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Ranking uncertainty: Wave climate variability versus model uncertainty in probabilistic assessment of coastline change

Published on Jun 1, 2020in Coastal Engineering3.85
· DOI :10.1016/J.COASTALENG.2020.103673
Anna Kroon1
Estimated H-index: 1
(TU Delft: Delft University of Technology),
Matthieu de Schipper10
Estimated H-index: 10
(TU Delft: Delft University of Technology)
+ 1 AuthorsStefan Aarninkhof20
Estimated H-index: 20
(TU Delft: Delft University of Technology)
Abstract
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.
  • References (48)
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References48
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#1Alan Kasprak (USGS: United States Geological Survey)H-Index: 8
#2Nathaniel D. Bransky (NAU: Northern Arizona University)H-Index: 1
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Abstract Change detection of high resolution topographic data is commonly used in river valleys to quantify reach- and site-scale sediment budgets by estimating the erosion/deposition volume, and to interpret the geomorphic processes driving erosion and deposition. Field survey data are typically collected as point clouds that are often converted to gridded raster datasets and the ultimate choice of grid resolution is left to the user. This choice may have important implications for both the qua...
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#1Anna Kroon (TU Delft: Delft University of Technology)H-Index: 1
#2Matthieu de Schipper (TU Delft: Delft University of Technology)H-Index: 10
Last. Stefan Aarninkhof (TU Delft: Delft University of Technology)H-Index: 20
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Thirty one percent (31%) of the world's coastline consists of sandy beaches and dunes that form a natural defense protecting the hinterland from flooding. A common measure to mitigate erosion along sandy beaches is the implementation of sand nourishments. The design and acceptance of such a mitigating measure require information on the expected evolution at time scales from storms to decades. Process-based morphodynamic models are increasingly applied, together with morphodynamic acceleration te...
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Shoreface nourishments are commonly applied for coastal maintenance, but their behaviour is not well understood. Bathymetric data of 19 shoreface nourishments located at alongshore uniform sections of the Dutch coast were therefore analyzed and used to validate an efficient method for predicting the erosion of shoreface nourishments. Data shows that considerable cross-shore profile change takes place at a shoreface nourishment, while an impact at the adjacent coast is hard to distinguish. The co...
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#1Gonéri Le CozannetH-Index: 17
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Sandy shorelines are constantly evolving, threatening frequently human assets such as buildings or transport infrastructure. In these environments, sea-level rise will exacerbate coastal erosion to an amount which remains uncertain. Sandy shoreline change projections inherit the uncertainties of future mean sea-level changes, of vertical ground motions, and of other natural and anthropogenic processes affecting shoreline change variability and trends. Furthermore, the erosive impact of sea-level...
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#2Nguyen Thanh Hoan (Royal HaskoningDHV)H-Index: 1
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Mega-nourishments, aiming at providing long-term coastal safety, nature qualities and recreational space, have been applied recently at the Holland coast and are considered at various other places in the world. Methods to quickly evaluate the potential and lifetime of these coastal mega nourishments are therefore very much desired, which is the main objective of this research. Two types of mega nourishments can be distinguished: feeder-type mega nourishments may erode freely to feed adjacent coa...
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Stochastic descriptions and simulations of oceanographic variables are essential for coastal and marine engineering applications. In the past decade, copula-based approaches have become increasingly popular for estimating the multivariate distribution of some variables at the peak of a storm along with its duration. The modeling of the storm shape, which contributes to its impact, is often simplified. This article proposes a vine-copula approach to characterize hourly significant wave heights an...
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Abstract Prediction of the potential impact of an extreme storm-sequence on coastal resilience and the subsequent rate of post-storm recovery is a fundamental goal for coastal scientists, engineers and managers alike. This paper presents a methodology for forecasting shoreline change over annual time-scales, including the prediction of the potential impact of storm sequences and the subsequent post-storm recovery. The methodology utilises an archive of measured or modelled wave data to produce m...
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Abstract Advanced numerical models used to predict coastal change at a variety of time and spatial scales often contain many free parameters that require calibration to the available field data. At present, little guidance (beyond the adoption of the default values provided) is available in the field of coastal engineering to inform the selection of best-fit parameter values. Common calibration techniques can often lack a rigorous quantification of model sensitivity to parameters and parameter-i...
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