Evapotranspiration (ET) is an important component of the hydrological cycle. Understanding the ET process has become of fundamental importance given the scenario of global change and increasing water use, especially in the agricultural sector. Determining ET over large agricultural areas is a limiting factor due to observational data availability. In this regard, remote sensing data has been used to estimate ET. In this study, we evaluated the Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface ET product estimates (hereafter MOD16 ET – MODIS Global Terrestrial Evapotranspiration Product) over two rice paddy areas in Southern Brazil, through the ET measured using the eddy covariance technique (hereafter EC). The energy balance components were evaluated during fallow and flooded seasons showing latent heat flux dominates in both seasons. The results showed that MOD16 ET underestimated EC measurements. Overall, the RMSE (root mean square error) ranged between 13.40 and 16.35 mm 8-day⁻¹ and percent bias (PBIAS) ranged between −33.7% and −38.7%. We also assessed the ET (measured and estimated) main drivers, with EC yielding higher correlation against observed net radiation (R_n) and global radiation (R_g), followed by air temperature (Temp) and vapor pressure deficit (VPD), whilst MOD16 ET estimates yielded higher correlation against leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR). The MOD16 algorithm was forced with meteorological measurements but the results did not improve as expected, suggesting a low sensitivity to meteorological inputs. Our results indicated when a water layer was present over the soil surface without vegetation (LAI around zero), the largest differences between EC measurements and MOD16 ET were found. In this period, the expected domain of soil evaporation was not observed in MOD16 ET physical processes partition, indicating the algorithm was not able to detect areas with high soil moisture. In general, the MOD16 ET product presented low accuracy when compared against experimental measurements over flooded rice paddy, suggesting more studies are necessary, in order to reduce uncertainties associated to the land cover conditions.

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.

Soil thermal conductivity is an important parameter for understanding soil heat transfer. It is difficult to measure in situ with available instruments. This work aims to propose a numerical model to estimate the thermal conductivity from the experimental measurements of soil heat flux and soil temperature. The new numerical model is based on the Fourier Law adding a constant empirical parameter to minimize the uncertainties contained in the data from field experiments. Numerically, the soil thermal conductivity is obtained by experimental linear data fitting by the Least Squares Method (LSM). This method avoids numerical indetermination when the soil temperature gradient or soil heat flux is very close to zero. The new model is tested against the different numerical methodology to estimate the soil heat flux and validated with field experimental data. The results indicate that the proposed model represents the experimental data satisfactorily. In addition, we show the influence of the different methodologies on evaluating the dependence of the thermal conductivity on the soil water content.

Knowledge of soil thermal properties (diffusivity (k) and conductivity (λ)) is important to understand the soil–plant–atmosphere interaction related to the physical and biological processes associated with energy transfer and greenhouse gas exchanges. The incorporation of all the physical processes that occur in the energy transfer in the soil is a challenge in order to correctly estimate soil thermal properties. In this work, experimental measurements of soil temperature and soil heat flux obtained in a silty clay loam soil covered by native grassland located in the Brazilian Pampa biome were used to estimate soil thermal properties using different methods including the influence of the soil water content at different soil depths in heat transfer processes. The λ was estimated using the numerical solution of the Fourier equation by the Gradient and Modified Gradient methods. For the surface layer, the results for both models show large variability in daily values, but with similar values for the annual mean. For λ at different soil depths, both models showed an increase of approximately 50% in the λ value in the deeper layers compared to the surface layer, increasing with depth in this soil type. The k was estimated using analytical and numerical methods. The analytical methods showed a higher variability and overestimated the values of the numerical models from 15% to 35%. The numerical models included a term related to the soil water content. However, the results showed a decrease in the mean value of k by only 2%. The relationship between thermal properties and soil water content was verified using different empirical models. The best results for thermal conductivity were obtained using water content in the surface layer (R² > 0.5). The cubic model presented the best results for estimating the thermal diffusivity (R² = 0.70). The analyses carried out provide knowledge for when estimating soil thermal properties using different methods and an experimental dataset of soil temperature, heat flux and water content, at different soil depths, for a representative soil type of the Brazilian Pampa biome.

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.

O arroz é um dos principais grãos produzidos no mundo, demandando grande volume de d’água através do cultivo de irrigação por inundação. Estimar a evapotranspiração (ET) para áreas de arroz irrigado através de medidas in situ ainda é um fator limitante, devido ao alto custo dos equipamentos e mão de obra especializada. O método Penman-Monteith (PM) é conhecido como sendo o mais completo, porém necessita de um grande número de informações. O método Priestley-Taylor (PT) representa uma simplificação de PM, utilizando um menor número de variáveis ambientais e energia disponível no sistema. Na literatura, diversos trabalhos têm apresentado calibrações para o coeficiente α de PT. No entanto, essas calibrações devem ser realizadas de acordo com as características climáticas e da superfície de cada região. Desta forma, o objetivo deste trabalho é propor uma calibração do coeficiente de PT para estimativas de ET no arroz irrigado, visando a utilização de um método mais simples. Para isso, foram utilizados dois sítios experimentais no sul do Brasil. Os resultados simulados foram comparados com dados experimentais obtidos pela técnica de Eddy Covariance (EC), resultando em um valor de calibração de 1,22 do parâmetro α, mostrando-se eficiente para estimativas de ET em áreas de arroz irrigado.