Majority of global river flow sustained by groundwater (2024)

References

  1. Alley, W. M., Healy, R. W., LaBaugh, J. W. & Reilly, T. E. Flow and storage in groundwater systems. Science 296, 1985–1990 (2002).

    Article CAS Google Scholar

  2. Winter, T. C., Harvey, J. W., Franke, O. L. & Alley, W. M. Ground Water and Surface Water: A Single Resource Circular 1139 (USGS, 1998); https://doi.org/10.3133/cir1139

  3. Dunne, T. & Leopold, L. B. Water in Environmental Planning (Macmillan, 1978).

  4. Beven, K. The era of infiltration. Hydrol. Earth Syst. Sci. 25, 851–866 (2021).

    Article Google Scholar

  5. Horton, R. E. Remarks on hydrologic terminology. EOS Trans. Am. Geophys. Union 23, 479–482 (1942).

    Article Google Scholar

  6. Sánchez-Murillo, R. Natural and Human Influences on Baseflow Regimes: A Physically-Based and Geochemical Analysis. PhD dissertation, Univ. Idaho (2014).

  7. Jasechko, S., Seybold, H., Perrone, D., Fan, Y. & Kirchner, J. W. Widespread potential loss of streamflow into underlying aquifers across the USA. Nature 591, 391–395 (2021).

    Article CAS Google Scholar

  8. Jasechko, S. et al. Global aquifers dominated by fossil groundwaters but wells vulnerable to modern contamination. Nat. Geosci. 10, 425–429 (2017).

    Article CAS Google Scholar

  9. Jasechko, S., Kirchner, J. W., Welker, J. M. & McDonnell, J. J. Substantial proportion of global streamflow less than three months old. Nat. Geosci. 9, 126–129 (2016).

    Article CAS Google Scholar

  10. Berghuijs, W. R. & Slater, L. J. Groundwater shapes North American river floods. Environ. Res. Lett. 18, 034043 (2023).

    Article Google Scholar

  11. Miller, M. P., Buto, S. G., Susong, D. D. & Rumsey, C. A. The importance of base flow in sustaining surface water flow in the Upper Colorado River basin. Water Resour. Res. 52, 3547–3562 (2016).

    Article Google Scholar

  12. Beck, H. E. et al. Global patterns in base flow index and recession based on streamflow observations from 3394 catchments. Water Resour. Res. 49, 7843–7863 (2013).

    Article Google Scholar

  13. Rodell, M. et al. The global land data assimilation system. Bull. Am. Meteorol. Soc. 85, 381–394 (2004).

    Article Google Scholar

  14. IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridhe Univ. Press, 2021).

  15. Clark, M. P. et al. Improving the representation of hydrologic processes in Earth system models. Water Resour. Res. 51, 5929–5956 (2015).

    Article Google Scholar

  16. Genereux, D. Quantifying uncertainty in tracer-based hydrograph separations. Water Resour. Res. 34, 915–919 (1998).

    Article Google Scholar

  17. Lott, D. A. & Stewart, M. T. Base flow separation: a comparison of analytical and mass balance methods. J. Hydrol. 535, 525–533 (2016).

    Article Google Scholar

  18. Lyne, V. & Hollick, M. Stochastic time-variable rainfall-runoff modelling. In Proc. Institute of Engineers Australia National Conference 89–93 (Institute of Engineers Australia, 1979).

  19. Nathan, R. J. & McMahon, T. A. Evaluation of automated techniques for base flow and recession analyses. Water Resour. Res. 26, 1465–1473 (1990).

    Article Google Scholar

  20. Gonzales, A. L., Nonner, J., Heijkers, J. & Uhlenbrook, S. Comparison of different base flow separation methods in a lowland catchment. Hydrol. Earth Syst. Sci. 13, 2055–2068 (2009).

    Article Google Scholar

  21. Rutledge, A. T. Computer Programs for Describing the Recession of Ground-Water Discharge and for Estimating Mean Ground-Water Recharge and Discharge from Streamflow Records-Update (USGS, 1998); https://doi.org/10.3133/wri984148

  22. Santhi, C., Allen, P. M., Muttiah, R. S., Arnold, J. G. & Tuppad, P. Regional estimation of base flow for the conterminous United States by hydrologic landscape regions. J. Hydrol. 351, 139–153 (2008).

