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A Reference Guide for Utilities

i

EPA/600/R-06/028 

December 2005 

Water Distribution System Analysis:


Field Studies, Modeling and Management


A Reference Guide for Utilities 

U. S. Environmental Protection Agency 

Office of Research and Development


National Risk Management Research Laboratory


Water Supply and Water Resources Division


Cincinnati, Ohio


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A Reference Guide for Utilities 

Notice 

Any opinions expressed in this document/reference guide for utilities are those of the author(s) and 

do not, necessarily, reflect the official positions and policies of the U.S. Environmental Protection 

Agency (EPA). Any mention of products or trade names does not constitute recommendation for use 

by EPA. This document has been reviewed in accordance with EPA’s peer and administrative review 

policies and approved for publication. 

ii 
A Reference Guide for Utilities 

Foreword 

The U.S. Environmental Protection Agency (EPA) is charged by Congress with protecting the Nation’s 

land, air, and water resources. Under a mandate of national environmental laws, the Agency strives 

to formulate and implement actions leading to a compatible balance between human activities and 

the ability of natural systems to support and nurture life. To meet this mandate, EPA’s research program 

is providing data and technical support for solving environmental problems today and building a 

science knowledge base necessary to manage our ecological resources wisely, understand how 

pollutants affect our health, and prevent or reduce environmental risks in the future. 

The National Risk Management Research Laboratory (NRMRL) is the Agency’s center for investigation 

of technological and management approaches for preventing and reducing risks from pollution that 

threaten human health and the environment. The focus of the Laboratory’s research program is on 

methods and their cost-effectiveness for prevention and control of pollution to air, land, water, and 

subsurface resources; protection of water quality in public water systems; remediation of contaminated 

sites, sediments and groundwater; prevention and control of indoor air pollution; and restoration of 

ecosystems. NRMRL collaborates with both public and private sector partners to foster technologies 

that reduce the cost of compliance and to anticipate emerging problems. NRMRL’s research provides 

solutions to environmental problems by: developing and promoting technologies that protect and 

improve the environment; advancing scientific and engineering information to support regulatory 

and policy decisions; and providing the technical support and information transfer to ensure 

implementation of environmental regulations and strategies at the national, state, and community 

levels. 

This publication has been produced as part of the Laboratory’s strategic long-term research plan. It is 

published and made available by EPA’s Office of Research and Development to assist the user 

community and to link researchers with their clients. 

Sally Gutierrez, Director 

National Risk Management Research Laboratory 

iii 
A Reference Guide for Utilities 

Table of Contents Overview 

Overview of distribution system 

infrastructure, general water quality issues 

and management approaches 

Summary discussion of various hydraulic 

and water quality modeling tools 

Outline for planning and execution of a 

tracer study to perform distribution system 

evaluation 

Techniques for calibration and validation of 

distribution system models 

Framework of available options for 

monitoring distribution system water quality 

Introduction to the use of geospatial 

technology for water distribution systems 

Real world applications of distribution 

system modeling approaches 

Chapter 1


Chapter 2


Chapter 3


Chapter 4


Chapter 5


Chapter 6


Chapter 7


iv 
A Reference Guide for Utilities 

Table of Contents 

Notice ....................................................................................................................................................... ii


Foreword ..................................................................................................................................................iii


Table of Contents Overview .................................................................................................................... iv


Table of Contents ..................................................................................................................................... v


List of Tables ............................................................................................................................................ ix


List of Figures .......................................................................................................................................... ix


Acronyms and Abbreviations ................................................................................................................ xii


Acknowledgments.................................................................................................................................. xv


1.0 Introduction .................................................................................................................................. 1-1


1.1 Distribution System - Infrastructure Design and Operation .................................................. 1-3


1.1.1 Key Infrastructure Components ..................................................................................... 1-3


1.1.1.1 Storage Tanks/Reservoirs ..................................................................................... 1-3


1.1.1.2 Pipe Network ........................................................................................................ 1-3


1.1.1.3 Valves .................................................................................................................... 1-3


1.1.1.4 Pumps ................................................................................................................... 1-4


1.1.1.5 Hydrants and Other Appurtenances .................................................................... 1-4


1.1.2 Basic Design and Operation Philosophy ....................................................................... 1-4


1.1.2.1 Pipe-Network Configurations ............................................................................... 1-5


1.1.2.2 Multiple Source Configuration ............................................................................ 1-5


1.1.2.3 Impact of System Design and Operation on Water Quality ................................ 1-5


1.2 Water Quality Problems and Issues ....................................................................................... 1-6


1.3 Regulatory Framework ........................................................................................................... 1-7


1.4 Assessment and Management of Water Quality ................................................................... 1-8


1.5 Advanced Tools for Water Quality Management ................................................................ 1-11


1.6 Report Organization................................................................................................................ 1-11


1.7 Summary .............................................................................................................................. 1-11


References ..................................................................................................................................... 1-13


2.0 Modeling Water Quality in Drinking Water Distribution Systems ............................................... 2-1


2.1 Distribution System Network Hydraulic Modeling ................................................................ 2-1


2.1.1 History of Hydraulic Modeling ....................................................................................... 2-1


2.1.2 Overview of Theoretical Concepts ................................................................................ 2-2


2.1.3 Basic Hydraulic Model Input Characterization .............................................................. 2-3


2.1.3.1 Pipe Network Inputs ............................................................................................. 2-3


2.1.3.2 Water Demand Inputs ........................................................................................... 2-3


2.1.3.3 Topographical Inputs ............................................................................................ 2-5


2.1.3.4 Model Control Inputs ........................................................................................... 2-5


2.1.3.5 Extended Period Simulation (EPS) Solution Parameters .................................... 2-5


2.1.4 General Criteria for Model Selection and Application .................................................. 2-6


2.1.4.1 Developing a Basic Network Model ..................................................................... 2-6


2.1.4.2 Model Calibration and Validation ......................................................................... 2-6


2.1.4.3 Establishing Objectives and Model Application .................................................. 2-6


2.1.4.4 Analysis and Display of Results ............................................................................ 2-7


2.2 Modeling Water Quality In Distribution System Networks .................................................... 2-7


2.2.1 History of Water Quality Modeling................................................................................. 2-7


2.2.2 Theoretical Concepts for Water Quality Modeling ........................................................ 2-8


2.2.3 Water Quality Model Inputs and Application .............................................................. 2-10


2.3 Hydraulic and Water Quality Modeling Software................................................................ 2-11


2.3.1 EPANET Software ........................................................................................................ 2-11


2.3.2 Commercial Hydraulic-Water Quality Modeling Software .......................................... 2-12


2.4 Additional Modeling Tools .................................................................................................... 2-12


2.4.1 Storage Modeling Tools ............................................................................................... 2-12


2.4.1.1 CFD Models ........................................................................................................ 2-13


2.4.1.2 Compartment Models ........................................................................................ 2-14


2.4.1.3 Physical Scale Models ........................................................................................ 2-14


2.4.2 Transient Analysis Software ......................................................................................... 2-15


v 
A Reference Guide for Utilities 

2.4.3 Optimization Tools ........................................................................................................ 2-15


2.4.3.1 Optimizing Calibration ........................................................................................ 2-15


2.4.3.2 Design Optimization ........................................................................................... 2-16


2.4.3.3 Optimization of Operation .................................................................................. 2-17


2.4.4 Probabilistic Models ..................................................................................................... 2-17


2.5 Summary and Conclusions ................................................................................................. 2-17


References ..................................................................................................................................... 2-19


3.0 Tracer Studies for Distribution System Evaluation ..................................................................... 3-1


3.1 Planning and Designing a Distribution System Tracer Study .............................................. 3-2


3.1.1 Establishing Study Objectives and Time-Line ............................................................... 3-2


3.1.2 Forming a Study Team................................................................................................... 3-2


3.1.3 Defining Study Area Characteristics .............................................................................. 3-2


3.1.4 Selecting Tracer Material ............................................................................................... 3-3


3.1.4.1 Fluoride ................................................................................................................. 3-3


3.1.4.2 Calcium Chloride .................................................................................................. 3-4


3.1.4.3 Sodium Chloride .................................................................................................. 3-5


3.1.4.4 Other Chemicals That May be Added as Tracers ................................................ 3-5


3.1.4.5 Naturally or Normally Occurring Tracers ............................................................ 3-5


3.1.4.6 Comparison of Tracers ......................................................................................... 3-6


3.1.5 Selecting Field Equipment and Procedures .................................................................. 3-8


3.1.5.1 Injection Pump(s) .................................................................................................. 3-8


3.1.5.2 Tracer Storage and Dosage Rate Measurement ................................................. 3-8


3.1.5.3 Distribution System Flow Rate Measurement ..................................................... 3-9


3.1.5.4 Field Measurement of Tracer Concentration ....................................................... 3-9


3.1.6 Developing a Detailed Study Design .......................................................................... 3-10


3.1.7 Addressing Agency and Public Notification................................................................ 3-11


3.2 Executing a Tracer Study ..................................................................................................... 3-11


3.2.1 Procurement, Setup, Testing and Disinfection of Study Equipment ......................... 3-11


3.2.2 Installation of Field Equipment and Testing ................................................................. 3-12


3.2.3 Tracer Dosage and Injection Duration Calculations .................................................... 3-13


3.2.4 Dry Runs and Planned Tracer Injection Event(s) ......................................................... 3-13


3.2.5 Real Time Field Assessments, Sampling, and Analysis .............................................. 3-13


3.2.6 Equipment De-Mobilization, Initiation of Data Collection, Reduction, and Verification


Process ......................................................................................................................... 3-14


3.3 Tracer Study Costs ............................................................................................................... 3-14


3.4 Summary, Conclusions and Recommendations ................................................................. 3-15


References ..................................................................................................................................... 3-17


4.0 Calibration of Distribution System Models .................................................................................. 4-1


4.1 Hydraulic and Water Quality Model Calibration .................................................................... 4-2


4.1.1 Hydraulic Model Calibration .......................................................................................... 4-2


4.1.2 Water Quality Model Calibration .................................................................................... 4-2


4.2 Static Calibration and Dynamic Calibration........................................................................... 4-4


4.2.1 Steady-State Calibration Methods ................................................................................. 4-4


4.2.1.1 C-Factor Tests ....................................................................................................... 4-4


4.2.1.2 Fire-Flow Tests ...................................................................................................... 4-5


4.2.1.3 Chlorine Decay Tests ............................................................................................ 4-5


4.2.2 Dynamic Calibration Methods ....................................................................................... 4-6


4.3 Manual Calibration and Automated Calibration .................................................................... 4-7


4.4 Case Studies ........................................................................................................................... 4-7


4.4.1 Case 1 - Small-Suburban, Dead-End System ............................................................... 4-8


4.4.2 Case 2 - Large-Suburban Pressure Zone .................................................................... 4-10


4.5 Future of Model Calibration ................................................................................................. 4-13


4.5.1 Calibration Standards .................................................................................................. 4-13


4.5.2 Technological Advances .............................................................................................. 4-14


4.6 Summary and Conclusions ................................................................................................. 4-14


References ..................................................................................................................................... 4-15


vi 
A Reference Guide for Utilities 

5.0 Monitoring Distribution System Water Quality ........................................................................... 5-1


5.1 Establishing Monitoring Objective(s) ..................................................................................... 5-1


5.1.1 Regulatory Driven Monitoring ....................................................................................... 5-2


5.1.2 Security Related Monitoring .......................................................................................... 5-2


5.1.3 Process Control-Related Monitoring.............................................................................. 5-3


5.1.4 Water Quality Characterization ...................................................................................... 5-3


5.1.5 Multi-Purpose Use of Monitoring Data .......................................................................... 5-3


5.2 Monitoring Techniques .......................................................................................................... 5-3


5.2.1 Manual Grab Sampling .................................................................................................. 5-3


5.2.2 Automated/Online Monitoring ....................................................................................... 5-4


5.3 Monitoring Equipment Overview .......................................................................................... 5-4


5.3.1 Physical Monitors ........................................................................................................... 5-4


5.3.2 Chemical Monitors ......................................................................................................... 5-4


5.3.3 Biological Monitors ......................................................................................................... 5-4


5.4 Establishing Monitoring Requirements .................................................................................. 5-5


5.4.1 Monitoring Parameters ................................................................................................... 5-5


5.4.2 Number and Location of Monitors ................................................................................ 5-5


5.4.2.1 Number of Monitors............................................................................................. 5-6


5.4.2.2 Optimal Monitor Locations .................................................................................. 5-6


5.4.3 Monitor Characteristics .................................................................................................. 5-7


5.4.4 Amenability to Remote Monitoring and SCADA Integration ........................................ 5-8


5.4.4.1 Online Sampling/Control Devices ........................................................................ 5-9


5.4.4.2 SCADA or Remote Monitoring Network ............................................................. 5-9


5.4.4.3 Field Wiring and Communication Media ............................................................. 5-9


5.5 Engineering and Evaluating a Remote Monitoring System ............................................... 5-10


5.5.1 Remote Monitoring System Evaluation ....................................................................... 5-11


5.6 Monitoring Case Studies ...................................................................................................... 5-11


5.6.1 Rural Community Application ...................................................................................... 5-11


5.6.2 Washington D.C. Remote Monitoring Network ........................................................... 5-11


5.6.3 Tucson Water Monitoring Network ............................................................................. 5-12


5.7 Summary and Conclusions ................................................................................................. 5-14


References ..................................................................................................................................... 5-15


6.0 Geospatial Technology for Water Distribution Systems ............................................................. 6-1


6.1 History of Geospatial Data Management ............................................................................... 6-2


6.1.1 Mapping, Surveying, and Remote Sensing .................................................................. 6-2


6.1.2 CADD .............................................................................................................................. 6-4


6.1.3 GIS .................................................................................................................................. 6-4


6.1.4 DEMs ............................................................................................................................... 6-5


6.1.5 Database Management Systems ................................................................................... 6-6


6.1.6 Facility Management ...................................................................................................... 6-6


6.2 GIS Principles ......................................................................................................................... 6-6


6.2.1 GIS Features ................................................................................................................... 6-6


6.2.2 Topology ......................................................................................................................... 6-7


6.2.3 Map Projections, Datum, and Coordinate Systems ...................................................... 6-8


6.2.4 GIS Database Design ...................................................................................................... 6-8


6.2.5 Management of GIS ....................................................................................................... 6-8


6.3 Geospatial Data Management in the Water Industry ............................................................ 6-9


6.3.1 CADD .............................................................................................................................. 6-9


6.3.2 GIS .................................................................................................................................. 6-9


6.3.3 CIS .................................................................................................................................. 6-9


6.3.4 SCADA ............................................................................................................................ 6-9


6.3.5 LIMS ................................................................................................................................ 6-9


6.3.6 Support Technology ...................................................................................................... 6-9


6.4 Integration of Geospatial Data Management and Modeling ............................................... 6-10


6.4.1 Model Integration Taxonomy ....................................................................................... 6-10


6.4.2 Issues in Integrating GIS and Water Distribution System Models .............................. 6-11


vii 
A Reference Guide for Utilities 

6.5 Use of GIS in Water Utilities - Case Studies ......................................................................... 6-11


6.5.1 Use of GIS at LVVWD ................................................................................................... 6-11


6.5.1.1 Pressure Complaint Resolution .......................................................................... 6-13


6.5.1.2 Water Main Break Analysis ................................................................................. 6-13


6.5.2 Geo-coding for Demand Forecasting and Allocation at Denver Water...................... 6-13


6.6 Summary .............................................................................................................................. 6-17


References ..................................................................................................................................... 6-18


7.0 Real-World Applications – Planning, Analysis, and Modeling Case Studies .............................. 7-1


7.1 Analysis of Waterborne Outbreak – Gideon, Missouri .......................................................... 7-1


7.1.1 Gideon Case Study Overview ........................................................................................ 7-1


7.1.2 The Gideon Water System Setup .................................................................................. 7-1


7.1.3 EPA Field Study .............................................................................................................. 7-2


7.1.4 Distribution System Evaluation ...................................................................................... 7-2


7.1.5 Case Study Summary and Conclusions ....................................................................... 7-3


7.2 Reconstructing Historical Contamination Events - Dover Township (Toms River), NJ ........ 7-4


7.2.1 Case Study Overview ..................................................................................................... 7-4


7.2.2 Overall Modeling Approach ........................................................................................... 7-5


7.2.3 Simulation Techniques ................................................................................................... 7-6


7.2.4 Simulation Results and Conclusions ............................................................................. 7-7


7.3 Application of Water Distribution System Modeling in Support of a Regulatory Requirement ...... 7-9


7.3.1 IDSE Requirements Overview ...................................................................................... 7-10


7.3.2 Example Application of Modeling in the IDSE Process .............................................. 7-10


7.4	 Use of Water Distribution System Models in the Placement of Monitors to Detect Intentional


Contamination ...................................................................................................................... 7-13


7.4.1 Red Team-Blue Team Exercise ..................................................................................... 7-13


7.4.2 Application of Optimization Model .............................................................................. 7-14


7.4.3 Case Summary ............................................................................................................. 7-15


7.5 Case Study – Use of PipelineNet Model ................................................................................ 7-15


7.5.1 Overview ....................................................................................................................... 7-16


7.5.2 Model Calibration ......................................................................................................... 7-16


7.5.3 Monitoring Site Location Methodology ...................................................................... 7-16


7.5.4 Response and Mitigation Tools .................................................................................... 7-17


7.5.4.1 Consequence Assessment Tool ......................................................................... 7-17


7.5.4.2 Isolation Tool ....................................................................................................... 7-17


7.5.4.3 Spatial Database Display Tool ............................................................................ 7-17


7.5.5 Case Summary ............................................................................................................. 7-17


7.6	 Use of Threat Ensemble Vulnerability Assessment (TEVA) Program for Drinking Water


Distribution System Security ............................................................................................... 7-18


7.6.1 TEVA Overview ............................................................................................................. 7-18


7.6.1.1 Stochastic Modeling........................................................................................... 7-19


7.6.1.2 Impact Analysis ................................................................................................... 7-19


7.6.1.3 Threat Mitigation Analysis .................................................................................. 7-19


7.6.2 Application of TEVA to a Water Distribution System for Optimal Monitoring ........... 7-19


7.6.2.1 Simulation Overview .......................................................................................... 7-20


7.6.2.2 TEVA Analysis Approach ................................................................................... 7-20


7.6.2.3 TEVA Analysis Results ........................................................................................ 7-21


7.6.3 Case Summary .............................................................................................................. 7-22


7.7 Field Testing of Water-Distribution Systems in Support of an Epidemiologic Study ........ 7-22


7.7.1 Case Study Overview ................................................................................................... 7-22


7.7.2 Field Work ..................................................................................................................... 7-23


7.7.2.1 C-Factor and Fire-Flow Tests .............................................................................. 7-23


7.7.2.2 Tracer Test and Continuous Measurements ...................................................... 7-23


7.7.3 Additional Test Procedures .......................................................................................... 7-24


7.7.4 Case Study Summary .................................................................................................. 7-25


7.8 Chapter Summary ................................................................................................................ 7-25


References ..................................................................................................................................... 7-25


viii 
A Reference Guide for Utilities 

List of Tables 

Table Page 

Table 1-1. Selected Rules and Regulations Dealing with Distribution Systems (Not Inclusive) ..... 1-9


Table 2-1. Available Hydraulic and Water Quality Network Modeling Software Packages .......... 2-13


Table 2-2. Example CFD Modeling Software Packages ................................................................. 2-14


Table 2-3. Example Transient Modeling Software Packages ......................................................... 2-15


Table 3-1. Tracer Characteristics (adapted from Teefy, 1996).......................................................... 3-7


Table 3-2. Equipment Costs ............................................................................................................ 3-14


Table 3-3. Representative Labor Hours for a Range of Studies .................................................... 3-15


Table 4-1 Calibration/Input Requirements for Water Quality Models ............................................. 4-3


Table 4-2 Draft Calibration Criteria for Modeling (based on ECAC, 1999) ................................... 4-13


Table 5-1. Federal Distribution System Water Quality Monitoring Requirements .......................... 5-2


Table 7-1. Master Operating Criteria Used to Develop Operating Schedules for the Historical Water


Distribution System, Dover Township Area, NJ (from Maslia et al., 2001) .................... 7-6


Table 7-2. Field Data Collected During Fire-Flow Test at Site H02 ................................................. 7-24


List of Figures 

Figure Page 

Figure 1-1. Water Treatment Process at the Miller Plant on the Ohio River (Adapted from: GCWW 

2005). ................................................................................................................................ 1-1


Figure 1-2. Distribution System Interactions that Affect Water Quality (Adapted from: MSU, 2005). . 1-3


Figure 1-3. Total Number and Proportion of U.S. Waterborne Diseases Associated with Water


Distribution System Deficiencies. .................................................................................... 1-6


Figure 1-4. Evolution of Federal Drinking Water Regulations - Timeline. ......................................... 1-8


Figure 2-1. Simple Link-Node Representation of a Water Distribution System. ............................... 2-2


Figure 2-2. A Flow Chart for Estimating Future Water Demand Based on Land-Use Methodology. .. 2-5


Figure 2-3. EPANET Graphical Output Showing Flow and Pressure. .............................................. 2-7


Figure 2-4. Sample EPANET Time Series Plots of Flow, Pressure, and Tank Water Level. ............. 2-7


Figure 2-5. EPANET Sample Tabular Outputs (at time 10.00 hrs). .................................................... 2-8


Figure 2-6. Illustration of the Evolution of Hydraulic and Water Quality Models. ............................ 2-9


Figure 2-7. Graphical Output from a CFD Model Showing Tracer Concentration in a Tank. ....... 2-14


Figure 2-8. A Large Physical Model of a Tank (Source: Bureau of Reclamation Laboratory). ...... 2-14


Figure 2-9. Graphical Output Based on 3-D Laser Induced Fluorescence with a Physical Scale


Model Showing Mixing in Tank (Source: Georgia Tech). ............................................ 2-15


Figure 2-10. Negative Pressure Transient Associated with a Power Outage. .................................. 2-16


Figure 2-11a. Velocity Field at a Junction............................................................................................ 2-18


Figure 2-11b. Tracer Concentration at a Junction. ............................................................................. 2-18


Figure 3-1. Flow Calibration Tube. ..................................................................................................... 3-9


Figure 3-2. Automated Monitoring Station. .................................................................................... 3-10


Figure 3-3. Tracer Injection Setup (Storage Tank, Calibration Tube and Feed Pump). ................. 3-11


Figure 4-1. Conceptual Representation of Calibration. ..................................................................... 4-1


Figure 4-2. Effects of the Initial Water Age on the Modeled Results................................................. 4-3


Figure 4-3. Schematic of Standard Two-Gage C-Factor Test Setup................................................. 4-4


Figure 4-4. Schematic of Parallel Hose C-Factor Test Setup. ............................................................ 4-4


Figure 4-5. Fire-Flow Test Setup. ....................................................................................................... 4-5


Figure 4-6. A Hydrant Being Flowed with a Diffuser as Part of a Fire-Flow Test. ............................. 4-5


Figure 4-7. Schematic Representation of Small-Suburban Dead-End System. ............................... 4-8


Figure 4-8. Empirical Relationship Between Chloride and Conductivity. ......................................... 4-8


Figure 4-9. Sample Chloride Data Used at One Station for Calibration. .......................................... 4-9


Figure 4-10. Comparison of Model Versus Field Results for Continuous Monitor Location CM-18 at


Various Calibration Stages. ............................................................................................. 4-9


ix 
A Reference Guide for Utilities 

Figure 4-11. Calibration of “Looped Portion.” ................................................................................... 4-10


Figure 4-12. Schematic Representation of Case 2 Study Location. ................................................. 4-11


Figure 4-13a. Modeled Flows Compared to Measured Flows Before Calibration. ............................ 4-11


Figure 4-13b. Modeled Flows Compared to Measured Flows After Calibration. ............................... 4-11


Figure 4-14a. Chloride Concentration for Calibration Event at Continuous Monitor Location CM-59. 4-12


Figure 4-14b. Chloride Concentration for Validation Event at Continuous Monitor Location CM-59. .. 4-12


Figure 5-1. Wastewater Injection: Free Chlorine and Associated Grab Sample Results. ................ 5-6


Figure 5-2. Theoretical Example of Benefits from Monitors. ............................................................ 5-6


Figure 5-3. Pareto-Optimal Cost Effectiveness Diagram. ................................................................... 5-7


Figure 5-4. Fort Reno #2 Remote Sampling System. ..................................................................... 5-12


Figure 5-5. WASA Remote Monitoring System Layout and Data Transmission Scheme. ............ 5-12


Figure 5-6. Monitoring Data for Fort Reno Tank. ............................................................................. 5-13


Figure 5-7. City of Tucson Water Quality Zone Map. ...................................................................... 5-13


Figure 5-8. Continuous Water Quality Monitoring Station. ............................................................ 5-13


Figure 6-1. Mercator’s Map of the World in 1569 (Whitfield, 1994). ................................................. 6-2


Figure 6-2. Landsat Thematic MapperTM Images of the Missouri River Floodplain Near Glasgow,


Missouri. (USGS, 1993).................................................................................................... 6-4


Figure 6-3. Typical Inputs and Results of Current GIS Packages. ..................................................... 6-5


Figure 6-4. Digital Terrain Model of Mount St. Helens after Eruption in 1980 (R. Horne, 2004). ..... 6-6


Figure 6-5. Map of Pressure Zone Showing Three Types of GIS Vector Data. ............................... 6-6


Figure 6-6. Regional Land Cover Characterization as a Raster Database (USGS, 1992). ................ 6-7


Figure 6-7. Triangulation of Elevation (Z) Data. ................................................................................. 6-7


Figure 6-8a. Typical Representation of a Pipe Section in GIS........................................................... 6-11


Figure 6-8b. Typical Representation of a Pipe Section in a Network Model. ................................... 6-11


Figure 6-9. LVVWD Distribution System Growth. ........................................................................... 6-12


Figure 6-10. Conceptual Relationship Model for Integration. ........................................................... 6-12


Figure 6-11. One-to-One Relationship Between GIS and Network Modeling Data. ......................... 6-12


Figure 6-12. Pressure Complaint Resolution – GIS Parcel/Account Search Window. ..................... 6-13


Figure 6-13. Pressure Complaint Resolution – Parcel and Hydrant Location................................... 6-14


Figure 6-14. Pressure Complaint Resolution – Model and Field Pressure Comparison. ................. 6-14


Figure 6-15. Water Main Break Analysis – Valve Isolation. ................................................................ 6-15


Figure 6-16. Water Main Break Analysis – Impacted Customer List. ................................................ 6-15


Figure 6-17. Water Main Break Analysis – Comparison of Junction Pressures - Before and After


Shutdown. ..................................................................................................................... 6-16


Figure 6-18. GIS Geo-coding - Metered Sales Demand Allocation Procedure. ............................... 6-17


Figure 7-1. Comparison of Early Confirmed Cases of Salmonella Positive Sample Versus the


Estimated Distribution of Tank Water During the First 6 Hours of the Flushing Program. 7-3


Figure 7-2. Investigation Area, Dover Township, Ocean County, NJ (modified from Maslia et al.,


2001). ................................................................................................................................ 7-4


Figure 7-3. Distribution System Zones – Woburn, MA (May 1969). ................................................. 7-5


Figure 7-4. Three-Dimensional Representation of Monthly Production of Water, Dover Township


Area, NJ (from Maslia et al., 2001). ................................................................................. 7-6


Figure 7-5. Areal Distribution of Simulated Proportionate Contribution of Water from the Parkway


Wells (22, 23, 24, 26, 28, 29) to Locations in the Dover Township Area, NJ, July 1988


Conditions (from Maslia et al., 2001)............................................................................... 7-8


Figure 7-6. Simulated Proportionate Contribution of Water from Wells and Well Fields to Selected


Locations, Dover Township Area, NJ, July 1988 Conditions (from Maslia et al., 2001). ... 7-8


Figure 7-7. Estimated Upper 97.5 Percent Credibility Limit for Annual Perchlorate Intake by One


Plaintiff (Grayman, 2004). ................................................................................................ 7-9


Figure 7-8. Average Water Age in the Distribution System Over Last 24 Hours of a 2-Week


Simulation. ..................................................................................................................... 7-11


Figure 7-9. Minimum Water Age in the Distribution System Over Last 24 Hours of a 2-Week


Simulation. ..................................................................................................................... 7-11


Figure 7-10. Diurnal Water Age at Node J-456. ................................................................................. 7-12


Figure 7-11. Minimum Chlorine Residual in Distribution System Over Last 24 Hours of a 2-Week


Simulation. ..................................................................................................................... 7-12


x

A Reference Guide for Utilities 

Figure 7-12. Zones Representing Potential Monitoring Locations for IDSE Based on Modeling. ... 7-12


Figure 7-15. Contaminant Concentration Just Downstream of Contaminant Introduction Location


Figure 7-16. Contaminant Concentration Far Downstream of Contaminant Introduction Location


Figure 7-20. Hypothetical System Showing High Score Areas (>27) Overlain with Hospitals and


Figure 7-21. Display of Low-Velocity Pipes, Oversized Pipes, and Current Monitoring Stations Using


Figure 7-23. Comparison of 24-Hour, 48-Hour, and Real-Time, Continuous Contamination


Monitoring Systems for the Reduction in Mean Infections for a 24-Hour Contaminant


Figure 7-24. Comparison of 24-Hour, 48-Hour, and Real-Time, Continuous Contamination


Figure 7-26. Continuous Recording Pressure Logger Mounted on Brass Shutoff Valve and Hydrant


Figure 7-29. Arrival Times of the Calcium Chloride Tracer at Monitoring Locations in Hadnot Point


Figure 7-13. Water Distribution System Characteristics. ................................................................... 7-13


Figure 7-14. Allowable Contaminant Introduction Locations. .......................................................... 7-14


(Node 121). ..................................................................................................................... 7-14


(Node 143). ..................................................................................................................... 7-14


Figure 7-17. Monitoring Locations Selected by the Optimization Model......................................... 7-15


Figure 7-18. Hypothetical Water Distribution System Showing Pipelines. ....................................... 7-16


Figure 7-19. Conceptual Diagram Showing the Ranking and Prioritization Methodology.............. 7-16


Schools. ......................................................................................................................... 7-17


the Spatial Database Display Tool. ................................................................................ 7-18


Figure 7-22. Threat Ensemble Vulnerability Assessment Framework............................................... 7-18


Attack. ............................................................................................................................. 7-21


Monitoring Systems for the Reduction in the Maximum Number of Infections for a 24­


Hour Contaminant Attack. ............................................................................................. 7-21


Figure 7-25. Water Distribution Systems Serving U.S. Marine Corps Base, Camp Lejeune, NC. ... 7-22


Adapter Cap Used for Fire-Flow and C-Factor Tests..................................................... 7-23


Figure 7-27. Location of Fire Hydrants Used in Fire-Flow Test at Site H02. ...................................... 7-23


Figure 7-28. Horiba W-23XD Dual Probe Ion Detector Inside Flow Cell. .......................................... 7-24


WTP Area, May 25, 2004. .............................................................................................. 7-25


xi 
A Reference Guide for Utilities 

Acronyms and Abbreviations 

AC Alternating Current 

ACCNSCM Arsenic and Clarifications to 

Compliance and New Source 

Contaminant Monitoring 

ADAPT Areal Design and Planning Tool 

ADEQ Arizona Department of 

Environmental Quality 

Al2(SO4)

3 Aluminum Sulfate 

AM/FM Automated Mapping (or Asset 

Management)/Facilities 

Management 

AMR Automated Meter Reading 

ASCE American Society of Civil Engineers 

ATSDR Agency for Toxic Substances and 

Disease Registry 

AWWA American Water Works Association 

AwwaRF Awwa Research Foundation 

C Coefficient of Roughness 

ClO4 Perchlorate Anion 

CaCl2 Calcium Chloride 

CAD Computer-Aided Design 

CADD Computer-Aided Design and 

Drafting 

CDC Centers for Disease Control and 

Prevention 

CFD Computational Fluid Dynamics 

CIS Customer Information System 

CM Continuous Monitoring 

COGO Coordinated Geometry 

CRT Cathode Ray Tube 

CWS Contamination Warning System 

DBP Disinfection By-Products 

DBPR1 Disinfectant By-Product Rule ­

Stage 1 

DBPR2 Disinfectant By-Product Rule ­

Stage 2 

DC Direct Current 

D.C. District of Columbia 

DEM Digital Elevation Model 

DIME Dual Independent Map Encoding 

DLG Digital Line Graph 

DSOP Distribution System Water Quality 

Optimization Plan 

DSS Distribution System Simulator 

DTM Digital Terrain Model 

DWQM Dynamic Water Quality Model 

EBMUD East Bay Municipal Utility District 

EDM Electronic Distance Measurement 

EMPACT Environmental Monitoring for Public 

Access and Community Tracking 

EOSAT Earth Observation Satellite 

EPA U.S. Environmental Protection 

Agency 

EPS Extended Period Simulation 

ESSA Environmental Science Services 

Administration 

EWS Environmental Warning System 

FC Fecal Coliform 

FeCl3 Ferric Chloride 

FOH Federal Occupational Health 

GA Genetic Algorithm 

gal Gallon 

GBF Geographic Base File 

GC Gas Chromatograph 

GCWW Greater Cincinnati Water Works 

GIS Geographic Information System 

GPD Gallons Per Day 

gpm Gallons Per Minute 

GPS Global Positioning System 

GRASS Geographic Resources Analysis 

Support System 

GUI Graphical User Interface 

GWR Ground Water Rule 

HAA Haloacetic Acid 

HAA5 The five Haloacetic Acids 

HACCP Hazard Analysis Critical Control 

Point 

HGL Hydraulic Grade Line 

HSPP Health and Safety Project Plan 

ICR Information Collection Rule 

IDSE Initial Distribution System 

Evaluation 

IESWTR Interim Enhanced Surface Water 

Treatment Rule 

ILSI International Life Sciences Institute 

I/O Input/Output 

ISE Ion Selective Electrode 

ISO Insurance Services Office 

LCR Lead and Copper Rule 

xii 
A Reference Guide for Utilities 

LIFO Last In/First Out 

LIMS Laboratory Information 

Management System 

LIS Land Information System 

LT1ESWTR Long Term 1 Enhanced Surface 

Water Treatment Rule 

LT2ESWTR Long Term 2 Enhanced Surface 

Water Treatment Rule 

LVVWD Las Vegas Valley Water District 

ma Milli-Amperes 

MCL Maximum Contaminant Level 

MCLG Maximum Contaminant Level Goal 

MDL Minimum Detection Limit 

MDNR Missouri Department of Natural 

Resources 

MDOH Missouri Department of Health 

MGD Million Gallons per Day 

mg/L milligrams per liter 

MIT Massachusetts Institute of 

Technology 

MOC Master Operating Criteria 

MRDLG Maximum Residual Disinfectant 

Level Goals 

MS Mass Spectrometer 

MSU Montana State University 

NaCl Sodium Chloride 

NAD27 North American Datum of 1927 

NAD83 North American Datum of 1983 

NAPP National Aerial Photography 

Program 

NASA National Aeronautics and Space 

Administration 

NFPA National Fire Protection Association 

NHAP National High Altitude Photography 

NIPDWR National Interim Primary Drinking 

Water Regulations 

NJDHSS New Jersey Department of Health 

and Senior Services 

NMWD North Marin Water District 

NPL National Priorities List 

NPWA North Penn Water Authority 

NOM Naturally Occurring Organic (and/ 

or Inorganic) Matter 

NRC National Research Council 

O&M Operations and Maintenance 

OCMS Online Contaminant Monitoring 

System 

ODBC Open Database Connectivity 

ORD Office of Research and Development 

ORP Oxidation Reduction Potential 

PAB3D A Three-Dimensional Computational 

Fluid Dynamics Model developed by 

Analytical Services & Materials, Inc. 

PC Personal Computer 

PDD Presidential Decision Directive 

PHRP Public Health Response Plan 

PL Public Law 

POE Point of Entry 

POGA Progressive Optimality Genetic 

Algorithm 

psi Pounds Per Square Inch 

PVC Polyvinyl Chloride 

PWS Public Water System 

QA Quality Assurance 

QAPP Quality Assurance Project Plan 

QC Quality Control 

RDBMS Relational Database Management 

Systems 

RDWR Radon in Drinking Water Rule 

SAN Styrene Acrylonitrile 

SCADA Supervisory Control and Data 

Acquisition 

SCCRWA South Central Connecticut Regional 

Water Authority 

SDMS	 Spatial Database Management System 

SDWA	 Safe Drinking Water Act 

SDWAA	 Safe Drinking Water Act Amendments 

SMP	 Standard Monitoring Program 

SNL	 Supply Node Link 

SOP	 Standard Operating Procedure 

SPC	 State Plane Coordinates 

SSS	 System Specific Study 

SVOC	 Semivolatile Organic Compound 

SWTR	 Surface Water Treatment Rule 

SYMAP	 Synagraphic Mapping 

T&E	 Test and Evaluation 

TCE	 Trichloroethylene 

TCR	 Total Coliform Rule 

TEVA	 Threat Ensemble Vulnerability 

Assessment 

THM	 Trihalomethane 

TIGER	 Topologically Integrated Geographic 

Encoding and Referencing 

xiii 
A Reference Guide for Utilities 

TIN Triangulated Irregular Network 

TIROS1 Television and Infrared Observation 

Satellite 1 

TOC Total Organic Carbon 

TT Treatment Technique 

TTHM Total Trihalomethane 

TV Television 

U.S. United States 

UF Ultrafiltration 

UHF Ultra High Frequency 

UV Ultraviolet 

UV-Vis Ultraviolet-Visible 

USGS United States Geological Survey 

UTM Universal Transverse Mercator 

VHF Very High Frequency 

WASA Water and Sewer Authority 

WATERS Water Awareness Technology 

Evaluation Research and Security 

WQP Water Quality Parameter 

WRC Water Research Centre 

WSSM Water Supply Simulation Model 

WSTP Wells, Storage Tanks, and Pumps 

WTP Water Treatment Plant 

xiv 
A Reference Guide for Utilities 

Acknowledgments 

EPA would like to acknowledge the principal authors of the document, “Water Distribution System 

Analysis: Field Studies, Modeling, and Management,” a Reference Guide for Utilities. The authors 

were Mr. Srinivas Panguluri, P.E. (Shaw Environmental, Inc.), Dr. Walter M. Grayman, P.E. (W.M. 

Grayman Consulting Engineer), and Dr. Robert M. Clark, P.E., D.E.E. (Environmental Engineering and 

Public Health Consultant). The work was performed under EPA Contract No. EP-C-04-034, Work 

Assignment No. 0-08 and 1-08, with Shaw Environmental, Inc. 

EPA acknowledges the significant contributions from Mr. David J. Hartman and Dr. Yeongho Lee, 

P.E., of the Greater Cincinnati Water Works. Mr. Hartman and Dr. Lee served as external reviewers 

and directly contributed to the development of portions of this document. Greater Cincinnati Water 

Works also participated significantly in the field tracer tests that served as a foundation for the 

development of this document. 

EPA acknowledges the peer reviews by the following utility personnel: Mr. William R. Kirkpatrick and 

Mr. Ronald B. Hunsinger of East Bay Municipal Utility District, and Mr. Arnold Strasser, P.E., of Denver 

Water. 

EPA also acknowledges the peer reviews provided for this document by Dr. Fran A. DiGiano, P.E., of 

University of North Carolina, Mr. Morris L. Maslia, P.E., D.E.E., of the Agency for Toxic Substances 

and Disease Registry, Dr. Edward A. McBean, P.E., of University of Guelph, Dr. Vanessa L. Speight, 

P.E., of Malcolm Pirnie, Ms. L. Michelle Moore of National Drinking Water Clearinghouse, and Mr. E. 

Radha Krishnan, P.E., of Shaw Environmental, Inc. 

EPA also gratefully acknowledges the following contributors to this document: Mr. Daniel R. Quintanar 

of Tucson Water, Ms. Laura B. Jacobson, P.E., Mr. Sridhar Kamojjala, P.E., and Mr. Mao Fang, P.E., of 

Las Vegas Valley Water District, Mr. Morris L. Maslia, P.E., D.E.E., of the Agency for Toxic Substances 

and Disease Registry, Dr. William B. Samuels, P.E., of SAIC, Dr. Mark W. LeChevallier and Dr. Kala K. 

Fleming of American Water. 

EPA Contributors 

Ms. Shirley J. Gibson functioned as Project Officer of EPA Contract No. EP-C-04-034, Ms. Lucille M. 

Garner served as Work Assignment Manager and Mr. Craig L. Patterson, P.E., served as the Alternate 

Work Assignment Manager and technical reviewer for this document. Mr. Roy C. Haught served as 

EPA Technical Advisor. Mr. Jonathan G. Herrmann, P.E., Mr. Kim R. Fox, P.E., D.E.E., and Dr. James 

A. Goodrich performed technical reviews of the document. Mr. Stephen M. Harmon was the EPA 

Quality Assurance Manager, and was responsible for the quality assurance review of the document. 

The document was also reviewed by many other EPA reviewers including the following: Mr. Blake L. 

Atkins, Mr. William H. Davis, Ms. Jean E. Dye, Mr. Robert J. Janke, Mr. Bruce A. Macler, Ms. Jill R. 

Neal, Dr. Lewis A. Rossman, P.E., Mr. Kenneth H. Rotert, Dr. Irwin J. Silverstein, P.E., D.E.E., and Ms. 

Elin A. Warn. 

xv 
Chapter 1 

Introduction 

Drinking water utilities in the United States (U.S.) 

and throughout the world face the challenge of 

providing water of good quality to their consumers. 

Frequently, the water supply is derived from surface 

water or groundwater sources that may be subject to 

naturally occurring or accidentally introduced 

contamination (ILSI, 1999; Gullick et al., 2003). In 

other cases, routine upstream waste discharges or 

purposeful contamination of the water can diminish 

the quality of the water. The treated water may be 

transmitted through a network of corroded or 

deteriorating pipes. All of these factors can result 

in degradation in the quality of the water delivered 

to customers. 

In the U.S., drinking water quality has to comply 

with federal, state, and local regulations. This is 

based on selected physical, chemical, and biological characteristics of the water. The U.S. Environmental Protection Agency (EPA) has promulgated 

many drinking water standards under the Safe 

Drinking Water Act (SDWA) of 1974. These rules 

and regulations require that public water systems 

(PWSs) meet specific guidelines and/or numeric 

standards for water quality. The SDWA defines a 

PWS as a system that serves piped water to at least 

25 persons or 15 service connections for at least 60 

days each year. For the purposes of this reference 

guide, PWSs are referred to as utilities. 

The SDWA has established two types of numeric 

standards. The first type of standard is enforceable 

and referred to as a maximum contaminant level 

(MCL). The other non-enforceable standard is 

referred to as a maximum contaminant level goal 

(MCLG). MCLGs are set at a level at which no known 

or anticipated adverse human health effects occur. 

Where it is not economically or technologically 

feasible to determine the level of a contaminant, a 

treatment technique (TT) is prescribed by EPA in lieu 

of establishing an MCL. For example, Giardia is a 

microbial contaminant that is difficult to measure. To 

ensure proper removal, experimental work has 

established optimum treatment conditions for the 

water at a specified pH, temperature, and chlorine 

concentration for a specified length of time to achieve 

a fixed level of inactivation. 

Compliance with MCL and TT requirements is 

typically ensured by requiring that water utilities 

periodically monitor various characteristics of the 

treated water. In summary, the EPA Guidelines and 

Standards are designed to ensure that drinking 

water is adequately treated and managed by water 

A Reference Guide for Utilities 

Removing contaminants from drinking water can be 

expensive. Depending upon the type and level of 

contaminant(s) present in the source water, utilities can 

choose from a variety of treatment processes. These 

individual processes can be arranged in a “treatment 

train” (a series of processes applied in a sequence). 

The most commonly used treatment processes include 

coagulation/flocculation, sedimentation, filtration, and 

disinfection. Some water systems also use ion exchange, membrane separation, ozonation, or carbon 

adsorption for treatment. The basic treatment options 

are briefly discussed later in this chapter. As an 

example, Figure 1-1 depicts the water treatment 

process implemented by the Greater Cincinnati Water 

Works (GCWW) at the Miller Plant on the Ohio River. 

Presettling Final settling occurs, removes most solids water prepared 

Further settling for final treatment 

occurs in reservoir 

pH adjusted 

Settling aids added Ohio River 

Intake 

Pumps 

To distribution


system


Granular activated Sand and

gravel filter 

water 

Furnace 

cleans carbon 

for reuse 

organics 

carbon removes 

pH adjusted, 

chlorine added, 

fluoride added 

Reservoir 

Figure 1-1. Water Treatment Process at the Miller 

Plant on the Ohio River (Adapted from: GCWW 2005). 

utilities to support public safety, protect public 

health, and promote economic growth (Clark and 

Feige, 1993). 

Disinfection of drinking water is considered to be one 

of the major public health advances of the 20th 

century. The successful application of chlorine as a 

disinfectant was first demonstrated in England. In 

1908, Jersey City (NJ) initiated the use of chlorine for 

water disinfection in the U.S. This approach subsequently spread to other locations, and soon the rates 

of common epidemics such as typhoid and cholera 

dropped dramatically. Today, disinfection is an 

essential part of a drinking water treatment train. 

Chlorine, chlorine dioxide, and chloramines are most 

1-1 
A Reference Guide for Utilities 

While disinfectants are effective in controlling many 

microorganisms, they can react with naturally occurring 

organic (and/or inorganic) matter (NOM) in the treated 

and/or distributed water to form potentially harmful 

disinfection byproducts (DBPs). Many of these DBPs 

are suspected of causing cancer, reproductive, and 

developmental problems in humans. To minimize the 

formation of DBPs, EPA has promulgated regulations 

that specify maximum residual disinfectant level goals 

(MRDLGs) for chlorine (4 milligrams per liter [mg/L] as 

chlorine), chloramines (4 mg/L as chlorine), and 

chlorine dioxide (0.8 mg/L as chlorine dioxide). In 

addition, MCLs for the DBPs total trihalomethanes 

(TTHMs) and haloacetic acids (HAA5) have been 

established as 0.080 and 0.060 mg/L, respectively. The 

TTHMs include chloroform, bromodichloromethane, 

dibromochloromethane and bromoform. The HAA5 

include monochloroacetic acid, dichloroacetic acid, 

trichloroacetic acid, monobromoacetic acid and 

dibromoacetic acid. In order to meet these requirements, utilities may need to remove the DBP precursor 

material from the water prior to disinfection by applying appropriate treatment techniques or modify their 

disinfection process. 

often used because they are very effective disinfectants, and residual concentrations can be maintained 

in the water distribution system. Some utilities (in the 

U.S. and Europe) use ozone and chlorine dioxide as 

oxidizing agents for primary disinfection prior to the 

addition of chlorine or chlorine dioxide for residual 

disinfection. The Netherlands identifies ozone as the 

primary disinfectant, as well as common use of 

chlorine dioxide, but typically uses no chlorine or 

other disinfectant residual in the distribution system 

(Connell, 1998). 

Prior to the passage of the SDWA of 1974, most 

Some important distribution system water quality 

concerns are: maintenance of proper disinfectant levels; 

minimization of DBP formation; turbidity, taste, color, 

and odor issues; distribution tank mixing and utilization; main repair and pressure stabilization; flow 

management; cross-connection control and back-flow 

prevention. 

Some water quality goals are contradictory. For 

example, an important goal is to maintain a positive 

disinfectant residual in order to protect against microbial contamination. However, DBPs (TTHMs) will 

increase as water moves through the network as long as 

disinfectant residual and NOM is available. Other DBPs 

(HAA5) are degraded biologically when free chlorine or 

chloramines are nearly absent. 

drinking water utilities focused on meeting drinking water standards at the treatment plant, even 

though it had long been recognized that water 

quality can deteriorate in a distribution system. 

The SDWA introduced a number of MCLs that must 

be measured at various monitoring points in the 

distribution system. Consequently, water quality in 

the distribution system became a focus of regulatory action and of major interest to drinking water 

utilities. Subsequently, utilities worked with 

various research organizations (including EPA) to 

understand the impact of the distribution system on 

water quality. The collective knowledge from this 

research has been applied to improve the quality of 

water delivered to the consumer (Clark and 

Grayman, 1998). 

Prior to September 11, 2001 (9/11), few water utilities 

were using online monitors in a distribution system as a 

means of ensuring that water quality was being maintained and addressed in cases of deviation from established ranges. Now the enhanced focus on water 

security has led EPA and water utilities to collectively 

evaluate commercial technologies to remotely monitor 

the distribution system water quality in real-time. As a 

part of an evolutionary process, in the future, these 

monitoring technologies are expected to be integrated 

with computer modeling and geospatial technologies. 

This evolution of monitoring and modeling technologies can potentially minimize the risks from drinking 

water contaminants in distribution systems. 

This reference guide has been prepared to provide 

information to drinking water utilities and researchers on the state of the art for distribution system 

management and modeling. Guidance is provided 

on the application of advanced modeling tools that 

can enhance a utility’s ability to better manage 

distribution system water quality. This introductory chapter provides the basic concepts, which 

include: 

•	 Distribution system – infrastructure design and


operation (definitions and overview).


•	 Water quality problems and issues (a brief


review).


•	 Regulatory framework (an overview). 

•	 Assessment and management of water quality


(current practices).


•	 Advanced tools for water quality management


(in distribution systems).


Subsequent chapters will provide more details on 

related concepts and tools. 

1-2 
1.1 Distribution System ­

Infrastructure Design and 

Operation 

Distribution system infrastructure is a major asset of a 

water utility, even though most of the components are 

either buried or located inconspicuously. Drinking 

water distribution systems are designed to deliver 

water from a source (usually a treatment facility) in 

the required quantity, quality, and at satisfactory 

pressure to individual consumers in a utility’s service 

area. In general, to continuously and reliably move 

water between a source and a customer, the system 

would require storage reservoirs/tanks, and a network 

of pipes, pumps, valves, and other appurtenances. 

This infrastructure is collectively referred to as the 

drinking water distribution system (Walski et al., 2003). 

1.1.1 Key Infrastructure Components 

A detailed description of the various distribution 

system infrastructure components is readily available 

from other sources and beyond the scope of this 

document. However, for the purposes of establishing 

the basics, this section includes a brief discussion of 

the uses of the major components, their characteristics, general maintenance requirements, and desirable 

features. 

1.1.1.1 Storage Tanks/Reservoirs 

Tanks and reservoirs are used to provide storage 

capacity to meet fluctuations in demand, to provide 

reserves for fire-fighting use and other emergency 

situations, and to equalize pressures in the distribution system. The most frequently used type of storage 

facility is the elevated tank, but other types of tanks 

and reservoirs include in-ground tanks and open or 

closed reservoirs. Materials of construction include 

concrete and steel. An issue that has drawn a great 

deal of interest is the problem of water turnover 

within storage facilities. Much of the water volume in 

storage tanks is dedicated to fire protection. Unless 

utilities make a deliberate effort to exercise (fill and 

draw) their tanks, or to downsize the tanks when the 

opportunity presents itself, there can be both water 

aging and water mixing problems. The latter can lead 

to stratification and/or large stagnant zones within the 

water volume. Some of these issues will be discussed 

later in this document. 

1.1.1.2 Pipe Network 

The system of pipes or “mains” that carry water from 

the source (such as a treatment plant) to the consumer 

is often categorized as transmission/trunk, distribution, and service mains. Transmission/trunk mains 

usually convey large amounts of water over long 

distances, such as from a treatment facility to a 

storage tank within the distribution system. Distribution mains are typically smaller in diameter than the 

A Reference Guide for Utilities 

transmission mains and generally follow city streets. 

Service mains are pipes that carry water from the 

distribution main to the building or property being 

served. Service lines can be of any size, depending 

on how much water is required to serve a particular 

customer, and are sized so that the utility’s design 

pressure is maintained at the customer’s property for 

the desired flows. The most commonly used pipes 

today for water mains are ductile iron, pre-stressed 

concrete, polyvinyl chloride (PVC), reinforced 

plastic, and steel. In the past, unlined cast iron pipe 

and asbestos-cement pipes were frequently used. 

Even a medium-sized water utility may have thousands of miles of pipes constructed from various types 

of materials, ranging from new, lined or plastic pipes 

to unlined pipes that are more than 50 years old. Over 

time, biofilms and tubercles attached to pipe walls can 

result in both loss of carrying capacity and a significant 

loss of disinfectant residual, thereby adversely 

affecting water quality (Clark and Tippen, 1990). 

Figure 1-2 depicts the various distribution system 

interactions that may adversely affect water quality. 

Figure 1-2. Distribution System Interactions that Affect 

Water Quality (Adapted from: MSU, 2005). 

The mains should be placed in areas along the public 

right of way, which provides for ease of access, 

installation, repair, and maintenance. Broken or 

leaking water mains should be repaired as soon as 

possible to minimize property damage and loss of 

water. In the past, it has been standard practice to 

maintain the carrying capacity of the pipe in the 

distribution system as high as possible to provide the 

design flow and keep pumping costs as low as 

possible. However, there has been recent concern that 

excess capacity can lead to long residence times and 

thus contribute to deterioration in water quality. 

1.1.1.3 Valves 

There are two general types of valves in a distribution 

system: isolation valves and control valves. Isolation 

valves are used in the distribution system to isolate 

sections for maintenance and repair and are typically 

1-3 
A Reference Guide for Utilities 

located in a system so that the areas isolated will 

cause a minimum of inconvenience to other service 

areas. Maintenance of the valves is one of the major 

activities carried out by a utility. Many utilities have 

a regular valve-turning program in which a percentage 

of the valves are opened and closed on a regular basis. 

It is desirable to turn each valve in the system at least 

once per year. In large systems, this may or may not 

be practical, but periodic exercise and checking of 

valve operations should occur. This practice minimizes the likelihood that valves will become inoperable due to corrosion. The implementation of such a 

program ensures that, especially during an emergency, 

water can be shut off or diverted and that valves have 

not been inadvertently closed. 

Control valves are used to regulate the flow or 

pressure in a distribution system. Typical types of 

control valves include pressure-reducing valves, 

pressure-sustaining valves, flow-rate control valves, 

throttling valves, and check valves. 

1.1.1.4 Pumps 

Pumps are used to impart energy to the water in order 

to boost it to higher elevations or to increase pressure. 

Routine maintenance, proper design and operation, 

and testing are required to insure that they will meet 

their specific objectives. Pump tests are typically run 

every five to ten years to check the head-discharge 

relationship for the pump. Many system designers 

recommend two smaller pumps instead of one large 

pump to ensure redundancy. 

1.1.1.5 Hydrants and Other Appurtenances 

Hydrants are primarily a part of the fire-fighting 

infrastructure of a water system. Although water 

utilities usually have no legal responsibility for fire 

flow, developmental requirements often include fire 

flows, and thus, distribution systems are designed to 

support needed fire flows where practical (AWWA, 

1998). Proper design, spacing, and maintenance are 

needed to insure an adequate flow to satisfy firefighting requirements. Fire hydrants are typically 

exercised and tested periodically by water utility or 

fire department personnel. Fire-flow tests are conducted periodically to satisfy the requirements of the 

Insurance Services Office (ISO, 2003) or as part of a 

water distribution system calibration program. Other 

appurtenances in a water distribution system include 

blow-off valves and air release valves. 

1.1.2 Basic Design and Operation Philosophy 

A detailed understanding of “how water is used” is 

critical to understanding water distribution system 

design and operation. Almost universally, the manner 

in which industrial and residential customers use 

water drives the overall design and operation of a 

water distribution system. Generally, water use varies 

Conservative design philosophies, aging water supply 

infrastructure, and increasingly stringent drinking water 

standards have resulted in concerns over the viability of 

drinking water systems in the U.S. Questions have been 

raised over the structural integrity of these systems as 

well as their ability to maintain water quality from the 

treatment plant to the consumer. The Clean Water and 

Drinking Water Infrastructure Gap Analysis (EPA 2002), 

which identified potential funding gaps between 

projected needs and spending from 2000 through 2019, 

estimated a potential 20-year funding gap for drinking 

water capital, and operations and maintenance, ranging 

from $45 billion to $263 billion, depending on spending levels. Based on current spending levels, the U.S. 

faces a shortfall of $11 billion annually to replace aging 

facilities and comply with safe drinking water regulations. Federal funding for drinking water in 2005 

remained level at $850 million—less than 10% of the 

total national requirement (ASCE, 2005). Parts of many 

systems are approaching or exceed 100 years old, and an 

estimated 26 percent of the distribution system pipe in 

this country is unlined cast iron and steel in poor 

condition. At current replacement rates for distribution 

system components, it is projected that a utility will 

replace a pipe every 200 years (Kirmeyer et al., 1994). 

Grigg, NS, 2005, provides comprehensive guidance to 

utilities on how to assess options for distribution system 

renewal. Grigg’s report contains a knowledge base on 

condition assessment, planning and prioritization, and 

renewal methods. 

both spatially and temporally. Besides customer 

consumption, a major function of most distribution 

systems is to provide adequate standby fire-flow 

capacity (Fair and Geyer, 1971). For this purpose, fire 

hydrants are installed in areas that are easily accessible by fire fighters and are not obstacles to pedestrians and vehicles. The ready-to-serve requirements for 

fire fighting are governed by the National Fire 

Protection Association (NFPA) that establishes 

standards for fire-fighting capacity of distribution 

systems (NFPA, 2003). In order to satisfy this need for 

adequate standby capacity and pressure (as mentioned 

earlier), most distribution systems use standpipes, 

elevated tanks, and large storage reservoirs. Additionally, most distribution systems are “zoned.” 

Zones are areas or sections of a distribution system of 

relatively constant elevation. Zones can be used to 

maintain relatively constant pressures in the system 

over a range of ground elevations. Sometimes, zone 

development occurs as a result of the manner in which 

the system has expanded. 

The effect of designing and operating a system to 

maintain adequate fire flow and redundant capacity 

can result in long travel times for water between the 

1-4 
Non-potable waters (e.g., sea, river, and lake water) 

without adequate treatment have been used for fire 

protection for many years, often with disastrous results. 

