The objective of this project was to develop and test a risk-based methodology to evaluate threats to critical installation assets and quantify the potential loss of mission performance when installation capabilities were impacted by a combination of rising sea levels and coastal storm hazards.

Technical Approach

The project’s step-wise risk assessment approach used predictive inferences to quantify vulnerabilities of critical assets based on their exposure, sensitivity, and adaptive capacity to withstand storm forcings (tidal fluctuations, waves, winds, surge, sedimentation, saltwater intrusion, flooding, etc.) exacerbated by sea level rise (SLR). Hierarchical aggregations of assets were then arrayed in a relational network to capture interdependencies, and service interruptions were monitored from a systems perspective to capture the overall risk to mission performance.

The approach began with an environmental and geomorphological characterization of the baseline conditions of a region. The project team then mapped the potential changes likely to occur to the coastline under a variety of SLR scenarios in a Geographic Information System (GIS) to better visualize the system’s response to the combination of inundation and vegetative switching. High fidelity numerical models were used to simulate coastal storms and assess regional, nearshore, surface, and subsurface conditions under a range of SLR scenarios. These models generated a series of resultant forcings (winds, waves, surge, etc.) that impacted both the installation and its surrounding environs. An installation-specific Asset Capability Network (ACN) model was created and used to capture the unique position, condition, and interdependencies of the installation’s critical infrastructure in supporting the mission. An assessment of possible damages to the installation network was undertaken, and risks of mission impairment were then quantified using probabilistic Bayesian analyses under the various storm and SLR scenarios.

The project team selected the Naval Station Norfolk (NSN) in Hampton Roads, Virginia (located at the mouth of the Chesapeake Bay, North Atlantic coast of the United States), to test the efficacy of the approach. All modeling efforts for the case study focused on a series of 25 scenarios comprised of five prescribed SLR conditions ranging from 0.0 m to 2.0 m (by 2100) in combination with five simulated coastal storms ranging in intensity from 1-yr to 100-yr return intervals. In addition, three historical nor’easters were incorporated into the storm analysis (at the request of NSN managers) to capture the localized impacts of these unique storms, but were omitted from the risk-based analysis due to time and budgetary constraints.


This project produced a robust, scientifically informed risk-based approach that is applicable to coastal military installations threatened by coastal hazards and rising sea levels. As part of this effort, a series of step-wise procedures were established to couple multiple high fidelity coastal storm models with installation-specific asset models and regional ecosystem response models to systematically assess risks to mission in a probabilistic manner using Bayesian networking.

The successful test of the framework at NSN clearly illustrated the efficacy of the procedures and the benefits of deploying a risk-based approach. Numerous products were generated for the test case. For example, each model application generated a series of 25 forcings datasets and accompanying high resolution maps that captured the existing conditions and quantified the storm forcings (winds, waves, surge, etc.) impacting the area under the various SLR scenarios. Accompanying these analyses, several associated GIS-based products (model meshes, digital elevation models, land use cover classifications) have been produced for the study area. Although not a primary objective, the test case also generated a series of GIS-based maps of forcings (winds, waves, surge, flooding, etc.) for the entire Hampton Roads area (for each of the SLR-storm scenarios studied) that can now be used to assess vulnerability of assets both inside and outside the installation, supporting community efforts to address the threats of SLR and coastal storm hazards from a regional perspective. The asset network model developed for the site offered a unique, highly detailed systems perspective of the installation’s service production (i.e., electric supply, water supply, waste removal, etc.), and has now been stored in the GIS database for use in future management and operations activities by the installation’s personnel. The Bayesian model developed for the test site now holds more than 13,000 conditional probabilities characterizing the fragility of the assets with regards to their location, condition, and structural composition. The relational Bayesian network quantifies impacts to capabilities and the risks to mission performance due to exposure to storm hazards and SLR.

Based on the analysis of NSN’s site-specific vulnerabilities, the project team found sea level rise to be a significant and pervasive threat multiplier to mission sustainability, significantly increasing loadings on built infrastructure, and dramatically increasing risks to system capabilities and service provisioning. Using the framework, the project team was able to identify several critical systems on the study site that were particularly vulnerable and likely to be incapacitated once sea levels rise above 1.0 meter on the site. The results show that the probabilities of damage to infrastructure and losses in mission performance increased dramatically once 0.5 meters of SLR was experienced, indicating a “tipping point,” or threshold, that should be considered when undertaking future planning or operational activities on the installation.


The analytical framework of this project can be used to evaluate relative performance of existing conditions, future no-action conditions, as well as structural and non-structural risk mitigating alternatives to sustain critical assets and mission capabilities at an actionable scale under a wide range of SLR and storm scenarios. Deploying this approach, installations can identify critical thresholds where minor mission impairment annoyances (on the order of approximately 1-2 hour delays in performance) evolve into catastrophic events (i.e., on the order of weeks or months). Once communicated to the planners and managers both on- and off-site in an actionable construct through maps and network diagrams, installations can consider altering the status quo to incorporate proactive management strategies to prevent or anticipate impairments based on the risks (i.e., regret management). Moreover, military leadership can use these experiences to develop new guidance and policy to proactively address systemic, commonly occurring failures across the range of the military’s holdings. In effect, this project offers a robust, scientifically defensible approach that transparently communicates potential risks to installations, while helping policymakers develop guidance to promote military readiness and sustainability in the face of climate change and sea level rise.

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