Articles | Volume 18, issue 20
https://doi.org/10.5194/gmd-18-7321-2025
© 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-18-7321-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A component-based modular treatment of the soil–plant–atmosphere continuum: the GEOSPACE framework (v.1.2.9)
Concetta D'Amato
CORRESPONDING AUTHOR
Center Agriculture Food Environment – C3A, University of Trento, Trento, Italy
Department of Civil, Environmental and Mechanical Engineering – DICAM, University of Trento, Trento, Italy
Niccolò Tubini
Department of Civil, Environmental and Mechanical Engineering – DICAM, University of Trento, Trento, Italy
Riccardo Rigon
Center Agriculture Food Environment – C3A, University of Trento, Trento, Italy
Department of Civil, Environmental and Mechanical Engineering – DICAM, University of Trento, Trento, Italy
Related authors
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Niccolò Tubini, Concetta D'Amato, Olaf David, and Christian Massari
Hydrol. Earth Syst. Sci., 26, 4773–4800, https://doi.org/10.5194/hess-26-4773-2022, https://doi.org/10.5194/hess-26-4773-2022, 2022
Short summary
Short summary
The
Digital Earth(DE) metaphor is very useful for both end users and hydrological modelers. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate information technology infrastructure. It is remarked that DARTHs have to, by construction, support the open-science movement and its ideas.
Niccolò Tubini and Stephan Gruber
EGUsphere, https://doi.org/10.5194/egusphere-2025-2649, https://doi.org/10.5194/egusphere-2025-2649, 2025
Short summary
Short summary
This research introduces a new model for simulating how melting ground ice in permafrost reshapes the land surface over time. It shows that small differences in soil and the depth where ice is found can cause large differences in how the ground sinks or rises. This helps improves our ability to predict future impacts on terrain, ecosystems, and infrastructure as the climate warms.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
Short summary
Short summary
We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Niccolò Tubini, Concetta D'Amato, Olaf David, and Christian Massari
Hydrol. Earth Syst. Sci., 26, 4773–4800, https://doi.org/10.5194/hess-26-4773-2022, https://doi.org/10.5194/hess-26-4773-2022, 2022
Short summary
Short summary
The
Digital Earth(DE) metaphor is very useful for both end users and hydrological modelers. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate information technology infrastructure. It is remarked that DARTHs have to, by construction, support the open-science movement and its ideas.
Niccolò Tubini and Riccardo Rigon
Geosci. Model Dev., 15, 75–104, https://doi.org/10.5194/gmd-15-75-2022, https://doi.org/10.5194/gmd-15-75-2022, 2022
Short summary
Short summary
This paper presents WHETGEO and its 1D deployment: a new physically based model simulating the water and energy budgets in a soil column. WHETGEO-1D is intended to be the first building block of a new customisable land-surface model that is integrated with process-based hydrology. WHETGEO is developed as an open-source code and is fully integrated into the GEOframe/OMS3 system, allowing the use of the many ancillary tools it provides.
Niccolò Tubini, Stephan Gruber, and Riccardo Rigon
The Cryosphere, 15, 2541–2568, https://doi.org/10.5194/tc-15-2541-2021, https://doi.org/10.5194/tc-15-2541-2021, 2021
Short summary
Short summary
We present a new method to compute temperature changes with melting and freezing – a fundamental challenge in cryosphere research – extremely efficiently and with guaranteed correctness of the energy balance for any time step size. This is a key feature since the integration time step can then be chosen according to the timescale of the processes to be studied, from seconds to days.
