Analysis of the Snow Water Equivalent Distribution and the Snowmelt Rate Factor Using the Snow Model and Satellite Information of Snow in the Tohoku District, Japan

Abstract. 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.

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Evaluation of CLASS Snow Simulation over Eastern Canada

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0153.1 ◽

2017 ◽

Vol 18 (5) ◽

pp. 1205-1225 ◽

Cited By ~ 19

Author(s):

Diana Verseghy ◽

Ross Brown ◽

Libo Wang

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Water Equivalent ◽

Climate Model ◽

Regional Climate ◽

Eastern Canada ◽

Fractional Snow Cover ◽

Forest Regions ◽

Snow Water ◽

Snow Model

Abstract The Canadian Land Surface Scheme (CLASS), version 3.6.1, was run offline for the period 1990–2011 over a domain centered on eastern Canada, driven by atmospheric forcing data dynamically downscaled from ERA-Interim using the Canadian Regional Climate Model. The precipitation inputs were adjusted to replicate the monthly average precipitation reported in the CRU observational database. The simulated fractional snow cover and the surface albedo were evaluated using NOAA Interactive Multisensor Snow and Ice Mapping System and MODIS data, and the snow water equivalent was evaluated using CMC, Global Snow Monitoring for Climate Research (GlobSnow), and Hydro-Québec products. The modeled fractional snow cover agreed well with the observational estimates. The albedo of snow-covered areas showed a bias of up to −0.15 in boreal forest regions, owing to neglect of subgrid-scale lakes in the simulation. In June, conversely, there was a positive albedo bias in the remaining snow-covered areas, likely caused by neglect of impurities in the snow. The validation of the snow water equivalent was complicated by the fact that the three observation-based datasets differed widely. Also, the downward adjustment of the forcing precipitation clearly resulted in a low snow bias in some regions. However, where the density of the observations was high, the CLASS snow model was deemed to have performed well. Sensitivity tests confirmed the satisfactory behavior of the current parameterizations of snow thermal conductivity, snow albedo refreshment threshold, and limiting snow depth and underlined the importance of snow interception by vegetation. Overall, the study demonstrated the necessity of using a wide variety of observation-based datasets for model validation.

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

Journal of Hydrology ◽

10.1016/j.jhydrol.2016.05.046 ◽

2016 ◽

Vol 540 ◽

pp. 26-39 ◽

Cited By ~ 13

Author(s):

David J. Dziubanski ◽

Kristie J. Franz

Keyword(s):

Snow Water Equivalent ◽

Snow Water ◽

Snow Model

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The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

10.5194/hess-2019-514 ◽

2019 ◽

Author(s):

Abbas Fayad ◽

Simon Gascoin

Keyword(s):

