Evaluation of groundwater quality for agricultural under different conditions using water quality indices, partial least squares regression models, and GIS approaches

Abstract

Abstract Evaluating grouLindwater quality and associated hydrochemical properties is critical to manage groundwater resources in arid and semiarid environments. The current study examined groundwater quality and appropriateness for agriculture in the alluvial aquifer of Makkah Al-Mukarramah Province, Saudi Arabia, utilizing several irrigation water quality indices (IWQIs) such as irrigation water quality index (IWQI), total dissolved solids (TDS), sodium adsorption ratio (SAR), potential salinity (PS), magnesium hazard (MH), and residual sodium carbonate (RSC) assisted by multivariate modeling and GIS tools. One hundred fourteen groundwater wells were evaluated utilizing several physicochemical parameters, which indicating that the primary cation and anion concentrations were as follows: Na+ > Ca2+ > Mg2+ > K+, and Cl− > SO4 2˗ > HCO3 ˗ > NO3 ˗ > CO3 2˗, respectively, reflecting Ca–HCO3, Na–Cl, and mixed Ca–Mg–Cl–SO4 water facies under the stress of evaporation, saltwater intrusion, and reverse ion exchange processes. The IWQI, TDS, SAR, PS, MH, and RSC across two studied regions had mean values of 64.86, 2028.53, 4.98, 26.18, 38.70, and − 14.77, respectively. For example, the computed IWQI model indicated that approximately 31% of samples fell into the no restriction range, implying that salinity tolerance crops should be avoided, while approximately 33% of samples fell into the low to moderate restriction range, and approximately 36% of samples fell into the high to severe restriction range for irrigation, implying that moderate to high salt sensitivity crops should be irrigated in loose soil with no compacted layers. The partial least squares regression model (PLSR) produced a more accurate assessment of six IWQIs based on values of R 2 and slope. In Val. datasets, the PLSR model generated strong estimates for six IWQIs with R 2 varied from 0.72 to 1.00. There was a good slope value of the linear relationship between measured and predicted for each parameter and the highest slope value (1.00) was shown with RSC. In the PLSR models of six IWQIs, there were no overfitting or underfitting between the measuring, calibrating, and validating datasets. In conclusion, the combination of physicochemical characteristics, WQIs, PLSR, and GIS tools to assess groundwater suitability for irrigation and their regulating variables is beneficial and provides a clear picture of water quality.

Keywords

• Irrigation water quality
• Multivariate modeling
• Groundwater aquifer
• Physicochemical parameters
• Hydrochemical facies
• GIS techniques