A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Ranking uncertainty: Wave climate variability versus model uncertainty in probabilistic assessment of coastline change
Abstract Sand nourishments are increasingly applied as adaptive coastal protection measures. Predictions of the evolution of these nourishments and their impact on the surrounding coastline contain many uncertainties. The sources that add to this uncertainty can be delineated between intrinsic and epistemic uncertainty, i.e. inevitably in the system or related to knowledge limitations. Effects of intrinsic uncertainty (e.g. due to wave climate variability) on coastal evolution can be significant. In studying these effects, it has often been assumed that intrinsic uncertainty is dominant over epistemic uncertainty (e.g. introduced by the model), yet the magnitude of both contributions have not been explicitly quantified to assess the validity of this assumption. This paper examines the relative importance of intrinsic and epistemic uncertainty in coastline modeling of a large-scale nourishment. It uses a probabilistic framework in which sediment transport is considered to be a function of random wave forcing (intrinsic) and model (epistemic) uncertainty, calculating transport using a one-line model. The test case for this analysis is the mega-nourishment, the Sand Engine, located in the Netherlands. The applied wave climate variability is obtained from long term wave observations, whereas model uncertainty is quantified using the Generalized Likelihood Uncertainty Estimation (GLUE) method relying on monthly observations. We find that the confidence intervals on predicted volume losses increase substantially when including both intrinsic and epistemic sources of uncertainty. A global sensitivity analysis shows that ignoring model uncertainty would underestimate the variance by at least 50% after a 2.5-year simulation period for the Sand Engine, hence producing significant overconfidence in the results. These findings imply that for coastal modeling purposes a dual approach should be considered, evaluating both epistemic and intrinsic uncertainties.
Highlights Wave climate variability and model uncertainty both influence prediction uncertainty. Model uncertainty can be estimated from observations through the GLUE method. Wave climate variability explains less than half of the predicted variance after 2.5 yr. Assessing only wave climate variability can produce significant overconfident results. Evaluating both intrinsic and epistemic uncertainty is essential in predicting shoreline evolution.
Ranking uncertainty: Wave climate variability versus model uncertainty in probabilistic assessment of coastline change
Abstract Sand nourishments are increasingly applied as adaptive coastal protection measures. Predictions of the evolution of these nourishments and their impact on the surrounding coastline contain many uncertainties. The sources that add to this uncertainty can be delineated between intrinsic and epistemic uncertainty, i.e. inevitably in the system or related to knowledge limitations. Effects of intrinsic uncertainty (e.g. due to wave climate variability) on coastal evolution can be significant. In studying these effects, it has often been assumed that intrinsic uncertainty is dominant over epistemic uncertainty (e.g. introduced by the model), yet the magnitude of both contributions have not been explicitly quantified to assess the validity of this assumption. This paper examines the relative importance of intrinsic and epistemic uncertainty in coastline modeling of a large-scale nourishment. It uses a probabilistic framework in which sediment transport is considered to be a function of random wave forcing (intrinsic) and model (epistemic) uncertainty, calculating transport using a one-line model. The test case for this analysis is the mega-nourishment, the Sand Engine, located in the Netherlands. The applied wave climate variability is obtained from long term wave observations, whereas model uncertainty is quantified using the Generalized Likelihood Uncertainty Estimation (GLUE) method relying on monthly observations. We find that the confidence intervals on predicted volume losses increase substantially when including both intrinsic and epistemic sources of uncertainty. A global sensitivity analysis shows that ignoring model uncertainty would underestimate the variance by at least 50% after a 2.5-year simulation period for the Sand Engine, hence producing significant overconfidence in the results. These findings imply that for coastal modeling purposes a dual approach should be considered, evaluating both epistemic and intrinsic uncertainties.
Highlights Wave climate variability and model uncertainty both influence prediction uncertainty. Model uncertainty can be estimated from observations through the GLUE method. Wave climate variability explains less than half of the predicted variance after 2.5 yr. Assessing only wave climate variability can produce significant overconfident results. Evaluating both intrinsic and epistemic uncertainty is essential in predicting shoreline evolution.
Ranking uncertainty: Wave climate variability versus model uncertainty in probabilistic assessment of coastline change
Kroon, Anna (author) / de Schipper, Matthieu A. (author) / van Gelder, Pieter H.A.J.M. (author) / Aarninkhof, Stefan G.J. (author)
Coastal Engineering ; 158
2020-02-21
Article (Journal)
Electronic Resource
English
Editorial: Climate Change and Variability, Uncertainty and Decision-Making (ev960111)
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