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Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands

Abstract

The African continent is facing one of the driest periods in the past three decades as well as continued deforestation. These disturbances threaten vegetation carbon (C) stocks and highlight the need for improved capabilities of monitoring large-scale aboveground carbon stock dynamics. Here we use a satellite dataset based on vegetation optical depth derived from low-frequency passive microwaves (L-VOD) to quantify annual aboveground biomass-carbon changes in sub-Saharan Africa between 2010 and 2016. L-VOD is shown not to saturate over densely vegetated areas. The overall net change in drylands (53% of the land area) was −0.05 petagrams of C per year (Pg C yr−1) associated with drying trends, and a net change of −0.02 Pg C yr−1 was observed in humid areas. These trends reflect a high inter-annual variability with a very dry year in 2015 (net change, −0.69 Pg C) with about half of the gross losses occurring in drylands. This study demonstrates, first, the applicability of L-VOD to monitor the dynamics of carbon loss and gain due to weather variations, and second, the importance of the highly dynamic and vulnerable carbon pool of dryland savannahs for the global carbon balance, despite the relatively low carbon stock per unit area.

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Fig. 1: Relationships between carbon density in biomass and VOD in sub-Saharan Africa.
Fig. 2: Changes in carbon stocks for 2010–2016.
Fig. 3: Shrub die-off in Senegal.
Fig. 4: Observed and simulated carbon dynamics.
Fig. 5: Hovmöller diagrams showing anomalies (z score) for Africa for each year and latitude.
Fig. 6: Climate as a driver of carbon stock dynamics.

References

  1. Brandt, M. et al. Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa. Nat. Ecol. Evol. 1, 0081 (2017).

    Article  Google Scholar 

  2. Rudel, T. K. The national determinants of deforestation in sub-Saharan Africa. Phil. Trans. R. Soc. B 368, 20120405 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Ciais, P. et al. The carbon balance of Africa: synthesis of recent research studies. Phil. Trans. R. Soc. A 369, 2038–2057 (2011).

    CAS  Article  PubMed  Google Scholar 

  4. Williams, C. A. et al. Africa and the global carbon cycle. Carbon Balance Manag. 2, 3 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Rodriguez-Veiga, P., Saatchi, S., Wheeler, J., Tansey, K. & Balzter, H. in Earth Observation for Land and Emergency Monitoring (ed. Balzter, H.) Ch. 2, 5–32 (John Wiley & Sons, Chichester, 2017).

  6. Réjou-Méchain, M. et al. Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks. Biogeosciences 11, 6827–6840 (2014).

    Article  Google Scholar 

  7. Reiche, J. et al. Combining satellite data for better tropical forest monitoring. Nat. Clim. Change 6, 120–122 (2016).

    Article  Google Scholar 

  8. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    CAS  Article  PubMed  Google Scholar 

  9. Hill, M. J. & Hanan, N. P. Ecosystem Function in Savannas: Measurement and Modeling at Landscape to Global Scales (CRC Press, Boca Raton, London, New York, 2010).

  10. Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage. 259, 660–684 (2010).

    Article  Google Scholar 

  11. Niang, I. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 1199–1265 (IPCC, Cambridge Univ. Press, 2014).

  12. Bastin, J.-F. et al. The extent of forest in dryland biomes. Science 356, 635–638 (2017).

    CAS  Article  PubMed  Google Scholar 

  13. Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).

    CAS  Article  PubMed  Google Scholar 

  14. Yue, C. et al. Vegetation greenness and land carbon-flux anomalies associated with climate variations: a focus on the year 2015. Atmos. Chem. Phys. 17, 13903–13919 (2017).

    CAS  Article  Google Scholar 

  15. Carvalhais, N. et al. Global covariation of carbon turnover times with climate in terrestrial ecosystems. Nature 514, 213–217 (2014).

