HR: 09:30h
AN: H41I-07    [Abstracts]
TI: Using Ensemble Kalman Filter to Simulate Groundwater Flow and Solute Transport in Heterogeneous Media with Unknown Contamination Sources
AU: * Hu, X B
EM: hu@gly.fsu.edu
AF: Florida State University, 108 Carraway Building, Tallahassee, FL 32306, United States
AU: Huang, C
EM: huangcl@lzb.ac.cn
AF: Chinese Academy of Sciences Chinese Academy of Sciences, 322 Dong-Gang Road Arid and Cold Regions Environmental Institute, Lanzhou, Gan 73000, China
AU: Li, X
EM: lixin@ns.lzb.ac.cn
AF: Chinese Academy of Sciences Chinese Academy of Sciences, 322 Dong-Gang Road Arid and Cold Regions Environmental Institute, Lanzhou, Gan 73000, China
AU: Ye, M
EM: mingye@scs.fsu.edu
AF: Florida State University, 108 Carraway Building, Tallahassee, FL 32306, United States
AB: Hydraulic conductivity distribution and plume initial source are two important factors to affect solute transport in a naturally heterogeneous medium. Due to current economic and technologic limitations, hydraulic conductivity can only be measured at limited locations in a field. Therefore, its spatial distribution in a complex heterogeneous medium is generally uncertain. In many groundwater contamination sites, solute initial conditions are generally unknown. The plume distributions are available only at sometimes after the contaminations occurred. The uncertain spatial distribution of the hydraulic conductivity field and plume of the initial condition will lead to uncertain predictions to groundwater flow and solute transport in subsurface. In this study, a data assimilation method is developed for calibrating a hydraulic conductivity field and improving solute transport prediction with unknown initial condition. Ensemble Kalman filter (EnKF) is used to update the model parameter, hydraulic conductivity, and model 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 will 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 will well capture the plume evolution process in downstream.
DE: 1805 Computational hydrology
DE: 1829 Groundwater hydrology
DE: 1832 Groundwater transport
DE: 1869 Stochastic hydrology
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting