14B.3 YUCATÁN HURRICANE HAZARD ASSESSMENT: A GIS METHODOLOGY FOR MODELING HURRICANE HAZARDS Amanda Weigel* and Dr. Robert Griffin University of Alabama in Huntsville, Huntsville, Alabama 1. INTRODUCTION 1.1 HURRICANE HAZARDS The Yucatán region spans across portions of Hurricane hazards selected for this analysis Mexico, Guatemala, Honduras, El Salvador, and include the common hurricane hazards: high Belize (Figure 1). Historically, this region has winds, storm surge flooding, and non-storm surge proven to have an active tropical climatology as it flooding (NOAA, 1999). Additionally, rainfall has been hit by numerous large scale, high triggered landslides were selected as the fourth magnitude hurricanes. Paleo-climatological hazard since portions of the study area are prone evidence has dated hurricane activity back 5,000 to landslides as numerous related damages and years through sediment core sampling taken from deaths have been reported from past hurricanes. low energy water bodies along coastal Belize (McCloskey and Keller, 2008). Recently, this 2. DATA AND PRE-PROCESSING region has been affected by high magnitude storms including Hurricane Mitch, Keith, Dean, Data sets selected for this analysis were and Andrew, which caused recording breaking determined based on a balance between their flooding, billions of dollars in damages, and over geographic coverage, spatial resolution, data size, 10,000 fatalities (IFRC, 2000; NOAA, 2009). and processing time. Table 1 outlines all datasets Tropical activity in the past century has and their associated spatial resolution, temporal, demonstrated this area to be extremely and geographic coverage. susceptible to hurricanes. The Global Administrative Areas (GADM) Because the Yucatán regions spans across dataset provides fine resolution global country portions of five countries with varying economic boundaries. For this analysis GADM was selected statuses and political policies, it is difficult to to derive the extent of the study area because it assess the risk of hurricane induced hazards provided the finest resolution data of global throughout this area as detailed historical and coastlines. predictive atmospheric model data is unavailable. Hurricane track data was acquired from the This work presents a methodology for using a National Oceanic and Atmospheric Administration Geographic Information System (GIS) to combine (NOAA) National Climatic Data Center. The various layer components for analysis of International Best Track Archive for Climate susceptibility to hurricane hazards using recorded stewardship (IBTrACS) hurricane tracks were hurricane information, and land surface selected for this analysis due to its availability in a characteristics. variety of GIS compatible formats, its endorsement by the World Meteorological Organization Tropical Cyclone Programme, and its detailed data on an array of hurricane parameters for storms dating back to 1851. Storms used for this analysis were condensed to a time period from 1970-2011 since hurricane tracks recorded prior to the 1970s are considered less accurate because weather observation methods, particularly satellites, were not available to provide a real time method for monitoring storm conditions (Davis, 2011). Figure 1: Study area map depicting the Yucatán Shuttle Radar Topography Mission (SRTM) Region data was selected as the topographic dataset and acquired from the USGS Earth Explorer data Corresponding author address: Amanda Weigel, distribution site. SRTM data files were mosaicked Univ. of Alabama in Huntsville, Dept. of Atmospheric together, masked to remove erroneous data Science, Huntsville, AL; 35805 e-mail: ranges, filled to remove sinks, and corrected by [email protected] removing negative elevation. Table 1: Data set information River network locations were acquired from Maximum winds within a hurricane occur in a the USGS Hydrological data and maps based on region called the radius of maximum wind (RMW) Shuttle Elevation Derivatives at multiple Scales located just outside the eye wall, and varies in (HydroSHEDS) data, which provides global distance from the center depending on the hydrographic information derived from SRTM data strength and diameter of the storm’s eye for regions where it is unavailable. River network (Bettinger, Merry, and Kepinstall, 2009). On shapefiles for Central America were acquired at 15 average, the RMW occurs approximately 50 km arc-second resolution and clipped down to the from the storm’s center (NOAA, 1999), where extent of study area. several hurricane radial wind profile studies have The Harmonized World Soil Database is a modeled hurricane wind speeds peaking near this collaboration between ISRIC, the European Soil distance (Wood et al., 2012; Holland, Belanger Bureau Network, and the Institute of Soil Science, and Fritz, 2010; Wang and Rosowsky, 2012; Chinese Academy of Sciences to combine and Vickery et al. 2009; Taramelli et al. 2013). make soil information available globally. Soil type Hurricane data were reduced to 75 storms passing data was acquired for the Yucatán region at 30 