Uncertainty quantification of flood mitigation predictions and implications for decision making

Abstract

Abstract. Reduction of water levels during river floods is key in preventing damage and loss of life. Computer models are used to design ways to achieve this and assist in the decision making process. However, the predictions of computer models are inherently uncertain, and it is currently unknown to what extent that uncertainty affects predictions of flood mitigation strategies. In this study, we quantify the uncertainty of flood mitigation interventions on the Dutch River Waal, based on 39 different sources of uncertainty and twelve intervention designs. The aim of each intervention is to reduce flood water levels. Our objective is to investigate the uncertainty of model predictions of intervention effect and to establish relationships to aid in decision making. We show that the uncertainty of an intervention can be adequately described by the newly introduced relative uncertainty metric, defined as the ratio between the confidence interval and the expected effect. Using this metric, we show that intervention effect uncertainty behaves like a traditional backwater curve with a constant relative uncertainty value. In general, we observe that uncertainty scales with effect: high flood level decreases have high uncertainty and conversely, small effects are accompanied by small uncertainties. However, different interventions with the same expected effect do not necessarily have the same uncertainty. For example, our results show that the large-scale but relatively ineffective intervention of floodplain smoothing by removing vegetation, has much higher uncertainty compared to alternative options. Finally, we show that for a defined standard of acceptable uncertainty, interventions need to be over-designed to meet this standard, and by how much. In general, we conclude that the uncertainty of model predictions is not large enough to invalidate model-based decision making, nor small enough to neglect altogether. Instead, uncertainty information can be used to improve intervention design and enrich the decision making process.