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Following the significant coastal changes caused by Hurricane Sandy in 2012, engineered berm-dunes were constructed along the New Jersey coastline to enhance protection from future storms. Following construction, property values on Long Beach Island, NJ, increased in three beachfront communities. The projects were financed entirely through federal disaster assistance, but the percentage of future maintenance costs must be covered by local communities. Whether communities are willing or capable of financially contributing to maintenance remains unclear because (i) some homeowners prefer ocean views over the protection afforded by the berm-dune structures, and (ii) stakeholder risk perceptions can change over time. To investigate the relationships between berm-dune geometries, values of coastal protection, and ocean view values, we developed a geo-economic model of the natural and anthropogenic processes that shape beach and dune morphology. The model results suggest that coastal communities may exhibit significant differences in their capabilities to maintain engineered dunes depending on stakeholder wealth and risk perception. In particular, communities with strong preferences for ocean views are less likely to maintain large-scale berm-dune structures over the long term. If these structures are abandoned, the vulnerability of the coast to future storms will increase.
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Improvement of empirical formulas to estimate the reduction effects by berms on irregular wave runup over a dune-berm coast
This study formulates the reduction effects of a sandy berm on irregular wave runup over a dune-berm coast. The numerical experiments by Park and Cox (2016) are closely re-examined to develop an empirical formula describing the variability of reduction effects of a sandy berm over a broad range of conditions. Based on a sequence of regression analyses, the reduction effects are expressed as a reduction factor in terms of normalized berm width, normalized surge level, and wave steepness in deep water. The comparison with the numerical experiments demonstrates that the regression formula can satisfactorily reproduce the variability of the reduction effects over the range of numerical experiments. The analysis of prediction uncertainty demonstrated that the derived formula reproduced the reduction effects observed in the numerical experiments with negligible bias and a 90% confidence interval of approximately ±20% relative error. In addition, conversion formulas between representative runup values based on different statistical definitions are derived to enable consistent comparisons between them. The proposed reduction formula is implemented into three empirical runup models that are applicable to the quick estimations of irregular wave runup on a dune-berm coast: the models by Park and Cox (2016), Stockdon et al. (2006), and Mase et al. (2013). Consistent comparisons were conducted among the empirical predictions and numerical experiments based on the statistical conversion formulas. Combined with the proposed reduction formula, all three models well reproduced the normalized 2% runup in the numerical experiments over a wide range of conditions. On the other hand, the uncertainty in the runup prediction appeared in different forms depending on the selected model. When the proposed reduction formula was implemented in the modified Park and Cox (2016) and modified Stockdon et al. (2006) models, the uncertainty was described by a log-normal distribution of the error ratio between the empirical predictions and numerical experiments. Quantitatively, these two models predicted 90% of the normalized runup on a dune within a range of relative error of less than approximately 20–30%. When the proposed reduction formula was combined with the model by Mase et al. (2013), the uncertainty followed a normal distribution of the residual error between the empirical predictions and numerical experiments. On the normalized runup, the model prediction indicated a small conservative bias (+0.05) and a root-mean-square error of 0.13.
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- Award ID(s):
- 2103713
- PAR ID:
- 10579337
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Coastal Engineering
- Volume:
- 176
- Issue:
- C
- ISSN:
- 0378-3839
- Page Range / eLocation ID:
- 104166
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.coastaleng.2022.104166
