Abstract
Human intervention in rivers is increasingly supported by computer model predictions. Due to limited observations of extreme events and the challenges related to predicting the future, it is recommended to quantify the uncertainty of model predictions. However, this is is not done in practice as the computational costs for such computations are often prohibitative. It is therefore unknown how model uncertainty affects model predictions of human intervention, even though human intervention in rivers has a large impact on society and the environment. The aim of this thesis is to improve the understanding of uncertainty surrounding model predictions of effect studies. We approached this as follows.
First, we developed a method to significantly decrease the computational burden of uncertainty quantification (80%95% in our case studies). Second, we quantified the uncertainty of archetypical spatial interventions and demonstrated that the uncertainty (expressed as the 90% confidence interval) of the effect is between 20% to 40% of the mean effect. So, for an intervention with a average water level decrease of 20 cm, 90% percent of model simulations will be between 17 cm and 23 cm. Next, we studied whether calibration could potentially mitigate the sources of uncertainty. Results showed that this was not the case; in fact, we showed that errors in model assumptions (such as a too simplistic vegetation model) increase uncertainty. Finally we compare model simulations with thirty years of hydraulic observations. Results showed that the uncertainty in observations are too great to discern the effect of human intervention. The accuracy of model simulations could therefore not independently be verified.
The recommended approach going forward is to explicitly quantify and communicate the uncertainty of model predictions. The methods and insights developed in this thesis contribute toward this goal.
First, we developed a method to significantly decrease the computational burden of uncertainty quantification (80%95% in our case studies). Second, we quantified the uncertainty of archetypical spatial interventions and demonstrated that the uncertainty (expressed as the 90% confidence interval) of the effect is between 20% to 40% of the mean effect. So, for an intervention with a average water level decrease of 20 cm, 90% percent of model simulations will be between 17 cm and 23 cm. Next, we studied whether calibration could potentially mitigate the sources of uncertainty. Results showed that this was not the case; in fact, we showed that errors in model assumptions (such as a too simplistic vegetation model) increase uncertainty. Finally we compare model simulations with thirty years of hydraulic observations. Results showed that the uncertainty in observations are too great to discern the effect of human intervention. The accuracy of model simulations could therefore not independently be verified.
The recommended approach going forward is to explicitly quantify and communicate the uncertainty of model predictions. The methods and insights developed in this thesis contribute toward this goal.
Original language  English 

Qualification  Doctor of Philosophy 
Awarding Institution 

Supervisors/Advisors 

Thesis sponsors  
Award date  28 Nov 2019 
Place of Publication  Enschede 
Publisher  
Print ISBNs  9789036548823 
DOIs  
Publication status  Published  28 Nov 2019 
Keywords
 river
 intervention
 Human intervention
 Hydraulic modeling
 Hydraulic modelling
 morphodynamic modeling
 Uncertainty
 Uncertainty Analysis
 Uncertainty quantification
 Probabilistic Analysis
 Bayesian nonparametric
 Glue
 calibration
 civil engineering
 river engineering
 Flood mitigation
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Prizes

Best paper award
U. Ji (Recipient), Koen Daniël Berends (Recipient), Minsang Cho (Recipient) & Ellis Penning (Recipient), 2017
Prize