Creation of an Empirical Energy-Balance Based Snow Module Simulating Both Snowmelt and Snow Accumulation for Mountain Hydrology

In Nordic and mountainous regions, hydrological processes are more complex than for regular rainfall-driven watersheds. Snow accumulates in winter, acting as a reservoir, and melts during late spring and summer. In order to take into account these additional natural processes present in mountainous watersheds, snow modules have been created in order to help rainfall-runoff models to simulate river discharge. Many empirical degree-day snow models have been designed to simulate snowmelt and river discharge when coupled to a rainfall runoff model, but few of them simulate correctly the amount of snow water equivalent (SWE) at point scale. Simulating correctly not only the amount of snowmelt but also the water content of the snowpack has several potential advantages: it allows improving the model reliability and performance for short-term and long-term prediction, spatial regionalization, and it makes it possible to perform data assimilation using observed snow measurements. The objective of our study is to create a new simple empirical snow module, with a structure allowing the use of snow data for calibration or assimilation. We used a model structure close to the snow model defined by M.T. Walter (2005) where each of the processes of the energy balance is parameterized using only temperature and precipitation data. The conductive fluxes into the snowpack have been modeled using analyticalsolutions to the heat equation with phase change. This model which is in-between the degree-day and the physical energy-balance approaches. It has the advantages to use only temperature and precipitation which arewidely available data and to take account of energy balance processes without being computationally intensive. Another advantage is that all state variables of the model should be comparable with observable measurements.For the moment, the snow module has been parameterized at point scale and has been tested over Switzerland and the US, using MeteoSwiss and SNOTEL USGS datasets. Further developments on the model aim to extend the daily-point scale model to a basin scale with a sub-daily time scale using satellites and point scale snow data for calibration and evaluation.