Global terrestrial water storage and drought severity under climate change

Abstract

Terrestrial water storage (TWS) modulates the hydrological cycle and is a key determinant of water availability and an indicator of drought. While historical TWS variations have been increasingly studied, future changes in TWS and the linkages to droughts remain unexamined. Here, using ensemble hydrological simulations, we show that climate change could reduce TWS in many regions, especially those in the Southern Hemisphere. Strong inter-ensemble agreement indicates high confidence in the projected changes that are driven primarily by climate forcing rather than land and water management activities. Declines in TWS translate to increases in future droughts. By the late twenty-first century, the global land area and population in extreme-to-exceptional TWS drought could more than double, each increasing from 3% during 1976–2005 to 7% and 8%, respectively. Our findings highlight the importance of climate change mitigation to avoid adverse TWS impacts and increased droughts, and the need for improved water resource management and adaptation.

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Fig. 1: Impact of climate change on TWS.
Fig. 2: Uncertainty in TWS simulations.
Fig. 3: Seasonal TWS variations averaged over the selected IPCC SREX regions.
Fig. 4: Projected changes in occurrence and time evolution of droughts under RCP6.0.
Fig. 5: PDF of monthly TWS-DSI for the late twenty-first century.

Data availability

The model results are freely available from the ISIMIP project portal (https://www.isimip.org/outputdata/) and the two GRACE products used for model evaluation can be obtained from http://www2.csr.utexas.edu/grace/ and https://podaac.jpl.nasa.gov/GRACE. The processed data used to generate the figures in the main text are available on CUAHSI HydroShare and Figshare (https://doi.org/10.6084/m9.figshare.13218710).

Code availability

All figures were produced using the freely available visualization libraries in Python 3.5 (such as Matplotlib), and statistical analysis was performed using built-in functions in Python 3.5. The relevant portions of the computer code used to process the results and develop the figures are available at https://doi.org/10.5281/zenodo.4266999.

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Acknowledgements

Y.P. and F.F. acknowledge support from the National Science Foundation (CAREER Award, grant no. 1752729). H.M.S. and C.-E.T. acknowledge support from the German Federal Ministry of Education and Research (BMBF, grant no. 01LS1711F). J.L. acknowledges support from the Strategic Priority Research Program of Chinese Academy of Sciences (grant no. XDA20060402) and the National Natural Science Foundation of China (41625001 and 51711520317). N.H. acknowledges support from the ERTDF (2RF-1802) of the ERCA, Japan. Y.W. is supported by the European Union under the Horizon 2020 EUCP project (grant no. 776613) and the JPI Climate and European Union under the ISIpedia project (grant no. 690462). W.T. acknowledges support from the Uniscientia Foundation and the ETH Zurich Foundation (Fel-45 15-1). H.K. acknowledges the Integrated Research Program for Advancing Climate Models (TOUGOU) JPMXD0717935457 from MEXT and the Grantin-Aid for Specially promoted Research 16H06291 from JSPS, Japan.

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Y.P. conceived the research. F.F. processed model results, conducted the analyses and prepared graphics. Y.P. and F.F. interpreted the results, and all authors discussed and commented on the outcome. Y.P. prepared the draft with contributions from F.F., and all authors commented on and edited the manuscript.

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Correspondence to Yadu Pokhrel.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Craig Ferguson, Tara Troy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Continent-based model skill and independence weights (see Methods for details) for 27 ensemble members.

The weights are temporally static.

Extended Data Fig. 2 Continent-based pairwise inter-model distance matrix for ensemble simulations and GRACE observations.

Each row or column associates with a single ensemble member or GRACE observations, and each cell represents a pairwise distance of that member compared to the others. Distances are evaluated based on the root mean squared error (RMSE) of TWS seasonal cycle (calculated for 2002–2016 period by combining the results from HIST simulations with RCP2.6), spatially averaged over each continent. The distance for each member is normalized by the mean of pair-wise distances for all members. Lower values of the pairwise distance between two members indicate a better agreement between the two members, and vice versa.

Extended Data Fig. 3 Spatial patterns of change in precipitation by the mid- (2030–2059) and late- (2070–2099) twenty-first century under RCP 2.6 and 6.0.

Shown are the absolute differences in the 30-year mean (mm/year) between the two future periods and historical baseline period of 1976–2005, calculated as the mean of the results from four Global Climate Models (GCMs) used to drive the hydrological models: HadGEM2-ES, GFDL-ESM2M, IPSL-CM5A-LR, and MIROC5. Note that Greenland is masked out. The graph on the right of each panel shows the latitudinal mean.

