Xu, Yuanhao, Lin, Kairong, Hu, Caihong, Chen, Xiaohong, Zhang, Jingwen, Xiao, Mingzhong, and Xu, Chong‐Yu
The formation of floods, as a complex physical process, exhibits dynamic changes in its driving factors over time and space under climate change. Due to the black‐box nature of deep learning, its use alone does not enhance understanding of hydrological processes. The challenge lies in employing deep learning to uncover new knowledge on flood formation mechanism. This study proposes an interpretable framework for deep learning flood modeling that employs interpretability techniques to elucidate the inner workings of a peak‐sensitive Informer, revealing the dynamic response of floods to driving factors in 482 watersheds across the United States. Accurate simulation is a prerequisite for interpretability techniques to provide reliable information. The study reveals that comparing the Informer with Transformer and LSTM, the former showed superior performance in peak flood simulation (Nash‐Sutcliffe Efficiency over 0.6 in 70% of watersheds). By interpreting Informer's decision‐making process, three primary flood‐inducing patterns were identified: Precipitation, excess soil water, and snowmelt. The controlling effect of dominant factors is regional, and their impact on floods in time steps shows significant differences, challenging the traditional understanding that variables closer to the timing of flood event occurrence have a greater impact. Over 40% of watersheds exhibited shifts in dominant driving factors between 1981 and 2020, with precipitation‐dominated watersheds undergoing more significant changes, corroborating climate change responses. Additionally, the study unveils the interplay and dynamic shifts among variables. These findings suggest that interpretable deep learning, through reverse deduction, transforms data‐driven models from merely fitting nonlinear relationships to effective tools for enhancing understanding of hydrological characteristics. Plain Language Summary: The formation of floods is often dynamically influenced by multiple driving factors. Traditional methods based on statistics or hydrological models struggle to clearly understand flood mechanisms due to their limitations. Although deep learning has become an effective tool for flood modeling, its black‐box nature makes it difficult to enrich understanding of flood processes through it. Our proposed interpretable deep learning offers a perspective to unveil the dynamic drivers of floods by extracting patterns from deep learning in a reverse deduction approach. We started with models that perform best on flood extremes and identified the dominant factors and variations of floods across 482 watersheds in the United States. These variations exhibit regional characteristics, with precipitation playing a more significant role and showing more pronounced trends in watersheds where floods are primarily driven by precipitation. We found that variables from 2 days before a flood could have a greater impact than those from the previous days. Furthermore, we revealed the combined impact of variables on flooding through deep learning, showcasing their dynamic changes. Interpretable deep learning explores a new avenue to derive new hydrological insights from inverse data, helping hydrologists better understand natural physical processes. Key Points: Proposed a framework for interpretable time series deep learning based on optimal performance to reveal the dynamic drivers of floodsUnveiled the changes in the dominant driving factors of floods and the roles of driving factors at different moments before a floodElucidated the dynamic responses of floods to the interactions among driving factors [ABSTRACT FROM AUTHOR]