Articles | Volume 28, issue 5
https://doi.org/10.5194/hess-28-1215-2024
© Author(s) 2024. 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-28-1215-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Mar 2024
Research article |
| 14 Mar 2024
| 14 Mar 2024
Disentangling coastal groundwater level dynamics in a global dataset
Annika Nolte
CORRESPONDING AUTHOR
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany
Ezra Haaf
Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden
Benedikt Heudorfer
Division of Hydrogeology, Institute of Applied Geosciences, Karlsruhe Institute of Technology, Karlsruhe, Germany
Steffen Bender
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany
Jens Hartmann
Institute for Geology, Center for Earth System Research and Sustainability, Universität Hamburg, Hamburg, Germany
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Jens S. Hammes, Jens Hartmann, Johannes A. C. Barth, Tobias Linke, Ingrid Smet, Mathilde Hagens, Philip A. E. Pogge von Strandmann, Tom Reershemius, Bruno Casimiro, Arthur Vienne, Anna A. Stoeckel, Ralf Steffens, and Dirk Paessler
EGUsphere, https://doi.org/10.5194/egusphere-2025-5402, https://doi.org/10.5194/egusphere-2025-5402, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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To test enhanced weathering's efficacy, we ran a two-year greenhouse study under warm, wet conditions, comparing several rock additives across farm soils. We tracked alkalinity and cation soil pools. Soil type was decisive: acidic, low-buffer soils exported more additional alkalinity, while alkaline or pH neutral soils retained it in cation pools. The results point to where enhanced weathering can deliver durable carbon removal and underscore the need for long, well-controlled trials.
Marc Ohmer, Tanja Liesch, Bastian Habbel, Benedikt Heudorfer, Mariana Gomez, Patrick Clos, Maximilian Nölscher, and Stefan Broda
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-321, https://doi.org/10.5194/essd-2025-321, 2025
Revised manuscript accepted for ESSD
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We present a public dataset of weekly groundwater levels from more than 3,000 wells across Germany, spanning 32 years. It combines weather data and site-specific environmental information to support forecasting groundwater changes. Three benchmark models of varying complexity show how data and modeling approaches influence predictions. This resource promotes open, reproducible research and helps guide future water management decisions.
Allanah Joy Paul, Mathias Haunost, Silvan Urs Goldenberg, Jens Hartmann, Nicolás Sánchez, Julieta Schneider, Niels Suitner, and Ulf Riebesell
Biogeosciences, 22, 2749–2766, https://doi.org/10.5194/bg-22-2749-2025, https://doi.org/10.5194/bg-22-2749-2025, 2025
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Ocean alkalinity enhancement (OAE) is being assessed for its potential to absorb atmospheric CO2 and store it for a long time. OAE still needs comprehensive assessment of its safety and effectiveness. We studied an idealised OAE application in a natural low-nutrient ecosystem over 1 month. Our results showed that biogeochemical functioning remained mostly stable but that the long-term capability for storing carbon may be limited at high alkalinity concentration.
Arthur Vienne, Patrick Frings, Jet Rijnders, Tim Jesper Suhrhoff, Tom Reershemius, Reinaldy P. Poetra, Jens Hartmann, Harun Niron, Miguel Portillo Estrada, Laura Steinwidder, Lucilla Boito, and Sara Vicca
EGUsphere, https://doi.org/10.5194/egusphere-2025-1667, https://doi.org/10.5194/egusphere-2025-1667, 2025
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Our study explores Enhanced Weathering (EW) using basalt rock dust to combat climate change. We treated corn-planted mesocosms with varying basalt amounts and monitored them for 101 days. Surprisingly, we found no significant inorganic carbon dioxide removal (CDR). However, rock weathering was evident through increased exchangeable bases. While immediate inorganic CDR benefits were not observed, basalt amendment may enhance soil health and potentially long-term carbon sequestration.
Niels Suitner, Jens Hartmann, Selene Varliero, Giulia Faucher, Philipp Suessle, and Charly A. Moras
EGUsphere, https://doi.org/10.5194/egusphere-2025-381, https://doi.org/10.5194/egusphere-2025-381, 2025
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Alkalinity leakage limits the efficiency of ocean alkalinity enhancement. Drivers of this process remain unquantified, restricting accurate assessments. The induced runaway process can be modeled using surface area and aragonite oversaturation as key factors. This study proposes a framework for improving predictability of alkalinity loss due to runaway precipitation, emphasizing the need for field experiments to validate theoretical models concerning dilution and particle sinking processes.
