1

U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2012–1182

Predicting Sea-Level Rise Vulnerability of

Terrestrial Habitat and Wildlife of the

Northwestern Hawaiian Islands
COVER

Photographs: 

Black Noddy, Anous minutus marcusi, and chick (by T. Speetjens, U.S. Fish and Wildlife Service); 

Red-footed Booby, Sula sula rubripes (by C. Cornett, U.S. Fish and Wildlife Service); 

Brown Booby, Sula leucogaster plotus, and chick (by T. Speetjens, U.S. Fish and Wildlife Service); 

Great Frigatebird, Fregata minor palmerstoni (by W. Taylor, U.S. Fish and Wildlife Service); and 

Laysan Teal, Anas laysanensis (by A. Boyd, U.S. Fish and Wildlife Service). 

Map: Topographic image of Laysan Island (PhotoStat copyrighted image, used with permission).
Open-File Report 2012–1182

U.S. Department of the Interior

U.S. Geological Survey

Predicting Sea-Level Rise Vulnerability

of Terrestrial Habitat and Wildlife of the

Northwestern Hawaiian Islands

Edited by Michelle H. Reynolds, Paul Berkowitz, Karen N. Courtot, and Crystal M. Krause
ii

U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

This report and any updates to it are available online at:

http://pubs.usgs.gov/of/2012/1182/

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copyright owners to reproduce any copyrighted materials contained within this report.

Suggested citation:

Reynolds, M.H., Berkowitz, P., Courtot, K.N., and Krause, C.M., eds., 2012, Predicting sea-level 

rise vulnerability of terrestrial habitat and wildlife of the Northwestern Hawaiian Islands: U.S. 

Geological Survey Open-File Report 2012–1182, 139 p.
    iii

Contents

Executive Summary........................................................................................................1

Chapter 1—Climate Change Vulnerability Assessment of the 

 Low-Lying Northwestern Hawaiian Islands

Introduction .............................................................................................................3

Materials and Methods............................................................................................5

Study Area ......................................................................................................5

Remote Sensing Data......................................................................................8

Climate Data..................................................................................................10

Sea-Level Rise ..............................................................................................10

Land Cover Classification ..............................................................................11

Land Cover Change.......................................................................................13

Avifauna Nesting Habitat Classification .........................................................13

Uncertainty and Model Assumptions .............................................................13

Results ..................................................................................................................15

Kure Atoll .......................................................................................................15

Midway Atoll...................................................................................................24

Pearl and Hermes Atoll..................................................................................37

Lisianski Island ..............................................................................................51

Laysan Island.................................................................................................56

French Frigate Shoals....................................................................................62

Discussion.............................................................................................................69

Acknowledgments.................................................................................................71

Chapter 2—Sea-Level Rise and Wave-Driven Inundation Models for Laysan Island 

Introduction ...........................................................................................................72

Methods ................................................................................................................75

 Storm Wave Models.......................................................................................75

Primary Wave Sources ..................................................................................75

Elevation and Bathymetry ..............................................................................75

Modeled Water Surfaces and Wave Run-up ..................................................79

Coral-Reef Accretion .....................................................................................79

Estimating Mean Sea Level ...........................................................................82

GIS Analysis ..................................................................................................82

Hydrogeology.................................................................................................82

Land Cover and Habitat Loss ........................................................................83

Nesting Habitat and Distribution ....................................................................83

Nesting Phenology and Temporal Overlap with Storm Season .....................84

Breeding Bird Abundance..............................................................................84

Wildlife Population Dynamics in Response to SLR........................................85

Uncertainty in SLR Models for Laysan Island................................................85

Limitations of Wildlife Data.............................................................................87
iv

Results ..................................................................................................................87

Sea-Level Rise and Wave-Driven Water Levels..............................................87

Passive and Dynamic Inundation ...................................................................88

Land Cover on Laysan Island.........................................................................99

Inundation by Land Cover Class ....................................................................99

Impacts of SLR on Nesting Habitat .............................................................101

Vulnerability of Breeding Birds to Storm Conditions.....................................120

Population Dynamics, Carrying Capacity, and Population Vulnerability........122

Discussion...........................................................................................................122

Systematic Underestimation of Vulnerability from Passive Models ..............122

Additional Increments of SLR.......................................................................122

Chronological Implications of Dynamic Inundation Models ..........................122

Frequency of Inundation Events ...................................................................123

Future Geomorphology ................................................................................123

Vulnerability to Outlier Storm Events ...........................................................123 

Succession of Vegetation .............................................................................124

Thresholds of Vulnerability for Nesting Habitat.............................................124

Implications of SLR for Laysan’s Wildlife Populations...................................124

Future Research Directions..........................................................................125

Conclusions.........................................................................................................125

Acknowledgments ...............................................................................................126

References Cited........................................................................................................127

Appendix 1—USGS lidar data collection specifications .........................................136

Appendix 2—General circulation model data downscaled for analysis 

 of predicted temperature and precipitation ......................................................137

Appendix 3—Nesting descriptions of land birds and seabirds ............................137

Figures

1.1. Global mean sea level between 1850 and 2100 .................................................3

1.2. Map of the Hawaiian Archipelago........................................................................5

1.3. Schematic showing the lidar data collection process..........................................9

1.4. Map showing the location of Kure Atoll .............................................................19

1.5. Green Island, Kure Atoll, land cover classification map ....................................20

1.6. Topographic map of Green Island, Kure Atoll....................................................21

1.7. Green Island, Kure Atoll, passive inundation scenario maps ............................22

1.8. Maps showing the location of Midway Atoll.......................................................24

1.9. Sand Island, Midway Atoll, land cover classification .........................................26

1.10. Spit Island, Midway Atoll, land cover classification map....................................27

1.11. Eastern Island, Midway Atoll, land cover classification map .............................28

1.12. Topographic map of Sand Island, Midway Atoll.................................................29

1.13. Sand Island, Midway Atoll, passive inundation scenario maps .........................30

1.14. Topographic map of Spit Island, Midway Atoll...................................................32

1.15. Spit Island, Midway Atoll, passive inundation scenario maps ...........................33

1.16. Topographic map of Eastern Island, Midway Atoll.............................................35

1.17. Eastern Island, Midway Atoll, passive inundation scenario maps.....................36
  v

1.18. Maps showing the location of Pearl and Hermes Atoll......................................37

1.19. North Island, Pearl and Hermes Atoll, land cover classification map................38

1.20. Southeast Island, Pearl and Hermes Atoll, land cover classification map.........39

1.21. Seal-Kittery Island, Pearl and Hermes Atoll, land cover classification map ......40

1.22. Topographic map of North Island, Pearl and Hermes Atoll ...............................43

1.23. Topographic map of Little North Island, Pearl and Hermes Atoll.......................44

1.24. Topographic map of Southeast Island, Pearl and Hermes Atoll ........................45

1.25. Topographic map of Grass Island, Pearl and Hermes Atoll...............................46

1.26. Topographic map of Seal-Kittery Island, Pearl and Hermes Atoll......................47

1.27. Pearl and Hermes Atoll passive inundation scenario maps ..............................48

1.28. Maps showing the location of Lisianski Island ..................................................51

1.29. Lisianski Island land cover classification map. ..................................................52

1.30. Topographic map of Lisianski Island .................................................................53

1.31. Lisianski Island passive inundation scenario maps...........................................54

1.32. Maps showing location of Laysan Island...........................................................56

1.33. Laysan Island land cover classification map .....................................................57

1.34. Topographic map of Laysan Island....................................................................58

1.35. Laysan Island passive inundation scenario maps.............................................59

1.36. Laysan Island passive inundation scenario maps for five sea levels.................60

1.37. Topographic map of Tern Island, French Frigate Shoals ...................................63

1.38. Topographic map of Trig Island, French Frigate Shoals.....................................63

1.39. Topographic map of East Island, French Frigate Shoals...................................64

1.40. Topographic map of Gin Island, French Frigate Shoals.....................................65

1.41. Topographic map of Little Gin Island, French Frigate Shoals............................66

1.42. Topographic map of Shark, Round and Disappearing Islands 

of French Frigate Shoals.................................................................................67

1.43. French Frigate Shoals passive inundation scenario maps................................68

2.1. Observed trends in sea level between 1993 and 2010 .....................................73

2.2. Schematic diagram showing the variation in waves and 

resulting wave-driven run-up at two sea levels .............................................74

2.3. Schematic diagram comparing (1) GIS-based passive 

inundation models with (2) dynamic wave-driven inundation models ...........76

2.4. Time series showing monthly variations in Pacific Ocean wave heights, 

wave periods, and wind speeds ...................................................................77

2.5. Compass plot of variation in wave height in meters ..........................................78

2.6. Compass plot of variation in wave period in seconds .......................................78

2.7. Compass plot of variation in wind speed in meters per second ........................79

2.8. Seamless 100-meter resolution bathymetric grid of Laysan Island...................80

2.9. Seamless 20-meter resolution bathymetric and 

topographic grid of Laysan Island .................................................................81

2.10. Conceptual model describing how global climate change may 

influence the environment and avifauna of Laysan Island ..............................86

2.11. Map of Laysan Island depicting modeled wave-driven water levels ..................88

2.12. Overlay of passive inundation zones for five sea-level rise scenarios 

at Laysan Island..............................................................................................90

2.13. Overlay of wave-driven inundation for five sea-level rise scenarios 

at Laysan Island..............................................................................................91
vi

2.14. Chart of inundation extent in hectares at Laysan Island 

for five sea-level rise scenarios.......................................................................92

2.15. Map depicting potential inundation extent at Laysan Island 

under a sea-level rise scenario. ......................................................................93

2.16. Map depicting potential inundation extent at Laysan Island under 

a sea-level rise scenario.................................................................................94

2.17. The inundation pattern documented by U.S. Fish and Wildlife 

Service field staff on Laysan Island ................................................................95

2.18. Overlay of wave-driven inundation extents on Laysan Island............................96

2.19. Overlay of passive inundation zones on Laysan Island for five 

sea-level rise scenarios ..................................................................................97

2.20. Overlay of wave-driven inundation including potential inundation from rising 

groundwater on Laysan Island for five sea-level rise scenarios......................98

2.21. Land cover classes for Laysan Island .............................................................100

2.22. Overlay of Black-footed Albatross nesting habitat at Laysan Island................105

2.23. Overlay of Laysan Albatross nesting habitat at Laysan Island........................106

2.24. Overlay of Masked Booby and Brown Booby nests mapped 

on Laysan Island...........................................................................................108

2.25. Overlay of the shrub-nesting areas for Red-footed Booby and 

Great Frigatebird at Laysan Island................................................................111

2.26. Overlay of Sooty Tern colonies at Laysan Island.............................................113

2.27. Overlay of Laysan Teal and Laysan Finch nesting habitat at Laysan Island ...115

2.28. Breeding phenology of 20 birds at Laysan Island ...........................................117

2.29. Population abundance graphs for five species ...............................................121

Tables

1.1. Known breeding distribution of avifauna, endangered plants, 

and other wildlife in the Northwestern Hawaiian Islands.................................6

1.2. Northwestern Hawaiian Islands airborne lidar data collection information..........9

1.3. Aerial imagery sources and collection dates.....................................................10

1.4. Climate change general circulation models ......................................................10

1.5. National Oceanic and Atmospheric Administration tide values.........................11

1.6. Land cover class definitions with plant species for each class..........................12

1.7. Land cover classes identified for the low-lying Northwestern 

Hawaiian Islands from satellite imagery........................................................13

1.8. Known avifauna nesting habitat by land cover class ..........................................14

1.9. Mean and maximum elevations of Northwestern Hawaiian Islands ..................16

1.10. Global breeding distribution for species of the 

Northwestern Hawaiian Islands ......................................................................17

1.11. Conservation status assessments and population counts of 

species breeding in the Northwestern Hawaiian Islands ...............................18

1.12. Predicted average annual maximum temperature and total precipitation .........19

1.13. Total land area and percent change in area for each of six land cover types at 

Green Island, Kure Atoll..................................................................................23

1.14. Total potential nesting habitat area and percent change in 

nesting habitat area for breeding avifauna at Green Island, Kure Atoll...........23
    vii

1.15. Total land area and percent change in area for each of ten land cover 

types at Sand Island, Midway Atoll .................................................................25

1.16. Total land area and percent change in area for each of six land cover 

types at Spit Island, Midway Atoll ...................................................................25

1.17. Total land area and percent change for each of five land cover 

types at Eastern Island, Midway Atoll .............................................................25

1.18. Total potential nesting habitat area and percent change in nesting 

habitat area for breeding avifauna at Sand Island, Midway Atoll ....................31

1.19. Total potential nesting habitat area and percent change in nesting 

habitat area for breeding avifauna at Spit Island, Midway Atoll.......................31

1.20. Total potential nesting habitat area and percent change for breeding 

avifauna at Eastern Island, Midway Atoll, .......................................................34

1.21. Total land area and percent change for each of four land cover 

types at North Island, Pearl and Hermes Atoll................................................41

1.22. Total land area and percent change for each of two land cover 

types at Little North Island, Pearl and Hermes Atoll .......................................41

1.23. Total land area and percent change for each of six land cover 

types at Southeast Island, Pearl and Hermes Atoll.........................................41

1.24. Total land area and percent change for each of three land cover 

types at Grass Island, Pearl and Hermes Atoll ...............................................42

1.25. Total land area and percent change for each of four land cover 

types at Seal-Kittery Island, Pearl and Hermes Atoll......................................42

1.26. Total potential nesting habitat area and percent change for 

breeding avifauna at North Island, Pearl and Hermes Atoll............................49

1.27. Total potential nesting habitat area and percent change for 

breeding avifauna at Little North Island, Pearl and Hermes Atoll ...................49

1.28. Total potential nesting habitat area and percent change for 

breeding avifauna at Southeast Island, Pearl and Hermes Atoll.....................49

1.29. Total potential nesting habitat area and percent change for 

breeding avifauna at Grass Island, Pearl and Hermes Atoll ...........................50

1.30. Total potential nesting habitat area and percent change for 

breeding avifauna at Seal-Kittery Island, Pearl and Hermes Atoll ..................50

1.31. Total land area and percent change for each of five land cover 

types at Lisianski Island..................................................................................51

1.32. Total potential nesting habitat area and percent change for 

breeding avifauna at Lisianski Island ..............................................................55

1.33. Total potential nesting habitat area and percent change for 

breeding avifauna at Laysan Island ...............................................................61

2.1. Recent published estimates of sea-level rise (in meters) by the end 

of the 21st century. .........................................................................................73

2.2. Inundation extent for five sea-level scenarios and three 

modeling approaches .....................................................................................89

2.3. Area for each land cover class on Laysan Island, including percentage of 

total terrestrial area and percentage of total island area. ...............................99

2.4. Inundated areas and percent change for Laysan Island, tallied by 

land cover class for five sea-level rise scenarios .........................................101

2.5. Inundation of 2009–2010 nesting habitat at Laysan Island for 

Black-footed Albatross and Laysan Albatross...............................................104
viii

2.6. Inundation of 2009 nest sites at Laysan Island for Masked Booby 

and Brown Booby..........................................................................................107

2.7. Inundated 2008 nesting habitat at Laysan Island for the Red-footed 

Booby and Great Frigatebird.........................................................................110

2.8. Inundated colony areas at Laysan Island for Sooty Tern...................................112

2.9. Inundated nesting habitat at Laysan Island for Laysan 

Teal and Laysan Finch..................................................................................114

2.10. Life-history traits for 20 avian species on Laysan Island.................................116

2.11. Population parameter estimates for the Gompertz model ..............................120

Conversion Factors and Datum

SI to Inch/Pound

Multiply By To obtain

Area

square meter (m2

) 0.0002471 acre

hectare (ha) 2.471 acre

square kilometer (km2

) 247.1 acre

hectare (ha) 0.003861 square mile (mi2

)

square kilometer (km2

) 0.3861 square mile (mi2)

Length

millimeter (mm) 0.03937 inch (in)

centimeter (cm) 0.3937 inch (in)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Rate

millimeter per year (mm/yr) 0.03937 inch per year (in/yr)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=(1.8×°C)+32

Vertical coordinate information is referenced to local mean sea level.

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Altitude, as used in this report, refers to distance above the vertical datum.

Universal Transverse Mercator (UTM), zones 1–3, is the coordinate system.

Elevation data were provided relative to mean sea level (MSL) in meters. 
    ix

Abbreviations and Acronyms

AR4 Fourth Assessment Report, Intergovernmental Panel on Climate Change

BCC Birds of Conservation Concern

BFAL Black-footed Albatross

BRBO Brown Booby

Deltares Dutch Institute for Delta Technology

DEM Digital elevation model

DLNR Hawai‘i Department of Land and Natural Resources

GCM General circulation model

GPS Global Positioning System

GRFR Great Frigatebird

GRS 80 Geodetic Reference System 1980

HTDP Horizontal Time Dependent Positioning

HWM High-water mark

IMU Inertial measurement unit

IPCC Intergovernmental Panel on Climate Change

ITRF00 International Terrestrial Reference Frame of 2000

IUCN International Union for Conservation of Nature

LAAL Laysan Albatross

LADU Laysan Teal

LAFI Laysan Finch

lidar Light detection and ranging

loran Long-range aide to navigation

MABO Masked Booby

MHW Mean high water

MIC Mean incubation count

MLLW Mean lower low water

MLW Mean low water

NGS National Geodetic Survey

NOAA National Oceanic and Atmospheric Administration

NWHI Northwestern Hawaiian Islands

NWR National wildlife refuge

OPUS Online positioning user service

PDOP Positional dilution of precision

PIBHMC Pacific Island Benthic Habitat Mapping Center

PMNM Papahānaumokuākea Marine National Monument

RFBO Red-footed Booby

RMSE Root-mean-squared error

RTK Real-time kinematic

SD Standard deviation

SLOSH Sea, Lake, and Overland Surges from Hurricanes

SLR Sea-level rise

SOTE Sooty Tern

TIN Triangulated irregular network

UNESCO United Nations Educational Scientific and Cultural Organization

USACE U.S. Army Corps of Engineers

USFWS U.S. Fish and Wildlife Service

USGS U.S. Geological Survey

WGS 84 World Geodetic System 1984
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Predicting Sea-Level Rise Vulnerability

of Terrestrial Habitat and Wildlife of the

Northwestern Hawaiian Islands

Wildlife, storm, and oceanographic data allowed for an assessment of the phenological and spatial vulnerability of Laysan 

Island’s breeding bird species to SLR and storms. 

Using remote sensing and geospatial techniques, we 

estimated topography, classified vegetation, modeled SLR, and 

evaluated a range of climate change scenarios. On the basis 

of high-resolution airborne data collected during 2010−11 

(root-mean-squared error = 0.05–0.18 m), we estimated the 

maximum elevation of 20 individual islands extending from 

Kure Atoll to French Frigate Shoals (range: 1.8–39.7 m) 

and computed the mean elevation (1.7 m, standard deviation 

1.1m) across all low-lying islands. We also analyzed general 

climate models to describe rainfall and temperature scenarios 

expected to influence adaptation of some plants and animals 

for this region. Outcomes for the NWHI predicted an increase 

in temperature of 1.8–2.6 degrees Celsius (°C) and an annual 

decrease in precipitation of 24.7–76.3 millimeters (mm) across 

the NWHI by 2100. 

Our models of passive SLR (excluding wave-driven 

effects, erosion, and accretion) showed that approximately 4 

percent of the total land area in the NWHI will be lost with 

scenarios of +1.0 m of SLR and 26 percent will be lost with 

+2.0 m of SLR. Some atolls are especially vulnerable to SLR. 

For example, at Pearl and Hermes Atoll our analysis indicated 

substantial habitat losses with 43 percent of the land area 

inundated at +1.0 m SLR and 92 percent inundated at +2.0 

m SLR. Across the NWHI, seven islands will be completely 

submerged with +2.0 m SLR. The limited global ranges of 

some tropical nesting birds make them particularly vulnerable to climate change impacts in the NWHI. Climate change 

scenarios and potential SLR impacts presented here emphasize 

the need for early climate change adaptation and mitigation 

planning, especially for species with limited breeding distributions and/or ranges restricted primarily to the low-lying 

NWHI: Cyperus pennatiformis var. bryanii, Black-footed 

Albatross (Phoebastria nigripes), Laysan Albatross (P. immutabilis), Bonin Petrel (Pterodroma hypoleuca), Gray-backed 

Tern (Onychoprion lunatus), Laysan Teal (Anas laysanensis), 

Edited by Michelle H. Reynolds, Paul Berkowitz, Karen N. Courtot, and Crystal M. Krause

Executive Summary

If current climate change trends continue, rising sea 

levels may inundate low-lying islands across the globe, 

placing island biodiversity at risk. Recent models predict a rise 

of approximately one meter (1 m) in global sea level by 2100, 

with larger increases possible in areas of the Pacific Ocean. 

Pacific Islands are unique ecosystems home to many endangered endemic plant and animal species. The Northwestern 

Hawaiian Islands (NWHI), which extend 1,930 kilometers 

(km) beyond the main Hawaiian Islands, are a World Heritage 

Site and part of the Papahānaumokuākea Marine National 

Monument. These NWHI support the largest tropical seabird 

rookery in the world, providing breeding habitat for 21 

species of seabirds, 4 endemic land bird species and essential 

foraging, breeding, or haul-out habitat for other resident and 

migratory wildlife. In recent years, concern has grown about 

the increasing vulnerability of the NWHI and their wildlife 

populations to changing climatic patterns, particularly the 

uncertainty associated with potential impacts from global sealevel rise (SLR) and storms. 

In response to the need by managers to adapt future 

resource protection strategies to climate change variability 

and dynamic island ecosystems, we have synthesized and 

down scaled analyses for this important region. This report 

describes a 2-year study of a remote northwestern Pacific atoll 

ecosystem and identifies wildlife and habitat vulnerable to 

rising sea levels and changing climate conditions. A lack of 

high-resolution topographic data for low-lying islands of the 

NWHI had previously precluded an extensive quantitative 

model of the potential impacts of SLR on wildlife habitat. The 

first chapter (chapter 1) describes the vegetation and topography of 20 islands of Papahānaumokuākea Marine National 

Monument, the distribution and status of wildlife populations, and the predicted impacts for a range of SLR scenarios. 

Furthermore, this chapter explores the potential effects of SLR 

on wildlife breeding habitats for each island. The subsequent chapter (chapter 2) details a study of the Laysan Island 

ecosystem, describing a quantitative model that incorporates SLR, storm wave, and rising groundwater inundation. 
2    Predicting Sea-Level Rise Vulnerability of Terrestrial Habitat and Wildlife of the Northwestern Hawaiian Islands

Laysan Finch (Telespiza cantans), and Hawaiian monk seal 

(Monachus schauinslandi). Furthermore, SLR scenarios that 

include the effects of wave dynamics and groundwater rise 

may indicate amplified vulnerability to climate change driven 

habitat loss on low-lying islands.

In chapter 2, we incorporated the combined effects of 

SLR, dynamic wave-driven inundation, and rising groundwater in a quantitative study specifically for the Laysan Island 

ecosystem. This is the first hydrodynamic model to simulate 

the combined impacts of SLR and wave-driven inundation in 

the NWHI. We developed a high-resolution digital elevation 

model (mean vertical accuracy of 0.32 m) for the island. Then 

using recent satellite imagery, geospatial models, and historical oceanographic, storm, and biological data we estimated 

potential inundation extent, habitat loss, and wildlife population impacts for a range of potential SLR scenarios (0.00, 

+0.50, +1.00, +1.50, and +2.00 m) that may occur over the 

next century. Additionally, we estimated the carrying capacity 

of Laysan Island for five species based on the available population monitoring data and described how potential losses 

in nesting habitat could influence population dynamics for 

Black-footed Albatross, Laysan Albatross, Red-footed Booby 

(Sula sula), Laysan Teal, and Laysan Finch. For some other 

seabird populations (Masked Booby, S. dactylatra; Brown 

Booby, S. leucogaster; Great Frigatebird, Fregata minor; and 

Sooty Tern, Onychoprion fuscata), we used recent colony 

distribution data, land cover maps, and nesting behavior to 

estimate potential losses of nesting habitat from SLR and 

wave-driven inundation. 

We observed far greater potential impacts of SLR to 

wildlife with the dynamic wave-driven modeling approach 

than with the passive modeling approach. Depending on SLR 

scenario and coastal orientation, during storms under a +2.00 

m SLR scenario, the wave-driven inundation model predicted 

three times more inundation than the passive model (17.2 

percent of total terrestrial area versus 4.6 percent, respectively). Large-wave events generally added 1 m of water 

height to passive inundation surfaces, therefore our dynamic 

models (during storm events) forecasted comparable inundation extents earlier than passive models. Although wave-driven 

water levels were highest in the northwest quadrant of Laysan 

Island, the greatest extent of inundation occurred in the southeast where coastal dunes less than 3 m above mean sea level 

provide little protection from wave-driven inundation.

When wave-driven inundation was included in the SLR 

model for Laysan Island greater nesting habitat loss and potential impacts on wildlife population dynamics were predicted. 

The consequences of habitat loss due to SLR may be worse for 

species with colonies in the wave-exposed coastal zones (for 

example, Black-footed Albatross) and for populations already 

near the island’s carrying capacity (for example, Laysan Teal). 

Species whose peak incubation and chick-rearing periods 

coincide with seasonally high wave heights also will be 

increasingly vulnerable, especially those species nesting on 

the ground in areas vulnerable to inundation, such as Graybacked Tern and Black-footed Albatross. Other species that 

have space for population growth, or are not restricted to a 

narrow range of habitat types on Laysan (for instance, Sooty 

Terns), may be less sensitive to habitat loss from SLR over the 

next century. 

Our assessments of inundation risk, habitat loss, and 

wildlife species vulnerability synthesize current knowledge about individual islands and contribute to a broader 

understanding of the impacts of inundation from SLR 

and storm-induced waves. Yet, most NWHI and their bird 

populations lack monitoring data to evaluate adaptations to 

and impacts of climate change. Exceptions include some 

data sets from long-term monitoring of wildlife populations, 

tides, or weather at French Frigate Shoals, Laysan Island, 

and Midway Atoll. These data sets are potentially valuable 

baselines, which could be informative for adaptive learning 

(integrating management and science) to predict, adapt, 

and mitigate the effects of climate change on NWHI wildlife in the future. This study provides the first quantitative 

vulnerability assessment for all of the low-lying NWHI, and 

results identify biological communities, locales, and resident 

endangered species of Papahānaumokuākea Marine National 

Monument expected to be at risk from SLR. This report is 

also intended as a reference for managers and conservation 

planners, a tool to identify and potentially reduce uncertainty, and a starting place for developing climate change 

monitoring priorities and future scientific studies. 
-200 

0 

200 

400 

600 

800 

1000 

1200 

1850 1900 1950 2000 2050 2100 

Sea level change, in millimeters 

Year 

Reconstructed Global Mean Sea Level, 

Church and White, 2011 

Historical 

Records 

Future Projections 

Chapter 1

Climate Change Vulnerability Assessment of the

Low-Lying Northwestern Hawaiian Islands

By Crystal M. Krause1, Karen N. Courtot1, Paul Berkowitz2, Jamie Carter3, and Michelle H. Reynolds1

Introduction

Rapid climate change, namely the global trend of atmospheric warming, is an important factor expected to contribute 

to numerous extinctions of plant and animal species across the 

globe. Although recent studies describe the natural adaptive 

capacity of many species including phenological changes in 

reproduction, seasonal wildlife migration, and shifting range 

boundaries (Parmesan, 2006; Lawler and others, 2009), many 

ecosystems have been identified as vulnerable to negative 

impacts of climate change. Arctic tundra, wetland coastal 

communities, and small island ecosystems are recognized as 

particularly vulnerable and may require intensive mitigation to 

prevent species losses due to climate change and other stressors 

(Intergovernmental Panel on Climate Change (IPCC), 2007). 

1

 U.S. Geological Survey, Pacific Island Ecosystems Research Center, 

Kīlauea Field Station, Hawai‘i National Park, HI 96718.

2

 Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo, Hilo, 

HI 96720.

3

 National Oceanic and Atmospheric Administration, Pacific Services Center, Honolulu, HI 96813.

Sea level represents a very sensitive index of global 

climate change (Cazenave and Llovel, 2010). Warming of 

the earth’s atmosphere affects sea level by (1) raising oceanic 

temperatures and causing thermal expansion of ocean waters, 

(2) increasing freshwater inputs from glacial melting, and 

(3) altering the mass balance of major ice sheets, resulting in 

increased global water volumes. Recent estimates of sealevel rise (SLR) that incorporate Greenland and Antarctic ice 

melt predict that SLR could exceed 1.0 meter (m) by 2100 

(Rahmstorf, 2007; Vermeer and Rahmstorf, 2009), with more 

rapid increases if major collapses of the polar ice sheets occur 

(Overpeck and others, 2006). From 1962 through 1990, global 

sea levels have risen at a mean rate of 1.5 ± 0.5 millimeter 

per year (mm/yr). Since 1990, the trend has increased to 3.2 

± 0.4 mm/yr (Merrifield and others, 2009); further acceleration is possible (Church and White, 2006; Jevrejeva and 

others, 2008; Rahmstorf, 2010). Recent satellite altimetry has 

shown that in the western Pacific Ocean, including areas in the 

vicinity of the Northwestern Hawaiian Islands (NWHI), much 

higher rates of SLR (5–10 mm/yr) have occurred from 1992 

Figure 1.1. Global mean sea level between 1850 

and 2100, modified from Cazenave and Llovel (2010). 

The thick black line indicates historical sea levels 

and the red line shows sea levels from observed tide 

gauge data (Church and White, 2011). The purple, 

blue, and orange areas show sea-level rise projections 

and the increasing uncertainty associated with 

projections after 2050, based on three different climate 

models from the Intergovernmental Panel on Climate 

Change (IPCC, 2007): purple without scaled-up 

ice sheet discharge, blue with scaled-up ice sheet 

discharge, and orange after Rahmstorf’s (2007) semiempirical approach to projecting future sea-level rise.
4    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

to 2011(Leuliette, 2012). Historical trends in sea level for the 

past two centuries as well as three different projections of sea 

level for this century (2000–2100) are shown in fig. 1.1 (IPCC, 

2007; Rahmstorf, 2007; Cazenave and Llovel, 2010).

The Intergovernmental Panel on Climate Change (IPCC) 

Fourth Assessment Report (AR4; 2007) identified several 

potential impacts of SLR, including reductions in island size, 

loss of freshwater resources, damage to coral reefs, declines 

in fisheries, deterioration of marine resources, and erosion of 

shorelines. Additionally, increases in sea surface temperatures 

may increase the intensity of storms, amplifying the effects 

of SLR with storm-generated impacts and flooding (IPCC, 

2007; Mousavi and others, 2011). The combination of SLR, 

storm surge, wave run-up (that is, the maximum vertical 

extent of waves on a beach), and coastal erosion poses risks to 

low-lying islands in the Pacific, as well as to the species that 

inhabit them. 

Few studies have examined the consequences of 

SLR on the biodiversity of low-lying islands, with most 

studies analyzing the effects of SLR using passive inundation scenarios (also known as “bathtub” models; Baker 

and others, 2006; Zhang and others, 2011). A recent global 

assessment of the effects of SLR on biodiversity predicted 

inundation of 0.7 percent of the world’s terrestrial area (1.1 

million square kilometers, km2

) under a +1.0 m SLR scenario 

(Menon and others, 2010). However, the Menon study relied 

on relatively coarse biological and topographic data and 

produced first-order estimates of passive inundation for all 

terrestrial ecoregions globally.

In the central Pacific, rapid climate change threatens 

Hawai‘i’s wildlife in numerous ways, including increased 

transmission of avian malaria for Hawaiian native forest 

birds (Benning and others, 2002; LaPointe and others, 2010), 

threats to coral reefs such as bleaching and ocean acidification 

(Jokiel and Brown, 2004), and SLR-induced habitat loss on 

small islands (Baker and others, 2006). Worst-case scenarios 

of SLR predict inundation of many low-lying islands and 

high extinction risk for many island endemics (that is, 

species confined to and indigenous to the region). Climate 

change projections for the main Hawaiian Islands include 

a 5–10-percent reduction in rainfall during the wet season 

(November–April) and a 5-percent increase in rainfall during 

the dry season (May–October), primarily due to changes 

in mid-latitude cyclone frequency and trade wind patterns, 

respectively (Timm and Diaz, 2009). Daily temperature ranges 

in the main Hawaiian Islands are decreasing while average 

ambient temperatures at sea level are projected to increase by 

2100 (IPCC, 2007). Changes in precipitation and temperature 

will likely affect vegetation distributions and composition 

(Juvik and others, 2011) and potentially increase ecosystem 

vulnerability to invasion by non-native species (Masters and 

Norgrove, 2010).

The Northwestern Hawaiian Islands (NWHI) extend 

1,930 kilometers (km) beyond the main Hawaiian Islands 

and consist of Kure Atoll (Mokupāpapa), Midway Atoll 

(Pihemanu), Pearl and Hermes Atoll (Holoikauaua), Lisianski 

Island (Papa‘āpoho), Laysan Island (Kauō), Maro Reef 

(Ko‘anako‘a, Nalukākala; largely submerged), Gardner 

Pinnacles (Pūhāhonu), French Frigate Shoals (Kānemiloha‘i), 

Mokumanamana (Necker Island), and Nihoa. The geographic 

isolation of the NWHI has led to the development of an 

extraordinary range of intact marine and terrestrial ecosystems that include a high degree of endemism (Conant and 

others, 1984; Friedlander and others, 2009). The NWHI are 

home to many endangered endemic plant and animal species 

and support the largest tropical seabird rookery in the world 

(United Nations Educational Scientific and Cultural Organization (UNESCO), 2010). For a recent assessment of the diverse 

marine ecosystems of the NWHI, see Friedlander and others 

(2009). The NWHI provide habitat for over 14 million federally protected tropical seabirds (Fefer and others, 1984), 4 

resident land bird species, and 90 percent of Hawai‘i’s subpopulation of green turtles (Chelonia mydas); this area is also 

designated critical habitat for the Hawaiian monk seal (Monachus schauinslandi) and five species of endangered plants 

(National Oceanic and Atmospheric Administration (NOAA), 

1986; 1988; U.S. Fish and Wildlife Service (USFWS), 2003; 

2005). The NWHI are also an important migration stopover or 

overwintering habitat for at least 29 species of migratory birds 

of the Pacific flyway (Pyle and Pyle, 2009). 

The terrestrial ecosystems of the remote NWHI remained 

largely intact until the late 1800s, when guano mining, feather 

and egg harvesting, and mammalian introductions contributed to population declines and the extinction of numerous 

endemic species (Bryan, 1911; Farrell, 1928; Bailey, 1956). 

All of the NWHI except for Midway Atoll, which was under 

U.S. military jurisdiction at the time, were protected by the 

1909 Bird Reserve designation (Executive Order 1019). Additional conservation designations occurred later, including the 

creation of the Hawaiian Islands National Wildlife Refuge in 

1940 (Presidential Proclamation 2416), Midway Atoll National 

Wildlife Refuge in 1996 (Executive Order 13022), and 

Papahānaumokuākea Marine National Monument (PMNM) in 

2006 (Presidential Proclamation 8112). Papahānaumokuākea 

Marine National Monument was designated a World Heritage 

Site in 2010 (UNESCO, 2010). The NWHI conservation area 

encompasses 362,061 km2

 of oceanic and terrestrial habitat 

(Friedlander and others, 2009). 

In order to describe terrestrial habitat vulnerabilities 

to SLR across this remote archipelago, we collected new 

topographic data for Kure Atoll, Midway Atoll, Pearl and 

Hermes Atoll, Lisianski Island, Laysan Island (see chap. 2), 

and French Frigate Shoals (see Hatfield and others, 2012), as 

well as compiled species distribution and habitat information 

for breeding birds, endangered plants, and other wildlife. We 

then modeled the potential impacts of climate change and SLR 

in order to describe possible effects of inundation on wildlife 

habitat of the NWHI with an emphasis on breeding birds.
Materials and Methods    5

Materials and Methods

Study Area

Papahānaumokuākea Marine National Monument, 

including small islands, atolls, submerged banks, and 

reefs, is the largest conservation area in the United States 

(Papahānaumokuākea Marine National Monument, 2008; 

fig.1.2). The land area included in this study consists of four 

multi-island coral atolls (Kure, Midway, Pearl and Hermes, 

and French Frigate Shoals), two carbonate islands (Lisianski 

and Laysan), and two basalt islands (Mokumanamana and 

Nihoa). The diverse ecosystems of the NWHI support 21 

breeding seabirds (table 1.1) and 17 terrestially-breeding 

species of animals and plants listed as endangered, threatened, or species of concern by the USFWS, State of Hawai‘i, 

or International Union for Conservation of Nature including: 

Hawaiian monk seal or ‘ilio-holo-i-ka-uaua, green turtle 

or honu, Laysan Teal (Anas laysanensis), Laysan Finch or 

‘Ainohu kauo (Telespiza cantans), Nihoa Finch (T. ultima), 

Nihoa Millerbird (Acrocephalus familiaris subsp. kingi), 

Short-tailed Albatross (Phoebastria albatrus), Black-footed 

Albatross or Ka‘upu (P. nigripes), Laysan Albatross or Mōlī 

(P. immutabilis), Christmas Shearwater (Puffinus nativitatis), 

Tristram’s Storm-petrel (Oceanodroma tristrami), Amaranthus brownii, presumed extinct kamanomano (Cenchrus 

agrimoniodes var. laysanensis), Cyperus pennatiformis var.

bryanii, lou‘lu (Pritchardia remota), Schiedea verticillata, 

and ‘ohai (Sesbania tomentosa) (PMNM, 2008; Friedlander 

and others, 2009). Additionally, the islands are winter habitat 

for regionally significant populations of four migratory 

shorebirds: Pacific Golden-plover or Kolea (Pluvialis fulva), 

Bristle-thighed Curlew or Kioea (Numenius tahitiensis), 

Wandering Tattler or ‘Ulili (Tringa incana), and Ruddy 

Turnstone or ‘Akekeke (Arenaria interpres; Engilis and 

Naughton, 2004; PMNM, 2008).

Figure 1.2. Map of the Hawaiian Archipelago with the main Hawaiian Islands (Kaua‘i to Hawai‘i) and Papahänaumokuäkea 

Marine National Monument (PMNM; 362,061 square kilometers), the largest conservation area in the United States. 
Table 1.1. Known breeding distribution of avifauna, endangered plants, and other wildlife in the Northwestern Hawaiian Islands.

