Articles | Volume 12, issue 1
https://doi.org/10.5194/esd-12-211-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-12-211-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How modelling paradigms affect simulated future land use change
Institute of Meteorology and Climate Research, Atmospheric
Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany
Ian Holman
School of Water, Energy and Environment, Cranfield University, Vincent Building, Bedford MK43 0AL, UK
Mark Rounsevell
Institute of Meteorology and Climate Research, Atmospheric
Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany
School of Geosciences, University of Edinburgh, Edinburgh EH8 9XP, UK
Related authors
Yongchao Zeng, Calum Brown, Mohamed Byari, Joanna Raymond, Thomas Schmitt, and Mark Rounsevell
Geosci. Model Dev., 18, 4983–5013, https://doi.org/10.5194/gmd-18-4983-2025, https://doi.org/10.5194/gmd-18-4983-2025, 2025
Short summary
Short summary
Understanding environmental policy interventions is challenging due to complex institutional actor interactions. Large language models (LLMs) offer new solutions by mimicking the actors. We present InsNet-CRAFTY v1.0, a multi-LLM-agent model coupled with a land system model, simulating competing policy priorities. The model shows how LLM agents can simulate decision-making in institutional networks, highlighting both their potential and limitations in advancing land system modelling.
Yongchao Zeng, Calum Brown, Joanna Raymond, Mohamed Byari, Ronja Hotz, and Mark Rounsevell
Earth Syst. Dynam., 16, 423–449, https://doi.org/10.5194/esd-16-423-2025, https://doi.org/10.5194/esd-16-423-2025, 2025
Short summary
Short summary
This study explores using large language models (LLMs) to simulate policy-making in land systems. We integrated LLMs into a land use model and simulated LLM-powered institutional agents steering meat production by taxation. The results show LLMs can generate boundedly rational policy-making behaviours that can hardly be modelled using conventional methods; LLMs can offer the reasoning behind policy actions. We also discussed LLMs’ potential and challenges in large-scale simulations.
Yongchao Zeng, Calum Brown, Mohamed Byari, Joanna Raymond, Thomas Schmitt, and Mark Rounsevell
Geosci. Model Dev., 18, 4983–5013, https://doi.org/10.5194/gmd-18-4983-2025, https://doi.org/10.5194/gmd-18-4983-2025, 2025
Short summary
Short summary
Understanding environmental policy interventions is challenging due to complex institutional actor interactions. Large language models (LLMs) offer new solutions by mimicking the actors. We present InsNet-CRAFTY v1.0, a multi-LLM-agent model coupled with a land system model, simulating competing policy priorities. The model shows how LLM agents can simulate decision-making in institutional networks, highlighting both their potential and limitations in advancing land system modelling.
Yongchao Zeng, Calum Brown, Joanna Raymond, Mohamed Byari, Ronja Hotz, and Mark Rounsevell
Earth Syst. Dynam., 16, 423–449, https://doi.org/10.5194/esd-16-423-2025, https://doi.org/10.5194/esd-16-423-2025, 2025
Short summary
Short summary
This study explores using large language models (LLMs) to simulate policy-making in land systems. We integrated LLMs into a land use model and simulated LLM-powered institutional agents steering meat production by taxation. The results show LLMs can generate boundedly rational policy-making behaviours that can hardly be modelled using conventional methods; LLMs can offer the reasoning behind policy actions. We also discussed LLMs’ potential and challenges in large-scale simulations.
Maliko Tanguy, Michael Eastman, Eugene Magee, Lucy J. Barker, Thomas Chitson, Chaiwat Ekkawatpanit, Daniel Goodwin, Jamie Hannaford, Ian Holman, Liwa Pardthaisong, Simon Parry, Dolores Rey Vicario, and Supattra Visessri
Nat. Hazards Earth Syst. Sci., 23, 2419–2441, https://doi.org/10.5194/nhess-23-2419-2023, https://doi.org/10.5194/nhess-23-2419-2023, 2023
Short summary
Short summary
Droughts in Thailand are becoming more severe due to climate change. Understanding the link between drought impacts on the ground and drought indicators used in drought monitoring systems can help increase a country's preparedness and resilience to drought. With a focus on agricultural droughts, we derive crop- and region-specific indicator-to-impact links that can form the basis of targeted mitigation actions and an improved drought monitoring and early warning system in Thailand.
