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Future socio-ecosystem productivity threatened by compound drought–heatwave events

Nature Sustainability volume 6, pages 259–272 (2023)Cite this article

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Abstract

Compound drought–heatwave (CDHW) events are one of the worst climatic stressors for global sustainable development. However, the physical mechanisms behind CDHWs and their impacts on socio-ecosystem productivity remain poorly understood. Here, using simulations from a large climate–hydrology model ensemble of 111 members, we demonstrate that the frequency of extreme CDHWs is projected to increase by tenfold globally under the highest emissions scenario, along with a disproportionate negative impact on vegetation and socio-economic productivity by the late twenty-first century. By combining satellite observations, field measurements and reanalysis, we show that terrestrial water storage and temperature are negatively coupled, probably driven by similar atmospheric conditions (for example, water vapour deficit and energy demand). Limits on water availability are likely to play a more important role in constraining the terrestrial carbon sink than temperature extremes, and over 90% of the global population and gross domestic product could be exposed to increasing CDHW risks in the future, with more severe impacts in poorer and more rural areas. Our results provide crucial insights towards assessing and mitigating adverse effects of compound hazards on ecosystems and human well-being.

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Fig. 1: Anomalies of composite water–heat–carbon variables during extreme climatic events.
Fig. 2: Recent changes in frequency and intensity of CDHWs as well as related socio-economic exposure.
Fig. 3: Anomalies of water, heat and carbon fluxes due to concurrent hot–drought conditions during historical and future periods as estimated by climate models.
Fig. 4: Future changes in the characteristics of CDHWs and socio-economic exposure to CDHWs under model simulations.
Fig. 5: Projected JRP of historical 50-year bivariate CDHWs and socio-economic exposure.

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Data availability

The CMIP5-based TWS simulations are freely available from the ISIMIP project portal (https://data.isimip.org/search/tree/ISIMIP2b/InputData/climate/). The three GRACE/GRACE-FO products are available from http://www2.csr.utexas.edu/grace/, https://grace.jpl.nasa.gov/data/get-data/ and https://earth.gsfc.nasa.gov. The long-term reconstructed TWS data are available on Figshare (https://doi.org/10.6084/m9.figshare.7670849). The TWS simulations under CMIP6 are available at the repository in the Open Science Framework (https://osf.io/hy96r/); this dataset cannot be accessed now, because the data are in an embargo period and currently shared only among the ISIMIP participants. The SPEI dataset is available at https://spei.csic.es/database.html. The GLDAS-2.2 data are available at https://ldas.gsfc.nasa.gov/gldas/forcing-data. The ERA5 reanalysis data are from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. The GLEAM 3.5a data are from https://www.gleam.eu/. The FLUXNET2015 dataset is from https://fluxnet.org/data/fluxnet2015-dataset/. The gridded SIF dataset is from https://doi.org/10.17605/OSF.IO/8XQY6, and the gridded GPP dataset is available from https://data.tpdc.ac.cn/en/data/582663f5-3be7-4f26-bc45-b56a3c4fc3b7/. The global gridded population data are available from https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals-rev11; the global gridded GDP data and the GDP per capita data are available from https://datadryad.org/stash/dataset/doi:10.5061/dryad.dk1j0. The BEST dataset is available at Berkeley Earth (http://berkeleyearth.org/data/).

Code availability

The R (version 4.1.0) code used for producing Figs. 1–5 and the MATLAB (version 2020a) code used for data analysis are available at the repository in the Open Science Framework (https://osf.io/dnuxv/).

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Acknowledgements

J.Y. acknowledges support from the National Natural Science Foundation of China (grant no. 52009091). L.S. acknowledges support from UK Research and Innovation (grant no. MR/V022008/1). Y.P. acknowledges support from the National Science Foundation (CAREER Award, grant no. 1752729). J.Y. is also supported by the Fundamental Research Funds for the Central Universities (grant no. 2042022kf1221). The numerical calculations in this paper were performed on the supercomputing system in the Supercomputing Center of Wuhan University.

