WorldWideScience

Sample records for modeling snow accumulation

  1. 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.

  2. Snow accumulation/melting model (SAMM for integrated use in regional scale landslide early warning systems

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    G. Martelloni

    2013-03-01

    Full Text Available We propose a simple snow accumulation/melting model (SAMM to be applied at regional scale in conjunction with landslide warning systems based on empirical rainfall thresholds. SAMM is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a conservation of mass equation is solved to model snowpack thickness and an empirical equation for the snow density. The model depends on 13 empirical parameters, whose optimal values were defined with an optimisation 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 about the additional benefit of a relatively easy implementation. After performing a cross validation and a comparison with two simpler temperature index models, we simulated an operational employment in a regional scale landslide early warning system (EWS and we found that the EWS forecasting effectiveness was substantially improved when used in conjunction with SAMM.

  3. Modelization of the Current and Future Habitat Suitability of Rhododendron ferrugineum Using Potential Snow Accumulation.

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    Benjamin Komac

    Full Text Available Mountain areas are particularly sensitive to climate change. Species distribution models predict important extinctions in these areas whose magnitude will depend on a number of different factors. Here we examine the possible impact of climate change on the Rhododendron ferrugineum (alpenrose niche in Andorra (Pyrenees. This species currently occupies 14.6 km2 of this country and relies on the protection afforded by snow cover in winter. We used high-resolution climatic data, potential snow accumulation and a combined forecasting method to obtain the realized niche model of this species. Subsequently, we used data from the high-resolution Scampei project climate change projection for the A2, A1B and B1 scenarios to model its future realized niche model. The modelization performed well when predicting the species's distribution, which improved when we considered the potential snow accumulation, the most important variable influencing its distribution. We thus obtained a potential extent of about 70.7 km(2 or 15.1% of the country. We observed an elevation lag distribution between the current and potential distribution of the species, probably due to its slow colonization rate and the small-scale survey of seedlings. Under the three climatic scenarios, the realized niche model of the species will be reduced by 37.9-70.1 km(2 by the end of the century and it will become confined to what are today screes and rocky hillside habitats. The particular effects of climate change on seedling establishment, as well as on the species' plasticity and sensitivity in the event of a reduction of the snow cover, could worsen these predictions.

  4. Modelization of the Current and Future Habitat Suitability of Rhododendron ferrugineum Using Potential Snow Accumulation

    Science.gov (United States)

    Komac, Benjamin; Esteban, Pere; Trapero, Laura; Caritg, Roger

    2016-01-01

    Mountain areas are particularly sensitive to climate change. Species distribution models predict important extinctions in these areas whose magnitude will depend on a number of different factors. Here we examine the possible impact of climate change on the Rhododendron ferrugineum (alpenrose) niche in Andorra (Pyrenees). This species currently occupies 14.6 km2 of this country and relies on the protection afforded by snow cover in winter. We used high-resolution climatic data, potential snow accumulation and a combined forecasting method to obtain the realized niche model of this species. Subsequently, we used data from the high-resolution Scampei project climate change projection for the A2, A1B and B1 scenarios to model its future realized niche model. The modelization performed well when predicting the species’s distribution, which improved when we considered the potential snow accumulation, the most important variable influencing its distribution. We thus obtained a potential extent of about 70.7 km2 or 15.1% of the country. We observed an elevation lag distribution between the current and potential distribution of the species, probably due to its slow colonization rate and the small-scale survey of seedlings. Under the three climatic scenarios, the realized niche model of the species will be reduced by 37.9–70.1 km2 by the end of the century and it will become confined to what are today screes and rocky hillside habitats. The particular effects of climate change on seedling establishment, as well as on the species’ plasticity and sensitivity in the event of a reduction of the snow cover, could worsen these predictions. PMID:26824847

  5. Assessment and application of a snowblow modelling approach for identifying enhanced snow accumulation in areas of former glaciation

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    Mills, Stephanie; Smith, Michael; Le Brocq, Anne; Ardakova, Ekaterina; Hillier, John; Boston, Clare

    2016-04-01

    The redistribution of snow by wind can play an important role in providing additional mass to the surface of glaciers and can, therefore, have an impact on the glacier's surface mass balance. In areas of marginal glaciation, this local topo-climatic effect may be prove crucial for the initiation and survival of glaciers, whilst it can also increase heterogeneity in the distribution of snow on ice caps and ice sheets. We present a newly developed snowblow model which calculates spatial variations in relative snow accumulation that result from variations in topography. We apply this model to areas of former marginal glaciation in the Brecon Beacons, Wales and an area of former plateau icefield glaciation in the Monadhliath, Scotland. We can then determine whether redistribution by snow can help explain variations in the estimated equilibrium line altitudes (ELAs) of these former glaciers. Specifically, we compare the areas where snow is modelled as accumulating, to the reconstructed glacier surface, which is based on mapped moraines believed to be of Younger Dryas age. The model is applied to 30 m resolution DEMs and potential snow accumulation is simulated from different wind directions in order to determine the most likely contributing sector. Total snow accumulation in sub-set areas is then calculated and compared to the reconstructed glacier area. The results suggest that areas with larger amounts of snow accumulation often correspond with those where the ELA is lower than surrounding glaciers and vice versa, in both the marginal and icefield setting, suggesting that the role of snowblow in supplying additional mass to the surface of glaciers is significant.

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

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    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.

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

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    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

  8. Coupling stable isotope and satellite to inform a snow accumulation and melt model for data poor, semi-arid watersheds

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    Hublart, Paul; Sproles, Eric; Soulsby, Chris; Tetzlaff, Doerthe; Hevía, Andres

    2016-04-01

    At the most basic level watersheds catch, store, and release water. In semi-arid northern central Chile (29°-32°) snow and glacier melt dominate these basic hydrological stages. In this region precipitation is typically limited to three to five events per year that falls as snow in the High Cordillera at elevations above 3000 m a.s.l. The rugged topography and steep gradient makes snowfall rates highly variable in space and time. Despite its critical importance for water supply, high elevation meteorological data and measurements of snowpack are scarce due to limited winter access above 3000 m a.s.l. Due to the critically limited understanding of catch, store, and release processes most conceptual watershed models for this region remain speculative, are prone to over-parameterization, and greatly inhibits hydrological prediction in the region. Focused on two headwater watersheds of the Elqui River basin (1615-6040 m a.s.l., 429-566 km2) this study couples stable isotope and Moderate Resolution Imaging Spectrometer (MODIS) data to develop an improved conceptual model of how semi-arid mountain watersheds catch, store, and release water. MODIS snow-cover and land surface temperature data are used to inform an enhanced temperature-index Snow Accumulation and Melt (SAM) model. The use of remotely-sensed temperature data as input to this model is evaluated by comparison with an interpolated dataset derived from a few available meteorological stations. The outputs from the SAM model are used as inputs to a conceptual catchment model including two water stores (one standing for surface/subsurface processes and the other for deeper groundwater storage). The model is calibrated and evaluated from a Bayesian perspective using discharge data measured at the catchment outlets over a 15-year period (2000-2015). Stable isotope data collected during 2015-2016 is applied to better constrain model outputs. The combination of MODIS-based and isotope-based information proves very

  9. Modelling of snow exceedances

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    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.

  10. Quantification of uncertainties in snow accumulation, snowmelt, and snow disappearance dates

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    Raleigh, Mark S.

    Seasonal mountain snowpack holds hydrologic and ecologic significance worldwide. However, observation networks in complex terrain are typically sparse and provide minimal information about prevailing conditions. Snow patterns and processes in this data sparse environment can be characterized with numerical models and satellite-based remote sensing, and thus it is essential to understand their reliability. This research quantifies model and remote sensing uncertainties in snow accumulation, snowmelt, and snow disappearance as revealed through comparisons with unique ground-based measurements. The relationship between snow accumulation uncertainty and model configuration is assessed through a controlled experiment at 154 snow pillow sites in the western United States. To simulate snow water equivalent (SWE), the National Weather Service SNOW-17 model is tested as (1) a traditional "forward" model based primarily on precipitation, (2) a reconstruction model based on total snowmelt before the snow disappearance date, and (3) a combination of (1) and (2). For peak SWE estimation, the reliability of the parent models was indistinguishable, while the combined model was most reliable. A sensitivity analysis demonstrated that the parent models had opposite sensitivities to temperature that tended to cancel in the combined model. Uncertainty in model forcing and parameters significantly controlled model accuracy. Uncertainty in remotely sensed snow cover and snow disappearance in forested areas is enhanced by canopy obstruction but has been ill-quantified due to the lack of sub-canopy observations. To better quantify this uncertainty, dense networks of near-surface temperature sensors were installed at four study areas (≤ 1 km2) with varying forest cover in the Sierra Nevada, California. Snow presence at each sensor was detected during periods when temperature was damped, which resulted from snow cover insulation. This methodology was verified using time-lapse analysis and

  11. Simulation of snow accumulation and melt in needleleaf forest environments

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    C. R. Ellis

    2010-02-01

    Full Text Available Drawing upon numerous field studies and modelling exercises of snow processes, the Cold Regions Hydrological Model (CRHM was developed to simulate the four season hydrological cycle in cold regions. CRHM includes modules describing radiative, turbulent and conductive energy exchanges to snow in forest and open environments, as well as provide account for losses from canopy snow sublimation and rain evaporation. Due to the physical-basis and rigorous testing of each module, there is a minimal need for model calibration. To evaluate CRHM, simulations of snow accumulation and melt were compared to observations collected at paired forest and clearing sites of varying latitude, elevation, forest cover density, and climate. Overall, results show that CRHM is capable of characterising the variation of snow accumulation between forest and open sites, achieving a model efficiency of 0.57, with the lowest efficiencies at the forest sites. Simulations of canopy sublimation losses slightly overestimated observed losses from a weighed cut tree, giving a model efficiency of 0.41 for daily losses. Good model performance was demonstrated in simulating energy fluxes to snow at the clearings, but performance was degraded from this under forest canopies due to errors in simulating daily net longwave radiation. However, expressed as cumulative energy to snow over the winter, simulated values were 96% and 98% of that observed at forest and clearing sites, respectively. Overall, good model prediction of the substantial variations in mass and energy between forest and clearing sites suggests that CRHM may be useful as an analytical or predictive tool for snow processes in needleleaf forests.

  12. Simulation of snow accumulation and melt in needleleaf forest environments

    Directory of Open Access Journals (Sweden)

    C. R. Ellis

    2010-06-01

    Full Text Available Drawing upon numerous field studies and modelling exercises of snow processes, the Cold Regions Hydrological Model (CRHM was developed to simulate the four season hydrological cycle in cold regions. CRHM includes modules describing radiative, turbulent and conductive energy exchanges to snow in open and forest environments, as well as account for losses from canopy snow sublimation and rain evaporation. Due to the physical-basis and rigorous testing of each module, there is a minimal need for model calibration. To evaluate CRHM, simulations of snow accumulation and melt were compared to observations collected at paired forest and clearing sites of varying latitude, elevation, forest cover density, and climate. Overall, results show that CRHM is capable of characterising the variation in snow accumulation between forest and clearing sites, achieving a model efficiency of 0.51 for simulations at individual sites. Simulations of canopy sublimation losses slightly overestimated observed losses from a weighed cut tree, having a model efficiency of 0.41 for daily losses. Good model performance was demonstrated in simulating energy fluxes to snow at the clearings, but results were degraded from this under forest cover due to errors in simulating sub-canopy net longwave radiation. However, expressed as cumulative energy to snow over the winter, simulated values were 96% and 98% of that observed at the forest and clearing sites, respectively. Overall, the good representation of the substantial variations in mass and energy between forest and clearing sites suggests that CRHM may be useful as an analytical or predictive tool for snow processes in needleleaf forest environments.

  13. A conceptual, distributed snow redistribution model

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    Frey, S.; Holzmann, H.

    2015-11-01

    When applying conceptual hydrological models using a temperature index approach for snowmelt to high alpine areas often accumulation of snow during several years can be observed. Some of the reasons why these "snow towers" do not exist in nature are vertical and lateral transport processes. While snow transport models have been developed using grid cell sizes of tens to hundreds of square metres and have been applied in several catchments, no model exists using coarser cell sizes of 1 km2, which is a common resolution for meso- and large-scale hydrologic modelling (hundreds to thousands of square kilometres). In this paper we present an approach that uses only gravity and snow density as a proxy for the age of the snow cover and land-use information to redistribute snow in alpine basins. The results are based on the hydrological modelling of the Austrian Inn Basin in Tyrol, Austria, more specifically the Ötztaler Ache catchment, but the findings hold for other tributaries of the river Inn. This transport model is implemented in the distributed rainfall-runoff model COSERO (Continuous Semi-distributed Runoff). The results of both model concepts with and without consideration of lateral snow redistribution are compared against observed discharge and snow-covered areas derived from MODIS satellite images. By means of the snow redistribution concept, snow accumulation over several years can be prevented and the snow depletion curve compared with MODIS (Moderate Resolution Imaging Spectroradiometer) data could be improved, too. In a 7-year period the standard model would lead to snow accumulation of approximately 2900 mm SWE (snow water equivalent) in high elevated regions whereas the updated version of the model does not show accumulation and does also predict discharge with more accuracy leading to a Kling-Gupta efficiency of 0.93 instead of 0.9. A further improvement can be shown in the comparison of MODIS snow cover data and the calculated depletion curve, where

  14. Accumulation of hydroxycinnamic acid amides in winter wheat under snow.

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    Jin, Shigeki; Yoshida, Midori; Nakajima, Takashi; Murai, Akio

    2003-06-01

    It was found that the content of antifungal compounds p-coumaroylagmatine [1-(trans-4'-hydroxycinnamoylamino)-4-guanidinobutane] and p-coumaroyl-3-hydroxyagmatine [1-(trans-4'-hydroxycinnamoylamino)-3-hydroxy-4-guanidinobutane] in the crown of winter wheat (Triticum aestivum L. cv Chihokukomugi) significantly increased under snow cover. This finding suggests that the accumulation of these hydroxycinnamic acid amides was caused by winter stress and related to protecting the plant against snow mold under snow cover.

  15. A stratification model of surface snow at Dome Fuji Station, Antarctica

    OpenAIRE

    2002-01-01

    A stratification model of surface snow on the ice sheet, which includes snow density evolution, is proposed. Using the temperature profile in the surface snow layer obtained at Dome Fuji Station, Antarctica, snow density evolution under various accumulation conditions was simulated. It is demonstrated that water vapor diffusion is very important for the snow density evolution, and temperature and accumulation at the snow surface are the most important factors that determine the future snow de...

  16. A stratification model of surface snow at Dome Fuji Station, Antarctica

    OpenAIRE

    2002-01-01

    A stratification model of surface snow on the ice sheet, which includes snow density evolution, is proposed. Using the temperature profile in the surface snow layer obtained at Dome Fuji Station, Antarctica, snow density evolution under various accumulation conditions was simulated.It is demonstrated that water vapor diffusion is very important for the snow density evolution, and temperature and accumulation at the snow surface are the most important factors that determine the future snow den...

  17. Development of a methodology to evaluate probable maximum snow accumulation using a regional climate model: application to Quebec, Canada, under changing climate conditions

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    Klein, I. M.; Rousseau, A. N.; Gagnon, P.; Frigon, A.

    2012-12-01

    Probable Maximum Snow Accumulation (PMSA) is one of the key variables used to estimate the spring probable maximum flood. A robust methodology for evaluating the PMSA is imperative so the resulting spring probable maximum flood is neither overestimated, which would mean financial losses, nor underestimated, which could affect public safety. In addition, the impact of climate change needs to be considered since it is known that solid precipitation in some Nordic landscapes will in all likelihood intensify over the next century. In this paper, outputs from different simulations produced by the Canadian Regional Climate Model are used to estimate PMSAs for southern Quebec, Canada (44.1°N - 49.1°N; 68.2°W - 75.5°W). Moisture maximization represents the core concept of the proposed methodology; precipitable water being the key variable. Results of stationary tests indicate that climate change will not only affect precipitation and temperature but also the monthly maximum precipitable water and the ensuing maximization ratio r. The maximization ratio r is used to maximize "efficient" snowfall events; and represents the ratio of the 100-year precipitable water of a given month divided by the snowstorm precipitable water. A computational method was developed to maximize precipitable water using a non-stationary frequency analysis. The method was carefully adapted to the spatial and temporal constraints embedded in the resolution of the available simulation data. For example, for a given grid cell and time step, snow and rain may occur simultaneously. In this case, the focus is restricted to snow and snow-storm-conditions only, thus rainfall and humidity that could lead to rainfall are neglected. Also, the temporal resolution cannot necessarily capture the full duration of actual snow storms. The threshold for a snowstorm to be maximized and the duration resulting from considered time steps are adjusted in order to obtain a high percentage of maximization ratios below

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

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    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

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    Koenig, Lora S.; Ivanoff, Alvaro; Alexander, Patrick M.; MacGregor, Joseph A.; Fettweis, Xavier; Panzer, Ben; Paden, John D.; Forster, Richard R.; Das, Indrani; McConnell, Joesph R.; Tedesco, Marco; Leuschen, Carl; Gogineni, Prasad

    2016-08-01

    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 GHz) airborne snow radar data collected as part of NASA's Operation IceBridge between 2009 and 2012. We use a semiautomated 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 %. A comparison of the radar-derived accumulation rates and contemporaneous ice cores shows that snow radar captures both the annual and long-term mean accumulation rate accurately. A comparison with outputs from a regional climate model (MAR) 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. Constraining snow model choices in a transitional snow environment with intensive observations

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    Wayand, N. E.; Massmann, A.; Clark, M. P.; Lundquist, J. D.

    2014-12-01

    The performance of existing energy balance snow models exhibits a large spread in the simulated snow water equivalent, snow depth, albedo, and surface temperature. Indentifying poor model representations of physical processes within intercomparison studies is difficult due to multiple differences between models as well as non-orthogonal metrics used. Efforts to overcome these obstacles for model development have focused on a modeling framework that allows multiple representations of each physical process within one structure. However, there still exists a need for snow study sites within complex terrain that observe enough model states and fluxes to constrain model choices. In this study we focus on an intensive snow observational site located in the maritime-transitional snow climate of Snoqualmie Pass WA (Figure 1). The transitional zone has been previously identified as a difficult climate to simulate snow processes; therefore, it represents an ideal model-vetting site. From two water years of intensive observational data, we have learned that a more honest comparison with observations requires that the modeled states or fluxes be as similar to the spatial and temporal domain of the instrument, even if it means changing the model to match what is being observed. For example, 24-hour snow board observations do not capture compaction of the underlying snow; therefore, a modeled "snow board" was created that only includes new snow accumulation and new snow compaction. We extend this method of selective model validation to all available Snoqualmie observations to constrain model choices within the Structure for Understanding Multiple Modeling Alternatives (SUMMA) framework. Our end goal is to provide a more rigorous and systematic method for diagnosing problems within snow models at a site given numerous snow observations.

  1. Tracking Forest and Open Area Effects on Snow Accumulation by Unmanned Aerial Vehicle Photogrammetry

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    Lendzioch, T.; Langhammer, J.; Jenicek, M.

    2016-06-01

    Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents

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

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    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.

  3. Creation of an Empirical Energy-Balance Based Snow Module Simulating Both Snowmelt and Snow Accumulation for Mountain Hydrology

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    Riboust, P.; Le Moine, N.; Thirel, G.; Ribstein, P.

    2015-12-01

    In Nordic and mountainous regions, hydrological processes are more complex than for regular rainfall-driven watersheds. Snow accumulates in winter, acting as a reservoir, and melts during late spring and summer. In order to take into account these additional natural processes present in mountainous watersheds, snow modules have been created in order to help rainfall-runoff models to simulate river discharge. Many empirical degree-day snow models have been designed to simulate snowmelt and river discharge when coupled to a rainfall runoff model, but few of them simulate correctly the amount of snow water equivalent (SWE) at point scale. Simulating correctly not only the amount of snowmelt but also the water content of the snowpack has several potential advantages: it allows improving the model reliability and performance for short-term and long-term prediction, spatial regionalization, and it makes it possible to perform data assimilation using observed snow measurements. The objective of our study is to create a new simple empirical snow module, with a structure allowing the use of snow data for calibration or assimilation. We used a model structure close to the snow model defined by M.T. Walter (2005) where each of the processes of the energy balance is parameterized using only temperature and precipitation data. The conductive fluxes into the snowpack have been modeled using analyticalsolutions to the heat equation with phase change. This model which is in-between the degree-day and the physical energy-balance approaches. It has the advantages to use only temperature and precipitation which arewidely available data and to take account of energy balance processes without being computationally intensive. Another advantage is that all state variables of the model should be comparable with observable measurements.For the moment, the snow module has been parameterized at point scale and has been tested over Switzerland and the US, using MeteoSwiss and SNOTEL USGS

  4. Linking snowfall and snow accumulation to generate spatial maps of SWE and snow depth

    Science.gov (United States)

    Broxton, Patrick D.; Dawson, Nicholas; Zeng, Xubin

    2016-06-01

    It is critically important but challenging to estimate the amount of snow on the ground over large areas due to its strong spatial variability. Point snow data are used to generate or improve (i.e., blend with) gridded estimates of snow water equivalent (SWE) by using various forms of interpolation; however, the interpolation methodologies often overlook the physical mechanisms for the snow being there in the first place. Using data from the Snow Telemetry and Cooperative Observer networks in the western United States, we show that four methods for the spatial interpolation of peak of winter snow water equivalent (SWE) and snow depth based on distance and elevation can result in large errors. These errors are reduced substantially by our new method, i.e., the spatial interpolation of these quantities normalized by accumulated snowfall from the current or previous water years. Our method results in significant improvement in SWE estimates over interpolation techniques that do not consider snowfall, regardless of the number of stations used for the interpolation. Furthermore, it can be used along with gridded precipitation and temperature data to produce daily maps of SWE over the western United States that are comparable to existing estimates (which are based on the assimilation of much more data). Our results also show that not honoring the constraint between SWE and snowfall when blending in situ data with gridded data can lead to the development and propagation of unrealistic errors.

  5. Influence of microscale in snow distributed modelling in semiarid regions

    OpenAIRE

    Pimentel Leiva, Rafael

    2015-01-01

    This work focuses on the importance of the microscale snow distribution in the modelling of the snow dynamics in semiarid regions. Snow over these areas has particular features that further complicate its measuring, monitoring and modelling (e.g. several snowmelt cycles throughout the year and a very heterogeneous distribution). Most extended GIS-based calculation of snowmelt/accumulation models must deal with non-negligible scales effects below the cell size, which may result ...

  6. Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes

    NARCIS (Netherlands)

    He, M.; Hogue, T.S.; Franz, K.J.; Margulis, S.A.; Vrugt, J.A.

    2011-01-01

    The National Weather Service (NWS) uses the SNOW17 model to forecast snow accumulation and ablation processes in snow-dominated watersheds nationwide. Successful application of the SNOW17 relies heavily on site-specific estimation of model parameters. The current study undertakes a comprehensive

  7. Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes

    NARCIS (Netherlands)

    He, M.; Hogue, T.S.; Franz, K.J.; Margulis, S.A.; Vrugt, J.A.

    2011-01-01

    The National Weather Service (NWS) uses the SNOW17 model to forecast snow accumulation and ablation processes in snow-dominated watersheds nationwide. Successful application of the SNOW17 relies heavily on site-specific estimation of model parameters. The current study undertakes a comprehensive sen

  8. Modelling high-resolution snow cover precipitation supply for German river catchments with SNOW 4

    Science.gov (United States)

    Böhm, Uwe; Reich, Thomas; Schneider, Gerold; Fiedler, Anett

    2013-04-01

    Formation of snow cover causes a delayed response of surface to precipitation. Both melting of snow and release of liquid water retained within the snow cover form precipitation supply which contributes to runoff and infiltration. The model SNOW 4 is developed to simulate snow cover accumulation and depletion and the resulting precipitation supply on a regular grid. The core of the model is formed by a set of equations which describe the snow cover energy and mass balance. The snow surface energy balance is calculated as a result of the radiation balance and the heat fluxes between atmosphere, soil and snow cover. The available melting heat enters the mass balance computation part of the model and melting of snow or freezing of liquid water within the snow layer takes place depending on its sign. Retention, aging and snow cover regeneration are taken into consideration. The model runs operationally 4 times a day and provides both a snow cover and precipitation supply analysis for the last 30 hours and a forecast for up to 72 hours. For the 30-hour analysis, regionalised observations are used both to define the initial state and force the model. Hourly measurements of air temperature, water vapour pressure, wind speed, global radiation or sunshine duration and precipitation are interpolated to the model grid. For the forecast period, SNOW 4 obtains the required input data from the operational products of the COSMO-EU weather forecast model. The size of a grid box is 1km2. The model area covers a region of 1100x1000km2 and includes the catchments of the German rivers completely. The internal time step is set to 1 hour. Once a day, the compliance between model and regionalized snow cover data is assessed. If discrepancies exceed certain thresholds, the model must be adjusted by a weighted approach towards the observations. The model simulations are updated every six hours based on the most recent observations and weather forecasts. The model works operationally since

  9. Analyzing the importance of wind-blown snow accumulations on Mount

    Science.gov (United States)

    Nestler, Alexander; Huss, Matthias; Ambartsumian, Rouben; Hambarian, Artak; Mohr, Sandra; Santi, Flavio

    2013-04-01

    Armenia's climate has a predominantly continental character with high amounts of precipitation and low temperatures during wintertime and a lack of precipitation together with high temperatures during summer. On the volcano Mount Aragatz, snow is relocated by strong winds into massive accumulations between 2500 and 4100 m a.s.l. during the winter season. These snow accumulations appear every winter in regular patterns as cornices on the lee side of sharp edges, such as those of ridges and canyons, which are arranged in a radial manner around the central crater. The biggest cornices almost outlast the hot period and provide considerable amounts of melt water until they disappear completely by the end of August. Snow melt water is known to have a high economic importance for agriculture on the slopes of Mount Aragatz and in the surroundings of Armenia's captial Yerewan. The aim of this study is to estimate the quantity of water naturally stored as snow on Mount Aragatz, and to what degree the use of geotextiles can prolong the lives of these snow accumulations. The characteristics and the spatial distribution of snow cornices on Mount Aragatz were determined using classical glaciological methods in June/July 2011 and 2012, involving snow depth soundings, water equivalent measurements and snow melt monitoring using ablation stakes, together with GPS mappings and classifications obtained from satellite images of the snow cornices. The combination of these data with ASTER DEMs and local weather data allows the modelling of the formation of wind-driven snow accumulations. Statistical relationships between the measured extent and volume of the snow cornices and surface parameters such as slope, aspect and curvature are established. In order to analyze the meltdown of the snow accumulations and the consequent impacts on runoff generation and the hydrological regime, a glacio-hydrological model integrating topographic parameters and meteorological data is applied. The

  10. Annual Greenland accumulation rates (2009–2012 from airborne Snow Radar

    Directory of Open Access Journals (Sweden)

    L. S. Koenig

    2015-12-01

    Full Text Available Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet (GrIS through increasing surface melt, emphasizing the need to closely monitor surface mass balance (SMB in order to improve sea-level rise predictions. Here, we quantify accumulation rates, the largest component of GrIS SMB, at a higher spatial resolution than currently available, using Snow Radar stratigraphy. We use a semi-automated method to derive annual-net accumulation rates from airborne Snow Radar data collected by NASA's Operation IceBridge from 2009 to 2012. An initial comparison of the accumulation rates from the Snow Radar and the outputs of a regional climate model (MAR shows that, in general, the radar-derived accumulation matches closely with MAR in the interior of the ice sheet but MAR estimates are high over the southeast GrIS. Comparing the radar-derived accumulation with contemporaneous ice cores reveals that the radar captures the annual and long-term mean. The radar-derived accumulation rates resolve large-scale patterns across the GrIS with uncertainties of up to 11 %, attributed mostly to uncertainty in the snow/firn density profile.

  11. Annual Greenland accumulation rates (2009-2012) from airborne Snow Radar

    Science.gov (United States)

    Koenig, L. S.; Ivanoff, A.; Alexander, P. M.; MacGregor, J. A.; Fettweis, X.; Panzer, B.; Paden, J. D.; Forster, R. R.; Das, I.; McConnell, J.; Tedesco, M.; Leuschen, C.; Gogineni, P.

    2015-12-01

    Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet (GrIS) through increasing surface melt, emphasizing the need to closely monitor surface mass balance (SMB) in order to improve sea-level rise predictions. Here, we quantify accumulation rates, the largest component of GrIS SMB, at a higher spatial resolution than currently available, using Snow Radar stratigraphy. We use a semi-automated method to derive annual-net accumulation rates from airborne Snow Radar data collected by NASA's Operation IceBridge from 2009 to 2012. An initial comparison of the accumulation rates from the Snow Radar and the outputs of a regional climate model (MAR) shows that, in general, the radar-derived accumulation matches closely with MAR in the interior of the ice sheet but MAR estimates are high over the southeast GrIS. Comparing the radar-derived accumulation with contemporaneous ice cores reveals that the radar captures the annual and long-term mean. The radar-derived accumulation rates resolve large-scale patterns across the GrIS with uncertainties of up to 11 %, attributed mostly to uncertainty in the snow/firn density profile.

  12. TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

    Directory of Open Access Journals (Sweden)

    T. Lendzioch

    2016-06-01

    Full Text Available Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1 developing a high resolution Digital Elevation Model during snow-free and 2 during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this

  13. Simulating wind-affected snow accumulations at catchment to basin scales

    Science.gov (United States)

    Winstral, Adam; Marks, Danny; Gurney, Robert

    2013-05-01

    In non-forested mountain regions, wind plays a dominant role in determining snow accumulation and melt patterns. A new, computationally efficient algorithm for distributing the complex and heterogeneous effects of wind on snow distributions was developed. The distribution algorithm uses terrain structure, vegetation, and wind data to adjust commonly available precipitation data to simulate wind-affected accumulations. This research describes model development and application in three research catchments in the Reynolds Creek Experimental Watershed in southwest Idaho, USA. All three catchments feature highly variable snow distributions driven by wind. The algorithm was used to derive model forcings for Isnobal, a mass and energy balance distributed snow model. Development and initial testing took place in the Reynolds Mountain East catchment (0.36 km2) where R2 values for the wind-affected snow distributions ranged from 0.50 to 0.67 for four observation periods spanning two years. At the Upper Sheep Creek catchment (0.26 km2) R2 values for the wind-affected model were 0.66 and 0.70. These R2 values matched or exceeded previously published cross-validation results from regression-based statistical analyses of snow distributions in similar environments. In both catchments the wind-affected model accurately located large drift zones, snow-scoured slopes, and produced melt patterns consistent with observed streamflow. Models that did not account for wind effects produced relatively homogenous SWE distributions, R2 values approaching 0.0, and melt patterns inconsistent with observed streamflow. The Dobson Creek (14.0 km2) application incorporated elevation effects into the distribution routine and was conducted over a two-dimensional grid of 6.67 × 105 pixels. Comparisons with satellite-derived snow-covered-area again demonstrated that the model did an excellent job locating regions with wind-affected snow accumulations. This final application demonstrated that the

  14. Complex Wind-Induced Variations of Surface Snow Accumulation Rates over East Antarctica

    Science.gov (United States)

    Das, I.; Scambos, T. A.; Koenig, L.; van den Broeke, M.; Lenaerts, J.

    2015-12-01

    Accurate quantification of surface snow-accumulation over Antarctica is important for mass balance estimates and climate studies based on ice core records. Using airborne radar, lidar and thresholds of surface slope, modeled surface mass balance (SMB) and wind fields, we have predicted continent-wide distribution of wind-scour zones over Antarctica. These zones are located over relatively steep ice surfaces formed by ice flow over bedrock topography. Near-surface winds accelerate over these steeper slopes and erode and sublimate the snow. This results in numerous localized regions (typically ≤ 200 km2) with reduced or negative surface accumulation. Although small zones of re-deposition occur at the base of the steeper slope areas, the redeposited mass is small relative to the ablation loss. Total losses from wind-scour and wind-glaze areas amounts to tens of gigatons annually. Near the coast, winds often blow significant amounts of surface snow from these zones into the ocean. Large uncertainties remain in SMB estimates over East Antarctica as climate models do not adequately represent the small-scale physical processes that lead to mass loss or redistribution over the wind-scour zones. In this study, we also use Operation IceBridge's snow radar data to provide evidence for a gradual ablation of ~16-18 m of firn (~200 years of accumulation) from wind-scour zones over the upper Recovery Ice Stream catchment. The maximum ablation rates observed in this region are ~ -54 kg m-2 a-1 (-54 mm water equivalent a-1). Our airborne radio echo-sounding analysis show snow redeposition downslope of the wind-scour zones is <10% of the cumulative mass loss. Our study shows that the local mass loss is dominated by sublimation to water vapor rather than wind-transport of snow.

  15. Snow hydrology in a general circulation model

    Science.gov (United States)

    Marshall, Susan; Roads, John O.; Glatzmaier, Gary

    1994-01-01

    A snow hydrology has been implemented in an atmospheric general circulation model (GCM). The snow hydrology consists of parameterizations of snowfall and snow cover fraction, a prognostic calculation of snow temperature, and a model of the snow mass and hydrologic budgets. Previously, only snow albedo had been included by a specified snow line. A 3-year GCM simulation with this now more complete surface hydrology is compared to a previous GCM control run with the specified snow line, as well as with observations. In particular, the authors discuss comparisons of the atmospheric and surface hydrologic budgets and the surface energy budget for U.S. and Canadian areas. The new snow hydrology changes the annual cycle of the surface moisture and energy budgets in the model. There is a noticeable shift in the runoff maximum from winter in the control run to spring in the snow hydrology run. A substantial amount of GCM winter precipitation is now stored in the seasonal snowpack. Snow cover also acts as an important insulating layer between the atmosphere and the ground. Wintertime soil temperatures are much higher in the snow hydrology experiment than in the control experiment. Seasonal snow cover is important for dampening large fluctuations in GCM continental skin temperature during the Northern Hemisphere winter. Snow depths and snow extent show good agreement with observations over North America. The geographic distribution of maximum depths is not as well simulated by the model due, in part, to the coarse resolution of the model. The patterns of runoff are qualitatively and quantitatively similar to observed patterns of streamflow averaged over the continental United States. The seasonal cycles of precipitation and evaporation are also reasonably well simulated by the model, although their magnitudes are larger than is observed. This is due, in part, to a cold bias in this model, which results in a dry model atmosphere and enhances the hydrologic cycle everywhere.

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

    Science.gov (United States)

    Sommer, Christian; Lehning, Michael; Mott, Rebecca

    2015-12-01

    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.

  17. 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.

  18. Constraints on Snow Accumulation and Firn Density in Greenland Using GPS Receivers

    Science.gov (United States)

    Larson, K. M.; Wahr, J. M.; Kuipers Munneke, P.

    2014-12-01

    Data from three continuously-operating GPS sites and located on the interior of the Greenland ice sheet are analyzed. In each case, the GPS antenna has been placed on a pole that is set in the firn layer above the ice. Traditionally these kinds of GPS installations are used as base stations or to estimate the local horizontal speed and direction of the ice sheet. However, these data are also sensitive to the vertical displacement of the pole as it moves through the firn layer. A new method developed to measure snow depth variations with reflected GPS signals is applied to these GPS data from Greenland. This method provides a constraint on the vertical distance between the GPS antenna and the surface snow layer. The vertical positions and snow surface heights are then used to assess output from surface accumulation and firn densification models, showing agreement better than 10% at the sites with the longest records. Comparisons between the GPS reflection method and in situ snow sensors at the Dye 2 site show excellent agreement, capturing the dramatic changes observed in Greenland during the 2012 summer melt season. The GPS vertical measurements and snow surface layer estimates can help validate surface elevation results obtained using satellite altimetry.

  19. Changes in Greenland ice sheet elevation attributed primarily to snow accumulation variability

    Science.gov (United States)

    McConnell; Arthern; Mosley-Thompson; Davis; Bales; Thomas; Burkhart; Kyne

    2000-08-24

    The response of grounded ice sheets to a changing climate critically influences possible future changes in sea level. Recent satellite surveys over southern Greenland show little overall elevation change at higher elevations, but large spatial variability. Using satellite studies alone, it is not possible to determine the geophysical processes responsible for the observed elevation changes and to decide if recent rates of change exceed the natural variability. Here we derive changes in ice-sheet elevation in southern Greenland, for the years 1978-88, using a physically based model of firn densification and records of annual snow accumulation reconstructed from 12 ice cores at high elevation. Our patterns of accumulation-driven elevation change agree closely with contemporaneous satellite measurements of ice-sheet elevation change, and we therefore attribute the changes observed in 1978-88 to variability in snow accumulation. Similar analyses of longer ice-core records show that in this decade the Greenland ice sheet exhibited typical variability at high elevations, well within the long-term natural variability. Our results indicate that a better understanding of ice-sheet mass changes will require long-term measurements of both surface elevation and snow accumulation.

  20. 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.

  1. 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.

  2. 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

  3. 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 typically form at night and in polar re

  4. 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 typically form at night and in polar re

  5. 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 typically form at night and in polar

  6. Importance of maximum snow accumulation for summer low flows in humid catchments

    Science.gov (United States)

    Jenicek, M.; Seibert, J.; Zappa, M.; Staudinger, M.; Jonas, T.

    2015-07-01

    The expected increase of air temperature will increase the ratio of liquid to solid precipitation during winter and, thus decrease the amount of snow, especially in mid-elevation mountain ranges across Europe. The decrease of snow will affect groundwater recharge during spring and might cause low streamflow values in the subsequent summer period. To evaluate these potential climate change impacts, we investigated the effects of inter-annual variations in snow accumulation on summer low flow and addressed the following research questions: (1) how important is snow for summer low flows and how long is the "memory effect" in catchments with different elevations? (2) How sensitive are summer low flows to any change of winter snowpack? To find suitable predictors of summer low flow we used long time series from 14 alpine and pre-alpine catchments in Switzerland and computed different variables quantifying winter and spring snow conditions. We assessed the sensitivity of individual catchments to the change of maximum snow water equivalent (SWEmax) using the non-parametric Theil-Sen approach as well as an elasticity index. In general, the results indicated that maximum winter snow accumulation influenced summer low flow, but could only partly explain the observed inter-annual variations. One other important factor was the precipitation between maximum snow accumulation and summer low flow. When only the years with below average precipitation amounts during this period were considered, the importance of snow accumulation as a predictor of low flows increased. The slope of the regression between SWEmax and summer low flow and the elasticity index both increased with increasing mean catchment elevation. This indicated a higher sensitivity of summer low flow to snow accumulation in alpine catchments compared to lower elevation catchments.

  7. On the Secular Trend in Snow Accumulation from the Mount Logan Ice Core

    Science.gov (United States)

    Moore, K.; Alverson, K.; Holdsworth, G.

    2001-12-01

    We present an analysis of a 250 year long annually resolved record of snow accumulation from a high altitude site on Mount Logan in the Yukon Territory of Canada. Previous work has shown that the snow accumulation time series exhibits a statistically significant correlation with indices of ENSO activity. In addition, snow accumulation at the site is associated with anomalies in both the tropospheric and land temperatures over Northwestern North America. Over the 250 years of the time series, annual snow accumulation at the site has increased by approximately 15%. Using a variety of statistical tests, we show that this secular trend is highly significant especially during the period from 1800 onwards. We will discuss the implications that this secular trend has on variability in ENSO activity and on trends in surface and tropospheric temperatures since 1800.

  8. Modeling the spatial variability of snow instability with the snow cover model SNOWPACK

    Science.gov (United States)

    Richter, Bettina; Reuter, Benjamin; Gaume, Johan; Fierz, Charles; Bavay, Mathias; van Herwijnen, Alec; Schweizer, Jürg

    2016-04-01

    Snow stratigraphy - key information for avalanche forecasting - can be obtained using numerical snow cover models driven by meteorological data. Simulations are typically performed for the locations of automatic weather station or for virtual slopes of varying aspect. However, it is unclear to which extent these simulations can represent the snowpack properties in the surrounding terrain, in particular snow instability, which is known to vary in space. To address this issue, we implemented two newly developed snow instability criteria in SNOWPACK relating to failure initiation and crack propagation, two fundamental processes for dry-snow slab avalanche release. Snow cover simulations were performed for the Steintälli field site above Davos (Eastern Swiss Alps), where snowpack data from several field campaigns are available. In each campaign, about 150 vertical snow penetration resistance profiles were sampled with the snow micro-penetrometer (SMP). For each profile, SMP and SNOWPACK- based instability criteria were compared. In addition, we carried out SNOWPACK simulations for multiple aspects and slope angles, allowing to obtain statistical distributions of the snow instability at the basin scale. Comparing the modeled to the observed distributions of snow instability suggests that it is feasible to obtain an adequate spatial representation of snow instability without high resolution distributed modeling. Hence, for the purpose of regional avalanche forecasting, simulations for a selection of virtual slopes seems sufficient to assess the influence of basic terrain features such as aspect and elevation.

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

    Science.gov (United States)

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

    2017-07-01

    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 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.

  10. Snow accumulation variability at altitude of 7010 m a.s.l. in Muztag Ata Mountain in Pamir Plateau during 1958-2002

    Science.gov (United States)

    Duan, Keqin; Xu, Baiqin; Wu, Guanjian

    2015-12-01

    Precipitation over high mountain is significant for glacier development and river runoff in arid Tarim Basin in the northwest China. However, a coherent perspective of precipitation variability at high-altitude in Tarim Basin has not been presented until now. Here, a 41-m ice core at altitude of 7010 m a.s.l. was drilled and gotten to determine annual snow accumulation rates at Muztag Ata Mountain in the headwater of Tarim River in Pamir Plateau during summer 2003. Using strong seasonally variation of oxygen isotope and β-radioactivity reference layers, the core was dated reliably back to 1958 and resulted in a 45-year record between 1958 and 2002. The mean annual snow accumulation was about 605 mm water equivalent, which is almost 10 times than precipitation in piedmont with elevation below 3000 m a.s.l. The snow accumulation is characterized by high values in 1960s and early 1970s, followed by a drop in the middle 1970s and a recent decreasing trend. Further analysis suggests the upstream zonal flow variation as the major mechanism linking the regional snow accumulation fluctuation to macroscale circulation conditions. During the high snow accumulation years, the westerly winds between 30 and 50°N from the Mediterranean Sea to Pamir Plateau are weakened. The decreased upstream westerly winds generate anomalous cyclonic flows in the Pamir Plateau, which results in an enhanced advection of moisture from the tropics to the vicinity of Muztag Ata Mountain and is thus consistent with enhanced snow accumulation at the core site. During the low snow accumulation years, the above processes are reversed. On the basis of this high-altitude snow accumulation and temperature, a linear regression model is established to simulate annual mass balance in Muztag Ata region. Results reveal that snow accumulation conditions largely determine the annual mass-balance, which has stronger impaction on Tarim River runoff.

  11. Regional distribution and variability of model-simulated Arctic snow on sea ice

    Science.gov (United States)

    Castro-Morales, Karel; Ricker, Robert; Gerdes, Rüdiger

    2017-09-01

    Numerical models face the challenge of representing the present-day spatiotemporal distribution of snow on sea ice realistically. We present modeled Arctic-wide snow depths on sea ice (hs_mod) obtained with the MITgcm configured with a single snow layer that accumulates proportionally to the thickness of sea ice. When compared to snow depths derived from radar measurements (NASA Operation IceBridge, 2009-2013), the model snow depths are overestimated on first-year ice (2.5 ± 8.1 cm) and multiyear ice (0.8 ± 8.3 cm). The large variance between model and observations lies mainly in the limitations of the model snow scheme and the large uncertainties in the radar measurements. In a temporal analysis, during the peak of snowfall accumulation (April), hs_mod show a decline between 2000 and 2013 associated to long-term reduction of summer sea ice extent, surface melting and sublimation. With the aim of gaining knowledge on how to improve hs_mod, we investigate the contribution of the explicitly modeled snow processes to the resulting hs_mod. Our analysis reveals that this simple snow scheme offers a practical solution to general circulation models due to its ability to replicate robustly the distribution of the large-scale Arctic snow depths. However, benefit can be gained from the integration of explicit wind redistribution processes to potentially improve the model performance and to better understand the interaction between sources and sinks of contemporary Arctic snow.

  12. A Creep Model for High Density Snow

    Science.gov (United States)

    2017-04-01

    Director of ERDC-CRREL was Dr. Lance Hansen, and the Director was Dr. Robert E. Davis. COL Bryan S. Green was Commander of ERDC, and Dr. David W...Station, Green - land, and that will be founded on a compacted snow surface. The defor- mation of snow under a constant load (creep deformation, or...developed in this study are enough similar to the generalized creep model used in the ABAQUS finite element software that the ABAQUS creep model was used

  13. Modelling the spatial distribution of snow water equivalent at the catchment scale taking into account changes in snow covered area

    Directory of Open Access Journals (Sweden)

    T. Skaugen

    2011-12-01

    Full Text Available A successful modelling of the snow reservoir is necessary for water resources assessments and the mitigation of spring flood hazards. A good estimate of the spatial probability density function (PDF of snow water equivalent (SWE is important for obtaining estimates of the snow reservoir, but also for modelling the changes in snow covered area (SCA, which is crucial for the runoff dynamics in spring. In a previous paper the PDF of SWE was modelled as a sum of temporally correlated gamma distributed variables. This methodology was constrained to estimate the PDF of SWE for snow covered areas only. In order to model the PDF of SWE for a catchment, we need to take into account the change in snow coverage and provide the spatial moments of SWE for both snow covered areas and for the catchment as a whole. The spatial PDF of accumulated SWE is, also in this study, modelled as a sum of correlated gamma distributed variables. After accumulation and melting events the changes in the spatial moments are weighted by changes in SCA. The spatial variance of accumulated SWE is, after both accumulation- and melting events, evaluated by use of the covariance matrix. For accumulation events there are only positive elements in the covariance matrix, whereas for melting events, there are both positive and negative elements. The negative elements dictate that the correlation between melt and SWE is negative. The negative contributions become dominant only after some time into the melting season so at the onset of the melting season, the spatial variance thus continues to increase, for later to decrease. This behaviour is consistent with observations and called the "hysteretic" effect by some authors. The parameters for the snow distribution model can be estimated from observed historical precipitation data which reduces by one the number of parameters to be calibrated in a hydrological model. Results from the model are in good agreement with observed spatial moments

  14. Modelling the spatial distribution of snow water equivalent at the catchment scale taking into account changes in snow covered area

    Science.gov (United States)

    Skaugen, T.; Randen, F.

    2011-12-01

    A successful modelling of the snow reservoir is necessary for water resources assessments and the mitigation of spring flood hazards. A good estimate of the spatial probability density function (PDF) of snow water equivalent (SWE) is important for obtaining estimates of the snow reservoir, but also for modelling the changes in snow covered area (SCA), which is crucial for the runoff dynamics in spring. In a previous paper the PDF of SWE was modelled as a sum of temporally correlated gamma distributed variables. This methodology was constrained to estimate the PDF of SWE for snow covered areas only. In order to model the PDF of SWE for a catchment, we need to take into account the change in snow coverage and provide the spatial moments of SWE for both snow covered areas and for the catchment as a whole. The spatial PDF of accumulated SWE is, also in this study, modelled as a sum of correlated gamma distributed variables. After accumulation and melting events the changes in the spatial moments are weighted by changes in SCA. The spatial variance of accumulated SWE is, after both accumulation- and melting events, evaluated by use of the covariance matrix. For accumulation events there are only positive elements in the covariance matrix, whereas for melting events, there are both positive and negative elements. The negative elements dictate that the correlation between melt and SWE is negative. The negative contributions become dominant only after some time into the melting season so at the onset of the melting season, the spatial variance thus continues to increase, for later to decrease. This behaviour is consistent with observations and called the "hysteretic" effect by some authors. The parameters for the snow distribution model can be estimated from observed historical precipitation data which reduces by one the number of parameters to be calibrated in a hydrological model. Results from the model are in good agreement with observed spatial moments of SWE and SCA

  15. Atmospheric Controls of Snow Accumulation on Glaciers and Ice Caps in High Asia

    Science.gov (United States)

    Scherer, D.; Curio, J.

    2015-12-01

    Snowfall is the major contributor to snow accumulation on glaciers and ice caps. Unfortunately, its quantification is rather difficult, both by observations and by numerical modelling. Field measurements of snowfall are generally problematic, and particularly inaccurate in mountainous regions. This holds true also for data from remote sensing systems like the TRMM. Numerical modelling of precipitation in general, and of snowfall in particular, is depending on parameterization of sub-grid processes occurring at a wide range of spatial scales. The scarcity of reliable observational data on snowfall required to test and validate the relevant parameterization schemes is one of the major obstacles for deepening our understanding of atmospheric controls of snow accumulation on glaciers and ice caps. In addition, the often made assumption that easy-to-measure snow accumulation equals snowfall is not valid in areas where other processes like snowdrift or avalanches cause snow deposition or erosion. Besides a general discussion of the above-mentioned problems, the presentation will focus on results obtained from a gridded atmospheric data set, i.e., the so-called High Asia Refined analysis (HAR), covering the study region by two nested domains of 30 km and 10 km grid spacing. Starting from autumn 2000, three-hourly (30 km) and hourly (10 km) data are available for a comprehensive set of atmospheric variables (see www.klima.tu-berlin.de/HAR). HAR data was used to analyse annual and seasonal patterns of precipitation and atmospheric water transport, as well as to drive numerical models for surface mass balance of glaciers and ice sheets. A new study, which is the main subject of this presentation, reveals specific regimes of dynamic controls of precipitation in different regions of High Asia. One of the striking results is that the analysis identified a specific regime that is able to explain some of the atmospheric controls behind the so-called Karakoram anomaly (glaciers in

  16. Modeling of AC arc inside wet snow

    Energy Technology Data Exchange (ETDEWEB)

    Hemmatjou, H.

    2006-07-01

    Overhead transmission lines cover long distances over a broad range of topographic relief, climates, and environments. As such, the high voltage equipment is subject to pollution, wet snow and atmospheric icing. Each of these factors have been the source of power outages recorded on power transmission lines. Electric arcs can develop on outdoor insulators until they cause a total flashover. This study involved the modeling of flashover in snow-covered insulators to better understand how electric discharges initiate inside snow and how they develop into flashover. The main objective of this thesis was to develop a mathematical model to predict the flashover voltage of snow-covered insulator surfaces and to ultimately design adequate insulators for cold regions. The results obtained through mathematical modeling were in good agreement with those obtained in experiments.

  17. Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

    OpenAIRE

    2013-01-01

    Snow grain size is a key parameter for modeling microwave snow emission properties and the surface energy balance because of its influence on the snow albedo, thermal conductivity and diffusivity. A model of the specific surface area (SSA) of snow was implemented in the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS) version 3.4. This offline multilayer model (CLASS-SSA) simulates the decrease of SSA based on snow age, snow temperature and t...

  18. Can a snow structure model estimate snow characteristics relevant to reindeer husbandry?

    Directory of Open Access Journals (Sweden)

    Sirpa Rasmus

    2014-02-01

    Full Text Available Snow affects foraging conditions of reindeer e.g. by increasing the energy expenditures for moving and digging work or, in contrast, by making access of arboreal lichen easier. Still the studies concentrating on the role of the snow pack structure on reindeer population dynamics and reindeer management are few. We aim to find out which of the snow characteristics are relevant for reindeer in the northern boreal zone according to the experiences of reindeer herders and is this relevance seen also in reproduction rate of reindeer in this area. We also aim to validate the ability of the snow model SNOWPACK to reliably estimate the relevant snow structure characteristics. We combined meteorological observations, snow structure simulations by the model SNOWPACK and annual reports by reindeer herders during winters 1972-2010 in the Muonio reindeer herding district, northern Finland. Deep snow cover and late snow melt were the most common unfavorable conditions reported. Problematic conditions related to snow structure were icy snow and ground ice or unfrozen ground below the snow, leading to mold growth on ground vegetation. Calf production percentage was negatively correlated to the measured annual snow depth and length of the snow cover time and to the simulated snow density. Winters with icy snow could be distinguished in three out of four reported cases by SNOWPACK simulations and we could detect reliably winters with conditions favorable for mold growth. Both snow amount and also quality affects the reindeer herding and reindeer reproduction rate in northern Finland. Model SNOWPACK can relatively reliably estimate the relevant structural properties of snow. Use of snow structure models could give valuable information about grazing conditions, especially when estimating the possible effects of warming winters on reindeer populations and reindeer husbandry. Similar effects will be experienced also by other arctic and boreal species.

  19. Snow

    Institute of Scientific and Technical Information of China (English)

    小雅

    2011-01-01

    雪花,雪花,白又凉。雪花,雪花,来了又走。啊,雪花!你去哪儿?我不知道,我不知道,飘到哪儿。%Snow, snow, White and cold. Snow, snow, Come and go. Oh, snow! Where do you go? I don't know, I don't know. Where I go.

