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基于集合卡尔曼滤波的水文模型状态变量反馈校正方法

王文鹏 何坫鹏 巫义锐 邱鹏 张馨月 刘波

水力发电学报2024,Vol.43Issue(10):17-31,15.
水力发电学报2024,Vol.43Issue(10):17-31,15.DOI:10.11660/slfdxb.20241002

基于集合卡尔曼滤波的水文模型状态变量反馈校正方法

State variable feedback-correction method of hydrological model based on ensemble Kalman filter

王文鹏 1何坫鹏 2巫义锐 3邱鹏 4张馨月 2刘波2

作者信息

  • 1. 河海大学 水文水资源学院,南京 210024||水利部水利大数据重点实验室,南京 211100
  • 2. 河海大学 水文水资源学院,南京 210024
  • 3. 水利部水利大数据重点实验室,南京 211100||河海大学 计算机与软件学院,南京 211100
  • 4. 重庆市水文监测总站,重庆 401120
  • 折叠

摘要

Abstract

The ensemble Kalman filter approach has been used to correct the state variable in hydrological models.Difficulties of its application include how to select the state variable for correction,whether or not to synchronize parameter correction with the state variable,and how to set up the filter algorithm's hyperparameters.To address these issues,we take the calibrated GR5J model for the Qijiang River basin as a prototype tool to assimilate observed streamflows and correct model state variables using feedback correction.We use synthesis experiments and rolling forecast tests to examine the impacts of state variable selection,model parameter disruption,and hyperparameter optimization of the filter algorithm on forecast accuracy.The results suggest that while the biased initial state could be specified,the ensemble Kalman filter does raise forecast accuracy;otherwise,a better way is to fix the runoff generation variable and the flow confluence variable simultaneously to avoid overcorrection on model states.In the case of biased model parameters,it is best to identify the parameter first and then adjust the state variable.Increasing the ensemble members and warm-up periods generally improve correction accuracy,but the impacts of model noises and observation noises on the correction accuracy are non-monotonic.The filter algorithm is superior to the warm-up method,though its forecast accuracy decreases with an increasing forecast period.The findings would help apply the state correction method to operational forecasting.

关键词

集合卡尔曼滤波/水文模型/洪水预报/数据同化/实时校正

Key words

ensemble Kalman filter/hydrological model/flood forecast/data assimilation/real-time correction

分类

水利科学

引用本文复制引用

王文鹏,何坫鹏,巫义锐,邱鹏,张馨月,刘波..基于集合卡尔曼滤波的水文模型状态变量反馈校正方法[J].水力发电学报,2024,43(10):17-31,15.

基金项目

国家重点研发计划项目(2021YFB3900601) (2021YFB3900601)

水利部重大科技项目(SKS-2022132) (SKS-2022132)

江苏省水利科技项目(2022002) (2022002)

水力发电学报

OA北大核心CSTPCD

1003-1243

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