Raoul A. Collenteur, Ezra Haaf, Mark Bakker, Tanja Liesch, Andreas Wunsch, Jenny Soonthornrangsan, Jeremy White, Nick Martin, Rui Hugman, Ed de Sousa, Didier Vanden Berghe, Xinyang Fan, Tim J. Peterson, Jānis Bikše, Antoine Di Ciacca, Xinyue Wang, Yang Zheng, Maximilian Nölscher, Julian Koch, Raphael Schneider, Nikolas Benavides Höglund, Sivarama Krishna Reddy Chidepudi, Abel Henriot, Nicolas Massei, Abderrahim Jardani, Max Gustav Rudolph, Amir Rouhani, J. Jaime Gómez-Hernández, Seifeddine Jomaa, Anna Pölz, Tim Franken, Morteza Behbooei, Jimmy Lin, and Rojin Meysami
Hydrol. Earth Syst. Sci., 28, 5193–5208, https://doi.org/10.5194/hess-28-5193-2024, https://doi.org/10.5194/hess-28-5193-2024, 2024
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We show the results of the 2022 Groundwater Time Series Modelling Challenge; 15 teams applied data-driven models to simulate hydraulic heads, and three model groups were identified: lumped, machine learning, and deep learning. For all wells, reasonable performance was obtained by at least one team from each group. There was not one team that performed best for all wells. In conclusion, the challenge was a successful initiative to compare different models and learn from each other.
Niels Suitner, Giulia Faucher, Carl Lim, Julieta Schneider, Charly A. Moras, Ulf Riebesell, and Jens Hartmann
Biogeosciences, 21, 4587–4604, https://doi.org/10.5194/bg-21-4587-2024, https://doi.org/10.5194/bg-21-4587-2024, 2024
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Recent studies described the precipitation of carbonates as a result of alkalinity enhancement in seawater, which could adversely affect the carbon sequestration potential of ocean alkalinity enhancement (OAE) approaches. By conducting experiments in natural seawater, this study observed uniform patterns during the triggered runaway carbonate precipitation, which allow the prediction of safe and efficient local application levels of OAE scenarios.
Benedikt Heudorfer, Tanja Liesch, and Stefan Broda
Hydrol. Earth Syst. Sci., 28, 525–543, https://doi.org/10.5194/hess-28-525-2024, https://doi.org/10.5194/hess-28-525-2024, 2024
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We build a neural network to predict groundwater levels from monitoring wells. We predict all wells at the same time, by learning the differences between wells with static features, making it an entity-aware global model. This works, but we also test different static features and find that the model does not use them to learn exactly how the wells are different, but only to uniquely identify them. As this model class is not actually entity aware, we suggest further steps to make it so.
Nele Lehmann, Hugues Lantuit, Michael Ernst Böttcher, Jens Hartmann, Antje Eulenburg, and Helmuth Thomas
Biogeosciences, 20, 3459–3479, https://doi.org/10.5194/bg-20-3459-2023, https://doi.org/10.5194/bg-20-3459-2023, 2023
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Riverine alkalinity in the silicate-dominated headwater catchment at subarctic Iskorasfjellet, northern Norway, was almost entirely derived from weathering of minor carbonate occurrences in the riparian zone. The uphill catchment appeared limited by insufficient contact time of weathering agents and weatherable material. Further, alkalinity increased with decreasing permafrost extent. Thus, with climate change, alkalinity generation is expected to increase in this permafrost-degrading landscape.
Mingyang Tian, Jens Hartmann, Gibran Romero-Mujalli, Thorben Amann, Lishan Ran, and Ji-Hyung Park
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-131, https://doi.org/10.5194/bg-2023-131, 2023
Manuscript not accepted for further review
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Effective water quality management in the Elbe River from 1984 to 2018 significantly reduced CO2 emissions, particularly after Germany's reunification. Key factors in the reduction include organic carbon removal and nutrient management, with nitrogen control being more critical than phosphorus for the restoration of ecosystem capacity. Unpredictable influxes of organic carbon and the relocation of emissions from wastewater treatment can cause uncertainties for CO2 removals.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
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In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Jens Hartmann, Niels Suitner, Carl Lim, Julieta Schneider, Laura Marín-Samper, Javier Arístegui, Phil Renforth, Jan Taucher, and Ulf Riebesell
Biogeosciences, 20, 781–802, https://doi.org/10.5194/bg-20-781-2023, https://doi.org/10.5194/bg-20-781-2023, 2023
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CO2 can be stored in the ocean via increasing alkalinity of ocean water. Alkalinity can be created via dissolution of alkaline materials, like limestone or soda. Presented research studies boundaries for increasing alkalinity in seawater. The best way to increase alkalinity was found using an equilibrated solution, for example as produced from reactors. Adding particles for dissolution into seawater on the other hand produces the risk of losing alkalinity and degassing of CO2 to the atmosphere.
