EPA-600/2-76-218
August 1976
Environmental Protection Technology Series
icipal Environmental Research Laborato
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into five series. These five broad
categories Were established to facilitate further development and application of
environmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The five series are:
; . [ • .'.•,:. •*!"•,;• ;v ,! <•• • :•• /;,(>!' /; 4,, ",","••...,*•" M ; " :
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
This report has been assigned to the ENVIRONMENTAL PROTECTION
TECHNOLOGY series. This series describes research performed to develop and
demonstrate instrumentation, equipment, and metnodblogy to repair'or prevent
environmental degradation from point and non-point sources of pollution. This
work provides the new or improved technology required for the control and
treatment of pollution sources to meet environmental quality standards.
iiiliii 11 iiii
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/2-76-218
August 1976
DEVELOPMENT AND APPLICATION OF A
SIMPLIFIED STORMWATER MANAGEMENT MODEL
by
John A. Lager
Theodor Didriksson
George B. Otte
Metcalf & Eddy, Inc.
Palo Alto, California 94303
Grant NO. Y005141
Project Officer
Anthony N. Tafuri
Storm and Combined Sewer Section
Wastewater Research Division
Municipal Environmental Research Laboratory (Cincinnati)
Edison, New Jersey 08817
MUNICIPAL ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OHIO 45268
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DISCLAIMER
This report has been reviewed by the Municipal Environmental
Research Laboratory, U.S. Environmental Projection Agency,
and approved for publication. Approval does not signify
that the contents necessarily reflect the views and policies
of the U.S. Environmental Protection Agency, nor does
mention of trade names or commercial products constitute
endorsement or recommendation for use.
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'FOREWORD
The Environmental Protection Agency was created, .because ;pf,
increasing public and government concern aboutithe ..d'angers,
of pollution 'to the health _ and . "'we If,are, .of .the. American,
people. Noxious air, '-foul water, 'and 'spoiled land are.
tragic testimony to the deterioration' of our;, natural
envi ronment.- ' The complexi ty of that ,e.nvironmeht and t'he,
interplay between its components require a concentrated;"and
integrated attack on the problem.
Research and development is that necessary first step in
problem solution and it involves defining the problem,
measuring its impact, and searching for solutions. The
Municipal Environmental Research Laboratory develops new and
improved technology and systems for the prevention,
treatment, and management of wastewater and solid and
hazardous waste pollutant discharges from municipal and
community sources, for the preservation and treatment of
public drinking water supplies and to minimize the adverse
economic, social, health, and aesthetic effects of
pollution. This publication is one of the products of that
research; a most vital communications link between the
researcher and the user community.
The deleterious effects of storm sewer discharges and
combined sewer overflows upon the nation's waterways have
become of increasing concern in recent times. Efforts to
alleviate the problem depend upon characterization of these
flows in both a quantity and quality sense. This report
describes the development and application of a simplified
stormwater management model that can be used to provide an
inexpensive, flexible tool for planning and preliminary
sizing of stormwater facilities.
Francis T. Mayo
Director
Municipal Environmental Research Laboratory
iii
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ABSTRACT
A simplified stormwater management model has been created to
provide an inexpensive, flexible tool for planning and
preliminary sizing of stormwater facilities.
The model delineates a methodology to be used in the
management of stormwater and consists of a series of
interrelated tasks that combine small computer programs and
hand computations. The model successfully introduces time
and probability into stormwater analysis, promotes total
system consciousness on the part of the user, and assists in
establishing size-effectiveness relationships for
facilities.
Throughout this report, data from the City of Rochester, New
York, is presented and analyzed as a working example,
This report was submitted in partial fulfillment of Grant
No. YOO5141 to the Monroe County Division of Pure Waters
under the sponsorship of the Environmental Protection
Agency. Work was completed as of June 1976.
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CONTENTS
Page
SECTION I - INTRODUCTION .,...........; 1
Purpose ..... .... .. 1
Tasks 2
Organization 2
SECTION II - CONCLUSIONS 3
SECTION III - RECOMMENDATIONS . . 4
SECTION IV - MODEL DEVELOPMENT 5
SECTION V - DATA PREPARATION . . . 8
System Schematic ._...' 8
Overflows 8
Drainage Areas or Subareas .. . .. 9
Interceptors ........ i . ... 9
Example of a System Schematic 11
Quantity and Quality 11
Quantity 11
Quality 15
Example of Quantity and Quality Data 18
SECTION VI - RAINFALL CHARACTERIZATION 19
Collection of Rainfall Data 19
Correlation of Rainfall Data 20
Definition of Discrete Storm Events 21
Ranking of Design Parameters from Each Storm 21
Computer Program Logic and Input-Output
Requirements 24
Storm Event Definition Program (EVENT) 27
Snowfall Inclusion Program (SNOWIN) 27
Minor Storm Event Exclusion Program (EXCLUD) .. 31
Storm Event Sequence List Program (LISTSQ) .... 31
Sorting and Ranking Program (SORT) 34
Listing of Ranked Files Program (LISTRK) 34
SECTION VII - STORAGE-TREATMENT BALANCE 41
v
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CONTENDS (Continued)
SECTION VII (Concluded)
Page
Characteristics of the Storage-Treatment Program.. 41
Concept of the Program . i ..... 41
Operational Controls and Data Requirements .... 41
Output Data and Application Philosophy!: ••'-.'•>..'.:... 43
Computer Program Logic and Input-Output
Requirements 44
Example of Storage-Treatment Program Application ... 51
Data Development . . . . ..... .; 54
Alternative Analysis 54
Comparison of Daily and Hourly Analysis 60
SECTION VIII - OVERFLOW QUALITY ASSESSMENT 63
Regression Analysis 63
Procedure ..........;.,........ 64
Example of Equations .,. 65
Analysis of Averages 66
Procedure , 6.6
Examples of Averages 67
SECTION IX - RECEIVING WATER RESPONSE 71
Characteristics of the Receiving Water Program 71
Limitations of the Receiving Water Program 72
Specific Requirements of the Receiving Water
Program 72
Example of the Genesee River Program Applied to
Stormwater Overflows 73
SECTION X - APPLICATION OF THE SIMPLIFIED
STORMWATER MANAGEMENT MODEL 80
Computer Requirements 81
Hardware Requirements 82
Cost of Computer Usage 82
Application to Storm Sewer and Nonurban Areas 82
Data Preparation » 84
Rainfall Analysis 84
Storage-Treatment Balance 84
Quality Assessment 85
Receiving Water Response 85
Simplified and Complex Modeling 85
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CONTENTS (Concluded)
Page
SECTION XI - OTHER STORMWATER CONSIDERATIONS . 87
Sludges . .. 87
Nonstructural Alternatives 90
Source Control Alternatives . . 91
System Control Alternatives 91
REFERENCES 93
APPENDIX A - EXAMPLE OF MONITORING DATA FROM
ROCHESTER, NEW YORK . . . . • • 95
APPENDIX B - PROGRAM LISTING AND LIST OF VARIABLES 99
APPENDIX C - DETERMINATION OF K FACTOR 127
APPENDIX D - OVERFLOW QUALITY ASSESSMENT-ALTERNATE
METHODS 130
GLOSSARY * 136
Vll
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FIGURES
No. Page
1 Interrelationship of Tasks in the
Simplified Stormwater Management Model 5
2 PERT Diagram of Simplified Stormwater
Management Model 6
3 Types of In-System Storage .. 10
4 Example of Drainage Subareas and
Overflow Locations . 12
5 Example of Functional Elements of
Sewerage System 14
6 Example of System Schematic 17'
7 Programs for Rainfall Analysis . . 22
8 Example Curve - Storm Magnitude
vs. Frequency 25
9 Example Curve - Storm Intensity
vs. Frequency .' 25
10 Example Curve - Storm Duration
vs. Frequency 26
11 Example Curve - Percent of Storms
Having Maximum 1-hr Intensity
vs. Hour After Start of Storm 26
12 Flow Chart for Storm Event Definition
Program (EVENT) . 29
13 Flow Chart for Snowfall Inclusion
Program (SNOWIN) 32
14 Flow Chart for Minor Storm Event
Exclusion Program (EXCLUD) 33
Vlll
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FIGURES (Concluded)
No.
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
C-l
Page
Flow Chart for Storm Event Sequence
Listing Program (LISTSQ) 35
Flow Chart for Listing of Ranked Files
Program (LISTRK) 39
Concept of Storage-Treatment Program 42
Flow Chart for Control Block of
Storage-Treatment Program 45
Basic Flow Chart for Subroutines '
of Storage-Treatment Program ; 47
Example of System Schematic - Existing
Rochester West Side Interceptors 55
Frequency of Occurrence of Runoff
and Overflows ......' 56
Rochester West Side - Alternative 1 57
Example of System Schematic - Modified
Rochester West Side Interceptors
58
Rochester West Side - Alternative 2 59
Example of System Schematic - Alternative 2 '...*.. 60
Comparison of Hourly and Daily Analysis 62
Example of Overflow Quality Trends . 70
Genesee River Reaches for the Receiving .
Water Program . 76
Computed Dissolved Oxygen in Genesee River
80
Comparison of Rochester Data with
Imperviousness Predicting Equation 129
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TABLES
NO,
1
2
3
4
6
7
8
9
10
11
12
13
14
15
16
17
Example of Drainage Subarea Characteristics;
Example of Calculation of Wet-Weather Flow
Capacity
Example of In-System Storage by Subarea
Example of Subarea Characteristics Used
in Storage-Treatment Task
Example of Use of Storm Event Ranking —
2-Year Storm
Format for Hourly Rainfall Data
Format for Storm Data
Format for Snowfall Data
Example of Output from Storm Event
Sequence Listing Program (LISTSQ)
Example of Output from Listing of
Ranked Files Program (LISTRK) ...
Format for Control Block Data of
Storage-Treatment Program
Format for HOUCRD Subroutine Data
Format for DLYCRD Subroutine Data
Format for DLYTAP Subroutine Data
Format for HOUTAP Subroutine Data .......
Example of Output from Storage-Treatment
Program for Daily Analysis
Example of Output from Storage-Treatment
Program for Hourly Analysis
Page
13
15
16
16
23
28
28
31
36
40
46
49
50
50
51
52
53
x
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TABLES (Continued)
No. Page
18 Comparison of Daily and Hourly Time
Increment Analysis 61
19 Equations for Quality Projection 65
20 Average Overflow Quality by Subarea 67
21 Example of Average Overflow Quality
by Subarea and Time Increment ................. 68
2.2 Format for Receiving Water Program Data ......... 74
23 Example of Receiving Water Program
Input Data for Base Condition ................. 75
24 Example of'Data for Overflows from
Storm on June 22, 1973 77
25 Example of Receiving Water Program :
Input Data from Storm on June 22, 1973 78
26 Example of Output from Receiving Water
Program for Storm on June 22, 1973 79
27 Approximate Computer Cost for Simplified
Stormwater Management Model 83
28 Composition of Sludge from the Treatment of
Combined Sewage Overflows . ..'. . .... ; 88
29 Nonstructural Control Alternatives 90
A-l Example of Monitoring Data from
Rochester, New York 96
B-l Rainfall Task - Program Listing for EVENT 100
B-2 Rainfall Task - List of Variables for EVENT . . 103
B-3 Rainfall Task - Program Listing for SNOWIN 104
B-4 Rainfall Task - List of Variables for SNOWIN , 105
B-5 Rainfall Task - Program Listing for EXCLUD 106
B-6 Rainfall Task - List of Variables for EXCLUD 107
B-7 Rainfall Task - Program Listing for LISTSQ ...... 108
9- , : . '.*•.,..
xi
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TABLES (Concluded)
No. Page
B-8 Rainfall Task - List of Variables for LISTSQ..... 110
B-9 Rainfall Task - Program Listing for LISTRK Ill
B-10 Rainfall Task - List of Variables for LISTRK 112
B-ll Storage-Treatment Task - Program Listing 113
B-12 Storage Treatment Task - List of Variables....... 125
D-l Quality Constants for Loading from
Sewer Systems 131
D-2 Quality Constants for Concentrations from
. Sewer Systems 132
D-3 Regression Coefficient fi 134
D-4 Regression Coefficient f2 • - • • 134
D-5 Regression Coefficient f3 134
XII
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ABBREVIATIONS
ADWF
avg
BOD .
COD
ft/ft
hr
in.
mgd
mg/kg
mg/1
mil gal.
min
NOD
SS, ss
TSS
VS
VS. , vs.
yr
average dry-weather flow
average -
biochemical oxygen demand
5-day biochemical oxygen demand
chemical oxygen demand
feet per foot
hour
inch
million gallons per day
milligrams per kilogram,
milligrams per liter
million gallons
minute
nitrogenous oxygen demand
suspended solids
total suspended solids
volatile solids
versus
year
xiii
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ACKNOWLEDGMENTS
Many persons have contributed to this report. Metcalf &
Eddy, Inc., gratefully acknowledges the cooperation of
O'Brien & Gere Engineers, Inc., and the Monroe County
Division of Pure Waters. This report has been prepared as ;a
subcontracted portion of a demonstration project being
conducted by ,O'Brien & Gere.
Especially acknowledged is the assistance and guidance
provided by Frank Dr.ehwing, Vice President, Dr. Cornelius
Murphy, Managing Engineer, and David Carleo, , Project
Engineer, of O'Brien & Gere, Inc.
Appreciation is expressed to Dr. Gerald McDonald, Director,
and Robert Hallenbeck, Chief of Technical Operation, of the
Monroe County Division of Pure Waters for their cooperation
and assistance. -•••-•
The support of this effort by the Storm and Combined Sewer
Section, (Edison, New Jersey) of the USEPA, Municipal
Environmental Research .Laboratory, Cincinnati, Ohio, and
especially of Anthony Tafuri, Project Officer,"' and Richard
Field, Chief, for their guidance, suggestions, and
contributions is acknowledged with gratitude.
This report has been prepared in the Western 'Regional Office
of Metcalf & Eddy, Inc.', in Palo Alto, California, by George
Otte and Theodor Didriksson under the direction o'f John
Lager, Vice President.
xiv
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SECTION I .
INTRODUCTION
Computer modeling of stormwater systems is currently
achieving a high degree of precision and complexity. The
complex models provide very valuable data for the design and
final sizing of stormwater facilities. At the same time,
the existing models , are extremely expensive to set up and
operate, requiring large blocks of time on extremely large
computer systems. A void has thus appeared in the area of
computer modeling. A tool is needed for the planning and
preliminary sizing of facilities. This tool must be
inexpensive to set up and use, flexible enough to be
applicable to a variety of system configurations, and
accurate even though , only very moderate expenditures are
made for data collection and preparation.
PURPOSE
The purpose of this report is to delineate an approach
methodology to be used for the management of stormwater that
meets these criteria. The approach is formulated as a
simplified stormwater management model. The model consists
of a series of uncomplicated interrelated tasks that can be
used either singly or together. This permits the user to
build on his individual data strengths and to focus on
individual study objectives,
The goals of this simplified model are:
• To introduce time and probability to stormwater
analyses
• To promote total system consciousness on the part of
the user or reviewer
• To establish size-effectiveness relationships
Just as time and probability analyses are important in
sizing water supply impoundments and safe yields, they
are—or should be—equally important in determining the
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effective use of stormwater facilities, Since total capture
is not a necessary goal, as it is in flood control works,
for example, there is greater latitude in facility sizing
and staged implementation. The trick is to determine the
relative merits of alternatives, a task for which modeling
is ideally suited,
This model is -based on the premise that the simplest model
that will do the job is usually the best, and has as its
primary target the breaking down of data into a form that is
meaningful to the user. In so doing, a degree of precision
is sacrificed for breadth of coverage, Because pf the low
cost of the model (both for setup and execution), multiple
assumptions can be tested with relative ease and over a
short period of time,
TASKS
In this simplified model five tasks are performed:
• Data preparation
• Rainfall characterization
• Storage-treatment balance
• Overflow-quality assessment
• Receiving water response
Each task actually is a combination of small computer
programs and hand computations.
ORGANIZATION
In the presentation of the five tasks in this report, the
logic of the analysis is discussed, the computer program
logic in the form of flow charts and the computer
input-output requirements are documented, and examples are
presented.
Throughout this report a system of combined sewers is
analyzed. The City of Rochester, New York, is used as a
working example. The data on Rochester were supplied by the
Monroe County Division of Pure Waters.
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SECTION II
CONCLUSIONS
1, A schematic of the existing system of stormwater
facilities, outlining major conduits and overflow
discharge locations and sizes, is an essential part of
the data preparation.
2. Overflow quantities and qualities must be measured to
provide information for calibration of the model.
3. Rankings can be prepared from long historical records
for important storm parameters, such as magnitude,
duration, and intensity.
4. Frequency of occurrence curves are easily generated from
the ranking of storm parameters.
5. The interrelationship between containment of runoff in
storage and the capacity of treatment plant or
interceptors can be quickly reviewed using the
storage-treatment computer program.
6. The quantity, frequency and duration of overflows can be
accurately tabulated by the storage-treatment program
because it uses real rainfall records for a long period
of time as the data source. .
7. The quality of overflows can be predicted on the basis
of storm characteristics using linear regression
techniques..
8. Gross averages of the quality data .by subarea can
provide an indication of overflow quality and areal
trends in overflow quality.
9, The receiving water analysis provides the final test of
a control alternative to determine if an ^adequate
solution has been reached.
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SECTION III
1.
2.
3.
4.
5.
RECOMMENDATIONS
The simplified stormwater management model should be
implemented as a preliminary design and planning tool.,
The rainfall characterization should be used to check
design storms" and to provide historical perspective on
storm events as they occur.
The storage-treatment program should be used repeatedly
to analyze various combinations of storage capacities
and interceptor rates to determine possible'optimum
conditions.
be determined
Areal trends in overflow quality should
using simple statistical techniques.
Any_promising control alternative should be tested on
reliable and operating receiving water simulation.
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SECTION IV
MODEL DEVELOPMENT
The simplified stormwater management model is composed of
five tasks. In this chapter an overview of each task is
presented, The interrelationship of the tasks, highlighted
in Figure 1, is also discussed,
1. DATA PREPARATION
2. RAINFALL CHARACTERIZATION
3. STORAGE-TREATMENT BALANCE
4. OVERFLOW QUALITY ASSESSMENT-
5. RECEIVING WATER RESPONSE
FIGURE 1. INTERRELATIONSHIP OF TASKS
THE SIMPLIFIED STORMWATER MANAGEMENT MODEL
A PERT diagram for the simplified model is presented in
Figure 2, This diagram, developed for the Rochester
project, illustrates the tying of the various tasks together
while focusing on the results. The broken lines in the
diagram indicate where information is exchanged between
tasks and where critical decision points are reached in the
flow of work.
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Task 1—DATA is the data preparation task of simplified
modeling, In this task, the questions, what do we have and
how does it work, are answered, A schematic diagram of the
system is synthesized, and data on overflow quantities and
qualities are collected. The data are collected for the
primary purpose of calibrating other tasks in the simplified
model. These data feed into both the storage-treatment task
(Task 3) and the overflow quality task (Task 4).
Task 2—RAINFALL is the rainfall characterization task of
the model. In this task, the raw rainfall data are
collected and analyzed. The emphasis is on the ranking of
critical rainfall characteristics—the design-sensitive
parameters. The results of this rainfall characterization
depend to a great extent on obtaining data for a long period
of record (approximately 20 years), While not every
community has such long records, ways of synthesizing these
data from other available long historical records in concert
with local data are discussed. The actual rainfall record
is a critical input for the storage-treatment task (Task 3).
Task 3—STOR-TREAT is an assessment of the storage-treatment
balance, In this task, rainfall is imposed on the city and
its system of separate or combined sewers. The
interrelationship between storage volumes and interceptor or
treatment plant capacities is analyzed. The primary output
from this task is the time and volume of stormwater that is
overflowing from the system. This output is a significant
input to the river response task (Task 5),
Task 4—OVERF-QUALITY is an evaluation of the quality of
potential overflows from a system of interceptors or
treatment facilities. The .data prepared in Task 1 are
analyzed using ; statistical techniques to develop trends.
The magnitude of overflow constituents is predicted for
input into the river response task (Task 5).
Task 5—RIVER is the task in which the response of the
receiving waters to overflows is determined. Overflow
volumes determined in Task 3 are paired with overflow
qualities developed in Task 4 to become loadings on the
river. The receiving waters are analyzed with the best
simulation that is available. For Rochester, a model of the
Genesee River, prepared by O'Brien and Gere, is used.
