DEVELOPMENT OF EXTENDED PERIOD SIMULATION MODEL
FOR THE CITY OF MODESTO
A Project
Presented to the faculty of the Department of Civil Engineering
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF SCIENCE
in
Civil Engineering
by
Glenn Durgesh Prasad
FALL
2012
© 2012
Glenn Durgesh Prasad
ALL RIGHTS RESERVED
ii
DEVELOPMENT OF EXTENDED PERIOD SIMULATION MODEL
FOR THE CITY OF MODESTO
A Project
by
Glenn Durgesh Prasad
Approved by:
__________________________________, Committee Chair
Dr. Saad Merayyan
__________________________________, Second Reader
Dr. Kurt Ohlinger
____________________________
Date
iii
Student: Glenn Durgesh Prasad
I certify that this student has met the requirements for format contained in the University format
manual, and that this Project is suitable for shelving in the Library and credit is to be awarded for
the Project.
__________________________, Graduate Coordinator
Dr. Cyrus Aryani, P.E., G.E.
Department of Civil Engineering
iv
___________________
Date
Abstract
of
DEVELOPMENT OF EXTENDED PERIOD SIMULATION MODEL
FOR THE CITY OF MODESTO
by
Glenn Durgesh Prasad
Through this project, an Extended Period Simulation (EPS) model was developed
in order to provide the City of Modesto (City) with an easily adaptable tool that
incorporates advanced supervisory controls within the existing distribution system, while
predicting the water distribution system behavior within a reasonable accuracy. Once
created, the EPS model was used to develop an understanding of the general water
system behavior during varying demand conditions. Through specific case studies, the
EPS model was further used to evaluate two important water resource planning concepts
currently explored by the City. Hence, this Project is divided into three main parts: (1)
Development of an Extended Period Simulation (EPS) Model; (2) Case Study 1: Surface
Water Optimization Source Tracing Model; and (3) Case Study 2: Aquifer Storage and
Recovery (ASR) Hydraulic EPS Model.
Since an understanding of the water system was critical, each task within the
Project was developed through close communication with City’s operations, planning and
v
design staff. Extensive pressure and flow data collected by the City was incorporated
into the EPS model development. Prior to the model calibration, a brief GIS-based water
system assessment was conducted that exclusively examined the City’s distribution pipe
network, in terms of its material and age. During this time, the City Water Operations
staff was consulted regarding the general condition of the City’s water pipe network.
The EPS model was calibrated with an overall accuracy of 10% utilizing more
than 500 measured flow and pressure data points. While the model predicted well in
North Modesto, there are still model refinements necessary in the South Modesto Area.
Primarily, tank control data will be needed to further refine model calibration.
The Surface Water Optimization Source Tracing Model outlined the extent of
surface water delivery within the City’s distribution system in a given 24 hour period.
Surface water, which is conveyed through the Modesto Irrigation District (MID)
transmission main was only delivered within close proximity to the mains. This was
primarily occurring because the receiving system was also supplied by groundwater wells
operating at similar pressure levels. The City should consider using the model for a
future surface water optimization study, which may look at the reevaluation of the well
cluster operation systems.
The Aquifer Storage and Recovery (ASR) Hydraulic EPS model demonstrated
that the City could supply up to 10 million gallons per day (MGD) of available excess
surface water during winter conditions for an ASR program to an area immediately west
of the City, near West Tank. The EPS model indicated that during summer demands, if
vi
the western portion of the City was selected for the ASR program, the City could face
difficulties in extracting ASR water and supplying it to the distribution system, with the
current well cluster supervisory controls in place. It is recommended that the City should
tailor the cluster controls to allow for desired ASR water supply. The evaluation in this
project was only from a hydraulic distribution system standpoint and no hydrogeologic
analysis was performed to confirm aquifer characteristics within the proposed location.
This analysis was conducted as a demonstration to the City of the nature of a future ASR
Facility Layout Analysis that should be performed as part of an ASR Feasibility Study.
_______________________, Committee Chair
Dr. Saad Merayyan
_______________________
Date
vii
DEDICATION
To My Wife and My Parents
viii
ACKNOWLEDGEMENTS
I would like to express my gratitude towards everyone who helped me complete this
Project. I would like to convey my appreciation to Dr. Saad Merayyan, for his guidance
and the Department of Civil Engineering at California State University Sacramento, for
accepting my Project.
This Project would not have been possible without the support of the Utility Planning and
Projects Department of the City of Modesto, led by Rich Ulm. A special thanks to Jack
Bond for heading the Capital Planning water modeling efforts and authorizing the
development of the Extended Period Simulation Model for the City. I would like to
furthermore thank the entire Water Operations Division of the City for their input, and
trust in the City's water model.
I would like to convey my sincere appreciation towards my parents for believing in me
and helping me achieve various goals in my life, including the completion of this Project.
Thanks to my brother for becoming my motivation in life. I wish to express my love and
gratitude towards my five year old nephew, Amon, for spilling juice over my Project
Report and thereby accelerating my efforts towards a new horizon. Finally, for good
reason, I am deeply indebted to my wife, Chaya for her support all the way.
ix
TABLE OF CONTENTS
Page
Dedication ............................................................................................................................. viii
Acknowledgments.................................................................................................................... ix
List of Tables ......................................................................................................................... xii
List of Figures ....................................................................................................................... xiii
Abbreviations ........................................................................................................................... xv
Chapter
1. INTRODUCTION ...................... ……………………………………………………….. 1
1.1 Background ............................................................................................................ 1
1.2 Water Supply Overview ........................................................................................ 1
1.3 Water Demand Overview ...................................................................................... 3
1.4 Unaccounted for Water ........................................................................................... 3
1.5 Water System Operations Overview ....................................................................... 4
2. LITERATURE REVIEW ................................................................................................... 7
3. DEVELOPMENT OF EXTENDED PERIOD SIMULATION MODEL ........................ 10
3.1 Earlier Steady State Model Description ............................................................... 10
3.2 Water Model Updates ........................................................................................... 11
3.3 Field Data Collection ............................................................................................ 12
3.4 Well Cluster Controls ........................................................................................... 13
3.5 Summer Diurnal Patterns ...................................................................................... 17
3.6 Model Simulation Options and Time Settings ...................................................... 19
3.7 EPS Model Calibration ......................................................................................... 20
x
3.7.1 Calibration Methodology ...................................................................... 20
3.7.2 Water System Pipe Condition Assessment ........................................... 23
3.7.3 Model Run and Results ......................................................................... 28
4. CASE STUDY 1: SURFACE WATER SUPPLY OPTIMIZATION USING
SOURCE TRACING MODEL .................................................................................... 32
4.1 Importance of Surface Water Optimization .......................................................... 32
4.1.1 Surface Water Availability and Reliability ........................................................ 32
4.1.2 Surface Water Quality versus Groundwater Quality ......................................... 33
4.1.3 Groundwater Management / Replenishment...................................................... 34
4.2 Surface Water Optimization Source Tracing Model............................................. 34
4.4 Well Field Optimization Project Phase II ............................................................. 38
5. CASE STUDY 2: AQUIFER STORAGE AND RECOVERY HYDRAULIC EPS
MODEL ............................................................................................................................. 40
5.1 Importance of an Aquifer Storage and Recovery Project ..................................... 40
5.2 Purpose and Limitations of the ASR Hydraulic EPS Model ................................ 42
5.3 Development of the ASR Hydraulic EPS Model .................................................. 45
5.4 Evaluation of ASR Water Diversion Point Locations .......................................... 46
5.5 ASR Hydraulic EPS Modeling Results................................................................. 47
6. CONCLUSIONS AND RECOMMENDATIONS ........................................................... 51
Appendix A: Model Data Entry for Well Cluster Controls ..................................................... 55
Appendix B: Water Pipe Grouping Maps for Calibration ....................................................... 85
Appendix C: Surface Water Optimization Source Tracing Model .......................................... 97
References .............................................................................................................................. 111
xi
LIST OF TABLES
Tables
Page
1.
