WorldWideScience

Sample records for modeling snow accumulation

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

    Science.gov (United States)

    Kinar, N. J.

    2017-05-01

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

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

    OpenAIRE

    Punge, H. J.; Gallée, H.; Kageyama, M.; Krinner, G.

    2012-01-01

    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 ...

  3. Improved climate model evaluation using a new, 750-year Antarctic-wide snow accumulation product

    Science.gov (United States)

    Medley, B.; Thomas, E. R.

    2017-12-01

    Snow that accumulates over the cold, dry grounded ice of Antarctica is an important component of its mass balance, mitigating the ice sheet's contribution to sea level. Secular trends in accumulation not only result trends in the mass balance of the Antarctic Ice Sheet, but also directly and indirectly impact surface height changes. Long-term and spatiotemporally complete records of snow accumulation are needed to understand part and present Antarctic-wide mass balance, to convert from altimetry derived volume change to mass change, and to evaluate the ability of climate models to reproduce the observed climate change. We need measurements in both time and space, yet they typically sample one dimension at the expense of the other. Here, we develop a spatially complete, annually resolved snow accumulation product for the Antarctic Ice Sheet over the past 750 years by combining a newly compiled database of ice core accumulation records with climate model output. We mainly focus on climate model evaluation. Because the product spans several centuries, we can evaluate model ability in representing the preindustrial as well as present day accumulation change. Significant long-term trends in snow accumulation are found over the Ross and Bellingshausen Sea sectors of West Antarctica, the Antarctic Peninsula, and several sectors in East Antarctica. These results suggest that change is more complex over the Antarctic Ice Sheet than a simple uniform change (i.e., more snowfall in a warming world), which highlights the importance of atmospheric circulation as a major driver of change. By evaluating several climate models' ability to reproduce the observed trends, we can deduce whether their projections are reasonable or potentially biased where the latter would result in a misrepresentation of the Antarctic contribution to sea level.

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

    Directory of Open Access Journals (Sweden)

    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.

  5. 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.

  6. 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 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.

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

    Directory of Open Access Journals (Sweden)

    H. J. Punge

    2012-11-01

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

  8. Measuring and modelling the impact of the bark beetle forest disturbance on snow accumulation and ablation at a plot scale

    Science.gov (United States)

    Jenicek, Michal; Matejka, Ondrej; Hotovy, Ondrej

    2017-04-01

    The knowledge of water volume stored in the snowpack and its spatial distribution is important to predict the snowmelt runoff. The objective of this study was to quantify the role of different forest structures on the snowpack distribution at a plot scale during snow accumulation and snow ablation periods. Special interest was put in the role of the forest affected by the bark beetle (Ips typographus). We performed repeated detailed manual field survey at selected mountain plots with different canopy structure located at the same elevation and without influence of topography and wind on the snow distribution. The forest canopy structure was described using parameters calculated from hemispherical photographs, such as canopy closure, leaf area index (LAI) and potential irradiance. Additionally, we used shortwave radiation measured using CNR4 Net radiometers placed in plots with different canopy structure. Two snow accumulation and ablation models were set-up to simulate the snow water equivalent (SWE) in plots with different vegetation cover. First model was physically-based using the energy balance approach, second model was conceptual and it was based on the degree-day approach. Both models accounted for snow interception in different forest types using LAI as a parameter. The measured SWE in the plot with healthy forest was on average by 41% lower than in open area during snow accumulation period. The disturbed forest caused the SWE reduction by 22% compared to open area indicating increasing snow storage after forest defoliation. The snow ablation in healthy forest was by 32% slower compared to open area. On the contrary, the snow ablation in disturbed forest (due to the bark beetle) was on average only by 7% slower than in open area. The relative decrease in incoming solar radiation in the forest compared to open area was much bigger compared to the relative decrease in snowmelt rates. This indicated that the decrease in snowmelt rates cannot be explained only

  9. Distributed calibrating snow models using remotely sensed snow cover information

    Science.gov (United States)

    Li, H.

    2015-12-01

    Distributed calibrating snow models using remotely sensed snow cover information Hongyi Li1, Tao Che1, Xin Li1, Jian Wang11. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China For improving the simulation accuracy of snow model, remotely sensed snow cover data are used to calibrate spatial parameters of snow model. A physically based snow model is developed and snow parameters including snow surface roughness, new snow density and critical threshold temperature distinguishing snowfall from precipitation, are spatially calibrated in this study. The study region, Babaohe basin, located in northwestern China, have seasonal snow cover and with complex terrain. The results indicates that the spatially calibration of snow model parameters make the simulation results more reasonable, and the simulated snow accumulation days, plot-scale snow depth are more better than lumped calibration.

  10. Determination of snowmaking efficiency on a ski slope from observations and modelling of snowmaking events and seasonal snow accumulation

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    Spandre, Pierre; François, Hugues; Thibert, Emmanuel; Morin, Samuel; George-Marcelpoil, Emmanuelle

    2017-04-01

    The production of Machine Made (MM) snow is now generalized in ski resorts and represents the most common method of adaptation for mitigating the impact of a lack of snow on skiing. Most investigations of correlations between snow conditions and the ski industry's economy focus on the production of MM snow though not one of these has taken into account the efficiency of the snowmaking process. The present study consists of observations of snow conditions (depth and mass) using a Differential GPS method and snow density coring, following snowmaking events and seasonal snow accumulation in Les Deux Alpes ski resort (French Alps). A detailed physically based snowpack model accounting for grooming and snowmaking was used to compute the seasonal evolution of the snowpack and compared to the observations. Our results show that approximately 30 % of the water mass can be recovered as MM snow within 10 m from the center of a MM snow pile after production and 50 % within 20 m. Observations and simulations on the ski slope were relatively consistent with 60 % (±10 %) of the water mass used for snowmaking within the limits of the ski slope. Losses due to thermodynamic effects were estimated in the current case example to be less than 10 % of the total water mass. These results suggest that even in ideal conditions for production a significant fraction of the water used for snowmaking can not be found as MM snow within the limits of the ski slope with most of the missing fraction of water. This is due to site dependent characteristics (e.g. meteorological conditions, topography).

  11. Airborne-radar and ice-core observations of annual snow accumulation over Thwaites Glacier, West Antarctica confirm the spatiotemporal variability of global and regional atmospheric models

    NARCIS (Netherlands)

    Medley, B.; Joughin, I.; Das, S.B.; Steig, E. J.; Conway, H.; Gogineni, S.; Criscitiello, A.S.; McConnell, J.R.; Smith, B.E.; van den Broeke, M.R.; Lenaerts, J.T.M.; Bromwich, D.H.; Nicolas, J.P.

    2013-01-01

    We use an airborne-radar method, verified with ice-core accumulation records, to determine the spatiotemporal variations of snow accumulation over Thwaites Glacier, West Antarctica between 1980 and 2009. We also present a regional evaluation of modeled accumulation in Antarctica. Comparisons between

  12. Snow as an accumulator of air pollutants

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    Robert T. Brown

    1976-01-01

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

  13. Modelling of snow exceedances

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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.

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

    Science.gov (United States)

    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

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

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    Glen E. Liston; Kelly. Elder

    2006-01-01

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

  16. 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.

  17. Regional Antarctic snow accumulation over the past 1000 years

    Science.gov (United States)

    Thomas, Elizabeth R.; Melchior van Wessem, J.; Roberts, Jason; Isaksson, Elisabeth; Schlosser, Elisabeth; Fudge, Tyler J.; Vallelonga, Paul; Medley, Brooke; Lenaerts, Jan; Bertler, Nancy; van den Broeke, Michiel R.; Dixon, Daniel A.; Frezzotti, Massimo; Stenni, Barbara; Curran, Mark; Ekaykin, Alexey A.

    2017-11-01

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

  18. Regional Antarctic snow accumulation over the past 1000 years

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    E. R. Thomas

    2017-11-01

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

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

    International Nuclear Information System (INIS)

    Helfricht, K.

    2014-01-01

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

  20. Spatial Distribution And Synoptic Conditions Of Snow Accumulation And Snow Ablation In The West Siberian Plain

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    Bednorz Ewa

    2015-09-01

    Full Text Available The mean duration of snow coverage in the West Siberian Plain is approximately eight months in the north to about five months in the south. While the period of intense snow melting is short (one or two months between March and May, snow accumulation persists for most of the cold season. Snow accumulation is associated with negative anomalies of sea level pressure, which means increased cyclonal activity and weaker than normal Siberian High. Much lower anomalies of sea level pressure occur during snow ablation. This suggests smaller influence of air circulation on snow cover reduction in spring.

  1. 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.

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

    Science.gov (United States)

    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

  3. Modeling of falling snow properties for snowpack models: towards a better link between falling snow and snow on the ground

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    Vionnet, Vincent; Milbrandt, Jason; Yamaguchi, Satoru; Morrison, Hugh

    2017-04-01

    Falling snow is made of different types of solid hydrometeors including single ice crystals, aggregates at different riming stages and graupel. This variability results from complex in-cloud processes (deposition, riming and aggregation) and sub-cloud processes (melting and sublimation). It has consequences for the properties of fresh fallen snow accumulating on the ground including density and specific surface area (SSA). These properties strongly affect the evolution of snow on the ground by modifying for example the albedo and the thermal conductivity. Detailed snowpack models such as Crocus or SNOWPACK use near-surface wind speed, air temperature and humidity to compute the density and SSA of falling snow. The shape, size and degree of riming of falling solid hydrometeors are not directly taken into account, which limit the accurate determination of properties of fresh fallen snow. An alternative is offered by new bulk cloud microphysics schemes implemented in numerical weather prediction system that can be used to drive snowpack model. In particular, the scheme P3 (Predicted Particle Properties) proposes an innovative representation of all ice-phase particles by predicting several physical properties (e.g. size, rime fraction, rime density …). In this study, we first theoretically analyze the strengths and limitations of P3 to represent the density and SSA of falling snow. Parameterizations are then proposed to derive density and SSA from P3 output. Finally, P3 implemented in the Canadian Global Environmental Multiscale (GEM) weather prediction model is used to simulate snowfall events observed at the Falling Snow Observatory (Nagoaka, Japan). Model predictions are compared with (i) observations of the type of falling snow particles derived from disdrometer data and (ii) manual measurements of fresh fallen snow density and SSA. This study is a first step towards the development of a more integrated approach between falling snow and snow on the ground.

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

    Directory of Open Access Journals (Sweden)

    Alexander Nestler

    2014-07-01

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

  5. 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.

  6. The performance of a simple degree-day estimate of snow accumulation to an alpine watershed

    Science.gov (United States)

    R. A. Sommerfeld; R. C. Musselman; G. L. Wooldridge; M. A. Conrad

    1991-01-01

    We estimated the yearly snow accumulation to the Glacier Lakes Ecosystems Experiments Site (GLEES) for the winters of 1987-88, 1988-89, and 1989-90, using a simple degree-day model developed by J. Martinec and A. Rango. Comparisons with other data indicate that the estimates are accurate. In particular, a calibration with an intensive snow core-probe survey in 1989-90...

  7. Greenland Ice Sheet Mass Balance Reconstruction. Part I: Net Snow Accumulation (1600–2009)

    NARCIS (Netherlands)

    Box, J.E.; Cressie, N.; Bromwich, D.H.; Jung, J.-H.; van den Broeke, M.R.|info:eu-repo/dai/nl/073765643; van Angelen, J.H.|info:eu-repo/dai/nl/325922470; Forster, R.R.; Miège, C.; Mosley-Thompson, E.; Vinther, B.; McConnell, J.R.

    2013-01-01

    Ice core data are combined with Regional Atmospheric Climate Model version 2 (RACMO2) output (1958–2010) to develop a reconstruction of Greenland ice sheet net snow accumulation rate, ^At(G), spanning the years 1600–2009. Regression parameters from regional climate model (RCM) output regressed on 86

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

    Science.gov (United States)

    Valero, Cesar Vera; Wever, Nander; Christen, Marc; Bartelt, Perry

    2018-03-01

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

  9. Evaluation of forest snow processes models (SnowMKIP2)

    Science.gov (United States)

    Nick Rutter; Richard Essery; John Pomeroy; Nuria Altimir; Kostas Andreadis; Ian Baker; Alan Barr; Paul Bartlett; Aaron Boone; Huiping Deng; Herve Douville; Emanuel Dutra; Kelly Elder; others

    2009-01-01

    Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation...

  10. Spatial and temporal variation in snow accumulation in the Central Spanish Pyrenees

    Science.gov (United States)

    López-Moreno, I.; Beguería, S.; García-Ruiz, J. M.

    2003-04-01

    Water stored in winter snowpack represents a valuable resource in mountainous regions. The distribution of the snow determines the availability of water resources during snowmelt period, as well as the development of the economy based on winter sports. This work analyses the main factors that explain the variation in snow accumulation in the Central Spanish Pyrenees. Data about evolution of snowpack is provided by the measurement of 106 sticks installed in the study area from 1985. The snow depth is measured in two moments of the year, at the beginning of March and at the end of April or beginning of May. Snow depth, of both measurements moments, has been mapped using linear regression between snow depth and different topographic and geographic variables derived from the digital terrain model. In order to improve the estimation interpolated residuals values have been subtracted. The variability of the interannual snow distribution has been analysed with a factorial analysis in order to obtain different annual patterns of snowpack distribution. Relation between the dominant winter weather-type and the different patterns, provided by the factorial analysis, has been studied. The weather-type classification has been obtained using the Jenkinson and Collison system based on daily series of sea level atmospheric pressure measured around the Iberian Peninsula with a 5º x 10º scale. Thus, it is possible to know the influence of the prevailing winter synoptic situations in the snowpack distribution. The results show that topographic and geographic variables have a high capacity to explain the spatial distribution of the average snow cover during the study period. Nevertheless, the annual snow accumulation is the result of the arrival of frontal disturbances that come from different directions. By that, it is complex to relate the different snow distribution patterns to the main winter synoptic conditions of each year.

  11. 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

  12. Spatial and temporal evolution of snow water equivalent in the seasonal snow pack in Hemavan based on field measurements of Snow Accumulation compiled for ASAR validation

    Science.gov (United States)

    Ingvander, S. M.; Brown, I.

    2012-12-01

    Estimating the snow water equivalent (SWE) of the seasonal snowpack is key information for the prediction of spring flood rates and the contribution to water reservoirs in Hydro-power production. This is particularly important in Northern Sweden where 40% of the power generation is from hydropower sources (2004). By determining the frequency and amplitude of the landscape topography and in the field measure how snow is accumulated in this landscape we can increase the accuracy of the estimation of SWE in the Swedish mountain regions. Understanding the distribution of snow depth in micro scale (sub meter scale) is our basis for extrapolating the information to kilometre scale based on digital elevation models and weighted by the land cover in the area. The micro scale analysis will then be extrapolated over a larger region by using DEM, remotely sensed data such as ASAR (Advanced Synthetic Aperture Radar) C-band, MODIS (Moderate Resolution Imaging Spectroradiometer) optical data and field data sampled in macro scale (kilometre scale) in reference areas. By establishing the relationship between accumulation patterns and physical parameters in the landscape, atmosphere and the volume area ration in snow melt, a model of accumulation patterns in different types of reference areas can be produced. This information can then be applied to satellite imagery and help the understanding of information in different scales and types of satellite imagery by upscaling from high resolution field data to derive new satellite algorithms. Snow cover mapping is an area of interest on national and international levels with ESA committed to data provision and the data provided in this project will be made available for validation of future ESA projects such as the BIOMASS and CoREH2O project.

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

    Science.gov (United States)

    Roth, Travis R.; Nolin, Anne W.

    2017-11-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Science.gov (United States)

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

    2010-01-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Directory of Open Access Journals (Sweden)

    Françoise Martz

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

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Directory of Open Access Journals (Sweden)

    K. Förster

    2018-02-01

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

  20. Modeling the Seasonality of Snow Cover in Naryn Oblast, Kyrgyzstan.

    Science.gov (United States)

    Tomaszewska, M. A.; Henebry, G. M.

    2016-12-01

    Vertical transhumance practiced by herders in the highlands of Kyrgyzstan is strongly affected by timing of snow melt in high-elevation summer pastures. To model snow cover seasonality, we explore a novel approach through the synergistic use of "frost degree-days" obtained from MODIS land surface temperature data and the normalized difference snow index (NDSI) derived from over 16 years of Landsat imagery (2000-2015). From the fitted parameter coefficients of a convex quadratic model linking NDSI to accumulated frost degree-days (AFDD), we calculated two key metrics—the Peak Height of the NDSI and the Thermal Time (in AFDD) to the Peak Height—to examine the interannual variation in the timing of snow cover onset, snow melt, and snow cover duration. We discuss the strengths and limitations of this modeling approach to snow cover seasonality as well as demonstrate how it complements the land surface phenology modeling for understanding climatic influences on the highland pastures of Naryn oblast in Central Kyrgyzstan.

  1. 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.

  2. 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.

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

    Czech Academy of Sciences Publication Activity Database

    Šípek, Václav; Tesař, Miroslav

    2014-01-01

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

  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

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

    Science.gov (United States)

    Ibuki, R.; Harada, K.

    2017-12-01

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

  7. Transformations of snow chemistry in the boreal forest: Accumulation and volatilization

    Science.gov (United States)

    Pomeroy, J.W.; Davies, T.D.; Jones, H.G.; Marsh, P.; Peters, N.E.; Tranter, M.

    1999-01-01

    This paper examines the processes and dynamics of ecologically-important inorganic chemical (primarily NO3-N) accumulation and loss in boreal forest snow during the cold winter period at a northern and southern location in the boreal forest of western Canada. Field observations from Inuvik, Northwest Territories and Waskesiu, Saskatchewan, Canada were used to link chemical transformations and physical processes in boreal forest snow. Data on the disposition and overwinter transformation of snow water equivalent, NO3-, SO42- and other major ions were examined. No evidence of enhanced dry deposition of chemical species to intercepted snow was found at either site except where high atmospheric aerosol concentrations prevailed. At Inuvik, concentrations of SO42- and Cl- were five to six times higher in intercepted snow than in surface snow away from the trees. SO4-S and Cl loads at Inuvik were correspondingly enhanced three-fold within the nearest 0.5 m to individual tree stems. Measurements of snow affected by canopy interception without rapid sublimation provided no evidence of ion volatilization from intercepted snow. Where intercepted snow sublimation rates were significant, ion loads in sub-canopy snow suggested that NO3- volatized with an efficiency of about 62% per snow mass sublimated. Extrapolating this measurement from Waskesiu to sublimation losses observed in other southern boreal environments suggests that 19-25% of snow inputs of NO3- can be lost during intercepted snow sublimation. The amount of N lost during sublimation may be large in high-snowfall, high N load southern boreal forests (Quebec) where 0.42 kg NO3-N ha-1 is estimated as a possible seasonal NO3- volatilization. The sensitivity of the N fluxes to climate and forest canopy variation and implications of the winter N losses for N budgets in the boreal forest are discussed.This paper examines the processes and dynamics of ecologically-important inorganic chemical (primarily NO3-N) accumulation

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

    Directory of Open Access Journals (Sweden)

    E. E. Stigter

    2017-07-01

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

  9. 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.

  10. Snow model design for operational purposes

    Science.gov (United States)

    Kolberg, Sjur

    2017-04-01

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

  11. GIS- and field data based modeling of snow water equivalent in shrub tundra

    Directory of Open Access Journals (Sweden)

    Yu. A. Dvornikov

    2015-01-01

    Full Text Available An approach for snow water equivalent (SWE modelling in tundra environments has been developed for the test area on the Yamal peninsula. Detailed mapping of snow cover is very important for tundra areas under continuous permafrost conditions, because the snow cover affects the active layer thickness (ALT and the ground temperature, acting as a heat-insulating agent. The information concerning snow cover with specific regime of accumulation can support studies of ground temperature distribution and other permafrost related aspects. 

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

    Directory of Open Access Journals (Sweden)

    Michal Jeníček

    2017-02-01

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

  13. Winter precipitation and snow accumulation drive the methane sink or source strength of Arctic tussock tundra.

    Science.gov (United States)

    Blanc-Betes, Elena; Welker, Jeffrey M; Sturchio, Neil C; Chanton, Jeffrey P; Gonzalez-Meler, Miquel A

    2016-08-01

    Arctic winter precipitation is projected to increase with global warming, but some areas will experience decreases in snow accumulation. Although Arctic CH4 emissions may represent a significant climate forcing feedback, long-term impacts of changes in snow accumulation on CH4 fluxes remain uncertain. We measured ecosystem CH4 fluxes and soil CH4 and CO2 concentrations and (13) C composition to investigate the metabolic pathways and transport mechanisms driving moist acidic tundra CH4 flux over the growing season (Jun-Aug) after 18 years of experimental snow depth increases and decreases. Deeper snow increased soil wetness and warming, reducing soil %O2 levels and increasing thaw depth. Soil moisture, through changes in soil %O2 saturation, determined predominance of methanotrophy or methanogenesis, with soil temperature regulating the ecosystem CH4 sink or source strength. Reduced snow (RS) increased the fraction of oxidized CH4 (Fox) by 75-120% compared to Ambient, switching the system from a small source to a net CH4 sink (21 ± 2 and -31 ± 1 mg CH4  m(-2)  season(-1) at Ambient and RS). Deeper snow reduced Fox by 35-40% and 90-100% in medium- (MS) and high- (HS) snow additions relative to Ambient, contributing to increasing the CH4 source strength of moist acidic tundra (464 ± 15 and 3561 ± 97 mg CH4  m(-2)  season(-1) at MS and HS). Decreases in Fox with deeper snow were partly due to increases in plant-mediated CH4 transport associated with the expansion of tall graminoids. Deeper snow enhanced CH4 production within newly thawed soils, responding mainly to soil warming rather than to increases in acetate fermentation expected from thaw-induced increases in SOC availability. Our results suggest that increased winter precipitation will increase the CH4 source strength of Arctic tundra, but the resulting positive feedback on climate change will depend on the balance between areas with more or less snow accumulation than they are currently

  14. 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.

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

    Science.gov (United States)

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

    2017-12-01

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

  16. 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.

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

    Science.gov (United States)

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

    2001-01-01

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

  18. 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.

  19. Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

    Science.gov (United States)

    Zheng, Z.; Kirchner, P. B.; Bales, R. C.

    2016-01-01

    Airborne light detection and ranging (lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak-snow-accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow-accumulation zone, with a total surveyed area of over 106 km2. The percentage of pixels with at least one snow-depth measurement was observed to increase from 65-90 to 99 % as the sampling resolution of the lidar point cloud was increased from 1 to 5 m. However, a coarser resolution risks undersampling the under-canopy snow relative to snow in open areas and was estimated to result in at least a 10 cm overestimate of snow depth over the main snow-accumulation region between 2000 and 3000 m, where 28 % of the area had no measurements. Analysis of the 1 m gridded data showed consistent patterns across the four sites, dominated by orographic effects on precipitation. Elevation explained 43 % of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14 % over the elevation range of 1500-3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under-canopy areas increased with elevation in the rain-snow transition zone (1500-1800 m) and was about 35 ± 10 cm above 1800 m. Lidar has the potential to transform estimation of snow depth across mountain basins, and including local canopy effects is both feasible and important for accurate assessments.

  20. A comparison study of two snow models using data from different Alpine sites

    Science.gov (United States)

    Piazzi, Gaia; Riboust, Philippe; Campo, Lorenzo; Cremonese, Edoardo; Gabellani, Simone; Le Moine, Nicolas; Morra di Cella, Umberto; Ribstein, Pierre; Thirel, Guillaume

    2017-04-01

    The hydrological balance of an Alpine catchment is strongly affected by snowpack dynamics. Melt-water supplies a significant component of the annual water budget, both in terms of soil moisture and runoff, which play a critical role in floods generation and impact water resource management in snow-dominated basins. Several snow models have been developed with variable degrees of complexity, mainly depending on their target application and the availability of computational resources and data. According to the level of detail, snow models range from statistical snowmelt-runoff and degree-day methods using composite snow-soil or explicit snow layer(s), to physically-based and energy balance snow models, consisting of detailed internal snow-process schemes. Intermediate-complexity approaches have been widely developed resulting in simplified versions of the physical parameterization schemes with a reduced snowpack layering. Nevertheless, an increasing model complexity does not necessarily entail improved model simulations. This study presents a comparison analysis between two snow models designed for hydrological purposes. The snow module developed at UPMC and IRSTEA is a mono-layer energy balance model analytically resolving heat and phase change equations into the snowpack. Vertical mass exchange into the snowpack is also analytically resolved. The model is intended to be used for hydrological studies but also to give a realistic estimation of the snowpack state at watershed scale (SWE and snow depth). The structure of the model allows it to be easily calibrated using snow observation. This model is further presented in EGU2017-7492. The snow module of SMASH (Snow Multidata Assimilation System for Hydrology) consists in a multi-layer snow dynamic scheme. It is physically based on mass and energy balances and it reproduces the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass

  1. A continuum model for meltwater flow through compacting snow

    Science.gov (United States)

    Meyer, Colin R.; Hewitt, Ian J.

    2017-12-01

    Meltwater is produced on the surface of glaciers and ice sheets when the seasonal energy forcing warms the snow to its melting temperature. This meltwater percolates into the snow and subsequently runs off laterally in streams, is stored as liquid water, or refreezes, thus warming the subsurface through the release of latent heat. We present a continuum model for the percolation process that includes heat conduction, meltwater percolation and refreezing, as well as mechanical compaction. The model is forced by surface mass and energy balances, and the percolation process is described using Darcy's law, allowing for both partially and fully saturated pore space. Water is allowed to run off from the surface if the snow is fully saturated. The model outputs include the temperature, density, and water-content profiles and the surface runoff and water storage. We compare the propagation of freezing fronts that occur in the model to observations from the Greenland Ice Sheet. We show that the model applies to both accumulation and ablation areas and allows for a transition between the two as the surface energy forcing varies. The largest average firn temperatures occur at intermediate values of the surface forcing when perennial water storage is predicted.

  2. Using Gridded Snow Covered Area and Snow-Water Equivalence Spatial Data Sets to Improve Snow-Pack Depletion Simulation in a Continental Scale Hydrologic Model

    Science.gov (United States)

    Risley, J. C.; Tracey, J. A.; Markstrom, S. L.; Hay, L.

    2014-12-01

    Snow cover areal depletion curves were used in a continuous daily hydrologic model to simulate seasonal spring snowmelt during the period between maximum snowpack accumulation and total melt. The curves are defined as the ratio of snow-water equivalence (SWE) divided by the seasonal maximum snow-water equivalence (Ai) (Y axis) versus the percent snow cover area (SCA) (X axis). The slope of the curve can vary depending on local watershed conditions. Windy sparsely vegetated high elevation watersheds, for example, can have a steeper slope than lower elevation forested watersheds. To improve the accuracy of simulated runoff at ungaged watersheds, individual snow cover areal depletion curves were created for over 100,000 hydrologic response units (HRU) in the continental scale U.S. Geological Survey (USGS) National Hydrologic Model (NHM). NHM includes the same components of the USGS Precipitation-Runoff-Modeling System (PRMS), except it uses consistent land surface characterization and model parameterization across the U.S. continent. Weighted-mean daily time series of 1-kilometer gridded SWE, from Snow Data Assimilation System (SNODAS), and 500-meter gridded SCA, from Moderate Resolution Imaging Spectroradiometer (MODIS), for 2003-2014 were computed for each HRU using the USGS Geo Data Portal. Using a screening process, pairs of SWE/Ai and SCA from the snowmelt period of each year were selected. SCA values derived from imagery that did not have any cloud cover and were >0 and <100 percent were selected. Unrealistically low and high SCA values that were paired with high and low SWE/Ai ratios, respectively, were removed. Second order polynomial equations were then fit to the remaining pairs of SWE/Ai and SCA to create a unique curve for each HRU. Simulations comparing these new curves with an existing single default curve in NHM will be made to determine if there are significant improvements in runoff.

