Atmospheric downward longwave radiation flux (L↓) is a variable that directly influences the surface net radiation and consequently, weather and climatic conditions. Measurements of L↓ are scarce, and the use of classical models depending on some atmospheric variables may be an alternative. In this paper, we analyzed L↓ measured over the Brazilian Pampa biome. This region is located in a humid subtropical climate zone and characterized by well defined seasons and well distributed precipitation. Furthermore, we evaluated the performance of the eleven classical L↓ models for clear sky with one-year experimental data collected in the Santa Maria experimental site (SMA) over native vegetation and high relative humidity throughout the year. Most of the L↓ estimations, using the original coefficients, underestimated the experimental data. We performed the local calibration of the L↓ equations coefficients over an annual period and separated them into different sky cover classifications: clear sky, partly cloudy sky, and cloudy sky. The calibrations decreased the errors, especially in cloudy sky classification. We also proposed the joint calibration between the clear sky emissivity equations and cloud sky correction function to reduce errors and evaluate different sky classifications. The results found after these calibrations presented better statistical indexes. Additionally, we presented a new empirical model to estimate L↓ based on multiple regression analysis using water vapor pressure and air temperature. The new equation well represents partial and cloudy sky, even without including the cloud cover parameterization, and was validated with the following five years in SMA and two years in the Cachoeira do Sul experimental site (CAS). The new equation proposed herein presents a root mean square error ranging from 13 to 21 Wm⁻² and correlation coefficient from 0.68 to 0.83 for different sky cover classifications. Therefore, we recommend using the novel equation to calculate L↓ over the Pampa biome under these specific climatic conditions.

The Brazilian Pampa biome has natural pastures that have been used for centuries for cattle grazing. This is considered a sustainable system because it combines the conservation of natural vegetation and high-quality meat production, protecting the biome from commercial agriculture’s advances. However, whether it is a source or a sink of carbon dioxide (CO₂) has yet to be evaluated. Hence, this study aimed to quantify the net ecosystem exchange (NEE) of the CO₂ of a natural pasture of the Pampa biome used for livestock production. The experimental area is located in a subtropical region of southern Brazil, where eddy covariance (EC) measurements were conducted from 2015 to 2021 in a rotational cattle grazing system. The seven months of the warm season (September to March) were characterized as CO₂ absorbers, while the five months of the cold season (April to August) were CO₂ emitters. Throughout the six years and with complete data, the ecosystem was an absorber of atmospheric CO₂, with an average value of −207.6 g C m⁻² year⁻¹. However, the significant interannual variability in NEE was observed, with cumulative values ranging from −82.0 to −385.3 g C m⁻² year⁻¹. The results suggest the coupling of climatic conditions to pasture management can be the factor that modulated the NEE interannual variability. The cattle raising system on the natural pastures of the Pampa absorbs CO₂, which is further evidence of its sustainability and need for conservation.

Energy and water exchange between the surface and the atmosphere are important drivers to Earth’s climate from local to global scale. In this study, the energy dynamic and the biophysical mechanisms that control the energy partitioning over a natural grassland pasture over the Brazilian Pampa biome are investigated using two micrometeorological sites located 300 km apart, in Southern Brazil. The latent heat flux, LE, was the main component of the energy balance in both autumn-winter (AW) and spring-summer (SS) periods. Annually, approximately 60% of the available energy is used for evapotranspiration (ET). However, the Bowen ratio presents seasonal variability greater in AW than SS. Global radiation, Rg, is the atmospheric variable controlling LE and sensible heat flux, H. Hysteresis curves in the daily cycle were observed for ET and surface conductance, Cs, regarding the environmental variables, net radiation, vapor pressure deficit, and air temperature. Among the variables analyzed in the Pampa biome, surface conductance and evapotranspiration respond more strongly to the vapor pressure deficit. The hysteresis cycles formed by ET and conductance show a substantial biophysical control in the ET process. The results obtained here allowed a comprehension of the biophysical mechanisms involved in the energy partition process in natural grassland. Therefore, this study can be used as a base for research on land-use changes in this unique ecosystem of the Pampa biome.

Paddy fields are significant anthropogenic sources of methane (CH₄) emissions. In southern Brazil, rice is grown in lowland flooded areas once a year, followed by a long fallow period. This study aimed to measure CH₄ fluxes in a rice paddy field in southern Brazil during the rice-growing season of 2015/2016 and the following fallow period. The fluxes were estimated using the eddy covariance (EC) technique and soil chamber (SC). Diurnal and seasonal variations of CH₄ fluxes and potential meteorological drivers were analyzed. The CH₄ fluxes showed distinct diurnal variations in each analyzed subperiod (vegetative, reproductive, pre-harvest, no rice, and land preparation), characterized by a single-peak diurnal pattern. The variables that most influenced methane emissions were air and surface temperatures. In the growing season, the rice vegetative stage was responsible for most of the measured emissions. The accumulated annual emission estimated was 44.88 g CH₄ m⁻² y⁻¹, being 64% (28.50 g CH₄ m⁻²) due to the rice-growing season and 36% (16.38 g CH₄ m⁻²) due to the fallow period. These results show the importance of including fallow periods in strategies to mitigate methane emissions in flood irrigated rice-growing areas.

Devido a necessidade de reduzir as emissões de gases do efeito estufa em sistemas agropecuários, estudos estão sendo realizados nos campos do bioma Pampa afim de conhecer a dinâmica das trocas de carbono para propor formas de mitigação. Neste estudo, calibramos um modelo para a estimativa da troca líquida de CO2 (NEE) nos campos nativos do bioma pampa utilizados para pecuária, com base nas equações clássicas de respiração do ecossistema e assimilação de carbono pela fotossíntese. Para tanto, serão utilizados dados de NEE obtidos através da técnica Eddy Covariance (EC) num campo nativo no sítio de Santa Maria nos anos de 2015 e 2016 para obter os parâmetros utilizados no modelo. Os parâmetros foram obtidos para cada estação do ano e o modelo foi avaliado para os anos de 2019 e 2020 tanto para o sítio de Santa Maria, quanto para o sítio de Aceguá, distante cerca de 300km de Santa Maria. Os resultados mostraram que o modelo foi capaz de estimar o NEE para o sítio de Santa Maria com média de R² = 0,80 e RMSE 0,08 g C m-2 30 min-1, e para o sítio de Aceguá com R² = 0,75 e RMSE 0,10 g C m-2 30 min-1. No entanto, o modelo teve maior R² e maior RMSE nos períodos de verão e primavera e menor no inverno e outono em ambos os sítios. Este modelo pode ser usado para estimativas do NEE CO2 nos campos nativos do bioma Pampa como base para previsão de absorção/emissão de CO2 nas diferentes estações do ano.