Abstract
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 |
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| Qualification | Doctor of Philosophy |
| Awarding Institution |
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| Supervisors/Advisors |
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| Thesis sponsors | |
| Award date | 28 Nov 2019 |
| Place of Publication | Enschede |
| Publisher | |
| Print ISBNs | 978-90-365-4882-3 |
| DOIs | |
| Publication status | Published - 28 Nov 2019 |
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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
Cite this
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Human intervention in rivers : quantifying the uncertainty of hydraulic model predictions. / Berends, Koen Daniël.
Enschede : University of Twente, 2019. 192 p.Research output: Thesis › PhD Thesis - Research UT, graduation UT
TY - THES
T1 - Human intervention in rivers
T2 - quantifying the uncertainty of hydraulic model predictions
AU - Berends, Koen Daniël
PY - 2019/11/28
Y1 - 2019/11/28
N2 - 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.
AB - 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.
KW - river
KW - intervention
KW - Human intervention
KW - Hydraulic modeling
KW - Hydraulic modelling
KW - morphodynamic modeling
KW - Uncertainty
KW - Uncertainty Analysis
KW - Uncertainty quantification
KW - Probabilistic Analysis
KW - Bayesian nonparametric
KW - Glue
KW - calibration
KW - civil engineering
KW - river engineering
KW - Flood mitigation
U2 - 10.3990/1.9789036548823
DO - 10.3990/1.9789036548823
M3 - PhD Thesis - Research UT, graduation UT
SN - 978-90-365-4882-3
PB - University of Twente
CY - Enschede
ER -