Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-75-2022
© Author(s) 2022. 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-15-75-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
07 Jan 2022
Model description paper |
| 07 Jan 2022
| 07 Jan 2022
Implementing the Water, HEat and Transport model in GEOframe (WHETGEO-1D v.1.0): algorithms, informatics, design patterns, open science features, and 1D deployment
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano 77, 38123 Trento, Italy
Riccardo Rigon
Center Agriculture Food Environment, University of Trento, Via Mesiano 77, 38123 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.
Shima Azimi, Manuela Girotto, Riccardo Rigon, Gaia Roati, Silvia Barbetta, and Christian Massari
EGUsphere, https://doi.org/10.5194/egusphere-2026-793, https://doi.org/10.5194/egusphere-2026-793, 2026
Short summary
Short summary
Even ground-based precipitation observations, often considered the most reliable, can introduce substantial uncertainty into snow modeling due to sparse gauge coverage at high elevations in mountainous catchments.This challenge motivates the present study, in which we propose a data assimilation framework that integrates satellite-based snow depth into a hydrological model to correct snowfall estimates over the Italian Alps, with implications for water management in data-scarce mountain regions.
John Mohd Wani, Kelly E. Gleason, Matteo Dall’Amico, Federico Di Paolo, Stefano Tasin, Gaia Roati, Marco Brian, Francesco Tornatore, and Riccardo Rigon
EGUsphere, https://doi.org/10.5194/egusphere-2025-5520, https://doi.org/10.5194/egusphere-2025-5520, 2025
Preprint archived
Short summary
Short summary
A 30-year study of Italy's Po River Basin reveals dramatic loss in snow below 2000 meters elevation, with some areas losing over 30 % of snow-water storage. The snow season is becoming shorter, mainly because snow arrives later in winter rather than melting earlier in spring. This represents a fundamental shift from stable to volatile conditions, with major implications for water security in one of Europe's second most climate sensitive areas.
Concetta D'Amato, Niccolò Tubini, and Riccardo Rigon
Geosci. Model Dev., 18, 7321–7355, https://doi.org/10.5194/gmd-18-7321-2025, https://doi.org/10.5194/gmd-18-7321-2025, 2025
Short summary
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.
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.
Cited articles
Abera, W., Formetta, G., Borga, M., and Rigon, R.: Estimating the water budget components and their variability in a pre-alpine basin with JGrass-NewAGE, Adv. Water Resour., 104, 37–54, https://doi.org/10.1016/j.advwatres.2017.03.010, 2017a. a
Abera, W., Formetta, G., Brocca, L., and Rigon, R.: Modeling the water budget of the Upper Blue Nile basin using the JGrass-NewAge model system and satellite data, Hydrol. Earth Syst. Sci., 21, 3145–3165, https://doi.org/10.5194/hess-21-3145-2017, 2017b. a
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements, Fao, Rome, FAO Irrigation and drainage paper 56, 300(9), D05109, 1998. a
Ashby, S. F. and Falgout, R. D.: A parallel multigrid preconditioned conjugate gradient algorithm for groundwater flow simulations, Nucl. Sci. Eng., 124, 145–159, https://doi.org/10.13182/NSE96-A24230, 1996. a
