EPA-600/2-84-109a
Final Draft
November 1981
Tenth Printing
March 1987
STORM WATER MANAGEMENT MODEL
USER'S MANUAL
Version III
By
Wayne C. Huber, James P. Heaney, Stephan J. Nix,
Robert E. Dickinson and Donald J. Polmann
Department of Environmental Engineering Sciences
University of Florida
Gainesville, Florida 32611
Project No. CR-805664
Project Officer
Douglas C. Ammon
Storm and Combined Sewer Section
Systems and Engineering Evaluation Branch
Wastewater Research Division
Municipal Environmental Research Laboratory
Cincinnati, Ohio 45268
MUNICIPAL- ENVIRONMENTAL RESEARCH CENTER
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OHIO 45268
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FOREWARD
The Environmental Protection Agency was created because of increasing
public and governmental concern about the dangers of pollution to the
health and welfare of the American people. Noxious air, foul water, and
spoiled land are tragic testimony to the deterioration of our natural
environment. The complexity of that environment and the 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.
Mathematical models are an important tool for use in analysis of
quantity and quality problems resulting from urban storm water runoff
and combined sewer overflows. This report is an updated user's manual
and documentation for one of the first of such models, the EPA Storm
Water Management Model (SWMM). Detailed instructions on the use of the
model are given and its use is illustrated with case studies.
Francis T. Mayo, Director
Municipal Environmental Research
Laboratory
iii
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PREFACE
There is seemingly never enough time to write a manual and document
a computer program in the manner it deserves. This SWMM Version III
User's Manual has been under preparation in one form or another for six
years, yet it will be found upon examination that some aspects of the
model are covered in much more detail than others, and some coverage is
downright sparse, notably case studies. The tendency to add "one more
thing" to the model has unfortunately proven all too irresistable, to
the detriment of as much testing and demonstration as would be desirable.
Nonetheless, with a few exceptions most coding is not radically new;
much deals just with improved input/output. Hence, much reliance has
been placed on the continuous testing of the model during its development
over the past 12 years.
Writing of the various text has also been accomplished in steps.
Hence, references on various topics, e.g., snowmelt, storage/treatment,
were current at the time of writing which may have been two or three
years ago. SWMM quality routines are most continually in a state of
flux due to new developments in the literature. The user should remain
familiar with current information and alter his/her modeling practices
as necessary.
Some parts of the model are completely new, some are similar if not
identical to the first release in 1971. The most significant change
since the 1975 Version II release is the inclusion of the Extended
Transport Block for quantity routing. This is documented separately by
Camp, Dresser and McKee in an addendum to this volume. Other major
changes include: continuous simulation, completely revised storage/
treatment routines, snowmelt, surface quality generation, a statistical
analysis block, updated graphical and tabular output, and scour-
deposition computations in the Transport Block. Parts of the model that
remain basically unchanged include the flow routing techniques of the
Runoff and Transport Blocks. From a software point of view, the program
is no shorter, but nor is it much longer for any particular block.
An attempt has been made to provide adequate information in this
manual for most users to conceptualize a stormwater problem and simulate
it using SWMM. As a result, some of the text is rather lengthy, approach-
ing a hydrology textbook in style in places. Unfortunately, it will
still be the user's responsibility to seek out the proper references for
additional information on modeling, especially when dealing with water
quality.
iv
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The authors hope that the user is not "put off" by the length of this
volume and the size of the SWMM program. Aside from the fact that it
requires a large computer core (about 400 K bytes) the program may often be
easily and usefully run with a minimum of input, say a dozen data cards.
For small systems in which time step requirements are not severe, the model
is very economical as well, and is within the reach of most users. It is by
no means the only engineering tool of its kind available, but it has bene-
fited greatly from its longevity and feedback from model users. The authors
hope such feedback will continue, and earnestly solicit suggestions for
improvements. Although no major support for model changes is likely to be
forthcoming, the EPA Storm Water and Water Quality Model Users Group (form-
erly the SWMM Users Group) remains a convenient forum. Announcements of
corrections, changes and new options will be made through that group, man-
aged by Mr. Thomas 0. Barnwell, EPA, Athens, Georgia 30605.
For the May 198^, Second Printing, minor editorial corrections have been
made on the following pages: xxvi, 1-7, 1-10, 2-4, 2-5, 2-6, 2-7, 2-8, 2-
12, 2-13, 4-87, 4-139, 4-154, 7-3, 8-1, 9-2, 9-3, 9-7, 9-11, 9-18, 1-6, IV-
5, IV-7, IV-10, IV-13, IV-19.
For the July 1982 Third Printing minor editorial corrections have been
made on the following pages: 4-144, 6-50, 7-7, IV-10, IV-17.
For the October 1982 Fourth Printing minor editorial correction have
been made on the following pages: 1-3, 1-8, 1-9, 1-10, 1-14, 1-15, 2-17, 2-
18, 4-28, 4-30, 4-41, 4-134, 4-138, 4-143, 4-154, 6-44, 6-50, 6-51, 6-72, 7-
3, 7-4, 10-15, 10-17, IX-1, IX-2.
For the January 1983 Fifth Printing minor editorial correction have
been made on the following page: 1-15, 4-157, 7-2, III-3-
For the July 1983 Sixth Printing minor editorial corrections have been
made on the following pages: 1-11, 1-12, 1-13, 1-14, 1-15, 4-138, 4-143, 4-
144, 4-145, 4-146, 6-56, 7-29, 9-9, 9-13, 10-8, 10-12.
For the October 1983 Seventh Printing minor editorial corrections have
been made on the following pages: ii, 3-4, 10-3, 10-9.
For the May 1984 Eighth Printing minor editorial corrections have been
made on the following page: 6-58.
For the September 1984 Ninth Printing minor editorial corrections have
been made on the following pages: 2-13, 4-19, 7-22.
For the March 1987 Tenth Printing minor editorial corrections have been
made on the following pages: 4-131, 4-147, 6-21, 9-5, 10-3, 10-6, 10-8, 10-
10, 10-11, 10-16.
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ABSTRACT
The EPA Storm Water Management Model (SWMM) is a comprehensive
mathematical model for simulation of urban runoff quantity and quality
in storm and combined sewer systems. All aspects of the urban hydrologic
and quality cycles are simulated, including surface runoff, transport
through the drainage network, storage and treatment, and receiving water
effects. (The latter component is currently under revision by the EPA.)
This volume applies to Version III of SWMM and is an update of two earlier
User's Manuals issued in 1971 and 1975. It should be coupled with
Addendum I in order to run the Extran Block (detailed hydraulic flow
routing) developed by Camp, Dresser and McKee.
Detailed descriptions are provided herein for all blocks (except
the Receiving Water Block): Runoff, Transport, Storage/Treatment,
Combine, Statistics and Graph (part of the Executive Block). The latter
three blocks are "service" blocks while the first three are the principal
computational blocks. In addition, extensive documentation of new
procedures is provided in the text and in several appendices.
This report was submitted in partial fulfillment of cooperative
agreement CR-805664 by the University of Florida under the sponsorship
of the U.S. Environmental Protection Agency. Work was completed as of
November 1981.
vi
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CONTENTS
Foreward iii
Preface iv
Abstract v±
Figures xv
Tables ; xxii
Acknowledgments xxy
Sections
1 Introduction ]__]_
Urban Runoff Analysis 1-1
Urban Runoff Models 1-1
Objectives 1-1
Screening Models 1-1
Planning Models 1-2
Design Models 1-2
Operational Models 1-3
Other Models . 1-3
Development of the Storm Water Management Model 1-3
Overall SWMM Description 1-4
Overview 1-4
Service Blocks 1-7
Executive Block ..... 1-7
Combine Block . 1-7
Statistics Block 1-7
Total Simulation 1-7
Detailed SWMM Summary 1-8
User Requirements 1-8
Computer Facilities 1-8
Data Requirements 1-8
Verification and Calibration 1-9
Metrification 1-9
When Should SWMM Be Used?. 1-9
2 Executive Block, Graph Routines and System Requirements 2-1
Block Description 2-1
Functions 2-1
Program Operation 2-1
Interfacing Between Computational Blocks 2-3
vii
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CONTENTS (continued)
Initial Job Set-Up 2-7
Computer System Requirements 2-7
Program Compilation, Execution Time and Cost 2-7
Fortran 2-9
Logical Units 2-9
Sample Job Control Language (JCL) 2-H
Overlay Procedures 2-14
Dummy Subroutines 2-14
Scratch Data Sets 2-15
Instructions For Data Preparation 2-18
Block Selection 2-18
Graph Routine 2-18
Capabilities 2-18
Input Parameters and Options 2-21
Examples 2-22
3 Combine Block 3-1
Block Description 3-1
Instructions for Data Preparation 3-1
Collate 3-1
Combine 3-4
Quality Options 3-4
Timing 3-4
4 Runoff Block 4-1
Block Description 4-1
Introduction 4-1
Program Operation 4-2
Interfacing and the Use of Off-line Computer Storage. . . . 4-2
Instructions For Data Preparation 4-5
Introduction 4-5
Basic Runoff Data Sources 4-5
Importance of Runoff Block Data 4-5
Meteorological Data 4-5
Surface Quantity Data 4-5
Surface Quality Data 4-6
Default Parameters 4-6
General Input and Control Data (Card Groups A1-B2) 4-6
Meteorological Data Processing (Card Groups C1-F1) 4-7
Snowmelt Data 4-7
Air Temperatures 4-14
Precipitation Data 4-15
Evaporation Data (Card Fl) 4-21
viii
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CONTENTS (continued)
Surface Quantity Input Data (Card Groups G1-I3) 4-22
Runoff Flow Routing Procedures and Options 4-22
Input Data Preparation 4-22
Discretization of the Catchment 4-23
Gutter/Pipe Parameters (Card Groups Gl and G2) 4-29
Subcatchment Parameters (Card Groups HI and H2) 4-30
Subcatchment Aggregation and Lumping 4-56
Snowtnelt Parameters (Card Groups 11-13) 4-59
Surface Quality Input Data (Card Groups J1-L1) 4-69
Overview of Quality Procedures 4-69
Quality Simulation Credibility 4-70
Quality Constituents 4-71
Land Use Data (Card Group J2) 4-73
Buildup 4-73
Washoff 4-88
Related Buildup-Washoff Studies 4-103
Rating Curve 4-104
Street Cleaning 4-105
Catchbasins 4-108
Constituent Fractions 4-114
Precipitation Contributions 4-116
Urban Erosion 4-120
Subcatchment Quality Data (Card Group LI) 4-125
Overall Sensitivity to Quality Parameters 4-129
Print Control (Card Groups Ml and M2) 4-133
5 Extended Transport Block 5-1
6 Transport Block 6-1
Block Description 6-1
Introduction 6-1
Broad Description of Flow Routing 6-1
Broad Description of Quality Routing 6-3
Program Operation 6-4
Off-Line Files 6-5
Instructions For Data Preparation 6-5
Introduction 6-5
Transport 6-7
Categories of Data 6-7
Step 1. Theoretical Data 6-7
Step 2. The Physical Representation of the Sewer System. 6-9
Step 3. Input Data and Computational Controls 6-21
Quality 6-22
Constituents 6-22
Decay 6-22
Routing 6-23
Scour and Deposition 6-23
ix
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CONTENTS (continued)
Internal Storage Model 6-25
Step 1. Call 6-25
Step 2. Storage Description: Part 1 6-25
Step 3. Storage Description: Part 2 6-25
Step 4. Initial Conditions 6-25
Infiltration Model 6-26
Description 6-26
Dry Weather Infiltration (DINFIL) 6-29
Residual Melting Ice and Frost Infiltration (SINFIL). . . 6-29
Antecedent Precipitation (RINFIL) 6-31
High Groundwater Table (GINFIL) 6-31
Apportionment of Infiltration 6-32
Quality of Infiltration 6-33
Data Needs 6-33
Summary of Infiltration Procedures 6-33
Dry Weather Flow Model. . . 6-34
Methodology 6-34
Quantity Estimates 6-36
Quality Estimates 6-38
Summary of Dry Weather Flow Requirements 6-42
Initialization 6-44
7 Storage/Treatment Block 7-1
Block Description 7-1
Introduction 7-1
Program Operation 7-1
Use of Off-line Computer Storage 7-3
Instructions For Data Preparation 7-4
Preliminary Information 7-4
Title 7-4
General Information 7-4
Starting Time and Print Instructions. 7-6
Evaporation Data 7-6
Pollutant Characterizations 7-6
Storage/Treatment Unit Information 7-8
General Unit Information 7-10
Pollutant Removal 7-10
Detention Unit Data 7-15
Cost Data 7-18
Input Waste Stream 7-20
Flow and Pollutant Data 7-20
Altering The Program Size 7-20
8 Receiving Water Block 8-1
x
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CONTENTS (continued)
9 Statistics Block 9-1
Block Description 9_1
Introduction 9-1
Program Operation 9-1
Output Options 9-3
Potential for Output 9-3
Sequential Series of Events 9-3
Table of Magnitude, Return Period and Frequency 9-4
Graph of Magnitude vs. Return Period or Graph of
Magnitude vs. Frequency 9-4
Moments 9-4
Preparation of Input Data 9-4
Extent of Data 9-4
Card Group Al 9.5
Card Group Bl 9-5
Card Group B2 9.5
Card Groups C1-C4 9-7
Card Groups D1-D4 9-7
Card Group El 9-7
Card Group Fl 9-7
Card Groups Gl and G2 9-8
Computations 9-8
Return Period and Frequency 9-8
Moments 9-8
Messages/Labels 9-10
Analysis of Rainfall Data 9-12
10 References 10-1
Appendices
I Continuous Simulation 1-1
Continuous and Single Event Simulation 1-1
Continuous SWMM Overview 1-2
Snowmelt 1-2
Input Data 1-3
Catchment Schematization 1-3
Output 1-3
Runoff Block. 1-3
Storage/Treatment Block 1-4
Dry-Period Regeneration 1-4
Quantity 1-4
Quality 1-4
Continuous SWMM Compared to STORM 1-6
xi
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CONTENTS (continued)
II SWMM Snowmelt Routines II-l
Introduction II-l
Overview II-2
Snow Depths II-2
Single Event Simulation II-2
Continuous Simulation II-2
Pollutant Simulation II-3
Snow and Temperature Generation From NWS Tapes II-3
National Weather Service (NWS) Data II-3
Creation of Hourly Temperatures II-5
Generation of Snowfall Intensities II-9
Gage Catch Deficiency Correction II-9
Structure of Precipitation-Temperature Data Set II-9
Output Options 11-12
Single Event SWMM 11-12
Subcatchment Schematization 11-12
Land Surface - Snow Cover Combinations 11-12
Redistribution and Simulation of Snow Removal 11-13
Array Restrictions 11-15
Melt Calculations 11-15
Theory of Snowmelt 11-15
Introduction 11-15
Snowpack Heat Budget , 11-18
Melt Prediction Techniques 11-18
Choice of Predictive Method 11-19
SWMM Melt Equations 11-20
Heat Exchange During Non-Melt Periods 11-22
Areal Extent of Snow Cover 11-24
Introduction 11-24
Areal Depletion Curves. 11-25
Use of Value of ASC 11-29
Liquid Water Routing in Snow Pack 11-29
Net Runoff 11-29
Effect of Snow on Infiltration and Surface Parameters . . . 11-31
Quality Interactions 11-31
Pollutant Accumulation 11-31
Snowmelt Quality 11-31
Pollutant Loadings 11-33
Adjustments for Presence of Snow 11-33
Possible Loading Rates 11-33
Street Sweeping 11-33
Other Considerations 11-34
Data Requirements 11-34
New Parameters 11-34
Sensitivity 11-34
xii
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CONTENTS (continued)
Output H-35
Temperature and Snowfall Generation 11-35
Runoff Simulation Output. 11-35
Programming Notes 11-35
Subroutines 11-35
Variable Names 11-35
Computer Time 11-35
Computer Size Requirements 11-37
III Reduction Of Energy Balance Equation To Degree-Day Equation. . .111-1
Purpose. III-l
Energy Budget III-l
Short Wave Radiation, H III-2
Heat Conduction Throughr6round, H III-2
Net Long Wave Radiation, H 1 . . ? III-2
Convective Heat Transfer, fi III-3
Condensation Heat Transfer,°H III-A
Heat Advection By Rain, H . ? III-4
Combined Equations ...?.. III-5
Numerical Example III-6
IV Storage/Treatment Simulation IV-1
Objectives IV-1
Program Developement And Overview IV-1
Development IV-1
Overview IV-2
Simulation Techniques IV-2
Introduction IV-2
Flow Routing IV-4
Detention vs. Instantaneous Throughflow IV-4
Detention Units IV-4
Residual Flow IV-3
Evaporation IV-8
Instantaneous Throughflow IV-8
Pollutant Routing IV-9
Complete Mixing IV-9
Plug Flow IV-11
Instantaneous Throughflow IV-11
Pollutant Characterization IV-12
Pollutant Removal IV-12
Characterized By Magnitude IV-12
Characterized by Particle Size-Specific Gravity or
Settling Velocity Distribution IV-15
Cost Calculations IV-22
Summary IV-23
xiii
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CONTENTS (concluded)
V Other Runoff Block Revisions V-l
Infiltration V-l
Introduction V-l
Evaporation V-l
Integrated Morton's Equation V-l
Cumulative Infiltration V-l
Summary of Procedure. V-5
Regeneration of Infiltration Capacity V-6
Program Variables V-9
Green-Ampt Equation V-9
Infiltration During Rainfall Events V-9
Recovery of Infiltration Capacity (Redistribution).... V-ll
Program Variables V-13
Subcatchment Runoff Calculations V-13
Overland Flow V-13
Gutter/Pipes V-18
VI Transport Block Scour And Deposition VI-1
Introduction VI-1
Methodology and Assumptions VI-2
Overview VI-2
Assumptions VI-2
Shields' Criterion VI-3
Particle Size Distribution VI-8
Inflows and Junctions VI-12
VII Example Analysis Of Urban Runoff Quality Data For Modeling
Applications VII-1
VIII Miscellaneous Transport Block Tables VIII-1
IX Integrated Form Of Complete Mixing Quality Routing ....... IX-1
xiv
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FIGURES
Number Page
1-1 Overview of SWMM Model Structure, Indicating Linkages
Among the Five Computational Blocks 1-6
2-1 Relationship of Executive Block to Other Blocks 2-2
2-2 Detailed Organization of SWMM Interfacing File 2-5
2-3 Fortran Statements Required to Generate an Interface
File. 2-6
2-4 Hypothetical Card Arrangement for a Comprehensive SWMM
Run 2-19
3-1 Combination of SWMM Runs for Overall Lancaster Simulation . 3-2
3-2 Hypothetical Drainage Network to be Collated 3-3
3-3 Hypothetical Drainage Network to be Combined 3-3
4-1 Structure of Runoff Block Subroutines 4_4
4-2 Gage Catch Deficiency Factor (SCF) Versus Wind Speed. . . . 4-8
4-3 Card Image of National Weather Service Card Deck 345,
"Summary of Day" 4-10
4-4 Actual Areal Depletion Curve for Natural Area 4-12
4-5 Effect on Snow Cover of Areal Depletion Curves 4-13
4-6 Comparison of Synthetic Versus Actual Storm Patterns,
Chicago 4-17
4-7 Card Image of National Weather Service Card Deck 488,
"Hourly Precipitation" 4-20
4-8 Northwood (Baltimore) Drainage Basin "Fine" Plan 4-24
4-9 Northwood (Baltimore) Drainage Basin "Coarse" Plan 4-25
4-10 Effect of Coarsening Subcatchment System, Northwood
(Baltimore) . 4-26
xv
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FIGURES (continued)
Number Page
4-11 Subcatchment Schematization 4-32
4-12 Non-linear Reservoir Representation of Subcatchment .... 4-34
4-13 Idealized Subcatchment - Gutter Arrangement Illustrating
the Subcatchment Width. 4-35
4-14 Different Subcatchment Shapes to Illustrate Effect of
Subcatchment Width 4-37
4-15 Subcatchment Hydrographs for Different Shapes of Figure
4-14 4-38
4-16 Irregular Subcatchment Shape for Width Calculation 4-40
4-17 Percent Imperviousness Versus Developed Population
Density for Large Urban Areas . . 4-43
4-18 Depression Storage vs. Catchment Slope 4-47
4-19 Soil Conservation Service Soil Survey Interpretation
for Conestoga Silt Loam (found near Lancaster, PA). . . . 4-50
4-20 Effect of Changing the Level of Discretization on the
Width of Overland Flow 4-58
4-21 Effect on Hydrographs of Changing Subcatchment Width for
West Toronto Area 4-60
4-22 Seasonal Variation of Melt Coeffecients for Continuous
Simulation 4-62
4-23 Degree-Day Equations for Snow Melt 4-64
4-24 Freezing Point Depression Versus Roadway Salting Chemical
Concentration 4-66
4-25 Illustration of Snow Redistribution Fractions 4-67
4-26 Layout of Quality Constitutent Headings 4-72
4-27 Non-linear Buildup of Street Solids 4-76
4-28 Buildup of Street Solids in San Jose 4-77
4-29 Comparison of Linear and Three Non-linear Buildup
Equations 4-82
xv i
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FIGURES (continued)
Number Page
4-30 Washoff of Street Solids by Flushing with a Sprinkler
System 4-90
4-31 Time Variation of Runoff Rate Used in Example of Table
4-20 and Figures 4-32 to 4-35 4-93
4-32 Time History of Concentration and Subcatchment Load
(PSHED) for Case 1 Runoff (Figure 4-31) 4-94
4-33 Time History of Concentration and Subcatchment Load
(PSHED) for Case 2 Runoff (Figure 4-31) 4-95
4-34 Time History of Concentration and Subcatchment Load
(PSHED) for Case 3 Runoff (Figure 4-31) 4-96
4-35 Time History of Concentration and Subcatchment Load
(PSHED) for Case 4 Runoff (Figure 4-31) 4-97
4-36 Simulated Load Variations within a Storm as a Function
of Runoff Rate 4-100
4-37 Variation of BOD5, TSS and NO +NO -N Load and
Concentration tor Storm of 11/17/74 for View Ridge 1
Catchment, Seattle 4-101
4-38 Hypothetical Time Sequence of Linear Buildup and Street
Sweeping 4-109
4-39 Representative Catchbasin Designs 4-110
4-40 Catchbasin Flushing Characteristics 4-113
4-41 Nationwide Annual Loadings of NH,+N + NO.-N in
Precipitation 4-119
4-42 Nomograph for Calculation of Soil Erodability Factor, K . . 4-123
4-43 Interaction of Buildup Parameters and Storm Interevent
Time . 4-130
4-44 Relationship Between RCOEF and WASHPO for 90 Percent
Washoff During a Storm Event of Runoff Depth d 4-132
4-45 Data Deck for the Runoff Block 4-135
6-1 Structure of Transport Block Subroutines 6-2
6-2 Data Deck for the Transport Block 6-6
xvii
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FIGURES (continued)
Number Page
6-3 The Intersection of the Straight Line and the Normalized
Flow-Area Curve as Determined in Route 6-8
6-4 Sewer Cross Sections 6-12
6-5 Cunnette Section 6-18
6-6 Example Particle Size Distributions for Pollutants found
on Street Surfaces 6-24
6-7 Typical Drainage Basin in which Infiltration is to be
Estimated 6-27
6-8 Components of Infiltration 6-28
6-9 Prescribed Melting Period 6-30
6-10 Determination of Subcatchment and Identification to
Estimate Sewage at 8 Points 6-35
6-11 Representative Daily Flow Variation 6-39
6-12 Representative Hourly Flow Variation 6-39
7-1 Storage/Treatment Block 7-2
7-2 Data Deck for the Storage/Treatment Block 7-5
7-3 Flows Into, Through, and Out of a Storage/Treatment Unit. . 7-11
7-4 Storage/Treatment Plant Configurations 7-12
9-1 Structure of Statistics Block Subroutines 9-2
9-2 Structure of the Data Deck 9-6
II-l Card Image of NWS Card Deck 345, "WBAN Summary of Day". . . II-4
II-2 Sinusoidal Interpolation of Hourly Temperatures I1-6
II-3 Typical Gage Catch Deficiency Correction 11-10
II-4 Structure of Precipitation-Temperature Data Set Used
Internally in Runoff for Continuous Simulation 11-11
II-5 Subcatchment Schematization With and Without Snowmelt
Simulation 11-14
xviii
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FIGURES (continued)
Numbers Page
II-6 Redistribution of Snow During Continuous Simulation .... 11-16
II-7 Seasonal Variation of Melt Coefficients 11-23
II-8 Typical Areal Depletion Curve for Natural Area and
Temporary Curve for New Snow 11-26
II-9 Effect on Snow Cover of Areal Depletion Curves 11-27
11-10 Schematic of Liquid Water Routing Through Snow Pack .... n-30
IV-1 Flows Into, Through and Out of a Storage/Treatment Unit . . IV-3
IV-2 Time-Varying Inflow and Outflow Rates for a Reservoir . . . IV-4
IV-3 Well-Mixed, Variable Volume Reservoir IV-10
IV-4 Reduction in Volatile Solids in Raw Sludge IV-14
IV-5 Camp's Sediment Trap Efficiency Curves IV-19
IV-6 Limiting Cases in Sediment Trap Efficiency IV-19
V-l Horton Infiltration Curve and Typical Hyetograph. ..... V-3
V-2 Cumulative Infiltration, F, is the Integral of f, i.e.,
the Area Under the Curve. V-4
V-3 Regeneration (Recovery) of Infiltration Capacity During
Dry Time Steps V-7
V-4 Subcatchment Schematization for Overland Flow Calculations. V-14
V-5 Non-linear Reservoir Model of Subcatchment V-15
V-6 Depth Parameters for Trapezoidal Gutter and Circular Pipe . V-19
VI-1 Shields' Diagram for Definition of Incipient Motion .... VI-4
VI-2 Linear and Parabolic Approximation of Shields' Diagram. . . VI-6
VI-3 Particle Size Distribution for a Pollutant VI-9
VI-4 Truncation of Particle Size Distribution During Scour and
Deposition VI-10
xix
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FIGURES (continued)
Numbers Page
VII-1 Flow and Quality Data for Seattle, Washington View Ridge
I Catchment (Single Family Residential, Separate Sewers),
Event 1, Event 6, Event 7 VII-2
VII-2 Flow and Quality Data for Seattle, Washington South Seattle
Catchment (Industrial, Separate Sewers), Event 3, Event
4, Event 5 VII-3
VII-3 Flow and Quality Data for Seattle, Washington Southcenter
Catchment (Shopping Center, Separate Sewers), Event 2,
Event 3, Event 7 VII-4
VII-4 BOD Load Vs. Flow VII-5
VII-5 TSS Load Vs. Flow VII-6
VII-6 N02-N + NO--N Load Vs. Flow VII-7
VII-7 Log1() BOD Load Vs. Log1Q Flow VII-8
VII-8 L°8io TSS Load Vs' Log10 Flow VII-9
VII-9 Log1Q N02-N + N03-N Load Vs. Log1Q Flow VII-10
VII-10 Log1Q Load Vs. Log1Q Flow for All Events VII-11
V1I-11 L°8in Load Vs* L°8in Runoff for A11 Events BOD, TSS and
NO^-N + N03-N . . VII-12
VII-12 L°8in Load/Area Vs. Login Runoff for All Events BOD, TSS,
& N02-N + N03-N .... VII-13
VII-13 Flow and Quality Data for Stevens Avenue Catchment (Single
and Multi-Family Residential, Combined Sewers),
Lancaster, Pennsylvannia VII-14
VII-14 BOD Concentration Vs. Flow, Event 1, Event 2, Event 6 ... VII-15
VII-15 TSS Concentration Vs. Flow, Event 1, Event 2, Event 6 ... VII-16
VII-16 NO -N Concentration Vs. Flow, Event 1, Event 2, Event 6 . . VII-17
VII-17 BOD Load Vs. Flow, Event 1, Event 2, Event 6 VII-18
VII-18 TSS Load Vs. Flow, Event 1, Event 2, Event 6 VII-19
VII-19 NO.-N Load Vs. Flow, Event 1, Event 2, Event 6 VII-20
xx
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FIGURES (concluded)
Number Page
VII-20 Log Concentration Vs. Log Flow for All Events, BOD,
TSS, N03-N ........ . ............... VII-21
L°81n Load Vs' Login Flow for A11 Events» BOD» TSS»
"
1n ' in
NO"-N ..... 7 .................... VII-22
xxi
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TABLES
Number Page
1-1 Summary of EPA Storm Water Management Model (SWMM)
Characteristics 1-11
2-1 Interface Limitations for Each Computational Block 2-4
2-2 Lengths of SWMM Blocks 2-7
2-3 Example Execution Times and Costs 2-8
2-4 Possible Fortran Modifications for CDC Machines 2-10
2-5 Sample Job Control Language for Compilation and Execution
of SWMM 2-12
2-6 Scratch Data Sets Required by SWMM 2-16
2-7 Summary of Control Words and Corresponding Action for
Main Program 2-20
2-8 Executive Block Card Data 2-23
3-1 Combine Block Card Data 3-5
4-1 Runoff Data Set Allocations 4-3
4-2 Snowpack Free Water Holding Capacity 4-9
4-3 Flow Routing Characteristics of Runoff, Transport and
Extended Transport Blocks 4-28
4-4 Subcatchment Surface Classification 4-31
4-5 Estimate of Manning's Roughness Coefficients 4-44
4-6 Recent European Depression Storage Data 4-45
4-7 Values of f^ for Hydrologic Soil Groups 4-49
4-8 Rate of Decay of Infiltration for Different Values of a . . 4-52
4-9 Representative Values for f 4-53
xxii
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TABLES (continued)
Number
4-10 Typical Values of IMD (SMDMAX) for Various Soil Types . . . 4-55
4-11 Typical Values of S (SUCT) for Various Soil Types 4-56
4-12 Guidelines for Levels of Service in Snow and Ice Control. . 4-68
4-13 Measured Dust and Dirt (DD) Accumulation in Chicago by
the APWA in 1969 4-73
4-14 Milligrams of Pollutant Per Gram of Dust and Dirt (Parts
Per Thousand By Mass) For Four Chicago Land Uses From
1969 APWA Study 4-74
4-15 Buildup Equations and Units for Dust and Dirt 4-78
4-16 Buildup Equations for Constituents 4-79
4-17 Units for Card Input of Constituent Parameters, Card
Group J3 4-80
4-18 Nationwide Data on Linear Dust and Dirt Buildup Rates
and on Pollutant Fractions 4-84
4-19 Guidelines for Chemical Application Rates for Snow
Control 4-89
4-20 Parameters Used for Washoff Equation Example 4-98
4-21 Removal Efficiencies from Street Cleaner Path for Various
Street Cleaning Programs 4-107
4-22 Constituent Concentrations in San Francisco Catchbasins . . 4-112
4-23 Rainfall and Runoff Concentrations For A Residential Area
Near Fort Lauderdale, Florida 4-117
4-24 Representative Concentrations in Rainfall 4-118
4-25 Cropping Management Factor, C 4-126
4-26 Erosion Control Practice Factor, P, for Construction
Sites 4-127
4-27 Measured Curb Length Density for Various Land Uses 4-128
4-28 Runoff Block Card Data 4-136
xxiii
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TABLES (continued)
Numbers
Page
6-1 Different Element Types Supplied with the Storm Water
Management Model 6-10
6-2 Summary of Area Relationships and Required Conduit
Dimensions 6-15
6-3 Parameters Required For Non-Conduits 6-17
6-4 RINFIL Equations For Three Study Areas 6-32
6-5 Land Use Classification 6-37
6-6 Transport Block Card Data 6-45
7-1 Particle Size Distributions 7-9
7-2 Program Variables Available for Pollutant 7-13
7-3 Storage/Treatment Block Card Data 7-22
9-1 Labels and Units 9-11
9-2 Statistics Block Card Data 9-13
1-1 Hourly Event Ranking by Rain, Flow and BOD for Two Year
Simulation of Lake Calhoun Catchment, Minneapolis .... 1-5
11-1 Time Zones and Standard Meridians II-8
11-2 Subcatchraent Surface Classification 11-13
II-3 Guidelines for Levels of Service in Snow and Ice Control. . 11-17
II--i Guidelines fcr Chsnical Application R=tes 11-32
II-5 Salting Rates Used in Ontario 11-33
II-6 Comparative Computer Runs of Continuous SWMM with
Snowmelt (Runoff Block only) 11-36
IV-1 Geometric and Hydraulic Data for Hypothetical Reservoir . . 1V-6
VI1I-1 Average Monthly Degree-days for Cities in the United
States (Base 65F) .VIII-2
V1II-2 Guide for Establishing Water Usage in Commercial Subareas .VIII-6
VHI-3 Guide for Establishing Water Usage in Industrial Subareas .VIII-8
xx iv
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ACKNOWLEDGEMENTS
Maintenance and updating of the EPA SWMM has been continuous since
its inception in 1969-70. Over the several intervening years, many
individuals have contributed to its improvement, most notably EPA col-
leagues Richard Field, Harry Torno, Chi-Yuan Fan, Doug Ammon and Tom
Barnwell. Harry Torno and Tom Barnwell have also managed the SWMM Users
Group, through which many helpful suggestions for improvements have
come, including those from the large contingent of Canadian users.
Regarding specific components of SWMM Version III, the Green-Ampt
infiltration routines were reviewed, programmed and tested by Dr. Russell
G. Mein, Department of Civil Engineering, Monash University, Clayton,
Victoria, Australia while on a sabbatical at the University of Florida.
He also provided valuable review and testing of other model components.
The earliest implementation of continuous simulation in the Runoff and
Storage/Treatment Blocks was done by George F. Smith, now with the
Office of Hydrology, National Weather Service, Silver Spring, Maryland.
Basic formulation of the snowmelt routines was done following the work
of Proctor and Redfern, Ltd. and James F. MacLaren, Ltd., Toronto, who
were under contract to the Ontario Ministry of the Environment and the
Canadian Environmental Protection Service. Runoff Block surface quality
changes were the subject of masters research at the University of Florida
by Douglas C. Ammon, now with the EPA, Storm and Combined Sewer Branch,
Cincinnati. Revision of Transport Block scour/deposition routines is
based on work with Dennis Lai, Clinton-Bogert Associates, Fort Lee, New
Jersey. Many lasting improvements in SWMM programming were made by W.
Alan Peltz, now with General Electric, Atlanta.
Several others contributed to changes in the model. The card ID
system and the user-defined default values and ratios were suggested by
the Corps of Engineers, Hydrologic Engineering Center, Davis, California.
The programming basis has been aided by Dr. William James, McMaster
University, Hamilton, Ontario and exposure to his FASTSWMM programs.
Emphasis upon proper use and objectives of SWMM modeling has been enhanced
by conversations with the late Murray McPherson, Marblehead, Massachusetts,
Eugene Driscoll, Oakland, New Jersey, Dr. Dominic DiToro, Manhattan
College, New York City, John Mancini, Lincoln, Nebraska, Dr. Paul Wisner,
Ottawa University, Charles Howard, Vancouver, B.C., and several others.
UF is additionally grateful to Reinhard Sprenger, Templeton Engineering,
Winnipeg, for improvements to the Extran Block, to Christian Eicher,
Gore and Storrie, Ltd., Toronto, for several important corrections to
the overall program, to Robert Johnson, Lehigh University, Bethlehem,
Pennsylvania, for comments on the compatability with CDC machines, to
Tom Jewell, Union College, Schenectady, New York, for analysis of surface
xxv
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washoff and other comments, and to Tom Meinholz and Richard Race, formerly
of Envirex, Inc., Milwaukee, for suggestions on making the program more
suitable to prototype configurations.
The Extended Transport Block has been an invaluable addition to the
SWMM package. Developed by Water Resources Engineers (now a part of
Camp, Dresser and McKee), Extran may be the most widely used portion of
SWMM. Dr. Larry Roesner and the late Dr. Robert Shubinski of COM, Annan-
dale, Virginia have given generously of their time in enhancing Extran
and in making other useful suggestions to SWMM modeling.
At the University of Florida, salutary programming and testing has
been conducted by J. Jay Santos, Efi Foufoula, Michael Kennedy, Kelly
Nead and Christina Neff. Typing has been performed by Linda Trawick,
Jeanette Heeb, Kim Karr and the College of Engineering Word Processing
Center. Figures were drafted by Terri Schubert, Micky Hartnett and
Anelia Crawford. Computations were performed at the Northeast Regional
Data Center on the University of Florida campus, Gainesville.
xxvi
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SECTION 1
INTRODUCTION
URBAN RUNOFF ANALYSIS
Urban runoff quantity and quality constitute problems of both a
historical and current nature. Cities have long assumed the responsibil-
ity of control of stormwater flooding and treatment of point sources
(e.g., municipal sewage) of wastewater. Within the past two decades,
the severe pollution potential of urban non-point sources, principally
combined sewer overflows and stormwater discharges, has been recognized,
both through field observation and federal legislation. The advent of
modern, high speed computers has led to the development of new, complex,
sophisticated tools for analysis of both quantity and non-point pollution
problems. The EPA Storm Water Management Model, SWMM, developed in
1969-71, was one of the first of such models, but is by no means the
only one. Since its original development, it has been continually
maintained and updated, and is perhaps the best known and most widely
used of the available urban runoff quantity/quality models.
Many of the changes that have occured to SWMM during the past ten
years have been poorly documented and not readily visible to users.
This volume includes documentation (of both the theory and programming
details) of major changes to the model since its original development.
This documentation is located primarily in the appendices whereas the
text consists primarily of the User's Manual. Theory that underlies
unchanged parts of the model may still be reviewed in the original
documentation (Metcalf and Eddy et al., 1971a, 1971b, 1971c, 1971d) plus
intermediate reports (Huber et al., 1975, Heaney et al., 1975).
URBAN RUNOFF MODELS
Objectives
Models are generally used for studies of quantity and quality-
problems associated with urban runoff in which four broad objectives may
be identified: screening, planning, design and operation. Each objective
typically produces models with somewhat different characteristics, and
the different models overlap to some degree.
Screening Models
Screening models are preliminary, "first-cut" ("Level I"), desktop
procedures that require no computer. They are intended to provide a
1 1-1
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first estimate of the magnitude of urban runoff quantity and quality
problems, prior to an investment of time and resources into more complex
computer based models. Only after the screening model indicates its
necessity should one of the latter models be used. Examples of screening
models include SWMM Level I procedures (Heaney et al., 1976, Heaney and
Nix, 1977) and others: Howard (1976), Hydroscience (1976, 1979), Chan
and Bras (1979).
Planning Models
Planning models are used for an overall assessment of the urban
runoff problem as well as estimates of the effectiveness and costs of
abatement procedures. They may also be used for "first-cut" analyses of
the rainfall-runoff-process and illustrate trade-offs among various
control options. They are typified by relatively large time steps
(hours) and long simulation times (months and years), i.e., continuous
simulation. Data requirements are kept to a minimum and their mathemati-
cal complexity is low.
Various continuous simulation models are reviewed in Appendix I.
SWMM has had this capability since 1976, following the earliest work of
the Stanford Watershed Model (Crawford and Linsley, 1966) and the later
widely used Corps of Engineers STORM model (Roesner et al., 1974, HEC,
1977a).
A planning model may also be run to identify hydrologic events that
may be of special interest for design or other purposes. These storm
events may then be analyzed in detail using a more sophisticated design
model. Planning or longterm models may also be used to generate initial
conditions (i.e., antecedent conditions) for input to design models.
They may occasionally be coupled to continuous receiving water models
as well; for example, SWMM and STORM may be used as input to Medina's
(1979) Level III Receiving Water Model.
Design Models
Design models are oriented toward the detailed simulation of a
single storm event. They provide a complete description of flow and
pollutant routing from the point of rainfall through the entire urban
runoff system and often into the receiving waters as well. Such models
may be used for accurate predictions of flows and concentrations anywhere
in the rainfall/runoff system and can illustrate the detailed and exact
manner in which abatement procedures or design options affect them. As
such, these models are a highly useful tool for determining least-cost
abatement procedures for both quantity and quality problems in urban
areas. Design models are generally used for simulation of a single
storm event and are typified by short time steps (minutes) and short
simulation times (hours). Data requirements may be moderate to very
extensive depending upon the particular model employed.
In its original form (Metcalf and Eddy et al., 1971a, 1971b, 1971c,
1971d), SWMM was strictly a design model. However, as described above,
2 1-2
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it may now be used in both a planning and design mode. In addition, it
has acquired additional design potential through inclusion of the Extended
Transport Model, EXTRAN, developed by Camp, Dresser and McKee (formerly
Water Resources Engineers). EXTRAN is probably the most sophisticated
program available in the public domain for detailed hydraulic analysis
of sewer systems (Shubinski and Roesner, 1973, Roesner et al., 1981).
Operational Models
Operational models are used to produce actual control decisions
during a storm event. Rainfall is entered from telemetered stations and
the model is used to predict system responses a short time into the
future. Various control options may then be employed, e.g., in-system
storage, diversions, regulator settings.
These models are frequently developed from sophisticated design
models and applied to a particular system. Examples are operational
models designed for Minneapolis-St. Paul (Bowers et al., 1968) and
Seattle (Leiser, 1974).
Other Models
SWMM is by no stretch of the imagination the only urban runoff
model available, or necessarily the preferred one under many circum-
stances. Many other urban runoff models have been described in the
literature and are too numerous to list here. However, good comparative
reviews are available, e.g., Brandstetter (1977), Chu and Bowers (1977),
Huber and Heaney (1979, 1980). In spite of the scores of models described
in the literature, Huber and Heaney (1979, 1980) identified only 14
operational (i.e., documented, tested and available for general use)
water quality models. A much larger number of strictly hydrologic and
hydraulic models is available.
DEVELOPMENT OF THE STORM WATER MANAGEMENT MODEL
Under the sponsorship of the Environmental Protection Agency, a
consortium of contractors Metcalf and Eddy, Incorporated, the Univer-
sity of Florida, and Water Resources Engineers, Incorporated developed
in 1969-71 a comprehensive mathematical model capable of representing
urban stormwater runoff and combined sewer overflow phenomena. The SWMM
portrays correctional devices in the form of user-selected options for
storage and/or treatment with associated estimates of cost. Effectiveness
is portrayed by computed treatment efficiencies and modeled changes in
receiving water quality.
The original project report is divided into four volumes. Volume
I, the "Final Report" (Metcalf and Eddy etal., 1971a), contains the
background, justifications, judgements, and assumptions used in the
model development. It further includes descriptions of unsuccessful
modeling techniques that were attempted and recommendations for forms of
user teams to implement systems analysis techniques most effectively.
Although many modifications and improvements have since been added to
i 1-3
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the SWMM, the material in Volume I still accurately describes much of
the theory behind updated versions. Documentation of some of the proce-
dures included in the 1975 Version II release of SWMM is provided by
Heaney et al. (1975).
Volume II, "Verification and Testing," (Metcalf and Eddy et al.,
1971b), describes the methods and results of the application of the
original model to four urban catchments.
Volume III, the "User's Manual" (Metcalf and Eddy et al., 1971c)
contains program descriptions, flow charts, instructions on data prepara-
tion and program usage, and test examples. This was updated in 1975 by
the Version II User's Manual (Huber et al., 1975). This present report
supersedes both of these previous two documents.
Volume IV, "Program Listing" (Metcalf and Eddy et al., 1971d),
lists the entire original program and Job Control Language (JCL) as used
in the demonstration runs. Since many routines in the updated version
are similar or identical to the original, it is still a useful reference,
but on the whole should be disregarded since the present coding is in
many cases, completely different.
All three original contractors have continued to modify and improve
the SWMM, as have numerous other users since its release. Through EPA
research grants, the University of Florida has conducted extensive
research on urban runoff and SWMM development, and has evolved into an
unofficial "clearinghouse" for SWMM improvements. There has clearly
been a large benefit from the fact that SWMM is in the public domain and
non-proprietary since the present version reflects the input and critical
assessments of over ten years of user experience. Of course, lingering
"bugs" are the responsibility of the present report authors alone.
As described earlier, this report is both a SWMM Version III User's
Manual and also documentation of new procedures. As much as possible,
input formats for large card groups (e.g., input of subcatchment informa-
tion) are compatible with earlier versions and will not necessarily have
to be rearranged. However, some changes are quite visible, such as a
two-column identifier on each card (in the manner of Hydrologic Engineer-
ing Center programs). Hence, it must be assumed by the user that all
input must be prepared anew for this SWMM version.
OVERALL SWMM DESCRIPTION
Overview
The comprehensive Storm Water Management Model uses a high speed
digital computer to simulate real storm events on the basis of rainfall
(hyetograph) inputs and system (catchment, conveyance, storage/treatment,
and receiving water) characterization to predict outcomes in the form of
quantity and quality values. The simulation technique that is, the
dynamic representation of the physical systems identifiable x^ithin the
/. 1-4
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model was selected because it permits relatively easy interpretation
and great versatility and flexibility in model representation of prototype
situations.
Since the program objectives are particularly directed toward both
complete time and spatial effects and also to gross effects (such as
total pounds of pollutant discharged in a given storm), it is considered
essential to work with both continuous curves (magnitude versus time),
referred to as hydrographs and "pollutographs," and with daily, monthly,
annual and total simulation summaries (for continuous simulation).
An overview of the Model structure is shown in Figure 1-1. In
simplest terms the program is constructed in the form of "blocks" as
follows:
1) The input sources:
The Runoff Block generates surface runoff based on arbi-
trary rainfall hyetographs, antecedent conditions, land
use, and topography. Dry-weather flow and infiltration
into the sewer system may be optionally generated using
the Transport Block.
2) The central core:
The Transport and Extended Transport Blocks carry and
combine the inputs through the sewer system.
3) The correctional devices:
The Storage/Treatment Block characterizes the effects of
control devices upon flow and quality. Elementary cost
computations are also made.
4) The effect (receiving waters):
The Receiving Block routes hydrographs and pollutographs
through the receiving waters, which may consist of a
stream, river, lake, estuary, or bay.
Quality constituents for simulation may be arbitrarily chosen for
any of the blocks, although the different blocks have different con-
straints on the number and type of constituents that may be modeled.
The Extended Transport Block is the only block that does not simulate
water quality.
AM hi'l Ir.'iled In Klyuru 1-1, the Transport, Extended Transport and
Storage/Treatment Blocks may all use input and provide output to any
1-5
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OFF-LINE
CARDS
OFF-LINE
CARDS
OFF-LINE
CARDS
STORAGE/
TREATMENT
I
ON
Figure 1-1. Overview of SWMM Model Structure, Indicating Linkages Among the Five
Computational Blocks.
-------
Service Blocks
Executive Block
In addition to the five computational blocks mentioned above, two
service blocks are utilized, the Executive and Combine Blocks. The
Executive Block assigns logical units (disk/tape/drum), determines the
block or sequence of blocks to be executed, and, on call, produces
graphs of selected results on the line printer. All access to the
computational blocks and transfers between them must pass through the
MAIN program of the Executive Block. Transfers are accomplished on
offline devices (disk/tape/drum) which may be saved for multiple trials
or permanent record using appropriate job control language (JCL).
Combine Block
This block allows the manipulation of data sets (files stored on
offline devices) in order to aggregate results of previous runs for
input into subsequent blocks. In this manner large, complex drainage
systems may be partitioned for simulation in smaller segments.
Statistics Block
Output from continuous simulation can be enormous if results for
every time step are printed. Even the monthly and annual summaries
contain more information than may easily be assimilated. The Statistics
Block has the capability to review the time step output from a continuous
(or single event) simulation, separate output into discrete storm events,
rank the events according to almost any desired criterion (e.g., peak or
average runoff rate, pollutant load, etc.), assign empirical frequencies
and return periods to runoff and pollutant parameters, tabulate and
graph the results, and calculate statistical moments. Output from this
block can thus be used to identify key events for further study and for
many other screening and analytical purposes.
Total Simulation
In principle, the capability exists to run all blocks together in a
given computer execution, although from a practical and sometimes neces-
sary viewpoint (due to computer core limitations), typical runs usually
involve only one or two computational blocks together with the Executive
Block. This approach may be used to avoid overlay and, moreover, allow
for examination of intermediate results before continuing the computa-
tions. Further, it permits the use of intermediate results as start-up
data in subsequent execution runs, thereby avoiding the waste of repeating
the computations already performed.
This manual expands on these block descriptions by providing for
each block:
1) descriptions of the program operation,
7 1-7
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2) instructions on data preparation with tables for data
card input requirements, and
3) examples of the application of procedures described with
sample I/O information reproduced.
There are two exceptions. The user's manual and documentation for
the Extended Transport Block has been prepared by Camp, Dresser and
McKee (Roesner et al., 1981) as an addendum to this report and is avail-
able as a separate document. Thus, Section 5 of this report merely
introduces EXTRAN.
Similarly, the Receiving Block is currently (May 1981) undergoing
extensive revisions-at the EPA Athens, Georgia, Environmental Research
Laboratory. This revised routine will combine the best features of the
EPA Dynamic Estuary Model and its many derivatives, such as the Receiving
Block. Hence, documentation from the Athens EPA effort will be utilized
as a supplement to this report when available in the future, and Section
8 only outlines RECEIV capabilities.
Detailed SWMM Summary
A concise description of most features of SWMM is given in Table 1-
1, adapted from similar tables prepared by Huber and Heaney (1979,
1980). Except for the Receiving Block, an indication of almost all
modeling techniques is included in the table.
USER REQUIREMENTS
Computer Facilities
A large, high-speed computer is required for operation of the SWMM,
such as an IBM 370, Amdahl 470, UNIVAC 1108 or CDC 6600. The largest of
the blocks requires on the order of 90,000 words of storage. Through
considerable efforts, users have been able to adapt different blocks of
the program to various mini-computers, but only with extensive use of
off-line storage and increase in execution time. Execution time is
discussed in Section 2.
Data Requirements
As will be seen from a review of following sections, the data
requirements for the SWMM may be extensive. Collection of the data from
various municipal and other offices within a city is possible to accom-
plish within a few days. However, reduction of the data for input to
the model is time consuming and may take up to three man-weeks for a
large area (e.g., greater than 2000 acres). On an optimistic note,
however, most of the data reduction is straight forward (e.g., tabulation
of slopes, lengths, diameters, etc., of the sewer system). The SWMM is
flexible enough to allow different modeling approaches to the same area,
and a specific, individual modeling decision upstream in the catchment
will have little effect on the predicted results at the outfall.
8 1-8
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Verification and Calibration
The SWMM is designed as a "deterministic" model, in that if all
input parameters are accurate, the physics of the processes are simulated
sufficiently well to produce accurate results with minimal calibration.
This concept fails in practice because the input data and the numerical
methods are not accurate enough for many real applications. Furthermore,
many computational procedures within the model are based upon limited
data themselves, especially surface quality predictions.
As a result it is essential that local verification/calibration
data be available at specific application sites to lend credibility to
the predictions of any urban runoff model. These data are usually in
the form of measured flows and concentrations at outfalls or combined
sewer overflow locations. Note that quality measurements without accom-
panying flows are of little value. The SWMM has sufficient parameters
that may be "adjusted," particularly in the Runoff Block, such that
calibrating the model against measured data is usually readily accom-
plished.
METRIFICATION
Use of metric units for input and output of data and results is now
allowed in the Runoff, Transport and Storage/Treatment Blocks as an
alternative to U.S. customary units. (Metric I/O to the Extran and
Receive Blocks may be added in the future.) For the most part, the
metric units are used strictly for I/O; all internal quantity calculations
are still performed in ft-sec units. (These units will apply to program
generated error messages printed during the simulation.) Most quality
calculations use conventional concentration units (e.g., mg/1) and loads
may be given in both pounds and kilograms, depending on the particular
subroutine, although pounds-will not be used if metric I/O is specified.
Thus, metric units (e.g., m /sec or, in some places, cms for brevity)
are obtained merely by a conversion factor for printing.
No attempt has been made to conform to SI standards or even customary
metric units for some parameters. For instance, because of output
format complications, metric pipe diameters are requested and printed in
meters instead of the more usual millimeters. However, all units are
clearly stated for both card input and printed output. It should be a
simple task to convert to other metric alternatives.
WHEN SHOULD SWMM BE USED?
SWMM is a large, relatively sophisticated hydrologic, hydraulic and
water quality simulation program. It is not appropriate for all applica-
tions or for all manner of personnel. For instance, hydrologic routing
(e.g., prediction of runoff from rainfall) may be performed simply using
standard techniques (e.g., unit hydrographs, linear reservoirs) described
in hydrology texts and suitable for programmable hand-held calculators
(e.g., Croley, 1977) or micro-computers (e.g., Golding, 1981). In
addition, many other, smaller Fortran programs are available for urban
9 1-9
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hydrologic simulation that may be entirely suitable for a given problem
and much easier to implement on a given computer system. Notable among
the hundreds of such programs are the Corps of Engineers, Hydrologic
Engineering Center program STORM (Roesner et al., 1974, HEC, 1977a) for
continuous simulation and the Illinois State Water Survey program ILLUDAS
(Terstreip and Stall, 1974) for single event simulation and pipe sizing.
Both have good documentation and user support and have been extensively
tested and utilized by engineers other than the model developers.
SWMM has similar attributes but is certainly more formidable in
terms of its size and capabilities. Who, then, should use SWMM and for
what purposes? Some guidelines are given below for when SWMM probably
is appropriate:
1. The engineer is knowledgeable of the modeling techniques
(e.g., non-linear reservoirs, kinematic waves, St. Venant equations,
buildup-washoff equations). An appreciation for how physical processes
may be simulated in a Fortran program is a necessity. As a corollary,
the engineer is assumed to be familiar with the problem to be solved and
with customary techniques for handling it. A clear problem definition
is a prerequisite to any solution methodology.
2. By virtue of its size (e.g., sewer system with hundreds of
pipes) or complexity (e.g., hydraulic controls, backwater) a simpler
technique or model will not work. It may be borne in mind, however,
that SWMM may also be used as a very simple "black box" model with
minimal input data, at the expense of computer overhead to manage the
program size and off-line files.
3. Quality is to be simulated. Although there are other models
that also simulate quality, SWMM is perhaps the most flexible of any.
4. A large body of literature on theory and case studies is
desired. Since SWMM was originally introduced in 1971, a wealth of such
information is available, including citation in hydrology texts (e.g.,
Viessman et al., 1977, Wanielista, 1978).
The primary technical disadvantage of SWMM is its lack of routines
for soil moisture accounting and sub-surface flow routing, e.g., as in
HSPF (Johanson et al., 1980). It is thus seldom suitable for largely
pervious, non-urban areas.
While any number of other examples could be presented for when SWMM
should not be used, attention is drawn to just one: the user is already
familiar with an adequate alternative technique or model. It is far
more important for the engineer/user to understand the methodology being
utilized than it is for a model such as SWMM to be employed on the
premise of a more sophisticated technique. In the final analysis, the
engineer/analyst is responsible for the decisions made using any technique
of analysis; the technique or model is only a tool that must be clearly
understood by those using it.
10 i-
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Table 1-1. Summary of EPA Storm Water Management Model (SWMM) Character-
istics
Applicable Land Drainage Area
(1) Urban; (2) General nonurban; (3) Unsuitable for lands requiring soil
moisture accounting and generation of base flow from ground water.
Time Properties
(1) Single event or continuous simulation; both modes have an unlimited
number of time steps, although former usually £ 200, depending on portions
of model utilized; (2) Precipitation: input at arbitrary time intervals
for single event simulation (typically 1-15 min) and usually at one-hour
intervals for continuous simulation; for snowmelt daily inax-min tempera-
tures required for continuous, temperatures at arbitrary intervals for
single event; (3) Output at time step intervals (or multiples); daily,
monthly, annual, and total summaries for continuous simulation; (A) Time
step arbitrary for single event (typically 5 minutes) and usually one
hour for continuous, Extran transport model time step depends on stability
criteria, may be as small as a few seconds.
Space Properties
(1) Small to large multiple catchments: (a) surface: lumped simulation of
overland flow with allowance for up to 200 subcatchments and six input
hyetographs, up to 200 gutter/pipes may be simulated by non-linear reservoir
routing, (b) channel/pipes: one-dimensional network, up to 159 conduit/
nonconduit elements for original transport model, up to 187 conduits in
Extrau transport model, up to two in-line storage units in original trans-
port model, (c) catchment area may be disaggregated and modeled sequentially
for simulation of areas too large for existing SWMM dimensions; (2) Storage/
treatment simulated separately, receiving input from upstream routing; (3)
Output from surface, channel/pipe, or storage/treatment simulation may
serve as new input for further simulation by the latter two modules.
Physical Processes
(1) Flow derived from precipitation and/or snowmelt; snow accumulation
and melt simulated using temperature-index methods developed by National
Weather Service, snow redistribution (e.g., plowing, removal) may be
simulated; (2) Overland flow by nonlinear reservoir using Manning's
equation and continuity, depression storage, integrated Horton or Green-
Anipt infiltration (lost from system), recovery of depression storage via
evaporation between storms during continuous simulation, also expon-
ential recovery of infiltration capacity; (3) Channel/pipes: (a) nonlinear
11 1-11
-------
reservoir formulation for gutter/pipes in surface runoff module, includes
translation and attenuation effects, (b) kinematic wave formulation in
original transport model assumes cascade of conduits, cannot simulate
backwater over more than one conduit length, surcharging handled by
storing water at surcharged junction pending available flow capacity;
(c) Extran transport model solves complete St. Venant equations including
effects of backwater, flow reversal, surcharging, looped connections,
pressure flow, (d) infiltration and dry weather flow may enter conduit
of either transport simulation; (A) Storage routing using modified Puls
method assuming horizontal water surface, outlets include pumps, weirs,
orifices; (5) Surface quality on basis of linear or non-linear buildup
of dust/dirt or other constituents during dry-weather and associated
pollutant fractions, power-exponential washoff with decay parameter a
power function of runoff rate; optional concentration prediction as
power function of flow rate only (rating curve); erosion by Universal
Soil Loss Equation; (6) Dry-weather flow quantity and quality on basis
of diurnal and daily variation, population density and other demographic
parameters, buildup of suspended solids in conduits by dry weather
deposition using Shield's criterion; (7) Quality routing by advection
and mixing in conduits and by plug flow or complete mixing in storage
units, scour and deposition of suspended solids in conduits (original
transport model) using Shield's criterion; (8) Storage/treatment device
simulated as series-parallel network of units, each with optional storage
routing; (9) Treatment simulation: (a) use of arbitrary user-supplied
removal equations (e.g., removal as exponential function of residence
time); (b) use of sedimentation theory coupled with particle size-
specific gravity distribution for constituents.
Chemical Processes
(1) Ten arbitrary conservative constituents in Runoff module, rainfall
quality included, choice of concentraton units is arbitrary; erosion of
"sediment" is optional; (2) Four constituents may be routed through the
original transport module (with optional first order decay), three
through the storage/treatment module and none through Extran (quantity
only).
Biological Processes
(1) Coliform washoff may be included; (2) Biological treatment may be
simulated.
Economic Analysis
Amortized capital plus operation and maintenance costs for control units
are determined.
12
-------
Mathematical Properties
(1) Deterministic model; (2) Surface quantity: iterative solution of
coupled continuity and Manning equations, Green-Ampt or integrated form
of Horton infiltration (infiltration rate proportional to cumulative
infiltration, not time); (3) Surface gutter/pipe routing: non-linear
reservoir assuming water surface parallel to invert; (4) Channel/pipes:
(a) original transport: implicit finite difference solution to modified
kinematic wave equation, (b) Extran transport: explicit finite difference
solution of complete St. Venant equations, stability may require short
time step; (5) Storage/sedimentation: modified Puls method requires
table look-up for calculation of outflow ; (6) Surface quality, quality
routing and treatment: algebraic equations, no iterations required once
flows and conduit volumes are known.
Computational Status
(1) Coded in Fortran IV, approximately 25,000 statements long; (2) Has
been run on IBM 370 series, UNIVAC 1108, CDC 6600, Amdahl 470, VAX 11/780,
Prime 550 and others, may be run in modular form (surface runoff, original
or Extran transport,storage/treatment, receiving water, plus executive
and service routines, e.g., plotting, file combining), largest module
requires about 90,000 words or 360K bytes of storage; (3) Available on a
magnetic tape; (4) Requires up to five off-line storage files.
Input Data Requirements
(1) Historical or synthetic precipitation record, uses National Weather
Service precipitaton tapes for continuous simulation, monthly evaporation
rates; for snowmelt: daily max-min (continuous) or time-step (single
event) temperatures, monthly wind speeds, melt coefficients and base
melt temperatures, snow distribution fractions and areal depletion
curves (continuous only), other melt paremeters; (2) Surface quantity:
area, imperviousness, slope, width, depression storage and Manning's
roughness for pervious and impervious areas; Horton or Green-Ampt infil-
tration parameters;(3) Channel/pipe quantity: linkages, shape, slope,
length, Manning's roughness, (Extran transport also requires invert and
ground elevation, storage volumes at manholes and other structures),
geometric and hydraulic parameters for weirs, pumps, orifices, storages,
etc., infiltration rate; (4) Storage/sedimentation quantity geometry,
hydraulic characteristics of outflows (5) Surface quality: (Note: several
parameters are optional, depending upon methods used) land use, total
curb length, catchbasin volume and initial pollutant concentrations,
street sweeping interval, efficiency and availability factor, dry days
prior to initial precipitation, dust/dirt and/or pollutant fraction
parameters for each land use or pollutant rating curve coefficients,
initial pollutant surface loadings, exponential and power washoff coeffi-
cients, erosion parameters for Universal Soil Loss Equation, if simulated;
(6) Dry weather flow on basis of diurnal and daily quantity/quality
13 1-13
-------
variations; population density, other demographic parameters; (7) Optional
particle size distribution, Shields parameter and decay coefficients for
channel/ pipe quality routing; (8) Storage/treatment: parameters defining
pollutant removal equations, parameters for individual treatment options,
e.g., particle size distribution, maximum flow rates, size of unit,
outflow characteristics, optional dry-weather flow data when using
continuous simulation; (9) Storage/treatment costs: parameters for
capital and operation and maintenance costs as function of flows, volumes
and operating time; (10) Data requirements for individual modules much
less than for run of whole model; large reduction in data requirements
possible by aggregating (lumping) of subcatchments and channel/pipes,
especially useful for continuous simulation; (11) Metric units optional
for all I/O.
Ease of Application
(1) Nonproprietary model available from EPA, Athens, GA or University of
Florida, Department of Environmental Engineering Sciences, Gainesville;
(2) Updated user's manual and thorough documentation of most routines
published as EPA reports, no one report covers all model aspects; (3)
Test cases documented in several EPA and other reports; (4) Short course
proceedings also useful for model applications; (5) U.S. and Canadian
users groups with newsletters and semi-annual meetings permit publication
of changes; (6) Due to its age (originally published in 1971), availabil-
ity, documentation, examples of SWMM usage are widely available in the
literature; (7) Frequent model update/corrections/improvements are
often difficult to learn about, new model released approximately on a
bi-annual basis; (8) Size of model most frequent deterrent to use,
however, see item 10 above under Input Data Requirements; (9) Initial
model setup often moderately difficult due to size.
Output and Output Format
(1) Input data summary including precipitation; (2) Hydrographs and
pollutographs (concentrations and loads versus time) at any point in
system on time step or longer basis, no stages or velocities printed;
(3) Extran transport also outputs elevation of hydraulic grade line; (4)
Surcharge volumes and required flow capacity, original transport model
will resize conduits to pass required flow (optional) ; (5) Removal in
storage/ treatment units, generated sludge quantities; (6) Summaries of
volumes and pollutant loads for simulation period, continuity check,
initial and final pounds of solids in conduit elements; (7) Daily (option
al), monthly, annual and total summaries for continuous simulation, plus
ranking of 50 highest hourly precipitation runoff and pollutant values;
(8) Line printer plots of hyetographs, hydrographs, and pollutographs;
(9) Costs of simulated storage /treatment options; (10) Statistical
analysis of continuous (or single event) output for frequency analysis,
moments and identification of critical events.
-------
Linkages to Other Models
(1) SWMM contains its own receiving water model, RECEIV; (2) Individual
modules and the total SWMM model have been linked to the HEC STORM
model, the QUAL-II model, simplified receiving water models, and others;
(3) Individual modules (e.g., surface runoff, receiving water) have been
altered by various groups.
Personnel Requirements
(1) Environmental engineer familiar with urban hydrological processes
for data reduction and model analysis; (2) Systems programmer for model
setup and off-line storage file usage.
Costs
(1) Model available upon receipt of cost of magnetic tape plus a nominal
charge (about $125 from University of Florida or no other charge from
EPA); (2) Data assembly and preparation may require multiple man-weeks
for a large catchment or urban area; (3) Example computer execution
costs given in user's manual, on the order of $20 for a surface runoff
and transport run for a single storm event with about 50 subcatchments
and channel/pipes, use of Extran transport can be more costly due to
short time step, continuous simulation of one subcatchment with snowtne.lt
for two years costs about $20; (4) Extensive calibration may be required
to duplicate measured quality results, quantity calibration relatively
simple; (5) National Weather Service precipitation tapes for continuous
simulation cost about $200 for a 25-year hourly record.
Model Accuracy
(1) Quantity simulation may be made quite accurate with relatively
little calibration; (2) Quality simulation requires more extensive
calibration using measured pollutant concentrations, quality results
will almost certainly be very inaccurate without local measurements; (3)
Extran transport accurately simulates backwater, flow reversal, surcharg-
ing, pressure flow; original transport routines may be used at less cost
if these conditions not present; (4) Sensitivity to input parameters
depends upon schematization, however, surface quality predictions are
most sensitive to pollutant loading rates.
15
-------
SECTION 2
EXECUTIVE BLOCK, GRAPH ROUTINES AND
SYSTEM REQUIREMENTS
BLOCK DESCRIPTION
Functions
The Executive Block performs three functions:
1) assignment of logical un:i.ts and files,
2) control of the sequencing of computational blocks, and
3) graphing of data files by the line printer.
No computations as such are performed. The relationship of the Executive
Block to other blocks is shown in Figure 2-1.
Program Operation
The Executive Block assigns logical units and files, and controls the
computational block(s) to be executed. These functions depend on reading in
a few data cards which must be supplied according to the needs of a given
computer run.
Since the various blocks use logical devices for input and output of
computations, the Executive Block has provision for assigning logical unit
numbers by reading two data cards. Logical units and data sets are dis-
cussed later. The first card may contain up to 20 integer numbers, corres-
ponding to 10 input and 10 output unit:;. It is not necessary, however, to
make such a larj;e number of assignments for the usual run; in fact, there
have been few occasions during the development and testing of the model when
more than four units have been needed. The files that are produced on these
units are saved (during the run) for use by a subsequent computational block;
also, the information contained in then can be examined directly by using
the graphing capability of the Executive Block. The other unit assignments
on the second data card are for scratch files, i.e., files that are gener-
ated and used during execution of the program, and are erased at the end of
the run. Again, there is provision for up to five such units, but only one
or two are typically needed. The unit numbers are passed from the Executive
Block to all pertinent blocks in the labeled COMMON/TAPES/.
16 2-1
-------
- DATA CARD
INPUT. TYPICAL
To
foATA ()
\l
PROGRAMS
EXECUTIVE BLOCK
MAY REQUIRE)
OUTPUT FILE
FROM
ANOTHER
8LOCK
MAY REQUIRE
OUTPUT FILE
FROM ANOTHER
BLOCK
COMPUTATIONAL
BLOCKS
Figure 2-1. Relationship of Executive Block to Other Blocks
N>
-------
The graphing subroutines enable hydrographs and pollutographs to be
plotted on the line printer for selected locations for output data files
(e.g., predicted results) as well as measured data that are input by the
user. Predicted and measured results may be plotted on one graph for com-
parison. Operation of the graph routines is described later. The subrou-
tines include GRAPH, called from the main executive program only, in which
control information, titles, and measured data, if any, are read.
Subroutine HYSTAT is called from GRAPH for computation of hydrograph
volumes, durations, peak flows, and times of peaks. This is done for both
predicted and measured hydrographs.
Subroutine CURVE is called from GRAPH to coordinate the actual plotting.
It is also called directly from the Runoff, Extran and Receive blocks, in
which case control information, titles, etc. are supplied from those blocks,
bypassing subroutine GRAPH. Subroutine CURVE performs the following opera-
tions :
1) Determines maximum and minimum values of arrays to be
plotted, calculates the range of values, and selects
appropriate scale intervals using subroutine SCALE.
2) Computes vertical axis labels based upon the cal-
culated scales.
3) Computes horizontal axis labels based upon the cal-
culated scales.
4) Joins individual parts of the curve using subroutine
PINE.
5) Outputs final plot usinjj subroutine PPLOT.
Subroutine SCALE calculates range;; and scaling factors. Subroutine
PINE joins two coordinate locations witti appropriate characters in the out-
put image array A of PPLOT. Subroutine PPLOT initializes the plotting array,
stores individual locations, and outputs the final image array A for the
printer plot.
INTERFACING BETV/EEN COMPUTATIONAL BLOCKS
Data may be transferred or interfaced from one block to another through
the use of the tape/disk assignments on card group *1. The interface tape/
disk (file) header consists of descriptive titles, the user-supplied pollu-
tant and unit names, the simulation starting date and time, the name of the
block generating; the interface file, the number of time steps and time step
size, the total catchment or service area, and the number of locations (in-
lets, outfalls, elements, etc.) and pollutants found on the interface file.
Also included are the location identifiers for which flow and pollutant data
are transferred, a conversion factor for converting the flow dimensions found
18 2-3
-------
on the interface file to the internal SWMM dimension of cfs and the type of
pollutant concentration units. Following the file header are the flow and
pollutant data for each time step (up to the total number of time steps) for
each of the specified locations. The detailed organization of the interfac-
ing file is shown in Figure 2-2, and example Fortran statements that will
write such a file are shown in Figure 2-3. These figures may be used as
guidelines for users who may wish to write or read an interfacing file with
a program of their own. Further information on required pollutant
identifiers, etc. may be found in the Runoff Block card input data descrip-
tion.
The title and the values for the starting date and time from the first
computational block are not altered by any subsequent block encountered by
the Executive Block. All other data may (depending on the block) be altered
by subsequent blocks. The individual computational blocks also have limita-
tions on what data they will accept from an upstream block and pass to a
downstream block. These limitations are summarized in Table 2-1. Detailed
discussions are presented in the computational block sections.
