Articles | Volume 18, issue 2
https://doi.org/10.5194/gmd-18-547-2025
© 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-18-547-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
31 Jan 2025
Model description paper |
| 31 Jan 2025
| 31 Jan 2025
SERGHEI v2.0: introducing a performance-portable, high-performance, three-dimensional variably saturated subsurface flow solver (SERGHEI-RE)
College of Civil Engineering, Tongji University, Shanghai, China
Gregor Rickert
Institute of Geoecology, Technische Universität Braunschweig, Braunschweig, Germany
Na Zheng
College of Civil Engineering, Tongji University, Shanghai, China
Zhibo Zhang
College of Civil Engineering, Tongji University, Shanghai, China
Ilhan Özgen-Xian
Institute of Geoecology, Technische Universität Braunschweig, Braunschweig, Germany
Daniel Caviedes-Voullième
Simulation and Data Lab Terrestrial Systems, Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
Institute of Bio- and Geosciences Agrosphere (IBG-3), Forschungszentrum Jülich, Jülich, Germany
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Cited articles
Bassetto, S., Cancès, C., Enchéry, G., and Tran, Q.-H.: On several numerical strategies to solve Richards' equation in heterogeneous media with finite volumes, Comput. Geosci., 26, 1297–1322, https://doi.org/10.1007/s10596-022-10150-w, 2022. a
Beegum, S., Šimůnek, J., Szymkiewicz, A., Sudheer, K., and Nambi, I. M.: Updating the Coupling Algorithm between HYDRUS and MODFLOW in the HYDRUS Package for MODFLOW, Vadose Zone J., 17, 1–8, https://doi.org/10.2136/vzj2018.02.0034, 2018. a
Brandhorst, N., Erdal, D., and Neuweiler, I.: Coupling saturated and unsaturated flow: comparing the iterative and the non-iterative approach, Hydrol. Earth Syst. Sci., 25, 4041–4059, https://doi.org/10.5194/hess-25-4041-2021, 2021. a
Caviedes-Voullième, D., García-Navarro, P., and Murillo, J.: Verification, conservation, stability and efficiency of a finite volume method for the 1D Richards equation, J. Hydrol., 480, 69–84, https://doi.org/10.1016/j.jhydrol.2012.12.008, 2013. a, b, c
Caviedes-Voullième, D., Morales-Hernández, M., Norman, M. R., and Özgen-Xian, I.: SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics, Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, 2023. a, b, c
Caviedes-Voullième, D., Morales-Hernández, M., and Özgen Xian, I.: SERGHEI (2.0), Zenodo [code], https://doi.org/10.5281/zenodo.13166466, 2024. a
Celia, M. A., Bouloutas, E. T., and Zarba, R. L.: A general mass‐conservative numerical solution for the unsaturated flow equation, Water Resour. Res., 26, 1483–1496, https://doi.org/10.1029/WR026i007p01483, 1990. a
Chang, C., Deringer, V. L., Katti, K. S., Van Speybroeck, V., and Wolverton, C. M.: Simulations in the era of exascale computing, Nat. Rev. Mater., 8, 309–313, https://doi.org/10.1038/s41578-023-00540-6, 2023. a
Chávez-Negrete, C., Domínguez-Mota, F., and Santana-Quinteros, D.: Numerical solution of Richards equation of water flow by generalized finite differences, Comput. Geotech., 101, 168–175, https://doi.org/10.1016/j.compgeo.2018.05.003, 2018. a, b, c
Condon, L. E., Markovich, K. H., Kelleher, C. A., McDonnell, J. J., Ferguson, G., and McIntosh, J. C.: Where is the bottom of the watershed?, Water Resour. Res., 56, e2019WR026010, https://doi.org/10.1029/2019WR026010, 2020. a
Coon, E. T., Svyatskiy, D., Jan, A., Kikinzon, E., Berndt, M., Atchley, A. L., Harp, D. R., Manzini, G., Shelef, E., Lipnikov, K., Garimella, R., Xu, C., Moulton, J. D., Karra, S., Painter, S. L., Jafarov, E., and Molins, S.: Advanced Terrestrial Simulator (ATS), USDOE Office of Science (SC) [code], https://doi.org/10.11578/dc.20190911.1, 2019. a
D'Haese, C. M. F., Putti, M., Paniconi, C., and Verhoest, N. E. C.: Assessment of adaptive and heuristic time stepping for variably saturated flow, Int. J. Numer. Meth. Fl., 53, 1173–1193, https://doi.org/10.1002/fld.1369, 2007. a, b
El-Kadi, A. I. and Ling, G.: The Courant and Peclet Number criteria for the numerical solution of the Richards Equation, Water Resour. Res., 29, 3485–3494, https://doi.org/10.1029/93WR00929, 1993. a
