Dual state-parameter estimation of hydrological models using ensemble Kalman filter
Dual state-parameter estimation of hydrological models using ensemble Kalman filter
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DOI：
10.1016/j.advwatres.2004.09.002
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发表时间：
2005-02-01
影响因子：
4.7
通讯作者：
Houser, PR
中科院分区：
文献类型：
--
作者：
Moradkhani, H;Sorooshian, S;Houser, PR
水文模型有两种：用于理解物理过程的模型和用于预测的模型。这项研究涉及后者，例如，在已知系统当前状态和模型参数的情况下，建模者使用它来预测未来某个时间的径流。在这方面，需要对参数和状态变量进行良好的估计，以使模型能够生成准确的预测。针对水文模型参数和状态变量的序贯估计问题，提出了一种基于集合卡尔曼滤波(EnKF)的双重状态参数估计方法。讨论了用于系综产生的扰动因子的识别和系综大小的选择的系统方法。Dual EnKF方法引入了许多新的特征：(1)可以同时估计模型状态和参数：(2)算法是递归的，因此不需要存储所有过去的信息。与批量校准程序中的情况一样；以及(3)可以适当地处理各种不确定性源。包括输入、输出和参数的不确定性。利用概念性降雨径流模型验证了双重EnKF方法在集合径流预报中的适用性和有效性。(C)2004爱思唯尔有限公司。保留所有权利。
Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses, the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state-parameter estimation approach is presented based on the Ensemble Kalman Fater (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can he estimated simultaneously: (2) the algorithm is recursive and therefore does not require storage of all past information. as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed. including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using conceptual rainfall-runoff model. (C) 2004 Elsevier Ltd. All rights reserved.