[Data compiled from published and unpublished sources (USFWS data). See atoll- and island-specific footnotes for sources]

Kure Atolla

Black-footed Albatross Phoebastria nigripes X X X X X X X X X X X X 

Laysan Albatross Phoebastria immutabilis X X X X X X X X X X X X X 

Short-tailed Albatross Phoebastria albatrus X

Bonin Petrel Pterodroma hypoleuca X X X X X X X X X X 

Bulwer's Petrel Bulweria bulwerii X X X X X X 

Wedge-tailed Shearwater Puffinus pacificus X X X X X X X X X X 

Christmas Shearwater Puffinus nativitatis X X X X X X X X 

Tristram's Storm-petrel Oceanodroma tristrami X X X X X X X 

Red-tailed Tropicbird Phaethon rubricauda rubricauda X X X X X X X X X X X X 

White-tailed Tropicbird Phaethon lepturus dorotheae XMasked Booby Sula dactylatra personata X X X X X X X X X X X X 

Brown Booby Sula leucogaster plotus X X X X X X 

Red-footed Booby Sula sula rubripes X X X X X X X X 

Great Frigatebird Fregata minor palmerstoni X X X X X X X X 

Little Tern Sterna albifrons sinensis X XGray-backed Tern Onychoprion lunatus X X X X X X X X X 

Sooty Tern Onychoprion fuscata oahuensis X X X X X X X X 

Blue Noddy Procelsterna cerulean saxatilis X 

Brown Noddy Anous stolidus pileatus X X X X X X X X X X X X 

Black Noddy Anous minutus marcusi X X X X X X X X 

White Tern Gygis alba candida X X X X X X X X X 

Laysan Teal Anas laysanensis X X X

Laysan Finch Telespiza cantans X X

Nihoa Finch Telespiza ultima 

Nihoa Millerbirdg Acrocephalus familiaris kingi Hawaiian monk seal Monachus schauinslandi X X X X X X X X X X X X Hawaiian green turtleh Chelonia mydas X X X X X X X X X X X X * Amaranthus brownii 

* Cyperus pennatiformis var. bryanii X

*Lou'lu Pritchardia remota 

* Schiedea verticillata 

*‘Ōhai Sesbania tomentosa 

North Island

Midway Atollb

Southeast Island

Pearl and Hermes Atollc, d

Trig Island

Frenc

Grass Island Scientific name

Lisianski Island

c

Species

Green Island

Sand Island

Spit Island

Eastern Island

Seal-Kittery Island

Laysan Island

Tern Island

f

Gardner Pinnacles

c

aVanderlip and others (2008). bUSFWS unpublished data, Klavitter (2006), P. Leary, USFWS, written commun. (2011). cFriedlander and others (2009). dWegmann and Kropidlowski 

(2002). eP. Hartzell, USFWS, written commun. (2011). fHammond and others (2010), Kristof and others (2011). gTwenty-four individuals translocated to Laysan Island in September 2011 

and successful breeding is anticipated in 2012 (USFWS, 2011b). hHawaiian green turtle population comprises a unique genetic stock (Balazs and Chaloupka, 2004). *Plant distributions are 

of extant populations and do not include outplantings and introductions on other islands. [USFWS, U.S. Fish and Wildlife Service]
Materials and Methods    7

Kure Atoll (Mokupäpapa) 28°23'33" N and 

178°17'36" W

Kure is the northernmost coral atoll in the world, the 

westernmost land mass of the NWHI, and approximately 

2,400 km northwest of Honolulu. The nearest land is Sand 

Island, Midway Atoll, 92 km to the southeast. Kure Atoll 

includes Green Island and several small sand spits. Seventeen seabird species breed at Green Island (table 1.1), 

excluding the single pair of endangered Short-tailed Albatross, which has not bred successfully yet (C. Vanderlip, 

Hawai‘i Department of Land and Natural Resources 

[DLNR], written commun., 2011).

Kure Atoll was visited by feather hunters during the 19th 

century and is the site of numerous shipwrecks from that era 

(Woodward, 1972). The U.S. Coast Guard began construction 

of a long-range navigation (loran) radar station in 1960; the 

station was in use until 1992 (Rauzon, 2001). In 1981, Kure 

was designated a State wildlife sanctuary. In 1993 the Hawai‘i 

DLNR began restoration projects including the eradication of 

Polynesian rats (Rattus exulans; Rauzon, 2001), a predator of 

Hawaiian birds and plants (Kepler, 1967).

Midway Atoll (Pihemanu) 28°11'41"–28°16'50" 

N and 177°18'38"–177°25'38" W

Midway is a coral atoll situated between Kure Atoll 

to the northwest and Pearl and Hermes Atoll to the southeast. Midway Atoll consists of three islands: Sand, Spit, and 

Eastern. Eighteen seabird species and a reintroduced population of endangered Laysan Teal breed at Midway Atoll 

(table1.1). Midway Atoll is the only location in the NWHI 

where the endangered Short-tailed Albatross has bred successfully (USFWS, 2011a). Two additional seabird species (Bulwer’s Petrel [Bulweria bulwerii] and Tristram’s Storm-petrel) 

are recolonizing Midway, but nesting has not been confirmed 

(P. Leary, USFWS, written commun., 2011). Additionally, 

the current status of Bryan’s Shearwater (Puffinus bryani), 

a species newly described from a specimen first collected at 

Midway Atoll in 1963, is unknown (Pyle and others, 2011).

Sand and Eastern Islands have been significantly altered 

by human activity since the early 1900s (McDermond and 

Morgan, 1993). Midway has been occupied since coming 

under the jurisdiction of the U.S. in 1903 (Bryan, 1938). As a 

national defense area, significant development of the islands 

occurred, including the addition of seawalls, a harbor, piers, 

runways, dredge and fill operations, numerous buildings, and 

species introductions between the 1930s and 1990s (SpeuldaDrews, 2010). In 1993 the naval air station was decommissioned, and in 1996 jurisdiction was transferred to the U.S. 

Department of the Interior as a National Wildlife Refuge 

(Speulda-Drews, 2010). Introduced black rats (Rattus rattus), 

predators of Hawaiian birds and plants (Fisher and Baldwin, 

1946), were eradicated by 1997 (Rauzon, 2001).

Pearl and Hermes Atoll (Holoikauaua) 

27°45'52"–27°58'36" N and 175°42'05"–

176°02'11" W

Pearl and Hermes is a coral atoll situated between 

Midway Atoll (160 km to the northwest) and Lisianski Island 

(265 km to the southeast). Pearl and Hermes Atoll currently 

comprises five islands: North, Little North (made up of two 

sections separated by water), Southeast, Grass, and SealKittery, plus several sand spits. Eighteen seabird species and a 

translocated population of endangered Laysan Finch breed at 

Pearl and Hermes Atoll (table 1.1).

As on the other atolls, Pearl and Hermes has a history 

of human impacts. The extent of human disturbance from 

feather collecting and guano mining in the early 1900s at Pearl 

and Hermes is not clear, although there is evidence of human 

occupation and rabbit (Oryctolagus cuniculus) introduction and 

subsequent extirpation (Wetmore 1923 unpub. personal journal 

reproduced in Olson, 1996). In 1927, black-lip pearl oysters 

were found in the atoll and 150 tons were removed in three 

years; by 1969 only one oyster could be found (Amerson and 

others, 1974). During World War II, a small U.S. Navy encampment occupied Pearl and Hermes Atoll (Amerson and others, 

1974). Several species translocations have been made at Pearl 

and Hermes, with failed establishment of Laysan Rail in 1929 

(Baldwin, 1945), Laysan Teal in 1967, and successful establishment of Laysan Finch 1967 (Amerson and others, 1974). 

Lisianski Island (Papa‘äpoho) 26°03'48" N and 

173°57'56" W

Lisianski, the third largest island within the monument, 

is a raised atoll situated between Pearl and Hermes Atoll and 

Laysan Island. Sixteen seabird species breed at Lisianski Island 

(table 1.1). Early disturbances at Lisianski included multiple 

shipwrecks (Ward, 1967), guano mining (Bryan, 1942), and 

feather collecting (Clapp and Wirtz, 1975; Olson and Ziegler, 

1995). The introduction of mice (Mus musculus) in the 1800s 

and later rabbits destroyed much of the flora and fauna on the 

island (Wetmore 1923 unpub. personal journal reproduced in 

Olson, 1996) until de-vegetation resulted in the die-off of the 

mammals. The mammal-caused habitat destruction and hungry 

shipwrecked mariners contributed to the extirpation of the 

Laysan Teal population at Lisianski (Olson and Ziegler, 1995). 

Laysan Island (Kauö) 25°46'11" N and 

171°44'00" W

Laysan is situated between Lisianski Island to the northwest and Maro Reef to the southeast; a detailed description of 

Laysan Island is provided in chap. 2. Laysan lies approximately 

1,600 km northwest of Honolulu, has 17 seabird species and 

3 resident endangered land birds: Laysan Teal, Laysan Finch, 

and a newly (2011) translocated population of Nihoa Millerbird 
8    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

(USFWS, 2011b; table 1.1). The endemic endangered plant 

Cyperus pennatiformis var. bryanii is found on Laysan Island.

Guano miners in the early 1900s introduced rabbits and 

other mammals to Laysan Island; these mammals devastated 

the flora and fauna (Wetmore 1923 unpub. personal journal 

reproduced in Olson, 1996). The Tanager Expedition in 1923 

reduced the rabbit population and documented the island as 

barren (Wetmore, 1925). Soon after the expedition the rabbits 

and other introduced mammals at Laysan died off from starvation (Wetmore, 1925). Laysan has been protected as a bird 

reserve since 1909.

French Frigate Shoals (Känemiloha‘i) 

23°37'18"–23°52'50" N and 166°03'14"–

166°20'04" W

French Frigate Shoals is an open atoll located near the 

midpoint of the Hawaiian archipelago. French Frigate Shoals is 

approximately 975 km northwest of Honolulu. The nearest land, 

Mokumanamana, lies 168 km to the southeast. French Frigate 

Shoals currently comprises nine islands: Shark, Tern, Trig, 

Round, East, La Perouse Pinnacle (a basalt rock formation), 

Gin, Little Gin, and Disappearing. Currently, 16 seabird species 

breed at Tern Island, 13 at East Island, 6 at La Perouse Pinnacle, 

4 at Gin Island, and 3 at Trig and Little Gin islands (table 1.1). 

Although green turtles are distributed in tropical oceans globally 

(Bowen and others, 1992), 90 percent of the Hawaiian subpopulation nests at French Frigate Shoals, and of those, 50 percent 

nest at East Island (Balazs, 1976; Balazs and Chaloupka, 2004; 

Tiwari and others, 2010). 

The islands of French Frigate Shoals were not subjected 

to guano mining and feather collecting, but were occupied by 

the U.S. military during World War II and served as a loran 

radar station from 1944–1979 (Amerson, 1971; Friedlander 

and others, 2009). The U.S. Coast Guard decommissioned 

Tern Island and transferred management of the island to the 

USFWS in 1979 (Rauzon, 2001). An unsuccessful translocation of Nihoa Finch occurred in 1967 (Amerson, 1971). 

Mokumanamana (Necker Island) 23°34'32" N 

and 164°42'02" W

Mokumanamana is a small basalt island (18.6 ha) with 

several peaks higher than 70 m; the highest peak is 82 m 

(Evenhuis and Eldredge, 2004). Mokumanamana is approximately 750 km northwest of Honolulu. The nearest land is to 

the north at French Frigate Shoals. The island is known for its 

religious and cultural importance, linking early Tahitian and 

Hawaiian cultures (Kikiloi, 2010). Sixteen seabird species 

breed at Mokumanamana (table 1.1).

Nihoa 23°3'36" N and 161°55'24" W

Nihoa is a basalt rock island of 63.1 ha at the southernmost end of the NWHI chain. Nihoa is approximately 250 

km northwest of Honolulu. The nearest land is Kaula Island, 

near Kaua‘i. Two high cliffs of Nihoa, which reach 269 m and 

256 m, are unique topographically among the NWHI (Evenhuis and Eldredge, 2004). Nihoa was colonized by Hawaiians between A.D. 1000 and A.D. 1700 and has more than 80 

known culturally significant sites (Evenhuis and Eldredge, 

2004). Seventeen seabird species; 2 endangered land birds, 

Nihoa Finch and Nihoa Millerbird; and 4 endangered plants, 

Amaranthus brownii, Pritchardia remota, Schiedea verticillata, and Sesbania tomentosa, are found on Nihoa (table 1.1).

La Perouse Pinnacle, Gardner Pinnacles, and the islands 

of Mokumanamana and Nihoa were not included in the SLR 

assessments because of their high elevation and low vulnerability to inundation.

Remote Sensing Data

Light Detection and Ranging (lidar) 

Lidar is an active remote sensing technique that measures 

the length of time between transmitted laser pulses and the 

detection of their reflected signals (USGS, 2010). The length 

of time is converted to a distance value that allows us to 

determine the elevation of any targeted surface (for example, 

bare earth, vegetation canopy). Although lidar sensors may be 

mounted on various objects (fixed-wing aircraft, helicopters, 

watercraft, tripods on the ground, etc.), the size, vast distances, 

and ecological fragility within the project area warranted an 

airborne application. We collected terrestrial lidar data of the 

study area using a Beechcraft Queen Air twin engine plane 

(Aerial Surveying, Inc.) that departed from Kaua‘i Island and 

then based survey flights from Midway Atoll National Wildlife 

Refuge (NWR) July 22–25, 2010 (table 1.2).

Lidar Data Collection

Two Riegl sensors, models LMS-Q140i and LMS-Q240, 

were used to acquire more than 20,000 laser pulses per second 

with a ground scan swath of 400 m in a downward direction 

below the aircraft (fig. 1.3). The instrument settings yielded a 

pulse density of approximately 2 points per square meter(m2). 

Aircraft position heading, pitch, and roll were measured 200 

times per second (200 hertz, Hz) from an onboard inertial 

measurement unit (IMU). Our study design specifications for 

the lidar data collection are given in appendix 1. A Global 

Positioning System (GPS) based ground survey, comprising 

1,678 real-time kinematic points on Sand Island (Midway 

Atoll), occurred concurrently with the airborne lidar data 

collection. The lidar data points were processed and tested for 

accuracy against the real-time kinematic survey points.

Lidar Data Processing

During data processing and analysis, lidar points above 

the ground surface were removed, creating a comprehensive 
Table 1.2. Northwestern Hawaiian Islands airborne lidar data collection information, including root-mean-squared error 

(RMSE) and standard deviation (SD) values measured between ground survey control points and lidar points.

[The 80-percent (%) confidence interval refers to the digital elevation model vertical accuracy]

Date Hawaii Standard Timea RMSE 

(meters)

Mean 

(meters) SD (meters)

80% 

Confidence 

interval

Kure Atoll July 23, 2010 1118 – 1207 0.11 0.08 0.09 0.20

Midway Atoll July 23, 2010 1230 – 1457 0.05 0.00 0.05 0.09

Pearl and Hermes Atoll July 25, 2010 1205 – 1246 0.12 -0.10 0.06 0.22

Lisianski Island July 24, 2010 1636 – 1705 0.18 0.00 0.18 0.32

Laysan Island July 24, 2010 1436 – 1505 0.09 0.00 0.09 0.16

French Frigate Shoals July 22, 2010 1200 – 1330 0.05 0.00 0.05 0.09

a

Hawaii Standard Time is the standard time in the 10th time zone west of Greenwich, reckoned at the 150th meridian west; used 

in Hawai`i and the western Aleutian Islands. Times are given using a 24-hour clock.

Materials and Methods    9

data set of points from various surfaces (for example, bare 

earth, vegetation, buildings) to be classified. Automated 

identification of points was done using MARS 7 software 

(Merrick, 2011); manual removal of outliers followed and 

consisted of visual inspection and editing, particularly in areas 

where the automated methodology was known to be deficient 

(for example, dense vegetation). First-return points and the 

ground-classified points were used to create separate triangulated irregular networks (TINs) that were then converted 

into two 1.0-m cell size (pixel grid) digital elevation models 

(DEMs), a first-return DEM and a bare-earth DEM. We used 

the North American Datum of 1983 (NAD83) as the horizontal 

datum and the Universal Transverse Mercator (UTM), zones 

1–3, as the coordinate system. Elevation data were provided 

relative to mean sea level (MSL) in meters. 

Lidar Quality Assessment/Quality Control

An accuracy assessment was conducted to compare 

known points on the ground with those points generated from 

our lidar data (unpub. data, NOAA, 2011a). A dual-frequency 

L1/L2 Thales Z-Max GPS receiver was used as a base station. 

The base station was set up on existing geodetic markers when 

possible and collected data continuously throughout each 

day of survey activity. Two single-frequency L1 ProMark 

3 GPS receivers were used as roving instruments to collect 

elevation data points. A minimum of 20 points were surveyed 

on each island or atoll area (in the case of the smaller atoll 

islets) to allow for a statistically rigorous analysis of accuracy. The locations selected for the GPS survey points met the 

following criteria: (1) flat, bare ground at least 4 m in diameter, (2) absence of tall vegetation within 5 m of the point, 

and (3) stable ground in areas assumed to be unaffected by the 

severe winter 2010–11 storms or the March 11, 2011 tsunami 

(USFWS, 2011c).

The data were then processed for the lidar accuracy 

assessment. The GPS data were imported into Ashtech’s 

GNSS Solutions software (version 3.60.1) for post-processing 

and data adjustment. The GPS base station locations were 

Figure 1.3. Schematic showing the lidar (light detection and

ranging) data collection process, which combines a Global 

Positioning System (GPS), an inertia measurement unit (IMU), 

and light pulses from a laser. Lidar data are often collected 

from aircraft to allow for a rapid collection of points (more than 

70,000 per second) over a large spatial area (reproduced from 

U.S. Department of Agriculture Forest Service, http://forsys.cfr.

washington.edu/JFSP06/lidar_technology.htm).

processed through the online positioning user service 

(OPUS) of the National Geodetic Survey (NGS) to obtain 

both NAD83 and International Terrestrial Reference Frame 

of 2000 (ITRF00) positions. The ITRF00 ellipsoid heights 

were used if known heights were not available. The two-step 

data processing produced corrected positions for the base 

station points and adjusted positions for the roving points. The 

OPUS-derived base station coordinates were entered in GNSS 
Digital Globe 

QuickBird WorldView-2

Kure Atoll July 2008 -

Midway Atoll March 2009 August 2011

Pearl and Hermes Atoll October 2007 December 2010

Lisianski Island January 2008 March 2011

Laysan Island January 2009 May 2010

French Frigate Shoals November 2005 August 2011

Table 1.3. Satellite imagery sources and collection dates. 

[All images georeferenced to NAD 83, UTM zones 1–3]

Table 1.4. Climate change general circulation models used to predict temperature and precipitation through 2100 

(Intergovernmental Panel on Climate Change, IPCC, 2007). 

Agency Model name

Canadian Centre for Climate Modeling and Analysis, Canada CCCMA CGCM3.1.T63

Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, United States GFDL CM2.0

Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, United States GFDL CM2.1

Max Planck Institute for Meteorology, Germany MPI ECHAM5

Meteorological Research Institute, Japan Meteorological Agency, Japan MRI CGCM2.3.2A

Hadley Centre for Climate Prediction and Research, Met Office, United Kingdom UKMO HADCM3

10    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Solutions to assign their positions and allow the roving points 

to adjust accordingly. 

Each position was also determined in relation to two 

vertical datums: NAD83/GRS80 and WGS84. OPUS was 

used to generate fixed positions for the base stations. These 

positions were compared against known geodetic controls 

where available. In those cases, the published NGS datasheet 

of precise latitude and longitude positions were processed 

using NGS’ Horizontal Time Dependent Positioning (HTDP) 

tool to update the positions with known velocities. The 

resulting positions included information about the region, 

island, latitude, longitude, UTM zone, and ellipsoid heights 

(GRS80 and WGS84). 

Data accuracy was derived from the absolute root-meansquared error (RMSE), a statistical measure of variability 

similar to standard deviation. In a nonbiased data set the two 

values will be the same (NOAA, 2009). RMSE is calculated 

directly from the differences between the ground control 

points and the lidar ellipsoidal elevations (table 1.2).

Aerial and Satellite Imagery

We acquired aerial imagery for the NWHI from multiple 

sources (table 1.3). All images were georeferenced to 

NAD83 as the horizontal datum and UTM zones 1–3 as the 

coordinate system. We purchased Digital Globe QuickBird 

and WorldView-2 satellite imagery (Digital Globe Inc., 

2010). Lastly, we acquired additional WorldView-2 stereopair imagery from PhotoSat Information Ltd. (2010) for 

Laysan (also see chap. 2), Trig, Little Gin, Round Islands, 

and La Perouse Pinnacle. PhotoSat also provided DEMs 

utilizing a geophysical processing system (Mitchell and 

MacNabb, 2010); these DEMs supplemented our lidarderived DEMs.

Climate Data

To better understand possible climate impacts for the 

NWHI, we analyzed future climates from general circulation models (GCMs) with interpolated climate surfaces of 

baseline data (Tabor and Williams, 2010). There are a number 

of different GCMs, and emission scenarios for future atmospheric concentrations of greenhouse gasses. Thus, projected 

climate for a given period in the future depends on the GCM 

model and emission scenario used. We used six GCMs and 

scenarios from the IPCC AR4 (2007; table 1.4) appropriate 

for the Hawaiian Islands. Timm and Diaz (2009) favored these 

models for accurate representation of inter-annual to decadal 

variability of precipitation patterns around the main Hawaiian 

Islands and extra-tropical to tropical teleconnection regions 

(distant areas influenced by the same climate anomalies).

Due to the coarse resolution (approximately 300-km cell 

size) of GCM data, we “downscaled” our data by computing 

the absolute difference between GCM output and baseline 

data (Tabor and Williams, 2010). The application of downscaling calibrates climate data to a resolution appropriate for 

the NWHI. These downscaled values were then applied to 

WorldClim data, high-resolution (1-km cell size) interpolated 

climate surfaces, derived from monthly averages of temperature and precipitation from a large number of global, regional, 

national, and local weather sources, and presented as a 30-year 

average (1960–1990). These data sources are available 

through the WorldClim database (see http://www.worldclim.

org; Hijmans and others, 2005). We present results for the 

predicted change in the annual average maximum temperature 

and total annual precipitation in 2100.

Sea-Level Rise

We used our lidar-derived DEMs (1-m cell size) as elevation grids for the land areas of Kure Atoll, Midway Atoll, Pearl 

and Hermes Atoll, Lisianski Island, Laysan Island, and French 

Frigate Shoals (Shark, Tern, Gin, and Disappearing Islands). 
Table 1.5. National Oceanic and Atmospheric Administration tide values 

(http://tidesandcurrents.noaa.gov/data) for each atoll, expressed relative to 

mean lower low water, used to calculate mean high water (MHW). 

[Where possible, verified tides were used to determine the tide at the time of data collection; 

otherwise predicted tides, scaled to the Honolulu tide gauge, were used. All values in meters]

MHW from 

benchmark 

Tide at time 

of lidar 

Lidar sea 

level Lidar MHW 

Kure Atoll 0.4 0.2 0.0 0.2

Midway Atoll 0.4 0.3 0.0 0.1

Pearl and Hermes Atoll 0.4 0.3 0.0 0.1

Lisianski Island 0.2 0.3 0.0 -0.1

Laysan Island 0.2 0.1 0.0 0.1

French Frigate Shoals 0.3 0.2 0.0 0.2

Materials and Methods    11

For islands of French Frigate Shoals without lidar data (Trig, 

East, Round, Little Gin, and La Perouse Pinnacle), we used 

satellite-derived DEMs (Mitchell and MacNabb, 2010; PhotoSat 

Information Ltd., 2010). To model inundation from SLR, we 

used a passive inundation model to inundate grid cells below 

an elevation threshold, while accounting for connectivity to a 

water source (Li and others, 2009), generally the ocean or, in the 

case of Laysan, the interior lake. Specifically, only those cells 

connected by an eight-neighbor relationship to the ocean (or 

interior lake) were inundated. By applying this constraint, lowlying elevations below a given threshold do not flood unless 

they are hydrologically connected to a water source. More 

complex and dynamic models that incorporate wave run-up 

require detailed bathymetric data that were not available for all 

islands. We describe a dynamic wave-driven inundation model 

for Laysan Island in chap. 2.

Our models did not predict ecological wetland succession or incorporate wetland response to projected changes in 

precipitation. However, for the interior lake of Laysan Island, 

we modeled SLR using two different lake (i.e., groundwater) 

responses: (1) no change in lake level or groundwater, and 

(2) a concurrent rise in groundwater equal to SLR. Under the 

latter scenario, if sea level rises +1.0 m, we predicted groundwater would also rise +1.0 m.

We used ArcGIS (version 10.0; ESRI 2010) to model 

four scenarios of SLR: +0.5, +1.0, +1.5, and +2.0 m. We 

referenced each SLR scenario to mean high water (MHW, 

table 1.5), which was based on NOAA tidal predictions from 

the nearest available data collection station or nearest location with predicted tidal data (http://tidesandcurrents.noaa.

gov/tide_predictions). Sand Island at Midway Atoll has the 

only tide gauge in the NWHI with a long-term time series 

of tide data. Tidal predictions from Midway Atoll (based on 

the tidal epoch 1983–2001; NOAA Station ID 1619910 Sand 

Island, Midway Islands) were used for Midway, Kure, and 

Pearl and Hermes atolls. Tidal predictions (NOAA Station ID 

1619645, scaled from the Honolulu tide gauge, and NOAA 

Station ID 1612340) were used for the islands of Laysan 

and Lisianski. For all islands in French Frigate Shoals, we 

used tide predictions from East Island (NOAA Station ID 

1619222, scaled from the Honolulu tide gauge, NOAA 

Station ID 1612340).

Land Cover Classification

We classified land cover for Kure Atoll, Midway Atoll, 

Pearl and Hermes Atoll, Lisianski Island, and French Frigate 

Shoals using QuickBird satellite imagery collected during 

2005–2009; for Laysan Island, we used WorldView-2 satellite imagery from 2010 (Digital Globe 2011; table 1.3). To 

classify satellite imagery digitally, we used the IsoCluster 

unsupervised classification tool in ArcGIS (ESRI, 2010); this 

tool classifies pixels based on color, texture, tone, pattern, and 

associated information (Xie and others, 2008). Where speciesspecific plant delineations were available from USFWS or 

Hawai‘i DLNR, we incorporated these data into vegetation 

maps (HI DLNR unpub. data, USFWS unpub. data, Vanderlip 

and others, 2007; Cornett and others, 2008; Vanderlip and 

others, 2008; Boyd and others, 2009; Kristof and others, 

2011). Verbesina encelioides distribution was mapped and 

tabulated from field data collected in 2007 at Green Island, 

Kure Atoll (Vanderlip and others, 2007); 2004 at Sand and 

Eastern Islands, Midway Atoll (Laniawe, 2004); and 2003 at 

Southeast Island, Pearl and Hermes Atoll (Sprague, 2003). 

However, active management may influence the distribution of 

invasive species from year to year. 

We classified land cover across the NWHI into 17 categories (table 1.6). General categories include a combination of 

vegetation classes (tree/shrub, mixed shrub, grass/herbaceous 

cover, vine/ground cover, wetland vegetation, and partially 

vegetated former runways), unvegetated areas (mudflats, 

standing water, bare ground, hard pan, and beach), and human 

infrastructure. Species-specific vegetation categories include 

three tree species (Casuarina equisetifolia, Cocos nucifera,

and Tournefortia argentea), one shrub (Pluchea indica), and 

one herbaceous invasive plant (Verbesina encelioides). Verbesina encelioides is a dynamic and pervasive plant distributed 
Land cover class General description Species included

Tree/shrub Continuous tree or shrub cover, multiple native and non-native species of 2–20 meters tall

Capparis sandwichiana, Casuarina equisetifo

Coccoloba uvifera, Cocos nucifera, Hibiscus 

bidentatum o-waihiense, Pisonia grandis, Plu

taccada, Terminalia catappa, Tournefortia ar

Casuarina equisetifolia Non-native tree species 10–20 meters tall Casuarina equisetifoliaCocos nucifera Polynesian tree species with potential habitat in understory Cocos nuciferaPluchea spp. Non-native invasive shrub species up to 2 meters tall, Hawaii noxious weed designation Pluchea indica, Pluchea carolinensis

Tournefortia argentea Polynesian coastal tree species with broad spreading crown Tournefortia argentea

Mixed shrub Low-density shrub cover, multiple native and non-native species

Capparis sandwichiana, Casuarina equisetifo

Coccoloba uvifera, Cocos nucifera, Hibiscus 

bidentatum o-waihiense, Pisonia grandis, Plu

taccada, Terminalia catappa, Tournefortia ar

Grass/herbaceous cover High-density grass and herbaceous cover, multiple native and non-native species, some speices up

to 1.5 meters tall 

Bidens alba, Cenchrus echinatus, Chamaesyc

Cynodon dactylon, Digitaria ciliaris, Eleusin

Eragrostis variabilis, Euphorbia heterophylla

Lepturus repens, Lobularia maritima, Malva 

sandwicensium, Psilotum nudum, Setaria ver

americanum, Solanum nelsonii, Sonchus oler

Verbesina encelioides Non-native invasive erect annual herb, 0.3 to 1.7 meters Verbesina encelioides

Wetland vegetation Wetland plant species surviving in saturated conditions

Batis maritima, Capparis sandwichiana, Cyplaevigatus, Cyperus pennatiformis var. bryan

Heliotropium currasavicum, Pluchea indica, 

Partially vegetated former runwaya Dilapidated runway with some vegetation Fimbristylis cymosa, Tribulus cistoides, VerbBare ground Bare ground inland of beach area (without wave swash) UnvegetatedHard panb Former guano mining site with minimal vegetation Portulaca spp., Sporobolus pyramidatus, Fim

Beach Coastal land subject to wave swash under typical conditions, typically sand, coral or rock Unvegetated

Wetland (unvegetated)b Primarily unvegetated mudflats UnvegetatedWetland (standing water) Perennially inundated area UnvegetatedHuman structuresc Runways, buildings, roads, seawalls, etc. UnvegetatedaOnly present on Green Island, Kure Atoll, and Eastern Island, Midway Atoll. 

bOnly present on Laysan Island. cBuildings and runways are only found on Green, Sand, Eastern and Tern Islands.

Low-lying vines and low herbaceous groundcover, multiple native and non-native species

Vine: Boerhavia repens, Cassytha filiformis, I

caprae, Sicyos maximowiczii, Sicyos pachyca

Coronopus didymus, Heliotropium procumbe

Vine/ground cover Portulaca lutea, Portulaca oleracea, Tribulu

Table 1.6. Land cover class definitions with plant species for each class. [General description used to identify and delineate each land cover class from satellite image analysis. Land cover was classified from QuickBird (Kure AtoAtoll [March 2009], Pearl and Hermes Atoll [October 2007], Lisianski Island [January 2008], French Frigate Shoals [November 2005]), and WorldView-2 (Lsatellite imagery using IsoCluster methods (ESRI, 2011). Plant species were described from Wagner and others (2005) and multiple botanical reviews: Ku2001; Vanderlip and others, 2007; Vanderlip and others, 2008), Midway Atoll (Starr and Martz, 1999a; Klavitter, 2006; Starr and others, 2006; Starr and othHermes Atoll (Starr and Martz, 1999a), Lisianski Island (Starr and Martz, 1999a), Laysan Island (Starr and Martz, 1999a; Kristof and others, 2011), and Fr(Starr and Martz, 1999a)]
Kure Atoll Green Island

Sand IslandSpit IslandEastern IslandNorth IslandSoutheast IslandGrass IslandSeal-Kittery IslandShark IslandTern IslandTrig IslandEast IslandRound IslandGin IslandLittle Gin IslandDisappearing Island

Tree/shrub X X X X X X X X

Mixed shrub X X X X

Grass/herbaceous cover X X X X X X X X X X X

Vine/ground cover X X X X X X X X X

Wetland vegetation X X

Partially vegetated former runway X X

Bare ground X X X X X X X X X X X X X X X X

Hard pan X

Beach X X X X X X X X X X X X X X X X X X

Wetland (unvegetated) X

Wetland (standing water) X X X X X X

Human structures X X X X X

French Frigate Shoals

Land cover

Lisianski IslandLaysan Island

Midway Atoll Pearl and Hermes Atoll

Table 1.7. Land cover classes identified for the low-lying Northwestern Hawaiian Islands from satellite imagery. 

[Land cover was classified from QuickBird (Kure Atoll [July 2008], Midway Atoll [March 2009], Pearl and Hermes Atoll [October 2007], Lisianski 

Island [January 2008], French Frigate Shoals [November 2005]) and WorldView-2 (Laysan Island [May 2010]) satellite imagery using IsoCluster 

methods (ESRI, 2011)]

Materials and Methods    13

across many land cover classes. Tables 1.6 and 1.7 provide 

additional details on land cover categories (also see chap. 2 for 

further details on Laysan Island land cover).

We did not perform a rigorous quantitative accuracy 

assessment of our land cover classification due to the logistical 

constraints of collecting ground-truth data. Our accuracy assessment of land cover classification included visual inspection and 

comparison with other historical mapping data, field reports, 

and past botanical surveys (Starr and Martz, 1999b; Starr and 

Martz, 1999a; Starr and others, 2001; Klavitter, 2006; Starr and 

others, 2006; Starr and others, 2008). We also solicited expert 

opinion from field biologists for general classification accuracy (E. Flint, USFWS; P. Hartzell, USFWS [French Frigate 

Shoals]; T. Speetjens, USFWS [Laysan Island]; M. Stelmach, 

USFWS [Laysan Island]; and C. Vanderlip, Hawai‘i DLNR 

[Kure Atoll]). 

Land Cover Change

We quantified potential land cover change from SLR 

using a static vegetation response model. The static vegetation response model predicts that land cover does not shift 

inland as water levels rise but, rather, that inundated land 

cover is lost. We overlaid the land cover classification with 

the inundation grids for each SLR scenario (+0.5, +1.0, +1.5, 

and +2.0m) and subtracted the inundation zone from the land 

cover classification in ArcGIS. 

Avifauna Nesting Habitat Classification

Land cover classes were designated as nesting habitat on 

the basis of species-specific nesting behavior (see Land Cover 

Classification section and appendix 3). We calculated speciesspecific potential nesting area as the sum of all utilized land 

cover classes (table 1.8) without differentiating between vertical 

habitat structures within land cover classes. For example, tree/

shrub habitat was quantified equally for Great Frigatebird, 

which nest on top of trees and shrubs, and Red-tailed Tropicbird, which nest on the ground beneath trees and shrubs. We 

assumed all known nesting habitat types were equally utilized 

by a given species, ignoring habitat preferences and competition 

that may vary with habitat availability. Resolution of remotely 

sensed imagery and seasonal variation in vegetation cover 

prevented fine-scale nesting habitat classification. 

Uncertainty and Model Assumptions

Scenarios are sequences of postulated events based on 

specific assumptions. Future SLR is subject to changing environmental conditions, such as carbon dioxide (CO2) emissions, 

changes in temperature, ice sheet dynamics, glacial retreat, and 

oceanic heat uptake (IPCC, 2007). Therefore, uncertainty in the 

magnitude and rate of climate change grows with projections 

further into the future (fig.1.1). Additionally, passive inundation 

models have inherent uncertainties. For example, our methods 

depict physical conditions at a single moment in time, but since 
Species Tree/shrub Casuarina equisetifolia Pluchea indica Tournefortia argentea Mixed shrub Grass/ herbaceous cover Vine/ ground cover Wetland vegetation Partially vegetated former runway Bare groun

Black-footed Albatross X XXX XXX Laysan Albatross X XXX XXX

Short-tailed Albatross X XXX X X Bonin Petrel X X X X XXX Bulwer's Petrel X Wedge-tailed Shearwater X X X X XXX X Christmas Shearwater X X X X XXX X Tristram's Storm-petrel X X X X XXX X Red-tailed Tropicbird X X X X X X X White-tailed Tropicbird X Masked Booby X X X X Brown Booby X X X X Red-footed Booby X X X X X Great Frigatebird X X X X Little Tern X X X Gray-backed Tern X X X Sooty Tern X X X X Brown Noddy X X X X XXX X X Black Noddy X X X X X White Tern X X X X 

Laysan Teal X Xc X X XXX Laysan Finch X X X XXX Xd Nihoa Finch X X X XXX Nihoa Millerbirde X X X XXX

aOccurs only on Laysan Island, only species known to nest in habitat type demarked. 

bUnder raised buildings at Tern Island, French Frigate Shoals, only. 

cSingle C. equisetifolia at Laysan Island not classified as potential habitat for Laysan Teal. 

dOn Southeast Island, Pearl and Hermes Atoll, only. 

eExpected habitat use from newly translocated populations to Laysan Island.

Table 1.8. Known avifauna nesting habitat by land cover class identified from satellite imagery on the low-lying Northwestern Hawaiia[See appendix 3 for details on nesting behavior]
Results    15

the coastal zone is dynamic, changes in inundation fluctuate with 

coastal processes, such as changes in sedimentation and erosion 

(NOAA, 2010b). 

DEM accuracy is estimated as RMSE (see chap. 2). Our 

passive SLR models do not include wave-driven inundation, 

so the impact of habitat loss and direct effects to resident wildlife during periods of high-wave energy are not considered. In 

chap. 2 we compare passive inundation models to wave-driven 

inundation models for a range of SLR scenarios for Laysan 

Island. We discuss the assumptions and uncertainty of inundation modeling in greater detail in chap. 2. 

In addition to the uncertainties in SLR predictions, the 

lack of baseline wildlife population data, and the complex 

interactions among climatic and biotic components of this 

system influence SLR effects on community and ecosystem 

dynamics. Most NWHI also lack historical land cover data 

to analyze change from SLR; however, historical accounts at 

French Frigate Shoals describe past vegetation communities on 

now barren islands (for example, Trig and Gin) and completely 

inundated islands (for example, Whale-Skate; Amerson, 1971; 

Wetmore 1923 unpub. personal journal reproduced in Olson, 

1996). Rising sea levels are expected to cause substantial reductions in beach and shoreline land cover area in other systems 

(Aiello-Lammens and others, 2011). Webb and Kench (2010) 

provide evidence of geomorphic change throughout the Pacific 

including the degree to which islands shift positions on reef 

platforms due to various factors and processes such as SLR. 

Evidence from historical observations and aerial photographs 

of French Frigate Shoals indicated island boundary shifts and 

inundation (see Hatfield and others, 2012). 

Given the model assumptions described above, predictions from SLR models should not be treated as absolute, but 

should be used as insight to better understand vulnerability 

and risks to island species, so that climate change mitigation 

and adaptation strategies can be planned to protect biodiversity. As we learn more about the state of this system under 

changing climate conditions, models can be improved and 

updated with new information to reduce uncertainty. In the 

following section, we report model results from SLR scenarios 

and downscaled climate projections for individual islands.

Results

The maximum elevation of the low-lying NWHI ranges 

from 1.8 m at Spit (Midway Atoll) and Little Gin Islands 

(French Frigate Shoals) to 11 m at Sand Island (Midway Atoll; 

table 1.9). La Perouse Pinnacle (French Frigate Shoals), a basalt 

rock, has the highest elevation measured (39.7 m, table1.9). 

Thirty-seven percent of the total land area analyzed (excluding 

La Perouse Pinnacle) was below 2 m; and 87 percent was below 

5 m. With passive SLR, just over 4 percent of the total land area 

was predicted to be inundated at +1.0 m and nearly 26percent at 

+2.0 m (table 1.9). Among the 25 native avian species breeding 

in the NWHI, we identified two endemic land birds found only 

on islands with mean elevations less than 4.5 m (Laysan Teal 

and Laysan Finch) and four seabird species with global breeding 

distributions largely limited to the NWHI (Black-footed 

and Laysan albatross, Bonin Petrel, and Gray-backed Tern; 

tables1.10 and 1.11). The State of Hawai‘i, USFWS, or international conservation committees have listed 11 of the 25 breeding 

bird species in the NWHI as species of conservation concern or 

at risk of extinction due to multiple threats (table1.11).

Across the NWHI, climate change models indicated that 

increasing temperatures and decreasing precipitation were likely 

in the future. Overall, annual average maximum temperature was 

predicted to increase by approximately 2.2°C (SD 0.2) across 

the NWHI by 2100 (table 1.12). The largest increase in annual 

average maximum temperature of 2.6°C (SD 1.7) was predicted 

for Kure, Midway, and Pearl and Hermes Atolls and Lisianski 

Island. In the following sections, results of land cover classification, elevation models, SLR, vegetation response, habitat projections, and climate models are presented for each atoll.

