The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method
The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method
复制标题
利用新安江降雨径流模型和双状态参数估计方法进行径流估计
DOI：
10.1016/j.jhydrol.2012.12.011
复制
发表时间：
2013-02
影响因子：
6.4
通讯作者：
Fu, Xiaolei
中科院分区：
文献类型：
--
作者：
Lu, Haishen;Hou, Ting;Horton, Robert;Zhu, Yonghua;Chen, Xi;Jia, Yangwen;Wang, Wen;Fu, Xiaolei
为了用水文模型准确地估计洪水，必须知道模型参数和初始状态变量。为了使模型能够进行准确的估计，需要对参数和初始状态变量进行良好的估计。新安江降雨径流（RR）模型在中国湿润和半湿润地区得到广泛应用。本文对新安江RR模型参数的敏感性以及状态变量与输出流量的相关性进行了评价。为了减少流量数据误差、模型结构误差和参数不确定性的影响，将新安江RR模型与智能优化算法和数据同化方法相结合，对中国罗河先测后不测的流量进行估算，进行参数定标。利用粒子群优化算法和集合卡尔曼滤波数据同化的三种变化（状态变量同化、参数同化和同时双重同化）对洛河和未测量流域的模型参数进行了批量估计。在这种情况下，模型参数被设置为等于中位数。结果表明，当采用10年数据优化算法确定参数值时，双集合卡尔曼滤波器显著改善了模拟结果。当参数采用中值时，状态变量同化对估计结果影响不大，而参数同化对模拟结果有正向影响。双集合卡尔曼滤波器也显著改善了模拟结果。同化的时间尺度对双同化的模拟结果影响不大，但对状态变量同化和参数同化的影响较大。
To accurately estimate floods with hydrological models, the model parameters and the initial state variables must be known. Good estimations of parameters and initial state variables are required to enable the models to make accurate estimations. The Xinanjiang rainfall-runoff (RR) model has been widely used in humid and semi-humid regions in China. In this paper, we evaluate the sensitivity of the Xinanjiang RR model parameters and the correlation between the state variables and the output streamflow. In order to reduce the impact of streamflow data error, model structural error and parameter uncertainty, the Xinanjiang RR model is coupled with intelligent optimization algorithms and a data assimilation method to estimate the streamflow in the Luo River in China that was first considered as gauged, and then as ungauged for parameter calibration. Model parameters are estimated in batch using a particle swarm optimization algorithm and three variations of ensemble Kalman Filter data assimilation (the state variable assimilation, parameter assimilation and the dual assimilation at the same time) for the Luo River, and the ungauged basin. In this case, the model parameters are set equal to median values. The results show that when parameter values are determined by an optimization algorithm using 10years of data, the dual ensemble Kalman Filter notably improves the simulated results. When the parameters adopt the median, the state variable assimilation has little effect on the estimation results, but the parameter assimilation positively affects the simulated results. The dual ensemble Kalman Filter also notably improves the simulated results. The time scale of assimilation has little effect on simulation results for the dual assimilation, but it has a large effect on the state variable assimilation and the parameter assimilation.
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影响因子：
4.7
作者：
Moradkhani, H;Sorooshian, S;Houser, PR
通讯作者：
Houser, PR
影响因子：
6.4
作者：
G. Kuczera
通讯作者：
G. Kuczera
影响因子：
--
作者：
Changsheng Chen;P. Malanotte‐Rizzoli;Jun Wei;R. Beardsley;Z. Lai;P. Xue;Siwei Lyu;Qichun Xu;J. Qi;G. Cowles
通讯作者：
Changsheng Chen;P. Malanotte‐Rizzoli;Jun Wei;R. Beardsley;Z. Lai;P. Xue;Siwei Lyu;Qichun Xu;J. Qi;G. Cowles
DOI：
10.1098/rsta.2002.1008
发表时间：
2002-07
期刊：
Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences
影响因子：
--
作者：
P. Young
通讯作者：
P. Young
DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
--
作者：
A. Engelbrecht
通讯作者：
A. Engelbrecht