  3. Statistical modelling of the snow depth distribution in open alpine terrain

    Directory of Open Access Journals (Sweden)

    T. Grünewald

    2013-08-01

    Full Text Available The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m, that 30 to 91% of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing, and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could only explain 23% of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.

  4. A probabilistic model for snow avalanche occurrence

    Science.gov (United States)

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

    2009-04-01

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

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

    Science.gov (United States)

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

    2017-11-01

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

  6. Analysis of snow feedbacks in 14 general circulation models

    Science.gov (United States)

    Randall, D. A.; Cess, R. D.; Blanchet, J. P.; Chalita, S.; Colman, R.; Dazlich, D. A.; Del Genio, A. D.; Keup, E.; Lacis, A.; Le Treut, H.; Liang, X.-Z.; McAvaney, B. J.; Mahfouf, J. F.; Meleshko, V. P.; Morcrette, J.-J.; Norris, P. M.; Potter, G. L.; Rikus, L.; Roeckner, E.; Royer, J. F.; Schlese, U.; Sheinin, D. A.; Sokolov, A. P.; Taylor, K. E.; Wetherald, R. T.; Yagai, I.; Zhang, M.-H.

    1994-10-01

    Snow feedbacks produced by 14 atmospheric general circulation models have been analyzed through idealized numerical experiments. Included in the analysis is an investigation of the surface energy budgets of the models. Negative or weak positive snow feedbacks occurred in some of the models, while others produced strong positive snow feedbacks. These feedbacks are due not only to melting snow, but also to increases in boundary temperature, changes in air temperature, changes in water vapor, and changes in cloudiness. As a result, the net response of each model is quite complex. We analyze in detail the responses of one model with a strong positive snow feedback and another with a weak negative snow feedback. Some of the models include a temperature dependence of the snow albedo, and this has significantly affected the results.

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

    Science.gov (United States)

    He, Minxue; Hogue, Terri S.; Franz, Kristie J.; Margulis, Steven A.; Vrugt, Jasper 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 sensitivity and uncertainty analysis of SNOW17 model parameters using forcing and snow water equivalent (SWE) data from 12 sites with differing meteorological and geographic characteristics. The Generalized Sensitivity Analysis and the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm are utilized to explore the parameter space and assess model parametric and predictive uncertainty. Results indicate that SNOW17 parameter sensitivity and uncertainty generally varies between sites. Of the six hydroclimatic characteristics studied, only air temperature shows strong correlation with the sensitivity and uncertainty ranges of two parameters, while precipitation is highly correlated with the uncertainty of one parameter. Posterior marginal distributions of two parameters are also shown to be site-dependent in terms of distribution type. The SNOW17 prediction ensembles generated by the DREAM-derived posterior parameter sets contain most of the observed SWE. The proposed uncertainty analysis provides posterior parameter information on parameter uncertainty and distribution types that can serve as a foundation for a data assimilation framework for hydrologic models.

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

    Data.gov (United States)

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

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

    Science.gov (United States)

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

    2001-01-01

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

  10. Evaluation of gridded snow water equivalent and satellite snow cover products for mountain basins in a hydrologic model

    Science.gov (United States)

    Dressler, K.A.; Leavesley, G.H.; Bales, R.C.; Fassnacht, S.R.

    2006-01-01

    The USGS precipitation-runoff modelling system (PRMS) hydrologic model was used to evaluate experimental, gridded, 1 km2 snow-covered area (SCA) and snow water equivalent (SWE) products for two headwater basins within the Rio Grande (i.e. upper Rio Grande River basin) and Salt River (i.e. Black River basin) drainages in the southwestern USA. The SCA product was the fraction of each 1 km2 pixel covered by snow and was derived from NOAA advanced very high-resolution radiometer imagery. The SWE product was developed by multiplying the SCA product by SWE estimates interpolated from National Resources Conservation Service snow telemetry point measurements for a 6 year period (1995-2000). Measured SCA and SWE estimates were consistently lower than values estimated from temperature and precipitation within PRMS. The greatest differences occurred in the relatively complex terrain of the Rio Grande basin, as opposed to the relatively homogeneous terrain of the Black River basin, where differences were small. Differences between modelled and measured snow were different for the accumulation period versus the ablation period and had an elevational trend. Assimilating the measured snowfields into a version of PRMS calibrated to achieve water balance without assimilation led to reduced performance in estimating streamflow for the Rio Grande and increased performance in estimating streamflow for the Black River basin. Correcting the measured SCA and SWE for canopy effects improved simulations by adding snow mostly in the mid-to-high elevations, where satellite estimates of SCA are lower than model estimates. Copyright ?? 2006 John Wiley & Sons, Ltd.

  11. 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.

  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. 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.

  14. 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

  15. Snow bedforms: A review, new data, and a formation model

    Science.gov (United States)

    Filhol, Simon; Sturm, Matthew

    2015-09-01

    Snow bedforms, like sand bedforms, consist of various shapes that form under the action of wind on mobile particles. Throughout a year, they can cover up to 11% of the Earth surface, concentrated toward the poles. These forms impact the local surface energy balance and the distribution of precipitation. Only a few studies have concentrated on their genesis. Their size ranges from 2 cm (ripple marks) to 2.5 m tall (whaleback dunes). We counted a total of seven forms that are widely recognized. Among them sastrugi, an erosional shape, is the most widespread. From laser scans, we compared scaling of snow versus sand barchan morphology. We found that both have proportionally the same footprint, but snow barchans are flatter. The key difference is that snow can sinter, immobilizing the bedform and creating an erodible material. Using a model, we investigated the effect of sintering on snow dune dynamics. We found that sintering limits their size because it progressively hardens the snow and requires an ever-increasing wind speed to maintain snow transport. From the literature and results from this model, we have reclassified snow bedforms based on two parameters: wind speed and snow surface conditions. The new data show that snow dune behavior mirrors that of sand dunes, with merging, calving, and collision. However, isolated snow barchans are rare, with most of the snow surfaces encountered in the field consisting of several superimposed bedforms formed sequentially during multiple weather events. Spatially variable snow properties and geometry can explain qualitatively these widespread compound snow surfaces.

  16. 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.

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

    Science.gov (United States)

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

    2012-04-01

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

  18. Modeling Snow Regime in Cores of Small Planetary Bodies

    Science.gov (United States)

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

    2017-12-01

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

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

    Directory of Open Access Journals (Sweden)

    A. Roy

    2013-06-01

    Full Text Available 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 the temperature gradient under dry snow conditions, while it considers the liquid water content of the snowpack for wet snow metamorphism. We compare the model with ground-based measurements from several sites (alpine, arctic and subarctic with different types of snow. The model provides simulated SSA in good agreement with measurements with an overall point-to-point comparison RMSE of 8.0 m2 kg–1, and a root mean square error (RMSE of 5.1 m2 kg–1 for the snowpack average SSA. The model, however, is limited under wet conditions due to the single-layer nature of the CLASS model, leading to a single liquid water content value for the whole snowpack. The SSA simulations are of great interest for satellite passive microwave brightness temperature assimilations, snow mass balance retrievals and surface energy balance calculations with associated climate feedbacks.

  20. Evaluation of snow and frozen soil parameterization in a cryosphere land surface modeling framework in the Tibetan Plateau

    Science.gov (United States)

    Zhou, J.

    2017-12-01

    Snow and frozen soil are important components in the Tibetan Plateau, and influence the water cycle and energy balances through snowpack accumulation and melt and soil freeze-thaw. In this study, a new cryosphere land surface model (LSM) with coupled snow and frozen soil parameterization 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.

  1. 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

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

    Science.gov (United States)

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

    2016-12-01

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

  3. A triple-moment blowing snow-atmospheric model and its application in computing the seasonal wintertime snow mass budget

    Directory of Open Access Journals (Sweden)

    J. Yang

    2010-06-01

    Full Text Available Many field studies have shown that surface sublimation and blowing snow transport and sublimation have significant influences on the snow mass budget in many high latitude regions. We developed a coupled triple-moment blowing snow-atmospheric modeling system to study the influence of these processes on a seasonal time scale over the Northern Hemisphere. Two simulations were performed. The first is a 5 month simulation for comparison with snow survey measurements over a Saskatchewan site to validate the modeling system. The second simulation covers the 2006/2007 winter period to study the snow mass budget over the Northern Hemisphere. The results show that surface sublimation is significant in Eurasian Continent and the eastern region of North America, reaching a maximum value of 200 mm SWE (Snow Water Equivalent. Over the Arctic Ocean and Northern Canada, surface deposition with an average value of 30 mm SWE was simulated. Blowing snow sublimation was found to return up to 50 mm SWE back to the atmosphere over the Arctic Ocean, while the divergence of blowing snow transport contributes only a few mm SWE to the change in snow mass budget. The results were further stratified in 10 degree latitudinal bands. The results show that surface sublimation decreases with an increase in latitude while blowing snow sublimation increases with latitude. Taken together, the surface sublimation and blowing snow processes was found to distribute 23% to 52% of winter precipitation over the three month winter season.

  4. 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

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

    Science.gov (United States)

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

    2012-12-01

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

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

    Science.gov (United States)

    Varade, D. M.; Dikshit, O.

    2017-12-01

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

  7. Simulation of Seasonal Snow Microwave TB Using Coupled Multi-Layered Snow Evolution and Microwave Emission Models

    Science.gov (United States)

    Brucker, Ludovic; Royer, Alain; Picard, Ghislain; Langlois, Alex; Fily, Michel

    2014-01-01

    The accurate quantification of SWE has important societal benefits, including improving domestic and agricultural water planning, flood forecasting and electric power generation. However, passive-microwave SWE algorithms suffer from variations in TB due to snow metamorphism, difficult to distinguish from those due to SWE variations. Coupled snow evolution-emission models are able to predict snow metamorphism, allowing us to account for emissivity changes. They can also be used to identify weaknesses in the snow evolution model. Moreover, thoroughly evaluating coupled models is a contribution toward the assimilation of TB, which leads to a significant increase in the accuracy of SWE estimates.

  8. Lasting effects of snow accumulation on summer performance of large herbivores in alpine ecosystems may not last.

    Science.gov (United States)

    Mysterud, Atle; Austrheim, Gunnar

    2014-05-01

    One of the clearest predictions from the IPCC is that we can expect much less snow cover due to global warming in the 21st century, especially in the lower alpine areas. In alpine ecosystems, snow accumulation in depressions gives rise to distinct snow-bed vegetation types, assumed to play a key role in ecosystem function. A delayed plant phenology yields high-quality forage in late summer for wild and domestic herbivores. Yet, the mechanistic pathways for how declining snow may affect future performance of large herbivores beyond the effect of phenology remain poorly documented. Here, we link unique individual-based data on diet choice, habitat selection and performance of domestic sheep over a 10-year period to manually GPS-recorded spatial positions of snow cover in early summer (0.57% to 43.3% in snow beds on 1st of July) in an alpine ecosystem. Snowy winters gave a higher proportion of easily digestible herbs in the diet and a more variable use of snow-bed and meadow vegetation types resulting in faster growing lambs. These patterns were consistent between two density treatment levels although slightly more marked for diet at low density, suggesting that effects of simple mitigation efforts such as managing population numbers will be meagre. Our study thus yields novel insight into the strong impact of melting snow on ecosystem function in alpine habitats, which are likely to affect productivity of both domestic and wild ungulate populations. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

  9. Verifying Snow Photochemistry Models by Combining Actinometry and UV Radiometry

    Science.gov (United States)

    Phillips, G. J.; Simpson, W. R.

    2003-12-01

    In the past few years, experiments and modeling studies have shown that chemical processes in the snow pack may have significant impacts on the chemistry of the atmosphere. A number of these studies have suggested that solar UV radiation penetrating the snow pack is the driving force for some of these chemical processes. Owing to the need to quantify the extent of the photochemical processing in the snow pack, radiative transfer models have been developed for use in the snow pack. The goal of this study is the development and performance testing of radiative transfer models constrained with photometric measurements of UV irradiance. To test these models, we compare the modeled photolysis rates to photolysis rates measured by chemical actinometry within an artificial snow-like scattering medium. This snow analog was made by flash freezing small droplets of a dilute actinometer solution in liquid nitrogen. Radiative transfer models based on both the delta-Eddington and the DISORT methods were used. The DISORT model was TUV, developed by Lee-Taylor and Madronich. To use these models, measurements of the snow analog's optical properties are needed. The light attenuation and intensity were measured in the scattering medium using a cosine- response sensor coupled to a spectrometer by fiber optic. With these optical properties constraining the model, we can then predict photolysis rates as a function of depth within the snow analog. The veracity of the radiative transfer models in the absence of light scattering was tested using 2-nitrobenzaldehyde (NBA) as an actinometer in liquid solution. The model results were found to have good agreement with the actinometer in this control experiment. The photolysis rate of NBA with respect to depth was measured from the conversion of the NBA to 2-nitrosobenzoic acid. The snow analog was irradiated with a UV light source. After exposure, samples of the snow analog were collected with respect to depth and the conversion of the

  10. A COMPARISON OF OBSERVATION WITH MODELING FOR ALBEDO AND TRANSMITTANCE OF SNOW

    OpenAIRE

    アオキ, テルオ; セコ, カツモト; アオキ, タダオ; フカボリ, マサシ; Teruo, AOKI; Katsumoto, SEKO; Tadao, AOKI; Masashi, FUKABORI

    1994-01-01

    Snow surface albedo and transmittance inside the snow have been investigated by observation and modeling. Observations were taken by a grating type spectrometer at Tokamachi in March 1993. The observed snow was old and very wet. Microscope photo-graphs of snow grains taken at this time indicate that snow grain is spherical particles with size of about 1.0μm. Surface albedo and transmittance of snow by a multiple scattering model for the atmosphere-snow system with pure snow grain size of 1.0μ...

  11. Estimating Snow and Glacier Melt in a Himalayan Watershed Using an Energy Balance Snow and Glacier Melt Model

    Science.gov (United States)

    Sen Gupta, A.; Tarboton, D. G.; Racoviteanu, A.; Brown, M. E.; Habib, S.

    2014-12-01

    to just using precipitation when considering streamflow availability. Overall, for a 10-year model run, the water equivalent of snow accumulation is 2.46 m compared to 7.13 m of glacier melt over the basin, suggesting a net loss in glacier mass.

  12. 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

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Science.gov (United States)

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

    2017-04-01

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

  15. Erosion dynamics of powder snow avalanches - model of frontal entrainment

    Science.gov (United States)

    Louge, Michel; Sovilla, Betty

    2013-04-01

    We analyze entrainment at the head of powder snow avalanches (PSA) behaving as an eruption current. Instead of invoking an erosion model or other fitted parameters, the analysis assumes that erosion is sustained by a massive blow-out arising as the snow cover is fluidized by the very pore pressure gradients that the avalanche induces within the snow pack. The stability of a mass balance involving snow cover and flow in the PSA's head region then sets frontal speed, height, mixed-mean density, snowpack fluidization depth, frontal impact pressure and static pressure. We show that acceleration of the front is insensitive to local slope, but effectively depends on the rate of change in cloud width. We compare predictions with data collected at the Vallee de la Sionne.

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

    Science.gov (United States)

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

    2012-04-01

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

  17. An improved snow scheme for the ECMWF land surface model: Description and offline validation

    Science.gov (United States)

    Emanuel Dutra; Gianpaolo Balsamo; Pedro Viterbo; Pedro M. A. Miranda; Anton Beljaars; Christoph Schar; Kelly Elder

    2010-01-01

    A new snow scheme for the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model has been tested and validated. The scheme includes a new parameterization of snow density, incorporating a liquid water reservoir, and revised formulations for the subgrid snow cover fraction and snow albedo. Offline validation (covering a wide range of spatial and...

  18. Study on Assessment Model of Classification for Snow Disasters in Tibet Plateau

    Science.gov (United States)

    Jia, L., Sr.; Xiao, T.; Wang, C.; Du, J.; Chen, D.; Zhou, Z.

    2017-12-01

    Based on Tibetan Plateau snow observation data from 39 meteorological stations during 1979-2013, we found 4 indexes for assessment model, as snow depth, the max snow depth, snow cover areas and snow duration respectively. The recurrence period of snow disasters and event distance function were calculated by normal probability density function. And the assessment model classification for snow disasters was also studied. The main contents of this research are summarized as follows: (1) The assessment model of classification for Snow Disasters was build. The snow depth, the max snow depth, snow cover areas and snow duration were selected as indexes for model, and the standard of Classification was found. The four indexes form the evaluation vector, thus euclidean distance was calculated to classify the snow disasters form the first to fifth class snow disaster. (2) The assessment of snow disasters was studied. In 370 cases of heavy snowfall in four seasons, the first class snow disaster occurred 257 times, and 69.46% of the whole cases. The second to fourth class snow disaster occurred 22.44%, 4.05% respectively. The probability of snow disaster is highest in spring, and the probability is similar in autumn and winter. (3) The average of snow depth in the fourth class snow disaster was 2.56cm, the max of snow depth is 7.45cm, the snow cover areas is 0.17 and snow duration is 57.2d. There are 4, 7, 4 times of the fourth class snow disaster in 1980s, 1990s, 2000s respectively, and 9 times occurred in winter. In December 1981, there was the fourth class snow disaster lasted for three months. During 2006-2013, up to the third class snow disaster were occurred every year, and the fourth class snow disaster occurred 4 times. In 21st century, although the times of the class snow disaster was descend, but it still has hugely damages at plateau areas. Key words: Snow Disasters; Tibet plateau; Classification; Assessment Model Acknowledgements: This study was supported by

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

    Science.gov (United States)

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

    2013-05-01

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

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

    OpenAIRE

    A. Kokhanovsky

    2013-01-01

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

  1. Geochemistry Model Validation Report: External Accumulation Model

    Energy Technology Data Exchange (ETDEWEB)

    K. Zarrabi

    2001-09-27

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

  2. Geochemistry Model Validation Report: External Accumulation Model

    International Nuclear Information System (INIS)

    Zarrabi, K.

    2001-01-01

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

  3. A multiphysical ensemble system of numerical snow modelling

    Science.gov (United States)

    Lafaysse, Matthieu; Cluzet, Bertrand; Dumont, Marie; Lejeune, Yves; Vionnet, Vincent; Morin, Samuel

    2017-05-01

    Physically based multilayer snowpack models suffer from various modelling errors. To represent these errors, we built the new multiphysical ensemble system ESCROC (Ensemble System Crocus) by implementing new representations of different physical processes in the deterministic coupled multilayer ground/snowpack model SURFEX/ISBA/Crocus. This ensemble was driven and evaluated at Col de Porte (1325 m a.s.l., French alps) over 18 years with a high-quality meteorological and snow data set. A total number of 7776 simulations were evaluated separately, accounting for the uncertainties of evaluation data. The ability of the ensemble to capture the uncertainty associated to modelling errors is assessed for snow depth, snow water equivalent, bulk density, albedo and surface temperature. Different sub-ensembles of the ESCROC system were studied with probabilistic tools to compare their performance. Results show that optimal members of the ESCROC system are able to explain more than half of the total simulation errors. Integrating members with biases exceeding the range corresponding to observational uncertainty is necessary to obtain an optimal dispersion, but this issue can also be a consequence of the fact that meteorological forcing uncertainties were not accounted for. The ESCROC system promises the integration of numerical snow-modelling errors in ensemble forecasting and ensemble assimilation systems in support of avalanche hazard forecasting and other snowpack-modelling applications.

  4. Validation of snow characteristics and snow albedo feedback in the Canadian Regional Climate Model simulations over North America

    Science.gov (United States)

    Fang, B.; Sushama, L.; Diro, G. T.

    2015-12-01

    Snow characteristics and snow albedo feedback (SAF) over North America, as simulated by the fifth-generation Canadian Regional Climate Model (CRCM5), when driven by ERA-40/ERA-Interim, CanESM2 and MPI-ESM-LR at the lateral boundaries, are analyzed in this study. Validation of snow characteristics is performed by comparing simulations against available observations from MODIS, ISCCP and CMC. Results show that the model is able to represent the main spatial distribution of snow characteristics with some overestimation in snow mass and snow depth over the Canadian high Arctic. Some overestimation in surface albedo is also noted for the boreal region which is believed to be related to the snow unloading parameterization, as well as the overestimation of snow albedo. SAF is assessed both in seasonal and climate change contexts when possible. The strength of SAF is quantified as the amount of additional net shortwave radiation at the top of the atmosphere as surface albedo decreases in association with a 1°C increase in surface temperature. Following Qu and Hall (2007), this is expressed as the product of the variation in planetary albedo with surface albedo and the change in surface albedo for 1°C change in surface air temperature during the season, which in turn is determined by the strength of the snow cover and snowpack metamorphosis feedback loops. Analysis of the latter term in the seasonal cycle suggests that for CRCM5 simulations, the snow cover feedback loop is more dominant compared to the snowpack metamorphosis feedback loop, whereas for MODIS, the two feedback loops have more or less similar strength. Moreover, the SAF strength in the climate change context appears to be weaker than in the seasonal cycle and is sensitive to the driving GCM and the RCP scenario.

  5. 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.

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

    International Nuclear Information System (INIS)

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

    1998-01-01

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

  7. Linking pollen deposition and snow accumulation on the Alto dell'Ortles glacier (South Tyrol, Italy) for sub-seasonal dating of a firn core

    Science.gov (United States)

    Festi, Daniela; Carturan, Luca; Kofler, Werner; dalla Fontana, Giancarlo; de Blasi, Fabrizio; Cazorzi, Federico; Bucher, Edith; Mair, Volkmar; Gabrielli, Paolo; Oeggl, Klaus

    2017-04-01

    Dating of ice cores from non-polar glaciers is challenging and often problematic. Yet, a proper timescale is essential for a correct interpretation of the proxies measured in the cores. Here we present a multi-disciplinary approach developed to obtain a high resolution timescale for a 10 m firn core retrieved from the Alto dell'Ortles Glacier (Italy). Results indicate that the core encompasses five accumulation years. A high resolution timescale was established by means of statistical analyses, comparing glacier pollen assemblages with daily pollen monitoring assemblages from Solda (base of Mt. Ortles). Ortles snow samples are characterised by their depth and pollen spectra, while Solda's samples are characterised by their pollen spectra and specific date. Thus, by finding for an Ortles sample the most similar Solda's sample according to their pollen content, we established a direct depth-to-day link. In this way every snow sample containing pollen has been dated. Finally, the timescale was compared with results from a mass balance model run at the drilling site. The comparison of the two independent dating methods enabled a better understanding of depositional and post depositional processes affecting pollen, dD, snow and firn at the study site. Finally, we provide an example of useful application of the timescale related to the direct comparison of measured meteorological parameters and the stable isotopes composition of the core.

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

    Science.gov (United States)

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

    2017-12-01

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

  9. 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.

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

    Directory of Open Access Journals (Sweden)

    A. Kokhanovsky

    2013-08-01

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

  11. 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.

  12. Application and Evaluation of a Snow Energy and Mass Balance Distributed Model in the Merced and Tuolumne River Watersheds of the Sierra Nevada, California

    Science.gov (United States)

    Roche, J. W.; Rice, R.; Marks, D. G.

    2015-12-01

    Characterization of snow accumulation and melt in forested environments is a key component of the hydrological cycle in mountainous regions, yet is often over-simplified in hydroecological watershed modeling due to computational limits and paucity of input data. A key first step in addressing this deficiency is to assess potential improvements in predicting snow water equivalent using a land surface model (full energy and mass balance of snow pack) over a sufficiently large geographic area to address societal questions centered on forest change and water yield of river basins. The snow energy and mass balance model ISNOBAL was applied over a 14,300 km2 region encompassing the Merced and Tuolumne River watersheds in the Sierra Nevada at a 1-hour time step and 100-meter spatial resolution for water years 2010-2014. Results show a bias toward a slight over-estimation of snow water equivalent when compared to manual snow course measurements, though cumulative melt was consistent with summed river gage data and independent estimates of evapotranspiration. Ratios of gaged runoff to water available for runoff from the base of the snowpack ranged from 0.45 to 0.59 for three different gages. Modeled results were compared to independently-derived estimates of snow water equivalent from MODIS Snow Covered Area reconstruction and airborne LiDAR estimates of snow depth, highlighting potential deficiency in SCA reconstructions, and the use of LiDAR derived snow surface maps providing snow distribution patterns in physically based models. Further, ISNOBAL highlighted trends in snow distribution patterns and snowmelt timing in an average (2010), above average (2011), and below average (2014) year.

  13. 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

  14. Vegetation controls on northern high latitude snow-albedo feedback: Observations and CMIP5 model simulations

    OpenAIRE

    Loranty, MM; Berner, LT; Goetz, SJ; Jin, Y; Randerson, JT

    2014-01-01

    The snow-masking effect of vegetation exerts strong control on albedo in northern high latitude ecosystems. Large-scale changes in the distribution and stature of vegetation in this region will thus have important feedbacks to climate. The snow-albedo feedback is controlled largely by the contrast between snow-covered and snow-free albedo (Δα), which influences predictions of future warming in coupled climate models, despite being poorly constrained at seasonal and century time scales. Here, ...

  15. 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.

  16. Comparison among physical process based snow models in estimating SWE and upwelling microwave emission

    Science.gov (United States)

    Li, D.; Durand, M. T.; Margulis, S. A.

    2012-12-01

    Snowpack serves as a critical water resource and an important climate indicator. Accurately estimating snow water equivalent (SWE) and melt timing has both civil and scientific merits. Physical process based multi-layer land surface models (LSM) characterize snowpack by tracking the energy balance and mass balance in each layer. However, in terms of the number of layers used to model the snowpack stratigraphy, as well as the complexity of the simulated mass/energy exchanges in each single layer, significant variances exist among different LSMs. Previous work has largely focused on assessing the impact of layering and stratigraphy representation on mass and energy balance, with little attention paid to the implications of these factors on predicted microwave brightness temperature (Tb). In this paper, three LSMs with varying snow layer schemes: SSiB (3-layer), CoLM (5-layer), and SNOWPACK (N-layer), are coupled to the Microwave Emission from Multi-Layer Snowpacks (MEMLS) radiative transfer model (RTM) to simulate the snowpack mass/energy budgets and microwave signature over a full season. The simulations are performed at five in-situ gage locations in the Kern River Basin, Sierra Nevada, CA where it is known that large snow events occur that can be problematic to represent using a small number of snow layers. A particular emphasis is placed on assessment of the impact of layering scheme on the results. Preliminary results show that even for SSiB which has a relative simple empirical layering scheme, the modeled annual SWE could be highly correlated with the in-situ SWE (r¬2=0.91) if the precipitation bias is corrected, also, the comparison between the Tb simulated by SSiB+MEMLS and the downscaled AMSR-E Tb measurements shows a correlation coefficient of 0.94 during the snow accumulation season (Oct to Apr) if the grain growth parameters and the soil snow reflectivity is properly calibrated. Future work includes comparing SWE and Tb from all threemodels and

  17. 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.

  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. 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.

  20. Method to determine snow albedo values in the ultraviolet for radiative transfer modeling.

    Science.gov (United States)

    Schwander, H; Mayer, B; Ruggaber, A; Albold, A; Seckmeyer, G; Koepke, P

    1999-06-20

    For many cases modeled and measured UV global irradiances agree to within +/-5% for cloudless conditions, provided that all relevant parameters for describing the atmosphere and the surface are well known. However, for conditions with snow-covered surfaces this agreement is usually not achievable, because on the one hand the regional albedo, which has to be used in a model, is only rarely available and on the other hand UV irradiance alters with different snow cover of the surface by as much as 50%. Therefore a method is given to determine the regional albedo values for conditions with snow cover by use of a parameterization on the basis of snow depth and snow age, routinely monitored by the weather services. An algorithm is evolved by multiple linear regression between the snow data and snow-albedo values in the UV, which are determined from a best fit of modeled and measured UV irradiances for an alpine site in Europe. The resulting regional albedo values in the case of snow are in the 0.18-0.5 range. Since the constants of the regression depend on the area conditions, they have to be adapted if the method is applied for other sites. Using the algorithm for actual cases with different snow conditions improves the accuracy of modeled UV irradiances considerably. Compared with the use of an average, constant snow albedo, the use of actual albedo values, provided by the algorithm, halves the average deviations between measured and modeled UV global irradiances.