    Article Google Scholar

  23. Wolock, D. M. Base-Flow Index Grid for the Conterminous United States (USGS, 2003); http://pubs.er.usgs.gov/publication/ofr03263

  24. Zhang, J. et al. Large-scale baseflow index prediction using hydrological modelling, linear and multilevel regression approaches. J. Hydrol. 585, 124780 (2020).

    Article Google Scholar

  25. Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).

    Article CAS Google Scholar

  26. Hall, A., Cox, P., Huntingford, C. & Klein, S. Progressing emergent constraints on future climate change. Nat. Clim. Change 9, 269–278 (2019).

    Article Google Scholar

  27. Shiogama, H., Watanabe, M., Kim, H. & Hirota, N. Emergent constraints on future precipitation changes. Nature 602, 612–616 (2022).

    Article CAS Google Scholar

  28. Lian, X. et al. Partitioning global land evapotranspiration using CMIP5 models constrained by observations. Nat. Clim. Change 8, 640–646 (2018).

    Article Google Scholar

  29. Mortatti, J., Moraes, J. M., Rodrigues, J., Victoria, R. L. & Martinelli, L. A. Hydrograph separation of the Amazon River using 18O as an isotopic tracer. Sci. Agric. 54, 167–173 (1997).

    Article CAS Google Scholar

  30. Yang, W., Xiao, C. & Liang, X. Technical note: analytical sensitivity analysis and uncertainty estimation of baseflow index calculated by a two-component hydrograph separation method with conductivity as a tracer. Hydrol. Earth Syst. Sci. 23, 1103–1112 (2019).

    Article Google Scholar

  31. Arnold, J. G. & Allen, P. M. Automated methods for estimating baseflow and ground water recharge from streamflow records. J. Am. Water Resour. Assoc. 35, 411–424 (1999).

    Article Google Scholar

  32. Gleeson, T. et al. GMD perspective: the quest to improve the evaluation of groundwater representation in continental- to global-scale models. Geosci. Model Dev. 14, 7545–7571 (2021).

    Article Google Scholar

  33. Berghuijs, W. R., Luijendijk, E., Moeck, C., van der Velde, Y. & Allen, S. T. Global recharge data set indicates strengthened groundwater connection to surface fluxes. Geophys. Res. Lett. 49, e2022GL099010 (2022).

    Article Google Scholar

  34. Decker, M. Development and evaluation of a new soil moisture and runoff parameterization for the CABLE LSM including subgrid-scale processes. J. Adv. Model. Earth Syst. 7, 1788–1809 (2015).

    Article Google Scholar

  35. Brunke, M. A. et al. Implementing and evaluating variable soil thickness in the Community Land Model, Version 4.5 (CLM4.5). J. Clim. 29, 3441–3461 (2016).

    Article Google Scholar

  36. Lawrence, D. M. et al. The Community Land Model Version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).

    Article Google Scholar

  37. Tashie, A., Pavelsky, T. & Kumar, M. A calibration-free groundwater module for improving predictions of low flows. Water Resour. Res. 58, e2021WR030800 (2022).

    Article Google Scholar

  38. Guo, Q. et al. Description of MATSIRO6. UTokyo Repository https://doi.org/10.15083/0002000181 (2021).

  39. Beven, K. & Germann, P. Macropores and water flow in soils. Water Resour. Res. 18, 1311–1325 (1982).

    Article Google Scholar

  40. Beven, K. & Germann, P. Macropores and water flow in soils revisited. Water Resour. Res. 49, 3071–3092 (2013).

    Article Google Scholar

  41. Gharari, S. et al. Improving the representation of subsurface water movement in land models. J. Hydrometeorol. 20, 2401–2418 (2019).

    Article Google Scholar

  42. Fan, Y. et al. Hillslope hydrology in global change research and Earth system modeling. Water Resour. Res. 55, 1737–1772 (2019).

    Article Google Scholar

  43. Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).

    Article CAS Google Scholar

  44. Hartmann, A., Gleeson, T., Wada, Y. & Wagener, T. Enhanced groundwater recharge rates and altered recharge sensitivity to climate variability through subsurface heterogeneity. Proc. Natl Acad. Sci. USA 114, 2842–2847 (2017).

    Article CAS Google Scholar

  45. Gudmundsson, L. & Seneviratne, S. I. Observation-based gridded runoff estimates for Europe (E-RUN version 1.1). Earth Syst. Sci. Data 8, 279–295 (2016).

    Article Google Scholar

  46. Xie, J., Liu, X., Bai, P. & Liu, C. Rapid watershed delineation using an automatic outlet relocation algorithm. Water Resour. Res. 58, e2021WR031129 (2022).