However, reclaimed wastewater (in cases where its 

quality is better managed than the aforementioned 

unregulated sources) has been effectively used for 

providing fire protection (AwwaRF, 2002). St. Petersburg, FL, has been operating such a system to bolster 

fire-protection capacity since 1976. The reclaimed 

water hydrants are distinguished from potable water 

hydrants by color and their special valves. If the 

reclaimed water system is designed for fire protection, 

the potable water piping can have a very small diameter 

and investments can be made in higher quality pipe 

materials, which, with much shorter residence time in 

the system, would vastly improve the quality of the 

water at the tap. With this in mind, where retrofitting 

one of the two systems is necessary, it might be wiser to 

use the existing potable water system for the reclaimed 

water and retrofit with new, high-quality, smaller, 

potable water lines (Okun, D., 1996). 

treatment plant and the consumer. These long travel 

times and low velocities may be detrimental to 

meeting the drinking water MCLs. Long residence 

times may lead to formation of DBPs, loss of disinfectant 

residuals, bacterial growth, and formation of biofilm. 

1.1.2.1 Pipe-Network Configurations 

The branch and grid/loop are the two basic configurations for most water distribution systems. A branch 

system is similar to that of a tree branch with smaller 

pipes branching off larger pipes throughout the 

service area. This type of system is most frequently 

used in rural areas, and the water has only one 

possible pathway from the source to the consumer. A 

grid/loop system is the most widely used configuration in large municipal systems and consists of 

interconnected pipe loops throughout the area to be 

served. In this type of system, there are several 

pathways that the water can follow from the source to 

the consumer. Transmission mains are typically 20 to 

24 inches in diameter or larger. Dual-service mains 

that serve both transmission and distribution purposes 

are normally 12 to 20 inches in diameter. Distribution mains are usually 6 to 12 inches in diameter in 

every street. Service lines are typically 1 inch in 

diameter. Specific pipe sizes can vary depending on 

the extent of the distribution system and the magnitude of demand. Looped systems provide a high 

degree of reliability should a line break occur, 

because the break can be isolated with little impact 

on consumers outside the immediate area (Clark and 

Tippen, 1990; Clark et al., 2004). 

1.1.2.2 Multiple Source Configuration 

Many systems serve communities with multiple 

A Reference Guide for Utilities 

sources of supply, such as a combination of wells and/or 

surface sources. In a grid/looped system, this configuration will influence water quality in a distribution 

system due to the effect of mixing of water from these 

different sources. These interactions are a function of 

complex system hydraulics (Clark et al., 1988; Clark 

et al., 1991a). Water quality models can be very 

useful in defining mixing and blending zones within 

water utility distribution networks. Mixing of water 

in a network can result in taste and odor problems or 

other water quality problems and can influence 

maintenance, repair, and rehabilitation procedures. 

1.1.2.3 Impact of System Design and Operation 

on Water Quality 

Based on the design and configuration of a particular 

system, there are many opportunities for water quality 

to change as water moves between the treatment plant 

and the consumer. These unwanted changes may 

occur due to various reasons including: failures at the 

treatment barrier, transformations in the bulk phase, 

corrosion and leaching of pipe material, biofilm 

formation, and mixing between different sources of 

water. Many researchers have investigated the factors 

that influence water quality deterioration once it 

enters the distribution system. It has been well 

documented that bacteriological growth can cause 

taste-and-odor problems, discoloration, slime buildup, 

and economic problems, including corrosion of pipes 

and bio-deterioration of materials (Water Research 

Centre, 1976). Bacterial numbers tend to increase 

during distribution and are influenced by several 

factors, including bacterial quality of the finished 

water entering the system, temperature, residence 

time, presence or absence of a disinfectant residual, 

construction materials, and availability of nutrients 

for growth (Geldreich et al., 1972; LeChevallier et al., 

1987; Maul et al., 1985a and b; Zhang and DiGiano, 

2002; Camper et al., 2003). 

It is difficult and expensive to study the problems 

caused by system design and configuration in fullscale systems. For example, one approach to 

studying residual chlorine levels in dead-end or 

low-flow situations would be to construct a pilotscale pipe system to simulate the phenomena. 

Another approach would be to use mathematical 

hydraulic and water quality models for simulation. 

For either of these approaches to work, they must 

be properly configured and/or calibrated to closely 

simulate a full-scale system. A combination of 

these approaches may be used to assess various 

operational and design decisions, to determine the 

impacts resulting from the inadvertent or deliberate 

introduction of a contaminant into the distribution 

system, and to assist in the design of systems to 

improve water quality. 

1-5 
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Figure 1-3. Total Number and Proportion of U.S. Waterborne Diseases Associated with Water Distribution 

System Deficiencies. 

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A Reference Guide for Utilities 

In pipes, it has been found that chlorine can be lost 

through both the interaction with NOM in the bulk 

phase and with pipe walls themselves in transporting 

finished water. This mechanism for loss of chlorine may 

be even more serious than long residence times in 

tanks. The pipe wall demand, possibly due to biofilm 

and tubercles, may use up the chlorine very rapidly in a 

distribution system. Maintaining adequate levels of 

disinfectant residual may require routine cleaning/ 

replacement of pipes and intensive treatment (Clark et 

al., 1993a). 

in 1989 and a Salmonella outbreak in Gideon, 

Missouri, in 1993. These two examples, discussed 

later in Chapter 7, illustrate the importance of the 

multiple-barrier concept. In both cases, the water 

source was un-disinfected groundwater and the 

utility’s infrastructure was breached, allowing 

contaminants to enter the system. This contamination 

resulted in major waterborne outbreaks. Water quality 

modeling was used in both cases to identify the 

source of the outbreaks and to study the propagation 

of the outbreak through the distribution network 

(Clark et al., 1993a and b). 

1.2 Water Quality Problems and 

Issues 

Drinking water treatment in the U. S. has played a 

major role in reducing waterborne disease. For 

example, the typhoid death rate for a particular year 

in the 1880s was 158 per 100,000 in Pittsburgh, PA, 

compared with 5 per 100,000 in 1935. Such dramatic 

reductions in waterborne disease outbreaks were 

brought about by the application of drinking water 

standards and engineering “multiple barriers” of 

protection. The multiple-barrier concept includes the 

use of conventional treatment (e.g., sand filtration) in 

combination with disinfection to provide safe and 

aesthetically acceptable drinking water. The residual 

disinfectant levels served to protect the water quality 

within the distribution system prior to its delivery to 

the consumer (Clark et al., 1991b). 

Despite the passage of the SDWA, waterborne outbreaks still occur. Two extensively studied examples 

of waterborne disease in the U.S. were an Escherichia 

coli O157:H7 (E. coli) outbreak in Cabool, Missouri, 

One useful outcome of the outbreaks in Missouri is 

that the ensuing investigative studies have typically 

led to the development and enhancement of scientific 

analysis techniques. For example, the Gideon 

Salmonella outbreak conclusions were based on 

statistical studies performed by Centers for Disease 

Control and Prevention (CDC) and corroborated by 

water quality modeling performed by EPA. The study 

provides an example of how tools such as water 

quality models can be used to reliably study contaminant propagation in a distribution system (Clark et al., 

1996). Both the Gideon and Cabool incidents were 

associated with source water contamination, inadequate treatment, and breeches in the distribution 

system. 

These types of problems are not just isolated incidents 

of infrastructure breakdowns. In fact, several problems with drinking water systems in the U. S. have 

been identified by researchers. The National Research 

Council (NRC, 2005) examined the causes of waterborne outbreaks reported by various investigators 

between 1971 and 2004. Figure 1-3 presents the total 

number and proportion of waterborne diseases 

associated with distribution system deficiencies 

1-6 
On December 16, 1974, the U. S. Congress passed the 

SDWA, which authorized the EPA to promulgate 

regulations which would “protect health to the extent 

feasible, using technology, treatment techniques, and 

other means, which the Administrator determines are 

generally available (taking costs into consideration)...”(SDWA, 1974). As a result, a set of regulations 

was promulgated in 1975 which became effective June 

24, 1977. These were known as the National Interim 

Primary Drinking Water Regulations (NIPDWR). The 

NIPDWR established MCLs for 10 inorganic contaminants, six organic contaminants, turbidity, coliform, 

radium-226, radium-228, gross alpha activity, and 

man-made radionuclides. The NIPDWR also established monitoring and analytical requirements for 

determining compliance. 

(extracted from the NRC report). As the figure 

reveals, overall there is a general decrease in the 

total number of waterborne disease outbreaks 

during the reported period. However, there is a 

general increase in the percentage of outbreaks that 

are associated with distribution system deficiencies. 

The NRC report attributes this increase in percentage of outbreaks (attributable to distribution system 

deficiencies) to the lack of historical regulatory 

focus on distribution systems. 

1.3 Regulatory Framework 

Concerns about waterborne disease and uncontrolled water pollution resulted in federal water 

quality legislation starting in 1893 with the 

passage of the Interstate Quarantine Act and 

continuing to 1970 under the stewardship of the 

U.S. Public Health Service (AWWA, 1999). Even 

though significant advances were made to eliminate 

waterborne disease outbreaks during that period, 

the focus of drinking water concerns began to 

change with the formation of the EPA in late 1970. 

By the 1970s, more than 12,000 chemical compounds were known to be in commercial use and 

many more were being added each year. Many of 

these chemicals cause contamination of groundwater and surface water, and are known to be carcinogenic and/or toxic. The passage of the SDWA of 

1974 was a reflection of concerns about chemical 

contamination. In this section, a brief overview of 

the regulatory framework is presented. A detailed 

history of the evolution of the federal drinking 

water regulations is beyond the scope of this 

document. 

Early in the history of the SDWA, the major focus of 

EPA was to implement the Act and to initiate the 

regulatory process. The first MCL established 

A Reference Guide for Utilities 

under the SDWA was the TTHM Rule in 1979. 

However, after several years of developing regulations, it became obvious that the rulemaking 

process must extend beyond a focus on MCLs at the 

treatment plant and into the distribution system. 

Many water utilities in the U.S. using surface 

supplies were experiencing waterborne outbreaks, 

especially from Giardia. The 1986 SDWA Amendments laid the groundwork for the promulgation of 

the Total Coliform Rule (TCR) and the Surface 

Water Treatment Rule (SWTR) in 1989. The 1986 

SDWA Amendments also set forth an aggressive 

plan to eliminate lead from PWSs and resulted in 

the promulgation of the Lead and Copper Rule 

(LCR) in 1991. These actions therefore extended 

the SDWA beyond its focus on the treatment plant 

and into the distribution system (Owens, 2001). 

A summary of the evolution of federal drinking water 

regulation since the passage of the SDWA in 1974 is 

presented in Figure 1-4. In addition to the rules and 

regulations promulgated under the SDWA, security 

has recently become an issue for the water utility 

industry. Security of water systems is not a new issue. 

The potential for natural, accidental, and purposeful 

contamination of water supplies has been the subject 

of many studies. For example, in May 1998, President 

Clinton issued Presidential Decision Directive (PDD) 

63 that outlined a policy on critical infrastructure 

protection, including our nation’s water supplies. 

However, it was not until after September 11, 2001, 

that the water industry focused on the vulnerability of 

the nation’s water supplies to security threats. In 

recognition of these issues, President George W. Bush 

signed the Public Health Security and Bioterrorism 

Preparedness and Response Act of 2002 (Bioterrorism 

Act) into law in June 2002 (PL107-188). Under the 

requirements of the Bioterrorism Act, community 

water systems (CWSs) serving more than 3,300 people 

are required to prepare vulnerability assessments and 

emergency response plans. CWSs are PWSs that 

supply water to the same population throughout the 

year. 

Table 1-1 summarizes the key requirements of the 

regulations presented in Figure 1-4 from a distribution system compliance perspective. 

Many of the tools and techniques discussed in this 

reference guide can assist in complying with the 

rules and regulations and security issues discussed 

above. Water quality modeling techniques can be 

used to identify points in the distribution system 

that experience long retention times, which can in 

turn represent locations in the system that may 

experience chlorine residual loss, excessive 

formation of DBPs, and the formation of biofilms. 

Chlorine residual loss, in conjunction with biofilm 

1-7 
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Figure 1-4. Evolution of Federal Drinking 

Water Regulations - Timeline. 

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A Reference Guide for Utilities 

Meeting and balancing the requirements of the 

various regulations can provide a significant challenge to water utilities. In some cases, regulations 

provide guidance or requirements that could result in 

contradictory actions. For example, the SWTR 

requires the use of chlorine or some other disinfectant. 

However, chlorine or other disinfectants interact with 

NOM in treated water to form DBPs. Similarly, raising 

the pH of treated water will assist in controlling 

corrosion but may increase the formation of TTHMs. 

Various analytical tools, such as water quality models, 

can provide the utility with information and an 

understanding that helps the utility in balancing the 

contradictory requirements of some regulations. 

formation, may result in the sporadic occurrence of 

coliforms (“indicator” organisms associated with 

bacteriologically polluted water). Models can be 

used to define mixing zones where blending water 

from two or more sources results in water quality 

problems. Specifically, water quality modeling 

tools may assist utilities in complying with the 

TCR, SWTR, IESWTR, LT1ESWTR, and LCR. 

Modeling can assist in identifying parts of the 

system with high TTHM formation potential 

(DBPR1) and meeting the Initial Distribution 

System Evaluation (IDSE) requirements of the 

DBPR2 (see the IDSE Case Study in Chapter 7). In 

addition, modeling techniques can assist in 

tracking contamination from cross-connections and 

other accidental or deliberate contamination events 

such as a waterborne outbreak. 

1.4 Assessment and Management 

of Water Quality 

Water utilities treat nearly 34 billion gallons of water 

every day (EPA, 1999). Generally, surface water 

systems require more treatment than groundwater 

1-8 
A Reference Guide for Utilities 

Table 1-1. Selected Rules and Regulations Dealing with Distribution Systems (Not Inclusive) 

Regulation Key Distribution System Requirements 

SDWA Gives EPA the authority to establish national primary and secondary drinking water regulations 

(MCLs and MCLGs). 

NIPDWR The NIPDWR which was adopted at the passage of the SDWA required that representative 

coliform samples be collected throughout the distribution system. 

TTHM Established a standard for TTHMs as 0.1 mg/L. 

86SDWAA Established the MCLG concept. 

TCR Regulates coliform bacteria which are used as surrogate organisms to indicate whether or not 

treatment is effective and system contamination is occurring. 

SWTR Requires using chlorine or some other disinfectant. 

LCR Monitoring for compliance with the LCR is based entirely on samples taken at the consumer’s tap. 

ICR Provided data to support the interim and long-term enhanced SWTR, and Stage 2 DBP rule. 

96SDWAA Has many provisions dealing with distribution systems, including the role that surface water 

quality can play in influencing the quality of distributed water. 

IESWTR Provisions to enhance protection from pathogens, including Cryptosporidium, and intended to 

prevent increases in microbial risk while large systems comply with the DBPR1. 

DBPR1 Has lowered the standard for TTHMs from 0.1 mg/L to 0.08 mg/L. This standard applies to all

community water supplies in the U. S. and requires monitoring and compliance at selected points 

in the distribution system. 

LT1ESWTR Provisions to enhance protection from pathogens, including Cryptosporidium, and prevent 

increases in microbial risk for systems serving less than 10,000 people while they comply with 

the DBPR1. 

systems because they are directly exposed to the 

atmosphere, runoff from rain and melting snow, and 

other industrial sources of contamination. Water 

utilities use a variety of treatment processes to remove 

contaminants from drinking water prior to distribution. The selected treatment combination is based on 

the contaminants found in the source water of that 

particular system. The general techniques include: 

•	 Coagulation/Flocculation: This process 

removes dirt and other particles suspended in 

the water. In this process, alum, iron salts, and/ 

or synthetic organic polymers are added to the 

water to form sticky particles called “floc,” 

which attract the suspended particles. 

•	 Sedimentation: In this process, the flocculated 

particles are gravity-settled and removed from 

the water. 

•	 Filtration: Many water treatment facilities use 

filtration to remove the smaller particles from 

the water. These particles include: clays and 

silts, natural organic matter, precipitates from 

other treatment processes in the facility, iron 

and manganese, and microorganisms. Filtration 

clarifies the water and enhances the 

effectiveness of disinfection. 

•	 Disinfection: Water is disinfected at the water 

treatment plant (or at the entry to the 

distribution system) to ensure that microbial 

contaminants are inactivated. Secondary 

disinfection is practiced in order to maintain a 

residual in the distribution system. 

Once the treated water enters the distribution system, 

a number of processes may occur that can adversely 

impact the water quality delivered to consumers. As 

the water enters a network of buried pipes, valves, 

joints, meters, and service lines, it is subject to 

disruptions such as water hammer (transient pressure 

shock wave), aging (at dead ends and large tanks), 

corrosion, cross-connections, leaching of toxic 

chemicals, intrusion of pathogens, and pipeline 

breaks. Some of these events may be regular occurrences, such as water aging, loss of chlorine residual 

in dead ends, or deposition of sedimentation in 

stagnant areas. Others may be rare or unusual events. 

Any of these events can cause the water quality to 

deteriorate and pose a potential public health risk. 

Some routine distribution system design changes and 

maintenance or operational procedures that can help 

to prevent or reduce the effects of such events include 

the following: 

•	 Tank Mixing: Inadequate mixing in a tank can 

lead to stagnant areas containing older water 

1-9 
A Reference Guide for Utilities 

Maintaining water quality in a drinking water distribution system while assuring adequate disinfection and 

reducing DBPs is a significant challenge for many 

drinking water utilities. This challenge will be even 

greater under the more stringent requirements of the 

LT2ESWTR and the DBPR2. Utilities that use chorine 

as their primary disinfectant and that have elevated 

organic levels in their treated water, long detention 

times, and/or warm water may have difficulty in meeting 

these regulations. The Las Vegas Valley Water District is 

conducting research to explore the feasibility of employing “targeted” distribution system treatment systems. 

This type of targeted system (or systems) would utilize 

small-scale water treatment technology to reduce the 

concentration of disinfection byproducts in those areas 

that might exceed the SDWA MCLs established under 

the LT2ESWTR and DBPR2. These systems are intended to be designed and operated in conjunction with 

a water quality/hydraulic model which would be used to 

predict where these decentralized treatment systems 

should be located. If the treatment technology is 

relatively mobile, it could be moved based on model 

predictions to locations where MCL violations are 

likely to occur. In addition, these types of systems 

would be valuable should a security threat arise. 

that has lost its disinfectant residual. Changes 

in operations (e.g., exercising the tank) or 

modifications to inlet-outlet configurations can 

improve mixing. 

•	 Re-chlorination: Some parts of a distribution 

system may experience long travel times from 

the treatment plant resulting in loss of chlorine 

residual. Installation of booster chlorination 

facilities at these locations can sometimes be an 

effective means of insuring an adequate residual 

in these areas. 

•	 Conventional Flushing: This procedure 

generally involves opening hydrants in an area 

until the water visibly runs clear. The object of 

this action would be to quickly remove 

contaminated water; however, it would not 

likely be effective in removal of contaminants 

that become attached to the pipe surfaces. 

Flushing only provides a short-term remedy. 

•	 Unidirectional Flushing: This procedure 

involves the closure of valves and opening of 

hydrants to concentrate the flow in a limited 

number of pipes. Flow velocities are 

maximized so that shear velocity near the pipe 

wall is maximized. It is intended to be done in 

a progressive fashion, proceeding outward from 

the source of water in the system so that 

flushing water is drawn from previously flushed 

Federal and state drinking water regulations are designed 

to provide a water supply to consumers that meets 

minimum health-based requirements. However, water 

utilities may choose to implement programs that go 

beyond current federal, state, and local regulatory 

requirements to increase the water quality and reduce the 

potential for contamination in water systems. There are 

several methods and guidelines that have been designed 

to assist utilities in providing water of a quality that 

exceeds the minimum requirements. These methods 

include: Hazard Analysis Critical Control Point 

(HACCP), source water optimization, and distribution 

system water quality optimization plans (DSOP). 

DSOP is one example of a framework for evaluating and 

improving programs that affect distribution system water 

quality (Friedman et al., 2005). Aspects of the DSOP 

include evaluation of conditions within the distribution 

system, creation of improved documentation, and 

enhancement of communication between the various 

utility functions that impact water quality in the distribution system. DSOPs address both regulatory/compliance issues and customer issues related to aesthetic 

properties of drinking water. The DSOP approach was 

piloted at three water utilities and a general template was 

developed that can be used by small, medium, and large 

utilities. The following ten steps are identified as part of 

the development of a DSOP: 

1. Formation of a committee to discuss distribution 

system issues of interest/concern and to guide the 

process of DSOP development. 

2. Identification of water quality and operating goals. 

3. Completion of a distribution system audit. 

4. Comparison of audit results to industry best


management practices.


5. Development of a list of utility needs for


optimizing distribution system water quality.


6. Prioritization of DSOP elements based on relative 

contribution towards improving water quality and 

precluding water quality degradation or 

contamination. 

7. For each priority DSOP element, compilation of 

applicable standard operating procedures (SOPs) 

and ongoing programs that provide information 

related to the condition of the distribution system 

and water quality. 

8. Development and implementation of priority


programs.


9. Periodic review of programs and goals developed 

as part of the DSOP. 

10. Development of revised SOPs that describe the 

optimized approach. 

DSOP and other aforementioned methodologies are still 

in their early stages of application in the water supply 

industry and will require further evaluation to determine 

their effectiveness in meeting the goals to improve water 

quality in drinking water systems. 

1-10 
reaches. No special equipment is required; 

however, some planning time is required to 

determine the flushing zones, the valves and 

hydrants to be operated, and the duration of the 

flushing exercise for each zone. 

•	 Valve Exercising Program: A routine program to 

exercise isolation valves can have several 

positive effects. These include identifying (and 

repairing) malfunctioning valves and 

identifying valves that are in an inappropriate 

setting (e.g., closed valves that are expected to 

be open). 

•	 Cross-Connection Control Program: An 

inspection program intended to ensure no 

interconnection(s) between the drinking water 

and wastewater systems in homes and buildings. 

Examples of routine maintenance and operation 

procedures for pipe cleaning include the following 

(AwwaRF, 2004): 

•	 Air Scouring, Swabbing and Abrasive pigging: Air


scouring, swabbing, and abrasive pigging are


progressively more aggressive cleanup techniques


that involve more specialized equipment and skills.


A few water utilities have implemented these


methods using their own staff; typically, these


methods are contracted to specialty firms.


Implementation of these methods would require


installation of new pipeline appurtenances (e.g., pig


launching and receiving stations; pigging is not


recommended for cast iron pipes).


•	 Chemical/Mechanical Cleaning and Lining:


Chemical cleaning involves the recirculation in an


isolated pipe section of proprietary acids and


surfactants to remove scale and deposits, while


mechanical cleaning is accomplished by dragged


scrapers. These techniques are typically applied in


the rehabilitation of older unlined cast iron pipe


which, over time, have become scaled and


tuberculated. These cleaning operations are


typically followed by an in-situ application of a


thin cement mortar or epoxy lining to ensure lasting


protection.


If the symptoms persist after the application of these 

techniques, the pipes are usually replaced. 

1.5 Advanced Tools for Water 

Quality Management 

Recent advancements in computation and instrumentation technologies have led to the availability 

of advanced tools that are already beginning to 

improve a utility’s ability to effectively manage 

A Reference Guide for Utilities 

water quality in distribution systems. These 

computational advancements have led to the 

development of software models that can simulate 

the behavior of distribution system networks. Water 

distribution system models (such as EPANET) have 

become widely accepted both within the water 

utility industry and the general research arena for 

simulating both hydraulic and water quality 

behavior in water distribution systems. The 

advancements in instrumentation and Supervisory 

Control and Data Acquisition (SCADA) systems 

now enable the utilities to monitor and control 

various water quality parameters from a remote 

location in real-time within a distribution system 

network. Furthermore, recent advances in Geographic Information Systems (GIS) technology have 

led to the integration of remote monitoring network 

models with GIS layers. This combination provides 

utilities a visual tool to efficiently manage both 

water quality and distribution system assets such as 

pipes, pumps, and valves. 

1.6 Report Organization 

Various chapters of this reference guide will 

describe modeling and monitoring tools for 

effectively managing water quality in drinking 

water distribution systems. Examples and protocols 

for effectively applying water quality models for 

understanding and resolving water quality issues in 

networks will be presented. Another important 

aspect of effectively applying water quality models 

is to ensure that they are properly and periodically 

calibrated. Tracer tests are one of the most effective techniques for calibrating a water quality 

model. Modeling techniques, when combined with 

advanced monitoring and geospatial technologies, 

can play a vital role in managing water quality in 

distribution systems. Chapter 2 provides an 

overview on modeling of distribution systems for 

water quality. Chapter 3 describes techniques for 

conducting tracer studies in distribution systems. 

Chapter 4 presents data analysis techniques for 

effectively calibrating a distribution system model 

using tracer or other field data. Chapter 5 provides 

an overview of monitoring techniques and technologies available for monitoring water quality. 

Chapter 6 introduces geospatial technology and its 

relation to water distribution systems. Finally, 

Chapter 7 is a compilation of real-world applications of water quality modeling and monitoring for 

planning, analysis and simulation of historical 

events. 

1.7 Summary 

Distribution system infrastructure is a major asset of 

most water utilities. It serves many important 

functions in a community, such as promoting eco1-11 
A Reference Guide for Utilities 

The information presented in this reference guide is 

intended for a general technical audience. The 

various chapters provide an overview of the state-ofart techniques for managing water quality in distribution systems. For a more comprehensive case-specific 

solution, the reader should refer to text books in 

specific subject areas and/or consult with water 

quality professionals. The following is a brief listing 

of recommended books (listed in alphabetical order 

by title): 

1. Advanced Water Distribution Modeling and 

Management. T.M. Walski, D.V. Chase, D.A. 

Savic, W. Grayman, S. Beckwith, and E. Koelle. 

Haestad Press, Waterbury, CT. 2003. 

2. Comprehensive Water Distribution Systems 

Analysis Handbook. P.B. Boulos, K.E. Lansey, 

and B.W. Karney. MWHSOFT, Inc., Pasadena, CA. 

2004. 

3. Computer Modeling of Water Distribution


Systems (M32), AWWA. 2004.


4. GIS Applications for Water, Wastewater, and 

Stormwater Systems. U. Shamsi. CRC Press. 2005. 

5. Hydraulics of Pipeline Systems. 	B.E. Larock, R.W. 

Jeppson, G.Z. Watters. CRC Press. 1999. 

6. Microbial Quality of Water Supply in Distribution 

Systems. Edwin E. Geldreich. CRC Press. 1996 

7.	 Modeling, Analysis and Design of Water 

Distribution Systems. L. Cesario. AWWA. 1995. 

8. Modeling Water Quality in Drinking Water


Distribution Systems. R.M. Clark and W.M.


Grayman. AWWA. 1998.


9. Online Monitoring for Drinking Water Utilities. 

Edited by E. Hargesheimer, O. Conio, and J. 

Popovicova. AwwaRF – CRS ProAqua. 2002. 

10.	 Safe Drinking Water: Lessons from Recent 

Outbreaks in Affluent Nations. S.E. Hrudey and 

E.J. Hrudey, IWA Publishing. 2004. 

11.	 Water Distribution Systems Handbook. Edited by 

L.W. Mays, McGraw Hill. 2000. 

12.	 Water Supply Systems Security. Edited by L.W. 

Mays, McGraw Hill. 2004. 

nomic growth, supporting public safety, and protecting public health. In order for a community to grow 

and prosper, it must have the physical infrastructure to 

provide basic services such as water supply. In 

addition to the economic implications of adequate 

water supply, water systems play a critical role in 

supporting public safety through the provision of fire 

protection capacity. Frequently, insurance rates in a 

community are tied to the fire protection capability of 

the water system. Water systems play a key role in 

protecting a community’s public health by providing 

safe drinking water to water consumers. 

1-12 
A Reference Guide for Utilities 

References


American Society of Civil Engineers (ASCE). 

“Report Card for America’s Infrastructure, Drinking 

Water.” ASCE, Reston, VA. 2005. 

American Water Works Association (AWWA). AWWA 

Manual M31, Distribution System Requirements for 

Fire Protection, AWWA, Denver, CO. 1998. 

AWWA. Water Quality & Treatment – A Handbook of 

Community Water Supplies. Edited by R.D. 

Letterman, McGraw-Hill, NY. 1999. 

American Water Works Association Research Foundation (AwwaRF). Standard Operating Procedures for 

Decontamination of Water Infrastructure. March, 

2004. 

AwwaRF. Impacts of Fire Flow on Distribution 

System Water Quality, Design, and Operation. 2002. 

Camper, A.K., K. Brastrup, A. Sandvig, J. Clement, C. 

Spencer, and A.J. Capuzzi. “Impact of distribution 

system materials on bacterial regrowth.” Journal of 

AWWA, 95(7):107-121. 2003. 

Clark, R.M., W.M. Grayman, and R.M. Males. 

“Contaminant Propagation in Distribution Systems,” 

Journal of Environmental Engineering, ASCE, 

114:(2). 1988. 

Clark, R.M., W.M. Grayman, and J.A. Goodrich. 

“Water Quality Modeling: Its Regulatory Implications.” Proceedings, AwwaRF/EPA Conference on 

Water Quality Modeling in Distribution Systems, 

Cincinnati, OH. 1991a. 

Clark, R.M., D.J. Ehreth, and J.J. Convery. “Water 

Legislation in the US: An Overview of the Safe 

Drinking Water Act,” Toxicology and Industrial 

Health, 7(516):43-52. 1991b. 

Clark, R.M., and W.A. Feige. “Meeting the Requirements of the Safe Drinking Water Act,” in Strategies 

and Technologies for Meeting the Requirements of 

the SDWA. Edited by Robert M. Clark and R. Scott 

Summers, Technomics, Publishing Company, Inc., 

Lancaster, PA. 1993. 

Clark, R.M., W.M. Grayman, R.M. Males, and A.F. 

Hess, 1993a. “Modeling Contaminant Propagation in 

Drinking Water Distribution Systems,” Journal of 

Environmental Engineering, ASCE, 119(2):349-364. 

1993. 

Clark, R.M., J.A. Goodrich, and L.J. Wymer. “Effect of 

the Distribution System on Drinking Water Quality,” 

Journal of Water Supply Research and Technology ­

Aqua, 42(1):30-38. 1993b. 

Clark, R.M., E.E. Geldreich, K.R. Fox, E.W. Rice, C.H. 

Johnson, J.A. Goodrich, J.A. Barnick, and F. 

Abdesaken. “Tracking a Salmonella serovar 

Typhimurium Outbreak in Gideon, Missouri: Role of 

Contaminant Propagation Modeling,” Journal of 

Water Supply Research and Technology - Aqua, 

45(4):171-183. 1996. 

Clark, R.M., and W.M. Grayman. Modeling Water 

Quality in Drinking Water Distribution Systems. 

AWWA, Denver, CO. 1998. 

Clark, R.M., W.M. Grayman, S.G. Buchberger, Y. Lee, 

and D.J. Hartman. “Drinking Water Distribution 

Systems: An Overview,” in Water Supply Systems 

Security. Edited by Larry W. Mays, McGraw-Hill, NY. 

pp 4.1- 4.49. 2004 

Clark, R.M., and D. L. Tippen. “Water Supply” in 

Standard Handbook of Environmental Engineering. 

Edited by Robert A. Corbitt, McGraw-Hill, NY. pp 

5.173-5.220. 1990. 

Connell, G.F. “European Water Disinfection Practices 

Parallel U.S. Treatment Methods,” Drinking Water 

and Health Quarterly. August 1, 1998. http:// 

www.clo2.com/reading/waternews/european.html 

EPA. The Clean Water and Drinking Water Infrastructure Gap Analysis. EPA - Office of Water, 

Washington DC. September 2002. 

EPA. 25 Years of the Safe Drinking Water Act: 

History & Trends. EPA 816-R-99-007. December 

1999. 

Fair, G.M., and J.C. Geyer. “Water Supply and WasteWater Disposal,” John Wiley and Sons, Inc., NY. 1971. 

Friedman, M., G. Kirmeyer, G. Pierson, S. Harrison, K. 

Martel, A. Sandvig, and A. Hanson. Development of 

Distribution System Water Quality Optimization 

Plans. AwwaRF/AWWA. Denver, CO. 2005. 

Geldreich, E.E., H.D. Nash, D.J. Reasoner, and R.H. 

Taylor. “The Necessity of Controlling Bacterial 

Populations in Potable Water: Community Water 

Supply,” Journal of AWWA, 64:596-602. 1972. 

1-13 
A Reference Guide for Utilities 

Greater Cincinnati Water Works. “Water Treatment,” 

http://www.cincinnati-oh.gov/water/pages/-3283-/. 

2005. 

Grigg, NS. Assessment and Renewal of Water 

Distribution Systems. AwwaRF Report # 91025F, 

June 2005. 

Gullick, R.W., W.M. Grayman, R.A. Deininger, and 

R.M. Males. “Design of Early Warning Monitoring 

Systems for Source Waters,” Journal of AWWA, 

95(11):58-72. 2003. 

Insurance Services Office (ISO). Fire Suppression 

Rating Schedule. Insurance Services Office, NY. 

2003. 

International Life Sciences Institute. Early Warning 

Monitoring to Detect Hazardous Events in Water 

Supplies. Edited by T.M. Brosnan, Washington, D.C. 

1999. 

Kirmeyer, G.J., W. Richards, and C. Dery-Smith. An 

Assessment of Water Distribution Systems and 

Associated Needs. AwwaRF. 1994. 

LeChevallier, M.W., T.M. Babcock, and R.G. Lee. 

“Examination and Characterization of Distribution 

System Biofilms,” Applied and Environmental 

Microbiology, 53:2714-2724. 1987. 

Maul, A., A.H. El-Shaarawi, and J.C. Block. “Heterotrophic Bacteria in Water Distribution Systems - I. 

Spatial and Temporal Variation.” The Science of the 

Total Environment, 44:201-214. 1985a. 

Maul, A., A.H. El-Shaarawi, and J.C. Block. “Heterotrophic Bacteria in Water Distribution Systems - II. 

Sampling Design for Monitoring.” The Science of the 

Total Environment, 44:215-224. 1985b. 

Montana State University (MSU) Center for Biofilm 

Engineering. Image provided by Pat Dirckx. 2005. 

National Fire Protection Association (NFPA). Fire 

Protection Handbook, 19th edition. Edited by A.E. 

Cote, National Fire Protection Association, Quincy, 

MA. 2003. 

NRC. Public Water Supply Distribution Systems: 

Assessing and Reducing Risks – First Report. The 

National Academies Press, Washington, DC. 2005. 

Okun, D. “Distributing reclaimed water through dual 

systems,” Journal of AWWA, 89 (11), pp. 52-64. 1996. 

Owens, J. A Review of Federal Drinking Water 

Regulations in the U.S., in Controlling Disinfection 

By-Products and Microbial Contaminants in Drinking 

Water. Edited by Robert M. Clark and Brenda Boutin, 

EPA-600-R-01-110. pp 2-1 to 2-14. December 2001. 

PDD 63. Critical Infrastructure Protection. The 

White House, Washington D.C. May 22, 1998. 

PL107-188. Public Health Security and Bioterrorism 

Preparedness and Response Act of 2002. 

Safe Drinking Water Act (SDWA), 1974, Public Law 

93-523. 

Walski, T. M, D.V. Chase, D.A. Savic, W.M. Grayman, 

S. Beckwith, and E. Koelle. Advanced Water Distribution Modeling and Management. Haestad Press, 

Waterbury, CT. pp 1-4. 2003. 

Water Research Centre. “Deterioration of Bacteriological Quality of Water During Distribution,” Notes 

on Water Research No. 6. 1976. 

Zhang, W., and F.A. DiGiano. “Comparison of 

Bacterial Regrowth in Distribution Systems Using 

Free Chlorine and Chloramine: A Statistical Study of 

Causative Factors.” Water Research, 36:6:1469-1482. 

2002. 

1-14 
A Reference Guide for Utilities 

Chapter 2 

Modeling Water Quality in Drinking Water 

Distribution Systems 

This chapter covers the use of models to simulate the flow and water quality conditions in a distribution 

system network. Models are mathematical or physical approximations of a real-world system and can be 

used to study the behavior of actual system(s). A variety of computer software modeling tools are now 

available to perform these simulations. These tools are now commonly used by trained engineers and 

scientists to study and improve water distribution system network design and operation. 

Water distribution system models have become 

widely accepted within the water utility industry as a 

mechanism for simulating the hydraulic and water 

quality behavior in water distribution system networks. Current water distribution modeling software 

is powerful, sophisticated and user-friendly. Many 

software packages are integrated with GIS and 

Computer Aided Design (CAD) technology in order to 

facilitate model construction and storage and display 

of model results. Early network models simulated 

only steady-state hydraulic behavior. In the 1970s, 

modeling capability was expanded to include 

Extended Period Simulation (EPS) models that could 

accommodate time-varying demand and operations. 

Subsequently, in the early 1980s, investigators began 

introducing the concept of water quality modeling. 

Most water distribution system modeling software 

packages now routinely incorporate water quality 

simulation capability. More recently, transient 

models for simulating water hammer (a transient 

phenomenon) and tank mixing/aging models have 

either been incorporated into or integrated with water 

distribution system models. Algorithms have been 

developed that enable users to optimize water system 

design and operation, assist in model calibration, and 

perform probabilistic analyses. Each of these model 

types are briefly described later in this chapter. 

Water distribution system models are more commonly 

being used to replicate the behavior of a real or 

proposed system for a variety of purposes including: 

capital investment decisions, development of master 

plans, estimation of fire protection capacity, design of 

new systems and extension or rehabilitation of 

existing systems, energy management, water quality 

studies, various event simulations and analysis, 

optimal placement of sensors, and daily operations. 

The costs associated with constructing and maintaining a distribution system model may be more easily 

justified if it is used for a variety of applications by a 

water utility (Grayman, 2000). 

2.1 Distribution System Network 

Hydraulic Modeling 

The network hydraulic model provides the foundation 

for modeling water quality in distribution systems. 

This subsection provides a brief history of hydraulic 

modeling, an overview of theoretical concepts, basic 

model inputs, and general criteria for selection and 

application. 

2.1.1 History of Hydraulic Modeling 

Hardy Cross first proposed the use of mathematical 

methods for calculating flows in complex networks 

(Cross, 1936). This manual, iterative procedure was 

used throughout the water industry for almost 40 

years. With the advent of computers and computerbased modeling, improved solution methods were 

developed for utilizing the Hardy Cross methodology. 

The improved implementations of this method were in 

widespread use by the 1980s (Wood, 1980a). 

Also, in the early 1980s, the concept of modeling 

water quality in distribution system networks was 

developed based on steady-state formulations (Clark 

et al., 1986). By the mid-1980s, water quality models 

were developed that incorporated the dynamic 

behavior of water networks (Grayman et al., 1988). 

The usability of these models was greatly improved in 

the 1990s with the introduction of the public domain 

EPANET model (Rossman, 2000) and other Windowsbased commercial water distribution system models. 

Initially, hydraulic models simulated flow and 

pressures in a distribution system under steady-state 

conditions where all demands and operations remained constant. Since system demands (and 

consequently the flows in the water distribution 

network) vary over the course of a day, EPS models 

were developed to simulate distribution system 

behavior under time-varying demand and operational 

conditions. These models have now become ubiquitous within the water industry and are an integral part 

of most water system design, master planning, and fire 

flow analyses. 

2-1 
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A Reference Guide for Utilities 

2.1.2 Overview of Theoretical Concepts 

The theory and application of hydraulic models is 

thoroughly explained in many widely available 

references (Walski et al., 2003; American Water Works 

Association, 2004; Larock et al., 2000). Essentially, 

three basic relations are used to calculate fluid flow in 

a pipe network. These relationships are: 

•	 Conservation of Mass: This principle requires 

that the sum of the mass flows in all pipes 

entering a junction must equal the sum of all 

mass flows leaving the junction. Because water 

is essentially an incompressible fluid, 

conservation of mass is equivalent to 

conservation of volume. 

•	 Conservation of Energy: There are three types 

of energy in a hydraulic system: kinetic energy 

associated with the movement of the fluid, 

potential energy associated with the elevation, 

and pressure energy. In water distribution 

networks, energy is referred to as “head” and 

energy losses (or headlosses) within a network 

are associated primarily with friction along pipe 

walls and turbulence. 

•	 Pipe Friction Headloss: A key factor in 

evaluating the flow through pipe networks is 

the ability to calculate friction headloss 

(Jeppson, 1976). Three empirical equations 

commonly used are the Darcy-Weisbach, the 

Hazen-Williams, and the Manning equations. 

All three equations relate head or friction loss in 

pipes to the velocity, length of pipe, pipe 

diameter, and pipe roughness. A fundamental 

relationship that is important for hydraulic 

analysis is the Reynolds number, which is a 

function of the kinematic viscosity of water 

(resistance to flow), velocity, and pipe diameter. 

The most widely used headloss equation in the 

U.S. is the Hazen-Williams equation. Though 

the Darcy Weisbach equation is generally 

considered to be theoretically more rigorous, 

the differences between the use of these two 

equations is typically insignificant under most 

circumstances. 

A distribution system is represented in a hydraulic 

model as a series of links and nodes. Links represent 

pipes whereas nodes represent junctions, sources, 

Hydraulic models represent the basic underlying 

equations (conservation of mass and conservation of 

energy) as a series of linear and non-linear equations. 

Because of the non-linearity, iterative solution methods 

are commonly used to numerically solve the set of 

equations. The most common numerical method 

utilized is the Newton-Raphson method. 

Source 

Pump 

LEGEND 

Tank 

Valve 

Junction node 

Pipe link 

Figure 2-1. Simple Link-Node Representation of a 

Water Distribution System. 

tanks, and reservoirs. Valves and pumps are represented as either nodes or links depending on the 

specific software package. Figure 2-1 illustrates a 

simple link-node representation of a water distribution system. 

As mentioned previously, there are two types of 

analyses that may be conducted on drinking water 

distribution systems: steady-state and EPS. In a 

steady-state analysis, all demands and operations are 

treated as constant over time and a single solution is 

generated. In the EPS mode, variations in demand, 

tank water levels, and other operational conditions 

are simulated by a series of steady-state analyses that 

are linked together. Each steady-state solution in the 

EPS mode involves a separate solution of the set of 

non-linear equations. EPS is used as the basis for 

Conservation of Mass: The conservation of mass 

principle for hydraulic analysis requires that the sum of 

the mass flow in all pipes entering a junction must 

equal the sum of all mass flows leaving the junction. 

In EPS, if storage is involved, a term for describing the 

accumulation of water at those nodes is included. 

Mathematically, the principle can be represented as 

follows: 

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2-2 
P V 2	 2 P V hL + hM	 Z (Equation 2-2) 1 + 1 + 1 + h = + Z 2 + 2

P 2 + 2g 2g

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water quality modeling. Though the EPS solution 

does introduce some approximations and ignores the 

transient phenomena resulting from sudden changes 

(e.g., a pump being turned on), these more refined 

assumptions are generally not considered significant 

for most distribution system studies. 

Conservation of Energy: The conservation of energy


principle requires that the difference in energy


between two points in a network must be the same


regardless of flow path. For hydraulic analysis, this


principle can be represented in terms of head as


follows:


where 

Z1 and 2 = elevation at points 1 and 2, respectively, in ft (m) 

2 2 P1 and 2 = pressure at points 1 and 2, respectively, in lb/ft (N/m )

3 3 = fluid (water) specific weight, in lb/ft (N/m ) 

V1 and 2 = velocity at points 1 and 2, respectively, in ft/s (m/s) 

2 2 g = acceleration due to gravity, in ft/sec (m/sec ) 

hP = pumping head gain, in ft (m) 

hL = head loss in pipes, i n ft (m) 

hM = head lossdue to minor losses, in ft (m) 

Pipe-friction headloss: The equation most commonly 

used in modeling software for computation of pipefriction headloss is the Hazen-Williams equation 

represented as follows: 

2.1.3 Basic Hydraulic Model Input


Characterization


Building a network model, particularly if a large 

number of pipes are involved, is a complex process. 

The following categories of information are needed to 

construct a hydraulic model: 

•	 Characteristics of the pipe network components 

(pipes, pumps, tanks, valves). 

A Reference Guide for Utilities 

•	 Water use (demands) assigned to nodes


(temporal variations required in EPS).


•	 Topographic information (elevations assigned 

to nodes). 

•	 Control information that describes how the 

system is operated (e.g., mode of pump 

operation). 

•	 Solution parameters (e.g., time steps, tolerances 

as required by the solution techniques). 

Commonly used methods for these inputs are briefly 

described in the following subsections. 

2.1.3.1 Pipe Network Inputs 

Construction of the pipe network and its characteristics may be done manually or through use of existing 

spatial databases stored in GIS or CAD packages. 

Most commonly, GIS or CAD packages are used in 

this process and are described in more detail in 

Chapter 6. The initial step in constructing a network 

model is to identify pipes to be included in the 

model. Nodes are usually placed at pipe junctions, or 

at major facilities (tanks, pumps, control valves), or 

where pipe characteristics change in diameter, “C”­

value (roughness), or material of construction. Nodes 

may also be placed at locations of known pressure or 

at sampling locations or at locations where water is 

used (demand nodes). The required pipe network 

component information includes the following: 

•	 pipes (length, diameter, roughness factor), 

•	 pumps (pump curve), 

•	 valves (settings), and 

•	 tanks (cross section information, minimum and 

maximum water levels). 

2.1.3.2 Water Demand Inputs 

Water consumption or water demand is the driving 

force behind the operation of a water distribution 

system. Any location at which water leaves the 

system can be characterized as a demand on the 

system. The water demands are aggregated and 

assigned to nodes, which represents an obvious 

simplification of real-world situations in which 

individual house taps are distributed along a pipe 

rather than at junction nodes. It is important to be 

able to determine the amount of water being used, 

where it is being used, and how this usage varies with 

time (Walski et al., 2003). Demand may be estimated 

by a count of structures of different types using a 

representative consumption per structure, meter 

readings and the assignment of each meter to a node, 

and to general land use. A universal adjustment factor 

should be used to account for losses and other 

unaccounted water usage so that total usage in the 

2-3 
A Reference Guide for Utilities 

Early software packages limited the number of pipes that 

could be included due to computer storage restrictions. 

This led to the concept of “skeletonizing” a network or 

including only those pipes that were considered to be 

the most important. The degree of skeletonization that is 

acceptable should depend upon the ultimate use of the 

model. For example, master plans and energy studies 

might be based on the use of skeletonized networks. 

Other applications, such as water quality modeling and 

designing flushing programs, require a model that 

includes more pipes. Though there is no national 

standard for skeletonization, the EPA draft guidance 

issued for modeling to support the IDSE under DBPR2 

suggests inclusion of (EPA, 2003): 

•	 At least 50 percent of total pipe length in the


distribution system.


•	 At least 75 percent of the pipe volume in the


distribution system.


•	 All 12-inch diameter and larger pipes. 

•	 All 8-inch and larger pipes that connect pressure 

zones, influence zones from different sources, 

storage facilities, major demand areas, pumps, and 

control valves, or are known or expected to be 

significant conveyors of water. 

•	 All 6-inch and larger pipes that connect remote 

areas of a distribution system to the main portion 

of the system. 

•	 All storage facilities with controls or settings 

applied to govern the open/closed status of the 

facility that reflect standard operations. 

•	 All active pump stations with realistic controls or 

settings applied to govern their on/off status that 

reflect standard operations. 

•	 All active control valves or other system features 

that could significantly affect the flow of water 

through the distribution system (e.g., 

interconnections with other systems, valving 

between pressure zones). 

A case study presented in Section 7.3.1 illustrates the 

use of models in support of IDSE. 

Most modern software packages support an unlimited 

number of pipes; however, skeletonization is still 

frequently used in order to reduce the modeling effort. A 

minimal skeletonization should include all pipes and 

features of major concern. 

model corresponds to total production. 

In order to use a model in the EPS mode, information 

on temporal variations in water usage over the period 

being modeled are required. Spatially different 

temporal patterns can be applied to the individual 

network nodes. The best available information 

should be used for developing temporal patterns in 

order to make EPS most effective. For example, some 

users may have continuous water metering data, while 

others may use literature values as a first approximation for estimating residential temporal patterns. 

Temporal patterns also vary with climate. For example, lawn watering in summer months will cause a 

spike in usage of water during that time period. In 

some cases, information from SCADA systems can be 

used to estimate system-wide temporal patterns. 

A typical hierarchy for assigning demands includes 

the following: 

•	 Baseline Demands: Baseline demands usually 

correspond to consumer demands and 

unaccounted-for-water associated with average 

day conditions. This information is often 

acquired from a water utility’s existing records, 

such as customer meter and billing records. 

Although the spatial assignment of these 

demands is extremely important and should 

include the assignment of customer classes such 

as industrial, residential, and commercial use, 

actual metering data should be used when 

available. 

•	 Seasonal Variation: Water use typically varies 

over the course of the year with higher demands 

occurring in warmer months. When developing 

a steady-state model, the baseline (average day) 

demand can be modified by multipliers in order 

to reflect other conditions such as maximum 

day demand, peak-hour demand, and minimum 

day demand. 

•	 Fire Demands: Water provided for fire services 

can be the most important consideration in 

developing design standards for water systems. 

Typically, a system is modeled corresponding to 

maximum-use conditions, with needed fire-flow 

added to a single node at a time. It is not 

uncommon for a requirement that multiple 

hydrants be flowing simultaneously. 

•	 Diurnal Variation: All water systems are 

unsteady due to continuously varying demands. 

It is important to account for these variations in 

order to achieve an adequate hydraulic model. 

Diurnal varying demand curves should be 

developed for each major consumer class or 

geographic zones within a service area. For 

example, diurnal demand curves might be 

developed for industrial establishments, 

commercial establishments, and residences. 

Large users such as manufacturing facilities 

may have unique usage patterns. 

2-4 
Future water use: For design and planning purposes, a 

water system must be examined under future conditions. 

In situations where a system is largely currently built 

out, future demands may be estimated by developing 

global or regional multipliers that are applied to current 

demands. However, in new or developing areas, existing 

water use does not provide a useful basis for estimating 

future demands. Alternative approaches use population-based projections, socioeconomic modeling, and 

land-use methods (Johnson and Loux, 2004). 

In estimating future demands for use in a network 

model, the most appropriate method is generally the 

land-use method. The land-use method is based on 

mapping land uses and then applying a water-use factor 

to each land-use category. When applied to existing 

situations or in historical reconstruction of water 

systems, aerial photographs are most commonly used as 

the base map for identifying land-use categories. For 

development of future demands, land–use maps can be 

obtained from planners. The land-use methodology is 

depicted in Figure 2-2. 

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Figure 2-2. A Flow Chart for Estimating Future Water 

Demand Based on Land-Use Methodology. 

Land-use unit demands or water-use factors are typically 

developed in units of gallons per day (GPD) per acre 

from local historical consumption data or from available 

regional information. GIS technology is frequently 

used as a means of developing and manipulating the 

land-use polygons and assigning the calculated demands to the model nodes. 

2.1.3.3 Topographical Inputs 

Hydraulic models use elevation data to convert heads 

to pressure. Actual pipe elevations should be used to 

establish the correct hydraulic gradeline. Elevations 

A Reference Guide for Utilities 

are assigned to each node in a network where pressure 

information is required. Various techniques are used 

to determine elevation information including the 

following: 

•	 Topographical maps: Paper topographical maps 

produced by the United States Geological 

Survey (USGS) or other local agencies may be 

used to manually interpolate elevations for 

nodes. The relative accuracy depends upon the 

degree of topography in the area, the contour 

elevations on the map, and the manual takeoff 

methods used. 

•	 Digital elevation models (DEM): USGS and 

other agencies produce digital files containing 

topographical information. When used with 

various software tools, elevation information 

can be directly interpolated and assigned to 

nodes based on the coordinates of the nodes. 

The accuracy of this process depends upon the 

degree of detail in the DEM. 

•	 Global Positioning Systems (GPS) or other field 

survey methods: Standard field surveying 

techniques or modern surveying methods using 

a GPS satellite can be used to measure 

elevations at nodes. The modern GPS units can 

calculate elevation by using four or more 

satellites. However, elevation is the most 

difficult calculation for a GPS unit, and 

depending upon the location surveyed, it may 

be prone to significant error. 

2.1.3.4 Model Control Inputs 

In order to apply an EPS model, it is necessary to 

define a set of rules that tells the model how the water 

system operates. This may be as simple as specifying 

that a particular pump operates from 7:00 AM to 

10:00 AM each day. Alternatively, it may be a set of 

complex “logical controls” in which operations such 

as pump off/on, pump speed, or valve status are 

controlled using Boolean operators (including ifthen-else logic) for factors such as tank water levels, 

node pressures, system demand, and time of day 

(Grayman and Rossman, 1994). For water systems 

that operate automatically based on a set of rules, 

determination of these rules are quite straightforward. 

For manual systems, the rules must be determined by 

interviews with system operators. 

2.1.3.5 Extended Period Simulation (EPS) 

Solution Parameters 

Solution techniques used to iteratively solve the set 

of non-linear equations typically have various global 

parameters that control the solution technique. These 

parameters may be time-step lengths for EPS runs or 

tolerance factors that tell the model when a solution is 

considered to have converged. The user must specify 

2-5 
A Reference Guide for Utilities 

the values for the solution parameters, or (as is 

frequently done) accept the default values that are 

built into the software products. The specific solution 

parameters vary between solution techniques and 

specific software products. 

2.1.4 General Criteria for Model Selection and 

Application 

The initial step in modeling is to define the basic 

scope and needs of the modeling process and to select 

an appropriate software package that will satisfy both 

the specific needs of the current project and likely 

future needs. Factors that may enter into the selection 

of a software package include: 

•	 technical features, 

•	 training/support and manuals, 

•	 user interface, 

•	 integration with other software (such as GIS, 

CAD), 

•	 compatibility with EPANET, 

•	 cost, and 

•	 response from existing users. 

A summary of major available hydraulic-water quality 

modeling software is provided in Section 2.3.2. Once 

a suitable model has been selected, the following 

steps should be followed in applying network models 

(Clark and Grayman, 1998): 

•	 Develop the basic network model. 

•	 Calibrate and validate the model. 

•	 Establish clear objectives and apply the model 

in a manner to meet the objectives. 

•	 Analyze and display the results. 

2.1.4.1 Developing a Basic Network Model 

The basic network model inputs should be first 

characterized using the techniques described in 

Section 2.1.3. The model should be developed based 

on accurate, up-to-date information. Information 

should be entered carefully and checked frequently. 

Following the entry of the data, an initial run of the 

model should be made to check for reasonableness. 

2.1.4.2 Model Calibration and Validation 

Calibration is an integral aspect of the art of modeling 

water distribution systems. Model calibration is the 

process of adjusting model input data (or, in some 

cases, model structure) so that the simulated hydraulic 

and water quality output sufficiently mirrors observed 

field data. Depending on the degree of accuracy 

desired, calibration can be difficult, costly, and timeconsuming. The extent and difficulty of calibration 

are minimized by developing an accurate set of basic 

inputs that provide a good representation of the real 

network and its components. 

A traditional technique for calibration is the use of 

“fire-flow” tests. In a fire-flow test, the system is 

stressed by opening hydrants to increase flows in 

small parts of the system. This results in increased 

headloss in pipes in the vicinity of the test. Pressures 

and flow are then measured in the field. Model 

parameters, such as roughness factors (C), demands, 

and valve positions, are adjusted so that the model 

adequately reflects the field data. Another common 

calibration technique is to measure predicted tank/ 

reservoir levels derived from computer simulations 

against actual tank levels during a given period of 

record. For example, using water level, pressure, or 

flow data from SCADA systems or from on-line 

pressure and tank-level recorders, model parameters 

(such as roughness, water demands, and pump 

controls) can be adjusted in the simulation model 

until the model results match the actual tank level 

and other continuous information for the defined 

criteria. The resulting optimal parameter values 

should be checked to ensure that the values are 

realistic. Sophisticated commercial hydraulic models, 

such as those listed in Section 2.4, may also incorporate optimization components that aid the user in 

selecting system parameters resulting in the best 

match between observed system performance and 

model results (Walski, 2003). 

Model validation is the step that follows calibration 

and uses an independent field data set to verify that 

the model is well calibrated. In the validation step, 

the calibrated model is run under conditions differing 

from those used for calibration and the results 

compared to field data. If the model results closely 

approximate the field results (visually) for an appropriate time period, the calibrated model is considered 

to be validated. Significant deviations indicate that 

further calibration is required. A variety of calibration and validation techniques suitable to both large 

and small water utilities are discussed in Chapter 4 of 

this document. 

Another rigorous methodology for calibration and validation is the use of tracers. Concentrations of naturally 

occurring materials or added chemical tracers may be 

measured in the field and the results used to calibrate 

hydraulic and water quality models. This methodology is 

further described in Chapter 3 of this document. 

2.1.4.3 Establishing Objectives and Model 

Application 

Prior to applying the model, the specific modeling 

objectives should be clearly established. The objectives may include specification of particular water 

demand and operational modes. Based on these 

2-6 
A Reference Guide for Utilities 

specifications, a series of scenarios can be defined and 

the model applied appropriately. Some software 

products contain a scenario manager that helps the 

user to define and manage a large number of specific 

model runs. Additional scenarios can be developed in 

order to test the sensitivity of the system to variations 

in model parameters that are not known with certainty. 

2.1.4.4 Analysis and Display of Results 

Water distribution system models generate a large 

amount of output. The amount of calculated information increases with increasing model size and, for 

EPS, the duration of the model run. Modern water 

distribution system analysis software typically 

provides a range of graphical and tabular displays 

that help the user wade through the large amount of 

output data so that it may be efficiently analyzed. 

Figures 2-3, 2-4, and 2-5 contain examples of various 

graphical and tabular outputs generated by the 

EPANET software. These outputs represent a small 

subset of types of graphics generated by most 

modeling software. The output should be analyzed to 

ensure that the model is operating properly and to 

extract the information required in order to analyze 

the specific problem being studied. 

2.2 Modeling Water Quality In 

Distribution System Networks 

Water quality models use the output of hydraulic 

models in conjunction with additional inputs 

(described later in this section) to predict the temporal 

and spatial distribution of a variety of constituents 

within a distribution system. These constituents 

include: 

•	 The fraction of water originating from a


particular source.


