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.
%0 Journal Article
%1 6a9361dc0480459398e6cd66270951ed
%A Jiang, Shijie
%A Tarasova, Larisa
%A Yu, Guo
%A Zscheischler, Jakob
%D 2024
%I American Association for the Advancement of Science (AAAS), New York
%J Science advances
%K FIS_scads imported yaff
%N 13
%R 10.1126/sciadv.adl4005
%T Compounding effects in flood drivers challenge estimates of extreme river floods
%V 10
%X 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.
@article{6a9361dc0480459398e6cd66270951ed,
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.},
added-at = {2024-11-28T16:27:18.000+0100},
author = {Jiang, Shijie and Tarasova, Larisa and Yu, Guo and Zscheischler, Jakob},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2a657fdd1a190dbd2b3085e1048b14a42/scadsfct},
doi = {10.1126/sciadv.adl4005},
interhash = {23324d472e916879d0ff6575d8453a8b},
intrahash = {a657fdd1a190dbd2b3085e1048b14a42},
issn = {2375-2548},
journal = {Science advances},
keywords = {FIS_scads imported yaff},
language = {English},
month = mar,
note = {Publisher Copyright: {\textcopyright} 2024 American Association for the Advancement of Science. All rights reserved.},
number = 13,
publisher = {American Association for the Advancement of Science (AAAS), New York},
timestamp = {2025-07-29T10:29:43.000+0200},
title = {Compounding effects in flood drivers challenge estimates of extreme river floods},
volume = 10,
year = 2024
}