Bancheri, M., Serafin, F., Bottazzi, M., Abera, W., Formetta, G., and Rigon, R.: The design, deployment, and testing of kriging models in GEOframe with SIK-0.9.8, Geosci. Model Dev., 11, 2189–2207, https://doi.org/10.5194/gmd-11-2189-2018, 2018. a
Benard, P., Zarebanadkouki, M., and Carminati, A.: Physics and hydraulics of the rhizosphere network, J. Plant Nutr. Soil Sc., 182, 5–8, https://doi.org/10.1002/jpln.201800042, 2019. a
Bisht, G. and Riley, W. J.: Development and verification of a numerical library for solving global terrestrial multiphysics problems, J. Adv. Model. Earth Sy., 11, 1516–1542, https://doi.org/10.1029/2018MS001560, 2019. a, b, c, d
Bloch, J.: Effective Java: Programming Language Guide, Addison-Wesley
Professional, ISBN: 9780201310054, 2001. a
Bottazzi, M., Bancheri, M., Mobilia, M., Bertoldi, G., Longobardi, A., and Rigon, R.: Comparing evapotranspiration estimates from the GEOframe-Prospero model and three empirical models under different climate conditions, Water, 13, 1221–1243, https://doi.org/10.3390/w13091221, 2021. a, b
Brooks, R. H. and Corey, T. A.: Hydraulic properties of porous media
Hydrology Paper No. 3, Civil Engineering Department, Colorado State University, Fort Collins, CO, 1964. a
Brugnano, L. and Casulli, V.: Iterative solution of piecewise linear systems, SIAM J. Sci. Comput., 30, 463–472, https://doi.org/10.1137/070681867, 2008. a, b
Brugnano, L. and Casulli, V.: Iterative solution of piecewise linear systems and applications to flows in porous media, SIAM J. Sci. Comput., 31, 1858–1873, https://doi.org/10.1137/08072749X, 2009. a
Casulli, V.: A high-resolution wetting and drying algorithm for free-surface hydrodynamics, Int. J. Numer. Meth. Fl., 60, 391–408, https://doi.org/10.1002/fld.1896, 2009. a
Casulli, V.: A coupled surface-subsurface model for hydrostatic flows under saturated and variably saturated conditions, Int. J. Numer. Meth. Fl., 85, 449–464, https://doi.org/10.1002/fld.4389, 2017. a
Casulli, V. and Zanolli, P.: High resolution methods for multidimensional advection–diffusion problems in free-surface hydrodynamics, Ocean Model., 10, 137–151, https://doi.org/10.1016/j.ocemod.2004.06.007, 2005. a, b, c, d
Casulli, V. and Zanolli, P.: Iterative solutions of mildly nonlinear systems, J. Comput. Appl. Math., 236, 3937–3947, https://doi.org/10.1016/j.cam.2012.02.042, 2012. a, b, c
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
Chistyakov, V.: On mappings of bounded variation, J. Dyn. Control Syst., 3, 261, https://doi.org/10.1007/BF02465896, 1997. a
Clark, M. P., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D., Gochis, D. J., Hooper, R. P., Kumar, M., Leung, L. R., Mackay, D. S., Maxwell, R. M., Shen, C., Swenson, S. C., and Zeng, X.: Improving the representation of hydrologic processes in Earth System Models, Water Resour. Res., 51, 5929–5956, https://doi.org/10.1002/2015WR017096, 2015a. a
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A unified approach for process-based hydrologic modeling: 1. Modeling concept, Water Resour. Res., 51, 2498–2514, https://doi.org/10.1002/2015WR017198, 2015b. a, b
Clark, M. P., Zolfaghari, R., Green, K. R., Trim, S., Knoben, W. J. M.,
Bennet, A., and Nijssen, B.: The numerical implementation of land models:
problem formulation and laugh tests, J. Hydrometeorol., 22, 1627–1648, https://doi.org/10.1175/JHM-D-20-0175.1, 2021. a, b, c
Constantz, J. and Murphy, F.: The temperature dependence of ponded infiltration under isothermal conditions, J. Hydrol., 122, 119–128, https://doi.org/10.1016/0022-1694(91)90175-H, 1991. a, b
