Climate defines the hydrological cycle of each region and climate change will undoubtedly affect the recharge processes of the world’s water tables and the water resources currently available at the basin and microbasin scale. The objective of the present paper is to evaluate future changes in evapotranspiration and flows from the Sardinas River basin in North Durango, Mexico. The Rural Genius Model (GR2M) is an aggregated monthly hydrological model, which is used to reconstruct flows from precipitation and evapotranspiration by applying two functions: a production function and a transfer function. A transfer function has been used under four shared socioeconomic pathway scenarios (126, 245, 370, and 585). Pettitt and Mann–Kendall statistical tests were used to determine trends, which were identified by the breakpoint in the evapotranspiration and flow time series. Results showed that under climate change scenarios, evapotranspiration shows an increase over time. Under the climate scenario, SSP 126, and the application of the statistical test in the flow series show an increasing trend with a break in May for 2090, with a mean of 1658 mm before and 2238 mm after the break, with an excess of 34.98%. The flow under the SSP 245 climate scenario predicts a mean flow of 1703.11 mm and a break in May of the 2090 horizon, with a mean before and after the break of 1624 mm and 2168 mm, respectively, with an excess of 33.49%. Under the SSP 370 scenario, the mean is expected to be 1710.81 mm, with a break in May 2090, before and after means of 1633 mm and 2166 mm, respectively, with an excess of 32.63%. Under climate change scenario SSP 585, the mean expected will be 1701.43 mm and the break in the flow series will occur in May of the 2090 horizon, with a mean of 1628 mm before the break and 2132 mm after, with a flow excess of 30.95%. The results of this study can be a basis for decision-makers for better management and protection of water resources in northern Durango, Mexico.

Canopy fuels determine the characteristics of the entire complex of forest fuels due to their constant changes triggered by the environment; therefore, the development of appropriate strategies for fire management and fire risk reduction requires an accurate description of canopy forest fuels. This paper presents a method for mapping the spatial distribution of canopy fuel loads (CFLs) in alignment with their natural variability and three-dimensional spatial distribution. The approach leverages an object-based machine learning framework with UAV multispectral data and photogrammetric point clouds. The proposed method was developed in the mixed forest of the natural protected area of “Sierra de Quila”, Jalisco, Mexico. Structural variables derived from photogrammetric point clouds, along with spectral information, were used in an object-based Random Forest model to accurately estimate CFLs, yielding R² = 0.75, RMSE = 1.78 Mg, and an average ${Bias}_{rel}$ = 18.62%. Canopy volume was the most significant explanatory variable, achieving a mean decrease in impurity values greater than 80%, while the combination of texture and vegetation indices presented importance values close to 20%. Our modelling approach enables the accurate estimation of CFLs, accounting for the ecological context that governs their dynamics and spatial variability. The high precision achieved, at a relatively low cost, encourages constant updating of forest fuels maps to enable researchers and forest managers to streamline decision making on fuel and forest fire management.

One of the conifers that survived after the last glaciation is the Pseudotsuga menziesii (Mirb.) Franco. Due to the gradual increase in temperature, this species was forced to move from the south to the north and to higher elevation, causing a fragmented and intermittent distribution in Mexico. The main objective of this study was to model and identify suitable areas for the future conservation of the P. menziesii in Mexico. The specific objectives of this research were: (i) to model the habitat suitability of P. menziesii in Mexico, (ii) to identify the most relevant environmental variables based on its current and future habitat suitability (2030, 2050, 2070 and 2090) and (iii) to suggest areas for the conservation of the species in Mexico. Records were compiled from different national and international sources. Climate and topographic variables were used. With MaxEnt software version 3.4.3 (Phillips, New York, NY, USA) 100 distribution models were obtained, where the model showed an area under the curve of 0.905 for training and 0.906 for validation and partial ROC of 1.95 and Z reliable (p < 0.01), with TSS values > 0.80. The current area of the P. menziesii was 31,580.65 km². The most important variables in the current and future distribution were maximum temperature of the hottest month, precipitation of the coldest trimester and average temperature of the coldest trimester. The percentage of permanence (resilience) for the 2030, 2050, 2070 and 2090 climate horizons was 49.79%, 25.14%, 17.45% and 16.46%, respectively, for the SSP 245 scenario. On the other hand, for the SSP 585 scenario and the analyzed horizons, the percentage resilience in areas of suitable habitat zones was 41.45%, 27.42%, 9.82% and 2.89%.

