MODEL-BASED COASTAL HAZARD ADAPTATION FRAMEWORK

Coastal regions throughout the world are experiencing the impacts of sea-level rise and increased coastal hazards from inundation, storms, and erosion. Many U.S. states are requiring coastal communities to develop practical and effective adaptation strategies to accommodate future hazards under anticipated sea level rise scenarios. The protection of built and natural resources and human life ultimately depends on the success of these adaptation strategies. In order to address this need, our team has developed a science-based framework that uses a variety of modeling approaches to support decision making. The resulting robust multi-phase decision framework allows for potential adaptation strategies to be quantitatively screened, evaluated, and planned in advance based on stakeholder-driven inputs utilizing the best available science. By applying a quantitative decision framework, the consequences and uncertainties of adaptation pathways can be more rigorously evaluated by coastal communities.

The first three phases of the framework involve 1) an evaluation of the existing coastal condition and identification of the natural and built resources in a community; 2) determining the temporal horizons for the for assessment of sea-level rise, associated coastal hazards (e.g., erosion, storms), and adaptation strategies; and 3) identifying the key community objectives for resource protection and evaluation of adaptation response strategies. Adaptation response strategies include protect, accommodate, retreat, and no action.

In phase four, modeling approaches are used to evaluate combinations of adaptation strategies for their likelihood of success and to identify potentially unforeseen consequences. The final evaluation requires knowledge of the physical processes driving coastal response to sea-level rise and other coastal hazards. Various models (e.g. conceptual, analytical, numerical) are available to evaluate likely outcomes of adaptation strategy implementation and the type of model chosen can be determined by the level of uncertainty tolerance. If uncertainty tolerance is high, simple models such as conceptual models or one-line models can be applied and evaluated. For lower uncertainty tolerances, more complex numerical or probabilistic-type models can be advantageous.