Shuang Gao, Jörg Schwinger, Jerry Tjiputra, Ingo Bethke, Jens Hartmann, Emilio Mayorga, and Christoph Heinze
Biogeosciences, 20, 93–119, https://doi.org/10.5194/bg-20-93-2023, https://doi.org/10.5194/bg-20-93-2023, 2023
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We assess the impact of riverine nutrients and carbon (C) on projected marine primary production (PP) and C uptake using a fully coupled Earth system model. Riverine inputs alleviate nutrient limitation and thus lessen the projected PP decline by up to 0.7 Pg C yr−1 globally. The effect of increased riverine C may be larger than the effect of nutrient inputs in the future on the projected ocean C uptake, while in the historical period increased nutrient inputs are considered the largest driver.
Doris E. Wendt, John P. Bloomfield, Anne F. Van Loon, Margaret Garcia, Benedikt Heudorfer, Joshua Larsen, and David M. Hannah
Nat. Hazards Earth Syst. Sci., 21, 3113–3139, https://doi.org/10.5194/nhess-21-3113-2021, https://doi.org/10.5194/nhess-21-3113-2021, 2021
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Managing water demand and supply during droughts is complex, as highly pressured human–water systems can overuse water sources to maintain water supply. We evaluated the impact of drought policies on water resources using a socio-hydrological model. For a range of hydrogeological conditions, we found that integrated drought policies reduce baseflow and groundwater droughts most if extra surface water is imported, reducing the pressure on water resources during droughts.
Cited articles
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017.
Alfarrah, N. and Walraevens, K.: Groundwater overexploitation and seawater intrusion in coastal areas of arid and semi-arid regions, Water, 10, 143, https://doi.org/10.3390/w10020143, 2018.
AQUASTAT: Percentage of irrigated area serviced by groundwater (Global), FAO-UN Land and Water Division [data set], https://data.apps.fao.org/catalog/dataset/d49db282-7c50-46c2-b210-3e197d767da3 (last access: 5 January 2024), 2021.
Ascott, M. J., Macdonald, D. M. J., Black, E., Verhoef, A., Nakohoun, P., Tirogo, J., Sandwidi, W. J. P., Bliefernicht, J., Sorensen, J. P. R., and Bossa, A. Y.: In Situ Observations and Lumped Parameter Model Reconstructions Reveal Intra-Annual to Multidecadal Variability in Groundwater Levels in Sub-Saharan Africa, Water Resour. Res., 56, D05109, https://doi.org/10.1029/2020WR028056, 2020.
Barbarossa, V., Huijbregts, M. A. J., Beusen, A. H. W., Beck, H. E., King, H., and Schipper, A. M.: FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015, figshare [data set], https://doi.org/10.6084/m9.figshare.c.3890224.v1, 2018.
Barthel, R., Haaf, E., Giese, M., Nygren, M., Heudorfer, B., and Stahl, K.: Similarity-based approaches in hydrogeology: proposal of a new concept for data-scarce groundwater resource characterization and prediction, Hydrogeol. J., 24, 3392, https://doi.org/10.1007/s10040-021-02358-4, 2021.
Barthel, R., Haaf, E., Nygren, M., and Giese, M.: Systematic visual analysis of groundwater hydrographs: potential benefits and challenges, Hydrogeol. J., 30, 359–378, https://doi.org/10.1007/s10040-021-02433-w, 2022.
Baulon, L., Allier, D., Massei, N., Bessiere, H., Fournier, M., and Bault, V.: Influence of low-frequency variability on groundwater level trends, J. Hydrol., 606, 127436, https://doi.org/10.1016/j.jhydrol.2022.127436, 2022a.
Baulon, L., Massei, N., Allier, D., Fournier, M., and Bessiere, H.: Influence of low-frequency variability on high and low groundwater levels: example of aquifers in the Paris Basin, Hydrol. Earth Syst. Sci., 26, 2829–2854, https://doi.org/10.5194/hess-26-2829-2022, 2022b.
Bear, J.: Hydraulics of Groundwater, Dover Publications Inc., New York, ISBN 0-486-453355-3, 2007.
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Miralles, D. G., McVicar, T. R., Schellekens, J., and Bruijnzeel, L. A.: Global-scale regionalization of hydrologic model parameters, Water Resour. Res., 52, 3599–3622, https://doi.org/10.1002/2015WR018247, 2016.
Berendrecht, W., van Vliet, M., and Griffioen, J.: Combining statistical methods for detecting potential outliers in groundwater quality time series, Environ. Monit. Assess., 195, 85, https://doi.org/10.1007/s10661-022-10661-0, 2022.
Bergen, K. J., Johnson, P. A., Hoop, M. V. de, and Beroza, G. C.: Machine learning for data-driven discovery in solid Earth geoscience, Science, 363, 6433, https://doi.org/10.1126/science.aau0323, 2019.
