Articles | Volume 29, issue 10
https://doi.org/10.5194/hess-29-2293-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-2293-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Substantial root-zone water storage capacity observed by GRACE and GRACE/FO
Department of Earth and Spatial Sciences, University of Idaho, Moscow, ID 83843, US
Erica L. McCormick
Department of Earth System Science, Stanford University, Stanford, CA 94305, US
Geruo A
Department of Earth System Science, University of California, Irvine, CA 92617, US
Alexandra G. Konings
Department of Earth System Science, Stanford University, Stanford, CA 94305, US
Bailing Li
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, US
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, US
Related authors
No articles found.
Anne Springer, Gabriëlle De Lannoy, Matthew Rodell, Yorck Ewerdwalbesloh, Helena Gerdener, Mehdi Khaki, Bailing Li, Fupeng Li, Maike Schumacher, Natthachet Tangdamrongsub, Mohammad J. Tourian, Wanshu Nie, and Jürgen Kusche
EGUsphere, https://doi.org/10.5194/egusphere-2025-2058, https://doi.org/10.5194/egusphere-2025-2058, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
The GRACE and GRACE Follow-On satellites monitor changes in Earth's water storage by observing gravity variations. By integrating these observations into hydrological models through data assimilation, estimates of groundwater, soil moisture, and hydrological trends are improved, helping to monitor droughts, floods, and human water use. This review highlights recent advances in GRACE data assimilation, identifies key challenges, and discusses future directions with upcoming satellite missions.
Tao Yuan, Shijie Zhong, and Geruo A
Geosci. Model Dev., 18, 1445–1461, https://doi.org/10.5194/gmd-18-1445-2025, https://doi.org/10.5194/gmd-18-1445-2025, 2025
Short summary
Short summary
Earth and other planets deform under various forces. Numerical modeling is critical in understanding the nature of various dynamic deformation processes. This article introduces a newly developed open-source package, CitcomSVE-3.0, which efficiently solves the viscoelastic deformation of planetary bodies. We present benchmark results against a semi-analytical code. With its accuracy and efficiency, CitcomSVE-3.0 could advance research in planetary and climatic sciences.
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
Short summary
Short summary
By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
David N. Dralle, W. Jesse Hahm, K. Dana Chadwick, Erica McCormick, and Daniella M. Rempe
Hydrol. Earth Syst. Sci., 25, 2861–2867, https://doi.org/10.5194/hess-25-2861-2021, https://doi.org/10.5194/hess-25-2861-2021, 2021
Short summary
Short summary
Root zone water storage capacity determines how much water can be stored belowground to support plants during periods without precipitation. Here, we develop a satellite remote sensing method to estimate this key variable at large scales that matter for management. Importantly, our method builds on previous approaches by accounting for snowpack, which may bias estimates from existing approaches. Ultimately, our method will improve large-scale understanding of plant access to subsurface water.
Yanlan Liu, Nataniel M. Holtzman, and Alexandra G. Konings
Hydrol. Earth Syst. Sci., 25, 2399–2417, https://doi.org/10.5194/hess-25-2399-2021, https://doi.org/10.5194/hess-25-2399-2021, 2021
Short summary
Short summary
The flow of water through plants varies with species-specific traits. To determine how they vary across the world, we mapped the traits that best allowed a model to match microwave satellite data. We also defined average values across a few clusters of trait behavior. These form a tractable solution for use in large-scale models. Transpiration estimates using these clusters were more accurate than if using plant functional types. We expect our maps to improve transpiration forecasts.
Caroline A. Famiglietti, T. Luke Smallman, Paul A. Levine, Sophie Flack-Prain, Gregory R. Quetin, Victoria Meyer, Nicholas C. Parazoo, Stephanie G. Stettz, Yan Yang, Damien Bonal, A. Anthony Bloom, Mathew Williams, and Alexandra G. Konings
Biogeosciences, 18, 2727–2754, https://doi.org/10.5194/bg-18-2727-2021, https://doi.org/10.5194/bg-18-2727-2021, 2021
Short summary
Short summary
Model uncertainty dominates the spread in terrestrial carbon cycle predictions. Efforts to reduce it typically involve adding processes, thereby increasing model complexity. However, if and how model performance scales with complexity is unclear. Using a suite of 16 structurally distinct carbon cycle models, we find that increased complexity only improves skill if parameters are adequately informed. Otherwise, it can degrade skill, and an intermediate-complexity model is optimal.
