Assessing uncertainties in land cover projections

Global Change Biology

Volumn 23, Issue 2, 2017, Pages 767-781

  Alexander, Peter ^a,b   Prestele, Reinhard ^c   Verburg, Peter H ^c   Arneth, Almut ^d   Baranzelli, Claudia ^e   Batista e Silva, Filipe ^e   Brown, Calum ^a   Butler, Adam ^f   Calvin, Katherine ^g   Dendoncker, Nicolas ^h   Doelman, Jonathan C ⁱ   Dunford, Robert ^j,k   Engström, Kerstin ^l   Eitelberg, David ^c   Fujimori, Shinichiro ^m   Harrison, Paula A ^k   Hasegawa, Tomoko ^m   Havlik, Petr ⁿ   Holzhauer, Sascha ^a   Humpenöder, Florian ^o   Jacobs Crisioni, Chris ^e   Jain, Atul K ^p   Krisztin, Tamás ⁿ   Kyle, Page ^g   Lavalle, Carlo ^e   Lenton, Tim ^q   Liu, Jiayi ^f   Meiyappan, Prasanth ^p   Popp, Alexander ^o   Powell, Tom ^q   Sands, Ronald D ^r   Schaldach, Rüdiger ^s   Stehfest, Elke ⁱ   Steinbuks, Jevgenijs ^t   Tabeau, Andrzej ^u   van Meijl, Hans ^u   Wise, Marshall A ^g   Rounsevell, Mark D A ^a   more..

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b SCOTTISH AGRICULTURAL COLLEGE   (United Kingdom)

^c VU UNIVERSITY AMSTERDAM   (Netherlands)

^d KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

^e EUROPEAN COMMISSION   (Belgium)

^f BIOMATHEMATICS AND STATISTICS SCOTLAND   (United Kingdom)

^g PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

^h UNIVERSITY OF NAMUR   (Belgium)

ⁱ PBL NETHERLANDS ENVIRONMENTAL ASSESSMENT AGENCY   (Netherlands)

^j UNIVERSITY OF OXFORD   (United Kingdom)

^k CENTRE FOR ECOLOGY AND HYDROLOGY   (United Kingdom)

^l LUND UNIVERSITY   (Sweden)

^m NATIONAL INSTITUTE FOR ENVIRONMENTAL STUDIES   (Japan)

ⁿ INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS   (Austria)

^o POTSDAM INSTITUTE FOR CLIMATE IMPACT RESEARCH   (Germany)

^p UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^q UNIVERSITY OF EXETER   (United Kingdom)

^r ECONOMIC RESEARCH SERVICE   (United States)

^s UNIVERSITY OF KASSEL   (Germany)

^t WORLD BANK   (United States)

^u WAGENINGEN UNIVERSITY   (Netherlands)

more..

Author keywords

cropland; land cover; land use; model inter comparison; uncertainty

Indexed keywords

ADAPTIVE MANAGEMENT; AGRICULTURAL LAND; CLIMATE CHANGE; ENVIRONMENTAL ASSESSMENT; LAND COVER; LAND USE CHANGE; UNCERTAINTY ANALYSIS;

ASTRONOMY; CLIMATE; CLIMATE CHANGE; FORECASTING; PLANT; UNCERTAINTY;

CLIMATE; CLIMATE CHANGE; EARTH (PLANET); FORECASTING; PLANTS; UNCERTAINTY;

EID: 84982237065     PISSN: 13541013     EISSN: 13652486     Source Type: Journal    
DOI: 10.1111/gcb.13447     Document Type: Article

References (65)

1. Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Second International Symposium on Information Theory (eds Petrov BN, Csaki BF), pp. 267–281. Academiai Kiado, Budapest.
2. Alexander P, Rounsevell MDA, Dislich C, Dodson JR, Engström K, Moran D (2015) Drivers for global agricultural land use change: the nexus of diet, population, yield and bioenergy. Global Environmental Change, 35, 138–147.
3. van Asselen S, Verburg PH (2013) Land cover change or land-use intensification: simulating land system change with a global-scale land change model. Global Change Biology, 19, 3648–3667.
4. Baranzelli C, Jacobs-crisioni C, Batista F, Castillo CP, Barbosa A, Torres JA, Lavalle C (2014) The Reference scenario in the LUISA platform – Updated configuration 2014 Towards a Common Baseline Scenario for EC Impact Assessment procedures. Luxembourg Publications Office of the European Union.
5. Bates D, Mächler M, Bolker BM, Walker SC (2015) Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67, 1–48.
6. Brovkin V, Boysen L, Arora VK et al. (2013) Effect of anthropogenic land-use and land-cover changes on climate and land carbon storage in CMIP5 projections for the twenty-first century. Journal of Climate, 26, 6859–6881.
7. Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods and Research, 33, 261–304.
8. Busch G (2006) Future European agricultural landscapes—What can we learn from existing quantitative land use scenario studies? Agriculture, Ecosystems and Environment, 114, 121–140.
9. Butler MP, Reed PM, Fisher-vanden K, Keller K, Wagener T (2014) Environmental modelling & software Identifying parametric controls and dependencies in integrated assessment models using global sensitivity analysis. Environmental Modelling and Software, 59, 10–29.
10. Calvin K, Wise M, Clarke L, Edmonds J, Kyle P, Luckow P, Thomson A (2013) Implications of simultaneously mitigating and adapting to climate change: initial experiments using GCAM. Climatic Change, 117, 545–560.
11. Chen L, Dirmeyer PA (2016) Adapting observationally based metrics of biogeophysical feedbacks from land cover/land use change to climate modeling. Environmental Research Letters, 11, 034002.
12. Dendoncker N, Bogaert P, Rounsevell M (2006) A statistical method to downscale aggregated land use data and scenarios. Journal of Land Use Science, 1, 63–82.
13. Engström K, Rounsevell MDA, Murray-Rust D et al. (2016) Applying Occam's Razor to global agricultural land use change. Environmental Modelling and Software, 75, 212–229.
14. FAOSTAT (2015) Resources/Land (2015-12-16). Food and Agriculture Organization of the United Nations, Rome, Italy.
15. Foley JA, Ramankutty N, Brauman KA et al. (2011) Solutions for a cultivated planet. Nature, 478, 337–342.
16. Fujimori S, Masui T, Matsuoka Y (2012) AIM/CGE [basic] manual Discussion paper series (Vol. 2012-01). Center for Social and Environmental Systems.
17. Goldewijk KK (2001) Estimating global land use change over the past 300 years: the HYDE Database. Global Biogeochemical Cycles, 15, 417.
18. Harrison PA, Holman IP, Berry PM (2015) Assessing cross-sectoral climate change impacts, vulnerability and adaptation: an introduction to the CLIMSAVE project. Climatic Change, 128, 153–167.
19. Havlík P, Valin H, Herrero M, Obersteiner M, Schmid E, Rufino MC (2014) Climate change mitigation through livestock system transitions. Proceedings of the National Academy of Sciences, 111, 3709–3714.
20. Houghton RA, House JI, Pongratz J et al. (2012) Carbon emissions from land use and land-cover change. Biogeosciences, 9, 5125–5142.
21. Howden SM, Soussana J-F, Tubiello FN, Chhetri N, Dunlop M, Meinke H (2007) Adapting agriculture to climate change. Proceedings of the National Academy of Sciences, 104, 19691–19696.
22. Hurtt GC, Chini LP, Frolking S et al. (2011) Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change, 109, 117–161.
23. IIASA (2015) SSP Database (version 0.93). International Institute for Applied Systems Analysis, Laxenburg, Austria.
24. IPCC (2000) Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK.
25. Kastner T, Rivas MJI, Koch W, Nonhebel S (2012) Global changes in diets and the consequences for land requirements for food. Proceedings of the National Academy of Sciences of the United States of America, 109, 6868–6872.
26. von Lampe M, Willenbockel D, Ahammad H et al. (2014) Why do global long-term scenarios for agriculture differ? An overview of the AgMIP global economic model intercomparison. Agricultural Economics, 45, 3–20.
27. Langsrud Ø (2003) anova for unbalanced data: use type II instead of Type III sums of squares. Statistics and Computing, 13, 163–167.
28. Le Quéré C, Moriarty R, Andrew RM, Peters GP, Ciais P, Friedlingstein P, Jones SD (2015) Global carbon budget 2014. 47–85.
29. Levis S (2010) Modeling vegetation and land use in models of the earth system. Wiley Interdisciplinary Reviews: Climate Change, 1, 840–856.
30. Mahmood R, Pielke RA, Hubbard KG et al. (2014) Land cover changes and their biogeophysical effects on climate. International Journal of Climatology, 34, 929–953.
31. van Meijl H, van Rheenen T, Tabeau A, Eickhout B (2006) The impact of different policy environments on agricultural land use in Europe. Agriculture, Ecosystems and Environment, 114, 21–38.
32. Meiyappan P, Dalton M, O'Neill BC, Jain AK (2014) Spatial modeling of agricultural land use change at global scale. Ecological Modelling, 291, 152–174.
