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How robust are future projections of forest landscape dynamics? Insights from a systematic comparison of four forest landscape models.

Authors :
Petter, Gunnar
Mairota, Paola
Albrich, Katharina
Bebi, Peter
Brůna, Josef
Bugmann, Harald
Haffenden, Austin
Scheller, Robert M.
Schmatz, Dirk R.
Seidl, Rupert
Speich, Matthias
Vacchiano, Giorgio
Lischke, Heike
Source :
Environmental Modelling & Software. Dec2020, Vol. 134, pN.PAG-N.PAG. 1p.
Publication Year :
2020

Abstract

Projections of landscape dynamics are uncertain, partly due to uncertainties in model formulations. However, quantitative comparative analyses of forest landscape models are lacking. We conducted a systematic comparison of all forest landscape models currently applied in temperate European forests (LandClim, TreeMig, LANDIS-II, iLand). We examined the uncertainty of model projections under several future climate, disturbance, and dispersal scenarios, and quantified uncertainties by variance partitioning. While projections under past climate conditions were in good agreement with observations, uncertainty under future climate conditions was high, with between-model biomass differences of up to 200 t ha−1. Disturbances strongly influenced landscape dynamics and contributed substantially to uncertainty in model projections (~25–40% of observed variance). Overall, model differences were the main source of uncertainty, explaining at least 50% of observed variance. We advocate a more rigorous and systematic model evaluation and calibration, and a broader use of ensemble projections to quantify uncertainties in future landscape dynamics. • The first systematic comparison of forest landscape models is presented. • Variance of model projections under several future scenarios is substantial. • Model differences explain most of the simulated variance. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13648152
Volume :
134
Database :
Academic Search Index
Journal :
Environmental Modelling & Software
Publication Type :
Academic Journal
Accession number :
146873994
Full Text :
https://doi.org/10.1016/j.envsoft.2020.104844