Andrew F. Feldman, Daniel J. Short Gianotti, Alexandra G. Konings, Pierre Gentine, and Dara Entekhabi
Biogeosciences, 18, 831–847, https://doi.org/10.5194/bg-18-831-2021, https://doi.org/10.5194/bg-18-831-2021, 2021
Short summary
Short summary
We quantify global plant water uptake durations after rainfall using satellite-based plant water content measurements. In wetter regions, plant water uptake occurs within a day due to rapid coupling between soil and plant water content. Drylands show multi-day plant water uptake after rain pulses, providing widespread evidence for slow rehydration responses and pulse-driven growth responses. Our results suggest that drylands are sensitive to projected shifts in rainfall intensity and frequency.
Nataniel M. Holtzman, Leander D. L. Anderegg, Simon Kraatz, Alex Mavrovic, Oliver Sonnentag, Christoforos Pappas, Michael H. Cosh, Alexandre Langlois, Tarendra Lakhankar, Derek Tesser, Nicholas Steiner, Andreas Colliander, Alexandre Roy, and Alexandra G. Konings
Biogeosciences, 18, 739–753, https://doi.org/10.5194/bg-18-739-2021, https://doi.org/10.5194/bg-18-739-2021, 2021
Short summary
Short summary
Microwave radiation coming from Earth's land surface is affected by both soil moisture and the water in plants that cover the soil. We measured such radiation with a sensor elevated above a forest canopy while repeatedly measuring the amount of water stored in trees at the same location. Changes in the microwave signal over time were closely related to tree water storage changes. Satellites with similar sensors could thus be used to monitor how trees in an entire region respond to drought.
A. Anthony Bloom, Kevin W. Bowman, Junjie Liu, Alexandra G. Konings, John R. Worden, Nicholas C. Parazoo, Victoria Meyer, John T. Reager, Helen M. Worden, Zhe Jiang, Gregory R. Quetin, T. Luke Smallman, Jean-François Exbrayat, Yi Yin, Sassan S. Saatchi, Mathew Williams, and David S. Schimel
Biogeosciences, 17, 6393–6422, https://doi.org/10.5194/bg-17-6393-2020, https://doi.org/10.5194/bg-17-6393-2020, 2020
Short summary
Short summary
We use a model of the 2001–2015 tropical land carbon cycle, with satellite measurements of land and atmospheric carbon, to disentangle lagged and concurrent effects (due to past and concurrent meteorological events, respectively) on annual land–atmosphere carbon exchanges. The variability of lagged effects explains most 2001–2015 inter-annual carbon flux variations. We conclude that concurrent and lagged effects need to be accurately resolved to better predict the world's land carbon sink.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., and Arkin, P.: The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), NOAA PSL, Boulder, Colorado, USA [data set], https://psl.noaa.gov/data/gridded/data.gpcp.html (last access: 14 May 2025), 2003.
Bachofen, C., Tumber-Dávila, S. J., Mackay, D. S., McDowell, N. G., Carminati, A., Klein, T., Stocker, B. D., Mencuccini, M., and Grossiord, C.: Tree water uptake patterns across the globe, New Phytol., 242, 1891–1910, https://doi.org/10.1111/nph.19762, 2024.
Baldocchi, D., Ma, S., and Verfaillie, J.: On the inter- and intra-annual variability of ecosystem evapotranspiration and water use efficiency of an oak savanna and annual grassland subjected to booms and busts in rainfall, Glob. Change Biol., 27, 359–375, https://doi.org/10.1111/gcb.15414, 2021.
Balland, V., Pollacco, J. A., and Arp, P. A.: Modeling soil hydraulic properties for a wide range of soil conditions, Ecol. Model., 219, 300–316, 2008.
Callahan, R. P., Riebe, C. S., Sklar, L. S., Pasquet, S., Ferrier, K. L., Hahm, W. J., Taylor, N. J., Grana, D., Flinchum, B. A., and Hayes, J. L.: Forest vulnerability to drought controlled by bedrock composition, Nat. Geosci., 15, 714–719, 2022.
Chen, Y., Velicogna, I., Famiglietti, J. S., and Randerson, J. T.: Satellite observations of terrestrial water storage provide early warning information about drought and fire season severity in the Amazon, J. Geophys. Res.-Biogeo., 118, 495–504, https://doi.org/10.1002/jgrg.20046, 2013.
