cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14282005
title: Calibration and Validation of the AquaCrop Model to Estimate Maize Production in Campos Gerais, Paraná State, Brazil

description: Abstract Crop productivity evaluation with models simulations can help in the prediction of harvests and in the understanding of the interactions resulting from the soil-plant-atmosphere continuum. The aim of this study was to calibrate and validate the AquaCrop model for maize crop in the edaphoclimatic conditions of Campos Gerais region, Paraná State, Brazil. The analyses were carried out for maize crop with model input data (climate, crop, soil and soil management) obtained from the ABC Foundation Experimental Station in Castro, Ponta Grossa and Socavão. The climate in the region is humid subtropical, with rainfall evenly distributed. The relief varies from flat to gently undulating. The period analyzed in the calibration and validation process comprised 2011 to 2016 and 2012 to 2016 harvests, respectively. The data used in the calibration of AquaCrop was different from those used in the validation process. Observed and simulated yields were evaluated by simple linear regression analyses, absolute and relative errors, correlation coefficient (r), concordance (d) and performance (c) indexes. The calibration of AquaCrop was satisfactory in the locations studied for maize crop, obtaining absolute errors varying from 6 to 121 kg ha–1. The highest calibration errors occurred in Castro. However, the errors were not enough to reduce the performance in the validation process for this localitie. The model validation resulted in “excellent” performance in all locations evaluated. The AquaCrop can be used to predict the maize yield with acceptable accuracy in the Campos Gerais Region, Paraná State, Brazil.
DOI: 10.6084/m9.figshare.14282003.v1
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.
DOI: 10.6084/m9.figshare.14282006
title: Persistence and Comprehensiveness of Extreme Precipitation Events in the South of Brazil: Space Variability and Atmospheric Patterns

description: Abstract The southern region of Brazil is characterized, due to its geographical location, by the diversity of meteorological systems operating throughout the year, resulting in adverse weather conditions. This work analyzes the extreme events of precipitation, characterizing them according to their persistence and comprehensiveness, and the related atmospheric patterns. The development of a computational algorithm allowed the identification and knowledge of the spatial comprehensiveness of the extreme precipitation events, which were classified in a comprehensive and noncomprehensive character, resulting in four precipitation groups identified according to the criteria of intensity, persistence and comprehensiveness. The results showed that summer is the preferred season for the occurrence of extreme events in all the analyzed categories, a greater number of events occur between the northern Rio Grande do Sul and Paraná, and for the cases of Persistent Rain, the Santa Catarina coast is a region favorable to this type of event. Among the main atmospheric processes for the occurrence of extreme events with a comprehensive character are the humidity availability in the lower layers of the troposphere, along with jet in high level and a dug in medium levels.
DOI: 10.6084/m9.figshare.14281679
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.14282006.v1
title: Persistence and Comprehensiveness of Extreme Precipitation Events in the South of Brazil: Space Variability and Atmospheric Patterns

description: Abstract The southern region of Brazil is characterized, due to its geographical location, by the diversity of meteorological systems operating throughout the year, resulting in adverse weather conditions. This work analyzes the extreme events of precipitation, characterizing them according to their persistence and comprehensiveness, and the related atmospheric patterns. The development of a computational algorithm allowed the identification and knowledge of the spatial comprehensiveness of the extreme precipitation events, which were classified in a comprehensive and noncomprehensive character, resulting in four precipitation groups identified according to the criteria of intensity, persistence and comprehensiveness. The results showed that summer is the preferred season for the occurrence of extreme events in all the analyzed categories, a greater number of events occur between the northern Rio Grande do Sul and Paraná, and for the cases of Persistent Rain, the Santa Catarina coast is a region favorable to this type of event. Among the main atmospheric processes for the occurrence of extreme events with a comprehensive character are the humidity availability in the lower layers of the troposphere, along with jet in high level and a dug in medium levels.
DOI: 10.6084/m9.figshare.14282009
title: Spatio-temporal Characterization of Droughts in Northeast based on the Analysis of the SPI Index

