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Data Series 601

A Bayesian Network to Predict Vulnerability to Sea-Level Rise: Data Report

By Benjamin T. Gutierrez, Nathaniel G. Plant, and E. Robert Thieler

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During the 21st century, sea-level rise is projected to have a wide range of effects on coastal environments, development, and infrastructure. Consequently, there has been an increased focus on developing modeling or other analytical approaches to evaluate potential impacts to inform coastal management. This report provides the data that were used to develop and evaluate the performance of a Bayesian network designed to predict long-term shoreline change due to sea-level rise. The data include local rates of relative sea-level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline-change rate compiled as part of the U.S. Geological Survey Coastal Vulnerability Index for the U.S. Atlantic coast. In this project, the Bayesian network is used to define relationships among driving forces, geologic constraints, and coastal responses. Using this information, the Bayesian network is used to make probabilistic predictions of shoreline change in response to different future sea-level-rise scenarios.

First posted November 2011

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    Contains: Compressed archive of data files in tab-delimited text format. Refer to the metadata files for more information.

For additional information contact:
Benjamin T. Gutierrez,
U.S. Geological Survey
384 Woods Hole Road
Woods Hole, MA, 02543

Part or all of this report is presented in Portable Document Format (PDF); the latest version of Adobe Reader or similar software is required to view it. Download the latest version of Adobe Reader, free of charge.

Suggested citation:

Gutierrez, B.T., Plant, N.G., and Thieler, E.R., 2011, A Bayesian network to predict vulnerability to sea-level rise: data report. U.S. Geological Survey Data Series 601, available at:




Development of the Bayesian Network

Bayesian Networks

The THK99 Dataset

Mapping Bayesian Network Predictions

Geospatial Data



References Cited

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