Advantages of Physically Based Flood Frequency Analysis with Long-Term Simulations for Iowa

Alexander Michalek, Felipe Quintero, Gabriele Villarini, Witold F. Krajewski

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Flood frequency estimation in the United States has been primarily driven by statistical analysis for the past one hundred years. For ungauged locations, the regionalized equations are developed to provide annual exceedance probability discharge estimates. These equations establish a relationship between discharge quantiles and drainage area through statistical regression. Predictors consisting of catchment physical properties can also be included based on minimization of residuals. For Iowa, only one-third of developed regional equations use a climatic parameter (i.e., precipitation), which is a critical driver of hydrologic processes. The authors explore an alternative approach to regional flood quantile estimation analysis by analyzing the performance of the Iowa Flood Center's physically based, calibration-free, and spatially distributed Hillslope-Link Model (HLM). They conducted continuous simulations for a 40-year period across the state of Iowa. Compared to the observations, the HLM can accurately capture the observed magnitude of annual maximum discharge, making it a viable physically based alternative to regional regression. In the study, regional flood quantile estimation is conducted at 445 ungauged communities to compare flood frequency estimates using HLM simulations with regionally developed regression equations. The results show similar discharge values between simulation and regional regression models for all annual exceedance probability where regional equations contain rainfall as a predictor. However, in areas where regional equations are only based on catchment properties, regional regression equations overestimated discharge for all quantiles. These results highlight inconsistencies in current regional regression equations for flood quantile estimates in Iowa and provide support for the reevaluation of flood quantile estimates with physically based hydrologic models.

Original languageEnglish (US)
Article number05022021
JournalJournal of Hydrologic Engineering
Volume27
Issue number12
DOIs
StatePublished - Dec 1 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Water Science and Technology
  • General Environmental Science
  • Environmental Chemistry
  • Civil and Structural Engineering

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