•	 The age of water (e.g., duration since leaving 

the source). 

•	 The concentration of a non-reactive constituent 

or tracer compound either added to or removed 

from the system (e.g., chloride or fluoride). 

•	 The concentration of a reactive compound 

including the concentration of a secondary 

disinfectant with additional input of its loss rate 

(e.g., chlorine or chloramines) and the 

concentration of disinfection by-products with 

their growth rate (e.g., THMs). 

The following subsection provides a brief history of 

water quality modeling, an overview of theoretical 

concepts related to water quality modeling, basic 

model inputs, and model application. 

2.2.1 History of Water Quality Modeling 

The use of models to determine the spatial pattern of 

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Flow and Pressure. 

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Figure 2-4. Sample EPANET Time Series Plots of Flow, 

Pressure, and Tank Water Level. 

2-7 
A Reference Guide for Utilities 

Figure 2-5. EPANET Sample Tabular Outputs 

(at time 10.00 hrs). 

water quality in a distribution system resulting from 

sources of differing quality was suggested by Wood 

(1980b) in a study of slurry flow in a pipe network. 

The steady-state hydraulic model was extended by 

solving a series of simultaneous equations at each 

node. In a generalization of this formulation, Males 

et al., (1985) used simultaneous equations to calculate the spatial distribution of variables that could be 

The ability to model the transport and fate of the water 

constituents in a distribution system can help utility 

managers perform a variety of water quality studies. 

Examples include: 

•	 Locating and sizing storage tanks and modifying 

system operation to reduce water age. 

•	 Modifying system design and operation to 

provide a desired blend of waters from different 

sources. 

•	 Finding the best combination of: i) pipe 

replacement, relining, and cleaning; ii) reduction 

in storage holding time; iii) location and 

injection rate of booster stations to maintain 

desired disinfectant levels throughout the 

system. 

•	 Assessing and minimizing the risk of consumer 

exposure to disinfectant by-products. 

•	 Assessing system vulnerability to incidents of 

external contamination. 

•	 Designing a cost-efficient, routine monitoring 

program to identify water quality variations and 

potential problems. 

associated with links and nodes such as concentration, travel times, costs, and others. This model, 

called SOLVER, was a component of the Water 

Supply Simulation Model (WSSM), an integrated data 

base management, modeling, and display system that 

was used to model water quality in networks (Clark 

and Males, 1986). A more general “marching out” 

solution was proposed by Males et al., (1988). 

Although steady-state water quality models provided 

some general understanding of water quality behavior 

in distribution systems, the need for models that 

would represent contaminant dynamics was recognized. This resulted in the introduction of three such 

dynamic models in the mid-1980s (Clark et al., 1986; 

Liou and Kroon, 1986; and Hart et al., 1986). 

The history and proliferation of water quality modeling in distribution systems can be traced back to two 

expert workshops that were convened in 1991 and in 

2003. The results of these workshops are presented in 

AWWARF/USEPA (1991) and Powell et al., (2004). 

Figure 2-6 illustrates the evolution of hydraulic and 

water quality models since the 1930s. 

2.2.2 Theoretical Concepts for Water Quality 

Modeling 

Various water quality processes are occurring in water 

distribution systems that can lead to introduction of 

contaminants and water quality transformations (see 

Figure 1-2, presented earlier in Chapter 1) as water 

moves through the distribution system. Cross 

connections, failures at the treatment barrier, and 

2-8 
A Reference Guide for Utilities 

Water Quality

Model History

Hydraulic

Model History 

Integration with GIS, SCADA 

2000 optimization techniques

Powerful PC-based models 1990

1980 Water quality models

1970

1960 Advent of computer models 

1950

1940

1930 Hardy Cross Method 

Figure 2-6. Illustration of the Evolution of Hydraulic 

and Water Quality Models. 

transformations in the bulk phase can all degrade 

water quality. Corrosion, leaching of pipe material, 

biofilm formation, and scour can occur at the pipe 

wall to degrade water quality. Bacteriological quality 

changes may cause aesthetic problems involving taste 

and odor development, discolored water, and other 

adverse impacts. 

In addition to the basic hydraulic modeling equations 

presented earlier in this chapter, the water quality 

models utilize various mathematical equations that 

are based on conservation of constituent mass. These 

models represent the following phenomena occurring 

in a distribution system (Rossman et al., 2000): 

•	 Advective transport of mass within pipes: A 

dissolved substance will travel down the length 

of a pipe with the same average velocity as the 

carrier fluid while at the same time reacting 

(either growing or decaying) at some given rate. 

Longitudinal dispersion is not an important 

transport mechanism in turbulent flow, which is 

normal inside transmission mains under most 

operating conditions. It may, however, be an 

important factor in dead-end pipes or in low and 

intermittent flow scenarios. 

•	 Mixing of mass at pipe junctions: All water 

quality models assume that, at junctions 

receiving inflow from two or more pipes, the 

mixing of fluid is complete and instantaneous. 

Thus, the concentration of a substance in water 

leaving the junction is simply the flowweighted sum of the concentrations in the 

inflowing pipes. 

•	 Mixing of mass within storage tanks: Most 

water quality models assume that the contents 

of storage tanks are completely mixed. See the 

discussion in Section 2.4.1 for further details 

and alternative representations. 

•	 Reactions within pipes and storage tanks: 

While a substance moves down a pipe or resides 

in storage, it can undergo reaction. The rate of 

reaction, measured in mass reacted per volume 

of water per unit of time, will depend on the 

type of water quality constituent being 

modeled. Some constituents, such as fluoride, 

do not react and are termed “conservative.” 

Other constituents, such as chlorine residual, 

decay with time; while the generation of DBPs, 

such as THMs, may increase over time. Some 

constituents, such as chlorine, will react with 

materials both in the bulk liquid phase and at 

the liquid-pipe wall boundary. 

Water quality models represent these phenomena 

(transport within pipes, mixing at junctions and 

storage tanks, and reaction kinetics in the bulk liquid 

phase and at the liquid-pipe wall boundary) with a set 

of mathematical equations. These equations are then 

solved under an appropriate set of boundary and 

initial conditions to predict the variation of water 

quality throughout the distribution system. 

Several solution methods are available for dynamic 

water quality models (Rossman and Boulos, 1996). 

All of these methods require that a hydraulic analysis 

be run first to determine how flow quantities and 

directions change from one time period to another 

throughout the pipe network. The water quality 

constituent is subsequently routed through each pipe 

link and then mixed at downstream nodes with other 

inflows into the node. For non-conservative substances, concentrations are continuously adjusted to 

accommodate the decay or growth of the constituent 

with time. This concentration is then released from 

the node into its out-flowing pipes. This process 

continues for all pipes and for the duration of the 

simulation. 

The methods described above are also applied when 

modeling water age and source-tracing in water 

quality models. Water age is equivalent to modeling 

a reactive constituent that ages and combines linearly. 

For example, for every hour that a “packet” of water 

spends in a tank, its age will increase by one hour. 

Additionally, combining a volume of water that is 

four days old with a similar volume of water that is 

eight days old will result in an average age of six 

days. When modeling the fraction of water coming 

from a designated source (source tracing), this 

parameter is modeled as a conservative substance and 

is linearly combined. For example, combining a 

volume of water that is entirely from the designated 

source with a similar volume of water from a different 

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Q Cki	 kj@x =L 

Cij@x =0 = k (Equation 2-6) Q kj


k


where 

Cij@x =0 = the concentration at the start of the link


 connecting node i to node j in mg/L (i.e.,where x=0)


Ckj@x = L = the concentration at the end of a link, in mg/L 

Q kj = flow from k to i 

A Reference Guide for Utilities 

Modeling the movement of a contaminant within the 

distribution systems as it moves through the system 

from various points of entry (e.g., wells or treatment 

plants) to water users is based on three principles: 

•	 Conservation of mass within differential lengths 

of pipe. 

•	 Complete and instantaneous mixing of the water 

entering pipe junctions. 

•	 Appropriate kinetic expressions for the growth or 

decay of the substance as it flows through pipes 

and storage facilities. 

This change in concentration can be expressed by the 

following differential equation: 

According to Equation 2-5, the rate at which the mass of 

material changes within a small section of pipe equals 

the difference in mass flow into and out of the section 

plus the rate of reaction within the section. It is assumed that the velocities in the links are known 

beforehand from the solution to a hydraulic model of 

the network. In order to solve Equation 2-5, one needs 

to know Cij at x=0 for all times (a boundary condition) 

and a value for kij. 

Equation 2-6 represents the concentration of material 

leaving the junction and entering a pipe: 

Equation 2-6 states that the concentration leaving a 

junction equals the total mass of a substance flowing 

into the junction divided by the total flow into the 

junction. 

source will provide a mixed volume calculated as 50 

percent from the designated source. 

2.2.3 Water Quality Model Inputs and 

Application 

In addition to the basic hydraulic model inputs 

described in Section 2.1.3, the water quality models 

require the following data elements to simulate the 

behavior in a distribution system: 

•	 Water Quality Boundary Conditions - A water 

quality model requires the quality of all 

external inflows to the network and the water 

quality throughout the network be specified at 

the start of the simulation. Data on external 

inflows can be obtained from existing source 

monitoring records when simulating existing 

operations or could be set to desired values to 

investigate operational changes. Initial water 

quality values can be estimated based on field 

data. Alternatively, best estimates can be made 

for initial conditions. Then the model is run for 

a sufficiently long period of time under a 

repeating pattern of source and demand inputs 

so that the initial conditions, especially in 

storage tanks, do not influence the water quality 

predictions in the distribution system. The 

water age and source tracing options only 

require input from the hydraulic model. 

•	 Reaction Rate Data – For non-conservative 

substances, information is needed on how the 

constituents decay or grow over time. 

Modeling the fate of a residual disinfectant is 

one of the most common applications of 

network water quality models. The two most 

frequently used disinfectants in distribution 

systems are chlorine and chloramines (a reactant 

of chlorine and ammonia). Free chlorine is 

more reactive than chloramine and its reaction 

kinetics have been studied more extensively. 

Studies have shown that there are two separate 

reaction mechanisms for chlorine decay, one 

involving reactions within the bulk fluid and 

another involving reactions with material on or 

released from the pipe wall (Vasconcelos et al., 

1997). Bulk decay is typically represented as a 

first order exponential decay function with a 

single decay coefficient specified to represent 

the decay over time. In some circumstances, this 

function does not adequately represent the 

observed decay characteristics, and more 

complex formulations may be used to describe 

the decay. Wall reaction represents the 

disinfectant decay due to contact with 

oxidizeable substances at the pipe wall, such as 

corrosion products or biofilm. The most widely 

used approach for representing wall demand 

considers two interacting processes – transport 

2-10 
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Storage tanks are usually modeled as completely


mixed, variable volume reactors in which the changes


in volume and concentration over time are as follows:


Many algorithms and methods exist for the numerical 

solution of fluid flows described by the Navier-Stokes 

equations. These algorithms can be classified as 

Eulerian or Lagrangian and as either time-driven or 

event-driven. In a Eulerian method, the movement of 

the fluid is viewed from a stationary grid as the water 

moves through the system. On the contrary, in a 

Lagrangian method, the analysis is viewed from a 

framework that is moving with the flow. Time-driven 

methods assess the system at fixed time steps. Eventdriven methods evaluate the system only when there is 

a discrete change in water quality such as a pulse of 

water with different concentrations entering or leaving 

a pipe. Various methodologies combine either Eulerian 

or Lagrangian solutions (or hybrid combinations of 

these two cases) with either time-driven or event-driven 

procedures. 

of the disinfectant from the bulk flow to the 

wall and interaction with the wall (Rossman et 

al., 1994). Recent studies have suggested that 

this formulation may not adequately represent 

the actual wall demand processes and that 

further research is needed (Clark et al., 2005; 

Grayman et al., 2002; DiGiano and Zhang, 

2004). There has been little study on the nature 

of the wall reaction in chloraminated systems. 

A limited amount of modeling of the growth of 

DBPs (most notably THMs) has been performed 

assuming an exponential growth approaching a 

maximum value corresponding to the THM 

formation potential. Both the formation 

potential and the growth rate constant must be 

specified in this type of model (Clark et al., 

A Reference Guide for Utilities 

1996). There has been extensive research on 

biofilm formation in distribution systems and 

this has led to the development of several 

theoretical models of this phenomenon (Powell 

et al., 2004). However, these models are 

generally quite complex involving many 

parameters that are difficult to determine, and 

thus are not ready for inclusion in a general 

water distribution system model. 

The following section provides an overview of 

available software for hydraulic and water quality 

modeling. 

Distribution system water quality models are generally 

limited to tracking the dynamics of a single component 

(e.g., chlorine, water age) at a time when the selected 

component is transported throughout the network of 

pipes and storage tanks. Such models do not consider 

interactions between different components in the 

flowing water or complex reactions between components that are transported with the water and surface 

components that are fixed to the pipe wall. This can be a 

significant limitation when modeling reactive components, for example when chlorine residual is modeled for 

a case where there are multiple sources with significant 

differences in water quality characteristics. Another 

more complex example that is not adequately represented by the single-species model is modeling of DBP 

formation. A solution to this deficiency is a generalpurpose, multi-species capability that is being added to 

EPANET (Uber et al., 2004). This addition will allow 

users to program their own chemical/physical/biological 

reactions in EPANET with almost unlimited interaction 

capability between multiple species. 

2.3 Hydraulic and Water Quality 

Modeling Software 

A variety of software packages are available to 

perform hydraulic and water quality modeling. A 

majority of these packages utilize the EPANET 

formulation as the basic computation engine. A full 

discussion of individual software packages is beyond 

the scope of this document. The following subsections briefly describe the EPANET model and 

summarize the features of other available software. 

2.3.1 EPANET Software 

EPANET was initially developed in 1993 as a 

distribution system hydraulic-water quality model to 

support research efforts at EPA (Rossman et al., 1994). 

The development of the EPANET software has also 

satisfied the need for a comprehensive public-sector 

model and has served as the hydraulic and water 

quality “engine” for many commercial models. 

2-11 
A Reference Guide for Utilities 

EPANET can be used for both steady-state and EPS 

hydraulic simulations. In addition, it is designed to 

be a research tool for modeling the movement and fate 

of drinking water constituents within distribution 

systems. EPANET can be operated in the SI (metric) 

or British systems of measurement. 

The water quality routines in EPANET can be used to 

model concentrations of reactive and conservative 

substances, changes in age of water and travel time to 

a node, and the percentage of water reaching any node 

from any other node. Outputs from EPANET include: 

•	 color-coded network maps, 

•	 time series plots, and 

•	 tabular reports. 

Example outputs from EPANET were previously 

presented in Figures 2-3, 2-4, and 2-5. 

2.3.2 Commercial Hydraulic-Water Quality 

Modeling Software 

In addition to EPANET, there are several commercial 

software packages that are widely used in the U.S. and 

internationally. Most of these packages are based on 

the EPANET formulation and include value-added 

components as parts of GUI that increase the capability of the software. Examples of such value-added 

components that are part of one or more of the 

commercially available software packages include: 

•	 Scenario manager: Manage inputs and outputs 

of a group of model runs. 

•	 Calibration optimization: Utilize genetic 

algorithm optimization technique to determine 

model parameters that best fit a set of field data. 

•	 Design optimization: Utilize genetic algorithm 

optimization techniques to select pipe sizes that 

minimize costs or other selected objectives. 

•	 Integration with GIS or CAD: Water distribution 

model directly integrates with GIS or CAD to 

assist in constructing or updating model and 

In addition to the standard use of EPANET in a 

Windows environment using the graphical user 

interface (GUI), the functionality of EPANET can be 

accessed through the EPANET toolkit. The toolkit is a 

series of open source routines available in both Visual 

Basic and C (programming language) that can be used 

as is or modified and accessed from a user’s own 

computer program. This powerful capability has been 

widely used throughout the world to support both 

research and specific applications in the field of water 

distribution system analysis. 

display results. 

•	 Flexible output graphics: Provides convenient 

ways to modify parameters for graphical 

displays of output data. 

•	 Energy management: Calculates energy use for 

a selected alternative. 

•	 Automated fire-flow analysis: Assesses the 

availability of fire flow at a range of nodes and 

determines whether a system meets fire-flow 

requirements. 

•	 Water security and vulnerability assessment 

methods, skeletonization, and demand 

allocation tools. 

Table 2-1 provides a summary listing of major 

commercial software and a Web link where additional 

details may be obtained on specific features and 

current version availability/pricing. 

2.4 Additional Modeling Tools 

In addition to standard hydraulic and water quality 

modeling of distribution systems, there are several 

other related types of models that can be used to 

assess hydraulic and water quality behavior in 

distribution systems. These include: storage modeling 

tools, transient (water hammer) modeling tools, 

optimization tools, and probabilistic models. Each of 

these types of models are briefly described and 

demonstrated in the following sections. 

2.4.1 Storage Modeling Tools 

An important aspect of water quality and contaminant 

propagation in drinking water distribution systems is 

the effect of system storage. Most utilities use some 

type of ground or elevated storage system to process 

water during time periods when treatment facilities 

would otherwise be idle. It is then possible to 

distribute and store water at one or more locations in 

the service area closest to the user. 

The principal advantage of distribution storage is that 

it equalizes demands on supply sources, production 

works, and transmission and distribution mains. As a 

result, the sizes or capacities of these elements may be 

minimized and peak power tariff periods for pumping 

can often be avoided. Additionally, system flows and 

pressures are improved and stabilized to better serve 

the customers throughout the service area. Finally, 

reserve supplies are provided in the distribution 

system for emergencies, such as fire fighting and 

power outages. 

In most municipal water systems, less than 25 percent 

of the volume of the storage in tanks is actively used 

(on a daily basis) under routine conditions. As the 

2-12 
A Reference Guide for Utilities 

Table 2-1. Available Hydraulic and Water Quality Network Modeling Software Packages 

Network Modeling Software Company EPANET 

Based Website 

AQUIS Seven Technologies www.7t.dk/aquis 

EPANET EPA X www.epa.gov/ord/nrmrl/wswrd/epanet.html 

InfoWater H2ONET/H2OMAP MWHSoft X www.mwhsoft.com 

InfoWorks WS Wallingford Software www.wallingfordsoftware.com 

MikeNet DHI, Boss International X www.dhisoftware.com/mikenet 

Pipe2000 University of Kentucky www.kypipe.com 

PipelineNet SAIC, TSWG X www.tswg.gov/tswg/ip/pipelinenettb.htm 

SynerGEE Water Advantica www.advantica.biz 

WaterCAD/WaterGEMS Haestad Methods X www.haestad.com 

STANET Fisher-Uhrig Engineering www.stanet.net 

Wadiso GLS Eng. Software X www.wadiso.com 

water level drops, tank controls require high-service 

pumps to start in order to satisfy demand and refilling 

of the tanks. The remaining water in the tanks (70 to 

75 percent) is normally held in reserve as dedicated 

fire or emergency storage. This water tends to be 

stagnant and may cause water quality problems. 

Storage tanks and reservoirs are the most visible 

components of a water distribution system, but are 

often the least understood in terms of their effect on 

water quality. Although these facilities can play a 

major role in providing hydraulic reliability for fire 

fighting needs and in providing reliable service, they 

may also serve as vessels for unwanted complex 

chemical and biological changes that may result in 

the deterioration of water quality. These storage tanks 

and reservoirs also contribute to increased residence 

time in drinking water systems. This increased 

residence time can contribute to the loss of disinfectant residuals and cause subsequent growth of 

microorganisms. Modeling can provide information 

on what will happen in existing, modified or proposed 

distribution system tanks and reservoirs under a range 

of operating situations (Grayman et al., 2004a). 

Three primary types of models are used for representing storage tanks and reservoirs: computational fluid 

dynamics (CFD) models, compartment models, and 

physical scale models. In mathematical models, 

equations are written to simulate the behavior of 

water in a tank or reservoir. These models range from 

detailed representations of the hydraulic mixing 

phenomena in the facility called CFD models to 

simplified conceptual representations of the mixing 

behavior called compartment or systems models. 

Physical scale models are constructed from materials 

such as wood or plastic. Dyes or chemicals are used 

to trace the movement of water through the model. 

2.4.1.1 CFD Models 

CFD models use mathematical equations to simulate 

flow patterns, heat transfer, and chemical reactions. 

Partial differential equations representing conservation of mass, momentum, and energy are solved 

numerically for a two- or three-dimensional grid that 

approximates the geometry of the tank. CFD modeling has been used widely in the chemical, nuclear, and 

mechanical engineering fields, and in recent years has 

emerged as a modeling tool in the drinking water 

industry (Grayman and Arnold, 2003). CFD models 

can be used to simulate temperature variations, 

unsteady hydraulic and water quality conditions, and 

decay of constituents in storage facilities. Significant experience is required to apply CFD models, and 

model run times of many hours, days, or even weeks 

are required for complex situations. Figure 2-7 depicts 

a graphical output from a CFD model showing the 

concentration throughout a tank at a snapshot in time 

resulting from a tracer that has been injected into the 

inflow. 

Many generalized CFD software packages are 

available that can be used to construct CFD models of 

tanks. Examples of such packages are listed in Table 

2-2. These packages vary in terms of capabilities, 

solution methods, ease of use, and support. Prior to 

selection of a package, the specific needs and 

capabilities of the user should be carefully evaluated. 

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A Reference Guide for Utilities 

Figure 2-7. Graphical Output from a CFD Model 

Showing Tracer Concentration in a Tank. 

Generally the purchase or lease of these packages is 

significant (typically on the order of $25,000 per 

year) and significant training/expertise is required to 

effectively apply them. 

2.4.1.2 Compartment Models 

Compartment models are a class of models in which 

physical processes (i.e., the mixing phenomena in the 

tank or reservoir) are represented by highly conceptual, empirical relationships. This type of model is 

also referred to as a black box model, or input-output 

model. Since such models do not use detailed 

mathematical equations to describe the movement of 

water within the tank, they rely on engineering 

judgment or upon field data and past experience to 

define the parameters that control the behavior of the 

model. Compartment models are used in water 

distribution network models to represent mixing in 

tanks and reservoirs. Various assumptions can be 

made in these models about the mixing behavior in 

tanks including complete and instantaneous mixing, 

plug flow, last-in/first-out (LIFO) behavior, and multiTable 2-2. Example CFD Modeling Software Packages 

compartment models. Both conservative substances 

and substances that decay according to a first-order 

decay function may be simulated in addition to 

simulation of water age. Compartment models are 

relatively easy to use and run in seconds as opposed 

to the long run times of CFD models. 

Compartment models of tanks are available as part of 

most water distribution system models. EPANET and 

several of its derivative commercial models allow the 

user to select from four options – a complete mix 

model, a plug flow first-in/first-out (FIFO) model, a 

LIFO (short circuiting) model and a two-compartment 

model. A stand-alone model called CompTank 

provides a wide range of alternatives and allows the 

user to model water age and reactive or conservative 

substances over a long period of time (Grayman et al., 

2000). This model uses tank inflow and outflow 

information that is generally available from SCADA 

records as its primary input. 

2.4.1.3 Physical Scale Models 

Physical scale models provide a relatively inexpensive mechanism for studying the mixing characteristics of tanks. In a physical scale model, a tracer 

chemical is added to the inflow (or internally within 

the model) and the movement of the tracer is monitored during the experiment (Grayman et al., 2000). 

Tracer substances include visible dyes, which are 

appropriate for developing a qualitative understanding of mixing behavior, and chemicals (e.g., calcium 

chloride) that can be measured by sensors in the tanks 

and used for quantitative assessments. Use of tracers 

of different density or careful control of temperature 

of the tracer can be used to study the impacts of 

thermal variations on mixing. Laws of similitude in 

hydraulics must be followed in order to account for 

the scaling effects. Scale models can vary in size and 

complexity from small tabletop models to large-scale 

models built in hydraulics laboratories. Figure 2-8 

depicts such a large-scale model. 

CFD Package Company Website 

CFD-ACE CFD Research Corp. www.cfdrc.com 

Cfdesign Blue Ridge Numerics www.brni.com 

CFX Ansys, Inc. www.software.aeat.com/cfx 

-3DFLOW Flow Science, Inc. www.flow3d.com 

Fluent Fluent, Inc. www.fluent.com 

Phoenics CHAM www.cham.co.uk 

SWIFT AVL www.avl.com 

Sinda/Fluint C&R Technologies www.crtech.com 

PAB3D Analytical Services & 

Materials 

www.asm-usa.com Figure 2-8. A Large Physical Model of a Tank (Source: 

Bureau of Reclamation Laboratory). 

2-14 
A Reference Guide for Utilities 

In an advanced technology form of physical scale 

modeling, three-dimensional laser induced fluorescence is being used to provide detailed measurements 

of mixing in tanks (Roberts and Tian, 2002). Figure 2­

9 shows an illustration of output from this technology. 

2.4.2 Transient Analysis Software 

A hydraulic transient is a rapid change in pressure 

associated with a pressure wave that moves rapidly 

through a piping system. A transient can be caused 

by a variety of events, such as rapid operation of a 

valve (including fire hydrants) or rapid pump starts 

and stops. If the magnitude of the resulting 

pressure wave is large enough and adequate 

transient control measures are not in place, a 

transient can cause a water hammer leading to 

failure of hydraulic components. It can also lead to 

instantaneous low or negative pressures that can 

result in intrusion of untreated water into the pipe, 

potentially resulting in contamination. Transient 

events are highly dynamic and sophisticated. 

Mathematical models are required to analyze their 

movement in a distribution system. 

Several commercial software packages for performing 

transient analysis in water distribution systems are 

available. Examples of such software are listed in 

Table 2-3. The technical capabilities, user interface, 

solution methods, graphical display, and technical 

support and training vary considerably among the 

packages. 

2.4.3 Optimization Tools 

Optimization tools allow the user to evaluate a large 

number of options and to select the specific alternative that gives the best results in terms of predefined 

objective functions. In the area of water distribution 

system analysis, optimization models are used for 

calibration, design, and operational purposes. These 

applications are briefly described in the following 

subsections. 

2.4.3.1 Optimizing Calibration 

Calibration of a water distribution system model 

involves adjustments in various model parameters so 

that the model agrees with field measurements of flow 

and pressure. Such a tool is used most frequently with 

flow and pressure measurements taken during flow 

(hydrant) tests to stress the system. Parameters that 

are typically adjusted include roughness factors, 

demands, and status of isolation valves. 

Figure 2-9. Graphical Output Based on 3-D Laser Induced Fluorescence with a Physical Scale Model Showing 

Mixing in Tank (Source: Georgia Tech). 

Table 2-3. Example Transient Modeling Software Packages 

Transient Modeling Software Company Website 

AQUIS Surge Seven Technologies www.7t.dk/aquis 

HAMMER Haestad Methods www.haestad.com 

Hytran v3.0 Hytran Solutions www.hytran.net 

Impulse Applied Flow Technology www.aft.com/products/impulse 

InfoSurge, H2OSurge MWHSoft www.mwhsoft.com 

2-15 
A Reference Guide for Utilities 

The production of transient low-and negativepressures in otherwise pressurized drinking water 

supply distribution systems creates the opportunity 

for contaminated water to enter the pipe from 

outside. Such events may be caused by the sudden 

shutdown of pumps or by other operational events 

such as flushing, hydrant use, and main breaks. 

Figure 2-10 illustrates an event that results in a 

negative pressure transient for 22 seconds caused 

by a power outage associated with a lightning 

strike. 

In a series of research projects (LeChevallier et al., 

2003; Gullick et al., 2004), the frequency and 

location of low-and negative-pressures in representative distribution systems were measured under 

normal operating conditions and during specific 

operational events. These investigators also 

confirmed that fecal indicators and culturable 

human viruses were present in the soil and water 

exterior to the distribution system pipes. Their 

research shows that a well-calibrated hydraulic 

surge model can be used to simulate the occurrence 

of pressure transients under a variety of operational 

scenarios, and a model can also be used to determine optimal mitigation measures. 

Although there are insufficient data to indicate 

whether pressure transients pose a substantial risk 

to water quality in the distribution system, mitigation techniques can be implemented. These 

techniques include the maintenance of an effective 

disinfectant residual throughout the distribution 

system, leak control, redesign of air relief venting, 

installation of hydro-pneumatic tanks, and more 

rigorous application of existing engineering 

standards. Pressure (psi)

negative 4.4 psi 

(± 2.0 psi) 

for 20 seconds 

Figure 2-10. Negative Pressure Transient Associated 

with a Power Outage. 

Use of manual adjustment techniques may involve 

many tedious runs of a distribution system model 

until the resulting predicted flows and pressures 

approximate the values observed in the field. When 

an optimization model is applied, the user defines an 

objective function, such as minimizing the square of 

the difference between observed and predicted values 

(for pressure and flow). The optimization algorithm 

then uses some type of controlled search method to 

identify the set of model parameters that will result in 

the best results (i.e., minimize the error). The user will 

generally set constraints on parameters so that the 

resulting values are reasonable. For example, the user 

may specify that the allowable range for the roughness factor for a certain set of ductile iron pipes range 

between 90 and 120. 

Over the past 40 years, various techniques have been 

applied as part of automated calibration methods 

(Rahal et al., 1980; Walski et al., 2003). The most 

common optimization technique in use today couples 

a hydraulic model with an optimization routine using 

genetic algorithms. Genetic algorithms are based on 

the theory of genetics in which successive population 

trials are generated with the “fittest” ones surviving 

to breed and evolve into increasingly desirable 

offspring solutions. The fitness of a solution is based 

on the objective functions that were previously 

described. Genetic algorithm-based calibration tools 

are available as optional components of several water 

distribution system analysis software packages. 

2.4.3.2 Design Optimization 

In a manner analogous to the calibration optimization 

technique described above, design optimization 

techniques evaluate a large number of distribution 

system design options and select the one that 

provides the best solution (Lansey, 2000). Schaake 

and Lai (1969) first proposed such an approach and 

applied it to the design of major transmission lines 

providing water to New York City. Since that time, 

numerous papers have been written on the subject 

(Walski et al., 2003) and have included a variety of 

techniques such as linear programming, dynamic 

programming, mixed integer programming, heuristic 

algorithms, gradient search methods, enumeration 

methods, genetic algorithms, and simulated annealing. In recent years, genetic algorithm methods have 

been favored for this problem and have been widely 

used in a variety of situations and are included in 

several commercial software packages. The user 

should, however, be aware that genetic algorithms do 

not guarantee optimality. These algorithms must be 

run several times to ensure near optimal solutions. 

Typically, design optimization tools limit a user to 

choose from designated piping options and to size 

the pipes to meet present and future demands. Cost 

minimization is the most common objective function. 

2-16 
A Reference Guide for Utilities 

Additionally, some researchers have incorporated 

reliability and capacity considerations (Mays, 1989). 

2.4.3.3 Optimization of Operation 

Models can also be used to optimize operations of a 

distribution system (Goldman et al., 2000). The most 

common areas of operation where such models have 

been applied are in energy management and water 

quality. Chase et al. (1994) describe a computer 

program to control energy costs that incorporates a 

hydraulic model, a pump optimization program, and 

an interface. In the water quality area, Uber et al. 

(2003) used optimization techniques to determine 

optimal location and operation of chlorine booster 

stations. Jentgen et al. (2003) implemented a prototype energy and water quality management system at 

Colorado Springs Utilities. This system combines a 

simplified distribution system model and an optimization routine to adjust operation of the water system 

and power generation system in near real-time. 

2.4.4 Probabilistic Models 

Hydraulic and water quality models of distribution 

systems are deterministic models. For a set of network 

parameters and specific operations and demands, the 

model produces a single set of resulting flows and 

pressures. However, there is uncertainty in many of 

the aspects of these models including parameters such 

as roughness, demands, actual inside diameter of 

pipes, valve settings, and system controls. This 

uncertainty is generally due to both imperfect 

knowledge and natural variability. An emerging 

procedure is to embed a deterministic network model 

within a probabilistic framework and to examine the 

effect of uncertainty on the results. 

The most common approach to incorporating uncertainty in models is the use of a Monte Carlo simulation (Vose, 2000). In this method, probability 

distributions are assigned to model parameters to 

represent the uncertainty associated with each 

parameter. The distribution system model is then run 

many times with parameter values being randomly 

drawn from the probability distributions. The results 

of many iterations are combined to determine the 

most likely result and a distribution of results. This 

approach has been used in legal cases where historical 

contamination events have been reconstructed 

(Grayman et al., 2004b), in evaluation of the impacts 

of purposeful contamination (Murray et al., 2004) and 

modeling bacterial regrowth in distribution systems 

(DiGiano and Zhang, 2004). 

2.5 Summary and Conclusions 

Acquiring and utilizing the proper data is very 

important for implementing water distribution system 

models. The key inputs include the characterization 

of the pipe network (e.g., pipes, pumps, tanks, and 

valves), water-demand information (temporal variations are required in EPS), topographic information 

(elevations assigned to nodes), control information 

that describes how the system is operated, and EPS 

solution parameters (e.g., time steps, tolerances as 

required by the solution techniques). Periodic 

calibration and validation of a model is important to 

achieve optimum results. 

Models have become widely accepted within the 

water utility industry as a mechanism to simulate the 

hydraulic and water quality behavior of a real or 

proposed distribution system. They are routinely 

used for a number of tasks including capital investment decisions, master plan development, and fire 

protection capacity design. Furthermore, these 

models have become very sophisticated and typically 

simulate both hydraulic and water quality behavior. 

Many modeling packages are integrated with GIS or 

CAD. Some software packages incorporate water 

hammers and tank mixing. EPANET is a public sector 

hydraulic/water quality model developed by EPA. 

EPANET also serves as the computation engine for 

many of the commercial models used by water 

utilities throughout the country. In addition to 

EPANET and EPANET-based water distribution 

system models, there are several other tools available 

to users for studying specific needs, such as transient 

analysis and optimization analysis. 

To successfully apply a model to study a problem, one 

should clearly define the objectives and select an 

appropriate tool. Thereafter, understanding the 

accuracy of the input data and limitations of the 

model will enable the user to better interpret the 

results of the analysis and develop appropriate 

solutions. 

2-17 
A Reference Guide for Utilities 

Many of the assumptions and methodologies in use 

today in water distribution system modeling date 

back to the early work of Hardy Cross (1936). With 

the monumental increase in computational power 

and improvements in the ability to measure flow in 

experimental distribution systems, it is natural that 

some of the basic assumptions are being examined 

and challenged. Three notable examples of active 

research areas include the following: 

• Distribution system water quality models 

currently assume advective flow that results 

in water quality pulses moving through a 

pipe without spreading out longitudinally. 

Lee and Buchberger (2001) have studied 

pipe flow and found that dispersion has a 

significant effect on concentration profiles, 

especially in cases of intermittent laminar 

flow. Lee (2004) developed an analytical 

equation which describes the unsteady 

dispersion of changing flow velocity in 

pipes based on the classic one-dimensional 

advection-dispersion equation by Taylor 

(1953). Tzatchkov et al., (2002) have 

developed an extension to the standard 

EPANET model that includes dispersion. 

• In distribution system models, deterministic 

demands are assigned to nodes. Buchberger 

et al., (2003) monitored water use at the 

individual home and neighborhood level 

and found that there are significant shortterm variations in water use. They have 

developed a model that represents water 

use as a series of pulses which can be 

simulated using a Poisson Rectangular 

Pulse model to capture the natural 

variability associated with water use. 

• Distribution system models currently 

assume complete mixing at a junction. As 

a result, if there are two pipes with flow 

entering the junction and two pipes 

through which the flow exits, the chemical 

content of the water in the two exiting 

pipes will be identical and represent an 

average of the characteristics of the two 

entering pipes. Van Bloemen Waanders et 

al., (2005) have tested this assumption 

using both laboratory analysis and CFD 

modeling. Figure 2-11a depicts the 

velocity field at a junction. Figure 2-11b 

presents the corresponding tracer 

concentrations at that junction. The figures 

indicate that the complete mix assumption 

would lead to some inaccuracy in 

computing chemical transport in a 

distribution system. 

Velocity (m/s) 1.9 

1.7 

1.5 

1.3 

1.1 

0.9 

0.7 

0.5 

0.3 

0.1 

Figure 2-11a. Velocity Field at a Junction. Figure 2-11b. Tracer Concentration at a Junction. 

1.0 

Tracer 

0.8 

0.6 

0.4 

0.2 

0.0 

2-18 
A Reference Guide for Utilities 

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Networks. Department of Civil Engineering, University of Kentucky, Lexington, KY. 1980a. 

Wood, D.J. “Slurry Flow in Pipe Networks.” Journal 

of Hydraulics, ASCE, 106(1). May 1980b. 

2-21 
A Reference Guide for Utilities 

Chapter 3 

Tracer Studies for Distribution System Evaluation 

Tracers have been used for decades to determine 

flow, travel time, and dispersion in surface waters 

and groundwater. Tracers can be of various types, 

ranging from a physical object that can be visually 

detected in a stream or river to dyes or other 

chemicals whose concentrations can be monitored 

using special instrumentation. Fluorescent dyes 

have been used for many years to measure velocity 

and tidal movement in streams and estuaries. Use 

of tracers to understand the hydraulic movement in 

drinking water treatment unit processes or distribution systems is a more recent development. When 

tracers are used in drinking water, care must be 

taken to ensure that they will have no adverse 

health effects and that their use does not result in 

any violations of primary and/or secondary drinking water MCLs. 

Tracers have been used in drinking water to 

estimate the travel time through various water 

treatment unit processes including clearwells 

(Teefy and Singer, 1990; Teefy, 1996; DiGiano et 

al., 2005). Tracer studies have also been conducted 

in distribution system tanks and reservoirs in an 

attempt to understand their mixing characteristics 

(Grayman et al., 1996; Boulos et al., 1996). They 

have also been used in water distribution networks 

to provide insight into the complex movement of 

water in a distribution system, to determine travel 

times, and to assist in calibration of distribution 

system hydraulic models (Clark et al., 1993; 

DeGiano et al., 2005; Vasconcelos et al., 1997; 

Grayman, 2001). For example, Boccelli et al. 

(2004) and Sautner et al. (2005) have used dual 

tracers injected into water distribution systems to 

assess travel time and characterize flow patterns in 

support of epidemiological investigations. With the 

recent interest in homeland security issues, tracers 

are being used to simulate the movement and 

impacts of accidental or intentional contamination 

of water distribution systems (Panguluri et al., 

2005). 

Conducting a distribution system tracer study 

involves (1) injecting the tracer into a pipe upstream of the area to be studied, (2) shutting off or 

reducing a continuous chemical feed at the water 

treatment plant, or (3) use of a naturally occurring 

substance in source water. The concentration is 

measured over time at various locations in the 

water distribution network as it moves through the 

study area. To be successful, a tracer study requires 

careful planning and implementation. This chapter 

provides information and guidance on planning 

and conducting tracer studies in drinking water 

distribution systems. 

Tracer studies in distribution systems may provide a 

wide variety of useful information, including the 

following: 

•	 Calculating travel time, residence time, or water 

age in a network. 

•	 Calibrating a hydraulic model. 

•	 Defining zones in a network served by a 

particular source and/or assessing the degree of 

blending with water from other sources. 

•	 Determining the impacts of accidental or


intentional contamination.


•	 Identifying appropriate sampling locations 

within the water distribution network. 

Tracer studies may also assist water utilities in 

complying with various regulatory requirements. For 

example, the DBPR2 IDSE draft Guidance Manual 

(EPA, 2003a) recognizes the use of tracers as a means 

of calibrating models and predicting residence time as 

a partial substitute for required field monitoring. 

Several rules and regulations (both existing and 

proposed) are currently being reviewed, such as the 

TCR and a proposed distribution system rule. Water 

quality modeling and model calibration are likely to 

play a role in the development and/or promulgation 

of these rules. 

The scope of a tracer study may vary considerably 

depending upon the study needs, size, and complexity of the distribution network being evaluated. A 

study area may consist of a single stretch of pipe, an 

entire neighborhood, a portion of a large distribution 

system, a pressure zone, or in some cases, the entire 

distribution network. The resources required to 

conduct a tracer study will vary with the extent, 

complexity of the study, and the test equipment used. 

Careful planning and implementation are critical in 

all cases to ensure meaningful results. Section 3.1 of 

this chapter contains information that can be used 

during the planning phases of a tracer study. Section 

3.2 provides a summary of the tasks associated with 

executing a tracer study. Section 3.3 presents typical 

costs associated with conducting a tracer study. 

Finally, Section 3.4 presents a summary, conclusions, 

and recommendations for this chapter. The use of 

tracer study data for model calibration/validation is 

described in Chapter 4. 

3-1 
A Reference Guide for Utilities 

3.1 Planning and Designing a 

Distribution System Tracer 

Study 

The initial step in any tracer study is a planning and 

design phase during which study-specific logistical 

details are identified and addressed. These details 

should be presented in a comprehensive manner in a 

planning document or work plan that can be reviewed 

and commented on by parties that may have an 

interest in the tracer study (e.g., team members, water 

utility staff and managers, and state regulatory 

officials). Planning and design-phase elements may 

include the following: 

• Establishing study objectives and timeline. 

• Forming a study team. 

• Defining study area characteristics. 

• Selecting tracer material. 

• Selecting field equipment and procedures. 

• Developing a detailed study design. 

• Addressing agency and public notification. 

The details of each of these tasks are described in the 

following sub-sections. 

3.1.1 Establishing Study Objectives and TimeLine 

A clear statement of the study objectives should be 

developed, even before logistical planning begins. 

For example, an objective statement might read 

“determine travel times from the Lincoln Water 

Treatment Plant to key locations (transmission mains 

and representative local mains) in the Washington 

Pressure Zone under typical summer operation.” Such 

a statement provides a clear understanding of the 

study’s overall goals and objectives. A study objective may also be more specific and define additional 

key elements such as tracer material, dosage, and 

injection duration. 

Depending upon the objective, an approximate timeline (schedule) for the study should be formulated. 

Frequently, external constraints such as weather, 

system operation, and availability of personnel/ 

equipment may influence this timeline. In other 

cases, the project timeline may depend upon the 

specific objective of the study. For example, if the 

maximum community exposure to a contamination 

event is being studied, the timeline should be 

consistent with the season and time during which the 

event is likely to occur. If the study is intended to 

identify locations in the system where the lowest 

chlorine residuals are found, the study should be 

conducted during a period when minimum chlorine 

residuals occur. However, it is not always possible to 

conduct a tracer study to match system conditions 

that coincide with the study time-frame. Therefore, a 

reasonable alternative is to use the tracer to calibrate a 

study-area-specific network model, under a given set 

of conditions, that can be used to simulate other 

critical events under different conditions. 

In mid-western U. S., October-November is the best 

time-frame to conduct a tracer study in a residential 

area. During this time, the utility has greater operational 

flexibility because it is not stressed by high demands, 

weather is conducive to outdoor activity, and cold 

weather pipe breaks are minimal. 

3.1.2 Forming a Study Team 

A “tracer study team” should be formed at the 

beginning of the project. Depending on the size and 

scope of the study, the size of the team may vary from 

as few as three members to a sizable group of as many 

as twenty members. However, the range of functions 

and responsibilities that must be considered are 

approximately the same in all types of studies. The 

team makeup must include members with expertise for 

planning and carrying out the following activities 

and functions: understanding study area distribution 

system and treatment operations; conducting preliminary modeling studies; selecting, acquiring, and 

installing field equipment; managing and organizing 

field crews; performing field sampling; conducting 

laboratory analysis; analyzing and reporting results; 

and performing communications and notifications. 

Study teams may be made up of water utility personnel, consulting engineering firm personnel, contractor 

staff, students from universities, and in some cases, 

federal or state governmental agency employees. 

Specific responsibilities and roles should be assigned 

to each team member. It is recommended that the 

study team meet on a regular basis to ensure that the 

task deadlines are met and the study objectives are 

achievable. If the tracer study includes new or neverbefore used equipment, training sessions for study 

team members should be included as part of study 

timeline and activities. 

3.1.3 Defining Study Area Characteristics 

After the study team is formed, perhaps the first task 

to be undertaken is to identify the key characteristics 

of the study area. These characteristics include: the 

piping system network, pumping and storage operations, inflow and outflow through study area boundaries, temporal and spatial variations in water consumption, presence of large water users that may 

significantly impact water use patterns, and the 

3-2 
A Reference Guide for Utilities 

When planning a tracer study, the effects of distribution system tanks and reservoirs should be considered 

(Grayman at al., 2004). When a tracer enters a tank in 

the inflow, it mixes with the distributed water and then 

exits the tank at a different concentration during the 

subsequent draw cycles. Mixing in the tank may be 

rapid and complete or there may be short-circuiting or 

plug flow behavior that affects the concentration in the 

effluent. Various mathematical tools such as CFD 

models may be applied to estimate the mixing characteristics of a tank and the effects on tracer concentration during discharge periods (Grayman et al., 2004). 

Distribution system models such as EPANET allow the 

user to simulate mixing in tanks by several alternative 

conceptual and simplified models such as completely 

and instantaneously mixed, short circuiting, plug flow, 

and multiple compartment mixing. The effects of tanks 

can impact the needed tracer dosage rate and injection 

duration and the subsequent sampling frequency and 

duration in parts of the distribution system impacted 

by the tank. During the tracer study, the impacts of 

mixing in the tank can be determined by sampling in 

the inflow and outflow lines, and in some cases, 

internally within the tank. 

geography and local features associated with the 

study area that could potentially constrain field 

activities. 

A large commercial user such as a golf course in the 

neighborhood may impact the study events. 

There are several tools and procedures that can be 

applied to improve the team’s understanding of the 

target water distribution system area prior to conducting the tracer study. If a hydraulic model of the 

distribution system (under study) is available, it 

would be very helpful to use the model to simulate 

the tracer study under expected conditions. Examination of documents, such as master plans or operational 

reports, can also shed light on how the water system 

behaves. The study team or key members of the study 

team should also tour the study site with as-built pipe 

drawings to identify potential locations for safely 

installing field injection equipment, as well as flow 

and tracer monitoring equipment. 

3.1.4 Selecting Tracer Material 

Criteria that can influence the selection of a particular 

tracer include: 

•	 regulatory requirements, 

•	 analytical methods and instruments available 

for measuring tracer concentration, 

•	 injection and storage requirements, 

•	 chemical reactivity, 

•	 chemical composition of the finished water, 

•	 overall cost, and 

•	 public perception. 

Ideally, a tracer should be inexpensive, nonreactive 

with both water and distribution system materials, 

safe to drink when dissolved in water, easily dispersed 

in water, aesthetically acceptable to customers, able to 

meet all drinking water regulations, and inexpensively and accurately monitored in the field by 

manual and automated methods. There is no one 

tracer that will meet all of these criteria for a given 

study. Frequently, there are tradeoffs among the 

criteria listed above that must be assessed when 

selecting a tracer. The tracer to be used in the study 

should be determined early in the planning stage, and 

approval for its use received from the water utility and 

state regulatory agencies. 

Tracers may fall into three broad categories: a 

chemical that is normally added to the water during 

the treatment process and that may be temporarily 

shut off during the study; a chemical that is added to 

the water by the team during the study; or a naturally 

occurring substance in the source water that may be 

adjusted in some manner to create a tracer. 

The most commonly used tracers are fluoride, calcium 

chloride, and sodium chloride. 

3.1.4.1 Fluoride 

Fluoride is frequently added to water supplies 

because of its health benefits, but can be turned off 

for short periods, thereby making the non-fluoridated water a tracer in the system. When fluoridation is not practiced, fluoride can be added to the 

water system and used as a tracer by injection. It is 

especially popular with utilities that routinely add 

fluoride as part of the treatment process, because 

little effort is required to turn the fluoride off and 

on. When the fluoride feed is shut off, a front of 

low-fluoride water (or no fluoride if there is no 

natural background concentration) becomes the 

tracer. A second tracer test (or a continuation of the 

initial test) can be performed when the fluoride feed 

Fluoride can interact with coagulants that have been 

added during treatment and in some circumstances 

can interact with pipe walls leading to non-conservative behavior. Thus, when used in systems that do not 

generally fluoridate, a field test should be performed 

to determine possible interactions with pipes. 

3-3 
A Reference Guide for Utilities 

is turned back on, thus making it possible to 

generate two sets of tracer data in one study. 

The MCL for fluoride is 4 mg/L. However, if the 

secondary MCL of 2 mg/L is exceeded, customers 

must be notified. Background levels of fluoride can 

vary significantly and actually exceed the secondary 

MCL in some geographic areas. 

In cases where a utility is not permitted to completely 

shut off the fluoride feed, it may be feasible to 

increase the fluoride feed prior to the tracer study and 

to reduce the fluoride feed during the test. Care 

should be exercised to avoid exceeding the secondary 

MCL. However, there must be a sufficient change in 

the fluoride concentration feed in order to trace the 

change through the system. Thus, for example, a 

decrease in feed concentration from 1.2 mg/L to 0.8 

mg/L may not be sufficient, but a decrease in concentration from 1.5 mg/L to 0.5 mg/L may be adequate. A 

change in fluoride dosage may have to be preapproved by state regulators. Depending upon the 

duration of the study, the state agency may choose to 

allow a temporary shutoff or set a specific lowest 

allowable-fluoride-concentration requirement. 

In most treatment plants, fluoride is injected prior to a 

final clearwell. As a result, when the feed is shut off as 

a part of the tracer study, there is both a time delay 

and a gradual change in concentration in the clearwell 

discharge as the non-fluoridated and fluoridated water 

mix. Therefore, wherever and whenever possible, the 

clearwell should be operated at minimum water levels 

during the tracer test in order to achieve a relatively 

sharp front of non-fluoridated water leaving the 

In a study conducted in the Cheshire service area of the 

South Central Connecticut Regional Water Authority 

(SCCRWA) in 1989, the fluoride feed was turned off to 

provide a tracer to validate a hydraulic and water 

quality model of their water distribution system (Clark 

et al., 1991). This study was among the first applications of water quality models in the world. SCCRWA 

normally added fluoride at a level of approximately 1 

mg/L. For purposes of the model validation study, the 

fluoride feed was turned off for a period of 7 days and 

then turned back on with sampling occurring for an 

additional 7 days. This approach yielded, in effect, two 

tracer fronts. During the study, grab samples were taken 

every few hours at 16 hydrants, two well fields, one 

tank, four continuous analyzer sites, and daily at 19 

“deadend” sites. Additionally, experimental units were 

installed at a few sheltered sites to automatically 

measure fluoride concentrations and to take discrete 

samples for later analysis. A total of 2,150 fluoride grab 

samples were taken during the study and analyzed in 

the laboratory. 

treatment plant. It is also important to evaluate the 

impact of travel through finished water storage 

reservoirs on the concentration of tracer during the 

study. An alternative is to inject fluoride solution 

(e.g., sodium fluoride) at a point in the main transmission line downstream of the clearwell where both flow 

and injection rate can be simultaneously monitored 

and measured. 

Ion-selective electrodes (ISE) can be used in conjunction with data loggers to provide continuous monitoring capability. At present, however, these instruments 

are relatively expensive (approximately $5,000 to 

$10,000 each) and have only been used extensively 

in large-scale tracer studies (Maslia et al., 2005; 

Sautner et al., 2005). Generally, grab samples are 

taken and analysis is performed manually in the field 

or laboratory. 

Under some circumstances, fluoride is not a fully 

conservative chemical. In one study (Vasconcelos et 

al., 1996) in a system that did not normally fluoridate, 

a 13-hour pulse (step input of limited duration) of 

fluoride was injected into the feed line of a pressure 

zone. Field measurements of fluoride concentrations 

in the zone during the study indicated a significant 

loss of fluoride. It was postulated that some of the 

fluoride was deposited on the pipe wall. In a 

followup study, this problem was virtually eliminated 

by injecting fluoride over a period of several days 

prior to the actual study in order to pre-condition the 

pipes. 

3.1.4.2 Calcium Chloride 

Calcium chloride (CaCl2

) has been used in many 

tracer studies throughout the U.S. It is considered to 

be safe and relatively easy to handle. Generally, a 

food grade substance is required. It can be purchased 

as a liquid (typically a 30 to 35% solution) or as a 

powder that can be mixed with water to form a 

solution. 

If calcium chloride is chosen as a tracer, the study 

personnel should be aware of the secondary drinking 

water MCL for chloride (250 mg/L). A target that is 

less than the secondary MCL should be set in order to 

provide a safety factor. Where chloride levels are 

high, calcium chloride may not be an appropriate 

choice for a tracer. 

Grayman et al., (2000) utilized calcium chloride as a 

tracer in two studies of mixing in distribution system 

tanks. In both studies, the chemical was injected into 

the inflow pipe of the tank during the fill cycles, and 

conductivity and chloride were measured at locations 

within the tank. Calcium chloride has recently been 

used in several distribution system studies (Panguluri 

et al., 2005; Maslia et al., 2005; and Sautner et al., 

2005). 

3-4 
Calcium chloride can be monitored by measuring 

conductivity, or by measuring the calcium or chloride 

ion (Standard Methods, 1998). Conductivity is 

typically the easiest of these parameters to measure 

and is most amenable to inexpensive continuous 

monitors. However, conductivity is not a truly linear 

parameter (i.e., if a beaker of water of conductivity 100 

mS/cm is combined with a like volume of water with a 

conductivity of 300 mS/cm, the conductivity of the 

resulting solution will not be exactly 200 mS/cm). As 

a result, distribution system models (that all assume 

linearity) can only approximately represent conductivity. Therefore, when using conductivity as the 

measured parameter, the options are to accept the 

linear approximation or convert conductivity to a true 

linear parameter such as chloride or calcium. If the 

former option is chosen, the amount of resulting error 

should be established in laboratory tests of waters of 

varying conductivity. If the latter option is chosen, 

the relationship between conductivity and chloride (or 

calcium) must be established in the laboratory. It 

should also be noted that most field devices are set up 

to measure specific conductance instead of conductivity (conductivity is temperature sensitive, whereas 

specific conductance is referenced to 25°C). For the 

purposes of this document, conductivity is assumed to 

represent specific conductance. 

3.1.4.3 Sodium Chloride 

Sodium chloride (NaCl) can be used as a tracer and 

has many characteristics similar to calcium chloride 

in that it can be traced by monitoring for conductivity 

or for the concentration of the chloride or sodium ion. 

The allowable concentration for sodium chloride is 

also limited by the secondary MCL for chloride and 

the potential health impacts of elevated sodium 

In a recent tracer study in Hillsborough County, 

Florida, two separate tracer chemicals were used to 

study the movement of water in a large distribution 

system (Boccelli et al., 2004). Approximately 2,200 

gallons of a saturated NaCl solution was injected into 

the finished water of a treatment plant as a series of 

four pulses ranging in duration from 1 to 3 hours over 

a 24-hour period. Simultaneously, the normal fluoride 

feed was shut off at the plant. Continuous conductivity monitors were installed at 14 locations in the 

distribution system to monitor for the NaCl tracer. 

Grab samples were taken to monitor the low fluoride 

front as it moved through the system and to evaluate 

water quality changes. The resulting extensive 

hydraulic and water quality database is being used to 

calibrate a hydraulic and water quality model of the 

system (Boccelli and Uber, 2005). 

A Reference Guide for Utilities 

levels. EPA reports that taste thresholds for sodium 

vary significantly among individuals, ranging from 

30 to 460 mg/L (EPA, 2003b). 

3.1.4.4 Other Chemicals That May be Added as 

Tracers 

Other chemicals added as part of a tracer study 

include lithium chloride and chlorine. Lithium 

chloride is a popular tracer in the United Kingdom but 

is used less frequently in the U.S., partly because of 

the public perception of lithium as a medical pharmaceutical. There are no field techniques for measuring 

lithium, and it is not easily amenable to automated 

continuous measurement. Samples must be collected 

and lithium concentrations measured in the laboratory. 

Chlorine is commonly used as a disinfectant in many 

water systems. Because chlorine is reactive, it will 

decay over time. Under some circumstances, however, 

it can be used effectively as a tracer. It is most 

effective in a water where chlorine is not highly 

reactive (low decay rate) with either the water or 

distribution system material, and where the concentration levels can be increased above the normal level to 

create a front of water with a high chlorine concentration propagating through the system. However, in no 

case should the chlorine or chloramine be decreased 

to a level that may affect the disinfection process 

(Ferguson and DiGiano, 2005). Again, any tracer 

study should first be approved by the state regulators. 

3.1.4.5 Naturally or Normally Occurring Tracers 

Perhaps the most difficult part of conducting a tracer 

study is obtaining permission to add a chemical and 

then injecting the tracer into the system at a concentration consistent with regulations. Much of this 

effort can be avoided if there is a natural tracer 

available. Natural tracers are generally site-specific, 

but many options do exist and should be explored. 

The most common situation is the existence of 

multiple sources of water with different chemical 

signatures or if a change is planned in the chemical 

signature at a single source. Examples of these 

situations are described below. 

Some of the chemical signatures that may be used to 

differentiate between sources include THM concentrations, hardness, conductivity, and treatment 

coagulant. Sampling in the distribution system for 

these “tracers” will provide information on zones 

served by each of the sources and the extent and 

variation of the mixing that takes place in these zones 

over time. Alternatively, if one water source can be 

turned off for a period of time until the other source 

has reached chemical equilibrium throughout the 

system, the original source can be turned back on and 

used as a tracer as it propagates through the system. 

One of the first uses of natural tracers was in the North 

3-5 
A Reference Guide for Utilities 

Making a major change in the incoming water supply 

such as a change in source water or modifying treatment 

may provide an opportunity to conduct a tracer test. 

The increased use of chloramines as a secondary 

disinfectant, to reduce the formation of DBPs, introduces another potential tracer opportunity. When a 

water utility switches from chlorine to chloramines (or 

vice versa), the chemical signature of the water changes 

and can be monitored by measuring both free and total 

chlorine. Namely, with chloramination, total chlorine is 

typically much higher than free chlorine, while with free 

chlorination, free and total chlorine will typically be 

very similar. A tracer study can be conducted when a 

system first adopts chloramination. Alternatively, many 

water utilities routinely switch back from 

chloramination to chlorine (e.g., annually for a month) 

in order to kill ammonia-oxidizing bacteria and thus 

reduce the chances of nitrification. This provides a 

recurring opportunity to conduct such a tracer study. 

