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

Climatic Change volume 131, pages 349–361 (2015)Cite this article

Abstract

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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References

  • Baker JD, Littnan CL, Johnston DW (2006) Potential effects of sea level rise on the terrestrial habitats of endangered and endemic megafauna in the Northwestern Hawaiian Islands. Endanger Species Res 2:21–30. doi:10.3354/esr002021

    Article  Google Scholar 

  • Bellard C, Lederc C, Courchamp F (2013) Impact of sea level rise on the 10 insular biodiversity hotspots. Glob Ecol Biogeogr 23:203–212. doi:10.1111/geb.12093

    Article  Google Scholar 

  • Bruland GL (2008) Coastal wetlands: function and role in reducing impact of land-based management. In: Fares A, El-Kadi AI (eds) Coastal watershed management. WIT Press, Southhampton, pp 85–124

    Chapter  Google Scholar 

  • Bruun P (1962) Sea level rise as a cause of shoreline erosion. Proceedings of the American Society of Civil Engineers. J Waterways Harbors Div 88:117–130

  • Church JA, Clark PU, Cazenave A et al (2013) Sea level change. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group 1 to the fifth assessment of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

  • Cooper H, Chen Q (2013) Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment: a case study in Kaului, Maui. Clim Chang 121:635–647. doi:10.1007/s10584-013-0987-x

    Article  Google Scholar 

  • Cooper JAG, Pilkey OH (2004) Sea level rise and shoreline retreat: time to abandon the Bruun Rule. Glob Planet Chang 43:157–171. doi:10.1016/j.gloplacha.2004.07.001

    Article  Google Scholar 

  • Cooper H, Chen Q, Fletcher CH, Barbee MM (2013a) Assessing vulnerability due to sea-level rise in Maui, Hawaii. Clim Chang 116:547–563. doi:10.1007/s10584-012-0510-9

    Article  Google Scholar 

  • Cooper HM, Chen Q, Fletcher CH, Barbee M (2013b) Sea-level rise vulnerability mapping for adaptation decisions using LiDAR DEMs. Prog Phys Geogr. doi:10.1177/0309133313496835

    Google Scholar 

  • Donner SD, Webber S (2014) Obstacles to climate change adaptation decisions: a case study of sea-level rise and coastal protection measures in Kiribati. Sustain Sci 9:331–345. doi:10.1007/s11625-014-0242-z

    Article  Google Scholar 

  • Ellison JC (2009) Wetlands of the pacific island region. Wetl Ecol Manag 17:169–206. doi:10.1007/s11273-008-9097-3

    Article  Google Scholar 

  • Fletcher CH, Romine BM, Genz AS, Barbee MM, Dyer M, Anderson TR, Lim SC, Vitousek S, Bochicchio C, Richmond BM (2013) National Assessment of Shoreline Change: Historical Shoreline Change in the Hawaiian Islands: U.S. Geological Survey Open-File Report 2011-1051, 55 p. (http://pubs.usgs.gov/of/2011/1051)

  • Fuentes MMPB, Cinner JE (2010) Using expert opinion to prioritize impacts of climate change on sea turtles. J Environ Manag 9:2511–2518. doi:10.1016/j.jenvman.2010.07.013

    Article  Google Scholar 

  • Galbraith H, Jones R, Park R, Clough J, Herrod-Julius S, Harrington B, Page G (2002) Global climate change and sea level rise: potential losses of intertidal habitat for shorebirds. Waterbirds 25:173–183. doi:10.1675/1524-4695(2002)025[0173:GCCASL]2.0.CO;2

    Article  Google Scholar 

  • Gedan KB, Kirwan ML, Wolanski E, Barbier EB, Silliman BR (2011) The present and future role of coastal wetland vegetation in protecting shorelines: answering recent challenges to the paradigm. Clim Chang 106:7–29. doi:10.1007/s10584-010-0003-7

    Article  Google Scholar 

  • Gesch D (2013) Consideration of vertical uncertainty in elevation-based sea-level rise assessments: Mobile Bay, Alabama case study. J Coast Res 63:197–210. doi:10.2112/SI63-016.1