  20. Operational snow mapping with simplified data assimilation using the seNorge snow model

    Science.gov (United States)

    Saloranta, Tuomo M.

    2016-07-01

    Frequently updated maps of snow conditions are useful for many applications, e.g., for avalanche and flood forecasting services, hydropower energy situation analysis, as well as for the general public. Numerical snow models are often applied in snow map production for operational hydrological services. However, inaccuracies in the simulated snow maps due to model uncertainties and the lack of suitable data assimilation techniques to correct them in near-real time may often reduce the usefulness of the snow maps in operational use. In this paper the revised seNorge snow model (v.1.1.1) for snow mapping is described, and a simplified data assimilation procedure is introduced to correct detected snow model biases in near real-time. The data assimilation procedure is theoretically based on the Bayesian updating paradigm and is meant to be pragmatic with modest computational and input data requirements. Moreover, it is flexible and can utilize both point-based snow depth and satellite-based areal snow-covered area observations, which are generally the most common data-sources of snow observations. The model and analysis codes as well as the "R" statistical software are freely available. All these features should help to lower the challenges and hurdles hampering the application of data-assimilation techniques in operational hydrological modeling. The steps of the data assimilation procedure (evaluation, sensitivity analysis, optimization) and their contribution to significantly increased accuracy of the snow maps are demonstrated with a case from eastern Norway in winter 2013/2014.

  1. A two thousand year annual record of snow accumulation rates for Law Dome, East Antarctica

    Directory of Open Access Journals (Sweden)

    J. Roberts

    2014-11-01

    AD 663–704, AD 933–975 and AD 1429–1468 were below average. The calculated snow accumulation rates show good correlation with atmospheric reanalysis estimates, and significant spatial correlation over a wide expanse of East Antarctica, demonstrating that the Law Dome record captures larger scale variability across a large region of East Antarctica well beyond the immediate vicinity of the Law Dome summit. Spectral analysis reveals periodicities in the snow accumulation record which may be related to ENSO and Interdecadal Pacific Oscillation frequencies.

  2. Monitoring and modelling snow avalanches in Svalbard

    Science.gov (United States)

    Humlum, O.; Christiansen, H.; Neumann, U.; Eckerstorfer, M.; Sjöblom, A.; Stalsberg, K.; Rubensdotter, L.

    2009-04-01

    Monitoring and modelling snow avalanches in Svalbard Ole Humlum 1,3, Hanne H. Christiansen 1, Ulrich Neumann 1, Markus Eckerstorfer 1, Anna Sjöblom 1, Knut Stalsberg 2 and Lena Rubensdotter 2. 1: The University Centre in Svalbard (UNIS). 2: Geological Survey of Norway (NGU) 3: University of Oslo Ground based transportation in Svalbard landscape all takes place across mountainous terrain affected by different geomorphological slope processes. Traffic in and around the Svalbard settlements is increasing, and at the same time global climate models project substantial increases in temperature and precipitation in northern high latitudes for coming century. Therefore improved knowledge on the effect of climatic changes on slope processes in such high arctic landscapes is becoming increasingly important. Motivated by this, the CRYOSLOPE Svalbard research project since 2007 has carried out field observations on snow avalanche frequency and associated meteorological conditions. Snow avalanches are important geomorphic agents of erosion and deposition, and have long been a source of natural disasters in many mid-latitude mountain areas. Avalanches as a natural hazard has thereby been familiar to inhabitants of the Alps and Scandinavia for centuries, while it is a more recent experience in high arctic Svalbard. In addition, overall climate, topography and especially high winter wind speeds makes it difficult to apply snow avalanche models (numerical or empirical) developed for use at lower latitudes, e.g. in central Europe. In the presentation we examplify results from the ongoing (since winter 2006-07) monitoring of snow avalanches in Svalbard along a 70 km long observational route in the mountains. In addition, we present observations on the geomorphological impact of avalanches, with special reference to the formation of rock glaciers. Finally, we also present some initial results from numerical attempts of snow avalanche risk modelling within the study area.

  3. Snow modeling using SURFEX with the CROCUS snow scheme for Norway

    Science.gov (United States)

    Vikhamar-Schuler, D.; Müller, K.

    2012-04-01

    In 2010 a research project was initiated with the aim to investigate methods to establish a regional snow avalanche forecasting system for Norway. A part of this project concerns snow models that simulate snow stratigraphy and physical parameters in the snow pack. For this purpose we have used the CROCUS snow scheme within the land surface model SURFEX for the location of 18 weather stations in Norway. We have carried out a sensitivity study of available meteorological data. Few weather stations have measurements of all the parameters used by the model on an hourly basis. Therefore it is interesting to investigate if certain parameters can be replaced by short-term prognoses from the operational weather prediction models (Unified Model-4 km, HARMONIE-4 km and postprocessed prognoses of temperature and precipitation). This study indicates that short-term prognoses of radiation, air humidity, wind and air pressure may replace observations without loosing the quality of the snow simulations. For all stations the modeled snow depth is validated with the observed snow depth for the last 2-3 winter seasons. Our results show that the modeled snow depth is most sensitive to precipitation and air temperature. Overall, very good estimates of the snow depth are obtained using the CROCUS snow scheme, except for very wind exposed stations. Temperatures within the snowpack were compared with observations of snow temperature at the Filefjell station, showing promising results. A cold bias was observed, but daily variations were reasonably modeled. During the winter 2011/2012 a series of snow stratigraphy observations from the Filefjell station is carried out for validation purposes of other intra-snowpack physical properties (density, liquid water content, temperature, grain type).

  4. Snow on Arctic sea ice: model representation and last decade changes

    Directory of Open Access Journals (Sweden)

    K. Castro-Morales

    2015-10-01

    Full Text Available Together with sea ice, Arctic snow has experienced vast changes during the last decade due to a warming climate. Thus, it is relevant to study the past and present changes of Arctic snow to understand the implications to the sea ice component, precipitation, heat and radiation budgets. In this study, we analyze the changes of snow depth between 2000 and 2013 at regional scale represented in an Arctic coupled sea ice-general circulation model. We evaluate the model performance by direct comparison of the modeled snow depths (hs_mod to snow depths from radar measurements from the NASA Operation IceBridge (hs_OIB during the flight campaigns completed from 2009 to 2013. Despite the description of the snow in our model is simple (i.e. single layer without explicit snow redistribution processes as in many current sea-ice models; the latitudinal distribution of hs_mod in the western Arctic is in good agreement to observations. The hs_mod is on average 3 cm thicker than hs_OIB in latitudes > 76° N. According to the model results, the hs in 2013 decreased 21 % with respect to the multi-year mean between 2000 and 2013. This snow reduction occurred mainly in FYI dominated areas, and is in good agreement to the year-to-year loss of sea ice, also well reproduced by the model. In a simple snow mass budget, our results show that 65 % of the yearly accumulated snow is lost by sublimation and snowmelt due to the heat transfer between the snow/ice interface and the atmosphere. Although the snow layer accumulates again every year, the long-term reduction in the summer sea-ice extent ultimately affects the maximum spring accumulation of snow. The model results exhibit a last decade thinning of the snowpack that is however one order of magnitude lower than previous estimates based on radar measurements. We suggest that the later is partially due to the lack of explicit snow redistribution processes in the model, emphasizing the need to include these in current sea

  5. An ice core record of net snow accumulation and seasonal snow chemistry at Mt. Waddington, southwest British Columbia, Canada

    Science.gov (United States)

    Neff, P. D.; Steig, E. J.; Clark, D. H.; McConnell, J. R.; Pettit, E. C.; Menounos, B.

    2011-12-01

    We recovered a 141 m ice core from Combatant Col (51.39°N, 125.22°W, 3000 m asl) on the flank of Mt. Waddington, southern Coast Mountains, British Columbia, Canada. Aerosols and other impurities in the ice show unambiguous seasonal variations, allowing for annual dating of the core. Clustered melt layers, originating from summer surface heating, also aid in the dating of the core. Seasonality in water stable isotopes is preserved throughout the record, showing little evidence of diffusion at depth, and serves as an independent verification of the timescale. The annual signal of deuterium excess is especially well preserved. The record of lead deposition in the core agrees with those of ice cores from Mt. Logan and from Greenland, with a sharp drop-off in concentration in the 1970s and early 1980s, further validating the timescales. Despite significant summertime melt at this mid-latitude site, these data collectively reveal a continuous and annually resolved 36-year record of snow accumulation. We derived an accumulation time series from the Mt. Waddington ice core, after correcting for ice flow. Years of anomalously high or low snow accumulation in the core correspond with extremes in precipitation data and geopotential height anomalies from reanalysis data that make physical sense. Specifically, anomalously high accumulation years at Mt. Waddington correlate with years where "Pineapple Express" atmospheric river events bring large amounts of moisture from the tropical Pacific to western North America. The Mt. Waddington accumulation record thus reflects regional-scale climate. These results demonstrate the potential of ice core records from temperate glaciers to provide meaningful paleoclimate information. A longer core to bedrock (250-300 m) at the Mt. Waddington site could yield ice with an age of several hundred to 1000 years.

  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. Assessing the Sensitivity of Satellite-Derived Estimates of Ice Sheet Mass Balance to Regional Climate Model Simulations of Snow Accumulation and Firn Compaction

    Science.gov (United States)

    Briggs, K.; Shepherd, A.; Horwath, M.; Horvath, A.; Nagler, T.; Wuite, J.; Muir, A.; Gilbert, L.; Mouginot, J.

    2015-12-01

    Surface mass balance (SMB) estimates from Regional Climate Models (RCMs) are fundamental for assessing and understanding ice sheet mass trends. Mass budget and altimetry assessments rely on RCMs both directly for estimates of the SMB contribution to the total mass trend, and indirectly for ancillary data in the form of firn compaction corrections. As such, mass balance assessments can be highly sensitive to RCM outputs and therefore their accuracy. Here we assess the extent to which geodetic measurements of mass balance are sensitive to RCM model outputs at different resolutions. We achieve this by comparing SMB dependent estimates of mass balance from the mass budget method and altimetry, with those from satellite gravimetry that are independent of SMB estimates. Using the outputs of the RACMO/ANT 2.3 model at 5.5 km and 27 km horizontal spatial resolution, we generate estimates of mass balance using the mass budget method and altimetry for the Western Palmer Land region of the Antarctic Peninsula between 2003 and 2014. We find a 19% increase in the long-term (1980 to 2014) mean annual SMB for the region when enhancing the model resolution to 5.5 km. This translates into an approximate 50% reduction in the total mass loss from 2003 to 2014 calculated with the mass budget method and a 15% increase in the altimetry estimate. The use of the enhanced resolution product leads to consistency between the estimates of mass loss from the altimetry and the mass budget method that is not observed with the coarser resolution product, in which estimates of cumulative mass fall beyond the relative errors. Critically, when using the 5.5 km product, we find excellent agreement, both in pattern and magnitude, with the independent estimate derived from gravimetry. Our results point toward the crucial need for high resolution SMB products from RCMs for mass balance assessments, particularly in regions of high mass turnover and complex terrain as found over the Antarctic Peninsula.

  8. Development of a land surface model with coupled snow and frozen soil physics

    Science.gov (United States)

    Wang, Lei; Zhou, Jing; Qi, Jia; Sun, Litao; Yang, Kun; Tian, Lide; Lin, Yanluan; Liu, Wenbin; Shrestha, Maheswor; Xue, Yongkang; Koike, Toshio; Ma, Yaoming; Li, Xiuping; Chen, Yingying; Chen, Deliang; Piao, Shilong; Lu, Hui

    2017-06-01

    Snow and frozen soil are important factors that influence terrestrial water and energy balances through snowpack accumulation and melt and soil freeze-thaw. In this study, a new land surface model (LSM) with coupled snow and frozen soil physics was developed based on a hydrologically improved LSM (HydroSiB2). First, an energy-balance-based three-layer snow model was incorporated into HydroSiB2 (hereafter HydroSiB2-S) to provide an improved description of the internal processes of the snow pack. Second, a universal and simplified soil model was coupled with HydroSiB2-S to depict soil water freezing and thawing (hereafter HydroSiB2-SF). In order to avoid the instability caused by the uncertainty in estimating water phase changes, enthalpy was adopted as a prognostic variable instead of snow/soil temperature in the energy balance equation of the snow/frozen soil module. The newly developed models were then carefully evaluated at two typical sites of the Tibetan Plateau (TP) (one snow covered and the other snow free, both with underlying frozen soil). At the snow-covered site in northeastern TP (DY), HydroSiB2-SF demonstrated significant improvements over HydroSiB2-F (same as HydroSiB2-SF but using the original single-layer snow module of HydroSiB2), showing the importance of snow internal processes in three-layer snow parameterization. At the snow-free site in southwestern TP (Ngari), HydroSiB2-SF reasonably simulated soil water phase changes while HydroSiB2-S did not, indicating the crucial role of frozen soil parameterization in depicting the soil thermal and water dynamics. Finally, HydroSiB2-SF proved to be capable of simulating upward moisture fluxes toward the freezing front from the underlying soil layers in winter.

  9. Snow Cover on the Arctic Sea Ice: Model Validation, Sensitivity, and 21st Century Projections

    Science.gov (United States)

    Blazey, Benjamin Andrew

    The role of snow cover in controlling Arctic Ocean sea ice thickness and extent is assessed with a series of models. Investigations with the stand alone Community Ice CodE (CICE) show, first, a reduction in snow depth triggers a decrease in ice volume and area, and, second, that the impact of increased snow is heavily dependent on ice and atmospheric conditions. Hindcast snow depths on the Arctic ice, simulated by the fully coupled Community Climate System Model (CCSM) are validated with 20th century in situ snow depth measurements. The snow depths in CCSM are found to be deeper than observed, likely due to excessive precipitation produced by the component atmosphere model. The sensitivity of the ice to the thermal barrier imposed by the biased snow depth is assessed. The removal of the thermodynamic impact of the exaggerated snow depth increases ice area and volume. The initial increases in ice due to enhanced conductive flux triggers feedback mechanisms with the atmosphere and ocean, reinforcing the increase in ice. Finally, the 21st century projections of decreased Arctic Ocean snow depth in CCSM are reported and diagnosed. The changes in snow are dominated by reduced accumulation due to the lack of autumn ice cover. Without this platform, much of the early snowfall is lost directly to the ocean. While this decrease in snow results in enhanced conductive flux through the ice as in the validation sensitivity experiment, the decreased summer albedo is found to dominate, as in the CICE stand alone sensitivity experiment. As such, the decrease in snow projected by CCSM in the 21st century presents a mechanism to continued ice loss. These negative (ice growth due decreased insulation) and positive (ice melt due to decreased albedo) feedback mechanisms highlight the need for an accurate representation snow cover on the ice in order to accurately simulate the evolution of Arctic Ocean sea ice.

  10. Recent trends in Antarctic snow accumulation from Polar MM5 simulations.

    Science.gov (United States)

    Monaghan, Andrew J; Bromwich, David H; Wang, Sheng-Hung

    2006-07-15

    Polar MM5, a mesoscale atmospheric model optimized for use over polar ice sheets, is employed to simulate Antarctic accumulation in recent decades. Two sets of simulations, each with different initial and boundary conditions, are evaluated for the 17yr period spanning 1985-2001. The initial and boundary conditions for the two sets of runs are provided by the (i) European Centre for Medium-Range Weather Forecasts 40 year Reanalysis, and (ii) National Centres for Environmental Prediction-Department of Energy Atmospheric Model Intercomparison Project Reanalysis II. This approach is used so that uncertainty can be assessed by comparing the two resulting datasets. There is broad agreement between the two datasets for the annual precipitation trends for 1985-2001. These generally agree with ice core and snow stake accumulation records at various locations around the continent, indicating broad areas of both upward and downward trends. Averaged over the continent the annual trends are small and not statistically different from zero, suggesting that recent Antarctic snowfall changes do not mitigate current sea-level rise. However, this result does not suggest that Antarctica is isolated from the recent climate changes occurring elsewhere on Earth. Rather, these are expressed by strong seasonal and regional precipitation changes.

  11. Assimilation of AMSR-E snow water equivalent data in a spatially-lumped snow model

    Science.gov (United States)

    Dziubanski, David J.; Franz, Kristie J.

    2016-09-01

    Accurately initializing snow model states in hydrologic prediction models is important for estimating future snowmelt, water supplies, and flooding potential. While ground-based snow observations give the most reliable information about snowpack conditions, they are spatially limited. In the north-central USA, there are no continual observations of hydrologically critical snow variables. Satellites offer the most likely source of spatial snow data, such as the snow water equivalent (SWE), for this region. In this study, we test the impact of assimilating SWE data from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument into the US National Weather Service (NWS) SNOW17 model for seven watersheds in the Upper Mississippi River basin. The SNOW17 is coupled with the NWS Sacramento Soil Moisture Accounting (SACSMA) model, and both simulated SWE and discharge are evaluated. The ensemble Kalman filter (EnKF) assimilation framework is applied and updating occurs on a daily cycle for water years 2006-2011. Prior to assimilation, AMSR-E data is bias corrected using data from the National Operational Hydrologic Remote Sensing Center (NOHRSC) airborne snow survey program. An average AMSR-E SWE bias of -17.91 mm was found for the study basins. SNOW17 and SAC-SMA model parameters from the North Central River Forecast Center (NCRFC) are used. Compared to a baseline run without assimilation, the SWE assimilation improved discharge for five of the seven study sites, in particular for high discharge magnitudes associated with snow melt runoff. SWE and discharge simulations suggest that the SNOW17 is underestimating SWE and snowmelt rates in the study basins. Deep snow conditions and periods of snowmelt may have introduced error into the assimilation due to difficulty obtaining accurate brightness temperatures under these conditions. Overall results indicate that the AMSR-E data and EnKF are viable and effective solutions for improving simulations

  12. High resolution modelling of snow transport in complex terrain using simulated wind fields

    Directory of Open Access Journals (Sweden)

    M. Bernhardt

    2008-07-01

    Full Text Available Snow transport is one of the most dominant processes influencing the snow cover accumulation and ablation in high alpine mountain environments. Hence, the spatial and temporal variability of the snow cover is significantly modified with respective consequences on the total amount of water in the snow pack, on the temporal dynamics of the runoff and on the energy balance of the surface. For the presented study we used the snow transport model SnowTran-3D in combination with MM5 (Penn State University – National Center for Atmospheric Research MM5 model generated wind fields. In a first step the MM5 wind fields were downscaled by using a semi-empirical approach which accounts for the elevation difference of model and real topography, as well as aspect, inclination and vegetation. The target resolution of 30 m corresponds to the resolution of the best available DEM and land cover map. For the numerical modelling, data of six automatic meteorological stations were used, comprising the winter season (September–August of 2003/04 and 2004/05. In addition we had automatic snow depth measurements and periodic manual measurements of snow courses available for the validation of the results. In this paper we describe the downscaling of the wind fields and discuss the results of the snow transport simulations with respect to the measurements and remotely sensed data.

  13. Limitations of modeling snow in ski resorts

    Science.gov (United States)

    Steiger, Robert; Abegg, Bruno

    2016-04-01

    The body of literature on snow modeling in a ski area operations context has been growing over the last decades in an accelerating speed. The majority of snow model applications for ski areas can be found in the climate change impacts literature. These studies differ in many aspects: the type of model used; the meteorological variables used in the models; the spatial and temporal resolution of the meteorological variables; the method how the climate change signal is derived and applied in the model concept; the number of climate models and emission scenarios used and consequently the handling of uncertainties; the indicators used to interpret the impacts for the skiing tourism industry; the incorporation of adaptation measures (e.g. snowmaking); and the geographical scale of analysis. In this contribution we will present a review of approaches used for modeling snow conditions in a ski area context. The major limitations both from a scientific as well as from a users' perspective will be discussed and solutions for shortcomings of existing approaches will be presented.

  14. Uncertainty in alpine snow mass balance simulations due to snow model parameterisation and windflow representation

    Science.gov (United States)

    Musselman, K. N.; Pomeroy, J. W.; Essery, R.; Leroux, N.

    2013-12-01

    Despite advances in alpine snow modelling there remain two fundamental areas of divergent scientific thought in estimating alpine snow mass balances: i) blowing snow sublimation losses, and ii) wind flow representation. Sublimation calculations have poorly understood humidity feedbacks that vary considerably and mathematical representations of alpine windflow vary in complexity - these differences introduce uncertainty. To better estimate and restrain this uncertainty, a variety of physically based, spatially distributed snowmelt models that consider the physics of wind redistribution and sublimation of blowing snow were evaluated for their ability to simulate seasonal snow distribution and melt patterns in a windy alpine environment in the Canadian Rockies. The primary difference in the snow models was their calculation of blowing snow sublimation losses which ranged from large to small estimates. To examine the uncertainty introduced by windflow calculations on the snow model simulations, each model was forced with output from windflow models of varying computational complexity and physical realism from a terrain-based empirical interpolation of station observations to a simple turbulence model to a computational fluid dynamics model that solves for the Navier-Stokes equations. The high-resolution snow simulations were run over a 1 km2 spatial extent centred on a ridgetop meteorological station within the Marmot Creek Research basin, Alberta, Canada. The three windflow simulations all produced reasonable results compared to wind speeds measured on two opposing slopes (bias better than ×0.3 m s-1; RMSE errors were greatest when forced with output from the empirical wind model and smallest using output from either of the two turbulence models. Simulations with higher blowing snow sublimation rates tended to better match measured SWE at multiple scales, confirming that alpine blowing snow sublimation is an important component of the snow mass balance in this region

  15. New estimations of precipitation and surface sublimation in East Antarctica from snow accumulation measurements

    Energy Technology Data Exchange (ETDEWEB)

    Frezzotti, Massimo; Gragnani, Roberto; Proposito, Marco [l' Energia e l' Ambiente, ' Progetto Clima Globale' , Ente per le Nuove Tecnologie, Rome (Italy); Pourchet, Michel; Gay, Michel; Vincent, Christian; Fily, Michel [CNRS, Laboratoire de Glaciologie et Geophysique de l' Environnement, Saint Martin d' Heres (France); Flora, Onelio [University of Trieste, Dipartimento di Scienze Geologiche, Ambientali e Marine, Trieste (Italy); Gandolfi, Stefano [University of Bologna, Dipartimento di Ingegneria delle Strutture, dei Trasporti, delle Acque, del Rilevamento, del Territorio, Bologna (Italy); Urbini, Stefano [Istituto Nazionale di Geofisica e Vulcanologia, Rome (Italy); Becagli, Silvia; Severi, Mirko; Traversi, Rita; Udisti, Roberto [University of Florence, Dipartimento di Chimica, Florence (Italy)

    2004-12-01

    Surface mass balance (SMB) distribution and its temporal and spatial variability is an essential input parameter in mass balance studies. Different methods were used, compared and integrated (stake farms, ice cores, snow radar, surface morphology, remote sensing) at eight sites along a transect from Terra Nova Bay (TNB) to Dome C (DC) (East Antarctica), to provide detailed information on the SMB. Spatial variability measurements show that the measured maximum snow accumulation (SA) in a 15 km area is well correlated to firn temperature. Wind-driven sublimation processes, controlled by the surface slope in the wind direction, have a huge impact (up to 85% of snow precipitation) on SMB and are significant in terms of past, present and future SMB evaluations. The snow redistribution process is local and has a strong impact on the annual variability of accumulation. The spatial variability of SMB at the kilometre scale is one order of magnitude higher than its temporal variability (20-30%) at the centennial time scale. This high spatial variability is due to wind-driven sublimation. Compared with our SMB calculations, previous compilations generally over-estimate SMB, up to 65% in some areas. (orig.)

  16. Role of Forcing Uncertainty and Background Model Error Characterization in Snow Data Assimilation

    Science.gov (United States)

    Kumar, Sujay V.; Dong, Jiarul; Peters-Lidard, Christa D.; Mocko, David; Gomez, Breogan

    2017-01-01

    Accurate specification of the model error covariances in data assimilation systems is a challenging issue. Ensemble land data assimilation methods rely on stochastic perturbations of input forcing and model prognostic fields for developing representations of input model error covariances. This article examines the limitations of using a single forcing dataset for specifying forcing uncertainty inputs for assimilating snow depth retrievals. Using an idealized data assimilation experiment, the article demonstrates that the use of hybrid forcing input strategies (either through the use of an ensemble of forcing products or through the added use of the forcing climatology) provide a better characterization of the background model error, which leads to improved data assimilation results, especially during the snow accumulation and melt-time periods. The use of hybrid forcing ensembles is then employed for assimilating snow depth retrievals from the AMSR2 (Advanced Microwave Scanning Radiometer 2) instrument over two domains in the continental USA with different snow evolution characteristics. Over a region near the Great Lakes, where the snow evolution tends to be ephemeral, the use of hybrid forcing ensembles provides significant improvements relative to the use of a single forcing dataset. Over the Colorado headwaters characterized by large snow accumulation, the impact of using the forcing ensemble is less prominent and is largely limited to the snow transition time periods. The results of the article demonstrate that improving the background model error through the use of a forcing ensemble enables the assimilation system to better incorporate the observational information.

  17. Role of forcing uncertainty and background model error characterization in snow data assimilation

    Directory of Open Access Journals (Sweden)

    S. V. Kumar

    2017-06-01

    Full Text Available Accurate specification of the model error covariances in data assimilation systems is a challenging issue. Ensemble land data assimilation methods rely on stochastic perturbations of input forcing and model prognostic fields for developing representations of input model error covariances. This article examines the limitations of using a single forcing dataset for specifying forcing uncertainty inputs for assimilating snow depth retrievals. Using an idealized data assimilation experiment, the article demonstrates that the use of hybrid forcing input strategies (either through the use of an ensemble of forcing products or through the added use of the forcing climatology provide a better characterization of the background model error, which leads to improved data assimilation results, especially during the snow accumulation and melt-time periods. The use of hybrid forcing ensembles is then employed for assimilating snow depth retrievals from the AMSR2 instrument over two domains in the continental USA with different snow evolution characteristics. Over a region near the Great Lakes, where the snow evolution tends to be ephemeral, the use of hybrid forcing ensembles provides significant improvements relative to the use of a single forcing dataset. Over the Colorado headwaters characterized by large snow accumulation, the impact of using the forcing ensemble is less prominent and is largely limited to the snow transition time periods. The results of the article demonstrate that improving the background model error through the use of a forcing ensemble enables the assimilation system to better incorporate the observational information.

  18. 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.

  19. Data sets for snow cover monitoring and modelling from the National Snow and Ice Data Center

    Science.gov (United States)

    Holm, M.; Daniels, K.; Scott, D.; McLean, B.; Weaver, R.

    2003-04-01

    A wide range of snow cover monitoring and modelling data sets are pending or are currently available from the National Snow and Ice Data Center (NSIDC). In-situ observations support validation experiments that enhance the accuracy of remote sensing data. In addition, remote sensing data are available in near-real time, providing coarse-resolution snow monitoring capability. Time series data beginning in 1966 are valuable for modelling efforts. NSIDC holdings include SMMR and SSM/I snow cover data, MODIS snow cover extent products, in-situ and satellite data collected for NASA's recent Cold Land Processes Experiment, and soon-to-be-released ASMR-E passive microwave products. The AMSR-E and MODIS sensors are part of NASA's Earth Observing System flying on the Terra and Aqua satellites Characteristics of these NSIDC-held data sets, appropriateness of products for specific applications, and data set access and availability will be presented.

  20. Changes in mid-troposphere snow accumulation on Mt. Logan, Yukon, over the last three centuries

    OpenAIRE

    Holdsworth, Gerald

    1990-01-01

    EXTRACT (SEE PDF FOR FULL ABSTRACT): A net snow accumulation time series is presented. It is derived from a 102.5 m ice core retrieved from Mt. Logan at an altitude of 5340 m a.s.l. Annual increments are identified using stable isotopes, trace chemistry, and beta activity. ... The resulting time series of nearly 300 years seems to indicate a lower mean accumulation from AD 1700 to the mid-19th century than after that time. The last 100 years of the series correlates significantly with cer...

  1. Snow Metamorphism and Albedo Process (SMAP) model for climate studies: Model validation using meteorological and snow impurity data measured at Sapporo, Japan

    Science.gov (United States)

    Niwano, Masashi; Aoki, Teruo; Kuchiki, Katsuyuki; Hosaka, Masahiro; Kodama, Yuji

    2012-09-01

    We developed a multilayered physical snowpack model named Snow Metamorphism and Albedo Process (SMAP), which is intended to be incorporated into general circulation models for climate simulations. To simulate realistic physical states of snowpack, SMAP incorporates a state-of-the-art physically based snow albedo model, which calculates snow albedo and solar heating profile in snowpack considering effects of snow grain size and snow impurities explicitly. We evaluated the performance of SMAP with meteorological and snow impurities (black carbon and dust) input data measured at Sapporo, Japan during two winters: 2007-2008 and 2008-2009, and found SMAP successfully reproduced all observed variations of physical properties of snowpack for both winters. We have thus confirmed that SMAP is suitable for climate simulations. With SMAP, we also investigated the effects of snow impurities on snowmelt at Sapporo during the two winters. We found that snowpack durations at Sapporo were shortened by 19 days during the 2007-2008 winter and by 16 days during the 2008-2009 winter due to radiative forcings caused by snow impurities. The estimated radiative forcings due to snow impurities during the accumulation periods were 3.7 W/m2 (it corresponds to albedo reduction in 0.05) and 3.2 W/m2 (albedo reduction in 0.05) for the 2007-2008 and 2008-2009 winters, respectively. While during the ablation periods they were 25.9 W/m2 (albedo reduction in 0.18) and 21.0 W/m2 (albedo reduction in 0.17) for each winter, respectively.

  2. Importance of temporal resolution of meteorological forcings for physics-based snow modeling

    Science.gov (United States)

    Sohrabi, M.; Benjankar, R. M.; Kumar, M.; Marks, D. G.; Kormos, P.; Tonina, D.

    2015-12-01

    In alpine regions, snow delays hydrological responses to precipitation and controls initiation and length of the growing season. Therefore, precise simulations of snow accumulation and melt are crucial for understanding hydrological dynamics and predicting hydrologic response from watersheds. These predictions are important for water resource management and for ecological studies of vegetation distribution, growth and for wildlife habitat. Snow models require fine temporal resolution of meteorological inputs to capture diurnal changes. However, lack of meteorological data at fine-temporal resolution may force the use of coarser than hourly data. The objective of this work is to understand what sort of information can be lost over the watershed depending on the temporal resolution of meteorological inputs, for a range of hydroclimatic and topographic conditions. To address this goal, a spatially distributed and physics-based snow model (iSnobal) was run using 1-, 3- and 6-hourly meteorological inputs for a wet, average and a dry year over Boise River Basin (BRB), Idaho, USA. Simulated snow variables such as Snow Water Equivalent (SWE) and Surface Water Input (SWI - melt draining from the snowcover plus rain on bare ground) were averaged over 3 elevation bands including rain dominated (≤1400m), rain-snow transition (>1400 and ≤1900m) and snow dominated (>1900m). Except at the rain dominated band, using 6-hr inputs causes considerable overestimation of SWE and SWI, particularly in the wet year. The results show that at the rain-snow transition and snow dominated bands at least 3-hr meteorological data are necessary for snow modeling, due to strong diurnal changes in meteorological variables at these elevations. However, using course temporal resolution data for the rain dominated band made only a small difference in results.

  3. An Integrated Snow Radiance and Snow Physics Modeling Framework for Cold Land Surface Modeling

    Science.gov (United States)

    Kim, Edward J.; Tedesco, Marco

    2006-01-01

    Recent developments in forward radiative transfer modeling and physical land surface modeling are converging to allow the assembly of an integrated snow/cold lands modeling framework for land surface modeling and data assimilation applications. 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. Together these form a flexible framework for self-consistent remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. Each element of this framework is modular so the choice of element can be tailored to match the emphasis of a particular study. For example, within our framework, four choices of a FRTM are available to simulate the brightness temperature of snow: Two models are available to model the physical evolution of the snowpack and underlying soil, and two models are available to handle the water/energy balance at the land surface. Since the framework is modular, other models-physical or statistical--can be accommodated, too. 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 at the NASA Goddard Space Flight Center. The advantages of such an integrated modular framework built on the LIS will be described through examples-e.g., studies to analyze snow field experiment observations, and simulations of future satellite missions for snow and cold land processes.

  4. Comparison of a coupled snow thermodynamic and radiative transfer model with in situ active microwave signatures of snow-covered smooth first-year sea ice

    Science.gov (United States)

    Fuller, M. C.; Geldsetzer, T.; Yackel, J.; Gill, J. P. S.

    2015-11-01

    Within the context of developing data inversion and assimilation techniques for C-band backscatter over sea ice, snow physical models may be used to drive backscatter models for comparison and optimization with satellite observations. Such modeling has the potential to enhance understanding of snow on sea-ice properties required for unambiguous interpretation of active microwave imagery. An end-to-end modeling suite is introduced, incorporating regional reanalysis data (NARR), a snow model (SNTHERM89.rev4), and a multilayer snow and ice active microwave backscatter model (MSIB). This modeling suite is assessed against measured snow on sea-ice geophysical properties and against measured active microwave backscatter. NARR data were input to the SNTHERM snow thermodynamic model in order to drive the MSIB model for comparison to detailed geophysical measurements and surface-based observations of C-band backscatter of snow on first-year sea ice. The NARR variables were correlated to available in situ measurements with the exception of long-wave incoming radiation and relative humidity, which impacted SNTHERM simulations of snow temperature. SNTHERM snow grain size and density were comparable to observations. The first assessment of the forward assimilation technique developed in this work required the application of in situ salinity profiles to one SNTHERM snow profile, which resulted in simulated backscatter close to that driven by in situ snow properties. In other test cases, the simulated backscatter remained 4-6 dB below observed for higher incidence angles and when compared to an average simulated backscatter of in situ end-member snow covers. Development of C-band inversion and assimilation schemes employing SNTHERM89.rev4 should consider sensitivity of the model to bias in incoming long-wave radiation, the effects of brine, and the inability of SNTHERM89.Rev4 to simulate water accumulation and refreezing at the bottom and mid-layers of the snowpack. These impact

  5. Modeling the snow cover in climate studies: 2. The sensitivity to internal snow parameters and interface processes

    Science.gov (United States)

    Loth, Bettina; Graf, Hans-F.

    1998-05-01

    In order to find an optimal complexity for snow-cover models in climate studies, the influence of single snow processes on both the snow mass balance and the energy fluxes between snow surface and atmosphere has been investigated. Using a sophisticated model, experiments were performed under several different atmospheric and regional conditions (Arctic, midlatitudes, alpine regions). A high simulation quality can be achieved with a multilayered snow-cover model resolving the internal snow processes (cf. part 1,[Loth and Graf, this issue]). Otherwise, large errors can occur, mostly in zones which are of paramount importance for the entire climate dynamics. Owing to simplifications of such a model, the mean energy balance of the snow cover, the turbulent heat fluxes, and the long-wave radiation at the snow surface may alter by between 1 W/m2 and 8 W/m2. The snow-surface temperatures can be systematically changed by about 10 K.

  6. Consequences of declining snow accumulation for water balance of mid-latitude dry regions

    Science.gov (United States)

    Schlaepfer, Daniel R.; Lauenroth, William K.; Bradford, John B.

    2012-01-01

    Widespread documentation of positive winter temperature anomalies, declining snowpack and earlier snow melt in the Northern Hemisphere have raised concerns about the consequences for regional water resources as well as wildfire. A topic that has not been addressed with respect to declining snowpack is effects on ecosystem water balance. Changes in water balance dynamics will be particularly pronounced at low elevations of mid-latitude dry regions because these areas will be the first to be affected by declining snow as a result of rising temperatures. As a model system, we used simulation experiments to investigate big sagebrush ecosystems that dominate a large fraction of the semiarid western United States. Our results suggest that effects on future ecosystem water balance will increase along a climatic gradient from dry, warm and snow-poor to wet, cold and snow-rich. Beyond a threshold within this climatic gradient, predicted consequences for vegetation switched from no change to increasing transpiration. Responses were sensitive to uncertainties in climatic prediction; particularly, a shift of precipitation to the colder season could reduce impacts of a warmer and snow-poorer future, depending on the degree to which ecosystem phenology tracks precipitation changes. Our results suggest that big sagebrush and other similar semiarid ecosystems could decrease in viability or disappear in dry to medium areas and likely increase only in the snow-richest areas, i.e. higher elevations and higher latitudes. Unlike cold locations at high elevations or in the arctic, ecosystems at low elevations respond in a different and complex way to future conditions because of opposing effects of increasing water-limitation and a longer snow-free season. Outcomes of such nonlinear interactions for future ecosystems will likely include changes in plant composition and productivity, dynamics of water balance, and availability of water resources.

  7. Arctic Snow Microstructure Experiment for the development of snow emission modelling

    Science.gov (United States)

    Maslanka, William; Leppänen, Leena; Kontu, Anna; Sandells, Mel; Lemmetyinen, Juha; Schneebeli, Martin; Proksch, Martin; Matzl, Margret; Hannula, Henna-Reetta; Gurney, Robert

    2016-04-01

    The Arctic Snow Microstructure Experiment (ASMEx) took place in Sodankylä, Finland in the winters of 2013-2014 and 2014-2015. Radiometric, macro-, and microstructure measurements were made under different experimental conditions of homogenous snow slabs, extracted from the natural seasonal taiga snowpack. Traditional and modern measurement techniques were used for snow macro- and microstructure observations. Radiometric measurements of the microwave emission of snow on reflector and absorber bases were made at frequencies 18.7, 21.0, 36.5, 89.0, and 150.0 GHz, for both horizontal and vertical polarizations. Two measurement configurations were used for radiometric measurements: a reflecting surface and an absorbing base beneath the snow slabs. Simulations of brightness temperatures using two microwave emission models, the Helsinki University of Technology (HUT) snow emission model and Microwave Emission Model of Layered Snowpacks (MEMLS), were compared to observed brightness temperatures. RMSE and bias were calculated; with the RMSE and bias values being smallest upon an absorbing base at vertical polarization. Simulations overestimated the brightness temperatures on absorbing base cases at horizontal polarization. With the other experimental conditions, the biases were small, with the exception of the HUT model 36.5 GHz simulation, which produced an underestimation for the reflector base cases. This experiment provides a solid framework for future research on the extinction of microwave radiation inside snow.

  8. 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.

  9. 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.

  10. 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

  11. Phase-field modeling of dry snow metamorphism.

    Science.gov (United States)

    Kaempfer, Thomas U; Plapp, Mathis

    2009-03-01

    Snow on the ground is a complex three-dimensional porous medium consisting of an ice matrix formed by sintered snow crystals and a pore space filled with air and water vapor. If a temperature gradient is imposed on the snow, a water vapor gradient in the pore space is induced and the snow microstructure changes due to diffusion, sublimation, and resublimation: the snow metamorphoses. The snow microstructure, in turn, determines macroscopic snow properties such as the thermal conductivity of a snowpack. We develop a phase-field model for snow metamorphism that operates on natural snow microstructures as observed by computed x-ray microtomography. The model takes into account heat and mass diffusion within the ice matrix and pore space, as well as phase changes at the ice-air interfaces. Its construction is inspired by phase-field models for alloy solidification, which allows us to relate the phase-field to a sharp-interface formulation of the problem without performing formal matched asymptotics. To overcome the computational difficulties created by the large difference between diffusional and interface-migration time scales, we introduce a method for accelerating the numerical simulations that formally amounts to reducing the heat- and mass-diffusion coefficients while maintaining the correct interface velocities. The model is validated by simulations for simple one- and two-dimensional test cases. Furthermore, we perform qualitative metamorphism simulations on natural snow structures to demonstrate the potential of the approach.

  12. 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.

  13. NORMALIZED DIFFERENCE SNOW INDEX SIMULATION FOR SNOW-COVER MAPPING IN FOREST BY GEOSAIL MODEL

    Institute of Scientific and Technical Information of China (English)

    CAO Yun-gang; LIU Chuang

    2006-01-01

    The snow-cover mapping in forest area is always one of the difficult points for optical satellite remote sensing. To investigate reflectance variability and to improve the mapping of snow in forest area, GeoSail model was used to simulate the reflectance of a snow-covered forest. Using this model, the effects of varying canopy density, solar illumination and view geometry on the performance of the MODIS (Moderate-resolution Imaging Spectroradiometer)snow-cover mapping algorithm were investigated. The relationship between NDSI (Normalized Difference Snow Index), NDVI (Normalized Difference Vegetation Index) and snow fraction was discussed in detail. Results indicated that the weak performance would be achieved if fixed criteria were used for different regions especially in the complicated land cover components. Finally, some suggestions to MODIS SNOWMAP algorithm were put forward to improve snow mapping precision in forest area based on the simulation, for example, new criteria should be used in coniferous forest, that is, NDSI greater than 0.3 and NDVI greater than zero. Otherwise, a threshold on view zenith angle may be used in the criteria such as 45°.

  14. Observations and modelling of snow avalanche entrainment

    Directory of Open Access Journals (Sweden)

    B. Sovilla

    2002-01-01

    Full Text Available In this paper full scale avalanche dynamics measurements from the Italian Pizzac and Swiss Vallée de la Sionne test sites are used to develop a snowcover entrainment model. A detailed analysis of three avalanche events shows that snowcover entrainment at the avalanche front appears to dominate over bed erosion at the basal sliding surface. Furthermore, the distribution of mass within the avalanche body is primarily a function of basal friction. We show that the mass distribution in the avalanche changes the flow dynamics significantly. Two different dynamical models, the Swiss Voellmy-fluid model and the Norwegian NIS model, are used to back calculate the events. Various entrainment methods are investigated and compared to measurements. We demon-strate that the Norwegian NIS model is clearly better able to simulate the events once snow entrainment has been included in the simulations.

  15. Revisiting Runoff Model Calibration: Airborne Snow Observatory Results Allow Improved Modeling Results

    Science.gov (United States)

    McGurk, B. J.; Painter, T. H.

    2014-12-01

    Deterministic snow accumulation and ablation simulation models are widely used by runoff managers throughout the world to predict runoff quantities and timing. Model fitting is typically based on matching modeled runoff volumes and timing with observed flow time series at a few points in the basin. In recent decades, sparse networks of point measurements of the mountain snowpacks have been available to compare with modeled snowpack, but the comparability of results from a snow sensor or course to model polygons of 5 to 50 sq. km is suspect. However, snowpack extent, depth, and derived snow water equivalent have been produced by the NASA/JPL Airborne Snow Observatory (ASO) mission for spring of 20013 and 2014 in the Tuolumne River basin above Hetch Hetchy Reservoir. These high-resolution snowpack data have exposed the weakness in a model calibration based on runoff alone. The U.S. Geological Survey's Precipitation Runoff Modeling System (PRMS) calibration that was based on 30-years of inflow to Hetch Hetchy produces reasonable inflow results, but modeled spatial snowpack location and water quantity diverged significantly from the weekly measurements made by ASO during the two ablation seasons. The reason is that the PRMS model has many flow paths, storages, and water transfer equations, and a calibrated outflow time series can be right for many wrong reasons. The addition of a detailed knowledge of snow extent and water content constrains the model so that it is a better representation of the actual watershed hydrology. The mechanics of recalibrating PRMS to the ASO measurements will be described, and comparisons in observed versus modeled flow for both a small subbasin and the entire Hetch Hetchy basin will be shown. The recalibrated model provided a bitter fit to the snowmelt recession, a key factor for water managers as they balance declining inflows with demand for power generation and ecosystem releases during the final months of snow melt runoff.

  16. Modelling avalanche danger and understanding snow depth variability

    OpenAIRE

    2010-01-01

    This thesis addresses the causes of avalanche danger at a regional scale. Modelled snow stratigraphy variables were linked to [1] forecasted avalanche danger and [2] observed snowpack stability. Spatial variability of snowpack parameters in a region is an additional important factor that influences the avalanche danger. Snow depth and its change during individual snow fall periods are snowpack parameters which can be measured at a high spatial resolution. Hence, the spatial distribution of sn...

  17. Arctic Snow Microstructure Experiment for the development of snow emission modelling

    Directory of Open Access Journals (Sweden)

    W. Maslanka

    2015-12-01

    Full Text Available The Arctic Snow Microstructure Experiment (ASMEx took place in Sodankylä, Finland in the winters of 2013–2014 and 2014–2015. Radiometric, macro-, and microstructure measurements were made under different experimental conditions of homogenous snow slabs, extracted from the natural seasonal taiga snowpack. Traditional and modern measurement techniques were used for snow macro- and microstructure observations. Radiometric measurements of the microwave emission of snow on reflector and absorber bases were made at frequencies 18.7, 21.0, 36.5, 89.0 and 150.0 GHz, for both horizontal and vertical polarizations. Two measurement configurations were used for radiometric measurements: a reflecting surface and an absorbing base beneath the snow slabs. Simulations of brightness temperatures using two microwave emission models were compared to observed brightness temperatures. RMSE and bias were calculated; with the RMSE and bias values being smallest upon an absorbing base at vertical polarization. Simulations overestimated the brightness temperatures on absorbing base cases at horizontal polarization. With the other experimental conditions, the biases were small; with the exception of the HUT model 36.5 GHz simulation, which produced an underestimation for the reflector base cases. This experiment provides a solid framework for future research on the extinction of microwave radiation inside snow.

  18. Calibration of a distributed snow model using MODIS snow covered area data

    Science.gov (United States)

    Franz, Kristie J.; Karsten, Logan R.

    2013-06-01

    Spatial ground-based observations of snow are often limited at the watershed-scale, therefore the snow modeling component of a hydrologic modeling system is often calibrated along with the rainfall-runoff model using watershed discharge observations. This practice works relatively well for lumped modeling applications when the accuracy of sub-watershed processes is generally not of concern. However, with the increasing use of distributed models, realistic representation of processes, such as snow areal depletion, become more important. In this study, we test the use of snow covered area (SCA) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite for calibration of four key parameters in the distributed US National Weather Service (NWS) SNOW17 model in the North Fork of the American River basin in California, USA. Three tests are conducted; two rely solely on MODIS SCA data and one includes discharge in the calibration procedure. The three calibrations are compared to the use of parameters obtained from the NWS California Nevada River Forecast Center (CNRFC). The calibration approach that utilizes both MODIS SCA and discharge data produces the most accurate spatial (gridded) SCA and basin discharge simulations but not the best SCA summary statistics. In general it was found that improvement in simulated SCA when averaged and evaluated by elevation zone using standard summary statistics, does not necessarily coincide with more accurate discharge simulations.

  19. Inter-Model Diagnostics for Two Snow Models Across Multiple Western U.S. Locations and Implications for Management

    Science.gov (United States)

    Houle, E. S.; Livneh, B.; Kasprzyk, J. R.

    2014-12-01

    In the western United States, water resource management is increasingly reliant on numerical modeling of hydrological processes, namely snow accumulation and ablation. We seek to advance a framework for providing model diagnostics for such systems by combining an improved understanding of model structural differences (i.e., conceptual vs. physically based) and parameter sensitivities. The two snow models used in this study are SNOW-17, a conceptual degree-day model, and the Variable Infiltration Capacity (VIC) snow model, which is physically based and solves the full water and energy balances. To better understand the performance of these models, several approaches will be used. For the conceptual model, global sensitivity analysis methods (e.g., Sobol' and Method of Morris), and a multi-objective calibration will be applied to identify important parameters and show calibrated parameter values. For the physically based model, we will contribute a novel exploration of some parameters that can be adjusted within the model, including the liquid water holding capacity, the density of newly fallen snow, and the snow roughness. Additionally, the VIC model will be run with explicit radiation inputs at selected sites. For each model run, snow sensitivities and errors (i.e., snow water equivalent results) will be translated into estimated changes in annual water yield for the study areas. Accurately predicting water yield is essential for water management, and it is used here as a practical measure to determine the importance of model parameter sensitivity and calibration. The analysis will be conducted across a range of snow-dominated locations representing a variety of climates across the western United States (e.g. continental, maritime, alpine).

  20. 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

  1. ESCIMO.spread (v2): parameterization of a spreadsheet-based energy balance snow model for inside-canopy conditions

    OpenAIRE

    Marke, T.; E. Mair; K. Förster; F. Hanzer; J. Garvelmann; Pohl, S.; M. Warscher; Strasser, U.

    2015-01-01

    This article describes the extension of the spreadsheet-based point energy balance snow model ESCIMO.spread by (i) an advanced approach for precipitation phase detection, (ii) a concept for cold and liquid water storage consideration and (iii) a canopy sub-model that allows to quantify the effect of a forest canopy on the meteorological conditions inside the forest as well as the simulation of snow accumulation and ablation inside a forest stand. To provide ...

  2. Multi-objective calibration of a spatially semi-distributed rainfall runoff model and its snow water equivalent module.

    Science.gov (United States)

    Valent, Peter; Výleta, Roman; Danáčová, Michaela; Sleziak, Patrik; Kotríková, Katarína

    2016-04-01

    The snow cover is an important environmental and water management factor in mid latitudes. From the water management point of view the impact of the water accumulated in the snow cover is significant mainly during the spring season when it's melting causes a significant flooding threat when melting is accompanied by precipitation (rain on snow floods). Modelling of spatial and temporal distribution of the snow water equivalent is therefore an important component of rainfall-runoff models. The main objective of this work was to study the possibility to include information on the spatial distribution of the snow cover into runoff modelling and evaluate the quality of the simulation of both of the snow water equivalent and catchment runoff. A conceptual semi-distributed rainfall-runoff model was used in order to model the snow water equivalent in a daily time step. In order to calibrate and validate the model a multi-calibration techniques were used taking into account both runoff from the catchment and the observed values of the snow water equivalents and snow heights in elevation and vegetation zones. The multi-objective calibration linearly combines two optimization functions and aggregates them into one. While the first optimization function compares observed and simulated flows, the second one is based on an indirect comparison of a snow water equivalent simulated by a rainfall-runoff model and the snow cover heights measured in rainfall gauges within the catchment. The aim of the paper is to optimize the ratio of the weights in the optimization. The methodology was tested on the Upper Hron River catchment, which could be considered as a mountainous catchment.

  3. Modelling the isotopic composition of snow using backward trajectories : a particular precipitation event in Dronning Maud Land, Antarctica

    NARCIS (Netherlands)

    Helsen, MM; Van de Wal, RSW; Van den Broeke, MR; Kerstel, ERT; Masson-Delmotte, [No Value; Meijer, HAJ; Reijmer, CH; Scheele, MP; Jacka, J

    2004-01-01

    We consider a specific accumulation event that occurred in January 2002 in western Dronning Maud Land, Antarctica. Snow samples were obtained a few days after accumulation. We combine meteorological analyses and isotopic modelling to describe the isotopic composition of moisture during transport. Ba

  4. Role of snow cover on urban heat island intensity investigated by urban canopy model with snow effects

    Science.gov (United States)

    Sato, T.; Mori, K.

    2015-12-01

    Urban heat islands have been investigated around the world including snowy regions. However, the relationship between urban heat island and snow cover remains unclear. This study examined the effect of snow cover in urban canopy on energy budget in urban areas of Sapporo, north Japan by 1km mesh WRF experiments. The modified urban canopy model permits snow cover in urban canopy by the modification of surface albedo, surface emissivity, and thermal conductivity for roof and road according to snow depth and snow water equivalent. The experiments revealed that snow cover in urban canopy decreases urban air temperature more strongly for daily maximum temperature (0.4-0.6 K) than for daily minimum temperature (0.1-0.3 K). The high snow albedo reduces the net radiation at building roof, leading to decrease in sensible heat flux. Interestingly, the cooling effect of snow cover compensates the warming effect by anthropogenic heat release in Sapporo, suggesting the importance of snow cover treatment in urban canopy model as well as estimating accurate anthropogenic heat distributions. In addition, the effect of road snow clearance tends to increase nocturnal surface air temperature in urban areas. A possible role of snow cover on urban heat island intensity was evaluated by two experiments with snow cover (i.e., realistic condition) and without snow cover in entire numerical domain. The snow cover decreases surface air temperature more in rural areas than in urban areas, which was commonly seen throughout a day, with stronger magnitude during nighttime than daytime, resulting in intensifying urban heat island by 4.0 K for daily minimum temperature.

  5. Effects of snow accumulation on soil temperature and change of salinity in frozen soil from laboratory experiments

    Science.gov (United States)

    Harada, K.; Sato, E.; Ishii, M.; Nemoto, M.; Mochizuki, S.

