scholarly journals Comments on Subgrid snow depth coefficient of variation within complex mountainous terrain

2016 ◽  
Author(s):  
Anonymous
Keyword(s):  
Coefficient Of Variation ◽  
Snow Depth ◽  
Mountainous Terrain

scholarly journals Subgrid snow depth coefficient of variation spanning alpine to sub-alpine mountainous terrain

2021 ◽  
Author(s):  
Graham A. Sexstone ◽  
Steven R. Fassnacht ◽  
Juan I. López-Moreno ◽  
Christopher A. Hiemstra
Keyword(s):  
Coefficient Of Variation ◽  
Land Surface ◽  
Snow Depth ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
Scale Modeling ◽  
Roughness Elements ◽  
Wide Range ◽  
Performance Statistics ◽  
Snowpack Properties

Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets spanning alpine to sub-alpine mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was the dominant driver of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influence seasonal snow variability. Subgrid CVds was also strongly related to topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for characterizing the variability of CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes.

scholarly journals Subgrid snow depth coefficient of variation within complex mountainous terrain

2016 ◽  
Author(s):  
Graham A. Sexstone ◽  
Steven R. Fassnacht ◽  
Juan Ignacio López-Moreno ◽  
Christopher A. Hiemstra
Keyword(s):  
Coefficient Of Variation ◽  
Land Surface ◽  
Snow Depth ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
Scale Modeling ◽  
Roughness Elements ◽  
Wide Range ◽  
Performance Statistics ◽  
Snowpack Properties

Abstract. Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets from mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was an important factor of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influences seasonal snow variability. Subgrid CVds was also correlated with topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two simple statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for parameterizing CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes.

scholarly journals The impact of initial conditions on convection-permitting simulations of a flood event over complex mountainous terrain

2020 ◽  
Vol 24 (2) ◽  
pp. 771-791 ◽  
Author(s):  
Lu Li ◽  
Marie Pontoppidan ◽  
Stefan Sobolowski ◽  
Alfonso Senatore
Keyword(s):  
Snow Depth ◽  
Peak Flow ◽  
Initial Conditions ◽  
Flood Event ◽  
Precipitation Pattern ◽  
Mountainous Terrain ◽  
Western Norway ◽  
Snow Conditions ◽  
The Impact ◽  
Hbv Model

Abstract. Western Norway suffered major flooding after 4 d of intense rainfall during the last week of October 2014. While events like this are expected to become more frequent and severe under a warming climate, convection-permitting scale models are showing their skill with respect to capturing their dynamics. Nevertheless, several sources of uncertainty need to be taken into account, including the impact of initial conditions on the precipitation pattern and discharge, especially over complex, mountainous terrain. In this paper, the Weather Research and Forecasting Model Hydrological modelling system (WRF-Hydro) is applied at a convection-permitting scale, and its performance is assessed in western Norway for the aforementioned flood event. The model is calibrated and evaluated using observations and benchmarks obtained from the Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The calibrated WRF-Hydro model performs better than the simpler conceptual HBV model, especially in areas with complex terrain and poor observational coverage. The sensitivity of the precipitation pattern and discharge to poorly constrained elements such as spin-up time and snow conditions is then examined. The results show the following: (1) the convection-permitting WRF-Hydro simulation generally captures the precipitation pattern/amount, the peak flow volume and the timing of the flood event; (2) precipitation is not overly sensitive to spin-up time, whereas discharge is slightly more sensitive due to the influence of soil moisture, especially during the pre-peak phase; and (3) the idealized snow depth experiments show that a maximum of 0.5 m of snow is converted to runoff irrespective of the initial snow depth and that this snowmelt contributes to discharge mostly during the rainy and the peak flow periods. Although further targeted experiments are needed, this study suggests that snow cover intensifies the extreme discharge instead of acting as a sponge, which implies that future rain-on-snow events may contribute to a higher flood risk.

scholarly journals Commentary on comparison of MODIS snow cover and albedo products with ground observations over the mountainous terrain of Turkey

2006 ◽  
Vol 3 (6) ◽  
pp. 3655-3673 ◽  
Author(s):  
A. Ü. Şorman ◽  
Z. Akyürek ◽  
A. Şensoy ◽  
A. A. Şorman ◽  
A. E. Tekeli
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Spatial And Temporal Variability ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
In Situ Data ◽  
Snow Cover Extent

