In this research, vegetation indices (VIs) were analyzed as indicators of the spatio-temporal variation of vegetation in a semi-arid region. For a better understanding of this dynamic, interactions between vegetation and climate should be studied more widely. To this end, the following methodology was proposed: (1) acquire the NDVI, EVI, SAVI, MSAVI, and NDMI by classification of vegetation and land cover categories in a monthly period from 2014 to 2018; (2) perform a geostatistical analysis of rainfall and temperature; and (3) assess the application of ordinary and uncertainty least squares linear regression models to experimental data from the response of vegetation indices to climatic variables through the BiDASys (bivariate data analysis system) program. The proposed methodology was tested in a semi-arid region of Zacatecas, Mexico. It was found that besides the high values in the indices that indicate good health, the climatic variables that have an impact on the study area should be considered given the close relationship with the vegetation. A better correlation of the NDMI and EVI with rainfall and temperature was found, and similarly, the relationship between VIs and climatic factors showed a general time lag effect. This methodology can be considered in management and conservation plans of natural ecosystems, in the context of climate change and sustainable development policies.

The presence of arsenic in groundwater constitutes a hazard for the environment and human health, and the determination of its source has become a global challenge, which can be approached by defining the natural background levels (NBL) in conjunction with the indicator kriging method, with the aim of delineating anthropogenically contaminated areas. However, having a unique value of NBL for large areas can generate interpretation errors. This research integrates the determination of the flow systems present in the Calera Aquifer, and the definition of the natural background levels in each flow system by making estimation maps in ArcGIS using two databases, 10 years apart, to evaluate the spatio-temporal variation of arsenic in groundwater. The results indicate a notable increase in the probability of exceeding the arsenic NBL, mainly in the intermediate flow, which may be due to movement resulting from mining activities as well as a mixture of regional and intermediate flows caused by the extraction of water for agriculture and drinking water supplies. The presented values exceed the maximum limits allowed for human consumption, as stated by the World Health Organization.

Advances in convolutional neural networks (CNNs) provide novel and alternative solutions for water quality management. This paper evaluates state-of-the-art optimization strategies available in PyTorch to date using AlexNet, a simple yet powerful CNN model. We assessed twelve optimization algorithms: Adadelta, Adagrad, Adam, AdamW, Adamax, ASGD, LBFGS, NAdam, RAdam, RMSprop, Rprop, and SGD under default conditions. The AlexNet model, pre-trained and coupled with a Multiple Linear Regression (MLR) model, was used to estimate the quantity black pixels (suspended solids) randomly distributed on a white background image, representing total suspended solids in liquid samples. Simulated images were used instead of real samples to maintain a controlled environment and eliminate variables that could introduce noise and optical aberrations, ensuring a more precise evaluation of the optimization algorithms. The performance of the CNN was evaluated using the accuracy, precision, recall, specificity, and F_Score metrics. Meanwhile, MLR was evaluated with the coefficient of determination ($R^2$), mean absolute and mean square errors. The results indicate that the top five optimizers are Adagrad, Rprop, Adamax, SGD, and ASGD, with accuracy rates of 100% for each optimizer, and $R^2$ values of 0.996, 0.959, 0.971, 0.966, and 0.966, respectively. Instead, the three worst performing optimizers were Adam, AdamW, and NAdam with accuracy rates of 22.2%, 11.1% and 11.1%, and $R^2$ values of 0.000, 0.148, and 0.000, respectively. These findings demonstrate the significant impact of optimization algorithms on CNN performance and provide valuable insights for selecting suitable optimizers to water quality assessment, filling existing gaps in the literature. This motivates further research to test the best optimizer models using real data to validate the findings and enhance their practical applicability, explaining how the optimizers can be used with real data.

One-third of the global population depends on groundwater for drinking, which is an even larger proportion for arid regions. The integration of isotopic and geophysical applications has been very useful in understanding the process of groundwater recharge. The aim of this study is to define a conceptual model that describes groundwater functions within an aquifer located in a semi-arid region by identifying recharge patterns based on the isotopic characteristics of: Rainfall, surface water, shallow and deep groundwater, and incorporating regional geophysical data. We demonstrated that rainfall was affected by sub-cloud evaporation and altitude. Shallow and deep modern groundwater samples were clustered and exhibited similar evolution from rainfall. However, different groups recharged from different precipitation sources compared to the local one. In the current study, we analyzed the isotopic evolution of deep groundwater over a 10-year period, which was mainly affected by the incorporation of different flows with different isotopic signatures and the hydrodynamics of the area. We performed two geoelectrical sections in the study area to improve the understanding of the hydrogeological setting and water movement patterns. The new conceptual model should help stakeholders in the context of water management policies for the study area.

The estimation of the hydraulic parameters of an aquifer such as the hydraulic conductivity is somehow complicated due to its heterogeneity, on the other hand field and laboratory tests are both time consuming and costly. The use of geostatistical-based techniques for data assimilation could represent an alternative tool that allows the use of space-time aquifer behaviour to characterize hydraulic conductivity heterogeneity. In this paper, a spatiotemporal bivariate methodology was implemented combining historical hydraulic head data with hydraulic conductivity sparse data in order to obtain an estimate of the spatial distribution of the latter variable. This approach takes advantage of the correlation between the hydraulic conductivity (K) and the hydraulic head (H) behaviour through time. In order to evaluate this approach, a synthetic experiment was constructed through a transitory numerical flow-model that simulates hydraulic head values in a horizontally-heterogeneous aquifer. Geostatistical tools were used to describe the correlation between simulated spatiotemporal data of hydraulic head and the spatial distribution of the hydraulic conductivity in a group of model nodes. Subsequently, the Kalman filter was used to estimate the hydraulic conductivity values at nonsampled sites. The results showed acceptable differences between estimated and synthetic hydraulic conductivity data, with low estimate error variances (predominating the 1 m²/day² value for K for all the cases, however, the smallest number of cells with values above 2 m²/day² correspond to the bivariate spatiotemporal case) and the best agreement between the estimated errors and the selected model variance (SMSE values of 0.574 and 0.469) were found for the bivariate cases, which suggests that the implemented methodology could be used for reducing calibration efforts, particularly when the hydraulic parameters data are scarce.