William Rust, John P. Bloomfield, Mark Cuthbert, Ron Corstanje, and Ian Holman
Hydrol. Earth Syst. Sci., 26, 2449–2467, https://doi.org/10.5194/hess-26-2449-2022, https://doi.org/10.5194/hess-26-2449-2022, 2022
Short summary
Short summary
We highlight the importance of the North Atlantic Oscillation in controlling droughts in the UK. Specifically, multi-year cycles in the NAO are shown to influence the frequency of droughts and this influence changes considerably over time. We show that the influence of these varying controls is similar to the projected effects of climate change on water resources. We also show that these time-varying behaviours have important implications for water resource forecasts used for drought planning.
William Rust, Mark Cuthbert, John Bloomfield, Ron Corstanje, Nicholas Howden, and Ian Holman
Hydrol. Earth Syst. Sci., 25, 2223–2237, https://doi.org/10.5194/hess-25-2223-2021, https://doi.org/10.5194/hess-25-2223-2021, 2021
Short summary
Short summary
In this paper, we find evidence for the cyclical behaviour (on a 7-year basis) in UK streamflow records that match the main cycle of the North Atlantic Oscillation. Furthermore, we find that the strength of these 7-year cycles in streamflow is dependent on proportional contributions from groundwater and the response times of the underlying groundwater systems. This may allow for improvements to water management practices through better understanding of long-term streamflow behaviour.
Cited articles
Alexander, P., Prestele, R., Verburg, P. H., Arneth, A., Baranzelli, C.,
Batista e Silva, F., Brown, C., Butler, A., Calvin, K., Dendoncker, N., Doelman, J. C., Dunford, R., Engström, K., Eitelberg, D., Fujimori, S.,
Harrison, P. A., Hasegawa, T., Havlik, P., Holzhauer, S., Humpenöder, F., Jacobs-Crisioni, C., Jain, A. K., Krisztin, T., Kyle, P., Lavalle, C., Lenton, T., Liu, J., Meiyappan, P., Popp, A., Powell, T., Sands, R. D.,
Schaldach, R., Stehfest, E., Steinbuks, J., Tabeau, A., van Meijl, H., Wise,
M. A., and Rounsevell, M. D. A.: Assessing uncertainties in land cover
projections, Global Change Biol., 23, 767–781, https://doi.org/10.1111/gcb.13447, 2017.
Appel, F. and Balmann, A.: Human behaviour versus optimising agents and the
resilience of farms – Insights from agent-based participatory experiments
with FarmAgriPoliS, Ecol. Complex., 40, 100731,
https://doi.org/10.1016/j.ecocom.2018.08.005, 2019.
Arneth, A., Brown, C., and Rounsevell, M. D. A.: Global models of human
decision-making for land-based mitigation and adaptation assessment, Nat. Clim. Change, 4, 550–557, https://doi.org/10.1038/nclimate2250, 2014.
Audsley, E., Trnka, M., Sabaté, S., Maspons, J., Sanchez, A., Sandars, D., Balek, J., and Pearn, K.: Interactively modelling land profitability to
estimate European agricultural and forest land use under future scenarios of
climate, socio-economics and adaptation, Climatic Change, 128, 215–227,
https://doi.org/10.1007/s10584-014-1164-6, 2015.
Baldos, C. and Hertel, T. W.: Looking back to move forward on model validation: insights from a global model of agricultural land use Related content Climate adaptation as mitigation: the case of agricultural investments, Environ. Res. Lett., 8, 034024, https://doi.org/10.1088/1748-9326/8/3/034024, 2013.
Brown, C., Brown, E., Murray-Rust, D., Cojocaru, G., Savin, C., and Rounsevell, M.: Analysing uncertainties in climate change impact assessment
across sectors and scenarios, Climatic Change, 128, 293–306,
https://doi.org/10.1007/s10584-014-1133-0, 2014a.
