RESUMO
Rice is one of the world's major food crops. Changes in major climatic factors such as temperature, rainfall, solar radiation and carbon dioxide (CO2) concentration have an important impact on rice growth and yield. However, many of the current studies that predict the impact of future climate change on rice yield are affected by uncertainties such as climate models, climate scenarios, model parameters and structure, and showing great differences. This study was based on the assessment results of the impact of climate change on rice in the future of 111 published literature, and comprehensively analyzed the impact and uncertainty of climate change on rice yield. This study utilized local polynomial (Loess) regression analysis to investigate the impact of changes in mean temperature, minimum temperature, maximum temperature, solar radiation, and precipitation on relative rice yield variations within a complete dataset. A linear mixed-effects model was used to quantitatively analyze the relationships between the restricted datasets. The qualitative analysis based on the entire dataset revealed that rice yields decreased with increasing average temperature. The precipitation changed between 0 and 25 %, it was conducive to the stable production of rice, and when the precipitation changed >25 %, it would cause rice yield reduction. The change of solar radiation was less than -1.15 %, the rice yield increases with the increase of solar radiation, and when the change of solar radiation exceeds -1.15 %, the rice yield decreases. Elevated CO2 concentrations and management practices could mitigate the negative effects of climate change. The results of a quantitative analysis utilizing the mixed effects model revealed that average temperature, precipitation, CO2 concentration, and adaptation methods all had a substantial impact on rice production, and elevated CO2 concentrations and management practices could exert positive influences on rice production. For every 1 °C and 1 % increase in average temperature and precipitation, rice yield decreased by 3.85 % and 0.56 %, respectively. For every 100 ppm increase in CO2 concentration, rice yield increased by 7.1 %. The variation of rice yield under different climate models, study sites and climate scenarios had significant variability. Elevated CO2 concentrations and management practices could compensate for the negative effects of climate change, benefiting rice production. This study comprehensively collected and analyzed a wide range of literature and research, which provides an in-depth understanding of the impacts of climate change on rice production and informs future research and policy development.
Assuntos
Mudança Climática , Produtos Agrícolas , Oryza , Oryza/crescimento & desenvolvimento , Produtos Agrícolas/crescimento & desenvolvimento , Dióxido de Carbono/análise , Modelos Climáticos , Temperatura , Agricultura/métodosRESUMO
Investments in renewable energy sources are increasing in several countries, especially in wind energy, as a response to global climate change caused by the burning of fossil fuels for electricity generation. Thus, it is important to evaluate the Regional Climate Models that simulate wind speed and wind power density in promising areas for this type of energy generation with the least uncertainty in recent past, which is essential for the implementation of wind farms. Therefore, this research aims to calculate the wind power density from Regional Climate Models in areas at Northeast of Brazil from 1986 to 2005. Initially, the ECMWF-ERA5 reanalysis data was validated against observed data obtained from Xavier. The results were satisfactory, showing a strong correlation in areas of Ceará and Rio Grande do Norte (except during the SON season), and some differences in relation to the wind intensity registered by observed data, particularly during the JJA season. Then, the Regional Climate Models RegCM4.7, RCA4 and Remo2009 were validated against the ECMWF-ERA5 reanalysis data, with all models successfully representing the wind speed pattern, especially from December to May. Four specific areas in Northeast of Brazil were selected for further study. In these areas, the RCMs simulations were evaluated to identify the RCM with the best statistical indices and consequently the lowest associated uncertainty for each area. The selected RCMs were: RegCM4.7_HadGEM2 (northern coastal of Ceará and northern coastal of Rio Grande do Norte) and RCA4_Miroc (Borborema and Central Bahia). Finally, the wind power density was calculated from the selected RCM for each area. The northern regions of Rio Grande do Norte and Ceará exhibited the highest wind power density.