Summary of Well Cluster Operating Parameters............................................... 15
2.
Pipe Categorization Based on Material and Age Groups .................................. 25
xii
LIST OF FIGURES
Figures
Page
1.
Surface Water Supply to the City of Modesto ................................................... 2
2.
City of Modesto Water System Layout ............................................................. 6
3.
Input of Rule Control Data for Cluster-Based Operation .................................. 14
4.
June, 14 2011 Water Production Demands – Global Summer Diurnal Curve .. 18
5.
EPS Model Simulation Options ......................................................................... 19
6.
EPS Model Simulation Time Options................................................................ 20
7.
Calibration Objective Functions Used by Calibrator® ...................................... 21
8.
Junction Pressure Entered into the EPS Model.................................................. 22
9.
MID Pipe Flow Data Entered into the EPS Model ............................................ 22
10.
Old Water Pipeline with Extensive Sedimentation............................................ 23
11.
Water Operations Staff Removing Sand Out of a Water Main ......................... 24
12.
Pipe Group 1 (AC, CI: 1980 – Present) ............................................................. 26
13.
Pipe Group 2 (AC, CI: 1940 – 1979) ................................................................. 27
14.
Entering Pipe Group Categorization into the EPS Model ................................. 28
15.
Calibration Options ............................................................................................ 29
16.
Exporting Calibrator® Results to Active Pipe Set ............................................ 30
17.
Surface Water Optimization Source Tracing Model (Hour 09:00) ................... 36
18.
Surface Water Optimization Source Tracing Model (Hour 24:00) ................... 37
19.
City of Modesto Water Demand versus Available Water Supplies in AFY...... 41
xiii
Figures
Page
20.
Aquifer Storage and Recovery Components Diagram....................................... 44
21.
January 14, 2011 Demand Pattern for ASR Hydraulic EPS Model .................. 46
22.
Winter Demands versus Suggested ASR Operations ........................................ 49
xiv
ABBREVIATIONS
AFY................................................................Acre-foot per Year
ARTDA ..........................................................Amended and Restated Delivery Agreement
CIP .................................................................Capital Improvement Projects
DWR ..............................................................California Department of Water Resources
EPS.................................................................Extended Period Simulation
GA ..................................................................Genetic Algorithm
MG .................................................................Million Gallons
MGD ..............................................................Million Gallons per Day
MID ................................................................Modesto Irrigation District
MRWTP .........................................................Modesto Regional Water Treatment Plant
PRV ................................................................Pressure Reducer Valve
PSI ..................................................................Pounds per Square Inch
SCADA ..........................................................Supervisory Control and Data Acquisition
UWMP ...........................................................Urban Water Management Plan
WFO-P2 .........................................................Well Field Optimization Project Phase II
xv
1
CHAPTER 1
INTRODUCTION
1.1 Background
The City of Modesto (City) is the largest urban water purveyor in the Modesto subbasin
of the San Joaquin Hydrologic basin. The City serves water to more than 230,000
constituents (including Modesto, Empire and Salida), as well as major industries that
provide goods and supplies worldwide. A constant and reliable urban water supply is
necessary for a sustainable urban environment, and is also vital to its economic growth.
1.2 Water Supply Overview
The City’s water supply comprises a 30 million gallon per day (MGD) surface water
supply, along with more than a hundred groundwater wells. The surface water supply is
provided by Modesto Irrigation District (MID) through an Amended and Restated
Transfer and Delivery Agreement (ARTDA). MID is one of the oldest irrigation districts
in California and has pre-1914 water rights on the Tuolumne River. Surface water from
the Tuolumne River is treated about fourteen miles east of the City, at the MID Regional
Water Treatment Plant (MRWTP) and delivered solely to the City, connecting into the
western side of its distribution system (see Figure 1).
2
0
3
1.3 Water Demand Overview
In the 2010 Water System Engineer’s Report (Engineer’s Report), it was estimated that
the City’s average daily demand (using a 10 year average) was 70.3 MGD, while the
maximum and peak hour demands were 117.5 MGD and 165.2 MGD respectively. The
total base water demand comprises residential, industrial, commercial, and institutional
customers. During the development of the Engineer’s Report, the 50 top users within the
City were identified and their annual water usage was quantified. Most of these users
were industrial and institutional water users.
1.4 Unaccounted for Water
Currently, the City’s water system is not entirely metered. For this reason, measuring
actual demand for each customer is not possible. Nonetheless, the City has a very robust
Supervisory Control and Data Acquisition (SCADA) system that measures water
production from each well, as well as continuous pressures and flows at key locations
throughout the City. Based on data from other similar cities, it had been estimated in the
Engineer’s Report that the City could be losing about 15% of the total water produced.
These losses are termed “Unaccounted for Water” and are mainly attributed to losses due
to water leaks, pipe repairs, pipeline flushing, hydrant testing and fire flows. Hence the
City has established the following relationship:
Water Produced = Water Supplied = Water Demand + Unaccounted for Water
4
1.5 Water System Operations Overview
The surface water supply from the MRWTP is conveyed 14 miles west into the Terminal
Reservoirs, which comprises two five million gallon (MG) tanks. Surface water from the
Terminal Reservoirs is then conveyed through about 14 miles of pipeline running through
the City, ranging from 24 inch to 48 inch diameter, known as the MID Transmission
Main. The City has limited control over the quantity of surface water supplied through
the Terminal Reservoir. The City's operation staff maintains daily communication with
MID staff to ensure proper surface water delivery. The surface water supply is conveyed
throughout the City through the MID Transmission Main, as shown in Figure 2. This
water is then regulated through several Pressure Reducer Valves (PRVs) to ensure
appropriate pressure reduction before entering the City's water distribution system.
The groundwater wells are grouped into 20 operational clusters. Each cluster comprises a
collection of groundwater wells that operate based on pressure measurements at a single
common pressure point. The collections of wells within a cluster have a specified
operating sequence, which was defined based on field experience of existing and past
City staff. The wells are sequenced such that the highest priority well is operated
continually to satisfy system demands. If the pressure point reading within a cluster goes
below the set point (50 - 60 psi in most cases), then the second well in the sequence turns
on. If the target pressure at the pressure point is met, then the second well shuts off.
Otherwise, the third well turns on. This process is repeated until all the wells within the
cluster are brought online as needed. The steady state model developed for the City did
5
not have the capability of activating wells based on the City’s defined cluster sequences.
The decision of which wells to bring online was primarily at the modeler's discretion.
The Extended Period Simulation (EPS) model developed through this study provided the
model this capability.
