Ensemble Kalman filter based data assimilation in the Delft3D-BLOOM lake eutrophication model

被引：2
作者：
Liu Z. [1 ]
Li Z. [3 ]
Hu L. [2 ]
Lin Y. [2 ]
Chen Q. [2 ,3 ]
机构：
[1] College of Hydraulic and Environmental Engineering, Three Gorges University, Yichang
[2] Center for Eco-Environmental Research, Nanjing Hydraulic Research Institute, Nanjing
[3] Research Center for Eco-Environment Sciences, Chinese Academy of Sciences, Beijing
来源：
Hupo Kexue/Journal of Lake Sciences | 2017年 / 29卷 / 05期
关键词：
Data assimilation; Ensemble Kalman filter; Eutrophication model; Lake; Lake Taihu;
D O I：
10.18307/2017.0505
中图分类号：
学科分类号：
摘要：
Numerical eutrophication model is an important tool to predict and manage the ecosystem of lakes and reservoirs. However, the objective errors of the model are always vital problems the users concerned. Data assimilation, which connects observations and model simulations, can effectively improve the accuracy of models. Ensemble Kalman filter (EnKF), which is one of the most widely used methods for data assimilation, is suitable for nonlinear system and has high computation efficiency. In this research, the Delft3D-BLOOM was taken as the eutrophication model, and Lake Taihu was taken as the study case. After numerical testing, the ensemble size was set to 100, the observation error variance was set to 1%, and the simulation error variance was set to 10%. Two data assimilation modes, assimilation of model state variables and synchronous assimilation of both state variables and key parameters, were examined. The results showed that the fitness between model simulation and observation was slightly improved when the state variable was updated. When both the state variables and parameters were assimilated, the fitness was significantly improved. The study provides a promising approach in using EnKF to improve the simulation accuracy of complex eutrophication models. © 2017 by Journal of Lake Sciences.
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页码：1070 / 1083
页数：13
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