Blumstock, M., Tetzlaff, D., Dick, J. J., Nuetzmann, G., and Soulsby, C.: Spatial organization of groundwater dynamics and streamflow response from different hydropedological units in a montane catchment, Hydrol. Process., 30, 3735–3753, https://doi.org/10.1002/hyp.10848, 2016.
Börker, J., Hartmann, J., Amann, T., and Romero-Mujalli, G.: Global Unconsolidated Sediments Map Database v1.0 (shapefile and gridded to 0.5° spatial resolution), PANGEA [data set], https://doi.org/10.1594/PANGAEA.884822, 2018.
Bosserelle, A. L., Morgan, L. K., Dempsey, D. E., and Setiawan, I.: Shallow groundwater characterisation and hydrograph classification in the coastal city of Ōtautahi/Christchurch, New Zealand, Hydrogeol. J., 1–24, https://doi.org/10.1007/s10040-023-02745-z, 2023.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Caliński, T. and Harabasz, J.: A dendrite method for cluster analysis, Commun. Stat.-Theor. M., 3, 1–27, 1974.
Campello, R. J., Kröger, P., Sander, J., and Zimek, A.: Density-based clustering, Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 10, e1343, https://doi.org/10.1002/widm.1343, 2020.
CIESIN: Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11, NASA Socioeconomic Data and Applications Center (SEDAC) [data set], https://doi.org/10.7927/H49C6VHW, 2018.
Costall, A. R., Harris, B. D., Teo, B., Schaa, R., Wagner, F. M., and Pigois, J. P.: Groundwater Throughflow and Seawater Intrusion in High Quality Coastal Aquifers, Sci. Rep.-UK, 10, 9866, https://doi.org/10.1038/s41598-020-66516-6, 2020.
Davies, D. L. and Bouldin, D. W.: A cluster separation measure, IEEE T. Pattern Anal., 224–227, https://doi.org/10.1109/TPAMI.1979.4766909, 1979.
Díaz-Alcaide, S. and Martínez-Santos, P.: Review: Advances in groundwater potential mapping, Hydrogeol. J., 27, 2307–2324, https://doi.org/10.1007/s10040-019-02001-3, 2019.
Donnelly, C., Ernst, K., and Arheimer, B.: A comparison of hydrological climate services at different scales by users and scientists, Clim. Serv., 11, 24–35, https://doi.org/10.1016/j.cliser.2018.06.002, 2018.
Elshall, A. S., Arik, A. D., El-Kadi, A. I., Pierce, S., Ye, M., Burnett, K. M., Wada, C. A., Bremer, L. L., and Chun, G.: Groundwater sustainability: a review of the interactions between science and policy, Environ. Res. Lett., 15, 93004, https://doi.org/10.1088/1748-9326/ab8e8c, 2020.
Esri: ArcGIS Pro, version 3.0.3, Esri, https://www.esri.com (last access: 6 March 2024), 2022.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, in: Proc. Second Int. Conf. Knowl. Discov. Data Mining (KDD-96), 226–231, https://cdn.aaai.org/KDD/1996/KDD96-037.pdf (last access: 6 March 2024), 1996.
Famiglietti, J. S.: The global groundwater crisis, Nat. Clim. Change, 4, 945–948, https://doi.org/10.1038/nclimate2425, 2014.
Fan, Y., Li, H., and Miguez-Macho, G.: Global patterns of groundwater table depth, Science, 339, 940–943, https://doi.org/10.1126/science.1229881, 2013.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L.: The shuttle radar topography mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Ferguson, G. and Gleeson, T.: Vulnerability of coastal aquifers to groundwater use and climate change, Nat. Clim. Change, 2, 342–345, https://doi.org/10.1038/nclimate1413, 2012.
Giese, M., Haaf, E., Heudorfer, B., and Barthel, R.: Comparative hydrogeology – reference analysis of groundwater dynamics from neighbouring observation wells, Hydrolog. Sci. J., 65, 1685–1706, https://doi.org/10.1080/02626667.2020.1762888, 2020.
Gleeson, T., Wagener, T., Döll, P., Zipper, S. C., West, C., Wada, Y., Taylor, R., Scanlon, B., Rosolem, R., Rahman, S., Oshinlaja, N., Maxwell, R., Lo, M.-H., Kim, H., Hill, M., Hartmann, A., Fogg, G., Famiglietti, J. S., Ducharne, A., de Graaf, I., Cuthbert, M., Condon, L., Bresciani, E., and Bierkens, M. F. P.: GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models, Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, 2021.