Cited articles
Allen, R. G.: A Penman for All Seasons, J. Irrig. Drain. Eng., 112, 348–368, 1986. a
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56, Vol. 56, FAO Rome, ISBN 92-5-104219-5, https://www.fao.org/3/x0490e/x0490e00.htm (last access: 1 October 2025), 1998. a
Anderson, M. C., Kustas, W. P., and Norman, J. M.: Upscaling and downscaling – A regional view of the soil–plant–atmosphere continuum, Agron. J., 95, 1408–1423, 2003. a
Argent, R. M.: An overview of model integration for environmental applications – components, frameworks and semantics, Environ. Modell. Softw., 19, 219–234, 2004. a
Asadollahi, M., Nehemy, M. F., McDonnell, J. J., Rinaldo, A., and Benettin, P.: Toward a closure of catchment mass balance: Insight on the missing link from a vegetated lysimeter, Water Resour. Res., 58, e2021WR030698, https://doi.org/10.1029/2021wr030698, 2022. a
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions, in: Progress in Photosynthesis Research: Volume 4 Proceedings of the VIIth International Congress on Photosynthesis Providence, Rhode Island, USA, 10–15 August 1986, edited by: Biggins, J., 221–224, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-017-0519-6_48, 1987. a, b
Bancheri, M., Rigon, R., and Manfreda, S.: The GEOframe-NewAge Modelling System Applied in a Data Scarce Environment, Water, 12, 86, https://doi.org/10.3390/w12010086, 2020. a
Benettin, P., Queloz, P., Bensimon, M., McDonnell, J. J., and Rinaldo, A.: Velocities, residence times, tracer breakthroughs in a vegetated lysimeter: a multitracer experiment, Water Resour. Res., 55, 21–33, https://doi.org/10.1029/2018wr023894, 2019. a
Benettin, P., Nehemy, M. F., Asadollahi, M., Pratt, D., Bensimon, M., McDonnell, J. J., and Rinaldo, A.: Tracing and closing the water balance in a vegetated lysimeter, Water Resour. Res., 57, https://doi.org/10.1029/2020wr029049, 2021a. a
Benettin, P., Nehemy, M. F., Cernusak, L. A., Kahmen, A., and McDonnell, J. J.: On the use of leaf water to determine plant water source A proof of concept, Hydrol. Process., 35, https://doi.org/10.1002/hyp.14073, 2021b. a
Berti, G.: Generic software components for Scientific Computing, PhD thesis, Naturwissenschaften und Informatik der Brandenburgischen Technischen Universität Cottbus, https://www.researchgate.net/publication/239065936_Generic_software_components_for_Scientific_Computing#fullTextFileContent (last access: 1 October 2025), 2000. a, b
Beyer, M., Hamutoko, J. T., Wanke, H., Gaj, M., and Koeniger, P.: Examination of deep root water uptake using anomalies of soil water stable isotopes, depth-controlled isotopic labeling and mixing models, J. Hydrol., 566, 122–136, 2018. a
Bierkens, M. F. P.: Global hydrology 2015: State, trends, and directions, Water Resour. Res., 51, 4923–4947, https://doi.org/10.1002/2015WR017173, 2015. a
Bonan, G.: Climate Change and Terrestrial Ecosystem Modeling, Cambridge University Press, https://doi.org/10.1017/9781107339217, 2019. a
Bonan, G. B., Lucier, O., Coen, D. R., Foster, A. C., Shuman, J. K., Laguë, M. M., Swann, A. L. S., Lombardozzi, D. L., Wieder, W. R., Dahlin, K. M., Rocha, A. V., and SanClements, M. D.: Reimagining Earth in the Earth system, J. Adv. Model. Earth Sy., 16, e2023MS004017, https://doi.org/10.1029/2023MS004017, 2024. a
