant growth. The actively taken up element can for example be an element essential to plant, but not available in high enough concentration by passive uptake alone, like the major nutrients N and P or an element that very well resembles a plant nutrient, like Cs resembles K. Active uptake of trace element may occur alone or in addition to passive uptake. Normal mechanism for the active uptake is molecular diffusion from the soil solution to the roots or via any other organism living in symbiosis with the roots like the mycorrhiza. Also a model approach describing adsorption was introduced. CoupModel dynamically couples and simulates the flows of water, heat, carbon and nitrogen in the soil/plant/atmosphere system. Any number of plants may be defined and are divided into roots, leaves, stem and grain. The soil is considered in one vertical profile that may be represented into a maximum of 100 layers. The model is the windows-successor and integrated version of the DOS-models SOIL and SOILN, which have been widely used on different ecosystems and climate regions during 25 years time period. To this soil/plant/atmosphere model were introduced a module describing accumulation of a radionuclide in the biosphere originating from groundwater contamination. The

  3. Modelling nitrogen saturation and carbon accumulation in heathland soils under elevated nitrogen deposition

    International Nuclear Information System (INIS)

    Evans, C.D.; Caporn, S.J.M.; Carroll, J.A.; Pilkington, M.G.; Wilson, D.B.; Ray, N.; Cresswell, N.

    2006-01-01

    A simple model of nitrogen (N) saturation, based on an extension of the biogeochemical model MAGIC, has been tested at two long-running heathland N manipulation experiments. The model simulates N immobilisation as a function of organic soil C/N ratio, but permits a proportion of immobilised N to be accompanied by accumulation of soil carbon (C), slowing the rate of C/N ratio change and subsequent N saturation. The model successfully reproduced observed treatment effects on soil C and N, and inorganic N leaching, for both sites. At the C-rich upland site, N addition led to relatively small reductions in soil C/N, low inorganic N leaching, and a substantial increase in organic soil C. At the C-poor lowland site, soil C/N ratio decreases and N leaching increases were much more dramatic, and soil C accumulation predicted to be smaller. The study suggests that (i) a simple model can effectively simulate observed changes in soil and leachate N; (ii) previous model predictions based on a constant soil C pool may overpredict future N leaching; (iii) N saturation may develop most rapidly in dry, organic-poor, high-decomposition systems; and (iv) N deposition may lead to significantly enhanced soil C sequestration, particularly in wet, nutrient-poor, organic-rich systems. - Enhanced carbon sequestration may slow the rate of nitrogen saturation in heathlands

  4. Snow deposition and melt under different vegetative covers in central New York

    Science.gov (United States)

    A. R. Eschner; D. R. Satterlund

    1963-01-01

    Two-thirds of the annual runoff from watersheds in the Allegheny Plateau of central New York comes from the snow-or snow and rain that falls in December through April. Although the amounts of precipitation in this period are fairly uniform from year to year, the proportion that falls as snow varies; so does the amount that accumulates on the ground, and its duration...

  5. Evaluation and Application of Gridded Snow Water Equivalent Products for Improving Snowmelt Flood Predictions in the Red River Basin of the North

    Science.gov (United States)

    Schroeder, R.; Jacobs, J. M.; Vuyovich, C.; Cho, E.; Tuttle, S. E.

    2017-12-01

    Each spring the Red River basin (RRB) of the North, located between the states of Minnesota and North Dakota and southern Manitoba, is vulnerable to dangerous spring snowmelt floods. Flat terrain, low permeability soils and a lack of satisfactory ground observations of snow pack conditions make accurate predictions of the onset and magnitude of major spring flood events in the RRB very challenging. This study investigated the potential benefit of using gridded snow water equivalent (SWE) products from passive microwave satellite missions and model output simulations to improve snowmelt flood predictions in the RRB using NOAA's operational Community Hydrologic Prediction System (CHPS). Level-3 satellite SWE products from AMSR-E, AMSR2 and SSM/I, as well as SWE computed from Level-2 brightness temperatures (Tb) measurements, including model output simulations of SWE from SNODAS and GlobSnow-2 were chosen to support the snowmelt modeling exercises. SWE observations were aggregated spatially (i.e. to the NOAA North Central River Forecast Center forecast basins) and temporally (i.e. by obtaining daily screened and weekly unscreened maximum SWE composites) to assess the value of daily satellite SWE observations relative to weekly maximums. Data screening methods removed the impacts of snow melt and cloud contamination on SWE and consisted of diurnal SWE differences and a temperature-insensitive polarization difference ratio, respectively. We examined the ability of the satellite and model output simulations to capture peak SWE and investigated temporal accuracies of screened and unscreened satellite and model output SWE. The resulting SWE observations were employed to update the SNOW-17 snow accumulation and ablation model of CHPS to assess the benefit of using temporally and spatially consistent SWE observations for snow melt predictions in two test basins in the RRB.

