Evapotranspiration (ET) is one of the least understood components of the hydrological cycle. Its applications are varied, from agricultural, ecological and hydrological monitoring, to control of the evolution of climate change. The goal of this work was to analyze the influence that uncertainties in the estimate of land surface temperature (Ts) can cause on ET estimates by S-SEBI model in the Pampa biome area. Also, the specificities of native grassland of Pampa biome related to energy balance were analyzed. The results indicate that the daily evapotranspiration is higher when the pixel Ts is lower, which also shows the influence of land use on the variability of ET. The results demonstrated that the S-SEBI is less dependent on Ts estimation than other models reported in the literature, such as the SEBS, which not exceed 0.5 mm/day in grasslands. The evapotranspiration variability between forest and grassland were lower than expected, demonstrating that the Pampa biome have in Rio Grande do Sul the same importance that forests regarding to the processes of the hydrological cycle, since it covers 63% of the State.

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.

A relação entre produção de alimentos e a preservação do meio ambiente tem se tornado um tema de debate internacional, principalmente relacionado a emissões de gases do efeito estufa. Desta forma, torna-se cada vez mais importante conhecer potenciais fontes e sumidouros destes gases, avaliando principalmente o balanço de Carbono em área de agricultura e pecuária. No Rio Grande do Sul a atividade agropecuária é extremamente importante, cultural e economicamente, ocupando grande parte do estado, principalmente dentro do bioma Pampa, este que conta com grandes áreas de pastagens naturais aptas a servirem de forragem para os animais. Neste trabalho, quantificamos as trocas de CO2 através da metodologia Eddy Covariance em dois sítios experimentais de pastagem nativa do bioma Pampa utilizados para criação de gado, no sistema rotativo, que visa manter a diversidade da pastagem nativa. Com dados de um ano, mostramos que ambos os sítios foram sumidouros de CO2, variando de -82,4 g C m-2 a-1 a -282,3 g C m-2 a-1, devido às diferenças nas condições ambientais e composição da vegetação. Assim, a criação de gado em pastagem natural do bioma pampa pode contribuir para compensar as emissões de gases do efeito estufa em outros setores da agropecuária.