Assuntos
Vento , Brasil , Modelos Climáticos , Energia Renovável , Mudança Climática , Estações do AnoRESUMO
Various problems may arise during the road transportation of one-day-old broiler chickens from hatcheries to rearing houses. In this study, the transportation vehicles of a private company operating in the Bursa Region were physically examined, and the climate parameters of the trailer were observed. During these observations the exposure of animals to heat stress was measured, and the loss of life during transportation was revealed. Thirteen data logger values were placed in the trailer and their readings were recorded. While the highest heat stress is in the summer and the heat stress is the highest in the front and middle parts of the trailer, the least are in the first row and the last row in vehicles that use natural ventilation in the summer and mechanical air conditioning in the winter.(AU)
Assuntos
Animais , Bem-Estar do Animal , Galinhas/fisiologia , Meios de Transporte/métodos , Ventilação/métodos , Transtornos de Estresse por Calor/diagnóstico , Modelos ClimáticosRESUMO
Climate models for the 21st century project further reduction in the warm season precipitation and more frequent droughts across Mexico. In the possible scenario of enhanced aridity from global warming, the δ18O (-10.6 to -6.3 ) and δ2H (-71.1 to -57.1 ) compositions and deuterium-excess (0.2-14.6) of shallow groundwater from two different basins (Sandia and El Potosi) with similar geological and geomorphological settings were considered to evaluate the influences of early summer rainfall and later summer tropical storms on aquifers at water-scarce southeast margin of the Chihuahuan Desert. Groundwater of the Sandia Basin was recharged mainly from tropical storms. Higher CO2 partial pressure (log pCO2: -2.70 to -1.61) caused more gypsum dissolution (Ca-Mg-SO4 facies) and the effect of irrigation return flow (Ca-Mg-Cl facies) was minor. Even though the El Potosi Basin is in proximity, its groundwater was recharged from both the early and late summer precipitations. The multivariate factor analysis helped to separate the process of rock-water interactions from the recharge seasonality. Gypsum dissolution was less as the partial pressure of CO2 was comparatively lower (log pCO2: -3.01 to -2.15), and the ion exchange along with carbonate mineral dissolutions led to Ca-Mg-HCO3 facies. Over-exploitation under the condition of reduced warm season rainfall would continue to enhance the salinity of groundwater in this region. Hence, the drought mitigation policies should prioritize sustainability of the depleted aquifers and cultivation of salinity resistant crops.
Assuntos
Água Subterrânea , Poluentes Químicos da Água , Modelos Climáticos , Monitoramento Ambiental , México , Água , Poluentes Químicos da Água/análiseRESUMO
Se comparan las métricas de 37 modelos climáticos globales (GCMs, por sus siglas en inglés) de la Fase 6 del Proyecto de Intercomparación de Modelos Acoplados (CMIP6) con el objetivo de simular el clima de Guatemala del periodo de 1971 al 2014. La temperatura y precipitación mensual fue comparada con los datos de observación de la Unidad De Investigación Climática de la Universidad del este de Anglia (CRU). Se generó un ranquin de modelos basado en la menor distancia entre tres dimisiones basado en tres métricas; Coeficiente de Correlación de Pearson (CCP), Error medio cuadrático (RMSE) y Desviación estándar (DS). Este ordenamiento coincide con los mejores valores de eficiencia Nash-Sutcliffe (NSE) para temperatura y eficiencia Kling-Gupta (KGE) para la precipitación, demás se calculan las métricas; coeficiente de correlación de Spearman (CCS), errores de sesgo medio (MBE) y el absoluto medio (MAE). Para precipitación los primeros 5 modelos presentan valores KGE de entre 0.5 y 0.7, el CCP y CCS entre 0.7 a 0.8 comparados con CRU. Para temperatura los primeros 5 modelos presenta valores de NSE de entre 0.5 a 0.6, CCP y CCS de 0.8. Los modelos sobreestiman levemente la temperatura y subestiman la precipitación. Los modelos con mejor habilidad fueron CIESM para temperatura¼ y el modelo IPSL-CM6A-LR para precipitación. Adicionalmente se compara el promedio de 66 estaciones locales con CRU, presentando un KGE de 0.51, CCP de 0.77 para precipitación y NSE de -0.17 y un CCP de 0.20 para temperatura. Finalmente, se presenta una tabla con los 10 primeros modelos para cada variable.
Metrics from 37 global climate models (GCMs) from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) with the purpose of simulating the climate of Guatemalan from 1971 to 2014. Monthly temperature and precipitation were compared with data from observation of the Climatic Research Unit of the University of East Anglia (CRU). A ranking of models was generated based on the shortest distance between three resignations based on three metrics; Pearson's Correlation Coefficient (PCC), Root Mean Square Error (RMSE), and Standard Deviation (SD). This ordering coincides with the best values of Nash-Sutcliffe efficiency (NSE) for temperature and Kling-Gupta efficiency (KGE) for precipitation; other metrics are calculated; Spearman's correlation coefficient (CCS), mean bias errors (MBE), and mean absolute error (MAE). For precipitation, the first 5 models present KGE values between 0.5 and 0.7, the CCP and CCS between 0.7 and 0.8 compared to CRU. For temperature, the first 5 models present NSE values between 0.5 to 0.6, CCP, and CCS of 0.8. The models slightly overestimate temperature and underestimate precipitation. The models with the best ability were CIESM for temperature and the IPSL-CM6A-LR model for precipitation. Additionally, the average of 66 local stations is compared with CRU, presenting a KGE of 0.51, CCP of 0.77 for precipitation, and NSE of -0.,17, and a CCP of 0.20 for temperature. Finally, a table is presented with the first 10 models for each variable.