  6. A model of nonlinear strain and damage accumulation in polymer composites

    Directory of Open Access Journals (Sweden)

    A. N. Ruslantsev

    2014-01-01

    Full Text Available This paper presents a model to predict a nonlinear strain of the carbon laminate; the model is based on the relations between the theory of laminated plates and the non-linear approximation of deformation curve of unidirectional layer at the shear in the layer plane. The explicit expressions of stiffness and compliance matrices were obtained via multiplying the matrices that correspond to the elastic characteristics by the matrices, considering the non-linear properties of the laminate. The paper suggests an approximation option for the non-linear properties of the layer at the shear using an exponential function. Some considerations on damage accumulation in carbon laminates were made.

  7. Snow, ice and solar radiation

    NARCIS (Netherlands)

    Kuipers Munneke, P.

    2009-01-01

    The snow-covered ice sheets of Antarctica and Greenland reflect most of the incoming solar radiation. The reflectivity, commonly called the albedo, of snow on these ice sheets has been observed to vary in space and time. In this thesis, temporal and spatial changes in snow albedo is found to depend

  8. Evaluation of the most suitable threshold value for modelling snow glacier melt through T- index approach: the case study of Forni Glacier (Italian Alps)

    Science.gov (United States)

    Senese, Antonella; Maugeri, Maurizio; Vuillermoz, Elisa; Smiraglia, Claudio; Diolaiuti, Guglielmina

    2014-05-01

    Glacier melt occurs whenever the surface temperature is null (273.15 K) and the net energy budget is positive. These conditions can be assessed by analyzing meteorological and energy data acquired by a supraglacial Automatic Weather Station (AWS). In the case this latter is not present at the glacier surface the assessment of actual melting conditions and the evaluation of melt amount is difficult and degree-day (also named T-index) models are applied. These approaches require the choice of a correct temperature threshold. In fact, melt does not necessarily occur at daily air temperatures higher than 273.15 K, since it is determined by the energy budget which in turn is only indirectly affected by air temperature. This is the case of the late spring period when ablation processes start at the glacier surface thus progressively reducing snow thickness. In this study, to detect the most indicative air temperature threshold witnessing melt conditions in the April-June period, we analyzed air temperature data recorded from 2006 to 2012 by a supraglacial AWS (at 2631 m a.s.l.) on the ablation tongue of the Forni Glacier (Italy), and by a weather station located nearby the studied glacier (at Bormio, 1225 m a.s.l.). Moreover we evaluated the glacier energy budget (which gives the actual melt, Senese et al., 2012) and the snow water equivalent values during this time-frame. Then the ablation amount was estimated both from the surface energy balance (MEB from supraglacial AWS data) and from degree-day method (MT-INDEX, in this latter case applying the mean tropospheric lapse rate to temperature data acquired at Bormio changing the air temperature threshold) and the results were compared. We found that the mean tropospheric lapse rate permits a good and reliable reconstruction of daily glacier air temperature conditions and the major uncertainty in the computation of snow melt from degree-day models is driven by the choice of an appropriate air temperature threshold. Then

  9. Data set: A modeling dataset that spans the rain - snow transition zone: Johnston Draw catchment, Reynolds Creek Experimental Watershed, Idaho, USA

    Science.gov (United States)

    Hydrometeorological data from the rain-to-snow transition zone in mountain basins are limited. As the climate warms, the transition from rain to snow in mountain regions is moving to higher elevations, and these changes are altering the timing of water delivery to the downstream streams, lakes and w...

  10. Validation of MODIS snow cover images over Austria

    Directory of Open Access Journals (Sweden