Abstract. The MODerate-resolution Imaging Spectroradiometer (MODIS) snow cover product was evaluated by Parajka and Blösch (2006) over the territory of Austria. The spatial and temporal variability of the MODIS snow product classes are analyzed, the accuracy of the MODIS snow product against numerous in situ snow depth data are examined and the main factors that may influence the MODIS classification accuracy are identified in their studies. The authors of this paper would like to provide more discussion to the scientific community on the "Validation of MODIS snow cover images" when similar methodology is applied to mountainous regions covered with abundant snow but with limited number of ground survey and automated stations. Daily snow cover maps obtained from MODIS images are compared with ground observations in mountainous terrain of Turkey for the winter season of 2002–2003 and 2003–2004 during the accumulation and ablation periods of snow. Snow depth and density values are recorded to determine snow water equivalent values at 19 points in and around the study area in Turkey. Comparison of snow maps with in situ data show good agreement with overall accuracies in between 62 to 82 percent considering a 2-day shift during cloudy days. Studies show that the snow cover extent can be used for forecasting of runoff hydrographs resulting mostly from snowmelt for a mountainous basin in Turkey. MODIS-Terra snow albedo products are also compared with ground based measurements over the ablation stage of 2004 using the automated weather operating stations (AWOS) records at fixed locations as well as from the temporally assessed measuring sites during the passage of the satellite. Temporarily assessed 20 ground measurement sites are randomly distributed around one of the AWOS stations and both MODIS and ground data were aggregated in GIS for analysis. Reduction in albedo is noticed as snow depth decreased and SWE values increased.

scholarly journals Satellite products of incoming solar and longwave radiations used for snowpack modelling in mountainous terrain

2017 ◽  
Author(s):  
Louis Quéno ◽  
Fatima Karbou ◽  
Vincent Vionnet ◽  
Ingrid Dombrowski-Etchevers
Keyword(s):  
Snow Depth ◽  
Radiative Forcing ◽  
Altitudinal Gradient ◽  
Longwave Radiation ◽  
Mountainous Terrain ◽  
Meteorological Model ◽  
Near Surface ◽  
French Alps ◽  
System A

Abstract. In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiations. In this study, a thorough evaluation of the incoming solar and longwave radiation products (DSSF and DSLF) derived from the Meteosat Second Generation satellite was undertaken in the French Alps and the Pyrenees. The satellite products were compared with forecast fields from the meteorological model AROME and with analyses fields from the SAFRAN system. A new satellite-derived product (DSLFnew) was developed by combining satellite observations and AROME forecasts. An evaluation against in situ measurements showed lower errors for DSSF than AROME and SAFRAN in terms of solar irradiances. For longwave irradiances, contrasted results falling in the range of uncertainty of sensors did not enable us to select the best product. Spatial comparisons of the different datasets over the Alpine and Pyrenean domains highlighted a better representation of the spatial variability of solar fluxes by DSSF and AROME than SAFRAN. We also showed that the altitudinal gradient of longwave irradiance is too strong for DSLFnew and too weak for SAFRAN. These datasets were then used as radiative forcing together with AROME near-surface forecasts to drive distributed snowpack simulations by the model Crocus in the French Alps and the Pyrenees. An evaluation against in-situ snow depth measurements showed higher biases when using satellite-derived products, despite their quality. This effect is attributed to some error compensations in the atmospheric forcing and the snowpack model. However, satellite-derived radiation products are judged beneficial for snowpack modelling in mountains, when the error compensations are solved.

scholarly journals Influence of Mountain Spruce Forest Dieback on Snow Accumulation and Melt

2019 ◽  
Vol 67 (1) ◽  
pp. 59-69 ◽  
Author(s):  
Martin Bartík ◽  
Ladislav Holko ◽  
Martin Jančo ◽  
Jaroslav Škvarenina ◽  
Michal Danko ◽  
...  
Keyword(s):  
Coefficient Of Variation ◽  
Snow Depth ◽  
Large Scale ◽  
Snow Accumulation ◽  
Spruce Forest ◽  
Snow Density ◽  
Forest Dieback ◽  
Dead Trees ◽  
Opposite Order ◽  
Disturbed Forest

Abstract Large-scale forest dieback was reported in recent decades in many parts of the world. In Slovakia, the most endangered species is Norway spruce (Picea Abies). Spruce dieback affects also indigenous mountain forests. We analysed changes in snow cover characteristics in the disturbed spruce forest representing the tree line zone (1420 m a.s.l.) in the Western Tatra Mountains, Slovakia, in five winter seasons 2013-2017. Snow depth, density and water equivalent (SWE) were measured biweekly (10-12 times per winter) at four sites representing the living forest (Living), disturbed forest with dead trees (Dead), forest opening (Open) and large open area outside the forest (Meadow). The data confirmed statistically significant differences in snow depth between the living and disturbed forest. These differences increased since the third winter after forest dieback. The differences in snow density between the disturbed and living forest were in most cases not significant. Variability of snow density expressed by coefficient of variation was approximately half that of the snow depth. Forest dieback resulted in a significant increase (about 25%) of the water amount stored in the snow while the snowmelt characteristics (snowmelt beginning and time of snow disappearance) did not change much. Average SWE calculated for all measurements conducted during five winters increased in the sequence Living < Dead < Meadow < Open. SWE variability expressed by the coefficient of variation increased in the opposite order.

