Impacts to a range of community resource physical assets are presented to identify current and emerging risk hotspots.
Coastal Flood Time Horizon:
Impact Type:
Some description about the selected option above
Impacts to a range of assets, systems, and networks vital to state, regional, and national activities are presented to identify current and emerging risk hotspots.
Coastal Flood Time Horizon:
Impact Type:
Some description about the selected option above
Impacts to natural coastal and aquatic environments that provide fish and wildlife habitat, water quality and flood reduction benefits, and numerous ecosystem services to the surrounding region.
Coastal Flood Time Horizon:
Impact Type:
Some description about the selected option above
Notes and Limitations
Community Resources considered in the analyses include: businesses and employers (commercial, industrial, educational, agricultural, religious, and non-profit owned facilities), residential neighborhoods, and tribal resources.
Impacts to these assets from exacerbating coastal hazards were quantified using different metrics, depending on the availability and quality of asset level data. Details on the analyses conducted are available in the Impact Assessment Methodology Appendix to the Coastal Resilience Master Plan.
A standard summarization grid (1,375 ft x 1,375 ft) was used to facilitate comparison of impacts across geography and time. A clustered rank approach, using the k-means clustering algorithm, was applied to the raw impact scores, at each summary grid cell, to understand relative priority areas within each impact type, time horizon and area of interest. Ranks were calculated three times: relative to the entire coastal region, relative to each planning district or regional commission, and relative to each locality. Ranks are calculated by considering raw impact scores across all time horizons.
Notes and Limitations
The impacts to assets and systems considered under the Critical Sectors theme were identified using the Cybersecurity & Infrastructure Security Agency (CISA) framework for critical infrastructure sectors.
Impacts to these assets from exacerbating coastal hazards were quantified using different metrics, depending on the availability and quality of asset level data. Details on the analyses conducted are available in the Impact Assessment Methodology Appendix to the Coastal Resilience Master Plan.
Criticality is considered a binary threshold for this assessment, with assets, either included or excluded from the analysis. Future iterations could expand upon the understanding of criticality to consider varying levels of value and importance, which would be incorporated through additional data, analysis, and stakeholder input. Additionally, improved risk quantification for built environment
A standard summarization grid (1,375 ft x 1,375 ft) was used to facilitate comparison of impacts across geography and time. A clustered rank approach, using the k-means clustering algorithm, was applied to the raw impact scores, at each summary grid cell, to understand relative priority areas within each impact type, time horizon and area of interest. Ranks were calculated three times: relative to the entire coastal region, relative to each planning district or regional commission, and relative to each locality. Ranks are calculated by considering raw impact scores across all time horizons.
Notes and Limitations
The natural infrastructure impact approach has several limitations, which could be addressed in future iterations of the CRMP. In reality, beaches and dune systems will respond to SLR in a much more dynamic way than represented in the current impact assessment. For example, beaches and dune systems can migrate landward similar to migrating marshes, given proper sediment supply and adequate room for retreat. These complex and dynamic processes were not accounted for in this assessment, and therefore likely overpredict the loss of these systems in response to SLR.
Similarly, the approach to mapping the conversion and loss of wetland habitat has several limitations. Firstly, it does not incorporate future changes in coastal geomorphology or account for development pressures. Modeling the response of tidal wetlands to sea level rise requires sophisticated calculations that consider multiple factors, including land slope, and sediment accretion, erosion, among others. However, simple land cover change models can identify areas that are vulnerable to habitat damage or loss. The land cover change model assumes that specific types of wetlands can exist with a certain amount of water and salinity. As sea levels rise, low-lying wetlands may become inundated frequently enough that the ecosystem is effectively “lost” to open water. Wetlands at higher elevations may experience more frequent inundation but may be able to migrate landward.
Details on the analyses conducted are available in the Impact Assessment Methodology Appendix to the Coastal Resilience Master Plan.
A standard summarization grid (1,375 ft x 1,375 ft) was used to facilitate comparison of impacts across geography and time. A clustered rank approach, using the k-means clustering algorithm, was applied to the raw impact scores, at each summary grid cell, to understand relative priority areas within each impact type, time horizon and area of interest. Ranks were calculated three times: relative to the entire coastal region, relative to each planning district or regional commission, and relative to each locality. Ranks are calculated by considering raw impact scores across all time horizons.