Dong, J., Lei, F., and Crow, W. T.: Land transpiration-evaporation partitioning errors responsible for modeled summertime warm bias in the central United States, Nat. Commun., 13, 336, https://doi.org/10.1038/s41467-021-27938-6, 2022.
Espeleta, J. F., West, J. B., and Donovan, L. A.: Species-specific patterns of hydraulic lift in co-occurring adult trees and grasses in a sandhill community, Oecologia, 138, 341–349, https://doi.org/10.1007/s00442-003-1460-8, 2004.
Esteban, E. J. L., Castilho, C. V., Melgaço, K. L., and Costa, F. R. C.: The other side of droughts: wet extremes and topography as buffers of negative drought effects in an Amazonian forest, New Phytol., 229, 1995–2006, https://doi.org/10.1111/nph.17005, 2021.
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B., and Otero-Casal, C.: Hydrologic regulation of plant rooting depth, P. Natl. Acad. Sci. USA, 114, 10572–10577, https://doi.org/10.1073/pnas.1712381114, 2017.
Federer, C., Vörösmarty, C., and Fekete, B.: Sensitivity of annual evaporation to soil and root properties in two models of contrasting complexity, J. Hydrometeorol., 4, 1276–1290, 2003.
Feng, W., Zhong, M., Lemoine, J.-M., Biancale, R., Hsu, H.-T., and Xia, J.: Evaluation of groundwater depletion in North China using the Gravity Recovery and Climate Experiment (GRACE) data and ground-based measurements, Water Resour. Res., 49, 2110–2118, https://doi.org/10.1002/wrcr.20192, 2013.
Friedl, M. and Sulla-Menashe, D.: MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://lpdaac.usgs.gov/products/mcd12c1v006/ (last access: 14 May 2025), 2015.
Gao, H., Hrachowitz, M., Schymanski, S. J., Fenicia, F., Sriwongsitanon, N., and Savenije, H. H. G.: Climate controls how ecosystems size the root zone storage capacity at catchment scale, Geophys. Res. Lett., 41, 7916–7923, https://doi.org/10.1002/2014GL061668, 2014.
Gao, H., Hrachowitz, M., Wang-Erlandsson, L., Fenicia, F., Xi, Q., Xia, J., Shao, W., Sun, G., and Savenije, H. H. G.: Root zone in the Earth system, Hydrol. Earth Syst. Sci., 28, 4477–4499, https://doi.org/10.5194/hess-28-4477-2024, 2024.
Gebremichael, M., Krajewski, W. F., Morrissey, M., Langerud, D., Huffman, G. J., and Adler, R.: Error Uncertainty Analysis of GPCP Monthly Rainfall Products: A Data-Based Simulation Study, J. Appl. Meteorol., 42, 1837–1848, https://doi.org/10.1175/1520-0450(2003)042<1837:Euaogm>2.0.Co;2, 2003.
Ghiggi, G., Gudmundsson, L., and Humphrey, V.: G-RUN: Global Runoff Reconstruction, figshare [data set], https://figshare.com/articles/dataset/GRUN_Global_Runoff_Reconstruction/9228176 (last access: 14 May 2025), 2019.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: G-RUN ENSEMBLE: A Multi-Forcing Observation-Based Global Runoff Reanalysis, Water Resour. Res., 57, e2020WR028787, https://doi.org/10.1029/2020WR028787, 2021.
Giardina, F., Gentine, P., Konings, A. G., Seneviratne, S. I., and Stocker, B. D.: Diagnosing evapotranspiration responses to water deficit across biomes using deep learning, New Phytol., 240, 968–983, https://doi.org/10.1111/nph.19197, 2023.
Gou, J. and Soja, B.: Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms, Nature Water, 2, 139–150, https://doi.org/10.1038/s44221-024-00194-w, 2024.
Goulden, M. L. and Bales, R. C.: California forest die-off linked to multi-year deep soil drying in 2012–2015 drought, Nat. Geosci., 12, 632–637, https://doi.org/10.1038/s41561-019-0388-5, 2019.
Hahm, W. J., Rempe, D., Dralle, D., Dawson, T., and Dietrich, W.: Oak transpiration drawn from the weathered bedrock vadose zone in the summer dry season, Water Resour. Res., 56, e2020WR027419, https://doi.org/10.1029/2020WR027419, 2020.
