Modeling sea-level rise and wave-driven
inundation on Laysan Island:
a geospatial approach
Paul Berkowitz, HCSU, UHH
pberk...
Laysan Island, Papahānaumokuākea Marine National
Monument (PMNM)
• Carbonate island
• Max. elevation ~ 10 m
• Central hyper-saline
lake
• Highest dunes in north
& west; lowest in east
• N...
Research Objective: Estimate habitat loss on Laysan
Island over the next century due to SLR and wave-driven
inundation.
Pa...
Geospatial processing steps:
1. Generate seamless topo/bathy grids used by wave-
models (Delft3D)
2. Assign wave-driven wa...
Step 1. Generate topo/bathy grids for Delft3D model
PIBHMC depth grid
Navigational Charts 1-m DEM
Digitized Coastline
Inpu...
Step 2. Assign wave-driven water levels to coastal stations
Step 3. Project water-levels
orthogonally up beach slope.
Step 4. Delineate
inundation extent.
Step 5. Account for
topography.
Results:
1. Passive models
underestimate inundation
extent during periods of
high-wave ene...
Step 6. Overlay inundation extent with habitat to assess
vulnerability (9 spp.; 5 SLR scenarios; 2 gw assumptions)
Masked ...
Conclusions for Laysan:
1. Passive & wave-driven models predict very different
levels of inundation.
2. Most impacts occur...
Acknowledgements
Contributors: Michelle Reynolds, Principal Investigator, USGS Pacific Island
Ecosystem Research Center (P...
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Modeling sea-level rise and wave-driven inundation on Laysan Island: a geospatial approach

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  • Traditional GIS application in a research context.
    Not cutting edge mobile app.
  • 1. 1600 km NW of Honolulu (1900 km NW of Hilo!), 700 km SE of Midway.
  • Estimated age = 20.7 million yrs ago.
    Area = 412 ha, including a 74.2 ha lake zone.
    Reef crest = very shallow barrier reef offshore with lagoon inside. More typical in the NWHI.
  • Most research to date uses passive SLR models. Passive models systematically underestimate inundation during periods of high-wave energy. We tried to take it one step further & incorporate wave-driven inundation during high-wave events (top 5% of conditions during winter, i.e., Dec-Feb). Examine the combined effect.

    We don’t know how much SLR will occur in the next 100 yrs, so we examined 4 scenarios of SLR (0 – 2 m in half-meter increments). Chip Fletcher uses 1 m as a planning target, so that’s a good estimate. Recent study by Joughin estimates 3-4 m rise in 200-500 yrs as W. Antarctic sheet melts.

    Wave-driven water levels include wave set-up (rise in mean water level above still water level inshore of initial point of wave-breaking produced by wave-driven momentum flux) & wave run-up (max. vertical extent of wave uprush on beach).
  • Seamless topo/bathy is prerequisite for Delft3D (Netherlands software). Worked with oceanographers in USGS Coastal & Marine Geology Program.
    Wave models output wave run-up and set-up heights in grid format. Assign to coastline points.
    3. Project heights upward based on topography for 430 coastal points (spaced 20 m apart) around island.
    4. Connect the dots.
    5. Account for 3 basins that collect water.
  • Pacific Island Benthic Habitat Mapping Center 20-m bathy grid (IKONOS & multibeam sonar) has data gaps. Fill in gaps with navigational chart spot data.
    Convert all data to points & interpolate.
    Kriging is one of many interpolation techniques available is ArcGIS (Geostatistical Analyst). IDW and Spline methods are deterministic (assign values based on surrounding measured values).
    Kriging fits a geostatistical model to data, based on statistical relationship between measured points. Appropriate when you know there’s spatial autocorrelation. Ordinary (most general) vs. universal (remove trend). It’s a complicated method --- go read some stats books!
  • The Delft3D wave module computes wave-driven water levels from oceanographic data (ACE hindcast data on wave height, period, wind speed), bathymetry, and topography (beach slope).
    Delft3D outputs water levels as a grid that does NOT line up with coast.
    In ArcGIS, use nearest neighbor techniques to assign calculated water levels to 430 points on the coastline (i.e., at MSL) spaced 20 m apart.
    Figure shows wave run-up (including set-up) for 2 different SLR scenarios (0.0 m and +2.0 m SLR) at these coastal points.
    Run-up is greater at higher sea level, since the offshore reef attenuates less wave energy as reef becomes deeper (e.g., high tide at 4 mi. in Keaukaha).
    Next step: figure out how far inland these water levels go.
  • Shore normal transects computed by (a) drawing regression line thru 11 adjacent coastal pts (200 m curved coastline), and (b) then taking the negative reciprocal to determine perpendicular direction. For GIS nerds: regression slope and perpendicular slope computed in EXCEL, then data was converted to pt & line GIS features using a Python script.
    Then we projected wave-driven water levels up beach slope along shore normal transects until land elevation > run-up height. Gives HWM on each transect.
    Delineate inundation extent by connecting high-water marks (pt-to-line conversion). We considered inundation at MHW for each SLR scenario to show the worst-case daily scenario.
    Counter-intuitively, inundation is greatest where vertical wave run-up heights are lower (SE side) because dunes are lowest here. Where vertical run-up is highest (NW side), dunes protect interior from flooding. Starting at a SLR of 1.5 m (tipping point), run-up breaches the low-lying dunes in the SE.