The relative success of stormwater control alternatives can
be checked at two major points in the simplified model,
After the storage-treatment task has been performed, the
duration, volume, and frequency of overflows can be checked
to determine the impact of an alternative. And, after the
river task has been completed, the impact on the receiving
water of a control alternative can also be checked,
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SECTION V • - • <
DATA PREPARATION ., .<.
The establishment of a firm data base is a very -iimportant
step in the modeling process. The data that are collected
must answer the questions: What do we have and how does it
work? In answering these questions, input for the
storage-treatment task and the overflow- quality task will
be developed.
SYSTEM SCHEMATIC
A good way to gain an understanding of the sewer system and
its relationship to the existing overflow points is to
prepare a schematic of the system showing the overflows,
drainage areas associated with the overflows, and the
pertinent interceptor capacities. An essential first step
in developing these data is to acquire the best and most
recent sewer and storm drainage maps for the region under
investigation.
Overflows
Overflows are defined as any point on the collection and
interceptor system specifically designed to permit excess
flows to bypass routing to the treatment plant. .Some of the
important characteristics of the overflows as they relate to
the system schematic are:
• Location of the overflows on the interceptor system
• The hydraulic capacity of the overflows and/or
regulating structures that control the overflows
• The capacity of any restrictions within the
interceptor system that restrict flow to the overflow
The overflows and their related characteristics
identified by a unique numbering system.
should be
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Drainage Areas or Subareas
Drainage areas or subareas are defined by delineating the
sewered area that is tributary to a particular overflow
structure (one overflow for each subarea). These drainage
subareas fit ,together so that the entire sewered area is
subdivided. The significant characteristics of each
drainage subarea are:
* The total surface area
• Percent of the subarea that is impervious
• Percent distribution of the industrial, commercial,
and residential (single-family and multifamily) land
uses
• Average slope of the ground
• Average dry-weather flow
Interceptors
The interceptor system is the last feature of the system
that is developed within the schematic. The specific
aspects of the interceptor systems that are analyzed are:
• The components that connect each subarea to the
treatment plant . .. '
• The maximum capacity of these components
• The capacity of components that are particularly
restrictive in the system near an overflow ,
• The available in-system storage
The maximum capacities of the interceptor system are often
calculated using Manning's equation and assuming
unsurcharged, open channel flow. If the system can
surcharge, significantly higher flowrates can occur. The
true maximum would therefore be for flow under surcharged
conditions which would probably occur during a heavy storm.
Two types of in-system storage can be created for storm
flows by an overflow structure, as illustrated in Figure 3.
The type A configuration of an overflow usually does not
provide a significant amount of storage, while the type B
configuration can retain a large volume in the system before
an overflow occurs.
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DEPTH OF
' STORM -FLOW
DEPTH OF
N ADWP
TYPE A
LEGEND
VOLUME OF
IN-SYSTEM STORAGE
DEPTH OF
STORM FLOW
TYPE B
FIGURE 3. TYPES OF IN-SYSTEM STORAGE
10
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Each of the foregoing items—overflows, drainage areas, and
interceptors—are connected in the system schematic.
Example of a System Schematic
Maps of the combined sewer system of the City of Rochester
were carefully studied. The overflows on the system were
noted, and the drainage subareas were defined, as shown in
Figure 4, The important characteristics of each subarea are
presented in Table 1, along with the average dry-weather
flow for each subarea, !
The interceptor system that connects the subareas and the
overflows is presented in Figure 5, The number in
parenthesis indicates the maximum flow that each segment of
the system can carry. Of particular interest is the
connection of Subareas 8 and 9 to the main interceptor,
This connection is made via a siphon under the Genesee River
that has very limited capacity. These types of
constrictions can significantly affect both the number of
times that overflows occur and the volume of wastewater that
overflows, so they must be identified in the schematic,
An example of the calculation of wet-weather flow capacity
is presented in Table 2, The sum of the average dry-weather
flow (Column 2) from each subarea is subtracted from the
maximum interceptor capacity (Column 3) to determine the
real available wet-weather capacity (Column 4),
An example of the calculation of the available in-system
storage for each subarea is summarized in Table 3, Most of
the overflows have the type A configuration and therefore
very little in-system storage is available,
A summary of the important characteristics that will be used
in the storage-treatment task for each subarea is presented
in Table 4, The schematic of the Rochester sewer system is
illustrated in Figure 6,
QUANTITY AND QUALITY
Quantity and quality data,.which are usually derived from
the monitoring of overflows, are necessary for the
calibration and development of the tasks,
Quantity
The first step in the monitoring of overflows is flow
measurement within the sewer system such that both the
overflow and intercepted flow can be determined, This
11
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VANLARE
TREATMENT
PLANT
SUBAREA NUMBER .
c .
OVERFLOW NUMBER
OVERFLOW LOCATION
SUBAREA WITH SEPARATE
STORM SEWERS
FIGURE 4. EXAMPLE OF DRAINAGE SUBAREAS
AND OVERFLOW LOCATIONS
12
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Table 1. EXAMPLE OF DRAINAGE SUBAREA CHARACTERISTICS
Sub-
area
6a
7
8
9
16
17
18
21
22
25
25Wa
26
23
29
31
50a
Total
area,
acres
1,277 '
715
984
2,603
826
235
541
821
569
348
1,390
554 ,
. 778
1-,V430
1,592
1,720
Land use.
%
Residential
Single- Multi-
family family
19.3
83.9
34.5
52.5
50.0
83.8
93.7
79.4
59.8
30.0
50.0
30.0
65.0
65.0
50.0
65.0
1.3
1.0
2.2
0
9.4
3.8
0.6
0
25.3
9.9
10.0
9.9
10.0
10.0
10.0
20.0 -
Commercial
1.9
7.3
47.0 *"
4.1
33.8
2.1.7-
3.8
9.0
6.7
44.9
20.0
44.9
10.0
10.0
20.0
5.0
Industrial Open
65.
0.
3.
37.
,1.
0
0
6.
4.
5.
10.
5.
4.
5.
15.
5.
8
2
2
1
1
8
9
0
0
2
9
0
0
0
11.8
5.5
13.2
6.4
5.7
10.2
2.2
4.9
3.3
10.2
10.0
9.9
10.0
10.0
5.0
5.0
Average
. slope,
ft/ft
0.0074
0.0118
0.0066
0.0060
0.0070
0.0067
0.0073
0.0065
0.0070
0.0080
0.0150,
0.0100
0.0100'
0.0100
O.OIO'O
0.0150
Impervious
area, %
55
. 50
45
50
55
•" ,40
40
35
50
80
35
65
50
55
47
40
.0
.0
.a •
•0
.0
.0
.0
.0
.0
.o.-;
•0,
• o' ;-
.0 '
• p
.0
.0
ADWF
(maximum
avg) , mgd
7.06
3.21
6.36
14.00
5.78
1.33
2.60
4.60
3.41
4.50
6.01
5.91
4.36
.'•' 7.86
10,13
11.90
a. Serviced by separate storm sewers.
information will determine the total runoff from the area
for a particular storm and can be used to calculate or check
the "K factor" (gross runoff coefficient,) The K factor is
used in the task to calculate the total runoff from
rainfall.
The flow data that are developed can include faulty data
because- of equipment and maintenance weaknesses, It is
essential, therefore, to screen the data carefully to ensure
a reasonable correlation between predicted and measured
values, Time history (variation of a parameter over a
specific time period) and volumes of runoff can be compared
with rainfall records to check if the runoff actually
reflects the real storm event,
13
-------
LAKE: ONTARIO
LEGEND
SUBAREA NUMBER
OVERFLOW NUMBER
MONITORING LOCATION
ELMWOOD AVENUE
PUMPING STATION
FIGURE 5. EXAMPLE OF FUNCTIONAL ELEMENTS
OF SEWERAGE SYSTEM
14
-------
Table 2. EXAMPLE OF CALCULATION
OF WET-WEATHER FLOW CAPACITY,
mgd
1
Subarea
No.
West side
system
17 and 18
25
16
8 and 9
22
21
7
6
East side
system
26
31
28 and 29
ADWF,
maximum
avg
(1)
3.9
4.5
5.8
20.4
3.4
44. ?b
3.2
7.0
5. 9
22.0
12.2
Sum of
ADWF
(2)
3.9
8.4
14.2
34.6
38.0
82.7
85.9
92.9
5.9
27.9°
40.1°
Maximum
interceptor
capacity
(3)
416
123
47
35
84.7
173.4
. 10.0
184
200
200
Available
wet-weather
capacity
(4)
412.1
114.6
32.8
14. 6a
46.7
90.7
6.8a
100.0
200
200
a. The limiting segment is not on the main
interceptor.
b. Of this amount, 4.6 mgd is from Subarea 21;
40.1 mgd is from the East side trunk sewer.
c. The equivalent of ADWF is carried by the
east side trunk sewer.
Quality
The best quality data from a monitoring program would
reflect the time history through various storm events for
each overflow location. The variations in quality through
time indicate the magnitude of the "first-flush" phenomenon,
The measurement of quality for each subarea reflects the
impact of the mix of various land uses on the wastewater
discharged from each subarea,
The use of composite or grab samples from overflows, by
subarea, can be substituted for the complete time-history
measurements. This may cause a distortion in the results
because the first-flush phenomenon, if it occurs, is not
acknowledged; yet it provides an insight into the real
15
-------
Table 3. EXAMPLE OF IN-SYSTEM STORAGE BY SUBAREA
Subarea
" " No.
7
8
9
16
17
18
21
22
25
26
28
29
31
Description
Maplewood Park
Lake and Lexington
West side trunk
Mill and Factory
Plymouth and RR
Brooks
Norton at Seth Green
Carthage
Central
Court
Screenhouse
Densmore bypass
Thomas Creek
Storage
volume,
mil gal.
0.009
0.004
0.006
0.011
0.004
0.005
0.298
0.035
0.007
* • * • •
0.250
0.026
0.023
Table 4. EXAMPLE OF SUBAREA CHARACTERISTICS
USED IN STORAGE-TREATMENT TASK
Downstream
Subarea , Area, Impervious In-system interceptor
No. ? acres area, % storage, mil gal. capacity, mgd
West side
system
17 and 18
25
16
8 and 9
22
21
7
6
Bast side
system
26
31
28 and 29
776
423
650
3,666
569
800
726
554
1,592
2,178
40.0
80.0
55.0
48.0
50.0
35.0
50.0
65.0
47.0
53.0
0.01
6.01
0.04
0.30
0.02
0.28
412.1
114.6
32.8
14.6a
46.7
90.7
100.0
751-
200
200
a. The limiting segment is not on the main interceptor.
b. This information is Jiot required because the area is
serviced by a. separate storm sewer.
c. Estimated.
16
-------
VANLARE
TREATMENT PLANT
LEGEND
31) SUBAREA NUMBER
OVERFLOW
INTERCEPTOR
CAPACITY, MGD
STORAGE VOLUME,
MIL GAL.
(200)
(200)
FIGURE 6. EXAMPLE OF SYSTEM -SCHEMATIC
17
-------
quality of the overflow. Whether or not the first- flush
phenomenon occurs is dependent upon catchment area and storm
characteristics.
Measurements on a single overflow can also be extrapolated
into results for the entire region. This compromises the
impact of land uses on overflow quality and further reduces
the reliability of quality modeling.
It is essential to have some quality measurements to
evaluate the quality of the overflows. "Textbook" values
provide very little knowledge of the overflow quality caused
by a particular region's climate and terrain.
The reliability of the quality analysis is directly related
to the data that are developed. The more complete the data,
the more reliable the analysis. A careful review of the
data is important, and any major deviations should be
readily explainable.
Example of Quantity and Quality Data
The City of Rochester created a high-quality monitoring
system to carefully measure the overflow on the combined
sewer system. This system measured the quantity and the
quality of the overflows. All of the data handling is via
paper tapes with computer processing and printing of the
output. An example of this elaborate output is presented in
Appendix A.
The Rochester data correlate rainfall, overflow quantity,
and overflow quality for each subarea. Although there are
some weaknesses in the collection and the data, the results
do represent what can be collected. One important parameter
that was not measured is the quantity of water intercepted
for any of the storms. Otherwise, the Rochester data are
more complete than required for the simplified modeling
effort. Measurement of a few of the most important quality
constituents and of overflow and interceptor quantity as
these factors vary in time through the storm would be the
ideal. The most common quality constituents of importance
are biochemical oxygen demand (BOD) or chemical oxygen
demand (COD) , nitrogen, and phosphorus. Coliform or fecal
coliform could also be measured to reflect bacterial
contamination.
18
-------
SECTION VI
RAINFALL CHARACTERIZATION
Rainfall characterization provides valuable insight into the
characteristics of rainfall that occurs in* an area. The
specific goal of this task is to create a ranking of the
design parameters. Four important analyses are performed:
• Collection of reliable historic rainfall data
• Correlation of rainfall data to study area
• Definition of discrete storm events
• Ranking of design parameters from each storm
These analyses can be accomplished with the aid of a
computer. The analyses as well as the computer program
logic and input;-output requirements will be discussed in
this chapter.
COLLECTION OF RAINFALL DATA
Data from rainfall records over a long period of time are
essential for the characterization of storm events and
future analysis. If at least 20 years of records are
analyzed, statistically valid results are generated.
Rainfall records for storm definition should be available on
an hourly basis, i.e., a specific intensity for each hour of
rainfall for each day of record over the period of record.
The hourly intensity is short enough to record a variation
in rainfall intensity for the length of most storms but long
enough to be manageable within the framework of simplified
modeling.
Rainfall data are available from many sources—fire
departments, sewage treatment plants, water treatment
plants, and local water supply facilities. The most readily
obtainable data and the most compatible data with computer
analysis are obtained from U.S. Weather Bureau records,
19
-------
either through tape files or published daily and hourly
summaries. Tapes are issued by: ' ,
U.S. Department of Commerce
National Climatic Center
NOAA Environmental Data Service •
Federal Building
Asheville, NC 28801
Tel. (704) 258-2850
Data are available on two record files: Deck 488-USWB
HOURLY PRECIPITATION and Deck 345-WBAN SUMMARY OP DAY.
These stations range in number from 1 in Delaware to 19 in
Texas. The period of record is generally from 1948-1949 to
the current date with some gaps. Tapes are furnishesd on 9
track-800 BPI, unless otherwise specified, and are forwarded
air parcel post. (Recent experience with these tapes has
been excellent. The two tape files for Rochester were
ordered and received within 15 days for a combined cost of
approximately $140).
CORRELATION OF RAINFALL DATA
The rainfall data that are available on tapes are from
Weather Bureau primary gages. The closest primary gage may
or may not be close enough to the area being studied to
portray local rainfall conditions accurately. Local rain
gage data from one of the sources mentioned earlier, or from
a gage specifically set up for comparison purposes, can be
used to check local performance with the Weather Bureau
primary gage. If a major difference is found, it may be
possible to apply a factor to the Weather Bureau data.
In Rochester, 12 local rain gages, which recorded the
rainfall in O.1-inch increments, were set up across the
city. Records from these rain gages were compared with
those of the primary gage, located at the Rochester Airport,
which records rainfall to the nearest 0,01 inch, On the
basis of an analysis of - 19 storms between January and
August, 1975, the Weather Bureau gage recorded an average of
0.44 inch per storm and the local gages recorded an average
of 0.51 inch per storm. Thus, the Weather Bureau gage
recorded magnitudes 14 percent lower than the average
magnitudes recorded by the in-town gages. The Weather
Bureau gage also records durations of storms 46 percent
longer than the average durations recorded by thes in-town
gages. The Weather Bureau gage recorded an average of 8.05
hours per storm, while the local gages recorded an average
duration of only 5.5 hours per storm. The difference in
duration is mostly due to the lag inherent in the measuring
20
-------
equipment,. The local gage must accumulate 0.10 Inch of rain
before signaling the start of a storm, While the local rain
gages exhibited some variation in results, they indicated
that rainfall across the entire city is fairly uniform,
DEFINITION OP DISCRETE STORM EVENTS
The hourly rainfall record is a continuous record of
rainfall and can be segregated into discrete storm events,
This segregation is essential to the characterization of a
particular storm event, For this analysis, Metcalf & Eddy
has defined a storm event as starting with the first
measurable rainfall after a minimum of 6 hours with no
rainfall and ending when a gap in measured rainfall
(precipitation) of at least 6 hours is first encountered.
Trace rainfall amounts are disregarded. The 6-hour gap was
selected to ensure relative independence between events. In
addition to.defining the storm, a check for the presence, of
snowfall for each storm event can be made, and, in the
process of listing the storm events, the annual totals of
important characteristics can also be tabulated. Each of
these tasks is accomplished by a separate computer program.,
The flow of these programs is illustrated in Figure 7.
For each event in the historical record, the .following are
noted and punched on data cards or filed on disk: date,
starting hour, duration, total rainfall, maximum hourly
rainfall and the hour in which it occurred, elapsed days
since the previous storm, and occurrences of excessive
precipitation and snow.
RANKING OF DESIGN PARAMETERS FROM EACH STORM
The sorting and ranking of the storm events develops the
data in a format from which characteristics of the storm can
be readily obsexved, The items that can be examined are
those characterized in the storm event definition,
Careful observation of the ranked characteristics can
provide valuable information on the nature of the rainfall
that occurs in an area. An example of the first page of the
ranking by magnitudetof the Rochester rainfall is presented
in Table 5, The storms that are highlighted are those that
would have a recurrence interval of approximately 2 years,.
The events with a different recurrence
calculated using the following formula:
interval can be
RC =
N + 1
M
21
-------
DAILY RAINFALL HOURLY RAINFALL
TAPE OF WEATHER BUiEAU RAINFALL
RECORD.
TRANSFER TO DISK STORAGE AND
EDIT FOR DIRECT ACCESS.
RAINFALL RECORD ON DISK.
STORM EVENT DEFINITION
PROGRAM (EVENT).
SNOWFALL INCLUSION PROGRAM
(SNOWINHOPTIONAL),
MINOR STORM EVENT EXCLUSION
PROGRAM (EXCLUD)(OI»TIONAL).
STORM EVENT SEQUENCE LIST
PROGRAM(LISTSQ).
SORTING PROGRAM (IBM PACKAGE
PROGRAM'.OS SORT/MERGE PROGRAM
-GC 28-6543).
LISTING OF RANKED FILES
PROGRAM (LISTRK).
FIGURE 7. PROGRAMS FOR RAINFALL ANALYSIS
22
-------
iaooooooc!oooaoooc;aooaoooooooaoooaooooo
zzzzzzzzzzz SB jff«zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz
i^4 ram
OOOO>-«OOOO.-lO O ~< -I
„ V <•
jlffi<jsserSatasasS?;
BtoUtaozoHO,
ujuiO3-^3O3.
23
-------
where M = number of event (13 for RC = 2)
N = number of years of record (25 for Rochester)
RC = recurrence interval in years
Several other facts can be gleaned from careful review of
the data: "~~
« The number of storms having a total rain of less than
0.1O inch
« The number of storms having duration greater than 24
hours
9 The average number of days between storms
• The number of storms starting between midnight and 6
a.m., or at any one particular hour
The list of questions is limited only by the imagination of
the user. The more these rankings are studied, the more
useful the tool becomes. Examples of frequency curves, on
the basis of ranked data for the rainfall in Rochester, are
presented in Figures 8, 9, 10, and 11. These curves can be
compared with curves for a design storm.
The validity of these curves is directly related to the
length of the period of record that is being analyzed. The
longer the period of record, the greater the statistical
significance.
COMPUTER PROGRAM LOGIC AND INPUT-OUTPUT REQUIREMENTS
Five small computer programs are used to perform the
complete analysis of the rainfall records:
1. Storm Event Definition Program (EVENT)
2. Snowfall Inclusion Program (SNOWIN),
3. Minor Storm Event Exclusion Program (EXCLUD)
4. Storm Event Sequence List Program (LISTSQ)
5. Listing of Ranked Files Program (LISTRK)
These programs are normally used in the sequence listed.
The Snowfall Inclusion Program and the Minor Storm Event
Exclusion Program are optional. The input and output data
24
-------
1 0
25 YEARS OF RECORD
ROCHESTER AIRPORT GAGE
0.1 0.2 0.40.6 1 2 46810
OCCURRENCES PER YEAR
20
40 60 80 100
FIGURE 8. EXAMPLE CURVE - STORM MAGNITUDE VS. FREQUENCY
i o
^ 0.8
0. 6
0. 4
0. 2
0. 1
25 YEARS OF RECORD
ROCHESTER AIRPORT GAG!