Kure Atoll

Island Information

Green Island (90.1 hectares, ha) and several small sand 

spits compose Kure Atoll (fig. 1.4). The small spits do not 

provide breeding habitat for nesting birds but provide haulout habitat for Hawaiian monk seals and green turtles. Land 

cover consisted of seven classes: tree/shrub including Scaevola 

taccada and Tournefortia argentea; grass/herbaceous vegetation 

including Cenchrus echinatus, Chamaesyce spp., Chenopodium 

murale, Cynodon dactylon, Eleusine indica, Eragrostis paupera,

Eragrostis variabilis, Fimbristylis cymosa, Lobularia maritima,

Pseudognaphalium sandwicensium, and Verbesina encelioides; 

vine/ground cover including Boerhavia repens, Cassytha filiformis, Ipomoea indica, Ipomoea pes-caprae, Sicyos maximowiczii, and Tribulus cistoides; partially vegetated former runway; 

bare ground; beach; and human structures including buildings 

(table 1.13; fig. 1.5). Green Island imagery was dominated by 

grass/herbaceous cover (including Verbesina encelioides, an 

invasive species of concern), covering 47 percent (42.1 ha) of 

the island in 2010 (table 1.13). Only 5 percent (4.1 ha) and 21 

percent (19.2 ha) of Green Island consisted of bare ground and 

tree/shrub, respectively (table 1.13).

Sea-Level Rise

Green Island had a maximum elevation of 7.3 m and mean 

of 2.8 m (SD 2.0; table 1.9, fig. 1.6). More than one-sixth (17 

percent) of the total land area was below 2.0 m elevation and 

90 percent was below 4.0 m. The passive inundation model 

showed a 9 percent loss of total island area at +1.0 m SLR 

and 14 percent at +2.0 m SLR (table 1.13, fig. 1.7). Almost no 

vegetated area was lost at less than +2.0 m SLR, at which point 

only 0.3 percent was lost. Beach area, however, was reduced 

by 50 percent at +1.0 m SLR and 80 percent at +2.0 m SLR 

(table 1.13). Seabird nesting habitat was reduced by less than 8 

percent for all seabirds (table 1.14). 
Table 1.9. Mean and maximum elevations of Northwestern Hawaiian Islands, referenced to mean high water (MHW), and derived from 

(DEMs; 2010, 2011). [Model accuracy is reported as the root-mean-squared error (RMSE) for each island; RMSE calculated from ellipsoidal heights. Land area and percent change w+1.5, and +2.0 meters of sea-level rise, assuming passive inundation. Abbreviations: m, meters; ha, hectares; %, percent; SD, standard deviation]

0.0 m

Atoll Island RMSE (m) Mean ± SD Max Area (ha) Area (ha) % change Area (ha) % change Area (ha) % 

Kure Atoll Green 0.11 2.8 ± 2.0 7.3 90.1 84.6 -6.1 82.4 -8.6 79.8 Midway Atoll Sand 0.05 2.5 ± 1.2 11.0 456.8 452.2 -1.0 448.7 -1.8 442.2 

Eastern 0.05 2.1 ± 0.6 7.5 133.6 131.4 -1.7 128.5 -3.8 122.1 Spit 0.05 0.8 ± 0.4 1.8 5.3 4.6 -13.2 1.4 -73.6 0.0 

Pearl and Hermes Atoll North 0.12 1.4 ± 0.5 2.5 8.4 7.5 -10.7 6.4 -23.8 4.4 

Little North 0.12 0.9 ± 0.4 2.3 3.2 2.3 -28.1 1.1 -65.6 0.3 Southeast 0.12 1.0 ± 0.6 2.5 18.4 14.1 -23.4 7.7 -58.2 3.2 Grass 0.12 1.3 ± 0.7 2.3 3.2 2.8 -13.3 2.2 -31.3 1.2 Seal-Kittery 0.12 1.3 ± 0.7 2.6 13.7 11.4 -16.9 9.5 -30.7 5.2 Lisianski 0.18 3.8 ± 1.3 7.6 147.1 145.1 -1.4 143.7 -2.3 142.1 

0.09 4.3 ± 2.3 10.7 339.4 338.3 -0.3 335.8 -1.1 331.2 0.09 4.3 ± 2.3 10.7 339.4 337.7 -0.5 317.0 -6.6 276.8 

French Frigate Shoals Shark 0.05 1.2 ± 0.3 1.9 0.3 0.3 0.0 0.2 -33.3 0.0 

Tern 0.05 2.3 ± 0.5 3.4 13.8 13.7 -0.7 13.2 -4.3 12.5 Trigb 0.15 0.5 ± 0.8 2.5 1.4 1.3 -7.1 1.1 -21.4 0.8 Eastb 0.15 2.3 ± 0.5 3.1 2.8 2.8 0.0 2.8 0.0 2.7 Roundb 0.15 1.0 ± 0.6 2.0 0.1 0.1 0.0 0.0 -100 0.0 La Perouse Pinnacleb 0.15 19.5 ± 12.1 39.7 0.3 * * * * * Gin 0.05 1.6 ± 0.5 2.9 1.7 1.4 -17.6 0.9 -47.1 0.3 Little Ginb 0.15 1.0 ± 0.4 1.8 1.6 1.5 -6.2 1.1 -31.3 0.2 Disappearing 0.05 0.6 ± 0.6 2.3 0.4 0.2 -50.0 0.1 -75.0 0.0 

All atolls and islandsc 1.7 ± 1.1 11.0 1241.3 1215.6 -2.1 1186.8 -4.4 1148.2 Laysan Islanda (with groundwater rise)

DEM Elevation (m) 2010 +0.5 m +1.0 m +1.5 m 

Laysan Islanda (without groundwater rise)aTerrestrial area only, lake zone mudflats and open water lake excluded. bAlternative data source, PhotoSat DEM. cTotal without groundwater rise on Laysan Island; does 

Pinnacle. *Area loss due to SLR not assessed for La Perouse Pinnacle.
Species NWHI

Main 

Hawaiian 

Islands

Tropical - Subtropical North 

Pacific: Other

Tropical - Subtropical South 

Pacific

Tropical to Subtropical Atlantic Trop

trop

Black-footed Albatross XX X X

Laysan Albatross XX X X

Short-tailed Albatross X XBonin Petrel XX XBulwer's Petrel X X X X X 

Wedge-tailed Shearwater X X X X Christmas Shearwater X X X X

Tristram's Storm-petrel X XRed-tailed Tropicbird X X X X 

White-tailed Tropicbird X X X X XMasked Booby X X X X

Brown Booby X X X X X 

Red-footed Booby X X X X X 

Great Frigatebird X X X X X 

Little Tern X X X X 

Gray-backed Tern XX X X

Sooty Tern X X X X 

Brown Noddy X X X X X 

Black Noddy X X X X X

Blue Noddy X X X

White Tern X X X X X 

Laysan Teal XXLaysan Finch XXNihoa Finch XNihoa Millerbird XHawaiian monk seal XHawaiian green turtle X XAmaranthus brownii XCyperus pennatiformis var. bryanii XXPritchardia remota XSchiedea verticillata XSesbania tomentosa X X

XX

Table 1.10. Global breeding distribution for species of the Northwestern Hawaiian Islands (NWHI), compiled from Fefer and (1984), Friedlander and others (2009), The Birds of North America Online (A. Poole, editor), and U.S. Fish and Wildlife Servic1998, 2007, 2008a, 2010) species recovery plans. [Species marked with a bold double X are expected to be highly vulnerable to habitat loss from sea-level rise due to their limited global breeding distrlargely occurring in the low-lying NWHI (also see table 1.11 and see table 1.1 for sub-species designations)]
IUCN Nature Serve U.S. Federal State of Hawai`i

NWHI breeding 

pairs reported

Proportion of global breedin

population restricted to NW

Black-footed Albatross Vulnerable G3 - Vulnerable BCC Threatened 64,000 (2011)a > 0.95aLaysan Albatross Near Threatened G3 - Vulnerable BCC 590,179 (2011)a > 0.95aShort-tailed Albatross Vulnerable G1 - Critically Imperiled Endangered Endangered 2 (2012)b, c < 0.05gBonin Petrel Least Concern Not evaluated 396,150 0.5–0.95gBulwer's Petrel Least Concern G4 - Apparently Secure 92,370 0.05–0.5 gWedge-tailed Shearwater Least Concern G4 - Apparently Secure 228,800 0.05–0.5hChristmas Shearwater Least Concern G3 - Vulnerable BCC 2,815 0.05–0.5hTristram's Storm-petrel Near Threatened G3 - Vulnerable BCC 6,030 0.05–0.5hRed-tailed Tropicbird Least Concern G4 - Apparently Secure 12,800 0.05–0.5hWhite-tailed Tropicbird Least Concern G5 - Secure 5 < 0.05hMasked Booby Least Concern G5 - Secure 2,215 Data neededBrown Booby Least Concern G5 - Secure 425 < 0.05hRed-footed Booby Least Concern G5 - Secure 7,450 0.05–0.5hGreat Frigatebird Least Concern G4 - Apparently Secure 10,345 0.05–0.5hLittle Tern Least Concern Not evaluated 20d < 0.05gGray-backed Tern Least Concern G3 - Vulnerable 43,225 0.5–0.95hSooty Tern Least Concern G5 - Secure 1,190,400 0.05–0.5hBlue Noddy Least Concern G4 - Apparently Secure 3,780 Data neededBrown Noddy Least Concern G5 - Secure 76,250 0.05–0.5hBlack Noddy Least Concern G5 - Secure 15,050 0.05–0.5hWhite Tern Least Concern G4 - Apparently Secure Threatened 25,215 0.05–0.5gLaysan Teal Critically Endangered G1 - Critically Imperiled Endangered Endangered 500–800 (2011)e 1Laysan Finch Vulnerable G1 - Critically Imperiled Endangered Endangered 5,000–20,000f 1

Nihoa Finch Critically Endangered G1 - Critically Imperiled Endangered Endangered 2,100–3,550f 1

Nihoa Millerbird Critically Endangered G1 - Critically Imperiled Endangered Endangered 250–999f 1Hawaiian monk seal Critically Endangered G2 - Imperiled Endangered Endangered *1,200–1,300i 0.5–0.95iHawaiian green turtle Endangered G3 - Vulnerable Endangered Endangered *61,000j 0.5–0.95jAmaranthus brownii Critically Endangered G1 - Critically Imperiled Endangered Endangered *> 40k 1Cyperus pennatiformis var. bryanii G1 - Critically Imperiled Endangered Endangered *488l 1Pritchardia remota Endangered G1 - Critically Imperiled Endangered Endangered *680m 1Schiedea verticillata G1 - Critically Imperiled Endangered Endangered *359m 1

Sesbania tomentosa G2 - Imperiled Endangered Endangered *1,600–2,000n Data needed

Common name

Status Population

*Denotes individuals, not breeding pairs. aFlint (2011). bUSFWS unpub. data. cC. Vanderlip, Hawai`i DLNR, written commun. dFriedlander and others (2009). eTotal number of mature 

individuals, USFWS/USGS, unpublished data. fTotal number of mature individuals, from Birdlife International (www.Birdlife.org). gCalculated from values of NWHI breeding pairs and global 

values from Birdlife International (where total mature birds reported for global population, half of value used for breeding pairs) or, hThe Birds of North America Online 

(http://bna.birds.cornell.edu/bna). iAntonelis and others (2006). jBalazs and Chaloupka (2004). kUSFWS (2007). lUSFWS (2008a). mUSFWS (1998). nUSFWS (2010).

Table 1.11. Conservation status assessments and population counts of species breeding (propagating) in the Northwestern Hawa[List compiled from the International Union for Conservation of Nature (IUCN; http://www.iucnredlist.org), Nature Serve (http://www.natureserve.org), the USFWS gram (USFWS; http://fws.gov/endangered/) and its list of Birds of Conservation Concern (BCC; USFWS, 2008b), and the State of Hawai‘i (Department of Land ahttp://hawaii.gov/dlnr/dofaw). Unless otherwise cited, population counts are not validated, but they are assumed to be indicators of breeder abundance; however, than 28 years old. These population counts were compiled from a variety of sources, including the most recent survey of all NWHI conducted in 1978-1982 (Fefetain updates to abundance indices when available (for details, see Pyle and Pyle, 2009). The sampling error, detection probability, and confidence interval of repounknown, and population values for many islands have not been updated in 28 years. Abbreviations: USFWS, U.S. Fish and Wildlife Service; USGS, U.S. Geolog
Table 1.12. Predicted (2100) annual average maximum temperature and total precipitation from interpolated climate surfaces. 

[Interpolation conducted with WorldClim data (Hijmans and others, 2005) at a central location at each atoll or island. Predictions from six general 

circulation models are presented (Intergovernmental Panel on Climate Change (IPCC) 2007). Abbreviations: SD, standard deviation; °C, degrees 

Celsius; mm, millimeters; see table 1.4 for model name descriptions]

Annual ± SD

CCCMA 

CGCM3.1.T63 ± SD GFDL CM2.0 ± SD GFDL CM2.1 ± SD

MPI 

ECHAM5 ± SD

MRI 

CGCM2.3.2A ± SD

UKMO 

HADCM3 ± SD

Average maximum temperature ( ̊C)

 Kure Atoll 23.4 0.0 1.8 1.2 2.2 1.4 2.5 1.6 2.3 1.5 2.6 1.7 1.9 1.2

 Midway Atoll 23.6 0.0 1.8 1.2 2.1 1.4 2.5 1.7 2.3 1.5 2.6 1.7 1.9 1.2

 Pearl and Hermes Atoll 23.5 0.0 1.8 1.2 2.1 1.4 2.6 1.7 2.3 1.5 2.6 1.7 1.9 1.3

 Lisianski Island 23.5 0.0 1.8 1.2 2.1 1.4 2.6 1.7 2.3 1.5 2.6 1.7 2.0 1.3

 Laysan Island 24.5 0.1 1.9 1.3 2.5 1.6 2.0 1.3 2.3 1.5 2.5 1.6 2.0 1.3

 French Frigate Shoals 25.3 0.1 2.0 1.3 1.9 1.2 2.4 1.5 2.3 1.5 2.2 1.4 2.2 1.4

Total precipitation (mm)

 Kure Atoll 818.7 1.3 -85.6 55.7 -99.8 65.0 -84.8 55.3 -56.0 36.5 -27.9 18.2 -103.5 67.4

 Midway Atoll 801.6 1.3 -89.3 58.2 -107.0 69.7 -87.6 57.1 -64.6 42.1 -28.9 18.9 -87.0 56.7

 Pearl and Hermes Atoll 812.1 1.5 -90.3 58.8 -121.3 79.0 -91.6 59.6 -91.5 59.6 -44.0 28.7 -67.7 44.1

 Lisianski Island 846.6 1.7 -91.9 59.9 -138.7 90.3 -95.5 62.2 -117.2 76.3 -51.4 33.5 -54.2 35.3

 Laysan Island 778.8 5.6 -97.8 63.7 -158.1 103.0 -120.7 78.6 -126.6 82.5 -81.5 53.1 -4.3 2.8

 French Frigate Shoals 577.2 3.5 -89.0 58.0 -51.4 33.5 -26.7 17.4 -34.8 22.7 -66.0 43.0 119.5 77.8

WorldClim 

(1960–1990) Predicted Change (2100)

Results    19

Figure 1.4. Map showing the location of Kure Atoll, the northernmost coral atoll in the world. Kure Atoll consists of Green 

Island and several small sand spits.
20    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.5. Green Island, Kure Atoll, land cover classification map developed using primarily 

unsupervised classification methods from a Digital Globe QuickBird satellite image (July 2008). Verbesina 

encelioides distribution mapped from 2007 field data (Vanderlip and others, 2007). Active management to 

remove V. encelioides after 2007 has substantially altered the distribution and abundance of this invasive 

species. Additional details on land cover and species composition are given in table 1.13.
Figure 1.6. Topographic map of Green Island, Kure Atoll, showing mean high water (MHW) and 2-meter 

(m) contour lines with a graph of the percentage of land in 0.5-m elevation bins. Elevations determined 

from U.S. Geological Survey lidar data (2010) with a vertical root-mean-squared error of 0.11 m.

Results    21
22    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.7. Green Island, Kure Atoll, passive inundation scenario maps for five sea levels: mean high water 

through +2.0 m (meters) sea-level rise (SLR) at 0.5-m increments. Inundation scenarios are based on U.S. 

Geological Survey lidar-derived elevations with a root-mean-squared error of 0.11 m.
0.0 m

Land cover Area (ha) Area (ha) % change Area (ha) % change Area (ha) % change Area (ha) % change

Tree/shrub 19.2 19.2 0.0 19.2 0.0 19.2 0.0 19.1 -0.5

Grass/herbaceous cover/Verbesina 

encelioides 42.1 42.1 0.0 42.1 0.0 42.0 -0.2 42.0 -0.2

Vine/ground cover 3.3 3.3 0.0 3.3 0.0 3.3 0.0 3.3 0.0

Partially vegetated former runway 6.0 6.0 0.0 6.0 0.0 6.0 0.0 6.0 0.0

Bare ground 4.1 4.1 0.0 4.0 -2.4 4.0 -2.4 3.8 -7.3

Beach 15.4 9.9 -35.7 7.7 -50.0 5.3 -65.6 3.1 -79.9

Total island size 90.1 84.6 -6.1 82.3 -8.6 79.8 -11.4 77.3 -14.2

Change from SLR

+0.5 m +1.0 m +1.5 m +2.0 m

Table 1.13. Total land area and percent change in area for each of six land cover types at Green Island, Kure Atoll, 

with four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters). 

[Areas calculated from Digital Globe QuickBird satellite imagery (July 2008). Abbreviations: m, meters; ha, hectares; %, percent]

Area (ha)

Species 0.0 m +0.5 m +1.0 m +1.5 m +2.0 m

Black-footed Albatross 55.5 0.0 -0.2 -0.4 -0.7

Laysan Albatross 55.5 0.0 -0.2 -0.4 -0.7

Short-tailed Albatross 55.5 0.0 -0.2 -0.4 -0.7

Bonin Petrel 64.6 0.0 0.0 -0.2 -0.3

Bulwer’s Petrel 4.1 0.0 -2.4 -2.4 -7.3

Wedge-tailed Shearwater 68.7 0.0 -0.1 -0.3 -0.7

Christmas Shearwater 70.6 0.0 0.0 -0.1 -0.3

Tristram’s Storm-petrel 68.7 0.0 -0.1 -0.3 -0.7

Red-tailed Tropicbird 67.3 0.0 0.0 -0.1 -0.3

Masked Booby 55.5 0.0 -0.2 -0.4 -0.7

Brown Booby 55.5 0.0 -0.2 -0.4 -0.7

Red-footed Booby 19.2 0.0 0.0 0.0 -0.5

Great Frigatebird 19.2 0.0 0.0 0.0 -0.5

Gray-backed Tern 13.4 0.0 -0.7 -0.7 -2.2

Sooty Tern 55.5 0.0 -0.2 -0.4 -0.7

Brown Noddy 74.7 0.0 -0.1 -0.3 -0.7

Black Noddy 19.2 0.0 0.0 0.0 -0.5

White Tern 19.2 0.0 0.0 0.0 -0.5

Percent Change from SLR

Table 1.14. Total potential nesting habitat area and percent change in nesting habitat area for 

breeding avifauna at Green Island, Kure Atoll, with four sea-level rise (SLR) scenarios (+0.5, +1.0, 

+1.5, and +2.0 meters). 

[Areas calculated from land cover classes with Digital Globe QuickBird satellite imagery (July 2008). We assumed percent change based on uniform density across nesting habitat for each species. Abbreviations: m, meters; ha, hectares]

Results    23
24    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Midway Atoll

Island Information

Sand, Spit, and Eastern Islands compose Midway Atoll 

(fig. 1.8). Sand Island was 456.8 ha, Spit Island was 5.3 ha, 

and Eastern Island was 133.6 ha. Land cover consisted of nine 

general classes: tree/shrub including Casuarina equisetifolia, 

Coccoloba uvifera, Hibiscus tiliaceus, Lepidium bidentatum, 

Scaevola taccada, Terminalia catappa, Tournefortia argentea; 

mixed shrub; grass/herbaceous cover including Cenchrus echinatus, Eragrostis paupera, Eragrostis variabilis, Euphorbia 

spp., Fimbristylis cymosa, Lepturus repens, Lobularia maritime, Pseudognaphalium sandwicensium, Psilotum nudum, 

Solanum nelsonii, Verbesina encelioides; vine/ground cover 

including Bidens alba, Boerhavia repens, Cassytha filiformis, 

Ipomoea indica, Ipomoea pes-caprae, Portulaca lutea, and 

Tribulus cistoides; partially vegetated former runway; bare 

ground; beach; wetland (standing water); and human structures including buildings, roads, and runways (tables 1.15−17; 

figs. 1.9–11). Two species-specific land cover classes based on 

historical distribution data included: Casuarina equisetifolia 

and Tournefortia argentea (table 1.15). At Sand Island the 

dominant land cover categories were grass/herbaceous cover 

(133.8 ha) and human structures including tarmac (129.3 ha; 

table 1.15). Spit Island land cover consisted mostly of bare 

ground (2.3 ha), grass/herbaceous cover (1.1 ha), mixed shrub 

(0.8 ha), and beach (0.8 ha; table 1.16). Grass/herbaceous 

cover (74.4 ha) and partially vegetated runway (31.6 ha) 

predominated at Eastern Island (table 1.17). 

Sea-Level Rise 

Sand Island

The maximum elevation of Sand Island was 11.0 m with 

a mean of 2.5 m (SD 1.2; table 1.9). Of the total land area, 

42 percent of Sand Island was below 2.0 m elevation and 90 

percent below 4.0 m (fig. 1.12). With the passive inundation 

model, 2 percent of total area was lost at +1.0 m SLR and 39 

percent at +2.0 m SLR (table 1.15, fig. 1.13). Loss of vegetated area was minimal (less than 1 percent) at less than +2.0 

m SLR, at which point 33 percent of total vegetated area was 

lost. Beach area, however, was reduced by 31 percent at +1.0 

m SLR and 71 percent at +2.0 m SLR (table 1.15). Less than 2 

percent of potential nesting habitat for bird species was lost at 

+1.5 m SLR; however, at +2.0 m SLR 32–38 percent was lost 

(table 1.18). 

Spit Island

The maximum elevation of Spit Island was 1.8 m with 

a mean of 0.8 m (SD 0.4; table 1.9). Of all the islands of 

Midway Atoll, proportional land area loss was greatest at Spit 

Island with 74 percent lost at +1.0 m SLR and complete inundation at +1.5 m SLR (table 1.16, figs. 1.14−15). Vegetated 

area losses were 62 percent at +1.0 m SLR and 100 percent at 

+1.5 m SLR (table 1.16). Beach and wetland were completely 

inundated at +1.0 m SLR (table 1.16), and all nesting habitat 

was lost at +1.5 m SLR (table 1.19).

Figure 1.8. Maps showing the location of Midway Atoll, a coral atoll located between Kure Atoll to the northwest 

and Pearl and Hermes Atoll to the southeast. Sand, Spit, and Eastern Islands are the only permanent land masses at 

Midway Atoll.
0.0 m

Land cover Area (ha) Area (ha) % change Area (ha) % change Area (ha) % change Area (ha) % change

Tree/shrub 36.6 36.6 0.0 36.5 -0.3 36.5 -0.3 26.0 -29.0

Casuarina equisetifolia 65.4 65.3 -0.2 65.3 -0.2 65.0 -0.6 40.9 -37.5

Tournefortia argentea 4.4 4.4 0.0 4.4 0.0 4.4 0.0 4.4 0.0

Mixed shrub 16.6 16.6 0.0 16.6 0.0 16.5 -0.6 11.9 -28.3

Grass/herbaceous cover 133.8 133.7 -0.1 133.6 -0.1 133.1 -0.5 89.5 -33.1

Vine/ground cover 29.5 29.4 -0.3 29.2 -1.0 29.0 -1.7 19.0 -35.6

Bare ground 16.7 16.6 -0.6 16.3 -2.4 15.8 -5.4 12.1 -27.5

Beach 22.3 18.1 -18.8 15.3 -31.4 11.4 -48.9 6.4 -71.3

Wetland (standing water) 2.2 2.2 0.0 2.2 0.0 2.2 0.0 0.9 -59.1

Human structures 129.3 129.3 0.0 129.3 0.0 128.3 -0.8 65.7 -49.2

Total island size 456.8 452.2 -1.0 448.7 -1.8 442.2 -3.2 276.8 -39.4

Change from SLR

+0.5 m +1.0 m +1.5 m +2.0 m

Table 1.15. Total land area and percent change in area for each of ten land cover types at Sand Island, Midway Atoll, with 

four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters). 

[Areas calculated from Digital Globe QuickBird satellite imagery (March 2009) and historical field data (Laniawe, 2004). The land cover class “partially vegetated former runway” was not classified at Sand Island. Abbreviations: m, meters; ha, hectares; %, percent] 

Results    25

Eastern Island

The maximum elevation of Eastern Island was 7.5 m with 

a mean of 2.1 m (SD 0.6; table 1.9). Of the total land area of 

Eastern Island, 42 percent was below 2.0 m elevation and 99 

percent was below 3.5 m (fig. 1.16). With the passive inundation model, 4 percent of total area was lost at +1.0 m SLR and 

39 percent at +2.0 m SLR (table 1.17, fig. 1.17). Total beach 

area was reduced by 45 percent and bare ground by 15 percent 

at +1.0 m SLR; at +2.0 m SLR all land cover classes were 

reduced by more than one-quarter (table 1.17). Habitat losses 

ranged from 2 to 5 percent for all bird species at +1.5 m SLR 

(table 1.20). However, at +2.0 m SLR breeding habitat for all 

species was expected to be reduced by 29–37 percent (table 

1.20, fig. 1.17). 

0.0 m

Land cover Area (ha) Area (ha) % change Area (ha) % change Area (ha) % change Area (ha) % change

Mixed shrub 0.8 0.8 0.0 0.4 -50.0 0.0 -100 0.0 -100

Grass/herbaceous cover 1.1 1.1 0.0 0.3 -72.7 0.0 -100 0.0 -100

Vine/ground cover 0.2 0.2 0.0 0.1 -50.0 0.0 -100 0.0 -100

Bare ground 2.3 2.2 -4.3 0.6 -73.9 0.0 -100 0.0 -100

Beach 0.8 0.2 -75.0 0.0 -100 0.0 -100 0.0 -100

Wetland (standing water) 0.1 0.1 0.0 0.0 -100 0.0 -100 0.0 -100

Total island size 5.3 4.6 -13.2 1.4 -73.6 0.0 -100 0.0 -100

Change from SLR

+0.5 m +1.0 m +1.5 m +2.0 m

Table 1.16. Total land area and percent change in area for each of six land cover types at Spit Island, Midway Atoll, with 

four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters). 

[Areas calculated from Digital Globe QuickBird satellite imagery (March 2009). Abbreviations: m, meters; ha, hectares; %, percent]

0.0 m

Land cover Area (ha) Area (ha) % change Area (ha) % change Area (ha) % change Area (ha) % change

Tree/shrub 16.1 16.1 0.0 16.1 0.0 15.7 -2.5 11.1 -31.1

Grass/herbaceous cover 74.4 74.3 -0.1 74.2 -0.3 71.2 -4.3 46.1 -38.0

Partially vegetated former runway 31.6 31.6 0.0 31.5 -0.3 31.5 -0.2 23 -27.2

Bare ground 1.3 1.2 -7.7 1.1 -15.4 0.8 -38.5 0.3 -76.9

Beach 10.2 8.2 -19.9 5.6 -45.1 2.8 -72.4 0.7 -93.1

Total island size 133.6 131.4 -1.7 128.5 -3.8 122.1 -8.6 81.2 -39.2

Change from SLR

+0.5 m +1.0 m +1.5 m +2.0 m

Table 1.17. Total land area and percent change for each of five land cover types at Eastern Island, Midway Atoll with four 

sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters). 

[Areas calculated from Digital Globe QuickBird satellite imagery (March 2009). Abbreviations: m, meters; ha, hectares; %, percent]
Figure 1.9. Sand Island, Midway Atoll, land cover classification map developed using primarily unsupervised 

classification methods from Digital Globe QuickBird satellite imagery (March 2009). Casuarina equisetifolia, 

Tournefortia argentea, Verbesina encelioides distribution mapped from historical field data (Laniawe, 2004). 

Additional details on land cover and species composition are given in table 1.15. Active management to remove 

C. equisetifolia, T. argentea, and V. encelioides has substantially altered the vegetation distribution. Land cover 

classification derived from 2011 imagery will be available in Storlazzi and others (2012, in prep., U.S. Geological 

Survey Open-File Report).

26    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands
Figure 1.10. Spit Island, Midway Atoll, land cover classification map developed using primarily 

unsupervised classification methods from Digital Globe QuickBird satellite imagery (March 2009). Additional 

details on land cover are given in table 1.16.

Results    27
28    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.11. Eastern Island, Midway Atoll, land cover classification map developed using primarily unsupervised 

classification methods from Digital Globe QuickBird satellite imagery (March 2009). Verbesina encelioides

distribution mapped from historical field data (Laniawe, 2004). Additional details on land cover and species 

composition are given in table 1.17.
Figure 1.12. Topographic map of Sand Island, Midway Atoll, showing mean high water (MHW) and 2-meter (m) contour lines percentage of land in 0.5-m elevation bins. Elevations were determined from U.S. Geological Survey lidar data (2010) with a ve

squared error of 0.05 m.
30    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.13. Sand Island, Midway Atoll, passive inundation scenario maps for five sea levels: mean high 

water through +2.0 m (meters) sea-level rise (SLR) at 0.5-m increments. Inundation scenarios are based on 

U.S. Geological Survey lidar-derived elevations with a root-mean-squared error of 0.05 m.
Area (ha)

Species 0.0 m +0.5 m +1.0 m +1.5 m +2.0 m

Black-footed Albatross 4.4 -2.3 -68.2 -100 -100

Laysan Albatross 4.4 -2.3 -68.2 -100 -100

Bonin Petrel 2.1 0.0 -61.9 -100 -100

Christmas Shearwater 2.1 0.0 -61.9 -100 -100

Red-tailed Tropicbird 1.9 0.0 -63.2 -100 -100

Masked Booby 3.6 -2.8 -72.2 -100 -100

Brown Booby 3.6 -2.8 -72.2 -100 -100

Red-footed Booby 0.8 0.0 -50.0 -100 -100

Great Frigatebird 0.8 0.0 -50.0 -100 -100

Gray-backed Tern 2.5 -4.0 -72.0 -100 -100

Brown Noddy 4.4 -2.3 -68.2 -100 -100

Percent Change from SLR

Table 1.19. Total potential nesting habitat area and percent change in nesting habitat area for 

breeding avifauna at Spit Island, Midway Atoll, with four sea-level rise (SLR) scenarios (+0.5, 

+1.0, +1.5, and +2.0 meters). 

[Areas calculated from land cover classes with Digital Globe QuickBird satellite imagery (March 2009). We 

assumed percent change based on uniform density across nesting habitat for each species. Abbreviations: m, 

meters; ha, hectares]

Results    31

Area (ha)

Species 0.0 m +0.5 m +1.0 m +1.5 m +2.0 m

Black-footed Albatross 262.0 -0.2 -0.4 -1.0 -33.8

Laysan Albatross 262.0 -0.2 -0.4 -1.0 -33.8

Bonin Petrel 286.3 -0.1 -0.2 -0.6 -33.0

Wedge-tailed Shearwater 303.0 -0.1 -0.4 -0.9 -32.7

Red-tailed Tropicbird 256.8 -0.1 -0.2 -0.5 -32.7

White-tailed Tropicbird 65.4 -0.2 -0.2 -0.6 -37.5

Little Tern 180.0 -0.2 -0.5 -1.2 -33.0

Brown Noddy 303.0 -0.1 -0.4 -0.9 -32.7

Black Noddy 123.0 -0.1 -0.2 -0.5 -32.4

White Tern 129.0 -0.1 -0.2 -0.5 -33.0

Laysan Teal 286.3 -0.1 -0.2 -0.6 -33.0

Percent Change from SLR

Table 1.18. Total potential nesting habitat area and percent change in nesting habitat area for 

breeding avifauna at Sand Island, Midway Atoll, with four sea-level rise (SLR) scenarios (+0.5, 

+1.0, +1.5, and +2.0 meters). 

[Areas calculated from land cover classes with Digital Globe QuickBird satellite imagery (March 2009). We assumed 

percent change based on a uniform density across nesting habitat for each species. Abbreviations: m, meters; ha, 

hectares]
32    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.14. Topographic map of Spit Island, Midway Atoll, showing mean high water (MHW) and 1-meter (m) 

contour lines with a graph of the percentage of land in 0.5-m elevation bins. Elevations were determined from 

U.S. Geological Survey lidar data (2010) with a vertical root-mean-squared error of 0.05 m.
Results    33

Figure 1.15. Spit Island, Midway Atoll, passive inundation scenario maps for five sea levels: mean high water 

through +2.0 m (meters) sea-level rise (SLR) at 0.5-m increments. Inundation scenarios are based on U.S. Geological 

Survey lidar-derived elevations with a root-mean-squared error of 0.05 m.
34    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Area (ha)

Species 0.0 m +0.5 m +1.0 m +1.5 m +2.0 m

Black-footed Albatross 107.3 -0.2 -0.5 -3.5 -35.3

Laysan Albatross 107.3 -0.2 -0.5 -3.5 -35.3

Short-tailed Albatross 107.3 -0.2 -0.5 -3.5 -35.3

Bonin Petrel 90.5 -0.1 -0.2 -4.0 -36.8

Wedge-tailed Shearwater 91.8 -0.2 -0.4 -4.5 -37.4

Christmas Shearwater 122.1 -0.1 -0.2 -3.0 -34.3

Red-tailed Tropicbird 122.1 -0.1 -0.2 -3.0 -34.3

Masked Booby 107.3 -0.2 -0.5 -3.5 -35.3

Brown Booby 107.3 -0.2 -0.5 -3.5 -35.3

Red-footed Booby 16.1 0.0 0.0 -2.5 -31.1

Great Frigatebird 16.1 0.0 0.0 -2.5 -31.1

Gray-backed Tern 32.9 -0.3 -0.9 -1.8 -29.2

Sooty Tern 107.3 -0.2 -0.5 -3.5 -35.3

Brown Noddy 123.4 -0.2 -0.4 -3.4 -34.8

Black Noddy 16.1 0.0 0.0 -2.5 -31.1

White Tern 16.1 0.0 0.0 -2.5 -31.1

Laysan Teal 90.5 -0.1 -0.2 -4.0 -36.8

Percent Change from SLR

Table 1.20. Total potential nesting habitat area and percent change for breeding avifauna at 

Eastern Island, Midway Atoll, with four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 

meters).

[Areas calculated from land cover classes with Digital Globe QuickBird satellite imagery (March 2009). We assumed 

percent change from a uniform density across nesting habitat for each species. Abbreviations: m, meters; ha, hectares]
Figure 1.16. Topographic map of Eastern Island, Midway Atoll, showing mean high water (MHW) and 1-meter (m) congraph of the percentage of land in 0.5-m elevation bins. Elevations were determined from U.S. Geological Survey lidar d

vertical root-mean-squared error of 0.05 m.
36    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.17. Eastern Island, Midway Atoll, passive inundation scenario maps for five sea levels: mean high 

water through +2.0 meters (m) sea-level rise (SLR) at 0.5-m increments. Inundation scenarios are based on U.S. 

Geological Survey lidar-derived elevations with a root-mean-squared error of 0.05 m.
Results    37

Pearl and Hermes Atoll

Island Information

North, Little North, Southeast, Grass, and Seal-Kittery 

Islands and several small sand spits constitute Pearl and 

Hermes Atoll (fig. 1.18). Southeast Island was the largest 

island of the atoll (18.4 ha), followed by Seal-Kittery Island 

(13.7 ha), North Island (8.4 ha), Little North Island (3.2 ha), 

and Grass Island (3.2 ha; table 1.9). Vegetation was only 

found on the three largest islands: North, Southeast, and 

Figure 1.18. Maps showing the location of Pearl and Hermes Atoll, a coral atoll located between Midway Atoll to the 

northwest and Lisianski Island to the southeast. Pearl and Hermes Atoll comprises multiple sand spits and five islands: 

North, Little North, Southeast, Grass, and Seal-Kittery.

Seal-Kittery. Land cover consisted of six classes: grass/

herbaceous cover including Cenchrus echinatus, Cynodon 

dactylon, Eragrostis paupera, Eragrostis variabilis,

Lepturus repens, Setaria verticillata, Solanum americanum,

Solanum nelsonii, Sonchus oleraceus, Verbesina encelioides;

vine/ground cover including Boerhavia repens, Coronopus 

didymus, Ipomoea pes-caprae, Portulaca lutea, Portulaca 

oleracea, Sicyos maximowiczii, and Tribulus cistoides; 

wetland vegetation including Sesuvium portulacastrum; bare 

ground; beach; and wetland (standing water; tables 1.21–25, 

figs. 1.19–21). The dominant land cover of the atoll was 

beach totaling 23.9 ha across the islands (tables 1.21–25).
Figure 1.19. North Island, Pearl and Hermes Atoll, land cover classification map developed using 

primarily unsupervised classification methods from Digital Globe QuickBird satellite imagery (October 

2007). Additional details on land cover are given in table 1.21.

38    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands
Figure 1.20. Southeast Island, Pearl and Hermes Atoll, land cover classification map developed using primarily unsupervise

methods from Digital Globe QuickBird satellite imagery (October 2007). Verbesina encelioides distribution was mapped base2003 field data (Sprague 2003). Additional details on land cover and species composition are given in table 1.23.
Figure 1.21. Seal-Kittery Island, Pearl and Hermes Atoll, land cover classification map developed using primarily unsupervise

classification methods from Digital Globe QuickBird satellite imagery (October 2007). Additional details on land cover are given 
0.0 m

Land cover Area (ha) Area (ha) % change Area (ha) % change Area (ha) % change Area (ha) % change

Grass/herbaceous cover 0.2 0.2 0.0 0.2 0.0 0.1 -50.0 0.0 -100

Vine/ground cover 0.6 0.6 0.0 0.6 0.0 0.5 -16.7 0.1 -83.3

Bare ground 1.9 1.9 0.0 1.9 0.0 1.5 -21.1 0.3 -84.2

Beach 5.7 4.8 -15.8 3.7 -35.1 2.3 -59.6 0.5 -91.2

Total island size 8.4 7.5 -10.7 6.4 -23.8 4.4 -47.6 0.9 -89.3

+0.5 m +1.0 m +1.5 m +2.0 m

Change from SLR

Table 1.21. Total land area and percent change for each of four land cover types at North Island, Pearl and Hermes Atoll, 

with four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters). 

[Areas calculated from Digital Globe QuickBird satellite imagery (October 2007). Abbreviations: m, meters; ha, hectares; %, percent]

Results    41

0.0 m

Land cover Area (ha) Area (ha) % change Area (ha) % change Area (ha) % change Area (ha) % change

Bare ground 0.3 0.3 0.0 0.3 0.0 0.3 0.0 0.0 -100

Beach 2.9 2.0 -31.0 0.8 -72.4 0.0 -100 0.0 -100

Total island size 3.2 2.3 -28.1 1.1 -65.6 0.3 -90.6 0.0 -100

+0.5 m +1.0 m +1.5 m +2.0 m

Change from SLR

Table 1.22. Total land area and percent change for each of two land cover types at Little North Island, Pearl and Hermes 

Atoll, with four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters). 