Hahm, W. J., Dralle, D. N., Rempe, D. M., Bryk, A. B., Thompson, S. E., Dawson, T. E., and Dietrich, W. E.: Low Subsurface Water Storage Capacity Relative to Annual Rainfall Decouples Mediterranean Plant Productivity and Water Use From Rainfall Variability, Geophys. Res. Lett., 46, 6544–6553, https://doi.org/10.1029/2019GL083294, 2019.
Hain, C. R., Crow, W. T., Anderson, M. C., and Yilmaz, M. T.: Diagnosing Neglected Soil Moisture Source–Sink Processes via a Thermal Infrared–Based Two-Source Energy Balance Model, J. Hydrometeorol., 16, 1070–1086, https://doi.org/10.1175/JHM-D-14-0017.1, 2015.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5 (last access: 14 May 2025), 2023.
Hulsman, P., Keune, J., Koppa, A., Schellekens, J., and Miralles, D. G.: Incorporating Plant Access to Groundwater in Existing Global, Satellite-Based Evaporation Estimates, Water Resour. Res., 59, e2022WR033731, https://doi.org/10.1029/2022WR033731, 2023.
Humphrey, V., Zscheischler, J., Ciais, P., Gudmundsson, L., Sitch, S., and Seneviratne, S. I.: Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage, Nature, 560, 628–631, https://doi.org/10.1038/s41586-018-0424-4, 2018.
Jackson, R. B., Moore, L. A., Hoffmann, W. A., Pockman, W. T., and Linder, C. R.: Ecosystem rooting depth determined with caves and DNA, P. Natl. Acad. Sci. USA, 96, 11387–11392, https://doi.org/10.1073/pnas.96.20.11387, 1999.
Jiménez-Rodríguez, C. D., Sulis, M., and Schymanski, S.: Exploring the role of bedrock representation on plant transpiration response during dry periods at four forested sites in Europe, Biogeosciences, 19, 3395–3423, https://doi.org/10.5194/bg-19-3395-2022, 2022.
Koirala, S., Jung, M., Reichstein, M., de Graaf, I. E. M., Camps-Valls, G., Ichii, K., Papale, D., Ráduly, B., Schwalm, C. R., Tramontana, G., and Carvalhais, N.: Global distribution of groundwater-vegetation spatial covariation, Geophys. Res. Lett., 44, 4134–4142, https://doi.org/10.1002/2017GL072885, 2017.
Koppa, A., Rains, D., Hulsman, P., Poyatos, R., and Miralles, D. G.: A deep learning-based hybrid model of global terrestrial evaporation, Nat. Commun., 13, 1912, https://doi.org/10.1038/s41467-022-29543-7, 2022.
Kuzyakov, Y. and Razavi, B. S.: Rhizosphere size and shape: Temporal dynamics and spatial stationarity, Soil Biol. Biochem., 135, 343–360, https://doi.org/10.1016/j.soilbio.2019.05.011, 2019.
Li, B., Rodell, M., and Famiglietti, J. S.: Groundwater variability across temporal and spatial scales in the central and northeastern U.S, J. Hydrol., 525, 769–780, https://doi.org/10.1016/j.jhydrol.2015.04.033, 2015.
Li, B., Rodell, M., Kumar, S., Beaudoing, H. K., Getirana, A., Zaitchik, B. F., de Goncalves, L. G., Cossetin, C., Bhanja, S., and Mukherjee, A.: Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges, Water Resour. Res., 55, 7564–7586, 2019.
Liu, P.-W., Famiglietti, J. S., Purdy, A. J., Adams, K. H., McEvoy, A. L., Reager, J. T., Bindlish, R., Wiese, D. N., David, C. H., and Rodell, M.: Groundwater depletion in California's Central Valley accelerates during megadrought, Nat. Commun., 13, 7825, https://doi.org/10.1038/s41467-022-35582-x, 2022.
Livneh, B. and Hoerling, M. P.: The Physics of Drought in the U.S. Central Great Plains, J. Climate, 29, 6783–6804, https://doi.org/10.1175/JCLI-D-15-0697.1, 2016.
