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Majority of global river flow sustained by groundwater

Nature Geoscience volume 17, pages 770–777 (2024)Cite this article

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Abstract

Groundwater-sustained baseflow is a vital source of river flow, especially during dry seasons. The proportion of river flow sustained by baseflow—the baseflow index—is essential for assessing fluvial nutrient cycling and contaminant transport. However, the global baseflow index remains highly uncertain, with current Earth system model simulations ranging from 12% to 94%. Here we estimate the global baseflow index to be 59% ± 7% based on an emergent constraint approach, which integrates 50 Earth system models with baseflow indices derived from streamflow observations in 15,567 basins. Our observational constraint indicates that at least 21% ± 3% of precipitation recharges groundwater, which is approximately double the figure reported in the Sixth Assessment Report of the United Nations Intergovernmental Panel on Climate Change. Thus, our research suggests a more active role of groundwater in the global water cycle than most Earth system models currently simulate. We present evidence that the considerable disagreement in simulated baseflow stems from unrealistic and varied model representations of infiltration, aquifer structure and groundwater dynamics. These processes should be prioritized so that models can capture active groundwater–river connections.

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Fig. 1: Observation-based and CMIP6-simulated baseflow indices in 15,496 small basins.
Fig. 2: Emergent constraints on the baseflow index.
Fig. 3: Contribution of environmental drivers to baseflow index spatial variations.
Fig. 4: Global spatial distribution of baseflow index dominant factors.

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Data availability

Our research benefits from the following data sources: (1) daily streamflow records for 48,651 basins from 9 national agencies and 3 research databases: Australian Bureau of Meteorology (http://www.bom.gov.au/waterdata/), Brazilian Agência Nacional de Águas (https://zenodo.org/record/3964745)76, National Water Data Archive of Canada (https://wateroffice.ec.gc.ca/), Chilean Center for Climate and Resilience Research (https://doi.org/10.1594/PANGAEA.894885), Chinese Ministry of Water Resources (http://www.cjh.com.cn/), Indian Water Resources Information System (https://indiawris.gov.in/wris/#/RiverMonitoring), Spanish Centro de Estudios Hidrográficos (https://ceh.cedex.es/anuarioaforos/demarcaciones.asp), UK National River Flow Archive (https://nrfaapps.ceh.ac.uk/nrfa/nrfa-api.html), US National Water Information System (https://waterdata.usgs.gov/nwis), Network for the Arctic Region (ArcticNET) database (https://www.r-arcticnet.sr.unh.edu/), the European Water Archive (https://ne-friend.bafg.de/servlet/is/7413/), the Global Runoff Data Centre (https://www.bafg.de/GRDC); (2) the ERA5-Land monthly dataset (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means); (3) the CMIP6 historical outputs for the 50 ESMs (https://esgf-node.llnl.gov/search/cmip6/); (4) 3 run-off simulations from G-RUN ENSEMBLE (https://doi.org/10.6084/m9.figshare.12794075)77, driven by precipitation from Climatic Research Unit gridded Time Series Version 4.04 (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.04/), Global Soil Wetness Project Phase 3 (https://doi.org/10.20783/DIAS.501) and Princeton Global Forcing Version 3 (https://prep-next.github.io/data/GDFC/products.html); (5) the global baseflow index map using artificial neural networks to extend from 3,394 basins to global grids (https://www.gloh2o.org/gscd/); (6) the Noah Land Surface Model outputs from the Global Land Data Assimilation System Version 2.0 (https://ldas.gsfc.nasa.gov/gldas); (7) static properties listed in Extended Data Table 5: Multi-Error-Removed Improved-Terrain digital elevation model (https://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/), topographic wetness index from Geomorpho90m (https://doi.org/10.1594/PANGAEA.899135), soil properties form SoilGrids 2.0 (https://soilgrids.org/), logarithmic permeability from global hydrogeology (https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP2/TTJNIU) and bedrock depth map (https://files.isric.org/soilgrids/former/2017-03-10/data/BDTICM_M_250m_ll.tif).

