Assimilating multi-source data into a three-dimensional hydro-ecological dynamics model using Ensemble Kalman Filter

被引:23
作者
Chen, Cheng [2 ]
Huang, Jiacong [2 ,3 ]
Chen, Qiuwen [1 ,2 ]
Zhang, Jianyun [1 ]
Li, Zhijie [4 ]
Lin, Yuqing [2 ]
机构
[1] Nanjing Hydraul Res Inst, State Key Lab Water Resources & Hydropower Dev, Nanjing 210029, Jiangsu, Peoples R China
[2] Nanjing Hydraul Res Inst, Ctr Eco Environm Res, Nanjing 210029, Jiangsu, Peoples R China
[3] Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing 210008, Jiangsu, Peoples R China
[4] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Beijing 100085, Peoples R China
关键词
Cyanobacterial biomass; Three-dimensional ecological dynamics model; Data assimilation; Ensemble kalman filter; HARMFUL ALGAL BLOOMS; HYDRODYNAMIC-PHYTOPLANKTON MODEL; MAPPING CYANOBACTERIAL BLOOMS; SHALLOW LAKE TAIHU; CHLOROPHYLL-A; MODIS OBSERVATIONS; WATER-QUALITY; FORECAST; SALMO; ECOPHYSIOLOGY;
D O I
10.1016/j.envsoft.2019.03.028
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:188 / 199
页数:12
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