Modeling Impact of Intertidal Foreshore Evolution on Gravel Barrier Erosion and Wave Runup with XBeach-X

This paper provides a sensitivity analysis around how characterizing sandy, intertidal foreshore evolution in XBeach-X impacts on wave runup and morphological change of a vulnerable, composite gravel beach. The study is motivated by a need for confidence in storm-impact modeling outputs to inform coastal management policy for composite beaches worldwide. First, the model is run with the sandy settings applied to capture changes in the intertidal foreshore, with the gravel barrier assigned as a non-erodible surface. Model runs were then repeated with the gravel settings applied to obtain wave runup and erosion of the barrier crest, updating the intertidal foreshore from the previous model outputs every 5, 10 and 15 min, and comparing this with a temporally static foreshore. Results show that the scenario with no foreshore evolution led to the highest wave runup and barrier erosion. The applied foreshore evolution setting update is shown to be a large control on the distribution of freeboard values indicative of overwash hazard and barrier erosion by causing an increase (with 5 min foreshore updates applied) or a decrease (with no applied foreshore updating) in the Iribarren number. Therefore, the sandy, intertidal component should not be neglected in gravel barrier modeling applications given the risk of over- or under-predicting the wave runup and barrier erosion.

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Main Factor Sensitivity Analysis Based on Response Surface Model Updating of Port Crane Structure

Journal of Coastal Research ◽

10.2112/si73-029.1 ◽

2015 ◽

Vol 73 ◽

pp. 166-172 ◽

Cited By ~ 4

Author(s):

Weiguo Zhang ◽

Yuan Liu

Keyword(s):

Sensitivity Analysis ◽

Response Surface ◽

Model Updating ◽

Response Surface Model ◽

Surface Model ◽

Main Factor

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The Sensitivity Analysis, Optimization and Uncertainty Assessment of the Land Surface Model Parameters

2010 International Conference on Intelligent Computation Technology and Automation ◽

10.1109/icicta.2010.299 ◽

2010 ◽

Author(s):

Gaoli Su ◽

Qinhuo Liu ◽

Fangping Deng ◽

Xiaozhou Xin

Keyword(s):

Sensitivity Analysis ◽

Land Surface ◽

Land Surface Model ◽

Uncertainty Assessment ◽

Surface Model ◽

Model Parameters

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Decision making in Dutch coastal research based on coastal management policy assumptions

10.1680/cm.65147.291 ◽

2020 ◽

Author(s):

Quirijn J. Lodder ◽

Jill H. Slinger ◽

Zheng Bing Wang ◽

Carola van Gelder

Keyword(s):

Decision Making ◽

Coastal Management ◽

Management Policy ◽

Coastal Research

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Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-2555-2011 ◽

2011 ◽

Vol 8 (2) ◽

pp. 2555-2608 ◽

Cited By ~ 2

Author(s):

E. H. Sutanudjaja ◽

L. P. H. van Beek ◽

S. M. de Jong ◽

F. C. van Geer ◽

M. F. P. Bierkens

Keyword(s):

Sensitivity Analysis ◽

Land Surface ◽

Large Scale ◽

Groundwater Modeling ◽

Land Surface Model ◽

Water Levels ◽

Surface Model ◽

Groundwater Model ◽

Model Output ◽

Global Datasets

Abstract. Large-scale groundwater models involving aquifers and basins of multiple countries are still rare due to a lack of hydrogeological data which are usually only available in developed countries. In this study, we propose a novel approach to construct large-scale groundwater models by using global datasets that are readily available. As the test-bed, we use the combined Rhine-Meuse basin that contains groundwater head data used to verify the model output. We start by building a distributed land surface model (30 arc-second resolution) to estimate groundwater recharge and river discharge. Subsequently, a MODFLOW transient groundwater model is built and forced by the recharge and surface water levels calculated by the land surface model. Although the method that we used to couple the land surface and MODFLOW groundwater model is considered as an offline-coupling procedure (i.e. the simulations of both models were performed separately), results are promising. The simulated river discharges compare well to the observations. Moreover, based on our sensitivity analysis, in which we run several groundwater model scenarios with various hydrogeological parameter settings, we observe that the model can reproduce the observed groundwater head time series reasonably well. However, we note that there are still some limitations in the current approach, specifically because the current offline-coupling technique simplifies dynamic feedbacks between surface water levels and groundwater heads, and between soil moisture states and groundwater heads. Also the current sensitivity analysis ignores the uncertainty of the land surface model output. Despite these limitations, we argue that the results of the current model show a promise for large-scale groundwater modeling practices, including for data-poor environments and at the global scale.

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