Articles | Volume 29, issue 9
https://doi.org/10.5194/hess-29-2201-2025
© 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-29-2201-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 May 2025
Research article |
| 12 May 2025
| 12 May 2025
CONCN: a high-resolution, integrated surface water–groundwater ParFlow modeling platform of continental China
Chen Yang
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Zitong Jia
College of Water Sciences, Beijing Normal University, Beijing, China
Wenjie Xu
Institute of Geological Survey, China University of Geosciences, Wuhan, China
Zhongwang Wei
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Xiaolang Zhang
Department of Geosciences, Florida Atlantic University, Boca Raton, USA
Department of Geography, National University of Singapore, Singapore, Singapore
Jeffrey McDonnell
School of Environment and Sustainability, Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada
School of Geography, Earth & Environmental Sciences, University of Birmingham, Birmingham, UK
North China University of Water Resources and Electric Power, Zhengzhou, China
Laura Condon
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, USA
Yongjiu Dai
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Department of Civil and Environmental Engineering, High Meadows Environmental Institute, Integrated GroundWater Modeling Center, Princeton University, Princeton, USA
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To manage Earth's water resources effectively amid climate change, it is crucial to understand both surface and groundwater processes. We developed a new modeling system that combines two advanced tools, ParFlow and LIS (Land Information System)/Noah-MP, to better simulate both land surface and groundwater interactions. By testing this integrated model in the Upper Colorado River Basin, we found it improves predictions of hydrologic processes, especially in complex terrains.
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Groundwater strongly influences how water and energy move between land and air, yet most large-scale climate and Earth system models treat it too simply. We reviewed 20 years of work combining a detailed groundwater model, ParFlow, with land surface models, showing ways groundwater shapes energy and water cycles. We also updated this model link, improving its performance, and proposed a flexible framework to support future advances.
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Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
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Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura E. Condon
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Preprint archived
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Long Short-Term Memory (LSTM) is a widely-used machine learning (ML) model in hydrology. However, it is difficult to extract knowledge from it. We propose HydroLSTM which represents processes analogous to a hydrological reservoir. Models using HydroLSTM perform similarly to LSTM but require fewer cell states. The learned parameters are informative about the dominant hydroclimatic characteristics of a catchment. Our results demonstrate how hydrological knowledge is encoded in the new structure.
Aniket Gupta, Alix Reverdy, Jean-Martial Cohard, Basile Hector, Marc Descloitres, Jean-Pierre Vandervaere, Catherine Coulaud, Romain Biron, Lucie Liger, Reed Maxwell, Jean-Gabriel Valay, and Didier Voisin
Hydrol. Earth Syst. Sci., 27, 191–212, https://doi.org/10.5194/hess-27-191-2023, https://doi.org/10.5194/hess-27-191-2023, 2023
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Patchy snow cover during spring impacts mountainous ecosystems on a large range of spatio-temporal scales. A hydrological model simulated such snow patchiness at 10 m resolution. Slope and orientation controls precipitation, radiation, and wind generate differences in snowmelt, subsurface storage, streamflow, and evapotranspiration. The snow patchiness increases the duration of the snowmelt to stream and subsurface storage, which sustains the plants and streamflow later in the summer.
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022, https://doi.org/10.5194/essd-14-5267-2022, 2022
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SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-345, https://doi.org/10.5194/hess-2022-345, 2022
Publication in HESS not foreseen
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As the stress on water resources from climate change grows, we need models that represent water processes at the scale of counties, states, and even countries in order to make viable predictions about things will change. While such models are powerful, they can be cumbersome to deal with because they are so large. This research explores a novel way of increasing the efficiency of large-scale hydrologic models using an approach called Simulation-Based Inference.
Jennie C. Steyaert and Laura E. Condon
EGUsphere, https://doi.org/10.5194/egusphere-2022-1051, https://doi.org/10.5194/egusphere-2022-1051, 2022
Preprint archived
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All river systems in the US are impacted by dams, yet analyses are limited by a lack of data. We use the first national dataset of reservoir data to analyze reservoir storage trends from 1980–2019. We show that reservoir storage has decreased over the past 40 years. The range in monthly storage has increased over time in drier regions and decreased in wetter ones. Lastly, we find that most regions have reservoir storage that takes longer to recover from and are therefore more vulnerable.
Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
Hydrol. Earth Syst. Sci., 26, 4147–4167, https://doi.org/10.5194/hess-26-4147-2022, https://doi.org/10.5194/hess-26-4147-2022, 2022
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Tracer-aided hydrological models are useful tool to reduce uncertainty of hydrological modeling in cold basins, but there is little guidance on the sampling strategy for isotope analysis, which is important for large mountainous basins. This study evaluated the reliance of the tracer-aided modeling performance on the availability of isotope data in the Yarlung Tsangpo river basin, and provides implications for collecting water isotope data for running tracer-aided hydrological models.
Ziqi Lin, Yongjiu Dai, Umakant Mishra, Guocheng Wang, Wei Shangguan, Wen Zhang, and Zhangcai Qin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-232, https://doi.org/10.5194/essd-2022-232, 2022
Manuscript not accepted for further review
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Spatial soil organic carbon (SOC) data is critical for predictions in carbon climate feedbacks and future climate trends, but no conclusion has yet been reached on which dataset to be used for specific purposes. We evaluated the SOC estimates from five widely used global soil datasets and a regional permafrost dataset, and identify uncertainties of SOC estimates by region, biome, and data sources, hoping to help improve SOC/soil data in the future.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
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Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Mary M. F. O'Neill, Danielle T. Tijerina, Laura E. Condon, and Reed M. Maxwell
Geosci. Model Dev., 14, 7223–7254, https://doi.org/10.5194/gmd-14-7223-2021, https://doi.org/10.5194/gmd-14-7223-2021, 2021
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Modeling the hydrologic cycle at high resolution and at large spatial scales is an incredible opportunity and challenge for hydrologists. In this paper, we present the results of a high-resolution hydrologic simulation configured over the contiguous United States. We discuss simulated water fluxes through groundwater, soil, plants, and over land, and we compare model results to in situ observations and satellite products in order to build confidence and guide future model development.
Yaoping Wang, Jiafu Mao, Mingzhou Jin, Forrest M. Hoffman, Xiaoying Shi, Stan D. Wullschleger, and Yongjiu Dai
Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, https://doi.org/10.5194/essd-13-4385-2021, 2021
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We developed seven global soil moisture datasets (1970–2016, monthly, half-degree, and multilayer) by merging a wide range of data sources, including in situ and satellite observations, reanalysis, offline land surface model simulations, and Earth system model simulations. Given the great value of long-term, multilayer, gap-free soil moisture products to climate research and applications, we believe this paper and the presented datasets would be of interest to many different communities.
Jun Zhang, Laura E. Condon, Hoang Tran, and Reed M. Maxwell
Earth Syst. Sci. Data, 13, 3263–3279, https://doi.org/10.5194/essd-13-3263-2021, https://doi.org/10.5194/essd-13-3263-2021, 2021
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Existing national topographic datasets for the US may not be compatible with gridded hydrologic models. A national topographic dataset developed to support physically based hydrologic models at 1 km and 250 m over the contiguous US is provided. We used a Priority Flood algorithm to ensure hydrologically consistent drainage networks and evaluated the performance with an integrated hydrologic model. Datasets and scripts are available for direct data usage or modification of processing as desired.
Chris M. DeBeer, Howard S. Wheater, John W. Pomeroy, Alan G. Barr, Jennifer L. Baltzer, Jill F. Johnstone, Merritt R. Turetsky, Ronald E. Stewart, Masaki Hayashi, Garth van der Kamp, Shawn Marshall, Elizabeth Campbell, Philip Marsh, Sean K. Carey, William L. Quinton, Yanping Li, Saman Razavi, Aaron Berg, Jeffrey J. McDonnell, Christopher Spence, Warren D. Helgason, Andrew M. Ireson, T. Andrew Black, Mohamed Elshamy, Fuad Yassin, Bruce Davison, Allan Howard, Julie M. Thériault, Kevin Shook, Michael N. Demuth, and Alain Pietroniro
Hydrol. Earth Syst. Sci., 25, 1849–1882, https://doi.org/10.5194/hess-25-1849-2021, https://doi.org/10.5194/hess-25-1849-2021, 2021
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This article examines future changes in land cover and hydrological cycling across the interior of western Canada under climate conditions projected for the 21st century. Key insights into the mechanisms and interactions of Earth system and hydrological process responses are presented, and this understanding is used together with model application to provide a synthesis of future change. This has allowed more scientifically informed projections than have hitherto been available.