Brown, C., Murray-Rust, D., Van Vliet, J., Alam, S. J., Verburg, P. H., and
Rounsevell, M. D.: Experiments in globalisation, food security and land use
decision making, PLoS One, 9, e114213, https://doi.org/10.1371/journal.pone.0114213, 2014b.
Brown, C., Brown, K., and Rounsevell, M.: A philosophical case for process-based modelling of land use change, Model. Earth Syst. Environ., 2, 1–12, https://doi.org/10.1007/s40808-016-0102-1, 2016.
Brown, C., Alexander, P., Holzhauer, S., and Rounsevell, M. D. A.: Behavioral models of climate change adaptation and mitigation in land-based sectors, Wiley Interdisciplin. Rev.: Clim. Change, 8, e448, https://doi.org/10.1002/wcc.448, 2017.
Brown, C., Alexander, P., and Rounsevell, M.: Empirical evidence for the
diffusion of knowledge in land use change, J. Land Use Sci., 13, 269–283, https://doi.org/10.1080/1747423X.2018.1515995, 2018a.
Brown, C., Holzhauer, S., Metzger, M. J., Paterson, J. S., and Rounsevell, M.: Land managers' behaviours modulate pathways to visions of future land
systems, Reg. Environ. Change, 18, 831–845, https://doi.org/10.1007/s10113-016-0999-y, 2018b.
Brown, C., Alexander, P., Arneth, A., Holman, I., and Rounsevell, M.: Achievement of Paris climate goals unlikely due to time lags in the land
system, Nat. Clim. Change, 9, 203–208, https://doi.org/10.1038/s41558-019-0400-5, 2019a.
Brown, C., Seo, B., and Rounsevell, M.: Societal breakdown as an emergent
property of large-scale behavioural models of land use change, Earth Syst.
Dynam., 10, 809–845, https://doi.org/10.5194/esd-10-809-2019, 2019b.
Castella, J.-C. and Verburg, P. H.: Combination of process-oriented and
pattern-oriented models of land-use change in a mountain area of Vietnam,
Ecol. Model., 202, 410–420, https://doi.org/10.1016/j.ecolmodel.2006.11.011, 2007.
Chouinard, H. H., Paterson, T., Wandschneider, P. R., and Ohler, A. M.: Will
farmers trade profits for stewardship? Heterogeneous motivations for farm
practice selection, Land Econ., 84, 66–82, https://doi.org/10.3368/le.84.1.66, 2008.
Couclelis, H.: Modeling frameworks, paradigms, and approaches, in: Geogr. Inf. Syst. Environ. Model., Prentice Hall, London, 2002.
de Coninck, H., Revi, A., Babiker, M., Bertoldi, P., Buckeridge, M., Cartwright, A., Dong, W., Ford, J., Fuss, S., and Hourcade, J.-C.: Chapter 4: Strengthening and Implementing the Global Response, available at: http://pure.iiasa.ac.at/id/eprint/15516/ (last access: 19 February 2021), 2018.
Elsawah, S., Guillaume, J. H. A., Filatova, T., Rook, J., and Jakeman, A. J.:
A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: From
cognitive maps to agent-based models, J. Environ. Manage., 151, 500–516,
https://doi.org/10.1016/j.jenvman.2014.11.028, 2015.
Elsawah, S., Filatova, T., Jakeman, A. J., Kettner, A. J., Zellner, M. L.,
Athanasiadis, I. N., Hamilton, S. H., Axtell, R. L., Brown, D. G., Gilligan,
J. M., Janssen, M. A., Robinson, D. T., Rozenberg, J., Ullah, I. I. T., and
Lade, S. J.: Eight grand challenges in socio-environmental systems modeling,
Socio-Environm. Syst. Model., 2, 16226, https://doi.org/10.18174/sesmo.2020a16226, 2020.
Estoque, R. C., Ooba, M., Togawa, T., and Hijioka, Y.: Projected land-use
changes in the Shared Socioeconomic Pathways: Insights and implications,
Ambio, 49, 1972–1981, https://doi.org/10.1007/s13280-020-01338-4, 2020.
Filatova, T., Verburg, P. H., Parker, D. C., and Stannard, C. A.: Spatial agent-based models for socio-ecological systems: Challenges and prospects, Environ. Model. Softw., 45, 1–7, https://doi.org/10.1016/j.envsoft.2013.03.017, 2013.
