<p>Attaining a comprehensive and reliable water balance of snow-dominated alpine catchments is fundamental for a holistic representation of the hydrological and hydrogeological processes. In fact, their contribution to the water balance is extremely important for the water resources management and for a reliable estimation of groundwater recharge. A major limitation to the elaboration of these balances in alpine terrain are the difficultly of data acquisition as well as the limited presence of meteorological stations. These two factors considerably increase the uncertainty of water balances. Remotely sensed data can provide valuable information for the balance elaboration at a regional scale. &#160;Among the satellite data available, the Sentinel data, collected in the ESA missions in the last 6 years, has provided free and global access of observations including optical, thermal, and microwave sensors with high spatial and temporal resolutions.</p><p>In the present work, we estimated groundwater recharge (R) for the last two hydrologic years (from March 2018 to March 2020), based on satellite data. For this purpose, the most recent methods and databases based on satellite observations were tested: &#160;time series of the precipitation (P), the snow water equivalent (SWE), and the evapotranspiration (ET) were retrieved in an extensive Alpine catchment (26,000 km<sup>2</sup>) located in northern Italy. Daily precipitation was calculated from PERSIANN-Cloud Classification System (PERSIANN-CCS, Hong et al. 2004) database at the resolution of 4.0 km. &#160;ET was estimated with the combined use of Sentinel 2 and 3 satellites (Guzinski et al., 2020) at a resolution of 20 m and with weekly return period. The weekly SWE was calculated starting from Sentienl 1 (C-SNOW database, Lievens et al., 2019) and Sentienl 2, at the spatial resolution of 30 m.</p><p>Based on available measurements of P, ET, and snow depth in the catchment, the uncertainty of the hydrologic estimations was quantified. We further carried out a sensitivity analysis, considering the physiographic parameters (altitude, slope, and aspect) and the seasonal conditions. For SWE estimates, an altitude-dependent effect and a lower accuracy in the snowmelt phase have been observed. The results show that the adopted satellite-based methods allow obtaining consistent and physically realistic values of recharge, with relatively low uncertainty.</p><p>References:</p><ul><li>Guzinski, R., Nieto, H., Sandholt, I., & Karamitilios, G. (2020). Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion. Remote Sensing, 12(9), 1433.</li>
<li>Hong, Y., Hsu, K. L., Sorooshian, S., & Gao, X. (2004). Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. Journal of Applied Meteorology, 43(12), 1834-1853.</li>
<li>Lievens, H., Demuzere, M., Marshall, H. P., Reichle, R. H., Brucker, L., Brangers, I., ... & Jonas, T. (2019). Snow depth variability in the Northern Hemisphere mountains observed from space. Nature communications, 10(1), 1-12.</li>
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A method for determining types of weather fronts based on cloud classification results from MODIS satellite data