Cited articles
Bailey, R. T., Wible, T. C., Arabi, M., Records, R. M., and Ditty, J.: Assessing regional-scale spatio-temporal patterns of groundwater–surface water interactions using a coupled SWAT-MODFLOW model, Hydrol. Process., 30, 4420–4433, https://doi.org/10.1002/hyp.10933, 2016.
Belleflamme, A., Goergen, K., Wagner, N., Kollet, S., Bathiany, S., El Zohbi, J., Rechid, D., Vanderborght, J., and Vereecken, H.: Hydrological forecasting at impact scale: the integrated ParFlow hydrological model at 0.6 km for climate resilient water resource management over Germany, Frontiers in Water, 5, 1183642, https://doi.org/10.3389/frwa.2023.1183642, 2023.
Bianchi, M., Scheidegger, J., Hughes, A., Jackson, C., Lee, J., Lewis, M., Mansour, M., Newell, A., O'Dochartaigh, B., Patton, A., and Dadson, S.: Simulation of national-scale groundwater dynamics in geologically complex aquifer systems: an example from Great Britain, Hydrolog. Sci. J., 69, 572–591, https://doi.org/10.1080/02626667.2024.2320847, 2024.
Cao, G. L., Zheng, C. M., Scanlon, B. R., Liu, J., and Li, W. P.: Use of flow modeling to assess sustainability of groundwater resources in the North China Plain, Water Resour. Res., 49, 159–175, https://doi.org/10.1029/2012wr011899, 2013.
Chen, J., Sudicky, E. A., Davison, J. H., Frey, S. K., Park, Y. J., Hwang, H. T., Erler, A. R., Berg, S. J., Callaghan, M. V., Miller, K., Ross, M., and Peltier, W. R.: Towards a climate-driven simulation of coupled surface-subsurface hydrology at the continental scale: a Canadian example, Can. Water Resour. J., 45, 11–27, https://doi.org/10.1080/07011784.2019.1671235, 2020.
Collins, G. and Reddy, G.: China's Growing Water Crisis, Foreign Affairs, The Council on Foreign Relations, Inc., https://www.foreignaffairs.com/china/chinas-growing-water-crisis (last access: 9 May 2025), 2022.
Condon, L. E. and Maxwell, R. M.: Modified priority flood and global slope enforcement algorithm for topographic processing in physically based hydrologic modeling applications, Comput. Geosci.-UK, 126, 73–83, https://doi.org/10.1016/j.cageo.2019.01.020, 2019.
Condon, L. E., Kollet, S., Bierkens, M. F. P., 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, e2020WR029500, https://doi.org/10.1029/2020WR029500, 2021.
Cosgrove, B., Gochis, D., Flowers, T., Dugger, A., Ogden, F., Graziano, T., Clark, E., Cabell, R., Casiday, N., Cui, Z., Eicher, K., Fall, G., Feng, X., Fitzgerald, K., Frazier, N., George, C., Gibbs, R., Hernandez, L., Johnson, D., Jones, R., Karsten, L., Kefelegn, H., Kitzmiller, D., Lee, H., Liu, Y., Mashriqui, H., Mattern, D., McCluskey, A., McCreight, J. L., McDaniel, R., Midekisa, A., Newman, A., Pan, L., Pham, C., RafieeiNasab, A., Rasmussen, R., Read, L., Rezaeianzadeh, M., Salas, F., Sang, D., Sampson, K., Schneider, T., Shi, Q., Sood, G., Wood, A., Wu, W., Yates, D., Yu, W., and Zhang, Y.: NOAA's National Water Model: Advancing operational hydrology through continental-scale modeling, J. Am. Water Resour. As., 60, 247–272, https://doi.org/10.1111/1752-1688.13184, 2024.
Dai, Y., Xin, Q., Wei, N., Zhang, Y., Shangguan, W., Yuan, H., Zhang, S., Liu, S., and Lu, X.: A Global High-Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling, J. Adv. Model. Earth Sy., 11, 2996–3023, https://doi.org/10.1029/2019ms001784, 2019.
