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

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.

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

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.

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 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 The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China

2019 ◽  
Vol 11 (16) ◽  
pp. 1879
Author(s):  
Jianwei Yang ◽  
Lingmei Jiang ◽  
Liyun Dai ◽  
Jinmei Pan ◽  
Shengli Wu ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Depth Estimation ◽  
Snow Cover Area ◽  
Dual Channel ◽  
Defense Meteorological Satellite Program ◽  
Snow Water ◽  
Microwave Imager

The long-term variations in snow depth are important in hydrological, meteorological, and ecological implications and climatological studies. The series of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) instruments onboard the Defense Meteorological Satellite Program (DMSP) platforms has provided a consistent 30+ year data record of global observations that is well-suited for the estimation of snow cover, snow depth, and snow water equivalent (SWE). To maximize the use of this continuous microwave observation dataset in long-term snow analysis and obtain an objective result, consistency among the SSM/I and SSMIS sensors is required. In this paper, we evaluated the consistency between the SSM/I and SSMIS concerning the observed brightness temperature (Tb) and the retrieved snow cover area and snow depth from January 2007 to December 2008, where the F13 SSM/I and the F17 SSMIS overlapped. Results showed that Tb bias at 19 GHz spans from −2 to −3 K in snow winter seasons, and from −4 to −5 K in non-snow seasons. There is a slight Tb bias at 37 GHz from −2 to 2 K, regardless of season. For 85 (91) GHz, the bias presents some uncertainty from the scattering effect of the snowpack and atmospheric emission. The overall consistency between SSM/I and SSMIS with respect to snow cover detection is between 80% and 100%, which will result in a maximum snow cover area difference of 25 × 104 km2 in China. The inconsistency in Tb between SSM/I and SSMIS can result in a −2 and −0.67 cm snow depth bias for the dual-channel and multichannel algorithms, respectively. SSMIS tends to yield lower snow depth estimates than SSM/I. Moreover, there are notable bias differences between SSM/I- and SSMIS-estimated snow depths in the tundra and taiga snow classes. Our results indicate the importance of considering the Tb bias in microwave snow cover detection and snow depth retrieval and point out that, due to the sensitivity of bias to seasons, it is better to do the intercalibration with a focus on snow-covered winter seasons. Otherwise, the bias in summer will disturb the calibration coefficients and introduce more error into the snow retrievals if the seasonal difference is not carefully evaluated and separated.

scholarly journals Improving the snow physics of WEB-DHM and its point evaluation at two SnowMIP alpine sites

2010 ◽  
Vol 7 (3) ◽  
pp. 3481-3519 ◽  
Author(s):  
M. Shrestha ◽  
L. Wang ◽  
T. Koike ◽  
Y. Xue ◽  
Y. Hirabayashi
Keyword(s):  
Surface Temperature ◽  
Liquid Water ◽  
Snow Depth ◽  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Bias Error ◽  
Snow Albedo ◽  
Basin Scale ◽  
Snow Water ◽  
Snow Physics

Abstract. The snow physics of a distributed biosphere hydrological model, referred to as the Water and Energy Budget based Distributed Hydrological Model (WEB-DHM) is improved by incorporating the three-layer physically based energy balance snowmelt model of Simplified Simple Biosphere 3 (SSiB3) and the Biosphere-Atmosphere Transfer Scheme (BATS) albedo scheme. WEB-DHM with improved snow physics (WEB-DHM-S) can simulate the variability of snow density, snow depth and snow water equivalent, liquid water and ice content in each layer, prognostic snow albedo, diurnal variation in snow surface temperature, thermal heat due to conduction and liquid water retention. The performance of WEB-DHM-S is evaluated at two alpine sites of the Snow Model Intercomparison Project with different climate characteristics: Col de Porte in France and Weissfluhjoch in Switzerland. The simulation results of the snow depth, snow water equivalent, surface temperature, snow albedo and snowmelt runoff reveal that WEB-DHM-S is capable of simulating the internal snow process better than the original WEB-DHM, with the root mean square error and bias error being remarkably reduced. Although WEB-DHM-S is only evaluated at a point scale for the simulation of snow processes, this study provides a benchmark for the application of WEB-DHM-S in cold regions in the assessment of the basin-scale snow water equivalent and seasonal discharge simulation for water resources management.