    Article  Google Scholar 

  • Halpern BS, Selkoe KA, Fiorenza M, Kappel CV (2007) Evaluating and ranking vulnerability of global marine ecosystems to anthropogenic threats. Conserv Biol 21:1301–1315. doi:10.1111/j.1523-1739.2007.00752.x

    Article  Google Scholar 

  • Hameed SO, Holzer KA, Doerr AN, Baty JH, Schwartz MW (2013) The value of a multi-faceted climate change vulnerability assessment to managing protected lands: Lessons from a case study in Point Reyes National Seashore. J Environ Manag 121:37–47. doi:10.1016/j.jenvman.2013.02.034

    Article  Google Scholar 

  • Hands EB (1979) Changes in rates of shore retreat, Lake Michigan, 1967–1976. Coastal Engineering Research Center, Technical Memorandum No. 79–4. (71 pp.)

  • Horton BPS, Rahmstorf R, Engelhart SE, Kemp AC (2014) Expert assessment of sea-level rise by AD 2100 and AD 2300. Quat Sci Rev 84:1–6. doi:10.1016/j.quascirev.2013.11.002

    Article  Google Scholar 

  • Hossell JE, Ellis NE, Harley MJ, Hepburn IR (2003) Climate change and nature conservation: Implications for policy and practice in Britain and Ireland. J Nat Conserv 11:67–73. doi:10.1078/1617-1381-00034

    Article  Google Scholar 

  • Intergovernmental Panel on Climate Change (IPCC) (2013) Climate Change 2013 The Physical Science Basis. In: Stocker TF, Qin D, Plattner G-K, Tignor MMB, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds), Working Group 1 Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Available online at: http://www.climatechange2013.org/images/report/WG1AR5_Frontmatter_FINAL.pdf. Accessed 18 August, 2014

  • Kirwan ML, Guntenspergen GR, D’Alpaos A, Morris JT, Mudd SM, Temmerman S (2010) Limits on the adaptability of coastal marshes to rising sea level. Geophys Res Lett 37:L23401. doi:10.1029/2010GL045489

    Article  Google Scholar 

  • Kopp RE, Horton RM, Little CM, Mitrovica JX, Oppenhelmer M, Rasmussen DJ, Strauss BH, Tebaldi C (2014) Probabilistic 21st and 22nd century-level projections at a global network of tide-gauge sites. Earth’s Future. doi:10.1002/2014EF000239

    Google Scholar 

  • Kuan WK, Jin G, Xin P, Robinson C, Bibbes B, Li L (2012) Tidal influence on seawater intrusion in unconfined coastal aquifers. Water Resour Res 48:1–11. doi:10.1029/2011WR010678

  • Mimura N, Nobuoka H (1995) Verification of the Bruun Rule for the estimate of shoreline retreat caused by sea-level rise. In: Dally ER, Zeidler RB (eds) Coastal dynamics 95. American Society of Civil Engineers, New York, pp 607–616

    Google Scholar 

  • Mitsova D, Esnard A, Li Y (2012) Using dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments. J Coast Conserv 16:355–372. doi:10.1007/s11852-012-0206-3

    Article  Google Scholar 

  • Mora C, Frazier AG, Longman RJ et al (2013) The projected timing of climate departure from recent variability. Nature 502:183–187. doi:10.1038/nature12540

    Article  Google Scholar 

  • National Oceanic Atmospheric Administration (NOAA) 2010 Mapping inundation uncertainty. http://csc.noaa.gov/digitalcoast/_/pdf/ElevationMappingConfidence.pdf. Accessed 13 March 2014

  • Norcross ZM, Fletcher CH, Merrified M (2002) Annual and interannual changes on a reef-fringed pocket beach: Kailua Bay, Hawaii. Mar Geol 190:553–580. doi:10.1016/S0025-3227(02)00481-4

    Article  Google Scholar 

  • Poulter B, Haplin PN (2008) Raster modeling of coastal flooding from sea-level rise. Int J Geogr Inf Sci 22:167–182. doi:10.1080/13658810701371858

    Article  Google Scholar 

  • Richardson MS, Gatti RC (1999) Prioritizing wetland restoration activity within a Wisconsin watershed using GIS modeling. J Soil Water Conserv 54:537–542

