Aquifer Vulnerability Assessment for Sustainable Groundwater Management Using DRASTIC

• Published in: Water (Basel) Vol. 9; no. 10; p. 792
• Main Authors: Jang, Won, Engel, Bernard, Harbor, Jon, Theller, Larry
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2017
• Subjects: aquifer vulnerability assessment; Aquifers; binary classifier calibration method; Case studies; Computer applications; Computer simulation; Computer-generated environments; Contamination; DRASTIC; Electric properties; Environmental monitoring; Gene mapping; genetic algorithm; Genetic algorithms; Geographic information systems; Groundwater; Groundwater management; Groundwater pollution; Groundwater quality; Groundwater recharge; Hydrogeology; Identification methods; Indiana; Management; Natural resources; Nitrates; Permeability coefficient; Pollution; Pollution monitoring; Quality assessment; Quality control; Soil contamination; Soil water; Vadose water; Water; Water depth; Water monitoring; Water pollution; Water quality; Water, Underground; Indiana
• DOI: 10.3390/w9100792
• ISSN: 2073-4441

Groundwater management and protection has been facilitated by computational modeling of aquifer vulnerability and monitoring aquifers using groundwater sampling. The DRASTIC (Depth to water, Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone media, and hydraulic Conductivity) model, an overlay and index GIS model, has been used for groundwater quality assessment because it relies on simple, straightforward methods. Aquifer vulnerability mapping identifies areas with high pollution potential that can be areas for priority management and monitoring. The objectives of this study are to demonstrate how aquifer vulnerability assessment can be achieved using DRASTIC with high resolution data. This includes calibrating DRASTIC weights using a binary classifier calibration method with a genetic algorithm (Bi-GA), identifying areas of high potential aquifer vulnerability, and selecting potential aquifer monitoring sites using spatial statistics. The aquifer vulnerability results from DRASTIC using Bi-GA were validated with a well database of observed nitrate concentrations for a study area in Indiana. The DRASTIC results using Bi-GA showed that approximately 42.2% of nitrate detections >2 ppm are within “High” and “Very high” vulnerability areas (representing 3.4% of study area) as simulated by DRASTIC. Moreover, 53.4% of the nitrate detections were within the “Moderate” vulnerability class (26.9% of study area), and only 4.3% of the nitrate detections were within the “Low” vulnerability class (60.1% of study area). Nitrates >2 ppm were not detected at all within the “Very low” vulnerability class (9.6% of area). “High” and “Very high” vulnerability areas should be regarded as priority areas for groundwater monitoring and efforts to prevent groundwater contamination. This case study suggests that the approach may be applicable to other areas as part of efforts to target groundwater management efforts.