6
7
CHAPTER 2
LITERATURE REVIEW
The Literature Review conducted during this Project, was primarily used to research
available information on the development of Extended Period Simulation (EPS) models,
evaluate the technological advances in water modeling, and compare the relative status of
the City of Modesto’s water system model. Critical review of some technical
publications was also performed and compared with the challenges faced by the City to
expand on its modeling capabilities.
Water distribution system models invariably attempt to solve two fundamental equations
in pipe network hydraulics. These two equations are the Continuity Equation and the
Momentum Equation. While these two equations have remained the focus of hydraulic
models for decades, our ability to solve these equations has dramatically changed
throughout history. The AWWA Manual M32: Computer Modeling of Water Distribution
Systems, a brief history of computer modeling is described in the first Chapter. The
authors explain how before 1960’s, manual calculations were primarily used for water
modeling. This was often a laborious task that was only practical for solving single
looped systems. As computers advanced, so did our ability to solve water distribution
models quickly and easily. The release of EPANET by the United States Environment
Protection Agency in 1993 as a public-domain software (Rossman, 1993) not only made
8
it accessible to water resource engineer’s worldwide, but also opened doors for other
software developers to expand its capabilities. In M32, authors indicate how present day
software packages have focused on Geographic Information Systems (GIS) as to allow
the centralization of data to reduce the effort required to develop models. Present day
water system models also have the capability to receive real time data from SCADA
systems. This includes the InfoWater® software package developed by Innovyze that
was used for this Project.
In Water Distribution System Handbook, Larry Mays et al. explores the linking of
SCADA systems with distribution system models. In this book, a model with such
linkage is explained to serve as a tool for operator training, emergency response
preparation, energy management and understanding water quality behavior. Chapter 16
of this book also explains various optimization models that can be developed after the
model is integrated with field measurements. The authors of the Water Distribution
System Handbook later acknowledge that “several water utilities in the United States are
using hydraulic network models in their operations, but few are actually allowing system
operators to use the models” (Mays, 2000). The City of Modesto falls under the latter of
the two types of utilities, where operations staffs are not utilizing the relevant capabilities
of the model to the fullest. The primary reason for this is the limitations of the City’s
steady state (snapshot in time) hydraulic model. The City’s current model was developed
in order to create a Capital Improvements Program (CIP) and not to solve ongoing (nearterm) operational issues. Lack of modeling expertise is another reason why the model
9
has not been utilized by operations staff. While technical adequacy may be attained by
implementing staff training programs, the City’s Water Operations division’s bigger
challenge will be dedicating consistent financial resources towards the development,
maintenance and purchase of equipment needed to utilize a system that incorporates the
hydraulic model as the core element in their decision making process.
The City’s most current report that deals intensively with hydraulic modeling is the 2010
Water Systems Engineer’s Report. The 2010 Joint Urban Water Management Plan also
lays out the City’s roadmap in terms of planning for water supplies and demonstrating
compliance with state-mandated conservation programs. None of these documents have
specifically stressed the need for an Extended Period Simulation Model for the City. The
2010 Engineer’s Report does include Water System Evaluation (Category 17), a $4.9M
program, as one of its CIP’s that will fund “as-needed” water system studies. This
program will also fund the maintenance of the hydraulic modeling software and hydraulic
model updates. Both reports have demonstrated the use of existing 30 MGD surface
water supply and have highlighted the need for additional 30 MGD (total of 60 MGD)
surface water supply in the near future. An Extended Period Simulation model will be
needed to conduct necessary analysis to ensure proper delivery operations and
conveyance of the additional surface water supply. Future conjunctive use projects such
as Aquifer Storage and Recovery (ASR) will also require the need for the EPS Model.
10
CHAPTER 3
DEVELOPMENT OF EXTENDED PERIOD SIMULATION MODEL
3.1 Earlier Steady State Model Description
The City’s earlier model was a steady state model (snapshot in time), which was a
planning level model primarily developed to help the City setup its Capital Improvement
Projects (CIP) Program. In the 2005 City of Modesto Hydraulic Model Update, a
software evaluation was conducted that compared a number of software packages based
on user-friendliness, analysis capabilities, demand allocation methods and cost. During
this evaluation, the H2ONET computer package developed by Innovyze (formally
MWHSoft), was selected. In 2009, the City decided to bring the model in-house and a
brief re-evaluation was conducted. After 2005, the City had expanded on its GIS
capabilities, and therefore, it was most appropriate to convert the model to the GIScompatible version of H2ONET, which is InfoWater®. The InfoWater® software
package was utilized in this study.
The early steady state InfoWater® model contained six main scenarios (i.e. Existing
Average Day, Existing Maximum Day plus Fire Flow, Existing Peak Hour, Future
Average Day, Future Maximum Day plus Fire Flow, and Future Peak Hour Scenarios).
In order to develop a particular scenario, the corresponding water production was
calculated based on land use designations and water duty factors and the demands were
11
distributed on relevant demand nodes. The top fifty water users within the City were also
identified and their corresponding water demand was quantified and applied separately to
the appropriate node within the steady state model. Once the demands were assigned, the
status of wells throughout the system was adjusted to achieve the desired flow at the MID
terminal reservoir. It was assumed (and confirmed from measurements) that during an
Average Day scenario, the MID terminal reservoir typically produced 30 MGD, whereas,
during a Peak Hour scenario, the MID Terminal Reservoirs could produce up to 45
MGD. The decision as to which wells will remain on or off was made by the modeler,
considering several factors such as pump efficiency, proximity to the MID terminal
reservoirs and most importantly, the response to MID terminal reservoir flows. For
example, if the computed flow from terminal reservoir was 35 MGD during Average Day
Demand conditions, wells were turned on to provide more groundwater supply and
therefore reduce surface water supply needed to 30 MGD. If turning on a well didn’t
provide any computed flow increase at the MID terminal reservoir, another well was tried
until the desired flow at MID terminal reservoir was achieved. This decision making
process, which was necessary each time the demand had considerably increased or
decreased, was a major drawback of the earlier steady state model.
3.2 Water Model Updates
All water system improvements made after the delivery of the Steady State Model in
2009 was added to the model. This included activating recently added constructed PRVs
along the MID transmission main, as well as Tank 12 (West Tank) and its booster pump
12
stations. The water model update was performed in collaboration with the City's Water
Design staff. All infrastructure activated in the model was confirmed consistent with the
as-built plans for the water system improvements. Water Operations field crews, who
had installed pipelines were consulted as needed.
3.3 Field Data Collection
The City collects and stores thousands of data points throughout the day through its
SCADA system. At the end of each day, this data is archived and is shared with various
City departments that deal with water system operations, design and planning. Examples
of stored data include:
1. Monthly flow data for each well
2. Monthly and hourly flow data by system (Note: The City serves other areas
outside the City of Modesto water service area)
3. Hourly pressure and flow measurements for each well cluster
4. Daily wells out of service reports
Due to the vast amounts of data collected and reported each day, it was logical to develop
the EPS model in such a way that included flexibility to update and re-calibrate the model
(if needed) based on any given demand conditions. Field data collected for June 14, 2011
was used for the development and calibration of the EPS model. Demands on this day
ranged from a low of about 60 MGD to a high of about 95 MGD. Hence it should be
noted that the calibration performed is valid within these demand ranges. Although 2011
13
had been a low water use year, it was still selected during the development because for
this Project, it was more important to keep the model updated to current conditions, than
simulating maximum demand conditions. January 2011 data was used for the winter
model created for the Aquifer Storage and Recovery (ASR) demonstration model.
