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Matthew Cahalan Georgia Water Resources Conference Presentation

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This is the poster I presented at the 2015 Georgia Water Resources Conference. It focuses on my M.S. thesis research that seeks to answer this fundamental question: "why do sinkholes form where they do?". This question was answered using an improved remote sensing sinkhole mapping procedure, integration of many datasets (i.e., hydrologic, anthropogenic, geologic, geomorphologic, and hydrogeologic), and spatial statistics (i.e., ordinary least squares and geographically weighted regression). This poster / my presentation was voted as one of the top 3 posters at the conference.

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Matthew Cahalan Georgia Water Resources Conference Presentation

  1. 1. An Integrated Approach for Subsidence Monitoring and Sinkhole Formation in the Karst Terrain of Dougherty County, Georgia Matthew Cahalan1 and Adam Milewski1 1 Department of Geology, University of Georgia, Athens, GA, 30602, USA Abstract: The Upper Floridan Aquifer (UFA) is an important water source for agricultural and municipal purposes in Dougherty County. However, prolonged water demand, dissolution of the carbonate aquifer (Ocala Limestone), and short and long-term fluctuations in precipitation, surface water discharge, and groundwater levels have placed many areas at risk for sinkhole formation and broader scale land deformation (i.e., subsidence) in the covered karst terrain. Evaluating the causes of sinkhole formation and subsidence is essential for reducing risk within karst settings. In this study, we evaluate the controlling factors on subsidence feature formation within a 71 mi2 area of southern Dougherty County, Georgia, by applying a sinkhole delineation and regression analysis procedure. ArcGIS 10.1 software was used to process digital elevation models (DEMs) from 1999 (10m) and 2011 (1m and resampled 10m) to locate subsidence features that formed between 1999 and 2011. Different types of subsidence features were delineated based on area values from the high spatial resolution LiDAR DEM (1m). Results show that small-scale subsidence features (i.e., cover collapse sinkholes less than 100 m2) form close to streams in areas with relatively thin overburden thickness. This is most likely due to flooding and subsequent liquefaction processes transporting overburden material into subsurface voids. Our conceptual model of subsidence feature controlling factors, or independent variables, included geologic, topographic, hydrologic, anthropogenic, and hydrogeologic variables. Regression analysis allows you to examine the influences of various factors driving observed spatial patterns. Ordinary least squares (OLS) and geographically weighted regression (GWR) statistical models were applied to the recently formed subsidence features between 1999 and 2011 to determine the factors that are most influential in subsidence feature development. The OLS global regression tool was applied to examine if we had a properly specified model with limited biases. OLS results showed that distance to lineaments, elevation, aspect, distance to roads, and overburden thickness were statistically significant (p-value < 0.05), or the most influential controlling factors in subsidence feature density. The 11 included independent variables did not show any redundancy (Variance Inflation Factors < 7.5). Overall, the OLS model explained 42% of the spatial variation of subsidence feature density (R2 = 0.42). GWR models, a local form of regression that illustrates how relationships vary across space by calculating a regression equation for each feature in the study area, were applied. GWR analysis resulted in a R2 value of 0.70 when 9 of the independent variables were included in the model (slope, aspect, land use, overburden thickness, distance to lineaments, distance to roads, distance to surface water features, distance to rivers, and distance to wetlands). . Subsidence Feature Delineation and Regression Procedure OLS Regression Results Area of InvestigationObjectives: 1. Apply statistical regression models (OLS and GWR) to evaluate the influences of topographic, hydrologic, geologic, hydrogeologic, and anthropogenic variables on subsidence feature formation. 2. Locate recently formed (1999 – 2011) subsidence features using a semi-automated mapping procedure. Acknowledgements • Randy Weathersby – Albany GIS • UGA Provost Fund for financial support of the project Subsidence Feature Density Maps Fig. 1 – Outlined study area location in southern Dougherty County, GA (71 mi2). Fig. 4 – 1999 (10m). Fig. 5 – 2011 (10m). Fig. 6 – Subsidence features that formed between 1999 and 2011. Subsidence Feature Types Fig. 3 - Processing procedure for the 1999 and 2011 digital elevation models (DEMs), which were used to locate subsidence features and analyze the factors controlling their development in the covered karst setting of southern Dougherty County, Georgia. Fig. 9 – Lidar-derived map of subsidence features within study area. Cover subsidence features are > 100 m2, and cover collapse features are < 100 m2. Conclusions The proposed processing procedure was successful at delineating sinkholes temporally and attributing their formation to various factors spatially. Regression results showed that the increase in subsidence features between 1999 and 2011 was primarily influenced by geologic (i.e., distance to lineaments and overburden thickness), topographic (i.e., elevation and aspect), and anthropogenic (i.e., distance to roads) factors. The cover-collapse features’ distributions along streams is evidence of liquefaction processes occurring in areas with shallow overburden thickness being a primary control on their formation, while cover subsidence features form in areas with greater overburden thickness at higher elevations. Data acquisition Digital elevation model Fill in sinks to spill level Digital elevation models, geology, land use/land cover, soils, hydrography, fractures, overburden thickness Climatic, stream discharge, well levels, roads, urban structures, aerial imagery Generate a “fill- difference” raster Validate with aerial imagery and previous subsidence feature maps Attribute all variables to subsidence feature points Perform ordinary least squares and geographically weighted regression analysis Begin regression procedure on mapped subsidence features Create layers for each independent variable 1. Land use/land cover 2. Overburden thickness 3. Upper Floridan Aquifer fluctuations 4. Distance to fractures 5. Distance to streams 6. Distance to roads 7. Distance to lakes and ponds 8. Distance to wetlands 9. Elevation 10. Slope 11. Aspect Apply threshold filters based on depth, geometry, area, and intersection with human structures Convert to polygon layer Calculate subsidence feature point density (dependent variable) Subsidence Feature Development Fig. 2 – Subsidence features can be triggered by flooding events (i.e., liquefaction) and drought or groundwater withdrawal (i.e., loss of hydrostatic support). Dobecki and Upchurch, 2006 Category Description Source Method OLS p-value Elevation NED 10m DEM Raster to polygon 0.016007* Topography Aspect NED 10m DEM ArcMap Aspect tool 0.049568* Slope NED 10m DEM ArcMap Slope tool 0.287726 Distance to ponds Georgia GIS Clearinghouse ArcMap Generate Near Table tool 0.560277 Hydrology Distance to streams National Hydrography Dataset ArcMap Generate Near Table tool 0.153236 Distance to wetlands National Wetland Inventory ArcMap Generate Near Table tool 0.235599 Distance to lineaments Brook and Allison (1986) ArcMap Generate Near Table tool 0.000403* Overburden thickness Borehole data and USGS cross-sections (48 wells) Empirical Bayesian Kriging interpolation (RMS = 2.42) 0.000000* Hydrogeology Upper Floridan Aquifer fluctuations USGS well data (12 wells) Kriging interpolation (RMS = 3.22) 0.211642 Land use National Land Cover Dataset Raster to polygon 0.339056 Distance to roads Georgia GIS Clearinghouse ArcMap Generate Near Table tool 0.001548* Fig. 8 – Independent variable descriptions. Statistically significant variables (p-values < 0.05) are in bold. Independent Variables Fig. 7 – Maps of independent variables used in the ordinary least squares and geographically weighted regression models. Geology Anthropogenic G e o l o g y H y d r o l o g y A n t h r o H y d r o g e o l o g y Density (features/km2)

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