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Parameter uncertainty and sensitivity analysis of water quality model in Lake Taihu, China

Author

Listed:
  • Jiang, Long
  • Li, Yiping
  • Zhao, Xu
  • Tillotson, Martin R.
  • Wang, Wencai
  • Zhang, Shuangshuang
  • Sarpong, Linda
  • Asmaa, Qhtan
  • Pan, Baozhu

Abstract

Lake Taihu was chosen as a case for parameter uncertainty and sensitivity analysis of water quality simulation in large shallow lakes. Forty parameters in Environmental Fluid Dynamic Code model (EFDC) were filtered and analyzed. The results showed that parameters had a considerable influence on simulation and three groups of parameters related to algal kinetics (i.e. PMc, BMRc and PRRc), light (KeChl) and temperature (KTG1c) were very sensitive. For shallow lakes with frequent algal blooms, light extinction due to Chlorophyll-a is also a sensitive parameter. While the temperature effect coefficient for algal growth is sensitive for lakes with seasonal temperature variation. Sensitive parameters and their relevant uncertainty varied spatially. For high nutrients and algae concentration subareas, temperature was more likely to be a limiting factor, whereas sensitive factors could be light in lower concentration subareas. Since most sensitive parameters were related to algae, uncertainty in simulation increased with increasing algal kinetic processes over time and varied in different subareas. Lower nutrients and algae concentration subareas were more easily influenced by model parameters while nearshore areas were highly influenced by boundary conditions. For better simulation of water quality, variable stoichiometry phytoplankton models should be considered and zooplankton need to be integrated into the model explicitly rather than a fixed predation rate.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecomod:v:375:y:2018:i:c:p:1-12
    DOI: 10.1016/j.ecolmodel.2018.02.014
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    References listed on IDEAS

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    Cited by:

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    2. Linda Sarpong & Yiping Li & Eyram Norgbey & Amechi S. Nwankwegu & Yue Cheng & Salifu Nasiru & Isaac Kwesi Nooni & Victor Edem Setordjie, 2020. "A Sediment Diagenesis Model of Seasonal Nitrate and Ammonium Flux Spatial Variation Contributing to Eutrophication at Taihu, China," IJERPH, MDPI, vol. 17(11), pages 1-14, June.
    3. Shen, Jian & Qin, Qubin & Wang, Ya & Sisson, Mac, 2019. "A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading," Ecological Modelling, Elsevier, vol. 398(C), pages 44-54.
    4. Ruichen Xu & Yong Pang & Zhibing Hu & Xiaoyan Hu, 2022. "The Spatiotemporal Characteristics of Water Quality and Main Controlling Factors of Algal Blooms in Tai Lake, China," Sustainability, MDPI, vol. 14(9), pages 1-17, May.
    5. Yuan Zhou & Yongkang Lv & Jing Dong & Jin Yuan & Xiaomei Hui, 2024. "Sensitivity Analysis of Urban Landscape Lake Transparency Based on Machine Learning in Taiyuan City," Sustainability, MDPI, vol. 16(16), pages 1-18, August.
    6. Hanane Rhomad & Karima Khalil & Khalid Elkalay, 2023. "Water Quality Modeling in Atlantic Region: Review, Science Mapping and Future Research Directions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 451-499, January.
    7. Jiancai Deng & Fang Chen & Weiping Hu & Xin Lu & Bin Xu & David P. Hamilton, 2019. "Variations in the Distribution of Chl- a and Simulation Using a Multiple Regression Model," IJERPH, MDPI, vol. 16(22), pages 1-16, November.

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