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Efficient irrigation water management is critical in regions subject to high hydroclimatic variability, where agricultural production strongly depends on how water resources are allocated and used. The objective of this study was to quantify the effect of distributed irrigation water volumes on maize production and to evaluate differences in irrigation performance among irrigation districts (IDs) in Sinaloa, Mexico, for the period 1999–2022. Official records from the National Water Commission (CONAGUA) for seven IDs were analyzed using a fixed-effects panel data model based on a balanced panel database constructed from annual irrigation water distribution and maize production statistics, which control for unobserved district-level heterogeneity and interannual variability to identify the water–production relationship. In addition, a Relative Water Performance Index (RWPI) based on water productivity was computed to standardize comparisons across districts and agricultural cycles characterized by contrasting hydroclimatic conditions, including the extreme frost event of 2011 and the severe drought of 2012. The results show a positive and statistically significant relationship between irrigation water volumes and maize production, along with marked structural heterogeneity across districts. The RWPI captured generalized performance declines during extreme years and differentiated districts according to productive stability, reflecting management adjustments, as well as crop rotation or reconversion strategies. Overall, the combined econometric and index-based approach provides quantitative evidence to support adaptive irrigation water management under climate variability.

In coastal regions, the combined effects of natural processes, human activity, and climate change have caused shoreline changes that may increase in the future. The assessment of these changes is essential for forecasting their future position for proper management. In this context, shoreline changes in the Gulf of California (GC), Mexico, have received little attention and no previous studies have addressed future forecasting. In this study, the researchers assessed the historical shoreline changes to forecast the long-term shoreline positions. To address this, shoreline data were obtained from Landsat satellite images for the years 1981, 1993, 2004, 2010, and 2020. The Net Shoreline Movement (NSM), Linear Regression Rate (LRR), End Point Rate (EPR), and Weighted Linear Regression (WLR) geo-spatial techniques were applied to estimate the shoreline change rate by using a Digital Shoreline Analysis System (DSAS) in the GIS environment. A Kalman filter model was used to forecast the position of the shoreline for the years 2030 and 2050. The results show that approximately 72% of the GC shoreline is undergoing steady erosion, and this trend is continuing in the future. This study has provided valuable and comprehensive baseline information on the state of the shoreline in the GC that can guide coastal engineers, coastal managers, and policymakers in Mexico to manage the risk. It also provides both long-term and large-scale continuous datasets that are essential for future studies focused on improving the shoreline forecast models.

This paper presents the implementation of a parallelization strategy using the OpenMP library, while developing a simulation tool based on a cellular automaton (CA) to run urban growth simulations. The characterization of an urban growth model CA is shown and it consists of a digitization process of the land use in order to get all the necessary elements for the CA to work. During the first simulation tests we noticed high processing times due to large quantity of calculations needed to perform one single simulation, in order to minimize this we implemented a parallelization strategy using the fork-join model in order to optimize the use of available hardware. The results obtained show a significant improvement in execution times in function of the number of available cores and map sizes, as a future work, it is planned to implement artificial neural networks in order to generate more complex urban growth scenarios.

Selective logging in the tropics is a major driver of forest degradation by altering forest structure and function, including significant losses of aboveground carbon. In this study, we used a 30-year Landsat time series (1985–2015) to analyze forest degradation and carbon emissions due to selective logging in a Forest Reserve of the Venezuelan Amazon. Our work was conducted in two phases: the first, by means of a direct method we detected the infrastructure related to logging at the sub-pixel level, and for the second, we used an indirect approach using buffer areas applied to the results of the selective logging mapping. Pre- and post-logging forest inventory data, combined with the mapping analysis were used to quantify the effects of logging on aboveground carbon emissions for three different sources: hauling, skidding and tree felling. With an overall precision of 0.943, we demonstrate the potential of this method to efficiently map selective logging and forest degradation with commission and omission errors of +7.6 ± 4.5 (Mean ± SD %) and −7.5% ± 9.1 respectively. Forest degradation due to logging directly affected close to 24,480 ha, or about ~1% of the total area of the Imataca Forest Reserve. On average, with a relatively low harvest intensity of 2.8 ± 1.2 trees ha−1 or 10.5 ± 4.6 m3 ha−1, selective logging was responsible for the emission of 61 ± 21.9 Mg C ha−1. Lack of reduced impact logging guidelines contributed to pervasive effects reflected in a mean reduction of ~35% of the aboveground carbon compared to unlogged stands. This research contributes to further improve our understanding of the relationships between selective logging and forest degradation in tropical managed forests and serves as input for the potential implementation of projects for reducing emissions from deforestation and forest degradation (REDD+).