Penn Water Authority (NPWA) located in Lansdale, PA 

(Clark and Coyle, 1990). A field research project was 

conducted by EPA and NPWA that resulted in the 

development of a series of models that were used to 

study contaminant propagation in the water distribution system. The utility used a combination of 

groundwater with high levels of hardness and surface 

water containing higher levels of THMs. This resulted 

in two sources of water with very different quality 

characteristics. By monitoring changes in water 

quality that occurred at selected sampling points in 

the utility network, it was possible to use hardness 

and THM concentrations as tracers to validate the 

model. 

Another case occurred in the North Marin Water 

District (NMWD) in northern California (Clark et al., 

1994) where natural differences in water characteristics were used to serve as a tracer for validation of a 

water distribution system model. In this EPAsponsored study, the utility used two sources of water 

with dramatically different water quality characteristics. The first source, Stafford Lake, has a very high 

humic content and thus has a very high THM formation potential. The other source is the North Marin 

Aqueduct with a very low humic content and thus a 

very low THM formation potential. The model was 

further validated by predicting chlorine residual 

losses at various points in the network. In a follow-up 

study supported by AwwaRF (Vasconcelos et al., 

1997), the investigators used sodium as a tracer to 

validate the model. 

DiGiano and Carter (2001) and DiGiano et al. (2005) 

traced the flow from two separate treatment plant 

sources at the same time by simultaneously reducing 

the fluoride feed at one plant while changing the 

coagulant added at the other plant. Normally, ferric 

chloride (FeCl3

) was used as a coagulant at both 

plants. During the tracer study, the coagulant at one 

plant was changed to aluminum sulfate [Al2

(SO4)3]. 

Fluoride, sulfate, and chloride were measured 

throughout the distribution system. 

Water utilities should carefully examine their particular system to determine if a natural tracer is available 

or if source-chemical signatures may be modified to 

be used as a tracer. 

Sweetwater Authority in southern California took 

advantage of a normal changeover in source water 

quality to perform a tracer study in their distribution 

system (Hatcher et al., 2004). In this case, the utility 

semi-annually changes the primary source of their water 

supply from local Sweetwater Reservoir raw water to 

water provided by the California Aqueduct. These two 

sources have very different chemical characteristics; 

most significantly, the organic carbon content (i.e., 

humic and fulvic acids) of Sweetwater Lake water is 

much higher compared to the raw aqueduct water. The 

measurement of molecular organic carbon absorbance at 

254 nanometers, utilizing an ultra-violet-visible (UVVIS) spectrophotometer, is a surrogate measurement for 

the organic carbon content in water. UV-254 measurements were taken from grab samples at the treatment 

plant and at 28 sites within the distribution system over 

the five-day changeover period. The distribution 

system sites included most of the TCR sampling sites in 

addition to selected tanks. The resulting database was 

used to assess the movement of water in the system, the 

travel time throughout the system, boundary zones in 

the distribution system between areas served by the 

surface water plant and secondary sources, and calibration/validation of the distribution system model. 

3.1.4.6 Comparison of Tracers 

Teefy (1996) investigated tracer alternatives for use in 

studies of residence time in clearwells and described 

the chemical characteristics of the individual tracers. 

Table 3-1 summarizes various chemical characteristics 

identified in that report.

There are advantages and disadvantages associated 

with each of the general types of tracers: conservative 

(non reactive) tracers, reactive tracers, chemicals that 

are normally added to the water but can be turned off, 

and natural chemical signatures in the finished water. 

Conservative tracers are more easily modeled than 

non-conservative tracers. Natural tracers or chemicals 

that can be turned off are easier to use than injected 

chemicals. Certain chemicals are more amenable to 

continuous monitors. These and other factors should 

all be considered when selecting a tracer for a study. 

3-6 
A Reference Guide for Utilities 

Table 3-1. Tracer Characteristics (adapted from Teefy, 1996) 

Fluoride Calcium Sodium Lithium Chloride 

Commonly 

available forms 

H2

SiF6 

NaF 

Na2

SiF6 

CaCl2 NaCl dry LiCl CaCl2 

NaCl 

KCl 

Analytical IC, AA, AA, AA, IC, 

methods ISE, IC, IC, IC, ISE, 

SPADNS method ICP, 

EDTA titration 

Conductivity 

ICP, 

FEP 

Conductivity 

ICP, 

FEP 

AgNO3 titration, 

Hg(NO3

)

2 

titration 

Typical 

chemical cost 

Food-grade 

H2

SiF6 

$7.6/100 lb ­

23.97% liquid1 

$140/55 gallons 

- 49% liquid2 

Food-grade 

CaCl2 

$150/55 gallons 

- 35% liquid3 

Food-grade NaCl 

$12/50 lb4 

$6/50 lb 5 

Lab-grade LiCl6 

$22 - $48/500g7 

Food-grade NaCl 

$12/50 lb4 

$6/50 lb5 

Typical 

analytical cost 

per sample 

$188 (IC) 

$1610 (IC) 

$1211 (ISE) 

$2512 (IC) 

$108 (ICP) 

$1210 (ICPMS) 

$511 (ICP) 

$108 (ICP) 

$1210 (ICPMS) 

$511 (ICP) 

$128 (ICP9

) 

$1210 (ICPMS) 

$611 (AA13) 

$188 (IC) 

$1610 (IC) 

$1210 (EPA 325.3) 

$1211 (IC) 

Typical 0-4 mg/L Varies greatly (1­ Varies greatly (1­ Usually below 5 Varies greatly (1­

background 300 mg/L), use 500 mg/L) mg/L 250 mg/L) 

levels in water only when low 

distribution 

systems 

Regulatory 4 mg/L SDWA None known. 20 mg/L for None known. 250 mg/L 

limits MCL, 2 mg/L See limits for restricted diet See limits for secondary 

secondary MCL chloride. (EPA chloride. standard 

recommendation) 

1 Provided by Lucier Chemical Industries (LCI), Ltd., http:// 

www.lciltd.com 

2 Provided by Bonded Chemicals, Inc., http://www.chemgroup.com/ 

bci.htm 

3 Provided by Benbow Chemical Packaging, Inc., http:// 

www.benbowchemical.com 

4 Provided by Skidmore Sales and Distributing Company, Inc., http:/ 

/www.skidmore-sales.com 

5 Provided by Ulrich Chemical, Inc., http://www.ulrichchem.com 

6 Food grade LiCl is not available. 

7 Provided by Science Kit & Boreal Laboratories, http:// 

www.sciencekit.com 

8 Provided by Severn Trent Laboratories (STL) North Canton, Ohio, 

http://www.stl-inc.com. Prices are based on a large sample volume 

(> 500 samples). 

9 STL North Canton Laboratory is not certified for Lithium test in 

Ohio. 

10 Provided by SPL Laboratories, Inc., http://www.spl-inc.com 

Prices are based on a large sample volume (> 500 samples). 

11 Provided by Environmental Enterprises, Inc., http:// 

www.eeienv.com Prices are based on a large sample volume (> 

500 samples). 

12 Provided by FOH Environmental Laboratory for the CDC study at 

Camp Lejeune, NC. http://www.foh.dhhs.gov/. The analytical cost 

per sample includes cost for providing a sample bottle and report. 

13 Environmental Enterprises, Inc. is not certified for Lithium test. 

Note:Tracers 

CaCl calcium chloride 2 

H SiF hydrofluosilicic acid 2 6 

KCl potassium chloride


LiCl lithium chloride


NaF sodium fluoride


Na SiF sodium silicofluoride
 2 6 

NaCl sodium chloride 

Analytical methods 

AA atomic absorption spectrometry 

AgNO3 silver nitrate 

EDTA ethylenediaminetetraacetic acid 

FEP flame emission photometric method 

Hg(NO3)2 mercuric nitrate 

IC ion chromatography 

ICP inductively coupled plasma 

ISE ion selective electrode 

SPADNS Trisodium (4,5-Dihydroxy-3-[(psulfophenyl)-2,7-) naphthalene 

disulfonic acid 

3-7 
A Reference Guide for Utilities 

After investigating tracer options and selecting the 

most appropriate tracer, the governing state drinking 

water agency should be contacted. The agency 

should be provided with the specifics regarding the 

proposed study including location(s), proposed timeline(s) and selected tracer material. Once agreement 

has been reached and consent is received, the study 

team can then proceed with the next steps in the 

planning process. 

3.1.5 Selecting Field Equipment and Procedures 

Once a tracer has been selected and approval has been 

received from the appropriate water utility managers 

and regulatory agencies, specialized equipment must 

be identified and procured, including injection 

pumps, temporary tracer storage tanks, and various 

flow and tracer monitoring equipment (e.g., tracer 

chemical, reagents, and/or sample bottles). Vendors 

should be contacted for technical information, 

equipment availability, and cost quotations for the 

required field equipment and analytical instrumentation. The major decisions to be made and the items to 

be purchased prior to the execution of the study are 

discussed in the following subsections. 

3.1.5.1 Injection Pump(s) 

Pumps that are typically used in drinking water 

applications can be broadly classified as centrifugal 

pumps or positive displacement pumps. The centrifugal pumps produce a head and a flow by increasing 

the velocity of the liquid with the help of a rotating 

vane impeller. The positive displacement pumps 

operate by alternating between filling a cavity and 

displacing the volume of liquid in the cavity. The 

positive displacement pumps deliver a constant 

volume of liquid (for a given speed) against varying 

discharge pressure or head. By design, the positive 

displacement pumps are better suited to serve as an 

injection pump for a tracer study. Examples of 

positive displacement pumps include: rotary lobe, 

progressing cavity, rotary gear, piston, diaphragm, 

screw, and chemical metering pumps (e.g., bellows, 

diaphragm, piston, and traveling cylinder). 

Selection of the most appropriate positive displacement pump depends upon the injection rate, the 

pressure in the receiving system, the chemical 

characteristics of the tracer, and local experience and 

preferences. Two types of positive displacement 

pumps have generally been used in tracer studies: 

gear pumps and metering pumps. The final selection 

depends upon viscosity of the tracer material, 

variability of pressure in the main, dosage accuracy 

needs, and other local factors. Furthermore, to control 

the drive speed (i.e., dosage), these pumps are 

equipped with alternating current (AC) or direct 

current (DC) motor. If a pump has an AC motor, 

frequency is adjusted; if it is equipped with a DC 

motor, voltage is adjusted to control speed. 

EPA has used gear pumps equipped with variable 

frequency drives in the past with success for conducting tracer studies. Other studies have reported success 

with metering pumps with variable speed or variable 

stroke controllers. The pump should be sized in 

accordance with the anticipated tracer dosage (for 

more details, see Tracer Dosage and Injection Duration Section 3.2.3) and pressure range in the main 

pipe for the selected injection location(s) in the study 

area. Depending upon the location and dosage 

requirements, more than one size of pump may be 

needed (excluding backup pumps). 

3.1.5.2 Tracer Storage and Dosage Rate 

Measurement 

Tracers are available in dry or liquid form. If purchased as a powder, provisions for mixing the powder 

with water must be made. If the tracer is purchased in 

liquid form, it typically comes in either 55-gallon 

drums or in larger containers such as a 330-gallon 

tote. If only a small amount of tracer is needed, a 

single 55-gallon drum will typically suffice. For 

greater accuracy, it is recommended that the tracer be 

transferred from 55-gallon drums to a suitably sized 

day tank with a sight glass (used to periodically 

monitor the total tracer volume dosed). It is easiest to 

pump the tracer from a single container rather than 

having to switch the pump from container to container during the injection process. Details on tracer 

dosage calculations are presented in Section 3.2.3. 

If a metering pump is purchased, care must be taken so 

that the pump flow rate is calibrated for the specific 

tracer solution (by the vendor). Furthermore, the 

During a tracer study when a tracer chemical is being 

injected into the system, in order to meet water quality 

regulations and to simplify the modeling, it may be 

desirable to maintain a constant tracer concentration in 

the receiving pipe. This can be accomplished by 

monitoring the resulting concentration in the receiving 

pipe and manually adjusting the tracer injection rate or 

through the use of a closed-loop system for automatically controlling the injection rate based on flow in the 

receiving pipe. The automated process is most 

effective at a location where the flow in the pipe is 

varying relatively slowly and where a flow meter 

exists. A typical situation is the use of an existing 

venturi meter that generates a 4-20 milli-ampere (ma) 

signal. This signal can be used as input to a controller 

that has been calibrated and programmed to control the 

stroke or speed of a variable stroke or speed injection 

pump. If the flow in the receiving pipe is varying 

rapidly over a large flow range, it is difficult for the 

closed-loop system to respond quickly. 

3-8 
A Reference Guide for Utilities 

variable area flow meters (rotameters - with floats 

contained in an upright conical tube) are relatively 

inaccurate for measuring tracer dosage even after 

adjustments are made for density and viscosity. 

Figure 3-1 shows a “flow tube” that can easily be 

custom fabricated and calibrated to accurately 

measure the rate of tracer injection. It is recommended that the supply tank also be marked to keep 

track of the tracer fluid level. Times should be noted 

at each mark so that it is possible to create a mass 

balance for the tracer injected during the study. 

5 gal 

4 gal 

3 gal 

2 gal 

1 gal 

from to 

storage injection 

tank pump inlet 

Figure 3-1. Flow Calibration Tube. 

3.1.5.3 Distribution System Flow Rate 

Measurement 

In order to calculate the concentration of the tracer in 

the receiving pipe, it is necessary to know the flow 

rate in the pipe, the injection rate of the tracer, the 

injected concentration of the tracer, and the background concentration in the water before tracer is 

added. Flow rate should be measured continuously, 

because variations in pipe flow rate can affect tracer 

concentration. These fluctuations in flow can be 

accommodated by manually adjusting the tracer 

injection rate in the field or through the use of a flowpaced injection pump that responds to the flow in the 

receiving pipe. 

Placement of additional flow meters or other flow 

measuring devices at various points in the system is 

recommended. This information will be very useful 

during the post-tracer modeling studies and is 

invaluable in calibrating a network hydraulic model. 

If the existing system does not have an adequate 

number of flow meters for purposes of a tracer study, 

installation of additional meters is recommended. 

Various types of flow meters may be used to 

measure flow in pipes. They are categorized as either 

non-intrusive or intrusive meters. Portable ultrasonic 

flow meters are non-intrusive and provide reasonably 

accurate data if the pipe material is conductive and 

relatively non-tuberculated. The ultrasonic flow 

meter requires suitable upstream/downstream straight 

runs of pipe. Insertion flow meters are also an option 

for measuring pipe flow rates. Insertion meters are 

intrusive, and may be magnetic (magmeters) that are 

flange coupled to the pipe or have propellers that 

must be inserted through a hole in the pipe. All meters 

require that the receiving main pipe be exposed (via 

excavation) or that an existing vault be used. If the 

injection location is in the vicinity of a reservoir/tank 

and the water level changes are available in real time, 

it may, in some instances, serve as a rough surrogate 

for in-pipe flow measurement. The selected method of 

flow measurement must be field tested. 

Depending upon the size of the reservoir/tank and the 

local demand, the reservoir level changes may not be 

fast or accurate and precise enough to determine the 

flow rate in real time. 

3.1.5.4 Field Measurement of Tracer 

Concentration 

Tracer concentration may be measured in the field 

using either automated monitors that analyze a 

sample at a preset frequency, by collecting “grab” 

samples, or a combination of both. Grab samples can 

be manually analyzed in the field or in the laboratory. 

If grab sampling is used during a tracer study, the 

sampling team will generally traverse a circuit of 

several sampling locations. Using such an approach 

will generally yield a sampling frequency of one 

sample per station every one to three hours for an 

average-sized residential neighborhood (unless 

multiple crews are used). Some of the factors that will 

influence sampling frequency include the speed at 

which the tracer is moving within the distribution 

system, the number of sampling crews participating in 

the study, the number of sampling sites selected, the 

time of the day, and the distance between sampling 

sites. Equipment requirements for grab sampling are 

minimal and may include the following: coolers, ice, 

labeled sample bottles, log books, and temperature 

blanks. If samples are to be analyzed in the field, the 

sampling teams will need the appropriate analytical 

equipment. If samples are to be analyzed in the 

laboratory, the team will need the means to properly 

store and transport samples to a central laboratory. 

The quality assurance project plan (QAPP) may 

require duplicate or split samples for some or all of 

the primary samples. When taking a grab sample, care 

must be taken to flush the tap for a sufficient time to 

ensure that the sample is representative of the 

distribution main rather than the service lines. 

3-9 
A Reference Guide for Utilities 

Reliance solely on grab sampling may be impractical 

if the study area is large, the tracer front is moving 

rapidly, or a high frequency of sampling is desired. In 

these cases, continuous automated monitoring may be 

the best choice although some grab samples for 

quality assurance and quality control are recommended. If calcium chloride or sodium chloride is the 

tracer selected, an online specific-conductivity meter 

equipped with an associated data logger is recommended. Automated monitors are available if 

chlorine residual is used as a tracer. There are also 

automated monitors available if fluoride is used as a 

tracer, but there has been relatively limited use under 

field conditions. Since most automated monitors 

require a continuous side stream (rather than being 

inserted directly into a main), the drainage flow from 

the monitor must be discharged into a sewer, into the 

street and subsequently into a storm drain, or into a 

pervious area. This discharge can be an added 

complication during cold weather when it may freeze. 

Since this discharge stream is generally chlorinated or 

chloraminated, regulations may control discharge into 

natural water courses. Additionally, this discharge 

flow may have to be accounted for if the data set is 

being used to calibrate a distribution system model, 

and the quantity of discharge through a particular 

meter is significant relative to the demand in the 

vicinity of the meter. If the total drainage discharge is 

significant for the purposes of modeling, provisions 

for continuously or manually measuring the amount 

of flow being bypassed are needed. 

Potential grab and online sampling sites include: 

dedicated sampling taps, hydrants, pump stations, 

tank inlet-outlet lines, and faucets located inside or 

outside of buildings. Figure 3-2 depicts an automated 

monitoring station used by EPA. This figure illustrates the case where the sampling tap is allowed to 

Figure 3-2. Automated Monitoring Station. 

EPA and GCWW have pioneered the use of online 

monitors as a central focus for distribution system 

tracer studies. In a series of field tests, EPA and GCWW 

injected calcium chloride tracer into the water system 

and followed the movement of the tracer using automated conductivity meters strategically placed 

throughout the study area. Three separate studies were 

conducted in a large water system representing a small 

highly urbanized area, a small dead-end suburban area, 

and a large suburban pressure zone. Based on the 

success of these studies, similar tracer studies have 

been conducted utilizing a combination of online 

monitors and grab samples by the CDC using both 

fluoride and sodium chloride as tracers in Hillsborough 

County, Florida (Boccelli et al., 2004) and by the Agency 

for Toxic Substances and Disease Registry (ATSDR) using 

fluoride and calcium chloride at a large military base in 

North Carolina (Maslia et al., 2005; Sautner et al., 2005). 

run continuously throughout the study with the water 

going to a drain. The flow rate to or through the 

sampling tap must be sufficient to minimize the travel 

time from the main to the monitor. 

Online, automated sampling programs should be 

complemented with a grab sampling program to add a 

degree of confidence in measured data and to supplement field data at additional locations or at the 

automated monitor stations if they fail to record 

correctly. 

3.1.6 Developing a Detailed Study Design 

A key element in planning and designing a tracer 

study is the preparation of a study design document. 

This document serves as the overall plan for conducting a tracer study and thus, the roadmap for execution 

of the study. Three important study-specific parts of 

the design plan that may be required before the 

execution phase are a QAPP, a Health and Safety 

Project Plan (HSPP), and a contingency plan. The 

contingency plan describes the actions to be taken if 

unexpected events occur; for example, if distribution 

system concentrations of the tracer exceed the MCL 

for chloride or fluoride. The HSPP should at a 

minimum define the job hazards that might be 

encountered and the controls, protective equipment, 

sample handling and work practices, safety review 

procedures, and emergency procedures to be employed during the study. 

The QAPP should clearly define the project objectives, 

organization, experimental approach, sampling 

procedures, analytical methods, protocols, instrument 

calibration requirements, data reporting, data reduction, 

and data verification procedures. 

3-10 
A Reference Guide for Utilities 

3.1.7 Addressing Agency and Public 

Notification 

Appropriate agencies, including fire and police 

departments, should be notified prior to the commencement of field activities. With heightened 

awareness of security, all people participating should 

have a valid identification and contact information. A 

standard statement concerning the study should be 

developed and provided to all team members in case 

they receive inquires at the study site. This same 

statement should be used by utility personnel to 

answer any telephone inquires that might be received. 

A summary information card may be provided to the 

study participants that could be handed out to the 

public during the study (if requested). This minimizes 

the risks of mis-communication. 

If the injection site or installation of meters requires 

excavation, the study team must obtain the necessary 

permits and approvals. This is especially important if 

any of the sites are in a residential neighborhood or 

near a busy street or road. Care should be taken in all 

cases to provide adequate traffic control. Safety is of 

paramount consideration. 

3.2 Executing a Tracer Study 

The team should first become familiar with the 

detailed study design documents discussed in Section 

3.1.6. Based on these documents, there are several 

tasks that need to be completed during the execution 

phase of a tracer study. These tasks include: 

•	 Procurement, setup, testing, and disinfection of 

study equipment (including pumps, storage 

tanks, chemicals, reagents, tubing, connectors, 

and continuous tracer monitoring stations). 

•	 Installation of field equipment and testing (both 

flow and tracer monitoring equipment to 

confirm study-specific distribution system 

operation and flow stability). 

•	 Tracer dosage and injection duration


calculations.


•	 “Dry runs” and planned tracer injection events. 

•	 Real-time field assessments, sampling, and


analysis.


•	 Equipment demobilization, initiation of data 

collection, reduction, and verification process. 

These specific execution subtasks are further discussed in the following sub-sections. 

A tailgate safety meeting before commencement of any 

field work is the best method to increase awareness. 

3.2.1 Procurement, Setup, Testing and 

Disinfection of Study Equipment 

Field equipment identified under Section 3.1.5 and its 

subsections should be procured on a timeline such 

that the items arrive several weeks before the planned 

study date, especially the monitoring and injection 

equipment that may require assembly. An early 

arrival will ensure that the equipment can be properly 

configured and tested before field use. 

Unless pre-calibrated flow-paced injection equipment 

is purchased (or if the study does not require injection 

equipment – as in the case of using naturally/ 

normally occurring tracers), the study team should 

obtain an appropriate injection pump setup. Figure 3­

3 shows a picture of a tracer injection system used by 

EPA for field tests. This setup should be calibrated in 

the lab to compute the speed-specific dosage rate 

using the tracer solution. If appropriate, a flowcalibration tube should also be fabricated to confirm 

the flow in the field. Figure 3-3 also depicts a flowtube used by EPA. 

Figure 3-3. Tracer Injection Setup (Storage Tank, 

Calibration Tube and Feed Pump). 

storage tank 

calibration 

tube 

feed pump 

3-11 
A Reference Guide for Utilities 

Concurrently, if applicable, the team should initiate 

the fabrication of the automated tracer monitoring 

stations. These stations are typically equipped with a 

probe for measuring the tracer (or a surrogate parameter such as conductivity), associated data logger, and 

batteries (for powering the probe and the data logger). 

If accurate measurement of flow through the automated monitoring station is needed, it should be 

augmented with a household-style water meter and 

logger. The equipment should be housed in a secure 

lock box to protect it during the field study. Figure 3­

2 shows an automated monitoring station used by 

EPA and GCWW to conduct a tracer study. The entire 

setup should be tested in the lab to ensure proper 

operation and battery capacity to maintain uninterrupted operation. 

The grab sampling, laboratory equipment, tracer 

storage tanks, transportation equipment, and 

arrangements should be procured and set up. The 

field equipment hookup, including interconnections between the tracer storage tank, injection 

pump, and flow-tube, should be leak tested. The 

equipment used for injection should be properly 

disinfected and tested prior to field deployment to 

ensure that no microbiological contamination 

results from the field tests. 

If ultrasonic flow meters are procured for field 

deployment, the equipment should be set up in a lab 

environment to confirm the individual component 

operation and approximate battery life. The existing 

flow and data acquisition systems to be used in the 

field study should be sampled for data accuracy and 

field communication. 

During the lab testing phase of the field equipment, 

the entire field (and backup) crew should familiarize 

themselves with proper operating procedures for the 

equipment they are designated to operate. 

One procedure for equipment disinfection is to 

prepare approximately 50 gallons of 50 ppm chlorine 

disinfectant solution. This solution is then recirculated through the injection pump setup for about 

15 minutes. Thereafter, continuously flush the 

injection pump using de-ionized water for about 15 

minutes. Collect a water sample at the end of the 

flush cycle and send it for bacteriological analysis 

(Coliform and E. coli) to insure that the disinfection 

procedure was successful. For the purposes of 

sampling, use sterile sample bottles with a dechlorinating agent (e.g., sodium thiosulfate). The dechlorinating agent is added to remove any residual 

chlorine or other halogen that may continue the 

disinfection process in the sample and yield incorrect 

test results. 

3.2.2 Installation of Field Equipment and 

Testing 

Prior to the commencement of field activity, a brief 

“tailgate” health and safety meeting should be 

conducted at the beginning of each day to remind the 

crew of potential job hazards. Mobilization of field 

equipment for excavations (if required – for installing 

main flow meters) should be initiated to allow for the 

flow monitoring devices to be installed prior to the 

scheduled injection event(s). This time lag will vary 

according to the needs of the specific study and could 

range from several days to several weeks. The early 

installation of flow meters will allow the study team 

to capture actual field flow data for performing any 

revisions to tracer dosage computations and preliminary hydraulic modeling analysis. The flow meter 

installation location should meet the manufacturer’s 

recommendations for upstream and downstream 

straight lengths of undisturbed pipe. The excavations 

should be performed in accordance with the HSPP. 

Appropriate drainage for the excavated pits should be 

arranged in case rain is forecast during the study 

period. 

The measured field flow data should be utilized to 

confirm the stability and range of flow at the injection 

location and other major branches of the system where 

flow is monitored. It may be necessary to operate the 

distribution system under specified conditions in 

order to achieve optimum results during the study. 

The operational changes that may be required 

include: scheduled cycling of tank levels, pumps, 

and valves. Time required for the deployment of the 

automated monitoring stations prior to the start of the 

tracer tests is dependent upon several factors, including the number of monitoring stations, the distances 

between stations, the ease of attaching the stations to 

the sampling hydrants, and the effort required to 

calibrate the monitoring equipment. If feasible and 

consistent with normal operating policies, the system 

should be operated to avoid frequent abrupt changes 

in flow such as would be associated with a pump that 

was cycling on and off very rapidly. 

A day or two prior to the execution of the tracer 

injection event, the study team should fully deploy 

the continuous monitoring stations (if used). These 

stations should be hooked up at the designated 

sampling locations and data logs should be checked 

to ensure data are being collected. Flow through a 

monitoring station should be sufficient to minimize 

the time delay in detecting the injected tracer 

between the main and the sampling location. Experience has shown that 1 to 2 gallons per minute (gpm) is 

usually sufficient. The field crew should also test the 

coverage and reliability of field communication 

devices (such as cellular phones) in the designated 

study area. 

3-12 
3.2.3 Tracer Dosage and Injection Duration 

Calculations 

Factors affecting the amount of tracer required for the 

study include the duration of the injection, the flow 

rate in the receiving pipe, and the target concentration in the distributed water. This target concentration should be consistent with drinking water 

standards. For example, if fluoride is being injected 

(into a system that does not fluoridate) with a secondary MCL of 2 mg/L, a reasonable target concentration 

level is 80% of the MCL, i.e., 1.6 mg/L. The injection 

rate should be set to meet that goal. 

Using the principle of material balance, the resulting 

tracer concentration in a receiving pipe downstream 

of the point of injection can be calculated as follows: 

QD = QU + QT ( Equation 3-1) 

(CB •QU ) + (CT •QT ) (Equation 3-2) CD = QD 

Where 

QD = flow downstream of injection point, L3/T 

QU = flow upstream of injection point, L3/T 

QT = flow of tracer solution, L3/T 

CD = concentration of tracer material downstream

of injection point, M/L3 

CB = background concentration of tracer material in 

distributed water, M/L3 

CT = tracer concentration, M/L3 

Equation 3-1 represents continuity and Equation 3-2 

represents conservation of mass. As written, these 

equations are independent of units for mass (M), 

length (L), and time (T) as long as consistent units are 

used for computations. However, when tracer 

concentrations, injection rates, and injection 

duration are used to calculate the required volume of 

tracer material purchased, units for flow, concentration, and time must be commensurate or appropriate 

conversion factors must be employed. 

For some tracers, the allowable concentration in the 

distributed water may be controlled by one of the 

dissolved ions that are part of the tracer. For example, 

if calcium chloride is the selected tracer, the concentration of the chloride ion in the distributed water 

controls the amount of tracer that may be injected. 

Injection duration depends upon the size and complexity of the distribution system, and the modeling 

objectives of the study. A typical duration can range 

from one hour in a small or branched system, to eight 

A Reference Guide for Utilities 

hours or more in a larger, looped system. Some 

studies have reported success with a series of pulses. 

However, if the duration of the injection is too short 

or the series of pulses too close together in time, it is 

difficult to separate the tracer fronts as they traverse 

different paths at different velocities through the 

looped systems. The presence of tanks can also 

impact the needed tracer duration since active filling 

and drawing can dampen the resulting tracer concentration as it moves through the system. 

The injection equipment should be located close to 

the main in order to minimize the tracer travel time to 

the main. Alternatively, the travel time should be 

compensated for during the appropriate phases of the 

study evaluation. 

3.2.4 Dry Runs and Planned Tracer Injection 

Event(s) 

Before the planned full-scale tracer injection event is 

actually carried out, the project team should consider 

conducting a smaller duration dry run injection to 

confirm the system operation and expected levels of 

tracer concentration. If continuous monitors are to be 

used in the study, then during the dry run some or all 

of the monitors should be installed and tested. The 

timing and duration of the dry run should be such that 

the injected pulse should be short and clear the 

system well before the actual event is initiated. 

The dry run serves as a final systems check and 

provides the study team an opportunity to make any 

necessary last minute changes prior to the actual 

study. Thereafter, the actual full-scale injection event 

should be conducted as planned. 

3.2.5 Real Time Field Assessments, Sampling, 

and Analysis 

While the injection event is ongoing, the study team 

should carefully monitor the tracer concentration at 

the immediate downstream location of the injection to 

ensure that there are no significant deviations in the 

expected versus observed concentrations in the field. 

Field crews should communicate directly with the 

system operations. It is critical that the field personnel are aware of any changes in system operations that 

may affect the study. Unanticipated changes in water 

demand may cause the tracer concentration to exceed 

target concentration levels. In such an event, the field 

crew should be trained to take measures to minimize 

any adverse effects. The preventive measures may 

include lowering (or stopping) the injection rate, or 

achieving appropriate dilution by means of rerouting 

water through the distribution system (as appropriate). 

Furthermore, any such tracer concentration 

exceedances should be confirmed by performing field 

grab sample analysis to make sure that the exceedance 

is real and not an instrument anomaly. Until the 

3-13 
A Reference Guide for Utilities 

results are confirmed, it is best to err on the safe side 

and take preventive measures to maintain water quality. 

Periodically, the field crew should take grab samples 

and inspect the continuous monitoring stations to 

ensure that the equipment is operating properly. The 

grab samples should be appropriately handled and 

analyzed in the field or transported to the laboratory 

for further analysis. The sampling and monitoring 

effort should continue well past the conclusion of the 

injection event until the tracer is expected (and 

observed) to have moved out of the system. This may 

take a period of 24 to 48 hours or more after completion of the injection event. 

During the course of the sampling event, it is very 

useful to examine and assess the field data on a near 

real-time basis. Questions that should be asked 

include “Are the results reasonable?” “Is the tracer 

moving through the system at a speed consistent with 

predictions?” Based on this assessment, modifications may be made in terms of injection rate, grab 

sampling frequency, or study duration. 

3.2.6 Equipment De-Mobilization, Initiation of 

Data Collection, Reduction, and 

Verification Process 

After the scheduled injection event(s) are completed, 

the field crew should download the data (including 

flow and tracer concentrations) from the various 

monitoring devices. The data should be spot checked 

against field grab sampling data to ensure that there 

are no time anomalies or gaps in the data log and the 

readings match relatively well. 

After the field sampling events are completed, the 

crew should de-mobilize the equipment, remove the 

automated monitoring stations, refill any excavations, 

and restore the system operations to their normal 

conditions. 

Downloaded data from the field should be processed 

according to the QAPP and used for further modeling 

and analysis. The use of field data in calibration and 

validation of hydraulic and water quality models is 

discussed further in Chapter 4. 

3.3 Tracer Study Costs 

In general, the cost of conducting a tracer study is 

proportional to the study area size, number of 

monitoring sites, study duration, sophistication and 

amount of equipment, and complexity of post-study 

analysis. If a study incorporates an injected tracer and 

the use of continuous monitors, it can be much more 

expensive initially than a study using a natural tracer 

and grab samples. However, the injection equipment 

and continuous monitoring equipment can be reused 

at various locations. These are the cost tradeoffs 

between purchase of automated monitoring equipment and labor associated with grab sampling. In 

some cases, a larger dataset derived from an automated monitor is necessary for a detailed analysis. 

Cost data presented in this section are intended to 

provide the basis for this type of analysis. For the 

purposes of this chapter, the overall costs have been 

broken down into two distinct categories: equipment 

and labor. Material costs are only a fraction of the 

total, and therefore, have been combined and included with equipment costs for simplicity. 

Table 3-2 lists typical equipment and material costs 

for those items that may be used in tracer studies. The 

unit costs can be easily scaled to the needs of a 

specific study. Chemical tracer costs, including 

analytical costs, were provided earlier in Table 3-1. 

Costs may vary widely among studies. For example, 

if it is necessary to purchase or rent a storage tank or a 

Table 3-2. Equipment Costs 

Equipment & Material Unit Cost ($) 

Injection pump $1,000 - $5,000 

Flow meter (ultrasonic meter for 

main pipes) 

$7,000 - $9,000 

Excavation, rigging and backfill 

(equipment rental per site) 

$1,500 

Lab chemicals, batteries and 

plumbing supplies (lump sum*) 

$1,000 - $5,000 

Automated monitoring box (self 

constructed) 

< $200 

Online conductivity ISE, meter 

and logger 

$800 - $1,500 

Automated monitoring station 

water flow meter 

$600 - $800 

Online fluoride meter $5,000 - $10,000 

Safety equipment (e.g., vests, 

first aid kits, rain gear, and 

flashlights) 

$500 - $1,000 

Communication equipment (e.g., 

radios and GPS) 

$500 - $1,000 

Hydrant equipment (e.g., 

wrenches, caps, and hoses) 

$1,000 - $2,000 

Transportation (e.g., rental 

vehicles) 

$500 - $2,000 

Tracer storage tanks (depending 

upon volume and material) 

$500 - $1,000 

3-14 
A Reference Guide for Utilities 

truck, costs will be higher if these types of items are 

not readily available. If the study team elects to 

analyze samples in-house rather than using an outside 

laboratory, the team should balance the cost of labor, 

and the cost of additional reagents and chemicals 

against the cost of performing the analyses at an 

outside commercial laboratory. Labor costs may be 

even more variable than equipment and material costs 

and are a function of the size and complexity of the 

study. In order to provide an easy basis for comparison, the labor costs are presented in labor hours (Table 

3-3) and include a combination of engineers and 

technicians. Labor hours have been estimated for low, 

medium, and high-end studies. These estimates are 

obtained from actual field studies, as described below. 

This approach should allow utilities to make sitespecific cost estimates. 

Table 3-3. Representative Labor Hours 

for a Range of Studies 

Activity Low-End Medium High-End 

Planning 27 274 480 

Setup - 150 520 

Field study 51 604 370 

Laboratory 

analysis 8 160 120 

Post-study 

assessment 24 212 740 

Total 110 1,400 2,230 

A typical example of a low-end tracer study is 

provided by the Sweetwater Authority distribution 

system in Southern California (see second sidebar in 

Section 3.1.4.5, page 3-6). The Sweetwater system 

covers a service area of 28 square miles. The utility 

was able to take advantage of a naturally occurring 

tracer and used grab samples taken at 28 existing 

dedicated sampling sites over a period of 5 days. A 

study performed in the 21-square-mile Cheshire 

service area of the South Central Connecticut 

Regional Water Authority in 1989 (see second sidebar 

in Section 3.1.4.1 on page 3-4) provides an example 

of a medium-level tracer study. In this case, the 

normal fluoride feed was shut off for a period of 7 

days (and then turned back on) and grab samples were 

taken at intervals of a few hours at 23 sites over a 

period of 14 days. An example of a high-end study is 

provided by a two-phased field investigation conducted in two suburban areas of GCWW. The first area 

is a small (<1 square mile) dead-end system, and the 

second area, a 12-square-mile pressure zone. A 

calcium chloride tracer was injected and monitored 

using a combination of automated conductivity 

meters and grab samples. In the smaller area, 20 

meters were used and monitoring was conducted over 

a 24-hour period. In the second area, 33 meters were 

used and two separate tracer injections were conducted over a period of 5 days. Including both 

studies, a total of 725 grab samples were taken and 

analyzed for conductivity, chloride, and calcium. 

Flow was monitored at four locations using ultrasonic 

flow meters. 

Table 3-3 presents estimated labor hours for these 

types of studies. They are divided into the planning 

phase (as described in Section 3.1); setup, field work, 

and laboratory analysis that together make up the 

execution phase (see Section 3.2); and the post-study 

modeling, assessment, and report phase. As illustrated in this table, there is a significant variation in 

the labor hours required to conduct a tracer study. For 

example, the low-end labor costs resulted due to the 

following study characteristics: naturally occurring 

tracer was used, no new equipment was purchased, 

existing routine monitoring sites were used, and only 

a limited post-study assessment was made. The 

medium-sized study included the following characteristics: a chemical that was routinely added (fluoride) 

to the water distribution system was used as the tracer 

(by shutting it off), the study required a much longer 

period to complete, and since it was the first major 

tracer study in the distribution system, it required 

significant planning. The high-end study included 

the following characteristics: it was the first major 

tracer study employing wide-scale use of continuous 

monitors; a non-naturally occurring, non-routinely 

added chemical was injected as a tracer; and significant time was required for acquiring and installing the 

equipment. For purposes of this study, a very detailed 

post-study data assessment involving processing of 

tracer study data, pipe network model calibration and 

report preparation required significant labor expenditures. Examples of model calibration efforts associated with tracer studies are presented in Chapter 4. 

3.4 Summary, Conclusions and 

Recommendations 

Tracers and tracing techniques have been used for 

many years in a number of engineering applications 

to estimate stream velocity and retention time in 

water and water supply unit processes. More recently, 

tracers have been used for calibrating drinking water 

distribution system hydraulic and water quality 

models. For the purposes of this document, it is 

assumed that tracer studies are used to calibrate and 

validate network models. The calibrated and validated network models are then used to estimate other 

parameters such as water age and travel times. 

However, the data from a tracer study can be directly 

used to estimate some specific parameters such as 

water age (DiGiano et al., 2005). A comprehensive 

3-15 
A Reference Guide for Utilities 

summary of potential uses and regulatory applications 

for tracer studies is provided in the first subsection of 

this chapter. Drinking water tracers might include 

chemicals that are injected into a water distribution 

pipe, the temporary shutoff of a chemical additive 

currently being added to treated water (such as 

fluoride), or significant changes in concentration of 

disinfectants, DBPs, or natural compounds. The tracer 

methodology selected would significantly impact the 

overall costs of the study. Probably, the most expensive option would be to inject a chemical tracer, 

monitor it using leased or purchased online instrumentation, and conduct the study using contractor 

staff. The least expensive approach would be to take 

advantage of a natural tracer, monitor the progress of 

the tracer by grab sampling, and conduct the study 

using primarily in-house staff. Once a tracer “injection” methodology has been selected, careful planning and execution will ensure the success of the study. 

When planning a tracer study, if the specific steps 

outlined in this chapter are followed, they should 

greatly increase the potential for a successful study. 

These steps include: establishing clear study objectives, forming a study team, defining the study area 

characteristics, carefully selecting an appropriate 

tracer, selecting the proper field equipment, developing key planning documents, and ensuring that the 

public and affected agencies are notified. Application of a distribution and water quality model during 

the planning stage is highly recommended to simulate 

the approximate behavior that will be expected 

during the actual tracer event. 

During the execution phase of the study, the following issues should be addressed: procurement of 

equipment and materials; setup, testing and disinfection of the procured equipment; availability of 

analytical instrumentation and laboratory facilities; 

and, finally, the installation, testing, and operation of 

field equipment. During the execution phase, it is 

important to review and understand how tracer 

dosages and injection duration are to be implemented. 

Dry runs are highly recommended as a means of 

debugging the procedures prior to a full study. 

Distribution system tracer studies have been conducted 

for over 15 years, but recent technology developments 

have improved the efficiency of these studies and 

provide promise for greatly expanded applications in 

the future. Specific components that will fuel this 

expanded use include the following: continuous 

monitors that can be easily adapted for use in distribution systems are being developed and tested, in part in 

response to water security concerns; automated meter 

reading (AMR) equipment is being installed by many 

utilities and could provide more detailed temporal and 

spatial consumption data for hydraulic models; 

advanced analysis software is evolving that will 

facilitate the use of large amounts of continuous data in 

calibrating distribution system models; and with 

increased availability of these technologies, costs are 

expected to decrease so that larger utilities can afford to 

purchase and routinely use the equipment, and consulting engineers can affordably offer these services to 

smaller utilities. 

During the field study, it is important that the study 

team be able to assess the progress of the tracer, in real 

time, as it propagates through the system. Concise 

and consistent communications between tracer study 

team members, test coordinator, and water utility staff, 

is critical al all times during the test. 

In the future, it is highly likely that advances currently on the horizon will result in significant 

increased use of both online tracer (or water quality) 

monitors and flow monitoring instrumentation. As 

the on-line technology becomes more widely used in 

drinking water, the use of network water quality 

models will also be more widely accepted. Online 

monitoring in conjunction with water quality 

modeling will provide an in-depth understanding of 

the manner in which water quality changes can be 

monitored in a drinking water distribution system. 

Also, given the current climate of concern over 

distribution water quality from both a regulatory and 

security viewpoint, it is reasonable to assume that 

there will be increased interest in applying this type of 

technology in the water industry. 

3-16 
A Reference Guide for Utilities 

References


Boccelli, D.L., F. Shang, J. G. Uber, A. Orcevic, D. 

Moll, S. Hooper, M. Maslia, J. Sautner, B. Blount, and 

F. Cardinali. “Tracer Tests for Network Model 

Calibration.” Proceedings, ASCE-EWRI Annual 

Conference. 2004. 

Boccelli, D., and J. Uber. “Incorporating Spatial 

Correlation in a Markov Chain Monte Carlo Approach for Network Model Calibration.” Proceedings, 

ASCE-EWRI World Water & Environmental Resources Congress, Anchorage, AK. 2005. 

Boulos, P.F., W.M. Grayman, R.W. Bowcock, J.W. 

Clapp, L.A. Rossman, R.M. Clark, R.A. Deininger, and 

A.K. Dhingra. “Hydraulic Mixing and Free Chlorine 

Residuals in Reservoirs,” Journal of AWWA, 88(7):48­

59. 1996. 

Clark, R.M., W.M. Grayman, R. Males, and A. F. Hess. 

“Modeling Contaminant Propagation in DrinkingWater Distribution Systems,” Journal of Environmental Engineering, ASCE, 119(2):349-364. 1993. 

Clark, R.M., W.M. Grayman, J.A. Goodrich, R.A. 

Deininger, and A.F. Hess. “Field Testing Distribution 

of Water Quality Models,” Journal of AWWA, 

83(7):67-75. 1991. 

Clark, R.M., G. Smalley, J.A. Goodrich, R. Tull, L.A. 

Rossman, J.T. Vasconcelos, and P.F. Boulos. “Managing Water Quality in Distribution Systems: Simulating TTHM and Chlorine Residual Propagation,” 

Journal of Water Supply Research and Technology – 

Aqua, 43(4):182-191. 1994. 

Clark, R.M., and J.A. Coyle. “Measuring and Modeling Variations in Distribution System Water Quality,” 

Journal of AWWA, 82(8):46-53. 1990. 

DiGiano, F.A., and G. Carter. “Tracer Studies to 

Measure Water Residence Time in a Distribution 

System Supplied by Two Water Treatment Plants.” 

Proceedings, AWWA Annual Conference. 2001. 

DiGiano, F.A., W. Zhang, and A. Travaglia. “Development of the mean residence time from tracer studies in 

distribution systems.” Journal of Water Supply: 

Research and Technology - Aqua 54:1-14. 2005. 

EPA. The Stage 2 DBPR Initial Distribution System 

Evaluation Guidance Manual. 2003a. 

EPA. Drinking Water Advisory: Consumer Acceptability Advice and Health Effects Analysis on Sodium. 

Available at: http://www.epa.gov/safewater/ccl/pdfs/ 

reg_determine1/support_cc1_sodium_dwreport.pdf. 

2003b. 

Ferguson, B.A. and F.A. DiGiano. “Impact of temporary switches from monochloramine to free chlorine 

on water quality in distribution systems.” Proceedings, AWWA Annual Conference. 2005. 

Grayman, W.M., R.A. Deininger, A. Green, P.F. Boulos, 

R.W. Bowcock, and C.C. Godwin. “Water Quality and 

Mixing Models for Tanks and Reservoirs,” Journal of 

AWWA, 88(7):60-73. 1996. 

Grayman, W.M., L.A. Rossman, C. Arnold, R.A. 

Deininger, C. Smith, J.F. Smith, and R. Schnipke. 

Water Quality Modeling of Distribution System 

Storage Facilities. AwwaRF and AWWA. Denver, CO. 

2000. 

Grayman, W.M. “Use of Tracer Studies and Water 

Quality Models to Calibrate a Network Hydraulic 

Model,” Current Methods, 1(1):38-42, Haestad Press, 

Waterbury, CT. 2001. 

Grayman, W.M., L. A. Rossman, R. A. Deininger, C. D. 

Smith, C. N. Arnold, and J. F. Smith. “Mixing and 

Aging of Water in Distribution System Storage 

Facilities,” Journal of AWWA, 96(9):70-80. 2004. 

Hatcher, M.D., W. M. Grayman, C. D. Smith, and M. 

Mann. “Monitoring and Modeling of the Sweetwater 

Authority Distribution System to Assess Water 

Quality.” Proceedings, AWWA Annual Conference. 

2004. 

Maslia, M.L., J.B. Sautner, C. Valenzuela, W.M. 

Grayman, M.M. Aral, and J.W. Green, Jr. “Use of 

Continuous Recording Water-Quality Monitoring 

Equipment for Conducting Water-Distribution System 

Tracer Tests: The Good, the Bad and the Ugly.” 

Proceedings, ASCE-EWRI World Water & Environmental Resources Congress, Anchorage, AK. 2005. 

Panguluri, S., R. Krishnan, L. Garner, C. Patterson, Y. 

Lee, D. Hartman, W. Grayman, R. Clark and H. Piao. 

“Using Continuous Monitors for Conducting Tracer 

Studies in Water Distribution Systems.” Proceedings, 

ASCE-EWRI World Water & Environmental Resources Congress, Anchorage, AK. 2005. 

Sautner, J.B., M.L. Maslia, C. Valenzuela, W.M. 

Grayman, M.M. Aral, and J.W. Green, Jr. “Field 

Testing of Water Distribution Systems at U.S. Marine 

Corps Base, Camp Lejeune, North Carolina, in 

Support of an Epidemiological Study.” Proceedings, 

ASCE-EWRI World Water & Environmental Resources Congress, Anchorage, AK. 2005. 

3-17 
A Reference Guide for Utilities 

AWWA. Standard Methods for the Examination of 

Water and Wastewater, 20th Edition. Edited by: 

Lenore S. Clrsceri, Arnold E. Greenberg, and Andrew 

D. Eaton, American Pubic Health Association/AWWA/ 

Water Environment Federation, pp 2-44 to 2-45. 

1998. 

Teefy, S.M., and P.C. Singer. “Performance Testing 

and Analysis of Tracer Tests to Determine Compliance 

of a Disinfection Scheme with the SWTR.” Journal of 

AWWA, 82(12):88-98. 1990. 

Teefy, S.M. Tracer Studies in Water Treatment 

Facilities: A Protocol and Case Studies. AwwaRF 

and AWWA, Denver, CO. 1996. 

Vasconcelos, J.J., L.A. Rossman, W.M. Grayman, P.F. 

Boulos, and R.M. Clark. “Kinetics of Chlorine 

Decay,” Journal of AWWA, 89(7):54-65. 1997. 

Vasconcelos, J., P. Boulos, W. Grayman, L. Kiene, O. 

Wable, P. Biswas, A. Bahri, L. Rossman, R. Clark, and 

J. Goodrich. Characterization and Modeling of 

Chlorine Decay in Distribution Systems. AwwaRF. 

1996. 

3-18

A Reference Guide for Utilities 

Chapter 4 

Calibration of Distribution System Models 

Water distribution system models can be used in a 

wide variety of applications to support design, 

planning, and analysis tasks. Since these tasks may 

result in engineering decisions involving significant 

investments, it is important that the model used be an 

acceptable representation of the “real world” and that 

the modeler have confidence in the model predictions. In order to determine whether a model represents the real world, it is customary to measure 

various system values (e.g., pressure, flow, storage 

tank water levels, and chlorine residuals) during field 

studies and then compare the field results to model 

predictions. If the model adequately predicts the field 

measurements under a range of conditions for an 

extended period of time, the model is considered to be 

calibrated. If there are significant discrepancies 

between the measured and modeled data, further 

calibration is needed. There are no general standards 

for defining what is adequate or what is a significant 

discrepancy. However, it is recognized that the level 

of calibration required will depend on the use of the 

model. A greater degree of calibration is required for 

models that are used for detailed analysis, such as 

design and water quality predictions, than for models 

used for more general planning purposes (e.g., master 

planning). 

All models are approximations of the actual systems 

that are being represented. In a network model, both 

the mathematical equations used in the model and the 

specific model parameters are only numerical approximations. For example, the Hazen-Williams equation 

used to describe friction headloss is an empirical 

relationship that was derived based on laboratory 

experiments (Williams and Hazen, 1920). Furthermore, the roughness parameter (C-factor) used in the 

Hazen-Williams equation that modelers assign to each 

pipe is not known with total certainty because it is 

not feasible to examine and test every pipe in the 

system. The goal in calibration is to reduce uncertainty in model parameters to a level such that the 

accuracy of the model is commensurate with the type 

of decisions that will be made based on model 

predictions. 

The types of model calibration associated with water 

distribution system analysis can be categorized in 

several ways. The nomenclature depends upon the 

adjusted parameters and the technique employed. In 

general, calibration can be categorized (or referenced) 

as follows: 

• Hydraulic and water quality model calibration. 

The concept of calibration can be compared to fine 

tuning an old fashioned television (TV) set. One knob 

on the TV is used for tuning the channel while other 

knobs are adjusted to improve color, sharpness, contrast, 

and hue. However, in calibrating a network model, there 

are far more knobs to adjust as illustrated in Figure 4-1. 

Adjustment knobs 

Field data 

Initial model results 

Model results after calibration 

Figure 4-1. Conceptual Representation of Calibration. 

Some of the knobs may be used to adjust roughness 

coefficients for pipes, other knobs to adjust demands 

assigned to nodes, while still other knobs may control 

valve positions, pump curves, or other parameters that 

are not known with complete certainty. Calibrating a 

model is an arduous task because there are many knobs 

that can be adjusted. Finding the combination of 

parameters that results in the best agreement between 

measured and modeled results is difficult. This process 

is complicated by the fact that there may not be a single 

best set of parameters. Extending the TV analogy, the 

knobs may be adjusted in order to get the best reception 

for one channel. However, when the channel is changed, 

the knobs may need to be adjusted to improve the 

reception for the new channel. Similarly, with a network 

model, a set of parameters may give the best match for 

one set of data while other parameters may give better 

results for another set of data. Therefore, it is recommended that a modeler first calibrate the model using 

one or more sets of field data and then validate it with 

an independent set of field data. 

4-1 
A Reference Guide for Utilities 

•	 Static (steady state) or dynamic (extended


period simulation) calibration.


•	 Manual or automated calibration. 

Hydraulic calibration refers to the process of adjusting the parameters that control the hydraulic behavior 

of the model. Similarly, water quality calibration 

relates to the process of adjusting parameters used in 

the water quality portion of the model. Static or 

steady-state calibration relates to calibration of a 

model that does not vary over time, or using data that 

is collected representing a snapshot in time. Dynamic 

or EPS calibration uses time-varying data in the 

calibration process. Manual calibration relies upon 

the user to investigate the effects of a range of 

possible parameter values. Automated calibration 

employs optimization techniques to find the set of 

parameters that results in the “best” match between 

measured and modeled results. 

It should be noted that the specific application 

method and availability of some of these techniques 

will vary depending upon the software used for 

modeling and the available network model information. Therefore, only the general techniques employed in each of these types of calibration are 

discussed in the following sections. Then, some 

example case studies are presented to illustrate their 

use. The final section in this chapter discusses future 

trends in calibration and the possibility of general 

calibration standards. 

4.1 Hydraulic and Water Quality 

Model Calibration 

Hydraulic calibration is essential for any model 

simulation to be meaningful. Furthermore, the 

distribution system water quality models work in 

concert with the hydraulic model and utilize the flow 

and velocity information calculated by the hydraulic 

model. Thus, if the hydraulic model is not properly 

calibrated and results in inaccurate flow and velocity 

estimates, the water quality model will not perform 

correctly. In fact, water quality modeling is very 

sensitive to the underlying hydraulic model. Frequently, a hydraulic model that has been calibrated 

sufficiently for applications such as master planning 

may require additional calibration before it is 

appropriate for use in water quality modeling. The 

following subsections describe the parameters and 

techniques employed for hydraulic and water quality 

model calibration. 

4.1.1 Hydraulic Model Calibration 

Hydraulic behavior refers to flow conditions in pipes, 

valves and pumps, and pressure/head levels at 

junctions and tanks. Parameters that are typically set 

and adjusted include pipe roughness factors, minor 

losses, demands at nodes, the position of isolation 

valves (closed or open), control valve settings, pump 

curves, and demand patterns. When intially establishing and adjusting these parameters, care should be 

taken to keep the values for the parameters within 

reasonable bounds. For example, if local experience 

shows that the roughness factor for a 20-year old 

ductile iron pipe typically falls within a range from 

100 to 130, a value that is not within or close to that 

range should not be used just to improve the agreement between the measured and modeled data. Use of 

unreasonable values may lead to a better match for 

one set of data, but will typically not provide a robust 

set of parameters that would apply in other situations. 

Proper calibration requires that adjustments be made 

to the correct parameters. A common mistake occurs 

when adjustments are incorrectly made in one set of 

parameters in order to match the field results while the 

parameters that are actually incorrect are left untouched. This process is referred to as “compensating 

errors” and should obviously be avoided. Field 

verification of suspect parameters (e.g., open or closed 

valves) can reduce confusion created by compensating errors. 

An example of compensating errors is an adjustment in 

roughness factors in order to compensate for a closed 

isolation valve in the system that is represented as open, 

or partially open, in the model. In this case, unreasonably low values for the Hazen-Williams roughness 

coefficients are typically introduced in order to force a 

large headloss in the pipes that are actually closed. 

Though this may result in approximating the pressure 

measurements made in the field, it will introduce other 

errors in flow and velocity calculations. Compensating 

errors may also result from incorrectly adjusting 

demands or other parameters. 

4.1.2 Water Quality Model Calibration 

Subsequent to the proper calibration of a hydraulic 

model, additional calibration of parameters in a water 

quality model may be required. The following 

parameters are used by water quality models that may 

require some degree of calibration: 

•	 Initial Conditions: Defines the water quality


parameter (concentration) at all locations in the


distribution system at the start of the


simulation.


•	 Reaction Coefficients: Describes how water 

quality may vary over time due to chemical, 

biological or physical reactions occurring in the 

distribution system. 

•	 Source Quality: Defines the water quality


characteristics of the water source over the time


period being simulated.


4-2 
A Reference Guide for Utilities 

The details of calibration depend upon Table 4-1. Calibration/Input Requirements for Water Quality Models 

the type and application of the water 

quality model. Calibration requirements for each type of modeling are 

described below and summarized in 

Table 4-1. 

•	 Water age: No explicit water 

quality calibration can be 

performed because there are no 

reaction coefficients. Estimates 

of initial water age in tanks and 

reservoirs are desirable in order 

to shorten the length of the 

simulation. Source water age is usually set to 

zero for all sources. Water age can be especially 

sensitive to inflow-outflow rates for tanks, 

mixing characteristics of tanks, and travel times 

in dead-end pipes. 

When modeling a tank, an important parameter is the 

initial age of the water in the tank at the start of the 

simulation. This value cannot be measured in the field 

but can be estimated by dividing the tank volume by 

the volume of water that is exchanged each day. 

Frequently, modelers will just assume that the initial 

age is zero and run the model for a long period until it 

has reached a dynamic equilibrium. This occurs when 

the initial water in the tank has been flushed out 

entirely through the fill and draw process. The following figure (Figure 4-2) shows the effects of the initial 

water age on the modeled results. As illustrated, a 

good initial estimate for water age (120 hours in this 

case) results in a much shorter time period until the 

dynamic equilibrium is reached. In fact, in this case 

when the initial age was input as zero hours, the model 

did not even come close to reaching dynamic equilibrium during the simulation period and would have 

required a much longer run duration to reach the same 

point. 

0 

20 

40 

60 

80 

100 

120 

140 

0 10 20 30 40 50 60 

Hours 

Water age (hours) 

Initial age 0 hours Initial age 120 hours 

Figure 4-2. Effects of the Initial Water Age on the 

Modeled Results. 

Model Application Initial 

Conditions 

Reaction 

Coefficients 

Source 

Quality 

Water age YES NO Usually NO 

Source tracing YES NO Usually NO 

Conservative constituent YES NO YES 

Reactive constituent YES YES YES 

•	 Source tracing: No explicit water quality 

calibration can be performed because there are 

no reaction coefficients. Estimates of initial 

conditions in tanks for percentage of water 

coming from a source are desirable in order to 

shorten the length of the simulation. Values for 

sources are usually set to zero for all sources 

except for the specific source being traced. 

