Editorial: Sorghum and pearl millet as climate resilient crops for food and nutrition security

Palak Chaturvedi Mahalingam Govindaraj Govindan Velu Wolfram Weckwerth (2022, [Artículo])

Climate Smart Crops Foxtail Millet CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING FINGER MILLET FOOD SECURITY SETARIA ITALICA GENETIC RESOURCES PEARL MILLET SORGHUM

Historical use of water resources. Civil works evolution in Zacatecas state

Carlos Bautista-Capetillo Georgia González-Pérez Hiram Badillo-Almaraz (2021, [Artículo, Artículo])

Availability and demand are essential aspects for the human being when planning is made to provide water to the different sectors that may have need of it; still, the demand of suitable volume of water increases day by day, while the supply decreases gradually. In this inverse relationship, anthropogenic and environmental dynamics are decisive to guarantee the needs of the population, specifically due to the climatic transformations evidenced in recent decades. Throughout history, the state of Zacatecas has suffered the ravages of extreme environmental events, mainly those related to drought. Likewise, but on a lesser extent, severe floods have occurred that have caused socioeconomic damage. In this work, the climatic variations of temperature and precipitation and their influence on the evolution of hydraulic systems for the supply of drinking water in the municipality of Nochistlán de Mejía, Zacatecas are analyzed during the period 1930-2015.

drinking water supply historical development of waterworks climate and its transformations Abasto de agua potable desarrollo histórico de obras hidráulicas clima y sus transformaciones CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA

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)

Control de sistemas usando aprendizaje de máquina

Systems control using machine learning

Jesús Martín Miguel Martínez (2023, [Tesis de maestría])

El aprendizaje por refuerzo es un paradigma del aprendizaje de máquina con un amplio desarrollo y una creciente demanda en aplicaciones que involucran toma de decisiones y control. Es un paradigma que permite el diseño de controladores que no dependen directamente del modelo que describe la dinámica del sistema. Esto es importante ya que en aplicaciones reales es frecuente que no se disponga de dichos modelos de manera precisa. Esta tesis tiene como objetivo implementar un controlador óptimo en tiempo discreto libre de modelo. La metodología elegida se basa en algoritmos de aprendizaje por refuerzo, enfocados en sistemas con espacios de estado y acción continuos a través de modelos discretos. Se utiliza el concepto de función de valor (Q-función y función V ) y la ecuación de Bellman para resolver el problema del regulador cuadrático lineal para un sistema mecánico masa-resorte-amortiguador, en casos donde se tiene conocimiento parcial y desconocimiento total del modelo. Para ambos casos las funciones de valor son definidas explícitamente por la estructura de un aproximador paramétrico, donde el vector de pesos del aproximador es sintonizado a través de un proceso iterativo de estimación de parámetros. Cuando se tiene conocimiento parcial de la dinámica se usa el método de aprendizaje por diferencias temporales en un entrenamiento episódico, que utiliza el esquema de mínimos cuadrados con mínimos cuadrados recursivos en la sintonización del crítico y descenso del gradiente en la sintonización del actor, el mejor resultado para este esquema es usando el algoritmo de iteración de valor para la solución de la ecuación de Bellman, con un resultado significativo en términos de precisión en comparación a los valores óptimos (función DLQR). Cuando se tiene desconocimiento de la dinámica se usa el algoritmo Q-learning en entrenamiento continuo, con el esquema de mínimos cuadrados con mínimos cuadrados recursivos y el esquema de mínimos cuadrados con descenso del gradiente. Ambos esquemas usan el algoritmo de iteración de política para la solución de la ecuación de Bellman, y se obtienen resultados de aproximadamente 0.001 en la medición del error cuadrático medio. Se realiza una prueba de adaptabilidad considerando variaciones que puedan suceder en los parámetros de la planta, siendo el esquema de mínimos cuadrados con mínimos cuadrados recursivos el que tiene los mejores resultados, reduciendo significativamente ...

Reinforcement learning is a machine learning paradigm with extensive development and growing demand in decision-making and control applications. This technique allows the design of controllers that do not directly depend on the model describing the system dynamics. It is useful in real-world applications, where accurate models are often unavailable. The objective of this work is to implement a modelfree discrete-time optimal controller. Through discrete models, we implemented reinforcement learning algorithms focused on systems with continuous state and action spaces. The concepts of value-function, Q-function, V -function, and the Bellman equation are employed to solve the linear quadratic regulator problem for a mass-spring-damper system in a partially known and utterly unknown model. For both cases, the value functions are explicitly defined by a parametric approximator’s structure, where the weight vector is tuned through an iterative parameter estimation process. When partial knowledge of the dynamics is available, the temporal difference learning method is used under episodic training, utilizing the least squares with a recursive least squares scheme for tuning the critic and gradient descent for the actor´s tuning. The best result for this scheme is achieved using the value iteration algorithm for solving the Bellman equation, yielding significant improvements in approximating the optimal values (DLQR function). When the dynamics are entirely unknown, the Q-learning algorithm is employed in continuous training, employing the least squares with recursive least squares and the gradient descent schemes. Both schemes use the policy iteration algorithm to solve the Bellman equation, and the system’s response using the obtained values was compared to the one using the theoretical optimal values, yielding approximately zero mean squared error between them. An adaptability test is conducted considering variations that may occur in plant parameters, with the least squares with recursive least squares scheme yielding the best results, significantly reducing the number of iterations required for convergence to optimal values.

