Uncertainity and Arbitrariness in Ecosystems Modelling: A Lake Modelling Example

1382 43 1423 4 2805 document 1423 WP-80-087.pdf application/pdf c8102ebd70a4f62728e8c441905063a3 MD5 1154027 2016-01-15 01:48:11 https://pure.iiasa.ac.at/id/eprint/1382/1/WP-80-087.pdf 1382 1 1 text en public

WP-80-087.pdf

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WP-80-087.pdf

http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/1423 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/1423 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/1423 archive 351 disk0/00/00/13/82 2016-01-15 01:48:10 2021-08-27 17:09:53 2016-01-15 01:48:10 monograph show 4 Fedra K. 1049 Straten G. van AL1730 Beck M.B. 562 Uncertainity and Arbitrariness in Ecosystems Modelling: A Lake Modelling Example pub iis_mod iis_wat prog_ren Mathematical models of ecosystems are considerable simplifications of reality, and the data upon which they are based are usually scarce and uncertain. Calibration of large, complex models depends upon arbitrary assumptions and choices, and frequently calibration procedures do not deal adequately with the uncertainty in the data describing the system under study. Since much of the uncertainty and arbitrariness in ecological modeling is inevitable, because of both practical as well as theoretical limitations, model-based predictions should at least reveal their dependence on, and sensitivity to, uncertainty and arbitrary assumptions. The paper proposes a method that explicitly takes into account the uncertainty associated with data for modeling. By reference to a qualitative and somewhat vague definition of system behavior in terms of allowable ranges, an ensemble of acceptable parameter vectors for the model may be identified. This contrasts directly with a more conventional approach to model calibration, in which a quantitative (squared-error) criterion is minimized and through which a supposedly "unique" and "best" set of parameters can be derived. The ensemble of parameter vectors is then used for the simulation of a multitude of future systems behavior patterns, so that the uncertainty in the initial data and assumptions is preserved, and the thus predicted future systems response can be interpreted in a probabilistic manner. 1980-05 published WP-80-087 WP-80-087 1241 2771 2986 public working_paper IIASA, Laxenburg, Austria 44 FALSE info:eu-repo/semantics/book