Extended Data Fig. 4 Spatial patterns of change in air temperature by the mid- (2030–2059) and late- (2070–2099) twenty-first century under RCP 2.6 and 6.0.

Shown are the differences in the 30-year mean (Kelvin) between the two future periods and historical baseline period of 1976–2005, calculated as the mean of the results from four GCMs used to drive the hydrological models: HadGEM2-ES, GFDL-ESM2M, IPSL-CM5A-LR, and MIROC5. Note that Greenland is masked out. The graph on the right of each panel shows the latitudinal mean.

Extended Data Fig. 5 Spatial patterns of seasonal TWS anomalies from models and GRACE data.

Shown are the seasonal averages (December-February (DJF), March-May (MAM), June-August (JJA), and September-November (SON)) of the simulated (multi-model ensemble mean) and GRACE-based monthly TWS deviation from the mean for the GRACE period (2002–2016). Model results for the 2002–2005 period are taken from the historical simulations (see Supplementary Table 2), and for 2006–2016 from RCP2.6 runs (2005soc). Anomalies are calculated by using the mean for 2002–2016 period for both model results and GRACE data. Note that we use the simple ensemble average (not the weighted mean) for these comparisons to provide an unbiased evaluation of the models and to ensure that the model-GRACE agreement is not a result of the weighting that is based on the GRACE data. The results from RCP6.0 (not shown) are almost identical to that shown here. GRACE data shown are the mean of mascon products76 from two processing centers: the Center for Space Research (CSR) at the University of Texas at Austin (http://www2.csr.utexas.edu/grace/) and NASA Jet Propulsion Laboratory (JPL; https://podaac.jpl.nasa.gov/GRACE).

Extended Data Fig. 6 Soil moisture (SM) component contribution ratio (CCR16,78).

The background map depicts the spatial variability of SM CCR (the ratio of seasonal amplitude of SM to that of TWS; see Methods) based on the ensemble mean results for the historical baseline period (HIST; 1976–2005). The insets present the SM CCR averaged over the IPCC SREX regions for the historical baseline period, mid-twenty-first century (2030–2059), and late-twenty-first century (2070–2099); results from both RCPs (RCP 2.6 and 6.0) are shown. Evidently, and as discussed in the main text, SM CCR shows a large spatial variability.

Extended Data Fig. 7 Probability density function of monthly standardized precipitation index (SPI29; see Methods).

Shown are ensemble simulations grouped for different cases (that is, HIST, PIC, RCP2.6, and RCP6.0). Labels are indicated in the inset for the entire globe; x-axis labels indicate the SPI. A description of SREX regions (background map) is provided in Supplementary Fig. 3.

Extended Data Fig. 8 Probability density function of monthly standardized runoff drought index (SRI33; see Methods).

Shown are ensemble simulations grouped for different cases (that is, HIST, PIC, RCP2.6, and RCP6.0). Labels are indicated in the inset for the entire globe; x-axis labels indicate the SRI. A description of SREX regions (background map) is provided in Supplementary Fig. 3.

Extended Data Fig. 9 Probability density function of monthly soil moisture drought index calculated based on Zhao et al. (ref. 5), that is, by using only soil moisture instead of total TWS.

Shown are ensemble simulations grouped for different cases (that is, HIST, PIC, RCP2.6, and RCP6.0). Labels are indicated in the inset for the entire globe; x-axis labels indicate the soil moisture drought index. A description of SREX regions (background map) is provided in Supplementary Fig. 3.

Extended Data Fig. 10 Probability density function of monthly soil moisture drought index (SMI31,32; see Methods).

Shown are ensemble simulations grouped for different cases (that is, HIST, PIC, RCP2.6, and RCP6.0). Labels are indicated in the inset for the entire globe; x-axis labels indicate the SMI. A description of SREX regions (background map) is provided in Supplementary Fig. 3. Note the different y-axis scale for MED.

Supplementary information

Supplementary Information

Supplementary Tables 1–4 and Figs. 1–5.

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Pokhrel, Y., Felfelani, F., Satoh, Y. et al. Global terrestrial water storage and drought severity under climate change. Nat. Clim. Chang. (2021). https://doi.org/10.1038/s41558-020-00972-w

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