A total of 480 pressure point data was collected for the project. This included hourly
pressure data for each of the 20 well clusters (24 hours per cluster x 20 well clusters
equals 480 data points). In addition, hourly flow data corresponding to the MID pipeline
was collected for 24 hours on January 2011.
3.4 Well Cluster Controls
One of the significant enhancements to the Model during this Project was the addition of
the Well Cluster Controls. These controls were simulated as Rule Controls within
InfoWater®. Unlike Simple Controls in InfoWater®, Rule Controls allow for complex
instructions to mimic the decision making capability that currently exists in the field
through the City's SCADA system. A total of 68 Rule Controls were added to the model
to allow wells to turn on or off based on the pressure point readings and their operating
sequence. Figure 3 shows how the user may enter and edit rule controls into the
InfoWater® computer program.
14
Figure 3: Input of Rule Control Data for Cluster-Based Operations
The remaining Rule Control entries have been provided in Appendix A. All Rule
Controls entered within the model were given equal priority. Table 1 summarizes the
cluster operations data that was used to develop the Rule Controls. The City wishes to
maintain system pressure around 60 psi and therefore, the wells operating within a cluster
are designed to meet the target delivery pressure.
15
Cluster #
Cluster 1
Cluster 1
Cluster 2
Cluster 2
Cluster 2
Cluster 2
Cluster 2
Cluster 2
Cluster 2
Cluster 3
Cluster 3
Cluster 3
Cluster 3
Cluster 3
Cluster 3
Cluster 3
Cluster 3
Cluster 4
Cluster 4
Cluster 4
Cluster 4
Cluster 4
Cluster 5
Cluster 5
Cluster 5
Cluster 5
Cluster 6
Cluster 6
Cluster 6
Cluster 6
Cluster 6
Cluster 6
Cluster 7
Cluster 7
Cluster 7
Cluster 7
Cluster 7
Cluster 7
Cluster 8
Cluster 8
Cluster 8
Cluster 8
Cluster 8
Cluster 9
Cluster 9
Cluster 9
Cluster 10
Cluster 10
Cluster 10
Well #
Well 226
Well 42
Well 25
Well 43
Well 262
Well 48
Well 264
Well 50
Well 24
Well 54
Well 259
Well 58
Well 52
Well 39
Well 46
Well 37
Well 62
Well 241
Well 237
Well 236
Well 34
Well 44
Well 8
Well 17
Well 16
Well 56
Well 14
Well 18
Well 21
Well 267
Well 269
Well 300
Well 65
Well 32
Well 41
Well 265
Well 278
Well 45
Well 3
Well 57
Well 4
Well 7
Well 2
Well 36
Well 1
Well 6
Well 40
Well 211
Well 59
Sequence
Offline
1
1
2
3
4
5
6
7
1
2
3
4
5
6
7
8
1
2
3
4
5
1
2
3
4
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
1
2
3
1
2
3
Pressure Point (Model
Node)
16046
16046
17534
17534
17534
17534
17534
17534
17534
16468
16468
16468
16468
16468
16468
16468
16468
11064
11064
11064
11064
11064
14650
14650
14650
14650
14274
14274
14274
14274
14274
14274
10040
10040
10040
10040
10040
10040
11502
11502
11502
11502
11502
14192
14192
14192
13286
13286
13286
Table 1: Summary of Well Cluster Operating Parameters
Setting
(psi)
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
16
Cluster #
Cluster 10
Cluster 10
Cluster 10
Cluster 11
Cluster 11
Cluster 11
Cluster 11
Cluster 11
Cluster 11
Cluster 11
Cluster 12
Cluster 12
Cluster 12
Cluster 12
Cluster 12
Cluster 13
Cluster 13
Cluster 13
Cluster 13
Cluster 13
Cluster 13
Cluster 14
Cluster 14
Cluster 14
Cluster 14
Cluster 14
Cluster 14
Cluster 15
Cluster 15
Cluster 15
Cluster 16
Cluster 16
Cluster 16
Cluster 16
Cluster 19
Cluster 19
Cluster 19
Cluster 19
Cluster 20
Cluster 20
Cluster 20
Cluster 20
Cluster 20
Cluster 21
Cluster 21
Cluster 22
Cluster 22
Cluster 22
Well #
Well 307
Well 204
Well 47
Well 53
Well 229
Well 301
Well 304
Well 10
Well 232
Well 283
Well 29
Well 30
Well 223
Well 305
Well 287
Well 38
Well 100
Well 55
Well 49
Well 19
Well 66
Well 212
Well 279
Well 225
Well 292
Well 308
Well 291
Well 247
Well 285
Well 312
Well 250
Well 313
Well 297
Well 288
Well 299
Well 298
Well 281
Well 280
Well 293
Well 277
Well 310
Well 294
Well 296
Well 33
Well 22
Well 61
Well 64
Well 51
Sequence
4
5
6
1
2
3
4
5
6
7
1
2
3
4
5
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
1
2
3
4
1
2
3
4
1
2
3
4
5
1
2
1
2
3
Pressure Point (Model
Node)
13286
13286
13286
12748
12748
12748
12748
12748
12748
12748
13620
13620
13620
13620
13620
18932
18932
18932
18932
18932
18932
13498
13498
13498
13498
13498
13498
19634
19634
19634
2282
2282
2282
2282
2394
2394
2394
2394
13470
13470
13470
13470
13470
17210
17210
20034
20034
20034
Setting
(psi)
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
Table 1: Summary of Well Cluster Operating Parameters (Continued)
17
It is recommended that the City re-evaluate and prioritize all of its wells based on factors
such as motor age, operational efficiency, impacts to groundwater levels etc. The cluster
controls should then be redefined. Further effort should be pursued to integrate the MID
Terminal Reservoirs into the cluster operating controls. This will help the City to
maximize surface water and thereby, execute an efficient conjunctive use program. The
City is currently in the process of evaluating all its wells through the Well Field
Optimization Project Phase II. This project will collect data regarding all the wells, and
evaluate them based on performance. Finally, a database along with a web-based
interface will be developed as part of a separate Well Field Optimization Project that will
centralize all collected data and allow City staff to determine different well operation
configurations (clusters) that will help the City meet a particular objective (such as
minimize pumping cost, minimize impact to groundwater, maximize production etc).
Further details regarding this project are provided in Chapter 4.
3.5 Summer Diurnal Patterns
Global system-wide water production information was gathered through the SCADA
system and a global demand pattern was calculated. This demand pattern was applied
globally on every demand node within the Model. This way, the final overall demand
pattern was the same as that observed in the field. Figure 4 shows the final diurnal curve
that was developed based on system-wide water production.
18
While this method of applying overall demand pattern is convenient and adequate for the
purposes of this project, it is recommended that more refined demand pattern assignments
be applied as part of City’s future work. This work will entail calculating demand
patterns, either through clusters (zones) or based on land use. Once the City starts
metering its customer’s water use, cluster based or land use based demands should be
calculated and applied to the EPS model to make the demand simulation more realistic.
