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.