Using data assimilation method to calibrate a heterogeneous conductivity field and improve solute transport prediction with an unknown contamination source |
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| Authors: | Chunlin Huang Bill X Hu Xin Li Ming Ye |
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| Institution: | (1) Cold and Arid Regions Environmental and Engineering Research Institute, CAS, 730000 Lanzhou, China;(2) Department of Geological Sciences, Florida State University, Tallahassee, FL 32306, USA;(3) Department of Scientific Computing, Florida State University, Tallahassee, FL 32306, USA; |
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| Abstract: | 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. |
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