Optimizing flood risk mitigation measures at river scale

The optimization of flood risk mitigation measures necessitates the estimation of flood risk without and with a wide range of combinations of mitigation measures. This flood risk estimation is a complex task, involving climate, hydrological, hydraulic and economic process components. Moreover, these components do not form a linear process chain but interact - for example through local protection measures and potential flood protection failures. Additionally, flood risk assessment is accompanied by significant natural and model uncertainties.

We developed a probabilistic model capable of efficiently estimating the flood risk at river scale [1]. It explicitly models the interplay among flood process components and mitigation measures, making it well suited to estimate the benefit of individual mitigation measures or combinations thereof. In the latter case, the model captures the joint effect of mitigation measures, rather than summing their independent benefits.

Decision making in flood risk management involves numerous possible mitigation measures, multiple conflicting objectives (such as minimizing costs versus maximizing risk reduction), and generally very high uncertainties. In this contribution, we present how the flood risk model can support decision making for the selection of flood mitigation measures. Identifying pareto-optimal combinations of mitigation measures at river scale poses different challenges: a combinatorially large design space, partly discrete optimization variables, substantial natural and model uncertainties and large variance in the sample-based risk estimates. We present an optimization framework tailored for these settings, which balances between the robustness of sample-based flood estimates and the convergence behavior of the optimization given computational constraints.