June 14, 2011 Water Production
June 14, 2011 Water Production
100.0
90.0
Water Production (MGD)
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
23.00 - 24.00
22.00 - 23.00
21.00 - 22.00
20.00 - 21.00
19.00 - 20.00
18.00 - 19.00
17.00 - 18.00
16.00 - 17.00
15.00 - 16.00
14.00 - 15.00
13.00 - 14.00
12.00 - 13.00
11.00 - 12.00
9.00 - 10.00
10.00 - 11.00
8.00 - 9.00
7.00 - 8.00
6.00 - 7.00
5.00 - 6.00
4.00 - 5.00
3.00 - 4.00
2.00 - 3.00
1.00 - 2.00
0.00 - 1.00
0.0
Time Interval
Figure 4: June 14, 2011 Water Production Demands – Global Summer Diurnal Curve
19
3.6 Model Simulation Options and Time Settings
The Simulation Options in the model were altered for the EPS model as shown in the
figure below. It is in the simulation options where rule controls are enabled.
Figure 5: EPS Model Simulation Options
20
Figure 6: EPS Model Simulation Time Options
3.7 EPS Model Calibration
3.7.1 Calibration Methodology
The EPS model was calibrated using the Calibrator® module within the InfoWater®
Suite package. The Calibrator® module uses Advanced Genetic Algorithm (GA) to solve
an implicit nonlinear optimization problem, subject to user specified constraints. The
three choices for the objective function are provided below:
21
(Source: Innovyze, Inc. 2007)
Figure 7: Calibration Objective Functions Used by Calibrator®
Type 1 objective function has been recommended in the Calibrator® User Guide and was
selected for calibration of the EPS model. The Calibrator® allows the user to enter
measured pressure and flow data, which is then matched with simulated results of the
EPS model by adjusting the pipe roughness values within similar defined Pipe Groups.
All 480 data points collected for the pressure points on June 14, 2011 were entered into
the model for calibration. Twenty four (24) MID hourly pipe flow data for this day was
also entered for calibration. The following figures show how the pressure points and
MID pipe data points were entered into the EPS model.
22
Figure 8: Junction Pressure Entered into the EPS Model
Figure 9: MID Pipe Flow Data Entered into the EPS Model
23
3.7.2. Water System Pipe Condition Assessment
Before the final calibration process, a brief qualitative condition assessment was done on
the City’s aging distribution pipeline system, using the model database and the City’s
Geographic Information System (GIS) database. Water Operations staff was also
consulted regarding the general condition of pipelines in the City and they indicated that
old steel pipes in the City’s distribution system were the primary concern. Cast Iron and
steel pipelines often have corrosion or sedimentation, which in some cases is quite
extensive since some pipes are nearly eighty years old. A few pictures of smaller pipes
within the system indicate approximately 30 - 50% blockage in flow area.
Figure 10: Old Water Pipeline with Extensive Sedimentation
24
In some cases, pipelines close to wells have sedimentation from sands that originate from
within the well casings. Figure 11 shows Water Operations staff removing sand from the
water main during an installation.
Figure 11: Water Operations Staff Removing Sand Out of a Water Main.
Reasonable effort was made to incorporate these factors affecting pipelines and their
calibration as closely as possible into the EPS model. All pipes in the model had to be
classified into groups (of similar C factors) based on the pipe material and age. This
categorization is tabulated in Table 2.
25
Pipe Roughness
Pipe Groups
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Material
AC, CI
AC, CI
AC, CI
AC, CI
CL
DI, DIP
DI, DIP
DI, DIP
DI, DIP
GI
GI
GI
GI
PVC
PVC
PVC
PVC
UNKOWN
UNKOWN
UNKOWN
UNKOWN
Year from
1980
1940
1939
unknown
unknown
1980
1940
1939
unknown
1980
1940
1939
unknown
1980
1940
1939
unknown
1980
1940
1939
unknown
Year to
Present
1979
and less
unknown
unknown
Present
1979
and less
unknown
Present
1979
and less
unknown
Present
1979
and less
unknown
Present
1979
and less
unknown
Lower
Range
115
90
60
60
60
80
70
60
60
95
70
60
60
125
100
60
60
125
100
60
60
Upper
Range
140
115
90
140
130
100
80
70
100
120
95
70
120
150
125
100
150
150
125
100
150
Result
(Calibrator
Module)
140
105
72
140
75
96
78
62
100
95
85
70
120
150
125
76
150
135
125
68
96
Table 2: Pipe Categorization Based on Material and Age Groups
Existing information within the City’s GIS system was used to visualize areas where
pipes were older. GIS data was queried to classify the water pipes. Results of this spatial
analysis have been shown in Figure 12 and 13. A map of each pipe group tabulated
above has been provided in the Appendix.
26
27
28
3.7.3 Model Run and Results
The pipe database within the EPS model was update according to the Pipe Group
categorization. Figure 14 shows how the categorization information that was entered into
the Calibrator® Module. The module was then run using the options shown in Figure 15.
Figure 14: Entering Pipe Group Categorization into the EPS Model
29
Figure 15: Calibration Options
The EPS model achieved the desired fitness threshold of 10% in 65 trials, using the
Advanced Genetic Algorithm. The average global difference in pressure measurements
was 4.2 %. The calibrated values for pipe roughness were applied to the pipe dataset
from within the EPS model. Figure 16 shows how results from the Calibrator® were
directly exported to the active pipe set within the EPS model.
30
Figure 16: Exporting Calibrator® Results to Active Pipe Set
Refinements were made as necessary to simulate field conditions as closely as possible.
A calibration result within the required tolerance level was reached. However, while the
model predicted well in some areas, other areas within the City, particularly the South
Modesto area, had lower pressure zones than what was measured in field conditions. One
main reason for discrepancy is that the model uses decisions based on clusters, as entered
in the Rule Controls. It is not uncommon for City Water Operations to switch to hand
control and operate clusters manually. The second reason for discrepancy could be
related to the tank operations. SCADA based controls (such as filling schedules and
pressure set points) were not added to the tanks as part of this Project. This work is
31
recommended as part of future improvements to the EPS model. Results at individual
pressure points throughout the distribution system have been provided in the Appendix.
32
CHAPTER 4
CASE STUDY 1: SURFACE WATER SUPPLY OPTIMIZATION USING SOURCE
TRACING MODEL
4.1 Importance of Surface Water Optimization
The City currently gets 30 MGD of surface water supply from the Modesto Irrigation
District, through an Amended and Restated Transfer and Delivery Agreement (ARTDA).
The first water delivery was made in 1995 and an expansion of the treatment plant is
nearly complete, which will provide the City with an additional 30 MGD of surface water
supply in the near future (bringing the City’s total surface water supply availability to 60
MGD). At the moment, no other City within the Modesto groundwater subbasin or the
neighboring Turlock groundwater subbasin (totaling approximately 927 square miles) has
surface water available to its customers. There are three main reasons why the City
wishes to optimize its surface water supplies, as described in the following sub-sections.
