In the shadow area, an example intervention along the Waal river that replaced all the vegetation with a meadow which offers very little resistance for the water to flow (Source: Google Earth).
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Tool for efficient uncertainty estimation and key insights to aid the interpretation of model results by decision-makers.
Rivers are continuously changing, both naturally and through human intervention. Project advisors generally use numerical models to predict the maximum decrease of the water levels due to large-scale river interventions such as side channels or floodplain vegetation changes. Predictions are generally affected by the limited understanding about the system behavior and the changes in model parameters. However, trying all possible combinations for the model setting is computationally demanding and therefore not feasible for every-day practice. Is model uncertainty so large that model results are not useful, or is it so small that it can be ignored? We reduce the computational time of estimating model uncertainty to get an indication of how does the it influence the predicted effects for flood safety.
Key goals: Collaborative Governance Fundamental understanding Integrated management
To estimate the computational time for the estimation of model uncertainty, we developed the Open-Source Python package ‘coral’ (Correlated Output Regression Analysis). The usefulness of this tool is demonstrated in various scientific papers, showing a potential reduction in the time required for a model evaluation of over 90%. To test the efficiency of our tool we quantified uncertainty for a large variety of different interventions such as side channel construction and floodplain vegetation smoothing. The comparison of these estimations provide insight for modelers and decision makers.
For whom and where?
- For modelers who want to quantify model uncertainty in their designs;
- For decision makers who want to understand how model uncertainty influences design choices.
Large-scale intervention to improve flood safety in a low-land river system such as the Dutch river Waal.
Innovative new data: comparing the effect including model uncertainty.
Data-collection methods: Numerical modeling Process-based modeling
Temporal scale: 1-10years
Application and findings
See the list of research outputs here.
The uncertainty analysis range from simple (1D) to more sophisticated models both in rivers and estuaries (2D and a 3D hydro-morphodynamic model). The Coral or statistical regression analysis approach can be applied to many different practical situations for example. Moreover, the uncertainty of model predictions for the design of river interventions such the ones implemented in the Netherlands is large enough to be relevant for decision makers.
Status for day-to-day practice
Tool and examples are available to incorporate model uncertainty into engineering projects.
Key locations: Maas River (NL) Waal River (NL)
Applications to different kind of projects, expansion to multidimensional analysis.
Last modified: 29/01/2019
Explore the contact details to get to know more about the researchers, the supervisory team and the organizations that contribute to this project.
As soon as available, explore the storyline to get to know more about the main methods or prototype tools that were developed within this project.
Explore the output details for available publications to get a glance of the innovative components and implications to practice as well as the links to supporting datasets.
Uncertainty quantification of flood mitigation predictions and implications for decision making
Using a new approach, we have now quantified the uncertainty of twelve interventions along the River Waal. We demonstrate that the choice between interventions can be different when uncertainty is taken into account.
14/11/2018 by Koen Berends et al.
Contains: Dataset access
Efficient uncertainty quantification for impact analysis of human interventions in rivers
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