Using a Bayesian Network to Predict Shoreline-Change Vulnerability to Sea-Level Rise for the Coasts of the United States

  • Gutierrez B
  • Plant N
  • Pendleton E
  • et al.
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Sea-level rise is an ongoing phenomenon that is expected to continue and is projected to have\ra wide range of effects on coastal environments and infrastructure during the 21st century and beyond.\rConsequently, there is a need to assemble relevant datasets and to develop modeling or other analytical\rapproaches to evaluate the likelihood of particular sea-level rise impacts, such as coastal erosion,\rand to inform coastal management decisions with this information. This report builds on previous work\rthat compiled oceanographic and geomorphic data as part of the U.S. Geological Survey’s Coastal\rVulnerability Index (CVI) for the U.S. Atlantic coast, and developed a Bayesian Network to predict\rshoreline-change rates based on sea-level rise plus variables that describe the hydrodynamic and geologic\rsetting. This report extends the previous analysis to include the Gulf and Pacific coasts of the\rcontinental United States and Alaska and Hawaii, which required using methods applied to the USGS\rCVI dataset to extract data for these regions. The Bayesian Network converts inputs that include observations\rof local rates of relative sea-level change, mean wave height, mean tide range, a geomorphic\rclassification, coastal slope, and observed shoreline-change rates to calculate the probability of the\rshoreline-erosion rate exceeding a threshold level of 1 meter per year for the coasts of the United States.\rThe calculated probabilities were compared to the historical observations of shoreline change to evaluate\rthe hindcast success rate of the most likely probability of shoreline change. Highest accuracy was\rdetermined for the coast of Hawaii (98 percent success rate) and lowest accuracy was determined for the\rGulf of Mexico (34 percent success rate). The minimum success rate rose to nearly 80 percent (Atlantic\rand Gulf coasts) when success included shoreline-change outcomes that were adjacent to the most likely\routcome. Additionally, the probabilistic approach determines the confidence in calculated outcomes as\rthe probability of the most likely outcome. The confidence was highest along the Pacific coast and it\rwas lowest along the Alaskan coast.




Gutierrez, B. T., Plant, N. G., Pendleton, E. A., & Thieler, E. R. (2014). Using a Bayesian Network to Predict Shoreline-Change Vulnerability to Sea-Level Rise for the Coasts of the United States. Open-File Report, (2014–1083), 32. Retrieved from

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