A method for aggregating state variables in large ecosystem models
Jock Lawrie and
John Hearne
Mathematics and Computers in Simulation (MATCOM), 2008, vol. 79, issue 3, 368-378
Abstract:
Simplifying large ecosystem models is essential if we are to understand the underlying causes of observed behaviours. However, such understanding is often employed to achieve simplification. This paper introduces a method for model simplification that can be applied without requiring intimate prior knowledge of the system. Its utility is measured by the resulting values of given model diagnostics relative to those of the original model. The method uses a least-squares criterion to identify sets of state variables that can be aggregated, and then generates a modified model structure and accompanying parameters that enable these variables to be replaced with the aggregates. The method is applied to a model of the nitrogen cycle in Port Phillip Bay, Victoria, Australia. Aside from reducing the model’s order, the method enables the reduced model to retain an ecological interpretation, and reveals insights into the system’s structure.
Keywords: Large ecosystem models; Aggregation; Partial proper orthogonal decomposition (search for similar items in EconPapers)
Date: 2008
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378475408000074
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:79:y:2008:i:3:p:368-378
DOI: 10.1016/j.matcom.2008.01.001
Access Statistics for this article
Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens
More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().