湖泊藻类动态模型数据同化模式的改进
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X524

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国家重点研发计划(2018YFC0830800);江苏省双创团队(SC917001);南京水利科学研究院创新团队(Y917020)


Modify of data assimilation model for lake algae dynamic model
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    摘要:

    以太湖为研究区域,采用2014—2016年的水环境生态监测数据,率定了三维水生态动力学模型(3DHED),模拟了太湖蓝藻生物量的时空变化;通过融入遥感数据建立了基于集合卡尔曼滤波(EnKF)的蓝藻生物量预测数据同化(DA)模式,同时提出了一种改进数据同化(mDA)的策略,降低了遥感数据不确定性的影响,显著提升了模型模拟精度。结果表明:相比3DHED蓝藻生物量的模拟结果,DA模拟结果的均方根误差均值降低了10.4%,IOA均值增加了48.8%;mDA在DA基础上对蓝藻生物量的模拟精度进一步提升,其均方根误差均值为1.16 mg/L,在DA基础上降低了8.6%,IOA均值为0.71,在DA基础上增加了10.9%,并有效提升了对蓝藻生物量峰值的捕捉能力,表明提出的mDA方法能有效减小原DA模式中遥感观测数据误差的影响,提升水华模拟精度。

    Abstract:

    Taking Taihu Lake as the research area, a three-dimensional hydro-ecological dynamics(3DHED)model was developed to simulate the spatiotemporal variation of cyanobacteria biomass in Taihu Lake based on the water environmental ecological monitoring data from 2014 to 2016. A data assimilation(DA)model of cyanobacteria biomass prediction based on ensemble Kalman filter(EnKF)was established by integrating remote sensing data. At the same time, a modified data assimilation(mDA)strategy is proposed to reduce the impact of remote sensing data uncertainty and significantly improve the simulation accuracy of the model. The results showed that compared with 3DHED imulation results, the RMSE of DA simulation results decreased by 10. 4%, and the IOA increased by 48. 8%. The simulation accuracy of mDA for cyanobacteria biomass based on DA was further improved. The mean RMSE of mDA was 1. 16 mg/L, which was 8. 6% lower than that of DA, and the mean IOA was 0. 71, which was 10. 9% higher than that of DA. mDA effectively enhanced the ability to capture the peak biomass of cyanobacteria, which means that the proposed method can effectively reduce the influence of remote sensing data error in the original DA model and improve the accuracy of bloom simulation.

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李港,陈诚,何欣霞,等.湖泊藻类动态模型数据同化模式的改进[J].水资源保护,2021,37(4):156-165.(LI Gang, CHEN Cheng, HE Xinxia, et al. Modify of data assimilation model for lake algae dynamic model[J]. Water Resources Protection,2021,37(4):156-165.(in Chinese))

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  • 在线发布日期: 2021-07-21
  • 出版日期: 2021-07-20