Bottazzi, M., Bancheri, M., Mobilia, M., Bertoldi, G., Longobardi, A., and Rigon, R.: Comparing evapotranspiration estimates from the GEOframe-Prospero model with Penman–Monteith and Priestley-Taylor approaches under different climate conditions, Water, 13, 1221, https://doi.org/10.3390/w13091221, 2021. a, b, c, d, e, f, g, h, i, j
Brunet, Y.: Turbulent Flow in Plant Canopies: Historical Perspective and Overview, Bound.-Lay. Meteorol., 177, 315–364, 2020. a
Cassiani, G., Boaga, J., Vanella, D., Perri, M. T., and Consoli, S.: Monitoring and modelling of soil–plant interactions: the joint use of ERT, sap flow and eddy covariance data to characterize the volume of an orange tree root zone, Hydrol. Earth Syst. Sci., 19, 2213–2225, https://doi.org/10.5194/hess-19-2213-2015, 2015. a
Chen, M., Voinov, A., Ames, D. P., Kettner, A. J., Goodall, J. L., Jakeman, A. J., Barton, M. C., Harpham, Q., Cuddy, S. M., DeLuca, C., Yue, S., Wang, J., Zhang, F., Wen, Y., and Lü, G.: Position paper: Open web-distributed integrated geographic modelling and simulation to enable broader participation and applications, Earth Sci. Rev., 207, 103223, https://doi.org/10.1016/j.earscirev.2020.103223, 2020. a
Collins, N., Theurich, G., DeLuca, C., Suarez, M., Trayanov, A., Balaji, V., Li, P., Yang, W., Hill, C., and da Silva, A.: Design and Implementation of Components in the Earth System Modeling Framework, Int. J. High Perform. Comput. Appl., 19, 341–350, 2005. a
Craig, J. R., Brown, G., Chlumsky, R., Jenkinson, R. W., Jost, G., Lee, K., Mai, J., Serrer, M., Sgro, N., Shafii, M., Snowdon, A. P., and Tolson, B. A.: Flexible watershed simulation with the Raven hydrological modelling framework, Environ. Modell. Softw., 129, 104728, https://doi.org/10.1016/j.envsoft.2020.104728, 2020. a
Cranko Page, J., Abramowitz, G., De Kauwe, M. G., and Pitman, A. J.: Are Plant Functional Types fit for purpose?, Geophys. Res. Lett., 51, https://doi.org/10.1029/2023GL104962, 2024. a
Dai, Y., Dickinson, R. E., and Wang, Y.-P.: A Two-Big-Leaf Model for Canopy Temperature, Photosynthesis, and Stomatal Conductance, J. Climate, 17, 2281–2299, 2004. a
Daly, E., Porporato, A., and Rodriguez-Iturbe, I.: Coupled Dynamics of Photosynthesis, Transpiration, and Soil Water Balance. Part II: Stochastic Analysis and Ecohydrological Significance, J. Hydrometeorol., 5, 559–566, 2004. a
D'Amato, C. and Rigon, R.: Ecohydrological simulation using GEOSPACE-1D model, Zenodo [data set], https://doi.org/10.5281/zenodo.14269721, 2024. a, b, c, d
D'Amato, C., Tubini, N., and Rigon, R.: OMS project for GEOSPACE-1D v.1.2.9, Zenodo [data set], https://doi.org/10.5281/zenodo.16779840, 2025a. a
D'Amato, C., Tubini, N., and Rigon, R.: GEOSPACE-1D v.1.2.9, Zenodo [code], https://doi.org/10.5281/zenodo.16779108, 2025b. a
D'Amato, C., Benettin, P., Rinaldo, A., and Rigon, R.: Unlocking GEOSPACE capabilities: A comprehensive soil–plant–atmosphere framework, in preparation, 2025c. a
David, O., Lloyd, W., Rojas, K., Arabi, M., Geter, F., Ascough, J., Green, T., Leavesley, G., and Carlson, J.: Modeling-as-a-Service (MaaS) using the Cloud Services Innovation Platform (CSIP), in: International Congress on Environmental Modelling and Software, https://scholarsarchive.byu.edu (last access: 1 October 2025), 2014. a
de Pury, D. G. G.: SCALING PHOTOSYNTHESIS AND WATER USE FROM LEAVES TO PADDOCKS, PhD thesis, The Australian National University, http://hdl.handle.net/1885/13261 (last access: 1 October 2025), 1995. a