Arid and semiarid regions are geographic units that cover approximately 43% of the earth’s surface worldwide, and conditions of extreme drought and reduced vegetation cover predominate in these regions. In Mexico, arid and semiarid ecosystems cover more than half of the territory, with desertification, mainly caused by anthropogenic activities and climatic events, as the main problem in these regions. The present research aimed to assess, identify, and classify arid and semiarid zones by employing a methodology based on multicriteria evaluation analysis (MCA) using the weighted linear combination (WLC) technique and geographic information systems (GIS) in the hydrological administrative regions (HARs) of the North Pacific, Northwest, and Baja California Peninsula, located in Northwest Mexico. Data related to aridity, desertification, degradation, and drought were investigated, and the main factors involved in the aridity process, such as surface temperature, soil humidity, precipitation, slopes, orientations, the normalized difference vegetation index (NDVI), and evapotranspiration, were obtained. For the standardization of factors, a fuzzy inference system was used. The weight of each factor was then determined with the analytical hierarchy process (AHP). To delimit arid regions, the classification of arid zones proposed by the United Nations Environment Program (UNEP) was used, and the result was an aridity suitability map. To validate the results, the sensitivity analysis method was applied. Quantitative and geospatial aridity indicators were obtained at the administrative hydrological level and by state. The main results indicated that semiarid and dry subhumid zones predominated, representing 40% and 43% of the surface of the study area, respectively, while arid regions represented 17%, and humid regions represented less than 1%. In addition, of the states for which 100% of the surface lay in the study area, it was observed that Baja California and Baja California Sur had the largest arid and semiarid zones, while subhumid regions predominated in Sonora and Sinaloa.

The risk of flooding is a destructive natural hazard, and it is increasing due to heavy rainfall and anthropogenic factors. Hydrologic–hydraulic models serve as valuable tools for flood forecasting and predicting future flow patterns. These models evaluate and simplify processes in ungauged basins. In this study, three hydrologic models (soil conservation service [SCS], Snyder, and Temez) were used to calculate synthetic unit hydrographs for the Humaya and Tamazula River (H-T-R) basin. Additionally, the flows derived from the three models were simulated in Hydrological Engineering Center River Analysis System for various return periods (2, 5, 10, 25, 50, and 100 years). The accuracy of the models SCS, Snyder, and Temez was evaluated using the root-mean-square error (1162.44, 144.76, and 2890.6); Nash–Sutcliffe efficiency (−51.12, 0.19, and −312.28); (0.97, 0.94, and 0.94), and kappa index (0.8534, 0.9895, and 0.7155), respectively. The data used in this study were obtained from a hydrometric station located on the Culiacan River. The main findings indicate that the Snyder model demonstrated a better predictive capability compared to the Temez and SCS models, albeit with a tendency to overestimate. Simulated flood depths are deeper in the upstream areas, particularly upstream from the Musala Island bifurcation on the Tamazula River, with values of 11.82 m for SCS, 9.76 m for Snyder, and 13.5 m for Temez. The simulation revealed potential overflow zones along the Tamazula River, particularly at the channel bifurcation and near the confluence with the Humaya River, during the 100 year return period simulation.

The last ten years have shown that Climate Change (CC) is a major global issue to attend to. The integration of its effects into coastal impact assessments and adaptation plans has gained great attention and interest, focused on avoiding or minimizing human lives and asset losses. Future scenarios of mean sea level rises and wave energy increase rates have then been computed, but downscaling still remains necessary to assess the possible local effects in small areas. In this context, the effects of CC on the wave climate in the Gulf of California (GC), Mexico, have received little attention, and no previous studies have tackled the long-term trend of wave climate at a regional scale. In this paper, the long-term trends of the wave height, wave period and wave energy in the GC were thus investigated, using the fifth-generation climate reanalysis dataset (ERA5). The long-term shoreline evolution was also examined from historical Landsat images, so as to identify erosional hotspots where intervention can be prioritized. The results indicate that both the mean and extreme wave regimes in the GC are getting more energetic and that two-thirds of the coast is suffering chronic erosion. A discrepancy between the trends of the wave period and wave height in some regions of the Gulf was also found. Finally, the importance of natural processes, human activity and CC in the shoreline change is highlighted, while addressing the need for future permanent field observations and studies in the GC.

