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An improved Ensemble Kalman Filter for optimizing parameters in a coupled phosphorus model for lowland polders in Lake Taihu Basin, China

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  • Huang, Jiacong
  • Gao, Junfeng

Abstract

Ensemble Kalman Filter (EnKF) is potential in optimizing parameters of an environmental model, but may lead to a worse performance of the model in case that improper parameters were updated. To overcome this weakness, EnKF was improved by coupling with a dynamic and multi-objective sensitivity analysis. The improved EnKF was applied to update the parameters of a coupled phosphorus model for simulating phosphorus dynamics of Polder Jian located in Lake Taihu Basin, China. Two parameters that were most sensitive to particulate and dissolved phosphorus were identified at each sub-period, and were then updated using EnKF. To evaluate the performance of the improved EnKF, four simulations with different parameter update strategies were implemented, and compared with measured data. The simulation with the improved EnKF well simulated DP dynamics in Polder Jian with a d value of 0.65 and a RMSE value of 0.015mg/L. This model fit is better than that of other three simulations with different parameter update strategies, implying a success of the improved EnKF in updating parameters of the coupled phosphorus model. This improved EnKF has the advantage to update several parameters simultaneously, and can be applied in other models with minimal changes.

Suggested Citation

  • Huang, Jiacong & Gao, Junfeng, 2017. "An improved Ensemble Kalman Filter for optimizing parameters in a coupled phosphorus model for lowland polders in Lake Taihu Basin, China," Ecological Modelling, Elsevier, vol. 357(C), pages 14-22.
    • Handle: RePEc:eee:ecomod:v:357:y:2017:i:c:p:14-22
    DOI: 10.1016/j.ecolmodel.2017.04.019
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    References listed on IDEAS

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    Cited by:

    1. Huang, Jiacong & Chen, Qiuwen & Peng, Jian & Gao, Junfeng, 2020. "Quantifying the cost-effectiveness of nutrient-removal strategies for a lowland rural watershed: Insights from process-based modeling," Ecological Modelling, Elsevier, vol. 431(C).
    2. Xizhi Nong & Dongguo Shao & Yi Xiao & Hua Zhong, 2019. "Spatio-Temporal Characterization Analysis and Water Quality Assessment of the South-to-North Water Diversion Project of China," IJERPH, MDPI, vol. 16(12), pages 1-23, June.
    3. Kvamsdal, Sturla & Maroto, José M. & Morán, Manuel & Sandal, Leif K., 2017. "A bridge between continuous and discrete-time bioeconomic models: Seasonality in fisheries," Ecological Modelling, Elsevier, vol. 364(C), pages 124-131.
    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.

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