4.1.1
Surface Water Availability and Reliability
The City obtains surface water from the Modesto Irrigation District, which
was formed on July 23, 1887, becoming the second irrigation district to be
established under the California Irrigation Districts Act (Wright Act). During
its early years, the MID developed numerous pre-1914 rights for the
Tuolumne River water. Pre 1914 water rights are ranked high in the hierarchy
33
of California’s water rights system, as perceived by the California State Water
Resource Control Board. The ARTDA provides the City with a solid legal
framework to assure reliable delivery of surface water when needed. In
contrast, groundwater supplies are very unreliable, in terms of its water
quality and availability.
4.1.2
Surface Water Quality versus Groundwater Quality
Surface water needs to be optimized due to its high water quality. Surface
water delivered to the City originates from the Tuolumne River, which has a
1,880 square mile watershed that extends to the High Sierra. Most of the
river’s flow comes from snowmelt, making this a high raw water quality
source. Furthermore, the raw water is treated at the Modesto Regional Water
Treatment Plant (owned and operated by the Modesto Irrigation District). The
30 MGD plant is a conventional treatment facility providing flocculation,
sedimentation, and filtration, along with ozonation for primary disinfection,
thus bringing water quality to Title 22 drinking water standards (City of
Modesto, 2010).
Groundwater quality, on the other hand, continually faces regulatory
challenges due to water contamination. The City’s groundwater supply is
affected by a range of contaminants, including Arsenic, Nitrates,
Perchloroethylene (PCE), Gross Alpha (indicator of a radionuclide source)
34
and Uranium. More than half of the wells offline for contaminants are due to
Uranium and Gross Alpha, which is very expensive and difficult to treat.
Therefore, in terms of water quality, it is logical for the City to optimize the
use of high quality surface water as much as possible.
4.1.3
Groundwater Management/ Replenishment
Groundwater replenishment is another reason to optimize surface water needs.
Groundwater is a resource that is shared with other agricultural and municipal
agencies, as well as private well owners within the groundwater subbasins.
Over-pumping from any one agency within the groundwater basin can cause a
basin overdraft, directly affecting other groundwater purveyors within the
subbasin. To avoid over-pumping, the City practices in-lieu banking, which is
a conjunctive use strategy for agencies utilizing both surface and groundwater.
The objective of in-lieu banking is to mostly utilize surface water during
colder months and allow for groundwater replenishment. Then in summer
months, surface water can be used in conjunction with groundwater to meet
high peaking demand conditions.
4.2 Surface Water Optimization Source Tracing Model
As part of this project, a Surface Water Optimization Model was developed within
InfoWater® to demonstrate the use of the EPS model in order to answer operational
questions related to the optimization of surface water. Once the EPS model was built, the
35
source tracing option was used to trace the movement of surface water from the MID
Terminal Reservoirs.
Results of the Source Tracing Model indicate that surface water moves more quickly
across the City through the transmission main, then it does in the distribution systems
close to the Terminal Reservoir. The source tracing model was used to provide a
visualization of the surface water movement within a 24 hour period. This visual aid can
be used to explain to Water System Operators how they can alter their cluster controls for
better spread and consumption of surface water. Figure 17 and 18 shows simulation
results at the 9th and 24th hour respectively, of the MID surface water source tracing
model. The dots indicate the nodes where all or a portion of the water is surface water
from MID terminal reservoir). During low demand periods of January and February
2012, it was noticed that the MID Terminal Reservoir was only able to deliver 20 MGD
of surface water flow. With the help of the source tracing model, decisions can be made
as to which wells to turn off to allow more surface water consumption. Note that
although the City uses 20 MGD, the City is still charged for the full 30 MGD whether it
is used or not.
36
37
38
4.4 Well Field Optimization Project Phase II
The Well Field Optimization Project Phase II is a state funded basin-wide (Modesto
Subbasin) project led by the City of Modesto as the lead agency, with an objective to
provide more efficient operations of the well field systems. Other agencies involved in
the Project include the City of Oakdale, Riverbank and the Stanislaus County. While this
work is beyond the scope of the current study, there are various elements of the Well
Field Project that can be used to enhance the developed EPS model.
A facilities inventory for all the wells in the City of Modesto was completed from various
sources. Data collected for the City included information such as well logs/ construction
information, pump motor ratings, soil tests, water quality, hydrogeologic information of
the underlying aquifers, well efficiency and well level information. Once the data is
collected, it will be compiled into a centralized database system. All the wells will be
closely evaluated based their performance and condition and will then be categorized into
four main categories as indicated below:

Category A: Online wells that are in good operating condition (these wells are
efficient, and have no known water quality concerns and do not require
rehabilitation).

Category B: Online wells with marginal or poor operating condition (would
benefit from rehabilitation).
39

Category C: Offline wells with good operating potential; if rehabilitated or put to
alternate use, such as irrigation. The best use of the well will be recommended
(these wells have a high pumping capacity and would be beneficial if brought
online).

Category D: Offline wells with poor operating condition. The wells are low
producing wells that will not have a significant contribution to the water supply
system (should be either completely rehabilitated or abandoned and destroyed).
Once the all wells have been ranked, a web-based Decision Support System (DSS) will
be developed by in-house City of Modesto Information Technology staff with guidance
from the Capital Planning team. Eventually, a web-based DSS will be developed that
will allow modelers as well as water operations staff to determine the best set of wells to
operate to meet a particular objective, such as, minimizing energy costs due to pumping,
minimizing groundwater level impacts, maximizing production etc. The web-based DSS
application was selected as opposed to a stand-alone application, since the web-based
DSS will be more accessible for data inquiries and updates. This Well Field
Optimization Project Phase II will be critical while re-evaluating the cluster system. Best
operation clusters obtained from the DSS will then be incorporated into the EPS model
for verification of hydraulic performance under varying winter, summer and emergency
demand conditions.
40
CHAPTER 5
CASE STUDY 2: AQUIFER STORAGE AND RECOVERY HYDRAULIC EPS
MODEL
5.1 Importance of an Aquifer Storage and Recovery Project
In California, various forms of Aquifer Storage and Recovery (ASR) have been studied,
explored and in some cases partially or fully implemented. For the purposes of this case
study, ASR is defined as the direct injection of potable water into deeper aquifers, for
future recovery from the same location to satisfy higher seasonal demands. As stated
earlier in Chapter 4.1.3, the City wishes to use conjunctive use strategies, supplying
winter demands with surface water (allowing groundwater to replenish) and summer
demands with both groundwater and surface water. Figure 19 from the City’s 2005
Urban Water Management Plan (UWMP) shows the water supply and demand
projections that were reported to DWR. It can be seen that based on these early
projections, the City will continue to have surplus water for about 20 years after
construction of the Phase 2 of the MRTWP. It should be noted Phase II did not come
online in 2008, as anticipated in the 2005 UWMP. The Phase II plant is nearly
constructed but the actual surface water delivery is still being worked about between
MID and the City of Modesto.
41
Figure 19: City of Modesto Water Demand versus Available Water Supplies in AFY
(Source: City of Modesto, 2005 UWMP)
The City along with the City of Ceres and the City of Turlock, are planning the Regional
Surface Water Supply Project (RSWSP), which will construct another water treatment
plant to serve its constituents within the neighboring Turlock Subbasin. Because the
treatment plants are built considering buildout demand projections, in early years
following construction, the City will have a water surplus.
