Articles | Volume 18, issue 4
https://doi.org/10.5194/gmd-18-1287-2025
© 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-18-1287-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
03 Mar 2025
Development and technical paper |
| 03 Mar 2025
| 03 Mar 2025
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
Jordi Buckley Paules
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Simone Fatichi
Department of Civil and Environmental Engineering, National University of Singapore, Singapore
Bonnie Warring
Grantham Institute on Climate Change and the Environment, Imperial College London, London, UK
Athanasios Paschalis
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Related authors
No articles found.
Jianning Ren, Zhaoyang Luo, Xiangzhong Luo, Stefano Galelli, Athanasios Paschalis, Valeriy Ivanov, Shanti Shwarup Mahto, and Simone Fatichi
EGUsphere, https://doi.org/10.5194/egusphere-2025-4570, https://doi.org/10.5194/egusphere-2025-4570, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Southeast Asia’s water and carbon fluxes remain poorly understood due to limited field observations and modelling. Using available data and computer models, we show the region is mostly energy-limited: evapotranspiration is controlled by relative humidity, while plant productivity is driven by solar radiation. In some particular areas, such as the Tibetan Plateau, savannas, and dry deciduous forests, water availability is the main limiting factor.
Yue Zhu, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi
Nat. Hazards Earth Syst. Sci., 25, 2271–2286, https://doi.org/10.5194/nhess-25-2271-2025, https://doi.org/10.5194/nhess-25-2271-2025, 2025
Short summary
Short summary
This study addresses the challenge of accurately predicting floods in regions with limited terrain data. By utilising a deep learning model, we developed a method that improves the resolution of digital elevation data by fusing low-resolution elevation data with high-resolution satellite imagery. This approach not only substantially enhances flood prediction accuracy, but also holds potential for broader applications in simulating natural hazards that require terrain information.
Shanti Shwarup Mahto, Simone Fatichi, and Stefano Galelli
Earth Syst. Sci. Data, 17, 2693–2712, https://doi.org/10.5194/essd-17-2693-2025, https://doi.org/10.5194/essd-17-2693-2025, 2025
Short summary
Short summary
The MSEA-Res database offers an open-access dataset tracking absolute water storage for 186 large reservoirs across Mainland Southeast Asia from 1985 to 2023. It provides valuable insights into how reservoir storage grew by 130 % between 2008 and 2017, driven by dams in key river basins. Our data also reveal how droughts, like the 2019–2020 event, significantly impacted water reservoirs. This resource can aid water management, drought planning, and research globally.
Yiran Wang, Naika Meili, and Simone Fatichi
Hydrol. Earth Syst. Sci., 29, 381–396, https://doi.org/10.5194/hess-29-381-2025, https://doi.org/10.5194/hess-29-381-2025, 2025
Short summary
Short summary
In this study, we use climate model simulations and process-based ecohydrological modeling to assess the effects of solar radiation changes on hydrological variables. Results show that direct changes in solar radiation without the land–atmosphere feedback primarily affects sensible heat with limited effects on hydrology and vegetation. However, including land–atmosphere feedbacks exacerbates the effects of radiation changes on evapotranspiration and modifies vegetation productivity.
Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Shahab Aldin Shojaeezadeh, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti
Hydrol. Earth Syst. Sci., 28, 3391–3433, https://doi.org/10.5194/hess-28-3391-2024, https://doi.org/10.5194/hess-28-3391-2024, 2024
Short summary
Short summary
Pedotransfer functions (PTFs) are used to predict parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically relies on the nature of the datasets for training the PTFs and the physical comprehensiveness of the models. This roadmap paper is addressed to PTF developers and users and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTF research.
Shuyue Li, Bonnie Waring, Jennifer Powers, and David Medvigy
Biogeosciences, 21, 455–471, https://doi.org/10.5194/bg-21-455-2024, https://doi.org/10.5194/bg-21-455-2024, 2024
Short summary
Short summary
We used an ecosystem model to simulate primary production of a tropical forest subjected to 3 years of nutrient fertilization. Simulations parameterized such that relative allocation to fine roots increased with increasing soil phosphorus had leaf, wood, and fine root production consistent with observations. However, these simulations seemed to over-allocate to fine roots on multidecadal timescales, affecting aboveground biomass. Additional observations across timescales would benefit models.