Code availability

The 12 baseflow separation methods are implemented as a Python package at https://github.com/xiejx5/baseflow. The automatic basin boundary delineation algorithm was shared at https://github.com/xiejx5/watershed_delineation. We express our gratitude to the following open-source projects for their valuable contributions: (1) the ESM Evaluation Tool (ESMValTool) used for CMIP6 data downloading and processing (https://github.com/ESMValGroup/ESMValTool); (2) the XGBoost Python package (https://github.com/dmlc/xgboost); (3) the SHAP Python package (https://github.com/slundberg/shap).

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Acknowledgements

This work received support from the National Key Research and Development Program of China (grant no. 2023YFC3206600 to X.L.) and the National Natural Science Foundation of China (grant no. 41922050 to X.L.).

Author information

Author notes

  1. Jiaxin Xie

    Present address: Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany

Authors and Affiliations

  1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Jiaxin Xie, Xiaomang Liu, Kaiwen Wang & Changming Liu

  2. University of Chinese Academy of Sciences, Beijing, China

    Jiaxin Xie

  3. Bren School of Environmental Science and Management, University of California, Santa Barbara, CA, USA

    Scott Jasechko

  4. Department of Earth Sciences, Free University Amsterdam, Amsterdam, Netherlands

    Wouter R. Berghuijs

  5. ELLIS Unit Jena, Jena, Germany

    Markus Reichstein

  6. Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany

    Markus Reichstein, Martin Jung & Sujan Koirala

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Contributions

J.X. conceived this study, performed all calculations and wrote initial drafts of the manuscript. X.L. supervised the study and frequently discussed the results. S.J. and W.R.B. provided critical insights into the data analysis and made major contributions to text revisions. K.W. and C.L. contributed to streamflow data collection. M.R., M.J. and S.K. provided substantial input into data interpretation and method validation, leading to a more robust assessment. All authors discussed the results and edited the manuscript.

Corresponding author

Correspondence to Xiaomang Liu.

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The authors declare no competing interests.

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Nature Geoscience thanks Arik Tashie and Hamidreza Mosaffa for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Compile of daily streamflow observations for 48,651 basins from 9 national agencies and 3 research databases.

The 9 national agencies are labeled in the Figure, and the three research databases are Network for the Arctic Region (ArcticNET), European Water Archive (EWA), and Global Runoff Data Centre (GRDC). We eliminated duplicates in the three research databases using the procedures outlined in Gudmundsson & Seneviratne45. The 48,651 basin boundaries were delineated using an automatic outlet relocation method46. Daily streamflow observations were downloaded in February 2023 from the links listed in the Acknowledgments.

Extended Data Fig. 2 Simulated baseflow index for three ESMs in 15,496 small basins.

(a) baseflow indices calculated from daily streamflow observations using an average of 12 baseflow separation methods. (b–d) baseflow indices extracted from (b) CESM2, (c) MIROC, and (d) ACCESS-ESM1-5. (e) Probability density distribution of the 15,496 baseflow indices. (f–h) Scatter plots comparing the observation-based baseflow indices with those from (f) CESM2, (g) MIROC6, and (h) ACCESS-ESM1-5. MAPE stands for mean absolute percentage error.

Extended Data Fig. 3 Temporal coverage of streamflow records.

