谢先红等:A partitioned update scheme for state-parameter estimation of distributed hydrologic models based on the ensemble Kalman filter
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2014-08-22

A partitioned update scheme for state-parameter estimation of distributed hydrologic models based on the ensemble Kalman filter
作者:Xie, XH (Xie, Xianhong)[ 1,2,3 ] ; Zhang, DX (Zhang, Dongxiao)[ 2,3 ]
WATER RESOURCES RESEARCH
卷: 49  期: 11  页: 7350-7365
DOI: 10.1002/2012WR012853
出版年: NOV 2013

摘要
Sequential data assimilation methods, such as the ensemble Kalman filter (EnKF), provide a general framework to account for various uncertainties in hydrologic modeling, simultaneously estimating dynamic states and model parameters with a state augmentation technique. But this technique suffers from spurious correlation for impulse responses, such as the rainfall-runoff process, especially in the case of high-dimensional state spaces containing various parameters. This paper presents a partitioned forecast-update scheme based on the EnKF to reduce the degree of freedom of the high-dimensional state space and to correctly capture covariances between states and parameters. In this update scheme, the parameter set is partitioned into several types according to their sensitivities, and each type of sensitive parameter is estimated in an individual loop by repeated forecast and assimilation. We test this scheme with a synthetic case and a distributed hydrologic model concerning the real case of the Zhanghe river basin in China. The results from the synthetic experiments show that this new scheme can retrieve optimal parameter values and represent the correlations in a more stable manner when compared with the standard state augmentation technique. The real case further demonstrates the robustness of the partitioned update scheme for state and parameter estimation owing to the low estimation errors of streamflow in the assimilation and the prediction periods.

通讯作者地址: Xie, XH (通讯作者)
Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Peking Univ, Coll Engn, ERE, Beijing 100871, Peoples R China
[ 3 ] Peking Univ, Coll Engn, SKLTCS, Beijing 100871, Peoples R China