Table 2-1
Block
Interface Limitations for Each Computational Block.
a
Input
Output
Runoff
Transport
Extended
Transport
Storage/ t
Treatment
Receive
80 elements (inlets),
4 pollutants
187 junctions (inlets),
no pollutants (ignored
if on the file)
10 elements (inlets
or non-conduits),
3 pollutants
50 elements (inlets non-
conduits or outfall),
6 pollutants
200 elements (inlets), .
10 pollutants
80 elements (non-
conduits), 4 pollutants
187 junctions
10 elements,
3 pollutants
The interface file may contain up to 10 pollutants from which a
lesser number may be selected by following blocks. The number of
pollutants found on the output file from any block is the lesser
of the number in the input file or that specified on cards within
the block.
Although the Storage/Treatment Block will read and write data for as
many as 10 elements, the data for only one element have passed through
the storage/treatment plant; the rest are unchanged from the input
file. 19 2-4
-------
VARIABLE NAME
DESCRIPTION
ho
O
FROM FIRST (TITLEZ(I).I = 1,38)
COMPUTATIONAL IDATEZ
BLOCK TZERO
FROM (TITLE1(I),I = 1,38)
INTERFACING SOURCE(I),I = 1,5)
COMPUTATIONAL NSTEPS
BLOCK DTSEC
NLOCAT
NPOLL
TRIBA
(LOCNOS(K).K = l.NLOCAT)
((PNAME(L,J),L = 1,2),J = 1,NPOLL
((PUNIT(L,J),L = 1,2),J = 1,NPOLL
(NDIM(J).J = 1,NPOLL)
QCONV
FLOW ANU POLLUTANT TIME
DATA FOR EACH
LOCATION, REPEAT
FOR EACH TIME I DATE
STEP
TIMDAY
Title from first computational block; formal is 38A4.
Starting date; six-digit number, year-month-day, e.g.,
July 20, 1979 is 790720.
Starting time of day, hours and fraction, e.g., 5:30P.M. is 17.5.
Title from interfacing computational block; format is 38A4.
Name of interfacing computational block; format is 5A4.
Number of time steps of flow/pollutant data on interface file.
Time step size, seconds.
Number of locations (inlets, manholes, outfalls, etc.) on inter-
face file.
Number of pollutants on interface file. .
Tributary or service area, acres.
Location numbers for which flow/pollutant data are found on
interface file.
NPOLL pollutant names; format for each is 2A4.
NPOLL pollutants units, e.g., mg/1, HPN/1, JTU, (jmho, etc., format
for each is 2A4.
Parameter to indicate type of pollutant concentration units.
=0, mg/1.
=1, "other quantity" per liter, e.g., for bacteria, units could
be MPN/1.
=2, other concentration units, e.g., JTU, (.imlio, °C, pll.
Conversion factor to obtain units of flow of cfs, (multiply values
on interface file by QCONV to get cfs).
Running time, beginning at TZERO, hours and fraction. First entry
on file is at TZERO + DTSEC/3600. TIME is continuous and is not reset
at end of a day.
Date for this time step, six-digit number, year-month-day, e.g.,
November 24, 1979 is 791124.
Time of day for this time step, hours and fraction, e.g., I2:45A.M.
is 0.75.
(Q(K).(POLL(J.K),J=l,NPOLL),K=1,
NtOCAT;
Flow and pollutant loads for NLOCAT locations at this time step.
Q(K) must be the instantaneous flow .it tliis time (i.e., .->t end of
tine step) in units of length/time. POLL(J.K) is the flow rate
tines the concentration (instantaneous value at cnii of time step)
for J'th pollutant at K'th location, e.g., units of cfc mg/1 or
m/sec -JTUC.
^Unformatted unless specified. Use an integer or real value as indicated by the variable name.
cEljpsed time is thus TIME-TZERO.
If units other than cfs are used for flow, this will be accounted for by multiplication by parameter QCONV.
Figure 2-2. Detailed Organization of SWMM Interfacing File.
-------
FILE WRITE(HOUT) (TITLEZ(I),1=1,38)
HEADER WRITE(NOUT) IDATEZ.TZERO
WRITE(NOUT) (TTTLEl(I),1=1,38)
WRITE(NOUT) (SOURCE(I),I=5),NSTEPS,DTSEC,NLOCAT,NPOLL,TRIBA
WRITE(NOUT) (LOCNOS(K),K=1,NLOCAT)
WRITE(NOUT) ((PNAME(L,J),L=1,2),J=1,NPOLL)
WRITE(NOUT) ((PUNIT(L,J),L=1,2),J=1,NPOLL)
WKITE(NOUT) (ND1M(J),J=1,NPOLL)
WUITE(NOUT) QCONV
NOUT is the interface file or logical unit number (see the subsection
"INITIAL JOB SET-UP").
FLOW AND POLLUTANT IF(NPOLL.GT.O) WRITE(NOUT)TIME, IDATE.TIMDAY, (Q(K), (I'OLL(J ,K) , J = l,
DATA FOR EACH NPOLL) ,K = 1 .NLOCAT)
LOCATION: REPEAT
FOR EACH TIME
STEP IF(NPOLL.LT.l) WRITE(NOUT)TIME,IDATE.TIMDAY,(O(K),K=1,NLOCAT)
Note: The interface file should be set up (using JCL) for variable block sizes (VBS).
The time step read/write statements must include IF statements to test for the
appearance of pollutants.
Figure 2-3. Fortran Statements Required to Generate an Interface File,
t-o
I
-------
INITIAL JOB SET-UP
Computer System Requirements
The Storm Water Management Model can be run on a machine having core
storage capacity of approximately 400 K bytes (or equivalent) and using
overlay. Current core requirements of each block are shown in Table 2-2.
In addition, the program uses peripheral storage devices which may consist
of disk, tape, or drum units, depending on the machine configuration.
The program has undergone detailed testing on the University of Florida
Amdahl 470 V/6-II running under OS/MVS Release 3.7 JES2 NJE Release 3 (as of
September 1979). Thus, SWMM should be compatible with any IBM or Amdahl
machine. Experience has shown that the program may be run fairly easily on
UNIVAC machines but can require minor programming changes for use on CDC
equipment (see discussion of Fortran, below). In any event, users are
regretfully warned that there are undoubtedly many "bugs" remaining in the
program, subtle and otherwise, for which they must be on the alert.
Table 2-2. Lengths of SWMM Blocks (in bytes)3
(Lengths are for overlay structure shown in Table 2-5.)
Length with
Block Length Alone Executive/Graph
Executive/Graph 142,100
Runoff 226,900 369,000
Transport 95,300 237,400
Extran 135,500 277,600
Storage/Treatment 98,000 240,100
Combine 22,000 164,100
Statistics 173,700 315,800
On the UF IBM system, approximately 20 K bytes are added to the storage
requirement for each off-line file accessed. E.g., if the Runoff Block
accesses three different off-line files, it requires about 60,000
additional bytes of storage.
Includes all required IBM library and overhead procedures.
Program Compilation, Execution Time and Cost
A sample of the compilation and execution times with run costs for
separate program blocks is shown on Table 2-3. This table incorporates the
22 2-7
-------
Table 2-3. Example Execution Times and Costs (See also Table II-6.)
KJ
OJ
Block/
Schematization
Runoff
7 Subcat. (ICRAIN = 1)
1 Subcat. (ICRAIN = 2)
Runoff + Transport
29 Subcat. , 34 Cutter/
pipes, 50 Elements
Storage /Treatment
1 Ueten. unit 1)
(plug flow)
3)
No. Time
Steps
3624
17,520
100
8760
50
100
Simulation No. Quality
Duration Constituents
5 mo.
24 mo.
100 min.
12 mo.
50 hr.
100 hr.
6
6
8 (Runoff)
4 (Transp.)
2
1
1
CPU"
Seconds
14
14
10 . 9
1H . 4
1.13
1.34
Cost,
$
4.50
4.50
4.09
5.97
1.17
1.67
'Compilation time not included.
b ,
Costs are total costs at
approximately
half the
commercial rate, Incl
uding Cl'U
time, disk
T/0, printing, etc.
"Runs 1) and 3) used removal equations. Run 2) used a particle size distribution.
-------
savings which were made by storing compiled blocks of the program in a per-
manent job library (load modules). At most computer installations, there is
a daily or monthly charge for storing load modules. If the SWIM is going to
be used more than a few times, it would be advisable to use load modules.
From the Central Processing Unit (CPU) and execution times in this
table, it may be possible to extrapolate a time and cost estimate for other
machines. A systems analyst can obtain these figures.
Fortran
All coding is in Fortran IV. Attempts have been made to follow Ameri-
can National Standards Institute Fortran (1978) but some instructions may be
machine dependent. Wetzel and Johnson (1976) describe programming modifica-
tions that were necessary in order to run SWMM on a CDC 6400. Although some
of their modifications have been incorporated into the present version,
others might still be necessary. Examples are shown in Table 2-4.
At the University of Florida, the program is routinely used with the
Fortran H extended compiler. Soir.c but not all of the program has also been
run under Fortrzm G.
Logical Units
Logical units or file numbers are simply the numbers assigned to vari-
ous input/output devices for use in the program. At most installations,
logical units for the card reader, line printer and card punch are as given
below:
I/O Device Logical Unit
Card Reader 5
Line Printer 6
Card Punch 7
SWMM is programmed under the assumption that the card reader and line printer
are so defined (.no cards are punched by SWMM). However, in an attempt to
allow versatility, all READ and WRITE statements use parameters N5 and N6
for the logical unit numbers of the card reader and line printer respectively.
These are define:d only once,
N5 = 5
N6 = 6
near the beginning of the Executive Block MAIN program and passed to all
other blocks through the labeled COMMON/TAPES/. Thus, if other logical
units are required for the card reader and line printer, the only program-
ming changes required should be at the above location.
Other I/O units include tapes, disks and drums. Such devices are typi-
cally assigned logical units between 1 and 20 (but not 5, 6 or 7) by appro-
priate JCL.
24 2-9
-------
Table 2-4. Possible Fortran Modifications for CDC Machines
N)
Ul
IBM Machines
STOP 1000
ARSIN(Y)
READ(MTAPE,END=300)
300 CONTINUE
FORMAT (' ***)
CDC Machines
STOP '1000'
ASIN(Y)
READ(MTAPE)
IF (EOF(MTAPEJ) 300,200
200 CONTINUE
300 CONTINUE
FORMAT (4H ***)
Comment
Without the delimeters,
the statement number may
be interpreted in octal
on CDC.
Different names of library
functions for arc-sine.
End of file check is
different.
The asterisk is sometimes
a delimeter on CDC.
I
I1
o
-------
Job Contvol Language (JCL)
As a rule, JCL is highly machine dependent; in fact, it often differs
on two identical machines at different installations. Therefore, the SWMM
cannot include JCL that is universally applicable. The following remarks,
however, may be useful in gaining insijjht into what is involved on systems
puch as an IBM 370/165 or Amdahl 470.
Usually, the interfacing file and scratch file assignments require JCL
to be supplied tor each unit. The rules for such JCL must be ascertained
from the systems programmers at the installation, since there is consider-
able variation in unit number availability, etc. In general, one should
only set up the units needed in a given run, since there may be a charge for
file space that is reserved, even if it: is not used.
Most users will prefer to store a compiled version of SWMM on a disk
rather than to compile and execute from cards or tape. Such an example is
shown in Table 2-5. This example is for an IBM operating system such as is
used on the University of Florida's Amdahl 470.
The following is a description of Table 2-5.
Line 0 is the job card unique to t.he University of
Florida Computing Center.
Lines 3-4 are for execution and overlay of the SWMM
sourer roding mid lo;id modules (compiled pot lions
stored on disks).
Lines 5-10 contain dummy subroutines to be compiled,
instead of retrieving them from disk storage.
Lines 12-14 describe the load module data set. The
SWMM program is stored on a data set named
"UF.A0063473.NEW.SWMM".
Lines 16-86 describe the overlay structure used at
the University of Florida (see below). The compiled
version of each subroutine is stored in the data
set with a name the same as the subroutine name.
"INCLUDE" cards list subroutines «md "INSERT" cards
list labeled common blocks.
Lines 87-100 describe scratch disl: files to estab-
lish unit numbers to be used for Interface files
and scratch file assignments in the Executive Block.
Other JCL could be used to save a file after run-
ning the program; these will be "lost" when the run
is over. An example of a disk unit number is
//GO.FXXF001 DD... where XX stands for the symbolic
unit number.
26 2-11
-------
Table 2-5. Sample Job Control Language for Compilation and Execution of
SVJMM.
JCL is for IBM Operating System OS/MVS Release 3.8. Also shown is the
overlay structure for subroutines and the location of sample input data.
Files RBDATA, TBDATA, BLOCK, STRDAT and LABELS are Block Data subroutines,
0000 //SWMMIII JOB (1006,31*73,15,9,0),'SWMM JCL',CLASS-A,REGION-UOOK
0001 /'PASSWORD
0002 /'ROUTE PRINT LOCAL
0003 // EXEC FORTXCLE,LPARM"'LIST,MAP,OVLY'
OOOU //FORT.SYSIN 00 *
0005 SUBROUTINE RECEIV
0006 RE-TURN
0007 END
0008 SUBROUTINE COMB IN
0009 RETURN
0010 END
0011 /
0012 //LKED.SYSLMOD DO SPACE = (CYL,(5,1,1))
0013 //LKED.SYSUT1 DO SPACE-(CYL,(5,2))
0011* //LKED.LIB DO DSN-UF.A0063U73.NEW.SWMM,DISP-SHR
0015 //LKED.SYSIN DD *
0016 INCLUDE LIB(MAIN,GRAPH,CURVE,PPLOT,HYSTAT)
0017 INCLUDE LI8(PINE,SCALE )
0018 OVERLAY ALPHA
0019 INCLUDE LIB(RUNOFF,RBDATA)
0020 INSERT Tl MER,SUBCAT,QUALTY,CO.SHED,DETA I L,GUTCOM
0021 OVERLAY BETA
0022 INCLUDE LIB(HYDRO,WSHED,GUTTER,GQUAL,GAMP)
0023 INCLUDE LIB(MELT,FINDSC,AREAL)
002U INCLUDE LIB(QSHEO,BUILD)
0025 OVERLAY GAMMA
0026 INCLUDE LIB(RHYDRO)
0027 OVERLAY DELTA
0028 INCLUDE LLB(CTRAIN)
0029 OVERLAY DELTA
0030 INCLUDE LIB(QHYDRO,ERROR)
0031 OVERLAY GAMMA
0032 INCLUDE LIB(HCURVE)
0033 OVERLAY BETA
00314 INCLUDE LIB(PRINTR)
0035 OVERLAY ALPHA
0036 INCLUDE LIB(TRANS,TBDATA,FIRST,FINDA,RADH,VEL)
0037 INCLUDE LIB(NEWTON,CIRCLE,DEPTH,PSI,DPSI)
0038 INCLUDE LIB(ROUTE,QUAL,PCT)
0039 INCLUDE LIB(TSTORG,TINTRP)
OOUO INSERT TST
OOlil INSERT DRWF, TABLES, NAMES, PSIDPS,NEW81
OOU2 OVERLAY BETA
001*3 INCLUDE LIB(SLOP)
OOU4 OVERLAY BETA
UOU5 INCLUDE LIB(TSTRDT)
OOU6 OVERLAY BETA
00U7 INCLUDE LIB(INFIL)
OOU8 OVERLAY BETA
OOU9 INCLUDE LIB(FILTH)
0050 OVERLAY BETA
0051 INCLUDE LIB(DWLOAD)
0052 OVERLAY BETA
0053 INCLUDE LIB(INITAL)
005U OVERLAY BETA
0055 INCLUDE LIB(PRIUTF,PRINTUJ
(continued)
27
2-12
-------
Table 2-5 (concluded).
Sample Job Control Language for Compilation and
Execution of SWIM.
0056
0057
0058
0059
0060
0061
UOG2
0063
006U
006S
0066
0067
0068
0069
0070
0071
0072
0073
007<»
0075
0076
0077
0078
0079
0030
0081
0082
0083
008'*
0085
0086
0037
0088
0039
0091
0092
0093
009U
009S
U096
0097
0093
0099
0100
0101
0102
0103
010U
0105
0106
0107
0108
0109
0110
0111
0112
0113
0114
0115
INSERT NEW80
OVERLAY ALPHA
INCLUDE L I 3(EXTRAN, 3LOCK,TRANSX )
INSERT FILES, OD
INSERT BND,HYFLOW,CONTR, PIPE, TRAP, STORE, OUT, EX STAT,SURCHG
INSERT ELEV,JUNC,URF,WEIR,PUI-.P
OVERLAY SETA
INCLUDE LIB(INDATA)
OVERLAY GAMMA
INCLUDE LIB(TIDCF)
OVERLAY 3ETA
INCLUDE LIB(INFLOW)
OVERLAY SETA
INCLUDE L I B(DEPTHX,HYDRAD )
OVERLAY GAMMA
INCLUDE LIBUOUND)
OVERLAY GAMMA
INCLUDE LIB(HEAO)
OVERLAY SETA
INCLUDE LIB(OUTPUT)
OVERLAY ALPHA
INCLUDE LI3(STRT)
INSERT 51, S2
OVERLAY BETA
INCLUDE LIB(STRDAT)
OVERLAY BETA
INCLUDE LIB(STCOST)
OVERLAY BETA
INCLUDE LI B(CONTRL,UNI T, PLUuS , EQUATE , INTERP)
OVERLAY ALPHA
INCLUDE LI B( STATS, SORT, MOMENT , SBTABL , PO I NTS , LABELS )
//GO.FT01F001 DD UNI T-SYSDA, SPACE=< TRK, ( 100. 10 ) ) ,
//GO.FT02F001 DD UN I T = i>YSDA , SPACE = ( TRK, ( 100, 10 ) ) ,
//GO.FT03F001 DD UN I T-SYSDA, SPACE=( TRK, ( 100, 10 ) ) ,
// VOL-SER"WORK01,OCB«(RECFM«VBS,BLKSIZE«U2UO,3UFNO-1)
//GO.FTOUF001 DD UN I T'SYSDA, SPACE = ( TRK, { 100 , 10 ) ) ,
//GO.FT08F001 00 UN I T = SYSDA , SPACE = ( TKK, ( 100, 10 ) ) ,
// VOL=»SER=WORK01,DCB = (RECFM=»V6S,aLKSIZE=i42UO,BUFNO=»l)
//GO.FT09F001 DO UNI T = 3YSOA , SPACE = ( TRK, ( 100, 10 )),
// VOL = SER=WORK01,OCB = (RECFM»VBS,BLKSI ZE't»2i»0,aUFNO = l)
//GO.FT10F001 00 UN I T=SYSDA, SPACE = ( TRK, ( 100 , 10 ) ) ,
// VOL=SER=WORK01,DCB=(RECFM-VBS,ULKSIZE=i»2UO,BUFNO=l)
//GO.SYSIN DD *
0 9 9 10 10 9 9 10
1 2 3 U 8
RUNOFF
........ RUNOFF DATA
TRANSPORT
........ TRANSPORT DATA
FXTRAN
....... .EXTRAN DATA
STORAGE
........ STORAGE TREATMENT DATA
STATS
........ STATS BLOCK DATA
ENDPROGRAM
/'EOF
28
2-13
-------
Lines 102-": 14 contain data for the run (described
below).
Note that the Extran Block uses the same graph subroutines as does the
rest of SWUM. It is unnecessary to us<; a separate set of graph routines
just for that block.
Overlay Procedures
The SWMM model was constructed as an overlay program because it can
then be executed in an area of main storage that is not large enough to
contain the entire model at one time. The linkage editor subdivides the
model so that it can be loaded and executed segment by segment. The total
main storage required is approximately 400 K bytes using the overlay
structure illustrated in Table 2-5.
First, there is the root segment which is always in main storage and
includes all the; subroutines of the Executive Block.
Second, there is a group of subroutines on the same level, (ALPHA
level), which is: called a segment. Only one ALPHA segment can be in main
storage at a time with the root segment: (Executive Block).
Next, there is another group of subroutines on the next lower level,
(BETA level). Only one BETA segment can occupy main storage simultaneously
with the ALPHA segment. Thence follow GAMMA and DELTA segments which behave
similarly.
With the JCL and overlay setup as shown, all the SWMM blocks can be run
either independently or sequentially in a continuous string. The Combine
and Receive Blocks are the only blocks that cannot be run for this example.
They could be run simply by removing the dummy subroutine cards at the
beginning of the JCL and then by adding an OVERLAY ALPHA and INCLUDE
LIB(COMBIN) card, etc. A systems programmer would be helpful in setting up
the overlay on other machines. For instance, Wetzel and Johnson (1976)
describe a segmented load procedure for CDC machines.
Dummy Subroutines
Use of dummy subroutines is not confined to the Combine and Receive
Blocks. Rather, they may be used to avoid compiling or having in permanent
storage any of t.he subroutines called by the SWMM Executive Block. These
sub-programs art: RUNOFF, TRANS (Transport), RECEIV (Receive), STRT
(Storage/Treatment), COMBIN (Combine), EXTRAN (Extended Transport), STATS
(Statistics) and GRAPH (graphing routine). The latter routine is always
required and is part of the Executive Mock. All others are needed only for
specific applications and may be "dummied" in the manner of the Combine
Block (as in Table 2-5), if not required.
29 2-14
-------
3cratch Data SeLs
A "scratch data set" is simply of}:-line storage (e.g., disk or drum)
that is used during program execution. However, the contents are erased
(lost) at the er.d of the simulation. These are established by assignment of
logical units in. lines 87-100 in Table 2-5 using IBM JCL, for example.
Alternatively, it may be wished to save output from a block for future runs
of subsequent blocks. In this case, appropriate JCL must be provided tor
this purpose.
Ordinarily, scratch data sets will be used for parameters JIN, JOUT and
NSCRAT of the Executive Block. With only two exceptions, parameters JIN and
JOUT are used only to transfer data between blocks, as explained elsewhere
within this section. One exception is that a JIN data set may be needed for
rainfall input from a tape to the RunoJlf Block when continuous SWMM is being
used. The second exception is that a JOUT data set must be used to store
processed event data in the Statistics Block. (This data set cannot be
transferred to other SWMM blocks.) The NSCRAT files are used for miscella-
neous tasks within each block, most typically to store output for later
printing. Currrnt requiremcnts are shown in Table 2-6.
The following presents a detailed explanation of the scratch data sets
used for the parameters for the example of Table 2-5. These are used to
make a typical run of the SWMM. The unit numbers assigned to the various
data sets are arbitrary. Any desired values compatible with the descrip-
tions of lines £7-100, Table 2-5, could be used. Furthermore, the following
definitions assf.me Runoff, Transport, Storage/Treatment and Statistics are
to be run in order. However, various sequences may be used, and the param-
eters would correspond to the sequence defined in lines 104-114 of Table 2-5.
Line 101 tells t.he computer that input data follow. Line 102 is tape/disk
assignments and corresponds to card group *1 of the Executive Block Card Data
Section. Line 102 may be interpreted as follows:
JIN(l), JOUT(l), JIN(2), JOUT(2), JIN(3), JOUT(3), JIN(4), JOUT(4)
0 9 9 10 10 9 9 10
Here, JIN(N) = I refers to an input device or file and JOUT(N) = I refers to
an output device or file. For example, a typical read statement in a FORTRAN
program may be READ(I,80). The I is replaced by the symbolic unit number of
an input device (e.g., card reader). As discussed previously, on most com-
puter systems, I is equal to 5 for reading cards and 6 or 7 for writing or
punching output. The same applies to JIN(N) = I or JOUT(N) = I where I is
substituted with, the symbolic unit number of an input or output device such
as a tape or disk unit, as defined by lines 87-100. Since the numbers 5, 6,
and 7 have standard meanings, their descriptions are omitted.
JIN(l) = unit number of tape/disk input into the first
block to be run (Runoff Block). JIN(l) = 0
means there is no tape/disk input.
30 2-15
-------
Table 2-6. Scratch Dat;i Sets Required by SWIM
1. Runoff Block
NSCRAT(l) - Always required.
NSCRAT(2) - Required for continuous SWIM.
NGCI\AT(3) - Required for continuous SWKH wiLii suowmeit.
NSCRAT(4) - Required for single event SWIM if it is
desired to avoid limitation of 200 rainfall
hyetograph entries.
2. Transport Block
NSCRAT(l) - Always required.
NSCRAT(2) - Always required.
3. Storage/Treatment Block
None required.
r%
4. Receiving Water Block
NSCRAT(l) - Always required (quantity and quality),
NSCMT(2) - Required for quality simulation.
NSCRAT(3) j Required for quality simulation if
NSCRAT(4) ) restart option is used.
5. Extended Transport Block (EXTRAN)
NSCRAT(l) - Always required.
NSCRAT(2) - Always required.
6. Combine Block
None required.
7. Statistics Block
None required.
Also, a value for JOUT must be specified for Receiving Block quantity.
However, a value for JOUT must bt; specified for the processing of
event data in the Statistics Block.
31 2-16
-------
JOUT(l) = i-nit number of tape/disk output from the first
block to be run (Runoff Block). JOUT(l) = 9
means there is such output to be saved and lines
97-98 describe the disk utilized.
JIN(2) = unit number of tape/disk input to the second
Mock to be run (Transport Block). (This is
ripKnia 1 1 IT 1-ho C2!T!S 3S tJli! OUt^lit. "Ul^bcr frC.TJ
the preceding block.) JIN(2) = 9 means there
is such input (from the Runoff Block) and lines
97-98 describe the disk utilized.
JOUT(2) = unit number of tape/disk output from the second
Mock to be run (Transport Block). JOUT(2) = 10
means there is such output to be saved and
lines 99-100 describe the disk utilized.
JIN(3) = unit number of the tape/'disk input to the third
Mock to be run (Storage/Treatment Block).
(This is normally the same as the output unit
number from the preceding block.) JIN(3) =
10 means there is such i.nput (from the Trans-
port Block) and lines 99-100 describe the disk
utilized.
JOUT(3) = unit number of the tape/'disk output from the
third block to be run (Storage/Treatment Block).
JOUT(3) = 9 means there is such output to be
saved. (Note that RunoJif output will be written
over.)
JIN(4) = unit number of the tape/disk input to the fourth
block to be run (Statistics Block). (This is
normally the same as th«: output unit number
from the preceding bloc):.)
JOUT(4) = unit number of tape/disk output from the fourth
block to be run (Statistics Block), JOUT(4) = 10.
(Note that Transport output will be written over.)
JIN(5) = JIN(IO) and JOUT(5) - JOUT(IO) allow more than
just four blocks to be run sequentially and
are defined similarly iJ: required.
Line 103 if scratch tape/disk assignments and corresponds to card group
*2 of the Executive Block Card Data. Line 103 may be interpreted as follows:
NSCRAT(l), NSCRAT(2), NSCRAT(3), IISCRAT(4), NSCRAT(5),
12348
Here, NSCRAT(N) = I refers to an input/output device or file. I is substi-
tuted with the symbolic unit number of an input/output device such as a tape
32
2-17
-------
or disk unit defined in lines 87-100. There should be a scratch tape/disk
assignment for ^fSCRAT(l) through NSCRAT(5). Most blocks do not use all
NSCRAT(I) tape/disk assignments (see Table 2-6); however, there is no stor-
age or CPU time charged for the ones not used at most installations.
INSTRUCTIONS FOR DATA PREPARATION
Block Selection
The instructions for data preparation are divided into two parts cor-
responding to control of the SWUM block selection and graphing capability.
Figure 2-4 and Table 2-8 at the end of these instructions give the procedure
for data card preparation.
The program controls the computational block(s) to be executed by read-
ing alphanumeric information, CNAME, on sentinel cards. Thus, for example,
CNAME might be RUNOFF. The program compares this word with a dictionary of
such words (first eight characters). If a match is found, as it would be in
tl'-is case, control is passed to the appropriate block. Here, for example, ^
call would be made to the Runoff Block. After execution of the Runoff Block,
control is returned to the Executive Block.
The program again reads a sentinel data card, which might indicate that
another block is to be executed. For example, if the Transport Block is to
be executed, the control word TRANSPORT would be given, etc. If results are
to be graphed, t.he control word GRAPH would be on the sentinel card, or, if
the run is to be terminated, the word KNDPROGRAM is given on the card. A
summary of the control words and corresponding action is given in Table 2-7.
The use of control words on sentinel cards allows considerable flexi-
bility in utilisation of the Storm Wator Management Model. The most common
type of run involves execution of one of the computational blocks along with
the graphing of results on the line printer. Thus, for the Runoff Block,
such a run would be made by appropriate; use of the words RUNOFF, GRAPH, and
ENDPROGRAM. If the entire model were 1:0 be run with graphical output at the
end of, say for example, the Transport and Storage/Treatment Blocks, the
sequence would be RUNOFF, TRANSPORT, GRAPH, STORAGE, GRAPH, and ENDPROGRAM.
Graph Routine
Capabilities --
When called from the Executive Block, the graph routines will plot pre-
dicted and/or measured hydrographs and pollutographs for specified locations.
Such hydrographs and pollutographs will be called simply "graphs" in the
following discussion. Predicted graph:; can be generated by the Runoff,
Transport, Extended Transport, and Storage/Treatment blocks. Thus, their
output files may be input to the graph routines. Measured graphs (or data
otherwise input on cards by the user) may also be plotted whether or not
predicted graphs are produced. Thus, i:he graph routines may be treated as
stand-alone programs and used independently of the other SWMM blocks. When
33 2-18
-------
/CNAME = ENDPROGRAM
RECEIVING DATA CARDS
/CNAME= RECEIVING
L
GRAPH DATA CARDS
L
CNAME GRAPH
STORAGE/TREATMENT DATA CARDS
CNAME = STORAGE
EXTENDED TRANSPORT DATA CARDS
CNAME =EXTRAN
GRAPH DATA CARDS
L
CNAME = GRAPH
L
TRANSPORT BLOCK DATA CARDS
L
CNAME =TRANSPORT
CRUNOFF BLOCK DATA CARDS
CNAME = RUNOFF
SCRATCH TAPE ASSIGNMENTS
(^INPUT/OUTPUT TAPE ASSIGNMENTS
Figure 2-4. Hypothetical Card Arrangement for a Comprehensive SWMM
Run.
34 2-19
-------
Table 2-7. Summary of Control Words and Corresponding Action
for MAIN Program
Control word Action to be taken
RUNOFF Execute Runoff Block
TRANSPORT Execute Transport Block
EXTRAN Execute Extended Transport
(Extran) Block
STORAGE Execute Storage/Treatment Block
RECEIVING Execute Receiving Water Block
COMBINE Execute Combine Block
STATS Execute Statistics Block
GRAPH Produce graphs on line printer
ENDPROGRAM Terminate run
Any other word Terminate run
.Program compares first eight characters only.
Up to 20 more cards will be examined i:or a possible match prior to
terminating the run.
35 2-20
-------
predicted and measured graphs are available for the same location, they will
be overprinted en one plot for comparison purposes. This greatly facilitates
calibration work.
A few simple statistics are also computed for each hydrograph and are
printed below each plot: volume, peak, time of peak, and duration. When
both the measured and predicted hydrographs are plotted on the same plot,
the above statistics and differences (absolute and percent) are also given
for the overlapping time period.
The final plot is produced by printing of array "A", dimensioned 51
(vertical) by 101 (horizontal). Both l.he vertical and horizontal scales are
determined on the basis of the range ol: the input data. The left-most plot-
ting location (at the left vertical axis) corresponds to the graph value at
the start of the. simulation (TZERO) . When more than 100 points are to be
plotted, the program selects points su<:h that the final output will consist
of up to 100 points. For instance, if 200 equally spaced (in time) data
points were input, only every other ono would be plotted. The actual choice
of points plotted depends on the number read in, their range, and whether
points are '"uuiicheii" in some portion 03" the plot.
The particular graph routines used in SWUM "fill in" between separated
points, horizontally or vertically, to form a continuous line. Thus, it is
sometimes difficult to determine exactly the points that were input for
plotting, except, that they are usually the end points of line segments.
Input Parameters', and Options --
Graphs (hydrographs or pollutographs) may l>e p Lotted for only one Loca-
tion at a time. Overprinting of graph;; from several locations on just one
plot is not allowed.
The routine will plot graphs of measured data, predicted data or both
for a location. If measured and predicted graphs are both supplied for the
same location, they will be overprinted on one plot for comparison.
Plots for up to ten locations for predicted and ten locations for mea-
sured may be requested during one call to GRAPH. The locations need not be
the same. For example, the routine may be used to plot only predicted or
only measured graphs. If there is a n«:ed to plot more than ten locations,
GRAPH may simply be called again.
The routine: will always plot hydrographs (when supplied) but will plot
pollutographs only if NQP > 0 (card Al). Any three from a group of up to
ten pollutants may be plotted (card Bl), assuming the pollutant is available.
Any group of pollutants (as many as ten) may be input to GRAPH. The param-
eter IPOL selects the IPOLth pollutant on the interfacing tape.
The time scales for input of measured graphs need not be the same as
for predicted graphs, nor does the tim<: spacing of measured graphs have to
be constant. The plot will run from the minimum to the maximum times of the
predicted or measured graphs. Several options are available for input of
the t.imes associated with graph ordinal'.es (card Dl).
36 2-21
-------
Input of Use horizontal axis labe). is not required, and it always reads
"Time of Day, in Hours". The times ar«: actually elapsed time, beginning at
the start of the simulation (TZERO). Conversion to hour of day does not
include a reset at midnight. Thus, if a simulation period begins at, say,
10 p.m. and lasts four hours, the graph abscissa will run from 22 to 26
hours.
Vertical axis labels are either "Flow in els" or else the name and
units of the pollutant being plotted. All pollutographs are plotted in
concentration units. The same units are required for both predicted and
measured pollutograph inputs. Hydrographs are plotted in units of cfs, (or
m /sec if METRIC = 1) and measured data must be entered in these same units.
"Loadographs" (e.g., Ib/min vs. time) cannot be plotted.
The input format of all measured data may be supplied by the user (card
E2). This should considerably facilitate the use of data already prepared
under an arbitrj-.ry format. In addition, the number of data values per card
may be varied (LCARD on card El).
Measured dc.ta may be read from cards (MEAS=1) or may be previously
stored as card Images on off-line file number MFILE and read from that file
(MEAS=2 on card Al). Retrieval from fi.le MFILE may avoid reading voluminous
card data more than once. Of course, appropriate job control language must
be supplied by t.he user to ensure the permanent storage of file MFILE.
Examples --
Sample input data are shown in conjunction with Runoff, Transport and
Storage/Treatment runs.
37 2-22
-------
Table 2-8. Executive Block Card Data
Card Card
Group Format Columns
Description
Variable Default
Name Value
*1 2014 1-4
5-8
9-12
I/O tape/disk assignments.
Input tape assignment for first block
to be run.
Output tape assignment for first block
to be run.
Input tape assignment for second block
JIN(l)
JOUT(l)
JIN(2)
0
0
0
13-16
to be run (usually the same as the output
tape from first block).
Output tape for second block to be run.
JOUT(2)
77-80
Output tape for tenth block to be run.
JOUT(IO)
*2 514 1-4
5-8
9-12
13-16
17-20
Scratch tape-disk assignments.
First scratch tape assignment.
Second scratch tape assignment.
Third scratch tape assignment.
Fourth scratch tape assignment.
Fifth scratch tape assignment.
NSCRAT(l)
NSCRAT(2)
NSCRAT(3)
NSCRAT(4)
NSCRAT(S)
0
0
0
0
0
REPEAT CARD *3 FOR EACH BLOCK TO BE CALLED,
AFTER EXECUTION OF PRECEDING BLOCK.
Control cards indicating which blocks in
the program are to be called.
38
2-23
-------
Table 2-8 (continued). Executive Block Card Data
Card Card
Group Format Columns
Description
Variable
Name
Default
Value
*3 3A4 1-12 Name of block to be called. Names
must start in column 1. All blocks
may be called more than once if over-
lay is not used or, if overlay is used,
one or more blocks may be repeated if
overlay is set up for this. See sub-
section "Initial Job Set-Up".
CNAME = RUNOFF for Runoff Block,
= TRANSPORT for Transport Block,
= EXTRAN for Extended Transport
(Extran) Block,
= RECEIVING for Receiving Water
Block,
= STORAGE for Storage/Treatment Block,
= COMBINE for Combine Block,
= STATS for Statistics Block,
= GRAPH for GRAPH subroutine,
= ENDPROGRAM for ending the SWMM
simulation.
CNAME
None
Card *3 is the last Executive Block card
unless the graph routines are called.
From card *3, control is passed to the
appropriate block. When execution of
that block is complete, control re-
turns to card *3.
Read card groups A1-E3 only if GRAPH
has been called on card *3.
General graph information.
Al 2X 1-2 Card identifier = Al.
13 3-5 File (logical unit) where predicted
graph information is stored. Will
usually - JOUT value of desired block.
If zero, only measured data will he
plotted.
lU.utk
STAFF.
39
2-24
-------
Table 2-8 (continued). Executive Block Card Data
Card
Group
Card
Format Columns
Description
Variable
Name
Default
Value
615 6-10 Number of locations (e.g., inlets) NPLOT
for which predicted hydrographs (and
pollutographs) are to be plotted.
Maximum = 10.
11-15 Input and plot measured data. MEAS
= 0, No measured data (on cards)
to be plotted.
= 1, Read (and plot) data from cards.
= 2, Read (and plot) data stored as
card images on file MFILE.
The following two parameters are not required if MEAS = 0.
16-20 File (logical unit) where measured MFILE
data are stored. Not required if
MEAS 5 1. (If zero, defaults to
card reader.)
21-25 Number of locations (e.g., inlets, man- MPLOT
holes) for which measured data are to
be input and plotted (MEAS = 1,2).
Maximum = 10.
N5
26-30 Number of pollutants graphed. NQP
(Maximum = 5).
31-35 Metric units used for input-output. METRIC
= 0, U.S. customary units.
= 1, Metric units used, indicated in
brackets [ ] in remainder of Cable.
Pollutant selection card.
IF NQP = 0 (CARD Al), SKIP TO CARD Cl.
OTHERWISE, REPEAT CARD Bl NQP TIMES.
Bl 2X 1-2 Card group identifier = Bl. -- Blank
13 3-5 Pollutant identifier from sequence IPOL 0
on interfacing file. E.g., if IPOL(l)
= 3, first pollutant graphed will be
third on interfacing file. User
must know sequence, as determined from
input to preceding block (e.g., card
group J3 of Runoff Block). If IPOL
= 0, pollutant is not found on inter-
facing file and is input only from
cards.
2-25
-------
Table 2-8 (continued). Executive Block Card Data
Card Card
Group Format Columns
Description
Variable
Name
Default
Value
*** If IPOL j* 0, omit all the following ***
parameters since they will be obtained
from the interfacing file. For discus-
sion of these parameters, see card group
J3 of the Runoff Block and its discus-
sion.
2A4 6-13 Pollutant name
2A4 14-21 Pollutant units.
14 22-25 Type of units.
= 0, mg/1.
= 1, "other" per liter, e.g., MPN/1.
= 2, other concentration units, e.g.,
JTU, pH.
PNAME(l)
PNAME(2)
PUNIT(l)
PUNITC2)
NDIM
Blank
Blank
Cl
2X
13
915
1-2
3-5
6-10
IF NPLOT = 0, SKIP TO CARD C2.
Locations (e.g., inlets, manholes) for
plotting of predicted output. Supply
NPLOT values, (maximum of 10).
Card identifier = Cl.
First location to be plotted.
Second location to be plotted.
IPLOT(l)
IPLOT(2)
Blank
None
None
Last location to be plotted.
I PLOT
(NPLOT)
None
IF MPLOT = 0, SKIP TO CARD Dl.
Locations (e.g., inlets, manholes) for
input and/or plotting of measured data.
Supply MPLOT values, (maximum of 10).
41
2-26
-------
Table 2-8 (continued). Executive Block Card Data
Card
Group
C2
Format
2X
13
Card
Columns
1-2
3-5
Description
Card identifier = C2.
First location to be input and
Variable
Name
--
KPLOT(l)
Default
Value
Blank
None
plotted.
915 6-10 Second location to be input and
plotted.
KPLOT(2)
None
Last location to be input and
plotted.
KPLOT
(MPLOT)
None
Title Card.
Dl
2X
16A4
1-2
3-66
Card identifier = Dl.
Title to be printed at bottom of
each plot.
Blank
TITL Blank
IF MPLOT = 0, SKIP REMAINING CARD
GROUPS. OTHERWISE READ MPLOT GROUPS
OF CARD GROUP(S) El (AND POSSIBLY E2
AND E3) A TOTAL OF NQP + 1 TIMES.
First, for measured hydrograph, MPLOT
groups of cards El, E2 and E3:
El 2X 1-2 Card identifier -El.
13 3-5 Measured data for this graph and
location corresponding to sequence
on card C2.
= 0, No measured data to be entered
for this location.
= 1, Input measured data according to
remaining parameters and format of
card El.
MDATA
Blank
0
42
2-27
-------
Table 2-8 (continued). Executive Block Card Data
Card Card
Group Format Columns
Description
Variable
Name
Default
Value
315 6-10
11-15
16-20
3F10.0 21-30
31-40
41-50
The following parameters are not required
if MDATA = 0.
Number of graph ordinates per card
(MTIME > 0) or pairs of time-graph
ordinates per card (MTIME = 0).
Maximum = 16.
Option for time of graph ordinates.
= 0, Enter a time with each ordin-
ate. Cease input of time-ordinate
pairs when entered time is > TQUIT.
> 0, The time for each ordinate will be
computed starting at TMZERO and using
time increment OTMHR. Read a total of
MTIME ordinates. Maximum =201
ordinates for either case.
Units of time if MTIME= 0. Not
required if MTIME > 0.
= 0, Time is in minutes.
= 1, Time is in hours.minutes
(i.e., decimal point between
hours and minutes).
= 2, Time is in decimal hours (and
may have values > 24).
Initial time (decimal hours) of
measured data if MTIME > 0. Value
of TMZERO is added to times entered
if MTIME = 0. May be used to pro-
vide a time offset for measured
data, avoiding revision of their
times.
A time greater than TQUIT ends entry
of time-ordinate pairs if MTIME = 0.
Not required if MTIME > 0.
Time increment (hours) if times of
graph ordinates are calculated
(MTIME > 0). Not required if
MTIME = 0.
LCARD
MTIME
None
MUNIT
TMZERO
0.0
TQUIT
DTMHR
0.0
0.0
43
2-23
-------
Table 2-8 (concluded). Executive Block Card Data
Card
Group
Card
Format Columns
Description
Variable
Name
Default
Value
Card groups E2 and E3 are not re-
quired if MDATA - 0 for this graph
and location.
E2 2X 1-2 Card identifier = £2.
19A4 3-78 Format by which measured data of
card group E3 will be read. In-
clude beginning and final paren-
theses. If card is left blank
the default format will be used.
FIRMAT
Blank
(2X, F8.0,
7F10.0)
*** Note: If MEAS <1 (card Al) this card will be read ***
from the card reader (unit N5). Otherwise, the
formatted read will be from unit number HFILE
(card Al).
E3
Card identifier is optional.
Time (if MTIME = 0) and graph ordinate,
LCARD pairs (if MTIME = 0) per card,
according to format of card E2.
Entries are stopped when a time is
> TQUIT (this time is not included
as a data entry). If MTIME > 0,
only YVAL will be read, a total of
MTIME values, LCARD values per card
according to format of card E2.
Units of hydrograph ordinate must
be cfs [tn /sec if METRIC = 1), and
pollutograph ordinates must be
concentrations corresponding to
NDIM of card group Bl.
TIMX 0.0
(optional)
and
YVAL 0.0
Repeat card groups El, E2 and E3 for remaining MPLOT-1 locations for measured
hydrograph inputs. Then, input MPLOT groups of cards El, E2 and E3 for first
pollutograph, second pollutograph, etc., up to NQP pollutographs. Note,
data for the MPLOT locations must appear in the order in which the locations
were entered on card C2. There will be a total of MPLOT-(NQP-t-l) entries of
card group(s) El (and possibly E2 and E3).
At the end of graph input, control is returned to card *3 of the Executive
Block.
44
2-29
-------
SECTION 3
COMBINE BLOCK
BLOCK DESCRIPTION
In order to add the capability of modeling larger areas, the Combine
Block has been ;idded to the Storm Water Management Model. This block has
two main objectives.
The first objective is to collate different data sets into one, e.g.,
two separate output data sets, one Transport and one Storage/Treatment, are
to be input into the Receiving Water Block. The Combine Block would be used
to collate the two output data sets into one which, in turn, would be input
into the Receiving Water Block.
The second objective is to combine different data sets and nodes into a
single data set and one node, e.g., using the Transport Block on two differ-
ent drainage networks gives two separate output data sets. Both data sets
go to the same treatment facility at the same inlet node. This program
would be used to combine the two different Transport output data sets into
one data set with a single node which uhen could be input into the
Storage/Treatment Block.
The Combine Block can be used in a number of different ways and gives
the Storm Water Management Model the capability of simulating the largest
and most diverse cities. For example, Figure 3-1 shows how the Combine
Block was used on a combination of SWMI1 runs for Lancaster, Pennsylvania.
INSTRUCTIONS FOR DATA PREPARATION
Collate
The first objective is to collate two different output data sets from
Runoff, Transport, Storage/Treatment, Kxtran, or any combination thereof.
This new data set could then be used a:; input into any block (Transport,
Extran, Storage/Treatment or Receiving Water), except Runoff. For example
(Figure 3-2), an output file from Transport area 'A' with manhole numbers 5,
6, 12 was collated with an output file from Transport area 'B1 with manhole
numbers 1, 3, 6. 19. Manhole number 6 is common between both output data
sets, therefore the hydrographs and po.Llutographs from both manholes are
added together. The new output file produced from the Combine Block has
manhole numbers 1, 3, 5, 6, 12, 19. This new data set could then be used as
Input to any other block, including Transport itself.
45 3-1
-------
SOUTH
A
RUNOFF a
SOUTH
B
RUNOFF a
STEVENS
AVE.
RUNOFF ft
TRANSPORT
RUNOFF a
TRANSPORT
STEVENS
AVE.
TREATMENT
COMBINE
SOUTH
COMBINE
SOUTH a
STEVENS
SILO
SOUTH
TREATMENT
PLANT
NORTH
TREATMENT
PLANT
COLLATE
SOUTH, NORTH
a STEVENS
OVERFLOW
RECEIVING WATER
Figure 3-1. Combination of SWMM Runs for Overall Lancaster
Simulation.
3-2
-------
AREA 'A*
AHEA V
Figure 3-2. Hypothetical Drainage Network to Be Collated.
Figure 3-3. Hypothetical Drainage Network to Be Combined.
47
3-3
-------
Combine
The Combine section combines two different files and output locations
into a single file with one output location. For example (Figure 3-3), an
output file from Transport area 'X' with manhole number 16 and an output
data set from Transport area 'Y' with Manhole number 23 are to be used as
input into the Receiving Water Block junction number 14. The Combine por-
tion of the Combine Block would be used to combine the two output data sets
into one data se;t with one location, assigned the new number, 14. This
number would correspond to the junction number of the Receiving Water Block.
The Combine Block card data are shown in Table 3-1.
Quality Options
The two different input files may have different quality constituents,
especially if a Runoff file is combined/collated with a Transport file, etc.
The user is responsible for knowing the: contents of each input file and may
specify on card Cl the constituents to be used from each. For instance, if
BOD,, is the first constituent to be placed on the output file, and if it is
the third on file 1 and seventh ou filf: 2, then NP032(1) - 3 and NPOS2(i) -
7. The description (name, units and type of units) will be copied from the
first input file. Constituents not accessed will not be placed on the
output file.
If a constituent is contained on one file but not the other, it may
still be used. However, the file for which the constituent position (NPOS1
or NPOS2) is zero will be assumed to have zero concentration for that con-
stituent.
If NPOLL = 0 on card Al no quality constituents will be placed on the
new output file regardless of whether they are on the input files.
Timing
Both input files must utilize the same time step. An error message
will be printed if they differ. If the starting time (TZERO) is different
for the two input files, the output fi.le will begin at the earlier TZERO
using zeroes for the other file until its series begins. Similarly, if one
input file ends before the other, zeroes will be used until the end of the
other file.
Files
The two off-line files (unit numbers) used as input are specified by
parameters NDATAS(l) and NDATAS(2), and the output file by parameter NDOUT.
Note that Executive Block parameters JIN and JOUT are not used at all by the
Combine Block. Nor does the Combine Block advance the "counter" for param-
eters JIN and JOUT as all other blocks do. For instance, if the following
sequence were performed: 1. Runoff-A, 2. Runoff-B, 3. Combine (Runoff-A
and Runoff-B), 4. Transport, then JIN(3) and JOUT(3) would refer to the
Transport Block run and not to the Combine Block run.
48 3-4
-------
Table 3-1. Combine Block Card Data
Card
Group
Al
Bl
B2
B3
Format
2X
13
15
2X
2X
19A4
2X
13
15
2X
13
15
Card
Columns
1-2
3-5
6-10
1-2
3-4
5-80
1-2
3-5
6-10
1-2
3-5
6-10
Description
Card identifier = Al.
Program control.
= 1, Collate only.
= 2, Combine only.
Number of quality constituents to be
placed on new file.
Title cards: two cards with heading
to be printed on output.
Card identifier = Bl.
Skip.
Title, 2 cards, to be placed as
first title on output file.
Card identifier.
Output file number.
Node number on output file for combined
location. (Not required if ICOMB = 1.)
Input data set numbers.
Card identifier.
First input file number.
Second input file number.
Variable Default
Name Value
Blank
ICOMB I
NPOLL 0
Blank
Blank
TITLE Blank
Blank
NDOUT None
NODEOT None
Blank
NDATAS(l) None
NDATAS(2) None
3-5
-------
Table 3-1 (continued). Combine Block Card Data
Card Card
Group Format Columns
Cl 2X 1-2
2013 3-5
6-8
Description
Pollutant identification card not
required if NPOLL = 0.
Card identifier = Cl.
Constituent 1 position on file 1.
Constituent 1 position on file 2.
Constituent NPOLL position on file 1.
Constituent NPOLL position on file 2.
Variable
Name
--
NPOSl(l)
NPOS2U)
NPOS1 (NPOLL)
NPOS2(NPOLL)
Default
Value
Blank
0
0
0
0
END OF COMBINE BLOCK CARDS.
Prugi.im now seeks new input from
Card *3 of Executive Block.
50
3-6
-------
SECTION 4
RUNOFF ISLOCK
BLOCK DESCRIPTION
Introduction
The Runoff Block has been developed to simulate both the quantity and
quality runoff phenomena of a drainage basin and the routing of flows and
contaminants to the major sewer lines. It represents the basin by an aggre-
gate of idealized subcatchments and gutters. The program accepts an arbi-
trary rainfall or snowfall liyetograph mid makes a step by step accounting oi
snowmelt, infiltration losses in pervious areas, surface detention, overland
flow, gutter flow, and the constituents washed into inlets leading to the
calculation of a number of inlet hydroj;raphs and poliutographs.
The Runoff Block may be run in the; single event or continuous mode.
With the slight exception of snowmelt, all computations are done identically
for the two cases. In addition, continuous SWMM produces extra daily,
monthly and annual summary output which single event SWMM does not. The main
difference between single event and continuous operation is basically that
the latter uses data sets (off-line storage) for storage of precipitation and
temperature input data instead of dimensioned arrays, thus eliminating any
restriction on the number of time step;;. Even in the single event mode, the
only time step limitation is imposed by the allowed input of only 200 precipi-
tation and air temperature values. Since the input time intervals for precip-
itation and air temperatures do not have to equal the computational time
step, this may impose a limit for single event simulation of either greater
or less than 200 time steps. (The limit of 200 precipitation values may be
bypassed by setting file NSCRAT(4) ^ 0. )
The overall catchment may be divided into a maximum of 200 subcatchments
for single event, simulation and 30 for continuous simulation although such
high limits are only rarely needed. These, in turn, may drain into a maximum
of 200 (or 30) gutter/pipes plus inlets. Inlet flows and poliutographs may
be placed on the interfacing file for input to subsequent blocks. However,
these blocks have their own limitations on the number of inflow locations
they will accept. See Table 2-1 tor details.
This section describes the program operation of the Runoff Block, pro-
vides instructions on data preparation and input data card formats, shows
sample runs, and presents the results of a calibration of the Runoff Block.
51 4-1
-------
Program Operation
The relationships among the subroutines which make up the Runoff Block
are shown in Figure 4-1. Subroutine RUNOFF is called by the Executive Block
to gain entrance to the Runoff Block. The program prints "ENTRY MADE TO
RUNOFF MODEL" and then acts as the driver routine for the block. Subroutine
RUNOFF directly calls subroutines HYDRO and PRINTR. Although BLOCK DATA is
not actually r> subroutine, it is .automatically activated by RUNOFF and
initializes some variables. Subroutine PRINTR reads tape headers, and
prints the table headings and results of the quantity and quality simula-
tions .
Subroutine HYDRO computes the liydrograph ordinates and the watershed
quality contributions with the assistance of six core subroutines, i.e.,
RHYDRO, QHYDRO. WSHED, QSHED, BUILD and GUTTER. It initializes all vari-
ables to zero before calling RHYDRO to read in the rainfall hyetograph and
information concerning the inlet drainage basin. Subroutine CTRAIN is
called from RHYDRO if the continuous simulation mode is selected. Its
purpose is to read long-term precipitation/temperature histories from Na-
tional Weather Service (NWS) magnetic tapes. Tf quality is to be simulated,
subroutine QHYDRO is called for input of parameters and subroutine ERROR
prints its error messages. QSHED and BUILD are then called to initializs
the watershed constituent loads. Next HYDRO sets up an ordering array to
sequence the computational order for gutter/pipes according to the upstrea-n
and downstream relationships.
HYDRO then sets up a DO loop to compute the hydrograph ordinate for
each incremental time step. In each step, subroutine WSHED is first called
to calculate the rate ot water flowing out of: the idealized subcatchments.
If snowmelt is simulated subroutines AREAL and MELT are called from WSHED
and subroutine FINDSC from AREAL. Additionally, subroutine GAMP is called
from WSHED if the Green-Ampt model is used to simulate infiltration. If
quality is to be simulated, QSHED and BUILD are called to compute the water-
shed quality contributions from catchbasins, erosion, dust and dirt, and
other sources. GUTTER is then called to compute the instantaneous water
depth and flow rate for the gutter/pipes atid to route the flow. Water
flowing into the inlet point, be it from gutter/pipes or direct drainage
from subcatchments, is summed for a hydrograph ordiuate. A continuity check
is then made for the disposition of rainfall water in the form of runoff,
detention, and infiltration and evaporation losses. The error in continuity
is computed and printed as a percentage of precipitation. With the assist-
ance of subroutine HCURVE, HYDRO plots the rainfall hyetograpli nnd the
runoff hydrograph for the total drainage basin. Subroutine GQUAL routes
quality in each gutter/pipe for the flow values computed in subroutine
GUTTER.
Interfacing and the Use of Off-line Computer Storage
The Runoff Block transfers hydrographs and pollutographs for as many as
200 inlets and 10 constituents through an assigned tape or disk to other
SWMM blocks (see Section 2). However, the other blocks may only accept part
of this output. These restrictions may be circumvented by making a single
52 4-2
-------
IEXECUTIVE
| BLOCK
^
Dl IKIflCP
WUINUrr
-fc
^
Ul YHRft
PRINTR
BLOCK
5ATA
,^_
*r
_>
^
u/cupn
DU vnon
nn T urtu
^
QHYDRO
OSHED
GUTTFR
ILJpl IQWP
nL-unvc.
^^
«^^
^.
*
MELT 1
ARFAI L -iJ FINH9P 1
/^ A ft J D 1
oAM r 1
P TD AIM 1
O 1 nAIIM 1
FRROR
BUILD 1
fiOIIAI
PI IOV/P AMH
uunvc. MIMU
PLOT ROUTINES
I
u>
Figure 4-1. Structure of Runoff Block Subroutines.
-------
run of the Runoff Block and generating a permanent data set that will allow
several runs of other blocks utilizing different portions of the output. If
this is the first computational block, the title, and values for the start-
ing date and time and time step size will remain throughout all subsequent
blocks.
One or two scratch data set are required for the single-event mode
and as many as three scratch data sets are required for the coutiiiuous mode;
see Table 4-1. In the continuous mode the additional data sets are used to
provide the program with a continuous feed of precipitation data so that
there is effectively no limit on the length of the simulation.
Table 4-1 Runoff Data Set Allocations
Q
JIN(l) = Input unit for NWS precipitation tape, read only, required
for continuous SWIM only (ICFAIN = 1 or 4 only, card Bl).
NSCRAT(l) = Scratch data set, always required. Used for temporary
storage of output data to be printed.
NSCRAT(2) = Data set used for storage of processed precipitation val-
ues (and temperatures if snowmelt is used) for continuous
SWIM, ICRAIN / 0. File can be saved and used as input for
subsequent Runoff runs if desired, (ICRAIN = 2), thus avoid-
ing reprocessing of precipitation (and temperature) records.
Can also be used to contain precipitation dnln for single
event SWNM (ICRA1N=0) thus avoidi.ng time step restrictions.
NSCRAT(3) = Input data set for NWS temperature tape, read only, re-
quired only for continuous SWIM with snowmelt, ICRAIN = 1 or
4 and ISNOW = 2.
NSCRAT(4) = Temporary storage of precipitation input data; may be used to
avoid possible limit of 200 time steps with single event SWMM.
a
JOUT(l) = Output unit for transfer of Runoff results to subsequent
blocks. Not required if no subsequent blocks are used or
graphing is not desired.
aSubscript "one'" is used if Runoff is the first block run in a SWIM simula-
tion. See explanation of Executive Block (Section 2).
Although files NSCRAT(2) arid NSCRAT(3) are protected for an individual run
of continuous SWMM using Runoff and Storage/Treatment, care should be taken
during subsequent SWIM runs to insure that other blocks using scratch files
do not accidently access the same files and write over them, thus eliminat-
ing them.
54
-------
INSTRUCTIONS FOJ-' DATA PREPARATION
Introduction
Instructions on the use of the Runoff Block are divided into five
subsections; general input and control data, meteorological data processing,
surface quantity, surface quality and print control. These subsections
follow the order of the input data carus sliowu in Ta'ule 4-28 at liie end of
this section. Figure 4-45 shows the layout of the data cards. The user
should refer to the latest documentation in the appendices and the original
SWMM documentation (Metcalf and Eddy el: al., 1971a). Many individual param-
eters are explained in more detail in the footnotes to Table 4-28.
Basic Runoff Data Sources
Importance of Runoff Block Data --
The Runoff Block forms the source of runoff and quality hydrographs and
pollutographs for most SWMM applications. Although the other SWMM blocks
allow direct input, of hydrographs and pollutcgraphs frcm cards, either
bypassing the interfacing file or in addition to it, in most cases these
will be generated by the conversion of rainfall/snowmelt into runoff and
pollutant loads in the Runoff Block. Hence, the input data for this block
are probably the most important in the model.
Key data requirements and sources are mentioned during discussions of
individual card groups later. However, the general types of data are dis-
cussed briefly at this point.
Meteorological Data --
Precipitation data are usually obtained from on-site gages maintained
by an agency that has performed rainfall-runoff monitoring such as a local
consulting firm, 208 agency, or city, county, state or federal agency. In
the unfortunate event of a missing rain gage, precipitation data are then
obtained from the nearest National Weather Service (NWS) station. The
fundamental data are precipitation hyet.ographs for the duration of the
simulation. (See subsequent discussion for use of synthetic rainfall data.)
When .snowmelt is simulated, air temperatures and wind speed are needed in
addition.
Surface Quantity Data --
Flow routing data are usually derived from topographic maps, aerial
photos and drainage system plans. These are customarily obtained from the
local agency responsible for drainage, usually the city or county. Especi-
ally for topographic maps, there is gr«;at variation in the quality of such
data. Some cities, for instance, have 1:200 scale topographic maps complete
with outlines of roads and structures. Slopes are easily derived from the
one or two foot contours found on such maps. In other cities, the only
contour information available may be the 1:24000 scale USGS quadrangle maps
from which gross parameter estimation is often the only possibility. Seekers
55 4-5
-------
of basic quantity data must be prepared to spend one to five days at the
municipal engineer's office to locate needed maps, plans etc. in public
files.
A significant problem remains: the reliability of such data sources.
Most municipal offices contain design drainage drawings, but recent as-built;
information is very rare. In older cities, design drawings may date back
several decades and only serve as a guide to what actually exists in tin:
field. This most often affects sewer slopes and cross sections (due to
deterioration of old sewers). Finally, combined sewer regulators and other
hydraulic control locations are often different from design drawings because
of deterioration and maintenance. In many instances, hydraulic connection:;
exist that are not included on any plans because of pragamatic action of
maintenance crews. .In summary, all sucli data should be field checked.
Surface Quality Data
Data required to formulate pollutographs are the most conceptual and
ambiguous of any SWMM input data. Such data and their possible sources art;
discussed later. At this point it is only re-emphasized th?t unless aetnaI
field sampling of runoff quality has been performed, typically by a 208 o;r
pollution control agency, the credibility of predicted quality results
cannot be established.
Default Parameters
A characteristic of past SWMM versions has been the inclusion of de-
fault values for many quantity and quality parameters. Although this prac-
tice is continued in a few instances, it is now discouraged, either by
removal of a default option altogether, or else by forcing the user to
insert the default parameters. This latter option is easily accomplished
through the use of "default" and "ratio" cards -- see footnotes 23 and 24 to
Table 4-28. Thus, the user is encouraged at least to consider the values or"
Manning's n, depression storage or infiltration parameters, for instance.
Representative values and guidelines for selection of such parameters arc
included in this volume.
General Input and Control Data (Card Groups Al - B2)
The first three card groups are concerned with a label for the output:
and general operating parameters. The labels of card Al will be placed on
the interfacing file for future identification of the output. Most in--
dividual parameters are self-explanatory. However, further information on
several parameters (e.g., infiltration) may be found in subsequent discus-
sions of those topics. The computational time step, DELT, is discussed in
conjunction with rainfall data and flow routing (surface quantity) param-
eters. Also, the user should avoid printing large amounts of unnecessary
output and use the parameters IRPRNT, ICNTNS, and IPRDAY judiciously (es-
pecially for continuous simulations).
56
-------
Meteorological Data Processing (Card Groups Cl-fl)
Snowmelt Data --
General Parameters Card groups Cl through Fl are used to read all per-
tinent meteorological data. Within these cards, groups C1-C5 are concerned
with snowmelt, if simulated. Additional snowmelt parameters are found in
card groups 11-13. Since snowmelt procedures are discussed in detail for
those card groups and in Appendix II, only minimal information is given
here.
On card Cl, the watershed elevation is used only to compute average
atmospheric pressure, which in turn has only a minimal effect on results.
Hence, it is not a "sensitive" parameter. The free water holding capacity
of a snow pack is the volume of water (as a depth, in inches) within the
pack that can be held as liquid melt prior to releasing runoff. In the
model it simply acts as an intermediate reservoir; the larger its volume,
the greater the delay in the appearance of runoff following the conversion
of snow to liquid water. Unfortunately, as is the case for most snowmelt
parameters, very few data exist that permit estimation of this parameter in
urhan areas, let alone make distinctions among three types of snow covered
areas as required on card Cl. However, some available information is sum-
marized in Table 4-2.
In natural areas, a surface temperature (SNOTMP) of 34 to 35°F (1-2°C)
provides the dividing line between equal probabilities of rain and snow
(Eagleson, 1970, Corps at Engineers, 1956). However, parameter SNOTMP on
card Cl might need to be somewhat lover in urban areas due to warmer surface
temperatures.
The snow gage correction factor accounts for the error in snow gage
measurements. The value of SCF is usually greater than 1.0 (the gage tend:;
to underestimate the catch) and increases as a function of wind speed.
Representative values are shown in Figure 4-2 (Anderson, 1973). In prac-
tice, SCF can be used as a calibration factor to account for gains or losses
of snow if data are available to determine it.
During non-melt periods (i.e., sub-freezing weather) the temperature of
the snow pack follows the air temperature, but with a delay, since tempera-
ture changes cannot occur instantaneously. Heat exchange and temperature
changes during this period are explained in Appendix II with reference to
equations 11-15 and 11-16. The weighting factor, TIPM, is an indicator of
the thickness of the "surface" layer of the snow pack. Values of TIPM < O.I
give significant weight to temperatures over the past week or more and would
indicate a deeper layer (thus inhibiting heat transfer) than TIPM values
greater than about 0.5 which would essentially only give weight to tempera-
tures during the past day. In other words the pack will both warm and cool
faster (i.e., track the air temperatures) with higher values of TIPM.
Anderson (1973) states that TIPM =0.5 has given reasonable results in
natural catchments, although there is some reason to believe that lower
values may be appropriate. No data exist for urban areas.
57 4-7
-------
00
OQ
C
t
a>
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> 0)
i-n (W
rr ro
fD
* O
CD
> rr
3 O
a. y
ro
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w n>
O >T>
3 H-
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Tl
01
n
on
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a.
SCF-GAGE CATCH DEFICCNCY CORRECTION FACTOR FOR
b I* S u. SNOWFALL
I
00
I I I i i I i i i i
-------
Heat transfer within the snow pack is less during non-melt periods than
during melt periods due to the presecce of liquid water in the pack for the
latter case. Parameter RNM simply multiplies melt coefficients (describe-1
for card groups 11-13) to produce a lower "negative melt coefficient" for
use during non-melt periods. A typical value for natural areas is 0.6, with
values for urban areas likely to be somewhat higher because of the higher
density of urban packs. The higher the value of RNM, the more rapid is tin?
heat gain ou loss of the pack in response Lo aii LempeLaLiut: changes.
The catchment latitude and th2 longitude correction (described in
footnote 14 to Table 4-28) are used only to compute hours of daylight for
the catchment. Computations are insensitive to small errors in these values.
Table 4-2 Snowpack Free Water Holding Capacity
(Anderson, 1973, Corps cf Engineers, 1956)
Model input (card Cl) is
FWFRAC = FW /WSNOW
max
where FWFRAC = free water holding capacity as a fraction of snowpack
depth,
FW = maximum depth of free water stored in pack, inches, and
max v i > »
WSNOW = snowpack depth, inches water equivalent.
Snowpack Conditions FWFRAC
Typical deep pack 0.02-0.05
(WSNOW > 10 in.)
Typical shallow early 0.05-0.25
winter pack
Typical shallow spring pack 0.20-0.30
or with slush layer
FWFRAC increases as pack density increases, pack depth decreases, slush
layer increases, ground slope decreases.
NWS Temperature Data -- Continuous SWMM requires a complete time history
of daily maximum and minimum temperatures, from which hourly temperatures
are synthesized by sinusoidal interpolation (see Appendix II). These max-
min temperatures are on the NWS card deck 345, "WBAN Summary of Day", as
shown in Figure 4-3. A magnetic tape containing these card images is
available for most first-order NWS stations and others within the U.S.
from the NOAA National Weather Records Center in Asheville, NC (phone
59 4-9
-------
O.RD LLCK 345 ftBA.-J SUi^H/lY jp
I
M
O
o oid ol+'b'of+o bio b o b'o bid
i io o o o o'b o
o j i i i < :i i i
- M 1 1 1 1 ill 111 ri 1 1| i i;i 1,1 i;i i.i
i i lii 11 n ii i4i is ii i
:22 2 2! 22:2 2!2 2:2 2'2
0 0 fliO 0 fl!""0 00 0 0 00 0; ) » n .1 00,01
PUNCH IN
44 44444444444444444 ,,«
COLUMNS
55 55555555555.555555
66 66E6666S6666666S 6i~~6 6' 666 6 6
M m 111 n 111 n i m 11 n n i
38 8.8888888,883888888 88 88.888
99! 99999999999999999; 99 99999
i i < ill M> i in "I" u "'U 11 mil " anin n I«!BJ» n .7iaiO|ii u:n 11 is a n a i» «i4i liiiiiuiit'it-ii'ii «-uui «:sn» u »isi ii'iiw tiuii Mumt ii u ft « M;;J u n,u n n »
Figure 4-3. Card Image of National Weather Service Card Deck 3A5, "Summary
of Dav".
-------
704, 258 2850). A record of 25 years costs approximately $100. Such
a record, corresponding to the precipitation record, is required for
continuous simulation of snowmelt. Values are interpolated for missing
dates. Parameter LOCAT3 is seen to be located in columns 1-5 of card
deck 345 (Figure 4-3).
Wind Data -- Wind speeds, entered on card C2, are used only for melt cal-
culations during periods.of rainfall (equation II-S). The higher the val-
ues of wind speed, the greater are the convective and condensation melt
terms. Of course, if the simulation covers a large city, the wind speeds
entered on card C2 can only be considered gross estimates of actual high-
ly variable speeds. Average monthly speeds are often available from cli-
matological summaries (e.g., NOAA, 1974).
Areal Depletion Curves -- Areal depletion curves account for the variation
in actual snow covered area that occurs following a snowfall. They are
explained in detail in Appendix II, some of which is repeated herein.
In most snowmelt models, it is assumed that there is a depth, SI,
above which the::e will always be 100 percent cover. (Values of ST are
input in card group 12.) In some models, the value of SI is adjusted
during the simulation; in SWMM it remains constant. The amount of snow
present at any t;ime is indicated by the parameter WSNOW, which is the
depth (water equivalent) over the snow covered areas of each subcatchment.
This depth is nou-diniensionalized by S[ for use in calculating the frac-
tion of areas that is snow covered, ASC. Thus, an areal depletion curve
(ADC) is a plot of WSNOW/SI versus ASC; a typical ADC for a natural catch-
ment is shown in Figure 4-4. For values of the ratio AWESI = WSNOW/SI
greater than 1.0, ASC = 1.0, that is, the area is 100 percent snow covered.
Some of the implications of different functional forms of the ADC
may be seen in Figure 4-5. Since the program maintains snow quantities,
WSNOW, as the depth over the total area, AT, then the actual snow depth,
WS, and area covered, AS, are related by continuity,
WSNOW AT = WS AS (4-1)
where WSNOW = depth of snow over total area AT, ft water
equivalent,
AT = total area, ft ,
WS = actual snow depth, ft.water equivalent, and
AS = snow covered area, ft .