Dai, Y., Wei, N., Yuan, H., Zhang, S., Shangguan, W., Liu, S., Lu, X., and Xin, Y.: Evaluation of soil thermal conductivity schemes for use in land surface modeling, J. Adv. Model. Earth Sy., 11, 3454–3473, https://doi.org/10.1029/2019MS001723, 2019. a, b, c
D'Amato, C.: BrokerGEO, GitHub [code], available at: https://github.com/geoframecomponents/BrokerGEO, last
access: 20 April 2021. a
D'Amato, C., Tubini, N., Benettin, P., Rinaldo, A., Noto, V., and Rigon, R.:
Investigation of a critical zone column with the GEOframe Plant Atmosphere
Continuum Estimator (GEO-SPACE), Hydrol. Earth Syst. Sci., in
preparation, 2022. a
David, O.: CSV Data Files, codeBeamer [data set], available at: https://alm.engr.colostate.edu/cb/wiki/16970, last access: 1 April 2021. a
David, O., Ascough II, J. C., Lloyd, W., Green, T. R., Rojas, K., Leavesley, G. H., and Ahuja, L. R.: A software engineering perspective on environmental modeling framework design: The Object Modeling System, Environ. Modell. Softw., 39, 201–213, https://doi.org/10.1016/j.envsoft.2012.03.006, 2013. a, b, c, d, e, f, g, h, i, j, k
Di Nucci, C.: Theoretical derivation of the conservation equations for single phase flow in porous media: a continuum approach, Meccanica, 49, 2829–2838, https://doi.org/10.1007/s11012-014-0022-y, 2014. a
Dong, Y., McCartney, J. S., and Lu, N.: Critical review of thermal conductivity models for unsaturated soils, Geotech. Geol. Eng., 33, 207–221, https://doi.org/10.1007/s10706-015-9843-2, 2015. a
Dunne, T. and Black, R. D.: An experimental investigation of runoff production in permeable soils, Water Resour. Res., 6, 478–490, https://doi.org/10.1029/WR006i002p00478, 1970. a
Eckel, B.: Thinking in JAVA, Prentice Hall Professional, 2003. a
Endrizzi, S., Gruber, S., Dall'Amico, M., and Rigon, R.: GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects, Geosci. Model Dev., 7, 2831–2857, https://doi.org/10.5194/gmd-7-2831-2014, 2014. a, b
Engeler, I., Franssen, H. H., Müller, R., and Stauffer, F.: The importance of coupled modelling of variably saturated groundwater flow-heat transport for assessing river–aquifer interactions, J. Hydrol., 397, 295–305, https://doi.org/10.1016/j.jhydrol.2010.12.007, 2011. a, b
Formetta, G., Rigon, R., Chávez, J. L., and David, O.: Modeling shortwave solar radiation using the JGrass-NewAge system, Geosci. Model Dev., 6, 915–928, https://doi.org/10.5194/gmd-6-915-2013, 2013. a, b, c, d
Formetta, G., Antonello, A., Franceschi, S., David, O., and Rigon, R.: Hydrological modelling with components: A GIS-based open-source framework, Environ. Modell. Softw., 55, 190–200, https://doi.org/10.1016/j.envsoft.2014.01.019, 2014a. a, b, c, d
Formetta, G., Kampf, S. K., David, O., and Rigon, R.: Snow water equivalent modeling components in NewAge-JGrass, Geosci. Model Dev., 7, 725–736, https://doi.org/10.5194/gmd-7-725-2014, 2014b. a
Formetta, G., Bancheri, M., David, O., and Rigon, R.: Performance of site-specific parameterizations of longwave radiation, Hydrol. Earth Syst. Sci., 20, 4641–4654, https://doi.org/10.5194/hess-20-4641-2016, 2016. a, b
Frampton, A., Painter, S. L., and Destouni, G.: Permafrost degradation and subsurface-flow changes caused by surface warming trends, Hydrogeol. J., 21, 271–280, https://doi.org/10.1007/s10040-012-0938-z, 2013. a