Dai, Y. J., Zeng, X. B., Dickinson, R. E., Baker, I., Bonan, G. B., Bosilovich, M. G., Denning, A. S., Dirmeyer, P. A., Houser, P. R., Niu, G. Y., Oleson, K. W., Schlosser, C. A., and Yang, Z. L.: The Common Land Model, B. Am. Meteorol. Soc., 84, 1013–1023, https://doi.org/10.1175/Bams-84-8-1013, 2003.
David, C. H., Maidment, D. R., Niu, G.-Y., Yang, Z.-L., Habets, F., and Eijkhout, V.: River Network Routing on the NHDPlus Dataset, J. Hydrometeorol., 12, 913–934, https://doi.org/10.1175/2011JHM1345.1, 2011.
de Graaf, I., Condon, L., and Maxwell, R.: Hyper-Resolution Continental-Scale 3-D Aquifer Parameterization for Groundwater Modeling, Water Resour. Res., 56, e2019WR026004, https://doi.org/10.1029/2019WR026004, 2020.
de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H., and Bierkens, M. F. P.: A high-resolution global-scale groundwater model, Hydrol. Earth Syst. Sci., 19, 823–837, https://doi.org/10.5194/hess-19-823-2015, 2015.
de Graaf, I. E. M., van Beek, R. L. P. H., Gleeson, T., Moosdorf, N., Schmitz, O., Sutanudjaja, E. H., and Bierkens, M. F. P.: A global-scale two-layer transient groundwater model: Development and application to groundwater depletion, Adv. Water Resour., 102, 53–67, https://doi.org/10.1016/j.advwatres.2017.01.011, 2017.
Delsman, J. R., Mulder, T., Romero Verastegui, B., Bootsma, H., Zitman, P., Huizer, S., and Oude Essink, G. H. P.: Reproducible construction of a high-resolution national variable-density groundwater salinity model for the Netherlands, Environ. Modell. Softw., 164, 105683, https://doi.org/10.1016/j.envsoft.2023.105683, 2023.
Devitt, L., Neal, J., Wagener, T., and Coxon, G.: Uncertainty in the extreme flood magnitude estimates of large-scale flood hazard models, Environ. Res. Lett., 16, 064013, https://doi.org/10.1088/1748-9326/abfac4, 2021.
Eilander, D., van Verseveld, W., Yamazaki, D., Weerts, A., Winsemius, H. C., and Ward, P. J.: A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models, Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, 2021.
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.
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.
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, D10125, https://doi.org/10.1029/2006JD008111, 2007.
Feng, D., Fang, K., and Shen, C.: Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales, Water Resour. Res., 56, e2019WR026793, https://doi.org/10.1029/2019WR026793, 2020.
Ferguson, G., McIntosh, J. C., Jasechko, S., Kim, J.-H., Famiglietti, J. S., and McDonnell, J. J.: Groundwater deeper than 500 m contributes less than 0.1 % of global river discharge, Commun. Earth Environ., 4, 48, https://doi.org/10.1038/s43247-023-00697-6, 2023.
Foster, L. M., Williams, K. H., and Maxwell, R. M.: Resolution matters when modeling climate change in headwaters of the Colorado River, Environ. Res. Lett., 15, 104031, https://doi.org/10.1088/1748-9326/aba77f, 2020.
Friedl, M. and Sulla-Menashe, D.: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061, NASA EOSDIS Land Processes Distributed Active Archive Center [dataset], https://doi.org/10.5067/MODIS/MCD12Q1.061, 2022.
Gleeson, T., Moosdorf, N., Hartmann, J., and van Beek, L. P. H.: A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity, Geophys. Res. Lett., 41, 3891–3898, https://doi.org/10.1002/2014gl059856, 2014.
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.
Gochis, D. J., Yu, W., and Yates, D. N.: The WRF-Hydro model technical description and user's guide, version 3.0, NCAR Technical Document, 120 pages, the National Center for Atmospheric Research, Boulder, CO, USA, 2015.
Han, J., Miao, C., Gou, J., Zheng, H., Zhang, Q., and Guo, X.: A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations, Earth Syst. Sci. Data, 15, 3147–3161, https://doi.org/10.5194/essd-15-3147-2023, 2023.
