32
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references5

          • Record: found
          • Abstract: found
          • Article: not found

          Climate change effects on agriculture: economic responses to biophysical shocks.

          Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Climate change mitigation through livestock system transitions.

            Livestock are responsible for 12% of anthropogenic greenhouse gas emissions. Sustainable intensification of livestock production systems might become a key climate mitigation technology. However, livestock production systems vary substantially, making the implementation of climate mitigation policies a formidable challenge. Here, we provide results from an economic model using a detailed and high-resolution representation of livestock production systems. We project that by 2030 autonomous transitions toward more efficient systems would decrease emissions by 736 million metric tons of carbon dioxide equivalent per year (MtCO2e⋅y(-1)), mainly through avoided emissions from the conversion of 162 Mha of natural land. A moderate mitigation policy targeting emissions from both the agricultural and land-use change sectors with a carbon price of US$10 per tCO2e could lead to an abatement of 3,223 MtCO2e⋅y(-1). Livestock system transitions would contribute 21% of the total abatement, intra- and interregional relocation of livestock production another 40%, and all other mechanisms would add 39%. A comparable abatement of 3,068 MtCO2e⋅y(-1) could be achieved also with a policy targeting only emissions from land-use change. Stringent climate policies might lead to reductions in food availability of up to 200 kcal per capita per day globally. We find that mitigation policies targeting emissions from land-use change are 5 to 10 times more efficient--measured in "total abatement calorie cost"--than policies targeting emissions from livestock only. Thus, fostering transitions toward more productive livestock production systems in combination with climate policies targeting the land-use change appears to be the most efficient lever to deliver desirable climate and food availability outcomes.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Land cover change or land-use intensification: simulating land system change with a global-scale land change model

              Land-use change is both a cause and consequence of many biophysical and socioeconomic changes. The CLUMondo model provides an innovative approach for global land-use change modeling to support integrated assessments. Demands for goods and services are, in the model, supplied by a variety of land systems that are characterized by their land cover mosaic, the agricultural management intensity, and livestock. Land system changes are simulated by the model, driven by regional demand for goods and influenced by local factors that either constrain or promote land system conversion. A characteristic of the new model is the endogenous simulation of intensification of agricultural management versus expansion of arable land, and urban versus rural settlements expansion based on land availability in the neighborhood of the location. Model results for the OECD Environmental Outlook scenario show that allocation of increased agricultural production by either management intensification or area expansion varies both among and within world regions, providing useful insight into the land sparing versus land sharing debate. The land system approach allows the inclusion of different types of demand for goods and services from the land system as a driving factor of land system change. Simulation results are compared to observed changes over the 1970-2000 period and projections of other global and regional land change models.
                Bookmark

                Author and article information

                Contributors
                reinhard.prestele@vu.nl
                Journal
                Glob Chang Biol
                Glob Chang Biol
                10.1111/(ISSN)1365-2486
                GCB
                Global Change Biology
                John Wiley and Sons Inc. (Hoboken )
                1354-1013
                1365-2486
                08 June 2016
                December 2016
                : 22
                : 12 ( doiID: 10.1111/gcb.2016.22.issue-12 )
                : 3967-3983
                Affiliations
                [ 1 ] Environmental Geography GroupDepartment of Earth Sciences Vrije Universiteit Amsterdam De Boelelaan 1087 1081 HV AmsterdamThe Netherlands
                [ 2 ] School of GeoSciencesUniversity of Edinburgh Drummond Street Edinburgh EH89XPUK
                [ 3 ] Department Atmospheric Environmental Research (IMK‐IFU)Karlsruhe Institute of Technology Kreuzeckbahnstr. 19 82467 Garmisch‐PartenkirchenGermany
                [ 4 ] Joint Global Change Research InstitutePacific Northwest National Laboratory College Park MD 20740USA
                [ 5 ]PBL Netherlands Environmental Assessment Agency P.O. Box 303 3720 AH BilthovenThe Netherlands
                [ 6 ] Department of Geography and Ecosystem ScienceLund University Sölvegatan 12 LundSweden
                [ 7 ] Center for Social and Environmental Systems ResearchNational Institute for Environmental Studies 16‐2 Onogawa Tsukuba Ibaraki 305‐8506Japan
                [ 8 ] Ecosystem Services and Management ProgramInternational Institute for Applied Systems Analysis A‐2361 LaxenburgAustria
                [ 9 ]Potsdam Institute for Climate Impact Research (PIK) P.O. Box 60 12 03 14412 PotsdamGermany
                [ 10 ] Department of Atmospheric SciencesUniversity of Illinois Urbana IL 61801USA
                [ 11 ] Resource and Rural Economics DivisionEconomic Research Service US Department of Agriculture Washington DC 20250USA
                [ 12 ] Center for Environmental Systems ResearchUniversity of Kassel Wilhelmshöher Allee 47 D‐34109 KasselGermany
                [ 13 ] LEIWageningen University and Research Centre P.O. Box 29703 2502 LS The HagueThe Netherlands
                [ 14 ]Swiss Federal Research Institute WSL Zürcherstrasse 111 CH‐8903 BirmensdorfSwitzerland
                Author notes
                [*] [* ]Correspondence: Reinhard Prestele, tel. +31 20 59 88710, fax +31 20 59 89553, e‐mail: reinhard.prestele@ 123456vu.nl
                Article
                GCB13337
                10.1111/gcb.13337
                5111780
                27135635
                d6c7abcd-c4f0-48d3-960b-f5e27e693dd3
                © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 04 February 2016
                : 11 April 2016
                Page count
                Figures: 7, Tables: 2, Pages: 17, Words: 10380
                Funding
                Funded by: European Research Council
                Award ID: 603542
                Funded by: ERC grant GLOLAND
                Award ID: 311819
                Categories
                Primary Research Article
                Primary Research Articles
                Custom metadata
                2.0
                gcb13337
                December 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.7 mode:remove_FC converted:16.11.2016

                land‐use allocation,land‐use change,land‐use model uncertainty,map comparison,model intercomparison,model variation

                Comments

                Comment on this article