Escenarios futuros de eventos extremos de precipitación y temperatura en México

Future changes of precipitation and temperature extremes in Mexico

Ernesto Ramos Esteban (2024, [Tesis de maestría])

Diferentes estudios a escala mundial indican un incremento en frecuencia de eventos climáticos extremos debido al calentamiento global y sugieren que podrían intensificarse en el futuro. El objetivo de este trabajo es analizar los posibles cambios de 12 índices climáticos extremos (ICE) de precipitación y temperatura en 15 regiones de México, el sur de los Estados Unidos y Centroamérica para un período histórico (1981-2010), un futuro cercano (2021-2040), un futuro intermedio (2041-2060) y un futuro lejano (2080-2099). Se utilizó el reanálisis ERA5 como referencia en la evaluación histórica de los modelos climáticos globales (MCG) y para las proyecciones se analizaron los ICE de diez MCG del Proyecto de Intercomparación de Modelos Climáticos, fase 6 (CMIP6), de acuerdo con dos escenarios de Vías Socioeconómicas Compartidas (SSPs), uno de bajas emisiones (SSP2-4.5) y otro de altas emisiones (SSP3-7.0). Los MCG reproducen muy bien los índices extremos de temperatura histórica y los días consecutivos secos, pero subestiman la lluvia promedio y la lluvia extrema en las zonas más lluviosas desde el centro de México hasta Centroamérica. Históricamente, se observaron tendencias positivas de las temperaturas extremas (TXx y TNn) en todas las regiones, pero sólo en algunas regiones fueron significativas, mientras que los índices de lluvia extrema (R95p, R10mm y R20mm) presentaron tendencias negativas, pero pequeñas. Las proyecciones indican que las temperaturas extremas podrían seguir incrementándose en el futuro, desde 2° C hasta 5° C a mitad y final de siglo, respectivamente. La contribución de la precipitación extrema arriba del percentil 95 (R95p) se podría incrementar entre un 10 % y 30 %, especialmente en la región subtropical, mientras que la precipitación podría disminuir en las regiones tropicales. Este estudio es el primero que analiza los cambios futuros de índices extremos del CMIP6 a escala regional (en 15 regiones) de México, el sur de Estados Unidos y Centroamérica.

Global-scale studies indicate an increase in the frequency of extreme weather events due to global warming and suggest that they could further intensify in the future. This study aims to assess potential changes in 12 extreme climate indices (ECI) related to precipitation and temperature in 15 regions in Mexico, the southern United States, and Central America for different periods: a historical period (1981-2010), a near future (2021-2040), an intermediate future (2041-2060), and a far future (2080-2099). The ERA5 reanalysis was used as a reference for the historical evaluation of global climate models (GCMs), and ECI from ten GCMs of phase 6 (CMIP6) from the Coupled Model Intercomparison Project were employed for the projections and examined under two Shared Socioeconomic Pathways (SSPs) scenarios, one characterized by low emissions (SSP2-4.5) and another representing high greenhouse gas emissions (SSP3-7.0). The GCMs reproduce historical extreme temperature indices and consecutive dry days very well. However, they underestimate average and extreme rainfall from central Mexico to Central America in the wetter areas. Historically, positive trends in extreme temperatures (TXx and TNn) were observed across all regions. However, statistical significance was only present in certain regions, while extreme rainfall indices (R95p, R10mm, and R20mm) exhibited small negative trends. The projections suggest that extreme temperatures could continue to increase in the future, from 2°C to 5°C by the mid and late century, respectively. The contribution of extreme precipitation above the 95th percentile (R95p) could increase by 10% to 30%, particularly in the subtropical regions, while precipitation might decrease in tropical regions. This study is the first to analyze future changes in extreme indices from CMIP6 at a regional scale (across 15 regions) in Mexico, the southern United States, and Central America.

Centroamérica, CMIP6, escenarios SSP, extremos climáticos, intercomparación de modelos climáticos, México Central America, climate extremes, CMIP6, intercomparison of climate models, Mexico, SSP scenarios CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS DE LA TIERRA Y DEL ESPACIO OCEANOGRAFÍA OCEANOGRAFÍA FÍSICA (VE R 5603 .04) OCEANOGRAFÍA FÍSICA (VE R 5603 .04)

Hot Extremes and Climatological Drought Indicators in the Transitional Semiarid-Subtropical Region of Sinaloa, Northwest Mexico

OMAR LLANES CARDENAS OSCAR GERARDO GUTIERREZ RUACHO Iván Hernández Romano ENRIQUE TROYO DIEGUEZ (2022, [Artículo])