Freeze, R. A. and Harlan, R. L.: Blueprint for a physically-based, digitally-simulated hydrologic response model, J. Hydrol., 9, 237–258, 1969. a
GEOframe: FairUse&Publication Policy_v1.pdf, OSF, available at: https://osf.io/wgdyq/, last access: 21 December 2021a. a
GEOframe: WHETGEO-1D, available at: https://geoframe.blogspot.com/2021/05/whetgeo-1d.html, last access: 18 May 2021b. a
GEOframe Summer School: http://geoframe.blogspot.com/2021/06/geoframe-summer-school-2021-gss2021.html, last access: 21 December 2021. a
Germann, P. and Beven, K.: Water flow in soil macropores I. An experimental approach, J. Soil Sci., 32, 1–13, https://doi.org/10.1111/j.1365-2389.1981.tb01681.x, 1981. a
Greenspan, D. and Casulli, V.: Numerical Analysis for Applied Mathematics, Science, and Engineering, Addison Wesley, https://doi.org/10.1201/9780429493393, 1988. a
Gugole, F., Dumbser, M., and Stelling, G.: An efficient three-dimensional semi-implicit finite volume scheme for the solution of coupled free-surface and variably saturated sub-surface flow, in: EGU General Assembly Conference Abstracts, 600, 2018. a
Hall, C. A., Saia, S. M., Popp, A. L., Dogulu, N., Schymanski, S. J., Drost, N., van Emmerik, T., and Hut, R.: A Hydrologist's Guide to Open Science, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2021-392, in review, 2021. a
Hay, L. E., Leavesley, G. H., Clark, M. P., Markstrom, S. L., Viger, R. J., and Umemoto, M.: Step wise, multiple objective calibration of a hydrologic model for a snowmelt dominated basin 1, J. Am. Water Resour. As., 42, 877–890, https://doi.org/10.1111/j.1752-1688.2006.tb04501.x, 2006. a, b
Horton, R. E.: The role of infiltration in the hydrologic cycle, EOS T. Am. Geophys. Un., 14, 446–460, https://doi.org/10.1029/TR014i001p00446, 1933. a
Hrachowitz, M., Benettin, P., Van Breukelen, B. M., Fovet, O., Howden, N. J., Ruiz, L., Van Der Velde, Y., and Wade, A. J.: Transit times–The link between hydrology and water quality at the catchment scale, WIREs Water, 3, 629–657, https://doi.org/10.1002/wat2.1155, 2016. a
IDEAS: Methodologies and “How To”, available at:
https://ideas-productivity.org/ideas-classic/how-to/, last access:
20 October 2021. a
Jones, J. E. and Woodward, C. S.: Newton–Krylov-multigrid solvers for large-scale, highly heterogeneous, variably saturated flow problems, Adv. Water Resour., 24, 763–774, https://doi.org/10.1016/S0309-1708(00)00075-0, 2001. a
Kelley, C. T.: Solving nonlinear equations with Newton's method, SIAM, https://doi.org/10.1137/1.9780898718898, 2003. a
Kennedy, J. and Eberhart, R.: Particle swarm optimization, in: Proceedings of
ICNN'95-international conference on neural networks, Perth, WA, Australia, 27 November–1 December 1995, IEEE, vol. 4, 1942–1948, 1995. a
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
Kosugi, K.: General model for unsaturated hydraulic conductivity for soils with lognormal pore-size distribution, Soil Sci. Soc. Am. J., 63, 270–277, https://doi.org/10.2136/sssaj1999.03615995006300020003x, 1999. a
Kurylyk, B. L. and Watanabe, K.: The mathematical representation of freezing and thawing processes in variably-saturated, non-deformable soils, Adv. Water Resour., 60, 160–177, https://doi.org/10.1016/j.advwatres.2013.07.016, 2013. a
Liu, S., Lu, L., Mao, D., and Jia, L.: Evaluating parameterizations of aerodynamic resistance to heat transfer using field measurements, Hydrol. Earth Syst. Sci., 11, 769–783, https://doi.org/10.5194/hess-11-769-2007, 2007. a