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

    Directory of Open Access Journals (Sweden)

    P. Räisänen

    2017-12-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

  4. 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

  5. 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

    2017-10-01

    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

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

    Directory of Open Access Journals (Sweden)

    Caixin Wang

    2015-08-01

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

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

    Science.gov (United States)

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

    2017-04-01

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

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

    Science.gov (United States)

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

    2015-12-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

  10. 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.

  11. An Evaluation of High-Resolution Regional Climate Model Simulated Snow Cover Using Satellite Data (With Implications for the Simulated Snow-Albedo Feedback)

    Science.gov (United States)

    Minder, J. R.; Letcher, T.

    2015-12-01

    Snow cover often exhibits large spatial variability over mountainous regions where variations in elevation, aspect, vegetation, winds, and orographic precipitation all modulate snow cover. Under climate change, reductions in mountain snow cover are likely to substantially amplify regional warming via the snow-albedo feedback. To capture this important feedback it is crucial that regional climate models (RCMs) adequately simulate spatial and temporal variations in snow cover. Snow cover simulated by high-resolution RCMs over the central Rocky Mountains of the United States is evaluated. RCM simulations were conducted using the Weather Research and Forecasting (WRF) model on a 4 km horizontal grid forced by reanalysis boundary conditions over a seven-year time period. A pair of simulations is considered that differ in the domain size (regional vs. continental) and the land surface model (Noah vs. Noah-MP) employed. RCM output is compared with high-resolution gridded satellite analyses of surface albedo and fractional snow cover derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Results reveal that both RCMs are generally successful at reproducing the observed seasonal cycle and interannual variability of snow extent over the high terrain of the Rockies. However, in simulations using the Noah land surface model (LSM), sub-grid scale fractional snow covered area of grid cells containing snow is systematically too high compared to observations, often exceeding observations by more than 0.2. This bias in fractional snow cover leads to a substantial positive bias in regional surface albedo. Simulations using the Noah-MP LSM produce more realistic variations in fractional snow cover and surface albedo, likely due to its more-realistic treatment of canopy effects. We quantify how differences in simulated snow cover affect the strength of the snow-albedo feedback under climate change. Both RCMs were used to conduct representative 7-year simulations of a

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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

  17. Spatial and temporal variability of snow accumulation rate on the East Antarctic ice divide between Dome Fuji and EPICA DML

    Directory of Open Access Journals (Sweden)

    S. Fujita

    2011-11-01

    Full Text Available To better understand the spatio-temporal variability of the glaciological environment in Dronning Maud Land (DML, East Antarctica, a 2800-km-long Japanese-Swedish traverse was carried out. The route includes ice divides between two ice-coring sites at Dome Fuji and EPICA DML. We determined the surface mass balance (SMB averaged over various time scales in the late Holocene based on studies of snow pits and firn cores, in addition to radar data. We find that the large-scale distribution of the SMB depends on the surface elevation and continentality, and that the SMB differs between the windward and leeward sides of ice divides for strong-wind events. We suggest that the SMB is highly influenced by interactions between the large-scale surface topography of ice divides and the wind field of strong-wind events that are often associated with high-precipitation events. Local variations in the SMB are governed by the local surface topography, which is influenced by the bedrock 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 % than averages over longer periods of 722 a or 7.9 ka before AD 2008. A similar increasing trend has been reported for many inland plateau sites in Antarctica with the exception of several sites on the leeward side of the ice divides.

  18. 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

  19. ALBEDO MODELS FOR SNOW AND ICE ON A FRESHWATER LAKE. (R824801)

    Science.gov (United States)

    AbstractSnow and ice albedo measurements were taken over a freshwater lake in Minnesota for three months during the winter of 1996¯1997 for use in a winter lake water quality model. The mean albedo of new snow was measured as 0.83±0.028, while the...

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

    Science.gov (United States)

    Sidler, R.

    2014-12-01

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

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

    Science.gov (United States)

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

    2014-12-01

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

  2. 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.

  3. Vegetation controls on northern high latitude snow-albedo feedback: observations and CMIP5 model simulations.

    Science.gov (United States)

    Loranty, Michael M; Berner, Logan T; Goetz, Scott J; Jin, Yufang; Randerson, James T

    2014-02-01

    The snow-masking effect of vegetation exerts strong control on albedo in northern high latitude ecosystems. Large-scale changes in the distribution and stature of vegetation in this region will thus have important feedbacks to climate. The snow-albedo feedback is controlled largely by the contrast between snow-covered and snow-free albedo (Δα), which influences predictions of future warming in coupled climate models, despite being poorly constrained at seasonal and century time scales. Here, we compare satellite observations and coupled climate model representations of albedo and tree cover for the boreal and Arctic region. Our analyses reveal consistent declines in albedo with increasing tree cover, occurring south of latitudinal tree line, that are poorly represented in coupled climate models. Observed relationships between albedo and tree cover differ substantially between snow-covered and snow-free periods, and among plant functional type. Tree cover in models varies widely but surprisingly does not correlate well with model albedo. Furthermore, our results demonstrate a relationship between tree cover and snow-albedo feedback that may be used to accurately constrain high latitude albedo feedbacks in coupled climate models under current and future vegetation distributions. © 2013 John Wiley & Sons Ltd.

  4. Describing macro-scale structure of the snow cover by a dynamic-stochastic model

    Directory of Open Access Journals (Sweden)

    A. N. Gelfan

    2015-01-01

    Full Text Available Possibilities to investigate the spatial structure of snow cover by means of dynamic-stochastic model are discussed in this article. Basin of the Cheboksary reservoir (area of 376 500 sq.km was used as an example. Results of numerical experiments show that our dynamic-stochastic model of the snow cover formation reproduces a snow field structure with adequate accuracy. The fractal dimensions of the modeled fields are in good correspondence with respective dimensions of fields obtained from data of the in situ observations.

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

    Science.gov (United States)

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

    2013-05-01

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

  6. Development of a Cryosphere Land Surface Model with Coupled Snow and Frozen Soil Processes

    Science.gov (United States)

    Wang, L.; Sun, L.; Yang, K.; Tian, L.

    2015-12-01

    In this study, a land surface model with coupled snow and frozen soil physics has been developed by improving the formulations of snow and frozen soil for a hydrologically-improved land surface model (HydroSiB2). First, an energy-balance based 3-layer snow model has been incorporated into the HydroSiB2 (hereafter HydroSiB2-S) for an improved description of internal processes of snow pack. Second, a universal and simplified soil model has been coupled with HydroSiB2-S to enable the calculation of soil water freezing and thawing (hereafter HydroSiB2-SF). In order to avoid the instability caused by the uncertainty in estimating water phase changes, enthalpy is 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 rigorously evaluated at two typical sites over Tibetan Plateau (one snowy and the other non-snowy, with both underlying frozen soil). At the snowy site in northeast TP (DY in the upper Hei River), HydroSiB2-SF demonstrated significant improvements over HydroSiB2-F (that is the model same as HydroSiB2-SF but using the original single-layer snow module of HydroSiB2), showing the importance of snow internal processes described by 3-layer snow parameterization. At the non-snowy site in southwest TP (Ngari, extremely dry), HydroSiB2-SF gave reasonable simulations of soil water phase changes while HydroSiB2-S did not, indicating the crucial role of frozen soil module in depicting the soil thermal and water dynamics. Finally, HydroSiB2-SF was proved capable of simulating upward moisture fluxes towards freezing front from the unfrozen soil layers below in winter.

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

    Science.gov (United States)

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

    2014-03-01

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

  8. 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

  9. 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.

  10. 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.

  11. 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; hide

    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.

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  13. Forecasting of rain-on-snow events in alpine region using a fully coupled atmosphere/snowpack/hydrology model

    Science.gov (United States)

    Vionnet, V.; Fortin, V.; Dimitrijevic, M.; Abrahamowicz, M.; Gauthier, N.; Garnaud, C.; Bélair, S.; Milbrandt, J.; Pomeroy, J. W.

    2017-12-01

    Rain-on-snow (ROS) events occur when rain falls on a snowpack and present a strong flooding potential. An accurate estimation of the amount and timing of snowpack runoff is crucial for hydro-meteorological forecasters and remains a challenge. Indeed, during ROS events, snowpack runoff is strongly influenced by the complex evolution of meteorological and snowpack conditions. In this study, the ability of the distributed hydrology platform GEM-Hydro to forecast hydro-meteorological conditions during ROS events in complex terrain is assessed. GEM-Hydro couples the GEM (Global Environmental Multi-scale) atmospheric model with the SVS (Soil, Vegetation and Snow) land-surface scheme and the WATROUTE routing scheme. GEM-Hydro is applied to the ROS event of 19-22 June 2013 in the Canadian Rockies that contributed to the devastating streamflow observed in Calgary (Alberta, Canada) and the surrounding region. A substantial snowpack remained at high elevations in the region due to a late May snowfall, snow redistribution by wind and avalanches and relatively cool spring temperatures. Relatively warm rainfall at the beginning of the storm changed to snowfall towards the end, resulting in a complex ROS and snow accumulation event. The impact of model resolution on the quality of the forecast is firstly tested using three configurations of the system at 2.5, 1 and 0.25 km. The sensitivity to horizontal resolution of simulated wind-driven turbulent energy fluxes between the atmosphere and snow surface is especially discussed since these fluxes often drive the high melt rates during ROS events. Additional simulations with a version of SVS including the multi-layer snowpack scheme Explicit Snow (SVS-ES) have then been carried out and compared to results obtained with the default version of SVS featuring a single layer snow scheme. In particular, the impact of the initialization of snowpack variables on simulated runoff is presented. This work is the first step towards the

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

    Science.gov (United States)

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

    2017-12-01

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

  15. Evaluating uncertainties in modelling the snow hydrology of the Fraser River Basin, British Columbia, Canada

    Science.gov (United States)

    Islam, Siraj Ul; Déry, Stephen J.

    2017-03-01

    This study evaluates predictive uncertainties in the snow hydrology of the Fraser River Basin (FRB) of British Columbia (BC), Canada, using the Variable Infiltration Capacity (VIC) model forced with several high-resolution gridded climate datasets. These datasets include the Canadian Precipitation Analysis and the thin-plate smoothing splines (ANUSPLIN), North American Regional Reanalysis (NARR), University of Washington (UW) and Pacific Climate Impacts Consortium (PCIC) gridded products. Uncertainties are evaluated at different stages of the VIC implementation, starting with the driving datasets, optimization of model parameters, and model calibration during cool and warm phases of the Pacific Decadal Oscillation (PDO). The inter-comparison of the forcing datasets (precipitation and air temperature) and their VIC simulations (snow water equivalent - SWE - and runoff) reveals widespread differences over the FRB, especially in mountainous regions. The ANUSPLIN precipitation shows a considerable dry bias in the Rocky Mountains, whereas the NARR winter air temperature is 2 °C warmer than the other datasets over most of the FRB. In the VIC simulations, the elevation-dependent changes in the maximum SWE (maxSWE) are more prominent at higher elevations of the Rocky Mountains, where the PCIC-VIC simulation accumulates too much SWE and ANUSPLIN-VIC yields an underestimation. Additionally, at each elevation range, the day of maxSWE varies from 10 to 20 days between the VIC simulations. The snow melting season begins early in the NARR-VIC simulation, whereas the PCIC-VIC simulation delays the melting, indicating seasonal uncertainty in SWE simulations. When compared with the observed runoff for the Fraser River main stem at Hope, BC, the ANUSPLIN-VIC simulation shows considerable underestimation of runoff throughout the water year owing to reduced precipitation in the ANUSPLIN forcing dataset. The NARR-VIC simulation yields more winter and spring runoff and earlier decline

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

    OpenAIRE

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2018-03-01

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

  18. Modelling of stream flow in snow dominated Budhigandaki ...

    Indian Academy of Sciences (India)

    60

    at the downstream part of the basin. The catchment area experiences short winter monsoon between. Nov-Feb. Relative humidity reaches maximum during the monsoon season. The Himalayan zone contributes to the stream flow in the dry season by snow and ice melt. The Central Mahabharata zone, which account for the ...

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

    Science.gov (United States)

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

    2013-06-01

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

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

    Science.gov (United States)

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

    2016-12-01

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

  1. 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...

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    Georgeta BĂETU

    2014-07-01

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

  5. Modelling hydrologic impacts of light absorbing aerosol deposition on snow at the catchment scale

    Science.gov (United States)

    Matt, Felix N.; Burkhart, John F.; Pietikäinen, Joni-Pekka

    2018-01-01

    Light absorbing impurities in snow and ice (LAISI) originating from atmospheric deposition enhance snowmelt by increasing the absorption of shortwave radiation. The consequences are a shortening of the snow duration due to increased snowmelt and, at the catchment scale, a temporal shift in the discharge generation during the spring melt season. In this study, we present a newly developed snow algorithm for application in hydrological models that allows for an additional class of input variable: the deposition mass flux of various species of light absorbing aerosols. To show the sensitivity of different model parameters, we first use the model as a 1-D point model forced with representative synthetic data and investigate the impact of parameters and variables specific to the algorithm determining the effect of LAISI. We then demonstrate the significance of the radiative forcing by simulating the effect of black carbon (BC) deposited on snow of a remote southern Norwegian catchment over a 6-year period, from September 2006 to August 2012. Our simulations suggest a significant impact of BC in snow on the hydrological cycle. Results show an average increase in discharge of 2.5, 9.9, and 21.4 %, depending on the applied model scenario, over a 2-month period during the spring melt season compared to simulations where radiative forcing from LAISI is not considered. The increase in discharge is followed by a decrease in discharge due to a faster decrease in the catchment's snow-covered fraction and a trend towards earlier melt in the scenarios where radiative forcing from LAISI is applied. Using a reasonable estimate of critical model parameters, the model simulates realistic BC mixing ratios in surface snow with a strong annual cycle, showing increasing surface BC mixing ratios during spring melt as a consequence of melt amplification. However, we further identify large uncertainties in the representation of the surface BC mixing ratio during snowmelt and the subsequent

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

    Czech Academy of Sciences Publication Activity Database

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

    2011-01-01

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

  7. Modeling Road Vulnerability to Snow Using Mixed Integer Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Rodriguez, Tony K [ORNL; Omitaomu, Olufemi A [ORNL; Ostrowski, James A [ORNL; Bhaduri, Budhendra L [ORNL

    2017-01-01

    As the number and severity of snowfall events continue to grow, the need to intelligently direct road maintenance during these snowfall events will also grow. In several locations, local governments lack the resources to completely treat all roadways during snow events. Furthermore, some governments utilize only traffic data to determine which roads should be treated. As a result, many schools, businesses, and government offices must be unnecessarily closed, which directly impacts the social, educational, and economic well-being of citizens and institutions. In this work, we propose a mixed integer programming formulation to optimally allocate resources to manage snowfall on roads using meteorological, geographical, and environmental parameters. Additionally, we evaluate the impacts of an increase in budget for winter road maintenance on snow control resources.

  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. Modelling snow cover duration improves predictions of functional and taxonomic diversity for alpine plant communities.

    Science.gov (United States)

    Carlson, Bradley Z; Choler, Philippe; Renaud, Julien; Dedieu, Jean-Pierre; Thuiller, Wilfried

    2015-11-01

    Quantifying relationships between snow cover duration and plant community properties remains an important challenge in alpine ecology. This study develops a method to estimate spatial variation in energy availability in the context of a topographically complex, high-elevation watershed, which was used to test the explanatory power of environmental gradients both with and without snow cover in relation to taxonomic and functional plant diversity. Snow cover in the French Alps was mapped at 15-m resolution using Landsat imagery for five recent years, and a generalized additive model (GAM) was fitted for each year linking snow to time and topography. Predicted snow cover maps were combined with air temperature and solar radiation data at daily resolution, summed for each year and averaged across years. Equivalent growing season energy gradients were also estimated without accounting for snow cover duration. Relationships were tested between environmental gradients and diversity metrics measured for 100 plots, including species richness, community-weighted mean traits, functional diversity and hyperspectral estimates of canopy chlorophyll content. Accounting for snow cover in environmental variables consistently led to improved predictive power as well as more ecologically meaningful characterizations of plant diversity. Model parameters differed significantly when fitted with and without snow cover. Filtering solar radiation with snow as compared without led to an average gain in R(2) of 0·26 and reversed slope direction to more intuitive relationships for several diversity metrics. The results show that in alpine environments high-resolution data on snow cover duration are pivotal for capturing the spatial heterogeneity of both taxonomic and functional diversity. The use of climate variables without consideration of snow cover can lead to erroneous predictions of plant diversity. The results further indicate that studies seeking to predict the response of alpine

  10. 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.

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

    Science.gov (United States)

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

    2013-08-01

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

  12. Coupling of a Detailed Snow Model to WRF-Hydro for Glacier Mass Balance and Glacier Runoff Studies

    Science.gov (United States)

    Eidhammer, T.; Gochis, D.; Barlage, M. J.; Rasmussen, R.

    2017-12-01

    Studies of mass balance in glaciers in complex terrain show that elevation gradients and complex topography in many glaciated regions lead to large variations in temperature, precipitation, winds (and thereby wind deflection, transport and deposition of dry snow during the accumulation season) and net radiative exchange across the glacier. Therefore, proper simulation of the non-homogenous, non-stationary, evolution of a glacier requires much finer resolution of atmospheric processes than typical global or regional climate models can provide. Furthermore, regional `atmosphere-only' models typically do not have the detailed information about runoff routing processes, which are important components in the hydrological cycle. Glacier melt contributes to discharge especially during summer when the magnitude of the summer peak river flow depends greatly on the contribution of melt water from snow and ice to the total river flow. This contribution from glaciers to total flow plays a key role in the glacier-fed rivers in populated regions where summer flows are crucial for irrigation, human consumption and energy production. We have incorporated the detailed Crocus snow model, as a glacier mass balance model, into the Noah-MP land model, within the Weather and Research Forecasting - Hydro (WRF-Hydro) modelling system. By linking a surface mass balance glacier model to the WRF-Hydro system (WRF-HydroGlac), the interactions between the energy, water and mass balance budgets over glaciated river basins can be better depicted and projected future impacts, better understood. We will demonstrate the WRF-HydroGlac model with a mass balance and snowpack/glacier runoff study of a highly observed Norwegian glacier (Hardangerjokulen).

  13. Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model

    Directory of Open Access Journals (Sweden)

    Ally M. Toure

    2018-02-01

    Full Text Available The NASA Catchment land surface model (CLSM is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA. Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF observations from the Moderate Resolution Imaging Spectroradiometer (MODIS. Moreover, a computationally-efficient empirical scheme is designed to improve CLSM estimates of SCF, snow depth, and snow water equivalent (SWE through the assimilation of MODIS SCF observations. Results show that data assimilation (DA improved SCF estimates compared to the open-loop model without assimilation (OL, especially in areas with ephemeral snow cover and mountainous regions. A comparison of the SCF estimates from DA against snow cover estimates from the NOAA Interactive Multisensor Snow and Ice Mapping System showed an improvement in the probability of detection of up to 28% and a reduction in false alarms by up to 6% (relative to OL. A comparison of the model snow depth estimates against Canadian Meteorological Centre analyses showed that DA successfully improved the model seasonal bias from −0.017 m for OL to −0.007 m for DA, although there was no significant change in root-mean-square differences (RMSD (0.095 m for OL, 0.093 m for DA. The time-average of the spatial correlation coefficient also improved from 0.61 for OL to 0.63 for DA. A comparison against in situ SWE measurements also showed improvements from assimilation. The correlation increased from 0.44 for OL to 0.49 for DA, the bias improved from −0.111 m for OL to −0.100 m for DA, and the RMSD decreased from 0.186 m for OL to 0.180 m for DA.

  14. Simulations of snow distribution and hydrology in a mountain basin

    Science.gov (United States)

    Hartman, M.D.; Baron, Jill S.; Lammers, R.B.; Cline, D.W.; Band, L.E.; Liston, G.E.; Tague, C.

    1999-01-01

    We applied a version of the Regional Hydro-Ecologic Simulation System (RHESSys) that implements snow redistribution, elevation partitioning, and wind-driven sublimation to Loch Vale Watershed (LVWS), an alpine-subalpine Rocky Mountain catchment where snow accumulation and ablation dominate the hydrologic cycle. We compared simulated discharge to measured discharge and the simulated snow distribution to photogrammetrically rectified aerial (remotely sensed) images. Snow redistribution was governed by a topographic similarity index. We subdivided each hillslope into elevation bands that had homogeneous climate extrapolated from observed climate. We created a distributed wind speed field that was used in conjunction with daily measured wind speeds to estimate sublimation. Modeling snow redistribution was critical to estimating the timing and magnitude of discharge. Incorporating elevation partitioning improved estimated timing of discharge but did not improve patterns of snow cover since wind was the dominant controller of areal snow patterns. Simulating wind-driven sublimation was necessary to predict moisture losses.

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Science.gov (United States)

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

    2018-04-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Directory of Open Access Journals (Sweden)

    N. A. N. Bertler

    2018-02-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  20. 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.

  1. Interpretation of Snow-Climate Feedback as Produced by 17 General Circulation Models

    Science.gov (United States)

    Cess, R. D.; Potter, G. L.; Zhang, M.-H.; Blanchet, J.-P.; Chalita, S.; Colman, R.; Dazlich, D. A.; del Genio, A. D.; Dymnikov, V.; Galin, V.; Jerrett, D.; Keup, E.; Lacis, A. A.; Le Treut, H.; Liang, X.-Z.; Mahfouf, J.-F.; McAvaney, B. J.; Meleshko, V. P.; Mitchell, J. F. B.; Morcrette, J.-J.; Norris, P. M.; Randall, D. A.; Rikus, L.; Roeckner, E.; Royer, J.-F.; Schlese, U.; Sheinin, D. A.; Slingo, J. M.; Sokolov, A. P.; Taylor, K. E.; Washington, W. M.; Wetherald, R. T.; Yagai, I.

    1991-08-01

    Snow feedback is expected to amplify global warming caused by increasing concentrations of atmospheric greenhouse gases. The conventional explanation is that a warmer Earth will have less snow cover, resulting in a darker planet that absorbs more solar radiation. An intercomparison of 17 general circulation models, for which perturbations of sea surface temperature were used as a surrogate climate change, suggests that this explanation is overly simplistic. The results instead indicate that additional amplification or moderation may be caused both by cloud interactions and longwave radiation. One measure of this net effect of snow feedback was found to differ markedly among the 17 climate models, ranging from weak negative feedback in some models to strong positive feedback in others.

  2. Snow Fun.

    Science.gov (United States)

    Finlay, Joy

    1988-01-01

    Describes several learning activities that can be done with children in the snow. Includes shake paintings, snow sculpture, snow "snakes," snow-ball contests, an igloo experience, and how to make snowshoes. (TW)

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

    Directory of Open Access Journals (Sweden)

    T. Skaugen

    2016-09-01

    Full Text Available 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 SD_G 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 SD_G 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. On the importance of sublimation to an alpine snow mass balance in the Canadian Rocky Mountains

    Directory of Open Access Journals (Sweden)

    M. K. MacDonald

    2010-07-01

    Full Text Available A modelling study was undertaken to evaluate the contribution of sublimation to an alpine snow mass balance in the Canadian Rocky Mountains. Snow redistribution and sublimation by wind, snowpack sublimation and snowmelt were simulated for two winters over an alpine ridge transect located in the Canada Rocky Mountains. The resulting snowcover regimes were compared to those from manual snow surveys. Simulations were performed using physically based blowing snow (PBSM and snowpack ablation (SNOBAL models. A hydrological response unit (HRU-based spatial discretization was used rather than a more computationally expensive fully-distributed one. The HRUs were set up to follow an aerodynamic sequence, whereby eroded snow was transported from windswept, upwind HRUs to drift accumulating, downwind HRUs. That snow redistribution by wind can be adequately simulated in computationally efficient HRUs over this ridge has important implications for representing snow transport in large-scale hydrology models and land surface schemes. Alpine snow sublimation losses, in particular blowing snow sublimation losses, were significant. Snow mass losses to sublimation as a percentage of cumulative snowfall were estimated to be 20–32% with the blowing snow sublimation loss amounting to 17–19% of cumulative snowfall. This estimate is considered to be a conservative estimate of the blowing snow sublimation loss in the Canadian Rocky Mountains because the study transect is located in the low alpine zone where the topography is more moderate than the high alpine zone and windflow separation was not observed. An examination of the suitability of PBSM's sublimation estimates in this environment and of the importance of estimating blowing snow sublimation on the simulated snow accumulation regime was conducted by omitting sublimation calculations. Snow accumulation in HRUs was overestimated by 30% when neglecting blowing snow sublimation calculations.

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

    Science.gov (United States)

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

    2017-11-01

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

  6. Differences Between the HUT Snow Emission Model and MEMLS and Their Effects on Brightness Temperature Simulation

    Science.gov (United States)

    Pan, Jinmei; Durand, Michael; Sandells, Melody; Lemmetyinen, Juha; Kim, Edward J.; Pulliainen, Jouni; Kontu, Anna; Derksen, Chris

    2015-01-01

    Microwave emission models are a critical component of snow water equivalent retrieval algorithms applied to passive microwave measurements. Several such emission models exist, but their differences need to be systematically compared. This paper compares the basic theories of two models: the multiple-layer HUT (Helsinki University of Technology) model and MEMLS (Microwave Emission Model of Layered Snowpacks). By comparing the mathematical formulation side-by-side, three major differences were identified: (1) by assuming the scattered intensity is mostly (96) in the forward direction, the HUT model simplifies the radiative transfer (RT) equation into 1-flux; whereas MEMLS uses a 2-flux theory; (2) the HUT scattering coefficient is much larger than MEMLS; (3 ) MEMLS considers the trapped radiation inside snow due to internal reflection by a 6-flux model, which is not included in HUT. Simulation experiments indicate that, the large scattering coefficient of the HUT model compensates for its large forward scattering ratio to some extent, but the effects of 1-flux simplification and the trapped radiation still result in different T(sub B) simulations between the HUT model and MEMLS. The models were compared with observations of natural snow cover at Sodankyl, Finland; Churchill, Canada; and Colorado, USA. No optimization of the snow grain size was performed. It shows that HUT model tends to under estimate T(sub B) for deep snow. MEMLS with the physically-based improved Born approximation performed best among the models, with a bias of -1.4 K, and an RMSE of 11.0 K.

  7. Modelling the impact of climate and landscape changes on snow distribution and melt in regions with limited data

    Science.gov (United States)

    Comeau, L. E.; Essery, R.; Dugmore, A.

    2010-12-01

    The quantity and distribution of snow across landscapes and timing of the spring snowmelt is key to a diverse range of processes, from the hydrological cycle and glaciation through to ecological and human-environment interactions. Many snow-covered landscapes are remote, inaccessible and lack observation data, especially at high resolutions and spanning multi-decadal time periods. Models are therefore valuable tools for understanding and simulating temporal and spatial variations in snow cover. The aim is to determine the most robust method of modelling snow distribution and melt across regional landscapes with limited data availability, and to apply models to understand and project variations in snow cover as a result of landscape and climate change. Physically based, high resolution snow distribution and melt models are tested through fieldwork in Sweden and Norway at research sites with detailed landscape and climate data. The impact of pseudo-limiting input data spatially and temporally on model performance and uncertainty is assessed. Methods of snow model transferral (including parameter estimation and transfer) between areas of different spatial scales and over varying time periods are explored alongside the effects on model uncertainty, with the use of additional field data from research sites in North America and Finland. The impact of variations in topography, vegetation and climate on snow distribution and melt is assessed through both fieldwork and model application, with exploration of the implications for landscape processes and populations.

