Advantages of Physically Based Flood Frequency Analysis with Long-Term Simulations for Iowa
Publication: Journal of Hydrologic Engineering
Volume 27, Issue 12
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.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported in part by the Iowa Department of Transportation (Project No. 20-SPR2-002). The opinions, findings, and conclusions expressed in this publication are those of the author and not necessarily those of the Iowa Department of Transportation or the United States Department of Transportation, Federal Highway Administration. Support by the Iowa Flood Center, IIHR—Hydroscience & Engineering, and the US Army Corps of Engineers’ Institute for Water Resources is gratefully acknowledged. We thank Nancy Barth (USGS) for her suggestions on PeakFQ. Additionally, we thank Renato Amorim, Hanbeen Kim, and Taereem Kim, and three anonymous reviewers for their suggestions and comments on the paper’s content.
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Journal of Hydrologic Engineering
Volume 27 • Issue 12 • December 2022
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© 2022 American Society of Civil Engineers.
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Received: Apr 28, 2022
Accepted: Jul 28, 2022
Published online: Sep 30, 2022
Published in print: Dec 1, 2022
Discussion open until: Feb 28, 2023
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