Abrar Habib, Athanasios Paschalis, Adrian P. Butler, Christian Onof, John P. Bloomfield, and James P. R. Sorensen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-27, https://doi.org/10.5194/hess-2023-27, 2023
Preprint withdrawn
Short summary
Short summary
Components of the hydrological cycle exhibit a “memory” in their behaviour which quantifies how long a variable would stay at high/low values. Being able to model and understand what affects it is vital for an accurate representation of the hydrological elements. In the current work, it is found that rainfall affects the fractal behaviour of groundwater levels, which implies that changes to rainfall due to climate change will change the periods of flood and drought in groundwater-fed catchments.
Stefano Manzoni, Simone Fatichi, Xue Feng, Gabriel G. Katul, Danielle Way, and Giulia Vico
Biogeosciences, 19, 4387–4414, https://doi.org/10.5194/bg-19-4387-2022, https://doi.org/10.5194/bg-19-4387-2022, 2022
Short summary
Short summary
Increasing atmospheric carbon dioxide (CO2) causes leaves to close their stomata (through which water evaporates) but also promotes leaf growth. Even if individual leaves save water, how much will be consumed by a whole plant with possibly more leaves? Using different mathematical models, we show that plant stands that are not very dense and can grow more leaves will benefit from higher CO2 by photosynthesizing more while adjusting their stomata to consume similar amounts of water.
Stefan Fugger, Catriona L. Fyffe, Simone Fatichi, Evan Miles, Michael McCarthy, Thomas E. Shaw, Baohong Ding, Wei Yang, Patrick Wagnon, Walter Immerzeel, Qiao Liu, and Francesca Pellicciotti
The Cryosphere, 16, 1631–1652, https://doi.org/10.5194/tc-16-1631-2022, https://doi.org/10.5194/tc-16-1631-2022, 2022
Short summary
Short summary
The monsoon is important for the shrinking and growing of glaciers in the Himalaya during summer. We calculate the melt of seven glaciers in the region using a complex glacier melt model and weather data. We find that monsoonal weather affects glaciers that are covered with a layer of rocky debris and glaciers without such a layer in different ways. It is important to take so-called turbulent fluxes into account. This knowledge is vital for predicting the future of the Himalayan glaciers.
Martina Botter, Matthias Zeeman, Paolo Burlando, and Simone Fatichi
Biogeosciences, 18, 1917–1939, https://doi.org/10.5194/bg-18-1917-2021, https://doi.org/10.5194/bg-18-1917-2021, 2021
Lianyu Yu, Simone Fatichi, Yijian Zeng, and Zhongbo Su
The Cryosphere, 14, 4653–4673, https://doi.org/10.5194/tc-14-4653-2020, https://doi.org/10.5194/tc-14-4653-2020, 2020
Short summary
Short summary
The role of soil water and heat transfer physics in portraying the function of a cold region ecosystem was investigated. We found that explicitly considering the frozen soil physics and coupled water and heat transfer is important in mimicking soil hydrothermal dynamics. The presence of soil ice can alter the vegetation leaf onset date and deep leakage. Different complexity in representing vadose zone physics does not considerably affect interannual energy, water, and carbon fluxes.
Cited articles
Amanullah: Specific leaf area and specific leaf weight in small grain crops wheat, rye, barley, and oats differ at various growth stages and NPK source, J. Plant Nutr., 38, 1694–1708, https://doi.org/10.1080/01904167.2015.1017051, 2015.
Ansarifar, J., Wang, L., and Archontoulis, S. V.: An interaction regression model for crop yield prediction, Sci. Rep., 11, 17754, https://doi.org/10.1038/s41598-021-97221-7, 2021.
Aubinet, M., Moureaux, C., Bodson, B., Dufranne, D., Heinesch, B., Suleau, M., Vancutsem, F., and Vilret, A.: Carbon sequestration by a crop over a 4-year sugar beet/winter wheat/seed potato/winter wheat rotation cycle, Agr. Forest Meteorol., 149, 407–418, https://doi.org/10.1016/j.agrformet.2008.09.003, 2009.
