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Comprehensive approach integrating remote sensing, machine learning, and physicochemical parameters to detect hydrodynamic conditions and groundwater quality deterioration in non-rechargeable aquifer systems

Affiliations
  • 1Institute of Environmental Management, Faculty of Earth Science, University of Miskolc, 3515, Miskolc, Egyetemváros, Hungary.
  • 2Geology Department, Faculty of Science, Beni-Suef University, Beni-Suef, 65211, Egypt.
  • 3Department of Mineralogy and Geology, University of Debrecen, 4032 Debrecen, Hungary.
  • 4Department of Geology, Tanta University, 31527, Tanta, Egypt.
  • 5Division of Water Resources and Arid Land, Department of Hydrogeochemistry, Desert Research Center, Cairo, Egypt.
  • 6Center for Water Supply Studies, Texas A&M University-Corpus Christi, Corpus Christi, TX, 78412, USA.
  • 7Faculty of Earth Science, Beni-Suef University, 62511, Egypt.
  • 8Department of Earth and Ocean Sciences, University of North Carolina Wilmington, 601 South College Road, Wilmington, NC, 28403-5944, USA.
  • 9Geology Department, Faculty of Science, Tanta University, Tanta, 31527, Egypt.
  • 10Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia.

Published on:

July 18, 2024

Abstract

The current study integrates remote sensing, machine learning, and physicochemical parameters to detect hydrodynamic conditions and groundwater quality deterioration in non-rechargeable aquifer systems. Fifty-two water samples were collected from all water resources in Siwa Oasis and analyzed for physical (pH, T°C, EC, and TDS) chemical (SO , HCO , NO , Cl, CO , SiO, Mg, Na, Ca, and K), and trace metals (AL, Fe, Sr, Ba, B, and Mn). A digital elevation model supported by machine learning was used to predict the change in the land cover (surface lake area, soil salinity, and water logging) and its effect on water quality deterioration. The groundwater circulation and interaction between the deep aquifer (NSSA) and shallow aquifer (TCA) were detected from the pressure-depth profile of 27 production wells penetrating NSSA. The chemical facies evolution in the aquifer systems were (Ca-Mg-HCO) in the first stage (freshwater of NSSA) and changed to (Na-Cl) type in the last stage (brackish water of TCA and springs). Support vector machine successfully predicted the rapid increase of the hypersaline lake area from 22.6 km to 60.6 km within 30 years, which deteriorated a large part of the cultivated land, reflecting the environmental risk of over-extraction of water for irrigation of agricultural land by flooding technique and lack of suitable drainage network. The waterlogging in the study was due to a reduction in the infiltration rate (low permeability) of the soil and quaternary aquifer. The cause of this issue could be a complete saturation of agricultural water with chrysotile, calcite, talc, dolomite, gibbsite, chlorite, Ca-montmorillonite, illite, hematite, kaolinite and K-mica (saturation index >1), giving the chance of these minerals to precipitate in the pore spaces of the soil and decrease the infiltration rate. The NSSA is appropriate for irrigation, whereas TCA is inappropriate due to potential salinity and magnesium risks. The best way to manage water resources in Siwa Oasis could be to use underground drip irrigation and combine water with TCA and NSSA.

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