Hotspots of uncertainty in land use and land cover change projections: a global scale model comparison

Reinhard Prestele, Peter Alexander, Mark Rounsevell, Almut Arneth, Katherine Calvin, Jonathan Doelman, David Eitelberg, Kerstin Engström, Shinichiro Fujimori, Tomoko Hasegawa, Petr Havlik, Florian Humpenöder, Atul K. Jain, Tamás Krisztin, Page Kyle, Prasanth Meiyappan, Alexander Popp, Ronald D. Sands, Rüdiger Schaldach, Jan Schüngel & 5 others Elke Stehfest, Andrzej Tabeau, Hans van Meijl, Jasper van Vliet, Peter H. Verburg

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Abstract

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.
Original languageEnglish
Pages (from-to)3967-3983
JournalGlobal Change Biology
Volume22
Issue number12
DOIs
Publication statusPublished - 2016

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Land use
land cover
land use
environmental assessment
comparison
Uncertainty
modeling
Biodiversity
economic conditions
Analysis of variance (ANOVA)
Model structures
biome
Water resources
Regression analysis
variance analysis
boreal forest
tropical forest
Data structures
pasture
regression analysis

Cite this

Prestele, R., Alexander, P., Rounsevell, M., Arneth, A., Calvin, K., Doelman, J., ... Verburg, P. H. (2016). Hotspots of uncertainty in land use and land cover change projections: a global scale model comparison. Global Change Biology, 22(12), 3967-3983. https://doi.org/10.1111/gcb.13337
Prestele, Reinhard ; Alexander, Peter ; Rounsevell, Mark ; Arneth, Almut ; Calvin, Katherine ; Doelman, Jonathan ; Eitelberg, David ; Engström, Kerstin ; Fujimori, Shinichiro ; Hasegawa, Tomoko ; Havlik, Petr ; Humpenöder, Florian ; Jain, Atul K. ; Krisztin, Tamás ; Kyle, Page ; Meiyappan, Prasanth ; Popp, Alexander ; Sands, Ronald D. ; Schaldach, Rüdiger ; Schüngel, Jan ; Stehfest, Elke ; Tabeau, Andrzej ; van Meijl, Hans ; van Vliet, Jasper ; Verburg, Peter H. / Hotspots of uncertainty in land use and land cover change projections: a global scale model comparison. In: Global Change Biology. 2016 ; Vol. 22, No. 12. pp. 3967-3983.
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abstract = "Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.",
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Prestele, R, Alexander, P, Rounsevell, M, Arneth, A, Calvin, K, Doelman, J, Eitelberg, D, Engström, K, Fujimori, S, Hasegawa, T, Havlik, P, Humpenöder, F, Jain, AK, Krisztin, T, Kyle, P, Meiyappan, P, Popp, A, Sands, RD, Schaldach, R, Schüngel, J, Stehfest, E, Tabeau, A, van Meijl, H, van Vliet, J & Verburg, PH 2016, 'Hotspots of uncertainty in land use and land cover change projections: a global scale model comparison' Global Change Biology, vol. 22, no. 12, pp. 3967-3983. https://doi.org/10.1111/gcb.13337

Hotspots of uncertainty in land use and land cover change projections: a global scale model comparison. / Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark; Arneth, Almut; Calvin, Katherine; Doelman, Jonathan; Eitelberg, David; Engström, Kerstin; Fujimori, Shinichiro; Hasegawa, Tomoko; Havlik, Petr; Humpenöder, Florian; Jain, Atul K.; Krisztin, Tamás; Kyle, Page; Meiyappan, Prasanth; Popp, Alexander; Sands, Ronald D.; Schaldach, Rüdiger; Schüngel, Jan; Stehfest, Elke; Tabeau, Andrzej; van Meijl, Hans; van Vliet, Jasper; Verburg, Peter H.

In: Global Change Biology, Vol. 22, No. 12, 2016, p. 3967-3983.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Hotspots of uncertainty in land use and land cover change projections: a global scale model comparison

AU - Prestele, Reinhard

AU - Alexander, Peter

AU - Rounsevell, Mark

AU - Arneth, Almut

AU - Calvin, Katherine

AU - Doelman, Jonathan

AU - Eitelberg, David

AU - Engström, Kerstin

AU - Fujimori, Shinichiro

AU - Hasegawa, Tomoko

AU - Havlik, Petr

AU - Humpenöder, Florian

AU - Jain, Atul K.

AU - Krisztin, Tamás

AU - Kyle, Page

AU - Meiyappan, Prasanth

AU - Popp, Alexander

AU - Sands, Ronald D.

AU - Schaldach, Rüdiger

AU - Schüngel, Jan

AU - Stehfest, Elke

AU - Tabeau, Andrzej

AU - van Meijl, Hans

AU - van Vliet, Jasper

AU - Verburg, Peter H.

PY - 2016

Y1 - 2016

N2 - Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.

AB - Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.

U2 - 10.1111/gcb.13337

DO - 10.1111/gcb.13337

M3 - Article

VL - 22

SP - 3967

EP - 3983

JO - Global Change Biology

JF - Global Change Biology

SN - 1354-1013

IS - 12

ER -