Lloyd, W., David, O., Ascough II, 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, https://doi.org/10.1016/j.envsoft.2011.03.011, 2011. a
Lu, N.: Generalized soil water retention equation for adsorption and capillarity, J. Geotech. Geoenviron., 142, 04016051, https://doi.org/10.1061/(ASCE)GT.1943-5606.0001524, 2016. a
Mcdonough, J. M.: Introductory Lectures on Turbulence: Physics, Mathematics and Modeling, Mechanical Engineering Textbook Gallery, 2, available at: https://uknowledge.uky.edu/me_textbooks/2 (last access: 21 December 2021), 2007. a
Menard, C. B., Essery, R., Krinner, G., Arduini, G., Bartlett, P., Boone, A., Brutel-Vuilmet, C., Burke, E., Cuntz, M., Dai, Y., Decharme, B., Dutra, E., Fang, X., Fierz, C., Gusev, Y., Hagemann, S., Haverd, V., Kim, H., Lafaysse, M., Marke, T., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Schädler, G., Semenov, V. A., Smirnova, T., Strasser, U., Swenson, S., Turkov, D., Wever, N., and Yuan, H.: Scientific and human errors in a snow model intercomparison, B. Am. Meteorol. Soc., 102, E61–E79, https://doi.org/10.1175/BAMS-D-19-0329.1, 2020. a
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
Muskat, M. and Meres, M. W.: The flow of heterogeneous fluids through porous media, Physics, 7, 346–363, https://doi.org/10.1063/1.1745403, 1936. a
National Research Council: Basic Research Opportunities in Earth Science, The National Academies Press, Washington, DC, https://doi.org/10.17226/9981, 2001. a
Newman, S.: Building Microservices: Designing Fine-Grained Systems, ISBN-10: 1491950358, 2015. a
Nicolsky, D. and Romanovsky, V. E.: Modeling long-term permafrost degradation, J. Geophys. Res.-Earth, 123, 1756–1771, https://doi.org/10.1029/2018JF004655, 2018. a
Nobel, P. S.: Physicochemical & environmental plant physiology, Academic Press, ISBN 0-12-520025-0, 1999. a
Ochsner, T. E., Horton, R., and Ren, T.: A new perspective on soil thermal properties, Soil Sci. Soc. Am. J., 65, 1641–1647, https://doi.org/10.2136/sssaj2001.1641, 2001. 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, b
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
Paniconi, C. and Wood, E. F.: A detailed model for simulation of catchment scale subsurface hydrologic processes, Water Resour. Res., 29, 1601–1620, https://doi.org/10.1029/92WR02333, 1993. a
PC Progress Discussion Forums: https://www.pc-progress.com/forum/viewtopic.php?f=3&t=3632, last access: 20 April 2021. a
Peckham, S. D., Hutton, E. W., and Norris, B.: A component-based approach to integrated modeling in the geosciences: The design of CSDMS, Comput. Geosci., 53, 3–12, https://doi.org/10.1016/j.cageo.2012.04.002, 2013. a
Penna, D., Hopp, L., Scandellari, F., Allen, S. T., Benettin, P., Beyer, M., Geris, J., Klaus, J., Marshall, J. D., Schwendenmann, L., Volkmann, T. H. M., von Freyberg, J., Amin, A., Ceperley, N., Engel, M., Frentress, J., Giambastiani, Y., McDonnell, J. J., Zuecco, G., Llorens, P., Siegwolf, R. T. W., Dawson, T. E., and Kirchner, J. W.: Ideas and perspectives: Tracing terrestrial ecosystem water fluxes using hydrogen and oxygen stable isotopes – challenges and opportunities from an interdisciplinary perspective, Biogeosciences, 15, 6399–6415, https://doi.org/10.5194/bg-15-6399-2018, 2018. a
Priestley, C. H. B. and Taylor, R. J.: On the assessment of surface heat flux and evaporation using large-scale parameters, Mon. Weather Rev., 100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972. a
