Articles | Volume 18, issue 11
https://doi.org/10.5194/gmd-18-3241-2025
© 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-18-3241-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Energy Geosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA
Benjamin J. Andre
Energy Geosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA
Jeffrey N. Johnson
Energy Geosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA
Cohere Consulting, LLC, Seattle, WA 98105, USA
Glenn E. Hammond
Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Benjamin N. Sulman
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Konstantin Lipnikov
Applied Mathematics and Plasma Physics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Marcus S. Day
Computational Science Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
James J. Beisman
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Daniil Svyatsky
Applied Mathematics and Plasma Physics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Hang Deng
Department of Energy and Resources Engineering, Peking University, Peking, 100871, China
Peter C. Lichtner
Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA
Carl I. Steefel
Energy Geosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA
J. David Moulton
Applied Mathematics and Plasma Physics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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Cited articles
Andre, B., Molins, S., Johnson, J., and Steefel, C.: Alquimia, U.S. Department of Energy Office of Scientific and Technical Information, https://doi.org/10.11578/DC.20210416.49, 2013. a, b
Andre, B., Molins, S., Johnson, J., and Steefel, C. I.: Alquimia, GitHub, https://github.com/LBL-EESA/alquimia-dev (last access: 25 May 2025), 2015. a
Balos, C. J., Day, M., Esclapez, L., Felden, A. M., Gardner, D. J., Hassanaly, M., Reynolds, D. R., Rood, J. S., Sexton, J. M., Wimer, N. T., and Woodward, C. S.: SUNDIALS time integrators for exascale applications with many independent systems of ordinary differential equations, Int. J. High Perform. C., 39, 123–146, https://doi.org/10.1177/10943420241280060, 2025. a
Bao, C., Li, L., Shi, Y., and Duffy, C.: Understanding watershed hydrogeochemistry: 1. Development of RT?Flux?PIHM, Water Resour. Res., 53, 2328–2345, https://doi.org/10.1002/2016WR018934, 2017. a
Bartlett, R., Demeshko, I., Gamblin, T., Hammond, G., Heroux, M. A., Johnson, J., Klinvex, A., Li, X., McInnes, L. C., Moulton, J. D., Osei-Kuffuor, D., Sarich, J., Smith, B., Willenbring, J., and Yang, U. M.: xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit, Supercomputing Frontiers and Innovations, 4, 69–82, https://doi.org/10.14529/jsfi170104, 2017. a, b
Beisman, J., Maxwell, R., Navarre-Sitchler, A., Steefel, C., and Molins, S.: ParCrunchFlow: an efficient, parallel reactive transport simulation tool for physically and chemically heterogeneous saturated subsurface environments, Comput. Geosci., 19, 403–422, https://doi.org/10.1007/s10596-015-9475-x, 2015. a, b, c
Bethke, C. M.: The Geochemist's Workbench® Release 17 ChemPlugin™ User's Guide, Aqueous Solutions, LLC, Champaign, Illinois, https://www.gwb.com/pdf/GWB/ChemPluginUsersGuide.pdf (last access: 24 May 2025), 2024. a
CFFI: CFFI documentation – CFFI 1.15.1 documentation, https://cffi.readthedocs.io/en/latest/ (last access: 24 May 2025), 2023. a
Chang, E., Zavarin, M., Beverly, L., and Wainwright, H.: A chemistry-informed hybrid machine learning approach to predict metal adsorption onto mineral surfaces, Appl. Geochem., 155, 105731, https://doi.org/10.1016/j.apgeochem.2023.105731, 2023. a
Charlton, S. R. and Parkhurst, D. L.: Modules based on the geochemical model PHREEQC for use in scripting and programming languages, Comput. Geosci., 37, 1653–1663, https://doi.org/10.1016/j.cageo.2011.02.005, 2011. a
