Development of an integrated irrigation water quality index (IIWQIndex) model

There are many irrigation water quality indices used to assess water suitability, despite some of their limitations. Hence, it is imperative to develop a water quality index to evaluate the irrigation water more accurately. This study has aimed to emerge an Integrated Irrigation Water Quality Index (IIWQIndex) using the sub-index and aggregated equations. This proposed index model was considered to be improved and updated in four aspects: the verified desirable and permissible value of parameters, maximum hazard class, used a modified rating system of parameters, and diversified parameters were considered. The proposed IIWQIndex model classified irrigation water into five categories, i.e., rejection, poor, moderate, good, and excellent. This model assessed two types of water to justify the model by categorizing the irrigation waters. The calculated results showed that the index values were 75.77 and 36.51, and the water category fell under ‘good’ and ‘rejected’ for the calcite (Ca-HCO3) and sodic (Na-Cl) water, respectively. Besides, this index model satisfactorily evaluated different types of water datasets of eight geographic locations in the world. The study illustrated that the IIWQIndex evaluated values and water categories were rational and exhaustive to predict the suitability of irrigation water.

Multivariate Analysis to Assess the Quality of Irrigation Water in a Semi-Arid Region of North West of Algeria : Case of Ghrib Dam

Dams are critical to agriculture, industry, and the needs of humans and wildlife. This study evaluates the water quality of the Ghrib dam in north west of Algeria, using Irrigation Water Quality Index (IWQI), sodium absorption rates (SAR) and multivariate statistical methods (Clustering and principal component analysis). The study concerns the analysis of physical and chemical parameters (pH, EC, O2, TUR, Ca, Mg, HCO3, Na, K, BOD, DCO, Cl−, PO4, SO3. NH4 et NO3) which were measured at twelve selected points along the dam over 8 periods (dry and wet periods) using standard methods. Irrigation Water Quality Index values in the dam were found to be between 41 and 59, according to classifications for different water uses, values below 60 indicate that water is of poor quality for irrigation and treatment is recommended to make dam water more suitable for irrigation. The results of water analysis in our study area reveal the presence of acute pollution which is certainly caused by direct releases of either industrial or domestic origin, and we note that this pollution remains variable depending on the collection periods. Also, Chloride-calcium and sulfate facies are the most dominant in sampling periods for dam water, resulting in poor water quality for irrigation. In addition, water is, therefore, highly mineralized and is likely to be suitable for irrigation of certain species (cucumbers...) that are well tolerant to salt and on well-drained and leached soils.

New Regression Model for Estimating Irrigation Water Quality Index

The common tool to evaluate the groundwater quality is so-called the Irrigation Water Quality Index (IWQI). In the present study, the IWQI model developed by Meireles is used to assess the quality of groundwater in Karbala - Najaf plateau, Iraq. The quality parameters that collected from groundwater tests of 78 wells in the study area are; EC, Na+1, Mg+2, Ca+2, Cl-1, and HCO3-1. The SPSS software program is employed to develop a non-linear regression model of the IWQI for the study area. The results show that the IWQI values for 90% of groundwater wells fall within the Severe Restriction (SR) category, which means that it’s only suitable for irrigation of high salt tolerance crops. While its remaining 10% of the wells are in the High Restriction (HR) category, and this means it is suitable for irrigation of moderate to high salt tolerance crops. The calibration and validation for the developed model presented that this model has a good estimation of the IWQI values. Due to flexible and unbiased, the current study recommends to use the proposed model to estimate the IWQI.