Snow Cover ◽

Liquid Water ◽

Snow Depth ◽

Snow Water Equivalent ◽

Mountain Regions ◽

Snow Cover Area ◽

Water Percolation ◽

Snow Water ◽

Snow Model ◽

Snow Mass

Abstract. In many Mediterranean mountain regions, the seasonal snowpack is an essential yet poorly known water resource. Here, we examine, for the first time, the spatial distribution and evolution of the snow water equivalent (SWE) during three snow seasons (2013–2016) in the coastal mountains of Lebanon. We run SnowModel (Liston and Elder, 2006a), a spatially-distributed, process-based snow model, at 100 m resolution forced by new automatic weather station (AWS) data in three snow-dominated basins of Mount Lebanon. We evaluate a recent upgrade of the liquid water percolation scheme in SnowModel, which was introduced to improve the simulation of the snow water equivalent (SWE) and runoff in warm maritime regions. The model is evaluated against continuous snow depth and snow albedo observations at the AWS, manual SWE measurements, and MODIS snow cover area between 1200 m and 3000 m a.s.l.. The results show that the new percolation scheme yields better performance especially in terms of SWE but also in snow depth and snow cover area. Over the simulation period between 2013 and 2016, the maximum snow mass was reached between December and March. Peak mean SWE (above 1200 m a.s.l.) changed significantly from year to year in the three study catchments with values ranging between 73 mm and 286 mm we (RMSE between 160 and 260 mm w.e.). We suggest that the major sources of uncertainty in simulating the SWE, in this warm Mediterranean climate, can be attributed to forcing error but also to our limited understanding of the separation between rain and snow at lower-elevations, the transient snow melt events during the accumulation season, and the high-variability of snow depth patterns at the sub-pixel scale due to the wind-driven blown-snow redistribution into karstic features and sinkholes. Yet, the use of a process-based snow model with minimal requirements for parameter estimation provides a basis to simulate snow mass SWE in non-monitored catchments and characterize the contribution of snowmelt to the karstic groundwater recharge in Lebanon. While this research focused on three basins in the Mount Lebanon, it serves as a case study to highlight the importance of wet snow processes to estimate SWE in Mediterranean mountain regions.

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Climate sensitivity of snow water equivalent and snowmelt runoff in a Himalayan catchment

10.5194/tc-2016-216 ◽

2016 ◽

Cited By ~ 1

Author(s):

Emmy E. Stigter ◽

Niko Wanders ◽

Tuomo M. Saloranta ◽

Joseph M. Shea ◽

Marc F.P. Bierkens ◽

...

Keyword(s):

Kalman Filter ◽

Snow Cover ◽

Climate Sensitivity ◽

Snow Depth ◽

Snow Water Equivalent ◽

Snowmelt Runoff ◽

Snow Water ◽

Snow Model ◽

Increasing Temperature

Abstract. 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 this provides no 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 meteorological 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. Landsat 8 and MOD10A2 snow cover maps 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. The approach of modelling snow depth in a Kalman filter framework allows for data-constrained estimation of SWE rather than snow cover alone and this has great potential for future studies in the Himalayas. Climate sensitivity tests with the optimized snow model show a strong decrease in SWE in the valley with increasing temperature. However, at high elevation a decrease in SWE 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. Finally the climate sensitivity study revealed that snowmelt runoff increases in winter and 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.

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SnowClim v1.0: High-resolution snow model and data for the western United States

10.5194/gmd-2021-407 ◽

2021 ◽

Author(s):

Abby C. Lute ◽

John Abatzoglou ◽

Timothy Link

Keyword(s):

Climate Change ◽

United States ◽

Spatial Resolution ◽

Snow Water Equivalent ◽

High Spatial Resolution ◽

Western United States ◽

Climate Data ◽

Western Us ◽

Snow Water ◽

Snow Model

Abstract. Seasonal snowpack dynamics shape the biophysical and societal characteristics of many global regions. However, snowpack accumulation and duration have generally declined in recent decades largely due to anthropogenic climate change. Mechanistic understanding of snowpack spatiotemporal heterogeneity and climate change impacts will benefit from snow data products that are based on physical principles, that are simulated at high spatial resolution, and that cover large geographic domains. Existing datasets do not meet these requirements, hindering our ability to understand both contemporary and changing snow regimes and to develop adaptation strategies in regions where snowpack patterns and processes are important components of Earth systems. We developed a computationally efficient physics-based snow model, SnowClim, that can be run in the cloud. The model was evaluated and calibrated at Snowpack Telemetry sites across the western United States (US), achieving a site-median root mean square error for daily snow water equivalent of 62 mm, bias in peak snow water equivalent of −9.6 mm, and bias in snow duration of 1.2 days when run hourly. Positive biases were found at sites with mean winter temperature above freezing where the estimation of precipitation phase is prone to errors. The model was applied to the western US using newly developed forcing data created by statistically downscaling pre-industrial, historical, and pseudo-global warming climate data from the Weather Research and Forecasting (WRF) model. The resulting product is the SnowClim dataset, a suite of summary climate and snow metrics for the western US at 210 m spatial resolution (Lute et al., 2021). The physical basis, large extent, and high spatial resolution of this dataset will enable novel analyses of changing hydroclimate and its implications for natural and human systems.

