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.

We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the “model-free” variational analysis (VA)-based image enhancement approach and the “model-based” descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations.