From Snow Depth to Streamflow: Reducing Snowfall Uncertainty in Alpine Headwaters with Sentinel-1 based snow depth retrievals

In mountainous regions, the sparse distribution of precipitation gauges at high elevations is a major source of uncertainty in snowfall estimation. This matters beyond the local scale: uncertainties originating in headwater areas propagate through hydrological modelling, affecting the estimation of all water balance components downstream. Yet establishing dense gauge networks in complex mountain terrain remains logistically and economically challenging — which makes it worthwhile to ask whether remote sensing can fill the gap.

This study assimilates Sentinel-1 C-band snow depth observations into the snow module of the GEOframe hydrological model, coupled with a snow-density scheme, to jointly update snow depth, snow water equivalent (SWE), and snowfall estimates. The method is applied to two key Alpine catchments: the Aosta River catchment and the headwaters of the Piemonte catchment in the upper Po River basin. Both are critical contributors of snowmelt-driven discharge to the Po Valley — sustaining its agricultural water supply — and both suffer from limited high-elevation gauge coverage.

Results show that assimilating satellite-derived snow depth systematically increases snowfall estimates across elevation gradients relative to the model's partitioned snowfall, and substantially improves simulated river discharge during the snowmelt season. Notably, similar improvements persist in years without active data assimilation, suggesting that the approach has a lasting positive influence on model state and performance.

This work¹ has been submitted to The Cryosphere and is currently under review.

References

Azimi, S., Girotto, M., Rigon, R., Roati, G., Barbetta, S., and Massari, C.: From Snow Depth to Streamflow: Reducing Snowfall Uncertainty in Alpine Headwaters with Sentinel-1 Based Snow Depth Retrievals, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-793, 2026.

Girotto, Manuela, Giuseppe Formetta, Shima Azimi, Claire Bachand, Marianne Cowherd, Gabrielle De Lannoy, Hans Lievens, et al. 2024. “Identifying Snowfall Elevation Patterns by Assimilating Satellite-Based Snow Depth Retrievals.” The Science of the Total Environment 906 (167312): 167312. https://doi.org/10.1016/j.scitotenv.2023.167312.

Shima Azimi's Thesis and Presentation at graduation

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A new tool for correcting the spatial and temporal pattern of global precipitation products across mountainous terrain: precipitation and hydrological analysis

This study primarily aims to integrate global precipitation data into hydrological models at the catchment scale, a common practice in hydrological research. Specifically, the study investigates how biased spatial and temporal patterns in precipitation data affectmodel performance and uncertainty. The European Meteorological Observations(EMO) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) global datasets are utilized as inputs for the GEOframe-NewAGE hydrological model to simulate the hydrological processes of the mountainous Aosta Valley catchment in northwestern Italy. The uncertainty of the hydrological model forced with global precipitation data is assessed using a proposed method called Empirical Conditional Probability (EcoProb). The results show that, although traditional performance metrics suggest similar outcomes for the model forced with EMO and CHIRPS, the proposed uncertainty analysis reveals higher uncertainty when CHIRPS is used as the precipitation input. To leverage all useful information in the global precipitation data, the spatial correlation of CHIRPS was combined with a subset of raingauges using the EcoProb method to modify the EMO precipitation data. This approach enabled the integration of the advantages of EMO and CHIRPS, which offer higher temporal and spatial correlation with ground observation, respectively, into a unified precipitation product. The combined dataset, referred to as the EcoProbSet product in this study, outperformed both the CHIRPS and EMO products, reducing the uncertainty introduced into hydrological models compared to the original global datasets.

You can find the paper preprint by clicking on the Figure above.