0.1 0.2 0.40.6 1
4 6810
20
40 6080100
OCCURRENCES PER YEAR
FIGURE 9. EXAMPLE CURVE - STORM INTENSITY VS. FREQUENCY
25
-------
ID
<=> 8
ui
Q
ui o
ui
o
X
a
ui
. 0.8
CO
2! 0. B
o
»—
CO
u. ••«
0. 2
0. t
25 YEARS OF RECORD
ROCHESTER AIRPORT GAGE
O.I 0.2 0.40.6 1 2 4 6810
OCCURRENCES PER YEAR
20
40 60 80 100
FIGURE 10. EXAMPLE CURVE - STORM DURATION VS. FREQUENCY
JU
CO
S 20
o
»—
CO
u.
o
t—
z
UI
g 10
tu
Qu
«•••
••§•
^•^
mmm
25 YEARS OF RECORD
ROCHESTER AIRPORT GAGE
i
T
1
j
8 10 12 14 16 1
HOUR AFTER START OF STORM
8 20
I
i
i
!
m|i
FIGURE 11. EXAMPLE CURVE - PERCENT OF STORMS HAVING
MAXIMUM 1-HOUR INTENSITY VS. HOUR AFTER START OF STORM
26
-------
for each program are compatible; therefore, the programs can
be run in any desired sequence. The programs are written in
FORTRAN, and, while not extremely complex, they could be
used most effectively by a person with the ability to
manipulate the input and output files on the computer system
that is being used. The complete listing of these programs
is given in Appendix B.
Storm Event Definition Program (EVENT)
EVENT, the first program in the sequence, is used to perform
the initial translation of the hourly record into storm
events using the prescribed definition. The program listing
for EVENT is presented in Table B-l and Table B-2 is a list
of variables for EVENT. The input for this program is the
hourly rainfall record in the format listed in Table 6,
which corresponds with the format used on the Weather
Bureau's hourly tapes.
The flow chart displaying the program logic is presented in
Figure 12. This program initially reads the hourly rainfall
records one day at a time and inspects it for errors. The
program then starts checking if rain is occurring and when
the last rain occurred. If it just started raining, the
interval between storms is checked. The peak hourly
intensity for the storm is also checked. The
characteristics of each storm are accumulated. When the
storm ends, the characteristics of the storm are recorded on
disk in the format listed in Table 7. The. program is
terminated when all of the data have been read.
The output format for this program is compatible with the
input format for each of the succeeding programs.
Therefore, any of the following programs can be used. If
the user would like to see the results of this analysis, the
Storm Event Sequence List Program or the Listing of. Ranked
Files Program could be used with the output file that has
been created.
Snowfall Inclusion Program (SNOWIN)
The input for the SNOWIN program consists of both the output
from the Storm Event Definition Program and data from the
Weather Bureau daily rainfall records. The program listing
for SNOWIN is presented in Table B-3 and a list of variables
for SNOWIN is shown in Table B-4. The storm event data are
in the format presented in Table 7. The daily rainfall
records are in the format presented in Table 8, which is
compatible with the Weather Bureau daily rainfall tapes. In
this program the daily - records are inspected for days on
which snowfall occurs, and these days are matched with storm
27
-------
Table 6. FORMAT FOR HOURLY RAINFALL DATA
Card
group Forjnat
312
11
12P3.2
12P3.2
12
Card
group Format
14
313
2F6.2
213
12
13
15
15
Card
columns
7-12
(7-8)
(9-10)
(11-12)
13
14-16
16-18
•
•
47-49
14-16
•
•
47-49
79-80
Table
Card
columns
2-5
6-8
9-11
12-14
15-20
21-26
27-29
30-32
33-34
35-37
43-47
43-47
Description
Date of rainfall
Year (last 2 digits)
Month
Day
Switch indicating time of day;
0 indicates a.m. and 1 indicates p.m.
Quantity of rainfall in Hour 1
Quantity of rainfall in Hour 2
Quantity of rainfall in Hour 12
Quantity of rainfall in Hour 13
Quantity of rainfall in Hour 24
Day of next recorded rainfall
7. FORMAT FOR STORM DATA
Description
Number of year of storm
Number of month of storm
Day of month of storm
Duration of storm
Total rainfall for storm
Maximum 1-hour intensity for storm
Number of hours into storm that
peak intensity occurs
Number of days between storms
Snowfall index
Clock hour for start, of storm
Sequence numbers
Magnitude sequence number
Variable
name
NY (I)
MO (I)
KID (I)
NX (I)
FR(I,1)
FR(I,2)
E'R(I,12) .
FR(I,13)
•
FR(I,24)
NEXT (I)
Variable
name
NYA/IYE
MOA/MON
NDA/NDA
LD/NDU
FRS/'TR
FAX/TMR
ITA/NHR
LLD/NDT
IS/ISN
IRT/IHR
IFXX/NRR/IPP
IQ/IFXX
28
-------
READ DATA IN 24-HOUR BLOCKS:
YEAR, MONTH. DAY, HOUR,
RAINFALL.AND DAY OF NEXT
RAINFALL.
DATA CHECK: EXCESSIVE NUMBER
OF DAYS BETWEEN STORMS.
ERROR MESSAGE.
TERMINATE PROGRAM.
DATA CHECK: IS STORM OCCURRENCE
AT END OF DAY?
IS STORM STARTING?
DETERMINE HOUR, DAY, MONTH,
AND YEAR OF START OF STORM
AND DAYS SINCE LAST STORM.
IS IT RAINING?
HAS IT NOT RAINED FOR
MORE THEN 6 HOURS?
SIGNAL END OF STORM.
FIGURE 12. FLOW CHART FOR STORM EVENT
DEFINITION PROGRAM (EVENT)
29
-------
IS THIS THE PEAK INTENSITY
FOR THIS STORM?
DETERMINE WHEN THIS PEAK OCCURS
AFTER START OF STORM.
DETERMINE TOTAL RAINFALL
AND DURATION OF STORM.
DID STORM OCCUR?
RECORD ON DISK: STORM EVENT
HAS ALL DATA BEEN READ?
TERMINATE PROGRAM.
FIGURE 12. (CONCLUDED)
30
-------
events. An index is set if snowfall is present or not
present for a particular storm. The flow chart for this
program is presented in Figure 13. The output is in the
format presented in Table 7, and is listed on a disk for
future reference.
Table 8. FORMAT FOR SNOWFALL DATA
Card
group
Card
Format columns
312 6-11
(6-7)
(8-9)
(10-11)
F3.1 22-24
Description
Date of precipitation
Year (last 2 digits)
Month
Day
Quantity of snowfall
Variable
name
NYB
MOB
NDB
SNOW
Minor Storm Event Exclusion Program (EXCLUD)
The function of the EXCLUD program is to eliminate the very
small rainfalls that occur from the storm file. This
reduces the amount of data to be sorted and ranked, These
small storms are sometimes the tailing or leading edge of a
large storm that became isolated due to the rigid
application of the 6-hour storm event definition, or they
are parts of large storms that fell somewhere else in the
region and are just passing through. In either case, in
Rochester these storms amount to an average of 51
occurrences, for a total of 1.21 inches of rain annually, or
4.1 percent of the total annual rainfall. Metcalf & Eddy
has defined small storms as those with rainfall amounting to
O.05 inches or less. The program logic is presented in
Figure 14. The program also uses the format presented in
Table 7 for both input and output. The input and output is
handled on disks. A program listing for EXCLUD is presented
in Table B-5 and the list of variables for EXCLUD is
presented in Table B-6.
Storm Event Sequence List Program (LISTSQ)
The output from the Snowfall Inclusion Program or the Minor
Events Exclusion Program is usually used with the LISTSQ
31
-------
READ STORM EVENT DATA.
READ SNOWFALL DATA:
YEAR, MONTH, DAY, SNOWFALL.
COMPARE RAIN DATE WITH SNOW DATE.
SET INDEX: NO SNOW IS PRESENT.
SET INDEX: SNOW IS PRESENT.
RECORD ALL STORM EVENT DATA
AND ADD SNOW INDEX.
HAS ALL DATA BEEN READ?
TERMINATE PROGRAM.
FIGURE 13. FLOW CHART FOR SNOWFALL
INCLUSION PROGRAM (SNOWIN)
32
-------
READ : STORM EVENT DATA.
DOES THE STORM HAVE LESS THEN 0.05
INCH OF RAIN ?
RECORD: EDITED STORM EVENT DATA.
HAS ALL DATA BEEN READ?
TERMINATE PROGRAM.
FIGURE 14. FLOW CHART FOR MINOR STORM EVENT
EXCLUSION PROGRAM (EXCLUD)
33
-------
program in the format presented in Table 7. The logic for
the program is presented in Figure 15. Its primary function
is to provide a chronological listing of the storm event
data. In the program, data on the duration of storms'; the
total rainfall, and the maximum hourly intensity for each
year are accumulated and listed at the end of each year.
When all of the data have been read, the program is
terminated. The results of this analysis are printed by
this program. An example of the output for the year 1962 is
presented in Table 9. Table B-7 is a program listing for
LISTSQ. Table B-8 is a list of variables for LISTSQ.
Sorting and Ranking Program (SORT)
The SORT program used to analyze rainfall is a package
program developed by IBM (OS SORT/MERGE Program GC28-6543)»
An important characteristic of this program is that it sorts
the data on the basis of a particular characteristic and
carries along with it the remaining characteristics of the
particular storm. , .
This program uses the same input-output format presented in
Table 7, and also uses disks for input and output files.
Listing of Ranked Files Program (LISTRK)
The LISTRK program provides output from any of the previous
programs. The program listing for LISTRK is shown in Table
B-9 and Table B-10 is a list of variables for LISTRK. The
program logic is presented in Figure 16. The program reads
disk files in the format presented in Table 7, assigns a
sequence number, and prints the information on the line
printer. An example of the output is presented in Table 1O.
This output has been ranked by maximum 1-hour intensity.
34
-------
READ
READ STORM EVENT DATA.
ERROR
STOP
YES
WRITE
STOP
J
WRITE
DATA CHECK: MONTHS IN YEAR.
ERROR MESSAGE.
TERMINATE PROGRAM.
CHECK FOR END OF YEAR.
LIST: TOTAL DURATION OF RAIN
FOR YEAR, TOTAL ANNUAL RAINFALL,
AND MAXIMUM INTENSITY RAINFALL
FOR YEAR.
CALCULATE: TOTAL DURATION OF RAIN
FOR YEAR, TOTAL ANNUAL RAINFALL,
AND MAXIMUM INTENSITY.
LIST: STORM EVENT DATA.
t
HAS DATA FOR EACH STORM BEEN READ?
TERMINATE PROGRAM.
FIGURE 15. FLOW CHART FOR STORM EVENT
SEQUENCE LISTING PROGRAM (LISTS.Q)
35
-------
I
s a
K W
O EH
E-i W
W H
EH O
Ql fjfj
o O
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38
-------
READ:RANKED STORM EVENT DATA.
ASSIGN SEQUENCE NUMBERS AND INDEX
COUNTERS.
LIST:RANKED STORM EVENT DATA.
HAS ALL DATA BEEN READ?
TERMINATE PROGRAM.
FIGURE 16. FLOW CHART FOR LISTING OF RANKED
FILES PROGRAM (LISTRK)
39
-------
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40
-------
SECTION VII
" .' '*
STORAGE-TREATMENT BALANCE
In the storage-treatment task, rainfall is converted into
runoff and overflows. In this chapter the general
characteristics of the program will be presented, the
program logic and specific input and output requirements
will be discussed, and examples using portions of the City
of Rochester will be modeled to illustrate the utility and
versatility of the program.
CHARACTERISTICS OF THE STORAGE-TREATMENT PROGRAM
The program is described in three parts: (1) the concept
upon which it is based, (2) the major operational controls
and general data requirements, and (3) the philosophy or
method of approach to its application and the usefulness of
its output.
Concept of the Program
The concept of the storage-treatment program is presented
graphically in Figure 17. In the program rainfall is
converted into runoff using a K factor (gross runoff
coefficient). This runoff is stored in a specific storage
volume that is drained by a specific treatment rate. The
treatment rate could be determined by an actual treatment
facility or an interceptor capacity. When the runoff
exceeds the storage capacity and the treatment rate, an
overflow occurs. .
Operational Controls and Data Requirements
The program, can function on either a daily or hourly time
step. The daily time step is used initially for analysis
based on the entire period of record. For specific periods
of interest—including critical storms—the analysis may be
performed on the hourly time step.
The starting of the treatment rate also can be controlled.
In the daily analysis, the percentage of the full treatment
41
-------
x
R
U
N
0
F
EFFLUENT
FIGURE 17. CONCEPT OF STORAGE-TREATMENT PROGRAM
rate for the first day of rain can be controlled. This
control can be used to reflect the uncertainty of when a
storm starts during the day or the length of time required
to start a treatment facility. In the hourly analysis, the
start of the treatment rate can be delayed a specified
number of hours to reflect the real time required for
start-up of a stormwater treatment facility.
42
-------
The program, when used with a computer system capable of
on-line storage of input and output files, can also be used
to analyze a system of linked subareas. The treatment rate
for a system would represent the capacity of the 'interceptor
system between subareas. The program has the ability to
create a time-varying interceptor capacity for upstream
areas based on the runoff from downstream areas and
downstream interceptor capacities.
The major data requirements for the program are simply the
land area, the K factor, the storage capacity, and the
treatment rate. The area of land -for a particular subarea
is developed as described in Section V. The K factor can be
developed and checked from (1) quantity measurements as
described in Section V; (2) analysis of detailed computer
programs, such as the Storm Water Management Model (SWMM);
or (3) other traditional empirical equations based on land
use or impervious area (as illustrated in Appendix C). The
storage capacity should be the real in-system storage, again
as presented in Section V, added to the existing or proposed
tunnel, cavern, and/or diked storage volume. The treatment
rate is the existing or proposed available peak wet-weather
treatment capacity or excess interceptor capacity
immediately downstream from the subarea.
The other input variables trigger the operational controls.
The factor for starting of the treatment rate is the most
significant of these triggers. This factor would normally
be zero when an interceptor is downstream rather than a
treatment facility. There is a switch for daily or hourly
analysis, and the number of upstream interceptors converging
on the area must also be noted.
Output Data and Application Philosophy
The output from the program is the record of the time and
volume of overflows and runoff, arid a summation of these
parameters. The summation is terminated at the end of each
year for the daily analysis and at the end of each month for
the hourly analysis.
The program is intended primarily for use in the analysis of
alternatives and general evaluation of the number of
occurrences and volumes of overflows.
The storage capacities or treatment rate can be varied.
Generally, one parameter is varied while the other
parameters are held constant, so that curves can be
generated to indicate the impact of the particular variable
on the system.
43
-------
The base or uncontrolled condition is run initially to
provide a base for comparison of the control approaches,
The success of an overflow abatement program can be measured
by a reduction in the volume, duration, and number of
overflows. The objectives of the control philosophy should
be defined early in the analysis and should be correlated to
to some improvement in receiving water quality.
The program operates on the real rainfall records, and
therefore it internally takes into account the synergistic
effects of storms coming close together with overlapping
demands on storage capacities. If the period of rainfall
records is long enough—say 2O to 50 years—the runoff,
overflow volumes, and durations can be filed and ranked, and
statistically significant frequency of occurrence curves can
be generated.
A series of linked subareas are analyzed by independent
computer runs. The analysis starts with the subarea closest
to the treatment plant or discharge point and proceeds
upstream through the series of subareas. The important data
are passed from one computer run to the next by means of
files stored on the computer system.
COMPUTER PROGRAM LOGIC AND INPUT-OUTPUT REQUIREMENTS
The storage-treatment program is written in FORTRAN computer
language. This program records information on input and
output files and therefore can be most productive when used
by a person familiar with the manipulation of files on the
computer system. The complete listing of this program is
presented in Appendix B.
The program is composed of a control block with four
subroutines:
» HOUCRD - Hourly analysis from card input
9 DLYCRD - Daily analysis from card input
9 DLYTAP - Daily analysis from tape input
» HOUTAP - Hourly analysis from tape input
These subroutines are quite similar; the major differences
are in the types of computer input used (cards or magnetic
tape) and in the time steps being analyzed (daily or
hourly). The logic for the control block is presented in
Figure 18. The format for the control block input data is
presented in Table 11.
44
-------
READ
DECISION
DAILY
HOURLY
DECISION.
TAPE
CARD
GENERAL DATA: AREA, RUNOFF
COEFFICIENT, MINIMUM STORAGE,
NUMBER OF YEARS TO BE ANALYZED,.
DAILY/HOURLY SWITCH,, TAPE/CARD
SWITCH. INFLOW FROM UPPER REACHES.
FACTOR FOR TREATMENT PLANT FLOW
ON FIRST DAY OF STORM.
IS TIME PERIOD OF ANALYSIS
DAILY OR HOURLY?
ARE DAILY RAINFALL RECORDS ON
TAPE/DISK OR CARDS ?
CALL SUBROUTINE DLYCRD.
CALL SUBROUTINE DLYTAP.
ARE HOURLY RAINFALL RECORDS ON
TAPE/DISK OR CARDS.
CALL SUBROUTINE HOUCRD.
CALL SUBROUTINE HOUTAP.
FIGURE 18. FLOW CHART FOR CONTROL BLOCK OF
STORAGE-TREATMENT PROGRAM
45
-------
Table 11. FORMAT FOR CONTROL BLOCK DATA
OF STORAGE-TREATMENT PROGRAM
Card
group
Card
Format columns
5A4 1-4
5-8
•
Description
Identifier of first area to be
analyzed
Identifier of second area to be
analyzed
Variable
name
ARtf(l)
ARN(2)
•
5F8.0
12
311.
14
IX
FIDO
17-20
21-28
29-36
37-44
45-52
53-60
61-62
63
64
65
66-69
70
71-80
Identifier of last area to be
analyzed
Area of area to be analyzed
Gross runoff coefficient "K factor"
Maximum volume of storage available
Treatment rate or adjacent down-
stream interceptor capacity
Minimum volume of storage
Number of years or months of
record to be analyzed
Switch 0 for daily analysis;
1 for hourly analysis
Switch 0 for card input;
1 for'tape input
Number of interceptors converging
at point of runoff
Years or months to be analyzed
Factor used to determine volume
of runoff routed to treatment
plant on first day of rain
ARN(5)
AREA
COEF
STMAX
TREAT
STOPS
NYEAR
NSS
IOTAP
IPFL
MYEAR
TFAC
The four subroutines function on basically the same logic,
which is presented in Figure 19. The slight differences in
input requirements and other minor functional differences in
the four subroutines are described in the discussion that
follows.
46
-------
GENERAL DATA FROM COMMON BLOCK.
START CALCULATIONS FOR A YEAR/MONTH.
YEAR/MONTH TO BE ANALYZED,(NUMBER OF DATA CARDS)
RAINFALL DATA: MONTH/DAY, DAY/HOUR. QUANTITY
OF RAINFALL. (10 DAYS RECORDED
ON EACH DATA CARD).
DATA CHECK: NUMBER OF DAYS IN MONTH/HOURS
IN DAY. NUMBER OF MONTHS IN
YEAR/DAYS IN MONTH.
ERROR MESSAGE.
TERMINATE PROGRAM.
START CALCULATIONS ON DAILY/HOURLY BASIS.
PROCEED TO NEXT DAY'S DATA.
RAINFALL CHECK: DID RAINFALL OCCUR IN THIS DAY/
HOUR?
STORAGE CHECK: is AVAILABLE STORAGE
EMPTY?
DETERMINE QUANTITY OFRAINFALL AND RUNOFF.
PUT RUNOFF INTO STORAGE.
FIGURE 19. BASIC FLOW CHART FOR SUBROUTINES
OF STORAGE-TREATMENT PROGRAM
47
-------
IS TREATMENT OPERATIONAL FOR THIS CYCLE?
REDUCE STORAGE AT RATE'OF TREATMENT.
START TREATMENT FOR NEXT CYCLE.
IS MAXIMUM STORAGE EXCEEDED?
CALCULATE QUANTITY OF WATER IN EXCESS OF
MAXIMUM STORAGE AND DECLARE IT TO BE
OVERFLOW.
CALCULATE: TOTAL RAINFALL FOR MONTH/EIAY,
QUANTITY OF RUNOFF, OVERFLOW
TREATED, STORED, AND DAYS/HOURS
OF RAIN AND OVERFLOW TO DATE
FOR YEAR/MONTH.
LIST: DATE (MONTH AND DAY)/
-------
In the HOUCRD subroutine, the input data are read from
computer cards in the format presented in Table 12, Of the
four subroutines, this one has the most limited capacity.
Subareas cannot be connected for analysis, and the treatment
rate cannot be turned off for the first hours of, a storm.
