Dual state-parameter optimal estimation of one-dimensional open channel model using ensemble Kalman filter

Lai, Ruixun; Fang, Hongwei; He, Guojian; Yu, Xin; Yang, Ming; Wang, Ming

doi:10.1016/s1001-6058(11)60397-2

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…Recently, the ensemble KF, which is capable of dealing efficiently with nonlinear models, has become a popular approach for assimilating observational data for improving hydraulic models in real-time flood forecasting (Andreadis et al, 2007;Lai et al, 2013;Neal et al, 2007Neal et al, , 2009Paiva et al, 2013;Schumann et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach

Zhang

Fang

et al. 2017

Environmental Modelling & Software

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Reliable real-time probabilistic flood forecasting is critical for effective water management and flood protection all over the world. In this study, we develop a real-time probabilistic channel flood-forecasting model by combining a channel hydraulic model with the Bayesian particle filter approach. The new model is tested in Highlights • A real-time flood-forecasting model is proposed by assimilating real-time stage observations into a hydraulic model.

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Section: Introductionmentioning

confidence: 99%

A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach

Zhang

Fang

et al. 2017

Environmental Modelling & Software

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“…The model used in this paper has been applied to fine suspended loads and to hyperconcentrated flows in the Yellow River (Fang et al 2008;Lai et al 2013), but with some simplifications in sediment carrying capacity.…”

Section: Governing Equations and Discretizationmentioning

confidence: 99%

“…The iterative step, β, was assumed as a constant value 0.002. The assimilation step was 150 h and the time step was 600 s. These initial values of the parameters, including Manning's roughness coefficient, recovery coefficient, and sediment carrying capacity, were calibrated using data obtained from historical floods and considered to be reasonable for use in the lower Yellow River (Lai et al 2013(Lai et al , 2014.…”

Section: Study Area and Boundary Conditionmentioning

confidence: 99%

“…The ensemble Kalman filter is an improved edition of the Kalman filter, which uses Monte Carlo theory to estimate the model error (Evensen 2009). For decades, the ensemble Kalman filter has received considerable attention over a wide range of subjects including ocean circulation, numerical weather forecasting, and hydrology (e.g., Moradkhani et al 2005;Lai et al 2013;Lewis et al 2006). The variational-based algorithm implements the minimum distance between the observations and predictions, while, at the same time, taking an explicit dynamic system as a constraint (Le Dimet and Talagrand 1986).…”

Section: Introductionmentioning

confidence: 99%

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Variational-Based Data Assimilation to Simulate Sediment Concentration in the Lower Yellow River, China

et al. 2016

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The heavy sediment load of the Yellow River makes it difficult to simulate sediment concentration using classic numerical models. In this paper, on the basis of the classic one-dimensional numerical model of open channel flow, a variational-based data assimilation method is introduced to improve the simulation accuracy of sediment concentration and to estimate parameters in sediment carrying capacity. In this method, a cost function is introduced first to determine the difference between the sediment concentration distributions and available field observations. A one-dimensional suspended sediment transport equation, assumed as a constraint, is integrated into the cost function. An adjoint equation of the data assimilation system is used to solve the minimum problem of the cost function. Field data observed from the Yellow River in 2013 are used to test the proposed method. When running the numerical model with the data assimilation method, errors between the calculations and the observations are analyzed. Results show that (1) the data assimilation system can improve the prediction accuracy of suspended sediment concentration; (2) the variational inverse data assimilation is an effective way to estimate the model parameters, which are poorly known in previous research; and (3) although the available observations are limited to two cross sections located in the central portion of the study reach, the variational-based data assimilation system has a positive effect on the simulated results in the portion of the model domain in which no observations are available.

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Improving the real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using the particle filter

Fang

Lai

et al. 2018

J Hydrodyn

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Dual state-parameter optimal estimation of one-dimensional open channel model using ensemble Kalman filter

Cited by 12 publications

References 13 publications

A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach

A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach

Variational-Based Data Assimilation to Simulate Sediment Concentration in the Lower Yellow River, China

Improving the real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using the particle filter

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