scholarly journals Simulation of the snowmelt runoff contributing area in a small alpine basin

2010 ◽  
Vol 7 (1) ◽  
pp. 971-1003 ◽  
Author(s):  
C. M. DeBeer ◽  
J. W. Pomeroy
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Spatial Scales ◽  
Snowmelt Runoff ◽  
Point Scale ◽  
Cold Regions ◽  
Covered Area ◽  
Basin Scale ◽  
Physically Based ◽  
Snow Water

Abstract. Simulation of areal snowmelt and snow-cover depletion over time can be carried out by applying point-scale melt rate computations to distributions of snow water equivalent (SWE). In alpine basins, this can be done by considering these processes separately on individual slope units. However, differences in melt timing and rates arise at smaller spatial scales due to the variability in SWE and snowpack cold content, which affects the timing of melt initiation, depletion of the snow-cover and spatial extent of the snowmelt runoff contributing area (SRCA). This study examined the effects of variability in SWE, internal energy and applied melt energy on melt rates and timing, and snow-cover depletion in a small cold regions alpine basin over various scales ranging from point to basin. Melt rate computations were performed using a physically based energy balance snowmelt routine (Snobal) in the Cold Regions Hydrological Model (CRHM) and compared with measurements at three meteorological stations over a ridge within the basin. At the point scale, a negative association between daily melt rates and SWE was observed in the early melt period, with deeper snow requiring greater energy inputs to initiate melt. SWE distributions over the basin (stratified by slope) were measured using snow surveys and repeat LiDAR depth estimates, and used together with computed melt rates to simulate the areal snow-cover depletion. Comparison with observations from georeferenced oblique photographs showed an improvement in simulated areal snow-cover depletion curves when accounting for the variability in melt rate with depth of SWE in the early melt period. Finally, the SRCA was characterized as the product of the snow-covered area and the fraction of the SWE distribution undergoing active melt on each slope unit. Results for each slope were then aggregated to give the basin scale SRCA. The SRCA is controlled by the variability of melt amongst slope units and over individual SWE distributions, the variability of SWE and the resulting snow-cover depletion patterns over the basin.

scholarly journals Simulation of the snowmelt runoff contributing area in a small alpine basin

2010 ◽  
Vol 14 (7) ◽  
pp. 1205-1219 ◽  
Author(s):  
C. M. DeBeer ◽  
J. W. Pomeroy
Keyword(s):  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Spatial Scales ◽  
Snowmelt Runoff ◽  
Point Scale ◽  
Cold Regions ◽  
Energy Inputs ◽  
Basin Scale ◽  
Physically Based ◽  
Snow Water

Abstract. Simulation of areal snowmelt and snowcover depletion over time can be carried out by applying point-scale melt rate computations to distributions of snow water equivalent (SWE). In alpine basins, this can be done by considering these processes separately on individual slope units. However, differences in melt timing and rates arise at smaller spatial scales due to the variability in SWE and snowpack cold content, which affects the timing of melt initiation, depletion of the snowcover and spatial extent of the snowmelt runoff contributing area (SRCA). This study examined the effects of variability in SWE, internal energy and applied melt energy on melt rates and timing, and snowcover depletion in a small cold regions alpine basin over various scales ranging from point to basin. Melt rate computations were performed using a physically based energy balance snowmelt routine (Snobal) in the Cold Regions Hydrological Model (CRHM) and compared with measurements at 3 meteorological stations over a ridge within the basin. At the point scale, a negative association between daily melt rates and SWE was observed in the early melt period, with deeper snow requiring greater energy inputs to initiate melt. SWE distributions over the basin (stratified by slope) were measured using snow surveys and repeat LiDAR depth estimates, and used together with computed melt rates to simulate the areal snowcover depletion. Comparison with observations from georeferenced oblique photographs showed an improvement in simulated areal snowcover depletion curves when accounting for the variability in melt rate with depth of SWE in the early melt period. Finally, the SRCA was characterized as the product of the snowcovered area and the fraction of the SWE distribution undergoing active melt and producing an appreciable runoff quantity on each slope unit. Results for each slope were then aggregated to give the basin scale SRCA. The SRCA is controlled by the variability of melt amongst slope units and over individual SWE distributions, the variability of SWE, and the resulting snowcover depletion patterns over the basin.

scholarly journals The Response of Areal Snow Cover to Climate Change in a Snowmelt—Runoff Model

1997 ◽  
Vol 25 ◽  
pp. 232-236 ◽  
Author(s):  
A. Rango
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Major Influence ◽  
Snowmelt Runoff ◽  
Snow Cover Extent ◽  
Physically Based ◽  
Depletion Rate ◽  
Snow Water ◽  
Runoff Model

The cryosphere is represented in some hydrological models by the arcal extent of snow cover, a variable that has been operationally available in recent years through remote sensing. In particular, the snowmelt runoff model (SRM) requires the remotely sensed snow-cover extent as a major input variable. The SRM is well-suited for simulating the hydrological response of a basin to hypothetical climate change because it is a non-calibrated model. In order to run the SRM in a climate-change mode, the response of the areal snow cover to a change in climate is critical, and must be calculated as a function of elevation, precipitation, temperature, and snow-water equivalent. For the snowmelt-runoff season, the effect of climate change on conditions in the winter months has a major influence. In a warmer climate, winter may experience more rain vs snow events, and more periods of winter snowmelt that reduce the snow water equivalent present in the basin at the beginning of spring snow melt. As a result, the spring snowmelt runoff under conditions of climate warming will be affected not only by different temperatures and precipitation, but also by a different snow cover with a changed depletion rate. A new radiation-based version of the SRM is under development that will also take changes in cloudiness and humidity into account, making climate-change studies of the cryosphere even more physically based.

