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Sea-level Rise Hazards and Decision Support

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Responding to Coastal Change sea-level rise, SLR, USGS, geology, hydrology, geography, shoreline change, coastal wetlands, piping plovers

Photograph of arial view of Fenwick and Assateatgue Island
Figure 1. Aerial view of Fenwick and Assateague Islands along the Maryland coast. Rising sea-level will increase the likelihood of erosion, wetland losses, and property and infrastructure damages in the 21st century. Photo source: Jane Thomas, IAN Image Library ( ).

The "Sea-level Rise Hazards and Decision Support" project assesses the potential impacts of sea-level rise and provides tools for coastal management decision making.   Historical and recent observations of coastal change are combined with model simulations of coastal environments such as barrier islands, wetlands, and coastal aquifers. A Bayesian network integrates the information to evaluate the probability of sea-level rise impacts and communicate these to managers who face decisions to avoid, mitigate, or adapt to future hazards. 

Project Overview

This project brings together scientists from the disciplines of geology, hydrology, geography, biology, and ecology to address the effects of sea-level rise (SLR) on the Nation’s coasts. The project synthesizes information on coastal environments and uncertainties in knowledge of coastal processes into a Bayesian statistical analysis framework. The Bayesian approach allows researchers to evaluate the probability of a number of sea-level rise impacts, and provides information that can be used for decision making.

The general nature of the changes that can occur on ocean coasts in response to SLR are widely recognized. It is difficult, however, to predict exactly what changes may occur in response to a specific rise in sea level at a particular location or point in time. The ability to predict the extent of these changes is limited by uncertainties in both currently available data that describes the coastal environment, as well as gaps in understanding of some of the driving processes that contribute to coastal change. Additionally, the cumulative impacts of physical and biological change on the quantity and quality of coastal habitats are not well understood. Potential societal responses to sea-level rise are also uncertain. Nonetheless, coastal managers need actionable information to make decisions to avoid, mitigate, or adapt to future hazards.  

Although projections of sea-level rise for the 21st century vary widely, future impacts will be significant and include:

  • land loss from inundation and erosion,
  • migration of coastal landforms,
  • increased elevation, duration and frequency of storm-surge flooding,
  • wetland losses,
  • changes in coastal aquifer hydrology, and
  • changes to coastal habitat.

Consequently, assessing the vulnerability of the nation’s coastal regions to sea-level rise and predicting how this will vary in the future requires information representing physical, biological, and social factors that describe landscape and habitat changes, as well as the ability of society and its institutions to adapt.

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. This information can also identify research needed to improve predictive skill.

Click on the links on the left to learn more about the various components of this project.



         Rob Thieler
                  Woods Hole Coastal and Marine Science Center
                  384 Woods Hole Road
                  Woods Hole, MA 02543

         Nathaniel Plant
                  St. Petersburg Coastal and Marine Science Center
                  600 Fourth Street South
                  St. Petersburg, Florida 33701
                  727-803-8747 x3072

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