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Global patterns of drought recovery

Nature volume 548, pages 202–205 (2017)Cite this article

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Abstract

Drought, a recurring phenomenon with major impacts on both human and natural systems1,2,3, is the most widespread climatic extreme that negatively affects the land carbon sink2,4. Although twentieth-century trends in drought regimes are ambiguous5,6,7, across many regions more frequent and severe droughts are expected in the twenty-first century3,7,8,9. Recovery time—how long an ecosystem requires to revert to its pre-drought functional state—is a critical metric of drought impact. Yet the factors influencing drought recovery and its spatiotemporal patterns at the global scale are largely unknown. Here we analyse three independent datasets of gross primary productivity and show that, across diverse ecosystems, drought recovery times are strongly associated with climate and carbon cycle dynamics, with biodiversity and CO2 fertilization as secondary factors. Our analysis also provides two key insights into the spatiotemporal patterns of drought recovery time: first, that recovery is longest in the tropics and high northern latitudes (both vulnerable areas of Earth’s climate system10) and second, that drought impacts11 (assessed using the area of ecosystems actively recovering and time to recovery) have increased over the twentieth century. If droughts become more frequent, as expected, the time between droughts may become shorter than drought recovery time, leading to permanently damaged ecosystems and widespread degradation of the land carbon sink.

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Figure 1: Response functions for drought recovery time.
Figure 2: Spatial pattern of drought recovery time.
Figure 3: Decadal changes in drought recovery.

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Acknowledgements

Funding for this research was provided by the National Science Foundation (NSF) grant DEB EF-1340270. C.R.S. was also supported by National Aeronautics and Space Administration (NASA) grants NNX12AK12G, NNX12AP74G, NNX10AG01A and NNX11AO08A. J.B.F. contributed to this paper from the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Government sponsorship acknowledged. Support was provided to J.B.F. by NASA grants NNN13D504T (CARBON), NNN13D202T (INCA), and NNN13D503T (SUSMAP). Funding for the MsTMIP activity was provided through NASA grant NNX10AG01A. Data management support for preparing, documenting and distributing model driver and output data was performed by the Modeling and Synthesis Thematic Data Center at Oak Ridge National Laboratory (http://nacp.ornl.gov), with funding through NASA grant NNH10AN681. Finalized MsTMIP datasets are archived at the Oak Ridge National Laboratory Distributed Active Archive Center (http://daac.ornl.gov). This is MsTMIP contribution number 10.

Author information

Authors and Affiliations

  1. Woods Hole Research Center, Falmouth, 02540, Massachusetts, USA

    Christopher R. Schwalm

  2. Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, 86011, Arizona, USA

    Christopher R. Schwalm & George Koch

  3. Department of Biology, University of Utah, Salt Lake City, 84112, Utah, USA

    William R. L. Anderegg

  4. Department of Global Ecology, Carnegie Institution for Science, Stanford, 94305, California, USA

    Anna M. Michalak & Yuanyuan Fang

  5. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, 91109, California, USA

    Joshua B. Fisher

  6. DendroLab and Graduate Program of Ecology, Evolution, and Conservation Biology (EECB), University of Nevada-Reno, Reno, 89557, Nevada, USA

    Franco Biondi

  7. Department of Biology, University of New Mexico, Albuquerque, 87131, New Mexico, USA

    Marcy Litvak

  8. Informatics and Computing Program, Northern Arizona University, Flagstaff, 86011, Arizona, USA

    Kiona Ogle

  9. Rocky Mountain Research Station, US Forest Service, Ogden, 84401, Utah, USA

    John D. Shaw

  10. Arable Labs Inc., 40 North Tulane Street, Princeton, 08542, New Jersey, USA

    Adam Wolf

  11. School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, 86011, Arizona, USA

    Deborah N. Huntzinger

  12. National Snow and Ice Data Center, Boulder, 80309, Colorado, USA

    Kevin Schaefer

  13. Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, 37831, Tennessee, USA

    Robert Cook & Yaxing Wei

  14. School of Forest Resources, University of Maine, Orono, 04469, Maine, USA

    Daniel Hayes

  15. Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, 99354, Washington, USA

    Maoyi Huang

  16. Department of Atmospheric Sciences, University of Illinois, Urbana, 61801, Illinois, USA

    Atul Jain

  17. International Center for Climate and Global Change Research and School of Forestry and Wildlife Sciences, Auburn University, Auburn, 36849, Alabama, USA

    Hanqin Tian

Authors
  1. Christopher R. Schwalm
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Contributions

C.R.S. and W.R.L.A. designed the analysis. C.R.S. carried out the analysis and wrote the manuscript with contributions from all authors. W.R.L.A., A.M.M. and J.B.F. contributed to the framing of the paper. D.N.H. is the overall lead of the MsTMIP effort; R.C., J.B.F., A.M.M., K.S., C.R.S., Y.F. and Y.W. serve as the MsTMIP core team. D.H., M.H., A.J. and H.T. contributed to MsTMIP results.

Corresponding author

Correspondence to Christopher R. Schwalm.