description: Abstract Here we characterize the spatio-temporal patterns of droughts in the Brazilian Nordeste through a Principal Component Analysis (PCA) of the Standardized Precipitation Index (SPI), obtained from rainfall data for the 1980-2013 period. In the first PC, that responds to 42% of the data variability, we observe a homogeneous drought throughout the region. The second PC is responsible for 11% of the data variance and presents a north-south dipole pattern. From the historical series of the two PCs, we objectively identified the most severe and extreme droughts, which coincided with the driest years reported in the literature, such as 1982 and 1993. However, to the best of our knowledge this is the first work to show the exact location and area of Nordeste hit by each extreme drought identified. We also show the less intense droughts that do not appear in the literature, such as those that took place in 1981, 1996 and 1998 and hit between 20% and 36% of the region. Moreover, a cross-correlation analysis of the PCS and the NINO3.4 and tropical Atlantic dipole indexes revealed that the influence of such indices on the droughts in the regions act at different temporal scales with distinguished spatial impacts.
DOI: 10.6084/m9.figshare.14282011
title: Coastal Wind Measurements and Power Assessment Using a LIDAR on a Pier

description: Abstract This paper reports the first LIDAR measurements obtained on a fishing pier in Santa Catarina, southern Brazil. A laboratory was built to monitor the vertical structure, time and directional variability of winds in the transitional region between the ocean and the continent. A meteorological station provided pressure, temperature, wind and humidity data. Prevailing winds tend to blow from the NE along the coastline, strongly influenced by the South Atlantic Subtropical High Pressure center. Sea and land breezes had significant amplitude. The offshore wind component (sea breeze) was generally active from 11:00 to 20:00 h; the onshore component (land breeze) from 22:00 to 09:00 h. Weak vertical shear and increasing wind speeds were typically observed in the early afternoon, while stronger shear and the highest wind speeds tend to occur in the late afternoon. Winds profiles from the ocean sector were usually more intense and less sheared. The average power of wind turbines was estimated. The local resource is nearly 2.5 times greater than that of a site located 9.3 km inland. The use of coastal laboratories was demonstrated to be a cost-saving solution for long-term monitoring of the country's wind resources.
DOI: 10.6084/m9.figshare.14282009.v1
title: Spatio-temporal Characterization of Droughts in Northeast based on the Analysis of the SPI Index

description: Abstract Here we characterize the spatio-temporal patterns of droughts in the Brazilian Nordeste through a Principal Component Analysis (PCA) of the Standardized Precipitation Index (SPI), obtained from rainfall data for the 1980-2013 period. In the first PC, that responds to 42% of the data variability, we observe a homogeneous drought throughout the region. The second PC is responsible for 11% of the data variance and presents a north-south dipole pattern. From the historical series of the two PCs, we objectively identified the most severe and extreme droughts, which coincided with the driest years reported in the literature, such as 1982 and 1993. However, to the best of our knowledge this is the first work to show the exact location and area of Nordeste hit by each extreme drought identified. We also show the less intense droughts that do not appear in the literature, such as those that took place in 1981, 1996 and 1998 and hit between 20% and 36% of the region. Moreover, a cross-correlation analysis of the PCS and the NINO3.4 and tropical Atlantic dipole indexes revealed that the influence of such indices on the droughts in the regions act at different temporal scales with distinguished spatial impacts.
DOI: 10.6084/m9.figshare.14282011.v1
title: Coastal Wind Measurements and Power Assessment Using a LIDAR on a Pier

description: Abstract This paper reports the first LIDAR measurements obtained on a fishing pier in Santa Catarina, southern Brazil. A laboratory was built to monitor the vertical structure, time and directional variability of winds in the transitional region between the ocean and the continent. A meteorological station provided pressure, temperature, wind and humidity data. Prevailing winds tend to blow from the NE along the coastline, strongly influenced by the South Atlantic Subtropical High Pressure center. Sea and land breezes had significant amplitude. The offshore wind component (sea breeze) was generally active from 11:00 to 20:00 h; the onshore component (land breeze) from 22:00 to 09:00 h. Weak vertical shear and increasing wind speeds were typically observed in the early afternoon, while stronger shear and the highest wind speeds tend to occur in the late afternoon. Winds profiles from the ocean sector were usually more intense and less sheared. The average power of wind turbines was estimated. The local resource is nearly 2.5 times greater than that of a site located 9.3 km inland. The use of coastal laboratories was demonstrated to be a cost-saving solution for long-term monitoring of the country's wind resources.
DOI: 10.6084/m9.figshare.14282005.v1
title: Calibration and Validation of the AquaCrop Model to Estimate Maize Production in Campos Gerais, Paraná State, Brazil

