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Woody-biomass projections and drivers of change in sub-Saharan Africa


Africa’s ecosystems have an important role in global carbon dynamics, yet consensus is lacking regarding the amount of carbon stored in woody vegetation and the potential impacts to carbon storage in response to changes in climate, land use and other Anthropocene risks. In this study, we explore the socioenvironmental conditions that have shaped the contemporary distribution of woody vegetation across sub-Saharan Africa and evaluate ecosystem response to multiple scenarios of climate change, anthropogenic pressures and fire disturbance. Our projections suggest climate change will have a small but negative effect on above-ground woody biomass at the continental scale, and the compounding effects of population growth, increasing human pressures and socioclimatic-driven changes in fire behaviour further exacerbate climate-driven trends. Relatively modest continental-scale trends obscure much larger regional perturbations, with climatic and anthropogenic factors leading to increased carbon storage potential in East Africa, offset by large deficits in West, Central and Southern Africa.

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Fig. 1: Satellite-derived estimates of contemporary (2005) above-ground woody biomass.
Fig. 2: Sensitivity of above-ground woody biomass to socioenvironmental conditions.
Fig. 3: Projected change in above-ground woody biomass relative to contemporary estimates.
Fig. 4: Sensitivity of burned area to socioenvironmental conditions.
Fig. 5: Projected above-ground woody-biomass changes in response to climate change, fire and human land use.
Fig. 6: Anthropogenic pressure scores for sub-Saharan Africa.

Data availability

The datasets used for this analysis can be accessed as described below.

(1) Woody cover and biomass data24 are available as GeoTiff files from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC;

(2) Aridity data were provided by Feng and Fu12.

(3) The Human Footprint map31 is available as a GeoTiff file from Dryad (

(4) Future projections of human population density based on Shared Socioeconomic Pathways37 are available as GeoTiff files from the Socioeconomic Data and Application Center (SEDAC;

(5) Contemporary estimates of burned area are available as GeoTiff files and were acquired from Kahiu and Hanan30.

(6) HYSOGs52 data are available as GeoTiff files from the ORNL DAAC (

(7) Shuttle Radar Topography Mission elevation data were acquired from the United States Geological Survey Earth Explorer (

(8) The biophysical regions were derived from The Nature Conservancy Terrestrial Ecoregions and are provided as GIS shapefiles (

(9) The biomass prediction maps65 and R code66 are available from Figshare.

Code availability

This analysis was performed using the R programming language and ArcGIS. R code is available for download from Figshare at The gridded projection maps are available for download at


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This research was supported by the US National Aeronautics and Space Administration (NASA) as part of the NASA Carbon Cycle Science program (Grant no. NNX17AI49G). We thank S. Kumar, B. Lind, H. Omari, C. Toth, P. J. Burch Stickel and R. Wojcikiewicz for their contributions to this analysis.

Author information




Funding acquisition: N.P.H. and L.P. Conceptualization: C.W.R., N.P.H. and L.P. Data assimilation, analysis and visualizations: C.W.R. Writing, reviewing and editing: C.W.R., N.P.H., L.P., W.J., Q.Y. and J.A.

Corresponding author

Correspondence to C. Wade Ross.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Martin Brandt, Zhihua Liu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Socio-environmental data.

a, Long-term mean-annual aridity based on the historical data12 (1981–2010) and (b) climate zones, with H.A., hyper-arid; A., arid; S.A., semi-arid; S.H., Sub-humid; H., humid. c, Satellite-derived estimates of mean annual (2003–2015) burned-area (%)30. d, Digital elevation model49. e, The contemporary human footprint index31. f) Hydrologic soil groups32, with A corresponds to low runoff-potential soils (for example, sands); B, moderately low runoff-potential; C, moderately high runoff-potential; D, high runoff-potential (for example, clays).

Extended Data Fig. 2 Biophysical regions and countries of sub-Saharan Africa.

a, Biophysical regions were derived by aggregating The Nature Conservancy Terrestrial Ecosystems46 into broader classes. b, Country borders were mapped using the R sf62 package.

Extended Data Fig. 3 Human population density.

a, Population density (people per km-2) for 2010 and (b) projected population density for 2100 under the ‘middle of the road’ Shared Socioeconomic Pathways (SSP2)37.

Extended Data Fig. 4 Above-ground woody biomass (Mg ha-1).

a, Satellite-derived estimates of above-ground woody biomass24. b, Predicted above-ground biomass representing the baseline (that is, contemporary) estimates. c, End of century empirical projection of woody biomass in response to RCP 4.5 and assumptions regarding population growth and fire regime changes. d, End of century empirical projection of woody biomass in response to RCP 8.5 and assumptions regarding population growth and fire regime changes. Data are available for download65 as GeoTiffs.

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Supplementary Tables 1–5 and references.

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Ross, C.W., Hanan, N.P., Prihodko, L. et al. Woody-biomass projections and drivers of change in sub-Saharan Africa. Nat. Clim. Chang. 11, 449–455 (2021).

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