A framework for the assessment of qualitative and quantitative sustainable development of groundwater system

The groundwater mitigation issues and moving toward sustainable quality and quantity conditions of the aquifers are primary water resources management objectives in arid and semi-arid regions. A framework is required to assess groundwater restoration strategies' impacts on the aquifer's qualitative and quantitative sustainability. In this study, two new indices are introduced to evaluate the state of aquifer sustainability. The aquifer quantity restoration index was determined by using the results of the MODFLOW, considering the differences between the simulated and the desired water levels and the storage volumes for restoration scenarios, and the percentage of obtaining the desired level at the end of the simulated period. The aquifer quality restoration index was defined based on the DRASTIC-LU aquifer vulnerability and the percentage of the desired total dissolved solids (TDS) goals at the end of the simulated period using the Bayesian networks model. Six restoration groundwater scenarios in 10 years were assessed to examine both proposed indices. Scenarios are comprised of a one to 3.5% reduction in agriculture groundwater withdrawal. The aquifer quantitative restoration index resulting from the MODFLOW simulation indicated that 2.5% of the decrease in groundwater withdrawal satisfied the aquifer's sustainability. Furthermore, the qualitative sustainability index showed that the reduction in groundwater withdrawal has enormous impacts on the aquifer's central area and marginal parts, where the groundwater is dramatically over-exploited. Indices proposed can be utilized for the assessment of achieving sustainable management in groundwater by restoration scenarios.

Automated summary

The study introduces two new indices to evaluate aquifer sustainability, focusing on assessing groundwater restoration strategies' impacts on the aquifer's qualitative and quantitative sustainability. By using MODFLOW simulations and Bayesian networks models, the indices provide valuable insights into achieving sustainable management in groundwater restoration scenarios through reduction in groundwater withdrawal and other strategies.