GEO-SPATIAL TECHNOLOGY USE TO MODEL FLOODING POTENTIAL IN CHESTATEE RIVER WATERSHED Sarah Skelton1 and Sudhanshu S Panda2AUTHORS: 1Undergraduate student (Environmental Science), Institute of Environmental Spatial Analysis, Gainesville State College, 3820Mundy Mill Road, Oakwood, GA 30566; 2Assistant Professor, GIS/Env. Sc., Institute of Environmental Spatial Analysis, Gainesville StateCollege, 3820 Mundy Mill Road, Oakwood, GA 30566; email@example.comREFERENCE: Proceedings of the 2009 Georgia Water Resources Conference, held April 27–29, 2009, at the University of Georgia. Abstract: Since 2002, the National Weather Service happening in many parts of the world (Panda, 2008).uses Flash Flood Monitoring Program (FFMP) and Flash Flash flooding induced by sudden surged storm eventsFlood Guidance (FFG) to predict flash flood events. has recently become a norm in the world (Lee and Lee,However, these programs contain several deficiencies for 2003; Hudson and Colditz, 2003). In this present decade,several forecast areas in the nation. Developing a GIS it is a fact that most of the federal disasters in Unitedbased model that incorporates basin physiographic States are due to Flooding. Hurricane Katrina and thischaracteristics will allow the hydrologist to better predict year’s flood in Iowa are few examples. Therefore,flash flood events. In this study, we have developed an reliable flood models are a necessity to allow emergencyautomated geospatial model to determine the flooding managers and city planners to obtain advance warning inpotential for the upper Chestatee River watershed, severe storm situations and get prepared for theLumpkin and White Counties, northeast Georgia. The eventuality (Knebl et al., 2005). Flood inundationdynamic GIS parameters used in the model development modeling would also help planners and insurance folks toare slope and flow accumulation (30 m DEM derived), take major decision to safeguard public’s interest (Bates,land cover and vegetation (University of Georgia), soil 2004).hydrologic and drainage classification (NRCS Geographic information systems (GIS) are currentlySTATSGO), and precipitation. All these layers were being used to help model flooding potential andtransformed to raster datasets of same resolution if they inundation. In 2004, Robayo et al. developed a tool thatwere not raster, using the essential attribute field combines NEXRAD (Next Generation Weather Radar)responsible for flooding potential analysis. Each dataset rainfall time series data with GIS in hydrological(model parameter) was assigned weights at the time of modeling. The Map-to-Map tool creates an ArcHydroreclassifying, a ranking of least flood potential (1) to model and an interface data model for all models thatmost flood potential (9). Finally, each individual layers share data with GIS to output a floodplain map. Dyhousewere overlayed with a weighted overlay analysis et al. (2008) have developed mechanism to modelconducted through map algebra application. For the floodplain using HEC-RAS software. Knebl et al. (2005)weighted overlay analysis, each layer was given certain combined GIS, NEXRAD rainfall data, and aweights judged by their influence in flooding potential. hydrological model to examine flooding of the SanFinal output obtained was raster cells with indices of 1 Antonio River basin. The hydrological model converted(least potential) to 9 (most potential). The final flooding excess precipitation into overland flow and channelpotential map was presented as colored map with scale runoff. A similar study of flood inundation wasfrom 1 to 9. This automated model can be easily performed at Eskilstuna, Sweden, again combining GISreplicated in any other watershed in the nation by with a hydrodynamic model to obtain flood information.changing the input parameters. This study used the hydrodynamic modeling tool MIKE 21, which allows for a complete analysis of floodingKey words: DEM, FFMP, GIS, Model Builder, flooding impacts (Yang and Rystedt, 2002). However, all thesepotential, map algebra, weighted overlay software used to model floodplain are cumbersome and sometimes difficult to work with. The National Weather Service (NWS) currently uses INTRODUCTION the Flash Flood Monitoring Program (FFMP) to predict flooding events. Within each hydrologic basin is a radar Due to global warming, the oceans thermohaline bin and rainfall rates and accumulation is based oncirculation pattern is changing. It is known as an El-Nino amount of reflectance. Then the average rainfall value isand La-Nina effect. Therefore, the ocean surface compared to the Flash Flood Guidance generating a flashtemperature is changing geographically. As a result, flood index. However, FFMP program hydrologistunlike earlier time, more than usual precipitation is predicts flooding events for 8-digit HUC basins. When
