Hydrogeological conceptual site models : data analysis and visualization 9781439852286, 1439852286

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Hydrogeological Conceptual Site Models Data Analysis and Visualization

Hydrogeological Conceptual Site Models Data Analysis and Visualization Neven Kresic Alex Mikszewski

Boca Raton London New York

CRC Press is an imprint of the Taylor & Francis Group, an informa business

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CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2013 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20120502 International Standard Book Number-13: 978-1-4398-5228-6 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com

To Angela, Joanne, and Miles

Contents Preface ........................................................................................................................................... xiii Authors ...........................................................................................................................................xv 1. Introduction .............................................................................................................................1 1.1 Historical Example........................................................................................................3 1.2 Example Uses of This Book .........................................................................................9 References ............................................................................................................................... 10 2. Conceptual Site Models ...................................................................................................... 11 2.1 Deinition...................................................................................................................... 11 2.2 Physical Proile ............................................................................................................ 13 2.2.1 Geomorphology (Topography) .................................................................... 14 2.2.2 Hydrology ....................................................................................................... 29 2.2.3 Climate (Hydrometeorology) ....................................................................... 37 2.2.4 Land Use and Land Cover ............................................................................ 40 2.2.5 Water Budget .................................................................................................. 46 2.3 Hydrogeology .............................................................................................................. 48 2.3.1 Aquifers in Unconsolidated Sediments ...................................................... 49 2.3.1.1 Alluvial Aquifers ............................................................................ 50 2.3.1.2 Basin-Fill Aquifers ......................................................................... 55 2.3.1.3 Blanket Sand-and-Gravel Aquifers.............................................. 61 2.3.1.4 Aquifers in Semiconsolidated Sediments................................... 62 2.3.1.5 Glacial-Deposit Aquifers ...............................................................63 2.3.2 Sandstone Aquifers........................................................................................65 2.3.3 Fractured-Bedrock Aquifers ........................................................................ 68 2.3.4 Karst Aquifers ................................................................................................ 79 2.3.5 Basaltic and Other Volcanic Rock Aquifers ............................................. 104 2.3.6 Aquitards ...................................................................................................... 108 References ............................................................................................................................. 112 3. Data Management, GIS, and GIS Modules ................................................................... 117 3.1 Introduction ............................................................................................................... 117 3.2 Data Management for GIS ....................................................................................... 121 3.2.1 Data Management Failures ........................................................................ 121 3.2.1.1 Working with the Wrong Units .................................................. 121 3.2.1.2 Working with Unknown or Mixed Coordinate Systems........ 123 3.2.1.3 Erroneous Query Results ............................................................ 124 3.2.1.4 Data Entry Ineficiency ................................................................ 126 3.2.2 Data Management Systems ........................................................................ 126 3.2.2.1 Deine the Project Objective ....................................................... 127 3.2.2.2 Determine the Quantity and Type of Field and Laboratory Data to Be Collected ................................................ 129

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3.2.2.3 Perform Required Data Collection and Analysis .................... 130 3.2.2.4 Present Study Conclusions ......................................................... 131 3.3 Introducing the Geodatabase .................................................................................. 133 3.3.1 Tables ............................................................................................................. 133 3.3.2 Feature Classes ............................................................................................. 135 3.3.3 Rasters ........................................................................................................... 135 3.4 Basics of Geodatabase Design ................................................................................. 138 3.4.1 Table Format and Querying ....................................................................... 139 3.4.1.1 Select Query .................................................................................. 141 3.4.1.2 Cross-Tab Query ........................................................................... 143 3.4.1.3 Forms ............................................................................................. 146 3.4.2 Data Linkage with GIS ................................................................................ 147 3.4.3 Errors in Geodatabase Design ................................................................... 148 3.4.3.1 Fields with Wrong Data Type ..................................................... 148 3.4.3.2 Spaces in Data Field Names ........................................................ 148 3.4.3.3 Misspellings and Format Discrepancies................................... 148 3.4.3.4 Cross-Tab Query Dificulties ...................................................... 148 3.4.3.5 Inconsistent or Unknown Coordinate Systems ....................... 149 3.5 Working with Coordinate Systems ........................................................................ 149 3.6 Data Visualization and Processing with ArcGIS ................................................. 154 3.6.1 Why Visualize Data? ................................................................................... 154 3.6.2 Analog versus Digital Data ........................................................................ 156 3.6.3 Data Visualization in ArcMap ................................................................... 160 3.6.3.1 Data View ...................................................................................... 160 3.6.3.2 Layout View .................................................................................. 170 3.6.4 Geoprocessing in ArcMap .......................................................................... 170 3.6.4.1 Querying Tools ............................................................................. 170 3.6.4.2 Labeling Tools ............................................................................... 173 3.6.4.3 Editing Tools ................................................................................. 174 3.6.4.4 Georeferencing Tools ................................................................... 176 3.6.4.5 Analysis Tools ............................................................................... 176 3.6.4.6 Measurement Tools ...................................................................... 178 3.6.4.7 Data Management Tools .............................................................. 180 3.7 GIS Modules for Hydrogeological Data Analysis ................................................ 182 3.7.1 Statistical Analyses ...................................................................................... 183 3.7.1.1 Visual Sample Plan ...................................................................... 183 3.7.1.2 FIELDS Rapid Assessment Tool Software ................................ 184 3.7.1.3 Spatial Analysis and Decision Assistance ................................ 185 3.7.1.4 ArcToolbox .................................................................................... 187 3.7.1.5 ProUCL .......................................................................................... 187 3.7.1.6 Monitoring and Remediation Optimization System .............. 188 3.7.1.7 Other Tools .................................................................................... 190 3.7.2 Geostatistics and Contouring .................................................................... 190 3.7.3 Boring Logs and Cross Sections ................................................................ 190 3.7.4 Proprietary Environmental Database Systems........................................ 192 3.7.5 General Notes on Computer Modules ...................................................... 193 References ............................................................................................................................. 196

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4. Contouring ...........................................................................................................................199 4.1 Introduction ............................................................................................................... 199 4.2 Contouring Methods ................................................................................................ 201 4.2.1 Manual Contouring ..................................................................................... 201 4.2.2 Contouring with Computer Programs ..................................................... 202 4.2.3 Spatial Interpolation Models ...................................................................... 208 4.2.3.1 Deterministic Models .................................................................. 208 4.2.3.2 Geostatistical Models................................................................... 212 4.2.3.3 Trend and Anisotropy ................................................................. 213 4.2.3.4 Error and Uncertainty Analysis ................................................. 218 4.3 Kriging ........................................................................................................................222 4.3.1 Variography ..................................................................................................225 4.3.1.1 Semivariogram Curve-Fitting .................................................... 229 4.3.1.2 Search Neighborhood .................................................................. 232 4.3.1.3 Modeling Techniques .................................................................. 233 4.3.2 Kriging Prediction Standard Error ........................................................... 233 4.3.2.1 Nugget Effect and Prediction Standard Error ......................... 237 4.3.3 Types of Kriging........................................................................................... 239 4.3.3.1 Ordinary, Simple, and Universal Kriging ................................ 239 4.3.3.2 Cokriging....................................................................................... 240 4.3.3.3 Indicator Kriging .......................................................................... 241 4.3.3.4 Point and Block Kriging .............................................................. 242 4.4 Contouring Potentiometric Surfaces ..................................................................... 243 4.4.1 Importance of Conceptual Site Model ...................................................... 243 4.4.2 Heterogeneity and Anisotropy .................................................................. 246 4.4.3 Inluence of Hydraulic Boundaries ........................................................... 250 4.5 Contouring Contaminant Concentrations............................................................. 256 4.5.1 Importance of Conceptual Site Model ...................................................... 257 4.5.2 Example Application ................................................................................... 261 4.5.2.1 Default Parameters ....................................................................... 263 4.5.2.2 Data Exploration ........................................................................... 266 4.5.2.3 Lognormal Kriging with Anisotropy ....................................... 271 4.5.2.4 Lognormal Kriging with Trend Removal ................................. 274 4.5.2.5 Model Comparison ...................................................................... 277 4.5.2.6 Advanced Detrending and Cokriging ...................................... 281 4.5.2.7 Advanced Uncertainty Analysis ................................................ 283 4.5.3 Summary....................................................................................................... 283 4.6 Grid and Contour Conversion Tools ...................................................................... 285 4.6.1 Converting Contour Maps to Grid Files ................................................... 285 4.6.2 Converting Grid File Types ........................................................................ 287 4.6.3 Extracting Grid Values to Points................................................................ 288 4.6.4 Appropriate Use of Spatial Analyst .......................................................... 289 References ............................................................................................................................. 291 5. Groundwater Modeling.....................................................................................................293 5.1 Introduction ............................................................................................................... 293 5.2 Misuse of Groundwater Models ............................................................................. 294

