Assessing Liquid Water Content in a Seasonal Snowpack: A Comparative Analysis of Satellite Observations and the HyS Model

Understanding the spatial and temporal variations in the liquid water content (LWC) of alpine snowpacks is crucial for assessing short-term water availability, which influences hazards such as wet snow avalanches and river floods. Accurate monitoring and forecasting of snow wetness play a vital role in applications ranging from avalanche risk assessment to hydropower management and flood prediction, particularly when integrated with hydrological models.

Remote sensing provides valuable observations of snowpack properties, with Sentinel-1 satellites offering C-band synthetic aperture radar (SAR) data at high spatial and temporal resolutions, enabling the detection of wet snow. Meanwhile, snow models like HyS (De Michele et al. 2013) can simulate the liquid water content of the snowpack.

This study focuses on evaluating the discrepancies between satellite-derived wet-snow products and modeled LWC estimates. Specifically, we compare (1) Sentinel-1-based wet-snow retrievals and (2) HyS model simulations. The analysis is conducted for the Mallero basin, a mid-sized alpine watershed where snowmelt and glacier ablation significantly impact seasonal river discharge, particularly in spring and summer.

The results indicate a strong overall agreement between Sentinel-1 data and HyS model outputs. Short periods of divergence between the two datasets are further analyzed to investigate potential physical processes that may not be fully captured by the model.