they venture into flood predictions in micro-watersheds,they face problem with basin connectivity and dataauthenticity. The current FEMP program also haveproblem in identification of correct individual basinphysiography. Incorporating GIS into flash flood prediction willgreatly improve the accuracy of the NWS warningsystem for any spatial area vulnerable to flash floods.Developing a geospatial model in ArcGIS ModelBuilderwould also enhance the ability of layman with simpleGIS knowhow to predict flooding probability in any areaof concern. The preliminary GIS model contains intrinsicparameters of soil, vegetation, land cover, slope, andflow accumulation. Running a model based on theseintrinsic parameters creates a static map of potentialflooding. To make the map more dynamic and useful tothe NWS precipitation data is added at the end of themodel. The overall goal of this study is to generate aflash flood index ranging from least potential to flood (1)to greatest potential to flood (9) for Chestatee Riverbasin. MATERIALS AND METHODSStudy area The study area for this paper is the upper Chestatee Figure 1: Study area map of Chestatee RiverRiver watershed in portions of Lumpkin and White watershed, Georgia.Counties, northeast Georgia (Figure 1). It is a 10-digitHUC (0313000105) watershed and a subwatershed of GIS and other Data Layers used in the studyLake Sidney Lanier Watershed in northeast Georgia. The Raw data sets used in this study include 10-digit HUCChestatee River is a major tributary of the Chattahoochee basin shapefile downloaded from the United StatesRiver, which flows into Lake Lanier. It begins at the Geological Survey (USGS) data server; 30 m DEMsconfluence of Dicks Creek and Frogtown Creek in obtained from Gainesville State College spatial server;northeastern Lumpkin County of Georgia and flows land cover and vegetation raster, GLUT 2005 (Landsat-down by the county seat and town of Dahlonega (Figure derived classification, Georgia Land Use Trend Program1). City of Clermont is just a mile below to the southern University of Georgia, College of Agricultural andsite of the study area (Figure 1). The watershed is few Environmental Sciences, Natural Resources Spatialmiles north of Gainesville city of Hall County, GA. Analysis Laboratory [http://narsal.uga.edu/glut.html]); and STATSGO soil data downloaded from the National Resource Conservation Service (NRCS) Data Gateway. The annual average rainfall data was collected from the rain gauges inside and the adjacent areas. Spatial layers preparation for analysis Study area delineation: At the outset all data sets were reprojected into Geographic Coordinate System NAD 1983, UTM Zone 17N using projections and transformation tools available with ArcToolbox (ESRI, Redlands, CA). The Chestatee 10-digit HUC (0313000105) watershed was selected from the 8-digit HUC Lake Lanier watershed and exported as a new shapefile (figure 1). The study area shape file was used as a mask to generate other GIS layers used for the study.
soil feature layer to raster format, it was made sure that Precipitation data preparation: Precipitation is a the raster cells were 30 meter to be compatible with themajor player in flooding potential mapping (Chow et al., DEM and NLCD LULC data. Then both soil drainage1988). With high annual rainfall, there is potential for and soil infiltration raster layers were clipped to the studymore flooding. Therefore using precipitation data is area boundary using the Extract by Mask tool ofessential. There were three gauging stations (National ArcToolbox (Figure3). Each raster were reclassified to aWeather Service) around the watershed. The annual rank from 1 to 9 (Table 1) based on the drainability andaverage rainfalls for these three rain gauges were 64, 65, infiltration rate of the soil texture. Finally, the drainageand 66 inches, respectively. A point shape file was and hydrologic rasters were combined based on the valuecreated in ArcGIS with the average precipitation value as (numeric) fields using the weighted overlay tool (Figurean attribute in its attribute table. Then the ‘Inverse 3). Values were scaled from 1 (least potential) to 9 (mostDistance Weighted’ surface interpolation technique was potential) to match the evaluation scale of 1 to 9 by 1.used to create an interpolation raster from the pointshapefile. Thiessen polygon technique can be used toobtain the distributed precipitation data for non-recordedlocations. Then the Extract by Mask tool was used to clipthe precipitation raster to the study area (Figure 2).Finally, the precipitation raster was reclassified into ascale of 1 to 9 with the class with the lowest rainfallamount getting a value of 1 and the highest one getting avalue of 9. It was performed by classifying theprecipitation raster with Equal Interval classificationtechnique with nine classes while performing thereclassification on the raster. Figure 3: Geospatial model for soil raster data preparation to include in flooding potential modeling.Figure 2: Geospatial model to develop precipitation Table 1. Soil raster based on the drainage andraster for flooding potential modeling. hydrologic group fields of the joined attribute table Soil data preparation: Soil is another important STATSGO Classification Cell Value Rank (1 to 9)factor in flooding potential mapping (Brady & Weil, Drainage2004). Soil permeability, and drainage ability are the W (Well, Int. water holding 1 5important soil characteristics that determine the amount capacity) P (Poorly, Low hydraulicof runoff and overland water storage. Therefore, using conductivity) 2 9these soil characteristics is necessary. However, NoData 3 NoDatapreparing data compatible to GIS spatial analysis is a SE (Somewhat excessively, low 4 1delicate task. To make it simpler, a geospatial model was water holding capacity)developed in ArcGIS 9.2 ModelBuilder so that with Hydrologic Groupsingle click of Run button, the required soil D (Very slow infiltration rates, 1 9 clayey soils or impervious layer)characteristics layer would be created. C (Slow infiltration rates) 2 1 As all the data layers should be in raster format to B (Moderate infiltration rates) 3 5help in the model development, the soil vector data layer NoData 4 NoDatawas converted to two different raster using the soilinfiltration (hydrologic group) and soil drainage fields, Weighted topography raster layer preparation:respectively (Figure 3). While converting the STATSGO Slope and flow accumulation are the essential
