Evangelina Avila-Aceves
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Evangelina Avila-Aceves has a total of 19 co-authors. Here are the top co-authors by number of shared publications:
- Wenseslao Plata-Rocha
Facultad de Ciencias de la Tierra y el Espacio, Universidad Autónoma de Sinaloa, Culiacán, Mexico
10 shared publications - Yedid Guadalupe Zambrano-Medina
Facultad de Ciencias de la Tierra y el Espacio, Universidad Autónoma de Sinaloa, Culiacán 80013, Sinaloa, Mexico
10 shared publications - Lidia Yadira Perez-Aguilar
Universidad Autónoma de Sinaloa
8 shared publications - Sergio Alberto Monjardin-Armenta
Facultad de Ciencias de la Tierra y el Espacio, Universidad Autónoma de Sinaloa, Culiacán, Mexico
8 shared publications - Jesús Gabriel Rangel-Peraza
División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/Instituto Tecnológico de Culiacán, Juan de Dios Batíz 310. Col. Guadalupe, Culiacán 80220, Mexico
4 shared publications - Ramón Fernando López-Osorio
Facultad de Informática Culiacán, Universidad Autónoma de Sinaloa, Culiacán 80013, Sinaloa, Mexico
3 shared publications
Latest Publications
- Open Access
Evaluating Tropospheric Mapping Functions for GPS-Derived PWV in a Tropical Region: Insights from Southwestern Mexico
Global Navigation Satellite Systems (GNSS) have emerged as a reliable and cost-effective tool for estimating atmospheric precipitable water vapor (PWV), particularly in regions with limited meteorological instrumentation. In this study, the performance of three tropospheric mapping functions—the Global Mapping Function (GMF), Niell Mapping Function (NMF), and Vienna Mapping Function 1 (VMF1)—was evaluated for PWV estimation using GPS observations collected during the 2009–2011 period in southwestern Mexico, a region characterized by high atmospheric variability and frequent extreme weather events. GPS data from three stations (TECO, COL2, and PENA) were processed using the GAMIT/GLOBK 10.71 software, and the resulting PWV estimates were validated against independent radiosonde observations and the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth-Generation Reanalysis (ERA5) data. The results show that GPS-derived PWV successfully captures the seasonal variability of atmospheric water vapor, with maximum values during the summer rainy season. High correlations were obtained with both radiosonde and ERA5 data, particularly at the TECO station (R = 0.95–0.99), where RMSE values ranged from 3.27 to 5.46 mm and BIAS values from to mm. In contrast, larger discrepancies were observed at COL2 and PENA, mainly due to horizontal separation and altitude differences relative to the radiosonde site, highlighting the importance of spatial representativeness during validation. Among the evaluated mapping functions, no single model consistently outperformed the others across all stations, years, and reference datasets. Nevertheless, GMF and NMF generally exhibited more stable and consistent performance, whereas VMF1 showed greater variability under the adopted processing strategy. Additionally, a clear relationship was identified between PWV and precipitation records, indicating that increases in PWV coincided with periods of intense rainfall and suggesting its potential as an indicator of atmospheric conditions favorable for precipitation events. Overall, this study shows that GPS-derived PWV can reproduce the seasonal variability of atmospheric water vapor under the adopted processing strategy and demonstrates the importance of mapping function selection and spatial representativeness for accurate PWV estimation.Open AccessEvaluating Tropospheric Mapping Functions for GPS-Derived PWV in a Tropical Region: Insights from Southwestern Mexico
Global Navigation Satellite Systems (GNSS) have emerged as a reliable and cost-effective tool for estimating atmospheric precipitable water vapor (PWV), particularly in regions with limited meteorological instrumentation. In this study, the performance of three tropospheric mapping functions—the Global Mapping Function (GMF), Niell Mapping Function (NMF), and Vienna Mapping Function 1 (VMF1)—was evaluated for PWV estimation using GPS observations collected during the 2009–2011 period in southwestern Mexico, a region characterized by high atmospheric variability and frequent extreme weather events. GPS data from three stations (TECO, COL2, and PENA) were processed using the GAMIT/GLOBK 10.71 software, and the resulting PWV estimates were validated against independent radiosonde observations and the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth-Generation Reanalysis (ERA5) data. The results show that GPS-derived PWV successfully captures the seasonal variability of atmospheric water vapor, with maximum values during the summer rainy season. High correlations were obtained with both