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Title: Sequential ensemble-based optimal design for parameter estimation

Journal Article · · Water Resources Research
DOI:https://doi.org/10.1002/2016WR018736· OSTI ID:1356516
 [1];  [1];  [2];  [1];  [3]
  1. Zhejiang Univ., Hangzhou (China)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Univ. of California, Riverside, CA (United States)
+ Show Author Affiliations

The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. Furthermore, the effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
National Natural Science Foundation of China (NSFC); Fundamental Research Funds for the Central Universities (China); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
AC05-76RL01830; 41371237; 41571215; 2016QNA6008
OSTI ID:
1356516
Report Number(s):
PNNL-SA-123579; KJ0401000
Journal Information:
Water Resources Research, Vol. 52, Issue 10; ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 14 works
Citation information provided by
Web of Science

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Joint inversion of physical and geochemical parameters in groundwater models by sequential ensemble-based optimal design journal February 2018

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54 ENVIRONMENTAL SCIENCES
ensemble Kalman filter
experimental design
parameter estimation
unsaturated flow