Effects of statistical sampling errors on flood‐damage‐reduction project evaluation

Abstract

Risk‐based decision making of flood‐damage‐reduction (FDR) projects evaluates different design alternatives that have uncertain inundation–reduction benefits and costs. Uncertainties in FDR projects arise from, but are not limited to, the natural randomness of hydrological events, knowledge deficiency in hydrologic models, and the parameters, among others. This study investigates how the flood damage estimation is affected by the epistemic uncertainty resulting from using finite flood data in defining the flood‐frequency relationship and its effects on risk‐based decision making. A Monte Carlo simulation is applied in the study to simulate the epistemic uncertainty associated with the sampling error of the flood magnitude. The model parameter uncertainty is explicitly considered in the estimation of statistical features of flood damage. A recently developed decision rule on the basis of expected opportunity loss (EOL) is applied to the risk‐based evaluation of the relative merits of several competing flood mitigation projects. EOL‐based decision rule has the advantages of considering a decision maker's risk‐aversion attitude and incorporating more complete statistical features of project outcomes, including their correlations. The influence of the model parameter uncertainty on the project evaluation results is examined through an example FDR project with five design alternatives in which flood magnitude follows a Gumbel distribution.

Risk Analysis in Design of a Multi-Section Flood Control Levee System: Assessing the Components Coordination and Probability Distribution

“…Hydrologic and hydraulic uncertainties have been studied as well in the design of levee systems , highway drainage (Tung and Bao 1990), river diversion (Afshar et al 1994), flood structural design (Qi et al 2015), and flood risk zoning (Song et al 2018;Kundzewicz 2019). Economic uncertainty, along with hydrologic and hydraulic uncertainties, have been used in flood damage reduction (USACE 1996;Su and Tung 2016) and coastal flood estimation projects (de Moel et al 2012 and.…”

Flood‐risk analysis based on a stochastic differential equation method

“…One is based on stochastic simulation and the other is analytical method (J. Chen, Zhong, Xu, & Zhao, 2015). In stochastic simulation (Apel, Thieken, Merz, & Blöschl, ; L. Chen, Singh, Lu, Zhang, Zhou, & Guo, ; Diao & Wang, ; Kuo, Yen, Hsu, & Lin, ; Peng, Chen, Yan, & Yu, ; Su & Tung, ; Zhang & Tan, ; Zou, Zhou, Zhou, Song, Guo, & Liu, ; Zou, Zhou, Zhou, Song, Guo, Deng, Yang, & Liao, ), the test samples of different kinds of uncertainties, quantified using their probability distributions, are generated simultaneously, and the corresponding flood risk caused by these uncertainties is estimated. This method needs to generate a large number of samples and involves a large amount of computation.…”

Uncertainty analysis and risk‐based design of detention basin without damage function

“…The EOL‐based decision criterion intrinsically embraces the idea of “damage control” because the opportunity loss represents decision‐maker's “regret” for unfavorable consequence of making an erroneous decision. Based on EOL criterion, the effect of the coexistence of aleatory uncertainty of floods and epistemic uncertainty from sampling errors of a chosen distribution model on levee system design was examined by Su and Tung [, ]. Alternatively, an innovative procedure to account for the contribution of aleatory and epistemic (including data, distribution model, and parameters) uncertainties is to develop upper and lower bounds of the total cost curve through Dempster‐Shafer theory of evidence [ Qi et al ., ].…”