[Areas calculated from Digital Globe QuickBird satellite imagery (October 2007). Abbreviations: m, meters; ha, hectares; %, percent]

0.0 m

Land cover Area (ha) Area (ha) % change Area (ha) % change Area (ha) % change Area (ha) % change

Grass/herbaceous cover/Verbesina encelioides 6.6 6.6 0.0 5.1 -22.7 2.3 -65.2 0.4 -93.9

Vine/ground cover 2.0 2.0 0.0 1.5 -25.0 0.7 -65.0 0.1 -95.0

Wetland vegetation 1.9 1.4 -26.3 0.0 -100 0.0 -100 0.0 -100

Bare ground 0.5 0.5 0.0 0.3 -40.0 0.1 -80.0 0.0 -100

Beach 7.2 3.4 -52.8 0.8 -88.9 0.1 -98.6 0.0 -100

Wetland (standing water) 0.2 0.2 0.0 0.0 -100 0.0 -100 0.0 -100

Total island size 18.4 14.1 -23.4 7.7 -58.2 3.2 -82.6 0.5 -97.3

+0.5 m +1.0 m +1.5 m +2.0 m

Change from SLR

Table 1.23. Total land area and percent change for each of six land cover types at Southeast Island, Pearl and Hermes 

Atoll, with four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters). 

[Areas calculated from Digital Globe QuickBird satellite imagery (October 2007). Abbreviations: m, meters; ha, hectares; %, percent]
0.0 m

Land cover Area (ha) Area (ha) % change Area (ha) % change Area (ha) % change Area (ha) % change

Grass/herbaceous cover 0.5 0.5 0.0 0.5 0.0 0.5 0.0 0.2 -60.0

Bare ground 0.7 0.7 0.0 0.7 0.0 0.6 -14.3 0.1 -87.5

Beach 2.0 1.6 -19.6 1.0 -50.0 0.1 -95.0 0.0 -100

Total island size 3.2 2.8 -13.3 2.2 -31.3 1.2 -62.8 0.3 -90.6

+0.5 m +1.0 m +1.5 m +2.0 m

Change from SLR

Table 1.24. Total land area and percent change for each of three land cover types at Grass Island, Pearl and Hermes 

Atoll, with four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters). 

[Areas calculated from Digital Globe QuickBird satellite imagery (October 2007). Abbreviations: m, meters; ha, hectares; %, percent]

42    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Sea-Level Rise

The mean elevation of the islands of Pearl and Hermes 

Atoll was 1.2 m (SD 0.2, RMSE 0.12; table 1.9). Maximum 

elevation of islands in the atoll ranged from 2.3 m at Grass 

Island to 2.6 m at Seal-Kittery Island (table 1.9). Maximum, 

mean, and standard deviation of elevation for all islands are 

presented in table 1.9. Of the total land area of Pearl and 

Hermes Atoll (excluding sand spits), 92 percent was below 

2.0m elevation. At least 85 percent of the land area of each 

island of the atoll was below 2.0 m elevation (figs. 1.22–26).

Of the low-lying atolls and islands of the NWHI, proportional land area loss was greatest at Pearl and Hermes Atoll 

where 43 percent of total land area was expected to be lost 

at +1.0 m SLR and 92 percent lost at +2.0 m SLR. Among 

the islands of Pearl and Hermes Atoll, land area losses 

expected from passive inundation varied (tables 1.21–25). The 

greatest losses in the atoll were predicted for Little North and 

Southeast Islands at which 66 percent and 58 percent of total 

land area, respectively, was lost at +1.0 m SLR; at +2.0m SLR 

more than 97 percent was lost at both islands (tables 1.22–23). 

Vegetated area across the atoll was expected to be reduced 

by 32 percent at +1.0 m SLR and 92 percent at +2.0 m SLR, 

while beach was expected to be reduced by 66 percent at 

+1.0m SLR and 98 percent at +2.0 m SLR (tables 1.21–25). 

No seabird breeding habitat was predicted to be lost at 

+0.5m SLR across the atoll. For bird species at North, Little 

North, and Grass Islands the limited breeding habitat (2.7 ha or 

less; tables 1.26–30) was reduced by 60–100 percent at +2.0m 

SLR. At Southeast Island, breeding habitat for all species, 

including the endangered Laysan Finch, was reduced by 23–37 

percent at +1.0 m SLR and by more than 94 percent at +2.0m 

SLR (table 1.28). The relatively small amount of potential 

habitat for Bulwer’s Petrel (0.5 ha) and Gray-backed Tern 

(2.5 ha) at Southeast Island was reduced by 40 percent and 28 

percent, respectively, at +1.0 m SLR (table 1.28). 

0.0 m

Land cover Area (ha) Area (ha) % change Area (ha) % change Area (ha) % change Area (ha) % change

Grass/herbaceous cover 0.3 0.3 0.0 0.3 0.0 0.2 -33.3 0.1 -67.7

Vine/ground cover 0.2 0.2 0.0 0.2 0.0 0.2 0.0 0.1 -52.4

Bare ground 7.1 7.1 0.0 7.1 0.0 4.5 -36.6 1.9 -73.2

Beach 6.1 3.8 -37.7 1.9 -68.9 0.3 -95.1 0.0 -100

Total island size 13.7 11.4 -16.9 9.5 -30.7 5.2 -62.0 2.1 -84.7

+0.5 m +1.0 m +1.5 m +2.0 m

Change from SLR

Table 1.25. Total land area and percent change for each of four land cover types at Seal-Kittery Island, Pearl and Hermes 

Atoll, with four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters). 

[Areas calculated from Digital Globe QuickBird satellite imagery (October 2007). Abbreviations: m, meters; ha, hectares; %, percent] 
Figure 1.22. Topographic map of North Island, Pearl and Hermes Atoll, showing mean high water (MHW) 

and 1-meter (m) contour lines with a graph of the percentage of land in 0.5-m elevation bins. Elevations were 

determined from U.S. Geological Survey lidar data (2010) with a vertical root-mean-squared error of 0.12 m.

Results    43
44    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.23. Topographic map of Little North Island, Pearl and Hermes Atoll, showing mean 

high water (MHW) and 1-meter (m) contour lines with a graph of the percentage of land in 0.5-m 

elevation bins. Elevations were determined from U.S. Geological Survey lidar data (2010) with a 

vertical root-mean-squared error of 0.12 m.
Figure 1.24. Topographic map of Southeast Island, Pearl and Hermes Atoll, showing mean high water (MHW) and 1-meter a graph of the percentage of land in 0.5-m elevation bins. Elevations were determined from U.S. Geological Survey lidar data 

root-mean-squared error of 0.12 m.
Figure 1.25. Topographic map of Grass Island, Pearl and Hermes Atoll, showing mean high water (MHW) and 1-meter (m

with a graph of the percentage of land in 0.5-m elevation bins. Elevations were determined from U.S. Geological Survey lidwith a vertical root-mean-squared error of 0.12 m.
Figure 1.26. Topographic map of Seal-Kittery Island, Pearl and Hermes Atoll, showing mean high water (MHW) and 1-mlines with a graph of the percentage of land in 0.5-m elevation bins. Elevations were determined from U.S. Geological Surv

(2010) with a vertical root-mean-squared error of 0.12 m.
48    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.27. Pearl and Hermes Atoll passive inundation scenario maps for five islands at three sea levels: 

mean high water (MHW), +1.0 and +2.0 meters (m) sea-level rise (SLR). Inundation scenarios are based on U.S. 

Geological Survey lidar-derived elevations with a root-mean-squared error of 0.12 m.
Area (ha)

Species 0.0 m +0.5 m +1.0 m +1.5 m +2.0 m

Black-footed Albatross 2.7 0.0 0.0 -22.2 -85.2

Laysan Albatross 2.7 0.0 0.0 -22.2 -85.2

Red-tailed Tropicbird 0.2 0.0 0.0 -50.0 -100.0

Masked Booby 2.7 0.0 0.0 -22.2 -85.2

Brown Booby 2.7 0.0 0.0 -22.2 -85.2

Sooty Tern 2.7 0.0 0.0 -22.2 -85.2

Brown Noddy 2.7 0.0 0.0 -22.2 -85.2

Percent Change from SLR

Table 1.26. Total potential nesting habitat area and percent change for breeding avifauna at 

North Island, Pearl and Hermes Atoll, with four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, 

and +2.0 meters). 

[Areas calculated from land cover classes with Digital Globe QuickBird satellite imagery (October 2007). We assumed 

percent change from a uniform density across nesting habitat for each species. Abbreviations: m, meters; ha, hectares]

Area (ha)

Species 0.0 m +0.5 m +1.0 m +1.5 m +2.0 m

Black-footed Albatross 0.3 0.0 0.0 0.0 -100

Laysan Albatross 0.3 0.0 0.0 0.0 -100

Masked Booby 0.3 0.0 0.0 0.0 -100

Percent Change from SLR

Table 1.27. Total potential nesting habitat area and percent change for breeding avifauna at 

Little North Island, Pearl and Hermes Atoll, with four sea-level rise (SLR) scenarios (+0.5, +1.0, 

+1.5, and +2.0 meters). 

[Areas calculated from land cover classes with Digital Globe QuickBird satellite imagery (October 2007). We assumed 

percent change from a uniform density across nesting habitat for each species. Abbreviations: m, meters; ha, hectares]

Area (ha)

Species 0.0 m +0.5 m +1.0 m +1.5 m +2.0 m

Black-footed Albatross 9.1 0.0 -24.2 -65.9 -94.5

Laysan Albatross 9.1 0.0 -24.2 -65.9 -94.5

Bonin Petrel 8.6 0.0 -23.3 -65.1 -94.2

Bulwer’s Petrel 0.5 0.0 -40.0 -80.0 -100.0

Wedge-tailed Shearwater 9.1 0.0 -24.2 -65.9 -94.5

Tristram’s Storm-petrel 9.1 0.0 -24.2 -65.9 -94.5

Red-tailed Tropicbird 6.6 0.0 -22.7 -65.2 -93.9

Masked Booby 9.1 0.0 -24.2 -65.9 -94.5

Brown Booby 9.1 0.0 -24.2 -65.9 -94.5

Little Tern 9.1 0.0 -24.2 -65.9 -94.5

Gray-backed Tern 2.5 0.0 -28.0 -68.0 -96.0

Sooty Tern 9.1 0.0 -24.2 -65.9 -94.5

Brown Noddy 9.1 0.0 -24.2 -65.9 -94.5

Laysan Finch 10.5 -4.8 -37.1 -71.4 -95.2

Percent Change from SLR

Table 1.28. Total potential nesting habitat area and percent change for breeding avifauna at 

Southeast Island, Pearl and Hermes Atoll, with four sea-level rise (SLR) scenarios (+0.5, +1.0, 

+1.5, and +2.0 meters). 

[Areas calculated from land cover classes with Digital Globe QuickBird satellite imagery (October 2007). We assumed 

percent change from a uniform density across nesting habitat for each species. Abbreviations: m, meters; ha, hectares]

Results    49
Area (ha)

Species 0.0 m +0.5 m +1.0 m +1.5 m +2.0 m

Black-footed Albatross 1.2 0.0 0.0 -8.3 -75.0

Laysan Albatross 1.2 0.0 0.0 -8.3 -75.0

Bonin Petrel 0.5 0.0 0.0 0.0 -60.0

Wedge-tailed Shearwater 1.2 0.0 0.0 -8.3 -75.0

Christmas Shearwater 0.5 0.0 0.0 0.0 -60.0

Red-tailed Tropicbird 0.5 0.0 0.0 0.0 -60.0

Masked Booby 1.2 0.0 0.0 -8.3 -75.0

Brown Booby 1.2 0.0 0.0 -8.3 -75.0

Gray-backed Tern 0.7 0.0 0.0 -14.3 -85.7

Sooty Tern 1.2 0.0 0.0 -8.3 -75.0

Brown Noddy 1.2 0.0 0.0 -8.3 -75.0

Percent Change from SLR

Table 1.29. Total potential nesting habitat area and percent change for breeding avifauna at 

Grass Island, Pearl and Hermes Atoll, with four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, 

and +2.0 meters). 

[Areas calculated from land cover classes with Digital Globe QuickBird satellite imagery (October 2007). We 

assumed percent change from a uniform density across nesting habitat for each species. Abbreviations: m, meters; 

ha, hectares]

50    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Area (ha)

Species 0.0 m +0.5 m +1.0 m +1.5 m +2.0 m

Black-footed Albatross 7.6 0.0 0.0 -35.5 -72.4

Laysan Albatross 7.6 0.0 0.0 -35.5 -72.4

Bonin Petrel 0.5 0.0 0.0 -20.0 -60.0

Wedge-tailed Shearwater 7.6 0.0 0.0 -35.5 -72.4

Christmas Shearwater 0.5 0.0 0.0 -20.0 -60.0

Red-tailed Tropicbird 0.3 0.0 0.0 -33.3 -66.7

Masked Booby 7.6 0.0 0.0 -35.5 -72.4

Brown Booby 7.6 0.0 0.0 -35.5 -72.4

Gray-backed Tern 7.3 0.0 0.0 -35.6 -72.6

Brown Noddy 7.6 0.0 0.0 -35.5 -72.4

Percent Change from SLR

Table 1.30. Total potential nesting habitat area and percent change for breeding avifauna at 

Seal-Kittery Island, Pearl and Hermes Atoll, with four sea-level rise (SLR) scenarios (+0.5, +1.0, 

+1.5, and +2.0 meters). 

[Areas calculated from land cover classes with Digital Globe QuickBird satellite imagery (October 2007). We 

assumed percent change from a uniform density across nesting habitat for each species. Abbreviations: m, meters; 

ha, hectares; %, percent]
0.0 m

Land cover Area (ha) Area (ha) % change Area (ha) % change Area (ha) % change Area (ha) % change

Tree/shrub 10.6 10.6 0.0 10.6 0.0 10.6 0.0 10.6 0.0

Grass/herbaceous cover 96.2 96.1 -0.1 96.1 -0.1 96.1 -0.1 95.9 -0.3

Vine/ground cover 17.2 17.1 -0.6 17.1 -0.6 17.1 -0.6 17.1 -0.6

Bare ground 11.9 11.9 0.0 11.9 0.0 11.8 -0.8 11.6 -2.5

Beach 11.2 9.4 -16.1 7.9 -29.5 6.6 -41.1 5.2 -53.6

Total island size 147.1 145.1 -1.4 143.7 -2.3 142.2 -3.3 140.4 -4.6

+0.5 m +1.0 m +1.5 m +2.0 m

Change from SLR

Table 1.31. Total land area and percent change for each of five land cover types at Lisianski Island with four sea-level 

rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters). 

[Areas calculated from Digital Globe QuickBird satellite imagery (January 2008). Abbreviations: m, meters; ha, hectares; %, percent]

Results    51

Lisianski Island

Island Information

Lisianski Island measured 147.1 ha in area (table 1.9, 

fig. 1.28). Five land cover classes were identified on Lisianski: 

tree/shrub including Chenopodium oahuense, Pisonia grandis,

Scaevola taccada, and Tournefortia argentea; grass/herbaceous cover including Cenchrus echinatus, Eragrostis variabilis, Lepturus repens, and Solanum americanum; vine/ground 

cover including Boerhavia repens, Ipomoea indica, Ipomoea 

pes-caprae, Nama sandwicensis, Portulaca lutea, Sicyos 

pachycarpus, and Tribulus cistoides; bare ground; and beach 

(table 1.31, fig. 1.29). The dominant land cover was grass/

herbaceous cover at 96.2 ha (65 percent; table 1.31). 

Figure 1.28. Maps showing the location of Lisianski Island, a low sandy island situated between Pearl and Hermes 

Atoll to the northwest and Laysan Island to the southeast.

Sea-Level Rise

The maximum elevation of Lisianski Island was 7.6 m 

with a mean of 3.8 m (SD 1.3, RMSE 0.18; table 1.9). Of 

Lisianski’s total land area, 5 percent was below 2.0 m, 59 

percent was below 4.0 m, and 93 percent was below 6.0 m 

elevation (fig. 1.30). Total area loss of 2 percent at +1.0 m 

SLR and 5 percent at +2.0 m SLR was expected for Lisianski 

Island with the passive inundation model (table 1.31, fig. 

1.31). Vegetated area loss was 0.2 percent at +1.0 m SLR and 

0.3 percent at +2.0 m SLR (table 1.31). Only beach area was 

reduced by more than 3 percent due to inundation at +2.0 m 

SLR (table 1.31). Less than 3 percent of avian breeding habitat 

was expected to be lost for any species at or below +2.0 m 

SLR (table 1.32).
52    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.29. Lisianski Island land cover classification map developed using primarily unsupervised 

classification methods from Digital Globe QuickBird satellite imagery (January 2008). Additional details on 

land cover are given in table 1.31.
Results    53

Figure 1.30. Topographic map of Lisianski Island showing mean high water (MHW) and 1-meter (m) 

contour lines with a graph of the percentage of land in 0.5-m elevation bins. Elevations were determined 

from U.S. Geological Survey lidar data (2010) with a vertical root-mean-squared error of 0.18 m.
54    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.31. Lisianski Island passive inundation scenario maps for five sea levels: mean high water through 

+2.0 meters (m) sea-level rise (SLR) at 0.5-m increments. Inundation scenarios are based on U.S. Geological 

Survey lidar-derived elevations with a root-mean-squared error of 0.18 m.
Results    55

Area (ha)

Species 0.0 m +0.5 m +1.0 m +1.5 m +2.0 m

Black-footed Albatross 125.3 -0.2 -0.2 -0.2 -0.6

Laysan Albatross 125.3 -0.2 -0.2 -0.2 -0.6

Bonin Petrel 124.0 -0.2 -0.2 -0.2 -0.3

Bulwer’s Petrel 11.9 0.0 0.0 -0.8 -2.5

Wedge-tailed Shearwater 135.9 -0.1 -0.1 -0.2 -0.5

Christmas Shearwater 124.0 -0.2 -0.2 -0.2 -0.3

Red-tailed Tropicbird 106.8 -0.1 -0.1 -0.1 -0.3

Masked Booby 125.3 -0.2 -0.2 -0.2 -0.6

Brown Booby 125.3 -0.2 -0.2 -0.2 -0.6

Red-footed Booby 10.6 0.0 0.0 0.0 0.0

Great Frigatebird 10.6 0.0 0.0 0.0 0.0

Gray-backed Tern 29.1 -0.3 -0.3 -0.7 -1.4

Sooty Tern 125.3 -0.2 -0.2 -0.2 -0.6

Brown Noddy 135.9 -0.1 -0.1 -0.2 -0.5

Black Noddy 10.6 0.0 0.0 0.0 0.0

White Tern 10.6 0.0 0.0 0.0 0.0

Percent Change from SLR

Table 1.32. Total potential nesting habitat area and percent change for breeding avifauna at 

Lisianski Island with four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters). 

[Areas calculated from land cover classes with Digital Globe QuickBird satellite imagery (January 2008). We based 

percent change on a uniform density across nesting habitat for each species. Abbreviations: m, meters; ha, hectares]
56    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Laysan Island

Island Information

The total area of Laysan Island was 413.6 ha, of which 

339.4 ha were terrestrial; the lake and mudflat area accounted 

for the remaining 74.2 ha (table 1.9, fig. 1.32). Eleven land 

cover classes were identified on Laysan: tree/shrub including 

Scaevola taccada; mixed shrub; grass/herbaceous cover 

including Eragrostis variabilis and Fimbristylis cymosa; vine/

ground cover including Boerhavia repens, Ipomoea pescaprae, Sicyos ssp., and Tribulus cistoides; wetland vegetation 

including Cyperus laevigatus, Heliotropium currasavicum,

and Sesuvium portulacastrum; bare ground; hard pan; beach; 

wetland (unvegetated); wetland (standing water); and human 

structure (fig. 1.33, see table 2.3). Four additional speciesspecific land cover classes based on distribution data included: 

Casuarina equisetifolia, Cocos nucifera, Pluchea indica, and 

Tournefortia argentea. The lake zone at Laysan comprised 

two land cover classes: (1) unvegetated wetland area including 

mudflats and (2) standing water. Though this area varies in 

size seasonally, from visual inspection of the May 18, 2010, 

WorldView-2 image (Digital Globe, 2010), the lake zone 

was made up of 34.2 ha of unvegetated area and 40.0 ha of 

standing water. The dominant land cover at Laysan Island was 

bare ground (129.3 ha; table 2.3).

Figure 1.32. Maps showing location of Laysan Island, a low carbonate island situated between Lisianski Island to 

the northwest and French Frigate Shoals to the southeast.
Results    57

Figure 1.33. Laysan Island land cover classification map developed using primarily unsupervised classification 

methods from Digital Globe WorldView-2 satellite imagery (May 2010). Species-specific plant delineations mapped 

from U.S. Fish and Wildlife Service (unpub. data). See chap. 2 for additional details on land cover and species 

composition.
58    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Sea-Level Rise

The maximum elevation of Laysan Island was 10.7 m with 

a mean of 4.3 m (SD 2.3, RMSE 0.09; table 1.9) excluding the 

lake zone. The mean elevation of Laysan Island including the 

lake zone was 3.5 m (SD 2.6). Of the terrestrial area of Laysan 

Island (excludes lake zone), 31 percent was below 2.0 m elevation and 50 percent below 3.5 m (fig. 1.34). Scenarios of passive 

inundation that did not incorporate rising groundwater showed 

total area losses of 1 percent at +1.0 m SLR and 4 percent at 

+2.0 m SLR (fig. 1.35, table 1.9). In contrast, total area loss 

with passive inundation and a concurrent groundwater rise was 

expected to be 7 percent at +1.0 m SLR and 24 percent at +2.0 m 

SLR (fig. 1.36, table 1.9). 

Models without rising lake levels indicated that less than 

4 percent of breeding habitat would be lost across all SLR 

scenarios (table 1.33a). If lake levels were to rise with SLR, 

greater losses in breeding habitat would occur for all bird 

species, especially shrub-nesting species with as much as 63 

percent habitat loss at +2.0 m SLR (table 1.33b).

Figure 1.34. Topographic map of Laysan Island showing mean high water (MHW) and 2-meter (m) contour lines 

with a graph of the percentage of land in 0.5-m elevation bins. Elevations were determined from U.S. Geological 

Survey lidar data (2010) with a vertical root-mean-squared error of 0.09 m.
Results    59

Figure 1.35. Laysan Island passive inundation scenario maps for five sea levels: mean high water through +2.0m 

(meters) sea-level rise (SLR) at 0.5-m increments, assuming no change in groundwater lake levels. Inundation 

scenarios are based on U.S. Geological Survey lidar-derived digital elevations with a root-mean-squared error of 0.09 m.
60    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.36. Laysan Island passive inundation scenario maps for five sea levels: mean high water through 

+2.0 meters (m) sea-level rise (SLR) at 0.5-m increments, assuming groundwater rise in lake levels. Inundation 

scenarios are based on U.S. Geological Survey lidar-derived elevations with a root-mean-squared error of 0.09 m.
Area (ha)

Species 0.0 m +0.5 m +1.0 m +1.5 m +2.0 m

Black-footed Albatross 280.1 0.0 -1.0 -2.4 -3.8

Laysan Albatross 283.5 0.0 -1.0 -2.4 -3.7

Bonin Petrel 165.4 0.0 -1.0 -2.4 -3.8

Bulwer's Petrel 133.6 0.0 -1.0 -2.4 -3.7

Wedge-tailed Shearwater 299.0 0.0 -1.0 -2.4 -3.8

Christmas Shearwater 165.4 0.0 -1.0 -2.4 -3.8

Tristram's Storm-petrel 299.0 0.0 -1.0 -2.4 -3.8

Red-tailed Tropicbird 111.2 0.0 -1.1 -2.4 -3.9

Masked Booby 266.4 0.0 -1.0 -2.4 -3.7

Brown Booby 263.0 0.0 -1.0 -2.4 -3.8

Red-footed Booby 36.0 0.0 -1.1 -2.2 -3.9

Great Frigatebird 36.0 0.0 -1.1 -2.2 -3.9

Gray-backed Tern 191.2 0.0 -1.0 -2.4 -3.7

Sooty Tern 263.0 0.0 -1.0 -2.4 -3.8

Brown Noddy 299.0 0.0 -1.0 -2.4 -3.8

Black Noddy 36.0 0.0 -1.1 -2.2 -3.9

White Tern 28.0 0.0 -1.1 -2.1 -3.9

Laysan Teal 165.4 0.0 -1.0 -2.4 -3.8

Laysan Finch 165.4 0.0 -1.0 -2.4 -3.8

Nihoa Millerbird* 165.4 0.0 -1.0 -2.4 -3.8

*

Expected habitat use from newly translocated population to Laysan Island.

Percent Change from SLR

Table 1.33. Total potential nesting habitat area and percent change for breeding avifauna at 

Laysan Island with four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters) using 

(a) current lake levels and (b) lake levels rising with groundwater and SLR. 

[Areas calculated from land cover classes with Digital Globe WorldView-2 satellite imagery (May 2010). We computed percent change based on uniform species density across nesting habitat for each species. (a) Laysan Island, assuming current lake levels; (b) Laysan Island, assuming rising lake levels with sea-level rise. Abbreviations: m, meters; ha, hectares]

a.

Results    61
Area (ha)

Species 0.0 m +0.5 m +1.0 m +1.5 m +2.0 m

Black-footed Albatross 280.1 -0.1 -2.5 -9.7 -14.2

Laysan Albatross 283.5 -0.1 -2.5 -9.9 -15.0

Bonin Petrel 165.4 -0.2 -6.0 -23.2 -30.8

Bulwer's Petrel 133.6 0.0 -0.1 -0.3 -0.9

Wedge-tailed Shearwater 299.0 -0.1 -3.4 -13.0 -17.4

Christmas Shearwater 165.4 -0.2 -6.0 -23.2 -30.8

Tristram's Storm-petrel 299.0 -0.1 -3.4 -13.0 -17.4

Red-tailed Tropicbird 111.2 -0.3 -8.5 -32.9 -41.9

Masked Booby 266.4 -0.1 -1.8 -7.2 -12.0

Brown Booby 263.0 -0.1 -1.7 -7.0 -11.2

Red-footed Booby 36.0 -0.3 -15.3 -56.9 -63.1

Great Frigatebird 36.0 -0.3 -15.3 -56.9 -63.1

Gray-backed Tern 191.2 -0.1 -0.5 -1.6 -4.2

Sooty Tern 263.0 -0.1 -1.7 -7.0 -11.2

Brown Noddy 299.0 -0.1 -3.4 -13.0 -17.4

Black Noddy 36.0 -0.3 -15.3 -56.9 -63.1

White Tern 28.0 -0.4 -14.3 -45.0 -52.5

Laysan Teal 165.4 -0.2 -6.0 -23.2 -30.8

Laysan Finch 165.4 -0.2 -6.0 -23.2 -30.8

Nihoa Millerbird* 165.4 -0.2 -6.0 -23.2 -30.8

*

Expected habitat use from newly translocated population to Laysan Island.

Percent Change from SLR

Table 1.33. Total potential nesting habitat area and percent change for breeding avifauna at 

Laysan Island with four sea-level rise (SLR) scenarios (+0.5, +1.0, +1.5, and +2.0 meters) using 

(a) current lake levels and (b) lake levels rising with groundwater and SLR.—Continued 

[Areas calculated from land cover classes with Digital Globe WorldView-2 satellite imagery (May 2010). We computed percent change based on uniform species density across nesting habitat for each species. (a) Laysan Island, assuming current lake levels; (b) Laysan Island, assuming rising lake levels with sea-level rise. Abbreviations: m, meters; ha, hectares]

b.

62    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

French Frigate Shoals

Island Information

We report elevation data and present topographic 

maps for French Frigate Shoals (Shark, Tern, Trig, Round, 

East, Gin, Little Gin, Disappearing Islands and La Perouse 

Pinnacle). For additional details on SLR models, land cover 

analysis, vegetation response, projected seabird habitat 

change analysis, and seabird population dynamics, see 

Hatfield and others (2012). Aside from Tern (13.8 ha) and 

East (2.8 ha) Islands, the islands of French Frigate Shoals 

were less than 2 ha in area (table 1.9). Tern, Trig, East, Gin, 

and Little Gin Islands support breeding seabirds.

Sea-Level Rise

The maximum elevations of Shark, Tern, Trig, East, 

Round, Gin, Little Gin, and Disappearing Islands range from 

1.8 m (Little Gin) to 3.4 m (Tern), while the mean elevations for these islands range from 0.5 m (Trig) to 2.3 m (both 

Tern and East; table 1.9, figs. 1.37–42). Thirty-five percent 

of the total land area of French Frigate Shoals was below 2.0 

m and 99 percent was below 3.0 m, excluding La Perouse 

Pinnacle. Five of the nine islands of French Frigate Shoals 

were expected to be entirely inundated at +2.0 m SLR with 

the passive inundation model (table 1.9, fig. 1.43). However, 

total land area loss across French Frigate Shoals was expected 

to be 12 percent at +1.0 m SLR and 32 percent at +2.0 m SLR 

(excluding La Perouse).
Figure 1.37. Topographic map of Tern Island, French Frigate Shoals, showing mean high water (MHW) and 

1-meter (m) contour lines with a graph of the percentage of land in 0.5-m elevation bins. Elevations were determined 

from U.S. Geological Survey lidar data (2010) with a vertical root-mean-squared error of 0.05 m.

Figure 1.38. Topographic 

map of Trig Island, 

French Frigate Shoals, 

showing mean high water 

(MHW) and 1-meter 

(m) contour lines with a 

graph of the percentage 

of land in 0.5-m elevation 

bins. Elevations were 

determined from PhotoSat 

(2011) with a vertical rootmean-squared error of 

0.15 m.

Results    63
64    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.39. Topographic map of East Island, French Frigate Shoals, showing mean high water (MHW) and 1-meter (m) 

contour lines with a graph of the percentage of land in 0.5-m elevation bins. Elevations were determined from PhotoSat 

(2011) with a vertical root-mean-squared error of 0.15 m.
Results    65

Figure 1.40. Topographic map of Gin Island, French Frigate Shoals, showing mean high water 

(MHW) and 1-meter (m) contour lines with a graph of the percentage of land in 0.5-m elevation bins. 

Elevations were determined from U.S. Geological Survey lidar data (2010) with a vertical root-meansquared error of 0.05 m.
66    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.41. Topographic map of Little Gin Island, French Frigate Shoals, showing mean high water (MHW) 

and 1-meter (m) contour lines with a graph of the percentage of land in 0.5-m elevation bins. Elevations were 

determined from PhotoSat (2011) with a vertical root-mean-squared error of 0.15 m.
Results    67

Figure 1.42. Topographic map of Shark, Round and Disappearing Islands of French Frigate Shoals, showing 

mean high water (MHW) and 1-meter (m) contour lines with a graph of the percentage of land in 0.5-m elevation 

bins. Elevations were determined from U.S. Geological Survey lidar (Shark and Disappearing [2011]) and 

PhotoSat (Round [2011]) data with a vertical root-mean-squared error of 0.05–0.15 m.
68    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 1.43. French Frigate Shoals passive inundation scenario maps for eight islands at three sea levels: mean high 

water (MHW), +1.0 and +2.0 meters (m) sea-level rise (SLR). Nesting habitat was mapped from 2011 field data (U.S. Fish 

and Wildlife Service, unpub. data). Inundation scenarios are based on U.S. Geological Survey lidar- and PhotSat-derived 

elevations with root-mean-squared errors of 0.05–0.15 m.
Discussion    69

Discussion

The first quantitative assessment of SLR for the NWHI 

(Baker and others, 2006) included only three atolls: Pearl 

and Hermes Atoll (North, Little North, Southeast, Grass, and 

Seal-Kittery Islands), Lisianski Island, and French Frigate 

Shoals (only the islands of Trig, East, Gin, and Little Gin). 

The Baker and others (2006) study examined three different 

SLR scenarios (+0.09, +0.48, and +0.88 m) based on previous 

IPCC forecasts and Church and others (2001). Baker and 

others (2006) analyzed the extent of inundation at each SLR 

scenario for both MLW and spring tide, using topography 

derived from ground-surveying techniques and interpolation. 

In contrast, our study examined a wider range of sealevel scenarios ranging from 0.0 to +2.0 m and twice as many 

islands. Since our study emphasized terrestrial avian habitat, 

we conducted our inundation analysis relative to MHW, which 

is similar to (although somewhat lower than) spring tides. 

Lastly, our digital elevations were derived from airborne lidar 

data instead of from ground surveys of the waterline and berm. 

While the ground surveying techniques used by Baker and 

others (2006) generally produced data of excellent accuracy 

at each survey point, spatial coverage was limited and, thus, 

required extensive interpolation to generate comprehensive 

topography. In short, given the differences in SLR scenarios 

and tidal datums, exact comparisons between our findings and 

the Baker and others (2006) results are impossible, although 

generalizations can be made. 

The calculated total land area for the islands included 

in both studies differed. The area calculated from our lidar

data (201.5 ha) was 6 percent greater than the area used in the 

Baker and others (2006) study (189.3 ha). At +0.50 m SLR 

and MHW, we predicted a 6-percent loss in total land area 

for all islands, whereas Baker and others (2006) predicted a 

9-percent loss at +0.48 m SLR and spring tide. At +1.00 m 

SLR and MHW, we predicted a 13-percent loss in total area, 

whereas Baker and others (2006) predicted an 18-percent loss 

at +0.88 m SLR and spring tide. Overall, our passive SLR 

models showed slightly less inundation. However, given that 

the SLR scenarios, tidal datums, and elevation models differ 

between studies, these are small differences. For some of the 

smaller islands, particularly Trig (1.4 ha) and Gin (1.7 ha), 

it appears that a factor in the difference is variation in initial 

island sizes between studies, most likely a result of shifting 

sand, currents, and dynamic island morphology. Consequently, 

comparable areal losses observed in both studies often result 

in large differences in an island’s proportion of area lost to 

SLR. Although land cover was not classified in the Baker and 

others (2006) study, we expect differences in predicted inundation to occur primarily in the beach land cover class due to 

wave influences and dynamic morphology.

Geomorphic changes such as island migration, accretion 

and erosion, as identified by Webb and Kench (2010) for other 

locations in the Pacific, have not been examined for the NWHI 

and are not included in our predictions. The findings of Webb 

and Kench (2010) suggest that up to a point reef islands are 

geomorphically resilient landforms that have remained fairly 

stable in the last 20–60 years (Webb and Kench, 2010). 

Predictions of land area loss and wildlife impacts from 

SLR varied among the islands of the NWHI. The primary 

breeding habitats of the endangered Hawaiian monk seal, 

are beach, bare ground near the coast, and vegetation behind 

beaches used for shelter (Antonelis and others, 2006; NOAA 

2007). Thus, Hawaiian monk seal habitat will likely be sensitive to changes in land cover as a result of beach loss and 

coastal erosion. The primary nesting area of green turtle is 

coastal bare ground habitat, often within 8 m of the high tide 

line (Niethammer and others, 1997). Because approximately 

90 percent of the Hawaiian population of green turtles nest at 

French Frigate Shoals (Balazs, 1976; Balazs and Chaloupka, 

2004; Tiwari and others, 2010), the decreases in nesting area 

predicted at + 1.0 m SLR as well as the complete inundation of five islands at +2.0 m SLR, are likely to limit nesting 

habitat for this population if philopatric behavior prevents 

their dispersal. As sea levels rise and coastal habitat is inundated, vegetation along the coastline, groundwater levels, 

and turtle nesting density will likely change. These changes 

along with increasing temperatures are expected to negatively impact turtle nesting (Niethammer and others, 1997; 

Tiwari and others, 2010).

Across the NWHI, nesting habitat losses predicted with 

the passive inundation model were similar among avian 

species, despite differences in habitat use. We expect that 

additional avian breeding habitat will be lost if land cover 

shifts dramatically with rising sea levels (LaFever and 

others, 2007), largely because of the encroachment of beach 

into areas that are currently breeding habitat with vegetation. Under such conditions nesting habitat losses may not 

occur uniformly across species. Avian species of the NWHI, 

such as the Sooty Tern and Brown Noddy, that nest in a 

broad range of habitat types, do not typically nest coastally, 

and have broad global distributions will likely be the most 

resilient to SLR and additional climate change effects in the 

region. 

Reductions in limited habitat types could, however, 

disproportionally impact habitat specialists that may have 

limited adaptation potential to respond to habitat changes 

(Laidre and others, 2008). Specifically, shrub habitat across 

the NWHI was a limited land cover at most atolls and was 

entirely absent from most of the smaller islets (for example, 

all of Pearl and Hermes Atoll and Shark, Trig, Gin, Little 

Gin, Round, and Disappearing Islands of French Frigate 

Shoals). As a result, we expect that even small losses in 

shrub habitat due to SLR and other potential climate change 

impacts (for example, more frequent storm surge) may negatively affect the dependent shrub-nesting avifauna by further 

limiting nest-site availability. However, White Terns that 

readily nest on artificial structures, large rocks, and debris 

where available (Rauzon and Kenyon, 1984) may be best 

able to adapt to the loss of shrubs. 
70    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Also specialized in their nesting habitat use are the 

burrow- and crevice- nesting species of the NWHI (Bonin 

Petrel, Bulwer’s Petrel, Tristram’s Storm-petrel, and Wedgetailed Shearwater) that select nesting habitat according to 

soil types or substrate and depths for their subterranean nests 

(McClelland and others, 2008; Moore, 2009). Although data 

limitations prevented us from determining species-specific 

habitat based on soil characteristics, evidence suggests that 

potential habitat for the burrowing species is geographically 

limited at each atoll (McClelland and others, 2008; Moore, 

2009). Inundation of substrates favorable for subterranean 

nests may limit or exclude these species on vulnerable lowlying islands, depending on the spatial relationship between 

soil characteristics and inundation patterns.

The effects of habitat reduction would be further exacerbated at islands where populations are at or near carrying 

capacity with density-dependent populations (Gaston and 

others, 2003). Although very limited avian population data 

are available throughout the NWHI, studies indicate that the 

Laysan Teal at Laysan Island and several seabird species at 

Tern Island are already at maximum carrying capacity (Seavy 

and others, 2009; Hatfield and others, 2012). Life-history 

constraints that may influence adaptation to SLR are further 

discussed in chapter 2.

Habitat loss on islands that provide nesting habitat for 

globally important avian populations could have tremendous 

impacts even for species that utilize a broad range of habitat 

types. For example, at Southeast Island, Pearl and Hermes 

Atoll, one of only two islands in the chain where the endemic 

Laysan Finch persists, 95 percent of finch nesting habitat is 

expected to be lost here at +2.0 m SLR. The Laysan Finch is 

not capable of unaided inter-atoll dispersal, thus habitat loss 

at Pearl and Hermes Atoll would likely restrict the range of 

this endangered species to Laysan Island, an island vulnerable 

under higher scenarios of SLR (see chap. 2). 

At Midway Atoll, substantial habitat loss is predicted for 

several globally important populations: the endangered Laysan 

Teal, one of the largest colonies of Bonin Petrel (Moore, 

2009), and the world’s largest colonies of Black-footed and 

Laysan albatross, representing one-third and three-quarters of 

the world’s breeding populations, respectively (USFWS data, 

Flint, 2011). The second largest populations of Black-footed 

and Laysan albatross, the founder population of Laysan Teal 

and Laysan Finch, and a newly translocated population of 

Nihoa Millerbird are all found on Laysan Island. Although 

high coastal dunes may protect much of Laysan Island from 

some of the impacts of SLR over the next 100 years, stochastic 

and extreme events are risks for the globally important populations of these species (see also chap. 2). 

