Journal
WATER RESOURCES MANAGEMENT
Volume 35, Issue 2, Pages 757-774Publisher
SPRINGER
DOI: 10.1007/s11269-021-02764-z
Keywords
Cost-benefit analysis; Optimal design flood; Total expected cost function; Metropolis-Hastings algorithm; AM series; PD series
Categories
Funding
- Ministry of Education [2018R1D1A1B07040409]
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Design flood frequency analysis is essential for designing hydraulic structures, with quantifying uncertainty in this analysis becoming increasingly important. Cost-benefit analysis can assist in selecting a single design flood, and it was found in this study that the optimal design floods obtained through cost-benefit analysis considering parameter uncertainty were generally larger than those obtained through traditional flood frequency analysis.
Design flood via flood frequency analysis provides basic information for designing hydraulic structures. Quantification of uncertainty in flood frequency analysis has become an important issue during the past three decades. However, few studies have considered practical procedures for selecting a single design flood in the uncertainty range. Cost-benefit analysis can be incorporated to select a single design flood by calculating the optimal value in the total expected cost function. In particular, in this study, the relationship between conventional flood frequency analysis and cost-benefit analysis is addressed. Additionally, the parameter uncertainty is quantified by the Metropolis-Hastings algorithm to find the optimal design floods considering parameter uncertainty. The annual maximum (AM) series and partial duration (PD) series were used to identify the effect of various types of data. The optimal design floods obtained by the cost-benefit analysis considering parameter uncertainty were systematically larger than the design flood obtained by the conventional flood frequency analysis. Regarding the types of data, the generalized Pareto distribution (GPD) had the largest values in all return periods, while the Gumbel distribution had the smallest values in all cases.
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