Abstract
Alpine regions are highly sensitive to the impacts of climate change, with snowmelt dynamics playing a crucial role in their hydrological processes. A representative variable of the snowmelt is the snow water equivalent (SWE). However, SWE measurements are rare and limited to point scales, making it difficult to obtain accurate spatialized estimates. For this reason, remote sensing products offer a unique opportunity to provide spatialized observations. Recently, using optical remote sensing data from MODIS, Landsat and Sentinel-2, SAR data from Sentinel-1 and in situ observations, Premier et al. (2021) developed a multi-source data method to reconstruct daily snow cover area (SCA) maps at high spatial resolution (20 m). In this work, we investigate the effectiveness of combining this approach with a semi-distributed hydrological model (GEOframe) (Formetta et al., 2014) for reconstruct SWE at high spatial resolution (20 m) in the alpine catchment of Dischma, Kanton Graubünden, Switzerland (~40 km²). Modelled results are compared against both observed discharge and high-resolution SWE maps reconstructed using snow depth data retrieved by airplane photogrammetry of Bührle et al. (2022) and then converted into SWE maps using the approach of Jonas et al. (2009).
The GEOframe model can reproduce with high accuracy the observed discharge (KGE=0.904, NSE=0.823). However, being a semi-distributed model, modelled SWE spatial patterns are too coarse and less accurate. We find that the most effective SWE downscaling approach is based on the combination of topographic parameters and the snow persistency estimated by the novel approach of Premier et al. (2021). Comparing SWE estimates based on the novel proposed approach against the observations, we find a mean bias error of + 27.27 mm and a correlation of 0.624. Results suggest that our new method can reproduce SWE spatial patterns quite well, but at the same time the catchment-averaged SWE is bound to the water mass balance estimated by the hydrological model.
The presented approach could be seen by a two-fold perspective. Either a downscaling procedure to improve the capability of a semi-distributed hydrological model to estimate high-resolution SWE pattern in mountain regions, or a method to estimate SWE from multi-source satellite observations using the constraint on catchment-scale water budget coming from a hydrological model.