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

Abstract

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.

Land Use Effects on Climate: Current State, Recent Progress, and Emerging Topics

“…For reconstructing land use, country-reported data on agricultural areas and forest harvest, often from FAOSTAT [85] and the Forest Resource Assessments [86], is typically included to isolate the anthropogenic signal [82]. Integrating better satellite-based and country-reported data on a global scale is clearly a promising path towards reducing the uncertainties around land use dynamics, but also the translation of country-reported data to land use dynamics as input to models could be substantially improved: Rosan et al [87] point out that retrospective updates in data reported by Brazil to FAO caused discrepancies between agricultural expansion and forest cover changes, which were attributed to the FAO "other land" category and thus remained unused by current global datasets [88], although they may contain part of a land use-induced deforestation signal. Additional uncertainties arise from the translation of land use data into land cover conversion [89].…”

How Well Do We Understand the Land‐Ocean‐Atmosphere Carbon Cycle?

“…Carbon fluxes are based on country‐level surveys of vegetation and soil carbon density for different forest ecosystems and response curves for temporal carbon dynamics following disturbance and recovery, for example, legacy fluxes and regrowth. More recently, satellite‐based biomass data are being used in book‐keeping approaches (e.g., Rosan et al., 2021) to more accurately reflect spatial variation in carbon stocks, and implicitly include the influence of environmental factors.…”

How Well Do We Understand the Land-Ocean-Atmosphere Carbon Cycle?

“…Carbon fluxes are based on country-level surveys of vegetation and soil carbon density for different forest ecosystems and response curves for temporal carbon dynamics following disturbance and recovery, for example, legacy fluxes and regrowth. More recently, satellite-based biomass data are being used in book-keeping approaches (e.g., Rosan et al, 2021) to more accurately reflect spatial variation in carbon stocks, and implicitly include the influence of environmental factors.…”