•	 Conservative constituents: No explicit water 

quality calibration can be performed because 

there are no reaction coefficients. Estimates of 

initial conditions in tanks for concentrations of 

the conservative constituents can usually be 

determined from field data and are desirable in 

order to shorten the length of the simulation. 

Values for sources are set to the typical 

concentrations found in the source. 

•	 Reactive constituents: For reactive constituents, 

both the form of the reaction equation and the 

reaction coefficients must be provided. When 

modeling chlorine or chloramine decay, the 

most common formulation is a first order decay 

equation including both bulk and wall decay 

coefficients. Values for these coefficients 

typically require laboratory and field analysis 

and calibration in order to match model results 

to the concentrations measured in the field. 

Correspondingly, THMs, a group of DBPs 

formed when water is chlorinated or 

chloraminated, generally increase in 

concentration with time (Vasconcelos et al., 

1996). This process is frequently represented as 

a first order growth function that asymptotically 

approaches a limiting value representative of 

maximum concentration reached when all of the 

NOM has reacted or all of the chlorine has been 

consumed. Both the limiting value and the rate 

of growth must be determined in this case. 

Water quality modeling is very sensitive to the 

hydraulic representation of the system. To reiterate, 

hydraulic calibration that may be sufficient for some 

hydraulic simulation may require additional calibration when used as a basis for water quality modeling. 

4-3 
A Reference Guide for Utilities 

4.2 Static Calibration and


Dynamic Calibration


Just as water distribution system models can be run 

in a steady-state or an extended period mode, 

calibration can be performed in either a static mode 

using a steady-state model or in a dynamic mode 

using an extended period model. A common 

approach is to perform a static calibration first 

followed by EPS, to enhance the static calibration 

through a dynamic calibration. The options and 

procedures for these two types of calibration are 

described below. 

4.2.1 Steady-State Calibration Methods 

The two most common approaches used in calibrating a steady-state hydraulic model are C-factor tests 

and fire-flow tests. For water quality models of 

chlorine/chloramines, a test procedure for estimating 

bulk and wall demand may be employed. In all of 

these cases, field data is collected under controlled 

conditions and then applied to determine the model 

parameters that result in the best fit of the model to 

the field data. 

4.2.1.1 C-Factor Tests 

C-factor tests (sometimes called head loss tests) are 

performed to estimate the appropriate C-factors to be 

used in a hydraulic model. The C-factor represents 

the roughness of the pipe in the widely used HazenWilliams friction equation. Typically, such tests are 

performed on a set of pipes that are representative of 

the range of pipe materials, pipe age, and pipe 

diameters found in the water system that is being 

studied. The results of the tests are then used to assign 

C-factors for other pipes of similar characteristics. 

In a field test, a homogeneous section of pipe between 

400 and 1,200 feet long is initially isolated. Subsequently, flow, pipe length, and head loss are measured 

in the field. Typically, nominal pipe diameters are 

The underlying concept for a C-factor test is that all 

factors in the Hazen-Williams friction equation can be 

measured in the field and the equation can then be 

solved for the unknown C-factor. It can also be used 

to account for minor losses that occur through distribution system components (e.g., valves, fittings). The 

following equation is the Hazen-Williams equation 

(Equation 2-3) arranged to solve for roughness. 

C = 8.71 V D-0.63 (H/L)-0.54 (Equation 4-1) 

where 

C = roughness factor 

V = velocity in feet per second 

D = pipe diameter in inches 

H = head loss in feet 

L = pipe length in feet 

Flowed 

Fire Hydrant #1 

Length 

X 

Hydrant Fire Hydrant #2 

Flow 

Pitot Gage 

Closed Valve 

Figure 4-3. Schematic of Standard Two-Gage CFactor Test Setup. 

Differential 

pressure gage Flowed Pitot Gage Fire Hydrant #1 

Length 

X 

Hydrant Fire Hydrant #2 

Flow 

Closed Valve 

Small diameter hose 

Figure 4-4. Schematic of Parallel Hose CFactor Test Setup. 

taken from system maps and these values are used 

along with flow rate to calculate velocity. There are 

two alternative methods for determining head loss in 

the pipe section: a two-gage method (Figure 4-3) and 

a parallel hose method (Figure 4-4). With the twogage method, pressure is read at hydrants located at 

the upstream and downstream end of the section and 

used along with elevation difference between the ends 

to calculate head loss. With the parallel hose method, 

a small-diameter hose is used to connect the two 

hydrants to a differential pressure gage to directly 

measure the difference in pressure. The two end 

hydrants should be spaced far enough apart and there 

should be sufficient flow so that there is a pressure 

drop of at least 15 pounds per square inch (psi) for a 

two-gage test or a 3-psi pressure drop for a parallel 

hose test (McEnroe et al., 1989). In both cases, a 

hydrant downstream of the test section is opened to 

induce flow and a sufficient pressure drop. Multiple 

downstream hydrants may also be employed to induce 

a greater flow and larger pressure drop. Typically, a 

pitot gage (as shown in Figures 4-3 and 4-4) is 

attached to the flowing hydrants to measure the flow 

rate. It is important to ensure that all flow between 

hydrants is accounted for (i.e., any connections that 

may bleed water into or out of the test section). The 

two-gage method is the more commonly used 

approach. The parallel hose method requires more 

4-4 
A Reference Guide for Utilities 

specialized equipment, but is inherently more 

accurate and may be used when a large pressure drop 

cannot be achieved. Note that the valve is closed 

downstream of the flowing hydrant. 

As noted above, an assumption is made that the pipe 

diameter has not diminished from its original nominal 

diameter due to tuberculation on the pipe walls. If 

that assumption is not valid, the calculated C-factors 

will be lower than expected. If very low C-factors are 

calculated based on a field C-factor test, it is recommended that further actions be taken in order to 

determine the effective diameter of representative 

pipes. These actions could include direct inspection 

of sample pipes or use of calipers inserted into the 

pipe to measure the effective pipe diameter. 

4.2.1.2 Fire-Flow Tests 

Fire-flow tests are routinely performed by water 

utilities to determine the ability of the system to 

deliver large flows needed to fight fires. In such a 

test, fire hydrants are opened, the flow through the 

hydrants measured and pressures measured at adjacent 

hydrants (see Figure 4-5). The high demands caused 

by the open hydrants lead to high flows and increased 

head loss in pipes in the area around the hydrants. 

Under these conditions, the system is stressed and the 

capacity of the system to deliver these flows is very 

sensitive to the roughness of the pipes. 

These fire-flow tests can also be very effective as a 

calibration methodology. In this case, in addition to 

the standard information routinely collected as part of 

a fire-flow test (flows and pressures), information is 

collected on the general state of the system such as 

pump and valve operation, tank water levels, and 

general system demand. The distribution system 

model is then run under the system conditions 

observed during the test and adjustments made in 

roughness factors (or other parameters) so that the model 

adequately represents the data measured in the field. 

P=42 psi 

P=55 psi 

Q=400 gpm 

P=36 psi P=52 psi 

Figure 4-5. Fire-Flow Test Setup. 

Figure 4-6. A Hydrant Being Flowed with a Diffuser 

as Part of a Fire-Flow Test. 

Figure 4-6 illustrates an example setup for a fire-flow 

test. The diffuser attached to the hydrant in the figure 

includes a pitot gage used to measure the flow. The 

cage diffuses the flow and prevents any objects in the 

stream from being projected out at high speed. 

In the case shown in Figure 4-5, only a single hydrant 

is opened, with the flow measured at that hydrant and 

pressure measurements made at four hydrants. 

Additional hydrants may be flowed and monitored as 

part of a fire-flow test for calibration (see Case Study 

in Section 7.7). 

4.2.1.3 Chlorine Decay Tests 

Chlorine bulk reaction and wall reaction (or demand) 

testing procedures can be used to determine the 

reaction parameters used in water quality models. 

Bottle tests measure the rate of chlorine reaction that 

occurs in the bulk flow independent of wall effects. 

This procedure is performed by first measuring the 

chlorine at a representative location such as in the 

effluent from a water treatment plant. Then several 

bottles are filled with the same water and kept at a 

constant temperature. Separate bottles are subsequently opened at intervals of several hours (or days) 

and the chlorine content is measured. The resulting 

record of chlorine at different times is used to estimate 

the bulk reaction rate. See AWWA (2004) for a more 

complete protocol for this test. 

The purpose of the chlorine decay field testing 

procedure is to estimate the chlorine wall demand 

coefficient for representative pipes in the distribution 

system. The method described here involves the 

measurement of chlorine concentrations in a pipe 

segment under controlled flow conditions and use of 

the resulting chlorine measurements to determine the 

wall reaction rate for that pipe segment. The method 

is designed to be complementary with C-factor testing 

so that it can be conducted in conjunction with a Cfactor test. The method is considered to be experimental 

4-5 
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A Reference Guide for Utilities 

and feasible only for pipes that are expected to have 

relatively high wall reaction values, such as smaller 

diameter unlined cast iron pipes. For the smaller 

diameter unlined cast iron pipes, pipe sections with a 

length in the range of 1,500 to 2,000 feet will be 

required to estimate wall demand. For other types of 

pipes that typically have low wall decay factors (e.g., 

plastic and new pipes), the required length of the pipe 

may be so long as to make this test impractical. Other 

factors that should be considered in selecting sites 

include the following: 

•	 Ability to measure flow in the pipe. 

•	 Ability to valve off the pipe segments. 

•	 Presence of a reasonable chlorine residual 

(preferably > 0.4 mg/L) at the upstream end of 

the pipe segment. 

•	 Ability to vary flow in the pipe over a 

reasonable flow range (e.g., for a 6” pipe, a 

range of flows of 100 to 500 gpm would be 

desirable). 

•	 Ability to estimate the actual pipe diameter for 

the pipe segment. 

For the selected pipe segment, major lateral(s) and 

downstream segments should be valved off to control 

flow in the pipe. Two or three sampling points should 

be established along the segment of interest (upstream, downstream, and an optional midpoint). 

Typically, these would be taps on fire hydrants. Prior 

to the testing, the taps should be run for several 

minutes to clean out the line. The approximate travel 

time through the pipe should be calculated and 

chlorine measurements taken from upstream to 

downstream so that approximately the same parcel of 

water is sampled at each station. Flow measurements 

can be made at any location within the segment. 

The test should be repeated for three flow values: a 

low flow rate, a medium flow rate, and a high flow 

rate. During each flow test, chlorine residual should 

be measured at each of the two or three sampling 

points. Since relatively small variations in chlorine 

concentration are expected, a good quality field 

chlorine meter should be employed and three replicates should be taken at each sampling point for each 

flow test. Following the field analysis, a spreadsheet 

can be used to back calculate the resulting wall 

reaction coefficients, or a water distribution model 

can be used to determine the wall reaction coefficient 

through trial and error. 

4.2.2 Dynamic Calibration Methods 

Dynamic calibration methods are associated with the 

use of an EPS model. The dynamic calibration 

methods include: (1) comparison of modeled results 

If measured and modeled records of tank water levels 

do not agree well, the relationship between the two 

traces can provide clues as to the potential problems. 

In the example depicted below, the timing of the fill 

and draw cycles in the measured and modeled results 

are quite close but the modeled and measured depth of 

the fill cycles vary significantly. This suggests that the 

system demands may be in error, resulting in an 

incorrect amount of flow entering the tank. 

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In the second example illustrated below, the magnitude 

of the change in water level is quite close in the 

modeled and measured results, but the timing of the fill 

and draw cycles differ. This is typically caused by 

errors in the pumping controls in the model, resulting 

in pumps being turned on and off at the wrong time. 

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to measurements made in the field over time, and (2) 

tracer studies. In both cases, model parameters are 

adjusted so that the model adequately reproduces the 

observed behavior in the field. Tracer studies are 

discussed in detail in Chapter 3. 

Comparison of modeled and measured data can be 

used for calibration of both hydraulic and water 

quality models. The most commonly measured 

hydraulic data are tank water levels, flows, and 

pressures. Frequently, this information is routinely 

reported through SCADA systems to a database and 

can be extracted. In other cases, continuous flow 

meters or pressure gages must be installed to collect 

data during a test period. Generally, tank water level 

data and flow measurements are the most useful form 

of data for calibrating an extended period model. 

Under average water use conditions, temporal 

variations in pressure measurements typically vary 

over a relatively small range and then only in 

response to variations in tank water levels. As a result, 

they are less useful in calibrating model parameters. 

If pressure measurements are going to be used for 

4-6 
A Reference Guide for Utilities 

dynamic calibration, the system must be stressed by 

conducting fire-flow tests during the testing period. 

The primary model parameters that are adjusted 

during dynamic calibration are: demand patterns, 

pump schedules and pump curves, control valve 

settings, and the position (open or closed) of isolation 

valves. 

Dynamic calibration procedures using tracer study 

data is discussed via a case study in Section 4.4 of 

this chapter. Dynamic calibration can also be used for 

calibrating water quality parameters, such as the wall 

demand coefficient for computing chlorine residuals. 

Generally, water quality field studies are performed in 

conjunction with field hydraulic studies or with a 

tracer study. For chlorine models, measurements of 

chlorine are taken at frequent intervals in the field at 

representative sites. These may include dedicated 

sampling taps, hydrants, tank inlet/outlets, or other 

accessible sites. Continuous chlorine meters may also 

be used. During the model calibration process, the 

model is first calibrated for hydraulic parameters, and 

water quality coefficients are subsequently adjusted 

so that the model results match the field data. 

4.3 Manual Calibration and 

Automated Calibration 

The aforementioned process of adjusting model 

parameters so that the model reproduces the hydraulic 

and/or water quality results measured in the field can 

involve a significant amount of effort in large or 

complex systems. As discussed earlier in this chapter, 

there are many parameters that can be adjusted in the 

model and the combinations of possible parameter 

values can sometimes appear to be quite overwhelming. Typically, a manual trial and error approach is 

used. The most influential parameters can be identified based on sensitivity analysis and then adjusted to 

see if they improve the results. This process is 

continued until an acceptable level of calibration is 

achieved or until budgetary constraints dictate 

closure. It is not unusual for many (dozens or even 

hundreds) separate model runs to be made in this 

process. 

An extension to the manual calibration process is an 

automated approach that allows the computer to 

search through different combinations of model 

parameters (with a realm of realistic values) and to 

select the set of parameters that results in the best 

match between measured and modeled results. The 

development of this type of program has been the 

topic of many studies over the past 25 years (Walski 

et al., 2003). 

Automated methods require a formal definition of an 

objective function for measuring how good a particular solution is. Generally, the value of a solution is 

measured by a statistic that reflects the deviation 

between measured and modeled results in flow and 

pressure. A commonly used objective function is 

minimization of the square root of the weighted 

summation of the squares of the differences between 

observed and predicted values. The weighting is used 

to establish a relationship between the errors associated with flow and pressure. For example, the user 

may choose a 1-psi error in pressure prediction to be 

equivalent in value to a 10-gpm error in flow. 

In most automated methods, the user also groups 

pipes by common characteristics, such as age, 

material, and nodes, into common demand characteristics such as residential or commercial. The user then 

specifies a range of allowable values for pipe roughness factors or a range of multipliers applied to the 

existing roughness factors. Similarly, a range of 

allowable demand multipliers is also specified, as are 

potential pipes where an existing isolation valve may 

be closed. The optimization routine is then applied 

and the roughness, demands, and isolation valve 

positions are selected that result in the minimum error. 

Though manual calibration still remains the predominant methodology, automated calibration methods are 

becoming more available in commercial modeling 

packages. It is likely that as the automated calibration methods are refined, the technology will expand 

for routine use with EPS hydraulic and water quality 

models. 

4.4 Case Studies 

In order to illustrate some of the calibration methods 

described earlier in this chapter, two case studies are 

presented in this subsection. The two case studies are 

similar in general methodology but differ in the 

overall scale and specifics of the study area. In both 

cases, the distribution system model that was used as 

a starting point for the calibration exercise was part of 

a skeletonized model extracted from unspecified 

portions of the GCWW distribution system. 

Most larger urban water systems generally have at 

least a skeletonized model of their distribution 

system. It should be noted that (as discussed in 

Chapters 2 and 3 of this report) a skeletonized model 

denotes a model that includes only a major subset of 

actual pipes rather than all pipes in the distribution 

system. The extracted system model was modified 

and converted to EPANET format for use in this 

project. The modifications included: addition of key 

pipes, updates to consumer demand data, and an 

interconnection between the case study area and the 

full system by a fixed grade node (reservoir). These 

portions of the base model had been previously 

calibrated using various dynamic calibration methods 

and were used for routine water utility work. For the 

4-7 
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A Reference Guide for Utilities 

purposes of calibration, separate field studies were 

conducted in each study area. 

In both field studies, a food-grade conservative tracer 

(calcium chloride) was introduced into the system and 

its movement through the system was monitored by 

both grab sampling and continuous monitoring (CM) 

stations installed at key locations in the distribution 

systems. The CM stations were installed at hydrants 

which were left partly open for the duration of the 

study to minimize travel time between the main and 

sampling location. Each open hydrant was added as a 

new demand node in the EPANET network model. 

Additionally, several ultrasonic flow meters were 

installed to provide continuous flow measurements at 

key locations. The general procedures, methodology, 

and instrumentation used in these field studies are 

consistent with those presented in Chapter 3. 

4.4.1 Case 1 - Small-Suburban, Dead-End 

System 

This system is part of a larger pressure zone. It was 

selected because of the relatively compact size and 

simple structure, fed by a single supply pipe with no 

additional storage. As a result, the movement of the 

tracer was relatively rapid through the system and it 

could be monitored with continuous meters placed at 

several locations. The general layout of this subsystem, the location of the injection site, and the monitoring locations for this study are shown in Figure 4-7. 

The calcium chloride tracer was injected as two 

pulses, a two-hour pulse followed by a 2.5-hour 

period of no injection and then followed by a higher 

concentration pulse of two hours duration. The 

injection rate and the resulting concentration of the 

tracer in the distribution system just downstream of 

Compliance with state and federal regulations during a 

tracer study is obviously quite important. In order to 

ensure that the tracer will not exceed allowable levels, 

it is necessary to monitor information such as the rate 

of injection of the tracer, the flow in the receiving pipe, 

and the resulting concentration in the receiving pipe. 

Frequently, a safety factor for the injection rate is 

included to account for uncertainty. In this field study, 

the tracer injection rate was very low and the flow 

meter on the injection pump provided approximate 

values. Chloride concentrations were monitored at a 

suitable location approximately 100 feet downstream 

of the injection point with a travel time of approximately 10 minutes. Due to unexpected variations in 

flow through the pipe, delay in measurements, and 

CM07 

CM09 

CM12 

CM16 

CM20 

CM19 

CM18 

CM17 

CM15 

CM14 

CM13 

CM11 CM10

CM01 

CM06 

CM05 

CM08 

CM02 CM03 CM04 

Flow gage 

Conductivity meter (CM 1-20) 

Injection point 

Figure 4-7. Schematic Representation of 

Small-Suburban Dead-End System. 

the injection point were carefully monitored to ensure 

that the resulting chloride concentration did not 

exceed the secondary maximum contaminant level 

(MCL) of 250 mg/L for chloride. 

The movements of the tracer pulses were monitored 

by using both manual sampling and continuous 

conductivity meters located throughout the distribution system. Additionally, four ultrasonic flow meters 

were installed in the study area to provide continuous 

flow measurements at key locations within the 

distribution system. 

In preparation for the calibration process, the conductivity readings were converted to chloride concentrations using a relationship developed in the laboratory. 

Figure 4-8 shows the relationship between conductivity and chloride and the best-fit linear and polynomial relationships between them. This conversion was 

necessary because conductivity is not a truly linear 

parameter and, as a result, cannot be simulated 

exactly in a water distribution system model. The 

converted continuous concentration readings were 

then compared to the manually collected data for 

quality control purposes. Figure 4-9 shows the 

resulting chloride data set that was used at one 

location as a basis for evaluating model predictions as 

part of the calibration process. 

The preliminary results indicated some discrepancy 

between the EPANET-model predicted values and the 

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experienced for a brief period before the injection rate Figure 4-8. Empirical Relationship Between was adjusted. Chloride and Conductivity. 

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Figure 4-10. Comparison of Model Versus Field Results 

for Continuous Monitor Location CM-18 at Various 

Calibration Stages. 

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of the junction (where the model predictions are 

compared with the field values), both concentration 

and predicted time of tracer arrival might not be in 

perfect agreement due to local variation in demands,

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local flow velocities, and dilution impacts. The sharp 

tracer fronts observed in this field study made it 

difficult to employ quantitative statistical measures 

(e.g., mean error, standard deviation, root mean square 

error). Therefore, a graphical (visual) approach was 

considered to be more suitable for model calibration 

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Figure 4-9. Sample Chloride Data Used at One 

Station for Calibration. 

actual field-measured values, indicating the need for 

model refinement and re-calibration to improve the 

prediction capability of the EPANET model. Therefore, EPANET modeling was performed to evaluate 

the following four levels of model refinements: 

•	 Level 1 (prior to calibration): A skeletonized 

EPANET model was used with the original


hourly demand pattern provided by GCWW and 

a time-step injection pattern of 60 minutes.


• Level 2: The same as Level 1, but a refined 10­

minute time-step pattern for injection was used 

along with the conversion of the original hourly 

demand patterns to 10-minute patterns. 

•	 Level 3: The same as Level 2 with a refined 

demand pattern for each node using the fieldmeasured flow data, addition of demand nodes 

representing water demand of the partially open 

hydrants, adjustment for a large industrial user 

of water in the study area (based on data 

obtained during the study), and the residential 

water billing information provided by GCWW. 

• Level 4: The same as Level 3 with a detailed 

all-pipe (non-skeletonized) EPANET model. 

The results of the four-stage model refinement and 

calibration process are shown in Figure 4-10 for a 

continuous monitoring location (CM-18) located on 

the main feeder pipe. As illustrated, the improvements in the demand estimates and inclusion of the 

system details in the all-pipe model resulted in a vast 

During the calibration and refinement process, various 

model inputs such as flow, demand, and pipe 

characteristics were adjusted to improve the model 

prediction. The EPANET model was considered to be 

calibrated for the area when the field data matched the 

model-predicted output to an acceptable degree based 

on visual observation. Depending upon the location 

improvement in the model’s prediction ability for that 

monitoring location. Similar results were found for 

most monitoring locations on the main pipe. 

4-9 
A Reference Guide for Utilities 

in this application. For example, if the prediction of 

the arrival time for the tracer differs by even a few 

minutes from the observed arrival time, use of these 

standard measures of error could result in a high 

number, even though the prediction could be viewed 

graphically as very good. 

The calibration of the “looped” portion (referring to 

the portion of the network on the bottom right hand 

side of Figure 4-7) of this network proved to be more 

difficult and the results for some monitoring locations 

on the looped piping were less satisfactory. The most 

problematic were continuous monitoring locations 

CM-02 and CM-04. Monitoring station CM-02 was 

located near the confluence of two separate loops, 

with the actual monitored connection being slightly 

offset from the junction node. Examination of the 

model results showed that flow reached that junction 

from both directions and small variations in the 

amount of flow in each of the loops resulted in very 

different travel times. As illustrated in Figure 4-11, 

this complex travel pattern along with the offset 

location of the monitoring station resulted in poor 

prediction of travel time to that station. Also, 

monitoring station CM-04 is located at the end of a 

dead-end pipe section and travel to this node is 

strongly influenced by demands at the very far end of 

the dead-end section. As illustrated in Figure 4-11, 

this resulted in a poor match of the peak concentration during the second pulse. It is also postulated that 

dispersion, which is not represented in EPANET, may 

have had an influence on the peak concentration due 

to the very low velocities in the dead end pipe. In 

some cases, this could also be caused by inaccurate Cfactors as applied to the distribution system. However 

(as illustrated in Figure 4-11), for monitoring location 

CM-03 located in the main part of the looping system, 

the model and field agreement was quite good. 

Case 1 data illustrates that, depending upon the level 

of refinement and calibration, there is a significant 

variation in the capability of a model to accurately 

represent the system. In general, the parts of the 

network that are configured as trees (main stem with 

branches) are more easily calibrated by making 

adjustments in demands. For looping parts of the 

system and at dead-ends, results are very sensitive to 

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CM-02 

CM-03 

CM-04 

Figure 4-11. Calibration of “Looped Portion.” 

small variations in demands and system configuration, leading to the possibility of significant prediction errors at some locations. Uncertainty in demand 

estimates can be a major source of error in the model 

estimates. 

4.4.2 Case 2 - Large-Suburban Pressure Zone 

Similar to Case 1, a field study and calibration 

exercise was carried out in a large-suburban, pressure 

zone. This area was selected in order to demonstrate 

the application of tracer studies and calibration 

techniques in a more complex area. The selected area 

contained multiple pumps and tanks. The selected 

distribution system area is representative of relatively 

complex, well-gridded systems found in many larger 

water systems. The layout of the system, the location 

of the injection site, and the monitoring locations are 

shown in Figure 4-12. 

Two separate tracer studies were performed in this 

zone. The first study was used to further calibrate the 

skeletonized model received from the water utility. 

The second study served as a validation event to test 

the veracity of the calibrated model. In the calibration 

event, the tracer was introduced directly into the main 

feed line servicing the entire area (characterized by 

higher flow/higher pressure). In the validation study, 

the tracer was pulsed. A total of 34 continuous 

conductivity meters were installed in the system. 

Four flow meters were temporarily installed to provide 

flow measurements at key locations. 

During the calibration study, the calcium chloride 

tracer was injected into the main feed line serving the 

4-10 
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Figure 4-13a. Modeled Flows Compared to Measured 

Flows Before Calibration. 

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Figure 4-13b. Modeled Flows Compared to Measured 

Flows After Calibration. 

A Reference Guide for Utilities 

EPANET model to accommodate for this development. Another possibility for the discrepancy was 

that the demand in this region was significantly 

higher than the average residential demand modeled 

in the area. To simulate this possibility, a sensitivity 

analysis was performed in which the modeled demand 

in this region was doubled. The model-predicted 

results improved significantly for this region based on 

these three adjustments. 

In Region 2 (CM52, CM53, CM55 and CM56), an 

opposite phenomenon to that in Region 1 was 

observed. The field data indicated that the tracer 

arrived several hours after the model’s prediction. 

One possible explanation was that this region had 

lower demand than the average residential demand 

modeled in this area. The flow meter data upstream of 

this location supported this theory as the EPANET 

predicted flow in this pipe was much higher than the 

field observed flow (see Figure 4-13a). To simulate 

this possibility, the local demand in this region was 

reduced by 30 percent in the model. The resultant 

flow matched the flow meter data (see Figure 4-13b). 

Also, similar to Region 1, it was found that a potential 

flow path had again been left out due to 

skeletonization of the model, which affected CM52. 

This pipe link was added to the model using the 

appropriate pipe parameters. Distribution mains 

between CM55 and CM53 were also found to have 

been upgraded since the EPANET network model was 

Injection Point 

Flow Monitoring Location 

Continuous Monitoring Sample Location 

CM-54 CM-53 Region 2 

CM-52

CM-51 CM-41 CM-42 Region 1 

CM-56 CM-45 CM-55 CM-50 CM-43 CM-44

Q21 CM-49 CM-46 

CM-47 

CM-57 CM-48 CM-37 CM-61


CM-59 CM-40
 CM-58 

CM-64 CM-32 Q23 CM-36 Q22

CM-60 CM-31 CM-33 CM-62 Q20 CM-39 

CM-63 Region 3 

CM-35 CM-34 

CM-38 

Figure 4-12. Schematic Representation of Case 2 

Study Location. 

area for a period of 6 hours. In the validation study, 

the tracer was pulsed by fill and draw cycles in a 

storage tank at the same location. In both cases, a 

target chloride concentration of 190 mg/L or lower 

was set in order to safely not exceed the 250 mg/L 

secondary MCL for chloride. 

During the calibration process, initial EPANET 

model simulations were reviewed in detail to 

determine the flow patterns around various monitoring locations and to attempt to identify causes 

for discrepancies in the observed and predicted 

values. A careful examination of the areas of 

significant discrepancies indicated that these were 

primarily limited to three geographic sub-regions 

within the skeletonized network. In addition to 

these three sub-regions, there were a few isolated 

locations where the predicted tracer pattern did not 

match the observed tracer pattern from the field 

study. The modeling team carefully examined each 

of these regions and addressed the zonal issues 

accordingly. The three sub-regions are shown in 

Figure 4-12. 

In Region 1 (CM42, CM43, and CM44), the field data 

indicated that the tracer arrived at these continuous 

monitoring locations several hours before the model’s 

prediction. On closer inspection, it was found that a 

potential flow path existed which was not included in 

the skeletonized model. While the pipe diameter was 

small, it significantly altered the hydraulic water flow 

path to that region. This missing pipe-link was added 

to the model, using the appropriate pipe parameters. 

Furthermore, the modeling team investigated the GIS 

database to see if there were any substantial changes 

in these areas since the time when the original water 

demand patterns were developed five years ago. The 

updated GIS information indicated a presence of 

recent housing development in that region. Therefore, additional demand nodes were entered into the 

4-11 
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Figure 4-14a. Chloride Concentration for Calibration 

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Figure 4-14b. Chloride Concentration for Validation 

Event at Continuous Monitor Location CM-59. 

A Reference Guide for Utilities 

developed for this area. The EPANET model pipes for 

this location were updated using the newer information. The model-predicted results improved significantly for this region based on these three adjustments. 

In Region 3 (CM34 and CM35), the field data 

indicated that the tracer arrived at locations CM34 

and CM35 several hours after the model’s predicted 

arrival time. However, the field-verified tracer arrival 

time matched the predicted tracer arrival time at 

location CM33 which is slightly upstream of these 

locations. Also, a review of the water flow pattern in 

this region indicated that the water traveled from 

CM33 towards CM34 and CM35 (at all times). Based 

on the demands in the EPANET model, the pipe 

lengths, and the regional water flow information, the 

delay in tracer arrival at CM34 and CM35 could not 

be explained. A closer inspection of the region 

revealed a complex grid of interconnected pipes in 

this region, which were skeletonized as two parallel 

pipes. This skeletonization eliminated a number of 

different possible hydraulic flow paths between CM 

33 and CM34/CM35. Also, in the EPANET model 

inputs, it appeared that the demand close to CM34 

and CM35 was set artificially higher (to account for 

the overall demand in the skeletonization process). 

This model setup resulted in the predicted faster tracer 

arrival times at CM34/CM35 than those observed in 

the field. To account for this anomaly, a few pipe 

segments from the master plan were added to the 

skeletonized model of this region to better simulate 

the actual grid demands near CM34 and CM35. This 

model adjustment resulted in better prediction of the 

tracer arrival times. 

During the calibration process, as demands were 

adjusted, a mass balance was performed for each hour 

to ensure that the net water demand in the study area 

remained the same, i.e., the increase in the demand at 

certain nodes was balanced by the reduced demand at 

other nodes to eliminate any net impact on water 

demand. In the final refinements, a multiplier of 2.0 

was used for the base demand in Region 1, and a 

multiplier of 0.7 for the base demand in Region 2. 

These refinements showed some improvement in the 

model’s ability to correctly predict the tracer arrival 

time and concentration. These calibration efforts 

resulted in a relatively well-calibrated network model. 

However, some local problems remained, especially in 

looped areas and areas that were branched off from the 

main lines. 

The substantial changes made to the EPANET 

skeletonized model representing the large area 

necessitated a validation process. Therefore, the 

calibrated EPANET model input file from the first 

event was used to validate the model’s capability to 

predict the results during the subsequent tracer 

addition. For the purposes of this validation, the data 

from the second set of pulsed injections was modeled 

using the calibrated EPANET network model for the 

study area to see how the predicted results compared 

with the continuous monitoring data collected during 

this event. The modeled and measured concentrations 

are compared in Figure 4-14a for the EPANET 

calibration. A similar comparison is shown in Figure 

4-14b for the validation study. 

Additionally for the purposes of this analysis, the 

EPANET predictions from the validation event were 

compared with the field results for each monitoring 

site and each site was given a grade as follows: 

•	 Very good match (within ±20 percent of the 

actual concentration and within ±1 hour of the 

actual tracer arrival time) 

•	 Moderate match (within ±30 percent of the 

actual concentration and within ±5 hours of the 

actual tracer arrival time) 

•	 Poor match (greater than ±30 percent of the 

actual concentration or greater than ±5 hours of 

the actual tracer arrival time). 

Of the 34 monitoring sites in this study area for the 

validation event, 15 received a grade of very good 

match, 14 were in the moderate match category, and 5 

received the lowest grade of poor match. In general, it 

was found that better matches occurred on larger pipes 

serving large populations, while the poorest matches 

occurred in more localized loops serving fewer 

4-12 
customers. These results are, in general, quite similar 

to the results obtained for the calibration event, and 

most problems repeatedly occurred at the same 

locations for both events. The validation event 

results confirm the fact that the calibrated EPANET 

network model can now be used to predict the 

outcome of a separate event to the same degree of 

accuracy. 

4.5 Future of Model Calibration 

Calibration continues to be a major focus of most 

modeling efforts. It can provide a model that may be 

used with greater confidence and produce results that 

are commensurate with the important decisions that 

are made based on the application of the model. 

However, there is significant room for improvements 

in calibration methodologies and in developing a 

standardized set of calibration protocols. This has led 

to an active research program in this area that is 

expected to continue into the future. 

4.5.1 Calibration Standards 

The following issues are raised frequently in the field 

of distribution system modeling: 

•	 extent of calibration needed for various


applications, and


•	 standards for calibration. 

Though these are very reasonable questions, straight 

forward answers are usually not readily available. 

There is general agreement in the modeling profession 

that the amount and degree of calibration required for 

a model should depend upon the intended use of the 

A Reference Guide for Utilities 

model (Engineering Computer Applications Committee [ECAC], 1999). Some applications such as design 

and water quality analysis typically require a high 

degree of calibration, while other uses, such as master 

planning, can be performed with a model that has not 

been calibrated to such a high standard. However, 

there are no universally accepted standards. 

In the United Kingdom, there are performance criteria 

for modeling distribution systems (Water Authorities 

Association and WRc, 1989). These are expressed in 

terms of the ability to reproduce field-measured flows 

and pressures within the model, as shown below. 

Flow 

1. ±5 percent of measured flow when flows are 

more than ±10 percent of total demand 

(transmission lines). 

2. ±10 percent of measured flow when flows are 

less than ±10 percent of total demand 

(distribution lines). 

Pressure 

1. 0.5 m (1.6 ft) or 5 percent of head loss for 85 

percent of test measurements. 

2. 0.75 m (2.31 ft) or 7.5 percent of head loss for 

95 percent of test measurements. 

3. 2 m (6.2 ft) or 15 percent of head loss for 100 

percent of test measurements. 

In 1999, the AWWA Engineering Computer Applications Committee developed and published a set of 

draft criteria for modeling. These were not intended 

as true calibration standards, but rather as a starting 

point for discussion on modeling needs. These criteria 

are summarized in the following table (Table 4-2). 

Table 4-2. Draft Calibration Criteria for Modeling (based on ECAC, 1999) 

Intended 

Use 

Level of 

Detail 

Type of 

Simulation 

Number of 

Pressure 

Readings1 

Accuracy of 

Pressure 

Readings 

Number of 

Flow 

Readings 

Accuracy of 

Flow 

Readings 

Long-Range 

Planning 

Low Steady-State 

or EPS 

10% of Nodes ±5 psi for 

100% Readings 

1% of Pipes ± 10% 

Design Moderate to Steady-State 5% - 2% of ±2 psi for 90% 3% of Pipes ± 5% 

High or EPS Nodes Readings 

Operations Low to High Steady-State 

or EPS 

10% - 2% of 

Nodes 

±2 psi for 90% 

Readings 

2% of Pipes ± 5% 

Water 

Quality 

High EPS 2% of Nodes ±3 psi for 70% 

Readings 

5% of Pipes ± 2% 

1 The number of pressure readings is related to the level of detail as illustrated in the table below. 

Level of Detail Number of Pressure Readings 

Low 10% of Nodes 

Moderate 5% of Nodes 

High 2% of Nodes 

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A Reference Guide for Utilities 

At this point, there is no clear movement toward 

establishing calibration standards. However, it is 

likely that the need for further guidance in this area 

will increase as the extent and sophistication of 

modeling continues to expand. 

4.5.2 Technological Advances 

Research is continuing in two areas that strongly 

influence the likelihood of improved calibration of 

water distribution systems models: monitoring 

technology and optimization techniques. The 

available optimization techniques (and those under 

development) have been briefly discussed in this 

chapter and in Chapter 2. Active research and 

development areas include optimization techniques 

for water quality calibration, EPS models, and use of 

tracer data. Areas of research, development, and 

experimental applications in monitoring technology 

include less expensive meters that can be inserted 

into pipes in the distribution system and automated 

monitoring for use in conjunction with tracer studies 

(as discussed in Chapter 3). 

4.6 Summary and Conclusions 

Water distribution system models can be used for a 

number of purposes. Many of these uses result in 

engineering decisions that involve significant 

investments. It is therefore important that the model 

represent the “real world.” Calibration techniques can 

be used to ensure that the mathematical representation 

of the system, or model, adequately simulates the 

system. 

Calibrating a model is a difficult task because there 

are many parameters that can be adjusted and finding 

the combination of parameters that result in the best 

agreement between measured and modeled results is 

often challenging. It is recommended that the model 

be calibrated using one set or more of field data and 

subsequently validated with an independent set of 

field data. 

Calibration of water distribution system models can 

be viewed in many dimensions. Hydraulic calibration 

is used to adjust the parameters associated with 

hydraulic simulations, while water quality calibration 

is applied to reaction rates and other parameters that 

control the water quality simulation. Static or steadystate calibration methods are used with steady-state 

models and data collected at instantaneous snapshots 

in time, while dynamic calibration is conducted with 

extended-period simulation models and time-series 

data. Manual calibration techniques involve manual 

application of models in a trial-and-error mode, while 

automated calibration uses the power of the computer 

to search a wide range of solutions and to select the 

set of parameters that best achieve a stated objective. 

Automated methods can reduce much of the tedium 

During the calibration process, it is important to 

eliminate various sources of errors in modeling. As a 

first pass, a modeler should check for typographical 

errors, accuracy of affected piping layout and material, 

general system flow, velocity values, and distribution 

system demands. Thereafter, one should look into other 

sources of errors such as skeletonization, valve position, geometric node placement anomalies, SCADA 

data errors, and pump performance. 

associated with calibration but require the modeler to 

formally define a quantitative objective function for 

measuring how well the model matches the field data. 

Such automated methods are becoming more available in commercial modeling packages. 

Two case studies are presented in this chapter. The 

case studies differ in terms of the overall scale of the 

study area. In both cases, the distribution system 

model that was used as a starting point for the 

calibration exercise was part of a skeletonized model. 

The results demonstrate the need for adequate model 

calibration. 

The extent of calibration and calibration techniques 

are a major issue in most modeling efforts. There is 

significant potential for improvements in calibration 

methodologies and in standardization of calibration. 

This has led to an active and continuing research 

program in this important area. 

4-14 
A Reference Guide for Utilities 

References


AWWA. Computer Modeling of Water Distribution 

Systems (M32). AWWA, Denver, CO. 2004. 

ECAC. “Calibration Guidelines for Water Distribution System Modeling.” Proceedings, AWWA ImTech 

Conference, New Orleans, LA. April 18-21, 1999. 

McEnroe, B.M., D.V. Chase, and W.W. Sharp. “Field 

Testing Water Mains to Determine Carrying Capacity.” Miscellaneous Paper EL-89, U.S. Army Engineer 

Waterways Experiment Station. Vicksburg, MS. 

1989. 

Vasconcelos, J.J., L.A. Rossman, W.M. Grayman, P.F. 

Boulos, and R.M. Clark. Characterization and 

Modeling of Chlorine Decay in Distribution Systems. 

AWWA and AwwaRF, Denver, CO. 1996. 

Walski, T. M, D.V. Chase, D.A. Savic, W.M. Grayman, 

S. Beckwith, and E. Koelle. Advanced Water Distribution Modeling and Management. Haestad Press, 

Waterbury, CT. pp 268-278. 2003. 

Water Authorities Association and WRc. Network 

Analysis – A Code of Practice. WRc, Swindon, UK. 

1989. 

Williams, G.S., and A. Hazen. Hydraulic Tables. John 

Wiley & Sons, NY. 1920. 

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A Reference Guide for Utilities 

Chapter 5 

Monitoring Distribution System Water Quality 

Monitoring a water supply system and its various 

components facilitates the gathering of data about the 

state of the system (physical, operational and water 

quality). If the state of the system has minimal 

changes in time or space, a simple monitoring system 

may be sufficient to define and manage the system 

characteristics. However, if there is potential for 

significant variation in the state of the system, the 

monitoring system must be adequately designed to 

capture that variability. Thereafter, depending upon 

the type and magnitude of variability, an appropriate 

response can be provided to restore the “normal” 

system state. This chapter will focus on monitoring 

water quality-related parameters in a distribution 

system. 

In a distribution system, water quality may vary due 

to factors such as normal patterns in water consumption, seasonal variations, source water quality, 

components of the distribution system, operation of 

the system, retention time in storage, travel time in 

the piping system, or the condition of the system 

itself. Variability may also result from unusual 

occurrences, such as intentional/accidental intrusions 

of contaminants, or chemical processes such as 

nitrification. Design of a water quality monitoring 

program must take into account both the nature of the 

variability and the manner in which monitoring data 

will be used. In other words, the objective of the 

monitoring program must be defined along with 

appropriate output or reporting requirements. 

In general, monitoring systems can be defined based 

on the uses or needs of the monitoring program, the 

general type of monitoring to be performed (manual 

grab sampling and/or continuous automated online 

monitoring), or the specific monitoring equipment 

characteristics. It is important to first establish a clear 

objective(s) for monitoring. Thereafter, depending 

upon the availability of funding, need, and expertise, 

one should select the appropriate sampling 

technique(s) and monitoring equipment. Once an 

appropriate monitoring system has been selected and 

implemented, it is important to operate and maintain 

the program to achieve optimal results and benefits. 

However, the system should be flexible enough so 

that it can be modified in case it does not meet the 

original objective(s). 

This chapter discusses the various drivers or objectives for monitoring followed by a summary of 

available monitoring techniques. An overview of 

monitoring equipment is presented followed by 

guidelines for establishing monitoring requirements 

(e.g., selection of parameters, number and locations of 

monitors, and monitor characteristics). Some guidance for engineering and evaluating remote monitoring systems is also presented, along with some EPAsponsored monitoring case studies. The chapter 

concludes with a summary and a listing of references. 

The recent studies involving the use of online continuous monitoring systems have resulted in large streams 

of data that document the minute-by-minute changes 

in water quality that exist at various points in the 

water networks. The application of this technology 

has the potential for providing new insights as to how 

water distribution systems may be operated and 

designed to improve water quality. However, these 

systems will require a relatively high level of sophistication in terms of data management, including the 

capability to generate real-time reports, graphical and 

visual representation of information, and compliance 

reports for meeting drinking water standards. Some of 

these data streams may well reveal excursions in water 

quality that constitute violations of current or future 

drinking water standards, or a security-related incident. This type of information may put pressure on 

drinking water utilities and regulatory agencies to take 

remedial action, possibly on an emergency basis, even 

when such actions may not be fully justified (or 

warranted). However, careful planning and negotiations with appropriate regulatory authorities to define 

these potential “excursions” and the proper corrective 

action to be taken would prevent any misunderstandings and minimize or eliminate the potential for 

unjustified enforcement or response actions. 

5.1 Establishing Monitoring 

Objective(s) 

In order to define and implement an effective monitoring plan, clear objectives must be established. 

Collecting data just for the sake of accumulating 

information is not cost effective. In drinking water 

systems, there are several specific reasons to collect 

data and, typically, the monitoring system is tailored 

to meet one or more of these needs. The objectives of 

monitoring distribution systems can be broadly 

classified into the following five uses: 

• regulatory driven monitoring, 

• security related monitoring, 

• process control related monitoring, 

5-1 
A Reference Guide for Utilities 

Table 5-1. Federal Distribution System Water Quality Monitoring Requirements 

Regulation Monitoring Requirement(s) 

TCR Samples must be collected at sites that are representative of the water quality 

throughout the distribution system based on a site plan that is subject to review by 

the primacy regulatory agency. 

The minimum number of samples that must be collected per month depends on the 

population served by the system. 

For each positive total coliform sample, there are repeat sampling requirements, 

additional analyses, and an increased number of routine samples. 

SWTR and IESWTR Disinfectant residuals must be measured at TCR monitoring sites. 

LCR All systems serving a population > 50,000 people must do water quality parameter 

(WQP) monitoring. 

The number of sample sites for Pb/Cu and WQP monitoring is based on system size. 

DBPR2 The IDSE requirement of DBPR2 in turn requires the establishment of a Standard 

Monitoring Program (SMP). The SMP will require one year of data on THMs and

Haloacetic Acids (HAAs). The number of sampling locations is based on utility size 

and source characteristics. Modeling can reduce sampling requirements. 

•	 water quality characterization (e.g., general, 

baseline, or other research-related monitoring), 

and 

•	 multi-purpose (a combination of above) use of 

monitoring data. 

The following subsections present the overall scope 

of each of these five objectives. 

5.1.1 Regulatory Driven Monitoring 

Various federal, state, or other governmental agencies 

have regulations that specify distribution system 

monitoring requirements. An overall review of federal 

regulations impacting distribution systems was 

presented in Chapter 1. The specific federal distribution system monitoring requirements (existing and 

proposed) are summarized in Table 5-1. In some 

cases, states have imposed more stringent criteria and 

monitoring requirements. 

5.1.2 Security Related Monitoring 

Assessments performed by utilities and various 

research studies have identified that water distribution systems are vulnerable to intentional (or accidental) contamination. In addition to “hardening” 

systems in order to deter intentional contamination, 

monitoring as part of an early warning system (EWS) 

has emerged as a logical approach to cope with 

potential contamination events. There are no existing 

or proposed standards for such monitoring. However, 

it is well recognized that monitors will need to be 

sufficiently sensitive to a broad range of potential 

contaminants and appropriately located to detect a 

contamination event within a reasonable time. 

Additionally, as detailed in EPA’s Response Protocol 

Toolbox (EPA, 2003-2004), monitors must be an 

integral part of an emergency response management 

plan in order to be effective. Extensive research and 

development is underway on monitor development, 

calibration, and placement in response to the perceived security monitoring needs. 

Currently, EPA has an ongoing test program to evaluate the potential of sensors monitoring routine online 

water quality parameters, such as pH, oxidation 

reduction potential (ORP), free chlorine, total organic 

carbon (TOC), conductivity, and turbidity, to serve as 

rapid detection devices for detecting contamination 

events in distribution systems. Online monitors were 

selected because response time is critical for achieving 

the objective of providing early warning. Both benchand pilot-scale studies are being conducted at the 

Water Awareness Technology Evaluation Research and 

Security (WATERS) Center within the EPA’s Test and 

Evaluation (T&E) Facility in Cincinnati, Ohio. The 

bench-scale runs are designed to identify the detection 

threshold of each sensor for specific contaminants. 

The pilot-scale runs are designed to evaluate overall 

response of the selected sensors by injecting known 

quantities of potential contaminants into the distribution system simulator (DSS). For this purpose, several 

pilot-scale DSSs have been fabricated and used for 

these test runs. The sensor data are collected continuously and archived electronically to establish stable 

baseline conditions and to also record sensor responses 

to injected contaminants. Grab samples are collected 

periodically before and after injection of contaminants 

to confirm the sensor results. 

5-2 
A Reference Guide for Utilities 

5.1.3 Process Control-Related Monitoring 

Monitors can also be used in a distribution system to 

provide real-time or near real-time information on 

water quality that can then be used to control treatment processes at a treatment plant or in the distribution system. The use of continuous chlorine monitors 

in the distribution system to control disinfectant feed 

rates at the plant or at in-distribution system booster 

chlorination stations are examples of this type of 

monitoring (Uber et al., 2003). 

5.1.4 Water Quality Characterization 

Information from long-term monitoring of distribution 

systems can be used to develop baseline trends in 

water quality for that system. Such information is 

useful in evaluating a water supply system and for 

planning upgrades or modifications to system design 

or operation. 

Additionally, if this information is appropriately 

distributed, it builds consumer confidence and helps 

to keep customers up to date about the water quality 

so that they can use this information to make decisions about protecting their health. Currently, there 

are no standards or guidelines for this type of monitoring. However, for this information to be useful and 

cost-effective, a regular program for examining and 

analyzing the collected information is essential. 

5.1.5 Multi-Purpose Use of Monitoring Data 

Monitoring can be an expensive undertaking in terms 

of capital costs, as well as operation and maintenance 

(O&M) costs, including labor. Costs include the 

purchase and upkeep of equipment, laboratory 

analysis, labor, and consumable supplies. The 

investment in monitoring and automated monitoring 

systems is justifiable if the resulting data are used for 

more than one objective. For example, if data 

collected for security purposes can also be used for 

process control, it should be easy to justify potentially large investments in automated monitoring 

equipment. Monitoring systems should be properly 

designed in order to meet multi-purpose requirements. 

5.2 Monitoring Techniques 

The two major factors in designing and implementing 

an effective monitoring program are sampling 

techniques and equipment selection. This section 

focuses on available monitoring techniques. Samples 

can be collected and analyzed in two ways: grab 

samples and/or by automated online monitoring. 

Automated monitors (continuous or discrete) are 

sometimes supplemented with automated samplers 

that can collect both discrete and composite water 

samples for further analysis at a later date/time. Grab 

samples are collected manually and analyzed in the 

field or in the laboratory. Grab samples are laborintensive in comparison to automated sampling and 

provide snapshot information about the system at the 

time of sample collection. Automated monitoring 

uses online instrumentation, and data is collected by 

means of sensors and automated data loggers. They 

can also be tied to a SCADA System. High-end 

monitors require a higher capital expense for the 

purchase and maintenance of sensors, data acquisition, data communication, data storage, and dataprocessing hardware and software. However, this type 

of monitoring provides a continuous time-series 

profile of changes in water quality. Both automated 

and grab sampling can be incorporated into a comprehensive monitoring plan. These techniques are 

further discussed in the following subsections. 

5.2.1 Manual Grab Sampling 

Historically, routine water quality monitoring in 

distribution systems has been carried out through 

manual grab samples followed by analysis in the field 

or in the laboratory. Essentially, all regulatory 

monitoring is still carried out by this method. For 

example, samples required for large community water 

supply systems under the SWTR are manually 

The equipment routinely required in a manual grab 

sampling program includes field sampling equipment 

(e.g., chlorine meter), safety equipment (vests, rain gear, 

and flashlights), and laboratory equipment. Consumable supplies include sampling containers, reagents, 

and marking pens. One should identify the needs and 

availability of equipment and supplies and investigate 

various sources for equipment. Because equipment 

malfunction or loss is possible, some redundancy in 

equipment is appropriate. Some important functions to 

consider when establishing a field sampling program 

include the following: 

•	 Establish a systematic and organized method for 

all sampling and data recording. Take notes to 

document all aspects of the process. 

•	 Provide training to sampling crews and specify 

these training requirements in the sampling 

program plan. 

•	 Contingency planning is important; therefore, 

consider the potential for equipment 

malfunction, illness of crew members, 

communication problems, severe weather, 

malfunction, and customer complaints. 

•	 Establish a communications protocol to 

coordinate actions. A means of communication 

is needed to respond to unexpected events. 

Alternatives include radios, cellular phones, 

walkie-talkies, or a coordinator in a vehicle to 

circulate among field crews. 

•	 Calibrate field analytical equipment before and 

during the sampling activity. 

5-3 
A Reference Guide for Utilities 

collected at sites within the distribution system and 

tested for disinfectant levels in the field. Samples taken 

to satisfy the requirements of the TCR are also manually 

collected in the field and subsequently analyzed in the 

laboratory. Manual sampling is labor-intensive and the 

number of samples that can be collected is limited by 

availability of personnel and analysis costs. However, 

they are specified by some regulations. Potentially 

important events that may occur between the routine 

grab samples may be lost (e.g., process upset). Also, 

there is a potential for dismissing unusual grab 

sampling results as some type of manual monitoring 

error (Hargesheimer et al., 2002). 

5.2.2 Automated/Online Monitoring 

As stated in the report, “Online Monitoring for 

Drinking Water Utilities” (Hargesheimer et al., 2002), 

“There is an evolution from grab-sample monitoring 

to online monitoring as sampling, analysis, data 

processing, and control functions become more 

automated.” Online monitoring requires a mechanism 

for moving the sample water from the distribution 

system to an instrument, appropriate instrumentation 

for analyzing the water, a mechanism for communicating the results, and a means of assessing the results of 

the monitoring. Additionally, the instrumentation 

must be periodically calibrated and maintained for 

quality control/quality assurance. 

In the past, distribution system online monitors were 

typically housed in a controlled environment with 

sample lines from the distribution system to the 

instrument. This resulted in most instrumentation 

being located at facilities such as tanks and pump 

stations. The instrumentation was sometimes connected to a SCADA system so that results could be 

communicated to a central office. More recently, 

some instrumentation is available that is designed for 

installation in manholes or for direct insertion into 

water mains. 

The American Society of Civil Engineers (ASCE), in 

concert with other leading organizations, entered into 

a cooperative agreement with the EPA to develop 

standards documents and guidance aimed at enhancing 

the physical security of the nation’s water and wastewater/stormwater systems. Under this agreement, 

ASCE is leading the effort to develop guidelines for 

designing an online contaminant monitoring system 

(OCMS). The Interim Voluntary Guidelines for 

Designing an OCMS were published in December 

2004 (ASCE, 2004). This document provides comprehensive information on several topics, including 

rationale for OCMS and system design basics, selection and siting of instruments, data analysis, and use of 

distribution system models. 

5.3 Monitoring Equipment 

Overview 

In general, monitors can be categorized by the types 

of parameters (contaminants, agents, and characteristics) that the monitor is used to measure. For establishing water quality, the monitors are designed to 

measure one or more parameters that represent 

physical, chemical, and/or biological characteristics 

of the system. Typically, in manual grab sampling 

programs, hand-held physical and/or chemical 

parameter measuring devices are used. These handheld devices are carried to the sampling location 

along with appropriate containers to collect water 

samples for performing more complex chemical and 

biological analyses in a laboratory. The online 

sampling devices are more complex devices that are 

designed to automatically measure, record, and 

display specific physical, chemical, or biological 

parameters. A brief overview of these devices is 

presented in the following subsections. 

5.3.1 Physical Monitors 

Physical monitors are used to measure the physical 

characteristics of the water in a distribution system. 

They include a variety of instrumentation that 

measures various macro characteristics, such as flow, 

velocity, water level, pressure, and other intrinsic 

physical characteristics. Examples of intrinsic 

physical characteristics include pH, turbidity, color, 

conductivity, hardness, alkalinity, radioactivity, 

temperature, fluorescence, UV254, and ORP. In 

general, physical monitors tend to be relatively 

inexpensive, quite durable, and readily available. 

5.3.2 Chemical Monitors 

Chemical monitors are used to detect and measure 

inorganic or organic chemicals that may be present in 

the water. A wide range of chemicals may be of 

interest, and a large variety of technologies can be 

used. A specific technology or multiple technologies 

must be properly selected for a particular chemical or 

a group of chemicals of interest. Examples of 

chemical monitors include, but are not limited to 

residual chlorine monitor, TOC analyzer, and gas 

chromatograph/mass spectrometer (GC/MS). Typically, the same general type of technology may be 

available in either automated online monitoring 

capability or to support manual grab sample analysis. 

5.3.3 Biological Monitors 

Biological monitors (bio monitors) include biosensors and bio-sentinels. Bio-sensors detect the 

presence of biological species of concern, such as 

some forms of algae or pathogens. The general 

operating principles of bio-sensors may include 

photometry, enzymatic, and/or some form of biochemical reaction. The bio-sentinels use biological 

5-4 
A Reference Guide for Utilities 

organisms as sentinels to determine the likely 

presence of chemical toxicity in a water sample. 

In general, bio-sentinels cannot be used to identify 

the presence of a specific toxic contaminant – rather 

only that there is some form of toxic contaminant 

present. Most bio-sentinels operate by observing the 

behavior of selected organisms. Examples of such 

organisms include: fish, mussels, daphnia, heterotrophic bacteria, and algae. When the sentinel 

organism senses the presence of toxicity, it reacts in 

some unusual manner. Bio-sentinel instruments 

respond to these reactions and note that an unusual 

event is occurring. This application is somewhat 

analogous to the use of indicator organisms (e.g., total 

coliforms) to indicate the water quality in the distribution system. 

While bio-sensors can be directly applied in distribution systems without pretreatment of the sample, the 

bio-sentinels are typically used in source waters. This 

is because most organisms are sensitive to the 

presence of chlorine (or other disinfectants) in the 

water. Therefore, if a bio-sentinel is proposed to be 

used for distribution system monitoring, the water 

must be de-chlorinated prior to entering the biosentinel instrument. Dechlorination may affect 

detection reliability and the chemical characteristics 

of the water. Also, the bio-sentinels require a protected housing environment along with some sort of 

nutritional supply to keep the sentinel organism alive 

and healthy. 

5.4 Establishing Monitoring 

Requirements 

Selection of the types, numbers, and locations of 

monitors is dependent on the nature of the monitoring 

program desired. These requirements depend upon 

the overall monitoring objectives and the distribution 

system site-specific requirements. For example, a 

monitor used for regulatory purposes may need to 

monitor different constituents than one used as part of 

a process control or security system. Similarly, a 

different monitor may be needed for a utility that uses 

chlorine as the disinfectant compared to one that uses 

chloramine. The site-specific monitoring requirements can be evaluated and represented in the 

following terms: 

•	 monitoring parameters, 

•	 number and location of monitors, 

•	 nonitor characteristics (e.g., detection limits, 

sampling frequency, cost, false negatives/false 

positives), and 

•	 amenability to remote monitoring and SCADA 

integration. 

These requirements are further discussed in the 

following subsections. 