Quarteroni, A., Sacco, R., and Saleri, F.: Numerical mathematics, vol. 37,
Springer Science and Business Media, ISBN-10: 3642071015, 2010. a
Raupach, M. and Thom, A. S.: Turbulence in and above plant canopies, Annu. Rev. Fluid Mech., 13, 97–129, https://doi.org/10.1146/annurev.fl.13.010181.000525, 1981. a
Regenass, D., Schlemmer, L., Jahr, E., and Schär, C.: It rains and then? Numerical challenges with the 1D Richards equation in kilometer-resolution land surface modelling, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2021-426, in review, 2021. a
Richards, L. A.: Capillary conduction of liquids through porous mediums, Physics, 1, 318–333, https://doi.org/10.1063/1.1745010, 1931. a
Richardson, L. F.: Weather prediction by numerical process, Cambridge University Press, https://doi.org/10.1002/qj.49704820311, 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, https://doi.org/10.1175/JHM497.1, 2006a. a, b, c
Rigon, R., Ghesla, E., Tiso, C., and Cozzini, A.: The HORTON machine: a system for DEM analysis The reference manual, Tech. rep., University of Trento, ISBN 9788884431479, available at: http://hdl.handle.net/11572/48576 (last access: 21 December 2021), 2006b. a
Rigon, R., Bancheri, M., Formetta, G., and de Lavenne, A.: The geomorphological unit hydrograph from a historical-critical perspective, Earth Surf. Proc. Land., 41, 27–37, https://doi.org/10.1002/esp.3855, 2016a. a
Rigon, R., Bancheri, M., and Green, T. R.: Age-ranked hydrological budgets and a travel time description of catchment hydrology, Hydrol. Earth Syst. Sci., 20, 4929–4947, https://doi.org/10.5194/hess-20-4929-2016, 2016b. a
Rizzoli, A., Svensson, M., Rowe, E., Donatelli, M., Muetzelfeldt, R., van der Wal, T., van Evert, F., and Villa, F.: Modelling framework (SeamFrame) requirements, Tech. rep., SEAMLESS, 2006. a
Roe, P. L.: Approximate Riemann solvers, parameter vectors, and difference schemes, J. Comput. Phys., 43, 357–372, https://doi.org/10.1016/0021-9991(81)90128-5, 1981. a
Romano, N., Brunone, B., and Santini, A.: Numerical analysis of one-dimensional unsaturated flow in layered soils, Adv. Water Resour., 21, 315–324, https://doi.org/10.1016/S0309-1708(96)00059-0, 1998. a, b
Ronan, A. D., Prudic, D. E., Thodal, C. E., and Constantz, J.: Field study and simulation of diurnal temperature effects on infiltration and variably saturated flow beneath an ephemeral stream, Water Resour. Res., 34, 2137–2153, https://doi.org/10.1029/98WR01572, 1998. a, b
Saito, H., Šimůnek, J., and Mohanty, B. P.: Numerical analysis of coupled water, vapor, and heat transport in the vadose zone, Vadose Zone J., 5, 784–800, https://doi.org/10.2136/vzj2006.0007, 2006. a
Shewchuk, J. R.: An Introduction to the Conjugate Gradient Method Without the Agonizing Pain, available at: https://www.bibsonomy.org/bibtex/2175fea4e807e879ebcb76b79a9992437/timo (last access: 21 December 2021), 1994. a
Šimůnek, J., Van Genuchten, M. Th., and Šejna, M.: The HYDRUS-1D software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media. Version 3.0, HYDRUS Softw., Ser. 1, Dep. of Environ. Sci., Univ. of California, Riverside, CA, 2012. a
Sophocleous, M.: Analysis of water and heat flow in unsaturated-saturated porous media, Water Resour. Res., 15, 1195–1206, https://doi.org/10.1029/WR015I005P01195, 1979. a