    2008-12-01

    In order to clarify the effect of snow depth on the ground temperature, snowfalls were occurred on soil samples using an artificial snowfall machine in the laboratory and variations of soil temperatures up to 30cm were measured during snowfall. The snow types used here were dendrites (type A) and sphere (type B). The snow depths on the soil surface were 10cm and 30cm for each snow type, so four deferent experimental results were obtained. At each experiment, two samples with deferent initial volumetric water content were prepared, about 10% and 20%. The initial soil temperature was set to 5°C and temperature in the laboratory was kept at -10°C. Soil temperatures were measured at the depths of 0cm, 10cm, 20cm and 30cm during the snowfall, and continuous measurements were conducted for ten hours after the stop of snowfall. From the experiments, it is confirmed that the soil temperature strongly depended on the depths of snow on the surface, density and water content. The soil sample using the type A with the depth of 30cm snow accumulation had the highest temperature at the surface, followed by the type A with 10cm snow, type B with 30cm snow and type B with 10cm snow. It was also pointed that temperature of the high water content samples showed the high temperature decrease compared with the low water one due to the high heat capacity except for the sample using type A with 10cm snow. Numerical calculation will be needed to explain these results. In addition, another experiment will be carried out to clarify the change of salinity during soil freezing with snow accumulation. The method to measure the salinity of soil is to measure the electrical conductivity of soil and volumetric water content at the same depth. The temperature condition in the cooling bath is ranged between -10 and 5°C and changed in 24 hours. Firstly, the temperature profiles will be measured to detect the frozen front, then measurements will start and discuss the results.

  6. Integrated snow and hydrology modeling for climate change impact assessment in Oregon Cascades

    Science.gov (United States)

    Safeeq, M.; Grant, G.; Lewis, S.; Nolin, A. W.; Hempel, L. A.; Cooper, M.; Tague, C.

    2014-12-01

    In the Pacific Northwest (PNW), increasing temperatures are expected to alter the hydrologic regimes of streams by shifting precipitation from snow to rain and forcing earlier snowmelt. How are such changes likely to affect peak flows across the region? Shifts in peak flows have obvious implications for changing flood risk, but are also likely to affect channel morphology, sediment transport, aquatic habitat, and water quality, issues with potentially high economic and environmental cost. Our goal, then, is to rigorously evaluate sensitivity to potential peak flow changes across the PNW. We address this by developing a detailed representation of snowpack and streamflow evolution under varying climate scenarios using a cascade-modeling approach. We have identified paired watersheds located on the east (Metolius River) and west (McKenzie River) sides of the Cascades, representing dry and wet climatic regimes, respectively. The tributaries of these two rivers are comprised of contrasting hydrologic regimes: surface-runoff dominated western cascades and deep-groundwater dominated high-cascades systems. We use a detailed hydro-ecological model (RHESSys) in conjunction with a spatially distributed snowpack evolution model (SnowModel) to characterize the peak flow behavior under present and future climate. We first calibrated and validated the SnowModel using observed temperature, precipitation, snow water equivalent, and manual snow survey data sets. We then employed a multi-objective calibration strategy for RHESSys using the simulated snow accumulation and melt from SnowModel and observed streamflow. The Nash-Sutcliffe Efficiency between observed and simulated streamflow varies between 0.5 in groundwater and 0.71 in surface-runoff dominated systems. The initial results indicate enhanced peak flow under future climate across all basins, but the magnitude of increase varies by the level of snowpack and deep-groundwater contribution in the watershed. Our continuing effort

  7. Electromagnetic reflection from multi-layered snow models

    Science.gov (United States)

    Linlor, W. I.; Jiracek, G. R.

    1975-01-01

    The remote sensing of snow-pack characteristics with surface installations or an airborne system could have important applications in water-resource management and flood prediction. To derive some insight into such applications, the electromagnetic response of multilayered snow models is analyzed in this paper. Normally incident plane waves at frequencies ranging from 1 MHz to 10 GHz are assumed, and amplitude reflection coefficients are calculated for models having various snow-layer combinations, including ice layers. Layers are defined by thickness, permittivity, and conductivity; the electrical parameters are constant or prescribed functions of frequency. To illustrate the effect of various layering combinations, results are given in the form of curves of amplitude reflection coefficients versus frequency for a variety of models. Under simplifying assumptions, the snow thickness and effective dielectric constant can be estimated from the variations of reflection coefficient as a function of frequency.

  8. Accumulation variability over a small area in east Dronning Maud Land, Antarctica, as determined from shallow firn cores and snow pits : some implications for ice-core records

    NARCIS (Netherlands)

    Karlof, Lars; Isaksson, Elisabeth; Winther, Jan-Gunnar; Gundestrup, Niels; Meijer, Harro A. J.; Mulvaney, Robert; Pourchet, Michel; Hofstede, Coen; Lappegard, Gaute; Pettersson, Rickard; Van den Broeke, Michiel; Van De Wal, Roderik S. W.

    2005-01-01

    We investigate and quantify the variability of snow accumulation rate around a medium-depth firn core (1160 m) drilled in east Dronning Maud Land, Antarctica (75 degrees 00'S, 15 degrees 00'E; 3470 m h.a.e. (ellipsoidal height)). We present accumulation data from five snow pits and five shallow (20

  9. Accumulation variability over a small area in east Dronning Maud Land, Antarctica, as determined from shallow firn cores and snow pits : some implications for ice-core records

    NARCIS (Netherlands)

    Karlof, Lars; Isaksson, Elisabeth; Winther, Jan-Gunnar; Gundestrup, Niels; Meijer, Harro A. J.; Mulvaney, Robert; Pourchet, Michel; Hofstede, Coen; Lappegard, Gaute; Pettersson, Rickard; Van den Broeke, Michiel; Van De Wal, Roderik S. W.

    2005-01-01

    We investigate and quantify the variability of snow accumulation rate around a medium-depth firn core (1160 m) drilled in east Dronning Maud Land, Antarctica (75 degrees 00'S, 15 degrees 00'E; 3470 m h.a.e. (ellipsoidal height)). We present accumulation data from five snow pits and five shallow (20

  10. A regional climate model hindcast for Siberia – assessing the added value of snow water equivalent using ESA GlobSnow and reanalyses

    Directory of Open Access Journals (Sweden)

    K. Klehmet

    2012-11-01

    Full Text Available This study analyzes the added value of a regional climate model hindcast of CCLM compared to global reanalyses in providing a reconstruction of recent past snow water equivalent (SWE for Siberia. Consistent regional climate data in time and space is necessary due to lack of station data in that region. We focus on SWE since it represents an important snow cover parameter in a region where snow has the potential to feed back to the climate of the whole Northern Hemisphere. The simulation was performed in a 50 km grid spacing for the period 1948 to 2010 using NCEP Reanalysis 1 as boundary forcing. Daily observational reference data for the period of 1987–2010 was obtained by the satellite derived SWE product of ESA DUE GlobSnow that enables a large scale assessment. The analyses includes comparisons of the distribution of snow cover extent, example time series of monthly SWE for January and April, regional characteristics of long-term monthly mean, standard deviation and temporal correlation averaged over subregions. SWE of CCLM is compared against the SWE information of NCEP-R1 itself and three more reanalyses (NCEP-R2, NCEP-CFSR, ERA-Interim. We demonstrate a significant added value of the CCLM hindcast during snow accumulation period shown for January for many subregions compared to SWE of NCEP-R1. NCEP-R1 mostly underestimates SWE during whole snow season. CCLM overestimates SWE compared to the satellite-derived product during April – a month representing the beginning of snow melt in southern regions. We illustrate that SWE of the regional hindcast is more consistent in time than ERA-Interim and NCEP-R2 and thus add realistic detail.

  11. Acidobacteria dominate the active bacterial communities of Arctic tundra with widely divergent winter-time snow accumulation and soil temperatures.

    Science.gov (United States)

    Männistö, Minna K; Kurhela, Emilia; Tiirola, Marja; Häggblom, Max M

    2013-04-01

    The timing and extent of snow cover is a major controller of soil temperature and hence winter-time microbial activity and plant diversity in Arctic tundra ecosystems. To understand how snow dynamics shape the bacterial communities, we analyzed the bacterial community composition of windswept and snow-accumulating shrub-dominated tundra heaths of northern Finland using DNA- and RNA-based 16S rRNA gene community fingerprinting (terminal restriction fragment polymorphism) and clone library analysis. Members of the Acidobacteria and Proteobacteria dominated the bacterial communities of both windswept and snow-accumulating habitats with the most abundant phylotypes corresponding to subdivision (SD) 1 and 2 Acidobacteria in both the DNA- and RNA-derived community profiles. However, different phylotypes within Acidobacteria were found to dominate at different sampling dates and in the DNA- vs. RNA-based community profiles. The results suggest that different species within SD1 and SD2 Acidobacteria respond to environmental conditions differently and highlight the wide functional diversity of these organisms even within the SD level. The acidic tundra soils dominated by ericoid shrubs appear to select for diverse stress-tolerant Acidobacteria that are able to compete in the nutrient poor, phenolic-rich soils. Overall, these communities seem stable and relatively insensitive to the predicted changes in the winter-time snow cover.

  12. Microwave snow emission modeling uncertainties in boreal and subarctic environments

    Directory of Open Access Journals (Sweden)

    A. Roy

    2015-10-01

    Full Text Available This study aims to better understand and quantify the uncertainties in microwave snow emission models using the Dense Media Radiative Theory-Multilayer model (DMRT-ML with in situ measurements of snow properties. We use surface-based radiometric measurements at 10.67, 19 and 37 GHz in boreal forest and subarctic environments and a new in situ dataset of measurements of snow properties (profiles of density, snow grain size and temperature, soil characterization and ice lens detection acquired in the James Bay and Umijuaq regions of Northern Québec, Canada. A snow excavation experiment – where snow was removed from the ground to measure the microwave emission of bare frozen ground – shows that small-scale spatial variability in the emission of frozen soil is small. Hence, variability in the emission of frozen soil has a small effect on snow-covered brightness temperature (TB. Grain size and density measurement errors can explain the errors at 37 GHz, while the sensitivity of TB at 19 GHz to snow increases during the winter because of the snow grain growth that leads to scattering. Furthermore, the inclusion of observed ice lenses in DMRT-ML leads to significant improvements in the simulations at horizontal polarization (H-pol for the three frequencies (up to 20 K of root mean square error. However, the representation of the spatial variability of TB remains poor at 10.67 and 19 GHz at H-pol given the spatial variability of ice lens characteristics and the difficulty in simulating snowpack stratigraphy related to the snow crust. The results also show that for ground-based radiometric measurements, forest emission reflected by the surface leads to TB underestimation of up to 40 K if neglected. We perform a comprehensive analysis of the components that contribute to the snow-covered microwave signal, which will help to develop DMRT-ML and to improve the required field measurements. The analysis shows that a better consideration of ice lenses and

  13. Modeling drifting snow in Antarctica with a regional climate model: 2. Results

    NARCIS (Netherlands)

    Lenaerts, J.T.M.; van den Broeke, M.R.

    2012-01-01

    This paper presents a model study of the impact of drifting snow on the lower atmosphere, surface snow characteristics, and surface mass balance of Antarctica. We use the regional atmospheric climate model RACMO2.1/ANT with a high horizontal resolution (27 km), equipped with a drifting snow routine

  14. Modeling drifting snow in Antarctica with a regional climate model: 2. Results

    NARCIS (Netherlands)

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

    2012-01-01

    This paper presents a model study of the impact of drifting snow on the lower atmosphere, surface snow characteristics, and surface mass balance of Antarctica. We use the regional atmospheric climate model RACMO2.1/ANT with a high horizontal resolution (27 km), equipped with a drifting snow routine

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

    Science.gov (United States)

    Bock, Josué; Savarino, Joël; Picard, Ghislain

    2016-04-01

    Snowpack is a multiphase (photo)chemical 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 firn. However, the current understanding of these physicochemical processes remains very poor. Several modelling studies have attempted to reproduce (photo)chemical reactions inside snow grains, but these required strong assumptions to characterise snow reactive properties, which are not well defined. Physical processes such as adsorption, solid state diffusion and co-condensation also affect snow chemical composition. We developed a model including a physically based parameterisation of these air-snow exchange processes for nitrate. This modelling study divides into two distinct parts: firstly, surface concentration of nitrate adsorbed onto snow is calculated using existing isotherm parametrisation. Secondly, bulk concentration of nitrate in solid solution into the ice matrix is modelled. In this second approach, solid state diffusion drives the evolution of nitrate concentration inside a layered spherical snow grain. A physically-based parameterisation defining the concentration at the air-snow interface was developed to account for the the co-condensation process. The model uses as input a one-year long time series of atmospheric nitrate concentration measured at Dome C, Antarctica. The modelled nitrate concentration in surface snow is compared to field measurements. We show that on the one hand, the adsorption of nitric acid on the surface of the snow grains fails to fit the observed variations. During winter and spring, the modelled adsorbed concentration of nitrate is 2.5 and 8.3-fold higher than the measured one, respectively. A strong diurnal variation driven by the temperature cycle and a peak occurring in early spring are two other

  16. The value of snow cover maps for hydrological model calibration in snow dominated catchments in Central Asia

    Science.gov (United States)

    Duethmann, Doris; Güntner, Andreas; Peters, Juliane; Vorogushyn, Sergiy

    2013-04-01

    This study aims at investigating the value of snow cover data in addition to discharge data for the calibration of a hydrological model in six headwater catchments of the Karadarya basin, Central Asia. If a hydrological model is to be used for the investigation of potential impacts of climate change, it is important that also internal variables are simulated correctly. Snow melt is of particular relevance, as it is probably the most important runoff generation process in these catchments. The study investigates whether there is a trade-off between good simulations with respect to discharge and with respect to snow cover area. Furthermore, we are interested in the information content of snow cover data, i.e. how many snow cover images would be sufficient for effective calibration of a hydrological model. As suitable precipitation data for the study area are only available up to 1990, MODIS snow cover data could not be used and we instead resorted to AVHRR data. Processing of the AVHRR snow cover data is time consuming, because georeferencing has to be performed manually. If only few images could already exclude parameter sets resulting in low model performance with respect to snow cover area, this would be a very valuable piece of information. In order to investigate this, a varying number of snow cover images is used for model calibration within a Monte-Carlo framework, and the effect on model performance with respect to snow cover area in the validation period is evaluated. The selected study period is 1986-1989, in which both AVHRR data and other input data are available. It is split into two parts with up to around 20 snow cover scenes for model calibration and about the same number for model validation. In most of the catchments we found only a small trade-off between good simulations with respect to discharge and with respect to snow cover area, but if the parameters were selected based on the discharge objective function only, this could also include

  17. Estimation of snow water equivalent using a radiance assimilation scheme with a multi-layered snow physical model

    Science.gov (United States)

    Mounirou Toure, Ally

    The feasibility of a radiance assimilation using a multi-layered snow physical model to estimate snow physical parameters is studied. The work is divided in five parts. The first two chapters are dedicated to the literature review. In the third chapter, experimental work was conducted in the alpine snow to estimate snow correlation (for microwave emission modelling) using near-infrared digital photography. We made microwave radiometric and near-infrared reflectance measurements of snow slabs under different experimental conditions. We used an empirical relation to link near-infrared reflectance of snow to the specific surface area (SSA), and converted the SSA into the correlation length. From the measurements of snow radiances at 21 and 35 GHz, we derived the microwave scattering coefficient by inverting two coupled radiative transfer models (RTM) (the sandwich and six-flux model). The correlation lengths found are in the same range as those determined in the literature using cold laboratory work. The technique shows great potential in the determination of the snow correlation length under field conditions. In the fourth chapter, the performance of the ensemble Kalman filter (EnKF) for snow water equivalent (SWE) estimation is assessed by assimilating synthetic microwave observations at Ground Based Microwave Radiometer (GBMR-7) frequencies (18.7, 23.8, 36.5, 89 vertical and horizontal polarization) into a snow physics model, CROCUS. CROCUS has a realistic stratigraphic and ice layer modelling scheme. This work builds on previous methods that used snow physics model with limited number of layers. Data assimilation methods require accurate predictions of the brightness temperature (Tb) emitted by the snowpack. It has been shown that the accuracy of RTMs is sensitive to the stratigraphic representation of the snowpack. However, as the stratigraphic fidelity increases, the number of layers increases, as does the number of state variables estimated in the assimilation

  18. Comparison of various remote sensing snow products in a distributed hydrological model

    Science.gov (United States)

    Berezowski, Tomasz; Chormański, Jarosław; Batelaan, Okke

    2014-05-01

    With the development of remote sensing, more and more data series with spatially distributed snow cover become available. These data can be obtained for free, from many sources varying in spatial and temporal resolution, the length of the time series and the method of acquisition (VIS-NIR or microwave sensors). A popular use of remotely sensed snow distribution data is in hydrological modelling. However, a suitability test of different remote sensing snow products for hydrological models was so far not conducted. In this work, some of the most common remote sensing snow products (MOD10A1, IMS , GLOBSNOW and AMSR-E_DySno) are used as input data in the WetSpa distributed hydrological model. Each of the snow products has different properties and is based on different algorithms, which makes the analysis interesting and multidimensional. The area of research is the Biebrza River catchment - located in north-eastern Poland, comprising approximately 7000 km2. Biebrza is a natural river with a snow melt regime, making it very suitable for this kind of analysis. In total 6 modelling scenarios were conducted (4 with remote sensing data, 1 standard approach - temperature threshold for snow accumulation and melting, 1 based on snow data from meteorological stations). Each model was calibrated against discharge with the Shuffled Complex Evolution (SCE) algorithm. The calibration was repeated three times for each model to make sure that the global optimum was found. The calibration and validation periods were both 3 years long. The next stage was a comparison with the GLUE uncertainty analysis for each of the models, on a shorter, one-year period. The best model in terms of Nash-Sutcliffe efficiency and r2 was using the MOD10A1 data; however, the models using GLOBSNOW SWE and the standard approach received similar scores. In terms of the model bias the best results were obtained for the IMS and MOD10A1 data. Nevertheless, the lowest root mean square error was found for the

  19. Biases in modeled surface snow BC mixing ratios in prescribed-aerosol climate model runs

    OpenAIRE

    Doherty, S. J.; C. M. Bitz; M. G. Flanner

    2014-01-01

    Black carbon (BC) in snow lowers its albedo, increasing the absorption of sunlight, leading to positive radiative forcing, climate warming and earlier snowmelt. A series of recent studies have used prescribed-aerosol deposition flux fields in climate model runs to assess the forcing by black carbon in snow. In these studies, the prescribed mass deposition flux of BC to surface snow is decoupled from the mass deposition flux of snow water to the surface. Here we compare progn...

  20. A predictive model for the spectral "bioalbedo" of snow

    Science.gov (United States)

    Cook, J. M.; Hodson, A. J.; Taggart, A. J.; Mernild, S. H.; Tranter, M.

    2017-01-01

    We present the first physical model for the spectral "bioalbedo" of snow, which predicts the spectral reflectance of snowpacks contaminated with variable concentrations of red snow algae with varying diameters and pigment concentrations and then estimates the effect of the algae on snowmelt. The biooptical model estimates the absorption coefficient of individual cells; a radiative transfer scheme calculates the spectral reflectance of snow contaminated with algal cells, which is then convolved with incoming spectral irradiance to provide albedo. Albedo is then used to drive a point-surface energy balance model to calculate snowpack melt rate. The model is used to investigate the sensitivity of snow to algal biomass and pigmentation, including subsurface algal blooms. The model is then used to recreate real spectral albedo data from the High Sierra (CA, USA) and broadband albedo data from Mittivakkat Gletscher (SE Greenland). Finally, spectral "signatures" are identified that could be used to identify biology in snow and ice from remotely sensed spectral reflectance data. Our simulations not only indicate that algal blooms can influence snowpack albedo and melt rate but also highlight that "indirect" feedback related to their presence are a key uncertainty that must be investigated.

  1. Reflectance Modeling for Real Snow Structures Using a Beam Tracing Model

    Directory of Open Access Journals (Sweden)

    Martin Schneebeli

    2008-05-01

    Full Text Available It is important to understand reflective properties of snow, for example for remote sensing applications and for modeling of energy balances in snow packs. We present a method with which we can compare reflectance measurements and calculations for the same snow sample structures. Therefore, we first tomograph snow samples to acquire snow structure images (6 x 2 mm. Second, we calculated the sample reflectance by modeling the radiative transfer, using a beam tracing model. This model calculates the biconical reflectance (BR derived from an arbitrary number of incident beams. The incident beams represent a diffuse light source. We applied our method to four different snow samples: Fresh snow, metamorphosed snow, depth hoar, and wet snow. The results show that (i the calculated and measured reflectances agree well and (ii the model produces different biconical reflectances for different snow types. The ratio of the structure to the wavelength is large. We estimated that the size parameter is larger than 50 in all cases we analyzed. Specific surface area of the snow samples explains most of the difference in radiance, but not the different biconical reflectance distributions. The presented method overcomes the limitations of common radiative transfer models which use idealized grain shapes such as spheres, plates, needles and hexagonal particles. With this method we could improve our understanding for changes in biconical reflectance distribution associated with changes in specific surface area.

  2. Used of observed snow in the Snomod model

    Science.gov (United States)

    Sorteberg, H. K.

    2009-04-01

    For the hydroelectric industry in Norway, it is important to know exactly what resources are available at all times. The correct volume of snow reserves and the accurate forecasting of the spring flood volume can provide the best basis for maximising production values. The forward market can fluctuate considerably, and it is therefore important to know what is available at the right time. For many years, the Snomod model has been used to calculate snow reserves and to forecast the spring flood volume. Snomod is based on a regression equation between the annual observations of inflow and one or more precipitation series. Manual snow measurements are used in both Snomod and the HBV model and other models to estimate the correct snow reserves. In operational use, Snomod is updated manually with the snow estimate that is considered to be correct. Following the winter of 2007-2008, analyses were carried out to determine how accurate the forecasting was. The analyses were based on comparing the spring flood volume forecast with the observed spring flood volume using the ‘observed precipitation' precipitation scenario. Such analyses can tell us something about the quality of the model results for this winter. Analyses have been carried out for 18 models using Snomod. When the results from the analyses are compared with the spring floods, the spring flood volume has been forecast accurately for most of the models with observed precipitation when observed snow has been used in the forecasting process. The results indicate that nine of the models are very good, five are good and two are reasonable. Only one model produced a poor forecast of the spring flood volume. If a corresponding analysis without correction for observed snow is carried out, and the observed spring flood is compared with the forecast spring flood, the results are not as good. This may stem from the fact that during the spring of 2008 there were higher levels of evaporation during the melting season than

  3. Observations and model simulations of snow albedo reduction in seasonal snow due to insoluble light-absorbing particles during 2014 Chinese survey

    Science.gov (United States)

    Wang, Xin; Pu, Wei; Ren, Yong; Zhang, Xuelei; Zhang, Xueying; Shi, Jinsen; Jin, Hongchun; Dai, Mingkai; Chen, Quanliang

    2017-02-01

    A snow survey was carried out to collect 13 surface snow samples (10 for fresh snow, and 3 for aged snow) and 79 subsurface snow samples in seasonal snow at 13 sites across northeastern China in January 2014. A spectrophotometer combined with chemical analysis was used to quantify snow particulate absorption by insoluble light-absorbing particles (ILAPs, e.g., black carbon, BC; mineral dust, MD; and organic carbon, OC) in snow. Snow albedo was measured using a field spectroradiometer. A new radiative transfer model (Spectral Albedo Model for Dirty Snow, or SAMDS) was then developed to simulate the spectral albedo of snow based on the asymptotic radiative transfer theory. A comparison between SAMDS and an existing model - the Snow, Ice, and Aerosol Radiation (SNICAR) - indicates good agreements in the model-simulated spectral albedos of pure snow. However, the SNICAR model values tended to be slightly lower than those of SAMDS when BC and MD were considered. Given the measured BC, MD, and OC mixing ratios of 100-5000, 2000-6000, and 1000-30 000 ng g-1, respectively, in surface snow across northeastern China, the SAMDS model produced a snow albedo in the range of 0.95-0.75 for fresh snow at 550 nm, with a snow grain optical effective radius (Reff) of 100 µm. The snow albedo reduction due to spherical snow grains assumed to be aged snow is larger than fresh snow such as fractal snow grains and hexagonal plate or column snow grains associated with the increased BC in snow. For typical BC mixing ratios of 100 ng g-1 in remote areas and 3000 ng g-1 in heavy industrial areas across northern China, the snow albedo for internal mixing of BC and snow is lower by 0.005 and 0.036 than that of external mixing for hexagonal plate or column snow grains with Reff of 100 µm. These results also show that the simulated snow albedos by both SAMDS and SNICAR agree well with the observed values at low ILAP mixing ratios but tend to be higher than surface observations at high ILAP

  4. 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

  5. Modeling drifting snow in Antarctica with a regional climate model: 1. Methods and model evaluation

    NARCIS (Netherlands)

    Lenaerts, J.T.M.; van den Broeke, M.R.; Déry, S. J.; van Meijgaard, E.; van de Berg, W.J.; Palm, S.P.; Sanz Rodrigo, J.

    2012-01-01

    To simulate the impact of drifting snow on the lower atmosphere, surface characteristics and surface mass balance (SMB) of the Antarctic ice sheet regional atmospheric climate model (RACMO2.1/ANT) with horizontal resolution of 27 km is coupled to a drifting snow routine and forced by ERA-Interim fie

  6. Modeling drifting snow in Antarctica with a regional climate model: 1. Methods and model evaluation

    NARCIS (Netherlands)

    Lenaerts, J.T.M.|info:eu-repo/dai/nl/314850163; van den Broeke, M.R.|info:eu-repo/dai/nl/073765643; Déry, S. J.; van Meijgaard, E.; van de Berg, W.J.|info:eu-repo/dai/nl/304831611; Palm, S.P.; Sanz Rodrigo, J.

    2012-01-01

    To simulate the impact of drifting snow on the lower atmosphere, surface characteristics and surface mass balance (SMB) of the Antarctic ice sheet regional atmospheric climate model (RACMO2.1/ANT) with horizontal resolution of 27 km is coupled to a drifting snow routine and forced by ERA-Interim fie

  7. Random fluctuations of snow accumulation over antarctica and their relation to sea level change

    Energy Technology Data Exchange (ETDEWEB)

    Remy, F.; Testut, L.; Legresy, B. [LEGOS (CNRS-CNES-UPS), Toulouse (France)

    2002-07-01

    Short-term changes in the volume of ice sheets as analyzed by radar altimetry may not be related to long-term climatic change. Indeed, the large relaxation time of an ice sheet induces a low-frequency response to random fluctuations of snow accumulation. However, the time scale of the response is big compared to the average human lifetime and the effect of these random fluctuations on sea level change may be important even if they are not linked to climatic change. In this study, the relaxation time of an ice sheet is expressed with respect to the ice thickness, surface slope and ice velocity. These parameters are deduced from the precise topography derived from the geodetic cycle of the ERS1 radar altimeter. The variance of the induced effect on ice elevation is found to be around 3 m over a 30-year scale and with a maximum of 10 m in Wilkes Land and in the western part of the West Antarctic ice sheet. Near the coast, this effect can mask a climatic signal and thus be critical for altimetric mass balance surveys. The estimated changes in Antarctica's elevation between the Seasat (1978) and ERS (1993) epochs could be explained at least partially by such processes. In terms of sea level change over the 30-year scale, the standard deviation of the induced effect is 8 {+-} 2.8 cm. Finally, we show that the probability of a present-day, induced sea level rise of between 0.5 and 1 mm/year over a 30-year time scale is estimated at 10% {+-} 10%, with coastal areas accounting for half of this signal. (orig.)

  8. Modelling the influence of elevation and snow regime on winter stream temperature in the rain-on-snow zone

    Science.gov (United States)

    Leach, J.; Moore, D.

    2015-12-01

    Winter stream temperature of coastal mountain catchments influences fish growth and development. Transient snow cover and advection associated with lateral throughflow inputs are dominant controls on stream thermal regimes in these regions. Existing stream temperature models lack the ability to properly simulate these processes. Therefore, we developed and evaluated a conceptual-parametric catchment-scale stream temperature model that includes the role of transient snow cover and lateral advection associated with throughflow. The model provided reasonable estimates of observed stream temperature at three test catchments. We used the model to simulate winter stream temperature for virtual catchments located at different elevations within the rain-on-snow zone. The modelling exercise examined stream temperature response associated with interactions between elevation, snow regime, and changes in air temperature. Modelling results highlight that the sensitivity of winter stream temperature response to changes in climate may be dependent on catchment elevation and landscape position.

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

    Science.gov (United States)

    Bock, Josué; Savarino, Joël; Picard, Ghislain

    2016-10-01

    Snowpack is a multiphase (photo)chemical 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 (photo)chemical 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 spring are two other

  10. Corruption of parameter behavior and regionalization by model and forcing data errors: A Bayesian example using the SNOW17 model

    Science.gov (United States)

    He, Minxue; Hogue, Terri S.; Franz, Kristie J.; Margulis, Steven A.; Vrugt, Jasper A.

    2011-07-01

    The current study evaluates the impacts of various sources of uncertainty involved in hydrologic modeling on parameter behavior and regionalization utilizing different Bayesian likelihood functions and the Differential Evolution Adaptive Metropolis (DREAM) algorithm. The developed likelihood functions differ in their underlying assumptions and treatment of error sources. We apply the developed method to a snow accumulation and ablation model (National Weather Service SNOW17) and generate parameter ensembles to predict snow water equivalent (SWE). Observational data include precipitation and air temperature forcing along with SWE measurements from 24 sites with diverse hydroclimatic characteristics. A multiple linear regression model is used to construct regionalization relationships between model parameters and site characteristics. Results indicate that model structural uncertainty has the largest influence on SNOW17 parameter behavior. Precipitation uncertainty is the second largest source of uncertainty, showing greater impact at wetter sites. Measurement uncertainty in SWE tends to have little impact on the final model parameters and resulting SWE predictions. Considering all sources of uncertainty, parameters related to air temperature and snowfall fraction exhibit the strongest correlations to site characteristics. Parameters related to the length of the melting period also show high correlation to site characteristics. Finally, model structural uncertainty and precipitation uncertainty dramatically alter parameter regionalization relationships in comparison to cases where only uncertainty in model parameters or output measurements is considered. Our results demonstrate that accurate treatment of forcing, parameter, model structural, and calibration data errors is critical for deriving robust regionalization relationships.

  11. Impact of Rain Snow Threshold Temperature on Snow Depth Simulation in Land Surface and Regional Atmospheric Models

    Institute of Scientific and Technical Information of China (English)

    WEN Lijuan; Nidhi NAGABHATLA; L(U) Shihua; Shih-Yu WANG

    2013-01-01

    This study investigates the impact of rain snow threshold (RST) temperatures on snow depth simulation using the Community Land Model (CLM) and the Weather Research and Forecasting model (WRF coupled with the CLM and hereafter referred to as WRF_CLM),and the difference in impacts.Simulations were performed from 17 December 1994 to 30 May 1995 in the French Alps.Results showed that both the CLM and the WRF_CLM were able to represent a fair simulation of snow depth with actual terrain height and 2.5℃ RST temperature.When six RST methods were applied to the simulation using WRF_CLM,the simulated snow depth was the closest to observations using 2.5℃ RST temperature,followed by that with Pipes',USACE,Kienzle's,Dai's,and 0℃ RST temperature methods.In the case of using CLM,simulated snow depth was the closest to the observation with Dai's method,followed by with USACE,Pipes',2.5℃ RST temperature,Kienzle's,and 0℃ RST temperature method.The snow depth simulation using the WRF_CLM was comparatively sensitive to changes in RST temperatures,because the RST temperature was not only the factor to partition snow and rainfall.In addition,the simulated snow related to RST temperature could induce a significant feedback by influencing the meteorological variables forcing the land surface model in WRF_CLM.In comparison,the above variables did not change with changes in RST in CLM.Impacts of RST temperatures on snow depth simulation could also be influenced by the patterns of temperature and precipitation,spatial resolution,and input terrain heights.

  12. Distributed, explicit modeling of technical snow production and ski area management with the hydroclimatological model AMUNDSEN

    Science.gov (United States)

    Hanzer, Florian; Marke, Thomas; Strasser, Ulrich

    2016-04-01

    In this presentation, a module for simulating technical snow production in ski areas coupled to the spatially distributed physically based hydroclimatological model AMUNDSEN is presented. The module explicitly considers individual snow guns and distributes the produced snow along the slopes. The amount of snow produced by each device is a function of its type, of wet-bulb temperature at the location, of ski area infrastructure (in terms of water supply and pumping capacity), and of snow demand. An empirical rule in the modeling for snow production, derived from common snowmaking practices, splits the winter season into a period of maximum snowmaking and a successive period of selective on-demand snowmaking. The model is exemplarily set up for a ski area in the Schladming region (Austrian Alps) using actual snowmaking infrastructure data. Integration of these data as model variables, as well as stakeholder-defined indicators and thresholds, have been implemented as defined interfaces in a coupled component model architecture. Comparison of the model results with recordings of snowmaking operation and satellite-derived snow cover maps indicate that the model is capable of accurately simulating the real-world snowmaking practice, and the combined natural and technical snow conditions on the slopes. The explicit consideration of individual snow guns and ski area infrastructure makes the model a valuable tool for scenario applications, e.g. to assess the effects of different ski area management strategies and changes in snowmaking infrastructure for climate change impact studies.

  13. The impact of snow nitrate photolysis on boundary layer chemistry and the recycling and redistribution of reactive nitrogen across Antarctica and Greenland in a global chemical transport model

    Science.gov (United States)

    Zatko, Maria; Geng, Lei; Alexander, Becky; Sofen, Eric; Klein, Katarina

    2016-03-01

    , and O3 in Greenland compared to Antarctica because of Greenland's proximity to pollution sources. The degree of nitrogen recycling in the snow is dependent on the relative magnitudes of snow-sourced NOx fluxes versus primary NO3- deposition. Recycling of snow NO3- in Greenland is much less than in Antarctica Photolysis-driven loss of snow NO3- is largely dependent on the time that NO3- remains in the snow photic zone (up to 6.5 years in Antarctica and 7 months in Greenland), and wind patterns that redistribute snow-sourced reactive nitrogen across Antarctica and Greenland. The loss of snow NO3- is higher in Antarctica (up to 99 %) than in Greenland (up to 83 %) due to deeper snow photic zones and lower snow accumulation rates in Antarctica. Modeled enrichments in ice-core δ15N(NO3-) due to photolysis-driven loss of snow NO3- ranges from 0 to 363 ‰ in Antarctica and 0 to 90 ‰ in Greenland, with the highest fraction of NO3- loss and largest enrichments in ice-core δ15N(NO3-) at high elevations where snow accumulation rates are lowest. There is a strong relationship between the degree of photolysis-driven loss of snow NO3- and the degree of nitrogen recycling between the air and snow throughout all of Greenland and in Antarctica where snow accumulation rates are greater than 130 kg m-2 a-1 in the present day.

  14. Modelled and measured energy exchange at a snow surface

    Science.gov (United States)

    Halberstam, I.

    1979-01-01

    Results of a model developed at JPL for the energy interchange between the atmosphere and the snow are compared with measurements made over a snowfield during a warm period of March, 1978. Both model and measurements show that turbulent fluxes are considerably smaller than the radiative fluxes, especially during the day. The computation of turbulent fluxes for both model and data is apparently lacking because of problems inherent in the stable atmosphere.

  15. A comparison of winter mercury accumulation at forested and no-canopy sites measured with different snow sampling techniques

    Science.gov (United States)

    Nelson, S.J.; Johnson, K.B.; Weathers, K.C.; Loftin, C.S.; Fernandez, I.J.; Kahl, J.S.; Krabbenhoft, D.P.

    2008-01-01

    Atmospheric mercury (Hg) is delivered to ecosystems via rain, snow, cloud/fog, and dry deposition. The importance of snow, especially snow that has passed through the forest canopy (throughfall), in delivering Hg to terrestrial ecosystems has received little attention in the literature. The snowpack is a dynamic system that links atmospheric deposition and ecosystem cycling through deposition and emission of deposited Hg. To examine the magnitude of Hg delivery via snowfall, and to illuminate processes affecting Hg flux to catchments during winter (cold season), Hg in snow in no-canopy areas and under forest canopies measured with four collection methods were compared: (1) Hg in wet precipitation as measured by the Mercury Deposition Network (MDN) for the site in Acadia National Park, Maine, USA, (2) event throughfall (collected after snowfall cessation for accumulations of >8 cm), (3) season-long throughfall collected using the same apparatus for event sampling but deployed for the entire cold season, and (4) snowpack sampling. Estimates (mean ?? SE) of Hg deposition using these methods during the 91-day cold season in 2004-2005 at conifer sites showed that season-long throughfall Hg flux (1.80 ??g/m2) Mercury deposition at the MDN site (0.91 ??g/m2) was similar to that measured at other no-canopy sites in the area using the other methods, but was 3.4 times less than was measured under conifer canopies using the event sampling regime. This indicates that snow accumulated under the forest canopy received Hg from the overstory or exhibited less re-emission of Hg deposited in snow relative to open areas. The soil surface of field-scale plots were sprayed with a natural rain water sample that contained an Hg tracer (202Hg) just prior to the first snowfall to explore whether some snowpack Hg might be explained from soil emissions. The appearance of the 202Hg tracer in the snowpack (0-64% of the total Hg mass in the snowpack) suggests that movement of Hg from the soil

  16. A new remote sensing model for retrieving snow depth within 30 centimeters using MODIS data

    Science.gov (United States)

    Li, Sanmei; Liu, Yujie; Huang, Zhen; Fu, Hua

    2006-12-01

    Snow depth, a very significant factor in agriculture and climate research, is one of the most important parameters for snow amount calculation. It is proved there is a good linear relationship between snow depth and snow surface reflectance in visible to short-infrared window channels when snow has a depth within 30cm, which makes it possible to retrieve snow depth using AVHRR or MODIS data and station-measured snow-depth data. This paper mainly introduces the principle theory and process to establish a snow-depth retrieval model within 30cm using EOS/MODIS visible to short-infrared window channels' data and station-measured data, considering snow characteristics in different physical states and various complex underneath conditions including DEM, land cover such as grassland, forest, cropland and so on. Based on snow characteristics and underneath conditions, snow is devided into many types: old dry snow in flat grassland, new dry snow in flat grassland, old dry snow in mountainous grassland, old dry snow in flat cropland and so on. Fourteen kinds of snow have been modeled respectively in this retrieval model. Through 4 years validation in XinJiang Province of China since 2002, the precision of snow-depth retrieval model using MODIS visible to short-infrared channels' data can reach more than 80%. In flat area with single underneath condition, where wind power can be ignored, the model can always get a better precision. On the contrary, in mountainous forests, the precision of the model is not that good.

  17. Recent Increases in Snow Accumulation and Decreases in Sea-Ice Concentration Recorded in a Coastal NW Greenland Ice Core

    Science.gov (United States)

    Osterberg, E. C.; Thompson, J. T.; Wong, G. J.; Hawley, R. L.; Kelly, M. A.; Lutz, E.; Howley, J.; Ferris, D. G.

    2013-12-01

    A significant rise in summer temperatures over the past several decades has led to widespread retreat of the Greenland Ice Sheet (GIS) margin and surrounding sea ice. Recent observations from geodetic stations and GRACE show that ice mass loss progressed from South Greenland up to Northwest Greenland by 2005 (Khan et al., 2010). Observations from meteorological stations at the U.S. Thule Air Force Base, remote sensing platforms, and climate reanalyses indicate a 3.5C mean annual warming in the Thule region and a 44% decrease in summer (JJAS) sea-ice concentrations in Baffin Bay from 1980-2010. Mean annual precipitation near Thule increased by 12% over this interval, with the majority of the increase occurring in fall (SON). To improve projections of future ice loss and sea-level rise in a warming climate, we are currently developing multi-proxy records (lake sediment cores, ice cores, glacial geologic data, glaciological models) of Holocene climate variability and cryospheric response in NW Greenland, with a focus on past warm periods. As part of our efforts to develop a millennial-length ice core paleoclimate record from the Thule region, we collected and analyzed snow pit samples and short firn cores (up to 20 m) from the coastal region of the GIS (2Barrel site; 76.9317 N, 63.1467 W) and the summit of North Ice Cap (76.938 N, 67.671 W) in 2011 and 2012, respectively. The 2Barrel ice core was sampled using a continuous ice core melting system at Dartmouth, and subsequently analyzed for major anion and trace element concentrations and stable water isotope ratios. Here we show that the 2Barrel ice core spanning 1990-2010 records a 25% increase in mean annual snow accumulation, and is positively correlated (r = 0.52, p<0.01) with ERA-Interim precipitation. The 2Barrel annual sea-salt Na concentration is strongly correlated (r = 0.5-0.8, p<0.05) with summer and fall sea-ice concentrations in northern Baffin Bay near Thule (Figure 1). We hypothesize that the positive

  18. 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.

  19. Arctic Snow Microstructure Experiment for the development of snow emission modelling

    OpenAIRE

    Maslanka, William; Leppänen, Leena; Kontu, Anna; Sandells, Mel; Lemmetyinen, Juha; Schneebeli, Martin; Proksch, Martin; Matzl, Margret; Hannula, Henna-Reetta; Gurney, Robert

    2016-01-01

    The Arctic Snow Microstructure Experiment (ASMEx) took place in Sodankylä, Finland in the winters of 2013–2014 and 2014–2015. Radiometric, macro-, and microstructure measurements were made under different experimental conditions of homogenous snow slabs, extracted from the natural seasonal taiga snowpack. Traditional and modern measurement techniques were used for snow macro- and microstructure observations. Radiometric measurements of the microwave emission of s...

  20. A snow and ice melt seasonal prediction modelling system for Alpine reservoirs

    Science.gov (United States)

    Förster, Kristian; Oesterle, Felix; Hanzer, Florian; Schöber, Johannes; Huttenlau, Matthias; Strasser, Ulrich

    2016-10-01

    The timing and the volume of snow and ice melt in Alpine catchments are crucial for management operations of reservoirs and hydropower generation. Moreover, a sustainable reservoir operation through reservoir storage and flow control as part of flood risk management is important for downstream communities. Forecast systems typically provide predictions for a few days in advance. Reservoir operators would benefit if lead times could be extended in order to optimise the reservoir management. Current seasonal prediction products such as the NCEP (National Centers for Environmental Prediction) Climate Forecast System version 2 (CFSv2) enable seasonal forecasts up to nine months in advance, with of course decreasing accuracy as lead-time increases. We present a coupled seasonal prediction modelling system that runs at monthly time steps for a small catchment in the Austrian Alps (Gepatschalm). Meteorological forecasts are obtained from the CFSv2 model. Subsequently, these data are downscaled to the Alpine Water balance And Runoff Estimation model AWARE running at monthly time step. Initial conditions are obtained using the physically based, hydro-climatological snow model AMUNDSEN that predicts hourly fields of snow water equivalent and snowmelt at a regular grid with 50 m spacing. Reservoir inflow is calculated taking into account various runs of the CFSv2 model. These simulations are compared with observed inflow volumes for the melting and accumulation period 2015.

  1. Towards an Aassimilation of MODIS VIS/NIR reflectance into the detailed snow model SURFEX/ISBA-Crocus

    Science.gov (United States)

    Charrois, L.; Cosme, E.; Dumont, M.; Lafaysse, M.; Morin, S.; Libois, Q.; Picard, G.; Arnaud, L.

    2014-12-01

    SURFEX/ISBA-Crocus is a physically based multi-layer snowpack model used for numerous scientific and operational applications such as avalanche risk forecast. Although some snowpack models simulations usually performed reasonably well, differences with real snowpack still exist and may be due to various origins such as weather forcing input. Yet, no snow observations are assimilated into the snow model SURFEX/ISBA-Crocus so that the simulation error is accumulated over the winter season. Some efforts will be done to assimilate data from visible and near-infrared imagers into the snowpack model to improve the snowpack simulations. The new optical scheme of SURFEX/ISBA-Crocus, called TARTES, allows the use of reflectance as diagnostic variables of the model. These reflectance are sensitive to snow properties such as specific surface area (SSA) and impurity content. They are measured by the MODIS spectroradiometer and can thus be used in an assimilation framework to account for the high spatial and temporal variability of the snow cover in mountainous areas. Prior to assimilation, we used ensemble methods to find the best assimilation scheme to be implemented. The distribution of model errors is investigated together with the relationship between simulated reflectance and model prognostic variables (density, SSA, …). First tests of reflectance assimilation were then carried out using a particle filter and MODIS measurements at Col du Lautaret (French Alps). The impact of the assimilation has been evaluated in terms of simulated snow properties.

  2. Markov models for accumulating mutations

    CERN Document Server

    Beerenwinkel, Niko

    2007-01-01

    We introduce and analyze a waiting time model for the accumulation of genetic changes. The continuous time conjunctive Bayesian network is defined by a partially ordered set of mutations and by the rate of fixation of each mutation. The partial order encodes constraints on the order in which mutations can fixate in the population, shedding light on the mutational pathways underlying the evolutionary process. We study a censored version of the model and derive equations for an EM algorithm to perform maximum likelihood estimation of the model parameters. We also show how to select the maximum likelihood poset. The model is applied to genetic data from different cancers and from drug resistant HIV samples, indicating implications for diagnosis and treatment.

  3. Biases in modeled surface snow BC mixing ratios in prescribed aerosol climate model runs

    OpenAIRE

    Doherty, S. J.; C. M. Bitz; M. G. Flanner

    2014-01-01

    A series of recent studies have used prescribed aerosol deposition flux fields in climate model runs to assess forcing by black carbon in snow. In these studies, the prescribed mass deposition flux of BC to surface snow is decoupled from the mass deposition flux of snow water to the surface. Here we use a series of offline calculations to show that this approach results, on average, in a~factor of about 1.5–2.5 high bias in annual-mean surface snow BC mixing ratios in three ...

  4. Evaluation of the Snow Simulations from the Community Land Model, Version 4 (CLM4)

    Science.gov (United States)

    Toure, Ally M.; Rodell, Matthew; Yang, Zong-Liang; Beaudoing, Hiroko; Kim, Edward; Zhang, Yongfei; Kwon, Yonghwan

    2015-01-01

    This paper evaluates the simulation of snow by the Community Land Model, version 4 (CLM4), the land model component of the Community Earth System Model, version 1.0.4 (CESM1.0.4). CLM4 was run in an offline mode forced with the corrected land-only replay of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land) and the output was evaluated for the period from January 2001 to January 2011 over the Northern Hemisphere poleward of 30 deg N. Simulated snow-cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the Canadian Meteorological Centre (CMC) daily snow analysis products, snow depth from the National Weather Service Cooperative Observer (COOP) program, and Snowpack Telemetry (SNOTEL) SWE observations. CLM4 SCF was converted into snow-cover extent (SCE) to compare with MODIS SCE. It showed good agreement, with a correlation coefficient of 0.91 and an average bias of -1.54 x 10(exp 2) sq km. Overall, CLM4 agreed well with IMS snow cover, with the percentage of correctly modeled snow-no snow being 94%. CLM4 snow depth and SWE agreed reasonably well with the CMC product, with the average bias (RMSE) of snow depth and SWE being 0.044m (0.19 m) and -0.010m (0.04 m), respectively. CLM4 underestimated SNOTEL SWE and COOP snow depth. This study demonstrates the need to improve the CLM4 snow estimates and constitutes a benchmark against which improvement of the model through data assimilation can be measured.

  5. Distributed snow and rock temperature modelling in steep rock walls using Alpine3D

    Science.gov (United States)

    Haberkorn, Anna; Wever, Nander; Hoelzle, Martin; Phillips, Marcia; Kenner, Robert; Bavay, Mathias; Lehning, Michael

    2017-02-01

    In this study we modelled the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and thus on rock temperatures in two rugged, steep rock walls on the Gemsstock ridge in the central Swiss Alps. The heterogeneous snow depth distribution in the rock walls was introduced to the distributed, process-based energy balance model Alpine3D with a precipitation scaling method based on snow depth data measured by terrestrial laser scanning. The influence of the snow cover on rock temperatures was investigated by comparing a snow-covered model scenario (precipitation input provided by precipitation scaling) with a snow-free (zero precipitation input) one. Model uncertainties are discussed and evaluated at both the point and spatial scales against 22 near-surface rock temperature measurements and high-resolution snow depth data from winter terrestrial laser scans.In the rough rock walls, the heterogeneously distributed snow cover was moderately well reproduced by Alpine3D with mean absolute errors ranging between 0.31 and 0.81 m. However, snow cover duration was reproduced well and, consequently, near-surface rock temperatures were modelled convincingly. Uncertainties in rock temperature modelling were found to be around 1.6 °C. Errors in snow cover modelling and hence in rock temperature simulations are explained by inadequate snow settlement due to linear precipitation scaling, missing lateral heat fluxes in the rock, and by errors caused by interpolation of shortwave radiation, wind and air temperature into the rock walls.Mean annual near-surface rock temperature increases were both measured and modelled in the steep rock walls as a consequence of a thick, long-lasting snow cover. Rock temperatures were 1.3-2.5 °C higher in the shaded and sunny rock walls, while comparing snow-covered to snow-free simulations. This helps to assess the potential error made in ground temperature modelling when neglecting snow in steep bedrock.

  6. Assimilation of satellite observed snow albedo in a land surface model

    NARCIS (Netherlands)

    Malik, M.J.; Velde, van der R.; Vekerdy, Z.; Su, Z.

    2012-01-01

    This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado

  7. Assimilation of satellite observed snow albedo in a land surface model

    NARCIS (Netherlands)

    Malik, M.J.; van der Velde, R.; Vekerdy, Z.; Su, Zhongbo

    2012-01-01

    This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park

  8. Assimilation of satellite observed snow albedo in a land surface model

    NARCIS (Netherlands)

    Malik, M.J.; van der Velde, R.; Vekerdy, Z.; Su, Zhongbo

    2012-01-01

    This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado

  9. Dynamic Equilibrium Inter-annual Snow Modeling for Wyoming using Reconstructed Regional Atmospheric Conditions

    Science.gov (United States)

    Ohara, N.; Johnson, R. J.

    2015-12-01

    The inland glacier retreat has often been considered as one of clearest evidences of the global warming last several decades. However, when we try to model the evolution of the inland inter-annual snow storage including glaciers using a standard energy and mass balance snow model, it is impossible to keep the snow storage constant under a constant climate condition. This study treats the inland glaciers as a dynamic equilibrium system that remains constant under static climate condition. We introduced a sub-grid scale parameterization that moves snow/ice from high elevation areas to valleys as the equilibrating factor of the system. This movement of snow/ice occurs by means of wind re-distribution, avalanches, and glaciation. The physically-based model of a dynamic equilibrium snow system at a regional scale was applied to the entire state of Wyoming for long-term simulation. The developed snow model, named RegSnow model, was coupled with the Weather Research and Forecasting (WRF) model to estimate the snow surface energy fluxes during the 33-year-long historical period for transient model calibration. The RegSnow model predicted that 82.2% of interannual snow and ice storage in Wyoming may disappear by 2100 under the RCP4.5 emission scenario based on the climate projection by CMIP5 GCMs.

  10. Modeling chemistry in and above snow at Summit, Greenland – Part 1: Model description and results

    Directory of Open Access Journals (Sweden)

    J. L. Thomas

    2010-12-01

    Full Text Available Sun-lit snow is increasingly recognized as a chemical reactor that plays an active role in uptake, transformation, and release of atmospheric trace gases. Snow is known to influence boundary layer air on a local scale, and given the large global surface coverage of snow may also be significant on regional and global scales.

    We present a new detailed one-dimensional snow chemistry module that has been coupled to the 1-D atmospheric boundary layer model MISTRA, we refer to the coupled model as MISTRA-SNOW. The new 1-D snow module, which is dynamically coupled to the overlaying atmospheric model, includes heat transport in the snowpack, molecular diffusion, and wind pumping of gases in the interstitial air. The model includes gas phase photochemistry and chemical reactions both in the interstitial air and the atmosphere. Heterogeneous and multiphase chemistry on atmospheric aerosol is considered explicitly. The chemical interaction of interstitial air with snow grains is simulated assuming chemistry in a liquid (aqueous layer on the grain surface. The model was used to investigate snow as the source of nitrogen oxides (NOx and gas phase reactive bromine in the atmospheric boundary layer in the remote snow covered Arctic (over the Greenland ice sheet as well as to investigate the link between halogen cycling and ozone depletion that has been observed in interstitial air. The model is validated using data taken 10 June–13 June, 2008 as part of the Greenland Summit Halogen-HOx experiment (GSHOX. The model predicts that reactions involving bromide and nitrate impurities in the surface snow at Summit can sustain atmospheric NO and BrO mixing ratios measured at Summit during this period.

  11. Snow accumulation variability in Adelie Land (East Antarctica) derived from radar and firn core data. A 600 km transect from Dome C

    Science.gov (United States)

    Verfaillie, D.; Fily, M.; Le Meur, E.; Magand, O.; Jourdain, B.; Arnaud, L.; Favier, V.