In terms of parameters shown on the ADC, equation 4-1 may be rearranged
to read
AWESI - "SNOW _ WS AS _ WS
AWEbl - SI - SI AT - si ' AbL (4-2)
61 4-11
-------
AWESI =
WSNOW/SI
TYPICAL
ADC FOR
NATURAL AREA
TEMPORARY ADC
FOR NEW SNOW
0.5 -
+ SNEW
SBWS
^ AWE
0.5
ASC* AS/AT
Figure 4-4. Actual Areal Depletion Curve for Natural Area. (After
Anderson, 1973, p. 3-15).
62
4-12
-------
AREAL DEPLETION CURVES
AWESI=
WSNOW/SI
4-5a
DEFINITION SKETCH
t»
.SNOW PACK BARE
' CATCHMENT
SNOW
DEPTH
WS
4"5b AREA
AS AT
4-5c
4-5d
4-5e C
4-5f D
4-5g
SI
0 0.5 1.0
ASC = AS/AT
INITIAL MELT PROGRESSING
WSNOW-AT = WS-AS
WSNOW . WS AS
SI AT
t.
AWESI= 1.0
SI
SI
SI
SI
SI
»l
0.8
0.6
0.3
A
1
A
T
'
T
1-
'
AT ,
j
AT
-
1
[
_
n
i
AT
Figure 4-5. Effect on Snow Cover of Areal Depletion Curves.
63
4-13
-------
Equation 4-2 ca:i be used to compute the actual snow depth, WS, from known
ADC parameters, if desired. It is unnecessary to do this in the program,
but it is helpful in understanding the curves of Figure 4-5. Thus,
WS = . 31 (4-3)
Consider the three ADC curves, B, C and D. For case B, AWESI is always
less than ASC; hence, WS is always less than SI as shown in Figure 4-5d.
For case C, AWESI = ASC, hence WS = SI, as shown in Figure 4-5e. Finally,
for case D, AWESI is always greater than ASC; hence WS is always greater
than SI, as shown in Figure 4-5f. Constant values of ASC at 100 percent
cover and 40 percent cover are illustrated in Figures 4-5c, curve A, and
Figure 4-5g, case E, respectively. At a given time (e.g, t, in Figure 4-
5), the area of each snow depth-area curve is the same and equal to AWESI
SI, (e.g., 0.3 SI for time t.).
Curve B on Figure 4-5a is the most common type of ADC occurring in
nature, as shown in Figure 4-4. The convex curve D requires some mech-
anism for raising snow levels above their original depth, SI. In nature,
drifting might provide such a mechanism; in urban areas, plowing and
windrowing could cause a similar effect. It is seen that such a convex
curve acts to delay melt because of the inhibiting effect on heat trans-
fer of deep snow packs. A complex curve could be generated to represent
specific snow removal practices in a city. However, the program utilizes
only one ADC curve for all impervious areas and only one ADC curve for
all pervious areas. This limitation should not hinder an adequate simula-
tion since the effects of variations in individual areas are averaged out
in the city-wide scope of most continuous simulations.
The two ADC curves for impervious (card C3) and pervious (card C4)
areas are input by the user, as are values of SI for each subcatchment
(card 12). The program does not require the ADC curves to pass through
the origin, AWESI=ASC=0; they may intersect the abscissa at a value of
ASC > 0 when ASC = 0.
The preceding paragraphs have centered on the situation where a depth
of snow greater than or equal to SI has fallen and is melting. (The ADC
curves are not employed until WSNOW becomes less than SI.) The situation
when there is new snow is discussed in Appendix II.
Air Temperatures --
For single event snowmelt simulation, air temperatures are input in
card group C5. These may be obtained from instrumentation at the catch-
ment or from the nearest NWS station. The temperatures are constant over
the time interval DTAIR (card B2). Hence, DTAIR should be an integer multi-
ple of DELT.
64 4-14
-------
Fteeipilalion Data --
Choice of Rainfall Data -- Without doubt, rainfall data are the single
most important group of hydrologic data required by SWtlM. Yet, they are
often prepared as an afterthought without proper consideration of the
implications of their choice. The following discussion will briefly des-
cribe options for rainfall input and their consequences. Only rainfall
is considered since for snow it is the physics of snowmplr rather than
snowfall which is important in determining runoff.
SWMM requires a hyetograph of rai.-ifall intensities versus time for
the period of simulation. For single event simulation this is usually a
single storm, and data for up to six g-sges may be entered (if the user is
fortunate enougli to have multiple gages for the catchment). For continu-
ous simulation, hourly data from only one gage are required; these are
usually obtained from the nearest NWS station. Thus, for continuous simu-
lation, the options are fewer since a satisfactory generator of a synthet-
ic hourly rainfall sequence is not usually available, or probably even desir-
able. Hence, a historical rainfall sequence is used.
For single event simulation, on the other hand, synthetic sequences
are indeed an option in lieu of historical records. However, several pit-
falls exist in the use of synthetic hyiitographs that may not be obvious at
first thought. As a prelude, consider the objectives of hydrologic quan-
tity and quality modeling.
Modeling Objectives -- These were treated broadly in Section 1. More spe-
cifically, models might be used to aid in urban drainage design for protec-
tion against flooding for a certain return period (e.g., five or ten years),
or to protect against pollution of receiving waters at a certain frequency
(e.g., only one combined sewer overflow per year). In these contexts, the
frequency or return period needs to be associated with a very specific pa-
rameter. That is, for rainfall one may speak of frequency distributions
of interevent times, total storm depth, total storm duration or average
storm intensity, all of which are different (Eagleson, 1970, pp. 183-190).
Traditional urban drainage techniques often utilize frequencies of depths
given durations, taken from intensity-duration-frequency (IDF) curves,
which are really conditional frequency distributions. But for the above
objectives, and in fact, for almost all urban hydrology work, the frequen-
cies- of runoff and quality parameters are required, not those of rainfall
at all. Thus, one may speak of the frequencies of maximum flow rate, to-
tal runoff volume or duration or of total pollutant loads. These distri-
butions are in no way the same as for similar rainfall parameters, although
they may be related through analytical methods (Howard, 1976, Chan and Bras,
1979, Hydroscience, 1980). Finally, for pollution control, the real inter-
est may lie in the frequency of water quality standards violations in the
receiving water, which leads to further complications.
Ideally an analyst would develop costs and benefits for designs at
several frequencies in preparation for an economic optimization. In prac-
tice, it is often difficult to accomplish this for even one case.
65 4-15
-------
However, continuous simulation offers an excellent, if not the only
method for obtaining the frequency of events of interest, be they related
to quantity or quality. Advantages are discussed in Section 1 and Appen-
dix I along with several other models besides SWIM that will perform simi-
lar tasks. But continuous simulation has the disadvantages of a higher
cost and the need for a continuous rainfall record. This has led to the
use of a "design storm" or "design rainfall" or "design event" in a single
event simulation instead of continuous. Of course, this iuea long preceded
continuous simulation, before the advent of modern computers. It is the
choice of the design event, as it will be termed herein, that may lead to
problems.
Design Events -- Two methods of obtaining design events are considered:
1) use of a historical sequence and 2) generation of a synthetic sequence.
Synthetic sequences are usually constructed by the following steps:
1. A storm duration is chosen, either on an arbitrary basis or to
coincide with the assumed catchment time of concentration, t , i.e., equi-
librium time during a steady rainfall at which outflow equals a constant
fraction of inflow (or outflow equals inflow on a catchment without
losses). The latter method itself has difficulties because of the depen-
dence of t on rainfall intensity and other parameters (Eagelson, 1970).
2. A return period is chosen in order to select the total storm depth
for the specified duration from intensity-duration-frequency (IDF) curves.
3. A time history for the storm is assumed, usually on the basis of
historical percentage mass curves. If peak intensities are at the begin-
ning of the storm, the hyetograph takes on the appearance of a decaying
exponential curve. If the peak intensities are near the middle, a "circus
tent" hyetograph results (Figure 4-6). The hyetograph is shaped such that
depths (or average intensities) for any duration centered about the peak
match those from the IDF curve.
4. The continuous hyetograph of Figure 4-6 must then be segmented in-
to a histogram for input to most digital models.
This procedure was apparently first detailed by Keifer and Chiu (1957)
and then by Tholin and Keifer (1960) in Chicago. It has since been emu-
lated by many others.
Many problems with this procedure have been enumerated for construc-
tion of synthetic hyetographs (McPherson, 1978, Patry and McPherson, 1979)
and the underlying Rational Method and IDF curves on which it is based
(McPherson, 1969). These reports may be seen for details, but some key
points are given here:
1. IDF curves themselves may consist of components of several differ-
ent storms. They in no way represent the time history of a real storm.
2. When frequencies are assigned to total storm depths (independent
of duration) they generally do not coincide with the conditional frequen-
cies of depth given duration obtained from IDF curves. For instance, the
two historical storms shown on Figure 4-6 for comparison with the "5-year"
synthetic storm of 2.28 in (58 mm) have return periods (based on total
depth) of 4.6 and 5.8 years, but total depths of only 1.61 and 1.85 in
(41 and 47 mm), respectively. Thus, IDF curves cannot be used to assign
66 4-16
-------
I
h^
-J
o
X
UJ
Q.
UJ
X
O
z
2
UJ
8
7
N
4
3
2
1
0
i .1
> STORM OF JULY 16, 1914,
1
|
I
«*^
r>
_ TE =4.6-YEARS( 1913-1935) |
ON BASIS OF TOTAL
DEPTH (1.6HN.K
'^
f,
_^
^^
r""^ i
1
4-
/
/
"'/
': /
|
1
1
'
\
f«
{
P
t
\
1
\
1 1 II 1
-
|
>
VT- ^
^ STORM OF JUNE 13, 1926,
I
i
...
! \s TE = 5.8- YEARS (1913-1935)
: ? ON BASIS OF TOTAL
\
j i DEPTH (1.85-IN.)
\i *"
$
\
\
\
\
\
\
1 \
\ "5-YEAR" SYNTHETIC STORM
\ y (TOTAL DEPTH = 2.28- IN.)
>v
1 1 1 1 1
20
40
60 80
TIME, MINUTES
100
120
140
160
ISO
Figure 4-6.
Comparison of Synthetic Versus Actual Storm Patterns, Chicago.
1978, p. 111.)
(After McPherson,
-------
frequencies to r;torm volumes. If synthetic hyetographs are thence used for
studies of detention storage or pollutant loads, where volumetric considera-
tions are key, no frequency should be assigned to the results.
3. Although the time history assigned to a synthetic storm may repre-
sent an average of many storms, there is often considerable variability (see
P. Bock, Discussion of Tholin and Keif<;r, 1960). If a frequency could be
assigned to a synthetic storm, it would probably be considerably rarer than
its nominal frequency, because the joint probability of all time sequences
within the storm corresponding to those; of an IDF curve is very low. The
two historical storms shown on Figure '»-6 certainly do not mimic the synthet-
ic storm.
4. Antecedent conditions must sti3.1 be chosen arbitrarily when using a
design event (either a synthetic or historical storm).
5. A synthetic design event is om: that "never really happened."
McPherson (1978) emphasizes the need to design with a real (historical)
event to insure credibility in the eyes of the public.
6. There is: evidence that synthetic design events may produce an over-
design if the objective is a design for a given return period. Marsalek
(1978a,b) has compared continuous simulation results of flood peaks and
volumes versus return period with results obtained by single event simula-
tions using the same model with input of n-year synthetic events of the type
described earlier. Flood peaks are always higher for the synthetic events.
Flood volumes are higher for most synthetic events, depending on the method
of generation ol" the event, because the return periods assigned to the
synthetic volumes are incorrect.
Design Event Alt.ernatives -- In spite of all of its problems, use of a
design event may still be required. Fortunately, there are ways in which
this may be accomplished satisfactorily.
Foremost among these is the use ol: continuous simulation as a screening
tool. As stated earlier, continuous simulation for several years of a large
catchment with inclusion of spatial detail can be expensive. Instead,
representative smaller catchments may be simulated from which critical
events may be selected for a more detailed, single event simulation. Thus,
from a simple long-term continuous simulation, critical subsets may be iden-
tified for further analysis. Walesh and Snyder (1979) present ideas along
this line.
Continuous simulation may also be used to "calibrate" a synthetic de-
sign event. That is, the design hyetoj;raph may be adjusted such that it
produces flows or volumes that correspond for its return period to those
produced by a continuous simulation run. This has been done in studies in
northern Virginia (Shubinski and Fitch, 1976) and Denver (B. Urbonas, per-
sonal communication, 1979). Proper adjustment of antecedent conditions can
also cause results from synthetic desijjn events to match historical results
(Wenzel and Voorhees, 1978).
In any event, several storm events should be processed for design
considerations. These may be selected from a continuous simulation run, as
suggested above or chosen from the historical record on another basis. For
urban drainage or flood control design, it may be desirable to choose a
68 4-18
-------
particular, well-known local rainfall event and make sure that a design will
handle that storm.
Calibration of the model remains important for any application. It has
been suggested (M. Terstriep, personal communication, 1979) that use of a
synthetic design event for analysis of a new system may not be any worse
Lhan using historical data in an uncalxbrated model.
National Weather Service Precipitation Data -- Hourly precipitation values
(including water equivalent of snowfall depths) are available for 25 year
periods for most, first-order NWS stations around the U.S. (Similar data are
available in Canada from the Atmospheric Environment Service.) Magnetic
tapes containing card images of NWS Card Deck 488, "USWB Hourly Precipita-
tion" (Figure 4-7) are available from the NOAA National Weather Records
Center in Asheville, NC (phone 704, 259 0682) at a cost of about $150. The
same data are also available for shorter periods for multiple cities.
'Having obtained these data for a continuous simulation, they are read
directly from the tape in subroutine CTRAIN. The required NWS ID number is
rraH on card Dl, Starting dates for the simulation are given on card Bl,
ending dates on card Dl. The station name (card Dl) is only for printing
purposes.
The highest. 50 hourly rainfall depths are printed following the pre-
cipitation tape processing. These may be used to aid in selection of events
of interest. li ICRAIN = 4 (card Bl), input ceases at card Dl, and pro-
cessed precipitation (and temperature) data may be reviewed prior to the
rest of the simulation run, if desired.
Card Input of Precipitation Data -- For single event simulation, precipita-
tion hyetographs may be input for up to six gages in card groups El and E2.
Any one of the six gages may then be asisigned to a subcatchment. The time
interval for input of hyetograph intensities, THISTO, (the same for all
hyetographs) must be either equal to the computation time step, DELT (Card
B2), or an integer multiple or integer fraction (e.g., 1/2, 1/5, etc.)
thereof. If THISTO is an integer fraction of DELT, the average intensity
over time step E'ELT is used in computations.
Up to 200 hyetograph ordinates may be entered without using a
scratch file (NSCRAT(2)). Depending on the relationship between DELT and
THISTO, this may impose a limitation on the run less than, more than or
equal to 200 time steps. When ICRAIN - 3 (card Bl) or when NSCRAT(4) $ 0,
the precipitation data input from cards: are stored on an off-line file
instead of in an array, eliminating any limit on the number of time steps.
Temporal Rainfall Variations -- The required time detail for rainfall hyeto-
graphs is a function of the catchment response to rainfall input. Small,
steep, smooth, impervious catchments have fast response times, and vice
versa. As a generality, shorter time increment data are preferable to
longer time increment data, but for a large (e.g., 10 square miles, 2.6 km )
69 4-19
-------
fss
i:
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1 3 3 1 1 i ! 1 i 3 1 3 ! 1 3 3 ) J 1 3 I
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Figure 4-7. Card Image of National Weather Service Card Deck
488, "Hourly Precipitation".
70
4-20
-------
subcatchment (coarse schematization), even the hourly inputs used for con-
tinuous simulation may be appropriate.
The rain g.ige itself is usually the limiting factor. It is possible to
reduce data from 24-hour charts from standard weighing-bucket gages to
obtain 5 minute increment data, and some USGS gages produce no better than 5
minute values. Shorter time increment data may usually be obtained only
from tipping bucket gage installations.
Of course, the necessary time increment for rainfall is closely coupled
to the computational time step, DELT. This is discussed subsequently in
conjunction with flow routing parameters.
Spatial Rainfall Variations -- Even for small catchments, runoff and con-
sequent model predictions (and prototype measurements) may be very sensitive
to spatial variations of the rainfall. For instance, thunderstorms (convec-
tive~.rainfall) may be very localized, and nearby gages may have very dissimi-
lar readings. For modeling accuracy (or even more specifically, for a
successful calibration of SWMM), it is essential that rain gages be located
within and adjacent to the catchment.
SWMM inputs the spatial variability by the assignment of one of up to
six gages to a particular subcatchment. (Clearly, there is no point in the
input of more gage data than there are subcatchments.) If multiple gages
are available, this is a much better procedure than is the use of spatially
averaged (e.g., Thiessen weighted) data, because averaged data tend to have
short-term time variations removed (i.e., rainfall pulses are "lowered" and
"spread out"). In general, if the rainfall is uniform spatially, as mignt
be expected from cyclonic (e.g., frontal) systems, these spatial considera-
tions are not as important. In making this judgment, the storm size and
speed in relation to the total catchment must be considered. It should be
noted that a moving or "kinematic" storm may only be simulated in SWMM by
using multiple gages. Storm motion may very significantly affect hydrographs
at the catchment outlet (Yen and Chow, 1968, Surkan, 1974, James and Drake,
1980).
Evaporation Data (Card Fl) --
An average monthly evaporation rate is required for the month being
simulated in the single event mode, or for all months in the continuous
mode. This rate is subtracted from rainfall and snowmelt intensities at
each time step and is also used to replenish surface depression storage but
for no other purposes. For instance, it is not used to account for sublima-
tion from snow or exfiltration from soil. Evaporation data may usually be
obtained from climatological summmaries (NOAA, 1974) or NWS or other pan
measurements (e.g., from NWS Climatological Data). Single event simulations
are usually insensitive to the evaporation rate, but evaporation can make up
a significant component of the water budget during continuous simulation.
71 4-21
-------
Surface Quantity Input Data (Card Groups G1-I3)
Runoff Flow Routing Procedures and Options --
Card groups Gl through 13 input data used to establish flow routing and
snowmelt parameters for the Runoff Block. Snowmelt will be discussed subse-
quently. Flow routing is accomplished using three types of elements:
1. subcatchmcnt elements (overland flow),
2. gutter elements (trapezoidal channel flow), and
3. pipe elements (circular channel flow).
Subcatchment elements receive rainfall and snowmelt, account for losses due
to evaporation and infiltration (via Morton's equation or the Green-Ampt
equation), and permit surface depression storage to account for losses such
as ponding or retention on grass or pavement. Flow from subcatchments is
always into gutter/pipe elements or inlets. A tree-network of gutter/ pipes
may be used to simulate smaller drainage elements of the sewer system. If
they are used, they route hydrographs (and pollutographs) from subcatchments
to inlets (e.g., entry points to the main sewer system). Inlet flows are
placed on the interfacing file for t.ransmi tal to subsequent SWMM blocks.
However, the Runoff Block is often used by itself if the more sophisticated
routing procedures of the Transport or Extended Transport Blocks are not
required (discussed below).
Flow routing for both subcatchments and gutter/pipes is accomplished by
approximating them as non-linear reservoirs. This is simply a coupling of a
spatially lumped continuity equation with Manning's equation. A detailed
description is presented in Appendix VI and the SWMM Final Report (Metcalf
and Eddy et al., 1971a). Should the capacity of a gutter/pipe be exceeded,
"surcharge" is indicated, and excess water is stored at the upstream end
until the gutter/pipe can accept it.
Input Data Preparation --
Preparation of these input data cards requires two tasks: 1) dis-
cretization of the physical drainage system and 2) estimation of the co-
efficients necessary to characterize the catchment. These tasks require
varying amounts of effort depending on the level of detail desired by the
user.
Very useful additional information for these tasks is contained in
short course proceedings prepared by the University of Massachusetts (Di-
Giano et al., 1977). The Runoff Block example is particularly good because
of the emphasis on data reduction from typical municipal maps and plans.
The SWMM user i;> encouraged to review these proceedings for alternative
explanations and examples. Further useful information is contained in the
User's Manual prepared for Canadian SWMM applications (Proctor and Redfern
and James F. MacLaren, 1977) and in tha SWMM-related references discussed in
Section 1.
72
-------
Discretization of the Catchment --
Definition -- Discretization is a procedure for the mathematical abstraction
of the physical drainage system. For the computation of hydrographs, the
drainage basin may be conceptually represented by a network of hydraulic
elements, i.e., subcatchments, gutters, and pipes. Hydraulic properties of
each element are then characterized by various parameters, such as size,
slope, and roughness coefficient.
Subcatchments represent idealized runoff areas with uniform slope.
Parameters such as roughness values, depression storage and infiltration
values are taken as constant for the area and usually represent averages,
although pervious and impervious areas have different characteristics within
the model. If roofs drain onto pervious areas, such as lawns, they are
usually considered part of the pervious area, although conceivably, they
could be treated as miniature subcatchinents themselves.
Discretization begins with the identification of drainage boundaries
using a topographic map, the location of major sewer inlets using a sewer
system map, and the selection of those gutter/pines to be included in the
Runoff Block system. Note that discretization of the sewer system involves
choices that affect elements to be used in either of the subsequent Trans-
port Blocks (see below). An example will illustrate some of these points.
Example -- Two possible discretizations of the Northwood catchment in Balti-
more (Tucker, 1968, Huber et al., 1979) are indicated in Figures 4-8 and 4-9
(Metcalf and Eddy et al., 1971a). A "fine" approach was used in Figure 4-8,
resulting in 12 subcatchments and 13 pipes leading to one inlet. In Figure
4-9, a "coarse" discretization was used, resulting in five subcatchments and
no gutter/pipes. "Storm Conduits" shown in Figure 4-9 could either be
simulated by the Transport Block or ignored, feeding all subcatchment flows
to the one inlet. The outlet to the creek then represents the downstream
point in the simulation. (This could lead, in a larger system, to inlets in
the Transport Model.)
A comparison of hydrographs produced by the two methods is shown in
Figure 4-10 (Metcalf and Eddy et al., 1971a), in which the differences are
relatively minor. Additional calibration effort could bring the two schema-
tizations into better agreement with each other and with the measured hydro-
graph. Techniques for this purpose ar= discussed later as are techniques
for aggregation of subcatchments.
Required Amount of Detail -- It is anticipated that only a very coarse
discretization will be used for continuous simulation. Although up to 30
subcatchments and gutter/pipes or inlets are allowed, a typical continuous
simulation might include only one subcatchment and no gutter/pipes. This
economy in the amount of detail simulated is prompted to save computer time
and because detail simply is not required for continuous simulation which
serves as a screening and planning tool (see Section 1 and Appendix I).
Moreover, reasonable agreement is possible between hydrographs produced by
coarse and fine schematizations as will be discussed later under "subcatch-
ment aggregation".
73 4-23
-------
Rain Gage #2
b
DRAINAGE AREA BOUNDARY
~~- SUBCATCHMENT BOUNDARY
SUBCATCHMENT NUMBER
Figure 4-8. Northwood (Baltimore) Drainage Basin "Fine" Plan. (After
Metcalf and Eddy et al., 1971a, p. 50)
74
4-24
-------
\'.rr.
Rain Gage *2
b
DRAINAGE AREA BOUNDARY !
SU8CATCHMENT BOUNDARY
STORM CONDUIT
INLET
SUBCATCHMENT NUMBER
Figure 4-9. Northwood (Baltimore) Drainage Basin "Coarse" Plan.
(After Metcalf and Eddy et al., 1971a, p. 51)
75
4-25
-------
60
50
40
tn
u_
o
u.
o
30
20
10
NORTHWOOD (BALTIMORE)
STORM OF AUGUST I, 1965 AM
AREA 47.4 ACRES
12- SUBCATCHMENT SYSTEM
\ 5- SUBCATCHMENT SYSTEM
OBSERVED
10 20 30 40 50 60 70 80 SO
TIME (MIN)
i
NJ
Figure 4-10. Effect of Coarsening Subcatchment System, Northwood (Baltimore). (After
Metcalf and Eddy et al., 1971a, p.74)
-------
Should flow routing be desired during continuous simulation, Runoff
Block gutter/pipes may be used. (The Transport Blocks are intended pri-
marily for a detailed storm event analysis.)
For single event simulation, the amount of detail should be the minimum
consistent with requirements for within-catchment information. Obviously,
no information can be obtained about upstream surcharging if the upstream
conduits are not simulated aac! subcatchments are not provided to feed liiein.
In addition, sufficient detail needs to be provided to allow within-system
control options to be tried for different areas and land uses. If, however,
the primary objective is simply to produce a hydrograph and pollutograph at
the outlet, utilizing a single rain gage, then one subcatchment will often
(but not always) serve as well as many.
A final constraint on the amount of detail is dictated by personnel
requirements for data reduction. Once data resources (e.g., maps, plans)
are gathered, discretization of the catchment can occupy one to three per-
son-days (a longer time for more subcatchments) with perhaps an additional
15 to 30 minutes per subcatchment for their input parameters. Finally,
there is no one "right" way to accomplish the discretization,
especially since decisions at this stage can be compensated for during the
later calibration phase.
Choice of Sewer System Flow Routing -- There are many criteria that influence
the choice of the block used for sewer system routing: Runoff, Transport or
Extended Transport. Several of these are give in Table 4-3; much more
extensive information is contained in the sections of this report and SWMM
documentation (Metcalf and Eddy et al., 1971a, Roesner et al., 1981) pertain-
ing to each block.
Regarding flow routing methods, no backwater effects can be calculated
(i.e., in an upstream direction) in the Runoff and Transport Blocks because
each conduit element simply provides an inflow to a downstream element with
no effect of the latter on the former. Thus, both Runoff and Transport
Block routing acts as a "cascade" of elements, each discharging into the
next with no other interactions. On the other hand, the solution of the
complete St. Venant (gradually varied flow) equations by the Extended Trans-
port Block provides for backwater effects and much more, as indicated in
Table 4-3. This is at the cost of considerable extra complexity and com-
puter time.
As a practical matter, the Runoff Block is often used to simulate
smaller diameter pipes, e.g., less than 30 in (762 mm) and one of the two
Transport Blocks for the larger trunk sewer system. The larger the catch-
ment being simulated, the less important becomes the simulation of small
conduits, far upstream. Conduits of less than a 12 in (305 mm) diameter are
rarely simulated. Also, in spite of the fact that Runoff Block trapezoidal
conduits are called "gutters", it should almost never be necessary to simu-
late flow in a roadside curb and gutter channel, unless the catchment is
extremely small.
77 4-27
-------
Table 4-3. Flow Routing Characteristics of Runoff, Transport and Extended
Transport Blocks.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
Flow routing method
Relative computational expense
for identical network
schematizations
Attenuation of hydrograph peaks
Time displacement of hydrograph
peaks
In-conduit storage
Backwater or downstream control
effects
Flow reversal
Surcharge
Pressure flow
Branching tree network
Network with looped connections
Number of pre-programmed
conduit shapes
Alternative hydraulic elements
(e.g., pumps, weirs, regulators)
Dry weather flow and infiltra-
tion generation (base flow)
Pollutograph routing
Solids scour/deposition
Card input of hydrographs/
pollutographs
Runoff
Block
Non-linear
reservoir,
cascade of
conduits
Low
Yes
Weak
Yes
No
No
Weak
No
Yes
No
2
No
No
Yes
No
No
Transport
Block
Kinematic
wave,
cascade of
conduits
Moderate
Yes
Yes
Yes
Noa
No
Weak
No
Yes
No
13
Yes
Yes
Yes
Yes
Yes
Extended
Transport
Block
Complete
equations,
interactive
conduit
network
High
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
6
Yes
Yes
No
No
Yes
Backwater may be simulated as a horizontal water surface behind a storage
element.
78
4-28
-------
Numbering Schemes -- Subcatchments may be assigned any numbers between 1 and
9999. This is true also for gutter/pipes and inlets except that inlet
numbers corresponding to Transport Block manholes must be less than or equal
to 1000. Also, Receive Block junctions have a maximum number equal to the
number of junctions. Thus, inflows to such junctions must be numbered
accordingly. To be on the safe side, it is often a good idea to reserve
relatively low numbers for inlets, etc. that are transfered to subsequent
blocks.
Internally, the Runoff Block assigns subscripts (internal numbers) in
the order in which the gutter/pipes or subcatchment cards are read in. Some
error messages use these numbers. It is not necessary to state specifically
the inlets to be transfered to subsequent blocks, since all inlets at the
downstream end of any subcatchment-gutter/pipe flow routing chain are placed
in that category and are printed out.
Within the above confines, considerable latitude exists for numbering
schemes. Thus, subcatchments may feed gutter/pipes with the same number;
subcatchments or gutter/pipes may be given numbers in a certain range (e.g.,
200 - 299) based on certain characteristics; etc. The Transport Block
numbering scheme allows even more latitude since it includes non-conduits
(e.g., manholes).
Gutter/Pipe Parameters (Card Groups Gl and G2) --
Routing and Timti Step Considerations -- As mentioned earlier, the non-linear
reservoir method of gutter/pipe flow routing is described in Appendix V and
the original documentation volume (Metcalf and Eddy et al., 1971a). Since
the formulation produces a spatially "Lumped" configuration (i.e., there is
no dependence upon longitudinal distance for a given gutter/pipe element),
flows introduced at the "upstream end" of such an element are distributed
horizontally over the entire water suface area. The implication is that a
concentrated inflow into one "end" of a simulated gutter/pipe is a reason-
able approximation to the true situation in which gutter/pipes receive
distributed inflows along their lengths.
At each time step, an iterative (Hewton-Raphson) scheme is used to
solve the non-linear difference equation used to approximate the differ-
ential equation of the non-linear reservoir. Depending upon the choice of
parameters for the gutter/pipe and the size of the time step, DELT, conver-
gence problems can arise. These usually occur with a gutter/pipe of rela-
tively small volume (e.g., short length and diameter) coupled with too long
a time step. In such a case, the continuity equation may try to force a
negative volume to be calculated because there is not enough "real water" in
the gutter/pipe to satisfy the outflow rate. In such a case, the volume and
outflow are set to zero and a warning message is printed by subroutine
GUTTER. This correction only approximates the true occurrence and results
in a small error in continuity.
Most convergence problems of this or other natures can be cured either
by decreasing the time step or increasing the gutter/pipe dimensions.
79
4-29
-------
Analysis of a similar finite difference approximation for a linear reservoir
routing technique indicates that negative volumes cannot occur if
DELT < _ , __ (4.4)
where DELT = computational time step, sec,
V = volume of water in gutter/pipe, ft ,
Q = outflow from gutter/pipe, cfs,
GLEN = length of gutter/pipe, ft, and
V = velocity of flow through gutter/pipe, ft/sec.
The requirement of equation 4-4 is thus a suggestion for Runoff Block gut-
ter/pipe routing, since a stability analysis of the non-linear routing
scheme has not been accomplished. Note, however, that if gutter/pipes are
used for continuous simulation, with a one-hour time step, their dimensions
should be quite large.
As a general rule, accuracy of the scheme increases as the time step
decreases, hut it should seldom be necessary to decrease the time step to
less than one minute; most often DELT is between one and five minutes. The
choice of the time step is also linked to the rainfall hyetograph input,
discussed earlier.
Parameter Selection -- Most gutter/pipe parameters are self explanatory and
little interpretation is needed. The slope and roughness are combined into
one parameter for further use in the program, using Manning's equation.
Thus ,
GCON = i VG3 (4-5)
where GCON = routing parameter,
G6 = Manning's roughness, n, and
G3 = invert slope, ft/ft.
Thus, equivalent changes in the routing can be made through changes in
either the slope or roughness.
The invert slope is usually given on drainage maps or may easily be
calculated from invert elevations and conduit lengths. Tables of Manning's
roughness coefficients are given in many references; see for instance Chow
(1959) or ASCE-WPCF (1969).
Subcatchment Parameters (Card Groups HI and H2) --
Subcatchment Schematization -- Many hydrologic models obtain a distributed
effect (spatially) by subdividing the overall catchment into subcatchments ,
predicting runoff from the subcatchments on the basis of their individual
properties, and combining their outflows using a flow routing scheme. This
80 4-30
-------
procedure is followed in SWMM, in which subcatchments are idealized mathe-
matically as spatially lumped, non-linear reservoirs, and their outflows are
routed via the gutter/pipe (or a subsequent Transport Block) network.
Each subcatchment is schematized as in Figure 4-11, in which three or
four subareas (depending on whether snowmelt is simulated) are used to
represent different surface properties as enumerated in Table 4-4. The
slope of the idealised subcatchment is in the direction perpendicular to the
width. Flow from each subarea moves directly to a gutter/pipe or inlet and
does not pass over any other subarea. (Thus, it is not possible to route
runoff from roofs over lawn surfaces, for instance.) The width of the
pervious subarea, A2, is the entire subcatchment width, whereas the widths
of the impervious subareas, Al, A3, A4, are in proportion to the ratio of
their area to the total impervious area, as implied in Figure 4-11. Speci-
fication of each subarea is through th-s use of parameters WAREA and WW(3)
on Card HI, PCTZER on Card B2 and.SNNl on Card II. If desired, any sub-
catchment may consist entirely of any one (or more) types of subareas.
Table 4-4. Subcatchment Surface Classification
Subarea Perviousness
Depression
Storage Single Event
Snow Cover and Extent
Continuous
Al
Impervious
Yes
Bare
A2
A3
A4
Pervious
Impervious
Impervious
Yes
No
Yes
Constant fraction,
SNCP, of area is
snow covered.
Bare
100% covered.
Normally bare,
but may have snow
cover over 100%
of Subarea Al
plus Subarea A3.
Snow covered sub-
ject to areal
depletion curve.
Same as Subarea
Al.
Snow covered
subject to areal
depletion curve.
Of course, real subcatchments seldom exhibit the uniform rectangular
geometries showri in Figure 4-11. In terms of the flow routing, all
geometrical properties are merely parameters (as explained below) and no
inherent "shape" can be assumed in the non-linear reservoir technique.
However, in terms of parameter selection, the conceptual geometry of
Figure 4-11 is useful because it aids In explaining the flow routing.
Routing and Time Step Considerations -- The routing and time step discussion
given earlier for gutter/pipes applies almost identically for subcatchments.
A detailed explanation of the non-linear reservoir equations is also
given in Appendix V. The routing is performed separately for each of
81
4-31
-------
WITH SNOWMELT
SUBCATCHMENT
WIDTH
SNOW COVERED
(Single Event)
AREAL DEPLETION CURVE
FOR A2.A4
(Continuous)
NORMALLY
BARE
WITHOUT SNOWMELT
PCTZER = A3/(AH-A3)
TO INLET OR GUTTER/PIPE
Figure 4-11. Subcatchment Schematization. Flows from pervious
and total impervious subareas go directly to gutter/
pipe or inlet. (E.g., flow from the pervious
subarea does not travel over impervious area.)
82
4-32
-------
the three or four subareas of the subcatchment. The same comments
apply regarding convergence and time step considerations as given with
reference to equation 4-4, except that V and Q are the volume and outflow
of water, respectively, from each subarea within the subcatchment. In fact,
convergence problems are rarely encountered during subcatchment routing
because total subcatchment volumes (area times depth) are usually large
compared to outflow volumes.
Parameter selection is aided with reference to Figure 4-12 in which
the subcatchment "reservoir" is shown in relation to inflows and outflows
(or losses). The outflow to gutter/pipes and inlets is computed as the
product of velocity (from Manning's equation based on the difference
between total depth and depression storage), depth and width,
Q = w . 12(d . d)5/3 sl/2 (4.5)
where Q = WFLOW = subcatchment (or subarea) outflow, cfs,
W = WW(1) = subcatchment width, ft,
n = WW(5) or WW(6) = Manning's roughness coefficient,
d = WDEPTH = water depth, ft,
d = WSTORE = depth of depression (retention) storage, ft, and
SP = WSLOPE = slope, ft/ft.
The Fortran parameters listed above are the Runoff Block parameters.
When combined with the continuity equation (see Appendix V) and divided
by the surface area, a new routing parameter is defined for the pervious
and total impervious suhcatchment areas and used in all subsequent calcu-
lations :
WCON = - W.' 1-49 S1/2 (4-6)
A n
where WCON = routing parameter used in subroutine WSHED, ft-sec units,
and
A = surface area of pervious or total impervious subarea,
ft2.
Note that the width, slope and roughness parameters are combined into
one parameter. Thus, equivalent changes may be caused by appropriate
alteration of any of the three parameters. (However, see further com-
ments below on the subcatchment width.) Flows computed in the Runoff
Block and transfered to subsequent blocks are instantaneous values at
the end of a time step.
Subcatchment Width If overland flow is visualized (Figure 4-11) as
running downslope off an idealized, rectangular catchment, then the
width of the subcatchment (card HI) is the physical width of overland
flow. This may be further seen in Figure 4-13 in which the lateral flow
per unit width, qT , is computed and multiplied by the width to obtain
the total inflow into the gutter. (As mentioned previously, the SWMM
gutter/pipes can only receive a concentrated inflow, however, and do not
receive a distributed inflow in a specific fashion.) Note also, in
83 4-33
-------
oo
RAINFALL,
EVAPORATION SNOWMELT
t
V*--
/////////////'/ '/'
\
.. _ .
/ v
r e?
/
5/3
) s
INFILTRATION
Figure 4-12. Non-linear Reservoir Representation of Subcatchment.
i
OJ
-------
UNIFORM RAINFALL INTENSITY I
q » RATE OF OVERLAND FLOW/UNIT WIDTH
W = 2t - TOTAL WIDTH OF OVERLAND FLOW
Figure 4-13.
Idealized Subcatchment-Gutter Arrangement Illustrating
the Subcatchment Width.
85
4-35
-------
Figure 4-13, that for this idealized case, if the two sides of the
subcatchment are symmetrical, the total width is twice the length of the
drainage channel.
Since real subcatchments will not be rectangular with properties of
symmetry and uniformity, it is necessary to adopt other procedures to
obtain the width for more general cases. This is of special importance,
because if the dope and roughness arc fixed, (sec equation 4-6), the
width can be used to alter the hydrograph shape.
For example, consider the five different subcatchment shapes shown
on Figure 4-14. Catchment hydraulic properties, routing parameters and
time of concentration are also given. The latter is calculated using
the kinematic wave formulation (Eagleson, 1970, p. 340),
'c =
a i
where t = time of concentration, sec,
L = subeatchment length, ft,
i* = rainfall excess (rainfall minus losses), ft/sec, and
a,m = kinematic wave parameters.
The kinematic wave formulation assumes that the runoff per unit width
(velocity times depth) from the subcatchment is
qL = ad"1 (4-8)
2
where q. = flow per unit width, ft /sec, and
d = depth of flow, ft.
Parameters a and m depend upon the velocity equation used for normal
flow. For Manning's equation,
a = l^i S1/2 (4-9)
n
and
m = 5/3 (4-10)
Note that the units of a depend upon the value of m, and for Manning's
equation, feet-second units should be used for all calculations. The
subcatchment length may be computed for the assumed rectangular shape
simply by dividing the area by the width.
Finally, note the dependence of time of concentration upon the
rainfall intensity. As i* increases, t decreases. The calculation
using equation 4-7 is consistent with the definition of t given earlier:
t is the time to equilibrium, at which inflow equals outflow (for an
impervious catchment).
Outflow hydrographs for continuous rainfall and for rainfall of
duration 20 min are shown on Figure 4-15. These were computed by the
Runoff Block non-linear reservoir equations (Appendix V) using a time
86 4-36
-------
1
1
1 I
A
i 4 * *
4 IB
D
C
-*
|
1
1
I
W
i
\
+
E
k
r
I
i
1
1
^H
Slope =0.01
Imperviousness = 100%
Depression Storage = 0
n = 0.02
Equilibrium outflow = i*A = 0.926 cfs
DELT = 5 min= 300 sec
i* = Rainfall = 1.0 in/hr = 0.000023148 ft/sec
Shape
A
-B
C
D
E
A
-------
oo
oo
.e-
I
u>
00
EQUILIBRIUM OUTFLOW» 0.926 CfS
DURATION'00
DURATION »20min
10 15 20 25 30 35 40 45 50 55 60 65
TIME, min
Figure 4-15. Subcatchment Hydrographs for Different Shapes of Figure 4-14.
-------
step of 5 mil:. Clearly, as the subcatchment width is narrowed (i.e.,
the outlet is constricted), the time to equilibrium increases. Thus, it
is achieved quite rapidly for cases A and B and more slowly for cases C,
D and E. The kinematic wave computation of t (Figure 4-14) is not partic-
ularly accurate for the non-linear reservoirs in which the asymptotic valuu
of equilibrium outflow is approached exponentially. However, it may be
used for guidance.
Two routing effects may be observed. A storage effect is very
noticeable, especially when comparing hydrographs A and E for a duration
of 20 min. The subcatchment thus behaves in the familiar manner of a
reservoir. For case E, the outflow is constricted (narrow); hence, for
the same amount of inflow (rainfall) more water is stored and less
released. For case A, on the other hand, water is released rapidly and
little is stored. Thus case A has both the fastest rising and recession
limbs of the hydrographs.
A shape effect is also evident. Theoretically, all the hydrographs
peak simultaneously (at the cessation of rainfall). However, a largu
width (e.g., case A) will cause equilibrium outflow to be achieved
rapidly, producing a flat-topped liydrograph for the remainder of the
(constant) rainfall. Thus, for a catchment schematized with several
subcatchments and subject to variable rainfall, increasing the widths
can tend to cause peak flows to occur sooner. In general, however,
shifting hydrograph peaks in time is difficult to achieve through adjust-
ment of Runoff Block flow routing parameters. The time distribution of
runoff is far and away the most {sensitive to the time distribution of
rainfall. Further discussion of t.he effect of subcatchment width on
hydrograph shapes will be given below under "Subcatchment Aggregation
and Lumping" .
What is the best estimate of subcatchment width? If the subcatchment
has the appearance of Figure 4-13, then the width is approximately twice
the length of the main drainage channel through the catchment. This is
perhaps the best single approximation. However, if the drainage channel
is on the side of the catchment as in Figure 4-14, the width is just
equal to the length of the channel.
Most real subcatchments will be irregular in shape and have a
drainage channel which is off center, as in Figure 4-16. A simple way
of handling this case is given by the University of Massachusetts Proce-
edings (DiGiano et al., 1977). A skew factor is computed,
A " A
where y = skew factor, 0 £ y £ 1.0, _
A. ~ area to one side of channel, ft ,
A? ~ area to other side of channel, ft , and
A = total area, ft .
89
-------
DIRECTION
OF OVERLAND
FLOW
MAIN
DRAINAGE
CHANNEL
= A
Figure 4-16. Irregular Subcatchment Shape for Width Calculation.
(After DiGiano et al.» 1977, p. 165.)
90
4-40
-------
Then the width is simply weighted between the two limits of S. or 2$. as,
W = (2 - y)£ (4-12)
where W = subcatchment width, ft, and
£ = length of main drainage channel, ft.
To reiterate, changing the subcatchment width changes the routing
parameter WCON of equation 4-6. Thus, identical effects to those discus-
sed above may be created by appropriate variation of the roughness and
slope.
Subcatchment Area -- In principle, the catchment and subcatchment area
can be defined by constructing drainage divides on topographic maps. In
practice, this may or may not be easy because of the lack of detailed
contour information and the presence of unknown inflows and outflows.
This may be most noticeably brought to the modeler's attention when the
measured runoff volume exceeds the measured rainfall volume. Since the
latter depends upon the catchment area, the area may be at fault.
From the modeling standpoint, there are no upper or lower bounds on
subcatchment area (other than to avoid convergence problems, as discussed
earlier). Subcatchments are usually chosen to coincide with different
land uses and with drainage divides. Further guidance is given later
while discussing subcatchment aggregation.
Imperviousness -- The percent imperviousness of a subcatchment is another
parameter that can, in principle, be measured accurately from aerial
photos or land use maps. In practice, such work tends to be tedious,
and it is common to make careful measurements for only a few representa-
tive areas and extrapolate to the rest.
Care must be taken to insure that impervous areas are hydraulically
(directly) connected to the drainage system. For instance, if rooftops
drain onto adjacent pervious areas, they should not be treated as
impervious in the Runoff Block. On the other hand, if a driveway drains
to a street and thence to a stormwater inlet, the driveway would be
considered to be hydraulically connected. Rooftops with downspouts
connected directly to a sewer are definitely hydraulically connnected.
Should rooftops be treated as "pervious", the real surrounding
pervious area is subject to more incoming water than rainfall alone and
thus might produce runoff sooner than if rainfall alone were considered.
In the unlikely event that this effect is important (a judgment based on
infiltration parameters) it could be modeled by altering the infiltration
parameters or by treating such pervious areas as separate subcatchments ,
and increasing their rainfall by the ratio of roof area plus pervious to
pervious alone. Since the roof areas would then not be simulated,
continuity would be maintained.
Further information on the concept of hydraulically connected
impervious areas is contained in USGS studies (Jennings and Doyle, 1978)
and documentation of the ILLUUAS model (Terstriep and btali, 19/4).
91 4-41
-------
For continuous simulation in which very large subcatchments are
being used, even spot calculations of imperviousness may be impractical.
Instead, regression formulations have been developed in several studies
(Graham et al., 1974, Stankowski, 1974, Manning et al., 1977, Sullivan et
al., 1978). These typically relate percent imperviousness to population
density, and are compared in Figure 4-17 (Heaney et al., 1977). The New
Jersey equation (Stankowski, 1974) is perhaps the most representative,
I = 9.6 PD<0'53 - °'0391 10S10 PV (4-13)
where I = WW(3) = imperviousness, percent, and
PD , = population density in developed portion of the urbanized
area, persons per acre.
The "developed portion" excludes large segments of undeveloped (i.e.,
natural or agricultural) lands that may lie within the area being simulated.
Also note that the relationships shown in Figure 4-17 were all developed
for large (city-wide) urban areas as a whole. Their use may be tenuous
for smaller sub-basins;.
Slope -- The subcatchment slope should reflect the average along the
pathway of overland flow to inlet locations. For a simple geometry
(e.g., Figures 4-13 and 4-14) the calculation is simply the elevation
difference divided by the length of flow. For more complex geometries,
several overland flow pathways may be determined, their slopes calculated,
and a weighed slope computed using a path-length weighted average. Such
a procedure is described in the University of Massachusetts Proceedings
(DiGiano et al., 1977, pp. 101-102).
Manning's Roughness Coefficient, n -- Values of Manning's roughness
coefficient, n, are not as well known for overland flow as for channel
flow because of the considerable variability in ground cover for the
former, transitions between laminar and turbulent flow, very small
depths, etc. Most studies indicate that for a given surface cover, n
varies inversely in proportion to depth, discharge or Reynolds"s number.
Such studies may be consulted for guidance (Petryk and Bosmajian, 1975,
Chen, 1976, Christensen, 1976, Graf and Chhun, 1976, Turner et al., 1978,
Emmett, 1978), or generalized values used (Chow, 1959, Crawford and Linsley,
1966). Successful use of the Stanford Watershed Model (Crawford and Linsley,
1966) was accomplished with the values given in Table 4-5.
92 4-42
-------
persons/hectare
30 40 50
70
80
GRAHAM ET AL.,
WASHINGTON, O.C
NEW JERSEY,
567 MUNICIPALITIES
WASHINGTON.D.C
O ONTARIO
IMPERVIOUSNESS DUE TO STREETS ONLY
05 10 15 20 25
DEVELOPED POPULATION DENSITY, PDd, persons/acre
Figure 4-17. Percent Imperviousness Versus Developed Population Density
. for Large Urban Areas. (After Heaney, et al., 1977, p. 105)
93 4.43
-------
Table 4-5. Estimate of Manning's Roughness Coefficients. (Crawford and
Linsley, 1966)
Ground Cover Manning's n for
Overland Flow
Smooth asphalt 0.012
Asphalt or concrete paving 0.014
Packed clay 0.03
Light turf 0.20
Dense turf 0.35
Dense shrubbery
and forest litter 0.4
Depression Storage Depression (retention) storage is a volume that
must be filled prior to the occurrence of runoff on both pervious and
impervious areas (see Figure 4-12); a good discussion is presented by
Viessman et al. (1977). It represents a loss or "initial abstraction"
caused by such phenomena as surface ponding, surface wetting, intercep-
tion and evaporation. In some models, "depression storage" also includes
infiltration in pervious areas. In the Runoff Block, water stored as
depression storage on pervious areas is subject to infiltration (and
evaporation), so that it is continuously and rapidly replenished.
Water stored in depression storage on impervious areas is depleted only
by evaporation. Hence, replenishment typically takes much longer.
As described earlier (e.g., Table 4-3), a percent PCTZER (card B2)
of the impervious area is assigned zero depression storage in order to
promote immediate runoff. This percentage is the same for all subcatch-
ments. Should variation among subcatchments be desired, PCTZER may be set
to zero, and zero values for WSTORE entered in card group HI as needed.
Depression storage may be derived from rainfall runoff data for
impervious areas by plotting runoff volume (depth) as the ordinate
against rainfall volume as the abscissa for several storms. The rainfall
intercept at zero runoff is the depression storage. Data obtained in
this manner from 18 urban European catchments (Falk and Niemczynomicz, 1978,
Kidd, 1978a, Van den Berg, 1978) are summarized in Table 4-6. The very
small catchments (e.g., less than 1 ac or 0.40 ha) were primarily roadway
tributaries to stormwater inlets and catchbasins.
94 4-44
-------
Table 4-6 Recent European Depression Storage Data (Kidd, 1978b)
vo
Ui
Catchment
Name
Lelystad Housing
Lelystad Parking
Ennerdale Two
Ennerdale Three
Bishopdale -Two
Hyde Green One
Hyde Green Two
School Close One
School Close Two
Lund 1:75
Klostergarden 1:
Klostergarden 1:
Klostergarden 2:
Klostergarden 2:
Klostergarden 3:
Klostergarden 3:
Klostergarden 4:
Klostergarden 4:
Area
Lot3
76
77
76
77
76
77
76
77
Country
Netherlands
Netherlands
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Area
(ac)
4
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.94
.73
.088
.022
.146
.120
.209
.113
.177
.072
.081
.083
.020
.019
.076
.102
.068
.069
Paved Imperviousness Slope
Area (*) (%)
(ac)
2.17
1.73
0.079
0.022
0.111
0.08S
0.103
0.070
0.097
0.072
0.081
0.083
0.020
0.019
0.076
0.102
0.068
0.069
44
100
89
100
76
71
49
62
55
100
100
100
100
100
100
100
100
100
0
0
3
3
2
2
2
1
0
2
0
2
3
4
3
2
1
1
.5
.5
.1
.0
.4
.2
.0
.7
.9
.1
.9
.3
.3
.1
.1
.3
.6
.9
Depression No. of
Storage Events
(in)
0.059
0.035
0.020
0.016
0.018
0.019
0.020
0.009
0.026
0.005
0.041
0.020
0.019
0.013
0.022
0.022
0.020
0.022
10
10
6
9
11
7
8
11
11
11
11
13
11
12
11
13
10
13
Reference
Van den Berg, 1978
' it
Kidd, 1978a
11
II
II
II
II
M
Falk and Niemczyaomi.cz, 1978
ii
ii
ti
ii
ti
ti
M
II
brick pavement. Other catchments have primarily asphalt pavement.
-------
The data were aggregated and a regression of depression storage
versus slope performed as part of a workshop (Kidd, 1978b). The data
are plotted in Figure 4-18 along with the relationship developed by the
workshop,
d = 0.0303 S"°'49 , (r = -0.85) (4-14)
where d = WSTORE = depression storage, in, and
S = WSLOPE = catchment slope, percent.
Viessman et al. (1977, p. 69) illustrate a similar but linear plot in
which depression storage values for "four small impervious areas" range
from 0.06 to 0.11 in (1.5 to 2.8 mm), considerably higher than the
values shown in Figure 4-18. The reason for this discrepancy is not
known, but it appears that the recent European data may be better suited
to provide depression storage estimates, mainly because of their extent.
Separate values of depression storage for pervious and impervious
f»reas are required for input in card group HI. Representative values
for the latter can probably be obtained from the European data just
discussed. Pervious area measurements are lacking; most reported values
are derived from successful simulation of measured runoff hydrographs.
Although pervious area values are expected to exceed those for impervious
areas, it must; bs remembered that the infiltration loss, often included
as an initial abstraction in simpler models, is computed explicitly in
SWMM. Hence, pervious area depression storage might best be represented
as an interception loss, based on the type of surface vegetation. Many
interception estimates are available for natural arid agricultural areas
(Viessman et al., 1977, Linsley et al., 1949). For grassed urban
surfaces a value of 0.10 in (2.5 mm) may be appropriate.
As mentioned earlier, several studies have determined depression
storage values in order to achieve successful modeling results. For
instance, Hicks (1944) in Los Angeles used values of 0.20, 0.15 and 0.10
in (5.1, 3.8, 2.5 mm) for sand, loam and clay soils, respectively, in
the urban area. Tholin and Kiefer (1960) used values of 0.25 and 0.0625
in (6.4 and 1.6 mm) for pervious and impervious areas, respectively, for
their Chicago hydrograph method. Miller and Viessman (1972) give an
initial abstraction (depression storage) of between 0.10 and 0.15 in
(2.5 and 3.8 mm) for four composite urban catchments.
In SWMM, depression storage may be treated as a calibration
parameter, particularly to adjust runoff volumes. If so, extensive
preliminary work to obtain an accurate a priori value may be point-
less since the value will be changed during calibration anyway.
Infiltration* - Infiltration from pervious areas may be computed by
either the Horton (1933, 1940) or Green-Ampt (1911) equations described
*The infiltration section was prepared by Dr. Russell G. Mein, Monash
University, Clayton, Victoria, Australia.
96 4-46
-------
0.07
0.0<
UJ
CD
< 0.05
o
co
0.04
CO
CO
UJ
a: 0.03
CL
ui
o
< 0.02
UJ
0. 01
I
x HOLLAND
o UNITED KINGDOM
o SWEDEN
dp = 0.0303' S
(r= -0.85)
-0.49
I
1
AVERAGE2
I
3 4
SLOPE, percent
Figure 4-18.
Depression Storage vs. Catchment Slope (after Kidd,
1978b). See Table 4-6 for catchment data.
97
4-47
-------
below. A complete description of the theoretical background and programming
details for both is given in Appendix V. The user has the option of which
method to be used for all subcatchments (parameter INFILM, card Bl). Param-
eters required by the two methods are quite different.
Morton Infiltration Infiltration capacity as a function of time is given
by Horton (1933, 1940) as
where f = infiltration capacity into soil, ft/sec,
f = minimum or ultimate value of f (WLMIN), ft/sec,
f = maximum or initial value of f (WLMAX) , ft/sec,
t = time from beginning of storm, sec, and
a = decay coefficient, (DECAY), sec
This equation describes the familiar exponential decay of infiltration
capacity evident during heavy storms. However, the program does not use
equation 4-15 directly; rather the integrated form is used in order to avoid
an unwarranted reduction in f during periods of light rainfall. Details
are given in Appendix V.
Required parameters for card group HI are f (WLMAX) , f (MLMIN) and a
(DECAY). In addition a parameter used to regenerate infiltration capacity
(REGEN, card B2) is required for continuous simulation. Although the Horton
infiltration equation is probably the best-known of the several infiltration
equations available, there is little to help the user select values of
parameters f and a for a particular application, (fortunately, some guid-
ance can be found for the value of f ) . Since the actual values of f and a
00 O
(and often f^) depend on the soil, vegetation, and initial moisture content,
ideally these parameters should be estimated using results from field infil-
trometer tests for a number of sites of the watershed and for a number of
antecedent wetness conditions. If it is not possible to use field data to
find estimates of f , f , and a for each sub-catchment, the following guide-
lines should be helpful.
The U.S. Soil Conservation Service (SCS) has classified most soils into
Hydrologic Soil Groups, A, B, C, and D, dependent on their limiting infiltra-
tion capacities, f . A listing of the groupings for more than 4000 soil
types can be ffind is t'r.s 5C5 HT'irrloeT H2niV?:r. ''Ir'I. rr . ^.^~~.Z6-". 2
similar listing is also given in the Handbook of Applied Hydrology (Ogrosky
and Mockus , 1964, pp. 21.12-21.25), but the former reference also gives
alternative groupings for some soil types depending on the degree of drain-
ase of the 3 M?;-:.!.
98
-------
The best source of information about a particular soil type is a
publication entitled "Soil Survey Interpertations" available from the
local SCS office. Information on the soil profile, the soil properties,
its suitability for a variety of uses, its erosion and crop yield
potential, and other data is included on the sheet provided. A copy of
the listing for Conestoga silt loam is shown in Figure 4-19.
Alternatively, values lor f^ accoidiag to Musgrave (1955) ate given
in Table 4-7. To help select a value within the range given for each
soil group, the user should consider the texture of the layer of least
hydraulic conductivity in the profile. Depending on whether that layer
is sand, loam, or clay, the fw value should be chosen near the top,
middle, and bottom of the range respectively. For example, the data
sheet for Conestoga silt loam identifies it as being in Hydrology Group
B which puts thfB estimate of f^ into the range 0.15-0.30 in/hr, (3.8-7.6
mm/hr). Examination of the texture of the layers in the soil profile
indicates that they are silty in nature, suggesting that the estimate of
the f value should be in the low end of the range, say 0.15 - 0.20
in/hr, (3.8 - 5.1 mm/hr). A sensitivity test on the f^ value will
indicate the importance of this parameter to the overall result.
Table 4-7. Values of f for Hydrologic Soil Groups (Musgrave, 1955)
oo
Hydrologic Soil f
Group (in/nr)
A
B
C
D
0.45 -
0.30 -
0.15 -
0.05 -
0.30
0.15
0.05
0
For any field infiltration test the rate of decrease (or "decay")
of infiltration capacity, a, from the initial value, f , depends on the
initial moisture content. Thus the a value determined for the same soil
will vary from test to test.
It is postulated here that, if f is always specified in relation
to a particular soil moisture condition (e.g., dry) and for moisture
contents other than this the time scale is changed accordingly (i.e.,
time "zero" is adjusted to correspond with the constant f ), then a can
be considered a constant for the soil independent of initial moisture
content. Put another way, this means that infiltration curves for the
same soil, but different antecedent conditions, can be made coincident
if they are moved along the time axis. Butler (1957) makes a similar
assumption.
99 4-49
-------
tO',.1 1
M vtf 0 I - <
»'ti c cor Wki it
CKA CRI
CECB CECJ
CXCH CECE
CECC CECO
Penntylvanl* 1
ULBA Ill8
SOIL SURVEY INTERPRETATIONS
,.,. T/28/71 «...,^. ,.rh «O,L: COIBTOOA .lit lota
Miir otiemrtioii ^
careoai ichlet and phyll
boat 75 Inchee.
Hydrology Group B
UAP SYMBOLS: . . ". ' f f'1 "» ' ' - /-»;
, irall'ttralned upland ell* forced fro* weathered edceeeoaa or ehaly llveaUne and eal-
U. They have a allt loan (mrfac* layer, a allty clay loan avbaoll. Bedrock oeeori at
Irngalion Group' 1 Drainage Group IA
ESTIMATED PHYSICAL AND CHEMICAL PROPERTIES
Depth To Bedrock illi 5*
Depth To Seasonal High Watef fable ill), b* flood Hazard: lone
".'"I*1"1 Classification cCoaise Percent Passing Sieve--- Range in *v"llal"e Shrink -
Inches Fixlior V... Moisture _. .. . ..
. ' USDA Teilure
Surface
0-15 SIL
15-d3 SICL
li>75 SH SIL
75* ...
TOPSOIL
GOOD to 15 Inchaa
Un.
t*d AASHO * . ' ' * Inches* 'Hf Potential
J in 4 , / inn i i(.uinni) ( ,4t Tiff) ) (.Or'Tim) (fi.ino> SO'I
ML A-h - 90-100 90-100 80-100 60-90 0.63-2.0 .16-. 20 k. 5-4.5 lo»
MI.CL »-u. 0-5 90-100 90-100 80-100 60-90 0.63-2.0 .12-. 16 li.5-5.$ Isv
MH.CH 5.6
7
GH.ML A-2 0-15 35-90 35-85 3WS 30-80 0.63-2.0 .06-. 10 5.6-7.8 low
CL.HM A-5
CH A-7
SUITABILITY OF SOIL AS A SOURCE OF
SAND AND GRAVEL ROAOFILL
DUSOITAHLZ FAIR to FOORi A-li. 5. 6, 7
SOIL FEATURES AFFECTING SPECIFIED ENGINEERING USES
!ls«
Hif-i ji« Hold Loeil'M
Ponds-Reservoir Arei
Ponds-Emrilrihfiienls
Oninift
So'inhlti lirijahon
Tciricesor Diversions
Gnssed filemiyi
1 mill Crldi«|
Piotliif CcnstiuCI'QA
«nd Uiifllenlnce
Major Soil Feature AMeclmg Use
Moderate potential froat action) euta and fllla needed
Moderate pen.
Fair to poor aUblllty and eo«pactlent fair to poor realatanea to DiolBi
RA
Moderate intalie raite; aoderate pen.) high available colatnre capacity
Fair to poor liability
High arallable mo la tore capacity! aodarate fertility
Fair tra/fleablllty
All featur
i ,
Use
Septic Tank
Filter Fields
Sow.ige Lagoons
Low Buildings
With Basements
Lawns
Mi
L.mdscaning
P.ukinB Lots
and Streets
m Subdivisions
Sanitary Land Fills
Phase
j£
0-7*
&
A
?i§5*
5*
5-35*
Q.-6
t-in
Ut35(
9m are faTorable
SOIL LIMITATIONS FOR COMMUNITY DEVELOPMENT
Degree ot Limitation Mal<" Soil Feature Allectmg Use
SLIGHT mwc
MODERATE Slope) HCWC
SEVERE Slope
MODERATE Moderate pen.
10DERATF. Slopei aoderat* pan.
SETF.fU: Slope
5UGHT
MODERATE -Slope
SETTRE Slope
SUOKT
10DERATE Slop*
SEVERE Slope
5UOHT
10DERATE Slop*
SETERE Slope
3UGMT
WDERATE Slope
JEVFRZ Slope
Figure 4-19a . ,
Figure 4-19. Soil Conservation Service Soil Survey Interpretation for
Conestoga Silt Loam (found near Lancaster, PA ).
100
4-50
-------
SOIL cores TPOA
SOIL LIMITATIONS FOR RECREATIONAL USES
Use
Phase
tepee a Limitation
M)|0> Soil FeJiuies Aiiecung use
Campsite* -Tents
SLIGHT
MODERATE
SETIKE ..
Slop*
SlOD*
Campsites Trailers
SLIQHT
HOCEKjm
SEVTRI
Slop*
Slope
Lm> Building-,
11- 'S*
SUCHT
NnDIWTE
Slop*
Slon*
Pains and Tiails
0-1M
1*-»S<
25-JSI
SLIGHT
MODERATE
SEVTRK
Slope
Slop*
'icnic and Play Aieas
1S-35*
SLIGHT
MODERATE
SEVERE
Slop*
Slooe
Athletic Fields
losx
SLIGHT
MODERATE
SETEM
Slop*
Slope
Coll Fairways
o-
*
11- 1«
SLIGHT
MOCEKATI
SEVERE
(underlie on erod*d ph«te)
Slop* («e«r« on eroded phi»)
Slom
LAND CAPABILITY. SOIL LOSS FACTORS. AND ESTIMATED "B" MANAGEMENT YIELDS
Soil Phase
Capability
Soil Loss Factors
T/K
Corn
bu.
Oals
bu.
Wheat
bu.
SOT-
Alfalfa
Clover-
Grass
T.
Pasture
Bint-
Grill
c. t.d.
rui
GUIS-
Lt|u»t
c.i.d.
ZS-351
I
II*
lilt
X,II.
IT*
IT*
TI*
n*
.1.3
.U3
.1.3
.U3
.1.3
.Ii3
.1.3
ll
U
3
U
3
li
3
li
9.3
9.3
7.0
9.3
7.0
9.3
7.0
9.3
135
135
125
125
110
UO
80
80
75
75
65
65
50
50
liS
liS
UO
UO
US
liS
35
35
5.5
5.5
5.0
5.0
U.S
b.S
3.5
3.5
3.5
3.5
3.0
3.0
160
160
160
160
135
135
115
115
315
315
285
:85
255
125
WOODLAND
Soil Phase
3-St
Eionon
Hniid
Equig.
Rnlncl.
Conge!.
C R
Imd
Thro*
Species To Favor in - -
Natural
Plantation
Species and Site Index
Ord.
Group
id*
RO
A
SH
IT
TP
W
L
RSp
WP
U. 85*
SO
A
SH
Ex. 95*
IP
WILDLIFE
Soil Phase
Wiidlile Habitat Elements
^
Ciogt
r.n»
«
(Id Hub
Ugiind
Plinli
liett.
SKiitbV
V.ntl
Pllnll
Will) HfrB
Idling
PIWIl
SKlllo*
tiifi
Of».
Pondt
Kinds of Wilrtlile Habitat
oodUid"
it'll,
HJ6.UI
rll.init
tioMe
HJiUKI
o-x
1
1 Good1 J fo
J Po
4 V.r, Poor
Figure 4-l9b
101
4-51
-------
Values of « found in the literature (Viessman et ai., 1977,
Linsley et al., 1975, Overton and Meadows, 1976, Wanielista, 1978)
range from 0.67 to 49 hr. Nevertheless most of the values cited
appear to be in the range 3-6 hr (0.00083 - 0.00167 sec" ). The
evidence is not clear as to whether there is any relationship between
soil texture and the a value although several published curves seem
to indicate a lower a value for sandy_soils. If_9<> field data are
available, an estimate of 0.00115 sec (4.14 hr ) could he used.
Use of such an estimate implies that, under ponded conditions, the
infiltration capacity will fall 98 percent of the way towards its
minimum value in the first hour, a not uncommon observation. Table
4-8 shows the rate of decay of infiltration for several values of a.
Table 4-8. Rate of Decay of Infiltration for Different Values of a.
hr-S
2
3
4
5
r value ,
C (sec -1)
(0.00056)
(0.00083)
(0.00111)
(0.00139)
Percent of decline of infiltration
capacity towards limiting value f^
after 1 hour.
76
95
98
99
The initial infiltration capacity, f , depends primarily on soil
type, initial moisture content, and surface vegetation conditions. For
example, Linsley et al. (1975) present data which show, for a sandy loam
soil, a 60 to 70 percent reduction in the f value due to wet initial
conditions. They also show that lower f values apply for a loam soil
than for a sandy loam soil. As to the effect of vegetation, Jens and
McPherson (1964, pp. 20.20 - 20.38) list data which show that dense grass
vegetation nearly doubles the infiltration capacities measured for bare
soil surfaces.
For the assumption that the decay coefficient a is independent of
initial moisture content to hold, f must be specified for the dry soil
condition. The continuous version of SWMM automatically calculates the
f value applicable for wetter conditions as part of the moisture accoun-
ting routine. However, the user of the single-event version of SWMM is
required to specify the f value for the storm in question, which may be
less than the value for dry soil conditions.
Published values of f vary depending on the soil, moisture, and
vegetation conditions for the particular test measurement. The f
values listed in Table 4-9 can be used as a rough guide. Interpolation
between the values may be required.
102
-------
Table 4-9. Representative Values for f .
A. DRY soils (with little or no vegetation):
Sandy soils: 5 in/hr
Loam soils: 3 in/hr
Clay soils: 1 in/hr
B. DRY soils (with dense vegetation):
Multiply values given in A. by 2 (after Jens and
McPherson, 1964)
C. MOIST soils (change from dry f value required for single
event model version of SWMM only):
Soils which have drained but not dried out (i.e., field
capacity): divide values from A and B by 3.
Soils close to saturation: choose value close to f
value.
Soils which have partially dried out: divide values
from A and B by 1.5-2.5.
For continuous simulation, infiltration capacity will be regenerated
(recovered) during dry weather. SWMM performs this function whenever
there are dry time steps - no precipitation or surface water - according
to the following equation (see Figure V-3, Appendix V).
-0,(t-t )
f=f-(£-f)e W (4-16)
P O O <*>'
where a, = decay coefficient for the recovery curve, sec , and
t ~ hypothetical projected time at which f = f on the recovery
curve, sec.
In the absence of better knowledge of Of,, it is taken to be a constant
fraction or multiple of or,
a, = R a (4-17)
d
where R = constant ratio, probably « 1.0, (implying a "longer" drying
curve than wetting curve). The parameter R is represented in the program
by REGEN (card B2).
On well drained porous soils (e.g., medium to coarse sands), recovery
of infiltration capacity is quite rapid and could well be complete in a
couple of days. For heavier soils, the recovery rate is likely to be
slower, say 7 to 14 days. The choice of the value can also be related
to the interval between a heavy storm and wilting of vegetation. The
value of ot, is then,
«, = 0.02/0 (4-1.R)
a
103 4-53
-------
where a, = Rof = recovery curve decay coefficient, day , and
D = number of days required for the soil to dry out (recover).
The factor of 0.02 in equation A"JL8 assumes 98 percent recovery of
infiltration capacity (i.e., e ' = 0.98). The value of R may then be
calculated from equation 4-17. For example, for a = 4.14 day and
drying times of 3, 7 and 14 days, values of R are 1.61 x 10 , 6.90 x
10 and 3.45 x 10" respectively.
Green-Ampt Infiltration -- The second infiltration option is the Green-Ampt
equation (1911), which, although not as well known as the Horton equation,
has the advantage of physically based parameters which, in principle,
can be predicted a priori. The Mein-Larson (1973) formulation of the
Green-Ampt equation is a two-stage model. The first step predicts the
volume of water, F , which will infiltrate before the surface becomes
saturated. From tnis point onward, infiltration capacity, f , is pre-
dicted directly by the Green-Ampt equation. Thus, ^
For F < F
s
f = i and
IMD
for i > K
i/K - 1 ' "s
(4-19)
No calculation of F for i < K
s - s
For F > F :
s
S IMD
f = £p and f = Kg(l + JL- ) (4-20)
where f - infiltration rate, ft/sec,
f = infiltration capacity, ft/sec,
i := rainfall intensity, ft/sec,
F ;= cumulative infiltration volume, this event, ft,
F - cumulative infiltration volume required to cause surface
saturation, ft,
S = average capillary suction at the wetting front (SUCT), ft water,
IMD = initial moisture deficit for this event (SMDMAX), ft/ft, and
K = saturated hydraulic conductivity of soil, (HYDCON) ft/sec.
