资源环境科技发展态势分析平台

Estimating the probabilities of rare floods in mountainous watersheds is challenging due to the hydrometeorological complexity of seasonally‐varying snowmelt and soil moisture dynamics as well as spatiotemporal variability in extreme precipitation. Design storm methods and statistical flood frequency analyses often overlook these complexities and how they shape the probabilities of rare floods. This study presents a process‐based approach that combines gridded precipitation, stochastic storm transposition (SST), and physics‐based distributed rainfall‐runoff modeling to simulate flood peak and volume distributions up to the 10,000‐year recurrence interval and to provide insights into the hydrometeorological drivers of those events. The approach is applied to a small mountainous watershed in the Colorado Front Range in the United States. We show that storm transposition in the Front Range can be justified under existing definitions of regional precipitation homogeneity. The process‐based results show close agreement with a statistically based mixture distribution that considers underlying flood drivers. We further demonstrate that antecedent conditions and snowmelt drive frequent peak discharges and rarer flood volumes, while the upper tail of the flood peak distribution appears to be controlled by heavy rainfall and rain‐on‐snow. In particular, we highlight the important role of early‐fall extreme rainfall in controlling rare flood peaks (but not volumes), despite only one such event having been observed in recent decades. Notwithstanding issues related to the accuracy of gridded precipitation datasets, these findings highlight the potential of SST and process‐based modeling to help understand the relationships between flood drivers and flood frequencies.

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