Güler, C., Kurt, M. A., Alpaslan, M., and Akbulut, C.: Assessment of the impact of anthropogenic activities on the groundwater hydrology and chemistry in Tarsus coastal plain (Mersin, SE Turkey) using fuzzy clustering, multivariate statistics and GIS techniques, J. Hydrol., 414–415, 435–451, https://doi.org/10.1016/j.jhydrol.2011.11.021, 2012.
Guppy, L., Uyttendaele, P., Villholth, K. G., and Smakhtin, V.: Groundwater and Sustainable Development Goals: Analysis of Interlinkages, UNU-INWEH Report Series, Issue 04, United Nations University, 26 pp., https://doi.org/10.53328/JRLH1810, 2018.
Haaf, E. and Heudorfer, B.: Groundwater dynamics indices (0.1), Zenodo [data set], https://doi.org/10.5281/zenodo.1486058, 2018.
Haaf, E., Giese, M., Heudorfer, B., Stahl, K., and Barthel, R.: Physiographic and Climatic Controls on Regional Groundwater Dynamics, Water Resour. Res., 56, e2019WR026545, https://doi.org/10.1029/2019WR026545, 2020.
Haaf, E., Giese, M., Reimann, T., and Barthel, R.: Data-driven Estimation of Groundwater Level Time-Series at Unmonitored Sites Using Comparative Regional Analysis, Water Resour. Res., 59, e2022WR033470, https://doi.org/10.1029/2022WR033470, 2023.
Haehnel, P., Rasmussen, T. C., and Rau, G. C.: Technical note: Removing dynamic sea-level influences from groundwater-level measurements, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2023-54, in review, 2023.
Hartmann, J. and Moosdorf, N.: Global Lithological Map Database v1.0 (gridded to 0.5° spatial resolution), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.788537, 2012.
Heudorfer, B., Haaf, E., Stahl, K., and Barthel, R.: Index-Based Characterization and Quantification of Groundwater Dynamics, Water Resour. Res., 55, 5575–5592, https://doi.org/10.1029/2018WR024418, 2019.
Huggins, X., Gleeson, T., Serrano, D., Zipper, S., Jehn, F., Rohde, M. M., Abell, R., Vigerstol, K., and Hartmann, A.: Overlooked risks and opportunities in groundwatersheds of the world's protected areas, Nat. Sustain., 6, 855–864, https://doi.org/10.1038/s41893-023-01086-9, 2023.
Huscroft, J., Gleeson, T., Hartmann, J., and Börker, J.: Compiling and mapping global permeability of the unconsolidated and consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0). [Supporting Data], Borealis [data set], https://doi.org/10.5683/SP2/TTJNIU, 2018.
Johnson, T. D. and Belitz, K.: Assigning land use to supply wells for the statistical characterization of regional groundwater quality: correlating urban land use and VOC occurrence, J. Hydrol., 370, 100–108, https://doi.org/10.1016/j.jhydrol.2009.02.056, 2009.
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., and Kessler, M.: Climatologies at high resolution for the earth's land surface areas, Sci. Data, 4, 1–20, https://doi.org/10.1038/sdata.2017.122, 2017.
Klingler, C., Schulz, K., and Herrnegger, M.: LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, Earth Syst. Sci. Data, 13, 4529–4565, https://doi.org/10.5194/essd-13-4529-2021, 2021.
Knoll, L., Breuer, L., and Bach, M.: Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning, Sci. Total Environ., 668, 1317–1327, https://doi.org/10.1016/j.scitotenv.2019.03.045, 2019.
Knowling, M. J., Werner, A. D., and Herckenrath, D.: Quantifying climate and pumping contributions to aquifer depletion using a highly parameterised groundwater model: Uley South Basin (South Australia), J. Hydrol., 523, 515–530, https://doi.org/10.1016/j.jhydrol.2015.01.081, 2015.
Kulp, S. A. and Strauss, B. H.: CoastalDEM: A global coastal digital elevation model improved from SRTM using a neural network, Remote Sens. Environ., 206, 231–239, https://doi.org/10.1016/j.rse.2017.12.026, 2018.
Lee, S., Lee, K.-K., and Yoon, H.: Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors, Hydrogeol. J., 27, 567–579, https://doi.org/10.1007/s10040-018-1866-3, 2019.
Lehner, B. and Grill, G.: Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems, Hydrol. Process., 27, 2171–2186, https://doi.org/10.1002/hyp.9740, 2013.
Lehr, C. and Lischeid, G.: Efficient screening of groundwater head monitoring data for anthropogenic effects and measurement errors, Hydrol. Earth Syst. Sci., 24, 501–513, https://doi.org/10.5194/hess-24-501-2020, 2020.
