Using Sentinel-1 wet snow maps to inform fully-distributed physically-based snowpack models

Abstract

Abstract. Distributed energy and mass-balance snowpack models at sub-kilometric scale have emerged as a tool for snow-hydrological forecasting over large areas. However, their development and evaluation often rely on a handful of well observed sites on flat terrain with limited topographic representativeness. Validation of such models over large scales in rugged terrain is therefore necessary. Remote sensing of wet snow has always been motivated by its potential utility in snow hydrology. However, its concrete potential to enhance physically based operational snowpack models in real time remains unproven. Wet snow maps could potentially help refining the temporal accuracy of simulated snowmelt onset, while the information content of remotely sensed snow cover fraction pertains predominantly to the ablation season. In this work, wet snow maps, derived from Sentinel-1 and snow cover fraction (SCF) retrieval from Sentinel-2 are compared against model results from a fully distributed energy-balance snow model (FSM2oshd). The comparative analysis spans the winter seasons from 2017 to 2021, focusing on the geographic region of Switzerland. We use the concept of wet snow line (WSL) to compare Sentinel-1 wet snow maps with simulations. We show that while the match of the model with flat-field snow depth observation is excellent, the WSL reveals insufficient snow melt in the southern aspects. Amending the albedo parametrization within FSM2oshd allowed achieving earlier melt in such aspects preferentially, thereby reducing WSL biases. Biases with respect to Sentinel-2 snow line (SL) observations were also substantially reduced. These results suggest that wet snow maps contain valuable real-time information for snowpack models, nicely complementing flat-field snow depth observations, particularly in complex terrain and at higher elevations. The persisting correlation between wet snow line and snow line biases provides insights into refined development, tuning and data assimilation methodologies for operational snow-hydrological modelling.

Seasonal Snow-Atmosphere Modeling: Let's do it

“…Prior studies using the FSM2oshd model have found modeled early season melt to be heavily dependent on the snow albedo at these elevations (Cluzet et al, 2024), suggesting that improvements to modeled albedo may yield better maps of early season melt.…”

“…One potential explanation for this is lower albedos predicted by the FSM2oshd model. The albedo scheme used here is a prognostic one, which differs from the scheme used for the operational snow forecast over Switzerland (Mott et al, 2023;Cluzet et al, 2024). The operational scheme was specifically developed to increase snow albedos at higher elevations.…”

Mapping Seasonal Snow Melting in Karakoram Using SAR and Topographic Data

“…Large scale wet snow maps, especially the ones with high spatial-temporal resolution, have significant implications for hydrological studies, water resource Management, and climate impact assessments (Helmert et al, 2018). Snow data obtained from remote sensing and field site stations have been proven to be fundamental for the development, calibration, and validation of snowpack, hydrology, and runoff prediction models (Schmugge et al, 2002;Andreadis and Lettenmaier, 2006;Dressler et al, 2006;Griessinger et al, 2019;Cluzet et al, 2024).…”

Comment on egusphere-2024-942

“…Large scale wet snow maps, especially the ones with high spatial-temporal resolution, have significant implications for hydrological studies, water resource Management, and climate impact assessments (Helmert et al, 2018). Snow data obtained from remote sensing and field site stations have been proven to be fundamental for the development, calibration, and validation of snowpack, hydrology, and runoff prediction models (Schmugge et al, 2002;Andreadis and Lettenmaier, 2006;Dressler et al, 2006;Griessinger et al, 2019;Cluzet et al, 2024).…”