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| Titel |
Upscaling with the dynamic two-layer classification concept (D2C): TreeMig-2L, an efficient implementation of the forest-landscape model TreeMig |
| VerfasserIn |
J. E. M. S. Nabel |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 11 ; Nr. 8, no. 11 (2015-11-05), S.3563-3577 |
| Datensatznummer |
250116658
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| Publikation (Nr.) |
 copernicus.org/gmd-8-3563-2015.pdf |
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| Zusammenfassung |
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. |
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