A Method of Mapping Sinkhole Susceptibility Using a Geographic Information System: A Case Study for Interstates in the Karst Counties of Virginia
Todd, Alexandra, Civil Engineering - School of Engineering and Applied Science, University of Virginia
Burden, Lindsay, Department of Civil Engineering, University of Virginia
Karst terrain is landscape underlain by limestone that has been chemically dissolved by acidic groundwater, producing subsurface voids that pose risks for sinkholes if the overlaying soil can no longer support its own weight and collapses. The western counties of Virginia are heavy in karst due to their natural, geographic boundary of the western Ridge Province and the eastern Blue Ridge Mountain Range. As a result, the Commonwealth of Virginia Hazard Mitigation Plan recommends that the Virginia Department of Transportation (VDOT) develop a method to determine the roadways and regions most susceptible to experiencing sinkholes, in an effort to reduce the number of reported sinkhole damage to property. While many noninvasive methods exist to detect subsurface voids, such as electric resistivity imaging, ground penetrating radar, and seismic surveys, these methods are time consuming and costly.
This study proposes the use of a geographic information system (GIS) to create a susceptibility map, pinpointing regions in the karst counties of Virginia, in particular, along interstates, most susceptible to future sinkhole development, determined by five factors that have previously been shown to play a role in the acceleration of sinkhole formation in Virginia: bedrock type, proximity to fault lines, drainage class, slope of incised river banks, and minimum soil depth to bedrock. The analysis compares a 1:24,000 scale map of existing sinkholes developed by Virginia Department of Mines Minerals and Energy (DMME) geologist, David Hubbard, with a series of risk maps representing differing combinations of each of the five risk factors to determine which weighted combination is most appropriate to use for a final representative risk map. The layers representing each risk factor are created using publicly available tabular and spatial data taken from the USDA Soil Survey Geographic (SSURGO) Database, the USGS National Map, the USGS Mineral Resources Online Data, and the National Weather Service. The final combination choice will provide an idea of the corresponding factor’s influence on predicting sinkhole risk regions. This investigation identified the following results for karst terrain in Virginia: (1) bedrock type has the most significant impact on predicting sinkhole risk, (2) proximity to faults plays a minimal, yet present, role in determining sinkhole risk, (3) drainage class is the second most influential factor in sinkhole formation behind bedrock type, (4) slope of incised river bank plays no role in the formation of sinkholes in Virginia, and (5) depth of overlying soil to bedrock has an existent yet insignificant effect on sinkhole development. The results display how this new inexpensive and efficient method of predicting sinkhole susceptibility can highlight the influence of natural features that trigger sinkhole and provide a map that can be used by local transportation departments as a general guideline to visualize regions along heavily trafficked interstates most and least at risk for sinkhole collapse. A benefit to this methodology is that the new technique can be adjusted to accommodate for sinkhole susceptibility in regions across the world, by simply adjusting the input risk layers to consider sinkhole risk potential based on the specific geology of a particular region.
MS (Master of Science)
Risk, Sinkholes, Mapping, Karst, GIS
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