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.
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Using Physically Based Synthetic Peak Flows to Assess Local and Regional Flood Frequency Analysis Methods
Although prior studies have evaluated the role of sampling errors associated with local and regional methods to estimate peak flow quantiles, the investigation of epistemic errors is more difficult because the underlying properties of the random variable have been prescribed using ad‐hoc characterizations of the regional distributions of peak flows. This study addresses this challenge using representations of regional peak flow distributions derived from a combined framework of stochastic storm transposition, radar rainfall observations, and distributed hydrologic modeling. The authors evaluated four commonly used peak flow quantile estimation methods using synthetic peak flows at 5,000 sites in the Turkey River watershed in Iowa, USA. They first used at‐site flood frequency analysis using the Pearson Type III distribution with L‐moments. The authors then pooled regional information using (1) the index flood method, (2) the quantile regression technique, and (3) the parameter regression. This approach allowed quantification of error components stemming from epistemic assumptions, parameter estimation method, sample size, and, in the regional approaches, the number of
- Award ID(s):
- 1749638
- NSF-PAR ID:
- 10452128
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 55
- Issue:
- 11
- ISSN:
- 0043-1397
- Page Range / eLocation ID:
- p. 8384-8403
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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