Author information

Authors and Affiliations

  1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China

    Jiabo Yin, Shenglian Guo & Lihua Xiong

  2. Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA

    Pierre Gentine

  3. Earth Institute, Columbia University, New York, NY, USA

    Pierre Gentine

  4. School of Geography and the Environment, University of Oxford, Oxford, UK

    Louise Slater

  5. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China

    Lei Gu

  6. Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI, USA

    Yadu Pokhrel

  7. Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Japan

    Naota Hanasaki

  8. School of International and Public Affairs, Columbia University, New York, NY, USA

    Wolfram Schlenker

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Contributions

J.Y. conceived and designed the study. J.Y. processed the model simulations and reanalysis data. N.H. conducted the H08 simulations. J.Y., P.G., L.S., L.G., Y.P., S.G., L.X. and W.S. contributed to the data analysis and interpretation. J.Y. drafted the manuscript. All authors edited the manuscript.

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Correspondence to Jiabo Yin.

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Extended data

Extended Data Fig. 1 Relationship between daily maximum near-surface temperature (Tmax) and terrestrial water storage (TWS) or root-zone soil moisture (SM) during 2002–2020.

a-d, Pearson’s correlation coefficient between: monthly GRACE/GRACE-FO ensemble mean TWS and ERA5 Tmax (a), monthly reconstructed TWS and Tmax from Berkeley Earth Surface Temperatures (b), daily ERA5 SM and Tmax (c), daily GLEAM SM and ERA5 Tmax (d). Insets in a-d show the histogram of the correlation coefficient, with the dashed vertical line representing the median value. The graph on the right of each panel shows the latitudinal median. e-h, Mean probability of each percentile bin across all land grid cells (excluding Greenland and Antarctica in all analyses).

Extended Data Fig. 2 Coupling of Tmax and monthly TWS from the three GRACE/GRACE-FO solutions dataset and their impacts on terrestrial carbon uptake.

a-c, Probability of each percentile bin of Tmax and monthly TWS across 73 flux tower sites. d-f, Mean anomalies of GPP for each percentile bin of Tmax and TWS. g-i, Mean anomalies of TER for each percentile bin of Tmax and TWS. j-l, Mean anomalies of NEP for each percentile bin of Tmax and TWS. The three columns represent the GRACE/GRACE-FO TWS data produced from JPL, CSR and GSFC, respectively. At each site, anomalies of GPP, TER, and NEP are calculated as the difference between the daily values in extreme events and the mean daily values in the warm season (defined as days when running 7-day mean temperatures are higher than the 60th percentile of daily temperature for the site).

Extended Data Fig. 3 Anomalies of SIF and GPP during extreme climatic events.

a, b, Anomalies of SIF (a) and GPP (b) during extreme heat events. c-d, Anomalies of SIF (c) and GPP (d) during extreme dry events. e, f, Anomalies of SIF (e) and GPP (f) in concurrent heat and dry conditions. At each grid, anomalies of SIF (GPP) are calculated as the difference between the 4-day (8-day) values in extreme events and the mean 4-day (8-day) values in the warm season. Dry conditions are identified using GRACE/GRACE-FO ensemble mean TWS data, and the heat conditions are identified by ERA5 Tmax. Insets show the histogram of the anomalies, with the dashed vertical line representing the median value. The graph on the right shows the latitudinal median value.

Extended Data Fig. 4 Temporal dynamics of the GDP exposures to CDHW in 21 Giorgi climate regions.

Each panel has a cluster of 21 grey lines, which show the ensemble of the regional GDP exposures in all regions. The black line in each figure represents the exposure value in each region, and the color lines represent trends of GDP exposures during recent, past and entire periods. The droughts are identified by reconstructed TWS data, and the heatwaves are detected by using Tmax from the BEST dataset. The * indicates the trend is significant (p < 0.05) detected by Mann-Kendall test.

