scholarly journals A virtual network for estimating daily new snow water equivalent and snow depth in the Swiss Alps

2010 ◽  
Vol 51 (54) ◽  
pp. 32-38 ◽  
Author(s):  
Luca Egli ◽  
Tobias Jonas ◽  
Jean-Marie Bettems
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Practical Importance ◽  
Cost Effective ◽  
Numerical Weather Prediction Model ◽  
Swiss Alps ◽  
Virtual Network ◽  
Systematic Bias ◽  
Snow Water

AbstractDaily new snow water equivalent (HNW) and snow depth (HS) are of significant practical importance in cryospheric sciences such as snow hydrology and avalanche formation. In this study we present a virtual network (VN) for estimating HNW and HS on a regular mesh over Switzerland with a grid size of 7 km. The method is based on the HNW output data of the numerical weather prediction model COSMO-7, driving an external accumulation/melting routine. The verification of the VN shows that, on average, HNW can be estimated with a mean systematic bias close to 0 and an averaged absolute accuracy of 4.01 mm. The results are equivalent to the performance observed when comparing different automatic HNW point estimations with manual reference measurements. However, at the local scale, HS derived by the VN may significantly deviate from corresponding point measurements. We argue that the VN presented here may introduce promising cost-effective options as input for spatially distributed snow hydrological and avalanche risk management applications in the Swiss Alps.

scholarly journals Seasonal development of spatial snow-depth variability across different scales in the Swiss Alps

2011 ◽  
Vol 52 (58) ◽  
pp. 216-222 ◽  
Author(s):  
Luca Egli ◽  
Nena Griessinger ◽  
Tobias Jonas
Keyword(s):  
Spatial Variability ◽  
Snow Depth ◽  
Weather Prediction ◽  
Ground Truth ◽  
Numerical Weather Prediction Model ◽  
Swiss Alps ◽  
Seasonal Development ◽  
Precipitation Pattern ◽  
Scaling Analysis ◽  
Snow Hydrology

AbstarctThe depth of snow cover is temporally and spatially heterogeneous at different scales in Alpine regions. For snow hydrology/climatology the spatial variability of snow depths is a key parameter for capturing the total amount of snow in a given area. Here a scale analysis of the spatial variability of snow depths during the accumulation period is investigated. The development of the variability is characterized by a parameter, β, describing the relationship between the standard deviation and the mean of snow depths. The analysis includes two datasets: (1) 141 snow-depth point measurements representing flat-field observations, and (2) snow precipitation from the numerical weather prediction model COSMO-7. The results reveal that β is almost invariant at scales between 10 and 300 km. The COSMO-7 data exhibit the same scale invariance above 50 km, indicating that the spatial variability of snow depths is formed by the precipitation pattern at these scales. The scaling analysis of β allows determination of the absolute accuracy of estimating the total amount of snow in a given area and helps to validate different snow models or remote-sensing techniques by ground truth verification.

Evaluation of the snow cover in the Soil, Vegetation, and Snow (SVS) land surface model

2021 ◽  
Author(s):  
Gonzalo Leonardini ◽  
François Anctil ◽  
Vincent Vionnet ◽  
Maria Abrahamowicz ◽  
Daniel F. Nadeau ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Land Surface Model ◽  
Late Winter ◽  
Surface Model ◽  
Lower Performance ◽  
Snow Water

AbstractThe Soil, Vegetation, and Snow (SVS) land surface model was recently developed at Environment and Climate Change Canada (ECCC) for operational numerical weather prediction and hydrological forecasting. This study examined the performance of the snow scheme in the SVS model over multiple years at ten well-instrumented sites from the Earth System Model-Snow Model Intercomparison Project (ESM-SnowMIP), which covers alpine, maritime and taiga climates. The SVS snow scheme is a simple single-layer snowpack scheme that uses the force-restore method. Stand-alone, point-scale verification tests showed that the model is able to realistically reproduce the main characteristics of the snow cover at these sites, namely snow water equivalent, density, snow depth, surface temperature, and albedo. SVS accurately simulated snow water equivalent, density and snow depth at open sites, but exhibited lower performance for subcanopy snowpacks (forested sites). The lower performance was attributed mainly to the limitations of the compaction scheme and the absence of a snow interception scheme. At open sites, the SVS snow surface temperatures were well represented but exhibited a cold bias, which was due to poor representation at night. SVS produced a reasonably accurate representation of snow albedo, but there was a tendency to overestimate late winter albedo. Sensitivity tests suggested improvements associated with the snow melting formulation in SVS.

scholarly journals On measuring snow ablation rates in alpine terrain with a mobile GPR device

2017 ◽  
Author(s):  
Nena Griessinger ◽  
Franziska Mohr ◽  
Tobias Jonas
Keyword(s):  
Ground Penetrating Radar ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Repeated Measurements ◽  
Swiss Alps ◽  
Promising Technique ◽  
Snow Water ◽  
Ground Penetrating ◽  
Good Agreement

Abstract. Ground penetrating radar (GPR) has become a promising technique in the field of snow hydrological research. It is commonly used to measure snow depth, density, and water equivalent over large distances or along gridded snow courses. Having built and tested a mobile light-weight setup, we demonstrate that GPR is capable of accurately measuring snow ablation rates in complex alpine terrain. Our setup was optimized for efficient measurements and consisted of a common-mid-point assembly with four pairs of antennas mounted to a plastic sled, which was small enough to permit safe and convenient operations. Repeated measurements were taken during the 2014/15 winter season along ten profiles within two valleys located in the eastern Swiss Alps. Resulting GPR-based data of snow depth and water equivalent as well as their respective change rates over time were in good agreement with concurrent manual measurements, in particular if accurate alignment between repeated overpasses could be achieved (root-mean-square error of 4.5 cm for snow depth, 25 mm for snow water equivalent, and 4.4 cm and 26 mm for the respective change rates). With its suitability for alpine terrain and the achieved accuracy, the presented setup could become a valuable tool to validate snowmelt models or to complement lidar-based snow surveys.