Hartmann, A., Gleeson, T., Wada, Y., and Wagener, T.: Enhanced groundwater recharge rates and altered recharge sensitivity to climate variability through subsurface heterogeneity, P. Natl. Acad. Sci. USA, 114, 2842–2847, https://doi.org/10.1073/pnas.1614941114, 2017.
Hartmann, J. and Moosdorf, N.: The new global lithological map database GLiM: A representation of rock properties at the Earth surface, Geochem. Geoph. Geosy., 13, Q12004, https://doi.org/10.1029/2012GC004370, 2012.
Hellwig, J., de Graaf, I. E. M., Weiler, M., and Stahl, K.: Large-Scale Assessment of Delayed Groundwater Responses to Drought, Water Resour. Res., 56, e2019WR025441, https://doi.org/10.1029/2019WR025441, 2020.
Henriksen, H. J., Troldborg, L., Nyegaard, P., Sonnenborg, T. O., Refsgaard, J. C., and Madsen, B.: Methodology for construction, calibration and validation of a national hydrological model for Denmark, J. Hydrol., 280, 52–71, https://doi.org/10.1016/S0022-1694(03)00186-0, 2003.
Hoch, J. M., Sutanudjaja, E. H., Wanders, N., van Beek, R. L. P. H., and Bierkens, M. F. P.: Hyper-resolution PCR-GLOBWB: opportunities and challenges from refining model spatial resolution to 1 km over the European continent, Hydrol. Earth Syst. Sci., 27, 1383–1401, https://doi.org/10.5194/hess-27-1383-2023, 2023.
Hu, L., Xu, Z., and Huang, W.: Development of a river-groundwater interaction model and its application to a catchment in Northwestern China, J. Hydrol., 543, 483–500, https://doi.org/10.1016/j.jhydrol.2016.10.028, 2016.
Huang, C., Zheng, X., Tait, A., Dai, Y., Yang, C., Chen, Z., Li, T., and Wang, Z.: On using smoothing spline and residual correction to fuse rain gauge observations and remote sensing data, J. Hydrol., 508, 410–417, https://doi.org/10.1016/j.jhydrol.2013.11.022, 2014.
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), Geophys. Res. Lett., 45, 1897–1904, https://doi.org/10.1002/2017GL075860, 2018.
Jiang, X., Ma, R., Ma, T., and Sun, Z.: Modeling the effects of water diversion projects on surface water and groundwater interactions in the central Yangtze River basin, Sci. Total Environ., 830, 154606, https://doi.org/10.1016/j.scitotenv.2022.154606, 2022.
Jiang, Y.: China's water scarcity, J. Environ. Manage., 90, 3185–3196, https://doi.org/10.1016/j.jenvman.2009.04.016, 2009.
Jiménez, C., Prigent, C., Mueller, B., Seneviratne, S. I., McCabe, M. F., Wood, E. F., Rossow, W. B., Balsamo, G., Betts, A. K., Dirmeyer, P. A., Fisher, J. B., Jung, M., Kanamitsu, M., Reichle, R. H., Reichstein, M., Rodell, M., Sheffield, J., Tu, K., and Wang, K.: Global intercomparison of 12 land surface heat flux estimates, J. Geophys. Res.-Atmos., 116, D02102, https://doi.org/10.1029/2010JD014545, 2011.
Kim, N. W., Chung, I. M., Won, Y. S., and Arnold, J. G.: Development and application of the integrated SWAT–MODFLOW model, J. Hydrol., 356, 1–16, https://doi.org/10.1016/j.jhydrol.2008.02.024, 2008.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Kollet, S. J. and Maxwell, R. M.: Integrated surface-groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model, Adv. Water Resour., 29, 945–958, 2006.
Kollet, S. J. and Maxwell, R. M.: Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model, Water Resour. Res., 44, W02402, https://doi.org/10.1029/2007wr006004, 2008.
Lancia, M., Yao, Y., Andrews, C. B., Wang, X., Kuang, X., Ni, J., Gorelick, S. M., Scanlon, B. R., Wang, Y., and Zheng, C.: The China groundwater crisis: A mechanistic analysis with implications for global sustainability, Sustainable Horizons, 4, 100042, https://doi.org/10.1016/j.horiz.2022.100042, 2022.