LfU-SH: Hydrogeologische Räume und Teilräume bezogen auf die oberflächennahen Wasserleiter, Landesamt für Umwelt des Landes Schleswig-Holstein, https://umweltportal.schleswig-holstein.de/trefferanzeige?docuuid=d7a7934c-d125-4fc4-93cc-08f34a2a3d7c (last access: 30 March 2023), 2003.
Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand, M., Beames, P., Burchard-Levine, V., Maxwell, S., Moidu, H., Tan, F., and Thieme, M.: Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution, Sci. Data, 6, 283, https://doi.org/10.1038/s41597-019-0300-6, 2019.
Lischeid, G., Dannowski, R., Kaiser, K., Nützmann, G., Steidl, J., and Stüve, P.: Inconsistent hydrological trends do not necessarily imply spatially heterogeneous drivers, J. Hydrol., 596, 126096, https://doi.org/10.1016/j.jhydrol.2021.126096, 2021.
Liu, Q., Gui, D., Zhang, L., Niu, J., Dai, H., Wei, G., and Hu, B. X.: Simulation of regional groundwater levels in arid regions using interpretable machine learning models, Sci. Total Environ., 831, 154902, https://doi.org/10.1016/j.scitotenv.2022.154902, 2022.
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I.: From Local Explanations to Global Understanding with Explainable AI for Trees, Nat. Mach. Intell., 2, 56–67, https://doi.org/10.1038/s42256-019-0138-9, 2020.
Mangor, K., Drønen, N. K., Kærgaard, K. H., and Kristensen, S. E.: Shoreline Management Guidelines, 4th ed., 462 pp., ISBN 0-486-453355-3, 2017.
Martens, K., van Camp, M., van Damme, D., and Walraevens, K.: Groundwater dynamics converted to a groundwater classification as a tool for nature development programs in the dunes, J. Hydrol., 499, 236–246, https://doi.org/10.1016/j.jhydrol.2013.06.045, 2013.
Martínez, M. L., Intralawan, A., Vázquez, G., Pérez-Maqueo, O., Sutton, P., and Landgrave, R.: The coasts of our world: Ecological, economic and social importance, Ecol. Econ., 63, 254–272, https://doi.org/10.1016/j.ecolecon.2006.10.022, 2007.
McMillan, H.: Linking hydrologic signatures to hydrologic processes: A review, Hydrol. Process., 34, 1393–1409, https://doi.org/10.1002/hyp.13632, 2020.
Mishra, N., Khare, D., Gupta, K. K., and Shukla, R.: Impact of land use change on groundwater – a review, Adv. Water Resour. Prot., 2, 28–41, 2014.
Moeck, C., Grech-Cumbo, N., Podgorski, J., Bretzler, A., Gurdak, J. J., Berg, M., and Schirmer, M.: A global-scale dataset of direct natural groundwater recharge rates: A review of variables, processes and relationships, Sci. Total Environ., 717, 137042, https://doi.org/10.1016/j.scitotenv.2020.137042, 2020.
Moosdorf, N. and Oehler, T.: Societal use of fresh submarine groundwater discharge: An overlooked water resource, Earth-Sci. Rev., 171, 338–348, https://doi.org/10.1016/j.earscirev.2017.06.006, 2017.
Müller Schmied, H., Cáceres, D., Eisner, S., Flörke, M., Herbert, C., Niemann, C., Peiris, T. A., Popat, E., Portmann, F. T., Reinecke, R., Shadkam, S., Trautmann, T., and Döll, P.: The global water resources and use model WaterGAP v2.2d – Standard model output, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.918447, 2020.
Narvaez-Montoya, C., Mahlknecht, J., Torres-Martínez, J. A., Mora, A., and Bertrand, G.: Seawater intrusion pattern recognition supported by unsupervised learning: A systematic review and application, Sci. Total Environ., 864, 160933, https://doi.org/10.1016/j.scitotenv.2022.160933, 2023.
Nimmo, J. R., Perkins, K. S., Plampin, M. R., Walvoord, M. A., Ebel, B. A., and Mirus, B. B.: Rapid-Response Unsaturated Zone Hydrology: Small-Scale Data, Small-Scale Theory, Big Problems, Front. Earth Sci., 9, e12552, https://doi.org/10.3389/feart.2021.613564, 2021.
Nölscher, M., Mutz, M., and Broda, S.: Multiorder hydrologic Position for Europe - a Set of Features for Machine Learning and analysis in Hydrology, Sci. Data, 9, 662, https://doi.org/10.1038/s41597-022-01787-4, 2022.
Nolte, A.: Disentangling coastal groundwater level dynamics in a global dataset – data, Zenodo [data set], https://doi.org/10.5281/zenodo.8173404, 2023.
