Groundwater occurrence in semi-arid regions is variable in space and time due to climate patterns, terrain features, and aquifer properties. Thus, accurate delineation of Groundwater Potential Zones (GWPZs) is essential for sustainable water resources management in these environments. The present research aims to delineate and assess GWPZs in a semi-arid basin of San Luis Potosi (SLP), Mexico, through the integration of Remote Sensing (RS), Geographic Information System (GIS), and Analytic Hierarchy Process (AHP). Seven thematic layers (geology, lineament density, land use and land cover, topographic wetness index (TWI), rainfall, drainage density, and slope) were generated in raster format. After the AHP procedure and rank assignment, the thematic layers were integrated using the raster calculator to obtain the GWPZs map. The results indicated that 68.21% of the area is classified as low groundwater potential, whereas 26.30% is classified as moderate. Validation was done by assessing the water residence time data from 15 wells distributed in the study area. Furthermore, the Receiver Operating Characteristics (ROC) curve was obtained, indicating a satisfactory accuracy prediction (AUC = 0.677). This study provides valuable information for decision-makers regarding the conservation and sustainable management of groundwater resources.

Groundwater quality and availability are essential for human consumption and social and economic activities in arid and semiarid regions. Many developing countries use wastewater for irrigation, which has in most cases led to groundwater pollution. The Mezquital Valley, a semiarid region in central Mexico, is the largest agricultural irrigation region in the world, and it has relied on wastewater from Mexico City for over 100 years. Limited research has been conducted on the impact of irrigation practices on groundwater quality on the Mezquital Valley. In this study, 31 drinking water wells were sampled. Groundwater quality was determined using the water quality index (WQI) for drinking purposes. The hydrogeochemical process and the spatial variability of groundwater quality were analyzed using principal component analysis (PCA) and K-means clustering multivariate geostatistical tools. This study highlights the value of combining various approaches, such as multivariate geostatistical methods and WQI, for the identification of hydrogeochemical processes in the evolution of groundwater in a wastewater irrigated region. The PCA results revealed that salinization and pollution (wastewater irrigation and fertilizers) followed by geogenic sources (dissolution of carbonates) have a significant effect on groundwater quality. Groundwater quality evolution was grouped into cluster 1 and cluster 2, which were classified as unsuitable (low quality) and suitable (acceptable quality) for drinking purposes, respectively. Cluster 1 is located in wastewater irrigated zones, urban areas, and the surroundings of the Tula River. Cluster 2 locations are found in recharge zones, rural settlements, and seasonal agricultural fields. The results of this study strongly suggest that water management strategies that include a groundwater monitoring plan, as well as research-based wastewater irrigation regulations, in the Mezquital Valley are warranted.

Identifying contributing factors of potential recharge zones is essential for sustainable groundwater resources management in arid regions. In this study, a data matrix with 66 observations of climatic, hydrogeological, morphological, and land use variables was analyzed. The dominant factors in groundwater recharge process and potential recharge zones were evaluated using K-means clustering, principal component analysis (PCA), and geostatistical analysis. The study highlights the importance of multivariate methods coupled with geospatial analysis to identify the main factors contributing to recharge processes and delineate potential groundwater recharge areas. Potential recharge zones were defined into cluster 1 and cluster 3; these were classified as low potential for recharge. Cluster 2 was classified with high potential for groundwater recharge. Cluster 1 is located on a flat land surface with nearby faults and it is mostly composed of ignimbrites and volcanic rocks of low hydraulic conductivity (K). Cluster 2 is located on a flat lowland agricultural area, and it is mainly composed of alluvium that contributes to a higher hydraulic conductivity. Cluster 3 is located on steep slopes with nearby faults and is formed of rhyolite and ignimbrite with interbedded layers of volcanic rocks of low hydraulic conductivity. PCA disclosed that groundwater recharge processes are controlled by geology, K, temperature, precipitation, potential evapotranspiration (PET), humidity, and land use. Infiltration processes are restricted by low hydraulic conductivity, as well as ignimbrites and volcanic rocks of low porosity. This study demonstrates that given the climatic and geological conditions found in the Sierra de San Miguelito Volcanic Complex (SSMVC), this region is not working optimally as a water recharge zone towards the deep aquifer of the San Luis Potosí Valley (SLPV). This methodology will be useful for water resource managers to develop strategies to identify and define priority recharge areas with greater certainty.

High cost, long-range communication, and anomaly detection issues are associated with IoT systems in water quality monitoring. Therefore, this work proposes a prototype for a water quality monitoring system (IoT-WQMS) based on IoT technologies, which include in the system architecture a LoRa repeater and an anomaly detection algorithm. The system performs the data collection, data storage, anomaly detection, and alarm sending remotely and in real-time for the information to be captured by the multisensor node. The LoRa repeater allowed the spatial coverage of the LoRa communication to extend, making it possible to reach a place where originally there was no coverage with a single LoRa transmitter due to topography and line of sight. The prototype performed well in terms of packet loss rate, transmission time, and sensitivity, extending the long-range wireless communication distance. Indoor multinode testing validation for 29 days of the mean absolute error for average relative errors of water temperature, pH, turbidity, and total dissolved solids (TDS) were 0.65%, 0.30%, and 14.33%, respectively. The anomaly detector identified all erroneous data events due to node sensor recalibration and water recirculation pump failures. The IoT-WQMS increased the reliability of monitoring through the timely identification of any sensor malfunctions and extended the LoRa signal range, which are relevant features in the scope of in situ and real-time water quality monitoring.