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On non-linear sensitivity of marine biological models to parameter variations

Author

Listed:
  • Chu, Peter C.
  • Ivanov, Leonid M.
  • Margolina, Tetyana M.

Abstract

Marine biological models are usually complex with many free parameters. Parameter prioritization (based on contribution to model output) is important for system management but difficult. A variance-based sensitivity analysis is developed in this paper using the Sobol’–Saltelli sensitivity indices, which measure the relative importance of each parameter (or group of parameters) and range these parameters along their contribution to output variability. To reduce the number of degrees of freedom, the model output is decomposed using the warping functions or irreversible predictability time. A simple three-component [nutrients, phytoplankton and zooplankton (NPZ)] model with 23 parameters for reproducing annual phytoplankton cycle of the Black Sea is taken as the example to show the usefulness and procedure of the sensitivity analysis. Single and total sensitivity indices showed strong sensitivity of the biological model to the light limitation of the phytoplankton growth. This agrees well with physical intuition. However, ranging model parameters along their contributions to model output variability does not follow exactly the physical intuition when model-related errors from large perturbations of the parameters are not small. For example, the model output becomes very sensitive to the nutrient stock parameterization for certain combinations of the light-related factors.

Suggested Citation

  • Chu, Peter C. & Ivanov, Leonid M. & Margolina, Tetyana M., 2007. "On non-linear sensitivity of marine biological models to parameter variations," Ecological Modelling, Elsevier, vol. 206(3), pages 369-382.
    • Handle: RePEc:eee:ecomod:v:206:y:2007:i:3:p:369-382
    DOI: 10.1016/j.ecolmodel.2007.04.006
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    References listed on IDEAS

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    1. Daniel Gervini & Theo Gasser, 2004. "Self‐modelling warping functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 959-971, November.
    2. Saltelli, A. & Andres, T. H. & Homma, T., 1993. "Sensitivity analysis of model output : An investigation of new techniques," Computational Statistics & Data Analysis, Elsevier, vol. 15(2), pages 211-238, February.
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    Cited by:

    1. Bar Massada, Avi & Carmel, Yohay, 2008. "Incorporating output variance in local sensitivity analysis for stochastic models," Ecological Modelling, Elsevier, vol. 213(3), pages 463-467.
    2. Jiang, Long & Li, Yiping & Zhao, Xu & Tillotson, Martin R. & Wang, Wencai & Zhang, Shuangshuang & Sarpong, Linda & Asmaa, Qhtan & Pan, Baozhu, 2018. "Parameter uncertainty and sensitivity analysis of water quality model in Lake Taihu, China," Ecological Modelling, Elsevier, vol. 375(C), pages 1-12.
    3. Kumar, Vijay & Kumari, Beena, 2015. "Mathematical modelling of the seasonal variability of plankton and forage fish in the Gulf of Kachchh," Ecological Modelling, Elsevier, vol. 313(C), pages 237-250.
    4. Platt, Trevor & White, George N. & Zhai, Li & Sathyendranath, Shubha & Roy, Shovonlal, 2009. "The phenology of phytoplankton blooms: Ecosystem indicators from remote sensing," Ecological Modelling, Elsevier, vol. 220(21), pages 3057-3069.

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