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Dryland productivity under a changing climate

Nature Climate Change volume 12, pages 981–994 (2022)Cite this article

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Abstract

Understanding dryland dynamics is essential to predict future climate trajectories. However, there remains large uncertainty on the extent to which drylands are expanding or greening, the drivers of dryland vegetation shifts, the relative importance of different hydrological processes regulating ecosystem functioning, and the role of land-use changes and climate variability in shaping ecosystem productivity. We review recent advances in the study of dryland productivity and ecosystem function and examine major outstanding debates on dryland responses to environmental changes. We highlight often-neglected uncertainties in the observation and prediction of dryland productivity and elucidate the complexity of dryland dynamics. We suggest prioritizing holistic approaches to dryland management, accounting for the increasing climatic and anthropogenic pressures and the associated uncertainties.

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Fig. 1: Global dryland distributions, dryland vegetation greenness and dryland productivity.
Fig. 2: Global dryland land use and land-use changes.
Fig. 3: The key drivers and major uncertainties of dryland dynamics.
Fig. 4: Global dryland vegetation trends.
Fig. 5: Cropland gain and loss between 2003 and 2019 in global drylands.
Fig. 6: Dryland productivity modelling uncertainties.

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Data availability

The advanced very high-resolution radiometer GIMMS-NDVI3g is available at https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v0. Global Land Surface Satellite (GLASS) LAI can be obtained from http://www.glass.umd.edu/Download.html. The aridity index dataset is available at https://cgiarcsi.community/data/global-aridity-and-pet-database/. Moderate-resolution imaging spectroradiometer (MODIS) based EVI and GPP datasets are available from the NASA Land Processes Distributed Active Archive Center at https://lpdaac.usgs.gov. The MODIS NPP dataset is available from https://lpdaac.usgs.gov/products/mod17a3hgfv006. Ku-band VOD datasets are available from https://zenodo.org/record/2575599#.XyLqfLdME0M. European Space Agency- (ESA-) based land-use/land-cover product is available from https://www.esa-landcover-cci.org/. Light response function- (LRF-) based GPP data are available from https://doi.org/10.17894/ucph.b2d7ebfb-c69c-4c97-bee7-562edde5ce66. Light-use efficiency model- (EC-LUE-) based GPP data can be obtained from https://doi.org/10.6084/m9.figshare.8942336.v3. Eddy covariance flux tower data are available for SW US sites from the AmeriFlux database (http://ameriflux.lbl.gov) and for Australian sites from the FLUXNET 2015 database (https://fluxnet.org/data/fluxnet2015-dataset/). More information on the TRENDY MIP and related simulations is available at https://sites.exeter.acuk/trendy/.

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Acknowledgements

We acknowledge support from Division of Earth Sciences of National Science Foundation (EAR‐1554894). N.M. acknowledges funding from NASA Carbon Cycle Program grant no. 80NSSC21K1709, S.M. from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 101001608), G.V. from European Commission and Swedish Research Council for Sustainable Development FORMAS (grant 2018-02787) for funding in the frame of the collaborative international consortium iAqueduct, financed under the 2018 Joint call, and M.C.R. from the EU PRIMA Programme under Horizon 2020 European Union’s Framework Programme for Research and Innovation (NEXUS-NESS an Art.185 initiative grant no, 2042). We thank the TRENDY v.7 modellers for providing simulations and the AmeriFlux, OzFlux and FLUXNET site principal investigators for providing the in situ eddy covariance flux tower CO2 and ET fluxes used to produce Fig. 6.

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Authors and Affiliations

  1. Department of Earth Sciences, Indiana University–Purdue University Indianapolis, Indianapolis, IN, USA

    Lixin Wang

  2. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Wenzhe Jiao

  3. Department of Geography and Environment, Western University, London, Ontario, Canada

    Natasha MacBean

  4. Department of Biology, Western University, London, Ontario, Canada

    Natasha MacBean

  5. Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy

    Maria Cristina Rulli

  6. Department of Physical Geography, Stockholm University, Stockholm, Sweden

    Stefano Manzoni

  7. Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

    Stefano Manzoni

  8. Department of Crop Production Ecology, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden

    Giulia Vico

  9. Department of Environmental Science Policy and Management, University of California, Berkeley, CA, USA

    Paolo D’Odorico

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  1. Lixin Wang
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Correspondence to Lixin Wang.

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Wang, L., Jiao, W., MacBean, N. et al. Dryland productivity under a changing climate. Nat. Clim. Chang. 12, 981–994 (2022). https://doi.org/10.1038/s41558-022-01499-y

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