The hourly time increment is used,
In the DLYCRD subroutine, the input data are read from
computer cards 'in the format presented in Table 13, which is
very similar to the format of the hourly data, Connected
subareas cannot be analyzed, but the treatment can be
adjusted on the first day of rain. Calculations are made on
a daily time step.
In the DLYTAP subroutine, the input data are read from
magnetic tape or disks in the format presented in Table 14,
which is compatible with the Weather Bureau's daily rainfall
record tapes, This subroutine can be used with all of the
operational controls described earlier in this chapter. The
daily time increment is used.
Table 12. FORMAT FOR HOUCRD SUBROUTINE DATA
Card
group
Format
2110
10(212, F4. 2)
(212)
(F4.2)
(212)
(F4.2)
Card
columns
1-10
2-20.
1-8
(1-2)
(3-4)
(5-8)
9-16
(9-10)
(11-12)
(13-16)
17-24
Description
Number of month to be analyzed
Number of data cards to be read
First hour and quantity of rainfall
Day
Hour
Quantity
Second hour and quantity of rainfall
Day
Hour . .
Quantity
Third hour and quantity of rainfall
Variable
name
MYEAR
NCARD
MON(l)
MDAT{1)
RAIN(l)
MON(2)
MDAT(2)
RAIN (2)
73-80 Tenth hour and quantity of rainfall
Ten hours and quantities on each
data card
49
-------
Table 13. FORMAT FOR DLYCRD SUBROUTINE- DATA
Card
group
Format'
Card
columns
Description
Variable
name
2iio 1-10 Number of year to be analyzed MYEAR
11-20 Number of data cards to be read NCARD
10{2F2,F4.2) 1-8 First date and quantity of rainfall
(212) (1-2) Month MON(l)
(3-4) Day MDAT(l)
(F4.2) (5-8) Quantity RAIN(l)
9-16 Second date and quantity of rainfall
(212) (9-10) Month MON(2)
(11-12) Day MDAT(2)
(F4.2) (13-14) Quantity RAIN(2)
17-24 Third date and quantity of rainfall
73-80 Tenth date and quantity of rainfall
Ten dates and quantities on each
data card
Table 14. FORMAT FOR DYLTAP SUBROUTINE DATA
Card Card
group Format columns
Variable
name
312 6-11 Date of precipitation
(6-7) Year (last 2 digits) NYZ
(8-9) Month MON
(10-11) Day MDAT
F4.2 18-21 Quantity of rain for day RAIN
50
-------
In the HOUTAP subroutine, the input data are read in the
format presented in Table 15, which is compatible with the
Weather Bureau's hourly tapes. Again, all of the
operational controls can be used. The time increment is
hourly,
In all of the subroutines, basically the same output format
is used. The daily and hourly times are recorded slightly
differently, An example of the daily output is presented in
Table 16, and an example of the hourly output is presented
in Table 17.
EXAMPLE OF STORAGE-TREATMENT PROGRAM APPLICATION
The storage-treatment program was used on the City of
Rochester system of combined sewers to analyze the
performance of the existing system as well as to evaluate
the suggested overflow control alternatives. Examples of
the results are presented in this section,
Table 15. FORMAT FOR HOUTAP SUBROUTINE DATA
Card Card
group Format columns
Description
Variable
name
312 7-12 Date of rainfall
(7-8) Year (last 2 digits) NY(I)
(9-10) Month MO (I)
(11-12) Day ND(I)
II 13 Switch indicating time of day; NX(I)
0 indicates a.m. and 1 indicates p.m.
12F3.2 14-16 Quantity of rainfall in Hour 1 FR(I,1)
16-18 Quantity of rainfall in Hour 2 FR(I,2)
47-49 Quantity of rainfall in Hour 12
12F3.2 14-16 Quantity of rainfall in Hour 13
FR(I,12)
FR(I,13)
12
47-49 Quantity of rainfall in Hour 24
79-80 Day of next recorded rainfall
FR(I,24)
NEXT(I)
51
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Data Development
The characteristics of the system were developed in Table 4,
which contains the basic information on subarea
characteristics. These characteristics must be considered
fixed during the alternative analysis so that the critical
parts of the alternatives can be analyzed,
In the City of Rochester, the subareas 'that drain directly
to the Genesee River were analyzed. Initially, the areas
indicated in the schematic shown in Figure 20 were analyzed
to determine the level of runoff control offered by the
existing sewer system. The output from the program was
translated into a plot showing the frequency of occurrence
of runoff and overflows versus volume as presented in Figure
21.
Several alternatives were analyzed. The results from each
analysis were translated into plots showing the frequency of
occurrence versus volume. Two of the alternatives,
identified as Rochester West Side Alternatives 1 and 2,
appear to have a distinct advantage in reducing the number
of overflows.
Alternative Analysis
In West Side Alternative 1, large drainage-storage tunnels
would be connected to a modified interceptor system. The
drainage tunnels would be constructed in the City of
Rochester as shown in Figure 22. The flow capacities of the
modified interceptor sewer and the size of the
drainage-storage tunnels are indicated. The
drainage-storage tunnels could be connected to the main
interceptor by pumps or by some other flow-controlling
structure. This alternative is presented schematically in
Figure 23.
In West Side Alternative 2, an interceptor tunnel would be
used along the river to connect the drainage-collector
tunnels. This interceptor tunnel would be connected to the
existing interceptor downstream of the city. This
connection would also have a controlled discharge rate. The
configuration of this alternative is presented in Figure
24, and the schematic is presented in Figure 25,
A major assumption of these two alternatives is that each
subarea, when improved, will have a single point of
overflow.
54
-------
©
LEGEND
SUBAREA
OVERFLOW
VANLARE
TREATMENT PLANT
/onn-v INTERCEPTOR
(.ZOO.) CAPACITY, MGD
STORAGE VOLUME.
MIL GAL.
* THESE VALUES DO NOT REFLECT
MOST RECENT ANALYSIS OF SEWER
SYSTEM PRESENTED IN TABLE 5-4
FIGURE 20. EXAMPLE OF SYSTEM SCHEMATIC -
EXISTING ROCHESTER WEST SIDE INTERCEPTORS
55
-------
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56
-------
7,000
VANLARE
/ TREATMENT
PLANT
8,000 (12)-
13,000
SUBAREA BOUNDARY
DRAINAGE TUNNEL
LENGTH AND
(DIAMETER). FT
MOD IFIED INTERCEPTOR
FIGURE 22. ROCHESTER WEST SIDE - ALTERNATIVE 1
57
-------
VANLARE
TREATMENT PLANT
LEGEND
31 } SUBAREA
OVERFLOW
INTERCEPTOR
CAPACITY. MGD
I - 1 STORAGE VOLUME,
I _ I MIL GAL.
* THESE VALUES DO NOT REFLECT
HOST RECENT ANALYSIS OF SEWER
SYSTEM PRESENTED IN TABLE 5-4
FIGURE 23. EXAMPLE OF SYSTEM SCHEMATIC -
MODIFIED ROCHESTER WEST SIDE INTERCEPTORS
58
-------
5,000 (10)
2,000/(16)
2,000 (18)
— 4,000 (16)
8,000 (12) J6,000 (16)
/-^_!/'-K ^~
^2,000 (16)
13,000/(16)
U-
7,000 (12
LEGEND
SUBAREA BOUNDARY
DRAINAGE TUNNEL
LENGTH AND
(DIAMETER). FT
FIGURE 24. ROCHESTER WEST SIDE - ALTERNATIVE 2
59
-------
The results from the analysis, presented in Figure 21, are
based on use of the daily time step and the last 20 years of
record from the Weather Bureau tapes. The volume of runoff
and overflow for each day that runoff or overflow occurred
was ranked, and the the frequency of occurrence curves were
developed. These curves are based on real rainfall for a
long period of record and therefore present statistically
valid data,
Comparison of Daily and Hourly Analysis
Two critical periods were analyzed using the hourly interval
on Alternative 2. These periods covered three of the
largest storms in the period of record. The hourly
analysis, as_expected, provides more sensitive data than the
daily analysis. These two analyses are compared in Table
18.
The daily and hourly results are compared graphically in
Figure 26. One of the periods analyzed represents a large
storm that was known locally as hurricane Agnes,
The concept of simplified modeling and the degree of
precision in the basic assumption of the input data limit
analysis on any finer time step than hourly. The input data
to the model are limited by three assumptions: (1) that
rainfall over the subarea is uniform, (2) that the K factor
is a gross runoff coefficient, and (3) that travel times in
the sewers (displacement of peak flow) are not accounted
for.
VANLARE
TREATMENT PLANT
(100)
f.21.2:
(8-9,16,25^
1,17-18,26^
31
, INTERCEPTOR
i CAPACITY.MGD
STORAGE VOLUME,
MIL GAL.
FIGURE 25. EXAMPLE OF SYSTEM SCHEMATIC - ALTERNATIVE 2
60
-------
Table 18. COMPARISON OF DAILY AND HOURLY
TIME INCREMENT ANALYSIS3
Test Period 1 Test Period 2
Number of days
Number of days
of rainfall
Total rainfall, in.
Percentage above
20-yr average
91
29
11.4
50.9
92
33
11.1
32.5
Total runoff, mil gal
Total overflow
volume, mil gal.a
Overflow, % of
total runoffa
Total number of
days of rain
Total number of days
of overflow9
a. Totals represent
b. Storm of January
Daily
increment
.a 2,679.3
238.7
8.9
62
3
sum of Test Periods
22-23 overflowed on
Hourly
increment
2,679.3
383.6
14.3
62
5b
1 and 2.
both days
when computed hourly. The second added overflow
occurred on June 29-30 from a 6-hr storm that
started 3 hrs before midnight and ended 3 hrs
after midnight. The daily simulation distributed
the storm's impact over, 48 hrs; thus, no overflow.
The hourly simulation properly compacted the
storm to 6 hrs and the system capacity was ex-
ceeded between 12:00 midnight and 3:00 a.m. on
June 30.
61
-------
50-
40-
30-
o
«g 480-] 20-
j 380-
S 240-
;f 120-
* 0-
10-
n -
X 240-i 10-1
^ t2oJ S
Sg I =
o -J o -
TOTAL RUNOFF RATE
r
Jt:-y-
S
'\
TREATMENT
rJr^T ' "r1^1 ""
_i u u i 1
91
30-
o: 480-i a 20-
7 380-
i 240-
C3
_, 120-
31 0-
i^j
3E
10-
Q-
OVERFLOW
1
n
Ir1
-.- ri r
n
1
-Jh— ^_.
_|.
t E G E N (1
™
HOURLY CALCULATIONS
„ DAILY SUMMATION OF
\
1 HOURLY RESULTS
DAILY RESULTS
n
LUL.__.
1 1 "-"• — ••••— -|
T! iya.-pas|_j
f
t
•L ' ' '
111
ll
90-
12 24 12 24 12
6/20 6/21 8/22
24
12 24 12
6/23 6/24
?4
FIGURE 26. COMPARISON OF HOURLY AND DAILY ANALYSIS
62
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SECTION VIII
OVERFLOW QUALITY ASSESSMENT
The assessment of overflow quality can be approached by two
methods. In the first method, dirt and nutrient suspension
and transportation are taken into consideration as in SWMM
and STORM, In the second method, regression techniques and
statistical manipulations of observations are used to
extrapolate quality parameters from existing data. The
second method is more direct and quite consistent with the
concept of simplified modeling. A third method using
geographic and demographic data based on nationally
collected statistics also can be used. This technique is
summarized in Appendix D,
Using the second method, two analyses are performed in this
chapter to derive quality characteristics. In the first
analysis, regression equations are developed on the basis of
measured data that have been collected. In the second
analysis, averages of selected subsets of the measured data
are used to develop quality parameters.
(A third analysis based on polynominal regression techniques
is presented in Appendix D. This third technique was not
applied successfully in Rochester.)
The quality characteristics are developed in the form of a
concentration that can be paired with an overflow volume.
The volume overflowing is a function of the storm
characteristics, the configuration of the drainage area, and
the control alternative. This volume is calculated in the
storage-treatment task. The quality of the overflow is also
a function of the storm characteristics and the drainage
area configuration.
REGRESSION ANALYSIS
The regression analysis is described
procedure and example of equations.
in two parts:
63
-------
Procedure
Regression analysis is part of the broad area of fitting
curves to measured data. In this analysis, equations based
on linear regression are developed. (Specific equations
based on the data acquired in Rochester are used in this
discussion) .
The linear regression analysis creates equations of the
following form:
= ax
bx
CX
where
x
1'
a.
Q, = quality parameter
x = measured parameters
z = calculated regression coefficient
By using logarithms of the measured values and the same type
of linear regression analysis, an equation of the following
form can be developed:
Ql =
The quality of the regression analysis can be evaluated by
checking the correlation of the values predicted from the
equation with the real measured values using the correlation
coefficient. The correlation coefficient has a maximum
value of one and a minimum value of zero. The closer the
calculated coefficient is to one, the better the correlation
and, therefore, the better the predicting equation. These
analyses can be performed with a package computer program, a
programmable desk-top calculator, or hand calculations using
matrix techniques.
Prom experience in the analysis of combined sewer overflows,
it is known that the quality of the overflow varies with
many factors. The most significant factors are the
intensity of the rainfall, the antecedent condition (days
since last rain), and the duration of the storm. Land use
parameters, such as population density, quantity of
commercial-industrial areas, and street cleaning policies,
can also affect overflow quality.
64
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Example of Equations
The regression equations are based on the quality data that
are collected, as described in Section V, In the following
analysis, two data sets are used, Data Set 1 (not included)
contains the average of the composite samples from each
subarea for each storm, This data set covers 29 storms
between March 1974 and August 1975; two to seven subareas
were averaged for each storm.
Data Set 2 (not included) contains all of the composite
samples of all of the storms for each of the subareas. This
data set covers the same storm period as Data Set 1 and is
the unaveraged data set used to create Data Set 1, It
contains 142 points covering 29 storms and 12 subareas,
Equations were fitted to these data using a package computer
program that performs linear regression analysis, Several
sets of equations to project COD, suspended solids, and
nitrogenous oxygen demand (NOD) were developed. In the
first and best set of equations, the rainfall
characteristics and Data Set 1 were used to create equations
to project the quality parameters. These equations are
presented in Table 19, The quality parameters projected
from these equations were correlated with Data Set 1 to
check the equations. The correlation coefficients are not
Table 19. EQUATIONS FOR QUALITY PROJECTION
Equation
Correlation
coefficient
0.0705 v-0.0761 y-O.'tp?
COD = 50.17 Xj
TSS = 159.62 Xj'220 X2-°-31tS X3-°-329
NOD = 1.89 X,-°-191 X,-0-*39 X;0-"9*
0.257
0.181
0.487
where COD = chemical oxygen demand, mg/1
TSS = total suspended solids, mg/1
NOD = nitrogenous oxygen demand, mg/1
Xi = number of days since last rain, days
X2 = duration of rainfall, hr
X3 = average intensity of rainfall, in./hr
65
-------
high because stormwater quality is highly variable, and the
data that were developed from the sampling program had some
major irregularities.
Attempts were also made to develop equations including
population density to reflect the impact of land use
patterns on stormwater quality. These equations were
correlated with Data Set 2. The correlation coefficient
indicated that there was essentially no correlation between
the measured values and the predicted values from these
equations. This lack of correlation may be because of
irregularities in the data and because of the particular
blend of land uses in the City of Rochester.
The composite samples used for the regression analysis were
composited from individual samples taken at regular
intervals in time starting with the beginning of an overflow
occurrence. The individual samples that were taken are not
directly related to flow and do not reflect quality
variations that are flow related. A possibly more realistic
composite would be one that is sampled proportionately with
flow. A flow proportionate average could also be calculated
if both the time of sample and volume of overflow were
correlated. This correlation was not effectively achieved
even with the extensive data collected in Rochester.
ANALYSIS OF AVERAGES
The analysis of averages is also described
procedure and example of averages.
Procedure
in two parts:
A large volume of data is generated from a sampling program.
These data can be manipulated in several ways. One of the
easiest ways is to calculate averages of meaningful subsets
of the data. Two of the significant averages are (1) gross
averages of the data by subarea and (2) averages for time
increments by subarea.
These subarea averages can be ranked to indicate trends.
The significant land use or surface characteristics can also
be ranked. If these rankings are indicated on a simplified
map of the study area, areal trends in overflow quality can
be noted. The areas that create the most pollution can be
assigned a high priority when projects to relieve overflows
are to be designed and constructed.
While averaging by time increment and subarea can provide
information on trends, it also can have another more
valuable use. Because of the first-flush phenomenon, the
66
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concentrations of the overflow change, If the first flush
is captured by a storage facility, then only the
higher-quality wastewater overflows, This time increment
average can provide more realistic quality values,
Examples of Averages
In Table 20, the average data from 31 storms in 1974 and
1975 are presented by subarea. Approximately 500 data
points were averaged for each subarea. The values in Table
20 are arithmetic averages of the measured values. The
geometric mean is usually used to characterize these
phenomena, but, because of the large number of zero values
from both actual measurement and inoperative equipment, the
geometric mean is not truly valid and the arithmetic value
more closely reflects the data.
In Table 21, the data have been averaged in time increments
from the start of the storm and by subarea. These values
are also arithmetic averages, From the data in Table 21,
the phenomenon of decreasing concentrations of pollutants
with time through the storm can be clearly seen,
Table 20. AVERAGE OVERFLOW QUALITY BY SUBAREA
mg/1
Total Biochemical Total Nitrogenous
Subarea inorganic oxygen suspended oxygen
No. phosphate demand solids demand
7
8
9
16
17
18
21
22
25
26
28
31
1.42
1.28
1.37
1.37
1.38
1.37
1.34
1.35
1.04
1.08
2.85
0.50
161
158
178
188
186
183
203
205
53
52
66
60
316
319
358
366
358
354
381
• • *
• • •
205
291
168
4.09
4.21
4.42
4.31 '
4.29
4.25
4.04
4.06
2.35
2.27
3.38
2.41
67
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Table 21. EXAMPLE OF AVERAGE OVERFLOW
QUALITY BY SUBAREA AND TIME INCREMENT
BOD, mg/1
Subarea
7
8
9
16
17
18
21
22
25
26
28
29
31
0-30
rain
224
208
14 6b
152
13 lb
143
83
a
75
48
74
33
196
30-60
min
136
63
17 6b
159
c
140
45
a
48
68
61
34
105
60-90
min
66
53
147b
138
14 Ob
65
43
a
65
17
55
39
83
90+
min
— a
59
122
55
23
50
75
a
44
13
62
22
47
0-30
min
578
247
c
260
114
368
147
836
435
685
270
165
251
TSS, mg/1
30-60
min
506
136 •
200b
357
— c
586
101
559
347
a
231
137
216
60-90
min
216
137
22 8b
718
469b
229
80
539
316
429
244
158
227
90+
min
198
210
219
313
129
142
127,
425
123
117
239
120
140
a.
b.
Average values are extremely high due to data
irregularities and therefore are not valid.
Insufficient data for statistically significant
results.
c. No data recorded.
In Figure 27, the rankings of population density and the
runoff coefficients are presented along with the rankings of
some significant quality parameters (based on data in Table
20). The land use-population density ranking was developed
by creating a weighted average of the ranking of
commercial-industrial land and the ranking of the population
density. This means that the area with the greatest
population density and the most commercial-industrial land
would be the source of the greatest pollution, In this
figure, the low numbers imply the worst conditions. The
68
-------
five areas with the lowest numbers^ in each category are
shaded for emphasis and to highlight any trends that may be
present.
The area served by the West Side trunk sewer (Area 9—shaded
in the lower half of Figure 27) are not the sources of
highly concentrated pollutants that might be expected
(possibly because of the location of the overflow and system
characteristics). The information presented in Figure 27
can assist the decision maker in translating an abundance of
data into specific problems in various parts of the study
area.
69
-------
12
(21
f
..L 1
(1 7,
11
10
(31.
LAND USE-POPULATION DENSITY
13 j
:1
7
I (R
10
11
CHEMICAL OXYGEN DEMAND -
TOTAL SUSPENDED SOLIDS
12
10
(21.
•t.*
Lit,;
12,
11
?'',fto. 8M2 9
RUNOFF COEFFICIENT
(PERCENT IMPERVIOUS)
(21
,10
11
(25
12
TOTAL INORGANIC PHOSPHATE
LEGEND: LOWEST RANKING NUMBER GIVEN TO HIGHEST CONCENTRATION
POPULATION, OR PERCENTAGE OF COMMERCIAL INDUSTRIAL '
AREA. RANKS 1-5 SHADED FOR EMPHASIS.