Farthing, M. W. and Ogden, F. L.: Numerical Solution of Richards' Equation: A Review of Advances and Challenges, Soil Sci. Soc. Am. J., 81, 1257–1269, https://doi.org/10.2136/sssaj2017.02.0058, 2017. a, b
Forsyth, P., Wu, Y., and Pruess, K.: Robust numerical methods for saturated-unsaturated flow with dry initial conditions in heterogeneous media, Adv. Water Resour., 18, 25–38, https://doi.org/10.1016/0309-1708(95)00020-J, 1995. a, b
Hammond, G. E., Lichtner, P. C., and Mills, R. T.: Evaluating the performance of parallel subsurface simulators: An illustrative example with PFLOTRAN, Water Resour. Res., 50, 208–228, https://doi.org/10.1002/2012WR013483, 2014. a
Hokkanen, J., Kollet, S., Kraus, J., Herten, A., Hrywniak, M., and Pleiter, D.: Leveraging HPC accelerator architectures with modern techniques — hydrologic modeling on GPUs with ParFlow, Comput. Geosci., 25, 1579–1590, https://doi.org/10.1007/s10596-021-10051-4, 2021. a, b
Hou, J., Pan, Z., Tong, Y., Li, X., Zheng, J., and Kang, Y.: High-efficiency and high-resolution numerical modeling for two-dimensional infiltration processes, accelerated by a graphics processing unit, Hydrogeol. J., 30, 637–651, https://doi.org/10.1007/s10040-021-02444-7, 2022. a
Kirkland, M. R., Hills, R. G., and Wierenga, P. J.: Algorithms for solving Richards' equation for variably saturated soils, Water Resour. Res., 28, 2049–2058, https://doi.org/10.1029/92WR00802, 1992. a, b, c, d
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, https://doi.org/10.1016/j.advwatres.2005.08.006, 2006. a
Kuffour, B. N. O., Engdahl, N. B., Woodward, C. S., Condon, L. E., Kollet, S., and Maxwell, R. M.: Simulating coupled surface–subsurface flows with ParFlow v3.5.0: capabilities, applications, and ongoing development of an open-source, massively parallel, integrated hydrologic model, Geosci. Model Dev., 13, 1373–1397, https://doi.org/10.5194/gmd-13-1373-2020, 2020. a
Lai, W. and Ogden, F. L.: A mass-conservative finite volume predictor–corrector solution of the 1D Richards' equation, J. Hydrol., 523, 119–127, https://doi.org/10.1016/j.jhydrol.2015.01.053, 2015. a, b, c, d
Li, Z.: Test cases for SERGHEI v2.0 (SERGHEI-RE), Zenodo [data set], https://doi.org/10.5281/zenodo.13282882, 2024. a
Li, Z., Hodges, B. R., and Shen, X.: Modeling hypersalinity caused by evaporation and surface-subsurface exchange in a coastal marsh, J. Hydrol., 618, 129268, https://doi.org/10.1016/j.jhydrol.2023.129268, 2023. a
Mao, W., Zhu, Y., Ye, M., Zhang, X., Wu, J., and Yang, J.: A new quasi-3-D model with a dual iterative coupling scheme for simulating unsaturated-saturated water flow and solute transport at a regional scale, J. Hydrol., 602, 126780, https://doi.org/10.1016/j.jhydrol.2021.126780, 2021. a, b
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. a
Morway, E. D., Niswonger, R. G., Langevin, C. D., Bailey, R. T., and Healy, R. W.: Modeling Variably Saturated Subsurface Solute Transport with MODFLOW-UZF and MT3DMS, Groundwater, 51, 237–251, https://doi.org/10.1111/j.1745-6584.2012.00971.x, 2013. a, b
Mualem, Y.: A new model for predicting the hydraulic conductivity of unsaturated porous media, Water Resour. Res., 12, 513–522, https://doi.org/10.1029/WR012i003p00513, 1976. a
Orgogozo, L., Renon, N., Soulaine, C., Hénon, F., Tomer, S., Labat, D., Pokrovsky, O., Sekhar, M., Ababou, R., and Quintard, M.: An open source massively parallel solver for Richards equation: mechanistic modelling of water fluxes at the watershed scale, Comput. Phys. Commun., 185, 3358–3371, https://doi.org/10.1016/j.cpc.2014.08.004, 2014. a
Özgen-Xian, I., Molins, S., Johnson, R. M., Xu, Z., Dwivedi, D., Loritz, R., Mital, U., Ulrich, C., Yan, Q., and Steefel, C. I.: Understanding the hydrological response of a headwater-dominated catchment by analysis of distributed surface–subsurface interactions, Sci. Rep., 13, 4669, https://doi.org/10.1038/s41598-023-31925-w, 2023. a
Paniconi, C. and Putti, M.: A comparison of Picard and Newton iteration in the numerical solution of multidimensional variably saturated flow problems, Water Resour. Res., 30, 3357–3374, https://doi.org/10.1029/94WR02046, 1994. a
