Herval Alves Ramos Filho

Reference evapotranspiration (ET0) is a fundamental hydrological variable for quantifying crop water requirements, conducting watershed water balance assessments, and optimizing irrigation management. However, the limited availability of high-quality meteorological data hinders the accurate determination of ET0 across most regions of Brazil. Therefore, this study aimed to estimate ET0 in areas lacking meteorological observations by employing ERA5 reanalysis climate data and artificial neural network (ANN) models, as well as to evaluate its spatial and temporal variability. Observational data from 32 automated weather stations in the State of Mato Grosso, Brazil, were utilized to estimate ET0 using the Penman-Monteith FAO56 method. These estimates were linked to ERA5 meteorological data, including global solar radiation and top-of-atmosphere radiation, using ANN models. The Mann-Kendall test was applied to detect trends in the ET0 time series for the period 1980–2019. In Mato Grosso, the Pantanal biome exhibited the highest ET0 values from October to March, followed by the Cerrado (Brazilian Savanna) and Amazon biomes. Statewide, the peak evapotranspirative demand occurred in August, September, and October, whereas April, May, and June recorded the lowest values. All biomes in Mato Grosso contained areas with statistically significant increasing trends in ET0. The proposed models demonstrated satisfactory error metrics for ET0 estimation, and the methodological approach enabled a robust assessment of the spatiotemporal dynamics of this hydrological variable, facilitating ET0 estimation even in data-scarce regions.

Estimating global radiation (GR) is crucial for assessing solar energy potential, understanding surface energy balance, and forecasting agricultural production. However, several regions require additional monitoring and sparse sensor networks. The ERA5-ECMWF reanalysis is a viable alternative for estimating meteorological elements in unmonitored areas. This study aimed to train an artificial neural network (ANN) model to estimate GR based on ERA5 data and map its distribution in the study area. We utilized GR data from 32 automatic weather stations of the Brazilian National Institute of Meteorology in Mato Grosso, Brazil, for model training. The model input consisted of ERA5 air temperature, precipitation data, and top-of-atmosphere solar radiation (R₀) calculated from the latitude and day of the year. The calibrated model demonstrated high accuracy, with Nash–Sutcliffe and Kling–Gupta efficiency indices exceeding 0.99. This enabled the generation of historical time series and maps of GR spatial distribution in the study area. The results demonstrate that—as input for the ANN—ERA5 data enables precise and accurate estimation of GR distribution, even in locations without meteorological stations.

A mineração é uma das atividades humanas mais impactantes, sendo desta forma, a origem de inúmeros conflitos socioambientais. Neste contexto, este estudo teve o objetivo de caracterizar os impactos causados pela lavra de minério de ferro da empresa britânica Brazil Iron em Piatã-BA, e assim fomentar o debate sobre as reais perspectivas de sustentabilidade de empreendimentos desta natureza no território da Chapada Diamantina. Para a execução da análise ambiental, foram utilizadas ferramentas de Sensoriamento Remoto e de SIG, como o Shuttle Radar Topography Mission (SRTM), a plataforma Planet e o software Quantum GIS 3.16 LTR, que auxiliaram na obtenção de imagens da área avaliada e na produção de um Modelo Digital de Elevação. O estudo evidenciou os principais impactos e riscos gerados pela atividade mineradora no local, bem como definiu suas zonas mais críticas.

The aim of this study was to develop artificial neural network (ANN) models to predict floods in the Branco River, Amazon basin. The input data for the models included the river levels and the average rainfall within the drainage area of the basin, which was estimated from the remotely sensed rainfall product PDIRnow. The hourly water level data used in the study were recorded by fluviometric telemetric stations belonging to the National Agency of Water. The multilayer perceptron was used as the neural framework of the ANNs, and the number of neurons in each layer of the model was determined via optimization with the SCE-UA algorithm. Most of the fitted ANN models showed Nash–Sutcliffe efficiency index values greater than 0.9. It is possible to conclude that the ANNs are effective for predicting the flood levels of the Branco River, with horizons of 6, 12 and 24 h; thus, constituting a viable option for use in river-flood warning systems in the Amazon basin. For the forecast with a 24-h horizon, it is essential to include the average rainfall of the basin that accumulated over the last 48 h as input data into the ANNs, along with the levels measured by the streamflow stations. The indirect rainfall estimates provided by PDIRnow are an excellent alternative as input data for ANN models used to predict floods and constitute a viable solution for regions where the density of rain gauge stations is low, as is the case in the Amazon basin.