Driving Hazards and Surface Hydrology Delaware County, Indiana

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1 Driving Hazards and Surface Hydrology Delaware County, Indiana ANTHONY D. PHILLIPS Department of Geography* Ball State University, Muncie, Indiana ABSTRACT Hazardous driving conditions exist during any type of heavy precipitation event, particularly during thunderstorms. Severe thunderstorms have the capability of producing particularly heavy amounts of rainfall with rates of two inches per hour or more. This amount of rainfall in such a short amount of time can result in streams and creeks quickly filing their banks and overflowing. Nearby roadways can quickly be covered, making them dangerous to traverse. Using a Geographical Information System (GIS), Light Detection and Ranging (LIDAR) data, and general survey techniques, hazardous road-stream intersections can be located and categorized. 1. INTRODUCTION During heavy precipitation events, such as thunderstorms or extratropical cyclones, runoff can quickly cause streams and creeks to overflow and flood neighboring roadways. This flooding usually results in an impasse for motorists trying to get to their destination. However, some drivers find it necessary to cross flooded roads, which could lead to a serious accident or even death. Turn Around, Don t Drown (TADD) is a slogan used by the National Weather Service (NWS) to advise motorist not to traverse flooded roadways and bridges. Numerous accidents and deaths occur each year when automobiles are swept downstream after attempting a crossing. Using a Geographical Information System (GIS), it is possible to map roads and streams to locate hazardous crossings. Field work and general surveying techniques will allow for quick measurements to determine the flood stage at any particular road-stream intersection. Corresponding author address: Mr. Anthony D. Phillips, Department of Geography, Ball State University, Muncie, IN In addition, data obtained by Light Detection and Ranging (LIDAR) can help verify these measurements, and may potentially eliminate the need for field surveys altogether. 1.2 Delaware County, Indiana Delaware County is located in east-central Indiana and covers approximately 1,025.6 square kilometers, of which, 7.8 square kilometers (0.66%) is surface hydrology 1. Two major surface hydrology features are the White River (West Fork) and the Mississinewa River (Fig. 1). The West Fork of the White River flows east to west through the county seat at Muncie while the Mississinewa River flows through northern Delaware County (east to west). Currently, only one United States Geological Survey (USGS) stream gauge exists in Delaware County, which is located along the White River at Muncie. Current flood categories include: Action Stage: 7 feet Flood Stage: 9 feet Moderate Flood Stage: 12 feet Major Flood Stage: 15 feet 1 American Fact Finder. U.S. Census Bureau. Retrieved: 10-Dec

2 Figure 1. The Wabash River Watershed and its tributaries. Image courtesy of Karl Musser. Major highways within Delaware County include: Interstate 69, U.S. Route 35, and Indiana State Routes (SR) 3, 28, 32, 67, 167, and 332. With the exception of I-69 and SR 167, each of these roads (in addition to other local roads) will be the focus of analysis using GIS, namely ESRI s ArcGIS The location of these roads in conjunction with nearby streams will provide an overview of possible locations impacted by flooding. 2. METHODS Road and stream networks were mapped for Delaware County using data obtained from the Department of Geography at Ball State University. Upon adding the data to ArcMap, the Intersect tool (found under Arc Toolbox>Analysis Tools>Overlay) was used to locate where roads and streams intersect (Fig. 2). Each feature was added to the Input Features and the Output Type was changed from INPUT to POINT. The result is a point shapefile that clearly locates all roadstream intersection points for the county, hereafter known as the flood stage shapefile. To this shapefile, the road shapefile and the stream shapefile were joined. The Indiana Department of Transportation (INDOT) conducts traffic counts at various locations within each county. This information was collected from INDOT s website as a printed map, which contained street names and

3 Figure 2. ArcMap application with Arc Toolbox and the Intersect tool open. Note that the road shapefile (dc_streets) and the stream shapefile (dc_hydrology) have been added and the Output Type has been selected as POINT. The XY Tolerance can be used to locate streams within a certain distance of a roadway. Annual Average Daily Traffic (AADT) count statistics. An edit session was conducted within ArcMap and the AADT count data was manually added to the flood stage shapefile. Several hundred point locations exist and need to be narrowed down in the interest of time. The AADT data helped to accomplish this by limiting further research to those roads that were frequently used by commuters. Of those roads that had AADT data, 41 road-stream intersections were identified for field surveying. 2.1 Field Work and Surveys The author s Chevy Tahoe was outfitted with GPS equipment and a laptop running the ArcGIS suite. ArcMap has the capability for GPS tracking, which made finding each of the 41 survey points very convenient. Data collection at each point began by taking a distance measurement from the stream edge to the author s eye-level, which is shown as line d in Figure 3. This measurement was to the nearest half-foot and was taken using a basic 50 feet measuring tape. Next, a clinometer was used to determine the angle of depression from the author s eye-level to the stream edge (represented by θ in Figure 3). With some basic trigonometry, it is possible to find exactly how high a stream would have to rise to flood the nearby roadway. This can be found using the following equation: FS = [d sin θ] 6 Where FS is the resulting flood stage in feet, d is the distance to the stream s edge, and θ is the angle of depression. The answer is subtracted by six feet to displace the author s height (which is exactly six feet from eye-level to ground). Notice that this equation will work for any angle, such as a bridge measurement taken

