Spatiotemporal Variability of Groundwater Quality for Irrigation: A Case Study in Mimoso Alluvial Valley, Semiarid Region of Brazil

Alluvial aquifers are vital for agricultural communities in semiarid regions, where groundwater quality is often constrained by seasonal and spatial salinity variations. This study employed geostatistical methods to analyze the spatial and temporal variability of electrical conductivity (EC) and the sodium adsorption ratio (SAR) and elaborate an indicative quality map in the Mimoso Alluvial Aquifer, Pernambuco, Brazil. Groundwater samples were collected and analyzed for cations, total hardness (TH), and the percentage of sodium (PS). Moreover, the relation between EC and the SAR was used to determine the groundwater quality for irrigation. Cation concentrations followed the order Ca2+ > Mg2+ > Na+ > K+. EC and the SAR exhibited medium to high variability, with spatial dependence ranging from moderate to strong, and presented a strong cross-spatial dependence. Results showed that sequential Gaussian simulation (SGS) provided a more reliable groundwater classification for agricultural purposes compared to kriging methods, enabling a more rigorous evaluation. Based on the strong geostatistical cross correlation between EC and RAS, a novel water quality index was proposed, properly identifying regions with lower groundwater quality. The resulting spatial indicator maps classified groundwater as suitable (64.7%), restricted use (2.08%) and unsuitable (2.38%) for irrigation. The groundwater quality maps indicated that groundwater was mostly suitable for agriculture, except in silty areas, also corresponding to regions with low hydraulic conductivity at the saturated zone. Soil texture, rainfall, and water extraction significantly influenced spatial and temporal patterns of groundwater quality. Such correlations allow a better understanding of the groundwater quality in alluvial valleys, being highly relevant for water resources management in semiarid areas.