scholarly journals Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

2021 ◽  
Vol 11 (18) ◽  
pp. 8365
Author(s):  
Liming Gao ◽  
Lele Zhang ◽  
Yongping Shen ◽  
Yaonan Zhang ◽  
Minghao Ai ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Implicit Scheme ◽  
Snow Water ◽  
Irtysh River ◽  
Simulation Results ◽  
The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

Improved Northern Hemisphere Snow Water Equivalent product from passive microwave remote sensing and in situ data

2021 ◽  
Author(s):  
Colleen Mortimer ◽  
Lawrence Mudryk ◽  
Chris Derksen ◽  
Kari Luojus ◽  
Pinja Venalainen ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Snow Density ◽  
Snow Season ◽  
Snow Water ◽  
Snow Mass

<p>The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.</p><p>Previous GlobSnow SWE products used a constant snow density of 0.24 kg m<sup>-3</sup> to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.</p><p>Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m<sup>-3</sup> early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.</p>

scholarly journals Evaluation of CLASS Snow Simulation over Eastern Canada

2017 ◽  
Vol 18 (5) ◽  
pp. 1205-1225 ◽  
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.

scholarly journals Incorporation of satellite-derived snow-cover area in spatial snowmelt modeling for a large area: determination of a gridded degree-day factor

2013 ◽  
Vol 54 (62) ◽  
pp. 205-213 ◽  
Author(s):  
Yoshihiro Asaoka ◽  
Yuji Kominami
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Sensible Heat Flux ◽  
Spatial Uncertainty ◽  
Large Area ◽  
Snow Cover Area ◽  
Degree Day ◽  
Snow Water ◽  
Snowmelt Modeling

AbstractSpatial degree-day factors (DDFs) are required for spatial snowmelt modeling over large areas by the degree-day method. We propose a method to obtain DDFs by incorporating snow disappearance dates (SDDs), derived from 10 day composites of Satellite Pour l’Observation de la Terre (SPOT)/VEGETATION data, into the degree-day method. This approach allowed determination of DDFs for each gridpoint so as to better reflect regional characteristics than use of spatially constant DDFs obtained from point measurements. Simulations at six observation sites successfully accounted for variations in snow water equivalent (SWE), even at elevations different from the closest measurement site. These results suggest that incorporating satellite-derived SDDs into the degree-day method decreases spatial uncertainty compared with the use of spatially constant DDFs. Application of our method to Japanese cold regions revealed that gridded DDFs were negatively correlated with accumulated positive degree-days (APDDs) and were high only when APDDs were low. These results imply that high DDFs resulted from the dominant contribution of solar radiation to snowmelt at low temperatures and that low DDFs resulted from a relatively high contribution of sensible heat flux at high temperatures. The proposed method seems to adequately account for the main energetic components of snowmelt during the snow-cover season over large areas.

scholarly journals Changes in Snow Storage in the Upper Hron River Basin (Slovakia)

2014 ◽  
Vol 10 (2) ◽  
pp. 145-160
Author(s):  
Katarína Kotríková ◽  
Kamila Hlavčová ◽  
Róbert Fencík
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Satellite Images ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Kendall Test ◽  
Measured Data ◽  
Time Step ◽  
Snow Storage ◽  
Snow Water

Abstract An evaluation of changes in the snow cover in mountainous basins in Slovakia and a validation of MODIS satellite images are provided in this paper. An analysis of the changes in snow cover was given by evaluating changes in the snow depth, the duration of the snow cover, and the simulated snow water equivalent in a daily time step using a conceptual hydrological rainfall-runoff model with lumped parameters. These values were compared with the available measured data at climate stations. The changes in the snow cover and the simulated snow water equivalent were estimated by trend analysis; its significance was tested using the Mann-Kendall test. Also, the satellite images were compared with the available measured data. From the results, it is possible to see a decrease in the snow depth and the snow water equivalent from 1961-2010 in all the months of the winter season, and significant decreasing trends were indicated in the months of December, January and February

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