WSL Institute for Snow and Avalanche Research SLFDavos Switzerland

Switzerland

WSL Institute for Snow and Avalanche Research SLFDavos Switzerland

Switzerland
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Matzl M.,WSL Institute for Snow and Avalanche Research SLFDavos Switzerland | Schneebeli M.,WSL Institute for Snow and Avalanche Research SLFDavos Switzerland
Water Resources Research | Year: 2017

We performed X-ray microtomographic observations of wet-snow metamorphism during controlled continuous melting and melt-freeze events in the laboratory. Three blocks of snow were sieved into boxes and subjected to cyclic, superficial heating or heating-cooling to reproduce vertical water infiltration patterns in snow similarly to natural conditions. Periodically, samples were taken at different heights and scanned. Results suggest that wet-snow metamorphism dynamics are highly heterogeneous even in an initially homogeneous snowpack. Consistent with previous work, we observed an increase with time in the thickness of the ice structure, which is a measure of grain size. However, this was coupled with large temporal scatter between consecutive measurements of the specific surface area and of the statistical moments of grain thickness distributions. Because of marked differences in the right tail, grain thickness distributions did not show shape invariance with time, contrary to previous analyses. In our experiments, wet-snow metamorphism showed two strikingly different patterns: homogeneous coarsening superimposed by faster heterogeneous coarsening in areas that were affected by preferential percolation of water. Liquid water movement in snow and fast structural evolution may be thus intrinsically coupled by early formation of preferential flow at local scale. These observations suggest that further experiments are highly needed to fully understand wet-snow metamorphism and infiltration patterns in a natural snowpack. © 2017. American Geophysical Union.


Magnusson J.,Norwegian Institute for Water Research | Winstral A.,WSL Institute for Snow and Avalanche Research SLFDavos Switzerland | Stordal A.S.,IRISStavanger Norway | Jonas T.,WSL Institute for Snow and Avalanche Research SLFDavos Switzerland
Water Resources Research | Year: 2016

Data assimilation can help to ensure that model results remain close to observations despite potential errors in the model, parameters, and inputs. In this study, we test whether assimilation of snow depth observations using the particle filter, a generic data assimilation method, improves the results of a multilayer energy-balance snow model, and compare the results against a direct insertion method. At the field site Col de Porte in France, the particle filter reduces errors in SWE, snowpack runoff, and soil temperature when forcing the model with coarse resolution reanalysis data, which is a typical input scenario for operational simulations. For those variables, the model performance after assimilation of snow depths is similar to model performance when forcing with high-quality, locally observed input data. Using the particle filter, we could also estimate a snowfall correction factor accurately at Col de Porte. The assimilation of snow depths also improves forecasts with lead-times of, at least, 7 days. At further 40 sites in Switzerland, the assimilation of snow depths in a model forced with numerical weather prediction data reduces the root-mean-squared-error for SWE by 64% compared to the model without assimilation. The direct insertion method shows similar performance as the particle filter, but is likely to produce inconsistencies between modeled variables. The particle filter, on the other hand, avoids such limitations without loss of performance. The methods proposed in this study efficiently reduces errors in snow simulations, seems applicable for different climatic and geographic regions, and are easy to deploy. © 2016. American Geophysical Union. All Rights Reserved.


Magnusson J.,WSL Institute for Snow and Avalanche Research SLFDavos Switzerland | Wever N.,WSL Institute for Snow and Avalanche Research SLFDavos Switzerland | Helbig N.,WSL Institute for Snow and Avalanche Research SLFDavos Switzerland | Winstral A.,WSL Institute for Snow and Avalanche Research SLFDavos Switzerland | Jonas T.,WSL Institute for Snow and Avalanche Research SLFDavos Switzerland
Water Resources Research | Year: 2015

Much effort has been invested in developing snow models over several decades, resulting in a wide variety of empirical and physically based snow models. For the most part, these models are built on similar principles. The greatest differences are found in how each model parameterizes individual processes (e.g., surface albedo and snow compaction). Parameterization choices naturally span a wide range of complexities. In this study, we evaluate the performance of different snow model parameterizations for hydrological applications using an existing multimodel energy-balance framework and data from two well-instrumented alpine sites with seasonal snow cover. We also include two temperature-index snow models and an intensive, physically based multilayer snow model in our analyses. Our results show that snow mass observations provide useful information for evaluating the ability of a model to predict snowpack runoff, whereas snow depth data alone are not. For snow mass and runoff, the energy-balance models appear transferable between our two study sites, a behavior which is not observed for snow surface temperature predictions due to site-specificity of turbulent heat transfer formulations. Errors in the input and validation data, rather than model formulation, seem to be the greatest factor affecting model performance. The three model types provide similar ability to reproduce daily observed snowpack runoff when appropriate model structures are chosen. Model complexity was not a determinant for predicting daily snowpack mass and runoff reliably. Our study shows the usefulness of the multimodel framework for identifying appropriate models under given constraints such as data availability, properties of interest and computational cost. © 2015. American Geophysical Union. All Rights Reserved.


Helbig N.,WSL Institute for Snow and Avalanche Research SLFDavos Switzerland | van Herwijnen A.,WSL Institute for Snow and Avalanche Research SLFDavos Switzerland
Water Resources Research | Year: 2017

Snow depth is an important variable for a variety of models including land-surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) most often at lower or mid elevation. While these sites have provided a wealth of information, various studies have questioned the representativity of such flat field measurements for the surrounding topography, especially in alpine regions. Using highly resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees, we developed two parameterizations to estimate domain-averaged snow depth in coarse-scale model applications over complex topography using easy to derive topographic parameters. The first parameterization uses a commonly applied linear lapse rate. Removing the dominant elevation gradient in mean snow depth revealed remaining underlying correlations with other topographic parameters, in particular the sky view factor. The second parameterization combines a power law elevation trend scaled with the subgrid parameterized sky view factor. Using a variety of statistic measures showed that the more complex parameterization performs better when using mean high-resolution flat field snow depth. The performances slightly decreased when formulating the parameterizations for a single flat field snow depth measurement. Nevertheless, the more complex parameterization still outperformed the linear lapse rate model. As the parameterization was developed independently of a specific geographic region, we suggest it could be used to assimilate flat field snow depth or snowfall into coarse-scale snow model frameworks. © 2017. American Geophysical Union.

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