In the Netherlands flood protection is immensely important for the safety of the nation. The shocking outcome of the 1953 flooding proves this point. In modern days, the development of socioeconomic and climate change factors casts doubt on the effectiveness of conventional approaches to flood risk management. Consequently, this project explored new approaches to flood risk management. An analysis of climate change effects led to estimation of future loading conditions. Subsequently, a detailed hydrodynamic analysis was conducted. It highlighted the significant levels of uncertainty that climate change introduces into loading conditions. Also, it confirmed the team’s perception, that the Westkapelle case region requires additional safety measures to guarantee an acceptable level of safety in the future. But how to guarantee the acceptable level of safety in the most efficient way? The team adopted the concept of robustness to find an answer. In a keynote publication Mens (2015) describes robustness in the following way: "Robust flood risk systems have some degree of resistance and some degree of resilience: the system can withstand some floods (no response), and for other (larger) floods impacts are limited and the system can recover quickly from the flood impact (response and recovery)." The team set out to include robustness as an integral part of the design process to handle uncertainties. The project shall be seen as an explorative study how this can be done, revolving around Westkapelle as a case study that proves the methodology’s feasibility. Robustness and uncertainty were included on multiple levels throughout the design process. Firstly, the range of uncertainties was quantified. Secondly, the effect, that single parameters have on the magnitude of uncertainties, was assessed. Thirdly, the system’s capacity was analysed to find the required overtopping reduction for guaranteeing sufficient safety. Fourthly, constructive measures were assessed on their robustness potential and satisfaction of stakeholder needs via a Multi Criteria Analysis (MCA). The MCA was then employed to select the type of constructive and non constructive measures to achieve the required levels of overtopping and safety. With the information on uncertainties, the measures were combined to form a robust design, consisting of living breakwater, dike heightening, surface protection and two policy measures. Probabilistic analysis was also done to see the sensitivity of the failure probability to sea level rise in different loading and design scenarios. A thorough comparison between the conventional design, that has been applied to the project location, and the robust design followed. The robust design came out on top. Robustness was found to be an effective tool in countering uncertainties. Where conventional design methodologies are lacking flexibility and precision, the robust design methodology makes use of the system and its resilience to find an optimal solution. Its applicability may not be limited to flood risk management only but stretch out to other civil engineering disciplines.

The increasing frequency and intensity of extreme events due to global warming and climate change is increasing flood risk. To act, rather than react, nature-based solutions (NBS) involving vegetation and wetlands are being explored on top of conventional solutions like dikes. WHY? There was a dire need for global study quantifying the potential of vegetation in reducing flood risk and eventually make a decision support tool which enables quick assessments about flood risk reduction in a vegetated hydrodynamic system. WHAT? The developed tool can predict flood risk anywhere in the world without rigorous modeling through user defined conditionalization of in-situ hydrodynamic or vegetation characteristics. HOW? Multivariate dependence among parameters of schematized system can exhibit characteristics of vegetated hydrodynamics. To ensure global representation of vegetated hydrodynamics a copula-based multivariate stochastic model has been developed which caters global ranges of each parameter, their probability distributions and the inter-parameter dependencies through ranked correlations. Numerical modeling has been carried out through XBeach non-hydrostatic model by resolving full spectrum of high and low frequency waves. A non-parametric Bayesian network-based flood risk prediction tool has been developed from the synthetic dataset developed from the simulations. SO? Bulk results conclude that saltmarshes attenuates waves by 87% and mangroves by 94% as a mean value. Wave attenuation, flood risk reduction and wave run-up manifests maximum dependence on offshore wave height, water depth, drag coefficient, vegetation height, frontal width, and forest length and least on offshore slope and vegetation density. NOW? The flood risk prediction tool would help decision makers in implementing NBS, in making better informed decisions about early warnings and policy making related to flood risk reduction and climate change adaptation by incorporating vegetation. NOVELTY? To the author's knowledge no such study exists which captures natural variability of hydrodynamics and vegetation together in a probabilistic model over global scales. Additionally, no such study exist which applies non-parametric Bayesian networks to predict flood risk. The dependence modeling of global vegetated hydrodynamic environments is also unique which skims out the most critical parameters.