De Pury, D. G. G. and Farquhar, G. D.: Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models, Plant Cell Environ., 20, 537–557, https://doi.org/10.1111/j.1365-3040.1997.00094.x, 1997. a, b
Donovan, L. A. and Sperry, J.: Scaling the soil–plant–atmosphere continuum: from physics to ecosystems, Trends Plant Sci., 5, 510–512, 2000. a
Evaristo, J. and McDonnell, J. J.: Prevalence and magnitude of groundwater use by vegetation: a global stable isotope meta-analysis, Sci. Rep., 7, 44110, https://doi.org/10.1038/srep44110, 2017. a
Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., and Torboton, D: An overview of current applications, challenges, and future trends in distributed process-based models in hydrology, J. Hydrol., 537, 45–60, 2016. a, b
Finnigan, J. J., Shaw, R. H., and Patton, E. G.: Turbulence structure above a vegetation canopy, J. Fluid Mech., 637, 387–424, 2009. a
Fisher, R. A. and Koven, C. D.: Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems, J. Adv. Model. Earth Sy., 12, e2018MS001453, https://doi.org/10.1029/2018MS001453, 2020. a, b
Fowler, M.: UML Distilled: A Brief Guide to the Standard Object Modeling Language, 3rd edn., Addison-Wesley Professional, Boston, MA, ISBN 978-0321193681, 2004. a
Gardner, H. and Manduchi, G.: Design Patterns for e-Science, Springer Science & Business Media, ISBN 9783540680901, 2007. a
Giraud, M., Gall, S. L., Harings, M., Javaux, M., Leitner, D., Meunier, F., Rothfuss, Y., van Dusschoten, D., Vanderborght, J., Vereecken, H., Lobet, G., and Schnepf, A.: CPlantBox: a fully coupled modelling platform for the water and carbon fluxes in the soil–plant–atmosphere continuum, in silico Plants, 5, diad009, https://doi.org/10.1093/insilicoplants/diad009, 2023. a
Holling, C. S.: Adaptive Environmental Assessment and Management, International Institute for Applied Systems Analysis, ISBN 0471996327, 1978. a
Katul, G., Lai, C.-T., Schäfer, K., Vidakovic, B., Albertson, J., Ellsworth, D., and Oren, R.: Multiscale analysis of vegetation surface fluxes: from seconds to years, Adv. Water Resour., 24, 1119–1132, 2001. a
Katul, G. G., Oren, R., Manzoni, S., Higgins, C., and Parlange, M. B.: Evapotranspiration: A Process Driving Mass Transport and Energy Exchange in the Soil-Plant-Atmosphere-Climate System, Rev. Geophys., 50, https://doi.org/10.1029/2011RG000366, 2012. a
Kerches Braghiere, R.: Improving the treatment of vegetation canopy architecture in radiative transfer schemes, PhD thesis, University of Reading, https://centaur.reading.ac.uk/82394/ (last access: 1 October 2025), 2018. a
Kramer, P. J.: Plant and Soil Water Relationships: A Modern Synthesis, Q. Rev. Biol., 45, 218–218, 1970. a
Lai, C.-T. and Katul, G.: The dynamic role of root-water uptake in coupling potential to actual transpiration, Adv. Water Resour., 23, 427–439, 2000. a
Lehmann, A., Giuliani, G., Ray, N., Rahman, K., Abbaspour, K. C., Nativi, S., Craglia, M., Cripe, D., Quevauviller, P., and Beniston, M.: Reviewing innovative Earth observation solutions for filling science-policy gaps in hydrology, J. Hydrol., 518, 267–277, 2014. a
Leuning, R.: Modelling Stomatal Behaviour and and Photosynthesis of Eucalyptus grandis, Funct. Plant Biol., 17, 159–175, 1990. a
Lhomme, J.-P.: Stomatal control of transpiration: Examination of the Jarvis-type representation of canopy resistance in relation to humidity, Water Resour. Res., 37, 689–699, 2001. a