Bilionis, I., Drewniak, B. A., and Constantinescu, E. M.: Crop physiology calibration in the CLM, Geosci. Model Dev., 8, 1071–1083, https://doi.org/10.5194/gmd-8-1071-2015, 2015.
Boas, T., Bogena, H., Grünwald, T., Heinesch, B., Ryu, D., Schmidt, M., Vereecken, H., Western, A., and Hendricks Franssen, H.-J.: Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0, Geosci. Model Dev., 14, 573–601, https://doi.org/10.5194/gmd-14-573-2021, 2021.
Bonan, G. B., Lawrence, P. J., Oleson, K. W., Levis, S., Jung, M., Reichstein, M., and Swenson, S. C.: Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data, J. Geophys. Res.-Biogeo., 116, G02014, 116, https://doi.org/10.1029/2010JG001593, 2011.
Bonetti, S., Sutanudjaja, E. H., Mabhaudhi, T., Slotow, R., and Dalin, C.: Climate change impacts on water sustainability of South African crop production, Environ. Res. Lett., 17, 084017, https://doi.org/10.1088/1748-9326/ac80cf, 2022.
Boote, K. J., Jones, J. W., White, J. W., Asseng, S., and Lizaso, J. I.: Putting mechanisms into crop production models, Plant Cell Environ., 36, 1658–1672, https://doi.org/10.1111/pce.12119, 2013.
Botter, M., Zeeman, M., Burlando, P., and Fatichi, S.: Impacts of fertilization on grassland productivity and water quality across the European Alps under current and warming climate: insights from a mechanistic model, Biogeosciences, 18, 1917–1939, https://doi.org/10.5194/bg-18-1917-2021, 2021.
Buckley, J.: Model and Data for the T&C-CROP Validation Paper: T&C-CROP: Representing mechanistic crop growth with a terrestrial biosphere model (T&C,v1.5): Model formulation and validation, Zenodo [data set], https://doi.org/10.5281/zenodo.13343701, 2024.
Buysse, P., Bodson, B., De Debacq, A., Ligne, A., Heinesch, B., Manise, T., Moureaux, C., and Aubinet, M.: Carbon budget measurement over 12 years at a crop production site in the silty-loam region in Belgium, Agr. Forest Meteorol., 246, 241–255, https://doi.org/10.1016/j.agrformet.2017.07.004, 2017.
Cammarano, D., Jamshidi, S., Hoogenboom, G., Ruane, A. C., Niyogi, D., and Ronga, D.: Processing tomato production is expected to decrease by 2050 due to the projected increase in temperature, Nat. Food, 3, 437–444, https://doi.org/10.1038/s43016-022-00521-y, 2022.
Cassman, K. G. and Grassini, P.: A global perspective on sustainable intensification research, Nat. Sustain., 3, 262–268, https://doi.org/10.1038/s41893-020-0507-8, 2020.
Cernusak, L. A.: Gas exchange and water-use efficiency in plant canopies, Plant Biol., 22, 52–67, https://doi.org/10.1111/plb.12939, 2020.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration — a model that includes a laminar boundary-layer, Agr. Forest Meteorol., 54, 107–136, https://doi.org/10.1016/0168-1923(91)90002-8, 1991.
Collatz, G. J., Ribas-Carbo, M., and Berry, J. A.: Coupled photosynthesis-stomatal conductance model for leaves of C4 plants, Funct. Plant Biol., 19, 519–538, https://doi.org/10.1071/PP9920519, 1992.
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, https://doi.org/10.1175/1520-0442(2004)017<2281>2.0.CO;2, 2004.
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.
Dietiker, D., Buchmann, N., and Eugster, W.: Testing the ability of the DNDC model to predict CO2 and water vapour fluxes of a Swiss cropland site, Agr. Ecosyst. Environ., 139, 396–401, https://doi.org/10.1016/j.agee.2010.09.002, 2010.
Di Paola, A., Valentini, R., and Santini, M.: An overview of available crop growth and yield models for studies and assessments in agriculture, J. Sci. Food Agric., 96, 709–714, https://doi.org/10.1002/jsfa.7359, 2016.