  • Passive & wave-driven inundation models show large differences in inundation extent on Laysan at +1.5 m SLR and +2.0 m SLR.

    We don’t know the exact volume of water that will penetrate inland during these events (not computed in wave modeling software --- depends on storm/swell characteristics), but it’s theoretically possible to inundate the faint blue shaded area (34% of terrestrial area) if run-up volumes are high enough. We know from staff on Laysan that these areas flood during both large wave events & high rainfall events.
  • We ran a series of overlays of inundation extent & habitat for 9 spp of birds, 5 different SLR scenarios (0 – 2 m), & 2 different GW assumptions (no rise in lake level vs. lake level rise comparable to SLR due to percolation from ocean).
    No hydrological studies on Laysan, but research from other Pacific islands suggests that the water table & consequently lake level may rise with sea level.
    In terms of methods, since it’s repetitive, we used Model Builder & Python for processing. Output = GDBs since area tabulates automatically.
    Conclusions: Wide range of predicted impacts depending on spp & modeling scenario. Main impacts for wave-driven scenarios:
    (a) BFAL: -19% habitat loss at 2.0m SLR. -7% loss at 1.5m SLR assuming wave-driven inundation. Philopatric.
    (b) SOTE : adaptable, not very vulnerable <= 2 m since this species will alter nest sites.
    (c) MABO: vulnerable to wave-driven inundation in SE. Lose -18% and -34% of nests at 1.5 and 2.0m SLR, assuming wave-driven model.
    (b) GRFB and RFBO vulnerable to GW rise since >65% of their shrub-nesting habitat is in low-lying interior at >= 1.5m SLR.
    (e) LADU: -27 and -36% habitat loss at 1.5 and 2.0m SLR respectively, assuming wave-driven inundation.
  • In terms of time, 1.5 m SLR may be 150 years in the future. No one knows for sure, but it’s coming given system inertia.
    Spp. vulnerabilities are actually very complicated: temporal component (nesting season vs. high surf season), nesting behavior adaptation, etc. Leave that to the biologists!
    More generally, for all islands passive modeling efforts may greatly understate impacts to vegetation and habitat. Further refinements should be considered.
  • Modeling sea-level rise and wave-driven inundation on Laysan Island: a geospatial approach

    1. 1. Modeling sea-level rise and wave-driven inundation on Laysan Island: a geospatial approach Paul Berkowitz, HCSU, UHH pberkowitz@usgs.gov May 20, 2014
    2. 2. Laysan Island, Papahānaumokuākea Marine National Monument (PMNM)
    3. 3. • Carbonate island • Max. elevation ~ 10 m • Central hyper-saline lake • Highest dunes in north & west; lowest in east • No well-defined reef crest WorldView-2 image (Digital Globe 2010)
    4. 4. Research Objective: Estimate habitat loss on Laysan Island over the next century due to SLR and wave-driven inundation. Passive (“bathtub”) inundation model vs. Wave-driven inundation model.
    5. 5. Geospatial processing steps: 1. Generate seamless topo/bathy grids used by wave- models (Delft3D) 2. Assign wave-driven water levels to coastal points 3. Project water-levels orthogonally up beach slope 4. Delineate inundation extent 5. Account for topography 6. Overlay inundation extent & seabird habitat
    6. 6. Step 1. Generate topo/bathy grids for Delft3D model PIBHMC depth grid Navigational Charts 1-m DEM Digitized Coastline Input Data Output Grids Kriging 100-m resolution 20-m resolution
    7. 7. Step 2. Assign wave-driven water levels to coastal stations
    8. 8. Step 3. Project water-levels orthogonally up beach slope. Step 4. Delineate inundation extent.
    9. 9. Step 5. Account for topography. Results: 1. Passive models underestimate inundation extent during periods of high-wave energy. 2. Starting at ~ 1.5 m SLR, wave run-up (during periods of high-wave energy) will breach dunes and start to fill the interior basins.
    10. 10. Step 6. Overlay inundation extent with habitat to assess vulnerability (9 spp.; 5 SLR scenarios; 2 gw assumptions) Masked & Brown Booby Laysan TealGreat Frigatebird & Red-footed Booby Sooty TernLaysan Albatross
    11. 11. Conclusions for Laysan: 1. Passive & wave-driven models predict very different levels of inundation. 2. Most impacts occur at SLR > 1.5 m. 3. Vulnerability varies by species distribution, temporal factors, & species life history. Overall Conclusions: Since passive models may significantly understate impacts, more complicated models should be considered.
    12. 12. Acknowledgements Contributors: Michelle Reynolds, Principal Investigator, USGS Pacific Island Ecosystem Research Center (PIERC); Curt Storlazzi, USGS Pacific Coastal and Marine Science Center; Karen Courtot and Crystal Krause, USGS PIERC; Jeff Hatfield, USGS Patuxent Wildlife Research Center; Jamie Carter, NOAA; Matt Stelmach and Tawn Speetjens, USFWS. Contributing organizations: Papahānaumokuākea Marine National Monument; USGS-Deltares co-operative. Funding sources: USGS National Climate Change and Wildlife Center, USGS PIERC, USFWS Pacific Islands Refuges Inventory and Monitoring Report on-line: http://pubs.usgs.gov/of/2012/1182/ pberkowitz@usgs.gov

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