    Article Google Scholar

  47. Lehner, B. et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Front. Ecol. Environ. 9, 494–502 (2011).

    Article Google Scholar

  48. Friedl, M. A. et al. Global land cover mapping from MODIS: algorithms and early results. Remote Sens. Environ. 83, 287–302 (2002).

    Article Google Scholar

  49. Scanlon, B. R. et al. Global water resources and the role of groundwater in a resilient water future. Nat. Rev. Earth Environ. 4, 87–101 (2023).

    Article Google Scholar

  50. Tashie, A., Pavelsky, T. & Emanuel, R. E. Spatial and temporal patterns in baseflow recession in the Continental United States. Water Resour. Res. 56, e2019WR026425 (2020).

    Article Google Scholar

  51. Ghiggi, G., Humphrey, V., Seneviratne, S. I. & Gudmundsson, L. G-RUN ENSEMBLE: a multi-forcing observation-based global runoff reanalysis. Water Resour. Res. 57, e2020WR028787 (2021).

    Article Google Scholar

  52. Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).

    Article Google Scholar

  53. Kim, H. Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1) (DIAS, 2017); https://doi.org/10.20783/DIAS.501

  54. He, X., Pan, M., Wei, Z., Wood, E. F. & Sheffield, J. A global drought and flood catalogue from 1950 to 2016. Bull. Am. Meteorol. Soc. 101, E508–E535 (2020).

    Article Google Scholar

  55. Gleeson, T., Moosdorf, N., Hartmann, J. & van Beek, L. P. H. A glimpse beneath earth’s surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity. Geophys. Res. Lett. 41, 3891–3898 (2014).

    Article Google Scholar

  56. Gleeson, T., Befus, K. M., Jasechko, S., Luijendijk, E. & Cardenas, M. B. The global volume and distribution of modern groundwater. Nat. Geosci. 9, 161–167 (2016).

    Article CAS Google Scholar

  57. Reick, C. H. et al. JSBACH 3—the land component of the MPI Earth System Model: documentation of version 3.2. MPG PuRe https://doi.org/10.17617/2.3279802 (2021)

  58. Righi, M. et al. Earth System Model Evaluation Tool (ESMValTool) v2.0—technical overview. Geosci. Model Dev. 13, 1179–1199 (2020).

    Article Google Scholar

  59. Low Flow Studies Report No. 1 Research Report (Institute of Hydrology, 1980); http://nora.nerc.ac.uk/id/eprint/9093/

  60. Sloto, R. A. & Crouse, M. Y. HYSEP: A Computer Program for Streamflow Hydrograph Separation and Analysis (USGS, 1996); https://doi.org/10.3133/wri964040

  61. Boughton, W. The Australian water balance model. Environ. Model. Softw. 19, 943–956 (2004).

    Article Google Scholar

  62. Chapman, T. G. Comment on ‘Evaluation of automated techniques for base flow and recession analyses’ by R. J. Nathan and T. A. McMahon. Water Resour. Res. 27, 1783–1784 (1991).

  63. Chapman, T. G. & Maxwell, A. I. Baseflow separation-comparison of numerical methods with tracer experiments. In Proc. Hydrology and Water Resources Symposium 1996: Water and the Environment 539–545 (Institution of Engineers Australia, 1996).

  64. Eckhardt, K. How to construct recursive digital filters for baseflow separation. Hydrol. Process. 19, 507–515 (2005).

    Article Google Scholar

  65. Furey, P. R. & Gupta, V. K. A physically based filter for separating base flow from streamflow time series. Water Resour. Res. 37, 2709–2722 (2001).

    Article Google Scholar

  66. Tularam, G. A. & Ilahee, M. Exponential smoothing method of base flow separation and its impact on ontinuous loss estimates. Am. J. Environ. Sci. 4, 136–144 (2008).

    Article Google Scholar

  67. Willems, P. A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models. Environ. Model. Softw. 24, 311–321 (2009).

    Article Google Scholar

  68. Brutsaert, W. Long-term groundwater storage trends estimated from streamflow records: climatic perspective. Water Resour. Res. 44, W02409 (2008).

    Article Google Scholar

  69. Rammal, M. et al. Technical note: an operational implementation of recursive digital filter for base flow separation. Water Resour. Res. 54, 8528–8540 (2018).