Due to the surplus water supplies, apart from in-lieu banking of groundwater, the City
wishes to explore ASR options. Water banked using ASR can not only be used to satisfy
42
high summer demands, but can also be available during drought conditions, when MID
cuts back on municipal surface water supply due to higher delivery priorities to its
agricultural customers.
5.2 Purpose and Limitations of the ASR Hydraulic EPS Model
The goal of this Evaluation is to demonstrate how the EPS model can be used to identify
potential surface water diversion points within the City’s water distribution system, as
well as evaluate the overall system hydraulic performance. Since an ASR diversion will
probably induce the highest single point demand (diversion) on the distribution system,
which can have adverse pressure impacts, a thorough hydraulic performance of the
distribution system simulating ASR operations is critical. The ASR operations and
scheduling should be done such that the ASR demand impacts to the distribution system
are minimal.
A successful ASR project requires a favorable target aquifer(s), in terms of storage
capacity and geochemistry. Such projects also require great financial commitment.
Figure 20 shows the various components that could be involved in an ASR Project.
Determining target aquifers within the groundwater subbasins, and exploring the current
regulatory setting in Northern California is beyond the scope of this evaluation and is
recommended in a preliminary feasibility study. The work highlighted in this Project
should be performed between the ASR feasibility study and the future pilot
43
demonstration project. The ASR Hydraulic EPS model developed as part of this Project
should be used to perform future ASR diversion-point alternative studies.
44
45
5.3 Development of the ASR Hydraulic EPS Model
The ASR Hydraulic EPS model was used in evaluating a location within the distribution
system for ASR diversion, as well as quantifying the amount of ASR injection that the
hydraulic system can tolerate at the evaluated location (strictly looking at distribution
system hydraulics). In order to conduct the evaluation, a winter EPS model simulating
ASR injection (demand), and a summer EPS model demonstrating ASR supply into the
distribution system had to be developed. This development was straightforward with the
well cluster rule controls in place as part of work performed within this project. For the
winter injection scenario, January 14, 2012 demand conditions were used in the model
because this was the most recent winter day for which data was collected at the time of
analysis. The demand pattern was developed from the collected field data and has been
shown in Figure 21.
46
January 2011 Water Production
January 2011 Water Production
70.0
Water Production (MGD)
60.0
50.0
40.0
30.0
20.0
10.0
23.00 - 24.00
22.00 - 23.00
21.00 - 22.00
20.00 - 21.00
19.00 - 20.00
18.00 - 19.00
17.00 - 18.00
16.00 - 17.00
15.00 - 16.00
14.00 - 15.00
13.00 - 14.00
12.00 - 13.00
11.00 - 12.00
10.00 - 11.00
9.00 - 10.00
8.00 - 9.00
7.00 - 8.00
6.00 - 7.00
5.00 - 6.00
4.00 - 5.00
3.00 - 4.00
2.00 - 3.00
1.00 - 2.00
0.00 - 1.00
0.0
Time Interval
Figure 21: January 14, 2011 Demand Pattern for ASR Hydraulic EPS Model
5.4 Evaluation of ASR Water Diversion Point Locations
The Surface Water Optimization Source Tracing Model developed in Chapter 4 was used
to evaluate potential ASR diversion point locations. Based on the EPS source tracing
model results (Figures 17 and 18), it was seen that surface water quickly reaches the
northeast portion of the distribution system. Since this zone generally has higher
pressures, a diversion point at this location will not adversely affect the distribution
system pressures based on modeling results. Alternatively, more suitable ASR location
could be at the western portion of the City, near Tank 12, where surface water is
conveyed by the MID transmission main. This location would be ideal because surface
47
water stored as part of the ASR project can be recovered to not only supply North
Modesto, but also South Modesto. Currently, water that is obtained from MID (Modesto
Subbasin) is not sent to South Modesto (Turlock Subbasin) due to place of use conditions
pertaining to MID’s water rights. If ASR water is injected in the deeper aquifers of the
western portion of the City, groundwater rights of the City may be exercised upon
extraction. This means that the water injected could be put to beneficial use in South
Modesto, which has immense water supply deficiencies, as well as a multitude of water
quality issues. The City is advised to conduct a thorough water rights evaluation
surrounding this issue as part of a future feasibility study.
A secondary advantage of selecting the western portion of the City (west of Highway 99)
is that this area has been identified by USGS as having a 200 feet deep corcoran clay
layer. Due to these clay lenses, this area may have some semi-confined or confined
aquifers that may be suitable for an ASR project. Until more comprehensive
hydrogeological studies are conducted as part of the preliminary ASR feasibility study,
these assumptions remain speculative.
5.5 ASR Hydraulic EPS Modeling Results
The ASR Hydraulic EPS model showed that during winter demand conditions, a single
diversion point in western Modesto could handle an average daily ASR demand of 10
MGD, without causing significant impacts to the water distribution system.
48
On January 14, 2012, MID terminal reservoirs provided the City with an average of 20
MGD, with production lowering to 11 MGD at 6:00 am. The modeling results show that
an additional 10 MGD can be supplied by MID and banked on the western portion of the
City near West Tank during winter season. However, the modeling results indicate that
the system cannot handle a constant average 10 MGD ASR diversion throughout the 24
hours and maintain distribution system pressures within normal operating range (i.e.
pressures above 40 psi). Hence, a more realistic ASR schedule had to be developed.
It is logical that when the winter demands are higher, more water should be used to
satisfy customer demand and less water should be diverted for ASR. Likewise, when the
winter demands are lower, there is less need for surface water and therefore, this can be
banked. Using the EPS Hydraulic model, a viable ASR schedule was developed as
shown in Figure 22, with injection rates ranging from 7 MGD to 12 MGD. Notice that
during higher demands, the ASR injection flow diversion is lowered, whereas it is
increased during lower demand periods.
49
70
60
Demand (MGD)
50
40
Winter Demand
ASR Operations
30
20
10
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time
Figure 22: Winter Demands versus Suggested ASR Operations
The schedule indicated above shows an average injection rate of 10 MGD, which would
be equivalent to an ASR program with approximately 7 wells, injecting at a rate of 1,000
gpm. If such a schedule is continued for a 3 month period, a total storage of 2,734 ac-ft
will be attained. To bring the results into perspective, this is equivalent to about a third
of the City of Roseville’s long term ASR program goal of 10,000 ac-ft per year (Source:
http://www.roseville.ca.us/eu/water_utility/aquifer_storage_n_recovery.asp).
The summer EPS model showed limitations of the system to receive 10 MGD at the
connection point from the ASR system to the distribution system. With it’s proximity to
the West Tank, its supply was limited to 2.5 MGD. This limitation also appears to be due
50
to the City’s current well cluster operation sequences. This issue should be resolved by
reevaluating the well clusters in a future study.
51
CHAPTER 6
CONCLUSIONS AND RECOMMENDATIONS
The Project detailed in this report was divided into three main parts: (1) Development of
an Extended Period Simulation (EPS) Model; (2) Case Study 1: Surface Water
Optimization Source Tracing Model; and (3) Case Study 2: Aquifer Storage and
Recovery (ASR) Hydraulic EPS Model. The conclusions and final recommendations
reached as part of this study have been summarized below.
