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Evapotranspiration frequently increases during droughts

Nature Climate Change volume 12, pages 1024–1030 (2022)Cite this article

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Abstract

Changes in evapotranspiration (ET) affect water availability and ecosystem health. Higher evaporative demand during drought acts to increase ET, but droughts also reduce the moisture supply necessary for ET, limiting predictions of even the sign of ET anomalies. Drought-driven increases in ET (\({\rm{ET}}_{{\rm{drought}}}^ +\)) are of particular concern because they quickly deplete water resources, causing flash droughts and acute stress on ecosystems. Here, using a water balance approach, we show that \({\rm{ET}}_{{\rm{drought}}}^ +\) is globally widespread, occurring in 44.4% of drought months. The sign of ET’s drought response depends most on the magnitude of precipitation and total water storage anomalies, rather than its location. The Coupled Model Intercomparison Project Phase 6 Earth system models underestimate the \({\rm{ET}}_{{\rm{drought}}}^ +\) probability by nearly one-half, and more so in drier regions, primarily due to missing representations of soil structure effects on soil evaporation, as well as incorrectly parameterized plant and soil traits. These processes should be prioritized to reduce model uncertainties in the water–energy–food nexus.

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Fig. 1: \(P({\rm{ET}}_{{\rm{drought}}}^ + )\) comparison between our observations and CMIP6 ESM simulations.
Fig. 2: Distribution of the underestimation of \(P({\rm{ET}}_{{\rm{drought}}}^ + )\) in ESMs.
Fig. 3: Variable importance for \(P({\rm{ET}}_{{\rm{drought}}}^ + )\) in our observations.
Fig. 4: Responses of \(P({\rm{ET}}_{{\rm{drought}}}^ + )\) to climate and vegetation properties.

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Data availability

GRACE and GRACE-FO TWS data are available from the NASA JPL (https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/). The GPCP version 2.3 combined precipitation dataset is available at https://psl.noaa.gov/data/gridded/data.gpcp.html. ERA5 reanalysis is available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. MODIS LAI data are available at https://lpdaac.usgs.gov/products/mcd15a3hv006/. MODIS land cover data are available at https://lpdaac.usgs.gov/products/mcd12c1v006/. Runoff data are available at https://www.bafg.de/GRDC. CMIP6 model outputs are available at https://esgf-node.llnl.gov/projects/cmip6/. The FLUXNET2015 dataset is available at https://fluxnet.org/data/fluxnet2015-dataset/. Elevation data are available at https://neo.gsfc.nasa.gov/view.php?datasetId=SRTM_RAMP2_TOPO. Ecosystem rooting depth data are available at https://wci.earth2observe.eu/thredds/catalog/usc/root-depth/catalog.html. Groundwater table depth data are available at http://thredds-gfnl.usc.es/thredds/catalog/GLOBALWTDFTP/catalog.html. The data necessary to reproduce the main results are provided at https://doi.org/10.5281/zenodo.6842054 (ref. 61).

Code availability

The computer code necessary to reproduce the main results is provided at https://doi.org/10.5281/zenodo.6842054 (ref. 61).

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Acknowledgements

We are grateful to D. Kennedy for helpful comments on the CMIP6 model results. M.Z. and A.G.K. were supported by NASA Terrestrial Ecology award 80NSSC18K0715 through the New Investigator Program and by the NASA Modeling, Analysis, and Prediction program under award 80NSSC21K1523.

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Authors and Affiliations

  1. Department of Earth System Science, Stanford University, Stanford, CA, USA

    Meng Zhao & Alexandra G. Konings

  2. Department of Earth System Science, University of California, Irvine, Irvine, CA, USA

    Geruo A

  3. School of Earth Sciences, The Ohio State University, Columbus, OH, USA

    Yanlan Liu

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Contributions

A.G.K. and M.Z. conceived of the study. M.Z. conducted the analyses with A.G.K. and G.A. M.Z. and A.G.K. wrote the initial draft of the manuscript. All authors edited the manuscript.

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Correspondence to Meng Zhao.

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Extended data

Extended Data Fig. 1 Number of analyzed drought months for each year.

Number of drought months that are detected using our drought definition for each year during 2003–2020. Basemap from Natural Earth (https://www.naturalearthdata.com/).

Extended Data Fig. 2 The robustness of the runoff assumption.