"The main goal of this study was to explore the historical and recent spatial concurrence between the frequency (F), duration (D) and intensity (I) of hot extremes (HEs) and the frequency and evolution of meteorological drought in the region of Sinaloa. Based on the values of daily maximum temperatura (Tmax) and precipitation obtained from CLImate COMputing for the interval April–October of a historical period (1963–2000) and a recent period (1982–2014), the HE and the standardized precipitation index (SPI) were calculated on one-month (SPI-1) and four-month (SPI-4) timescales. Spearman rank correlation coefficients (Sr) were used to obtain the significant concurrences (SCs) between HEs and SPI-1, and HEs and SPI-4. El Quelite weather station showed the highest historical SCs between HEs and SPI-1 (−0.66≤Sr≤−0.57). Jaina is the only station that showed SCs with all four indicators of HEs and SPI-4 (−0.47≤Sr≤−0.34). In this study, the concurrence between HEs and SPI-1, and HEs and SPI-4 was determined for the first time. These are phenomena that can decrease the crop yield, particularly for rainfed crops such as maize, sesame and sorghum in the region commonly known as “the breadbasket of Mexico."

frequency and evolution of meteorological droughts, the breadbasket of Mexico, Sinaloa CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS DE LA TIERRA Y DEL ESPACIO CLIMATOLOGÍA CLIMATOLOGÍA REGIONAL CLIMATOLOGÍA REGIONAL

“Mexico half way of making”: Carlos Chávez and the foundation of the National Institute of Fine Arts (INBA) (1945-1947)

Ana Garduño (2023, [Artículo, Artículo])

I am focusing on the interaction between a cultural agent, Carlos Chávez, and the government elite, related to the circumstances of the enunciation of a public and official organism, INBA (The National Institute of Fine Arts), because I assume that political relations are fundamental to understand a process that endorsed the official culture as an appendage of politics. In spite of its relevance, this subject has not been studied. Based on archival documentation (at the National Archive of Mexico and the archive of INBA) I am examining the cultural policies derived from the foundation of INBA. Due to the current pandemic situation, it was not possible to consult other documental sources. I am formulating that the concept of “high culture” was at the core of a wider conflict between governmental interests and those of representative social actors, and I conclude that this obstacle would have conferred a major political and symbolical importance to the Institute, and consequently, would restrict its budget.

Carlos Chávez INBA Agentes culturales Políticas institucionales Centralización artística HUMANIDADES Y CIENCIAS DE LA CONDUCTA HUMANIDADES Y CIENCIAS DE LA CONDUCTA Foundation of the National Institute of Fine Arts in Mexico (INBA), Cultural agents Institutional policies Artistic centralization

Optimizing nitrogen fertilizer and planting density levels for maize production under current climate conditions in Northwest Ethiopian midlands

Kindie Tesfaye Dereje Ademe Enyew Adgo (2023, [Artículo])

This study determined the most effective plating density (PD) and nitrogen (N) fertilizer rate for well-adapted BH540 medium-maturing maize cultivars for current climate condition in north west Ethiopia midlands. The Decision Support System for Agrotechnology Transfer (DSSAT)-Crop Environment Resource Synthesis (CERES)-Maize model has been utilized to determine the appropriate PD and N-fertilizer rate. An experimental study of PD (55,555, 62500, and 76,900 plants ha−1) and N (138, 207, and 276 kg N ha−1) levels was conducted for 3 years at 4 distinct sites. The DSSAT-CERES-Maize model was calibrated using climate data from 1987 to 2018, physicochemical soil profiling data (wilting point, field capacity, saturation, saturated hydraulic conductivity, root growth factor, bulk density, soil texture, organic carbon, total nitrogen; and soil pH), and agronomic management data from the experiment. After calibration, the DSSAT-CERES-Maize model was able to simulate the phenology and growth parameters of maize in the evaluation data set. The results from analysis of variance revealed that the maximum observed and simulated grain yield, biomass, and leaf area index were recorded from 276 kg N ha−1 and 76,900 plants ha−1 for the BH540 maize variety under the current climate condition. The application of 76,900 plants ha−1 combined with 276 kg N ha−1 significantly increased observed and simulated yield by 25% and 15%, respectively, compared with recommendation. Finally, future research on different N and PD levels in various agroecological zones with different varieties of mature maize types could be conducted for the current and future climate periods.

