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Adaptation and multiple parameter optimization of the simulation model SALMO as prerequisite for scenario analysis on a shallow eutrophic Lake

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  • Chen, Qiuwen
  • Zhang, Chengcheng
  • Recknagel, Friedrich
  • Guo, Jing
  • Blanckaert, Koen

Abstract

SALMO (Simulation by means of an Analytical Lake Model) describes the seasonal dynamics of PO4-P, NO3-N, detritus, chlorophyta, bacillariophyta, cyanophyta and cladocerans of the epilimnion and hypolimnion of stratified lakes by complex ordinary differential equations. This study adapted the model for shallow polymictic lakes and developed a real-coded genetic algorithm to optimize key parameters identified by sensitivity analysis. Meiliang Bay of Taihu Lake is taken as a study case, and 5 years data were collected for model calibration and validation. Given the good performance of the adapted and optimized SALMO, the model was applied to analyze four management scenarios for the Meiliang Bay, including 50% reduction of external nutrient loads, 50% reduction of internal nutrient loads, 50% reduction of zooplankton mortality and 1°C increase of water temperature. The study showed: the adapted SALMO model is applicable to shallow polymictic and hypertrophic lakes; the real-coded genetic algorithm significantly improved the model efficiency and accuracy; reduction of nutrients from sediment release appears to be a key measure for controlling cyanobacteria blooms in Lake Taihu; biomanipulation is very delicate and can only be implemented with sufficient investigation and great caution; the increase of algae biomass due to water temperature rise indicates extra potential threat from climate change to lake ecosystems.

Suggested Citation

  • Chen, Qiuwen & Zhang, Chengcheng & Recknagel, Friedrich & Guo, Jing & Blanckaert, Koen, 2014. "Adaptation and multiple parameter optimization of the simulation model SALMO as prerequisite for scenario analysis on a shallow eutrophic Lake," Ecological Modelling, Elsevier, vol. 273(C), pages 109-116.
    • Handle: RePEc:eee:ecomod:v:273:y:2014:i:c:p:109-116
    DOI: 10.1016/j.ecolmodel.2013.11.006
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    References listed on IDEAS

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    1. Law, Tony & Zhang, Weitao & Zhao, Jingyang & Arhonditsis, George B., 2009. "Structural changes in lake functioning induced from nutrient loading and climate variability," Ecological Modelling, Elsevier, vol. 220(7), pages 979-997.
    2. Recknagel, Friedrich & van Ginkel, Carin & Cao, Hongqing & Cetin, Lydia & Zhang, Byron, 2008. "Generic limnological models on the touchstone: Testing the lake simulation library SALMO-OO and the rule-based Microcystis agent for warm-monomictic hypertrophic lakes in South Africa," Ecological Modelling, Elsevier, vol. 215(1), pages 144-158.
    3. Recknagel, Friedrich & Ostrovsky, Ilia & Cao, Hongqing & Zohary, Tamar & Zhang, Xiaoqing, 2013. "Ecological relationships, thresholds and time-lags determining phytoplankton community dynamics of Lake Kinneret, Israel elucidated by evolutionary computation and wavelets," Ecological Modelling, Elsevier, vol. 255(C), pages 70-86.
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    4. Cao, Hongqing & Recknagel, Friedrich & Bartkow, Michael, 2016. "Spatially-explicit forecasting of cyanobacteria assemblages in freshwater lakes by multi-objective hybrid evolutionary algorithms," Ecological Modelling, Elsevier, vol. 342(C), pages 97-112.
    5. Zhang, Xiaoqing & Recknagel, Friedrich & Chen, Qiuwen & Cao, Hongqing & Li, Ruonan, 2015. "Spatially-explicit modelling and forecasting of cyanobacteria growth in Lake Taihu by evolutionary computation," Ecological Modelling, Elsevier, vol. 306(C), pages 216-225.

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