H44E:
Uncertainty Quantification and Reduction in Hydrogeological Forecasting and Inversion III

Session ID#: 4943

Thursday, 18 December 2014: 4:00 PM-6:00 PM
3020 (Moscone West)
Chairs:  Souheil M Ezzedine, Univ California LLNL, Livermore, United States and Daniel M Tartakovsky, Stanford University, Department of Energy Resources Engineering, Stanford, CA, United States
Primary Convener:  Souheil M Ezzedine, Univ California LLNL, Livermore, United States
Co-conveners:  Niklas Linde, University of Lausanne, Institute of Earth Sciences, Lausanne, Switzerland, Philippe Renard, University of Neuchâtel, Centre for Hydrogeology and Geothermics, Neuchâtel, Switzerland and Daniel M Tartakovsky, Stanford University, Energy Science and Engineering, Stanford, United States
OSPA Liaison:  Daniel M Tartakovsky, Stanford University, Department of Energy Resources Engineering, Stanford, CA, United States
Co-Sponsor(s):
  • A - Atmospheric Sciences
  • GC - Global Environmental Change
  • MR - Mineral and Rock Physics
  • NG - Nonlinear Geophysics
Index Terms:

1816 Estimation and forecasting [HYDROLOGY]
1846 Model calibration [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1869 Stochastic hydrology [HYDROLOGY]
Virtual Option?: No
Swirl Theme: Characterizing Uncertainty

Abstracts Submitted to this Session:

H44E-01
Incorporating Pore-Scale Data in Field-Scale Uncertainty Quantification: A Multi-Scale Bayesian Approach (Invited) (5921)
Matteo Icardi, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; University of Texas at Austin, Institute for Computational Engineering and Sciences, Austin, TX, United States
H44E-02
Transient Hydraulic Tomography in the Field: 3-D K Estimation and Validation in a Highly Heterogeneous Unconfined Aquifer (17310)
David L Hochstetler1, Warren Barrash1,2 and Peter K Kitanidis1, (1)Stanford, Civil and Environmental Engineering, Stanford, CA, United States, (2)Boise State University, Geosciences, Boise, ID, United States
H44E-03
Hierarchical Acceleration of Multilevel Monte Carlo Methods for Computationally Expensive Simulations in Reservoir Modeling (17849)
Guannan Zhang, Oak Ridge National Laboratory, Oak Ridge, United States, Dan Lu, Oak Ridge National Laboratory, Computational Sciences and Engineering Division, Oak Ridge, United States and Clayton Webster, Oak Ridge National Laboratory, Oak Ridge, TN, United States
H44E-04
Inverse Groundwater Flow Modelling Via Conditional Simulation (7242)
Sebastian Hörning, University of Stuttgart, Stuttgart, Germany and Andras Bardossy, University of Stuttgart, Chair of Hydrology and Geohydrology, Stuttgart, Germany
H44E-05
CDF solutions of diffusion equation with random inputs (Invited) (5973)
Francesca Boso, University of Calif San Diego, La Jolla, CA, United States and Daniel M Tartakovsky, Stanford University, Department of Energy Resources Engineering, Stanford, CA, United States
H44E-06
A Comparison of Three Stochastic Approaches for Parameter Estimation and Prediction of Steady-State Groundwater Flow: Nonlocal Moment Equations and Monte Carlo Method Coupled with Ensemble Kalman Filter and Geostatistical Stochastic Inversion. (26308)
Jessica Vanessa Briseño-Ruiz1, Abel F. Hernández2, Eric Morales-Casique3, Graciela del Socorro S Herrera4 and Oscar Escolero-Fuentes1, (1)Instituto de Geología, Universidad Nacional Autónoma de México, Geología Regional, México, D.F., Mexico, (2)Instituto de Investigaciones Eléctricas, Gerencia de Geotermia, Cuernavaca, Mor., Mexico, (3)Instituto de Geología, Universidad Nacional Autónoma de México, Geología Regional, México, Mexico, (4)Universidad Nacional Autónoma de México, Instituto de Geofísica, México, D.F., Mexico
H44E-07
Characterizing Roughness and Connectivity Properties of Aquifer Conductivity Using the Method of Anchored Distributions (MAD) (19062)
Falk Hesse1, Jon Edward Sege1, Carlos Murillo2, Sabine Attinger3 and Yoram Rubin4, (1)University of California Berkeley, Berkeley, CA, United States, (2)Brigham Young University, Provo, UT, United States, (3)Helmholtz Centre for Environmental Research GmbH – UFZ, Leipzig, Germany, Computational Hydrosystems, Leipzig, Germany, (4)Univ California Berkeley, Berkeley, CA, United States
H44E-08
Optimal Observation Network Design for Model Discrimination using Information Theory and Bayesian Model Averaging (13859)
Hai V Pham, Louisiana State University, Department of Civil and Environmental Engineering, Baton Rouge, LA, United States and Frank T-C Tsai, Louisiana State University, Department of Civil and Environmental Engineering, Baton Rouge, Louisiana, United States
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