radiosonde and ERA5 data, particularly at the TECO station (R = 0.95–0.99), where RMSE values ranged from 3.27 to 5.46 mm and BIAS values from to mm. In contrast, larger discrepancies were observed at COL2 and PENA, mainly due to horizontal separation and altitude differences relative to the radiosonde site, highlighting the importance of spatial representativeness during validation. Among the evaluated mapping functions, no single model consistently outperformed the others across all stations, years, and reference datasets. Nevertheless, GMF and NMF generally exhibited more stable and consistent performance, whereas VMF1 showed greater variability under the adopted processing strategy. Additionally, a clear relationship was identified between PWV and precipitation records, indicating that increases in PWV coincided with periods of intense rainfall and suggesting its potential as an indicator of atmospheric conditions favorable for precipitation events. Overall, this study shows that GPS-derived PWV can reproduce the seasonal variability of atmospheric water vapor under the adopted processing strategy and demonstrates the importance of mapping function selection and spatial representativeness for accurate PWV estimation. - Open Access
- 7 May 2026
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 15 (5) , 200
Spatial Analysis of Land Cover Degradation Processes Associated with Aridity in Northwestern Mexico Using Geographically Weighted Regression
Aridity is a key climatic factor influencing ecosystem dynamics and land degradation in arid and semi-arid regions. This study analyzes the spatial relationship between aridity and land cover degradation in northwestern Mexico during 2005–2020 using a Geographically Weighted Regression (GWR) model, complemented by spatial autocorrelation techniques including Moran’s I and Local Indicators of Spatial Association (LISA). Aridity was derived from climatic data, and land cover transitions were used as proxies for degradation. The results indicate that the study area is predominantly characterized by arid and semi-arid conditions, where degradation-related transitions are strongly concentrated. In particular, transitions from shrubland to grassland (59.53%) and from shrubland to bare soil (93.60%) occur primarily under arid conditions, highlighting the high vulnerability of these ecosystems to water deficit. The GWR model explains approximately 49.5% of the spatial variability in degradation. However, residual analysis shows strong spatial autocorrelation (Moran’s I = 0.72, p < 0.001), indicating spatially structured patterns not fully captured by the model. These findings demonstrate that, although aridity is a key driver, additional factors influence degradation patterns.
- 7 May 2026
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 15 (5) , 200
Open AccessSpatial Analysis of Land Cover Degradation Processes Associated with Aridity in Northwestern Mexico Using Geographically Weighted Regression
Aridity is a key climatic factor influencing ecosystem dynamics and land degradation in arid and semi-arid regions. This study analyzes the spatial relationship between aridity and land cover degradation in northwestern Mexico during 2005–2020 using a Geographically Weighted Regression (GWR) model, complemented by spatial autocorrelation techniques including Moran’s I and Local Indicators of Spatial Association (LISA). Aridity was derived from climatic data, and land cover transitions were used as proxies for degradation. The results indicate that the study area is predominantly characterized by arid and semi-arid conditions, where degradation-related transitions are strongly concentrated. In particular, transitions from shrubland to grassland (59.53%) and from shrubland to bare soil (93.60%) occur primarily under arid conditions, highlighting the high vulnerability of these ecosystems to water deficit. The GWR model explains approximately 49.5% of the spatial variability in degradation. However, residual analysis shows strong spatial autocorrelation (Moran’s I = 0.72, p < 0.001), indicating spatially structured patterns not fully captured by the model. These findings demonstrate that, although aridity is a key driver, additional factors influence degradation patterns.
- Open Access
- 9 February 2026
- Published by MDPI AG in Agriculture
- Vol. 16 (4) , 399
Water Use and Maize Productivity Under Climate Variability in Irrigation Districts of Sinaloa, Mexico: A Panel Econometric Analysis
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.
- 9 February 2026
- Published by MDPI AG in Agriculture
- Vol. 16 (4) , 399
Open AccessWater Use and Maize Productivity Under Climate Variability in Irrigation Districts of Sinaloa, Mexico: A Panel Econometric Analysis
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.