Extended Data Fig. 5 Anomalies of GPP, TER and NEP due to extreme heat or drought conditions in the GFDL-CLM4.5 model.

a-i, Anomalies of carbon fluxes in the historical period (a-c), RCP 2.6 (d-f) and RCP 6.0 (g-i) due to extreme heat conditions (monthly Tmax above the 90th percentile). j-r, Anomalies of carbon fluxes in the historical period (j-l), RCP2.6 (m-o) and RCP6.0 (p-r) due to droughts (TWS-DSI < −0.8). The graph on the right shows the latitudinal median and 90% confidence interval. The TWS and carbon fluxes are projected by CLM4.5 model with bias-corrected GFDL-GCM2M outputs.

Extended Data Fig. 6 Future changes in characteristics of CDHW and heatwaves.

Insets in each figure show the histogram of the relative change percentages, with the dashed vertical line representing the mean value. Stippling denotes regions where the sign of the relative changes is consistent with the sign of the multi-model means (as shown in the figure) in at least 80% of GCM-THM models. These results are derived from the ISIMIP2b multiple impacts model ensemble.

Extended Data Fig. 7 Future changes in the characteristics of CDHW and socioeconomic exposure to CDHW under CMIP6.

a-l, Relative changes in the frequency (a-c), average duration (d-f), average severity (g-i) and coincidence rate (j-l) of CDHW from the historical to the future periods. m-p, Temporal dynamics of the global average coincidence rate (m), exposed land area (n), exposed population (o) and exposed GDP (p). Insets in a-l show the histogram of the relative change percentages, with the dashed vertical line representing the mean value. Stippling in a-l denotes regions where the sign of the relative changes is consistent with the sign of the multi-model means (as shown in the figure) in at least 80% of GCMs. In m-p, the shading represents ±1 standard deviation, and only the historical exposures linking to SSP126 TWS data are presented. For projecting CDHW, the TWS is simulated by driving H08 forced by five bias-corrected GCMs under CMIP6.

Extended Data Fig. 8 Projected JRP of historical 50-year bivariate CDHW and socioeconomic exposure under CMIP6.

a-c, Average JRP in the future period under a non-stationary bivariate framework. d-i, Population (d-f) and GDP (g-i) exposure due to increasing risk of bivariate CDHW in the future period. j-l, Temporal dynamics of the global average exposed land area (j), population (k) and GDP (l) due to increasing CDHW risk; the solid curve and shading indicate multi-model mean ± SD. m, Boxplot of updated JRP of the historical 50-year CDHW in different Giorgi climate regions under SSP585; the centre line indicates median value, and the box bounds (whiskers) indicate 25th/75th percentile (min/max) values. n, Average contribution ratios of seven uncertainty sources in different Giorgi climate regions and in the global landmass (Glob). Stippling in a-i denotes regions where the sign of the JRP is consistent with the sign of the multi-model means (as shown in the figure) in at least 80% of GCMs. For projecting CDHW, the TWS is simulated by driving H08 forced by five bias-corrected GCMs under CMIP6.

Extended Data Fig. 9 CDHW coincidence rate and socioeconomic exposures to CDHW in rich versus poor areas.

a-f, Temporal dynamics of the global average coincidence rate (a, b), and exposed GDP fraction (c, d) and population fraction (e, f) to CDHW. g-i, Average coincidence rate (g), GDP exposure fraction (h) and population exposure fraction (i) during 2070–2099 in different Giorgi climate regions under SSP585. In a-f, the shading represents ±1 standard deviation, and only the historical exposures linking to SSP126 TWS data are presented. For projecting CDHW, the TWS is simulated by driving H08 forced by five bias-corrected GCMs under CMIP6. Rich (poor) areas are identified where the 2015-year GDP per capita exceeds (is below) the 80th (20th) percentile values in different regions.

Extended Data Fig. 10 Gridded Gross Domestic Product (GDP) per capita (purchasing power parity) in constant 2011 international US dollars (USD) for six typical years during 1990–2015.

a-f, GDP per capita in year of 1990 (a), 1995 (b), 2000 (c), 2005 (d), 2010 (e) and 2015 (f).

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Supplementary Figs. 1–30, Texts 1 and 2, and Tables 1–3.

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Yin, J., Gentine, P., Slater, L. et al. Future socio-ecosystem productivity threatened by compound drought–heatwave events. Nat Sustain 6, 259–272 (2023). https://doi.org/10.1038/s41893-022-01024-1

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