A MODIS-based energy balance to estimate evapotranspiration for clear-sky days in Brazilian tropical savannas

Evapotranspiration (ET) plays an important role in global climate dynamics and in primary production of terrestrial ecosystems; it represents the mass and energy transfer from the land to atmosphere. Limitations to measuring ET at large scales using ground-based methods have motivated the development of satellite remote sensing techniques. The purpose of this work is to evaluate the accuracy of the SEBAL algorithm for estimating surface turbulent heat fluxes at regional scale, using 28 images from MODIS. SEBAL estimates are compared with eddy-covariance (EC) measurements and results from the hydrological model MGB-IPH. SEBAL instantaneous estimates of latent heat flux (LE) yielded r 2= 0.64 and r2 = 0.62 over sugarcane croplands and savannas when compared against in situ EC estimates. At the same sites, daily aggregated estimates of LE were r 2 = 0.76 and r2 = 0.66, respectively. Energy balance closure showed that turbulent fluxes over sugarcane croplands were underestimated by 7% and 9% over savannas. Average daily ET from SEBAL is in close agreement with estimates from the hydrological model for an overlay of 38,100 km2 (r2 = 0.88). Inputs to which the algorithm is most sensitive are vegetation index (NDVI), gradient of temperature (dT) to compute sensible heat flux (H) and net radiation (Rn). It was verified that SEBAL has a tendency to overestimate results both at local and regional scales probably because of low sensitivity to soil moisture and water stress. Nevertheless the results confirm the potential of the SEBAL algorithm, when used with MODIS images for estimating instantaneous LE and daily ET from large areas

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

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 land‐cover 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.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151879/1/eco2126_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151879/2/eco2126.pd

A multi-data assessment of land use and land cover emissions from Brazil during 2000–2019

Brazil is currently the largest contributor of land use and land cover change (LULCC) carbon dioxide net emissions worldwide, representing 17%–29% of the global total. There is, however, a lack of agreement among different methodologies on the magnitude and trends in LULCC emissions and their geographic distribution. Here we perform an evaluation of LULCC datasets for Brazil, including those used in the annual global carbon budget (GCB), and national Brazilian assessments over the period 2000–2018. Results show that the latest global HYDE 3.3 LULCC dataset, based on new FAO inventory estimates and multi-annual ESA CCI satellite-based land cover maps, can represent the observed spatial variation in LULCC over the last decades, representing an improvement on the HYDE 3.2 data previously used in GCB. However, the magnitude of LULCC assessed with HYDE 3.3 is lower than estimates based on MapBiomas. We use HYDE 3.3 and MapBiomas as input to a global bookkeeping model (bookkeeping of land use emission, BLUE) and a process-based Dynamic Global Vegetation Model (JULES-ES) to determine Brazil's LULCC emissions over the period 2000–2019. Results show mean annual LULCC emissions of 0.1–0.4 PgC yr−1, compared with 0.1–0.24 PgC yr−1 reported by the Greenhouse Gas Emissions Estimation System of land use changes and forest sector (SEEG/LULUCF) and by FAO in its latest assessment of deforestation emissions in Brazil. Both JULES-ES and BLUE now simulate a slowdown in emissions after 2004 (−0.006 and −0.004 PgC yr−2 with HYDE 3.3, −0.014 and −0.016 PgC yr−2 with MapBiomas, respectively), in agreement with the Brazilian INPE-EM, global Houghton and Nassikas book-keeping models, FAO and as reported in the 4th national greenhouse gas inventories. The inclusion of Earth observation data has improved spatial representation of LULCC in HYDE and thus model capability to simulate Brazil's LULCC emissions. This will likely contribute to reduce uncertainty in global LULCC emissions, and thus better constrains GCB assessments

Contrasting carbon cycle responses to dry (2015 El Niño) and wet (2008 La Niña) extreme events at an Amazon tropical forest

