| 引用本文: | 张帅,彭福利,季雨来,张京,张奇谋,李琪,钱瑞,齐凌艳,黄佳聪.耦合敏感参数实时识别的新型数据同化算法研究——以湖泊藻类模拟为例.湖泊科学,2022,34(6):1877-1889. DOI:10.18307/2022.0608 |
| Zhang Shuai,Peng Fuli,Ji Yulai,Zhang Jing,Zhang Qimou,Li Qi,Qian Rui,Qi Lingyan,Huang Jiacong.A new data assimilation method coupled with real-time detection of sensitive parameters: An example of phytoplankton modeling in lakes. J. Lake Sci.2022,34(6):1877-1889. DOI:10.18307/2022.0608 |
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| 耦合敏感参数实时识别的新型数据同化算法研究——以湖泊藻类模拟为例 |
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张帅1,2, 彭福利3, 季雨来2, 张京2, 张奇谋2, 李琪2, 钱瑞2, 齐凌艳1,4, 黄佳聪2
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1.安徽师范大学地理与旅游学院, 芜湖 241003;2.中国科学院南京地理与湖泊研究所, 中国科学院流域地理学重点实验室, 南京 210008;3.中国环境监测总站, 北京 100012;4.资源环境与地理信息工程安徽省工程技术研究中心, 芜湖 241003
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| 摘要: |
| 数据同化是提升复杂机理过程模型精度的关键技术之一，而湖泊藻类模型的敏感参数具有随时间动态变化的特征，导致数据同化过程中无法精准更新某一时段的敏感参数，影响数据同化的模型精度提升效果.针对上述问题，本研究耦合了参数敏感性分析与集合卡尔曼滤波，研发了一种能够实时识别模型敏感参数的新型数据同化算法；为验证研发算法的效率，依托巢湖的高频水质自动监测数据，测试算法对藻类动态模型的精度提升效果.测试结果表明：研发算法能够精准跟踪模型敏感参数的动态变化，并根据监测数据实时更新模型敏感参数，实现了水质高频自动监测数据与藻类动态模型的深度融合，藻类生物量模拟精度提升了55%，即纳什系数（NSE）从0.49提升到0.76，模拟精度提升效果也显著优于传统数据同化算法（NSE=0.63）.研发算法可应用于其它水生态环境模型的数据同化，为水生态环境相关要素的精准模拟预测提供关键技术支撑. |
| 关键词: 巢湖 集合卡尔曼滤波 参数敏感性分析 模型 |
| DOI：10.18307/2022.0608 |
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| 基金项目:中国科学院青年创新促进会项目（2019313）、安徽省自然科学基金青年项目（1908085QD151）、江苏省水利科技项目（2019025，2020042，2020032，2021036）和安徽师范大学大学生创新创业训练计划项目（202010370193）联合资助. |
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| A new data assimilation method coupled with real-time detection of sensitive parameters: An example of phytoplankton modeling in lakes |
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Zhang Shuai1,2, Peng Fuli3, Ji Yulai2, Zhang Jing2, Zhang Qimou2, Li Qi2, Qian Rui2, Qi Lingyan1,4, Huang Jiacong2
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1.College of Geography and Tourism, Anhui Normal University, Wuhu 241003, P. R. China;2.Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P. R. China;3.China National Environmental Monitoring Centre, Beijing 100012, P. R. China;4.Engineering Technology Research Center of Resources Environment and GIS, Wuhu 241003, P. R. China
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| Abstract: |
| Data assimilation is a critical method to improve the performance of complex process-based models. However, the sensitive parameters for lake models are generally changing over time. Therefore, it is challenging to accurately update the sensitive parameters for a specific period, which affects the performance of data assimilation. To address the problem, this study developed a new data assimilation method by coupling the methods of parameter sensitivity analysis and the Ensemble Kalman Filter. The new method aimed to identify the model's sensitive parameters in real time. To evaluate its performance, we collected the high-frequency water quality automatic monitoring data of Lake Chaohu, and investigated the performance improvement of a phytoplankton dynamic model using a new data assimilation method. Our investigation results showed that the developed method was able to identify the sensitive parameters of the model in each simulation period, and updated them based on the measured data to achieve better performance. The simulation accuracy of phytoplankton biomass increased by 55%, i.e., the Nash-Sutcliffe Efficiency (NSE) increased from 0.49 to 0.76. This performance is better than that of the traditional data assimilation method (NSE=0.63). The method can be applied to the data assimilation of other ecological and environmental models, technically supporting an accurate prediction of environmental and ecological factors. |
| Key words: Lake Chaohu Ensemble Kalman Filter parameter sensitivity analysis model |
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