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Predicting coastal cliff erosion using a Bayesian probabilistic model

Marine Geology

By:
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DOI: 10.1016/j.margeo.2010.10.001

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Abstract

Regional 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. ?? 2010.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Predicting coastal cliff erosion using a Bayesian probabilistic model
Series title:
Marine Geology
DOI:
10.1016/j.margeo.2010.10.001
Volume
278
Issue:
1-4
Year Published:
2010
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
140
Last page:
149
Number of Pages:
10