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

    Science.gov (United States)

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

    2016-12-01

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

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  10. Application of a Process Based Hydrologic Model in a Snow Dominant WaterShed: Upper Feather River Basin in California

    Science.gov (United States)

    Chung, F. I.; Kadir, T.; Galef, J.

    2008-12-01

    Milly et al. in a recent article (Science, Vol319, 1February, 2008, pp573-574) declared that "stationarity is dead." They went on stating, "Finding a suitable successor is crucial for human adaptation to changing climate." California's Department of Water Resources' (DWR's) search for a suitable successor led to the conclusion that a "temperature based approach" might be a good candidate to replace or supplement the traditional "precipitation based" hydrology. In this paper application of a physically based model that begins with ambient air temperature is presented. The projections of precipitation by various GCM's are wide spread and uncertainties on the wetness (or dryness) are abound whereas the future temperature projections, through also wide spread, are unanimous in directional sense-going up or getting warmer over time. Noting this robust nature of the future temperature projections and also noting that the cause of the future precipitation changes is due to the rising temperature, the authors take an approach that the temperature, rather than the precipitation, should be the commencing point in the development of the changing future hydrology. We claim that the main cause of the "death" of the stationarity in a snow dominant high elevation watershed is the warming temperature. Therefore, by commencing with the temperature in the hydrologic process, either the form of precipitation or the melting of the accumulated snow can be captured and the non-stationary future hydrology can be generated for water resources planning and management. The USGS under a contract to DWR completed development of the Precipitation-Runoff Modeling System (PRMS) application for simulating daily streamflow for the Upper Feather River Basin. PRMS simulates all the major snowmelt/precipitation related physical processes including snowpack accumulation/melting, sublimation, evapotranspiration, surface runoff, subsurface flow, and ground water flow. The model was calibrated for Water

  11. Estimation of the spatiotemporal dynamics of snow covered area by using cellular automata models

    Science.gov (United States)

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

    2017-07-01

    Given the need to consider the cryosphere in water resources management for mountainous regions, the purpose of this paper is to model the daily spatially distributed dynamics of snow covered area (SCA) by using calibrated cellular automata models. For the operational use of the calibrated model, the only data requirements are the altitude of each cell of the spatial discretization of the area of interest and precipitation and temperature indexes for the area of interest. For the calibration step, experimental snow covered area data are needed. Potential uses of the model are to estimate the snow covered area when satellite data are absent, or when they provide a temporal resolution different from the operational resolution, or when the satellite images are useless because they are covered by clouds or because there has been a sensor failure. Another interesting application is the simulation of SCA dynamics for the snow covered area under future climatic scenarios. The model is applied to the Sierra Nevada mountain range, in southern Spain, which is home to significant biodiversity, contains important water resources in its snowpack, and contains the most meridional ski resort in Europe.

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

    Directory of Open Access Journals (Sweden)

    M. Niwano

    2018-02-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

  14. What Does a Multilayer Canopy Model Tell Us About Our Current Understanding of Snow-Canopy Unloading?

    Science.gov (United States)

    McGowan, L. E.; Paw U, K. T.; Dahlke, H. E.

    2017-12-01

    In the Western U.S., future water resources depend on the forested mountain snowpack. The variations in and estimates of forest mountain snow volume are vital to projecting annual water availability; yet, snow forest processes are not fully known. Most snow models calculate snow-canopy unloading based on time, temperature, Leaf Area Index (LAI), and/or wind speed. While models crudely consider the canopy shape via LAI, current models typically do not consider the vertical canopy structure or varied energetics within multiple canopy layers. Vertical canopy structure influences the spatiotemporal distribution of snow, and therefore ultimately determines the degree and extent by which snow alters both the surface energy balance and water availability. Within the canopy both the snowpack and energetic exposures to the snowpack (wind, shortwave and longwave radiation, turbulent heat fluxes etc.) vary widely in the vertical. The water and energy balance in each layer is dependent on all other layers. For example, increased snow canopy content in the top of the canopy will reduce available shortwave radiation at the bottom and snow unloading in a mid-layer can cascade and remove snow from all the lower layers. We examined vertical interactions and structures of the forest canopy on the impact of unloading utilizing the Advanced Canopy-Atmosphere-Soil-Algorithm (ACASA), a multilayer soil-vegetation-atmosphere numerical model based on higher-order closure of turbulence equations. Our results demonstrate how a multilayer model can be used to elucidate the physical processes of snow unloading, and could help researchers better parameterize unloading in snow-hydrology models.

  15. 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

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

    Science.gov (United States)

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

    2015-11-15

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

  17. Combining model and satellite data to investigate the effect of light absorbing impurities on snow melt and discharge generation

    Science.gov (United States)

    Matt, F.; Burkhart, J. F.

    2017-12-01

    Light absorbing impurities in snow and ice (LAISI) originating from atmospheric deposition enhance snow melt by increasing the absorption of solar 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 effects as simulated in numerical models have large uncertainties. These uncertainties originate 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. This leads to high uncertainties in the additional energy absorbed by the snow due to the presence of LAISI (the so called radiative forcing of LAISI), a key variable in understanding snowpack energy-balance dynamics. In this study, we present an approach combining distributed model simulations on the catchment scale and remotely sensed radiative forcing from LAISI in order to evaluate and improve model predictions. In a case study, we assess the effect of LAISI on snow melt and discharge generation in a high mountain catchment located in the western Himalaya using the distributed hydrologic model, Shyft. The snow albedo is hereby calculated from a radiative transfer model for snow, taking the increased absorption of solar radiation by LAISI into account. LAISI mixing ratios in snow are determined from atmospheric aerosol deposition rates. To asses the quality of our simulations, we model the instantaneous clear sky radiative forcing at MODIS overpass times, and compare it to the MODIS Dust Radiative Forcing in Snow (MODDRFS) satellite product. By scaling the deposition input to the model, we can optimize the simulated radiative forcing towards the satellite observations.

  18. Using Distributed Snow Data to Evaluate and Improve the Performance of the Distributed Soil Hydrology Vegetation Model (DHSVM): a test case from the northeastern U.S

    Science.gov (United States)

    del Peral, A.; Wemple, B.

    2011-12-01

    Internal validation of physically-based hydrologic models using distributed field measurements has been increasingly recognized as an important approach for testing model performance and improving model skill. We are evaluating the performance of the Distributed Soil Hydrology Vegetation Model (DHSVM) to model snowmelt and runoff in mountainous watersheds in the northeastern U.S. Our test cases include a forested control watershed and a watershed managed as an alpine ski area. Empirical results from these watersheds show substantial differences in annual water yield between the watersheds that are highly correlated to annual snowpack magnitude. An underlying objective of our research is to explore the effects of ski area development, in particular the size, spatial arrangement and orientation of ski runs and base village development on runoff production. In this application, we use distributed snow pack measurements to validate distributed model simulations of snow accumulation and melt. We also explore the use of an automated model calibration approach, utilizing our distributed snow measurements, to improve model skill. Future work will make use of these exploratory simulations as we attempt to model the effects of ski area development under different design scenarios and climate conditions.

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

    Science.gov (United States)

    Räisänen, P.; Luomaranta, A.; Järvinen, H.; Takala, M.; Jylhä, K.; Bulygina, O. N.; Luojus, K.; Riihelä, A.; Laaksonen, A.; Koskinen, J.; Pulliainen, J.

    2014-12-01

    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 melting snow.

  20. Spring Snow-Albedo Feedback Analysis Over the Third Pole: Results From Satellite Observation and CMIP5 Model Simulations

    Science.gov (United States)

    Guo, Hui; Wang, Xiaoyi; Wang, Tao; Ma, Yaoming; Ryder, James; Zhang, Taotao; Liu, Dan; Ding, Jinzhi; Li, Yue; Piao, Shilong

    2018-01-01

    The snow-albedo feedback is a crucial component in high-altitude cryospheric change but is poorly quantified over the Third Pole, encompassing the Karakoram and Tibetan Plateau. Here we present an analysis of present-day and future spring snow-albedo feedback over the Third Pole, using a 28 year satellite-based albedo and the latest climate model simulations. We show that present-day spring snow-albedo feedback strength is primarily determined by the decrease in albedo due to snow metamorphosis, rather than that due to reduced snow cover in the Karakoram, but not found in Southeastern Tibet. We further demonstrate an emergent relationship between snow-albedo feedback from the seasonal cycle and that from climate change across models. Combined with contemporary satellite-based snow-albedo feedback from seasonal cycle, this relationship enables us to estimate that the feedback strength for the Karakoram with a relatively high glaciated area is -2.42 ± 0.48% K-1 under an unmitigated scenario, which is much stronger than that for Southeastern Tibet (-1.64 ± 0.48% K-1) and for the Third Pole (-0.89 ± 0.44% K-1), respectively. Moreover, it is noteworthy that the magnitude of the constrained strength is only half of the unconstrained model estimate for the Third Pole, suggesting that current climate models generally overestimate the feedback of spring snow change to temperature change based on the unmitigated scenario.

  1. 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

  2. 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).

  3. 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.

  4. 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.

  5. 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...

  6. Snow on Sea Ice Workshop - An Icy Meeting of the Minds: Modelers and Measurers

    Science.gov (United States)

    2015-09-30

    Roberts. Measurers: Don Perovich, Rob Massom, Melinda Webster, Oliver Dammann, Charlie Parr III, Alexandra Arnsten. Topics discussed included...with models. RESULTS A spatially heterogeneous and temporally changing snow cover resides atop Arctic and Antarctic sea ice for much of the...must be accurately represented to properly treat the polar sea ice covers and related feedbacks in climate models, but that is a difficult

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

    Science.gov (United States)

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

    2014-05-01

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

  8. Multiple Scattering of Laser Pulses in Snow Over Ice: Modeling the Potential Bias in ICESat Altimetry

    Science.gov (United States)

    Davis, A. B.; Varnai, T.; Marshak, A.

    2010-01-01

    The primary goal of NASA's current ICESat and future ICESat2 missions is to map the altitude of the Earth's land ice with high accuracy using laser altimetry technology, and to measure sea ice freeboard. Ice however is a highly transparent optical medium with variable scattering and absorption properties. Moreover, it is often covered by a layer of snow with varying depth and optical properties largely dependent on its age. We describe a modeling framework for estimating the potential altimetry bias caused by multiple scattering in the layered medium. We use both a Monte Carlo technique and an analytical diffusion model valid for optically thick media. Our preliminary numerical results are consistent with estimates of the multiple scattering delay from laboratory measurements using snow harvested in Greenland, namely, a few cm. Planned refinements of the models are described.

  9. 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.

  10. Snow reliability in ski resorts considering artificial snowmaking

    Science.gov (United States)

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

    2009-04-01

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

  11. A Creep Model for High-Density Snow

    Science.gov (United States)

    2017-04-01

    plotted as an additional parameter. From these data , I extracted the trend in Y vs. ρ and provide this as a look-up table in ABAQUS (Table 2). Table 2...could be used in the standard ABAQUS creep model. Comparing model-predicted strain and settlement to published data shows that the secondary creep...Available laboratory data helped to determine the parameters for these models. These models were recast into a form compatible with the ABAQUS finite

  12. Modelling of stream flow in snow dominated Budhigandaki ...

    Indian Academy of Sciences (India)

    60

    constant, initial and constant, exponential, Green-Ampt, SCS (Soil Conservation Service) Curve. Number, Soil Moisture Accounting and Smith-Parlange model for simulation ... Meteorological Organization (WMO) with regard to runoff simulation (WMO 1986). 3.2(a) Model Structure. Each day, the model computes the runoff ...

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

  15. Discrepancies between two measurements and two model approaches for liquid water flow in snow

    Science.gov (United States)

    Wever, N.; Schmid, L.; Heilig, A.; Fierz, C. G.; Lehning, M.

    2014-12-01

    Liquid water flow in snowpacks is a complicated process to measure or to simulate in snowpack models, although it is important for assessing, for example, soil moisture variations, streamflow discharge or wet snow avalanche formation. The measurement site Weissfluhjoch (WFJ) is equipped with instruments recording meteorological conditions and snowpack properties, including a snow lysimeter and an upward looking ground penetrating radar (upGPR). The upGRP, among other capabilities, is able to track the progress of the melt water front through the seasonal snowpack at WFJ, whereas the snow lysimeter only records liquid water runoff from the snowpack. The 1 dimensional physics-based snowpack model SNOWPACK has recently been extended with a solver for Richards equation, which provides a demonstrable improvement in simulating snowpack runoff, especially on the hourly time scale, when compared to a simpler bucket-type approach. Here, we compare the two measurement methods and the two snowpack simulations for four snow seasons with respect to the progress of the melt water front through the snowpack and snowpack runoff. We show that in the studied period, snowpack runoff in the melt season starts before the arrival of the melt water front at the bottom of the snowpack as detected by the upGPR. This discrepancy is in the order of several days to 1-2 weeks. The agreement between measured and modeled snowpack runoff is higher, although modeled snowpack runoff is still lagging several days from observed runoff, depending on the used water transport scheme. This demonstrates that the early start of snowpack runoff is likely associated with the existence of preferential flow paths. The modeled progress of the melt water front is faster than observed in the upGPR data. This contributes to a better predicition of the onset of snowpack runoff, but may have consequences for the representation of the internal snowpack in the model. The study highlights the extreme difficulties in

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

    Science.gov (United States)

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

    2017-12-01

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

  17. Mapping snow depth return levels: smooth spatial modeling versus station interpolation

    Directory of Open Access Journals (Sweden)

    J. Blanchet

    2010-12-01

    Full Text Available For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965–1966 to 2007–2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

  18. 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

  19. Linking livestock snow disaster mortality and environmental stressors in the Qinghai-Tibetan Plateau: Quantification based on generalized additive models.

    Science.gov (United States)

    Li, Yijia; Ye, Tao; Liu, Weihang; Gao, Yu

    2018-06-01

    Livestock snow disaster occurs widely in Central-to-Eastern Asian temperate and alpine grasslands. The effects of snow disaster on livestock involve a complex interaction between precipitation, vegetation, livestock, and herder communities. Quantifying the relationship among livestock mortality, snow hazard intensity, and seasonal environmental stressors is of great importance for snow disaster early warning, risk assessments, and adaptation strategies. Using a wide-spatial extent, long-time series, and event-based livestock snow disaster dataset, this study quantified those relationships and established a quantitative model of livestock mortality for prediction purpose for the Qinghai-Tibet Plateau region. Estimations using generalized additive models (GAMs) were shown to accurately predict livestock mortality and mortality rate due to snow disaster, with adjusted-R 2 up to 0.794 and 0.666, respectively. These results showed that a longer snow disaster duration, lower temperatures during the disaster, and a drier summer with less vegetation all contribute significantly and non-linearly to higher mortality (rate), after controlling for elevation and socioeconomic conditions. These results can be readily applied to risk assessment and risk-based adaptation actions. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. 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

  1. 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.

  2. Snow accumulation rate on Qomolangma (Mount Everest), Himalaya: synchroneity with sites across the Tibetan Plateau on 50-100 year timescales

    Science.gov (United States)

    Kaspari, Susan; Hooke, Roger Leb.; Mayewski, Paul Andrew; Kang, Shichang; Hou, Shugui; Qin, Dahe

    Annual-layer thickness data, spanning AD 1534-2001, from an ice core from East Rongbuk Col on Qomolangma (Mount Everest, Himalaya) yield an age-depth profile that deviates systematically from a constant accumulation-rate analytical model. The profile clearly shows that the mean accumulation rate has changed every 50-100 years. A numerical model was developed to determine the magnitude of these multi-decadal-scale rates. The model was used to obtain a time series of annual accumulation. The mean annual accumulation rate decreased from ˜0.8 m ice equivalent in the 1500s to ˜0.3 m in the mid-1800s. From ˜1880 to ˜1970 the rate increased. However, it has decreased since ˜1970. Comparison with six other records from the Himalaya and the Tibetan Plateau shows that the changes in accumulation in East Rongbuk Col are broadly consistent with a regional pattern over much of the Plateau. This suggests that there may be an overarching mechanism controlling precipitation and mass balance over this area. However, a record from Dasuopu, only 125 km northwest of Qomolangma and 700 m higher than East Rongbuk Col, shows a maximum in accumulation during the 1800s, a time during which the East Rongbuk Col and Tibetan Plateau ice-core and tree-ring records show a minimum. This asynchroneity may be due to altitudinal or seasonal differences in monsoon versus westerly moisture sources or complex mountain meteorology.

  3. 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.

  4. 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

  5. Basic Snow Pressure Calculation

    Science.gov (United States)

    Hao, Shouzhi; Su, Jian

    2018-03-01

    As extreme weather rising in recent years, the damage of large steel structures caused by weather is frequent in China. How to consider the effect of wind and snow loads on the structure in structural design has become the focus of attention in engineering field. In this paper, based on the serious snow disasters in recent years and comparative analysis of some scholars, influence factors and the value of the snow load, the probability model are described.

  6. FRACTAL MODEL OF DAMAGE ACCUMULATION IN SOLID BODES

    Directory of Open Access Journals (Sweden)

    Alim. Abed Al-Zobaede

    2014-01-01

    Full Text Available The paper considers a model of damage accumulation in parts of machines and structures which is based on a theory of fractals. Hidden process of destruction prior to the formation of macroscopic cracks is usually associated with the accumulation of micro-damages. Various models of damage accumulation and crack growth under the influence of power and thermal loads. However, models describing the accumulation process of micro-damages and their outgrowth into macro-crack are practically non-existent. Fractal structures with self-similarity are an adequate model of the fracture process. The MacDonald correlation function describing the medium structure allows to present the self-similarity of structures within a certain range of scales.The paper reviews models of damage accumulation near an opening in a composite medium and at layer boundaries. The Cantor model in a forward algorithm and a backward algorithm have been used in order to describe the model of damage accumulation. As it is known, the Cantor fractal (Cantor dust is obtained by using a recursive algorithm being applied to fracture mechanics can be regarded as a model of stepwise formation of dispersed micro-damages. The process of damage accumulation (latent destruction phase and its transition in the formation process of macro-cracks and their unification in a through-thickness crack can be described, for example, by the Paris' law.

  7. An accumulator model of semantic interference

    NARCIS (Netherlands)

    van Maanen, Leendert; van Rijn, Hedderik

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

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

    Science.gov (United States)

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

    2017-06-01

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

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

    Directory of Open Access Journals (Sweden)

    K. Rankinen

    2004-01-01

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

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

    International Nuclear Information System (INIS)

    Varnai, T.; Cahalan, R. F.

    2009-01-01

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

  11. Snow Leopard

    Indian Academy of Sciences (India)

    the Central Asian mountains and the Indian Himalayan re- gion. Owing to their ... ographical range and associated ecological, social, and cultural ... Central Asia. Snow Leopard: Morphology. The ability of snow leopards to camouflage with the surrounding landscape of rocks, sparse low vegetation, and snow is crucial for.

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

    Science.gov (United States)

    Luce, C. H.; Lute, A.

    2017-12-01

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

  13. 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

  14. 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.

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

    Science.gov (United States)

    Turchaninova, A.

    2012-04-01

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

  16. Computer kinetic modelling of radionuclide accumulation in Marine organisms

    International Nuclear Information System (INIS)

    Quintella, H.M.; Santimateo, D.; Paschoa, A.S.

    1977-01-01

    Continuous System Modelling Program (CSMP) is used to simulate the first step of the kinetic of a radionuclide in a food chain by using the exponential model of accumulation from water-to-algae based on data found in the literature. The use of computer modelling as a tool for environmental studies is discussed as far as economical advantages and future applications are concerned

  17. Wind drifted snow influence on the water and mass balance in the mountainous catchment "Modry potok", the Giant Mountains, Czech Republic.

    Science.gov (United States)

    Dvorak, I. J.; Fottova, D.; Tesar, M.; Kocianova, M.; Harcarik, J.

    2009-04-01

    There are very specific components of the water balance in the mountain headwater regions. Beside the point of cloud- and fog-water deposition it is mainly accumulation of water in the snow cover drifted into the watershed by the wind. Uneven distribution of the snow cover over the mountainous terrain is a well known phenomenon in all alpine and arctic areas. The result of this uneveness is a mosaic of microhabitats with various snow depths, different melting dates and snow free periods. Wire probes can be reliably used up to snow depths of 3 m only. To get more realistic data, two digital models using kinematic carrier phase-based GPS measurements were developed: (1) a model for snow surface data, applied at the end of winter seasons from 2000 to 2008, and (2) a model for the underlying snow free ground surface, applied after the snow melting in August 2000. These two models, overlaid in the GIS environment, have identified snow depths. For the creation of digital elevation models (DEMs), the TOPOGRID command in ArcInfo was used, which generated a grid of elevations from 3-D point, line, and polygon data. The snow depths were obtained and snow maps constructed accordingly. These "snow" results can be used for more realistic estimation of water content of snow in the watershed, distribution of snow depth during the winter seasons and define the water and mass balance more precisely. The objectives of this study were to highlight water storage in the snow-beds and show the GPS kinematic measurements as a contribution to understand more the snow accumulating and melting processes in the Modry potok catchment (2,62 km2, 1010 - 1554 m a.s.l.) in the Giant Mts. The research is supported by the Ministry of the Environment of the Czech Republic (SP/1a6/151/07) and by the Krkonose National Park Administration in Vrchlabi.

  18. Joint DEnKF-albedo assimilation scheme that considers the common land model subgrid heterogeneity and a snow density-based observation operator for improving snow depth simulations

    Science.gov (United States)

    Xu, Jianhui; Zhang, Feifei; Zhao, Yi; Shu, Hong; Zhong, Kaiwen

    2016-07-01

    For the large-area snow depth (SD) data sets with high spatial resolution in the Altay region of Northern Xinjiang, China, we present a deterministic ensemble Kalman filter (DEnKF)-albedo assimilation scheme that considers the common land model (CoLM) subgrid heterogeneity. In the albedo assimilation of DEnKF-albedo, the assimilated albedos over each subgrid tile are estimated with the MCD43C1 bidirectional reflectance distribution function (BRDF) parameters product and CoLM calculated solar zenith angle. The BRDF parameters are hypothesized to be consistent over all subgrid tiles within a specified grid. In the SCF assimilation of DEnKF-albedo, a DEnKF combining a snow density-based observation operator considers the effects of the CoLM subgrid heterogeneity and is employed to assimilate MODIS SCF to update SD states over all subgrid tiles. The MODIS SCF over a grid is compared with the area-weighted sum of model predicted SCF over all the subgrid tiles within the grid. The results are validated with in situ SD measurements and AMSR-E product. Compared with the simulations, the DEnKF-albedo scheme can reduce errors of SD simulations and accurately simulate the seasonal variability of SD. Furthermore, it can improve simulations of SD spatiotemporal distribution in the Altay region, which is more accurate and shows more detail than the AMSR-E product.

  19. 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

  20. Unexpected Patterns in Snow and Dirt

    Science.gov (United States)

    Ackerson, Bruce J.

    2018-01-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

  2. Diagnosing the inter-model spread in snow water equivalent for CMIP5 over Southwest Asia

    Science.gov (United States)

    Mankin, J. S.; Scherer, M.; Diffenbaugh, N. S.

    2012-12-01

    Recent analysis of the CMIP5 set of integrations has highlighted a wide divergence in the models' ability to resolve historical observations of snow water equivalent (SWE) throughout the Northern Hemisphere. However, despite the difficulty of resolving SWE in hindcasts, there exists a consistent signal in the magnitude of SWE decline under the RCP8.5 forcing scenario among the CMIP5. Separately, our work has established that lower yields in irrigated wheat induce Afghan farmers to plant more opium poppy, a more drought resistant crop planted at the same time. In Southwest and Central Asia, subsistence and industrial agriculture rely on irrigation supplied by runoff from upland snowmelt, and crop yields, including those of wheat and poppy, are influenced by this water availability. If water availability attenuates driving yield declines in staple crops like wheat, farmers in Afghanistan can be driven to cultivate more opium poppy in response—a crop that has a complex influence on stability and conflict there. Bounding the ensemble uncertainty in model simulations of SWE is an important step in assessing the ways in which farmer decisions have and will be constrained. Therefore, diagnosing the sources of model divergence in this important metric for subsistence and large-scale agriculture in Southwest and Central Asia is a first step for improving model resolution of such processes in projections of climate change. We present initial results that quantify the extent to which snow albedo feedback (SAF) parameterizations among the models in CMIP5 influence SWE simulation uncertainties over Southwest and Central Asia.

  3. A model for estimating carbon accumulation in cork products

    Directory of Open Access Journals (Sweden)

    Ana C. Dias

    2014-08-01

    Full Text Available Aim of study: This study aims to develop a calculation model for estimating carbon accumulation in cork products, both whilst in use and when in landfills, and to apply the model to Portugal as an example.Area of study: The model is applicable worldwide and the case-study is Portugal.Material and methods: The model adopts a flux-data method based on a lifetime analysis and quantifies carbon accumulation in cork products according to three approaches that differ on how carbon stocks (or emissions are allocated to cork product consuming and producing countries. These approaches are: stock-change, production and atmospheric-flow. The effect on carbon balance of methane emissions from the decay of cork products in landfills is also evaluated.Main results: The model was applied to Portugal and the results show that carbon accumulation in cork products in the period between 1990 and 2010 varied between 24 and 92 Gg C year-1. The atmospheric-flow approach provided the highest carbon accumulation over the whole period due to the net export of carbon in cork products. The production approach ranked second because exported cork products were mainly manufactured from domestically produced cork. The net carbon balance in cork products was also a net carbon accumulation with all the approaches, ranging from 5 to 81 Gg C eq year-1.Research highlights: The developed model can be applied to other countries and may be a step forward to consider carbon accumulation in cork products in national greenhouse gas inventories, as well as in future climate agreements.Keywords: Atmospheric-flow approach; Greenhouse gas balance; Modelling; Production approach; Stock-change approach.

  4. 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.

  5. 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.

  6. 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 (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.

  7. Snow Matters

    DEFF Research Database (Denmark)

    Gyimóthy, Szilvia; Jensen, Martin Trandberg

    2018-01-01

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

  8. A vertically integrated snow/ice model over land/sea for climate models. I - Development. II - Impact on orbital change experiments

    Science.gov (United States)

    Neeman, Binyamin U.; Ohring, George; Joseph, Joachim H.

    1988-01-01

    A vertically integrated formulation (VIF) model for sea ice/snow and land snow is discussed which can simulate the nonlinear effects of heat storage and transfer through the layers of snow and ice. The VIF demonstates the accuracy of the multilayer formulation, while benefitting from the computational flexibility of linear formulations. In the second part, the model is implemented in a seasonal dynamic zonally averaged climate model. It is found that, in response to a change between extreme high and low summer insolation orbits, the winter orbital change dominates over the opposite summer change for sea ice. For snow over land the shorter but more pronounced summer orbital change is shown to dominate.

  9. 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.

  10. 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.

  11. 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.

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Directory of Open Access Journals (Sweden)

    R. Boesch

    2016-06-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

  15. Representation of vegetation effects on the snow-covered albedo in the Noah land surface model with multiple physics options

    OpenAIRE

    S. Park; S. K. Park

    2015-01-01

    Snow albedo plays a critical role in calculating the energy budget, but parameterization of the snow surface albedo is still under great uncertainty. It varies with snow grain size, snow cover thickness, snow age, forest shading factor and other variables. Snow albedo of forest is typically lower than that of short vegetation; thus snow albedo is dependent on the spatial distributions of characteristic land cover and on the canopy density and structure. In the No...