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5.3

Types of Groundwater Models................................................................................300 5.3.1 Analytical Models ........................................................................................ 301 5.3.1.1 BIOCHLOR Case Study ...............................................................304 5.3.2 Numerical Models ....................................................................................... 312 5.4 Numerical Modeling Concepts ............................................................................... 314 5.4.1 Initial Conditions, Boundary Conditions, and Water Fluxes ................ 316 5.4.2 Dispersion and Diffusion ........................................................................... 319 5.5 Model Calibration, Sensitivity Analysis, and Error ............................................. 326 5.6 Modeling Documentation and Standards .............................................................334 5.7 MODFLOW-USG ....................................................................................................... 335 5.7.1 Description of Method ................................................................................ 336 5.7.2 Input and Output ......................................................................................... 339 5.7.3 Benchmarking and Testing ........................................................................ 339 5.8 Variably Saturated Models ......................................................................................340 5.9 GIS and Numerical Modeling Software ................................................................342 References .............................................................................................................................343 6. Three-Dimensional Visualizations ................................................................................347 6.1 Introduction ............................................................................................................... 347 6.2 3D Conceptual Site Model Visualizations .............................................................348 6.2.1 3D Views of Geologic Model ...................................................................... 353 6.2.2 4D Views of Groundwater Chemistry ...................................................... 357 6.2.3 Views of 3D Plumes and Soil Plumes ....................................................... 361 6.2.4 Specialty 3D Visualizations ........................................................................ 361 Citations ................................................................................................................................ 365 7. Site Investigation ................................................................................................................367 7.1 Data and Products in Public Domain .................................................................... 368 7.1.1 USGS Data and Publications ...................................................................... 369 7.1.2 State GIS Data ............................................................................................... 370 7.2 Database Coordination............................................................................................. 371 7.3 Georeferencing .......................................................................................................... 372 7.3.1 Georeferencing AutoCAD Data ................................................................. 372 7.3.2 Georeferencing Raster Data ....................................................................... 376 7.4 Developing a Site Basemap ...................................................................................... 381 7.5 Developing and Implementing Sampling Plans .................................................. 382 7.5.1 Developing Sampling Plans ....................................................................... 382 7.5.1.1 Systematic Planning to Balance Cost and Risk ........................ 382 7.5.1.2 Example Application of Visual Sample Plan ............................ 385 7.5.2 Implementing Sampling Plans .................................................................. 393 7.5.2.1 Data Collection ............................................................................. 393 7.5.2.2 Real-Time Data Management, Analysis, and Visualization..... 399 7.6 Example Visualizations for Site Investigation Data .............................................404 7.6.1 Plan-View Maps ...........................................................................................404 7.6.2 Boring Logs and Cross Sections ................................................................ 411 7.6.3 Graphs and Charts....................................................................................... 418 7.7 Toxic Gingerbread Men and Other Confounders................................................. 421 References .............................................................................................................................422

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8. Groundwater Remediation ...............................................................................................425 8.1 Introduction ...............................................................................................................425 8.2 Pump and Treat .........................................................................................................430 8.2.1 Introduction ..................................................................................................430 8.2.2 Design Concepts ..........................................................................................430 8.2.3 System Optimization ...................................................................................442 8.3 In Situ Remediation...................................................................................................444 8.3.1 Introduction ..................................................................................................444 8.3.2 In Situ Thermal Treatment ..........................................................................445 8.3.2.1 Design Concepts ...........................................................................445 8.3.2.2 Case Study ..................................................................................... 449 8.3.3 In Situ Chemical Oxidation ........................................................................ 455 8.3.3.1 Design Concepts ........................................................................... 455 8.3.3.2 Case Study ..................................................................................... 460 8.3.4 Bioremediation and Monitored Natural Attenuation ............................ 466 8.3.4.1 In Situ Bioremediation ................................................................. 466 8.3.4.2 Monitored Natural Attenuation ................................................. 471 8.4 Alternative Remedial Endpoints and Metrics ...................................................... 479 8.4.1 Motivation ..................................................................................................... 479 8.4.2 Technical Impracticability .......................................................................... 481 8.4.3 Risk-Based Cleanup Goals.......................................................................... 494 8.4.4 Mass Flux ...................................................................................................... 497 8.5 The Way Forward: Sustainable Remediation ........................................................ 503 References ............................................................................................................................. 511 9. Groundwater Supply.......................................................................................................... 517 9.1 Integrated Water Resources Management ............................................................ 517 9.2 Groundwater Supply ................................................................................................ 526 9.2.1 Groundwater Quantity ............................................................................... 529 9.2.2 Groundwater Quality .................................................................................. 537 9.2.2.1 Protection of Groundwater .........................................................540 9.2.3 Groundwater Extraction .............................................................................544 9.3 Groundwater Sustainability .................................................................................... 550 9.3.1 Sustainable Groundwater Use Case Study: Plant Washington ............. 557 9.3.2 Sustainable Groundwater Use: Conclusion.............................................. 563 References .............................................................................................................................564

Preface From their origins, exploration and inquiry in the Earth sciences have been dependent on conceptual models and data visualizations to test theories and convey indings to the general public. One can appreciate the power and importance of conceptual graphics by lipping through the pages of a National Geographic magazine. Data visualization is inextricably linked to quantitative spatial data analysis—the two major forms of which, for the Earth sciences, are statistical interpolation and modeling. Data analysis and visualization are invaluable in assessing the eficacy of current regulatory and consulting practices to ensure that political and technical interventions related to the management of groundwater resources and contaminated sites are evidence based and lead to desirable outcomes. This book covers conceptual site model development, data analysis, and visual data presentation for hydrogeology and groundwater remediation. While this book is technical in nature, equations and advanced theoretical discussions are minimized with the focus instead placed on key concepts and practical data analysis and visualization strategies. As a result, we believe that nontechnical stakeholders involved in groundwater projects will ind this book interesting and relevant as well. We sincerely hope that the reader’s academic or professional practice, whatever that may be, beneits from the tips and techniques contained herein. We wish to thank Hisham Mahmoud, Don Chandler, Dave Goershel, Dan Grogan, Allen Kibler, Leonard Ledbetter, Ann Massey, Larry Neal, and Steve Youngs of AMEC for their continuing support and advice, and Ted Chapin and Karl Kasper of Woodard & Curran for their support in the completion of this book.

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Authors Neven Kresic is a hydrogeology practice leader at AMEC Environment and Infrastructure, Inc., an international engineering and consulting irm. He is a professional geologist and professional hydrogeologist working for a variety of national and international clients, including industry, mining, water utilities, government agencies, and environmental law irms. Neven holds a bachelor’s degree in hydrogeological engineering, a master’s degree in hydrogeology, and a PhD in geology, all from the University of Belgrade. Before coming to the United States as a Senior Fulbright Scholar at the U.S. Geological Survey in Reston, Virginia, and George Washington University in Washington, DC, Neven was a professor of groundwater dynamics and hydrogeology at the University of Belgrade. He serves on the management committee of the Groundwater Management and Restoration Specialty Group of the International Water Association, co-chairs the Karst Commission of the International Association of Hydrogeologists, and is a past vice president for international affairs of the American Institute of Hydrology. Alex Mikszewski is a licensed professional environmental engineer in the Commonwealth of Massachusetts, where he works for Woodard & Curran, Inc. He holds a bachelor’s degree in civil and environmental engineering from Cornell University and a master’s degree in environmental engineering and science from The Johns Hopkins University. He was a NNEMS fellow in the U.S. EPA Ofice of Superfund Remediation and Technology Innovation. As a consultant, Alex has developed groundwater models for clients in the public and private sectors in settings ranging from southeastern New Hampshire to the semiarid groundwater basins of Southern California. His experience in statistics and geostatistics involves the use of computer software to design defensible sampling plans at Superfund sites, delineate contaminant concentrations in soil and groundwater, assess surface water–groundwater interactions, and evaluate the effects of pumping in multiple-aquifer systems. Alex has hands-on experience with a variety of remedial technologies, including in situ chemical oxidation, soil vapor extraction, in situ thermal remediation, monitored natural attenuation, and pump and treat.

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1 Introduction The physics of groundwater low, geochemistry, contaminant fate and transport, groundwater remediation, and groundwater resources development and management are all subjects that have been covered extensively in innumerable textbooks. Thus, a student or a practicing groundwater professional has access to a wealth of information regarding hydrogeological theory. A strong technical background in hydrogeology and related disciplines, such as luid mechanics, forms the foundation for a successful career in academia or the public or private sectors. However, this is typically where the education ends, and continued development is generally only possible by obtaining real-world experience in ield hydrogeology, quantitative spatial data analysis, and data visualization that includes mapping. The novice groundwater professional may also ind that there are critical hydrogeological concepts applicable at varying investigatory scales that are not typically covered in conventional textbooks. The political and regulatory framework that a hydrogeologist must operate within is another area where improved educational materials are desirable but lacking. The intention of this book is to ill the void in hydrogeological literature through identiication and explanation of key concepts in professional hydrogeology and to provide practical guidance and real-life examples related to the following applications: • • • • • • • •

Hydrogeological conceptual site models (Chapter 2) Data management and geographic information systems (Chapter 3) Contouring (Chapter 4) Groundwater modeling (Chapter 5) Three-dimensional visualization (Chapter 6) Site investigation (Chapter 7) Groundwater remediation (Chapter 8) Groundwater supply (Chapter 9)

Data visualization is underestimated in its importance to the practice of groundwater professionals. Eficient and clear presentation to stakeholders with varying technical backgrounds is essential to the success of any project. The content and design of visual presentations depend on the target audience, which, in the case of professional hydrogeology, may include: • • • • •

Regulators such as the United States Environmental Protection Agency (US EPA) Commercial and industrial clients Attorneys involved in litigation or real-estate transactions Juries Communities affected by a contaminated site or a water supply project 1

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Hydrogeological Conceptual Site Models

