Abstract
Flood frequency estimation forms the basis for engineering design of hydraulic structures, including bridges and culverts, local and regional development planning, and flood insurance. In the United States, the Water Resources Council recommends using the Log-Pearson Type III (LP3) distribution as a standard for use with the annual peak flow data. However, researchers have argued for the use of more than one streamflow value in a year thus increasing the sample size and decreasing the sampling error in the estimates of the flood quantiles. In this study, conducted over Iowa, the authors revisit the method proposed by Donald Turcotte and others to use power-law distribution applied to streamflow peak values for events separated by a time window. In contrast to those earlier studies, the authors applied formal statistical approach based on the maximum likelihood method and Kolmogorov-Smirnov statistic for parameter estimation. They also propose a novel simulation framework for the estimation of the sampling uncertainty of the power-law distribution. They apply the methodology to streamflow data from 62 USGS stream gauges in Iowa. The key finding of the study is that low-probability quantile estimates using Turcotte’s method result in conservative estimates when compared with LP3 distribution confirming the earlier outcomes.
Original language | English |
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Pages (from-to) | 2013-2022 |
Number of pages | 10 |
Journal | Stochastic Environmental Research and Risk Assessment |
Volume | 37 |
Issue number | 5 |
DOIs | |
State | Published - May 2023 |
Externally published | Yes |
Funding
We would like to thank the Iowa Flood Center (IFC) for funding this work and providing all resources. We would like to recognize USGS for providing stream flow data to conduct this study. We thank our colleague, Dr. Grzegorz Ciach for his valuable comments on an early version of the manuscript. LO is currently with WEST Consultants, SV is currently with the University of Maryland Center for Environmental Science, and GP is currently with Vanderbilt University.
Keywords
- Flooding
- Hydrology
- Risk assessment
- Statistics