scholarly journals Supplementary material to "Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile"

2021 ◽  
Author(s):  
Annelies Voordendag ◽  
Marion Réveillet ◽  
Shelley MacDonell ◽  
Stef Lhermitte
Keyword(s):  
Snow Depth ◽  
Model Comparison ◽  
Point Scale ◽  
Supplementary Material ◽  
Snow Model ◽  
Semi Arid

scholarly journals Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile

2021 ◽  
Author(s):  
Annelies Voordendag ◽  
Marion Réveillet ◽  
Shelley MacDonell ◽  
Stef Lhermitte
Keyword(s):  
Model Calibration ◽  
Model Comparison ◽  
Meteorological Forcing ◽  
Local Point ◽  
Physically Based ◽  
Order Of Magnitude ◽  
Snow Model ◽  
Snow Physics ◽  
Semi Arid

Abstract. Physically-based snow models provide valuable information on snow cover evolution and are therefore key to provide water availability projections. Yet, uncertainties related to snow modelling remain large as a result of differences in the representation of snow physics and meteorological forcing. While many studies focus on evaluating these uncertainties, issues still arise, especially in environments where sublimation is the main ablation process. This study evaluates a case study in the semi-arid Andes of Chile and aims to compare two snow models with different complexities, SNOWPACK and SnowModel, at a local point, over one snow season. Their sensitivity relative i) to physical calibration for albedo and fresh snow density and ii) to forcing perturbation is evaluated based on ensemble approaches. Results indicate larger uncertainty depending on the model calibration than between the two models (even though the significant differences in their physical complexity). We also confirm the importance of albedo parameterization, even though ablation is driven by sublimation. SnowModel is particularly sensitive to this choice as it strongly affects both the sublimation and the melt rates. However, the day of snow-free snow surface is not sensitive to the parameterization as it only varies every eight days. The albedo parameterization of SNOWPACK has stronger consequences on melt at the end of the season leading to a date difference of the end of the season of 41 days. However, despite these differences, the sublimation ratio ranges are in agreement for the two models: 42.7–63.5 % for SnowModel and 51.3 and 64.6 % for SNOWPACK, and are related to the albedo calibration choice for the two models. Finally, the sensitivity of both models to the forcing data was in the same order of magnitude and highly influenced by the precipitation uncertainties.

scholarly journals Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile

2021 ◽  
Vol 15 (9) ◽  
pp. 4241-4259
Author(s):  
Annelies Voordendag ◽  
Marion Réveillet ◽  
Shelley MacDonell ◽  
Stef Lhermitte
Keyword(s):  
Model Comparison ◽  
Roughness Length ◽  
Meteorological Forcing ◽  
Local Point ◽  
Physically Based ◽  
Order Of Magnitude ◽  
Snow Model ◽  
Snow Physics ◽  
Semi Arid

Abstract. Physically based snow models provide valuable information on snow cover evolution and are therefore key to provide water availability projections. Yet, uncertainties related to snow modelling remain large as a result of differences in the representation of snow physics and meteorological forcing. While many studies focus on evaluating these uncertainties, no snow model comparison has been done in environments where sublimation is the main ablation process. This study evaluates a case study in the semi-arid Andes of Chile and aims to compare two snow models with different complexities, SNOWPACK and SnowModel, at a local point over one snow season and to evaluate their sensitivity relative to parameterisation and forcing. For that purpose, the two models are forced with (i) the most ideal set of input parameters, (ii) an ensemble of different physical parameterisations, and (iii) an ensemble of biased forcing. Results indicate large uncertainties depending on forcing, the snow roughness length z0, albedo parameterisation, and fresh snow density parameterisation. The uncertainty caused by the forcing is directly related to the bias chosen. Even though the models show significant differences in their physical complexity, the snow model choice is of least importance, as the sensitivity of both models to the forcing data was on the same order of magnitude and highly influenced by the precipitation uncertainties. The sublimation ratio ranges are in agreement for the two models: 36.4 % to 80.7 % for SnowModel and 36.3 % to 86.0 % for SNOWPACK, and are related to the albedo parameterisation and snow roughness length choice for the two models.

scholarly journals Simulation of Snow on Arctic Sea Ice Using a Coupled Snow–Ice Model

2010 ◽  
Vol 11 (1) ◽  
pp. 199-210 ◽  
Author(s):  
Yi-Ching Chung ◽  
Stéphane Bélair ◽  
Jocelyn Mailhot
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Snow Depth ◽  
Single Layer ◽  
Coupled System ◽  
The Arctic ◽  
Ice Thickness ◽  
The Arctic Ocean ◽  
Snow Model ◽  
Ice Model

