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dc.contributor.authorHapke, Cheryl J.
dc.contributor.authorPlant, Nathaniel G.
dc.date.accessioned2011-02-28T20:10:42Z
dc.date.available2011-02-28T20:10:42Z
dc.date.issued2010-10-10
dc.identifier.citationMarine Geology 278 (2010): 140-149en_US
dc.identifier.urihttp://hdl.handle.net/1912/4358
dc.descriptionThis paper is not subject to U.S. copyright. The definitive version was published in Marine Geology 278 (2010): 140-149, doi:10.1016/j.margeo.2010.10.001.en_US
dc.description.abstractRegional coastal cliff retreat is difficult to model due to the episodic nature of failures and the along-shore variability of retreat events. There is a growing demand, however, for predictive models that can be used to forecast areas vulnerable to coastal erosion hazards. Increasingly, probabilistic models are being employed that require data sets of high temporal density to define the joint probability density function that relates forcing variables (e.g. wave conditions) and initial conditions (e.g. cliff geometry) to erosion events. In this study we use a multi-parameter Bayesian network to investigate correlations between key variables that control and influence variations in cliff retreat processes. The network uses Bayesian statistical methods to estimate event probabilities using existing observations. Within this framework, we forecast the spatial distribution of cliff retreat along two stretches of cliffed coast in Southern California. The input parameters are the height and slope of the cliff, a descriptor of material strength based on the dominant cliff-forming lithology, and the long-term cliff erosion rate that represents prior behavior. The model is forced using predicted wave impact hours. Results demonstrate that the Bayesian approach is well-suited to the forward modeling of coastal cliff retreat, with the correct outcomes forecast in 70–90% of the modeled transects. The model also performs well in identifying specific locations of high cliff erosion, thus providing a foundation for hazard mapping. This approach can be employed to predict cliff erosion at time-scales ranging from storm events to the impacts of sea-level rise at the century-scale.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.publisherElsevier B.V.en_US
dc.relation.urihttps://doi.org/10.1016/j.margeo.2010.10.001
dc.subjectCoastal erosionen_US
dc.subjectCoastal cliffsen_US
dc.subjectBayesianen_US
dc.subjectPredictive modelen_US
dc.subjectSouthern Californiaen_US
dc.titlePredicting coastal cliff erosion using a Bayesian probabilistic modelen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.margeo.2010.10.001


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