Filatova, T., Polhill, J. G., and van Ewijk, S.: Regime shifts in coupled socio-environmental systems: Review of modelling challenges and approaches, Environ. Model. Softw., 75, 333–347, 2016.
Fonoberova, M., Fonoberov, V. A., and Mezić, I.: Global
sensitivity/uncertainty analysis for agent-based models, Reliab. Eng. Syst.
Safe., 118, 8–17, https://doi.org/10.1016/j.ress.2013.04.004, 2013.
Fronzek, S., Carter, T. R., Pirttioja, N., Alkemade, R., Audsley, E., Bugmann, H., Flörke, M., Holman, I., Honda, Y., Ito, A., Janes-Bassett,
V., Lafond, V., Leemans, R., Mokrech, M., Nunez, S., Sandars, D., Snell, R.,
Takahashi, K., Tanaka, A., Wimmer, F., and Yoshikawa, M.: Determining sectoral and regional sensitivity to climate and socio-economic change in
Europe using impact response surfaces, Reg. Environ. Change, 19, 679–693, https://doi.org/10.1007/s10113-018-1421-8, 2019.
Gostoli, U. and Silverman, E.: Sound behavioural theories, not data, is what makes computational models useful. Review of Artificial Societies and Social Simulation, available at: https://rofasss.org/2020/04/22/sound-behavioural-theories/ (last access: 19 February 2021), 2020.
Hamilton, H., Henry, R., Rounsevell, M., Moran, D., Cossar, F., Allen, K.,
Boden, L., and Alexander, P.: Exploring global food system shocks, scenarios
and outcomes, Futures, 123, 102601, https://doi.org/10.1016/j.futures.2020.102601, 2020.
Harrison, P. A., Holman, I. P., and Berry, P. M.: Assessing cross-sectoral
climate change impacts, vulnerability and adaptation: an introduction to the
CLIMSAVE project, Climatic Change, 128, 153–167, https://doi.org/10.1007/s10584-015-1324-3, 2015.
Harrison, P. A., Dunford, R. W., Holman, I. P., and Rounsevell, M. D. A.:
Climate change impact modelling needs to include cross-sectoral interactions, Nat. Clim. Change, 6, 885–890, https://doi.org/10.1038/nclimate3039, 2016.
Harrison, P. A., Hauck, J., Austrheim, G., Brotons, L., Cantele, M., Claudet, J., and Turok, J.: Chapter 5: Current and future interactions between nature and society, in: The IPBES regional assessment report on biodiversity and ecosystem services for Europe and Central Asia, edited by: Rounsevell, M., Fischer, M., Torre-Marin Rando, A., and Mader, A., IPBES Secretariat, Bonn, Germany, 571–658, 2018.
Harrison, P. A., Dunford, R. W., Holman, I. P., Cojocaru, G., Madsen, M. S.,
Chen, P. Y., Pedde, S., and Sandars, D.: Differences between low-end and
high-end climate change impacts in Europe across multiple sectors, Reg. Environ. Change, 19, 695–709, https://doi.org/10.1007/s10113-018-1352-4, 2019.
Holman, I., Audsley, E., Berry, P., Brown, C., Bugmann, H., Clarke, L.,
Cojocaru, G., Dunford, R., Fronzek, S., Harrison, P. A., Honda, Y., Janes, V., Kovats, S., Lafond, V., Lobanova, A., Madsen, M. S., Mokrech, M., Nunez,
S., Savin, C., and Wimmer, F.: Modelling Climate Change Impacts, Adaptation
and Vulnerability in Europe: IMPRESSIONS project deliverable, IMPRESSIONS project, 2017a.
Holman, I. P., Brown, C., Janes, V., and Sandars, D.: Can we be certain about
future land use change in Europe? A multi-scenario, integrated-assessment
analysis, Agric. Syst., 151, 126–135, https://doi.org/10.1016/j.agsy.2016.12.001, 2017b.
Holzhauer, S., Brown, C., and Rounsevell, M.: Modelling dynamic effects of
multi-scale institutions on land use change, Reg. Environ. Change, 19, 733–746, https://doi.org/10.1007/s10113-018-1424-5, 2019.
Houet, T., Schaller, N., Castets, M., and Gaucherel, C.: Improving the simulation of fine-resolution landscape changes by coupling top-down and
bottom-up land use and cover changes rules, Int. J. Geogr. Inf. Sci., 28,
1848–1876, https://doi.org/10.1080/13658816.2014.900775, 2014.
Huber, R., Bakker, M., Balmann, A., Berger, T., Bithell, M., Brown, C., Grêt-Regamey, A., Xiong, H., Le, Q. B., Mack, G., Meyfroidt, P., Millington, J., Müller, B., Polhill, J. G., Sun, Z., Seidl, R., Troost, C., and Finger, R.: Representation of decision-making in European agricultural agent-based models, Agric. Syst., 167, 143–160, 2018.
IMPRESSIONS Project: IMPRESSIONS Integrated Assessment Platform version 2 (IAP2), available at: http://www.impressions-project.eu/show/IAP2_14855 (last access: 19 February 2021), 2019.
Kay, S., Graves, A., Palma, J. H. N., Moreno, G., Roces-Díaz, J. V., Aviron, S., Chouvardas, D., Crous-Duran, J., Ferreiro-Domínguez, N.,
García de Jalón, S., Măcicăşan, V., Mosquera-Losada, M.
R., Pantera, A., Santiago-Freijanes, J. J., Szerencsits, E., Torralba, M.,
Burgess, P. J., and Herzog, F.: Agroforestry is paying off – Economic evaluation of ecosystem services in European landscapes with and without
agroforestry systems, Ecosyst. Serv., 36, 100896,
https://doi.org/10.1016/J.ECOSER.2019.100896, 2019.
Kebede, A. S., Dunford, R., Mokrech, M., Audsley, E., Harrison, P. A., Holman, I. P., Nicholls, R. J., Rickebusch, S., Rounsevell, M. D. A., Sabaté, S., Sallaba, F., Sanchez, A., Savin, C., Trnka, M., and Wimmer,
F.: Direct and indirect impacts of climate and socio-economic change in
Europe: a sensitivity analysis for key land- and water-based sectors, Climatic Change, 128, 261–277, https://doi.org/10.1007/s10584-014-1313-y, 2015.
Kling, C. L., Arritt, R. W., Calhoun, G., and Keiser, D. A.: Integrated Assessment Models of the Food, Energy, and Water Nexus: A Review and an Outline of Research Needs, Annu. Rev. Resour. Econ., 9, 143–163, 2017.
Kok, K., Pedde, S., Gramberger, M., Harrison, P. A., and Holman, I. P.: New
European socio-economic scenarios for climate change research: operationalising concepts to extend the shared socio-economic pathways, Reg.
Environ. Change, 19, 643–654, https://doi.org/10.1007/s10113-018-1400-0, 2019.
Lawrence, D. M., Hurtt, G. C., Arneth, A., Brovkin, V., Calvin, K. V., Jones, A. D., Jones, C. D., Lawrence, P. J., de Noblet-Ducoudré, N., Pongratz, J., Seneviratne, S. I., and Shevliakova, E.: The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design, Geosci. Model Dev., 9, 2973–2998, https://doi.org/10.5194/gmd-9-2973-2016, 2016.
Ligmann-Zielinska, A., Church, R., and Jankowski, P.: Spatial optimization as
a generative technique for sustainable multiobjective land-use allocation,
Int. J. Geogr. Inf. Sci., 22, 601–622, https://doi.org/10.1080/13658810701587495, 2008.
Ligmann-Zielinska, A., Kramer, D. B., Cheruvelil, K. S., and Soranno, P. A.:
Using uncertainty and sensitivity analyses in socioecological agent-based
models to improve their analytical performance and policy relevance, PLoS
One, 9, e109779, https://doi.org/10.1371/journal.pone.0109779, 2014.
Low, S. and Schäfer, S.: Is bio-energy carbon capture and storage (BECCS) feasible? The contested authority of integrated assessment modeling, Energ. Res. Soc. Sci., 60, 101326, https://doi.org/10.1016/j.erss.2019.101326, 2020.
Meiyappan, P., Dalton, M., O'Neill, B. C., and Jain, A. K.: Spatial modeling
of agricultural land use change at global scale, Ecol. Model., 291, 152–174, https://doi.org/10.1016/j.ecolmodel.2014.07.027, 2014.
Meyfroidt, P., Chowdhury, R., de Bremond, A., Ellis, E. C., Erb, K.-H., Filatova, T., Garrett, R. D., Grove, J. M., Heinimann, A., Kuemmerle, T., Kull, C. A., Lambin, E. F., Landon, Y., le Polain de Waroux, Y., Messerli, P., Müller, D., Nielsen, J. Ø., Peterson, G. D., Rodriguez García, V., Schlüter, M., Turner, B. L., and Verburg, P. H.: Middle-range theories of land system change, Global Environ. Change, 53, 52–67, 2018.
Millington, J. D. A., Demeritt, D., and Romero-Calcerrada, R.: Participatory
evaluation of agent-based land-use models, J. Land Use Sci., 6, 195–210, https://doi.org/10.1080/1747423X.2011.558595, 2011.
Müller, B., Balbi, S., Buchmann, C. M., de Sousa, L., Dressler, G.,
Groeneveld, J., Klassert, C. J., Le, Q. B., Millington, J. D. A., Nolzen, H., Parker, D. C., Polhill, J. G., Schlüter, M., Schulze, J., Schwarz, N., Sun, Z., Taillandier, P., and Weise, H.: Standardised and transparent model descriptions for agent-based models: Current status and prospects, Environ. Model. Softw., 55, 156–163, https://doi.org/10.1016/j.envsoft.2014.01.029, 2014.
Müller, B., Hoffmann, F., Heckelei, T., Müller, C., Hertel, T. W.,
Polhill, J. G., van Wijk, M., Achterbosch, T., Alexander, P., Brown, C.,
Kreuer, D., Ewert, F., Ge, J., Millington, J. D. A., Seppelt, R., Verburg, P. H., and Webber, H.: Modelling food security: Bridging the gap between the
micro and the macro scale, Global Environ. Change, 63, 102085,
https://doi.org/10.1016/j.gloenvcha.2020.102085, 2020.
Murray-Rust, D., Brown, C., van Vliet, J., Alam, S. J., Robinson, D. T., Verburg, P. H., and Rounsevell, M.: Combining agent functional types, capitals and services to model land use dynamics, Environ. Model. Softw., 59, 187–201, https://doi.org/10.1016/j.envsoft.2014.05.019, 2014.
Newland, C. P., Maier, H. R., Zecchin, A. C., Newman, J. P., and van Delden,
H.: Multi-objective optimisation framework for calibration of Cellular
Automata land-use models, Environ. Model. Softw., 100, 175–200,
https://doi.org/10.1016/j.envsoft.2017.11.012, 2018.
Obermeister, N.: Local knowledge, global ambitions: IPBES and the advent of multi-scale models and scenarios, Sustainabil. Sci., 14, 843–856, 2019.
O'Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., Levy, M., and Solecki, W.: The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century, Global
Environ. Change, 42, 169–180, https://doi.org/10.1016/j.gloenvcha.2015.01.004, 2017.
Papadimitriou, L., Holman, I. P., Dunford, R., and Harrison, P. A.: Trade-offs are unavoidable in multi-objective adaptation even in a post-Paris Agreement world, Sci. Total Environ., 696, 134027,
https://doi.org/10.1016/j.scitotenv.2019.134027, 2019.
Pedde, S., Kok, K., Hölscher, K., Frantzeskaki, N., Holman, I., Dunford,
R., Smith, A., and Jäger, J.: Advancing the use of scenarios to understand society's capacity to achieve the 1.5 degree target, Global Environ. Change, 56, 75–85, https://doi.org/10.1016/J.GLOENVCHA.2019.03.010, 2019.
Polhill, J. G. and Gotts, N. M.: Ontologies for transparent integrated human-natural system modelling, Landsc. Ecol., 24, 1255–1267,
https://doi.org/10.1007/s10980-009-9381-5, 2009.
