Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1463-2025
© 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/gmd-18-1463-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Water Table Model (WTM) (v2.0.1): coupled groundwater and dynamic lake modelling
Department of Earth and Environmental Sciences, University of Illinois Chicago, Chicago, IL, USA
Department of Earth and Environmental Sciences, University of Minnesota, Minneapolis, MN, USA
Lamont–Doherty Earth Observatory, Columbia University, New York, NY, USA
Andrew D. Wickert
Department of Earth and Environmental Sciences, University of Minnesota, Minneapolis, MN, USA
St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, MN, USA
Sektion 4.6: Geomorphologie, GFZ Helmholtz-Zentrum für Geoforschung, Potsdam, Germany
Richard Barnes
National Energy Research Scientific Computing Center (NERSC), Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Jacqueline Austermann
Lamont–Doherty Earth Observatory, Columbia University, New York, NY, USA
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Richard Barnes, Kerry L. Callaghan, and Andrew D. Wickert
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Existing ways of modeling the flow of water amongst landscape depressions such as swamps and lakes take a long time to run. However, as our previous work explains, depressions can be quickly organized into a data structure – the depression hierarchy. This paper explains how the depression hierarchy can be used to quickly simulate the realistic filling of depressions including how they spill over into each other and, if they become full enough, how they merge into one another.
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Matias Romero, Shanti B. Penprase, Maximillian S. Van Wyk de Vries, Andrew D. Wickert, Andrew G. Jones, Shaun A. Marcott, Jorge A. Strelin, Mateo A. Martini, Tammy M. Rittenour, Guido Brignone, Mark D. Shapley, Emi Ito, Kelly R. MacGregor, and Marc W. Caffee
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Sea level was significantly higher during the Pliocene epoch, around 3 million years ago. The present-day elevations of shorelines that formed in the past provide a data constraint on the extent of ice sheet melt and the global sea level response under warm Pliocene conditions. In this study, we identify 10 escarpments that formed from wave-cut erosion during Pliocene times and compare their elevations with model predictions of solid Earth deformation processes to estimate past sea level.
Andrew D. Wickert, Jabari C. Jones, and Gene-Hua Crystal Ng
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For over a century, scientists have used a simple algebraic relationship to estimate the amount of water flowing through a river (its discharge) from the height of the flow (its stage). Here we add physical realism to this approach by explicitly representing both the channel and floodplain, thereby allowing channel and floodplain geometry and roughness to these estimates. Our proposed advance may improve predictions of floods and water resources, even when the river channel itself changes.
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We can measure glacier flow and sliding velocity by tracking patterns on the ice surface in satellite images. The surface velocity of glaciers provides important information to support assessments of glacier response to climate change, to improve regional assessments of ice thickness, and to assist with glacier fieldwork. Our paper describes Glacier Image Velocimetry (GIV), a new, easy-to-use, and open-source toolbox for calculating high-resolution velocity time series for any glacier on earth.
Richard Barnes, Kerry L. Callaghan, and Andrew D. Wickert
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Existing ways of modeling the flow of water amongst landscape depressions such as swamps and lakes take a long time to run. However, as our previous work explains, depressions can be quickly organized into a data structure – the depression hierarchy. This paper explains how the depression hierarchy can be used to quickly simulate the realistic filling of depressions including how they spill over into each other and, if they become full enough, how they merge into one another.