- 1 February 2026
- Published by Springer Nature
- p. 57-77
Evolution of Coastal Erosion in the Gulf of California During the Anthropocene Epoch and Climate Change
- 1 February 2026
- Published by Springer Nature
- p. 57-77
Evolution of Coastal Erosion in the Gulf of California During the Anthropocene Epoch and Climate Change
- 2 January 2026
- Published by Springer Nature
- p. 3-36
Soil Degradation: Causes, Impacts, and Mitigation Strategies in the Context of Climate Change
- 2 January 2026
- Published by Springer Nature
- p. 3-36
Soil Degradation: Causes, Impacts, and Mitigation Strategies in the Context of Climate Change
- 22 November 2025
- Published by Springer Nature
- p. 83-104
Assessment Soil Surface Erosion by USLE and RUSLE Geospatial Models: A Review
- 22 November 2025
- Published by Springer Nature
- p. 83-104
Assessment Soil Surface Erosion by USLE and RUSLE Geospatial Models: A Review
- 1 January 2025
- Published by Springer Nature
- p. 39-57
Remote Sensing in Agriculture: Advances in Satellite Sensors and Unmanned Aerial Vehicles
- 1 January 2025
- Published by Springer Nature
- p. 39-57
Remote Sensing in Agriculture: Advances in Satellite Sensors and Unmanned Aerial Vehicles
- Open Access
- 1 November 2024
- Published by Revista de Investigacion en Tecnologias de la Informacion in Revista de Investigación en Tecnologías de la Información
- Vol. 12 (28) , 54-70
Aplicación de evaluación multicriterio para modelar factores climáticos y ambientales en la identificación de regiones áridas en el noroeste de México
In Mexico, arid regions cover more than half of the territory, with desertification, degradation and droughts being the main problems in these ecosystems, caused mainly by climatic events and anthropogenic activities. The objective of this research was to apply multicriteria assessment (MCE) techniques to model factors that favor the increase in aridity, and to be able to identify areas with different levels of aridity in the Northwest region of Mexico by 2023. The Analytical Hierarchy Method (AHP) and Weighted Linear Summation (WLC) were used in a Geographic Information Systems (GIS) environment. The modeling was carried out with satellite images, which were: Temperature, Soil Moisture, Precipitation, Slopes, Normalized Differential Vegetation Index (NDVI), Digital Elevation Model (DEM), Vegetation Cover and Evapotranspiration. The results show that thanks to the effective integration of the factors with the EMC technique, regions with different levels of aridity could be identified; the northern and northwestern regions have a greater arid surface area with 41%, while the semi-arid regions, located in the west, represent 30% of the surface area, while the sub-humid regions were located in the southeast with 27% of the surface area.
- 1 November 2024
- Published by Revista de Investigacion en Tecnologias de la Informacion in Revista de Investigación en Tecnologías de la Información
- Vol. 12 (28) , 54-70
Open AccessAplicación de evaluación multicriterio para modelar factores climáticos y ambientales en la identificación de regiones áridas en el noroeste de México
In Mexico, arid regions cover more than half of the territory, with desertification, degradation and droughts being the main problems in these ecosystems, caused mainly by climatic events and anthropogenic activities. The objective of this research was to apply multicriteria assessment (MCE) techniques to model factors that favor the increase in aridity, and to be able to identify areas with different levels of aridity in the Northwest region of Mexico by 2023. The Analytical Hierarchy Method (AHP) and Weighted Linear Summation (WLC) were used in a Geographic Information Systems (GIS) environment. The modeling was carried out with satellite images, which were: Temperature, Soil Moisture, Precipitation, Slopes, Normalized Differential Vegetation Index (NDVI), Digital Elevation Model (DEM), Vegetation Cover and Evapotranspiration. The results show that thanks to the effective integration of the factors with the EMC technique, regions with different levels of aridity could be identified; the northern and northwestern regions have a greater arid surface area with 41%, while the semi-arid regions, located in the west, represent 30% of the surface area, while the sub-humid regions were located in the southeast with 27% of the surface area.
- 7 August 2024
- Published by Springer Nature
- p. 1-20
The Impact of Climate Change on Crop Productivity and Adaptation and Mitigation Strategies in Agriculture
- 7 August 2024
- Published by Springer Nature
- p. 1-20
The Impact of Climate Change on Crop Productivity and Adaptation and Mitigation Strategies in Agriculture