Drewniak, B., Song, J., Prell, J., Kotamarthi, V. R., and Jacob, R.: Modeling agriculture in the Community Land Model, Geosci. Model Dev., 6, 495–515, https://doi.org/10.5194/gmd-6-495-2013, 2013.
Dufranne, D., Moureaux, C., Vancutsem, F., Bodson, B., and Aubinet, M.: Comparison of carbon fluxes, growth, and productivity of a winter wheat crop in three contrasting growing seasons, Agr. Ecosyst. Environ., 141, 133–142, https://doi.org/10.1016/j.agee.2011.02.023, 2011.
Dumont, B., Heinesch, B., Bodson, B., Bogaerts, G., Chopin, H., De Ligne, A., Demoulin, L., Douxfils, B., Engelmann, T., Faurès, A., Longdoz, B., Manise, T., Orgun, A., Piret, A., and Thyrion, T.: ETC L2 Fluxnet (half-hourly), Lonzee, 2017-12-31–2022-12-31, ICOS RI, https://meta.icos-cp.eu/resources/stations/ES_BE-Lon (last access: 26 Febuary 2025), 2023.
Dury, S., Bendjebbar, P., Hainzelin, E., Giordano, T., and Bricas, N. (Eds.): Food Systems at Risk: New Trends and Challenges, FAO, CIRAD, and European Commission, Rome, Montpellier, Brussels, https://doi.org/10.19182/agritrop/00080, 2019.
Ecosystem Thematic Centre, Buchmann, N., Emmel, C., Eugster, W., and Maier, R.: Fluxnet Product, Oensingen crop, 2003-12-31–2020-12-31, Miscellaneous, Ecosystem Thematic Centre [data set], https://doi.org/10.18160/1Y8J-NKQ3, 2021.
Eurostat: Agriculture, forestry and fishery statistics, 2020 edition, European Union, https://ec.europa.eu/eurostat/web/products-statistical-books/-/ks-fk-20-001 (last access: 27 February 2025), 2020.
FAO, IFAD, UNICEF, WFP, and WHO: The State of Food Security and Nutrition in the World 2022, Repurposing Food and Agricultural Policies to Make Healthy Diets More Affordable, FAO, Rome, https://doi.org/10.4060/cc0639en, 2022.
Farquhar, G. D., von Caemmerer, S. V., and Berry, J. A.: A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, https://doi.org/10.1007/BF00386231, 1980.
Fatichi, S., Ivanov, V. Y., and Caporali, E.: Simulation of future climate scenarios with a weather generator, Adv. Water Resour., 34, 448–467, https://doi.org/10.1016/j.advwatres.2010.12.013, 2011.
Fatichi, S., Ivanov, V. Y., and Caporali, E.: A mechanistic ecohydrological model to investigate complex interactions in cold and warm water-controlled environments: 1. Theoretical framework and plot-scale analysis, J. Adv. Model. Earth Sy., 4, M05002, https://doi.org/10.1029/2011MS000086, 2012.
Fatichi, S., Leuzinger, S., Paschalis, A., Langley, J. A., Donnellan Barraclough, A., and Hovenden, M. J.: Partitioning direct and indirect effects reveals the response of water-limited ecosystems to elevated CO2, P. Natl. Acad. Sci. USA, 113, 12757–12762, https://doi.org/10.1073/pnas.1605036113, 2016.
Fatichi, S., Manzoni, S., Or, D., and Paschalis, A.: A mechanistic model of microbially mediated soil biogeochemical processes: A reality check, Global Biogeochem. Cy., 33, 620–648, https://doi.org/10.1029/2018GB006077, 2019.
Fatichi, S., Or, D., Walko, R., Vereecken, H., Young, M. H., Ghezzehei, T., Hengl, T., Kollet, S., Agam, N., and Avissar, R.: Soil structure – an important omission in Earth System Models, Nat. Commun., 11, 522, https://doi.org/10.1038/s41467-020-14411-z, 2020.
Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O'Connell, C., Ray, D. K., West, P. C., and Balzer, C.: Solutions for a cultivated planet, Nature, 478, 337–342, https://doi.org/10.1038/nature10452, 2011.
Fraser, L. H.: TRY—A plant trait database of databases, Glob. Change Biol., 26, 189–190, https://doi.org/10.1111/gcb.14869, 2020.
