Connecting Hydrometeorological Processes to Low‐Probability Floods in the Mountainous Colorado Front Range. Issue 4 (22nd April 2021)

• Record Type: Journal Article
• Authors: Yu, Guo; Wright, Daniel B.; Holman, Kathleen D.
• Is Part Of: Water resources research. Volume 57:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-04-22
• Subjects: hydrometeorological processes -- mountainous watershed -- flood frequency distribution -- stochastic storm transposition -- Colorado Front Range; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2021WR029768
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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. Key Points: Seasonally varying hydrometeorological drivers lead to "mixture distributions" of floods in mountainous watersheds Distributions of flood peaks and volumes up to the 10, 000‐year recurrence interval were created using a process‐based simulation approach Rare peak discharges have different physical drivers than more common ones; drivers of flood volumes differ from those of peaks