4 Figure 3. Taking an angle measurement alongside a stream in northern Pulaski County, Virginia. From the author s eye-level to the ground is exactly six feet. The distance from the author s eye-level to the stream edge is represented by d, the angle of depression is θ, and the height water would need to rise to induce flooding is FS. straight down at 90 (thus the sinθ term cancels), in which case we re left with: FS = d 6 This will provide an accurate measurement of how high a stream must rise in order to reach the roadway and begin flooding. Each of the 41 points within Delaware County were evaluated by the method above. This took the greatest amount of time as the weather was uncooperative on the survey days chosen. 2.2 LIDAR Data LIDAR data was generously provided by the Delaware County GIS Department. This information was helpful in verifying field measurements taken by the method previously described and may possible be a better, more economical way to conduct such a survey. LIDAR data was imported into ArcMap as.las shapefiles. These were point shapefiles, which resulted in many thousands of points being displayed at any one time (Fig. 4). Each point has a corresponding elevation value which was determined by the height and position of the aircraft conducting the survey. By comparing the elevation values along a roadway to the elevation values of the nearby stream or creek, the flood stage height can be calculated. For example, SR 67 crosses the Mississinewa River near Albany. By selecting several points along the bridge that crosses the river, we can find that the elevation of the bridge surface is approximately 919 feet (Fig. 5). Next, by selecting several points along the riverbank, the elevation of the Mississinewa River is determined to be 903 feet (Fig. 6). Recall that

5 Figure 4. LIDAR data loaded into ArcMap as a point shapefile. SR 67 is shown where it crosses the Mississinewa River near Albany. Note the lack of elevation change along the roadway. Figure 5. Selecting points along SR 67 to determine elevation height.

6 Figure 6. Selecting points along the Mississinewa River to determine water elevation. LIDAR returns measurements at or above the surface, it cannot penetrate the ground (or water); thus the lowest elevation observed along the river must be the location of the water s surface. LIDAR returns additional information about above-ground objects as well, such as trees or buildings, such objects represent different classes and return values. This is why some points selected along the river have an elevation 50+ feet above the roadway, these are likely trees. Returning to the example, the flood stage can be calculated by subtracting the elevation of the river from the elevation of the bridge/roadway: effectively be used to measure the height rise needed to cause road flooding. A comparison of the data collected in the field to data calculated by LIDAR indicates that the average standard deviation between the two was Many of the calculations done in the field were within a foot of measurements taken by LIDAR. 3. RESULTS Table 1 includes information about each survey point. The following is a list of the headers and their meaning in Table 1: FS = Elevation Road Elevation Stream FS = FS = 16 This is an accurate measurement on the part of LIDAR as the field survey measurement was also 16 feet. It is safe to say that LIDAR can FID: Survey point number. STREAM_NAME: Name of the watercourse ROAD_NAME: Name of the nearby roadway TRAFFIC_DI: Direction of traffic movement AADT: Average Annual Daily Traffic for 2001 Rd_Elev: Road elevation taken from LIDAR Wt_Elev: Stream elevation taken from LIDAR Distance: Rd_Elev minus Wt_Elev (feet)

7 My_dist: Field calculation of flood stage (feet) FS: Final calculation of flood stage (feet). Additionally, ArcMap was used to create a Google Earth file (.kml) which includes information about each survey point as a clickable icon. This was accomplished by the use of an extension called Export to KML. The flood stage symbology used in this.kml file is as follows: Thus, locations identified as red squares require between zero and five feet of water rise to cause road flooding. Several options were required to be selected within the Export to KML extension. Once opening Export to KML, the layer selected was the flood stage shapefile. Next, FID was selected for the attribute for labeling features. Within the options menu, under the tab Export Options, the KML Layer Description was filled in using standard HTML code (Fig. 7). Under the tab Labeling and Description Options, FID was selected as the attribute for naming each feature and the following HTML code was inserted into the Feature description expression (Fig. 8): The final.kml file can be found at the following web address (or through the corresponding author s address): County_DrivingHazards.kml 4. CONCLUSION Previous research related to road flooding from nearby streams required field work measurements to accurately calculate the amount of water rise needed for flooding. LIDAR data seems to be a viable alternative to such field work if it is available. Currently only select states and counties have their own LIDAR data due to the expense of gathering the information and subsequent storage. However, if it is available, it can be analyzed using ArcGIS. The information obtained from calculating the flood stage for road-stream intersection points can help researchers locate possible areas of impact from flooding. The impacts to local commuters can also be quickly assessed. Future work will need to be done to devise a shortcut or possible ArcMap extension that would calculate flood stage values from LIDAR data automatically, without the need for manual computation. <b>point ID:</b> [FID]<br /> <b>flood Stage:</b> [FS] feet<br /> <br /> <b>lat:</b> [Lat]<br /> <b>lon:</b> [Lon]<br /> <b>road:</b> [ROAD_NAME]<br /> <b>stream:</b> [NAME]<br /> <b>2001 Annual Average Daily Traffic:</b> [AADT]<br /> The information in brackets [ ] corresponds to the appropriate column name found under the attributes for the flood stage shapefile.

8 Figure 7. The Export to KML extension running with the Options menu opened. Note the boxes that are checked and the HTML code used for the KML Layer Description. Figure 8. Export to KML extension options under Labeling and Description Options tab. Note the HTML code used for the Feature description expression and the attribute selected for naming each feature.