S
Infiltration is thus related to the volume of water infiltrated as well
as to the moisture conditions in the surface soil zone. Full computa-
tional details are given in Appendix V.
104 4-54
-------
Like the Horton equation, the Green-Ampt infiltration equation has
three parameters to be specified S (SUCT), K (HYDCON) and IMD (SMDMAX).
Again, estimates based on any available field data should take precedence
over the following guidelines. No default values are provided.
The "Soil Survey Interpretation" sheet (see Figure 4-19) available
for most soils from the SCS shows values of "permeability" (hydraulic
conductivity) for the soil, K . However these values are taken from
data for disturbed samples ana tend to be highly variable. For example,
for Conestoga silt loam the values range from 0.63 to 2.0 in/hr (16 to
51 uun/hr). A butter guide for the K values is as given for parameter
fw for the Horton equation; theoretically these parameters (i.e., f^ and
K ) should be equal for the same soil. Note that, in general, the range
of K values encountered will be of the order of a few tenths of an inch
per Hour.
The moisture deficit, IMD, is defined as the fraction difference
between soil porosity and actual moisture content. Sandy soils tend to
have lower porosities than clay soils, but drain to lower moisture
contents between storms because the water is not held so strongly in the
soil pores. Consequently, values of IMD for dry antecedent conditions
tend to be higher for sandy soils than for clay soils. This parameter
is the most sensitive of the three parameters for estimates of runoff
from pervious areas (Brakensiek and Onstad, 1977); hence, some care
should be taken in determining the best IMD value to use. Table 4-10,
derived from Clapp and Hornberger (1973), gives typical values of IMD
for various soil types.
Table 4-10 Typical Values of IMD (SMDMAX) for Various Soil Types
Soil Typical IMD at
Texture Soil Wilting Point
Sand 0.34
Sandy Loam 0.33
Silt Loam 0.32
Loam 0.31
Sandy Clay Loam 0.26
Clay Loam 0.24
Clay 0.21
These IMD valuer, would be suitable for input to continuous SWMM; the
soil type selected should correspond to the surface layer for the parti-
cular subcatchment. For single event SWMM the values of Table 4-10
would apply only to very dry antecedent conditions. For moist or wet
antecedent conditions lower values of IMD should be used. When estimating
the particular value it should be borne in mind that sandy soils drain
more quickly than clayey soils, i.e., for the same time since the previous
event, the IMD value for a sandy soil will be closer in value to that of
Table 4-10 than it would be for a clayey soil.
105 4-55
-------
The average capillary suction, S , is perhaps the most difficult
parameter to measure. It can be derived from soil moisture - conductivity
data (Mein and Larsen, 1973) but such data are rare for most soils. Chu
(1978) gives average values of the product of S -IMD for a range of soils,
but these are not based on measurements. Fortunately the results obtained
are not highly sensitive to the estimate of S (Brakensiek and Onstad, 1977)
The approximate values which follow result from a survey of the literature
(Mein and Lairsea, 1973, Brakensiek and Ouslad, 1977, Clapp and Horuberger,
1978, Chu, 1978). Published values vary considerably and conflict; however,
a range of 2 to 15 in (50 to 380 mm) covers virtually all soil textures.
Table 4-11 summarizes the published values.
Table 4-11. Typical values of S (SUCT) for Various Soil Types.
Soil Texture Typical Values
for S (inches)
Sand 4
Sandy Loam 8
Silt Loam 12
Loam 8
Clay Loam 10
Clay 7
It is very difficult to give satisfactory estimates of infiltration
equation parameters that will apply to all soils encountered. Whichever
infiltration equation is used, the user should be prepared to adjust
preliminary estimates in the light of any available data whether they be
infiltrometer tests, measurements of runoff volume, or local experience.
Subcatchment Aggregation and Lumping --
As discussed earlier, it is desirable to represent the total catch-
ment by as few subcatchments as possible, consistent with the need for
hydraulic detail within the catchment. That is, if the only interest is
in hydrographs and pollutographs at the catchment outlet, as is likely
for continuous simulation, then one subcatchment should suffice for the
simulation (although up to 30 can be used for continuous simulation).
For a single event, detailed simulation, the number of subcatchments
needed is a function of the amount of hydraulic detail (e.g., backwater,
surcharging, routing, storage) that must be modeled. In addition,
enough detail must be simulated to allow non-point source controls to be
evaluated (e.g., detention, street sweeping). Finally, multiple subcatch-
ments are the only means by which a moving (kinematic) storm may be
simulated. Coupled spatial and temporal variations in rainfall can signi-
ficantly alter predicted hydrographs (Yen and Chow, 1968, Surkan, 1974,
James and Drake, 1980).
106 4-56
-------
Clearly, the required volume of input data (and personnel time)
decreases as the number of subcatchments decreases. How then, can sub-
catchments be aggregated or "lumped" to provide hydrographs and polluto-
graphs that are equivalent to more detailed simulations?
The most complete study of this question is contained in the Canadian
SWMM report (Proctor and Redfern and J. F. MacLaren, 1976a) in which the
effect, of lumping is compared on real .ind hypothetical catchments. Similar
work has been performed independently by Smith (1975). In both studies it
is shown that a single equivalent lumped catchment can be formulated by
proper adjustment of the subcatchment width.
In SWMM, Runoff and Transport simulation of the drainage network
(i.e., conduits and channels) adds storage to the system and thus attenuates
and somewhat delays the hydrograph pea.ks. When the drainage network is
removed from the simulation, subcatchment runoff feeds "instantaneously"
into inlets, with consequent higher and earlier hydrograph peaks. The
key to aggregation of subcatchments is thus the replacement of the lost
storage. This :Ls best accomplished through variation of the subcatchment
width, although the r.amo effect could be achieved through variation of
the slope or roughness (see discussion of equation 4-6). However, it is
assumed that reasonable average values of the latter two parameters for
the total catchment may be obtained by weighting individual subcatchment
values by their respective areas. (For the roughness an area-weighted
harmonic mean may be used, although it is probably an unnecessary refinement)
Hence, the subcatchment width is a more logical parameter to be adjusted.
It was shown in the discussion of the subcatchment width, that
reducing its value increases storage on the subcatchment. Hence, as
subcatchments aire aggregated and drainage network storage lost, the
total catchment width, i.e., the sum o£ the subcatchment widths, must be
reduced accordingly. This may be seen in Figure 4-20 for a very schema-
tized drainage network in which the subcatchment widths are nominally
twice the length of the drainage conduits (Smith, 1975). The lumped
catchment could be represented by a single subcatchment, as in the
bottom sketch oi: Figure 4-20, in which the width is approximately twice
the length of the main drainage channel. Experience indicates (Smith,
1975, Proctor and Redfern and J. F. MacLaren, 1976a) that good results
can be obtained with no channel/conduic network. However, the Canadian
study (Proctor and Redfern and J. F. MacLaren, 1976a) does illustrate
the routing effect of an "equivalent pipe" in the Transport Block. Note
that if the storm duration is long compared to the catchment time of
concentration, and if the rainfall intensity is constant, the peak flows
obtained for either a lumped or detailed simulation will be about the same,
since equilibrium outflow must ultimately result (see the discussion of
Figure 4-15).
Several examples of lumping using real rainfall data on real catch-
ments are shown by Proctor and Redfern and James F. MacLaren (1976a) and
Smith (1975). An instructive example for the 2330 ac (943 ha) West Toronto
107 4-57
-------
/A
! I
X -- »
c:
E?
WIDTH = 5 3/8x2
f
1
N,
%
J
f
}
> J
\
1
J
WIDTH=3Vsx2
WIDTH=V8*2
I UNIT
Figure 4-20. Effect of Changing the Level of Discretization on the
Width of Overland Flow. (After Smith, 1975, p. 57.)
108
4-58
-------
area is taken from the former reference and shown in Figure 4-21. A
Runoff-Transport simulation using 45 subcatchments and including the
drainage network is compared with three Runoff-only simulations with no
drainage network. The best agreement, in terms of matching of peak
flows, between the detailed and lumped simulations occured for a single
subcatchment width of 60,000 ft (18,000 m) which is about 1.7 times the
length of the main trunk conduit in the actual system. Even if a factor
of two had been used (i.e., a width of 70,600 ft ut 21,500 in) as a first
guess, agreement would not be bad. The timing of the peaks for the
single subcatchment representation is somewhat early, but adequate for
most purposes. Recall that it is difficult to change the timing of
subcatchment hydrograph peaks by changing only the width.
It is assumed that when subcatchments are aggregated, other para-
meters required on card HI are simply areally weighted. When this is
done, very little difference in runoff volume occurs between the aggre-
gated and detailed representations. Differences that do result are usu-
ally-'from water that remains in storage and has not yet drained off of the
lumped catchment, or from very slightly increased infiltration on the
lumped catchment, again due to the longer presence of standing water on
pervious areas (because of the reduced width).
To summarize, many subcatchments may be aggregated into a single
lumped or equivalent subcatchment by using areally weighted subcatchment
parameters and by adjustment of the subcatchment width. The lumped
subcatchment width should be approximately twice the length of the main
drainage channel (e.g., the trunk sewer) through the catchment in order
to match hydrograph peaks. The effect on runoff volume should be minimal.
Runoff quality predictions are affected by aggreagtion of sub-
catchments to the extent that hydrographs and surface loadings are
changed. When .ireal weighted averages of the latter are used for a
lumped catchment, total storm loads are essentially the same as for a
detailed simulation. Pollutographs of concentration versus time then
vary only because of hydrograph variations.
Snowmelt Parameters (Card Groups 11-13) --
Overview of Procedures Following the earlier work of the Canadian
SWMM study by Proctor and Redfern and James F. MacLaren (1976a, 1976b,
1977) snowmelt simulation has been added for both single event and
continuous simulation. Since snowmelt computations are explained in
detail in Appendix II, only an outline is given here. Most techniques
are drawn from Anderson's (1973) work for the National Weather Service
(NWS). For continuous simulation, daily max-min temperatures from the
NWS "WBAN Summary of the Day, Deck 345" are converted to hourly values
by sinusoidal interpolation, as explained earlier.
Urban snow removal practices may be simulated through "redistribution
fractions" input for each subcatchment (discussed below), through altera-
tion of the melt coefficients and base temperatures for the regions of
109 4-59
-------
800
900
1000
II 00
WEST TORONTO AREA
STORM OF SEPT. 23,1973
DETAILED SIMULATION
45 SUBCATCHMENTS
Wo = 207,196 FT.
RUNOFF 8 TRANSPORT
I SUBCATCHMENT
Wo* 130,000 FT.
SIMULATION
USING RUNOFF < a a
BLOCK ONLY
I SUBCATCHMENT
Wo = 60,000 FT.
I SUBCATCHMENT
Wo = 40.000 FT.
Wo * OVERLAND FLOW WIDTH
6:00
10'00
1140 IZ'OO
TIME IN HOURS
1300
14=00
Figure 4-21.
Effect on Hydrographs of Changing Subcatchment Width
for West Toronto Area. (After Proctor and Redfern and
J.F. MacLaren, 1976a, p. 216.)
110
4-60
-------
each subcatchmeut, and through the areal depletion curves used for
continuous simulation. Anderson's temperature-index and heat balance
melt equations are used for melt computations during dry and rainy
periods, respectively. For continuous simulation, the "cold content" of
the pack is maintained in order to "ripen" the snow before melting.
Routing of melt water through the snow pack is performed as a simple
reservoir routing procedure, as in the Canadian study.
The presence of a snow pack is assumed to have no effect on overland
flow processes beneath it. Melt is routed in the same manner as rain-
fall.
Subcatchment Schematization When snowmelt is simulated, a fourth
subarea is added to each subcatchment as illustrated in Figure 4-11.
The properties of each subarea are described in Table 4-4. The main
purpose of the fourth subarea is to permit part of the impervious area
(subarea A4) to be continuously snow covered (e.g., due to windrowing or
dumping) and part (subareas Al plus A3) to be "normally bare" (e.g.,
streets and sidewalks that are plowed). However, during continuous
simulation, the normally bare portion can also have snow cover up to an
amount WEPLOW (card 12) inches water equivalent (in. w.e.). (All snow
depths and calculations are in terms of the equivalent depth of liquid
water.) The snow covered and normally bare impervious areas are determined
from fraction SNN1 (card II). During single event simulation, subarea
A4 retains 100 percent snow cover until it has all melted. During
continuous simulation, an areal depletion curve, discussed earlier, is
used.
Similarly, for single event simulation, a fraction SNN2 (card II)
of the pervious area remains 100 percent snow covered. During continuous
simulation, the whole pervious area is subject to an areal depletion
curve.
Initialization Initial snow depths (inches water equivalent) may be
entered using parameters SNN3, SNN4 (card II) and SNN7 (card 12). This
is likely to be the only source of snow for a single event simulation
although snowfall values may be entered as negative precipitation in
card group E2. During continuous simulation, the effect of initial
conditions will die out, given a simulation of a few months.
No liquid runoff will leave the snow pack until its free water
holding capacity (due to its porosity) has been exceeded. The available
volume is a constant fraction, FWFRAC (card Cl) of the snow depth, WSNOW.
Hence, initial values of free water, FW, should maintain the inequality
FW < FWFRAC WSNOW (4-21)
Melt Equations During periods of no rainfall, snowmelt is computed by
a degree-day or temperature index equation,
SMELT = DHM (TA - TBASE) (4-22)
111 4-61
-------
DHMAX
DHM
DHMIN
250
200
Dec. 21
DAY NUMBER-
June2l
Figure 4-22. Seasonal Variation of Melt Coefficients for Continuous Simulation.
-P-
N>
-------
where SMELT = snowmelt rate, in. w.e./hr,
DHM = melt coefficient, in. w.e./hr-°F,
TA = air temperature, °F, and
TBASE = snowmelt base temperature, °F.
There is no melt when TA £ TBASE. For single event simulation, the melt
coefficient, DID!, remains constant. For continuous simulation it is
allowed to vary sinusoidally from a minimum value on December 21 to a
maximum value on June 21 (see Figure 4-22) in order to reflect seasonal
changes.
Melt coefficients and base melt temperatures may be determined both
theoretically and experimentally. Considering the former, it is possible
to first write a snowmelt equation from a heat budget formulation that
includes all relevant terms: change in snow pack heat storage, net
short wave radiation entering pack, coaduction of heat to the pack from
underlying ground, net (incoming minus outgoing) longwave radiation
entering pack, convective transport of sensible heat from air to pack,
release of latent heal: of vaporization by condensation of atmospheric
water vapor, and advection of heat to snow pack by rain. (It is assumed
here that the pack is "ripe", i.e., just at the melting point, so that
rain will not freeze and release its latent heat of fusion.) The equation
may then be linearized about a reference air temperature and reduced to
the form of equation 4-22. Exactly this procedure is followed in a
detailed example presented in Appendix III.
Alternatively, observed melt, in inches per time interval, may be
plotted against temperature for that time interval, and a linear relation-
ship developed of the form of equation 4-22. An often-cited such develop-
ment for natural areas is illustrated in Figure 4-23 taken from the Corps
of Engineers (1956). Viessman et al. (1977) also present a good discussion
of degree-day equations. In the highly desirable but unlikely event
that snowmelt data are available, the experimental procedure of Figure
4-23 is probabaLy best for urban areas due to the considerable variation
of snow pack and meteorological conditions that will be encountered,
making reasonable theoretical assumptions more difficult.
For natural areas, considerable range in melt coefficients exits,
on the order of 0.0006 to 0.008 in/hr-°F (0.03 to 0.4 mm/hr-°C). Although
base melt temperatures are nominally near the freezing point (i.e., 32°F
or 0°C) they may be considerably lower depending on the exposure of the
site and meteorological conditions. For instance, for the linearization
performed in Appendix III a base melt temperature of 9°F (-13°C) was
computed, which is valid only over the range of air temperatures used
in the linearization (approximately 30 to 40°F or -1 to 5°C).
If the effects of snow removal practices (e.g., street salting) and
land surface factors are known, different melt coefficients and base melt
temperatures may be entered for the different snow covered subareas of a
subcatchment. For instance, street salting lowers the freezing point in
113 . 4-63
-------
z
u
z
ul
2
O
Z
O
J4
Note:
£quo/ions ore opplicoble only /o
rang? of temperatures shewn on
the diagram.
AZ 4« 5O 14
MEAN DAILY TEMPERATURE (T),*r
(a) MEAN TEMPERATURE INDEX
Figure 4-23. Degree-Day Equations for Snow Melt. (After
Corps of Engineers, 1956, plate 6-4).
114
4-64
-------
proportion to the concentration of the chemical. Handbook values (Chemical
Rubber Co., 1976, pp. D218 - D267) for freezing point depression are plotted
versus concentration in Figure 4-24 for several common roadway salting
chemicals. Thus, the base melt temperature computed for pure water might
be lowered by an amount taken from Figure 4-24 if an idea is known about
the likely concentration on the roadway. The concentration will depend
upon the amount of chemical applied and the amount of snowfall and might
cot be easily computed. An interesting alternative would be to let S'mlfl
predict it!
During periods (i.e., time steps) with rainfall, good assumptions
can be made about relevant meteorological parameters for the complete
heat balance melt equation. It then replaces the degree-day equation
for "wet" time steps. A detailed explanation may be found in reference
to equation III-8 in Appendix III. Melt during these time steps is
linearly proportional to air temperature and wind speed.
Areal Depletion Parameters -- In the earlier discussion of areal depletion
curves it was noted that there would be 100 percent cover above a depth
of SI inches water equivalent. Values of SI for jmnervious an.H pervious
areas are read on card 12.
For natural areas, Anderson (1973) recommends that a distinction be
made on the basis of areal homogeneity. For a very heterogeneous area
there are likely to be areas that receive little snow, or else it will
quickly melt. The value of SI for such areas might be about the maximum
depth anticipated. For homogeneous areas a much lower value would be
appropriate.
No specific information is available for urban areas; however, they
are likely to be quite heterogeneous, especially if large, aggregated
subcatchments are being used for the continuous simulation. Hence, a
high value is probably indicated. Whichever values are used, they
should be consistent with the form of the areal depletion curves entered
in card groups C3 and C4. In general (depending somewhat on the areal
depletion curve), the higher the value of SI, the more "stacked up" on a
catchment is th£ snow, and snowmelt will occur at a lower rate over a
longer time.
Snow Redistribution The program allows (during continuous simulation)
snow that falls on the normally bare impervious areas to be redistributed
according to th^ fractions given as SFRAC on card 12. This is intended
to simulate plowing and other snow removal practices in urban areas.
Snow depths above WEPLOW inches water equivalent are thus redistributed
according to Figure 4-25.
The value of WEPLOW depends upon the level of service given the
particular impervious area. That is, at what snow depth do removal
practices start? Some guidelines are provided by Richardson et al. (1974)
in Table 4-12.
115 4-65
-------
30
28
26
24
22
Z20
O
0)18
(O
II-
0
0.
UJI4
O
1-12
Z
g,o
O 8
Z
N 6
LJ
o:
u.
Q
I I
AMMONIUM SULFATE - (NH4) S0
12
CALCIUM CHLORIDE-CaCI2-2H20
POTASSIUM CHLORIDE- KCI
CaCI-2H 0
X
MAGNESIUM CHLORlDE-MgCI-6H20
m
SODIUM CHLORIDE-NaCI
UREA-NH2CONH2
I
NaCI
I
24 6 8 10 12 14 16 18 20
CONCENTRATION, % by weight
Figure 4-24. Freezing Point Depression Versus Roadway Salting Chemical
Concentration. Compiled from data from CRC (1976).
116
4-66
-------
Al = IMPERVIOUS AREA WITH DEPRESSION STORAGE
A2= PERVIOUS AREA
A3= IMPERVIOUS AREA WITH ZERO DEPRESSION STORAGE
A4= SNOW COVERED IMPERVIOUS AREA
Al +A3= NORMALLY BARE
SFRAC (5)
AMOUNT TRANSFERRED
IS FRACTION OF SNOW
ABOVE WEPLOW INCHES
WATER EQUIVALENT
SFRAC (3)
PERVIOUS IN
LAST SUBCATCHMENT
SFRAC (4)
OUT OF SIMULATION
Figure.4-25. Illustration of Snow Redistribution Fractions.
-------
Table 4-12.
Road Classification
Low-Speed Ilultilane
Urban Expressway
Guidelines for Levels of Service in Snow and Ice Control,
1974.)
(Richardson et al.,
2. High-Speed
4-Lane Divided Highways
Interstate System
ADT greater than 10,000
3. Primary Highways
Undivided 2 and 3 lanes
ADT 500 -- 5000
4. Secondary Roads
ADT less than 500
Level of Service
Full Pave- Full Pavement
Snow Depth to Max. Snow Depth n«=t Clear of Clear of Ice
Start Plowing on Pavement Snow After After Storm
(Inches) (Inches) Storm (Hours) Hours
Roadway routinely patrolled during storms 0.5 to I
All traffic lanes treated with chemicals
All lanes (including breakdown lanes) operable
at all tines but ac reduced speeds
Occasional patches of well-sanded snow pack
Roadway repeatedly cleared by echelons
of plows to minimize traffic disruption
Clear pavement obtained as soon as possible
Roadway routinely patrolled during storms l
Driving and passing lanes treated with chemicals
Driving lane operabj2 at all times at reduced
speeds
Passing lane operable depending on equipment
availability
Clear pavement obtained as soon as possible
Roadway is routinely patrolled during storms 1
Mostly clear pavement after storm stops
Hazardous areas receive treatment of chemicals
or abrasive
Remaining snow and ice removed when thawing occurs
Roadway is patrolled at least once during a storm 2
Bare left-wheel track with intermittent snow cover
Hazardous areas are plowed and treated with chemicals
or abrasives as a first order of work
Full width of road is cleared as equipment becomes
available
12
1.5
12
2.5
24
J>
00
-------
The five fractions, SFRAC, should sum to 1.0 and are defined on
the basis of the ultimate fate of the removed snow. For instance, if
snow is plowed from a street onto an adjacent impervious or pervious
area, fractions SFRAC(l) or SFRAC(2) would be appropriate. It may also
be transfered to the last subcatchment (e.g., a dumping ground) or
removed from the simulation (i.e., removed from the total catchment)
altogether. Finally, it may be converted to immediate melt. Should
variations in saow removal practices need to be simulated, uiffereut
subcatchments can be established for different purposes and the frac-
tions varied accordingly.
Surface Quality Input Data (Card Groups Jl-Ll)
Overview of Quality Procedures --
For most SWUM applications, the Runoff Block is the origin of water
quality constituents. Although effects of dry-weather flow and scour and
deposition may be included in the Transport Block, (dry-weather flow quality
may also be included in the Storage/treatment Block), the generation of
quality constituents (e.g., pollutants) in the storm water itself can
only be included in the Runoff Block.
Several mechanisms constitute the genesis of stormwater quality, most
notably buildup and washoff. In an impervious urban area, it is usually
assumed that a supply of constituents is built up on the land surface dur-
ing dry weather preceding a storm. Such a buildup may or may not be a
function of time and factors such as traffic flow, dry fallout arid street
sweeping. With the storm the material is then washed off into the drainage
system. The physics of the washoff may involve rainfall energy, as in some
erosion calculations, or may be a function of bottom shear stress in the
flow as in sediment transport theory. Most often, however, washoff is
treated by an empirical equation with slight physical justification.
As an alternative to the use of a buildup-washoff formulation, quality
loads (i.e., mass/time) may be generated by a rating curve approach in which
loads are proportional to flow to some power. Such an approach may also be
justified physically and is often easier to calibrate using available data.
Another quality source is catchbasins. These are treated in SWMM as
a reservoir of constituents in each subcatchment available to be flushed
out during the storm.
Erosion of "solids" may be simulated directly by the Universal Soil
Loss Equation (USLE). Since it was developed for long term predictions
(e.g., seasonal or annual loads), its use during a storm event in SWMM
is questionable. But it is convenient since many data are available to
support it.
A final source of constituents is in the precipitation itself. Much
more monitoring exists of precipitation quality at present than in the past,
and precipitation can contain surprisingly high concentrations of many
parameters. This is treated in SWMM by permitting a constant concentration
of constituents ia precipitation.
119 4-69
-------
Many constituents can appear in either dissolved or solid form (e.g.,
BOD, nitrogen, phosphorus) and may adsorbed onto other constituents (e.g.,
pesticides onto "solids") and thus be generated as a portion of such other
constituents. To treat this situation, any constituent may be computed as
a fraction ("potency factor") of another. For instance, five percent of
the suspended solids load could be added to the (soluble) BOD load. Or
several particle size - specific gravity ranges could be generated, with
other constituents consisting of fractions of each.
Up to ten quality constituents may be simulated in the Runoff Block.
All are user supplied, with appropriate parameters for each. All are
transferred to the interface file for transmittal to subsequent SWMM blocks,
but not all may be used by the blocks; see the documentation for each block.
Up to five user supplied land uses may be entered to characterize dif-
ferent subcatchments. Street sweeping is a function of land use, and indi-
vidual constituents. Constituent buildup may be a function of land use or
else fixed for each constituent. Considerable flexibility thus exists.
When gutter/piper, are included, quality constituents are routed through
them assuming complete mixing within each gutter/pipe at each time step. No
scour, deposition or decay-interaction during routing is simulated in the
Runoff Block.
Output consists of pollutographs (concentrations versus time) at de-
sired locations along with total loads, and flow-weighted concentration
means and standard deviations. The pollutographs may be plotted using the
graph routines of the Executive Block (Section 2). In addition, summaries
are printed for each constituent describing its overall mass balance for the
simulation for the total catchment, i.e., sources, removals, etc. These sum-
maries are the most useful output for continuous simulation runs.
In the following material, the processes described above will be dis-
cussed in more detail. The various parameters will be related to individual
card groups as appropriate.
Quality Simulation Credibility
Although the conceptualization of the quality processes is not diffi-
cult, the reliability and credibility of quality parameter simulation is
very difficult to establish. In fact, quality predictions by SWMM or almost
any other surface runoff model are almost useless without local data for the
catchment being simulated to use for calibration and verification. If such
data are lacking, results may still be used to compare relative effects of
changes, but parameter magnitudes (i.e., actual values of predicted concen-
trations) will forever be in doubt. This is in marked contrast to quantity
prediction for which reasonable estimates of hydrographs may be made in
advance of calibration.
Moreover, as will be discussed, there is disagreement in the literature
as to what are the important and appropriate physical and chemical mecha-
nisms that should be included in a model to generate surface runoff quality.
120 4_70
-------
The objective in the Runoff Block has been to provide flexibility in mecha-
nisms and the opportunity for calibration. But this places a considerable
burden on the user to obtain adequate data for model usage and to be famil-
iar with quality mechanisms that may apply to the catchment being studied.
This burden may often be ignored, leading ultimately to model results
being discredited.
In the end then, there is no substitute for local uata, that is,
flow and concentration measurements, with which to calibrate and verify
the quality predictions. Without such data, little reliability can be
placed in the predicted magnitudes of quality parameters.
Quality Constituents --
The number and choice of constituents to be simulated must reflect the
user's needs, potential for treatment and receiving water impacts, etc. Al-
most any constituent measured by common laboratory or field tests can be
included, up to a total of ten. The name and concentration units are entered.
as A-format variables in card group J3. These will be passed to subsequent
blocks (see Section .?) and are used as column headings for tabular output
of concentrations, as illustrated in Figure 4-26. This heading style is
used in both the Runoff and Transport Blocks.
Options for concentration units are reasonably broad and broken into
three categories, indicated by parameter NDIM in card group J3. Host con-
stituents are measurable in units of milligrams per liter, mg/1. Although
parameters such as metals, phosphorus or trace organics are often given as
micrograms per liter, (Jg/1, the output of concentrations for NDIM=0 is F10.3
(allowing for three decimal places), and it is expected to be compatible with
reported values of such parameters. Thus, the use of mg/1 should suffice for
all parameters for which the "quantity" of the parameter is measured as a mass
(e.g., mg).
A notable exception to the use of mass units is for bacteria, for which
constituents such as coliforms, fecal strep etc. are given as a number or
count per volume, e.g., MPN/1. Setting NDIM=1 accounts for these units (or
any other type of "quantity" per liter, including mass if desired). Con-
centration output for these constituents is given an E9.3 format.
A third category covers parameters with specialized concentration-
type units such as pH, conductivity ((Jmho), turbidity (JTU), color (PCU),
temperature (°C), etc. These are simulated using NDIM=2. For these param-
eters, interpretation of concentration results is straightforward, but "total
mass" or "buildup" is mostly conceptual. Since loads (e.g., mass/time) are
transmitted in terms of concentration times flow rate, whichever concentra-
tion units are used, proper continuity of parameters is readily maintained.
Of course, simulation of a parameter such as temperature could only be done
to the zeroth approximation in any event since all Runoff Block constituents
are conservative.
121 4-71
-------
PNAME(I.K)
PNAME (2,K)
,PNAME(I,KH
PNAME (2.K-H)
PUNIT(I,K)
PUNIT(2,K)
PUNIT(I,K+I)
PUNIT(2,K»I)
FIELD WIDTH =10 FOR
CONCENTRATION OUTPUT,
E.G..FI0.3 OR IX, E9.3
Figure 4-26. Layout of Quality Constituent Headings. Parameters
PNAME and PUNIT are entered in card group J3, Table
4-28.
122
4-72
-------
Land Use Data (Card Group J2J --
Each subcatchment must be assigned only one of up to five user supplied
land uses. The number of the land use is used as a program subscript, so at
least one land use card must be entered. Street sweeping is a function of
land use and constituent (discussed subsequently). Constituent buildup may
be a function of land use depending on the type of buildup calculation spec-
ified for each i.n card group J3. The buildup parameters DDLIM, DDPQW, and
DDFACT in card group J2 are used only when constituent buildup will be a
function of "dust and dirt" buildup. This is discussed in detail below.
The land use name, LNAME, will be printed in the output using eight
columns. The land use types are completely arbitrary, but they could re-
flect those for which data are available and, of course, those found in
the catchment, or an aggregate thereof.
Buildup --
Background -- One of the most influential of the early studies of stormwater
pollution was conducted in Chicago by the American Public Works Association
(1969). As part of this project, street surface accumulation of "dust and
dirt" (anything passing through a quarter inch mesh screen) was measuredxby
sweeping with brooms and vacuum cleaners. The accumulations were measured
for different land uses and curb lengths, and the data were normalized in
terms of pounds of dust and dirt per dry day per 100-ft of curb or gutter.
These well known results are shown in Table 4-13 and imply that dust and
dirt buildup is a linear function of time.
Table 4-13 Measured Uust and Dirt (DD) Accumulation in Chicago by
the APWA in 1969 (APWA, 1969).
Type
1
2
3
4
5
Land Use
Single Family Residential
Multi-Family Residential
Commercial
Industrial
Undeveloped or Park
Pounds DD/dry day 100 ft-curb
0.7
2.3
3.3
4.6
1.5
The dust and dirt samples were analyzed chemically, and the fraction of
sample consisting of various constituents for each of four land uses was
determined, leading to the results shown in Table 4-14.
123 4-73
-------
Table 4-14 Milligrams of Pollutant Per Gram of Dust and Dirt (Parts
Per Thousand By Mass) For Four Chicago Land Uses From 1969
APWA Study (APWA, 1969).
Land Use Type
Single Family Multi-Family
Parameter Residential Residential Commercial Industrial
BOD5
COD
Total Coliforms3
Total N
Total PO, (as PO.)
5.0
40.0
1.3xl06
0.48
0.05
3.6
40.0
2.7xl06
0.61
0.05
7.7
39.0
1.7xl06
0.41
0.07
3.0
40.0
l.OxlO6
0.43
0.03
ft
Units for coliforms are MPN/gram.
From the values shown in the table, the buildup of each constituent (also
linear with time) can be computed simply by multiplying dust and dirt by
the appropriate fraction. Since the APWA study was published during the
original SWMM project (1969-1971), it represented the state of the art at
the time and was used extensively in the development of the surface quality
routines (Metcalf and Eddy et al., 1971a, Section 11). In fact, the
formulation and data may still be used in this present version of SWMM
should the user wish to rely upon highly site specific results for Chicago.
Needless to say, unless the application is in Chicago this is not recom-
mended. Several useful studies have been conducted since the pioneering
APWA work which permit much more selectivity.
Of course the whole buildup idea essentially ignores the physics of
generation of pollutants from sources such as street pavement, vehicles,
atmospheric fallout, vegetation, land surfaces, litter, spills, anti-skid
compounds and chemicals, construction, and drainage networks. Lager et al.
(1977a) consider each source in turn and give guidance on buildup rates.
But the rates that are (optionally) entered into the Runoff Block only
reflect the aggregate of all sources.
Available Studies The 1969 APWA study (APWA, 1969) was followed by sev-
eral more efforts, notably AVCO (1970) reporting extensive data from Tulsa,
Sartor and Boyd (1972) reporting a cross section of data-from ten US cities,
and Shaheen (1975) reporting data for highways in the Washington, D.C. area.
Pitt and Amy (1973) followed the Sartor and Boyd (1972) study with an anal-
ysis of heavy metals on street surfaces from the same ten U.S. cities. More
recently, Pitt (1979) reports extensive data gathered both on the street
surface and in runoff for San Jose. A drawback of the earlier studies is
that it is difficult to draw conclusions from them on the relationship
between street surface accumulation and stormwater concentrations since
they were seldom measured simultaneously.
124 4-74
-------
Amy et al. (1975) provide a summary of data available in 1974 while
Lager et al. (1977a) provide a similar function as of 1977 without the
extensive data tabulations given by Amy et al. Perhaps the most compre-
hensive summary of surface accumulation and pollutant fraction data is
provided by Manning et al. (1977) in which the many problems and facets of
sampling and measurements are also discussed. For instance, some data are
obtained by sweeping, others by flushing; the particle size characteristics
and degree of removal fro.-s the street surface differ for each method. Seme
results of Manning et al. (1977) will be illustrated later. Surface accumu-
lation data may be gleaned, somewhat less directly, from references on
loading functions that include McElroy et al. (1976), Heaney et al. (1977)
and Huber et al. (1979, 1980).
Ammon (1979) has summarized many of these and other studies, specific-
ally in regard to application to SWMM. For instance, there is evidence to
suggest several buildup relationships as alternatives to the linear one, and
these relationships may change with the constituent being considered. Upper
limits for buildup are also likely. Several options for both buildup and
washoff are investigated by Ammon, and his results are partially the basis
for formulations in this version of SWMM. Recently. Jewell et a].. (1980)
also provide a useful critique of methods available for simulation of sur-
face runoff quality and ultimately suggest statistical analysis as the
proper alternative. Many of the problems and weaknesses with extensive data
and present modeling formulations are pointed out by Sonnen (1980) along
with guidelines for future research.
To summarize, many studies and voluminous data exist with which to
formulate buildup relationships, most of which are purely empirical and
data-based, ignoring the underlying physics and chemistry of the generation
processes. Nonetheless, they represent what is available, and modeling
techniques in SWMM are designed to accommodate them in their heuristic form.
Buildup Formulations Most data, as will be seen, imply linear buildup
since they are given in units such as Ib/ac-day or lb/100 ft curb-day. As
stated earlier, the Chicago data that were used in the original SWMM formu-
lation assumed a linear buildup. However, there is ample evidence that
buildup can be nonlinear; Sartor and Boyd's (1972) data are most often cited
as examples (Figure 4-27). More recent data from Pitt (Figure 4-28) for San
Jose indicate almost linear accumulation, although some of the best fit
lines indicated in the figure had very poor correlation coefficients, rang-
ing from 0.35 § r ^ 0.9. Even in data collected as carefully as in the San
Jose study, the scatter (not shown in the report) is considerable. Thus,
the choice of the best functional form is not obvious. Whipple (1977) has
criticized the linear buildup formulation included in the original SWMM,
although it is somewhat irrelevant since the user has long been able to
insert his/her own desired initial loads, calculated by whatever procedure
was desired, in card group LI. However, linear buildup between storms
during continuous simulation has indeed been used in SWMM, until this
present version.
The choice of the proper functional form must ultimately be the re-
sponsibility of the user. The program provides three options for dust and
dirt buildup (Table 4-15) and three for individual constituents (Table
4-16). They arr the same three, as seen from the tables, namely:
125 4-75
-------
01 234567
ELAPSED TIME SINCE LAST CLEANING BY SWEEPING OR RAIN (do/.)
10
Figure 4-27. Non-linear Buildup of Street Solids. (After Sartor and
Boyd, 1972, p. 206.)
126
4-76
-------
NJ
O
Z
o
<
o
CO
CO
Q
_J
O
CO
2500-
2000
1500
1000
500-
_, Keyes - oil and screens
____-- Downtown - poor asphalt (winter only)
Keyes good asphalt
Tropicana good asphalt
Downtown good asphalt (winter only)
1.0
20 30
i
40
i
50
i
60
70
DAYS SINCE LAST CLEANED
Figure 4-28. Buildup of Street Solids in San Jose. (After Pitt, 1979, p. 29.)
-------
Table 4-15. Buildup Equations and Units for Dust and Dirt.
Enter parameters on Card Group J2.
DO = Dust and Dirt, Ib. t = time, days.
For metric input substitute kg for Ib,
ha for ac and km for 100-ft.
METHOD
(Card Group
0
1
2
METHOD
0
1
2
J2)
Type
Power-Linear
Exponential
Michaelis-Menton
Units for Card Input of:
JACGUT DDLIM
0
1
2
0
1
2
0
1
2
lb-(100 ft curb)"1
Ib-ac"1
Ib
lb-(100 ft curb)"1
Ib-ac"1
Ib
lb-(100 ft curb)"1
Ib-ac"1
Ib
Equation
DD = DDFACT tDDPOW
DD < DDLIM
DD=DDLIM.(l-e"DDPOW-t
DD = DDLIM -t/(DDFACT+t)
DDPOW
Dimensionless
Dimensionless
Dimensionless
day"1
day"1
d.iy
Not Used
Not Used
Not Used
Equation Number
(4-23)
) (4-24)
(4-25)
DDFACT
lb-(100 ft-curb)"1-day"DUPOW
.. -1 . -DDPOW
Ib'ac 'day
.. . -DDPOW
Ib-day
Not Used
Not Used
Not Usrd
day
day
day
Parameters DDLIM, DDPOW, and DDFACT are single subscripted by land use, J.
128
4-78
-------
N>
I
~J
VO
Table 4-16. Buildup Equations for Constituents.
Eater Parameters on Card Group J3.
PSHED = Constituent quantity. t = time, days.
For parameter units, see Table 4-17.
KALC
(Card Group J3)
1
2
3
Type
Power-Linear
Exponential
Michaelis-Menton
Equation
PSHED = QFACT(3)-tQFACT(2)
PSHED < QFACT(l)
PSHED = QFACT(l)-(l-e~^FACT'2
r-IED - ^"dM
PoIIED (QFACT(3)+t)
Equation Number
(4-26)
l't) (4-27)
(4-28)
Parameters QFACT are doubly subscripted. Second subscript is constituent number, K.
-------
U)
O
Table 4-17. Units for Card Input of Constituent Parameters, Card Group J3.
Define Q. and Q, = Constituent quantity as follows:
NDIM Q
Qj = Ib, Q2 = rag
Q. = Q, = 10 Other quantity, e.g., 10 -MPN For metric input substitute
kg for Ib, ro for ft , ha for
Q = Q. = Concentration x ft , e.g., JTU ft ac and km for 100-ft.
For KALC = 4, buildup parameters are not required.
For KALC = 0, QFACT(J.K) = Q2/8DD for J = 1 to JLAND and g_D = grams dust and dirt. (E.g., see Table 4-14)
Otherwise:
KALC
KACGUT
QFACT(1,K)
QFACT(2,K)
QFACT(3,K)
1
2
3
0
1
2
0
1
2
0
1
2
-1
Q '(100 ft-curb)
Q -ac"1
1
Q,
Qj-OOO ft-curb)"1
Q -ac
Q
Qj-UOO ft-curb)"1
Qj-ac"1
Ql
Dimensionless
Dimensionless
Dimensionless
day
*
. i
day
day
Not Used
Not Used
Not Used
Q -(100 ft-curbrW^2-*
q!..c-1.d!y-5FACT<2'«
Qi1.day-QFACT(2,K)
Not Used
Not Used
Not Used
day
day
day
QFACT(4,K) and QFACT(S.K) not required for KALC f 0.
.O
I
Co
O
-------
1. power-linear,
2. exponential, or
3. Michaelis-Menton.
Linear buildup is simply a subset of a power function buildup. The shapes
of the three functions are compared in Figure 4-29 using the dust and dirt
parameters (card group J2) as examples, and a strictly arbitrary assignment
of numerical values to the parameters. Exponential and Michaeiis-Meutoii
functions have clearly defined asymptotes or upper limits. Upper limits
for linear or power function buildup may be imposed if desired. "Instan-
taneous buildup" may be easily achieved using any of the formulations with
appropriate parameter choices. For instance, if it were desired to always
have a fixed amount of dust and dirt available, DDLIM, at the beginning of
any storm event (i.e., after any dry time step during continuous simula-
tion), then linear buildup could be used with DDPOW =1.0 and DUFACT e^ual
to a large number ^ DDLIM/DELT. Linear buildup is "cheapest" to run in
terms of computer time.
It is apparent in Figure 4-29 that different options may be used to
accomplish the same objective (e.g., nonlinear buildup); the choice may
well be made on the basis of available data to which one or the other func-
tional forms have been fit. If an asymptotic form is desired, either the
exponential or Michaelis-Menton option may be used depending upon ease of
comprehension of the parameters. For instance, for exponential buildup
the exponent (i.e., DDPOW for dust and dirt or QFACT(2,K) for a constit-
uent) is the familiar exponential decay constant. It may be obtained from
the slope of a semi-log plot of buildup versus time. As a numerical ex-
ample, if its value were 0.4 day , then it would take 5.76 days to reach
90 percent of the maximum buildup (see Figure 4-29).
For Michaelis-Menton buildup the parameter DDFACT for dust and dirt (or
QFACT(3,K) for a constituent) has the interpretation of the half-time con-
stant, that is, the time at which buildup is half of the maximum (asymp-
totic) value. For instance, DD = 50 Ib at t = 0.9 days for curve 4 in
Figure 4-29. If the asymptotic value is known or estimated, the half-
time constant may be obtained from buildup data from the slope of a plot
of DD versus t*(DDLIM-DD), using dust and dirt as an example. Generally,
the Michaelis-Menton formulation will rise steeply (in fact, linearly for
small t) and then approach the asymptote slowly.
The power function may be easily adjusted to resemble asymptotic
behavior, but it must always ultimately exceed the maximum value (if used).
The parameters are readily found from a log-log plot of buildup versus time.
This is a common way of analyzing data, (e.g., Miller et al., 1978, Ammon,
1979, Smolenyak, 1979, Jewell et al., 1980, Wallace, 1980).
Prior to the beginning of the simulation, buildup occurs over DRYDAY
days for both single event and continuous simulation. During the simula-
tion, buildup will occur during dry time steps (runoff less than 0.0005
in./hr or 0.013 mm/hr) only for continuous simulation.
131 4-81
-------
120
DDLIM =100
EON. NO. EQUATION
4-23 DD=30t
I LINEAR
2 POWER
3 EXPONENTIAL
4-23 DD= 40t
-0.4t
4-24 DD= I00{l-e
4 MICHAELIS-MENTON 4-25 DD =
6 8
TIME, days
Figure 4-29. Comparison of Linear and Three Non-linear Buildup
Equations. "Dust and dirt," DD, is used as an example.
Numerical values have been chosen arbitrarily.
132
4-82
-------
For a given constituent, buildup may be computed 1) as a fraction of
dust and dirt, or 2) individually for the constituent. If the first option
is used (KALC=0 on card group J3) then the rate of buildup will depend upon
the fraction and the functional form used for a given land use. In other
words, the functional form could vary with land use for a given constituent.
If the second option is used (1 i KALC ^ 3 on card group J3) the buildup
function will be the same for all land uses (and subcatchments) for a
given constituent. Of course, each constituent may use any of the options.
Catchment characteristics (i.e., area or gutter length) may be included
through the use of parameters JACGUT (card group J2) or KACGUT (card group
J3), as described in Tables 4-15 and 4-17.
Units for dust and dirt buildup parameters are reasonably straight-
forward and explained in Table 4-15. For example, if linear buildup was
assumed using the Chicago APWA data (APWA, 1969), values for DDFACT could
be taken directly from Table 4-13 for different land uses. Parameters
JACGUT would equal zero. A limiting buildup (DDLIM) of so many lb/100 ft-
curb could be entered if desired, and for linear buildup, DDPOW = 1.0.
Units for constituent buildup parameters Append upon parameter NDIM,
that is, the units for the buildup parameters depend upon the units of
the constituent. When NDIM = 0 and the constituent concentration is
simply mg/1 (mass per volume), then buildup units are straightforward
and given as pounds. When NDIM = 1, concentrations are given as some
other quantity psr volume, usually a bacteria count such as MPN/1. In
this case buildup is simply in millions of MPN. The scaling is included
to facilitate entry of large numbers.
When NDIM = 2, constituent concentrations are given in specialized
units such as pH, JTU, PCU, °C, etc. "Buildup" of such parameters is
rarely referred to; instead, a much more viable option is the use of
a rating curve that gives load (i.e., concentration times flow) directly
as a function of flow (discussed subsequently). However, the buildup
option may be used with such constituents if desired. Within the Runoff
Block, concentrations are ultimately computed in subroutine GUTTER by
dividing a load (quantity per second) by a flow rate (cubic feet per
second). Thus, if the quantity has units of concentration times cubic
feet, the proper conversion will be made. This is the reason for the
peculiar units requested in Table 4-17. The authors of this report
have not seen data placed in this form, but such an analysis would be
straightforward and analogous to computations of total mass in pounds
(obtained by summing flow rate times concentration) for constituents
measured in mg/1.
Buildup Data -- Data with which to evaluate buildup parameters are available
in most of the references cited earlier under "available studies." Manning
et al. (1977) have perhaps the best summary of linear buildup rates; these
are presented in Table 4-18. It may be noted that dust and dirt buildup
varies considerably among three different studies. Individual constituent
buildup may be taken conveniently as a fraction of dust and dirt from the
entries in Table 4-18, or they may be computed explicitly. It is apparent
that although a large number of constituents have been sampled, little
distinction can be made on the basis of lau«J uses lot' inosL uf Lhem.
133 A'83
-------
Table 4-18.
Nationwide Data on Linear Dust and Dirt Buildup Rates
and on Pollutant Fractions. (After Manning et al., 1977,
pp. 138-140.)
PolluUflt
Land Uil Categories
Oust and Oirt
Accumulation
Ib/curb-mi/day
kg/curb-km/dty
Chicago'1 >
Washington'2'
Multi-City'3'
All Data
BOO mg/kg
COO mg/kg
Total N-N
(mg/kg)
Kjeldahl N
(mg/kg)
N03
(mg/kg)
NO2-N
(mg/kg)
Total PO4
(mg/kg)
Mean
Range
No. of Obs
Mean
Range
No. of Obs
Mean
Range
No. of Obs
Mean
Range
No. of Obs
Mean
Range
No. of Obs
Mean
Range
No. of Obs
Mean
Range
No. of Obs
Mean
Range
No. of Obs
Mean
Range
No. of Obs
Mean
Range
No. of Obs.
Mean
Range
No. of Obs
Single Family
Residential
35(10)
19-96(5-27)
60
..
-
-
182(51)
3-950(1-268)
14
62(17)
3-950(1-268)
74
5.260
1.720-9,430
59
39.250
18.300-72.800
59
460
325-525
59
-
--
-
--
Multiple Family
Residential
109(31)
62-153(17-43)
93
..
-
-
157(44)
8-770(2-217)
8
113(32)
8-770(2-217)
101
3.370
2.030-6320
93
41.970
24.600-61.300
93
550
356-961
93
Commercial
181(51)
71-326(80-151)
126
134(38)
35-365(10-103)
22
45(13)
3-260(1-73)
10
116(47)
3-365(1-103)
158
7,190
1.280-14.540
102
61.730
24.800-498.410
102
420
323-480
80
640
230-1,790
22
24
10-35
21
0
0
15
170
9O- 340
21
Industrial
325192)
284-536(80- 161)
55
-
288(81)
4-1,500(1-423)
12
319(90)
4-1,500(1-423)
67
2,920
2,820-2,950
56
25,080
23.000-31.800
38
430
410-431
38
-
All Data
158(44)
19-536(5-15)
334
134(38)
35-365(10-103)
22
175(49)
3-1.500(1-423)
44
159(45)
3-1,500(1-423)
400
5,030
1.288-14.540
292
46.120
18.300498.410
292
480
323-480
270
640
230-1.790
22
24
10-35
21
15
0
15
170
90-340
21
134
4-84
-------
Table 4-18. (Continued)
Pollutant
Land Uu Categories
P04-P
(mg/kg)
Chlorides
(mg/kg)
Asbestos
fibers/lb
(fibers/kg)
Ag
Img/kg)
As
(mg/Vgl
Ba
(mg/Vg)
CD
(mg/kg)
Cr
Img/kg)
Cu
(mg/kg)
Fe
(mg/kg)
'a
(mg/kg)
Mn
(mg/kg)
Ni
(mg/kg)
Singlt Family
Residential
Mean 49
Range 20-109
No. of Obs 59
Mean
Range
No. of Obs
Mean
Range
No. of Obs
Mean
Range
No. of Obs
Mean
Range
No. of Obs
Mean
Range
No. of Cbs
Mean 3.3
Range 0-8.8
No. of Obs 14
Mean 200
Range 111-325
No. of Obs 14
Mean 91
Range 33-150
No. of Obs 14
Mean 21.280
Range 11,000-48,000
No. of Obs 14
Mean
Range
No. of Obs
Mean 450
Range 250-700
No. of Obs 14
Mean 38
Range 0-120
No. of Obs 14
Multiple Family
58
20-73
93
..
-
..
60
0-142
101
220
100-370
22
57.2x10'(126x108)
26
14-30
38
..
-
0-172.5x1o'(0-380x10*l
-
..
-
2.7
0.3-6.0
8
180
75-325
8
73
34-170
8
18,500
11,000-25.000
8
340
230-450
8
18
0-80
8
16
200
0-600
3
0
0
3
38
0-80
8
2.9
0-9.3
22
140
10-430
30
95
25-810
30
21.580
5,000-44.000
10
0.02
0-0.1
6
380
160-540
10
94
6-170
30
' -
-
-
..
-
3.6
0.3-11.0
13
240
159-335
13
87
32-170
13
22.540
14,000-43.000
13
-
430
240-620
13
44
1-120
13
All Data
53
0-142
291
220
100-370
22
57.2x106(126x106)
0- 172.5x1 0s (0-380x1 06 1
16
200
0-600
3
0
0
3
38
0-80
8
3.1
0-11.0
57
180
10-430
65
90
25-810
65
21.220
5.000-48.000
45
0.02
0-0.1
6
410
160-700
45
62
1-170
65
135
4-85
-------
Table 4-18. (Concluded)
Pollutant
Pb
(mg/Vg)
Sb
(mg/Vg)
Se
(mg/kg)
Sn
(mg/kg)
Sr
(mg/kg)
Zn
(mg/kg)
Fecal Strep
No./gram
Fecal Coli
No./gram
Total Coli
No./gram
Single Family
Residential
Mean 1,570
Range 220-5.700
No. of Obi 14
Mean
Range
No. of Obi
Mean
Range
No. of Obj
Mean
Range
No. of Ots
Mean 32
Range 5-110
No. of Ob* 14
Mean 310
Range 110-810
No. of Obi 14
Geo. Mean
Range
No. of Obi
Geo. Mean 82.500
Range 26-130.000
No. of Obi 65
Geo. Mean 891.000
Range 25.000-3.000.000
No. of Obt 65
Multiple Family
Residential
1.980
470-3.700
8
..
-
-
..
-
..
-
18
12-24
8
280
210-490
8
38,800
1.500-1.000.000
96
1.900.000
Land Ute Categoriei
Commercial Induitrial All Data
2.330
0-7.600
29
54
50-60
3
0
0
3
17
0-50
3
17
7-38
10
690
90-3,040
30
370
44-2,420
17
36.900
140-970.000
84
1.000.000
80.000-5.600.000 1 8.000-3.500.000
97
85
1.590
260-3.500
13
-
13
0-24
13
280
140-450
13
30,700
67-530.000
42
419.000
27,000-2.600.000
43
1.970
0-7.600
64
54
50-60
3
0
0
3
17
0-50
3
21
0-110
45
470
90-3,040
65
370
44-2,420
17
94,700
26-1.000.000
287
1.070.000
18.000-5.600.000
290
136
4-86
-------
As an example, suppose options METHOD = 0 and KALC = 0 are chosen
in card groups J2 and J3 and "all data" are used from Table 4-18 to com-
pute_dust and dirt parameters. Since the data are given as lb"curb mile "
day , linear buildup is assumed, and commercial land use DD buildup would
be DDFACT = 2.2 lb"(100 ft curb)" "day" (i.e., 116/52.8, where 52.8 is the
number of hundreds of feet in a mile). DDPOW would equal 1.0 and no data
are available to set an upper limt, DDLIM. Parameter JACGUT = 0 so that
the loading rate will be multiplied by the curb length for each subcatch-
ment. Constituent fractions are available from the table. For instance,
QFACT values for commercial land use would be 7.19 mg/g for BOD-, 0.06 mg/g
for total phosphorus, 0.00002 mg/g for Hg, and 0.0369 10 MPN/g for fecal
coliforms. Direct loading rates could be computed for each constituent
as an alternative. For instance, with KALC = 1 for BOD- and KACGUT =0, .
parameter QFACT(3,K) would equal 2.2 x 0.00719 = O.OlSS^lb " (100 ft curb)"
11 day"1.
It must be stressed once again that the generalized buildup data of
Table 4-18 are merely informational, and are never a substitute for local
sampling or even a calibration using measured concentrations. They may
serve as a first trial value for a calibration, however. In this respect
it is important to point out that concentrations and loads computed by the
Runoff Block are usually linearly proportional to buildup rates. If twice
the quantity is available at the beginning of a storm, then concentrations
and loads will be doubled. Calibration is probably easiest with linear
buildup parameters, but it depends on the rate at which the limiting build-
up, i.e., DDLIM or QFACT(1,K), is approached. If the limiting value is
reached during the interval between most storms, then calibration using
it will also have almost a linear effect on concentrations and loads. It
is apparent that the interaction between the interevent time of storms
(i.e., dry days) and the effect of buildup is accomplished using the rate
constants, DDPOW and DDFACT for dust and dirt and QFACT(2,K) and QFACT(3,K)
for constituents. This is discussed further subsequently under "Overall
Sensitivity to Quality Parameters."
Almost all of the above loading data are from samples of storm
water, not combined sewage. Although some loadings may be inferred
from concentration measurements of combined sewage (e.g., Huber et al.,
1979, 1980, Wallace, 1980), they are not directly related to most surface
accumulation measurements. Thus, if buildup data alone are used in com-
bined sewer areas, buildup rates will probably be multiples of the values
listed, for example, in Table 4-18. The proper factor will most easily
be found by calibration with local concentration measurements. Alternatively
the dry-weather flow mixing and scour routines in the Transport Block may be
used to increase combined sewer concentrations. However, mixing of dry-
weather flow with storm water has a negligible effect on concentrations dur-
ing high flows, and the scour routine is highly empirical and adds a second
calibration step. Hence, the easiest option for combined sewers is probably
to calibrate as described earlier. Calibration may also be achieved using
the rating curve approach.
137
4-87
-------
When snowmelt is simulated, some of the ten constituents may be used
to represent deicing chemicals; several common roadway "salts" are listed
in Figure 4-24. Application of such chemicals varies depending upon depth
of snowfall and local practice. Loading rates are discussed in Appendix
II and other references (Proctor and Fedfern and J.F. MacLaren, 1976a, 1976b,
Field et al., 1973, Richardson et al., 1974, Ontario Ministry of the Environ-
ment, 1974). For instance, guidelines of the type proposed by Richardson et
al. (1974) arc used in many cities and are given in Table 4-19. Summaries
are also given by Manning et al. (1977) and Lager et al. (1977a).
Since for most deicing chemicals the principal source is direct appli-
cation during snow events, there is little or no buildup during snow-free
periods. Parameter LINKUP (card group J3) may be used to simulate this
effect for continuous simulation. Of course, for single event simulation,
buildup may be computed directly by the user and input in card group LI or
computed by any of the equations just discussed. Since there is only one
storm simulated (ordinarily) there is no need for inter-storm buildup.
Washoff --
Definition -- Washoff is the process of erosion or solution of constituents
from a subcatchment surface during a period of runoff. If the water depth
is more than a few millimeters, processes of erosion may be described by
sediment transport theory in which the mass flow rate of sediment is pro-
portional to flow and bottom shear stress, and a critical shear stress can
be used to determine incipient motion of a particle resting on the bottom
of a stream channel, e.g., Graf (1971), Vanoni (1975). Such a mechanism
might apply in street gutters and larger channels. For thin overland flow,
however, rainfall energy can also cause particle detachment and motion. This
effect is often incorporated into predictive methods for erosion from pervious
areas (Wischmeier and Smith, 1958) and may also apply to washoff from imper-
vious surfaces, although in this latter case, the effect of a limited supply
(buildup) of the material must be considered.
Washoff Formulation -- Ammon (1979) reviews several theoretical approaches
for urban runoff washoff and concludes that although the sediment transport
based theory is attractive, it is often insufficient in practice because of
lack of data for parameter (e.g., shear stress) evaluation, sensitivity to
time step and discretization and because simpler methods usually work as
well (still with some theoretical basis) and are usually able to duplicate
observed washoff phenomena. Among the latter, the most oft-cited results
are those of Sartor and Boyd (1972), shown in Figure 4-30, in which con-
stituents were flushed from streets using a sprinkler system. From the
figure it would appear that an exponential relationship could be developed
to describe washoff of the form
POFF(t) = PSHED0(l-e"kt) (4-29)
where POFF = cumulative amount washed off at time, t,
PSHED0 = initial amount of quantity on surface at t=0, and
k = coefficient.
138
-------
Table 4-19. Guidelines for Chemical Application Rates for Snow Control. (Richardson et
al., 1974.)
WEATHER CONDITIONS
APPLICATION RATE (Pounds of material per mile of 2-laoe road or 2-lanes of divided)
INSTRUCTIONS
wait at least 0.5
hour before
Temperature
30* F and
above
Pavement
Conditions
Wet
Precipitation
Snow
Low- and High-Speed
Haiti lane Divided
300 salt
Two and Three-Lane
Primary
300 salt
Two-Lane
Secondary
300 aalt
25-30*F
20-25'F
Co
vo
15-20'F
Dry
Wet
below 15° P
Dry
Sleet or Freezing Rain 200 salt
200 salt
Wet Snow or Sleet
Freezing Rain
Wet Snow or Sleet
Freezing Rain
Dry Snow
Wet Snow or Sleet
Dry Snow
Initial at (00 salt initial at 400 salt
repeat at 200 salt repeat at 200 salt
initial at 300 salt initial at 300 salt
repeat at 200 salt repeat at 200 salt
initial at 500 salt initial at 500 salt
repeat at 250 salt repeat at 250 salt
initial at 400 salt
repeat at 300 salt
plow
500 of 3:1 Salt/
Calcium Chloride
plow
initial at 400 salt
repeat at 300 salt
plow
500 of 3:1 Salt/
Calcium Chloride
pie
200 salt
Initial at 400 salt
repeat at 200 salt
initial at 300 aalt
repeat at 200 salt
1200 of 5:1
Sand/Salt; repeat
same
plow
1200 of 5:1 Sand
plow
- reapply as necessary
- wait at least 0.5
hour before plowing;
repeat
- repeat as necessary
- wait about 0.7S hour
before plowing;
repeat
- repeat as necessary
treat hazardous
areas with 1200 of
20:1 Sand/Salt
wait about one hour
before plowing;
continue plowing
until storm ends;
then repeat
application
treat hazardous area
with 1200 of 20:1
Sand/Salt
.e-
i
oo
vO
-------
i
vO
O
1.00
.10
r
i
s
;" .ooi
I
z .0001
0 I
FLUSHING TIME (Inun)
0 I
FLUSHING TIME (hounj
0 I
FLUSHING TIME (hcur.)
10.00 ,=
0 I
FLUSHING T1M{ (houn)
0 I
FLUSHING TIME (knn)
Figure 4-30. Washoff of Street Solids by Flushing with a Sprinkler System. (After
and Boyd, 1972, pp. 86-87.)
Sartor
-------
POFF is shown ats the ordinate of Figure 4-30. Alternatively, since the
amount remaining, PSHED(t), equals PSHED0-POFF, then
PSHED(t) = PSHED0e"kt (4-30)
where PSHED(t) = quantity remaining on surface at time, t,
PSHED0 = initial amount of quantity, and
k = coefficient.
It is clear that the coefficient, k, is a function of both particle size and
runoff rate. An analysis of the Sartor and Boyd (1972) data by Ammon (1979)
indicates that k increases with runoff rate, as would be expected, and de-
creases with particle size.
The Sartor and Boyd data lend credibility to the washoff assumption
included in the original SWMM release (and all versions to date), that the
rate of washoff (e.g., mg/sec) at any time is proportional to the remaining
quantity,
= -k . PSHED (4-31)
The solution of equation 4-31 is equation 4-30. This was first proposed
by Mr. Allen J. Burdoin, a consultant to Metcalf and Eddy, during the orig-
inal SWMM development. The coefficient k may be evaluated by assuming it is
proportional to runoff rate, r:
k = RCOEF r (4-32)
where RCOEF = washoff coefficient, in , and
r = runoff rate over subcatchment, in/hr.
Burdoin assumed that one-half inch of total runoff in one hour would wash
off 90 percent of the initial surface load, leading to the now familiar
value of RCOEF of 4.6 in . (The actual time distribution of intensity
does not affect the calculation of RCOEF.)
Even in the original SWMM release, this formulation did not adequately
fit some data, and as a "correction," availability factors of the form
AV = a + brc (4-33)
where AV = availability factor, and
a,b,c = coefficients,
were multiplied by equation 4-29 in order to match measured suspended solids
concentrations in Cincinnati and San Francisco (Metcalf and Eddy et al.,
1971a, Section 11). The primary difficulty is that use of equations 4-31
and 4-32 will always produce decreasing concentrations as a function of time
regardless of the time distribution of runoff. This is counter-intuitive,
since it is expected that high runoff rates during the middle of a storm
141 4-91
-------
might indeed produce higher concentrations than those preceding. This may
be explained by observing that concentrations are calculated by dividing the
load rate (e.g., mg/sec.) to obtain the quantity per volume (e.g., mg/1).
Thus,
PSHED . RCOEF r PSHED 0/ ,
= const. -- T - (4-34)
AT
where C = concentration, quantity/volume,
Q = AT = flow rate, cfs,
A = subcatchment area, ac,
r = runoff rate, in/hr.,
and the constant incorporates conversion factors. Clearly, the concentra-
tion will always decrease with time since the runoff rate, r, divides out of
the equation and the quantity remaining, PSHED, continues to decrease. This
problem is overcome in the present version of SWMM by making washoff at each
time step, POFF, proportional to runoff rate to a power, WASHPO,
-POFF(t) = = -HCOEFX - rWASHPO - PSHED (4-35)
where POFF = constituent load washed off at time, t, quantity/sec
(e.g. , mg/sec),
PSHED = quantity of constituent available for washoff at time, t,
(e.g., mg) , -WASHPO -1
RCOEFX = washoff coefficient = RCOEF/3600, (in/hr) - sec , and
r = runoff rate, in/hr.
It may be seen that if equation 4-35 is divided by runoff rate to obtain
concentration, then concentration is now proportional to r . Hence,
if the increase in runoff rate is sufficient, concentrations can increase
during the middle of a storm even if PSHED is diminished. (Equation 4-35
was first suggested in a 1974 report to the Boston District Corps of Engi-
neers, authorship unknown).
There are two parameters to be determined, RCOEF and WASHPO. Avail-
ability factors of the form of equation 4-33 are no longer used since there
is sufficient flexibility for calibration using only equation 4-35. Of
course, the original SWMM methodology can be recovered using WASHPO = 1.0.
Effects of Parameters The effect of different values for RCOEF and WASHPO
on PSHED and concentration is shown for four temporal distributions of run-
off (Figure 4-31) in Figures 4-32 to 4-35. The basis for the calculations
and plotted values is given in Table 4-20. It may be seen that concentra-
tions may be made to increase with increasing runoff rate during the middle
of a storm by increasing the value of WASHPO. However, perhaps counter in-
tuitively, a larger value of WASHPO generally yields lower concentrations
and higher values of PSHED. This is because the runoff rates used for the
142 4-92
-------
CASE I
CASE 2
CO
-O
1
1.00
k.
.£ °-75
v-"o. 50
0.25
0
^ 1.00
_c
.£ 0.75
»- 0.50
0.25
n
_
^-,
) 20 40 6
TIME , min
CASE 3
.
-
/
^ 1.00
.E 0.75
»- 0.50
0.25
0 °C
L. 1.00
.E 0.75
»- 0.50
0.33
0.25
_
-
\
i i i i i
) 20 40 61
TIME , min
CASE 4
.
-
**
0 20 40
TIME , min
60
20 40
TIME , mm
60
Figure 4-31. Time Variation of Runoff Rate Used in Example of Table 4-20 and Figures 4-32
to 4-35
-------
10 2O 30 40 50 60
TIME, min
RCOEF = 2
TIME, min
RCOEF = 5
5O 60
TIME, min
RCOEF = 10
.p-
I
10
9
°o 8
x 7
cr>
E 6
o" 5
UJ
I 4
°- 3
2
I
0
10
20 30 40
TIME, min
RCOEF = 2
50 60
10 2O 30 40
TIME, min
RCOEF = 5
IO
2O 30 4O
TIME, min
RCOEF = IO
5O 6O
Figure 4-32. Time History of Concentration and Subcatchment Load (PSHED)
for Case 1 Runoff (Figure 4-31) .
WASHPO = I
WASHPO = 2
WASHPO = 5
-------
1
o
X
^
o>
E
o"
2
O
0
IU
9
8
7
6
5
4
3
2
1
r\
-
-
_
-
-
"* ~^^^
" *" "!V~
, , T T I __ _
10 20 30 40 50 60
TIME, min
RCOEF = 2
IO 20 3O 40
TIME, min
RCOEF .= 5
50 60
TIME, min
RCOEF = 10
I
v£>
U1
O
10
2O 30 4O
TIME, min
RCOEF = 2
10
20 3O 40
TIME, min
RCOEF = 5
50 60
10
20 30 40
TIME, min
RCOEF = 10
5O 60
Figure 4-33. Time History of Concentration and Subcatchment Load (PSHED)
for Case 2 Runoff (Figure 4-31).
WASHPO=I
WASHPO = 2
WASHPO = 5
-------
IU
9
7o 8
x 7
^ 6
? 5
z 4
O
<-> 3
2
1
°(
-
-
-
-
-
.
^ * '
? *? "? i i
3 10 20 30 40 50 6
TIME, min
RCOEF = 2
TIME, min
RCOEF = 5
10 20 30 40 5O
TIME, min
RCOEF = 10
60
10 20 30 40
TIME, min
RCOEF = 2
50
10
o
O1
E
a
UJ
CO
Q.
10 20 3O 4-0
TIME, min
RCOEF = 5
10
9
"b 8
x 7
f 6
a" 5
UJ
s 4
°- 3
2
I
0
0 IO 2O 30 40
TIME, min
RCOEF = 10
Figure 4-34. Time History of Concentration and Subcatchment Load (PSHED)
for Case 3 Runoff (Figure 4-31).
5O 60
WASHPO = I
WASHPO= 2
WASHPO=5
-------
1
0
X
v.
o>
6
o
2
O
O
in
i \j
9
8
7
6
5
4
3
2
1
-
-
-
_
-
-
-
~----^___^__^^
;i___J__^ * * * .
'III!
0 10 2O 3O 40 50 6
TIME, min
RCOEF = 2.
10
9
To 8
x 7
S 6
8 3
10 2O 30 40 50 60
TIME, min
RCOEF = 5
20 3O 40 50 6O
TIME, min
RCOEF = 10
o
LU
(ft
Q.
_L
o
LU
X
0.
10 20 30 4O 50 60
TIME, min
RCOEF = 2
Figure 4-35. Time History of Concentration and Subcatchment Load (PSHED)
for Case 4 Runoff (Figure 4-31).
20 30 40
TIME, min
RCOEF = 10
* WASHPO = I
«* WASHPO = 2
WASHPO = 5
-------
example are all less than 1.0 in/hr. (25.4 mm/hr.) and decrease in magni-
tude when raised to a power. The reverse will be true for values of t < 1.0.
But many storms will have r > 1.0 throughout their durations. Increasing
the value of RCOEF always increases concentrations. (See also the subse-
quent discussion under "Overall Sensitivity to Quality Parameters".)
In subroutine QSHED of the Runoff Block, washoff load rates (e.g.,
ing/sec) are computed instantaneously at the end of a time step using
equation 4-35. They are subsequently combined with other possible in-
flow loads to a gutter/pipe or inlet before dividing by the total inflow
rate to obtain a concentration. The remaining constituent load on the sub-
catchment at the end of a time step is determined by using the average power
of the runoff rate over the time step,
r(t)WASHPO + f(t+At)WASHPO
PSHED(t+At) = PSHED(t) e 2 (4-36)
Table 4-20 Parameters Used for Washcff Equation Example
Equations Used:
RrnFF Af . n s . rrft+AMWASHPO + rmWASHP0l
PSHED(t+At) = PSHED(t) e KLU" at "^ intro; r r^ J
C(t+At)= Const. RCOEFX . r(t+At)WASHPO-l. pSH£D(t+At)
where PSHED(t) = mg on catchment,
PSHED(O) = 1000 mg,
C(t) = concentration, mg/1,
RCOEFX = RCOEF/3600,
Const. = 0.0353 ft /I, (utilizing 1 ac-in/hr ~ 1 cfs),
A =1 ac,
At - 0.16667 hr. (10 min),
r(t) = runoff rate in in/hr. (Figure 4-31).
