Science Center Objects

 The Bayesian statistical framework is ideal for using data sets derived from historical or modern observations such as long-term shoreline change or wetland accretion/elevation trends. This information can be combined with model simulations and used to define the relationships between key variables in coastal environments. 

A Bayesian network provides a means of integrating these data to evaluate competing hypotheses regarding the relationships between forcing factors (e.g., rate of SLR, suspended sediment concentration, elevation change), and responses (e.g., shoreline change, wetland vertical accretion, water table change). This framework allows scientists to make probabilistic predictions of the future state of coastal environments for outcomes such as shoreline change, wetland survival, and changes in the depth to groundwater. The predictions also have estimates of outcome uncertainty that can be expressed as both numbers (e.g., 90%) and words (e.g., very likely). The ability to communicate SLR impacts in terms of a probabilistic prediction can improve scientists’ ability to support decision making and evaluate specific management questions about alternatives for addressing SLR.A Bayesian network provides a means of integrating these data to evaluate competing hypotheses regarding the relationships between forcing factors (e.g., rate of SLR, suspended sediment concentration, elevation change), and responses (e.g., shoreline change, wetland vertical accretion, water table change). This framework allows scientists to make probabilistic predictions of the future state of coastal environments for outcomes such as shoreline change, wetland survival, and changes in the depth to groundwater. The predictions also have estimates of outcome uncertainty that can be expressed as both numbers (e.g., 90%) and words (e.g., very likely). The ability to communicate SLR impacts in terms of a probabilistic prediction can improve scientists’ ability to support decision making and evaluate specific management questions about alternatives for addressing SLR.

Conceptual diagram demonstrating how Bayesian networks used in this project incorporate data and knowledge

Conceptual diagram demonstrating how Bayesian networks used in this project incorporate data and knowledge to provide predictions with decision-support applications

(Credit: Erika Lentz, Woods Hole Coastal and Marine Science Center. Public domain.)