An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions
Abstract
Abstract Ensemble smoother (ES) has been widely used in inverse modeling of hydrologic systems. However, for problems where the distribution of model parameters is multimodal, using ES directly would be problematic. One popular solution is to use a clustering algorithm to identify each mode and update the clusters with ES separately. However, this strategy may not be very efficient when the dimension of parameter space is high or the number of modes is large. Alternatively, we propose in this paper a very simple and efficient algorithm, i.e., the iterative local updating ensemble smoother (ILUES), to explore multimodal distributions of model parameters in nonlinear hydrologic systems. The ILUES algorithm works by updating local ensembles of each sample with ES to explore possible multimodal distributions. To achieve satisfactory data matches in nonlinear problems, we adopt an iterative form of ES to assimilate the measurements multiple times. Numerical cases involving nonlinearity and multimodality are tested to illustrate the performance of the proposed method. It is shown that overall the ILUES algorithm can well quantify the parametric uncertainties of complex hydrologic models, no matter whether the multimodal distribution exists.
- Authors:
-
[1];
[2];
[3];
[4];
[1]
- Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University Hangzhou China
- Department of Mathematics and School of Mechanical Engineering Purdue University West Lafayette IN USA
- Pacific Northwest National Laboratory Richland WA USA
- Department of Environmental Sciences University of California Riverside CA USA
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1433557
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Water Resources Research
- Additional Journal Information:
- Journal Name: Water Resources Research Journal Volume: 54 Journal Issue: 3; Journal ID: ISSN 0043-1397
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Zhang, Jiangjiang, Lin, Guang, Li, Weixuan, Wu, Laosheng, and Zeng, Lingzao. An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions. United States: N. p., 2018.
Web. doi:10.1002/2017WR020906.
Zhang, Jiangjiang, Lin, Guang, Li, Weixuan, Wu, Laosheng, & Zeng, Lingzao. An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions. United States. https://doi.org/10.1002/2017WR020906
Zhang, Jiangjiang, Lin, Guang, Li, Weixuan, Wu, Laosheng, and Zeng, Lingzao. Sat .
"An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions". United States. https://doi.org/10.1002/2017WR020906.
@article{osti_1433557,
title = {An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions},
author = {Zhang, Jiangjiang and Lin, Guang and Li, Weixuan and Wu, Laosheng and Zeng, Lingzao},
abstractNote = {Abstract Ensemble smoother (ES) has been widely used in inverse modeling of hydrologic systems. However, for problems where the distribution of model parameters is multimodal, using ES directly would be problematic. One popular solution is to use a clustering algorithm to identify each mode and update the clusters with ES separately. However, this strategy may not be very efficient when the dimension of parameter space is high or the number of modes is large. Alternatively, we propose in this paper a very simple and efficient algorithm, i.e., the iterative local updating ensemble smoother (ILUES), to explore multimodal distributions of model parameters in nonlinear hydrologic systems. The ILUES algorithm works by updating local ensembles of each sample with ES to explore possible multimodal distributions. To achieve satisfactory data matches in nonlinear problems, we adopt an iterative form of ES to assimilate the measurements multiple times. Numerical cases involving nonlinearity and multimodality are tested to illustrate the performance of the proposed method. It is shown that overall the ILUES algorithm can well quantify the parametric uncertainties of complex hydrologic models, no matter whether the multimodal distribution exists.},
doi = {10.1002/2017WR020906},
journal = {Water Resources Research},
number = 3,
volume = 54,
place = {United States},
year = {Sat Mar 10 00:00:00 EST 2018},
month = {Sat Mar 10 00:00:00 EST 2018}
}
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