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| 001 | 21350 | ||
| 005 | 20200702121620.0 | ||
| 024 | 7 | _ | |2 DOI |a 10.1016/j.jhydrol.2012.01.037 |
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| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 690 |
| 084 | _ | _ | |2 WoS |a Engineering, Civil |
| 084 | _ | _ | |2 WoS |a Geosciences, Multidisciplinary |
| 084 | _ | _ | |2 WoS |a Water Resources |
| 100 | 1 | _ | |0 P:(DE-HGF)0 |a Li, L. |b 0 |
| 245 | _ | _ | |a Jointly mapping hydraulic conductivity and porosity by assimilating concentration data via ensemble Kalman filter |
| 260 | _ | _ | |a Amsterdam [u.a.] |b Elsevier |c 2012 |
| 300 | _ | _ | |a 152 - 169 |
| 336 | 7 | _ | |a Journal Article |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) |
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| 336 | 7 | _ | |a article |2 DRIVER |
| 440 | _ | 0 | |0 3413 |a Journal of Hydrology |v 428 |x 0022-1694 |
| 500 | _ | _ | |3 POF3_Assignment on 2016-02-29 |
| 500 | _ | _ | |a The authors gratefully acknowledge the financial support by ENRESA (Project 0079000029) and the Spanish Ministry of Science and Innovation (Project CGL2011-23295). Extra travel Grants awarded to the first and second author by the Ministry of Education (Spain) are also acknowledged. Dr. Jichun Wu and an anonymous reviewer are grateful acknowledged for their comments which helped improving the final version of the manuscript. |
| 520 | _ | _ | |a Real-time data from on-line sensors offer the possibility to update environmental simulation models in real-time. Information from on-line sensors concerning contaminant concentrations in groundwater allow for the real-time characterization and control of a contaminant plume. In this paper it is proposed to use the CPU-efficient Ensemble Kalman Filter (EnKF) method, a data assimilation algorithm, for jointly updating the flow and transport parameters (hydraulic conductivity and porosity) and state variables (piezometric head and concentration) of a groundwater flow and contaminant transport problem. A synthetic experiment is used to demonstrate the capability of the EnKF to estimate hydraulic conductivity and porosity by assimilating dynamic head and multiple concentration data in a transient flow and transport model. In this work the worth of hydraulic conductivity, porosity, piezometric head, and concentration data is analyzed in the context of aquifer characterization and prediction uncertainty reduction. The results indicate that the characterization of the hydraulic conductivity and porosity fields is continuously improved as more data are assimilated. Also, groundwater flow and mass transport predictions are improved as more and different types of data are assimilated. The beneficial impact of accounting for multiple concentration data is patent. (C) 2012 Elsevier B.V. All rights reserved. |
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| 653 | 2 | 0 | |2 Author |a Data assimilation |
| 653 | 2 | 0 | |2 Author |a Stochastic transport |
| 653 | 2 | 0 | |2 Author |a Ensemble Kalman filter |
| 653 | 2 | 0 | |2 Author |a Multiple concentration data |
| 653 | 2 | 0 | |2 Author |a Hydraulic conductivity and porosity |
| 653 | 2 | 0 | |2 Author |a Heterogeneity |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Zhou, H.Y. |b 1 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Gomez-Hernandez, J.J. |b 2 |
| 700 | 1 | _ | |0 P:(DE-Juel1)138662 |a Hendricks-Franssen, H.J. |b 3 |u FZJ |
| 773 | _ | _ | |0 PERI:(DE-600)1473173-3 |a 10.1016/j.jhydrol.2012.01.037 |g Vol. 428-429, p. 152 - 169 |p 152 - 169 |q 428-429<152 - 169 |t Journal of hydrology |v 428-429 |x 0022-1694 |y 2012 |
| 856 | 7 | _ | |u http://dx.doi.org/10.1016/j.jhydrol.2012.01.037 |
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