A GIS-based statistical model for assessing groundwater susceptibility index in shallow aquifer in Central Tunisia (Sidi Bouzid basin)

A comprehensive approach for assessing the shallow aquifer Susceptibility Index (SI) to pollution was proposed by combining the Vulnerability Index (VI) and Quality Index (QI) in Sidi Bouzid basin in Central Tunisia. Hydrochemical investigation showed that nitrate concentrations and total dissolved solid (TDS) values of the Mio-Plio-Quaternary (MPQ) aquifer in the study area were ranging from 14.3 to 111 mg/l and 1218 to 6202 mg/l successively. VI was first estimated using either a generic DRASTIC model or DRASTIC-LU model by adding land use (LU) factor, with preset factor weights; these weights were later adjusted using a single parameter sensitivity analysis (SPSA) or two different statistical methods: canonical analysis of principal coordinates (CAP) and partial least squares (PLS). Compared to the generic models, the weight of the factor impact of vadose zone (I) is equal to 5 remained the highest for all the other models, except for DRASTIC one using a CAP weight adjustment technique where the weight of I is equal to 1. DRASTIC-LU and DRASTIC-LU-CAP models predicted the widest (VILU − min=89, VILU − max=206) and narrowest (VILU − CAP − min=59, VILU − CAP − max=125) VI range, respectively. VI obtained by different weight adjustment techniques significantly correlated with nitrate concentrations with a significant correlation coefficient, higher than 0.50. Based on a model selection criterion, correlation between vulnerability indices and nitrate concentration, DRASTIC-LU-CAP may be recommended as the best model. QI was assessed by simply adding the concentration of some major elements (Cl−,Na+,NO3−\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {Cl}^{-},{Na}^{+},{NO}_3^{-} $$\end{document},SO42−\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {SO}_4^{2-} $$\end{document}) and electric conductivity (EC) transformed into ordinal classes (1–5). Groundwater SI maps for both drinking and irrigation water generated into a GIS-based map showed that a great part of the study area had a high SI to pollution.