It is the hope of the authors that this book will be interesting and useful to any of the above stakeholders involved in groundwater projects. While this book is technical in nature, equations and advanced theoretical discussions are minimized, with the focus placed on key concepts, practical data analysis, and visualization strategies. In addition, concepts are presented throughout this book related to the current state of the hydrogeological practice, focusing on prevailing ideologies and recommendations for improvement. These topics are often controversial, and the authors hope  that  this book provokes thought and discussion on how we can evolve current policies and practices to achieve better outcomes at a lesser cost to society. The authors have no agenda or underlying motivation in these discussions, and it should be noted that this book was completed without inancial support from any public or private entity. One example of a thought-provoking topic similar to others included in this book is the current regulatory policy related to arsenic (As) in private drinking-water supplies in eastern New England. Arsenic occurs naturally in metasedimentary bedrock units in the region that are extensively tapped by private water-supply wells. In 2003, it was estimated that more than 100,000 people across eastern New England were using private water supplies with arsenic concentrations above the federal maximum contaminant level (MCL) of 10 µg/L (Ayotte et al. 2003). This represents a widespread exposure to a chemical at dangerously high levels. Figure 1.1 is a map of arsenic concentrations measured at private bedrock wells in southeastern New Hampshire during a 2003 study performed by the United States Geological Survey. Despite these alarming data, arsenic is not regulated by the state of New Hampshire in private drinking-water wells, and there are no current requirements to even test existing wells for the contaminant. In 2010, a bill (HB 1685) that would have made it a requirement to test new wells and wells involved in home sales was killed by the New Hampshire Legislature (Susca and Klevens 2011). In contrast to this policy of allowing arsenic exposure, environmental regulations require the expenditure of millions of dollars to remediate Superfund and state-led contaminated sites where the exposure often constitutes a very low risk (e.g., one in a million excess lifetime cancer risk) or is hypothetical in nature (e.g., potential future consumption of groundwater). For example, at the Visalia Pole Yard Superfund site, well over $20 million was spent to remediate groundwater contamination that was not posing an actual risk (see Chapter 8 for a more detailed discussion). This is a classic example of policy that permits self-inlicted risk while disproportionately targeting externally inlicted risk, ignoring the relative costs and beneits of the overall outcome. One potential declaration of this ideology is When protecting human health and the environment, it is not our place to address risk related to naturally occurring contamination or individual lifestyle choices, but we will act aggressively to remedy any minimal level of risk caused by a third-party agent.

The reader should consider how this logic impedes efforts to protect human health and the environment. Developing sound conceptual models and using effective data analysis and visualization tools can help address problems even at this philosophical scale; practicing groundwater professionals are encouraged to use their expertise to be active agents of change. A historical example of the power of these methods is provided in the following section.

Introduction

3

FIGURE 1.1 Arsenic concentrations in private bedrock wells in southeastern New Hampshire and grouped geologic units showing the percentage of wells with concentrations of arsenic greater than the current MCL of 0.010 mg/L. (Modiied from USGS, 2003. Arsenic Concentrations in Private Bedrock Wells in Southeastern New Hampshire. US Department of the Interior, USGS Fact Sheet 051-03.)

1.1 Historical Example It is likely that most hydrogeologists, environmental engineers, epidemiologists, and medical doctors have heard the famous story of Dr. John Snow and the Broad Street pump in mid-19th-century London. Dr. Snow has been voted the greatest doctor of all time by Hospital Doctor magazine (edging out Hippocrates) and is also known as the father of modern epidemiology (Frerichs 2011). The authors have also heard him referred to as the irst environmental engineer. While Dr. Snow was a pioneering anesthesiologist, he is best known for his staunch advocacy of the waterborne theory of cholera transmission, and his innovative work in this area led to his great posthumous fame. In the mid-19th century, cholera outbreaks were common in the cities of the industrial revolution, spreading rapidly through densely settled areas and inlicting frighteningly

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high mortality rates. At the time, the spread of cholera and most other diseases was blamed on foul inner-city air. This conceptual model for disease transmission by odors was termed the miasmatic theory and was widely accepted by sanitation professionals, public oficials, and Parliament in London by the late 1840s (Johnson 2006). Dr. Snow will forever be remembered for his ight against this lawed, superstition-based theory. Dr. Snow’s interest in cholera was likely spurred by the London cholera outbreak of 1848–1849, which killed 50,000 people (Johnson 2006). The doctor became obsessed with the disease and, during that outbreak, developed an original conceptual model for cholera transmission based on his knowledge and experience as a medical doctor. He reasoned that cholera is fundamentally a diarrheic disease of the gut and, therefore, is caused by something ingested rather than inhaled. Where advocates of the miasmatic theory argued that cholera was a poison inhaled and circulated through the blood, causing fever, Dr. Snow argued that the pathology of cholera is caused by dehydration from severe diarrhea (Koch 2011). He further built his argument on waterborne transmission through two populationbased studies conducted during the 1848–1849 epidemic. His indings were communicated through a landmark 1849 publication On the Mode and Communication of Cholera. While Dr. Snow’s work garnered much public interest, it was generally concluded, at the time, that his publication failed to provide suficient evidence linking cholera to water supply. He therefore stewed for an additional ive years before getting another chance at conclusively proving the accuracy of his conceptual model. This opportunity came in the form of another cholera outbreak in the Soho neighborhood centered on the famous Broad Street pump (Johnson 2006). The Soho outbreak was particularly swift and virulent, yet both Dr. Snow and his rival working for the Board of Health, Reverend Henry Whitehead, were able to conduct rigorous, on-the-ground data collection during the outbreak itself. Armed with his correct conceptual model, Dr. Snow collected site-speciic data linking the spread of disease to the Broad Street pump. He presented his immediate indings to the Board of Governors of St. James Parish, and the evidence was compelling enough to convince the board to remove the handle from the pump, thereby eliminating public access to the well. The action was met with jeers by the observing public. While the data indicate that the outbreak was already waning by the time of the pump handle removal, Dr. Snow’s actions likely contributed to its decline and, at the minimum, prevented a second wave of disease spread (Tufte 1997). The toll of the cholera outbreak was devastating; 90 out of the 896 Broad Street residents died within two weeks (Johnson 2006). Seizing the opportunity to further promote his theory, Dr. Snow quickly compiled his data on the Soho outbreak for scientiic publication. He summarized his indings in a now famous map originally presented to the Epidemiological Society in December 1854 and included as Figure 1.2. Cholera deaths are represented as thick black bars, which are clearly clustered around the Broad Street pump. While many declare that Dr. Snow’s map “solved the mystery” of cholera (e.g., FlowingData 2007), it was not used to get the pump handle removed (Dr. Snow’s weight of on-the-ground evidence was suficient to get a desperate board to try anything), and it did not convince the board or the general public of the waterborne theory of cholera transmission. The impact of the map has therefore been somewhat exaggerated. The miasmatic theory persisted for several decades after Dr. Snow’s work until it was replaced by the germ theory, and German scientist Robert Koch isolated the cholera microbe in 1883. Ironically, Vibrio cholerae had already been identiied in 1854—the same year as the Soho outbreak—by the Italian Fillipo Pacini, a inding that was largely ignored by his contemporaries but later acknowledged by the parasite’s renaming in 1965 to Vibrio cholerae Pacini 1854 (Johnson 2006).

Introduction

5

FIGURE 1.2 Dr. John Snow’s famous map of the 1854 Broad Street cholera outbreak irst presented in December 1854 and later published in 1855. Available at http://www.ph.ucla.edu/epi/snow.html.

This fascinating story is presented in detail in the work of Johnson (2006). It has been summarized here to provide a historical example of how conceptual models, data analysis, and data visualization can be used to tackle even the most dificult scientiic and societal problems. Dr. Snow developed a conceptual model based on his professional knowledge, collected and analyzed data quantitatively, and presented his results in an effective visualization (which also served as an additional test on his original theory). However, as previously stated, Dr. Snow unfortunately did not solve the mystery as his contemporaries remained unconvinced. Koch (2011) proposes that Dr. Snow could have used more detailed quantitative analysis to bolster his study and potentially win over even the most ardent miasma believers. Dr. Snow did not calculate relative mortality rates in the individual pump catchments, which is a form of quantitative analysis that Koch (2011) asserts was practiced at the time. A rendering of Dr. Snow’s data created using modern mapping

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FIGURE 1.3 Cholera deaths per 1000 persons for the pump catchments in the area of the 1854 cholera outbreak. (Mortality rates and approximate georeferenced catchment, cholera death, and pump locations from Koch, T., Disease Maps: Epidemics on the Ground, University of Chicago Press, Chicago, 2011, 330 pp.). World Street Map sources: Esri, DeLorme, NAVTEQ, TomTom, USGS, Intermap, iPC, NRCAN, Esri Japan, METI, Esri China (Hong Kong), Esri (Thailand).

techniques is presented as Figure 1.3, including clear delineation of the pump catchments and labeling the number of cholera deaths per 1000 persons in each catchment. The mortality per 1000 persons in the Broad Street pump catchment (149 per 1000 persons) clearly overwhelms the rates of the adjacent catchments (Koch 2011). It is important to note that Dr. Snow produced a second version of his original map that innovatively used a Voronoi diagram to delineate the area where the Broad Street pump was the closest source of water. This results in a similar effect to the catchment-area delineations presented in Figure 1.3. If Dr. Snow had performed these mortality calculations and presented them in such a manner, might he have ended the cholera debate once and for all? The authors believe it is highly unlikely. While the addition of mortality rates does enhance the visualization, it often takes generations for entrenched ideologies to be purged from the public mind. In