Assuntos
Chuva , Estações do Ano , Temperatura , Modelos Climáticos , Mudança Climática/estatística & dados numéricos , Secas , Guatemala , Cromatografia Gasosa-Espectrometria de Massas/métodosAssuntos
Mudança Climática/estatística & dados numéricos , Ecossistema , Calor Extremo/efeitos adversos , Oceanos e Mares , Animais , Organismos Aquáticos , Pressão Atmosférica , Brasil , Mudança Climática/mortalidade , Modelos Climáticos , Cadeia Alimentar , Abastecimento de Alimentos , Previsões , Kelp , Alimentos Marinhos , Austrália OcidentalRESUMO
Temperature extreme indices were analyzed for five continental regions of southern South America defined according to their climatic characteristics. Gridded observations, reanalysis, and global-coupled climate models from CMIP5 were used with the approach of temperature extreme trend attribution analysis on fixed-threshold and percentile-based temperature extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The largest positive trends are exhibited in the tropical nights index, and a clear anthropogenic signal is evident in the subtropical region. In the subtropical central Andes, there is a decrease in the frost days index and increases in the tropical nights and summer days indices, and an anthropogenic signal is evident. In the Patagonian region, all trends from the historical runs were significant, while the ones from the natural experiment were nonsignificant, showing the marked effect of anthropogenic forcing in this region in the extreme temperature events. Projected changes in extreme indices for the 21st century are consistent with a warming climate, and larger changes are expected in the warm nights index.
Assuntos
Mudança Climática , Modelos Climáticos , Temperatura , Previsões , Geografia , Modelos Teóricos , América do SulRESUMO
Este documento presenta un análisis comparativo de los resultados de un modelo de simulación de clima, datos de reanálisis regionales y datos locales de precipitación y temperatura estacional de veintitrés estaciones me-teorológicas de Guatemala, para detectar señales de la habilidad del modelo a reproducir el clima estacional en un periodo de 3 años (1998-2000). La simulación se realizó con un modelo climático regional (MCR), para su reducción de escala dinámica, las condiciones de frontera se obtuvieron de los datos de reanálisis ERA-Interim. El modelo utilizado fue RegCM, versión 4, y se comparó con los datos de precipitación y temperatura de la Base de datos CRU a nivel regional centroamericano y a nivel nacional con tres instituciones que generan datos globales (CRU, TRMM y GPCP) y los datos locales. Los esquemas convectivos utilizados fueron el esquema de Grell sobre tierra y Emanuel sobre el océano, con 50 km de resolución espacial. Los ajustes realizados a las parametrizaciones generaron buen desempeño a nivel regional Centroamericano y a nivel Guatemala a pesar de perder habilidad en algunas regiones y meses. El modelo reproduce adecuadamente el comportamiento de la precipitación estacional en la mayor parte de la temporada lluviosa. Subestima la temperatura a nivel regional, pero a nivel Guatemala muestra buen ajuste. La comparación con los datos locales observados muestra que el modelo se ajusta para el periodo en estudio; pero, es necesario realizar más experimentos con distintas resoluciones espaciales y temporales y evaluar la persistencia del modelo.
This document presents the results of an analysis on the comparison of the results of a climate simulation model, regional reanalysis data and local data on precipitation and seasonal temperature from twenty-three meteoro-logical stations in Guatemala, to detect signs of the ability of the model to reproduce the seasonal climate over a period of 3 years (1998-2000). The simulation was performed with a regional climate model (RCM), for its dynamic scale reduction, the boundary conditions were obtained from the ERA-Interim reanalysis data. The model used was RegCM, version 4, and it was compared with the precipitation and temperature data from the CRU Database at the Central American regional level and at the national level with three institutions that generate global data (CRU, TRMM and GPCP) and local data. The convective schemes used were the scheme of Grell on land and Emanuel on the ocean, with 50 km of spatial resolution. The adjustments made to the settings generated good performance at the Central American regional level and at the Guatemala level, despite losing skill in some regions and months. The model adequately reproduces the behavior of seasonal precipitation in most of the rainy season. It underestimates the temperature at the regional level but at the Guatemala level it shows a good fit. The comparison with the observed local data shows that the model fits for the period under study, but it is necessary to carry out more experiments with different spatial and temporal resolutions and to evaluate the persistence of the model.