Upscaling and Inverse Modeling of Groundwater Flow and Mass ...

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170 BIBLIOGRAPHY Deutsch, C. V., Journel, A. G., 1992. GSLIB, Geostatistical Software Library and User’s Guide. Oxford University Press, New York. Deutsch, C. V., Journel, A. G., 1998. GSLIB, Geostatistical Software Library and User’s Guide, 2nd Edition. Oxford University Press, New York. Devegowda, D., Arroyo-Negrete, E., Datta-Gupta, A., 2010. Flow relevant covariance localization during dynamic data assimilation using EnKF. Advances in Water Resources 33 (2), 129–145. Evensen, G., 2003. The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean dynamics 53 (4), 343–367. Fu, J., Gómez-Hernández, J. J., 2009. A Blocking Markov Chain Monte Carlo Method for Inverse Stochastic Hydrogeological Modeling. Mathematical Geosciences 41 (2), 105–128. Gaspari, G., Cohn, S., 1999. Construction of correlation functions in two and three dimensions. Quarterly Journal of the Royal Meteorological Society 125 (554), 723–757. Gómez-Hernández, J., Wen, X., 1998. To be or not to be multi-Gaussian? A reflection on stochastic hydrogeology. Advances in Water Resources 21 (1), 47–61. Gómez-Hernández, J. J., Journel, A. G., 1993. Joint sequential simulation of multi-Gaussian fields. Geostatistics Troia 92 (1), 85–94. Gómez-Hernández, J. J., Sahuquillo, A., Capilla, J. E., 1997. Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data, 1, Theory. Journal of Hydrology 203 (1–4), 162–174. Gómez-Hernández, J. J., Srivastava, R. M., 1990. ISIM3D: an ANSI-C three dimensional multiple indicator conditional simulation program. Computers & Geosciences 16 (4), 395–440. Goovaerts, P., 1997. Geostatistics for natural resources evaluation. Oxford University Press, USA. Hamill, T., Whitaker, J., Snyder, C., 2001. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Monthly Weather Review 129 (11), 2776–2790. Harbaugh, A. W., Banta, E. R., Hill, M. C., McDonald, M. G., 2000. MODFLOW-2000, the U.S. Geological Survey modular ground-water model. U.S. Geological Survey, Branch of Information Services, Reston, VA, Denver, CO.
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Upscaling and Inverse Modeling of G
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c⃝ Copyright by Liangping Li 2011
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Abstract The need to reduce the com
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improved as more data is assimilate
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Resumen La necesidad de reducir el
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Por último, en el tercer bloque, e
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Resum La necessitat de reduir el co
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flux i el transport (conductivitat
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Acknowledgements I want to thank my
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xx CONTENTS 3 Transport Upscaling U
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xxii CONTENTS
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xxiv LIST OF FIGURES 2.7 Flow compa
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xxvi LIST OF FIGURES 4.4 Reference
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xxviii LIST OF FIGURES
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1.1 Motivation and Objectives 1 Int
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CHAPTER 1. INTRODUCTION 3 Chapter 2
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6 CHAPTER 2. A COMPARATIVE STUDY OF
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8 CHAPTER 2. A COMPARATIVE STUDY OF
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10 CHAPTER 2. A COMPARATIVE STUDY O
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38 BIBLIOGRAPHY Berkowitz, B., Sche
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40 BIBLIOGRAPHY Gómez-Hernández,
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42 BIBLIOGRAPHY Salamon, P., Fernà
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44 CHAPTER 3. TRANSPORT UPSCALING U
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70 BIBLIOGRAPHY Dagan, G., 1994. Up
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72 BIBLIOGRAPHY Lawrence, A. E., Sa
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74 BIBLIOGRAPHY Zhang, Y., 2004. Up
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76 CHAPTER 4. MODELING TRANSIENT GR
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100 CHAPTER 4. MODELING TRANSIENT G
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102 BIBLIOGRAPHY Durlofsky, L. J.,
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104 BIBLIOGRAPHY Tran, T., 1996. Th
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106 CHAPTER 5. JOINTLY MAPPING HYDR
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