Introduction

7

some cases, it takes extreme acts of self-sacriice, such as self-experimentation, to prove the validity of a scientiic concept. While not necessary for cholera, self-experimentation was critical in demonstrating the role of the mosquito in yellow fever transmission. The yellow fever saga is brilliantly chronicled by Crosby (2006). If Dr. Snow had voluntarily consumed cholera-impacted water, or conducted a study using other human subjects, maybe the transition to the waterborne theory would have been expedited. However, apart from martyrdom or unethical experimentation, Dr. Snow contributed as much as humanly possible to the ight against cholera. At the time of this writing, cholera has still not been eradicated, and a deadly outbreak continues in the Caribbean country of Haiti. As of July  31, 2011, there have been more than 400,000 reported cases of cholera associated with the epidemic in Haiti that began in fall 2010 (World Health Organization 2011). The reader may explore how entrenched ideologies have contributed to the persistence of this outbreak. The John Snow story is relevant to this publication for multiple reasons. For starters, it involves contaminated groundwater and associated impacts on public health. More importantly, though, it outlines the framework for conducting spatial scientiic studies that is the fundamental topic of this book. The key elements of this framework are • Conceptual model development based on education and experience in hydrogeology and available information from historical studies • Data collection at the site-speciic level • Spatial data analysis to evaluate the original conceptual model • Data visualization to present study conclusions and reine the conceptual model as needed A low chart illustrating the relationship of these elements is provided in Figure 1.4. Note that this framework is cyclical as it is valuable to perform data visualization or analysis irst before focusing on the conceptual model, particularly where historical data are limited or completely absent. However, without a conceptual model, data collection, analysis, and visualization are uninformed and can lead to erroneous interpretations. If Dr. Snow had blindly plotted the cholera deaths on his map without providing substantive technical and conceptual justiication for his theory, the map could have just as easily linked cholera

FIGURE 1.4 Flow chart of the framework for spatial investigation advanced by Dr. Snow and applicable to modern hydrogeology. Note that data analysis and visualization often occur cooperatively.

Hydrogeological Conceptual Site Models

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to a former plague burial site in the Broad Street area, which would have it nicely into the miasmatic model (Koch 2011). The failure to include conceptual models in hydrogeological studies results in the propagation of major errors in professional practice. Examples of such fundamental errors highlighted in this book are • • • •

Failure to identify karst and other predominant geological features (Chapter 2) Data management and technical mapping errors (Chapter 3) Default contouring with computer programs (Chapter 4) Blindly accepting models published by “authorities” or “experts” including government agencies (Chapter 5) • Performing groundwater remediation without a conceptual basis for the design (Chapter 8) It is the hope of the authors that this book educates groundwater professionals and stakeholders alike about these major errors. However, more important objectives are to encourage independent thinking about current groundwater issues and to promote the use of conceptual models and advanced data analysis and visualization tools to better solve hydrogeological problems. The breakdown of independent analysis and the failure to use appropriate conceptual and quantitative models are symptoms of groupthink, a term discussed further in Chapter 5. Groupthink has led to innumerable engineering failures including such disasters as the Space Shuttle Columbia accident in 2003. According to the Columbia Accident Investigation Board (CAIB), foam shedding from space shuttles was originally viewed as a potential safety issue early in the shuttle program. However, foam shedding occurred so frequently over the course of 112 missions without major incident that it was eventually accepted as a nuisance management issue rather than a signiicant hazard. Even when it became apparent from analytic evidence that the Columbia accident was caused by damage to the shuttle’s thermal protection system from a collision with detached foam debris, there remained “lingering denial” that foam could really be the root cause (CAIB 2003). As a result, the CAIB had to conduct impact and analysis testing using a real-life physical model to provide irrefutable proof that foam can inlict potentially catastrophic damage to shuttle paneling. Volume I of the CAIB report is included on the companion DVD for reference. In addition to the lawed notion that foam shedding was solely a maintenance problem, the report identiies many other factors that contributed to the fatal accident: • The use of a semiempirical quantitative model beyond its calibration range rather than a physics-based model • Poor communication of decision uncertainty and risk to National Aeronautics and Space Administration (NASA) management (see also Tufte [2006]) • Concern regarding jumping the chain of command • Fear of being ridiculed for expressing dissenting opinion • Decision-making processes that were obscured by scheduling metrics and political pressures All the above factors can similarly affect projects in hydrogeology and groundwater remediation, leading to engineering failure and associated consequences.

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1.2 Example Uses of This Book With the previously stated objectives in mind, it is useful to list several example scenarios of how this book may be used to assist different entities involved in groundwater projects. Example 1 A consulting irm has just been awarded a contract for a Phase II/Comprehensive Site Investigation Assessment at a former industrial facility. The primary component of the Phase II report is a conceptual site model (CSM), which will dictate where and how environmental data will be collected and what the signiicance of the data will be. This book can help the consultant develop an effective CSM for the site, leading to defensible characterization strategies and study conclusions. Concepts related to CSMs and site investigations are presented in Chapters 2 and 7. Data management and contouring are also key elements of Phase II investigations, which are discussed at length in Chapters 3 and 4, respectively. Example 2 A hydrogeologist becomes an expert witness in a lawsuit regarding the contamination of several public water-supply wells. The hydrogeologist develops a fate and transport groundwater model that demonstrates the client is not responsible for the contamination. For the upcoming trial, the hydrogeologist has been asked to produce simpliied graphics illustrating the principles behind the groundwater model and its overall conclusions. The hydrogeologist can use this book as a resource for producing data tables, graphs, maps, illustrations, and animations of modeling results that may be easily understood by the nontechnical trial jury. The hydrogeologist can also ind key insight in this book regarding the use of groundwater models in professional practice. Concepts and visualizations related to groundwater models are presented in Chapter 5. Chapter 6, covering three-dimensional visualizations, may also be useful for this application. Numerous examples including animations are provided on the companion DVD. Example 3 An environmental engineer is responsible for the design and operation of an in situ chemical oxidation (ISCO) and monitored natural attenuation (MNA) remedy at a highproile Superfund site. An initial round of ISCO injections at the contaminant source area has been completed. The potentially responsible parties (PRPs) paying for the cleanup have just asked the environmental engineer to demonstrate to the US EPA that the source area remediation has been completed to the extent practicable and that the remedy can fully transition to long-term MNA. Similarly, the US EPA has asked the engineer to verify that MNA processes are occurring at the site to substantiate this transition. The engineer can use this book to learn about key concepts in ISCO, technical impracticability, and MNA and also as a reference for developing compelling visualizations of ield data to justify remedial decisions to the US EPA. Groundwater remediation is discussed at length in Chapter 8. Example 4 A municipality has just completed a long-term pumping test at an extraction well that is being considered for use as a public water supply. The town hydrogeologist needs to present the results of the test at a town hall meeting to local conservation committees,

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state regulators, and the general public. A major concern of the conservation groups and regulators is the dewatering of a small river located near the extraction well site. The hydrogeologist can use this book to better understand surface water and groundwater interactions, which are described in Chapters 2, 4, and 9. In addition, this book can help the hydrogeologist perform groundwater modeling and contouring that assess the potential for induced iniltration under pumping conditions (Chapters 4 and 5). Lastly, the hydrogeologist can ind example visualizations throughout this book and the companion DVD that may be helpful in developing simpliied data tables, graphs, maps, and illustrations for the town hall meeting, helping nontechnical stakeholders clearly understand the study conclusions.

References Ayotte, J. D., Montgomery, D. L., Flanagan, S. M., and Robinson, K. W., 2003. Arsenic in groundwater in eastern New England: Occurrence, controls, and human health implications. Environ. Sci. Technol., 37(10), 2075–2083. Columbia Accident Investigation Board, 2003. The Columbia Accident Investigation Board Report: Volume I. The National Aeronautics and Space Administration and the Government Printing Ofice, Washington, DC, 248 pp. Crosby, M. C., 2006. The American Plague: The Untold Story of Yellow Fever, the Epidemic that Shaped our History. The Berkley Publishing Group, New York, 308 pp. FlowingData, 2007. John Snow’s Famous Cholera Map. Available at http://lowingdata .com/2007/09/12/john-snows-famous-cholera-map/. Accessed July 30, 2011. Frerichs, R. R., 2011. John Snow—A Historical Giant in Epidemiology. UCLA Department of Epidemiology, School of Public Health. Available at http://www.ph.ucla.edu/epi/snow.html. Accessed August 21, 2011. Johnson, S., 2006. The Ghost Map. Riverhead Books, New York, 299 pp. Koch, T., 2011. Disease Maps: Epidemics on the Ground. The University of Chicago Press, Chicago, 330 pp. Snow, J., 1855. On the Mode of Communication of Cholera, 2nd Edition. Churchill, London. Susca, P., and Klevens, C., 2011. NHDES Private Well Strategy Private Well Working Group. Drinking  Water and Groundwater Bureau, N.H. Department of Environmental Services (NHDES). Available at http://www.dartmouth.edu/~toxmetal/program-resources/research-translation/ arsenic consortium.html. Accessed August 15, 2011. Tufte, E. R., 1997. Visual Explanations: Images and Quantities, Evidence and Narrative. Graphics Press, Cheshire, 156 pp. Tufte, E. R., 2006. Beautiful Evidence. Graphics Press, Cheshire, 213 pp. United States Geological Survey (USGS), 2003. Arsenic Concentrations in Private Bedrock Wells in Southeastern New Hampshire. U.S. Department of Interior, USGS Fact Sheet 051-03. World Health Organization, 2011. Haiti: Cholera Epidemic Reached a Second Peak and Case Numbers Now Decreasing. Available at http://www.who.int/hac/crises/hti/en/. Accessed August 28, 2011.

2 Conceptual Site Models

2.1 Definition A hydrogeological conceptual site model (CSM) is a description of various natural and anthropogenic factors that govern and contribute to the movement of groundwater in the subsurface. Simply put, it is the answer to the following key questions: • • • • •

Where is the groundwater coming from? Through what type of porous media is it lowing? How much of it is there, and how fast is it lowing? Where is it going? How did the groundwater system behave in the past, and how will it change in the future based on both natural and anthropogenic inluences?