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

    African Journals Online (AJOL)

    Modeling sludge accumulation rates in lined pit latrines in slum areas of Kampala City, Uganda. ... African Journal of Environmental Science and Technology ... methods such as open defecation and emptying into storm drainages are employed which consequently contribute to environmental and health-related challenges.

  17. Snow Leopard

    Indian Academy of Sciences (India)

    Owing to their secretive nature and inaccessible habitat,little is known about its ecology and distribution. Due toits endangered status and high aesthetic value, the snow leopardis considered as an 'umbrella species' for wildlife conservationin the Indian Himalayas. This article summarizes thecurrent knowledge on snow ...

  18. Snow Leopard

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 22; Issue 7. Snow Leopard: Ecology and Conservation Issues in India. Abhishek Ghoshal. General Article Volume 22 Issue 7 July 2017 pp 677- ... Keywords. Ecology, carnivore, conservation, Himalayas, mammal, snow leopard, Panthera uncia, wildlife.

  19. 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

  20. 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.

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

    Science.gov (United States)

    Joyce, M.; Painter, T. H.; Bormann, K. J.; Berisford, D. F.; Lai-Norling, J.

    2017-12-01

    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.

  2. 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.

  3. Rain-on-snow events over North America based on two Canadian regional climate models

    Science.gov (United States)

    Il Jeong, Dae; Sushama, Laxmi

    2018-01-01

    This study evaluates projected changes to rain-on-snow (ROS) characteristics (i.e., frequency, rainfall amount, and runoff) for the future 2041-2070 period with respect to the current 1976-2005 period over North America using six simulations, based on two Canadian RCMs, driven by two driving GCMs for RCP4.5 and 8.5 emission pathways. Prior to assessing projected changes, the two RCMs are evaluated by comparing ERA-Interim driven RCM simulations with available observations, and results indicate that both models reproduce reasonably well the observed spatial patterns of ROS event frequency and other related features. Analysis of current and future simulations suggest general increases in ROS characteristics during the November-March period for most regions of Canada and for northwestern US for the future period, due to an increase in the rainfall frequency with warmer air temperatures in future. Future ROS runoff is often projected to increase more than future ROS rainfall amounts, particularly for northeastern North America, during snowmelt months, as ROS events usually accelerate snowmelt. The simulations show that ROS event is a primary flood generating mechanism over most of Canada and north-western and -central US for the January-May period for the current period and this is projected to continue in the future period. More focused analysis over selected basins shows decreases in future spring runoff due to decreases in both snow cover and ROS runoff. The above results highlight the need to take into consideration ROS events in water resources management adaptation strategies for future climate.

  4. Forest-snow interactions at Critical Zone Observatories of the Western U.S. (Invited)

    Science.gov (United States)

    Molotch, N. P.; Harpold, A. A.

    2013-12-01

    Forest structure exerts strong controls on the hydrology of mountainous regions by influencing snow accumulation, snowmelt, and partitioning of melt water to runoff and evapotranspiration. Predicting the hydrological fluxes and states of these montane systems has been limited by an inability to represent snow-vegetation interactions across complex terrain and variable climate. This is particularly important in Western U.S. forests, where recent evidence indicates forest disturbance from fire and insects has uneven impacts on water availability. Instrument clusters deployed in the Central and Southern Rockies and the Sierra Nevada reveal the dominant role of vegetation in controlling the timing and magnitude of snow accumulation and snowmelt. In this regard, vegetation structure largely controlled the distribution of snow accumulation with greater snow accumulation in open versus sub-canopy positions across the Critical Zone Observatory (CZO) network; 11% greater in the Jemez River Basin (JRB) CZO, 24% greater in the Boulder Creek (BC) CZO and 13% in the Southern Sierra (SS) CZO. Similarly, snow ablation rates were greater in open versus under-canopy positions; 17% greater at JRB, 6% greater in BC, and 2% greater in SS. The canopy structure had varying controls on the date of snow disappearance at the different sites. At JRB, snow disappeared an average of 3 days later in under canopy versus open positions. Conversely, at both the SS and BC snow persisted an average of 7 days longer in open versus under-canopy positions. Despite high inter-annual snowpack variability, the timing of peak soil moisture was strongly linked to the timing of snow disappearance at all sites. Peak soil moisture was nearly synchronous with snow disappearance at JRB and BC, with deviations of less than one week. Conversely, peak soil moisture consistently preceded snow disappearance by 1 to 2 weeks at the SS sites. These results highlight the importance of vegetation structure with regard to

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

    Science.gov (United States)

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

    2017-12-01

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

  6. Multilevel modeling of damage accumulation processes in metals

    Science.gov (United States)

    Kurmoiartseva, K. A.; Trusov, P. V.; Kotelnikova, N. V.

    2017-12-01

    To predict the behavior of components and constructions it is necessary to develop the methods and mathematical models which take into account the self-organization of microstructural processes and the strain localization. The damage accumulation processes and the evolution of material properties during deformation are important to take into account. The heterogeneity of the process of damage accumulation is due to the appropriate physical mechanisms at the scale levels, which are lower than the macro-level. The purpose of this work is to develop a mathematical model for analyzing the behavior of polycrystalline materials that allows describing the damage accumulation processes. Fracture is the multistage and multiscale process of the build-up of micro- and mesodefects over the wide range of loading rates. The formation of microcracks by mechanisms is caused by the interactions of the dislocations of different slip systems, barriers, boundaries and the inclusions of the secondary phase. This paper provides the description of some of the most well-known models of crack nucleation and also suggests the structure of a mathematical model based on crystal plasticity and dislocation models of crack nucleation.

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

    Science.gov (United States)

    Zerda-Sarmiento, Alvaro

    2012-06-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2011-12-01

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

  10. Use of satellite-derived data for characterization of snow cover and simulation of snowmelt runoff through a distributed physically based model of runoff generation

    Directory of Open Access Journals (Sweden)

    L. S. Kuchment

    2010-02-01

    Full Text Available A technique of using satellite-derived data for constructing continuous snow characteristics fields for distributed snowmelt runoff simulation is presented. The satellite-derived data and the available ground-based meteorological measurements are incorporated in a physically based snowpack model. The snowpack model describes 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 remote sensing data used in the model consist of products include the daily maps of snow covered area (SCA and SWE derived from observations of MODIS and AMSR-E instruments onboard Terra and Aqua satellites as well as available maps of land surface temperature, surface albedo, land cover classes and tree cover fraction. The model was first calibrated against available ground-based snow measurements and then applied to calculate the spatial distribution of snow characteristics using satellite data and interpolated ground-based meteorological data. The satellite-derived SWE data were used for assigning initial conditions and the SCA data were used for control of snow cover simulation. The simulated spatial distributions of snow characteristics were incorporated in a distributed physically based model of runoff generation to calculate snowmelt runoff hydrographs. The presented technique was applied to a study area of approximately 200 000 km2 including the Vyatka River basin with catchment area of 124 000 km2. The correspondence of simulated and observed hydrographs in the Vyatka River are considered as an indicator of the accuracy of constructed fields of snow characteristics and as a measure of effectiveness of utilizing satellite-derived SWE data for runoff simulation.

  11. Integrated simulation of snow and glacier melt runoff in a distributed biosphere hydrological modeling framework at Upper Indus Basin, Karakoram region

    Science.gov (United States)

    Shrestha, M.; Koike, T.; Xue, Y.; Wang, L.; Hirabayashi, Y.

    2014-12-01

    High mountain river basins in Hindukush Karakoram and Himalaya (HKH) regions are considered as 'water towers' of Asia with abundant source of fresh water as snow and glaciers. Upper Indus basin is one of the mega scale river basin in HKH region where snow and glaciermelt runoff is the major contributor to the annual runoff. The hostile climate, remote and extreme rough topography imposes many restraints regarding hydro-meteorological and glaciological observations, leading towards limited understanding of hydrological processes of river basins in this region. It is vital to integrate snow and glacier melt processes in a distributed biosphere hydrological framework to estimate the snow and glacier melt runoff and to quantify the river flow composition (snowmelt, glacier melt and rainfall contribution). An integrated system of distributed biosphere hydrological modeling framework with multilayer energy balance based snow and glaciermelt runoff schemes (WEB-DHM-S model) was implemented at Upper Indus basin (207300 km2) with a spatial resolution of 1 km and temporal resolution of an hour. Model input were meteorological forcing from Global Land Data Assimilation System (GLDAS), APHRODITE precipitation and de-trended gridded air temperature from observations. Simulations were carried out for two hydrological years (2002-2003). Discharge simulation results at multiple gauges showed good agreement with the observed one having Nash efficiency at 0.86. The spatial distribution of snow cover is simulated well as compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) derived eight-day maximum snow-cover extent data (MOD10A2). Model accuracy, overestimation error and underestimation error in snow cover simulation were obtained at 78%, 7% and 15% respectively. Uncertainty in precipitation was the main reason for the biases in seasonal variation of snow pixel errors. The model demonstrated its sound capability in comprehensive simulation of discharge with its flow

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

    Science.gov (United States)

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

    2017-12-01

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

  13. 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.

  14. Uncertainty analysis of impacts of climate change on snow processes: Case study of interactions of GCM uncertainty and an impact model

    Science.gov (United States)

    Kudo, Ryoji; Yoshida, Takeo; Masumoto, Takao

    2017-05-01

    The impact of climate change on snow water equivalent (SWE) and its uncertainty were investigated in snowy areas of subarctic and temperate climate zones in Japan by using a snow process model and climate projections derived from general circulation models (GCMs). In particular, we examined how the uncertainty due to GCMs propagated through the snow model, which contained nonlinear processes defined by thresholds, as an example of the uncertainty caused by interactions among multiple sources of uncertainty. An assessment based on the climate projections in Coupled Model Intercomparison Project Phase 5 indicated that heavy-snowfall areas in the temperate zone (especially in low-elevation areas) were markedly vulnerable to temperature change, showing a large SWE reduction even under slight changes in winter temperature. The uncertainty analysis demonstrated that the uncertainty associated with snow processes (1) can be accounted for mainly by the interactions between GCM uncertainty (in particular, the differences of projected temperature changes between GCMs) and the nonlinear responses of the snow model and (2) depends on the balance between the magnitude of projected temperature changes and present climates dominated largely by climate zones and elevation. Specifically, when the peaks of the distributions of daily mean temperature projected by GCMs cross the key thresholds set in the model, the GCM uncertainty, even if tiny, can be amplified by the nonlinear propagation through the snow process model. This amplification results in large uncertainty in projections of CC impact on snow processes.

  15. 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.

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

    International Nuclear Information System (INIS)

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

    1990-01-01

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

  17. 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.

  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. An exactly solvable, spatial model of mutation accumulation in cancer

    Science.gov (United States)

    Paterson, Chay; Nowak, Martin A.; Waclaw, Bartlomiej

    2016-12-01

    One of the hallmarks of cancer is the accumulation of driver mutations which increase the net reproductive rate of cancer cells and allow them to spread. This process has been studied in mathematical models of well mixed populations, and in computer simulations of three-dimensional spatial models. But the computational complexity of these more realistic, spatial models makes it difficult to simulate realistically large and clinically detectable solid tumours. Here we describe an exactly solvable mathematical model of a tumour featuring replication, mutation and local migration of cancer cells. The model predicts a quasi-exponential growth of large tumours, even if different fragments of the tumour grow sub-exponentially due to nutrient and space limitations. The model reproduces clinically observed tumour growth times using biologically plausible rates for cell birth, death, and migration rates. We also show that the expected number of accumulated driver mutations increases exponentially in time if the average fitness gain per driver is constant, and that it reaches a plateau if the gains decrease over time. We discuss the realism of the underlying assumptions and possible extensions of the model.

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

    Science.gov (United States)

    Suzuki, Kazuyoshi; Zupanski, Milija

    2018-01-01

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

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

    Directory of Open Access Journals (Sweden)

    L. Menzel

    2009-08-01

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

  2. Evaluation of the Snow Thermal Model (SNTHERM through Continuous in situ Observations of Snow’s Physical Properties at the CREST-SAFE Field Experiment

    Directory of Open Access Journals (Sweden)

    Jose A. Infante Corona

    2015-11-01

    Full Text Available Snowpack properties like temperature or density are the result of a complex energy and mass balance process in the snowpack that varies temporally and spatially. The Snow Thermal Model (SNTHERM is a 1-dimensional model, energy and mass balance-driven, that simulates these properties. This article analyzes the simulated snowpack properties using SNTHERM forced with two datasets, namely measured meteorological data at the Cooperative Remote Sensing Science and Technology-Snow Analysis and Field Experiment (CREST-SAFE site and the National Land Data Assimilation System (NLDAS. The study area is located on the premises of Caribou Municipal Airport at Caribou (ME, USA. The model evaluation is based on properties such as snow depth, snow water equivalent, and snow density, in addition to a layer-by-layer comparison of snowpack properties. The simulations were assessed with precise in situ observations collected at the CREST-SAFE site. The outputs of the SNTHERM model showed very good agreement with observed data in properties like snow depth, snow water equivalent, and average temperature. Conversely, the model was not very efficient when simulating properties like temperature and grain size in different layers of the snowpack.

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

    Science.gov (United States)

    Ala-aho, Pertti; Tetzlaff, Doerthe; McNamara, James P.; Laudon, Hjalmar; Soulsby, Chris

    2017-10-01

    Tracer-aided hydrological models are increasingly used to reveal fundamentals of runoff generation processes and water travel times in catchments. Modelling studies integrating stable water isotopes as tracers are mostly based in temperate and warm climates, leaving catchments with strong snow influences underrepresented in the literature. Such catchments are challenging, as the isotopic tracer signals in water entering the catchments as snowmelt are typically distorted from incoming precipitation due to fractionation processes in seasonal snowpack. We used the Spatially distributed Tracer-Aided Rainfall-Runoff (STARR) model to simulate fluxes, storage, and mixing of water and tracers, as well as estimating water ages in three long-term experimental catchments with varying degrees of snow influence and contrasting landscape characteristics. In the context of northern catchments the sites have exceptionally long and rich data sets of hydrometric data and - most importantly - stable water isotopes for both rain and snow conditions. To adapt the STARR model for sites with strong snow influence, we used a novel parsimonious calculation scheme that takes into account the isotopic fractionation through snow sublimation and snowmelt. The modified STARR setup simulated the streamflows, isotope ratios, and snow pack dynamics quite well in all three catchments. From this, our simulations indicated contrasting median water ages and water age distributions between catchments brought about mainly by differences in topography and soil characteristics. However, the variable degree of snow influence in catchments also had a major influence on the stream hydrograph, storage dynamics, and water age distributions, which was captured by the model. Our study suggested that snow sublimation fractionation processes can be important to include in tracer-aided modelling for catchments with seasonal snowpack, while the influence of fractionation during snowmelt could not be unequivocally

  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 density climatology across the former USSR

    Science.gov (United States)

    Zhong, X.; Zhang, T.; Wang, K.

    2014-04-01

    Snow density is one of the basic properties used to describe snow cover characteristics, and it is a key factor for linking snow depth and snow water equivalent, which are critical for water resources assessment and modeling inputs. In this study, we used long-term data from ground-based measurements to investigate snow density (bulk density) climatology and its spatiotemporal variations across the former Soviet Union (USSR) from 1966 to 2008. The results showed that the long-term monthly mean snow density was approximately 0.22 ± 0.05 g cm-3 over the study area. The maximum and minimum monthly mean snow density was about 0.33 g cm-3 in June, and 0.14 g cm-3 in October, respectively. Maritime and ephemeral snow had the highest monthly mean snow density, while taiga snow had the lowest. The higher values of monthly snow density were mainly located in the European regions of the former USSR, on the coast of Arctic Russia, and the Kamchatka Peninsula, while the lower snow density occurred in central Siberia. Significant increasing trends of snow density from September through June of the next year were observed, however, the rate of the increase varied with different snow classes. The long-term (1966-2008) monthly and annual mean snow densities had significant decreasing trends, especially during the autumn months. Spatially, significant positive trends in monthly mean snow density lay in the southwestern areas of the former USSR in November and December and gradually expanded in Russia from February through April. Significant negative trends mainly lay in the European Russia and the southern Russia. There was a high correlation of snow density with elevation for tundra snow and snow density was highly correlated with latitude for prairie snow.

  6. The ringed seal's last refuge and the importance of snow cover

    Science.gov (United States)

    Kelly, B. P.; Bitz, C. M.

    2010-12-01

    Ringed seals are strongly adapted to inhabiting seasonal ice cover throughout the Arctic Ocean, marginal seas, and some freshwater lakes. Their distribution has expanded and contracted with northern hemisphere ice cover and is expected to mirror declining ice cover in coming decades. Ringed seals require snow cover to provide shelter from extreme cold and from predators, and the southern extent of their range corresponds to the latitudes to which snow cover—sufficient to form and maintain subnivean lairs—extends. The lairs are especially critical to the survival of pups born and nursed under the snow in late March through May. Snow drifts 50 cm or deeper are necessary for lair occupation, and field measurements indicate that such drifting occurs only where average snow depths (on flat ice) exceed 20 cm. When snow depths are less, ringed seal pups freeze in their lairs and are vulnerable to predation by carnivores and birds. As the climate warms, winter precipitation is expected to increase in the Arctic Ocean, potentially favoring formation and occupation of lairs. At the same time, increasingly late ice formation is expected to decrease the overall accumulation of snow, an effect exacerbated by the high fraction of annual snow fall that occurs in autumn. Early snow melts also contribute to pup mortality and are likely to increase as the climate warms. We forecast April snow depths on Arctic sea ice through the year 2100 in seven runs of CCSM3. Despite predicted increases in winter precipitation in the Arctic, the model forecasted that the accumulation of snow on sea ice will decrease by almost 50% in this century. The timing of the onset of snow melt changes little in the projections, but the shallower snow pack will melt more quickly in the warmer climate. In almost all portions of the range, average snow depths are expected to be less than 20 cm and inadequate for successful rearing of ringed seal young by the end of the century and—in many locations

  7. Towards Improved Snow Water Equivalent Estimation via GRACE Assimilation

    Science.gov (United States)

    Forman, Bart; Reichle, Rofl; Rodell, Matt

    2011-01-01

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

  8. Snow-avalanche modeling and hazard level assessment using statistical and physical modeling, DSS and WebGIS: case study from Czechia

    Science.gov (United States)

    Blahut, J.; Balek, J.; Juras, R.; Klimes, J.; Klose, Z.; Roubinek, J.; Pavlasek, J.

    2014-12-01

    Snow-avalanche modeling and hazard level assessment are important issues to be solved within mountain regions worldwide. In Czechia, there are two mountain ranges (Krkonoše and Jeseníky Mountains), which suffer from regular avalanche activity every year. Mountain Rescue Service is responsible for issuing avalanche bulletins. However, its approaches are still lacking objective assessments and procedures for hazard level estimations. This lack is mainly caused by missing expert avalanche information system. This paper presents preliminary results from a project funded by the Ministry of Interior of the Czech Republic. This project is focused on development of an information system for snow-avalanche hazard level forecasting. It is composed of three main modules, which should act as a Decision Support System (DSS) for the Mountain Rescue Service. Firstly, snow-avalanche susceptibility model is used for delimiting areas where avalanches can occur based on accurate statistical analyses. For that purpose a waste database is used, containing more than 1100 avalanche events from 1961/62 till present. Secondly, a physical modeling of the avalanches is being performed on avalanche paths using RAMMS modeling code. Regular paths, where avalanches occur every year, and irregular paths are being assessed. Their footprint is being updated using return period information for each path. Thirdly, snow distribution and stability models (distributed HBV-ETH, Snowtran 3D, Snowpack and Alpine 3D) are used to assess the critical conditions for avalanche release. For calibration of the models data about meteo/snow cover data and snowpits is used. Those three parts are being coupled in a WebGIS platform used as the principal component of the DSS in snow-avalanche hazard level assessment.

  9. Modelling a soft composite accumulator for human mobility assist devices.

    Science.gov (United States)

    Shaheen, Robert; Doumit, Marc

    2018-04-01

    Research in the field of human mobility assist devices, aiming to reduce the metabolic cost of daily activities, is seeing the benefits of the exclusive use of accumulators to store and release energy during the gait cycle. The Pneumatic Artificial Muscle, used in a passive state, has proven to be a superior choice for these devices when compared to its alternatives, however, challenges regarding muscle pressure dissipation and a limited elongation potential have been identified. A recently developed, novel Soft Composite material has been shown to experimentally replicate the distinctive mechanical behaviour of the Pneumatic Artificial Muscle, without the need for internal pressurization. This paper presents two separate constitutive models to provide a closer insight into the behaviour of these Soft Composite accumulators. Both models were derived from methods involving finite elasticity theory and employed either a structural strain energy function of Holzapfel, Gasser, and Ogden's type or a phenomenological strain energy function of Fung's type. Both models were in good agreement with the experimental data that were collected through a modified extension-inflation test and, therefore, provide a basis for further examination as a Soft Composite design model. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-06-02

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

  11. MODIS/Terra+Aqua BRDF/Albedo Snow-free Model Parameters Daily L3 Global 0.05Deg CMG V006

    Data.gov (United States)

    National Aeronautics and Space Administration — Version 6 Bidirectional reflectance distribution function and Albedo (BRDF/Albedo) Model Snow Free Quality Parameters data set is a 5600 meter daily 16-day product....

  12. Modelling the snow distribution at two high arctic sites at Svalbard, Norway, and at an alpine site in central Norway

    NARCIS (Netherlands)

    Bruland, Oddbjørn; Liston, Glen E.; Vonk, Jorien; Sand, Knut; Killingtveit, Anund

    2004-01-01

    In Arctic regions snow cover has a major influence on the environment both in a hydrological and ecological context. Due to strong winds and open terrain the snow is heavily redistributed and the snow depth is quite variable. This has a significant influence on the snow cover depletion and the

  13. 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

    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 i...

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

    NARCIS (Netherlands)

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

    2016-01-01

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

  15. Improving daily streamflow forecasts in mountainous Upper Euphrates basin by multi-layer perceptron model with satellite snow products

    Science.gov (United States)

    Uysal, Gökçen; Şensoy, Aynur; Şorman, A. Arda

    2016-12-01

    This paper investigates the contribution of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Snow Cover Area (SCA) product and in-situ snow depth measurements to Artificial Neural Network model (ANN) based daily streamflow forecasting in a mountainous river basin. In order to represent non-linear structure of the snowmelt process, Multi-Layer Perceptron (MLP) Feed-Forward Backpropagation (FFBP) architecture is developed and applied in Upper Euphrates River Basin (10,275 km2) of Turkey where snowmelt constitutes approximately 2/3 of total annual volume of runoff during spring and early summer months. Snowmelt season is evaluated between March and July; 7 years (2002-2008) seasonal daily data are used during training while 3 years (2009-2011) seasonal daily data are split for forecasting. One of the fastest ANN training algorithms, the Levenberg-Marquardt, is used for optimization of the network weights and biases. The consistency of the network is checked with four performance criteria: coefficient of determination (R2), Nash-Sutcliffe model efficiency (ME), root mean square error (RMSE) and mean absolute error (MAE). According to the results, SCA observations provide useful information for developing of a neural network model to predict snowmelt runoff, whereas snow depth data alone are not sufficient. The highest performance is experienced when total daily precipitation, average air temperature data are combined with satellite snow cover data. The data preprocessing technique of Discrete Wavelet Analysis (DWA) is coupled with MLP modeling to further improve the runoff peak estimates. As a result, Nash-Sutcliffe model efficiency is increased from 0.52 to 0.81 for training and from 0.51 to 0.75 for forecasting. Moreover, the results are compared with that of a conceptual model, Snowmelt Runoff Model (SRM), application using SCA as an input. The importance and the main contribution of this study is to use of satellite snow products and data

  16. [Modeling of cotton boll maturation period and cottonseed biomass accumulation].

    Science.gov (United States)

    Li, Wen-Feng; Meng, Ya-Li; Zhao, Xin-Hua; Chen, Bing-Lin; Xu, Nai-Yin; Zhou, Zhi-Guo

    2009-04-01

    Field experiments with different maturity cotton cultivars and sowing dates were conducted at different sites to quantitatively study the effects of cultivar characteristics, weather conditions (air temperature and solar radiation), and crop management variable (N application rate) on the cotton boll maturation period and cottonseed biomass accumulation. The cotton boll maturation period was simulated by using the scale of physiological development time. Based on the hypothesis of sink-determined, the cottonseed biomass accumulation model was then developed. The subtending leaf N concentration of cotton boll was simulated with a semi-empirical equation, and used as the direct indicator of the N nutrition effect on cottonseed growth and development. The model was tested by independent field data obtained in the Yellow River Valley (Xuzhou and Anyang) and the lower reaches of Yangtze River Valley (Huaian) in 2005. The simulated values of boll maturation period showed reasonable agreement with observed values, with a root mean square error (RMSE) of 2.25 days for cultivar DSC-1, of 2.61 days for cultivar KC-1, and of 2.75 days for cultivar AC-33B. The RMSE of cottonseed dry mass prediction was 9.5 mg x seed(-1) for KC-1 and 8.2 mg x seed(-1) for AC-33B, indicating that the model had a good prediction precision.

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

    Science.gov (United States)

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

    2018-02-01

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

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

    International Nuclear Information System (INIS)

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

    2003-01-01

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

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

    CERN Document Server

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

    2003-01-01

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

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-12-01

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

  2. MODIS Snow-Cover Products

    Science.gov (United States)

    Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.; DiGirolamo, Nicole E.; Bayr, Klaus J.; Houser, Paul R. (Technical Monitor)

    2002-01-01

    On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. MODIS snow and ice products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to or enhancement of the currently-available operational products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set may be generated for long-term climate studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the products. The MODIS snow product suite begins with a 500-m resolution, 2330-km swath snow-cover map which is then gridded to an integerized sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to a climate-modeling grid (CMG) product at about 5.6-km spatial resolution, with both daily and 8-day composite products. Each pixel of the CMG contains fraction of snow cover from 40 - 100%. Measured errors of commission in the CMG are low, for example, on the continent of Australia in the spring, they vary from 0.02 - 0.10%. Near-term enhancements include daily snow albedo and fractional snow cover. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented to show some early validation work.

  3. Modeling Snow Aggregates and their Single Scattering Properties: Implications to Snowfall Remote Sensing

    Science.gov (United States)

    Nowell, H.; Liu, G.

    2012-12-01

    With the advent of satellites, we can now observe areas of the globe that have sparse to no ground data coverage. Both active and passive satellite sensors aboard satellites including CloudSat's Cloud Profiling Radar (CPR), Aqua's Advanced Microwave Scanning Radiometer (AMSR-E) and the upcoming Global Precipitation Measurement's (GPM) Dual-Frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) study ice and snow particles. A good retrieval algorithm for these satellite sensors can only be developed when the single scattering properties of the snowflakes are accurately calculated in radiative transfer models. This becomes crucial at frequencies at and above the W-band when aggregate ice crystals become detectable by satellite radiometers. Snowflakes are often modeled as spheres or oblate spheroids to ease the complexity of calculations, despite the fact that they are typically aggregates of crystals. For improved accuracy in satellite remote sensing, it is important to model snowflakes as close to nature as possible. Several recent studies model flakes as pristine crystal types [Liu, 2008], generate aggregate flakes as fractals [Ishimoto, 2008] or via the Monte Carlo method [Maruyama and Fujioshi, 2005]. Modeling snowflakes as pristine crystals, however, has the drawback of not accurately reflecting snowflakes as most tend to be aggregates of different crystal types. Other studies where aggregates are generated tend to overlook size-density relationships of aggregate flakes or other studied statistical parameters such as aspect ratio. In an effort to improve available single-scattering properties of aggregate flakes, we developed a new method of generating flakes. Starting out with a six-bullet rosette crystal of accurate size and density, aggregate flakes are generated with two different bullet rosette crystal sizes of 200 and/or 400 microns in maximum dimension. The flakes similarly follow size-density relationships of aggregate as determined from

  4. Antarctic snow and global climate

    International Nuclear Information System (INIS)

    Granberg, H.B.