    2012-07-01

    Polar ice sheets mass balance is a timely topic intensively studied in the context of global change and sea-level rise. However, obtaining mass balance estimates in Antarctica in particular, remains difficult due to various logistical problems. In the framework of the TASTE-IDEA program, labeled as an International Polar Year project, continuous Ground Penetrating Radar (GPR) measurements were carried out during a traverse realised in Adelie Land (East Antarctica) during the 2008-2009 austral summer between the Italo-French Dome C (DC) polar plateau site and French Dumont D'Urville (DdU) coastal station. The aim of this study was to process and interpret GPR data in terms of snow accumulation, to analyse its spatial and temporal variability along the DC-DdU traverse and compare it with historical data and modeling. The emphasis has been put on the last 300 yr, from the pre-industrial to recent time period. Beta-radioactivity counting and gamma spectrometry were studied in cores at LGGE laboratory, providing a depth-age calibration for radar measurements. Over the 600 km of usable GPR data, depth and snow accumulation were determined with the help of three distinct layers visible on the radargrams (≈1730, 1799 and 1941 AD). Preliminary results reveal a gradual accumulation increase towards the coast and the occurrence of previously undocumented undulating structures between 300 and 600 km from DC. Results agree fairly well with data from previous studies and modeling. Concluding on temporal variations is difficult because of the margin of error introduced by density estimation. This study should have various applications such as for model validation.

  12. Snow accumulation variability in Adelie Land (East Antarctica derived from radar and firn core data. A 600 km transect from Dome C

    Directory of Open Access Journals (Sweden)

    D. Verfaillie

    2012-07-01

    Full Text Available Polar ice sheets mass balance is a timely topic intensively studied in the context of global change and sea-level rise. However, obtaining mass balance estimates in Antarctica in particular, remains difficult due to various logistical problems. In the framework of the TASTE-IDEA program, labeled as an International Polar Year project, continuous Ground Penetrating Radar (GPR measurements were carried out during a traverse realised in Adelie Land (East Antarctica during the 2008–2009 austral summer between the Italo-French Dome C (DC polar plateau site and French Dumont D'Urville (DdU coastal station. The aim of this study was to process and interpret GPR data in terms of snow accumulation, to analyse its spatial and temporal variability along the DC-DdU traverse and compare it with historical data and modeling. The emphasis has been put on the last 300 yr, from the pre-industrial to recent time period. Beta-radioactivity counting and gamma spectrometry were studied in cores at LGGE laboratory, providing a depth-age calibration for radar measurements. Over the 600 km of usable GPR data, depth and snow accumulation were determined with the help of three distinct layers visible on the radargrams (≈1730, 1799 and 1941 AD. Preliminary results reveal a gradual accumulation increase towards the coast and the occurrence of previously undocumented undulating structures between 300 and 600 km from DC. Results agree fairly well with data from previous studies and modeling. Concluding on temporal variations is difficult because of the margin of error introduced by density estimation. This study should have various applications such as for model validation.

  13. Snow accumulation variability derived from radar and firn core data along a 600 km transect in Adelie Land, East Antarctic plateau

    Directory of Open Access Journals (Sweden)

    D. Verfaillie

    2012-11-01

    Full Text Available The mass balance of ice sheets is an intensively studied topic in the context of global change and sea-level rise. However – particularly in Antarctica – obtaining mass balance estimates remains difficult due to various logistical problems. In the framework of the TASTE-IDEA (Trans-Antarctic Scientific Traverses Expeditions – Ice Divide of East Antarctica program, an International Polar Year project, continuous ground penetrating radar (GPR measurements were carried out during a traverse in Adelie Land (East Antarctica during the 2008–2009 austral summer between the Italian–French Dome C (DC polar plateau site and French Dumont D'Urville (DdU coastal station. The aim of this study was to process and interpret GPR data in terms of snow accumulation, to analyse its spatial and temporal variability and compare it with historical data and modelling. The focus was on the last 300 yr, from the pre-industrial period to recent times. Beta-radioactivity counting and gamma spectrometry were applied to cores at the LGGE laboratory, providing a depth–age calibration for radar measurements. Over the 600 km of usable GPR data, depth and snow accumulation were determined with the help of three distinct layers visible on the radargrams (≈ 1730, 1799 and 1941 AD. Preliminary results reveal a gradual increase in accumulation towards the coast (from ≈ 3 cm w.e. a−1 at Dome C to ≈ 17 cm w.e. a−1 at the end of the transect and previously undocumented undulating structures between 300 and 600 km from DC. Results agree fairly well with data from previous studies and modelling. Drawing final conclusions on temporal variations is difficult because of the margin of error introduced by density estimation. This study should have various applications, including model validation.

  14. Semi-distributed snowmelt modeling and regional snow mapping using passive microwave radiometry

    Science.gov (United States)

    Singh, Purushottam Raj

    2002-01-01

    Two semi-distributed snowmelt models (SDSM-MTI and SDSM-EBM) developed to model the basin-scale snow accumulation and ablation processes at sub-basin scale, were applied to the Paddle River Basin (PRB) of central Alberta. SDSM-MTI uses a modified temperature index approach that consists of a weighted average of near surface soil (Tg) and air temperature (Ta) data. SDSM-EBM, a relatively data intensive energy balance model accounts for snowmelt by considering (a) vertical energy exchange in open and forested area separately; (b) snowmelt in terms of liquid and ice phases separately, canopy interception, snow density, sublimation, refreezing, etc, and (c) the snow surface temperature. Other than the "regulatory" effects of beaver dams, both models simulated reasonably accurate snowmelt runoff, SWE and snow depth for PRB. For SDSM-MTI, the advantage of using both Ta and Tg is partly attributed to T g showing a stronger correlation with solar and net radiation at PRB than Ta. Existing algorithms for retrieving snow water equivalent (SWE) from the Special Sensor Microwave/Imager (SSM/I) passive microwave brightness temperature data were assessed and new algorithms were developed for the Red River basin of North Dakota and Minnesota. The frequencies of SSM/I data used are 19 and 37 GHz in both horizontal and vertical polarization. The airborne gamma-ray measurements of SWE for 1989, 1988, and 1997 provided the ground truth for algorithm development and validation. Encouraging calibration results are obtained for the multivariate regression algorithms and dry snow cases of the 1989 and 1988 SSM/I data (from DMSP-F8). Similarly, validation results e.g., 1988 (1989 as calibration data), 1989 (1988 as calibration data), and 1997 (from DMSP-F10 and F13), are also encouraging. The non-parameric, Projection Pursuit Regression technique also gave good results in both stages. However, for the validation stage, adding a shift parameter to all retrieval algorithms was necessary

  15. Observations and modelling of snow avalanche entrainment

    OpenAIRE

    2002-01-01

    In this paper full scale avalanche dynamics measurements from the Italian Pizzac and Swiss Vallée de la Sionne test sites are used to develop a snowcover entrainment model. A detailed analysis of three avalanche events shows that snowcover entrainment at the avalanche front appears to dominate over bed erosion at the basal sliding surface. Furthermore, the distribution of mass within the avalanche body is primarily a function of basal fric...

  16. 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.

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

    Science.gov (United States)

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

    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 -0.027 g cm-3 to -0.004 g cm-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 136 mm, 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

  18. 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

  19. Modeling elastic tensile fractures in snow using nonlocal damage mechanics

    Science.gov (United States)

    Borstad, C. P.; McClung, D. M.

    2011-12-01

    The initiation and propagation of tensile fractures in snow and ice are fundamental to numerous important physical processes in the cryosphere, from iceberg calving to ice shelf rift propagation to slab avalanche release. The heterogeneous nature of snow and ice, their proximity to the melting temperature, and the varied governing timescales typically lead to nonlinear fracture behavior which does not follow the predictions of Linear Elastic Fracture Mechanics (LEFM). Furthermore, traditional fracture mechanics is formally inapplicable for predicting crack initiation in the absence of a pre-existing flaw or stress concentration. An alternative to fracture mechanics is continuum damage mechanics, which accounts for the material degradation associated with cracking in a numerically efficient framework. However, damage models which are formulated locally (e.g. stress and strain are defined as point properties) suffer from mesh-sensitive crack trajectories, spurious localization of damage and improper fracture energy dissipation with mesh refinement. Nonlocal formulations of damage, which smear the effects of the material heterogeneity over an intrinsic length scale related to the material microstructure, overcome these difficulties and lead to numerically efficient and mesh-objective simulations of the tensile failure of heterogeneous materials. We present the results of numerical simulations of tensile fracture initiation and propagation in cohesive snow using a nonlocal damage model. Seventeen beam bending experiments, both notched and unnotched, were conducted using blocks of cohesive dry snow extracted from an alpine snowpack. Material properties and fracture parameters were calculated from the experimental data using beam theory and quasi-brittle fracture mechanics. Using these parameters, a nonlocal isotropic damage model was applied to two-dimensional finite element meshes of the same scale as the experiments. The model was capable of simulating the propagation

  20. Snow water equivalent mapping in Norway

    Science.gov (United States)

    Tveito, O. E.; Udnæs, H.-C.; Engeset, R.; Førland, E. J.; Isaksen, K.; Mengistu, Z.

    2003-04-01

    In high latitude area snow covers the ground large parts of the year. Information about the water volume as snow is of major importance in many respects. Flood forecasters at NVE need it in order to assess possible flood risks. Hydropower producers need it to plan the most efficient production of the water in their reservoirs, traders to estimate the potential energy available for the market. Meteorologists on their side use the information as boundary conditions in weather forecasting models. The Norwegian meteorological institute has provided snow accumulation maps for Norway for more than 50 years. These maps are now produced twice a month in the winter season. They show the accumulated precipitation in the winter season from the day the permanent snow cover is established. They do however not take melting into account, and do therefore not give a good description of the actual snow amounts during and after periods with snowmelt. Due to an increased need for a direct measure of water volumes as snow cover, met.no and NVE initialized a joint project in order to establish maps of the actual snow cover expressed in water equivalents. The project utilizes recent developments in the use of GIS in spatial modeling. Daily precipitation and temperature are distributed in space by using objective spatial interpolation methods. The interpolation considers topographical and other geographical parameters as well as weather type information. A degree-day model is used at each modeling point to calculate snow-accumulation and snowmelt. The maps represent a spatial scale of 1x1 km2. The modeled snow reservoir is validated by snow pillow values as well traditional snow depth observations. Preliminary results show that the new snow modeling approach reproduces the snow water equivalent well. The spatial approach also opens for a wide use in the terms of areal analysis.

  1. The Relationship Between Snow Accumulation at Mt. Logan, Yukon, and Climate Variability in the North Pacific

    Science.gov (United States)

    Rupper, S.; Steig, E. J.; Roe, G.

    2004-05-01

    An ice core from Mt. Logan, Yukon, presents an opportunity to evaluate the degree to which ice core accumulation records can be interpreted as meaningful measures of interannual climate variability. Statistical analyses and comparisons with synoptic station data are used to identify the physical relationships between Mt. Logan ice core accumulation data and large scale atmospheric circulation. These analyses demonstrate that only the winters of high accumulation years have a robust connection with atmospheric circulation. There are no consistent relationships during anomalously low and average accumulation years. The wintertime of high accumulation years is associated with an enhanced trough-ridge structure at 500 hPa and in sea level pressure over the Northeast Pacific and Western Canada, consistent with increased southerly flow bringing in warmer, moister air to the region. While both storm (i.e. 2-6 days) and blocking (i.e. 15-20 days) events project onto the same climate pattern, only the big storm events give rise to the dynamical moisture convergence necessary for anomalous accumulation. Taken together these results suggest that while the Mt. Logan accumulation record is not a simple record of Pacific climate variability, anomalously high accumulation years are a reliable indicator of wintertime circulation and, in particular, of Northeast Pacific storms.

  2. Forcing the snow-cover model SNOWPACK with forecasted weather data

    Directory of Open Access Journals (Sweden)

    S. Bellaire

    2011-12-01

    Full Text Available Avalanche danger is often estimated based on snow cover stratigraphy and snow stability data. In Canada, single forecasting regions are very large (>50 000 km2 and snow cover data are often not available. To provide additional information on the snow cover and its seasonal evolution the Swiss snow cover model SNOWPACK was therefore coupled with a regional weather forecasting model GEM15. The output of GEM15 was compared to meteorological as well as snow cover data from Mt. Fidelity, British Columbia, Canada, for five winters between 2005 and 2010. Precipitation amounts are most difficult to predict for weather forecasting models. Therefore, we first assess the capability of the model chain to forecast new snow amounts and consequently snow depth. Forecasted precipitation amounts were generally over-estimated. The forecasted data were therefore filtered and used as input for the snow cover model. Comparison between the model output and manual observations showed that after pre-processing the input data the snow depth and new snow events were well modelled. In a case study two key factors of snow cover instability, i.e. surface hoar formation and crust formation were investigated at a single point. Over half of the relevant critical layers were reproduced. Overall, the model chain shows promising potential as a future forecasting tool for avalanche warning services in Canadian data sparse areas and could thus well be applied to similarly large regions elsewhere. However, a more detailed analysis of the simulated snow cover structure is still required.

  3. Principles of snow hydrology

    National Research Council Canada - National Science Library

    DeWalle, David R; Rango, Albert

    2008-01-01

    ... Hydrology describes the factors that control the accumulation, melting, and runoff of water from seasonal snowpacks over the surface of the earth. The book addresses not only the basic principles governing snow in the hydrologic cycle, but also the latest applications of remote sensing, and principles applicable to modelling streamflow from snowmelt across lar...

  4. Snow modeling within a multi-layer soil-vegetation-atmosphere model

    Science.gov (United States)

    McGowan, L. E.; Paw U, K. T.; Pyles, D. R.

    2014-12-01

    Estimates of snow depth, extent, and melt in the Sierra Nevada Mountain Range are critical to estimating the amount of water that will be available for crops during the growing season within California's Central Valley. Numerical simulations utilizing a fourth order turbulent closure transport scheme in a multi-layer soil-vegetation-atmosphere model, Advanced Canopy-Atmosphere-Soil algorithm (ACASA), were used to explore snow model improvements in the physics-based parameterization for the Sierra Nevada Range. A set of alterations were made separately to the existing snowpack model within ACASA focusing on improvements to snow cover simulations on complex terrain slopes and over varying canopy cover. Three winter seasons were simulated; a climatological average, dry, and wet winter. The simulated output from the models are compared to observations to determine which model alterations made the largest improvements to snow simulations.

  5. Better interpretation of snow remote sensing data with physics-based models

    Science.gov (United States)

    Sandells, M.; Davenport, I. J.; Quaife, T. L.; Flerchinger, G. N.; Marks, D. G.; Gurney, R. J.

    2012-12-01

    Interpretation of remote sensing data requires a model and some assumptions, and the quality of the end product depends on the accuracy and appropriateness of these. Snow is a vital component of the water cycle, both socially and economically, so accurate monitoring of this resource is important. However, the snow mass products from passive microwave data may have large errors in them, and were deemed too unreliable for consideration in the latest Intergovernmental Panel on Climate Change Assessment Report. The SSM/I passive microwave snow mass retrieval algorithm uses a linear brightness temperature difference model, and assumptions that snow has a fixed grain diameter of 0.8mm and density of 300 kg m-3. In reality, the properties of the snow vary in time and space depending on its thermal history, and scattering of microwave radiation is very sensitive to snow properties. If snow mass retrievals are to be made from remote sensing data, then these properties must be known rather well. Layered physics-based models are capable of simulating the evolution of profiles of temperature, water content in the snow or soil, and snow grain size. These simulations could be used to provide information to help understand remote sensing data. Additional information from other remote sensing sources could enhance the accuracy of the product. For example, surface snow grain size can be obtained from near-infrared reflectance observations, and these data can be used to constrain the physically-based model, as could thermal observations. Here, we will present a new method that could be used to derive better estimates of snow mass and soil moisture. The system is comprised of a physically-based model of the snow and soil to derive snow and soil properties, a snow microwave emission model to estimate the satellite observations and ancillary data to constrain the physically-based model. These components will be used to estimate snow mass from passive microwave data with data

  6. Modelling runoff in the northern boreal forest using SLURP with snow ripening and frozen ground

    Science.gov (United States)

    St. Laurent, M. E.; Valeo, C.

    2003-04-01

    of snow ripening in the snowmelt process, model predictions of spring freshet volume and timing were greatly improved. The modified SLURP model depleted the snowpack over shorter periods of time and thus, significantly raised model efficiencies in the snowmelt period for 12 of the 15 years. Snowmelt accumulation curves developed for the original and modified model were found to be landcover dependent. The Muskeg and Coniferous landcovers were found to have the smallest changes in snow depletion periods between the original and modified SLURP models.

  7. A Model for the Spectral Albedo of Snow. II: Snow Containing Atmospheric Aerosols.

    Science.gov (United States)

    Warren, Stephen G.; Wiscombe, Warren J.

    1980-12-01

    Small highly absorbing particles, present in concentrations of only 1 part per million by weight (ppmw) or less, can lower snow albedo in the visible by 5-15% from the high values (96-99%) predicted for pure snow in Part I. These particles have, however, no effect on snow albedo beyond 0.9 m wavelength where ice itself becomes a strong absorber. Thus we have an attractive explanation for the discrepancy between theory and observation described in Part I, a discrepancy which seemingly cannot be resolved on the basis of near-field scattering and nonsphericity effects.Desert dust and carbon soot are the most likely contaminants. But careful measurements of spectral snow albedo in the Arctic and Antarctic paint to a `grey' absorber, one whose imaginary refractive index is nearly constant across the visible spectrum. Thus carbon soot, rather than the red iron oxide normally present in desert dust, is strongly indicated at these sites. Soot particles of radius 0.1 m, in concentrations of only 0.3 ppmw, can explain the albedo measurements of Grenfell and Maykut on Arctic Ice Island T-3. This amount is consistent with some observations of soot in Arctic air masses. 1.5 ppmw of soot is required to explain the Antarctic observations of Kuhn and Siogas, which seemed an unrealistically large amount for the earth's most unpolluted continent until we learned that burning of camp heating fuel and aircraft exhaust indeed had contaminated the measurement site with soot.Midlatitude snowfields are likely to contain larger absolute amounts of soot and dust than their polar counterparts, but the snowfall is also much larger, so that the ppmw contamination does not differ drastically until melting begins. Nevertheless, the variations in absorbing particle concentration which will exist can help to explain the wide range of visible snow albedos reported in the literature.Longwave emissivity of snow is unaltered by its soot and dust content. Thus the depression of snow albedo in the

  8. 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

  9. Evaluation of snow-glide risk by modelling and on-site assessment

    Science.gov (United States)

    Leitinger, Georg; Meusburger, Katrin; Rüdisser, Johannes; Tasser, Erich; Höller, Peter

    2015-04-01

    Abandonment of agricultural practices on alpine grasslands lead to increasing snow-glide intensities due to lower surface roughness of the vegetation. Beneath the danger of snow-glide avalanches snow gliding leads to soil erosion and damaging of young trees at afforested sites. Especially in high altitudes afforestation is important to protect settlements and infrastructure against snow-gliding and glide avalanches. Snow-glide damages are therefore of particular danger for these afforestation sites. In the light of future climate change and warmer winter periods, studies already state increasing snow-glide risk and the occurrence of glide avalanches. This study presents an approach to evaluate snow-glide risk by combining the refined Spatial Snow Glide Model (SSGM) first published by Leitinger et al. (2008) and the Guidelines to Identify Snow-Glide Areas (GISGA) proposed by Höller (2012), an on-site risk analyses approach. First, GISGA was validated on the basis of corresponding snow-glide measurements. Second, a potential snow-glide map for an area in the Eastern Alps covering 20000 km² was modelled. The results revealed considerable areas of high snow-glide risk. Using the average amount of winter precipitation between 1990 and 2010 in the SSGM shows higher vulnerability for the northern part of the study area (Tyrol, Austria) than in the southern part (South Tyrol, Italy) because of lower winter precipitation. However, running the SSGM based on the highest winter precipitation registered in the study area between 1801 and 2003 exhibits the possibility of very high snow-glide risk for most parts of the study area with significant increasing risk in the southern part. Given the very probable future climate during winter periods with increasing temperatures but uncertain development of precipitation patterns, snow-glide activity and linked glide avalanches might further increase at least in areas and altitudes with solid precipitation. In combination with the

  10. Modeling angular-dependent spectral emissivity of snow and ice in the thermal infrared atmospheric window.

    Science.gov (United States)

    Hori, Masahiro; Aoki, Teruo; Tanikawa, Tomonori; Hachikubo, Akihiro; Sugiura, Konosuke; Kuchiki, Katsuyuki; Niwano, Masashi

    2013-10-20

    A model of angular-dependent emissivity spectra of snow and ice in the 8-14 μm atmospheric window is constructed. Past field research revealed that snow emissivity varies depending on snow grain size and the exitance angle. Thermography images acquired in this study further revealed that not only welded snow particles such as sun crust, but also disaggregated particles such as granular snow and dendrite crystals exhibit high reflectivity on their crystal facets, even when the bulk snow surface exhibits blackbody-like behavior as a whole. The observed thermal emissive behaviors of snow particles suggest that emissivity of the bulk snow surface can be expressed by a weighted sum of two emissivity components: those of the specular and blackbody surfaces. Based on this assumption, a semi-empirical emissivity model was constructed; it is expressed by a linear combination of specular and blackbody surfaces' emissivities with a weighting parameter characterizing the specularity of the bulk surface. Emissivity spectra calculated using the model succeeded in reproducing the past in situ measured directional spectra of various snow types by employing a specific weighting parameter for each snow type.

  11. Modeling IRA Accumulation and Withdrawals

    OpenAIRE

    Sabelhaus, John

    2000-01-01

    Empirical analysis of IRA accumulation and withdrawal patterns is limited because information about IRA balances and flows is not available for a sample of taxpayers. This paper combines survey data on IRA balances with individual tax return data on IRA flows to study IRA accumulation and withdrawal patterns across cohorts. The analysis shows that IRA rules such as penalties for early withdrawals and minimum distribution requirements have predictable effects on IRA flows. The estimated propen...

  12. Influence of snow cover changes on surface radiation and heat balance based on the WRF model

    Science.gov (United States)

    Yu, Lingxue; Liu, Tingxiang; Bu, Kun; Yang, Jiuchun; Chang, Liping; Zhang, Shuwen

    2016-07-01

    The snow cover extent in mid-high latitude areas of the Northern Hemisphere has significantly declined corresponding to the global warming, especially since the 1970s. Snow-climate feedbacks play a critical role in regulating the global radiation balance and influencing surface heat flux exchange. However, the degree to which snow cover changes affect the radiation budget and energy balance on a regional scale and the difference between snow-climate and land use/cover change (LUCC)-climate feedbacks have been rarely studied. In this paper, we selected Heilongjiang Basin, where the snow cover has changed obviously, as our study area and used the WRF model to simulate the influences of snow cover changes on the surface radiation budget and heat balance. In the scenario simulation, the localized surface parameter data improved the accuracy by 10 % compared with the control group. The spatial and temporal analysis of the surface variables showed that the net surface radiation, sensible heat flux, Bowen ratio, temperature and percentage of snow cover were negatively correlated and that the ground heat flux and latent heat flux were positively correlated with the percentage of snow cover. The spatial analysis also showed that a significant relationship existed between the surface variables and land cover types, which was not obviously as that for snow cover changes. Finally, six typical study areas were selected to quantitatively analyse the influence of land cover types beneath the snow cover on heat absorption and transfer, which showed that when the land was snow covered, the conversion of forest to farmland can dramatically influence the net radiation and other surface variables, whereas the snow-free land showed significantly reduced influence. Furthermore, compared with typical land cover changes, e.g., the conversion of forest into farmland, the influence of snow cover changes on net radiation and sensible heat flux were 60 % higher than that of land cover changes

  13. Simulation of snow microwave radiance observations using a coupled land surface- radiative transfer models

    Science.gov (United States)

    Toure, A. M.; Rodell, M.; Hoar, T. J.; Kwon, Y.; Yang, Z.; Zhang, Y.; Beaudoing, H.

    2013-12-01

    Radiance assimilation (RA) has been used in operational numerical weather forecasting for generating realistic initial and boundary conditions for the last two decades. Previous studies have shown that the same approach can be used to characterize seasonal snow. Since the penetration depth of microwaves depends essentially on snow physical properties, studies have also shown that for RA to be successful, it is crucial that the land surface model (LSM) represents with great fidelity snow physical properties such as the effective grain size, the temperature, the stratigraphy, the densification and the melt/refreeze processes. The Community Land Model version 4 (CLM4), the land model component of the Community Earth System Model (CESM), describes the physical, chemical, biological, and hydrological processes by which terrestrial ecosystems interact with climate across a variety of spatial and temporal scales. Sub-grid heterogeneity of the CLM4 is represented by fractional coverage of glacier, lake, wetland, urban, and vegetation land cover types. The vegetation portion is further divided into mosaic of plant functional types (pfts) each with its own leaf and stem area index and canopy height. Processes such as snow accumulation, depletion, densification, metamorphism, percolation, and refreezing of water are represented by a state-of-the-art multi-layer (up to five layers) snow model. Each snow layer is characterized by its thickness, ice mass, liquid water content, temperature, and effective grain radius. The model is considered to be one of the most sophisticated snow models ever within a general circulation model. One of the main challenges in simulating the radiance observed by a radiometer on-board a satellite is the spatial heterogeneity of the land within the footprint of the radiometer. Since CLM4 has the capability to represent the sub-grid heterogeneity, it is perfect candidate for a model operator for simulating the observed brightness temperature (Tb). The

  14. 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.

  15. Drifting snow measurements on the Greenland Ice Sheet and their application for model evaluation

    Directory of Open Access Journals (Sweden)

    J. T. M. Lenaerts

    2014-01-01

    Full Text Available This paper presents autonomous drifting snow observations performed on the Greenland Ice Sheet in the fall of 2012. High-frequency Snow Particle Counter (SPC observations at ~1 m above the surface provided drifting snow number fluxes and size distributions; these were combined with meteorological observations at six levels. We identify two types of drifting snow events: katabatic events are relatively cold and dry, with prevalent winds from the southeast, whereas synoptic events are short-lived, warm and wet. Precipitating snow during synoptic events disturbs the drifting snow measurements. Output of the regional atmospheric climate model RACMO2, which includes the drifting snow routine PIEKTUK-B, agrees well with the observed near-surface climate at the site, as well as with the frequency and timing of drifting snow events. Direct comparisons with the SPC observations at 1 m reveal that the model overestimates the typical size of drifting snow particles, as well as the horizontal snow transport at this level.

  16. Approximating snow surface temperature from standard temperature and humidity data: new possibilities for snow model and remote sensing validation (Invited)

    Science.gov (United States)

    Raleigh, M. S.; Landry, C.; Hayashi, M.; Quinton, W. L.; Lundquist, J. D.

    2013-12-01

    The snow surface skin temperature (Ts) is important in the snowmelt energy balance, land-atmosphere interactions, weak layer formation (avalanche risk), and winter recreation, but is rarely measured at observational networks. Reliable Ts datasets are needed to validate remote sensing and distributed modeling, in order to represent land-atmosphere feedbacks. Previous research demonstrated that the dew point temperature (Td) close to the snow surface approximates Ts well because air is saturated immediately above snow. However, standard height (2 to 4 m) measurements of the saturation temperatures, Td and wet-bulb temperature (Tw), are much more readily available than measurements of Ts or near-surface Td. There is limited understanding of how these standard height variables approximate Ts, and how the approximations vary with climate, seasonality, time of day, and atmospheric conditions (stability and radiation). We used sub-daily measurements from seven sites in varying snow climates and environments to test Ts approximations with standard height temperature and moisture. Td produced the lowest bias (-2.2 °C to +2.6 °C) and root mean squared error (RMSE) when approximating mean daily Ts, but tended to underestimate daily extremes in Ts. For comparison, air temperature (Ta) was biased +3.2 °C to +6.8 °C. Ts biases increased with increasing frequency in nighttime stability and daytime clear sky conditions. We illustrate that mean daily Td can be used to detect systematic input data bias in physically-based snowmelt modeling, a useful tool when validating spatially distributed snow models in data sparse regions. Thus, improved understanding of Td variations can advance understanding of Ts in space and time, providing a simple yet robust measure of surface feedback to the atmospheric energy budget.

  17. 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

  18. 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

  19. 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

  20. Modeling the elution of organic chemicals from a melting homogeneous snow pack.

    Science.gov (United States)

    Meyer, Torsten; Wania, Frank

    2011-06-01

    Organic chemicals are often released in peak concentrations from melting snow packs. A simple, mechanistic snowmelt model was developed to simulate and predict the elution of organic substances from melting, homogeneous snow, as influenced by chemical properties and snow pack characteristics. The model calculates stepwise the chemical transport along with the melt water flow in a multi-layered snow pack, based on chemical equilibrium partitioning between the individual bulk snow phases. The model succeeds in reproducing the elution behavior of several organic contaminants observed in previously conducted cold room experiments. The model aided in identifying four different types of enrichment of organic substances during snowmelt. Water soluble substances experience peak releases early during a melt period (type 1), whereas chemicals that strongly sorb to particulate matter (PM) or snow grain surfaces elute at the end of melting (type 2). Substances that are somewhat water soluble and at the same time have a high affinity for snow grain surfaces may exhibit increasing concentrations in the melt water (type 3). Finally, elution sequences involving peak loads both at the beginning and the end of melting are simulated for chemicals that are partially dissolved in the aqueous melt water phase and partially sorbed to PM (type 4). The extent of type 1 enrichment mainly depends on the snow depth, whereby deeper snow generates more pronounced concentration peaks. PM influences the elution behavior of organic chemicals strongly because of the very large natural variability in the type and amount of particles present in snow. Urban and road-side snow rich in PM can generate type 2 concentration peaks at the end of the melt period for even relatively water soluble substances. From a clean, melting snow pack typical for remote regions, even fairly hydrophobic chemicals can be released in type 1 mode while being almost completely dissolved in the aqueous melt water phase. The

  1. Simulation of the specific surface area of snow using a one-dimensional physical snowpack model: implementation and evaluation for subarctic snow in Alaska

    Directory of Open Access Journals (Sweden)

    H. W. Jacobi

    2009-09-01

    Full Text Available The specific surface area (SSA of the snow constitutes a powerful parameter to quantify the exchange of matter and energy between the snow and the atmosphere. However, currently no snow physics model can simulate the SSA. Therefore, two different types of empirical parameterizations of the specific surface area (SSA of snow are implemented into the existing one-dimensional snow physics model CROCUS. The parameterizations are either based on diagnostic equations relating the SSA to parameters like snow type and density or on prognostic equations that describe the change of SSA depending on snow age, snowpack temperature, and the temperature gradient within the snowpack. Simulations with the upgraded CROCUS model were performed for a subarctic snowpack, for which an extensive data set including SSA measurements is available at Fairbanks, Alaska for the winter season 2003/2004. While a reasonable agreement between simulated and observed SSA values is obtained using both parameterizations, the model tends to overestimate the SSA. This overestimation is more pronounced using the diagnostic equations compared to the results of the prognostic equations. Parts of the SSA deviations using both parameterizations can be attributed to differences between simulated and observed snow heights, densities, and temperatures. Therefore, further sensitivity studies regarding the thermal budget of the snowpack were performed. They revealed that reducing the heat conductivity of the snow or increasing the turbulent fluxes at the snow surfaces leads to a slight improvement of the simulated thermal budget of the snowpack compared to the observations. However, their impact on further simulated parameters like snow height and SSA remains small. Including additional physical processes in the snow model may have the potential to advance the simulations of the thermal budget of the snowpack and, thus, the SSA simulations.

  2. Simulation of the specific surface area of snow using a one-dimensional physical snowpack model: implementation and evaluation for subarctic snow in Alaska

    Science.gov (United States)

    Jacobi, H.-W.; Domine, F.; Simpson, W. R.; Douglas, T. A.; Sturm, M.

    2010-01-01

    The specific surface area (SSA) of the snow constitutes a powerful parameter to quantify the exchange of matter and energy between the snow and the atmosphere. However, currently no snow physics model can simulate the SSA. Therefore, two different types of empirical parameterizations of the specific surface area (SSA) of snow are implemented into the existing one-dimensional snow physics model CROCUS. The parameterizations are either based on diagnostic equations relating the SSA to parameters like snow type and density or on prognostic equations that describe the change of SSA depending on snow age, snowpack temperature, and the temperature gradient within the snowpack. Simulations with the upgraded CROCUS model were performed for a subarctic snowpack, for which an extensive data set including SSA measurements is available at Fairbanks, Alaska for the winter season 2003/2004. While a reasonable agreement between simulated and observed SSA values is obtained using both parameterizations, the model tends to overestimate the SSA. This overestimation is more pronounced using the diagnostic equations compared to the results of the prognostic equations. Parts of the SSA deviations using both parameterizations can be attributed to differences between simulated and observed snow heights, densities, and temperatures. Therefore, further sensitivity studies regarding the thermal budget of the snowpack were performed. They revealed that reducing the thermal conductivity of the snow or increasing the turbulent fluxes at the snow surfaces leads to a slight improvement of the simulated thermal budget of the snowpack compared to the observations. However, their impact on further simulated parameters like snow height and SSA remains small. Including additional physical processes in the snow model may have the potential to advance the simulations of the thermal budget of the snowpack and, thus, the SSA simulations.

  3. 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

  4. Drifting snow climate of the Greenland ice sheet: a study with a regional climate model

    NARCIS (Netherlands)

    Lenaerts, J.T.M.; van den Broeke, M.R.; van Angelen, J.H.; van Meijgaard, E.; Déry, S.J.

    2012-01-01

    This paper presents the drifting snow climate of the Greenland ice sheet, using output from a high-resolution ( 11 km) regional climate model. Because reliable direct observations of drifting snow do not exist, we evaluate the modeled near-surface climate instead, using automatic weather station (AW

  5. Drifting snow climate of the Greenland ice sheet: a study with a regional climate model

    NARCIS (Netherlands)

    Lenaerts, J.T.M.|info:eu-repo/dai/nl/314850163; van den Broeke, M.R.|info:eu-repo/dai/nl/073765643; van Angelen, J.H.; van Meijgaard, E.; Déry, S.J.

    2012-01-01

    This paper presents the drifting snow climate of the Greenland ice sheet, using output from a high-resolution ( 11 km) regional climate model. Because reliable direct observations of drifting snow do not exist, we evaluate the modeled near-surface climate instead, using automatic weather station (AW

  6. Investigating the effect and uncertainties of light absorbing impurities in snow and ice on snow melt and discharge generation using a hydrologic catchment model and satellite data

    Science.gov (United States)

    Matt, Felix; Burkhart, John F.

    2017-04-01

    Light absorbing impurities in snow and ice (LAISI) originating from atmospheric deposition enhance snow melt by increasing the absorption of short wave radiation. The consequences are a shortening of the snow cover duration due to increased snow melt and, with respect to hydrologic processes, a temporal shift in the discharge generation. However, the magnitude of these effects as simulated in numerical models have large uncertainties, originating mainly from uncertainties in the wet and dry deposition of light absorbing aerosols, limitations in the model representation of the snowpack, and the lack of observable variables required to estimate model parameters and evaluate the simulated variables connected with the representation of LAISI. This leads to high uncertainties in the additional energy absorbed by the snow due to the presence of LAISI, a key variable in understanding snowpack energy-balance dynamics. In this study, we assess the effect of LAISI on snow melt and discharge generation and the involved uncertainties in a high mountain catchment located in the western Himalayas by using a distributed hydrological catchment model with focus on the representation of the seasonal snow pack. The snow albedo is hereby calculated from a radiative transfer model for snow, taking the increased absorption of short wave radiation by LAISI into account. Meteorological forcing data is generated from an assimilation of observations and high resolution WRF simulations, and LAISI mixing ratios from deposition rates of Black Carbon simulated with the FLEXPART model. To asses the quality of our simulations and the related uncertainties, we compare the simulated additional energy absorbed by the snow due to the presence of LAISI to the MODIS Dust Radiative Forcing in Snow (MODDRFS) algorithm satellite product.

  7. 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.

  8. Snow and ice on Bear Lake (Alaska – sensitivity experiments with two lake ice models

    Directory of Open Access Journals (Sweden)

    Tido Semmler

    2012-03-01

    Full Text Available Snow and ice thermodynamics of Bear Lake (Alaska are investigated with a simple freshwater lake model (FLake and a more complex snow and ice thermodynamic model (HIGHTSI. A number of sensitivity experiments have been carried out to investigate the influence of snow and ice parameters and of different complexity on the results. Simulation results are compared with observations from the Alaska Lake Ice and Snow Observatory Network. Adaptations of snow thermal and optical properties in FLake can largely improve accuracy of the results. Snow-to-ice transformation is important for HIGHTSI to calculate the total ice mass balance. The seasonal maximum ice depth is simulated in FLake with a bias of −0.04 m and in HIGHTSI with no bias. Correlation coefficients between ice depth measurements and simulations are high (0.74 for FLake and 0.9 for HIGHTSI. The snow depth simulation can be improved by taking into account a variable snow density. Correlation coefficients for surface temperature are 0.72 for FLake and 0.81 for HIGHTSI. Overall, HIGHTSI gives slightly more accurate surface temperature than FLake probably due to the consideration of multiple snow and ice layers and the expensive iteration calculation procedure.

  9. Measurements of snow radiometric and microstructure properties over a transect of plot-scale field observations: Application to snow thermodynamic and passive microwave emission models (Invited)

    Science.gov (United States)

    Langlois, A.; Royer, A.; Montpetit, B.; Roy, A.; Derksen, C.

    2010-12-01

    Snow geophysical and thermophysical properties are known to be sensitive to climate variability and change and are of primary importance for hydrological and climatological processes in northern regions. Specifically, spatial and temporal variations of snow extent and thickness are good indicators of climate variability and change, and better tools are required to assess those changes from space. Numerous studies have looked at the linkages between passive microwave brightness temperatures (Tb) and snow thickness and water equivalent (SWE), but lingering uncertainties remain with regards to the effect of snow grain metamorphism on the microwave emission. Snow grains play an important role in the scattering mechanisms, but the lack of objectivity and repeatability in the measurement of snow grain morphology highlights the need for improved observations in order to fully exploit passive microwave radiometry. This work presents an innovative approach to measure and better define snow grains through accurate measurements of specific surface area (SSA) using near-infrared photography at 715 nm and laser measurements at 1310 nm. The relationship between infrared reflectance and snow grain morphology parameters measured from directional lighting photographs is also investigated. Using the theoretical snow albedo model of Kokhanovsky and Zege (2004), vertical SSA profiles are derived and coupled to snow thermodynamic and microwave emission models (SNOWPACK and MEMLS). Measurements of snow properties and microwave emission at 19 and 37 GHz were performed over a transect of 2 000 km in northerneastern Canada, from the dense boreal forest to arctic tundra. A series of plot-scale observations were performed every 40 km. Results show that with proper assessment of snow grains, simulations of brightness temperatures are improved when compared to field measurements from airborne passive microwave radiometers.

  10. MODELING OF THE SNOW LOAD ON THE ROOFS OF INDUSTRIAL BUILDINGS

    Directory of Open Access Journals (Sweden)

    Zolina Tat’yana Vladimirovna

    2016-08-01

    Full Text Available When designing load-bearing framework structures using the method of limiting states it is necessary to determine the maximum possible value of snow load for the entire period of operation of an industrial building for the possibility of transition. The magnitude of the snow load is randomly changed over the time, and therefore the most appropriate form of its display is a probabilistic model of random process. The authors have identified the most preferable approach to modeling of snow load. It consists in presenting a selective sequence of the year maximums in the form of a continuous random variable distributed according to the Gumbel law. Its parameters are expressed through the mathematical expectation and the standard sample set of meteorological observations. According to the calculated values of the parameters the authors have built a graphic interpretation of the law of distribution of this random variable. When building a model of the total snow load on the roof of a building the influence of various factors should be considered, such as: • snow shedding at a given roof slope; • snow movement caused by wind; • distribution of snow depending on the roof shape; • snow melting depending on the thermal characteristics of the roof; • the ability to drain meltwater from the surface of the roof. The resulting model of snow load is adapted for implementation using software complex “DINCIB-new” developed by the authors. The proposed approach to the modeling of the snow load on the roof of an industrial building allows correlating the repeatability period of its limit calculated value with the residual life of the research object. This has become possible due to the multiple implementation of an automated algorithm for calculating an industrial building, which was developed by the authors, with account of the varying values of snow load in relation to the corresponding mathematical expectation, with registering the quantities of

  11. Annual Greenland accumulation derived from airborne radar and comparisons to modeled and in situ data

    Science.gov (United States)

    Koenig, L.; Ivanoff, A.; Alexander, P. M.; MacGregor, J. A.; Cullather, R. I.; Nowicki, S.

    2015-12-01

    Mass loss across the Greenland Ice Sheet (GrIS) has accelerated in recent decades and recently a fundamental change in the nature of this mass loss has begun. The dominant GrIS mass-loss process has switched from ice dynamics to surface mass balance (SMB) processes, including melt generation and runoff. This recent shift further emphasizes the need to monitor and constrain SMB, which, across most of the GrIS, is dominated by accumulation. High resolution, near-surface radar data have shown good fidelity at mapping spatial patterns of accumulation to validate model outputs. To better constrain accumulation over the GrIS, we derive annual accumulation rates using NASA Operation IceBridge (OIB) Snow Radar data collected from 2009 through 2012. Accumulation is calculated using the radar-determined depth to an annual layer and the local snow/firn density profile. Up to 30 years of annual stratigraphy is observed in the interior of the ice sheet, near Summit Station, while only the past year is detectable in the ablation zone around the perimeter of the ice sheet. Annual layering is traced using a semi-automatic algorithm and mapped across large areas (tens of thousands of line kilometers). A combined measured and modeled density profile is used to convert the annual stratigraphy into accumulation. Modeled density profiles from the Modèle Atmosphérique Régional (MAR) model are shown to be less than half of in situ observations in the top 1 m of snow/firn and are, therefore, replaced with in situ measurements. Using a compilation of in situ measurements, the mean GrIS snow/firn density is found to be ~340 +/- 40 kg/m3 in the top 1 m. Error in the snow density profile represents the largest error in the radar-derived accumulation. The pattern of radar-derived accumulation rate compares well with MAR estimates, although the latter has a mean bias of 4.6 cm water equivalent, a root mean square error of 16.8 cm water equivalent and a correlation coefficient of 0.6 across

  12. 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

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

    Science.gov (United States)

    Wang, Tao; Peng, Shushi; Krinner, Gerhard; Ryder, James; Li, Yue; Dantec-Nédélec, Sarah; Ottlé, Catherine

    2015-01-01

    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 magnitude of

  14. Distributed modeling of snow cover mass and energy balance in the Rheraya watershed (High Atlas, Morocco)

    Science.gov (United States)

    Marchane, Ahmed; Gascoin, Simon; Jarlan, Lionel; Hanich, Lahoucine

    2016-04-01

    The mountains of the High Moroccan Atlas represent an important source of water for the neighboring arid plains. Despite the importance of snow in the regional water balance, few studies were devoted to the modeling of the snow cover at the watershed scale. This type of modeling is necessary to characterize the contribution of snowmelt to water balance and understanding its sensitivity to natural and human-induced climate fluctuations. In this study, we applied a spatially-distributed model of the snowpack evolution (SnowModel, Liston & Elder 2006) on the Rheraya watershed (225 km²) in the High Atlas in order to simulate the mass and energy balance of the snow cover and the evolution of snow depth over a full season (2008-2009). The model was forced by 6 meteorological stations. The model was evaluated locally at the Oukaimeden meteorological station (3230 m asl) where snow depth is recorded continuously. To evaluate the model at the watershed scale we used the daily MODIS snow cover products and a series of 15 cloud-free optical images acquired by the FORMOSAT-2 satellite at 8-m resolution from February to June 2009. The results showed that the model is able to simulate the snow depth in the Oukaimeden station for the 2008-2009 season, and also to simulate the spatial and temporal variation of of the snow cover area in the watershed Rheraya. Based on the model output we examine the importance of the snow sublimation on the water balance at the watershed scale.

  15. Acoustic waves in a Biot-type porous snow model: The fast slow wave in light snow

    CERN Document Server

    Sidler, Rolf

    2015-01-01

    Wave velocities, attenuation and reflection coefficients in snow can not be explained by the widely used elastic or viscoelastic models for wave propagation. Instead, Biot's model of wave propagation in porous materials should be used. However, the application of Biot's model is difficult due to the large property space of the underlying porous material. Here we use the properties of ice and air as well as empirical relationships to define the properties of snow as a function of porosity. This reduction allows to predict phase velocities and attenuation of the shear- and compressional-waves as functions of porosity or density. For light snow the peculiarity was found that the velocity of the compressional wave of the first kind is lower than the compressional wave of the second kind that is commonly referred to as the "slow" wave. The reversal of the velocities comes with an increase of attenuation for the first compressional wave. This is in line with the common observation that sound is strongly absorbed af...

  16. Test of newly developed conceptual hydrological model for simulation of rain-on-snow events in forested watershed

    Directory of Open Access Journals (Sweden)

    Si-min QU

    2013-01-01

    Full Text Available A conceptual hydrological model that links the Xin’anjiang hydrological model and a physically based snow energy and mass balance model, described as the XINSNOBAL model, was developed in this study for simulating rain-on-snow events that commonly occur in the Pacific Northwest of the United States. The resultant model was applied to the Lookout Creek Watershed in the H. J. Andrews Experimental Forest in the western Cascade Mountains of Oregon, and its ability to simulate streamflow was evaluated. The simulation was conducted at 24-hour and one-hour time scales for the period of 1996 to 2005. The results indicated that runoff and peak discharge could be underestimated if snowpack accumulation and snowmelt under rain-on-snow conditions were not taken into account. The average deterministic coefficient of the hourly model in streamflow simulation in the calibration stage was 0.837, which was significantly improved over the value of 0.762 when the Xin’anjiang model was used alone. Good simulation performance of the XINSNOBAL model in the WS10 catchment, using the calibrated parameter of the Lookout Creek Watershed for proxy-basin testing, demonstrates that transplanting model parameters between similar watersheds can provide a useful tool for discharge forecasting in ungauged basins.

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

    Science.gov (United States)

    Naha, Shaini; Thakur, Praveen K.; Aggarwal, S. P.

    2016-06-01

    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 using observed

  18. 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

  19. Impact of Model and Observation Error on Assimilating Snow Cover Fraction Observations

    Science.gov (United States)

    Arsenault, Kristi R.

    Accurately modeling or observing snow cover fraction (SCF) estimates, which represent fractional snow cover area within a gridcell, can help with better understanding earth system dynamics, improving weather and climate prediction, and providing end-use water solutions. Seeking to obtain more accurate snowpack estimates, high resolution snow cover fraction observations are assimilated with different data assimilation (DA) methods within a land surface model (LSM). The LSM simulates snowpack states, snow water equivalent and snow depth, to obtain improved snowpack estimates known as the analysis. Data assimilation experiments are conducted for two mountainous areas where high spatial snow variability occurs, which can impact realistic snowpack representation for different hydrological and meteorological applications. Consequently, the experiments are conducted at higher model resolutions to better capture this variability. This study focuses on four key aspects of how assimilating SCF observations may improve snowpack estimates and impact the LSM overall. These include investigating the role of data assimilation method complexity, evaluating the impact of model and observational errors on snow state analysis estimates, improving the model's SCF representation for assimilation using observation operators, and examining subsequent model state and flux impacts when SCF observations are assimilated. A simpler direct insertion (DI) and a more complex ensemble Kalman filter (EnKF) data assimilation method were applied. The more complex method proved to be superior to the simpler one; however, this method required accounting for more realistic observational and model errors. Also, the EnKF method required an ensemble of model forecasts, in which bias in the ensemble generation was found and removed. Reducing this bias improved the model snowpack estimates. Detection and geolocation errors in the satellite-based snow cover fraction observations also contributed to degrading

  20. The Effect of Errors in Snow Assimilation on Land Surface Modeling

    Science.gov (United States)

    Cosgrove, Brian A.; Houser, Paul R.; Atlas, Robert (Technical Monitor)

    2001-01-01

    The accurate portrayal of the hydrological cycle is extremely important in land surface modeling. Central to this effort is the treatment of snow, as errors in the representation of this quantity can impact practically all other modeled quantities through alterations in the water and energy balances. Although land surface model (LSM) simulations can benefit from the assimilation of snow cover and snow depth observations, they can be negatively impacted if such observations contain errors or if a model bias exists in the simulation of surface or soil temperatures. Both cases may lead to excessive melting or growth of snow packs, and to large alterations in both the energy and water balances. Such problems in the snow assimilation process, made evident by the repeated melting and replenishing of snow pack over significant areas of the United States, exists in the Eta Data Assimilation System and is a product of the EDAS system's direct insertion assimilation of snow data. Occurring on a 24 hour cycle, the repeated melting infuses the soil column with a large quantity of water that upsets the hydrological cycle. In an effort to quantify the impacts of such errors in snow assimilation on water and energy budgets, a series of Mosaic LSM simulations were performed over the 12 month period covering October 1998 to October 1999.

  1. Distinguishing Ice from Snow for Melt Modeling Using Daily Observations from MODIS

    Science.gov (United States)

    Rittger, K.; Brodzik, M. J.; Racoviteanu, A.; Barrett, A. P.; Khalsa, S. J. S.; Painter, T. H.; Armstrong, R. L.; Burgess, A. B.

    2014-12-01

    In Earth's mountainous regions, melt from both seasonal snow and glacier ice contributes to streamflow. Few in-situ observations exist that can help distinguish between the two components of melt, particularly across large mountain ranges. In this study, we analyze daily time series of MODIS data products to distinguish ice from snow as the seasonal snowpack recedes revealing firn and glacier ice surfaces. We run a temperature index melt model for the Hunza, a sub-basin of the Upper Indus basin using the MODIS data to discriminate between glacier ice and snow and partition the corresponding streamflow. During the ablation period, this high elevation mid-latitude snowpack receives intense incoming solar radiation resulting in snow grain growth and surface albedo decreases. To explore snow grain growth, we use estimates of grain size from both the MODIS Snow Covered Area and Grain Size Model (MODSCAG) and MODIS Dust Radiative Forcing in Snow (MODDRFS). To explore albedo reduction we use 2 standard albedo products from MODIS, the Terra Daily Snow Cover algorithm (MOD10A1) and Surface Reflectance BRDF/Albedo (MOD43). We use a threshold on the grain size and albedo products to discriminate ice from snow. We test the ability of the 4 MODIS products to discriminate snow from glacier ice using higher resolution data from the Landsat 8 sensor from July 5th and July 21st, 2013 for a subset of the study area in the Karakoram region of the Himalaya that includes the Yazghil and Hopper Glaciers that drain north and northeast in the Shimshall Valley, part of the Hunza River basin. Snow and glacier ice are mapped using band ratio techniques, and are then separated on the basis of broadband albedo values calculated from Landsat bands for comparison with MODIS-derived snow and glacier ice pixels. We run a temperature index melt model that uses gap filled snow covered area from MODSCAG and interpolated station temperature data for the Hunza River basin. The model outputs daily melt

  2. A comparison of two approaches to modelling snow cover dynamics at the Polish Polar Station at Hornsund

    Science.gov (United States)

    Luks, B.; Osuch, M.; Romanowicz, R. J.

    2012-04-01

    We compare two approaches to modelling snow cover dynamics at the Polish Polar Station at Hornsund. In the first approach we apply physically-based Utah Energy Balance Snow Accumulation and Melt Model (UEB) (Tarboton et al., 1995; Tarboton and Luce, 1996). The model uses a lumped representation of the snowpack with two primary state variables: snow water equivalence and energy. Its main driving inputs are: air temperature, precipitation, wind speed, humidity and radiation (estimated from the diurnal temperature range). Those variables are used for physically-based calculations of radiative, sensible, latent and advective heat exchanges with a 3 hours time step. The second method is an application of a statistically efficient lumped parameter time series approach to modelling the dynamics of snow cover , based on daily meteorological measurements from the same area. A dynamic Stochastic Transfer Function model is developed that follows the Data Based Mechanistic approach, where a stochastic data-based identification of model structure and an estimation of its parameters are followed by a physical interpretation. We focus on the analysis of uncertainty of both model outputs. In the time series approach, the applied techniques also provide estimates of the modeling errors and the uncertainty of the model parameters. In the first, physically-based approach the applied UEB model is deterministic. It assumes that the observations are without errors and that the model structure perfectly describes the processes within the snowpack. To take into account the model and observation errors, we applied a version of the Generalized Likelihood Uncertainty Estimation technique (GLUE). This technique also provide estimates of the modelling errors and the uncertainty of the model parameters. The observed snowpack water equivalent values are compared with those simulated with 95% confidence bounds. This work was supported by National Science Centre of Poland (grant no. 7879/B/P01

  3. 寒区积雪堆蚀路基的室内风洞试验研究%Studied on Depositing Erosion of Accumulated Snow on Subgrade Slope Through Indoor wind Tunnel Experiment in Cold Region

    Institute of Scientific and Technical Information of China (English)

    高瑜; 李驰

    2014-01-01

    以寒区公路路基作为研究对象,采用麸皮作为模型雪,通过室内风洞试验研究风雪流下雪粒子的起动,以及在路基不同部位的堆积,确定雪粒子沿路基坡面的堆积区域与路基断面之间的关系。试验结果表明,雪粒子在路基坡面的堆积区域与路基沿程风雪流的运动规律、雪粒子的天然密度、路基断面型式等密切相关。当路基模型高度不大于250mm时,雪粒子在路基迎风坡面和背风坡面堆积高度随路基高度的增加而增大,随路基边坡坡率的增加而增大。当路基边坡坡率为1∶1时,雪粒子在迎风坡面上的堆积高度约为路基模型高度的57.5%,在背风坡的堆积高度为路基模型高度的79%。当边坡坡率1∶2.5时,雪粒子在迎风坡面上的堆积高度为路基模型高度的34.4%;雪粒子在背风坡的堆积高度为路基模型高度的76.7%。%Taking the subgrade in cold region highway as the research object ,using bran as simu‐lant snow in indoor wind tunnel experiment ,the relationship between accumulation area of snow parti‐cles along the subgrade and embankment section was determined by analyzing experimental results . The experiment results were summarized for the movement and accumulation of snow particles ,depos‐iting erosion and carrying processes of snow particles in different parts along the subgrade slope .The results indicated that :the accumulation area of the snow particles in the subgrade slope surface was closely related to these factors including the moving rules of the wind -drift snow ,natural density of the snow particles and the sections of subgrade .When the subgrade height was no more than 250mm , the more subgrade height and the great slope gradient was ,the more accumulation areas along the windward and leeward slope surface .While the slope rate was 1∶1 ,accumulation height of snow par‐ticles along the windward slopes was 57 .5 percent

  4. Vegetation and Variable Snow Cover: Spatial Patterns of Shrubland, and Grassland Snow

    Science.gov (United States)

    Liston, G. E.; Hiemstra, C. A.; Strack, J. E.

