资源环境科技发展态势分析平台

River flooding is a major natural hazard worldwide, whose prediction is impaired by limited understanding of the interplay of processes triggering floods within large regions. In this study we use machine learning techniques such as decision trees and random forests to pinpoint spatio-temporal features of precipitation and catchment wetness states which led to floods among 177 267 rainfall-runoff events observed in 373 German river basins. In mountainous catchments with high annual precipitation rates and shallow soils, event rainfall characteristics primarily control flood occurrence, while wetness conditions and the spatial interplay between rainfall and catchment soil moisture drive flood occurrence even more than event rainfall volume in drier basins. The existence of a snow cover also enhances flood occurrence. The identified ingredients and regional flavors shed new light on the spatial dynamics of hydro-meteorological processes leading to floods and foster regional adaptation of flood management strategies and early warning systems.