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The water resources of the arid region are chemically governed by several factors, predominantly by evaporation, apart from other natural and anthropogenically driven factors. The current review examines the applications of geochemical models, software tools, and operator-splitting techniques, as well as challenges and limitations in groundwater studies of arid regions. There are two broad categories of approach: process-based models for assessing geochemical reactions or interactive advection/solute-transport models. The output of these modes can be used with Numerical transport models, geographic information systems, machine learning and artificial intelligence methods for predictive assessment, statistical and geostatistical methods. The review indicates that PHREEQC is the widely used software for forward and inverse modeling and speciation studies, while NETPATH is used for mass balance modeling along the flow path, and MODFLOW-MT3DMS dominates the coupled flow transport studies. Machine learning platforms-primarily MATLAB, Python, R, with ANN-based models, hybrid optimisation variants have been used recently for predicting water quality index, outperforming non-linear regressions and geostatistical methods. The chief application of these techniques addresses thermodynamic nature (dissolution/precipitation), causes and process of salanisation, contamination studies, impact of CO2 sequestration on groundwater quality and water quality assessment. The key challenges include data scarcity and monitoring limitations inherent to arid regions. Further, mass balance calculations are complicated by extreme evaporation rates, hydrogeological complications, which include a multilayered aquifer of different types, inter-aquifer leakage, assumptions for certain kinetic reactions and redox reactions. The application of ML with a small data set results in overfitting risks. Future directions of research to improve uncertainty quantification, adopting machine learning processes, enhancing the coupled flow and reactive transport models, and better representation of heterogeneity and kinetic processes.

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Review of Geochemical Modelling Approaches in Groundwater of Arid Regions: Software, Applications, Challenges, and Recommendations

  • 会议论文

  • 发表日期：

    2026年

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  • Chidambaram Sabarathinam,
  • Amjad Al-Rashidi,
  • Samayamanthula Dhanu Radha,
  • Adnan Akber,
  • Khaled Hadi,
  • Yogeesha Jayaramu,
  • Tariq Rashid

摘要

The water resources of the arid region are chemically governed by several factors, predominantly by evaporation, apart from other natural and anthropogenically driven factors. The current review examines the applications of geochemical models, software tools, and operator-splitting techniques, as well as challenges and limitations in groundwater studies of arid regions. There are two broad categories of approach: process-based models for assessing geochemical reactions or interactive advection/solute-transport models. The output of these modes can be used with Numerical transport models, geographic information systems, machine learning and artificial intelligence methods for predictive assessment, statistical and geostatistical methods. The review indicates that PHREEQC is the widely used software for forward and inverse modeling and speciation studies, while NETPATH is used for mass balance modeling along the flow path, and MODFLOW-MT3DMS dominates the coupled flow transport studies. Machine learning platforms-primarily MATLAB, Python, R, with ANN-based models, hybrid optimisation variants have been used recently for predicting water quality index, outperforming non-linear regressions and geostatistical methods. The chief application of these techniques addresses thermodynamic nature (dissolution/precipitation), causes and process of salanisation, contamination studies, impact of CO2 sequestration on groundwater quality and water quality assessment. The key challenges include data scarcity and monitoring limitations inherent to arid regions. Further, mass balance calculations are complicated by extreme evaporation rates, hydrogeological complications, which include a multilayered aquifer of different types, inter-aquifer leakage, assumptions for certain kinetic reactions and redox reactions. The application of ML with a small data set results in overfitting risks. Future directions of research to improve uncertainty quantification, adopting machine learning processes, enhancing the coupled flow and reactive transport models, and better representation of heterogeneity and kinetic processes.