Observation‐Masked Localized Assimilation of Satellite‐Derived Snow Cover Data for Enhancing Runoff Prediction in the Rhône River Basin, France

Abstract

Accurate estimation of snowpack dynamics is essential for predicting snowmelt‐driven hydrological processes in mountainous regions. Although data assimilation techniques can integrate remote sensing observations into snow models to reduce uncertainties, their operational implementation remains constrained by sparse and irregular coverage of satellite data, complexity of mountainous terrain, and the computational demands of high‐dimensional assimilation. In this study, an Observation‐Masked Localized Ensemble Kalman Filter (OML‐EnKF) framework was developed to restrict analysis updates to observed regions while preserving localized spatial covariance among nearby observations. This framework assimilated daily Copernicus snow cover fraction (SCF) observations into the Wflow_sbm distributed hydrological model across the Rhône River basin, France, for 2017–2019. We compared its performance against an open‐loop run, a direct insertion scheme, and a one‐dimensional EnKF. Results demonstrate that incorporating a snow module in the Wflow_sbm model improved seasonal discharge patterns. The OML‐EnKF produced spatially coherent snow water equivalent patterns consistent with topography and improved snow depth estimates in mid‐elevation regions. Both EnKF schemes refined SCF and improved discharge predictions across multiple gauged stations. Furthermore, temporal shifts in snowpack accumulation and ablation modulated runoff timing, with increased snowmelt contributions during winter (October–March) and diminished discharge in late spring (April–June). Sensitivity analyses showed that a moderate localization radius (0.025–0.1 ° ${}^{\circ}$ , approximately 2.8–11.1 km) effectively captured spatial correlations within the OML‐EnKF, whereas excessively restrictive radius values degraded performance. These findings show that the OML‐EnKF enhances snowpack characterization and discharge prediction in the Rhône River basin, offering a promising tool for water resource management in snow‐dominated basins.