    2001-01-01

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

  5. Snow clearance

    CERN Multimedia

    Mauro Nonis

    2005-01-01

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

  6. Snow Matters

    DEFF Research Database (Denmark)

    Gyimóthy, Szilvia; Jensen, Martin Trandberg

    2018-01-01

    attribute of high altitude mountain destinations. Hitherto, researchers mostly engaged with snowclad landscapes as a backstage; trying to deconstruct the complex symbolism and representational qualities of this elusive substance. Despite snow being a strategically crucial condition for tourism in the Alps...

  7. Snow Leopard

    Indian Academy of Sciences (India)

    around the same time in Kinnaur district. Studies on snow leop- ard habitats over the past two decades show that the economy of the region has rapidly shifted from traditional agro-pastoralism to market-driven agriculture. Consequently, human population growth, agricultural expanse, and excessive livestock grazing (Fig-.

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

    Science.gov (United States)

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

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    D. M. Kurhan

    2015-07-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

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

    Science.gov (United States)

    Christina Tague; Gordon E. Grant

    2009-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-12-01

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

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

    Science.gov (United States)

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

    2017-04-01

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

  14. Spatial and temporal variability in seasonal snow density

    KAUST Repository

    Bormann, Kathryn J.

    2013-03-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

  17. Comparison of Local Scale Measured and Modeled Brightness Temperatures and Snow Parameters from the CLPX 2003 by Means of a Dense Medium Radiative Transfer Theory Model

    Science.gov (United States)

    Tedescol, Marco; Kim, Edward J.; Cline, Don; Graf, Tobias; Koike, Toshio; Armstrong, Richard; Brodzik, Mary J.; Hardy, Janet

    2004-01-01

    Microwave remote sensing offers distinct advantages for observing the cryosphere. Solar illumination is not required, and spatial and temporal coverage are excellent from polar-orbiting satellites. Passive microwave measurements are sensitive to the two most useful physical quantities for many hydrological applications: physical temperature and water content/state. Sensitivity to the latter is a direct result of the microwave sensitivity to the dielectric properties of natural media, including snow, ice, soil (frozen or thawed), and vegetation. These considerations are factors motivating the development of future cryospheric satellite remote sensing missions, continuing and improving on a 26-year microwave measurement legacy. Perhaps the biggest issues regarding the use of such satellite measurements involve how to relate parameter values at spatial scales as small as a hectare to observations with sensor footprints that may be up to 25 x 25 km. The NASA Cold-land Processes Field Experiment (CLPX) generated a dataset designed to enhance understanding of such scaling issues. CLPX observations were made in February (dry snow) and March (wet snow), 2003 in Colorado, USA, at scales ranging from plot scale to 25 x 25 km satellite footprints. Of interest here are passive microwave observations from ground-based, airborne, and satellite sensors, as well as meteorological and snowpack measurements that will enable studies of the effects of spatial heterogeneity of surface conditions on the observations. Prior to performing such scaling studies, an evaluation of snowpack forward modelling at the plot scale (least heterogeneous scale) is in order. This is the focus of this paper. Many forward models of snow signatures (brightness temperatures) have been developed over the years. It is now recognized that a dense medium radiative transfer (DMRT) treatment represents a high degree of physical fidelity for snow modeling, yet dense medium models are particularly sensitive to

  18. The Distribution of Snow Black Carbon observed in the Arctic and Compared to the GISS-PUCCINI Model

    Science.gov (United States)

    Dou, T.; Xiao, C.; Shindell, D. T.; Liu, J.; Eleftheriadis, K.; Ming, J.; Qin, D.

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    T. Dou

    2012-09-01

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

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

    Science.gov (United States)

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

    2013-12-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

  2. Using geostatistical methods to estimate snow water equivalence distribution in a mountain watershed

    Science.gov (United States)

    Balk, B.; Elder, K.; Baron, Jill S.

    1998-01-01

    Knowledge of the spatial distribution of snow water equivalence (SWE) is necessary to adequately forecast the volume and timing of snowmelt runoff.  In April 1997, peak accumulation snow depth and density measurements were independently taken in the Loch Vale watershed (6.6 km2), Rocky Mountain National Park, Colorado.  Geostatistics and classical statistics were used to estimate SWE distribution across the watershed.  Snow depths were spatially distributed across the watershed through kriging interpolation methods which provide unbiased estimates that have minimum variances.  Snow densities were spatially modeled through regression analysis.  Combining the modeled depth and density with snow-covered area (SCA produced an estimate of the spatial distribution of SWE.  The kriged estimates of snow depth explained 37-68% of the observed variance in the measured depths.  Steep slopes, variably strong winds, and complex energy balance in the watershed contribute to a large degree of heterogeneity in snow depth.

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

    Directory of Open Access Journals (Sweden)

    L. Jin

    2009-08-01

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

  4. Heavy snow loads in Finnish forests respond regionally asymmetrically to projected climate change

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    I. Lehtonen

    2016-10-01

    Full Text Available This study examined the impacts of projected climate change on heavy snow loads on Finnish forests, where snow-induced forest damage occurs frequently. For snow-load calculations, we used daily data from five global climate models under representative concentration pathway (RCP scenarios RCP4.5 and RCP8.5, statistically downscaled onto a high-resolution grid using a quantile-mapping method. Our results suggest that projected climate warming results in regionally asymmetric response on heavy snow loads in Finnish forests. In eastern and northern Finland, the annual maximum snow loads on tree crowns were projected to increase during the present century, as opposed to southern and western parts of the country. The change was rather similar both for heavy rime loads and wet snow loads, as well as for frozen snow loads. Only the heaviest dry snow loads were projected to decrease over almost the whole of Finland. Our results are aligned with previous snowfall projections, typically indicating increasing heavy snowfalls over the areas with mean temperature below −8 °C. In spite of some uncertainties related to our results, we conclude that the risk for snow-induced forest damage is likely to increase in the future in the eastern and northern parts of Finland, i.e. in the areas experiencing the coldest winters in the country. The increase is partly due to the increase in wet snow hazards but also due to more favourable conditions for rime accumulation in a future climate that is more humid but still cold enough.

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

    Science.gov (United States)

    Grünewald, Thomas; Wolfsperger, Fabian

    2016-04-01

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

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

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    R. J. Schuldt

    2013-03-01

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

  7. Snow farming: conserving snow over the summer season

    Science.gov (United States)

    Grünewald, Thomas; Wolfsperger, Fabian; Lehning, Michael

    2018-01-01

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

  8. Snow farming: conserving snow over the summer season

    Directory of Open Access Journals (Sweden)

    T. Grünewald

    2018-01-01

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

  9. Evaluating controls on snow distribution in the eastern Chugach Mountains, Alaska

    Science.gov (United States)

    Wolken, G. J.; Wikstrom Jones, K.

    2017-12-01

    A detailed understanding of the spatial and temporal variability of alpine snow depth is important because of its strong influence on water resources, regional economies, and public safety. However, in complex terrain, strong orographic gradients and complicated topography-influenced wind fields produce complex accumulation patterns that are difficult to accurately quantify using traditional in situ and satellite-based approaches, and are challenging to model. In this study, we use repeat airborne photogrammetry, Structure from Motion (SfM) processing methods, and field-based measurements to produce continuous and accurate maps of end-of-winter snow depth and snow water equivalence (SWE) in the eastern Chugach Mountains, Alaska, during the period 2015-2017. We validate photogrammetry-derived snow depth maps using simultaneously acquired snow depth measurements recorded by project and citizen scientists. Patterns of snow distribution in the study area are largely controlled by orographic forcing, however, these patterns are strongly modulated by persistent synoptic weather systems that serve to weaken the climatological snow distribution pattern.

  10. MODIS Snow and Sea Ice Products

    Science.gov (United States)

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

    2004-01-01

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

  11. Snow darkening caused by black carbon emitted from fires

    Science.gov (United States)

    Engels, Jessica; Kloster, Silvia; Bourgeois, Quentin

    2014-05-01

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

  12. Mesocell study area snow distributions for the Cold Land Processes Experiment (CLPX)

    Science.gov (United States)

    Glen E. Liston; Christopher A. Hiemstra; Kelly Elder; Donald W. Cline

    2008-01-01

    The Cold Land Processes Experiment (CLPX) had a goal of describing snow-related features over a wide range of spatial and temporal scales. This required linking disparate snow tools and datasets into one coherent, integrated package. Simulating realistic high-resolution snow distributions and features requires a snow-evolution modeling system (SnowModel) that can...

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

    Directory of Open Access Journals (Sweden)

    H. G. Chan

    2018-02-01

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

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

    Science.gov (United States)

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

    2017-04-01

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

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

    Science.gov (United States)

    Luo, Xiao; Wang, Bin

    2018-03-01

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

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

    Science.gov (United States)

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

    1988-01-01

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

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

    Science.gov (United States)

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

    2014-03-01

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

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

    Science.gov (United States)

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

    2013-09-01

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

  19. Performance modeling and valuation of snow-covered PV systems: examination of a simplified approach to decrease forecasting error.

    Science.gov (United States)

    Bosman, Lisa B; Darling, Seth B

    2018-03-22

    The advent of modern solar energy technologies can improve the costs of energy consumption on a global, national, and regional level, ultimately spanning stakeholders from governmental entities to utility companies, corporations, and residential homeowners. For those stakeholders experiencing the four seasons, accurately accounting for snow-related energy losses is important for effectively predicting photovoltaic performance energy generation and valuation. This paper provides an examination of a new, simplified approach to decrease snow-related forecasting error, in comparison to current solar energy performance models. A new method is proposed to allow model designers, and ultimately users, the opportunity to better understand the return on investment for solar energy systems located in snowy environments. The new method is validated using two different sets of solar energy systems located near Green Bay, WI, USA: a 3.0-kW micro inverter system and a 13.2-kW central inverter system. Both systems were unobstructed, facing south, and set at a tilt of 26.56°. Data were collected beginning in May 2014 (micro inverter system) and October 2014 (central inverter system), through January 2018. In comparison to reference industry standard solar energy prediction applications (PVWatts and PVsyst), the new method results in lower mean absolute percent errors per kilowatt hour of 0.039 and 0.055%, respectively, for the micro inverter system and central inverter system. The statistical analysis provides support for incorporating this new method into freely available, online, up-to-date prediction applications, such as PVWatts and PVsyst.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-02-09

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

  1. Large scale snow water status monitoring: comparison of different snow water products in the upper Colorado basins

    Science.gov (United States)

    Artan, G.A.; Verdin, J.P.; Lietzow, R.

    2013-01-01

    We illustrate the ability to monitor the status of snowpack over large areas by using a~spatially distributed snow accumulation and ablation model in the Upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) datasets; remaining meteorological model input data was from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a~region of the Western United States that covers parts of the Upper Colorado Basin. We also compared the SWE product estimated from the Special Sensor Microwave Imager (SSM/I) and Scanning Multichannel Microwave Radiometer (SMMR) to the SNODAS and SNOTEL SWE datasets. Agreement between the spatial distribution of the simulated SWE with both SNODAS and SNOTEL was high for the two model runs for the entire snow accumulation period. Model-simulated SWEs, both with MPE and TRMM, were significantly correlated spatially on average with the SNODAS (r = 0.81 and r = 0.54; d.f. = 543) and the SNOTEL SWE (r = 0.85 and r = 0.55; d.f. = 543), when monthly basinwide simulated average SWE the correlation was also highly significant (r = 0.95 and r = 0.73; d.f. = 12). The SWE estimated from the passive microwave imagery was not correlated either with the SNODAS SWE or (r = 0.14, d.f. = 7) SNOTEL-reported SWE values (r = 0.08, d.f. = 7). The agreement between modeled SWE and the SWE recorded by SNODAS and SNOTEL weakened during the snowmelt period due to an underestimation bias of the air temperature that was used as model input forcing.

  2. The role of snow-surface coupling, radiation, and turbulent mixing in modeling a stable boundary layer over Arctic sea ice

    NARCIS (Netherlands)

    Sterk, H.A.M.; Steeneveld, G.J.; Holtslag, A.A.M.

    2013-01-01

    To enhance the understanding of the impact of small-scale processes in the polar climate, this study focuses on the relative role of snow-surface coupling, radiation and turbulent mixing in an Arctic stable boundary layer. We extend the GABLS1 (GEWEX Atmospheric Boundary-Layer Study 1) model

  3. Parameter optimization of a hydrologic model in a snow-dominated basin using a modular Python framework

    Science.gov (United States)

    Volk, J. M.; Turner, M. A.; Huntington, J. L.; Gardner, M.; Tyler, S.; Sheneman, L.

    2016-12-01

    Many distributed models that simulate watershed hydrologic processes require a collection of multi-dimensional parameters as input, some of which need to be calibrated before the model can be applied. The Precipitation Runoff Modeling System (PRMS) is a physically-based and spatially distributed hydrologic model that contains a considerable number of parameters that often need to be calibrated. Modelers can also benefit from uncertainty analysis of these parameters. To meet these needs, we developed a modular framework in Python to conduct PRMS parameter optimization, uncertainty analysis, interactive visual inspection of parameters and outputs, and other common modeling tasks. Here we present results for multi-step calibration of sensitive parameters controlling solar radiation, potential evapo-transpiration, and streamflow in a PRMS model that we applied to the snow-dominated Dry Creek watershed in Idaho. We also demonstrate how our modular approach enables the user to use a variety of parameter optimization and uncertainty methods or easily define their own, such as Monte Carlo random sampling, uniform sampling, or even optimization methods such as the downhill simplex method or its commonly used, more robust counterpart, shuffled complex evolution.

  4. Estimating Snow Water Equivalent In The Swedish Mountains Based On The Frequency And Amplitude Of The Local Topography

    Science.gov (United States)

    Ingvander, Susanne; Brown, Ian

    2013-12-01

    Estimating the snow water equivalent (SWE) of the seasonal snowpack is key information for the prediction of spring flood rates and the contribution to water reservoirs in Hydro-power production. By establishing the relationship between accumulation patterns and physical parameters in the landscape such as topographic features and vegetation, a model of accumulation patterns in different types of reference areas can be produced. However, detailed information on the spatial and temporal evolution of the snow pack is necessary in order to develop such an algorithm. By determining the frequency and amplitude of topography in the Swedish mountain regions and by measuring snow accumulation in these regions we can increase the accuracy of the estimation of SWE. This information can then aid the understanding of scaling issues over the region. Satellite imagery and products can then be used for up-scaling from high-resolution field data to derive new satellite algorithms in the future.

  5. Modeling Information Accumulation in Psychological Tests Using Item Response Times

    Science.gov (United States)

    Ranger, Jochen; Kuhn, Jörg-Tobias

    2015-01-01

    In this article, a latent trait model is proposed for the response times in psychological tests. The latent trait model is based on the linear transformation model and subsumes popular models from survival analysis, like the proportional hazards model and the proportional odds model. Core of the model is the assumption that an unspecified monotone…

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

    Science.gov (United States)

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

    2017-12-01

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

  7. Linking pollen deposition and snow accumulation on the Alto dell'Ortles glacier (South Tyrol, Italy) for sub-seasonal dating of a firn temperate core

    Science.gov (United States)

    Festi, Daniela; Carturan, Luca; Kofler, Werner; dalla Fontana, Giancarlo; de Blasi, Fabrizio; Cazorzi, Federico; Bucher, Edith; Mair, Volkmar; Gabrielli, Paolo; Oeggl, Klaus

    2017-04-01

    Dating of ice cores from temperate non-polar glaciers is challenging and often problematic. However, a proper timescale is essential for a correct interpretation of the proxies measured in the cores. Here, we introduce a new method developed to obtain a sub-seasonal timescale relying on statistically measured similarities between pollen spectra obtained from core samples and daily airborne pollen monitoring samples collected in the same area. This approach was developed on a 10 m core retrieved from the temperate-firn portion of Alto dell'Ortles glacier (Eastern Italian Alps), for which a 5-year annual/seasonal timescale already exists. The aim was to considerably improve this timescale, reaching the highest possible temporal resolution and testing the efficiency and limits of pollen as a chronological tool. A test of the new timescale was performed by comparing our results to the output (date of layer formation) of the mass balance model EISModel, during the period encompassed by the timescale. The correspondence of the results supports the new sub-seasonal timescale based on pollen analysis. This comparison also allows us to draw important conclusions on the post-depositional effects of meltwater percolation on the pollen content of the firn core as well as on the climatic interpretation of the pollen signal.

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

    Science.gov (United States)

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

    2013-04-01

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

  9. LS3MIP (v1.0) Contribution to CMIP6: The Land Surface, Snow and Soil Moisture Model Intercomparison Project Aims, Setup and Expected Outcome.

    Science.gov (United States)

    Van Den Hurk, Bart; Kim, Hyungjun; Krinner, Gerhard; Seneviratne, Sonia I.; Derksen, Chris; Oki, Taikan; Douville, Herve; Colin, Jeanne; Ducharne, Agnes; Cheruy, Frederique; hide

    2016-01-01

    The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow, and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth System Models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems).The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (LMIP, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (LFMIP, building upon the GLACE-CMIP blueprint).

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

    Science.gov (United States)

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

    2017-04-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  12. Deriving Snow Cover Metrics for Alaska from MODIS

    Directory of Open Access Journals (Sweden)

    Chuck Lindsay

    2015-09-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-11-01

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

  14. Everywhere and nowhere: snow and its linkages

    Science.gov (United States)

    Hiemstra, C. A.

    2017-12-01

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

  15. Using daily air temperature thresholds to evaluate snow melting occurrence and amount on Alpine glaciers by T-index models: the case study of the Forni Glacier (Italy)

    Science.gov (United States)

    Senese, A.; Maugeri, M.; Vuillermoz, E.; Smiraglia, C.; Diolaiuti, G.

    2014-10-01

    Glacier melt conditions (i.e., null surface temperature and positive energy budget) can be assessed by analyzing data acquired by a supraglacial automatic weather station (AWS), such as the station installed on the surface of Forni Glacier (Italian Alps). When an AWS is not present, the assessment of actual melt conditions and the evaluation of the melt amount is more difficult and simple methods based on T-index (or degree days) models are generally applied. These models require the choice of a correct temperature threshold. In fact, melt does not necessarily occur at daily air temperatures higher than 0 °C. In this paper, we applied both energy budget and T-index approaches with the aim of solving this issue. We start by distinguishing between the occurrence of snowmelt and the reduction in snow depth due to actual ablation (from snow depth data recorded by a sonic ranger). Then we find the daily average temperature thresholds (by analyzing temperature data acquired by an AWS on Forni Glacier) which, on the one hand, best capture the occurrence of significant snowmelt conditions and, on the other, make it possible, using the T-index, to quantify the actual snow ablation amount. Finally we investigated the applicability of the mean tropospheric lapse rate to reproduce air temperature conditions at the glacier surface starting from data acquired by weather stations located outside the glacier area. We found that the mean tropospheric lapse rate allows for a good and reliable reconstruction of glacier air temperatures and that the choice of an appropriate temperature threshold in T-index models is a very important issue. From our study, the application of the +0.5 °C temperature threshold allows for a consistent quantification of snow ablation while, instead, for detecting the beginning of the snow melting processes a suitable threshold has proven to be at least -4.6 °C.

  16. ESA GlobSnow Snow Water Equivalent (SWE)

    Data.gov (United States)

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

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

    Science.gov (United States)

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

    2014-01-01

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

  18. Climate controls on future Northern Hemisphere snow-dependent water availability

    Science.gov (United States)

    Mankin, J. S.; Diffenbaugh, N. S.

    2013-12-01

    Mountain ice and snowpack supplies water for up to 40 percent of the worlds' irrigation, plays a critical role in providing hydropower, and a host of ecosystem functions in forests, riparian, and downstream communities. Climate models project that global warming from increased greenhouse gas concentrations will induce important changes in the quantity and timing of runoff from these mountain systems. Given the strategic importance of seasonal snow accumulation and the timing of its runoff for downstream systems we ask two sets of model ensembles four questions: 1. What are projected trends in Northern Hemisphere snow accumulation under the RCP8.5 forcing scenario? 2. Where and when are such trends driven by temperature or precipitation variability? 3. For regions of the world where temperature variability dominates precipitation variability in accounting for the accumulation of snow, how much of the uncertainty across the ensembles is attributable to internal variability versus model uncertainty? 4. Given these uncertainties, what are the implications of future snow accumulation for the timing and runoff of irrigation water availability in snow-dependent regions? We analyze the spatial and temporal variation in the signal-to-noise ratio of trends in Northern Hemisphere snow accumulation and the factors that drive these trends. We use two sets of climate experiments for our analysis, a single model's 40-member ensemble from version 3 of the Community Climate System Model (CCSM3.0) and the set of models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) with at least four realizations for the same initialization and parameterization schemes. This experimental design, centered on each model's having an ensemble of integrations with only minor differences in the initial atmospheric state (in the case of CCSM3.0) or the atmospheric and ocean state (in the case of the CMIP5) and the same external forcing, provides a sample to estimate each model

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

    Directory of Open Access Journals (Sweden)

    A. I. Lantuh-Lyaschenko

    2010-04-01

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

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

    Science.gov (United States)

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

    2017-04-01

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

  1. Converting Snow Depth to SWE: The Fusion of Simulated Data with Remote Sensing Retrievals and the Airborne Snow Observatory

    Science.gov (United States)

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

    2015-12-01

    Snow cover monitoring has greatly benefited from remote sensing technology but, despite their critical importance, spatially distributed measurements of snow water equivalent (SWE) in mountain terrain remain elusive. Current methods of monitoring SWE rely on point measurements and are insufficient for distributed snow science and effective management of water resources. Many studies have shown that the spatial variability in SWE is largely controlled by the spatial variability in snow depth. JPL's Airborne Snow Observatory mission (ASO) combines LiDAR and spectrometer instruments to retrieve accurate and very high-resolution snow depth measurements at the watershed scale, along with other products such as snow albedo. To make best use of these high-resolution snow depths, spatially distributed snow density data are required to leverage SWE from the measured snow depths. Snow density is a spatially and temporally variable property that cannot yet be reliably extracted from remote sensing techniques, and is difficult to extrapolate to basin scales. However, some physically based snow models have shown skill in simulating bulk snow densities and therefore provide a pathway for snow depth to SWE conversion. Leveraging model ability where remote sensing options are non-existent, ASO employs a physically based snow model (iSnobal) to resolve distributed snow density dynamics across the basin. After an adjustment scheme guided by in-situ data, these density estimates are used to derive the elusive spatial distribution of SWE from the observed snow depth distributions from ASO. In this study, we describe how the process of fusing model data with remote sensing retrievals is undertaken in the context of ASO along with estimates of uncertainty in the final SWE volume products. This work will likely be of interest to those working in snow hydrology, water resource management and the broader remote sensing community.

  2. Wet meadow ecosystems contribute the majority of overwinter soil respiration from snow-scoured alpine tundra

    Science.gov (United States)

    Knowles, John F.; Blanken, Peter D.; Williams, Mark W.

    2016-04-01

    We measured soil respiration across a soil moisture gradient ranging from dry to wet snow-scoured alpine tundra soils throughout three winters and two summers. In the absence of snow accumulation, soil moisture variability was principally determined by the combination of mesotopographical hydrological focusing and shallow subsurface permeability, which resulted in a patchwork of comingled ecosystem types along a single alpine ridge. To constrain the subsequent carbon cycling variability, we compared three measures of effective diffusivity and three methods to calculate gradient method soil respiration from four typical vegetation communities. Overwinter soil respiration was primarily restricted to wet meadow locations, and a conservative estimate of the rate of overwinter soil respiration from snow-scoured wet meadow tundra was 69-90% of the maximum carbon dioxide (CO2) respired by seasonally snow-covered soils within this same catchment. This was attributed to higher overwinter soil temperatures at wet meadow locations relative to fellfield, dry meadow, and moist meadow communities, which supported liquid water and heterotrophic respiration throughout the winter. These results were corroborated by eddy covariance-based measurements that demonstrated an average of 272 g C m-2 overwinter carbon loss during the study period. As a result, we updated a conceptual model of soil respiration versus snow cover to express the potential for soil respiration variability from snow-scoured alpine tundra.

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

    Science.gov (United States)

    McCabe, Gregory J.; Wolock, David M.

    2010-01-01

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

  4. Modelling Accumulator Stripper Foil Heating for ESSNUSB Facility

    CERN Document Server

    Martini, Michel

    2015-01-01

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

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

    Czech Academy of Sciences Publication Activity Database

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

    2017-01-01

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

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

    Czech Academy of Sciences Publication Activity Database

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

    2017-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-01-15

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

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

    Science.gov (United States)

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

    2017-11-01

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

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

    Directory of Open Access Journals (Sweden)

    J. M. Cook

    2017-11-01

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

  10. Snow snake performance monitoring.

    Science.gov (United States)

    2008-12-01

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

  11. IMPLEMENTATION AND VALIDATION OF A FULLY IMPLICIT ACCUMULATOR MODEL IN RELAP-7

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Haihua [Idaho National Laboratory; Zou, Ling [Idaho National Laboratory; Zhang, Hongbin [Idaho National Laboratory; Martineau, Richard Charles [Idaho National Laboratory

    2016-01-01

    This paper presents the implementation and validation of an accumulator model in RELAP-7 under the framework of preconditioned Jacobian free Newton Krylov (JFNK) method, based on the similar model used in RELAP5. RELAP-7 is a new nuclear reactor system safety analysis code being developed at the Idaho National Laboratory (INL). RELAP-7 is a fully implicit system code. The JFNK and preconditioning methods used in RELAP-7 is briefly discussed. The slightly modified accumulator model is summarized for completeness. The implemented model was validated with LOFT L3-1 test and benchmarked with RELAP5 results. RELAP-7 and RELAP5 had almost identical results for the accumulator gas pressure and water level, although there were some minor difference in other parameters such as accumulator gas temperature and tank wall temperature. One advantage of the JFNK method is its easiness to maintain and modify models due to fully separation of numerical methods from physical models. It would be straightforward to extend the current RELAP-7 accumulator model to simulate the advanced accumulator design.

  12. Precipitation and snow cover in the Himalaya: from reanalysis to regional climate simulations

    Directory of Open Access Journals (Sweden)

    M. Ménégoz

    2013-10-01

    Full Text Available We applied a Regional Climate Model (RCM to simulate precipitation and snow cover over the Himalaya, between March 2000 and December 2002. Due to its higher resolution, our model simulates a more realistic spatial variability of wind and precipitation than those of the reanalysis of the European Centre of Medium range Weather Forecast (ECMWF used as lateral boundaries. In this region, we found very large discrepancies between the estimations of precipitation provided by reanalysis, rain gauges networks, satellite observations, and our RCM simulation. Our model clearly underestimates precipitation at the foothills of the Himalaya and in its eastern part. However, our simulation provides a first estimation of liquid and solid precipitation in high altitude areas, where satellite and rain gauge networks are not very reliable. During the two years of simulation, our model resembles the snow cover extent and duration quite accurately in these areas. Both snow accumulation and snow cover duration differ widely along the Himalaya: snowfall can occur during the whole year in western Himalaya, due to both summer monsoon and mid-latitude low pressure systems bringing moisture into this region. In Central Himalaya and on the Tibetan Plateau, a much more marked dry season occurs from October to March. Snow cover does not have a pronounced seasonal cycle in these regions, since it depends both on the quite variable duration of the monsoon and on the rare but possible occurrence of snowfall during the extra-monsoon period.

