cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14281987.v1
title: Changes in Land Use in the Western Amazon and the Microclima’s Response to the Occurrence of Extreme Events

description: Abstract The reports of the Intergovernmental Panel on Climate Change constantly reveal the possibility that climatic extremes will increase in the coming years. The Amazon Region has experienced these events frequently, which has contributed to the fact that more studies about its susceptibility are carried out. This study aimed to identify the microclimate response to droughts of 2005 and 2010 in areas of native forest and pasture in the Western Amazon by analyzing specific air humidity, air temperature and net radiation. The data used come from towers belonging to Large Scale Biosphere-Atmosphere Experiment in Amazonia. The results indicate that in the years studied there were significant changes in the variables studied at both sites, with reductions of approximately 16% in specific humidity and increases of up to 3.76% in temperature. However, the effects of the 2010 drought may have been softened in the forest due to the event being preceded by an extreme flood event (2009). The results show that the conversion of forest areas to pasture, together with extreme events, can interfere in the meteorological variables, being necessary the continuous study of this dynamics for the microclimatic implications to be elucidated.
DOI: 10.6084/m9.figshare.14281993
title: Trends and Variabilities in the Historical Series of Monthly and Annual Precipitation in Cerrado Biome in the Period 1977-2010

description: Abstract The objective of the present study was to analyze the annual and monthly precipitation historical series of the Cerrado biome (Brazil), in order to identify local and regional trends. Different trend detection methods were used for 125 rain gauge stations throughout the Cerrado during the period 1977-2010, including LOWESS (LW), Mann-Kendall (MK), Theil-Sen (TS), and Pettitt (PT). The MK test suggested negative trends for 71% of the stations, with 14% of them statistically significant, mainly in the southerly region. Positive trends were found in 28% of the stations, where 1% was significant. The TS test suggested that, in general, the annual total precipitation in Cerrado decreased by 8,4% during the studied period. The precipitation in the dry months (JJAS) decreased in 76% of the stations, indicating an intensification of the dry season in the region. The results were consistent, suggesting a general decreasing trend in the precipitation of the biome, during the studied period.
DOI: 10.6084/m9.figshare.14281988.v1
title: Biophysical parameters of different types of soil cover in a Cerrado area in the south of Mato Grosso

description: Abstract The Cerrado presents a variety of phytophysiognomies, with distinct characteristics of structure and distribution, which contributes to a heterogeneity in the interaction of solar radiation with the surface. Thus, the objective of this study was to evaluate the spatial and temporal variability of biophysical parameters in different types of soil cover of a Cerrado area in southern Mato Grosso state by remote sensing. Biophysical parameters Normalized Difference Vegetation Index (NDVI), albedo, surface temperature and radiation balance were obtained from the Cerrado phytophysiognomies: Campo Limpo, Campo Sujo, Cerradão and Cerrado stricto sensu; and in a pasture area. Estimates were made with TM Landsat 5 sensor images in 2009, 2010 and 2011. In the rainy season, NDVI and radiation balance were 44% and 15% higher and albedo and surface temperature were 18% and 12% smaller than dry, respectively. In places with higher NDVI and higher radiation balance, the lowest albedo and surface temperature were found. The different phytophysiognomies of the Cerrado had a distinct interaction with solar radiation at seasonal and spatial scales due to the variation of vegetation cover and water availability.
DOI: 10.6084/m9.figshare.14281991
title: Spatial and Seasonal Water Characterization of Mato Grosso do Sul with Grid Data

