Flood risk analysis of embanked river systems

Flood risk analysis of embanked river systems: Probabilistic systems approaches for the Rhine and Po rivers

Curran, A.N. (TU Delft Hydraulic Structures and Flood Risk)

Kok, M. (promotor)
de Bruijn, K.M. (copromotor)

Delft University of Technology

2020-12-04

Flood risk analysis focused on small sections or regions of embanked systems ignores the dy-namics of large-scale floods through time and space. Resulting measures are oftentimes inef-ficient due to an over estimation of risk or be-cause they simply transfer risk downstream due to increased localised protection upstream. This present work addresses this problem through a ‘system behaviour’ analysis, in which the com-plete river-dike floodplain system is assessed using stochastic simulations of flood events and defence failures. Such analysis can provide decision-makers with accurate estimates of local and system-wide risk from which efficient and effective FRM strate-gies can be developed. Furthermore, it creates a repository of realistic flood event simulations that inform emergency responders and provides data for future analyses. System behaviour analyses were implemented on two of the most developed floodplain regions of Europe; the Po River in Italy and the Dutch Rhine-Meuse delta. In both cases, the analysis underlined the importance of assessing risk of complete systems, as it provided more accurate estimates of current flood hazard, defence fail-ure probabilities and risk. The analysis can also be used in the evaluation of new measures such as dike strengthening and detention areas, both in the case studies and in other protected river systems. Finally, the development of map-based tools allows for a clear interpretation of the data for decision makers and researchers that wish to further investigate aspects of the system.

flood hazard
Probabilistic
System behaviour
River system
Embankments

https://doi.org/10.4233/uuid:3010a337-742c-4776-a747-2985085e981d

978-94-6421-121-4

Institutional Repository

doctoral thesis

© 2020 A.N. Curran