To what extent do flood-inducing storm events change future flood hazards?

M. Khanam, Giulia Sofia, E. Anagnostou
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Abstract

Abstract. Flooding is predicted to become more frequent in the coming decades because of global climate change. Recent literature has highlighted the importance of river morphodynamics in controlling flood hazards at the local scale. Abrupt and short-term geomorphic changes can occur after major flood-inducing storms. However, there is still a widespread lack of ability to foresee where and when substantial geomorphic changes will occur, as well as their ramifications for future flood hazards. This study sought to gain an understanding of the implications of major storm events for future flood hazards. For this purpose, we developed self-organizing maps (SOMs) to predict post-storm changes in stage–discharge relationships, based on storm characteristics and watershed properties at 3101 stream gages across the contiguous United States (CONUS). We tested and verified a machine learning (ML) model and its feasibility to (1) highlight the variability of geomorphic responses to flood-inducing storms across various climatic and geomorphologic regions across CONUS and (2) understand the impact of these storms on the stage–discharge relationships at gaged sites as a proxy for changes in flood hazard. The established model allows us to select rivers with stage–discharge relationships that are more prone to change after flood-inducing storms, for which flood recurrence intervals should be revised regularly so that hazard assessment can be up to date with the changing conditions. Results from the model show that, even though post-storm changes in channel conveyance are widespread, the impacts on flood hazard vary across CONUS. The influence of channel conveyance variability on flood risk depends on various hydrologic, geomorphologic, and atmospheric parameters characterizing a particular landscape or storm. The proposed framework can serve as a basis for incorporating channel conveyance adjustments into flood hazard assessment.
诱发洪水的风暴事件会在多大程度上改变未来的洪水危害?
摘要据预测,由于全球气候变化,洪水将在未来几十年内变得更加频繁。最近的文献强调了河流形态动力学在控制当地洪水灾害方面的重要性。在引发洪水的大风暴过后,地貌会发生突然的短期变化。然而,人们仍然普遍缺乏能力来预测何时何地会发生重大地貌变化,以及这些变化对未来洪水灾害的影响。本研究旨在了解大风暴事件对未来洪水灾害的影响。为此,我们开发了自组织地图 (SOM),根据风暴特征和整个美国 (CONUS) 3101 个溪流水文站的流域属性,预测风暴后阶段-排水关系的变化。我们测试并验证了一个机器学习(ML)模型及其可行性,该模型可(1)突出显示整个美国不同气候和地貌区域的地貌对引发洪水的暴风雨的反应的差异性,以及(2)了解这些暴风雨对测站的阶段-排泄关系的影响,以替代洪水危害的变化。通过已建立的模型,我们可以选择在洪水诱发风暴后阶段-排泄关系更容易发生变化的河流,并定期修订这些河流的洪水重现间隔,以便根据不断变化的条件进行最新的灾害评估。该模型的结果表明,尽管暴风雨后河道输送量的变化非常普遍,但其对洪水灾害的影响在整个美国大陆却各不相同。河道输送变化对洪水风险的影响取决于特定地貌或风暴的各种水文、地貌和大气参数。建议的框架可作为将河道输送调整纳入洪水灾害评估的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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