Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1126/sciadv.adl4005
Lizenz creative commons licence
Titel (primär) Compounding effects in flood drivers challenge estimates of extreme river floods
Autor Jiang, S.; Tarasova, L.; Yu, G.; Zscheischler, J. ORCID logo
Quelle Science Advances
Erscheinungsjahr 2024
Department CATHYD; CER
Band/Volume 10
Heft 13
Seite von eadl4005
Sprache englisch
Topic T5 Future Landscapes
Abstract 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.
dauerhafte UFZ-Verlinkung
Jiang, S., Tarasova, L., Yu, G., Zscheischler, J. (2024):
Compounding effects in flood drivers challenge estimates of extreme river floods
Sci. Adv. 10 (13), eadl4005 10.1126/sciadv.adl4005