The City’s earlier InfoWater® model was a steady state model (snapshot in time) that had
some limitations. The primary limitation outlined in this report deals with absence of any
SCADA controls setup in the model. While for a steady state model simulating a high
demand condition, a well configuration can be decided fairly quickly (since most wells
will be online), the task can get very cumbersome for an EPS model, where demands are
varying throughout the simulation. Through this project, it was determined that SCADA
controls that exist in the field can be entered into the InfoWater® software. However, the
process requires may require much research and close communication with staff
constantly dealing with water system operations. Furthermore, the task of entering
cluster control data is not straightforward and requires intimate knowledge of both the
distribution system, as well as the software (no support is provided for Rule Controls by
52
Innovyze, the vendor for InfoWater®). Nonetheless, once entered, the model will
become more accurate in terms of simulating field conditions.
The EPS model was calibrated with an overall accuracy of 10% between more than 500
measured and modeled flow and pressure data points. While the model predicted well in
North Modesto, there are still model refinements necessary in the South Modesto Area.
It is recommended that further updates to the model be made to include control data for
the water tanks. This data should incorporate tank filling and emptying schedules, as
well as low and high tank level information.
In Case Study 1, the Surface Water Optimization Source Tracing Model outlined the
extent of surface water delivery within the City’s distribution system. Surface water,
which is conveyed through the Modesto Irrigation District (MID) transmission main was
only delivered within close proximity to the mains. Other zones within the City were
mainly supplied by groundwater. Since treated surface water is of higher quality and
reliability, the City should optimize its use. It is highly recommended that the City reevaluate its cluster operations (including operating sequence) through the EPS model in
order to determine how to best optimize surface water and still meet other important
criteria, such as minimizing impacts to groundwater and minimizing energy costs related
to pumping. Other tools and data available to the City, such as the future web interface
that will be developed for the Well Field Optimization Project Phase II, will need to be
utilized in conjunction with the EPS model while re-evaluating the well clusters. In
53
general, well clusters should be modified to shutoff more wells to allow for surface water
in the needed areas within the City. The EPS model can also be used to examine well run
times to ensure that minimum runtime requirements can be met during the optimization.
Further effort should also be pursued to integrate the controls governing the MID
Terminal Reservoirs into the cluster operating controls. This will help the City to setup
an automated process to maximize surface water and thereby, execute an efficient
conjunctive use program. It is recommended that the optimization process be conducted
both for the existing conditions (30 MGD surface water supply) as well as the buildout
condition (60 MGD surface water supply).
In Case Study 2, the Aquifer Storage and Recovery (ASR) Hydraulic EPS model
demonstrated that the City could supply up to 10 million gallons per day (MGD) of
available excess surface water during winter conditions for an ASR program to an area
immediately west of the City, near West Tank. The EPS model indicated that during
summer demands, if the western portion of the City was selected for the ASR program,
the City could face difficulties in extracting ASR water and supplying it to the
distribution system, with the current well cluster supervisory controls in place. The
obvious solution for the City will be to tailor the cluster controls to allow for ASR water
supply. The evaluation in this project was only from a hydraulic distribution system
standpoint and no hydrogeologic analysis was performed to confirm aquifer
characteristics within the proposed location. This analysis was conducted as a
54
demonstration to the City of the nature of the future ASR Facility Layout Analysis that
should be performed as part of an ASR Feasibility Study.
Finally, the EPS model can be used for many other purposes that have not been discussed
in this project. Examples of studies that can be conducted by the City utilizing the EPS
model include water quality studies (maintaining chlorine residuals), blending studies,
system vulnerability studies, and energy cost studies. All of these studies will require the
City to establish protocols to keep the model updated and calibrated. Since the City
collects and stores pressure and flow data periodically, it is recommended that model recalibration and update protocols be established within the City. A model re-calibration
and update effort every three years is recommended to the City, unless major
improvements are made to the water system sooner. It is also recommended that during
such time, fire flow tests be conducted to supplement field data. Fire flow tests may be
able to help the modeler refine the model for fire flow scenarios, as well as detect any
valves that may be turned off within the distribution system. The model should be
maintained by the in-house modeling team, which should also be responsible of the
model updates and calibrations. Keeping stakeholders in mind, the model should also be
presented to other staff dealing with water system planning, design and operations in
order to increase their overall confidence and understanding of the capabilities of the EPS
model.
55
APPENDIX A
MODEL DATA ENTRY FOR WELL CLUSTER CONTROLS
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
APPENDIX B
WATER PIPE GROUPING MAPS FOR CALIBRATION
86
Pipe Group 1 (AC, CI: 1980 - Present)
Pipe Group 2 (AC, CI: 1940 - 1979)
87
Pipe Group 3 (AC, CI: 1939 and earlier)
Pipe Group 4 (AC, CI: Year Unknown)
88
Pipe Group 5 (CL: Year Unknown)
Pipe Group 6 (DI, DIP: 1980 – Present)
89
Pipe Group 7 (DI, DIP: 1940 – 1979)
Pipe Group 8 (DI, DIP: 1939 and earlier)
90
Pipe Group 9 (DI, DIP: Year Unknown)
Pipe Group 10 (GI: 1980 – Present)
91
Pipe Group 11 (GI: 1940 – 1979)
Pipe Group 12 (GI: 1939 and earlier)
92
Pipe Group 13 (GI: Year Unknown)
Pipe Group 14 (PVC: 1980 – Present)
93
Pipe Group 15 (PVC: 1940 – 1979)
Pipe Group 16 (PVC: 1939 and earlier)
94
Pipe Group 17 (PVC: Year Unknown)
Pipe Group 18 (Unknown Material: 1980 – Present)
95
Pipe Group 19 (Unknown Material: 1940 – 1979)
Pipe Group 20 (Unknown Material: 1939 and earlier)
96
Pipe Group 21 (Unknown Material: Unknown Year)
97
APPENDIX C
SURFACE WATER OPTIMIZATION SOURCE TRACING MODEL
98
99
100
101
102
103
104
105
106
107
108
109
110
111
REFERENCES
American Water Works Association. (2005). Manual of Water Supply Practice– M32s,
Computer Modeling of Water Distribution Systems. Denver, Colorado
City of Modesto Public Works Department, West Yost Associates. (2010). 2010 Water
System Engineer’s Report. Modesto, California: Author. Retrieved from
http://www.modestogov.com/uppd/reports/water/eir/systemab.asp
City of Modesto Public Works Department, Modesto Irrigation District, RMC. (2007).
Joint Urban Water Management Plan 2005 Update. Modesto, California: Author.
Retrieved from
http://www.modestogov.com/pwd/docs/reports/water/uwmp/0705_MID_UWMP.pdf
Innovyze, Inc. (2007). Help for Calibrator (Documentation Release 2, Build 0.08)
[Computer software]. Arcadia, California.
Mays, L.W., Bosserman, B., Bouchart, F., Chase, D. V., Clark, R., Geldreich, E. E.,
Goldman, F. E., Goulter, I., Grayman, W. M., Karney, B. W., Kirmeyer, G. J.,
Lansey, K., Lingireddy, S., Male, J. W., Martin, C. S., Ormsbee, L. E., Rossman, L.
A., Sakarya, A, Tung, Y., Uber, J., Walski, T. M., Ysusi, M. (2000). Water
Distribution System Handbook.