Spatial Vulnerability Assessment

Spatial Vulnerability Assessment
across MHI

Lansing Perng, Mariska Weijerman, Kirsten Leong, Lucia Hošeková, and Kirsten L.L. Oleson

Vulnerability Framework
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Figure 1: Conceptual model of vulnerability showing how exposure, sensitivity, and adaptive capacity combine to determine potential impacts and overall vulnerability.

Source: Marshall et al., 2009

We quantified vulnerability using a framework introduced by Marshall et al. (2009), where vulnerability is shaped by exposure, sensitivity, and adaptive capacity. Exposure refers to the extent to which a region, resource, or community experiences stressors. Sensitivity captures the susceptibility to harm from those stressors, including dependence on resource systems affected by climate change. Adaptive capacity represents the ability to cope with stress and recover. Together, these components determine the potential impact and overall vulnerability of ecological and social systems.

To construct indices for the three dimensions of vulnerability, we applied data envelopment analysis (DEA), a method that scores system performance across multiple indicators. DEA is flexible and data-driven, avoiding subjective weighting that can introduce bias by inferring weights directly from the data. Using this approach, we developed composite indices of exposure, sensitivity, and adaptive capacity to assess community vulnerability based on standard indicators. These indices were then combined into an overall vulnerability index. In this framework, higher exposure and sensitivity increase vulnerability, while adaptive capacity reduces it.

Constructing Vulnerability Indices
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Figure 2: Conceptual framework for the vulnerability index, combining exposure, sensitivity, and adaptive capacity into a single measure.
Indicators for Exposure Index
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Figure 3: Indicators from the Atlantis ecosystem model used to construct the exposure index, including ecological (biomass, diversity, coral cover, trophic level, megafauna abundance) and environmental (pH, temperature) projections.

To build the exposure index, we used projections from the Atlantis ecosystem model. Exposure reflects the degree to which reef systems are stressed by climate and ecological change, with higher stress corresponding to higher exposure. Most ecological indicators, such as biomass, diversity, and coral cover, reduce exposure when values are high, while temperature contributes positively, with higher temperatures increasing exposure. Together, these indicators capture how ecological and environmental conditions shape overall exposure across reef systems.

To build the sensitivity index, we used datasets that capture the degree of human dependence on reef resources. Sensitivity reflects how strongly communities are affected when reef conditions change, with higher dependence corresponding to higher sensitivity. Because time series data are not available, this index focuses on spatial variation across the MHI. These indicators provide a snapshot of the economic and social reliance on coral reef ecosystems.

Indicators for Sensitivity Index
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Figure 4: Data sources used to construct the sensitivity index, reflecting community dependence on reef resources through catch, revenue, participation, and site popularity.
Indicators for Adaptive Capacity Index
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Figure 5: Indicators used to construct the adaptive capacity index, grouped by the five sectors.

Source: Cinner et al., 2018

To build the adaptive capacity index, we used the framework introduced by Cinner et al. (2018), where adaptive capacity reflects the ability of communities to cope with stress and adapt to change. Indicators were taken from the American Community Survey and averaged across the most recent five years. Each of the five sectors in the framework was represented by a corresponding metric. For the assets index, income was used. For the flexibility index, occupational diversity was used. For the organization index, internet access was used. For the learning index, educational attainment was used. Agency was represented by English proficiency and the percentage of the population living under the poverty line. In this framework, higher values of the first five indicators are positively associated with adaptive capacity, while the percentage under the poverty line is negatively associated.

Sensitivity index results varied both across and within the MHI. Index values range from low (light yellow) to high (dark red), with values near 1 indicating the highest sensitivity. Communities in the top 10% of scores are labeled. Oʻahu exhibited consistently high sensitivity, with Waianae, Honolulu, Koʻolaupoko, and Waialua standing out due to high reliance on commercial fisheries and high recreational site popularity. Additional highly sensitive communities included Līhuʻe, Wailuku, Spreckelsville, and North Kona, where elevated scores were driven primarily by recreational fishery engagement.

Spatial Distribution of Sensitivity Index Values Across Communities in the MHI
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Figure 6: Communities are shaded from low (yellow) to high (red) sensitivity, with the top 10% of scores labeled.
Spatial Distribution of Adaptive Capacity Index Values Across Communities in the MHI
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Figure 7: Scores range from 0.83 to 1, with darker green indicating higher adaptive capacity. Cities in the lowest 10% of scores are labeled.