(3J) SUBAREA NUMBER
7 RANK NUMBER
FIGURE 27. EXAMPLE OF OVERFLOW QUALITY TRENDS
70
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SECTION IX
RECEIVING WATER RESPONSE
The impact of combined sewer overflows on the receiving
water is the most difficult and the most critical task that
must be performed. At the present time, a simplified
solution does not exist. The next alternative is to use the
best available simulation of the receiving water that has a
record of use in the area, Even when a program has a
history of use, it is important to define the specific
characteristics, limitations, and input-output requirements
of the program as they apply to the system of discharges and
receiving water being analyzed.
CHARACTERISTICS OF THE RECEIVING WATER PROGRAM
The basic approach and assumptions of the receiving water
program are important, There are many approaches that can
be used, and it is important to document the specific
approach so that any internal limitations of the program are
clearly understood.
Some of the most significant aspects of the approach are:
• The quality parameters that are analyzed
• The time frame of the program: steady state or
dynamic
• The basic equations that are used
• The source of reaction or decay rates
• The factors used for calibration of the program
For the City of Rochester, a program of the Genesee River
prepared by O'Brien and Gere for the Environmental
Protection Agency (Contract No, 68-01-1574) was used[l].
In this program a modified Streeter-Phelps formulation was
used to calculate steady-state dissolved oxygen
concentrations in the Genesee River, The purpose of the
71
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modification was to allow the use of separate decay rates
for carbonaceous material and for nitrogenous material. An
additional term was also added to the equation to calculate
the benthic oxygen demand. This benthic factor was used to
calibrate the program.
The decay coefficients used in the program were calculated
for each section of the river by the standard O'Connor
equation. These coefficients are based on existing river
characteristics and were not adjusted to calibrate the
program.
LIMITATIONS OF THE RECEIVING WATER PROGRAM
Many factors can limit the applicability of a program. The
most significant limitations are the range of flows and
conditions for which the program is calibrated. The program
could be calibrated for only summer conditions which may be
significantly different from winter conditions. Flows
during the spring rainy season and winter thaw may be
substantially different from flows during a dry spell in the
fall. If the program is not calibrated for the time of year
and flow conditions that are thought to be critical,
recalibration may be required, or a new program may have to
be found.
The Genesee River program was calibrated for an "average"
condition. This average condition is the period from
mid-July through mid-October of 1973. The program was also
operated under the Minimum Average Seven Consecutive Day
flow condition that is expected to recur once in a 10-year
period (MA7CD/10). Data on river flows and reaction rates
were developed for these two conditions. While these two
conditions represent critical flow period in the river, they
do not necessarily represent a typical condition when storm
overflows would occur.
The average condition that the Genesee River program was
calibrated for does not represent the river during storms.
Imposing simulated storm overflows on this average condition
will result in dissolved oxygen depletions greater than
those actually occurring during storms^ This deviation is
not expected to be extreme enough to warrant recalibration
of the program.
SPECIFIC REQUIREMENTS OF THE RECEIVING WATER PROGRAM
This program and the Genesee River are discussed at length
in the report prepared for the Environmental Protection
Agency titled The Investigation of Eleven Special Attention
Areas -in the Great Lakes Region - Genesee Rivev Basin [1] .
72
-------
The specific details of the program are presented in that
report. The program itself is written in FORTRAN and was
modified slightly for use in the simplified approach,
The format for the input data is presented in Table 22.
Most of the data that are required describe the river and
its reaction rates. The data for the base condition are
presented in Table 23. Only a small portion of these data
must be altered for input of a stormwater overflow.
The river was modeled by a series of "reaches." Each reach
is a segment of the river that has, at its beginning, a
pollution source, The reaches are shown in Figure 28.
For each reach the characteristics of the river and data on
any discharges to the river are input to the program, To
include a stormwater overflow, the loading from an overflow
is added to the existing parameters. This creates a new
data set that can be input to the program,
EXAMPLE OF THE GENESEE RIVER PROGRAM APPLIED
OVERFLOWS
TO STORMWATER
The data for the base condition, as mentioned earlier,
represent an average of the summer-fall flow conditions.
The overflow data are derived from the storage-treatment and
quality tasks.
The important flow and quality parameters for the overflow
from the existing interceptor system that occurred on June
22, 1973, are presented in Table 24. This information is
combined with the data for the base condition and forms a
new data set for the program. The data are combined on the
basis of a mass loading. This means that if a discharge
exists, the flows are added. The quality parameters are
combined by adding the individual flows multiplied by
quality concentration and divided by the total flow to get a
new quality value. This method was used for the Court SW
discharge. There was no storm flow for the Maplewood and
Sethgreen overflows, and the stormwater quality was
substituted for the existing discharge quality. (This
substitution may not be valid and would depend on the
specific relationship between the existing discharges and
the stormwater discharges), The new data set is presented
in Table 25,
A partial sample of the output for the condition that is
discussed is presented in Table 26. The dissolved oxygen
concentrations calculated for the portion of the river that
73
-------
Table 22. FORMAT FOR RECEIVING WATER PROGRAM DATA
Card Card
group Format columns
3X2 1-6
(1-2)
(3-4)
(5-6)
2F5.0 41-45
46-50
29A1 52-80
11 1
14A1 2-15
12F5.0 16-20
21-25
26-30
31-35
36-40
41-45
46-50
51-55
56-60
61-65
66-70
71-75
76-80
14F5.0 11-15
16-20
21-25
26-30
31-35
36-40
41-45
46-50
51-55
56-60
61-65
66-70
71-75
76-80
Description
Date of computer printout
Month
Day
Year
Calculation interval in river miles
Print interval in river mile
Title
Card number
Name of reach
Distance of reach
Velocity in reach
Time in reach
Streamflow
Dissolved oxygen level
Carbon oxygen demand
Nitrogen oxygen demand
Deoxygenation coefficient (carbon)
Deoxygenation coefficient (nitrogen)
Reaeration coefficient
Dispersion coefficient (estuary)
Benthic "demand
Bottom surface area
Dissolved oxygen concentration
at saturation
Distance before first reach
Velocity before first reach
Time before first reach
Streamflow
Dissolved oxygen level
Carbon oxygen demand
Nitrogen oxygen demand
Deoxygenation coefficient (carbon)
Deoxygenation coefficient (nitrogen)
Reaeration coefficient
Dispersion coefficient (estuary)
Benthic demand
Bottom surface area
Variable
name
NMO
NDAY
NYR
CINT
PINT
ITIT
LX
KCARD
XIN(l)
XIN(2)
XIN(3)
XIN(4)
x:iN(5)
XIN(6)
XIN(7)
XIN(8)
XIN(9)
XIN(IO)
XIN(ll)
XIN(12)
XIN(13)
DOSAT
DO
UO
TO
FO
D00
0DCO
0DNO
XK10
XK20
XK30
EO
BO
AO
74
-------
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75
-------
LAKE ONTARIO
CENTRAL CSO
COURT CSO
EASTMAN KODAK
MAPLEWOOD
LEXINGTON
IRONDEQUO
ST. PAUL SIP
SETHGREEN
CARTHAGE
BAUSCH-LOMB
PLYMOUTH
BROOKS CSO
SCOTTSVILLE
NOTE: REACHES IDENTIFIED BY
DISCHARGE NAMED AT
UPSTREAM END
AVON STP
FIGURE 28. 6ENESEE RIVER REACHES FOR
THE RECEIVING WATER PROGRAM
76
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Table 24. EXAMPLE OF DATA FOR OVERFLOWS
FROM STORM ON JUNE 22, 1973
Dissolved Carbonaceous Nitrogenous
Flow, oxygen, oxygen oxygen
Reach name mgd mg/1 demand," mg/1 demand, mg/1
Plymouth SW
Court SW
Central SW
Mill-Factory
Lexington
Sethgreen
Maplewood
16.29
8.88
5.14
46.93
77.73
3.5
3.8
3.5
3.5
3.5
3.5
3. 5
74.0
154.3
87.0
94.0
145.9
134. 0
82. 0
14.2
36.5
9.9
5.0
15.1
32 4
•J £• » *x
14 <3
j»** • y
a. Carbonaceous oxygen demand - 5-day biochemical oxygen
demand is used.
b. Nitrogenous oxygen demand - usually the concentration
of ammonia plus organic nitrogen is used. A stochio-
metric factor is used to calculate the oxygen demand
for these compounds.
was analyzed are presented graphically in Figure 29, The
dissolved oxygen concentrations for the base condition are
also indicated as a reference.
There are two specific limitations in the analysis of storm
flows. During storms the flow in the river would be higher
than the condition that is modeled due to rainfall over the
entire basin. The effect of this increase in flow cannot be
readily tabulated. The program also uses a plug flow type
of analysis. This means that a specific volume of water
moves downstream as a slug, with reactions occurring and
pollutants accumulating within this slug.
These two limitations are significant, and therefore this
particular program (type of model) should be used only to
indicate trends and not to tabulate the specific dissolved
oxygen concentration. A revision to this program, currently
being prepared by O'Brien and Gere, will make it possible to
analyze more constituents and will also result in a dynamic
representation of the river.
77
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78
-------
Table 26. EXAMPLE OF OUTPUT FROM RECEIVING WATER
PROGRAM FOR STORM ON JUNE 22, 1973
CX>35N DEMAND OXYGEN
CMBCNACECIS MTRCGENEOLS 6ENTHIC TOTAL DEFICIT LEVEL FLCW
ME DIST. MG/L POLNDS *G/L PCINDS MG/L PCUNCS fG/L PCUND'S MG/L HG/L MGO
co
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83 SETHGREEN SW
83
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83
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80
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SECTION X '••.:•"'
APPLICATION OF THE SIMPLIFIED STORMWATER MANAGEMENT MODEL
In the introduction the characteristics of a simplified
model were enumerated,
"...This tool must be inexpensive to set " up and use
flexible enough to be applicable to a variety of system
configurations, and accurate even though only very
moderate expenditures are made for data collection and
preparation."
In this chapter the specifics of how the simplified model
m<^\ t5?Se needs wil1 be Presented. Among the items that
will be discussed are:
• The computer size requirements and operating costs
• The ability of the simplified model to analyze various
drainage basins
• The relationship between simplified stormwater management
modeling and the more complex models.
COMPUTER REQUIREMENTS
In the simplified stormwater management model a high speed
digital computer is used in three tasks:
• Rainfall Characterization
• Storage-Treatment Balance
• Receiving Water Response
As described in Section I small independent computer
programs are used for each analysis. The use of small
programs effectively reduces the overall computer hardware
requirement of the simplified model.
81
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Hardware Requirements
The computer programs for the simplified stormwater
management model have been developed on an IBM 360/67
digital computer. The storage-treatment program (the most
complex program in the model) has been used successfully on
a XEROX 560 computer (approximately equal .to an IBM 1620)
and an IBM 113O. For continued use on an IBM 1130 some
program modifications would have to be made.
For general use of all of the simplified stormwater
management model's programs a computer with approximately
40K of core storage and a FORTRAN compiler would be
required. In addition, the programs use both disk and tape
peripheral storage devices and a card reader.
Cost of Computer Usage
The programs are currently being used on an IBM 370/168
computer. Approximate CPU (central processing unit) time,
execution time, and dollar cost for using this computer on
each of the programs is summarized in Table 27.
It is important to note that these costs represent a single
computational run for the computer. The rainfall analysis
will require several runs of the SORT and LISTRK programs.
One run of each program is required to provide a listing of
the ranking of each of the rainfall parameters. The
storage-treatment program also may be run many times in the
course of analyzing a system of alternatives. The actual
number of runs will vary significantly depending on the
configuration of the system being analyzed and the number of
control strategies that are investigated.
No costs or times are presented for the receiving water
analysis because these costs are dependent on the specific
river model used for the area being analyzed.
APPLICATION TO STORM SEWER AND NONURBAN AREAS
In this report primary attention has been directed at
applying the simplified stormwater management model to a
system of combined sewers. This simplified modeling
approach is equally valid for drainage basins served by
separate storm sewers and for nonurban drainage basins.
Because the simplified model is a series of interrelated
tasks, it is extremely flexible and can be readily adapted
to various types of drainage basins. The data preparation
task is the only task that is directly affected by the type
82
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Table 27. APPROXIMATE COMPUTER COST FOR
SIMPLIFIED STORMWATER MANAGEMENT MODEL
Program
CPUa
min
Exec
min
Cost""
$
Rainfall
characterization
TAPE-Disk
EVENT
SNOWIN
EXCLUD
LISTSQ
SORT
LISTRK
Storage-Treatment
20 years of record
DLYCRD
DLYTAP .
1 year of record
HOUCRD
HOUTAP
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.07
0.25;
0.60'
0.10
0.50
0.18
0.25
0.61
5.80
0.10
1.10
0.90
4.40
3.93
0.79
0.90
0.60
0.74
0.84
0.96
0.64
0.80
14.50
16.25
13,45
14.25
a. Actual computation time in computer
core not including the time needed
to execute the read and write (I/O)
statements or to run the peripheral
devices.
b. Time required in the computer
including I/O statements.
c. Cost based on use of IBM 370/168.
of basin being analyzed. The other tasks are affected only
by the data that is collected. The alternatives that are
analyzed with the model may also be significantly different
for a system of combined sewers than for a nonurban area,
but the basic principles of the analysis remain unchanged.
When applying the model to any area, the determination of
the K factor (see Section VII and Appendix C) and the
determination of the quality associated with the runoff (see
Section VIII and Appendix D) are portions of the model that
are accomplished by hand computations rather than by the
computer program.
83
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Data Preparation .
The system schematic that is developed for a drainage basin
reflects the type of basin being analyzed, and differs
significantly for combined sewers, separate storm sewers,
and nonurban areas. Yet the quantity and quality data
requirements for analysis of various drainage basins do not
change.
For separate sewer systems and nonurban areas, the point of
discharge to receiving waters that would be focused on is a
storm sewer outlet or the mouth of creeks rather than an
overflow structure on an interceptor. In fact, an
interceptor system would probably not exist for a system
that is affected only by flows during wet weather. The
drainage areas for a system of separate storm sewers or a
nonurban area would follow natural topography very closely
and may have a very low total percentage of impervious area.
The need for collection of quantity and quality information
is still the same for separate sewer systems and nonurban
areas as it is for combined sewers. It is necessary to
collect some data to provide a point from which the model
can be calibrated. For nonurban areas "textbook" values for
quality are a partial guide, but a few good quality samples
are valuable to provide a firm reference point.
Rainfall Analysis
The analysis of rainfall is completely independent of the
specific type of drainage basin being analyzed. Because
nonurban areas would possibly be remote from a primary
weather bureau gage, it is useful to check a reliable local
gage with the weather bureau gage. A factor may have to be
applied to the primary gage data to provide rain data
reflecting local conditions.
Storage-Treatment Balance
The analysis of alternatives and the use of the
storage-treatment program should be less complex for a
system of separate sewers or a nonurban area. The need for
running the storage-treatment program sequentially working
upstream along an interceptor may be completely eliminated
for nonurban areas. Each drainage area may be looked at
independently. Yet the ability to investigate an
alternative that considers collecting stormflows in an
interceptor with overflows at points on the system is still
within the capabilities of the program.
84
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Quality Assessment
The techniques of monitoring and analysis used to assess
stormwater quality are also independent of the drainage area
being analyzed, The actual quality values that are
generated may be significantly different for various
drainage basins particularly in the relationship between BOD
and suspended solids, but these values do not affect the
techniques being used. The quality assessment is
accomplished through a series of hand computations.
Receiving Water Response
The analysis of receiving water is based on a locally
available river model and again would be independent of the
specific drainage basin being analyzed.
SIMPLIFIED AND COMPLEX MODELING
The model that has been presented in this report is a
simplified stormwater management model. Complex stormwater
management models exist [2, 3] and have been used
extensively in several cities and parts of the country.
These two types of modeling effort are compatible, and in
fact, complementary,
The advantage of a simplified model is the ability to
process long periods of record and broad areal coverage at
low cost. The advantage of a detailed model is the ability
to make a comprehensive analysis of singular events and
systems with a corresponding increase in accuracy when
supported by a viable data base. •
An example of the difference between the simplified and the
complex modeling effort is clearly evident in the
development and use of design storms. In the rainfall
analysis of simplified modeling, hourly increments of
rainfall are examined and grouped into storms,. When these
storms are ranked and arrayed, statistical techniques can be
used to indicate the characteristics of design storms. The
entire analysis of alternatives, however, is performed on
the actual rainfall records for a selected extended period
of time. For complex modeling, rainfall for shorter time
increments would be examined, and critical intensities from
the entire period of record may be composited into a single
storm that would represent the design storm. .All
atlernatives would then be analyzed using this particular
design storm,, An alternative to the use of ,a design storm
is the use of an historical storm event for comparison of
sewer system modification alternatives.
85
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It is readily apparent that the design storm developed in
the course of a complex modeling effort is clearly a
precise, discrete, critical event, This kind of precision,
characteristic of complex modeling, is necessary for an
accurate technical evaluation of closely competing plans.
Yet in planning studies, the simplified models offer a
flexible screening device to identify consequential storm
events and potentially attractive alternatives.
Further, in the design phase, the complex and simplified
models can continue to interact providing valuable
information on the selected plan. The complex model can be
used to fix component size and configuration while the
simplified model can be used to test the decisions that are
made against the historical record.
86
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SECTION XI
OTHER STORMWATER CONSIDERATIONS
Within the broad area of stormwater management there are
many complex problems. Two areas not specifically addressed
by this simplified mathematical model are sludges generated
within stormwater systems and "nonstructural" alternatives
for stormwater quality improvement.
SLUDGES
Sludge is a byproduct of any water collection, and treatment
system and stormwater systems are not an exception.
Stormwater sludges are the solids that accumulate in the
stormwater system and the solids that are specifically
removed by stormwater treatment facilities. This area has
been studied in recent literature [4, 5].
Stormwater sludges are typically high in grit and silt, and
usually low in organic materials. These sludges also tend
to accumulate high concentrations of heavy metals as
compared with sludges from typical domestic sewage. Typical
composition of sludge from combined stormwater systems are
presented in Table 28.
Solids in the stormwater system are important in two ways:
(1) they are generally a settleable fraction of the flow and
can affect the capacity of facilities, and (2) solids are a
treatment and disposal problem.
The solids in stormwater that form the sludges are subject
to the basic rules governing settling of materials in
sewers. Typically, if velocities exceeding 2 feet per
second are maintained in the collection system, excessive
sedimentation should not occur. Problems with sludge
deposits can occur in stormwater storage or detention
facilities. Specific provisions must be made to provide for
cleaning of these facilities so that the total storage
volume will not be affected.
87
-------
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88
-------
The treatment and disposal of these sludges is a complex and
unique problem. Three major alternatives have been
investigated by recent research in this area:
• "Bleedback" to dry-weather facilities
• Onsite treatment
• Land disposal
Bleedback to dry-weather facilities is the most commonly
considered alternative. In this system, sludges that are
separated from storm flows are stored during the storm and
slowly metered into an existing dry-weather sewage treatment
facility after the storm has passed. This system is
constrained by the hydraulic and solids handling capacity of
the dry-weather.treatment facility.
The second alternative for sludge handling is onsite
treatment. In this alternative facilities for treating the
stormwater flows and solids would be constructed near the
point of discharge of the sewer or overflow. Most often
this onsite system takes the form of physical-chemical
treatment plant. The sludge is treated and dewatered using
mechanical equipment. Typical processes that may be
employed are:
• Dissolved air flotation
• Vacuum filtration
• Centrifugation
• Anaerobic digestion
• Gravity thickening
Land disposal is also being considered as a sludge handling
option. Initial investigations (4, 5) dictate sludge sta-
bilization prior to disposal and maximum loading rates based
on the sludge nitrogen and heavy metal concentrations. Re-
sidual solids from the other two alternatives must be placed
in a landfill or some other ultimate disposal site.
89
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NONSTRUCTURAL ALTERNATIVES
Nonstructural alternatives are those alternatives that
affect a reduction in quantity or improvement in quality of
of the runoff, yet can be implemented without construction
of major new facilities. These alternatives have been
discussed extensively in the literature [6, 7, 8]. The
possibilities are limited only by the imagination of those
charged with management of the stormwater facilities.
The nonstructural alternatives can generally be discussed in
two categories: (1) alternatives that control the source of
pollutants and runoff, and (2) alternatives that affect
control and management of pollutants and runoff within the
system of sewers. Some typical nonstructural control
alternatives are listed in Table 29.