5.4.1 Monitoring Parameters 

The parameters to be monitored depend strongly upon 

the specific use of the monitor and upon utilityspecific situations. For regulatory purposes, the 

regulations typically specify the minimum set of 

parameters that must be sampled. For each system, the 

regulating authority typically also specifies the 

monitoring locations and frequency. A utility may 

choose to analyze the water for additional parameters 

and/or increase the frequency of monitoring in order 

to address other water quality concerns. 

For security monitoring, there are no regulations or 

standards. Utilities can choose whether or not they 

want to perform such monitoring and select the 

parameters they will monitor. Generally, such 

monitoring will be limited by budgets and by 

technology. Research and development is being 

conducted on security monitoring systems, in 

conjunction with event detection platforms, that 

measure standard parameters, such as TOC, pH, 

turbidity, conductivity, chlorine, ORP and temperature. For both process control and security-related 

monitoring, instrument response time is critical. 

Therefore, online monitors are typically used in these 

types of applications. The parameters monitored vary 

widely depending upon the type of process and/or 

security monitoring. 

The goal of online monitoring for security purposes is 

to automatically analyze the data to determine (1) 

whether there is an indication of unusual contamination in the sample; and (2) what the likely contaminant is, based on the water quality signature of these 

parameters. 

5.4.2 Number and Location of Monitors 

For selecting monitoring locations in distribution 

systems, there are two related decisions: (1) how many 

monitors to place in the system, and (2) where to 

place them. The number of monitors is generally 

controlled by the monitoring objective (e.g., regulatory requirement) or by budgetary factors, while the 

location of monitors is a more complex issue that can 

be addressed in many ways. For example, for compliance with the TCR and the SWTR, there are specific 

requirements as to the number of samples that must be 

taken. For most other uses, the number of sampling 

points (or the number of monitors installed) is 

controlled by budgetary and financial constraints and 

through comparison to the benefits associated with 

the monitors. The following subsections summarize 

an approach that can be used when the established 

objectives do not clearly define the number and 

location of monitors. 

5-5 
A Reference Guide for Utilities 

EPA’s research in contamination warning systems (CWSs) at the T&E Facility is 

developing data based on bench- and pilot-scale experiments that reveal how 

traditional water quality parameters, if monitored online, can serve as triggers 

for contamination events. Figure 5-1 shows the response of several instruments 

to the injection of secondary wastewater into a DSS. 

Figure 5-1. Wastewater Injection: Free Chlorine and Associated Grab 

Sample Results. 

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5.4.2.1 Number of Monitors 

To select the optimum number of monitors for a 

distribution system, theoretically one can perform a 

simple cost-to-benefit analysis. If the overall life 

cycle benefits of each monitor exceed its life cycle 

costs, analysis would suggest that the monitor is 

justified. Life cycle costs represent both the capital 

and operational costs for the monitors. Depending 

upon the location-specific requirements, as the 

number of monitors increase, there may be economies 

of scale or the unit cost may actually increase 

disproportionately. The unit costs increase when the 

additional monitors are placed in less convenient 

locations where servicing and/or data communication 

costs are higher. Frequently, budgetary constraints 

may also limit the number of monitors that can be 

deployed, even if benefits 

justify their costs. 

Figure 5-2 is a graphical 

representation of benefits 

associated with increasing the 

number of monitors in a 

distribution system. This 

graph illustrates that typically 

after a basic network of 

monitors has been established 

for a distribution system, the 

incremental benefits gained 

by installing additional 

monitors follow the law of 

diminishing returns. The 

actual development of such a 

graph is difficult because of 

the need to explicitly quantify 

benefits. In the case of water 

security-related monitoring, 

one could measure the value 

based on population or 

sensitive facilities (e.g., 

hospitals) protected by use of 

online monitors. For other 

types of monitoring situations, quantification of 

benefits is more difficult. 

Though a formal cost-benefit analysis may not be 

feasible, this discussion provides a general framework 

that can informally guide the design of a monitoring 

network. 

5.4.2.2 Optimal Monitor Locations 

Historically, monitors/sensors have been placed in 

distribution systems to meet regulatory requirements. 

Their locations have been determined based on ease 

of access and a general intuitive assessment of 

representative locations. Lee et al. (1991) proposed a 

method for locating monitors, based on the concept of 

coverage, which is defined as the percentage of total 

demand that is sampled by a set of monitors. Various 

other researchers further addressed this issue using 

alternative mathematical methods (Kessler et al., 

1998). Though widely cited, these methodologies 

have rarely been applied in actual practice. However, 

following the attacks of September 11, 2001, there has 

Benefits 

Number of Monitors 

been a renewed interest in the development of 

monitoring technology and placement of monitors in 

the distribution system as a mechanism for detecting 

intentional contamination of distribution systems. 

Many current studies are applying optimization 

techniques to determine the optimal placement for 

monitors in distribution systems based on a defined 

objective function. Ostfeld (2004) and Ostfeld and Figure 5-2. Theoretical Example of Benefits from Salomons (2004) provide reviews of past work in this 

Monitors. area and present example mathematical formulations 

5-6 
A Reference Guide for Utilities 

A 17th century Italian economist, Vilfredo Pareto, 

developed a method for comparing alternatives. Based 

on his work, a situation is defined as being Paretooptimal if by reallocation you cannot make someone 

better off without making someone else worse off. This 

can be applied to evaluating monitors by examining the 

diagram (Figure 5-3) where various monitoring options 

are compared in terms of their cost and some measure of 

effectiveness. Just looking at alternatives A and B, we 

can say that A is better than B because it costs less and 

is more effective. By comparing all potential alternatives, we can define a Pareto front. All alternatives 

located on that front are better than alternatives located 

to the right and below the front. This provides a useful 

conceptual mechanism for evaluating alternative 

monitoring schemes. For additional information on the 

work of Pareto, see Johansson (1991). For more details 

on the application of Pareto’s concepts in the area of 

optimization related to water distribution system 

analysis, see Walski et al., 2003). 

Figure 5-3. Pareto-Optimal Cost Effectiveness 

Diagram. 

Cost 

Effectiveness 

Pareto front 

1 

2 

3 

4 

5 

6 

A

B

using genetic algorithm solution techniques. Their 

methodology finds an optimal layout of an early 

warning detection system comprised of a set of 

monitoring stations aimed at capturing contamination 

from external sources, nodes, or tanks under EPS 

conditions. Berry et al. (2004) developed an optimization program that considers the maximum volume 

of contaminated water exposure at a concentration 

higher than a defined safe level. The method uses an 

integer programming optimization technique to place 

a limited number of “perfect” sensors in the pipes or 

junctions of a water network so as to minimize the 

expected amount of exposure to the public before 

detection, assuming the attack occurs on a typical day. 

Watson et al. (2004) use mixed-integer linear programming models for sensor placement over a range 

of design objectives. Using two case studies, they 

Bahadur et al. (2003) describes an approach using 

PipelineNet in which GIS data and hydraulic model 

results are used to guide the manual placement of 

monitors in order to fulfill some general criteria. In a 

case study conducted with personnel at a water utility, 

25 potential monitoring sites were identified and 

subsequently reduced to two best sites using the GIS/ 

PipelineNet framework. This approach is more closely 

related to the traditional methods for locating monitors compared to the optimization techniques described in this section. 

showed that optimal solutions with respect to one 

design objective (e.g., population exposed) are 

typically highly sub-optimal with respect to other 

design objectives (e.g., time for detection). The 

implication is that robust algorithms for the sensor 

placement problem must carefully and simultaneously 

consider multiple, disparate design objectives. 

In general, the optimization methods described above 

are experimental approaches that have been applied 

only to hypothetical or small water systems and are 

based on assumptions about the availability of 

monitoring technology, ability to define explicit 

objective functions, and limited incorporation of the 

variability of water system operation. Further 

research and development is needed before this 

technology is ready for routine use. 

5.4.3 Monitor Characteristics 

The following characteristics of monitors must be 

evaluated prior to selecting an appropriate device: 

•	 Minimum detection limit (MDL) – The 

minimum detection limit is the lowest 

concentration or value at which the monitor can 

dependably detect the constituent of interest. 

The MDL can vary for different constituents, 

different technologies, or for different 

implementations of the same technology and 

constituent. 

•	 False negatives/false positives – Two forms of 

errors associated with a monitor are false 

positives and false negatives. A false positive 

exists when a monitor reports, incorrectly, that it 

has detected a constituent where none exists in 

reality. A false negative exists when a monitor 

reports, incorrectly, that a constituent was not 

detected when, in fact, it was present. False 

positives can lead to unneeded responses, and 

repeated false positives will lead to a lack of 

confidence in the instrument. Lack of detection 

associated with a false negative results in no 

response to a real contamination event and can 

expose consumers to contaminants in the 

system. 

5-7 
A Reference Guide for Utilities 

The heightened level of concern over the need to 

protect water distribution systems has led to the 

initiation of research into the development of CWSs for 

both source and finished waters (Clark et al., 2004a). 

CWSs are intended to reliably identify low probability/ 

high impact contamination events in source or distributed water. The International Life Sciences Institute 

(ILSI) developed a report (ILSI, 1999) focused on the 

development of environmental warning systems (EWSs) 

for source water. The same development principles 

apply to distribution systems. EWSs applied to 

distribution systems are commonly referred to as CWSs. 

The following design requirements for EWSs were 

identified by ILSI in their report: 

•	 provides warning in sufficient time to respond to 

a contamination event and prevent exposure of 

the public to the contaminant, 

•	 capable of detecting all potential contamination 

threats, 

•	 remotely operable, 

•	 identifies the point at which the contaminant was 

introduced, 

•	 generates a low rate of false positive and false


negative results,


•	 provides continuous, year-round surveillance, 

•	 produces results with acceptable accuracy and


precision,


•	 requires low skill and training, and 

•	 be affordable to the majority of public water


systems.


A key aspect of an effective EWS will be the need for it 

to operate in a remote monitoring and reporting mode. 

•	 Sampling frequency – The rate at which a 

monitor analyzes and reports a value is the 

sampling frequency. This may vary from a few 

seconds or less for an instrument such as a 

pressure gage to an hour or more for instruments 

that take longer periods to perform the analysis 

such as a gas chromatograph. For grab 

sampling, this delay may be even higher. Some 

instruments can be set for different sampling 

frequencies. More frequent sampling may result 

in higher operating costs, shorter battery life, 

increased data storage requirements, or 

increased communication needs. 

•	 Amenability to SCADA integration – The 

monitor’s ability to be online and integrated 

into some sort of SCADA or remote data 

acquisition system is critical if multiple remote 

locations are monitored simultaneously. Most 

current online monitors have analog (e.g., 4-20 

mA, 1-20 V) or digital signal (e.g., RS232, 

RS485) outputs that provide the ability to 

remotely collect and store data at a central 

location for analysis. 

•	 Operation and maintenance requirements – The 

operational requirements of monitors can vary 

significantly and may strongly impact the 

selection process. Issues include the electrical 

needs, expendable material needs (e.g., 

reagents, wear related components), temperature 

and humidity requirements, needs to handle 

waste streams from the monitor, and other 

factors related to the housing of the monitor. 

Similarly, the maintenance requirements of the 

monitors will also impact the selection process. 

Issues such as how frequently a technician must 

service the monitor in the field and the level of 

expertise required to service the device are 

important considerations when evaluating 

monitors. 

•	 Combinations of monitors – The ability of a 

monitoring system to reliably detect a 

contamination event generally increases with 

multiple monitors working in tandem. For 

example, a single monitor that reports a signal 

slightly above the noise level may easily be 

dismissed. However, if multiple monitors at 

several locations in close proximity or several 

instruments at the same location monitoring for 

different parameters all detect a potential event, 

a more forceful and rapid response is likely. An 

ongoing area of research is the development of 

data mining algorithms that can differentiate or 

detect a signal above background levels that are 

not normally observed in the monitored system. 

•	 Costs – The cost of monitoring systems can 

vary over several orders of magnitude. A single 

simple instrument monitoring for a physical 

parameter such as conductivity may cost less 

than $1,000. The cost of a multi-parameter 

physical monitor is typically in the 

neighborhood of $10,000. More complex 

instruments such as a TOC monitor or a GC/MS 

cost in the range of $25,000 to $90,000. The 

cost of more complex instruments or a 

monitoring station containing multiple 

instruments can easily exceed $100,000 in 

capital cost. Installation and ongoing 

maintenance costs are frequently site-specific 

and vary according to environmental 

conditions. 

5.4.4 Amenability to Remote Monitoring and 

SCADA Integration 

For a comprehensive network-wide water quality 

remote monitoring program, it is essential to ensure 

that the system and its monitored components are 

amenable to remote monitoring and SCADA integra5-8 
tion. The SCADA component adds the element of 

control to the monitored network. Most utilities have 

some sort of SCADA functionality to automate and 

monitor the key water treatment and/or distribution 

operations. The control logic is typically triggered 

based on a specified time and/or event. For example, 

the pumps may be set to fill a distribution system tank 

at midnight and when the tank level monitor detects 

that the tank is full (an event reported through the 

SCADA system), the control logic to turn off the 

pumps is initiated. This type of control logic can be 

enhanced to perform control functions based on 

detection of water quality change in the distribution 

system. However, to achieve this functionality, one 

needs to understand the following three major 

components of a remote monitoring and/or control 

system (or SCADA): 

•	 online sampling instruments (e.g., pH, ORP) 

and/or control devices (e.g., pump, valves), 

•	 SCADA or remote monitoring network, and 

•	 field wiring and communications media. 

These components are discussed briefly in the 

following subsections. 

Electric power is generally required for operating these 

components. If electric power is not readily available 

at the desired location where a monitor is to be installed, consider the costs for installing a suitable 

power apparatus (e.g., a solar panel, battery pack). 

5.4.4.1 Online Sampling/Control Devices 

Online sampling/control devices can be the most 

expensive component of a SCADA system. The 

sensors, switches, monitors, and controllers used in a 

SCADA system may vary widely, depending upon the 

parameters that need to be controlled and/or observed. 

The cost for online sampling devices can range from a 

few hundred dollars to over $100,000. Control units 

such as sample feed pumps or shut-off valves are less 

expensive (Panguluri et al., 1999). Costs associated 

with maintenance and calibration of the online 

sensors when planning the acquisition and implementation of a remote monitoring network should also be 

considered. 

5.4.4.2 SCADA or Remote Monitoring Network 

Larger utilities typically use some type of SCADA 

system for water distribution system control that can 

easily be integrated to include online sampling 

instrumentation in a cost-effective manner. Also, 

recent advances in electronic hardware and software 

technologies have resulted in several cost-effective 

SCADA alternatives for smaller systems. A microproA Reference Guide for Utilities 

Sensors and Transducers: A sensor responds to a 

physical and/or chemical stimulus, such as thermal 

energy, flow, light, chemical, pressure, magnetism, or 

motion. A transducer takes the measured physical and/ 

or chemical phenomenon (e.g., pressure, temperature, 

humidity, and flow) and converts it to an electrical 

signal. In each case, the electrical signals produced are 

proportional to a physical and/or chemical quantity 

being measured based on a pre-defined relationship. 

The electrical signals generated by transducers often 

require “conditioning.” Depending upon the transducer, a signal conditioner can be used to perform one 

or more conditioning functions, such as noise filtration, 

amplification, linearization, isolation, and excitation. 

cessor-based “smart” SCADA system can be used in 

remote locations by small system operators where 

direct online communication is expensive. Smart 

systems have higher initial costs, but overall costs are 

reduced since the communication costs (e.g. longdistance phone costs) are negligible because most of 

the burden is transferred from the main computer to 

the individual SCADA unit at the remote site 

(Panguluri et al., 1999). Newer SCADA units are 

fairly inexpensive, with capital costs ranging between 

$500 (PC card-based units and remote data collection 

nodes) and $5,000 (independent PC-based full 

SCADA units). 

The data acquisition hardware processes the digital 

and analog inputs/outputs from various online 

sampling and control devices. For monitoring 

systems, the hardware typically processes the analog 

data measured from various instruments and transfers 

it to a computer system for display, storage, and 

analysis. In a monitoring/control system (SCADA) 

scenario, the hardware would process both analog and 

digital inputs (typically from a field device) and 

outputs (to perform control functionality). The 

application software provides the operator the 

display, control, and analysis (trends and reports) of 

collected data. 

5.4.4.3 Field Wiring and Communication Media 

Depending upon availability, cost, user preference, 

and the relative location of the sensors to the data 

acquisition system, the communication media can be 

either wired (e.g., direct, phone line) or wireless (e.g., 

radio, cellular). In field environments, distributed 

input/output (I/O) is typically employed. A remote 

data acquisition hardware unit employed at the field 

location performs the appropriate signal conditioning 

and transmits the data to a central hub. More recently, 

mesh or grid computing systems are used in remote 

locations to add redundancy in cases of link failures. 

The field wiring between the sensor and the remote 

data acquisition hardware unit is usually direct 

wire. 

5-9 
A Reference Guide for Utilities 

Depending upon the area covered and availability, in 

some cases it may be preferable to use some form of 

radio communication devices. The available radio 

communication devices operate mostly in the very 

high frequency (VHF) or ultra high frequency (UHF) 

range. The VHF frequencies range between 30-300 

megahertz (MHZ) and the UHF frequencies range 

between 300-1,000 MHZ. In U.S., most of the 

available VHF/UHF radio frequencies are licensed. 

The unlicensed bands available include the industrial, 

scientific and medical device channels with frequency 

ranges between 902 - 928 and 2,400 - 2,484 MHZ. 

The unlicensed bands do not have any connection or 

monthly fee requirements. 

Typically, direct wire and phone line (including 

cellular) communication media are inexpensive. The 

primary limitations associated with selecting the 

communication media include installation and 

operating costs, which can vary between $200 (for a 

simple telephone or cellular modem) and several 

hundred dollars for a satellite-based system per 

location. Ongoing monthly operating costs can range 

from $25 for a phone line to approximately $200 per 

month for satellite-based services within the U. S (per 

monitored location). 

5.5 Engineering and Evaluating a 

Remote Monitoring System 

Once all of the basic requirements have been established (e.g., objectives, parameters, location) as 

outlined in the previous section (Section 5.4) and the 

requirements indicate a need for a system-wide remote 

monitoring program for water quality, the following 

additional site-specific needs should be evaluated for 

water quality monitoring in a distribution system 

(Panguluri et al., 1999): 

•	 What are the complexities of the distribution 

system (size, location)? 

•	 What locations are best suited for sampling and/ 

or control system installation? 

•	 Is sufficient flow and water pressure available 

for online instruments? 

•	 Is there an existing SCADA system available? 

•	 What types of communication media are


available at the selected locations?


•	 How many parameters are going to be 

monitored and/or controlled at each location? 

•	 What other site-specific information (e.g., 

availability of power, access, security) will be 

needed? 

Additonal factors to be considered are (Haught and 

Panguluri 1998): 

•	 system features (e.g., ease of operation,


customization, networkability, operator


security),


•	 cost (initial, training, service agreements, and


operation and maintenance), and


•	 vendor support (hardware and software


upgrades and remote diagnosis).


It is important that each site is evaluated individually 

for appropriate SCADA system selection. The cost of 

SCADA software has plummeted over the past few 

years. For example, the cost of one commercially 

available graphical (Windows-based) SCADA software 

package has dropped from $30,000 in the early 1990s 

to $2,000 today. 

Prior to selecting and implementing a remote monitoring network, one should evaluate the options carefully. Engineering a remote monitoring system is a 

difficult task that typically involves many factors: 

multi-dimensional objectives, changing needs, rapid 

Besides the aforementioned immediate needs (e.g., ease 

of operation, customization, networkability), SCADA 

system features include: 

•	 Scalability: This allows for future growth with 

respect to addition of I/O blocks with more 

channels or advance capabilities. These I/O 

channels are used to communicate with various 

field monitoring instruments (sensors) and control 

devices. 

•	 Local Memory: The SCADA hardware must also 

contain sufficient local memory to store the 

monitored data for extended periods of time in 

case of communication failures. 

•	 Remote operation and diagnosis: In the event of 

brownouts or blackouts, the field SCADA units 

should normally self-boot upon resumption of 

power supply. The field SCADA units should also 

allow for remote diagnosis. 

•	 Call-out feature: This feature allows the system’s 

software to notify appropriate personnel if 

problems develop with a treatment system or 

water quality. This feature can greatly enhance 

operator response in emergency situations and 

prevent costly shutdowns and loss of water and/or 

water quality. 

•	 Open Database Connectivity (ODBC): This 

feature allows for open communication with other 

databases and tools that can be integrated to 

provide additional features. The data then can 

also be used for network modeling. 

5-10 
technological change, conflicting technical claims, 

and budgetary constraints. The following subsection 

presents general methods for evaluating and assessing 

alternatives followed by a set of specific criteria for 

evaluating alternative monitoring systems. 

5.5.1 Remote Monitoring System Evaluation 

In order to justify a remote monitoring system and to 

select the best monitoring system, it would be ideal if 

one could evaluate the benefits derived from monitoring and compare them to costs and choose the system 

that maximizes net benefits subject to budgetary 

constraints. Depending on the uses of the monitoring 

data, monitoring benefits may be associated with: 

•	 reduced risks from an intentional or accidental 

contamination event, 

•	 improved understanding of the variation in 

water quality of a distribution system, 

•	 enhanced operation if the data are used as part 

of a process control system, and 

•	 increased compliance if the information is used 

for regulatory purposes. 

5.6 Monitoring Case Studies 

EPA has conducted research into the use of remote 

monitoring and control technology alternatives for 

many years. These projects have involved both water 

treatment systems and water distribution (Clark et al., 

2004b). The agency’s first research project that 

incorporated real-time monitoring at a remote 

location was conducted at the T&E Facility. The 

initial research was focused on evaluating SCADA 

systems for small drinking water package plants. The 

goal was to demonstrate that SCADA systems could 

be used to monitor and control several small plants 

remotely from a centralized location at one time 

(Haught and Panguluri, 1998). The following case 

studies represent some of the highlights of the 

research and collaboration with different water 

utilities. 

5.6.1 Rural Community Application 

In May 1991, EPA provided funding to support a 

research project titled “Alternative Low Maintenance Technologies for Small Water Systems in 

Rural Communities” (Goodrich et al., 1993). This 

project involved the installation of a small drinking 

water treatment package plant in a rural location in 

West Virginia. The primary objective of this study 

was to evaluate the cost-effectiveness of package 

plant technology in removing and disinfecting 

microbiological contaminants. The secondary 

objectives of this project included: remote monitoring and automation of the system to minimize the 

O&M costs, assessment of the community’s accepA Reference Guide for Utilities 

tance of such a system, ability to pay, and the effect 

of the distribution system on water quality at the 

tap. The following is a brief summary of the overall 

project. 

The treatment system was located in rural Coalwood 

(McDowell County), WV, approximately 12 miles 

from the McDowell County Public Services Division 

office. Prior to 1994, an aerator combined with a slow 

sand filter was being used for water treatment at this 

site. This combined unit had been operational for 

over 30 years and needed substantial repairs. The 

water flowed by gravity from an abandoned coal mine 

to an aerator built over a six-foot-diameter slow sand 

filter. A hypochlorinator provided disinfection of the 

treated water, and the water flowed by gravity through 

the distribution system to the consumer. The volume 

of water from the mine was considered sufficient for 

the small rural community. 

Based on a review of existing technology, EPA 

determined that a packaged ultrafiltration (UF) system 

would be ideally suited for this location. In 1992, a 

UF unit was purchased and installed at this site. In 

1996, EPA developed, installed, and tested a remote 

monitoring system at the site. The system used 

commercially available hardware along with EPAdeveloped software. The software was not userfriendly and the overall cost of ownership was very 

high. Therefore, in 1998, EPA updated the SCADA 

system with a scalable commercially available off-theshelf, user-friendly SCADA system. The total cost 

(including instrumentation, technical support, 

training, and set-up) was approximately $33,000. 

EPA installed similar SCADA systems at Bartley and 

Berwind sites in McDowell County, WV, for remote 

monitoring of water quality. 

5.6.2 Washington D.C. Remote Monitoring 

Network 

Following a number of coliform violations, EPA’s 

Region 3 office directed the Washington D.C. Water 

and Sewer Authority (WASA) to implement a number 

of corrective actions for its water distribution system 

(Clark et al., 1999). Remote monitoring of water 

quality parameters within the distribution system was 

identified as being one possible method for identifying water quality problems. Consequently in 1997, 

EPA initiated a study to install a remote network at 

various locations in Washington D.C. to monitor 

water quality within the distribution system (Meckes 

et al., 1998). The WASA staff teamed with EPA to 

select appropriate locations within the distribution 

system for installation of online sampling stations. 

Following are some of the study objectives: 

•	 development of methods to monitor real-time 

water quality at various locations within the 

5-11 
A Reference Guide for Utilities 

distribution system, 

•	 field evaluation of sensors and remote 

monitoring technologies for inclusion in the 

network, 

•	 development of effective methods to publish 

real-time data that enhanced consumer 

confidence, 

•	 evaluation of costs associated with


implementing such systems, and


•	 identification of potential problems and 

suggestions for remedial actions when 

implementing remote monitoring networks. 

Free chlorine, pH, temperature, and turbidity were 

selected as the monitored parameters based on the 

availability of online sensor technologies. The 

selection was based on the premise that these were 

parameters which could be reliably monitored 

continuously and the selected instruments required 

limited maintenance. Additionally, WASA used their 

SCADA system to track various operating parameters 

within the distribution system. During the evaluation, it was clear that use of the existing SCADA 

system to manage the monitored data provided clear 

advantages over other available systems. Using the 

existing SCADA system minimized long-term on-site 

support costs. 

After suitable location(s) were identified, customized 

sampling and monitoring systems were built. The 

remote monitoring system in Washington D.C. was 

implemented in three phases. In the first phase, a 

remote monitoring system was installed at the Fort 

Reno #2 tank (Figure 5-4), which provided security 

and easy access to the distribution system. Subsequently, based on initial success at this location, two 

other sites (Bryant Street and Blue Plains) were 

selected and added 

to the remote 

monitoring network 

in the second phase. 

The third phase 

involved the 

development of a 

Web-based application to publish the 

real-time data in 

order to enhance 

consumer confidence. 

Figure 5-5 shows the 

relationship between 

the SCADA system 

and the transmission 

of the data. AlFigure 5-4. Fort Reno #2 

Remote Sampling System. 

though WASA’s SCADA system used a proprietary 

operating system, it provided a personal computer 

(PC) link which was used to dump data into a regular 

PC for further processing. The hardware-based feature 

enabled tight security; an authorized end user could 

only copy the relevant data published on the PC and 

could not directly access the SCADA system. This 

feature also eliminated any potential interference 

between the sampling system data and other distribution system operations data. Unfortunately, the EPA 

funding for this study was terminated and, as a result, 

the systems and the Website are currently not operational. The overall project, however, did demonstrate 

that such systems could be developed and operated. 

Figure 5-6 shows some of the output data for the Fort 

Reno tank which indicates the loss of disinfectant 

chlorine levels at night. Clearly, this type of information can be used to improve system operations to 

better maintain the water quality. 

5.6.3 Tucson Water Monitoring Network 

Based on a grant received from the EPA’s Environmental Monitoring for Public Access and Community 

Figure 5-5. WASA Remote Monitoring System Layout and Data 

Transmission Scheme.

5-12 
Figure 5-6. Monitoring Data for Fort Reno Tank. 

Tracking (EMPACT) Program, the city of Tucson 

implemented a comprehensive water quality monitoring program. The city’s EMPACT goals included the 

following: implementing enhanced monitoring of the 

utility’s potable distribution system, providing the 

community with near real-time water quality information on Tucson Water’s Website 

(www.cityoftucson.org/water), and creating community partnerships to better inform water consumers 

about water quality and resource issues. The water 

quality monitoring and data collection tools provided 

through EMPACT also enables the utility to track and 

respond to real-time changes in system water quality. 

Tucson Water’s distribution system consists of one 

central drinking water distribution system that serves 

the majority of the customers and ten isolated 

drinking water distribution systems. All eleven 

drinking water distribution systems cover a service 

area of 300 square miles and serve 680,000 customers 

in the Tucson metropolitan area. The two types of 

source water that supply the central distribution 

system are native groundwater and renewable 

recharged surface water from the Colorado River. The 

source water that supplies the ten isolated distribution 

systems is groundwater. 

For the purposes of monitoring, the central distribution system is divided into ten water quality zones 

and each isolated distribution system is considered an 

individual water quality zone. Figure 5-7 shows the 

zone map. A water quality zone is defined as an area 

of the distribution system that is similar in water 

quality characteristics, water pressure, geographical, 

and political boundaries. Each water quality zone has 

a set number of dedicated sampling stations and 

points-of-entry (POE). The dedicated sampling 

stations monitor the quality of the drinking water in 

the distribution system before delivery to the customer. The POEs are usually individual wells that 

represent the water quality of a single well or in a few 

A Reference Guide for Utilities 

cases, combined POE systems that represent the 

collective blended water quality from a group of wells 

that directly supply Tucson’s drinking water. 

In total, there are 262 dedicated sampling stations and 

approximately 154 active POEs located within the 

multiple distribution systems. In addition, 22 online 

water quality stations (for monitoring: chlorine 

residual, total dissolved solids, pH, and temperature) 

are located throughout the central distribution system 

at strategic locations, such as reservoirs, well sites, 

and booster stations, as one of the primary objectives 

of the EMPACT program. 

Figure 5-7. City of Tucson Water Quality Zone Map. 

Figure 5-8 depicts a continuous water quality 

monitoring station. The monitoring frequency ranges 

from tri-annually (for grab sample locations) to every 

60 seconds (for continuous monitoring stations), 

depending on the location and specific monitoring 

program that is being utilized for that location. 

The comprehensive water quality monitoring program 

encompasses the entire distribution system. Source 

waters are monitored and sampled according to the 

Arizona Department of Water Resources and the 

Figure 5-8. Continuous Water Quality Monitoring Station. 

5-13 
A Reference Guide for Utilities 

Arizona Department of Environmental Quality 

(ADEQ) regulations, while the drinking water is 

monitored according to EPA and ADEQ regulations 

and drinking water standards. Drinking water is also 

evaluated against a set of consumer-established water 

quality goals. Special-purpose samples are taken to 

characterize and track changing trends in water 

quality for both source water and drinking water. All 

data sets are utilized to track and monitor changes in 

water quality to learn the baseline water quality 

operating parameter levels and also to be able to 

identify and react appropriately when a contamination event occurs. Most of the analysis is conducted 

by the utility’s water quality laboratory and all the 

results are tracked through the Water Quality Management Division. 

All 262 dedicated sampling locations are monitored 

at least once each month for total coliform and 

chlorine residual, while 26 other parameters are 

monitored once every three months. Based on the 

water quality measurements collected each month 

from these 262 sampling locations, the trends in water 

quality conditions are determined for each water 

quality zone and for the distribution system as a 

whole. This information can be found on the aforementioned Web site in the Water Quality section 

under Tucson’s Water Quality and Water Quality in 

My Neighborhood links. The water quality information displayed on two interactive maps shows data 

charts and tables for each location that is sampled 

under the Water Quality program. In addition, the 

information provided to all Tucson water customers in 

the annual water quality report or consumer confidence report is based on POE monitoring data. 

5.7 Summary and Conclusions 

Distribution system monitoring is intended to identify 

the spatial and temporal variations in water quality 

that take place in a drinking water system. Monitoring data can be used to satisfy various objectives, 

such as regulatory requirements, security requirements, or process control requirements. The costs of 

implementing such a system can best be justified if 

the resulting data can be used for more than one of the 

aforementioned objectives. 

A monitoring program can implement either routine 

grab sampling or continuous monitoring. A combined approach, utilizing both continuous and grab 

sampling data, may prove to be very effective as the 

basis for a comprehensive system-wide monitoring 

plan. In the past, distribution system online monitors 

were typically housed in a controlled environment 

with sample lines from the distribution system to the 

instrument. This resulted in most instrumentation 

being located at facilities such as tanks and pump 

stations. The instrumentation was sometimes connected to a SCADA system, so that results could be 

communicated to a central office. Recently, some 

instrumentation has been designed for installation in 

manholes or for direct insertion into water distribution system pipes. 

Vulnerability assessments performed by utilities and 

various research studies have identified that water 

distribution systems are vulnerable to intentional or 

accidental contamination. In addition to hardening 

systems to make it more difficult to contaminate a 

system, monitoring as part of a CWS has emerged as a 

logical approach to cope with potential contamination events. Monitors can also be used in a distribution system to provide real-time or near real-time 

information on water quality. The data can then be 

used to control treatment processes at a treatment plant 

or in the water distribution system. However, this type of 

program may not be practical for small systems. 

SCADA is widely used in industrial environments and 

by larger water utilities to control and monitor their 

individual facility operations. However, water 

utilities typically do not use available SCADA 

systems for conventional water quality monitoring. 

Water utilities typically monitor water quality 

parameters by performing grab sampling on a scheduled or random basis that provides a periodic snapshot of the overall system. Current drinking water 

regulations require all public water systems to 

implement water quality monitoring for total coliform 

to ensure that good quality water is provided to 

consumers (EPA, 1996). Since the regulations do not 

clearly specify that real-time monitoring of water 

quality is required, utilities have been reluctant to 

install and operate such devices. 

After the events of 9/11, utilities have become more 

interested in the potential for continuous water 

quality monitoring. SCADA systems can assist in this 

function by constantly monitoring water quality 

within drinking water distribution systems. These 

systems can potentially reduce the risk of security 

related threats or even non-security related threats, 

and detect undesirable water quality changes within a 

system (Meckes et al., 1998). 

Users should evaluate monitoring data appropriately for 

errors and inconsistencies before commencing actions 

based on acquired data. Each component in a monitoring system is a potential source of error. For example, a 

remote monitoring system could have data errors for one 

or more of the following reasons: instrument errors and 

spikes, SCADA data errors related to system failure, 

backfilling due to communication failure, timing errors, 

or missing data. It is important to validate data and 

understand routine changes in water quality due to 

system-specific operations. 

5-14 
A Reference Guide for Utilities 

Monitoring equipment should be chosen appropriately after establishing the monitoring requirements. 

The individual monitor characteristics, costs, and 

amenability to SCADA integration are key to effective 

implementation. Each system should be individually 

examined and engineered for implementation. 

References 

ASCE. Interim Voluntary Guidelines for Designing 

an Online Contaminant Monitoring System. Published by American Society of Civil Engineers, 

Reston, VA. 2004. 

Bahadur, R., W. Samuels, W. M. Grayman, D. Amstutz, 

and J. Pickus. “PipelineNet: A Model for Monitoring 

Introduced Contaminants in a Distribution System.” 

Proceedings, ASCE-EWRI World Water & Environmental Resources Congress, Philadelphia, PA. 2003. 

Berry, J., W. Hart, C. Phillips, and J. Uber. “A General 

Integer-Programming-Based Framework for Sensor 

Placement in Municipal Water Networks.” Proceedings, ASCE-EWRI World Water & Environmental 

Resources Congress, Salt Lake City, UT. 2004. 

Clark R.M., W.M. Grayman, S.G. Buchberger, Y. Lee, 

and D.J. Hartman. “Drinking Water Distribution 

Systems: An Overview” in Water Supply Systems 

Security. Edited by L. W. Mays, McGraw-Hill, NY, pp 

4.1-4.49. 2004a. 

Clark, R.M., S. Panguluri, and R.C. Haught. “Remote 

Monitoring and Network Models: Their Potential For 

Protecting U.S. Water Supplies,” in Water Supply 

Systems Security, edited by L. W. Mays, McGraw-Hill, 

NY, pp 14.1-14.22. 2004b. 

Clark, R.M., G.S. Rizzo, J.A. Belknap, C. Cochrane. 

“Water Quality and the Replacement and Repair of 

Drinking Water Infrastructure: The Washington, DC 

Case Study.” Journal of Water Supply Research and 

Technology - Aqua, 48(3):106-114. 1999. 

EPA. Drinking Water Regulations and Health 

Advisories. Office of Water, EPA 822-B-B-96-002. 

October, 1996 

EPA. “Response Protocol Toolbox.” Overview and 

Modules 1 through 6. Can be downloaded at: http:// 

cfpub.epa.gov/safewater/watersecurity/ 

home.cfm?program_id=8#response_toolbox. 2003­

2004. 

The monitoring case studies presented in this chapter 

demonstrate the manner in which effective monitoring 

systems can be implemented in small, medium, and 

large distribution systems. If the data are used for 

responding to a contamination threat, it is important 

to understand the movement of water in the system. 

Goodrich, J., J. Adams, and B. Lykins, Jr. “Ultrafiltration Membrane Application for Small System.” EPA 

National Risk Management Research Laboratory. 

1993. 

Haught, R. C., and S. Panguluri. “Selection and 

Management of Remote Telemetry Systems for 

Monitoring and Operation of Small Drinking Water 

Treatment Plants.” Proceedings, First International 

Symposium on Safe Drinking Water in Small Systems, 

May 10-13, 1998, Washington, D.C. USA. 1998. 

Hargesheimer, E., O. Conio, and J. Popovicova 

(Editors). Online Monitoring for Drinking Water 

Utilities. AwwaRF – CRS ProAqua. 2002. 

International Life Sciences Institute (ILSI). Early 

Warning Monitoring to Detect Hazardous Events in 

Water Supplies. ILSI Press, Washington D.C. 1999. 

Johansson, P. An Introduction to Modern Welfare 

Economics. Cambridge University Press, Cambridge, 

UK. 1991. 

Kessler, A., A. Ostfeld, and G. Sinai. “Detecting 

Accidental Contaminations in Municipal Water 

Networks.” Journal of Water Resources Planning and 

Management, ASCE. 124(4):192-198. 1998. 

Lee, B., R. Deininger, and R. Clark. “Locating 

Monitoring Stations in Water Distribution Systems.” 

Journal of AWWA, 83(7):60-66. July 1991. 

Meckes, M. C., J. S. Mattingly, G. J. Papadopoulos, M. 

Dosani, and S. Panguluri. “Real Time Water Quality 

Monitoring of a Water Distribution System Using 

Remote Telemetry.” Proceedings, AWWA Distribution 

System Symposium, Austin, Texas. September 20-22, 

1998. 

Ostfeld, A. “Optimal Monitoring Stations Allocations 

for Water Distribution System Security” in Water 

Supply Systems Security, edited by L. W. Mays, 

McGraw-Hill, NY, pp 16.1-16.15. 2004. 

5-15 
A Reference Guide for Utilities 

Ostfeld, A., and E. Salomons. “Optimal Layout of 

Early Warning Detection Stations for Water Distribution Systems Security.” Journal of Water Planning 

and Resources Management, ASCE. 130(5):377-385. 

2004. 

Panguluri, S., R. C. Haught, M. C. Meckes, and M. 

Dosani, 1999. “Remote water quality monitoring of 

drinking water treatment systems.” Proceedings, 

AWWA Water Quality Technology Conference, 

Denver, CO. November 1999. 

Uber, J., F. Shang, M. Ploycarpou, and Z. Wang. 

“Feedback Control of Booster Chlorination Systems.” 

AwwaRF, Denver, CO. 2003. 

Walski, T.M., D.V. Chase, D.A. Savic, W. Grayman, S. 

Beckwith, and E. Koelle. Advanced Water Distribution Modeling and Management. Haestad Press, 

Waterbury, CT. 2003. 

Watson, J., H. J. Greenberg, W. E. Hart. “A MultipleObjective Analysis of Sensor Placement Optimization 

in Water Networks.” Proceedings, ASCE-EWRI World 

Water & Environmental Resources Congress, Salt 

Lake City, UT. 2004. 

5-16

A Reference Guide for Utilities 

Chapter 6 

Geospatial Technology for 

Water Distribution Systems 

Section 6.1 provides a brief summary of the history and development of geospatial data management 

based on information extracted from many sources. This history is included in order to provide a context 

for the current geospatial data management methodologies in use today by utilities. Readers who are 

already familiar with the history may choose to skip Section 6.1 of this chapter. 

Geospatial data identifies the geographic location and 

characteristics of natural or constructed features and 

boundaries on the earth. This information may be 

derived from various sources of data, including 

remote sensing, mapping, and surveying technologies. More simply, geospatial data is any information 

in or on the earth that has a “where” component. This 

can be a house address, a street intersection on a map, 

a pump station with a coordinate location stored in a 

facilities list, or the location of the sampling tap on a 

diagram of a pump station. Thus, every object has a 

geospatial data component based on its location. 

Geospatial data provides a mechanism for incorporating geographic locations of various functions and 

facilities in a distribution systems analysis. The cost 

of incorporating map data into the water distribution 

systems discipline is decreasing, which enables a 

wider audience of users to perform powerful spatial 

analyses over time, such as master plan development, 

pipe break analysis, and locational information on 

sensitive subpopulations (e.g., nursing homes, 

schools). As these tools and datasets become more 

commonly used and shared among engineers, new 

efficiencies will be realized that will have a positive 

impact on water distribution system management. 

Water systems are by nature quite geographically 

extensive and the location of a particular component 

or feature may significantly affect its performance. 

Source watersheds can cover hundreds or thousands of 

square miles. Similarly, distribution systems can 

cover vast areas. The operation of a water system 

entails moving water from one location to another. 

Elevation (Z), the third dimension of location (along 

with the X and Y dimensions of a Cartesian coordinate system), is an important factor in designing and 

operating a water system. This illustrates that the 

management of a water system is inherently a 

geospatial issue. 

Because of the spatial nature of water systems, many 

aspects of managing a water system consist of using, 

managing, and displaying geospatial data. This has 

led to a variety of mechanisms ranging from maps and 

plans to sophisticated, computerized database 

management systems. The following is a list of some 

of the computerized data systems that water utilities 

typically use for managing their spatial data. These 

systems will be discussed in greater detail later in this 

chapter. 

•	 GIS – Geographic Information System. 

•	 CADD – Computer-Aided Design and Drafting. 

•	 AM/FM – Automated Mapping (or Asset


Management)/Facilities Management.


•	 CIS – Customer Information System. 

GIS

CADD GPS

SCADAGIS

CADD GPS

SCADA CIS CIS DEM DEM AM/FM AM/FM

•	 DEM – Digital Elevation Model. 

•	 GPS – Global Positioning System. 

•	 SCADA – Supervisory Control And Data 

Acquisition. 

•	 LIMS – Laboratory Information Management 

System. 

•	 LIS – Land Information System. 

•	 RDBMS – Relational Database Management 

System. 

•	 SDMS – Spatial Data Management System. 

6-1 
A Reference Guide for Utilities 

6.1 History of Geospatial Data 

Management 

Geospatial data is of interest in many professional 

fields and each of these fields has approached the 

issue of using and managing geospatial data in a 

different manner. Examples include the following: 

•	 Cartographers concentrate on making maps. 

•	 Surveyors emphasize accurate capture of 

locational information on natural and manmade 

land features. 

•	 Engineers use spatial data to draw construction 

plans and, more recently, use it as input for 

various types of models. 

•	 Planners use maps and spatial data to assess 

growth and to determine the suitability of land 

to support a particular type of development. 

•	 People in the public works area are concerned 

with managing assets such as streets, sewers, and 

water lines, which all have a spatial component. 

•	 Fields such as the military, engineering, mining, 

and hydrology are interested in topographic 

(elevation) data. 

As a result of these varying interests in “spatially 

arrayed data,” the tools and methodologies for 

managing these data have evolved from many 

directions and recently there has been a significant 

move towards integrating the basic concepts. A brief 

history of geospatial data management is presented 

below, organized by the various disciplines that have 

influenced this field. The needs of the water industry 

in the spatial arena cross each of these areas. Geospatial data management in the water industry will be 

discussed in greater detail in a later section of this 

chapter. 

6.1.1 Mapping, Surveying, and Remote Sensing 

Mapping is the oldest of the geospatial disciplines. 

Examples of maps date back many millenniums. A 

wall painting, dating back to around 6200 B.C. in 

Turkey, depicts the positions of the streets and houses 

of the town together with surrounding features such as 

the volcano close to the town. The Babylonians 

produced clay tablets containing maps that date back 

to around 1000 B.C. Other early maps were prepared 

by the Egyptians and Chinese (O’Connor and 

Robertson, undated). 

Many of the advances in map making are attributed to 

the Greeks. Around 350 B.C., Aristotle argued that 

the earth was a sphere and around 250 B.C., Eratosthenes accurately calculated the circumference of 

the earth. In 140 A.D., Ptolemy’s eight-volume Guide 

to Geography was written and provided the basic 

principles of cartography. It introduced the concept of 

map projections and attempted to map the known 

world, giving coordinates of the major places in a 

system akin to the present day latitude-longitude 

system. This document served as the definitive 

reference on geography for over a thousand years and 

was later translated into Latin and printed in 1475. 

The 16th century saw the introduction of globes and 

many improvements in the mathematical basis of 

cartography. Gerardus Mercator developed a wall 

map of the world in 1569 on 18 separate sheets (see 

Figure 6-1). In the “Mercator projection,” lines of 

longitude, lines of latitude, and rhomb lines all 

appear as straight lines on the map. This projection 

was a great aid to navigators and is still in use today. 

With the basis of cartography well in hand almost 500 

years ago, the cartographic methodologies continued 

to evolve. Additionally, emphasis was placed on 

methods of accurately establishing the coordinates of 

places of interest. This led to the field of surveying 

and, more recently, the field of remote sensing. 

Figure 6-1. Mercator’s Map of the World in 1569 

(Whitfield, 1994). 

“Surveying is the science and art of measuring 

distances and angles on or near the surface of the 

earth. It is an orderly process of acquiring data 

relating to the physical characteristics of the earth and 

in particular the relative position of points and the 

magnitude of areas. Evidence of surveying and 

recorded information exists from five thousand years 

ago in places such as China, India, Babylon, and 

Egypt” (Queensland Government, undated). Some 

key inventions in the area of surveying include the 

following: 

•	 Knotted ropes – Measuring device developed 

by the Egyptians and used in construction of 

the pyramids. 

•	 Levels – Mechanism developed by the 

Egyptians composed of a hanging “plumb bob” 

used to establish a level surface. 

6-2 
A Reference Guide for Utilities 

•	 Magnetic compass – Device for determining 

north direction. Developed by Chinese circa 

200 B.C. and composed of a magnetic 

“lodestone.” Later used by Chinese for 

navigation. 

•	 Theodolite - An instrument, graduated in 360 

degrees, used in the mid-1500s by an 

Englishman, Leonard Digges, to measure 

angles. 

•	 Alidade and Plane table - Sighting mechanism 

and flat table developed in 1590 for mapping 

the surface features of the earth. Attributed to 

Jean Praetorius. 

•	 Quadrant and Sextant - The quadrant is an 

apparatus developed in 1730 by John Hadley 

for measuring angles of celestial bodies. This 

led to the development of the sextant, which is 

a precision instrument made from brass or 

aluminum that is used for ocean navigation 

where celestial observations are taken to plot a 

ship’s position. 

•	 Transit - The transit is used to measure vertical 

and horizontal angles and may also be used for 

leveling; its chief elements are a telescope that 

can be rotated (transited) about a horizontal and 

about a vertical axis, spirit levels, and 

graduated circles supplemented by Vernier 

scales. Attributed to W.J. Young in Philadelphia 

in 1831. 

•	 Electronic Distance Measurement (EDM) – 

Starting in the 1950s, electronic distance 

measuring instruments were developed and 

have now largely replaced traditional methods 

for measuring distance. Horizontal distances 

are measured using a variety of instruments that 

employ a laser beam aimed at a reflector station. 

Low-cost instruments that employ sound waves 

or infrared beams are also available. 

•	 GPS - The use of GPS in surveying procedures is 

the most recent and revolutionary change to 

influence land measurement. GPS was designed 

and built and is operated and maintained by the 

U.S. Department of Defense. Originally called 

the Navstar GPS, it was first brainstormed at the 

Pentagon in 1973. In 1978, the first operational 

GPS satellite was launched; by the mid-1990s, 

the system was fully operational with 24 

satellites. The basic principle behind GPS is the 

measurement of distance between satellites and 

the receiver. The distance to at least 3 satellites 

must be known in order to find out a position. 

Satellites and receivers generate duplicate radio 

signals at exactly the same time. As satellite 

signals travel at the speed of light (186,000 

miles per second), they only take a few 

hundredths of a second to reach the GPS 

receiver. This difference and the speed at which 

the signal travels is used in the equation to find 

out the distance between the GPS receiver and 

the satellite (Radio Shack, 2004). GPS is now 

also being used to provide information on 

elevations. 

Local governments frequently store survey information on parcels in an LIS. This information can 

include property ownership, construction date, land 

assessment, and land taxation. This information may 

be linked to a computerized database system for 

storing the geographic coordinates of the parcels. 

Remote sensing refers to imagery from airplanes or 

satellites. Some early examples of remote sensing 

include: aerial photography from a balloon in 1859 

by Gaspard Felix Tournachon in an attempt to 

conduct a land survey; use of light cameras attached 

to pigeons in Bavaria in 1903 to monitor troop 

positions; photographs of San Francisco following the 

1906 earthquake by George Lawrence from a kite; and 

the work of a photographer who accompanied Wilbur 

Wright on one of his first demonstration flights in 

1909. More serious aerial photography was conducted 

during World War I and II and during the Cold War 

period. 

Aerial photography has become a staple item in the 

development of maps and documentation of land use 

changes. The National Aerial Photography Program 

(NAPP) is an interagency Federal effort coordinated 

by the USGS, which uses NAPP products to revise 

maps. Other agencies have varied uses for these 

photographs, which are taken on a 5- to 7-year cycle 

and produced to rigorous specifications. The NAPP 

effort encompasses the entire lower 48 states and 

Hawaii. The photos are acquired from airplanes flying 

at an altitude of 20,000 feet using a 6-inch focal 

length camera resulting in a scale of 1:40,000. Each 

9-inch by 9-inch photo (without enlargement) covers 

an area of slightly more than 5 miles on a side. The 

NAPP effort began in 1987 and replaced the National 

High Altitude Photography (NHAP) program which 

was initiated in 1980. Strict specifications regarding 

sun angle, cloud cover, minimal haze, stereoscopic 

coverage, and image inspection were followed and all 

NAPP photography is cloud-free (USGS, undated). 

Satellite remote sensing can be traced to the early 

days of the space age (National Aeronautics and 

Space Administration [NASA], undated). On April 1, 

1960, the Television and Infrared Observation 

Satellite (TIROS 1) was launched, which proved that 

satellites could observe Earth’s weather patterns. In 

1966, the Environmental Science Services Adminis6-3 
A Reference Guide for Utilities 

tration (ESSA) Satellites I and II gave the United 

States its first global weather satellite system. In 1972, 

NASA began the Landsat series with the launch of the 

Earth Resources Technology Satellite 1, which was 

later renamed Landsat 1 by NASA. Figure 6-2 

illustrates an image derived from Landsat. Subsequently, U.S. governmental satellites, such as Landsat 

7, are still gathering consistently calibrated imagery 

of the earth under the Earth Observation Satellite 

(EOSAT) program. Satellites of other governments 

(SPOT-France) and private satellites (GE, Digital 

Globe) have expanded the routine availability of 

imagery, and further enhanced the resolution of the 

data collected. 

Figure 6-2. Landsat Thematic MapperTM Images of 

the Missouri River Floodplain Near Glasgow, 

Missouri. (USGS, 1993). 

6.1.2 CADD 

Over the past quarter of a century, CADD has revolutionized the way in which engineers and architects 

perform their work. The basis for CADD was laid by 

Ivan Sutherland’s 1963 Ph.D. thesis at Massachusetts 

Institute of Technology (MIT) titled, “Sketchpad: A 

Man-machine Graphical Communications System” 

(Sutherland, 2003). Sutherland used a lightpen to 

create engineering drawings directly on the Cathode 

Ray Tube (CRT). His thesis laid out virtually all of 

the graphical human interface issues. Sketchpad 

pioneered the concepts of graphical computing, 

The acronyms CAD, CADD, CAM, and CAE refer to 

“computer aided” methodologies used in various fields 

of engineering. CAD can stand for “computer aided 

drafting” or “computer aided design”. CADD can 

mean “computer aided design and drafting” or “computer aided drafting and design”. CAE refers to 

computer aided engineering and CAM refers to 

“computer aided manufacturing”. The fields of CAD, 

CAM and CAE overlap and are frequently lumped into 

a single field of CAD/CAM/CAE. 

including memory structures to store objects, rubberbanding of lines, the ability to zoom in and out on the 

display, and the ability to make perfect lines, corners, 

and joints. This was the first GUI long before the term 

was coined. 

In the late 1960s and early 1970s, several companies 

were founded that developed and commercialized the 

concepts of CADD. In the 1980s, Autodesk (maker of 

AutoCAD) and Bentley Systems (Microstation) were 

founded and led to the wider availability of CADD on 

personal computers. Later in that decade, Parametric 

Technology Corp. produced a 3-dimensional design 

system. 

6.1.3 GIS 

GIS represents computerized systems for the storage, 

retrieval, manipulation, analysis, and display of 

geographically referenced data (Mark, 1997a). 

Though the term GIS was first coined by Roger 

Tomlinson, director of the Canada GIS in the early 

1960s, many of the concepts of GIS lie in the earlier 

fields of mapping and cartography. There are several 

fields and institutions that contributed to the GIS area 

in a non-linear manner over the past 40 years, 

resulting in the very powerful and widespread use of 

GIS today. Figure 6-3 shows the typical inputs and 

results of current GIS packages. 

The development of the Geographic Base File/Dual 

Independent Map Encoding (GBF-DIME) files by 

the U.S. Census Bureau in the 1960s was the first 

large-scale use of digital mapping by the government. This system led to the production of the 

Census Topologically Integrated Geographic 

Encoding and Referencing (TIGER) files. Important 

geographic work was also being done at universities throughout the 1950s and 1960s. A grid-based 

mapping program called Synagraphic Mapping 

(SYMAP), developed at the Laboratory for Computer Graphics and Spatial Analysis at the Harvard 

Graduate School of Design in 1966, was widely 

distributed and served as a model for later systems. 

Output from SYMAP was on a line printer. A 

companion program called SYMVU allowed for 

mapping of topographic and other data using a pen 

plotter. 

6-4 
A Reference Guide for Utilities 

Figure 6-3. Typical Inputs and Results of Current GIS Packages. 

From an application viewpoint, much of GIS modeling technology, as it is used today, is largely an 

outgrowth of planning approaches that are based on 

the work of Ian McHarg, as articulated in his book 

Design with Nature (McHarg, 1969). His manual 

methods involved overlaying a grid on the area to be 

studied, comparing and combining values for 

different types of attributes in a grid cell to determine 

the suitability of each grid cell for various uses. 

Attributes could include characteristics such as land 

slope and soil attributes. 

He demonstrated this process in his book by creating 

maps of different attributes on transparencies with the 

darkness proportional to the degree to which that 

attribute would support a particular use. For example, 

significantly sloping land would be represented as 

dark areas because it is difficult to build under these 

circumstances. Then the reader could physically 

overlay the transparencies and select the lighter areas 

which were most appropriate for development. When 

computerized, this became the basis of the common 

overlay analysis of GIS technology, which served as 

the basic modeling technology of GIS for many years. 

Another source for GIS technology was computerized 

photogrammetry and surveying. In the early 1960s, 

the desire to manipulate spatial data in a computer led 

to such well-known civil engineering programs as 

Coordinated Geometry (COGO), developed at MIT for 

calculating surveying analyses on coordinate data. At 

the same time, the concept of the Digital Terrain 

Model (DTM) was developed, in which the computer 

would be used to store a digital database representing 

the earth’s surface (see the next subsection for more 

details on DTM). 

In the past decade, development within the GIS 

community has been primarily associated with 

commercial enterprises that develop and market GIS 

software. Companies such as ESRI, Smallworld, 

Intergraph, Bentley, MapInfo, and AutoCAD dominate the GIS field today. Early important public 

domain or academic GIS packages such as Geographic 

Resources Analysis Support System (GRASS), 

developed by the U.S. Army Corps of Engineers and 

Clark University, have been largely replaced by the 

commercial software packages. 

6.1.4 DEMs 

DEMs and DTMs refer to representation of ground 

surfaces in a computer. Over the past 40 years, they 

6-5 
A Reference Guide for Utilities 

have been used to support applications such as 

highway design, sewer design, hydrologic analysis, 

mining calculations, and military applications (such 

as part of a guidance system for missiles). Various 

methods utilizing regular/irregular grids and triangulated irregular networks (TIN) (Mark, 1997b) have 

been employed to provide efficient representations of 

surfaces. DEMs have become a regular feature of 

today’s GIS packages. DEM databases are readily 

available for most of the U.S. from USGS and other 

sources. Figure 6-4 depicts a DTM output. 

Figure 6-4. Digital Terrain Model of Mount St. Helens after 

Eruption in 1980 (R. Horne, 2004). 

6.1.5 Database Management Systems 

Spatial data is composed of two forms of information: 

geographic coordinates and attribute information. As 

an example, in order to represent a water main, 

geographic information describes the coordinates of 

the start and end of the pipe and any curves or bends 

in the pipe. Attribute information may include the 

pipe diameter, length, material, age, and other data of 

interest. Typically, attribute data are stored in a 

relational database system that may be part of a GIS or 

an asset management system. The Relational 

Database model for database design was invented by 

Dr. E.F. Cobb in 1969 and published in Computer 

World in 1985. A relational database system is 

composed of a series of tables that are related through 

keywords. This model is considered to be highly 

efficient and minimizes errors. 

6.1.6 Facility Management 

Facilities management (or asset management as it is 

frequently called today) pertains to use of computer 

database and mapping technology to store and 

manage information related to physical assets in a 

water system. In the 1980s, the term AM/FM was used 

to describe the automation of mapping and the 

management of facilities represented on those maps. 

This typically involved the integration of CAD 

technology and database management technology. In 

addition to the water industry, AM/FM was used by 

the electric and gas industry, telecommunications 

industry, and other industries that maintained 

physical networks. In the 1990s, the focus of facilities management both shifted and expanded to 

encompass a broader management of geographic 

spatially arrayed data that are used and maintained by 

the various types of utilities. This shift brought a 

closer interdependence to the GIS field and frequently 

this broader area is now referred to as AM/FM/GIS 

(Cesario, 1995) or even more broadly as Geospatial 

Information Technology. 

