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.

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.

Dengue is the most important viral disease transmitted by mosquitoes, predominantly Aedes (Stegomyia) aegypti (L.) (Diptera:Culicidae). Forty percent of the world’s population is at risk of contracting the disease, and a large area of Mexico presents suitable environmental conditions for the life cycle of Ae. aegypti. In particular, the Central Mexican Highlands have a high population density, increasing the risk of transmission and propagation of dengue. In the present study, the potential distribution of Ae. aegypti was modeled under an ecological niche approach using the maximum entropy technique with the aim of determining the spatial risk distribution of dengue. The final model of five variables (minimum temperature of the coldest month |Bio6|, precipitation of the wettest month |Bio13|, precipitation seasonality |Bio15|, the normalized difference vegetation index (NDVI), and relative humidity) contributed to more than 90% of the model’s performance. The results of the potential distribution model were then compared with the number of dengue cases per locality during the 2009–2015 period considering four suitability of presence categories. Category 4 corresponded with the highest suitability of presence (0.747 to 1) and the greatest risk of dengue (odds ratio [OR] = 103.27; P < 0.001). In conclusion, the present ecological niche model represents an important tool for the monitoring of dengue and the identification of high-risk areas.