    2003-12-01

    areas and deposited into the shrub patches, where drifting on the lee sides of individual shrubs was apparent. As a result, the snow cover over graminoid species was shallow and ablated quickly. In contrast, shrubs accumulated deeper snow that persisted longer. Variable snow accumulation patterns, their respective effects on snowpack characteristics, ecosystem properties, and the implications of these variable snow regimes for climate models and in terms of vegetation cover change are described.

  5. Simulation of Snow Processes Beneath a Boreal Scots Pine Canopy

    Institute of Scientific and Technical Information of China (English)

    LI Weiping; LUO Yong; XIA Kun; LIU Xin

    2008-01-01

    A physically-based multi-layer snow model Snow-Atmosphere-Soil-Transfer scheme (SAST) and a land surface model Biosphere-Atmosphere Transfer Scheme (BATS) were employed to investigate how boreal forests influence snow accumulation and ablation under the canopy. Mass balance and energetics of snow beneath a Scots pine canopy in Finland at different stages of the 2003-2004 and 2004-2005 snow seasons are analyzed. For the fairly dense Scots pine forest, drop-off of the canopy-intercepted snow contributes, in some cases, twice as much to the underlying snowpack as the direct throughfall of snow. During early winter snow melting, downward turbulent sensible and condensation heat fluxes play a dominant role together with downward net longwave radiation. In the final stage of snow ablation in middle spring, downward net all-wave radiation dominates the snow melting. Although the downward sensible heat flux is comparable to the net solar radiation during this period, evaporative cooling of the melting snow surface makes the turbulent heat flux weaker than net radiation. Sensitivities of snow processes to leaf area index (LAI) indicate that a denser canopy speeds up early winter snowmelt, but also suppresses melting later in the snow season. Higher LAI increases the interception of snowfall, therefore reduces snow accumulation under the canopy during the snow season; this effect and the enhancement of downward longwave radiation by denser foliage outweighs the increased attenuation of solar radiation, resulting in earlier snow ablation under a denser canopy. The difference in sensitivities to LAI in two snow seasons implies that the impact of canopy density on the underlying snowpack is modulated by interannual variations of climate regimes.

  6. Evaluation of a physically-based snow model with infrared and microwave satellite-derived estimates

    Science.gov (United States)

    Wang, L.

    2013-05-01

    Snow (with high albedo, as well as low roughness and thermal conductivity) has significant influence on the land-atmosphere interactions in the cold climate and regions of high elevation. The spatial and temporal variability of the snow distribution on a basin scale greatly determines the timing and magnitude of spring snowmelt runoff. For improved water resources management, a physically-based distributed snow model has been developed and applied to the upper Yellow River Basin to provide the outputs of snow variables as well as streamflows from 2001 to 2005. Remotely-sensed infrared information from MODIS satellites has been used to evaluate the model's outputs of spatially-distributed snow cover extent (SCE) and land surface temperature (LST); while the simulated snow depth (SD) and snow water equivalent (SWE) have been compared with the microwave information from SSM/I and AMSR-E satellites. In general, the simulated streamflows (including spring snowmelt) agree fairly well with the gauge-based observations; while the modeled snow variables show acceptable accuracies through comparing to various satellite-derived estimates from infrared or microwave information.;

  7. Simulation of black carbon in snow and its climate impact in the Canadian Global Climate Model

    Science.gov (United States)

    Namazi, M.; von Salzen, K.; Cole, J. N. S.

    2015-09-01

    A new physically based parameterisation of black carbon (BC) in snow was developed and implemented in the Canadian Atmospheric Global Climate Model (CanAM4.2). Simulated BC snow mixing ratios and BC snow radiative forcings are in good agreement with measurements and results from other models. Simulations with the improved model yield considerable trends in regional BC concentrations in snow and BC snow radiative forcings during the time period from 1950-1959 to 2000-2009. Increases in radiative forcings for Asia and decreases for Europe and North America are found to be associated with changes in BC emissions. Additional sensitivity simulations were performed in order to study the impact of BC emission changes between 1950-1959 and 2000-2009 on surface albedo, snow cover fraction, and surface air temperature. Results from these simulations indicate that impacts of BC emission changes on snow albedos between these 2 decades are small and not significant. Overall, changes in BC concentrations in snow have much smaller impacts on the cryosphere than the net warming surface air temperatures during the second half of the 20th century.

  8. Simulation of black carbon in snow and its climate impact in the Canadian Global Climate Model

    Directory of Open Access Journals (Sweden)

    M. Namazi

    2015-07-01

    Full Text Available A new physically-based parameterization of black carbon (BC in snow was developed and implemented in the Canadian Atmospheric Global Climate Model (CanAM4.2. Simulated BC snow mixing ratios and BC snow radiative forcings are in good agreement with measurements and results from other models. Simulations with the improved model yield considerable trends in regional BC concentrations in snow and BC snow radiative forcings during the time period from 1950–1959 to 2000–2009. Increases in radiative forcings for Asia and decreases for Europe and North America are found to be associated with changes in BC emissions. Additional sensitivity simulations were performed in order to study the impact of BC emission changes between 1950–1959 and 2000–2009 on surface albedo, snow cover fraction, and surface air temperature. Results from these simulations indicate that impacts of BC emission changes on snow albedos between these two decades are small and not significant. Overall, changes in BC concentrations in snow have much smaller impacts on the cryosphere than the net warming surface air temperatures during the second half of the 20th century.

  9. 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.

  10. Wheels and Tracks in Snow. Second Validation Study of the CRREL Shallow Snow Mobility Model

    Science.gov (United States)

    1990-12-01

    Blaisdell and Charles E. Green December 1990 to 4), Vtt 91 3 19 105 For conversion of SI metric units to U.S./British customary units of measurement...algorithm, resistance attributable to snow compaction. Gross trac - which was incorporated into the second version of the tion (in kilopascals) can be...station ** 1030 trac0.851*(loa/area/1000)Ŕ.823 I in kPa 1040 trac =tracoarea*1000 I in N 1050 ! 1060 IF r$="n" OR r$="N" THEN trac -0 I no traction if not

  11. A current precipitation index-based model for continuous daily runoff simulation in seasonally snow covered sub-arctic catchments

    Science.gov (United States)

    Akanegbu, Justice O.; Marttila, Hannu; Ronkanen, Anna-Kaisa; Kløve, Bjørn

    2017-02-01

    A new precipitation index-based model, which includes a snow accumulation and melt component, has been developed to simulate hydrology in high latitude catchments. The model couples a point snowmelt model with a current precipitation index (CPI) formulation to simulate continuous daily runoff from catchments with seasonal snow cover. A new runoff conversion factor: CT and Lf, threshold flow factor ThQ and runoff transformation function Maxbas were introduced into the CPI equation, which converts and transforms the routed daily CPI into daily runoff and maintains the daily base flow in the catchment. The model was developed using twelve sub-arctic boreal catchments located above and below the Arctic Circle in northern Finland, representing a region with considerable seasonal snow cover. The results showed that the model can adequately simulate and produce the dynamics of daily runoff from catchments where the underlying physical conditions are not known. An open-access Excel-based model is provided with this paper for daily runoff simulations. The model can be used to estimate runoff in sub-arctic regions where little data is typically available but significant changes in climate are expected, with considerable shifts in the amount and timing of snowmelt and runoff.

  12. 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.

  13. 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.

  14. Assimilation of satellite information in a snowpack model to improve characterization of snow cover for runoff simulation and forecasting

    OpenAIRE

    2009-01-01

    A new technique for constructing spatial fields of snow characteristics for runoff simulation and forecasting is presented. The technique incorporates satellite land surface monitoring data and available ground-based hydrometeorological measurements in a physical based snowpack model. The snowpack model provides simulation of temporal changes of the snow depth, density and water equivalent (SWE), accounting for snow melt, sublimation, refreezing melt water and snow metamorphism processes with...

  15. Analysis of MODIS snow cover time series over the alpine regions as input for hydrological modeling

    Science.gov (United States)

    Notarnicola, Claudia; Rastner, Philipp; Irsara, Luca; Moelg, Nico; Bertoldi, Giacomo; Dalla Chiesa, Stefano; Endrizzi, Stefano; Zebisch, Marc

    2010-05-01

    Snow extent and relative physical properties are key parameters in hydrology, weather forecast and hazard warning as well as in climatological models. Satellite sensors offer a unique advantage in monitoring snow cover due to their temporal and spatial synoptic view. The Moderate Resolution Imaging Spectrometer (MODIS) from NASA is especially useful for this purpose due to its high frequency. However, in order to evaluate the role of snow on the water cycle of a catchment such as runoff generation due to snowmelt, remote sensing data need to be assimilated in hydrological models. This study presents a comparison on a multi-temporal basis between snow cover data derived from (1) MODIS images, (2) LANDSAT images, and (3) predictions by the hydrological model GEOtop [1,3]. The test area is located in the catchment of the Matscher Valley (South Tyrol, Northern Italy). The snow cover maps derived from MODIS-images are obtained using a newly developed algorithm taking into account the specific requirements of mountain regions with a focus on the Alps [2]. This algorithm requires the standard MODIS-products MOD09 and MOD02 as input data and generates snow cover maps at a spatial resolution of 250 m. The final output is a combination of MODIS AQUA and MODIS TERRA snow cover maps, thus reducing the presence of cloudy pixels and no-data-values due to topography. By using these maps, daily time series starting from the winter season (November - May) 2002 till 2008/2009 have been created. Along with snow maps from MODIS images, also some snow cover maps derived from LANDSAT images have been used. Due to their high resolution (manto nevoso in aree alpine con dati MODIS multi-temporali e modelli idrologici, 13th ASITA National Conference, 1-4.12.2009, Bari, Italy. [3] Zanotti F., Endrizzi S., Bertoldi G. and Rigon R. 2004. The GEOtop snow module. Hydrological Processes, 18: 3667-3679. DOI:10.1002/hyp.5794.

  16. An evaluation of high-resolution regional climate model simulations of snow cover and albedo over the Rocky Mountains, with implications for the simulated snow-albedo feedback

    Science.gov (United States)

    Minder, Justin R.; Letcher, Theodore W.; Skiles, S. McKenzie

    2016-08-01

    The snow-albedo feedback (SAF) strongly influences climate over midlatitude mountainous regions. However, over these regions the skill of regional climate models (RCMs) at simulating properties such as snow cover and surface albedo is poorly characterized. These properties are evaluated in a pair of 7 year long high-resolution RCM simulations with the Weather Research and Forecasting model over the central Rocky Mountains. Key differences between the simulations include the computational domain (regional versus continental) and land surface model used (Noah versus Noah-MP). Simulations are evaluated against high-resolution satellite estimates of snow cover and albedo from the Moderate Resolution Imaging Spectroradiometer. Both simulations generally reproduce the observed seasonal and spatial variability of snow cover and also exhibit important biases. One simulation substantially overpredicts subpixel fractional snow cover over snowy pixels (by up to 0.4) causing large positive biases in surface albedo, likely due in part to inadequate representation of canopy effects. The other simulation exhibits a negative bias in areal snow extent (as much as 19% of the analysis domain). Surface measurements reveal large positive biases in snow albedo (exceeding 0.2) during late spring caused by neglecting radiative effects of impurities deposited onto snow. Semi-idealized climate change experiments show substantially different magnitudes of SAF-enhanced warming in the two simulations that can be tied to the differences in snow cover in their control climates. More confident projections of regional climate change over mountains will require further work to evaluate and improve representation of snow cover and albedo in RCMs.

  17. Snow and sea ice thermodynamics in the Arctic: Model validation and sensitivity study against SHEBA data

    Institute of Scientific and Technical Information of China (English)

    CHENG Bin; Timo Vihma; ZHANG Zhan-hai; LI Zhi-jun; WU Hui-ding

    2008-01-01

    Evolution of the Arctic sea ice and its snow cover during the SHEBA year were simulated by applying a high-resolution thermodynamic snow/ice model (HIGHTSI). Attention was paid to the impact of albedo on snow and sea ice mass balance, effect of snow on total ice mass balance, and the model vertical resolution.The SHEBA annual simulation was made applying the best possible external forcing data set created by the Sea Ice Model Intercomparison Project. The HIGHTSI control run reasonably reproduced the observed snow and ice thickness. A number of albedo schemes were incorporated into HIGHTSI to study the feedhack processes between the albedo and snow and ice thickness. The snow thickness turned out to be an essential variable in the albedo parametetization. Albedo schemes dependent on the surface temperature were liable to excessive positive feedback effects generated by errors in the modelled surface temperature. The superimposed ice formation should be taken into account for the annual Arctic sea ice mass balance.

  18. Changing Arctic Snow Cover: A Review of Recent Developments and Assessment of Future Needs for Observations, Modelling, and Impacts

    Science.gov (United States)

    Bokhorst, Stef; Pedersen, Stine Hojlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W.; Brown, Ross D.; Ehrich, Dorothee; Essery, Richard L. H.; Heilig, Achim; Ingvander, Susanne; Johansson, Cecilia; Johansson, Margareta; Jonsdottir, Svala Ingibjorg; Inga, Niila; Luojus, Kari; Macelloni, Giovanni; Mariash, Heather; McLennan, Donald; Rosqvist, Gunhild Ninis; Sato, Atsushi; Savela, Hannele; Schneebeli, Martin; Sokolov, Aleksandr; Sokratov, Sergey A.; Terzago, Silivia; Vikhamar-Schuler, Dagrun; Williamson, Scott; Qui, 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 impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes 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 manage and adapt to natural hazards in the Arctic region.

  19. 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.

  20. Small-scale variation of snow in a regional permafrost model

    Directory of Open Access Journals (Sweden)

    K. Gisnås

    2016-06-01

    estimated fractions of sub-zero mean annual ground surface temperatures within ±10 %. We also find that snow distributions within areas of 1 km2 in Norwegian mountain environments are closer to a gamma than to a lognormal theoretical distribution. The modelled permafrost distribution seems to be more sensitive to the choice of distribution function than to the fine-tuning of the coefficient of variation. When incorporating the small-scale variation of snow, the modelled total permafrost area of mainland Norway is nearly twice as large compared to the area obtained with grid-cell average snow depths without a sub-grid approach.

  1. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-09-01

    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 wide variety of system designs and locations. A scattering of independent snow coverage models have been developed over the last 15 years; however, there has been very little effort spent on verifying these models beyond the system design and location on which they were based. Moreover, none of the major PV modeling software products have incorporated any of these models into their workflow. In response to this deficiency, we have integrated the methodology of the snow model developed in the paper by Marion et al. [1] 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 discuss our demonstration of the model's effectiveness at reducing error in annual estimations for two PV arrays. Following this, we use this new functionality in conjunction with a long term historical dataset to estimate average snow losses across the United States for a typical PV system design. The open availability of the snow loss estimation capability in SAM to the PV modeling community, coupled with our results of the nation-wide study, will better equip the industry to accurately estimate PV energy production in areas affected by snowfall.

  3. Mesoscale modeling of lake effect snow over Lake Erie - sensitivity to convection, microphysics and the water temperature

    NARCIS (Netherlands)

    Theeuwes, N.E.; Steeneveld, G.J.; Krikken, F.; Holtslag, A.A.M.

    2010-01-01

    Lake effect snow is a shallow convection phenomenon during cold air advection over a relatively warm lake. A severe case of lake effect snow over Lake Erie on 24 December 2001 was studied with the MM5 and WRF mesoscale models. This particular case provided over 200 cm of snow in Buffalo (NY), caused

  4. Clear-sky stable boundary layers with low winds over snow-covered surfaces Part I: A WRF model evaluation

    NARCIS (Netherlands)

    Sterk, H.A.M.; Steeneveld, G.J.; Vihma, T.; Anderson, P.S.; Bosveld, F.C.; Holtslag, A.A.M.

    2015-01-01

    In this paper we evaluated the Weather Research and Forecasting (WRF) mesoscale meteorological model for stable conditions at clear skies with low wind speeds. Three contrasting terrains with snow covered surfaces are considered, namely Cabauw (Netherlands, snow over grass), Sodankylä (Finland, snow

  5. Snow physics as relevant to snow photochemistry

    Directory of Open Access Journals (Sweden)

    F. Domine

    2007-05-01

    Full Text Available Snow on the ground is a complex multiphase photochemical reactor that dramatically modifies the chemical composition of the overlying atmosphere. A quantitative description of the emissions of reactive gases by snow requires the knowledge of snow physical properties. This overview details our current understanding of how those physical properties relevant to snow photochemistry vary during snow metamorphism. Properties discussed are density, specific surface area, optical properties, thermal conductivity, permeability and gas diffusivity. Inasmuch as possible, equations to parameterize these properties as a function of climatic variables are proposed, based on field measurements, laboratory experiments and theory. The potential of remote sensing methods to obtain information on some snow physical variables such as grain size, liquid water content and snow depth are discussed. The possibilities for and difficulties of building a snow photochemistry model by adapting current snow physics models are explored. Elaborate snow physics models already exist, and including variables of particular interest to snow photochemistry such as light fluxes and specific surface area appears possible. On the other hand, understanding the nature and location of reactive molecules in snow seems to be the greatest difficulty modelers will have to face for lack of experimental data, and progress on this aspect will require the detailed study of natural snow samples.

  6. Continuum cavity expansion and discrete micromechanical models for inferring macroscopic snow mechanical properties from cone penetration data

    Science.gov (United States)

    Ruiz, Siul; Capelli, Achille; van Herwijnen, Alec; Schneebeli, Martin; Or, Dani

    2017-08-01

    Digital cone penetration measurements can be used to infer snow mechanical properties, for instance, to study snow avalanche formation. The standard interpretation of these measurements is based on statistically inferred micromechanical interactions between snow microstructural elements and a well-calibrated penetrating cone. We propose an alternative continuum model to derive the modulus of elasticity and yield strength of snow based on the widely used cavity expansion model in soils. We compare results from these approaches based on laboratory cone penetration measurements in snow samples of different densities and structural sizes. Results suggest that the micromechanical model underestimates the snow elastic modulus for dense samples by 2 orders of magnitude. By comparison with the cavity expansion-based model, some of the discrepancy is attributed to low sensitivity of the micromechanical model to the snow elastic modulus. Reasons and implications of this discrepancy are discussed, and possibilities to enhance both methodologies are proposed.

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

    OpenAIRE

    2016-01-01

    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. ...

  8. Coupling of Dynamic Ice to a New Snow/Firn Layer in the GISS Climate Model

    Science.gov (United States)

    Fischer, R.; Aleinov, I. D.; Kelley, M.; Nowicki, S.

    2015-12-01

    A scheme is presented for thermo-mechanical coupling of a snow/firn layer with low spatial but high temporal resolution, on top of an ice sheet with high spatial but low temporal resolution. Central to the thermal coupling is gridpoints that are shared between the two models. Initial results are presented, based on a new implementation of the snow/firn layer in the GISS climate model.

  9. MISAWA Snow Accumulation Study

    Science.gov (United States)

    1991-02-01

    snowfall along the west coast of Honshu has been attributed primarily to orographic lifting ( Estoque and Ninomiya, 1976), a precipitation mechanism that... Estoque , M.A., and K. Ninomiya, "Numerical Simulation of Japan Sea Effect Snowfall," Tellus, 28, pp. 243-253, 1976. lshihara, K., "Study of Statistical

  10. Mercury accumulation in snow on the Idaho National Engineering and Environmental Laboratory and surrounding region, southeast Idaho, USA

    Science.gov (United States)

    Susong, D.D.; Abbott, M.L.; Krabbenhoft, D.P.

    2003-01-01

    Snow was sampled and analyzed for total mercury (THg) on the Idaho National Engineering and Environmental Laboratory (INEEL) and surrounding region prior to the start-up of a large (9-11 g/h) gaseous mercury emission source. The objective was to determine the effects of the source on local and regional atmospheric deposition of mercury. Snow samples collected from 48 points on a polar grid near the source had THg concentrations that ranged from 4.71 to 27.26 ng/L; snow collected from regional background sites had THg concentrations that ranged from 0.89 to 16.61 ng/L. Grid samples had higher concentrations than the regional background sites, which was unexpected because the source was not operating yet. Emission of Hg from soils is a possible source of Hg in snow on the INEEL. Evidence from Hg profiles in snow and from unfiltered/filtered split samples supports this hypothesis. Ongoing work on the INEEL is investigating Hg fluxes from soils and snow.

  11. Forest cover algorithms for estimating meteorological forcing in a numerical snow model

    Science.gov (United States)

    Hellström, Robert Å.

    2000-12-01

    The architectural properties of a forest are known to significantly modify meteorological forcing of snowcover. This project develops four numerical modules to simulate canopy processes including attenuation of solar radiation and wind speed, the mixed sky and canopy components of longwave irradiance, and precipitation interception by canopy elements. The four modules and a more realistic atmospheric stability algorithm were included in the Utah Energy Balance (UEB) snow model to estimate water equivalence beneath coniferous and defoliated deciduous forests in northern Michigan. Systematic underestimation of early season snow depth was attributed to the assumption of constant, seasonal average, snow density in the model's lumped treatment of the snowpack processes. The modified UEB model (UEBMOD) improved estimation of snow depth in a clearing and beneath the coniferous site, whereas UEB with original forest parameterizations performed best for the deciduous site.

  12. Modeling visible and near-infrared snow surface reflectance-simulation and validation

    Institute of Scientific and Technical Information of China (English)

    Hongyi Wu; Ling Tong

    2011-01-01

    Retrieving snow surface reflectance is difficult in optical remote sensing.Hence,this letter evaluates five surface reflectance models,including the Ross-Li,Roujean,Walthall,modified Rahman and Staylor models,in terms of their capacities to capture snow reflectance signatures using ground measurements in Antarctica.The biases of all the models are less than 0.0003 in both visible and near-infrared regions.Moreover,with the exception of the Staylor model,all models have root-mean-square errors of around 0.02,indicating that they can simulate the reflectance magnitude well.The R2 performances of the Ross-Li and Roujean models are higher than those of the others,indicating that these two models can capture the angle distribution of snow surface reflectance better.The bidirectional reflectance distribution flmction (BRDF) characterizes the angular distribution of surface reflection[1,2].It plays an important role in performing atmospheric correction,detecting land cover types,and calculating other biophysical parameters[3].Howcver,the retrieval of snow BRDF/albedo is always a difficult issue in the application of remotely sensed information.%Retrieving snow surface reflectance is difficult in optical remote sensing. Hence, this letter evaluates five surface reflectance models, including the Ross-Li, Roujean, Walthall, modified Rahman and Staylor models, in terms of their capacities to capture snow reflectance signatures using ground measurements in Antarctica. The biases of all the models are less than 0.0003 in both visible and near-infrared regions. Moreover, with the exception of the Staylor model, all models have root-mean-square errors of around 0.02, indicating that they can simulate the reflectance magnitude well. The R2 performances of the Ross-Li and Roujean models are higher than those of the others, indicating that these two models can capture the angle distribution of snow surface reflectance better.

  13. Energy Balance Modeling of Interannual Snow and Ice Storage in High Altitude Region by Dynamic Equilibrium Concept

    Science.gov (United States)

    Johnson, R. J.; Ohara, N.

    2014-12-01

    Snow models in the field of hydrologic engineering have barely incorporated the long-term effect of the inter-annual snow storage such as glaciers because the time scale of glacier dynamics is much longer than those of river flow and seasonal snowmelt. This study proposes an appropriate treatment for inland glaciers as systems in dynamic equilibrium that stay constant under a static climate condition. It is supposed that the snow/ice vertical movement from high elevation areas to valleys (lower elevation areas) by means of wind re-distribution, avalanches, and glaciation, may be considered as an equilibrator of the glacier system because it stimulates snow/ice ablation. The implicit physically-based modeling of such a dynamic equilibrium snow system is introduced and discussed for the long-term snow simulation at a regional scale. The developed model has been coupled with the Weather Research and Forecasting (WRF) model to compute the snow surface energy balance.

  14. Comparing snow models under current and future climates: Uncertainties and implications for hydrological impact studies

    Science.gov (United States)

    Troin, Magali; Poulin, Annie; Baraer, Michel; Brissette, François

    2016-09-01

    Projected climate change effects on snow hydrology are investigated for the 2041-2060 horizon following the SRES A2 emissions scenario over three snowmelt-dominated catchments in Quebec, Canada. A 16-member ensemble of eight snow models (SM) simulations, based on the high-resolution Canadian Regional Climate Model (CRCM-15 km) simulations driven by two realizations of the Canadian Global Climate Model (CGCM3), is established per catchment. This study aims to compare a range of SMs in their ability at simulating snow processes under current climate, and to evaluate how they affect the assessment of the climate change-induced snow impacts at the catchment scale. The variability of snowpack response caused by the use of different models within two different SM approaches (degree-day (DD) versus mixed degree-day/energy balance (DD/EB)) is also evaluated, as well as the uncertainty of natural climate variability. The simulations cover 1961-1990 in the present period and 2041-2060 in the future period. There is a general convergence in the ensemble spread of the climate change signals on snow water equivalent at the catchment scale, with an earlier peak and a decreased magnitude in all basins. The results of four snow indicators show that most of the uncertainty arises from natural climate variability (inter-member variability of the CRCM) followed by the snow model. Both the DD and DD/EB models provide comparable assessments of the impacts of climate change on snow hydrology at the catchment scale.

  15. Modeling the Variability and Importance of Snow Sublimation in the North-Central Colorado Rocky Mountains

    Science.gov (United States)

    Sexstone, G. A.; Clow, D. W.; Fassnacht, S. R.; Liston, G. E.; Hiemstra, C. A.; Knowles, J. F.

    2016-12-01

    In the western United States, where seasonal snowmelt is a critical water resource for ecological and anthropological needs, snow sublimation (sublimation) has been suggested by many studies to be an important component of the snow cover mass balance. However, few studies have evaluated the spatial and temporal variability of sublimation in complex mountainous environments. In this study, we use a process-based snow model (SnowModel) and eddy covariance (EC) measurements to evaluate the variability and importance of sublimation across the north-central Colorado Rocky Mountains for 5 water years (WY 2011 - WY 2015). In-situ EC observations of sublimation compare well with modeled sublimation at sites dominated by surface and canopy components of sublimation, but model verification of blowing sublimation in alpine areas was not feasible because these fluxes often occur when snow is in turbulent suspension, which cannot always be resolved by EC instrumentation. Model simulations showed substantial spatial and temporal variability of sublimation across the study domain. Sublimation rates were found to exhibit differences across landscape characteristics such as elevation and land cover. Land cover type was an important driver of snow sublimation variability, with substantial sublimation occurring in alpine and forested areas, and relatively lower sublimation occurring in open areas below treeline. Sublimation from forested areas accounted for the majority of modeled sublimation losses across the study domain and highlights the importance of sublimation from snow stored in the forest canopy in this region. Additionally, the interannual differences in total sublimation were strongly linked with seasonal snowfall amounts. Results from this study suggest that snow sublimation is a significant component of the winter water balance and have important implications for future water management and decision making.

  16. Long-term energy-balance modeling of interannual snow and ice in Wyoming using the dynamic equilibrium concept

    Science.gov (United States)

    Johnson, Ryan J.

    Many snow models in the field of hydrologic engineering do not incorporate the long-term effects of the interannual snow storage such as glaciers because glacier dynamics have a much longer timescale than river flow and seasonal snowmelt. This study proposes an appropriate treatment for inland glaciers as systems in dynamic equilibrium that remain constant under a static climate condition. This new method considers the vertical movement of snow/ice from high elevation areas to valleys as the equilibrating factor of the glacier system. The vertical movement of snow/ice occurs by means of wind re-distribution, avalanches, and glaciation. This paper introduces and discusses the physically-based modeling of such a dynamic equilibrium snow system for long-term snow simulation at a regional scale. We apply the regional snow model (RegSnow) to a domain containing the entire state of Wyoming and couple the model to the Weather Research and Forecasting (WRF) model to compute the snow surface energy-balance. RegSnow predicted that 82.2% of interannual snow and ice storage in Wyoming may disappear by 2100 using temperature increases projected by CMIP5 GCMs, under the RCP4.5 emission scenario.

  17. Hydrological response to Black Carbon deposition in seasonally snow covered catchments in Norway using two different atmospheric transport models

    Science.gov (United States)

    Matt, F.; Burkhart, J. F.; Pietikäinen, J. P.

    2015-12-01

    Black Carbon (BC) has been shown to significantly impact snow melt through lowering the albedo of snow and increasing the absorption rate of short wave radiation. Yet few studies have investigated the effect of the enhanced melt on hydrological variability. BC sources for Norway are rather remote and deposition rates low. However, once deposited on snow even low concentrations of BC can have a detectable effect on the snow melt. Variations in snow melt have a direct impact on the snow cover duration and the timing and magnitude of peak outflow. In this study, we use two different atmospheric transport models (the Lagrangian transport and dispersion model FELXPART and the regional aerosol-climate model REMO-HAM) and GAINS emissions to simulate deposition rates over Norway and Statkraft's Hydrologic Forecasting Toolbox (ShyFT) to simulate the impact of BC deposition on the seasonal snow melt. The Snow, Ice, and Aerosol Radiation (SNICAR) model coupled to the snow routine of the hydrological model is used to determine the albedo of the snow as a function of the BC concentration in two snow layers. To investigate the impact range of BC on the seasonal snow melt, we simulate the catchment hydrology of catchments in south-east, south-west and northern Norway under the impact of deposition rates from both transport models, respectively. Comparing the deposition rates from the two transport models, we observe large differences in the seasonal cycle which in turn results in a significantly different response in the snow melt. Furthermore, we investigate the overall impact of BC deposition on the snow melt and duration on a catchment scale for both transport models.

  18. 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.

  19. 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...

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

    Science.gov (United States)

    Voegeli, Christian; Lehning, Michael; Wever, Nander; Bavay, Mathias; Bühler, Yves; Marty, Mauro; Molnar, Peter

    2016-04-01

    Precise knowledge about the snow distribution in alpine terrain is crucial for various applications such as flood risk assessment, avalanche warning or water supply and hydropower. To simulate the seasonal snow cover development in alpine terrain, the spatially distributed, physics-based model Alpine3D is suitable. The model is often driven by spatial interpolations from automatic weather stations (AWS). As AWS are sparsely spread, the data needs to be interpolated, leading to errors in the spatial distribution of the snow cover - especially on subcatchment scale. With the recent advances in remote sensing techniques, maps of snow depth can be acquired with high spatial resolution and vertical accuracy. Here we use maps of the snow depth distribution, calculated from summer and winter digital surface models acquired with the airborne opto-electronic scanner ADS to preprocess and redistribute precipitation input data for Alpine3D to improve the accuracy of spatial distribution of snow depth simulations. A differentiation between liquid and solid precipitation is made, to account for different precipitation patterns that can be expected from rain and snowfall. For liquid precipitation, only large scale distribution patterns are applied to distribute precipitation in the simulation domain. For solid precipitation, an additional small scale distribution, based on the ADS data, is applied. The large scale patterns are generated using AWS measurements interpolated over the domain. The small scale patterns are generated by redistributing the large scale precipitation according to the relative snow depth in the ADS dataset. The determination of the precipitation phase is done using an air temperature threshold. Using this simple approach to redistribute precipitation, the accuracy of spatial snow distribution could be improved significantly. The standard deviation of absolute snow depth error could be reduced by a factor of 2 to less than 20 cm for the season 2011/12. The

  1. Theory and numerical modeling of electrical self-potential signatures of unsaturated flow in melting snow

    Science.gov (United States)

    Kulessa, B.; Chandler, D.; Revil, A.; Essery, R.

    2012-09-01

    We have developed a new theory and numerical model of electrical self-potential (SP) signals associated with unsaturated flow in melting snow. The model is applicable to continuous natural melt as well as transient flow phenomena such as meltwater pulses and is tested using laboratory column experiments. SP signals fundamentally depend on the temporal evolution of snow porosity and meltwater flux, electrical conductivity (EC), and pH. We infer a reversal of the sign of the zeta potential (a fundamental electrical property of grain surfaces in porous media) consistent with well-known elution sequences of ions that cause progressive increases and decreases in meltwater pH and EC, respectively. Injection of fully melted snow samples, containing the entire natural range of ions, into melting snow columns caused additional temporary reversals of the sign of the zeta potential. Widely used empirical relationships between effective saturation, meltwater fraction, EC, and pH, as well as snow porosity, grain size, and permeability, are found to be robust for modeling purposes. Thus nonintrusive SP measurements can serve as proxies for snow meltwater fluxes and the temporal evolution of fundamental snow textural, hydraulic, or water quality parameters. Adaptation of automated multisensor SP acquisition technology from other environmental applications thus promises to bridge the widely acknowledged gap in spatial scales between satellite remote sensing and point measurements of snow properties. SP measurements and modeling may therefore contribute to solving a wide range of problems related to the assessment of water resource availability, avalanche or flood risk, or the amplification of climatic forcing of ice shelf, ice sheet, or glacier dynamics.

  2. A dense medium electromagnetic scattering model for the InSAR correlation of snow

    Science.gov (United States)

    Lei, Yang; Siqueira, Paul; Treuhaft, Robert

    2016-05-01

    Snow characteristics, such as snow water equivalent (SWE) and snow grain size, are important characteristics for the monitoring of the global hydrological cycle and as indicators of climate change. This paper derives an interferometric synthetic aperture radar (InSAR) scattering model for dense media, such as snow, which takes into account multiple scattering effects through the Quasi-Crystalline Approximation. The result of this derivation is a simplified version of the InSAR correlation model derived for relating the InSAR correlation measurements to the snowpack characteristics of grain size, volume fraction, and layer depth as well as those aspects of the volume-ground interaction that affects the interferometric observation (i.e., the surface topography and the ratio of ground-to-volume scattering). Based on the model, the sensitivity of the InSAR correlation measurements to the snow characteristics is explored by simulation. Through this process, it is shown that Ka-band InSAR phase has a good sensitivity to snow grain size and volume fraction, while for lower frequency signals (Ku-band to L-band), the InSAR correlation magnitude and phase have a sensitivity to snow depth. Since the formulation depends, in part, on the pair distribution function, three functional forms of the pair distribution function are implemented and their effects on InSAR phase measurements compared. The InSAR scattering model described in this paper is intended to be an observational prototype for future Ka-band and L-band InSAR missions, such as NASA's Surface Water and Ocean Topography and NASA-ISRO Synthetic Aperture Radar missions, planned for launch in the 2020-2021 time frame. This formulation also enables further investigation of the InSAR-based snow retrieval approaches.

  3. Paint Pavement Marking Performance Prediction Model That Includes the Impacts of Snow Removal Operations

    Science.gov (United States)

    2011-03-01

    Hypothesized that snow plows wear down mountain road pavement markings. 2007 Craig et al. -Edge lines degrade slower than center/skip lines 2007...retroreflectivity to create the models. They discovered that paint pavement markings last 80% longer on Portland Cement Concrete than Asphalt Concrete at low AADT...retroreflectivity, while yellow markings lost 21%. Lu and Barter attributed the sizable degradation to snow removal, sand application, and studded

  4. Observation, Simulation, and Evaluation of Snow Dynamics in the Transitional Snow Zone

    Science.gov (United States)

    Wayand, Nicholas E.

    The frequent mid-winter accumulation and ablation cycles of snowpack within the rain-snow transitional zone play an important role for the maritime basins along the western U.S. mountain ranges. Representation of transitional snowpack within hydrological models has remained a challenge, largely because surface and meteorological conditions frequently remain near the freezing point, which allows large errors in modeled accumulation or ablation to result from small forcing or structural errors. This research aims to improve model representation of accumulation and ablation processes by utilizing new observations within the transitional snow zone combined with novel methods of model evaluation. (Abstract shortened by ProQuest.).

  5. Comparison of spatial extreme value models for snow depth extremes in Austria

    Science.gov (United States)

    Schellander, Harald; Hell, Tobias

    2017-04-01

    In Alpine regions like Austria a spatial representation of extreme snow depth is of crucial importance for numerous purposes such as the designing of construction projects. Extreme value theory builds the well-established foundation of modeling extremes. Two different approaches for the spatial modeling of snow depth extremes have been extensively investigated lately: Smooth Spatial Modeling (Blanchet and Lehning, 2010) and different classes of max-stable processes (Blanchet and Davison, 2011; Nicolet et al., 2015), both outperforming classical interpolation techniques. While max-stable models are generally considered as improvement over smooth modeling, the methods have not been compared in the context of extreme snow depth. In the present study a great variety of different GEV models is fitted to seasonal snow depth maxima measured at more than 200 Austrian weather stations. Return levels of smooth spatial models and several max-stable representations (Schlather, Brown-Resnick, Geometric Gaussian, Extremal-t) and covariance models (Powered Exponential, Brown, Whittle-Matern), also allowing for anisotropic extremal dependence are compared by a modified Anderson-Darling score and a normalized RMSE. Preliminary results show, that for snow depth extremes in Austria smooth spatial modeling and a version with extremal coefficients as covariates deliver slightly better scores than (an)-isotropic max-stable models.

  6. 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.

  7. Evaluating the potential of MSG-SEVIRI snow cover images for hydrological modeling

    Science.gov (United States)

    Surer, S.; Parajka, J.; Akyurek, Z.; Bloeschl, G.

    2012-12-01

    Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board of MSG (METEOSAT Second Generation) geostationary satellite enables snow cover monitoring at very high temporal resolution (15 min). It is a key component of the recent EUMETSAT programme for Satellite Application Facility on Support to operational Hydrology and Water Management (H-SAF) project. The main aim of the project is to develop and test new satellite products, which will comply the requirements for operational hydrology and water resources management. The objective of this study is (a) to compare snow cover product (H10) derived from MSG-SEVIRI with MODIS (MOD10A1) snow cover product, (b) to examine the MSG-SEVIRI snow mapping accuracy against in situ snow observations, (c) to test potential of H10 snow cover products for calibration and validation of a conceptual hydrologic model. We compare MSG-SEVIRI, MODIS grid maps and daily snow depth measurements at 272 climate stations over Austria in the period from October 2007 to June 2012. The results indicate that temporal merging of 15 minutes MSG-SEVIRI observations allows a reduction of cloud coverage at daily time scale. The relative number of days with cloud coverage in winter season is on average 35% for MSG-SEVIRI, compared to 65% for MODIS dataset. The coarser spatial resolution of MSG-SEVIRI, namely 0.05o, however, resulted in lower mapping accuracy. The overall snow cover mapping error is 5% for MODIS and 15% for MSG-SEVIRI, respectively. Our results showed that for MSG-SEVIRI dataset the underestimation errors dominate and tend to increase with increasing altitude of climate stations. Our results showed that for MSG-SEVIRI dataset the underestimation errors dominated. The potential of MSG-SEVIRI product (H10) for hydrological modeling is examined in two mountain catchments in Austria and Turkey. We will evaluate the potential of snow cover data from the MSG-SEVIRI for calibrating and validating a conceptual semi

  8. A Refined Methodology for Modelling Climate Change Impacts on Snow Sports Tourism

    Science.gov (United States)

    Demiroglu, O. Cenk; Turp, M. Tufan; Ozturk, Tugba; An, Nazan; Kurnaz, M. Levent

    2015-04-01

    Nature-based tourism is one of the most vulnerable sectors of the economy against climate change. Among its types, winter tourism stands out as the most critical due to the relatively high exposure and sensitivity of snow cover to the anthropogenic warming trends. In this study, we aim at improving previous works by Ozturk et al. where snow reliability of ski resorts have been examined through projections based on regional climate model outputs downscaled from various GCMs. Major improvements to these studies will be related to increasing the resolution, obtaining snow depth values from snow-water equivalent outputs, and hourly, instead of the daily, calculations of wet bulb temperatures. Daily snow depth values will be utilized for 100-days rule that looks for at least 100 days of snow cover at a minimum of 30 cm in order for a ski resort to be viable, whereas the wet bulb temperatures below -7 oC will indicate the snowmaking capacity. The domain of analysis will be the Balkans, the Middle East and the Caucasus. Therefore the spatial gap in the mostly Euro- and Amero-centric literature will also be improved. The domain will be modelled through RegCM 4.4.2 of the International Centre for Theoretical Physics basing its resolution on MPI-ESM-MR of Max Planck Institut für Meteorologie and the concentration scenario RCP 4.5 for a realistic tourism development future of 2020-2050.

  9. 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.

  10. On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model

    Science.gov (United States)

    Charrois, Luc; Cosme, Emmanuel; Dumont, Marie; Lafaysse, Matthieu; Morin, Samuel; Libois, Quentin; Picard, Ghislain

    2016-05-01

    This paper examines the ability of optical reflectance data assimilation to improve snow depth and snow water equivalent simulations from a chain of models with the SAFRAN meteorological model driving the detailed multilayer snowpack model Crocus now including a two-stream radiative transfer model for snow, TARTES. The direct use of reflectance data, allowed by TARTES, instead of higher level snow products, mitigates uncertainties due to commonly used retrieval algorithms.Data assimilation is performed with an ensemble-based method, the Sequential Importance Resampling Particle filter, to represent simulation uncertainties. In snowpack modeling, uncertainties of simulations are primarily assigned to meteorological forcings. Here, a method of stochastic perturbation based on an autoregressive model is implemented to explicitly simulate the consequences of these uncertainties on the snowpack estimates.Through twin experiments, the assimilation of synthetic spectral reflectances matching the MODerate resolution Imaging Spectroradiometer (MODIS) spectral bands is examined over five seasons at the Col du Lautaret, located in the French Alps. Overall, the assimilation of MODIS-like data reduces by 45 % the root mean square errors (RMSE) on snow depth and snow water equivalent. At this study site, the lack of MODIS data on cloudy days does not affect the assimilation performance significantly. The combined assimilation of MODIS-like reflectances and a few snow depth measurements throughout the 2010/2011 season further reduces RMSEs by roughly 70 %. This work suggests that the assimilation of optical reflectances has the potential to become an essential component of spatialized snowpack simulation and forecast systems. The assimilation of real MODIS data will be investigated in future works.

  11. Snow modelling in a glacierized catchment using scale-dependent calibration data

    Science.gov (United States)

    Engel, Michael; Comiti, Francesco; Penna, Daniele; Notarnicola, Claudia; Bertoldi, Giacomo

    2013-04-01

    Physically-based hydrological models that integrate a large amount of parameters often face the problem of equifinality. Thus, the application of such models to Alpine catchments with high spatial heterogeneity and complex hydrological behaviour is challenging. In this context, the distributed hydrological model GEOtop was employed to simulate snow dynamics in the period 2010 - 2012 at different spatial scales within the Saldur basin (Eastern Italian Alps). In this catchment, hydrometric, isotopic and sediment transport data at different spatial scales are available to validate the model and to assess the physical consistency of the model output. This work aims to: (i) assess the model validation at the plot scale in order to improve performances at the catchment scale; (ii) verify the usefulness of using multiple types of observations (snow, satellites, tracers, discharge) in order to assess the physical consistency and reduce the equifinality of the model output. At the plot scale, the model was calibrated by a manual sensitivity analysis on predicted snow heights, water equivalent and duration compared with the corresponding snow depth data of two meteorological stations (at 1930 m and 3035 m a.s.l.) in the study area. Selected snow parameters controlling snow reflectivity and snow aging were calibrated. In addition to traditional snow depth data, photosynthetically active radiation (PAR) sensor data were used to derive snow duration of other four meteorological stations In order to account for the spatial distribution of snow cover, best parameter settings of the plot scale models were transferred to catchment scale models. These models were assigned for two nested catchments, named LSG (19 km²) and USG (11 km²), which provided snow height and SWE in the catchment. At this scale, the model calibration was based on two types of remotely sensed snow maps: monthly Landsat images (30 m of resolution) and daily MODIS images (250 m of resolution). To support the

  12. Modeling multi-layer effects in passive microwave remote sensing of dry snow using Dense Media Radiative Transfer Theory (DMRT) based on quasicrystalline approximation

    Science.gov (United States)

    Liang, D.; Xu, X.; Tsang, L.; Andreadis, K.M.; Josberger, E.G.

    2008-01-01

    The Dense Media Radiative Transfer theory (DMRT) of Quasicrystalline Approximation of Mie scattering by sticky particles is used to study the multiple scattering effects in layered snow in microwave remote sensing. Results are illustrated for various snow profile characteristics. Polarization differences and frequency dependences of multilayer snow model are significantly different from that of the single-layer snow model. Comparisons are also made with CLPX data using snow parameters as given by the VIC model. ?? 2007 IEEE.

  13. Modeling of Electromagnetic Waves Scattering from Snow Covered First Year Sea Ice

    Science.gov (United States)

    Komarov, A. S.; Barber, D. G.; Isleifson, D. K.

    2011-12-01

    Modeling of electromagnetic wave interaction with sea ice is required for various remote sensing applications, such as an interpretation of Synthetic Aperture Radar (SAR) imagery over sea ice. In this study, we present numerical modeling of the Normalized Radar Cross Section (NRCS) at vertical and horizontal polarizations from snow covered First Year (FY) sea ice. We consider sea ice as a layered medium with an arbitrary profile of dielectric constant, and the snow cover as a homogeneous layer on the top of the sea ice. Surface scattering at the snow-sea ice interface was taken into account by the first-order approximation of the small perturbation method. We obtained an analytical formulation for radar cross-sections at vertical and horizontal polarizations and conducted numerical modeling of the backscattering characteristics. The solution derived for NRCSs includes reflection coefficients from snow and sea ice. The calculation of reflection coefficients from the stratified sea ice is considered separately as an auxiliary problem. In-situ geophysical properties of snow and sea ice collected during the Circumpolar Flow Lead (CFL) system study project were used to estimate the dielectric constants of snow and sea ice for several case studies. The dielectric constant of the sea ice was calculated using the Polder-van-Santen/de Loor (PVD) mixture model, while the dielectric constant of the snow was estimated using a Debye-like model. The calculated angular dependencies of the NRCSs (HH- and VV- polarizations) and co-polarization ratios were compared with in-situ C-band scatterometer measurements. These comparisons demonstrate a good agreement between simulated and observed scattering characteristics.

  14. Impacts of snow and organic soils parameterization on northern Eurasian soil temperature profiles simulated by the ISBA land surface model

    Science.gov (United States)

    Decharme, Bertrand; Brun, Eric; Boone, Aaron; Delire, Christine; Le Moigne, Patrick; Morin, Samuel

    2016-04-01

    In this study we analyzed how an improved representation of snowpack processes and soil properties in the multilayer snow and soil schemes of the Interaction Soil-Biosphere-Atmosphere (ISBA) land surface model impacts the simulation of soil temperature profiles over northern Eurasian regions. For this purpose, we refine ISBA's snow layering algorithm and propose a parameterization of snow albedo and snow compaction/densification adapted from the detailed Crocus snowpack model. We also include a dependency on soil organic carbon content for ISBA's hydraulic and thermal soil properties. First, changes in the snowpack parameterization are evaluated against snow depth, snow water equivalent, surface albedo, and soil temperature at a 10 cm depth observed at the Col de Porte field site in the French Alps. Next, the new model version including all of the changes is used over northern Eurasia to evaluate the model's ability to simulate the snow depth, the soil temperature profile, and the permafrost characteristics. The results confirm that an adequate simulation of snow layering and snow compaction/densification significantly impacts the snowpack characteristics and the soil temperature profile during winter, while the impact of the more accurate snow albedo computation is dominant during the spring. In summer, the accounting for the effect of soil organic carbon on hydraulic and thermal soil properties improves the simulation of the soil temperature profile. Finally, the results confirm that this last process strongly influences the simulation of the permafrost active layer thickness and its spatial distribution.

  15. Black carbon in snow in the upper Himalayan Khumbu Valley, Nepal: observations and modeling of the impact on snow albedo, melting, and radiative forcing

    Directory of Open Access Journals (Sweden)

    H.-W. Jacobi

    2014-10-01

    Full Text Available Black carbon (BC in the snow in the Himalayas has recently attracted considerable interest due to its impact on snow albedo, snow and glacier melting, regional climate and water resources. A single particle soot photometer (SP2 instrument was used to measure refractory BC (rBC in a series of surface snow samples collected in the upper Khumbu Valley in Nepal between November 2009 and February 2012. The obtained time series indicates annual cycles with maximum concentration before the onset of the monsoon season and fast decreases in rBC during the monsoon period. Measured concentrations ranged from a few ppb up to 70 ppb rBC. However, due to the handling of the samples the measured concentrations possess rather large uncertainties. Detailed modeling of the snowpack including the measured range and an estimated upper limit of rBC concentrations was performed to study the role of BC in the seasonal snowpack. Simulations were performed for three winter seasons with the snowpack model Crocus including a detailed description of the radiative transfer inside the snowpack. While the standard Crocus model strongly overestimates the height and the duration of the seasonal snowpack, a better calculation of the snow albedo with the new radiative transfer scheme enhanced the representation of the snow. However, the period with snow on the ground neglecting BC in the snow was still over-estimated between 37 and 66 days, which was further diminished by 8 to 15% and more than 40% in the presence of 100 or 300 ppb of BC. Compared to snow without BC the albedo is on average reduced by 0.027 and 0.060 in the presence of 100 and 300 ppb BC. While the impact of increasing BC in the snow on the albedo was largest for clean snow, the impact on the local radiative forcing is the opposite. Here, increasing BC caused an even larger impact at higher BC concentrations. This effect is related to an accelerated melting of the snowpack caused by a more efficient metamorphism

  16. Evaluation of the SMAP model-simulated snow internal physical properties at Sapporo, Japan from 2005 to 2015

    Science.gov (United States)

    Niwano, Masashi; Aoki, Teruo; Kuchiki, Katsuyuki; Matoba, Sumito; Kodama, Yuji; Tanikawa, Tomonori

    2016-04-01

    Temporal evolution of snow internal physical properties such as grain size, density, temperature, and water content are controlled by changes in meteorological conditions. On the other hand, in a snow covered area, surface atmospheric conditions are modulated in response to variations of snow albedo, which is affected by (optically equivalent) snow grain size as well as mass concentration of snow impurities such as black carbon and dust. Therefore, it is necessary for snowpack models incorporated in climate models to simulate realistic snow internal physical properties to perform accurate future climate prediction especially in the cryosphere. In this study, we evaluated snow internal physical properties at Sapporo (43° 05'N, 141° 21'E, 15 m a.s.l.), Japan from 2005 to 2015 simulated with a 1-D multilayered physical snowpack model SMAP (Snow Metamorphism and Albedo Process). The model was driven by quality controlled 30-min averaged data for air temperature, relative humidity, wind speed, surface pressure, snow depth, downward and upward shortwave radiant flux, downward longwave radiant flux, and ground surface soil heat flux. Simulation results were compared against the data obtained from snow pit works performed twice a week at Sapporo. First of all, the model-simulated column integrated SWE (snow water equivalent) were compared against in-situ measurements (273 data were available during the 10 winters). The results show that the model tends to underestimate SWE (mean error; ME was -19 mm); however, root mean square error (RMSE) was 34 mm, and these scores are better than those for simulations driven by not snow depth but precipitation (ME was less than -25 mm and RMSE was more than 40 mm). It suggests that the correction technique for precipitation measurements considering catch efficiency of a rain gauge is still insufficient. Next, the model-simulated profiles for snow density and snow temperature were compared against in-situ measurements. For this purpose

  17. Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

    Science.gov (United States)

    Pimentel, Rafael; Herrero, Javier; José Polo, María

    2017-02-01

    Subgrid variability introduces non-negligible scale effects on the grid-based representation of snow. This heterogeneity is even more evident in semiarid regions, where the high variability of the climate produces various accumulation melting cycles throughout the year and a large spatial heterogeneity of the snow cover. This variability in a watershed can often be represented by snow accumulation-depletion curves (ADCs). In this study, terrestrial photography (TP) of a cell-sized area (30 × 30 m) was used to define local snow ADCs at a Mediterranean site. Snow-cover fraction (SCF) and snow-depth (h) values obtained with this technique constituted the two datasets used to define ADCs. A flexible sigmoid function was selected to parameterize snow behaviour on this subgrid scale. It was then fitted to meet five different snow patterns in the control area: one for the accumulation phase and four for the melting phase in a cycle within the snow season. Each pattern was successfully associated with the snow conditions and previous evolution. The resulting ADCs were associated to certain physical features of the snow, which were used to incorporate them in the point snow model formulated by Herrero et al. (2009) by means of a decision tree. The final performance of this model was tested against field observations recorded over four hydrological years (2009-2013). The calibration and validation of this ADC snow model was found to have a high level of accuracy, with global RMSE values of 105.8 mm for the average snow depth and 0.21 m2 m-2 for the snow-cover fraction in the control area. The use of ADCs on the cell scale proposed in this research provided a sound basis for the extension of point snow models to larger areas by means of a gridded distributed calculation.