Coon, E., Svyatsky, D., Jan, A., Kikinzon, E., Berndt, M., Atchley, A., Harp, D., Manzini, G., Shelef, E., Lipnikov, K., Garimella, R., Xu, C., Moulton, D., Karra, S., Painter, S., Jafarov, E., and Molins, S.: Advanced Terrestrial Simulator, U.S. Department of Energy Office of Scientific and Technical Information, https://doi.org/10.11578/DC.20190911.1, 2019. a, b
De Lucia, M., Kühn, M., Lindemann, A., Lübke, M., and Schnor, B.: POET (v0.1): speedup of many-core parallel reactive transport simulations with fast DHT lookups, Geosci. Model Dev., 14, 7391–7409, https://doi.org/10.5194/gmd-14-7391-2021, 2021. a
Dubey, A., Almgren, A., Bell, J., Berzins, M., Brandt, S., Bryan, G., Colella, P., Graves, D., Lijewski, M., L”offler, F., O'Shea, B., Schnetter, E., Van Straalen, B., and Weide, K.: A survey of high level frameworks in block-structured adaptive mesh refinement packages, J. Parallel and Distr. Com., 74, 3217–3227, https://doi.org/10.1016/j.jpdc.2014.07.001, 2014. a, b, c
GitHub: GitHub Actions documentation, https://docs.github.com/en/actions (last access: 24 May 2025), 2024. a
Greskowiak, J., Gwo, J., Jacques, D., Yin, J., and Mayer, K. U.: A benchmark for multi-rate surface complexation and 1D dual-domain multi-component reactive transport of U(VI), Comput. Geosci., 19, 585–597, https://doi.org/10.1007/s10596-014-9457-4, 2015. a
Gupta, A. D., Lake, L. W., Pope, G. A., Sepehrnoori, K. T. U., and King, M. J. B. R.: High-Resolution Monotonic Schemes for Reservoir Fluid Flow Simulation, In Situ, (United States), 15, https://www.osti.gov/scitech/biblio/5799660 (last access: 24 May 2025), 1991. a
Hammond, G. E.: The PFLOTRAN Reaction Sandbox, Geosci. Model Dev., 15, 1659–1676, https://doi.org/10.5194/gmd-15-1659-2022, 2022. a, b, c, d
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
Jara, D., de Dreuzy, J.-R., and Cochepin, B.: TReacLab: An object-oriented implementation of non-intrusive splitting methods to couple independent transport and geochemical software, Comput. Geosci., 109, 281–294, https://doi.org/10.1016/j.cageo.2017.09.005, 2017. a
Jaysaval, P., Hammond, G. E., and Johnson, T. C.: Massively parallel modeling and inversion of electrical resistivity tomography data using PFLOTRAN, Geosci. Model Dev., 16, 961–976, https://doi.org/10.5194/gmd-16-961-2023, 2023. a
LaFond-Hudson, S. and Sulman, B.: Modeling strategies and data needs for representing coastal wetland vegetation in land surface models, New Phytol., 238, 938–951, https://doi.org/10.1111/nph.18760, 2023. a, b, c
Leal, A. M. M., Kyas, S., Kulik, D. A., and Saar, M. O.: Accelerating Reactive Transport Modeling: On-Demand Machine Learning Algorithm for Chemical Equilibrium Calculations, Transport Porous Med., 133, 161–204, https://doi.org/10.1007/s11242-020-01412-1, 2020. a
Li, L., Maher, K., Navarre-Sitchler, A., Druhan, J., Meile, C., Lawrence, C., Moore, J., Perdrial, J., Sullivan, P., Thompson, A., Jin, L., Bolton, E. W., Brantley, S. L., Dietrich, W. E., Mayer, K. U., Steefel, C. I., Valocchi, A., Zachara, J., Kocar, B., Mcintosh, J., Bao, C., Tutolo, B. M., Beisman, J., Kumar, M., and Sonnenthal, E.: Expanding the role of reactive transport models in critical zone processes, Earth-Sci. Rev., 165, 280–301, https://doi.org/10.1016/j.earscirev.2016.09.001, 2017. a, b
Lichtner, P. C.: The Quasi-Stationary State Approximation to Fluid/Rock Reaction: Local Equilibrium Revisited, in: Diffusion, Atomic Ordering, and Mass Transport: Selected Topics in Geochemistry, edited by: Ganguly, J., Advances in Physical Geochemistry, Springer US, New York, NY, 452–560, ISBN 978-1-4613-9019-0, https://doi.org/10.1007/978-1-4613-9019-0_13, 1991. a