Cited articles
Abatzoglou, J., Dobrowski, S., Parks, S., and Hegewisch, K.: Monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015, University of Idaho [data set], https://doi.org/10.7923/G43J3B0R, 2017. a
Amanambu, A. C., Obarein, O. A., Mossa, J., Li, L., Ayeni, S. S., Balogun, O., Oyebamiji, A., and Ochege, F. U.: Groundwater system and climate change: Present status and future considerations, J. Hydrol., 589, 125163, https://doi.org/10.1016/j.jhydrol.2020.125163, 2020. a
Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis., NOAA Technical Memorandum NESDIS NGDC-24, National Geophysical Data Center, NOAA [data set], https://doi.org/10.7289/V5C8276M, 2009. a, b
Ameli, A. A., McDonnell, J. J., and Bishop, K.: The exponential decline in saturated hydraulic conductivity with depth: a novel method for exploring its effect on water flow paths and transit time distribution, Hydrol. Process., 30, 2438–2450, https://doi.org/10.1002/hyp.10777, 2016. a
Anderson, D. G.: Iterative Procedures for Nonlinear Integral Equations, Journal of the ACM (JACM), 12, 547–560, https://doi.org/10.1145/321296.321305, 1965. a, b
Argus, D. F., Peltier, W. R., Drummond, R., and Moore, A. W.: The Antarctica component of postglacial rebound model ICE-6G_C (VM5a) based upon GPS positioning, exposure age dating of ice thicknesses, and relative sea level histories, Geophys. J. Int., 198, 537–563, https://doi.org/10.1093/gji/ggu140, 2014 (data available at: https://www.atmosp.physics.utoronto.ca/~peltier/data.php, last access: 4 March 2025). a
Balay, S., Gropp, W. D., McInnes, L. C., and Smith, B. F.: Efficient Management of Parallelism in Object Oriented Numerical Software Libraries, in: Modern Software Tools in Scientific Computing, edited by: Arge, E., Bruaset, A. M., and Langtangen, H. P., 163–202, Birkhäuser Press, https://doi.org/10.1007/978-1-4612-1986-6_8, 1997. a, b
Balay, S., Abhyankar, S., Adams, M. F., Benson, S., Brown, J., Brune, P., Buschelman, K., Constantinescu, E., Dalcin, L., Dener, A., Eijkhout, V., Faibussowitsch, J., Gropp, W. D., Hapla, V., Isaac, T., Jolivet, P., Karpeev, D., Kaushik, D., Knepley, M. G., Kong, F., Kruger, S., May, D. A., McInnes, L. C., Mills, R. T., Mitchell, L., Munson, T., Roman, J. E., Rupp, K., Sanan, P., Sarich, J., Smith, B. F., Zampini, S., Zhang, H., Zhang, H., and Zhang, J.: PETSc/TAO Users Manual, Tech. Rep. ANL-21/39 – Revision 3.18, Argonne National Laboratory, https://publications.anl.gov/anlpubs/2022/10/179042.pdf (last access: 28 February 2025), 2022a. a, b
Balay, S., Abhyankar, S., Adams, M. F., Benson, S., Brown, J., Brune, P., Buschelman, K., Constantinescu, E. M., Dalcin, L., Dener, A., Eijkhout, V., Faibussowitsch, J., Gropp, W. D., Hapla, V., Isaac, T., Jolivet, P., Karpeev, D., Kaushik, D., Knepley, M. G., Kong, F., Kruger, S., May, D. A., McInnes, L. C., Mills, R. T., Mitchell, L., Munson, T., Roman, J. E., Rupp, K., Sanan, P., Sarich, J., Smith, B. F., Zampini, S., Zhang, H., Zhang, H., and Zhang, J.: PETSc Web page, https://petsc.org/ (last access: 3 March 2023), 2022b. a, b
Barnes, R. and Callaghan, K. L.: Depression Hierarchy Source Code, Zenodo, https://doi.org/10.5281/zenodo.3238558, 2019. a, b, c
Barnes, R. and Callaghan, K. L.: Fill-Spill-Merge Source Code, Zenodo, https://doi.org/10.5281/zenodo.3755142, 2020. a, b, c
