Subsurface hydrology : data integration for properties and processes
著者
書誌事項
Subsurface hydrology : data integration for properties and processes
（Geophysical monograph, 171）
American Geophysical Union, c2007
大学図書館所蔵 全1件
注記
Includes bibliographical references
内容説明・目次
内容説明
Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 171.
Groundwater is a critical resource and the PrinciPal source of drinking water for over 1.5 billion people. In 2001, the National Research Council cited as a "grand challenge" our need to understand the processes that control water movement in the subsurface. This volume faces that challenge in terms of data integration between complex, multi-scale hydrologie processes, and their links to other physical, chemical, and biological processes at multiple scales. Subsurface Hydrology: Data Integration for Properties and Processes presents the current state of the science in four aspects:
Approaches to hydrologie data integration
Data integration for characterization of hydrologie properties
Data integration for understanding hydrologie processes
Meta-analysis of current interpretations
Scientists and researchers in the field, the laboratory, and the classroom will find this work an important resource in advancing our understanding of subsurface water movement.
目次
Preface
David W. Hyndman, Frederick D. Day-Lewis, and Kamini Singha vii
Introduction
Kamini Singha, David W. Hyndman, and Frederick D. Day-Lewis 1
I. Approaches to Data Integration
A Review of Geostatistical Approaches to Data Fusion
Clayton V. Deutsch 7
On Stochastic Inverse Modeling
Peter K. Kitanidis 19
II. Data Integration for Property Characterization
A Comparison of the Use of Radar Images and Neutron Probe Data to Determine the Horizontal Correlation Length of Water Content
Rosemary J. Knight, James D. Irving, Paulette Tercier, Gene J. Freeman, Chris J. Murray, and Mark L. Rockhold 31
Integrating Statistical Rock Physics and Sedimentology for Quantitative Seismic Interpretation
Per Avseth, Tapan Mukerji and Gary Mavko, and Ezequiel Gonzalez 45
A Geostatistical Approach to Integrating Data From Multiple and Diverse Sources: An Application to the Integration of Well Data, Geological Information, 3d/4d Geophysical and Reservoir-Dynamics Data in a North-Sea Reservoir
Jef Caers and Scarlet Castro 61
A Geostatistical Data Assimilation Approach for Estimating Groundwater Plume Distributions From Multiple Monitoring Events
Anna M. Michalak and Shahar Shlomi 73
A Bayesian Approach for Combining Thermal and Hydraulic Data
Allan D. Woodbury 89
Fusion of Active and Passive Hydrologic and Geophysical Tomographic Surveys: The Future of Subsurface Characterization
Tian-Chyi Jim Yeh, Cheng Haw Lee, Kuo-Chin Hsu, and Yih-Chi Tan 109
III. Data Integration to Understand Hydrologic Processes
Evaluating Temporal and Spatial Variations in Recharge and Streamflow Using the Integrated Landscape Hydrology Model (ILHM)
David W. Hyndman, Anthony D. Kendall, and Nicklaus R.H. Welty 121
Integrating Geophysical, Hydrochemical, and Hydrologic Data to Understand the Freshwater Resources on Nantucket Island, Massachusetts
Andee J. Marksamer, Mark A. Person, Frederick D. Day-Lewis, John W. Lane, Jr., Denis Cohen, Brandon Dugan, Henk Kooi, and Mark Willett 143
Integrating Hydrologic and Geophysical Data to Constrain Coastal Surficial Aquifer Processes at Multiple Spatial and Temporal Scales
Gregory M. Schultz, Carolyn Ruppel, and Patrick Fulton 161
Examining Watershed Processes Using Spectral Analysis Methods Including the Scaled-Windowed Fourier Transform
Anthony D. Kendall and David W. Hyndman 183
Integrated Multi-Scale Characterization of Ground-Water Flow and Chemical Transport in Fractured Crystalline Rock at the Mirror Lake Site, New Hampshire
Allen M. Shapiro, Paul A. Hsieh, William C. Burton, and Gregory J. Walsh 201
IV. Meta Analysis
Accounting for Tomographic Resolution in Estimating Hydrologic Properties from Geophysical Data
Kamini Singha, Frederick D. Day-Lewis, and Stephen Moysey 227
A Probabilistic Perspective on Nonlinear Model Inversion and Data Assimilation
Dennis McLaughlin 243
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