description: Abstract The use of global atmospheric reanalysis data (ERA-Interim) produced by the European Center for Medium Range Weather Forecasts (ECMWF) becomes an important tool for agricultural production regions, especially in scarce weather station areas. We sought to characterize the spatial and seasonal water variability of the state of Mato Grosso do Sul using meteorological reanalysis data from ERA-Interim. The study was conducted in Mato Grosso do Sul, located in the Midwest region of Brazil. Decendial rainfall and mean air temperature data were obtained from ERA-Interim between 1989 and 2015, totaling 566 points. We estimated soil water storage, water deficit and water surplus from the normal water balance of Thornthwaite and Mather (1955). In Mato Grosso do Sul there was spatial and seasonal variation of water balance components. Seasonal variation in soil water storage occurred with the change of seasons. The southern region of the state of Mato Grosso do Sul has the largest water resources, with reduced DEF, close to 100% ARM and high EXC. The largest water surpluses occur from December to March, while from August to October the lowest water surpluses occur in the state.
DOI: 10.6084/m9.figshare.14281993.v1
title: Trends and Variabilities in the Historical Series of Monthly and Annual Precipitation in Cerrado Biome in the Period 1977-2010

description: Abstract The objective of the present study was to analyze the annual and monthly precipitation historical series of the Cerrado biome (Brazil), in order to identify local and regional trends. Different trend detection methods were used for 125 rain gauge stations throughout the Cerrado during the period 1977-2010, including LOWESS (LW), Mann-Kendall (MK), Theil-Sen (TS), and Pettitt (PT). The MK test suggested negative trends for 71% of the stations, with 14% of them statistically significant, mainly in the southerly region. Positive trends were found in 28% of the stations, where 1% was significant. The TS test suggested that, in general, the annual total precipitation in Cerrado decreased by 8,4% during the studied period. The precipitation in the dry months (JJAS) decreased in 76% of the stations, indicating an intensification of the dry season in the region. The results were consistent, suggesting a general decreasing trend in the precipitation of the biome, during the studied period.
DOI: 10.6084/m9.figshare.14281994
title: The Impact of Microphysics Parameterization on Precipitation Forecast Using Radar Data Assimilation

description: Abstract Recent studies show that data assimilation improves the efficiency of weather forecast models, however, it is not properly quantified the impacts of radar data assimilation related to the physical model parameterizations, especially the microphysics. The goal of this study was to study the impact of the use of radar data with different microphysics parameterizations of the Weather Research and Forecasting (WRF) model with its data assimilation system (WRFDA-3DVAR) for cases of intense precipitation. The study area covers the South-western Brazil and Southeastern Paraguay. The simulations were done for three cases in 2014. The comparison is performed through the statistical metrics Fractional Skill Score (FSS) and Local Root Mean Square Error (LRMSE). Different microphysics parameterizations were tested when assimilating conventional and radar data for three events. Thus, we evaluated nine microphysical parameterizations in order to determine which one provides the most realistic short-term forecasts of meteorological fields over the radar coverage, as well as the relative impact of different microphysical parameterization and the assimilation of conventional and radar data. The positive impact of the radar data assimilation was in the average up to 20% in the FSS, while the positive impact among the microphysics options reached 70% in the FSS.
DOI: 10.6084/m9.figshare.14281994.v1
title: The Impact of Microphysics Parameterization on Precipitation Forecast Using Radar Data Assimilation

description: Abstract Recent studies show that data assimilation improves the efficiency of weather forecast models, however, it is not properly quantified the impacts of radar data assimilation related to the physical model parameterizations, especially the microphysics. The goal of this study was to study the impact of the use of radar data with different microphysics parameterizations of the Weather Research and Forecasting (WRF) model with its data assimilation system (WRFDA-3DVAR) for cases of intense precipitation. The study area covers the South-western Brazil and Southeastern Paraguay. The simulations were done for three cases in 2014. The comparison is performed through the statistical metrics Fractional Skill Score (FSS) and Local Root Mean Square Error (LRMSE). Different microphysics parameterizations were tested when assimilating conventional and radar data for three events. Thus, we evaluated nine microphysical parameterizations in order to determine which one provides the most realistic short-term forecasts of meteorological fields over the radar coverage, as well as the relative impact of different microphysical parameterization and the assimilation of conventional and radar data. The positive impact of the radar data assimilation was in the average up to 20% in the FSS, while the positive impact among the microphysics options reached 70% in the FSS.
DOI: 10.6084/m9.figshare.14281991.v1
title: Spatial and Seasonal Water Characterization of Mato Grosso do Sul with Grid Data