Barnes, R., Callaghan, K. L., and Wickert, A. D.: Computing water flow through complex landscapes – Part 2: Finding hierarchies in depressions and morphological segmentations, Earth Surf. Dynam., 8, 431–445, https://doi.org/10.5194/esurf-8-431-2020, 2020. a, b
Callaghan, K. L. and Wickert, A. D.: Computing water flow through complex landscapes – Part 1: Incorporating depressions in flow routing using FlowFill, Earth Surf. Dynam., 7, 737–753, https://doi.org/10.5194/esurf-7-737-2019, 2019. a
Callaghan, K. L., Barnes, R., and Wickert, A. D.: The Water Table Model (v2.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.10611076, 2024. a, b, c, d
Callaghan, K. L., Wickert, A. D., Barnes, R., and Austermann, J.: Water table: WTM steady-state water table simulations for LGM and present day, Hydroshare [data set], https://doi.org/10.4211/hs.9eaa891ef9c44a19b3d40cdfcb1fe824, 2025. a
Cardenas, M. B. and Jiang, X. W.: Groundwater flow, transport, and residence times through topography-driven basins with exponentially decreasing permeability and porosity, Water Resour. Res., 46, 1–9, https://doi.org/10.1029/2010WR009370, 2010. a
Charnock, H.: Wind stress on a water surface, Q. J. Roy. Meteor. Soc., 81, 639–640, https://doi.org/10.1002/qj.49708135026, 1955. a, b, c, d
Condon, L. E., Kollet, S., Bierkens, M. F., Fogg, G. E., Maxwell, R. M., Hill, M. C., Fransen, H. J. H., Verhoef, A., Van Loon, A. F., Sulis, M., and Abesser, C.: Global Groundwater Modeling and Monitoring: Opportunities and Challenges, Water Resour. Res., 57, 1–27, https://doi.org/10.1029/2020WR029500, 2021. a
Cuthbert, M. O., Gleeson, T., Moosdorf, N., Befus, K. M., Schneider, A., Hartmann, J., and Lehner, B.: Global patterns and dynamics of climate–groundwater interactions, Nat. Clim. Change, 9, 137–141, https://doi.org/10.1038/s41558-018-0386-4, 2019a. a, b, c
Cuthbert, M. O., Taylor, R. G., Favreau, G., Todd, M. C., Shamsudduha, M., Villholth, K. G., MacDonald, A. M., Scanlon, B. R., Kotchoni, D. O., Vouillamoz, J. M., Lawson, F. M., Adjomayi, P. A., Kashaigili, J., Seddon, D., Sorensen, J. P., Ebrahim, G. Y., Owor, M., Nyenje, P. M., Nazoumou, Y., Goni, I., Ousmane, B. I., Sibanda, T., Ascott, M. J., Macdonald, D. M., Agyekum, W., Koussoubé, Y., Wanke, H., Kim, H., Wada, Y., Lo, M. H., Oki, T., and Kukuric, N.: Observed controls on resilience of groundwater to climate variability in sub-Saharan Africa, Nature, 572, 230–234, https://doi.org/10.1038/s41586-019-1441-7, 2019b. a
Dalca, A., Ferrier, K., Mitrovica, J., Perron, J., Milne, G., and Creveling, J.: On postglacial sea level – III. Incorporating sediment redistribution, Geophys. J. Int., 194, 45–60, https://doi.org/10.1093/gji/ggt089, 2013. a
Dean, J. F., Middelburg, J. J., Röckmann, T., Aerts, R., Blauw, L. G., Egger, M., Jetten, M. S. M., de Jong, A. E. E., Meisel, O. H., Rasigraf, O., Slomp, C. P., in't Zandt, M. H., and Dolman, A. J.: Methane Feedbacks to the Global Climate System in a Warmer World, Rev. Geophys., 56, 207–250, https://doi.org/10.1002/2017RG000559, 2018. a
Decharme, B., Delire, C., Minvielle, M., Colin, J., Vergnes, J. P., Alias, A., Saint-Martin, D., Séférian, R., Sénési, S., and Voldoire, A.: Recent Changes in the ISBA-CTRIP Land Surface System for Use in the CNRM-CM6 Climate Model and in Global Off-Line Hydrological Applications, J. Adv. Model. Earth Sy., 11, 1207–1252, https://doi.org/10.1029/2018MS001545, 2019. a