Traditional photogrammetry techniques require the use of Ground Control Points (GCPs) to accurately georeference aerial images captured by unmanned aerial vehicles (UAVs). However, the process of collecting GCPs can be time-consuming, labor-intensive, and costly. Real-time kinematic (RTK) georeferencing systems eliminate the need for GCPs without deteriorating the accuracy of photogrammetric products. In this study, a statistical comparison of four RTK georeferencing systems (continuously operating reference station (CORS)-RTK, CORS-RTK + post-processed kinematic (PPK), RTK + dynamic RTK 2 (DRTK2), and RTK + DRTK2 + GCP) is presented. The aerial photo was acquired using a Dà-Jiāng Innovation Phantom 4 RTK. The digital photogrammetric processing was performed in Agisoft Metashape Professional software. A pair of global navigation satellite systems (GNSSs) receiving antennas model CHC x900 were used for the establishment of check points (CPs). The accuracy of photogrammetric products was based on a comparison between the modeled and CP coordinates. The four methods showed acceptable planimetric accuracies, with a root mean square error (RMSE) ranging from 0.0164 to 0.0529 m, making the RTK-CORS + PPK method the most accurate (RMSE = 0.0164 m). RTK-CORS + PPK, RTK-DRTK2, and RTK-DRTK2 + GCP methods showed high altimetric accuracies, with RMSE values ranging from 0.0201 to 0.0334 m. In general, RTK methods showed a high planimetric and altimetric accuracy, similar to the accuracy of the photogrammetric products obtained using a large number of GCPs.

This study evaluated seven vegetation indices for the monitoring of a cherry tomato crop using an unmanned aerial vehicle with a multispectral camera that measures in the green, red, and near-infrared spectral bands. A photogrammetric flight plan was designed to capture the spectral images every 2 weeks in two agricultural parcels identified as Treatment 1 ( ) and Treatment 2 ( ). The corresponding orthophotographs were obtained using digital photogrammetry techniques. Subsequently, vegetation indices were calculated for these orthophotographs. The mean and standard deviation of these indices were extracted, and a statistical analysis was performed to compare the vegetation indices and to analyze their behavior over time. Analysis of variance showed that the ratio vegetation index (RVI), green vegetation index (GVI), normalized difference vegetation index (NDVI), infrared percentage vegetation index (IPVI), green normalized difference vegetation index (GNDVI), and optimized soil-adjusted vegetation index (OSAVI) indices showed significant variation ( -value <0.05) over time. No statistically significant differences between the two treatments were found. IPVI, NDVI, and OSAVI showed less variation in pixel values. RVI, GVI, NDVI, IPVI, GNDVI, and OSAVI proved to be valuable tools for monitoring field crops since these indices responded to the crop growth kinetics.

The present study focuses on identifying and describing the possible proximate and underlying causes of deforestation and its factors using the combination of two techniques: (1) specialized consultation and (2) spatial logistic regression modeling. These techniques were implemented to characterize the deforestation process qualitatively and quantitatively, and then to graphically represent the deforestation process from a temporal and spatial point of view. The study area is the North Pacific Basin, Mexico, from 2002 to 2014. The map difference technique was used to obtain deforestation using the land-use and vegetation maps. A survey was carried out to identify the possible proximate and underlying causes of deforestation, with the aid of 44 specialized government officials, researchers, and people who live in the surrounding deforested areas. The results indicated total deforestation of 3938.77 km2 in the study area. The most important proximate deforestation causes were agricultural expansion (53.42%), infrastructure extension (20.21%), and wood extraction (16.17%), and the most important underlying causes were demographic factors (34.85%), economics factors (29.26%), and policy and institutional factors (22.59%). Based on the spatial logistic regression model, the factors with the highest statistical significance were forestry productivity, the slope, the altitude, the distance from population centers with fewer than 2500 inhabitants, the distance from farming areas, and the distance from natural protected areas.