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

集合卡尔曼滤波已应用于水文模型初始状态误差校正。如何选择校正变量、是否同步校正模型参数、如何优选滤波超参数是其应用难点。为此,以綦江流域GR5J模型为原型,同化实测流量反馈校正模型状态,通过合成实验和滚动预报实验,分析状态变量选择、模型参数扰动、滤波超参数对预报精度的影响。结果表明:准确识别有偏初始状态,集合卡尔曼滤波能提高预报精度;若难以识别,建议同步校正产流汇流状态变量,减少过校正。模型参数有偏时,应先识别模型参数,再校正模型状态。增加集合数和预热时长能提高校正精度,模型和观测噪声的影响具有非单调性;滤波校正效果随预见期延长而衰减,但要优于模型预热技术。该发现可作为作业预报应用状态校正法的参考。

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.