Olden, J. D., Kennard, M. J., and Pusey, B. J.: A framework for hydrologic classification with a review of methodologies and applications in ecohydrology, Ecohydrology, 5, 503–518, https://doi.org/10.1002/eco.251, 2012.
Otto, R.: Estimating groundwater recharge rates in the southeastern Holstein region, northern Germany, Hydrogeol. J., 9, 498–511, https://doi.org/10.1007/s100400100155, 2001.
Oude Essink, G. H. P., van Baaren, E. S., and de Louw, P. G. B.: Effects of climate change on coastal groundwater systems: A modeling study in the Netherlands, Water Resour. Res., 46, W00F04, https://doi.org/10.1029/2009WR008719, 2010.
Papacharalampous, G. and Tyralis, H.: Time Series Features for Supporting Hydrometeorological Explorations and Predictions in Ungauged Locations Using Large Datasets, Water, 14, 1657, https://doi.org/10.3390/w14101657, 2022.
Papacharalampous, G., Tyralis, H., Markonis, Y., and Hanel, M.: Hydroclimatic time series features at multiple time scales, J. Hydrol., 618, 129160, https://doi.org/10.1016/j.jhydrol.2023.129160, 2023.
Parisi, A., Alfio, M. R., Balacco, G., Güler, C., and Fidelibus, M. D.: Analyzing spatial and temporal evolution of groundwater salinization through Multivariate Statistical Analysis and Hydrogeochemical Facies Evolution-Diagram, Sci. Total Environ., 862, 160697, https://doi.org/10.1016/j.scitotenv.2022.160697, 2023.
Peng, J., Loew, A., Merlin, O., and Verhoest, N. E. C.: A review of spatial downscaling of satellite remotely sensed soil moisture, Rev. Geophys., 55, 341–366, https://doi.org/10.1002/2016RG000543, 2017.
Peters, C. N., Kimsal, C., Frederiks, R. S., Paldor, A., McQuiggan, R., and Michael, H. A.: Groundwater pumping causes salinization of coastal streams due to baseflow depletion: Analytical framework and application to Savannah River, GA, J. Hydrol., 604, 127238, https://doi.org/10.1016/j.jhydrol.2021.127238, 2022.
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021.
Post, V., Kooi, H., and Simmons, C.: Using hydraulic head measurements in variable-density ground water flow analyses, Groundwater, 45, 664–671, https://doi.org/10.1111/j.1745-6584.2007.00339.x, 2007.
Probst, P., Wright, M. N., and Boulesteix, A.-L.: Hyperparameters and tuning strategies for random forest, WIREs Data Min. Knowl. Discov., 9, e1301, https://doi.org/10.1002/widm.1301, 2019.
Python Software Foundation: Python language reference, version 3.7.11, https://www.python.org (last access: 6 March 2024), 2021.
R Core Team: R: A language and environment for statistical computing, https://www.R-project.org/ (last access: 6 March 2024), 2021.
Rajaee, T., Ebrahimi, H., and Nourani, V.: A review of the artificial intelligence methods in groundwater level modeling, J. Hydrol., 572, 336–351, https://doi.org/10.1016/j.jhydrol.2018.12.037, 2019.
Rau, G. C., Cuthbert, M. O., Post, V. E. A., Schweizer, D., Acworth, R. I., Andersen, M. S., Blum, P., Carrara, E., Rasmussen, T. C., and Ge, S.: Future-proofing hydrogeology by revising groundwater monitoring practice, Hydrogeol. J., 28, 2963–2969, https://doi.org/10.1007/s10040-020-02242-7, 2020.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.
Retike, I., Bikše, J., Kalvāns, A., Dēliòa, A., Avotniece, Z., Zaadnoordijk, W. J., Jemeljanova, M., Popovs, K., Babre, A., and Zelenkevičs, A.: Rescue of groundwater level time series: How to visually identify and treat errors, J. Hydrol., 605, 127294, https://doi.org/10.1016/j.jhydrol.2021.127294, 2022.
Riedel, T. and Weber, T. K. D.: Review: The influence of global change on Europe's water cycle and groundwater recharge, Hydrogeol. J., 28, 1939–1959, https://doi.org/10.1007/s10040-020-02165-3, 2020.
Rinderer, M., Meerveld, H. J., and McGlynn, B. L.: From Points to Patterns: Using Groundwater Time Series Clustering to Investigate Subsurface Hydrological Connectivity and Runoff Source Area Dynamics, Water Resour. Res., 55, 5784–5806, https://doi.org/10.1029/2018WR023886, 2019.
Rodriguez, E., Morris, C. S., and Belz, J. E.: A global assessment of the SRTM performance, Photogramm. Eng. Rem. S., 72, 249–260, https://doi.org/10.14358/PERS.72.3.249, 2006.
