Abstract

1. A QUANTITATIVE METHOD FOR RANKING THE GEOMORPHIC CONTROLS ON STORM SURGE PENETRATION ALONG THE MISSISSIPPI COAST DURING HURRICANE KATRINA Extreme High Moderate Low Damage pattern Land cover from air photo Flood zones Examples of pre-storm layers Rochelle F. Legaspi, Department of Geosciences, University of West Georgia, Carrollton, GA 30118, rlegasp1@my.westga.edu; Chester W. Jackson, Department of Geology, University of Georgia, Athens, GA 30602, cwjjr@uga.edu; David M. Bush, Department of Geosciences, University of West Georgia, Carrollton, GA 30118, dbush@westga.edu; Robert S. Young, Program for the Study of Developed Shorelines, Western Carolina University, Cullowhee, NC 28723, ryoung@wcu.edu Land surface slope Satellite imagery, air photos, DEM Tabular information (attribute table) Topographic map Harrison County Mississippi Long Beach Harrison County Mississippi Logistic regression is designed to describe probabilities associated with the values of the response variable, which in this study is the extent of hurricane damage. 3.2 mile study area NOAA Hurricane Katrina images (http://ngs.woc.noaa.gov/katrina/) stitched together using ERDAS Imagine showing study area. Study Area Pre-Katrina aerial photograph: GIS - Map Stacking Example Basic data structures for GIS Logistic regression gives maximum and minimum limits, ideal for hazards assessment Linear versus logistic analysis Photo from TerraServer USA Goals Determine which pre-storm geomorphic parameters correlate most closely with damage patterns observed after Hurricane Katrina (2005). It is believed that the alongshore variability is primarily controlled by various physical and geomorphic features of the shoreline and nearshore. Use this new information to produce a series of coastal hazard maps which can be used as predictors of damage from future storms. Provide data and maps to both public and private entities interested in learning about and managing coastal hazards. Methods Pre-storm geomorphic characteristics, such as digital elevation model, Q3 FEMA flood data, and measurement of slope were entered into a digital data base. This has entailed both hand digitizing of features and inputting data already in digital form into the database from Mississippi Automated Resource Information System. Each characteristic constitutes a single data layer defined in Arc Map. A map of storm damage has also been generated based on NOAA post-storm aerial photographs of Harrison County, Mississippi. Zones of overwash penetration, maritime vegetation, a no-damage layer, and total development destruction layer were digitized and entered into the database. The following coastal attributes have been quantified for each station: bathymetric slope, fetch, average inland slope, shoreline curvature, degree of vegetation cover, pre-storm beach width, and pre-storm dune volume/height. Finally, a statistical logistic regression analysis was performed using Minitab Splus, a statistical analysis program. The logistic regression analysis determines which of these geomorphic parameters of coastal attributes controlled the nature of the storm impact as measured by overwash penetration and/or total development destruction. Abstract Coastal hazard mapping has historically been based primarily on simple models of storm inundation (SLOSH model), erosion rates, or estimated setbacks from a control line. There is still only a rudimentary understanding of how the numerous geomorphic attributes of a particular stretch of shoreline control cross-shore storm energy. An analysis of the impacts of Hurricane Katrina along the Mississippi coast provides an important addition to that understanding—a ranking of the geomorphic controls on storm surge penetration (and thus property damage). These data augment the science of coastal storm processes while providing critically important guidance for future coastal management. Previous studies of geomorphic controls on storm damage in Florida and South Carolina indicate that along those coasts, site elevation provided the best protection against property damage, followed by dune height (in front of the site) and beach width. Each site is different and different factors may exert more control along the Mississippi coast. Ultimately, the new information can be used to produce a series of coastal hazard maps that can be easily accessed via the internet by both public and private entities interested in learning about and managing their coastal hazards. A regression analysis was used to determine which pre-storm coastal attributes influenced the observed post-storm storm-surge penetration and damage. Hurricane Katrina impacted a great length of the Gulf of Mexico Shoreline, and her effects were felt far inland. Preliminary results from Katrina have produced the following ranking: site elevation provided the best protection followed by dune height (in front of the site) and beach width. An on-going study of the impact of Hurricane Georges (1998) in Puerto Rico is exploring how consistent the results will be for a completely different geological setting. Having a quantitative understanding of coastal hazard risk is critical for producing accurate risk maps, as well as, for prioritizing spending on mitigation. Pre-Storm Data Layers Q3 and/or D-FIRMs (FEMA flood zones) Nearshore bathymetry DEM (Digital Elevation Model) Onshore and offshore slopes from DEM and bathymetry Air photos -number and types of houses/buildings -beach width -engineering structures -land use Satellite Imagery Storm Surge layer Enlargement of portion of above. Tabular information (attribute table)