Habitat loss from passive SLR will likely be compounded 

by other forecasted impacts of rapid climate change such as 

increased temperatures, decreased precipitation, and increased 

frequency of overwash events and severe storms (Richardson 

and others, 2009). In addition to erosion caused by wave action, 

some nesting habitat may be negatively influenced by more 

frequent wave overwash as many shrub and grass species are 

intolerant of saltwater; storm events may weaken or slow the 

rebound of vegetation that, in turn, may further increase erosion. 

Increased frequency of storms may also have direct 

impacts on avian breeding productivity and/or survival 

(Frederiksen and others, 2008). Storm waves and flooding 

have been the cause of large-scale failure of albatross nests 

and adult mortality at Laysan Island and Midway Atoll 

(Kristof and others, 2011; E. Flint, USFWS, oral commun., 

2011). Reproductive failure of Laysan Finches and Laysan 

Teal at Laysan Island during extensive storms and extreme 

events have also been documented (Morin, 1992a; Kristof and 

others, 2011), highlighting the potential negative impacts of 

increasing storm intensity on NWHI avifauna. 

Impacts to NWHI avifauna from rapid climate change may 

occur with predicted increases in climatic variability that may 

resemble extreme events similar to El Niño-Southern Oscillation events (IPCC, 2007). The six climate change scenarios 

examined all predicted increases in temperatures and decreases 

in precipitation. The NWHI endemic land birds have demonstrated strong demographic responses to climatic variability 

including reduced rainfall (Morin, 1992a; Reynolds, 2002; 

Work and others, 2004) and increased temperatures (Reynolds 

and others, 2007; Work and others, 2010). For seabirds, environmental stochasticity can alter the distribution of prey associated 

with specific oceanic water masses (Polovina and others, 2001; 

Bograd and others, 2004) and alter prey composition in foraging 

areas (Brodeur and others, 1999; Grebmeier and others, 2006). 

Indeed, many seabird species have reduced survival, productivity, and breeding frequency as a result of altered conditions in 

their offshore foraging grounds (Schreiber and Schreiber, 1984; 

Montevecchi and Myers, 1997; Vadenbosch, 2000; Smithers 

and others, 2004; Congdon and others, 2007; Frederiksen and 

others, 2008). As such, increased environmental stochasticity is 

potentially detrimental to the wildlife of the NWHI.

Our results provide the first quantitative assessment 

of inundation risk and habitat change for many of the lowlying islands of the NWHI using high-resolution topographic 

data. The potential changes in habitat as a result of SLR may 

threaten species dependent on the NWHI. Our results show 

areas most vulnerable to inundation, allowing managers to 

anticipate where risks are highest. Loss of vegetation may 

precede permanent inundation, as frequent overwash events 

often lead to shifts in vegetation patterns, plant dieback, 

accelerated erosion, and soil instability (Davidson-Arnott, 

2005). Past habitat restoration initiatives and shoreline protections have been shown to be beneficial to nesting seabirds 

throughout the NWHI (Hatfield and others, 2012). Although 

there are uncertainties regarding climate change impacts and 

ecosystem response, modeling applications provide tools to 

better understand the island areas and natural resources most 

vulnerable to inundation. There is a need for additional climate 

change adaptation strategies and planning for the four bird 

species we have identified as potentially in jeopardy from 

SLR, as well as the marine wildlife dependent on terrestrial 

breeding habitat of French Frigate Shoals and Pearl and 

Hermes Atoll, likely to be inundated before 2100. 
Acknowledgments    71

Acknowledgments

This research and publication was made possible by 

funding from the U.S. Geological Survey’s National Climate 

Change Wildlife Science Center and Pacific Island Ecosystems Research Center, National Oceanic and Atmospheric 

Administration’s Pacific Services Center and Coastal Services 

Center, and Papāhanaumokuākea Marine National Monument. 

We thank center directors D. Beard and G. Tribble for their 

support. We are grateful to the dedicated staff of the USFWS 

for long-term data collection and archives of the Hawaiian 

Islands National Wildlife Refuge. We thank J. Klavitter for 

facilitating the lidar flights and fuel transportation to Midway 

Atoll. We thank C. Littnan for helping to facilitate the lidar 

ground truth data via the Oscar Elton Sette. We thank E. Flint, 

P. Hartzell, J. Klavitter, R. Born, S. White, C. Rehkemper, and 

P. Leary from USFWS. We also thank C. Vanderlip, J. Parish, 

and J. Moore for sharing data and insights on seabird habitat, 

distribution, and abundance. We thank A. Kristof and M. 

Stelmach for data on Laysan Island land cover. We are grateful 

to S. Nash for extensive editing assistance and review of 

earlier drafts of this manuscript. We would also like to thank 

T. Speetjens, and S. Duffy for assistance in formatting and 

editing and K. Brinck for reviewing an early draft manuscript. 

We thank L. Fortini and J. Baker for reviewing this manuscript 

and G. Tribble for advice on atoll hydrology. We also thank 

L. Slack and J. Hendley (USGS, Menlo Park Publishing 

Service Center) for editing assistance.
72    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Chapter 2

in that time frame (Rahmstorf, 2007; Pfeffer and others, 2008; 

Fletcher, 2009; Vermeer and Rahmstorf, 2009). Beyond the 

year 2100, or perhaps sooner according to some scientists, rapid 

collapses of the Greenland and West Antarctic ice sheets could 

lead to a SLR of many meters (Overpeck and others, 2006).

Sea-level rise is particularly critical for unconsolidated 

coral atoll islets, many of which have maximum elevations of 

less than 3 m above present sea level, but SLR is also significant 

for larger carbonate islands such as Laysan. Rising sea levels 

will likely exacerbate the impacts of storms and wave action 

on coastlines, primarily by reducing wave-energy dissipation 

(wave breaking) at the reef crest and increasing water depth 

relative to hydrodynamic roughness (that is, how the irregular 

nature of the seabed imparts drag on water flow) over the 

reef flat (see fig.2.2). By reducing wave-energy dissipation

at the reef crest and over the reef flat, SLR will cause longerwavelength and larger waves to directly impact the coastline 

and potentially drive coastal erosion. These larger waves at 

the shoreline increase the potential for wave-driven inundation 

that can extend inland considerable distances. The maximum 

vertical extent of waves on a beach, referred to as wave “runup,” is primarily a function of wave height, wavelength, and 

beach slope. Because storm wave heights and wavelengths 

vary in time and space, and corals reefs are spatially heterogeneous, wave-driven and SLR-induced inundation will also vary 

spatially and temporally. This variation is particularly large for 

Pacific Ocean islands and atolls that are exposed to waves in 

excess of 5 m numerous times each year (U.S. Army Corps of 

Engineers (USACE), 2011). 

Studies to date, including those presented in chapter 1, 

Hatfield and others (2012), and work by Baker and others 

(2006), describe SLR threats for several NWHI using passive 

inundation models to simulate flooding of the islands (fig. 2.3). 

These passive models, often referred to as “bathtub” models, 

do not simulate the cumulative effects of SLR and stormdriven waves, or the associated impacts on the landscape or 

biodiversity. The National Oceanic and Atmospheric Administration’s (NOAA) Coastal Inundation Mapping Guidebook (2006; 2009) draws a distinction between two types of 

Sea-Level Rise and Wave-Driven Inundation Models

for Laysan Island

By Paul Berkowitz4, Curt D. Storlazzi5, Karen N. Courtot6, Crystal M. Krause6, and 

Michelle H. Reynolds6

Introduction

Seabird breeding colonies and resident endemic wildlife 

on low-lying islands may be threatened by sea-level rise (SLR) 

and storm waves (Baker and others, 2006; Hatfield and others, 

2012; also see chap. 1). In the central and western Pacific, 

many beaches and islands experience periodic inundation from 

storm-induced waves; however, until recently, for the Northwestern Hawaiian Islands (NWHI) we lacked basic information 

to predict the magnitude of physical and biological impacts of 

SLR and storm waves under either present or future sea-level 

conditions. New topographic and bathymetric data have allowed 

us to explore SLR scenarios and the combined impacts of SLR 

and storm waves for Laysan Island, one of the largest and most 

ecologically intact islands in the NWHI. 

Among the predicted impacts of climate change in the 

Hawaiian archipelago, including rising sea-surface and atmospheric temperatures, changing oceanic and atmospheric circulation, increased storm frequency, changes in primary productivity, 

shifting species distributions, and increased disease incidence 

(Benning and others, 2002; Parmesan, 2006; IPCC, 2007; Friedlander and others, 2009; Loarie and others, 2009), SLR has the 

potential to be one of the most influential. Satellite observations 

from 1993 to 2010 (Leuliette, 2012) show global SLR occurring 

at almost double the rate cited in the Intergovernmental Panel on 

Climate Change (IPCC, 2007) report. Above-average local SLR 

rates have been observed near Laysan Island and at the northwest end of the Hawaiian archipelago (fig 2.1; Leuliette, 2012). 

Recent estimates and syntheses that include both ice melting and 

thermal expansion suggest a global rise in sea level of about +1.0 

meters (m) (above 2000 levels) by the end of the 21st century 

(table 2.1); some studies suggest a more extreme rise of +2.0 m 

4

Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo, Hilo, HI 

96720.

5

U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa 

Cruz, CA, 95060.

6

U.S. Geological Survey, Pacific Island Ecosystems Research Center, Kīlauea 

Field Station, Hawai‘i National Park, HI 96718.
Figure 2.1. Observed trends in sea level between 1993 and 2010 (Leuliette, 2012), in millimeters per year (mm/yr). Note 

the high rates (greater than 5 mm/year, displayed in orange and red) observed in the central and western North Pacific 

Ocean, near the northwest end of Papahänaumokuäkea Marine National Monument. On this global map, we depicted 

Laysan Island’s approximate location with a small circle that falls just south of an area of rapid sea-level change. 

Study Estimated range of SLR by 2100 

(meters) Author’s notes

Vermeer and Rahmstorf, 

2009

+0.75 to +1.90 (above 1990 level) Central estimate = +1.24 m

Pfeffer and others, 2008 +0.8 to +2.0 (above 2008 level) More likely toward the lower end of 

range

Rahmstorf, 2007 +0.50 to +1.40 (above 1990 level)

Table 2.1. Recent published estimates of sea-level rise (in meters) by the end of the 21st century. 

[The numerical precision of each estimate reflects the original publication]

Acknowledgments    73
Figure 2.2. Schematic diagram showing the variation in waves and resulting wavedriven run-up at two sea levels. At higher sea levels, wave breaking at the reef crest 

decreases while water depth relative to hydrodynamic roughness over the reef flat 

increases. Consequently less wave energy is attenuated over the reef crest and reef flat, 

resulting in more wave energy (larger wave heights and longer wavelengths) reaching 

the shoreline, which results in higher wave-driven water levels at the shoreline.

= Atoll islet 

= Atoll carbonate platform 

= Present sea level 

= Future elevated sea level 

fore 

reef 

reef 

crest reef flat 

Waves generally break when their height (H) approaches 125% of the water depth, 

so as sea level increases, larger waves can propagate over the reef crest and 

onto the reef flat. Because wave-driven run-up (R) scales with wave height, as sea

level increases, there are larger waves on the reef flat and by the shoreline, and

thus larger run-up. 

Hfuture > Hnow 

Hnow 

Rfuture > Rnow

Rnow 

74    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

inundation modeling depending on the type of water-surface 

model employed (single-value and modeled water surfaces). 

The first type of model represents a passive approach, whereby 

all land below a certain elevation (for example, a level identified in the 2007 IPCC report) and hydrologically connected 

to the ocean is flooded. The second type of model, applied to 

Laysan, relies on a more complicated water-surface model 

to refine estimates of inundation depth and inundation extent 

(NOAA, 2009). The latter model is referred to as a dynamic or 

wave-driven inundation model.

Inundation scenarios derived from either passive 

or dynamic surfaces simplify the wide range of possible 

impacts of rising sea level. While passive inundation 

represents an important element of SLR, coastal areas and 

islands are likely to be affected by a broader, more complex 

and interrelated set of processes including the following: 

loss of land due to erosion; island breaching and segmentation; wetland flooding or accretion; saltwater intrusion; and 

increased frequency of storm flooding (Gesch and others, 

2009). The unique characteristics of a particular location 

affect the relative importance of each of these processes. 

If data were available, a more comprehensive modeling 

approach could include sediment transport, morphological 

changes to the island, density-driven flows, and currents. 

Data requirements for such a model would include substrate 

analyses, grain size, water current measurements, and 

detailed hydrodynamic roughness (Deltares (Dutch Institute for Delta Technology), 2011). In this study, we focused 

primarily on passive SLR and wave-driven inundation, 

employing a hydrodynamic numerical model to estimate the 

combined impacts of SLR and wave-driven inundation on 

the land cover, vegetation, and selected breeding birds of 

Laysan Island.

The geomorphological history and isolation of the 

NWHI have led to the development of extraordinary marine 

and terrestrial ecosystems, with a high degree of endemism (Conant and others, 1984). Laysan Island (25°46'11" 

N, 171°44'00" W, ) is the second largest landmass in the 

NWHI (based on recent ligar data, see chap 1). The biota 

of Laysan Island remained largely free of anthropogenic 

disturbance until the late 1800s. By 1900, human impacts 

to the island included guano mining and rabbit ranching, 

activities that triggered numerous species extinctions (Ely 

and Clapp, 1973). Protection of this ecosystem as a bird 

reserve occurred in 1909 (Executive Order 1019). Additional conservation measures have been implemented to 

protect the ecological integrity of this island, including the 

creation of the Hawaiian Islands and Midway Atoll national 

wildlife refuges (Executive Order 13022; National Wildlife 

Refuge System Administration Act, 1966, as amended) and 

Papahānaumokuākea Marine National Monument (PMNM; 

Presidential Proclamation 8031). Today (2012), Laysan 

supports 17 species of breeding seabirds and numerous 

endangered, threatened, and candidate vertebrate species 

of concern to the U.S. Fish and Wildlife Service (USFWS) 

or International Union for Conservation of Nature (IUCN): 
Methods    75

Hawaiian monk seals (Monachus schauinslandi), green 

turtles (Chelonia mydas), Black-footed Albatross (Phoebastria nigripes), Laysan Albatross (P. immutabilis), Christmas 

Shearwater (Puffinus nativitatis), Tristram’s Storm-petrel 

(Oceanodroma tristrami), Laysan Teal (Anas laysanensis), 

Laysan Finch (Telespiza cantans), and the recently introduced Nihoa Millerbird (Acrocephalus familiaris kingi; 

Friedlander and others, 2009; Kristof and others, 2011). 

Additionally, Laysan Island is designated as critical habitat 

for two endangered plants: Cyperus pennatiformis var. 

bryanii and Pritchardia remota (see chap. 1). 

Although Laysan Island is one of the older islands in 

the archipelago, estimated at 20.7 million years old (Clague, 

1996), it remains one of the higher islands in the NWHI 

with a maximum elevation of 10.7 m above mean sea level 

(see chap. 1). Laysan contrasts sharply with the lower-lying 

open and closed atolls in the region. Laysan has substantially more emergent land (412.0 hectares (ha), including 

the lake basin) than most islands within the monument, has 

the largest natural lake in the Hawaiian Islands, and has less 

lagoon habitat (approximately 57 square kilometers (km2

) in 

waters less than 100 m deep) than any nearby island or atoll, 

including Kure Atoll, Midway Atoll, Pearl and Hermes Atoll, 

Lisianski Island, and French Frigate Shoals (Friedlander and 

others, 2009). 

Laysan’s unique topographic and bathymetric characteristics will influence how climate change affects the future 

landscape of the island. Our study is the first to simulate 

wave-driven inundation with a three-dimensional hydrodynamic model to predict the combined impacts of SLR 

and wave-driven water levels on the physical features and 

selected biological resources of Laysan Island. We focus on 

the breeding birds of Laysan Island, yet this assessment of 

inundation from SLR and storm-generated waves also can 

be applied to identify vulnerable habitat for other species as 

well as to identify other vulnerable sites such as field camps 

and infrastructure.

Methods

Storm Wave Models

Ocean surface gravity waves (“storm-induced waves”) 

were simulated with a coupled wave and water-level hydrodynamic model, the Delft3D WAVE module (Deltares, 2011). This 

WAVE module has been shown to realistically model the propagation and breaking of waves over Pacific coral reefs (Lowe 

and others, 2005; Lowe and others, 2009; Hoeke and others, 

2011) and is described in detail by Booij and others (1999), 

Holthuijsen and others (1993), and Ris and others (1999). 

Physical processes such as bottom friction, depth-induced wave 

breaking, and wave-wave interactions are included in the simulations. The results of the wave simulation include wave height, 

peak spectral period (the time period between successive wave 

crests at which wave energy reaches its maximum), and mass 

fluxes (rate of mass flow per unit area). 

Primary Wave Sources

We developed a wave climatology based on primary 

wave sources (North Pacific, Trade Wind, South Pacific, and 

Kona storms) as well as hurricanes. No buoys with long-term 

directional wind and wave data exist for the PMNM; thus we 

used data from the U.S. Army Corps of Engineers (USACE, 

2011) Wave Information System for a location (28°00’ N, 

174°00’ W) approximately 375 kilometers (km) northwest 

of Laysan Island (figs. 2.4–7). These data were generated by 

running wave-forecast models on past events, hence the term 

“hindcast” data. Based on methodology described in detail by 

Storlazzi and Wingfield (2005) and Storlazzi and Reid (2010), 

we analyzed these data (hourly wave height, wave period, 

wave direction, wind speed, and wind direction) from 1981 

to 2004 for the top 5 percent storm conditions for winter and 

summer. The different sets of conditions helped to constrain 

the combinations of wave heights, wave periods, and wave 

directions, above which possible damage to terrestrial habitats might occur. In our final analysis for Laysan Island, we 

analyzed winter conditions since these seasonal conditions 

generally encompassed the worst-case scenario according to 

the USACE wave data. 

Elevation and Bathymetry

As boundary input to the wave and inundation models, 

we created two seamless topographic/bathymetric grids of 

differing scales. The first grid covered a broad area around 

Laysan Island to water depths of approximately 3,000 m and 

had a spatial resolution or grid cell size of 100 × 100 m (fig. 

2.8). The second grid had a finer spatial resolution (20 × 20m 

cell size) encompassing all land area plus the surrounding 

ocean to a depth of approximately 30 m (fig. 2.9). Extensive 

pre-processing was required to generate these topographic/

bathymetric grids from a wide range of data sources and 

formats including the following: bathymetric 20-m gridded 

data from the Pacific Island Benthic Habitat Mapping Center 

(PIBHMC, 2011), that is, synthesized pseudo-bathymetric 

grids derived from IKONOS satellite imagery (in depths of 

0–16 m) and multibeam sonar (in depths of 20–5,000 m); two 

raster navigational charts (19019 and 19442, sections 2–3) and 

one electronic navigational chart (US1HA01M) from NOAA’s 

Office of Coast Survey (2011); a bare-earth 1-m digital elevation model (DEM) derived from photogrammetric methods by 

PhotoSat Information Ltd. (2010); and a digitized coastline 

based on a May 18, 2010 WorldView-2 satellite photo from 

DigitalGlobe Inc. (2010). Lidar (light detection and ranging) 

elevation data were not available at the time of analysis; 

consequently we used elevation data derived from satellite 

stereo pairs. When two or more data sources covered the same 

area, we excluded the less reliable sources from analysis. In 

general, older navigational chart data were used only to fill 

in data gaps between the newer IKONOS-derived and multibeam sonar data. After converting all depths and elevations to 
76    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 2.3. Schematic diagram comparing (1) Geographic Information System (GIS)-based passive 

inundation models (also known as “bathtub" models) with (2) dynamic wave-driven inundation 

models that incorporate wave-driven water levels (employed in this study). Most sea-level rise models 

employ passive inundation modeling techniques, which do not account for wave-driven water levels. 

Abbreviations: m, meters.

Mean Sea Level = +0.0 m above Mean Sea Level

Beach Slope

Mean Sea Level

Beach Slope

Mean Sea Level

Beach Slope

Mean Sea Level

Set-Up (+0.1) + Run-Up (+0.4 m) = +0.5 m above Mean Sea Level

Set-Up+Run-up

(+0.5 m)

+1.0 m Sea-level Rise = +1.0 m above Mean Sea Level

+1.0 m Sea-level Rise

+1.0 m Sea-level Rise + Set-Up (+0.1) + Run-Up (+0.6 m) =

+1.8 m above Mean Sea Level

Set-Up+Run-up

(+0.8 m)

Total Water Level = Mean Sea Level + Sea-level Rise + Set-Up + Run-Up = Limit of Inundation

Set-Up = a rise in sea level above the mean sea level inshore of the initial point of wave-breaking produced by cross-shore gradients in momentum flux

Run-Up = maximum vertical extent that wave-driven swash reaches above the water level [mean sea level + set-up] 

1) GIS-based Passive Inundation Model

2) Dynamic Wave-driven Inundation Model

Present

Present

Future with Sea-level Rise

Future with Sea-level Rise
Methods    77

Feb Apr Jun Aug Oct Dec

2 

4 

6 

8 

10 

Mont h 

Wave height [meters]

BLUE = all data, RED = top 5% of dat a 

Feb Apr Jun Aug Oct Dec

6 

8 

10 

12 

14 

16 

18 

Mont h 

Wave period [seconds]

BLUE = all data, RED = top 5% of dat a 

Feb Apr Jun Aug Oct Dec

5 

10 

15 

20 

Mont h 

Wind speed [meters per second]

BLUE = all data, RED = top 5% of dat a 

Figure 2.4. Time series showing monthly variations in Pacific Ocean wave heights, wave 

periods, and wind speeds (means and 1-standard deviation error bars) by month, based on 

U.S. Army Corps of Engineers (2011) data for the years 1981 to 2004 at a location (28°00' N, 

174°00'W) approximately 375 kilometers northwest of Laysan Island. These hindcast data were 

used to simulate wave-driven water levels during periods of high-wave energy for various sealevel rise scenarios. 
78    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 2.5. Compass plot of variation in wave height in meters (m), by wave direction (or compass orientation), 

based on U.S. Army Corps of Engineers (2011) data for the years 1981 to 2004 at a location (28°00' N, 

174°00'W) approximately 375 kilometers northwest of Laysan Island. Frequency of occurrence (in percent) 

increases outward from the center of the compass plot. Wave height data, along with wave period (fig. 2.6) and 

wind speed (fig. 2.7) data, were used to simulate wave-driven water levels under various sea-level rise scenarios. 

Figure 2.6. Compass plot of variation in wave period in seconds (s), by wave direction (or compass orientation), 

based on U.S. Army Corps of Engineers (2011) data for the years 1981 to 2004 at a location (28°00'N, 174°00' W) 

approximately 375 kilometers northwest of Laysan Island. Frequency of occurrence (in percent) increases outward 

from the center of the compass plot. Wave period data, along with wave height (fig. 2.5) and wind speed (fig. 2.7) 

data, were used to simulate wave-driven water levels under various sea-level rise scenarios.
Methods    79

Figure 2.7. Compass plot of variation in wind speed in meters per second (m/s), by wave direction (or compass 

orientation), based on U.S. Army Corps of Engineers (2011) data for the years 1981 to 2004 at a location (28°00'N, 

174°00' W) approximately 375 kilometers northwest of Laysan Island. Frequency of occurrence (in percent) 

increases outward from the center of the compass plot. Wind speed data, along with wave height (fig. 2.5) and period 

(fig. 2.6) data, were used to simulate wave-driven water levels under various sea-level rise scenarios.

meters and a common datum, we generated two grids using 

the geostatistical interpolation technique of kriging (Childs, 

2004) in ArcGIS 10 (ESRI, 2010). These two grids represent 

boundary input for the Delft3D modeling software.

Modeled Water Surfaces and

Wave Run-up

We used the Delft3D WAVE module to generate parameters for our wave-driven inundation computations. Similar to 

other models that estimate water surfaces such as the Sea, Lake 

and Overland Surges from Hurricanes (SLOSH) model used by 

the National Hurricane Center (2011) to estimate storm surge, 

the Delft3D model produces a grid of output parameters over 

a wide spatial area. For Delft3D, output parameters include 

wave height, wavelength, and wave-induced set-up (a rise in 

mean water level inshore of the initial point of wave-breaking). 

We extracted these parameters for each grid cell for all present 

and future sea-level scenarios (0.00, +0.50, +1.00, +1.50, and 

+2.00 m), as defined on the basis of studies and projections 

from the IPCC (2007), Grinsted and others (2010), and Nicholls 

and Cazenave (2010). Using extracted wave height and wavelength data, we computed wave-induced run-up heights (height 

above still water levels reached by wave-driven swash) with 

the methodology of Walton and Ahrens (1989) and Stockdon 

and others (2006). Then for each cell, we summed SLR values, 

wave-induced set-up, and run-up heights into total water 

levels, or elevations of inundation. The method for projecting 

these water levels up the beach slope is discussed below (see 

Methods, GIS Analysis).

Coral-Reef Accretion

Our methodology and bathymetric grids reflect the 

current state of the reef without future accretion. Coral-reef 

accretion is the natural biological and sedimentological 

process of adding material to an existing reef. Potential 

vertical reef accretion was not included in this study because 

no data exist for Laysan and published vertical reef-flat 

accretion rates for reef flats exposed to open-ocean storm 

waves (1–4 mm/yr according to Buddemeier and Smith, 

1988; Montaggioni, 2005) are very small compared with the 

rates of projected SLR until 2100 (8–16 mm/yr according to 

Grinsted and others, 2010; Nicholls and Cazenave, 2010). 

These vertical accretion rates, which are not equal to coral 

growth rates, suggest that projected SLR will outpace potential vertical reef-flat accretion, resulting in a net increase in 

water depth over exposed reef flats on the order of 0.4–1.5 

m during the 21st century. At the northern end of the NWHI, 

where water temperatures are generally cooler and coral reef 

accretion rates slower, vertical accretion rates are close to 

zero; Kure Atoll has an accretion rate of 0.2 mm/yr, compared 
80    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 2.8. Seamless 100-meter (m) resolution bathymetric grid of Laysan Island within Papahänaumokuäkea 

Marine National Monument. Data were compiled from Pacific Island Benthic Habitat Mapping Center (PIBHMC, 

2011) bathymetric grids, National Oceanic and Atmospheric Administration Office of Coast Survey (2011) 

navigational charts, PhotoSat Information Ltd. (2010) digital elevation models, and DigitalGlobe Inc. (2010) 

WorldView-2 satellite imagery. This compilation represents the coarse input grid to the wave-driven inundation 

model.
Methods    81

Figure 2.9. Seamless 20-meter (m) resolution bathymetric and topographic grid of Laysan Island, the 

finer input grid to the wave-driven inundation model. Data were compiled from Pacific Island Benthic 

Habitat Mapping Center (PIBHMC, 2011) bathymetric grids, National Oceanic and Atmospheric 

Administration Office of Coast Survey (2011) navigational charts, PhotoSat Information Ltd. (2010) 

digital elevation models, and DigitalGlobe Inc. (2010) WorldView-2 satellite imagery.
82    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

to long-term accretion rates of 2 mm/yr in the wave-sheltered 

bays of the main Hawaiian Islands (for example, Hanauma and 

Kāne‘ohe; Grigg, 1998, 2008). 

Estimating Mean Sea Level

Prior to mapping inundation, we delineated the coastline 

(mean sea level, MSL) based on satellite imagery and predicted 

tides at the time of the photo (PhotoSat Information Ltd., 2010; 

NOAA, 2011b). Since no tide gauges or tidal epochs exist for 

Laysan, the predicted tide at the time of the photo (+0.064 m 

above mean lower low water, MLLW, based on scaled heights 

from the Honolulu gauge, NOAA Station ID 1612340) was 

assumed to represent the best estimate of tide at 10:30 a.m. 

local time, May 18, 2010. Scaled heights from the Honolulu 

gauge indicate that the predicted tide at the time of the photo 

would lie −0.065 m below predicted MSL (or +0.129 m above 

MLLW, scaled from Honolulu values), while mean high water 

(MHW) would lie +0.233 m above the MLLW mark. During 

the 24-hour period when satellite photos were taken, the tide 

gauge at Midway Atoll (NOAA Station ID 1619910) registered only slight differences (mean difference = 0.07 m over 24 

hours) between predicted and verified tides, indicating that no 

major oceanographic or meteorological phenomena would have 

affected tidal predictions for Laysan Island. For the purpose of 

mapping inundation, this analysis used MHW (defined as the 

average of all high water heights over a 19-year National Tidal 

Datum Epoch, a period of time that includes most tidal variation 

due to lunar and solar forces) as the vertical reference level for 

each SLR and wave-driven inundation scenario; thus we generally modeled the worst-case daily scenario.

GIS Analysis

The remaining inundation modeling involved a number 

of sequential steps, primarily carried out in a GIS framework using the North American Datum of 1983 (NAD83) as 

the horizontal control datum and the Universal Transverse 

Mercator (UTM) coordinate system, zone 2N, as the spatial 

reference. Vertical analysis referenced local sea level (generally MHW) as described above. 

First we linked our modeled wave-driven water-level 

surface (set-up + run-up), which was stored in a 20-m grid 

format, to the Laysan coastline using nearest neighbor 

techniques (ESRI, 2010). Nearest water-level values were 

assigned to coastal points spaced 20 m apart. We represented 

the Laysan coast with 430 points, each with a Delft3D 

wave-driven water-level value. For each SLR scenario, 

we projected inundation inland from each coastal point 

along shore-normal transects. Given the highly convoluted 

nature of the Laysan coastline, we determined shore-normal 

transects to lie in a direction perpendicular to alongshore 

coastal segments, rather than perpendicular to the instantaneous orientation at each 20-m-spaced coastal station. These 

coastal segments were drawn as regression lines based on the 

location of each coastal point and its ten nearest neighbors 

(five to each side); orthogonal transects were defined to run 

in the direction of the negative reciprocal of the regression 

slope. For each scenario, we projected combined SLR and 

wave-driven water levels orthogonally inland from each 

coastal point until land elevations exceeded combined water 

heights, with the highest point reached on each shore-normal 

transect representing the high-water marks (HWMs). We 

then delineated the extent of inundation by connecting the 

HWMs from all 430 transects.

For transects with much longer run-up distances than 

adjacent transects, we modeled water to flow into adjacent 

topographic depressions if these depressions were hydrologically connected and at lower elevations than the inundated 

transects. While the Delft3D model produces wave-driven 

water levels, it does not estimate water volumes; therefore, 

the volume of water (reaching the wave-driven water level) 

that is likely to flow into adjacent topographic depressions is 

unknown. Another unknown factor is the infiltration capacity 

of the land, which influences how much water reaches and 

settles in these basins rather than percolates into the ground. 

Thus in depicting the extent of inundation for SLR scenarios, 

we were able to map the initial inundation patterns as wavedriven water moved onshore, representing a transient state 

before wave-driven seawater volumes flowed laterally into 

adjacent depressions. We also were able to delineate the 

maximum extent of inundation if wave-driven water volumes 

were unlimited and no infiltration occurred. Additional 

complexities in surface hydrology and island morphology 

were not considered (see Discussion, Future Research Directions, for potential enhancements to these methods). 

For both passive and dynamic modeling approaches, we 

analyzed and mapped inundation extent within an ArcGIS 

framework under five potential sea-level scenarios (0.00, 

+0.50, +1.00, +1.50, and +2.00 m). The passive approach 

represents a reference level and depicts the amount of inundation due to SLR only, while the wave-driven water-level 

approach considers the additional effect of wave-driven set-up 

and run-up from the top 5 percent of winter storm events 

(USACE, 2011). As described above, all inundation maps 

depict flooding under a worst-case daily scenario at MHW.

Hydrogeology

No monitoring wells or hydrological data exist for 

Laysan and the hydraulic conductivity of various geologic 

formations remains unknown; therefore, the effect of SLR on 

the water table and consequently lake levels within Laysan’s 

central basin (as well as eastern basins) is uncertain. Precipitation is predicted to decrease in the Hawaiian Islands with 

climate change (IPCC, 2007), but we did not incorporate this 

in our scenarios of the lake’s projected hydrology. Because no 

paleoenvironmental evidence exists to indicate that this lake 

has dried out completely within the past 7,000 years, Athens 

and others (2007) concluded that some percolation of seawater 
Methods    83

into the lake is likely; however, since tidal fluctuation has 

not been observed in the lake (Ely and Clapp, 1973), these 

percolation rates must be low. If some degree of percolation 

occurs between the ocean and lake, we speculate that over the 

long-term, lake levels may rise at a similar rate to SLR rates, 

as observed in hydrological studies on the main Hawaiian 

Islands (Rotzoll and others, 2007; Gingerich, 2008). If this 

were to occur, rising groundwater may cause inundation of the 

low-lying areas adjacent to the lake. Thus, we explored and 

presented most of our results in two ways: (1) assuming no 

percolation of seawater to the lake, and (2) assuming sufficient 

percolation to allow lake levels to rise at similar rates to those 

of sea level. 

Land Cover and Habitat Loss

We defined and classified land cover for Laysan Island 

using a combination of USFWS data (Hammond and others, 

2010) and remotely sensed data using WorldView-2 satellite 

imagery (Digital Globe Inc., 2010). Whenever possible, we 

used ground-survey data (available for four plant species, the 

hard pan, and wetland area) to delineate land cover; otherwise unsupervised classification methods (whereby computer 

software executes a clustering process to classify pixels; see 

chap. 1) were used due to logistical constraints and limited 

ground-truth data for various habitat/land cover classes 

currently present on Laysan Island (Xie and others, 2008; 

see chap. 1 for additional details). In ArcGIS, we developed 

signature files (characterizing the reflectance of electromagnetic radiation from the earth’s surface) for each land cover 

class using the IsoCluster tool and performed the classification using maximum likelihood methods (ESRI, 2010). Field 

biologists were consulted to review the land cover categories, 

as well as to assess the accuracy of our land cover maps (M. 

Stelmach, USFWS, oral commun., 2011). In general, our land 

cover classes were similar to the categories used by USFWS 

field biologists (USFWS data; Hammond and others, 2010). 

By overlaying predicted inundation patterns with our remotely 

sensed land cover maps, we quantified the land cover classes 

likely to be inundated. 

The land cover classes consisted of 15 categories of 

varying specificity (also see chap. 1). Five categories represented broad vegetation classes: tree/shrub, mixed shrub, 

grass/herbaceous cover, vine/ground cover, and wetland 

vegetation. As the name implies, wetland vegetation typically occurred outside of the lake-zone mudflats and consisted 

of areas with wetland plant species (primarily sedges and 

matted vegetation). Four classes delineated the distribution of 

individual plant species: ironwood (Casuarina equisetifolia), 

coconut tree (Cocos nucifera), Indian camphorweed (Pluchea 

indica), and tree heliotrope (Tournefortia argentea). Three 

classes differentiated unvegetated areas into bare ground, hard 

pan, or beach. Beach consisted of the coastal band subject 

to tidal action and regular wave swash, while bare ground 

consisted of bare ground inland of the beach zone. Located on 

a flat, former guano-mining site, the hard pan was composed 

primarily of guano (Ely and Clapp, 1973). We divided wetland 

areas (that is, the lake zone) into two categories, depending 

on whether standing water existed at the time of the satellite 

image on May 18, 2010 (Digital Globe Inc., 2010). Lastly, we 

included a category for all human structures on the island. 

To estimate habitat-specific losses, we overlaid our land 

cover classes with five inundation patterns, one from each 

of the following five modeling approaches: a passive model, 

a wave-driven inundation model, a wave-driven inundation 

model with unlimited seawater volumes and no infiltration, 

a passive model with rising groundwater, and a wave-driven 

inundation model with rising groundwater. We chose not to 

analyze the combined effects of wave-driven inundation (with 

unlimited seawater volumes and no infiltration) and rising 

groundwater, as these two approaches generally inundated the 

same low-lying areas, rendering this modeling combination 

redundant. In presenting our results, we generally expressed 

area as a percent of total terrestrial area, which included all 

land cover classes except for unvegetated wetland (that is, 

lake zone mudflat and standing water) categories. 

Nesting Habitat and Distribution

Using descriptions of breeding bird habitat, we identified 

all land cover classes potentially used as nesting habitat and 

conducted analyses to identify SLR vulnerability for nine of 

the breeding bird species on Laysan Island with data (Blackfooted Albatross, Laysan Albatross, Masked Booby [Sula 

dactylatra], Brown Booby [S. leucogaster], Red-footed Booby 

[S. sula], Great Frigatebird [Fregata minor], Sooty Tern 

[Onychoprion fuscata], Laysan Teal, and Laysan Finch; see 

tables 1.7 and 1.8). Using existing geospatial data (described 

below), we overlaid predicted inundation areas on current 

nesting areas for the nine breeding bird species listed above 

(USFWS data; Morin and Conant, 2002; USFWS, 2005; 

Cornett and others, 2008; Boyd and others, 2009; Hammond 

and others, 2010; Reynolds and others, 2010). We assumed 

that these data provide an index of the breeding distribution 

and typical breeding abundance observed under current conditions, although in most cases these assumptions should be 

tested (Citta and others, 2007; Seavy and Reynolds, 2009; see 

appendix 3).

For Black-footed Albatross, we determined nesting area 

distributions from land cover maps, survey data, and 2010 

distribution maps (USFWS data; Kristof and others, 2010; see 

Methods, Breeding Bird Abundance). The Black-footed Albatross nesting area included all suitable habitat types determined 

by overlaying land cover and survey data. For the Laysan 

Albatross, nesting area was also determined based on land cover 

maps, long-term monitoring data, and recent surveys, essentially 

including suitable habitat types across the island.

For Masked and Brown booby, USFWS biologists 

mapped a sub-sample of nest sites on June 17–19, 2009 (163 

of 190 Masked Booby and 35 of 78 Brown Booby nests), 
84    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

recording location (with a Garmin GPS map 76 unit; USFWS 

data; Boyd and others, 2009). Since standard mean incubation 

count (MIC) methodology was not used on Laysan to index 

the abundance of nesting Masked and Brown booby and since 

both species have a protracted breeding season, we cannot use 

the data to evaluate population size (Citta and others, 2007; 

Seavy and Reynolds, 2009). Despite these data limitations, we 

assumed the limited sample was a representative index for the 

purposes of mapping and exploring the vulnerability of these 

breeding colonies to SLR and wave-driven inundation. 

Using similar survey methods, USFWS biologists 

conducted a survey of Red-footed Booby and Great Frigatebird nests on June 10–12, 2008 (USFWS data; Cornett and 

others, 2008). Using hundreds of GPS points and track files 

for these two shrub-nesting species, USFWS staff consolidated 

these data into 24 nesting colonies (USFWS data; Cornett and 

others, 2008). Although both species are not truly synchronous 

nesters (Citta and others, 2007) and some breeders would be 

missed during a two-day survey, this single sample allowed 

us to model the vulnerability of the assumed breeding distributions of these species to SLR-induced and wave-driven 

inundation at Laysan. 

Sooty Tern colonies were mapped by USFWS biologists on July 11–12, 2008; July 21, 23, 27–28, 2009; and 

summer 2010 (USFWS data; Cornett and others, 2008; Boyd 

and others, 2009; Hammond and others, 2010). During each 

survey, USFWS crew members walked the perimeter of Sooty 

Tern colonies, leaving a 5–7 m buffer that was removed during 

processing. Biologists did not collect density data during these 

surveys, so Sooty Tern abundance was not estimated. We 

overlaid inundation scenarios on spatial distributions from the 

3 years with colony data.