scholarly journals The response of areal snow cover to climate change in a snowmelt–runoff model

1997 ◽  
Vol 25 ◽  
pp. 232-236
Author(s):  
A. Rango
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Major Influence ◽  
Snowmelt Runoff ◽  
Snow Cover Extent ◽  
Physically Based ◽  
Snow Water ◽  
Runoff Model ◽  
Spring Snowmelt

The cryosphere is represented in some hydrological models by the areal extent of snow cover, a variable that has been operationally available in recent years through remote sensing. In particular, the snowmelt–runoff model (SRM) requires the remotely sensed snow-cover extent as a major input variable. The SRM is well-suited for simulating the hydrological response of a basin to hypothetical climate change because it is a non-calibrated model. In order to run the SRM in a climate-change mode, the response of the areal snow cover to a change in climate is critical, and must be calculated as a function of elevation, precipitation, temperature, and snow-water equivalent. For the snowmelt-runoff season, the effect of climate change on conditions in the winter months has a major influence. In a warmer climate, winter may experience more rain vs snow events, and more periods of winter snowmelt that reduce the snow water equivalent present in the basin at the beginning of spring snowmelt. As a result, the spring snowmelt runoff under conditions of climate warming will be affected not only by different temperatures and precipitation, but also by a different snow cover with a changed depletion rate. A new radiation-based version of the SRM is under development that will also take changes in cloudiness and humidity into account, making climate-change studies of the cryosphere even more physically based.

scholarly journals Estimating degree day factors from MODIS for snowmelt runoff modeling

2014 ◽  
Vol 11 (7) ◽  
pp. 8697-8735
Author(s):  
Z. H. He ◽  
J. Parajka ◽  
F. Q. Tian ◽  
G. Blöschl
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
New Method ◽  
Snowmelt Runoff ◽  
Degree Day ◽  
Covered Area ◽  
Runoff Modeling ◽  
Snow Water ◽  
Model Parameter Uncertainty

Abstract. Degree-day factors are widely used to estimate snowmelt runoff in operational hydrological models. Usually, they are calibrated on observed runoff, and sometimes on satellite snow cover data. In this paper, we propose a new method for estimating the snowmelt degree-day factor (DDFS) directly from MODIS snow covered area (SCA) and ground based snow depth data without calibration. Subcatchment snow volume is estimated by combining SCA and snow depths. Snow density is estimated as the ratio of observed precipitation and changes in the snow volume for days with snow accumulation. Finally, DDFS values are estimated as the ratio of changes in the snow water equivalent and degree-day temperatures for days with snow melt. We compare simulations of basin runoff and snow cover patterns using spatially variable DDFS estimated from snow data with those using spatially uniform DDFS calibrated on runoff. The runoff performances using estimated DDFS are slightly improved, and the simulated snow cover patterns are significantly more plausible. The new method may help reduce some of the runoff model parameter uncertainty by reducing the total number of calibration parameters.

scholarly journals Nimbus-7 SMMR Derived Global Snow Cover Parameters

1987 ◽  
Vol 9 ◽  
pp. 39-44 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Theoretical Calculations ◽  
Winter Season ◽  
Western China ◽  
High Plains ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

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.

scholarly journals Nimbus-7 SMMR Derived Global Snow Cover Parameters

1987 ◽  
Vol 9 ◽  
pp. 39-44 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Theoretical Calculations ◽  
Winter Season ◽  
Western China ◽  
High Plains ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Snow covers about 40 million km2 of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

scholarly journals Forward-Looking Assimilation of MODIS-Derived Snow-Covered Area into a Land Surface Model

2009 ◽  
Vol 10 (1) ◽  
pp. 130-148 ◽  
Author(s):  
Benjamin F. Zaitchik ◽  
Matthew Rodell
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Radiation Budget ◽  
Surface Model ◽  
Energy Fluxes ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

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

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

2016 ◽  
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.

scholarly journals Estimated seasonal snow cover and snowfall in Japan

1993 ◽  
Vol 18 ◽  
pp. 179-184
Author(s):  
Tsutomu Nakamura ◽  
Osamu Abe
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Heavy Snowfall ◽  
Seasonal Snow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Prone Area ◽  
Snow Water ◽  
Seasonal Snow Cover

The average amounts of seasonal snow cover and snowfall in Japan were calculated as 7.9 × 1013kg and 1.2 × 1014kg, respectively. The mass of seasonal snow cover of a heavy-snowfall winter, 1980–81 (56-Gosetsu), was calculated as 1.3 × 1014kg. The amount of 7.9 × 1013kg was converted to water equivalent of 230 mm on the whole snow-covered area, including snow-prone area. A mean of 370 mm in snow water equivalent was calculated for the snow area where mean snow depth on the ground was more than 10 cm.

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