Predicting coastal cliff erosion using a Bayesian probabilistic model

Abstract

a b s t r a c t a r t i c l e i n f oRegional 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.Published by Elsevier B.V.

Sea cliff instability susceptibility at regional scale: a statistically based assessment in the southern Algarve, Portugal

“…For low retreat rate cliff hazard estimation or assessment, the published work is very scarce and includes expert-based classification of indicators of near future instability (e.g. De Pippo et al, 2008), attempts to characterize the role of sets of conditioning factors, also weighted and combined according to local experience or expert opinion (Del Río and Gracia, 2008;Nunes et al, 2009), and use of Bayesian probabilistic models to forecast future cliff evolution based on past cliff retreat data and expert opinion on a limited set of conditioning factors (Milheiro-Oliveira, 2007;Hapke and Plant, 2010). One of the common shortcomings of these approaches lies in the non-objective assessment of the relative importance of the selected conditioning factors or indicators of future instability.…”

Brief communication "Evaluating European Coastal Evolution using Bayesian Networks"

“…The BN approach has been used in a variety of different applications, from studies of artificial intelligence to ecological systems (Berger, 2000), and recent work has applied this approach in the coastal domain (Hapke and Plant, 2010;Gutierrez et al, 2011;Plant and Holland, 2011). The current article, following Gutierrez et al (2011), presents the construction of a BN model (Sect.…”

Bluff Erosion Hazards and Construction Setbacks on the Great Lakes Coasts of the United States: A Review

“…Recent applications include cliff and landslide analysis [25], [26], and groundwater flow [27], both of which pertain directly to coastal bluff dynamics. Bayesian networks are being successfully used by the US Geological Survey on the Atlantic and Pacific coasts to better predict coastal change given a priori knowledge of physical conditions, controlling processes, and historical erosion rates [28], [29]. As inputs, Bayesian network models may use a "prior-behavior" parameter (such as historical bluff retreat); a set of initial-state parameters that define the system (such as bluff height, slope, and stratigraphy; beach geometry; and coastal engineering structures); and the dominant forcing agent causing bluff retreat (such as wave regime or groundwater flux).…”