Mastrotheodoros, T., Pappas, C., Molnar, P., Burlando, P., Manoli, G., Parajka, J., Rigon, R., Szeles, B., Bottazzi, M., Hadjidoukas, P., and Fatichi, S.: More green and less blue water in the Alps during warmer summers, Nat. Clim. Change, 10, 155–161, https://doi.org/10.1038/s41558-019-0676-5, 2020.
Maxwell, R. M. and Condon, L. E.: Connections between groundwater flow and transpiration partitioning, Science, 353, 377–380, https://doi.org/10.1126/science.aaf7891, 2016.
McCabe, G. J. and Markstrom, S. L.: A monthly water-balance model driven by a graphical user interface, US Geological Survey Reston, VA, USA, https://doi.org/10.3133/ofr20071088, 2007.
McCormick, E. L., Dralle, D. N., Hahm, W. J., Tune, A. K., Schmidt, L. M., Chadwick, K. D., and Rempe, D. M.: Widespread woody plant use of water stored in bedrock, Nature, 597, 225–229, https://doi.org/10.1038/s41586-021-03761-3, 2021.
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought frequency and duration to time scales, Proceedings of the 8th Conference on Applied Climatology, Anaheim, California, USA, 17–22 January 1993, 179–183, https://www.droughtmanagement.info/literature/AMS_Relationship_Drought_Frequency_Duration_Time_Scales_1993.pdf (last access: 14 May 2025), 1993.
Miguez-Macho, G. and Fan, Y.: Spatiotemporal origin of soil water taken up by vegetation, Nature, 598, 624–628, https://doi.org/10.1038/s41586-021-03958-6, 2021.
Miralles, D. G., Bonte, O., Koppa, A., Baez-Villanueva, O. M., Tronquo, E., Zhong, F., Beck, H. E., Hulsman, P., Dorigo, W., Verhoest, N. E., and Haghdoost S.: GLEAM4: global land evaporation and soil moisture dataset at 0.1 resolution from 1980 to near present, Scientific Data, 12, 416, https://doi.org/10.1038/s41597-025-04610-y, 2025a.
Miralles, D. G., Bonte, O., Koppa, A., Baez-Villanueva, O. M., Tronquo, E., Zhong, F., Beck, H. E., Hulsman, P., Dorigo, W., Verhoest, N. E. C., and Haghdoost, S.: GLEAM4 (v4.1), Zenodo [data set], https://doi.org/10.5281/zenodo.14056080, 2025b.
Miralles, D. G., Jiménez, C., Jung, M., Michel, D., Ershadi, A., McCabe, M. F., Hirschi, M., Martens, B., Dolman, A. J., Fisher, J. B., Mu, Q., Seneviratne, S. I., Wood, E. F., and Fernández-Prieto, D.: The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets, Hydrol. Earth Syst. Sci., 20, 823–842, https://doi.org/10.5194/hess-20-823-2016, 2016.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, 1970.
Naumburg, E., Mata-gonzalez, R., Hunter, R. G., McLendon, T., and Martin, D. W.: Phreatophytic Vegetation and Groundwater Fluctuations: A Review of Current Research and Application of Ecosystem Response Modeling with an Emphasis on Great Basin Vegetation, Environ. Manage., 35, 726–740, https://doi.org/10.1007/s00267-004-0194-7, 2005.
Novick, K. A., Ficklin, D. L., Baldocchi, D., Davis, K. J., Ghezzehei, T. A., Konings, A. G., MacBean, N., Raoult, N., Scott, R. L., Shi, Y., Sulman, B. N., and Wood, J. D.: Confronting the water potential information gap, Nat. Geosci., 15, 158–164, https://doi.org/10.1038/s41561-022-00909-2, 2022.
Orellana, F., Verma, P., Loheide II, S. P., and Daly, E.: Monitoring and modeling water-vegetation interactions in groundwater-dependent ecosystems, Rev. Geophys., 50, RG3003, https://doi.org/10.1029/2011RG000383, 2012.
Pérez-Ruiz, E. R., Vivoni, E. R., and Sala, O. E.: Seasonal carryover of water and effects on carbon dynamics in a dryland ecosystem, Ecosphere, 13, e4189, https://doi.org/10.1002/ecs2.4189, 2022.
Peterson, T. J., Saft, M., Peel, M. C., and John, A.: Watersheds may not recover from drought, Science, 372, 745–749, https://doi.org/10.1126/science.abd5085, 2021.