Friedlingstein, P., Joel, G., Field, C. B., and Fung, I.: Toward an allocation scheme for global terrestrial carbon models, Glob. Change Biol., 5, 755–770, https://doi.org/10.1046/j.1365-2486.1999.00269.x, 1998.
Gaupp, F., Hall, J., Mitchell, D., and Dadson, S.: Increasing risks of multiple breadbasket failure under 1.5 and 2 °C global warming, Agric. Syst., 175, 34–45, https://doi.org/10.1016/j.agsy.2019.05.010, 2019.
Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., and Toulmin, C.: Food security: The challenge of feeding 9 billion people, Science, 327, 812–818, https://doi.org/10.1126/science.1185383, 2010.
Haghighi, E., Shahraeeni, E., Lehmann, P., and Or, D.: Evaporation rates across a convective air boundary layer are dominated by diffusion, Water Resour. Res., 49, 1602–1610, https://doi.org/10.1002/wrcr.20166, 2013.
He, D., Wang, E., Wang, J., and Robertson, M. J.: Data requirement for effective calibration of process-based crop models, Agr. Forest Meteorol., 234, 136–148, https://doi.org/10.1016/j.agrformet.2016.12.015, 2017.
He, L., Lipson, D. A., Mazza Rodrigues, J. L., Mayes, M., Björk, R. G., Glaser, B., Thornton, P., and Xu, X.: Dynamics of fungal and bacterial biomass carbon in natural ecosystems: Site-level applications of the CLM-microbe model, J. Adv. Model. Earth Sy., 13, e2020MS002283, https://doi.org/10.1029/2020MS002283, 2021.
Heinesch, B., Bodson, B., Chopin, H., De Ligne, A., Demoulin, L., Douxfils, B., Engelmann, T., Faurès, A., Longdoz, B., Manise, T., Piret, A., and Thyrion, T.: Fluxnet Product, Lonzee, 2003-12-31–2020-12-31, https://hdl.handle.net/11676/ql2ZkJ2Xx4a4yOyG3cd5lsBS (last access: 26 February 2025), 2021.
Hollinger, D. Y. and Richardson, A. D.: Uncertainty in eddy covariance measurements and its application to physiological models, Tree Physiol., 25, 873–885, https://doi.org/10.1093/treephys/25.7.873, 2005.
Hörtnagl, L., Barthel, M., Buchmann, N., Eugster, W., Butterbach-Bahl, K., Díaz-Pinés, E., Zeeman, M., Klumpp, K., Kiese, R., Bahn, M., and Hammerle, A.: Greenhouse gas fluxes over managed grasslands in Central Europe, Glob. Change Biol., 24, 1843–1872, https://doi.org/10.1111/gcb.14079, 2018.
Hussain, S., Ulhassan, Z., Brestic, M., Zivcak, M., Zhou, W., Allakhverdiev, S. I., Yang, X., Safdar, M. E., Yang, W., and Liu, W.: Photosynthesis research under climate change, Photosynth. Res., 150, 5–19, https://doi.org/10.1007/s11120-021-00861-z, 2021.
Ingwersen, J., Högy, P., Wizemann, H. D., Warrach-Sagi, K., and Streck, T.: Coupling the land surface model Noah-MP with the generic crop growth model Gecros: Model description, calibration and validation, Agr. Forest Meteorol., 262, 322–339, https://doi.org/10.1016/j.agrformet.2018.06.023, 2018.
Jacquemin, I., Berckmans, J., Henrot, A. J., Dury, M., Tychon, B., Hambuckers, A., Hamdi, R., and François, L.: Using the CARAIB dynamic vegetation model to simulate crop yields in Belgium: Validation and projections for the 2035 horizon, Geo-Eco-Trop, 44, https://hdl.handle.net/2268/256139 (last access: 26 February 2025), 2021.
Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Tautenhahn, S., Werner, G. D., Aakala, T., Abedi, M., and Acosta, A. T.: TRY plant trait database – enhanced coverage and open access, Glob. Change Biol., 26, 119–188, https://doi.org/10.1111/gcb.14904, 2020.
Khanal, S., Kc, K., Fulton, J. P., Shearer, S., and Ozkan, E.: Remote sensing in agriculture – accomplishments, limitations, and opportunities, Remote Sens., 12, 3783, https://doi.org/10.3390/rs12223783, 2020.