When the groundwater is contaminated, a CSM also includes answers to similar general questions regarding the contaminant(s). ASTM International (2008), formerly the American Society for Testing and Materials, deines a conceptual site model for contaminated sites as follows: “A written or pictorial representation of an environmental system and the biological, physical, and chemical processes that determine the transport of contaminants from sources through environmental media to environmental receptors within the system.” An accurate CSM is critical in satisfying the ultimate goal of any project, which, in hydrogeological practice, typically involves a decision regarding water supply, protection of human health and the environment, or both. A schematic (qualitative) CSM of an alluvial hydrogeological system consisting of several water-bearing zones (aquifers, hydrostratigraphic units) is presented in Figure 2.1 together with major areas of groundwater recharge. Insets show schematic CSMs focused on groundwater contamination discovered at two sites. Although these three CSMs, one regional and two local, may have been developed independently, it is obvious that each would beneit greatly from incorporating information collected for seemingly different purposes at the other sites. In many instances, the success of a CSM will depend on the ability of the project team to gather relevant information at different scales and from different sources, and integrate it with the data collected during site-speciic investigations. This concept is discussed in detail in Chapter 7. The complexity and quantitative aspects of a CSM vary broadly depending on project goals and the investigative stages of data collection. For example, a preliminary model developed during early phases of water resource planning on a watershed scale may be qualitative in nature and limited to general descriptions of underlying aquifers, their likely 11

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FIGURE 2.1 Schematic regional and two local CSMs in an alluvial aquifer. (a) Three-phase contaminant plume developed from a leaky underground storage tank at a gas station. (Modiied from U.S. EPA 2000.) (b) Contaminant plumes developed from a source of DNAPLs at the land surface.

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recharge and discharge areas, and existing groundwater users. This information may be readily available from various government agencies such as geological surveys in a format appropriate for direct presentation to applicable decision makers. In contrast, a CSM adopted to serve as the basis for the design of a groundwater remediation (aquifer restoration) system is, by default, very detailed and quantitative. Such a CSM is usually the result of lengthy and expensive site-speciic investigations and includes quantiication of risk, time-dependent (changing in time) groundwater low rates and velocities, and fate and transport of contaminants. CSMs that include quantitative aspects are increasingly relying on mathematical models (analytic and numeric) during various stages of CSM development. Mathematical models enable testing of hypotheses and can make predictions on, for example, sustainable rates of groundwater extraction for water supply or probable contaminant concentrations at points of exposure. Quantitative models also allow evaluation of the uncertainty associated with management decisions. The main purpose of a CSM is to provide a single visual product composed of text, pictures, and, if necessary, animations, where all the information about the site is easily accessible and can be used for decision making at any stage of project implementation. At the same time, it is very important to understand that the CSM is a dynamic entity, continuously reined and updated. At the initial stage of the project, it is possible to have two or three competing preliminary CSMs because the readily available information may not lead to a deinitive concept. As the project progresses and new information is collected, the CSM becomes more detailed and quantitative, helps plan additional investigations, and focuses the project team on feasible solutions. These solutions (such as design of a well ield for water supply or a bioremediation system for aquifer restoration) will be possible only when there is consensus among all involved parties that the inal CSM accurately represents the hydrogeology of the site at the scale of interest—a concept further discussed below. The remainder of this chapter focuses on key physical elements of hydrogeological conceptual site models. Elements of the CSM related to contaminant exposure (i.e., the exposure or receptor proile) are discussed in Chapter 8.

2.2 Physical Profile The most important decision made very early in the process of developing a CSM is selection of appropriate temporal and spatial scales of data collection, both regional and local (site-speciic). When deciding about the right scales, it helps to understand that it is always easier to reduce the extent of investigation and drop some of the initially collected information than to expand the scale of investigation when the project is nearing the completion deadline. Whatever the case might be, one common but erroneous approach to describing the physical proile of a site is to view it as something less important and therefore not deserving of detailed discussion. Too many CSMs include very dry, cookie-cutter, brief sections on geomorphology (topography), hydrology, climate, and land cover/land use while failing to discuss their importance for the site hydrogeology. Often these discussions are blindly copied directly from previous reports or from regional-scale documents produced by the United States Geological Survey (USGS), the United States Environmental Protection Agency (U.S. EPA), or other federal or state government agencies. This practice adds zero value to the CSM, and can result in the propagation of serious conceptual errors when the copied text is not at all applicable to the scale of interest, or presents grossly

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dated or obsolete information. In addition, the key components of a site physical proile listed above are usually described in a static manner even though most, if not all, of them change in time as a result of various natural and anthropogenic factors. 2.2.1 Geomorphology (Topography) One important aspect of geomorphology is that, on a regional scale, natural directions of groundwater low can be related to surface topography. Just like surface water, groundwater lows from a higher hydraulic head toward a lower hydraulic head, that is, from pronounced topographic highs (hills) to pronounced topographic lows (valleys), respectively. This is generally true regardless of the underlying geology (rock type) with the occasional exceptions of conined aquifers when observed locally and aquifers developed in karstiied rocks (see Section 2.3.4). The concept is shown in Figure 2.2, which is an example from a guidance manual on developing conceptual hydrogeological models for fractured rock aquifers in the Piedmont and Mountain regions of North Carolina, USA. This manual of the North Carolina Department of Environment and Natural Resources explains, in detail, the importance of topography for determining groundwater recharge and discharge zones and low directions. Figure 2.2 shows that the path of natural groundwater movement is relatively short and almost invariably restricted to the zone underlying the topographic slope extending from a topographic divide to an adjacent stream. Thus, the concept of a local slope– aquifer system applies. On the opposite sides of an interstream topographic divide are two similar slope–aquifer systems as shown by A and B. The region as a whole is a

FIGURE 2.2 Conceptual view of double slope–aquifer systems and its compartments (C). All arrows indicate groundwater low directions. (Modiied from LeGrand, H. W., Sr., A Master Conceptual Model for Hydrogeological Site Characterization in the Piedmont and Mountain Region of North Carolina: A Guidance Manual, North Carolina Department of Environment and Natural Resources, Division of Water Quality, Groundwater Section, 2007.)

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network of slope–aquifers where an individual aquifer represents a unit of the groundwater low regime that is seemingly separated and free of impact from adjacent, similar units. Commonly, the slope–aquifer system includes smaller hill-and-dale conigurations that are observed as topographic spurs (ridges branching from a main ridge or mountain crest). Similar undulations, although of lesser amplitude, may also occur in the underlying water table and form important natural groundwater low-control features. The crests of the water table undulations represent natural groundwater divides within a slope–aquifer system and may limit the area of inluence of wells or the extent of contaminant plumes located within their boundaries (LeGrand 2007). Natural topography can be artiicially altered in many ways resulting in the creation of new topographic highs and lows and the redistribution of original groundwater low directions. Some well-known examples include landills, below which there is groundwater mounding caused by increased iniltration and waste leachate, and quarries and mine works, which act as new groundwater discharge zones. For example, Figure 2.3 shows the region of Butte, MT, which had already earned the nickname “The Richest Hill on Earth”

FIGURE 2.3 This image of the Berkeley Pit in Butte, MT, shows many features of the mine workings, such as the terraced levels and access roadways of the open mine pits (gray and tan sculptured surfaces). A large gray tailings pile of waste rock and an adjacent tailings pond appear to the north of the Berkeley Pit. Color changes in the tailings pond result primarily from changing water depth. This astronaut photograph ISS013-E-63766 was acquired August 2, 2006, with a Kodak 760C digital camera using an 800-mm lens and is provided by the ISS Crew Earth Observations experiment and the Image Science & Analysis Group, Johnson Space Center. (From NASA, Earth Observatory, http://earthobservatory.nasa.gov/, accessed March 2011.)

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by the end of the 19th century because of mining for gold, silver, and copper. Demand for electricity increased demand for copper so much that by World War I, the city of Butte was a boomtown. Well before World War I, however, copper mining had spurred the creation of an intricate network of underground drains and pumps to lower the groundwater level and continue the extraction of copper. Water extracted from the mines was so rich in dissolved copper sulfate that it was also mined (by chemical precipitation) for the copper it contained. In 1955, copper mining in the area expanded with the opening of the Berkeley Pit. The mine took advantage of the existing subterranean drainage and pump network to lower groundwater until 1982 when a new owner suspended operations. After the pumps were turned off, water from the surrounding rock basin began seeping into the pit. By the time an astronaut on the International Space Station took this picture, water in the pit was more than 275 meters (900 feet) deep. Because its water contains high concentrations of metals such as copper and zinc, the Berkeley Pit is listed as a federal Superfund site. The Berkeley Pit receives groundwater lowing through the surrounding bedrock and acts as a terminal pit or sink for these heavy metal–laden waters, which can be as strong as battery acid. Ongoing cleanup efforts include treating and diverting water at locations upstream of the pit to reduce inlow and decrease the risk of accidental release of contaminated water from the pit into local aquifers or surface streams (NASA 2011). Another key concept of geomorphology is that speciic landforms are closely related to the underlying geology, that is, rock types and the tectonic fabric that includes folds, fractures, faults, and other discontinuities in the rock mass. Together, the geology and the geomorphologic processes shaping the land surface play key roles in the formation of aquifers and the resulting characteristics of groundwater low at the site. Thanks to rapid developments in remote sensing technology and easy access to various Internet (online) sources of Earth imagery and digital elevation data, it is now possible to perform a very detailed visual analysis of geomorphologic features without even visiting a site. This, however, is not recommended by the authors—one should always make every attempt to visit the site he or she is working on. As emphasized throughout this book, site topography can be displayed in three dimensions, rotated and viewed from different angles by using a variety of commercial and public-domain computer programs. Aerial and satellite images, geologic maps, and other thematic maps can be easily draped over digital 3D topography and analyzed in the same fashion (Figures 2.4 and 2.5). In addition, every working professional should have ready access to Google Earth, which is now the default, free platform for visualization of land surface features. Figures 2.6 through 2.18 illustrate many beneits of analyzing remote sensing imagery and digital elevation models (DEMs) when developing hydrogeological CSMs. The Kunlun fault shown in Figure 2.6 is one of the gigantic strike-slip faults that bound the north side of Tibet. Left-lateral motion along the 1500-km (932-mi) length of the Kunlun has occurred uniformly for the last 40,000 years at a rate of 1.1 cm/year, creating a cumulative offset of more than 400 m. In this image, two splays of the fault are clearly seen crossing from east to west. The northern fault juxtaposes sedimentary rocks of the mountains against alluvial fans. Its trace is also marked by lines of vegetation, which appear red in the image. The southern, younger fault cuts through the alluvium. Box A shows wet ground caused by discharge of groundwater from the alluvial fans. The dark linear area in the outlined box B is wet ground where groundwater has ponded against the fault (NASA 2011). Songhua River just upstream (west) of the city of Harbin, China, is shown in Figure 2.7. The main stem of the river and its myriad channels appear deep blue, winding from bottom left toward center right. To the west of the river, shallow lakes appear electric blue.

