From Space to Subsurface: A Groundwater Story in Southwest Georgia

EarthzineAssessing Human Risk, DEVELOP 2015 Fall VPS, DEVELOP Virtual Poster Session, Original

This is a part of the 2015 Fall VPS. For more VPS articles, click here

GRACE-based groundwater storage time series from April 2002 to December 2009. Columns represent months and rows represent years in sequence for available data. Image Credit: Georgia Water Resources Team

GRACE-based groundwater storage time series from April 2002 to December 2009. Columns represent months and rows represent years in sequence for available data. Image Credit: Georgia Water Resources Team

Category: Assessing Human Risk

Project Team: Georgia Water Resources

Team Location: University of Georgia ‰ÛÒ Athens, Georgia

Authors:

Wenjing Xu

Jason Reynolds

Linli Zhu

Mingshu Wang

Doori Oh

Mentors/Advisors:

Dr. Adam Milewski (Department of Geology, University of Georgia)

Matthew Cahalan (Department of Geology, University of Georgia)

Abstract:

Groundwater from karst aquifers is the primary water source for domestic, industrial, and agricultural use in southwest Georgia. However, these aquifers are highly vulnerable to pollution due to their high geological conductivity. Groundwater storage and contamination risk monitoring can improve water consumption and protection management decisions. This project used an applied methodology that incorporated remote sensing data for groundwater monitoring. Specifically, the Gravity Recovery and Climate Experiment (GRACE) was used to estimate groundwater depth change from 2002 to 2009, which was correlated with sinkhole inventory data during this time period. The DRASTIC model was combined with sinkhole susceptibility maps generated by the Summer 2015 DEVELOP Georgia Disasters team to create corresponding groundwater contamination vulnerability maps. Building upon the traditional DRASTIC model, sinkhole susceptibility was incorporated as a multiplier term to calculate a final, modified DRASTIC index (DRASTICS). This augmented DRASTIC model will provide end-users working in karst aquifer systems a tool designed to enhance decision-making processes associated with managing groundwater contamination risks.

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