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Sensitivity analysis of the two dimensional application of the Generic Ecological Model (GEM) to algal bloom prediction in the North Sea

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  • Salacinska, K.
  • El Serafy, G.Y.
  • Los, F.J.
  • Blauw, A.

Abstract

Harmful algae can cause damage to co-existing organisms, tourism and farmers. Accurate predictions of algal future composition and abundance as well as when and where algal blooms may occur could help early warning and mitigating. The Generic Ecological Model is an instrument that can be applied to any water system (fresh, transitional or coastal) to calculate the primary production, chlorophyll-a concentration and phytoplankton species composition. It consists of physical, chemical and ecological model components which are coupled together to build one generic and flexible modelling tool. In this paper the model has been analyzed to assess sensitivity of the simulated chlorophyll-a concentration to a subset of ecologically significant input factors. Only a small number of approaches could be considered as suitable for several reasons including the model complexity, engagement of numerous interacting parameters and relatively long time of a single simulation. Thus, sensitivity analysis has been carried out with the use of the Morris method and later enriched by the computation of the correlation ratios of the selected parameters on the model response at more than a few locations in the modelled area. The obtained results are in agreement with expert knowledge of the ecological processes in the North Sea and correspond well with local characteristics.

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  • Salacinska, K. & El Serafy, G.Y. & Los, F.J. & Blauw, A., 2010. "Sensitivity analysis of the two dimensional application of the Generic Ecological Model (GEM) to algal bloom prediction in the North Sea," Ecological Modelling, Elsevier, vol. 221(2), pages 178-190.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:2:p:178-190
    DOI: 10.1016/j.ecolmodel.2009.10.001
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    References listed on IDEAS

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    1. Radboud J. Duintjer Tebbens & Kimberly M. Thompson & M. G. Myriam Hunink & Thomas A. Mazzuchi & Daniel Lewandowski & Dorota Kurowicka & Roger M. Cooke, 2008. "Uncertainty and Sensitivity Analyses of a Dynamic Economic Evaluation Model for Vaccination Programs," Medical Decision Making, , vol. 28(2), pages 182-200, March.
    2. Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769, August.
    3. Yoshie, Naoki & Yamanaka, Yasuhiro & Rose, Kenneth A. & Eslinger, David L. & Ware, Daniel M. & Kishi, Michio J., 2007. "Parameter sensitivity study of the NEMURO lower trophic level marine ecosystem model," Ecological Modelling, Elsevier, vol. 202(1), pages 26-37.
    4. Jacques, Julien & Lavergne, Christian & Devictor, Nicolas, 2006. "Sensitivity analysis in presence of model uncertainty and correlated inputs," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1126-1134.
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    1. Morris, David J. & Speirs, Douglas C. & Cameron, Angus I. & Heath, Michael R., 2014. "Global sensitivity analysis of an end-to-end marine ecosystem model of the North Sea: Factors affecting the biomass of fish and benthos," Ecological Modelling, Elsevier, vol. 273(C), pages 251-263.
    2. Yi, Xuan & Zou, Rui & Guo, Huaicheng, 2016. "Global sensitivity analysis of a three-dimensional nutrients-algae dynamic model for a large shallow lake," Ecological Modelling, Elsevier, vol. 327(C), pages 74-84.
    3. Wu, Guozheng & Xu, Zongxue, 2011. "Prediction of algal blooming using EFDC model: Case study in the Daoxiang Lake," Ecological Modelling, Elsevier, vol. 222(6), pages 1245-1252.
    4. Yao, Jianyu & Xiao, Peng & Zhang, Yunhuai & Zhan, Min & Cheng, Jiangwei, 2011. "A mathematical model of algal blooms based on the characteristics of complex networks theory," Ecological Modelling, Elsevier, vol. 222(20), pages 3727-3733.

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