• Energy Research

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.

Artículo EDITORIAL Front. Soil Sci., 11 de julio de 2023Sec. Biogeoquímica del Suelo y Ciclo de Nutrientes Volumen 3 - 2023 | https://doi.org/10.3389/fsoil.2023.1240930 Article ÉDITORIAL Front. Soil Sci., 11 juillet 2023Sec. Biogéochimie du sol et cycle des nutriments Volume 3 - 2023 | https://doi.org/10.3389/fsoil.2023.1240930 مقال تحريري Front. Soil Sci., 11 July 2023Sec. الكيمياء الحيوية للتربة وركوب الدراجات الغذائية المجلد 3 - 2023 | https://doi.org/10.3389/fsoil.2023.1240930 EDITORIAL article Front. Soil Sci., 11 July 2023Sec. Soil Biogeochemistry & Nutrient Cycling Volume 3 - 2023 | https://doi.org/10.3389/fsoil.2023.1240930

AbstractGlobally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil‐to‐atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS), is one of the largest carbon fluxes in the Earth system. An increasing number of high‐frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open‐source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long‐term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS, the database design accommodates other soil‐atmosphere measurements (e.g. ecosystem respiration, chamber‐measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

We studied the seasonal fluctuation of soil respiration (R(S)), and its root-dependent (R(R)) and basal (R(B)) components, in a Vitis vinifera (Chardonnay) vineyard. The R(S) components were estimated through independent field methods (y-intercept and trenching) and modeled on the basis of a Q(10) response to soil temperature, and fine and coarse root respiration coefficients. The effect of assimilate availability on R(R) was assessed through a trunk girdling treatment. The apparent Q(10) for R(R) was twice that of R(B) (3.5 vs 1.6) and increased linearly with increasing vine root biomass. The fastest R(R) of fine roots was during rapid fruit growth and the fastest R(R) of coarse roots was immediately following fruit development. R(S) was estimated at 32.6 kg ha(-1) d(-1) (69% as a result of R(R) ) for the hottest month and at 7.6 kg ha(-1) d(-1) (18% as a result of R(R)) during winter dormancy. Annual R(S) was low compared with other natural and cultivated ecosystems: 5.4 Mg ha(-1) (46% as a result of R(R)). Our estimates of annual vineyard R(S) are the first for any horticultural crop and suggest that the assumption that they are similar to those of annual crops or forest trees might lead to an overestimation.