Search for: All records

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.

 

Abstract. Floods are the product of complex interactions among processes includingprecipitation, soil moisture, and watershed morphology. Conventional floodfrequency analysis (FFA) methods such as design storms and discharge-basedstatistical methods offer few insights into these process interactions andhow they “shape” the probability distributions of floods. Understanding andprojecting flood frequency in conditions of nonstationary hydroclimate andland use require deeper understanding of these processes, some or all ofwhich may be changing in ways that will be undersampled in observationalrecords. This study presents an alternative “process-based” FFA approachthat uses stochastic storm transposition to generate large numbers ofrealistic rainstorm “scenarios” based on relatively short rainfall remotesensing records. Long-term continuous hydrologic model simulations are usedto derive seasonally varying distributions of watershed antecedentconditions. We couple rainstorm scenarios with seasonally appropriateantecedent conditions to simulate flood frequency. The methodology is appliedto the 4002 km2 Turkey River watershed in the Midwestern United States,which is undergoing significant climatic and hydrologic change. We show that,using only 15 years of rainfall records, our methodology can produce accurateestimates of “present-day” flood frequency. We found that shifts in theseasonality of soil moisture, snow, and extreme rainfall in the Turkey Riverexert important controls on flood frequency. We also demonstrate thatprocess-based techniques may be prone to errors due to inadequaterepresentation of specific seasonal processes within hydrologic models. Ifsuch mistakes are avoided, however, process-based approaches can provide auseful pathway toward understanding current and future flood frequency innonstationary conditions and thus be valuable for supplementing existing FFApractices.