Intercomparison of Sentinel-2 and modelled snow cover maps in a high-elevation Alpine catchment

Modelling runoff generation in high-elevation Alpine catchments requires detailed knowledge on the spatiotemporal distribution of snow storage. With Sentinel-2 MultiSpectral Instrument (MSI), it is possible to map snow cover with a high temporal and spatial resolution. In contrast to the coarse MODIS data, Sentinel-2 MSI enables the investigation of small-scale differences in snow cover duration in complex terrains due to gravitational redistribution (slope), energy balance and wind-driven redistribution (aspect). In this study, we describe the generation of high-resolution spatial and temporal snow cover data sets from Sentinel-2 images for a highelevation Alpine catchment and discuss how the data contribute to our understanding of the spatio-temporal snow cover distribution. The quality of snow and cloud detection is evaluated against in-situ snow observations and against other snow and cloud products. The main problem was in the false detection of snow in the presence of clouds and in topographically shaded areas. We then seek to explore the potential of the generated high-resolution snow cover maps in calibrating the gravitational snow redistribution module of a physically based snow model, especially for an area with a very data-scarce point snow observation network. Generally, the calibrated snow model is able to simulate both the mean snow cover duration with a high F1 accuracy score of > 0.9 and the fractional snow-covered area with a correlation coefficient of 0.98. The snow model is also able to reproduce spatio-temporal variability in snow cover duration due to surface energy balance dynamics, wind and gravitational redistribution.

Automated summary

This study utilizes Sentinel-2 MSI images to generate high-resolution snow cover maps in a high-elevation Alpine catchment. The data are analyzed to understand the spatiotemporal distribution of snow cover, and the quality of snow and cloud detection is evaluated against in-situ observations and other products. The generated snow cover maps are further used to calibrate a snow model, which successfully simulates snow cover duration and fractional snow-covered area, as well as the variability due to surface energy balance dynamics, wind, and gravitational redistribution.