scholarly journals Satellite-derived products of solar and longwave irradiances used for snowpack modelling in mountainous terrain

2020 ◽  
Vol 24 (4) ◽  
pp. 2083-2104 ◽  
Author(s):  
Louis Quéno ◽  
Fatima Karbou ◽  
Vincent Vionnet ◽  
Ingrid Dombrowski-Etchevers
Keyword(s):  
Snow Depth ◽  
Radiative Forcing ◽  
Altitudinal Gradient ◽  
Longwave Radiation ◽  
Mountainous Terrain ◽  
Meteorological Model ◽  
Near Surface ◽  
French Alps ◽  
System A

Abstract. In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiation. In this study, a thorough evaluation of the solar and longwave downwelling irradiance products (DSSF and DSLF) derived from the Meteosat Second Generation satellite was undertaken in the French Alps and the Pyrenees. The satellite-derived products were compared with forecast fields from the meteorological model AROME and with analysis fields from the SAFRAN system. A new satellite-derived product (DSLFnew) was developed by combining satellite observations and AROME forecasts. An evaluation against in situ measurements showed lower errors for DSSF than AROME and SAFRAN in terms of solar irradiances. For longwave irradiances, we were not able to select the best product due to contrasted results falling in the range of uncertainty of the sensors. Spatial comparisons of the different datasets over the Alpine and Pyrenean domains highlighted a better representation of the spatial variability of solar fluxes by DSSF and AROME than SAFRAN. We also showed that the altitudinal gradient of longwave irradiance is too strong for DSLFnew and too weak for SAFRAN. These datasets were then used as radiative forcing together with AROME near-surface forecasts to drive distributed snowpack simulations by the model Crocus in the French Alps and the Pyrenees. An evaluation against in situ snow depth measurements showed higher biases when using satellite-derived products, despite their quality. This effect is attributed to some error compensations in the atmospheric forcing and the snowpack model. However, satellite-derived irradiance products are judged beneficial for snowpack modelling in mountains, when the error compensations are solved.

scholarly journals Commentary on comparison of MODIS snow cover and albedo products with ground observations over the mountainous terrain of Turkey

2007 ◽  
Vol 11 (4) ◽  
pp. 1353-1360 ◽  
Author(s):  
A. Ü. Şorman ◽  
Z. Akyürek ◽  
A. Şensoy ◽  
A. A. Şorman ◽  
A. E. Tekeli
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Spatial And Temporal Variability ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
In Situ Data ◽  
Snow Cover Extent

Abstract. The MODerate-resolution Imaging Spectroradiometer (MODIS) snow cover product was evaluated by Parajka and Blösch (2006) over the territory of Austria. The spatial and temporal variability of the MODIS snow product classes are analyzed, the accuracy of the MODIS snow product against numerous in situ snow depth data are examined and the main factors that may influence the MODIS classification accuracy are identified in their studies. The authors of this paper would like to provide more discussion to the scientific community on the "Validation of MODIS snow cover images" when similar methodology is applied to mountainous regions covered with abundant snow but with limited number of ground survey and automated stations. Daily snow cover maps obtained from MODIS images are compared with ground observations in mountainous terrain of Turkey for the winter season of 2002–2003 and 2003–2004 during the accumulation and ablation periods of snow. Snow depth and density values are recorded to determine snow water equivalent (SWE) values at 19 points in and around the study area in Turkey. Comparison of snow maps with in situ data show good agreement with overall accuracies in between 62 to 82 percent considering a 2-day shift during cloudy days. Studies show that the snow cover extent can be used for forecasting of runoff hydrographs resulting mostly from snowmelt for a mountainous basin in Turkey. MODIS-Terra snow albedo products are also compared with ground based measurements over the ablation stage of 2004 using the automated weather operating stations (AWOS) records at fixed locations as well as from the temporally assessed measuring sites during the passage of the satellite. Temporarily assessed 20 ground measurement sites are randomly distributed around one of the AWOS stations and both MODIS and ground data were aggregated in GIS for analysis. Reduction in albedo is noticed as snow depth decreased and SWE values increased.

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