FIGURE 2.4 Portion of the geologic map from Hydrogeologic Atlas of the Hill Country Trinity Aquifer, Blanco, Hays, and Travis Counties, Central Texas. (Modiied from Wierman, D. A. et al., Hydrogeologic Atlas of the Hill Country Trinity Aquifer, Blanco, Hays, and Travis Counties, Central Texas, 2010.)

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FIGURE 2.5 Geologic map shown in Figure 2.4 draped over a DEM. (Courtesy of Gavin Hudgeons, AMEC.)

The surrounding landscape reveals the Manchurian Plain in shades of brown, crossed by pale lines (roads) and spots representing villages and towns. The extreme latness of the Manchurian Plain has caused the river to meander widely over time. The result of the meandering is that the river is surrounded by a wide plain that is illed with swirls and curves, showing paths the river once took (NASA 2011). The plain includes classic features of meandering rivers, such as oxbow lakes—semicircular lakes formed when a meander is cut off from the main channel by river-deposited sediment. As meandering rivers, such as this one, shift their positions across the valley bottom, they create a complicated pattern of heterogeneous sediment deposits of varying grain sizes both laterally and vertically. Consequently, groundwater low rates, directions, and velocities are quite convoluted, not just in the three-dimensional space but also in time because of changing water levels in the main river and its tributaries, including looding (see also Figure 4.40). Analysis of topography is particularly useful when deciphering possible geologic reasons for a certain type of surface drainage as illustrated schematically in Figure 2.8. Although topographic maps printed on paper will continue to be utilized for years by default (and in some parts of the world may still be the only available option), DEMs offer many advantages as illustrated in Figures 2.9 and 2.10. For example, areas denoted as A and B have a higher density of surface drainage features and steeper slopes (more closely spaced contours), which are clearly visible on both the topographic map in Figure 2.9 and the DEM in Figure 2.10. This difference may be the result of a less permeable rock type, different slopes of sedimentary layers, or some other geologic reason such as local uplifting (tectonic movement) that promotes vertical erosion. However, more subtle landforms in the central lood plain such as the oxbow lakes and river terraces that are clearly visible on the bottom of Figure 2.10 are less obvious on the printed map or not even depicted because of a relatively coarse contour interval of 20 ft. In contrast, the high-resolution DEM derived from Light Detection and Ranging (LiDAR) topographic data even shows rows of crops in some of the lood-plain ields. In terrains like this, a hydrogeologic site visit would focus

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FIGURE 2.6 Advanced Spaceborne Thermal Emission and Relection Radiometer (ASTER) satellite image of the Kunlun fault, north Tibet. This visible light and near infrared scene was acquired on July 20, 2000. (Courtesy of NASA/ GSFC/MITI/ERSDAC/JAROS and the U.S./Japan ASTER Science Team. From NASA, Earth Observatory, http:// earthobservatory.nasa.gov/, accessed March 2011.)

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FIGURE 2.7 ASTER image of the Songhua River upstream (west) of the city of Harbin, China, acquired on April 1, 2002. (Courtesy of NASA/GSFC/METI/ERSDAC/JAROS and the U.S./Japan ASTER Science Team. From NASA, Earth Observatory, http://earthobservatory.nasa.gov/, accessed March 2011.) F E

D

E D

F

D

E

F

F

D

FIGURE 2.8 Criteria for interpretation of drainage patterns on topographic maps and remote sensing imagery. Top left: (a)  Drainage density is high in less permeable rocks; (b) more permeable rocks have fewer drainage features; (c) surface drainage is disintegrated or missing in karstiied rocks. Top right: (a) Dendritic drainage is characteristic of homogeneous and isotropic geologic terrains; (b) rectangular drainage is common in folded, stratiied (layered) sedimentary rocks dissected by perpendicular fractures and faults; (c) circular drainage (ring pattern) is characteristic for domes or partially destroyed calderas. Bottom: Faults can be inferred from drainage features such as long, straight stream segments (a), aligned segments of neighboring streams that change low directions abruptly (b), and segments of different streams extending (aligning) over the ridges (c). (Modiied from Dimitrijevic´, M., Geolosko Kartiranje (Geological Mapping), ICS, Beograd, 1978.)

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FIGURE 2.9 Part of printed 1:24,000 USGS topographic quadrangle with a contour interval of 20 ft. Note denser surface drainage in areas A and B (see also Figure 2.8).

on the river terrace and other escarpments looking for springs, seeps, and rock (sediment) outcrops. Planning such a visit would greatly beneit from having a “living” 3D image of the topography. Another advantage of the DEM visualization is that it is likely easier for nontechnical audiences to understand than 2D contour maps, and it is therefore well suited for presentations and reports prepared for the general public. National Elevation Dataset (NED) high-resolution data from the USGS is typically derived from LiDAR technology or digital photogrammetry and is often break line enforced to account for linear relief features. If collected at a ground sample distance no coarser than 5  m, such data may also be available within the NED at a resolution of 1/9 arcsec, now

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FIGURE 2.10 Top: View of 3D surface created from high-resolution digital elevation data for the same area shown in Figure 2.9. Bottom: Enlarged view of the 3D surface showing ine detail such as roads, meanders, terrace scarps, and rows of crops.

downloadable for some portions of the United States (see USGS’s Seamless Data Warehouse at http://seamless.usgs.gov/ or The National Map Viewer at http://viewer.nationalmap .gov/viewer/). The new generation of USGS topographic maps at scale 1:24,000 (topographic quadrangles) is based on these high-resolution elevation data, incorporates high-resolution photo images, and has various layers of information all available in digital, georeferenced, pdf format. An example is shown in Figures 2.11 through 2.15. Both the digital map with all its layers, including the aerial photograph, and the accompanying high-resolution NED (digital raster ile) can be downloaded and analyzed as illustrated. The NED ile can be

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FIGURE 2.11 Part of the new 2011 edition of the USGS Lewisburg, WV, quadrangle showing contour and hydrography layers only.

contoured and displayed in 3D with some of the commonly used programs such as Surfer by Golden Software, Inc. or Esri ArcScene (see Chapter 4). Figure 2.11 shows contours and hydrography layers of a part of the new 2011 USGS Lewisburg, WV, quadrangle illustrating unique features of karst topography. Numerous sinkholes including deep, uvala-like closed depressions developed in the Greenbrier Limestone are visible in the left portion of the map and a smaller area in the southeast. Closed depressions, such as these, and an absence of surface drainage (lowing streams) are the main characteristics of mature karst terrains. In contrast, less permeable rocks such as shales of the McCrady Formation and Pocono Group in the central and eastern portions of the map have densely developed surface drainage. Figure 2.12 shows two views of the high-resolution NED for another portion of the same quadrangle and some of the adjacent areas where pronounced lines of sinkholes and other relief lineaments are clearly visible. These linear features, likely formed by faulting and contrasting lithology, are main indicators of preferential low paths within the underlying karst aquifer. The entire karst area of the Lewisburg quadrangle is draining at Davis Spring, the largest spring in West Virginia (the spring is located to the southeast on the adjacent Asbury, WV, quadrangle). Because of the legibility issues, printed topographic maps are limited by the contour interval even when a iner resolution of elevation data is available. This is illustrated with Figures 2.13 through 2.15. The maps’ contour interval of 20 ft is not suficient to depict smaller sinkholes, which are clearly visible on the aerial photograph. Having the NED ile of the same quadrangle enables contouring at any desired interval including 3 ft (Figure 2.14), which should sufice for identifying virtually all sinkholes visible on the aerial photograph. Better yet is to use a combination of contours and a color-shaded surface map (Figure 2.15), which can be rotated, zoomed in, and displayed with different vertical exaggeration. As a small incentive to our speleological (karst) colleagues, the authors will

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Hydrogeological Conceptual Site Models

FIGURE 2.12 3D view of a portion of the Lewisburg, WV, quadrangle and the adjacent areas to the west. Bottom: Blowup of the 3D surface showing ine detail depicted by the high-resolution NED.