  18. A storage model approach to the assessment of snow depth trends

    Science.gov (United States)

    Woody, Jonathan; Lund, Robert; Grundstein, Andrew J.; Mote, Thomas L.

    2009-10-01

    This paper introduces a stochastic storage model capable of assessing trends in daily snow depth series. The model allows for seasonal features, which permits the analysis of daily data. Breakpoint times, which occur when the observing station changes location or instrumentation, are shown to greatly influence estimated trend margins and are accounted for in this analysis. The model is fitted by numerically minimizing a sum of squares of daily prediction errors. Standard errors for the model parameters, useful in making trend inferences, are presented. The methods are illustrated in the analysis of a century of daily snow depth observations from Napoleon, North Dakota. The results here show that snow depths are significantly declining at Napoleon, with spring ablation occurring earlier, and that breakpoint features are very influential in deriving realistic trend estimates.

  19. Estimating the distribution of snow water equivalent using remotely sensed snow cover data and a spatially distributed snowmelt model: A multi-resolution, multi-sensor comparison

    Science.gov (United States)

    Molotch, Noah P.; Margulis, Steven A.

    2008-11-01

    Time series of fractional snow covered area (SCA) estimates from Landsat Enhanced Thematic Mapper (ETM+), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) data were combined with a spatially distributed snowmelt model to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3419 km 2). In this reconstruction approach, modeled snowmelt over each pixel is integrated during the period of satellite-observed snow cover to estimate SWE. Due to underestimates in snow cover detection, maximum basin-wide mean SWE using MODIS and AVHRR were, respectively, 45% and 68% lower than SWE estimates obtained using ETM+ data. The mean absolute error (MAE) of SWE estimated at 100-m resolution using ETM+ data was 23% relative to observed SWE from intensive field campaigns. Model performance deteriorated when MODIS (MAE = 50%) and AVHRR (MAE = 89%) SCA data were used. Relative to differences in the SCA products, model output was less sensitive to spatial resolution (MAE = 39% and 73% for ETM+ and MODIS simulations run at 1 km resolution, respectively), indicating that SWE reconstructions at the scale of MODIS acquisitions may be tractable provided the SCA product is improved. When considering tradeoffs between spatial and temporal resolution of different sensors, our results indicate that higher spatial resolution products such as ETM+ remain more accurate despite the lower frequency of acquisition. This motivates continued efforts to improve MODIS snow cover products.

  20. A model of the planetary boundary layer over a snow surface

    Science.gov (United States)

    Halberstam, I.; Melendez, R.

    1979-01-01

    A model of the planetary boundary layer over a snow surface has been developed. It contains the vertical heat exchange processes due to radiation, conduction, and atmospheric turbulence. Parametrization of the boundary layer is based on similarity functions developed by Hoffert and Sud (1976), which involve a dimensionless variable, dependent on boundary-layer height and a localized Monin-Obukhov length. The model also contains the atmospheric surface layer and the snowpack itself, where snowmelt and snow evaporation are calculated. The results indicate a strong dependence of surface temperatures, especially at night, on the bursts of turbulence which result from the frictional damping of surface-layer winds during periods of high stability, as described by Businger (1973). The model also shows the cooling and drying effect of the snow on the atmosphere, which may be the mechanism for air mass transformation in sub-Arctic regions.

  1. Microstructure representation of snow in coupled snowpack and microwave emission models

    Science.gov (United States)

    Sandells, Melody; Essery, Richard; Rutter, Nick; Wake, Leanne; Leppänen, Leena; Lemmetyinen, Juha

    2017-01-01

    This is the first study to encompass a wide range of coupled snow evolution and microwave emission models in a common modelling framework in order to generalise the link between snowpack microstructure predicted by the snow evolution models and microstructure required to reproduce observations of brightness temperature as simulated by snow emission models. Brightness temperatures at 18.7 and 36.5 GHz were simulated by 1323 ensemble members, formed from 63 Jules Investigation Model snowpack simulations, three microstructure evolution functions, and seven microwave emission model configurations. Two years of meteorological data from the Sodankylä Arctic Research Centre, Finland, were used to drive the model over the 2011-2012 and 2012-2013 winter periods. Comparisons between simulated snow grain diameters and field measurements with an IceCube instrument showed that the evolution functions from SNTHERM simulated snow grain diameters that were too large (mean error 0.12 to 0.16 mm), whereas MOSES and SNICAR microstructure evolution functions simulated grain diameters that were too small (mean error -0.16 to -0.24 mm for MOSES and -0.14 to -0.18 mm for SNICAR). No model (HUT, MEMLS, or DMRT-ML) provided a consistently good fit across all frequencies and polarisations. The smallest absolute values of mean bias in brightness temperature over a season for a particular frequency and polarisation ranged from 0.7 to 6.9 K. Optimal scaling factors for the snow microstructure were presented to compare compatibility between snowpack model microstructure and emission model microstructure. Scale factors ranged between 0.3 for the SNTHERM-empirical MEMLS model combination (2011-2012) and 3.3 for DMRT-ML in conjunction with MOSES microstructure (2012-2013). Differences in scale factors between microstructure models were generally greater than the differences between microwave emission models, suggesting that more accurate simulations in coupled snowpack-microwave model systems

  2. Tibetan Plateau Geladaindong black carbon ice core record (1843–1982: Recent increases due to higher emissions and lower snow accumulation

    Directory of Open Access Journals (Sweden)

    Jenkins Matthew

    2016-09-01

    Full Text Available Black carbon (BC deposited on snow and glacier surfaces can reduce albedo and lead to accelerated melt. An ice core recovered from Guoqu glacier on Mt. Geladaindong and analyzed using a Single Particle Soot Photometer (SP2 provides the first long-term (1843–1982 record of BC from the central Tibetan Plateau. Post 1940 the record is characterized by an increased occurrence of years with above average BC, and the highest BC values of the record. The BC increase in recent decades is likely caused by a combination of increased emissions from regional BC sources, and a reduction in snow accumulation. Guoqu glacier has received no net ice accumulation since the 1980s, and is a potential example of a glacier where an increase in the equilibrium line altitude is exposing buried high impurity layers. That BC concentrations in the uppermost layers of the Geladaindong ice core are not substantially higher relative to deeper in the ice core suggests that some of the BC that must have been deposited on Guoqu glacier via wet or dry deposition between 1983 and 2005 has been removed from the surface of the glacier, potentially via supraglacial or englacial meltwater.

  3. A model for the spatial distribution of snow water equivalent parameterized from the spatial variability of precipitation

    Science.gov (United States)

    Skaugen, Thomas; Weltzien, Ingunn H.

    2016-09-01

    Snow is an important and complicated element in hydrological modelling. The traditional catchment hydrological model with its many free calibration parameters, also in snow sub-models, is not a well-suited tool for predicting conditions for which it has not been calibrated. Such conditions include prediction in ungauged basins and assessing hydrological effects of climate change. In this study, a new model for the spatial distribution of snow water equivalent (SWE), parameterized solely from observed spatial variability of precipitation, is compared with the current snow distribution model used in the operational flood forecasting models in Norway. The former model uses a dynamic gamma distribution and is called Snow Distribution_Gamma, (SD_G), whereas the latter model has a fixed, calibrated coefficient of variation, which parameterizes a log-normal model for snow distribution and is called Snow Distribution_Log-Normal (SD_LN). The two models are implemented in the parameter parsimonious rainfall-runoff model Distance Distribution Dynamics (DDD), and their capability for predicting runoff, SWE and snow-covered area (SCA) is tested and compared for 71 Norwegian catchments. The calibration period is 1985-2000 and validation period is 2000-2014. Results show that SDG better simulates SCA when compared with MODIS satellite-derived snow cover. In addition, SWE is simulated more realistically in that seasonal snow is melted out and the building up of "snow towers" and giving spurious positive trends in SWE, typical for SD_LN, is prevented. The precision of runoff simulations using SDG is slightly inferior, with a reduction in Nash-Sutcliffe and Kling-Gupta efficiency criterion of 0.01, but it is shown that the high precision in runoff prediction using SD_LN is accompanied with erroneous simulations of SWE.

  4. Perennial snow and ice volumes on Iliamna Volcano, Alaska, estimated with ice radar and volume modeling

    Science.gov (United States)

    Trabant, Dennis C.

    1999-01-01

    The volume of four of the largest glaciers on Iliamna Volcano was estimated using the volume model developed for evaluating glacier volumes on Redoubt Volcano. The volume model is controlled by simulated valley cross sections that are constructed by fitting third-order polynomials to the shape of the valley walls exposed above the glacier surface. Critical cross sections were field checked by sounding with ice-penetrating radar during July 1998. The estimated volumes of perennial snow and glacier ice for Tuxedni, Lateral, Red, and Umbrella Glaciers are 8.6, 0.85, 4.7, and 0.60 cubic kilometers respectively. The estimated volume of snow and ice on the upper 1,000 meters of the volcano is about 1 cubic kilometer. The volume estimates are thought to have errors of no more than ?25 percent. The volumes estimated for the four largest glaciers are more than three times the total volume of snow and ice on Mount Rainier and about 82 times the total volume of snow and ice that was on Mount St. Helens before its May 18, 1980 eruption. Volcanoes mantled by substantial snow and ice covers have produced the largest and most catastrophic lahars and floods. Therefore, it is prudent to expect that, during an eruptive episode, flooding and lahars threaten all of the drainages heading on Iliamna Volcano. On the other hand, debris avalanches can happen any time. Fortunately, their influence is generally limited to the area within a few kilometers of the summit.

  5. Energy- and momentum-conserving model of splash entrainment in sand and snow saltation

    Science.gov (United States)

    Comola, Francesco; Lehning, Michael

    2017-02-01

    Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.

  6. 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

    for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes...... 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...

  7. Accuracy of physically based snow albedo model evaluated with measured data at Sapporo, Japan during five winters from 2006 to 2011

    Science.gov (United States)

    Aoki, T.; Kuchiki, K.; Niwano, M.; Kodama, Y.

    2011-12-01

    Physically based snow albedo model (PBSAM) to calculate broadband albedos and solar heating profile in a general circulation model was developed by Aoki et al. (2011), in which the accuracy for albedos was evaluated with the data of radiation budget and snow pit work performed at Sapporo during two winters from 2007 to 2009. The model calculates the broadband albedos for the visible, near-infrared (NIR), and shortwave bands for any snow layer structure of snow grain size, snow impurity concentrations, and snow water equivalent under any solar illumination condition. The estimated root mean square errors (RMSE) from the measured data were 0.047 for the visible albedo and 0.057 for the NIR albedo. In the paper, it is described that possible error causes for calculated albedos are (1) PBSAM faultiness; (2) inappropriately modeled snow layers structure (e.g., number of layers and depths of layer boundaries); (3) the assumption that the diffuse fractions of the visible and NIR bands are the same as the measured diffuse fraction of the shortwave radiation; (4) errors in the measured snow grain size and snow impurity concentrations; and (5) errors in the albedo measurements. Using the data obtained at Sapporo during five winters from 2006 to 2011, we further investigated the effects of snow grain size, mass concentrations of snow impurities (black carbon and dust), air temperature, snow surface temperature, snow depth, diffuse fraction of solar radiation, continuous snow cover days, wet snow days, new snow days, ice layer days, and albedo values themselves on the accuracy of calculated albedos for each winter. Among them, the best (worst) RMSE value of calculated albedos by PBSAM for each winter during five winters is 2008-2009 (2010-2011) for the visible albedo and 2007-2008 (2006-2007) for the NIR albedos. The estimated RMSE for each winter have a high correlation with continuous snow cover days and wet snow days for each winter, meaning that PBSAM error may increase

  8. Spatial Scaling of Snow Observations and Microwave Emission Modeling During CLPX and Appropriate Satellite Sensor Resolution

    Science.gov (United States)

    Kim, Edward J.; Tedesco, Marco

    2005-01-01

    Accurate estimates of snow water equivalent and other properties play an important role in weather, natural hazard, and hydrological forecasting and climate modeling over a range of scales in space and time. Remote sensing-derived estimates have traditionally been of the "snapshot" type, but techniques involving models with assimilation are also being explored. In both cases, forward emission models are useful to understand the observed passive microwave signatures and developing retrieval algorithms. However, mismatches between passive microwave sensor resolutions and the scales of processes controlling subpixel heterogeneity can affect the accuracy of the estimates. Improving the spatial resolution of new passive microwave satellite sensors is a major desire in order to (literally) resolve such subpixel heterogeneity, but limited spacecraft and mission resources impose severe constraints and tradeoffs. In order to maximize science return while mitigating risk for a satellite concept, it is essential to understand the scaling behavior of snow in terms of what the sensor sees (brightness temperature) as well as in terms of the actual variability of snow. NASA's Cold Land Processes Experiment-1 (CLPX-1: Colorado, 2002 and 2003) was designed to provide data to measure these scaling behaviors for varying snow conditions in areas with forested, alpine, and meadow/pasture land cover. We will use observations from CLPX-1 ground, airborne, and satellite passive microwave sensors to examine and evaluate the scaling behavior of observed and modeled brightness temperatures and observed and retrieved snow parameters across scales from meters to 10's of kilometers. The conclusions will provide direct examples of the appropriate spatial sampling scales of new sensors for snow remote sensing. The analyses will also illustrate the effects and spatial scales of the underlying phenomena (e.g., land cover) that control subpixel heterogeneity.

  9. Investigating the spread in surface albedo for snow-covered forests in CMIP5 models

    Science.gov (United States)

    Wang, Libo; Cole, Jason N. S.; Bartlett, Paul; Verseghy, Diana; Derksen, Chris; Brown, Ross; Salzen, Knut

    2016-02-01

    This study investigates the role of leaf/plant area index (LAI/PAI) specification on the large spread of winter albedo simulated by climate models. To examine the sensitivity of winter albedo to LAI, we perform a sensitivity analysis using two methods commonly used to compute albedo in snow-covered forests as well as diagnostic calculations within version 4.2 of the Canadian Atmospheric Model for which PAI is systematically varied. The results show that the simulated albedo is very sensitive to negative PAI biases, especially for smaller PAI values. The LAI and surface albedo of boreal forests in the presence of snow simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) models are evaluated using satellite observations. The evaluation of CMIP5 models suggest that inaccurate tree cover fraction due to improper plant functional type specification or erroneous LAI parameterization in some models explains, in part, an observed positive bias in winter albedo over boreal forest regions of the Northern Hemisphere. This contributes to a large intermodel spread in simulated surface albedo in the presence of snow over these regions and is largely responsible for uncertainties in simulated snow-albedo feedback strength. Errors are largest (+20-40%) in models with large underestimation of LAI but are typically within ±15% when simulated LAI is within the observed range. This study underscores the importance of accurate representation of vegetation distribution and parameters in realistic simulation of surface albedo.

  10. Investigating the spread of surface albedo in snow covered forests in CMIP5 models

    Science.gov (United States)

    Wang, Libo; Cole, Jason; Bartlett, Paul; Verseghy, Diana; Derksen, Chris; Brown, Ross; von Salzen, Knut

    2016-04-01

    This study investigates the role of leaf/plant area index (LAI/PAI) specification on the large spread of winter albedo simulated by climate models. To examine the sensitivity of winter albedo to LAI, we perform a sensitivity analysis using two methods commonly used to compute albedo in snow-covered forests as well as diagnostic calculations within version 4.2 of the Canadian Atmospheric Model for which PAI is systematically varied. The results show that the simulated albedo is very sensitive to negative PAI biases, especially for smaller PAI values. The LAI and surface albedo of boreal forests in the presence of snow simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) models are evaluated using satellite observations. The evaluation of CMIP5 models suggest that inaccurate tree cover fraction due to improper plant functional type specification or erroneous LAI parameterization in some models explains, in part, an observed positive bias in winter albedo over boreal forest regions of the Northern Hemisphere. This contributes to a large intermodel spread in simulated surface albedo in the presence of snow over these regions and is largely responsible for uncertainties in simulated snow-albedo feedback strength. Errors are largest (+20-40 %) in models with large underestimation of LAI but are typically within ±15% when simulated LAI is within the observed range. This study underscores the importance of accurate representation of vegetation distribution and parameters in realistic simulation of surface albedo.

  11. 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.

  12. A drought index accounting for snow

    Science.gov (United States)

    Staudinger, Maria; Stahl, Kerstin; Seibert, Jan

    2015-04-01

    The Standardized Precipitation Index (SPI) is the most widely used index to characterize and monitor droughts that are related to precipitation deficiencies. However, the SPI does not always deliver the relevant information for hydrological drought management when precipitation deficiencies are not the only reason for droughts as it is the case for example in snow influenced catchments. If precipitation is temporarily stored as snow, then there is a significant difference between meteorological and hydrological drought because the delayed release of melt water from the snow accumulation to the stream. In this study we introduce an extension to the SPI, the Standardized Snow Melt and Rain Index (SMRI), that captures both rain and snow melt deficits, which in effect modify streamflow. The SMRI does not require any snow data instead observations of temperature and precipitation are used to model snow. The SMRI is evaluated for seven Swiss catchments with varying degrees of snow influence. In particular for catchments with a larger component of snowmelt in runoff generation, we found the SMRI to be a good complementary index to the SPI to describe streamflow droughts. In a further step, the SPI and the SMRI were compared for the summer drought of 2003 and the spring drought of 2011 for Switzerland, using gridded products of precipitation and temperature including the entire country.

  13. Using MODIS snow cover and precipitation data to model water runoff for the Mokelumne River Basin in the Sierra Nevada, California (2000-2009)

    Science.gov (United States)

    Powell, Cynthia; Blesius, Leonhard; Davis, Jerry; Schuetzenmeister, Falk

    2011-05-01

    Climate change will affect snowpack and water supply systems in California, and methods for predicting daily stream flow help prepare for these changes. This research provides a daily model to predict stream flow based on snow cover and precipitation in the Mokelumne River Basin in the Sierra Nevada in California. The snow cover of the Mokelumne River Basin is monitored using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. Using data from these images as well as precipitation data from 2000 to 2009, we produced a predictive statistical model. The final results show that with an R2 of 0.71, the true natural flow (TNF) of the Mokelumne River is based on the daily area of snow cover in each of seven equal area elevation zones according to the time lag of that zone as well as the accumulated precipitation functioning as a proxy for snow depth. The capability of this model to predict water supply suggests the potential for developing new spatial hydrologic informational products based on MODIS and the probability of improving the accuracy of the prediction of hydrologic processes for water resource managers.

  14. Assimilation of Airborne Snow Observatory Snow Water Equivalent to Improve Runoff Forecasting Model Performance and Reservoir Management During Warm and Dry Winters

    Science.gov (United States)

    McGurk, B. J.; Painter, T. H.

    2015-12-01

    The Airborne Snow Observatory (ASO) NASA-JPL demonstration mission has collected detailed snow information for portions of the Tuolumne Basin in California for three years, 2013 - 2015. Both 2014 and 2015 were low snow years, and 2015 was exceptionally warm and analogous to future years after climate change. The ASO uses an imaging spectrometer and LiDAR sensors mounted in an aircraft to collect snow depth and extent data, and snow albedo. By combining ground and modeled density fields, the ~weekly flights over the Tuolumne produced both basin-wide and detailed sub-basin snow water equivalent (SWE) estimates that were provided to Hetch Hetchy Reservoir operators. The data were also assimilated into an hydrologic simulation model in an attempt to improve the accuracy and timing of a runoff forecasting tool that can be used to improve the management of Hetch Hetchy Reservoir, the source of 85% of the water supply for 2.6 million people on the San Francisco Peninsula. The USGS Precipitation Runoff Modeling System was calibrated to the 1181 square kilometer basin and simulation results compared to observed runoff with and without assimilation of ASO data. Simulated and observed were also compared with observed with both single updates associated with each flight, and with sequential updates from each flight. Sequential updating was found to improve correlation between observed and simulated reservoir inflows, and there by improve the ability of reservoir operators to more efficiently allocate the last half of the recession limb of snowmelt inflow and be assured of filling the reservoir and minimizing ecologically-damaging late season spills.

  15. Developing Snow Model Forcing Data From WRF Model Output to Aid in Water Resource Forecasting

    Science.gov (United States)

    Havens, S.; Marks, D. G.; Watson, K. A.; Masarik, M.; Flores, A. N.; Kormos, P.; Hedrick, A. R.

    2015-12-01

    Traditional operational modeling tools used by water managers in the west are challenged by more frequently occurring uncharacteristic stream flow patterns caused by climate change. Water managers are now turning to new models based on the physical processes within a watershed to combat the increasing number of events that do not follow the historical patterns. The USDA-ARS has provided near real time snow water equivalent (SWE) maps using iSnobal since WY2012 for the Boise River Basin in southwest Idaho and since WY2013 for the Tuolumne Basin in California that feeds the Hetch Hetchy reservoir. The goal of these projects is to not only provide current snowpack estimates but to use the Weather Research and Forecasting (WRF) model to drive iSnobal in order to produce a forecasted stream flow when coupled to a hydrology model. The first step is to develop methods on how to create snow model forcing data from WRF outputs. Using a reanalysis 1km WRF dataset from WY2009 over the Boise River Basin, WRF model results like surface air temperature, relative humidity, wind, precipitation, cloud cover, and incoming long wave radiation must be downscaled for use in iSnobal. iSnobal results forced with WRF output are validated at point locations throughout the basin, as well as compared with iSnobal results forced with traditional weather station data. The presentation will explore the differences in forcing data derived from WRF outputs and weather stations and how this affects the snowpack distribution.

  16. 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.

  17. ALPINE3D: a detailed model of mountain surface processes and its application to snow hydrology

    Science.gov (United States)

    Lehning, Michael; Völksch, Ingo; Gustafsson, David; Nguyen, Tuan Anh; Stähli, Manfred; Zappa, Massimiliano

    2006-06-01

    Current models of snow cover distribution, soil moisture, surface runoff and river discharge typically have very simple parameterizations of surface processes, such as degree-day factors or single-layer snow cover representation. For the purpose of reproducing catchment runoff, simple snowmelt routines have proven to be accurate, provided that they are carefully calibrated specifically for the catchment they are applied to. The use of more detailed models is, however, useful to understand and quantify the role of individual surface processes for catchment hydrology, snow cover status and soil moisture distribution.We introduce ALPINE3D, a model for the high-resolution simulation of alpine surface processes, in particular snow processes. The model can be driven by measurements from automatic weather stations or by meteorological model outputs. As a preprocessing alternative, specific high-resolution meteorological fields can be created by running a meteorological model. The core three-dimensional ALPINE3D modules consist of a radiation balance model (which uses a view-factor approach and includes shortwave scattering and longwave emission from terrain and tall vegetation) and a drifting snow model solving a diffusion equation for suspended snow and a saltation transport equation. The processes in the atmosphere are thus treated in three dimensions and are coupled to a distributed (in the hydrological sense of having a spatial representation of the catchment properties) one-dimensional model of vegetation, snow and soil (SNOWPACK) using the assumption that lateral exchange is small in these media. The model is completed by a conceptual runoff module. The model can be run with a choice of modules, thus generating more or less detailed surface forcing data as input for runoff generation simulations. The model modules can be run in a parallel (distributed) mode using a GRID infrastructure to allow computationally demanding tasks. In a case study from the Dischma Valley

  18. Sea salt aerosol from blowing snow on sea ice - modeling vs observation

    Science.gov (United States)

    Yang, Xin; Frey, Markus; Norris, Sarah; Brooks, Ian; Anderson, Philip; Jones, Anna; wolff, Eric; Legrand, Michel

    2016-04-01

    Blowing snow over sea ice, through a subsequent sublimation process of salt-containing blown snow particles, has been hypothesized as a significant sea salt aerosol (SSA) source in high latitudes. This mechanism has been strongly supported by a winter cruise in the Weddell Sea (during June-August 2013). The newly collected data, including both physical and chemical components, provide a unique way to test and validate the parameterisation used for describing the SSA production from blowing snow events. With updates to some key parameters such as snow salinity in a global Chemistry-transport model pTOMCAT, simulated SSA concentrations can be well compared with measured SSA data. In this presentation, I will report modeled SSA number density against collected data on board of Polarstern ship during the Weddell Sea cruise, as well as modeled SSA massive concentrations against those measured at both coastal sites such as Alert in the North and Dumont d'Urville (DDU) in the South and central Antarctic sites such as Concordia and Kohnen stations. Model experiments indicated that open ocean-sourced SSA could not explain the observed winter SSA peaks seen in most polar sites, while with sea ice-sourced SSA in the model, the winter peaks can be well improved indicating the importance of sea ice-sourced SSA as a significant contributor to the salts (Na+, Cl-) recorded in the ice core.

  19. Evaluation of the MSG-SEVIRI snow-cover product potential in hydrological modeling

    Science.gov (United States)

    Surer, Serdar; Parajka, Juraj; Akyurek, Zuhal; Blöschl, Günter

    2013-04-01

    Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board of METEOSAT Second Generation (MSG) geostationary satellite enables snow cover monitoring at very high temporal resolution of 15 min. It is one of the key components of the recent EUMETSAT program for Satellite Application Facility on Support to operational Hydrology and Water Management (H-SAF) Project. The main goal of the project is to develop, and test new satellite products in order to comply the requirements for operational hydrology and water resources management. The objective of this study is i) to compare snow cover product (H10) derived from MSG-SEVIRI with MODIS (MOD10A1) snow cover product, ii) to observe H10 product accuracy against in situ snow observations, and iii) to test potential of H10 product for calibration and validation of a conceptual hydrologic model. We compare MSG-SEVIRI, MODIS grid maps and daily snow depth measurements at 272 climate stations over Austria in the period from October 2007 to June 2012. The results indicate that temporal merging of 15 minutes MSG-SEVIRI observations allows a significant reduction of cloud coverage at daily time scale. The relative number of days with cloud coverage in winter season is on average 35% for MSG-SEVIRI, compared to 65% for MODIS dataset. The coarser spatial resolution of MSG-SEVIRI, namely 0.05o, however, resulted in lower mapping accuracy. The overall snow cover mapping error is 5% for MODIS and 15% for MSG-SEVIRI, respectively. Our results showed that for MSG-SEVIRI dataset, the underestimation errors dominate, and tend to increase with increasing altitude of climate stations. The potential of H10 for hydrological modeling is examined in two different mountain catchments, one in Austria, and the other in Turkey. We will evaluate the potential of snow H10 product for calibrating and validating a conceptual semi-distributed hydrological model. Our results will discuss the strength and weaknesses of H10 product in

  20. A long-term data set for hydrologic modeling in a snow-dominated mountain catchment

    Science.gov (United States)

    An hourly modeling data set is presented for the water years 1984 through 2008 for a snow-dominated headwater catchment. Meteorological forcing data and GIS watershed characteristics are described and provided. The meteorological data are measured at two sites within the catchment, and include pre...

  1. The contact density to characterize the mechanics of cohesive granular materials: application to snow microstructure modeling.

    Science.gov (United States)

    Gaume, Johan; Löwe, Henning

    2016-04-01

    Microstructural properties are essential to characterize the mechanics of loose and cohesive granular materials such as snow. In particular, mechanical properties and physical processes of porous media are often related to the volume fraction ν. Low-density microstructures typically allow for considerable structural diversity at a given volume fraction, leading to uncertainties in modeling approaches using ν-based parametrizations only. We have conducted discrete element simulations of cohesive granular materials with initial configurations which are drawn from Baxter's sticky hard sphere (SHS) model. This method allows to control independently the initial volume fraction ν and the average coordination number Z. We show that variations in elasticity and strength of the samples can be fully explained by the initial contact density C = νZ over a wide range of volume fractions and coordination numbers. Hence, accounting for the contact density C allows to resolve the discrepancies in particle based modeling between samples with similar volume fractions but different microstructures. As an application, we applied our method to the microstructure of real snow samples which have been imaged by micro-computed tomography and reconstructed using the SHS model. Our new approach opens a promising route to evaluate snow physical and mechanical properties from field measurements, for instance using the Snow Micro Penetrometer (SMP), by linking the penetration resistance to the contact density.

  2. 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

  3. The Relationship between Snow Accumulation at Mt. Logan, Yukon, Canada, and Climate Variability in the North Pacific.

    Science.gov (United States)

    Rupper, Summer; Steig, Eric J.; Roe, Gerard

    2004-12-01

    An ice core from Mt. Logan, Yukon, Canada, presents an opportunity to evaluate the degree to which ice core accumulation records can be interpreted as meaningful measures of interannual climate variability. Statistical analyses and comparisons with synoptic station data are used to identify the physical relationships between Mt. Logan ice core accumulation data and large-scale atmospheric circulation. These analyses demonstrate that only the winters of high accumulation years have a robust connection with atmospheric circulation. There are no consistent relationships during anomalously low and average accumulation years. The wintertime of high accumulation years is associated with an enhanced trough ridge structure at 500 hPa and in sea level pressure over the northeast Pacific and western Canada, consistent with increased southerly flow bringing in warmer, moister air to the region. While both storm (i.e., 2 6 days) and blocking (i.e., 15 20 days) events project onto the same climate pattern, only the big storm events give rise to the dynamical moisture convergence necessary for anomalous accumulation. Taken together, these results suggest that while the Mt. Logan accumulation record is not a simple record of Pacific climate variability, anomalously high accumulation years are a reliable indicator of wintertime circulation and, in particular, of northeast Pacific storms.

  4. Simulation of the melt season using a resolved sea ice model with snow cover and melt ponds

    Science.gov (United States)

    Skyllingstad, Eric D.; Shell, Karen M.; Collins, Lee; Polashenski, Chris

    2015-07-01

    A three-dimensional sea ice model is presented with resolved snow thickness variations and melt ponds. The model calculates heating from solar radiative transfer and simulates the formation and movement of brine/melt water through the ice system. Initialization for the model is based on observations of snow topography made during the summer melt seasons of 2009, 2010, and 2012 from a location off the coast of Barrow, AK. Experiments are conducted to examine the importance of snow properties and snow and ice thickness by comparing observed and modeled pond fraction and albedo. One key process simulated by the model is the formation of frozen layers in the ice as relatively warm fresh water grid cells freeze when cooled by adjacent, cold brine-filled grid cells. These layers prevent vertical drainage and lead to flooding of melt water commonly observed at the beginning of the melt season. Flooding persists until enough heat is absorbed to melt through the frozen layer. The resulting long-term melt pond coverage is sensitive to both the spatial variability of snow cover and the minimum snow depth. For thin snow cover, initial melting results in earlier, reduced flooding with a small change in pond fraction after drainage of the melt water. Deeper snow tends to generate a delayed, larger peak pond fraction before drainage.

  5. Evaluation of snow dynamics modelling on a pixel scale using terrestrial photography and Ensemble Transform Kalman Filtering

    Science.gov (United States)

    Pimentel, Rafael; José Pérez-Palazón, María; Herrero, Javier; José Polo, María

    2016-04-01

    Snow plays a crucial role in the hydrological regime in mountainous catchments, which increases in semiarid regions, where the recurrence of drought period makes it necessary to accurate the determination of the water availability from the snowpack. Physically based approaches constitute one of the best ways to reproduce the snow dynamics over these highly variable conditions. Moreover, they allow further understanding the processes involved, the snowpack behaviour and evolution. However, in some cases the complexity of the modelled process and the non-availability of all the required data for such models, avoid a correct representation of certain aspects. In these cases, data assimilation techniques can help to improve model performance and may also act as an indirect tool to understand the represented processes. This work assesses snow dynamics on a pixel scale (30x30m) in a Mediterranean site (Sierra Nevada Mountain, southern Spain) combining physical snow modelling (WiMMed, a physically based hydrological model developed for Mediterranean environments), ground sensing information (terrestrial photography) and data assimilation techniques (Ensemble Transform Kalman Filter), throughout a study period of two hydrological years: 2009-2010 and 2010-2011. Snow cover fraction and averaged snow depth were obtained from the terrestrial photography images and used as observations in the assimilation scheme. The model performance was evaluated using different combinations of the variables assimilated: 1) only snow cover fraction, 2) only snow depth, 3) and both variables. The results show how the assimilation enhances the model performance. This improvement is higher if the variable assimilated is snow depth, with RMSE= 0.14 m2m-2and RMSE=12.16 mm for snow cover and snow depth respectively. However, this enhancement varies throughout the study period. During short snowmelt cycles, for example, the assimilation of the snow cover fraction is the most efficient. Nevertheless

  6. Impact Analysis of Climate Change on Snow over a Complex Mountainous Region Using Weather Research and Forecast Model (WRF Simulation and Moderate Resolution Imaging Spectroradiometer Data (MODIS-Terra Fractional Snow Cover Products

    Directory of Open Access Journals (Sweden)

    Xiaoduo Pan

    2017-07-01

    Full Text Available Climate change has a complex effect on snow at the regional scale. The change in snow patterns under climate change remains unknown for certain regions. Here, we used high spatiotemporal resolution snow-related variables simulated by a weather research and forecast model (WRF including snowfall, snow water equivalent and snow depth along with fractional snow cover (FSC data extracted from Moderate Resolution Imaging Spectroradiometer Data (MODIS-Terra to evaluate the effects of climate change on snow over the Heihe River Basin (HRB, a typical inland river basin in arid northwestern China from 2000 to 2013. We utilized Empirical Orthogonal Function (EOF analysis and Mann-Kendall/Theil-Sen trend analysis to evaluate the results. The results are as follows: (1 FSC, snow water equivalent, and snow depth across the entire HRB region decreased, especially at elevations over 4500 m; however, snowfall increased at mid-altitude ranges in the upstream area of the HRB. (2 Total snowfall also increased in the upstream area of the HRB; however, the number of snowfall days decreased. Therefore, the number of extreme snow events in the upstream area of the HRB may have increased. (3 Snowfall over the downstream area of the HRB decreased. Thus, ground stations, WRF simulations and remote sensing products can be used to effectively explore the effect of climate change on snow at the watershed scale.

  7. 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

  8. 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

  9. A 7-year dataset for driving and evaluating snow models at an Arctic site (Sodankylä, Finland)

    Science.gov (United States)

    Essery, Richard; Kontu, Anna; Lemmetyinen, Juha; Dumont, Marie; Ménard, Cécile B.

    2016-06-01

    Datasets derived from measurements at Sodankylä, Finland, for driving and evaluating snow models are presented. This is the first time that such complete datasets have been made available for a site in the Arctic. The continuous October 2007-September 2014 driving data comprise all of the meteorological variables required as inputs for physically based snow models at hourly intervals: incoming solar and longwave radiation, snowfall and rainfall rates, air temperature, humidity, wind speed and atmospheric pressure. Two versions of the driving data are provided: one using radiation and wind speed measurements made above the height of the trees around the clearing where the evaluation data were measured and one with adjustments for the influence of the trees on conditions close to the ground. The available evaluation data include automatic and manual measurements of bulk snow depth and snow water equivalent, and profiles of snow temperature, snow density and soil temperature. A physically based snow model is driven and evaluated with the datasets to illustrate their utility. Shading by trees is found to extend the duration of both modelled and observed snow cover on the ground by several days a year.

  10. Assessing the application of a laser rangefinder for determining snow depth in inaccessible alpine terrain

    Directory of Open Access Journals (Sweden)

    J. L. Hood

    2010-01-01

    Full Text Available Snow is a major contributor to stream flow in alpine watersheds and quantifying snow depth and distribution is important for hydrological research. However, direct measurement of snow in rugged alpine terrain is often impossible due to avalanche and rock fall hazard. A laser rangefinder was used to determine the depth of snow in inaccessible areas. Laser rangefinders use ground based light detection and ranging technology but are more cost effective than airborne surveys or terrestrial laser scanning systems and are highly portable. Data was collected within the Opabin watershed in the Canadian Rockies. Surveys were conducted on one accessible slope for validation purposes and two inaccessible talus slopes. Laser distance data was used to generate surface models of slopes when snow covered and snow-free and snow depth distribution was quantified by differencing the two surfaces. The results were compared with manually probed snow depths on the accessible slope. The accuracy of the laser rangefinder method as compared to probed depths was 0.21 m or 12% of average snow depth. Results from the two inaccessible talus slopes showed regions near the top of the slopes with 6–9 m of snow accumulation. These deep snow accumulation zones result from re-distribution of snow by avalanches and are hydrologically significant as they persist until late summer.

  11. Assessing the application of a laser rangefinder for determining snow depth in inaccessible alpine terrain

    Directory of Open Access Journals (Sweden)

    J. L. Hood

    2010-06-01

    Full Text Available Snow is a major contributor to stream flow in alpine watersheds and quantifying snow depth and distribution is important for hydrological research. However, direct measurement of snow in rugged alpine terrain is often impossible due to avalanche and rock fall hazard. A laser rangefinder was used to determine the depth of snow in inaccessible areas. Laser rangefinders use ground based light detection and ranging technology but are more cost effective than airborne surveys or terrestrial laser scanning systems and are highly portable. Data were collected within the Opabin watershed in the Canadian Rockies. Surveys were conducted on one accessible slope for validation purposes and two inaccessible talus slopes. Laser distance data was used to generate surface models of slopes when snow covered and snow-free and snow depth distribution was quantified by differencing the two surfaces. The results were compared with manually probed snow depths on the accessible slope. The accuracy of the laser rangefinder method as compared to probed depths was 0.21 m or 12% of average snow depth. Results from the two inaccessible talus slopes showed regions near the top of the slopes with 6–9 m of snow accumulation. These deep snow accumulation zones result from re-distribution of snow by avalanches and are hydrologically significant as they persist until late summer.

  12. Air-snow transfer of nitrate on the East Antarctic Plateau - Part 2: An isotopic model for the interpretation of deep ice-core records

    Science.gov (United States)

    Erbland, J.; Savarino, J.; Morin, S.; France, J. L.; Frey, M. M.; King, M. D.

    2015-10-01

    Unraveling the modern budget of reactive nitrogen on the Antarctic Plateau is critical for the interpretation of ice-core records of nitrate. This requires accounting for nitrate recycling processes occurring in near-surface snow and the overlying atmospheric boundary layer. Not only concentration measurements but also isotopic ratios of nitrogen and oxygen in nitrate provide constraints on the processes at play. However, due to the large number of intertwined chemical and physical phenomena involved, numerical modeling is required to test hypotheses in a quantitative manner. Here we introduce the model TRANSITS (TRansfer of Atmospheric Nitrate Stable Isotopes To the Snow), a novel conceptual, multi-layer and one-dimensional model representing the impact of processes operating on nitrate at the air-snow interface on the East Antarctic Plateau, in terms of concentrations (mass fraction) and nitrogen (δ15N) and oxygen isotopic composition (17O excess, Δ17O) in nitrate. At the air-snow interface at Dome C (DC; 75° 06' S, 123° 19' E), the model reproduces well the values of δ15N in atmospheric and surface snow (skin layer) nitrate as well as in the δ15N profile in DC snow, including the observed extraordinary high positive values (around +300 ‰) below 2 cm. The model also captures the observed variability in nitrate mass fraction in the snow. While oxygen data are qualitatively reproduced at the air-snow interface at DC and in East Antarctica, the simulated Δ17O values underestimate the observed Δ17O values by several per mill. This is explained by the simplifications made in the description of the atmospheric cycling and oxidation of NO2 as well as by our lack of understanding of the NOx chemistry at Dome C. The model reproduces well the sensitivity of δ15N, Δ17O and the apparent fractionation constants (15ϵapp, 17Eapp) to the snow accumulation rate. Building on this development, we propose a framework for the interpretation of nitrate records

  13. Modelling Water Flow, Heat Transport, Soil Freezing and Thawing, and Snow Processes in a Clayey, Subsurface Drained Agricultural Field

    Science.gov (United States)

    Warsta, L.; Turunen, M.; Koivusalo, H. J.; Paasonen-Kivekäs, M.; Karvonen, T.; Taskinen, A.

    2012-12-01

    Simulation of hydrological processes for the purposes of agricultural water management and protection in boreal environment requires description of winter time processes, including heat transport, soil freezing and thawing, and snow accumulation and melt. Finland is located north of the latitude of 60 degrees and has one third to one fourth of the total agricultural land area (2.3 milj. ha) on clay soils (> 30% of clay). Most of the clayey fields are subsurface drained to provide efficient drainage and to enable heavy machines to operate on the fields as soon as possible after the spring snowmelt. Generation of drainflow and surface runoff in cultivated fields leads to nutrient and sediment load, which forms the major share of the total load reaching surface waters at the national level. Water, suspended sediment, and soluble nutrients on clayey field surface are conveyed through the soil profile to the subsurface drains via macropore pathways as the clayey soil matrix is almost impermeable. The objective of the study was to develop the missing winter related processes into the FLUSH model, including soil heat transport, snow pack simulation and the effects of soil freezing and thawing on the soil hydraulic conductivity. FLUSH is an open source (MIT license), distributed, process-based model designed to simulate surface runoff and drainflow in clayey, subsurface drained agricultural fields. 2-D overland flow is described with the diffuse wave approximation of the Saint Venant equations and 3-D subsurface flow with a dual-permeability model. Both macropores and soil matrix are simulated with the Richards equation. Soil heat transport is described with a modified 3-D convection-diffusion equation. Runoff and groundwater data was available from different periods from January 1994 to April 1999 measured in a clayey, subsurface drained field section (3.6 ha) in southern Finland. Soil temperature data was collected in two locations (to a depth of 0.8 m) next to the

  14. Snow Microwave Radiative Transfer (SMRT): A new model framework to simulate snow-microwave interactions for active and passive remote sensing applications

    Science.gov (United States)

    Loewe, H.; Picard, G.; Sandells, M. J.; Mätzler, C.; Kontu, A.; Dumont, M.; Maslanka, W.; Morin, S.; Essery, R.; Lemmetyinen, J.; Wiesmann, A.; Floury, N.; Kern, M.

    2016-12-01

    Forward modeling of snow-microwave interactions is widely used to interpret microwave remote sensing data from active and passive sensors. Though different models are yet available for that purpose, a joint effort has been undertaken in the past two years within the ESA Project "Microstructural origin of electromagnetic signatures in microwave remote sensing of snow". The new Snow Microwave Radiative Transfer (SMRT) model primarily facilitates a flexible treatment of snow microstructure as seen by X-ray tomography and seeks to unite respective advantages of existing models. In its main setting, SMRT considers radiation transfer in a plane-parallel snowpack consisting of homogeneous layers with a layer microstructure represented by an autocorrelation function. The electromagnetic model, which underlies permittivity, absorption and scattering calculations within a layer, is based on the improved Born approximation. The resulting vector-radiative transfer equation in the snowpack is solved using spectral decomposition of the discrete ordinates discretization. SMRT is implemented in Python and employs an object-oriented, modular design which intends to i) provide an intuitive and fail-safe API for basic users ii) enable efficient community developments for extensions (e.g. for improvements of sub-models for microstructure, permittivity, soil or interface reflectivity) from advanced users and iii) encapsulate the numerical core which is maintained by the developers. For cross-validation and inter-model comparison, SMRT implements various ingredients of existing models as selectable options (e.g. Rayleigh or DMRT-QCA phase functions) and shallow wrappers to invoke legacy model code directly (MEMLS, DMRT-QMS, HUT). In this paper we give an overview of the model components and show examples and results from different validation schemes.

  15. Pursuing the method of multiple working hypotheses to understand differences in process-based snow models

    Science.gov (United States)

    Clark, Martyn; Essery, Richard

    2017-04-01

    When faced with the complex and interdisciplinary challenge of building process-based land models, different modelers make different decisions at different points in the model development process. These modeling decisions are generally based on several considerations, including fidelity (e.g., what approaches faithfully simulate observed processes), complexity (e.g., which processes should be represented explicitly), practicality (e.g., what is the computational cost of the model simulations; are there sufficient resources to implement the desired modeling concepts), and data availability (e.g., is there sufficient data to force and evaluate models). Consequently the research community, comprising modelers of diverse background, experience, and modeling philosophy, has amassed a wide range of models, which differ in almost every aspect of their conceptualization and implementation. Model comparison studies have been undertaken to explore model differences, but have not been able to meaningfully attribute inter-model differences in predictive ability to individual model components because there are often too many structural and implementation differences among the different models considered. As a consequence, model comparison studies to date have provided limited insight into the causes of differences in model behavior, and model development has often relied on the inspiration and experience of individual modelers rather than on a systematic analysis of model shortcomings. This presentation will summarize the use of "multiple-hypothesis" modeling frameworks to understand differences in process-based snow models. Multiple-hypothesis frameworks define a master modeling template, and include a a wide variety of process parameterizations and spatial configurations that are used in existing models. Such frameworks provide the capability to decompose complex models into the individual decisions that are made as part of model development, and evaluate each decision in

  16. A New Formulation for Fresh Snow Density over Antarctica for the regional climate model Modèle Atmosphérique Régionale (MAR).

    Science.gov (United States)

    Tedesco, M.; Datta, R.; Fettweis, X.; Agosta, C.

    2015-12-01

    Surface-layer snow density is important to processes contributing to surface mass balance, but is highly variable over Antarctica due to a wide range of near-surface climate conditions over the continent. Formulations for fresh snow density have typically either used fixed values or been modeled empirically using field data that is limited to specific seasons or regions. There is also currently limited work exploring how the sensitivity to fresh snow density in regional climate models varies with resolution. Here, we present a new formulation compiled from (a) over 1600 distinct density profiles from multiple sources across Antarctica and (b) near-surface variables from the regional climate model Modèle Atmosphérique Régionale (MAR). Observed values represent coastal areas as well as the plateau, in both West and East Antarctica (although East Antarctica is dominant). However, no measurements are included from the Antarctic Peninsula, which is both highly topographically variable and extends to lower latitudes than the remainder of the continent. In order to assess the applicability of this fresh snow density formulation to the Antarctic Peninsula at high resolutions, a version of MAR is run for several years both at low-resolution at the continental scale and at a high resolution for the Antarctic Peninsula alone. This setup is run both with and without the new fresh density formulation to quantify the sensitivity of the energy balance and SMB components to fresh snow density. Outputs are compared with near-surface atmospheric variables available from AWS stations (provided by the University of Wisconsin Madison) as well as net accumulation values from the SAMBA database (provided from the Laboratoire de Glaciologie et Géophysique de l'Environnement).

  17. 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.

  18. 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.

  19. Modeling and monitoring avalanches caused by rain-on-snow events

    Science.gov (United States)

    Havens, S.; Marshall, H. P.; Trisca, G. O.; Johnson, J. B.; Nicholson, B.

    2014-12-01

    Direct-action avalanches occur during large storm cycles in mountainous regions, when stresses on the snowpack increase rapidly due to the load of new snow and outpace snow strengthening due to compaction. If temperatures rise above freezing during the storm and snowfall turns to rain, the near-surface snow undergoes rapid densification caused by the introduction of liquid water. This shock to the snowpack, if stability is near critical, can cause widespread immediate avalanching due to the large induced strain rates in the slab, followed by secondary delayed avalanches due to both the increased load as well as water percolation to the depth of a weak layer. We use the semi-empirical SNOow Slope Stability model (SNOSS) to estimate the evolution of stability prior to large avalanches during rain-on-snow events on Highway 21 north of Boise, Idaho. We have continuously monitored avalanche activity using arrays of infrasound sensors in the avalanche-prone section of HW21 near Stanley, in collaboration with the Idaho Transportation Department's avalanche forecasting program. The autonomous infrasound avalanche monitoring system provides accurate timing of avalanche events, in addition to capturing avalanche dynamics during some major releases adjacent to the array. Due to the remote location and low winter traffic volume, the highway is typically closed for multiple days during major avalanche cycles. Many major avalanches typically release naturally and reach the road, but due the complex terrain and poor visibility, manual observations are often not possible until several days later. Since most avalanche programs typically use explosives on a regular basis to control slope stability, the infrasound record of avalanche activity we have recorded on HW21 provides a unique opportunity to study large naturally triggered avalanches. We use a first-order physically based stability model to estimate the importance of precipitation phase, amount, and rate during major rain-on-snow

  20. 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...

  1. Hydrological modelling of alpine headwaters using centurial glacier evolution, snow and long-term discharge dynamics

    Science.gov (United States)

    Kohn, Irene; Vis, Marc; Freudiger, Daphné; Seibert, Jan; Weiler, Markus; Stahl, Kerstin

    2016-04-01

    The response of alpine streamflows to long-term climate variations is highly relevant for the supply of water to adjacent lowlands. A key challenge in modelling high-elevation catchments is the complexity and spatial variability of processes, whereas data availability is rather often poor, restricting options for model calibration and validation. Glaciers represent a long-term storage component that changes over long time-scales and thus introduces additional calibration parameters into the modelling challenge. The presented study aimed to model daily streamflow as well as the contributions of ice and snow melt for all 49 of the River Rhine's glaciated headwater catchments over the long time-period from 1901 to 2006. To constrain the models we used multiple data sources and developed an adapted modelling framework based on an extended version of the HBV model that also includes a time-variable glacier change model and a conceptual representation of snow redistribution. In this study constraints were applied in several ways. A water balance approach was applied to correct precipitation input in order to avoid calibration of precipitation; glacier area change from maps and satellite products and information on snow depth and snow covered area were used for the calibration of each catchment model; and finally, specific seasonal and dynamic aspects of discharge were used for calibration. Additional data like glacier mass balances were used to evaluate the model in selected catchments. The modelling experiment showed that the long-term development of the coupled glacier and streamflow change was particularly important to constrain the model through an objective function incorporating three benchmarks of glacier retreat during the 20th Century. Modelling using only streamflow as calibration criteria had resulted in disproportionate under and over estimation of glacier retreat, even though the simulated and observed streamflow agreed well. Also, even short discharge time

  2. Field measurements and modeling of wave propagation and subsequent weak layer failure in snow due to explosive loading

    Science.gov (United States)

    Simioni, Stephan; Sidler, Rolf; Dual, Jürg; Schweizer, Jürg

    2015-04-01

    Avalanche control by explosives is among the key temporary preventive measures. Yet, little is known about the mechanism involved in releasing avalanches by the effect of an explosion. Here, we test the hypothesis that the stress induced by acoustic waves exceeds the strength of weak snow layers. Consequently the snow fails and the onset of rapid crack propagation might finally lead to the release of a snow slab avalanche. We performed experiments with explosive charges over a snowpack. We installed microphones above the snowpack to measure near-surface air pressure and accelerometers within three snow pits. We also recorded pit walls of each pit with high speed cameras to detect weak layer failure. Empirical relationships and a priori information from ice and air were used to characterize a porous layered model from density measurements of snow profiles in the snow pits. This model was used to perform two-dimensional numerical simulations of wave propagation in Biot-type porous material. Locations of snow failure were identified in the simulation by comparing the axial and deviatoric stress field of the simulation to the corresponding snow strength. The identified snow failure locations corresponded well with the observed failure locations in the experiment. The acceleration measured in the snowpack best correlated with the modeled acceleration of the fluid relative to the ice frame. Even though the near field of the explosion is expected to be governed by non-linear effects as for example the observed supersonic wave propagation in the air above the snow surface, the results of the linear poroelastic simulation fit well with the measured air pressure and snowpack accelerations. The results of this comparison are an important step towards quantifying the effectiveness of avalanche control by explosives.

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

    Directory of Open Access Journals (Sweden)

    Y. Arnaud

    2009-09-01

    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 set 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 constrast 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.

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

    Science.gov (United States)

    Dumont, M.; Brissaud, O.; Picard, G.; Schmitt, B.; Gallet, J.-C.; Arnaud, Y.

    2010-03-01

    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.

  5. Gridded snow maps supporting avalanche forecasting in Norway

    Science.gov (United States)

    Müller, K.; Humstad, T.; Engeset, R. V.; Andersen, J.