Li, L., Yang, Z.-L., Matheny, A. M., Zheng, H., Swenson, S. C., Lawrence, D. M., Barlage, M., Yan, B., McDowell, N. G., and Leung, L. R.: Representation of plant hydraulics in the Noah-MP land surface model: Model development and multiscale evaluation, J. Adv. Model. Earth Sy., 13, https://doi.org/10.1029/2020MS002214, 2021. a
Lloyd, W., David, O., Ascough, J. C., Rojas, K. W., Carlson, J. R., Leavesley, G. H., Krause, P., Green, T. R., and Ahuja, L. R.: Environmental modeling framework invasiveness: Analysis and implications, Environ. Modell. Softw., 26, 1240–1250, 2011. a
Luo, X., Chen, J. M., Liu, J., Black, T. A., Croft, H., Staebler, R., He, L., Arain, M. A., Chen, B., Mo, G., Gonsamo, A., and McCaughey, H.: Comparison of Big-Leaf, Two-Big-Leaf, and Two-Leaf Upscaling Schemes for Evapotranspiration Estimation Using Coupled Carbon-Water Modeling, J. Geophys. Res.-Biogeo., 123, 207–225, https://doi.org/10.1002/2017JG003978, 2018. a
Manoli, G., Huang, C.-W., Bonetti, S., Domec, J.-C., Marani, M., and Katul, G.: Competition for light and water in a coupled soil-plant system, Adv. Water Resour., 108, 216–230, 2017. a
Mauder, M., Foken, T., and Cuxart, J.: Surface-Energy-Balance Closure over Land: A Review, Bound.-Lay. Meteorol., 177, 395–426, 2020. a
McDermid, S. S., Mearns, L. O., and Ruane, A. C.: Representing agriculture in Earth System Models: Approaches and priorities for development, J. Adv. Model. Earth Sy., 9, 2230–2265, https://doi.org/10.1002/2016MS000749, 2017. a
McGrath, M. J., Ryder, J., Pinty, B., Otto, J., Naudts, K., Valade, A., Chen, Y., Weedon, J., and Luyssaert, S.: A multi-level canopy radiative transfer scheme for ORCHIDEE (SVN r2566), based on a domain-averaged structure factor, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2016-280, 2016. a
Mencuccini, M., Manzoni, S., and Christoffersen, B.: Modelling water fluxes in plants: from tissues to biosphere, New Phytol., 222, 1207–1222, 2019. a
Miralles, D. G., Vilà-Guerau de Arellano, J., McVicar, T. R., and Mahecha, M. D.: Vegetation-climate feedbacks across scales, Ann. N.Y. Acad. Sci., 1544, 27–41, https://doi.org/10.1111/nyas.15286, 2025. a
Molz, F. J.: Models of water transport in the soil-plant system: A review, Water Resour. Res., 17, 1245–1260, 1981. a
Molz, F. J. and Remson, I.: Extraction term models of soil moisture use by transpiring plants, Water Resour. Res., 6, 1346–1356, 1970. a
Moore, R. V. and Hughes, A. G.: Integrated environmental modelling: achieving the vision, Geol. Soc. Lond. Spec. Publ., 408, 17–34, 2017. a
National Research Council, Commission on Geosciences, Environment, and Resources, Board on Earth Sciences and Resources, and Committee on Basic Research Opportunities in the Earth Sciences: Basic Research Opportunities in Earth Science, National Academies Press, ISBN 9780309071338, 2001. a
Nehemy, M. F., Benettin, P., Asadollahi, M., Pratt, D., Rinaldo, A., and McDonnell, J. J.: Dataset: The SPIKE II experiment – Tracing the water balance, Zenodo [data set], https://doi.org/10.5281/zenodo.4037240, 2020. a
Nehemy, M. F., Benettin, P., Asadollahi, M., Pratt, D., Rinaldo, A., and McDonnell, J. J.: Tree water deficit and dynamic source water partitioning, Hydrol. Process., 35, https://doi.org/10.1002/hyp.14004, 2021. a
Nilsen, E. and Orcutt, D.: The Physiology of Plants Under Stress, Abiotic Factors, Wiley, ISBN 978-0-471-03512-1, 1996. a