Table 29. NONSTRUCTURAL CONTROL ALTERNATIVES
Source Control Alternatives
Roof storage
Ponding
Porous pavements
Erosion control
Street cleaning
Deicing methods
Utilize natural drainage features
Collection System Control Alternatives
Sewer flushing
Inflow/infiltration
Sewer cleaning
Polymer injection
Inline storage
Remote Monitoring and Control
90
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Source Control Alternative
Three examples of source control measures that may be
important in an urban area are:
• Street sweeping
• Deicing methods
• Erosion control
Street Sweeping. There is a direct relationship between the
amount of dirt and grit that accumulates on city streets and
the quality of runoff. Recent research has shown that a
great portion of the pollution is related to fine materials
relatively unaffected by conventional broom type street
sweeping equipment. Modern vacuum type street cleaning
equipment can remove as much as 95 percent of the fine
materials. No parking signs can be posted indicating the
hours of street cleaning operation to maximize the
effectiveness of the cleaning equipment that is currently
being used.
Deicing Methods. In cold climates various chemicals have
beenusedon pavement for control of ice. These chemicals
enter the stormwater collection system as thawing occurs.
Recent studies in Michigan have suggested: (1) no salt
application on straight, flat sections, (2) better training
for operators of salt spreading equipment, and (3) keeping
records of salt use as a means of reducing salt consumption.
At a minimum, salts without highly toxic substances, such as
cyanide or chromium compounds, should be used. These toxic
chemicals have been added to some deicing salts as
anticaking agents or corrosion inhibitors, but have very
damaging effects on the environment.
Erosion Control. In an urban situation, a heavy load of
silt and dirt often can be generated by simply poor
management of a construction site. Graveling of entrance
roads to the site and control of runoff from the site during
construction can help minimize pollutants that enter the
sewer system.
System Control Alternatives
Two examples of system control alternatives for
urban area might be:
• Sewer cleaning and flushing
• Inflow/infiltration control
a typical
91
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Sewer Cleaning and Flushing. This control measure may have
a dual benefit, particularly on a system of combined sewers.
The capacity of a sewer system is inversely related to the
quantity of grit that deposits within it. A regular
cleaning program can maintain flow capacity throughout the
collection system, but special attention should be directed
at any area where deposits regularly occur along the main
interceptor lines. If the capacity of the main interceptors
are reduced by sediment , overflow will occur on the system
unnecessarily. The cleaning and flushing maintains capacity
in lines while at the same time reducing the total volume of
pollutants that will be scoured out of the lines and
discharged to receiving waters during periods of high flow.
Inflow/Infiltration Control. Inflow is the stormwater that
enters the system unnecessarily from the surface of the
ground. Examples of this may be roof leaders or area drains
connected directly to the sewer system. Infiltration is
groundwater that enters the sewer unnecessarily. Typically
infiltration occurs through leaking pipe joints and
structurally damaged pipe. It is somewhat costly to
determine sources of inflow/infiltration. Usually smoke
testing or television inspection is required. Quite often
though significant reductions in flow can be realized from a
thorough location and correction program.
92
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J
9
REFERENCES
1. Moffa, P.E. Water Pollution Investigation- Genessee River
and Rochester Area - Great Lakes Initial Contract Program.
EPA-905/9-74-016, U.S. Environmental Protection Agency, Cin-
cinnati, Ohio, January 1975. 234 pp.
2. Storm Water Management Model, Volume I, Final Report.
11024DOCO7/71, U.S. Environmental Protection Agency, Cincin-
ati, Ohio, July 1971. 353 pp.
Storm Water Management Model, Volume II, Final Report.
11024DOC08/71, U.S. Environmental Protection Agency, Cincin-
nati, Ohio, August- 1971. 173pp.
Storm Water Management Model, Volume III, Final Report.
11024DOCO9/71, U.S. Environmental Protection Agency, Cincin-
nati, Ohio, September 1971. 360 pp.
Storm Water Management Model, Volume IV, Final Report.
11024DOC10/71, U.S. Environmental Protection Agency, Cincin-
nati, Ohio, October 1971. 249 pp.
3. DiGiano, Francis T., et al. Short Course Proceedings, Appli-
cation of Stormwater Management Models, EPA-670/2-75-065,
U.S. Environmental Protection Agency, Cincinnati, Ohio, 1975.
427 pp.
4. Gupta, M.K., et al. Handling and Disposal of Sludges Arising
from Combined~Sewer Overflow Treatment - Phase 1. (Draft Re-
port), U.S. Environmental Protection Agency, Cincinnati,
Ohio, Contract No. 68-03-0242, 1975. 100 pp.
5. Clark, M.J. and Geinopoulos, A. Assessment of the Handling
and Disposal of Sludges Arising from Combined Sewer Overflow
Treatment. (Draft Report), U.S. Environmental Protection
Agency, Cincinnati, Ohio, Contract No. 68-03-0242, February
1976. 260 pp.
5. Lager, J.A. and Smith, W.G. Urban Stormwater Management and
Technology, an Assessment. EPA-670/2-74-040, U.S. Environ-
mental Protection Agency, Cincinnati, Ohio, June 1971.
447 pp.
93
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7. Edison Water Quality Laboratory, Environmental Impact of
Highway Deicing. 11040GKK06/71, U.S. Environmental Pro-
tection Agency, Cincinnati, Ohio, June 1971. Ill pp.
8. American Public Works Association. Control of Infiltration
and Inflow into Sewer Systems. 11022EFF12/70. U,,S. Envi-
ronmental Protection Agency, Cincinnati, Ohio, December
1970. 124 pp.
9. Heaney, J.P. et al. Nationwide Evaluation of Combined
Sewer Overflows and Urban Stormwater Discharges, Volume II:
Cost Assessment and Impacts. (Draft Report), U.S» Environ-
mental Protection Agency, Cincinnati, Ohio, Contract No.
68-03-0283, May 1976. 376 pp.
10. Metcalf & Eddy, Inc. Reconnaissance Study of Combined Sewer
Overflows and Storm Sewer Discharges. District of Columbia,
Department of Environmental Services Engineering and Construc-
tion Administration. March 1973.
11. Hydrologic Engineering Center, Corps of Engineers,, Urban
Stormwater Runoff: STORM. Generalized Computer Program.
723-58-L2520, 1975.
12. Davis, P.L. and Borchardt, F. Combined Sewer Overflow
Abatement Plan, Des Moines, Iowa. EPA-R2-73-170. U.S. En-
vironmental Protection Agency, Cincinnati, Ohio, 1974.
312 pp.
NOTE: The following report is recommended as a companion docu-
ment for this report:
Heaney, J.P. Stormwater Management Model Level I, Prelimin-
ary Screening Procedure for Wet-Weather Flow Planning.
(Draft Report), U.S. Environmental Protection Agency, Office
of Research and Development, Cincinnati, Ohio, June 1976.
94
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1
X:
Appendix A
EXAMPLE OF MONITORING DATA
FROM ROCHESTER, NEW YORK
95
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Appendix B
PROGRAM LISTING AND LIST OF VARIABLES
99
-------
Table B-l. RAINFALL TASK - PROGRAM LISTING FOR EVENT
//J4073 JOB 'C802,322','DIDRIKSSON'
/* SERVICE EXEC=U
//STEP1 EXEC WATFIV
//GO.FT08F001 DD DSN=C802.STORM1,UNIT=2314,
// DISP=( NEW, KEEP) ,SPACE=(TRK, (100,10) ,RLSE) ,
// DCB=(RECFM=FB,LRECL=80,BLKSIZE=3520)
//GO. SYS IN DD *
$ WATFIV
DIMENSION MON(12) ,NY(100) ,MO(100) ,ND(100) ,NX( 100) , FR(100,24) ,
*NEXT(100) ,FAX(100) ,FRS(100)',LD(100) ,NYA(100) ,MOA(100) ,NDA(100)
*ITA(100) ,IRT(100) ,LLD(100) ,NEE(100) ,NR(100) ,LCC(100)
DATA MON/31,28,31,30,31,30,31,31,30,31,30,31/
12
14
142
144
1444
145
146
15
16
161
1614
162
17
174
175
176
NLAST=0
NLM=0
LL=0
00111=1,100
LCC( I) =0
IiLD(I)=0
CONTINUE
•1=1
CONTINUE
MON(2)=28
CONTINUE
READ(5,144)-NY(I) ,
FORMAT(6X,3I2,I1,12F3.2)
IF(NY(I)-99)1444,8,8
CONTINUE
IF(NX(I}-1)142,145,142
CONTINUE
READ(5,146) (FR(I,J) ,J=13,24) ,NEXT(I)
FORMAT(13X,12F3.2,29X,I2)
NY(I)=NY(I)+1900
XL=NY(I)
XL=XL/4 .
(FR(I,J) ,J=1,12)
IF(XL-XXI,)16,15r16
MON(2)=29
CONTINUE
IF(MO(I)-NLM)161,1614,161
NE=NQ+NE
NLM=MO(I)
NQ=MON(NLM)
CONTINUE
NR(I) =ND(I)+NE
IC=MO(I)
CONTINUE
IF(I-100)176,176,174
CONTINUE
L=I-1
WRITE(6,175)NY(L) ,MO(t) ,ND(L)
FORMAT(1H , 'ERROR' ,312)
STOP
CONTINUE
GOTO14
100
-------
Table B-l (Continued)
18 CONTINUE
IC=ND(I)+1-NEXT(I)
IF(IC)21,17,21
21 CONTINUE
DO22 IA=1,100
FAX(IA)=0.
FRS(IA)=0
LD(IA)=0
22 CONTINUE
JX=0
JXX=0
JCC=0
DO40IA=1,I
DO38IB=1,24
IF(JXX)24,222,24
222 IF(FR(IA,IB))38,38,224
224 CONTINUE
JXX=1
JX?=JX+1
NEE(JX)=NR(IA)
IF(JX-1)228,226,228
226 LLD(JX)=NEE(JX)-NLAST
GOT023
228 LLD(JX)=NEE(JX)-NEE(JX-1)
23 CONTINUE
IRT(JX)=IB
NYA(JX)=NY(IA)
MOA(JX)=MO(IA)
NDA(JX)=ND(IA)
24 CONTINUE
IF(FR(IA,IB))25,25,26
25 JCC=JCC+1
LCC(JX)=JCC
IF(JCC-6)262,262,252
252 JXX=0
26 CONTINUE
NEE(JX)=NR(IA)
NLAST=NR(IA)
JCC=0
262 CONTINUE
IF(FR(IA,IB)-FAX(JX) )29,29,28
28 FAX(JX)=FR(IA,IB)
IF(MOA(JX)-MO(IA))282,286,282
282 LL=MOA(JX)
IFAC=ND(IA)+MON(LL)-NDA(JX)
GOTO288
286 CONTINUE
IFAC=ND(IA)-NbA(JX)
288 CONTINUE
ITA(JX)=IFAC*24+IB-IRT(JX)+1
29 CONTINUE
FRS(JX)=FRS(JX)+FR(IA,IB)
LD(JX)=LD(JX)-H
38 CONTINUE
40 CONTINUE
IF(JX)12,12,41
41 CONTINUE
DO414IA=1,JX
LD(IA)=LD(IA) -LCC(IA)
101
-------
Table B-l (Concluded)
414 CONTINUE
DO44 IA=1,JX
WRITE(8,42)NYA(IA),MOA(IA),NDA(IA),LD(IA),FRS(IA),PAX(IA)
*ITA(IA),LLD(IA),IRT(IA)
42 FORMAT(1H ,14,313,2F6.2,2I3,2X,I3)
44 CONTINUE
D05IA=1,100
5 LCC(IA)=0
GOTO12
8 CONTINUE
WRITE(8,82)
82 PORMATC 9999')
STOP
END
102
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Table B-3. RAINFALL TASK - PROGRAM LISTING FOR SNOWIN
//J4073 JOB (C802,322),'T.DIDRIKSSON"
// EXEC WATFIV
//GO.PT08F001 DD DSN=C802. STORMS, UNIT=2314,VOI,=SER=SYS17,DISP=OLD
//GO.FT09P001 DD DSN=C802.CARb.C345,UNIT=2314,VOL=SER=SYS14,DISlP=OI,D
//GO.FT10F001 DD DSN=C802.STORM2,UNIT=2314,DISP=(NEW,KEEP),
// SPACED(TRK,(50,2),RLSE),DCB=(RECFM=FB,LRECL=80,BLKSIZE=3520)
//GO.SYSIN DD *
$WATFIV
N=0
M=0
NRR=0
SNOW=0.
NDB=0
NYB=0
MOB=0
12 CONTINUE
READ(8,2)NYA,MOA,NDA,LD,FRS,FAX,ITA,LLD,IS,IRT
2 FORMAT(1X,I4,3I3,2F6.2,2I3,I2,I3,5X,I5)
NRR=NRR+1
NYX=NYA-1900
GOTO32
22 CONTINUE
READ(9,3)NYB,MOB,NDB,SNOW
3 FORMAT(5X,3I2,10X,F3.1)
32 CONTINUE
IF(NYA-3000)324,8,8
324 IF(NYB-99)325,64,64
325 IF(NY»-NYB)64,326,22
326 IF(MOA-MOB) 64,328,22
328 IF(NDA-NDB)64,6,22
6 IF(SNOW)64,64,66
64 IS=0
GOTO?
66 IS=1
7 CONTINUE
WRITE(10,2)NYA,MOA,NDA,I,D,FRS,FAX,ITA,LLD,IS,I!IT,NRR
GOTO12
8 CONTINUE
WRITE(10,9)NYA
9 FORMAT(IX,14)
STOP
END
104
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Table B-5. RAINFALL TASK - PROGRAM LISTING FOR EXCLUD
//J4073 JOB (C802,322),'T.DIDRIKSSON'
// EXEC WATFIV
//GO.FT08F001 DD DSN=C802.STORM2,UNIT=2314,VOL=SER=SYS13,DISP=OLD
//60.FT10F001 DD DSN=C802.STORM4,UNIT=2314,DISP=(NEW,KEEP) ,
// SPACE=(TRK,(50,2),RLSE),DCB=(RECFM=FB,Z,RECL=80,BLKSIZE=3520)
//GO.SYSIN DD *
$WATFIV
12 CONTINUE
READ(8,2)NYA,MOA,NDA,LD,FRS,FAX,ITA,I,LD,IS,IRT,NRR
2 FORMAT(1X,I4,3I3,2F6.2,2I3,I2,I3,5X,I5)
IF(NYA-3000)324,8,8
324 CONTINUE
IF(FRS-0.05)12,12,6
6 CONTINUE
WRITE(10,2)NYA,MOA,NDA,LD,FRS,FAX,ITA,I,LD,IS,IRT,NRR
GOTO12
8 CONTINUE
WRITE(10,9)NYA
9 FORHAT(1X,I4)
STOP
END
106
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Table B-7. RAINFALL TASK - PROGRAM LISTING FOR LISTSQ
258
246
248
25
//J4073TED JOB 'C802,322,1.0,10' , 'T DIDRIK'
/* PRINT COPIES=2
/* SERVICE CLASS=Q
//STEPl EXEC WATPIV
//GO.SYSIN DD *
$WATFIV
C SEQONCE ALL YEAR
DIMENSIONA(12) ,B(2)
DATA A/ 'JAN' , 'FEE' , 'MAR' , 'APR' , 'MAY* , 'JUNE' , 'JULY' , 'AUG' , 'SEPT' , '0
ICT' , 'NOV', 'DEC'/,B/'NO', 'YES'/
TTR=0.
1=0
NTDU=0
18
19
2
24
22
224
226
228
242
244
252
254
IL=*0
WRITE(6,18)
FORMAT( 1H1 ,//2X, 4HYEAR, 2X, 5HMONTH, 3X, 3HDAY, 2X, 6HDURAT. , 3X, 5HTOTA
1L,2X,8HMAX HOUR,2X, 10HHOUR AFTER, IX, 10HDAYS SINCE, 4X,6HEXCESS
23X,9HREAL TIME,4X,4HSNOW, 9X,8HSEQUNCE
3 /21X,5HHOURS,2X,8HRAINFALL, 1X,8HRAINFALL,4X,5HSTART,4X,10HLAST
4STORH,4X, 6HPRECIP ,3X,10HSTART HOUR,2X,8HINCLUDBD
CONTINUE
1=1+1
READ (5, 2) IYE,MON,NDA,NDU,TR,TMR,NHR,NDT,ISN,IHR,IFXX
IX=0
FORMAT(1X,I4,3I3,2F6.2,2I3,I2,I3,5X,I5)
IF(2000-IYE)9,9,24
CONTINUE
IF(MON)224,224,22
IF(MON-12)228,228,224
WRITE(6,226)
FORMAT(6H ERROR)
STOP
CONTINUE
ISN=ISN+1
IF(I-1}244, 242,244
IO>IYE
CONTINUE
IF(ICC-IYE) 252,258,252
WRITE(6,254)NTDU,TTR,TXR
FORMAT(////10X,I15,2F9.2)
IL=0
NTDU=0
TTR=0.
ICC^IYE
WRITE (6, 18)
CONTINUE
INN=INN+1
NTDU=NTDU+NDU
IF(THR-TXR) 248,248,246
TXR=TMR
CONTINUE
IF(IX-2)25,25,224
IF(ISN-2)272,272,224
108
-------
Table B-7 (Concluded)
272 CONTINUE
WRITE(6,26)IYE,A(MON),NDA,NDU,TR,TMR,NHR,NDT,B(IX),IHR,B(ISN),INN
26 FORMAT(1H ,16,2X,A4,15,17,4X,F6.2,3X,F6.2,4X,I7,5X,I6,8X,A4,5X,I7,
17X,A4,7X,I4)
IF(49-IL)28,8,8
28 CONTINUE
WRITE(6,18)
IL=0
8 CONTINUE
GOT019
9 CONTINUE
WRITE(6,254)NTDU,TTR,TXR
WRITE(6,92)
92 FORMAT(lHl)
STOP
END
109
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Table B-9. RAINFALL TASK - PROGRAM LISTING FOR LISTRK
//J4073TED JOB 'C802,322,1.,5','T DIDRIK'
/* PRINT COPIES=2
/* SERVICE CLASS=Q
//STEP! EXEC WATFIV
//GO.SYSIN DD *
$WATFIV
DIMENSIONA(12),B(2)
C RANK ALL YEAR
DATA A/'JAN','FEE','MAR','APR','MAY','JUNE','JULY','AUG','SEPT','o
ICT','NOV','DEC'/,B/'NO','YES'/
INN=0
IL=0
WRITE(6,18)
18 FORMAT(1H1,//2X,4HYEAR,2X,5HMONTH,3X,3HDAY,2X,6HDURAT. ,3X,5HTOTA
1L,2X,8HMAX HOUR,2X,10HHOUR AFTER,IX,10HDAYS SINCE,4X,6HEXCESS ,
23X,9HREAL TIME,4X,4HSNOW, 9X,6HRANKED,5X,9HRANKED BY,
3 /21X,5HHOURS,2X,8HRAINFALL,IX,8HRAINFALL,4X,5HSTART,4X,10HLAST
4STORM,4X, 6HPRECIP ,3X,10HSTART HOUR,2X>8HINCLUDED,
517X,9HMAGNITUDE///)
12 CONTINUE
READ(5,2) IYE,MON,NDA,NDU,TR,TMR,NHR,NDT,ISN,IHR,IPP,IFXX
2 FORMAT(I4,3I3,2F6.2,2I3,I2,I3,5X,I5,5X,I5)
IX=0
IF(2000-IYE)9,9,24
24 CONTINUE
IX=IX+1
ISN=ISN+1
INN=INN+1
IL=IL+1
WRITE(6,26) IYE,A(MON) ,NDA,NDU,TR,TMR,NHR,NDT,B(IX) ,IHR,B(ISN),INN
1,IFXX
26 FORMAT(1H ,16,2X,A4,I5,I7,4X,F6.2,3X,F6.2,4X,I7,5X,I6,8X,A4,5X,I7,
17X,A4,7X,I4,5X,I10)
IF(49-IL)28,8,8
28 CONTINUE
WRITE(6,18)
IL=0
8 CONTINUE
GOTO12
9 CONTINUE
WRITE(6,92)
92 FORMAT(1H1)
STOP
END
111
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Table B-ll. STORAGE-TREATMENT TASK - PROGRAM LISTING
//J4073TED JOB 'C802,322,l.,3','T DIDRIK'
//STEP1 EXEC FORTHC,FARM.FORT='MAP'
//GO.FT08F001 DD DSN=C802.STORM1,UNIT=2314,
// DISP=(NEW,KEEP) ,SPACE=(TRK,(100,10) ,RLSE) ,
// DCB=(RECFM=FB,LRECL=80,BLKSIZE=3520)
//GO.FT11F001 DD DSN=C802.STORMS,UNIT=2314,VOL=SER=SYS17,DISP=OLD
//GO.FT09F001 DD DSN=C802.CARD.C345,UNIT=2314,VOL=SER=SYS14,DISP=OLD
//GO.FT10F001 DD DSN=C802.STORM2,UNIT=2314,DISP=(NEW,KEEP),
//GO.SYSIN DD *
C STORAGE - TREATMENT
C THEODOR DIDRIKSSON
C METCALF & EDDY, INC., ENGINEERS
C 1029 CORPORATION WAY
C ' PALO ALTO, CALIF. 94303
DIMENSIONM(13),ARN(5),MXX(13)
COMMONM,AREA,COEF,STMAX,TREAT,STOPS,NYEAR,STOR,COEFX,
*NFL,LD,ND,TRD,FAC,IOTAP,MYEAR
DATA MXX/0,31,28,31", 30,31,30,31,31,30,31,30,31/
D011I=1,13
11 M(I)=MXX(I)
READ{5,12)(ARN(I),1=1,5),AREA,COEF,STMAX,TREAT,STOPS,NYEAR,
*NSS,IOTAP,IPFL,MYEAR,TFAC
C NSS=0 FOR DAILY & NSS=1 FOR HOURLY
C IOTAP=0 INPUT ON CARDS & IOTAP=1 INPUT ON TAPE OR DISK
C IPFL NUMBER OF INFLOW FROM UPPER REACHES BY INTERSEPTOR
C TFAC FACTOR TO DETERMIND VOLUMN ROUTED TO TREATMENT PLANT
C THE FIRST DAY OF RAINFALL OR NUMBER OF HOURS BEFORE
C TREATMENT STARTS FOR HOURLY SIMULATION
C NYEAR NUMBER OF YEARS OR MONTHS READ OF CARDS
C ARN NAME OR NUMBER OF THE AREA UNDER CONSIDERATION
12 FORMAT(5A4,5F8.0,I2,3I1,I4,1X,F10.0)
STOR=STOPS
COEFX=AREA*COEF* 0.027156
TRD=0.
FAC=1.
NFL=0
LD=0
ND=0
NFAC=TFAC
WRITE(6,13)(ARN(I),1=1,5)
13 FORMAT(1H4,60X,'ROCHESTER',10X,5A4, ///43X,4HAREA,8X,6HR
ItJNOFF,5X,11HMAX STORAGE,3X,14HTREATMENT RATE/43X,5HACRES,6X,10HCOE
2FFICENT,7X,2HMG,11X,3HMGD///)
- WRITE(6,132)AREA,COEF,STMAX,TREAT
132 FORMAT(1H ,38X,F10,0,5X,F7.2,4X, F10.0,4X,F10.0)
WRITE(6,14)
14 FORMAT(1H3,8sx,'METCALF & EDDY, INC., ENGINEERS'/
*85X,'1029 CORPORATION WAY'/85X,'PALO ALTO, CALIF. 94303')
IF(NSS)2,2,3
2 IF(IOTAP)22,22,24
22 CALL DLYCRD(TFAC)
GOTO4
24 CALL DLYTAP(IPFL,TFAC)
GOTO 4
3 IF(IOTAP)32,32,34
32 CALL HOUCRD
GOT04
34 CALL HOUTAP(IPFL,NFAC)
4 CONTINUE
113
-------
Table B-ll (Continued)
STOP
END
SUBROUTINE HOUCRD
COHMONM,AREA,COEP,STMAX,TREAT,STOPS,NYEAR,STOR,COEFX,
*NFL,LD,ND,TRD,FAC,IOTAP,MYEAR
DIMENSIONM(13),MON(10),MDAT(10),RAIN(10)
TREAT=TREAT/2 4.
D060I1=1,NYEAR
RUNOF=0.
NDR=0
NOV=0
CTD=<0.
STOS=STOPS
TRUN=0.
TOV=0.
TRE=0.
LSD=0
READ(5,14)MYEAR,NCARD
14 FORMAT(2110)
WRITE(6,142)MYEAR
142 FORMAT(1H1,///5X,5HMONTH,16,14X,
A 20HOCCURING ON THE HOUR,19X,SSHACCUMULATED FROM STA
1RT OF THE MONTH/24X,34H ,5X,
C5 6H""""*"""~""""'<~~"'M"-"--"""-"---~"--™—•"——"•——"—"-—"———•""——"-———-——••••"-"••••-"—//
D 6H DAY ,1X,4HHOUR,2X,4HRAIN,2X,4HRAIN,2X,6HRUNOFF,2
2X,7HSTORAGE,2X,8HOVERFLOW,2X,7HTREATED,5X,9HT. RUNOFF,2X,10HT.OVER
3FLOW,7H OVERFL,8H T.TREAT,7H TREAT,13H MAX STORAGE/13X,2HIN,3X,4
4HHOUR,4X,2HMG,6X,2HMG,8X,2HMG,7X,2HMG,12X,2HMG,10X,2HMG,5X,4HHOUR,
55X,2HMG,5X,4HHOUR,7X,2HMG//)
MY=MYEAR+1
MT=M(MY)
LTOT=0.
DO166II=1,MT
166 LTOT=I,TOT+24
DO50I2=1,NCARD
READ(5,168)(MON(J),MDAT(J),RAIN(J),J=1,10)
168 FORMAT(10(2I2/F4.2))
DO17J=1,10
IF( MON(J)-MT)1684,1684,1688
1684 IF(MDAT(J)-24)17,17,1688
1688 WRITE(6,1689)
1689 FORMAT(6H ERROR)
STOP
17 CONTINUE
003013=1,770
IF(NFL-0)178,178,176
176 NFL=NFL+1
178 CONTINUE
TRD=0.
OVFL=0.
RUNOF=0.
RIN=0.
IF(JC-10)18,18,32
18 CONTINUE
MX=MON(JC)
LSD=LSD-H
IF(LTOT-LSD)2604,21,21
21 CONTINUE
114
-------
Table B-ll (Continued)
IF(MX)26,26,214
214 CONTINUE
LTD=-24
DO22I4=1,MX
22 LTD=LTD+24
ND=LTD+MDAT(JC)
23 CONTINUE
IF(LSD-ND)26,24,26
24 RUNOF=COEFX*RAIN(JC)
NFL=NFL-H
TRUN=TRUN+RUNOF
RIN=RAIN(JC)
JC=JC+1
STOR=STOR+RUNOF
NDR=NDR+1
26 CONTINUE
IF(LSD-l)2601,2608,2601
2601 CONTINUE
LTD=0
DO2602IQ=1,MT
LTD=LTD+24
IF{LTD-LSD+1)2602,2604,2602
2602 CONTINUE
GOT02608
2604 CONTINUE
WRITE(6,2603)SRIN
2603 FORMAT(///11H TOTAL RAIN,F6.2)
SRIN=0.
IF(LTOT-LSD)60,2606,2606
2606 CONTINUE
WRITE(6,142)MXEAR
2608 CONTINUE
STORA=STOR
IF(NFL-l)271,271,263
263 STOR=STOR-TREAT
2'64 IF(STOR-STOPS) 266,266,27
266 NFL=0
STOR=STOPS
27 CONTINUE
TRD=STORA-STOR
CTD=CTD+TRD/TREAT
TRE=TRE+TRD
271 CONTINUE
STM=STOR-STMAX
IF(STM)274,274,272
272 NOV=NOV+1
OVFL=STM
TOV=TOV+STM
STOR=STMAX
274 CONTINUE
IF(STOS.LT.STOR)STOS=STOR
ILL=0
MXQ=0
DO276IL=1,MT
IF(MXQ-LSD)275,278,278
275 MQQ=MXQ
MXQ=MXQ+24
ILI,= ILL+1
276 CONTINUE
278 LSX=LSD-MQQ
115
-------
282
29
30
32
50
60
64
14
142
15
154
16
166
168
Table B.-ll (Continued)
SRIN=SRIN+RIN
WRITE (6, 28 2) ILL ,LSX,RIN,NDR,RUNOF,STOR,OVFL,TRD,TRUN,TOV,NOV,TRE
1 ,CTD,STOS
FORMAT(1H ,I3,I5,F6.2,I5,F9.2,F10.2,2F9.2,F14.2,F11.2,I6,1?10.2,
1F8.2,F10.2)
IF(JC-11}30,29,30
IF(I2-NCARD)32,30,32
CONTINUE
CONTINUE
CONTINUE
CONTINUE
WRITE (6, 64)
FORMAT (1H1)
STOP
END
SUBROUTINE DLYCRD(TFAC)
COMMONM, AREA, COEF,STMAX, TREAT, STOPS, NYEAR,STOR,COEFX,
*NFL,LD,ND,TRD,FAC,IOTAP,MYEAR
DIMENSIONM{13) ,MON(10) ,MDAT(10) ,RAIN(10)
D060I1=1,NYEAR
NDR=0
CTD=0.
SRIN=0.
RUNOF=0.
STOS=STOPS
TRUN=0.
TRE=0.
LSD=0
M(3)=28
READ(5,14)MYEAR,NCARD
FORMAT(2I10)
KRITE(6,142)MYEAR
FORMAT(1H1,///5X,4HYEAR,16,14X,
A 20HOCCURING ON THE DATE,19X,34HACCUMULATED FROM STA
1RT OF THE YEAR/24X,34H ,5X,
C56H • //
D 6H MONTH,1X,3HDAY,2X,4HRAIN,2X,4HRAIN,2X,6HRUNOFF,2
2X,7HSTORAGE,2X,8HOVERFLOW,2X,7HTREATED,5X,9HT. RUNOFF,2X,10HTJOVER
3FLOW,7H OVERFL,8H T.TREAT,7H TREAT,13H MAX STQRAGE/13X,2HIN,3X,4
4HDAYS,4X,2HMG,6X,2HMG,8X,2HMG,7X,2HMG,12X,2HMG,10X,2HMG,5X,4HDAYS,
55X,2HMG,5X,4HDAYS,7X,2HMG//)
XL=MYEAR
XL=XL/4.
XXL=L
IF(XL-XXL)16,15,16
M(3)=29
WRITE(6,154)MYEAR
FORMAT(10H LEAP YEARI10)
CONTINUE
LTOT=0.
0016611=1,13
LTOT=LTOT+M(II)
D050I2=1,NCARD
READ(5,168)(MON(J),MDAT(J),RAIN(J),J=1,10)
FORMAT(10(2I2,F4.2))
IF{ MON(J)-12)1684,1684,1688
116
-------
Table B-ll (Continued)
1684 IF(MDAT(J)-31)17,17,1688
1688 WRITE(6,1689)
1689 FORMAT(6H ERROR)
STOP
17 CONTINUE
JC-1
DO30I3=1,370
IF(NFL-0)178,178,176
176 NFL=NFL+1
178 CONTINUE
IF(TRD)1798,1798,1792
1792 IF(RUNOF)1798,1798,1793
1793 NFL=NFL+1
FAC=1.
1798 CONTINUE
TRD=0.
OVFL=0.
RUNOF=0.
RIN=0.
IF(JC-10)18,18,32
18 CONTINUE
MX=MON(JC)
LSD=LSD-H
IF(LTOT-LSD)2604,21,21
21 CONTINUE
IF(MX)26,26,214
214 CONTINUE
LTD=0
D022I4=1,MX
22 LTD=LTD+M(I4)
ND=LTD+MDAT(JC)
23 CONTINUE
IF(LSD-ND)26,24,26
24 RUNOF=COEFX*RAIN(JC)
NFL=NFL+1
TRUN=TRUN+RUNOF
RIN=RAIN(JC)
JC=JC+1
STOR=STOR+RUNOF
NDR=NDR+1
26 CONTINUE
IF(LSD-l)2601,2608,2601
2601 CONTINUE
LTD=0
DO2602IQ=1,13
LTD=LTD+M(IQ)
IF(LTD-LSD+1)2602,2604,2602
2602 CONTINUE
GOT02608
2604 CONTINUE
WRITE(6,2603)SRIN
2603 FORMAT(///11H TOTAL RAIN,F6.2)
SRIN=0.
IF(LTOT-LSD)60,2606,2606
2606 CONTINUE
WRITE(6,142)MYEAR
2608 CONTINUE
STORA=STOR
IF(NFL-1)271,271,263
263 STOR=STOR-TREAT*FAC
117
-------
Table B-ll (Continued)
264
266
27
271
2712
2718
2719
272
274
IF(STOR-STOPS)266,266,27
NFI,=0
STOR= STOPS
CONTINUE
TRD=STORA-STOR
CTD=CTD+TRD/TREAT
TRE=TRE+TRD
GOTO 2719
CONTINUE
IF(NFL-l) 2712, 2712, 2718
FAC=TFAC
GOTO 263
FAC=1
CONTINUE
STH=STOR-STMAX
IF(STM) 274, 274,272
NOV=NOV+1
OVFL=STH
TOV=TOV+STM
STOR=STMAX
CONTINUE
IF ( STOS . LT . STOR) STOS=STOR
ILL=0
MXQ=0
DO276IL=1,13 "
MXQ=MXQ+M(IL)
IF(MXQ-LSD)275,278,278
275 MQQ=MXQ
ILL=ILL+1
276 CONTINUE
278 LSX=LSD-MQQ
SRIN=SRIN+RIN
WRITE(6,282)ILL ,LSX,RIN,NDR,RUNOF,STOR,OVFL,TRD,TRUN,TOV,NOV,TRE
1 ,CTD,STOS
282 FORMAT(1H ,I3,I5,F6. 2,I5,F9.2,F10. 2,2F9. 2,F14. 2,F11.2,I6,1?10.2,
1F8.2,F10.2)
IF(JC-11)30,29,30
29 IF(I2-NCARD) 32,30,32
30 CONTINUE
32 CONTINUE
50 CONTINUE
60 CONTINUE
WRITE (6, 64)
64 FORMAT (1H1)
STOP
END
SUBROUTINE DLYTAP( IPFI,,TFAC)
COMMONM , AREA , COEF , STMAX , TREAT , STOPS , N YEAR, STOR , COEFX ,
*NFL,LD,ND,TRD,FAC,IOTAP,MYEAR
DIMENSIONM(13) ,MON(370) ,MDAT(370) ,RAIN(370) ,FINT(370) ,FP1(370) ,
*FP2(370) ,FP3(370)
READ(5,167)NZA,MON(1) ,MDAT(1) ,RAIN(1)
12 CONTINUE
MYEAR=NZA+1900
D0122IQ1=1,370
FP1(IQ1)=10000.
FP2(IQ1)=0.
FP3(IQ1)=0.
122 FINT(IQ1)=0.
IF(IPFL)14,14,124
118
-------
Table B-ll (Continued)
124 READ(11,1678)Jll
READ(11,1679)(FPl(JXX),JXX=1,J11)
14 CONTINUE
FAC=TFAC
NDR=0
NOV=0
CTD=0.
SRIN=0.
RUNOF=0.
STOS=STOPS
TRUN=0.
TOV=0.
TRE=0.
LSD=0
M(3)=28
WRITE{6,142)MYEAR
142 FORMAT(1H1,///85X,'METCALF & EDDY, INC., ENGINEERS /
*85X,'1029 CORPORATION WAY'/
*85X,'PALO ALTO,CALIF.94303'
* ,///5X,4HYEAR,I6,14X,
A 20HOCCURING ON THE DATE,19X,34HACCUMULATED FROM STA
1RT OF THE YEAR/24X,34H ; /5X,
C5 6H—"~—————————————-——'-———————————————————————— —————/ j
D 6H MONTH,1X,3HDAY,2X,4HRAIN,2X,4HRAIN,2X,6HRUNOFF,2
2X,7HSTORAGE,2X,8HOVERFLOW,2X,7HTREATED,5X,9HT, RUNOFF,2X,10HT.OVER
3FLOW,7H OVERFL,8H T.TREAT,7H TREAT,13H MAX STORAGE/13X,2HIN,3X,4
4HDAYS,4X,2HMG,6X,2HMG,8X,2HMG,7X,2HMG,12X,2HMG,10X,2HMG,5X,4HDAYS,
55X,2HMG,5X,4HDAYS,7X,2HMG//)
XL=MYEAR
XL=XL/4.
L=XL
XXL=L
IF(XL-XXL) 16,15,16
15 M(3)=29
WRITE(6,154)MYEAR
154 FORMAT(10H LEAP YEARI10)
16 CONTINUE
LTOT=0.
0016611=1,13
166 LTOT=LTOT+M(II)
J=l
1664 CONTINUE
J=J+1
READ(5,167)NYZ,MON(J),MDAT(J),RAIN(J)
167 FORMAT(5X,3I2,6X,F4.2)
IF(NYZ-NZA)1682,1664,1682
1674 CONTINUE
J=KK
NZA=NYZ
MON(1)=MON(J)
MDAT(1)=MDAT(J)
RAIN(1)=RAIN(J)
WRITE(8,1678)J11
1678 FORMAT(I10)
WRITE(8,1679) (FINT(IXX) ,IXX=1,J11)
1679 FORMAT(10F8.2)
WRITE(9,1678) Jll
WRITE(9,1679) (FP2(IXX) ,IXX=1,J11)
IF(NYZ-90)12,62,62
1682 CONTINUE
119
-------
Table B-ll (Continued)
KK=J
D017J=1,KK
IF( MON(J)-99)1683,17,1683
1683 IP( MON(J)-12)1684,1684,1688
1684 IF(MDAT(J)-31)17,17,1688
1688 WRITE(6,1689)MON(J),MDAT(J)
1689 FORMAT(6H ERROR,2110)
STOP
17 CONTINUE
JC=1
DO30I3=1,370
IF(NFL-0)178,178,176
176 NFL=NFL+1
178 CONTINUE
IF(TRD)1798,1798,1792
17.92 IF(RUNOF) 1798,1798,1793
1793 NFL=NFL+1
FAC=1.
1798 CONTINUE
TRD=0.
OVFL=0.
RUNOF=0.
RIN=0.
IF(JC-370)18,18,32
18 CONTINUE
MX=MON(JC)
LSD=LSD+1
IF(LTOT-LSD)2604,21,21
21 CONTINUE
IF(MX)26,26,214
214 CONTINUE
LTD=0
D022I4=1,MX
22 LTD=LTD+M(I4)
ND=LTD+MDAT(JC)
23 CONTINUE
IF(LSD-ND)26,24,26
24 RUNOF=COEFX*RAIN(JC)
RINT=FP1(JC)
NFL=NFL+1
TRUN=TRUN+RUNOF
RIN=RAIN(JC)
JC=JC+1
STOR=STOR+RUNOF
IF(RUNOF)25,26,25
25 CONTINUE
NDR=NDR+1
26 CONTINUE
IF(LSD-l)2601,2608,2601
2601 CONTINUE
LTD=0
DO2602IQ=1,13
LTD=LTD+M(IQ)
IF(LTD-LSD+1)2602,2604,2602
2602 CONTINUE
GOTO2608
2604 CONTINUE
WRITE(6,2603)SRIN
2603 FORMAT(///11H TOTAL RAIN,F6.2)
SRIN=0.
1.}
120
-------
Table B-11 CContinued)
IF(LTOT-LSD)60,2606,2606
2606 CONTINUE
WRITE(6,142)MYEAR
2608 CONTINUE
STORA=STOR
IF(NFL-l)271,271,263
263 CONTINUE
IF(TREAT-RINT) 2632,2632,2634
2632 TREET=TREAT
GOT02638
2634 TREET=RINT
2638 CONTINUE
STOR=STOR-TREET*FAC
264 IF(STOR-STOPS)266,266,27
266 NFL=0
STOR=STOPS
27 CONTINUE
TRD=STORA-STOR
IF(TREET)2704,2719,2704
2704 CONTINUE
CTD=CTD+TRD/TREET
TRE=TRE+TRD
GOTO 2719
271 CONTINUE
IF(NFL-l)2712,2712,2718
2712 FAC=TFAC
GOTO 263
2718 FAC=1.
2719 CONTINUE
STM=STOR-STMAX
IF(STM)274,274,272
272 NOV=NOV+1
OVFL=STM
TOV=TOV+STM
STOR=STMAX
274 CONTINUE
IF{STOS,LT.STOR) STOS=STOR
ILL=0
MXQ=0
D0276IL=1,13
MXQ=MXQ+M(IL)
IF(MXQ-LSD)275,278,278
275 MQQ=MXQ
ILL=ILL+1
276 CONTINUE
278 LSX=LSD-MQQ
SRIN=SRIN+RIN
IF(IPFL-2)2784,279,279
2784 CONTINUE
FINT(13)=TREET-TRD
GOTO 28
279 FINT(I3)=RINT-TRD
28 CONTINUE
FP2(I3)=RUNOF
J11=I3
WRITE(6,282)ILL ,LSX,RIN,NDR,RUNdF,STOR,OVFL,TRD,TRUN,TOV,NOV,TRE
1 ,CTD,STOS
282 FORMAT(1H ,I3,I5,F6.2,I5,F9.2,F10.2,2F9.2,F14.2,F11.2,I6,F10.2,
1F8.2,F10.2)
30 CONTINUE
121
-------
32
50
60
62
64
122
13
142
144
145
148
149
15
152
154
Table B-ll (Continued)
CONTINUE
CONTINUE
CONTINUE
GOTO1674
CONTINUE
WRITE (6, 64)
FORMAT (1H1)
RETURN
END
SUBROUTINE HOUTAP( IPFL,NFAC)
COMMONM , AREA , COEF , STMAX , TREAT , STOPS , N YEAR , STOR, COEPX ,
*NFr,,LD,ND,TRD,FAC,IOTAP,MYEAR
DIMENSIONM(13) ,MON(24) ,MDAT(24) ,RAIN(24)
TREAT=TREAT/ 2 4 .
CONTINUE
READ(5,148)MYEAR,MA,MD,NX,(RAIN(J),J=1,12)
IF(MYEAR-99)122,63,122
IF(NX-2)13,12,12
CONTINUE
SRIN=0.
RUNOF=0.
NDR=0
CTD=0.
STOS=STOPS
TRUN=0.
TRE=0.
LSD=0
WRITE(6,142)MA
FORMATC1H1,///5X,5HMONTH,I6,14X,
A 20HOCCURING ON THE HOUR,19X,35HACCUMULATED FROM STA
1RT OF THE MONTH/24X,34H ,,5v
C56H LJ/'/
D 6H DAY ,1X,4HHOUR,2X,4HRAIN,2X,4HRAIN,2X,6HRUNOFF,2
2X,7HSTORAGE,2X,8HOVERFLOW,2X,7HTREATED,5X,9HT. RUNOFF,2X,]0HT.OVER
3FLOW,7H OVERFI,,8H T.TREAT, 7H TREAT,13H MAX STORAGE/13X, 2IHIN, 3X,4
4HHOUR,4X,2HMG,6X,2HMG,8X,2HMG,7X,2HMG,12X,2HMG,10X,2HMG,5X,4HHOUR,
55X,2HMG,5X,4HHOUR,7X,2HMG//)
MY=MA+1
MT=M(MY)
LTOT=0.
0014411=1,MT
LTOT=LTOT+24
GOTO 15
CONTINUE
READ(5,148)IC,MA,MD,NX>(RAIN(J) ,J=1,12)
FORMAT(6X,312,II,12F3. 2)
IF(IC-99)149,63,149
CONTINUE
IF(NX-2)15,145,145
CONTINUE
READ(5,152)(RAIN(J),J=13,24),NEXT
FORMAT(13X,12F3.2,29X,I2)
DO154IA=1,24
MON(IA)=MD
MDAT(IA)=IA
CONTINUE
D017J=1,24
IF( MON(J)-MT) 1684,1684,1688
122
-------
Table B-ll (Continued)
1684 IF(MDAT(J)-24)17,17,1688
1688 WRITE(6,1689)
1689 FORMAT(6H ERROR)
STOP
17 CONTINUE
JC=1
003013=1,770
IF(NFL-NPAC)178,176,176
176 NFL=NFL+1
178 CONTINUE
TRD=0.
OVFL=0.
RUNOF=0.
RIN=0.
IF(JC-24)18,18,32
18 CONTINUE
MX=MON(JC)
LSD=LSD-fcl
IF(LTOT-LSD)2604,21,21
21 CONTINUE
IF(MX)26,26,214
214 CONTINUE
LTD=-24
D022I4=1,MX
22 LTD=LTD+24
ND=LTD+MDAT(JC)
23 CONTINUE
IF(LSD-ND)26,24,26
24 RUNOF=COEFX*RAIN{JC)
NFL=NFL+1
TRUN=TRUN+RUNOF
RIN=RAIN(JC)
JC=JC+1
STOR=STOR+RUNOF
IFCRIN)25,26,25
25 CONTINUE
NDR=NDR+1
26 CONTINUE
IF(LSD-l)2601,2608,2601
2601 CONTINUE
LTD=0
D02602IQ=1,MT
LTD=LTD+24
IF(LTD-LSD+1)2602,2604,2602
2602 CONTINUE
GOT02608
2604 CONTINUE
WRITE(6,2603)SRIN
2603 FORMAT(///11H TOTAL RAIN,F6.2)
SRIN=0.
IF(LTOT-LSD)60,2606,2606
2606 CONTINUE
WRITE(6,142)MA
2608 CONTINUE
STORA=STOR
IF(NFL-1)271,271,263
263 STOR=STOR~TREAT
264 IF(STOR-STOPS)266,266,27
266 NFL=0
STOR=STOPS
123
-------
Table B-ll (Concluded)
27
271
272
274
275
276
278
282
29
30
32
60
63
64
CONTINUE
TRENSTORA-STOR
CTD=CTD+TRD/TREAT
TRE=TRE+TRD
CONTINUE
STM=STOR-STMAX
IF(STM)274,274,272
NOV=NOV+1
OVFL=STM
TOV=TOV+STM
STOR=STMAX
CONTINUE
IF(STOS.LT.STOR) STOS=STOR
D0276IL=1,HT
IF(MXQ-LSD) 275,278,278
MQQ=MXQ
MXQ=MXQ-f24
ILL=ILL+1
CONTINUE
LSX=LSD-HQQ
SRIN=SRIN+RIM
WRITE(6,282) ILL ,LSX,RIN,NDR,RUNOF,STOR,OVFL,TRD,TRUN,TOV,NOV,TRE
1 ,CTD,STOS
FORMAT(1H ,I3,I5,F6. 2,I5,F9. 2,F10. 2,2F9. 2,F14. 2,F11. 2,I6,F10. 2,
1F8.2,F10.2)
IF(JC-25)30,29,30
IF(NEXT-1)32,30,32 ;
CONTINUE
CONTINUE
GOTO 145
CONTINUE
GOT012
WRITE(6,64)
FORMAT(lHl)
STOP
END
124
-------
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126
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Appendix C
DETERMINATION OF K FACTOR
127
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Appendix C
DETERMINATION OF K FACTOR
The gross runoff coefficient (K factor) is an integral part
of the storage-treatment program. This factor can be
determined from reliable measurements of rainfall and total
runoff quantities as-described in Section V. This factor
can also be predicted based on geographic data.
Recent research has developed a correlation between
population density and the fraction of impervious land in a
region [9]. This relationship is of the form:
1-9.6 PDd
(0-573 - °'0391
(1)
where I — imperviousness in percent
PD, - population density in developed portion
of urbanized area, persons/acre
The results from this equation can be checked with values
for a known region. in Figure C-l the actual data for
Rochester are plotted along with the curve resulting from
the predicting equation shown above.
The fraction of imperviousness can be used directly as a K
factor or it can be adjusted to reflect local conditions.
The computer model STORM[11,12] weights pervious and
impervious fractions with the following equation[9].
K = 0.15 (l-I) •*• 0.901
K - 0.15 + 0.751
(2)
where K = gross runoff coefficient
I = fraction imperviousness
The values of 0.15 and
STORM.
0.90 are default values used in
128
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LEGEND
PREDICTING EQUATION
© DATA POINT FOR- ROCHESTER
15 20 25 30 35
POPULATION DENSITY, PERSONS PER ACRE
FIGURE C-1 . COMPARISON OF ROCHESTER DATS'WITH
IMPERVIOUSNESS PREDICTING EQUATION
129
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Appendix D
OVERFLOW QUALITY ASSESSMENT-ALTERNATE METHODS
In this section two alternative procedures for computing the
quality of stormwater discharges from combined and separate
sewer systems are presented. The first method was developed
in the course of the nationwide assessment of the stormwater
problem [9], This method uses geographic and demographic
characteristics to predict quality parameter. The second
procedure that will be discussed is a regression technique.
QUALITY PREDICTION FROM GEOGRAPHIC AND DEMOGRAPHIC DATA
Stormwater quality is a function of land use, population
density, and precipitation. From the nationwide assessment
the following relationships were developed[9]:
Separate areas: Mg » a(i,j) x P x f(PD)
Coiribined areas: M,, = b(i,j) x P x f (PD)
(1)
(2)
M
a(i,j)
where M = lb of pollutant, Ib/acre-yr
constant for separate storm systems for
i land use and j constituent, lb/acre-iii.,
constant for combined storm systems for
i land use and j constituent, Ib/acre-in.
P = annual precipitation, in./yr
f (PD) = population density function
This equation when provided with appropriate constants
summarized in Table D-l, will calculate annual pollutant
loadings.
However, for use in the simplified mathematical model a
concentration of pollutants is required that can be paired
with the volume of overflow generated in the
130
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Table D-l. QUALITY CONSTANTS FOR
LOADINGS FROM SEWER SYSTEMS
Pounds per Acre-Inch
Pollutant,
Land use, i
Separate areas a(i,j)
Combined areas b(i,j)
"
1.
2.
3.
4.
1.
2.
3.
4.
Residential
Commercial
Industrial
Other
Residential
Commercial
Industrial
Other
1.
0
3
1
0
3
13
5
0
BOD5
.799
.20
.22
.18
.29
.2
.00
.47
2. SS
16
22
29
2
67
91
120
11
.3
.2
.1
.7
.2
.8
.0
.1
3.
9.
14.
14.
2.
38.
57.
59.
10.
VS
48
0
4
60
9
9
4
8
j
4
0
0
0
0
0
0
0
0
. P04*
.034
.076
.071
.010
.139
,312
.291
.041
5. N**
0.540
0.296
0.276
0.061
0.540
1.22
0.140
0.250
* Total P as PO4.
**Total N as N.
storage-treatment task. Following the format used in the
nationwide assessment the relationship would be of the form:
Separate areas: Qs = c(i,j) x f(PD)
Combined areas: Qc = d(i,j) x f(PD)
(3)
(4)
where Q = concentration of pollutants, mg/1
c(i,j) = constant for separate storm systems for
i land use and j constituent, mg/1
d(i,j) = constant for combined sewer systems for
i land use and j constituent, mg/1
f (PD) «* population density function
The constants a(if j) and b(i, j) were derived from surface
loading to be used with precipitation. These constants can
be adjusted to be used with runoff and for dimensional
131
-------
consistency with simplified modeling,
be effected by the following equation:
x P]/k
x F]/k
The conversion can
(5)
(6)
where F » 4.14, constant, [(mg/l)/(lb/acre-in.)]
k * 0.34 national average runoff coefficient
for seven cities [9]
These constants, summarized in Table D-2, can be used with
the following population density functions (f(PD)) to
predict quality values that can be used in the, simplified
mathematical model.
Residential
f(PD) = 0.142 + (0.218 x PD°'54)
Commercial and industrial
f(PD) = 1.0
Other (open and nonurban)
f(PD) - 0.142
(7)
(8)
(9)
Table D-2. QUALITY CONSTANTS FOR
CONCENTRATIONS FROM SEWER SYSTEMS
Pollutant, j
Land use, i
Separate areas, c(i,j)
Combined areas, d(i,j)
1.
2.
3.
4.
1.
2.
3.
4.
Residential
Commercial
Industrial
Other
Residential
Commercial
Industrial
Other
1. BOD5
10.
41.
15.
1.
42.
171.
64.
6.
4
5
8
5
7
3
9
1
2. SS
211.
288.
377.
35.
871.
1,191.
1,557.
144.
5
1
6
0
9
2
0
0
3. VS
123.
181.
186.
33.
123.
751.
770.
140.
0
7
8
7
0
3
8
1
4.
0.
0.
0.
0.
1.
4.
3.
0.
:po4*
44
98
91
13
80
05
78
521
5. N**
7.01
3.84
3.58
0.79
7.01
15.83
1.82
3.24
* Total P as PO4.
**Total N as N.
132
-------
An important fact to remember is that the values predicted
by these equations reflect the characteristics of the seven
cities on which sampling data was available from the
nationwide assessment. It would be very beneficial to have
sampling data collected that could be used for comparison
and/or adjustment of these equations to reflect local
conditions
QUALITY PREDICTION USING REGRESSION TECHNIQUES
In the body of the text two regression techniques, developed
for use in Rochester, were described. A third regression
analysis, originally developed for analyzing stormwater in
Washington, B.C., is presented here as another option for
predicting quality parameters[10]. The procedure is
outlined sequentially.
1. Compute suspended solids (degritted
from the following expression:
fraction) in mg/1
ss
400 x £]. x £2 x £3
(10)
where
400 = an average suspended solids value
that can be changed if local data
are available for a closer fit
f i = a function of days since the last
rain and time from the start of
overflow or discharge
f2 = a function of rainfall intensity
£3=3 funqtion of catchment population
density
For each time increment compute a value for ss reading f^,
±2, and fa from Tables D-3, D-4, and D-5, respectively.
The computed values will suffice, for both combined and
separate systems.
2. Compute BOD for separate storm drains from the following
expressions:
BOD,, (storm)
3
BOD- (storm)
5
.10.x ss for ss values equal to
or less than 300 mg/1
30 + (ss - 300) x .08 for ss values
greater than 300 mg/1
(12)
133
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Table D-3. REGRESSION COEFFICIENT f-.
Time since start of
overflow, hr/or min
1st hr/or less than 30 min
2nd hr/30-90 min
3rd hr/90-180 min
4fch-6th hr/180-360 min
7th-12th hr/360-720 min
13th or more hr/more than
720 min
0-6
1.2
.9
.6
.5
.4
.3
Days since
7-12
1.9
1.2
.7
.5
.4
.3
last
13-18
2.3
1.5
.7
.5
.4
.3
rainstorm
Over
18
2.6
1.7
.7
.5
.4
.3
Unknown
1.9
1.2
.7
.5
.4
.3
Table D-4. REGRESSION COEFFICIENT
Rainfall intensity, in./hr
.01-.09 .10-.20 .21-.50 Over .50
f 2 = .5
.9
1.2
1.5
Table D-5. REGRESSION COEFFICIENT f-
Populatiqn density, persons per acre
0.10 11-20 21-30 31-40 Over 40
.5
.65
.80
.95
1.0
134
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3. Compute BOD for combined overflows from the following
expression:
BOD5(comb.) = aD + (1 - a) x BODg(storm)
(13)
where a = proportion of combined flow attributed to
average dry-weather sanitary flow
D = average BOD5 concentration of dry-weather
sanitary flow
Note: Knowing the average dry-weather flow from the area in
mgd, the hourly rate is simply this value divided by 24.
Where a is therefore the hourly sanitary flow plus the storm
runoff that hour divided by the sum of the two,. A new a
must be computed each time step.
4. Compute total nitrogen (all forms as N, mg/1) for both
combined and separate systems from the following expression:
N = o.io BOD,
(14)
5. Finally, compute total phosphorus (all forms as P, mg/1)
for both combined and separate systems from the following
expression:
0.033 BODC
(15)
The above method gives concentrations in a general sense
only and should not be used for other than first level
approximations without substantial corroborating data.
Total coliforms can be computed similarly, but it is
doubtful that the effort is warranted considering the high
order numbers involved.
135
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GLOSSARY
Combined sewage—Sewage containing both domestic sewage and
surfacewateror stormwater, with or without industrial
wastes. Includes flow in heavily infiltrated sanitary sewer
systems as well as combined sewer systems.
Combined sewer—A sewer receiving both
runoff and municipal sewage.
intercepted surface
Combined sewer overflow—Flow from a combined sewer in
excess of the interceptor capacity that is discharged into a
receiving water.
First flush-—The condition, often occurring in storm sewer
discharges and combined sewer overflows, in which a
disproportionately high pollutional load is carried in the
first portion of the discharge or overflow.
Infiltration—The water entering a sewer system and service
connections from the ground, through such means as,, but not
limited to, defective pipes, pipe joints, connections, or
manhole walls. Infiltration does not include, and is
distinguished from, inflow.
Inflow—The water discharged into a sewer system arid service
connections from such sources as, but not limited to,, roof
leaders, cellar, yard, and area drains, foundation drains,
cooling water discharges, drains from springs and swampy
areas, manhole covers, cross connections from storm sewers
and combined sewers, catchbasins, stormwaters, surface
runoff, street wash waters, or drainage. Inflow does not
include, and is distinguished from, infiltration.
In-system—Within the physical confines of
network.
the sewer pipe
Interceptor—A sewer that receives dry-weather flow from a
number of transverse combined sewers and cidditional
predetermined quantities of intercepted surface runoff and
conveys such waters to a point for treatment.
136
-------
Municipal sewage—Sewage from a community which may be
composed of domestic sewage, industrial wastes, or both.
Overflow—(1) The flow discharging from a sewer resulting
from combined sewage, storm wastewater, or extraneous flows
and normal flows that exceed the sewer capacity. (2) The
location at which such flows leave the sewer.
Physical-chemical treatment processes—Means of treatment in
w.hich the removal, of pollutants is brought about primarily
by chemical clarification in conjunction with physical
processes. The process string generally includes
preliminary treatment, chemical clarification, filtration,
carbon adsorption, and disinfection.
Plug flow—The passage of liquid through a chamber such that
all increments of liquid move only in the direction of flow
and at equal velocity.
Pollutant-—Any harmful or objectionable material , in or
change in physical characteristic of water or sewage.
Sewer—A pipe or conduit generally closed, but normally ,npt
flowing full, for carrying sewage or other waste liquids.
Storm flow—Overland flow, sewer flow, or receiving stream
flow caused totally, or partially by surface r.unoff or
snowmelt. • ,
Storm, sewer—A sewer that carries intercepted surface
runoff, street wash and other wash'waters, or drainage, but
excludes domestic sewage and industrial wastes.
Storm,sewer discharge—Flow from a
discharged into a receiving water.
storm sewer that is
Stormwater—Water resulting from precipitation which either
•percolates into the soil, runs off freely from the surface,
or is captured by storm sewer, .combined sewer, and to a
limited degree sanitary sewer facilities.
Surcharge-—The flow condition occurring in closed, conduits
whenthe hydraulic grade line is above the crown of the
sewer.
Surface runoff—Precipitation -that falls onto the surfaces
of roofs, streets, ground, etc., and is not absorbed .or or
retained by that surface, thereby collecting and running
off.
137
-------
Urban runoff—surface runoff from an urban drainage area
that reaches a stream or other body of water or a sewer.
Wastewater—The spent water of a community.
Sewage and Combined Sewage.
See Municipal
138
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/2-76-218
3. RECIPIENT'S ACCESSION-NO.
4. TITLE AND SUBTITLE
5. REPORT DATE
Development and Application of a
Simplified Stormwater Management Model
August 1976 (Issuing Date)
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S) - •
John A. Lager, Theodor Didriksson, and
George B. Otte
8. PERFORMING ORGANIZATION REPORT NQ.
9. PERFORMING ORG \NIZATION NAME AND ADDRESS
Metcalf & Eddy, Inc.
1029 Corporation foay
P.O.Box 10-046
Palo Alto, California 94303
10. PROGRAM ELEMENT NO.
1BC611
II.CSMWJfcSK'GRANTNO.
Y005141
12. SPONSORING AGENCY NAME AND ADDRESS
"Municipal Environmental Research Laboratory
Office of Research and Development
U.S. Environmental P-rotection Agency
Cincinnati, Ohio 45268
13. TYPE OF REPORT AND PERIOD COVERED
Final Report
14. SPONSORING AGENCY CODE
EPA-ORD
15. SUPPLEMENTARY NOTES
Project Officer: Anthony N. Tafuri, 201/548-3347 x512 (8-342-7512)
16. ABSTRACT •
A simplified stormwater management model has been created to
provide an inexpensive, flexible tool for planning and
preliminary sizing of stormwater facilities.
The model delineates a methodology to be used in the
management of storrawater and consists of a series of
interrelated tasks that combine small computer programs and
hand computations. The model successfully introduces time
and probability into stormwater analysis, promotes total
system consciousness on the part of the user, and assists in
establishing size-effectiveness relationships for
facilities.
Throughout the report, data from the City of Rochester, ISiew
York, is presented and analyzed as a working example.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
*Combined sewers, Drainage, *Mathe-
matical models, *0verflows—sewers,
Regression analysis, *Runoff,
*Surface water runoff, *water crual-
ity
*Corabinec2 sewer
overflows, Drainage
systems, Pollution
abatement, *Storrrr
runoff, * Urban
hydrology
13B
18. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SE'CUfrl'TY"CLASS (ThisReport)"
UNCLASSIFIED
21. NO. OF PAGES
153
20. SECURITY CLASS (Thispage)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (9-73)
139
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