Using data assimilation method to calibrate a heterogeneous conductivity field and improve solute transport prediction with an unknown contamination source
文献类型：期刊论文
| 作者 | Huang, Chunlin2; Hu, Bill X.1; Li, Xin2; Ye, Ming1,3 |
| 刊名 | STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
 |
| 出版日期 | 2009-12-01 |
| 卷号 | 23期号:8页码:1155-1167 |
| 关键词 | Data assimilation Ensemble Kalman filter Solute transport Hydraulic conductivity Steady-state flow |
| ISSN号 | 1436-3240 |
| DOI | 10.1007/s00477-008-0289-4 |
| 通讯作者 | Hu, Bill X.(hu@gly.fsu.edu) |
| 英文摘要 | Hydraulic conductivity distribution and plume initial source condition are two important factors affecting solute transport in heterogeneous media. Since hydraulic conductivity can only be measured at limited locations in a field, its spatial distribution in a complex heterogeneous medium is generally uncertain. In many groundwater contamination sites, transport initial conditions are generally unknown, as plume distributions are available only after the contaminations occurred. In this study, a data assimilation method is developed for calibrating a hydraulic conductivity field and improving solute transport prediction with unknown initial solute source condition. Ensemble Kalman filter (EnKF) is used to update the model parameter (i.e., hydraulic conductivity) and state variables (hydraulic head and solute concentration), when data are available. Two-dimensional numerical experiments are designed to assess the performance of the EnKF method on data assimilation for solute transport prediction. The study results indicate that the EnKF method can significantly improve the estimation of the hydraulic conductivity distribution and solute transport prediction by assimilating hydraulic head measurements with a known solute initial condition. When solute source is unknown, solute prediction by assimilating continuous measurements of solute concentration at a few points in the plume well captures the plume evolution downstream of the measurement points. |
| 收录类别 | SCI |
| WOS关键词 | ENSEMBLE KALMAN FILTER ; LAND DATA ASSIMILATION ; HYDROLOGIC DATA ASSIMILATION ; INVERSE PROBLEM ; MODEL ; SYSTEM ; FLOW |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources |
| WOS类目 | Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000271752100008 |
| 出版者 | SPRINGER |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2556196 |
| 专题 | 寒区旱区环境与工程研究所 |
| 通讯作者 | Hu, Bill X. |
| 作者单位 | 1.Florida State Univ, Dept Geol Sci, Tallahassee, FL 32306 USA 2.CAS, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Peoples R China 3.Florida State Univ, Dept Comp Sci, Tallahassee, FL 32306 USA |
| 推荐引用方式 GB/T 7714 | Huang, Chunlin,Hu, Bill X.,Li, Xin,et al. Using data assimilation method to calibrate a heterogeneous conductivity field and improve solute transport prediction with an unknown contamination source[J]. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,2009,23(8):1155-1167. |
| APA | Huang, Chunlin,Hu, Bill X.,Li, Xin,&Ye, Ming.(2009).Using data assimilation method to calibrate a heterogeneous conductivity field and improve solute transport prediction with an unknown contamination source.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,23(8),1155-1167. |
| MLA | Huang, Chunlin,et al."Using data assimilation method to calibrate a heterogeneous conductivity field and improve solute transport prediction with an unknown contamination source".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 23.8(2009):1155-1167. |
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来源：寒区旱区环境与工程研究所
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