Kim, S. M. and Mendelsohn, R.: Climate change to increase crop failure in US, Environ. Res. Lett., 18, 014014, https://doi.org/10.1088/1748-9326/acac41, 2023.
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005.
Lawlor, D. W. and Mitchell, R. A. C.: The effects of increasing CO2 on crop photosynthesis and productivity: A review of field studies, Plant Cell Environ., 14, 807–818, https://doi.org/10.1111/j.1365-3040.1991.tb01444.x, 1991.
Leng, G. and Hall, J. W.: Predicting spatial and temporal variability in crop yields: An inter-comparison of machine learning, regression, and process-based models, Environ. Res. Lett., 15, 044027, https://doi.org/10.1088/1748-9326/ab7b24, 2020.
Leuning, R.: Modelling stomatal behaviour and photosynthesis of Eucalyptus grandis, Aust. J. Plant Physiol., 17, 159–175, https://doi.org/10.1071/PP9900159, 1990.
Leuning, R.: A critical appraisal of a combined stomatal-photosynthesis model for C3 plants, Plant Cell Environ., 18, 357–364, https://doi.org/10.1111/j.1365-3040.1995.tb00370.x, 1995.
Li, Z., Zhan, C., Hu, S., Ning, L., Wu, L., and Guo, H.: Implementation of a dynamic specific leaf area (SLA) into a land surface model (LSM) incorporated crop-growth model, Comput. Electron. Agric., 213, 108238, https://doi.org/10.1016/j.compag.2023.108238, 2023.
Lobell, D. B. and Asseng, S.: Comparing estimates of climate change impacts from process-based and statistical crop models, Environ. Res. Lett., 12, 015001, https://doi.org/10.1088/1748-9326/aa518a, 2017.
Lobell, D. B. and Burke, M. B.: On the use of statistical models to predict crop yield responses to climate change, Agr. Forest Meteorol., 150, 1443–1452, https://doi.org/10.1016/j.agrformet.2010.07.008, 2010.
Manoli, G., Meijide, A., Huth, N., Knohl, A., Kosugi, Y., Burlando, P., Ghazoul, J., and Fatichi, S.: Ecohydrological changes after tropical forest conversion to oil palm, Environ. Res. Lett., 13, 064035, https://doi.org/10.1088/1748-9326/aac54e, 2018.
Mastrotheodoros, T., Pappas, C., Molnar, P., Burlando, P., Manoli, G., Parajka, J., Rigon, R., Szeles, B., Bottazzi, M., Hadjidoukas, P., and Fatichi, S.: More green and less blue water in the Alps during warmer summers, Nat. Clim. Change, 10, 155–161, https://doi.org/10.1038/s41558-019-0676-5, 2020.
McGrath, J. M. and Lobell, D. B.: Regional disparities in the CO2 fertilization effect and implications for crop yields, Environ. Res. Lett., 8, 014054, https://doi.org/10.1088/1748-9326/8/1/014054, 2013.
Moustakis, Y., Papalexiou, S. M., Onof, C. J., and Paschalis, A.: Seasonality, intensity, and duration of rainfall extremes change in a warmer climate, Earth's Future, 9, e2020EF001824, https://doi.org/10.1029/2020EF001824, 2021.
Muller, B. and Martre, P.: Plant and crop simulation models: powerful tools to link physiology, genetics, and phenomics, J. Exp. Bot., 70, 2339–2344, https://doi.org/10.1093/jxb/erz175, 2019.
Niyogi, D. S. and Raman, S.: Comparison of four different stomatal resistance schemes using FIFE observations, J. Appl. Meteorol., 36, 903–917, https://doi.org/10.1175/1520-0450(1997)036<0903:COFDSR>2.0.CO;2, 1997.
Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G., and Lobell, D. B.: Anthropogenic climate change has slowed global agricultural productivity growth, Nat. Clim. Change, 11, 306–312, https://doi.org/10.1038/s41558-021-01000-1, 2021.
Osborne, T., Gornall, J., Hooker, J., Williams, K., Wiltshire, A., Betts, R., and Wheeler, T.: JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator, Geosci. Model Dev., 8, 1139–1155, https://doi.org/10.5194/gmd-8-1139-2015, 2015.