description: Abstract Crop productivity evaluation with models simulations can help in the prediction of harvests and in the understanding of the interactions resulting from the soil-plant-atmosphere continuum. The aim of this study was to calibrate and validate the AquaCrop model for maize crop in the edaphoclimatic conditions of Campos Gerais region, Paraná State, Brazil. The analyses were carried out for maize crop with model input data (climate, crop, soil and soil management) obtained from the ABC Foundation Experimental Station in Castro, Ponta Grossa and Socavão. The climate in the region is humid subtropical, with rainfall evenly distributed. The relief varies from flat to gently undulating. The period analyzed in the calibration and validation process comprised 2011 to 2016 and 2012 to 2016 harvests, respectively. The data used in the calibration of AquaCrop was different from those used in the validation process. Observed and simulated yields were evaluated by simple linear regression analyses, absolute and relative errors, correlation coefficient (r), concordance (d) and performance (c) indexes. The calibration of AquaCrop was satisfactory in the locations studied for maize crop, obtaining absolute errors varying from 6 to 121 kg ha–1. The highest calibration errors occurred in Castro. However, the errors were not enough to reduce the performance in the validation process for this localitie. The model validation resulted in “excellent” performance in all locations evaluated. The AquaCrop can be used to predict the maize yield with acceptable accuracy in the Campos Gerais Region, Paraná State, Brazil.
DOI: 10.6084/m9.figshare.14282014
title: Calibration of Methods to Estimate Solar Irradiance in Northeastern Pará

description: Abstract Two models aimed to estimate solar irradiance were calibrated in six locations in Northeastern Pará (Belém, Cametá, Conceição do Araguaia, Marabá, Soure, and Tucuruí). The first one is the equation of Angström-Prescott (AP), which requires observations of sunshine duration hours. The second model is a modified version of Hargreaves' radiation formula (MH), which requires observations of daily maximum and daily minimum air temperatures. Both models were calibrated to estimate daily and monthly solar radiation. The calibration of both equations for each season (i.e., dry season and wet season) in each location was also tested. AP has an average performance about 74% higher than MH for daily estimates (excluding Soure) and 83% higher than MH for monthly estimates (excluding Soure and Tucuruí). The use of seasonally calibrated equations slightly improves the performance of AP, measured by the performance index, by 0.68% and improves the performance of MH in most locations, when estimating daily solar radiation. The performance of both models is much higher when estimating monthly solar radiation than daily solar radiation, with an increase of the performance index of 10.95% for AP.
DOI: 10.6084/m9.figshare.14282015
title: Gap Filling in Historical Data of Daily Precipitation in Rio Grande do Sul

description: Abstract The gap filling in time series of precipitation is an important process for applications in hydrology, aiming the use of long series, avoiding that they are discarded. Thus, this study had as objective to gap filling in historical series of daily precipitation in Rio Grande do Sul, assisting in the use of these data in studies that require long term analysis. For that, we used historical series of 287 stations in the period between 1987 and 2016 and comparing the Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) methods, comparing to evaluating the filled values. An algorithm was developed to perform the following operations: i) identify the days with missing datas in each station; ii) identify the stations that can be used to fill each missing data; iii) identify all combinations of input to fill in failures in each station; iv) perform the adjustment/training of the MLR and ANN models; v) perform validation of the models based on the period without failure of each station. The main results indicate that the higher density of rainfall stations favors the process of filling of faults in historical series of precipitation, improving the quality of the filled series. Gap filling showed a higher coefficient of determination and lower mean absolute error using the MLR model in relation to RNA, possibly due to the strong linear correlation of the precipitation data of each site in relation to their neighborhood. The MLR model presented an average coefficient of determination (R2) of 0.697, while the RNA model obtained an average of 0.675. Considering the analysis by means of the mean absolute error (MAE), the mean values were 2.27 mm for MLR, while for RNA the error was 2.31 mm. It is concluded, considering the daily rainfall data set of RS, that there was a slight superiority of the RLM method in relation to RNA.
DOI: 10.6084/m9.figshare.14282015.v1
title: Gap Filling in Historical Data of Daily Precipitation in Rio Grande do Sul