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FIGURE 2.13 Karst features depicted with the 20-ft contours versus all features visible on the aerial photograph of the new 2011 edition of the Lewisburg, WV, quadrangle. The photograph has a resolution of approximately 0.5 m.

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Hydrogeological Conceptual Site Models

FIGURE 2.14 Contours of the same area shown in Figure 2.13 (top) created from the NED ile contour interval is 3 ft.

FIGURE 2.15 Contours with the 5-ft interval superimposed on the shaded color surface of the same area shown in Figures 2.13 and 2.14.

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FIGURE 2.16 Shaded relief map of the greater San Antonio, TX, area available from the National Atlas of the United States (www.nationalatlas.gov). Arrows indicate some of the lineaments visible on this small-scale map.

donate a signed copy of this book (and possibly some other goodies as well) to the irst speleological team that sends us a nice-looking georeferenced map that shows the spatial relationship between the land surface features visible on the previous igures and the longest explored cave in the area. For various, and sometimes mystifying reasons, geologic maps do not always show otherwise obvious features that may have an underlying geologic reason and are of critical importance to a hydrogeological conceptual site model. One such example is the Balcones Fault Zone in Texas, which is responsible for the current geometry of the Edwards Aquifer, one of the most extensive and proliic karst aquifers in the world. Most, if not all, geologic maps of this large area, at varying scales, only show faults of the northeast– southwest strike, which are universally interpreted as the most important geologically. However, even very general relief maps at small scales such as in Figure 2.16 clearly show long prominent lineaments trending in other directions, including perpendicular to the main northeast–southwest system of faults. Even a nongeologist would be able to identify northwest–southeast striking faults in this igure. In addition to being perfect candidates for preferential groundwater low paths within the Edwards Aquifer, these faults (deemed unimportant lineaments by some) may be transferring signiicant quantities of groundwater from the adjacent Trinity Aquifer to the Edwards Aquifer. Figure 2.17 illustrates advantages of DEMs for analyzing topographic lineaments in the midsection of the Balcones Fault Zone between San Antonio and Austin.

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FIGURE 2.17 Two DEM views of the central portion of the Balcones Fault Zone between San Antonio and Austin, TX. Some topographic lineaments may be more apparent when viewed from different angles. Note the two blue ellipses for orientation.

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2.2.2 Hydrology Surface water and groundwater are inseparable parts of the same hydrologic cycle and, as such, inluence each other at practical (site-speciic) scales for most projects. However, some working professionals do not always appreciate this simple fact and may, for example, study a groundwater problem without paying any attention to a surface stream in the vicinity. Even when a project at hand involves a deep, conined aquifer seemingly separated from the rest of the world, it is highly recommended to make an attempt to understand its recharge and discharge areas. For all projects involving unconined aquifers, it is mandatory to deine likely hydraulic roles of nearby surface water bodies (e.g., streams, lakes, drainage ditches) with respect to the movement of groundwater. In this sense, the term hydrology refers to low rates and stages (water elevation) of surface water bodies and their inluence on the exchange of low between surface water and groundwater. In the ield of water resources management, groundwater and surface water are increasingly seen as a single interconnected resource that must be managed holistically. Additional discussion regarding integrated management of surface water and groundwater resources, including examples of combined surface water–groundwater numeric models, is provided in Chapter 9. The majority of perennial surface streams would not have permanent low without groundwater contribution called baselow. Excessive withdrawal of groundwater may cause depletion or complete cessation of baselow and disappearance of wetlands abutting the surface stream in question. Conversely, changes in land use, such as urban development, may alter patterns of surface water runoff, increase average low in the receiving streams, and reduce aquifer recharge. Contamination of surface streams adjacent to well ields used for water supply may threaten groundwater quality if the contaminant enters the underlying aquifer, and discharge of contaminated groundwater into a surface water body may have negative impact on human health and the environment. Examples in Chapter 4 illustrate various hydraulic relationships between surface streams and underlying unconined aquifers including their importance for drawing contour maps of the potentiometric surface and determining groundwater low directions. However, depending on local hydrogeologic and climatic conditions and human impacts, such as stream regulation with dams and locks, the same stream may be losing or gaining water in different sections (reaches), and this pattern may change in time. Major unregulated meandering streams with large lood plains that are seasonally looded (see Figure 2.18) may have quite complicated surface water–groundwater relationships, especially if there are oxbow lakes and buried meanders. In addition, large streams are often regional groundwater discharge locations for deeper conined aquifers, which complicates things even further (Figure 2.19). Consequently, trying to determine representative groundwater low directions and contaminant plume geometry at a local site based solely on quarterly water level measurements may be a daunting, if not impossible, task. Streams without permanent gauges and suficient data records present special challenges when determining characteristic lows needed for various calculations. Long-term minimum baselow is of particular importance for various applications, such as determining maximum allowable loading rates of a groundwater contaminant still protective of in-stream water-quality standards. In the United States, this low is typically referred to as 7Q10, which means seven-day, consecutive low low with a 10-year return frequency (i.e., the lowest stream low for seven consecutive days that would be expected to occur once in 10 years). Flow measurement at successive stream segments is a common method of determining if the stream is losing or gaining water between the segments. However, because of the variability of low conditions in the same stream and the associated potential

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FIGURE 2.18 Two composite color satellite images of the White River, AR. ASTER on NASA’s Terra satellite captured the top image on April 7, 2008, while the river was still rising before reaching one of the worst lood levels on record. The bottom image is from April 14, 2006, when water levels were closer to normal. Bright green vegetation lanks the river in the 2006 image. Much of the land is forested and preserved in the Cache River National Wildlife Refuge. Around the forest, agricultural ields create a checkerboard of tan and green. Some of the ields are looded in the 2008 image. (NASA image created by Jesse Allen, using data provided courtesy of NASA/ GSFC/METI/ERSDAC/JAROS and U.S./Japan ASTER Science Team; caption by Holli Riebeek. From NASA, Earth Observatory, http://earthobservatory.nasa.gov/, accessed March 2011.)

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FIGURE 2.19 Groundwater low directions (shown with arrows) in a lood plain of a meandering stream, which also acts as a groundwater discharge zone for the regional aquifer. (Modiied from Kresic, N., Hydrogeology and Groundwater Modeling, Second Edition, CRC Press, Taylor & Francis Group, Boca Raton, FL, 807 pp., 2007.)

measurement errors, this method should be applied with great care in order to avoid false conclusions. For example, if only one set of measurements is made at a few locations with several or more hours separating them and the stream low is under the inluence of recent precipitation, the results would almost certainly be misleading because the low wave is moving rapidly. It is therefore best if the low measurements are performed after a long period without precipitation and the applied method is based on a continuous recording of the stream stage at successive stream segments. Flow hydrographs derived in this way provide information on the actual change of volume of water between the segments, which is the only real measure of gain or loss. One of the most precise methods for measuring low of smaller streams is tracer dilution gauging, which can be performed with a luorescent dye or, more simply, a salt solution. In addition, when accompanied with chemical analyses, it is very useful when determining if and where there is an increased inlow of contaminated groundwater. Figures 2.20 and 2.21 show results of a dye tracer study used to assess the dry-weather baselow of a stream and groundwater seepage inlows to the stream segment. The rhodamine dye was injected at the most upstream location of the study segment at a constant rate and constant known concentration. A water-quality meter with a rhodamine sensor was placed in-stream at the most downstream sampling location and programmed to continuously record rhodamine concentrations. The dye was injected continuously into the stream until the most downstream dye concentrations reached a plateau. Note that if salt solution were used as the tracer, one would measure in-stream conductivity at the downstream location. Once dye concentrations along the stream reached a plateau, surface water samples were collected at each sampling location and analyzed for rhodamine concentrations and concentrations of a constituent of concern (COC). Stream low rate was determined for each stream sampling location using the rhodamine concentration based on the observed dilution of the tracer. As can be seen in Figure 2.21, near the middle of the study reach, the concentration of the COC increases notably and then decreases gradually, indicating that an inlux of impacted groundwater to the stream is occurring along a preferential low path through a short stream segment.

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FIGURE 2.20 Rhodamine concentration at the downgradient location during an instream dye tracing study used to determine baselow in a small stream. (Courtesy of Lisa Pfau, Larry Neal, and Margaret Tanner, AMEC).

Another important aspect of determining baselow of surface streams is its application in water budget and groundwater recharge studies (Kresic and Mikszewski 2009). In natural long-term conditions and the absence of artiicial groundwater withdrawal, the rate of groundwater recharge in a drainage basin of a permanent gaining stream is equal to the rate of groundwater discharge. Assuming that all groundwater discharges into the surface stream, either directly or via springs, it follows that the stream baselow equals the groundwater recharge in the basin. This simple concept is illustrated in Figure 2.22.  )ORZ UDWH

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FIGURE 2.22 Estimation of aquifer recharge from surface stream baselow. (Modiied from Kresic, N., Hydrogeology and Groundwater Modeling, Second Edition, CRC Press, Taylor & Francis Group, Boca Raton, FL, 807 pp., 2007.)

