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Abstract

Extreme rainfall events in high mountain areas can trigger hydro-meteorological hazards like floods, debris-flows, and landslides, and affect water quality, flood hazards, and hydro-power production in downstream areas by mobilising and transporting substantial amounts of sediment. As such, there is a need to better understand rainstorm events in mountainous catchments, especially in light of the projected increases to their frequency and intensity with climate change. The quantification of rainfall extremes in high mountain areas is, however, challenging. Rain gauges are generally sparse and biased towards lower elevations, and are therefore often unable to adequately capture the spatial variability of heavy rainfall at higher elevations. Multi-source precipitation products that combine numerical weather prediction, radar and station data try to narrow this gap. Here, we analyse a multi-source gridded precipitation dataset, INCA, to detect and quantify heavy rainfall events in the High Ötztal Alps, Austria. Acknowledging that the dataset may be biased at higher elevations, we complement our analysis with rainfall data from a rather dense high-alpine network of independent weather stations maintained by various research institutions. We estimate event thresholds using a GEV distribution of catchment precipitation, which enables us to identify heavy rainfall event peaks. We constrain our analysis to the mainly snowfree period of April-October, as the catchment is hydrologically inactive outside of this period. Each rainstorm event is quantified in terms of intensity, duration, seasonality and spatiotemporal characteristics. Finally, we discuss observed hydro-morphological catchment responses to the detected occurrences of heavy rainfall.