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Constraining uncertainty and process-representation in an algal community lake model using high frequency in-lake observations

Page, Trevor; Smith, Paul J.; Beven, Keith J.; Jones, Ian D.; Elliott, J. Alex; Maberly, Stephen C.; Mackay, Eleanor B.; De Ville, Mitzi; Feuchtmayr, Heidrun. 2017 Constraining uncertainty and process-representation in an algal community lake model using high frequency in-lake observations. Ecological Modelling, 357. 1-13. 10.1016/j.ecolmodel.2017.04.011

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Abstract/Summary

Excessive algal blooms, some of which can be toxic, are the most obvious symptoms of nutrient enrichment and can be exacerbated by climate change. They cause numerous ecological problems and also economic costs to water companies. The process-representation of the algal community model PROTECH was tested within the extended Generalised Likelihood Uncertainty Estimation framework which includes pre-defined Limits of Acceptability for simulations. Testing was a precursor to modification of the model for real-time forecasting of algal communities that will place different demands on the model in terms of a) the simulation accuracy required, b) the computational burden associated with the inclusion of forecast uncertainties and c) data assimilation. We found that the systematic differences between the model’s representation of underwater light compared to the real lake systems studied and the uncertainties associated with nutrient fluxes will be the greatest challenges when forecasting algal blooms.

Item Type: Publication - Article
Digital Object Identifier (DOI): 10.1016/j.ecolmodel.2017.04.011
CEH Sections: Parr
ISSN: 0304-3800
Additional Keywords: algal bloom, forecasting, GLUE, PROTECH model, uncertainty
NORA Subject Terms: Ecology and Environment
Date made live: 18 Jul 2017 15:55 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/517340

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