Climate surfaces are digital representations of climatic variables from a region in the planet estimated via geographical interpolation techniques. Climate surfaces have multiple applications in research planning, experimental design, and technology transfer. Although high‐resolution climatologies have been developed worldwide, Mexico is one of the few countries that have developed several climatic surfaces. Here, we present an updated high‐resolution (30 arc sec) climatic surfaces for Mexico for the average monthly climate period 1910–2009, corresponding to monthly values of precipitation, daily maximum, and minimum temperature, as well as 19 bioclimatic variables derived from the monthly precipitation and temperature values. To produce these surfaces we applied the thin‐plate smoothing spline interpolation algorithm implemented in the ANUSPLIN software to nearly 5000 climate weather stations countrywide. As an additional product and unlike the previous efforts, we generated monthly standard error surfaces for the three climate parameters, which can be used for error assessment when using these climate surfaces. Our climate surface predicted slightly drier and cooler conditions than the previous ones. ANUSPLIN diagnostic statistics indicated that model fit was adequate. We implemented a more recent error assessment, a set of withheld stations to perform an independent evaluation of the model surfaces. We estimate the mean absolute error and mean error, with the withheld data and all the available data. Average RTGCV for monthly temperatures was of 1.26–1.12 °C and 24.67% for monthly precipitation, and a RTMSE of 0.48–0.56 °C and 11.11%. The main advantage of the surfaces presented here regarding the other three developed for the country is that ours cover practically the entire 20th century and almost the entire first decade of the 21st century. It is the most up to date high‐resolution climatology for the country.

Spatial assessments of historical climate change provide information that can be used by scientists to analyze climate variation over time and evaluate, for example, its effects on biodiversity, in order to focus their research and conservation efforts. Despite the fact that there are global climatic databases available at high spatial resolution, they represent a short temporal window that impedes evaluating historical changes of climate and their impacts on biodiversity. To fill this gap, we developed climate gridded surfaces for Mexico for three periods that cover most of the 20^th and early 21^st centuries: t₁-1940 (1910–1949), t₂-1970 (1950–1979) and t₃-2000 (1980–2009), and used these interpolated surfaces to describe how climate has changed over time, both countrywide and in its 19 biogeographic provinces. Results from our characterization of climate change indicate that the mean annual temperature has increased by nearly 0.2°C on average across the whole country from t₂-1970 to t₃-2000. However, changes have not been spatially uniform: Nearctic provinces in the north have suffered higher temperature increases than southern tropical regions. Central and southern provinces cooled at the beginning of the 20^th century but warmed consistently since the 1970s. Precipitation increased between t₁-1940 and t₂-1970 across the country, more notably in the northern provinces, and it decreased between t₂-1970 and t₃-2000 in most of the country. Results on the historical climate conditions in Mexico may be useful for climate change analyses for both environmental and social sciences. Nonetheless, our climatology was based on information from climate stations for which 9.4–36.2% presented inhomogeneities over time probably owing to non-climatic factors, and climate station density changed over time. Therefore, the estimated changes observed in our analysis need to be interpreted cautiously.

This work proposes a methodology whereby the selection of hydrologic and land-use cover change (LUCC) models allows an assessment of the proportional variation in potential groundwater recharge (PGR) due to both land-use cover change (LUCC) and some climate change scenarios for 2050. The simulation of PGR was made through a distributed model, based on empirical methods and the forecasting of LUCC stemming from a supervised classification with remote sensing techniques, both inside a Geographic Information System. Once the supervised classification was made, a Markov-based model was developed to predict LUCC to 2050. The method was applied in Acapulco, an important tourism center for Mexico. From 1986 to 2017, the urban area increased 5%, and by 2050 was predicted to cover 16%. In this period, a loss of 7 million m³ of PGR was assumed to be caused by the estimated LUCC. From 2017 to 2050, this loss is expected to increase between 73 and 273 million m³ depending on the considered climate change scenario, which is the equivalent amount necessary for satisfying the water needs of 6 million inhabitants. Therefore, modeling the variation in groundwater recharge can be an important tool for identifying water vulnerability, through both climate and land-use change.

Water is one of the primary resources for humans; however, clean water available for consumption is a constant problem worldwide. Nonetheless, environmental education is fundamental to promote social consciousness and orientate people to improve water management, and children are the main target as they are prone to adopt long-lasting habits. Therefore, this study aimed to implement an environmental education program on water conservation in 10-year-old students from two rural communities within the Nenetzingo River catchment in Mexico. The study was a three-step methodology that included diagnostics, education, and evaluation. It was found that the students that participated in the educational program enriched their knowledge, attitudes, and perception towards water. Therefore, implementing environmental education programs in young students can improve water use and conservation in Mexico. Additionally, the design used in this research allows identifying the target groups for immediate intervention.

Remote sensing plays a crucial role in modeling surface water quality parameters (WQPs), which aids spatial and temporal variation assessment. However, existing models are often developed independently, leading to uncertainty regarding their applicability. This study focused on two primary objectives. First, it aimed to evaluate different models for chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) in a surface water body, the J. A. Alzate dam, in the Mexican highland region (R² ≥ 0.78 and RMSE ≤ 16.1 mg/L). The models were estimated using multivariate regressions, with a focus on identifying dilution and dragging effects in inter-annual flow rate estimations, including runoff from precipitation and municipal discharges. Second, the study sought to analyze the potential scope of application for these models in other water bodies by comparing mean WQP values. Several models exhibited similarities, with minimal differences in mean values (ranging from −9.5 to 0.57 mg/L) for TSS, TN, and TP. These findings suggest that certain water bodies may be compatible enough to warrant the exploration of joint modeling in future research endeavors. By addressing these objectives, this research contributes to a better understanding of the suitability of remote sensing-based models for characterizing surface water quality, both within specific locations and across different water bodies.