    CAS  Article  PubMed  Google Scholar 

  16. Mermoz, S., Le Toan, T., Villard, L., Réjou-Méchain, M. & Seifert-Granzin, J. Biomass assessment in the Cameroon savanna using ALOS PALSAR data. Remote Sens. Environ. 155, 109–119 (2014).

    Article  Google Scholar 

  17. Gibbs, H. K., Brown, S., Niles, J. O. & Foley, J. A. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ. Res. Lett. 2, 045023 (2007).

    Article  Google Scholar 

  18. Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).

    CAS  Article  Google Scholar 

  20. Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).

    Article  Google Scholar 

  21. Adoption of the Paris Agreement (UNFCCC, 2015).

  22. Tian, F. et al. Remote sensing of vegetation dynamics in drylands: evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sens. Environ. 177, 265–276 (2016).

    Article  Google Scholar 

  23. Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5, 470–474 (2015).

    Article  Google Scholar 

  24. Kerr, Y. H. et al. Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sens. Environ. 180, 40–63 (2016).

    Article  Google Scholar 

  25. Wigneron, J.-P. et al. Modelling the passive microwave signature from land surfaces: a review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms. Remote Sens. Environ. 192, 238–262 (2017).

    Article  Google Scholar 

  26. Hollmann, R. et al. The ESA climate change initiative: satellite data records for essential climate variables. Bull. Am. Meteor. Soc. 94, 1541–1552 (2013).

    Article  Google Scholar 

  27. Brandt, M. et al. Woody vegetation die off and regeneration in response to rainfall variability in the West African Sahel. Remote Sens 9, 39 (2017).

    Article  Google Scholar 

  28. Houghton, R. A. & Nassikas, A. A. Global and regional fluxes of carbon from land use and land cover change 1850–2015. Glob. Biogeochem. Cycles 31, 456–472 (2017).

    CAS  Article  Google Scholar 

  29. Liu, J. et al. Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño. Science 358, eaam5690 (2017).

    Article  PubMed  Google Scholar 

  30. Kerr, Y. H. et al. Soil moisture retrieval from space: the soil moisture and ocean salinity (SMOS) mission. IEEE Trans. Geosci. Remote Sens. 39, 1729–1735 (2001).

    Article  Google Scholar 

  31. Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Global Forest Resource Assessment (FAO, 2016).

  33. Skowno, A. L. et al. Woodland expansion in South African grassy biomes based on satellite observations (1990–2013): general patterns and potential drivers. Glob. Change Biol. 23, 2358–2369 (2017).

    Article  Google Scholar 

  34. Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).

    Article  PubMed  Google Scholar 

  35. Stevens, N., Erasmus, B. F. N., Archibald, S. & Bond, W. J. Woody encroachment over 70 years in South African savannahs: overgrazing, global change or extinction aftershock?. Phil. Trans. R. Soc. B 371, 20150437 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Biederman, J. A. et al. CO2 exchange and evapotranspiration across dryland ecosystems of southwestern North America. Glob. Change Biol. 23, 4204–4221 (2017).

    Article  Google Scholar 

  37. Mbow, C. Biogeoscience: Africa’s greenhouse-gas budget is in the red. Nature 508, 192–193 (2014).

    CAS  Article  PubMed  Google Scholar 

  38. Ji, F., Wu, Z., Huang, J. & Chassignet, E. P. Evolution of land surface air temperature trend. Nat. Clim. Change 4, 462–466 (2014).

    Article  Google Scholar 

  39. Fernandez-Moran, R. et al. SMOS-IC: an alternative SMOS soil moisture and vegetation optical depth product. Remote Sens. 9, 457 (2017).

    Article  Google Scholar 

  40. Wigneron, J.-P. et al. L-band microwave emission of the biosphere (L-MEB) model: description and calibration against experimental data sets over crop fields. Remote Sens. Environ. 107, 639–655 (2007).

    Article  Google Scholar 

  41. Chave, J. et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145, 87–99 (2005).

    CAS  Article  PubMed  Google Scholar 

  42. Kerr, Y. H. et al. The SMOS soil moisture retrieval algorithm. IEEE Trans. Geosci. Remote Sens. 50, 1384–1403 (2012).