aprendizaje por refuerzo, control óptimo, control adaptativo, sistemas mecánicos, libre de modelo, dinámica totalmente desconocida, aproximación paramétrica, Q-learning, iteración de política reinforcement learning, optimal control, adaptive control, mechanical systems, modelfree, utterly unknown dynamics, parametric approximation, Q-learning, policy iteration INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ORDENADORES INTELIGENCIA ARTIFICIAL INTELIGENCIA ARTIFICIAL

Assessing the Response of Nematode Communities to Climate Change-Driven Warming: A Microcosm Experiment

RUTH GINGOLD WERMUTH (2013, [Artículo])

Biodiversity has diminished over the past decades with climate change being among the main responsible factors. One consequence of climate change is the increase in sea surface temperature, which, together with long exposure periods in intertidal areas, may exceed the tolerance level of benthic organisms. Benthic communities may suffer structural changes due to the loss of species or functional groups, putting ecological services at risk. In sandy beaches, free-living marine nematodes usually are the most abundant and diverse group of intertidal meiofauna, playing an important role in the benthic food web. While apparently many functionally similar nematode species co-exist temporally and spatially, experimental results on selected bacterivore species suggest no functional overlap, but rather an idiosyncratic contribution to ecosystem functioning. However, we hypothesize that functional redundancy is more likely to observe when taking into account the entire diversity of natural assemblages. We conducted a microcosm experiment with two natural communities to assess their stress response to elevated temperature. The two communities differed in diversity (high [HD] vs. low [LD]) and environmental origin (harsh vs. moderate conditions). We assessed their stress resistance to the experimental treatment in terms of species and diversity changes, and their function in terms of abundance, biomass, and trophic diversity. According to the Insurance Hypothesis, we hypothesized that the HD community would cope better with the stressful treatment due to species functional overlap, whereas the LD community functioning would benefit from species better adapted to harsh conditions. Our results indicate no evidence of functional redundancy in the studied nematofaunal communities. The species loss was more prominent and size specific in the HD; large predators and omnivores were lost, which may have important consequences for the benthic food web. Yet, we found evidence for alternative diversity-ecosystem functioning relationships, such as the Rivets and the Idiosyncrasy Model. © 2013 Gingold et al.

aquaculture, article, bacterivore, benthos, biodiversity, biomass, climate, community dynamics, controlled study, ecosystem, environmental temperature, microcosm, nematode, nonhuman, population abundance, species diversity, species richness, taxonomy CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS DE LA TIERRA Y DEL ESPACIO OCEANOGRAFÍA OCEANOGRAFÍA

Contrasting spatial patterns in active-fire and fire-suppressed mediterranean climate old-growth mixed conifer forests

Danny L. Fry  (2014, [Artículo])

In Mediterranean environments in western North America, historic fire regimes in frequent-fire conifer forests are highly variable both temporally and spatially. This complexity influenced forest structure and spatial patterns, but some of this diversity has been lost due to anthropogenic disruption of ecosystem processes, including fire. Information from reference forest sites can help management efforts to restore forests conditions that may be more resilient to future changes in disturbance regimes and climate. In this study, we characterize tree spatial patterns using four-ha stem maps from four old-growth, Jeffrey pine-mixed conifer forests, two with active-fire regimes in northwestern Mexico and two that experienced fire exclusion in the southern Sierra Nevada. Most of the trees were in patches, averaging six to 11 trees per patch at 0.007 to 0.014 ha-1, and occupied 27-46% of the study areas. Average canopy gap sizes (0.04 ha) covering 11-20% of the area were not significantly different among sites. The putative main effects of fire exclusion were higher densities of single trees in smaller size classes, larger proportion of trees (≥56%) in large patches (≥10 trees), and decreases in spatial complexity. While a homogenization of forest structure has been a typical result from fire exclusion, some similarities in patch, single tree, and gap attributes were maintained at these sites. These within-stand descriptions provide spatially relevant benchmarks from which to manage for structural heterogeneity in frequent-fire forest types.

article, climate, controlled study, ecosystem fire history, forest structure, geographic distribution, geographic mapping, land use, mathematical computing, mathematical model, Mexico, spatial analysis, taiga, United States, comparative study, conife CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA