An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

Abstract

Abstract Groundwater contamination risk mapping is one essential measure in groundwater management and quality control. The purpose of the present study is to address this mapping by means of a novel framework, which is more suitable for arid regions than other methods developed in previous work. Specifically, we integrate machine learning tools, interpolation and process-based models with a modified version of DRASTIC-AHP to evaluate groundwater vulnerability to nitrate contamination and to map this contamination in Jiroft plain, Iran. The DRASTIC model provides a tool for evaluating aquifer vulnerability by using seven parameters related to the hydrogeological setting (Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity), while the criteria ratings and weights of these parameters are evaluated by means of an Analytic Hierarchy Process (AHP). However, to obtain the risk map, the results about groundwater vulnerability are combined here with a contamination hazard map, which we estimate by applying ensemble modeling based, in part, on the occurrence probability predicted from Generalized Linear Model (GLM), Flexible Discriminant Analysis (FDA), and Support Vector Machine (SVM). Our integrated modeling framework provides an assessment of both regional patterns of groundwater contamination and an estimate of the impacts of the contamination based on socio-environmental variables, and is particularly suitable for applications based on limited amount of available data. The groundwater contamination risk map obtained from our case study shows that the central and southern regions of the Jiroft plain display high and very high contamination risk, which is associated with high production rate of urban waste in residential lands and an overuse of nitrogen fertilizers in agricultural lands. Therefore, our work is providing new modeling insights for the future assessment of groundwater contamination, with potential impacts for the management and control of water resources in arid and semi-arid environments.