(a) End year of streamflow records. The legend, which indicates the percentages of basin numbers, has been truncated below 40% to enhance readability. The blue bar reveals that streamflow records for 51% of the basins ended between 2016 and 2022. (b) Duration of streamflow records. 84% of the basins have streamflow records of at least 10 years. (c) Baseflow indices of 3,764 basins that have complete streamflow records for the 30 years from 1985 to 2014. We selected the period from 1985 to 2014 because this 30-year period has the largest number of basins with complete records. The spatial standard deviation of the 3,764 baseflow indices is 0.15. (d) Temporal standard deviation of baseflow indices. We performed baseflow separation methods over a 5-year moving window from 1985 to 2014, resulting in 26 baseflow indices for each basin. The standard deviation of the 26 baseflow indices was calculated to indicate decadal temporal variability. 68% of basins have a temporal standard deviation of less than 0.03, considerably smaller than the spatial standard deviation of 0.15. (e) We applied emergent constraints over a 5-year moving window across the 3,764 basins, resulting in 26 constrained global baseflow indices. The 26 constraints, depicted by the blue line, remain stable within the range of 0.58 to 0.59, demonstrating the stationarity of the emergent constraint approach. The grey boxes represent baseflow indices of the 3,764 basins in the given year displayed on the x-axis. The horizontal line within each box represents the mean baseflow index estimates, and the box lengths encompass the first to third quartiles. The whiskers of the boxes do not exceed 1.5 times the box lengths. (f) Density scatter plot of 30-year versus 5-year (1985–1989) baseflow indices. The slope of 0.98 and intercept of 0.01 suggest that 5-year streamflow records can well capture the global spatial variations of baseflow indices. The R2 value of 0.94 (p < 0.001) demonstrates that 5-year baseflow indices can well represent long-term baseflow indices, despite some discreteness introduced by temporal variability. (g) Density scatter plot of 30-year versus 10-year (1985–1994) baseflow indices. The x-axis represents the 30-year baseflow indices of the 3,764 basins between 1985 and 2014. The R2 value of 0.97 also has a p-value less than 0.001. All p-values in this study were derived from two-sided correlation tests, which check if the correlation is significantly different from zero based on the t-distribution, without adjusting for multiple comparisons.

Extended Data Fig. 4 Sensitivity of the constrained global (a) baseflow index and (b) R2 in response to the number of basins.

The x-axis represents the percentage of basins randomly sampled from a total of 15,496 basins. For each percentage, we performed random sampling and applied the emergent constraint approach in 100 iterations. The black line indicates the average constrained results obtained from the 100 iterations, and the grey shadow depicts the standard deviation across the iterations. The results demonstrate that the constrained global baseflow index remains stable at 59%, and the R2 of emergent constraint remains stable at 0.79, even when utilizing only 20% of the available basins. Constrained global (c) baseflow index and (d) R2 for different basin area groups. We used quartiles of basin areas to divide the 15,496 basins into four equal groups, namely small, medium, large, and extra large. The constrained baseflow index for these four groups ranges from 57% to 62%, and R2 ranges from 0.77 to 0.79. All groups yield a global baseflow index close to 59%, demonstrating that scaling from basins to grid cells may be a random error.

Extended Data Fig. 5 Emergent constraints in four major rivers.

(a) The Amazon River. (b) The Missouri River. (c) The Upper Colorado River. (d) The Orange River. Each scatter represents an ESM. For a specific ESM, the x-axis represents its simulated mean baseflow index across small basins in the major river, while the y-axis represents the global baseflow index. The black regression line depicts the relationship between the x- and y-axis variables for the 50 ESMs. All four regressions exhibit an R2 > 0.8 with p < 0.001. The vertical green line corresponds to the observation-based mean baseflow index across the small basins within the major river, calculated as an average from 12 baseflow separation methods. The horizontal blue dotted line indicates the new baseflow index estimate of the major river through emergent constraint. The blue box represents 12 constrained baseflow index estimates of the major river, which are derived by substituting 12 vertical green lines (not shown) from different baseflow separation methods into the regression relationship. The yellow box represents 50 global baseflow index estimates from the ESMs. The horizontal line within each box represents the mean baseflow index estimates, and the box lengths encompass the first to third quartiles. The whiskers of the boxes do not exceed 1.5 times the box lengths. Outliers beyond the whiskers are not displayed here. The first three rivers have tracer-based baseflow indices of 70%, 57%, and 56%, respectively. As for the last river (Orange River), emergent constraint decreased the original value of 56% ± 26% to 37% ± 5%. The new baseflow index appears to be more plausible due to the extensive presence of biological soil crusts78 in the Orange River.

Extended Data Table 1 The 50 CMIP6 Earth system models used in this study and their coupled land surface models (LSM)
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Extended Data Table 2 Emergent constraints in 17 major rivers
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Extended Data Table 3 Constrained baseflow indices in different climate zones
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Extended Data Table 4 Previous global groundwater recharge statistics
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Extended Data Table 5 Environmental drivers used as training data for baseflow index simulation
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Xie, J., Liu, X., Jasechko, S. et al. Majority of global river flow sustained by groundwater. Nat. Geosci. 17, 770–777 (2024). https://doi.org/10.1038/s41561-024-01483-5

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