Deep learning-constrained projection of global fluvial floods and their socioeconomic implications under global warming
Year: 2025
Type: article
Abstract: Fluvial floods characterized by either large volume or prolonged duration severely impede the sustainable development of socio-ecological systems, which may intensify with global warming. However, the co-evolution dynamics of joint fluvial flood characteristics and their socioeconomic implications under different global warming targets are poorly understood. Here, we combine a deep learning (DL)-constrained hybrid model, a statistical approach and climate models to evaluate future flood occurrence within a bivariate framework (i.e. volume and duration) across 8735 catchments. After evaluating the performance of the DL-constrained hybrid model, we project a doubling of bivariate fluvial flood hazard under 3.0 °C warming. We assess the uncertainty of fluvial flood projections and reveal that the global climate models and coupled factors are the major uncertainty sources. Global warming from 1.5 °C to 3.0 °C tends to amplify flood exposure of gross domestic product, population and agricultural area, by ∼97%, ∼46%, and ∼67% under a medium emission scenario (SSP370), respectively.
Source: Environmental Research Letters
Authors: Xi Huang, Jiabo Yin, Shengyu Kang, Manuela I Brunner, Weifeng Hao
Institutions: Wuhan University, University of Freiburg
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $3,045
OpenAlex ID: W4415753911