    2012-04-01

    We present gridded maps indicating key parameters for avalanche forecasting with a 1 km x 1 km resolution. Based on the HBV hydrology model, snow parameters are modeled based on observed and interpolated precipitation and temperature data. Modeled parameters include for example new snow accumulated the last 24 and 72 hours, snow-water equivalent, and snow-water content. In addition we use meteorological parameters from the UK weather prediction model "Unified Model" such as wind and radiation to model snow-pack properties. Additional loading in lee-slopes by wind-transport is modeled based on prevailing wind conditions, snow-water content and snow age. A depth hoar index accounts for days with considerable negative temperature gradients in the snow pack. A surface hoar index based on radiation and humidity is currently under development. The maps are tested against field reports from avalanche observers throughout Norway. All data is available via a web-platform that combines maps for geo-hazards such as floods, landslides and avalanches. The maps are used by the Norwegian avalanche forecasting service, which is currently in a test phase. The service will be operational by winter 2012/2013.

  6. Verification of Advances in a Coupled Snow-runoff Modeling Framework for Operational Streamflow Forecasts

    Science.gov (United States)

    Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

    2011-12-01

    The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.

  7. Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter

    Directory of Open Access Journals (Sweden)

    Milan Kalas

    2013-11-01

    Full Text Available Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA in physical distributed hydrological models is a possible source of improvement of snowmelt-related floods. In this study, the assimilation in the LISFLOOD model of the MODIS sensor SCA has been evaluated, in order to improve the streamflow simulations of the model. This work is realized with the final scope of improving the European Flood Awareness System (EFAS pan-European flood forecasts in the future. For this purpose daily 500 m resolution MODIS satellite SCA data have been used. Tests were performed in the Morava basin, a tributary of the Danube, for three years. The particle filter method has been chosen for assimilating the MODIS SCA data with different frequencies. Synthetic experiments were first performed to validate the assimilation schemes, before assimilating MODIS SCA data. Results of the synthetic experiments could improve modelled SCA and discharges in all cases. The assimilation of MODIS SCA data with the particle filter shows a net improvement of SCA. The Nash of resulting discharge is consequently increased in many cases.

  8. Assimilation of satellite information in a snowpack model to improve characterization of snow cover for runoff simulation and forecasting

    Directory of Open Access Journals (Sweden)

    L. S. Kuchment

    2009-08-01

    Full Text Available A new technique for constructing spatial fields of snow characteristics for runoff simulation and forecasting is presented. The technique incorporates satellite land surface monitoring data and available ground-based hydrometeorological measurements in a physical based snowpack model. The snowpack model provides simulation of temporal changes of the snow depth, density and water equivalent (SWE, accounting for snow melt, sublimation, refreezing melt water and snow metamorphism processes with a special focus on forest cover effects. The model was first calibrated against available ground-based snow measurements and then was applied to calculate the spatial distribution of snow characteristics using satellite data and interpolated ground-based meteorological data. The remote sensing data used in the model consist of products derived from observations of MODIS and AMSR-E instruments onboard Terra and Aqua satellites. They include daily maps of snow cover, snow water equivalent (SWE, land surface temperature, and weekly maps of surface albedo. Maps of land cover classes and tree cover fraction derived from NOAA AVHRR were used to characterize the vegetation cover. The developed technique was tested over a study area of approximately 200 000 km2 located in the European part of Russia (56° N to 60° N, and 48° E to 54° E. The study area comprises the Vyatka River basin with the catchment area of 124 000 km2. The spatial distributions of SWE, obtained with the coupled model, as well as solely from satellite data were used as the inputs in a physically-based model of runoff generation to simulate runoff hydrographs on the Vyatka river for spring seasons of 2003, 2005. The comparison of simulated hydrographs with the observed ones has shown that suggested procedure gives a higher accuracy of snow cover spatial distribution representation and hydrograph simulations than the direct use of satellite SWE data.

  9. Assimilation of satellite information in a snowpack model to improve characterization of snow cover for runoff simulation and forecasting

    Science.gov (United States)

    Kuchment, L. S.; Romanov, P.; Gelfan, A. N.; Demidov, V. N.

    2009-08-01

    A new technique for constructing spatial fields of snow characteristics for runoff simulation and forecasting is presented. The technique incorporates satellite land surface monitoring data and available ground-based hydrometeorological measurements in a physical based snowpack model. The snowpack model provides simulation of temporal changes of the snow depth, density and water equivalent (SWE), accounting for snow melt, sublimation, refreezing melt water and snow metamorphism processes with a special focus on forest cover effects. The model was first calibrated against available ground-based snow measurements and then was applied to calculate the spatial distribution of snow characteristics using satellite data and interpolated ground-based meteorological data. The remote sensing data used in the model consist of products derived from observations of MODIS and AMSR-E instruments onboard Terra and Aqua satellites. They include daily maps of snow cover, snow water equivalent (SWE), land surface temperature, and weekly maps of surface albedo. Maps of land cover classes and tree cover fraction derived from NOAA AVHRR were used to characterize the vegetation cover. The developed technique was tested over a study area of approximately 200 000 km2 located in the European part of Russia (56° N to 60° N, and 48° E to 54° E). The study area comprises the Vyatka River basin with the catchment area of 124 000 km2. The spatial distributions of SWE, obtained with the coupled model, as well as solely from satellite data were used as the inputs in a physically-based model of runoff generation to simulate runoff hydrographs on the Vyatka river for spring seasons of 2003, 2005. The comparison of simulated hydrographs with the observed ones has shown that suggested procedure gives a higher accuracy of snow cover spatial distribution representation and hydrograph simulations than the direct use of satellite SWE data.

  10. Assessing wet snow avalanche activity using detailed physics based snowpack simulations

    Science.gov (United States)

    Wever, N.; Vera Valero, C.; Fierz, C.

    2016-06-01

    Water accumulating on microstructural transitions inside a snowpack is often considered a prerequisite for wet snow avalanches. Recent advances in numerical snowpack modeling allow for an explicit simulation of this process. We analyze detailed snowpack simulations driven by meteorological stations in three different climate regimes (Alps, Central Andes, and Pyrenees), with accompanying wet snow avalanche activity observations. Predicting wet snow avalanche activity based on whether modeled water accumulations inside the snowpack locally exceed 5-6% volumetric liquid water content is providing a higher prediction skill than using thresholds for daily mean air temperature, or the daily sum of the positive snow energy balance. Additionally, the depth of the maximum water accumulation in the simulations showed a significant correlation with observed avalanche size. Direct output from detailed snow cover models thereby is able to provide a better regional assessment of dangerous slope aspects and potential avalanche size than traditional methods.

  11. Sensitivity of snow cover to horizontal resolution in a land surface model

    Science.gov (United States)

    Dutra, E.; Kotlarski, S.; Viterbo, P.; Balsamo, G.; Miranda, P. M. A.; Schär, C.

    2010-09-01

    Snow cover is a highly variable land surface condition that exerts a strong control on the heat and moisture budget of the overlying atmosphere. Modeling studies based on long integrations of global circulation models (GCM) are normally carried out at very low resolution (typically coarser than 100 km) due to their high computational demand. On local scales, snow cover plays an important socioeconomic role, ranging from water management applications to outdoor recreation. These latter applications vary in horizontal resolution from a few hundred meters to a few kilometers, where small scale topography, land cover and local circulation effects play a significant role. In this study our focus will be on horizontal scales ranging from typical GCM global climate modeling to high resolution global weather forecasts. In the land surface component of a GCM (land surface model - LSM), snow cover temporal and spatial variability is mainly determined by the overlying atmospheric conditions. However, once snowfall settles on the ground, the sub-grid scale variability associated with complex terrain and land cover variability (not resolved at the model resolution) is parameterized following simple physical and/or empirical relations. The present study intends to access the impact of horizontal resolution in the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model (HTESSEL). HTESSEL is forced by the ECMWF operational weather forecasts since March 2006 to December 2009 (runs in offline/stand-alone mode). The control run is carried out at the horizontal resolution of the forecasts at TL799 (gaussian reduced grid N400 -about 25 km). Two lower horizontal resolutions are then tested: TL255 (gaussian reduced grid - about 80 km, same as the ERA-Interim reanalysis), and TL95 (gaussian reduced grid N48 - about 200 km). The length of the simulations is rather small (only 46 months), however global meteorological forcing at 25 km can only be accessed through the

  12. Spatial sensitivity analysis of remote sensing snow cover fraction data in a distributed hydrological model

    Science.gov (United States)

    Berezowski, Tomasz; Chormański, Jarosław; Nossent, Jiri; Batelaan, Okke

    2014-05-01

    Distributed hydrological models enhance the analysis and explanation of environmental processes. As more spatial input data and time series become available, more analysis is required of the sensitivity of the data on the simulations. Most research so far focussed on the sensitivity of precipitation data in distributed hydrological models. However, these results can not be compared until a universal approach to quantify the sensitivity of a model to spatial data is available. The frequently tested and used remote sensing data for distributed models is snow cover. Snow cover fraction (SCF) remote sensing products are easily available from the internet, e.g. MODIS snow cover product MOD10A1 (daily snow cover fraction at 500m spatial resolution). In this work a spatial sensitivity analysis (SA) of remotely sensed SCF from MOD10A1 was conducted with the distributed WetSpa model. The aim is to investigate if the WetSpa model is differently subjected to SCF uncertainty in different areas of the model domain. The analysis was extended to look not only at SA quantities but also to relate them to the physical parameters and processes in the study area. The study area is the Biebrza River catchment, Poland, which is considered semi natural catchment and subject to a spring snow melt regime. Hydrological simulations are performed with the distributed WetSpa model, with a simulation period of 2 hydrological years. For the SA the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm is used, with a set of different response functions in regular 4 x 4 km grid. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different landscape features. Moreover, the spatial patterns of the SA results are related to the WetSpa spatial parameters and to different physical processes. Based on the study results, it is clear that spatial approach of SA can be performed with the proposed algorithm and the MOD10A1 SCF is spatially sensitive in

  13. UV albedo of arctic snow in spring

    Directory of Open Access Journals (Sweden)

    O. Meinander

    2008-02-01

    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 (67.37° N, 26.63° 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 0.5–0.7 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.

  14. 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

  15. Snow multivariable data assimilation for hydrological predictions in Alpine sites

    Science.gov (United States)

    Piazzi, Gaia; Thirel, Guillaume; Campo, Lorenzo; Gabellani, Simone; Stevenin, Hervè

    2017-04-01

    Snowpack dynamics (snow accumulation and ablation) strongly impacts on hydrological processes in Alpine areas. During the winter season the presence of snow cover (snow accumulation) reduces the drainage in the basin with a resulting lower watershed time of concentration in case of possible rainfall events. Moreover, the release of the significant water volume stored in winter (snowmelt) considerably contributes to the total discharge during the melting period. Therefore when modeling hydrological processes in snow-dominated catchments the quality of predictions deeply depends on how the model succeeds in catching snowpack dynamics. The integration of a hydrological model with a snow module allows improving predictions of river discharges. Besides the well-known modeling limitations (uncertainty in parameterizations; possible errors affecting both meteorological forcing data and initial conditions; approximations in boundary conditions), 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 several independent snow-related data sources (model simulations, ground-based measurements and remote sensed observations) in order to obtain the most likely estimate of snowpack state. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model strengthened by a multivariable DA framework for hydrological purposes. 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

  16. Voronoi diagram-based spheroid model for microwave scattering of complex snow aggregates

    Science.gov (United States)

    Honeyager, Ryan; Liu, Guosheng; Nowell, Holly

    2016-02-01

    Methods to model snow aggregate scattering properties at microwave frequencies can be divided into structurally explicit and implicit techniques. Explicit techniques, such as the discrete dipole approximation (DDA), determine scattering and backscatter cross-sections assuming full knowledge of a given snow particle's structure. Such calculations are computationally expensive. Implicit techniques, such as using the T-matrix method (TMM) with optically soft spheroids, model equivalent particles with variable mass, bulk density and aspect ratio according to an effective-medium approximation. It is highly desirable that there should be a good agreement between modeled aggregate cross-sections using both methods. A Voronoi bounding-neighbor algorithm is presented in this study to determine the bulk equivalent density of complex three-dimensional snow aggregates. While mass and aspect ratio are easily parameterized quantities, attempts to parameterize the bulk density of snowflakes have usually relied on a bounding ellipsoid, which can be determined from a flake's radius of gyration, root mean square mean or simply from its maximum diameter. We compared the Voronoi algorithm against existing bounding spheroid approaches and mass-effective density relations at ten frequencies from 10.65 to 183.31 GHz, using a set of 1005 aggregates with maximum dimensions from a few hundred microns to several centimeters. When using the Voronoi-determined effective density, the asymmetry parameter, scattering, and backscatter cross-sections determined using the TMM reasonably match those for DDA-computed snow aggregates. From Ku to W-band, soft spheroids can reproduce cross-sections for aggregates up to 9 mm in maximum dimension. Volume-integrated cross-sections always agree to within 25% of DDA. As the DDA is computationally expensive, this offers a fast alternative that efficiently evaluates scattering properties at microwave frequencies.

  17. Understanding and modeling the physical processes that govern the melting of snow cover in a tropical mountain environment in Ecuador

    Science.gov (United States)

    Wagnon, P.; Lafaysse, M.; Lejeune, Y.; Maisincho, L.; Rojas, M.; Chazarin, J. P.

    2009-10-01

    The ISBA/CROCUS coupled ground-snow model developed for the Alps and subsequently adapted to the outer tropical conditions of Bolivia has been applied to a full set of meteorological data recorded at 4860 m above sea level on a moraine area in Ecuador (Antizana 15 glacier, 0°28'S; 78°09'W) between 16 June 2005 and 30 June 2006 to determine the physical processes involved in the melting and disappearance of transient snow cover in nonglaciated areas of the inner tropics. Although less accurate than in Bolivia, the model is still able to simulate snow behavior over nonglaciated natural surfaces, as long as the modeled turbulent fluxes over bare ground are reduced and a suitable function is included to represent the partitioning of the surface between bare soil and snow cover. The main difference between the two tropical sites is the wind velocity, which is more than 3 times higher at the Antizana site than at the Bolivian site, leading to a nonuniform spatial distribution of snow over nonglaciated areas that is hard to describe with a simple snow partitioning function. Net solar radiation dominates the surface energy balance and is responsible for the energy stored in snow-free areas (albedo = 0.05) and transferred horizontally to adjacent snow patches by conduction within the upper soil layers and by turbulent advection. These processes can prevent the snow cover from lasting more than a few hours or a few days. Sporadically, and at any time of the year, this inner tropical site, much wetter than the outer tropics, experiences heavy snowfalls, covering all the moraine area, and thus limiting horizontal transfers and inducing a significant time lag between precipitation events and runoff.

  18. The effect of model scale in reconstructing snow water equivalent over complex terrain

    Science.gov (United States)

    Molotch, N. P.; Durand, M.; Margulis, S. A.

    2007-05-01

    Improving estimates of water and energy fluxes in the rugged landscapes of the American Cordillera is particularly challenging given the considerable topographic heterogeneity and paucity of observations within the region. In these complex systems, parameterizations of sub-grid variability in energy and mass transformations are highly sensitive to relationships between model spatial resolution and the correlation-length scale of the variable of interest - which is usually unknown. In the mountainous regions of the Western United States, the processes controlling the distribution of snow water equivalent are likely better known than any other hydrologic state. The observation record is relatively long (dating back to the early part of the 20th century) and snow cover is relatively easy to detect using remotely sensed observations in the visible and near infrared. Hence, distributed snowpack simulations provide an ideal case study for exploring relationships between process, model, and observation scales; potentially guiding efforts regarding other hydrologic states (e.g. soil moisture). To that end, this research uses a time series of fractional snow covered area (SCA) estimates from Landsat Enhanced Thematic Mapper (ETM+), Moderate Resolution Imaging Spectoradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) data, in combination with a spatially distributed snowmelt model, to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3,419 km2) of Colorado, USA. In this reconstruction approach, modeled snowmelt over each pixel is integrated over the time of satellite observed snow cover to estimate SWE. Despite the considerable differences in the magnitude of SWE in 2001 versus 2002, model performance - using ETM+ data aggregated to 100-m resolution - was robust with a mean absolute error (MAE) of 23% relative to observed SWE from intensive field campaigns. Model performance deteriorated when MODIS (MAE = 57%) and AVHRR (MAE = 90

  19. A Case Study of Using a Multilayered Thermodynamical Snow Model for Radiance Assimilation

    Science.gov (United States)

    Toure, Ally M.; Goita, Kalifa; Royer, Alain; Kim, Edward J.; Durand, Michael; Margulis, Steven A.; Lu, Huizhong

    2011-01-01

    A microwave radiance assimilation (RA) scheme for the retrieval of snow physical state variables requires a snowpack physical model (SM) coupled to a radiative transfer model. In order to assimilate microwave brightness temperatures (Tbs) at horizontal polarization (h-pol), an SM capable of resolving melt-refreeze crusts is required. To date, it has not been shown whether an RA scheme is tractable with the large number of state variables present in such an SM or whether melt-refreeze crust densities can be estimated. In this paper, an RA scheme is presented using the CROCUS SM which is capable of resolving melt-refreeze crusts. We assimilated both vertical (v) and horizontal (h) Tbs at 18.7 and 36.5 GHz. We found that assimilating Tb at both h-pol and vertical polarization (v-pol) into CROCUS dramatically improved snow depth estimates, with a bias of 1.4 cm compared to-7.3 cm reported by previous studies. Assimilation of both h-pol and v-pol led to more accurate results than assimilation of v-pol alone. The snow water equivalent (SWE) bias of the RA scheme was 0.4 cm, while the bias of the SWE estimated by an empirical retrieval algorithm was -2.9 cm. Characterization of melt-refreeze crusts via an RA scheme is demonstrated here for the first time; the RA scheme correctly identified the location of melt-refreeze crusts observed in situ.

  20. Infiltration under snow cover: Modeling approaches and predictive uncertainty

    Science.gov (United States)

    Meeks, Jessica; Moeck, Christian; Brunner, Philip; Hunkeler, Daniel

    2017-03-01

    Groundwater recharge from snowmelt represents a temporal redistribution of precipitation. This is extremely important because the rate and timing of snowpack drainage has substantial consequences to aquifer recharge patterns, which in turn affect groundwater availability throughout the rest of the year. The modeling methods developed to estimate drainage from a snowpack, which typically rely on temporally-dense point-measurements or temporally-limited spatially-dispersed calibration data, range in complexity from the simple degree-day method to more complex and physically-based energy balance approaches. While the gamut of snowmelt models are routinely used to aid in water resource management, a comparison of snowmelt models' predictive uncertainties had previously not been done. Therefore, we established a snowmelt model calibration dataset that is both temporally dense and represents the integrated snowmelt infiltration signal for the Vers Chez le Brandt research catchment, which functions as a rather unique natural lysimeter. We then evaluated the uncertainty associated with the degree-day, a modified degree-day and energy balance snowmelt model predictions using the null-space Monte Carlo approach. All three melt models underestimate total snowpack drainage, underestimate the rate of early and midwinter drainage and overestimate spring snowmelt rates. The actual rate of snowpack water loss is more constant over the course of the entire winter season than the snowmelt models would imply, indicating that mid-winter melt can contribute as significantly as springtime snowmelt to groundwater recharge in low alpine settings. Further, actual groundwater recharge could be between 2 and 31% greater than snowmelt models suggest, over the total winter season. This study shows that snowmelt model predictions can have considerable uncertainty, which may be reduced by the inclusion of more data that allows for the use of more complex approaches such as the energy balance

  1. Influence of Dust and Black Carbon on the Snow Albedo in the NASA Goddard Earth Observing System Version 5 Land Surface Model

    Science.gov (United States)

    Yasunari, Teppei J.; Koster, Randal D.; Lau, K. M.; Aoki, Teruo; Sud, Yogesh C.; Yamazaki, Takeshi; Motoyoshi, Hiroki; Kodama, Yuji

    2011-01-01

    Present-day land surface models rarely account for the influence of both black carbon and dust in the snow on the snow albedo. Snow impurities increase the absorption of incoming shortwave radiation (particularly in the visible bands), whereby they have major consequences for the evolution of snowmelt and life cycles of snowpack. A new parameterization of these snow impurities was included in the catchment-based land surface model used in the National Aeronautics and Space Administration Goddard Earth Observing System version 5. Validation tests against in situ observed data were performed for the winter of 2003.2004 in Sapporo, Japan, for both the new snow albedo parameterization (which explicitly accounts for snow impurities) and the preexisting baseline albedo parameterization (which does not). Validation tests reveal that daily variations of snow depth and snow surface albedo are more realistically simulated with the new parameterization. Reasonable perturbations in the assigned snow impurity concentrations, as inferred from the observational data, produce significant changes in snowpack depth and radiative flux interactions. These findings illustrate the importance of parameterizing the influence of snow impurities on the snow surface albedo for proper simulation of the life cycle of snow cover.

  2. Influence of Dust and Black Carbon on the Snow Albedo in the NASA Goddard Earth Observing System Version 5 Land Surface Model

    Science.gov (United States)

    Yasunari, Teppei J.; Koster, Randal D.; Lau, K. M.; Aoki, Teruo; Sud, Yogesh C.; Yamazaki, Takeshi; Motoyoshi, Hiroki; Kodama, Yuji

    2011-01-01

    Present-day land surface models rarely account for the influence of both black carbon and dust in the snow on the snow albedo. Snow impurities increase the absorption of incoming shortwave radiation (particularly in the visible bands), whereby they have major consequences for the evolution of snowmelt and life cycles of snowpack. A new parameterization of these snow impurities was included in the catchment-based land surface model used in the National Aeronautics and Space Administration Goddard Earth Observing System version 5. Validation tests against in situ observed data were performed for the winter of 2003.2004 in Sapporo, Japan, for both the new snow albedo parameterization (which explicitly accounts for snow impurities) and the preexisting baseline albedo parameterization (which does not). Validation tests reveal that daily variations of snow depth and snow surface albedo are more realistically simulated with the new parameterization. Reasonable perturbations in the assigned snow impurity concentrations, as inferred from the observational data, produce significant changes in snowpack depth and radiative flux interactions. These findings illustrate the importance of parameterizing the influence of snow impurities on the snow surface albedo for proper simulation of the life cycle of snow cover.

  3. Causes of snow instability variations at the basin scale

    Science.gov (United States)

    Reuter, Benjamin; Richter, Bettina; Schweizer, Jürg

    2016-04-01

    The alpine snow cover accumulates layers during characteristic meteorological events. The so formed stratigraphic features of the snowpack are known to influence avalanche release processes, such as failure initiation or crack propagation. Synoptic scale meteorological processes are altered by the underlying terrain, which causes micro-meteorological differences at smaller scales, such as the basin scale, for instance. Such micro-meteorological effects of complex snow surfaces were successfully modeled suggesting that the time is ripe to investigate their influence on snow instability. In other words, we aim at identifying the causes of spatial snow instability variations at the scale of a small basin. Over the past years we have compiled several field data sets for a small basin above Davos (Eastern Swiss Alps) covering 400 m by 400 m and consisting of snow penetration resistance profiles collected with the snow micro-penetrometer, terrain data and terrestrial laser scans. Each dataset holds about 150 vertical profiles sampled semi-randomly in the basin and captures the situation of a specific day, hence a particular avalanche situation. At those 150 point measurements the criteria for failure initiation and crack propagation were calculated and their spatial structure was analyzed. Eventually, we were able to model the distribution of snow instability in the basin by external drift kriging. We based the regression models on terrain and snow depth data. Slope aspect was the most prominent driver, but the number of significant covariates depended on the situation. Our results further suggest that the observed differences were caused by external influences possibly due to meteorological forcing as their residual autocorrelation ranges were shorter than the ones of the terrain. Repeating the geostatistical analysis with snow cover model output as covariate data, we were able to identify the causes of the snow instability patterns observed at the basin scale. The most

  4. Quasi-Biennial and Quasi-Decadal Variations in Snow Accumulation over Northern Eurasia and Their Connections to the Atlantic and Pacific Oceans.

    Science.gov (United States)

    Ye, Hengchun

    2001-12-01

    Spatial and temporal characteristics of winter snow depth variation over northern Eurasia and their connections to sea surface temperatures (SSTs) and associated atmospheric circulation anomalies, surface air temperatures, and precipitation are examined by using 60 yr (1936-95) of station data records. This study found that snow depth variation over the region east of the Caspian Sea and west of China, explaining 10.1% of total snow depth variance, has a quasi-biennial variability of about 2.5 yr. The snow depth variation over central European Russia and western-central Siberia, explaining 8.1% of the total snow depth variance, has a quasi-decadal variability of about 11.8 yr. The snow depth variation over the northern Ural Mountains, explaining 7.5% of the total snow depth variance has, variability of about 8 and 14 yr.The quasi-biennial snow depth variation is associated with SSTs over the northern North Pacific and tropical western Atlantic extending into the Gulf of Mexico. The associated atmospheric circulation pattern of Eurasia 1 (EU-1) and the Pacific-North American (PNA) pattern determine the surface air temperature conditions and thus snow depth at the biennial timescale. The quasi-decadal snow variation is associated with a well-known SST anomaly pattern over the Atlantic, having opposite SST variations in alternating latitudinal belts, and SSTs over the tropical Pacific Ocean. The associated atmospheric North Atlantic oscillation (NAO) and the circulation anomaly over central Siberia affect both surface air temperature and precipitation and thus snow depth anomaly on this quasi-decadal timescale. The results provide observational evidence of possible causes for snow depth variability over high-latitude regions.

  5. Simulating melt, runoff and refreezing on Nordenskiöldbreen, Svalbard, using a coupled snow and energy balance model

    Directory of Open Access Journals (Sweden)

    W. J. J. van Pelt

    2012-06-01

    Full Text Available A distributed energy balance model is coupled to a multi-layer snow model in order to study the mass balance evolution and the impact of refreezing on the mass budget of Nordenskiöldbreen, Svalbard. The model is forced with output from the regional climate model RACMO and meteorological data from Svalbard Airport. Extensive calibration and initialisation are performed to increase the model accuracy. For the period 1989–2010, we find a mean net mass balance of −0.39 m w.e. a−1. Refreezing contributes on average 0.27 m w.e. a−1 to the mass budget and is most pronounced in the accumulation zone. The simulated mass balance, radiative fluxes and subsurface profiles are validated against observations and are generally in good agreement. Climate sensitivity experiments reveal a non-linear, seasonally dependent response of the mass balance, refreezing and runoff to changes in temperature and precipitation. It is shown that including seasonality in climate change, with less pronounced summer warming, reduces the sensitivity of the mass balance and equilibrium line altitude (ELA estimates in a future climate. The amount of refreezing is shown to be rather insensitive to changes in climate.

  6. Simulating melt, runoff and refreezing on Nordenskiöldbreen, Svalbard, using a coupled snow and energy balance model

    Directory of Open Access Journals (Sweden)

    W. J. J. van Pelt

    2012-01-01

    Full Text Available A distributed energy balance model is coupled to a multi-layer snow model in order to study the mass balance evolution and the impact of refreezing on the mass budget of Nordenskiöldbreen, Svalbard. The model is forced with output of a regional climate model (RACMO and meteorological data from Svalbard Airport. Extensive calibration and initialisation are performed to increase the model accuracy. For the period 1989–2010, we find a mean net mass balance of −0.39 m w.e. a−1. Refreezing contributes on average 0.27 m w.e. a−1 to the mass budget and is most pronounced in the accumulation zone. The simulated mass balance, radiative fluxes and subsurface profiles are validated against observations and are generally in good agreement. Climate sensitivity experiments reveal a non-linear, seasonally dependent response of the mass balance, refreezing and runoff to changes in temperature and precipitation. Output of the climate sensitivity experiments is used in combination with temperature and precipitation time-series to extend mass balance time-series in the past and the future to obtain estimates for the period 1912–2085. It is shown that including seasonality in climate change, with less pronounced summer warming, has a major impact on future mass balance and ELA estimates. Due to compensating effects, the contribution of refreezing hardly changes in a future climate.

  7. 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

  8. Spatial and temporal variability of snow accumulation in Dronning Maud Land, East Antarctica, including two deep ice coring sites at Dome Fuji and EPICA DML

    Directory of Open Access Journals (Sweden)

    S. Fujita

    2011-08-01

    Full Text Available To better understand the spatio-temporal variability of the glaciological environment in Dronning Maud Land (DML, East Antarctica, investigations were carried out along the 2800-km-long Japanese-Swedish IPY 2007/2008 traverse. The route covers ice sheet ridges and two deep ice coring sites at Dome Fuji and EPICA DML. The surface mass balance (SMB distribution was derived based on analysis of isochrones within snow pits, firn cores and subsurface radar signals. The SMB averaged over various time scales in the Holocene was determined. This was then compared with various glaciological data. We find that the large-scale distribution of the SMB depends on the surface elevation, continentality and interactions between ice sheet ridges and the prevailing counterclockwise windfield in DML. A different SMB is found for the windward and leeward sides of the ridges. Local-scale variability in the SMB is essentially governed by bedrock topography which determines the local surface topography. In the eastern part of DML, the accumulation rate in the second half of the 20th century is found to be higher by 15 % compared to averages over longer periods of 722 a or 7.9 ka before AD 2008. A similar trend has been reported for many inland plateau sites in East Antarctica.

  9. Modeling the Soil Moisture Parametrization in a Snow Dominated Mountainous Region

    Science.gov (United States)

    Kikine, Daniel; Sensoy, Aynur; Sorman, Arda

    2016-04-01

    The study quantifies the effects of both the soil moisture accounting and the temperature index in the event based as well as the continuous simulation of a model in a snow dominated basin. Physically based watershed model parameters are required to reproduce the historical flows and forecast the stream flows. This study demonstrates that parameterization of hydrological model is a favorable approach to perform forecasting because it employs the relationship of the calibrated model parameters and those of the watershed's physical properties. With this consideration, the temperature index (degree-day) snowmelt and the soil moisture accounting models within the Hydrologic Engineering Center's hydrologic modeling system (HEC-HMS) are applied to the Upper Euphrates watershed. The versatile 14-parameter soil moisture accounting (SMA) algorithm is utilized for a better simulation and parameterization of the watershed. The methodology was exemplified by performing various independent simulations using the meteorological data and the observed stream discharges. The soil moisture parameters were calibrated and modified according to their statistical relationships with the land use for the 2002 - 2008 period, the obtained parameter set are then validated for the 2009 - 2012 period. Model outputs are evaluated in comparison to satellite derived soil moisture and snow water equivalent data. Deterministic Numerical Weather Prediction data are used together with the conceptual model to forecast runoff for the melting period of the year 2015.

  10. Modeling of snow avalanches for protection measures designing

    Science.gov (United States)

    Turchaninova, Alla; Lazarev, Anton; Loginova, Ekaterina; Seliverstov, Yuri; Glazovskaya, Tatiana; Komarov, Anton

    2017-04-01

    Avalanche protection structures such as dams have to be designed using well known standard engineering procedures that differ in different countries. Our intent is to conduct a research on structural avalanche protection measures designing and their reliability assessment during the operation using numerical modeling. In the Khibini Mountains, Russia, several avalanche dams have been constructed at different times to protect settlements and mining. Compared with other mitigation structures dams are often less expensive to construct in mining regions. The main goal of our investigation was to test the capabilities of Swiss avalanche dynamics model RAMMS and Russian methods to simulate the interaction of avalanches with mitigation structures such as catching and reflecting dams as well as to reach the observed runout distances after the transition through a dam. We present the RAMMS back-calculation results of an artificially triggered and well-documented catastrophic avalanche occurred in the town of Kirovsk, Khibini Mountains in February 2016 that has unexpectedly passed through a system of two catching dams and took the lives of 3 victims. The estimated volume of an avalanche was approximately 120,000 m3. For the calculation we used a 5 m DEM including catching dams generated from field measurements in summer 2015. We simulated this avalanche (occurred below 1000 m.a.s.l.) in RAMMS having taken the friction parameters (µ and ζ) from the upper altitude limit (above 1500 m.a.s.l.) from the table recommended for Switzerland (implemented into RAMMS) according to the results of our previous research. RAMMS reproduced the observed avalanche behavior and runout distance. No information is available concerning the flow velocity; however, calculated values correspond in general to the values measured in this avalanche track before. We applied RAMMS using an option of adding structures to DEM (including a dam in GIS) in other to test other operating catching dams in

  11. Obtaining 3d models of surface snow and ice features (penitentes) with a Xbox Kinect

    Science.gov (United States)

    Nicholson, Lindsey; Partan, Benjamin; Pętlicki, Michał; MacDonell, Shelley

    2014-05-01

    Penitentes are snow or ice spikes that can reach several metres in height. They are a common feature of snow and ice surfaces in the semi-arid Andes as their formation is favoured by very low humidity, persistently low temperatures and sustained high solar radiation. While the conditions of their formation are relatively well constrained it is not yet clear how their presence influences the rate of mass loss and meltwater production from the mountain cryosphere and there is a need for accurate measurements of ablation within penitente fields through time in order to evaluate how well existing energy balance models perform for surfaces with penitentes. The complex surface morphology poses a challenge to measuring the mass loss at snow or glacier surfaces as (i) the spatial distribution of surface lowering within a penitente field is very heterogeneous, and (ii) the steep walls and sharp edges of the penitentes limit the line of sight view for surveying from fixed positions. In this work we explored whether these problems can be solved by using the Xbox Kinect sensor to generate small scale digital terrain models (DTMs) of sample areas of snow and ice penitentes. The study site was Glaciar Tapado in Chile (30°08'S; 69°55'W) where three sample sites were monitored from November 2013 to January 2014. The range of the Kinect sensor was found to be restricted to about 1 m over snow and ice, and scanning was only possible after dusk. Moving the sensor around the penitente field was challenging and often resulted in fragmented scans. However, despite these challenges, the scans obtained could be successfully combined in MeshLab software to produce good surface representations of the penitentes. GPS locations of target stakes in the sample plots allow the DTMs to be orientated correctly in space so the morphology of the penitente field and the volume loss through time can be fully described. At the study site in snow penitentes the Kinect DTM was compared with the quality

  12. Application and improvement of a georadar system to assess areal snow distribution for advances in hydrological modeling

    Energy Technology Data Exchange (ETDEWEB)

    Marchand, Wolf-Dietrich

    2003-10-01

    A good understanding of the snow processes in the hydrological cycle is crucial in the design of hydrological models in cold regions. Problems are often connected to the missing information on snowcover in remote areas (where the largest amount of snow usually allocates) as well as the lack of ability to simulate or even predict the complex processes of the interaction between the snow and its environment. The objective of this study is to contribute to a further clarification of both of these obstacles. The most effective way of gathering snowcover information is the accomplishment of snow measurements. In recent years, a lot of emphasis has been put on satellite techniques. However, concerning the measurement of snowcover properties, which is more than snow covered area only, satellite techniques are still in the beginning stages and ground-truth data are needed. In many places, conventional ground-based measurements are still common practice. These measurements are warrantable, but traditional manual measurements cannot supply sufficient amounts of data for modern, computer based Geographic Information System (GIS) analyses and distributed hydrological modeling. Thus, it is well worth improving the ground based measuring methods. One goal of the presented research was the improvement of an existing radar system for snow depth measurements. An interface module was used to connect the radar to a Global Positioning System (GPS) receiver. One major advantage of the entire system was the ease of further use of snow data in the GIS. Every snow depth sample had a GPS position and a sufficient amount of data was warranted by the effectiveness of the radar. A study area was determined and snow data were collected for three consecutive winter seasons. clue purpose benefit A comparison study on ground-based and airborne snowradar was made. The results showed good congruence between both data sets, even if the performance of both techniques was different. The second aim of

  13. Modelled seasonal forecasts of snow water equivalent and runoff in alpine catchments

    Science.gov (United States)

    Förster, Kristian; Hanzer, Florian; Schöber, Johannes; Huttenlau, Matthias; Achleitner, Stefan; Strasser, Ulrich

    2016-04-01

    Seasonal forecasts of water balance components are becoming increasingly important for hydrological applications. These forecasts are typically derived from coupled atmosphere-ocean climate models, which enable physically based seasonal forecasts. In mountainous regions, however, topography is complex whilst typical spatial resolutions of the climate models are still comparably coarse, i.e in the data, ridges and valleys are not represented with sufficient accuracy. Therefore, seasonal predictions of atmospheric variables require consideration of representative gradients. We present first results of seasonal forecasts and re-forecasts processed by the NCEP (National Centers for Environmental Prediction) Climate Forecast System version 2 (CFSv2). These are prepared for monthly time steps in order to be used for ensemble runs of water balance simulation using the Alpine Water balance And Runoff Estimation model (AWARE). This model has been designed for monthly seasonal predictions in ice- and snowmelt dominated catchments. The study area is the Inn catchment in Tyrol/Austria, including its headwaters in Switzerland. Results are evaluated for both anomalies of meteorological input data (temperature and precipitation), as well as balance components including snow water equivalent and runoff, both simulated with AWARE. Based on model skill evaluations derived from forecasts and observations, the model chain CFSv2 - AWARE proves helpful to analyse possible future hydrological system states of mountainous catchments with emphasis on spatio-temporal snow cover evolution.

  14. The CryoMET project - combining deterministic and probabilistic downscaling to model snow depth over a wide range of scales

    Science.gov (United States)

    Westermann, S.; Berntsen, T.; Etzelmüller, B.; Gisnås, K.; Hagen, J. O.; Kristjansson, J. E.; Schuler, T.; Stordal, F.

    2012-12-01

    Snow is a crucial factor in arctic and high-mountain ecosystems, e.g. for the thermal regime of permafrost and the mass balance on glaciers. However, the snow depth and properties can vary considerably on small scales due to wind redistribution, which for instance leads to distinctly different soil temperatures in permafrost areas on distances of tens of meters. The spatial resolution of standard atmospheric models is clearly insufficient to capture such small-scale variability. CryoMET is a new collaborative project between atmospheric modeling, glacier and permafrost research groups funded by the Norwegian Research Council. It seeks to bridge the scale gap between coarsely-resolved Earth System Models providing climate projections and the process and impact scales on the ground, on which permafrost temperatures and glacier mass balance are projected to change. CryoMET will explore a seamless downscaling procedure for the variables snow depth and snow water equivalent. In a first step, we use the state-of-the-art regional model PolarWRF to downscale atmospheric variables, including precipitation, air temperature and wind speed, to the so-called interface scale, where these variables are constant to a good approximation. In CryoMET, we aim for a spatial resolution of 1 to 3 km, which is determined by the topography of the project's target areas in Norway and Svalbard. In a second step, we will employ probabilistic downscaling of the average snow water equivalent at the interface scale (as delivered by PolarWRF) using snow redistribution models, which can resolve small-scale variations of snow depth due to wind drift down to the meter scale. With probability density functions of snow depth, we can infer the distribution of environmental parameters affected by snow within one grid cell at the interface scale, e.g. of permafrost temperatures. Thus, CryoMET ultimately aims for a scaling concept capable of bridging up to five orders of magnitude in space without

  15. A Modeling Study of the Effects of Anomalous Snow Cover over the Tibetan Plateau upon the South Asian Summer Monsoon

    Institute of Scientific and Technical Information of China (English)

    刘华强; 孙照渤; 王举; 闵锦忠

    2004-01-01

    The effect of anomalous snow cover over the Tibetan Plateau upon the South Asian summer monsoon is investigated by numerical simulations using the NCAR regional climate model (RegCM2) into which gravity wave drag has been introduced. The simulations adopt relatively realistic snow mass forcings based on Scanning Multi-channel Microwave Radiometer (SMMR) pentad snow depth data. The physical mechanism and spatial structure of the sensitivity of the South Asian early summer monsoon to snow cover anomaly over the Tibetan Plateau are revealed. The main results are summarized as follows. The heavier than normal snow cover over the Plateau can obviously reduce the shortwave radiation absorbed by surface through the albedo effect, which is compensated by weaker upward sensible heat flux associated with colder surface temperature, whereas the effects of snow melting and evaporation are relatively smaller.The anomalies of surface heat fluxes can last until June and become unobvions in July. The decrease of the Plateau surface temperature caused by heavier snow cover reaches its maximum value from late April to early May. The atmospheric cooling in the mid-upper troposphere over the Plateau and its surrounding areas is most obvious in May and can keep a fairly strong intensity in June. In contrast, there is warming to the south of the Plateau in the mid-lower troposphere from April to June with a maximum value in May.The heavier snow cover over the Plateau can reduce the intensity of the South Asian summer monsoon and rainfall to some extent, but this influence is only obvious in early summer and almost disappears in later stages.

  16. Drifting snow measurements on the Greenland Ice Sheet and their application for model evaluation

    NARCIS (Netherlands)

    Lenaerts, J.T.M.; Smeets, C.J.P.P.; Nishimura, K.; Eijkelboom, M.; Boot, W.; van den Broeke, M.R.; van de Berg, W.J.

    2014-01-01

    This paper presents autonomous drifting snow observations performed on the Greenland Ice Sheet in the fall of 2012. High-frequency Snow Particle Counter (SPC) observations at 1m above the surface provided drifting snow number fluxes and size 5 distributions; these were combined with meteorological o

  17. Drifting snow measurements on the Greenland Ice Sheet and their application for model evaluation

    NARCIS (Netherlands)

    Lenaerts, J.T.M.; Smeets, C.J.P.P.; Nishimura, K.; Eijkelboom, M.; Boot, W.; van den Broeke, M.R.; van de Berg, W.J.

    2014-01-01

    This paper presents autonomous drifting snow observations performed on the Greenland Ice Sheet in the fall of 2012. High-frequency Snow Particle Counter (SPC) observations at 1m above the surface provided drifting snow number fluxes and size 5 distributions; these were combined with meteorological o

  18. An accumulator model of semantic interference

    NARCIS (Netherlands)

    van Maanen, Leendert; van Rijn, Hedderik

    2007-01-01

    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

  19. Effects of modeling decisions on cold region hydrological model performance: snow, soil and streamflow

    Science.gov (United States)

    Musselman, Keith; Clark, Martyn; Endalamaw, Abraham; Bolton, W. Robert; Nijssen, Bart; Arnold, Jeffrey

    2017-04-01

    Cold regions are characterized by intense spatial gradients in climate, vegetation and soil properties that determine the complex spatiotemporal patterns of snowpack evolution, frozen soil dynamics, catchment connectivity, and streamflow. These spatial gradients pose unique challenges for hydrological models, including: 1) how the spatial variability of the physical processes are best represented across a hierarchy of scales, and 2) what algorithms and parameter sets best describe the biophysical and hydrological processes at the spatial scale of interest. To address these topics, we apply the Structure for Unifying Multiple Modeling Alternatives (SUMMA) to simulate hydrological processes at the Caribou - Poker Creeks Research Watershed in the Alaskan sub-arctic Boreal forest. The site is characterized by numerous gauged headwater catchments ranging in size from 5 sq. km to 106 sq. km with varying extents (3% to 53%) of discontinuous permafrost that permits a multi-scale paired watershed analysis of the hydrological impacts of frozen soils. We evaluate the effects of model decisions on the skill of SUMMA to simulate observed snow and soil dynamics, and the spatial integration of these processes as catchment streamflow. Decisions such as the number of soil layers, total soil column depth, and vertical soil discretization are shown to have profound impacts on the simulation of seasonal active layer dynamics. Decisions on the spatial organization (lateral connectivity, representation of riparian response units, and the spatial discretization of the hydrological landscape) are shown to be as important as accurate snowpack and soil process representation in the simulation of streamflow. The work serves to better inform hydrological model decisions for cold region hydrologic evaluation and to improve predictive capacity for water resource planning.

  20. A 3-D thermal regime model suitable for cold accumulation zones of polythermal mountain glaciers

    Science.gov (United States)

    Gilbert, A.; Gagliardini, O.; Vincent, C.; Wagnon, P.

    2014-09-01

    Analysis of the thermal and mechanical response of high altitude glaciers to climate change is crucial to assess future glacier hazards associated with thermal regime changes. This paper presents a new fully thermo-mechanically coupled transient thermal regime model including enthalpy transport, firn densification, full-Stokes porous flow, free surface evolution, strain heating, surface meltwater percolation, and refreezing. The model is forced by daily air temperature data and can therefore be used to perform prognostic simulations for different future climate scenarios. The set of equations is solved using the finite element ice sheet/ice flow model Elmer/Ice. This model is applied to the Col du Dôme glacier (Mont Blanc area, 4250 m a.s.l., France) where a comprehensive data set is available. The results show that the model is capable of reproducing observed density and velocity fields as well as borehole temperature evolution. The strong spatial variability of englacial temperature change observed at Col du Dôme is well reproduced. This spatial variability is mainly a result of the variability of the slope aspect of the glacier surface and snow accumulation. Results support the use of this model to study the influence of climate change on cold accumulation zones, in particular to estimate where and under what conditions glaciers will become temperate in the future.

  1. Snow Depth Retrieval with UAS Using Photogrammetric Techniques

    Directory of Open Access Journals (Sweden)

    Benjamin Vander Jagt

    2015-07-01

    Full Text Available Alpine areas pose challenges for many existing remote sensing methods for snow depth retrieval, thus leading to uncertainty in water forecasting and budgeting. Herein, we present the results of a field campaign conducted in Tasmania, Australia in 2013 from which estimates of snow depth were derived using a low-cost photogrammetric approach on-board a micro unmanned aircraft system (UAS. Using commercial off-the-shelf (COTS sensors mounted on a multi-rotor UAS and photogrammetric image processing techniques, the results demonstrate that snow depth can be accurately retrieved by differencing two surface models corresponding to the snow-free and snow-covered scenes, respectively. In addition to accurate snow depth retrieval, we show that high-resolution (50 cm spatially continuous snow depth maps can be created using this methodology. Two types of photogrammetric bundle adjustment (BA routines are implemented in this study to determine the optimal estimates of sensor position and orientation, in addition to 3D scene information; conventional BA (which relies on measured ground control points and direct BA (which does not require ground control points. Error sources that affect the accuracy of the BA and subsequent snow depth reconstruction are discussed. The results indicate the UAS is capable of providing high-resolution and high-accuracy (<10 cm estimates of snow depth over a small alpine area (~0.7 ha with significant snow accumulation (depths greater than one meter at a fraction of the cost of full-size aerial survey approaches. The RMSE of estimated snow depths using the conventional BA approach is 9.6 cm, whereas the direct BA is characterized by larger error, with an RMSE of 18.4 cm. If a simple affine transformation is applied to the point cloud derived from the direct BA, the overall RMSE is reduced to 8.8 cm RMSE.

  2. 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

  3. Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach

    Directory of Open Access Journals (Sweden)

    J. Veitinger

    2013-09-01

    Full Text Available In alpine terrain, the snow covered winter surface deviates from its underlying summer terrain due to the progressive smoothing caused by snow accumulation. Terrain smoothing is believed to be an important factor in avalanche formation, avalanche dynamics and affects surface heat transfer, energy balance as well as snow depth distribution. To characterize the effect of snow on terrain we use the concept of roughness. Roughness is calculated for several snow surfaces and its corresponding underlying terrain for three alpine basins in the Swiss Alps characterized by low medium and high terrain roughness. To this end, elevation models of winter and summer terrain are derived from high-resolution (1 m measurements performed by airborne and terrestrial LIDAR. We showed that on basin scale terrain smoothing not only depends on mean snow depth in the basin but also on its variability. Terrain smoothing can be modelled in function of mean snow depth and its standard deviation using a power law. However, a relationship between terrain smoothing and snow depth does not exist on a pixel scale. Further we demonstrated the high persistence of snow surface roughness even in between winter seasons. Those persistent patterns might be very useful to improve the representation of a winter terrain without modelling of the snow cover distribution. This can potentially improve avalanche release area definition and in the long term natural hazard management strategies.

  4. Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

    Science.gov (United States)

    Berezowski, T.; Nossent, J.; Chormański, J.; Batelaan, O.

    2015-04-01

    As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction - SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a distributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter representing a 4 × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data.

  5. Modelling wet snow avalanche runout to assess road safety at a high-altitude mine in the central Andes

    Science.gov (United States)

    Valero, Cesar Vera; Wever, Nander; Bühler, Yves; Stoffel, Lukas; Margreth, Stefan; Bartelt, Perry

    2016-11-01

    Mining activities in cold regions are vulnerable to snow avalanches. Unlike operational facilities, which can be constructed in secure locations outside the reach of avalanches, access roads are often susceptible to being cut, leading to mine closures and significant financial losses. In this paper we discuss the application of avalanche runout modelling to predict the operational risk to mining roads, a long-standing problem for mines in high-altitude, snowy regions. We study the 35 km long road located in the "Cajón del rio Blanco" valley in the central Andes, which is operated by the Codelco Andina copper mine. In winter and early spring, this road is threatened by over 100 avalanche paths. If the release and snow cover conditions can be accurately specified, we find that avalanche dynamics modelling is able to represent runout, and safe traffic zones can be identified. We apply a detailed, physics-based snow cover model to calculate snow temperature, density and moisture content in three-dimensional terrain. This information is used to determine the initial and boundary conditions of the avalanche dynamics model. Of particular importance is the assessment of the current snow conditions along the avalanche tracks, which define the mass and thermal energy entrainment rates and therefore the possibility of avalanche growth and long runout distances.

  6. Modeling chemistry in and above snow at Summit, Greenland - Part 2: Impact of snowpack chemistry on the oxidation capacity of the boundary layer

    OpenAIRE

    Thomas, J. L.; J. E. Dibb; Huey, L. G.; J. Liao; D. Tanner; B. Lefer; Glasow, R.; J. Stutz

    2012-01-01

    The chemical composition of the boundary layer in snow covered regions is impacted by chemistry in the snowpack via uptake, processing, and emission of atmospheric trace gases. We use the coupled one-dimensional (1-D) snow chemistry and atmospheric boundary layer model MISTRA-SNOW to study the impact of snowpack chemistry on the oxidation capacity of the boundary layer. The model includes gas phase photochemistry and chemical reactions both in the interstitial air and the at...

  7. Modelling the system behaviour of wet snow avalanches using an expert system approach for risk management on high alpine traffic roads

    OpenAIRE

    Zischg, A.; Fuchs, S; M. Keiler; Meißl, G.

    2005-01-01

    International audience; The presented approach describes a model for a rule-based expert system calculating the temporal variability of the release of wet snow avalanches, using the assumption of avalanche triggering without the loading of new snow. The knowledge base of the model is created by using investigations on the system behaviour of wet snow avalanches in the Italian Ortles Alps, and is represented by a fuzzy logic rule-base. Input parameters of the expert system are numerical and li...

  8. 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

  9. Spatial sensitivity analysis of snow cover data in a distributed rainfall–runoff model

    Directory of Open Access Journals (Sweden)

    T. Berezowski

    2014-10-01

    Full Text Available As the availability of spatially distributed data sets for distributed rainfall–runoff modelling is strongly growing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis (SA method for snow cover fraction input data (SCF for a distributed rainfall–runoff model to investigate if the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focused on the relation between the SCF sensitivity and the physical, spatial parameters and processes of a distributed rainfall–runoff model. The methodology is tested for the Biebrza River catchment, Poland for which a distributed WetSpa model is setup to simulate two years of daily runoff. The SA uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT algorithm, which uses different response functions for each 4 km × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as: geomorphology, soil texture, land-use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for the spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model.

  10. Functioning of the avalanche starting zones which undergo snow-transport by wind: Field observations and computer modeling

    Science.gov (United States)

    Sivardière, F.; Castelle, T.; Guyomarc'h, G.; Mérindol, L.; Buisson, L.

    1995-11-01

    For two years, three French and Swiss laboratories have been making field observations and measurements on two high altitude slopes in a Northern French Alps site. The aim of this work is to study the functioning of the avalanche sites which, in their starting zones, undergo snow-transport by wind. The experimental site is located in the French Alps, at 2,800 m, above Grenoble. It is an open area, equipped with an automatic meteorological station and an altitude laboratory. The two slopes that are studied face East. One of them is artificially released but the other has a natural avalanche activity. The investigations concern: -snow deposition in avalanche starting zones; -temporal evolution of the snowpack characteristics; -avalanche release. For the field observations and measurements, continuous recording of the meteorological conditions on the site, photogrammetrical techniques and two snow depth profiles, as well as stratigraphical snow profiles and video are used. The computer modeling is based on existing computer models developed by the CEMAGREF-Nivologie (ELSA) and the CEN/Météo-France (SAFRAN-CROCUS-MEPRA), which analyse the snowpack and its stability. The field observations and measurements aim at improving snow-transport by wind modeling modules, in order to improve their whole analysis.