  13. POLSCAT Ku-Band Radar Remote Sensing of Terrestrial Snow Cover

    Science.gov (United States)

    Yueh, Simon; Cline, Donald; Elder, Kelly

    2008-01-01

    Characteristics of the POLSCAT data acquired from five sets of aircraft flights in the winter months of 2006-2008 for the second Cold Land Processes Experiment (CLPX-II) in Colorado are described in this paper. The data showed the response of the Ku-band radar echoes to snowpack changes for various types of background vegetation in the study site in north central Colorado. We observed about 0.15 to 0.5 dB increases in backscatter for every 1 cm of snow water equivalent (SWE) accumulation for areas with short vegetation. Based on a simplified radiative transfer model, the change detection technique is used to convert the temporal change of radar backscatter into SWE accumulation for dry snow conditions. The resulting SWE accumulation estimates are consistent with the in-situ SWE measurements, with about 2 to 3 cm Root-Mean-Square (RMS) difference for regions with sagebrush or pasture.

  14. Modeling, Optimization, and Detailed Design of a Hydraulic Flywheel-Accumulator

    Science.gov (United States)

    Strohmaier, Kyle Glenn

    Improving mobile energy storage technology is an important means of addressing concerns over fossil fuel scarcity and energy independence. Traditional hydraulic accumulator energy storage, though favorable in power density, durability, cost, and environmental impact, suffers from relatively low energy density and a pressure-dependent state of charge. The hydraulic flywheel-accumulator concept utilizes both the hydro-pneumatic and rotating kinetic energy domains by employing a rotating pressure vessel. This thesis provides an in-depth analysis of the hydraulic flywheel-accumulator concept and an assessment of the advantages it offers over traditional static accumulator energy storage. After specifying a practical architecture for the hydraulic flywheel-accumulator, this thesis addresses the complex fluid phenomena and control implications associated with multi-domain energy storage. To facilitate rapid selection of the hydraulic flywheel-accumulator dimensions, computationally inexpensive material stress models are developed for each component. A drive cycle simulation strategy is also developed to assess the dynamic performance of the device. The stress models and performance simulation are combined to form a toolset that facilitates computationally-efficient model-based design. The aforementioned toolset has been embedded into a multi-objective optimization algorithm that aims to minimize the mass of the hydraulic flywheel-accumulator system and to minimize the losses it incurs over the course of a drive cycle. Two optimizations have been performed - one with constraints that reflect a vehicle-scale application, and one with constraints that reflect a laboratory application. At both scales, the optimization results suggest that the hydraulic flywheel-accumulator offers at least an order of magnitude improvement over traditional static accumulator energy storage, while operating at efficiencies between 75% and 93%. A particular hydraulic flywheel-accumulator design

  15. Andes Mountain Snow Distribution, Properties, and Trend: 1979-2014

    Science.gov (United States)

    Mernild, Sebastian H.; Liston, Glen E.; Hiemstra, Christopher A.

    2015-04-01

    Andes snow presence, absence, properties, and water amount are key components of Earth's changing climate system that incur far-reaching physical ramifications. Modeling developments permit relatively high-resolution (4-km horizontal grid; 3-h time step) Andes snow estimates for 1979-2014. SnowModel, in conjunction with land cover, topography, and 35-years of NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) atmospheric reanalysis data, was used to create a spatially distributed, time-evolving, snow-related dataset that included air temperature, snow precipitation, snow-season timing and length, maximum snow water equivalent depth, and average snow density. Regional variability is a dominant feature of the modeled snow-property trends from an area northeast of Quito (latitude: 2.65°S to 0.23°N) to Patagonia (latitude: 52.15°S to 46.44°S). For example, the Quito area annual snow cover area changed -45%, -43% around Cusco (latitude: 14.75°S to 12.52°S), -5% east of Santiago (including the Olivares Basin), and 25% in Patagonia. The annual snow covered area for the entire Andes decreased 13%, mainly in the elevation band between 4,000-5,000 m a.s.l. In spite of strong regional variability, the data clearly show a general positive trend in mean annual air temperature and precipitation, and a decreasing trend in snow precipitation, snow precipitation days, and snow density. Also, the snow-cover onset is later and the snow-cover duration - the number of snow cover days - decreased.

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

    Science.gov (United States)

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

    2018-03-01

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

  17. Close packing effects on clean and dirty snow albedo and associated climatic implications

    Science.gov (United States)

    He, Cenlin; Takano, Yoshi; Liou, Kuo-Nan

    2017-04-01

    Previous modeling of snow albedo, a key climate feedback parameter, follows the independent scattering approximation (ISA) such that snow grains are considered as a number of separate units with distances longer than wavelengths. Here we develop a new snow albedo model for widely observed close-packed snow grains internally mixed with black carbon (BC) and demonstrate that albedo simulations match closer to observations. Close packing results in a stronger light absorption for clean and BC-contaminated snow. Compared with ISA, close packing reduces pure snow albedos by up to 0.05, whereas it enhances BC-induced snow albedo reduction and associated surface radiative forcing by up to 15% (20%) for fresh (old) snow, with larger enhancements for stronger structure packing. Finally, our results suggest that BC-snow albedo forcing and snow albedo feedback (climate sensitivity) are underestimated in previous modeling studies, making snow close packing consideration a necessity in climate modeling and analysis.

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

    Science.gov (United States)

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

    2016-01-01

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

  19. How to determine wet-snow instability

    Science.gov (United States)

    Reiweger, Ingrid; Mitterer, Christoph

    2017-04-01

    Processes leading to wet-snow instability are very complex and highly non-linear in time and space. Infiltrating water changes wet-snow strength and other mechanical properties. A high liquid water content presumably favors fracture propagation, which consequently has an influence on the formation of wet slab avalanches. The weakening of snow due to liquid water within the snowpack might be gradual (melt event) or sudden (rain-on-snow event). There are several feedback mechanisms between liquid water and snow stratigraphy, making the weakening process complex. We used modelled stability indices to determine periods with high wet-snow instability. These indices were either based on energy and mass balances indicating critical amounts of water within the snowpack or on simple hydro-mechanical relationships. In addition to the modelled indices, preliminary field studies investigated the fracture initiation and fracture propagation propensity within wet snowpacks. We therefore performed Rutschblock and propagation saw tests in faceted weak layers with different volumetric liquid water contents. Results of simulations and field experiments showed that a critical amount of liquid water combined with a pre-critical snow stratigraphy were relevant for wet-snow instability. The critical amount of water was assumed to drive both failure initiation and fracture propagation. The simulated indices and observed stability tests indicated a high wet-snow instability when the volumetric liquid water content within faceted weak layers exceeded 3. Within our propagation saw test measurements crack propagation propensity even slightly decreased at very low liquid water contents compared to completely dry conditions, presumably due to capillary forces. For liquid water contents higher than 3-4%, however, crack propagation propensity strongly increased, which we assume was due to the weakening of bonds between grains within the increasingly wet weak snow layer. Our results could be used

  20. The hydrological role of snow and glaciers in alpine river basins and their distributed modeling

    NARCIS (Netherlands)

    Verbunt, M.; Gurtz, J.; Jasper, K.; Lang, H.; Warmerdam, P.M.M.; Zappa, M.

    2003-01-01

    A temperature index approach including incoming solar radiation was used as a sub-model in the gridded hydrological catchment model WaSiM-ETH to simulate the melt rate of glacierized areas. Melt water and rainfall are transformed into glacier discharge by using linear reservoir approaches. The

  1. Linear and nonlinear aspects of snow albedo feedbacks in atmospheric models

    Science.gov (United States)

    Roads, J. O.

    1981-01-01

    Namias' hypothesis, that anomalous snowcover on the eastern side of the North American continent can generate an anomalous east coast low pressure system and an anomalous inland high pressure system, is consistent with the time-averaged anomalous response from a nonlinear, primitive equation channel model with an idealized, flat land-sea arrangement. An attempt to understand and describe this anomalous response in the nonlinear model as a linear response to anomalous diabatic heating was largely unsuccessful, primarily because the anomalous eddy fluxes were also important. This unsuccessful attempt to describe the nonlinear model's time averages by linear theory then motivated several comparisons between linear and nonlinear severely truncated quasi-geostrophic models. It was also found in these models that the eddy fluxes were extremely important for forcing or dissipating the stationary eddies.

  2. Diffusion versus linear ballistic accumulation: different models for response time with different conclusions about psychological mechanisms?

    Science.gov (United States)

    Heathcote, Andrew; Hayes, Brett

    2012-06-01

    Two similar classes of evidence-accumulation model have dominated theorizing about rapid binary choice: diffusion models and racing accumulator pairs. Donkin, Brown, Heathcote, and Wagenmakers (2011) examined mimicry between the Ratcliff diffusion (RD; Ratcliff & Smith, 2004) and the linear ballistic accumulator (LBA; Brown & Heathcote, 2008), the 2 least similar models from each class that provide a comprehensive account of a set benchmark phenomena in rapid binary choice. Where conditions differed only in the rate of evidence accumulation (the most common case in past research), simulations showed the models supported equivalent psychological inferences. In contrast, differences in 2 other parameters of key psychological interest, response caution (the amount of information required for a decision), and nondecision time, traded-off when fitting 1 model to data simulated from the other, implying the potential for divergent inferences about latent cognitive processes. However, Donkin, Brown, Heathcote, and Wagenmakers did not find such inconsistencies between fits of the RD and LBA models in a survey of data sets from paradigms using a range of experimental manipulations. We examined a further data set, collected by Dutilh, Vandekerckhove, Tuerlinckx, and Wagenmakers (2009), which used a manipulation not surveyed by Donkin, Brown, Heathcote, and Wagenmakers's practice. Dutilh et al.'s RD model fits indicated that practice had large effects on all three types of parameters. We show that in this case the LBA provides a different and simpler account of practice effects. Implications for evidence accumulation modelling are discussed.

  3. Snow and albedo climate change impacts across the United States Northern Great Plains

    Science.gov (United States)

    Fassnacht, S. R.; Cherry, M. L.; Venable, N. B. H.; Saavedra, F.

    2016-02-01

    In areas with a seasonal snowpack, a warmer climate could cause less snowfall, a shallower snowpack, and a change in the timing of snowmelt, all which could reduce the winter albedo and yield an increase in net short-wave radiation. Trends in temperature, precipitation (total and as snow), days with precipitation and snow, and winter albedo were investigated over the 60-year period from 1951 to 2010 for 20 meteorological stations across the Northern Great Plains. This is an area where snow accumulation is shallow but persistent for most of the winter (November to March). The most consistent trends were minimum temperature and days with precipitation, both of which increased at a majority of the stations. Among the stations included, a decrease in the modelled winter albedo was more prevalent than an increase. There was substantial spatial variability in the climate trends. For most variables, the period of record used influenced the magnitude and sign of the significant trends.

  4. Recent greenland accumulation estimated from regional climate model simulations and ice core analysis

    DEFF Research Database (Denmark)

    Dethloff, K.; Schwager, M.; Christensen, J. H.

    2002-01-01

    The accumulation defined as "precipitation minus evaporation" over Greenland has been simulated with the high-resolution limited-area regional climate model HIRHAM4 applied over an Arctic integration domain. This simulation is compared with a revised estimate of annual accumulation rate...... distribution over Greenland taking into account information from a new set of ice core analyses, based on surface sample collections from the North Greenland Traverse. The region with accumulation rates below 150 mm yr-1 in central-northwest Greenland is much larger than previously assumed and extends about...

  5. Comparison of Two Models for Damage Accumulation in Simulations of System Performance

    Energy Technology Data Exchange (ETDEWEB)

    Youngblood, R. [Idaho National Laboratory, Idaho Falls, ID (United States); Mandelli, D. [Idaho National Laboratory, Idaho Falls, ID (United States)

    2015-11-01

    A comprehensive simulation study of system performance needs to address variations in component behavior, variations in phenomenology, and the coupling between phenomenology and component failure. This paper discusses two models of this: 1. damage accumulation is modeled as a random walk process in each time history, with component failure occurring when damage accumulation reaches a specified threshold; or 2. damage accumulation is modeled mechanistically within each time history, but failure occurs when damage reaches a time-history-specific threshold, sampled at time zero from each component’s distribution of damage tolerance. A limiting case of the latter is classical discrete-event simulation, with component failure times sampled a priori from failure time distributions; but in such models, the failure times are not typically adjusted for operating conditions varying within a time history. Nowadays, as discussed below, it is practical to account for this. The paper compares the interpretations and computational aspects of the two models mentioned above.

  6. Modeling lipid accumulation in oleaginous fungi in chemostat cultures: I. Development and validation of a chemostat model for Umbelopsis isabellina

    NARCIS (Netherlands)

    Meeuwse, P.; Tramper, J.; Rinzema, A.

    2011-01-01

    Lipid-accumulating fungi may be able to produce biodiesel precursors from agricultural wastes. As a first step in understanding and evaluating their potential, a mathematical model was developed to describe growth, lipid accumulation and substrate consumption of the oleaginous fungus Umbelopsis

  7. Quantifying the impacts of snow on surface energy balance through assimilating snow cover fraction and snow depth

    Science.gov (United States)

    Meng, Chunlei

    2017-10-01

    Seasonal snow plays an important part in Earth's climate system. Snow cover regulates the land surface energy balance through altering the albedo of the land surface. To utilize the satellite-retrieved snow cover fraction (SCF) and snow depth (SD) data sufficiently and avoid inconsistency, this paper developed a very simple but robust quality control method to assimilate Fengyun satellite-retrieved SCF and SD simultaneously. The results show that the assimilation method which this paper implemented can not only utilize the satellite-retrieved SCF and SD data sufficiently but also avoid the inconsistency of them. Two experiments were designed and performed to quantify the impacts of snow on land surface energy balance using the integrated urban land model. With the increase of the SCF and SD, the net radiation decreased significantly during the day and increased a little at night; the sensible heat flux decreased significantly during the day; the evapotranspiration and ground heat flux decreased during the day too.

  8. Chemical elements contamination of snow cover in region of coal production 'Karazhyra'

    International Nuclear Information System (INIS)

    Evlampieva, E.P.; Panin, M.S.

    2008-01-01

    Peculiarities of space distribution of chemical elements in hardphase and water-soluble falls of snow cover in region of coal deposit 'Karazhyra' are investigated. The maximal, minimal and background areas of elements accumulation in the snow of this region and distribution of their cumulative rates are determined. The main pollutants of snow cover unto background level are revealed.

  9. Building a Cloud-based Global Snow Observatory

    Science.gov (United States)

    Li, X.; Coll, J. M.

    2016-12-01

    Snow covers some 40 percent of Earth's land masses year in and year out and constitutes a vitally important variable for the planet's climate, hydrology, and biosphere due to its high albedo and insulation. It affects atmospheric circulation patterns, permafrost, glacier mass balance, river discharge, and groundwater recharge (Dietz et al. 2015). Snow is also nature's igloo where species from microscopic fungi to 800-pound moose survive the winter each in its own way (Pauli et al. 2013; Petty et al. 2015). Many studies have found that snow in high elevation regions is particularly sensitive to global climate change and is considered as sentinel of change. For human beings, about one-sixth of the world's population depends on seasonal snow and glaciers for their water supply (Barnett et al. 2005) and more than 50% of mountainous areas have an essential or supportive role for downstream regions (Viviroli et al. 2007). Large snowstorms also have a major impact on society in terms of human life, economic loss, and disruption (Squires et al. 2014). Remote sensing provides a practical approach of monitoring global snow and ice cover change. Based on our comprehensive validation and assessment on MODIS snow products, we build a cloud-based Global Snow Observatory (GSO) using Google Earth Engine (GEE) to serve as a platform for global researchers and the general public to access, visualize, and analyze snow data and to build snowmelt runoff models for mountain watersheds. Specifically, we build the GSO to serve global MODIS daily snow cover data and their analyses through GEE on Google App Engine. The GSO provides users the functions of accessing and extracting cloud-gap-filled snow data and interactive snow cover change exploration. In addition to snow cover frequency (SCF), we also plan to develop several other snow cover parameters, including snow cover duration/days, snow cover onset dates, and snow cover melting dates, and to study the shift and trend of global snow

  10. Snow instability patterns at the scale of a small basin

    Science.gov (United States)

    Reuter, Benjamin; Richter, Bettina; Schweizer, Jürg

    2016-02-01

    Spatial and temporal variations are inherent characteristics of the alpine snow cover. Spatial heterogeneity is supposed to control the avalanche release probability by either hindering extensive crack propagation or facilitating localized failure initiation. Though a link between spatial snow instability variations and meteorological forcing is anticipated, it has not been quantitatively shown yet. We recorded snow penetration resistance profiles with the snow micropenetrometer at an alpine field site during five field campaigns in Eastern Switzerland. For each of about 150 vertical profiles sampled per day a failure initiation criterion and the critical crack length were calculated. For both criteria we analyzed their spatial structure and predicted snow instability in the basin by external drift kriging. The regression models were based on terrain and snow depth data. Slope aspect was the most prominent driver, but significant covariates varied depending on the situation. Residual autocorrelation ranges were shorter than the ones of the terrain suggesting external influences possibly due to meteorological forcing. To explore the causes of the instability patterns we repeated the geostatistical analysis with snow cover model output as covariate data for one case. The observed variations of snow instability were related to variations in slab layer properties which were caused by preferential deposition of precipitation and differences in energy input at the snow surface during the formation period of the slab layers. Our results suggest that 3-D snow cover modeling allows reproducing some of the snow property variations related to snow instability, but in future work all relevant micrometeorological spatial interactions should be considered.

  11. Is snow-ice now a major contributor to sea ice mass balance in the western Transpolar Drift region?

    Science.gov (United States)

    Graham, R. M.; Merkouriadi, I.; Cheng, B.; Rösel, A.; Granskog, M. A.

    2017-12-01

    During the Norwegian young sea ICE (N-ICE2015) campaign, which took place in the first half of 2015 north of Svalbard, a deep winter snow pack (50 cm) on sea ice was observed, that was 50% thicker than earlier climatological studies suggested for this region. Moreover, a significant fraction of snow contributed to the total ice mass in second-year ice (SYI) (9% on average). Interestingly, very little snow (3% snow by mass) was present in first-year ice (FYI). The combination of sea ice thinning and increased precipitation north of Svalbard is expected to promote the formation of snow-ice. Here we use the 1-D snow/ice thermodynamic model HIGHTSI forced with reanalysis data, to show that for the case study of N-ICE2015, snow-ice would even form over SYI with an initial thickness of 2 m. In current conditions north of Svalbard, snow-ice is ubiquitous and contributes to the thickness growth up to 30%. This contribution is important, especially in the absence of any bottom thermodynamic growth due to the thick insulating snow cover. Growth of FYI north of Svalbard is mainly controlled by the timing of growth onset relative to snow precipitation events and cold spells. These usually short-lived conditions are largely determined by the frequency of storms entering the Arctic from the Atlantic Ocean. In our case, a later freeze onset was favorable for FYI growth due to less snow accumulation in early autumn. This limited snow-ice formation but promoted bottom thermodynamic growth. We surmise these findings are related to a regional phenomenon in the Atlantic sector of the Arctic, with frequent storm events which bring increasing amounts of precipitation in autumn and winter, and also affect the duration of cold temperatures required for ice growth in winter. We discuss the implications for the importance of snow-ice in the future Arctic, formerly believed to be non-existent in the central Arctic due to thick perennial ice.

  12. Metal accumulation in the earthworm Lumbricus rubellus. Model predictions compared to field data

    International Nuclear Information System (INIS)

    Veltman, Karin; Huijbregts, Mark A.J.; Vijver, Martina G.; Peijnenburg, Willie J.G.M.; Hobbelen, Peter H.F.; Koolhaas, Josee E.; Gestel, Cornelis A.M. van; Vliet, Petra C.J. van; Jan Hendriks, A.

    2007-01-01

    The mechanistic bioaccumulation model OMEGA (Optimal Modeling for Ecotoxicological Applications) is used to estimate accumulation of zinc (Zn), copper (Cu), cadmium (Cd) and lead (Pb) in the earthworm Lumbricus rubellus. Our validation to field accumulation data shows that the model accurately predicts internal cadmium concentrations. In addition, our results show that internal metal concentrations in the earthworm are less than linearly (slope < 1) related to the total concentration in soil, while risk assessment procedures often assume the biota-soil accumulation factor (BSAF) to be constant. Although predicted internal concentrations of all metals are generally within a factor 5 compared to field data, incorporation of regulation in the model is necessary to improve predictability of the essential metals such as zinc and copper. - Earthworm metal concentrations are less than linearly related to total soil concentrations and predicted pore water concentrations

  13. Combined Study of Snow Depth Determination and Winter Leaf Area Index Retrieval by Unmanned Aerial Vehicle Photogrammetry

    Science.gov (United States)

    Lendzioch, Theodora; Langhammer, Jakub; Jenicek, Michal

    2017-04-01

    A rapid and robust approach using Unmanned Aerial Vehicle (UAV) digital photogrammetry was performed for evaluating snow accumulation over different small localities (e.g. disturbed forest and open area) and for indirect field measurements of Leaf Area Index (LAI) of coniferous forest within the Šumava National Park, Czech Republic. The approach was used to reveal impacts related to changes in forest and snowpack and to determine winter effective LAI for monitoring the impact of forest canopy metrics on snow accumulation. Due to the advancement of the technique, snow depth and volumetric changes of snow depth over these selected study areas were estimated at high spatial resolution (1 cm) by subtracting a snow-free digital elevation model (DEM) from a snow-covered DEM. Both, downward-looking UAV images and upward-looking digital hemispherical photography (DHP), and additional widely used LAI-2200 canopy analyser measurements were applied to determine the winter LAI, controlling interception and transmitting radiation. For the performance of downward-looking UAV images the snow background instead of the sky fraction was used. The reliability of UAV-based LAI retrieval was tested by taking an independent data set during the snow cover mapping campaigns. The results showed the potential of digital photogrammetry for snow depth mapping and LAI determination by UAV techniques. The average difference obtained between ground-based and UAV-based measurements of snow depth was 7.1 cm with higher values obtained by UAV. The SD of 22 cm for the open area seemed competitive with the typical precision of point measurements. In contrast, the average difference in disturbed forest area was 25 cm with lower values obtained by UAV and a SD of 36 cm, which is in agreement with other studies. The UAV-based LAI measurements revealed the lowest effective LAI values and the plant canopy analyser LAI-2200 the highest effective LAI values. The biggest bias of effective LAI was observed

  14. Snow water equivalent monitoring retrieved by assimilating passive microwave observations in a coupled snowpack evolution and microwave emission models over North-Eastern Canada

    Science.gov (United States)

    Royer, A.; Larue, F.; De Sève, D.; Roy, A.; Vionnet, V.; Picard, G.; Cosme, E.

    2017-12-01

    Over northern snow-dominated basins, the snow water equivalent (SWE) is of primary interest for spring streamflow forecasting. SWE retrievals from satellite data are still not well resolved, in particular from microwave (MW) measurements, the only type of data sensible to snow mass. Also, the use of snowpack models is challenging due to the large uncertainties in meteorological input forcings. This project aims to improve SWE prediction by assimilation of satellite brightness temperature (TB), without any ground-based observations. The proposed approach is the coupling of a detailed multilayer snowpack model (Crocus) with a MW snow emission model (DMRT-ML). The assimilation scheme is a Sequential Importance Resampling Particle filter, through ensembles of perturbed meteorological forcings according to their respective uncertainties. Crocus simulations driven by operational meteorological forecasts from the Canadian Global Environmental Multiscale model at 10 km spatial resolution were compared to continuous daily SWE measurements over Québec, North-Eastern Canada (56° - 45°N). The results show a mean bias of the maximum SWE overestimated by 16% with variations up to +32%. This observed large variability could lead to dramatic consequences on spring flood forecasts. Results of Crocus-DMRT-ML coupling compared to surface-based TB measurements (at 11, 19 and 37 GHz) show that the Crocus snowpack microstructure described by sticky hard spheres within DMRT has to be scaled by a snow stickiness of 0.18, significantly reducing the overall RMSE of simulated TBs. The ability of assimilation of daily TBs to correct the simulated SWE is first presented through twin experiments with synthetic data, and then with AMSR-2 satellite time series of TBs along the winter taking into account atmospheric and forest canopy interferences (absorption and emission). The differences between TBs at 19-37 GHz and at 11-19 GHz, in vertical polarization, were assimilated. This assimilation

  15. Snowdrift modelling for the Vestfonna ice cap, north-eastern Svalbard

    Directory of Open Access Journals (Sweden)

    T. Sauter

    2013-08-01

    Full Text Available The redistribution of snow by drifting and blowing snow frequently leads to an inhomogeneous snow mass distribution on larger ice caps. Together with the thermodynamic impact of drifting snow sublimation on the lower atmospheric boundary layer, these processes affect the glacier surface mass balance. This study provides a first quantification of snowdrift and sublimation of blowing and drifting snow on the Vestfonna ice cap (Svalbard by using the specifically designed snow2blow snowdrift model. The model is forced by atmospheric fields from the Polar Weather Research and Forecasting model and resolves processes on a spatial resolution of 250 m. The model is applied to the Vestfonna ice cap for the accumulation period 2008/2009. Comparison with radio-echo soundings and snow-pit measurements show that important local-scale processes are resolved by the model and the overall snow accumulation pattern is reproduced. The findings indicate that there is a significant redistribution of snow mass from the interior of the ice cap to the surrounding areas and ice slopes. Drifting snow sublimation of suspended snow is found to be stronger during spring. It is concluded that the redistribution process is strong enough to have a significant impact on glacier mass balance.

  16. Modeling Scale and Orientation-Dependent Effects in Snow Particles at Microwave Wavelengths

    Science.gov (United States)

    Honeyager, R. E.; Liu, G.; Nowell, H.

    2013-12-01

    With the advent of satellite-borne radar and radiometers, it is now possible to observe cloud processes throughout the globe with unprecedented levels of precision. However, interpreting the large amount of data generated by such instruments requires detailed understanding of how light is scattered in-cloud by ice. Unlike liquid water, ice exhibits complex shapes and orientation-dependent effects. This is of particularly great importance at microwave wavelengths, where ice aggregates are easily detected by radar and radiometers. When modeling such irregular particles, it is desirable to have a large collection of particle that resemble those found in nature. There is a tradeoff, however, in modeling fine features of particles and system resources (processing time and memory requirements). This becomes increasingly significant at particle sizes that are significantly larger than the considered wavelengths. Both pristine flakes and aspect-ratio correct bullet rosette aggregates were first considered using the Discrete Dipole Approximation. These initial flakes were subjected to a variety of decimation conditions, where adjacent dipoles were combined, thus producing equivalent particles with slightly lower fractal complexity. Calculations using such decimated particles were an order of magnitude faster, and exhibited scattering cross-sections to within twenty percent of initial values. Similar methods of approximating particles with dielectric-scaled ellipsoids and clusters of spherical particles were also examined using the T-matrix method. Many microwave radiative transfer models currently make the implicit assumption that all scattering sources are randomly oriented. This is generally true of the bulk atmosphere, but dynamical simulations suggest that this does not hold for asymmetric ice crystals in nonturbulent conditions. Ensembles of such particles were constructed according to established size, density and aspect ratio relationships. Relative orientation

  17. Investigating the dynamics of bulk snow density in dry and wet conditions using a one-dimensional model

    NARCIS (Netherlands)

    De Michele, C.; Avanzi, F.; Ghezzi, A.; Jommi, C.