Li, Z., Chen, Y., Yang, J., and Wang, Y.: Potential evapotranspiration and its attribution over the past 50 years in the arid region of Northwest China, Hydrol. Process., 28, 1025–1031, https://doi.org/10.1002/hyp.9643, 2014.
Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., and Wood, E. F.: Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches, Water Resour. Res., 55, 6499–6516, https://doi.org/10.1029/2019wr025287, 2019.
Livneh, B., Bohn, T. J., Pierce, D. W., Munoz-Arriola, F., Nijssen, B., Vose, R., Cayan, D. R., and Brekke, L.: A spatially comprehensive, hydrometeorological data set for Mexico, the U. S., and Southern Canada 1950–2013, Sci. Data, 2, 150042, https://doi.org/10.1038/sdata.2015.42, 2015.
Ma, N., Szilagyi, J., Zhang, Y., and Liu, W.: Complementary-Relationship-Based Modeling of Terrestrial Evapotranspiration Across China During 1982–2012: Validations and Spatiotemporal Analyses, J. Geophys. Res.-Atmos., 124, 4326–4351, https://doi.org/10.1029/2018JD029850, 2019.
Maxwell, R. M.: A terrain-following grid transform and preconditioner for parallel, large-scale, integrated hydrologic modeling, Adv. Water Resour., 53, 109–117, https://doi.org/10.1016/j.advwatres.2012.10.001, 2013.
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.
Miao, C., Gou, J., Fu, B., Tang, Q., Duan, Q., Chen, Z., Lei, H., Chen, J., Guo, J., Borthwick, A. G. L., Ding, W., Duan, X., Li, Y., Kong, D., Guo, X., and Wu, J.: High-quality reconstruction of China's natural streamflow, Sci. Bull., 67, 547–556, https://doi.org/10.1016/j.scib.2021.09.022, 2022.
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.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Moriasi, D. N., G. Arnold, J., W. Van Liew, M., L. Bingner, R., D. Harmel, R., and L. Veith, T.: Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations, T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007.
Niu, Z., He, H., Zhu, G., Ren, X., Zhang, L., and Zhang, K.: A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981–2015, Sci. Data, 7, 369, https://doi.org/10.1038/s41597-020-00693-x, 2020.
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.
Peng, S.: 1-km monthly precipitation dataset for China (1901–2022), National Tibetan Plateau Data Center [dataset], https://doi.org/10.5281/zenodo.3185722, 2020.
Pietz, D. A.: China's Water Challenges: National and Global Implications, Education About ASIA, 22, 8–14, 2017.
Regan, R. S., Juracek, K. E., Hay, L. E., Markstrom, S. L., Viger, R. J., Driscoll, J. M., LaFontaine, J. H., and Norton, P. A.: The U. S. Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States, Environ. Modell. Softw., 111, 192–203, https://doi.org/10.1016/j.envsoft.2018.09.023, 2019.
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, 2019.
Reinecke, R., Wachholz, A., Mehl, S., Foglia, L., Niemann, C., and Döll, P.: Importance of Spatial Resolution in Global Groundwater Modeling, Groundwater, 58, 363–376, https://doi.org/10.1111/gwat.12996, 2020.
Reinecke, R., Gnann, S., Stein, L., Bierkens, M., de Graaf, I., Gleeson, T., Essink, G. O., Sutanudjaja, E. H., Ruz Vargas, C., Verkaik, J., and Wagener, T.: Uncertainty in model estimates of global groundwater depth, Environ. Res. Lett., 19, 114066, https://doi.org/10.1088/1748-9326/ad8587, 2024.
Running, S., Mu, Q., Zhao, M., and Moreno, A.: MODIS/Terra Net Evapotranspiration Gap-Filled Yearly L4 Global 500m SIN Grid V061, NASA EOSDIS Land Processes Distributed Active Archive Center [dataset], https://doi.org/10.5067/MODIS/MOD16A3GF.061, 2021.
Schaap, M. G. and Leij, F. J.: Database-related accuracy and uncertainty of pedotransfer functions, Soil Sci., 163, 765–779, https://doi.org/10.1097/00010694-199810000-00001, 1998.
Schaefli, B. and Gupta, H. V.: Do Nash values have value?, Hydrol. Process., 21, 2075–2080, https://doi.org/10.1002/hyp.6825, 2007.