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in climate change projections: The role of internal variability, Clim. Dynam., 38, 527–546, https://doi.org/10.1007/s00382-010-0977-x, 2012. a
Döll, P., Fiedler, K., and Zhang, J.: Global-scale analysis of river flow alterations due to water withdrawals and reservoirs, Hydrol. Earth Syst. Sci., 13, 2413–2432, https://doi.org/10.5194/hess-13-2413-2009, 2009. a
Döll, P., Trautmann, T., Göllner, M., and Schmied, H. M.: A global-scale analysis of water storage dynamics of inland wetlands: Quantifying the impacts of human water use and man-made reservoirs as well as the unavoidable and avoidable impacts of climate change, Ecohydrology, 13, 1–18, https://doi.org/10.1002/eco.2175, 2020. a, b
Dunne, T. and Black, R. D.: An experimental investigation runoff production in permeable soils, Water Resour. Res., 6, 478–490, 1970. a
European Centre for Medium-Range Weather Forecasts: ERA5 Reanalysis (Monthly Mean 0.25 Degree Latitude-Longitude Grid), NCAR, https://doi.org/10.5065/P8GT-0R61, 2019. a
Fan, Y. and Miguez-Macho, G.: A simple hydrologic framework for simulating wetlands in climate and earth system models, Climate Dynamics, 37, 253–278, https://doi.org/10.1007/s00382-010-0829-8, 2011. a
Fan, Y., Miguez-Macho, G., Weaver, C. P., Walko, R., and Robock, A.: Incorporating water table dynamics in climate modeling: 1. Water table observations and equilibrium water table simulations, J. Geophys. Res.-Atmos., 112, 1–17, https://doi.org/10.1029/2006JD008111, 2007. a, b, c, d
Finch, J. and Calver, A.: Methods for the Quantification of Evaporation from Lakes. Prepared for the World Meteorological Organization's Commission for Hydrology, Oxfordshire, UK, 41 pp., 2008. a
Gleeson, T., Befus, K. M., Jasechko, S., Luijendijk, E., and Cardenas, M. B.: The global volume and distribution of modern groundwater, Nat. Geosci., 9, 161–164, https://doi.org/10.1038/ngeo2590, 2016. a
Hersbach, H.: Sea surface roughness and drag coefficient as functions of neutral wind speed, J. Phys. Oceanogr., 41, 247–251, https://doi.org/10.1175/2010JPO4567.1, 2011. a, b, c
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 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Horton, R. E. and Htrata, T.: Erosional development of streams and their drainage basins, hydrophysical approach to quantitative morphology, Nihon Ringakkai Shi/Journal of the Japanese Forestry Society, 37, 417–420, https://doi.org/10.11519/jjfs1953.37.9_417, 1955. a
Hu, S., Niu, Z., Chen, Y., Li, L., and Zhang, H.: Global wetlands: Potential distribution, wetland loss, and status, Sci. Total Environ., 586, 319–327, https://doi.org/10.1016/j.scitotenv.2017.02.001, 2017. a
Kendall, R. A., Mitrovica, J. X., and Milne, G. A.: On post-glacial sea level – II. Numerical formulation and comparative results on spherically symmetric models, Geophys. J. Int., 161, 679–706, https://doi.org/10.1111/j.1365-246X.2005.02553.x, 2005. a
Koirala, S., Yeh, P. J., Hirabayashi, Y., Kanae, S., and Oki, T.: Global-scale land surface hydrologic modeling with the representation of water table dynamics, J. Geophys. Res., 119, 75–89, https://doi.org/10.1002/2013JD020398, 2014. a
Kollet, S. J.: Influence of soil heterogeneity on evapotranspiration under shallow water table conditions: Transient, stochastic simulations, Environ. Res. Lett., 4, 035007, https://doi.org/10.1088/1748-9326/4/3/035007, 2009. a
