Predicting coastal cliff erosion using a Bayesian probabilistic model
MetadataShow full item record
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.
This 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.
Showing items related by title, author, creator and subject.
Gutierrez, Benjamin T.; Plant, Nathaniel G.; Thieler, E. Robert (American Geophysical Union, 2011-04-22)Sea level rise during the 21st century will have a wide range of effects on coastal environments, human development, and infrastructure in coastal areas. The broad range of complex factors influencing coastal systems ...
A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features Gieder, Katherina D.; Karpanty, Sarah M.; Fraser, James D.; Catlin, Daniel H.; Gutierrez, Benjamin T.; Plant, Nathaniel G.; Turecek, Aaron M.; Thieler, E. Robert (Elsevier, 2014-01-31)Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests ...
Gutierrez, Benjamin T.; Plant, Nathaniel G.; Thieler, E. Robert; Turecek, Aaron M. (John Wiley & Sons, 2015-12-04)Quantifying geomorphic variability of coastal environments is important for understanding and describing the vulnerability of coastal topography, infrastructure, and ecosystems to future storms and sea level rise. Here we ...