Evaluate for 36 combinations of four runoff rate distributions (Figure
4-31), three values of RCOEF and three values of WASHPO given below:
RCOEF, (in/hr.)"WASHP° hr"1 WASHPO
2 1
5 2
10 5
148 4-98
-------
This calculation is done prior to application of equation 4-35. The average
(trapezoidal rule) approximates the integral of r over the time step.
That the load rate of sediment is proportional to flow rate as in
equation 4-35 is supported by both theory and data. For instance, sediment
data from streams can usually be described by a sediment rating curve of
the form
G = aQb (4-37)
where G = sediment load rate, mg/sec,
Q = flow rate, cfs, and,
a,b = coefficients.
Due to a hysteresis effect, such relationships may vary during the passing
of a flood wave, but the functional form is evident in many rivers, e.g.,
Vanoni (1975), pp. 220-225, Graf (1971), pp. 234-241, and Simons and Senturk
(1977), p. 602. Of particular relevance to overland flow washoff is the
appearance of similar relationships describing sediment yield from a catch-
ment e.g., Vanoni (1975), pp. 472-481. The exponent b in equation 4-37
corresponds to the exponent WASHPO in equation 4-35, and the presence of
the quantity PS1IED in equations 4-35 reflects the fact that the total
quantity of sediment washed off of a largely impervious urban area is likely
to be limited to the amount built up during dry weather. Natural catchments
and rivers from which equation 4-37 is derived generally have no source limi-
tation.
The use of rating curves in their own right is an option in the Runoff
Block which will be discussed subsequently. At this point, however, results
from sediment transport theory can be used to provide guidance for the magni-
tude of parameters WASHPO and RCOEF in equation 4-35. Values of the exponent
b in equation 4-37 range between 1.1 and 2.6 for rivers and sediment yield
from catchments, with most values near 2.0. Typically, the exponent tends
to decrease (approach 1.0) at high flow rates (Vanoni, 1975, p. 476). In
the Runoff Block, constituent concentrations will follow runoff rates
better if WASHPO is higher (see Figures 4-32 to 4-35). A reasonable first
guess for WASHPO would appear to be in the range of 1.5-2.5.
Values of RCOEF are much harder to infer from the sediment rating curve
data since they vary in nature by almost five orders of magnitude. The issue
is further complicated by the fact that equation 4-35 includes the quantity
remaining to be washed off, PSHED, which decreases steadily during an event.
At this point it will suffice to say that values of RCOEF between 1.0 and 10
appear to give concentrations in the range of most observed values in urban
runoff. Both RCOEF and WASHPO may be varied in order to calibrate the model
to observed data.
The preceding discussion assumes that urban runoff quality constituents
will behave in some manner similar to "sediment" of sediment transport theory.
Since many constituents are in particulate form the assumption may not be too
bad. If the concentration of a dissolved constituent is observed to decrease
sLrongly with increasing flow rate, a value of WASHPO < 1.0 could be used.
149 4-99
-------
LU
1.0
0.8
Q6
u.
li. 0.2
o
2 0.0,
a:
o
0)
Ql
O QOI
0.1
0 12 24 36 48 60 72 84 96 108 I2O
TIME, min
RCOEF=3.WASHPO = 3
* RCOEF = I, WASHPO= I
a RCOEF -1 , WASHPO = 3
0.5
1.0
RUNOFF RATE, in/hr
RCOEF =
WASHPO=I
» WASHPO s 2
a WASHPO =
00 0.1 0.2 Q3 0.4 0.5 0.6 0.7 08 0.9 1.0
b RUNOFF RATE, in/hr
RCOEF = 3
WASHPO *
» WASHPO i 2
a WASHPO=
0.0 O.I 0.2 Q3 04 05 0.6 0.7 Ofl 09 I.O
RUNOFF RATE, in/hr
.p-
I
o
o
Figure 4-36.
Simulated Load Variations within a Storm as a Function of Runoff Rate. The initial
surface load is 1000 mg on a 1 ac catchment, and the time step is 5 min. The loop
effect is exagerated as RCOEF is increased (Figures b vs. d). The loops are
flattened when using a log-log scale (Figure c).
-------
0 I 2 3 4 5 6 7 8 9 10 12 14 16 18 20 22 24
FLOW, cfs
150
140
130
120
110
^.100
0*9°
C 80
70
Teo
050
Z 40
O 30
O20
BOD
0 I 2 3 4 5 6 7 8 9 10 12 14 16 18 20 22 24
FLOW, cf s
0 I 2 3 4 5 6 7 8 9 10 12 14 16 18 22 24
1.3
1.2
I.I
1.0
0.9
\ 0.8
O»0.7
O Q5
?g«
il-
^^ nn
i
\
\
H
11
1
'I>A
: £L__
' ' 1 J 1 1 1 1 1 1 1 1 1 1 1 L_ 1 1 '. ' ' ' ' .
FLOW, Cfs
FLOW, Cfs
o Figure 4-37. Variation of 8005, TSS and N02+N03-N Load and Concentration for Storm of 11/17/74 for View
Ridge 1 Catchment, Seattle (from Huber et al., 1979). Connected points trace time history.
(Figure continued, next page.)
-------
looooor
0>
CO
O
3
; loooo
1000
100
10
1.0
I I
O.I 0.5
FLOW, cfs
10 20
o
z
o
o
100
10
i.o
o.i -
N02+N03
O.I 0.5 10 20
£ FLOW, cfs
Figure 4-37(Continued). The log-log plots could form the basis for rating
curves, although the loop effect may only be simulated using
a washoff calculation. Compare with Figure 4-36 b and d.
Several more plots are shown in Appendix VII.
152
4-102
-------
Although tUe development has ignored the physics of rainfall energy in
eroding particles, the runoff rate, r, in equation 4-35 closely follows
rainfall intensity. Hence to some degree at least, greater washoff will be
experienced with greater rainfall rates. As an option, soil erosion lit-
erature could be surveyed to infer a value of WASHPO if erosion is pro-
portional to rainfall intensity to a power.
An idea of the relative effect of parameters RCCEF and WA3KFO lias been
shown in Figures 4-32 to 4-35. Another view is presented in Figure 4-36 in
which the time history of washoff is presented as a function of flow for
various parameter values and for a more realistic runoff hydrograph. By
variation of WASHPO especially, the shape of the curve may be varied to
match local data. A plot using such data (Figure 4-37) is illustrated
under the discussion of rating curves, and several such plots are given
in Appendix VII.
Related Buildup-Washoff Studies --
Several recent studies are directly related to the preceding discus-
sions of the SWMM Runoff Block water quality routines. Some of these have
been mentioned previously in the text, but it is worthwhile pointing out
those that are particularly relevant to SWMM modeling as opposed to data
collection and .analysis (although most of the studies do, of course, utilize
data as well). The following discussion is by no means exhaustive but does
include several studies that have simulated water quality using buildup-
washoff mechanisms, rating curves or both.
The U.S. Geological Survey (USGS) has performed comprehensive urban
hydrologic studies from both a data collection and modeling point of view.
For example, their South Florida urban runoff data are described and refer-
enced in the EPA Urban Rainfall-Runoff-Quality Data Base (Huber et al., 1979,
1980). Urban rainfall-runoff quantity may be simulated with the USGS dis-
tributed Routing Rainfall-Runoff Model (Dawdy et al., 1978, Alley et al.,
1980a) which is presently being appended to include simulation of water
quality. This will be accomplished using a separate program that uses the
quantity model results as input. These efforts are described by Alley
(1980) and Alley et al. (1980b). Alley (1981) also provides a method for
optimal estimation of washoff parameters using measured data. The USGS
procedures are based in part upon earlier work of Ellis and Sutherland
(1979). These four references all discuss the use of the original SWMM
buildup-washoff equations. An application of SWMM Runoff and Transport
Blocks to two Denver catchments during which buildup-washoff parameters
were calibrated is described by Ellis (1978) and Alley and Ellis (1979).
Work at the University of Massachusetts has developed procedures for
calibration of SWMM Runoff Block quality (Jewell et al., 1978a) and for
determination of appropriate washoff relationships for use in the Version
II SWMM release (Jewell et al., 1978b). Recently Jewell et al. (1980) and
Jewell and Adrian (1981) reviewed the supporting data base for buildup-
washoff relationships and advocate using local data to develop site spe-
cific equations for buildup and washoff. Most of their suggested forms
could be simulated using the available functional forms in SWMM.
153 4-103
-------
Since several other models use quality formulations similar to those
of SWMM, their documentation provides insight into choosing proper SWMM
parameters. In particular, most of the STORM calibration procedures
(Roesner et al., 1974, HEC, 1977a,b) can be applied also to SWMM (with
WASHPO=1). Recent inclusion of water quality simulation in ILLUDAS
(Terstriep et al., 1978, Han and Delleur, 1979) also is based on SWMM
procedures. Finally, modified SWMM routines have been used to simulate
v:ater quality in Houston (Diniz, 1978, Eedicnt et al., 1378).
Rating Curve
As discussed above, the washoff calculations may be avoided and load
rates computed for each sub catchment at each time step by a rating curve
method, analogous to equation 4-37,
POFF = RCOEF WLOV (4-38)
3
where WFLOW = subcatchment runoff, cfs, (or m /sec for metric input),
POFF = constituent load washed off at time, t, quantity/sec
(e.g. , rng/sec),
RCOEF = coefficient that includes correct units conversion, and
WASHPO = exponent.
Parameters RCOEF and WASHPO are entered for a particular constituent on card
J3. That these parameters apply to a rating curve is indicated by parameter
KWASH on card J3. Although used on a time step basis, the parameters for
equation 4-38 are customarily determined on a storm event basis, by plotting
total load versus total flow (Huber, 1980, Wallace, 1980).
Two differences are apparent between equations 4-35 and 4-38. First,
the former includes the quantity remaining on the surface, PSHED, in the
right-hand side of the equation, leading to an exponential-type decay of
the quantity in addition to being a function of runoff rate.
Second, the form of the runoff rate is different in the equations.
The power-exponential washoff, equation 4-35, uses a normalized runoff
rate, r, in in./hr over the total subcatchment surface (not just the
impervious part). The rating curve, equation 4-38, also uses the total
runoff, but in an unnormalized form, WFLOW, in cfs. Since data for a
particular catchment are often analyzed as a log-log plot of load versus
flow, equation 4-38 facilitates use of the best fit line. For example,
data for Seattle are plotted in Figure 4-37. In addition, Appendix VII
contains several other similar plots for three Seattle catchments and for
Lancaster, Pennsylvania.
Clearly, the rating curve will work better for some storms and param-
eters than for others. If the data plot primarily as a loop (Figure 4-37),
the power-exponential washoff formulation will work better since it tends to
produce lower loads at the end of storm events. But if the load versus flow
data tend to plot as a straight line on log-log paper, the rating curve
method should work better. On the basis of the previous discussion of
154 4-104
-------
rating curves based on sediment data, it is expected that the exponent,
WASHPO, would be in the range of 1.5-3.0 for constituents that behave like
particulates. For dissolved constituents, the exponent will tend to be
less than 1.0 since concentration often decreases as flow increases, and
concentration is proportional to flow to the power WASHPO-1. (Constant
concentration would use WASHPO = 1.0.) Much more variability is expected
for RCOEF. Should a value need to be entered for RCOEF that exceeds the
field width of five available using the F5.0 format, an E-format may still
be used with three place accuracy. For instance, the entry 243E3 will be
read as 243,000 using an F5.0 format. This may often be necessary, since
for constituents measured in mg/1 (NDIM=0), load rates in mg/sec will
usually be quite large.
The rating curve approach may be combined with constituent buildup if
desired. If KWASH=1 on card J3, constituents are generated according to
the rating curve with no upper limit. There is no buildup between storms
during continuous simulation, nor will measures like street sweeping have
any effect. Constituents will be generated solely on the basis of flow
rate.
Alternatively, with KWASH=2, the rating curve is still used, but the
maximum amount that can be removed is the amount built up prior to the
storm. It will have an effect only if this limit is reached, at which time
loads and concentrations will suddenly drop to zero. They will not assume
non-zero valu«s again until dry-weather time steps occur to allow buildup
(during continuous simulation). Street sweeping will have an effect if
the buildup limit is reached.
Street Cleaning
Street cleaning is performed in most urban areas for control of solids
and trash deposited along street gutters. Although it has long been assumed
that street cleaning has a beneficial effect upon the quality of urban
runoff, until recently, few data have been available to quantify this effect.
The best current study is probably that of Pitt (1979) in which street sur-
face loadings were carefully monitored along with runoff quality in order
to determine the effectiveness of street cleaning. According to Pitt,
frequent street cleaning on smooth asphalt surfaces (once or twice per day)
can remove up to 50 percent of the total solids and heavy metal yields of
urban runoff. Under more typical cleaning programs (once or twice a month),
less than 5 percent of the total solids and heavy metals in the runoff are
removed. Organics and nutrients in the runoff cannot be effectively control-
led by intensive street cleaning typically much less than 10 percent re-
moval, even for daily cleaning. This is because the latter originate pri-
marily in runoff and erosion from off-street areas during storms.
The removal effectiveness of street cleaning depends upon many factors
such as the type of sweeper, whether flushing is included, the presence of
parked cars, the quantity of total solids, and constituent being considered.
Data from which an analysis of most of these efforts can be performed are
available in Pitt (1979). For example, removal efficiencies for several
155 4-105
-------
constituents are shown in Table 4-21. Clearly, efficiencies are greater
for constituents that behave as particulates.
Within the Runoff Block, street cleaning (usually assumed to be sweep-
ing) is performed (if desired) prior to the beginning of the first storm
event and in between storm events (for continuous simulation). Unless
initial constituent loads are input in card group LI (or unless a rating
curve is used) a "mini-simulation" is performed for each constituent
during the dry days prior to a storm during which buildup and sweeping
are modeled. Starting with zero initial load, buildup occurs according
to the method chosen in card groups J2 and J3. Street sweeping occurs
at intervals of CLFREQ days (card J2). (During continuous simulation,
sweeping occurs between storms based on intervals calculated using dry
time steps only. A dry time step does not have runoff greater than 0.0005
in/hr (0.013 mm/hr), nor is snow present on the impervious area of the
catchment.) Removal occurs such that the fraction of constituent sur-
face load, PSHED, remaining on the surface is
REMAIN = 1.0 - AVSWP(J) REFF(K) (4-39)
where REMAIN = fraction of constituent (or dust and dirt) load
remaining on catchment surface,
AVSWP = availability factor (fraction) for land use J, and
REF.F = removal efficiency (fraction) for constituent K.
The removal efficiency differs for each constituent as seen in Table 4-21,
from which estimates of REFF may be obtained. The effect of multiple passes
must be included in the value of REFF. During the mini-simulation that
occurs prior to the initial storm or start of simulation "dust and dirt" is
also removed during sweeping using an efficiency REFFDD (card J2). It is
probably reasonable to assume that dust and dirt is removed similarly to
the total solids of Table 4-21. A non-linear effect is exhibited in Table
4-21, in which efficiencies tend to increase as the total solids on the
street surface increase. The Runoff Block algorithm does not duplicate
this effect. Rather, the same fraction is removed during each sweeping.
The availability factor, AVSWP, is intended to account for the frac-
tion of the catchment area that is actually sweepable^. For instance,
Heaney and Nix (1977) demonstrate that total imperviousness increases
faster as a function of population density than does imperviousness due
to streets only. Thus, the ratio of street surface to total impervious-
ness is one measure of the availability factor, and their relationship
is
AVSWP = 0.6 PDd ~°'2, PDd > 0.1 (4.40)
where AVSWP = availability factor, fraction, and
PD, = population density over developed area, persons/ac.
Such a relationship is reasonably a function of land use. Although a value
of AVSWP must be entered for each land use (card J2), the equation of Heaney
and Nix (1977) was developed only for an overall urban area. Thus, extra-
156 4-106
-------
Table 4-21. Removal Efficiencies from Street Cleaner Path for Various
Street Cleaning Programs.* (Pitt, 1979)
Sreet Cleaning
Program and
Street Surface Total
Loading Conditions Solids
Vacuum Street Cleaner
1 pass; 20 - 200 31
Ib/curb mile
total solids
2 passes 45
3 passes 53
Vacuum Street Cleaner
1 pass; 200 - 1,000 37
Ib/curb mile
total solids
2 passes 51
3 passes 58
Vacuum Street Cleaner
1 pass; 1000 - 10,000 48
Ib/curb mile
total solids
2 passes 60
3 passes 63
Mechanical Street Cleaner
I pass; 180 »' 1800 54
Ib/curb mile
total solids
2 passes 75
3 passes 85
Flusher 30
Mechanical Street Cleaner
followed by a flusher 80
Pestl-
BODj COD KN P04 cldes
24 16 26 8 33
35 22 37 12 50
41 27 .45 14 59
29 21 31 12 40'
.
42 29 46 17 59
47 35 51 20 67
38 33 43 20 57
50 42 54 25 72
52 44 57 26 75
40 31 40 20 40
58 48 58 35 60
69 59 69 46 72
(a) (a) (a) (a) (a)
(b) (b) (b) (b) (b)
Cd Sr Cu Nl Cr Zn Mn Pb Fe
23 27 30 37 34 34 37 40 40
34 35 45 54 53 52 56 59 59
40 48 52 63 '60 59 65 70 68
30 34 36 43 42 41 45 49 59
43 48 49 59 60 59 63 68 68
50 53 59 68 66 67 70 76 75
45 44 49 55 53 55 58 62 63
57 55 63 70 68 69 72 79 77
60 58 66 73 72 73 76 83 82
28 40 38 45 44 43 47 44 49
45 59 58 65 64 64 64 65 71
57 70 -69 76 75 75 79 77 82
(a) (a) (a) (a) (a) (a) (a) (a) (a)
(b) (b) (b) (b) (b) (b) (b) (b) (b)
(a) 15 » 40 percent estimated
(b) 35 100 percent estimated
*These removal values assume all the pollutants would He within the cleaner path (0 to 8 ft. from'the curb)
157 4-107
-------
polation to specific land uses should be done only with caution, but equation
4-40 is probably suitable for use on a large, aggregated catchment, such as
might be used for continuous simulation.
An alternative approach may be found in Pitt (1979) in which the issue
of parked cars is dealt with directly. Pitt shows that the percentage of
curb left uncle.med is essentially equal to the percentage of curb occupied
by parked cars. Thus, if typically 40 percent of the curb (length) is
occupied by parked cars, the availability factor would be about 0.60. In
many cities, parking restrictions on street cleaning days limit the length
of curb occupied during sweeping.
Parameter DSLCL (card J2) merely establishes the proper time sequence
for the "mini-simulation" prior to the start of the storm (or continuous
simulation). A hypothetical sequence of linear buildup and street sweeping
prior to a storm is sketched in Figure 4-38. Eventually an equilibrium
between buildup and sweeping will occur. For the example shown in Figure
4-38, this is when the removal, 0.32 PSHED, equals the weekly buildup,
0.3 x 10 7, or PSHED = 6.56 x 10 mg. If sweeping is scheduled for the
day of the start of the storm (DSCL = CLFREQ) it does not occur. (An
exception would be when the first day of a continuous simulation is a dry
day. Sweeping would then occur during the first time step.)
The SWUM user should bear in mind that although the model assumes
constituents to build up over the entire subcatchment surface, the sur-
face load, PSHED, is simply a lumped total in, say, mg (for NDIM = 0),
and there are no spatial effects on buildup or washoff. Hence, if it is
assumed that a particular constituent originates only on the impervious
portion of the catchment, loading rates and parameters can be scaled
accordingly. Likewise, AVSWP can be determined based on the characteriza-
tion of only the impervious areas described above. However, if a constituent
originates over both the pervious and impervious area of the subcatchment
(e.g., nutrients and organics) the removal efficiency, REFF, should be re-
duced by the average ratio of impervious to total area since it is indepen-
dent of land type. The availability factor, AVSWP, differs for individual
land uses but has the same effect on all constituents.
Catchbasins
Background Catchbasins are found in a large number of cities. They were
originally installed at storm water inlets to combined sewers to prevent
sewer clogging by trapping coarse debris and solids and to prevent emanation
of odors from the sewer by providing a water seal. There is no standard
design for Catchbasins; representative designs are shown in Figure 4-39.
The purpose of the deep well or sump is to trap solids by sedimentation
prior to stormwater entry into the sewer, which distinguishes Catchbasins
from stormwater inlets. The volume of the sump varies considerably with
design, ranging from 2.8 to 78 ft (0.08 - 2.21 m ). The volume is typic-
ally reduced by a large quantity of solids trapped in the sump, often by
more than 50 percent.
158 4-108
-------
4.5
Q>
E
<0
S
2.0
1.0
0.0
-25
BUILDUP RATE = 0.3 x |Q mg/day
AVSWP = 0.8
REFF = 0.4
32 PERCENT
REMOVAL
DRYDAY = 25
CLFREQ = 7 -^ DSLC = 3
START OF SIMULATION -,
i i /
-20
-15
-10
-5
TIME PRIOR TO STORM, days
Figure 4-38. Hypothetical Time Sequence of Linear Buildup and Street
Sweeping.
159
4-109
-------
*v ,.'.*. WWW^I^V^^^V^W^W^V^F
NEW YORK
SAN FRANCISCO
CAP.
-2 ft - 0 I*.
1 1 in.
ATLANTA
TORONTO
Figure 4-39. Representative Catchbasin Designs. (After Lager et al.,
1977b, p. 12.)
160
4-110
-------
A comprehensive examination of catchbasins and their effectiveness
for pollutant control is presented by Lager et al. (1977b). They con-
clude that:
"Existing catchbasins exhibit mixed performance with
respect to pollution control. The trapped liquid
purged from catchbasins to the sewers during each
storm generally has a high pollution content that
contributes to the intensification of first-flush
loadings. Countering this negative impact is the
removal of pollutants associated with the solids
retained in, and subsequently cleaned from, the
basin."
In fact according to their data, there is unlikely to be much removal
(treatment) at all in most cities because of infrequent maintenance;
the median cleaning frequency in 1973 was once per year. Without such
maintenance, solids accumulate in the sump until there is little removal
effectiveness, even for large particles. Lager et al. (1977b) conclude
that, with the possible exception of total solids and heavy metals,
catchbasins are of limited usefulness for pollution abatement, both because
of their ineffectiveness and because of their high maintenance costs. Hence,
their treatment potential is not modeled in SWMM. (If it is significant in
a given city, surface loadings could be correspondingly reduced.)
Modeling Approach -- The potential for a first flush of catchbasin material
is simulated by assuming that the sump contains at the beginning of a storm
a constituent load (e.g., mass, in mg, for NDIM = 0) given by
PBASIN = CBVOL BASINS CBFACT FACTS (4-41)
where PBASIN = subcatchment constituent load in catchbasins at
beginning of storm, mg for NDIM = 0,
CBVOL = individual catchbasin volume of sump, reduced by
quantity of stored solids, if know, ft ,
BASINS = number of basins in subcatchment,
CBFACT = constituent concentration in basin at beginning
of storm, mg/1 for NDIM = 0, and _
FACT3 = conversion factor, equals 28.3 I/ft for NDIM = 0.
Parameter CBVOL is entered on card Jl as an average for the entire catchment.
The number of basins in each subcatchment, BASINS, is entered in card group
LI. Numbers can be obtained knowing the general basin density for the
catchment in lieu of the more tedious method of counting every one. Consti-
tuent concentrations, CBFACT, are entered in card group J3 and should, of
course, be measured in the catchment under study. Literature values are
few. Samples from 12 San Francisco catchbasins (Sartor and Boyd, 1972) were
characterized by Lager et al. (1977b) by "casting out the extremes and
averaging," resulting in the values shown in Table 4-22.
161 4-111
-------
Tabltt 4-22 Constituent Concentrations in San Francisco
Catchbasins (Sartor and Boyd, 1972)
Constituent Concentration, mg/1
Average Range
COD
BOD5
Total-N
Total-P
6,400
110
8
0.2
153 - 143,000
5 - 1,500
0.5 - 33
<0.2
0
The values for COD and Total-N are consistent with a few samples reported by
Sartor and Boyd (1972) for Baltimore and Milwaukee, although the "phosphates"
concentration in these two cities was somewhat higher, 1.1 - 2.2 mg/1. The
concentration of BOD^ in seven Chicago catchbasins was measured by APWA
(1969). The average concentration for five commercial area basins was 126
mg/1, ranging from 35 to 225 mg/1. Two residential area basins yielded
BOD,, concentrations of 50 and 85 mg/1.
No data have been found to characterize other constituents within the
catchbasins themselves. In particular, suspended solids (SS) concentrations
can be expected to be high for particle sizes less than about 0.25 mm, on
the basis of flushing tests (Sartor and Boyd, 1972, Lager et al., 1977b).
Initial suspended and total solids concentrations of several thousand mg/1
are probably justified, although measurements by Waller (1971) during
storms in four residential catchbasins in Halifax indicate SS concentra-
tions in a range of 42 to 305 mg/1.
Flushing of stored constituents from catchbasin sumps is based on tests
conducted by APWA (1969) in which salt was used as a tracer and its rate of
flushing observed. Data and fitted equations are shown in Figure 4-40.
The basin behaves approximately as a completely mixed tank in which
d PBASIN WFLOW DDAC a
~dt = * k-BASINS PMSIN (
where PBASIN = constituent load remaining in the catchbasin as
a function of time, e.g., mg for NDIM = 0,
WFLOW = flow through the basin (runoff from the subcatchment),
cfs, 3
BASINS = volume of catchbasin sump, ft , and
k = constant to be determined from flushing tests.
When the flow rate is constant, equation 4-42 integrates to
162 4-112
-------
CO
V- = 47. 2 fr
Q = 7 cf m
© Q = 4 cfm
Q = I cfm
CO
Q.
OQ
CL
COMPLETE
MIXING,
SLOPE = 1.0
ORIGINAL SWMM,
SLOPE = 1/1.6
LEAST SQUARES,
SLOPE = 1/1.3
1.0
2.0 3.0
Q-t/₯
4.0
Figure 4-40. Catchbasin Flushing Characteristics. Data are from APWA
(1969).
163
4-113
-------
WFLOW
PBASIN = PEASIN e k*BASINS (4-43)
o
where PBASIN = initial catchbasin load.
o
It complete mixing occurs, k=l. For the Chicago tests this did not quite
occur, as seen in Figure 4-40. The original SWUM version used k=1.6, but
this does not give the best-fit line. Rather, a k value of 1.3 is con-
sistent with a least squares fit through the data points and is used in
this version of SWMM. (However, the difference is probably undetectable
in a simulation.)
During a runoff event, equation 4-42 is used to calculate the load
rate, dPBASIN/dt, at each time step. (Parameter BASINS represents the
total catchbasin volume for the subcatchment.) The remaining catchbasia
load is then computed by multiplying the load rate by DELT and subtracting
from PBASIN. This crude Euler integration is justified because of 1) the
weakness of field data and mixing assumptions, 2) the necessity for an
additional arrsiy arid computation time for a more sophisticated approxima-
tion, and 3) insensitivity of most simulations to catchbasin flushing.
The latter point will be discussed further subsequently.
Regeneration of Catchbasin Loads During continuous simulation, catchbasin
loads are regenerated to their original values, PBASIN at a rate PBASIN /
DRYBSN (e.g., mg/day) where DRYBSN is entered on card° Jl and is the time
required for complete regeneration from a zero load. No data are available
herein to establish a value for DRYBSN, but it is likely that catchbasins
are at "full strength" after only few days of dry weather.
Effect on Simulation -- It is the experience of the authors of this report
that catchbasins have a negligible effect on most simulation results. Typ-
ical drainage areas served by catchbasins range from 2.15 to 5.05 ac/basin
(0.85 to 2.05 ha/basin) in the U.S. (Lager et al., 1977b). Unless the area
served is low, surface loadings tend to overwhelm those from catchbasins.
Although they do contribute to a first flush effect, the most important
task in most simulations is to obtain a proper total storm load, to which
catchbasins are seldom strong contributors. Hence, excessive effort to pin
down catchbasin simulation parameters is seldom justified.
Constituent Fractions
Background -- As previously discussed, the original SWMM Runoff Block qual-
ity routines were based on the 1969 APWA study in Chicago (APWA, 1969). A
particular aspect of that study that led to modifications to the first
buildup-washoff formulation was that the Chicago quality data (e.g., Table
4-14) were reported for the soluble fraction only, i.e., the samples were
filtered prior to chemical analysis. Hence, they could not represent the
total content of, say, BOD , in the stormwater. In calibration of SWMM in
San Francisco and Cincinnati, five percent of predicted suspended solids was
added to BOD to account for the insoluble fraction. This provided a
reasonable BOD calibration in both cities.
164
-------
The Version II release of SWMM (Huber et al., 1975) followed the STORM
model (Roesner et al., 1974) and added to BOD , N and PO, fractions of both
suspended solids and settleable solids. Adding a fraction from settleable
solids is double counting however, since it is no more than a fraction of
suspended solids itself. Furthermore, all the fractions in SWMM and STORM
were basically just assumed from calibration exercises as opposed to being
measured from field samples.
Agricultural models, such as NFS (Donigian and Crawford, 1976) and ARM
(Donigian et al., 1977) also relate other constituent mass load rates and
concentrations to that of "solids," usually "sediment" predicted by an
erosion equation. The ratio of constituent to "solids" is then called a
"potency factor" and for some constituents is the only means by which their
concentrations are predicted. The approach works well when constituents are
transported in solid form, either as particulates or by adsorption onto soil
particles. This approach can also be used in SWMM. For instance, one con-
stituent could represent "solids" and be predicted by any of the means
available (i.e., buildup-washoff, rating curve, Universal Soil Loss Equa-
tion). Other constituents could then be treated simply as a fraction, Fl,
of "solids." The fractions (potency factors) are entered in card group
J4. As a refinement, two or more constituents could represent "solids" in
different particle size ranges, and fractions of each summed to predict
other constituents. Again, this approach will not work well for consti-
tuents that are transported primarily in a dissolved state (e.g., NO.,).
Available Information In an effort: to evaluate potency factors for vari-
ous constituents in both urban and agricultural runoff, Zison (1980) ex-
amined available data and developed regression relationships as a function
of suspended solids and other parameters. His only urban catchments were
three from Seattle, taken from the Urban Rainfall-Runoff-Quality Data Base
(Huber et al., 1979), for which several water quality and storm event param-
eters were available. Unfortunately, statistically meaningful results could
only be obtained using log-transformed data, and simple fractions of the
type required for input in card group J4 are seldom reported. Zison (1980)
acknowledged this and suggested that, model modifications might be made or
piecewise-linear approximation made to the power function relationship. In
any event, Zison related the total constituent concentration (not just the
nonsoluble portion) to other parameters. Hence, for their use in SWMM, the
buildup-washoff portion would need to be "zeroed out," (easily accomplished),
as suggested earlier.
Other reports also provide some insight as to potential values for the
constituent fractions. For instance, Sartor and Boyd (1972), Shaheen (1975)
and Manning et al. (1977), report particle size distributions for several
constituents. However, the distributions refer principally to fractions of
constituents appearing as "dust and dirt," not to fractions of total concen-
tration, soluble plus nonsoluble. Finally, Pitt and Amy (1973) give frac-
tions (and surface loadings) for heavy metals.
If constituent fractions are used in SWMM, local samples should identify
the soluble (filtrable) and nonsoluble fractions for the constituents of in-
terest. Alternatively, the fractions may be avoided altogether by treating
the buildup-washoff or rating curve approach as one for the total concentra-
tion, thus eliminating the need to break constituents into more than one
form. 165 4~115
-------
Effect in Runoff Block The fractions entered in card group J4 act only
in "one direction." That is, nothing is subtracted from, say, suspended
solids if it is a constituent that contributes to others. When the frac-
tions are used, they can contribute significantly to the concentration of
a constituent. For instance, if five percent of suspended solids is added
to BOD_, high SS concentrations will insure somewhat high BOD,, concentra-
tions, even if BOD,, loadings are small.
Units conversions must be accounted for in the fractions. For instance,
if a fraction of SS is added to total coliforms, units for Fl would be MPN
per mg of SS. In general, Fl has units of the "quantity" of KTO (e.g., MPN)
per "quantity" of constituent KFROM (e.g., mg).
The contributions from other constituents are the penultimate step in
subroutine QSHED. They occur after the Universal Soil Loss Equation cal-
culation, and the to-from constituents can include the contribution from
erosion if desired. Only the contribution from precipitation comes later
and thus cannot be included in the constituent fractions. Rather it is
added to the constituent load at the end of the chain of calculations,
an described below.
Precipitation Contributions --
Precipitation Chemistry There is now considerable public awareness of the
fact that precipitation is by no means "pure" and does not have character-
istics of distilled water. Low pH (acid rain) is the best known parameter
but many substances can also be found in precipitation, including brganics,
solids, nutrients, metals and pesticides. Compared to surface sources, rain-
fall is probably an important contributor mainly of some nutrients, although
it may contribute sustantially to other constituents as well. In particular,
Kluesener and Lee (1974) found ammonia levels in rainfall higher than in
runoff in a residential catchment in Madison, Wisconsin; rainfall nitrate
accounted for 20 to 90 percent of tae nitrate in stormwater runoff to Lake
Wingra. Mattraw and Sherwood (1977) report similar findings for nitrate
and total nitrogen for a residential area near Fort Lauderdale, Florida.
Data from the latter study are presented in Table 4-23 in which rainfall
may be seen to be an important contributor to all nitrogen forms, plus
COD, although the instance of a higher COD value in rainfall than in run-
off is probably anomalous.
In addition to the two references first cited, Weibel et al. (1964,
1966) report concentrations of constituents in Cincinnati rainfall (Table
4-24), and a summary is also given by Manning et al. (1977). A comprehen-
sive summary is presented by Brezonik (1975) from which it may be seen in
Table 4-24 that there is a wide range of concentrations observed in rain-
fall. Again, the most important parameters relative to urban runoff are
probably the various nitrogen forms.
Uttormark et al. (1974) provide annual nitrogen (and phosphorus)
precipitation loading values (kg/ha-yr) for many cities regionally for
the U.S. and Canada. Their nitrogen loadings are shown in Figure 4-41
166 4-116
-------
Table 4-23 Rainfall and Runoff Concentrations For A Residential Area Near
Fort Lauderdale, Florida (After Mattraw and Sherwood, 1977).
Rainfall, in.
Runoff, in.
Concentrations (mg/1):
Total N, rainfall
Total N, runoff
N03-N, rainfall
NO -N, runoff
*J
Org.-N, rainfall
Org.-N, runoff
NH3-N, rainfall
NH3-N, runoff
Total P, rainfall
Total P, runoff
COD, rainfall
COD, runoff
Storm
8/23/75
1.01
0.060
0.30
0.52
0.14
0.16
0.15
0.34
0.01
0.02
0.01
0.12
22
16
9/17/75
0.55
0.012
0.84
0.74
0.73
0.19
0.09
0.49
0.01
0.04
0.02
0.20
12
21
9/26/75
0.77
0.072
0.29
1.50
0.12
0.26
0.12
1.10
0.04
0.13
0.05
0.30
4
17
167 4-117
-------
Table 4-24 Representative Concentrations in Rainfall
Ft. Lauderdale3 Cincinnati "Typical Range"
Parameter (Mattraw and Sherwood, 1977) (Weibel et al., 1966) (Brezonik, 1975)
Acidity (pH) 3-6
Organics
BOD,, mg/1 1-13
COD, mg/1 4-22 16 9-16
TOC, mg/1 1-3 Few
Inorg. C, mg/1 0-2
Color, PCU 5-10
Solids
Total Solids, mg/1 18-24
Suspended Solids, mg/1 2-10 13
,_, Turbidity, JTU 4-7
c^
00 Nutrients
Org. N, mg/1 0.09-0.15 0.58 0.05-1.0
NH.-H, mg/1 0.01-0.04
NO,-N, mg/1 0.00-0.01
NCT-N, mg/1 0.12-0.73 1.27C 0.05-1.0
Total N, ng/1 0.29-0.84 0.2 -1.5
Crthophosphorus, mg/1 0.01-0.03 0.08 0.0-0.05
Total P, mg/1 0.01-0.05 0.02-0.15
Pesticides, pg/1 3-600 Few
Heavy metals, (Jg/1 Few
Lead, pg/1 30-70
aRange for three storms Average of 35 storms CSum of NH--N, N02-N, NO.-N
I
M
M
00
-------
WO
-C--
I
Figure 4-41. Nationwide Annual Loadings of NH^ -N + N03~N in Precipitation (after Uttormark
et al., 1974, p. 87). Dry fallout is not included.
-------
although it should be remembered that considerable seasonal variability
may exist. These may be easily converted to precipitation concentrations
required for SWMM input if the local rainfall is known, since kg/ha-yr f
cm/yr x 10 = mg/1. For instance, annual NH_-N + NCL-N loadings at Miami
are almost 2 kg/ha-yr from Figure 4-41, and annual rainfall is 60 in (152
cm). From the above, the inorganic nitrogen concentration is 10 x 2/152 -
0.13 mg/1 which compares quite favorably with the sum of NH,-N and NG--N
concentrations for two of the three Ft. Lauderdale storms given in Taole
4-23. For a better breakdown of nitrogen forms, see Table 17 of Uttormark
et al. (1974).
Effect in Runoff Block Constituent concentrations in precipitation are
entered in card group J3. All runoff, including snowmelt, is assumed to
have at least this concentration, and the precipitation load (e.g., mg/sec)
is calculated by multiplying by the runoff rate and adding to the load
already generated by other mechanisms. It may be inappropriate to add a
precipitation load to loads generated by a calibration of buildup-washoff
or rating curve parameters against measured runoff concentrations, since
the latter already reflect the sum of all contributions, land surface and
otherwise. But precipitation loads might well be included if starting with
buildup-washoff data from other sources.
For single event simulation, use of precipitation concentrations is a
simple way in which to account for the high concentrations of several con-
stituents found in snowpacks (Proctor and Redfern and James F. MacLaren,
1976b). It would be inappropriate for continuous simulation, however,
since such high concentrations in runoff would not be expected to persist
over the whole year. If this is the only method used to simulate melt
quality, howevor, a constant predicted concentration will result. Also,
caution should be used if simulating particulates (e.g., suspended solids)
or heavy metals since high concentrations in a snowpack do not necessarily
mean high concentrations in runoff, since the material may rapidly settle
during overland flow. -For instance, the very high lead concentrations (2 -
100 mg/1) found in snow windrows in urban areas are greatly reduced in the
melt runoff (0.05 - 0.95 mg/1), (Proctor and Redfern and James F. MacLaren,
1976b).
Urban Erosion
Background Erosion and sedimentation are often cited as a major problem
related to urban runoff. They not only contribute to degradation of land
surfaces and soil loss but also to adverse receiving water quality and sedi-
mentation in channels and sewer networks. Several ways exist to analyze
erosion from the land surface (e.g., Vanoni, 1975), the most sophisticated
of which include calculations of the shear stress exerted on soil particles
by overland flow and/or the influence of rainfall energy in dislodging them.
In keeping with the simplified quality procedures included in the rest of
the Runoff Block, a widely-used empirical approach, the Universal Soil Loss
Equation (USLE), has been adapted for use in SWMM. Full details and further
information on the USLE are given by Heaney et al. (1975).
170 4-120
-------
Universal Soil Loss Equation The USLE was derived from statistical analy-
ses of soil loss and associated data obtained in 40 years of research by the
Agricultural Research Service (ARS) arid assembled at the ARS runoff and soil
loss data center at Purdue University. The data include more than 250,000
runoff events at 48 research stations in 26 states, representing about
10,000 plot-years of erosion studies under natural rain. It was developed
by Wischmeier and Smith (1958) as an estimate of the average annual soil
erosion from rainstorms for a given upland area, L, expressed as the average
annual soil loss per unit area, (tons por acre per year):
L = R K LS C P . (4-44)
where R = the rainfall factor,
K = the soil erodibility factor,
LS = the slope length gradient ratio,
C = the cropping management factor or cover index factor, and
P = the erosion control practice factor.
This equation represents a comprehensive attempt at relating the major
factors in soil erosion. It is used in SWMM to predict the average soil
loss for a given storm or time period. It is recognized that the USLE was
not developed for making predictions based on specific rainfall events.
There are many random variables which tend to cancel out when predicting
individual storm yields. For example, the initial soil moisture condition,
or antecedent moisture condition, is a parameter which cannot be determined
directly and used reliably. It should be understood by the SWMM user that
equation 4-44 enables land management planners to estimate gross erosion
rates for a wide range of rainfall, soil, slope, crop, and management condi-
tions .
Input Parameters If erosion is to be simulated, it is so indicated by
parameter IROS on card Jl. Note that at least one other (arbitrary) quality
constituent must be simulated along with "erosion." No particular soil
characteristics (e.g., particle size distribution) are assigned to the
erosion parameter, and its title is "EROSION," with units of mg/1, in the
output. Erosion may be added to another constituent, e.g., suspended
solids, if desired using parameter IROSAD on card Jl. However, the
erosion parameter will also always be maintained as an individual param-
eter throughout the Runoff Block.
Other input parameters are:
1) the maximum 30-minute rainfall intensity of the storm (single-
event) or of the simulation period (continuous), RAINIT, (card
Jl),
2) the area of each subcatchment subject to erosion, ERODAR,
(card IC1),
3) the flow distance in feet from the point of origin of overland
flow over the erodible area to the point at which runoff enters
the gutter or inlet, ERLEN, (card Kl),
171 4-121
-------
4) the soil factor K, SOILF, (card Kl),
5) the cropping management factor C, CROPMF, (card Kl), and
6) the control practice factor P, CONTPF, (card Kl).
The source and use of these parameters is described below.
Rainfall Factor and Maximum Thirty Minute Intensity The rainfall factor,
R, of equation 4-44 is the product of the maximum thirty minute intensity
and the sum of the rainfall energy for the time of simulation. Rainfall
energy, E, is given by an empirical expression by Wischmeier and Smith
(1958),
E = 1(9.16 + 3.31 log RNINHR.) RNINHR. DELT (4-45)
J J
where E = total rainfall energy for time period of summation, 100-ft-ton/ac,
RNINHR. = rainfall intensity at time interval j, in/hr, and
J
DELT = time interval, hr, such that the product RNINHR DELT =
rainfall depth during the time interval.
The summation was performed over all time intervals with rainfall for a year
for the original USLE development; contours of R over the U.S. are given by
Wischmeier and Smith (1965). However, it can also be performed for an indi-
vidual storm. In SWMM this is performed on a time step basis; that is, S
is evaluated at each time step using the rainfall intensity at that time
step (no summation). The rainfall factor, R, is then
R = E RAINIT (4-46)
where RAINIT = maximum average 30-minute rainfall intensity for the
storm (single event) or the period of simulation (continuous),
in/hr.
RAINIT must be found from an inspection of the input hyetograph prior to
simulation. Computed in this manner, the rainfall factor does not account
for soil losses due to snowmelt or wind erosion. The units of R (100-ft-
ton-in/ac-hr) are generally meaningless since the soil factor, K, is de-
signed to cancel them. But the indicated units for RAINIT and RNINHH
(in/hr) must be used.
Erosion Area Parameter ERODAR (card Kl) represents the acres of the
subcatchment subject to erosion. This would ordinarily be less than or
equal to the pervious area of the subcatchment and could indicate land
that is barren or under construction.
Soil Factor -- The soil factor, K, is a measure of the potential credibility
of a soil and has units of tons per unit of rainfall factor, R. The soil
credibility nomograph shown in Figure 4-42 (Wischmeier et al., 1971) may
be used to find the value of the soil factor once five soil parameters have
172 4-122
-------
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Figure 4-42. Nomograph for Calculation of Soil Erodability Factor, K.
(After Wischmeier et al., 1971.)
173
4-123
-------
been estimated. These parameters are: percent silt plus very fine sand
(0.05-0.10 mm), percent sand greater than 0.10 mm, organic matter (O.M.)
content, structure, and permeability. To use the nomograph, enter on the
left vertical scale with the appropriate percent silt plus very fine sand.
Proceed horizontally to the correct percent sand curve, then move vertically
to correct organic matter curve. Moving horizontally to the right from this
point, the first approximation of K is given on the vertical scale. For soils
of fine granular structure and moderate.permeability, this first approximation
value corresponds to the final K value and the procedure is terminated. If
the soil structure and permeability is different than this, it is necessary
to continue the horizontal path to interact the correct structure curve, pro-
ceed vertically downward to the correct permeability curve, and move left to
the soil credibility scale to find K. This procedure is illustrated by the
dotted line on the nomograph. For a more complete discussion of this topic,
see Wischmeier et al. (1971).
A preferable and often simpler alternative to the use of the nomograph
of Figure 4-42 is to refer directly to the soil survey interpretation sheet
for the soil in question, on which may be found the value of the soil factor.
This is , illustrated :lu Figure 4-19 for Conestoga Silt Loam v?hereupon the K
value is given as 0.43. Since this is site-specific local information, it
is highly recommended. Local Agricultural Research Service and Soil Conser-
vation Service offices are available to obtain the soil survey interpreta-
tion sheets and to provide much other useful information.
Slope Length Gradient Ratio This parameter is an empirical function of
runoff length arid slope and is given by
LS = ERLEN0'5 (0.0076 + 0.53 WSLOPE + 7.6 WSLOPE2) (4-47)
where LS = slope length gradient ratio,
ERLEN = the length in feet from the point of origin of overland
flow to the point where the slope decreases to the extent
that deposition begins or to the point at which runoff
enters a defined channel, e.g., gutter/pipe or inlet, and
WSLOPE = the average slope over the given runoff length, ft/ft.
Parameter ERLEN is entered with the erosion parameters in card group Kl.
The slope, WSLOPE, is the same as for runoff calculations and will already
have been entered in card group HI.
In using 1:he average slope in calculating the LS factor, the predicted
erosion will be different from the actual erosion when the slope is not
uniform. Meyer and Kramer (1969) show that when the actual slope is convex,
the average slope prediction will underestimate the total erosion whereas
for a concave slope, the prediction equation will overestimate the actual
erosion. If possible, to minimize these errors, large eroding sites should
be broken up into areas of fairly uniform slope.
Cropping Management Factor -- This factor is dependent upon the type of
ground cover, the general management practice and the condition of the soil
over the area of concern. The C factor (CROPMF in card group Kl) is set
equal to 1.0 for continuous fallow ground which is defined as land that has
174 4-124
-------
been tilled and kept free of vegetation and surface crusting. Values for
the cropping management factor are given in Table 4-25 (Maryland Dept. of
Natural Resources, 1973). Again consultation with local soils experts is
recommended.
Control Practice Factor This is similar to the C factor except that P
(CONTPF in card group Kl) accounts for the erosion-control effectiveness of
superimposed practices such as contouring, terracing, compacting, sediment
basins and control structures. Values for the control practice factor for
construction sites are given in Table 4-26 (Ports, 1973). Agricultural
land use P factor values are given by Wischmeier and Smith (1965).
The C and P factors are the subject of much controversy among erosion
and sedimentation experts of the US Department of Agriculture (USDA) and
the Soil Conservation Service (SCS). These factors are estimates and many
have no theoretical or experimental justification. It has been suggested
that upper and lower limits be placed on these factors by local experts to
increase the flexibility of the USLE for local conditions.
The P factors in the upper portion of Table 4-26 were designated as
estimates when they were originally published. SCS scientists have found
no theoretical or experimental justification for factors significantly
greater than 1.0. Surface conditions 4, 6, 7 and 8 (P < 1.0) of Table 4-26
also are estimates with no experimental verification.
Subcatchment Quality Data (Card Group LI)
Introduction As discussed earlier while describing buildup and washoff
mechanisms, certain quality parameters are unique to each subcatchment and
are entered in this card group. These parameters are independent of the
quantity parameters entered in card group HI (except for subcatchment num-
ber, of course) and are not required if no quality simulation is performed.
Land Use -- Each subcatchment is assigned one of up to five land uses defined
in card group J2. Parameters entered for an individual land use will then
be used on the corresponding subcatchments.
Catchbasins The total number is entered for parameter BASINS. (See
earlier discussion of catchbasins.) In lieu of counting every one, BASINS
may be computed if the general catchbasin density is known, e.g., 0.2 - 0.5
per ac (0.5 - 1.2 per ha) for most cities (Lager et al., 1977b). When BASINS
= 0, no catchbasin computations are performed for the subcatchment.
Gutter Length Gutter or curb length, GQLEN, is used only for quality
calculations for which buildup parameters are normalized as lb/100-ft
curb, etc. (i.e., only when parameters JACGUT or KACGUT equal zero in
card groups J2 and J3). This parameter may be measured directly by scaling
the total length of streets off of maps and multiplying by two. As for other
parameters, this is most economically achieved by measurements in a few repre-
sentative areas and extrapolation to others.
175 4_125
-------
Table 4-25 Cropping Management Factor, C
(Maryland Dept. of Natural Resources, 1973)
Type of cover C Value
None (fallow) 1.00
Temporary seedings:
First sixty days 0.40
After sixty days 0.05
Permanent seedings:
First sixty days 0.40
After sixty days 0.05
Sod (laid immediately) 0.01
Rate of
Application
Mulch (tons/acre)
Hay or straw 0.5
1.0
1.5
2.0
Stone or gravel 15.0
60.0
135.0
240.0
Chemical mulches
First ninety days a
After ninety days a
Woodchips 2.0
4.0
12.0
20.0
25.0
C Value
0.35
0.20
0.10
0.05
0.80
0.20
0.10
0.05
0.50
1.00
0.80
0.30
0.10
0.06
0.05
Maximum
Allowable
Slope Length
20 feet
30
40
50
15
80
175
200
50
50
25
50
100
150
200
As recommended by manufacturer
I
I-1
NJ
-------
Table 4-26 Erosion Control Practice Factor, P, for Construction Sites
(Ports, 1973)
Surface Condition With no Cover Factor P
1. Compact, smooth, scraped with bulldozer
or scraper up and down hill 1.30
2. Same as above, except raked with bulldozer
root, raked up and down hill 1.20
3. Compact, smooth, scraped with bulldozer
or scraper across the slope 1.20
4. Same as above, except raked with bulldozer
root, raked across the slope 0.90
5. Loose, as in a disked plow layer 1.00
6. Rough irregular surface, equipment
tracks in all directions 0.90
7. Loose with rough surface greater than 12" depth 0.80
8. Loose with smooth surface greater than 12" depth 0.90
Structures
1. Small sediment basins:
0.04 basin/acre 0.50
0.06 basin/acre 0.30
2. Downstream sediment basins:
with chemical flocculants 0.10
without chemical flocculants 0.20
3. Erosion control structures:
normal rate usage 0.50
high rate usage 0.40
4. Strip building 0.75
177 4-127
-------
Curb length has been measured in several cities as a function of land
use. Results for Tulsa and for ten Ontario cities are shown in Table 4-27.
The Ontario results were compiled from aerial photographs. On a broad,
total urbanized area basis, curb length has been related to population
density, e.g., Graham et al. (1974) for the Washington, D.C. area. Manning
et al. (1977) augmented the Washington, D.C. data with data from six other
U.S. cities to develop the equation
PD
GD = 413 - 353 0.839
where GD = curb length density, ft/ac, and
PD = population density, persons/ac.
Subcatchment gutter length may then be obtained simply by
GQLEN = GD WAREA/100.
where GQLEN = gutter (curb) length, 100-ft, and
WAREA = subcatchment area, ac.
(4-48)
(4-49)
Equation 4-48 should only be used for large areas, such as an aggregated
subcatchment used for continuous simulation. Site specific data are always
preferred in any event.
Table 4-27. Measured Curb Length Density for Various Land Uses.
(Heaney et al., 1977, Sullivan et al., 1978)
Tulsa
mile/acre km/ha lOOft/acre
10 Ontario Cities
mile/acre tun/ha lOOft/acre
Residential
Commercial
Industrial
Park
Open
Institutional
0.076
0.081
0.062
0.042
0.016
-
0.30
0.32
0.17
0.17
0.063
-
4.0
4.3
2.2
2.2
0.85
-
0.042
0.057
0.025
-
0.015
0.030
0.17
0.23
0.099
-
0.059
0.12
2.2
3.0
1.3
-
0.79
1.60
178
4-128
-------
Constituent Loadings As an alternative to the several buildup options
available in card groups J2 and J3, initial desired constituent loads may be
entered on a per acre basis for each, subcatchment. Total initial loads are
then computed simply by multiplication by the subcatchraent area,
PSHED = pshed WAREA FACT1 (4-50)
where PSHED = initial surface constituent load, e.g., mg for NDIM=0,
pshed = loading entered on card group LI, e.g., Ib/ac for
NDIM=0,
WAREA = subcatchment area, ac, and
FACT1 = conversion factor, e.g., 453600 mg/lb for NDIM=0.
Loadings may be entered for any number of constituents. A loading entered
for one subcatchment does not affect buildup calculations on another for
which a zero loading is used.
For continuous simulation, constituents will buildup between storms,
(unless the rating curve option is used). These buildup parameters must be
entered in card groups J2 and J3. The initial loading will have no effect
after the first storm has ended except for a possible residual load (PSHED)
remaining on the surface. The loading parameters on card group LI are thus
most easily adapted to single event simulation. They also provide one
method of avoiding computation of an equivalent gutter length for land uses
such as parking lots (if that type of normalized loading rate is being used).
Overall Sensitivity to Quality Parameters
One of the advantages of computer simulation is that it permits examina-
tion of the interactions between the complex precipitation time series and
the various quantity and quality process of the catchment. It should be
borne in mind that quality buildup processes in the model occur only during
dry weather, and quality washoff processes occur only during storms (or
during runoff due to snowmelt). For the moment it will be assumed that the
rating curve approach is not being used.
As a general rule, predicted concentrations and total loads are most
sensitive to buildup rates. Twice the initial surface load usually means
that about twice the load in the runoff will occur. (An obvious qualifica-
tion is if washoff parameters are such that not all the material is washed
from the surface during most storm events.) For instance, if linear build-
up is used for dust and dirt, parameter DDFACT in card group J2 is a very
important parameter. But the upper limit to buildup also enters the pic-
ture.
Consider the sketch in Figure 4-43. If the limiting buildup quantity
is reached before a storm occurs, the results will be sensitive to the
buildup limit (i.e., DDLIM or QFACT(l)) but not the rate. On the other
hand, if the limit is not reached before a storm occurs, the results will
be sensitive to the buildup rate (i.e., DDFACT or QFACT(3)) but not the
limit. During continuous simulation the interevent time between storms
varies, typically with an exponential probability density function. But
179 4-129
-------
Q
LU
BUILDUP /
LIMIT /
TIME
INSENSITIVE TO
BUILDUP RATE
INSENSITIVE TO
BUILD UP LIMIT
INTEREVENT TIME
STORM OCCURRENCE
Figure 4-43. Interaction of Buildup Parameters and Storm Interevent
Time.
130
4-130
-------
examination of the average interevent item should permit a sensitivity
analysis of the type sketched in Figure 4-43- A similar argument could be
made using power, exponential or Michaelis-Menton buildup functions
The effect of street cleaning is also obviously related to average
interevent time. Clearly if the interval, CLPREQ, exceeds the storm inter-
event time, cleaning will have a decreasing effect. For example, for a
continuous simulation of Des Moines, Iowa, street cleaning had essentially
no effect for intervals greater than 20 days (Heaney et al., 1977). The
average interevent time for Des Moines is about 4 days.
Should it be desired to evaluate the average interevent time for pre-
cipitation, the computer program SYNOP may be used to process the National
Weather Service precipitation tapes. This is described in the EPA Area-wide
Assessment Procedures Manual (EPA, 1976). Alternatively the SWMM Statistics
Block may be used.
Total storm loads will be sensitive to washoff parameters as long as
they do not already produce 100 percent washoff during most storms. For
examples, in many SWMM applications in the past, parameters RCOEF and WASHPO
(equation 4-51) were set to 4.6 in"1 and 1.0, respectively. This resulted
in 90 percent washoff after 0.5 in (13 mm) of runoff (independent of the
time, as discussed earlier). Since most applications of single event SWMM
simulated storm events for which runoff was greater than 0.5 in (13 mm),
total loads were insensitive to increases in RCOEF and relatively insensi-
tive to decreases.
This may still be true for single event simulations of "large" storms
(i.e., depths greater than 0.5 in or 13 mm). But during continuous simula-
tion the median runoff depth is likely to be considerably less than 0.5 in
(13 mm), more on the order of 0.2 in (5 mm). Hence washoff coefficients
will be relatively more important for continuous simulation. As an indica-
tion of relative sensitivity, equation 4-36 can be rearranged for constant
runoff rate, r, and for 90 percent washoff (PSHED/PSHEDQ = 0.1) to give
RCOEF r WASHPO . t _ RCOEP . rWASHPO-1 . d = _m 0. 1 = 2.303 (4-51)
where RCOEF = washoff coefficient, in-WASHPO . hrWASHPO-1?
WASHPO = washoff power,
t = time (runoff duration), hr,
r = runoff rate, in/hr, and
d = storm runoff depth, in.
This relationship between RCOEF and WASHPO (linear on semi-log paper) is
shown for d = 0.2 and 0.5 in (5 and 13 mm) on Figure 4-44 for various values
of r. Note that for a half-inch of runoff, the familiar value for RCOEF of
4.6 is found for r = 1.0 in/hr or WASHPO = 1.0. The figure shows that for
runoff rates less that 1.0 in/hr (25 mm/hr) RCOEF must be increased as
WASHPO is increased to achieve the same percent washoff. The relationship
is reversed for r > 1.0 in/hr, but runoff rates this high occur only over
181 4-131
-------
100
0 .5
I 1.5 2 2.5 0
WASHPO
.5 I 1.5 2 2.5
Figure 4-44.
Relationship Between RCOEF and WASHPO for 90 Percent
Washoff During a Storm Event of Runoff Depth d. The
runoff rate is r.
182
4-132
-------
brief intervals during a year. In fact, average hourly rainfall intensi-
ties greater than 1.0 in/hr are rarely found in precipitation records.
Hence, during continuous simulation, if RCOEF or WASHPO is changed, the
other parameter should be increased if the same percentage total washoff
is desired. Manipulations similar to equation 4-51 may be performed if
a different percentage washoff is being considered.
During single event simulation it may occasionally be important to
match the pollutograph (concentration versus time) shape to measured data,
as well as the total storm load. The effect of RCOEF and WASHPO on pollu-
tographs has already been discussed and illustrated in Figures 4-32 to 4-36.
Generally, if the data show that concentrations tend to increase with flow
rate, especially late in the storm, then WASHPO should be greater than one.
If a rating curve approach is being used buildup parameters will have
no effect (KWASH = 1) or little effect (KWASH =2). In general, as WASHPO
increases beyond 1.0, the predicted loads and concentrations will closely
follow flow variations. If WASHPO is less than 1.0, concentration will be
inversely proportional to flow.
As has been discussed, catchbasins have only a small effect on total
storm load and affect pollutographs only during the first several time steps
of a storm. Their main effect is to enhance the first flush, if there is
one.
The constituent fractions (card group J4) are capable of having a
large effect on a few constituents if those constituents have added to
them a large loading. Thus, if suspended solids (SS) are high and five
percent of SS is added to BOD, BOD can also be high without any surface
loading. Since the fractions interrelate the constituents, it is often
easier to calibrate the model without them, although it may be more
physically realistic to include them.
Print Control (Card Groups Ml and M2)
Two types of printed output are available from the Runoff Block. Ex-
amples may be found subsequently in the section on case studies. Summary
tables listing total flow volumes and quality loads by source are always
printed. For continuous simulation options exist for the frequency of
summaries (daily or monthly and annual) as indicated by parameter IPRDAY
on card Bl. Caution should be used in order not to produce excessive
lines and pages of output.
The continuity check for quantity will ordinarily have an error of
less than 1 percent, due to roundoff and the method of summing (numerically
integrating) instantaneous flow rates. Should non-convergence messages
be encountered, the continuity error could be somewhat higher.
The second type of output available is on a time step basis. Single
event SWMM (IC.RAIN = 0) will print output for desired locations for the
total event duration. Since there is no limit on time steps, it is possible
183 4-133
-------
for this output to be lengthy. However, the number of time steps between
printing may be varied using parameter INTERV on card Ml.
For continuous simulation, time step print out is available for up to
five specified time periods. The choice of these time periods must be made
in advance and can be most reasonably accomplished by examination of the
precipitation record prior to running the total continuous simulation. Recall
that this is done by using ICRA1N = 4 for preprocessing of the precipitation
(and temperature) records.
For single event simulation (ICRAIN = 0) all time step flows and concen-
trations are instantaneous values at the indicated time. (In previous SWMM
versions they were averages over the preceding time step.) In addition to
the time step values, the total load, and flow-weighted averages and standard
deviations are printed for flow and each quality parameter.
For continuous simulation (ICRAIN > 0), flows are time averages over the
previous time step. It was necessary to adopt this scheme to reduce large
continuity errors that resulted when using a one hour time step for periods
of a year or more. Computationally, the procedure is the same as outlined in
Appendix V except that the average flow, Q, is found by insertion of the end-
of-time-step depth, d2, found by equation V-37 into the continuity equation
V-33. This is the way the Runoff Block formerly computed all flows and results
in very low continuity errors at the expense of other computational incon-
veniences.
The print control cards mark the end of Runoff Block input. A schematic
of all required card input is given in Figure 4-45. Control is now returned
to the Executive Block. For review of hydrographs and pollutographs and for
ease of calibration, use of the graph routines described in Section 2 is
highly recommended. Finally, continuous SWIM output may most conveniently
be summarized using the Statistics Block.
184
-------
jU.
/MI-M2 PRINT CONTROL
CARDS
/LI SUBCATCHMENT
CARDS
/,
KI-K2 EROSION CARDS
-J5 QUALITY CARDS
r
hi3 SNOWMELT CARDS
HI-H2 SUBCATCHMENT CARDS
GI-G2 GUTTER/PIPE CARDS
EVAPORATION DATA
L
EI-E2 PRECIPITATION DATA
r
Dl CONTINUOUS SIMULATION DATA
CI-C5 SNOWMELT PARAMETERS
r
BI-B2 CONTROL PARAMETERS
Al TITLE CARDS
RUNOFF (READ IN EXECUTIVE BLOCK)
Figure 4-A5. Data Deck for the Runoff Block
185 4-135
-------
Table 4-28. Runoff Block Card Data
Card
Group
Card
Format Columns
Description
Variable
Name
Default
Value
Two Title Cards
Al 2X 1-2 Card group identifier = Al. Blank
19A4 3-78 Title, heading to be printed TITLE Blanks
on output and carried to sub-
sequent SWMM blocks (two cards.)
First Control Card
Bl 2X 1-2 Card identifier = Bl. Blank
13 3-5 Continuous SWMM parameter. ICRAIN 0
= 0, Single event SWMM, continuous SWMM
not used.
***Values greater than zero indicate continuous SWMM***
= 1, Hourly precipitation values read as
card images from National Weather Service
(NWS) tape. Input unit is JIN(l). for
NWS tape.
= 2, Processed hourly precipitation values
(and temperatures if ISNOW = 2) are
read from unit NSCRAT(2). These values
were generated and saved from earlier run
when ICRAIN = 1- or 4.
= 3, Read precipitation values from cards, using
card groups El and E2. Not useable with
snowmelt, i.e., ISNOW must equal zero.
= 4, Same as ICRAIN = 1, except that program stops
after processing precipitation (and tempera-
ture) data. The only Runoff Block input
parameters required are those needed for this
processing. Card input ceases after card Dl.
186 4-136
-------
Table 4-28 (continued). Runoff Block Card Data
Card Card Description Variable Default
Group Format Columns Name Value
12 6-7 Metric input-output. METRIC 0
= 0, Use U.S. customary units.
= 1, Use metric units. Metric input
indicated in brackets [ ] in
remainder of Table 4-28.
Bl 13 8-10 Snowmelt parameter3 ISNOW 0
= 0, Snowmelt not simulated.
= 1, Snowmelt simulation for single event
SWMM. ICRAIN must equal zero.
= 2, Snowmelt simulation for continuous SWIM,
ICRAIN j« 0 or 3. When ICRAIN = 1 or 4,
NWS temperature tape is input on unit
NSCRAT(3). When ICRAIN = 2, use already
processed precipitation and temperature
data saved on NSCRAT(2).
615 11-15 Number of hyetographs (rain gages) (maxi- NRGAG 1
mum = 6, must be equal to only one for con-
tinuous SWMM).
16-20 Choice of infiltration equation. INFILM 0
= 0, Horton equation used.
= 1, Green-Ampt equation used.
21-25 Quality (or erosion) simulated? KWALTY 0
=0, No.
= 1, Yes.
4
26-30 Evaporation parameter. IVAP 0
= 0, Evaporation data not read in,
default rate used of 0.1 in./day
[3 mm/day|.
187 4-137
-------
Table 4-28 (continued). Runoff Block Card Data
Card Card Variable Default
Group Format Columns Description Name Value
= 1, Read monthly evaporation data on Card Fl.
31-35 Hour of day of start of storm (24-hour NHR 0
clock, midnight = 0). N.R. for ICRAIN j* 0.
36-40 Minute of hour of start of storm. N.R. NMN 0
for ICRAIN # 0.
3(3X,I2) 41-45 Day of month of start of simulation.5 NDAY 1
46-50 Month of start of simulation. MONTH 1
51-55 Year of start of simulation. IYRSTR None
315 56-60 Print control parameter for NWS pre- IRPRNT 0
cipitation and temperature data, (con-
tinuous SWUM). Used (and useable) when
ICRAIN = 1 or 4 only.
= 0, No print of any precipitation or
Irmprrntiire dat.i.
= 1, All hourly precipitation tl.ita printed.
When snowmelt is run, prints only daily
maximum and minimum temperatures, one
year per page, 6 days per line.
= 2, All hourly precipitation data printed.
When snowmelt is run, all generated
hourly temperatures are printed (in-
cluding summer values), two Lines per
day, 29 days per page (See also Card Dl).
61-65 Data set print control parameter. Used ICNTNS 0
to print data sets generated while pro-
cessing NWS precipitation and tempera-
ture data. Used only for possible de-
bugging, and only for ICRAIN t \ or 4.
= 0, Not used.
= 1, Prints data set, NSCRAT(2), with hourly
precipitation and temperature values on
it. Prints 4 lines per day.
188 4-138
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group
Card
Format Columns
Description
Variable
Name
Default
Value
66-70
= 2, Prints scratch data set of hourly tem-
peratures only, 3 lines per day.
Print-control for output of continuous
SWMM, (ICRAIN # 0). "Totals" below
refer to precipitation, runoff and all
quality parameters, for each inlet, to
a maximum of 30 inlets.
= 0, Monthly and annual totals only, one
year per page.
= 1, Daily, monthly and annual totals, two
months per page. Daily totals are
printed whenever there is non-zero
precipitation and/or runoff.
IPRDAY
B2
2X
18
1-2
3-10
Second Control Card
Card identifier = B2.
Number of time steps in simulation.
Blank
NSTEP 0
-
No maximum. Required when ICRAIN
= 0. Optional for ICRAIN >0. If
given as zero for ICRAIN >0, NSTEP
will be computed using beginning and
ending simulation dates.
4F5.0 11-15 Integration period (time step), rain.
Must be 60 min for ICRAIN = 1,2, or 4.
Percent of impervious area with zero
detention (immediate runoff).
For continuous SWMM, infiltration
capacity is regenerated using a Horton
type exponential rate constant equal to
REGEN-DECAY, where DECAY is the Horton
rate constant read in for each subcatch-
ment in card group HI. N.R. if ICRAIN =
0 or INFILM =1.
*** The following two parameters required only for ISNOW = 1. ***
26-30 Time interval between input of DTAIR
11-15
16-20
21-25
DELT
PCTZER
REGEN
15
31-35
Time interval between input of
air temperatures in card group C5.
Number of air temperatures read in
on card group C5.
NAIRT
None
25.0
0.01
0.0
189
4-139
-------
Table.4-28 (continued). Runoff Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
Cl
2X
F8.0
9F5.0
General Snow Input Data
IF ISHOW = 0 ON CARD Bl, SKIP TO CARD Dl.
1-2 Card identifier = Cl.
3-10 Average watershed elevation, ft. [m] HSL. ELEV
11-15 Ratio of free water holding capacity
to snow depth (in. or ram w.e.) on snow
covered impervious area.
16-20 Ratio of free water holding capacity to
snow depth (in. or mm w.e.) on snow
covered pervious area.
FWFRAC(l)
*** The following parameters are required only for ***
ISNOW = 2 (continuous SWMM).
21-25
26-30
31-35
36-40
41-45
Ratio of free water holding capacity to
snow depth (in. or mm w.e.) for snow on
normally bare impervious area.
Dividing temperature between snow and SNOTMP
rain, °F [°CJ. Precipitation occurring at
air temperatures above this value will
be rain, at or below will be snow.
Snow gage catch correction factor. Snow
depths computed from NWS precipitation
tape will be multiplied by this value.
SCF
Weight used to compute antecedent tempera- TIPM
ture index, 0 S TIPM S 1.0. Low values
(e.g., 0.1) give more weight to past tem-
peratures. Values 5 0.5 essentially give
weight to temperatures only during the
past day.
Ratio of negative melt coefficient to RNM
melt coefficient. "Negative melt coeffi-
cient" is used when snow is warming or
cooling below the base melt temperature
without producing liquid melt. RNM is
usually 3 1.0 with a typical value of
0.6.
Blank
0.0
0.0
FWFRAC(2) 0.0
FWFRAC(3) 0.0
0.0
1.0
0.0
0.6
190
4-140
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
15
46-50 Average latitude of watershed, degrees
north.
ANGLAT
51-55 Longitude correction, standard time.minus DTLONG
mean solar time, minutes (of time).
***The following parameter is not required if ICRAIN = 2***
(precipitation/temperature data set has been already
computed).
56-60 National Weather Service (NWS) Station LOCAT3
ID number for temperature tape, read in
on NSCRAT(3). Program must find a match
for this ID number.
0.0
0.0
None
C2
2X
F8.0
Monthly Wind Speeds
If ISNOW = 2, values are required for all months with
potential snow melt.
If ISNOW = 1, value is required only for month of the
event being simulated.
1-2
3-10
11F5.0 11-15
Card identifier = C2.
Average wind speed in January, miles/hr
[m/hrj.
WIND(l)
Average wind speed in February, miles/hr WIND(2)
[m/hrj.
