Literature DB >> 27474896

Assessing uncertainties in land cover projections.

Peter Alexander1,2, Reinhard Prestele3, Peter H Verburg3, Almut Arneth4, Claudia Baranzelli5, Filipe Batista E Silva5, Calum Brown1, Adam Butler6, Katherine Calvin7, Nicolas Dendoncker8, Jonathan C Doelman9, Robert Dunford10,11, Kerstin Engström12, David Eitelberg3, Shinichiro Fujimori13, Paula A Harrison11, Tomoko Hasegawa13, Petr Havlik14, Sascha Holzhauer1, Florian Humpenöder15, Chris Jacobs-Crisioni5, Atul K Jain16, Tamás Krisztin14, Page Kyle7, Carlo Lavalle5, Tim Lenton17, Jiayi Liu6, Prasanth Meiyappan16, Alexander Popp15, Tom Powell17, Ronald D Sands18, Rüdiger Schaldach19, Elke Stehfest9, Jevgenijs Steinbuks20, Andrzej Tabeau21, Hans van Meijl21, Marshall A Wise7, Mark D A Rounsevell1.   

Abstract

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.
© 2016 John Wiley & Sons Ltd.

Keywords:  cropland; land cover; land use; model inter-comparison; uncertainty

Mesh:

Year:  2016        PMID: 27474896     DOI: 10.1111/gcb.13447

Source DB:  PubMed          Journal:  Glob Chang Biol        ISSN: 1354-1013            Impact factor:   10.863


  10 in total

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  10 in total

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