Uncertainty quantification of flood mitigation predictions and implications for interventions

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 the effect 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 12 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 explore relationships that may aid in decisionmaking. We identified the relative uncertainty, defined as the ratio between the confidence interval and the expected effect, as a useful metric to compare uncertainty between different interventions. Using this metric, we show that intervention effect uncertainty behaves like a traditional backwater curve with an approximately 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 how a level of acceptable uncertainty can be defined and how this can affect the design of interventions. In general, we conclude that the uncertainty of model predictions is not large enough to invalidate model-based intervention design, nor small enough to neglect altogether. Instead, uncertainty information is valuable in the selection of alternative interventions.