LandClim is a spatially-​explicit forest landscape model that was developed to assess the importance of climatic effects, wildfire and management on historical and future forest dynamics.  

process-based modelforest dynamicsLANDIS



Initial contribute: 2021-05-24


ETH Zürich
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Application-focused categoriesNatural-perspectiveLand regions
Method-focused categoriesProcess-perspectiveBiological process calculation

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LandClim is a stochastic process-based model designed to study spatially explicit forest dynamics at the landscape scale over long time periods with a fine spatial resolution.

The model is a descendant of the raster-based LANDIS model (Mladenoff, 2004) with an improved tree growth and succession sub-model. The tree growth (‘stand scale’) processes are based on a simplified version of the forest gap model FORCLIM (Bugmann, 1994), in order to maintain computational efficiency at the larger scale. The model has been used to predict historic (Colombaroli et al., 2010, Henne et al., 2011), current (Schumacher et al., 2004) and future forest dynamics (Elkin et al., 2013, Temperli et al., 2013) in various parts of the world such as Central Europe (Briner et al., 2013), USA (Schumacher et al., 2006) or New Zealand (Thrippleton et al., 2014).

Overview of the model

Structure of the model

The design of LandClim reflects processes at two spatial scales: (1) the stand scale (with grid cells of 25m x 25 m) and (2) the landscape scale (large geographical areas up to several thousand hectares).

At the stand scale, the model simulates tree growth, recruitment and mortality within each grid cell, using species-specific life history parameters. The model uses a cohort approach; i.e., trees of the same age and species are simulated by one representative individual.

At the landscape scale, LandClim simulates processes that occur across grid cells, including seed dispersal, disturbances and forest management regimes (Temperli et al., 2013, Schumacher and Bugmann, 2006). Presently four disturbance types are implemented: fire, windthrow, bark beetle outbreaks and browsing pressure.




Prof. Dr. Harald Bugmann (2021). LandClim, Model Item, OpenGMS,


Initial contribute : 2021-05-24



ETH Zürich
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