Uncertainties in carbon emissions from land use and land cover change in Indonesia - I. B. M. Brasika; I. B. M. Brasika; P. Friedlingstein; P. Friedlingstein; S. Sitch; M. O'Sullivan; M. C. Duran-Rojas; T. M. Rosan; K. K. Goldewijk; J. Pongratz; J. Pongratz; C. Schwingshackl; et al. (Biogeosciences)
Indonesia is currently one of the three largest contributors of carbon emissions from land use and land cover change (LULCC) globally, together with Brazil and the Democratic Republic of the Congo. However, until recently, there were only limited reliable data available on LULCC across Indonesia, leading to a lack of agreement on the drivers, magnitudes, and trends of carbon emissions between different estimates. Accurate LULCC should improve robustness and reduce the uncertainties of carbon dioxide (CO2) emissions from land use change (ELUC) estimation. Here, we assess several cropland datasets that are used to estimate ELUC in dynamic global vegetation models (DGVMs) and bookkeeping models (BKMs). Available cropland datasets are generally categorized as either statistics-based, such as the Food and Agricultural Organization (FAO) annual statistical dataset, or satellite-based, such as the Mapbiomas dataset, which is derived from Landsat satellite images. Our results show that national-statistics-based and satellite-based estimates have little agreement on temporal variability and cropland area changes. On some islands, they show spatial similarity, but differences appear on the main islands such as Kalimantan, Sumatra, and Java. These differences lead to spatiotemporal uncertainty in carbon emissions. The different land cover forcings (national-statistics-based and satellite-based) in a single model (JULES-ES) result in ELUC uncertainties of about 0.08 [0.06 to 0.11] PgC yr−1. Furthermore, we found that uncertainties in ELUC estimates are also due to differences in the carbon cycle models in DGVMs, as DGVMs driven by the same land cover dataset show differences in ELUC estimates of 0.12 ± 0.02 PgC yr−1 with a 95 % confidence level and range [−0.04 to 0.35] PgC yr−1. This is consistent with other products such as BKMs that estimate 0.14 [0.12 to 0.15] PgC yr−1, with both having steady trends. We also compare the emissions with those from the National Greenhouse Gas Inventory (NGHGI) product. The NGHGI estimates (based on BUR3, the periodic official government report on greenhouses gases to UNFCCC) have much lower carbon emissions (0.06 ± 0.06 PgC yr−1), though with an increasing trend. These numbers double when we include emissions from peat fire and peat drainage: the DGVM ensemble indicates emissions of 0.23 ± 0.05 PgC yr−1, and BKMs indicate emissions of 0.24 ± 0.01 PgC yr−1. In contrast, emissions based on the Indonesian NGHGI remain much lower (BUR2: 0.18 ± 0.07 PgC yr−1; BUR3: 0.13 ± 0.10 PgC yr−1). Furthermore, emission peaks occur in years of moderate to strong El Niño events. Several improvements might reduce uncertainties in carbon emissions from LULCC in Indonesia, such as a combination of a satellite-based dataset with a national-statistics-based dataset, inclusion of peat-related emissions in DGVMs, and potentially explicit inclusion of palm oil in models, as this is a major crop in Indonesia. Overall, the analysis shows that carbon emissions have no decreasing trend in Indonesia. Therefore, deforestation and forest fire prevention remain vital for Indonesia.