Articles | Volume 19, issue 8
https://doi.org/10.5194/nhess-19-1737-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-19-1737-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Aug 2019
Research article |
| 13 Aug 2019
| 13 Aug 2019
Uncertainty quantification of flood mitigation predictions and implications for interventions
Department of Marine and Fluvial Systems, Twente Water Centre, University of Twente, P. O. Box 217, 7500 AE Enschede, the Netherlands
Department of River Dynamics and Inlands Shipping, Deltares, Boussinesqweg 1, 2629 HV Delft, the Netherlands
Menno W. Straatsma
Department of Physical Geography, Faculty of Geosciences, University of Utrecht, Princetonlaan 8, 3584 CS Utrecht,
the Netherlands
Jord J. Warmink
Department of Marine and Fluvial Systems, Twente Water Centre, University of Twente, P. O. Box 217, 7500 AE Enschede, the Netherlands
Suzanne J. M. H. Hulscher
Department of Marine and Fluvial Systems, Twente Water Centre, University of Twente, P. O. Box 217, 7500 AE Enschede, the Netherlands
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- Human interventions in a bifurcating river system: Numerical investigation and uncertainty assessment M. Gensen et al. 10.1111/jfr3.12762
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- Multidecadal Analysis of an Engineered River System Reveals Challenges for Model-Based Design of Human Interventions K. Berends et al. 10.3390/civileng2030032
- Preface: Advances in flood risk assessment and management C. Prieto et al. 10.5194/nhess-20-1045-2020
- Optimal sizing of the sediment replenishment capacity based on robust ergodic control of subordinator-driven dynamics H. Yoshioka & M. Tsujimura 10.1080/02331934.2022.2103414
- Computing River Discharge Using Water Surface Elevation Based on Deep Learning Networks W. Liu et al. 10.3390/w15213759
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- A Decision‐Relevant Factor‐Fixing Framework: Application to Uncertainty Analysis of a High‐Dimensional Water Quality Model Q. Wang et al. 10.1029/2022WR032194
- Predicting discharge capacity of vegetated compound channels: uncertainty and identifiability of one-dimensional process-based models A. Kiczko et al. 10.5194/hess-24-4135-2020
- Improving rating curve accuracy by incorporating water balance closure at river bifurcations M. Gensen et al. 10.1016/j.jhydrol.2022.127958
- Estimation of Parameters and Pooling in Nonlinear Flooding Event Scenarios with Bayesian Model F. Alduais et al. 10.1155/2022/6319197
1 citations as recorded by crossref.
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Short summary
River flooding is a major safety concern. Sophisticated models are used to design ways to decrease flood risk, but until recently it was not feasible to calculate how uncertain these model predictions are. Using a new approach, we have now quantified the uncertainty of 12 interventions along the River Waal. Results show significant but not problematically high uncertainty. We demonstrate that the choice between interventions can be different when uncertainty is taken into account.
River flooding is a major safety concern. Sophisticated models are used to design ways to...
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