Return Levels under Nonstationarity: The Need to Update Infrastructure Design Strategies
Publication: Journal of Hydrologic Engineering
Volume 24, Issue 1
Abstract
Recent studies propose different metrics for hydrologic design under nonstationarity, such as the effective return level, the expected waiting time (EWT)-based return level, the expected number of events (ENE)-based return level, the design life level (DLL), and the minimax design level (MDL). In this study, we formalize a method to test the credibility of such metrics in (1) developing precipitation intensity-duration-frequency relationships, (2) at-site design flood estimation, and (3) regional flood frequency analysis. The test relies on asymptotic normality assumptions and applies to the mean of the estimated return levels. Our results show that, based on historical records, point estimates or means of nonstationary design quantiles in all three applications are not significantly different from their traditional stationary counterparts when the associated uncertainties are considered. For example, in the application of at-site design flood estimation, although the estimated stationary 100-year flood is 32% and 29% lower in magnitude than the EWT- and ENE-based nonstationary 100-year return level, respectively, such a difference is not statistically significant. Further, enhanced model complexity is found to result in increased uncertainty in design levels under nonstationarity to at least twice the range obtained from a stationary analysis.
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Acknowledgments
This work was supported by the Department of Science and Technology (DST), Government of India’s INSPIRE Faculty Award, 2015–16 (DST/INSPIRE/04/2015/001548). All of the analysis was performed in the statistical language R (R Core Team 2016) using the packages extRemes, numDeriv, Kendall, bcp, abind, lubridate, foreach, doParallel, and ggplot2, and the R function multiplot. Contributors to these packages and functions are acknowledged. Discussions and inputs from Auroop Ganguly were insightful. Dan Cooley, Eric Gilleland, and Rick Katz assisted with a number of clarifications of their work related to the statistical extreme value theory. The authors also wish to thank Chingka Kalai for his specific inputs on regional frequency analysis. They also thank the Editor, Associate Editor, and three anonymous reviewers whose comments helped to significantly improve the paper.
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Journal of Hydrologic Engineering
Volume 24 • Issue 1 • January 2019
Copyright
©2018 American Society of Civil Engineers.
History
Received: Mar 8, 2018
Accepted: Aug 13, 2018
Published online: Oct 31, 2018
Published in print: Jan 1, 2019
Discussion open until: Mar 31, 2019
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