However, its application is not always straightforward, and it should be based on a thorough understanding of the geologic and hydrogeologic characteristics of the basin. The following examples illustrate some situations where baselow alone should not be used to estimate actual groundwater recharge (Kresic 2007): • Surface stream lows through a karst terrain where topographic and groundwater divides are not the same. The groundwater recharge based on baselow may be grossly overestimated or underestimated depending on the circumstances. • The stream is not permanent, or some river segments are losing water (either always or seasonally); locations and timing of the low measurements are not adequate to assess such conditions. • There is abundant riparian vegetation in the stream loodplain, which extracts a signiicant portion of groundwater via evapotranspiration. • There is discharge from deeper aquifers, which have remote recharge areas in other drainage basins. • A dam regulates the low in the stream. Most techniques for estimating baselow are based on graphical separation of surface stream hydrographs into two major components: low generated by surface and nearsurface runoff and low generated by discharge of groundwater. Although some professionals view this approach as a convenient iction because of its subjectivity and lack of rigorous theoretical basis, it does provide useful information in the absence of detailed (and expensive) data on many surface water runoff processes and drainage basin characteristics that contribute to streamlow generation. Risser et al. (2005) present a detailed application and comparison of two automated methods of hydrograph separation for estimating groundwater recharge based on data from 197 streamlow gauging stations in Pennsylvania. The two computer programs—PART and RORA (Rutledge 1993, 1998, 2000) developed by the USGS—are in public domain and available for free download from the USGS Web site. The PART computer program uses a hydrograph separation technique

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to estimate baselow from the streamlow record. The RORA computer program uses the recession-curve displacement technique of Rorabaugh (1964) to estimate groundwater recharge from each storm period. The RORA program is not a hydrograph-separation method; rather, recharge is determined from displacement of the streamlow–recession curve according to the theory of groundwater drainage. Rorabaugh’s method utilized by RORA is a one-dimensional analytical model of groundwater discharge to a fully penetrating stream in an idealized, homogenous aquifer with uniform spatial recharge. Because of the simplifying assumptions inherent to the equations, Halford and Mayer (2000) caution that RORA may not provide reasonable estimates of recharge for some watersheds. In fact, in some extreme cases, RORA may estimate recharge rates that are higher than the precipitation rates. Rutledge (2000) suggests that estimates of mean monthly recharge from RORA are probably less reliable than estimates for longer periods and recommends that results from RORA not be used at time scales smaller than seasonal (three months) because results differ most greatly from manual application of the recession-curve displacement method at small time scales. A method proposed by Pettyjohn and Henning (1979) includes the effects of riparian evapotranspiration and, therefore, usually provides lower estimates than the hydrographseparation technique based on recession-curve displacement (Rutledge 1992). Groundwater recharge estimates produced by the Pettyjohn–Henning and similar methods are sometimes called effective (or residual) groundwater recharge because the estimates represent the difference between actual recharge and losses to riparian evapotranspiration. As illustrated in Figure 2.23, graphical methods of baselow separation may not be applicable at all in some cases. A stream with alluvial sediments having signiicant bank storage

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capacity may, during loods or high river stages, lose water to the subsurface so that no baselow is occurring (Figure 2.23a). Or a stream may continuously receive baselow from a regional aquifer that has a different primary recharge area than the shallow aquifer and maintains a higher head than the stream stage (Figure 2.23b). Although one may attempt to graphically separate either of the two hydrographs using some common method, it would not be possible to make any conclusions as to the groundwater component of the surface stream low without additional ield investigations. One such ield method is hydrochemical separation of the streamlow hydrograph using dissolved chemical constituents or environmental tracers. It is often more accurate than simple graphoanalytical techniques because surface water and groundwater usually have signiicantly different chemical signatures (Kresic and Mikszewski 2009). The rate of low exchange between surface water and groundwater depends on two main factors: the hydraulic gradient between the two and the conductance of the riverbed (lakebed) sediments. This is schematically illustrated in Figure 2.24. The hydraulic gradient or the difference between the hydraulic head in the aquifer adjacent to the river and the river stage (hydraulic head of the river) is the same in all four cases but with different

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signs. In cases a and b, the hydraulic gradient is toward the river, which therefore gains water, whereas in cases c and d, the hydraulic gradient is from the river toward the aquifer (the river loses water). The lower conductance corresponds to more ines (e.g., silt) in the riverbed sediment and a lower hydraulic conductivity, resulting in a lower water lux between the aquifer and the river. Thicker low-permeable riverbed sediments will have the same effect as shown with cases b and d. All other things being equal, an increase in the hydraulic gradient will result in an increased lux of water between the aquifer and the river. As discussed earlier, one of the most important aspects of surface water stages is that they change in time. Which time interval will be used for their inevitable averaging depends upon the goals of each particular study. Seasonal or perhaps annual periods may be adequate for a long-term water supply evaluation when considering recharge from precipitation. When a hydraulic boundary is quite dynamic and the required accuracy of predictions is high, the time interval for describing a changing river (lake) stage may have to be much shorter. For example, Figure 2.25 shows a comparison of two time intervals used to model the interaction between a large river and a highly transmissive alluvial aquifer. The Columbia River stage at this site is dominated by higher frequency diurnal luctuations that are principally the result of water released at Priest Rapid Dam to match powergeneration needs. The magnitude of these diurnal river-stage luctuations can exceed the  5LYHU

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FIGURE 2.25 River water tracer concentrations at the end of a model simulation. 2D numeric model of interaction between the aquifer, vadose zone, and the Columbia River in the Hanford 300 area, Washington. Top: Hourly boundary conditions. Bottom: Monthly boundary conditions. (Modiied from Waichler, S. R., and Yabusaki, S. B., Flow and Transport in the Hanford 300 Area Vadose Zone–Aquifer–River System, Paciic Northwest National Laboratory, Richland, WA, 2005.)

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seasonal luctuation of monthly average river stages. During the simulation period, the mean 24-hour change (difference between minimum and maximum hourly values) in river stage was 0.48 m, and the maximum 24-hour change was 1.32 m. Groundwater levels are signiicantly correlated with river stage although with a lag in time and decreased amplitude of luctuations. A two-dimensional, vertical, cross-sectional model domain was developed to capture the principal dynamics of low to and from the river as well as the zone where groundwater and river water mix (Waichler and Yabusaki 2005). Running the model with hourly boundary conditions resulted in frequent direction and magnitude changes of water lux across the riverbed. In comparison, the velocity luctuations resulting from averaging the hourly boundary conditions over a day were considerably attenuated, and for the month average, luctuations were nonexistent. A similar pattern held for the river tracer, which could enter the aquifer and then return to the river later. Simulations based on hourly water-level boundary conditions predicted an aquifer– river water mixing zone that reached 150 m inland from the river based on the river tracer concentration contours. In contrast, simulations based on daily and monthly averaging of the hourly water levels at the river and interior model boundaries were shown to signiicantly reduce predicted river water intrusion into the aquifer, resulting in underestimation of the volume of the mixing zone. The relatively high-frequency river-stage changes associated with diurnal release schedules at the dams generated signiicant mixing of the river and groundwater tracers and lushing of the subsurface zone near the river. This mixing was the essential mechanism for creating a fully developed mixing zone in the simulations. Although the size and position of the mixing zone did not change signiicantly on a diurnal basis, they did change in response to seasonal trends in the river stage. The largest mixing zones occurred with the river-stage peaks in May–June and December–January, and the smallest mixing zone occurred in September when the river stage was relatively low (Waichler and Yabusaki 2005). Urban areas present special challenges for understanding relevant hydrologic factors and developing an accurate CSM. Examples include rerouted streams and illed streambeds resulting in complex groundwater low patterns that may not be easily deciphered based on current land surface topography. Considering these complicating factors is particularly important for interpretation of present shapes of groundwater contaminant plumes emanating from old historic source(s). A list of possible artiicial hydrographic features often only marginally (or not at all) described in CSMs includes storm water and drainage ditches, storm water collection basins, leaky sewer and water lines, culverts, and infrastructure tunnels. All of them can cause either local or regional effects and can signiicantly inluence groundwater recharge, discharge, and low directions. 2.2.3 Climate (Hydrometeorology) Climate is deined as an aggregate of weather conditions, representing a general pattern of weather variations at a location or in a region. It includes average weather conditions and the variability of elements and information on the occurrence of extreme events (Lutgens and Tarbuck 1995). The nature of both weather and climate is expressed in terms of basic elements, the most important of which are the temperature of the air, the humidity of the air, the type and amount of cloudiness, the type and amount of precipitation, the pressure exerted by the air, and the speed and direction of the wind. These elements constitute the variables by which weather patterns and climatic types are characterized (Lutgens and Tarbuck 1995). They also all inluence the water budget of an area primarily by affecting natural processes of aquifer recharge and aquifer discharge via precipitation and evapotranspiration.

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The main difference between weather and climate is the time scale at which these basic elements change. Weather is constantly changing, sometimes from hour to hour, and these changes create an almost ininite variety of weather conditions at any given time and place. In comparison, climate changes are more subtle and were, until relatively recently, considered important for time scales of hundreds of years or more and usually only discussed in academic circles. A more broad deinition of climate is that it represents the long-term behavior of the interactive climate system, which consists of the atmosphere, hydrosphere, lithosphere, biosphere, and cryosphere or the ice and snow that are accumulated on the Earth’s surface (Lutgens and Tarbuck 1995). At a minimum, and regardless of the project scale and scope, longterm precipitation data and air temperatures for the closest available climate-gauging station should be analyzed as they relate to the site’s groundwater. It is also generally recommended to include a rain gauge at hydrogeological study sites as precipitation can vary greatly over small distances. Barometric pressure is another parameter typically monitored by hydrogeologists at the site level, as many pressure transducers placed in monitoring or production wells are not vented to the atmosphere, which means that the barometric pressure must be subtracted from all measurements in order to obtain the water-level measurement. An example illustrating interconnections between recharge from precipitation and water table luctuations in a fractured rock aquifer is given by Harned (1989) and shown in Figure 2.26. In this example, the main period of groundwater recharge results from heavy rains in late winter when evapotranspiration is low. It is relected by a peak in the water table hydrograph appearing a few days after heavy rainfall in late March. The time

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