(a) Spatial distribution of 185 analyzed hydrological basins. Colors in the map differentiate basins. (b) The scatterplot of ET′ (calculated using a complete water balance) and \(P\prime - \frac{{dTWS}}{{dt}}\prime\) across all basin-drought months. The number in squared box represents the count of basin-month points in each quadrant. The accuracy of using the sign of \(P\prime - \frac{{dTWS}}{{dt}}\prime\) to approximate the sign of ET′ is (1030 + 1384)/(1030 + 261 + 1348 + 23) ≈ 0.9. Subplot (c) is like (b) but for basin-drought months with ZET’ > 0.25. (d) Accuracy of each basin’s determination of the sign of ET′ when neglecting runoff in the calculation of ET′. Each point is one of the basins shown in (a). The color of each point represents the average uncertainty of the ET′ estimate, calculated using Eq. (4) of the Methods (the same calculation as performed at the mascon-scale). The vertical line represents the area of a mascon. Basemap from Natural Earth (https://www.naturalearthdata.com/).

Extended Data Fig. 3 The ET anomaly error is dominated by the random error.

The frequency distribution of the ratio between ET’ random error (σET) and ET’ total observational uncertainty (\(\sqrt {\sigma _{{{{\mathrm{ET}}}}}^2 + |{{{\mathrm{R}}}}\prime |^2}\)) for 2662 basin-drought months across 185 hydrological basins with GRDC data. High values of the x-axis suggest that the total error in our water-balance estimate of ET’ is driven primarily by random errors in the ET estimates propagated from uncertainties in input P, \(\frac{{dTWS}}{{dt}}\), and R, rather than the propagated bias derived from systematically neglecting an R′ that has non-zero mean. Thus, high values of the ratio shown on the x-axis justify the assumption to neglect bias from R′ when propagating all error sources to the estimates of ET′.

Extended Data Fig. 4 \(P({\rm{ET}}_{{\rm{drought}}}^ + )\) error.

Standard error of \(P(ET_{drought}^ + )\). Inset shows the probability density function of each location’s \(P(ET_{drought}^ + )\) standard error across the globe. Basemap from Natural Earth (https://www.naturalearthdata.com/).

Extended Data Fig. 5 Ground-level observational analyses of \(P(ET_{drought}^ + )\).

(a) Spatial distribution of the 36 FLUXNET2015 sites used. (b) The frequency distribution of \(P(ET_{drought}^ + )\) of these sites. Basemap from Natural Earth (https://www.naturalearthdata.com/).

Extended Data Fig. 6 Climate of observed and model-simulated drought events.

Kernel density estimate of the joint probability distribution of ZTWS and ZP for our observations and CMIP6 ESMs.

Extended Data Fig. 7 Using PFT-averages for plant traits does not significantly change the distribution of \(P(ET_{drought}^ + )\).

Comparison between the constrained trait model run and the PFT-wide average trait run. Corresponding dots on the x-axis represents the global average value of \(P(ET_{drought}^ + )\) for each estimate.

Extended Data Fig. 8 Soil structure effect on bare-soil evaporation and plant transpiration.

(a–c) The probability of ET, bare-soil evaporation (E), and transpiration (T) increase during droughts as a function of the aridity index, respectively. (d-f) are similar to (a-c) but as a function of the LAImax. We calculated \(P(E_{drought}^ + )\) and \(P\left( {T_{drought}^ + } \right)\) in the same way as \(P(ET_{drought}^ + )\). That is, each is calculated as the number of drought months with positive E (or T) anomalies divided by the total number of drought months at each grid cell.

Extended Data Fig. 9 Drought intensity effect on \({{{\boldsymbol{P}}}}({{{\boldsymbol{ET}}}}_{{{{\boldsymbol{drought}}}}}^ + )\).

The kernel density estimation of \(P(ET_{drought}^ + )\) as a function of drought intensity (ZP and ZTWS).

Extended Data Fig. 10 Variable importance comparison between observations and ESMs.

Normalized variable importance in observation-derived random forests (bars) and ESM-derived random forests (colored dots). Blue color bars represent drought-specific variables and green color bars represent geographic variables. Colored dots follow the same color scheme of main Fig. 2.

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Zhao, M., A, G., Liu, Y. et al. Evapotranspiration frequently increases during droughts. Nat. Clim. Chang. 12, 1024–1030 (2022). https://doi.org/10.1038/s41558-022-01505-3

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