Land surface models diverge in their predictions of the Amazon forest\u27s response to climate change-induced droughts, with some showing a catastrophic collapse of forests, while others simulating resilience. Therefore, observations of tropical ecosystem responses to real-world droughts and other extreme events are needed. We report long-term seasonal dynamics of photosynthesis, respiration, net carbon exchange, phenology, and tree demography and characterize the effect of dry and wet events on ecosystem form and function at the Tapajós National Forest, Brazil, using over two decades of eddy covariance observations that include the 2015–2016 El Niño drought and La Niña 2008–2009 wet periods. We found strong forest responses to both ENSO events: La Niña saw forest net carbon loss from reduced photosynthesis (due to lower incoming radiation from increased cloudiness) even as ecosystem respiration (Reco) was maintained at mean seasonal levels. El Niño induced the opposite short-term effect, net carbon gains, despite significant reductions in photosynthesis (from a drought-induced halving of canopy conductance to CO2 and significant losses of leaf area), because drought suppression of Reco losses was even greater. However, long-term responses to the two climate perturbations were very different: transient during La Niña –the forest returned to its “normal” state as soon as the climate did, and long-lasting during El Niño –leaf area loss and associated declines in photosynthetic capacity (Pc) and canopy conductance were exacerbated and extended by feedbacks from higher temperatures and atmospheric evaporative demand and persisted for ∼3+ years after normal rainfall resumed. These findings indicate that these forests are more vulnerable to drought than to excess rain, because drought drives significant changes in forest structure (e.g., leaf-abscission and mortality) and ecosystem function (e.g. reduced stomatal conductance). As future Amazonian climate change increases frequencies of hydrological extremes, these mechanisms will determine the long-term fate of tropical forests

A multi-data assessment of land use and land cover emissions from Brazil during 2000-2019

Brazil is currently the largest contributor of land use and land cover change (LULCC) carbon dioxide net emissions worldwide, representing 17%-29% of the global total. There is, however, a lack of agreement among different methodologies on the magnitude and trends in LULCC emissions and their geographic distribution. Here we perform an evaluation of LULCC datasets for Brazil, including those used in the annual global carbon budget (GCB), and national Brazilian assessments over the period 2000-2018. Results show that the latest global HYDE 3.3 LULCC dataset, based on new FAO inventory estimates and multi-annual ESA CCI satellite-based land cover maps, can represent the observed spatial variation in LULCC over the last decades, representing an improvement on the HYDE 3.2 data previously used in GCB. However, the magnitude of LULCC assessed with HYDE 3.3 is lower than estimates based on MapBiomas. We use HYDE 3.3 and MapBiomas as input to a global bookkeeping model (bookkeeping of land use emission, BLUE) and a process-based Dynamic Global Vegetation Model (JULES-ES) to determine Brazil's LULCC emissions over the period 2000-2019. Results show mean annual LULCC emissions of 0.1-0.4 PgC yr-1, compared with 0.1-0.24 PgC yr-1 reported by the Greenhouse Gas Emissions Estimation System of land use changes and forest sector (SEEG/LULUCF) and by FAO in its latest assessment of deforestation emissions in Brazil. Both JULES-ES and BLUE now simulate a slowdown in emissions after 2004 (-0.006 and -0.004 PgC yr-2 with HYDE 3.3, -0.014 and -0.016 PgC yr-2 with MapBiomas, respectively), in agreement with the Brazilian INPE-EM, global Houghton and Nassikas book-keeping models, FAO and as reported in the 4th national greenhouse gas inventories. The inclusion of Earth observation data has improved spatial representation of LULCC in HYDE and thus model capability to simulate Brazil's LULCC emissions. This will likely contribute to reduce uncertainty in global LULCC emissions, and thus better constrains GCB assessments

Productivity and carbon allocation in a tropical montane cloud forest in the Peruvian Andes

Background: The slopes of the eastern Andes harbour some of the highest biodiversity on Earth and a high proportion of endemic species. However, there have been only a few and limited descriptions of carbon budgets in tropical montane forest regions.Aims

The linkages between photosynthesis, productivity, growth and biomass in lowland Amazonian forests

Understanding the relationship between photosynthesis, net primary productivity and growth in forest ecosystems is key to understanding how these ecosystems will respond to global anthropogenic change, yet the linkages among these components are rarely explored in detail. We provide the first comprehensive description of the productivity, respiration and carbon allocation of contrasting lowland Amazonian forests spanning gradients in seasonal water deficit and soil fertility. Using the largest data set assembled to date, ten sites in three countries all studied with a standardized methodology, we find that (i) gross primary productivity (GPP) has a simple relationship with seasonal water deficit, but that (ii) site-to-site variations in GPP have little power in explaining site-to-site spatial variations in net primary productivity (NPP) or growth because of concomitant changes in carbon use efficiency (CUE), and conversely, the woody growth rate of a tropical forest is a very poor proxy for its productivity. Moreover, (iii) spatial patterns of biomass are much more driven by patterns of residence times (i.e. tree mortality rates) than by spatial variation in productivity or tree growth. Current theory and models of tropical forest carbon cycling under projected scenarios of global atmospheric change can benefit from advancing beyond a focus on GPP. By improving our understanding of poorly understood processes such as CUE, NPP allocation and biomass turnover times, we can provide more complete and mechanistic approaches to linking climate and tropical forest carbon cycling