Journal of Geophysical Research: Earth Surface

Cover image for Vol. 121 Issue 7

Impact Factor: 3.318

ISI Journal Citation Reports © Ranking: 2015: 27/184 (Geosciences Multidisciplinary)

Online ISSN: 2169-9011

Associated Title(s): Journal of Geophysical Research

Statistical model predicts shoreline erosion rates due to sea level rise

While sea level rise in the face of global warming is a well-acknowledged threat, providing estimates of the local impact-the information needed by planners to develop effective strategies against the rising waters-has been difficult. Many attempts treat the global ocean as a giant bathtub, where increased water volume simply rises up and floods the land. Understandably, these approaches fall short of accurately estimating the impact of storms, sea level rise, and human influence on coastlines. The next extension toward an accurate long-term prediction of shoreline change necessarily includes a representation of the dynamic interaction between coastal features and the rising water. To create a model with these necessary qualities, Gutierrez et al. (2011) developed a Bayesian network that uses observations of historical oceanographic and geologic processes, coupled with knowledge of geographic features to calculate the probability of shoreline change for the Unites States' Atlantic coast. The model draws on measurements collected at 5-kilometer intervals along the shore representing between 50 and 100 years of observations. The authors used their model to retroactively predict the observed shoreline change. Clustering their estimates into bins representing five different rates of change, they found that it accurately predicted the bin for each 5-kilometer stretch of coastline 71% of the time. They found that knowledge of the local sea level rise rate was the most important factor contributing to a successful prediction. Their model makes predictions in terms of the probability of shoreline movement at different rates, which allows scientists to communicate the uncertainty in their findings and translate their predictions into terminology familiar to planners. The authors see their model as a framework for understanding climate impacts of the coast, where new observations and physical processes can be added to easily improve predictive ability.

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