Screening flood adaptation measures framework

Abstract

To reduce the flood risk in areas prone to flooding, possible adaptation measures must be investigated. In this thesis a Python-based framework has been developed that can screen flood adaptation measures as an alternative to the existing analytical method of Royal HaskoningDHV. The framework is limited to the costs and effects on the economic flood risk (the benefits) of eleven different measures such as levees, landfills, and flood proofing. First, the existing methods for screening measures were investigated. The resulting strong and missing elements were combined to form the functional requirements of the framework. The underlying script for the framework was elaborated with these requirements. Subsequently, the framework was applied to two case locations, namely Phu Loc (Vietnam) and Waal Eemhaven (Rotterdam, The Netherlands). Because these locations had already been developed by Royal HaskoningDHV, the results could be compared. The existing methods showed that clear results and the possibility for the user to select the measures and location are the main strengths. In addition, an uncertainty analysis and well-organized input table were found to be useful for the framework. The results of the simulations at the case locations were comparable to the results of the existing method of Royal HaskoningDHV for the measures considered. The framework is faster than the existing method and determines the optimal measure(s) and protection level. Therefore, it is a good alternative for screening the eleven considered measures. The framework can be supported with a hydraulic model to include more measures, for example river widening. It is recommended to add an extensive cost database to the framework, so that cost calculations and uncertainty analysis can be performed more easily in future applications.