Storm-scale characteristics governing extreme precipitation statistics in an Alpine region

Quantifying extreme precipitation is fundamental for effective flood risk management and climate change adaptation. This study seeks to advance the physical interpretation of extreme precipitation statistics by explicitly connecting the properties of statistical distributions to the characteristics of the underlying physical processes. High-temporal resolution observations from approximately 400 rain gauges and temperature stations distributed across the Alpine region are analyzed. Extreme precipitation depths are estimated for durations ranging from sub-hourly to daily, and for return periods of up to 100 years, using a non-asymptotic framework based on the duration maxima of independent meteorological events (storms). Key storm characteristics, such as peak and mean intensity, storm duration, temporal variability, temporal profile metrics, antecedent temperature, are derived and examined in relation to extreme precipitation statistics.

Preliminary findings reveal a strong dependence of extreme precipitation estimates on both topography and accumulation duration. At short timescales, extremes are more intense in lowland regions than in mountainous areas, indicating a reverse orographic effect, whereas the pre-Alpine zone exhibits larger extremes at longer durations. These spatial patterns are consistent with variations in the parameters governing storm intensity and tail behavior of the precipitation distributions. Distribution parameters exhibit weak to strong correlations with storm characteristics, varying across accumulation durations. At sub-hourly scales, the intensity and tail-heaviness parameters display opposite correlations with the same storm properties (that is, an antagonistic effect on return level estimates). Although at these durations the heavy storms are predominantly convective across the whole domain, our results indicate that local storm features play a key role in shaping the extreme precipitation distribution.

By exploring the links between storm structure and extreme precipitation statistics, this work contributes to a more robust characterization and improved prediction of precipitation extremes.

 

This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005).