研究人员利用可解释的机器学习来厘清驱动因素之间的复合效应，并量化它们对全球数千个集水区不同洪水量级的重要性。结果表明，在许多洪水中，复合效应无处不在。它们的重要性往往随着洪水的大小而增加，但这种增加的强度因流域条件而异。
传统的洪水分析可能低估了集水区的极端洪水灾害，在这些集水区，复合效应的贡献随洪水量级的变化而变化。总的来说，研究结果强调了在洪水风险预估中仔细考虑复合效应的必要性，以改善对极端洪水的估计。
据了解，气候变化下的河流洪水风险预估具有挑战性，主要是由于其受各种洪水驱动因素的相互作用和综合影响。然而，对这种复合效应及其相互作用的影响的更详细的定量分析仍未得到大规模的探索。
附：英文原文
Title: Compounding effects in flood drivers challenge estimates of extreme river floods
Author: Shijie Jiang, Larisa Tarasova, Guo Yu, Jakob Zscheischler
Issue&Volume: 2024-03-29
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.
DOI: adl4005
Source: https://www.science.org/doi/10.1126/sciadv.adl4005