Droughts are a climatic phenomenon with local, regional, and large-scale repercussions. Historical knowledge of droughts generated by modeled data allows the development of more accurate climate reconstructions to propose better approaches for the management of hydric resources. The objective of this research was to evaluate the association of precipitation and temperature with data from the NLDAS-002 to develop a reconstruction of droughts in the center of Chihuahua, Mexico using the SPEI from tree rings. We also identified the influence of ocean–atmospheric phenomena on the reconstructed drought index. The best association among chronologies was obtained with the earlywood band and accumulated seasonal precipitation from November of the previous year to June of the current year (r = 0.82, p < 0.05) and for temperature from January to July (r = −0.81, p < 0.05). The reconstructed drought index extended from 1775 to 2017 (243 years), where seven extreme drought events were identified. We found significant correlations between the reconstructed Standardized Precipitation Evapotranspiration Index and the Pacific Decadal Oscillation (r = 0.46, p < 0.05), Atlantic Multidecadal Oscillation (r = −0.34, p < 0.05), Multivariate El Niño Southern Oscillation Index (r = 0.29, p < 0.05), and Southern Oscillation Index (r = −0.22, p < 0.05). The historical reconstruction of hydroclimatology in the center of Chihuahua is important for planning a long-term assessment and for the management of water resources shared by Mexico and the United States.

Maize yield is highly sensitive to climate change and extreme weather events. In some locations, it is projected to decrease due to an increase in the average growing season temperature. The present study analyzes changes in temperature and precipitation extremes in the Comarca Lagunera located in Northern Mexico, using the ETCCDI indices. We examined a 40-year period (1980–2020) using daily and monthly climate data provided by the National Meteorological Service. The climate databases were subjected to quality control, homogenization, and data filling using Climatol, and the ETCCDI indices were obtained using RClimDex software. Results indicate that the climate variable that most influences climate change in Comarca Lagunera is temperature, with increases in both maximum and minimum values. This situation is accentuating the drought in the Comarca Lagunera, which is supported by the increase in temperature-based indices. Furthermore, precipitation is the primary variable influencing the yield of rainfed maize, while maximum temperature affects the yield of irrigated maize. These results indicate that irrigation is functioning as a climate change adaptation strategy, reducing the impact of extreme weather on maize productivity, which could have a negative impact on water productivity in the study region in the short term.

El nitrógeno es el nutriente más importante en cultivos forrajeros, debido a su participación en diversas reacciones bioquímicas en las diferentes etapas fenológicas de la planta. El objetivo del presente estudio fue desarrollar un modelo de regresión lineal múltiple para estimar el nitrógeno total (Nt) en planta de maíz a partir de imágenes satelitales. El porcentaje de Nt se determinó mediante tres muestreos de planta en cuatro parcelas experimentales. El modelo de estimación se obtuvo al procesar las imágenes satelitales Sentinel-2 de acuerdo a las fechas de muestreo, se calcularon 13 índices espectrales y se analizó la asociación entre los valores del contenido de nitrógeno y la reflectancia a través de: análisis de componentes principales (ACP), matriz de correlación y dendrograma. Los índices con mayor relación fueron MCARI/OSAVI, TCARI/OSAVI, MCARI/OSAVI RE y TCARI/OSAVI RE, esto permitió explicar más de 50% de la variabilidad del modelo propuesto y un EMC de 0.12. El presente estudio indicó que el cálculo de índices espectrales derivados de Sentinel-2 tiene gran potencial para conocer el estado nitrogenado del cultivo de maíz, sin embargo, para futuras investigaciones se sugiere obtener modelos de estimación de Nt por etapa fenológica del cultivo.