  11. 21st Century changes in snow climate in Northern Europe: a high-resolution view from ENSEMBLES regional climate models

    Energy Technology Data Exchange (ETDEWEB)

    Raeisaenen, Jouni [Department of Physics, P.O. Box 48 (Erik Palmenin aukio 1), University of Helsinki (Finland); Eklund, Joonas [Department of Physics, P.O. Box 48 (Erik Palmenin aukio 1), University of Helsinki (Finland); Finnish Meteorological Institute, P.O. Box 503 (Erik Palmenin aukio 1), Helsinki (Finland)

    2012-06-15

    Changes in snow amount in northern Europe are analysed from 11 regional model simulations of 21st century climate under the Special Report on Emissions Scenarios A1B scenario. These high-resolution models collectively indicate a future decrease in the water equivalent of the snow pack (SWE). Although winter precipitation increases, this is insufficient to compensate for the increased fraction of liquid precipitation and increased snowmelt caused by higher temperatures. The multi-model mean results suggest a slight increase in March mean SWE only locally in mountains of northern Sweden, and even there, snow is reduced earlier in winter and later in spring. The nature of the changes remains the same throughout the 21st century, but their magnitude increases with time as the greenhouse gas forcing grows larger. The geographical patterns of the change support the physically intuitive view that snow is most vulnerable to warming in areas with relatively mild winter climate. A similar relationship emerges when comparing the 11 simulations with each other: the ratio between the relative SWE decrease and winter mean temperature change is larger (smaller) for simulations with higher (lower) late 20th century winter temperatures. Despite the decrease in long-term mean SWE, individual snow-rich winters do occur in the simulations, but they become increasingly uncommon towards the end of the 21st century. (orig.)

  12. Downscaling of general circulation model outputs: simulation of the snow climatology of the French Alps and sensitivity to climate change

    Energy Technology Data Exchange (ETDEWEB)

    Martin, E. [Centre Nat. de Recherches Meteorologiques Centre d`Etudes de la Neige, St. Martin d`Heres (France). Meteo-France; Timbal, B. [Groupe de Meteorologie a Grande Echelle et Climat, Toulouse (France); Brun, E. [Centre Nat. de Recherches Meteorologiques Centre d`Etudes de la Neige, St. Martin d`Heres (France). Meteo-France

    1996-12-01

    A downscaling method was developed to simulate the seasonal snow cover of the French Alps from general circulation model outputs under various scenarios. It consists of an analogue procedure, which associates a real meteorological situation to a model output. It is based on the comparison between simulated upper air fields and meteorological analyses from the European Centre for medium-range weather forecasts. The selection uses a nearest neighbour method at a daily time-step. In a second phase, the snow cover is simulated by the snow model CROCUS at several elevations and in the different regions of the French Alps by using data from the real meteorological situations. The method is tested with real data and applied to various ARPEGE/climate simulations: the present climate and two climate change scenarios. (orig.). With 10 figs., 4 tabs.

  13. Downscaling of general circulation model outputs: simulation of the snow climatology of the French Alps and sensitivity to climate change

    Science.gov (United States)

    Martin, E.; Timbal, B.; Brun, E.

    1996-12-01

    A downscaling method was developed to simulate the seasonal snow cover of the French Alps from general circulation model outputs under various scenarios. It consists of an analogue procedure, which associates a real meteorological situation to a model output. It is based on the comparison between simulated upper air fields and meteorological analyses from the European Centre for Medium-Range Weather Forecasts. The selection uses a nearest neighbour method at a daily time-step. In a second phase, the snow cover is simulated by the snow model CROCUS at several elevations and in the different regions of the French Alps by using data from the real meteorological situations. The method is tested with real data and applied to various ARPEGE/Climat simulations: the present climate and two climate change scenarios.

  14. Probabilistic Modeling of Fatigue Damage Accumulation for Reliability Prediction

    Directory of Open Access Journals (Sweden)

    Vijay Rathod

    2011-01-01

    Full Text Available A methodology for probabilistic modeling of fatigue damage accumulation for single stress level and multistress level loading is proposed in this paper. The methodology uses linear damage accumulation model of Palmgren-Miner, a probabilistic S-N curve, and an approach for a one-to-one transformation of probability density functions to achieve the objective. The damage accumulation is modeled as a nonstationary process as both the expected damage accumulation and its variability change with time. The proposed methodology is then used for reliability prediction under single stress level and multistress level loading, utilizing dynamic statistical model of cumulative fatigue damage. The reliability prediction under both types of loading is demonstrated with examples.

  15. 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

  16. Influence of Air Temperature Difference on the Snow Melting Simulation of SWAT Model

    Science.gov (United States)

    YAN, Y.; Onishi, T.

    2013-12-01

    The temperature-index models are commonly used to simulate the snowmelt process in mountain areas because of its good performance, low data requirements, and computational simplicity. Widely used distributed hydrological model: Soil and Water Assessment Tool (SWAT) model is also using a temperature-index module. However, the lack of monitoring air temperature data still involves uncertainties and errors in its simulation performance especially in data sparse area. Thus, to evaluate the different air temperature data influence on the snow melt of the SWAT model, five different air temperature data are applied in two different Russia basins (Birobidjan basin and Malinovka basin). The data include the monitoring air temperature data (TM), NCEP reanalysis data (TNCEP), the dataset created by inverse distance weighted interpolation (IDW) method (TIDW), the dataset created by improved IDW method considering the elevation influence (TIDWEle), and the dataset created by using linear regression and MODIS Land Surface Temperature (LST) data (TLST). Among these data, the TLST , the TIDW and TIDWEle data have the higher spatial density, while the TNCEP and TM DATA have the most valid monitoring value for daily scale. The daily simulation results during the snow melting seasons (March, April and May) showed reasonable results in both test basins for all air temperature data. While R2 and NSE in Birobidjan basin are around 0.6, these values in Malinovka basin are over 0.75. Two methods: Generalized Likelihood Uncertainty Estimation (GLUE) and Sequential Uncertainty Fitting, version. 2 (SUFI-2) were used for model calibration and uncertainty analysis. The evolution index is p-factor which means the percentage of measured data bracketed by the 95% Prediction Uncertainty (95PPU). The TLST dataset always obtained the best results in both basins compared with other datasets. On the other hand, the two IDW based method get the worst results among all the scenarios. Totally, the

  17. Design of an optimal snow observation network to estimate snowpack

    Science.gov (United States)

    Juan Collados Lara, Antonio; Pardo-Iguzquiza, Eulogio; Pulido-Velazquez, David

    2016-04-01

    Snow is an important water resource in many river basins that must be taken into account in hydrological modeling. Although the snow cover area may be nowadays estimated from satellite data, the snow pack thickness must be estimated from experimental data by using some interpolation procedure or hydrological models that approximates snow accumulation and fusion processes. The experimental data consist of hand probes and snow samples collected in a given number of locations that constitute the monitoring network. Assuming that there is an existing monitoring network, its optimization may imply the selection of an optimal network as a subset of the existing network (decrease of the existing network in the case that there are no funds for maintaining the full existing network) or to increase the existing network by one or more stations (optimal augmentation problem). In this work we propose a multicriterion approach for the optimal design of a snow network. These criteria include the estimation variance from a regression kriging approach for estimating thickness of the snowpack (using ground and satellite data), to minimize the total snow volume and accessibility criteria. We have also proposed a procedure to analyze the sensitivity of the results to the non-snow data deduced from the satellite information. We intent to minimize the uncertities in snowpack estimation. The methodology has been applied to estimation of the snow cover area and the design of the optimal snow observation network in Sierra Nevada mountain range in the Southern of Spain. Acknowledgments: This research has been partially supported by the GESINHIMPADAPT project (CGL2013-48424-C2-2-R) with Spanish MINECO funds. We would also like to thank ERHIN program and NASA DAAC for the data provided for this study.

  18. Integrated network modelling for identifying microbial mechanisms of particulate organic carbon accumulation in coastal marine systems

    Science.gov (United States)

    McDonald, Karlie; Turk, Valentina; Mozetič, Patricija; Tinta, Tinkara; Malfatti, Francesca; Hannah, David; Krause, Stefan

    2016-04-01

    Accumulation of particulate organic carbon (POC) has the potential to change the structure and function of marine ecosystems. High abidance of POC can develop into aggregates, known as marine snow or mucus aggregates that can impair essential marine ecosystem functioning and services. Currently marine POC formation, accumulation and sedimentation processes are being explored as potential pathways to remove CO2 from the atmosphere by CO2 sequestration via fixation into biomass by phytoplankton. However, the current ability of scientists, environmental managers and regulators to analyse and predict high POC concentrations is restricted by the limited understanding of the dynamic nature of the microbial mechanisms regulating POC accumulation events in marine environments. We present a proof of concept study that applies a novel Bayesian Networks (BN) approach to integrate relevant biological and physical-chemical variables across spatial and temporal scales in order to identify the interactions of the main contributing microbial mechanisms regulating POC accumulation in the northern Adriatic Sea. Where previous models have characterised only the POC formed, the BN approach provides a probabilistic framework for predicting the occurrence of POC accumulation by linking biotic factors with prevailing environmental conditions. In this paper the BN was used to test three scenarios (diatom, nanoflagellate, and dinoflagellate blooms). The scenarios predicted diatom blooms to produce high chlorophyll a at the water surface while nanoflagellate blooms were predicted to occur at lower depths (> 6m) in the water column and produce lower chlorophyll a concentrations. A sensitivity analysis identified the variables with the greatest influence on POC accumulation being the enzymes protease and alkaline phosphatase, which highlights the importance of microbial community interactions. The developed proof of concept BN model allows for the first time to quantify the impacts of

  19. Assimilation of MODIS Snow Cover Through the Data Assimilation Research Testbed and the Community Land Model Version 4

    Science.gov (United States)

    Zhang, Yong-Fei; Hoar, Tim J.; Yang, Zong-Liang; Anderson, Jeffrey L.; Toure, Ally M.; Rodell, Matthew

    2014-01-01

    To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (2345N). Only minimal modifications are made in the higher-middle (4566N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snowmove poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological

  20. Fractional snow cover mapping from MODIS data using wavelet-artificial intelligence hybrid models

    Science.gov (United States)

    Moosavi, Vahid; Malekinezhad, Hossein; Shirmohammadi, Bagher

    2014-04-01

    This study was carried out to evaluate the wavelet-artificial intelligence hybrid models to produce fractional snow cover maps. At first, cloud cover was removed from MODIS data and cloud free images were produced. SVM-based binary classified ETM+ imagery were then used as reference maps in order to obtain train and test data for sub-pixel classification models. ANN and ANFIS-based modeling were performed using raw data (without wavelet-based preprocessing). In the next step, several mother wavelets and levels were used in order to decompose the original data to obtain wavelet coefficients. Then, the decomposed data were used for further modeling processes. ANN, ANFIS, wavelet-ANN and wavelet-ANFIS models were compared to evaluate the effect of wavelet transformation on the ability of artificial intelligence models. It was demonstrated that wavelet transformation as a preprocessing approach can significantly enhance the performance of ANN and ANFIS models. This study indicated an overall accuracy of 92.45% for wavelet-ANFIS model, 86.13% for wavelet-ANN, 72.23% for ANFIS model and 66.78% for ANN model. In fact, hybrid wavelet-artificial intelligence models can extract the characteristics of the original signals (i.e. model inputs) accurately through decomposing the non-stationary and complex signals into several stationary and simpler signals. The positive effect of fuzzification as well as wavelet transformation in the wavelet-ANFIS model was also confirmed.

  1. A spatiotemporal model for snow crab (Chionoecetes opilio) stock size in the southern Gulf of St. Lawrence

    DEFF Research Database (Denmark)

    Cadigan, Noel G.; Wade, Elmer; Nielsen, Anders

    2017-01-01

    We develop a high-resolution spatiotemporal model of stock size and harvest rates for snow crab (Chionoecetes opilio) in the southern Gulf of St. Lawrence, which supports an economically important fishery off the east coast of Canada. It is a spatial and weekly model during 1997–2014 that utilize...

  2. One decade of scientific studies of snow management on Austria's glacier ski resorts

    Science.gov (United States)

    Fischer, Andrea; Helfricht, Kay

    2016-04-01

    After the extremely warm summer of 2003, when melt affected Austria's glaciers up to the highest elevations, a scientific study on artificial modification of mass balance was initiated. It examined the effects of glacier covers and water injection, but also various grooming methods and snow accumulations based on monitoring and modelling of snow and energy balance. The results showed that covering the glacier was the most effective and cheapest method, saving about 70% of glacier melt in places. But covers are restricted to a small portion of the area, as they require high maintenance. In recent years, snow production and snow accumulation by wind drift have gained more and more importance, not only modifying glacier mass balance, but also guaranteeing an early season start. Initially about 35 ha of the glacier area (ski resort area and less than one per mille of the total glacier area in Austria) were covered and later the area was reduced as snow production possibilities increased. Snow depots are often used as fun parks for snow boarders. Glacier covers are not primarily used for keeping snow for early season start on ski tracks, but to maintain the surface, especially close to cable car infrastructure, at a constant elevation and slope. Despite glacier dynamics, glacier surfaces with snow management show reduced decrease of surface elevation , both on piste and along lift tracks.

  3. 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.

  4. Blowing snow at D17, Adélie Land, Antarctica: atmospheric moisture issues

    Directory of Open Access Journals (Sweden)

    H. Barral

    2014-06-01

    Full Text Available Three years of blowing snow and meteorological observations have been collected along a 7 m mast at site D17 in coastal Adélie Land, Antarctica. This is a region particularly exposed to katabatic winds. The atmospheric surface layer is often close to saturation because of the sublimation of the airborne snow particles. A systematic dry bias results in atmospheric models that ignore blowing snow and its moistening effects, and in meteorological analyses that use such model. The Crocus snow-pack model, including a parameterization for the erosion of surface snow by wind, reproduces the observed march of snow accumulation and ablation if the observed meteorology is used as input. Because of subsaturation, a 2.5 fold increase in surface sublimation is obtained if analyzed surface air meteorology is used. The sublimation obtained in the Crocus model poorly agrees with the moisture fluxes evaluated using the profile method along the mast. Moisture gradients are very weak, particularly when blowing snow saturates the air, to a point where measurement accuracy is an issue. Using the profile method, the measurement uncertainties are strongly amplified in case of strong wind. In such conditions, a single level bulk parameterization with surface energy balance closure as in the Crocus model is preferred. At D17, more than half of the total snow fall is removed by erosion and sublimation, both at the surface and, mainly, of airborne snow particles.

  5. Climate change impact assessment on mountain snow hydrology by water and energy budget-based distributed hydrological model

    Science.gov (United States)

    Bhatti, Asif M.; Koike, Toshio; Shrestha, Maheswor

    2016-12-01

    A water and energy budget-based distributed hydrological model with improved snow physics (WEB-DHM-S) was applied to elucidate the impact of climate change on mountain snow hydrology in the Shubuto River basin, Hokkaido, Japan. The simulated spatial distribution of snow cover was evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2) product, which revealed the model's capability for capturing the spatiotemporal variations in snow cover within the study area. Four Atmosphere Ocean General Circulation Models (AOGCMs) were selected and the SRESA1B emission scenario of the Intergovernmental Panel on Climate Change was used to describe climate predictions in the basin. All AOGCMs predict a future decrease in snowmelt contribution to total discharge 11-22% and an average decrease in SWE of 36%, with a shift in peak SWE by 4-14 days. The shift in runoff regime is broadly consistent between the AOGCMs with snowmelt-induced peak discharge expected to occur on average about two weeks earlier in the future hydrological year. The warming climate will drive a shift in runoff regime from a combined rainfall- and snowmelt-driven regime to one with a reduced contribution from snowmelt. The results of the study revealed that the model could be successfully applicable on the basin scale to simulate river discharge and snow processes and to investigate the effect of climate change on hydrological processes. This research contributes to improve the understanding of basin hydrological responses and the pace of change associated with climate variability.

  6. Is snow sublimation important in the alpine water balance?

    Directory of Open Access Journals (Sweden)

    U. Strasser

    2007-09-01

    Full Text Available In alpine terrain, snow sublimation as a component of the winter moisture budget represents a proportion of precipitation which does not contribute to melt. To quantify its amount we analyze the spatial pattern of snow sublimation at the ground, from a canopy and from turbulent suspension during wind-induced snow transport for a high alpine area in the Berchtesgaden National Park (Germany, and we discuss the efficiency of these processes with respect to seasonal snowfall. Therefore, we utilized hourly meteorological recordings from a network of automatic stations, and a distributed simulation framework comprising validated, physically based models. Meteorological data records were spatially distributed over the simulation domain by means of a quasi-physically based interpolation scheme that accounts for topographic influences on the distributed fields. The applied simulation tools were: a detailed model for shortwave and longwave radiative fluxes, a mass and energy balance model for the ground snow cover, a model for the microclimatic conditions within a forest canopy and related snow-vegetation interactions including snow sublimation from the surface of the trees, and a model for the simulation of wind-induced snow transport and related sublimation from suspended snow particles. For each of the sublimation processes, mass rates were quantified and aggregated over an entire winter season. Sublimation from the ground and from most canopy types are spatially relatively homogeneous and sum up to about 100 mm of snow water equivalent (SWE over the winter period. Accumulated seasonal sublimation due to turbulent suspension is small in the valley areas, but can locally, at very wind-exposed mountain ridges, add up to more than 1000 mm of SWE. The fraction of these sublimation losses of winter snowfall is between 10 and 90%.

  7. Mapping snow avalanche risk using GIS technique and 3D modeling in Ceahlau Mountain

    Science.gov (United States)

    Covasnianu, A.; Grigoras, I. R.; State, L. E.; Balin, D.; Hogas, S.; Balin, I.

    2009-04-01

    This study consisted in a precise mapping project (GPS field campaign and on-screen digitization of the topographic maps at 1:5.000 scale) of the Ceahlau mountain area in Romanian Carpathians in order to address the snow avalanche risk management, surveying and monitoring. Thus we considered the slope, aspect, altitude, landforms and roughness derived from a high resolute numerical terrain model (31 km2 at 1: 5.000 scale resulted in a spatial resolution of 3 m by the help of Topo to Raster tool). These parameters were classified according to a model applied into Tatra Mountains and used over Ceahlau Massive. The results were adapted and interpreted considering to the European Avalanche Hazard Scale. This work was made in the context of the elaboration of Risk Map and is directly concerning both the security of tourism activities but also the management of the Natural Park Ceahlau. The extension of this method to similar mountain areas is ongoing.

  8. Modelling of the densification of polar firn: characterization of the snow-firn transition

    OpenAIRE

    1997-01-01

    International audience; The transformation of dry snow to firn is described by the transition between densification by deformationless restacking and densification by power-law creep.The observed decrease with temperature of the density at the snow- firn transition seems to result from the competition between grain-boundary sliding and power-law creep. These two densification processess occur concurrenLly in snow, although there are probably micro-regions in which sliding alone occurs. Valida...

  9. 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.

  10. 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.

  11. Validation of the Antarctic Snow Accumulation and Ice Discharge Basal Stress Boundary in the South Eastern Region of the Ross Ice Shelf, Antarctica

    Science.gov (United States)

    Nelson, C. B.; King, K.

    2015-12-01

    The largest ice shelf in Antarctic, Ross Ice Shelf, was investigated over the years of (1970-2015). Near the basal stress boundary between the ice shelf and the West Antarctic ice sheet, ice velocity ranges from a few meters per year to several hundred meters per year in ice streams. Most of the drainage from West Antarctica into the Ross Ice Shelf flows down two major ice streams, each of which discharges more than 20 km3 of ice each year. Along with velocity changes, the warmest water below parts of the Ross Ice Shelf resides in the lowest portion of the water column because of its high salinity. Vertical mixing caused by tidal stirring can thus induce ablation by lifting the warm water into contact with the ice shelf. This process can cause melting over a period of time and eventually cause breakup of ice shelf. With changes occurring over many years a validation is needed for the Antarctic Snow Accumulation and Ice Discharge (ASAID) basal stress boundary created in 2003. After the 2002 Larsen B Ice Shelf disintegration, nearby glaciers in the Antarctic Peninsula accelerated up to eight times their original speed over the next 18 months. Similar losses of ice tongues in Greenland have caused speed-ups of two to three times the flow rates in just one year. Rapid changes occurring in regions surrounding Antarctica are causing concern in the polar science community to research changes occurring in coastal zones over time. During the research, the team completed study on the Ross Ice Shelf located on the south western coast of the Antarctic. The study included a validation of the ABSB vs. the natural basal stress boundary (NBSB) along the Ross Ice Shelf. The ASAID BSB was created in 2003 by a team of researchers headed by National Aeronautics and Space Administration Goddard Space Flight Center (NASA GSFC), with an aim of studying coastal deviations as it pertains to the mass balance of the entire continent. The point data file was aimed at creating a replica of the

  12. 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.

  13. 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.

  14. Improving snow process modeling with satellite-based estimation of near-surface-air-temperature lapse rate

    Science.gov (United States)

    Wang, Lei; Sun, Litao; Shrestha, Maheswor; Li, Xiuping; Liu, Wenbin; Zhou, Jing; Yang, Kun; Lu, Hui; Chen, Deliang

    2016-10-01

    In distributed hydrological modeling, surface air temperature (Tair) is of great importance in simulating cold region processes, while the near-surface-air-temperature lapse rate (NLR) is crucial to prepare Tair (when interpolating Tair from site observations to model grids). In this study, a distributed biosphere hydrological model with improved snow physics (WEB-DHM-S) was rigorously evaluated in a typical cold, large river basin (e.g., the upper Yellow River basin), given a mean monthly NLRs. Based on the validated model, we have examined the influence of the NLR on the simulated snow processes and streamflows. We found that the NLR has a large effect on the simulated streamflows, with a maximum difference of greater than 24% among the various scenarios for NLRs considered. To supplement the insufficient number of monitoring sites for near-surface-air-temperature at developing/undeveloped mountain regions, the nighttime Moderate Resolution Imaging Spectroradiometer land surface temperature is used as an alternative to derive the approximate NLR at a finer spatial scale (e.g., at different elevation bands, different land covers, different aspects, and different snow conditions). Using satellite-based estimation of NLR, the modeling of snow processes has been greatly refined. Results show that both the determination of rainfall/snowfall and the snowpack process were significantly improved, contributing to a reduced summer evapotranspiration and thus an improved streamflow simulation.

  15. Kinetic model of sucrose accumulation in maturing sugarcane culm tissue.

    Science.gov (United States)

    Uys, Lafras; Botha, Frederik C; Hofmeyr, Jan-Hendrik S; Rohwer, Johann M

    2007-01-01

    Biochemically, it is not completely understood why or how commercial varieties of sugarcane (Saccharum officinarum) are able to accumulate sucrose in high concentrations. Such concentrations are obtained despite the presence of sucrose synthesis/breakdown cycles (futile cycling) in the culm of the storage parenchyma. Given the complexity of the process, kinetic modelling may help to elucidate the factors governing sucrose accumulation or direct the design of experimental optimisation strategies. This paper describes the extension of an existing model of sucrose accumulation (Rohwer, J.M., Botha, F.C., 2001. Analysis of sucrose accumulation in the sugar cane culm on the basis of in vitro kinetic data. Biochem. J. 358, 437-445) to account for isoforms of sucrose synthase and fructokinase, carbon partitioning towards fibre formation, and the glycolytic enzymes phosphofructokinase (PFK), pyrophosphate-dependent PFK and aldolase. Moreover, by including data on the maximal activity of the enzymes as measured in different internodes, a growth model was constructed that describes the metabolic behaviour as sugarcane parenchymal tissue matures from internodes 3-10. While there was some discrepancy between modelled and experimentally determined steady-state sucrose concentrations in the cytoplasm, steady-state fluxes showed a better fit. The model supports a hypothesis of vacuolar sucrose accumulation against a concentration gradient. A detailed metabolic control analysis of sucrose synthase showed that each isoform has a unique control profile. Fructose uptake by the cell and sucrose uptake by the vacuole had a negative control on the futile cycling of sucrose and a positive control on sucrose accumulation, while the control profile for neutral invertase was reversed. When the activities of these three enzymes were changed from their reference values, the effects on futile cycling and sucrose accumulation were amplified. The model can be run online at the JWS Online

  16. The GOddard SnoW Impurity Module (GOSWIM) for the NASA GEOS-5 Earth System Model: Preliminary Comparisons with Observations in Sapporo, Japan

    Science.gov (United States)

    Yasunari, Teppei J.; Lau, K.-M.; Mahanama, Sarith P. P.; Colarco, Peter R.; daSilva, Arlindo M.; Aoki, Teruo; Aoki, Kazuma; Murao, Naoto; Yamagata, Sadamu; Kodama, Yuji

    2014-01-01

    The snow darkening module evaluating dust, black carbon, and organic carbon depositions on mass and albedo has been developed for the NASA Goddard Earth Observing System, Version 5 (GEOS-5) Earth System Model, as the GOddard SnoW Impurity Module (GOSWIM). GOSWIM consists of the snow albedo scheme from a previous study (Yasunari et al. 2011) with updates and a newly developed mass concentration scheme, using aerosol depositions from the chemical transport model (GOCART) in GEOS-5. Compared to observations at Sapporo, the numerical experiments, forced by observation-based meteorology and aerosol depositions from GOES-5, better simulated the seasonal migration of snow depth, albedos, and impurities of dust, BC, and OC in the snow surface. However, the magnitude of the impurities is underestimated, compared to the sporadic snow impurity measurements. Increasing the deposition rates of dust and BC could explain the differences on the snow darkening effect between observation and simulation. Ignoring BC deposition can possibly lead to an extension of snow cover duration in Sapporo for four days. Comparing the off-line GOSWIM and the GEOS-5 global simulations, we found that determining better local precipitation and deposition rates of the aerosols are key factors in generating better GOSWIM snow darkening simulation in NASA GEOS-5.

  17. Meltwater percolation and refreezing in compacting snow

    Science.gov (United States)

    Meyer, Colin; Hewitt, Ian

    2016-11-01

    Meltwater is produced on the surface of glaciers and ice sheets when the seasonal surface energy forcing warms the ice above its melting temperature. This meltwater percolates through the porous snow matrix and potentially refreezes, thereby warming the surrounding ice by the release of latent heat. Here we model this process from first principles using a continuum model. We determine the internal ice temperature and glacier surface height based on the surface forcing and the accumulation of snow. When the surface temperature exceeds the melting temperature, we compute the amount of meltwater produced and lower the glacier surface accordingly. As the meltwater is produced, we solve for its percolation through the snow. Our model results in traveling regions of meltwater with sharp fronts where refreezing occurs. We also allow the snow to compact mechanically and we analyze the interplay of compaction with meltwater percolation. We compare these models to observations of the temperature and porosity structure of the surface of glaciers and ice sheets and find excellent agreement. Our models help constrain the role that meltwater percolation and refreezing will have on ice-sheet mass balance and hence sea level. Thanks to the 2016 WHOI GFD Program, which is supported by the National Science Foundation and the Office of Naval Research.

  18. Snow/ice melt precipitation runoff modelling of glaciers in the Bhutan himalaya

    Science.gov (United States)

    Payer, T.; Leber, D.; Haeusler, H.; Brauner, M.; Wangda, D.

    2003-04-01

    After the 1994 outburst of the Luggye glacier lake in Northern Bhutan, the Royal Bhutanese Government initiated a project on GLOF risk reduction and mitigation measures in the Pho River headwaters. The approach to model snow/ice melt and precipitation of this high mountain system with few measured data only was a request of this integrated geoscientific project. The size of the test area for runoff modelling is about 50 km2 and comprises an interlinked glacier system of 3 glaciers and moraine dammed glacier lakes. The model proposed for the Luggye catchment area is based on records from a nearby weather station at Thanza (4150 m altitude) available for the years 2000-2001, a high resolution digital elevation model and the total surface discharge of the Luggye test area, calculated from the Luggye outlet hydrograph. Using the digital elevation model, the catchment area and the surface of the glaciers, up to 7000 m altitude, was divided into 100-meter elevation layers. Precipitation and mean air temperature from Thanza were extrapolated according to the best fitting results. Prominent single temperature and precipitation “events” recorded at Thanza can be related to discharge “events” in the Luggye hydrograph and allow for calculation of time lag and lapse rates. The modelled discharge is calculated as the total of the three simulated components, namely base flow, melt water from snow and ice, and precipitation. The model proposed can be interactively evaluated by best fitting of the model hydrograph to the measured hydrograph. This simulation was achieved using temperature lapse rates of -0.4 to -0.45 °C/100 m during the summer monsoon period, a degree/day factor of 6 to 8 l/(m2*day*°C), and the decrease of precipitation with approximately 7% per 100-meter altitude. Although many assumptions have to be taken for non-extreme weather conditions, the very good correlation of 0.97 between runoff modelling and the real discharge hydrograph highlights the

  19. Snow on Antarctic sea ice

    Science.gov (United States)

    Massom, Robert A.; Eicken, Hajo; Hass, Christian; Jeffries, Martin O.; Drinkwater, Mark R.; Sturm, Matthew; Worby, Anthony P.; Wu, Xingren; Lytle, Victoria I.; Ushio, Shuki; Morris, Kim; Reid, Phillip A.; Warren, Stephen G.; Allison, Ian

    2001-08-01

    Snow on Antarctic sea ice plays a complex and highly variable role in air-sea-ice interaction processes and the Earth's climate system. Using data collected mostly during the past 10 years, this paper reviews the following topics: snow thickness and snow type and their geographical and seasonal variations; snow grain size, density, and salinity; frequency of occurrence of slush; thermal conductivity, snow surface temperature, and temperature gradients within snow; and the effect of snow thickness on albedo. Major findings include large regional and seasonal differences in snow properties and thicknesses; the consequences of thicker snow and thinner ice in the Antarctic relative to the Arctic (e.g., the importance of flooding and snow-ice formation); the potential impact of increasing snowfall resulting from global climate change; lower observed values of snow thermal conductivity than those typically used in models; periodic large-scale melt in winter; and the contrast in summer melt processes between the Arctic and the Antarctic. Both climate modeling and remote sensing would benefit by taking account of the differences between the two polar regions.

  20. Incorporation of the Mass Concentration and the New Snow Albedo Schemes into the Global Forecasting Model, GEOS-5 and the Impact of the New Schemes over Himalayan Glaciers

    Science.gov (United States)

    Yasunari, Teppei

    2012-01-01

    Recently the issue on glacier retreats comes up and many factors should be relevant to the issue. The absorbing aerosols such as dust and black carbon (BC) are considered to be one of the factors. After they deposited onto the snow surface, it will reduce snow albedo (called snow darkening effect) and probably contribute to further melting of glacier. The Goddard Earth Observing System version 5 (GEOS-5) has developed at NASA/GSFC. However, the original snowpack model used in the land surface model in the GEOS-5 did not consider the snow darkening effect. Here we developed the new snow albedo scheme which can consider the snow darkening effect. In addition, another scheme on calculating mass concentrations on the absorbing aerosols in snowpack was also developed, in which the direct aerosol depositions from the chemical transport model in the GEOS-5 were used. The scheme has been validated with the observed data obtained at backyard of the Institute of Low Temperature Science, Hokkaido University, by Dr. Teruo Aoki (Meteorological Research Institute) et aL including me. The observed data was obtained when I was Ph.D. candidate. The original GEOS-5during 2007-2009 over the Himalayas and Tibetan Plateau region showed more reductions of snow than that of the new GEOS-5 because the original one used lower albedo settings. On snow cover fraction, the new GEOS-5 simulated more realistic snow-covered area comparing to the MODIS snow cover fraction. The reductions on snow albedo, snow cover fraction, and snow water equivalent were seen with statistically significance if we consider the snow darkening effect comparing to the results without the snow darkening effect. In the real world, debris cover, inside refreezing process, surface flow of glacier, etc. affect glacier mass balance and the simulated results immediately do not affect whole glacier retreating. However, our results indicate that some surface melting over non debris covered parts of the glacier would be

  1. Cartographic modeling of snow avalanche path location within Glacier National Park, Montana

    Science.gov (United States)

    Walsh, Stephen J.; Brown, Daniel G.; Bian, Ling; Butler, David R.

    1990-01-01

    Geographic information system (GIS) techniques were applied to the study of snow-avalanche path location within Glacier National Park, Montana. Aerial photointerpretation and field surveys confirmed the location of 121 avalanche paths within the selected study area. Spatial and nonspatial information on each path were integrated using the ARC/INFO GIS. Lithologic, structural, hydrographic, topographic, and land-cover impacts on path location were analyzed. All path frequencies within variable classes were normalized by the area of class occurrence relative to the total area of the study area and were added to the morphometric information contained within INFO tables. The normalized values for each GIS coverage were used to cartographically model, by means of composite factor weightings, avalanche path locations.

  2. Cartographic modeling of snow avalanche path location within Glacier National Park, Montana

    Science.gov (United States)

    Walsh, Stephen J.; Brown, Daniel G.; Bian, Ling; Butler, David R.

    1990-05-01

    Geographic information system (GIS) techniques were applied to the study of snow-avalanche path location within Glacier National Park, Montana. Aerial photointerpretation and field surveys confirmed the location of 121 avalanche paths within the selected study area. Spatial and nonspatial information on each path were integrated using the ARC/INFO GIS. Lithologic, structural, hydrographic, topographic, and land-cover impacts on path location were analyzed. All path frequencies within variable classes were normalized by the area of class occurrence relative to the total area of the study area and were added to the morphometric information contained within INFO tables. The normalized values for each GIS coverage were used to cartographically model, by means of composite factor weightings, avalanche path locations.

  3. Comparison of Digital Surface Models for Snow Depth Mapping with Uav and Aerial Cameras

    Science.gov (United States)

    Boesch, R.; Bühler, Y.; Marty, M.; Ginzler, C.

    2016-06-01

    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.

  4. NASA Airborne Snow Observatory: Measuring Spatial Distribution of Snow Water Equivalent and Snow Albedo

    Science.gov (United States)

    Joyce, M.; Painter, T. H.; Mattmann, C. A.; Ramirez, P.; Laidlaw, R.; Bormann, K. J.; Skiles, M.; Richardson, M.; Berisford, D. F.

    2015-12-01

    The two most critical properties for understanding snowmelt runoff and timing are the spatial and temporal distributions of snow water equivalent (SWE) and snow albedo. Despite their importance in controlling volume and timing of runoff, snowpack albedo and SWE are still largely unquantified in the US and not at all in most of the globe, leaving runoff models poorly constrained. NASA Jet Propulsion Laboratory, in partnership with the California Department of Water Resources, has developed the Airborne Snow Observatory (ASO), an imaging spectrometer and scanning LiDAR system, to quantify SWE and snow albedo, generate unprecedented knowledge of snow properties for cutting edge cryospheric science, and provide complete, robust inputs to water management models and systems of the future. This poster will describe the NASA Airborne Snow Observatory, its outputs and their uses and applications, along with recent advancements to the system and plans for the project's future. Specifically, we will look at how ASO uses its imaging spectrometer to quantify spectral albedo, broadband albedo, and radiative forcing by dust and black carbon in snow. Additionally, we'll see how the scanning LiDAR is used to determine snow depth against snow-free acquisitions and to quantify snow water equivalent when combined with in-situ constrained modeling of snow density.

  5. Multidecadal climate and seasonal snow conditions in Svalbard

    Science.gov (United States)

    Pelt, W. J. J.; Kohler, J.; Liston, G. E.; Hagen, J. O.; Luks, B.; Reijmer, C. H.; Pohjola, V. A.

    2016-11-01

    Svalbard climate is undergoing amplified change with respect to the global mean. Changing climate conditions directly affect the evolution of the seasonal snowpack, through its impact on accumulation, melt, and moisture exchange. We analyze long-term trends and spatial patterns of seasonal snow conditions in Svalbard between 1961 and 2012. Downscaled regional climate model output is used to drive a snow modeling system (SnowModel), with coupled modules simulating the surface energy balance and snowpack evolution. The precipitation forcing is calibrated and validated against snow depth data on a set of glaciers around Svalbard. Climate trends reveal seasonally inhomogeneous warming and a weakly positive precipitation trend, with strongest changes in the north. In response to autumn warming the date of snow onset increased (2 days decade-1), whereas in spring/summer opposing effects cause a nonsignificant trend in the snow disappearance date. Maximum snow water equivalent (SWE) in winter/spring shows a modest increase (+0.01 meters water equivalent (mwe) decade-1), while the end-of-summer minimum snow area fraction declined strongly (from 48% to 36%). The equilibrium line altitude is highest in relatively dry inland regions, and time series show a clear positive trend (25 m decade-1) as a result of summer warming. Finally, rain-on-snow in the core winter season, affecting ground ice formation and limiting access of grazing animals to food supplies, peaks during specific years (1994, 1996, 2000, and 2012) and is found to be concentrated in the lower lying coastal regions in southwestern Svalbard.

  6. Influence of Accumulated Snow Cover on Sand-Dust Weather over Eastern Hexi Corridor%河西走廊东部冬春季积雪对沙尘天气的影响

    Institute of Scientific and Technical Information of China (English)

    李玲萍; 李岩瑛; 王兵

    2011-01-01

    利用河西走廊东部5个气象站1961 -2007年逐月积雪深度、积雪日数和沙尘天气的常规观测资料,分析了河西走廊东部冬春季积雪深度、积雪日数和春夏季沙尘日数的时空变化特征,进而探讨河西走廊东部冬春季积雪与春夏季沙尘天气的关系.结果表明:受海拔高度、地理位置以及天气系统等影响,河西走廊东部积雪从东南向西北递减,高海拔地区的积雪多于低海拔地区;沙尘日数从西北向东南递减,低海拔地区的沙尘日数明显多于高海拔地区.河西走廊东部冬春季积雪与春夏季沙尘日数呈显著负相关.积雪深度与沙尘日数的负相关性高于积雪日数与沙尘日数的负相关性;冬春季积雪对春季沙尘的影响大于对夏季沙尘的影响;山区积雪与沙尘日数的相关性高于平原区积雪与沙尘日数的相关性.%Based on conventional observational data of monthly snow cover days and depths, as well as number of sand-dust weather days of five meteorological stations over Eastern Hexi Corridor from 1961-2007, the temporal-spatial variation characteristics of snow cover days and depths from winter to spring and number of sand-dust weather days from spring to summer were analyzed. The relationships between accumulated snow cover from winter to spring and sand-dust weather from spring to summer over Eastern Hexi Corridor were also discussed. The results show that the spatial distribution of snow cover depths and days decreased from the southeast to the northwest resulting from altitude changes, geographical location and weather influence. Snow cover days and depths were greater in high altitude areas than in low altitude areas. In contrast, the number of sand-dust weather days decreased from the northwest to the southeast. The number of sand-dust weather days were obviously more in low altitude areas than in high altitude areas. There were significantly negative correlations between number of

  7. The use of remotely-sensed canopy variables and ultrasonic snow depth sensors to improve the understanding of forest - snow interactions (Invited)

    Science.gov (United States)

    Varhola, A.; Coops, N.; Teti, P.; Weiler, M.

    2013-12-01

    Current methods to estimate snow accumulation and ablation at the plot and watershed levels can be improved as new technologies offer alternative approaches to more accurately monitor snow dynamics and their drivers. Here we conduct a meta-analysis of snow and vegetation data collected in British Columbia to explore the relationships between a wide range of forest structure variables --obtained from Light Detection and Ranging (LiDAR), hemispherical photography (HP) and Landsat Thematic Mapper-- and several indicators of snow accumulation and ablation estimated from manual snow surveys and ultrasonic range sensors. By merging and standardizing all the ground plot information available in the study area, we demonstrate how LiDAR-derived forest cover above 0.5 m was the variable explaining the highest percentage of absolute peak snow water equivalent (SWE) (33%), while HP-derived leaf area index and gap fraction (45° angle of view) were the best potential predictors of snow ablation rate (SAR) (explaining 57% of variance). This study reveals how continuous SWE data from ultrasonic sensors are fundamental to obtain statistically-significant relationships between snow indicators and structural metrics by increasing mean coefficient of determination by 20% when compared to manual surveys. The relationships between vegetation and some spectral indices from Landsat and snow indicators, not explored before, were almost as high as those shown by LiDAR or HP and thus point towards a new line of research with important practical implications. While the use of different data sources from two snow seasons prevented us from developing models with predictive capacity, a large sample size allowed us to identify outliers that weakened the relationships and suggest improvements for future research. A concise overview of the limitations of this and previous studies is provided along with propositions to consistently improve experimental designs to take advantage of remote sensing

  8. Using multi-year data to evaluate performance of one-layer and multi-layer models in snow hydrology: an example from Col De Porte

    Science.gov (United States)

    Avanzi, Francesco; De Michele, Carlo; Morin, Samuel; Carmagnola, Carlo Maria; Ghezzi, Antonio; Lejeune, Yves

    2016-04-01

    Snow mass dynamics prediction represents an important task for snow hydrologists, since snow on the ground influences local/global water availability and streamflow timing and amount. Different modeling tools have been formulated for decades to predict snowmelt runoff dynamics and therefore to integrate snow mass dynamics in watershed hydrology modeling. Typical variables of interest include snow depth, snow bulk density, snow water equivalent (SWE) and snowmelt runoff. All these variables have been monitored at several locations worldwide for several decades in order to evaluate model performance. As a result, several multi-year datasets are now available to perform extensive evaluation tests. In this presentation, we report an example of these evaluations by discussing the performance of two models of different complexity in reproducing observed data of snow dynamics at a site in French Alps (Col De Porte, 1325 m AMSL), where 18 continuous-time years of observations are available. We consider Crocus as an example of multi-layer physically-based complex models and HyS (De Michele et al. 2013) as an example of a one-layer temperature-index models. Using multi-year data allows us to compare models performance over long periods of time, thus considering different climatic and snow conditions. Moreover, the use of continuous-time data allows to evaluate models performance at different temporal resolutions. De Michele, C., Avanzi, F., Ghezzi, A., and Jommi, C.: Investigating the dynamics of bulk snow density in dry and wet conditions using a one-dimensional model, The Cryosphere, 7, 433-444, doi:10.5194/tc-7-433-2013, 2013.

  9. 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.

  10. Multi-Frequency Measured and Modeled Microwave Backscatter from a Highly Saline Snow Cover on Smooth First-Year Sea Ice

    Science.gov (United States)

    Nandan, V.; Geldsetzer, T.; Islam, T.; Yackel, J.; Gill, J. P. S.; Gunn, G. E.; Duguay, C. R.

    2015-12-01

    Monitoring Arctic sea ice and its snow cover variability is of prime importance in Cryosphere research. Snow cover plays major roles in the energy balance of Arctic sea ice and also required to understand the present condition and future behavior of first-year ice (FYI). Microwave remote sensing provides the most effective means to acquire near-real time thermodynamic information about snow cover on smooth FYI. Microwave interaction with snow-covered sea ice is a function of both snow and ice electro-thermo-physical properties such as shape, size and orientation of scatterers, surface roughness, complex dielectric constant as a function primarily of brine volume, and brine volume as a function of temperature, salinity and density; as well as microwave parameters such as incidence angle, polarization and wavelength. Fluctuations in snow cover thermodynamics affect microwave propagation, attenuation, and scattering through the influence that brine volume exerts on interfacial and volume characteristics of snow and ice layers. Previous studies exhibit reduced penetration depth and inaccurate snow thickness estimates, using a single-frequency approach (C-band), from highly saline snow covers. We present a case study based on an observational (Ku-, X- and C-band surface-based fully-polarimetric microwave scatterometer system) and theoretical multi-frequency approach (using first-order microwave scattering and penetration depth models), to understand the sensitivity of varying snow thermodynamics on microwave scattering and penetration. The study site is a 14cm highly saline snow cover over smooth FYI, near Resolute Bay, Nunavut, Canada (Figure 1), with in-situ snow property measurements acquired from 18th to 20th May 2012, when snow layer temperatures were found to be fluctuating (Figure 2). Preliminary results show variations in observed Ku-, X- and C-band VV backscatter (Figure 3) and penetration (Figure 5) for warm (18th and 20th May) and cold (19th May) snow cases

  11. ESCIMO.spread – a spreadsheet-based point snow surface energy balance model to calculate hourly snow water equivalent and melt rates for historical and changing climate conditions

    Directory of Open Access Journals (Sweden)

    T. Marke

    2010-05-01

    Full Text Available This paper describes the spreadsheet-based point energy balance model ESCIMO.spread which simulates the energy and mass balance as well as melt rates of a snow surface. The model makes use of hourly recordings of temperature, precipitation, wind speed, relative humidity, global and longwave radiation. The effect of potential climate change on the seasonal evolution of the snow cover can be estimated by modifying the time series of observed temperature and precipitation by means of adjustable parameters. Model output is graphically visualized in hourly and daily diagrams. The results compare well with weekly measured snow water equivalent (SWE. The model is easily portable and adjustable, and runs particularly fast: hourly calculation of a one winter season is instantaneous on a standard computer. ESICMO.spread can be obtained from the authors on request (contact: ulrich.strasser@uni-graz.at.

  12. 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.

  13. Artificial Neural Network Model of Hydrocarbon Migration and Accumulation

    Institute of Scientific and Technical Information of China (English)

    刘海滨; 吴冲龙

    2002-01-01

    Based on the dynamic simulation of the 3-D structure the sedimentary modeling, the unit entity model has been adopted to transfer the heterogeneous complex pas sage system into limited simple homogeneous entity, and then the traditional dyn amic simulation has been used to calculate the phase and the drive forces of the hyd rocarbon , and the artificial neural network(ANN) technology has been applied to resolve such problems as the direction, velocity and quantity of the hydrocarbo n migration among the unit entities. Through simulating of petroleum migration a nd accumulation in Zhu Ⅲ depression, the complex mechanism of hydrocarbon migra tion and accumulation has been opened out.

  14. 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.

  15. 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.

  16. 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.

  17. Incorporating microbial ecology into the metabolic modelling of polyphosphate accumulating organisms and glycogen accumulating organisms.

    Science.gov (United States)

    Oehmen, A; Carvalho, G; Lopez-Vazquez, C M; van Loosdrecht, M C M; Reis, M A M

    2010-09-01

    In the enhanced biological phosphorus removal (EBPR) process, the competition between polyphosphate accumulating organisms (PAO) and glycogen accumulating organisms (GAO) has been studied intensively in recent years by both microbiologists and engineers, due to its important effects on phosphorus removal performance and efficiency. This study addresses the impact of microbial ecology on assessing the PAO-GAO competition through metabolic modelling, focussing on reviewing recent developments, discussion of how the results from molecular studies can impact the way we model the process, and offering perspectives for future research opportunities based on unanswered questions concerning PAO and GAO metabolism. Indeed, numerous findings that are seemingly contradictory could in fact be explained by the metabolic behaviour of different sub-groups of PAOs and/or GAOs exposed to different environmental and operational conditions. Some examples include the glycolysis pathway (i.e. Embden-Meyerhof-Parnas (EMP) vs. Entner-Doudoroff (ED)), denitrification capacity, anaerobic tricarboxylic acid (TCA) cycle activity and PAOs' ability to adjust their metabolism to e.g. a GAO-like metabolism. Metabolic modelling may further yield far-reaching influences on practical applications as well, and serves as a bridge between molecular/biochemical research studies and the optimisation of wastewater treatment plant operation. Copyright © 2010 Elsevier Ltd. All rights reserved.

  18. 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.

  19. Modelling of interaction between a snow mantle and a flexible structure using a discrete element method

    Directory of Open Access Journals (Sweden)

    F. Nicot

    2002-01-01

    Full Text Available The search of improvement of protective techniques against natural phenomena such as snow avalanches continues to use classic methods for calculating flexible structures. This paper deals with a new method to design avalanche protection nets. This method is based on a coupled analysis of both net structure and snow mantle by using a Discrete Element Method. This has led to the development of computational software so that avalanche nets can be easily designed. This tool gives the evolution of the forces acting in several parts of the work as a function of the snow situation.

  20. Integrated simulation of snow and glacier melt in water and energy balance‐based, distributed hydrological modeling framework at Hunza River Basin of Pakistan Karakoram region

    National Research Council Canada - National Science Library

    Shrestha, Maheswor; Koike, Toshio; Hirabayashi, Yukiko; Xue, Yongkang; Wang, Lei; Rasul, Ghulam; Ahmad, Bashir

    2015-01-01

    Energy budget‐based distributed modeling of snow and glacier melt runoff is essential in a hydrologic model to accurately describe hydrologic processes in cold regions and high‐altitude catchments...

  1. Evidence accumulation as a model for lexical selection

    NARCIS (Netherlands)

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

    2015-01-01

    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

  2. Spatial distribution of stable water isotopes in alpine snow cover

    Directory of Open Access Journals (Sweden)

    N. Dietermann

    2013-07-01

    Full Text Available The aim of this study was to analyse and predict the mean stable water isotopic composition of the snow cover at specific geographic locations and altitudes. In addition, the dependence of the isotopic composition of the entire snow cover on altitude was analysed. Snow in four Swiss catchments was sampled at the end of the accumulation period in April 2010 and a second time during snowmelt in May 2010 and analysed for stable isotope composition of 2H and 18O. The sampling was conducted at both south-facing and north-facing slopes at elevation differences of 100 m, for a total altitude difference of approximately 1000 m. The observed variability of isotopic composition of the snow cover was analysed with stepwise multiple linear regression models. The analysis indicated that there is only a limited altitude effect on the isotopic composition when considering all samples. This is due to the high variability of the isotopic composition of the precipitation during the winter months and, in particular in the case of south-facing slopes, an enrichment of heavy isotopes due to intermittent melting processes. This enrichment effect could clearly be observed in the samples which were taken later in the year. A small altitudinal gradient of the isotopic composition could only be observed at some north-facing slopes. However, the dependence of snow depth and the day of the year were significant predictor variables in all models. This study indicates the necessity to further study the variability of water isotopes in the snow cover to increase prediction for isotopic composition of snowmelt and hence increase model performance of residence time models for alpine areas in order to better understand the accumulation processes and the sources of water in the snow cover of high mountains.

  3. 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.

  4. 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.

  5. 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.

  6. A one-dimensional heat transfer model of the Antarctic Ice Sheet and modeling of snow temperatures at Dome A, the summit of Antarctic Plateau

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    A vertical one-dimensional numerical model for heat transferring within the near-surface snow layer of the Antarctic Ice Sheet was developed based on simplified parameterizations of associated physical processes for the atmosphere, radiation, and snow/ice systems. Using the meteorological data of an automatic weather station (AWS) at Dome A (80°22′S, 70°22′E), we applied the model to simulate the seasonal temperature variation within a depth of 20 m. Comparison of modeled results with observed snow temperatures at 4 measurement depths (0.1, 1, 3, 10 m) shows good agreement and consistent seasonal variations. The model results reveal the vertical temperature structure within the near-surface snow layer and its seasonal variance with more details than those by limited measurements. Analyses on the model outputs of the surface energy fluxes show that: 1) the surface energy balance at Dome A is characterized by the compensation between negative net radiation and the positive sensible fluxes, and 2) the sensible heat is on average transported from the atmosphere to the snow, and has an evident increase in spring. The results are considered well representative for the highest interior Antarctic Plateau.

  7. 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.

  8. A meteorological and snow observational data set from Snoqualmie Pass (921 m), Washington Cascades, USA

    Science.gov (United States)

    Wayand, Nicholas E.; Massmann, Adam; Butler, Colin; Keenan, Eric; Stimberis, John; Lundquist, Jessica D.

    2015-12-01

    We introduce a quality controlled observational atmospheric, snow, and soil data set from Snoqualmie Pass, Washington, USA, to enable testing of hydrometeorological and snow process representations within a rain-snow transitional climate where existing observations are sparse and limited. Continuous meteorological forcing (including air temperature, total precipitation, wind speed, specific humidity, air pressure, and short and longwave irradiance) are provided at hourly intervals for a 24 year historical period (water years 1989-2012) and at half-hourly intervals for a more recent period (water years 2013-2015), separated based on the availability of observations. The majority of missing data were filled with biased-corrected reanalysis model values (using NLDAS). Additional observations include 40 years of snow board new snow accumulation, multiple measurements of total snow depth, and manual snow pits, while more recent years include subdaily surface temperature, snowpack drainage, soil moisture and temperature profiles, and eddy covariance-derived turbulent heat flux. This data set is ideal for testing hypotheses about energy balance, soil, and snow processes in the rain-snow transition zone.

  9. Role of blowing snow in snow processes in Qilian Mountainous region

    Institute of Scientific and Technical Information of China (English)

    HongYi Li; Jian Wang; XiaoHua Hao

    2014-01-01

    Blowing snow is an important part of snow hydrologic processes in mountainous region, however the related researches were rare for the Qilian mountainous region where blowing snow is frequent. Using the observation dataset in 2008 snow season in Binggou wa-tershed in Qilian mountainous region, we systematically studied the energy and mass processes of blowing snow by field observation and model simulation. The results include the analysis of snow observation, the occurrence probability of blowing snow, blowing snow transport and blowing snow sublimation. It was found that blowing snow was obvious in high altitude region (4,146 m), the snow redistribution phenomena was remarkable. In Yakou station in the study region, blowing snow was easily occurred in midwinter and early spring when no snowmelt, the blowing snow transport was dominated in this period;when snowmelt beginning, the occur-rence probability of blowing snow decreased heavily because of the increasing air temperature, melt, and refrozen phenomena. The blowing