topographic factors that guide the flood potential of When reclassifying continuous rasters (like flowspatial areas. Slope and flow accumulation data layers accumulation and slope) the values were grouped intocan be generated using the digital elevation model of the ranges using the equal interval classification scheme withstudy area. nine classes. For example, the interval of greatest flow The DEM raster was clipped to the study area accumulation received a rank of 9 and the interval ofboundary using the Extract by Mask tool of ArcToolbox. lowest flow accumulation received a rank of 1. After theThen the Slope and Flow Direction tools were used to reclassification both were overlaid using the weighteddevelop slope and flow direction raster, respectively, overlay tool to get the weighted topography. Slope wasfrom the DEM (Figure4). Flow Accumulation tool was weighted slightly more (60%) than flow accumulationused with the flow direction raster as input to produce the because slope has a large influence on flood potential.flow accumulation raster. Figure 4 is the geospatial model developed in ArcGIS 9.2 Each raster (slope and flow accumulation) was ModelBuilder to prepare the raster for the flood potentialassigned weights at the time of reclassifying, a ranking of modeling.least flood potential (1) to most flood potential (9).Figure 4: Geospatial model for weighted topograph raster data preparation to include in flooding potential modeling. Table 2: The reclass table to produce weighted Weighted vegetation raster layer preparation: The vegetation raster from the land-use data.vegetation is a major restraint for flooding because itreduces the runoff and helps in percolation. Therefore, Old Values New Valuesthe land-use raster was reclassified into new values for 41 1the old values as given to vegetation class according to 42 1the Anderson’s classification scheme (Table 2). Classes 43 141, 42, and43 are forest classes and they were assigned 81 3with the new value of 1. Similarly 91, wetted forest also 91 1was assigned with value 1. Pasture (81) and other wetted 92 3thin forest covers were given a value of 3 as shown in 93 3Table 2. Similarly the land-use raster was reclassified No Data No Datawith scores of 9 (highest for flood potential contribution)for 22 and 24 (urban/impervious classes) and 8 for thebare land classes as shown in Table 3. Thus, two new Table 3: The reclass table to produce weighted land-raster, weighted vegetation and land-use were created. use raster from the NLCD data.Again, the weighted vegetation raster was overlayed withthe reclassed soil raster to create the weighted vegetation- Old Values New Valuessoil raster. Both got 50% of weight while conducting theoverlay. Figure 5 is the geospatial model developed to 11 0prepare the weighted vegetation-soil raster. 22 9 24 9 31 8 34 8 41 0 42 0
number of inputs in the final weighted overlay. The final output of a flood potential index was a result of equally weighting (25% each) the weighted topography layer, weighted vegetation-soil layer, land cover, and precipitation data. The final comprehensive single geospatial model developed to obtain the flood potential map of the study area is given in Figure 6. Finally, once the flood potential map of the study area is produced, it was classified using the Natural Breaks (Jenks) classification technique into several classesFigure 5: Geospatial model for weighted vegetation according to the need of the user. In this study we haveraster data preparation. used five ranks, very low, low, medium, high, and very high potential areas, respectively, to represent the spatialFlood Potential Model Development areas of the watershed based on their vulnerability for Once, all the four raster layers (1) Reclassified flooding.precipitation, 2) Weighted topograph, 3) Weightedvegetation-soil, and 4) Reclassufied land-use) werecreated using the geospatial models developed, they wereoverlayed together to produce the final flooding potentialmap of the 10-digit HUC Chestatee watershed. Theseweighted layers were created in order to reduce theFigure 6: The comprehensive geospatial model to develop the flood potential map.
RESULTS AND DISCUSSIONS Individual layers were created according as theprocedures suggested in the Materials and Methodssection. Each layer was scored according to the scoringscheme as suggested. Figure 7 shows the maps of all thelayers associated with the preparation of weightedtopography raster preparation as workflow process. Thereclassed precipitation raster generated from theprecipitation point feature file is shown in Figures 8. Figure 8: The precipitation raster of the study area Figure 9 shows the NLCD LULC data and the process with which the reclassified vegetation and reclassified land-use raster were created. When the weighted soil layer and reclassified vegetation data were overlayed it produced the weighted vegetation-soil raster layer (Figure 10). Figure 9: NLCD LULC, reclassified land-use, and vegetation layer maps.Figure 7: Maps involved in the preparation ofweighted topography raster layer from DEM.