Overgaard, J., Rosbjerg, D., and Butts, M. B.: Land-surface modelling in hydrological perspective – a review, Biogeosciences, 3, 229–241, https://doi.org/10.5194/bg-3-229-2006, 2006. a
Peckham, S. D., Hutton, E. W. H., and Norris, B.: A component-based approach to integrated modeling in the geosciences: The design of CSDMS, Comput. Geosci., 53, 3–12, 2013. a
Pereira, L. S., Allen, R. G., Smith, M., and Raes, D.: Crop evapotranspiration estimation with FAO56: Past and future, Agr. Water Manage., 147, 4–20, 2015. a
Perrochet, P.: Water uptake by plant roots – A simulation model, I. Conceptual model, J. Hydrol., 95, 55–61, 1987. a
Poggi, D., Katul, G. G., and Albertson, J. D.: A note on the contribution of dispersive fluxes to momentum transfer within canopies, Bound.-Lay. Meteorol., 111, 615–621, 2004. a
Porporato, A., Daly, E., and Rodriguez-Iturbe, I.: Soil water balance and ecosystem response to climate change, Am. Nat., 164, 625–632, 2004. a
Prentice, I. C., Liang, X., Medlyn, B. E., and Wang, Y.-P.: Reliable, robust and realistic: the three R's of next-generation land-surface modelling, Atmos. Chem. Phys., 15, 5987–6005, https://doi.org/10.5194/acp-15-5987-2015, 2015. a
Queloz, P., Bertuzzo, E., Carraro, L., Botter, G., Miglietta, F., Rao, P., and Rinaldo, A.: Transport of fluorobenzoate tracers in a vegetated hydrologic control volume: 1. Experimental results, Water Resour. Res., 51, 2773–2792, https://doi.org/10.1002/2014WR016433, 2015. a
Rahman, J. M., Seaton, S. P., Perraud, J. M., Hotham, H., Verrelli, D. I., and Coleman, J. R.: It's TIME for a new environmental modelling framework, in: Proceedings of MODSIM 2003: International Congress on Modelling and Simulation, Townsville, Australia, vol. 4, 1727–1732, https://mssanz.org.au/MODSIM03/Volume_04/C05/03_Rahman.pdf (last access: 1 October 2025), 2003. a
Richards, L. A.: Capillary conduction of liquids through porous mediums, Physics, 1, 318–333, 1931. a
Richards, M. and Ford, N.: Fundamentals of software architecture, O'Reilly Media, Sebastopol, CA, ISBN 9781492043454, 2020. a
Richardson, L. F.: Weather Prediction by Numerical Process, Cambridge University Press, https://doi.org/10.1017/CBO9780511618291, 1922. a
Rigon, R., Bertoldi, G., and Over, T. M.: GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets, J. Hydrometeorol., 7, 371–388, 2006. a
Rigon, R., Formetta, G., Bancheri, M., Tubini, N., D'Amato, C., David, O., and Massari, C.: HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists, Hydrol. Earth Syst. Sci., 26, 4773–4800, https://doi.org/10.5194/hess-26-4773-2022, 2022. a, b, c
Rouson, D., Xia, J., and Xu, X.: Scientific Software Design: The Object-Oriented Way, The object-oriented way, Cambridge University Press, Cambridge, UK, ISBN 978-0521888134, 2011. a
Ryu, Y., Baldocchi, D. D., Kobayashi, H., van Ingen, C., Li, J., Black, T. A., Beringer, J., van Gorsel, E., Knohl, A., Law, B. E., and Roupsard, O.: Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales, Global Biogeochem. Cy., 25, https://doi.org/10.1029/2011GB004053, 2011. a, b
Schröder, T., Javaux, M., Vanderborght, J., Körfgen, B., and Vereecken, H.: Effect of local soil hydraulic conductivity drop using a three-dimensional root water uptake model, Vadose Zone J., 7, 1089–1098, 2008. a
