Identifying drivers of river floods using causal inference

River floods are among the most devastating natural hazards, causing thousands of deaths and billions of euros in damages every year. Floods can result from a combination of compounding drivers such as heavy precipitation, snowmelt, and high antecedent soil moisture. These drivers and the processes they govern vary widely both between catchments and between flood events within a catchment, making a causal understanding of the underlying hydrological processes difficult.

Modern causal inference methods, such as the PCMCI framework, are able to identify drivers from complex time series through causal discovery and build causally aware statistical models. However, causal inference tailored to extreme events remains a challenge due to data length limitations. To overcome data limitations, here we bridge the gap between synthetic and real world data using 1,000 years of simulated weather to drive as state-of-the-art hydrological model (the mesoscale Hydrological Model, mHM) over a wide range of European catchments. From the simulated time series, we extract high runoff events, on which we evaluate the causal inference approach. We identify the minimum data necessary for obtaining robust causal models, evaluate metrics for model evaluation and comparison, and compare causal flood drivers across catchments. Ultimately, this work will help establish best practices in causal inference for flood research to identify meteorological and catchment specific flood drivers in a changing climate.