Pongratz, J., Dolman, H., Don, A., Erb, K.-H., Fuchs, R., Herold, M., Jones, C., Kuemmerle, T., Luyssaert, S., Meyfroidt, P., and Naudts, K.: Models meet data: Challenges and opportunities in implementing land management in Earth system models, Global Change Biol., 2, 1470–1487, 2018.
Prestele, R., Alexander, P., Rounsevell, M. D. A., Arneth, A., Calvin, K.,
Doelman, J., Eitelberg, D. A., Engström, K., Fujimori, S., Hasegawa, T.,
Havlik, P., Humpenöder, F., Jain, A. K., Krisztin, T., Kyle, P., Meiyappan, P., Popp, A., Sands, R. D., Schaldach, R., Schüngel, J.,
Stehfest, E., Tabeau, A., Van Meijl, H., Van Vliet, J., and Verburg, P. H.:
Hotspots of uncertainty in land-use and land-cover change projections: a
global-scale model comparison, Global Change Biol., 22, 3967–3983,
https://doi.org/10.1111/gcb.13337, 2016.
Rogelj, J., Shindell, D., Jiang, K., Fifita, S., Forster, P., Ginzburg, V., Handa, C., Kheshgi, H., Kobayashi, S., Kriegler, E., Mundaca, L., Séférian, R. and Vilariño, M. V.: Mitigation Pathways Compatible with 1.5 ∘C in the Context of Sustainable Development, in: Global Warming of 1.5 ∘C. An IPCC Special Report on the impacts of global warming of 1.5 ∘C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, edited by: Masson-Delmotte, V., Zhai, P., Pörtner, H.-O., Roberts, D., Skea, P. R., Shukla, J., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, V., and Waterfield, T., IPCC Secretariat, Geneva, Switzerland, p. 82, 2018.
Rosa, I. M. D., Ahmed, S. E., and Ewers, R. M.: The transparency, reliability
and utility of tropical rainforest land-use and land-cover change models,
Global Change Biol., 20, 1707–1722, https://doi.org/10.1111/gcb.12523, 2014.
Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K.,
Almaatouq, A., Altschul, D. M., Brand, J. E., Carnegie, N. B., Compton, R. J., Datta, D., Davidson, T., Filippova, A., Gilroy, C., Goode, B. J., Jahani, E., Kashyap, R., Kirchner, A., McKay, S., Morgan, A. C., Pentland, A., Polimis, K., Raes, L., Rigobon, D. E., Roberts, C. V., Stanescu, D. M., Suhara, Y., Usmani, A., Wang, E. H., Adem, M., Alhajri, A., AlShebli, B.,
Amin, R., Amos, R. B., Argyle, L. P., Baer-Bositis, L., Büchi, M., Chung, B.-R., Eggert, W., Faletto, G., Fan, Z., Freese, J., Gadgil, T., Gagné, J., Gao, Y., Halpern-Manners, A., Hashim, S. P., Hausen, S., He, G., Higuera, K., Hogan, B., Horwitz, I. M., Hummel, L. M., Jain, N., Jin, K., Jurgens, D., Kaminski, P., Karapetyan, A., Kim, E. H., Leizman, B., Liu, N., Möser, M., Mack, A. E., Mahajan, M., Mandell, N., Marahrens, H., Mercado-Garcia, D., Mocz, V., Mueller-Gastell, K., Musse, A., Niu, Q., Nowak, W., Omidvar, H., Or, A., Ouyang, K., Pinto, K. M., Porter, E., Porter, K. E., Qian, C., Rauf, T., Sargsyan, A., Schaffner, T., Schnabel, L., Schonfeld, B., Sender, B., Tang, J. D., Tsurkov, E., van Loon, A., Varol, O., Wang, X., Wang, Z., Wang, J., Wang, F., Weissman, S., Whitaker, K., Wolters, M. K., Woon, W. L., Wu, J., Wu, C., Yang, K., Yin, J., Zhao, B., Zhu, C., Brooks-Gunn, J., Engelhardt, B. E., Hardt, M., Knox, D., Levy, K., Narayanan, A., Stewart, B. M., Watts, D. J., and McLanahan, S.: Measuring the predictability of life outcomes with a scientific mass collaboration, P. Natl. Acad. Sci. USA, 117, 201915006, https://doi.org/10.1073/pnas.1915006117, 2020.