6.2 GIS Principles 

Understanding the basic principles behind geographic 

information systems is difficult because of the breadth 

of the field, the rapid change in technology, and the 

lack of standardization for terminology. This section 

provides a general overview of the most significant 

GIS principles. 

6.2.1 GIS Features 

A GIS is composed of a group of objects or features 

that have both a locational description and a description of their characteristics or attributes. For example, 

a water tank can be identified by its location in terms 

of latitude-longitude or other coordinate systems and 

its characteristics, such as diameter, height, and type 

of construction. Similarly, a pipe can be described by 

its route, diameter, length, material, and age. More 

importantly, these attributes can be stored, updated, 

and analyzed in a database over time. 

Geographic features are stored in three general ways: 

vector, raster, or TIN. Though a geographic feature 

can frequently be stored in more than one way (e.g., as 

a vector or as raster), typically there is a preferred way 

to store each piece of information. Under the general 

area of vector representation, features can be stored as 

points, lines, or polygons. Figure 6-5 illustrates the 

three types of features in an example map of a water 

POLYGON 

LINE 

POINT 

Figure 6-5. Map of Pressure Zone Showing Three 

Types of GIS Vector Data. 

6-6 
distribution system pressure zone. Points are geographically represented as a single set of coordinates in two or three dimensions. A line can represent 

either a single, straight-line segment, identified by the 

coordinates of the two end points, or a series of 

connected line segments to represent a curved line. A 

polygon is defined by a closed set of line segments 

and identifies the area contained within the defined 

outer boundary. 

Raster data most commonly refers to a set of data that 

has been defined in terms of a regular square or 

rectangular grid system that is tied to a geographical 

coordinate system. Each grid cell can have one or 

more characteristics assigned to it. As an example of 

a raster data set, Figure 6-6 illustrates land use 

information derived from a satellite that is represented 

as a raster database. Other methodologies for storing 

raster data include scan lines and other regular grid 

cell configurations. 

Figure 6-6. Regional Land Cover Characterization 

as a Raster Database (USGS, 1992). 

The third general type of GIS feature is the TIN 

structure. As the name implies, a TIN structure is 

composed of a series of irregularly-sized triangular 

cells. TIN is most frequently used as a mechanism for 

storing topographical information, though it can also 

be used to store other discrete or continuous spatial 

data fields. The applicability of the TIN structure for 

storing topographical information lies in the simple 

geometric axiom that three points define a plane, as 

illustrated in Figure 6-7. As shown, a continuous 

surface can be represented by a faceted set of triangles 

with the sides of triangles representing topographical 

elements such as streams, ridges, and drainage divides 

(Grayman et al., 1975). 

A Reference Guide for Utilities 

The resolution and accuracy of a TIN database 

generally depends upon the size of the triangles 

relative to the degree of detail in the surface being 

represented. Various mathematical techniques can be 

used to construct a TIN representation with the most 

commonly used method for constructing a TIN from a 

series of points known as Delaunay triangulation 

(named after a Russian mathematician who invented 

the procedure in 1934). 

Within a GIS, features are organized as separate layers 

in a manner analogous to the original concept 

developed by Ian McHarg over 35 years ago. When 

viewing GIS data, layers can be turned on or off or 

moved forward or backward in order to better understand or view the spatial relationships. 

6.2.2 Topology 

An important characteristic of GIS is the concept of 

topology. Topology may be described as the 

locational interrelationship between features. Terms 

such as adjacency, intersection, and connectivity are 

all topological characteristics that describe how 

individual features interact. When we look at a map, 

our eyes and mind construct the topological linkages 

between features. Observations such as the 

Mississippi River forming the boundary between 

Illinois and Missouri, the Monongahela River and 

Allegheny River intersecting to form the Ohio River, 

or that a highway intersection between Interstate 95 

and Interstate 10 is completely contained within the 

state of Florida are all statements of topology. GIS 

Figure 6-7. Triangulation of Elevation (Z) Data. 

6-7 
A Reference Guide for Utilities 

constructs the topological relationship between 

individual features, and this capability is used in 

various analyses and modeling tasks. 

6.2.3 Map Projections, Datum, and Coordinate 

Systems 

Map projections, datum, and coordinate systems 

provide the mechanism for establishing a unique 

geographic location for a point on the earth’s surface. 

As scientists have known for centuries (or even 

millennia), the earth is approximately a spheroid, or 

in reality, an ellipsoid. However, when we are viewing 

maps or utilizing spatial data in a GIS, the earth is 

represented as a planar (flat) system. For maps or 

plans covering smaller areas, the distortion introduced by ignoring the curvature in the earth’s surface 

is generally insignificant. However, for maps and 

plans covering larger areas, this distortion would be 

unacceptable. With the wide availability of regional 

or national GIS databases, the necessity for accurately 

determining coordinates is paramount. 

The mechanism for converting a location on the earth 

surface to a flat surface is performed using a map 

projection. A map projection is a mathematical 

relationship for performing this conversion. There are 

many projections or relationships that can be used to 

make this conversion. Some of the more commonly 

used projections include: 

• State Plane Coordinates (SPC), 

• Universal Transverse Mercator (UTM), 

• Albers Equal Area, 

• Lambert Conformal Conic, and 

• Space Oblique Mercator. 

In each projection, the earth is divided into a series of 

zones. A best-fit, separate, planar coordinate system 

is established for each zone. When examining an area 

that straddles multiple zones, such as use of the SPC 

system with a metropolitan area that is in multiple 

states, coordinate conversions are needed in order to 

view the entire area in a single, consistent coordinate 

system. 

A final important issue in understanding projections 

and coordinate systems is the concept of a datum. 

Because of the complexity of the shape of the earth 

and the inability to exactly describe it mathematically, the earth has been historically modeled by a 

best-fit ellipsoid. The parameters of that ellipsoid are 

defined by key datum points located on or in the 

earth. For many years, the North American Datum, 

developed in 1927 (NAD27) that uses a point on the 

earth’s surface in Meade’s Ranch, Kansas, as an 

anchor, was the major standard. Most projections for 

North America used this datum. 

With improved mathematics and measurements of the 

earth, the North American Datum of 1983 (NAD83) 

was developed with a datum located within the earth. 

This has become the new standard for projections. As 

a result, a point having a particular coordinate using 

the NAD27 datum may be shifted by tens or hundreds 

of feet from a point with the same coordinate using 

the NAD83 datum. If this is not properly accounted 

for in a GIS system, a map that used the NAD27 datum 

would not properly overlay on a map using the 

NAD83 datum. There are several GIS utilities 

available that will properly convert datasets from one 

projection and datum into another, as well as some 

newer GIS programs that re-project datasets with 

different coordinate systems “on-the-fly.” In either 

case, it is the responsibility of the GIS user to know 

the projection and datum associated with each data 

source and to make the appropriate definition. 

6.2.4 GIS Database Design 

GIS concepts and software provide an opportunity 

and a platform for utilizing spatial data. However, in 

order to effectively store, analyze, and display the 

data, they must be arranged in an organized manner. 

Factors that affect database design include: the goals 

of the GIS implementation, the short- and long-term 

plans for the GIS, the type and number of users for the 

particular application, any existing industry-wide 

standards, and other application-specific factors. 

Zeiler (1999) discusses the issues associated with 

database design. Various industry groups are attempting to define generic data structures for a particular 

industry (such as the water utility industry) in order to 

facilitate data transfer and common usage of a GIS in 

that industry (Grise et al., 2000). 

6.2.5 Management of GIS 

In the early days of GIS development, the GIS was 

typically developed, managed, and used by a central 

core of a few people at a governmental or private 

organization. With the growth and acceptance of GIS, 

there are now frequently many GIS stations using a 

specific GIS at an agency, utility or consortium of 

utilities. Management of such a system and controls 

on the manner in which changes in the GIS are made 

are very important issues. 

GIS installations may be classified as a personal (or 

local) system or an enterprise system. In a personal 

system, the GIS is used and managed by an individual 

or a small group. On the other hand, an enterprise 

system may be used by dozens of users distributed 

throughout an agency and many locations. Though 

many of the management issues may be similar in 

these two scales of operation, the enterprise system 

presents a more challenging situation in terms of 

6-8 
A Reference Guide for Utilities 

managing the system. Issues such as personnel (user) 

assignment for changing data or backing up the 

system and interconnectivity between stations and 

users must be carefully spelled out in order to insure 

the integrity of the system. 

A common management model for large systems 

involving multiple users and locations is the combination of a central enterprise GIS with multiple local 

GIS installations. A central core of managers, who 

control any modifications to the database and 

maintain its integrity, maintains the central system. 

Local stations can either access the enterprise system 

on a read-only basis or can download copies of all or 

part of the database for their local use and modification. Specific protocols are then used if changes 

made at the local level are to be incorporated into the 

enterprise system. These protocols may include 

assigned responsibility at the local level to selected 

layers within the enterprise system. This form of 

management is relatively common with a county-wide 

or multi-utility agency managing an enterprise GIS 

and a water utility maintaining responsibility for the 

water system layers within the GIS. 

6.3 Geospatial Data Management 

in the Water Industry 

Because of the spatial extent and nature of water 

supply systems, management of geospatial data is an 

important task. This is accomplished through a series 

of systems under the overall umbrella of SDMS used 

to collect, store, and employ these spatial data. In 

some cases, these various systems are integrated; in 

other cases, they are independent systems. 

6.3.1 CADD 

CADD systems have long served as the basis for 

designing water distribution systems and facilities 

and for managing maps of the water system. Most 

utilities and consulting engineers use commercial 

packages such as AutoCAD, Intergraph or 

MicroStation. The CADD system may be organized 

around a collection of maps or plans with a local 

coordinate system for each plan or may utilize a 

regional coordinate system such as SPC. Many water 

utilities use water distribution system models that are 

integrated with CADD packages. 

6.3.2 GIS 

GIS has made significant inroads in supplementing or 

replacing CADD packages at many water utilities. GIS 

capabilities to store, access and map data are leading 

to increased usage of GIS in areas such as planning, 

facilities management, and management of customer 

and water quality data. Some water utilities share a 

GIS database with other entities, such as city or 

county governments, and other utilities, such as gas, 

electric, and telephone. At many utilities, GIS 

technology has also subsumed the capabilities that 

were formerly classified as AM/FM systems. Similarly, GIS systems may include an LIS as a means of 

storing land property, parcel, and ownership information and geographic descriptions. DEMs are also a 

regular feature in GIS packages. They provide a 

mechanism for storing topographical information. In 

the past few years, integration of GIS with water 

distribution system models has been a significant area 

of research and development in the water industry. 

6.3.3 CIS 

CIS provides a mechanism for storing and using 

information on water consumption by customers. The 

geographic component in a CIS is an address and/or a 

geographic coordinate. AMR systems facilitate 

collection of consumption data that can be stored in 

databases. Standard GIS “address matching” capabilities facilitate conversion of addresses to geographic 

coordinates. A geographically enabled CIS provides 

an excellent mechanism for automatically recording 

current consumption data to be used in water distribution system models. 

6.3.4 SCADA 

SCADA systems typically include capabilities to 

remotely access information on the state of the water 

system, to manually or automatically control components such as pumps and valves, and to store and 

display current or historical time-series data about 

system operation. A wide range of commercial 

SCADA hardware/software systems is available and 

can be tailored to the specific needs of the water 

utility. Each component that is referenced in a SCADA 

system can have a unique geographic identifier that 

can be used as a linkage to a GIS or other spatial data 

management systems. Research and development is 

underway related to integrating SCADA systems and 

hydraulic/water quality distribution system models so 

that these models can be used in real-time operation 

and emergencies. 

6.3.5 LIMS 

LIMS are computerized systems for managing samples 

in a laboratory. Such systems typically include a 

mechanism for storing, managing, displaying, and 

tracking samples. Since the origin of a sample must 

be identified both spatially and temporally, this 

information provides a means of associating LIMS 

data with other spatial database management systems. 

6.3.6 Support Technology 

Other technological advances related to spatial 

database management that are used by water utilities 

include GPS and RDBMS. GPS is a widely used 

technology in surveying and can be used for tagging 

field data with a geographic coordinate. RDBMS is a 

6-9 
A Reference Guide for Utilities 

general methodology for efficiently storing information as a series of related 2-dimensional tables. Most 

modern database management systems associated 

with GIS, LIMS, and other systems utilize the RDBMS 

structure. 

6.4 Integration of Geospatial Data 

Management and Modeling 

The concept of integrating water distribution 

system modeling with geospatial database management systems has been evolving over the past 

quarter of a century and continues to be a major 

focus of development in the water industry today. 

Early water distribution system models were standalone entities. In the very early models, input was 

provided by punch cards and output was in the 

form of printed tabular information. This cumbersome I/O gave way to input via terminals in the 

1980s and GUIs in the 1990s. The 1990s also saw 

the first commercial integration of water distribution system models with CADD followed by 

integration with GIS in the 2000s. 

The basis for integrating water distribution system 

models with geospatial data can be traced back to an 

early study that interfaced a planning level sewer 

design model with a TIN-based GIS called ADAPT 

(Areal Design And Planning Tool) (Grayman et. al., 

1975). This approach was called “geo-based modeling” and was subsequently applied to various other 

water engineering situations such as hydrologic 

modeling (Males and Gates, 1979). In these systems, 

a geo-based network representing sewer lines or 

streams was integrated with GIS elevation, land use, 

and soil data. This network directly interfaced with 

design and simulation models. 

In the 1980s, the same geo-based modeling concept 

was applied to water distribution system analysis 

through a series of EPA research projects. The Water 

Supply Simulation Model (WSSM) integrated a geobased, link-node system to several models including a 

hydraulic model, a steady-state water quality and cost 

allocation model, and various display and editing 

routines (Clark and Males, 1985). Subsequently, 

WSSM was expanded to include an interface to GIS 

files using AutoCAD. USGS digital line graph (DLG) 

files of road networks and DEMs were used within 

AutoCAD to create a detailed representation of the 

water distribution system. The resulting database was 

used to generate an input file for the Wadiso hydraulic model whose engine worked as a prototype for 

EPANET. 

In the past 10 years, commercial vendors of network 

modeling software working in conjunction with 

CADD and GIS vendors have led the integration of 

modeling software and spatial database technology. 

In the mid 1990s, hydraulic/water quality models 

were built to operate within AutoCAD. More recently, 

commercial modeling systems have been released as a 

version that are fully integrated and operate within 

the GIS environment. Commercial products include 

WaterGEMS (Haestad Methods/Bentley Systems) and 

InfoWater (MWHSoft). 

6.4.1 Model Integration Taxonomy 

The term “integration” can refer to a wide range of 

capabilities related to use of network models in 

conjunction with a spatial database system. Shamsi 

(2001) provides a taxonomy of three levels for modeldatabase integration. These are described as interchange, interface, and integration. 

Interchange provides a mechanism for transferring 

data between a spatial database such as GIS and a 

model. With interchange, there is no direct linkage 

between the two systems. Rather, they are run 

separately and information is extracted from one 

system and stored in an intermediate file that is 

subsequently accessed by the other system. In the 

direction of database to model, information stored in 

a GIS is used to generate a complete or partial dataset 

that is used as input to the model. In the other 

direction, output from a model is used as input into a 

GIS in order to display the results of the model 

application. Most commercial water distribution 

modeling packages can interchange data with CADD 

and GIS platforms. 

An interface involves a direct connection between the 

database and the model in order to transfer information in either direction. As is the case in interchange, 

the two systems still operate independently, but in 

this case, there is a direct linkage so that intermediate 

files are not necessary. Protocols and structures must 

be established and compatible within the two systems 

in order to support this interface. Current trends are 

directed towards open architecture in which information on the data structures for models and GIS are 

designed to data structures. For example, H2OMap 

and WaterCAD are standalone software packages 

which can directly interface with data in CADD and 

GIS platforms. 

True integration is the most sophisticated of the three 

methods. Ideally, the two systems work together 

seamlessly as a single entity. In such integration, 

either the model can operate within the spatial 

database software or the spatial database capability 

can be part of the model. For example, WaterCAD and 

H2ONET software packages are integrated and 

operate within AutoCAD. InfoWater and WaterGEMS 

software packages are integrated to operate within 

ArcGIS. 

6-10 
6 links – 3 nodes 

Figure 6-8b. Typical Representation of a Pipe Section in a 

Network Model. 

6.4.2 Issues in Integrating GIS and Water 

Distribution System Models 

As an evolving technology, there are still issues in 

truly integrating GIS technology with water distribution system models. These issues primarily revolve 

around the level of detail required in the two systems 

and the procedures for updating the model and the 

database. 

In most cases, a water utility GIS is used for many 

purposes including mapping, facility management, 

planning, and modeling support. As a result, there 

may be a great deal of detail in the GIS. For example, 

it may include hydrants, shutoff valves, water meters 

and household connections, air release valves, and 

other appurtenances. On the other hand, typically, 

water distribution system models do not explicitly 

include many of these components. This is shown 

graphically in Figure 6-8. In this case, only junction 

nodes and pipes are included in the model representation. As a result, the GIS representation includes 17 

links and 11 nodes, and the model representation 

includes 6 links and 3 nodes. The disparity between 

the two representations can increase by another order 

of magnitude if water meters and customer connections are included in the GIS. Various approaches are 

taken to deal with this situation. 

•	 A very detailed model is constructed that 

includes all of the elements in the GIS. This 

solution can result in a very large model with an 

excessive number of nodes and links. 

•	 The GIS representation goes through a 

consolidation (skeletonization) procedure to 

eliminate unneeded nodes and to aggregate the 

resulting links in order to construct the model 

representation. Though this results in a more 

appropriate model, it adds an intermediate step 

between the GIS and the model. Additionally, 

after the consolidation process, there is no 

longer a one-to-one correspondence between 

GIS and model features. This lack of 

correspondence leads to issues related to storing 

model output in the GIS and updating the model. 

•	 Multiple representations are maintained within 

the GIS for different uses. The detailed 

representation is the complete, base case and 

used for facility management while the 

skeletonized version is used for modeling. This 

approach has the limitation that requires 

changes in information to be made in multiple 

databases. 

•	 The basic link-node network (as used in the 

model) is maintained as the base case in the GIS 

and associates other components (such as 

hydrants) with links rather than structurally 

embedding them in the network. 

A Reference Guide for Utilities 

17 links – 11 nodes 

Figure 6-8a. Typical Representation of a Pipe Section in GIS. 

In any of the options described above, procedures for 

updating the GIS are essential. There are many issues 

associated with GIS updating such as authorization of 

specific users to make changes, nature of the changes 

as permanent or part of a ‘what-if’ modeling scenario, 

and frequency of replication of the two databases if 

separate model and GIS files are maintained. All of 

these should be carefully spelled out prior to designing and implementing a GIS. 

6.5 Use of GIS in Water Utilities ­

Case Studies 

This section presents the potential uses of GIS in the 

water utility industry. Case studies from the Las Vegas 

Valley Water District (LVVWD) and Denver Water are 

presented. 

6.5.1 Use of GIS at LVVWD 

Between 1989 and 2004, Las Vegas grew faster than 

any other metropolitan area in the U.S. As a result, 

LVVWD has more than doubled its service area 

population during this period. In 1989, the service 

area population was 558,000 and in 2004 it rose to 

1,209,000, representing an increase of 651,000 people 

serviced by LVVWD (Jacobsen and Kamojjala, 2005). 

Figure 6-9 is a GIS representation of the LVVWD 

distribution system growth between 1989 and 2004. 

To address a variety of issues related to this rapid 

growth, LVVWD integrated the functions of master 

planning, operational planning, and development 

review by integrating its GIS data with modeling, 

SCADA, and enterprise data (such as CIS, AM/FM and 

LIMS). Figure 6-10 presents the conceptual relationship model of these functions and potential integration benefits (Jacobsen et al., 2005). 

During the process of integration, LVVWD developed 

a one-to-one relationship between the GIS spatial data 

and its network model (Jacobsen and Kamojjala, 

2005). An example of this one-to-one relationship is 

6-11 
A Reference Guide for Utilities 

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shown in Figure 6-11. Depending on the size of the 

network, developing such a relationship and subsequent data integration has both advantages and 

Figure 6-9. LVVWD Distribution System Growth. disadvantages. 

ID 286814 

Figure 6-11. One-to-One Relationship Between GIS and Network Modeling Data. 

6-12 
A Reference Guide for Utilities 

The advantages include ease of search and retrieval 

with other data/applications, and ease of importation, 

development, and maintenance. For a large network 

model, the disadvantages include an increase in the 

runtime of the network model due to the addition of 

detailed components and relatively slow water quality 

simulations. To minimize this, LVVWD has taken an 

“all-pipes capable” approach where the distribution 

system is divided according to existing pressure zones 

and attached to an operational backbone network 

(skeletonized). Each of the zone models can be 

attached seamlessly to the backbone network for 

detailed hydraulic and water quality modeling. 

Examples of how GIS data are used by LVVWD on a 

day-to-day basis are presented below. 

6.5.1.1 Pressure Complaint Resolution 

Once a pressure complaint is received from a customer, the GIS data is searched for parcel and account 

information, together with modeled and measured 

pressure data in the vicinity of the complaint. Figure 

6-12 shows an example search window. Depending on 

the results of the analyses, a crew may be dispatched 

to trace field pressures and abnormal conditions from 

the water supply source to the customer location, and 

to install hydrant pressure recorders to capture 

dynamic variations. Upon retrieval of field information, model and field results are compared to identify 

possible problems. Figures 6-13 and 6-14 present 

examples resulting from this search (Jacobsen and 

Kamojjala, 2005). 

6.5.1.2 Water Main Break Analysis 

During a water main break, it is critical to quickly 

identify the distribution system valves that must be 

closed in order to minimize water loss, potential 

flooding, or possible contamination. Repairs must be 

performed quickly so that service can be resumed. 

Figure 6-15 illustrates the procedure for rapidly 

identifying the valves to be isolated utilizing the GIS 

and modeling tools. A list of affected customers is 

generated for appropriate notification (Figure 6-16). 

An analysis is performed to evaluate the impact on 

existing services. Figure 6-17 shows a comparison of 

pressures after a shutdown and identifies a lowerpressure area after the shutdown. Response to 

emergencies, such as main breaks, can be provided 

quickly and accurately using integrated GIS tools 

(Jacobsen and Kamojjala, 2005). 

6.5.2 Geo-coding for Demand Forecasting and 

Allocation at Denver Water 

Between January 1997 and December 2000, Denver 

Water conducted a treated water study to evaluate the 

transmission, pumping and storage system for 

capacity and the need for new facilities for capital 

planning and operations. Denver Water made 

extensive use of GIS tools for demand forecasting and 

Figure 6-12. Pressure Complaint Resolution – GIS Parcel/Account Search Window. 

6-13 
A Reference Guide for Utilities 

Parcel/Account Information 

Select Hydrant for 

Pressure Measurements 

Figure 6-13. Pressure Complaint Resolution – Parcel and Hydrant Location. 

Modeled and Measured Pressures at Hydrant 

As-Built Pipes in Model 

Figure 6-14. Pressure Complaint Resolution – Model and Field Pressure Comparison. 

6-14 
A Reference Guide for Utilities 

Figure 6-15. Water Main Break Analysis – Valve Isolation. 

Affected Accounts 

Figure 6-16. Water Main Break Analysis – Impacted Customer List. 

6-15 
A Reference Guide for Utilities 

Before Shutdown 

After Shutdown 

Figure 6-17. Water Main Break Analysis – Comparison of Junction Pressures - Before and After Shutdown. 

6-16 
allocation as part of this analysis. Specifically, 

existing demands were spatially allocated using 

address geo-coding (Strasser et al., 2000). 

Denver Water has been a completely metered 

system since 1992. Historic consumption data is 

available from 1993 to the present. Consumption 

data was extracted from the billing system, and 

imported into an MS Access database for use in 

various tasks including the treated water study. 

The extracted data included both customer address 

and customer class information (e.g., single family, 

multi-family, commercial, industrial, and public). 

The customer class information allowed the 

demand information to be aggregated by customer 

class. Because the service area was large (over 250 

square miles), it was important to identify the 

location of the consumption demand points. This 

is where the use of GIS became very important 

(Strasser et al., 2000). 

Denver Water used the “address geo-coding” feature 

available within ArcInfo which allowed for each 

customer or demand point to be identified on a base 

map. Using this process, a dot is placed on the base 

map representing each customer that could be 

positively geo-coded. The match rate was over 93 

percent. Those accounts that could not be geo-coded, 

mostly large accounts representing master meter 

accounts, and wholesale customers, were entered in 

the system manually. Results from the geo-coding 

process for one pressure zone are illustrated in Figure 

6-18. A quality control check was performed on the 

results of this geo-coding process by reconciling 

consumer demands with Denver Water’s annual 

statistical report (Strasser et al., 2000). 

A Reference Guide for Utilities 

6.6 Summary 

Use and management of geospatial data is an important aspect of the design and operation of water 

systems. This can be accomplished through a range of 

systems under the overall umbrella of SDMS utilized 

to collect, store, and use the spatial data. This 

umbrella covers not only the broad topics of GIS and 

CADD that are widely recognized as geospatial data 

systems, but also systems such as SCADA and LIMS 

that have a spatial component associated with all 

data. 

The area of spatial database management is continuing to evolve within the water industry. Just as the 

capabilities of the various individual components 

within the SDMS umbrella continue to expand, the 

integration of the various systems is an active area of 

development. Water distribution system analysis is a 

significant beneficiary of these improvements and 

integration. As a result, models can be built more 

quickly and in greater detail. Information on facilities and demands can be routinely updated. The 

results of a model application can be rapidly displayed and viewed along with other spatial data. The 

prospect of real-time application of models to assist in 

system operation under routine conditions or under 

emergency conditions is getting closer. 

Figure 6-18. GIS Geo-coding - Metered Sales Demand 

Allocation Procedure. 

6-17 
A Reference Guide for Utilities 

References


Clark, R.M., and R.M. Males. “Simulating Cost and 

Quality in Water Distribution.” Journal Water 

Resources Planning and Management, ASCE. 

111(4):454-466. 1985. 

Cesario, A.L. Modeling, Analysis, and Design of 

Water Distribution Systems. AWWA, Denver, CO. 

1995. 

Grayman, W.M., R.M. Males, W.E. Gates, and A.W. 

Hadder. “LandBased Modeling System for Water 

Quality Management Studies.” Journal of Hydraulics, ASCE, HY5. May 1975. 

Grise, S., E. Idolyantes, E. Brinton, B. Booth, and M. 

Zeiler. “Water Utilities ArcGIS Data Models.” ESRI 

Press, Redlands, CA. 2000. 

Horne, R. “Gallery of 3DEM Terrain Images.” http:// 

www.visualizationsoftware.com/3dem/gallery.html. 

2004. 

Jacobsen L., and S. Kamojjala. “Full System Models 

and GIS Integration.” AWWA Annual Conference and 

Exposition, San Francisco, June 2005. 

Jacobsen L., S. Kamojjala, and M. Fang. “Integrating 

Hydraulic and Water Quality Models with Other 

Utility Systems: A Case Study.” AWWA Information 

Management and Technology Conference, Denver, 

April 2005. 

Males, R.M., and W.E. Gates. “ADAPT: A Digital 

Terrain Model-Based Geographic Information 

System.” Symposium, Laboratory for Computer 

Graphics & Spatial Analysis, Harvard University, 

Cambridge, MA. 1979. 

Mark, D.M., N. Chrisman, A. U. Frank, P. H. McHaffie, 

and J. Pickles. “The GIS History Project.” UCGIS 

Summer Assembly, Bar Harbor, ME. http://www. 

geog.buffalo.edu/ncgia/gishist/bar_harbor.html. 

1997a 

Mark, D.M. “The History of Geographic Information 

Systems: Invention and Re-Invention of Triangulated 

Irregular Networks (TINs).” Proceedings, GIS/LIS ’97. 

http://www.geog.buffalo.edu/ncgia/gishist/ 

GISLIS97.html. 1997b. 

McHarg, I.L. Design with Nature. Published for the 

American Museum of Natural History by the Natural 

History Press. 1969. 

NASA. “Earth Observatory – Remote Sensing.” http:// 

earthobservatory.nasa.gov/Library/RemoteSensing/ 

remote.html. Undated. 

O’Connor, J.J., and E. F. Robertson. “The History of 

Maps.” http://geogdata.csun.edu/geogcourses/ 

history_of_maps.html. Undated. 

Queensland Government. “Maps and Compasses – 

The History of Surveying.” http://education.qld. 

gov.au/curriculum/area/maths/compass/html/surveying/suhis.html. Undated. 

Radio Shack. “A Guide to the Global Positioning 

System (GPS).” http://support.radioshack.com/ 

support_tutorials/gps/gps_main.htm. 2004. 

Shamsi, U.M. “GIS and Modeling Integration.” CE 

News, 13(6). 2001. 

Strasser, A., N. Diallo, and E.J. Koval. “Development 

& Calibration of Denver Water’s Hydraulic Models for 

a Treated Water Study.” AWWA National Conference, 

Denver, Colorado, 2000. 

Sutherland, I.E. “Sketchpad: A Man-machine 

Graphical Communications System.” Reprinted 

version of original 1963 thesis issued as a report by 

Cambridge University. UCAM-CL-TR574. Cambridge, UK. http://www.cl.cam.ac.uk/TechReports/ 

UCAM-CL-TR-574.pdf . 2003. 

USGS. “National Aerial Photography Program 

(NAPP)”. http://edc.usgs.gov/guides/napp.html. 

Undated. 

USGS. “Scientific Assessment and Strategy Team 

(SAST) Study of the Moberly Quadrangle.” http:// 

edc.usgs.gov/sast/moberly.html. 1993. 

USGS. “National Land Cover Characterization 

(NLCD) Database Program.” http://landcover.usgs. 

gov/images/glensfalls_web.jpg. 1992. 

Whitfield, P. The Image of the World: 20 Centuries of 

World Maps. Pomegranate Artbooks, San Francisco, 

CA, 144 p. 1994. 

Zeiler, M. Modeling Our World. ESRI Press, 

Redlands, CA. 1999. 

6-18 
A Reference Guide for Utilities 

Chapter 7 

Real-World Applications – 

Planning, Analysis, and Modeling Case Studies 

The previous chapters of this reference guide showcased several tools for analyzing water quality in 

drinking water distribution systems. Some of these 

chapters also have relevant case studies that relate to 

the individual topic of discussion for that chapter. 

This chapter focuses on the broader application of 

multiple tools to analyze real-world situations. The 

types of applications presented here include: reconstruction of historical contamination events, analysis 

of waterborne outbreaks of infectious diseases, 

regulatory compliance, monitoring systems location, 

and security. Each real-world application is presented 

as an individual case study and the portions related to 

water quality and analysis have been highlighted 

along with some specifics on techniques used in the 

analysis. 

7.1 Analysis of Waterborne 

Outbreak – Gideon, Missouri 

This case study is focused on evaluating the distribution of microbiologically contaminated water in a 

distribution system. The supporting investigations 

for this case study were primarily sponsored by the 

EPA, CDC, and the State of Missouri. 

Key Phrases to Characterize Case Study: water 

borne outbreak analysis, salmonella, tank contamination, hydraulic and water quality modeling, exposure 

modeling, contamination assessment, and flushing. 

7.1.1 Gideon Case Study Overview 

From November 1993 through January 1994, the 

Missouri Department of Health (MDOH) had identified 31 cases of laboratory-confirmed salmonellosis 

infections associated with a waterborne outbreak in 

Gideon, Missouri (Clark et al., 1996). The State 

Public Health Laboratories identified 21 of these 

isolates as dulcitol negative Salmonella serovar 

Typhimurium. Salmonella is a pathogenic bacterium 

that has been classified into several serotypes 

(common set of antigens). Salmonella serovar 

Typhimurium is among the most common Salmonella 

serovars causing salmonellosis in the U. S. Fifteen of 

the 31 laboratory culture-confirmed patients were 

hospitalized (including two patients hospitalized for 

other causes and who developed diarrhea while in the 

hospital). These 15 patients were admitted to 10 

different hospitals. Seven nursing home residents 

exhibiting diarrheal illness died; four of these 

patients were culture confirmed (the other three were 

not cultured). Two of the patients had positive blood 

cultures. Interviews conducted by the MDOH during 

this period suggested that there were no food exposures common to a majority of the patients. However, 

all of the ill persons, including the culture-confirmed 

patients, had consumed municipal water which 

supported the association. The MDOH reported their 

suspicion to the Missouri Department of Natural 

Resources (MDNR). 

7.1.2 The Gideon Water System Setup 

The Gideon municipal water system was originally 

constructed in the mid-1930s and obtained water from 

two adjacent, 1,300 ft deep wells. The well waters 

were not disinfected at the time of the outbreak. After 

the outbreak emergency, chlorination was initiated, 

and later a permanent chlorination system was 

installed. The distribution system consisted primarily 

of small-diameter (2-, 4-, and 6-inch) unlined, steel 

and cast iron pipe. Tuberculation and corrosion were 

major problems in the distribution pipes. Raw water 

temperatures were unusually high for a groundwater 

supply system (58°F), because the system overlies a 

geologically active fault. Under low flow or static 

conditions, the water pressure was close to 50 psi. 

However, under high flow or flushing conditions the 

pressure dropped dramatically. These sharp pressure 

drops were evidence of major problems in the Gideon 

distribution system. The municipal system had two 

elevated tanks. One tank was a 50,000 gallon (gal) 

tank (referred to as small tank) and the other was a 

100,000 gal tank (referred to as large tank). 

Initially, another 100,000 gal privately owned tank 

was suspected to be the cause of the outbreak (as it 

was in a state of disrepair) and connected to the city 

water system. However, subsequent investigations 

revealed that this private tank was connected via a 

backflow prevention valve to the city water system 

that was later confirmed to be functional. Furthermore, the Salmonella found in a sample collected at a 

hydrant matched the serovar of the patient isolate 

when analyzed by the CDC laboratory (comparing 

deoxyribonucleic acid [DNA] fragments using pulse 

field gel electrophoresis). Although the samples from 

the private tank sediment also contained Salmonella 

serovar Typhimurium dulcitol negative organisms, the 

isolate did not provide an exact DNA match with the 

other two isolates. No Salmonella isolates were found 

elsewhere in the system. Therefore, the subsequent 

EPA field investigations and modeling efforts focused 

on the two municipal tanks as the source of contamination. 

7-1 
A Reference Guide for Utilities 

7.1.3 EPA Field Study 

On January 14, 1994, an EPA field team, in conjunction with the CDC and the State of Missouri, initiated 

a field investigation that included a sanitary survey 

and microbiological analyses of samples collected on 

site. A system evaluation was also conducted in 

which EPANET was used to develop various scenarios 

to explain possible contaminant transport in the 

Gideon system. Prior to the Gideon outbreak, a 

similar waterborne disease outbreak in Cabool, 

Missouri, and subsequent advancements in water 

quality modeling firmly established the use of water 

quality models to analyze such events. 

The key analysis was focused on a flushing program 

conducted earlier by the utility in response to taste 

and odor complaints. A sequential flushing program 

was conducted on November 10, 1993, involving all 

50 hydrants in the system. The flushing program was 

started in the morning and continued through the 

entire day. Each hydrant was flushed for 15 minutes 

at an approximate rate of 750 gallons per minute 

(gpm). It was observed that the pump at one of the 

wells was operating at full capacity during the 

flushing program (approximately 12 hours), which 

would indicate that the municipal tanks were discharging during this period. 

During the evaluation, it was hypothesized that the 

taste and odor problems may have resulted from a 

thermal inversion that had taken place due to a sharp 

temperature drop prior to the day of the complaint. If 

stagnant or contaminated water were floating on the 

top of a tank, a thermal inversion could have caused 

this water to be mixed throughout the tank and to be 

discharged into the system resulting in taste and odor 

complaints (Fennel et al., 1974). As a consequence, 

the utility initiated the aforementioned city-wide 

flushing program. Turbulence in the tank from the 

flushing program could have stirred up the tank 

sediments that were subsequently transported into the 

distribution system. It is likely that the bulk water 

and/or the sediments were contaminated with Salmonella serovar Typhimurium. During the EPA field 

visit, a large number of pigeons (bird droppings are 

known to contain Salmonella) were observed roosting 

on the roof of the 100,000 gal municipal tank. 

7.1.4 Distribution System Evaluation 

The EPA study team evaluated the effects of distribution system design and operation, demand, and 

hydraulic characteristics on the possible propagation 

of contaminants in the system. Given the evidence 

from the lab samples and the results from the valve 

inspection of the private tank, it was concluded that 

the most likely contamination source was bird 

droppings in the large municipal tank. Therefore, the 

analysis concentrated on propagation of water from 

In 1991, a joint workshop sponsored by the EPA and 

AwwaRF recommended the application of water quality 

modeling techniques to evaluate waterborne disease 

outbreaks. The first opportunity to attempt this type of 

application arose as a result of an outbreak that occurred 

between December 15, 1989, and January 20, 1990, in 

Cabool, Missouri, population 2,090 (Geldreich et al., 

1992). During the outbreak, residents and visitors to 

Cabool experienced 243 cases of diarrhea (85 bloody) 

and six deaths. The illness and deaths were attributed to 

the pathogenic agent E. coli. serotype O157:H7. At the 

time of the outbreak, the water source was untreated 

groundwater. Shortly after the outbreak was identified, 

EPA was invited to send a team to conduct a research 

study with the goal of determining the underlying cause 

of the outbreak. 

Exceptionally cold weather prior to the outbreak 

contributed to two major water system line breaks and 43 

water meter replacements throughout the city area. The 

sewage collection lines in Cabool were located (for the 

most part) away from the drinking water distribution 

lines but did cross or were near to water lines in several 

locations. At the time of the outbreak, stormwater 

drained via open ditches along the sides of the streets 

and roads. During heavy rainfalls, sewage was observed 

to overflow manhole covers, and to overflow streets in 

several locations, parking lots and residential foundations. 

The Dynamic Water Quality Model (DWQM), developed 

by EPA, was applied to examine the movement of water 

and contaminants in the system (Grayman et al., 1988). 

Steady-state scenarios were examined, and a dynamic 

analysis of the movement of water and contaminants 

associated with meter replacement and the line breaks 

was conducted. Typical demand patterns were developed 

from available meter usage for each service connection, 

and it was found that the water demand was 65 percent of 

the average well production, indicating inaccurate 

meters, un-metered uses, and a high water loss in the 

system. 

The modeling effort revealed the pattern of illness 

occurrence was consistent with water movement patterns 

in the distribution system assuming two water line 

breaks. It was concluded that some disturbance in the 

system, possibly the two line breaks or 43 meter replacements, allowed contamination to enter the water system. 

Analysis showed the simulated contaminant movement 

covered 85 percent of the infected population. 

The application of DWQM proved to be a vital step in 

completing the analysis of the outbreak. The next 

opportunity to apply water modeling techniques occurred in 1994 as a result of a waterborne outbreak in 

Gideon, Missouri (Clark et al., 1996). In the intervening 

period, EPA had developed EPANET and Gideon 

provided an opportunity to test its application. 

7-2 
the large municipal tank in conjunction with the 

flushing program. Other possible sources of contamination, such as cross connections were also studied. 

The system layout, demand information, pump 

characteristic curves, tank geometry, flushing program, and other information needed for the modeling 

effort were obtained from maps and demographic 

information and numerous discussions with consulting engineers and city and MDNR officials. EPANET 

was used to conduct the contaminant propagation 

study (Rossman et al., 1994). 

The EPANET network model was calibrated by 

simulating flushing at the hydrants assuming a 

discharge of 750 gpm for 15 minutes. The “C” factors 

(pipe roughness – see Chapter 4) were adjusted until 

the head loss in the model matched head losses 

observed in the field. After the calibration, the 

hydraulic model was simulated for 48 hours. Thereafter, the flushing program was simulated starting at 8 a.m. 

on day 3, by sequentially imposing a 750 gpm 

demand on each hydrant for 15 minutes. Utilizing the 

TRACE option in EPANET, the percentages of water 

from both municipal tanks were calculated at each 

node over a period of 72 hours. 

During the simulation of the flushing program, the 

pump at one of the wells was operated (as previously observed) at full capacity, which was over 

800 gpm, and then reverted to cyclic operation. 

The simulation results showed that the tank 

elevation fluctuated for both municipal tanks, and 

both the tanks discharged during the flushing 

program. At the end of the flushing period, nearly 

25 percent of the water from the large municipal 

tank passed through the small municipal tank 

where it was again discharged into the system. The 

model predicted dramatic pressure drops during the 

flushing program. Based on the information 

available, it was felt that these modeling results 

replicated the conditions that existed during the 

flushing program closely enough to provide a basis 

for an analysis of water movement in the system. 

Data from the simulation study, the microbiological 

surveillance data, and the outbreak data were utilized 

to provide insight into the nature of both general 

contamination problems in the system and the 

outbreak itself. The water movement patterns showed 

the majority of the collected samples that were total 

coliform and fecal coliform (FC)-positive occurred at 

points within the zone of influence of the small and 

large tanks. During both the flushing program and for 

large parts of normal operation, these areas were 

predominately served by tank water, which confirmed 

the belief that the tanks are the source of the fecal 

contamination since there were positive FC samples 

A Reference Guide for Utilities 

prior to chlorination. Figure 7-1 shows the comparison of early confirmed cases of Salmonella positive 

sample versus the estimated distribution of tank water 

during the first six hours of the flushing program. 

Figure 7-1. Comparison of Early Confirmed Cases of 

Salmonella Positive Sample Versus the Estimated 

Distribution of Tank Water During the First 6 Hours 

of the Flushing Program. 

20% or more of Small Tank water 

20% or more of Large Tank water 

Gideon Schools – reflects increase in absentee level 

Hydrant with confirmed Salmonella 

Residences with confirmed case 

Homes called as part of CDC survey 

7.1.5 Case Study Summary and Conclusions 

Data from the CDC survey of the outbreak, in combination with the EPANET simulated water movement, 

were utilized to establish the possible source of 

contamination. An overlay of the CDC data on the 

water movement simulations showed that the areas 

served by the small and large tanks (during the first 

six hours of the flushing period) coincided with the 

earliest recorded infectious cases. Furthermore, the 

earliest recorded cases and the positive Salmonella 

hydrant sample were found in the area that was 

primarily served by the large tank, but outside the 

small tank’s area of influence. 

The investigators concluded that during the first six 

hours of the flushing period, the water that reached an 

infected resident and the Gideon School (the earliest 

reported infections) was almost totally from the large 

tank. Based on the results of the study, it appeared 

that the contamination had been occurring over a 

7-3 
A Reference Guide for Utilities 

period of time, which is consistent with the possibility of bird contamination. It is likely that the contaminant was pulled through the system during the 

flushing program. The application of EPANET to the 

outbreak proved to be a vital part of the study. 

7.2 Reconstructing Historical 

Contamination Events - Dover 

Township (Toms River), NJ 

This case study is focused on evaluating the distribution of chemically contaminated source water in a 

distribution system. The supporting investigations 

for this case study were primarily sponsored by the 

ATSDR. The investigations involved several other 

organizations. The major contributors included the 

New Jersey Department of Health and Senior Services 

(NJDHSS), the Multimedia Environmental Simulations Laboratory at the Georgia Institute of Technology, EPA’s National Risk Management Research 

Laboratory, and the U.S. Geological Survey. 

Key Phrases to Characterize Case Study: historical 

reconstruction, hydraulic and water quality modeling, 

exposure modeling, contamination assessment, source 

tracing, source contribution, model calibration, 

sensitivity analysis, genetic algorithm. 

7.2.1 Case Study Overview 

In August 1995, responding to an evaluation requested by the ATSDR, the New Jersey Department of 

Health (now NJDHSS) determined that the childhood 

cancer incidence rate in Dover Township (and the 

Toms River section) was higher than expected for all 

malignant cancers combined (brain and central 

nervous system cancer, and leukemia, Berry, 1995). In 

March 1996, NJDHSS and ATSDR developed a Public 

Health Response Plan (PHRP) describing actions 

these agencies would take to investigate the unexpected increase in childhood cancers and environmental concerns in Dover Township (NJDHSS and 

ATSDR, 1996). The PHRP included a list of several 

evaluations. One of the key evaluations was to 

identify potential environmental exposure pathways 

relative to two National Priorities List (NPL) sites in 

Dover Township (Figure 7-2) – Ciba-Geigy and Reich 

Farm. Figure 7-2 also shows the two public water 

supply well fields (Parkway and Holly) that were 

identified as potential routes of exposure. These well 

fields are not only located in the vicinity of the 

aforementioned NPL sites, but are also in areas where 

the statistically higher childhood cancer rates were 

established. 

The ensuing evaluations revealed the presence of a 

previously unidentified compound, styrene acrylonitrile (SAN), in the groundwater from the Parkway wellfield that could be traced to the Reich Farm NPL site. 

Figure 7-2. Investigation Area, Dover Township, Ocean 

County, NJ (modified from Maslia et al., 2001). 

Similarly, a search of historical records revealed 

contamination (primarily semivolatile organics 

[SVOCs]) of the Holly well fields that could be 

traced to the Ciba-Geigy NPL site. Furthermore, 

one of the hypotheses for the epidemiologic casecontrol study was that the higher cancer incident 

rate was related to the higher exposure to public 

water supplies with documented contamination (the 

Parkway and Holly well fields). To assist NJDHSS 

with the contaminated drinking water exposure 

assessment component of the epidemiologic study, 

ATSDR developed a water distribution model for 

the study area using the EPANET software. This 

network model was used to simulate historical 

characteristics of the water distribution system 

serving Dover Township from 1962–1996. Because 

there was a lack of historical contaminant-specific 

data during most of the period relevant to the 

epidemiologic study, the modeling effort focused 

on estimating the percentage of water that a study 

subject might have received from each well that 

supplied water to the impacted area. The following 

subsections present a brief overview of the water 

distribution modeling effort (both hydraulic and 

water quality) followed by a summary of findings 

and conclusions. 

7-4 
Prior to the ATSDR’s analysis of well field contamination in Dover Township and the potential linkages to 

childhood diseases, another study in Woburn, Massachusetts, heralded the era of such analyses. Though 

both the model and graphical presentations are primitive by today’s standards, they were effective in providing a quantitative basis for assessing the spread of 

contaminants in the distribution system. The following 

is a brief description of the Woburn analysis. 

In May 1979, the Massachusetts Department of Environmental Quality Engineering discovered that two wells 

(Wells G & H in the B Zone – See Figure 7-3) in 

Woburn, Massachusetts were contaminated with toxic 

chemicals. Subsequent analysis showed that parts of the 

city experienced elevated levels of childhood leukemia 

and other illnesses attributed to drinking water derived 

from these wells. This event resulted in legal action, a 

diverse set of scientific studies that are still ongoing, 

and the publication of a book entitled A Civil Action 

(Harr, 1995). Early steady-state distribution system 

hydraulic and water quality models were also used as a 

means to track the movement of the contaminated water 

in the distribution system under a range of operating 

and demand conditions (Murphy, 1986). The accompanying figure is one example of a plot resulting from this 

early modeling effort. As shown in Figure 7-3, based on 

the modeling, the city was divided into three zones for 

each scenario – the A zone that received no water from 

the contaminated wells, the B zone that received all of 

its water from the contaminated wells, and the C zone 

that received some of its water from the contaminated 

wells. 

Figure 7-3. Distribution System Zones – Woburn, MA 

(May 1969). 

A Reference Guide for Utilities 

7.2.2 Overall Modeling Approach 

Because of the lack of historical hydraulic and water 

quality information, the water distribution system was 

characterized using data gathered during an extensive 

field investigation in 1998. The 1998 field investigation consisted of two components: (1) determining 

spatial locations of distribution system facilities 

(wells, tanks, pump, and hydrants) and (2) equipping 

hydrants with continuous-recording digital data 

loggers and monitoring supply sources (wells, pumps, 

and tanks) to measure system responses during winter 

demand (March) and summer demand (August) 

periods. Twenty-five hydrants located throughout the 

distribution system were equipped with data loggers 

to simultaneously collect information on system 

response (Maslia et al., 2000). The collected response 

data included on-off cycling of groundwater wells, 

high service and booster pump operations, pressure 

variations, storage tank water-level fluctuations, and 

total production. 

A detailed “all-pipe” hydraulic network model was 

developed and calibrated to present-day conditions 

(1998) using the field investigation results. The 

reliability of the calibrated model was successfully 

demonstrated through a water quality simulation of 

the transport of a naturally occurring conservative 

element (barium) and a comparison of the results with 

data collected in March and April 1996 at 21 schools 

and 6 points of entry to the water distribution system. 

Thereafter, to describe the historical distribution 

system networks specific to the Dover Township area, 

databases were developed from diverse sources of 

information. These data sources included water utility 

pipeline installation records, quarterly billing records, 

NJDHSS groundwater well records, and annual water 

utility reports to the state board of public utilities. 

These data were applied to EPANET and simulations 

were conducted for each month of the historical 

period—January 1962 through December 1996 (420 

simulations). After completing those 35-year/420­

month analyses, source-trace analysis simulations 

were conducted to determine the percentage of water 

contributed by each well or well field operating 

during each month for all study subject locations. 

A review of the historical network configuration 

revealed that the water distribution system complexity increased significantly during this period. The 

model inputs were appropriately adjusted to account 

for these historical changes. For example, the 1962 

water distribution system was represented with an 

approximate peak production of 1.3 million 

gallons per day (MGD) produced from three wells that 

served nearly 4,300 customers (population ~17,200). 

By contrast, in 1996, the water distribution system 

had an approximate peak production of 13.9 MGD 

produced from 20 wells that served nearly 44,000 

7-5 
A Reference Guide for Utilities 

customers (population ~ 89,300). Appropriate 

adjustments were made to modeled pipe segments, 

storage reservoirs, and operational details. Grayman et 

al. (2004) present a more detailed 

account of the EPANET model 

input adjustments. Production data for the 420­

month historical 

period is graphically represented 

in Figure 7-4. 

Figure 7-4. Three-Dimensional Representation of Monthly 

Production of Water, Dover Township Area, NJ (from 

Maslia et al., 2001). 

To perform an extended period simulation (EPS) of 

the distribution of water for each of the 420 months of 

the historical period, information was required on 

network configuration, demand, and operational data. 

However, operational data prior to 1978 were unavailable, requiring the development of system operation 

parameters—designated as “master operating criteria 

(MOC).” The MOC is based on hydraulic engineering 

principles necessary to successfully operate distribution systems similar to the one serving the Dover 

Township area (Table 7-1). From 1978 forward, for 

selected years, operators of the water utility provided 

information on the generalized operating practices for 

a typical peak-demand (summer) and non-peak 

demand (fall) day. These guidelines were used in 

conjunction with the MOC to simulate a typical 24­

hour daily operation of the water distribution system 

for each month of the historical period. 

The model parameter of interest from the epidemiologic study perspective was the proportionate 

contribution of water from wells and well fields to 

locations throughout the historical pipeline networks. 

Thus, the distribution of water delivered to pipeline 

locations was the item of interest rather than the 

specific operations of the wells, storage tanks, and 

pumps (WSTP) that delivered the water. Normally, 

detailed WSTP operational inputs would be required 

for EPANET simulation. However, to simplify the 

simulation methodology and reduce data requirements, a “supply-node-link” (SNL) method of 

idealizing the WSTP combination was developed. In 

the SNL simulation method, an equivalent amount of 

water is supplied to the distribution system (based on 

estimated monthly demands and the typical daily 

operation of the systems). To demonstrate that the 

surrogate SNL simulation method supplies the 

distribution system with an equivalent amount of 

water when compared to the real-world WSTP 

simulation method, both simulation methods were 

applied to the present-day (1998) water distribution 

system for conditions existing in August 1998. The 

results obtained from these simulations produced 

nearly identical flows in the modeled system. 

7.2.3 Simulation Techniques 

Using the EPANET network model developed for the 

Dover Township area, hydraulic modeling was 

conducted whereby average network conditions were 

simulated for every month of the historical period 

Table 7-1. Master Operating Criteria Used to Develop 

Operating Schedules for the Historical Water Distribution 

System, Dover Township Area, NJ (from Maslia et al., 

2001) 

Parameter Criteria 

Pressure1 Minimum of 15 psi; maximum of 110 

psi at pipeline locations, including 

network end points 

Water level Minimum of 3 ft above bottom 

elevation of tank; maximum equal to 

elevation of top of tank; ending 

water level should equal the starting 

water level 

Hydraulic device 

on-line date 

June 1 of year installed to meet 

maximum-demand conditions 

On-and-off cycling: 

Manual operation 

Wells and high-service and booster 

pumps cannot be cycled on-and-off 

from 2200 to 0600 hours 

On-and-off cycling: 

Automatic 

operation 

Wells and high-service and booster 

pumps can be cycled on-and-off at 

any hour 

Operating hours Wells should be operated 

continuously for the total number of 

production hours, based on 

production data2 

1

Generally, for residential demand, minimum recommended 

pressure is about 20 psi. However, for some locations in 

the Dover Township area (mostly in areas near the end of 

distribution lines), lower pressures were simulated. 

2

See Maslia et al. (2001) for production data (Appendix B) 

and hours of operation (Appendix D) 

7-6 
(420 simulations). These simulations were completed 

under balanced flow conditions that utilized hydraulic engineering principles and conformed to the MOC 

(Table 7-1). Thereafter, using the results of the 

monthly network hydraulic simulations, water quality 

simulations (source-trace analysis) were conducted for 

each water source (point of entry) of the network in 

order to determine the monthly proportionate 

contribution of source water at all locations in the 

Dover Township area serviced by the water distribution system. 

EPANET is a dynamic water quality model that has 

the ability to compute the percentage of water 

reaching any point in the distribution system over 

time from a specified location (source) in the 

network. To estimate this proportionate contribution of water, a source location is assigned a value 

of 100 percent. The resulting solution provided by 

the water quality simulator in EPANET then 

becomes the percentage of flow at any location in 

the distribution system network (for example, a 

demand node) contributed by the source location of 

interest. For the purposes of this analysis, a sourcetrace analysis was conducted for every month of the 

historical period. Source nodes were assigned a 

value of 100 percent in order to estimate the 

proportionate contribution of water to locations in 

the historical distribution system networks. These 

initial conditions were fully propagated through 

most of the distribution system before retrieving 

the proportionate contribution results (Maslia et al., 

2000). Accordingly, for each monthly historical 

network model, 24-hour demand and operational 

patterns were defined and these patterns were 

repeated for approximately 1,200 hours to reach a 

state of stationary water-quality dynamics (dynamic 

equilibrium). For most of the analyses, hydraulic 

time steps of 1 hour and water-quality time steps of 

5 minutes were used within EPANET. For some 

monthly simulations, the water-quality time steps 

were reduced to 1 minute to ensure that the mass 

balance summed to ~100 percent (range of 98 to 

101 percent due to numerical approximations). 

With respect to the scheduling of groundwater well 

operations, the EPANET model was set to utilize 

pattern factors corresponding to the hourly operations 

of supply wells. These pattern factors along with the 

operational extremes of storage tank water levels were 

manually adjusted during each of the 420 monthly 

network simulations to achieve balanced flow 

conditions. This approach to simulation was designated as the manual adjustment process. A second 

simulation approach designated as the genetic 

algorithm (GA) approach was also utilized to achieve 

balanced flow conditions for each of the 420 monthly 

networks of the historical period. This approach 

A Reference Guide for Utilities 

required the development of an innovative methodology known as the progressive optimality genetic 

algorithm (POGA), which is an automated objective 

simulation technique (Aral et al., 2004a, b). The GA 

simulations utilized the balanced flow conditions 

obtained by the manual adjustment process as starting 

conditions. The GA technique was used to address 

the following key questions: 

•	 If a balanced flow operating condition was 

achieved using the manual adjustment process, 

was the resulting operating condition the only 

way the system could have been successfully 

operated? 

•	 Could alternative or additional operating 

conditions be defined such that system 

operations would also be satisfactory or even 

optimal? 

Thus, the POGA methodology was used in conjunction with EPANET to simulate alternative and 

possibly optimal water distribution system operations 

and to assess the effects of variations in system 

operations on the results of the proportionate contribution simulations. 

7.2.4 Simulation Results and Conclusions 

Figure 7-5 shows the aerial distribution of simulated 

proportionate contribution results for all model nodes 

(pipeline junctions) for the month of July 1988, using 

the Parkway well field as the point of entry (source 

point). The simulated proportionate contribution 

results are divided into six intervals (1 to 10 percent, 

10 to 25 percent, 25 to 50 percent, 50 to 75 percent, 

75 to 90 percent, and 90 to 100 percent) and a color is 

assigned to all nodes within each interval (results are 

not shown for negligible proportionate contributions 

of less than 1 percent). 

Simulated proportionate contribution results can also 

be viewed in terms of selected pipeline locations and 

the combination of wells or well fields that contribute 

water to that location. Five geographically distinct 

pipeline locations are selected from the historical 

networks to represent the spatial distribution of 

proportionate contribution results. These locations 

are identified on Figure 7-5 as locations A, B, C, D, 

and E. The simulated proportionate contribution of 

water for July 1988 corresponding to each pipeline 

location is shown graphically on Figure 7-6. The 

simulation results demonstrated that the contribution 

of water from wells and well fields varied by time and 

location. However, the results also showed that certain 

wells provided the predominant amount of water to 

locations throughout the Dover Township area. 

Additionally, although the pattern factors for some 

hours of operations showed marked differences, the 

simulated proportionate contributions of water using 

7-7 
A Reference Guide for Utilities 

Figure 7-5. Areal Distribution of Simulated Proportionate 

Contribution of Water from the Parkway Wells (22, 23, 24, 

26, 28, 29) to Locations in the Dover Township Area, NJ, 

July 1988 Conditions (from Maslia et al., 2001). 

A

B

C

D

E

Figure 7-6. Simulated Proportionate Contribution of Water 

from Wells and Well Fields to Selected Locations, Dover 

Township Area, NJ, July 1988 Conditions (from Maslia et 

al., 2001). 

pattern factors derived from the application of the 

POGA methodology showed little difference throughout the Dover Township area when compared to 

corresponding proportionate contribution of water 

simulated using the manual adjustment process. The 

results of sensitivity analyses conducted using the 

historical reconstruction process indicated the 

following: 

•	 There was a narrow range within which the 

historical water distribution systems could 

have successfully operated and still satisfy 

hydraulic engineering principles and the 

MOC. 

