Assessing the sustainability of groundwater quality for irrigation purposes using a fuzzy logic approach

Abstract

Deterioration of water quality poses significant threats to various aspects of life, particularly affecting agricultural irrigation, the primary source of food production for human consumption. In Iran, unregulated groundwater extraction for agriculture has resulted in a decline in groundwater quality, exemplified by a reduction in permitted wells for water exploitation in the Houmand-Absard aquifer from 47 to 20 over 18 years. Current water quality assessments for agricultural use often employ indicators focusing on specific ions, neglecting a comprehensive evaluation. This study introduces the Fuzzy Groundwater Quality Index (FGWQI), utilizing a fuzzy inference system model to appraise groundwater quality in the Houmand-Absard aquifer, specifically for irrigation in Iran. The FGWQI amalgamates five agriculture-oriented quality indicators: Sodium Adsorption Ratio (SAR), Sodium Percentage (Na%), Magnesium Hazard Ratio (MHR), Kelly's Index (KI), and Potential Salinity (PS). Comparative analyses between FGWQI and the widely used MHR were conducted on water samples collected from twenty stations during the 2021 seasons. Laboratory analyses of the samples determined parameters including Sodium, Calcium, Magnesium, Chloride, and Sulfate. The FGWQI model outperformed traditional indicators, notably MHR, demonstrating more realistic assessments. Despite MHR deeming water quality unsuitable in 47% of cases where FGWQI surpassed 60, FGWQI indicated permissible results (30–70), suggesting that, with proper groundwater management, a greater water resource allocation to agriculture is feasible. By adopting FGWQI over MHR, water wells deemed suitable for irrigation won't be decommissioned, offering farmers increased flexibility in groundwater utilization in the case study region.

Keywords

• Groundwater quality index
• Sustainability assessment
• Composite indicator
• Fuzzy logic
• MHR