Water planning in semi-arid regions depends on climate information that resolves both seasonal timing and topographic gradients. This study evaluated 15 CMIP6 models over Zacatecas, Mexico, and produced a 1 km historical dataset for 1985–2014 by statistically refining bias-corrected daily fields from NEX-GDDP-CMIP6. Downscaling was referenced to the CHELSA climatology: temperature was refined using an elevation-informed hybrid spline approach, whereas rainfall was downscaled with geographically weighted regression (GWR) to represent orographic gradients. The resulting fields were assessed against two independent observational baselines: an automated INIFAP network (2004–2014) and a conventional CONAGUA network (1985–2014). For temperature, BCC-CSM2-MR showed the highest performance, with a Pearson correlation of R = 0.94 for both T_max and T_min. A consistent network-dependent bias pattern was identified: the downscaled models overestimated the diurnal temperature range relative to INIFAP but underestimated it relative to CONAGUA, highlighting the influence of instrumentation and observational protocols on model evaluation. For rainfall, ACCESS-ESM1-5 reproduced the seasonal cycle and dominant orographic patterns, with a correlation of R = 0.611 despite the intrinsic stochasticity of semi-arid rainfall. The resulting 1 km fields provide a spatially consistent baseline for regional applications, including stochastic weather generation and impact models in complex semi-arid regions.

This study evaluates the human health risks associated with exposure to arsenic in groundwater from a semi-arid region of Mexico, focusing on concentration levels and their temporal variation. Arsenic concentrations were analyzed using ordinary kriging for spatial interpolation, along with descriptive and inferential statistical methods. Human health risk was assessed through the following two key indicators: the Hazard Quotient (HQ), which estimates non-carcinogenic risk by comparing exposure levels to reference doses and carcinogenic risk (CR), which represents the estimated lifetime probability of developing cancer due to arsenic exposure. The mean arsenic concentration across both study years was 0.0200 mg/L, with median values of 0.0151 mg/L in 2015 and 0.0200 mg/L in 2020. The average HQ was 2.13 in 2015 and 2.17 in 2020, both exceeding the safety threshold of one. Mean CR values were 0.00096 and 0.00097 for 2015 and 2020, respectively, with a consistent median of 0.00072 across both years. A t-test was applied to compare the distributions between years. Both HQ and CR values significantly exceeded the recommended safety limits (p < 0.05), indicating that groundwater in the study area poses a potential carcinogenic and non-carcinogenic health risk. These findings underscore the urgent need for water quality monitoring and the implementation of mitigation measures to safeguard public health in the region.

Pronounced spatio-temporal variability of rainfall in semi-arid basins remains a central challenge for rainfall–infiltration–runoff modeling, particularly in ungauged or newly instrumented catchments where continuous soil-moisture data are unavailable. This study evaluates the comparative predictive performance of the SCS-CN and Green-Ampt (GA) infiltration methods, implemented within HEC-HMS at the sub-basin scale, using eight rainfall–runoff analysis time windows recorded during the 2020–2025 rainy seasons within a monitoring network operational since October 2019 in an instrumented semi-arid basin in Mexico. A blind-validation framework was adopted: parameters for both methods were derived a priori from tabulated sources indexed by land use, hydrologic soil group, soil textural class, and locally supported by textural analysis at three depths per sub-basin, in situ testing of saturated hydraulic conductivity, and gravimetric determination of field capacity; the initial moisture content required by GA was set equal to the measured field capacity (θ_i = θ_fc) to equate initial conditions between the two methods. Spatially distributed rainfall was captured by four monitoring stations under a one-to-one gauge–sub-basin assignment scheme, with monthly rainfall depth varying from 22.8 to 204.9 mm across the four sub-basins. Both methods reproduced observed discharge with varying levels of agreement: SCS-CN yielded very good performance (Pearson R = 0.95; Nash–Sutcliffe efficiency NSE = 0.76), whereas Green-Ampt yielded moderate correlation but unsatisfactory NSE (R = 0.70; NSE = 0.45) against the Levelogger records. Contrary to the initial expectation that the physically based GA would outperform SCS-CN, SCS-CN yielded substantially higher performance across windows, with the two simulated discharge series differing by a mean absolute deviation of 36.9 m³/s. A systematic sensitivity analysis (±6%, ±10%, ±20% perturbations) revealed an asymmetric response: SCS-CN was highly sensitive to Curve Number perturbations (mean-deviation amplitude 65.9 m³/s), whereas Green-Ampt was nearly insensitive to its compound soil-hydraulic parameterization (amplitude 3.2 m³/s), indicating a structural limitation of the physically based method under blind validation.

Mineral scale deposits in groundwater-supplied drinking water distribution systems reduce hydraulic efficiency and increase maintenance costs. This study evaluated the effectiveness of citric acid as a descaling agent for natural scale deposits collected from the Benito Juárez and La Joya drinking water distribution systems in Zacatecas, Mexico. The samples were treated with citric acid solutions at concentrations of 1, 2, and 3 M (moles per liter) and characterized before and after treatment using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and inductively coupled plasma optical emission spectrometry (ICP-OES). In addition, statistical analyses were performed using analysis of variance (ANOVA) and Tukey’s test. The scale deposits were composed mainly of calcium carbonate and exhibited differences in their initial mineralogical composition between the two systems. Treatment with citric acid promoted the progressive dissolution of carbonate phases, resulting in mineralogical, chemical, and morphological changes. The 3 M solution produced the highest degree of mineral dissolution, whereas the 1 M and 2 M treatments showed statistically similar behavior. The results demonstrate that scale deposit removal efficiency is influenced by the initial composition of the scale deposits, providing a scientific basis for optimizing remediation strategies in drinking water distribution systems.

Water is expected to become one of the main issues of conflict throughout the 21st century. These scenarios are no longer a thing of the future. Currently, large cities, where an estimated two-thirds of the world's population lives, require large volumes of drinking water to meet their needs. Rainwater harvesting can become an alternative to reduce pressure on traditional sources. It is considered the cleanest natural source available and can be easily used and consumed both domestically, commercially, and industrially. This research aims to design a green wall through PET and considering the physical and chemical characteristics of rainwater. Recycled bottles and easily available materials are used, and it is connected to a harvesting system made of concrete and galvanized sheet metal. The parameters evaluated to determine water quality are physical characteristics [odor, color, turbidity], chemical [pH, EC, and hardness], and heavy metals. Most comply with Mexican and World Health Organization standards.