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Abstract
Abstract. Models used to investigate impacts of climatic changes on spatio-temporal vegetation dynamics need to balance required accuracy with computational feasibility. To enhance the computational efficiency of these models, upscaling methods are required that maintain key fine-scale processes influencing vegetation dynamics. In this paper, an adjustable method – the dynamic two-layer classification concept (D2C) – for the upscaling of time- and space-discrete models is presented. D2C aims to separate potentially repetitive calculations from those specific to single grid cells. The underlying idea is to extract processes that do not require information about a grid cell's neighbourhood to a reduced-size non-spatial layer, which is dynamically coupled to the original two-dimensional layer. The size of the non-spatial layer is thereby adaptive and depends on dynamic classifications according to pre-specified similarity criteria. I present how D2C can be used in a model implementation on the example of TreeMig-2L, a new, efficient version of the intermediate-complexity forest-landscape model TreeMig. To discuss the trade-off between computational expenses and accuracy, as well as the applicability of D2C, I compare different model stages of TreeMig-2L via simulations of two different application scenarios. This comparison of different model stages demonstrates that applying D2C can strongly reduce computational expenses of processes calculated on the new non-spatial layer. D2C is thus a valuable upscaling method for models and applications in which processes requiring information about the neighbourhood constitute the minor share of the overall computational expenses.
Highlights
Impact studies of climatic changes on spatio-temporal vegetation dynamics are often conducted with so-called dynamic vegetation models (DVMs)
DVMs are mainly implemented as time- and space-discrete models, simulating ecological processes that are key to vegetation dynamics, such as establishment, growth and mortality, usually under consideration of biotic and abiotic influences
4.1 Pre-structuring of the simulation areas. For both application scenarios the number of bioclimate types resulting from the pre-structuring was considerably smaller than the number of grid cells
Summary
Impact studies of climatic changes on spatio-temporal vegetation dynamics are often conducted with so-called dynamic vegetation models (DVMs). DVMs need to balance accuracy with computational feasibility and parametrisation requirements (Huntley et al, 2010; He et al, 2011). Modelled processes and their level of detail vary among DVMs, with a close link to the trade-off between spatial resolution and spatial extent of the simulation area. DVMs simulating small-scale processes with a fine spatial resolution ( < 1 km2) often have large computational expenses. Using a coarser spatial resolution to enable an increase in the spatial extent risks introducing strong biases, such as replacing rare with dominant forest types (He et al, 2011), or Published by Copernicus Publications on behalf of the European Geosciences Union
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