Blank
0.0
0.0
61-65 Average wind speed in December, miles/hr WINDU2) 0.0
[m/hr|.
191
4-141
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
C3
2X
F8.0
9F5.0
1-2
IF ISNOW = 1, ON CARD Bl, SKIP TO CARD GROUP C5
REQUIRED ONLY FOR ISNOW = 2
Areal Depletion Curve for Impervious Area*
Card identifier = C3.
16
3-10 Fraction of^area covered by snow (ASC) ADCI(l)
at "zero*" ratio of snow depth to
depth at 100 percent cover (AWESI).
11-15 Value of ASC for AWESI = 0.1. ADCI(2)
16-20 Value of ASC for AWESI =0.2. ADCI(3)
Blank
0.0
0.0
0.0
46-50 Value of ASC for AWESI = 0.8 ADCI(9) O.-O
51-55 Value of ASC for AWESI = 0.9. ADCI(IO) 0.0
Note: Program automatically assigns value of ADCI = 1.0 when AWESI = 1.0.
C4 2X 1-2
F8.0 3-10
9F5.0 11-15
16-20
Areal Depletion Curve for Pervious Area
Card identifier = C4.
Fraction of7area covered by snow (ASC)
at "zero+" ratio of snow depth to depth
at 100 percent cover (AWESI).
Value of ASC for AWESI =0.1
Value of ASC for AWESI =0.2
--
ADCP(l)
ADCP(2)
ADCP(3)
Blank
0.0
0.0
0.0
46-50 Value of ASC for AWESI = 0.8 ADCP(9) 0.0
51-55 Value of ASC for AWESI = 0.9 ADCP(IO) 0.0
Note: Program automatically assigns value of ADCP = 1.0 when AWESI = 1.0.
192
4-142
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
C5
Air Temperatures
READ CARD C5 ONLY IF 1SNOW
CARD Dl IF ISNOW = 2.
1. SKIP TO
(For ISNOW = 2, (continuous SWMM) air temperatures are computed using NWS
tapes). Read an air temperature for each time interval, ten values per card,
for a total of NAIRT values. (Maximum number of values = 200.)
2X 1-2 Card group identifier = C5. Blank
F8.0 3-10 Air temperature during time interval TAIR(l) 0.0
i, °F rcj.
9F5.0 11-15 Air temperature during time interval TAIR(2) 0.0
2, T I'd.
51-55 Air temperature during time interval TAIR(IO)
10, °F [°C].
Repeat card C5 until NAIRT values are read in.
8A4
26-57
Program must find a match for this ID
number on JIN(l) (precipitation tape).
Required only for ICRAIN = 1 or 4.
Station name.
RTITLE
***The following parameters required only to***
control print out of continuous temperature
data, ICRAIN = 1 or 4, ISNOW = 2, IRPRNT = 2
or 3 (card Bl).
0.0
IF ICRAIN = 0 ON CARD Bl, SKIP TO CARD El
Dl 2X
13
15
3X.I2
110
1-2
3-5
6-10
14-15
16-25
Card identifier Dl.
19
Day simulation ends.
19
Month simulation ends.
19
Year simulation ends.
NWS station identification number.1
DAYSTP
MONSTP
IYRSTP
LOCAT1
Blank
last day
of month
12
IYRSTR
None
Blank
193
4-143
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group
Dl
El
E2
Format
3X.I3
12
13
12
2X
18
F5.0
2X
F8.0
Card
Columns
Variable
Description Name
No temperature data printed during a year between
these dates. (Use equal dates to print for all
dates.)
61-63 Month to end print of temperature data. MOT1
64-65 Day of month to end print of temperature MDAY1
data.
66-68 Month to resume print of temperature MOT2
data.
69-70 Day of month to resume print of MDAY2
temperature data.
***End
1-2
3-10
11-15
1-2
3-10
of Runoff Block input if ICRAIN = 4***
IF ICRAIN = 1 OR 2 ON CARD Bl, SKIP CARDS El & E2
Rainfall Control Card
Card identifier = El
Number of data points for each hyetograph NHISTO
(Maximum = 200 for ICRAIN = 0 and NSCRAT(4)
= 0, no limit for ICRAIN = 3 or NSCRAT(4)
* O.)20
Time interval between values, rain. THISTO
REPEAT CARD GROUP E2 FOR EACH HYETOCRAPH,
UP TO NRGAG TIMES
Rainfall hyetograph cards: Read 10 intensities
per card, up to NHISTO values.
Card group identifier = E2
Rainfall intensity, first interval, RAIN(l)
Default
Value
4
1
10
1
Blank
None
None
Blank
0.0
in./hr [mm/hr].
9F5.0 11-15 Rainfall intensity, second interval, RAIN(2) 0.0
in./hr [mra/hr).
16-20 Rainfall intensity, third interval, RAIN(3) 0.0
in./hr [mm/hrj.
194
4-144
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
E2
51-55 Rainfall intensity, tenth interval,
in./hr [ram/hr].
RAIN(IO)
Note: IF ISNOW =1, snowfall during a time step may be entered as a
negative value. Units are in. [mm] water equivalent/hr.
0.0
Fl
SKIP THIS CARD IF IVAP * 1 ON CARD Bl
2X 1-2 Card identifier = Fl.
F8.0 3-10 Evaporation rate for month 1 (January),
In./day [mm/day].
11F5.0 11-15 Evaporation rate for month 2 (February),
in./day [mm/day].
VAP(l)
VAP(2)
Blank
o.o22
0.0
61-56 Evaporation rate for month 12 (December), VAP(12)
in./day [mm/day].
0.0
Gl
2X
18
215
1-2
3-10
11-15
16-20
REPEAT CARD Gl FOR EACH GUTTER/PIPE
Gutter/pipe cards: one card per gutter/pipe
(if none, leave out). Maximum number of
gutter/pipes plus inlets = 200.
Card group identifier = Gl
Gutter/pipe number.
= +number, gutter/pipe ID number.
= -1, new ratios, or"
= -2, new default values for values with*.
Cutter or inlet number for drainage.
(Max. of 30 different inlets for con-
tinuous SWMM.)
Cutter/pipe shape.
= 1 for gutter, (trapezoidal channel),
= 2 for circular pipe, 0?
= 3 for dummy gutter, infiou=outflow.~
NAMEC"
24
23,26
NPG = MP
Blank
195
4-145
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group
Gl
G2
Card
Format Columns
***The
7F8.0 21-28
29-36
37-44
45-52
53-60
61-68
69-76
Description
following parameters are N.R. if NP =
28
Bottom width of gutter , or pipe
diameter, ft [m] .
Length of gutter, ft [m] .
Invert slope, ft/ft [dimensionless)
Variable
Name
3***
GWIDTH=G1*
GLEN=G2*
G3*
Left-hand side slope,
ft/ft [dimensionless). 29GS1 = M*
I Horizontal/vertical.
' N.R. if NP # 1
Right-hand side slope,
ft/ft [dimensionless).
Manning's roughness coefficient.
Depth of gutter when full, ft [m) .
if NP t 1.
Blank card (except for identifier)
gutter cards: one card (must always
GS2 = G5*
G6*
N.R. DFULL=G7*
to terminate ,..
be included).
Default
Value
0.0
0.0
None
None
None
None
None
HI
2X
13
215
1-2
3-5
6-10
11-15
REPEAT CARD HI FOR EACH SUBCATCHMENT
Subcatchment Data: (Maximum of 200 different
subcatchments for single event SWIM, ICRAIN = 0,
and 30 for continuous SWUM, ICRAIN # 0).
Card group identifier = HI.
Hyetograph number (based on the order in JK
which they are input, in card group E2).
Subcatchment number NAME
= + number, Subcatchment ID number,
= -1, new ratio, or -,
= -2, new default for values with *
Gutter or inlet (manhole) number for NGTO
drainage (max. 30 different inlets for
continuous SWUM).
W3l
25,32
Blank
1
None
None
196
4-146
-------
Table 4-28 (continued). Runoff Block Card Data
Card Card . Variable Default
Group Format Columns Description Name Value
HI. 8F5.0 16-20 Width of subcatchment, ft |ml. This term W(l)* None
actually refers to the physical width
of overland flow in the subcatchment and _,
may be obtained as illustrated in the text.
21-25 Area of subcatchment, acres [ha]. WAREA=WW(2)* None
26-30 Percent iraperviousness of subcatchment, %.WW(3)* None
31-35 Ground slope, ft/ft [dimensionless]. WSLOPE=WW(4)* None
36-40 Impervious area.
41-45 Pervious area.
WW(5)* None
Roughness factor.
(Manning's n) W(6)* None
46-50 Impervious area.i WSTORE=WW(7)* None
> Depression storage, in.
51-55 Pervious area. ) [mm]. WSTORE=WW(8)* None
*** Horton equation parameters if INFILM = 0 (Card Bl) ***
56-60 Maximum (initial) infiltration rate, WLMAX-WW(9)* None
in./hr [mm/hr).
61-65 Minimum (asymptotic) infiltration rate, WLMIN=WW(10)*- None
in./hr [mm/hr).
F10.5 66-75 Decay rate of infiltration in Morton's DECAY=WW(11)* None
equation, I/sec.
*** Green-Ampt equation parameters if INFILM = 1 (Card Bl) ***
2F5.0 56-60 Capillary suction, inches (mm) of water. SUCT=WW(9)>'< None
61-65 Hydraulic conductivity of soil, in./hr HYDCON=WW( 10)*None
(mm/hrJ.
F10.5 66-75 Initial moisture deficit for soil, SMDMAX=WW(11)*None
volume air/volume voids.
H2 Blank card (except for identifier) to terminate
subcatchment cards: one card.
197 4-147
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
II
2X
13
1-2
3-5
15F5.0 6-10
11-15
16-20
21-25
26-30
31-35
36-40
IF ISNOW = 0, SKIP TO CARD Jl. IF ISNOW = 1,
READ ONLY CARD II AND TERMINATE WITH A BLANK
CARD (CARD 13). IF ISNOW = 2, READ BOTH CARDS
II AND 12, IN PAIRS, AND TERMINATE WITH ONE
BLANK CARD (CARD 13). ORDER OF SUBCATCHMENTS
MUST BE SAME AS IN CARD GROUP HI, AND THERE
MUST BE SNOW DATA CARD(S) FOR EACH ONE.
CAUTION - THERE IS A LIMIT OF 30 SUBCATCH-
MENTS WHEN ISNOW = 2.
NOTE THAT ALL SNOW DEPTH RELATED PARAMETERS
REFER TO DEPTH OF SNOW WATER EQUIVALENT (in. w.e.)
Subcatchment Snow Input Data
12.
Card group identifier = II.
Subcatchment number.
= + number, Subcatchment ID number.
= -1, new ratio. ^
= -2, new default value for parameter with *.
Blank
JK135(=NAMEW(N))36 None
Fraction of impervious area with 100
percent snow cover (ISNOW = 1) or
subject to areal depletion curve
(ISNOW = 2).
Fraction of pervious area subject
to 100 percent snow cover (ISNOW
= 1). N.R. if ISNOW = 2.
Initial snow depth on impervious
area that is normally snow covered,
SNN1
SNN2=SNCP(N)
0.0
0.0
SNN3=WSNOW(N,1) 0.0
,12.
in. [mmj water equivalent (in. or mm w.e.)
SNN4=WSNOW(N,2)
Initial snow depth on pervious area,
in. [mm] w.e.
Initial free water on snow covered
impervious area, in. (nun).
Initial free water on snow covered
pervious area, in. (mm|.
Melt coefficient (ISNOW = 1) or
maximum melt coefficient, occurring
June 21 (ISNOW = 2) for snow covered
impervious area, in. w.e./hr-°F
(mm w.e./hr-°C|.
0.0
0.0
SNN5=FW(N,1)
SNN6=FW(N,2) 0.0
SN(1)*=DHMAX(N,1) 0.0
198
4-148
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group Format
Card
Columns
Description
Variable
Name
Default
Value
II
41-45 Melt coefficient (ISNOW = 1) or
maximum melt coefficient, occuring
on June 21 (ISNOW =2) for snow
covered pervious area, in. w.e./hr-°F
(mm w.e./hr-°Cl.
SN(2)*=DHMAX(N,2) 0.0
46-50
51-55
12 2X 1-2
13 3-5
Snowmelt base temperature for snow
covered impervious area, °F [°CJ.
Snowmelt base temperature for snow
covered pervious area, °F [0C).
READ CARD 12 ONLY FOR ISNOW = 2
Card group identifier = 12.
34
Subcatchment number. If JK2 =+
SN(3)*=TBASE(N,1)
SN(4)*=TBASE(N,2)
JK235(=NAMEW(N))
0.0
0.0
Blank
None
I5F5.0 6-10
11-15
16-20
21-25
26-30
31-35.
36-40
number, then JK2 = subcatchment.,
number. If JK2 = -1, new ratio
or if JK2 -2, new default value
for parameters with *
Initial snow depth on impervious
area that is normally bare,
in. [mm] w.e.
Initial free water on impervious
area that is normally bare, in. [mm).
SNN7=WSNOW(N,3) 0.0
SNN8=FW(N,3)
0.0
Maximum melt coefficient, occurring SN(5)-=DHMAX(N,3) 0.0
on June 21 for snow on normally bare
impervious area, in. w.e./hr-°F
[mm w.e./hr-°C|.
Snowmelt base temperature for nor- SN(6)-=TBASE(N,3) 0.0
mally bare impervious area, °F [°C|.
Minimum melt coefficient occuring on SN(7)*=DHMIN(N,1) 0.0
Dec. 21 for snow covered impervious
area, in. w.e./hr.-°F (mm/v.e./hr-°C).
Minimum melt coefficient, occurring
on Dec. 21 for snow covered pervious
area, in. w.e./hr-°F [mm w.e./hr-°C|.
Minimum melt coefficient, occurring
on Dec. 21 for snow on normally bare
impervious area, in. w.e./hr-°F
[mm w.e./hr-°C|.
.2) 0.0
" = DI(MIN(N, J) 0.0
199
4-149
-------
Table 4-28 (continued). Runoff Block Card Data
Card Card Variable Default
Group Format Columns Description Name Value
12 41-45 Snow depth above which there is 100 SN(10)*=SI(N,1) 0.0
percent cover on snow covered imper-
vious areas, in.[mm] w.e.
46-50 Snow depth above which there is 100 SN(11)*=SI(N,2) 0.0
percent cover on snow covered pervious
areas, in.[mm] w.e.
51-55 Redistribution (plowing) depth on SNN9=WEPLOW(N) 0.0
normally bare impervious area, in.
w.e. Snow above this depth redis-
tributed according to fractions below.
Redistribution (plowing) fractions.
(See Figure 4-25.) Snow above WEPLOW in.
w.e. on normally bare impervious
area will be transferred to area(s)
indicated below. The five fractions
should sum to 1.0.
56-60 Fraction transferred to snow covered SNN10=SFRAC(N, 1) 0.0
impervious area.
61-65 Fraction transferred to snow covered SNN11=SFRAC(N,2) 0.0
pervious area.
66-70 Fraction transferred to snow covered SKN12=SFRAC(N,3) 0.0
pervious area in last subcatchment.
71-75 Fraction transferred out of watershed. SNN13=SFRAC(N,4) 0.0
76-80 Fraction converted to immediate melt SNN14=SFRAC(N,5) 0.0
on normally bare impervious area.
13 Terminate card group II (or II and 12) ,-
with one blank card (except for identifier).
200 4-150
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
Jl
2X
13
315
5F5.0
1-2
3-5
6-10
11-15
16-20
21-25
26-30
31-35
36-40
IF KWALTY * 1 (CARD Bl) SKIP TO CARD Ml
General Quality Control Card
Card identifier = Jl.
Number of quality constituents,
maximum = 10. Must have 1< NQS ,.
< 9 if erosion is simulated (IROS =1).
Number of land uses,
1 < JLAND < 5.
Erosion simulation parameter.
= 0, Erosion not simulated.
NQS
JLAND
IROS
= 1, Erosion of suspended solids simulated
using the Universal Soil Loss Equation.
Parameters input in card group Kl.
Output will be last quality constituent
(i.e., constituent NQS+1).
Option to add erosion constituent to IROSAD
constituent number IROSAD. E.g., if
IROSAD = 3, erosion will be added to
constituent 3 (perhaps suspended solids).
No addition if IROSAD = 0. N.R. if IROS = 0.
42
Number of dry days prior to start of DRYDAY
storm. Must be >0 for ICBAIN >0
(continuous simulation).
Average, individual catchbasin storage CBVOL
volume, ft [m J.
Dry days required to recharge catch- DRYBSN
basin concentrations to initial values
(CBFACT, card group J3). N.R. if
ICRAIN = 0. Must be > 0.
For erosion, highest average 30-minute RAINIT
rainfall intensity during the year
(continuous SWUM) or during the storm
(single event), in./hr. [mm/hr|. N.R. if
IROS = 0.
Blank
None
None
0.0
0.0
1.0
0.0
201
4-151
-------
Table 4-28 (continued). - Runoff Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
215
*** Street Sweeping Parameters ***
41-45 Street sweeping efficiency (removal), REFFDD
for "dust and dirt" fraction.
*** The following two variables are required only ***
for ICRAIN # 0 (continuous SWMM)
46-50 Day of year on which street,sweeping KLNBGN
begins (e.g., March 1 = 60)
51-55 Day of year on which street sweeping KLNEND
stops'(e.g., Nov. 30 = 334).
0.0
0
367
J2
2X
2A4
215
1-2
3-10
11-15
16-20
Land Use Cards
REPEAT FOR EACH LAND USE, TOTAL OF JLAND
CARDS. (MINIMUM =1, MAXIMUM =5.) LAND USE
1 WILL BE THAT AT FIRST CARD, LAND USE 2
WILL BE THAT OF SECOND CARD, ETC.
Card group identifier = J2.
Name of land use.
Buildup equation type
for "dust and dirt" (see text).
= -2, New default value, or
= -1, new ratio for parameters with
= 0, Power-linear.
= 1, Exponential.
= 2, Michaelis - Menton.
Functional dependence of buildup
parameters.
LNAME(l.J)
LNAME(2,J)
METHOD(J)
JACGUT(J)
= 0, Function of subcatchment gutter length.
= 1, Function of subcatchment area.
= 2, Constant.
Blank
Blank
202
4-152
-------
Table 4-28 (continued). Runoff Block Card Data
Card Card
Group Format Columns Description
Variable
Name
Default
Value
*** Following are up to three buildup parameters. ***
(See Table 4-15)
3F10.0 21-30 Limiting buildup quantity.
31-40 Power or exponent.
41-50 Coefficient.
***Street Sweeping Parameters ***
3F5.0 51-55 Cleaning interval, days.
56-60 Availability factor, fraction.
61-65 Days since last cleaning,
DSLCL < CLFREQ.
DDLIM(J)*
DDPOW(J)*
DDFACT(J)*
CLFREQ(J)*
AVSWP(J)*
DSLCL(J)*
1050
0.0
0.0
0.0
0.0
0.0
J3
2X
2A4
2A4
12
13
1-2
3-10
11-18
Constituent Cards
REPEAT FOR EACH CONSTITUENT, TOTAL OF NQS
CARDS. (MAXIMUM =10.) CONSTITUENT 1 WILL BE
THAT OF FIRST CARD, CONSTITUENT 2 THAT OF
SECOND CARD, ETC.
Card group identifier = J3.
/ Q
Constituent name.
Constituent units.
49
19-20 Type of units.
PNAME(1,K)
PNAME(2,K)
PUNIT(1,K)
PUNIT(2,K)
NDIM(K)
= 0, mg/1.
= 1, "other"per liter, e.g., MPN/1.
= 2, other concentration units, e.g., pH, JTU.
21-23 Type of buildup calculation.
24
50
KALC(K)
=-2, New default value , or
=-1, New ratio for paramters with
= 0, Buildup is fraction of "dust and
dirt" for each land use.
Blank
Blank
Blank
203
4-153
-------
Table 4-28 (continued). Runoff Block Card Data
Card Card Variable Default
Group Format Columns Description Name Value
= 1, Power-linear constituent buildup.
= 2, Exponential constituent buildup.
= 3, Michaelis-Menton constituent buildup.
= U, No buildup required (with KWASH =1).
12 24-25 Type of washoff calculation.50 KWASH(K) 0
= 0, Power-exponential.
= 1, Rating curve, no upper limit.
- 2, Rating curve, upper limit by buildup equation.
13 26-28 Functional dependence of buildup KACGUT(K) 0
parameters. N.R. for KALC = 0 or 4.
= 0, Function of subcatchment gutter length.
= 1, Function of subcatchment area.
= 2, Constant.
12 29-30 Linkage to snowmelt. N.R. if ICRAIN = 0 LINKUP(K) 0
or ISNOW = 0 or KALC = 4.
= 0, Linkage to snow parameters.
= 1, Constituent buildup during dry weather
only when snow is present,on impervious
surface of subcatchment.
-"'-'Following are up to five buildup parameters (see text and Tables 4-16 & 4-17)***
5F5.0 31-35 First buildup parameter, e.g., limit. QFACT(1,K)* 0.0
36-40 Second buildup parameter, e.g. power or QFACT(2,K)* 0.0
exponent.
41-45 Third buildup parameter, e.g. coefficient. QFACT(3,K)* 0.0
46-50 Fourth buildup parameter, N.R. if KALC ?! O.QFACT(4 ,K)* 0.0
51-55 Fifth buildup parameter, N.R. if KALC f 0. QFACT(5,K)* 0.0
''"'"'Following are two washoff or rating curve parameters.''"'"*
4F5.0 56-60 Power (exponent) for runoff rate. WASIIPO(K)-'- 0.0
61-65 Coefficient, an/hr)~WASHP° i,r~l RCOEF(K)* 0.0
,, ,. ,-WASHPO , -ii
[(mm/hr) hr '].
For washoff, see Table 4-20 and equation 4-35. For
rating curve see equation 4-38.
204
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
J4
***Miscellaneous parameters.***
66-70
71-75
76-80
Initial catchbasin concentration.
(Units according to NDIM.)
54
Concentration in precipitation.
(Units according to NDIM.)
Street sweeping efficiency
(removal) for this constituent, fraction.
CBFACT(K)*
CONCRN(K)*
REFF(K)*
0.0
0.0
0.0
Fractions For Contributions From Other Constituents
REPEAT THIS CARD UNTIL ALL DESIRED FRACTIONS ARE
ENTERED. END WITH A BLANK CARD.
2X 1-2 Card group identifier = J4. -- Blank
13 3-5 Number (from order in card group J3) KTO 0
of constituent to which fraction will
be added.
15 6-10 Number of constitutent from which KFROM 0
fraction is computed.
F10.0 11-20 Fraction of constitutent KFROM to be F1(KTO, 0.0
added to constituent KTO. KFROM)
J5
End this card group with a blank card (except
for identifier).
Kl
2X
1-2
Erosion Cards
IF IROS = 0 ON CARD Jl, SKIP TO CARD GROUP LI
REPEAT CARD Kl ONLY FOR EACH SUBCATCHHENT THAT
IS SUBJECT TO EROSION COMPUTATIONS. ORDER OF
CARDS IS ARBITRARY, BUT A MATCH MUST BE FOUND
OF SUBCATCHMENT NUMBER WITH A VALUE OF NAMEW
USED IN CARD GROUP HI.
Card group identifier = Kl.
Blank
205
4-155
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
18
5F5.0
K2
3-10
11-15
16-20
Subcatchment number.
= + number, Subcatchment 10 number.
=-1, New ratio , or 24
=-2, new default value for values with*.
Area of Subcatchment subject to erosion,
acres [ha].
Flow distance in feet [mj from point of
origin of overland flow over erodible
area to point at which runoff enters
gutter or inlet.
N=NAMEW
ERODAR*
ERLEN*
Terminate erosion input data with one
blank card (except for identifier).
None
0.0
0.0
21-25
26-30
31-35
Soil factor 'K'.
Cropping management factor 'C'.
Control practice factor 'P'.
SOILF*
CROPMF*
CONTPF*
0.0
0.0
0.0
LI
2X
18
15
Subcatchment Surface Quality Data Cards
IF NQS = 0, SKIP TO CARD Ml.
ONE CARD FOR EACH SUBCATCHMENT IS REQUIRED.
ORDER IS ARBITRARY, BUT A MATCH MUST BE FOUND
FOR EACH SUBCATCHMENT NUMBER (NAMEW) USED
EARLIER IN CARD GROUP HI.
1-2
3-10
11-15
Card group identifier = LI
Subcatchment number
= ^number, Subcatchment
= -1, New ratio. .
= -2, New default
ID number.
for values with *.
Land use classification. Must have
1 £ KL 5 5. Numbers correspond to
input sequence of card group J2.
N=NAMEW
KL
Blank
None
206
4-156
-------
Table 4-28 (continued). Runoff Block Card Data
Card
Group
Format
F5.0
F10.0
Card
Columns
16-20
21-30
Description
Number of catchbasins in subcatchment.
Total curb length within subcatchments.
Variable Default
Name Value
BA-=BASINS(N) 0.0
GQ*=GQLEN(N) 0.0
10F5.0 31-35
36-40
hundreds of feet [km]. May not
be required, depending on method used
to calculate constituent loadings (card
groups J2 and J3).
The following constituent loading values
may be input as an alternative to compu-
tation of loadings via methods specified
in card groups J2 and J3. For any non-
zero values read in, initial constituent
loadings will be calculated simply by
multiplication of the value by the sub-
catchment area. "Load" has units depend-
ing on value of NDIM (card group J3),
according to following table:
NDIH
0
1
2
Load
pounds (kg)
106 x quantity, e.g., 106 MPN
106 quantity x ft3, e.g., 10s pH-ft3
Initial loading, first constituent,
load/acre (load/ha).
Initial loading, second constituent,
load/acre [ load/ha|.
PSHED(1,N)* 0.0
PSHED(2,N)* 0.0
76-80 Initial loading, tenth constituent (if
used), load/acre [load/ha|.
PSHED(10,N)* 0.0
Gutter/Inlet Print Control
Ml 2X 1-2 Card identifier = Ml. -- Blank
13 3-5 Total number of gutters/inlets for which MPRNT 0
non-zero flows (,ind concentrations) arc
to be printed (maximum = 2001.J
207
4-157
-------
Table 4-28 (continued). Runoff Block Card Data
Card Card
Group Format Columns Description
15 6-10 Number of time-steps between
(Use INTERV = 1 for printing
time step.)
Variable
Name
printings. INTERV
at each
Default
Value
None
*** For continuous SWUM (ICRAIN = 1, 2, 3), detailed (time ***
step) printing may be obtained for up to five different
time periods. The following parameters N.R. if ICRAIN
= 0, (printing will occur for the total simulation), or
if NPRNT - 0.
1016 11-16 For starting printout date,
day, e.g., October 2, 1949 =
17-22 First stopping printout date
23-28 Second starting date.
29-34 Second stopping date.
35-40 Third starting date.
41-46 Third stopping date.
47-52 Fourth starting Hate.
53-58 Fourth stopping date.
59-64 Fifth starting date.
65-70 Fifth stopping date.
year, month STARTP(l)59
491002.
STOPPR(l)59
STARTP(2)
STOPPR(2)
STARTP(3)
.STOPPR(3)
STARTP(4)
STOPPR(4)
STARTP(5)
STOPPR(5)
None
None
0
0
0
0
0
0
0
0
M2
2X
13
1515
1-2
3-5
6-10
IF NPRNT = 0, SKIP CARD M2
Gutter/Inlet Print Cards: 16 Values/Card
(Same format for all cards)
Card group identifier = M2.
Gutter/inlet numbers for which flows and
concentrations are to be printed.
,60
Blank
None
IPRNT(l)
IPRNT(2) None
208
4-158
-------
Table 4-28 (continued). Runoff Block Card Data
Card Card Variable Default
Group Format Columns Description Name Value
11-15 IPRNT(3) None
IPRNT(NPRNT) None
***END OF RUNOFF BLOCK DATA CARDS***
At this point, program will seek new input data from the Executive Block.
209 4_159
-------
Footnotes 1;o Table 4-28
The main difference between "single event" and "continuous" SWMM
is basically that the latter uses data sets or offline storage
(e.g., disks) for storage of precipitation (and temperature) input
data instead of dimensioned arrays, thus eliminating any restric-
tion on the number of time steps. With the slight exception of
snowmelt, all computations are done identically for the two cases.
In addition, continuous SWMM produces extra daily, monthly and
annual summary output which single event SWMM does not. See also
footnotes 9, 10, and 20.
This option may be used, for example, if it is desired to review
the precipitation/temperature data prior to the simulation, perhaps
for location of critical time periods for detailed output using card
group Ml. To avoid the expense of reprocessing the NWS tapes, out-
put stored on unit NGCRAT(2) should be permaueuLly saved for later
use when ICRAIN = 2. This is done using appropriate job control
language instructions. When ICIlAIN = 4, only the following para-
meters are required on card groups Al - Dl; others may be left
blank (ze^ro), although the print: out of zeros may be misleading.
Required parameters (allowing for some default values) are: card Al,
TITLE; card Bl, ICRAIN (=4), ISNOW, INFILM, KWALTY, NDAY, MONTH,
IYRSTR, 1RPRNT, ICNTNS; card B2. DELT (=60.); card Cl, ELEV, SNOTMP,
SCF, ANGI.AT, UTLONG, LOCAT3; card C2, none; card C3, none; card C4,
none; card C5, none; card 1)1, DAYSTP, MONSTP, LYRSTP, LOCAT1,
RTITLE. Input data end after card Dl. The usual instructions for
skipping card groups should be followed, e.g., if ISNOW = 0, skip
card groups C1-C5.
The main differences between single event and continuous snowmelt
simulation follow. For single event SWMM, snow covered areas are
constant (areal depletion curves are used for continuous SWMM) and
input parameters are fewer. In addition, snowfall quantities are not
computed on the basis of air temperatures but may only be input, if
desired, as negative precipitation intensities on card group E2.
Melt coefficients are constant and there is no maintenance of the
cold content of the snow pack, nor is there redistribution (e.g.,
plowing) from normally bare areas. For continuous SWMM, melt
coefficients vary daily, from a maximum on June 21 to a minimum on
December 21. Both modes use the same melt equations and melt
routing procedures.
Evaporation is used to renew surface depression storage and is also
subtracted from rainfall and/or snowmelt at each time step. It has a
negligible effect on single event simulation, but is important for
continuous simulation. Evaporation is not used to deplete the snow
pack, i.e., it does not also ad: as sublimation, nor does it affect
regeneration of infiltration capacity.
210 4-160
-------
5. Used for information only for single event SWMM (ICRAIN = 0).
This parzimeter does not affect computations, but it is passed
to subsequent blocks.
6. Used as subscript for monthly w:ind speed and evaporation data
(card groups C2 aiul Fl).
7. Precipitation values printed one line per day, 57 days per page.
A line is: printed only for days with measureable precipitation and
for the first day of each month.
8. Conventional output from card groups Ml and M2 may also be used
with continuous SWMM.
9. There is now no limit on the nuwber of time steps for any SWMM block.
However, the allowable number oi" input values for precipitation and
temperature data may provide an effective limit of 200 time steps in
some cases. See card groups C5 and E2. When ICRAIN -! or 4,.(optional
for ICRAIN = 3) NSTEP will be calculated by program on the basis of
nearest precipitation (and temperature) data to specified beginning
and ending dates. (Ending date will always be reached, using zeroes
for missing precipitation and temperatures at end of record, if
required.) Note that continuous: SWMM will always start and stop at
midnight, such that computation!: are done for groups of whole days.
If ICRAIN = 2, (use previously processed and saved precipitation and,
possibly, temperature values), NSTEP must still be input and must be
less than or equal to value printed in output from prior run using
ICRAIN = 1 or 4.
10. A 60 minute time step must be used when accessing National Weather Ser-
vice (NWS) tapes (ICRAIN =1, 2, or 4) since they provide hourly data,
and the program is not equipped to average over longer time steps or
to treat the hourly values as a step function for shorter time steps.
This restriction, however, is due only to I/O complexity; all computa-
tions us«! DELT as a variable.
When ICRAIN = 3, no use is made of NWS tapes. DELT is arbitrary
for this case.
The nonli.near reservoir routing procedures used for subcatchment
overland flows and gutter/pipe flows is unconditionally stable
and independent of the time step. However, problems of conver-
gence arise occasionally for gutter/pipe routing when too large
a time step is used. These take the form of error messages print-
ed from subroutine GUTTER, and usually mean that flow values com-
puted at one or a few time step?* are slightly in error. It may
also result from an attempt to produce a negative outflow, neces-
sary to satisfy continuity, but not allowed by the program. These
problems can usually be eliminated by shortening the time step
(e.g. reduce DELT from, say, five minutes to two and one-half
211 4-161
-------
minutes), or, by increasing the volume of the offending gutter/
pipe (i.e., increase the length and/or the width/diameter). The
latter option would be the only recourse when using continuous
SWUM with a 60 minute time step; however, gutter/pipes will seldom
be used at all with continuous SWMM, thus avoiding any such problem.
Convergence problems almost never occur in the subcatchment over-
land flow routing. If nonconvergence messages are encountered
from subroutine WSHED, however, the time step could be shortened
or subcatchment area increased.
See also footnotes 1, 9 and 21.
11. This parameter allows immediate runoff, prior to filling depres-
sion storage on impervious areas. As PCTZER is increased, the
rising limb of the hydrograph steepens.
12. All snow depths are in inches [or mm] of water equivalent, "in.
[mm] w.e." One inch of snow watr.er equivalent equals a depth
of approximately 11 inches of new snow on the ground surface.
13. Values of. SCF are usually > 1.0 and increase as a function of wind
speed. See Figure 4-2. The value of SCF can also be used to
account for snow losses, such as interception and sublimation,
not included in program computations.
14. Compute UTLONG as follows: Determine standard meridian (SM) for
time zon« of catchment (e.g., EST = 75°W, CST = 90°W, MST = 105°W,
PST = 120°W). Let 0 = average longitude of catchment and A = 0 -
SM. Then DTLONG = 4 min/deg x A. Example: Minneapolis at 0 =
93°W has DTLONG = +12 min.
15. The NWS station ID for the temperature tape is not necessarily the
same as J:or the precipitation tape.
16. See Figures 4-4 and 4-5 for description of areal depletion curve.
17. Value of ADC may = zero, but curve need not pass through (0,0);
see Figure 4-4. Thus ADC can take on an arbitrary value for a
small departure of AWESI from zero.
18. In program, AWESI is the ratio of actual snow depth (WSNOW) to
depth at 100 percent cover (SI, read in card group 12).
19. Values are used computationally only for ICRAIN = 1 or 4. When
ICRAIN = 2 or 3, ending date may be optionally determined from
value of NSTEP. Card is required for continuous SWMM for these
cases only to be able to print out values on card.
212 4-162
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20. Scratch tile number 2, NSCRAT(2), is used for storage of processed
rainfall data for continuous simulation. NSCRAT(4) may also be
used for this purpose for single; event simulation, thus eliminating
the restriction of 200 rainfall values imposed by the rainfall array
dimension.
21. THISTO must be the same or an integer multiple of BELT (card B2)
or vice versa. (If BELT is an integer multiple of THISTO, the
rainfall values are averaged over the time step, DELT.)
22. If this card is read, the default value of 0.1 in./day [3 mm/day]
indicated on card Bl no longer applies, i.e., the default value
becomes zero.
23. Input values on this card indicated with asterisks are multiplied
by ratios, initially set equal to 1.0. If the ID number = -1,
non-zero data entries for parameters with asterisks will replace
old values of the ratios. Ratios may be altered or reset to 1..0
any number of times. The intention of the use of ratios is to
simplify sens.itivity analyses, etc., by allowing easy changes of
data values without repunching data cards. Ratios may be reset
any number of times and alter the indicated ratios to be applied
to all following data cards (until another ratio card is encountered).
24. Input parameters on this card indicated with asterisks will take
on default values if input values are zero. If the ID number =
-2, non-2:ero data entries for parameters with asterisks will
become new default values for all future entries of these para-
meters. Default values may be altered or reset to their original
values (except zero) any number of times. The indicated default
values apply to all following data cards (until another default
card is encountered).
It is not possible to reset a default value exactly to zero since
only non-zero values are changed. However, the value may be made
arbitrarily small by using_E-foi:mat data entries. For example,
1E-50 will be read as 10 in an F5.0 format.
25. Numbers may be arbitrarily chosen, such that 1 < NAMEG or NGTO
£ 9999. However, if an inlet number is to correspond to an inlet
manhole in the Transport Block, it must be < 1000. The maximum
total number of inlets must be < 80 for input to Transport, < 180
for input, to Extended Transport. £ 50 for input to Receive. There
is no restriction for input to Storage/Treatment except that the
block will select only one of the inlets on the interface file for
input. Others will be saved but ignored. Gutter/pipe numbers and
inlet numbers are contained in the same array and thus must be dis-
tinct frcm one another; however, they may duplicate subcatchment
numbers if desired. Each inlet is assigned a dummy gutter/pipe to
receive upstream flows. Hence, the total number of gutter/pipes plus
213 4-163
-------
inlets must be £ 200. Internal subscripts in the program for gutter/
pipe data are assigned in the order in which cards in card group Gl
are read in.
Of course it makes no sense to indicate a gutter/pipe with
nothing entering it. Thus, each one should have flow entering,
either from other gutter/pipe(s) or from subcatchment(s).
26. A maximum of five different gutter/pipes may feed to a single
gutter/pipe or to a single inlet:. If more are desired, a dummy
gutter/pipe may be used to provi.de five additional "feeds".
See footnote 27.
27. Dummy gutters may be used for two purposes: 1) to provide five
additionel "feeds" to a given gutter/pipe or inlet (see footnote
26) by placing it in series with the gutter/pipe or inlet
(although, of course, by placing it in series with the original
gutter/pipe or inlet, it uses one of the original five "feeds"),
or 2) to provide a location for print out of data. The latter
situation arises because outflcv;s from subcatchments may not bs
printed directly (using card groups Ml and M2), only inflows or
outflows to gutter/pipes or inflows to inlets. Hence, if a dummy
gutter/pipe is placed immediately downstream from a subcatchment,
the inflow (or outflow) to the dummy gutter/pipe is the outflow
from the subcatchment, (provided that that is the only subcatchment
feeding the dummy gutter/pipe).
28. A bottom width of zero for a gutter corresponds to a triangular
cross section.
29. A side slope of zero indicates a vertical wall.
30. Check for blank card is to see if ID number equals zero.
31. Numbers may be arbitrarily chosen such that 1 < NAMEW < 9999
except that when snow melt is simulated (ISNOW > 0), numbers must
be £ 999 since they are read by an 13 format in card groups II
and 12. Numbers may duplicate j;utter/pipe and inlet numbers if
desired. Internal subscripts in the program for subcatchment data
are assigned in the order in which cards in card group HI are
read in.
32. A maximum of five different subcatchments may feed to a single
gutter/pipe or to a single inlet:, (in addition to gutter/pipes
feeding the gutter/pipe or inlet:). If more "feeds" are desired,
a dummy gutter/pipe may be used to provide additional feeds.
See footnote 27.
33. The subcatchment width is a key calibration parameter, one of the
few that can significantly alter the shape of the hydrograph,
214 4-164
-------
rather than just the runoff volume. One way to think of the width
is the area of the subcatchment divided by the average path length
of overland flow (see Figure 4-13). The effect upon output hydro-
graphs is illustrated in Figure 4-15 and is approximately as fol-
lows. For rainfall durations less than the time of concentration,
(i.e., less than the equilibrium time of an impervious subcalcumeul,
at which inflow equals outflow), increasing the width effectively
provides a greater cross sectional area for outflow from the sub-
catchment., thus increasing the magnitude of the peak flow and
decreasing the time to peak. Decreasing the width has the oppos-
ite effect, and the subcatchmenl: surface acts more as a reservoir,
reducing and delaying the peak. For rainfall durations greater
than the time of concentration, the magnitude of the peak is
affected only minutely. The time to equilibrium conditions,
that is, the time of concentration, is reduced slightly for
larger widths.
The subcatchment width ran thus be used to incorporate
storage lost when pipes are removed from the simulation. For
instance, if only a coarse discretization of the total catchment
is desired, only a few or no pipes need be modeled. To account
for this lost storage in the system, the overall subcatchment
width is correspondingly reduced (see Figure 4-20). Whether for
one aggregated catchment or for a small individual subcatchment,
a reasonable approximation for determining the width is to use
twice the length of the main drjiinage channel in the catchment
(see Figure 4-20).
The same subcatchment width entered here is used for the pervious
area of the subcatchment and the total impervious area of the
subcatchment (see Figure 4-11).
34. Subcatchment number(s) entered on cards II and 12 must correspond
exactly to numbers and order of card group HI.
35. Numbers JK1 and JK2 must be the same.
36. Subscript N is the internal subcatchment number (subscript) determined
from the order in which subcatchment data are entered in card group HI.
37. Default value/ratio test is on parameter JK1. If JK1 = -1 or -2,
then both, cards II and 12 will be treated as changes of default
values/ratios.
38. "Normally bare" implies surfaces: such as roadways and sidewalks
that receive snowfall, but are subject to early snow removal.
39. "Last subcatchment" is last one entered in card group HI.
40. Test for blank card (zero) is on parameter JK1.
215 4-165
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41. The 10 or fewer constituents may be arbitrarily chosen (see text).
When erosion is simulated it is stored as the last constituent.
Hence, no more than 9 constituents may be simulated while using
the erosion routine. Furthermore, at least one constituent must
be simulated in addition to erosion in order to proceed correctly
through program loops.
42. This addition is performed before constituent fractions are added
(card group J4).
43. A "dry day" is not well defined, but may be considered as the number
of days prior to start of simulation, in which the cumulative rain-
fall is less than a specified value, e.g., 0.1 in. (3 mm).
44. Not required for ICRAIN = 0. For year-round sweeping, let KLNBGN
= 0 and KLNEND = 367. Leap years are not treated separately,
other than in maintaining the proper number of days in February
and in total annual days.
45. Sec the text for explanation and illustration of the various options
for buildup of dust and dirt. Depending on the form of buildup chosen
for each constituent (card group J3), the land use buildup parameters
may not be required.
46. If JACGUT = 0, parameters DDLIM and DDFACT will be multiplied by
GQLEN (card group LI) in 100-ft. If JACGUT = 1, parameters DDLIM
and DDFACT will be multiplied by WAREA (card group HI) in acres.
47. For continuous simulation, street sweeping occurs at intervals of CLFRKQ
days, computed during the simulation using dry time steps only (no
runoff and no unmelted snow on normally bare impervious areas). When
cleaning occurs, a fraction of each pollutant, REFF-AVSWP, is removed
from each subcatchment. The availability factor AVSWP, is intended to
account for the relative amount of subcatchment surface that consists
of streets, and therefore may be swept. See text.
At start of single-event and continuous simulations, streets are swept
approximately DRYDAY/CLFREQ times, each time removing a fraction
REFF-AVSWP. Parameter DSLCL esHablish.es proper backwards time
sequence.
48. The constituent names and units established in this card group will be
carried through to subsequent SWMM blocks. See Figure 4-26 for illus-
tration of how the A-format names and units will appear as headings.
49. Since most constituents are measurable in mass units, NDIM = 0
will be the most common. Since concentrations will be printed
using an F10.3 format, NDIM = 0 should suffice also for consti-
tuents whose concentrations are usually given in pg/1. The value
of NDIM basically affects conversion factors used in the program.
216 4-166
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50. See the text for full explanation of buildup-washoff equation
options and interpretation of parameters.
51. If KACGUT = 0, parameters QFACT(1,K) and QFACT(3,K) will be multi-
plied by GQLEN (card group LI) in 100-ft [km]. If KACGUT = 1, para-
meters QFACT(1,K) and QFACT(3,X) will be multiplied by WAREA
(card group HI) in acres [ha].
52. For instance, if chlorides are simulated, they might be only applied
for street salting when snow is present. The rate of buildup will
not be a function of the amount of snow, however.
53. For continuous SWMM, concentrations will be regenerated to this
value during dry time steps over a period of DRYBSN days,
(DRYBSN e:ntered on card Jl).
54. This concentration is assumed to be that of the runoff (and snow-
melt) before adding washoff loads. The preripi f.ati.on lo?»d is
always added regardless of the vashoff mechanism utilized,
unless of course, CONCRN = 0.
55. After computing and summing all loads except rainfall, a fraction
of any constituent may be added to any other. (No fractions are
removed, however.) This is intended to account for insoluble BOD
etc. if surface loadings are based only on insoluble portions, as
is true, for instance, for 1969 APWA data from Chicago. For
instance, 5 percent of suspended solids could be added to BOD.
Alternatively, different particle size ranges could be generated
as different constituents, and other constituents could consist
of fractions of the first group of different particle sizes. When
these fractions are used, concentrations can be drastically (and
subtly) increased if, for instance, suspended solids are high,
soluble I!OD is low and a fraction of 0.05 is used. The choice of
whether or not any fractions should be entered depends upon how
constituent data are being reported (e.g. total BOD or only the
soluble fraction) and on how it is desired to simulate each con-
stituent in SWMM.
56. See text for explanation of method of computation, parameters
and typical values. Also, there may be a need to consult with
local Soil Conservation Service or Agricultural Research Service
or state agricultural extension service experts for knowledge of
parameter values for particular areas.
A value of the "sediment delivery ratio" is sometimes included in
the U.S.L.E. computation. Since it is merely another multiplier,
if desired, it may be incorporated into the "K" or "C" or "P" fac-
tors.
57. See footnotes 46 and 51. This is the only use of parameter GQLEN.
217 4-167
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58. Zero flows are not printed to avoid voluminous output with contin-
uous SWMM. (There are no quality loads when flows are zero).
Thus, some care should be taken in examining the output, since
if a zero flow occurs in the middle of a single-event simulation,
for instance, it will not be listed. This can be determined
by inspecting the sequential tioie of day printed with each set
of values.
Care should still be taken when running continuous SWMM, since one
line of output will be generated for each hourly value of non-zero
flow, for each indicated location, within the indicated time span.
Hence, the potential exists for thousands of lines of output.
59. All printed values are instantaneous (flows and concentrations)
at the end of the preceding time; step.
60. These numbers correspond to numbers NAMEG and NGTO used in card
groups Gl and HI. They may be cither positive or negative. A
positive number will cause the total inflows to the indicated
gutter/pipe or inlet to be printed. A negative number will
cause the outflow to be printed. (Both a positive and negative
value for the same location may be used). Regardless of the
sign, only outflow concentrations are printed, however, since it
is computationally inconvenient to calculate the average inflow
concentration. Of course, for an inlet (or dummy gutter/pipe),
inflow values equal outflow values.
218 4-168
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SECTION 5
EXTENDED TRANSPORT BLOCK
Following development of the original SWMM model, Water Resources
Engineers (now, Camp, Dresser and McKee) participated in a study of the
proposed master plan for control of combined sewer overflows in San
Francisco. In order to analyze the complex hydraulics of that system,
they developed the WRE Transport Model (Shubinski and Roesner, 1973),
one that solves the coupled complete St. Venant equations and accounts
for phenomena such as backwater, looped connections, surcharging and.
pressure flow that were either not considered or treated in a very
simplified manner in the original Transport Model (Section 6). Through
subsequent work for EPA in other cities the WRE Transport Model was
acquired for the SWMM package and became known as the Extended Transport
Model or Extran. This model has few peers in its capacity for simulation
of the hydraulics of urban drainage system and is probably the most
sophisticated such model that is non-proprietary and available in the
public domain. (Similar proprietary models do exist.) Extran capabil-
ities are compared with those of Runoff and Transport in Table 4-3.
Extran has been part of the SWMM package since 1976. However, it
has been rather poorly documented and the quality portion has never been
used. In fact, the only (but very extensive) use for the model has been
for hydraulic analysis, and the quality routing has been formally removed
from the program. The state of the art in urban runoff quality modeling
is such that adequate simulation of pollutographs may be performed using
the simpler hydraulics of the Runoff and Transport Blocks.
Comprehensive new documentation of Extran for this Version III of
SWMM has been prepared by Camp, Dresser and McKee and is included as a
separate addendum to this User's Manual (Roesner et al., 1981). Full
details of the model are available therein. Interfacing between Extran
and the remainder of SWMM is performed as described in Section 2.
219 5-1
-------
SECTION 6
TRANSPORT BLOCK
BLOCK DESCRIPTION
Introduction
Flow routing through the sewer system may be accomplished in the Storm
Water Management Model (SWMM) by subroutine TRANS which is called from the
Executive Block program. TRANS has the responsibility of coordinating not
only routing of sewage quantities but also such functions as routing of
quality parameters (subrouting QUAL), estimating dry-weather flow (DWF)
(subrouting FILTH), estimating infiltration (subroutine INFIL), and calling
internal storage (subroutine TSTRDT). The relationships among the subrou-
tines which make up the Transport Block are shown in Figure 6-1. The pro-
gram is about 5,000 cards long, consisting of 24 subroutines and functions.
This section describes the Transport Block, provides instructions on
data preparation, and furnishes examples of program usage. Instructions are
provided for these subroutines requiring card input data, namely: transport,
internal storage, infiltration, and DWF. Examples, with sample I/O data,
are given for transport, infiltration, and DWF computations.
Broad Description of Flow Routing
Differences in flow routing techniques among the Runoff, Transport and
Extended Transort Blocks were described in Section 4 (e.g., Table 4-3); the
techniques increase in complexity in the order just listed. A brief descrip-
tion of techniques used in the Transport Block follows.
To categorize a sewer system conveniently prior to flow routing, each
component of the system is classified as a certain type of "element." All
elements in combination form a conceptual representation of the system in a
manner similar to that of links and nodes. Elements may be conduits, man-
holes, lift stations, overflow structures, or any other component of a real
system. Conduits themselves may be of different element types depending
upon their geometrical cross-section (e.g., circular, rectangular, horse-
shoe). A sequencing is first performed (in subroutine SLOP) to order the
numbered elements for computations. Flow routing then proceeds downstream
through all elements during each increment in time until the storm hydro-
graphs have been passed through the system.
The solution procedure is described in detail in the original SWMM
documentation (Metcalf and Eddy et al., 1971a) and basically follows a
kinematic wave approach in which disturbances are allowed to propagate only
220 6-1
-------
NJ
NJ
Figure 6-1. Structure of Transport Block Subroutines
i
N)
-------
in the downstream direction. As a consequence, backwater effects are not
modeled beyond the realm of a single conduit, and downstream conditions
(e.g., tide gates, diversion structure!;) will not affect upstream computa-
tions. Systems that branch in the downstream direction can be modeled using
"flow divider" elements to the extent i:hat overflows, etc., are not affected
by backwater conditions. Surcharging is modeled simply by storing excess
flows (over and above the fullflow conduit capacity) at the upstream manhole
until capacity exists to accept the stored volume. Pressure-flow coiiuiLions
are not explicitly modeled and no atteoipt is made to determine if ground
surface flooding exists. However, a message is printed at each time step
for each location at which surcharging occurs. The Transport Block has
proven its ability to model accurately flows in most sewer systems, within
the limitations discussed above, and as such it should be adequate for most
applications. However, it will not accurately simulate systems with exten-
sive interconnections or loops, system?! that exhibit flow reversals or
significant backwater effects, or systems in which surcharging must be
treated as a pressure-flow phenomenon; the Extended Transport Block should
be used for this purpose (Section 5).
An option in the program is the u:;e of the internal storage T.odel which
acts as a transport element. It is a scaled-down version of the Storage/
Treatment Block (Section 7) and provides the possibility of storagerouting
of the storm at one or two separate points within the sewer system
(restricted by computer core capacity). The program routes the flow through
the storage unit for each time step bailed on the continuity equation in a
manner analogous to flood routing through a reservoir. Extensive backwater
conditions may thus be modeled by treating portions of the sewer system as a
storage unit with a horizontal water surface.
Broad Description of Quality Routing
Up to four contaminants are also handled by the Transport Block. Con-
stituents may be introduced to the sewer system by any combination of four
means:
1) Storm-generated pollutographs computed by an upstream
block are transferred on tape/disk devices (the interface
file cf Section 2) to enter l.he system at designated inlet
manholes.
2) Storm-generated pollutographs may be entered on cards at
designated inlet manholes.
3) Residual bottom sediment in 1:he pipes may be resuspended
due to the flushing action ol: the storm flows (subroutine
DWLOAD).
4) For combined systems, dry-weather flow pollutographs (sub-
routine FILTH) may be entered at designated inlet manholes.
The Transport Block can receive input?; from the Runoff, Storage/Treatment,
Extrau, and Transport Block itself.
222 6-3
-------
The routing of the pollutants is then done for each time step by subroutine
QUAL. The maximum number of contaminants that can be routed is four. These
may be selected arbitrarily from the input file, except that the FILTH rou-
tine can only be used to generate suspended solids, BOD_ and total coliforms.
The scour/deposition routines may be used for any constituent.
Program Operation
Most of the input to TRANS is related to data needed to describe the
particular sewer system being modeled (e.g., dimensions, slopes, roughnesses,
etc.) and parameters needed to solve the governing flow routing equations.
Following input of these data, the sewer elements are sequenced for
computations in subroutine SLOP. Certain geometric and flow parameters are
then initialized in subroutine FIRST while others are initialized in TRANS.
The various program parameters and initialized variables describing the
elements are then printed. Parameters relating to the amount of data to be
stored and printed out are also read (from cards).
If indicated, infiltration values will be calculated in subroutine INFTT.
and DWF quantity and quality parameters will be calculated in subroutine FILTH.
Alternatively, user supplied values may simply be input at any manhole loca-
tion. If desired, subroutine DWLOAD then initializes constituent depositions,
and subroutine INITAL initializes flows and pollutant concentrations in each
element to values corresponding to a condition of dry-weather flow and
infiltration only.
The main iterations of the program consist of an outer loop on time
steps and an inner loop on element numbers in order to calculate flows and
concentrations in all elements at each time step. Inlet hydrographs and
pollutograph ordinates are read from the interface file and/or cards to
permit linear interpolation for values at each time step prior to entering
the loop of element numbers.
When in the loop on element numbers (with index I), the current sewer
element through which flows are to be routed, indicated by the variable M,
is determined from the vector JR(I). This array is calculated in subroutine
SLOP in a manner to insure that prior to flow routing in a given element,
all flows upstream will have been calculated.
When calculating flows in each element, the upstream flows are summed
and added to surface runoff, DWF, and infiltration entering at that element.
These latter three quantities are allowed to enter the system only at non-
conduits (e.g., manholes, flow dividers). If the element is a conduit, a
check for surcharging is made. If the inflow exceeds the conduit capacity,
excess flow is stored at the element just upstream (usually a manhole) and
the conduit is assumed to operate at full-flow capacity until the excess
flow can be transmitted. A message indicating surcharging is printed.
A simple hydraulic design routine is available at this point. If
desired (NDESN = 1), when a surcharge condition is encountered, the conduit
will be increased in size in standard increments (for circular pipes) or in
223
6-4
-------
six-inch width increments for rectangular conduits until capacity exists to
accept the flow. (Conduits that are neither circular nor rectangular will
be converted to circular if they need to be resized.) A message is printed
indicating the resizing, and a table of final conduit dimensions is printed
at the end of the simulation. This design operation will effectively elimi-
nate surcharging but will also minimize in-system storage within manholes,
etc. The net effect is to increase hydrograph peaks at the downstream end
of the system. An obvious conflict can thus exist between controls aimed at
curing in-system hydraulic problems and controls intended for pollution
abatement procedures at the outfall.
Flows are routed through each element in subroutine ROUTE and quality
parameters are routed in subroutine QUAL. When routing flows in conduits,
ROUTE may be entered more than once depending upon the value of ITER, the
number of iterations. It is necessary to iterate upon the solution in
certain cases because of the implicit nature of calculating the energy grade
line in ROUTE.
Upon completion of flow and quality routing at all time steps for all
elements. TRANS then, performs the task of outputting the various data.
Hydrograph and pollutograph ordinates for any specified outfall point(s) may
be written onto an interface file for further use by the Executive Block,
and subroutine PRINTQ (or PRINTF for flows only) is then called for printing
outflows for any of up to 80 desired elements.
Off-Line Files
The Transport Block uses two scratch data sts, NSCRAT(l) and NSCRAT(2),
for storage of input and output hydrographs and pollutographs prior to print-
ing. These are specified in the Executive Block using job control language
(JCL) appropriate to the computer system. No input data will be sought from
the interface file (Section 2) if JIN = 0, and no output will be placed on
the file if JOUT = 0.
INSTRUCTIONS FOR DATA PREPARATION
Introduction
Instructions for data preparation for the Transport Block have been di-
vided along the lines of the major components for clarity of the presenta-
tion. These components are: (1) Transport, (2) Quality, (3) Internal
Storage, (4) Infiltration and (5) Dry-Weather Flow. All data input card and
tape/disk sources enter the Transport Block through one of these components.
The typical data deck setup for the complete Transport Block is shown in
Figure 6-2. Transport data describe the physical characteristics of the
conveyance system. Quality data identify pollutants to be routed and their
characteristics. Internal Storage data describe a particular type of Trans-
port element. Infiltration and DWF data describe the necessary drainage area
characteristics to permit the computation of the respective inflow quantities
and qualities. Data card preparation and sequencing instructions for the
complete Transport Block are given at the end of these instructions in Table
6-6.
224 6-5
-------
/Rl CARD HYDROGRAPHS,
POLLUTOGRAPHS
LI-QI FILTH DATA
KI-K2 INFIL DATA
JI-J2 ELEMENT PRINT DATA
HI -I I ELEMENT I/O DATA
GI-G5 STORAGE ELEMENT DATA
Fl POLLUTANT DATA
El ELEMENT DATA
DI-D3 EXECUTION DATA
Cl TITLE
BI-B9 NEW ELEMENT DATA
Al NKLASS, KPRINT
Figure 6-2. Data Deck for the Transport Block
225
6-6
-------
Transport
Categories of Data
Use of the Transport program involves three primary steps:
1) Preparation of theoretical data for use by subroutines en-
gaged in hydraulic calculations in the program.
2) Preparation of physical data describing the combined sewer
system.
3) Generation of inlet hydrographs and pollutographs required
as input to the Transport Block and computational controls.
Data for Step 1 are supplied with the SWMM program for 13 different
conduit shapes, and it will only be necessary for the user to generate
supplemental data in special instances. These instances will occur only
when conduit sections of very unusual geometry are incorporated into the
system, Generation of such data will be discussed below.
The primary data requirements for the user are for Step 2, the physical
description of the combined sewer system, i.e., the tabulation of shape,
dimension, slope, and roughness parameters, which will be discussed in
detail below.
The data for Step 3 may be generated by cards, by an external block and
by subroutines 1NFIL and FILTH.
Step 1. Theoretical Data --
The first data read by TRANS describe the number and types of different
conduit shapes found in the system. Only in the case of a very unusual shape
should it become necessary to generate theoretical data to supplement the
data supplied by the program. The required data describe flow-area relation-
ships of conduits, as shown in Figure 6-3 through the parameters ANORM and
QNORM described below. A similar depth-area relationship is also required
using the parameter DNORM.
The flow-area data are generated from Manning's equation, normalized by
dividing by the corresponding equation for the conduit flowing full, -denoted
by the subscript f. Thus,
A-R2/3
Q/Q = ^r/V f(A/A ) (6-1)
A 'R '
Af Kf
where Q = flow, cfs, ~
A = flow area, ft , and
R = hydraulic radius, ft.
226
-------
1.0
A/Af =
Figure 6-3.
The Intersection of the Straight
Line and the Normalized Flow-Area
Curve as Determined in Route. The
ty-* Curve is Formed by Straight Line
Segments Delineated by the Variables
ANORM and QNORM, for Conduits with a
Tabular Q-A Relationship. Q Denotes
Flow, A Denotes Area, and the Subscript
f Denotes Values at Full-Flow. The
Line -Cj_ « -2 is Formed by the Program
from the Continuity Equation.
227
6-8
-------
For a given conduit shape (e.g., circular, rectangular, horseshoe), the hy-
draulic radius is a unique function of the area of flow; hence, Q/Qf (inter-
polated between values of QNORM) is a function only of A/Af (interpolated
between values of ANORM). This function is tabulated for circular conduits
in Appendix A of Chow (1959), for example, and on page 443 of Davis (1952)
for a Boston horseshoe section. It is shown in graphical form for several
conduit shapes in Chapter XI of Metcalf and Eddy (1914) from which some data
supplied with this program have been generated. A list of the conduit
shapes supplied with the Transport Block as well as all other element types
is given in Table 6-1. The conduits are illustrated in Figure 6-4. If y =
depth of flow, values of y/yf corresponding to A/A (ANORM) are tabulated as
the variable DNORM.
It will often be satisfactory to represent a shape not included in Table
6-1 by one of similar geometry. This"use of "equivalent" sewer sections will
avoid the problem of generating flow-area and depth-area data. An equivalent
section is defined as a conduit shape from Table 6-1 whose dimensions are
such that its cross-sectional area and the area of the actual conduit are
equal. Only very small errors should result from the flow routing when this
i:; done.
If it is desired to have the exact flow-area and depth-area relation-
ships, then the product AR2 3 must bi; found as a function of area. In gen-
eral, the mathematical description of the shape will be complex and the task
is most easily carried out graphically. Areas may be planimetered, and the
wetted perimeter measured to determine R. In addition, the depth may be
measured with a scale. The required flow-area relationship of Equation 6-1
may then be tabulated as can the depth-area relationship. The number of
points on the flow-area and depth-area curves required to describe the
curves is an input variable (MM and NN, respectively). Note that the nor-
malized flows (QNORM) and depths (DNORM) must be tabulated at points corres-
ponding to MM-1 and NN-1, respectively, equal divisions of the normalized
area axis (ANOF.M). If desired, the routing parameters stored in the program
may be listed by specifying KPRINT = 1 on card Al. The four pages of output
are seldom necessary during the simulations, however.
Step 2. The Physical Representation of the Sewer System --
General -- These data are the different element types of the sewer system
and their physical descriptions. The: system must first be as a network of
conduit lengths, joined at manholes (or other nonconduits). In addition,
either real or hypothetical manholes should delineate significant changes in
conduit geometry, dimensions, slope, or roughness. Finally, inflows to the
system (i.e., stormwater, wastewater, and infiltration) are allowed to enter
only at manholes (or other non-conduits). Thus, manholes must be located at
points corresponding to inlet points for hydrographs generated by an external
block and input points specified in subroutines FILTH and INFIL. In general,
the task of identifying elements of the sewer system will be done most con-
veniently in conjunction with the preparation of data for these other sub-
routines, especially the Runoff Block.
228 6-9
-------
Table 6-1. Different Element Types Supplied with the
Storm Water Management Model
NTYPE
Conduits
1 Circular
2 Rectangular
3 Phillips standard egg shape
4 Boston horseshoe
5 Gothic
6 Catenary
7 Louisville semielliptic
8 Basket-handle
9 Semi-circular
10 Modified basket-handle
11 Rectangular, triangular bottom
12 Rectangular, round bottom
13 Trapezoid
14,15 User supplied
Non-conduits
16 Manhole
17 Lift station
18 Flow divider
19 Storage unit
20 Flow divider - weir
21 Flow divider
22 Backwater element
229 6-10
-------
Each element (conduit or non-conduit) must be identified with a number
which may range from 1 to 1000. They need not be sequential or continuous.
Experience has shown that a schematic map showing the complete sewer network
and the numbering system will be very useful for debugging and identification
purposes. It is difficult to rely upon detailed (and often cluttered) sewer
plans alone.
Description of Conduits The 13 conduit shapes supplied with the SwMM are
shown in Figure 6-4. For each shape, the required dimensions are illustrated
in the figure and specified in Table 6-2. In addition, Table 6-2 gives the
formula for calculating the total cross-sectional area of the conduit.
Usually, the shape and dimensions of the conduit will be indicated oa
plans. It is then a simple matter to refer to Figure 6-4 for the proper con-
duit type and dimensions. If the shape does not correspond to any supplied
by the program, it will ordinarily smffice to choose a shape corresponding
most nearly to the one in question. For example, an inverted egg can be
reasonably approximated by a catenary section. The dimensions of the substi-
tute shape should be chosen so that the area of the substitute conduit and
that of the actual conduit are the same. This is facilitated by Table 6-2,
in which the area is given as a function of conduit dimensions. If desired,
the flow-depth area parameters for up to two additional conduit shapes may
be read in at the beginning of the program as discussed previously. (See
also Card Groups: B1-B9, Table 6-6.)
Occasionally, the conduit dimensions and area may be given, but the
shape not specified. It will sometimes be possible to deduce the shape from
the given information. For example, .3 conduit may have an area of 4.58 ft2.
(0.425 m2) and dimensions of 2 ft by 3 ft (0.6 by 1.0 m). First, assume that
the 2 foot dimension is the width and the 3 foot dimension is the depth of
the conduit. Second, note from Figure 6-4 that the ratio of depth to width
for an egg-shaped conduit is 1.5:1. Finally, the area of an egg-shaped con-
duit of 3 foot depth is 0.5105 x 9 == 4.59 ft2 (0.426 m2). It is concluded
that the conduit should be type 3 with GEOM1 = 3 ft.
Because of the limits on the ;;ize of the computer program, it will
usually not be possible to model every conduit in the drainage basin. Conse-
quently, aggregation of individual conduits into longer ones will usually be
the rule. Average slopes and sizes may be used provided that the flow capac-
ity of the aggregate conduit is not significantly less than that of any por-
tion of the real system. This is to avoid simulated surcharge conditions
that would not occur in reality. In general, flow calculations are rela-
tively insensitive to conduit lengths although with conduits over 4000 to
5000 ft (1200 m and 1500 m) long some loss of routing accuracy will result.
This is caused primarily when a large inflow enters a dry or nearly dry pipe,
often at the beginning of the simulation. A non-convergence error message
will be printed, but the resultant error is seldom significant. Conduit
lengths should always be separated by manholes (or other non-conduit type
elements). The conduit length should be measured from the center of the
adjacent manholes. A further means of simulating large systems lies in
simulating different portions with separate Transport runs and combining the
results using the Combine Block (see Section 3).
230 6-11
-------
T
Gl
G2.
Type 1: Circular
Type 2: Rectangular
Type 3: Phillips Standard
Egg Shape
Type 4: Boston Horseshoe
.IIIM I l!l I I .1.1 /L
Nj-j-U-U-u: f -DZ?
ci o? 03 C4 c: c; 01 oa 09 to
Rotiooftl-lt^-drjoU (kmrr.tvef Ihc filifi
to those of ttn t rtirc Setlii
Cl
I i i i <\: i;'.". i ! ! i-v> <"H"
T-i-r-!-: I-T'- : r-n-i-/ r- -r
nZJI
---H-hJffl-
* \-rrr\-
.. ^ ..-.J-JJ-LQI
^ihrF^
"-r
CO CJ Cl Cl C'« Ci Ci C7 0.6 0» 10 I.I
Type 5: Gothic Type 6: Catenary
Figure fa-4. Sewer Cross-Sections
231
6-12
-------
Gl
0 0.1 02 O.J 0.-; C5 ftt OJ 0.6 C.3 LO LI 12
Ralio of HyJroulcc EltTsnfs of f illtJ S^xtnf Is :n»c ef EnlireScclfon
'lype 7: Louisville SemiellipH.c
Gl
^^^^WKW+HT
r,c iXx|-rp.-l'/.-T!^TTl-Hh;-|-j-ri-
5 j.i. ^l-R\i^i^-Fffl-l---^lTP
Cl '.:!.
0.0 C.I
-- ' ri! I I i !
I O.i O.i 0.5 0.5 0.7 O.o 0.0 1.0 I.I
et the K.'druulitlk/nints of th; Fi'Cfd Sf3m«nt
lotKo^ee! the tntirc »;ction.
8: Basket-handle
OJ& 0.1 02 0.3 04 OS 0.& 07 0.5 &.9 10 U 12
Ro1ioefHyd''ou'i^ Elcm-ntsof the Filled Segment to
tho:-e of ihs Entire Scctic-n.
Type 9: Semi-circular
G2
T
Gl
Type 10: Modified Basket-handle
\
G3
Type 11: Rectangular, Triangular Bottom Type 12: Rectangular, Round Bottom
Figure 6-4 (continued). Sewer Cross-Sections
232
6-13
-------
61
G2
TYPE 13: TRAPEZOID
Figure 6-4 (continued). Sewer Cross-Section
233
6-14
-------
Table 6-2. Summary of Area Relationships and Required Conduit Dimensions.
NTYPE
1
2
3
4
5
6
7
8
9
10
11
12
Shape
Circular
Rectangular
Egg-shaped
Horseshoe
Gothic
Catenary
Seraielliptic
Basket-handle
Semi-circular
Modified basket-
handle
Rectangular,
triangular bottom
Rectangular,
round bottom
Area
(7t/4)(Gl)2
G1-G2
0.5105-(G1)2
0.829-(G1)2
0.655-(G1)2
0.703-(G1)2
0.785-(G1)2
0.786-(G1)2
1.27-(G1)2
G2(GU(n/8)G2)
G2(Gl-G3/2)
6 = 2-ARSIN
(G2/(2G3))
Required dimensions
(ft)
GEOM1 = Diameter
GEOM1 = Height
GEOM2 = Width
GEOM1 = Height
GEOM1 = Height
GEOM1 = Height
GEOM1 = Height
GEOM1 = Height
GEOM1 = Height
GEOM1 = Height
GEOM1 = Side height
GEOM2 = Width
GEOM1 = Height
GEOM2 = Width
GEOM3 = Invert height
GEOM1 = Side height
GEOM2 = Width
GEOM3 = Invert radius
13
Trapezoidal channel
Area = G1-G2
+ (G3T/2
(6-SIN(0))
G1(G2+G/G3)
GEOM1 = Depth
GEOM2 = Bottom width
GEOM3 = Side slope (ver-
tical/horizontal)
Refer to Figure 6-4 for definition of dimensions, Gl, G2,
Note that Gl = GEOM1, G2 = GEOM3, G3 = GEOM3.
and G3.
234
6-15
-------
Values of Manning's roughness may be known by engineers familiar with
the sewer system. Otherwise, they may be estimated from tables in many
engineering references (e.g., Chow, 1959, ASCE-WPCF, 1969) as a function of
the construction material and sewer conditions. The value may be adjusted
to account for losses not considered in the routing procedure (e.g., head
losses in manholes or other structures, roots, obstructions). However, the
flow routing is relatively insensitive to small changes in Manning's n.