Rempe, D. M. and Dietrich, W. E.: Direct observations of rock moisture, a hidden component of the hydrologic cycle, P. Natl. Acad. Sci. USA, 115, 2664–2669, https://doi.org/10.1073/pnas.1800141115, 2018.
Rodell, M., Velicogna, I., and Famiglietti, J. S.: Satellite-based estimates of groundwater depletion in India, Nature, 460, 999–1002, https://doi.org/10.1038/nature08238, 2009.
Rodell, M., Chao, B. F., Au, A. Y., Kimball, J. S., and McDonald, K. C.: Global biomass variation and its geodynamic effects: 1982–98, Earth Interact., 9, 1–19, 2005.
Rodell, M., Famiglietti, J. S., Wiese, D. N., Reager, J. T., Beaudoing, H. K., Landerer, F. W., and Lo, M. H.: Emerging trends in global freshwater availability, Nature, 557, 651–659, https://doi.org/10.1038/s41586-018-0123-1, 2018.
Rohde, M. M., Albano, C. M., Huggins, X., Klausmeyer, K. R., Morton, C., Sharman, A., Zaveri, E., Saito, L., Freed, Z., Howard, J. K., Job, N., Richter, H., Toderich, K., Rodella, A.-S., Gleeson, T., Huntington, J., Chandanpurkar, H. A., Purdy, A. J., Famiglietti, J. S., Singer, M. B., Roberts, D. A., Caylor, K., and Stella, J. C.: Groundwater-dependent ecosystem map exposes global dryland protection needs, Nature, 632, 101–107, https://doi.org/10.1038/s41586-024-07702-8, 2024.
Schlemmer, L., Schär, C., Lüthi, D., and Strebel, L.: A Groundwater and Runoff Formulation for Weather and Climate Models, J. Adv. Model. Earth Sy., 10, 1809–1832, https://doi.org/10.1029/2017MS001260, 2018.
Scott, R. L. and Biederman, J. A.: Critical Zone Water Balance Over 13 Years in a Semiarid Savanna, Water Resour. Res., 55, 574–588, https://doi.org/10.1029/2018WR023477, 2019.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture–climate interactions in a changing climate: A review, Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Speich, M. J. R., Lischke, H., and Zappa, M.: Testing an optimality-based model of rooting zone water storage capacity in temperate forests, Hydrol. Earth Syst. Sci., 22, 4097–4124, https://doi.org/10.5194/hess-22-4097-2018, 2018.
Stocker, B. D., Tumber-Dávila, S. J., Konings, A. G., Anderson, M. C., Hain, C., and Jackson, R. B.: Global patterns of water storage in the rooting zones of vegetation, Nat. Geosci., 16, 250–256, https://doi.org/10.1038/s41561-023-01125-2, 2023.
Stoy, P. C., El-Madany, T. S., Fisher, J. B., Gentine, P., Gerken, T., Good, S. P., Klosterhalfen, A., Liu, S., Miralles, D. G., Perez-Priego, O., Rigden, A. J., Skaggs, T. H., Wohlfahrt, G., Anderson, R. G., Coenders-Gerrits, A. M. J., Jung, M., Maes, W. H., Mammarella, I., Mauder, M., Migliavacca, M., Nelson, J. A., Poyatos, R., Reichstein, M., Scott, R. L., and Wolf, S.: Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities, Biogeosciences, 16, 3747–3775, https://doi.org/10.5194/bg-16-3747-2019, 2019.
Sulla-Menashe, D. and Friedl, M. A.: User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) product, USGS: Reston, VA, USA, 1–18, https://lpdaac.usgs.gov/documents/438/MCD12Q1_User_Guide_V51.pdf (last access: 14 May 2025), 2018.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L.: A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017RG000574, 2018.
Teuling, A. J., Seneviratne, S. I., Williams, C., and Troch, P. A.: Observed timescales of evapotranspiration response to soil moisture, Geophys. Res. Lett., 33, L23403, https://doi.org/10.1029/2006GL028178, 2006.
Thompson, S. E., Harman, C. J., Konings, A. G., Sivapalan, M., Neal, A., and Troch, P. A.: Comparative hydrology across AmeriFlux sites: The variable roles of climate, vegetation, and groundwater, Water Resour. Res., 47, W00J07, https://doi.org/10.1029/2010WR009797, 2011.
