• Energy Research

Matter and energy flux dynamics of wetlands are important to understand environmental processes that govern biosphere-atmosphere interactions across ecosystems. This study presents analyses about hourly interaction between wind speed and energy fluxes in Brazilian Wetlands - Mato Grosso - Brazil. This study was conducted in Private Reserve of Natural Heritage (PRNH SESC, 16º39'50''S; 56º47'50''W) in Brazilian Wetland. According to Curado et al. (2012), the wet season occurs between the months of January and April, while the June to September time period is the dry season. Results presented same patterns in energies fluxes in all period studied. Wind speed and air temperature presented same patterns, while LE was relative humidity presented inverse patterns of the air temperature. LE was predominant in all seasons and the sum of LE and H was above 90% of net radiation. Analyses of linear regression presented positive interactions between wind speed and LE, and wind speed and H in all seasons, except in dry season of 2010. Confidence coefficient regression analyses present statistical significance in all wet and dry seasons, except dry season of 2010, suggest that LE and H had interaction with other micrometeorological variables.

Funding for AmeriFlux data resources was provided by the U.S. Department of Energy's Office of Science. Davi de C. D. Melo was supported by the São Paulo State Research Foundation (FAPES) (grant 2016/23546-7) and by the Brazilian National Council for Scientific and Technological Development (CNPq) (project 409093/2018-1). Paulo Tarso S. Oliveira was supported by the Brazilian National Council for Scientific and Technological Development (CNPq) (grants 441289/2017-7 and 306830/2017-5) and the CAPES Print program. Rafael Rosolem would like to acknowledge the Brazilian Experimental datasets for MUlti-Scale interactions in the critical zone under Extreme Drought (BEMUSED) project [grant number NE/R004897/1] funded by the Natural Environment Research Council (NERC). Alvaro Moreno was financially supported by the NASA Earth Observing System MODIS project (grant NNX08AG87A) and the European Research Council (ERC) funding under the ERC Consolidator Grant 2014 SEDAL (Statistical Learning for Earth Observation Data Analysis, European Union) project under Grant Agreement 647423. Diego G. Miralles, Brecht Martens and Dominik Rains are supported by the European Research Council (ERC) DRY–2–DRY project (grant no. 715254) and the Belgian Science Policy Office (BELSPO) STEREO III ALBERI (grant no. SR/00/373) and ET–SENSE (grant. no SR/02/377) projects. Thiago R. Rodrigues was supported by the Brazilian National Council for Scientific and Technological Development (CNPq) with Bolsa de Produtividade em Pesquisa - PQ (Grant Number 308844/2018-1). Jorge Perez-Quezada and Mauricio Galleguillos were supported by the Chilean National Agency for Research and Development, grant FONDECYT 1211652. Rodolfo Nobrega and Anne Verhoef acknowledge support by the Newton/NERC/FAPESP Nordeste project: NE/N012488/1. Gabriela Posse acknowledges support by AERN 3632 and PNNAT 1128023 INTA Projects. Funding for site support: NPW tower: Brazilian National Institute for Science and Technology in Wetlands (INCT-INAU), Federal University of Mato Grosso (UFMT - PGFA and PGAT), University of Cuiabá (UNIC) and SESC-Pantanal; SDF tower: funded by the National Commission for Scientific and Technological Research (CONICYT, Chile) through grants FONDEQUIP AIC-37 and AFB170008 from the Associative Research Program. TF1 and TF2 towers: funded by the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy – EXC 177 'CliSAP - Integrated Climate System Analysis and Prediction' – contributing to the Center for Earth System Research and Sustainability (CEN) of Universität Hamburg and by DFG project KU 1418/6-1. MCR and BAL towers: funded by the National Council for Scientific and Technological Research (CONICET, Argentina) grants PIP-11220100100044 and PIP-11220130100347CO, and by the National Agency for the Scientific and Technological Promotion (ANPCyT, Argentina) grant PICT 2010-0554. JBF contributed to this research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged. JBF was supported in part by NASA: ECOSTRESS and SUSMAP. Copyright 2021. All rights reserved. CAA, CST, and ESEC Towers: funded by National Observatory of Water and Carbon Dynamics in the Caatinga Biome (INCT-NOWCDCB), Federal University of Pernambuco (UFPE), FACEPE (Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco) through the Project Caatinga-FLUX APQ 0062-1.07/15. Metadata of ‘Are remote sensing evapotranspiration models reliable across South American ecoregions?’ This document describes the file formatting and data used to run and evaluate the evapotranspiration models in this study. Because forcing data varies among models, each input file contains a different set of meteorological data placed within a folder named after the corresponding model. File format and time stamps Data files are CSV formatted with timestamps in the first column of the file. The following timestamps are used: GLEAM: Year (YYYY); Day of Year (DDD) PT-JPL: Year (YYYY); Month (MM); Day (DD) PM-MOD: Year (YYYY); Month (MM); Day (DD) PM-VI: Date (MM/DD/YYYY) Missing data Missing data are reported using ‘NaN’ as a replacement flag. Data for all days in a leap year are reported. Data format The column headers Name, Description and Units are adopted used in the data files to describe the following variables:: ETo, Penman-Monteith FAO-56 reference evapotranspiration (mm day-1); ETobs, Observed evapotranspiration (mm day-1); Rn, Surface Net Radiation (w m-2); Rg, Daylight shortwave Incoming Radiation (w m-2); Rgs_out, Shortwave Radiation - outgoing (w m-2); G, Soil heat flux (w m-2); P, Rainfall (mm day-1); T, Surface Air Temperature (ºC); Tmax, Maximum Temperature (ºC); Tmin, Minimum Temperature (ºC); Tday, Daytime Temperature (ºC); TminDay, Daytime Minimum Temperature (ºC); TminNight, Nighttime Minimum Temperature (ºC); Patm, Atmospheric Air Pressure (Pa); ea, Actual Vapor Pressure (kPa); es, Saturation Vapor Pressure (kPa); VPD, Vapor Pressure Deficit (kPa); eaDay, Daytime Actual Vapor Pressure (kPa); eaNight, Nighttime Actual Vapor Pressure (kPa); RH, Air Relative Humidity; RHDayTime, Daytime Air Relative Humidity; RHNightTime, Nighttime Air Relative Humidity; LAI, Leaf Area Index (m² m-²); SWC, Soil Water Content (mm m-1). Forcing data per model Each model requires a different set of forcing data, as follows: GLEAM: Rn, P, T, Rgs_out; PT-JPL: Tmax, Rn, RH (or ea); PM-MOD: Rg, Tday, TminDay, TminNight, RHDayTime, RHNighttime, eaDay, eaNight; PM-VI: ETo. Tower sites (IDs) and co-authors/PIs: SDF: J. P. Quezada and M. Galleguillos; TF1 and TF2: L. Kutzbach and D. Holl; GRO and SLU: G. Posse; BAL and MCC: M. Gassman and C. Perez; PDG, EUC and USR: O. Cabral; FM and SIN: J.S. Nogueira and T. Range; CAA: M. Moura; CST: A. C. D. Antonino; SJO: E. S. Souza and J. R. S. Lima; ESEC: B. Bezerra.

