Climate-sensitive Flood Frequency Analysis Based on Flood Event Characteristics

Understanding how flood frequency changes under non-stationary hydroclimatic conditions remains a key challenge in hydrology. This study presents a Bayesian process-based framework for flood frequency analysis that explicitly accounts for the seasonal dependence of rainfall–runoff processes and their sensitivity to climate change. The approach links an event-based rainfall–runoff model with probabilistic representations of storm, soil moisture, and catchment response, allowing the joint propagation of uncertainty from climate drivers to flood quantiles. The process-based structure of the framework also enables the disentangling of individual flood-generating mechanisms, such as the upward shift of the zero-degree isotherm, long-term changes in soil moisture regimes, and variations in precipitation intensity. The framework is implemented in Austrian hotspots, i.e. groups of similar catchments, using long-term hydrometeorological records and regional climate projections (EURO-CORDEX).

Results show that (i) changes in flood frequency are primarily driven by projected increases in precipitation intensity, while temperature and soil moisture act as modulators or amplifiers of this signal; (ii) the expected reduction in soil moisture tends to mitigate frequent floods but has limited influence on rare events; (iii) anticipated shift of flood peaks toward spring in alpine regions due to the rising 0°C line and enhanced snowmelt contribution. The proposed methodology provides a transferable tool for assessing climate-sensitive flood hazards in non-stationary environments.