Revealing how precipitation extremes impact river floods in a warming climate with interpretable machine learning

Abstract

An intensified hydrological cycle due to climate change is expected to increase precipitation extremes, but how river flood magnitudes will respond to this change remains disputed. Historically, there is only limited observational evidence that increasing precipitation extremes directly translate into systematically increased flood magnitudes. The incongruence between extreme precipitation and flooding is likely related to the compounding nature of various flooding drivers such as snowmelt and antecedent soil moisture. This complex interplay between flooding drivers makes it challenging to predict flood risks under warming. In order to better understand how precipitation extremes affect river floods in a warming climate, it is essential to disentangle the impacts of different drivers and conditions in flood generation. In this study, we employ an interpretable machine learning approach together with a large-sample hydrological dataset to identify the impact of various drivers in flood generation across a myriad of globally distributed catchments. We analyze how these impacts change with warmer temperatures and how – in response – their relationships with flood occurrence and magnitude change. The results indicate that increases in precipitation extremes have indeed contributed increasingly to flood generation in many regions over the historical period. The fact that flood magnitudes did not necessarily increase is likely a result of decreasing contributions of other drivers. We further investigate how future floods may change given the continuously rising trend of precipitation extremes. Overall, the study emphasizes the value of interpretable machine learning in helping understand how flood risks are likely to change in a warming climate.