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    Rights statement: This is the author’s version of a work that was accepted for publication in Ecological Modelling. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Ecological Modelling, 357, 2017 DOI: 10.1016/j.ecolmodel.2017.04.011

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

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<mark>Journal publication date</mark>10/08/2017
<mark>Journal</mark>Ecological Modelling
Volume357
Number of pages13
Pages (from-to)1-13
Publication StatusPublished
Early online date12/05/17
<mark>Original language</mark>English

Abstract

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.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Ecological Modelling. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Ecological Modelling, 357, 2017 DOI: 10.1016/j.ecolmodel.2017.04.011