•	 Daily operational variations over a month did 

not appreciably change the proportionate 

contribution of water from specific sources. 

Therefore, the reconstructed historical water 

distribution systems were determined to be the most 

plausible and realistic scenarios under which the 

1962–1996 historical water distribution systems 

were operated. The health scientists conducting 

the case-control epidemiologic study used the 

resulting percentage of water derived from the 

different sources to derive exposure indices for 

each study subject. 

The results from the case-control study showed that 

there was an association between prenatal exposure to 

contaminated community water and leukemia in 

female children (NJDHSS, 2003). For example, female 

leukemia cases were 5 times more likely to have 

occurred when exposed during the prenatal period to 

a high percentage of Parkway well water than were 

control children. The control children are those 

living in the study area, but were not exposed to the 

water from the contaminated well fields. These 

findings would not have been possible without the 

results derived from the innovative water distribution 

system modeling efforts. These efforts have led to 

developing new methods for evaluating the accuracy 

of modeling results and exposure classification 

techniques that are critical components of epidemiologic studies. Some of the innovations documented 

by the Dover Township historical reconstruction 

analysis are: 

•	 A new approach, proportionate contribution 

analysis, was developed that utilized water 

distribution system modeling and source 

tracing to quantify exposure on a monthly basis 

for all locations historically served by the 

distribution system. 

•	 Through the use of an innovative genetic 

algorithm approach (POGA), historical water 

distribution system operating schedules were 

7-8 
1970

197119721973197419751976197719781979198019811982 

Individual 3 

1.60E+06 

1.80E+06 

2.00E+06 

1.40E+06 

Intake, μg1.20E+06 

1.00E+06 

8.00E+05 

6.00E+05 

4.00E+05 

2.00E+05 

0.00E+00 

Year 

Figure 7-7. Estimated Upper 97.5 Percent Credibility 

Limit for Annual Perchlorate Intake by One Plaintiff 

(Grayman, 2004). 

A Reference Guide for Utilities 

The city of Redlands lies in the San Bernardino valley of California, approximately 60 miles east of central Los 

Angeles. In 1981, a routine analysis for chlorination byproducts revealed the presence of trichloroethylene 

(TCE) in a sample of water from the Redlands water system. Subsequent water quality analyses revealed that a 

number of wells supplying the city were contaminated with TCE. In 1997, the perchlorate anion (ClO4) was 

also detected in several wells. In 1996, the first of a series of lawsuits was filed in California State Court 

alleging that the source of these contaminants was a manufacturing facility located up-gradient from the most 

seriously contaminated wells. 

One of these lawsuits claimed that plaintiffs were harmed by exposure to toxic chemicals that were improperly 

disposed of at the manufacturing site and found their way into groundwater that was subsequently extracted 

through the city’s wells and delivered to water customers, including the plaintiffs. The plaintiffs’ burden of 

proof requires them to establish, among other things, that they were actually exposed to contaminated water at 

their homes, places of work, or other locations and that the amounts of contaminants that entered their bodies 

as the result of these exposures were sufficient to cause harm to them. 

To establish this proof, experts for the plaintiffs 

reconstructed the historical conditions in the water 

distribution system of the City of Redlands over a 

period from the mid 1950s to the late 1990s using 

the EPANET model. As part of litigation, several 

forensic reconstructions of water quality in the 

Redlands water distribution system were performed. 

The reconstruction involved estimates of both 

human exposure to toxic contaminants and wholebody intakes of these chemicals. Estimates of 

exposures and intakes were expressed as credibility 

intervals, which were calculated using Monte Carlo 

simulation techniques. As an example output from 

the analysis, Figure 7-7 illustrates the estimated 

upper 97.5 percent credibility limit of one 

plaintiff’s exposure to perchlorate. Similar information was developed to describe the lower 2.5 

percent credibility limit for each of the test plaintiffs in the case (Grayman et al., 2004). 

synthesized. Sensitivity analyses indicated 

that operating system changes did not 

appreciably change the proportionate 

contribution of water to Dover Township 

locations. 

•	 The association between exposure and disease 

would not have been possible without 

developing the integrated approach using 

environmental science, engineering 

evaluations, and epidemiologic analyses. 

Historical reconstruction of environmental exposure is 

not an easy task. The procedures and results summarized herein (and the detailed analyses in Maslia et 

al., [2001]) represent one of the most comprehensive, 

well-documented, and quality-controlled studies of its 

kind. Another example of public exposure assessment 

using modeling is related to the City of Redlands, 

California. 

7.3 Application of Water 

Distribution System Modeling 

in Support of a Regulatory 

Requirement 

The new DBPR2 regulation that is proposed for 

promulgation in the near future requires all water 

utilities that have a disinfectant residual in the 

distribution system to perform an IDSE unless they 

obtain a small-system or “40/30” waiver (EPA, 2003). 

Systems that can certify TTHM and HAA5 compliance data to be less than or equal to 40 μg/L for 

TTHM and 30 μg/L for HAA5 are not required to 

perform an IDSE. The goal of the IDSE is to identify 

compliance monitoring sites that may have high DBP 

levels in distribution systems. Utilities may choose to 

perform an SMP that involves extensive monitoring. 

Alternatively they may choose to perform a system7-9 
A Reference Guide for Utilities 

specific study (SSS) that uses historical data, distribution system models, or other analyses combined with 

minimal monitoring to evaluate TTHM and HAA5 

levels throughout the distribution system as the basis 

to select future compliance monitoring sites. This 

case study demonstrates how a hydraulic/water 

quality distribution system model can be applied to 

satisfy the IDSE requirements of an SSS. 

Key Phrases to Characterize Case Study: Regulatory modeling, IDSE, water quality modeling. 

7.3.1 IDSE Requirements Overview 

The IDSE guidance manual spells out a series of 

suggested minimum requirements for the use of a 

calibrated water distribution system hydraulic model 

to perform an SSS. In general, the water distribution 

system model should be more comprehensive for the 

purpose of an SSS than models typically used for 

long-range capital improvement program analysis 

(e.g., master planning). A calibrated hydraulic model 

intended for detailed distribution system design (e.g., 

for system improvements) or operational studies is 

likely to be adequate. Because systems are always 

changing (e.g., population growth, industry development in network area, aging of mains), it is important 

that the model generally reflect system conditions and 

demand at the time of the IDSE SSS. A model that has 

not been updated or calibrated in the last 5 to 10 

years is unlikely to be adequate for an SSS. The 

guidelines provided in the draft guidance manual are 

summarized below: 

•	 EPS model that has been recently calibrated 

using generally accepted methods. 

•	 An all-pipe model or skeletonized model that 

includes (a) at least 50 percent of total pipe 

length in the distribution system, (b) at least 75 

percent of the pipe volume in the distribution 

system, (c) all 12-inch-diameter and larger 

pipes, (d) all 8-inch and larger pipes that 

connect major facilities, (e) all 6-inch and larger 

pipes that connect remote areas of a distribution 

system, and (f) all active control valves or other 

system features that could significantly affect 

the flow of water through the distribution 

system. 

•	 Water demand should be allocated among the 

nodes of the model in a manner that reflects the 

actual spatial distribution of such demand 

throughout the system. 

•	 A system-specific, diurnal (24-hour) demand 

pattern should be applied to the overall system 

demand. 

•	 The model should accurately simulate seasonal 

system configurations and operational changes. 

Once the model is established, it is then run in EPS 

mode until a consistent, repeating temporal pattern of 

water age is established at all nodes of the model. 

Generally, the model should be run under high DBP 

formation conditions (typically summer months) to 

estimate residence times. Based on the modeled water 

age results, preliminary monitoring sites are identified 

near locations that satisfy the sampling site requirements. Sampling sites are selected to represent: 

•	 High-TTHM Sites: High TTHM values are 

expected at high-residence-time locations. 

These locations can be identified by reviewing 

the modeled water age at each node in the 

model. These sites are generally downstream of 

storage facilities and in remote locations. 

However, the regulation does not require 

extremes or non-representative sites to be 

sampled. 

•	 High-HAA5 Sites: The criteria and procedure 

for selecting high HAA5 sites using a hydraulic 

model are generally the same as those described 

above for selecting high-TTHM sites with one 

important difference: the sites chosen to 

represent high HAA5 should have a disinfectant 

residual sufficient to suppress bacteria which 

can degrade HAAs. 

•	 Average-Residence-Time Sites: Averageresidence-time sites can be selected from sites 

with residence times close to the flow-weighted 

mean of all nodal residence times. 

•	 Near-Entry-Point Sites: Modeled water age can 

be used to identify locations in the near vicinity 

to entry points into the water system. 

Requirements for the number of monitoring sites have 

not yet been finalized. As a result, this case study 

demonstrates the general usage of models for IDSE 

and relies upon the 2003 draft guidance issued by 

EPA to illustrate the usage of models. 

7.3.2 Example Application of Modeling in the 

IDSE Process 

The following example is a hypothetical case study 

based in large part on an actual water distribution 

system. The system purchases disinfected groundwater and serves approximately 15,000 people. Water 

enters the distribution system from two separate 

interconnections to a wholesale utility. The average 

demand is 2.2 MGD. A north interconnection operates 

intermittently and provides approximately 80 percent 

of the demand, while the south interconnection 

operates at all times and provides the remaining 20 

percent of the system demand. There is a 1.5-milliongallon storage tank. In order to comply with the Stage 

2 requirements, the draft proposed DBPR2 states that 

a total of six sites are required for a utility of this size 

7-10 
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Figure 7-8. Average Water Age in the Distribution 

System Over Last 24 Hours of a 2-Week Simulation. 

A Reference Guide for Utilities 

using groundwater: one representing a site near to the 

predominant entry, one representing a site with 

average residence time, two sites representing high 

TTHM conditions, and two representing high HAA5 

conditions. 

The water utility water distribution system model has 

been used extensively in the past for both hydraulic 

and water-quality studies. It is a skeletonized model 

that includes all 8-inch-diameter and larger pipes and 

all major facilities. The pipes in the model represent 

74 percent of the total length of pipe in the system 

and 86 percent of the total volume. The model has 

been previously calibrated based on two tracer studies 

and has been shown to have excellent agreement with 

observed field results. Demands have been assigned 

to nodes based on actual meter readings, and information from the SCADA system has been used to construct 

a typical diurnal water use pattern. The model is being 

operated in an EPS mode to simulate a 12-day period. 

The model has also been calibrated for use in simulating chlorine residual in the distribution system. 

A series of runs of the model were performed to help 

understand the movement of water and water quality 

transformations in the system. Specific simulations 

included water age and chlorine residual. The results 

of the water-age model run are shown in Figure 7-8. 

This plot shows the average water age throughout the 

distribution system over the last 24 hours of the 2­

week simulation. This period was selected to avoid 

the uncertainty associated with assigning initial water 

age in the system. The plot illustrates the nodes in the 

vicinity of the predominant northern interconnection 

that receive water with an average age of less than 2 

hours. As also shown, the average water age increases 

significantly for areas that are further from the 

interconnections. The demand flow-weighted average 

water age for delivered water was calculated to be 27 

hours. However, water age can also vary quite 

significantly over the course of a day in water 

systems, largely due to the impacts of storage tanks. 

This is illustrated in Figure 7-9, which depicts the 

minimum water age at each node over the same 24­

hour period. This is also shown in the plot in Figure 

7-10 for Node J-456 in the vicinity of the tank. The 

IDSE guidance does not require utilities to explicitly 

consider the effects of tanks on diurnal variations in 

water age, and thus on the formation of DBPs. If Node 

J-456 was selected as representative of high DBP 

because of its high water age, it would be expected 

that the DBPs would only be high during the part of 

the day when water is being discharged from the tank. 

The model was also used to determine the chlorine 

residual throughout the system. Figure 7-11 contains 

a plot of the minimum chlorine residual throughout 

the system. It is important to note areas with high 

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Figure 7-9. Minimum Water Age in the Distribution System 

Over Last 24 Hours of a 2-Week Simulation. 

7-11 
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Figure 7-11. Minimum Chlorine Residual in 

Distribution System Over Last 24 Hours of a 2-Week 

Simulation. 

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A Reference Guide for Utilities 

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Figure 7-10. Diurnal Water Age at Node J-456. 

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Figure 7-12. Zones Representing Potential 

Monitoring Locations for IDSE Based on Modeling. 

residence time and low residual for high-TTHM sites 

and high residence time and high residual for highHAA5 sites. This information can be used to avoid 

selecting monitoring sites that are to be used as 

representative of high HAA5 concentrations. The 

current ability to accurately model HAA5 in a 

distribution system is limited. However, research has 

shown that depressed chlorine residual can result in 

biodegradation of HAA5, thus lowering the HAA5 

concentrations even for older water. Figure 7-11 also 

shows a small area in the southwestern portion of the 

system that the model predicts to potentially experience chlorine residuals less than 0.2 mg/L of chlorine. 

Based on the modeling results, various zones were 

defined in the distribution system representing areas 

that are appropriate for different types of compliance 

monitoring requirements (Figure 7-12). In actual use, 

information generated by the model would be 

supplemented by historical field data. The zones 

shown in the plot include: 

1. Nodes in the vicinity of the predominant north 

connection with water age less than 2 hours 

representative of near entry locations; 

2. Nodes with average water age in the range of 21 

to 33 hours that represent locations that 

approximate the average residence time of 27 

hours; 

7-12

3. Nodes with residence times that exceed twice 

the average water age (> 54 hours) and have a 

minimum chlorine residual exceeding 0.20 mg/L 

representing potential high-HAA5 sites; and 

4. Other nodes with residence times that exceed 

twice the average water age (> 54 hours) 

representing potential high-TTHM sites. 

As illustrated by the case example, if a detailed, 

calibrated EPS model is available, the model represents an efficient means of defining the compliance 

monitor locations as required under the forthcoming 

regulation. 

7.4 Use of Water Distribution 

System Models in the 

Placement of Monitors to 

Detect Intentional 

Contamination 

The increasing concern over the potential for intentional contamination of a water distribution system 

has led to interest in the placement of monitors to 

detect contamination and to serve as part of a rapid 

detection system. Design of such monitoring systems 

must include decisions on the type, number and 

location for the monitors. Water distribution system 

models can play a significant role in the decision 

making by providing a quantitative mechanism for 

determining the movement of a contaminant through 

the distribution system and testing the effectiveness 

of a monitoring system design. 

To illustrate this application, a red team-blue team 

concept is used (Grayman et al., 2005). The red 

team-blue team concept is part of “war gaming” 

that is widely used today as a mechanism for 

training and development and testing of security 

plans. The red team acts as the aggressor and the 

blue team acts as the defenders. Each team has 

different types and amounts of information available to them and different rules or constraints that 

they must follow. In this case study, network 

models are used in two modes to assist in evaluating monitoring networks: 

1. As part of a red team-blue team exercise to 

demonstrate the effectiveness of manual 

selection of location of monitors as part of a 

CWS. 

2. As part of an optimization model to select the 

best locations for monitors based on a stated 

metric for measuring the effectiveness of the 

monitoring system. 

A Reference Guide for Utilities 

Key Phrases to Characterize Case Study: Water 

security, contamination, optimization, monitor 

placement 

7.4.1 Red Team-Blue Team Exercise 

In this simulated exercise, the red team attacks a water 

distribution system by adding a harmful chemical to 

the water. The red team is provided with limited 

information on the distribution system, a number of 

potential locations where they can inject a contaminant, and predetermined information on the characteristics of the contaminant (quantity and lethality of the 

contaminant). The blue team represents the water 

utility and attempts to protect the water system by 

installing three monitors as part of a CWS that detects 

contaminants. It is assumed that they have extensive 

information on the design and operation of the 

distribution system but no firm information on where 

the attackers may choose to introduce the contaminant or the nature of the contamination scenario. 

The water distribution system network used in this 

exercise is a skeletonized version of a major pressure 

zone of a water distribution system in California 

approximating the conditions (design and operation) 

in the mid 1990s. The system is fed by two sources; 

one that operates continuously and one that operates 

only during the day. There are three storage facilities 

located in the network. This network is one of the 

example networks provided as part of the EPANET 

model. The simulation performed in the exercise is a 

24-hour EPS starting at 7AM. The model representation of the network is shown in Figure 7-13. This 

figure also illustrates the relative nodal demands, and 

the typical flow directions and magnitude during the 

day. This plot is given only to the blue team to 

provide them with information on the design and 

operation of the system. The red team is provided 

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Figure 7-13. Water Distribution System Characteristics. 

7-13 
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Figure 7-15. Contaminant Concentration Just Downstream 

of Contaminant Introduction Location (Node 121). 

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Figure 7-16. Contaminant Concentration Far Downstream 

of Contaminant Introduction Location (Node 143). 

A Reference Guide for Utilities 

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node located 1500 feet immediately south of the 

injection point is shown. As expected, the contaminant moved very rapidly and reached this node in less 

than an hour after the injection. If a monitor was 

located at this node, and rapid analysis and response occurred, it could be very effective as an 

early warning for most of the distribution system. 

Figure 7-16 illustrates concentrations resulting 

from the same contaminant introduction location. 

This node is located near the eastern edge of the 

distribution system approximately 2.5 miles 

downstream of the contaminant introduction 

location. As illustrated, the concentration of the 

contaminant remained about the same but the travel 

time to this point was approximately 7 hours. For 

this injection scenario, a monitor located at this 

point would be relatively ineffective as a warning 

device for most of the distribution system because 

of the significant time lag. 

Figure 7-14. Allowable Contaminant Introduction Locations. 

only with a map of the distribution system showing 

eight potential sites that can be used to introduce a 

contaminant (Figure 7-14). 

Following the selection of points of attack by the red 

team and selection of monitor locations by the blue 

team, the contaminant introduction is simulated using 

the EPANET model. The movement and concentration 

of the contaminants are then viewed graphically by 

animating the movement of the contaminant in the 

distribution system in the EPANET model. The time 

history of contaminant concentrations is also viewed 

at selected nodes in the distribution system. The 

effectiveness of monitors is illustrated by graphing 

the concentrations of the contaminants at monitoring 

nodes and assessing whether (and how quickly) the 

monitors will serve their purpose of detecting the 

contaminant. Figures 7-15 and 7-16 show the 

concentrations resulting from 8 hours of contamination at node 123 starting at 11 a.m. 

In Figure 7-15, the resulting contamination at a 

In the exercise, most red team members tend to select 

contamination introduction locations that they 

perceive would result in the most widespread impacts. 

The most often selected sites were those near to the 

water sources. Little attention is generally given to 

the amount of dilution that would result at a particular location. Blue team members tend to select 

monitoring locations that cover a wide range of 

locations. Frequently, the three allowable monitors 

will be located in the north, central, and southern 

portions of the distribution system. 

7.4.2 Application of Optimization Model 

The optimization model used in this demonstration 

is a methodology developed by Ostfeld and 

Salomons (2004). The model links EPANET and a 

genetic algorithm in an overall framework for 

optimally allocating monitoring stations, aimed at 

detecting deliberate external contamination into 

water distribution system nodes. The model 

operates under extended period (unsteady) hydraulics and water quality conditions. The optimization 

routine determines the monitor placement to detect 

contaminants in order to minimize the exposure of 

7-14 
customers above an allowable minimum concentration. The algorithm can be used to study contamination of fixed duration, quantity, or location or 

can simulate contamination under stochastic 

conditions. There are several model parameters that 

can be specified to control the number of monitors, 

the allowable contaminant introduction locations, the 

characteristics of the event, and whether the event 

characteristics and demands are to be considered as 

stochastic variables. 

In one application of the model, it was assumed that 

the pollutant could be introduced at any single node 

of the distribution system at any time, all with the 

same injection probability. The following additional 

assumptions were made: 

•	 The maximum contamination exposure volume 

to the public above which an alarm signal of the 

monitoring stations is required is 25 gallons. 

•	 The water is considered contaminated above 1 

mg/L. 

•	 The pollutant flow discharge is 2 kg/min for 5 

minutes (i.e., a total of 10 kg of a solution of 100 

percent is introduced within a total of 5 minutes). 

•	 The pollutant flow discharge of the


contaminant introduced and the consumer


demands are deterministic.


•	 The monitoring stations are providing real-time 

data and detection alarms. 

•	 All monitoring stations have a detection


sensitivity of 1 mg/L.


•	 3 monitors are to be placed. 

The model suggests placing monitors at nodes 143, 

181, and 213 with a detection likelihood of 0.4354 

(i.e., there is a probability of about 44 percent that the 

contaminant will be detected prior to the consumption of more than 25 gallons at a concentration higher 

than 1 mg/L). The location of the monitors is shown 

in Figure 7-17. As illustrated, the selected monitor 

locations were relatively evenly spaced around the 

network. 

Other evaluated scenarios looked at a different 

number of allowable monitors, the allowable contaminant introduction locations, the critical exposure 

threshold, and representation of contaminant quantity 

and nodal demands as stochastic variables. Though 

the exact “optimal” locations varied slightly between 

the different runs, typically the monitors were placed 

throughout the network. However, the effectiveness of 

the monitoring network, as measured by the detection 

likelihood does vary considerably between scenarios. 

A Reference Guide for Utilities 

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Figure 7-17. Monitoring Locations Selected by the 

Optimization Model. 

7.4.3 Case Summary 

The red team – blue team exercise serves as a good 

mechanism for demonstrating both the dynamics of 

contaminant movement in the distribution system and 

the potential effectiveness of monitors. Application of 

the optimization model, both as a demonstration 

procedure and as a practical tool, provides a method 

that moves the monitor placement from a purely 

intuitive process to a quantitative procedure. Both the 

exercise and the optimization tool show the importance in minimizing delays in the detection, notification, and response process. Even an added delay of an 

hour or two can lead to a significant increase in the 

number of customers that would be impacted by a 

contamination event. 

7.5 Case Study – Use of 

PipelineNet Model 

This case study focuses on the application of the 

PipelineNet model, which incorporates both GIS and 

the EPANET model discussed in the previous chapters 

of this reference guide. The supporting investigations 

were primarily sponsored by the Awwa Research 

Foundation (AwwaRF) and EPA with work performed 

by a consulting firm (SAIC) and assistance from water 

utility personnel (Ron Hunsinger, Bill Kirkpatrick, 

Dave Rehnstrom) working at the East Bay Municipal 

Utility District (EBMUD), Oakland, CA. The text and 

figures are adapted from AwwaRF report 2922 

prepared by Bahadur et al. (2003). 

Key Phrases to Characterize Case Study: hydraulic 

and water quality modeling, placement of monitors, 

exposure modeling, contamination assessment, 

contamination response tools, geospatial analysis. 

7-15 
A Reference Guide for Utilities 

7.5.1 Overview 

PipelineNet is an EPANET/ArcView1 -based model, 

and uses the same hydraulic engine as EPANET. The 

EPANET portion of the model can simulate the fate 

and transport of potentially introduced contaminants 

in a water distribution system. The ArcView (or the 

GIS layer) portion of the model can relate the 

geospatial components of the simulated impact. The 

GIS layer allows for geo-features and map display 

with an overlay of model output. This feature is 

particularly useful in applications such as emergency 

response, determining optimal placement of sampling, 

and monitoring instruments. 

AwwaRF and EPA jointly funded a project to 

develop techniques to locate monitoring points in a 

distribution system, determine appropriate timing 

and frequency of monitoring, and establish monitoring techniques and relevant water quality 

parameters. For this purpose, a fully calibrated 

extended period simulation (EPS) network model 

hypothetically representing a portion of EBMUD 

was developed using PipelineNet. This study area 

network model represents 16 of the 123 pressure 

zones in the overall EBMUD distribution system. 

The study area contained 27 tanks, 748 miles of 

pipes, 62 pumps and 17,997 pipe segments with 

diameters equal to or greater than 2 inches. Figure 

7-18 shows a partial view of the hypothetical 

network of pipelines. 

7.5.2 Model Calibration 

The network model was calibrated by comparing 

the observed (SCADA data) and simulated 

(PipelineNet model) water level in 25 tanks located 

in the study area. The primary focus of the calibraFigure 7-18. Hypothetical Water Distribution System 

Showing Pipelines. 

1

Registered Trademark of ESRI 

tion was to match the shape of the observed water 

level in the tanks. The model calibration was 

performed for a 24-hour time interval using data 

measured on July 1, 2001. To further enhance 

calibration, the pump characteristic curves were 

used to achieve a good comparison between 

simulated and observed tank levels. The flow value 

of the characteristic curve was changed as necessary to reflect field conditions. Each pump was 

operated with time controls. 

7.5.3 Monitoring Site Location Methodology 

A hierarchical selection process was developed to 

locate monitoring stations in the distribution system. 

A three-step approach was employed based on model 

inputs, outputs, and GIS layers (see Figure 7-19). 

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Figure 7-19. Conceptual Diagram Showing the Ranking 

and Prioritization Methodology. 

In the first step (source prioritization factor), all the 

elements of the water distribution system are 

assumed to be available for monitoring. This 

universe is then reduced to a smaller set based on 

accessibility (location) and amenability (e.g., 

eliminating dead ends, crosses, tees, junctions with 

different pipe material) to monitoring. Initially, all 

nodes are considered available for monitoring and 

are assigned a score of 1. Subsequently, all the 

nodes, which are either not amenable or not 

accessible, are assigned a score of 0. This reduced 

the number of pipes available for monitoring from 

17,997 to 14,938. Therefore, only the 14,938 

nodes with a score equal to 1 are considered for 

Step 2 (described below). 

In the second step (distribution system response 

factor), the PipelineNet model is run to quantify the 

distribution system response in terms of flow, velocity, and pressure. Concentration of water quality 

parameters could also be considered in this ranking 

procedure but was not included as a factor in this case 

study. Each system response parameter has equal 

7-16 
weighting and is assigned an initial score of 1 for 

every pipe. Thereafter, based on the run, the scores 

are re-assigned values ranging between 1 and 10, 

where a score of 10 would indicate a higher level of 

concern. For any given parameter, the user can 

determine the distribution of scores over the parameter range. For example, a score range of 10 to 1 

could be distributed over a flow range of 0.001 to 100 

gpm. The PipelineNet model and Bahadur et al. 

(2003) provide some guidance for assigning scores, 

but the user can select any score based on the requirements of the analysis. 

In the third step (critical facilities and population 

density factor), user defined buffer zones (polygons) 

are created around critical facility locations. In 

addition, areas of low-, medium-, and high-population 

density are delineated by the creation of polygons. 

Pipes closest to the critical facilities and/or near high 

population density areas are assigned a score of 10. 

The total score for each pipe based on Steps 2 and 3 

are computed. These final scores are linked to the GIS 

pipeline layer. The user can identify areas where 

monitoring stations should be placed based on the 

display of pipes with high scores. Figure 7-20 shows 

the pipes in the hypothetical network with scores 

greater than 27 overlaid with critical facility locations. The methodology outlined above for selecting 

the location of monitoring stations is a subjective 

procedure that requires input and judgment from 

water utility personnel. It would not result in a 

common solution for all distribution systems but can 

incorporate the specific needs and objectives of the 

participating water utility. 

Figure 7-20. Hypothetical System Showing High Score 

Areas (>27) Overlain with Hospitals and Schools. 

A Reference Guide for Utilities 

7.5.4 Response and Mitigation Tools 

In addition to the monitoring site location methodology, three additional tools were developed as part of 

this case study to enhance the capability of 

PipelineNet in the areas of emergency response, 

mitigation, and normal operations. These three tools 

are briefly described in the following subsections. 

7.5.4.1 Consequence Assessment Tool 

The consequence assessment tool of PipelineNet 

provides the ability to quickly identify and quantify 

the population, infrastructure, and resources at risk 

from a contaminant event. For a defined contaminated area, this tool can calculate: 

•	 total population at risk, 

•	 number of taps contaminated, 

•	 miles of pipe contaminated, 

•	 total number of hospitals and beds for each 

hospital, and 

•	 total number of schools and student population. 

7.5.4.2 Isolation Tool 

The isolation tool of PipelineNet provides the ability 

to change the status (open or closed) of any pipe in 

the distribution system. After completing a water 

quality simulation and examining the contaminant 

distribution from the event, this tool could be used to 

close off one or more pipes to control the flow of 

water. The model would then be re-run, reflecting 

these new hydraulic conditions, and the output 

examined to determine if this mitigation step was 

successful in limiting the area of contamination. 

7.5.4.3 Spatial Database Display Tool 

PipelineNet’s spatial database display tool can 

overlay the EPANET model output with various 

geospatial properties. The spatial database display 

tool of PipelineNet has nineteen criteria to choose 

from. The users can select any combination(s) of the 

available criteria. For illustration purposes, Figure 7­

21 shows the display of three criteria: oversized pipes 

(diameter >30 inches), current monitoring locations, 

and low velocity (velocity < 0.001 FPS) pipes. 

7.5.5 Case Summary 

The case study demonstrates that the PipelineNet 

model can be used to perform a variety of practical 

analyses to locate monitoring systems. Three 

additional tools are available that enhance 

PipelineNet’s capability in the areas of emergency 

response, mitigation, and normal operations. However, to effectively utilize the PipelineNet model, the 

utility must have a calibrated EPS EPANET-based 

network model and utility-specific GIS data. At the 

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A Reference Guide for Utilities 

Figure 7-21. Display of Low-Velocity Pipes, Oversized 

Pipes, and Current Monitoring Stations Using the Spatial 

Database Display Tool. 

time this reference guide was being written, the model 

is slated to undergo additional enhancements to 

improve the following: contaminant database, 

consequence assessment, inclusion of time of travel, 

conversion from ArcView 3.2 to ArcGIS, and establishment of links to SCADA data. 

7.6 Use of Threat Ensemble 

Vulnerability Assessment 

(TEVA) Program for Drinking 

Water Distribution System 

Security 

In response to the increased focus on the vulnerability 

of drinking water systems to the intentional introduction of chemical, biological, or radiological contaminants, EPA is developing the Threat Ensemble 

Vulnerability Assessment (TEVA) Program. TEVA, 

when completed, will be capable of analyzing the 

vulnerabilities of drinking water distribution systems, 

measure public health and economic impacts, and 

design and evaluate threat mitigation and response 

strategies. TEVA is a probabilistic framework for 

assessing the vulnerability of a water utility to a 

variety of contamination attacks. Monte Carlo 

simulations generate ensembles of scenarios, and 

statistics are analyzed to explore the feasibility of 

scenarios, identify vulnerable areas of the water 

distribution network, and analyze the sensitivity of 

the model to various parameters. 

The TEVA team includes several individuals from 

various organizations. The key EPA TEVA leads are: 

Regan Murray, Robert Janke, and Jim Uber. 

Key Phrases to Characterize Case Study: hydraulic 

and water quality modeling, placement of monitors, 

vulnerability assessment, exposure modeling, 

contamination assessment, contamination response 

tools, probabilistic analysis, economic impact 

assessment, threat mitigation strategies. 

7.6.1 TEVA Overview 

TEVA incorporates a probabilistic framework for 

analyzing the vulnerability of drinking water distribution systems. Figure 7-22 outlines the major 

components of the framework: the stochastic modeling of scenarios, the analysis of potential impacts, and 

the assessment of threat mitigation strategies. Together, these three components present an integrated 

view of the vulnerability of a unique distribution 

system to a wide variety of contamination threats and 

the potential for a water utility to decrease this 

vulnerability through a set of mitigation strategies. 

Preliminary design and implementation has been 

completed for a core set of components. A longerterm research effort is being planned for the other 

components. 

Stochastic Modeling Scenarios 

Ensemble Database 

1 2 N 

Impact Analysis 

Threat Mitigation Analysis 

Assessment of Risk 

Reduction Strategies 

Identification of 

Vulnerable 

Populations, 

Regions, and Services 

Evaluation of Countermeasures 

Public Health 

Impacts 

Economic Impacts 

Select Scenario 

Simulate Scenario 

Figure 7-22. Threat Ensemble Vulnerability 

Assessment Framework. 

Without specific intelligence information, one cannot 

predict exactly how a terrorist group might sabotage a 

water system. Therefore, TEVA is based on a probabilistic analysis of a large number of likely threat 

scenarios. While the number of possible variations on 

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scenarios is nearly infinite, the vulnerability of the 

system can be assessed by selecting a “large enough” 

set of likely scenarios. TEVA creates a threat ensemble, or a set of contamination scenarios, based on 

varying the type of contaminant, the amount and 

concentration of the contaminant, the location of the 

contaminant introduction into the distribution 

system, and the duration of the contamination event. 

The vulnerability of the system is based on an 

assessment of the entire threat ensemble. The 

following subsections present an overview of the 

aforementioned three key modeling elements. 

7.6.1.1 Stochastic Modeling 

The stochastic modeling element involves three steps: 

selection of the threat ensemble, simulation of the 

ensemble, and storage of the output in the ensemble 

database. The threat ensemble is a collection of 

scenarios that will be simulated. One scenario may 

represent, for example, the injection of a 55-gallon 

drum containing a biotoxin mixture into one node of 

a particular distribution system with 1,000 nodes. 

This scenario can be repeated for each of the 1,000 

nodes, generating a threat ensemble of 1,000 scenarios. One could vary other parameters, such as 

contaminant type, quantity, concentration, location, 

or duration, to generate other threat ensembles. 

Current work is determining how to best select a large 

enough threat ensemble in order to accurately assess 

vulnerability. While a larger number of scenarios will 

allow for the consideration of more threats, constraints on computation time require that the number 

of scenarios be minimized. 

Next, each scenario in the threat ensemble is simulated using a network hydraulic and water quality 

model. In TEVA, an EPANET based network model is 

generated with all necessary data for running the 

simulations. The EPANET model currently includes 

first order decay of constituents. Soon to be completed upgrades to EPANET will allow modeling of 

the fate and transport of multiple dissolved constituents in distribution systems (Uber et al., 2004a). 

These upgrades will permit the modeling of reactions 

at the pipe wall and in the bulk flow and enable the 

inclusion of chemical reaction products, thereby 

resulting in more accurate estimates of human 

exposure and health risk. The results of the stochastic 

modeling of the threat ensemble are stored in the 

ensemble database, allowing for later analysis of the 

data in the other components of TEVA. 

7.6.1.2 Impact Analysis 

The Impact Analysis element uses the data stored in 

the ensemble database to estimate likely public health 

impacts and economic impacts. Public health impacts 

include injuries, disease, illness, and deaths. People 

can be exposed to contaminants from ingestion of 

A Reference Guide for Utilities 

water, inhalation of volatilized chemicals or particles, 

and/or contact with the skin. Depending on the 

contaminant, specific dose-response models can be 

employed to estimate the various health endpoints. 

For many threat agents, reliable data for such models 

are lacking, and the ensuing uncertainty in the results 

must be understood. For contagious diseases, 

dynamic models of disease transmission also must be 

included in order to accurately assess health impacts. 

Economic impacts include restoration costs (cleanup, 

treatment, remediation, and decontamination), denial 

of service costs (providing alternative sources of 

water), and other costs, such as medical costs (hospitalization, vaccines). Psychological costs related to 

consumers’ loss of trust in the water supply system are 

very difficult to estimate. The distribution of impacts 

will be calculated from the ensemble database, 

thereby providing an estimate of the expected impacts 

for the ensemble of threat scenarios. 

7.6.1.3 Threat Mitigation Analysis 

The Threat Mitigation element explores various 

mitigation strategies such as the use of early warning 

systems (sensors and data analysis tools), operational 

approaches (chlorine boosters, back-up equipment), 

and emergency response methods (isolation of part of 

the system, public notification). A range of mitigation strategies can be evaluated with the TEVA 

simulations using health risk and economic impact 

analyses to rank and select the best alternative for a 

set of scenarios (Uber et al., 2004b). This will enable a 

quantitative analysis of the benefits of implementing 

various strategies. 

7.6.2 Application of TEVA to a Water 

Distribution System for Optimal 

Monitoring 

The TEVA computational framework (Murray et al., 

2004, Uber et al., 2004b, Murray et al., 2005) has 

been applied to three sizes of distribution systems, 

each differing in population by approximately one 

order of magnitude. The results shown for this case 

study illustrate that TEVA has the potential to help 

water utilities assess the contaminants to which they 

are most vulnerable, identify the most vulnerable 

regions of their distribution systems, and select the 

most appropriate mitigation strategies for their system. 

Many different forms of contamination monitoring 

systems have been proposed, using water quality 

sensors, composite or grab sampling, and various 

numbers and locations of sensors. Any contamination 

monitoring and surveillance program will be budget 

constrained. Optimizing the placement of a fixed 

number of monitoring stations plays an important role 

in the design of the monitoring system. Selecting the 

best locations for conducting a routine sampling 

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A Reference Guide for Utilities 

program to serve as a monitoring and surveillance 

program for detection of intentional contamination 

can be considered an integer linear programming 

problem in which a quantity is optimized subject to a 

set of constraints (Berry 2005). Formal optimization 

methods and heuristic methods have been applied to 

solve such problems. In this case study, a Greedy 

heuristic algorithm is used to locate monitoring 

stations, given a defined budget or number of 

monitoring stations, in order to minimize health 

impacts. The following is an example of using TEVA 

to evaluate and optimize (in a limited fashion) the 

design of a contamination monitoring system for a 

water distribution system. 

7.6.2.1 Simulation Overview 

For the purposes of this analysis, an “all-pipes” 

EPANET network model for the sample distribution 

system was generated. There are 1,062 miles of pipe 

represented in this network model. This system 

contains approximately 12,000 nodes, has an average 

daily demand of approximately 20 million gallons, 

and an estimated population of 130,000. Approximately 6,000 potential sampling locations were 

selected randomly from the nearly 12,000 nodes by 

considering that each node had a 50 percent probability of inclusion. This pruning was used to make the 

problem less computationally intensive and emphasize that an optimal placement of monitoring stations 

will likely be difficult given legal, financial, or 

logistical constraints for placing and managing 

monitoring stations. 

To simulate a contamination scenario, many parameters must be specified, including characteristics of 

the contaminant, the contaminant-introduction 

scenario, and the consumption patterns of the 

population. In order to represent the range of possible 

parameter values, the TEVA computational framework 

uses simulation to vary parameters, such as contaminant type, quantity, concentration, location, rate, or 

duration, to generate threat ensembles (collections of 

many threat scenarios) which collectively can be 

analyzed for health impact statistics. All nodes (a 

grouping of service connections) in the distribution 

system are considered equally likely introduction 

points. Biological and chemical contaminants can be 

considered, and contaminant introduction can last 

from a few minutes to hours to days. For the purposes 

of this analysis, a biological agent was considered as 

the contaminant and the introduction duration was 24 

hours, at a rate of 8.675 liters per hour, and a mass rate 

of 1.45 x 1011 organisms per minute. 

The health impacts are affected by factors such as 

dose-response relationships, lethal doses, time-toonset of symptoms, time for effective medical 

intervention, and the time delay for transporting and 

analyzing samples in laboratories. Health impacts to 

a population will increase with an increase in the time 

required to implement an effective response for a 

known contamination event. Considering these 

factors, modeling and simulation analyses, such as 

those presented here, must be performed on a contaminant specific basis. The health impact statistics can 

include mean infections/illnesses or mean fatalities. 

Infection/illness is a function of the dose of organisms 

or contaminant ingested and the probability of illness 

caused by that dose, as determined from the contaminant dose-response curve (in this case, Salmonella). 

Mean infections/illnesses are statistically determined 

from the probabilistic analysis of all threat scenarios. 

Maximum infections resulted from introduction at the 

node delivering the maximum health impacts. 

Although there was one worst case node, there were 

approximately 60 threat scenarios (contaminant 

introduction locations), which delivered at least 50 

percent of the maximum lethality. The maximum 

number of lethalities was approximately 13,000. 

In this TEVA-simulated analysis, the benefits of two 

composite grab sampling programs were evaluated 

(daily and every 48 hours) and compared to the 

benefits provided by a system of real-time (inline, 

contaminant specific) sensors. The benefits of the 

sampling or monitoring programs are measured by the 

hypothetical reduction in public health impacts. In 

this analysis, sample location designs are based on 

minimizing the mean number of fatalities for 2 

sampling frequencies: 24 hours and 48 hours. Six 

sampling/sensor station placement scenarios were 

evaluated in this analysis: 5, 10, 15, 20, 30, and 40 

locations for each program. The Greedy algorithm 

used in this analysis will provide an optimal solution 

for minimizing public health impacts. 

For the purposes of this analysis, the grab samples are 

considered to be filtered samples. Filtered samples 

represent composited samples that have been collected and concentrated through a filtration device to 

better enable the collection and analysis of biological 

organisms. Also, real-time water quality monitors are 

assumed to detect chemical contaminants or biological organisms through the change in water quality, 

such as determined by the reduction of chlorine 

residual or increase in turbidity. These real-time and 

sampling-based analyses are considered ideal, 

meaning that detection limits were zero and the 

biological contaminant was always detected. 

7.6.2.2 TEVA Analysis Approach 

This analysis considers attacks at every non-zero 

demand node, totaling approximately 10,000. Only 

non-zero demand nodes are considered because they 

represent service connections that are using water 

from the distribution system on a regular basis and, 

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48 Hour Sampling Frequency (12 Hour Delay)


24 Hour Sampling Frequency (12 Hour Delay)


Real-Time Monitoring (Hourly Monitoring) 12 Hour Delay


Real-Time Monitoring (Hourly Monitoring) 4 Hour D elay


90000 

80000 

Figure 7-24. Comparison of 24-Hour, 48-Hour, and RealTime, Continuous Contamination Monitoring Systems for 

the Reduction in the Maximum Number of Infections for a 

24-Hour Contaminant Attack. Maximum Number of Infections (Number of Persons) 

70000 

60000 

50000 

40000 

30000 

20000 

10000 

0 

0 5 10 15 20 30 

Num ber of Sam pling/Sensor Stations 

40 

A Reference Guide for Utilities 

therefore, could possibly be used for contaminant 

introduction. Statistically analyzing the approximately 10,000 threat scenarios provides an estimate of 

the hypothetical health impacts in terms of average 

health impacts (e.g., fatalities or illnesses) and 

maximum impacts. Average impacts could be 

expected to result if a saboteur had no knowledge of 

where best to attack and simply randomly chose a 

node location for contaminant introduction. Maximum health impacts correspond to a relatively small 

set of contaminant introduction node locations (threat 

scenarios) that maximize health impacts to the 

associated receptors. 

The contaminants were modeled as tracers, i.e., free of 

hydrolysis, chlorination, pipe wall, or biofilm reactions, which would largely decrease the contaminant’s 

0 

500 

1000 

1500 

2000 

2500 

3000 

0 5 10 15 20 30 40 

Number of Sampling/Sensor Stations 

48 Hour Sampling Frequency (12 Hour Delay) 

24 Hour Sampling Frequency (12 Hour Delay) 

Real-Time Monitoring (Hourly Monitoring) 12 Hour Delay In Response 

Real-Time Monitoring (Hourly Monitoring) 4 Hour Delay in Response 

Mean Infections (No. of Persons) 

Figure 7-23. Comparison of 24-Hour, 48-Hour, and RealTime, Continuous Contamination Monitoring Systems for 

the Reduction in Mean Infections for a 24-Hour 

Contaminant Attack. 

effectiveness in causing harm to public health (an 

extended version of EPANET is undergoing testing to 

allow multi-species modeling of contaminants). 

Contaminants are modeled using a mass injection 

rate, zero volume added, which consequently does not 

influence the hydraulic properties of the network, i.e., 

flow increase, decrease, or reversal of flow. 

Hydraulic and water quality simulations were run for 

192 hours. The disease-causing agent or contaminant 

was considered to be a hypothetical, biological 

contaminant that is expected to cause infection (50 

percent of the time) in an adult when 10,000 or more 

of the organisms are ingested. The incubation period 

was assumed to be 24 hours, and the time for effective 

treatment was 48 hours after the onset of symptoms. 

After 72 hours, people either recovered or died. A 

sigmoidal dose-response curve was assumed for the 

ingestion of organisms with the untreated fatality rate 

at 16 percent of those infected. 

7.6.2.3 TEVA Analysis Results 

Figure 7-23 compares the reduction in mean infections provided by a routine 24-hour filtered sampling 

program, a routine 48-hour filtered sampling program, 

and a real-time, continuous, monitoring program. 

Similarly, Figure 7-24 compares the reduction in the 

maximum number of infections of the same 3 monitoring programs. Again, this scenario assumes contamination by an individual who understands distribution 

systems and has the knowledge and resources to 

determine the maximum impact location(s). 

It is assumed that the computer simulations and the 

monitoring/surveillance programs are successful in 

reducing public health impacts by preventing further 

consumption after detection. The results show that 

there is not a significant difference between daily (24­

hour) sampling and 48-hour sampling in terms of 

reducing health impacts. As expected, the continuous 

monitoring program detects the incident much earlier 

than the daily sampling program. Figure 7-23 shows 

an 80 percent reduction in mean infections is 

achieved using 40 real-time monitors, as compared to 

having zero monitors. The results show that it is 

important that the real-time program be integrated 

with a response protocol. A comparison of the two 

real-time, continuous monitoring cases (12-hour delay 

versus 4-hour delay in notifying the public) illustrates 

the importance of response time. Shortening the time 

needed to implement an effective response to reduce 

further exposure is critical for reducing the number of 

additional infections. 

The results of this case study also illustrate that, for 

this distribution system, strategically placing just 5 or 

10 sampling/sensor stations as part of a monitoring 

and surveillance system can have a significant effect 

on reducing potential health impacts from intentional 

contamination. 

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A Reference Guide for Utilities 

7.6.3 Case Summary 

TEVA is an integrated system intended to provide the 

capability for analyzing the vulnerabilities of 

drinking water distribution systems and to measure 

the public health and economic impacts. TEVA can 

be used to design and evaluate threat mitigation and 

response strategies related to events of intentional 

introduction of chemical, biological, or radiological 

contaminants into drinking water networks. MonteCarlo simulations generate ensembles of threat 

scenarios to identify vulnerable areas of the water 

distribution network. TEVA is based on the use of an 

all pipes EPANET network model. 

7.7 Field Testing of WaterDistribution Systems in 

Support of an Epidemiologic 

Study 

This case study is focused on the use of information 

collected as part of field studies to assist in the 

calibration of a hydraulic and water quality model 

of a distribution system. The information presented 

in this section is based on an ongoing investigation 

by the ATSDR at the U.S. Marine Corps Base, Camp 

Lejeune, NC (Camp Lejeune). These data are being 

collected to support an ongoing epidemiologic 

study at Camp Lejeune. The resulting calibrated 

model is needed to perform a historical reconstruction of the water system for the period of interest. 

This case study highlights the field methodologies 

employed to generate the information proposed for 

use in the calibration of the model. 

Key Phrases to Characterize Case Study: field 

studies, historical reconstruction, hydraulic and water 

quality modeling, model calibration. 

7.7.1 Case Study Overview 

ATSDR is conducting an epidemiologic study to 

determine if there is an association between exposure 

to contaminated drinking water and birth defects 

among children of women who lived at Camp Lejeune 

while they were pregnant between 1968 and 1985. 

Because of the paucity of historical water distribution 

system operational data, information based on the 

operation of present-day water distribution systems 

will be used for historical reconstruction. Present-day 

system operations will be modeled using waterdistribution system models. To calibrate the models 

against hydraulic and water quality parameters, field 

testing is being performed to gather data and information on hydraulic, fate and transport, and operational 

characteristics of the water distribution systems 

(Maslia et al., 2005; Sautner et al., 2005). The 

specific field activities are discussed later in Section 

7.7.2. 

Figure 7-25. Water Distribution Systems Serving U.S. 

Marine Corps Base, Camp Lejeune, NC. 

Camp Lejeune encompasses an area of about 164 

square miles, and is located in Jacksonville, Onslow 

County, North Carolina, bordering the Atlantic Ocean. 

The focus of the epidemiologic study is on exposure 

from water-distribution systems that historically 

served the military base’s housing—Camp Johnson, 

Tarawa Terrace, Holcomb Boulevard, and Hadnot 

Point (see Figure 7-25). Presently, there are two 

operating water treatment plants (WTPs) that provide 

water for the distribution systems of interest to the 

epidemiologic study: (1) the Holcomb Boulevard 

WTP that services the Camp Johnson, Tarawa Terrace, 

and Holcomb Boulevard areas of the distribution 

system, and (2) the Hadnot Point WTP that services 

the Hadnot Point area of the distribution system. 

Hadnot Point was the original WTP and at one time, 

serviced the entire base. The Holcomb Boulevard 

WTP presently services the rest of the military 

housing areas. A third plant, the Tarawa Terrace WTP, 

historically serviced the Tarawa Terrace and Camp 

Johnson areas, but this plant was shut down and 

replaced by a ground storage tank at Tarawa Terrace 

that receives water directly from the Holcomb 

Boulevard WTP. 

System pressures range from about 55–68 psi 

throughout the distribution systems. As topography is very flat, ranging from sea level to less than 

40 ft, hydraulic heads range from 140–160 ft 

resulting in a very mild hydraulic gradient. There 

are nine elevated storage tanks in the Holcomb 

Boulevard and Hadnot Point WTP service areas. 

The range in water level fluctuation for the elevated storage tanks is small; generally 1–6 ft. 

Excellent mapping information and a detailed GIS 

provide good information on the location and 

characteristics of the water system facilities. 

SCADA data are available that provide continuous 

7-22 
Figure 7-26. Continuous Recording Pressure Logger 

Mounted on Brass Shutoff Valve and Hydrant 

Adapter Cap Used for Fire-Flow and C-Factor Tests. 

information on plant discharges and tank water 

levels. However, individual buildings and residences are not metered. 

7.7.2 Field Work 

A variety of field activities are being performed to 

provide a better understanding of the operation of 

the water system and to provide information that 

can be used to calibrate a detailed water distribution system model. To date, these activities have 

included: 

•	 conducting C-factor and fire-flow tests, 

•	 recording system pressures and storage tank 

water levels over time, 

•	 tracer and associated travel time tests, and 

•	 recording continuous flow information at key 

locations. 

Several of these field activities were performed in 

tandem in order to provide an integrated understanding of the system operation and performance. 

7.7.2.1 C-Factor and Fire-Flow Tests 

C-factor tests and fire-flow tests were conducted in 

August 2004 at various sites at Camp Lejeune. 

Continuous pressure loggers (Figure 7-26) set to 

record pressure at 1-minute intervals were attached to 

hydrants. Standard analog pressure gages were also 

used as backup during the tests. Hydrant flows were 

measured using pitot gages installed on hydrants that 

were flowed during the tests. One of the pitot gages 

was integrated with a diffuser and cage to both diffuse 

the flow from the hydrant and to trap any solids to 

prevent damage from the flow (see Figure 4-6 in 

Chapter 4). The other pitot gage was a standard gage 

attached to the hydrant. 

A Reference Guide for Utilities 

Standard C-factor testing procedures were used to 

measure data needed to calculate the Hazen-Williams 

C-factors for eight sections representing a variety of 

pipe materials and diameters. 

Fire-flow tests are frequently used in the process of 

calibrating a hydraulic water distribution system 

model. One or more hydrants are opened and flowed 

to increase flows in the distribution system in the 

vicinity of the hydrants. Since friction losses increase 

exponentially, the higher flows can result in a 

significant lowering of the hydraulic grade line 

(HGL). In calibrating the model, the model is applied 

under the flow and operational conditions experienced during the fire-flow test and the pressures or 

hydraulic grade line observed in the field are compared to the model results. If there are significant 

differences between the model and field results, 

adjustments are made in model parameters in order to 

reduce the differences or calibrate the model. In the 

simplest configuration, a single hydrant is flowed and 

pressure read at another single hydrant. An alternative approach was used at Camp Lejeune to improve 

the labor efficiency and to collect more data. Continuous recording pressure gages were installed at up 

to six hydrants in the area being tested. Additionally, 

pitot gages were installed on two hydrants that were 

designated as hydrants to be flowed. Pressure was 

measured under several conditions: (1) static conditions at start of testing, (2) while each of the two 

hydrants was flowed separately, (3) while both 

hydrants were flowed together, and (4) static conditions at the end of testing. The results of such a test at 

one site are shown in Figure 7-27 and Table 7-2. 

7.7.2.2 Tracer Test and Continuous 

Measurements 

A field test was conducted May 24–27, 2004, in the 

Hadnot Point (Camp Lejeune) distribution system 

consisting of three activities: (1) injecting liquid 

CaCl , 35 percent by weight, into the transmission 2

main on the distribution system side of the WTP to 

achieve an elevated conductance and chloride 

concentration, and recording conductivity and 

chloride concentration using continuous recording 

Figure 7-27. Location of Fire Hydrants Used in Fire-Flow 

Test at Site H02. 

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A Reference Guide for Utilities 

Table 7-2. Field Data Collected During Fire-Flow Test at Site H02 

FF-H02-P0 

Pressure, Psi 

FF-H02-P1 

Pressure, Psi 

FF-H02-P2 

Pressure, Psi 

FF-H02-P3 

Pressure, Psi 

FF-H02-Q1 

Flow, gpm 

FF-H02-Q2 

Flow, gpm 

Static case 

(start) 

53.1 50.7 56.2 52.6 0 0 

Hydrant 1 

flowed 

41.4 37.3 46.8 42.9 773 0 

Hydrant 1+2 

flowed 

29.7 24.5 36.5 32.7 631 579 

Hydrant 2 

flowed 

43.9 40.7 48.1 44.1 0 747 

Static case 

(end) 

53.5 51.2 56.5 52.9 0 0 

1.0 psi = 6.8948 kPa; 1 gpm = 0.0639 L/S 

water-quality monitoring data loggers, (2) injecting 

a sodium fluoride solution into the transmission 

main to achieve an elevated fluoride concentration 

and monitoring fluoride concentration in the 

distribution system, and (3) monitoring distribution 

system pressures with continuous recording data 

loggers attached to selected hydrants and flows and 

tank water levels from SCADA data. In addition to 

continuously recording tracer concentrations and 

conductivity, grab samples were collected for 

quality assurance and quality control (QA/QC) 

purposes. Samples were analyzed at the Hadnot 

Point WTP by ATSDR staff and then also shipped 

to the Federal Occupational Health (FOH) environmental laboratory in Chicago, Illinois, for analysis. 

Twenty-seven hydrants were selected in the Hadnot 

Point area as monitoring locations. For monitoring 

conductivity and chloride and fluoride concentrations, nine hydrants were equipped with the Horiba 

W-23XD dual probe ion detector (Figure 7-28). For 

monitoring conductivity, nine hydrants were 

equipped with the Horiba W-21XD single probe ion 

detector, thus providing a total of 18 monitoring 

locations for continuously recording conductivity 

data. For pressure measurements, nine hydrants 

were equipped with continuous recording Dixon 

PR300 pressure data loggers (Figure 7-26). 

Results from the chloride injection were used to 

estimate arrival times of the tracer at different 

locations throughout the Hadnot Point WTP area. 

Of special interest are the extremely long arrival 

times—in excess of 16 hours—in the northwestern 

part of the of the Hadnot Point WTP area (Figure 7­

29, loggers C01, C02, and F01). Additionally, a 

comparison of arrival times of the calcium chloride 

tracer at logger location C04 with arrival times at 

loggers F04, F05, and F02, led investigators to 

consider that there may have been undocumented 

closed valves in the distribution system (the closed 

valves did not affect C-factor measurements). Posttest field verification by water utility staff confirmed the locations of closed valves, as indicated 

by the “•” symbol in Figure 7-29. 

7.7.3 Additional Test Procedures 

A second tracer test was conducted in the Holcomb 

Boulevard WTP area in September-October 2004. In 

this test, the normal fluoride feed was turned off for a 

period of a week and then turned back on and 

Figure 7-28. Horiba W-23XD Dual Probe Ion 

Detector Inside Flow Cell. 

7-24 
6.6 

C04 

9.7 

C06 

1.8 

F05 

2.5 

F04 

6.6 

C04 

19.5 

F01 

26.2 

C01 

26.3 

C02 

3.5 

F02 

3.4 

C03 

0.3 

F03 

3.6 

F07 

12.1 

C09 

4.5 

C08 

11.7 

F09 

5.3 

C07 

2.1 

F08 

9 

F06 

EXPLANATION 

Water Pipeline 

Water Body 

Roads 

Base Boundary 

Storage Tanks 

Elevated - controlling 

Elevated 

Ground - raw water 

Ground - treated water ATSDR Data Logger 

Front arrival time (hours) 

Logger ID

 F - Fluoride, Chloride

 C - Conductivity 

Figure 7-29. Arrival Times of the Calcium Chloride Tracer 

at Monitoring Locations in Hadnot Point WTP Area, May 

25, 2004. 

monitored for another week. Nine locations in the 

distribution system were equipped with the Horiba W23XD continuous recording, dual probe ion detector 

data logger. Minimal labor was required in support of 

this test. Additionally, 16 magnetic flow meters have 

been installed throughout the system and will be used 

in conjunction with future tracer tests to provide 

additional calibration information. 

References 

Aral, M. M., J. Guan, M.L. Maslia, J.B. Sautner, R.E. 

Gilig, J.J. Reyes, and R.C. Williams, “Optimal 

Reconstruction of Historical Water Supply to a 

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Aral, M. M., J. Guan, M.L. Maslia, J.B. Sautner, R.E. 

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Bahadur, R., W.B. Samuels, and J. Pickus. Case Study 

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A Reference Guide for Utilities 

7.7.4 Case Study Summary 

This case study presents results from preliminary 

field-test activities used to gather hydraulic and water 

quality data at Camp Lejeune. Field tests to date have 

included: (a) recording system pressures and storage 

tank water levels, and (b) conducting C-factor, fireflow, tracer, and travel time tests. The test data are 

being used to assist with hydraulic and water quality 

model calibration. They are also being used to plan 

and carry out a more refined, detailed field test of 

water distribution systems serving military base 

housing. These activities will assist in providing 

much-needed model parameter data for calibrating 

models of the present-day water distribution system. 

The present-day models are needed as a first step in 

reconstructing historical operations during the period 

between 1968 and 1985, as part of an ongoing 

epidemiologic study of childhood diseases at Camp 

Lejeune. 

7.8 Chapter Summary 

The case studies presented in this chapter illustrate 

the various ways in which the tools presented in 

this reference guide can be used. The case studies 

also demonstrate that, for each application, a 

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