Konikow, L. F.: Contribution of global groundwater depletion since 1900 to sea-level rise, Geophys. Res. Lett., 38, 1–5, https://doi.org/10.1029/2011GL048604, 2011. a
Lambeck, K., Rouby, H., Purcell, A., Sun, Y., and Sambridge, M.: Sea level and global ice volumes from the Last Glacial Maximum to the Holocene, P. Natl. Acad. Sci. USA, 111, 15296–15303, https://doi.org/10.1073/pnas.1411762111, 2014. a
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. a
Lemieux, J. M., Sudicky, E. A., Peltier, W. R., and Tarasov, L.: Dynamics of groundwater recharge and seepage over the Canadian landscape during the Wisconsinian glaciation, J. Geophys. Res.-Earth, 113, 1–18, https://doi.org/10.1029/2007JF000838, 2008. a
Märker, M. and Flörke, M.: Preliminary assessment of IPCC-SRES scenarios on future water resources using the WaterGAP 2 model, International Congress on …, 440–445, University of Kassel: Center of Environmental Systems Research, 2003. a
Maxwell, R. M., Condon, L. E., and Kollet, S. J.: A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3, Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, 2015. a, b, c, d
Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.: Estimating the volume and age of water stored in global lakes using a geo-statistical approach, Nat. Commun., 7, 1–11, https://doi.org/10.1038/ncomms13603, 2016. a
Monteith, J.: Evaporation and environment, Sym. Soc. Exp. Biol., 19, 205–234, 1965. a
Müller Schmied, H., Cáceres, D., Eisner, S., Flörke, M., Herbert, C., Niemann, C., Peiris, T. A., Popat, E., Portmann, F. T., Reinecke, R., Schumacher, M., Shadkam, S., Telteu, C.-E., Trautmann, T., and Döll, P.: The global water resources and use model WaterGAP v2.2d: model description and evaluation, Geosci. Model Dev., 14, 1037–1079, https://doi.org/10.5194/gmd-14-1037-2021, 2021. a
NCAR: Hybrid STATSGO/FAO (30-second for CONUS/5-minute elsewhere) Soil Texture, NCAR [data set], https://ral.ucar.edu/model/wrf-noah-modeling-system, last access: 4 March 2025. a
Neteler, M., Bowman, M. H., Landa, M., and Metz, M.: GRASS GIS: A multi-purpose open source GIS, Environ. Modell. Softw., 31, 124–130, https://doi.org/10.1016/j.envsoft.2011.11.014, 2012. a
Ni, S., Chen, J., Wilson, C. R., Li, J., Hu, X., and Fu, R.: Global Terrestrial Water Storage Changes and Connections to ENSO Events, Surv. Geophys., 39, 1–22, https://doi.org/10.1007/s10712-017-9421-7, 2018. a
NOAA: National Water Model: Improving NOAA's Water Prediction Services, p. 2, https://water.noaa.gov/assets/styles/public/images/wrn-national-water-model.pdf (last access: 28 February 2025), 2016. a
NOAA National Geophysical Data Center: ETOPO1 1 Arc-Minute Global Relief Model, NOAA National Centers for Environmental Information, https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ngdc.mgg.dem:316 (last access: 4 March 2025), 2009. a
Oleson, K., Lawrence, D., Bonan, G., Flanner, M., Kluzek, E., Lawrence, P., Levis, S., Swenson, S., and Thornton, P.: Technical Description of version 4.0 of the Community Land Model (CLM), NCAR Technical Note NCAR/TN-478+STR, p. 257, ISSN Electronic Edition 2153-2400, 2010. a
O'Neill, M. M. F., Tijerina, D. T., Condon, L. E., and Maxwell, R. M.: Assessment of the ParFlow–CLM CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States, Geosci. Model Dev., 14, 7223–7254, https://doi.org/10.5194/gmd-14-7223-2021, 2021. a
