Spatial and Temporal Verification of High-Resolution Modelled Rainfall Data for Urban Flood Risk Assessment

Reliable climate forecasts are crucial for adapting to future challenges, particularly in urban flood management, where pluvial flooding poses a significant threat. This study focuses on the verification and enhancement of rainfall data for urban flood modelling by analysing critical aspects such as total depth, intensities, seasonality, dry weather periods, and spatial distribution during extreme storm events.

In Graz, Austria, a network of 23 high-resolution precipitation measurement stations covering 120 km², including 13 stations with over a decade of data, was utilized to calibrate a regional climate model through a downscaling approach. This provided minute-level rainfall data for each station, enabling a detailed comparison of historical measurements from the past 10 years with climate model outputs for the current state of the climate. Subsequently, changes in key rainfall characteristics were assessed for the near future (2040–2050) and far future (2090–2100).

Our analysis evaluated yearly precipitation totals, spatial rainfall distribution, intensity-duration-frequency (IDF) functions, and the seasonality of extreme rainfall events. The results revealed promising alignment with historical data, though discrepancies were noted for shorter durations and seasonal shifts. Specifically, heavy rainfall events were projected to occur more frequently in autumn in the future, a trend absent in historical observations.

This study underscores the importance of statistically robust downscaling and verification techniques in blending observational and model-based forecasts to enhance the reliability of climate predictions. These advancements provide critical insights for urban flood resilience planning and illustrate the evolving nature of extreme rainfall under changing climatic conditions.