Modeling the Rate of Change in Irrigation Water Quality Between the Dry Season and Wet Season in Coastal Wells in the Municipality of Guasave, Sinaloa, Mexico

The goal of this study is to model the rate of change between the dry season and wet season in groundwater quality for irrigation in coastal wells in the municipality of Guasave, Sinaloa, Mexico. In 58 well-water samples (29 sampled in the dry season and 29 in the wet season), the following parameters were measured: pH, electrical conductivity (E.C.), total dissolved solids, temperature, salinity, static water level, Na+, Ca2+, Mg2+, K+, Cl−, SO42−, CO32−, and HCO3−. The original sodium adsorption ratio (SARor), corrected SARco, and adjusted SARad were calculated. Using Pearson (Pr) and Spearman coefficients, ordinary and repeated measures correlations were calculated for the dry season and the wet season. To identify differences between seasons, paired tests and Piper diagrams were used. Values of quality measures were modeled using multiple linear regressions (partial least squares). For the quality change rate, three predictive models were obtained. These three models presented the following Pearson coefficient values: 0.311 (E.C. vs. SARor), 0.266 (E.C. vs. SARco), and 0.318 (E.C. vs. SARad). Observed and predicted quality were numerically classified based on the E.C. vs. SARor, SARco, and SARad. Although the models did not register efficient validations, this methodology can serve as a basis for applying more powerful non-linear predictive methods in future research. The methodology can be applied to prevent soil contamination caused by irrigation with saline and sodic water, potentially boosting agricultural yields in the agricultural heart of Mexico.