Alpine regions are highly sensitive to climate change, and snow melt dynamics are crucial in their hydrological processes. A key variable is the snow water equivalent (SWE), but direct measurements are rare and limited, making spatial estimates difficult. Remote sensing provides spatial observations, yet lacks water mass balance information essential for hydrological modeling. We combine daily snow cover area (SCA) maps from multi-source optical data with the semi-distributed GEOframe model to reconstruct SWE at 20 m resolution in the alpine Dischma catchment, Switzerland (∼40 km²). Modeled SWE is redistributed using a matrix from a random forest (RF) trained on topographic features and snow cover duration. Modelled discharge is compared with outlet data, while redistributed SWE maps were compared with high-resolution SWE maps reconstructed using snow depth (HS) data generated from imagery captured by a survey camera mounted on a piloted aircraft and then converted into SWE maps. GEOframe reproduces discharge well (KGE = 0.904, NSE = 0.823) but provides coarse SWE patterns. Training RF with only one year and 25% of pixels yields reliable results. Our approach achieves mean bias error (MBE) of 67.42 mm and Pearson correlation coefficient (CORR) of 0.811, suggesting that our method is good for redistributing SWE in a mountainous region reproducing SWE spatial patterns quite well. Moreover, the average SWE of the catchment is now bound to the water mass balance estimated by the GEOframe model ensuring consistency with hydrological processes and enhancing the spatial and temporal representation of snow-melt dynamics.
Bozzoli et al. (Mon,) studied this question.