2. Qualitative Analysis: Overlays of assessed parameters on base of NOAA post-storm air photos Elevation (meters) The debris line (yellow) matches almost exactly with 5 meter contour, indicating a relationship with elevation. Slope The debris line (yellow) is the landward limit of major collection of water-borne debris, essentially a large wrack line. The debris line matches up fairly well with a modest increase in slope from 0-1 degree to 1-2 degrees. The steepest portion of the study area is right along the beach. 0 - 1 (degrees) 1 - 3 0 - 1 3 - 5 1 - 2 5 - 7 7 - 10 2 - 3 3 - 4 4 - 5 X-500 Flood Zone X Flood Zone The debris line (yellow) runs landward of the X-500 flood zone. Clearly the debris was washed from the X-500 zone into the X-zone . V A X500 X Slope greater than 1 degree The debris line (yellow) is nearly coincident with the X flood zone, the zone with the highest correlation with damage. The X-zone indicates the area that would be flooded in a 500-year event or greater giving an idea of the magnitude of Katrina. Elevation in blue, 0-5 meters Elevation in blue, 5-10 meters Q3 Flood Zones Examples of Geomorphic Parameters: Binary Logistic Regression Binary response: Buildings Destroyed Factors: Debris Field, Built up areas, Maritime Vegetation, and Flood Zone Covariates: DEM and Slope Response Information: Factor Levels Variable: Buildings Destroyed 1138 Extensive Debris present or not present Variable: Buildings Not Destroyed 1805 Built up areas present or not present Total 2943 Maritime Vegetation present or not present Flood Zone V, A, X500, X Logistic Regression Table: Odds Predictor Coef SE Coef Z P Ratio Debris Present -3.47487 0.206307 -16.84 0.000 0.03 Built up areas 1.66442 0.120389 13.83 0.000 5.28 Mar Veg Present -4.68865 0.325926 -14.39 0.000 0.01 Flood Zone A 2.11503 0.164835 12.83 0.000 8.29 X500 3.67971 0.311737 11.80 0.000 39.63 X 3.92412 0.242102 16.21 0.000 50.61 DEM -0.297976 0.106742 -2.79 0.005 0.74 Slope -0.184805 0.107235 -1.72 0.085 0.83 Interpretation of results: The P-value indicates the significance level of a factor/covariate. A p-value less than 0.05 (chosen level of significance) is considered significant. Here for all factors/covariates other than slope we have a significance implying that all factor estimates are different from zero and hence having a contribution to the response. The odds ratio for Built up areas, and flood zones A, X500, and X are large. The odds ratio indicates the effect of change on response due to change in factor. Hence for Built up areas, an odds ratio of 5.28 indicates that when there are buildings (that is factor =1), the odds of a building being destroyed is 5.28 times more than for areas with lower density of development. Similarly, we can conclude that the odds of a building being destroyed is 8 times more in flood zone A, 40 times more in flood zone X500, and 51 times more in flood zone X as compared to flood zone V. Findings/Conclusions I The results of the statistical logistic regression analysis were unexpected. It was anticipated that the statistical analysis would have shown that areas without forest cover and of lower elevation as pockets of total structural/development damage. And similarly, that forested areas of higher elevation with less damage to structural buildings. Using flood zones from FEMA as proxy for elevation it was expected that Flood Zones X and X500 would have less structural damage than Flood Zones V and A. However, the analysis actually showed that the X zone correlated most highly with structural damage. It is likely that V and A zones did not correlate strongly with damage because there were few buildings located in those zones. Although the hurricane impacts or energy flux were high in those zones, there was little or nothing to be damaged. In addition, most structures were located in the X500 zones, and there was near complete destruction of those structures, obviously causing a high correlation. Moreover, the regression analysis as it has been developed and applied to other areas (Hurricane Opal/Florida panhandle; Hurricane Hugo/Pawleys Island, SC; Hurricane Fran/Topsail Island, NC) were all weaker storm situations. The width of the study area for this Katrina study likely did not extend far enough inland to include zones of less than total damage. Findings/Conclusions II As a result, a qualitative analysis was undertaken. The NOAA post-storm photos showing damage were overlain with several pre-storm parameter layers in an attempt to visually identify correlations. See “Qualitative Analysis” box, above. This and similar evaluations in other geologic settings hopefully provide an important boost to the understanding of coastal storm processes and possibly of greater importance, this information will be invaluable to coastal managers seeking to delineate those shoreline areas that are most vulnerable to storm surge driven damage. The results will help delineate a coastal construction or setback line and form a sound geologic and oceanographic framework on which to base reconstruction and future planning efforts. It is further hoped that the methodology will serve as a model for future studies of hurricanes impacting other shorelines, the ranked list of geomorphic attributes which control storm energy can be applied elsewhere along the U. S. Gulf of Mexico and Atlantic coasts. The next obvious step is to extend the study area farther inland and along the coast; include wind damage and inland flooding; and perhaps to include areas of impact with higher versus lower slopes and higher elevation. Acknowledgments Thanks to John Congleton, Dr. Jeong C. Seong, and Dr. Rebecca Dodge of UWG Department of Geosciences, and Mike Rooker for their assistance in the Geospatial Analysis Lab. A special thanks to Dr. Ayona Chatterjee of the UWG Department of Mathematics for running the statistics. Funding for this project was provided by the National Science Foundation STEP grant #DUE-0336571, the UWG Student Research Assistantship Program, the UWG Faculty Research Grant program, and the UWG Sponsored Operations Faculty Research Enhancement Award program. Websites Cited http://terraserverusa.com/default.aspx - TerraServer USA http://datagateway.nrcs.usda.gov/ - United States Department of Agriculture Geospatial Data Gateway http://www.maris.state.ms.us/ - Mississippi Automated Resource Information System http://www.ncddc.noaa.gov/Katrina-2005/InteractiveMaps/ - National Oceanic and Atmospheric Administration/Katrina Impact Assessment