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Snow water equivalent modeling components in NewAge-JGrass

Geoscientific Model Development ◽

10.5194/gmd-7-725-2014 ◽

2014 ◽

Vol 7 (3) ◽

pp. 725-736 ◽

Cited By ~ 12

Author(s):

G. Formetta ◽

S. K. Kampf ◽

O. David ◽

R. Rigon

Keyword(s):

Goodness Of Fit ◽

Hydrological Modeling ◽

Snow Water Equivalent ◽

Short Wave ◽

Wave Radiation ◽

Radiation Balance ◽

Time Step ◽

Vector Mode ◽

Snow Water ◽

Snow Model

Abstract. This paper presents a package of modified temperature-index-based snow water equivalent models as part of the hydrological modeling system NewAge-JGrass. Three temperature-based snow models are integrated into the NewAge-JGrass modeling system and use many of its components such as those for radiation balance (short wave radiation balance, SWRB), kriging (KRIGING), automatic calibration algorithms (particle swarm optimization) and tests of goodness of fit (NewAge-V), to build suitable modeling solutions (MS). Similarly to all the NewAge-JGrass components, the models can be executed both in raster and in vector mode. The simulation time step can be daily, hourly or sub-hourly, depending on user needs and availability of input data. The MS are applied on the Cache la Poudre River basin (CO, USA) using three test applications. First, daily snow water equivalent is simulated for three different measurement stations for two snow model formulations. Second, hourly snow water equivalent is simulated using all the three different snow model formulae. Finally, a raster mode application is performed to compute snow water equivalent maps for the whole Cache la Poudre Basin.

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Insights into precipitation orographic enhancement from snow-course data and their value for improved hydrologic predictions

10.5194/egusphere-egu2020-1285 ◽

2020 ◽

Author(s):

Francesco Avanzi ◽

Giulia Ercolani ◽

Simone Gabellani ◽

Edoardo Cremonese ◽

Umberto Morra di Cella ◽

...

Keyword(s):

Snow Water Equivalent ◽

High Elevation ◽

Elevation Gradients ◽

Precipitation Distribution ◽

Orographic Enhancement ◽

Snow Water ◽

Snow Model ◽

Precipitation Gauge ◽

Lapse Rates ◽

High Elevations

<p>Precipitation enhancement along elevation gradients is the result of complex interactions between synoptic-circulation patterns and local topography. Since precipitation measurements at high elevation are often biased and sparse, predicting precipitation distribution in mountain regions is challenging, despite this being a key step of hydrologic-forecasting procedures and of water management in general. By acting as a natural precipitation gauge, the snowpack can provide useful information about precipitation orographic enhancement, but the information content of snow-course measurements in this regard has been generally underappreciated. We leveraged 70,000+ measurements upstream five reservoirs in Valle d&#8217;Aosta, Italy, to show how manual and radar snow courses can be used to estimate precipitation lapse rates and consequently improve predictions of hydrologic models. Snow Water Equivalent above 3000 m ASL can be more than 4-5 times cumulative seasonal precipitation below 1000 m ASL, with elevational gradients up to 1000 mm w.e. / km ASL. Enhancement factors estimated by blending precipitation-gauge and snow-course data are highly seasonal and spatially variable, with exponential or linear profiles with elevation depending on the year. Blended gauge - snow-course precipitation lapse rates can be used to infer precipitation in ungauged areas and compensate for elevation gradients in an iterative, two-step distribution procedure of precipitation based on modified Kriging. Coupling this precipitation-distribution procedure with a snow model (S3M) shows promising improvements in Snow Water Equivalent estimates at high elevations.</p>

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Estimating the Uncertainty in Spatial Estimates of Areal Snow Water Equivalent

