Spatial probabilistic multi-criteria decision making for assessment of flood management alternatives

Flood management alternatives are often evaluated on the basis of flood parameters such as depth and velocity. As these parameters are uncertain, so is the evaluation of the alternatives. It is thus important to incorporate the uncertainty of flood parameters into the decision making frameworks. This research develops a spatial probabilistic multi-criteria decision making (SPMCDM) framework to demonstrate the impact of the design rainfall uncertainty on evaluation of flood management alternatives. The framework employs a probabilistic rainfall-runoff transformation model, a two-dimensional flood model and a spatial MCDM technique. Thereby, the uncertainty of decision making can be determined alongside the best alternative. A probability-based map is produced to show the discrete probability distribution function (PDF) of selecting each competing alternative. Overall the best at each grid cell is the alternative with the mode parameter of this PDF. This framework is demonstrated on the Swannanoa River watershed in North Carolina, USA and its results are compared to those of deterministic approach. While the deterministic framework fails to provide the uncertainty of selecting an alternative, the SPMCDM framework showed that in overall, selection of flood management alternatives in the watershed is "moderately uncertain". Moreover, three comparison metrics, F fit measure, κ statistic, and Spearman rank correlation coefficient (ρ), are computed to compare the results of these two approaches. An F fit measure of 62.6%, κ statistic of 15.4-45.0%, and spatial mean ρ value of 0.48, imply a significant difference in decision making by incorporating the design rainfall uncertainty through the presented SPMCDM framework. The SPMCDM framework can help decision makers to understand the uncertainty in selection of flood management alternatives.