Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2017WR020906 |
An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions | |
Zhang, Jiangjiang1; Lin, Guang2,3; Li, Weixuan4; Wu, Laosheng5; Zeng, Lingzao1 | |
2018-03-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2018 |
卷号 | 54期号:3页码:1716-1733 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA |
英文摘要 | 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. |
英文关键词 | ensemble smoother inverse modeling multimodal distribution |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000430364900017 |
WOS关键词 | MONTE-CARLO-SIMULATION ; BAYESIAN EXPERIMENTAL-DESIGN ; SEQUENTIAL DATA ASSIMILATION ; KALMAN FILTER ; DIFFERENTIAL EVOLUTION ; INVERSE METHODS ; EFFICIENT ; FLOW ; PROBABILITY ; CALIBRATION |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/21207 |
专题 | 资源环境科学 |
作者单位 | 1.Zhejiang Univ, Coll Environm & Resource Sci, Inst Soil & Water Resources & Environm Sci, Zhejiang Prov Key Lab Agr Resources & Environm, Hangzhou, Zhejiang, Peoples R China; 2.Purdue Univ, Dept Math, W Lafayette, IN 47907 USA; 3.Purdue Univ, Sch Mech Engn, W Lafayette, IN 47907 USA; 4.Pacific Northwest Natl Lab, Richland, WA 99352 USA; 5.Univ Calif Riverside, Dept Environm Sci, Riverside, CA 92521 USA |
推荐引用方式 GB/T 7714 | Zhang, Jiangjiang,Lin, Guang,Li, Weixuan,et al. An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions[J]. WATER RESOURCES RESEARCH,2018,54(3):1716-1733. |
APA | Zhang, Jiangjiang,Lin, Guang,Li, Weixuan,Wu, Laosheng,&Zeng, Lingzao.(2018).An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions.WATER RESOURCES RESEARCH,54(3),1716-1733. |
MLA | Zhang, Jiangjiang,et al."An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions".WATER RESOURCES RESEARCH 54.3(2018):1716-1733. |
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