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

Trevor Page, Paul J. Smith, Keith J. Beven, Ian D. Jones, J. Alex Elliott, Stephen C. Maberly, Eleanor B. Mackay, Mitzi De Ville and Heidrun Feuchtmayr

Ecological Modelling, 2017, vol. 357, issue C, 1-13

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.

Keywords: Algal bloom; Forecasting; GLUE; PROTECH model; Uncertainty (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:357:y:2017:i:c:p:1-13

DOI: 10.1016/j.ecolmodel.2017.04.011

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