Ukkola, A. M., De Kauwe, M. G., Roderick, M. L., Burrell, A., Lehmann, P., and Pitman, A. J.: Annual precipitation explains variability in dryland vegetation greenness globally but not locally, Glob. Change Biol., 27, 4367–4380, https://doi.org/10.1111/gcb.15729, 2021.
Vereecken, H., Amelung, W., Bauke, S. L., Bogena, H., Brüggemann, N., Montzka, C., Vanderborght, J., Bechtold, M., Blöschl, G., Carminati, A., Javaux, M., Konings, A. G., Kusche, J., Neuweiler, I., Or, D., Steele-Dunne, S., Verhoef, A., Young, M., and Zhang, Y.: Soil hydrology in the Earth system, Nature Reviews Earth & Environment, 3, 573–587, https://doi.org/10.1038/s43017-022-00324-6, 2022.
Wang, S., Li, J., and Russell, H. A. J.: Methods for Estimating Surface Water Storage Changes and Their Evaluations, J. Hydrometeorol., 24, 445–461, https://doi.org/10.1175/JHM-D-22-0098.1, 2023a.
Wang, T., Wu, Z., Wang, P., Wu, T., Zhang, Y., Yin, J., Yu, J., Wang, H., Guan, X., Xu, H., Yan, D., and Yan, D.: Plant-groundwater interactions in drylands: A review of current research and future perspectives, Agr. Forest Meteorol., 341, 109636, https://doi.org/10.1016/j.agrformet.2023.109636, 2023b.
Wang-Erlandsson, L., Bastiaanssen, W. G. M., Gao, H., Jägermeyr, J., Senay, G. B., van Dijk, A. I. J. M., Guerschman, J. P., Keys, P. W., Gordon, L. J., and Savenije, H. H. G.: Global root zone storage capacity from satellite-based evaporation, Hydrol. Earth Syst. Sci., 20, 1459–1481, https://doi.org/10.5194/hess-20-1459-2016, 2016.
Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C., and Landerer, F. W.: Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res.-Sol. Ea., 120, 2648–2671, https://doi.org/10.1002/2014JB011547, 2015.
Wieder, W., Boehnert, J., Bonan, G., and Langseth, M.: Regridded Harmonized World Soil Database v1.2, Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA [data set], https://doi.org/10.3334/ORNLDAAC/1247, 2014.
Wiese, D. N., Landerer, F. W., and Watkins, M. M.: Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution, Water Resour. Res., 52, 7490–7502, https://doi.org/10.1002/2016WR019344, 2016.
Wiese, D. N., Yuan, D.-N., Boening, C., Landerer, F. W., and Watkins, M. M.: JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height Release 06 Coastal Resolution Improvement (CRI) Filtered Version 1.0, PO.DAAC, CA, USA [data set], https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/ (last access: 14 May 2025), 2018.
Yang, Y., Donohue, R. J., and McVicar, T. R.: Global estimation of effective plant rooting depth: Implications for hydrological modeling, Water Resour. Res., 52, 8260–8276, https://doi.org/10.1002/2016WR019392, 2016.
Zhao, M.: Substantial root-zone water storage capacity observed by GRACE and GRACE/FO, Zenodo [data set] and [code], https://doi.org/10.5281/zenodo.14970062, 2025.
Zhao, M., A, G., Liu, Y., and Konings, A. G.: Evapotranspiration frequently increases during droughts, Nat. Clim. Change, 12, 1024-1030, https://doi.org/10.1038/s41558-022-01505-3, 2022.
Zhao, M., A, G., Zhang, J., Velicogna, I., Liang, C., and Li, Z.: Ecological restoration impact on total terrestrial water storage, Nature Sustainability, 4, 56–62, https://doi.org/10.1038/s41893-020-00600-7, 2021.
Short summary
Root-zone water storage capacity (Sr) helps plants survive droughts and influences water and climate systems. Using GRACE (Gravity Recovery and Climate Experiment) satellite data, we estimated Sr globally and found that it exceeds 2 m soil storage in nearly half of the vegetated areas, far more than previously thought. Incorporating our Sr estimates into a global hydrological model improves evapotranspiration simulations, particularly during droughts, highlighting the value of our approach for advancing water resource and ecosystem modeling.
Root-zone water storage capacity (Sr) helps plants survive droughts and influences water and...