Peirce, J. J., Weiner, R. F., and Vesilind, P. A.: Environmental Pollution and Control, Butterworth-Heinemann, 4th Edn., https://doi.org/10.1016/B978-0-7506-9899-3.X5000-7, 1998. a
Peltier, W., Argus, D., and Drummond, R.: Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model, J. Geophys. Res.-Sol. Ea., 120, 450–487, https://doi.org/10.1002/2014JB011176, 2015 (data available at: https://www.atmosp.physics.utoronto.ca/~peltier/data.php, last access: 4 March 2025). a, b, c, d, e
Pokhrel, Y. N., Hanasaki, N., Yeh, P. J., Yamada, T. J., Kanae, S., and Oki, T.: Model estimates of sea-level change due to anthropogenic impacts on terrestrial water storage, Nat. Geosci., 5, 389–392, https://doi.org/10.1038/ngeo1476, 2012. a
Reinecke, R., Foglia, L., Mehl, S., Herman, J. D., Wachholz, A., Trautmann, T., and Döll, P.: Spatially distributed sensitivity of simulated global groundwater heads and flows to hydraulic conductivity, groundwater recharge, and surface water body parameterization, Hydrol. Earth Syst. Sci., 23, 4561–4582, https://doi.org/10.5194/hess-23-4561-2019, 2019a. a, b
Reinecke, R., Foglia, L., Mehl, S., Trautmann, T., Cáceres, D., and Döll, P.: Challenges in developing a global gradient-based groundwater model (G3M v1.0) for the integration into a global hydrological model, Geosci. Model Dev., 12, 2401–2418, https://doi.org/10.5194/gmd-12-2401-2019, 2019b. a, b, c, d, e
Ringeval, B., De Noblet-Ducoudré, N., Ciais, P., Bousquet, P., Prigent, C., Papa, F., and Rossow, W. B.: An attempt to quantify the impact of changes in wetland extent on methane emissions on the seasonal and interannual time scales, Global Biogeochem. Cy., 24, 1–12, https://doi.org/10.1029/2008GB003354, 2010. a
Sousa, M. R., Jones, J. P., Frind, E. O., and Rudolph, D. L.: A simple method to assess unsaturated zone time lag in the travel time from ground surface to receptor, J. Contam. Hydrol., 144, 138–151, https://doi.org/10.1016/j.jconhyd.2012.10.007, 2013. a
Sun, J., Wang, L., Peng, Z., Fu, Z., and Chen, C.: The Sea Level Fingerprints of Global Terrestrial Water Storage Changes Detected by GRACE and GRACE-FO Data, Pure Appl. Geophys., 179, 3493–3509, https://doi.org/10.1007/s00024-022-03123-8, 2022. a
Syed, T. H., Famiglietti, J. S., Rodell, M., Chen, J., and Wilson, C. R.: Analysis of terrestrial water storage changes from GRACE and GLDAS, Water Resour. Res., 44, W02433, https://doi.org/10.1029/2006WR005779, 2008. a
Tarboton, D.: Great Salt Lake Bathymetry, HydroShare, http://www.hydroshare.org/resource/582060f00f6b443bb26e896426d9f62a (last access: 28 February 2025), 2017. a
Valiantzas, J. D.: Simplified versions for the Penman evaporation equation using routine weather data, J. Hydrol., 331, 690–702, https://doi.org/10.1016/j.jhydrol.2006.06.012, 2006. a
Verpoorter, C., Kutser, T., Seekell, D. A., and Tranvik, L. J.: A global inventory of lakes based on high-resolution satellite imagery, Geophys. Res. Lett., 41, 6396–6402, https://doi.org/10.1002/2014GL060641, 2014. a, b
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, J. K., Mayorov, N., Nelson, A. R., Jones, E., Kern, R., Larson, E., Carey, C., Polat, L., Feng, Y., Moore, E. W., Van der Plas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nature Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Vörösmarty, C. J., Federer, C. A., and Schloss, A. L.: Potential evaporation functions compared on US watersheds: Possible implications for global-scale water balance and terrestrial ecosystem modeling, J. Hydrol., 207, 147–169, https://doi.org/10.1016/S0022-1694(98)00109-7, 1998. a
