Publication Date


Advisor(s) - Committee Chair

Dr. Jun Yan (Director), Dr. Chris Groves, Dr. David Keeling

Degree Program

Department of Geography and Geology

Degree Type

Master of Science


The management of stormwater runoff is a particular challenge for communities in karst regions. Most guidelines for compliance with regulations for stormwater monitoring and mapping pertain to non-karst environments. It can be argued that effective stormwater management is even more essential to karst regions because stormwater receives little or no natural filtration as it is transferred through conduits in the subsurface and the buildup of pollutants underneath can be detrimental to community and environmental health if not effectively mitigated. Because of the limited resources available to determine how stormwater runoff carries potential pollutants across the surface before being transferred to the karst subsurface and then reentering back on the surface across the landscape, this study aims to use geographic information systems (GIS) to investigate this problem. The objectives of this study are twofold. The first objective is to understand the transport mechanisms for stormwater runoff and how the movement through karst systems differs from non-karst systems, especially in regards to the surface and subsurface interactions. The second objective is to develop a general procedure for predicting stormwater runoff pathways in karst regions using GIS technologies and spatial analysis techniques – including identifying which data and techniques are essential to analyze surface and subsurface processes - to improve stormwater monitoring effectiveness.

The premise of this study is broken down into a conceptual model with three significant components: Surface Input (stormwater runoff on surface), Subsurface Transport (stormwater transport through subsurface), and Output to Surface (output of stormwater to the surface via springs). The first component utilizes Hydrological Analysis and Network Analysis techniques to determine stormwater runoff pathways from potential point-source pollutant sites across surface to injection points where runoff enters subsurface. The second component uses Spatial Interpolation Techniques and Hydrological Analysis to predict subsurface accumulation areas that collect runoff from injection points and subsurface conduit pathways to output locations. The third and final component examines the output of the runoff back to the surface and identifies the locations where stormwater runoff can be sampled.

The analyses of the Surface Input component proved to be effective in predicting the behavior of stormwater runoff between pollutant sites and their corresponding injection points. The analyses of the Subsurface Transport captured the overall patterns in the inferred dye tracing pathways that were used as the control dataset. The Output to Surface established the linkages among RCRA sites, their corresponding injection points and ultimately their output springs. These findings are very useful in developing informed stormwater sampling strategies and plans. In future investigations, these results could be verified with stormwater sampling and additional dye tracings and can be improved in two ways: more complete datasets of all stormwater features in the area – especially springs and drywells, and a more extensive and equally distributed dataset for groundwater depths across the study area to create a more accurate interpolated potentiometric surface.


Earth Sciences | Environmental Sciences | Geology | Sustainability