Spatiotemporal Assessment of Groundwater Quality in the Oum Rbia Watershed Using GIS-Pro and Water Quality Indices

Résumé

Groundwater analysis across the Oum Rbia watershed is currently hampered by technical constraints and high costs.This research aimed to produce comprehensive groundwater quality maps throughout the basin aquifers by integrating the water quality index (WQI) and microbiological quality index (MQI) with GIS-Pro for a spatiotemporal assessment of water quality.Twenty physicochemical parameters, including pH, temperature, conductivity, total dissolved solids, permanganate index, ammonium (NH₄⁺), major cations (Na⁺, K⁺, Ca²⁺, Mg²⁺, Mn²⁺), major anions (Cl⁻, HCO₃⁻, NO₂⁻, NO₃⁻, CO₃²⁻, SO₄²⁻), total hardness (TH), total alkalinity (TAC), and total iron (FeT) concentration were analyzed.Additionally, the microbiological parameters, such as the fecal streptococci, fecal coliforms, and total coliforms were investigated.Fieldwork conducted over twelve campaigns during the 2021 and 2022 seasons involved sample collection from fifty-four locations across the six aquifers of the watershed.The comprehensive database facilitated the calculation of both MQI and WQI.Kriging interpolation was utilized to create spatial estimates of these indices beyond the sampling points, enabling the generation of maps that visualize water quality across the study area.WQI indicated that groundwater in most of the studied basin is of excellent quality, though water quality deteriorates in the areas receiving wastewater discharge from urban, industrial, and agricultural activities.The MQI results revealed significant pathogenic germ contamination across a substantial portion of the watershed, intensifying during the summer due to such factors as temperature, river flow, human activities, and seasonal pollution sources.These maps enhance the understanding of water table information for non-experts as well as aid decision-makers in identifying critical areas and developing effective management strategies.However, complexities in water quality and training data influence the accuracy of ArcGIS-Pro predictions, potentially overlooking key factors if the data is insufficient.

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