Saltelli, A., Aleksankina, K., Becker, W., Fennell, P., Ferretti, F., Holst, N., Li, S., and Wu, Q.: Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices, Environ. Model. Softw., 114, 29–39, 2019.
Schwarze, J., Sophie Holst, G., and Mußhoff, O.: Do farmers act like
perfectly rational profit maximisers? Results of an extra-laboratory experiment, Int. J. Agric. Manage., 4, 11–20, https://doi.org/10.22004/ag.econ.262336, 2014.
Seo, B., Brown, C., and Rounsevell, M.: Evaluation and calibration of an
agent based land use model using remotely sensed land cover and primary
productivity data, in: International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., Valencia, 7472–7475, 2018.
Seo, B., Brown, C., and Rounsevell, M.: Competition for Resources between Agent Functional Types (CRAFTY), available at: https://landchange.earth/CRAFTY (last access: 19 February 2021), 2019.
Seppelt, R., Lautenbach, S., and Volk, M.: Identifying trade-offs between
ecosystem services, land use, and biodiversity: A plea for combining scenario analysis and optimization on different spatial scales, Curr. Opin. Environ. Sustain., 5, 458–463, https://doi.org/10.1016/j.cosust.2013.05.002, 2013.
Smith, P., Calvin, K., Nkem, J., Campbell, D., Cherubini, F., Grassi, G., Korotkov, V., Le Hoang, A., Lwasa, S., McElwee, P., Nkonya, E., Saigusa, N., Soussana, J. F., Taboada, M. A., Manning, F. C., Nampanzira, D., Arias-Navarro, C., Vizzarri, M., House, J., Roe, S., Cowie, A., Rounsevell, M., and Arneth, A.: Which practices co-deliver food security, climate change mitigation and adaptation, and combat land degradation and desertification?, Global Change Biol., 26, 1532–1575, 2019.
Sohl, T. L. and Claggett, P. R.: Clarity versus complexity: Land-use modeling as a practical tool fordecision-makers, J. Environ. Manage., 129, 235–243, https://doi.org/10.1016/j.jenvman.2013.07.027, 2013.
Strauch, M., Cord, A. F., Pätzold, C., Lautenbach, S., Kaim, A., Schweitzer, C., Seppelt, R., and Volk, M.: Constraints in multi-objective
optimization of land use allocation – Repair or penalize?, Environ. Model.
Softw., 118, 241–251, https://doi.org/10.1016/j.envsoft.2019.05.003, 2019.
Turner, P. A., Field, C. B., Lobell, D. B., Sanchez, D. L., and Mach, K. J.:
Unprecedented rates of land-use transformation in modelled climate change
mitigation pathways, Nat. Sustain., 1, 240–245, https://doi.org/10.1038/s41893-018-0063-7, 2018.
van Vliet, J., Bregt, A. K., Brown, D. G., van Delden, H., Heckbert, S., and Verburg, P. H.: A review of current calibration and validation practices in land-change modeling, Environ. Model. Softw., 82, 174–182, 2016.
Verburg, P. H. and Overmars, K. P.: Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model, Landsc. Ecol., 24, 1167–1181, https://doi.org/10.1007/s10980-009-9355-7, 2009.
Verburg, P. H., Alexander, P., Evans, T., Magliocca, N. R., Malek, Z.,
Rounsevell, M. D. A., and van Vliet, J.: Beyond land cover change: towards a new generation of land use models, Curr. Opin. Environ. Sustain., 38, 77–85,
https://doi.org/10.1016/j.cosust.2019.05.002, 2019.
Short summary
The variety of human and natural processes in the land system can be modelled in many different ways. However, little is known about how and why basic model assumptions affect model results. We compared two models that represent land use in completely distinct ways and found several results that differed greatly. We identify the main assumptions that caused these differences and therefore key issues that need to be addressed for more robust model development.
The variety of human and natural processes in the land system can be modelled in many different...
Altmetrics
Final-revised paper
Preprint