Schymanski, S. J. and Or, D.: Leaf-scale experiments reveal an important omission in the Penman–Monteith equation, Hydrol. Earth Syst. Sci., 21, 685–706, https://doi.org/10.5194/hess-21-685-2017, 2017. a, b, c
Serafin, F.: Enabling modeling framework with surrogate model- ing capabilities and complex networks, PhD thesis, University of Trento, https://hdl.handle.net/11572/369029 (last access: 1 October 2025), 2019. a
Silva, M., Matheny, A. M., Pauwels, V. R. N., Triadis, D., Missik, J. E., Bohrer, G., and Daly, E.: Tree hydrodynamic modelling of the soil–plant–atmosphere continuum using FETCH3, Geosci. Model Dev., 15, 2619–2634, https://doi.org/10.5194/gmd-15-2619-2022, 2022. a
Simard, S. W., Perry, D. A., Jones, M. D., Myrold, D. D., Durall, D. M., and Molina, R.: Net transfer of carbon between ectomycorrhizal tree species in the field, Nature, 388, 579–582, 1997. a
Staudinger, M., Stoelzle, M., Cochand, F., Seibert, J., Weiler, M., and Hunkeler, D.: Your work is my boundary condition!, J. Hydrol., 571, 235–243, 2019. a
Steudle, E.: Water uptake by plant roots: an integration of views, Plant Soil, 226, 45–56, 2000. a
Tubini, N. and Rigon, R.: Geoframepy 0.0.5, https://pypi.org/project/geoframepy/ (last access: 22 October 2021), 2021. a
Tubini, N., Gruber, S., and Rigon, R.: A method for solving heat transfer with phase change in ice or soil that allows for large time steps while guaranteeing energy conservation, The Cryosphere, 15, 2541–2568, https://doi.org/10.5194/tc-15-2541-2021, 2021. a, b, c, d
Vereecken, H., Weihermüller, L., Assouline, S., Šimůnek, J., Verhoef, A., Herbst, M., Archer, N., Mohanty, B., Montzka, C., Vanderborght, J., Balsamo, G., Bechtold, M., Boone, A., Chadburn, S., Cuntz, M., Decharme, B., Ducharne, A., Ek, M., Garrigues, S., Goergen, K., Ingwersen, J., Kollet, S., Lawrence, D. M., Li, Q., Or, D., Swenson, S., Vrese, P., Walko, R., Wu, Y., and Xue, Y.: Infiltration from the pedon to global grid scales: An overview and outlook for land surface modeling, Vadose Zone J., 18, 1–53, https://doi.org/10.2136/vzj2018.10.0191, 2019. a
Verhoef, A. and Egea, G.: Modeling plant transpiration under limited soil water: Comparison of different plant and soil hydraulic parameterizations and preliminary implications for their use in land surface models, Agr. Forest Meteorol., 191, 22–32, 2014a. a
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., 't Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18, 2016. a
York, L. M., Carminati, A., Mooney, S. J., Ritz, K., and Bennett, M. J.: The holistic rhizosphere: integrating zones, processes, and semantics in the soil influenced by roots, J. Exp. Bot., 67, 3629–3643, 2016. a
Yu, L.-Y., Cai, H.-J., Zheng, Z., Li, Z.-J., and Wang, J.: Towards a more flexible representation of water stress effects in the nonlinear Jarvis model, J. Integr. Agric., 16, 210–220, 2017. a
Short summary
This paper presents GEOSPACE and its 1D implementation: an open-source tool for simulating soil–plant–atmosphere continuum (SPAC) interactions. Using object-oriented programming, GEOSPACE modularizes SPAC processes, focusing on infiltration, evapotranspiration, and root water uptake. The 1D deployment integrates plant transpiration, soil evaporation, and root growth, providing a flexible and validated framework for ecohydrological modeling and applications.
This paper presents GEOSPACE and its 1D implementation: an open-source tool for simulating...