A PROBABILISTIC FRAMEWORK FOR EVALUATING SEA-LEVEL RISE IMPACTS

Assessing the vulnerability of the coastal zone to sea-level rise (SLR) requires integrating a variety of physical, biological, and social factors. These include landscape and habitat changes, as well as the ability of society and its institutions to adapt. For example, the range of physical and biological responses associated with SLR is poorly understood at some of the critical time and space scales required for decision making. Although the general nature of the changes that can occur on ocean coasts in response to SLR are widely recognized, predicting what changes may occur in response to a specific rise in sea level at a particular point in time is difficult. Similarly, the cumulative impacts of physical and biological change on the quantity and quality of coastal habitats are not well understood. Potential societal responses to SLR are also uncertain. Limitations in the ability to quantitatively predict outcomes at local, regional, and national scales affect whether, when, and how some decisions will be made. Thus, coastal managers require improved tools to understand and anticipate the magnitude and likelihood of future SLR impacts, as well as evaluate the consequences of different actions (or inaction).

Here we describe a Bayesian statistical analysis framework developed from a wide range of geologic, biologic, and hydrologic information on coastal systems and the related uncertainties in physical and process characterizations. Basic data sets are used as inputs to define the initial states of coastal systems, relevant forcing factors, prior behavior, and idealized model simulations. The Bayesian network is used to integrate these data to make probabilistic predictions of the future state of coastal environments for parameters such as shoreline change, wetland sustainability, and depth to groundwater in response to different SLR scenarios. Competing hypotheses regarding the relationships between forcing, the responses, and their interrelationships are evaluated and their uncertainties are compared. Results from the U.S. mid-Atlantic coastal region are used to explore different scenarios, as well as identify research needed to improve predictive skill. The Bayesian network approach provides an extensible framework to support decision making and evaluate specific management questions about alternatives for adapting to SLR.