The Global Climate Observing System (GCOS), cosponsored by the World Meteorological Organization (WMO) has categorized snow as one of the essential climate variables. This underlines the importance of snow related research questions, especially at a time when the issue of climate change is omnipresent, both in the scientific community and in everydaylife. Snowmelt research is essential as many people depend on snowmelt runoff from high alpine catchments. Little research has been done on snowmelt events from remote sensing applications, mostly covering small areas and compared to in situ measurements. This thesis compares snow meltevents from Sentinel-1 SAR backscatter data to river level fluctuations overa four-year period. Two different approaches have been investigated to determine the day ofsnow melt over all catchments. The two approaches differ in their threshold settings and mathematical calculations. The first method classifies wetsnow and identifies different states of the melting phase using a threshold, while the second method uses time series derivatives. After defining thecatchments region for 35 water stations, 35 different catchment polygons are obtained. It is found that using the derivative method for calculating the runoff day did not work as good as by detecting the absolute minimum of the backscatters time series. In addition, an elevation related threshold was introduced to discriminate the runoff date at different elevations. It was clearly noticeable that higher catchments drain later than lower situated catchments.