    Google Scholar 

  • Romine BM, Fletcher CH, Barbee MM, Anderson TR, Frazier LN (2013) Are beach erosion rates and sea-level rise related in Hawaii? Glob Planet Chang 108:149–157. doi:10.1016/j.gloplacha.2013.06.009

    Article  Google Scholar 

  • Rotzoll K, Fletcher CH (2012) Assessment of groundwater inundation as consequences of sea level rise. Nat Clim Chang 3:477–481. doi:10.1038/nclimate1725

    Article  Google Scholar 

  • Selkoe KA, Halpern BS, Toonen RJ (2008) Evaluating anthropogenic threats to Northwestern Hawaiian Islands. Aquat Conserv Mar Freshwat Ecosyst 18:1149–1165. doi:10.1002/aqc.961

    Article  Google Scholar 

  • Semlitsch RD, Bodie JR (1998) Are small, isolated wetlands expendable? Conserv Biol 12:1129–1133. doi:10.1046/j.1523-1739.1998.98166.x

    Article  Google Scholar 

  • Slangen ABA, Katsman CA, van de Wal RSW, Vermeersen LLA, Riva REM (2012) Towards regional projections of the twentyfirst century sea-level change based on IPCC SRES scenarios. Clim Dyn 38:1191–1201. doi:10.1007/s00382-011-1057-6

  • Spada G, Bamber JL, Hurkmans RTWL (2013) The gravitationally consistent sea-level fingerprint of future terrestrial ice lost. Geophys Res Lett 40:482–486. doi:10.1029/2012GL053000

  • Tebaldi C, Strauss BH, Zervas CE (2012) Modelling sea level rise impacts on storm surges along US coasts. Environ Res Lett 7:014032. doi:10.1088/1748-9326/7/1/014032

    Article  Google Scholar 

  • Thieler ER, Pilkey OH Jr, Young RS, Bush DM, Chai F (2000) The use of mathematical models to predict beach behavior for US coastal engineering: a critical review. J Coast Res 16:48–70

    Google Scholar 

  • U.S. Fish and Wildlife Service, 2011a. James Campbell National Wildlife Refuge Comprehensive Conservation Plan and Environmental Assessment. U.S. Fish and Wildlife Service, Haleiwa, Hawaii. Available from http://www.fws.gov/uploadedFiles/Region_1/NWRS/Zone_1/Oahu_Complex/James_Campbell/Documents/James%20Campbell%20NWR%20CCP%20%28final%29%2012-01-11.pdf (accessed March 2014)

  • U.S. Fish and Wildlife Service (2011b) Kealia pond national wildlife refuge comprehensive conservation plan and envrionmental assessment. U.S. Fish and Wildlife Service, Kihei

    Google Scholar 

  • Van Lonkhuyzen RA, Lagory KE, Kuiper JA (2004) Modeling the suitability of potential wetland mitigation sites with a geographic information system. Environ Manag 33:368–375. doi:10.1007/s00267-003-3017-3

    Article  Google Scholar 

  • Wetzel FT, Kissling WD, Beissmann H, Penn DJ (2012) Future climate change driven sea-level rise: secondary consequences from human displacement for island biodiversity. Glob Chang Biol 18:2707–2719. doi:10.1111/j.1365-2486.2012.02736.x

    Article  Google Scholar 

  • White D, Fennessy S (2005) Modeling the suitability of wetland restoration potential at the watershed scale. Ecol Eng 24:359–377. doi:10.1016/j.ecoleng.2005.01.012

    Article  Google Scholar 

  • Zhang K, Douglas BC, Leatherman SP (2004) Global warming and coastal erosion. Clim Chang 64:41–58. doi:10.1023/B:CLIM.0000024690.32682.48

    Article  Google Scholar 

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Acknowledgments

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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Authors and Affiliations

  1. SOEST/Geology and Geophysics, University of Hawai‘i, 1680 East West Road, Honolulu, HI, 96822, USA

    Haunani H. Kane, Charles H. Fletcher, L. Neil Frazer, Tiffany R. Anderson & Matthew M. Barbee

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  1. Haunani H. Kane
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  2. Charles H. Fletcher
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  3. L. Neil Frazer
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  5. Matthew M. Barbee
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Corresponding author

Correspondence to Haunani H. Kane.

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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)

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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). https://doi.org/10.1007/s10584-015-1377-3

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Keywords

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