Abstract The new Recherche Prévision Numérique (NEW-RPN) model, a coupled system including a multilayer snow thermal model (SNTHERM) and the sea ice model currently used in the Meteorological Service of Canada (MSC) operational forecasting system, was evaluated in a one-dimensional mode using meteorological observations from the Surface Heat Budget of the Arctic Ocean (SHEBA)’s Pittsburgh site in the Arctic Ocean collected during 1997/98. Two parameters simulated by NEW-RPN (i.e., snow depth and ice thickness) are compared with SHEBA’s observations and with simulations from RPN, MSC’s current coupled system (the same sea ice model and a single-layer snow model). Results show that NEW-RPN exhibits better agreement for the timing of snow depletion and for ice thickness. The profiles of snow thermal conductivity in NEW-RPN show considerable variability across the snow layers, but the mean value (0.39 W m−1 K−1) is within the range of reported observations for SHEBA. This value is larger than 0.31 W m−1 K−1, which is commonly used in single-layer snow models. Of particular interest in NEW-RPN’s simulation is the strong temperature stratification of the snowpack, which indicates that a multilayer snow model is needed in the SHEBA scenario. A sensitivity analysis indicates that snow compaction is also a crucial process for a realistic representation of the snowpack within the snow/sea ice system. NEW-RPN’s overestimation of snow depth may be related to other processes not included in the study, such as small-scale horizontal variability of snow depth and blowing snow processes.

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 Supplementary material to "Uncertainties in the spatial distribution of snow sublimation in the semi-arid Andes of Chile"

2019 ◽  
Author(s):  
Marion Réveillet ◽  
Shelley MacDonell ◽  
Simon Gascoin ◽  
Christophe Kinnard ◽  
Stef Lhermitte ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Supplementary Material ◽  
Semi Arid

Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: A multi-model comparison

2017 ◽  
Vol 202 ◽  
pp. 21-35 ◽  
Author(s):  
Heidi Webber ◽  
Pierre Martre ◽  
Senthold Asseng ◽  
Bruce Kimball ◽  
Jeffrey White ◽  
...  
Keyword(s):  
Heat Stress ◽  
Model Comparison ◽  
Canopy Temperature ◽  
Arid Environment ◽  
Irrigated Wheat ◽  
Semi Arid

scholarly journals Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

2016 ◽  
Author(s):  
Rafael Pimentel ◽  
Javier Herrero ◽  
María José Polo
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Scale Effects ◽  
Control Area ◽  
Sigmoid Function ◽  
Snow Cover Fraction ◽  
Snow Distribution ◽  
Snow Model ◽  
Snow Conditions ◽  
High Level

Abstract. Subgrid variability introduces non-negligible scale effects on the GIS-based representation of snow. This heterogeneity is even more evident in semiarid regions, where the high variability of the climate produces various accumulation melting cycles throughout the year and a large spatial heterogeneity of the snow cover. This variability in a watershed can often be represented by snow depletion curves (DCs). In this study, terrestrial photography (TP) of a cell-sized area (30 x 30 m) was used to define local snow DCs at a Mediterranean site. Snow cover fraction (SCF) and snow depth (h) values obtained with this technique constituted the two datasets used to define DCs. A flexible sigmoid function was selected to parameterize snow behaviour on this subgrid scale. It was then fitted to meet five different snow patterns in the control area: one for the accumulation phase and four for the melting phase in a cycle within the snow season. Each pattern was successfully associated with the snow conditions and previous evolution. The resulting DCs were able to capture certain physical features of the snow, which were used in a decision-tree and included in the point snow model formulated by Herrero et al. (2009). The final performance of this model was tested against field observations recorded over four hydrological years (2009–2013). The calibration and validation of this DC-snow model was found to have a high level of accuracy with global RMSE values of 84.2 mm for the average snow depth and 0.18 m2 m-2 for the snow cover fraction in the control area. The use of DCs on the cell scale proposed in this research provided a sound basis for the extension of point snow models to larger areas by means of a gridded distributed calculation.

scholarly journals Supplementary material to "Advances in altimetric snow depth estimates using bi-frequency SARAL/CryoSat-2 Ka/Ku measurements"

2021 ◽  
Author(s):  
Florent Garnier ◽  
Sara Fleury ◽  
Gilles Garric ◽  
Jérôme Bouffard ◽  
Michel Tsamados ◽  
...  
Keyword(s):  
Snow Depth ◽  
Supplementary Material
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