    Article Google Scholar

  70. Vogel, R. M. & Kroll, C. N. Estimation of baseflow recession constants. Water Resour. Manage. 10, 303–320 (1996).

    Article Google Scholar

  71. Cox, P. M., Huntingford, C. & Williamson, M. S. Emergent constraint on equilibrium climate sensitivity from global temperature variability. Nature 553, 319–322 (2018).

    Article CAS Google Scholar

  72. Sanderson, B. M. et al. The potential for structural errors in emergent constraints. Earth Syst. Dyn. 12, 899–918 (2021).

    Article Google Scholar

  73. Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (eds Krishnapuram, B. et al.) 785–794 (Association for Computing Machinery, 2016); https://doi.org/10.1145/2939672.2939785

  74. Gupta, H. V., Kling, H., Yilmaz, K. K. & Martinez, G. F. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J. Hydrol. 377, 80–91 (2009).

    Article Google Scholar

  75. Lundberg, S. M. & Lee, S.-I. In Proc. 31st International Conference on Neural Information Processing Systems (Ulrike von Luxburg, U. et al.) 4768–4777 (Curran Associates, 2017).

  76. Chagas, V. B. P. et al. CAMELS-BR: hydrometeorological time series and landscape attribues for 897 catchments in Brazil—link to files. Zenodo https://doi.org/10.5281/zenodo.3709337 (2020).

  77. Ghiggi, G. et al. G-RUN ENSEMBLE. figshare https://doi.org/10.6084/m9.figshare.12794075 (2021).

  78. Chen, N., Yu, K., Jia, R., Teng, J. & Zhao, C. Biocrust as one of multiple stable states in global drylands. Sci. Adv. 6, eaay3763 (2020).

    Article CAS Google Scholar

  79. Beck, H. E. et al. Present and future Köppen–Geiger climate classification maps at 1 km resolution. Sci. Data 5, 180214 (2018).

    Article Google Scholar

  80. Koirala, S., Yeh, P. J.-F., Hirabayashi, Y., Kanae, S. & Oki, T. Global-scale land surface hydrologic modeling with the representation of water table dynamics. J. Geophys. Res. Atmos. 119, 75–89 (2014).

    Article Google Scholar

  81. Müller Schmied, H. et al. The global water resources and use model WaterGAP v2.2d: model description and evaluation. Geosci. Model Dev. 14, 1037–1079 (2021).

    Article Google Scholar

  82. Sutanudjaja, E. H. et al. PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. Geosci. Model Dev. 11, 2429–2453 (2018).

    Article Google Scholar

  83. Lvovich, M. I. World water resources, present and future. GeoJournal 3, 423–433 (1979).

    Article Google Scholar

  84. Döll, P. & Fiedler, K. Global-scale modeling of groundwater recharge. Hydrol. Earth Syst. Sci. 12, 863–885 (2008).

    Article Google Scholar

  85. Wada, Y. et al. Global depletion of groundwater resources. Geophys. Res. Lett. 37, L20402 (2010).

    Article Google Scholar

  86. Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).

    Article Google Scholar

  87. Walsh, R. P. D. & Lawler, D. M. Rainfall seasonality: description, spatial patterns and change through time. Weather 36, 201–208 (1981).

    Article Google Scholar

  88. Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements (FAO, 1998).

  89. Milly, P. C. D. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946–949 (2016).

    Article Google Scholar

  90. Boussetta, S., Balsamo, G., Beljaars, A., Kral, T. & Jarlan, L. Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model. Int. J. Remote Sens. 34, 3520–3542 (2013).

    Article Google Scholar

  91. Yamazaki, D. et al. A high-accuracy map of global terrain elevations. Geophys. Res. Lett. 44, 5844–5853 (2017).

    Article Google Scholar

  92. Amatulli, G., McInerney, D., Sethi, T., Strobl, P. & Domisch, S. Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers. Sci. Data 7, 162 (2020).

    Article CAS Google Scholar

  93. Poggio, L. et al. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. SOIL 7, 217–240 (2021).

    Article CAS Google Scholar

  94. Huscroft, J., Gleeson, T., Hartmann, J. & Börker, J. Compiling and mapping global permeability of the unconsolidated and consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0). Geophys. Res. Lett. 45, 1897–1904 (2018).

    Article Google Scholar

  95. Shangguan, W., Hengl, T., Jesus de, J. M., Yuan, H. & Dai, Y. Mapping the global depth to bedrock for land surface modeling. J. Adv. Model. Earth Syst. 9, 65–88 (2017).

    Article Google Scholar

  96. Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

    Article Google Scholar

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