    2013-01-01

    The snowpack is a complicated multiphase mixture with mechanical, hydraulic, and thermal properties highly variable during the year in response to climatic forcings. Bulk density is a macroscopic property of the snowpack used, together with snow depth, to quantify the water stored. In seasonal

  18. Volcanic deposits in Antarctic snow and ice

    Science.gov (United States)

    Delmas, Robert J.; Legrand, Michel; Aristarain, Alberto J.; Zanolini, FrançOise

    1985-12-01

    Major volcanic eruptions are able to spread large amounts of sulfuric acid all over the world. Acid layers of volcanic origin were detected for the first time a few years ago by Hammer in Greenland ice. The present paper deals with volcanic deposits in the Antarctic. The different methods that can be used to find volcanic acid deposits in snow and ice cores are compared: electrical conductivity, sulfate, and acidity measurements. Numerous snow and ice samples collected at several Antarctic locations were analyzed. The results reveal that the two major volcanic events recorded by H2SO4, fallout in Antarctic ice over the last century are the eruptions of Krakatoa (1883) and Agung (1963), both located at equatorial latitudes in the southern hemisphere. The volcanic signals are found to be particularly well defined at central Antarctic locations apparently in relation to the low snow accumulation rates in these areas. It is demonstrated that volcanic sulfuric acid in snow is not even partially neutralized by ammonia. The possible influence of Antarctic volcanic activity on snow chemistry is also discussed, using the three recent eruptions of the Deception Island volcano as examples. Only one of them seems to have had a significant effect on the chemistry of snow at a location 200 km from this volcano. It is concluded that Antarctic volcanic ice records are less complicated than Greenland records because of the limited number of volcanos in the southern hemisphere and the apparently higher signal to background ratio for acidity in Antarctica than in Greenland.

  19. Snow chemistry of high altitude glaciers in the French Alps

    OpenAIRE

    MAUPETIT, FRANÇOIS; DELMAS, ROBERT J.

    2011-01-01

    Snow samples were collected as snowcores in the accumulation zone of four high altitude glaciers (2980–3540 m.a.s.l.) from each of the 4 highest mountain areas of the French Alps, during 3 consecutive years: 1989, 1990 and 1991. Sampling was performed in spring (∼ May), before the onset of late spring–summer percolation. The accumulated snow therefore reflects winter and spring conditions. A complementary sampling of fresh-snow was performed on an event basis, on one of the studied glaciers, ...

  20. Evaluation of SNODAS snow depth and snow water equivalent estimates for the Colorado Rocky Mountains, USA

    Science.gov (United States)

    Clow, David W.; Nanus, Leora; Verdin, Kristine L.; Schmidt, Jeffrey

    2012-01-01

    The National Weather Service's Snow Data Assimilation (SNODAS) program provides daily, gridded estimates of snow depth, snow water equivalent (SWE), and related snow parameters at a 1-km2 resolution for the conterminous USA. In this study, SNODAS snow depth and SWE estimates were compared with independent, ground-based snow survey data in the Colorado Rocky Mountains to assess SNODAS accuracy at the 1-km2 scale. Accuracy also was evaluated at the basin scale by comparing SNODAS model output to snowmelt runoff in 31 headwater basins with US Geological Survey stream gauges. Results from the snow surveys indicated that SNODAS performed well in forested areas, explaining 72% of the variance in snow depths and 77% of the variance in SWE. However, SNODAS showed poor agreement with measurements in alpine areas, explaining 16% of the variance in snow depth and 30% of the variance in SWE. At the basin scale, snowmelt runoff was moderately correlated (R2 = 0.52) with SNODAS model estimates. A simple method for adjusting SNODAS SWE estimates in alpine areas was developed that uses relations between prevailing wind direction, terrain, and vegetation to account for wind redistribution of snow in alpine terrain. The adjustments substantially improved agreement between measurements and SNODAS estimates, with the R2 of measured SWE values against SNODAS SWE estimates increasing from 0.42 to 0.63 and the root mean square error decreasing from 12 to 6 cm. Results from this study indicate that SNODAS can provide reliable data for input to moderate-scale to large-scale hydrologic models, which are essential for creating accurate runoff forecasts. Refinement of SNODAS SWE estimates for alpine areas to account for wind redistribution of snow could further improve model performance. Published 2011. This article is a US Government work and is in the public domain in the USA.

  1. Spatiotemporal Variability of Snow Depth across Eurasian Continent from 1966 to 2008

    Science.gov (United States)

    Zhong, X.; Zhang, T.; Wang, K.

    2013-12-01

    Snow depth is one of the important parameters of snow cover, and it affects the surface energy balance, assessment of snow water equivalent, ecosystem, soil temperatures, and water cycle as a whole. In this study, the long-term observed snow depth from 1972 meteorological stations and snow course sites were used to investigate snow depth climatology and its spatiotemporal variations over Eurasian Continent from 1966 to 2008. Preliminary results showed that snow depth was affected by latitude, which in general snow depth increased with the increasing latitude. The higher values of snow depth were found in the northeastern European Russia, the east of western Siberia, the west of central Siberia, Kamchatka Peninsula, and some areas of Sakhalin. While the lower snow accumulation occurred in most areas of China except for the north of Xinjiang Autonomous Region of China, Northeast China, and some regions of the southwestern Tibet. Both of the trends in inter-annual variability of annual mean snow depth and annual maximum snow depth were not significant. However, the long-term monthly mean snow depth had obvious increasing trends from February to May. There were similar spatial distributions of linear trend coefficients of annual mean snow depth and annual maximum snow depth across the former Soviet Union (USSR). The most significant trends of changes in annual mean snow depth and annual maximum snow depth were found between 40° to 70°N. The obvious trends of variability in monthly mean snow depth appeared in the areas between 50° to 60°N. The significant decreasing trends in monthly mean snow depth were observed in most areas of China from February to March. This may be largely influenced by climate change, which leads to an advancing of the end date of snow cover.

  2. The significance of vertical moisture diffusion on drifting snow sublimation near snow surface

    Science.gov (United States)

    Huang, Ning; Shi, Guanglei

    2017-12-01

    Sublimation of blowing snow is an important parameter not only for the study of polar ice sheets and glaciers, but also for maintaining the ecology of arid and semi-arid lands. However, sublimation of near-surface blowing snow has often been ignored in previous studies. To study sublimation of near-surface blowing snow, we established a sublimation of blowing snow model containing both a vertical moisture diffusion equation and a heat balance equation. The results showed that although sublimation of near-surface blowing snow was strongly reduced by a negative feedback effect, due to vertical moisture diffusion, the relative humidity near the surface does not reach 100 %. Therefore, the sublimation of near-surface blowing snow does not stop. In addition, the sublimation rate near the surface is 3-4 orders of magnitude higher than that at 10 m above the surface and the mass of snow sublimation near the surface accounts for more than half of the total snow sublimation when the friction wind velocity is less than about 0.55 m s-1. Therefore, the sublimation of near-surface blowing snow should not be neglected.

  3. Measured Black Carbon Deposition on the Sierra Nevada Snow Pack and Implication for Snow Pack Retreat

    Energy Technology Data Exchange (ETDEWEB)

    Hadley, O.L.; Corrigan, C.E.; Kirchstetter, T.W.; Cliff, S.S.; Ramanathan, V.

    2010-01-12

    Modeling studies show that the darkening of snow and ice by black carbon deposition is a major factor for the rapid disappearance of arctic sea ice, mountain glaciers and snow packs. This study provides one of the first direct measurements for the efficient removal of black carbon from the atmosphere by snow and its subsequent deposition to the snow packs of California. The early melting of the snow packs in the Sierras is one of the contributing factors to the severe water problems in California. BC concentrations in falling snow were measured at two mountain locations and in rain at a coastal site. All three stations reveal large BC concentrations in precipitation, ranging from 1.7 ng/g to 12.9 ng/g. The BC concentrations in the air after the snow fall were negligible suggesting an extremely efficient removal of BC by snow. The data suggest that below cloud scavenging, rather than ice nuclei, was the dominant source of BC in the snow. A five-year comparison of BC, dust, and total fine aerosol mass concentrations at multiple sites reveals that the measurements made at the sampling sites were representative of large scale deposition in the Sierra Nevada. The relative concentration of iron and calcium in the mountain aerosol indicates that one-quarter to one-third of the BC may have been transported from Asia.

  4. Modeling electron competition among nitrogen oxides reduction and N2 O accumulation in hydrogenotrophic denitrification.

    Science.gov (United States)

    Liu, Yiwen; Ngo, Huu H; Guo, Wenshan; Peng, Lai; Chen, Xueming; Wang, Dongbo; Pan, Yuting; Ni, Bing-Jie

    2018-04-01

    Hydrogenotrophic denitrification is a novel and sustainable process for nitrogen removal, which utilizes hydrogen as electron donor, and carbon dioxide as carbon source. Recent studies have shown that nitrous oxide (N 2 O), a highly undesirable intermediate and potent greenhouse gas, can accumulate during this process. In this work, a new mathematical model is developed to describe nitrogen oxides dynamics, especially N 2 O, during hydrogenotrophic denitrification for the first time. The model describes electron competition among the four steps of hydrogenotrophic denitrification through decoupling hydrogen oxidation and nitrogen reduction processes using electron carriers, in contrast to the existing models that couple these two processes and also do not consider N 2 O accumulation. The developed model satisfactorily describes experimental data on nitrogen oxides dynamics obtained from two independent hydrogenotrophic denitrifying cultures under various hydrogen and nitrogen oxides supplying conditions, suggesting the validity and applicability of the model. The results indicated that N 2 O accumulation would not be intensified under hydrogen limiting conditions, due to the higher electron competition capacity of N 2 O reduction in comparison to nitrate and nitrite reduction during hydrogenotrophic denitrification. The model is expected to enhance our understanding of the process during hydrogenotrophic denitrification and the ability to predict N 2 O accumulation. © 2017 Wiley Periodicals, Inc.

  5. Biodynamic modelling and the prediction of accumulated trace metal concentrations in the polychaete Arenicola marina

    International Nuclear Information System (INIS)

    Casado-Martinez, M. Carmen; Smith, Brian D.; DelValls, T. Angel; Luoma, Samuel N.; Rainbow, Philip S.

    2009-01-01

    The use of biodynamic models to understand metal uptake directly from sediments by deposit-feeding organisms still represents a special challenge. In this study, accumulated concentrations of Cd, Zn and Ag predicted by biodynamic modelling in the lugworm Arenicola marina have been compared to measured concentrations in field populations in several UK estuaries. The biodynamic model predicted accumulated field Cd concentrations remarkably accurately, and predicted bioaccumulated Ag concentrations were in the range of those measured in lugworms collected from the field. For Zn the model showed less but still good comparability, accurately predicting Zn bioaccumulation in A. marina at high sediment concentrations but underestimating accumulated Zn in the worms from sites with low and intermediate levels of Zn sediment contamination. Therefore, it appears that the physiological parameters experimentally derived for A. marina are applicable to the conditions encountered in these environments and that the assumptions made in the model are plausible. - Biodynamic modelling predicts accumulated field concentrations of Ag, Cd and Zn in the deposit-feeding polychaete Arenicola marina.

  6. Magnetic resonance imaging (MRI to study striatal iron accumulation in a rat model of Parkinson's disease.

    Directory of Open Access Journals (Sweden)

    Ana Virel

    Full Text Available Abnormal accumulation of iron is observed in neurodegenerative disorders. In Parkinson's disease, an excess of iron has been demonstrated in different structures of the basal ganglia and is suggested to be involved in the pathogenesis of the disease. Using the 6-hydroxydopamine (6-OHDA rat model of Parkinson's disease, the edematous effect of 6-OHDA and its relation with striatal iron accumulation was examined utilizing in vivo magnetic resonance imaging (MRI. The results revealed that in comparison with control animals, injection of 6-OHDA into the rat striatum provoked an edematous process, visible in T2-weighted images that was accompanied by an accumulation of iron clearly detectable in T2*-weighted images. Furthermore, Prussian blue staining to detect iron in sectioned brains confirmed the existence of accumulated iron in the areas of T2* hypointensities. The presence of ED1-positive microglia in the lesioned striatum overlapped with this accumulation of iron, indicating areas of toxicity and loss of dopamine nerve fibers. Correlation analyses demonstrated a direct relation between the hyperintensities caused by the edema and the hypointensities caused by the accumulation of iron.

  7. Modelling accumulation of marine plastics in the coastal zone; what are the dominant physical processes?

    Science.gov (United States)

    Critchell, Kay; Lambrechts, Jonathan

    2016-03-01

    Anthropogenic marine debris, mainly of plastic origin, is accumulating in estuarine and coastal environments around the world causing damage to fauna, flora and habitats. Plastics also have the potential to accumulate in the food web, as well as causing economic losses to tourism and sea-going industries. If we are to manage this increasing threat, we must first understand where debris is accumulating and why these locations are different to others that do not accumulate large amounts of marine debris. This paper demonstrates an advection-diffusion model that includes beaching, settling, resuspension/re-floating, degradation and topographic effects on the wind in nearshore waters to quantify the relative importance of these physical processes governing plastic debris accumulation. The aim of this paper is to prioritise research that will improve modelling outputs in the future. We have found that the physical characteristic of the source location has by far the largest effect on the fate of the debris. The diffusivity, used to parameterise the sub-grid scale movements, and the relationship between debris resuspension/re-floating from beaches and the wind shadow created by high islands also has a dramatic impact on the modelling results. The rate of degradation of macroplastics into microplastics also have a large influence in the result of the modelling. The other processes presented (settling, wind drift velocity) also help determine the fate of debris, but to a lesser degree. These findings may help prioritise research on physical processes that affect plastic accumulation, leading to more accurate modelling, and subsequently management in the future.

  8. An 18-yr long (1993–2011 snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt. for driving and evaluating snowpack models

    Directory of Open Access Journals (Sweden)

    S. Morin

    2012-07-01

    Full Text Available A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France. Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September, when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN, in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo and hourly (snow depth, albedo, runoff, surface temperature, soil temperature time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://dx.doi.org/10.1594/PANGAEA.774249 as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

  9. Analysis of snow-glacial historical and projected flows in Olivares river basin. Comparison between DHSVM and WEAP models.

    Science.gov (United States)

    Cepeda, Javier; Vargas, Ximena

    2017-04-01

    In the Andes Mountains, in central Chile, glaciers are a key element to both environment and economy, since they contribute highly to streamflow during the summer season. Many studies have been performed in order to understand the actual contribution of glacial-based streamflow and the expected response of glaciers to climatological alterations such as climate change. This work studies and analyses the historical and future streamflow on the Olivares river basin, located close to Chile's capital city, Santiago, under climatic change scenario RCP8.5. For this, we use two hydrological models with different topology, to have more consistency in the results, and analysing the differences because of the conceptualization of the processes and its spatial scale. DHSVM is a distributed, physically based model, while WEAP is a semi-distributed model that represents some processes conceptually and others physically based. Both models are calibrated considering streamflow and snow cover data from the period 2001-2012 at a daily scale. Additionally, comparisons between the modelled glacier area variations and LANDSAT images are performed to strengthen the calibration process. Climate change projections are obtained from five Global Circulation Models (GCM) under RCP8.5 scenario. Changes in glacier area, volume and glacial streamflow contribution to basin discharge are analysed, comparing two future time lapses, near-future period (2015-2044) and far-future (2045-2074), to a baseline period (1985-2004). The basin has an area of 543 km2, with elevations ranging from 1,528 to 6,024 m.a.s.l. and an important glacier presence. According to the National Glacier Cadastre developed by Chile Water Authority (DGA) in 2012, there are 80 uncovered glaciers within the basin, the most important being Juncal Sur, Olivares Alfa, Beta and Gamma. Glacier area represented 17% of the basin in 1985, while they made up only to 11% in 2015.The glaciers are located at altitudes ranging from 3,500 to

  10. Modeling and control of a hybrid wind-tidal turbine with hydraulic accumulator

    International Nuclear Information System (INIS)

    Fan, YaJun; Mu, AnLe; Ma, Tao

    2016-01-01

    This paper presents the modeling and control of a hybrid wind-tidal turbine with hydraulic accumulator. The hybrid turbine captures the offshore wind energy and tidal current energy simultaneously and stores the excess energy in hydraulic accumulator prior to electricity generation. Two hydraulic pumps installed respectively in wind and tidal turbine nacelles are used to transform the captured mechanical energy into hydraulic energy. To extract the maximal power from wind and tidal current, standard torque controls are achieved by regulating the displacements of the hydraulic pumps. To meet the output power demand, a Proportion Integration Differentiation (PID) controller is designed to distribute the hydraulic energy between the accumulator and the Pelton turbine. A simulation case study based on combining a 5 MW offshore wind turbine and a 1 MW tidal current turbine is undertaken. Case study demonstrates that the hybrid generation system not only captures all the available wind and tidal energy and also delivers the desired generator power precisely through the accumulator damping out all the power fluctuations from the wind and tidal speed disturbances. Energy and exergy analyses show that the energy efficiency can exceed 100% as the small input speeds are considered, and the exergy efficiency has the consistent change trends with demand power. Further more parametric sensitivity study on hydraulic accumulator shows that there is an inversely proportional relationship between accumulator and hydraulic equipments including the pump and nozzle in terms of dimensions. - Highlights: • A hybrid wind-tidal turbine is presented. • Hydraulic accumulator stores/releases the surplus energy. • Standard torque controls extract the maximal power from wind and tidal. • Generator outputs meet the electricity demand precisely. • Parametric sensitivity study on accumulator is implemented.

  11. Enhancement of the MODIS Daily Snow Albedo Product

    Science.gov (United States)

    Hall, Dorothy K.; Schaaf, Crystal B.; Wang, Zhuosen; Riggs, George A.

    2009-01-01

    The MODIS daily snow albedo product is a data layer in the MOD10A1 snow-cover product that includes snow-covered area and fractional snow cover as well as quality information and other metadata. It was developed to augment the MODIS BRDF/Albedo algorithm (MCD43) that provides 16-day maps of albedo globally at 500-m resolution. But many modelers require daily snow albedo, especially during the snowmelt season when the snow albedo is changing rapidly. Many models have an unrealistic snow albedo feedback in both estimated albedo and change in albedo over the seasonal cycle context, Rapid changes in snow cover extent or brightness challenge the MCD43 algorithm; over a 16-day period, MCD43 determines whether the majority of clear observations was snow-covered or snow-free then only calculates albedo for the majority condition. Thus changes in snow albedo and snow cover are not portrayed accurately during times of rapid change, therefore the current MCD43 product is not ideal for snow work. The MODIS daily snow albedo from the MOD10 product provides more frequent, though less robust maps for pixels defined as "snow" by the MODIS snow-cover algorithm. Though useful, the daily snow albedo product can be improved using a daily version of the MCD43 product as described in this paper. There are important limitations to the MOD10A1 daily snow albedo product, some of which can be mitigated. Utilizing the appropriate per-pixel Bidirectional Reflectance Distribution Functions (BRDFs) can be problematic, and correction for anisotropic scattering must be included. The BRDF describes how the reflectance varies with view and illumination geometry. Also, narrow-to-broadband conversion specific for snow on different surfaces must be calculated and this can be difficult. In consideration of these limitations of MOD10A1, we are planning to improve the daily snow albedo algorithm by coupling the periodic per-pixel snow albedo from MCD43, with daily surface ref|outanoom, In this paper, we

  12. Blowing snow detection from ground-based ceilometers : Application to East Antarctica

    NARCIS (Netherlands)

    Gossart, Alexandra; Souverijns, Niels; Gorodetskaya, Irina V.; Lhermitte, S.L.M.; Lenaerts, Jan T M; Schween, Jan H.; Mangold, Alexander; Laffineur, Quentin; van Lipzig, Nicole P. M.

    2017-01-01

    Blowing snow impacts Antarctic ice sheet surface mass balance by snow redistribution and sublimation. However, numerical models poorly represent blowing snow processes, while direct observations are limited in space and time. Satellite retrieval of blowing snow is hindered by clouds and only the

  13. Small scale variability of snow properties on Antarctic sea ice

    Science.gov (United States)

    Wever, Nander; Leonard, Katherine; Paul, Stephan; Jacobi, Hans-Werner; Proksch, Martin; Lehning, Michael

    2016-04-01

    Snow on sea ice plays an important role in air-ice-sea interactions, as snow accumulation may for example increase the albedo. Snow is also able to smooth the ice surface, thereby reducing the surface roughness, while at the same time it may generate new roughness elements by interactions with the wind. Snow density is a key property in many processes, for example by influencing the thermal conductivity of the snow layer, radiative transfer inside the snow as well as the effects of aerodynamic forcing on the snowpack. By comparing snow density and grain size from snow pits and snow micro penetrometer (SMP) measurements, highly resolved density and grain size profiles were acquired during two subsequent cruises of the RV Polarstern in the Weddell Sea, Antarctica, between June and October 2013. During the first cruise, SMP measurements were done along two approximately 40 m transects with a horizontal resolution of approximately 30 cm. During the second cruise, one transect was made with approximately 7.5 m resolution over a distance of 500 m. Average snow densities are about 300 kg/m3, but the analysis also reveals a high spatial variability in snow density on sea ice in both horizontal and vertical direction, ranging from roughly 180 to 360 kg/m3. This variability is expressed by coherent snow structures over several meters. On the first cruise, the measurements were accompanied by terrestrial laser scanning (TLS) on an area of 50x50 m2. The comparison with the TLS data indicates that the spatial variability is exhibiting similar spatial patterns as deviations in surface topology. This suggests a strong influence from surface processes, for example wind, on the temporal development of density or grain size profiles. The fundamental relationship between variations in snow properties, surface roughness and changes therein as investigated in this study is interpreted with respect to large-scale ice movement and the mass balance.

  14. Photopolarimetric Retrievals of Snow Properties

    Science.gov (United States)

    Ottaviani, M.; van Diedenhoven, B.; Cairns, B.

    2015-01-01

    Polarimetric observations of snow surfaces, obtained in the 410-2264 nm range with the Research Scanning Polarimeter onboard the NASA ER-2 high-altitude aircraft, are analyzed and presented. These novel measurements are of interest to the remote sensing community because the overwhelming brightness of snow plagues aerosol and cloud retrievals based on airborne and spaceborne total reflection measurements. The spectral signatures of the polarized reflectance of snow are therefore worthwhile investigating in order to provide guidance for the adaptation of algorithms currently employed for the retrieval of aerosol properties over soil and vegetated surfaces. At the same time, the increased information content of polarimetric measurements allows for a meaningful characterization of the snow medium. In our case, the grains are modeled as hexagonal prisms of variable aspect ratios and microscale roughness, yielding retrievals of the grains' scattering asymmetry parameter, shape and size. The results agree with our previous findings based on a more limited data set, with the majority of retrievals leading to moderately rough crystals of extreme aspect ratios, for each scene corresponding to a single value of the asymmetry parameter.

  15. Loropetalum chinense 'Snow Panda'

    Science.gov (United States)

    A new Loropetalum chinense, ‘Snow Panda’, developed at the U.S. National Arboretum is described. ‘Snow Panda’ (NA75507, PI660659) originated from seeds collected near Yan Chi He, Hubei, China in 1994 by the North America-China Plant Exploration Consortium (NACPEC). Several seedlings from this trip w...

  16. Automatic Calibration of a Distributed Rainfall-Runoff Model, Using the Degree-Day Formulation for Snow Melting, Within DMIP2 Project

    Science.gov (United States)

    Frances, F.; Orozco, I.

    2010-12-01

    This work presents the assessment of the TETIS distributed hydrological model in mountain basins of the American and Carson rivers in Sierra Nevada (USA) at hourly time discretization, as part of the DMIP2 Project. In TETIS each cell of the spatial grid conceptualizes the water cycle using six tanks connected among them. The relationship between tanks depends on the case, although at the end in most situations, simple linear reservoirs and flow thresholds schemes are used with exceptional results (Vélez et al., 1999; Francés et al., 2002). In particular, within the snow tank, snow melting is based in this work on the simple degree-day method with spatial constant parameters. The TETIS model includes an automatic calibration module, based on the SCE-UA algorithm (Duan et al., 1992; Duan et al., 1994) and the model effective parameters are organized following a split structure, as presented by Francés and Benito (1995) and Francés et al. (2007). In this way, the calibration involves in TETIS up to 9 correction factors (CFs), which correct globally the different parameter maps instead of each parameter cell value, thus reducing drastically the number of variables to be calibrated. This strategy allows for a fast and agile modification in different hydrological processes preserving the spatial structure of each parameter map. With the snowmelt submodel, automatic model calibration was carried out in three steps, separating the calibration of rainfall-runoff and snowmelt parameters. In the first step, the automatic calibration of the CFs during the period 05/20/1990 to 07/31/1990 in the American River (without snow influence), gave a Nash-Sutcliffe Efficiency (NSE) index of 0.92. The calibration of the three degree-day parameters was done using all the SNOTEL stations in the American and Carson rivers. Finally, using previous calibrations as initial values, the complete calibration done in the Carson River for the period 10/01/1992 to 07/31/1993 gave a NSE index of

  17. Determination of Micronutrient Accumulation in Greenhouse Cucumber Crop Using a Modeling Approach

    Directory of Open Access Journals (Sweden)

    Lino J. Ramírez-Pérez

    2017-11-01

    Full Text Available The control of micronutrient application in cucumber cultivation has great importance as they participate in many functions of metabolism. In addition, micronutrient application efficiency is fundamental to avoid periods of overconsumption or deficits in the crop. To determine micronutrient accumulation using a dynamic model, two cycles of Vitaly and Luxell cucumber crops were grown. During the development of the crop, micronutrient content (Fe, B, Mn, Cu, and Zn in the different organs of the cucumber plant was quantified. The model dynamically simulated the accumulation of biomass and micronutrients using climatic variables recorded inside the greenhouse as inputs. It was found that a decrease in photosynthetically active radiation and temperature significantly diminished the accumulation of biomass by the cucumber plants. On the other hand, the results demonstrated that the model efficiently simulated both the accumulation of biomass and micronutrients in a cucumber crop. The efficiency evaluation showed values higher than R2 > 0.95. This dynamic model can be useful to define adequate strategies for the management of cucumber cultivation in greenhouses as well as the application of micronutrients.

  18. Evaluating two concepts for the modelling of intermediates accumulation during biological denitrification in wastewater treatment.

    Science.gov (United States)

    Pan, Yuting; Ni, Bing-Jie; Lu, Huijie; Chandran, Kartik; Richardson, David; Yuan, Zhiguo

    2015-03-15

    The accumulation of the denitrification intermediates in wastewater treatment systems is highly undesirable, since both nitrite and nitric oxide (NO) are known to be toxic to bacteria, and nitrous oxide (N2O) is a potent greenhouse gas and an ozone depleting substance. To date, two distinct concepts for the modelling of denitrification have been proposed, which are represented by the Activated Sludge Model for Nitrogen (ASMN) and the Activated Sludge Model with Indirect Coupling of Electrons (ASM-ICE), respectively. The two models are fundamentally different in describing the electron allocation among different steps of denitrification. In this study, the two models were examined and compared in their ability to predict the accumulation of denitrification intermediates reported in four different experimental datasets in literature. The N-oxide accumulation predicted by the ASM-ICE model was in good agreement with values measured in all four cases, while the ASMN model was only able to reproduce one of the four cases. The better performance of the ASM-ICE model is due to that it adopts an "indirect coupling" modelling concept through electron carriers to link the carbon oxidation and the nitrogen reduction processes, which describes the electron competition well. The ASMN model, on the other hand, is inherently limited by its structural deficiency in assuming that carbon oxidation is always able to meet the electron demand by all denitrification steps, therefore discounting electron competition among these steps. ASM-ICE therefore offers a better tool for predicting and understanding intermediates accumulation in biological denitrification. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Snow observations in Mount Lebanon (2011–2016

    Directory of Open Access Journals (Sweden)

    A. Fayad

    2017-08-01

    Full Text Available We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km2 and an elevation up to 3088 m. The dataset consists of (1 continuous meteorological and