within 50km of the study area, a distance arc-second resolution. representative of the generalized location for the Accumulated monthly precipitation data was RMW. collected at 30 arc-second resolution from the A model was created using ArcGIS Model WorldClim data redistribution site to assess rainfall Builder to map the temporal radial wind profile of quantity and distribution. Data was collected for each storm. The hurricane wind profile model the span of the North Atlantic hurricane season iterated through hurricane track and applied the from June 1st through November 30th. Euclidean Distance tool to map a continuous hurricane extent using a generalized radius of 3. METHODOLOGY 250km based on previous studies (Wood et al., 2012; Holland, Belanger and Fritz, 2010; Wang Methods used to assess the risk of hurricane and Rosowsky, 2012; Vickery et al. 2009; induced hazards were adopted from Vahrson Taramelli et al. 2013; NOAA, 1991). A spatial (1994) and SERVIR (2012). For each hazard, a resolution of 10km was selected to manage set of underlying susceptibility factors were processing time. In order to map the radial wind determined based on previous methodologies distribution of each hurricane, a fuzzy membership from literature. Susceptibility factors were then function was applied to each storm extent dataset rescaled, and plugged into their associated hazard using the small function as it provided a line of equation. Table 2 outlines each hazard, the best fit to hurricane wind profiles. The function was determined susceptibility factors, and the hazard controlled by starting at the center of the storm, equations. setting the peak value at 50km out to represent the RMW, and then setting 250km as the spread 3.1 HIGH WIND to go towards zero, since the hurricane wind Hazard Susceptiblity Factors Variable Scale Hazard Equation Wind Wind Speed Swv 0-1 H Wind = Ave(Swv x Sd x Swd ) Distance from Eye Sd n/a Radial Wind Distribution Swd n/a Storm Surge Flooding Onshore Wind Speed Swvo 0-1 H Surge = (Swvo x Se c ) Low Lying Coastal Elevations Se c 1-0 Non-Storm Surge Flooding Elevation Above Drainage Se d 1-0 H Flood = (Se d x Sf x Sv) Storm Frequency Sf 0-1 Storm Velocity Sv 1-0 Rainfall Triggered Landslides Soil Type Sl 0-5 H Land = (Sr x Sl x Sh)(Sf r x Sv r ) Slope Sr 0-5 Soil Humidity Sh 1-5 Storm Frequency Rescaled Sf r 1-5 Storm Velocity Rescaled Sv r 1-0 Table 2: Hurricane hazard susceptibility factors and hazard equations speed drastically decreases with distance from the values originally set to one equal the maximum eye. sustained wind speed, and outer values gradually It was necessary to account for the temporal decrease to zero as a function of the small change in wind speed since hurricanes are function. dynamic systems, changing in strength as they To identify areas most at risk of experiencing move over different surface conditions. high winds, the average wind speed and Incremental wind speed measurements were frequency of hurricane occurrence were assessed. attributed to the radial wind. A 10km fishnet This was done through the use of Cell Statistics to covering the extent of the 50km distance from the average the wind profiles for all 75 storms to coastline was created at a resolution, then calculate the per pixel average wind speed. From snapped to a template raster to insure all pixels the calculated average, a Wind Speed Impact align in final processing. A near table was then Factor was created by rescaling average wind generated to identify grid cells closest to the speed values from 0-1. Areas closer to one have segmented portion of each hurricane track a higher risk of experiencing high wind speeds measurement. The hurricane track data was then from hurricanes. joined to the near table to attribute maximum sustained wind speed values to cells found closest 3.2 STORM SURGE FLOODING to the track. The near table was again joined to the original 10km fishnet and exported to a new To model storm surge, three factors were shapefile before being converted to raster format. considered: onshore wind speed, frequency of The resulting raster file covered the extent of the occurrence, and low lying coastal elevations. fishnet, containing segmented areas attributed to Storm surge is highly dependent on the strength of maximum sustained wind speed values. hurricane winds particularly on the right side of the Two components were used to assemble the storm where the counter clockwise rotation pushes final wind profile: the generated fuzzy membership water towards the coastline yielding higher water raster file and the raster conversion of the near depths (Ozcelik, Gorokhovich, and Doocy, 2012). table-fishnet join. Raster calculator was used to To model onshore wind speed, the Wind Profile attribute wind speed values to the fuzzy Model was adopted to only focus on the right side membership wind speed distribution file by of the storm. Cell Statistics were then used to multiplying the two files together. Fuzzy derive the per pixel average wind speed, before membership values range on a scale from zero to rescaling values from 0-1.