description: Abstract The use of global atmospheric reanalysis data (ERA-Interim) produced by the European Center for Medium Range Weather Forecasts (ECMWF) becomes an important tool for agricultural production regions, especially in scarce weather station areas. We sought to characterize the spatial and seasonal water variability of the state of Mato Grosso do Sul using meteorological reanalysis data from ERA-Interim. The study was conducted in Mato Grosso do Sul, located in the Midwest region of Brazil. Decendial rainfall and mean air temperature data were obtained from ERA-Interim between 1989 and 2015, totaling 566 points. We estimated soil water storage, water deficit and water surplus from the normal water balance of Thornthwaite and Mather (1955). In Mato Grosso do Sul there was spatial and seasonal variation of water balance components. Seasonal variation in soil water storage occurred with the change of seasons. The southern region of the state of Mato Grosso do Sul has the largest water resources, with reduced DEF, close to 100% ARM and high EXC. The largest water surpluses occur from December to March, while from August to October the lowest water surpluses occur in the state.
DOI: 10.6084/m9.figshare.14281996
title: Influence of the Macroclimatic Oscillations in the Gachaneca River Basin Weather Patterns; Boyaca-Colombia

description: Abstract To study the influence of macroclimatic oscillations in the Gachaneca river basin this work studies the variability of minimum and maximum temperature and standardized precipitation indices in the basin area between the years 1982 and 2015, and evaluated the relation among these variables with El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Quasi-Biennial Oscillation (OCB) by canonical correlation analysis. The results reveal that percentages of variance explained by macroclimatic phenomena in December-February range between 34% and 38%, in March-May between 15% and 20%, in June-August between 15% and 25% and in September- November between 21% and 34%. The most influential phenomenon is ENSO, followed by NAO. During DEF the study area is more exposed to dry events that increase the risk of depletion of water resources due to the warm phase of ENSO. The NAO can indirectly reinforce or mitigate drought events due to its relationship with the magnitude of the trade winds and the variability of the tropical Atlantic that have an impact on the position of the ITCZ and the convective processes of the region.
DOI: 10.6084/m9.figshare.14281997
title: Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil

description: Abstract This work aimed to select semivariogram models to estimate trends in monthly precipitation in Paraiba State-Brazil using ordinary kriging. The methodology involves the application of geostatistical interpolation of precipitation records of 51 years from 69 rainfall stations across the state. Analysis of semivariograms showed that specific months had a strong spatial dependence (Index of Spatial Dependence - IDE &lt; 25%). The trends were subjected to the following models: circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, K-Bessel and tetraspherical. The best fit models were selected by cross-validation and Error Comparison Index (ECI). Each data set (month) had a particular spatial dependence structure, which made it necessary to define specific models of semivariogram in order to enhance the adjustment of the experimental semivariogram. Besides, the monthly trend map was plotted to justify the chosen models.
DOI: 10.6084/m9.figshare.14281997.v1
title: Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil

description: Abstract This work aimed to select semivariogram models to estimate trends in monthly precipitation in Paraiba State-Brazil using ordinary kriging. The methodology involves the application of geostatistical interpolation of precipitation records of 51 years from 69 rainfall stations across the state. Analysis of semivariograms showed that specific months had a strong spatial dependence (Index of Spatial Dependence - IDE &lt; 25%). The trends were subjected to the following models: circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, K-Bessel and tetraspherical. The best fit models were selected by cross-validation and Error Comparison Index (ECI). Each data set (month) had a particular spatial dependence structure, which made it necessary to define specific models of semivariogram in order to enhance the adjustment of the experimental semivariogram. Besides, the monthly trend map was plotted to justify the chosen models.
DOI: 10.6084/m9.figshare.14281996.v1
title: Influence of the Macroclimatic Oscillations in the Gachaneca River Basin Weather Patterns; Boyaca-Colombia

