Implications of observation-enhanced energy-balance snowmelt simulations for runoff modeling of Alpine catchments

• Published in: Advances in water resources Vol. 133; p. 103410
• Main Authors: Griessinger, N., Schirmer, M., Helbig, N., Winstral, A., Michel, A., Jonas, T.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2019
• Subjects: Alpine catchments; Energy balance snow model; Meteorological forcing data; Runoff modeling; Snow observations; Snowmelt; Snow observations; Meteorological forcing data; Snowmelt; Runoff modeling; Alpine catchments; Energy balance snow model
• ISSN: 0309-1708; 1872-9657
• DOI: 10.1016/j.advwatres.2019.103410

•Incorporating observations into snow models improves streamflow predictions.•Best performances with a bias-detecting method within energy-balance snow models.•Snow-hydrological model frameworks benefit at a variety of Alpine catchments. Snow is an important component of the water balance of many mountain watersheds worldwide. In a warming climate, snowmelt modeling and consequent soil water input, is often challenged by complex conditions such as rain-on-snow situations. This is why detailed physics-based snow models are increasingly being used. These models however have much higher input data requirements, where in many cases accurate forcing fields are very difficult to provide. This study investigates whether the latest advances in the development of snow model framework actually translate into improved discharge simulations. To this end we integrated a distributed multi-layer energy-balance snow model with two recently developed methods of updating snow model mass and energy fluxes using snow observations to improve snow accumulation and depletion predictions. Surface water input from these simulations was used as input for subsequent streamflow modeling of 25 catchments in the Swiss Alps over four hydrological years. Our analysis clearly demonstrates the benefits of accurate snow simulations for hydrological modeling in Alpine catchments. Simulations that included the flux updates improved streamflow predictions, and offered best performance at high elevation, where snow most prominently affected watershed hydrology. These results were consistently achieved when analyzing model performance over entire hydrological years, over the snowmelt season only, and for individual events.