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Article: GAPPARD: A computationally efficient method of approximating gap-scale disturbance in vegetation models

TitleGAPPARD: A computationally efficient method of approximating gap-scale disturbance in vegetation models
Authors
Issue Date2013
Citation
Geoscientific Model Development, 2013, v. 6, n. 5, p. 1517-1542 How to Cite?
AbstractModels 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 veg. © Author(s) 2013.
Persistent Identifierhttp://hdl.handle.net/10722/268549
ISSN
2023 Impact Factor: 4.0
2023 SCImago Journal Rankings: 2.055
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorScherstjanoi, M.-
dc.contributor.authorKaplan, J. O.-
dc.contributor.authorThürig, E.-
dc.contributor.authorLischke, H.-
dc.date.accessioned2019-03-25T08:00:02Z-
dc.date.available2019-03-25T08:00:02Z-
dc.date.issued2013-
dc.identifier.citationGeoscientific Model Development, 2013, v. 6, n. 5, p. 1517-1542-
dc.identifier.issn1991-959X-
dc.identifier.urihttp://hdl.handle.net/10722/268549-
dc.description.abstractModels 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 veg. © Author(s) 2013.-
dc.languageeng-
dc.relation.ispartofGeoscientific Model Development-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleGAPPARD: A computationally efficient method of approximating gap-scale disturbance in vegetation models-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5194/gmd-6-1517-2013-
dc.identifier.scopuseid_2-s2.0-84884262961-
dc.identifier.volume6-
dc.identifier.issue5-
dc.identifier.spage1517-
dc.identifier.epage1542-
dc.identifier.eissn1991-9603-
dc.identifier.isiWOS:000326601300009-
dc.identifier.issnl1991-959X-

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