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Modeling sea-level rise vulnerability of coastal environments using ranked management concerns


Coastal erosion, salt-water intrusion, and flooding due to sea-level rise threaten to degrade critical coastal strand and wetland habitats. Because habitat loss is a measure of the risk of extinction, managers are keen for guidance to reduce risk posed by sea-level rise. Building upon standard inundation mapping techniques and suitability mapping, we develop a ranking system that models sea-level rise vulnerability as a function of six input parameters defined by wetland experts: type of inundation, time of inundation, soil type, habitat value, infrastructure, and coastal erosion. We apply this model under the mid-century and end-of-century RCP8.5 sea-level projection (0.30 m by 2057, and 0.74 m by 2100) according to the Intergovernmental Panel on Climate Change Fifth Assessment Report. To demonstrate this method, the model is applied to three coastal wetlands on the Hawaiian islands of Maui and O‘ahu. Each ranked input parameter is mapped upon a 2 m horizontal resolution raster and final vulnerability is obtained by calculating the weighted geometric mean of the input vulnerability scores. Areas that ranked with the ‘highest’ vulnerability should be the focus of future management efforts. The tools developed in this study can be a guide to prioritize conservation actions at flooded areas and initiate decisions to adaptively manage sea-level rise impacts.

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This project was supported by the U.S. Department of Interior Pacific Islands Climate Change Cooperative grant # 6661281, the School of Ocean Earth Science and Technology, and the Native Hawaiian Student Engineering Mentorship Program.

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Correspondence to Haunani H. Kane.

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Online Resource 1
figure 3

Example vulnerability maps for Kanaha State Wildlife Refuge. Vulnerability is defined and high confidence areas (80 % probability of flooding) are mapped for six input parameters; type of inundation (a), time of inundation (b), habitat value (c), soil type (d), infrastructure (e), and coastal erosion (f). Input parameter vulnerability maps are combined (g) and areas of the highest vulnerability (red and yellow) are identified as a subset of the total area inundated at 0.74 m by 2100 (blue). High vulnerability areas are mapped at high (80% probability of flooding) and low confidence (50%). (GIF 314 kb)

Online Resource 2
figure 4

Example vulnerability maps for James Campbell National Wildlife Refuge. Vulnerability is defined and high confidence areas (80 % probability of flooding) are mapped for six input parameters; type of inundation (a), time of inundation (b), habitat value (c), soil type (d), infrastructure (e), and coastal erosion (f). Input parameter vulnerability maps are combined (g) and areas of the highest vulnerability (red and yellow) are identified as a subset of the total area inundated at 0.74 m by 2100 (blue). High vulnerability areas are mapped at high (80% probability of flooding) and low confidence (50%). (GIF 357 kb)

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Kane, H.H., Fletcher, C.H., Frazer, L.N. et al. Modeling sea-level rise vulnerability of coastal environments using ranked management concerns. Climatic Change 131, 349–361 (2015).

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  • High Vulnerability
  • LiDAR Data
  • Wetland Habitat
  • Hydric Soil
  • Vulnerability Score