SERAP: Assessment of Shoreline Retreat in Response to Sea Level Rise

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The broad range of complex factors influencing coastal systems contribute to large uncertainties in predicting long-term sea level rise impacts. Researchers demonstrated the capabilities of a Bayesian network (BN) to predict long-term shoreline change associated with sea level rise and make quantitative assessments for predicting uncertainty. A BN was used to define relationships between drivin...

The broad range of complex factors influencing coastal systems contribute to large uncertainties in predicting long-term sea level rise impacts. Researchers demonstrated the capabilities of a Bayesian network (BN) to predict long-term shoreline change associated with sea level rise and make quantitative assessments for predicting uncertainty. A BN was used to define relationships between driving forces, geologic constraints, and coastal response for the U.S. Atlantic coast that include observations of local rates of relative sea level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline change rate. The BN was used to make probabilistic predictions of shoreline retreat in response to different future sea level rise rates.