Shangguan, W., Hengl, T., de Jesus, J. M., Yuan, H., and Dai, Y. J.: Mapping the global depth to bedrock for land surface modeling, J. Adv. Model. Earth Sy., 9, 65–88, 2017.
Smith, S., Ferguson, I., Engdahl, N., Gasper, F., Chennault, C., Triana, J. S. A., Avery, P., Rigor, P., Condon, L., Jourdain, S., Bennett, A., Artavanis, G., Maxwell, R., Kulkarni, K., Bansal, V., Beisman, J., de Rooij, R., Swilley, J., Lazzeri, D., Thompson, D., Coon, E., Bertolacci, I., Azeemi, M. F., Wagner, N.: parflow/parflow: ParFlow Version 3.13.0 (v3.13.0), Zenodo [code], https://doi.org/10.5281/zenodo.10989198, 2024.
Srivastava, V., Graham, W., Muñoz-Carpena, R., and Maxwell, R. M.: Insights on geologic and vegetative controls over hydrologic behavior of a large complex basin – Global Sensitivity Analysis of an integrated parallel hydrologic model, J. Hydrol., 519, 2238–2257, https://doi.org/10.1016/j.jhydrol.2014.10.020, 2014.
Strahler, A. N.: Quantitative analysis of watershed geomorphology, Eos T. Am. Geophys. Un., 38, 913–920, https://doi.org/10.1029/TR038i006p00913, 1957.
Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, Geosci. Model Dev., 11, 2429–2453, https://doi.org/10.5194/gmd-11-2429-2018, 2018.
Swilley, J. S., Tijerina-Kreuzer, D., Tran, H. V., Zhang, J., Yang, C., Condon, L. E., and Maxwell, R. M.: Continental Scale Hydrostratigraphy: Comparing Geologically Informed Data Products to Analytical Solutions, Groundwater, 62, 75–92, https://doi.org/10.1111/gwat.13354, 2023.
Tian, Y., Zheng, Y., Zheng, C., Xiao, H., Fan, W., Zou, S., Wu, B., Yao, Y., Zhang, A., and Liu, J.: Exploring scale-dependent ecohydrological responses in a large endorheic river basin through integrated surface water-groundwater modeling, Water Resour. Res., 51, 4065–4085, https://doi.org/10.1002/2015WR016881, 2015.
Tijerina, D., Condon, L., FitzGerald, K., Dugger, A., O'Neill, M. M., Sampson, K., Gochis, D., and Maxwell, R.: Continental Hydrologic Intercomparison Project, Phase 1: A Large-Scale Hydrologic Model Comparison Over the Continental United States, Water Resour. Res., 57, e2020WR028931, https://doi.org/10.1029/2020WR028931, 2021.
Tijerina-Kreuzer, D., Swilley, J. S., Tran, H. V., Zhang, J., West, B., Yang, C., Condon, L. E., and Maxwell, R. M.: Continental scale hydrostratigraphy: basin-scale testing of alternative data-driven approaches, Groundwater, 62, 93–110, https://doi.org/10.1111/gwat.13357, 2023.
Vergnes, J.-P., Caballero, Y., and Lanini, S.: Assessing climate change impact on French groundwater resources using a spatially distributed hydrogeological model, Hydrolog. Sci. J., 68, 209–227, https://doi.org/10.1080/02626667.2022.2150553, 2023.
Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H. P., Lin, H. X., and Bierkens, M. F. P.: GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model, Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, 2024.
Wang, K., Zhang, C., Chen, H., Yue, Y., Zhang, W., Zhang, M., Qi, X., and Fu, Z.: Karst landscapes of China: patterns, ecosystem processes and services, Landscape Ecology, 34, 2743–2763, https://doi.org/10.1007/s10980-019-00912-w, 2019.
Wang, X., Tian, Y., Yu, J., Lancia, M., Chen, J., Xiao, K., Zheng, Y., Andrews, C. B., and Zheng, C.: Complex Effects of Tides on Coastal Groundwater Revealed by High-Resolution Integrated Flow Modeling, Water Resour. Res., 59, e2022WR033942, https://doi.org/10.1029/2022WR033942, 2023.