    Article  Google Scholar 

  43. Fensholt, R. et al. Greenness in semi-arid areas across the globe 1981–2007 — an Earth Observing Satellite based analysis of trends and drivers. Remote Sens. Environ. 121, 144–158 (2012).

    Article  Google Scholar 

  44. Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles 19, GB1015 (2005).

    Article  Google Scholar 

  45. Wei, Y. et al. The North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project - Part 2: Environmental driver data. Geosci. Model Dev. 7, 2875–2893 (2014).

    Article  Google Scholar 

  46. Poulter, B. et al. Plant functional type mapping for earth system models. Geosci. Model Dev. 4, 993–1010 (2011).

    Article  Google Scholar 

  47. Poulter, B. et al. Plant functional type classification for earth system models: results from the European Space Agency’s land cover climate change initiative. Geosci. Model Dev. 8, 2315–2328 (2015).

    Article  Google Scholar 

  48. Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change 109, 117 (2011).

    Article  Google Scholar 

  49. Peng, S. et al. Sensitivity of land use change emission estimates to historical land use and land cover mapping. Glob. Biogeochem. Cycles 31, 626–643 (2017).

    CAS  Article  Google Scholar 

  50. Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11, 2027–2054 (2014).

    Article  Google Scholar 

  51. Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).

    Article  Google Scholar 

  52. Lamarque, J.-F. et al. Multi-model mean nitrogen and sulfur deposition from the atmospheric chemistry and climate model intercomparison project (ACCMIP): evaluation of historical and projected future changes. Atmos. Chem. Phys. 13, 7997–8018 (2013).

    Article  Google Scholar 

  53. Etheridge, D. M. et al. Natural and anthropogenic changes in atmospheric CO2 over the last 1000 years from air in Antarctic ice and firn. J. Geophys. Res. Atmos. 101, 4115–4128 (1996).

    CAS  Article  Google Scholar 

  54. Keeling, C. D., Whorf, T. P., Wahlen, M. & van der Plichtt, J. Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980. Nature 375, 666–670 (1995).

    CAS  Article  Google Scholar 

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Acknowledgements

This research work was funded by CNES (Centre National d’Etudes Spatiales) through the Science TEC (Terre Environment et Climat) program. M.B., F.T. and R.F. acknowledge the funding from the Danish Council for Independent Research (DFF) Grant ID: DFF–6111-00258. M.B. is supported by an AXA post-doctoral fellowship. We thank DigitalGlobe for providing commercial satellite data within the NextView license program. P.C., A.V. and J.P. acknowledge funding from the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P. T.T. was funded by the Swedish national space board (Dnr: 95/16). P.C. acknowledges additional support from the ANR ICONV CLAND grant. J.Chav. has benefited from “Investissement d’Avenir” grants managed by the French Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01 and TULIP, ref. ANR-10-LABX-0041), and from TOSCA funds from the CNES.

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J.-P.W., M.B., J.Chav., F.T. and R.F. designed the study. J.-P.W., A.A.-Y., N.R.-F., Y.K. and A.M. prepared the SMOS-IC data. P.C. and J.Chan. prepared the ORCHIDEE data, G.S. prepared the LPJ-GUESS data, C.T. prepared the high spatial-resolution satellite data. M.B., F.T. and W.Z. analysed the data. The results were interpreted by J.Chav., J.-P.W., T.T., J.P., P.C., L.V.R., K.R., C.M., L.F., A.V. and R.F. The manuscript was drafted by M.B., K.R., J.Chav., R.F., J.P.W. and P.C. with contributions by all authors.

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Correspondence to Martin Brandt or Jean-Pierre Wigneron.

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Brandt, M., Wigneron, JP., Chave, J. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat Ecol Evol 2, 827–835 (2018). https://doi.org/10.1038/s41559-018-0530-6

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