Hydrology Research ◽

10.2166/nh.1996.0011 ◽

1996 ◽

Vol 27 (5) ◽

pp. 295-312

Author(s):

Steven S. Carroll

Keyword(s):

River Basin ◽

Snow Water Equivalent ◽

Simulation Models ◽

The United States ◽

Hydrologic Simulation ◽

National Weather Service ◽

The West ◽

Snow Water ◽

Snow Model ◽

Weather Service

With the increased demand for water in the United States, particularly in the West, it is essential that water resources be accurately monitored. Consequently, the National Weather Service (NWS) maintains a set of conceptual, continuous, hydrologic simulation models used to generate extended streamflow predictions, water supply outlooks, and flood forecasts. A vital component of the hydrologic simulation models is a snow accumulation and ablation model that uses observed temperature and precipitation date to simulate snow cover conditions. The simulated model states are updated throughout the snow season using snow water equivalent estimates (estimates of the water content of snowpack) obtained from airborne and ground-based snow water equivalent data. The National Weather Service has developed a spatial geostatistical model to estimate the areal snow water equivalent in a river basin. The estimates, which are obtained for river basins throughout the West, are used to update the snow model. To facilitate accurate updating of the simulated snow water equivalent estimates generated by the snow model, it is necessary to incorporate measures of uncertainty of the areal snow water equivalent estimates. In this research, we derive the expression for the mean-squared prediction error of the areal snow water equivalent estimate and illustrate the methodology with an example from the Upper Colorado River basin.

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The role of liquid water percolation representation in estimating snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

Hydrology and Earth System Sciences ◽

10.5194/hess-24-1527-2020 ◽

2020 ◽

Vol 24 (3) ◽

pp. 1527-1542

Author(s):

Abbas Fayad ◽

Simon Gascoin

Keyword(s):

Snow Cover ◽

Liquid Water ◽

Snow Depth ◽

Snow Water Equivalent ◽

Mountain Regions ◽

Snow Cover Area ◽

Water Percolation ◽

Snow Water ◽

Snow Model ◽

Snow Mass

Abstract. In many Mediterranean mountain regions, the seasonal snowpack is an essential yet poorly known water resource. Here, we examine, for the first time, the spatial distribution and evolution of the snow water equivalent (SWE) during three snow seasons (2013–2016) in the coastal mountains of Lebanon. We run SnowModel (Liston and Elder, 2006a), a spatially distributed, process-based snow model, at 100 m resolution forced by new automatic weather station (AWS) data in three snow-dominated basins of Mount Lebanon. We evaluate a recent upgrade of the liquid water percolation scheme in SnowModel, which was introduced to improve the simulation of the SWE and runoff in warm maritime regions. The model is evaluated against continuous snow depth and snow albedo observations at the AWS, manual SWE measurements, and MODIS snow cover area between 1200 and 3000 m a.s.l. The results show that the new percolation scheme yields better performance, especially in terms of SWE but also in snow depth and snow cover area. Over the simulation period between 2013 and 2016, the maximum snow mass was reached between December and March. Peak mean SWE (above 1200 m a.s.l.) changed significantly from year to year in the three study catchments, with values ranging between 73 and 286 mm w.e. (RMSE between 160 and 260 mm w.e.). We suggest that the major sources of uncertainty in simulating the SWE, in this warm Mediterranean climate, can be attributed to forcing error but also to our limited understanding of the separation between rain and snow at lower-elevations, the transient snowmelt events during the accumulation season, and the high variability of snow depth patterns at the subpixel scale due to the wind-driven blown-snow redistribution into karstic features and sinkholes. Yet, the use of a process-based snow model with minimal requirements for parameter estimation provides a basis to simulate snow mass SWE in nonmonitored catchments and characterize the contribution of snowmelt to the karstic groundwater recharge in Lebanon. While this research focused on three basins in the Mount Lebanon, it serves as a case study to highlight the importance of wet snow processes to estimate SWE in Mediterranean mountain regions.

Download Full-text