Description of Non-Conduits -- The sewer system consists of many different
structures, each with its own hydraulic properties. Elements 16 through 22
are designed to simulate such structures. Data requirements for these ele-
ments are given in Table 6-3. Brief descriptions of these elements follow.
Manholes (NTYPE = 16) No physical data are required for manholes except
their numbers and upstream element numbers. Note that the number of upstream
elements is limited to three. If more than three branches of the system
should join at a point, two manholes could be placed in series, allowing a
total of five branches to joint at that point, etc. Flow routing is accom-
plished in manholes by specifying that the outflow equals the sum of tha
inflows.
As an alternative to the use of the more detailed infiltration (INFIL)
and dry-weather flow (FILTH) routines described later, flow and quality con-
stituents may be input at manholes to simulate baseflow conditions. This
input is constant over time and is allowed only at manholes and at no other
element types.
Lift Stations (NTYPE = 17) -- The data requirements for lift stations are
given in Table 6-3. It is assumed that the force main will remain full when
the pump is not operating, resulting in no time delay in the flow routing
(i.e., no time is required to fill the force main when the pump starts).
When the volume of sewage in the wet well reaches its specified capacity,
the pumps begin to operate at a constant rate. This continues until the wet
well volume equals zero. (Two-stage pumping may be simulated using a storage
element.)
Flow Dividers (NTYPE = 18 and 21) The routing procedure through these
elements is explained in the discussion below. Typical uses are given below.
1) Simple diversion structure - A type 18 flow divider may be
used to model a diversion structure in which none of the
flow is diverted until it reaches a specified value (GEOM1).
When the inflow is above this value, the non-diverted flow
(Q01) remains constant at its capacity, GEOM1, and the
surplus flow (Q02) is diverted.
2) Cunnette section - A type 21 flow divider may be used to
model a downstream cunnette section. The cunnette section
is considered as a separate circular conduit to be placed
parallel to the primary conduit as shown in Figure 6-5.
In order to model the cunnette as a semi-circle, the sep-
arate circular conduit is given a diameter (GEOM1) so that
235
6-16
-------
Table 6-3. Parameters Required for Non-Conduits.
N)
U)
NTYI'K Description DIST CEOM1 SLOPF. ROIIKH CEOH2 BARREL
16 Manhole Constant Inflow, Const, inflow Const. Inflow Const, inflow Const, inflow N.R.
cfs, [m /sec]. concen. of concen. of concen. of concen. of
pollutant 1. pollutant 2. pollutant 3. pollutant 4.
17 Lilt si, ic ion Pumping rate, Volume in wet N.R. N.R. N.R. N.R.
assumed constant, well at which
cfs, [m /sec]. pumps will
start, ft3,
[m3].
1H Flow divider N.R. Maximum undi- N.R. N.R. N.R. N.R.
verted flow.
Inflow in
excess of this
value is di-
verted, cfs,
|m3/sec].
19 Storage unit'' N.R. N.R. N.R. N.R. N.R. N.R.
JO Flow divider Maximum Inflow Weir height, Maximum inflow Weir constant Depth in struc- N.R.
witliuuc flow over above zero through whule times weir ture ac time of
the weir, cfs, flow depth, structure, cfs, length, ft, maximum inflow,
|m3/sec|. ft, [m]. [m3/sec|. |m). ft, [m].
.fl Fl.iu divider N.R. N.K. N.R. N.R. N.R. N.R.
(assigned in
program)
JJ ll.u-kw.ilcr N.K. N.K. N.K. N.K. N.R. N.R.
el. -Illl-lll
CEOM3
N.R.
N.R.
Number of element
into which flows the
undiverted flow (in-
clude decimal point).
N.R.
Number of element
into which flows the
undiverted flow (weir
flow is the diverted
flow).
Number of element
into which flows the
undiverted flow.
Element number of
downstream storage
uni t .
'I'nii:. .K-,-..i-.I ing in NI)IM, Card group Fl.
I
I1
^J
.lit .il p.ii .niu-i.-rs .ire iv.id in riiilisuiMicntly in (lard groups ill - c;5.
II: .Ml i-U-iui-iii:< ivi|uire .in clement under (MOK). three upstream element numbers (NUE), and type (NTYi'E). Parameters for conduits are de-fined
III l.ihl.- h-J.
-------
SECTION OF SEWER
WITH CUNNETTE
PRIMARY CONDUIT
CUNNETTE (TYPE I) \CUNNETTE (TYPE I)
FLOY/ DIVIDER (TYPE 2l)
a. SCHEMATIC OF HYPOTHETICAL FLOW DIVISION
CONDUIT WITH
CUNNETTE
PRIMARY
CONDUIT
CUNNETTE
b. SPLIT OF CONDUIT INTO PRIMARY CONDUIT AND CUNNETTE
Figure 6-5. Cunnette Section
237
6-18
-------
its area will be twice that of the actual total cunnette
flow area. (The distance, slope and roughness will be the
same as for the primary conduit.) A type 21 flow divider
is then the upstream element common to both conduits, as
shown in Figure 6-5. (The program assigns a value of GEOM1
of the flow divider equal to half the full flow capacity
of the circular pipe simulating the cunnette so that it
has the hydraulic characteristics of a semi-circle.) Any
flow higher than GEOM1 will be diverted to the primary con-
duit. Note that the parameter GEOM3 of the flow divider
will be the element number assigned to the cunnette sec-
tion. Note further that the element downstream from the
two parallel conduits must list them both as upstream ele-
ments .
3) Overbank flow - A type 18 flow divider can be used to
simulate flow into a main channel (undiverted flow) and
into a parallel overbank channel for simulation of flooded
conditions. Parameter GEOM1 would be set equal to the
main channel capacity. The channels could be of any shape
although two trapezoidal channels might be most appropriate
for many natural configurations.
Routing at Flow Dividers (NTYPE = 18 and 21) -- Both types will divide the in-
flow, QI, into two outflows, Q01 and Q02. The divider then acts as follows:
For 0 ^ QI ^ GEOM1, Q01 = QI
Q02 = 0.0
(6-2)
For GEOM1 g QI, Q01 = GEOM1
Q02 = QI - GEOM1
The undiverted outflow, Q01, will flow into the downstream element denoted by
GEOM3. (The element into which Q02 flows does not need to be specified).
Flow Divider (NTYPE = 20) This element is used to model a weir-type diver-
sion strucutre in which a linear relationship can adequately relate the flow
rate and the depth of flow into the weir structure. Input parameters are de-
fined in Table 6-3. The weir constant, incorporated into the variable .ROUGH,
can be varied to account for the type of weir. Typical values of the weir con-
stant are 3.3 for a broad crested weir and 4.1 for a side weir.
The flow divider behaves as a function of the inflow, QI, as follows:
For Q S QI g DIST, Q01 = QI
Q02 = 0.0 (6-3)
For DIST ^ QI, Q01 and Q02 are computed as follows:
1) Compute depth of flow above the weir, DH, assuming a
linear flow-depth relationship:
»II = (QI-DIST)'(GECM2-GEOM1)/(3LOFE-DIGT)
238 6-19
-------
2) Compute the diverted flow from the weir formula:
Q02 = ROUGH-DH1'5
3) Compute the undiverted flow:
Q01 = QI - Q02
Storage Unit (NTYPE 19) This element is specified only when internal
storage computations are required. Internal storage is modeled in a manner
similar to the detention unit of the Storage/Treatment Block. (See Section
7 and Appendix IV.) Internal storage is modeled as a completely-mixed unit
with a variety of outlet structures. However, as opposed to the detention
unit in the S/T Block, pollutant removal is not simulated other than by decay.
The storage unit must be described on card groups Gl to G5.
A storage unit may be placed anywhere in the sewer system where appreci-
able storage may exist, such as at an outflow or diversion structure. The
required data inputs are described later. It should be noted that the stor-
age area or "reservoir" may consist of a portion of the sewer system itself,
and area-depth relationships must be worked out accordingly.
Backwater Element (NTYPE = 22) -- This element may be used to model backwater
conditions in a series of conduits due to a flow control structure downstream.
The situation is modeled in a manner analogous to reservoir flood routing as
follows:
1) A storage element (NTYPE 19) is placed at the location of
the control structure. The type of storage element will
depend upon the structure (e.g., weir, orifice). One
inflow to this storage element is then from the conduit
just upstream.
2) If this water surface is extended horizontally upstream
from the flow control structure at the time of maximum
depth at the structure, it will intersect the invert slope
of thu sewer at a point corresponding to the assumed maxi-
mum length of backwater. The reach between this point and
the structure may encompass several conduit lengths. A
backwater element (NTYPE 22) is placed at this point of
maximum backwater, in place of a manhole, for instance.
3) The backwater element then diverts flow directly into the
storage element depending upon the volume of water (and
hence, the length of backwater) in the storage element.
If the backwater extends all the way to the backwater ele-
ment, the total flow is diverted to the storage element;
none is diverted to the conduits.
4) The amount of diverted flow (Q01) is assumed directly pro-
portional to the length of the backwater. The storage area
in reality consists of the conduits. Since most conduits
239
6-20
-------
can be assumed to have a constant width, on the average, the backwater
length is assumed proportional to the square root of the current storage
volume, obtained from the storage routine.
5) The parameter GEOM3 of the backwater element must contain the element
number of the downstream storage unit.
6) Parameters for the storage element are read in as usual. Note that the
depth-area values will correspond to the storage area of the upstream
conduits. Note also that the storage unit must list the backwater
element as one of its upstream elements, as well as the conduit
immediately upstream.
7) At each time step, the backwater element computes the ratio of current to
maximum storage volume in the downstream storage element. Call this
ratio r. Then
Q01 = QIJr
and (6-4)
Q02 = 01 - Q01
where Q01 = flow directly into storage unit,
Q02 = flow into intermediate conduits, and
Q1 = inflow to backwater element.
Step 3« Input Data and Computational Controls --
Options The basic input data, hydrographs and pollutographs are generated
outside of the Transport Block. However, certain operational controls are
available within Transport.
Choice of Time Step (DT) The size of the time step must be an integer
multiple or integer fraction of the time step used in the preceding block.
In tests of sensitivity (Metcalf and Eddy et al., 1971a), it was found that
except for very small values of DT (10 seconds), the output from Transport
is insensitive to the length of the time step. Between values of two
minutes and 30 minutes, hydrograph ordinates varied by less than one per-
cent. For extremely short time step values, the peak flow moved downstream
faster and never attained the maximum value that it had with a DT of two
minutes and longer. Within the range commonly needed by SWMM users (two
minutes to 30 minutes), the choice of time step will not significantly
affect results. However, continuity errors can occasionally arise if the
time step is "appreciably" longer than the travel time through any conduit.
"Appreciably" longer means about factor of two.
Choice of Number of Time Steps (NOT) The number of time steps is not
restricted. The program will use the number input in Transport (NDT) or the
number used by the preceding block, whichever gives the shorter simulation
time.
240 6-21
-------
Choice of Number of Iterations (NITER) -- The purpose of iterations in the
computations is to eliminate flow oscillations in the output. The flatter
pipe slopes (less than 0.001 ft/ft) require iterations of the flow routing
portion of the Transport Model to help dampen these oscillations. Four
iterations have proven to be sufficient in most cases.
Choice of Allowable Convergence Error (EPSIL) -- Convergence of the flow
routing procedure should not be any problem, and -the default value cf EPSIL,
0.0001, may be used. It will provide sufficient accuracy and result in only
a very minimal increase in computer time over larger values. The only conver-
gence problems that may exist can occur when flow enters a dry conduit. For
instance, this could occur at the beginning of a storm in a sewer with little
or no base flow. Messages to this effect will be printed if parameter NPRINT
^ 0. These may almost always be ignored since the default options in subrou-
tine ROUTE will continue program execution and only result in a very small
error in continuity (a fraction of a percent).
Alternate Hydrograph and Pollutograph Inputs Hydrograph and pollutographs
may be entered from a tape/disk file (e.g., as generated in the Runoff Block)
and/or entered from cards, using card groups II and Rl in Table 6-6. Param-
eters NCNTRL and NINPUT are set accordingly. Note that input from both cards
and tape/disk may be performed simultaneously. If, for some reason, input
from cards is not desired, a tape/disk file containing the specified input
values could be created and specified as an input file to Transport in place
of, say, a file: generated by the Runoff Block. The format of such a file is
described in Section 2.
Quality
Constituents
Up to four pollutants may be arbitrarily chosen for input and routing
by the Transport Block. Although these would often be chosen from the group
(up to ten) supplied by the Runoff Block (or another preceding block), they
do not have to be since card input may be used in addition to the interface
file. If the same pollutant is entered from both the interface file and from
cards, the description (name, name of units, type of units) from the inter-
face file must be used. If the pollutant is entered only from cards, this
description must be supplied on card group Fl. Further information on
pollutant description is contained in Section 4.
Each pollutant may be subjected to a first order decay during the rout-
ing process by supplying a first order decay coefficient, DECAY (based on
natural logarithms or base e). Although travel time through most sewer sys-
tems is short enough so that decay is seldom important, the user could sup-
ply, for example, a deoxygenation coefficient, K , for BOD if desired. Non-
conservative pollutants are not linked. The decay of one has no effect on
any other.
241 6-22
-------
Routing --
Routing of quality parameters is performed by using the integral solu-
tion for the output from a completely mixed conduit volume (Medina et al.,
1981). See Appendix IX for a derivation. Although this tends to introduce
artificial dispersion of concentration profiles, it is the most convenient
way in which to introduce new loadings at manholes along the system, as well
as to facilitate scour and deposition calculations. The quality routing
procedure is not subject to calibration directly. However, the routing
becomes closer to pure advection (plug flow) as the number of elements is
increased.
Scour and Deposition
The basis for these procedures is described in Appendix VI. Each
pollutant is assigned a specific gravity (SPG) and particle size distri-
bution, assumed to apply throughout the drainage system regardless of the
source of the pollutant, e.g., stormwater or dry-weather flow. If the
specific gravity is less than or equal to 1.0, the pollutant is considered
to be entirely suspended (or dissolved) and not subject to scour and
deposition. If all calibration is to be performed using Runoff Block
buildup-washoff parameters, for instance, it may be desirable to avoid the
complexity of simulating a second real but largely unknown source.
Typical particle size distributions (and interpretation of input param-
eters PSIZE and PGR) are illustrated in Figure 6-6. Such information should
be collected first hand at each catchment; secondary sources such as Sartor
and Boyd (1972), Shaheen (1975), Manning et al. (1977) and Pisano et al.
(1979) should be used only if local data are not available. During the simu-
lation scour and deposition are simulated using Shield's criterion to deter-
mine the critical diameter for incipient motion and deposition (see Appendix
VI). The kinematic velocity of water (GNU on Card D2) is a function of
temperature and used to calculate the boundary Reynolds number on Shield's
diagram (Graf, 1971, Vanoni, 1975). For each conduit, the critical diameter
is determined as a function of velocity, roughness and specific gravity. At
the same time, the maximum diameter of the suspended fraction and the minimum
diameter of the settled fraction is maintained. If the .critical diameter is
less than the maximum of the suspended material, more is settled; the settled
mass is determined by multiplying by a fraction determined from the particle
size distribution (Appendix VI). Similarly, if the critical diameter is
greater than the minimum of the settled material, more is suspended. The
settled material is thus assumed to have the particle size distribution of
the right hand tail of the total distribution (Figure 6-6), and the sus-
pended material has the distribution of the left hand side.
Decreasing the specific gravity (downwards toward 1.0) increases the
amount suspended and vice versa. As SPG approaches 1.0 closely the proce-
dure becomes very sensitive to SPG since there is a division by SPG-1.0.
Typical values of specific gravities of particulate matter in sewers range
from 1.1 for volatile material to 2.7 for sand and grit. The actual situa-
tion in which each particle size range may have its own specific gravity
can be handled by the Storage/Treatment Block, but not the Transport Block
242
6-23
-------
I 100
S2
UJ
80
CD
EXAMPLE INPUT PARAMETERS FOR BOD5
INDEX PSIZE (mm) PGR (%)
DASHED LINES ARE ASSUMED
I 2
PARTICLE SIZE, mm
Figure 6-6. Example Particle Size Distributions for Pollutants found
on Street Surfaces. (After Sartor and Boyd, 1972, p. 146.)
243
6-24
-------
(except that up to four different pollutants may be simulated). Since it is
only one parameter, calibration of the scour-deposition routine may be most
easily calibrated using SPG. Alternatively, a greater percentage of large
diameter material may be assigned a pollutant using the particle size
distribution if, for instance, more deposition were desired.
Unlike the previous Transport Model, continuity of pollutant mass is
now maintained during occur and deposition. In addition, larger particles
can settle upstream in flat conduits and be unavailable for downstream
settling. An initial settled mass in each conduit is computed prior to the
start of the simulation by running the routine for DWDAYS days (card D2)
prior to the storm event (or longer) simulation. This initial deposition is
assumed to start with a clean bed.
Although this scour-deposition routine is a far cry from the detailed
sewer sediment transport program developed by Sonnen (1977), it is reason-
ably simple, consistent and may be calibrated. And should the user desire,
it may be bypassed (using SPGS1.0), and all quality calibration performed in
the Runoff Block.
Internal Storage Model
Use of the internal storage routine involves four basic steps. A somewhat
more detailed data description may be found in Section 7.
Step 1. Call
The internal storage routine is called by subroutine TRANS when element
NTYPE 19 is specified. No more than two storage locations may be specified
in a single run.
Step 2. Storage Description: Part 1
Describe the manner in which the outlet depth-discharge relationship is
given (set of data pairs, power equation or pumps). See Appendix IV and
Section 7 for a more detailed description of this technique.
Step 3. Storage Description: Part 2
Describe the geometry of the unit with a set of depth-surface area
volume data triplets and the depth-discharge ^relationship with data pairs or
a power equation. See Appendix IV and Section 7 for more details.
Step 4. Initial Conditions
Describe the initial conditions of the unit with respect to volume and
pollutant concentrations.
244 6-25
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Infiltration Model
Description
The infiltration program, INFIL, has been developed to estimate infil-
tration into a .given sewer system based upon existing information about the
sewer, its surrounding soil and groundwater, and precipitation. It should
be bcrne in mind throughout that the accuracy of infiltration prediction is
dependent upon the accuracy and extent of data descriptive of infiltration
in the system being modeled.
Using these data, INFIL has been structured to accept estimates of
average daily infiltration inflows at discrete locations along the trunk
sewers of a given sewer system. A typical urban drainage basin in which
infiltration might be estimated is shown in Figure 6-7.
Since the Storm Water Management Model's principal use has been mainly
to simulate individual storms which cover a time period of less than a day,
average daily estimates from INFIL are calculated only once prior to sewer
flow routing. INFIL is called from subroutine TRANS by setting the variable,
NINFIL equal to 1, thus signaling the computer to estimate infiltration.
In fact, however, the user has most of the responsibility for infiltration
estimation, optionally using techniques described below. The program does
little more than apportion it properly.
For the purposes of analysis, infiltration is classified into four
categories, i.e., miscellaneous sources causing a base dry weather inflow,
frozen residual moisture, antecedent precipitation, and high groundwater.
The cumulative effects of the first three sources can be seen in Figure 6-8
which excludes surface runoff. Figure 6-8 shows total infiltration QINF as
the sum of dry weather infiltration DINFIL, wet weather infiltration RINFIL,
and melting residual ice and frost infiltration SINFIL. However, in cases
where the groundwater table rises about the sewer invert, it is assumed that
groundwater inflow GINFIL alone will be the dominant source of infiltration.
Thus, infiltration is defined as:
QINF =
DINFIL + RINFIL + SINFIL
or (6-5)
GINFIL for high groundwater table
Throughout the procedure for determining input variables, observations
and estimates based upon local data given preference over generalized esti-
mates for infiltration described below. Thus, the hierarchy for basing
estimates should be:
1) Use historical data for the study area under consider-
ation.
2) Use historical data for a nearby study area and adjust re-
sults accordingly.
245 6-26
-------
SEWERS
CO.'JC'jrfS TO V/HICH TOTAL
INfllTHAMON 13 APPORTIONED
DRAINAGE BASIH BOUNDARY
tiOfl-CC;iOU!T ELEMENT
Figure 6-7. Typical Drainage Basin in wh±ch
Infiltration is to be Estimated
246
6-27
-------
o
_i
u.
TIME
QINF =* Total infiltration
DINFIL = Dry v/cather infiltration
RINFIL = Wet v;eather infiltration
SIMFIL = Melting residual ice and snow infiltration
RSMAX = Residual moisture peak contribution
SMMDWF = Accounted for sev.igs flov/
Figure 6-8. Components of Infiltration
247
6-28
-------
3) Use estimates of local professionals.
4) Use generalized estimates based upon country-wide observa-
tions.
Infiltration - inflow studies (e.g., EPA, 1975) have been performed in many
cities and should provide much of the needed data.
Dry Weather Infiltration (DINFIL) --
If the study area under consideration has been gaged, base dry-weather
infiltration can be taken by inspection from the flow data. In the absence
of flow data, an estimate of the unit infiltration rate XLOCAL (gpm/inch-
diameter per mile) for dry weather must, be obtained from local professionals.
From data in the form of calculated values of DIAM and PLEN, Equation 6-6
can then be used to determine DINFIL (j;pm):
DINFIL = XLOCAL-DIAM-PLEN (6-6)
where DIAM = average sewer diamater, in., and
PLEN = pipe length, mi.
Values of XLOCAL range from 250 to 600 gpm/in-diameter per day (ASCE-WPCF,
1969) and may be even higher for laterals with many stubs and wyes. The
importance of local data cannot be over-emphasized.
Residual Melting; Ice and Frost Infiltration (SINFIL) --
SINF1L arises from residual precipitation such as snow as it melts fol-
lowing cold periods. Published data (American Soc. of Heating and Air Condi-
tioning Engineers) in the form of monthly degree days (sum of deviations
below 65°F) provide an excellent index as to the significance of SINFIL.
Average monthly degree-days for cities in the United States are reproduced
in Appendix VIII. The onset and duration of melting can be estimated by
noting the degree days NDD above and immediately below a value of 750.
Refer to Figure 6-9 for the following description.
Within subroutine INFIL, the beginning of melting, MLTBE, is taken as
the day on which NDD drops below 750. Next, MLTEN is determined so that Aj
equals A2. In the absence of evidence to the contrary, it is assumed that
the melting rate is sinusoidal. The maximum contribution RSMAX from residual
moisture can be determined from previous gaging of the study area or local
estimates. In either case, SINFIL is determined within the program by the
following equation:
SINFIL =
RSMAX-sin[180(NDYUD-MLTBE)/(MLTEN-MLTBE)]
0.0 if NDYUD is not: in melting period or if
NDD never exceeds 750.
(6-7)
248 6-29
-------
to
M
w
4UNE I JOLl
DATE
MLTlit-
MLTEH-
MLTBE
ML'J'EK
Day on which melting period begins
Day on which melting peripcl enda
Figure 6-9. Prescribed Melting Period
249
6-30
-------
where NDYUD = day on which infiltration estimate is desired,
RSMAX = residual moisture peak contribution, gpm,
MLTBE = beginning of melting period, day, and
MLTEN = end of melting period, day.
Note that RSMAX is a required input parameters, in addition to degree day
information.
/
Antecedent Precipitation (RINFIL) --
RINFIL depends upon antecedent precipitation occurring within nine days
prior to an estimate. If antecedent rainfall is unavailable or less than
about 0.25 in (6.4 mm), the RINFIL contribution to QINFIL is usually small.
For larger antecedent rainfall contributions, regression techniques offer
one method of estimating RINFIL. For example, during development of the
infiltration routine, available rainfall and infiltration data were examined
(Metcalf and Eddy et al., 1971a). For three areas in which sewer flow data
were not affected by melting, RINFIL was found to satisfy the following
linear relationship:
RINFIL = ALF + ALFO-RNO + ALF1-RN1 + ... + ALF9-RN9 (6-8)
where RINFIL = SWFLOW - DINFIL - SMMDWF, gpm,
ALFN = coefficient to rainfall for N days prior to estimate,
gpm/in,
RNN = precipitation on N days prior to estimate, in,
SWFLOW = daily average sewer flow excluding surface runoff, gpm,
arid
SMMDWF = otherwise accounted for sewage flow, gpm.
To determine the coefficients in Equation 6-8, a multiple linear regression
should be run on existing flow and rainfall data. For comparative purposes,
the results of regression analyses for study areas in three selected cities
(Lentz, 1963, McJtcalf and Eddy et al., 1971a) are given in Table 6-4.
High Groundwater Table (GINFIL) --
For locations and times of the year that cause the groundwater table to
be above the sewer invert, groundwater infiltration GINFIL supersedes contri-
butions from DINFIL, RINFIL, and SINFIL. GINFIN can be determined from his-
torical sewer flow data by inspection or regression analysis. For example,
a regression analysis could involve determination of the BETA coefficients
in Equation 6-9, or an alternative formulation could be investigated.
GINFIL = BETA + BETA1-GWHD + BETA2-GWHD2 + (6-9)
BETA3-GWHD
where GWHD = groundwater table elevation above sewer invert,
ft, and
BETAN = coefficient for terra N.
250 6-31
-------
Table 6-4 RINFIL Equations For Three Study Areas
Study Area Equation
3L-adenton, RINFIL = 4.1 + 2.9RNO + 17.5RN1 + 15.0RN2 +
Florida 12.8RN3 + 13.0RN4 + 10.4RN5 +
13.2RN6 + 10.1RN7 + 11.8RN8 + 9.5N9
Baltimore, RINFIL = 2.4 + 11.3RNO + 11.6RN1 + 5.5RN2 +
Maryland 6.4RN3 + 4.8RN4 + 3.6RN5 + 1.0RN6 +
1.5RN7 + 1.4RN8 + 1.8RN9
Springfield, RINFIL = 2.0 + 18.3RNO + 13.9RN1 + 8.9RN2 +
Missouri 5.5RN3 + 6.7RN4 + 16.RN5 + 5.2RN6 +
4.6RN7 + 4.4RN8 + 1.3RN9
Apportionment o£ Infiltration
Once an estimate of the total local infiltration QINF has been obtained,
this flow must be apportioned throughout the designated study area. The cri-
terion chosen for apportionment is an opportunity factor OPINF which repre-
sents the relative number and length of openings susceptible to infiltration.
Pipe joints constitute the primary avenue for entry of infiltration (Geyer
and Lentz, 1963). The number and length of joints is assumed to be propor-
tional to the relative surface area of each conduit. For each, an equivalent
circular pipe diameter will be proportional to the square root of its known
cross sectional area, ft. Then the fraction of total infiltration ("oppor-
tunity" for infiltration) allocated to each conduit, OPINF, is:
VA -DIST
OPINF =
I VAf.DIST (6-10)
2
where Af = cross sectional area of conduit, ft , and
DIST = conduit length, ft,
and the summation in the denominator is over all conduits. Trapezoidal chan-
nels are treated the same as all others. The apportioned infiltration enters
the system at the non-conduit element immediately upstream of the conduit.
This procedure allocates the most infiltration to the largest and long-
est conduits. Should local information dictate otherwise, infiltration may
be apportioned by the user and entered at appropriate manholes in card group
El.
251 6-32
-------
Infiltration developed using subroutine INFIL is held constant in time.
Should hourly or daily corrections be desired, infiltration can be incorpo-
rated into dry-weather flow (described below).
Quality of Infiltration --
Although infiltration is often assumed to be "clean" due to its origin
in the soil layers, in-conduit measurements usually indicate non-zero levels
of most parameters. These concentrations may be entered on card Kl.
Data Needs --
Hydrologic Data Concurrent historical rainfall, water table, and sewer
flow data of several weeks' duration are needed to completely describe infil-
tration. In addition, rainfall for several days prior to the flow estimate
is required for use in a regression equation for RINFIL. Of course, such
data would be required for many different storms for development of such an
equation.
Ideally, the rainfall record would be from a rain gage which is located
near the center of the study area and which records daily rainfall in inches.
If more than one rain gage is located within the study area, daily measure-
ments from all gages should be averaged. Missing data (e.g., from a malfunc-
tioning gage) or a total absence of measurements due to no gaging within the
study area can sometimes be overcome with measurements taken from a rain gage
located within .a few miles. If National Weather Service (NWS) Climatological
Data recorded at the nearest airport or federal installation are not avail-
able, contact the National Weather Records Center (Asheville, NC) for
assistance.
Should some other form of precipitation, e.g., snowfall, be encountered,
it will be necessary to convert this to equivalent rainfall. If estimates
are unavailable from the NWS, the ratio of ten inches of snow to one inch of
rain may be used.
Water table data should also be obtained from gaging within the study
area. However, shallow-well data from the US Geological Survey or state
geological office can be used to supplement missing data. Water table ele-
vations are not required if they are below the sewer inverts for the day on
which QINF is to be estimated.
Sewer Data Sewer flow data for regression analysis should be taken from
a gage located at the downstream point within the study area. Upstream gag-
ing may sometimes be used to estimate flows at the downstream point by simply
adjusting flows based upon respective surface area. Physical sewer data
(e.g., lengths, diameters) are taken from prior input used within TRANS to
route sewer flow.
Summary of Infiltration Procedures
Input Effective use of the Infiltration Model requires estimates of its
component flows, namely:
252 6-33
-------
DINFIL = dry weather infiltration,
RINFIL = wet weather infiltration,
SINFIL = melting residual ic« and snow, and
GINFIL = groundwater infiltration.
Step 1. Determine Groundwater Condition - If the groundwater table is pre-
dominantly above the sewer invert, all infiltration is attributed to this
source (GINFIL). In this case, au estimate o£ Lue LoLai infiltration is
made directly (in cfs for the total drainage basin) and read in card Kl.
This card followed by a blank card (card K2) would complete the infiltration
data input. If the groundwater table is not predominantly above the sewer
invert, proceed to Step 2.
Step 2. Build-Up Infiltration from Base Estimates -- From measurements,
historical data, or judgment, provide estimates of DINFIL and RINFIL. In
this case, GINFIL must be set equal to 0.0. Finally, if needed, provide the
peak residual moisture (RSMAX), and tht: 12 monthly degree-day totals taken
from Appendix VII or a local source.
Pry Weather Flov Model
Methodology
Subroutine FILTH has been developed as an option to estimate average
sewage flow and quality from residential, commercial, and industrial urban
areas. FILTH estimates sewage inputs at discrete locations along the trunk
sewers of any specified urban drainage basin. These estimates are calculated
from data describing drainage basin subsections (subcatchments and subareas)
under which the trunk sewer passes. In this routine, dry-weather flow quan-
tity and quality are developed from regression equations, as explained in
the documentation (Metcalf and Eddy et al., 1971a). The estimates are for
three specific quality parameters: BOIL, suspended solids (SS) and total
coliforms. Thus, if any different parameters are to be simulated, FILTH
cannot be used. However, if a fourth parameter is to be routed in addition
to BOD_, SS and total coliforms, FILTH can be used to provide estimates for
the first three but not the additional one. Also bear in mind that a con-
stant base flow for any parameter may be input at manholes in card group El.
When FILTH is not used, DWF estimates may be input at desired manholes,
as discussed previously. In fact, this; option may be routinely used in place
of FILTH whenever reasonable estimates are available for instream DWF quan-
tity and quality.
An example of a hypothetical sewer system and input situation for FILTH
is given in Figure 6-10. To avoid confusion with Runoff Block subcatchments,
all drainage basin subdivisions will be; referred to as subareas in the fol-
lowing discussion. As shown in the figure, an input manhole near the center
of each subarea is assumed to accept all sewage flow from that subarea.
Criteria for establishing subarea boundaries and input locations are dis-
cussed later in the text.
253
-------
X U\ MANHOLE
SEWER ELEMENT NUMBERS
SUBCATCHMENT OR SUBAREA
NUMBER
INPUT MANHOLE:
CONDUITS
SUBAREA BOUNDARIES
SU3CATCHMENT BOUNDARIES
Sewer and Subeatchnent Data
1. Manhole 32 is the cost downstream point.
2. Subcatchments 1,2,3, and 4 are single-family residential
areas, each 100 acres in size and each with water r.etering.
3. Sufccatch^ants 5 and 7 are 220-acre industrial areas.
4. Subarea 6 is a 250-acre park.
3. Subarea 5 is a 50-acre commercial area.
Snbareas 6 and 8 constitute a subcatchment draining to
input manhole nusber 21.
Resulting Data
8 sewage estimates
KTNUM, total subcatchments and subareas in drainage basin = S.
TOTA, total acres in drainage basin = 1,140.
KNUM,
subcatchment
or subarea
1
2
3
4
S
6
7
8
INPUT,
input manhole
number
3
17
29
8
26
21
24
21
KLAND,
land use
category
1
1
1
1
4
5
4
3
ASUB,
acres in
subcatchnent
or subarea
100
100
100
100
220
250
220
50
Figure 6-10. Determination of Subcatchment and Identifi-
cation to Estimate Sewage at 8 Points
254
6-35
-------
In the context of the SWMM, FILTH calculates daily sewage flow (cfs)
and characteristics (BOD , SS, and total coliforms) averaged over the entire
year for each subarea. FILTH is called from subroutine TRANS by setting the
parameter NFILTH equal to one. Flow and quality characteristics estimates
and corresponding manhole input numbers are then returned to TRANS where the
estimates undergo adjustment depending upon the day of the week and hour of
the day during which simulation is proceeding.
The subroutine may be omitted when modeling separate storm sewers
unless it is desired to generate a base flow with DWF characteristics.
FILTH is designed to handle an unrestricted number of inlet areas and indi-
vidual process flow contributors. As a safeguard against faulty data, how-
ever, a program interrupt is provided if the combined number exceeds 159,
which is a limit set by the Transport Model.
Quantity Estimates --
Three data categories are used to estimate sewage flow: (1) drainage
basin data, (2) subarea data, and (3) decision and adjustment parameters.
Study area data are TOTA, KTNUM and ADWF. KTNUM denotes the number of
subareas into which a drainage basin, having a surface area TOTA (acres), is
being divided. ADWF, which is optional depending upon its availability,
gives the average sewage flow (cfs) originating from the entire drainage
basin (e.g., average flow data from a treatment plant serving the study
area). When it is included, the predicted basin flow will be adjusted to
match this value.
Subarea data requirements consist of several options depending upon
availability and choice of input. Discussion later in the text will assist
in data tabulation by noting the order of preference where options exist.
Subarea data can be broken into three categories as follows: (1) identifi-
cation parameters, (2) flow data, and (3) estimating data.
1) Identification parameters -- Identification parameters are
KNUM, INPUT, and KLAND. KNUM identifies each subarea by a
number less than or equal to KTNUM. For each of the KTNUM
subareas, INPUT indicates the number of the manhole into
which DWF is assumed to enter. Land use within each sub-
area which approximately corresponds to zoning classifica-
tion, is categorized according to Table 6-5. KLAND serves
as an important factor in deciding subarea locations and
sizes. Figure 6-10 will assist in describing how the above
data are determined and tabulated.
255 6-36
-------
Table 6-5. Land Use Classification
KLAND
1 Single-family residential
2 Multi-family residential
3 Commercial
4 Industrial
5 Park and open area
2) Flow data - Flow data are optional inputs that eliminate
the need for using predictive equations. Two possible types
of flow data are average sewage flow measurements, SEWAGE,
and metered water use, WATER. Commercial or industrial
sewage flow or water use measurements should be input
using the variable SAQPF. Flows from commercial and in-
dustrial establishments located in residential or open
subareas may be included using SAQPF, also. Metering
at lift stations and other flow control structures within
the study area is occasionally available and should be
used whenever possible. Metered water use offers a more
available source of subarea flow data. Unfortunately,
considerable effort in locating, tabulating, and aver-
aging these data is often required.
3) Estimating data -- For each subarea where SEWAGE or WATER
measurements are not available estimated water use must be
used as an estimate of sewage flow. In the case of a fac-
tory or commercial establishment, estimates can be made by
multiplying the number of employees by an established co-
efficient (gpd per employee). In the case of a large fac-
tory or commercial establishment, one subarea may be estab-
lished with estimated water use tabulated as SAQPF for that
subarea. On the other hand, estimates of water use for
established non-residential areas (e.g., industrial parks
or shopping centers) may be summed and tabulated as SAQPF
for one large subarea. A list of the above mentioned co-
efficients is given in Appendix VIII.
In the case of residential areas, estimating data for each subarea are
METHOD, PRICE, ASUB, POPDEN, DWLINGS, FAMILY, and VALUE. Default values
and definitions of each of these are given in the description of input data.
Decision and adjustment parameters consist of DVDWF, HVDWF, KDAY, CPI, and
256 6-37
-------
CCCI. DVDWF and HVDWF are daily and hourly correction factors, respectively,
for DWF. DVDWF is comprised of seven numbers that are ratios of daily aver-
age sewage flows to weekly average flow. Likewise, HVDWF is comprised of 24
numbers that are ratios of hourly average sewage flows to daily average flow.
Both groups of numbers may be derived from observed flow variation patterns
throughout the country (e.g., Tucker, 1967, Portland Cement Association,
1968). Their use is to correct measured or estimated average sewage flow to
more accurate estimates depending upon the day and hour. Typical sewage flow
variations are shown in Figures 6-11 and 6-12. These flow patterns are only
examples; locally observed patterns more accurately describe local variations
and should be used when available.
KDAY denotes the day of the week at which simulation is to begin. As
the simulation proceeds, this value is continually updated. By using the
current day and hour, the appropriate values of DVDWF and HVDWF can be
multiplied by average flow to determine the correct value. KDAY ranges from
1 to 7 with Sunday being day number 1.
Two cost indices are employed to adjust current house valuations and
water prices to appropriate 1960 values and 1963 prices, respectively. This
is done because estimating equations within FILTH are based upon 1960 values
and 1963 prices. CPI, consumer price index, has been chosen to adjust water
price by multiplying water price by 1960 CPI divided by the current CPI.
CCCI, composite construction cost index, has been chosen to adjust house
valuations similarly. Both indices can be found in most libraries in jour-
nals on economic affairs (e.g., U.S. Dept. of Commerce, Survey of Current
Business and Statistical Abstracts of the United States).
Quality Estimates --
The purpose of the DWF quality computation is to apportion waste charac-
teristics (such as would be measured at a sewage treatment plant before
treatment) among the various subareas in the drainage basin under study, or
in the event no measured data are available, to estimate and apportion usable
average values. The apportionment is based upon the flow distribution, land
use, measured or estimated industrial flows, average family income, the use
or absence of garbage grinders, and infiltration.
Daily and hourly correction factors for concentrations of BOD , SS and
total coliforms are input in conjunction with those for flow variations.
All are expressed as ratios of instantaneous to annual or daily averages.
Card Nl includes the total number of subareas and process flow sources
to be processed along with the type case (whether the total DWF character-
istics are known or to be estimated), the number of process flow contribu-
tors, the cost indices, and the total drainage basin population. Depending
upon the instructions given, computations proceed along the Case 1 or Case 2
channel.
Case 1 In this instance, the total DWF quality charac-
teristics are known at a point well downstream in the sys-
tem. These characteristics may be obtained from treatment
257 6-38
-------
3
3 1,10
0)
t>0
M
S 1.00
-------
plant operating records (raw sewage) or by a direct sampling
program. The average daily concentrations are read into
the program for flow, BOD,., SS, and total coliforms (card
01). The total pounds per day of BOD and SS and the total
MPM per day of coliforms are then calculated. Then, infil-
tration and base flow are subtracted from the average daily
flow. Note that infiltration is computed in separate sub-
routine INTIL. If it is 110L executed a default of zero will
be assumed.
Next, the known process flow contributions (card group PI)
are summed and deducted from the daily totals, yielding a
further corrected flow, C2DWF (cfs), and characteristics,
C1BOD and C1SS (lb/day). This is the only use of the in-
put from card group PI. Process flow information must be
re-entered for each subarea, in card group Ql.
Finally, corrections based on regression equations, are
made for personal income variations, degree of commercial
use, and garbage grinder status (card 02). The DWF quan-
tity does not change but the characteristics obtain new,
average values, C2BOD and C2SS. Average concentrations of
the residual flow, A1BOD, A1SS, and A1COL1 are then computed.
Case 2 Here no direct measurements are available; thus,
estimates must be made or default values will be assumed.
A typical application of Case 2 would be in a situation
where several catchments are to be modeled, yet funds will
permit monitoring the DWF only in a single area. A1BOD,
A1SS, and A1COLI would be computed via the Case 1 sub-
routine for the known area and the results could be trans-
ferred as Case 2 for the remaining catchments.
Default values of A1BOD, A1SS, and A1COLI are 1300 Ib/day-
cfs (241 mg/1), 1420 Ib/day-cfs (263 mg/1) and 6.2 x 107
MPN/100 ml. These values assume 85 gal/capita-day (322
I/capita-day) domestic wastewater flow and 0.02 lb/capita-
day (0.09 kg/capita-day) for BOD5, 0.22 Ib/capita-day
(0.1 kg/capita-day) for SS and 200 billion MPN/capita-day
for total coliforms. All values assume average income
families. The default value for ADWF assumes 100 gal/capita-
day (376 I/capita-day) which includes an extra 15 gal/capita-
day (57 I/capita-day) for infiltration or other sources.
Following estimation of basin totals, average daily flow and quality
values are computed for each of the KTNUM subareas. Data are input in card
group Ql for estimation of water use and sewage quality as well as process
flow information for each subarea.
Dry weather flow quantity (DWF in cfs) is computed for each land use
on the basics of the following priorities:
259 6-40
-------
Priority Method
1 Measured average sewage flow (SEWAGE ^ 0.0).
2 Measured water use (WATER ^ 0).
3 Regression equations, for single and multiple-family
residential land use only.
The first two methods are really equivalent since DWF is simply equated to
either SEWAGE or WATER, in this order, for all land uses. Regression equa-
tions are employed as a third choice for residential land uses. As explained
in the documentation (Metcalf and Eddy et al., 1971a), DWF becomes a function
of the number of dwelling units within the subarea (DWLNGS) and other param-
eters as will be listed below. DWLNGS is required for all regression equa-
tions and is computed on the following basis:
Priority Method
1 Input on card 01
2 DWLNGS = POPDEN-ASUS/FAMILY
3 Default to 10 units per acre.
DWF is then computed using DWLNGS and input parameters as listed below:
METHOD = 1 METHOD = 2
PRICE = Q PRICE t 0
DWLNGS DWLNGS DWLNGS
VALUE PRICE FAMILY
CPI VALUE
VALUE
For each technique default values will be used where necessary. It may be
inferred that parameters not used in a regression equation may be omitted
from input. Note that VALUE is also used in each technique. It is adjusted
to the 1960 Composite Construction Cost Index, CCCI, by
VALUE = VALUE 103/CCCI (6-11)
Finally, the user is reminded that all inputs for the regression equations
can be avoided if either SEWAGE or WATER is known.
For commercial, industrial or undeveloped land uses parameter SEWAGE
or WATER is the only method used to input DWF, except that process flows
are added to the value of DWF previously computed, for all land uses. Thus,
they could constitute the only dry-weather flow source for non-residential
land use.
260 6-41
-------
Dry-weather flow quality starts with the average BOD and SS concentra-
tions (A1BOD and A1SS) previously computed for the entire subarea. These
are used for the concentrations of non-process flows for all subareas, with
two exceptions. First, for commercial and industrial areas, the average
concentrations are multiplied by 0.9. Second, the strengths of residential
flows are adjusted according to average family income, XINCOM, and percent
garbage grinders, PCGG, as explained in the documentation.
The process flow load (i.e. flow times concentration) is then added to
the loads just computed, for all land uses. For non-residential land use
process flows could constitute the only quality loads.
Finally, for all subareas, total coliforms are computed solely on the
basis of population using the average concentration, A1COLI, computed earlier
along with the total basin populations, POPULA, (card Nl) and subarea popula-
tions computed from POPDEN (card Ql). Thus, there will be a subarea contri-
bution of total coliforms only if POPDEN ^ 0.
For each of the KTNUM subareas, subtotals (cumulative up to this
area) of computed flov/s and quality will be printed for each subarea if
MSUBT ^ 1. Otherwise, only basin totals will be printed. If measured basin
averages have been input on card 01 (KASE =1) all subarea loads are adjusted
by a constant ratio such that the flow and concentrations computed from the
data of card group Ql will agree with the input averages.
Summary of Dry Weather Flow Requirements
Step 1. Establishing Subareas Establishment of the subareas constitutes
the initial step in applying subroutine FILTH. Both detail of input data
and assumptions made in developing FILTH imply constraints on the type, size,
and number of subareas. However, most important in subarea establishment is
the type of estimating data available and the maintenance of homogenous land
use.
Subareas should be located and sized to utilize existing sewer flow
measurements taken within the drainage basin. These measurements should be
recent and of sufficient duration to provide a current average sewage flow
value for the period of time during which simulation is to proceed. Measured
daily and hourly flow variation should be used in lieu of generalized values
described earlier in the text. A gaging site with less than 200 ac (81 ha)
contributing flow often provides a convenient data input situation. A sub-
area should be established upstream from the gage with average sewage flow
tabulated as SEWAGE for that subarea. It is convenient, though not neces-
sary, for the subareas to correspond to subcatchments in Runoff.
If metered water use is to be used to estimate sewage flow, subareas
should be located to coincide with meter reading zones or other zones used
by the water department that simplify data takeoff. Since water use would
be used to estimate sewage flow, average winter readings should be used to
minimize the effects of lawn sprinkling and other summer uses.
261
-------
If neither gaging nor metered water use are input, sewage characteris-
tics must be estimated. Subareas should then be established to yield appro-
priate input data for the residential estimating equations in FILTH. Zero
sewage flow is assumed from commercial, industrial, and parkland subareas
for which SEWAGE and WATER are zero and measurements of SAQPF are not given.
Since KLAND and VALUE are the significant variables in estimating subarea
sewage flow, subareas should be located and sized to include land with uni-
form land use and property valuation. To utilize existing census data, cub-
area boundaries should be made to coincide with census tract boundaries.
Criteria for establishing subareas are listed in the following summary:
1) Subaraas in general should:
a. be of homogenous land use;
b. be less than or equal to 159 in number; and
c. conform to the branched pipe network.
2) Subareas should be established to employ any existing
sewer flow measurements.
3) Subareas for which metered water use is used to estimate
sewag;e flow should be compatible with meter reading zones.
4) Residential subareas for which estimated water use is used
to estimate sewage flow should:
a. be uniform with respect to land use;
b. be uniform with respect to dwelling unit
valuation; and
c. coincide with census tracts.
Step 2. Collection of Data Other than the establishment of measured data
described earlier, the primary data source is the US Bureau of Census for
census tract information. This source provides readily available data on
population distribution, family income, and the number and relative age of
dwelling units. City records, aerial photographs, and on-site inspection
may be necessary to define land use activities, process flow, and dwelling
density variations within tracts.
Step 3. Data Tabulation -- Once subareas have been established, several
alternatives exist regarding data tabulation. An identification number KNUM
should be given to each subarea prior to data takeoff. However, once KNUM's
have been established, corresponding INPUT manhole numbers are selected from
a previously numbered schematic diagram of the trunk sewer. This numbered
schematic serves as the mechanism to coordinate runoff, infiltration, and
sewage inputs. Refer to the Transport discussion for additional information
about the numbered schematic. If water use estimates are necessary, land
262 6-43
-------
use should be determined from city zoning maps and the previously tabulated
values for KLAN.D.
ADWF should be tabulated as average drainage basin sewage flow. As the
ADWF, SEWAGE should be averaged from flow data for the appropriate month,
season, or year. ADWF, SAQPF, or SEWAGE may be obtained from routine or
specific gaging programs done by the city, consulting engineers, or other
agencies. SAQPF may be estimated for commercial and industrial areas using
water use coefficients (Appendix VIII). Also, SAQPF and WATER may be deter-
mined for all land use categories from water meter records.
Initialization
Following execution of subroutines INFIL and/or FILTH, flows and con-
centrations will be initialized to base flow values simply by summing flows
and loads at all junctions (non-conduits) in subroutine INITAL. Base flow
can thus originate from three sources: input at manholes, infiltration (sub-
routine INFIL), and/or dry-weather flow (subroutine FILTH). Inflows from
FILTH are always subject to the hourly and daily adjustment factors; inflows
from INFIL and manholes are not.
In addition, the buildup of settled pollutant fractions (if simulated)
in the sewer system is estimated using subroutine DWLOAD. For the particle
size distribution and specific gravity discussed earlier, daily "solids"
deposition is computed for DWDAYS dry-weather days (card D2) prior to the
simulation. The initial pounds of deposition are printed for each conduit.
This material is then eligible for erosion during the simulation (computed
in subroutine QUAL). Thus, if flows increase over their initial values, (as
expected during a storm) a "first flush" will be provided.
6-44
-------
Table 6-6. Transport Block Card Data
Card Card
Group Format Columns
Description
Variable
Name
Default
Value
Flow routing data for new shapes.
Al 2X 1-2 Card identifier = Al.
13 5 Number of sewer cross-sectional
shapes, in addition to the 13
program-supplied for which ele-
ment routing parameters are to
follow (maximum value = 2).
15 10 Control parameter for printing
out flow routing parameters for
all shapes, (about 500 lines).
= 0, Suppress printing.
= 1, To allow printing (for
all shapes, program-
supplied and additional)
NKLASS
KPRINT
Blank
0
Bl 2X
8A4
B2 2X
13
15
1-2
3-18
19-34
1-2
4-5
9-10
DELETE CARD GROUPS Bl TO B9 IF
NKLASS = 0
Card identifier = Bl.
16-letter name of shape 1.
16-letter name of shape 2.
Number of values of DNORM to be
supplied (maximum value =51,
minimum value = 2).
Card identifier = B2.
Number of values for shape 1.
Number of values for shape 2.
Blank
NAME(I,14) None
NAMEU.15) None
Blank
NN(I4) None
NN(15) None
264
6-45
-------
Table 6-6 (continued). Transport Block Card Data
Card
Group Format
B3 2X
13
15
BA 2X
F8.0
F10.0
B5 2X
F8.0
F10.0
B6 2X
F8.0
F10.0
Card
Columns
1-2
4-5
9-10
1-2
3-10
11-20
1-2
3-10
11-20
1-2
3-10
11-20
Description
Number of values of QNORM to
be read (maximum value = 51,
minimum value = 2).
Card identifier = B3.
Number of values for shape 1.
Number of values for shape 2.
Value of A/A, corresponding to
the maximum Q/Q, value for each
shape.
Card identifier = B4.
A/A, value for shape 1.
A/A, value for shape 2.
Maximum Q/Q, value for each shape.
Card identifier = B5.
Maximum Q/Q, value for shape 1.
Maximum Q/Qf value for shape 2.
Factor used to determine full flow
area for each shape, i.e., for use
in AFULL = AFACT(GEOMl)2 .
Card identifier = B6.
Factor for shape I.
Factor for shape 2.
Variable Default
Name Value
Blank
MM(14) None
MM(15) None
Blank
ALFMAX(14) None
ALFMAX(IS) None
Blank
PSIMAXU4) None
PSIMAXO5) None
AFACT
Blank
AFACT(14) None
AFACT(IS) None
265
6-46
-------
Table 6-6 (continued). Transport Block Card Data
Card Card
Group Format Columns
Description
Variable
Name
Default
Value
Factor used to determine full flow
hydraulic radius for each shape,
i.e., for use in equation
RADH = RFACT(GEOMl).
87
2X
F8.0
F10.0
1-2
3-10
11-20
Card identifier = B7.
Factor for shape 1.
Factor for shape 2.
--
RFACT(14)
RFACT(IS)
Blank
None
None
REPEAT CARD GROUP B8 FOR EACH
ADDED SHAPE.
1)8
2X
F8.0
7F10.0
1-2
3-10
11-20
Input of tabular data (depth of
flow, y, divided by total depth
of conduit, y (y/y,)) for each
added shape corresponding to the
NN-1 equal divisions of A/A, of
the conduit as. given by NN on
card group B2.
A
Card identifier = B8.
First value for y/y, for shape 1.
Second value of y/y, for shape 1.
Blank
DNORM(I.l) None
DNORM(I,2) None
Last'value of y/y, for shape 1.
(Total of NN(U)/8 + NN(15)/8
data cards)
DNORM(I,NN(I)) None
K'.'".}:'" ;.fi|;' .tWti1 .1!' >'(>' .-I".
A£DED JHArE
Input of tabular data (flow rate,
Q, divided by the flow rate of the
conduit running full, Qf(Q/Q,))
for each added shape corresponding
to the HM-1 equal divisions of A/A,
of the conduit as given by MM on
card group B3.
266
6-47
-------
Table 6-6 (continued). Transport Block Card Data
Card Card
Group Format Columns
Description
Variable
Name
Default
Value
B9
2X
F8.0
7F10.0
1-2
10
11-20
Card identifier = B9.
First value of Q/Q, for shape 1.
Second value of Q/Q, for shape 1.
Blank
QNORM(I,1) None
QNORM(I,2) None
Last value of Q/Q, for shape 1.
(Total of MM(14)/8 + MM(15)/8
data cards)
QNORM(I,MM(I)) None
Cl 2X
19A4
Dl 2X
18
Two title cards.
1-2 Card identifier = Cl.
3-78 Title, two cards with .
heading to be printed on output.
Execution control data.
1-2 Card identifier = Dl.
3-10 Total number of time-steps, no
Blank
TITLE Blank
Blank
NOT None
limit.
715 14-15 Total number of non-conduits ele- NINPUT None
ments into which there will be card
input of hydrographs and pollutographs
in card group Rl (maximum = 80).
19-20 Total number of non-conduit elements - NNYN None
at which input hydrographs and pollu-
tographs are to be printed out (maxi-
mum = 80, minimum - 0).
24-25 Total number of non-conduit elements NNPE None
at which routed hydrographs and
pollutographs are to be printed out
(maximum = 80, minimum =0).
26-30 Total number of non-conduit elements NOUTS None
at which flow is to be transferred
to a subsequent block by tape or
disk (maximum = 80).
267
6-48
-------
Table 6-6 (continued). Transport Block Card Data
Card Card
Group Format Columns
Description
Variable Default
Name Value
35 Control parameter for program- NPRINT
generated error messages occurring
in the execution of the flow
routing scheme. These errors do
not normally affect the program
execution.
= 0, messages suppressed.
= 1, messages printed.
40 Total number of pollutants being NPOLL
routed (maximum = 4, minimum = 0).
When NPOLL = 0, program will route
flows only and all quality operations
will be bypassed.
45 Total number of iterations to be used NITER
in routing subroutine (4 recommended).
4X,I6 50-55 Starting date of storm, year-raonth-day, IDATEZ
e.g., July 20, 1979 = 790720. Super-
seded by value on interface file from
previous block, if accessed.
215 56-60 Metric input/output. METRIC
= 0, U.S. customary units.
= 1, Metric units, indicated
in brackets [ ] among input
variables.
61-65 Print interval for input and output INTPRT
elements. Use 1 for printing at each
time step. Value of zero will result
in printing of total loads and moments
only.
D2 2X
F8.0
1-2
3-10
Execution control data.
Card identifier = D2.
Size,of-time-step for computation,
DT
Blank
None
5F10.0 11-20
Allowable error for convergence of
iterative methods in routing routine
(0.0001 recommended).
EPSIL
0.0001
268
6-49
-------
Table 6-6 (continued). Transport Block Card Data
Card Card Variable Default
Group Format Columns Description Name Value
21-30 Total number of days (dry-weather days) DWDAYS 0
prior to simulation during which solids
were not flushed from the sewers.
31-40 Starting time of day of storm, hours TZERO 0
and fraction, e.g., 5:30 PM is 17.5.
Superseded by value on interface file
from previous block, if accessed.
41-50 Kinematic viscosity of water, ft /sec GNU 10 -2
[cm /sec]. Required only if SPG > 1.0 '[10~ ]
for any of pollutants in card group Fl.
51-55 Total catchment area, ac [ha]. Superseded TRIBA 0.0
by value on interface file from previous
block, if accessed.
Execution control data.
D3 2X 1-2 Card identifier = D3. -- Blank
13 5 Control parameter specifying means NCNTRL 0
to be used in transferring inlet
hydrographs.
= 0, Input from a preceding
block, using interface file
(tape/disk) JIN (card input
optional).
= 1, Input from cards only.
315 10 Control parameter in estimating {2 NINFIL 0
groundwater infiltration inflows.
= 0, Infiltration not estimated (INFIL
not called and corresponding data
omitted).
= 1, Infiltration to be estimated (sub-
routine INFIL called).
269 6-5°
-------
Table 6-6 (continued). Transport Block Card Data
Card Card Variable Default
Group Format Columns Description Name Value
15 Control parameter in-estimating sani NFILTH 0
tary sewage inflow. If used with
quality simulation, the first three
pollutants must be BOD,, SS and Total
Colifonns.
= 0, Sewage inflows not estimated
(FILTH not called and correspond-
ing data omitted).
= 1, Sewage inflows to be estimated
(subroutine FILTH called).
20 Control parameter for hydraulic design NDESN 0
routine. (See pp. 6-4, 6-5.)
= 0, Hydraulic design routine is not
called.
= 1, Hydraulic design routine is to
be called.
REPEAT CARD GROUP El FOR EACH
NUMBERED SEWER ELEMENT (maximum
number of elements = 159). THESE
CARDS MAY BE READ IN ANY ORDER.
TERMINATE WITH A BLANK CARD.
Sewer element data.
El 2X 1-2 Card group identifier = El. -- Blank
514 3-6 External element number. No NOE None
element may be labeled with a
number greater than 1000, and
it must be a positive numeral.
However, numbering need not be
consecutive or continuous.
= + number, element,number.
= -1, new ratios, or
= -2, new default values for
values with*.
270
-------
Table 6-6 (continued). Transport Block Card Data
Card Card
Group Format Columns
Description
Variable Default
Name Value
EXTERNAL NUMBER(S) OF UPSTREAM
ELEMENT(S). UP TO THREE ARE
ALLOWED. A ZERO DENOTES NO UP-
STREAM ELEMENT (maximum value =
1000).
7-10 First of three possible upstream NUE(l)
elements.
11-14 Second of three possible upstream NUE(2)
elements.
15-18 Third of three possible elements. NUE(3)
7F8.3 23-30
31-38
*
19-22 Classification of element type. NTYPE
Obtain value from Table 6-1 or 6-3.
THE FOLLOWING VARIABLES ARE DEFINED
BELOW FOR CONDUITS ONLY. REFER TO
TABLE 6-3 FOR REQUIRED INPUT FOR
NON-CONDUITS.
None
None
None
None
Element length for conduit, ft. DIST*
(For manhole: constant inflow
into system, cfs [m /sec).)
First, characteristic dimension of GEOM1*
conduit, ft(ra). See Figure 6-4 and
Table 6-2 for definition.
(For manhole: Constant concentra-
tion of pollutant 1 in the inflow
if simulated. Units according to
NDIM, card group Fl.)
39-46 Invert slope of conduit, ft/100 ft SLOPE*
(i.e., percent).
(For manhole: constant concentration
of pollutant 2 in the inflow, if simu-
lated. Units according to NDIM, card
group Fl.)
47-54 Manning's roughness of conduit. ROUGH*
(For manhole: constant concentra-
tion of pollutant 3 in the inflow,
if simulated. Units according to
NDIM, card group Fl.)
0.0
0.0
0.0
0.0
271
6-52
-------
Table 6-6 (continued). Transport Block Card Data
Card Card ' Variable Default
Group Format Columns Description Name Value
55-62 Second characteristic dimension of GEOM2* None
conduit, ft[m). See Figure 6-4 and
Table 6-2 for definition. (Not
required for some conduit shapes.)
(For manhole: constant concentra-
tion of pollutant 4 in the inflow,
if simulated. Units according to
ND1M, card group Fl.)
63-70 Number of barrels for this element. BARREL* 1.0
The barrels are assumed to be identi- 16
cal in shape and flow characteristics.
(Must be integer £ 1.)
71-78 Third characteristic dimension of GEOM3* None
conduit, ft[m). See Figure 6-4
and Table 6-2 for definition.
(Not required for some conduit
shapes.)
E2 Blank card (except for identifier)
to end card group El. (Test is
whether NOE = 0.)
SKIP TO CARD Gl IF NPOLL = 0 (card Dl)
Quality input data.
READ NPOLL CARDS (NPOLL S 4).
POLLUTANT NUMBERS ASSIGNED BY
THE ORDER OF THESE CARDS.
Fl 2X 1-2 Card group identifier = Fl. ... Blank
13 3-5 Pollutant selector from interface KPOL 0
file. E.g., if KPOL = 7, seventh
constituent on interface file will
be this pollutant. User must know
contents of interface file from run-
ning preceding block. If KPOL = 0,
this pollutant is defined below and
not taken from interface file.
272
6-53
-------
Table 6-6 (continued). Transport Block Card Data
Card
Group Format
2A4
2A4
14
10F5.0
Card
Columns
A A A
6-13
14-21
22-25
26-30
31-35
Description
The following three parameters, ***
PNAME, PUNIT, NDIM not required
if KPOL t 0.
18
Pollutant name.
Pollutant units.
18
Type of units.
= 0, rag/1.
= 1, "other" per liter, e.g., MPN/1.
= 2, other concentration units, e.g.,
pH, JTU.
First order decay coefficient, day.
Specific gravity. If SPG > 1.0,
Variable
Name
PNAME
PUNIT
NDIM
DECAY
SPG
Default
Value
Blank
Blank
0
0.0
0.0
pollutant will be subject to
scour-deposition calculations.
*** The following parameters not ***
required if SPG S l.Q.
Particle size distribution.
First point, PSIZE(l) = 0.0 ram,
PGR(l) = 100.0 is automatically
included. See Figure 6-6.
36-40 Particle size, mm.
41-45 Percent greater than, %.
46-50 Particle size, mm.
51-55 Percent greater than, %.
56-60 Particle size, mm.
61-65 Percent greater than, %.
66-70 Particle size, mm.
71-75 Percent greater than, %.
76-80 Maximum particle size contained
in dry-weather flow input (either
through manholes or using subrou-
tine FILTH). Must be S PSIZE(5).
PSIZE(2)
PGR(2)
PSIZEP)
PGR(3)
PSIZE(4)
PGR(4)
PSIZEC5)
PGR(5)
PSDWF
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
273
6-54
-------
Table 6-6 (continued). Transport Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
Gl
2X
18
CARD GROUPS Gl THROUGH G5 ARE
FOR INTERNAL STORAGE (NTYPE =19).
OMIT IF INTERNAL STORAGE IS NOT
DESIRED AND SKIP TO CARD HI.
REPEAT CARD GROUPS G1-G5 FOR EACH
INTERNAL STORAGE ELEMENT, IS (maxi-
mum of 2).
1-2 Card identifier = Gl.
3-10 Outflow routing parameter.
= 0, The depth-outflow relation-
ship is described by as many as
sixteen data pairs on the G2
cards.
= 1, The depth-outflow relation-
ship is described by a single
power equation on card G3.
= 2, The depth-outflow relation-
ship is governed by two power
equations on card G3.
= 3, The depth-outflow relation-
ship is controlled by the pumps
described on card G4.
LOUT(IS)
Blank
0
Depth-surface area-volume-outflow
data cards. Each card contains a
column for a unit depth and the
corresponding values of area,
volume and treated outflow. The
column for outflow may be left
blank depending on the value of
LOUT(IS) on card Gl. If no
values for volume are entered
the program estimates volume
from the depth-surface area
relationship. Order the cards
from the bottom of the unit
(TSDEP(IS,1) = 0.0) to the
maximum depth (including
as many as sixteen cards.
End the card series with
a blank card.
274
6-55
-------
Table 6-6 (continued). Transport Block Card Data
Card
Group Format
G2 2X
F8.0
4F10.0
G3 2X
F8.0
6F10.0
Card
Columns
1-2
3-10
11-20
21-30
31-40
1-2
3-10
11-20
21-30
***
31-40
41-50
51-60
61-70
Description
Card identifier = G2.
A unit depth, ft [mj .
Surface area corresponding to the
above depth, ft (ra ] .
Volume. corresponding to the above depth,
ft3 In,3].
Outflow at the above depth, ft /sec
[in /sec].
Follow the last card (maximum depth) with
a blank card.
Depth-outflow power equation card.
Required only if LOUT(IS) = 1 or 2
(card Gl). (See Equation 7-3 1
Card identifier = G3.
Depth-outflow equation coefficient,
A,.
Depth-outflow equation minimum flow
depth, DQ.
Depth-outflow equation exponent, A .
The following parameters required ***
only if LOUT(IS) = 2 (card Gl).
Depth-outflow equation coefficient
for second outlet.
Depth-outflow equation minimum flow
depth for second outlet.
Depth-outflow equation exponent
for second outlet.
External element number into which
Variable
Name
--
TSDEP(IS,
TSAREAUS
TSTORE(IS
TSQOU(IS,
--
Aid)
D0(l)
A2(l)
- Al(2)
D0(2)
A2(2)
GEOM3
Default
Value
Blank
MM) None
,MM) None
,MM) None
MM) None
Blank
0.0
0.0
0.0
0.0
0.0
0.0
0.0
flows the outflow from the second
outlet. (Include decimal point.)
275
6-56
-------
Table 6-6 (continued). Transport Block Card Data
Card
Group
Card
Format Columns
Description
Variable
Name
Default
Value
G4
2X
F8.0
4F10.0
1-2
3-10
11-20
21-30
31-40
41-50
Outflow pumping card. Required
only if LOUT(IS) = 3 (card Gl).
Card identifier = G4.
Depth at which pumping rate TQPUMP(IS,I),
ft [m].
Depth at which pumping rate TQPUMP(IS,2)
begins, ft (m). Must be greater than or
equal to TDSTAR(IS.l).
Pumping rate when depth is greater than
or equal to TDSTAR(IS,1), ftj/sec [ra/sec].
Pumping rate when depth is greater than
or equal to TDSTAR(IS,2), ft /sec [m /sec).
Depth below which all pumping stops,
ft [m]. Must be less than or equal to
TDSTAR(IS.l).
the start of the simulation, ft [m ].
The initial pollutant concentration
are required only if STORL(IS) > 0.0.
The concentrations must be given with
dimensions consistent with those en-
tered in card group Fl.
4F10.0 11-20 Concentration of pollutant in the
storage unit at the start of the
simulation. Required only if
NPOLL 2 1.
21-30 Concentration of pollutant 2 in the
storage unit at the start of the
simulation. Required only if NPOLL
S 2.
Blank
TDSTAR(IS.l) None
TDSTARUS.2) None
TQPUMP(IS.l) None
TQPUMP(IS,2) None
TDSTOP(IS) None
Initial conditions in internal storage
element IS.
G5 2X 1-2 Card identifier = G5 .
F8.0 3-10 Total volume of water in unit at
Blank
STORL(IS) 0.0
PTCO(IS.l) 0.0
PTCO(IS',2) 0.0
276
6-57
-------
Table 6-6 (continued). Transport Block Card Data
Card Card
Group Format Columns
Description
Variable Default
Name Value
31-40 Concentration of pollutant 3 in the
storage unit at the "start of the
simulation. Required only if NPOLL
£ 3.
41-50 Concentration of pollutant 4 in the
storage unit at the start of the
simulation. Required only if NPOLL
= 4.
PTCO(IS,3) 0.0
PTCO(IS,4) 0.0
HI
2X
13
1515
1-2
3-5
6-10
SKIP TO CARD II IF NOUTS = 0 ON CARD Dl.
List of external non-conduit element
numbers at which outflows are to be
transferred to subsequent blocks for
a total of.NOUTS (Card Dl) non-conduit
elements.
Card identifier = HI.
First element number.
Second element number.
21
Blank
JN(1) None
JN(2) None
Last element number.
JN(NOUTS) None
II
2X
1-2
SKIP TO CARD Jl IF NINPUT = 0 (card Dl).
Non-conduit element numbers into
which hydrographs and pollutographs
(from card input using card group Rl)
enter the sewer system. These must be
in the order in which hydrograph and
pollutograph ordinates appear at each
time step2Q A total of NINPUT values
required.
Card identifier = II.
Blank
277
6-58
-------
Table 6-6 (continued). Transport Block Card Data
Card
Group
Format
13
1515
Card
Columns
3-5
6-10
Description
First element number.
Second element number.
Variable
Name
NORDER(l)
NORDER(2)
Default
Value
None
None
Last element number.
NORDER(NINPUT)None
Jl
2X
13
1515
1-2
3-5
6-10
SKIP TO CARD J2 IF NNYN = 0 (card Dl).
List of external non-conduit element
numbers at which input hydrographs
and pollutographs are to be stored
and printed out for a total of tflJYN
(card Dl) non-conduit elements.
Card identifier = Jl.
First input location number.
Second input location number.
NYN(l)
NYN(2)
Blank
None
None
Last input location number.
NYN(NNYN)
None
J2
2X
13
1-2
3-5
SKIP TO CARD Kl IF NNPE = 0 (card Dl).
List of external non-conduit element
numbers at which output hydrographs
and pollutographs are to be stored and
printed out for a total of NNPE (card
Dl) non-conduit elements.
Card identifier = J2.
First output location number.
NPE(l)
Blank
None
278
6-59
-------
Table 6-6 (continued). Transport Block Card Data
Card
Group Format
1515
Kl 2X
F8.0
7F10.0
K2 2X
13
Card
Columns
6-10
1-2
3-10
11-20
21-30
31-40
41-50
51-60
61-70
71-80
1-2
3-5
Description
Second output location number.
Last output location number.
IF SUBROUTINE INFIL IS TO BE CALLED
(NINFIL = 1), INSERT CARDS Kl THROUGH
K2, OTHERWISE OMIT.
Estimated infiltration.
Card identifier = Kl.
Base dry weather infiltration, cfs,
[m /sec).
Grqundwater infiltration, cfs
[n,3/sec].
Rainwater infiltration, cfs (ra /sec].
Peak residual moisture, cfs [m /sec].
Constant concentrations of pollutants
in infiltration. Not required if NPOLL
= 0. Units of each according to NDIM,
card group Fl.
Concentration of pollutant 1.
Concentration of polutant 2.
Concentration of pollutant 3.
Concentration of pollutant 4.
22
Monthly degree-days.
Card identifier = K2.
July degree-days.