description: Abstract The gap filling in time series of precipitation is an important process for applications in hydrology, aiming the use of long series, avoiding that they are discarded. Thus, this study had as objective to gap filling in historical series of daily precipitation in Rio Grande do Sul, assisting in the use of these data in studies that require long term analysis. For that, we used historical series of 287 stations in the period between 1987 and 2016 and comparing the Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) methods, comparing to evaluating the filled values. An algorithm was developed to perform the following operations: i) identify the days with missing datas in each station; ii) identify the stations that can be used to fill each missing data; iii) identify all combinations of input to fill in failures in each station; iv) perform the adjustment/training of the MLR and ANN models; v) perform validation of the models based on the period without failure of each station. The main results indicate that the higher density of rainfall stations favors the process of filling of faults in historical series of precipitation, improving the quality of the filled series. Gap filling showed a higher coefficient of determination and lower mean absolute error using the MLR model in relation to RNA, possibly due to the strong linear correlation of the precipitation data of each site in relation to their neighborhood. The MLR model presented an average coefficient of determination (R2) of 0.697, while the RNA model obtained an average of 0.675. Considering the analysis by means of the mean absolute error (MAE), the mean values were 2.27 mm for MLR, while for RNA the error was 2.31 mm. It is concluded, considering the daily rainfall data set of RS, that there was a slight superiority of the RLM method in relation to RNA.
DOI: 10.6084/m9.figshare.14282014.v1
title: Calibration of Methods to Estimate Solar Irradiance in Northeastern Pará

description: Abstract Two models aimed to estimate solar irradiance were calibrated in six locations in Northeastern Pará (Belém, Cametá, Conceição do Araguaia, Marabá, Soure, and Tucuruí). The first one is the equation of Angström-Prescott (AP), which requires observations of sunshine duration hours. The second model is a modified version of Hargreaves' radiation formula (MH), which requires observations of daily maximum and daily minimum air temperatures. Both models were calibrated to estimate daily and monthly solar radiation. The calibration of both equations for each season (i.e., dry season and wet season) in each location was also tested. AP has an average performance about 74% higher than MH for daily estimates (excluding Soure) and 83% higher than MH for monthly estimates (excluding Soure and Tucuruí). The use of seasonally calibrated equations slightly improves the performance of AP, measured by the performance index, by 0.68% and improves the performance of MH in most locations, when estimating daily solar radiation. The performance of both models is much higher when estimating monthly solar radiation than daily solar radiation, with an increase of the performance index of 10.95% for AP.
DOI: 10.6084/m9.figshare.14282018
title: Exploring the particularities of the method object-based in the precipitation forecast evaluation

description: Abstract Numerical Weather Prediction Models (NWPM) are based on computational methods applied to predict meteorological phenomena from the numerical integration of equations that describe the movement of the atmospheric components. Among the variables predicted by NWPM, precipitation is one of the most important, and it is also the most difficult to obtain good results in the positioning, intensity, and extension of the events. One of the sources of uncertainty in the quality of these forecasts is the evaluation method because conventional methods do not evaluate the spatial aspects appropriately. The objective of this study is to propose and validate a methodology of object-based diagnostic evaluation using Object-Based Diagnostic Evaluation (MODE) algorithm implemented in the Community System for the Evaluation of Numerical Models of Weather and Climate Prediction (SCANTEC). Experiments using idealized cases are presented, which allow validate the implementation of the method and show its advantages over conventional evaluation metrics. The results of this method are also presented in the comparison of the real precipitation forecasts generated by routinely used models in CPTEC as proof of concept, exemplifying its use to explore the available metrics. The results demonstrate the great potential of the object-based methodology for the evolution of the NWPM.
DOI: 10.6084/m9.figshare.14282018.v1
title: Exploring the particularities of the method object-based in the precipitation forecast evaluation

description: Abstract Numerical Weather Prediction Models (NWPM) are based on computational methods applied to predict meteorological phenomena from the numerical integration of equations that describe the movement of the atmospheric components. Among the variables predicted by NWPM, precipitation is one of the most important, and it is also the most difficult to obtain good results in the positioning, intensity, and extension of the events. One of the sources of uncertainty in the quality of these forecasts is the evaluation method because conventional methods do not evaluate the spatial aspects appropriately. The objective of this study is to propose and validate a methodology of object-based diagnostic evaluation using Object-Based Diagnostic Evaluation (MODE) algorithm implemented in the Community System for the Evaluation of Numerical Models of Weather and Climate Prediction (SCANTEC). Experiments using idealized cases are presented, which allow validate the implementation of the method and show its advantages over conventional evaluation metrics. The results of this method are also presented in the comparison of the real precipitation forecasts generated by routinely used models in CPTEC as proof of concept, exemplifying its use to explore the available metrics. The results demonstrate the great potential of the object-based methodology for the evolution of the NWPM.