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

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 duemore » 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. Furthermore, 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.« less
Authors:
 [1] ;  [2] ;  [2] ;  [3] ;  [4] ;  [5] ;  [1] ;  [6] ;  [7] ;  [7] ;  [8] ;  [9] ;  [10] ;  [8] ;  [4] ;  [10] ;  [9] ;  [11] ;  [12] ;  [12] more »;  [13] ;  [14] ;  [14] ;  [1] ;  [15] « less
  1. Vrije Univ. Amsterdam, Amsterdam (The Netherlands)
  2. Univ. of Edinburgh, Edinburgh (United Kingdom)
  3. Karlsruhe Institute of Technology, Garmisch-Partenkirchen (Germany)
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  5. PBL Netherlands Environmental Assessment Agency (The Netherlands)
  6. Lund Univ., Lund (Sweden)
  7. National Institute for Environmental Studies, Ibaraki (Japan)
  8. International Institute for Applied Systems Analysis, Laxenburg (Austria)
  9. Potsdam Institute for Climate Impact Research (PIK), Potsdam (Germany)
  10. Univ. of Illinois, Urbana, IL (United States)
  11. U.S. Dept. of Agriculture, Washington, D.C. (United States)
  12. Univ. of Kassel, Kassel (Germany)
  13. PBL Netherlands Environmental Assessment Agency, (The Netherlands)
  14. Wageningen Univ. and Research Centre (The Netherlands)
  15. Vrije Univ. Amsterdam, Amsterdam (The Netherlands); Swiss Federal Research Institute WSL, Birmensdorf (Switzerland)
Publication Date:
Report Number(s):
PNNL-SA-115917
Journal ID: ISSN 1354-1013; KP1703030
Grant/Contract Number:
AC05-76RL01830
Type:
Accepted Manuscript
Journal Name:
Global Change Biology
Additional Journal Information:
Journal Volume: 22; Journal Issue: 12; Journal ID: ISSN 1354-1013
Publisher:
Wiley
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; land use modeling; uncertainty; inter-model comparison; land-use allocation; land-use change; land-use uncertainty; map comparison; model intercomparison; model variation
OSTI Identifier:
1339860

Prestele, Reinhard, Alexander, Peter, Rounsevell, Mark D. A., Arneth, Almut, Calvin, Katherine, Doelman, Jonathan, Eitelberg, David A., Engstrom, Kerstin, Fujimori, Shinichiro, Hasegawa, Tomoko, Havlik, Petr, Humpenöder, Florian, Jain, Atul K., Krisztin, Tamas, Kyle, Page, Meiyappan, Prasanth, Popp, Alexander, Sands, Ronald D., Schaldach, Rudiger, Schungel, Jan, Stehfest, Elke, Tabeau, Andrzej, Van Meijl, Hans, Van Vliet, Jasper, and Verburg, Peter H.. Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison. United States: N. p., Web. doi:10.1111/gcb.13337.
Prestele, Reinhard, Alexander, Peter, Rounsevell, Mark D. A., Arneth, Almut, Calvin, Katherine, Doelman, Jonathan, Eitelberg, David A., Engstrom, Kerstin, Fujimori, Shinichiro, Hasegawa, Tomoko, Havlik, Petr, Humpenöder, Florian, Jain, Atul K., Krisztin, Tamas, Kyle, Page, Meiyappan, Prasanth, Popp, Alexander, Sands, Ronald D., Schaldach, Rudiger, Schungel, 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. United States. doi:10.1111/gcb.13337.
Prestele, Reinhard, Alexander, Peter, Rounsevell, Mark D. A., Arneth, Almut, Calvin, Katherine, Doelman, Jonathan, Eitelberg, David A., Engstrom, Kerstin, Fujimori, Shinichiro, Hasegawa, Tomoko, Havlik, Petr, Humpenöder, Florian, Jain, Atul K., Krisztin, Tamas, Kyle, Page, Meiyappan, Prasanth, Popp, Alexander, Sands, Ronald D., Schaldach, Rudiger, Schungel, Jan, Stehfest, Elke, Tabeau, Andrzej, Van Meijl, Hans, Van Vliet, Jasper, and Verburg, Peter H.. 2016. "Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison". United States. doi:10.1111/gcb.13337. https://www.osti.gov/servlets/purl/1339860.
@article{osti_1339860,
title = {Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison},
author = {Prestele, Reinhard and Alexander, Peter and Rounsevell, Mark D. A. and Arneth, Almut and Calvin, Katherine and Doelman, Jonathan and Eitelberg, David A. and Engstrom, Kerstin and Fujimori, Shinichiro and Hasegawa, Tomoko and Havlik, Petr and Humpenöder, Florian and Jain, Atul K. and Krisztin, Tamas and Kyle, Page and Meiyappan, Prasanth and Popp, Alexander and Sands, Ronald D. and Schaldach, Rudiger and Schungel, Jan and Stehfest, Elke and Tabeau, Andrzej and Van Meijl, Hans and Van Vliet, Jasper and Verburg, Peter H.},
abstractNote = {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. Furthermore, 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.},
doi = {10.1111/gcb.13337},
journal = {Global Change Biology},
number = 12,
volume = 22,
place = {United States},
year = {2016},
month = {5}
}