Habitat studies of Laysan Teal using radio telemetry 

(Reynolds, 2004; Reynolds and others, 2007) indicated that 

Laysan Teal use all vegetated habitat types that provide dense 

cover for nest concealment on Laysan, but they typically avoid 

some land cover classes while nesting: wetland vegetation, 

bare ground, hard pan, and beach (table 1.8). Laysan Finch 

also occupy all vegetated land cover types but primarily use 

bunch grass for nesting on Laysan Island (Morin and Conant, 

2002). Laysan Finch on Laysan Island generally avoid nesting 

in wetland vegetation, bare ground, hard pan, and beach, but 

they use vine/ground cover and shrubs for nesting in low densities (Morin and Conant, 2002; table 1.8). For both of these 

endangered endemics, we used our land cover classes to define 

potential nesting area (table 1.8) and assumed uniform densities; thus, we are likely to overestimate their nesting habitat. 

Nesting Phenology and Temporal

Overlap with Storm Season

To describe species-specific vulnerability, we integrated the seasonal variation and life-history characteristics 

of breeding birds with wave-energy data showing seasonal 

variation in mean high-wave and high-wind values from 1981 

to 2004 for a location (28°00' N, 174°00' W) approximately 

375 km northwest of Laysan Island (USACE, 2011). The 

frequency and magnitude of storm events may increase with 

SLR (Richardson and others, 2009). We used published literature, field reports, and historical nesting accounts at Laysan 

Island to develop an island-specific breeding phenology for 

20 bird species. To identify periods of adult presence, stage of 

reproductive investment, and potential risk to the populations, 

phenology was divided into seven periods with minimum (offpeak) or maximum (peak) number of expected breeders: (1) 

adults, off-peak; (2) adults, peak; (3) incubation, off-peak; (4) 

incubation, peak; (5) incubation and chick rearing; (6) chick 

rearing, peak; and (7) chick rearing, off-peak. 

To evaluate relative risks to eggs, chicks, or breeders to 

storm events, we grouped species according to nesting characteristics. The first group included Procellariiformes (Blackfooted Albatross, Laysan Albatross, Bonin Petrel [Pterodroma 

hypoleuca], Bulwer’s Petrel [Bulweria bulwerii], Wedge-tailed 

Shearwater [Puffinus pacificus], Christmas Shearwater, and 

Tristram’s Storm-petrel) that nest above ground, in crevices, 

or in subterranean burrows. These birds lay a single egg and 

are constrained to a single reproductive attempt (that is, they 

are not able to relay if their nests are lost; Warham, 1996). The 

second group consisted of other ground-nesting species (Redtailed Tropicbird [Phaethon rubricauda], Masked Booby, 

Brown Booby, Gray-backed Tern [Onychoprion lunatus], 

Sooty Tern, Brown Noddy [Anous stolidus], and Laysan Teal) 

that nest on the ground, in low vegetation, or under vegetation, 

and might relay if their nests are lost. The third group of treeand shrub-nesting species (Red-footed Booby, Great Frigatebird, Black Noddy [A. minutus], White Tern [Gygis alba], 

Laysan Finch, and Nihoa Millerbird) is also more flexible and 

often able to relay if eggs or chicks are lost.

Breeding Bird Abundance

For five species with a time series of abundance data 

(Black-footed Albatross, Laysan Albatross, Red-footed Booby, 

Laysan Teal, and Laysan Finch), we assumed uniform nest 

density across all suitable nesting habitats for the purposes 

of this model. We estimated current carrying capacity and 

inferred the potential impacts of inundation on population size 

(Citta and others, 2007; Reynolds and Citta, 2007; Hatfield 

and others, 2012). For Black-footed and Laysan Albatross, 

biologists surveyed nests annually on a series of systematic 

transects (divided into 5 × 50 m quadrats, which are rectangular plots used in ecological studies) between the high tide 

line and lake zone (USFWS data; Kristof and others, 2010). 

We used albatross data from 2001 through 2011 from quadrats 

that were counted in all years (n=185). Red-footed Booby 

have been surveyed once annually since 1991 (with no data 

collection in 1996); however, current methods do not implement the standard monitoring protocols needed to account for 

observation error, variable detection probability, seasonality, 

or asynchrony of these species (Citta and others, 2007). 
Methods    85

Laysan Teal population abundance has been monitored twice monthly using temporally systematic surveys 

and randomized start locations since 1991 (USFWS data; 

Marshall, 1991; Seavy and others, 2009). Since the mid-1980s, 

biologists have conducted a single annual count of Laysan 

Finch on 120 strip transects, 91.4 m long and 5.0 m wide, 

located along the edges of randomized grid squares across the 

island (USFWS data; McClung, 2005). Current Laysan Finch 

monitoring methods do not account for observation error or 

variable detection probability.

Wildlife Population Dynamics in

Response to SLR

Our analysis of vulnerability of nesting populations to 

SLR-induced and wave-driven inundation assumes wildlife may be limited by available habitat and that population 

dynamics (that is, reproduction, survival, and rate of population growth) may be influenced by SLR and wave-driven 

inundation. We developed a conceptual model to describe 

how changes in sea level, global circulation patterns, storm 

frequency and intensity, and wave-driven water levels may 

influence habitat availability, island carrying capacity, and 

avifauna population dynamics at Laysan Island (fig. 2.10). 

We used abundance time-series data to quantify population dynamics and estimate the observed carrying capacity and 

strength of density dependence (that is, when populations are 

at high numbers and approaching carrying capacity, population size may decline due to a lack of nest sites, overcrowding, 

and scarce resources). We used a state-space approach to 

investigate the population dynamics of Black-footed Albatross 

(2001–2011), Laysan Albatross (2001–2011), Red-footed 

Booby (1996–2011), Laysan Teal (1993–2010), and Laysan 

Finch (1983–2011). Using this approach, we estimated mean 

carrying capacity (the population abundance that the environment sustains, given currently available conditions) for 

each species. Because density dependence and environmental 

stochasticity (randomness) produce fluctuations in population abundance, carrying capacity often is described as a 

“stationary probability distribution,” rather than a point equilibrium (Dennis and Constantino, 1988; Dennis and others, 

2006). Density-dependent growth was modeled with the 

Gompertz equation (Edelstein-Keshat, 1988):

 Xt = a + cXt-1 + Et (1)

where

Xt is the natural logarithm of the true population size (Nt) 

at time t; 

 a is the rate of population growth (in the absence of 

density dependence); 

 c is the strength of density dependence; and 

Et is ~ N (0,σ2), the normally distributed unbiased error. 

The case where c = 1 is the density independent model, 

while c less than 1 implies density dependence, with smaller 

values of c implying greater density dependence (Staples and 

others, 2004; Staples and others, 2005; Dennis and others, 

2006). Et represents process variation generated by environmental stochasticity. Additionally, observation error was 

incorporated by assuming: 

 Yt = Xt + Ft (1)

where

Ft is ~ N (0, τ2); and 

 τ is the observation error (also called sampling error), 

as the true population cannot be counted perfectly. 

We then analyzed our count data using methods developed by Seavy and others (2009). The program WinBUGS 

(version 1.4.3, Lunn and others, 2000) was used to provide 

a Bayesian analysis (Link and Barker, 2010). Where data 

were sufficient, we summarized the posterior distributions 

for σ (process variation), τ (sampling error), a (intrinsic rate 

of increase), and c (strength of density dependence) by their 

means, standard deviations, medians, and credible intervals. 

We then calculated carrying capacities (K) and standard deviations using equations 47–51 from Dennis and others (2006).

Uncertainty in SLR Models for

Laysan Island

Three sources of uncertainty affect the vertical accuracy 

of our modeled inundation levels: (1) uncertainty in tidal 

elevations; (2) uncertainty in topographic elevations; and (3) 

uncertainty in wave-driven water levels (set-up + run-up). 

A robust tidal datum does not exist for Laysan since no tide 

gauge exists on the island. Thus, we used NOAA’s predicted 

tide (−0.065 m relative to MSL) at the moment the stereo-pair 

images were captured as a reference elevation and defined 

our MSL tidal datum relative to this level. The accuracy of 

NOAA’s predicted tides at Laysan Island is unknown since 

these data have not been verified. However, given the small 

tidal range in the Central Pacific, the relatively flat tidal levels 

within 6 hours of the time of the photos (less than 0.10 m 

range), and the small deviations between predicted and verified tides at Midway Atoll within 12 hours of the photo time 

(±0.07 m), we would expect NOAA’s predicted tide relative to 

predicted MSL to be within 0.10 m of the actual tide (relative 

to actual MSL). This uncertainty estimate expresses the relative difference between predicted tidal stage and actual tidal 

stage (that is, predicted tide relative to predicted MSL versus 

actual tide relative to actual MSL), not the absolute accuracy 

of the predicted tide relative to an ellipsoid, geoid, or other 

fixed surface. 

We estimated uncertainty in topographic elevation 

(0.32 m) using 27 survey-grade (less than 1 centimeter (cm) 

accuracy) GPS points collected in April 2011 (NOAA, 2011a; 

chap. 1). Surveyors located survey points in flat, open areas 

to ensure that horizontal uncertainty did not have a major 

effect on vertical accuracy since the root-mean-squared error 
Sea-level rise due to 

thermal expansion 

of ocean

Global 

Climate 

Change 

Changing 

atmospheric and 

oceanic circulation

Increased seawater 

volume due to ice loss 

Laysan 

Island

Change 

Rising 

groundwater

and lake level

Morphodynamic 

change

(e.g., erosion) 

Increased 

storm 

frequency & 

intensity

Periodic 

wavedriven 

inundation

Year-round 

passive 

inundation

Loss of terrestrial land

Degradation of 

vegetation and 

nesting habitat during 

storm events

Direct 

Effects on 

Avifauna 

Habitat 

Loss of 

nesting

habitat

Reproductive 

loss (eggs, 

chicks)

Mortality 

Projected 

Effects on 

Avifauna 

Population 

Dynamics 

Decreased 

carrying capacity (K)

Population 

growth 

rate

Population 

viability

86    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 2.10. Conceptual model describing how global climate change may 

influence the environment and avifauna of Laysan Island. Thermal expansion of the 

ocean, increased seawater volume, and changing global circulation patterns are 

likely to result in a range of impacts specific to Laysan Island. These impacts, in turn, 

are likely to have direct and indirect effects on avian population dynamics.
Results    87

(RMSE) affects our confidence in which areas of the island 

might be inundated. 

Lastly, we conducted sensitivity analysis to address 

uncertainty in wave-driven water levels and on the input 

parameters to the wave-driven inundation model. Our analysis 

indicated that modeled wave height and wave period were less 

than 2 percent of the mean values for wave height and wave 

period, with a standard deviation in beach slope of approximately 2 percent. We calculated the resulting mean error in 

wave-driven water levels as 0.11 m. 

Since these uncertainties (expressed as standard deviation, SD) are uncorrelated, we combined them into an overall 

uncertainty term (NOAA, 2010a):

SDTotal = (SDTidal2 + SDTopographic2 + SDRun-up2)

0.5 = 

 (0.102 + 0.322 + 0.112)

 0.5 (3)

From the uncertainty estimates above, we computed the 

overall vertical uncertainty or RMSE as 0.35 m. This vertical 

uncertainty has a variable effect on horizontal inundation extent, 

depending on topography; areas with gradual slopes exhibit 

more horizontal uncertainty than areas with steep slopes. 

Inundation extents (or boundaries) can be created from 

data of any quality and are not sensitive to accuracy, although 

data accuracy determines the area of confidence around 

inundation boundaries (NOAA, 2010a). NOAA uses a value 

of 80-percent confidence as the threshold between high and 

low confidence. By definition, the Z-score at the inundation 

boundary equals zero, with the probability of inundation on 

this boundary equaling 50 percent. At elevations above the 

inundation boundary, the probability of inundation decreases 

according to the normal probability distribution, such that 

a location +0.84 SDs above the inundation level has a 

20-percent probability of inundation (or 80-percent probability 

of remaining dry). Similarly, a location −0.84 SDs below 

the inundation level has an 80-percent probability of inundation (or 20-percent probability of remaining dry). Between 

these two thresholds lies an area of low confidence (less than 

80 percent), while beyond these thresholds are areas of high 

confidence (greater than 80 percent). 

As an example, consider a SLR scenario of +1.00 m and 

a RMSE of 0.35 m. At the inundation boundary (that is, at an 

elevation of +1.00 m), we observe a 50-percent probability 

of inundation. For an elevation of +0.71 m (Z = −0.84 SDs), 

we are highly confident (80 percent) that this elevation will 

experience flooding; likewise for an elevation of +1.29 m (Z = 

+0.84 SDs) we are highly confident this elevation will remain 

dry. Between 0.71 and 1.29 m lies an area of low (less than 80 

percent) confidence. 

None of the projected inundation boundaries were biased 

by positional uncertainty, although the true position of the 

boundary may have been higher or lower given the uncertainty 

in the model. For all sea-level scenarios under consideration 

(0.00, +0.50, +1.00, +1.50, and +2.00 m), if we wanted to 

maintain 80-percent confidence that the inundation boundary 

would remain dry, then as a precaution we would have had to 

add +0.29 m of elevation to the flood surface and allowed the 

horizontal boundary to adjust accordingly. While we could have 

adopted such a precautionary approach, we decided to analyze 

inundation extent using unbiased inundation boundaries only. 

Since we modeled multiple scenarios of SLR and presented a 

range of inundation boundaries, the effects of uncertainty on 

inundation extent can be visualized by examining changes in 

inundation extent at different increments of SLR. 

Limitations of Wildlife Data

Wildlife distribution and abundance data from Laysan 

have many limitations. First, most of our assembled distribution data was collected during a single year. In such cases, 

we assumed surveys occurred at the peak of the breeding 

season and represented typical distribution patterns for the 

species surveyed. For the five species with USFWS nesting 

distribution maps, GPS (Garmin GPS map 76 or 76CS) 

accuracy was generally within 10 m (RMSE) for all point 

locations (Masked and Brown booby), perimeter lines around 

colonies (Sooty Tern), or mixed data that combined points 

and track lines (Red-footed Booby and Great Frigatebird). 

For species whose distribution was estimated from land 

cover maps, long-term monitoring data, and recent surveys, 

the accuracy of our land cover map represented the greatest 

source of uncertainty. Due to logistical constraints, we could 

not perform a quantitative ground-based accuracy assessment on these land cover maps; however, Laysan field 

biologists reviewed the map qualitatively (M. Stelmach, 

USFWS, oral commun., 2011). As was the case with distribution data, we assumed abundance data were collected at 

the peak of the breeding season and represented a typical 

year in breeding abundance. Most species with time-series 

data from Laysan Island had large year-to-year variance, 

and monitoring methods prevented analyses to differentiate 

between sampling error and true population fluctuations, 

often referred to as process variation, or σ (Citta and others, 

2007; Seavy and Reynolds, 2009). 

Results

Depending on the SLR scenario considered and the 

modeling approach used, the potential physical and biological 

impacts of SLR on Laysan Island varied widely, with vulnerability ranging from negligible to significant.

Sea-Level Rise and Wave-Driven

Water Levels

Raw wave-driven water levels (set-up + run-up) from the 

Delft3D WAVE module are shown in figure 2.11 for two of the 

five SLR scenarios considered. Values represent vertical water 

levels at the shoreline before they are projected up the beach 

slope. Higher wave-driven water levels occurred for the +2.00 m 
88    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

scenario on all coastlines, regardless of exposure. Mean wavedriven water levels under the +2.00 m SLR scenario (fig. 2.11) 

exceeded wave-driven water levels under the +1.50 m scenario 

(not shown) by about 5 percent. In general, mean wave-driven 

water levels ranged from +0.98–1.22 m depending on SLR 

scenario, with higher wave-driven water levels occurring at 

higher SLR scenarios. As expected, for each SLR scenario, 

higher wave-driven water levels generally occurred on western 

exposures during the period of highest wave energy (Dec.-Feb.). 

Passive and Dynamic Inundation

Across all sea-level scenarios (0.00, + 0.50, + 1.00, + 

1.50, and + 2.00 m), the extent of inundation predicted under 

the passive modeling approach covered less area than the inundation extent forecasted by the dynamic modeling approach, 

particularly when considering unlimited volumes of wavedriven seawater and no infiltration (table 2.2, figs. 2.12–14). 

Under the passive modeling approach, each of the five SLR 

scenarios yielded a similar spatial pattern, with inundation 

progressing inland from the coast in the shape of concentric 

rings (fig. 2.12). Under this approach, none of the coastal 

dunes (which have heights of greater than 2.5 m above MSL) 

separating the ocean from the interior lake and mudflats were 

breached by seawater. As sea level increased, inundation area 

expanded more-or-less linearly, accelerating only slightly at 

the higher sea-level values (table 2.2, fig. 2.14). For all passive 

SLR scenarios, inundation was less than 5 percent of the 

island’s terrestrial area. 

Figure 2.11. Map of Laysan Island depicting modeled wave-driven water levels at 430 coastal points, spaced 20 

meters (m) apart, for the lowest and highest sea-level rise (SLR) scenarios: 0.00 m (left) and +2.00 m (right). Wavedriven water levels on the right exhibited higher values, as less wave-energy attenuation occurred across the reef crest 

and reef flat under higher sea-level conditions. These heights, which include both wave set-up and wave run-up, were 

projected up shore-normal transects to estimate inundation extent during large-wave events.
SLR

scenario

Area

(ha)

%

change

Area

(ha)

%

change

Area

(ha)

%

change

 0.00 m 0.4 -0.1 6.1 -1.8 6.1 -1.8

 + 0.50 m 3.1 -0.9 10.5 -3.1 10.5 -3.1

 + 1.00 m 6.7 -2.0 15.5 -4.6 15.5 -4.6

 + 1.50 m 10.8 -3.2 22.1 -6.5 115.8 -34.3

 + 2.00 m 15.5 -4.6 58.0 -17.2 136.2 -40.3

Passive inundation

due to SLR only

Dynamic inundation

due to SLR and

wave-driven water levels

Dynamic inundation

due to SLR and

wave-driven water levels, assuming 

unlimited seawater volumes and no 

infiltration

Table 2.2. Inundation extent for five sea-level scenarios and three modeling approaches: (a) passive inundation 

model showing inundation due to sea-level rise (SLR) only, (b) wave-driven inundation model showing inundation due 

to SLR and wave-driven water levels, and (c) wave-driven inundation model depicting inundation due to SLR and wavedriven water levels assuming unlimited seawater volumes and no infiltration of filled basins.

 [All values reflect inundation at mean high water (MHW) for the specified SLR scenario. For example, at present sea level (SLR = 0.00 m), 

inundation represents the area above mean sea level that is submerged at MHW. Percentages represent the percent change in terrestrial land, 

where terrestrial land (337.8 ha) equals total land area (412.0 ha) minus the two wetland (or lake zone) categories (74.2 ha). By definition, percent change due to inundation is negative, representing a reduction in terrestrial land. Abbreviations: ha, hectares; m, meters; %, percent]

Results    89

For the dynamic inundation model (fig. 2.13), where SLR 

was combined with wave set-up and run-up, similar patterns of 

concentric inundation occurred at SLR scenarios less than or 

equal to +1.00 m. For these scenarios (SLR = 0.00, +0.50 and 

+1.00 m), the wave-driven inundation model showed slightly 

wider bands of inundation than the passive inundation model, 

although none of the predicted inundation extents exceeded 

5 percent of the island’s terrestrial area (table 2.2), defined as 

total island area (412.0 ha) minus the entire lake zone (74.2 ha). 

For the higher SLR scenarios of +1.50 and +2.00 m 

(figs. 2.13, 15–16), wave-driven inundation patterns started 

to diverge considerably from passive inundation patterns, 

as wave-driven water levels began to breach the coastal 

dunes separating the ocean from the interior basins (that is, 

the central lake basin and the two depressions in the eastern 

desert). At +1.50 m SLR, the added effect of wave-driven 

water levels breached the dunes in two locations along the 

east coast, including one location where wave-driven water 

levels penetrated to the lake zone (fig. 2.13). The inundated 

area from these two breaches intersected the two topographic 

depressions flooded by the storm event of February 2011 

(figs. 2.13 and 2.17; Kristof and others, 2011). In terms of 

area, at +1.50 m SLR (fig. 2.15), the dynamic wave-driven 

inundation model (with limited seawater volumes and no 

infiltration) predicted losses in total terrestrial area of 22.1 ha 

(6.5 percent of the total terrestrial area), or more than double 

that from the passive approach (10.8 ha, or 3.2 percent). At 

+2.00 m SLR, the added effect of wave-driven water levels 

was more dramatic, as seawater propagating from the southern 

and eastern shorelines penetrated wide expanses of the lake 

zone. For the +2.00-m SLR scenario (fig. 2.16), the dynamic 

wave-driven model (with limited seawater volumes and no 

infiltration) predicted losses in terrestrial area of 58.0 ha (17.2 

percent of the terrestrial area), or greater than three times 

the inundation extent forecasted with the passive modeling 

approach (15.5 ha or 4.6 percent of terrestrial area). 

For the dynamic wave-driven inundation model at the 

lower SLR scenarios (+1.00 m or less), we observed no 

differences between initial wave-driven inundation and the 

theoretical maximum extent of inundation, assuming unlimited 

seawater volumes and no infiltration. This equality occurred 

since wave-driven water levels did not overtop the coastal 

dunes and penetrate to the low-lying interior areas. Once SLR 

reached +1.50 m, wave-driven inundation breached the dunes, 

allowing for greatly increased potential inundation, assuming 

unlimited volumes of wave-driven seawater and no infiltration. 

Under these assumptions, the theoretical maximum extent of 

inundation at +1.50 m SLR was 115.8 ha, representing 34.3 

percent of the island’s terrestrial area (figs. 2.15 and 2.18); at 

+2.00 m SLR, the theoretical maximum extent of inundation 

covered 136.2 ha, or 40.3 percent of the island’s terrestrial area 

(figs. 2.16 and 2.18). When compared to the observed flooding 

during the severe winter storm of February 2011 

(fig. 2.17; USFWS data; Kristof and others, 2011), our predicted 

maximum areas of inundation aligned closely (fig. 2.18). 

When we incorporated a concurrent rise in groundwater 

into the passive SLR model, inundation extents expanded 

considerably, especially in the area adjacent to the lake 

zone (fig. 2.19). For this model, the narrow band of passive 

coastal flooding was accompanied by a broad range of 

interior flooding depending on the SLR scenario considered. 

At the higher levels of SLR (+1.50 and +2.00 m), extensive 

inundation of the low-lying lake basin and eastern desert 

basins occurred in a pattern similar to the one observed for 
90    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 2.12. Overlay of passive inundation zones for five sea-level rise (SLR) scenarios at Laysan 

Island with land cover in the background (fig. 2.21 provides a clearer depiction of land cover). This map 

does not depict wave-driven inundation or inundation from groundwater percolation from the ocean. In 

general, under this modeling approach inundation occurs only along a narrow stretch of beach around 

the entire island. All areas represent inundation at mean high water. Abbreviations: m, meters.
Results    91

Figure 2.13. Overlay of wave-driven inundation for five sea-level rise (SLR) scenarios at Laysan Island with 

land cover in the background (fig. 2.21 provides a clearer depiction of land cover). This map does not depict 

inundation from unlimited volumes of wave-driven seawater, or inundation from groundwater percolation from 

the ocean. In general, under this modeling approach inundation occurs in wider bands along the beach, with 

major inland excursions along the south and eastern sides of the island under the higher SLR scenarios 

(+1.50 and +2.00 meters). All areas represent inundation at mean high water. Abbreviations: m, meters.
92    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 2.14. Chart of inundation extent in hectares (ha) at Laysan Island for five sea-level rise (SLR) scenarios 

and three different modeling approaches: (a) passive inundation model (also referred to as a “bathtub model") 

showing inundation due to SLR only, (b) wave-driven inundation model showing inundation due to SLR and 

wave-driven water levels, and (c) wave-driven inundation model depicting inundation due to SLR and wave-driven 

water levels assuming unlimited seawater volumes and no infiltration (see table 2.2). All values reflect inundation 

at mean high water. For a discussion of the uncertainty associated with the modeling approaches, see Methods 

(Uncertainty in SLR Models for Laysan Island).
Results    93

Figure 2.15. Map depicting potential inundation extent at Laysan Island under a sea-level rise (SLR) 

scenario of +1.50 meters (m) at mean high water. Relative to the passive inundation modeling approach, the 

dynamic inundation model that includes wave-driven water levels shows a wider band of coastal inundation, 

plus two locations in the east where inundation extends more than 400 m inland. If wave-driven seawater 

volumes were unlimited and no infiltration occurred, an area of 115.8 hectares (ha) could be inundated.
94    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 2.16. Map depicting potential inundation extent at Laysan Island under a sea-level rise (SLR) 

scenario of +2.00 meters (m) at mean high water. Relative to the passive inundation modeling approach, the 

dynamic inundation model that includes wave-driven water levels (medium blue) shows a wider band of coastal 

inundation, plus multiple locations in the east where inundation extends more than 400 m inland. If wave-driven 

seawater volumes were unlimited and no infiltration occurred, an area of 136.2 ha (hectares) could be inundated.
Results    95

Figure 2.17. The inundation pattern documented by U.S. Fish and Wildlife Service (USFWS) field staff 

on Laysan Island after a heavy rainfall (27.20 centimeters in 24 hours) and large-wave event on February 

12, 2011 (USFWS data; Kristof and others, 2011). The “average” lake zone identified in this USFWS map 

represents the entire lake zone. Typically this lake zone encompasses areas of standing water and mud 

flats, depending on recent hydrologic events (for example, heavy rainfall or drought).
Figure 2.18. Overlay of wave-driven inLaysan Island assuming unlimited seawinfiltration for five sea-level rise (SLR) sccover in the background (fig. 2.21 providof land cover). This pattern depicts the thextent of inundation during periods of higdoes not show inundation due to groundfrom the ocean. In comparison to the wamodel with limited wave-driven seawatethis model exhibits extensive inundation lake and in the eastern depressions for tof +1.50 and +2.00 meters (m). At the lo(0.00, +0.50, and +1.00 m), inundation is of whether seawater volumes are limited wave-driven seawater does not overtop penetrate to the interior basin.
Figure 2.19. Overlay of passive inundaIsland for five sea-level rise (SLR) scenathe background (fig. 2.21 provides a cleacover). This map depicts passive SLR inuocean (as shown in fig. 2.12) plus potentrising groundwater and an expanded lakpotential groundwater inundation under tresembles the inundation pattern exhibitinundation model when wave-driven seaunlimited. Both models forecast extensivlying areas near the central lake. Abbrev
Figure 2.20. Overlay of wave-driven inpotential inundation from rising groundwfor five sea-level rise (SLR) scenarios wbackground (fig. 2.21 provides a clearer For the higher SLR scenarios (+1.50 anabove inundation pattern covers substathe inundation pattern from the wave-drwith unlimited volumes of seawater and 
Results    99

the wave-driven inundation model with unlimited seawater 

volumes and no infiltration. In terms of overall area, at +2.00 

m SLR the passive inundation model (with rising groundwater) predicted a 27.4-percent reduction in terrestrial area. 

Lastly, the model that incorporated wave-driven inundation and groundwater rise (fig. 2.20) produced an inundation pattern that closely resembled the inundation pattern of 

the wave-driven inundation model with unlimited seawater 

volumes and no infiltration. In terms of total area, this model 

predicted a 36.1-percent reduction in terrestrial area.

Land Cover on Laysan Island

We identified and quantified land cover for Laysan Island 

prior to considering the overlays of land cover and inundation (table 2.3, fig. 2.21). At the time the satellite imagery 

was captured, the lake basin (including seasonal mudflats 

and standing water) covered 18.0 percent (74.2 ha) of total 

island area. Of the remaining terrestrial land, 77.6 percent was 

composed of an interspersed mix of grass/herbaceous cover 

(22.1 percent), vine/ground cover (17.2 percent), and bare 

ground (38.3 percent). None of the other land cover classes, 

including tree/shrub, mixed shrub, wetland vegetation, and 

beach, individually covered greater than 6 percent of Laysan’s 

terrestrial area. In general, land cover on Laysan featured a 

narrow band of beach around the island’s perimeter, a narrow 

band of wetland vegetation around the lake perimeter, broad 

areas of sandy bare ground in the north and east, and predominantly interspersed bunch grass (Eragrostis variabilis) and 

beach morning glory (Ipomoea pes-caprae) elsewhere (especially in the west; fig. 2.21).

Inundation by Land Cover Class

With the passive inundation model, the only land cover 

class to exhibit substantial losses was beach (fig. 2.12, table 

2.4a). Even at the highest SLR scenario (+2.00 m), only 

1.3 percent of the bare ground class was inundated under a 

passive modeling approach. Similar to the passive inundation model, the wave-driven inundation model (fig. 2.13, 

table 2.4b) exhibited losses predominantly to the beach area 

at SLR scenarios below +2.00 m. However, at +2.00 m SLR, 

96.6 percent of current beach area and nearly 20 percent 

of the bare ground class experienced periodic inundation 

during large-wave events. At the lower SLR scenarios (0.00, 

+0.50, and +1.00 m), no differences existed between the 

wave-driven inundation models with and without unlimited 

seawater volumes. For the SLR scenarios of +1.50 and +2.00 

m, where unlimited seawater volumes flowed outward into 

adjacent depressions, broad flooding occurred across several 

land cover categories (fig. 2.18, table 2.4c). For most land 

cover classes, the extent of flooding did not differ greatly 

Land cover class Area (ha)

% of total 

terrestrial 

area 

% of total 

island area

Tree/shrub 12.3 3.6 3.0

Casuarina equisetifolia 0.0 0.0 0.0

Cocos nucifera 0.0 0.0 0.0

Pluchea indica 8.3 2.5 2.0

Tournefortia argentea 0.7 0.2 0.2

Mixed shrub 18.0 5.3 4.4

Grass/herbaceous cover 74.8 22.1 18.2

Vine/ground cover 58.0 17.2 14.1

Wetland vegetation 13.8 4.1 3.4

Bare ground 129.3 38.3 31.4

Hard pan 3.1 0.9 0.7

Beach 19.4 5.7 4.7

Wetland (unvegetated) 34.2 --- 8.3

Wetland (standing water) 40.0 --- 9.7

Human structures 0.0 0.0 0.0

Total terrestrial area 337.8 100.0 ---

Total island area 412.0 --- 100.0

Table 2.3. Area of each land cover class on Laysan Island, 

including percentage of total terrestrial area and percentage of total 

island area. 

[The two unvegetated wetland classes are excluded from total terrestrial area. 

Abbreviations: ha, hectares; %, percentage]
100    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Figure 2.21. Land cover classes for Laysan Island, based on U.S. Fish and Wildlife Service ground-survey data 

(Hammond and others, 2010) and unsupervised classification of WorldView-2 satellite imagery taken on May 18, 

2010 (Digital Globe Inc., 2010). This map provides the basis for determining the type of land cover and habitat 

that may be inundated under various sea-level rise (SLR) scenarios and modeling approaches. This map displays 

land cover class without using semi-transparent colors, as in previous maps.
Table 2.4. Inundated areas and percent change for Laysan Island, tallied by land cover class for five sea-level rise (SLR) 

scenarios (0.00, +0.50, +1.00, +1.50, and +2.00 meters) under five modeling approaches: (a) passive inundation, (b) 

dynamic wave-driven inundation, (c) dynamic wave-driven inundation with unlimited seawater volumes and no infiltration, 

(d) passive inundation including rising groundwater levels, and (e) dynamic wave-driven inundation including rising 

groundwater levels. 

[All values reflect inundation at mean high water (MHW) for the specified SLR scenario. For example, at present sea level (SLR = 0.00 m), inundation 

represents the area above mean sea level that is submerged at MHW. Abbreviations: ha, hectares; %, percent; m, meters]

a. Passive Inundation Model

Land cover class

Area 

(ha) Area (ha) % change Area (ha) % change Area (ha) % change Area (ha) % change Area 

(ha) % change

Tree/shrub 12.3

Casuarina equisetifolia 0.0

Cocos nucifera 0.0

Pluchea indica 8.3

Tournefortia argentea 0.7

Mixed shrub 18.0

Grass/herbaceous cover 74.8

Vine/ground cover 58.0

Wetland vegetation 13.8

Bare ground 129.3 0.1 0.0 0.3 -0.2 0.4 -0.3 0.6 -0.5 1.6 -1.3

Hard pan 3.1

Beach 19.4 0.4 -1.9 2.8 -14.5 6.3 -32.3 10.1 -52.3 13.8 -71.3

Wetland (unvegetated) 34.2

Wetland (standing water) 40.0

Human structures 0.0

Total terrestrial area 337.8 0.4 -0.1 3.1 -0.9 6.7 -2.0 10.8 -3.2 15.5 -4.6

Total island area 412.0 0.4 -0.1 3.1 -0.8 6.7 -1.6 10.8 -2.6 15.5 -3.8

Change from SLR

0.00 m +0.50 m +1.00 m +1.50 m +2.00 m

Results    101

between the two highest scenarios (+1.50 and +2.00 m). 

Under both scenarios, losses were 100 percent for Cocos 

nucifera and Pluchea indica, and exceeded 60 percent for 

mixed shrub, 40 percent for grass/herbaceous cover, 90 

percent for wetland vegetation, 95 percent for hard pan, and 

85 percent for beach (table 2.4c). Managers with USFWS 

have been attempting to eradicate the invasive plant P. indica

(Hawaiian Ecosystems at Risk Project, 2010) since 2010; 

thus any future losses to this land cover class may apply to 

the restored wetland or shrub species that replace P. indica.

The models that incorporated groundwater (tables 2.4d and 

2.4e) exhibited similar losses in land cover types to the wavedriven inundation models with unlimited seawater volumes and 

no infiltration, as substantially the same low-lying areas would 

be inundated under either modeling approach. With the passive 

inundation model that includes rising groundwater, primarily 

wetland vegetation was inundated (38.6-percent loss) at +0.50 

m SLR, whereas at +1.00 m SLR, other classes including 

tree/shrub, C. nucifera, P. indica, and mixed shrub also were 

inundated (table 2.4d, fig. 2.19). The federally endangered plant 

Cyperus pennatiformis var. bryanii would be one of the species 

likely to experience substantial inundation under a scenario 

of rising groundwater (See chap. 1, Laysan Island Results, for 

additional details and C. pennatiformis var. bryanii distribution maps). At the higher SLR scenarios (+1.50 and +2.00 m), 

sizable losses to most land cover classes occurred. Lastly, for 

the wave-driven inundation model that incorporates rising 

groundwater (table 2.4e, fig. 2.20), specific losses by land cover 

class generally approached the magnitude of losses exhibited 

by the wave-driven inundation model with unlimited seawater 

volumes and no infiltration. 

Impacts of SLR on Nesting Habitat

Vulnerability of nesting habitat to SLR and wave-driven 

inundation varied according to species distribution and breeding 

ecology (figs. 2.22–28), as well as by the SLR scenario and 

modeling approach: (1) passive inundation model, (2) wavedriven inundation model, (3) wave-driven inundation model 

assuming unlimited seawater volumes and no infiltration, (4) 

passive inundation model with rising groundwater, and (5) 

wave-driven inundation model with rising groundwater. 
Table 2.4. Inundated areas and percent change for Laysan Island, tallied by land cover class for five sea-level rise (SLR) 

scenarios (0.00, +0.50, +1.00, +1.50, and +2.00 meters) under five modeling approaches: (a) passive inundation, (b) 

dynamic wave-driven inundation, (c) dynamic wave-driven inundation with unlimited seawater volumes and no infiltration, 

(d) passive inundation including rising groundwater levels, and (e) dynamic wave-driven inundation including rising 

groundwater levels. —Continued

[All values reflect inundation at mean high water (MHW) for the specified SLR scenario. For example, at present sea level (SLR = 0.00 m), inundation 

represents the area above mean sea level that is submerged at MHW. Abbreviations: ha, hectares; %, percent; m, meters]

b. Dynamic Wave-driven Inundation Model

Land cover class Area (ha) Area (ha) % change

Area 

(ha) % change Area (ha) % change Area (ha) % change

Area 

(ha) % change

Tree/shrub 12.3 0.0 -0.1 0.1 -1.0

Casuarina equisetifolia 0.0

Cocos nucifera 0.0

Pluchea indica 8.3 0.0 -0.1 1.0 -12.4

Tournefortia argentea 0.7 0.1 -7.1

Mixed shrub 18.0 1.0 -5.6

Grass/herbaceous cover 74.8 0.1 -0.1 6.9 -9.3

Vine/ground cover 58.0 0.1 -0.1 3.7 -6.3

Wetland vegetation 13.8 1.2 -8.4

Bare ground 129.3 0.4 -0.3 0.6 -0.5 1.5 -1.1 4.9 -3.8 25.2 -19.5

Hard pan 3.1

Beach 19.4 5.7 -29.4 9.9 -51.0 14.0 -72.2 17.0 -87.8 18.8 -96.6

Wetland (unvegetated) 34.2

Wetland (standing water) 40.0

Human structures 0.0

Total terrestrial area 337.8 6.1 -1.8 10.5 -3.1 15.5 -4.6 22.1 -6.5 57.9 -17.2

Total island area 412.0 6.1 -1.5 10.5 -2.5 15.5 -3.8 22.1 -5.4 57.9 -14.1

Change from SLR

0.00 m +0.50 m +1.00 m +1.50 m +2.00 m

c. Dynamic Wave-driven Inundation Model with Unlimited Seawater Volumes and No Infiltration

Land cover class Area (ha) Area (ha) % change

Area 

(ha) % change Area (ha) % change Area (ha) % change

Area 

(ha) % change

Tree/shrub 12.3 6.2 -50.3 6.4 -52.1

Casuarina equisetifolia 0.0

Cocos nucifera 0.0 0.0 -100.0 0.0 -100.0

Pluchea indica 8.3 8.3 -100.0 8.3 -100.0

Tournefortia argentea 0.7 0.1 -7.1

Mixed shrub 18.0 11.7 -64.9 12.2 -68.0

Grass/herbaceous cover 74.8 31.1 -41.6 33.9 -45.3

Vine/ground cover 58.0 8.7 -15.0 10.8 -18.5

Wetland vegetation 13.8 12.5 -90.8 12.7 -91.6

Bare ground 129.3 0.4 -0.3 0.6 -0.5 1.5 -1.1 17.2 -13.3 30.1 -23.2

Hard pan 3.1 3.0 -97.9 3.0 -98.9

Beach 19.4 5.7 -29.4 9.9 -51.0 14.0 -72.2 17.0 -87.8 18.8 -96.6

Wetland (unvegetated) 34.2

Wetland (standing water) 40.0

Human structures 0.0

Total terrestrial area 337.8 6.1 -1.8 10.5 -3.1 15.5 -4.6 115.8 -34.3 136.2 -40.3

Total island area 412.0 6.1 -1.5 10.5 -2.5 15.5 -3.8 115.8 -28.1 136.2 -33.1

Change from SLR

0.00 m +0.50 m +1.00 m +1.50 m +2.00 m

102    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands
Table 2.4. Inundated areas and percent change for Laysan Island, tallied by land cover class for five sea-level rise (SLR) 

scenarios (0.00, +0.50, +1.00, +1.50, and +2.00 meters) under five modeling approaches: (a) passive inundation, (b) 

dynamic wave-driven inundation, (c) dynamic wave-driven inundation with unlimited seawater volumes and no infiltration, 

(d) passive inundation including rising groundwater levels, and (e) dynamic wave-driven inundation including rising 

groundwater levels. —Continued

[All values reflect inundation at mean high water (MHW) for the specified SLR scenario. For example, at present sea level (SLR = 0.00 m), inundation 

represents the area above mean sea level that is submerged at MHW. Abbreviations: ha, hectares; %, percent; m, meters]

d. Passive Inundation Model Including Rising Groundwater Levels

Land cover class Area (ha) Area (ha) % change

Area 

(ha) % change Area (ha) % change Area (ha) % change

Area 

(ha) % change

Tree/shrub 12.3 0.9 -7.5 3.5 -28.7 5.1 -41.1 5.7 -46.4

Casuarina equisetifolia 0.0

Cocos nucifera 0.0 0.0 -1.9 0.0 -100.0 0.0 -100.0 0.0 -100.0

Pluchea indica 8.3 0.3 -4.0 6.5 -78.5 8.3 -100.0 8.3 -100.0

Tournefortia argentea 0.7

Mixed shrub 18.0 1.1 -5.9 5.8 -32.5 9.9 -55.2 10.9 -60.4

Grass/herbaceous cover 74.8 2.7 -3.6 7.6 -10.1 19.6 -26.2 26.2 -35.0

Vine/ground cover 58.0 0.4 -0.6 0.7 -1.3 2.8 -4.9 5.5 -9.6

Wetland vegetation 13.8 5.3 -38.6 9.9 -71.4 12.2 -88.1 12.4 -89.9

Bare ground 129.3 0.1 0.0 0.3 -0.2 0.4 -0.3 1.3 -1.0 6.9 -5.4

Hard pan 3.1 0.8 -26.8 2.8 -91.3

Beach 19.4 0.4 -1.9 2.8 -14.5 6.3 -32.3 10.1 -52.3 13.8 -71.3

Wetland (unvegetated) 34.2

Wetland (standing water) 40.0

Human structures 0.0

Total terrestrial area 337.8 0.4 -0.1 13.8 -4.1 40.8 -12.1 70.2 -20.8 92.6 -27.4

Total island area 412.0 0.4 -0.1 13.8 -3.4 40.8 -9.9 70.2 -17.0 92.6 -22.5

Change from SLR

0.00 m +0.50 m +1.00 m +1.50 m +2.00 m

e. Dynamic Wave-driven Inundation Model Including Rising Groundwater Levels

Land cover class Area (ha) Area (ha) % change

Area 

(ha) % change Area (ha) % change Area (ha) % change

Area 

(ha) % change

Tree/shrub 12.3 0.9 -7.5 3.5 -28.7 5.1 -41.1 5.7 -46.6

Casuarina equisetifolia 0.0

Cocos nucifera 0.0 0.0 -1.9 0.0 -100.0 0.0 -100.0 0.0 -100.0

Pluchea indica 8.3 0.3 -4.0 6.5 -78.5 8.3 -100.0 8.3 -100.0

Tournefortia argentea 0.7 0.1 -7.1

Mixed shrub 18.0 1.1 -5.9 5.8 -32.5 9.9 -55.2 11.2 -62.1

Grass/herbaceous cover 74.8 2.7 -3.6 7.6 -10.1 19.7 -26.3 28.5 -38.2

Vine/ground cover 58.0 0.4 -0.6 0.7 -1.3 2.9 -5.0 7.8 -13.4

Wetland vegetation 13.8 5.3 -38.6 9.9 -71.4 12.2 -88.1 12.6 -90.9

Bare ground 129.3 0.4 -0.3 0.6 -0.5 1.5 -1.2 5.5 -4.3 26.4 -20.4

Hard pan 3.1 0.8 -26.8 2.8 -91.3

Beach 19.4 5.7 -29.4 9.9 -51.0 14.0 -72.2 17.0 -87.8 18.8 -96.6

Wetland (unvegetated) 34.2

Wetland (standing water) 40.0

Human structures 0.0

Total terrestrial area 337.8 6.1 -1.8 21.2 -6.3 49.6 -14.7 81.4 -24.1 122.1 -36.1

Total island area 412.0 6.1 -1.5 21.2 -5.2 49.6 -12.0 81.4 -19.8 122.1 -29.6

Change from SLR

0.00 m +0.50 m +1.00 m +1.50 m +2.00 m

Results    103
Table 2.5. Inundation of 2009–2010 nesting habitat at Laysan Island (USFWS data; Kristof and others, 2010) for Blackfooted Albatross and Laysan Albatross under five sea-level rise (SLR) scenarios and five modeling approaches: (1) passive 

inundation, (2) dynamic wave-driven inundation, (3) dynamic wave-driven inundation with unlimited seawater volumes 

and no infiltration, (4) passive SLR including a comparable rise in groundwater, and (5) dynamic wave-driven inundation 

including a comparable rise in groundwater. 

