Assimilating multi-source data into a three-dimensional hydro-ecological dynamics model using Ensemble Kalman Filter
Assimilating multi-source data into a three-dimensional hydro-ecological dynamics model using Ensemble Kalman Filter
复制标题
使用集成卡尔曼滤波器将多源数据同化为三维水文生态动力学模型
DOI：
10.1016/j.envsoft.2019.03.028
复制
发表时间：
2019-07-01
影响因子：
4.9
通讯作者：
Lin, Yuqing
中科院分区：
文献类型：
--
作者：
Chen, Cheng;Huang, Jiacong;Lin, Yuqing
准确预测赤潮的时空格局是一个重要而又具有挑战性的问题。建立了湖泊蓝藻生物量的三维水生态动力学模型（3DHED），并应用Enhancement Kalman滤波器将多源数据同化到3DHED模型中进行模型改进。该模型应用于太湖，使用现场测量和遥感（RS）反演。进行了两个数据同化试验（EnKF 1和EnKF 2）。EnKF 1只同化了原地测量数据，而EnKF 2同化了原地测量数据和遥感数据。结果表明，3DHED模拟太湖蓝藻生物量的时空格局具有可接受的一致性指数（IOA）。EnKF 1显著提高了模型拟合度，将85%测量点的IOA提高到0.85，尤其是更好地捕捉峰值。与EnKF 1相比，EnKF 2在空间格局上有了更大的改善，说明同化多源数据有助于提高模型的性能。
Accurately predicting spatio-temporal patterns of algal bloom is important and also challenging. This study developed a three-dimensional hydro-ecological dynamics model (3DHED) to predict cyanobacterial biomass in lakes and applied Ensemble Kalman Filter to assimilate multi-source data into 3DHED for model improvement. The model was applied in Lake Taihu, using in-situ measurements and remote sensing (RS) retrievals. Two data assimilation experiments (named EnKF1 and EnKF2) were conducted. EnKF1 assimilated only in-situ measurements, while EnKF2 assimilated both in-situ measurements and RS data. The results revealed that 3DHED simulated the spatio-temporal patterns of cyanobacterial biomass in Taihu with an acceptable Index of Agreement (IOA). EnKF1 significantly improved the model fitness and increased the IOA of 85% measurement sites to 0.85, especially better captured the peak values. Compared with EnKF1, EnKF2 gave more improvements in spatial patterns besides model fitness, implying that assimilating multi-source data was helpful to improving the model performance.