Multi-year Hyper-high Resolution Snow Cover Simulation in a Mountainous Region in Central Spain (Peñalara Massif).

Abstract

<p>The snowpack over mountains represents an important source of water both in these areas and in adjacent lowlands. It also has a large impact on their economy since it affects tourism, communications, logistics and risks associated with its recreational use.  Snow cover in mid elevations is experiencing a significant decrease as a consequence of climate change (IPCC-2021) and it is becoming an important issue in the water management agenda. Despite its importance, there is a lack of understanding of its dynamics, due to the scarcity of properly distributed temporally and spatially mountain snowpack observations and the availability of specific simulation tools. This gap is even more pronounced in mediterranean mountainous regions, where the complex processes involved in snowpack growth and ablation, together with its high spatial variability, pose a challenge for the models. To overcome these challenges, a hyper-high resolution state-of-the-art chain model (SnowCast) has been developed and validated in Penalara Massif (Sierra de Guadarrama, Central Spain). It couples ERA5 atmospheric reanalysis (ECMWF) with the Intermediate Atmospheric Research model (ICAR, NCAR) and the Flexible Snow Model (FSM2, University of Edinburgh) along with own developed parametrizations and high resolution topographic forcing models. A multi-year simulation has been performed for this area and sensitivity tests have been performed with different resolutions and topographically induced air and soil forcings. Results after validation using intensive field work, automatic snowpack monitoring and satellite imagery look very promising. A temporal and spatial realistic representation of the snow cover will be presented along with an analysis of the performance of the model and a discussion about new processes to be implemented, additional validation techniques and future coupling with a hydrological model.</p> <p> </p>