In case of environmental analysis, weighted overlay with uniform weight allocations to spatial parameters may be not perfect for entire watershed spatial locations. For example, slope may not be as influential under canopy or in grassed areas as it would be for bare ground, fallow pasture, or in urban settings. Therefore, a matrix of coefficients or weights for each raster layer can be used. However, the studied watershed does not have that much variability in land-use, so individual weight factors for each raster was rightly used.Figure 10: Weighted vegetation-soil raster map. Figure 11 represents the flooding potential map of theChestatee watershed. From the analysis of the result itwas observed that most of the northern part of watershedis of low flood potential area. The southern part of thewatershed is more flat than the northern portion. It is alsocloser to the city area and hence devoid of vegetationcompared to the northern part that is of dense forestcover. There is not much area under the very highflooding potential category as observed from the imagevisual analysis. Table 4 shows the percentage of area ofstudy area under different flooding potential scale. Morethan 80% of the area is under the low to mediumvulnerability with respect to flooding potential. Rest ofthe 18% area is under high to very high flooding Figure 11: Flood potential map of the Chestateepotential area. Flood managers or insurance officers watershed.should be interested to develop these areas to decreasethe flooding potential in the watershed. However, it is to be noted that the use of annual Table 4: Percentage of area under different floodingprecipitation totals may be too coarse a resolution for potential category.accurate flood potential estimation, i.e., precipitationintensity on a given day and given period may vary Flooddramatically at two locations of a watershed. Therefore, Percentage of Potential Cell countsit would have been more appropriate to use sub-annual area scorehigh intensity precipitation records for accurate floodforecasting. One more note of this study is that the three Very low 1 0.00gauging station in the watershed may be not sufficient to Low 162,988 43.41reflect the actual spatial variability of rainfall in thewatershed. Therefore, if possible more number of rain Medium 143,418 38.20gauge stations should be used in analysis. We have High 35,240 9.39developed a procedure for flood mapping in a watershedthrough a developed geospatial model and the model can Very High 33,809 9.01be modified with precise information as mentioned here.
CONCLUSION Hudson, P.F., Colditz, R.R., 2003. Flood delineation in a large and complex alluvial valley, lower Panuco From this study, it was found that geospatial basin, Mexico. Journal of Hydrology 280, 229–245.technology has the best potential to undertake complex Knebl, M.R., Yang, Z.L., Hutchinson, K. and Maidment,environmental problems to analyze and provide results D.R., 2005. Regional scale flood modeling usingrequired for decision-making. This comprehensive flood NEXRAD rainfall, GIS, and HEC-HMS/RAS: a casepotential model developed in ArcGIS ModelBuilder study for the San Antonio River Basin Summer 2002could be easily handled by novice GIS users for decision storm event. Journal of Environmental Managementmaking. Again, as per advantage of the models 75, 325-336developed in ModelBuilder can be tweaked easily by Lee, K.S., Lee, S.I., 2003. Assessment of post-floodingreplacing inputs to obtained similar maps for other conditions of rice fields with multi-temporal satellitewatersheds or study areas. Therefore, the models SAR data. International Journal of Remote Sensing 24developed as part of this study could be easily replicated (17), 3457–3465.elsewhere. Panda, S.S., 2008. Precipitation. Encyclopedia of Global Warming and Climate Change, Ed. S. G. Philander. Sage Publications: Los Angeles, pp 823 - 825. REFERENCES Robayo, O., Whiteaker, T., Maidment, D., 2004. Converting a NEXRAD map to a floodplain map.Bates, P.D., 2004. Remote sensing and flood inundation Paper presented at the meeting of the American Water modeling. Hydrological Processes 18, 2593–2597. Resources Association, Nashville, TN.Brady, N.C. and Weil, R.R., 2004. Elements of Nature Yang, X. and Rystedt, B., 2002. Predicting Flood and Properties of Soils (2nd Edition). Upper Saddle Inundation and Risk Using GIS and Hydrodynamic River, NJ: Prentice Hall Inc. Model: A Case Study at Eskilstuna, Sweden. IndianChow, V.T., Maidment, D.R., and Mays, L.W., 1988. Cartographer, 183-191. Applied Hydrology. Newark, NJ: McGraw-Hill.Dyhouse, G.R., Hatchett, J., Benn, J., Ford, D., and Rhee, H., 2008. Floodplain Modeling with HEC-RAS. Haestad Methods, Inc., Watertown, CT.