Paniconi, C. and Putti, M.: Physically based modeling in catchment hydrology at 50: Survey and outlook, Water Resour. Res., 51, 7090–7129, https://doi.org/10.1002/2015WR017780, 2015. a, b
Phoon, K., Tan, T., and Chong, P.: Numerical simulation of Richards equation in partially saturated porous media: under-relaxation and mass balance, Geotech. Geol. Eng., 25, 525–541, https://doi.org/10.1007/s10706-007-9126-7, 2007. a
Richards, L. A.: Capillary conduction of liquids through porous mediums, Physics, 1, 318–333, 1931. a
Richardson, L. F.: Weather Prediction by Numerical Process, Cambridge University Press, Cambridge, UK, https://doi.org/10.1017/CBO9780511618291, 1922. a
Shen, C. and Phanikumar, M. S.: A process-based, distributed hydrologic model based on a large-scale method for surface–subsurface coupling, Adv. Water Resour., 33, 1524–1541, https://doi.org/10.1016/j.advwatres.2010.09.002, 2010. a
Šimůnek, J., Šejna, M., Saito, H., Sakai, M., and van Genuchten, M. T.: The HYDRUS-1D Software Package for Simulating theOne-Dimensional Movement of Water, Heat, andMultiple Solutes in Variably-Saturated Media v4.17, University of California Riverside, Riverside, California, https://www.pc-progress.com/Downloads/Pgm_hydrus1D/HYDRUS1D-4.17.pdf (last access: 4 August 2024), 2013. a, b
Šimůnek, J., van Genuchten, M. T., and Šejna, M.: Recent Developments and Applications of the HYDRUS Computer Software Packages, Vadose Zone J., 15, 1–15, https://doi.org/10.2136/vzj2016.04.0033, 2016. a
Trott, C. R., Lebrun-Grandié, D., Arndt, D., Ciesko, J., Dang, V., Ellingwood, N., Gayatri, R., Harvey, E., Hollman, D. S., Ibanez, D., Liber, N., Madsen, J., Miles, J., Poliakoff, D., Powell, A., Rajamanickam, S., Simberg, M., Sunderland, D., Turcksin, B., and Wilke, J.: Kokkos 3: Programming Model Extensions for the Exascale Era, IEEE T. Parallel Distr., 33, 805–817, https://doi.org/10.1109/TPDS.2021.3097283, 2022. a
van Genuchten, M.: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980. a
van Lier, Q. J. and Pinheiro, E. A. R.: An alert regarding a common misinterpretation of the van Genuchten α parameter, Rev. Bras. Ciênc. Solo, 42, e0170343, https://doi.org/10.1590/18069657rbcs20170343, 2018. a
Vogel, T.: Effect of the shape of the soil hydraulic functions near saturation on variably-saturated flow predictions, Adv. Water Resour., 24, 133–144, https://doi.org/10.1016/S0309-1708(00)00037-3, 2001. a
Warrick, A. W., Lomen, D. O., and Yates, S. R.: A Generalized Solution to Infiltration, Soil Sci. Soc. Am. J., 49, 34–38, https://doi.org/10.2136/sssaj1985.03615995004900010006x, 1985. a, b
Zha, Y., Tso, M. C.-H., Shi, L., and Yang, J.: Comparison of Noniterative Algorithms Based on Different Forms of Richards' Equation, Environ. Model. Assess., 21, 357–370, https://doi.org/10.1007/s10666-015-9467-1, 2016. a
Zha, Y., Yang, J., Yin, L., Zhang, Y., Zeng, W., and Shi, L.: A modified Picard iteration scheme for overcoming numerical difficulties of simulating infiltration into dry soil, J. Hydrol., 551, 56–69, https://doi.org/10.1016/j.jhydrol.2017.05.053, 2017. a
Zha, Y., Yang, J., Zeng, J., Tso, C. M., Zeng, W., and Shi, L.: Review of numerical solution of Richardson–Richards equation for variably saturated flow in soils, WIREs Water, 6, e1364, https://doi.org/10.1002/wat2.1364, 2019. a, b
Zhang, Z., Wang, W., Gong, C., Jim Yeh, T.-C., Duan, L., and Wang, Z.: Finite analytic method: Analysis of one-dimensional vertical unsaturated flow in layered soils, J. Hydrol., 597, 125716, https://doi.org/10.1016/j.jhydrol.2020.125716, 2021. a
Short summary
We introduce SERGHEI-RE, a 3D subsurface flow simulator with performance-portable parallel computing capabilities. SERGHEI-RE performs effectively on various computational devices: from personal computers to advanced clusters. It allows users to solve flow equations with multiple numerical schemes, making it adaptable to various hydrological scenarios. Testing results show its accuracy and performance, confirming that SERGHEI-RE is a powerful tool for hydrological research.
We introduce SERGHEI-RE, a 3D subsurface flow simulator with performance-portable parallel...
