Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review

The snow cover is a key component of land surface hydrology, especially in mountain areas where it governs the amount and timing of water availability in downstream areas. It is involved in relevant climate feedbacks and natural hazards such as avalanches and floods. Monitoring and forecasting snow cover characteristics is challenging. While snow cover extent is relatively easy to retrieve from satellite data, remote sensing retrievals of the snow water equivalent (SWE) is often inaccurate, particularly in complex mountainous terrain. Model-based snow cover estimates, driven by meteorological data, often bear significant uncertainties due to both input data and model errors. Data assimilation can combine both approaches to improve SWE estimates. In this paper, we review current state-of-the-art data assimilation methodologies used to optimally combine measurements with snow cover models in order to reduce uncertainties. The suitability of a given data assimilation method varies with the numerical complexity of snow models as well as the availability and the type of observations. This review describes the issues and challenges associated with data assimilation applied to the mountain snow cover, providing recommendations for existing and upcoming monitoring and prediction systems of snow hydrology in mountainous regions.