description: Abstract To study the influence of macroclimatic oscillations in the Gachaneca river basin this work studies the variability of minimum and maximum temperature and standardized precipitation indices in the basin area between the years 1982 and 2015, and evaluated the relation among these variables with El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Quasi-Biennial Oscillation (OCB) by canonical correlation analysis. The results reveal that percentages of variance explained by macroclimatic phenomena in December-February range between 34% and 38%, in March-May between 15% and 20%, in June-August between 15% and 25% and in September- November between 21% and 34%. The most influential phenomenon is ENSO, followed by NAO. During DEF the study area is more exposed to dry events that increase the risk of depletion of water resources due to the warm phase of ENSO. The NAO can indirectly reinforce or mitigate drought events due to its relationship with the magnitude of the trade winds and the variability of the tropical Atlantic that have an impact on the position of the ITCZ and the convective processes of the region.
DOI: 10.6084/m9.figshare.14281999
title: Climatic Index Associated with the Interannual Variability of Rice Yield in Rio Grande do Sul

description: Abstract The main of this work is to highlight climatic patterns that influence the interannual variability of rice yield in Rio Grande do Sul (RS). Series of rice yield data from 47 municipalities were separated into three groups according to their yield (low, medium and high) based on clustering analysis. Lag correlations between climatic indicators associated with teleconnections patterns and mean rice yield of each group evidenced the importance of each index for variability of the yield in RS. The most significant and persistent correlations with rice yield were the Quasi-Biennial Oscillation (negative), the Pacific Decadal Oscillation (negative) an atmospheric (positive) and oceanic (negative) components of the El Niño Southern Oscillation. In addition, Sea Surface Temperature anomalies in the Atlantic Ocean, between 20° S-30° S and 20° O-40° W (negative correlation) have a significant correlation with the rice yield. The influence of some of the climate indices had not yet been discussed from the agricultural point of view. These results can be a crucial information in decision-making and rice yield forecast in Rio Grande do Sul.
DOI: 10.6084/m9.figshare.14281679.v1
title: Dataset on the effect of vancomycin on metabolic changes in normal control mice

description: Here we treated normal control mice with vancomycin to disturb the gut microbiota and host metabolic phenotype. In order to study the effect of vancomycin on host metabolism, a <sup>1</sup>H NMR-based metabolomics approach was applied to analyze metabolic changes in faecal sample of normal control (CON) and vancomycin-treated normal control diabetic (CONV) mice at 3, 7 and 11 weeks. This study is submitted to Microbiome. Hong Zheng, Pengtao Xu, Qiaoying Jiang, Qingqing Xu, Yafei Zheng, Junjie Yan, Hui Ji, Jie Ning, Xi Zhang, Chen Li, Limin Zhang, Yuping Li, Xiaokui Li, Weihong Song, and Hongchang Gao. (2021). Depletion of acetate-producing bacteria from the gut microbiota facilitates cognitive impairment through the gut-brain neural mechanism in diabetic mice.
DOI: 10.6084/m9.figshare.14281999.v1
title: Climatic Index Associated with the Interannual Variability of Rice Yield in Rio Grande do Sul