Wada, Y.: Modeling Groundwater Depletion at Regional and Global Scales: Present State and Future Prospects, Surv. Geophys., 37, 419–451, https://doi.org/10.1007/s10712-015-9347-x, 2016. a
Wada, Y., Van Beek, L. P., Sperna Weiland, F. C., Chao, B. F., Wu, Y. H., and Bierkens, M. F.: Past and future contribution of global groundwater depletion to sea-level rise, Geophys. Res. Lett., 39, 1–6, https://doi.org/10.1029/2012GL051230, 2012. a
Wickert, A. D.: Potential open water evaporation from TerraClimate, Zenodo, https://doi.org/10.5281/zenodo.4391500, 2020. a, b
Wickert, A. D., Mitrovica, J. X., Williams, C., and Anderson, R. S.: Gradual demise of a thin southern Laurentide ice sheet recorded by Mississippi drainage, Nature, 502, 668–671, https://doi.org/10.1038/nature12609, 2013. a
Wiltshire, A. J., Duran Rojas, M. C., Edwards, J. M., Gedney, N., Harper, A. B., Hartley, A. J., Hendry, M. A., Robertson, E., and Smout-Day, K.: JULES-GL7: the Global Land configuration of the Joint UK Land Environment Simulator version 7.0 and 7.2, Geosci. Model Dev., 13, 483–505, https://doi.org/10.5194/gmd-13-483-2020, 2020. a
Yokohata, T., Kinoshita, T., Sakurai, G., Pokhrel, Y., Ito, A., Okada, M., Satoh, Y., Kato, E., Nitta, T., Fujimori, S., Felfelani, F., Masaki, Y., Iizumi, T., Nishimori, M., Hanasaki, N., Takahashi, K., Yamagata, Y., and Emori, S.: MIROC-INTEG-LAND version 1: a global biogeochemical land surface model with human water management, crop growth, and land-use change, Geosci. Model Dev., 13, 4713–4747, https://doi.org/10.5194/gmd-13-4713-2020, 2020. a
Zeng, X., Shajkh, M., Dai, Y., Dickinson, R. E., and Myneni, R.: Coupling of the Common Land Model to the NCAR Community Climate Model, J. Climate, 15, 1832–1854, https://doi.org/10.1175/1520-0442(2002)015<1832:COTCLM>2.0.CO;2, 2002. a
Zhang, X., Liu, L., Zhao, T., Chen, X., Lin, S., Wang, J., Mi, J., and Liu, W.: GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020, Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, 2023a. a, b, c, d, e
Zhang, Z., Poulter, B., Feldman, A. F., Ying, Q., Ciais, P., Peng, S., and Li, X.: Recent intensification of wetland methane feedback, Nat. Clim. Change, 13, 430–433, https://doi.org/10.1038/s41558-023-01629-0, 2023b. a
Zhu, P. and Gong, P.: Suitability mapping of global wetland areas and validation with remotely sensed data, Science China Earth Sciences, 57, 2283–2292, https://doi.org/10.1007/s11430-014-4925-1, 2014. a
Zotarelli, L. and Dukes, M.: Step by step calculation of the Penman-Monteith Evapotranspiration (FAO-56 Method), Institute of Food and Agricultural Sciences, 1–10, https://doi.org/10.32473/edis-ae459-2010, 2010. a, b, c
Short summary
We present the Water Table Model (WTM), a new model for simulating groundwater and lake levels at continental scales over millennia. The WTM enables long-term evaluations of water-table changes. As a proof of concept, we simulate the North American water table for the present and the Last Glacial Maximum (LGM), showing that North America held more groundwater and lake water during the LGM than it does today – enough to lower sea levels by 14.98 cm. The open-source code is available on GitHub.
We present the Water Table Model (WTM), a new model for simulating groundwater and lake levels...