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

Research output: Contribution to journalArticle

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 socioeconomic 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 and 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.

Details

Authors
  • Reinhard Prestele
  • Peter Alexander
  • Mark D A Rounsevell
  • Almut Arneth
  • Katherine Calvin
  • Jonathan Doelman
  • David A. 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
  • And 5 others
  • Elke Stehfest
  • Andrzej Tabeau
  • Hans Van Meijl
  • Jasper Van Vliet
  • Peter H. Verburg
Organisations
External organisations
  • Vrije Universiteit Amsterdam
  • University of Edinburgh
  • Karlsruhe Institute of Technology
  • Netherlands Environmental Assessment Agency; Bilthoven
  • National Institute for Environmental Studies of Japan
  • National Institute for Environmental Studies
  • International Institute for Applied Systems Analysis
  • University of Illinois at Urbana-Champaign
  • University of Kassel
  • Wageningen University
  • Swiss Federal Institute for Forest, Snow and Landscape Research
  • Pacific Northwest National Laboratory
  • Potsdam Institute for Climate Impact Research
  • United States Department of Agriculture (USDA)
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Climate Research
  • Environmental Sciences related to Agriculture and Land-use

Keywords

  • land-use allocation, land-use change, land-use model uncertainty, map comparison, model intercomparison, model variation
Original languageEnglish
Pages (from-to)3967-3983
Number of pages17
JournalGlobal Change Biology
Volume22
Issue number12
Publication statusPublished - 2016 Dec 1
Publication categoryResearch
Peer-reviewedYes