4.7 Article

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

期刊

ENVIRONMENTAL MODELLING & SOFTWARE
卷 134, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2020.104844

关键词

Forest landscape models; Model comparison; Variance partitioning; Disturbances; Dispersal; Future projections

资金

  1. COST (European Cooperation in Science and Technology) [FP1304]
  2. ANR ALIEN project [ANR-14-CE36-0001-01]
  3. Swiss National Science Foundation [SNF 163250]
  4. MIUR (Italian Ministry for Education, University and Research) in the PON Ricerca e Competitivita 2007-2013'' Program: ReCaS (Azione I - Interventi di rafforzamento strutturale) [PONa3_00052]
  5. MIUR (Italian Ministry for Education, University and Research) in the PON Ricerca e Competitivita 2007-2013'' Program: PRISMA (Asse II - Sostegno all'innovazione) [PON04a2_A]
  6. Austrian Science Fund through START grant [Y895-B25]
  7. Czech Ministry of Education, Youth and Sports [LD15158]
  8. Czech Academy of Sciences [RVO 67985939]
  9. Agence Nationale de la Recherche (ANR) [ANR-14-CE36-0001] Funding Source: Agence Nationale de la Recherche (ANR)

向作者/读者索取更多资源

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 dy-namics and contributed substantially to uncertainty in model projections (similar to 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.

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