投稿中心

审稿中心

编辑中心

期刊出版

网站地图

友情链接

引用本文:刘卓,李志杰,胡柳明,林育青,陈求稳.基于集合卡尔曼滤波的湖泊富营养化模型Delft3D-BLOOM数据同化.湖泊科学,2017,29(5):1070-1083. DOI:10.18307/2017.0505
LIU Zhuo,LI Zhijie,HU Liuming,LIN Yuqing,CHEN Qiuwen.Ensemble Kalman filter based data assimilation in the Delft3D-BLOOM lake eutrophication model. J. Lake Sci.2017,29(5):1070-1083. DOI:10.18307/2017.0505
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 5898次   下载 2384 本文二维码信息
码上扫一扫!
分享到: 微信 更多
基于集合卡尔曼滤波的湖泊富营养化模型Delft3D-BLOOM数据同化
刘卓1, 李志杰2, 胡柳明3, 林育青3, 陈求稳3,2
1.三峡大学水利与环境学院, 宜昌 443002;2.中国科学院生态环境研究中心, 北京 100085;3.南京水利科学研究院生态环境研究中心, 南京 210029
摘要:
富营养化模型是进行湖泊水环境质量预测和管理的重要工具,然而模型客观存在的误差一直是应用者关心的重要问题.数据同化作为连接观测数据与数值模型的重要方法,可以有效提高模型的准确性.集合卡尔曼滤波(EnKF)是众多数据同化算法中应用最为广泛的一种,可进行非线性系统的数据同化,并能有效降低数据同化的计算量.本研究以太湖作为具体实例,选择Delft3D-BLOOM作为富营养化模型,在数值实验确定EnKF集合数为100、观测误差方差为1%、模拟误差方差为10%的基础上分别进行模型状态变量同化以及状态变量与关键参数同步同化.结果显示,仅同化状态变量时,模型预测精度有所增加;同时同化状态变量和关键参数时,可显著提升模型在湖泊水环境质量预测中的精度.该研究为应用集合卡尔曼滤波以提高复杂的湖库富营养化模型模拟精度提供了有效的方法.
关键词:  集合卡尔曼滤波  富营养化模型  数据同化  湖泊  太湖
DOI:10.18307/2017.0505
分类号:
基金项目:国家自然科学基金项目(51579149,51609142)和江苏省水利科技项目(2016021)联合资助.
Ensemble Kalman filter based data assimilation in the Delft3D-BLOOM lake eutrophication model
LIU Zhuo1, LI Zhijie2, HU Liuming3, LIN Yuqing3, CHEN Qiuwen3,2
1.College of Hydraulic and Environmental Engineering, Three Gorges University, Yichang 443002, P. R. China;2.Research Center for Eco-Environment Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China;3.Center for Eco-Environmental Research, Nanjing Hydraulic Research Institute, Nanjing 210029, P. R. China
Abstract:
Numerical eutrophication model is an important tool to predict and manage the ecosystem of lakes and reservoirs.Howev-er,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 pa-rameters,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 im-proved.The study provides a promising approach in using EnKF to improve the simulation accuracy of complex eutrophication mod-els.
Key words:  Ensemble Kalman filter  eutrophication model  data assimilation  lake  Lake Taihu
分享按钮