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Application of a data assimilation method via an ensemble Kalman filter to reactive urea hydrolysis transport modeling

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Abstract

With growing importance of water resources in the world, remediations of anthropogenic contaminations due to reactive solute transport become even more important. A good understanding of reactive rate parameters such as kinetic parameters is the key to accurately predicting reactive solute transport processes and designing corresponding remediation schemes. For modeling reactive solute transport, it is very difficult to estimate chemical reaction rate parameters due to complex processes of chemical reactions and limited available data. To find a method to get the reactive rate parameters for the reactive urea hydrolysis transport modeling and obtain more accurate prediction for the chemical concentrations, we developed a data assimilation method based on an ensemble Kalman filter (EnKF) method to calibrate reactive rate parameters for modeling urea hydrolysis transport in a synthetic one-dimensional column at laboratory scale and to update modeling prediction. We applied a constrained EnKF method to pose constraints to the updated reactive rate parameters and the predicted solute concentrations based on their physical meanings after the data assimilation calibration. From the study results we concluded that we could efficiently improve the chemical reactive rate parameters with the data assimilation method via the EnKF, and at the same time we could improve solute concentration prediction. The more data we assimilated, the more accurate the reactive rate parameters and concentration prediction. The filter divergence problem was also solved in this study.

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Acknowledgments

This work is partly supported by the National Nature Science Foundation of China (Grant No. 51209187), the Fundamental Research Funds for the Central Universities (Grant No. 2652011286) and the National Nature Science Foundation of China Major Research Project (Grant No. 91125024).

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Authors and Affiliations

  1. Collage of Water Resources and Environmental Sciences/Key Laboratory of Groundwater Cycle and Environment Evolution, Ministry of Education, China University of Geosciences, Beijing, 100083, People’s Republic of China

    Juxiu Tong & Bill X. Hu

  2. Idaho National Laboratory, Carbon Resource Management Department, P.O. Box 1625, Idaho Falls, ID, 83415-2107, USA

    Juxiu Tong, Hai Huang & Luanjin Guo

  3. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, People’s Republic of China

    Juxiu Tong & Jinzhong Yang

  4. Department of Earth, Ocean and Atmospheric Science/Geological Sciences, Florida State University, Tallahassee, FL, 32306-4100, USA

    Juxiu Tong & Bill X. Hu

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  1. Juxiu Tong
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Correspondence to Bill X. Hu.

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Tong, J., Hu, B.X., Huang, H. et al. Application of a data assimilation method via an ensemble Kalman filter to reactive urea hydrolysis transport modeling. Stoch Environ Res Risk Assess 28, 729–741 (2014). https://doi.org/10.1007/s00477-013-0786-y

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