A probabilistic detailed level approach to flood risk assessment in the Scheldt estuary

Blanckaert, J.; Audoorn, L.; Audoorn, L.

basket (1): add | show

one publication added to basket [121075]

A probabilistic detailed level approach to flood risk assessment in the Scheldt estuary

Blanckaert, J.; Audoorn, L. (2005). A probabilistic detailed level approach to flood risk assessment in the Scheldt estuary, in: Proceedings of the Nineth International Symposium on Stochastic Hydraulics (ISSH), Nijmegen, The Netherlands, 23-25 May 2005.

In: (2005). Proceedings of the Nineth International Symposium on Stochastic Hydraulics (ISSH), Nijmegen, The Netherlands, 23-25 May 2005[s.n.]: Nijmgen

Available in	Authors
VLIZ: Open access 133371 [ download pdf ]
Document type: Conference paper

Keywords

Control > Flood control
Brackish water; Fresh water

Authors		Top
Blanckaert, J. Audoorn, L.

Abstract

It is commonly understood that computational limitations imply the adoption of considerablesimplifications in flood risk analysis. Those simplifications focus either on the hydrodynamic model, e.g.through substitution by a response surface, or on the boundary conditions, e.g. by ignoring some variables ordrawing a limited number of design storms with a set probability. Drawbacks of this approach include an unreliablequantification of uncertainties and a questionable extrapolation to extremely low probabilities. Thispaper covers an investigation towards the achievability of a Monte Carlo approach for flood risk assessmentby means of a hydrodynamic model. Efforts were made to stick to the well-known physical laws and interrelationships.This contribution fits in the framework of a societal cost-benefit analysis of flood protectionmeasures in the Scheldt basin. It can be concluded that, if computing power is substantially available, thequasi Monte Carlo method is a reliable and comprehensible tool for flood risk assessment.

All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy

Top | Authors

Datasets