Lichtner, P. C., Hammond, G. E., Lu, C., Karra, S., Bisht, G., Andre, B. N. C. F. A. R., Lawrence Berkeley National Lab. (LBNL), B., Mills, R. I. C., Univ. of Tennessee, K., and Kumar, J.: Pflotran User Manual: A Massively Parallel Reactive Flow and Transport Model for Describing Surface and Subsurface Processes, Tech. Rep. LA-UR–15-20403, Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States), Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States), Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States), OFM Research, Redmond, WA, USA, https://doi.org/10.2172/1168703, 2015. a
Maxwell, R. M. and Miller, N. L.: Development of a Coupled Land Surface and Groundwater Model, J. Hydrometeorol., 6, 233–247, https://doi.org/10.1175/JHM422.1, 2005. a
Maxwell, R. M., Putti, M., Meyerhoff, S., Delfs, J.-O., Ferguson, I. M., Ivanov, V., Kim, J., Kolditz, O., Kollet, S. J., Kumar, M., Lopez, S., Niu, J., Paniconi, C., Park, Y.-J., Phanikumar, M. S., Shen, C., Sudicky, E. A., and Sulis, M.: Surface-subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks, Water Resour. Res., 50, 1531–1549, https://doi.org/10.1002/2013WR013725, 2014. a
Mayer, K. U., Frind, E. O., and Blowes, D. W.: Multicomponent reactive transport modeling in variably saturated porous media using a generalized formulation for kinetically controlled reactions, Water Resour. Res., 38, 1174, https://doi.org/10.1029/2001WR000862, 2002. a
Molins, S.: Reactive Interfaces in Direct Numerical Simulation of Pore-Scale Processes, Rev. Mineral. Geochem., 80, 461–481, https://doi.org/10.2138/rmg.2015.80.14, 2015. a
Molins, S., Trebotich, D., Steefel, C. I., and Shen, C.: An investigation of the effect of pore scale flow on average geochemical reaction rates using direct numerical simulation, Water Resour. Res., 48, https://doi.org/10.1029/2011WR011404, 2012. a, b
Molins, S., Soulaine, C., Prasianakis, N. I., Abbasi, A., Poncet, P., Ladd, A. J. C., Starchenko, V., Roman, S., Trebotich, D., Tchelepi, H. A., and Steefel, C. I.: Simulation of mineral dissolution at the pore scale with evolving fluid-solid interfaces: review of approaches and benchmark problem set, Comput. Geosci., 25, 1285–1318, https://doi.org/10.1007/s10596-019-09903-x, 2020. a, b
Molins, S., Andre, B., Johnson, J., Hammond, G., Sulman, B., Lipnikov, K., Day, M., Beisman, J., Svyatskiy, D., Deng, H., Lichtner, P., Steefel, C., and Moulton, D.: Alquimia: A generic interface to biogeochemical codes – A tool for interoperable development, prototyping and benchmarking for multiphysics simulators, Zenodo [code], https://doi.org/10.5281/zenodo.11414442, 2024a. a
Molins, S., Trebotich, D., and Steefel, C. I.: Approaches for the simulation of coupled processes in evolving fractured porous media enabled by exascale computing, Comput. Sci. Eng., 26, 33–42, https://doi.org/10.1109/MCSE.2024.3403983, 2024b. a
Nardi, A., Idiart, A., Trinchero, P., de Vries, L. M., and Molinero, J.: Interface COMSOL-PHREEQC (iCP), an efficient numerical framework for the solution of coupled multiphysics and geochemistry, Comput. Geosci., 69, 10–21, https://doi.org/10.1016/j.cageo.2014.04.011, 2014. a
OpenFOAM: Open-source Field Operation And Manipulation, https://www.openfoam.com/ (last access: 24 May 2025), 2022. a
Parkhurst, D. L. and Wissmeier, L.: PhreeqcRM: A reaction module for transport simulators based on the geochemical model PHREEQC, Adv. Water Resour., 83, 176–189, https://doi.org/10.1016/j.advwatres.2015.06.001, 2015. a
Parkhurst, D. L., Kipp, K. L., and Charlton, S. R.: PHAST Version 2 – A program for simulating groundwater flow, solute transport, and multicomponent geochemical reactions, in: U.S. Geological Survey Techniques and Methods, 6-A35, 235, https://doi.org/10.3133/tm6A35, 2010. a
Prommer, H. and Post, V.: PHT3D: A ReactiveMulticomponent Transport Model for Saturated Porous Media, User's Manual v2.10, http://www.pht3d.org (last access: 24 May 2025), 2010. a
Simunek, J., Jacques, D., Langergraber, G., Bradford, S. A., Šejna, M., and Genuchten, M. T. v.: Numerical Modeling of Contaminant Transport Using HYDRUS and its Specialized Modules, J. Indian I. Sci., 93, 265–284, https://journal.iisc.ac.in/index.php/iisc/article/view/1224 (last access: 24 May 2025), 2013. a
