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Global managed aquifer recharge potential as a solution to water scarcity

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Abstract

Groundwater overexploitation from irrigated agriculture is accelerating in major food-producing regions, threatening long-term water and food security. Managed aquifer recharge (MAR), the intentional storage of available water in aquifers, could help buffer hydrologic extremes by diverting high flows for infiltration. Here we present a first-order global screening of spreading-based MAR potential across irrigated lands (2002–2021) by integrating Gravity Recovery and Climate Experiment-derived monthly groundwater depletion, high-magnitude flow and unsustainable irrigation water consumption. We estimate high-magnitude flow volumes under 90th and 95th percentile thresholds and weight them with a region-/monthly-specific feasibility coefficient representing infiltration suitability, evaporative competition and off-season crop-area availability. Globally, MAR could offset 4–6% of unsustainable irrigation, with hotspots reaching more than 50% offset (for example, Europe and Southeast Asia) but lower potential in basins such as the Ganges and the Central Valley (3–7%). Our results warrant subsequent regional assessments to evaluate technical, economic and governance/policy feasibility, as well as site-specific design.

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Fig. 1: Global net GWS volume change in irrigation regions, 2002–2021.
Fig. 2: GWS anomalies in selected irrigation regions, 2002–2021.
Fig. 3: Potential of MAR to offset unsustainable irrigation and its sensitivity to the HMF threshold definition.
Fig. 4: Sensitivity of MAR offset potential to different HMF capture-efficiency assumptions.
Fig. 5: Interannual share of unsustainable irrigation offset by MAR for the top 20 irrigation regions affected by groundwater depletion, 2002–2021.

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Data availability

Data supporting the findings of this study are available within the article and its Supplementary Information. Results are available via Zenodo at https://doi.org/10.5281/zenodo.15731808 (ref. 96).

Code availability

Data analysis was performed using Python 3.11.0, utilizing the following packages: PySheds (0.3.5), Xarray (2022.11.0), Cartopy (0.21.1), GeoPandas (0.12.1), Matplotlib (3.6.2), Pandas (1.5.2) and NumPy (1.23.5). Spatial figure preparation was also carried out in ArcGIS Pro (version 3.3.0, Esri). Custom scripts and functions developed for this study are available from the corresponding author upon reasonable request.

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Funding

L.R. discloses support of this work from Schmidt Sciences, LLC.

Author information

Authors and Affiliations

  1. Biosphere Sciences and Engineering, Carnegie Institution for Science, Stanford, CA, USA

    Andrea Citrini, Gang Zhao & Lorenzo Rosa

  2. Bureau of Economic Geology, Jackson School of Geosciences, University of Texas at Austin, Austin, TX, USA

    Bridget R. Scanlon & Ashraf Rateb

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

    Gang Zhao

  4. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy

    Matteo Sangiorgio

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  1. Andrea Citrini
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  2. Bridget R. Scanlon
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  4. Gang Zhao
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  5. Matteo Sangiorgio
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  6. Lorenzo Rosa
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Contributions

A.C. and L.R. conceived the study. A.C. led the analysis and wrote the paper. A.C., B.R.S. and L.R. designed the research with input from all authors. A.R. contributed key datasets and provided technical support for groundwater depletion assessment. G.Z. developed and processed essential geospatial datasets for surface water storage. M.S. contributed analytical tools and supported the development of the modelling framework for unsustainable irrigation. L.R. supervised the research activities and supported interpretation of results. All authors contributed to discussions and reviewed the final paper.

Corresponding author

Correspondence to
Lorenzo Rosa.

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The authors declare no competing interests.

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Nature Water thanks Mohammad Faiz Alam, Matthew Rodell and Joanne Vanderzalm for their contributions to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Basin-specific feasibility factors used in the MAR-scenario analysis.

Maps are shown at the irrigation-region scale. a, Multi-year median of the monthly feasibility coefficient (M(r,t)in [mathrm{0,1}]). b, Infiltration-suitability coefficient (Cinf (r)in [mathrm{0,1}]). c, Evapotranspiration coefficient (1-{C}_{{ET}}(r,t)in [mathrm{0,1}]), for which higher values indicate lower evapotranspiration competition and thus more favorable months for MAR. d, Off-season crop-area availability ({Ccrop}(r,t)in [mathrm{0,1}]). All coefficients are dimensionless; warmer colors generally denote greater feasibility, except in panel c, where the scale reflects lower evapotranspiration competition. Basemap data from Esri, Garmin International, Inc., the US Central Intelligence Agency (The World Factbook) and the National Geographic Society.