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Reviewer Information Nature thanks M. Migliavacca and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Variable importance scores.

a, Covariates by rank of variable importance. b, Covariates by value of relative variable importance. Scores allow the skill of a trained Random Forest to be decomposed and mapped to individual covariates. Box plots are based on all possible combinations of GPP dataset and integration time. The box range covers the 25th to 75th percentiles; whiskers extend from about the 0.50th to 99.50th percentiles; outlying values are given by open circles. Red crosses give the composite value from the weighted median with weights given by the length, in years, of each GPP dataset. Both panels are sorted by median value from smallest rank (highest importance) to largest rank (lowest importance). Variable importance is calculated as the total decrease in residual sum of squares, averaged over all trees, from splitting on the target covariate (see Methods). CO2 indicates CO2 fertilization (p.p.m.), LULCC is land-use and land-cover change relative to the pre-industrial baseline, MAP is mean annual precipitation 1971–2000, and MAT is mean annual temperature 1971–2000.

Extended Data Figure 2 Spatial gradients of recovery time.

a, Pre-drought GPP. b, GPP amplitude. c, MAP. d, MAT. Mapped values for a and b are based on the mean across all events (all GPP datasets, all integration times, and all time steps) by grid cell. For c and d the covariates from training the Random Forests algorithm (see Methods) are used directly. Recovery time is then projected spatially by matching response function values with each mapped factor by grid cell. To highlight spatial patterns values are expressed as difference in recovery time (∆t) relative to median recovery time by factor; red (blue) values indicate where a given factor is spatially associated with longer (shorter) drought recovery times. White areas are water; grey areas are barren or did not experience any relevant drought events.

Extended Data Figure 3 Enviroclimatic spatial gradients associated with recovery time.

a, Mask of normalized biodiversity ≤ 0.2; these areas show an increase in recovery time with decreasing biodiversity across space. b, MAP. c, MAT. d, LULCC. Values in parentheses give areal extent as percentage. Aggregated types include Barren (tundra, desert, polar desert, rock, ice), Tree/Grass (shrublands, savannah, riparian/riverine systems) and Forest (all forest types).

Extended Data Figure 4 Recovery time for moderate droughts.

a, Recovery time as a function of drought severity (mean SPEI by drought event) and return interval—calculated as the number of months between successive drought events for a given pixel but excluding the first drought event and droughts where no recovery is observed. This is a representative subsample of 5,000 events from MsTMIP using 1-month SPEI as shown. The grey surface is a smoothed surface to aid visual interpretation. b, Mean recovery time by 50 equidistant bins of return time and drought severity. c, Number of drought and recovery events for each bin in b; note log scale.

Extended Data Figure 5 Response functions of recovery time relative to burned area.

MsTMIP and upscaled FLUXNET, with 1-month SPEI integration time, for 1997–2010—overlap with GFED data44 used to calculate burned area—shown. The trained Random Forests algorithm used both fire regime43 and burned area44 in addition to the standard set of covariates. Burned area is highly skewed; its 90th percentile is 0.035, such that the data support is concentrated at smaller burned area fractions. Burned area was used in training the Random Forests algorithm, given by its maximum value, for a given pixel, during drought and subsequent recovery. Both fire regime and burned area have low importance scores and are ranked 12 and 13.5, respectively, out of 16 covariates.

Extended Data Figure 6 Spatial patterns of recovery time.

Panels show mean recovery time by grid cell for all combinations of GPP dataset and integration time. Colours denote recovery time as shown. Inset values show number of drought and recovery events, n. White areas are water, barren, or did not experience any relevant drought events.

Extended Data Figure 7 Response function similarity.

Panels a to n show similarity score (correlation) across all possible combinations of GPP dataset and integration time by covariate. Each panel gives the covariate name and median of all off-diagonal scores. Integration time and GPP dataset are abbreviated in the y-axis labels as number–letter couplets (the number indicates the integration time in months, the letter indicates the GPP dataset (M, MsTMIP; F, upscaled FLUXNET; R, remotely sensed MODIS); for example, 12R indicates MODIS using a 12-month SPEI integration time. Panel o gives the cumulative distribution function of all (n = 924) off-diagonal scores. Thin horizontal red lines show, from bottom to top, the 10th, 25th and 50th percentiles. Over 75% of all similarity scores are greater than 0.70, with an overall median of 0.90; only 7% of all scores are negative.

Extended Data Figure 8 Response function similarity.

Each colourmap pixel shows the similarity score (correlation) between the mirroring and fixed window approaches to calculate the pre-drought baseline across the common time frame for all three datasets (2000 to 2008). a, 3-month fixed window (overall similarity median of 0.97). b, 6-month fixed window (overall similarity median of 0.96). Integration time and GPP dataset are abbreviated as number–letter couplets as in Extended Data Fig. 7.

Extended Data Table 1 Skill of the trained Random Forests algorithm
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Schwalm, C., Anderegg, W., Michalak, A. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017). https://doi.org/10.1038/nature23021

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