scholarly journals Snowpack modelling in the Pyrenees driven by kilometric resolution meteorological forecasts

2016 ◽  
Author(s):  
Louis Quéno ◽  
Vincent Vionnet ◽  
Ingrid Dombrowski-Etchevers ◽  
Matthieu Lafaysse ◽  
Marie Dumont ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Dynamical Behaviour ◽  
Mountain Range ◽  
Orographic Effects ◽  
Snow Water ◽  
Analysis System ◽  
Precipitation Gauge

Abstract. Distributed snowpack simulations in the French and Spanish Pyrenees were carried out using the detailed snowpack model Crocus driven by the Numerical Weather Prediction system AROME at 2.5 km grid spacing, during four consecutive winters, from 2010 to 2014. The aim of this study was to assess the benefits of kilometric resolution atmospheric forcing to a snowpack model for describing the spatial variability of seasonal snow cover within a mountain range. The evaluation was performed through comparisons to ground-based measurements of snow depth, snow water equivalent and precipitation, to satellite snow cover images and to snowpack simulations driven by the analysis system SAFRAN. Snow depths simulated by AROME–Crocus exhibit an overall positive bias, particularly marked on the first summits near the Atlantic Ocean. The simulation of mesoscale orographic effects by AROME allows to capture a realistic regional snowpack variability, unlike SAFRAN–Crocus. The categorical study of daily snow depth variations gives a differentiated perspective of accumulation and ablation processes. Both models underestimate strong snow accumulations and strong snow depth decreases, which is mainly due to the non-simulated wind-induced erosion, the underestimation of strong melting and an insufficient settling after snowfalls. The problematic assimilation of precipitation gauge measurements is also emphasized, which raises the issue of a dedicated analysis to complement the benefits of AROME kilometric resolution and dynamical behaviour in mountainous terrain.

Estimating the snow water equivalent from snow depth measurements in the Swiss Alps

2009 ◽  
Vol 378 (1-2) ◽  
pp. 161-167 ◽  
Author(s):  
T. Jonas ◽  
C. Marty ◽  
J. Magnusson
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Swiss Alps ◽  
Snow Water

scholarly journals Snowpack modelling in the Pyrenees driven by kilometric-resolution meteorological forecasts

2016 ◽  
Vol 10 (4) ◽  
pp. 1571-1589 ◽  
Author(s):  
Louis Quéno ◽  
Vincent Vionnet ◽  
Ingrid Dombrowski-Etchevers ◽  
Matthieu Lafaysse ◽  
Marie Dumont ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Dynamical Behaviour ◽  
Mountain Range ◽  
Orographic Effects ◽  
Snow Water ◽  
Analysis System ◽  
Precipitation Gauge

Abstract. Distributed snowpack simulations in the French and Spanish Pyrenees are carried out using the detailed snowpack model Crocus driven by the numerical weather prediction system AROME at 2.5 km grid spacing, during four consecutive winters from 2010 to 2014. The aim of this study is to assess the benefits of a kilometric-resolution atmospheric forcing to a snowpack model for describing the spatial variability of the seasonal snow cover over a mountain range. The evaluation is performed by comparisons to ground-based measurements of the snow depth, the snow water equivalent and precipitations, to satellite snow cover images and to snowpack simulations driven by the SAFRAN analysis system. Snow depths simulated by AROME–Crocus exhibit an overall positive bias, particularly marked over the first summits near the Atlantic Ocean. The simulation of mesoscale orographic effects by AROME gives a realistic regional snowpack variability, unlike SAFRAN–Crocus. The categorical study of daily snow depth variations gives a differentiated perspective of accumulation and ablation processes. Both models underestimate strong snow accumulations and strong snow depth decreases, which is mainly due to the non-simulated wind-induced erosion, the underestimation of strong melting and an insufficient settling after snowfalls. The problematic assimilation of precipitation gauge measurements is also emphasized, which raises the issue of a need for a dedicated analysis to complement the benefits of AROME kilometric resolution and dynamical behaviour in mountainous terrain.

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 Snowpack variability and trends at long-term stations in northern Colorado, USA

2015 ◽  
Vol 371 ◽  
pp. 131-136 ◽  
Author(s):  
S. R. Fassnacht ◽  
M. Hultstrand
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Entire Period ◽  
Northern Colorado ◽  
Snow Water ◽  
Highly Correlated ◽  
The Individual

Abstract. The individual measurements from snowcourse stations were digitized for six stations across northern Colorado that had up to 79 years of record (1936 to 2014). These manual measurements are collected at the first of the month from February through May, with additional measurements in January and June. This dataset was used to evaluate the variability in snow depth and snow water equivalent (SWE) across a snowcourse, as well as trends in snowpack patterns across the entire period of record and over two halves of the record (up to 1975 and from 1976). Snowpack variability is correlated to depth and SWE. The snow depth variability is shown to be highly correlated with average April snow depth and day of year. Depth and SWE were found to be significantly decreasing over the entire period of record at two stations, while at another station the significant trends were an increase over the first half of the record and a decrease over the second half. Variability tended to decrease with time, when significant.

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.

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