Paschalis, A., Katul, G. G., Fatichi, S., Palmroth, S., and Way, D.: On the variability of the ecosystem response to elevated atmospheric CO2 across spatial and temporal scales at the Duke Forest FACE experiment, Agr. Forest Meteorol., 232, 367–383, https://doi.org/10.1016/j.agrformet.2016.09.003, 2017.
Paschalis, A., Bonetti, S., Guo, Y., and Fatichi, S.: On the uncertainty induced by pedotransfer functions in terrestrial biosphere modeling, Water Resour. Res., 58, e2021WR031871, https://doi.org/10.1029/2021WR031871, 2022.
Paschalis, A., De Kauwe, M. G., Sabot, M., and Fatichi, S.: When do plant hydraulics matter in terrestrial biosphere modelling?, Glob. Change Biol., 30, e17022, https://doi.org/10.1111/gcb.17022, 2024.
Peleg, N., Fatichi, S., Paschalis, A., Molnar, P., and Burlando, P.: An advanced stochastic weather generator for simulating 2-D high-resolution climate variables, J. Adv. Model. Earth Syst., 9, 1595–1627, https://doi.org/10.1002/2016MS000854, 2017.
Peng, B., Guan, K., Chen, M., Lawrence, D. M., Pokhrel, Y., Suyker, A., and Lu, Y.: Improving maize growth processes in the Community Land Model: Implementation and evaluation, Agr. Forest Meteorol., 250, 64–89, https://doi.org/10.1016/j.agrformet.2017.11.012, 2018.
Polley, H. W.: Implications of atmospheric and climatic change for crop yield and water use efficiency, Crop Sci., 42, 131–140, https://doi.org/10.2135/cropsci2002.1310, 2002.
Revill, A., Emmel, C., D’Odorico, P., Buchmann, N., Hörtnagl, L., and Eugster, W.: Estimating cropland carbon fluxes: A process-based model evaluation at a Swiss crop-rotation site, Field Crops Res., 234, 95–106, https://doi.org/10.1016/j.fcr.2019.02.006, 2019.
Roberts, M. J., Braun, N. O., Sinclair, T. R., Lobell, D. B., and Schlenker, W.: Comparing and combining process-based crop models and statistical models with some implications for climate change, Environ. Res. Lett., 12, 095010, https://doi.org/10.1088/1748-9326/aa7f33, 2017.
Saxton, K. E. and Rawls, W. J.: Soil water characteristic estimates by texture and organic matter for hydrologic solutions, Soil Sci. Soc. Am. J., 70, 1569–1578, https://doi.org/10.2136/sssaj2005.0117, 2006.
Semenov, M. A.: Impacts of climate change on wheat in England and Wales, J. Roy. Soc. Int., 6, 343–350, https://doi.org/10.1098/rsif.2008.0285, 2009.
Sheehy, J. E., Mitchell, P. L., and Ferrer, A. B.: Decline in rice grain yields with temperature: Models and correlations can give different estimates, Field Crops Res., 98, 151–156, https://doi.org/10.1016/j.fcr.2006.01.001, 2006.
Sheng, M., Liu, J., Zhu, A. X., Rossiter, D. G., Zhu, L., and Peng, G.: Evaluation of CLM-Crop for maize growth simulation over Northeast China, Ecol. Model., 377, 26–34, https://doi.org/10.1016/j.ecolmodel.2018.03.005, 2018.
Slater, L. J., Huntingford, C., Pywell, R. F., Redhead, J. W., and Kendon, E. J.: Resilience of UK crop yields to compound climate change, Earth Syst. Dynam., 13, 1377–1396, https://doi.org/10.5194/esd-13-1377-2022, 2022.
Steduto, P., Hsiao, T. C., Raes, D., and Fereres, E.: AquaCrop – The FAO crop model to simulate yield response to water: I. Concepts and underlying principles, Agron. J., 101, 426–437, https://doi.org/10.2134/agronj2008.0139s, 2009.