Westerhoff, R., White, P., and Miguez-Macho, G.: Application of an improved global-scale groundwater model for water table estimation across New Zealand, Hydrol. Earth Syst. Sci., 22, 6449–6472, https://doi.org/10.5194/hess-22-6449-2018, 2018.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., and Pavelsky, T. M.: MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset, Water Resour. Res., 55, 5053–5073, https://doi.org/10.1029/2019wr024873, 2019.
Yan, F., Shangguan, W., Zhang, J., and Hu, B.: Depth-to-bedrock map of China at a spatial resolution of 100 meters, Sci. Data, 7, 2, https://doi.org/10.1038/s41597-019-0345-6, 2020.
Yang, C., Li, H.-Y., Fang, Y., Cui, C., Wang, T., Zheng, C., Leung, L. R., Maxwell, R. M., Zhang, Y.-K., and Yang, X.: Effects of Groundwater Pumping on Ground Surface Temperature: A Regional Modeling Study in the North China Plain, J. Geophys. Res.-Atmos., 125, e2019JD031764, https://doi.org/10.1029/2019jd031764, 2020.
Yang, C., Maxwell, R., McDonnell, J., Yang, X., and Tijerina-Kreuzer, D.: The Role of Topography in Controlling Evapotranspiration Age, J. Geophys. Res.-Atmos., 128, e2023JD039228, https://doi.org/10.1029/2023JD039228, 2023a.
Yang, C., Tijerina-Kreuzer, D. T., Tran, H. V., Condon, L. E., and Maxwell, R. M.: A high-resolution, 3D groundwater-surface water simulation of the contiguous US: Advances in the integrated ParFlow CONUS 2.0 modeling platform, J. Hydrol., 130294, https://doi.org/10.1016/j.jhydrol.2023.130294, 2023b.
Yang, J. and Huang, X.: The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019, Earth Syst. Sci. Data, 13, 3907–3925, https://doi.org/10.5194/essd-13-3907-2021, 2021.
Yang, Y., Feng, D., Beck, H. E., Hu, W., Sengupta, A., Monache, L. D., Hartman, R., Lin, P., Shen, C., and Pan, M.: Global Daily Discharge Estimation Based on Grid-Scale Long Short-Term Memory (LSTM) Model and River Routing, ESS Open Archive, ESS Open Archive, https://doi.org/10.22541/essoar.169724927.73813721/v1, 2023c.
Yin, Z., Lin, P., Riggs, R., Allen, G. H., Lei, X., Zheng, Z., and Cai, S.: A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies, Earth Syst. Sci. Data, 16, 1559–1587, https://doi.org/10.5194/essd-16-1559-2024, 2024.
Yu, X., Luo, L., Hu, P., Tu, X., Chen, X., and Wei, J.: Impacts of sea-level rise on groundwater inundation and river floods under changing climate, J. Hydrol., 614, 128554, https://doi.org/10.1016/j.jhydrol.2022.128554, 2022.
Zafarmomen, N., Alizadeh, H., Bayat, M., Ehtiat, M., and Moradkhani, H.: Assimilation of Sentinel-Based Leaf Area Index for Modeling Surface-Ground Water Interactions in Irrigation Districts, Water Resour. Res., 60, e2023WR036080, https://doi.org/10.1029/2023WR036080, 2024.
Zhang, J., Condon, L. E., Tran, H., and Maxwell, R. M.: A national topographic dataset for hydrological modeling over the contiguous United States, Earth Syst. Sci. Data, 13, 3263–3279, https://doi.org/10.5194/essd-13-3263-2021, 2021.
Zhang, Y., Kong, D., Gan, R., Chiew, F. H. S., McVicar, T. R., Zhang, Q., and Yang, Y.: Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017, Remote Sens. Environ., 222, 165–182, https://doi.org/10.1016/j.rse.2018.12.031, 2019.
Zhao, K., Fang, Z., Li, J., and He, C.: Spatial-temporal variations of groundwater storage in China: A multiscale analysis based on GRACE data, Resources, Conserv. Recycling, 197, 107088, https://doi.org/10.1016/j.resconrec.2023.107088, 2023.
Short summary
We developed the first high-resolution, integrated surface water–groundwater hydrologic model of the entirety of continental China using ParFlow. The model shows good performance in terms of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision-making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
We developed the first high-resolution, integrated surface water–groundwater hydrologic model of...
