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Overview and Credits - Sea Level Rise Maps

Overview

The goal of this research was to simulate a theoretical global sea level rise of one to six meters with Geographic Information Systems (GIS), and to develop multiple products for visualizing the inundation and its effects, including static maps, map animations, and layers viewable in Google Earth.

Datasets

Sea level rise or inundation zones were calculated from the Global Land One-km Base Elevation (GLOBE) digital elevation model (DEM) (Hastings and Dunbar 1998), a raster elevation dataset covering the entire world. Cells in GLOBE have a spatial resolution of 30 arc seconds of latitude and longitude (approximately one kilometer at the Equator), with each land cell in the grid assigned an elevation value (meters) in whole number increments. Inundation zones depicted on all static map products and the regional maps in the map animations were derived from GLOBE. Inundation zones for the entire globe in the world map animation were calculated from the ETOPO2 raster elevation dataset developed by the National Geophysical Data Center (NGDC) (NGDC 2001), which has a coarser resolution of two minutes of latitude and longitude (approximately 1.8 km at the Equator).

Populations in the inundation zones were estimated from LandScan, a global population dataset developed by the Oak Ridge National Laboratory Global Population Project for estimating populations at risk (Dobson et al. 2000). LandScan was compiled from the best available population census data for each country that were then disaggregated into cells based on land cover type, proximity to roads, slope, and nighttime lights. The most recent version of LandScan (2004) was used for all population calculations in the inundation zones. LandScan may also be displayed under the inundation zone layers in the map animations.

Inundation zones were displayed on Natural Earth, a global shaded relief map with land cover tints, for all static maps and the map animations. Natural Earth was developed by Tom Patterson of the U. S. National Park Service and is available at http://www.shadedrelief.com/natural/index.html.

Methods for Delineating Inundation Zones

Potentially inundated areas were computed based on elevation and proximity to the current ocean shoreline.  To accomplish this, we developed a GIS raster analysis framework.  First, the algorithm flags all raster cells in the DEM that lie adjacent to the contiguous ocean.  Second, cells within that group of flagged cells whose elevation value is less than or equal to the desired sea level rise increment are selected and reassigned as ocean cells.  For example, to determine an inundation area for a sea level increase of one meter above the current sea level, all cells in the DEM that are adjacent to the ocean and that have a value less than or equal to one are selected and converted to water (i.e., they are inundated in the resulting output). 

Since the initial pass floods only those cells that are directly adjacent to the current ocean, the two-step procedure is repeated until all cells connected with cells adjacent to the ocean are inundated.  A large, flat coastal plain whose elevation is one meter above current sea level, for instance, would be inundated after the algorithm has completed several iterations, or until there are no more raster cells of a specific elevation that are adjacent to the current sea level.  The map animations make use of this ability to simulate the progression of inundation at steps smaller than whole meter increments by using several intermediate passes of the two-step algorithm.  Once sea level rise was simulated, we calculated a zone of inundation for each incremental rise.

Credits

This research was supported by was supported by the National Science Foundation under grant numbers 0424589, 0122520, and 0407827.

The following individuals at the Centers for the Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas and Haskell Indian Nations University contributed to this research:

University of Kansas

David Braaten, Nathaniel Haas, Xingong Li, RJ Rowley

Haskell Indian Nations University

Kalonie Hulbutta, John Kostelnick, Joshua Meisel

References

Dobson, J. E., Bright, E. A., Coleman, P. R., Durfee, R. C., and Worley, B. A. 2000. LandScan: A global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing 66: 849-857.

Hastings, D. A., and Dunbar, P. K. 1998. Development and assessment of the Global Land One-km Base Elevation Digital Elevation Model (GLOBE).  International Archives of Photogrammetry and Remote Sensing 32: 218-221.

National Geophysical Data Center. 2001. Topography data and images. WWW document, http://www.ngdc.noaa.gov/mgg/topo/topo.html.

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