A new data assimilation method coupled with real-time detection of sensitive parameters: An example of phytoplankton modeling in lakes

Abstract

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.;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.