Source data

Extended Data Fig. 2 Sensitivity of MAR offset potential to HMF capture-efficiency and threshold assumptions.

Percentage of unsustainable irrigation water consumption offset by MAR under three HMF capture-efficiency scenarios—100% (a,b), 50% (c,d) and 10% (e,f)—for the 90th-percentile HMF threshold (left column) and the percentage difference between the 90th and 95th percentile scenarios (right column). These scenarios represent varying assumptions about infrastructure and institutional capacity to capture and store high-magnitude flows. Grey hatching indicates areas where MAR was not evaluated because modeled unsustainable irrigation water consumption was zero and/or groundwater depletion was not detected. Basemap data from Esri, Garmin International, Inc., the US Central Intelligence Agency (The World Factbook) and the National Geographic Society.

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Extended Data Fig. 3 Accumulated high-magnitude flow and seasonal discharge variability in representative irrigation regions.

a, Global distribution of accumulated high-magnitude flow volume (cubic kilometres) from 2002 to 2021, based on the 90th-percentile threshold of daily discharge derived from GloFAS67. Darker blue-to-purple colors indicate higher cumulative HMF volumes. b–k, monthly discharge patterns in a selection of major irrigation regions heavily impacted by groundwater depletion, showing the median and interquartile range (25th-75th percentiles) of monthly discharge across the 2002–2021 period: b, Ganges River; c, Sabarmati River; d, Ziya River; e, Lower Yellow River; f, Indus River; g, Mekong Delta; h, Iran Central Plateau; i, Upper Tigris–Euphrates, j, California Central Valley; and k, Lower Tigris–Euphrates. Basemap data in a from Esri, Garmin International, Inc., the US Central Intelligence Agency (The World Factbook) and the National Geographic Society.

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Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–8, Table 1, Text 1: constraint diagnostics and Text 2: modelled monthly MAR dynamics.

Peer Review File (download PDF )

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Source Data Fig. 1 (download XLSX )

Volumetric (cubic kilometres) GWS change in irrigation regions (2002–2021), trend slope coefficients, areas and percentage (%) of irrigated land by region.

Source Data Fig. 2 (download XLSX )

Monthly long-term components of TWS and GWS anomalies (millimetres) for each irrigation region (2002–2021).

Source Data Fig. 3 (download XLSX )

Percentage of unsustainable irrigation offset by MAR across irrigation regions (feasibility-weighted scenario) for the 2002–2021 period considering both 90th and 95th percentile HMF scenarios.

Source Data Fig. 4 (download XLSX )

Percentage of unsustainable irrigation offset by MAR across irrigation regions for the 2002–2021 period considering both 90th and 95th percentile HMF scenarios (100%/50%/10% efficiency).

Source Data Fig. 5 (download XLSX )

Interannual percentage of unsustainable irrigation offset by MAR across irrigation regions (2002–2021) considering both 90th and 95th percentile HMF scenarios for the feasibility-weighted scenario.

Source Data Extended Data Fig. 1 (download XLSX )

Feasibility coefficient (b,t), Infiltration coefficient (b), evapotranspiration coefficient (b,t) and off-season area coefficient (b,t).

Source Data Extended Data Fig. 2 (download XLSX )

Percentage of unsustainable irrigation offset by MAR under varying HMF capture-efficiency scenarios (100%, 50% and 10%) and sensitivity to flow thresholds (90th and 95th percentile HMF scenarios) across irrigation regions (2002–2021).

Source Data Extended Data Fig. 3 (download XLSX )

Monthly accumulated HMF volume (cubic kilometres) by irrigation regions (2002–2021) considering both 90th and 95th percentile HMF scenarios.

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Citrini, A., Scanlon, B.R., Rateb, A. et al. Global managed aquifer recharge potential as a solution to water scarcity.
Nat Water (2026). https://doi.org/10.1038/s44221-026-00672-3

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