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Partial financial support for this project was provided by the National Science Foundation, grant CCF‐1539605.
Published In
Water Resources Research
Document Type
Article
Publication Date
3-25-2019
Subjects
Uncertainty -- Mathematical models, Hydrology -- Data processing, Streamflow -- Mathematical modeling, Hydrologic models -- Mathematical optimization
Abstract
This article presents a novel approach to couple a deterministic four‐dimensional variational (4DVAR) assimilation method with the particle filter (PF) ensemble data assimilation system, to produce a robust approach for dual‐state‐parameter estimation. In our proposed method, the Hybrid Ensemble and Variational Data Assimilation framework for Environmental systems (HEAVEN), we characterize the model structural uncertainty in addition to model parameter and input uncertainties. The sequential PF is formulated within the 4DVAR system to design a computationally efficient feedback mechanism throughout the assimilation period. In this framework, the 4DVAR optimization produces the maximum a posteriori estimate of state variables at the beginning of the assimilation window without the need to develop the adjoint of the forecast model. The 4DVAR solution is then perturbed by a newly defined prior error covariance matrix to generate an initial condition ensemble for the PF system to provide more accurate and reliable posterior distributions within the same assimilation window. The prior error covariance matrix is updated from one cycle to another over the main assimilation period to account for model structural uncertainty resulting in an improved estimation of posterior distribution. The premise of the presented approach is that it (1) accounts for all sources of uncertainties involved in hydrologic predictions, (2) uses a small ensemble size, and (3) precludes the particle degeneracy and sample impoverishment. The proposed method is applied on a nonlinear hydrologic model and the effectiveness, robustness, and reliability of the method is demonstrated for several river basins across the United States.
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DOI
10.1029/2018WR023629
Persistent Identifier
https://archives.pdx.edu/ds/psu/28346
Citation Details
Abbaszadeh, P., Moradkhani, H., & Daescu, D. N. (2019). The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework. Water Resources Research, 55, 2407–2431.
Included in
Civil Engineering Commons, Environmental Engineering Commons, Hydraulic Engineering Commons
Description
©2019. The Authors. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.