[Inundation of nesting habitat is expressed in area and percent change relative to the total habitat without inundation (listed in last row). Abbreviations: USFWS, U.S. Fish and Wildlife Service; m, meters; ha, hectares; %, percent]

Model

SLR

scenario

Total island

inundation 

area (ha)

Inundated 

2009–2010

habitat (ha)

% change in 

2009–2010

habitat 

Inundated 

2009–2010

habitat (ha)

% change in 

2009–2010

habitat

Passive inundation 0.00 m 0.4 0.0 0.0 0.1 0.0

 + 0.50 m 3.1 0.0 0.0 0.3 -0.1

 + 1.00 m 6.7 0.0 0.0 0.4 -0.1

 + 1.50 m 10.8 0.2 -0.1 0.6 -0.2

 + 2.00 m 15.5 1.2 -0.6 1.6 -0.5

Dynamic wave-driven inundation 0.00 m 6.1 0.0 0.0 0.4 -0.1

 + 0.50 m 10.5 0.2 -0.1 0.6 -0.2

 + 1.00 m 15.5 1.0 -0.5 1.5 -0.5

 + 1.50 m 22.1 4.6 -2.4 5.0 -1.7

 + 2.00 m 57.9 31.2 -16.3 38.0 -12.8

Dynamic wave-driven inundation, 0.00 m 6.1 0.0 0.0 0.4 -0.1

unlimited seawater and no infiltration + 0.50 m 10.5 0.2 -0.1 0.6 -0.2

 + 1.00 m 15.5 1.0 -0.5 1.5 -0.5

 + 1.50 m 115.8 25.7 -13.4 84.3 -28.4

 + 2.00 m 136.2 41.2 -21.5 102.7 -34.6

Passive + groundwater inundation 0.00 m 0.4 0.0 0.0 0.1 0.0

 + 0.50 m 13.8 0.0 0.0 9.8 -3.3

 + 1.00 m 40.8 0.2 -0.1 24.5 -8.2

 + 1.50 m 70.2 2.9 -1.5 46.7 -15.7

 + 2.00 m 92.6 11.7 -6.1 64.8 -21.8

Dynamic wave-driven + groundwater inundation 0.00 m 6.1 0.0 0.0 0.4 -0.1

 + 0.50 m 21.2 0.2 -0.1 10.1 -3.4

 + 1.00 m 49.6 1.3 -0.7 25.6 -8.6

 + 1.50 m 81.4 7.2 -3.8 51.1 -17.2

 + 2.00 m 122.1 35.3 -18.5 89.3 -30.1

Total terrestrial habitat (without inundation) 337.8 191.2 297.0

Black-footed Albatross Laysan Albatross

104    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Based on the spatial distribution of 2010–11 breeding 

season data, Black-footed Albatross (BFAL) nested on 191.2 

ha located primarily around the island’s perimeter (fig. 2.22). 

BFAL nested in all habitats, excluding areas of continuous 

tree/shrub cover, wetland vegetation, and the narrow strip of 

beach (subject to tidal fluctuations and typical wave swash). 

Under the passive modeling approach, less than 1 percent of 

BFAL nesting area experienced inundation at any SLR scenario 

considered (table 2.5). When groundwater rise was incorporated into the passive model, 6.1 percent of BFAL nesting 

area was inundated at the highest SLR scenario (+2.00 m); for 

all other SLR scenarios, less than 2 percent of their nesting 

area experienced inundation, as BFAL are not typically found 

nesting in the low-lying central basin. Using the wave-driven 

modeling approach with +2.00 m of SLR, inundation covered 

16.3 percent (limited seawater-volume case) or 21.5 percent 

(unlimited seawater-volume case) of the BFAL nesting area. 

At +1.50 m SLR, inundation covered 13.4 percent of the BFAL 

nesting area for the unlimited seawater scenario (table 2.5).

Current spatial distribution data based on surveys were 

not available for the Laysan Albatross (LAAL); however, 

using our land cover classification, we estimated their potential nesting habitat as 297.0 ha (fig. 2.23). Passive inundation 

models for all SLR scenarios (including the +2.00-m case) 
Figure 2.22. Overlay of Black-footed Albahabitat at Laysan Island, with combined inusea-level rise (SLR), wave-driven water levegroundwater. The combined background allof inundation extents from various models whabitat. By depicting inundation extent with symbols, the underlying nesting habitat remmay appear slightly darker. Biologists perfoBFAL nest sites in December 2009 and JanFish and Wildlife Service (USFWS) data; Kr2010); they also conducted direct counts. Bhectares (ha) located primarily around the iduring the 2010–11 breeding season. Undeapproach, less than 1 percent of BFAL nestinundation for all SLR scenarios. When grouincorporated into the passive model, 6.1 pearea was inundated at the highest SLR scem). With the wave-driven modeling approacinundation covered 16.3 percent (limited seor 21.5 percent (unlimited seawater-volume nesting area.
Figure 2.23. Overlay of Laysan Albatroat Laysan Island, with combined inundatlevel rise (SLR), wave-driven water levelsThe combined background allows for a cextents from various models with LAAL ndepicting inundation extent with semi-traunderlying nesting habitat remains visibleslightly darker. U.S. Fish and Wildlife Sersurveyed LAAL nests in 2010 (USFWS d2010). The displayed nesting habitat was maps and known nesting habitats. We espotential nesting habitat for LAAL as 297inundation models for all SLR scenarios loss of less than 1 percent. At +2.00 metmodeling approaches (including the wavgroundwater models) exhibited moderate LAAL habitat inundation (12.8 to 34.6 pewe considered either unlimited volumes ogroundwater, the +1.50 m scenario showhabitat losses (15.7 to 28.4 percent).
Results    107

predicted LAAL habitat inundation of less than 1 percent 

(table 2.5). At +2.00 m SLR, the other modeling approaches 

(including the wave-driven and groundwater approaches) 

exhibited moderate to substantial levels of LAAL habitat inundation (12.8 to 34.6 percent). Likewise, when we considered 

either unlimited volumes of seawater or rising groundwater, 

the +1.50-m scenario resulted in large habitat losses (15.7 to 

28.4 percent; table 2.5). 

The spatial distribution of LAAL nests overlapped the 

area vulnerable to inundation from winter storms, wave-driven 

water levels, and rising groundwater (USFWS data; Kristof 

and others, 2011). The inundation risks to potential habitat 

(that is, potential habitat based on land cover classes derived 

from satellite imagery rather than ground survey data) for 

BFAL and LAAL are similar. In general, potential habitat 

loss exceeded currently known BFAL nesting area loss (from 

ground survey data), as BFAL typically do not nest in the 

low-lying interior areas identified as habitat with land cover 

classification. 

Masked Booby (MABO) nests (n=163) were distributed 

throughout much of the island’s interior in 2009 (fig. 2.24). 

No MABO nests were inundated for any of the passive SLR 

scenarios up to +2.00 m SLR (table 2.6). For the wave-driven 

inundation model at the highest SLR scenario (+2.00 m), 

11.7 percent of the MABO nest sites experienced flooding. 

Incorporating rising groundwater into the passive model led to 

Model SLR

scenario

Total 

inundation 

area (ha)

% change

from 2009

nest sites

% change

from 2009

nest sites

Passive inundation 0.00 m 0.4 0.0 0.0

 + 0.50 m 3.1 0.0 0.0

 + 1.00 m 6.7 0.0 0.0

 + 1.50 m 10.8 0.0 0.0

 + 2.00 m 15.5 0.0 0.0

Dynamic wave-driven inundation 0.00 m 6.1 0.0 0.0

 + 0.50 m 10.5 0.0 0.0

 + 1.00 m 15.5 0.0 0.0

 + 1.50 m 22.1 0.0 0.0

 + 2.00 m 57.9 -11.7 -5.7

Dynamic wave-driven inundation, 0.00 m 6.1 0.0 0.0

unlimited seawater and no infiltration + 0.50 m 10.5 0.0 0.0

 + 1.00 m 15.5 0.0 0.0

 + 1.50 m 115.8 -39.3 -17.1

 + 2.00 m 136.2 -46.6 -22.9

Passive + groundwater inundation 0.00 m 0.4 0.0 0.0

 + 0.50 m 13.8 -2.5 0.0

 + 1.00 m 40.8 -9.2 -11.4

 + 1.50 m 70.2 -18.4 -11.4

 + 2.00 m 92.6 -27.0 -14.3

Dynamic wave-driven + groundwater inundation 0.00 m 6.1 0.0 0.0

 + 0.50 m 21.2 -2.5 0.0

 + 1.00 m 49.6 -9.2 -11.4

 + 1.50 m 81.4 -18.4 -11.4

 + 2.00 m 122.1 -34.4 -17.1

Masked Booby Brown Booby

Table 2.6. Inundation of 2009 nest sites at Laysan Island (USFWS data; Boyd and others, 2009) for Masked Booby 

(n=163) and Brown Booby (n=35) under five sea-level rise (SLR) scenarios and five modeling approaches: (1) passive 

inundation, (2) dynamic wave-driven inundation, (3) dynamic wave-driven inundation with unlimited seawater volumes 

and no infiltration, (4) passive SLR including a comparable rise in groundwater, and (5) dynamic wave-driven inundation 

including a comparable rise in groundwater.

 [U.S. Fish and Wildlife Service biologists mapped all nests June 17–19, 2009. [Inundation of nest sites is expressed as a percent change relative to 

the 2009 census numbers. Abbreviations: USFWS, U.S. Fish and Wildlife Service; m, meters; ha, hectares; %, percent]
Figure 2.24. Overlay of Masked Booby Brown Booby (BRBO, n=35) nests mappin 2009, with combined inundation from (SLR), wave-driven water levels, and risicombined background allows for a compextents from various models with displaytransparent satellite image appears in thGlobe Inc., 2010). U.S. Fish and Wildlife biologists conducted a complete census June 17–19, 2009 (USFWS data; Boyd aMABO nested across the island interior east and south, whereas BRBO nested psouthwestern edge of the island. Neither nests in the coastal inundation zone for aSLR scenarios considered; however, whgroundwater was modeled, 27.0 percent 14.3 percent of BRBO nests experiencemeters (m) SLR. For the wave-driven inulimited seawater volumes, no MABO or Binundated until SLR reached +2.00 m.
Results    109

greater inundation rates, with 18.4 percent and 27.0 percent of 

the nest sites flooded at +1.50 and +2.00 m SLR, respectively. 

Greater rates of inundation occurred with the wave-driven 

modeling approach assuming unlimited seawater volumes and 

no infiltration, with as much as 46.6 percent of the nest sites 

experiencing flooding at a SLR scenario of +2.00 m. 

Nest sites (n=35) of Brown Booby (BRBO) were concentrated inland of the protective dunes on the southwest coast in 

2009; consequently, no inundation of nest sites occurred under 

the passive model (fig. 2.24). When groundwater rise was 

modeled for a SLR of +2.00 m, 14.3 percent of the nest sites 

were inundated (table 2.6). Similarly, the wave-driven inundation 

model (assuming unlimited seawater volumes and no infiltration) 

at +2.00 m SLR exhibited a 22.9-percent loss in nest sites.

The same habitat classes were identified as potential 

nesting habitat (that is, habitat based on land cover classes, 

not survey data) for MABO (265.2 ha) and BRBO (262.1 ha), 

excluding the hard pan that was used by MABO only. Therefore, inundation risks to potential habitat for these two groundnesting boobies were almost equal. When incorporating 

wave-driven inundation, potential habitat loss (not shown 

in table 2.6) was less than current nest site losses for both 

species, as potential nesting habitat was distributed across the 

island, whereas current nest distribution was located primarily 

on the more vulnerable southern and eastern sides of the island 

(fig. 2.24). Of the potential ground-nesting booby habitat, 28.5 

percent (MABO) and 29.3 percent (BRBO) were inundated 

with the wave-driven model, assuming unlimited seawater 

volumes and no infiltration at +2.00 m SLR. Passive inundation models that incorporated rising groundwater predicted 

less inundation of current nest sites than potential habitat for 

MABO (14.8 percent) and similar levels of inundation for 

BRBO (15.6 percent). 

The nesting distribution of Red-footed Booby (RFBO; 

n=539) and Great Frigatebird (GRFR; n=1801) was nearly 

the same for each species in 2008 (fig. 2.25; USFWS data; 

Cornett and others, 2008), and generally included parts of 

the following land cover classes: tree/shrub, Casuarina 

equisetifolia (RFBO only), Pluchea indica, Tournefortia 

argentea, and mixed shrub. Under the passive modeling 

approach, we predicted no nesting habitat loss for these 

species, even at the highest SLR scenario considered. Under 

the wave-driven inundation model, 10.7 percent of the 2008 

habitat was inundated under a SLR scenario of +2.00 m 

(table 2.7). For the models that incorporated groundwater, 

minimal losses of habitat occurred until SLR exceeded 

+1.00 m, after which habitat losses for these species 

exceeded 50 percent. Similarly, the wave-driven inundation 

model with unlimited seawater volumes and no infiltration 

predicted no habitat loss for these species at SLR values less 

than or equal to +1.00 m, and extensive losses at SLR values 

of +1.50 and +2.00 m (table 2.7). 

Of the potential tree and shrub habitat used by these two 

species (39.3 ha, based on our land cover classification), less 

than 7 percent was inundated under both the passive inundation 

model and the wave-driven inundation model across all SLR 

values. However, when groundwater percolation was modeled 

(passive and wave-driven), inundation of potential RFBO and 

GRFR habitat exceeded 50 percent at +1.00 m SLR and 60 

percent at +2.00 m SLR (table 2.7). Across the entire range 

of SLR scenarios, a greater proportion of recent (that is, 2008) 

shrub habitat was inundated than potential shrub habitat, as the 

2008 nesting distribution was concentrated in shrubs located in 

low-lying lakeside areas vulnerable to flooding (fig. 2.25). 

Sooty Tern (SOTE) colonies for 2008, 2009, and 

2010 ranged in area from 24.7 to 31.7 ha, with the spatial 

distribution of colonies varying widely from year to year 

(fig. 2.26). No losses in colony area were observed under a 

passive modeling approach for any year or SLR value (table 

2.8). Likewise, the wave-driven inundation model exhibited virtually no losses in habitat, except for modest losses 

at the highest SLR scenario only. When seawater volumes 

were assumed to be unlimited, the wave-driven inundation model showed losses of 8.9 to 39.1 percent for the two 

highest values of SLR (+1.50 and +2.00 m); otherwise, 

at the lower SLR values no losses occurred. For the two 

models that incorporated rising groundwater, a wide range 

of habitat losses occurred, ranging from essentially zero to 

almost 30 percent, depending on the year and SLR scenario; 

in general, only the two highest SLR scenarios exhibited 

substantial losses of SOTE habitat under this modeling 

approach (table 2.8). 

Of the potential SOTE habitat (265.2 ha), based on land 

cover class, 14.3 percent and 29.3 percent were inundated at 

+2.00 m SLR using the passive model with rising groundwater 

and the wave-driven inundation model (assuming unlimited 

seawater volumes and no infiltration), respectively. In general, 

for the 3 years with data, potential habitat loss exceeded 

current nesting distribution loss, as potential habitat was 

distributed throughout the island including inundation-prone 

areas not recently used by SOTE (for example, low-lying interior areas subject to groundwater inundation and wave-driven 

seawater; table 2.8, fig. 2.26). 

For two of the resident endangered birds, Laysan Teal and 

Laysan Finch, we observed patterns of potential nesting habitat 

loss that resembled the habitat-loss patterns for the other species 

(table 2.9, fig. 2.27). Both species rely heavily on bunch grass 

for nesting, and showed no losses in nesting habitat under a 

passive modeling approach and only modest losses in habitat 

with the wave-driven models at the highest SLR scenario (+2.00 

m) only. When considering unlimited seawater volumes and 

no infiltration, large losses in nesting habitat occurred at the 

two highest SLR scenarios (approximately 40 percent for both 

species). Similarly, when we incorporated groundwater into our 

models, substantial losses in nesting habitat occurred: 14.1 to 

35.8 percent for both species at SLR scenarios of +1.00 to +2.00 

m (table 2.9). The nesting habitat adjacent to the central lake 

was most vulnerable to inundation from unlimited volumes of 

wave-driven seawater and from rising groundwater (fig. 2.27). 

Although the two species nest in the same habitat classes, as 

ground nesters the Laysan Teal may be more vulnerable than the 

Laysan Finch to periodic inundation. 
110    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Model SLR

scenario

Total 

inundation 

area (ha)

Inundated 

2008 habitat 

(ha)

% change in 

2008 habitat

Passive inundation 0.00 m 0.4 0.0 0.0

 + 0.50 m 3.1 0.0 0.0

 + 1.00 m 6.7 0.0 0.0

 + 1.50 m 10.8 0.0 0.0

 + 2.00 m 15.5 0.0 0.0

Dynamic wave-driven inundation 0.00 m 6.1 0.0 0.0

 + 0.50 m 10.5 0.0 0.0

 + 1.00 m 15.5 0.0 0.0

 + 1.50 m 22.1 0.0 0.0

 + 2.00 m 57.9 1.6 -10.7

Dynamic wave-driven inundation, 0.00 m 6.1 0.0 0.0

unlimited seawater and no infiltration + 0.50 m 10.5 0.0 0.0

 + 1.00 m 15.5 0.0 0.0

 + 1.50 m 115.8 10.3 -69.6

 + 2.00 m 136.2 10.6 -71.1

Passive + groundwater inundation 0.00 m 0.4 0.0 0.0

 + 0.50 m 13.8 0.1 -1.0

 + 1.00 m 40.8 8.1 -54.5

 + 1.50 m 70.2 10.2 -68.4

 + 2.00 m 92.6 10.2 -69.0

Dynamic wave-driven + groundwater inundation 0.00 m 6.1 0.0 0.0

 + 0.50 m 21.2 0.1 -1.0

 + 1.00 m 49.6 8.1 -54.5

 + 1.50 m 81.4 10.2 -68.5

 + 2.00 m 122.1 10.4 -69.9

Total terrestrial habitat (without inundation) 337.8 14.9

Red-footed Booby and Great 

Frigatebird

Table 2.7. Inundated 2008 nesting habitat at Laysan Island (USFWS data; Cornett and others, 2008) for the Red-footed 

Booby (RFBO) and Great Frigatebird (GRFR) under five sea-level rise (SLR) scenarios and five modeling approaches: 

(1) passive inundation, (2) dynamic wave-driven inundation, (3) dynamic wave-driven inundation with unlimited seawater 

volumes and no infiltration, (4) passive SLR including a comparable rise in groundwater, and (5) dynamic wave-driven 

inundation including a comparable rise in groundwater. 

[Inundated habitat is expressed in area and percent change relative to the total habitat without inundation (listed in last row). Note that for the 

purposes of this study, we assumed the colony distribution from 2008 was representative of long-term spatial patterns. In this table, the shrubnesting habitat of GRFR and RFBO are equal . Abbreviations: USFWS, U.S. Fish and Wildlife Service; m, meters; ha, hectares; %, percent]
Figure 2.25. Overlay of the shrufor Red-footed Booby (RFBO) and (GRFR) at Laysan Island, with comfrom passive sea-level rise (SLR), water levels, and rising groundwatbackground allows for a comparisoextents from various models with tColony areas appear lighter when layers. A semi-transparent satellite the background (Digital Globe Inc.and Wildlife Service (USFWS) bioa complete survey of these two sp2008, consolidating the data into 23.7 hectares (ha; USFWS data; Co2008). Both species occupied essenesting habitat. The nesting sites fwere found primarily near the centabove the wave-driven inundation reached +2.00 meters (m). These vulnerable to rising lake levels, asswere to rise with sea level.
112    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Table 2.8. Inundated colony areas at Laysan Island (USFWS data; Cornett and others, 2008; Boyd and others, 2009; 

Hammond and others, 2010) for Sooty Tern under five sea-level rise (SLR) scenarios and five modeling approaches: (1) 

passive inundation, (2) dynamic wave-driven inundation, (3) dynamic wave-driven inundation with unlimited seawater 

volumes and no infiltration, (4) passive SLR including a comparable rise in groundwater, and (5) dynamic wave-driven 

inundation including a comparable rise in groundwater. 

[Inundated habitat is expressed in area and percent change relative to the total habitat without inundation (listed by year in last row). Abbreviations: USFWS, U.S. Fish and Wildlife Service; m, meters; ha, hectares; %, percent]

Sooty Tern by Year

Model SLR

scenario

Total 

inundation 

area (ha) Inundated 

habitat (ha)

% change in 

habitat 

Inundated 

habitat (ha)

% change in 

habitat 

Inundated 

habitat (ha)

% change 

in habitat

Passive inundation 0.00 m 0.4 0.0 0.0 0.0 0.0 0.0 0.0

 + 0.50 m 3.1 0.0 0.0 0.0 0.0 0.0 0.0

 + 1.00 m 6.7 0.0 0.0 0.0 0.0 0.0 0.0

 + 1.50 m 10.8 0.0 0.0 0.0 0.0 0.0 0.0

 + 2.00 m 15.5 0.0 0.0 0.0 0.0 0.0 0.0

Dynamic wave-driven inundation 0.00 m 6.1 0.0 0.0 0.0 0.0 0.0 0.0

 + 0.50 m 10.5 0.0 0.0 0.0 0.0 0.0 0.0

 + 1.00 m 15.5 0.0 0.0 0.0 0.0 0.0 0.0

 + 1.50 m 22.1 0.0 0.0 0.1 -0.2 0.1 -0.3

 + 2.00 m 57.9 1.1 -3.7 2.7 -8.4 1.3 -5.3

Dynamic wave-driven inundation, 0.00 m 6.1 0.0 0.0 0.0 0.0 0.0 0.0

unlimited seawater and no infiltration + 0.50 m 10.5 0.0 0.0 0.0 0.0 0.0 0.0

 + 1.00 m 15.5 0.0 0.0 0.0 0.0 0.0 0.0

 + 1.50 m 115.8 10.4 -35.0 5.2 -16.5 2.2 -8.9

 + 2.00 m 136.2 11.6 -39.1 6.5 -20.5 2.6 -10.6

Passive + groundwater inundation 0.00 m 0.4 0.0 0.0 0.0 0.0 0.0 0.0

 + 0.50 m 13.8 0.3 1.0 0.0 0.0 0.0 0.0

 + 1.00 m 40.8 1.9 -6.5 0.6 -1.8 0.0 0.0

 + 1.50 m 70.2 5.7 -19.3 2.5 -8.0 0.1 -0.6

 + 2.00 m 92.6 8.0 -27.0 3.5 -11.0 1.2 -4.9

Dynamic wave-driven + groundwater inundation 0.00 m 6.1 0.0 0.0 0.0 0.0 0.0 0.0

 + 0.50 m 21.2 0.3 -1.0 0.0 0.0 0.0 0.0

 + 1.00 m 49.6 1.9 -6.5 0.6 -1.8 0.0 0.0

 + 1.50 m 81.4 5.7 -19.3 2.6 -8.2 0.2 -0.9

 + 2.00 m 122.1 8.8 -29.4 5.3 -16.6 1.9 -7.8

Total terrestrial habitat (without inundation) 337.8 29.8 31.7 24.7

2010 2009 2008
Figure 2.26. Overlay of Sooty Tat Laysan Island with combined inusea-level rise (SLR), wave-driven wgroundwater. The combined backgcomparison of inundation extents fwith the displayed colony locationsappear lighter when covered by inutransparent satellite image appears (Digital Globe Inc., 2010). Biologistcolonies in 2008, 2009, and 2010 UService data; Cornett and others, 2others, 2009; Hammond and otherdifferences in colony locations exisa wider range of nesting habitat typfidelity than other seabird species. of the colonies were located in the SLR scenarios +1.00 meters (m). Eassumptions (that is, a SLR of +2.0and groundwater inundation), only percent of the colony locations for respectively, were located in the inSOTE’s adaptability, it is likely that available habitat during extreme in
Table 2.9. Inundated potential nesting habitat at Laysan Island for Laysan Teal and Laysan Finch 

under five sea-level rise (SLR) scenarios and five modeling approaches: (1) passive inundation, 

(2) dynamic wave-driven inundation, (3) dynamic wave-driven inundation with unlimited seawater 

volumes and no infiltration, (4) passive SLR including a comparable rise in groundwater, and (5) 

dynamic wave-driven inundation including a comparable rise in groundwater. 

[Inundated habitat is expressed in area and percent change relative to the total habitat without inundation (listed in last 

row). Abbreviations: m, meters; ha, hectares; %, percent]

Model SLR

scenario

Total

inundation 

area (ha) Inundated

habitat (ha) 

% change in 

habitat 

Passive inundation 0.00 m 0.4 0.0 0.0

 + 0.50 m 3.1 0.0 0.0

 + 1.00 m 6.7 0.0 0.0

 + 1.50 m 10.8 0.0 0.0

 + 2.00 m 15.5 0.0 0.0

Dynamic wave-driven inundation 0.00 m 6.1 0.0 0.0

 + 0.50 m 10.5 0.0 0.0

 + 1.00 m 15.5 0.0 0.0

 + 1.50 m 22.1 0.2 -0.1

 + 2.00 m 57.9 12.8 -7.4

Dynamic wave-driven inundation, 0.00 m 6.1 0.0 0.0

unlimited seawater and no infiltration + 0.50 m 10.5 0.0 0.0

 + 1.00 m 15.5 0.0 0.0

 + 1.50 m 115.8 66.0 -38.3

 + 2.00 m 136.2 71.7 -41.6

Passive + groundwater inundation 0.00 m 0.4 0.0 0.0

 + 0.50 m 13.8 5.4 -3.1

 + 1.00 m 40.8 24.2 -14.1

 + 1.50 m 70.2 45.8 -26.6

 + 2.00 m 92.6 56.6 -32.9

Dynamic wave-driven + groundwater inundation 0.00 m 6.1 0.0 0.0

 + 0.50 m 21.2 5.4 -3.1

 + 1.00 m 49.6 24.2 -14.1

 + 1.50 m 81.4 45.9 -26.7

 + 2.00 m 122.1 61.6 -35.8

Total terrestrial habitat (without inundation) 337.8 172.2

Laysan Teal and Laysan Finch

114    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands
Figure 2.27. Overlay of Laysan Teal (LA(LAFI) potential nesting habitat at Laysan Isinundation from passive sea-level rise (SLRlevels, and rising groundwater. The combinfor a comparison of inundation extents from the displayed nesting habitat. By depicting semi-transparent symbols, the underlying nvisible, although may appear slightly lighterdetermined from land cover maps and prev(Reynolds, 2004; Reynolds and others, 200habitat occurred under a passive modeling modest losses occurred with the wave-driveSLR scenario (+2.00 meters, m) only. When seawater volumes and no infiltration, losses approximately 40 percent for both species ascenarios. Similarly, when we incorporated models, substantial losses occurred (14.1 tscenarios greater than or equal to +1.00 madjacent to the central lake was the most vfrom either unlimited volumes of wave-drivegroundwater. 
Species Nesting frequency Reclutch ability Maximum young per year Nest site fidelity Flexibility in habitat use Inter-atoll dispersal ability Overlap with peak storm season 

Black-footed Albatross1 Annual to Alternate No 1 High High Yes Breeding, peak 

Laysan Albatross2 Annual to Alternate No 1 High High Yes Breeding, peak Bonin Petrel3 Annual No 1 High Moderate Yes Breeding, peak 

Bulwer's Petrel4 Annual No 1 High Moderate Yes Not present 

Wedge-tailed Shearwater5 Annual to Alternate No 1 High Moderate Yes Not present Christmas Shearwater6 Annual to Alternate No 1 High High Yes Adults, off-peak 

Tristram's Storm-petrel7 Annual to Alternate No 1 High Moderate Yes Breeding, peak 

Red-tailed Tropicbird8 Annual to Alternate Yes 1 High Moderate Yes Breeding, off-peak Masked Booby9 Annual Yes 1 Low Moderate Yes Breeding, aseasonal 

Brown Booby10 Annual Yes 1 Moderate Moderate Yes Breeding, off-peak 

Red-footed Booby11 Annual Yes 1 Moderate Low Yes Breeding, off-peak Great Frigatebird12 Annual to Alternate Yes 1 Low Low Yes Breeding, peak 

Gray-backed Tern13 Annual to Alternate Yes 1 Moderate Moderate Yes Breeding, peak 

Sooty Tern14 Annual Yes 1 Low Moderate Yes Adults, off-peak Brown Noddy15 Annual Yes 1 Moderate High Yes Breeding, aseasonal 

Black Noddy16 Annual to Biannual Yes 2 Moderate Low Yes Breeding, aseasonal White Tern17 Annual to Biannual Yes 2 High Moderate Yes Breeding, aseasonal Laysan Teal18 Annual to Alternate Yes 6 Moderatea High No Breeding, aseasonal 

Laysan Finch19 Annual to Biannual Yes 4 Moderatea Moderate No Adults 

Nihoa Millerbirdb,20 Annual to Biannual Unknown Unknown High Moderate No Breeding, peak aLaysan	
  Teal	
  and	
  Finch	
  are	
  territorial	
  nesters,	
  not	
  colonial	
  breeders.	
  bData	
  for	
  the	
  recently	
  (September	
  2011)	
  translocated	
  population	
  of	
  Nihoa	
  Millerbird,	
  derived	
  from	
  data	
  from	
  Nihoa.	
  1Ri3Grant	
  and	
  others	
  (1983),	
  Moore	
  (2009).	
  4Amerson	
  and	
  Shelton	
  (1976),	
  Megyesi	
  and	
  O'Daniel	
  (1997),	
  Mougin	
  1990	
  in	
  Megyesi	
  and	
  O'Daniel	
  (1997).	
  5Ely	
  and	
  Clapp	
  (1973),	
  Shallenberger	
  19

(2002).	
  6Ely	
  and	
  Clapp	
  (1973),	
  Naughton	
  (1982),	
  Warham	
  (1996),	
  Seto	
  (2001).	
  7Marks	
  and	
  Leasure	
  (1992),	
  Slotterback	
  (2002),	
  McClelland	
  and	
  others	
  (2008).	
  8Fleet	
  (1972),	
  Amerson	
  and	
  She

(1978),	
  Grace	
  and	
  Anderson	
  (2009),	
  Kepler	
  1969	
  in	
  Grace	
  and	
  Anderson	
  (2009).	
  10Amerson	
  (1971),	
  Woodward	
  (1972),	
  Ely	
  and	
  Clapp	
  (1973),	
  Clapp	
  and	
  Wirtz	
  (1975),	
  Schreiber	
  and	
  Norton	
  (2

Nelson	
  (1978),	
  Schreiber	
  and	
  others	
  (1996),	
  Citta	
  and	
  others	
  (2007).	
  12Dearborn	
  and	
  Anders,	
  personal	
  commun.	
  in	
  Metz	
  and	
  Shreiber	
  (2002),	
  Ely	
  and	
  Clapp	
  (1973),	
  Metz	
  and	
  Schreiber	
  (200

(1976),	
  Mostello	
  and	
  others	
  (2000).	
  14Ely	
  and	
  Clapp	
  (1973),	
  Feare	
  (1976),	
  Saliva	
  and	
  Burger	
  (1989),	
  Schreiber	
  and	
  others	
  (2002),	
  Cornett	
  and	
  others	
  (2008),	
  Boyd	
  and	
  others	
  (2009),	
  Hammo

Morris	
  (1996),	
  Megyesi	
  and	
  Griffin	
  (1996).	
  16Ely	
  and	
  Clapp	
  (1973),	
  Gauger	
  (1999).	
  17Ashmole	
  (1968),	
  Ely	
  and	
  Clapp	
  (1973),	
  Niethammer	
  and	
  Patrick	
  (1998).	
  18Moulton	
  and	
  Marshall	
  (1996),	
  R

colonial	
  breeders.	
  19Morin	
  (1991),	
  Morin	
  (1992a,	
  b),	
  Morin	
  and	
  Conant	
  (2002).	
  Laysan	
  Finch	
  are	
  territorial	
  nesters,	
  not	
  colonial	
  breeders.	
  20Morin	
  and	
  others	
  (1997),	
  USFWS	
  (2011b).	
  [USFW

Adaptability V

Table 2.10. Life-history traits (nests/year, breeding season, nest site fidelity, etc.) for 20 avian species on Laysan Island, used to evaluate vulnerability to inundation. 

a

Laysan Teal and Finch are territorial nesters, not colonial breeders. bData for the recently (September 2011) translocated population of Nihoa Millerbird, derived from data from Nihoa. 1Rice and Kenyon (1962), Robbins (1

(1971). 3Grant and others (1983), Moore (2009). 4Amerson and Shelton (1976), Megyesi and O’Daniel (1997), Mougin 1990 in Megyesi and O’Daniel (1997). 5Ely and Clapp (1973), Shallenberger 1973 in Whittow (1997), W

and others (2002). 6Ely and Clapp (1973), Naughton (1982), Warham (1996), Seto (2001). 7Marks and Leasure (1992), Slotterback (2002), McClelland and others (2008). 8Fleet (1972), Amerson and Shelton (1976), Schreiber 

(1973), Nelson (1978), Grace and Anderson (2009), Kepler 1969 in Grace and Anderson (2009). 10Amerson (1971), Woodward (1972), Ely and Clapp (1973), Clapp and Wirtz (1975), Schreiber and Norton (2002), Tershy 199

and Clapp (1973), Nelson (1978), Schreiber and others (1996), Citta and others (2007). 12Dearborn and Anders, personal commun. in Metz and Shreiber (2002), Ely and Clapp (1973), Metz and Schreiber (2002), Citta and oth

Amerson and Shelton (1976), Mostello and others (2000). 14Ely and Clapp (1973), Feare (1976), Saliva and Burger (1989), Schreiber and others (2002), Cornett and others (2008), Boyd and others (2009), Hammond and other

(1973), Chardine and Morris (1996), Megyesi and Griffin (1996). 16Ely and Clapp (1973), Gauger (1999). 17Ashmole (1968), Ely and Clapp (1973), Niethammer and Patrick (1998). 18Moulton and Marshall (1996), Reynolds (

territorial nesters, not colonial breeders. 19Morin (1991), Morin (1992a, b), Morin and Conant (2002). Laysan Finch are territorial nesters, not colonial breeders. 20Morin and others (1997), USFWS (2011b). *Not modeled spat

distribution.[Abbreviations: USFWS, U.S. Fish and Wildlife Service]
Figure 2.28. Breeding phenology of 20 birds at Laysan Island compared to the monthly mean of the top 5 percent of wave heights an1-standard deviation error bars). Wave height and wind speed data were acquired from the U.S. Army Corps of Engineers (2011) Wave location (28°00’ N, 174°00’ W) approximately 375 kilometers (km) northwest of Laysan Island. The groups include (a) Procellariiformes Laysan Albatross, Bonin Petrel, Bulwer’s Petrel, Wedge-tailed Shearwater, Christmas Shearwater, and Tristram’s Storm-petrel) that nescrevices, or in subterranean burrows; (b) other ground-nesting species (Red-tailed Tropicbird, Masked Booby, Brown Booby, Gray-backeBrown Noddy, and Laysan Teal) that nest on the ground, on low vegetation, or under vegetation; and (c) tree- and shrub-nesting specieGreat Frigatebird, Black Noddy, White Tern, Laysan Finch, and Nihoa Millerbird). Breeding stage was divided into seven periods (listed based on adult presence and reproductive investment. Abbreviations: %, percent.

a.
Figure 2.28. Breeding phenology of 20 birds at Laysan Island compared to the monthly mean of the top 5 percent of wave heights an1-standard deviation error bars). Wave height and wind speed data were acquired from the U.S. Army Corps of Engineers (2011) Wave location (28°00’ N, 174°00’ W) approximately 375 kilometers (km) northwest of Laysan Island. The groups include (a) Procellariiformes Laysan Albatross, Bonin Petrel, Bulwer’s Petrel, Wedge-tailed Shearwater, Christmas Shearwater, and Tristram’s Storm-petrel) that nescrevices, or in subterranean burrows; (b) other ground-nesting species (Red-tailed Tropicbird, Masked Booby, Brown Booby, Gray-backeBrown Noddy, and Laysan Teal) that nest on the ground, on low vegetation, or under vegetation; and (c) tree- and shrub-nesting specieGreat Frigatebird, Black Noddy, White Tern, Laysan Finch, and Nihoa Millerbird). Breeding stage was divided into seven periods (listed based on adult presence and reproductive investment. Abbreviations: %, percent.—Continued

b.
Figure 2.28. Breeding phenology of 20 birds at Laysan Island compared to the monthly mean of the top 5 percent of wave heights an1-standard deviation error bars). Wave height and wind speed data were acquired from the U.S. Army Corps of Engineers (2011) Wave location (28°00’ N, 174°00’ W) approximately 375 kilometers (km) northwest of Laysan Island. The groups include (a) Procellariiformes Laysan Albatross, Bonin Petrel, Bulwer’s Petrel, Wedge-tailed Shearwater, Christmas Shearwater, and Tristram’s Storm-petrel) that nescrevices, or in subterranean burrows; (b) other ground-nesting species (Red-tailed Tropicbird, Masked Booby, Brown Booby, Gray-backBrown Noddy, and Laysan Teal) that nest on the ground, on low vegetation, or under vegetation; and (c) tree- and shrub-nesting specieGreat Frigatebird, Black Noddy, White Tern, Laysan Finch, and Nihoa Millerbird). Breeding stage was divided into seven periods (listed based on adult presence and reproductive investment. Abbreviations: %, percent.—Continued

c.
120    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Vulnerability of Breeding Birds to

Storm Conditions

Seasonal overlap of breeding birds with the period of 

highest wave energy and wind speed varied among species 

(table 2.10, fig. 2.28a–c). Of the seven Procellariiformes, the 

peak breeding period of four species (Black-footed Albatross, Laysan Albatross, Bonin Petrel, and Tristram’s Stormpetrel) overlapped the period of highest wave energy (Dec.–

Feb.) each year (fig. 2.28a). During this period, adults of 

these four species were generally present incubating eggs or 

brooding young chicks, making both adults and nest contents 

vulnerable to storm impacts. Of the winter-nesting Procellariiformes, Bonin Petrel and Tristram’s Storm-petrel nest 

in underground burrows and may be particularly vulnerable 

to flooding from storm wave inundation or heavy rainfall. 