Adaptive capacity scores were uniformly high across the MHI, ranging from 0.83 to 1, with the lowest 10% of scores concentrated in Lahaina on Maui and North Kohala, Pāpaʻikou-Wailea, Pāhoa-Kalapana, and Kaʻū on Hawaiʻi Island. Lahaina showed relatively low educational attainment and English proficiency; North Kohala had moderate values across indicators but none particularly high; Pāpaʻikou-Wailea had mostly below-average values aside from high occupational diversity; Pāhoa-Kalapana had the lowest income and highest poverty levels; and Kaʻū exhibited very low income and educational attainment along with high poverty. These results indicate that weaker performance in the assets, learning, and agency indices contributed most to reduced adaptive capacity in these communities.

The exposure index was constructed using Atlantis projections under a high-emission SSP3 climate scenario, comparing initial, mid-century, and end-of-century conditions benchmarked to the baseline. Initial exposure across the main Hawaiian Islands was low. By mid-century, exposure increased in several communities, with higher values concentrated in Maui County. By the end of the century, high-exposure communities were present across all islands except Maui, with values in the top 10% driven by declines in ecosystem integrity metrics, particularly reef fish diversity and biomass of herbivores and targeted fishery species, alongside relatively high temperature increases. Honolulu showed the highest exposure index of all communities by the end of the century.

Spatial Distribution of Exposure Index Values Across Communities in the MHI Under SSP3
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Figure 8: Index values are shown for initial, mid-century, and end-of-century conditions under SSP3, benchmarked to the baseline. Labeled communities represent the top 10% of exposure scores.
End-of-Century Exposure Index Values in the MHI Under SSP2
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Figure 9: Index values range from low (yellow) to high (red), with scores approaching 1 indicating the greatest exposure. Labeled communities represent those in the top 10% of index values, highlighting locations projected to face the highest climate-related stress by 2100.

Comparing end-of-century exposure across climate scenarios shows notable differences. Under SSP2, Honolulu and Kaʻū are no longer among the most highly exposed communities, while ʻEwa on Oʻahu and South Kona on Hawaiʻi Island remain in the top 10% of exposure index scores.

Under SSP1, exposure remained relatively low across the main Hawaiian Islands, with South Kona standing out as the only community to remain in the top 10% of scores across all climate scenarios.

End-of-Century Exposure Index Values in the MHI Under SSP1
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Figure 10: Index values range from low (yellow) to high (red). South Kona is the only community that remained in the top 10% of exposure scores across all climate scenarios.
Spatial Distribution of Vulnerability Index Values Across Communities in the MHI Under All SSP Scenarios
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Figure 11: Index values integrate exposure, sensitivity, and adaptive capacity, benchmarked for cross-scenario comparison. Labeled communities highlight those with the highest vulnerability scores at the end of the century.

We combined the exposure, sensitivity, and adaptive capacity indices to construct a vulnerability index that can be projected under alternative climate scenarios. Because exposure values were benchmarked, vulnerability can be directly compared across scenarios, and results show a clear increase in vulnerability with climate change intensity. In SSPs 1 and 2, South Kona and Waiʻanae were among the most vulnerable communities. South Kona’s consistently high exposure across all scenarios explains its elevated vulnerability in SSPs 1 and 2, although lower sensitivity buffered its score in SSP3 as more sensitive regions became highly exposed. Waiʻanae, which had the maximum sensitivity score and moderate exposure, showed high vulnerability in SSPs 1 and 2 but only moderate vulnerability in SSP3 as its exposure level did not increase substantially. In SSPs 2 and 3, ʻEwa and Honolulu emerged as the most vulnerable communities. ʻEwa’s combination of high exposure and above-average sensitivity produced consistently high vulnerability, while Honolulu’s vulnerability peaked in SSP3, where it combined maximum sensitivity with very high exposure.

Different communities exhibited high index scores across the three dimensions of vulnerability. Sensitivity was highest on Oʻahu, driven by high commercial fishery engagement and ocean recreation. Adaptive capacity was generally high across the MHI, though communities on Hawaiʻi Island were at the lower end of the range. Exposure increased over time, with the highest values observed under SSP3, and differences among scenarios were substantial. Together, these results demonstrate how combining exposure, sensitivity, and adaptive capacity provides an integrated assessment of vulnerability that reveals outcomes not captured by individual indices.

Urban communities such as Honolulu and ʻEwa, despite greater resources, showed high sensitivity due to reliance on marine resources and may benefit from strategies that reduce exposure. Rural communities generally had lower adaptive capacity, highlighting the need for capacity-building support. Four communities remained vulnerable across two of the three climate scenarios, with SSP3 consistently producing the highest scores. These spatially resolved indices provide a basis for targeted, scenario-informed adaptation, helping managers prioritize resources where they can be most effective.


Resources

Code Repository

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Presentation Slides

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