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Woods Hole Coastal and Marine Science Center

Woods Hole Coastal and Marine Science Center > Sea-Level Rise Hazards and Decision Support > 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.

Figure 1

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 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.

Because climate change will have significant impacts to natural and developed, coastal systems, , there is a growing demand for information that can be used to make climate-change related decisions. For example, where sea level rise poses a potential threat, natural resource managers need to identify coastal habitat, such as wetlands, that is at risk in order to formulate effective long-term management strategies. Local planners and managers also need to be able to identify infrastructure that could be at risk to make effective long-term adaptation or mitigation decisions.  In both cases, the decision makers need to consider alternative response measures and assess the benefits and costs of options.  Consequently, there is a need to develop decision frameworks that combine detailed and sometimes complicated scientific information in a way that improves the ability to translate it into decision making scenarios. These frameworks might take the form of software applications and databases that are evaluated according specific decision making guidelines, or research products that can be made available as web visualization or mapping services.  The term decision support is used commonly to refer to the effort to bring relevant scientific information into policy formulation and decision-making. A recent National Research Council report (NRC, 2009 ) defines decision support in this context as: “organized efforts to produce, disseminate, and facilitate the use of data and information in order to improve the quality and efficacy of climate-related decisions”.

Important conclusions from the NRC (2009) report include:

  • “The nature of climate change and the incompleteness of scientific understanding of its consequences mean that decision makers must expect to be surprised."
  • “When predictive certainty is elusive and probabilistic information is all that is available, decision making can benefit from an “uncertainty management” framework. This approach considers the range of plausible futures and the key characteristics of each, the best estimates of the likelihood of each, and the likely magnitudes of the associated consequences.”

Uncertainty in the future form and function of many coastal areas is due to uncertainty in the future forces that will drive long-term changes in these systems (e.g., rate and magnitude of sea level rise, changes in storminess) and due to limitations in our understanding how the controlling factors influence one another as this occurs (e.g., coastal erosion, habitat response, human response).

Decision makers depend on the future coastal environment having certain characteristics. For example, homeowners desire a home that is at low risk of loss due to coastal erosion. Land managers  may target parcels for acquisition that provide critical habitat for threatened and endangered species. Flora and fauna require specific habitat attributes in order to survive and flourish. Ideally, where information (even if it is somewhat uncertain) is available decision makers should be able to evaluate the probability of impacts to make informed decisions.


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Page Last Modified:Tuesday, 07-Jul-2015 13:50:03 EDT (GW)