Estimation of evapotranspiration for different land covers in a Brazilian semi-arid region: A case study of the Brígida River basin, Brazil

Santos, Celso Augusto Guimarães; Silva, Richarde Marques da; Silva, Alexandro Medeiros; Neto, Reginaldo Moura Brasil

doi:10.1016/j.jsames.2017.01.002

Cited by 35 publications

(31 citation statements)

References 36 publications

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“…For This higher sensitivity on the hot and dry endmember candidates was originally demonstrated by Bastiaanssen [53] and also observed by previous studies [58,114], where the endmembers play a key role in the SEBAL's internal calibration. The strong and accurate performance of the SEBAL and other surface energy balance models in heterogeneous areas was also demonstrated by other studies [115,116] also considering wet and dry climate conditions [61,95]. The importance of the selection of the endmembers for ET estimation was also highlighted [35,57,114], however, our results indicate that the influence of pixel selection is more relevant than the meteorological data used to force the model, making reanalysis data such as from MERRA-2 useful for surface energy balance models.…”

Section: Discussionsupporting

confidence: 66%

Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil

et al. 2020

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Evapotranspiration ( E T ) provides a strong connection between surface energy and hydrological cycles. Advancements in remote sensing techniques have increased our understanding of energy and terrestrial water balances as well as the interaction between surface and atmosphere over large areas. In this study, we computed surface energy fluxes using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm and a simplified adaptation of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for automated endmember selection. Our main purpose was to assess and compare the accuracy of the automated calibration of the SEBAL algorithm using two different sources of meteorological input data (ground measurements from an eddy covariance flux tower and reanalysis data from Modern-Era Reanalysis for Research and Applications version 2 (MERRA-2)) to estimate the dry season partitioning of surface energy and water fluxes in a transitional area between tropical rainforest and savanna. The area is located in Brazil and is subject to deforestation and cropland expansion. The SEBAL estimates were validated using eddy covariance measurements (2004 to 2006) from the Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA) at the Bananal Javaés (JAV) site. Results indicated a high accuracy for daily ET, using both ground measurements and MERRA-2 reanalysis, suggesting a low sensitivity to meteorological inputs. For daily ET estimates, we found a root mean square error (RMSE) of 0.35 mm day−1 for both observed and reanalysis meteorology using accurate quantiles for endmembers selection, yielding an error lower than 9% (RMSE compared to the average daily ET). Overall, the ET rates in forest areas were 4.2 mm day−1, while in grassland/pasture and agricultural areas we found average rates between 2.0 and 3.2 mm day−1, with significant changes in energy partitioning according to land cover. Thus, results are promising for the use of reanalysis data to estimate regional scale patterns of sensible heat (H) and latent heat (LE) fluxes, especially in areas subject to deforestation.

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Section: Discussionsupporting

confidence: 66%

Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil

et al. 2020

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“…Regarding the accuracy of the estimates, studies indicate relative errors ranging between~5% and 17%(Bastiaanssen, Pelgrum, et al, 1998;Bhattarai et al, 2012;Hemakumara, Chandrapala, & Moene, 2003;Kimura, Bai, Fan, Takayama, & Hinokidani, 2007). Further details of SEBAL can be found inBhattarai, Shaw, Quackenbush, Im, and Niraula (2016) andSantos, da Silva, Silva, and Neto (2017). Further details of SEBAL can be found inBhattarai, Shaw, Quackenbush, Im, and Niraula (2016) andSantos, da Silva, Silva, and Neto (2017).…”

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confidence: 99%

“…The primary sources of uncertainty in SEBAL are related to the determination of H and the low sensitivity of the model to soil moisture and water stress. Further details of SEBAL can be found inBhattarai, Shaw, Quackenbush, Im, and Niraula (2016) andSantos, da Silva, Silva, and Neto (2017).…”

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confidence: 99%

Effects of land‐cover changes on the partitioning of surface energy and water fluxes in Amazonia using high‐resolution satellite imagery

et al. 2019

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Spatial variability of surface energy and water fluxes at local scales is strongly controlled by soil and micrometeorological conditions. Thus, the accurate estimation of these fluxes from space at high spatial resolution has the potential to improve prediction of the impact of land-use changes on the local environment. In this study, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) data were used to examine the partitioning of surface energy and water fluxes over different landcover types in one wet year (2004) and one drought year (2005) in eastern Rondonia state, Brazil. The spatial variation of albedo, net radiation (Rn), soil (G) and sensible (H) heat fluxes, evapotranspiration (ET), and evaporative fraction (EF) were primarily related to the lower presence of forest (primary [PF] or secondary [SF]) in the western side of the Ji-Parana River in comparison with the eastern side, located within the Jaru Biological Reserve protected area. Water limitation in this part of Amazonia tends to affect anthropic (pasture [PA] and agriculture [AG]) ecosystems more than the natural land covers (PF and SF). We found statistically significant differences on the surface fluxes prior to and~1 year after the deforestation. Rn over forested areas is~10% greater in comparison with PA and AG. Deforestation and consequent transition to PA or AG increased the total energy (~200-400%) used to heat the soil subsurface and raise air temperatures. These differences in energy partitioning contributed to approximately three times higher ET over forested areas in comparison with nonforested areas. The conversion of PF to AG is likely to have a higher impact in the local climate in this part of Amazonia when compared with the change to PA and SF, respectively. These results illustrate the importance of conserving secondary forest areas in Amazonia.

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“…An example of land use change illustrates the difficulty of valuing human appropriated water flows. Santos et al (2017) found that in a Brazilian semi-arid region, evapotranspiration (ET) was highest in forested areas and dense savannah vegetation. Grassland and cropland had a considerably lower ET, and bare land lower still, especially in a wet year.…”

Section: Acknowledgementsmentioning

confidence: 99%

Bringing it all together: linking measures to secure nations’ food supply

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et al. 2017

Current Opinion in Environmental Sustainability

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Estimation of evapotranspiration for different land covers in a Brazilian semi-arid region: A case study of the Brígida River basin, Brazil

Cited by 35 publications

References 36 publications

Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil

Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil

Effects of land‐cover changes on the partitioning of surface energy and water fluxes in Amazonia using high‐resolution satellite imagery

Bringing it all together: linking measures to secure nations’ food supply

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