Life-history traits for species in this group (constrained to 

annual or alternate nesting frequency, inability to renest if 

nest contents are lost, and high nest-site fidelity) indicated 

that they have little capacity to adapt rapidly to direct loss of 

nests or nesting habitat (table 2.10). 

For other ground-nesting species, the coincidence of breeding 

season and high-wave energy was greatest for Gray-backed 

Tern (fig. 2.28b). Red-tailed Tropicbird and Masked Booby, as 

aseasonal breeders with limited egg-laying during the period of 

high-wave energy, exhibited less vulnerability to winter storm 

conditions (fig. 2.28b). For Brown Booby and Sooty Tern, peak 

nesting season typically did not coincide with the period of highest 

wave energy. Since Brown Noddy nest throughout the year, only a 

limited portion of their reproductive period overlapped with winter 

storm conditions (fig. 2.28b). For Laysan Teal, a year-round resident with a variable breeding season, overlap between breeding 

season and high-wave energy (Dec.–Feb.) is likely to occur in 

some but not all years (fig. 2.28b). Since Laysan Teal nested in 

widely distributed sites rather than in colonies, inundation risk for 

this species was dispersed spatially. In general, ground-nesting tern 

species display the greatest potential to renest after nest loss and 

show lower nest-site fidelity compared to Procellariiformes and 

some other species (table 2.10).

Tree- and shrub-nesting species at Laysan were present 

throughout the year and typically have protracted breeding 

seasons (fig. 2.28c). Generally more vulnerable to high winds 

than to inundation, the coincidence of breeding season with 

winter-storm conditions may result in nest loss for these species. 

Egg laying and incubation may occur during the period of highwave energy and high wind speeds for at least some part of the 

population of each shrub-nesting species (fig. 2.28c). Since the 

shrub-nesting species of Laysan are the least flexible in their 

nesting habitat (table 2.10), they may be the least capable of 

adapting to losses of nesting habitat.

Species Years Parameter Mean SD 2.5% CI Median 97.5% CI

BFAL 2001–2011 c -0.44 ±0.37 -1.1 0.46 0.38

K 48,057 ±5,918 - - -

LAAL 2001–2011 c 0.02 ±0.02 0.92 0.96 1.01

K 284,491 ±43,886 - - -

RFBO 1996–2011 c 0.04 ±0.55 -1.01 0.15 0.92

K 1395 ±518 - - -

LADU 1993–2010 c 0.68 ±0.10 0.45 0.69 0.87

0.11 ±0.04 0.03 0.11 0.2

0.07 ±0.04 0.03 0.06 0.16

K 434 ±72 - - -

LAFI 1983–2011 c -0.18 ±0.51 -1.04 -0.011 0.76

K 10,580 ±3,513 - - -

*Future abundance monitoring for Laysan bird populations could be improved with independent 

replicate counts, especially for the Red-footed Booby (Citta and others, 2007) and Laysan Finch. There 

is little correlation between population sizes among years for all species besides Laysan Teal, 

preventing reliable estimates of parameters. Replicate counts (either using maximum mean incubation 

count or other methods, see Dearborn and Anders, 2006) are needed to better clarify sampling variance 

and the other parameters for Laysan Finch (see Citta and others, 2007; Dennis and others, 2010).

Table 2.11. Population parameter estimates for the Gompertz model (c = the strength of 

density dependence, K = carrying capacity) with standard deviations (SD) and credible intervals 

(CI) for three seabird species (BFAL, Black-footed Albatross; LAAL, Laysan Albatross; and 

RFBO, Red-footed Booby) and two endangered resident birds (LADU, Laysan Teal; and LAFI, 

Laysan Finch) with abundance time-series data at Laysan Island. 

[Observation error (τ) and process variation (σ) are not reported for species if the estimate of c is close to zero 

(Dennis and others, 2010). High variation in abundance between years prevented reliable τ and σ estimates for 

most species]

*

 There is little correlation between population sizes among years for all species besides Laysan Teal, preventing reliable 

estimates of parameters. Future abundance monitoring for Laysan bird populations could be improved with independent replicate counts, especially for the Red-footed Booby (Citta and others, 2007) and Laysan Finch. Replicate counts (either using 

maximum mean incubation count or other methods, see Dearborn and Anders, 2006) are needed to better clarify sampling 

variance and the other parameters for Laysan Finch (see Citta and others, 2007; Dennis and others, 2010).

*

*

*

*
Results    121

Figure 2.29. Population abundance graphs for 

five species with long-term monitoring data: (a) 

Black-footed Albatross, (b) Laysan Albatross, 

(c) Red-footed Booby, (d) Laysan Teal, and (e) 

Laysan Finch. Abundance values are an index 

of population size derived from a subset of the 

population counted during monitoring surveys. 
122    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

Population Dynamics, Carrying Capacity,

and Population Vulnerability

We evaluated avian population vulnerability for the 

same five sea-level scenarios, assuming that changes in 

population abundance would be proportional to changes in 

species-specific nesting habitat. For species showing strong 

evidence of density dependence, this relationship is better 

supported. Time series of abundance from long-term monitoring are shown in fig. 2.29, parts (a) through (e). Most 

species’ time series were extremely variable and sampling 

error could not be estimated.

The observed carrying capacity of Laysan Island to support 

the breeding bird species with long-term monitoring data ranged 

from 434 ± 72 for Laysan Teal to 284,491 ± 43,886 for Laysan 

Albatross (table 2.11; carrying capacities expressed as means 

± standard deviations). The only species for which observation error could be measured, the Laysan Teal, showed strong 

evidence of density dependence (that is, already at carrying 

capacity; c=0.68; table 2.11); thus, this species is expected to 

respond to changes in habitat. It is assumed that species without 

strong evidence of density dependence (c greater than 1; table 

2.11) may respond more gradually or adapt to habitat change, as 

potential losses in current nesting habitat may be offset by population growth in those areas that are not inundated. In contrast 

to gradual habitat losses that may reduce the carrying capacity 

of wildlife populations, additive mortality of chicks and adults 

in habitats vulnerable to storm impacts may cause more rapid 

decreases in population abundance. 

Discussion

Systematic Underestimation of

Vulnerability from Passive Models

In comparison to the passive (or “bathtub”) inundation 

models, the dynamic wave-driven inundation models required 

far lower values of SLR to produce similar magnitudes of 

inundation as the passive models. We observed that as sea 

level increased and water depths over the reef crest and reef 

flats increased, wave-energy attenuation due to wave breaking 

and bottom friction declined, resulting in more wave energy 

(larger wave heights and longer wavelengths) reaching the 

shoreline. Thus as sea level rose in our models, wave-driven 

water levels rose simultaneously, exacerbating the effects 

of SLR. This positive feedback cycle suggests that passive 

models systematically underestimate the amount of inundation 

that will occur on annual timescales and that the larger the rise 

in sea level, the greater the degree of underestimation. 

Additional Increments of SLR

Although the passive models considered in this 

study (SLR less than or equal to +2.00 m) exhibited little 

inundation on Laysan Island for each SLR scenario, a 

potential threshold may be reached if an additional 0.50-m 

increment of SLR (+2.50 m) were considered. In such a case, 

inundation would increase dramatically as the low-lying 

coastal dunes in the south and east quadrants of the island 

would be overtopped at mean high tide. This scenario would 

flood the two topographic depressions in the eastern desert 

and portions of the central basin adjacent to the hyper-saline 

lake. Under a passive +2.50-m SLR scenario, the overall 

inundation pattern would resemble the theoretical maximum 

amount of flooding predicted by the wave-driven inundation 

model at the higher SLR scenarios (+1.50 and +2.00 m). 

Alternatively, if +1.50 m of passive SLR were combined 

with the mean wave-driven water level during high-surf 

events (+1.16 m at +1.50 m of SLR), then potential inundation during high-surf events might equate to a passive rise of 

+2.66 m, depending on the volume of wave-driven seawater 

and infiltration rates. In either case (that is, a passive SLR of 

+2.66 m or a dynamic rise of +1.50 m of SLR), the maximum 

extent of inundation would resemble the documented inundation from the severe February 2011 rainfall and large-wave 

event (USFWS data; Kristof and others, 2011), as wavedriven seawater would reach and begin to fill the low-lying 

interior basins. The documented flood event of February 2011 

provides an excellent reference level for the predicted inundation extents presented here and provides some validation of 

the general spatial patterns of our predictions. 

As sea level increases, wave-driven inundation becomes 

more likely to occur in complicated and broad patterns across 

the island’s interior, as noted when comparing the results for 

the wave-inundation models at +1.50 and +2.00 m SLR. Since 

the volume of seawater along all inundation swaths remains 

unknown, it is not clear how much seawater will reach the 

interior basins under either of these two SLR scenarios, only 

that such a worst-case inundation scenario reflects the flow 

characteristics of wave-driven seawater and is theoretically 

possible during large-wave events. Although the likelihood 

of achieving the maximum theoretical extent of inundation is 

unclear, as sea level rises and more inundation paths intersect 

the central lake, greater seawater volumes would reach the 

lake, increasing the likelihood of achieving the maximum 

extent of inundation. In the long-term, by the time SLR 

reaches the height of the southeastern coastal dunes (greater 

than or equal to +2.50 m above MSL), the maximum extent 

of inundation under the wave-driven modeling approach 

would become a moot point, as these basins would likely 

be connected to the ocean at that time. Future modeling that 

includes potential morphological changes to the island would 

provide a more accurate assessment of this likelihood.

Chronological Implications of

Dynamic Inundation Models

We investigated the effect of various SLR scenarios 

without assigning explicit dates to them, although we assumed 
Discussion    123

our scenarios represent a range of distinct possibilities over 

the next century, with +1.00 m SLR representing the most 

likely scenario (given our current knowledge of the state of this 

system; see Fletcher, 2009). One consequence of considering 

wave-driven inundation for Laysan Island was that for a given 

SLR value, wave-driven water levels generally added approximately 1 m of inundation depth (depending on SLR scenario) 

to passive inundation surfaces during large-wave events. Thus, 

since large-wave events have the potential to add water heights 

comparable to a century’s worth of SLR to our passive inundation surfaces, our wave-driven inundation models essentially 

forecasted comparable levels of inundation nearly a century 

earlier than passive models. Of course, with such a comparison, 

the passive model with +1.00 m of SLR depicted year-round 

inundation, while the wave-driven inundation model at present 

sea level forecasted similar levels of inundation only during the 

top 5 percent of storm-wave events. As the rate of SLR changes 

(due to changes in the rate of ice sheet melting, greenhouse gas 

emissions, etc.), wave-driven inundation will change accordingly as will the patterns and timing of inundation. In spite of 

these uncertainties over the next century, consideration of wavedriven inundation resulted in significant increases in inundation, with several areas experiencing periodic inundation many 

decades earlier than forecasted by passive modeling approaches.

Uncertainty in the rate of SLR has multiple components 

including a strong regional component. As noted by Stammer 

(2010), regional SLR is “most relevant” and “most complex.” 

While mass contributions to the ocean (primarily in the form of 

melting ice sheets, glaciers, and ice caps) spread rapidly around 

the world by pressure-related motions (known as barotropic 

motions), it may take many decades for sea level to respond to 

both the pressure- and temperature-related forces (in the form 

of a baroclinic response) from meltwater (Stammer, 2008; 

Gower, 2010; Church and others, 2011). Despite this potential 

time lag, recent studies have shown a wide range of regional 

SLR responses including (1) a high rate of SLR in the Western 

Pacific corresponding to increased trade winds in the tropical 

Pacific (Merrifield, 2011), and (2) falling sea level relative to 

the earth’s crust near Greenland and West Antarctica due to 

decreased weight of the ice sheets, diminished local gravitational forces, and vertical rebounding of the crust (Church and 

others, 2011). In short, given the complex set of forces operating 

on regional sea level, a high degree of uncertainty still exists 

regarding the timing of regional SLR in all areas of the world, 

including the Central Pacific. 

Frequency of Inundation Events

While flooding from storm-generated waves would occur 

only during approximately annual storm events and not perennially as it would under a scenario of passive SLR, these periodic 

injections of seawater into the lake could produce dramatic and 

permanent changes in lake chemistry and biology, depending upon 

the frequency of large-wave events. With salinities of 3−4 times 

that of the ocean and an ecosystem dominated by salt-tolerant 

species (including algae, Dunaliella spp.; brine shrimp, Artemia 

franciscana; and brine flies, Scatella sexnotata; Reynolds, 2002), 

the lake ecosystem is potentially vulnerable to large injections 

of seawater that could affect lake salinities. Based on interannual variability in climate (for instance, variability between 

El Niño and La Niña years), we would anticipate the frequency 

of large-wave events to vary greatly from year to year (USACE, 

2011). Likewise, the impacts of these large-wave events would 

vary annually, depending on event magnitude and the number of 

events per year. In addition to impacts on Laysan’s hyper-saline 

wetland ecosystem, injections of storm-wave water across the 

landscape could have significant impacts on surrounding land 

cover and vegetation, as observed during recent inundation events 

on Laysan in 2011 including the February storm and March 

tsunami (USFWS data; Kristof and others, 2011). If mass avian 

mortality events associated with some severe storms occurred 

more frequently, dramatic population declines could be expected. 

Species most at risk to increasing storm frequency and intensity 

are those attending young chicks during storm events (for instance, 

Black-footed Albatross, Laysan Albatross, Bonin Petrel, and Tristram’s Storm-petrel; see fig. 2.28).

Future Geomorphology

While the degree of groundwater connectivity between the 

ocean and lake remains unknown, since no hydrological studies 

have been conducted for Laysan Island, a comparable rise in 

groundwater and sea level remains a distinct possibility, especially 

over the decadal and centurial time periods relevant to climate 

change and SLR. When we incorporated rising groundwater into 

the modeling process, the lake area expanded considerably, as did 

the likelihood of wave-driven seawater reaching the lake zone 

during large-wave events. Under such a modeling scenario, the 

island took on a distinct atoll-like appearance (where parts of the 

island partially enclose a lagoon), providing insight into possible 

geomorphologic changes in the coming centuries. 

While the highest wave-driven water levels generally 

occurred on the west side of Laysan Island (fig. 2.11), the 

highest dunes (which exceed 10 m above MSL) were located on 

the western side of the island, generally preventing wave-driven 

seawater from penetrating much beyond the beach. In contrast, 

the southeast quadrant of the island had much lower coastal 

dunes (less than 3 m above MSL), offering the least protection 

from wave-driven water levels. Thus, in spite of generally lower 

wave-driven water levels in the southeast, our models indicated 

that the dunes would be breached here first. Assuming current 

island morphology, the ocean would be most likely to intersect 

Laysan’s interior lake in the southeast quadrant. 

Vulnerability to Outlier Storm Events

All modeled wave-driven water levels were calculated 

for the winter season (Dec.–Feb.) only; thus our predicted 

inundation extents relate to likely levels of inundation as a 

result of combined SLR and the largest 5 percent of winter 
124    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

waves. Based on historical wave data (USACE, 2011), winter 

on average produces the largest wave heights, longest wave 

periods, and highest resulting set-up and run-up values of the 

year. Since storm wave-driven water levels were computed 

based on the mean of the top 5 percent of winter waves, 

approximately 2.5 percent of these water levels exceeded 

calculated mean levels, often by a substantial amount. Consequently some future winter storms are likely to produce 

wave-driven water levels that greatly exceed the total water 

levels used in our inundation modeling. Additionally, although 

mean wave-driven water levels for winter were greater than 

that of any other season, a single storm event from another 

season (for example, a summer or fall hurricane) may produce 

wave-driven water levels in Laysan’s vulnerable southeast 

quadrant that greatly exceed winter levels along this section of 

coastline. Such an outlier event remains a possibility and could 

produce extreme wave-driven water levels similar in magnitude to the total water levels (4–9 m above MLLW) observed 

on Kaua‘i when Hurricane Iniki made landfall (Fletcher and 

others, 1995). For the island of Laysan, where the lowest 

coastal dunes lie in the south and east, a category-five hurricane approaching from the southeast represents the worst-case 

scenario in terms of wave-driven water levels and potential 

inundation. Since the first official record of a hurricane in 

Hawaiian waters in 1950, only five category-five hurricanes 

have occurred in the Central Pacific, with none of these 

making landfall in the NWHI, although Hurricane Patsy did 

approach Midway Atoll as a category-three storm (Businger, 

2012; Central Pacific Hurricane Center, 2012). More recently 

(in 2009), Hurricane Neki developed into a category-three 

hurricane but then weakened into a tropical storm as it crossed 

the PMNM; although no damage was caused, personnel were 

evacuated from Laysan Island during this storm (Kristof and 

others, 2010). Potentially damaging surf events from the south 

and east may occur, even if a hurricane does not make landfall 

at Laysan.

Succession of Vegetation

When analyzing vegetation loss, we evaluated the potential effects of inundation for existing land cover patterns only. 

However, we know that as the frequency and magnitude of 

inundation increases, land cover and vegetation patterns will 

shift accordingly (LaFever and others, 2007). For instance, as 

sea level rises and inundates existing beach areas, we expect 

the beach zone and coastal communities to shift upwards with 

the changing coastline. Likewise for wetland vegetation, a 

potential rise in lake levels is likely to result in an upward shift 

in wetland vegetation, rather than a complete disappearance of 

this land cover class. Simultaneously, other land cover classes 

may shift upwards as sea level rises, producing a cascade 

effect, at least for the land cover classes currently at lower 

elevations. For the interspersed bunch grass and groundcover 

at the higher elevations (up to 10 m above MSL), we do not 

anticipate much change due to SLR in the foreseeable future.

Thresholds of Vulnerability for

Nesting Habitat

Although the severity of impact varied among models 

and species, less than 10 percent of avian nesting areas 

were predicted to experience inundation by all models for 

SLR scenarios less than +1.00 m; thus below the +1.00-m 

threshold, our models predicted limited change across all 

avian nesting areas and habitats. However, at +2.00 m SLR, 

only the passive inundation model (without wave-driven 

inundation or groundwater percolation) predicted less than 10 

percent direct losses to avian nesting habitats.

The nesting areas for Black-footed Albatross and Brown 

Booby, which occur around the coastal perimeter of Laysan, 

were more vulnerable to wave-driven water levels than to rising 

groundwater. Conversely, the nesting habitat for Red-footed 

Booby and Great Frigatebird was more vulnerable to rising 

groundwater and interior lake levels than to wave-driven water 

levels, as much of their nesting habitat was located near the 

central lake; because these two species are shrub nesters, we 

anticipate rising groundwater to limit future shrub distribution 

rather than to inundate nests directly. Other avian species that 

were not modeled spatially in this study have similar distribution patterns on Laysan (for instance, along the island perimeter 

or lake perimeter), and consequently are expected to exhibit 

similar levels of vulnerability to inundation. For example, 

Tristram’s Storm-petrel, a burrow nester that breeds in a narrow 

band around the perimeter of the lake (Ely and Clapp, 1973; 

Marks and Leasure, 1992; McClelland and others, 2008), would 

be more vulnerable to rising groundwater (that would inundate 

underground nests) than to wave-driven seawater. Additionally, 

we anticipate habitat loss to affect the Black Noddy and White 

Tern, because these species nest in many of the same tree and 

shrub species as Red-footed Booby and Great Frigatebird.

Implications of SLR for Laysan’s

Wildlife Populations

We expect SLR to impact avian populations at Laysan 

Island by reducing carrying capacity and influencing population dynamics through population limitation and changes in 

nesting habitat; these factors might lead to smaller population 

sizes for some species. Although the time scale of SLR is 

uncertain, we expect the largest population effects from additive mortality due to increasing magnitude and frequency of 

extreme storm events, rather than from gradual habitat losses 

from passive inundation scenarios. We expect there to be 

habitat loss from SLR as well as habitat destruction from periodic high-wave events including permanent physical, chemical, and biological impacts that render some habitats unsuitable. How species adapt to catastrophic events and long-term 

changes in habitat availability will vary with inter-specific 

differences in breeding season phenology and life-history 

characteristics. Some wildlife species demonstrate resilience 

with the ability to adapt behavior, relocate to alternative 
Conclusions    125

habitats, and renest after nest loss. Availability of alternative 

habitat given inter- and intra-species competition on small 

islands also will influence how species adapt to reductions in 

habitat (that is, carrying capacity). 

Avian populations on Laysan may be adapted to deal 

with infrequent catastrophes and extreme inundation events, 

temporarily influencing fecundity and adult survival. During 

the severe winter storms of 2010 (Nov.–Dec.) and 2011 (Feb.), 

substantial flooding of low-lying areas inundated more than 

20,000 Laysan Albatross and 9,000 Black-footed Albatross 

nests (17 and 40 percent, respectively), resulting in failed nests 

and the mortality of adults (USFWS data; Kristof and others, 

2011; E. Flint, USFWS, oral commun.). Also in 2011, Laysan 

Teal produced no offspring, likely a result of severe habitat 

flooding during the February storm and March tsunami event. 

As sea levels rise, these storm-driven inundation events may 

become more frequent and more severe, with higher wavedriven water levels. Increased frequency and severity of such 

events can have significant population-level impacts on avian 

species over the long-term (Frederiksen and others, 2008).

Species that are not nesting during seasonal wave-driven 

inundation events may be able to adjust their nesting distribution to habitat loss. However, some seabird colonies may 

persist for years without reproductive success. For example, 

albatross with strong nest-site fidelity have continued to nest 

at Trig Island in French Frigate Shoals (area 1.5 ha; mean 

elevation 0.5 m) despite frequent overwash events (E. Flint, 

USFWS, oral commun.). At the other extreme, species such as 

Sooty Tern that exhibit higher inter-annual variation in colonial nesting area may be less vulnerable to habitat losses and 

more able to adapt readily to habitat changes.

The effects of habitat reduction could be exacerbated for 

species such as the Laysan Teal that are at or near carrying 

capacity because they are the most sensitive to small decreases in 

habitat abundance (Gaston and others, 2003). A decrease in habitat 

does not necessarily correspond to a proportional decrease in 

carrying capacity, as species may be able to adjust their behavior. 

For nonmigratory endemic land birds that meet all of their 

life-history requirements at Laysan Island, population dynamics 

could be affected by nesting habitat losses and changes in local 

food distribution and abundance (either positive or negative). In 

contrast, for seabirds that forage at sea and utilize the island for 

nesting only, the impact of habitat loss on carrying capacity is 

limited to nesting area abundance. Consequently, the proportional 

effects of habitat inundation on carrying capacity may differ 

greatly between seabirds and endangered endemic land birds. 

Future Research Directions

In future iterations of this modeling process, we hope 

to gain a better understanding of the physical processes that 

affect wave-driven water levels, including complexities 

such as hydrodynamic and terrestrial roughness (that impart 

frictional resistance on waves and run-up, potentially limiting 

run-up distances), scouring (which may channelize and 

enable greater run-up distances), and morphodynamic change 

(changes in beach and sea floor shape due to erosion and/

or deposition). Although our dynamic wave-driven inundation model represents a major advance over passive inundation models, it still has a number of technical limitations that 

could be addressed with refined treatment of hydrodynamic 

roughness (distribution of sand patches and coral species), 

seasonal variation in beach slope, low-latitude beach structure, 

and infiltration capacity. In order to address these concerns, 

high-resolution spatial and temporal data (including beach 

morphology, sedimentology, stratigraphy, and hydrogeology) 

would be required to better constrain these variables and thus 

reduce uncertainty in projected wave-driven water levels and 

inundation extents.

In terms of biological modeling, better avian monitoring data are needed to describe and predict the relationship between climate change and population change over 

time. Future population monitoring could be improved with 

independent replicate surveys, especially for the Laysan Finch 

and Red-footed Booby (Citta and others, 2007). More specifically, replicate counts (either using maximum MIC or other 

methods; Dearborn and others, 2001; Citta and others, 2007) 

could reduce sampling variance and better define other parameters (Dennis and others, 2010). The collection of additional 

species-specific distribution data would allow for detailed 

analysis of the spatial overlap between nest sites and inundation areas, making for improved species-specific vulnerability assessments to climate change. If resource limitations 

prevent monitoring of all species, the endangered species and 

the species we have identified as the most vulnerable to SLR 

could be targeted first, including those not currently monitored 

such as Gray-backed Tern, Tristram’s Storm-petrel, and Bonin 

Petrel. These species would serve as good vulnerability indicator species (Citta and others, 2007) because their breeding 

season overlaps with the period of highest wave energy and 

their life-history characteristics make them less able to adapt 

to rapid environmental change.

Conclusions

This study illustrated substantial differences between 

passive and dynamic inundation models for Laysan Island and 

provided the basis for assessing the vulnerability of Laysan’s 

habitat and avifauna to climate change. At the higher SLR 

scenarios (+1.50 m or greater), passive inundation models 

severely underestimated the extent of inundation predicted by 

wave-driven inundation models, as wave-driven water levels 

added approximately 1 m of elevation to passive inundation 

surfaces. Although periodic in nature, wave-driven inundation may cause long-term changes in land cover, vegetation 

structure and composition, lake chemistry and area, avian 

habitat, seabird population dynamics, and endangered endemic 

bird viability. Viewed chronologically, wave-driven inundation models predicted comparable inundation extents many 
126    1. Climate Change Vulnerability Assessment of the Low-Lying Northwestern Hawaiian Islands

decades earlier than forecasted by passive models. At SLR 

values greater than or equal to +1.50 m, a threshold was 

identified, above which coastal dunes were overtopped during 

periods of high-wave energy and large losses in avian habitat 

occurred, particularly for species nesting in low-lying interior 

basins such as Tristram’s Storm-petrel, Red-footed Booby, 

Great Frigatebird, Black Noddy, and White Tern. The nesting 

habitats of these species were highly vulnerable to large 

volumes of wave-driven seawater and to rising groundwater. 

In general, impacts on avian species varied widely, depending 

on modeling assumptions, nest-site distribution, breeding 

phenology, and life-history characteristics. Given current 

trends in SLR, the +1.50 m SLR threshold will not occur until 

the next century, allowing most avifauna decades to adapt 

to changing sea levels and managers time to plan for natural 

resource protection. 

These conclusions apply specifically to SLR on Laysan 

Island, as differing topography, bathymetry, morphology, and 

wildlife on other Pacific islands will result in island-specific 

impacts, vulnerabilities, and thresholds. Given Laysan’s 

relative size and elevation in comparison to other islands and 

atolls, we anticipate SLR and wave-driven inundation to cause 

more extensive impacts on other islands in the Pacific first 

(see chap. 1). However, given Laysan’s unique and diverse 

ecosystem, the impacts of SLR and wave-driven inundation on 

Laysan should not be underestimated.

Acknowledgments

We are grateful to the U.S. Geological Survey (USGS) 

National Climate Change and Wildlife Science Center, 

USGS Pacific Coastal and Marine Science Center, USGS 

Pacific Island Ecosystems Research Center, and the U.S. 

Fish and Wildlife Service (USFWS) Pacific Islands Refuges 

Inventory and Monitoring Program for grants funding this 

research. We also would like to thank the USFWS Hawaiian 

Islands National Wildlife Refuge, National Oceanic and 

Atmospheric Administration Pacific Services Center, and 

Papahānaumokuākea Marine National Monument for their 

overarching support of this project, the USGS-Deltares 

co-operative for numerical modeling support. Also, we extend 

special thanks to L. Fortini (Pacific Islands Climate Change 

Cooperative), C. Ostrander (Pacific Islands Ocean Observing 

System), and L. Mehrhoff (USFWS) for reviewing this 

chapter; M. Stelmach (USFWS) and A. Kristof (USFWS) for 

mapping assistance; J. Carter (NOAA) for accuracy assessment surveys; J. Hatfield (USGS Patuxent Wildlife Research 

Center) for statistical support; T. Speetjens (USFWS) for 

biological data; and S. Nash (Hawai‘i Cooperative Studies 

Unit) for editing assistance. We also thank L. Slack and J. 

Hendley (USGS, Menlo Park Publishing Service Center) for 

editing assistance.
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Appendix 1—USGS lidar (light detection and ranging) data

collection specifications for the Northwestern Hawaiian Islands

including Kure Atoll, Midway Atoll, Pearl and Hermes Atoll, Lisianski

Island, Laysan Island, and French Frigate Shoals

[Data were acquired July 22–25, 2010; abbreviations: <, less than; >, greater than; ≤, less than or equal to; ≥, greater than or equal to; m, meters; 

%, percent]

Data acquisition Survey design Minimum requirements

Laser pulse rate Up to 20,000 pulses per second (10,000 pulses for 

each of two instruments)

Returns per pulse First and last (up to 2) First and last (up to 2)

On-ground laser beam diameter Approx. 60 centimeters (cm)

Scan angle ±20 degrees ≤ ±20 degrees (data beyond this scan angle along 

the swath edge should not be included in the 

final product)

Aircraft altitude 244–305 meters (m) above ground

Aircraft speed 85–95 knots

Ground swath width 400 m

Swath overlap 50% sidelap (100% overlap) No voids between swaths.

No voids because of cloud cover or instrument 

failure.

<20% no-overlap area per island.

Aggregate pulse density 1–2/square meters (m2); for bare areas or sparsely 

vegetated areas, 3–4/m2 for more densely 

vegetated areas to increase the odds of having 

a pulse reflect off the ground 

1/m2 barring nonreflective areas (for example, open 

water):

• ≥85% design pulse density for entire project area 

• Within areas of swath overlap, no 30 × 30 m area 

with <50% design pulse density 

Flight line direction Bare areas or sparsely vegetated areas: opposing 

parallel lines, with at least one perpendicular 

flight line to tie parallel lines together; or

Densely vegetated areas: cross-hatch pattern to 

increase pulse density

GPS base-line length Approximately 1350 kilometers (km) (Midway 

Atoll to Tern Island)

GPS positional dilution of 

precision (PDOP)

≤3.0, ≥6 satellites in view ≤3.5, ≥6 satellites in view

Survey conditions Cloud and fog-free between aircraft and ground. 

No unusual flooding or inundation.

Accuracy Survey design Minimum requirements

Absolute accuracy <15 cm vertical, <10 cm horizontal (RMSE) ≤15 cm vertical (RMSE)

Between-swath reproducibility1 ≤15 cm vertical on horizontal surfaces (RMSE) 

Reproducibility of range measurements2

≤5 cm (RMSE)

Spatial reference framework

Vertical Datum WGS 84 for ellipsoid heights. Local MSL for orthometric heights (will be a challenge for islands without 

tidal gauges; heights may need to be tied to NGS benchmarks until a more accurate datum is developed 

and orthometric heights can be adjusted). 

Horizontal Datum NAD 83

Projection UTM, zone varies among islands (for example, Midway = 1N, Laysan = 2N)

Units Meters (UTM)

1

Extensive swath overlap allows for robust estimation of intra-survey reproducibility. Intra-survey measurement errors on flat ground are commonly 4–6 cm 

RMSEz, with an increase in Z errors as local slope increases. 2Evaluated by measuring departures from planarity of returns from planar surfaces (for example, 

the lake on Laysan Island). 
Appendix 1–3   137

because sub-surface conditions constrain where these species 

are able to establish a nest. 

Similarly, satellite imagery resolution prevented classification of low ground cover from vine vegetation. Two endangered 

land birds will utilize dense vine vegetation for nesting cover, 

but not low ground cover where nests are exposed (Morin, 

1991; Reynolds and others, 2007). Potential nesting habitat at 

Laysan Island is, therefore, overestimated using our land cover 

classification methods and ground-based methods are needed. 

At Pearl and Hermes Atoll, Laysan Finches utilize marine debris 

for nesting and occur on the beach edge; however, we could not 

quantify this “habitat” using satellite imagery.

Island-specific classification of human structures used 

for nesting habitat was necessary at Sand and Eastern Islands 

(Midway Atoll), and Tern Island (French Frigate Shoals). We 

classified buildings as habitat for White Tern at all islands 

as they commonly nest on window ledges and other flat 

surfaces off the ground (Rauzon and Kenyon, 1984). Buildings 

constructed on post-and-pier foundations at Tern Island were 

classified as nesting habitat for species that nest underneath 

them (Wedge-tailed Shearwater, Red-tailed Tropicbird, and 

Black Noddy; P. Hartzell, USFWS, written commun.). The 

decommissioned runway at Eastern Island is unsuitable for 

burrow-nesting birds because of hard-packed in-fill materials 

(Moore, 2009); therefore, we excluded this area from nesting 

habitat calculations for burrow-nesting birds (Bonin Petrel, 

Wedge-tailed Shearwater, and Tristram’s Storm-petrel). The 

decommissioned runway area at Kure Atoll was also excluded 

as potential nesting habitat for burrow-nesters. Paved surfaces 

were not included as nesting habitat for any species.

 Species-specific evaluations of potential nesting habitat 

loss accounted for inter-specific differences in vegetation 

classes used. However, species were generally considered as 

part of one of the following nesting groups, based on the type 

of land cover used in nesting: (1) bare ground or sparse low 

vegetation (Bulwer’s Petrel, Gray-backed Tern), (2) tree/shrub 

(White-tailed Tropicbird, Red-footed Booby, Great Frigatebird, Black Noddy, and White Tern), (3) within vegetation 

(Bonin Petrel, Christmas Shearwater, Red-tailed Tropicbird, 

Laysan Teal, Laysan Finch, Nihoa Finch, Nihoa Millerbird), 

or (4) with vegetation and bare ground (Black-footed Albatross, Laysan Albatross, Short-tailed Albatross, Wedge-tailed 

Shearwater, Tristram’s Storm-petrel, Masked Booby, Brown 

Booby, Little Tern, Sooty Tern, Brown Noddy). Habitat was 

not quantified for the Blue Noddy or Nihoa Finch that were 

found only on the higher islands of Nihoa, Mokumanamana, 

Gardner Pinnacles, and La Perouse Pinnacle. Breeding habitat 

for populations of non-native birds at Midway Atoll (Cattle 

Egret [Bubulcus ibis], Common Myna [Acridotheres tristis] 

and Common Canary [Serinus carius]) was not considered.

Nesting descriptions from:

Amerson, A.B., 1971, The natural history of French Frigate 

Shoals, Northwestern Hawaiian Islands: Atoll Research 

Bulletin, v. 150.

Appendix 2—General circulation

model data downscaled

for analysis of predicted

temperature and precipitation

The six general circulation models (GCM; table 1.4) 

analyzed for predictions of temperature and precipitation 

were downscaled for the Northwestern Hawaiian Islands. 

The models were downscaled using the delta change method 

(Tabor and Williams, 2010) for a scale appropriate for the 

NWHI. The A1B emission scenario was chosen to simulate 

future atmospheric concentrations of greenhouse gasses. 

This scenario is described as an intermediate case of emissions, where CO2 concentrations stabilize at 750 parts per 

million. The A1B scenario describes a future world of very 

rapid economic growth, low population growth, and the rapid 

introduction of new and more efficient technologies. Major 

underlying themes are convergence among regions, capacity 

building, and increased cultural and social interactions, with 

a substantial reduction in regional differences in per capita 

income. For each model, cell size resolution and model attributes are provided, as well as references. 

Appendix 3—Nesting

descriptions of land birds and

seabirds of the low-lying islands

of the Northwestern Hawaiian

Islands—Linking nesting habitat

to land cover classes by island

Species-specific nesting descriptions and land cover 

utilization vary by island. At Pearl and Hermes Atoll, we could 

not delineate habitat for Great Frigatebird and Red-footed 

Booby, the two shrub-nesting species known to nest here in 

recent years (USFWS data; Sprague, 2003). At this atoll, these 

two avian species have been documented nesting in relatively 

low-lying herbaceous vegetation such as Nelson’s horsenettle 

(Solanum nelsonii; Amerson and others, 1974). Satelliteimagery resolution was not sufficient to classify specific plant 

species preventing habitat differentiation. 

Land cover classification using aerial imagery did 

not allow for separation among soil types or bare ground 

substrates (for example, sand, rubble). The three burrowing 

species (Bonin Petrel, Wedge-tailed Shearwater, and Tristram’s Storm-petrel) establish nests where they are able to 

excavate and the soil is stable enough to maintain a burrow; 

Bulwer’s Petrel typically nest in crevices in coral rubble and 

rocks. Potential nesting habitat area for these four species 

is, therefore, overestimated using land cover classification 
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Appendix 1–3    139

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Menlo Park Publishing Service Center, California

Manuscript approved for publication, August 24, 2012
Reynolds and others—Predicting Sea-Level Rise Vulnerability of Terrestrial Habitat and Wildlife of the Northwestern Hawaiian Islands—Open–File Report 2012–1182