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.

Environmental isotope tracers have been a useful tool in providing new insights into hydrologic processes. In Mexico, there have been several studies reporting different values for ${\mathsf{\delta }}^{18}\mathrm{O}$ and ${\mathsf{\delta }}^2\mathrm{H}$ for certain geographical areas. The objective of this study is to achieve the isotopic characterization of rainfall and groundwater and to report the comprehensive understanding of groundwater flow processes around and within the Calera aquifer and, consequently, its potential recharge sites. The samples used for the stable isotope analysis ( ${\mathsf{\delta }}^{18}\mathrm{O},{\mathsf{\delta }}^2\mathrm{H}$ ) were measured using a GV-Isoprime isotope-ratio mass spectrometer at the Isotopy Laboratory of the Water Center for Latin America and the Caribbean. The $\mathsf{\delta }\mathrm{D}$ of precipitation ranged between −110.20‰ and 10.11‰, with a mean of −55.67‰ $\pm$ 27.81‰. The ${\mathsf{\delta }}^{18}\mathrm{O}$ ranged between −17.80‰ and 2.74‰, with a mean of −9.44‰ $\pm$ 4.74‰. The $\mathsf{\delta }\mathrm{D}$ of groundwater ranged between −81.92‰ and −36.45‰, with a mean of −66.05‰ $\pm$ 8.58‰. The ${\mathsf{\delta }}^{18}\mathrm{O}$ ranged between −18.26‰ and −8.84‰, with a mean of −12.35‰ $\pm$ 2.12‰. The local meteoric water line of the Zacatecas state is $\mathsf{\delta }\mathrm{D}=-2.03+5.68{\mathsf{\delta }}^{18}\mathrm{O}$ . The groundwater samples were clustered into four groups. The clustering of the samples led to the finding that streamflows play a significant role in the hydrological balance as a source of local recharge to the aquifer.

Hydrological cycle research requires a detailed analysis of the involved parameters to understand watershed behavior comprehensively. In recent decades, both Geographic Information Systems (GIS) and Digital Elevation Models (DEMs) were implemented and took a substantial role in watershed geomorphological parameterization; however, the variability of these instruments remains a challenge, together with high-resolution DEMs being unavailable, requiring digital processing to improve resolution. This research aims to merge DEMs and evaluate GIS geoprocessing algorithms to determine drainage networks and the geomorphological parametrization of a semiarid watershed. DEMs with resolutions of 1.5, 5, 12.5, and 30 m, the Jenson/Domingue (J/D) and Wang/Liu (W/L) fill algorithms; and D8, D, KRA, and MFD flow routing algorithms were used. One of the research findings proved that the divergences of the drainage networks are mainly attributed to filling algorithms and not flow routing algorithms; the shifts between the networks obtained in the processes reach horizontal distances up to 300 m. Since the water movement within the watershed depends on geomorphological characteristics, it is suggested that DEM-based hydrological studies specify both the resolution and the algorithms used in the parametrization to validate the rigidity of the research, improving estimate areas of high hydrological risk.

The great potential of the convolutional neural networks (CNNs) provides novel and alternative ways to monitor important parameters with high accuracy. In this study, we developed a soft sensor model for dynamic processes based on a CNN for the measurement of suspended solids and turbidity from a single image of the liquid sample to be measured by using a commercial smartphone camera (Android or IOS system) and light-emitting diode (LED) illumination. For this, an image dataset of liquid samples illuminated with white, red, green, and blue LED light was taken and used to train a CNN and fit a multiple linear regression (MLR) by using different color lighting, we evaluated which color gives more accurate information about the concentration of suspended particles in the sample. We implemented a pre-trained AlexNet model, and an MLR to estimate total suspended solids (TSS), and turbidity values in liquid samples based on suspended particles. The proposed technique obtained high goodness of fit ($R^2$ = 0.99). The best performance was achieved using white light, with an accuracy of 98.24% and 97.20% for TSS and turbidity, respectively, with an operational range of 0–800 ${\mathrm{mgL}}^{-1}$, and 0–306 NTU. This system was designed for aquaculture environments and tested with both commercial fish feed and paprika. This motivates further research with different aquatic environments such as river water, domestic and industrial wastewater, and potable water, among others.

In arid and semiarid regions, groundwater becomes the main source to meet the drinking water needs of large cities, food production, and industrial activities. For this reason, necessary studies must be carried out to estimate its quantity and quality, always seeking sustainable management, thus avoiding social conflicts or a decrease in the productive activities of humanity. This research explains the behavior of groundwater quality concerning arsenic speciation and its relationship with fluoride. The average total arsenic concentration of 19.95 µg/L and 20.29 µg/L is reported for the study period from 2015 to 2020, respectively, according to the Mexican standard. If the population drinks water directly, it is exposed to possible damage to health. The predominant arsenic species is As (V), with 95% and As (III) with 5%, this finding will allow us to define in greater detail the type of remediation that is required to reduce the content of this element in the water. Regarding the relationship between arsenic and fluorine, very small Pearson correlation coefficients of the order of 0.3241 and 0.3186 were found. The estimation of the space–time variation made it possible to identify the areas with the highest concentration of arsenic and fluorine, allowing the definition of the operating policies of these wells, thereby protecting the health of the inhabitants who consume this water.