Sonnenthal, E., Spycher, N., Xu, T., and Zheng, L.: TOUGHREACT V4.12-OMP and TReactMech V1.0 Geochemical and Reactive-Transport User Guide, https://escholarship.org/uc/item/8945d2c1 (last access: 24 May 2025), 2021. a
Sphinx: Welcome – Sphinx documentation, https://www.sphinx-doc.org/en/master/ (last access: 24 May 2025), 2024. a
Steefel, C. I., DePaolo, D. J., and Lichtner, P. C.: Reactive transport modeling: An essential tool and a new research approach for the Earth sciences, Earth Planet. Sc. Lett., 240, 539–558, https://doi.org/10.1016/j.epsl.2005.09.017, 2005. a
Steefel, C. I., Appelo, C. A. J., Arora, B., Jacques, D., Kalbacher, T., Kolditz, O., Lagneau, V., Lichtner, P. C., Mayer, K. U., Meeussen, J. C. L., Molins, S., Moulton, D., Shao, H., Šimůnek, J., Spycher, N., Yabusaki, S. B., and Yeh, G. T.: Reactive transport codes for subsurface environmental simulation, Comput. Geosci., 19, 445–478, https://doi.org/10.1007/s10596-014-9443-x, 2015. a, b, c, d, e, f, g, h, i
Sulman, B., Yuan, F., O'Meara, T., Graham, D., Gu, B., Herndon, E., and Zheng, J.: Simulated hydrological dynamics and coupled iron redox cycling impact methane production in an Arctic soil: Modeling Archive, ESS-Dive [code], https://doi.org/10.5440/1814844, 2020. a, b
Sulman, B., Wang, J., LaFond-Hudson, S., O'Meara, T., Yuan, F., Molins, S., Forbrich, I., Cardon, Z., and Giblin, A.: Model simulations of Plum Island Ecosystems LTER low marsh site using ELM-PFLOTRAN, ESS-Dive [code], https://doi.org/10.15485/1991625, 2023. a
Sulman, B. N., Yuan, F., O'Meara, T., Gu, B., Herndon, E. M., Zheng, J., Thornton, P. E., and Graham, D. E.: Simulated Hydrological Dynamics and Coupled Iron Redox Cycling Impact Methane Production in an Arctic Soil, J. Geophys. Res.-Biogeo., 127, e2021JG006662, https://doi.org/10.1029/2021JG006662, 2022. a, b, c
Sulman, B. N., Wang, J., LaFond-Hudson, S., O'Meara, T., Yuan, F., Molins, S., Hammond, G. E., Forbrich, I., Cardon, Z., and Giblin, A.: Integrating tide-driven wetland soil redox and biogeochemical interactions into a land surface model, J. Adv. Model. Earth Sy., 16, e2023MS004002, https://doi.org/10.1029/2023MS004002, 2024. a, b, c, d, e, f
Tang, G., Yuan, F., Bisht, G., Hammond, G. E., Lichtner, P. C., Kumar, J., Mills, R. T., Xu, X., Andre, B., Hoffman, F. M., Painter, S. L., and Thornton, P. E.: Addressing numerical challenges in introducing a reactive transport code into a land surface model: a biogeochemical modeling proof-of-concept with CLM–PFLOTRAN 1.0, Geosci. Model Dev., 9, 927–946, https://doi.org/10.5194/gmd-9-927-2016, 2016. a
van der Lee, J., De Windt, L., Lagneau, V., and Goblet, P.: Module-oriented modeling of reactive transport with HYTEC, Comput. Geosci., 29, 265–275, https://doi.org/10.1016/S0098-3004(03)00004-9, 2003. a
Wang, J., O'Meara, T., LaFond-Hudson, S., He, S., Maiti, K., Ward, E. J., and Sulman, B. N.: Subsurface Redox Interactions Regulate Ebullitive Methane Flux in Heterogeneous Mississippi River Deltaic Wetland, J. Adv. Model. Earth Sy., 16, e2023MS003762, https://doi.org/10.1029/2023MS003762, 2024. a, b, c
Zhang, Q., Deng, H., Dong, Y., Molins, S., Li, X., and Steefel, C.: Investigation of Coupled Processes in Fractures and the Bordering Matrix via a Micro-Continuum Reactive Transport Model, Water Resour. Res., 58, e2021WR030578, https://doi.org/10.1029/2021WR030578, 2022. a
Zhang, Q., Dong, Y., Molins, S., and Deng, H.: The Impacts of Micro-Porosity and Mineralogical Texture on Fractured Rock Alteration, Water Resour. Res., 60, e2023WR036266, https://doi.org/10.1029/2023WR036266, 2024. a
Short summary
Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Developing scientific software and making sure it functions properly requires a significant...