Advancing Flood Risk Screening

Research output: ThesisDissertation (TU Delft)

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Abstract

Manycoastal cities are struggling with a rapidly growing risk of flooding. The sizeand complexity of these cities often demand a coordinated strategy, consistingof a combination of flood risk reduction measures. A crucial part in the designprocess is the identification of effective flood risk management strategies. However,data and resources are often limited in these early stages of design, which ischaracterized by the many different options and measures that can be considered.The focus of this study is to identify the needs and challenges of this ‘floodrisk screening’ phase and develop and implement a model framework to supportdecision making in this stage. At the centre of the study is the developmentand application of such a model: the Flood Risk Reduction Evaluation andScreening, or FLORES, model. This dissertation includes two real-life casestudies which explain the structure and development of the FLORES model, aswell as two new applications in conceptual design – flood risk analysis basedon low-resolution data, and robust decision making – that are easier toimplement in flood risk management when combined with flood risk screeningmodels.The FLORESmodel has been implemented in two case studies, one in the USA and one in Mozambique.In the Houston-Galveston Bay Area, USA, the model showed the reliance of theentire region’s flood risk on the choices made at the coastal barriers.Especially the effectiveness of inland Nature-based Solutions heavily relied onthe placement and elevation of coastal structures. In Beira, Mozambique, coastalstructures are combined and compared with other measures, such as drainagesystems, retention, and early-warning systems. The use of flood risk screeningprovided insight into the effectiveness of individual measures, as well ascombinations of measures, and prioritized strategies based on predeterminedgoals. In both cities, these insights, combined with a better understanding ofthe local flood risk and how it is influenced by risk reducing measures andfuture scenarios, can be used to support decision makers in finding the mosteffective strategy going forward.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Jonkman, S.N., Supervisor
  • Kok, M., Supervisor
  • Timmermans, J.S., Advisor
Award date14 Jun 2022
Print ISBNs978-94-6421-779-7
DOIs
Publication statusPublished - 2022

Keywords

  • Flood risk
  • flood simulation
  • Adaptive design
  • Robust decision making
  • Flood risk management

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