33. Murray-Rust D, Brown C, van Vliet J, Alam SJ, Robinson DT, Verburg PH, Rounsevell M (2014) Combining agent functional types, capitals and services to model land use dynamics. Environmental Modelling and Software, 59, 187–201.
34. National Research Council (2014) Advancing Land Change Modeling: Opportunities and Research Requirements. The National Academy Press, Washington, DC, USA.
35. Nelson GC, Valin H, Sands RD et al. (2014) Climate change effects on agriculture: economic responses to biophysical shocks. Proceedings of the National Academy of Sciences of the United States of America, 111, 3274–3279.
36. Nishina K, Ito A, Falloon P et al. (2014) Decomposing uncertainties in the future terrestrial carbon budget associated with emission scenario, climate projection, and ecosystem simulation using the ISI-MIP result. Earth System Dynamics Discussions, 5, 1197–1219.
37. O'Neill BC, Kriegler E, Ebi KL et al. (2015) The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change, doi: 10.1016/j.gloenvcha.2015.01.004.
38. Parry ML, Rosenzweig C, Iglesias A, Livermore M, Fischer G (2004) Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Global Environmental Change, 14, 53–67.
39. Pindyck RS (2015) The Use and Misuse of Models for Climate Policy. National Bureau of Economic Research, Cambridge, MA, USA.
40. Pontius RG, Boersma W, Castella JC et al. (2008) Comparing the input, output, and validation maps for several models of land change. Annals of Regional Science, 42, 11–37.
41. Popp A, Humpenoeder F, Weindl I et al. (2014) Land-use protection for climate change mitigation. Nature Climate Change, 4, 2–5.
42. Powell T (2015) Closing loops to rebalance the global carbon cycle: Biomass flows modelling of global agricultural carbon fluxes.
43. Prestele R, Alexander P, Rounsevell M et al. (2016) Hotspots of uncertainty in land use and land cover change projections: a global scale model comparison. Global Change Biology, doi: 10.1111/gcb.13337.
44. Ramankutty N, Evan AT, Monfreda C, Foley JA (2008) Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles, 22, 1–19.
45. Robinson S, van Meijl H, Willenbockel D et al. (2014) Comparing supply-side specifications in models of global agriculture and the food system. Agricultural Economics, 45, 21–35.
46. Rose SK, Ahammad H, Eickhout B et al. (2012) Land-based mitigation in climate stabilization. Energy Economics, 34, 365–380.
47. Rounsevell MDA, Arneth A, Alexander P et al. (2014) Towards decision-based global land use models for improved understanding of the Earth system. Earth System Dynamics, 5, 117–137.
48. Saltelli A, Hombres BD (2010) Sensitivity analysis didn't help. A practitioner's critique of the Stern review. Global Environmental Change, 20, 298–302.
49. Sands RD, Jones CA, Marshall E (2014) Global Drivers of Agricultural Demand and Supply. ERR-174. Economic Research Service, U.S. Department of Agriculture.
50. Schaldach R, Alcamo J, Koch J, Kölking C, Lapola DM, Schüngel J, Priess JA (2011) An integrated approach to modelling land-use change on continental and global scales. Environmental Modelling and Software, 26, 1041–1051.
51. Schmitz C, van Meijl H, Kyle P et al. (2014) Land-use change trajectories up to 2050: insights from a global agro-economic model comparison. Agricultural Economics, 45, 69–84.
52. Smith P, Haberl H, Popp A et al. (2013) How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals? Global Change Biology, 19, 2285–2302.
53. Smith P, House JI, Bustamante M et al. (2016) Global change pressures on soils from land use and management. Global Change Biology, 22, 1008–1028.
54. Solomon S, Qin D, Manning M et al. (2007) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, USA.
55. Stehfest E, vanVuuren D, Kram T et al. (2014) Integrated Assessment of Global Environmental Change with IMAGE 3.0. Model description and policy applications. PBL Netherlands Environmental Assessment Agency. The Hague, Netherlands.
56. Steinbuks J, Hertel TW (2016) Confronting the food-energy-environment trilemma : global land use in the long run. Environmental and Resource Economics, 63, 545–570.
57. Sterling SM, Ducharne A, Polcher J (2012) The impact of global land-cover change on the terrestrial water cycle. Nature Climate Change, 3, 385–390.
58. Tilman D, Balzer C, Hill J, Befort BL (2011) Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences of the United States of America, 108, 20260–20264.
59. Verburg PH, Overmars KP (2009) Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecology, 24, 1167–1181.
60. Verburg PH, Neumann K, Nol L (2011) Challenges in using land use and land cover data for global change studies. Global Change Biology, 17, 974–989.
61. van Vuuren DP, Carter TR (2014) Climate and socio-economic scenarios for climate change research and assessment: reconciling the new with the old. Climatic Change, 122, 415–429.
62. van Vuuren DP, Edmonds J, Kainuma M et al. (2011) The representative concentration pathways: an overview. Climatic Change, 109, 5–31.
63. Walker WE, Harremoës P, Rotmans J, van der Sluijs JP, van Asselt MBA, Janssen P, Krayer von Krauss MP (2003) Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated Assessment, 4, 5–17.
64. Weinzettel J, Hertwich EG, Peters GP, Steen-Olsen K, Galli A (2013) Affluence drives the global displacement of land use. Global Environmental Change, 23, 433–438.
65. Yip S, Ferro CAT, Stephenson DB (2013) A simple, coherent framework for partitioning uncertainty in climate predictions. Journal of Climate, 24, 4634–4643.