Rousseeuw, P. J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 53–65, https://doi.org/10.1016/0377-0427(87)90125-7, 1987.
Sayre, R., Karagulle, D., Frye, C., Boucher, T., Wolff, N. H., Breyer, S., Wright, D., Martin, M., Butler, K., and van Graafeiland, K.: An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems, Glob. Ecol. Conserv., 21, e00860, https://doi.org/10.1016/j.gecco.2019.e00860, 2020.
Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H., and Dai, Y.: Mapping the global depth to bedrock for land surface modeling, J. Adv. Model. Earth Sy., 9, 65–88, https://doi.org/10.1002/2016MS000686, 2017.
Shen, C., Laloy, E., Elshorbagy, A., Albert, A., Bales, J., Chang, F.-J., Ganguly, S., Hsu, K.-L., Kifer, D., Fang, Z., Fang, K., Li, D., Li, X., and Tsai, W.-P.: HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community, Hydrol. Earth Syst. Sci., 22, 5639–5656, https://doi.org/10.5194/hess-22-5639-2018, 2018.
Sorensen, J. P. R., Davies, J., Ebrahim, G. Y., Lindle, J., Marchant, B. P., Ascott, M. J., Bloomfield, J. P., Cuthbert, M. O., Holland, M., Jensen, K. H., Shamsudduha, M., Villholth, K. G., MacDonald, A. M., and Taylor, R. G.: The influence of groundwater abstraction on interpreting climate controls and extreme recharge events from well hydrographs in semi-arid South Africa, Hydrogeol. J., 29, 2773–2787, https://doi.org/10.1007/s10040-021-02391-3, 2021.
Trabucco, A. and Zomer, R.: Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2, figshare [data set], https://doi.org/10.6084/m9.figshare.7504448.v3, 2019.
Tyralis, H., Papacharalampous, G., and Langousis, A.: A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources, Water, 11, 910, https://doi.org/10.3390/w11050910, 2019.
United Nations: The United Nations World Water Development Report 2022: Groundwater – Making the invisible visible, United Nations Educational, Scientific and Cultural Organization (UNESCO), https://doi.org/10.18356/9789210015363, 2022.
Vahdat-Aboueshagh, H., Tsai, F. T.-C., Bhatta, D., and Paudel, K. P.: Irrigation-Intensive Groundwater Modeling of Complex Aquifer Systems Through Integration of Big Geological Data, Front. Water, 3, 623476, https://doi.org/10.3389/frwa.2021.623476, 2021.
Wang, X., Smith, K., and Hyndman, R.: Characteristic-Based Clustering for Time Series Data, Data Min. Knowl. Disc., 13, 335–364, https://doi.org/10.1007/s10618-005-0039-x, 2006.
Winkler, K., Fuchs, R., Rounsevell, M. D. A., and Herold, M.: HILDA+ Global Land Use Change between 1960 and 2019, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.921846, 2020.
Worland, S. C., Steinschneider, S., Asquith, W., Knight, R., and Wieczorek, M.: Prediction and Inference of Flow Duration Curves Using Multioutput Neural Networks, Water Resour. Res., 55, 6850–6868, https://doi.org/10.1029/2018WR024463, 2019.
Wriedt, G.: Verfahren zur Analyse klimatischer und anthropogener Einflüsse auf die Grundwasserstandsentwicklung, Grundwasser, 22, 41–53, https://doi.org/10.1007/s00767-016-0349-5, 2017.
Wunsch, A., Liesch, T., and Broda, S.: Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles, Water Resour. Manag., 36, 39–54, https://doi.org/10.1007/s11269-021-03006-y, 2021.
Wunsch, A., Liesch, T., and Broda, S.: Deep learning shows declining groundwater levels in Germany until 2100 due to climate change, Nat. Commun., 13, 1221, https://doi.org/10.1038/s41467-022-28770-2, 2022.
Xanke, J. and Liesch, T.: Quantification and possible causes of declining groundwater resources in the Euro-Mediterranean region from 2003 to 2020, Hydrogeol. J., 30, 379–400, https://doi.org/10.1007/s10040-021-02448-3, 2022.
Yang, Y. and Chui, T. F. M.: Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods, Hydrol. Earth Syst. Sci., 25, 5839–5858, https://doi.org/10.5194/hess-25-5839-2021, 2021.
Short summary
This study examines about 8000 groundwater level (GWL) time series from five continents to explore similarities in groundwater systems at different scales. Statistical metrics and machine learning techniques are applied to identify common GWL dynamics patterns and analyze their controlling factors. The study also highlights the potential and limitations of this data-driven approach to improve our understanding of groundwater recharge and discharge processes.
This study examines about 8000 groundwater level (GWL) time series from five continents to...
