| Autor: |
Jiang S; Department of Compound Environmental Risks, Helmholtz Centre for Environmental Research, Leipzig, Germany.; Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany.; ELLIS Unit Jena, Jena, Germany., Tarasova L; Department Catchment Hydrology, Helmholtz Centre for Environmental Research, Halle (Saale), Germany., Yu G; Division of Hydrologic Sciences, Desert Research Institute, Las Vegas, NV, USA., Zscheischler J; Department of Compound Environmental Risks, Helmholtz Centre for Environmental Research, Leipzig, Germany.; Technische Universität Dresden, Dresden, Germany.; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden-Leipzig, Germany. |
| Abstrakt: |
Estimating river flood risks under climate change is challenging, largely due to the interacting and combined influences of various flood-generating drivers. However, a more detailed quantitative analysis of such compounding effects and the implications of their interplay remains underexplored on a large scale. Here, we use explainable machine learning to disentangle compounding effects between drivers and quantify their importance for different flood magnitudes across thousands of catchments worldwide. Our findings demonstrate the ubiquity of compounding effects in many floods. Their importance often increases with flood magnitude, but the strength of this increase varies on the basis of catchment conditions. Traditional flood analysis might underestimate extreme flood hazards in catchments where the contribution of compounding effects strongly varies with flood magnitude. Overall, our study highlights the need to carefully incorporate compounding effects in flood risk assessment to improve estimates of extreme floods. |