Maize Model Planting Density CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE MODELS SPACING NITROGEN FERTILIZERS YIELDS

Diseño de un sistema eléctrico resiliente: Una evaluación multiobjetivo considerando dimensiones económicas, tecnológicas y políticas

EDMUNDO MOLINA PEREZ (2023, [Tesis de maestría])

https://orcid.org/0000-0003-0774-3205

Esta tesis aborda el desarrollo futuro del Sistema Eléctrico Nacional (SEN) de México en un contexto donde el país ha experimentado políticas energéticas contrastantes en una década. Frente a la alternancia de políticas energéticas opuestas bajo los gobiernos de Enrique Peña Nieto y Andrés Manuel López Obrador, se emplea la técnica de Robust Decision Making (RDM) para evaluar la resiliencia de sus políticas frente a un amplio rango de futuros plausibles. El estudio se centra en evaluar el desempeño del SEN bajo la implementación de cada política en términos de margen de reserva, costos de producción y emisiones directas de gases de efecto invernadero en un contexto de incertidumbre profunda. Los resultados revelan que las políticas energéticas más resilientes se caracterizan por una significativa incorporación de nuevas capacidades de generación, diversificación tecnológica y uso extensivo de energías limpias. Se observa que la política energética actual podría ser vulnerable por no alinearse con estos criterios. La investigación subraya la necesidad de un debate público y la formulación de políticas basadas en análisis objetivos y evidencia, enfocándose en el bienestar y progreso nacional.

Maestro en Prospectiva Estratégica

CIENCIAS SOCIALES CIENCIAS ECONÓMICAS ECONOMÍA SECTORIAL ENERGÍA

Regional analysis of the wage discrimination in the indigenous workers in Mexico

Christian De la Luz-Tovar SIBYL ITALIA PINEDA SALAZAR (2023, [Artículo, Artículo])

The objective of this research is to estimate and decompose the wage gap between indigenous and non-indigenous workers by region in Mexico, to examine whether there are regional differences in the existing wage inequality that a priori affects the indigenous population and whether these differences can be attributed to the job profile of this group or by systematic labor discrimination against them. Using the data from the 2018 National Household Expenditure Revenue Survey (ENIGH-N) and the Oaxaca-Blinder decomposition, it was found that indigenous workers face a wage gap in all regions of the county. But, this gap is more pronounced in the center and south regions, where, on average, the associated component with labor discrimination has a percentage greater than 56. In contrast, in the north-central and northern regions of Mexico, the residual component is on average less than 33%, which suggests that the wage gap is explained by differences in productivity between groups.                         

Labor economics Ethnicity wage gap Indigenous population Regions Oaxaca-Blinder decomposition Economía laboral Brecha salarial étnica Población indígena Regiones Descomposición de Oaxaca-Blinder CIENCIAS SOCIALES CIENCIAS SOCIALES

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding

Regional analysis of the wage discrimination in the indigenous workers in Mexico

Christian De la Luz-Tovar SIBYL ITALIA PINEDA SALAZAR (2023, [Artículo, Artículo])

The objective of this research is to estimate and decompose the wage gap between indigenous and non-indigenous workers by region in Mexico, to examine whether there are regional differences in the existing wage inequality that a priori affects the indigenous population and whether these differences can be attributed to the job profile of this group or by systematic labor discrimination against them. Using the data from the 2018 National Household Expenditure Revenue Survey (ENIGH-N) and the Oaxaca-Blinder decomposition, it was found that indigenous workers face a wage gap in all regions of the county. But, this gap is more pronounced in the center and south regions, where, on average, the associated component with labor discrimination has a percentage greater than 56. In contrast, in the north-central and northern regions of Mexico, the residual component is on average less than 33%, which suggests that the wage gap is explained by differences in productivity between groups.                         

Labor economics Ethnicity wage gap Indigenous population Regions Oaxaca-Blinder decomposition Economía laboral Brecha salarial étnica Población indígena Regiones Descomposición de Oaxaca-Blinder CIENCIAS SOCIALES CIENCIAS SOCIALES

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Martin van Ittersum (2023, [Artículo])

Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY

Calidad de la democracia en los gobiernos delegacionales de izquierda: Un análisis de las implicaciones de las políticas sociales en la Magdalena Contreras.

MARIELA DIAZ SANDOVAL (2018, [Capítulo de libro])

En México el estudio sobre los gobiernos a nivel delegacional es una gran asignatura pendiente en las ciencias sociales. No obstante, desde diversas disciplinas existe un gran interés en entender las realidades locales. La llegada al poder, los conflictos entre diversas fuerzas políticas en la localidad, las relaciones que se tejen entre ciudadanos, intermediarios y gobernantes, la incidencia de las organizaciones de la sociedad civil, así como el ejercicio del gobierno, y otros aspectos pueden y deben ser analizados con miras a encontrar las diferencias y similitudes en este nivel de estudio.

Educación Derecho a la salud Derecho a la vivienda Sectores vulnerables CIENCIAS SOCIALES CIENCIA POLÍTICA ADMINISTRACIÓN PÚBLICA ADMINISTRACIÓN CIVIL