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Uncertainty quantification of flood mitigation predictions and implications for decision making

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Published on 14/11/2018 by Berends, K. D., Straatsma, M. W., Warmink, J. J., & Hulscher, S. J. M. H.

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Koen Berends


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Relative uncertainty at the location of the maximum effect for all interventions studied (top) and typical cross section indicating the river interventions (bottom). Source: Berends et. al (2018)

Innovative components

Computational models that take into account many of the river characteristics (i.e terrain geometry, vegetation, and hydraulic structures) are used to predict the water level decrease due to possible river interventions.  To calculate how uncertain are these model predictions are, thousands of runs may be necessary each with a calculation time that increases with the model complexity. By using a simplified approach (CORAL tool proposed in our first publication), we calculated the most probable value (90% confidence) to interpret the uncertainty estimations. As criteria to compare the effects of possible river interventions, we proposed the ‘relative uncertainty’ or ratio between the most probable water level (90% confidence) against the total water level decrease that is expected with given river intervention.

Implications to practice

Results show that different interventions with the same expected decrease in water level do not necessarily have the same uncertainty. We demonstrate that the choice between interventions can be different when the relative uncertainty is taken into account. As an example, we compared typical river interventions at a critical location such as the St Andries bend in the Dutch river Waal. We modeled a total of six interventions (see Figure). The comparison of river interventions shows that floodplain smoothing is by far the most uncertain intervention. A reduction in relative uncertainty could be achieved by minimizing the amount of change to existing floodplains while optimizing the expected flood level decrease.

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Related outputs

Efficient uncertainty quantification for impact analysis of human interventions in rivers

A novel method is presented to estimate model uncertainty with a reduced number of model evaluations.

14/06/2018 by Koen Berends et al.

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Contains: Dataset access Model or tool upon request

Last modified: 18/05/2019