Suyker, A., Verma, S., Burba, G., Arkebauer, T., Walters, D., and Hubbard, K.: Growing season carbon dioxide exchange in irrigated and rainfed maize, Agr. Forest Meteorol., 124, 1–13, https://doi.org/10.1016/j.agrformet.2004.01.011, 2004.
Suzuki, S., Nakamoto, H., Ku, M. S., and Edwards, G. E.: Influence of leaf age on photosynthesis, enzyme activity, and metabolite levels in wheat, Plant Physiol., 84, 1244–1248, https://doi.org/10.1104/pp.84.4.1244, 1987.
Ukkola, A. M., De Kauwe, M. G., Roderick, M. L., Abramowitz, G., and Pitman, A. J.: Robust future changes in meteorological drought in CMIP6 projections despite uncertainty in precipitation, Geophys. Res. Lett., 47, e2020GL087820, https://doi.org/10.1029/2020GL087820, 2020.
Van Klompenburg, T., Kassahun, A., and Catal, C.: Crop yield prediction using machine learning: A systematic literature review, Comput. Electron. Agric., 177, 105709, https://doi.org/10.1016/j.compag.2020.105709, 2020.
Waha, K., Müller, C., and Rolinski, S.: Separate and combined effects of temperature and precipitation change on maize yields in sub-Saharan Africa for mid-to late-21st century, Global Planet. Change, 106, 1–12, https://doi.org/10.1016/j.gloplacha.2013.02.009, 2013.
Wang, W., Pijl, A., and Tarolli, P.: Future climate-zone shifts are threatening steep-slope agriculture, Nat. Food, 3, 193–196, https://doi.org/10.1038/s43016-021-00454-y, 2022.
Wang, Y.-P. and Leuning, R.: A two-leaf model for canopy conductance, photosynthesis and portioning of available energy I: Model description and comparison with a multi-layered model, Agr. Forest Meteorol., 91, 89–111, https://doi.org/10.1016/j.agrformet.1998.01.004, 1998.
Williams, K., Gornall, J., Harper, A., Wiltshire, A., Hemming, D., Quaife, T., Arkebauer, T., and Scoby, D.: Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska, Geosci. Model Dev., 10, 1291–1320, https://doi.org/10.5194/gmd-10-1291-2017, 2017.
Wiltshire, A. J., Burke, E. J., Chadburn, S. E., Jones, C. D., Cox, P. M., Davies-Barnard, T., Friedlingstein, P., Harper, A. B., Liddicoat, S., Sitch, S., and Zaehle, S.: JULES-CN: a coupled terrestrial carbon–nitrogen scheme (JULES vn5.1), Geosci. Model Dev., 14, 2161–2186, https://doi.org/10.5194/gmd-14-2161-2021, 2021.
Wu, B., Zhang, M., Zeng, H., Tian, F., Potgieter, A. B., Qin, X., Yan, N., Chang, S., Zhao, Y., Dong, Q., and Boken, V.: Challenges and opportunities in remote sensing-based crop monitoring: A review, Nat. Sci. Rev., 10, nwac290, https://doi.org/10.1093/nsr/nwac290, 2023.
Wu, X., Vuichard, N., Ciais, P., Viovy, N., de Noblet-Ducoudré, N., Wang, X., Magliulo, V., Wattenbach, M., Vitale, L., Di Tommasi, P., Moors, E. J., Jans, W., Elbers, J., Ceschia, E., Tallec, T., Bernhofer, C., Grünwald, T., Moureaux, C., Manise, T., Ligne, A., Cellier, P., Loubet, B., Larmanou, E., and Ripoche, D.: ORCHIDEE-CROP (v0), a new process-based agro-land surface model: model description and evaluation over Europe, Geosci. Model Dev., 9, 857–873, https://doi.org/10.5194/gmd-9-857-2016, 2016.
Zhang, W., Liu, C., Zheng, X., Wang, K., Cui, F., Wang, R., Li, S., Yao, Z., and Zhu, J.: Using a modified DNDC biogeochemical model to optimize field management of a multi-crop (cotton, wheat, and maize) system: a site-scale case study in northern China, Biogeosciences, 16, 2905–2922, https://doi.org/10.5194/bg-16-2905-2019, 2019.
Short summary
We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
We present and validate enhancements to the process-based T&C model aimed at improving its...