According to data obtained from meteorological towers, Brazil has significantly increased temperature in the past 20 years, particularly in the North and Midwest regions. Vapor pressure deficit and evapotranspiration were also analyzed, showing an increase across the entire country, confirming that the air is becoming drier. This warming trend is part of the global climate change phenomenon caused by the rise of greenhouse gases in the atmosphere, fires, poor soil management practices, deforestation, and logging. The increase in temperature and dryness has profoundly impacted Brazil’s climate and ecosystems, leading to intensified extreme weather events and changes in the distribution of both animal and plant species. This study highlights the importance of utilizing meteorological tower data to monitor and understand the effects of climate change in Brazil. It emphasizes the need for immediate action to address its causes and mitigate its negative impacts.

Meteorological elements can affect the environment and cultures differently and may alter the natural development process contributing significantly to climate change. Meteorological variables of the Brazilian Pantanal were studied and used to determine evapotranspiration with fewer variables. It was found that artificial intelligence can substantially improve environmental modeling when alternative prediction techniques are used, resulting in lower project costs and more reliable results. This work tried to find the best combination by comparing machine learning techniques such as artificial neural networks, random forests, and support vector machines. A new model was created that depends on fewer climatic variables compared to the Penman–Monteith method (the standard method for estimating reference evapotranspiration) and can efficiently describe the reference evapotranspiration. Machine learning techniques are highly efficient for modeling environmental systems since they can process large amounts of data and find the best interactions between the parameters involved. In addition, more than 98% accuracy was obtained using fewer variables compared to the standard method when artificial neural networks are utilized.

This paper describes details of an electric field sensor calibrator. The electric field sensor (field mill) is used to measure the atmospheric electric field. This calibrator is a parallel plate capacitor capable of creating electric fields near the atmospheric condition of fair weather, ie, without clouds and also electric fields observed during storms. The details of this system and the methodologies for the calibration of the electric field sensors as well as a study of the electric field generated in the calibrator are presented. Results of measurements of the atmospheric electric field in different local conditions, fair weather and storms are also presented.

Research involving the flux of energy in the soil has been intensified in order to increase the understanding of the geophysical behavior of the Pantanal-Brazil. In present study was examined the seasonal variation of the thermal soil conductivity in the Pantanal for the study of energy flow in the soil to Pantanal region. The average values obtained by the Fourier equation showed that the soil thermal conductivity in the wet and dry seasons was 8.69 W.m-1.ºC-1 and 6.65 W.m-1.ºC-1 respectively. The seasonal variation of the thermal conductivity of the soil was 30.68% higher in the wet season than in the dry season due to soil moisture in the wet season. It was also noted that the seasonal variation of temperature in the soil layer was higher in the wet season than in the dry season due to a lower incidence of solar radiation in this season.