description: Abstract The main of this work is to highlight climatic patterns that influence the interannual variability of rice yield in Rio Grande do Sul (RS). Series of rice yield data from 47 municipalities were separated into three groups according to their yield (low, medium and high) based on clustering analysis. Lag correlations between climatic indicators associated with teleconnections patterns and mean rice yield of each group evidenced the importance of each index for variability of the yield in RS. The most significant and persistent correlations with rice yield were the Quasi-Biennial Oscillation (negative), the Pacific Decadal Oscillation (negative) an atmospheric (positive) and oceanic (negative) components of the El Niño Southern Oscillation. In addition, Sea Surface Temperature anomalies in the Atlantic Ocean, between 20° S-30° S and 20° O-40° W (negative correlation) have a significant correlation with the rice yield. The influence of some of the climate indices had not yet been discussed from the agricultural point of view. These results can be a crucial information in decision-making and rice yield forecast in Rio Grande do Sul.
DOI: 10.6084/m9.figshare.14282000
title: Seasonal Flow Forecast for the Orós Dam (Ceará, Brazil) Using Neural Networks and the Resampling Technique of K-neighbors

description: Abstract The objective of this work is to perform a comparative flow forecast for the Orós basin (Ceará, Brazil) using artificial neural networks (RNA) and k-neighbors re-sampling technique. The models were developed from the historical series of 100 years of hydrometeorological data (sea surface temperature and flows). Both use as predictors the temperatures of the North Atlantic, South Atlantic and Equatorial Pacific oceans, and forecast July in the next year’s rainy season (January to June). The k-neighbors model was elaborated from the identification of the closest neighbor years for the resampling of the approximation, since the RNA model was formulated from the synaptic and bias weights obtained in the training phase of the network. The Nash-Suttcliffe (E) efficiency coefficient, the coefficient of determination (R²), the Taylor diagram (2001) and the coefficient of determination (R²) were used for the validation step. maximum likelihood ratio. For all comparative variables, the neural model presented better values, indicating that this represents more efficiently the behavior of the flows to the reservoir.
DOI: 10.6084/m9.figshare.14282000.v1
title: Seasonal Flow Forecast for the Orós Dam (Ceará, Brazil) Using Neural Networks and the Resampling Technique of K-neighbors

description: Abstract The objective of this work is to perform a comparative flow forecast for the Orós basin (Ceará, Brazil) using artificial neural networks (RNA) and k-neighbors re-sampling technique. The models were developed from the historical series of 100 years of hydrometeorological data (sea surface temperature and flows). Both use as predictors the temperatures of the North Atlantic, South Atlantic and Equatorial Pacific oceans, and forecast July in the next year’s rainy season (January to June). The k-neighbors model was elaborated from the identification of the closest neighbor years for the resampling of the approximation, since the RNA model was formulated from the synaptic and bias weights obtained in the training phase of the network. The Nash-Suttcliffe (E) efficiency coefficient, the coefficient of determination (R²), the Taylor diagram (2001) and the coefficient of determination (R²) were used for the validation step. maximum likelihood ratio. For all comparative variables, the neural model presented better values, indicating that this represents more efficiently the behavior of the flows to the reservoir.
DOI: 10.6084/m9.figshare.14282003
title: Regionalization and Trend Analysis of Precipitation in Rio Grande do Norte Associated with SST Patterns

description: Abstract Precipitation presents high spatial and temporal variability, as in Northeast Brazil (NEB), which is characterized by long periods of droughts and episodes of excessive precipitation, which influences the water resources management. Specifically, Rio Grande do Norte (RN) state located in the eastern of NEB, the rainy season occurs in two periods. In this context, the main objective of this study is to define homogeneous areas of precipitation (HAPs) in the RN state to investigate linear trends and changes in which they occurred in the rainy season. It was used the accumulated precipitation from the fourth version of data from the University of Delaware, for the period 1900 to 2014. To identify the HAPs was used the cluster analysis. Trends were estimated by the Mann-Kendall test. The structural breaks was made by the Pettitt method. The results indicate three HAPs in the RN state: West, Seridó and Coast. The rainy season in the West and Seridó is concentrated between February and May; on the coast, between April and July. Trends are identified only on the coast with p-value &lt; 0.05, an increase in their mean was observed from 1964 and this is apparently associated with the modes of Atlantic variability.