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| Titel |
GAPPARD: a computationally efficient method of approximating gap-scale disturbance in vegetation models |
| VerfasserIn |
M. Scherstjanoi, J. O. Kaplan, E. Thürig, H. Lischke |
| 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 ; 6, no. 5 ; Nr. 6, no. 5 (2013-09-12), S.1517-1542 |
| Datensatznummer |
250084991
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| Publikation (Nr.) |
 copernicus.org/gmd-6-1517-2013.pdf |
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| Zusammenfassung |
| Models of vegetation dynamics that are designed for application at spatial
scales larger than individual forest gaps suffer from several limitations.
Typically, either a population average approximation is used that results in
unrealistic tree allometry and forest stand structure, or models have a high
computational demand because they need to simulate both a series of age-based
cohorts and a number of replicate patches to account for stochastic gap-scale
disturbances. The detail required by the latter method increases the number
of calculations by two to three orders of magnitude compared to the less
realistic population average approach. In an effort to increase the
efficiency of dynamic vegetation models without sacrificing realism, we
developed a new method for simulating stand-replacing disturbances that is
both accurate and faster than approaches that use replicate patches. The
GAPPARD (approximating GAP model results with a Probabilistic Approach
to account for stand Replacing Disturbances) method works by postprocessing
the output of deterministic, undisturbed simulations of a cohort-based
vegetation model by deriving the distribution of patch ages at any point in
time on the basis of a disturbance probability. With this distribution, the
expected value of any output variable can be calculated from the output
values of the deterministic undisturbed run at the time corresponding to the
patch age. To account for temporal changes in model forcing (e.g., as a
result of climate change), GAPPARD performs a series of deterministic
simulations and interpolates between the results in the postprocessing step.
We integrated the GAPPARD method in the vegetation model LPJ-GUESS,
and evaluated it in a series of simulations along an altitudinal transect of
an inner-Alpine valley. We obtained results very similar to the output of the
original LPJ-GUESS model that uses 100 replicate patches, but simulation time
was reduced by approximately the factor 10. Our new method is therefore
highly suited for rapidly approximating LPJ-GUESS results, and provides the
opportunity for future studies over large spatial domains, allows easier
parameterization of tree species, faster identification of areas of
interesting simulation results, and comparisons with large-scale datasets and
results of other forest models. |
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