Srivastava, R. and Yeh, T.-C. J.: Analytical solutions for one-dimensional, transient infiltration toward the water table in homogeneous and layered soils, Water Resour. Res., 27, 753–762, https://doi.org/10.1029/90WR02772, 1991. a, b, c, d
Tomin, P. and Lunati, I.: Investigating Darcy-scale assumptions by means of a
multiphysics algorithm, Adv. Water Resour., 95, 80–91,
https://doi.org/10.1016/j.advwatres.2015.12.013, 2016. a
Tubini, N. and Rigon, R.: GEOframePy, available at: https://pypi.org/project/geoframepy/, last access: 22 October 2021a. a
Tubini, N. and Rigon, R.: OMS project for WHETGEO-1D, v-1.0-beta, Zenodo [data set], https://doi.org/10.5281/zenodo.4749319, 2021b (data available at: https://github.com/GEOframeOMSProjects/OMS_Project_WHETGEO1D, last access: 21 December 2021). a, b
Tubini, N. and Rigon, R.: WHETGEO-1D v1.0-beta, Zenodo [code], https://doi.org/10.5281/zenodo.5112727, 2021c (data available at: https://github.com/geoframecomponents/WHETGEO-1D, last access: 21 December 2021). a
Tubini, N., Formetta, G., Rigon, R., Bancheri, M., Serafin, F., Bottazzi, M., Tasin, S., Dalla Torre, D., and D'Amato, C.: Day 1-2 WHETGEO-1D, OSF [code], https://doi.org/10.17605/OSF.IO/Z53C6, 2021a. a
Unidata: Chunking Data with NetCDF-4, available at:
https://www.unidata.ucar.edu/software/netcdf/workshops/2008/nc4chunking/index.html,
last access: 1 April 2021a. a
Unidata: Formats and Performance, available at: https://www.unidata.ucar.edu/software/netcdf/workshops/2008/performance/index.html, last access: 1 April 2021b. a
Unidata: NetCDF version 4.3.22, UCAR/Unidata Program Center, Boulder, CO, https://doi.org/10.5065/D6H70CW6, 2021. a
Van Genuchten, M. T.: 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
Vanderborght, J., Kasteel, R., Herbst, M., Javaux, M., Thiéry, D., Vanclooster, M., Mouvet, C., and Vereecken, H.: A set of analytical benchmarks to test numerical models of flow and transport in soils, Vadose Zone J., 4, 206–221, https://doi.org/10.2113/4.1.206, 2005. a, b, c
Walvoord, M. A. and Kurylyk, B. L.: Hydrologic impacts of thawing permafrost–A review, Vadose Zone J., 15, 1–20, https://doi.org/10.2136/vzj2016.01.0010, 2016.
a
Walvoord, M. A., Voss, C. I., and Wellman, T. P.: Influence of permafrost distribution on groundwater flow in the context of climate-driven permafrost thaw: Example from Yukon Flats Basin, Alaska, United States, Water Resour. Res., 48, W07524, https://doi.org/10.1029/2011WR011595, 2012. a
Whitaker, S.: Flow in porous media I: A theoretical derivation of Darcy's law, Transport Porous Med., 1, 3–25, https://doi.org/10.1007/BF01036523, 1986. a
Wierenga, P., Hagan, R. M., and Nielsen, D.: Soil temperature profiles during infiltration and redistribution of cool and warm irrigation water, Water Resour. Res., 6, 230–238, https://doi.org/10.1029/WR006i001p00230, 1970. a
Yang, Y., Wu, J., Zhao, S., Han, Q., Pan, X., He, F., and Chen, C.: Assessment of the responses of soil pore properties to combined soil structure amendments using X-ray computed tomography, Sci. Rep.-UK, 8, 695–705, https://doi.org/10.1038/s41598-017-18997-1, 2018. a
Zhang, S., Meurey, C., and Calvet, J.-C.: Identification of soil-cooling rains in southern France from soil temperature and soil moisture observations, Atmos. Chem. Phys., 19, 5005–5020, https://doi.org/10.5194/acp-19-5005-2019, 2019. a
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.
This paper presents WHETGEO and its 1D deployment: a new physically based model simulating the...