Variable Default
Name Value
NPE(2) None
NPE(NNPE) None
Blank
DINFIL 0.0
GINFIL 0.0
RINFIL 0.0
RSMAX 0.0
CPINF(l) 0.0
CPINF(2) 0.0
CPINF(3) 0.0
CPINF(4) 0.0
Blank
NDD(l) 0.0
279
6-60
-------
Table 6-6 (continued). Transport Block Card Data
Card Card
Group Format Columns
Description
Variable Default
Name Value
1115
6-10
August degree-days.
NDD(2)
0.0
56-60
June degree-days.
NDD(12)
0.0
LI
2X
F8.0
6F10.0
1-2
3-10
11-20
IF SUBROUTINE FILTH IS TO BE CALLED
(NFILTH =1), INSERT CARD GROUPS LI
THROUGH Ql, OTHERWISE OMIT.
Factors to correct yearly average
sewage flows to daily average by
accounting for daily variations
throughout a typical week.
Card identifier = LI.
Flow correction for Sunday.
Flow correction for Monday.
DVDWF(l)
DVDWF(2)
Blank
1.0
1.0
61-70
Flow correction for Saturday.
DVDWF(7)
1.0
L2
2X
F8.0
6F10.0
1-2
3-10
IF NPOLL = 0 SKIP TO CARD GROUP Ml.
(NOTE:. IF POLLUTANTS ARE SIMULATED
AND FILTH IS CALLED, FIRST THREE
POLLUTANTS MUST BE BOD , SUSPENDED
SOLIDS AND TOTAL COLIFORMS.
Factors to correct BOD yearly averages
to daily averages.
Card identifier = L2.
BOD correction for Sunday.
DVBOD(l)
Blank
1.0
61-70
BOD correction for Saturday.
DVBOD(7)
1.0
280
6-61
-------
Table 6-6 (continued). Transport Block Card Data
Card
Group Format
L3 2X
F8.0
6F10.0
Ml 2X
F8.0
7F10.0
Card
Columns Description
Factors for correction of yearly SS
averages to daily averages.
1-2 Card identifier = L3.
3-10 SS correction for Sunday.
61-70 SS correction for Saturday.
(No daily correction factors for coliforms)
Factors to correct daily average
sewage flow to hourly averages by
accounting for hourly variations
throughout a typical day (3 cards
needed) .
1-2 Card identifier = Ml.
3-10 Midnight to 1 a.m. factor (first card).
Variable Default
Name Value
Blank
DVSS(l) 1.0
DVSS(7) 1.0
Blank
HVDWF(l) t.O
1-10
8 a.m. to 9 a.m. factor (second card).
HVDWF(9)
1.0
1-10 4 p.m. to 5 p.m. factor (third card).
HVDWF(17)
1.0
M2
2X
1-2
IF NPOLL = 0 SKIP TO CARD GROUP Nl.
Factors for BOD hourly corrections
(3 cards needed).
Card group identifier = M2.
Blank
281
6-62
-------
Table 6-6 (continued). Transport Block Card Data
Card Card
Group Format Columns
F8.0 3-10
7F10.0
71-80
M3 2X 1-2
F8.0 3-10
7F10.0
71-80
M4 2X 1-2
F8.0 3-10
7F10.0
71-80
Description
Midnight to 1 a.m. factor (first card).
11 p.m. to midnight factor (third card).
Factors for SS hourly corrections
(3 cards needed).
Card group identifier = M3.
Midnight to 1 a.m. factor (first card).
11 p.m. to midnight factor (third card).
Factors for total coliforra hourly
corrections (3 cards needed).
Card group identifier = M4.
Midnight to 1 a.m. factor (first card).
11 p.m. to midnight factor (third card).
Variable Default
Name Value
HVBOD(l) 1.0
HVBOD(24) 1.0
Blank
HVSS(l) 1.0
HVSS(24) 1.0
Blank
HVCOLI(l) t.O
HVCOLI(24) 1.0
Study area data.
Nl 2X 1-2 Card group identifier = Nl.
13- 3-5 Total number of subareas within a given
study area in which sewage flow and
quality are to be estimated.
315 6-10 Indicator as to whether study area data,
such as treatment plant records, are to
be used to estimate sewage quality, i.e.
= I, Yes.
= 2, No.
Blank
KTNUM None
KASE 1
282
6-63
-------
Table 6-6 (continued). Transport Block Card Data
Card Card
Group Format Columns
Description
Variable Default
Name Value
11-15 Total number of process flows within NPF
the study area for which data are
included in one of the following card
groups.
16-20 Number indicating the day of the week KDAY
during which simulation begins (Sunday
= 1).
2F5.0 21-25
26-30
F10.0 31-40
01 2X 1-2
F8.0 3-10
Consumer Price Index.
Composite Construction Cost Index.
Total population in all areas,
thousands .
IF KASE = 1, INCLUDE CARD GROUPS
01, 02 AND PI.
24
Average study area data.
Card identifier = 01.
Total study area average sewage flow,
CPI 109.5
CCCI 103.0
POPULA None
Blank
ADWF 0.0
01
02
2X
F8.0
2F10.0
E10.2
2X
F8.0
1-2
3-10
11-20
21-30
31-40
1-2
3-10
Card identifier = 01.
Total study area average sewage flow, ADWF
e.g., from treatment plant records,
cfs [ra /sec].
Total study area average BOD, mg/1. ABOD
Total study area average SS, mg/1. ASUSO
Study area average total coliforms, ACOLI
MPN/100 ml.
Categorized study area data.
Card group identifier =02.
Total study area from which ABOD and TOTA
Blank
0.0
0.0
0.0
0.0
Blank
None
7F10.0 11-20
ASUSO were taken, acres [ha].
Total contributing industrial area,
acres [ha].
TINA
None
283
6-64
-------
Table 6-6 (continued). Transport Block Card Data
Card Card
Group Format Columns
Description
Variable Default
Name Value
21-30 Total contributing commercial area, TCA None
acres (ha).
*** Valuations for the following three ***
parameters are for 1963 dollars!
31-40 Total contributing high income (above TRHA None
$15,000) residential area, acres [ha].
4t-50 Total contributing average income TRAA None
(above $7,000 but below $15,000)
residential area, acres (ha].
51-60 Total contributing low income (below TRLA None
$4,000) residential area, acres [ha].
61-70 Total area from the above three resi- TRGGA None
dential areas that contribute addi-
tional waste from garbage grinders,
acres [ha].
71-80 Total park and open area within the TPOA None
study area, acres [ha].
IF PROCESS FLOW DATA ARE AVAILABLE
(NPF NOT EQUAL 0 AND KASE = 1), RE-
PEAT CARD GROUP PI FOR EACH PROCESS
FLOW (NPF cards). OTHERWISE, SKIP
TO CARD GROUP Ql.
Process flow characteristics.
PI 2X 1-2 Card group identifier = PI.
13 3-5 External manhole number into which INPUT
flow is assumed to enter (maximum
value = 1000, minimum value = 1).
3F10.3 6-15 Average daily process flow entering 2_ QPF
the study area system, cfs (m /sec].
16-25 Average daily BOD of process flow, BODPF
mg/1.
26-35 Average daily SS of process flow, SUSPF
mg/1.
Blank
None
None
0.0
O.U
284
6-65
-------
Table 6-6 (continued). Transport Block Card Data
Card
Group
Card
Format Columns
Description
Variable
Name
Default
Value
REPEAT CARD GROUP Ql FOR EACH OF THE
KTNUM SUBAREAS. THESE SUBAREAS DO
NOT NECESSARILY HAVE TO CORRESPOND TO
RUNOFF SUBCATCHMENTS.
Subarea data.
Ql 2X 1-2 Card group identifier = Ql.
13 3-5 Subarea number. KNUM
15 6-10 External number of the manhole into INPUT
which flow is assumed to enter for
subareas KNUM (maximum value = 1000,
minimum value = 1).
12 11-12 Predominant land use within subarea. KLAND
= 1, Single-family residential.
= 2, Multi-family residential.
= 3, Commercial.
= 4, Industrial.
= 5, Undeveloped or park lands.
311 13 Parameter indicating whether or not METHOD
water-usage within subarea KNUM is
metered.
= 1, Metered water use.
= 2, Incomplete or no metering.
14 Parameter indicating units in which KUNIT
water usage estimates (WATER) are
tabulated.
= 0, thousand gal/mo [10 ra /mo).
= 1, thousand ft /mo [10 m /moj.
Blank
None
None
285
6-66
-------
Table 6-6 (continued). Transport Block Card Data
Card Card
Group Format Columns
Description
Variable
Name
Default
Value
13F5.1
15 Subtotals printed after each subarea
input?
= 0, No.
= 1, Yes.
*** Several of the following parameters ***
are optional. See text.
Measured winter water use for sub-
MSUBT
16-20
21-25
26-30
31-35
36-40
41-45
46-50
51-55
56-60
61-65
area KNUM.in the units specified
by KUNIT. 3
Cost of the last thousand gal (10 ra ] of
water per billing period for an aver-
age consumer within subarea KNUM,
cents/1,000 gal [cents/10 m ].
Measured average sewage flow from
entire subarea KNUM, cfs [m /sec],
but not including process flows (SAQPF).
Population density within subarea
KNUM, persons/acre [pers/haj.
Total number of dwelling units within
subarea KNUM.
Number of people living in average_7
dwelling unit within subarea KNUM.
Market value of average dwelling unit
within subarea KNUM, thousands of
dollars.
Percentage of dwelling units pos-
sessing garbage grinders within sub-
area KNUM.
Income of average family living within
subarea KNUM, thousands of dollars per
year.
WATER
PRICE
SEWAGE
Total area within subarea KNUM, acres |ha|. ASUB
*** The next six parameters are not re- ***
required if KLAND > 2.
POPDEN
DWLNGS
FAMILY
VALUE
PCGG
XIHCOH
None
None
None
None
None
10.0/ac.
3.0
20.0
0.0
VALUE/2.5
286
6-67
-------
Table 6-6 (continued). Transport Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
66-70
71-75
76-80
Total industrial process flow origin- .. SAQPF
ating within subarea KNUM, cfs (m /sec).
BOD contributed from industrial process SABPF
flow originating within subarea KNUM,
rag/1.
SS contributed from industrial process SASPF
flow originating within subarea KNUM,
rag/1.
END OF FILTH DATA CARDS.
0.0
0.0
0.0
Include card group Rl only if NINPUT
# 0 (Card Dl).
REPEAT CARD Rl FOR EACH INLET FOR
FIRST TIME AND THEN REPEAT CARD Rl
FOR EACH INLET FOR SECOND TIME,
ETC. REPEAT THIS COMBINATION FOR
ALL INPUT TIMES. ORDER OF INLET
CARDS MUST BE THE SAME AS INDICATED
IN CARD GROUP II.
Hydrograph and pollutograph input
ordinates.
Rl 2X 1-2 Card group identifier = Rl
F8.0 3-10 Time of day, decimal hours, e.g.,
6:30 p.m. = 18.5. The first time
must equal time TZERO. The pro-
gram will automatically set TEO =
TZERO.for entries for the first
time.
TE2
Blank
0.0
or TZERO
5F10.0 11-20
21-30
31-40
Input flow for this.time step at
first inlet, cfs (m /sec).
Pollutant 1 for this time at
first inlet, concentration
according to NDIM (card Fl).
Pollutant 2 for this time at
first inlet, concentration
according to NDIM (card Fl).
QE2
PE2(l)
PE2(2)
0.0
0.0
0.0
287
6-68
-------
Table 6-6 (continued). Transport Block Card Data
Card Card . Variable Default
Group Format Columns Description Name Value '
41-50 Pollutant 3 for this time at first PE2(3) 0.0
inlet, concentration according to
NDIH (card Fl).
51-60 Pollutant I* for this time at PE2(4) 0.0
first inlet, concentration
according to NDIH (card Fl).
END OF TRANSPORT BLOCK DATA CARDS.
At this point the program seeks new
input from the Executive Block.
288 6-69
-------
Footnotes to Table 6-6
1. A/A,. = ANORM is the cross-sectional flow area divided by the cross-
sectional flow area of the pipe running full. Tabular values of ANORM
are generated in the program by dividing the ANORM axis (0.0 1.0) into
NN1 or MM1 equal divisions.
2. Q/Q, = QNORM is the flow rate divided by the flow rate of the conduit
flowing full.
3. y/y, = DNORM is the depth of flow, y, divided by the maximum flow
depth, y. (e.g., diameter of a circular conduit).
4. Repeated cards have same format, i.e., 2X, F8.0, 7F10.0.
5. The title from the first of any preceding blocks run will also be
printed.
6. The Transport Block time step doer: not have to equal that of a preced-
ing block. The total simulation time is NDT DT. If this is greater
than that of an input file, the simulation will end earlier. Although
there is no limit on the simulation time (number of time steps), output
is geared toward single events only. That is, daily or monthly totals
are not printed, and zeroes are not suppressed. Hence, output is
inconvenient for continuous simulation, although the Transport Block
can be run that way.
7. Inlet hydrcgraphs from a preceding block will automatically be accepted,
but a match must be found for each inlet (element) number on the inter-
face file with an element number tmtered in Transport.
8. These locations should include any elements for which graphical output
is desired since only locations ou the interface file may be plotted
using the Executive Block.
9. Except in unusual cases, these errors will only indicate that a small
continuity violation will occur. These errors can usually be cured by
shortening the time step or increasing the length of the conduit.
10. If the time step for Transport is different than that for a preceding
block, input hydrograph and pollut.ograph ordinates will be found by
linear interpolation at the required time.
11. If both card and interface file input is used, hydrographs and polluto-
graph loads entered at common locations will be summed. Transport can
be run without card hydrograph input if it were desired to route only
dry-weather flows (base flows).
12. Constant base flows and concentrations may be entered at manhole ele-
ments (Type 16) in card group El if desired, thus eliminating the need
to call FILTH or INFIL. They may still be called if desired;
289 6_70
-------
infiltration, dry-weather flow and base flow will be summed for entry
at non-conduits. Base flows and concentrations entered in card group
El will not be subject to daily and hourly correction factors.
13. "External" numbers are those assigned by the user to the various sewer
system components. "Internal" numbers are assigned within the program
in the order in which elements in card group El are read in. All input
to the Transport Model is in term;; of external numbers.
14. Input values on this card indicated with asterisks are multiplied by
ratios, initially set equal to 1.0. If the element number = 1, non-
zero data entries for parameters with asterisks will replace old values
of the ratios. Ratios may be altered or reset to 1.0 any number of
times. Th«; intention of the use of ratios is to simplify sensitivity
analyses, etc., by allowing easy changes of data values without
repunching data cards. The altered ratios apply to all subsequent
input cards (until changed by another ratio card).
15. Input parameters on this card indicated with asterisks will take on
default values if input values are zero. If the element number'= 2,
non-zero data entries for parameters with asterisks will become new
default values for all future entries of these parameters. Default
values may be altered or reset to their original values (except zero)
any number of times. It is not possible to reset a default value
exactly to zero since only non-zero values are changed. However, the
value may be made arbitrarily small by using E-format data entries.
For example, 0.10E-50 will be read as 10 in an F8.3 format. The
altered default values apply to all subsequent input cards (until
changed by another default value card).
16. For example, a two-barrelled conduit would consist of two identical
parallel conduits adjacent to each other, as in a double box culvert.
17. If NFILTH =; 1 (card D3) it will be assumed that the first pollutant is
BOD , the second is suspended solids and the third is total coliforms.
Hence, the selection indicated by NPOL must be in this order. The
fourth pollutant, if simulated, ii: unaffected by the value of NFILTH.
18. See the discussion in the Runoff Block.
19. Up to the five available points (including 0.0 mm, 100%) may be used to
define the particle size distribution. For instance, if a triangular
distribution were satisfactory with 0.1 mm being the largest particle
size, then PSIZE(2) = 0.1, PGR(2) = 0.0 would be the only entries
needed.
20. May require multiple cards.
21. Be careful in subsequent blocks to ensure that element numbers will
correspond to those transferred in interface file. However, any ele-
ment number may be placed on the interface file, e.g., for plotting
purposes.
290 6_71
-------
22. See Table YIII-1, Appendix VIII, for representative values. Degree-
days are not needed if RSMAX = 0.0 on card Kl, (but a blank card must
still be included for card K2). Degree days are in °F-day and there is
no metric input for this parameter.
23. When subroutine FILTH is used to generate dry-weather flow and pollu-
tants, the only pollutants included are BOD., suspended solids (SS) and
total colii'orms (T.C.). NPOLL must be 3 or" 4, and the three pollutants
entered in the given order in card group Fl. The fourth pollutant is
arbitrary (if used) and is not affected by FILTH calculations.
24. Predicted total average daily dry-weather flow from downstream end(s)
of system will be adjusted to these values.
25. If process flows are too large it is possible for ADWF - infiltration -
IQPF < 0 in which case the program defaults to KASE = 2.
26. Either SEWAGE or else WATER is required to generate dry-weather flow
from commercial areas. If both are zero, only process flows will be
considered. SEWAGE and WATER should = 0 for industrial land use; foi-
KLAND = 4, let entire flow be process flow.
27. DWLNGS and FAMILY can be used to calculate the population and then the
population density. If both are j'iven, the value of POPDEN will be
overridden.
28. These industrial process flows described by SAQPF, SABPF and SASPF will
be added to flows and quality already generated.
29. The program will interpolate linearly between entries of TE2 to obtain
intermediate values of flow and concentrations. Hence, the difference
between two time entries, TE2, should not be less than the time step,
DT, unless a step function change is desired. Time entries need not be
equally spaced, but the last time entered must extend past the end time
of the run. Time TZERO is read from the interface file or else entered
on card D2. If simulation extends beyond midnight (i.e., TE2 > 24),
continue the running time into the next day or days (i.e., let TE2 be
greater thzm 24). Note, the time TE2 must be the same for all inlets.
(The program will use the value for TE2 entered on the last inlet card.)
291 6-72
-------
SECTION 7
STORAGE/TREATMENT BLOCK
BLOCK DESCRIPTION
Introduction
The Storage/Treatment Block has been developed to simulate the routing
of flows and pollutants through a dry- or wet-weather storage/treatment
plant containing up to five units or processes. The model will accept any
number of time steps; therefore, a single-event or a continuous simulation
is possible. Each unit may be modeled as having delenlion or non-detention
characteristics. The various units may be linked in a variety of configur-
ations. Sludge handling may also be modeled using one or more units. Addi-
tionally, capital cost and operation and maintenance cost may be estimated
for each unit.
The S/T Block will route, in addition to flow, up to three different
pollutants. Th«:se pollutants may be input to the block from any external
block via off-line storage, directly from cards, or a combination of both.
Characterization of the pollutants may be by magnitude (i.e., concentration)
or by magnitude and a particle size/specific gravity or settling velocity
distribution. All input flows and pollutant concentrations are assumed to
be instantanous values. However, the instantaneous values at the beginning
and end of each time are used to compute average values for each time step.
Thus, the user is cautioned that the output from the S/T Block consist of
average values, not instantaneous values as in the rest of SWMM.
This section describes the program operations of the S/T Block, pro-
vides instructions for preparing input data cards, defines program .vari-
ables, and presents test applications. The user is referred to Appendix IV
for theoretical development and explanations.
Program Operation
The Storage/Treatment Block is a FORTRAN program of approximately 2000
statements in length and consists of eight subroutines. The relationships
among the S/T Block, the rest of SWMM and the various subroutines are shown
in Figure 7-1.
The subroutine STRT is called by the Executive Block to initiate the
operation of the: S/T Block. STRT provides the main driving loop for the
block and generally acts as the central coordinating subroutine. Input flow
and pollutants from any external block are read in this subroutine. Output
292 _
-------
EXECUTIVE
BLOCK
STRT
CONTRL
STRDAT
STCOST
Figure 7-1. Storage/Treatment Block.
I
NJ
-------
from the S/T Block transferred to other blocks is also handled by STRT
(through the Executive Block). The information transferred to other blocks
is discussed in the next paragraph. Subroutine STRDAT is called in STRT at
the start of a run and is responsible for reading the input data describing
the units, their configuration, the pollutant removal mechanisms, the method
of characterizing pollutants and the remainder of the data provided by the
user (excluding flow and pollutant inputs). STRDAT also prints the input
data for verification. Subroutine CONTRL is called each time-step from the
main driving loop in subroutine STRT. CONTRL directs flow and pollutants
between units and any subsequent block. CONTRL also coordinates the account-
ing and printout of detailed and summarized performance information. Flow
and pollutant inputs from cards are also read in this subroutine. Sub-
routine UNIT is called from subroutine CONTRL for each unit and is the heart
of the S/T Block. UNIT has the flexibility and capability to model deten-
tion and non-detention units with a variety of pollutant removal mechanisms,
residual removal schemes and outflow structures. Subroutine EQUATE is used
by UNIT to provide a variety of pollutant removal equations. Subroutine
INTERP is employed by UNIT for linear interpolation in routing flows through
detention units. Subroutine PLUGS is used by UNIT to model pollutant rout-
ing through a detention unit when perfect plug flow is specified. "Subroutine
STCOST is called from STRT to estimate the capital cost and operation and
maintenance cost for each unit.
Use of Off-line Computer Storage
No scratch data sets are required to run the Storage/Treatment Block.
However, disks or tapes may be used as an input source (from an external
block) or to transfer output to other blocks. This interfacing file con-
sists of descriptive titles, user-supplied pollutant names and dimensions,
the simulation starting date and time, the name of the external block gener-
ating the output (input) file, the number of time steps and time step size,
the total catchment or tributary area, the number of elements (inlets,
outfalls, nonconduits, etc.) and pollutants found on the output (input)
file, and the elements for which flow and pollutant data are placed (read
from) the output (input) file. This preliminary information is followed by
the flow and pollutant data for each time step (up to the total number of
time steps) for each of the specified elements. The user should refer to
Section 2 for a detailed description of the interfacing file.
Flow and up to three pollutants are transferred unchanged from an input
file to an output file if the data are from elements or are pollutants not
selected for use in the S/T Block. In other words, the flow and pollutant
data from an input file (external block) are altered only if they pass
through a storage/ treatment facility. However, the dimensions of the data
on the input file will be made to conform to the standard SWMM dimensions
before being placed on an output file (see Section 2). The methods of
selecting these data from particular external element numbers and processing
specific pollutants is discussed in the next subsection.
294
-------
INSTRUCTIONS FOJv' DATA PREPARATION
The Storage/Treatment Block is a user-intensive model, i.e., the user
should have a thorough knowledge of what he/she is modeling. This is an
obvious, but oft.en overlooked, axiom. This is especially true when a model
provides "default" values. The user is; herein required to describe each unit
in some detail with data of his own choosing. Storage/treatment performance
depends 011 local pollutant characteristics, flow rates, etc.; thus, the user
is encouraged to use local operating data, whenever possible, to aid in the
modeling effort.
Instructions for preparing the input data cards are presented below
with suggestions and examples. All entries not described in the text are
considered to be; self-explanatory or covered sufficiently in Table 7-3. The
user is referred to this table for input format and order. All card groups
are required unless otherwise stated. Figure 7-2 shows the general structure
of the data deck.
Preliminary Information
Title
Card Group Al -- This card group allows the user to print a descriptive
heading at the start of the printed output. The heading is also transferred
to the next block. Two cards are required.
General Informat.ion --
Card Group Bl -- The variable NOTAPE allows the user to specify the source
of flow and pollutants used for input to the S/T Block. If NOTAPE = 0, the
input is provided by an external input file (arranged by the Executive
Block). When NOTAPE = 1, the input is provided by card group Jl. If NOTAPE
= 2, the input is the sum of the entries for each time step from the external
file and card group Jl. The parameter JNS selects the external element
number (from an external file, NOTAPE " 0 or 2) from which flows and pollu-
tants will be taken and passed through the S/T Block. If the input is
provided solely by card group Jl (NOTAPE = 1 or 2), the user may label the
output of the S/T Block with an element number. When NOTAPE = 0 or 2, the
variables NDT and DS are read from the external input file. However, the
value of NDT from an external block may be altered by specifying a non-zero
value on card Bl. There is no limit on the number of time steps. This is
useful for extending the simulation beyond the limit of the external block
or input file. The value of DS from an external block will supersede any
value for DS entered on card Bl.
The variable NU specifies the number of storage/treatment units, in-
cluding residuals (sludge) handling units, to be modeled and the number of
times that card groups Fl through II are repeated. The model, as written,
is limited to five units. However, a J:ew program modifications can increase
this limit (see later discussion). The variable NP specifies the number of
pollutants to be routed through the storage/treatment system. If NP = 0,
then only flows are routed. A maximum of three pollutants may be selected
295 7_A
-------
( CARD GROUP J I, FLOW a POLLUTANT DATA
f CARD GROUP II, COST DATA
/T -. '.".-. - -.. -."--' - .. .^-TT^ = ,-_ --_ -~'-^/-, "."V..,..>_ .T_ __.-"_ '"---i ...
':: -.- _.. __ _ _j - ~~: -] -^-^^iT -~~-~~ _~ ~ - - : _ - -- - =- ..~ - ~
CARD GROUPS HI-H8, DETENTION UNIT DATA
fCARD GROUPS GI-G4 POLLUTANT REMOVAL
( CARD GROUPS FI-F2, GENERAL UNIT CHARACTERISTICS
fCARD GROUP D I, EVAPORATION DATA =
{ CARD GROUPS C I - C 2 , STARTING TIME Q PRINT INSTR.
( CARD GROUP B I. GENERAL DATA
f CARD GROUP AI , TITLE
( STORAGE
Figure 7-2. Data Deck for the Storage/Treatment Block
-------
for routing. The specific pollutants are selected on card El. However, NP
may not be greater than the number of pollutants transferred from other
blocks (NOTAPE - 0 or 2).
If ICOST = 1, the cost model is called and the capital and operation
and maintenance costs are computed for each unit. Cost equation parameters
are entered on card group Jl. The value of METRIC determines whether U.S.
customary or metric units aire used for all card input and the resulting
output. The value of TRIBA is the service area of the S/T plant. It does
not enter into the computations but is transferred to the next block.
Starting Time and Print Instructions -
Card Group Cl The values of IDATE and ITIME are used to start the date/
time algorithm of the S/T Block. The date (year, month, and day) and time
(hour, minutes, and seconds) are updated for each time step. If NOTAPE = 0
or. 2 (card Bl) the values of IDATE and ITIME read from the external file
supersede the values entered on card Cl.
The nspi" i^ cautioned against printing large quantities of unwanted
information. The first runs should have as little printout as possible
(ISUM = 0 and IDET = 0) to check for obvious errors in the input data. As
simulation efforts proceed, more detailed printouts may be desired.
Card Group C2 -- These cards (up to two) are used to enter the first and
last dates of the detailed print periods specified by NPR (card Cl).
Evaporation Data --
Card Group Dl -- Monthly evaporation rates are required to correct for evap-
oration from detention units. However, two cards must be included even if
there are no det.ention units. If needed, values of E(l) through E(12) are
entered for the months during which the simulation occurs; others may be
left blank.
Pollutant Characterizations
Card groups El through E6 are omitted if NP = 0 (card Bl).
Card Group El -- These cards (up to two) allow the user to select the NP
(card Bl) pollutants to be routed through the S/T Block. The variables
IPOLL(l), IPOLL(2), and IPOLL(3) are used to select pollutants from an ex-
ternal input file (NOTAPE = 0 or 2, card Bl). For example, if IPOLL(l) = 5,
then the first pollutant routed through the S/T Block is the fifth pollutant
found on the input file. If data from card group Jl are to be added to the
external input file data (NOTAPE = 2, card Bl), the pollutants entered on
that card group must be in the same order as specified by IPOLL(l), IPOLL(2),
and IPOLL(3).
Pollutants may be characterized by their magnitude alone (concentration)
or by their magnitude and a particle size/specific gravity or settling
velocity distribution by specifying IPART(IP) = 0 or 1, respectively. If
297
7-6
-------
IPART(IP) = 1, the user is required to enter these distributions on card
groups E2 through E6. Using these distributions, however, limits the type
of unit that may be modeled (discussed in later card groups).
The variables NDIMl(l), NDIM1(2), and NDIM1(3) are used to describe the
dimensions of the pollutant data on card group Jl when NOTAPE = 1. When
NOTAPE = 0 or 2 (card Bl) this information is provided by the external input
file. For example, if NDIMl(l) = 0, then the first pollutant has dimensions..
of mg/1. If NDIMl(l) = 1, then the first pollutant has dimensions of liter
This is used when pollutants such as coliforms are routed. When NDIMl(l) =
2, pollutant 1 has other concentration dimensions such as JTU, pmho, °C, pH,
etc.
The entry of pollutant names PNAME1(IN,1), PNAME1(IN,2), and PNAME1(IN,3)
and dimension names PUNIT1(IN,1), PUNIT1(IN,2), and PUNIT1(IN,3) is required
only when the source of flow and pollutant data is solely card group Jl
(NOTAPE = 1, card Bl). If flow and pollutant data are read from an external
input file (NOTAPE = 0 or 2, card Bl) this information is already provided.
Naturally, if NP = 2 (card Bl), for example, then the values for IPOLL
(3), IPART(3), NDIM1(3), PNAME1(IN,3), and PUNIT1(IN,3) are not required
under any circumstances. The order in which this pollutant information is
entered determines the numbering of the pollutants.
Card groups E2 through E6 are required only if IPART(IP) = 1 for any
pollutant. The user is referred to Appendix IV for a more detailed discus-
sion of this form of pollutant characterization.
Card Group E2 -- The variable NVS specifies the manner in which the particles
in the waste stream are classified. If NVS = 0, the particles are classi-
fied by size and specific gravity. The variable NNR specifies the number of
size/specific gravity ranges used to delineate the distribution (up to a
maximum of 10 ranges). The size/specific gravity classification remains
constant throughout the simulation for each pollutant characterized in this
manner. The size and specific gravity ranges are established in card groups
E3 and E4. These cards and card E5 (which enters waste stream temperature
data) provide the information with which an average settling velocity is
computed for each range (see Appendix IV for details). If NVS = 1, the
particles are classified by NNR settling velocity ranges specified on card
group E3. The average settling velocity for each size range is the average
of the range endpoints. Cards E4 and E5 are not required. As with the
size/specific gravity ranges, this classification remains constant throughout
the simulation for each pollutant characterized by settling velocity.
Obviously, a particle size or settling velocity distribution may change as
it passes through the storage/treatment plant. This is accomplished by
altering the pollutant fractions associated with the various size/specific
gravity or settling velocity ranges as they pass through the units. The
initial pollutant distributions are entered on the E6 cards. These distri-
butions remain constant, however, at the input to the S/T plant.
The results of a literature review to characterize the pollutants in
sanitary sewage, combined sewer overflows, and urban runoff by particle size
298 7-7
-------
and specific gravity are shown in Table 7-1. The data presented in Table
7-1 are not default values and are presented only as a guide in setting
up card groups E2 through E6. Local data, through sieve and/or settling
column analyses, are always preferred.
Card Group E3 -- These cards (up to two) are used to enter particle size (if
NVS = 0, card E2) or settling velocity (if NVS= 1, card E2) ranges. A maxi-
mum of ten ranges (as indicated by NNR, card E2) may be entered. The vari-
ables RAN(1,1) and RAN(1,2) through RAN(NNR,1) and RAN (NNR,2) represent the
lower and upper bounds of the diameters or settling velocities of particles
found in ranges 1 through NNR. The ranges entered on this card remain
constant through a simulation. When NVS = 0 (card E2), corresponding con-
stant values of specific gravity are read on card E4.
Card Group E4 These cards (up to two) are required only if NVS = 0(card
E2). The variables SPG(l) through SPG(NNR) represent the specific gravity
of the particle:? found in size ranges 1 through NNR(card E2). Literature
values are shown in Table 7-1.
Card Group E5 -- These cards (two) are required only if NVS = 0(cf»rd E2).
the variables TEMP(l) through TEMP(12) represent the average wastewater
temperatures for each month of the year. Water temperature has a direct ef-
fect on the settling velocity of a particle. This is reflected through the
viscosity of the wastewater which is a function of temperature. The user is
referred to Appendix IV for details.
Card Group E6 -- A set (consisting of up to two cards) of E6 cards is
required for each pollutant characterized by a particle size/specific grav-
ity or settling velocity distribution (IPART(IP) = 1, card El). For example,
if IPART(l) = l(card El) the variables PSD(1,1) through PSD(1, NNR) represent
the fraction of pollutant 1 found in the particle size/ specific gravity or
settling velocity ranges 1 through NNR entering unit 1 (all flows and pollu-
tants must enter the S/T plant at unit 1). Naturally, these fractions
should sum to 1.0. Table 7-1 contains some literature values for several
pollutants (particle size/specific gravity distributions only).
The distribution entering the S/T plant remains constant throughout the
simulation; however, it may change as the pollutants move through the var-
ious units. This is an approximation of the more probable situation in
which the plant influent distributions change with time. This limitation
was necessary due to the fact that no other SWMM blocks generate such dis-
tributions for input to the S/T Block. However, the S/T Block can be easily
modified to accept time-varying distributions should such a capability be
developed in the future.
Storage/Treatment Unit Information
Card groups Fl through II are repeated for each storage/treatment unit
(up to NU, card Bl). The unit number, I, is determined by the order in
which each set of card groups Fl through II appears. This numbering scheme
is important to the permissible configurations of units. There are two
rules for numbering units; 1) flows and pollutant exiting from one unit must
299
7-8
-------
Table 7-1. Particle Size Distributions.
SANITARY SEWAGE
Particle Size,
Microns
< 0.001
C.001-1
1-100
> 100
Total Solids
(1) (2) (3) (4)
69
6
11
14
68
6
11
15
64
7
11
18
64
7
12
17
64
7
29
Percent Weighted in Each Size Range
(5)
Volatile Solids
(1} (2) (3) (A)
50
9
18
23
42
9
13
25
41
10
19
30
31
14
24
31 .
(5)
46
11
43
Organic Nitrogen Total P
(6) (3) (7) (A)
53
A7
22
11
34
33
37
20
20
23
63
9
15
8
Specific gravity: Suspended solids, 0.80-1.60(6); Settleable Solids, excluding grit, 1.05-1.20(11); Grit, 2.65(11)
O
O
COMBINED SEWAGE
Particle Size,
Microns
< 74
74-295
295-991
991-3327
> 3327
Percent Weight
in Each Size RanRe
Suspended Solids
(8)
48
22
16
9
5
Specific gravity: Suspended solids, 0.80-
2.60(10); Settleable solids, excluding grit,
1.05-1.20(11); Grit, 2.65(11)
STORMWATER RUNOFF (STREET CONTAMINANTS)
Percent Weight
in Each Size Range
Particle Size,
Microns
< 43
43-104
104-246
246-840
840-2000
> 2000
Suspended Solids
(9)
14
11
18
22
14
21
Specific gravity: Same as reported by
reference 11 for combined sewage.
Numbers in parentheses refer to the literature cited below:
(1) Rickert and Hunter, 1967 (5) Matcalf and Eddy, Inc., 1972
(2) Rickert and Hunter, 1971 (6) Helfgott et al., 1970
(3) Hunter and Heulekekian, 1965 (7) Painter and Viney, 1959
(4) Heulekekian and.Balmat, 1959 (8) Envirogenics Co., 1970
(9) Sartor and Boyd, 1972
(10) Dalrymple et al., 1975
(11) Sullivan et al., 1974
-------
be directed to a unit with a number greater than its own; and 2) all flows
and pollutants entering the S/T system must enter unit 1. The flows enter-
ing and exiting a unit are shown in Figure 7-3. Several examples of
storage/treatment plant configurations are shown in Figure 7-4.
General Unit Information --
Card Croup Fl -- This card is used to enter the name of unit I.
Card Group F2 -- The variable IDENT(I) describes the unit I as a nondeten-
tion (IDENT(I) = 0) or detention process (IDENT(I) =1). If IDENT(I) = 1,
all or portions of card group H must be included.
Each unit is assigned a maximum inflow, QMAX(I), beyond which all flows
and pollutants are bypassed. However, this variable may be set to an ab-
normally high value for design purposes (i.e., responses at all possible
inputs) or set at a realistic value for modeling existing or proposed facil-
ities. The variable QRF(I) is used to specify the residual flow, as a
fraction of the inflow, for non-detention units only (IDENT(I) = 0).
The variables IDIREC(I,1), IDIREC(I,2) and IDIREC(I,3) are used to
direct bypassed flow and pollutants, treated outflow, and residuals from
unit I to other units. The values entered for these variables represent the
unit numbers to which these flows and pollutants are to be directed. Addi-
tionally, these: flows may be sent directly to the next block (e.g., Receiving
Block) or to ultimate disposal (which simply removes them from the simulation)
by specifying IDIREC(I,ID) = 100 or 200, respectively. The flows and pollu-
tants directed to the next block are summed for all the units and transferred
as a single stream. Any unit to which flows are directed must have a unit
number greater than the source unit (see Figure 7-4 for examples).
Pollutant Removal --
Pollutants are removed by settling or obstruction when characterized by
particle size/specific gravity or settling velocity distributions. When
they are characterized by magnitude (concentration) alone, removal is simu-
lated through removal equations. Card groups Gl through G3 are used to
establish these removal equations. When a particle size/specific gravity or
settling velocity distribution is used and the unit is classified as a
non-detention process, then a critical size or settling velocity is selected.
The model removes all particles with a size or settling velocity greater
than or equal to the critical size. Card G4 is used to enter this parameter.
Card groups Gl through G4 are repeated for each pollutant unless a
detention unit is specified (IDENT(l) = 1, card F2) and a particle size/
specific gravity or settling velocity distribution is specified for pollu-
tant IP (IPART(IP) = 1, card El). Naturally, these card groups are omitted
if no pollutants are routed (NP = 0, card Bl). Again, card groups Gl through
G3 are used only if the pollutant is characterized solely by magnitude.
Card G4 is used only if a pollutant is characterized by magnitude and a
particle size/specific gravity or settling velocity distribution (IPART(IP)
= 1, card El) and a non-detention unit is specified (IDENT(I) = 0, card F2).
301 7-10
-------
'tot
BYPASS EXCESS
(YES)
Q
by
LEGEND
Qtot
Qmax
Qby
Qin
Qout
Ores
Q
res
= TOTAL INFLOW, fi3/sec
* MAXIMUM ALLOWABLE INFLOW,
ft^sec
= BYPASSED FLOW, ft3/sec
= DIRECT INFLOW TO UNIT, ft3/sec
= TREATED OUTFLOW, ft3/sec
= RESIDUAL STREAM, ft3/cec
Figure 7-3. Flows Into, Through, and Out of a Storage/Treatment Unit.
302
7-11
-------
PLANT
INFLOW
Configuration- I
PLANT
INFLOW
Configuration- 2
'by
PLANT
INFLOW
Qi
Configuration-3
Figure 7-4. Storage/Treatment Plant Configurations.
303
7-12
-------
Table 7-2. Program Variables Available for Pollutant Removal Equations
Value of
INPUT(I,IP,K)3
(G2 cards)
Non-Detention Units,
IDENT(I) = 0 (card F2)
Detention Units, IDENT(I) = 1 (card F2)
Perfect plug flow is
used, to route poll-
utants, IROUTE(I)=0
(card 12)
each plug in
detention unit I,
seconds.
Complete mixing is
used to route pollu-
tants, IROUTE(I) = 1
(card 12)
0 Not used.
1 Not used.
Not used.
Detention time of
Not used.
Time step size,
seconds.
Concentration of pol- Initial concentration Not used.
lutant 1 passing through of pollutant 1 in each ,
unit I. plug in detention unit 1.
Concentration of pol- Initial concentration of
lutant 2 passing through pollutant 2 in each plug
unit I. in detention unit I.
Not used.
Concentration of pol- Initial concentration
lutant 3 passing through of pollutant 3 in each ,
unit I. plug in detention unit I.
Removal fraction of
pollutant 1 in unit I
(used only for pollu-
tants 2 and 3).
Removal fraction of
pollutant 1 for each
plug in detention
unit I (used only for
pollutants 2 and 3).
Not used.
Removal fraction of
pollutant 1 in deten-
tion unit I (used only
for pollutants 2 and
3).
Removal fraction of
pollutant 2 in unit
I (used only for
pollutant 3).
Inflow rate,
ft /sec [in /sec].
Removal fraction of
pollutant 2 for each
plug in detention
unit I (used only for
pollutant 3).
Not used.
Removal fraction of
pollutant 1 in de-
tention unit I (used
only for pollutant
3).
Not used.
I = unit number.
IP = pollutant number.
K = Subscript of x in equations 7-1.
Dimensions determined by NDIM(IP) on card El.
304
7-13
-------
Card Group Gl - A single flexible functional form is avaliable for use
as a pollutant removal equation (See Appendix IV):
/ ax a ax a ax a
R = (312e X2 + 313e X4 + a!4e X6
(a?x7 + agxg) a(( a^ BU\ aj6
+ al5e X9 X10 Xll / (7-D
where x. = removal equation variables,
a. = coefficients, and
J
R = removal fraction, 0 £ R £ 1.0.
Each removal equation variable, x., may represent, one of several parameters
available in the program at each time step and these options are discussed
below (card group G2 ). With these variables and the coefficients, a., the
user can develop the desired removal equation. The coefficients are entered
on card group GJ.
A maximum removal fraction is specified by RMX(I,IP). This is partic-
cularly useful for equations which mathematically generate values of the
removal fraction, R, that may exceed a reasonable value or 1.0. RMX(I,IP)
provides an upper bound on such equations.
Card Group G2 -- These cards (two) allow the user to assign various program
variables to the variables in equation 7-1. For example, if pollutant 1
(IP = 1) is to be removed in unit I by a removal equation, the values given
INPUT(I,1,1) through INPUT(I,1,11) assign program variables to the corre-
sponding variables x through x in equation 7-1. The program variables
available for inclusion are shown in Table 7-2. An example removal equation
is discussed below.
Card Group G3 -- The variables A(I,IP,1) through A(I,IP,16) represent the
variables a through a fi in equation 7-1 as applied to pollutant IP in unit
I. Two cards are required.
An example of applying equation 7-1 is provided by a suspended solids
removal equation used in an earlier version of the Storage/Treatment Block
for sedimentation (detention) units:
305 7-14
-------
where Rcc = suspended solids removal fraction, 0 ^ Rcc ^ R ,
DO DO max
R = maximum removal fraction,
HlclX
t, = detention time, seconds, and
k = first order decay coefficient, I/sec.
This equation can be constructed from equation 7-1 by setting a (or A(I,
IP,12)) = R , a (or A(I,IP,13)) = -R , a (or A(I,IP,3)) = -k,
a , (or A(r,lP,l6J7 = 1.0, and letting x = detention time, t, , by setting
INPUT(I,IP,3) = 1 (card group G2). All other coefficients, a (or
A(I,IP,J)), would equal zero. RMX(I,IP) (card Gl) would not DC necessary,
as R limits the value of R. Appendix IV contains other examples.
max
Card Group G4 -- The variable PSC(I) specifies a critical particle size (if
NVS = 0, card E2) or settling velocity (if NVS = 1, card E2) that denotes
the point above which all particles are removed from the influent. This
parameter is included primarily to model such non-detention units as micro-
soreens, fine screens, and coarse screens. An approximation of the removal
effectiveness of screens may be obtained by letting PSC(I) equal the aper-
ture size of the screen (see Appendix IV). Card G4 is required only if
IPART(IP) = 1 (card El) and IDENT(I) = 0 (card F2).
Detention Unit Data
Card groups HI through H8 are used to describe the special charac-
teristics of detention units. Sedimentation, dissolved air floatation,
chlorination, and sludge thickening are some of the processes that may be
modeled by a detention unit.
These cards primarily describe the hydraulic characteristics of a
detention unit and, thus, are required only if IDENT(I) = 1 (card F2).
Card Group HI -- The variable IROUTE(I) specifies the manner in which pol-
lutants are routed in detention unit I. When IROUTE(I) = 0 the unit routes
pollutants under the assumption of perfect plug flow. Perfect plug flow is
recommended for long, rectangular tanks where settling is the most important
removal mechanism and is required when any pollutant is characterized by a
particle size/specific gravity or settling velocity distribution (IPART(IP)
= 1, card El). Removed pollutants are accumulated in plug-flow units,
without decay, until removed by the residual flow. When IROUTE(I) = 1 the
unit routes pollutants under the assumption of perfect miximg. Complete
mixing is most applicable to small tanks where the primary purpose is to
thoroughly mix the contents (e.g., rapid-mix chlorination, flocculators, and
mixing tanks). Removed pollutant quantities are not allowed to accumulate
in completely-mixed units (i.e., no settling). The user is referred to
Appendix IV for further explanation.
The variable IOUT(I) is used to describe the depth-treated outflow
relationship that characterizes the discharge of treated outflow (e.g., weir
flow) from unit I. The user is given three options. The first (IOUT(I) = 0)
306 7-15
-------
is to provide the model with as many an sixteen data pairs describing the
depth-outflow relationship (entered on card group 13). The second option
(IOUT(I) = 1) is- to approximate the relationship by a power equation (entered
on card H4). The third option (IOUT(I)=2) specifies a constant pumping rate
between certain depths (entered on card H5).
In addition to treated outflow, a residual stream may be drawn from the
unit during periods1of no inflow or treated outflow. When a residual stream
occurs from a plug-flow unit the entire unit contents (including the removed
pollutant quantities) are mixed (i.e., the remaining plugs lose their iden-
tity) and drawn off until the unit is empty or inflow occurs. If inflow
begins before the unit is empty the remaining contents are placed in a
single plug for further routing. In a completely-mixed unit, the pollutant
concentrations in the residual flow are identical to the concentrations in
the treated outflow. Again, the flow is suspended when inflow occurs. The
variable IDRAW(l) simply specifies the conditions under which a residual
stream begins. If IDRAW(I) = 0, a residual stream is never drawn and the
accumulated pollutants (if IROUTE(I) = 0) remain in the unit. If IDRAW(I)
£ -1, the residuals are drawn off starting at every -IDRAW(I) time steps (but
Lhe flow is delayed if inflow and/or tj/ealed oulflow is in progress). This
option corresponds with the situation in which the unit is drained on a
regular (e.g., scheduled) basis. If II)RAW(I) 5 1, the residuals are drawn
after IDRAW(I) time steps of no inflow or treated outflow. The conditions
specified by IDKAW(I) ^ 1 apply directly to the case in which the unit
contents are drained after each runoff event.
The variable IRES(I) is used to describe the depth-residual flow re-
lationship that characterizes the draw off of Lhe residual stream from unit
I. If IRES(I) - 0, the user provides the model with as many as sixteen data
pairs describing; the depth-residual flow relationship (entered on the H3
cards). If IRES(I) = 1, the relationship is approximated by a power equa-
tion (entered on card H6).
Card Group H2 The parameters on thi;; card are required only when a part-
icle size/specific gravity or settling velocity distribution is used to
characterize any pollutant (IPART(IP) " 1, card El) and unit I is a plug-
flow detention unit (IROUTE(I) = 0, card HI).
The variable ALEN(I) represents the travel length for plugs in unit I.
The variable AMAN(I) is the Manning's roughness coefficient for the surfaces
of unit I and is commonly available for many materials. These values are
required for the pollutant removal algorithms used when a particle size/
specific gravity or settling velocity distribution characterizes a pollutant
(see Appendix IV).
Card Group H3 These cards are used to enter up to sixteen sets of data
describing the geometry and hydraulics of detention unit I. Each card
enters a value for depth, SDEPTH(I,MM), along with the corresponding values
of surface area, SAREA(I,MM), volume, 5>STORE(I,MM), treated outflow,
SQQOU(I,MM), and residual flow, SQQRSOi ,MM). The series is terminated by a
blank card.
307
-------
The only required parameters are DEPTH(I,MM) and SAREA(I,MM); the need
for the other parameters depends on other factors. If SSTORE(I,MM) is left
blank on every H3 card, the program will estimate the volume at each depth
by averaging the surface area at each depth and the lower adjacent depth,
multiplying by the difference in depth, and adding the result to the esti-
mated volume at the lower adjacent depth. A value for treated outflow,
SQQOU(I,MM), is required for each depth only if IOUT(I) = 0 (card HI). If
IOUT = 1 cr 2 (card III), this relationship is provided by c C2 (?-3)
3 3
where Q = treated outflow, ft /sec [m /sec],
C1, C_ = coefficients,
D = water depth in detention unit, ft [m], and
D- = depth below which there is no treated outflow, ft [m].
The user supplies the values of D , C , and C? (program variables DO, Cl,
and C2, respectively).
Two common outlet structures that may be modeled with equation 7-3 are
the orifice and the broad-crested weir. For example, a weir could be modeled
by letting C = 3.33 L where L = length of the weir in feet, C = 1.5, and
D- = depth at the bottom of the weir in feet. These substitutions yield the
familiar weir equation.
Card Group H5 -- This card is required only if pumping is specified for the
treated outflow from a detention unit (IOUT(I) = 2, card HI). The variables
DSTART(I,1) and DSTART(I,2) represent the depths at which the pumping rates
QPUMP(I,1) and QPUMP(I,2) begin. The variable DSTOP(I) specifies the depth
at which all pumping stops. In other words, the rate QPUMP(I,1) occurs when
308 7-17
-------
the depth is equal to or exceeds DSTART(I,1) and the rate QPUMP(I,2) occurs
when the depth is greater than or equal to DSTART(I,2). The pumping rate
reverts to the rate QPUMP(I,1) when the depth falls below DSTART(I,2) and
continues at th.at rate until the depth falls to DSTOP(I). The value of
DSTART(I,1) must be less than or equal to DSTART(I,2) and DSTOP(I) must be
less than or equal to DSTART (1,1).
Card Group H6 -- This card is required only if a pcv;er equation is used to
describe the depth-residual flow relationship (IRES(I) = 1, card HI). The
equation is
Qres = C3(D ' D1) (7'4)
3 3
where Q = residual flow, ft /sec [m /sec],
res
C«, C, = coefficients,
D = water depth in detention unit, ft [m] , and
DI = depth below which there is no residual flow, ft [m] .
The user supplies the values of DI , C_, and C, (program variables Dl, C3,
and C4, respectively). Recall that a residual flow occurs only when dicta-
ted by IDRAW(I) (card HI).
Card Group 117 -~ This card is used to indicate the build up of sludge in a
plug-flow detention unit (IROUTE(I) = 0, card HI). The user specifies the
pollutant used to calculate the sludge volume, NPSL(I); the concentration of
pollutant NPSL([) in sludge; and the depth at which a warning is given to
indicate that sludge has accumulated to an unacceptable level. The sludge
volume is increased by dividing the amount of pollutant NPSL(I) removed each
-tim^ step by SLDEN(I). The model assumes that the sludge volume has no
effect on the available storage volume and that no compression occurs. The
information on this card is only used to warn the user of a possible
maintenance/performance problem.
Card Group H8 -- This card specifies the total volume, WARN(I), and pollu-
tant concentrations, PCO(I,IP), present in the unit at the start of the
simulation. Obviously, the values of PCO(I,IP) are not required if NP = 0
(card Bl) or WARN(I) = 0.0.
Cost Data --
Card Group II This card is required only if ICOST = 1 (card Bl).
The capital cost for each unit is computed as a function of a design
flow or volume specified by the user or is calculated by the model as a
function of the maximum value recorded during the simulation.
309
-------
or
or
or
C = a Qb (7-5)
cap xmax v '
C = a(Q. )b (7-6)
cap xni max v
C = a Vb (7-7)
cap max
C = a(V , )b (7-8)
cap obs max v '
where C = initial capital cost, dollars,
cap
3 3
Q = maximum allowable inflow, ft /sec [m /sec],
FT13X
(Q. ) = maximum inflow encountered during the simulation,
in. m3x t-*_3 / r j / i
ft /sec [m /sec],
V = maximum allowable storage (detention units only),
max ~. j r J i
ft [m ],
(V , ) = maximum storage encountered during the simulation
obs max /-..... ... T ^ ,-..3 r 3, ,
(detention units only), ft [m J, and
a, b = coefficients (specified by the user).
Equations 7-5 through 7-8 differ only in the variable used to compute the
initial capital cost of unit I. The variable KPC(I,1) specifies which
variable is used (as shown in Table 7-3) and CC(I,1) and CC(I,2) represent
the coefficients a and b.
The operation and maintenance costs are calculated as a function of
the variables listed above and the total operating time (calculated as
the number of time steps with flow to or from the unit).
C = d Qf + hD (7-9)
om max op
or
or
C = d(Q. )f + hD (7-10)
om in max op
310 7-19
-------
or
C -- d Vf + hD (7-11)
om max op
C = d(V , )f + hD
om obs max op
where C = operation and maintenance cost, dollars,
D = total operating time during the simulation period, hours,
°^ and
d,f,h = coefficients (supplied by the user).
Equations 7-9 through 7-12 differ only in the variable used to compute the
operation and maintenance costs for unit I. The variable KPC(I,2) specifies
which variable is used (as shown in Table 7-3) and CC(I,3), CC(I,4), and
CC(Ij5) represent thi? coefficients d, j:, and h.
The user is cautioned not to misinterpret the cost calculated by the
model. For example, in a single-event simulation the calculated capital
cost can only be: considered an estimator of the true capital cost when the
simulated event is a design event. Likewise, when operating time is a
factor in computing operation and maintenance costs, the calculated costs
can be a valid estimator of the true costs only when a long-term simulation
is performed. Recent EPA publications provide useful information for the
proper selection of the coefficients required in equations 7-5 through 7-12
(EPA, 1976; Benjes, 1976).
Input Waste Stream
Flow and Pollutant Data --
Card group Jl is required only if NOTAPE = 1 or 2 (card Bl).
Card Group Jl This card is repeated for each time step including those
with no inflow. The pollutant concentrations, PCAR(IP), must be entered in
the same order as on card El and have the same dimensions specified by
NDIM(IP) and PUNIT(IN,IP) (card El). 11: NOTAPE = 2 (card Bl), the flow and
concentrations are added to the values from the external tape or disk. All
values are instantaneous flows or concentrations (at the end of the time
step).
ALTERING THE PROGRAM SIZE
The Storage/Treatment Block, as presently written, is capable of model-
ing a maximum of five S/T units. To alter this restriction requires that
the number 5, which appears in several lines of seven subrountines, be
changed to the desired maximum. The specific subroutines and lines (num-
bered in columns 72 through 80) are as follows:
7-20
-------
Subroutine STRT:
Subroutine STRDAT:
Subroutine CONTRL:
Subroutine UNIT:
Subroutine PLUGS:
Subroutine EQUATE:
Subroutine STCOST:
14 through 27.
4 through 18.
4 through 19 and 21 through 24,
4 through 23 and 25.
3
3 through 16.
4 through 17.
When a detention unit is modeled as a plug-flow reactor(IDENT(I) = 1,
card F2, and IROUTE(I) = 0, card HI) the maximum number of plugs allowed to
be in the unit at any one time is 50. The program will terminate if this
limit is exceeded. However, the user can increase this capacity by changing
the number 50 to the desired value in the following lines (numbered in
columns 72-80):
Subroutine UNIT:
18 through 21. 23, 25 and 29.
312
7-21
-------
Table 7-3. Storage/Treatment Block Card Data
Card
Group
Card Variable
Format Column Description Name
Default
Value
CARD GROUP Al; TITLE
Al
Title cards: two cards with heading to be
printed on output.
2X 1-2 Card identifier = Al.
2X 3-4 Skip
19A4 5-80 Title. TITLE2
Blank
Blank
None
CARD GROUP Bl; GENERAL DATA
Bl
2X 1-2 Card identifier = Bl.
13 3-5 Input data source. NOTAPE
Blank
0
= 0, Input is from an external input file.
= 1, Input is supplied on card group Jl.
= 2, Input is from an external input file
and card group Jl.
15 6-10 External element number from the outside JNS None
block (e.g., NOE in Transport Block)
which routes flow to the S/T Block. If
NOTAPE = 1, the value of JNS is placed on
the output file.
110 11-20 Total number of simulation time steps. NDT 0
F10.0 21-30 Size of time step, seconds. Required only DS None
if NOTAPE = 1.
415 31-35 Number of storage/treatment units. NU 1
(maximum = 5).
36-40 Number of pollutants routed (maximum = 3). NP 0
41-45 Cost calculations performance? [COST 0
= 0, No.
= 1, Yes.
313
7-22
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card
Group
Card
Format Column
Description
Variable
Name
Default
Value
46-50 Metric input-output. METRIC
= 0, Use U.S. customary units.
= 1, Use metric units. Metric input
indicated in brackets! )
F10.0 51-60 Service area, acres [ha]. Not required. SAREA
CARD GROUPS Cl AND C2; STARTING TIME AND PRINT INSTRUCTIONS
Cl
2X
18
F10.0
3110
1-2
3-10
11-20
21-30
31-40
41-50
Starting date/time and print instructions card.
Card identifier = Cl.
Date at beginning of simulation (6 digit IDATE
number; year, month, day -- e.g., March 10,
1979 = 790310).
Time at beginning of simulation (24-hour TIME
clock, e.g., 5:30 pm = 17.5.
Summary print control parameter. ISUM
= 0, Print a summary at the end of the
simulation only.
= 1, Print an annual summary and a summary
at the end of the simulation.
= 2, Print monthly and annual summaries and a
summary at the end of the simulation.
Detailed print control parameter. IDET
= 0, No detailed print of simulation results.
> 0, Detailed print of results is provided at
every time step that is a multiple of
IDET (e.g., IDET = 2 gives a detailed
report at every other time step) during
specified periods (see below and card
group C2).
Number of detailed print periods. Up to 8
periods may be specified (see C2 cards).
Required only if IDET > 0.
NPR
Blank
0
314
7-23
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card
Group
C2
Card Variable
Format Column Description Name
Detailed print period cards. NPR (card Cl)
periods must be specified. Only date to date
periods may be used (e.g., 790720 to 790806).
Required only if IDET > 0 (card Cl).
2X 1-2 Card group identifier = C2.
18 3-10 First detailed print period starting date ISTART(l)
(e.g., July 20, 1979 = 790720).
7110 11-20 First detailed print period ending date IEND(1)
(e.g., August 6, 1979 = 790806).
21-30 Repeat for second period, etc., up to NPR ISTARTU)
(card Cl) periods (may require two cards).
31-40 IEND(2)
Default
Value
Blank
None
None
None
None
CARD GROUP Dl; EVAPORATION DATA
Dl
2X 1-2
F8.0 3-10
7F10.0 11-20
Evaporation data cards. May leave blank if
there are no detention units (IDENT(I) = 0
for all units, see card F2). However, two
cards must be included.
Card group identifier = Dl.
Evaporation rate, January, in/day [mra/dayl. E(l)
Evaporation rate, February, in/day [mm/day]. E(2)
Blank
0.0
0.0
Mr* \tf*n I I *» r »*n f h imirtt h .
CARD GROUPS E1-E6; POLLUTANT CHARACTERIZATION.
REQUIRED ONLY IF NP > 0 (CARD Bl).
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card
Group
Card
Format Column
Description
Variable
Name
Default
Value
El
2X
311
1-2
3
2(2X,2A4) 8-15
18-25
2X.3I1 28
29
30
2(2X,2A4) 33-40
General pollutant characteristics card.
Card group identifier = El.
Pollutant 1 selector. Required only if
NOTAPE = 0 or 2 (card Bl). The value
selected depends on the order in which
the pollutants were placed on the ex-
ternal input file. For example, if
suspended solids was the third pollu-
tant listed on the file and it was
desired for use in the S/T Block,
then IPOLL(l) = 3.
Dimensions for pollutant 1. Required only KDIMl(l)
if NOTAPE = 1 (card Bl).
= 0, Dimensions are mg'/l.
= 1, Dimensions are liter
= 2, Other concentration dimensions are used
(e.g., JTU, pmho, °C, pH)
Blank
IPOLL(l) None
Particle size/specific gravity or settling
velocity distribution parameter.
= 0, Distribution not used to characterize
pollutant 1.
= 1, Distribution used to characterize
pollutant 1.
Pollutant 1 name. Required only if
NOTAPE = 1 (card Bl).
Pollutant 1 dimension label.
Required only if NOTAPE = 1 (card Bl).
Pollutant 2 selector. Required only
if NP g 2 and NOTAPE = 0 or 2 (card Bl).
See above.
Dimensions for pollutant 2. Required only
if NP g 2 and NOTAPE = 1 (card Bl). See
above.
Particle size/specific gravity or settling
velocity distribution parameter. Required
only if NP S 2 (card Bl). See above.
Pollutant 2 name. Required only if
NP 2 2 and NOTAPE = 1 (card Bl).
IPART(l) 0
PNAMEKIN.l) None
PUNIT1(IN,1) None
IPOLL(2) None
NDIMK2) 0
IPART(2) 0
PNAHE1UN.2) None
316
7-25
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card
Group
Card
Format Column
Description
Variable
Name
Default
Value
43-50 Pollutant 2 dimension label. Required
only if NP £ 2 and NOTAPE = 1 (card Bl).
2X.3I1 53 Pollutant 3 selector. Required only
if NP = 3 and NOTAPE = 0 or 2 (card Bl).
PUNIT1(IN,2) None
IPOLL(3) None
54
55
2(2X,2A4) 58-65
68-75
Dimensions for pollutant 3. Required only
if NP = 3 and NOTAPE = 1 (card Bl). See
above.
Particle size/specific gravity or settling
velocity distribution parameter. Required
only if NP = 3 (card Bl). See above.
Pollutant 3 name. Required only
if NP = 3 and NOTAPE = 1 (card Bl).
Pollutant 3 dimension label. Required
only if NP = 3 and NOTAPE = 1 (card Bl).
NDIM1(3) 0
IPART(3) 0
PNAMEKIN.3) None
PUNIT1UN.3) None
E2
2X
18
110
1-2
3-10
11-20
CARD GROUPS E2-E6 ARE REQUIRED ONLY IF IPART(IP)
= 1 (CARD El) FOR ANY POLLUTANTS.
Card identifier = E2.
Classification parameter. NVS
= 0, Particle size/specific gravity distri-
bution is used to classify particles
in waste stream.
= 1, Settling velocity distribution is used.
Number of particle size ranges or settling
velocities used to classify particles in
waste stream.
NNR
Blank
0
None
Particle size (if NVS =0, card E2) or
settling velocity (if NVS = 1, card E2)
range cards. May require up to three cards.
E3 2X 1-2 Card group identifier = E3. -- Blank
18 3-10 Lower bound of size or velocity RAN(1,1) None
range 1, microns or ft/sec [cm/secj.
7110 11-20 Upper bound of size or velocity range 1, RANI 1,2) None
microns or ft/sec (cm/sec|.
317
7-26
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card
Group
Card
Format Column
21-30
31-40
Description
Lower bound of size or velocity range 2,
microns or ft/sec [cm/sec].
Upper bound of size or velocity range 2,
Variable
Name
RAN(2,1)
RAN(2,2)
Default
Value
None
None
microns or ft/sec (cm/sec).
Repeat for each size or velocity range, up to
NNR (card E2) ranges.
Specific gravity cards. Required only if
NVS = 0 (card E2). May require up to
two cards.
E4 2X 1-2 Card group identifier = E4. -- Blank
F8.0 3-10 Specific gravity for parti
icles in size SPG(l) None
range 1.
7F10.0 11-20 Specific gravity for particles in size
range 2.
SPG(2)
None
Repeat for each size range, up to NNR
(card E2) ranges.
E5
2X
F8.0
-T II
Waste stream temperature cards. Required only
if NVS = 0 (card E2). Requires two cards.
1-2 Card group identifier = E5.
3-10 Waste stream temperature, January, °F (°Cl. TEMP(l)
Blank
None
- Li
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card
Group
Format
Card
Column
Description
Variable
Name
Default
Value
E6
2X 1-2
F8.0 3-10
7F10.0 11-20
Fraction of pollutant associated with
each particle size/specific gravity
or settling velocity range (card
group E3). Repeat these cards for each
pollutant for which IPART(IP) = Kcard
El). Each pollutant may require up to
two cards.
Card group identifier = E6.
Fraction of pollutant IP in range 1.
Fraction of pollutant IP in range 2.
PSD(IP,1)
PSD(IP,2)
Blank
None
None
Repeat for each range up to
NNR (card E2) ranges.
REPEAT CARD GROUPS F1-I1 FOR EACH UNIT I.
THERE WILL BE NU(CARD Bl) SETS. THE UNIT
NUMBER IS DICTATED BY THE ORDER IN WHICH
THE SETS OF CARD GROUPS F1-I1 ARE READ.
CARD GROUPS F1-F2; GENERAL UNIT CHARACTERISTICS
Fl
F2
2X
6A3
2X
18
1-2
3-20
1-2
3-10
Card group identifier = Fl.
Name of unit. UNAME(I.ID)
General unit parameters and flow directions.
Card group identifier = F2.
Detention modeling parameter. IDENT(l)
Blank
None
Blank
U
= 0, Unit is the non-detention type.
= 1, Unit is the detention type.
319
7-28
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card
Group
Format
Card
Column
Description
Variable
Name
Default
Value
2F10.0 11-20 Maximum inflow (above which bypass occurs), QMAX(l)
ft3/sec [m3/sec].
None
21-30 Residual flow as a fraction of the
inflow. Required only if IDENT(I) = 0.
Residual flows for detention units
(IDENT(I) = 1) are determined in card
groups HI, H3, and H6.
3110 31-40 Unit number to which bypass is directed
(must be greater than I).
= 2-5, Downstream S/T unit.
= 100, Next block.
= 200, Ultimate disposal.
41-50 Unit number to which treated outflow is
directed (must be greater than I). See
above.
51-60 Unit number to which residuals stream is
directed (must be greater than I). See
above. If IDRAW(I) = 0 (card 111), set
equal to any number greater than I.
QRF(I)
None
IDIREC(I,1) None
IDIRECU.2) None
II)1RF.C([,3) None
CARD GROUPS G1-G4; POLLUTANT REMOVAL
REQUIRED ONLY IF NP > 0 (card Bl).
Gl
REPEAT CAR!) GROUPS Gl - C3 TOR EACH
POLLUTANT FOR WHICH IPART(IP) = 0.
2X 1-2 Card identifier = Gl.
F8.0 3-10 Maximum removal fraction(S 1.0).
HI. ink
KMX(I.IF) None
Removal equation variable card (equation 7-1).
Requires two cards.
320
7-29
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card Card Variable Default
Group Format Column Description Name Name
G2 2X 1-2 Card group identifier =02. Blank
18 3-10 Program variable for equation variable INPUT(I ,IP, 1) 0
X1 '
= 0, Not used.
= 1
= 2
= 3
= 4
= 5
= 6
See Table 7-2 in text.
= 7
7110 11-20 Program variable for equation variable INPUTCI ,IP,2) 0
x.. See above.
Repeat for each program variable x..
G3
2X 1-2
F8.0 3-10
7F10.0 11-20
Equation coefficients cards. Requires
two cards. The coefficients must be
consistent with the units used (see METRIC,
card Bl).
Card group identifier = G3.
Value of coefficient a,
r
Value of coefficient a_
Repeat for each coefficient a..
Blank
0.0
0 0
G4
2X
1-2
Critical particle size or settling velo-
city card. Required only if IPART(IP) = 1
(card El) for any pollutant and unit I is
a non-detention unit, IDENT(I) = 0 (card
F2).
Card group identifier = G4.
lll.iiik
321
7-30
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card Card Variable Default
Group Format Column Description Name Value
F8.0 3-10 Critical particle size microns (if
NVS = 0, card E2), or settling velocity, PSC(I) None
ft/sec [cm/sec] (if NVS = 1, card E2).
CARD GROUP H1-H8; DETENTION UNIT DATA
REQUIRED ONLY IF IDENT(I) = 1 (CARD F2).
General detention unit parameters card.
HI 2X 1-2 Card group identifier = HI. -- Blank
18 3-10 Pollutant routing parameter. IROUTE(I) 0
= 0, Plug flow mode is used.
= 1, Complete mixing mode is used. (Note:
Particle size or settling velocity
distribution are not routed through
completely-mixed units.)
3110 11-20 Treated outflow routing parameter. IOUT(I) 0
= 0, The depth-treated outflow relation-
ship is described by as many as six-
teen data pairs on card group H3.
= 1, The depth-treated outflow relation-
ship is described by a power equa-
tion on card H4.
= 2, The depth-treated outflow relationship
is controlled by the pumps described on
card H5.
21-30 Residuals stream draw-off scheme. * IDRAW(I) 0
§ -1, A residual stream is drawn off __
starting at every -IDRAW(I) ~"
time step (if possible).
= 0, Residuals are never drawn off.
£ 1, A residuals stream (if available) is
drawn off only after IDRAW(I) time
steps of no inflow or treated outflow.
322 7-31
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card
Group
Format
Card
Column
Description
Variable
Name
Default
Value
31-40 Residual stream routing parameter.
Required only if IDRAW(I) f 0.
= 0, The depth-residual flow relationship
is described by as many as sixteen
data pairs on card group H3.
=1, The depth-residual flow relationship
is described by a power equation on
card H6.
IRES(I)
Detention unit (plug flow only) parameters
required when pollutants are characterized
by n particle size/specific gravity or
settling velocity distribution. Thus, this
card is required only if IF.\RT(.IP1 = I for
any pollutant (card El) and IROUTEII1 = 0
I card HI).
12 2X
K8 . 0
K 1 0 . 0
1-2
>10
11-20
Card group identifier = H2.
Travel length for plug flow, ft [m| .
Manning's roughness coefficient for
detention unit surfaces.
--
AI.EN(l)
AMMUU
Blank
'None
None
\{^
:\
F8.0
Depth-surface area-volume-treated out-
flow- residual flow data cards. Each
card contains a column for a unit depth
and the corresponding values of area,
volume, treated outflow, and residual
flow. The columns for treated outflow
and resulnal flow nay be left Mar.k Ae-
peiuluit on the values of tCVUP J"J
'.£T?v.P --ti c.ird HI. '. t -o va'.ues f:r
b,- as sany as sixt:':".\ .-av.U. :'
SCM >t-s wxth a Man's car.l.
v'avd jsrouv identifier r :'..
3-10 A unit depth, ft (m|.
i 1 .V.V.I
323
7-32
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card
Group
Card
Format Column
Description
Variable
Name
Default
Value
4F10.0 11-20 Surface area corresponding to the above SAREA(I,MM) None
depth, ft' [ni ].
21-30 Volume.corresponding to the above depth, SSTORE(I,MM) None
ftJ [in ].
31-40 Treated outflow at the above depth,
ftj/sec [in /sec].
SQQOU(I.MM) None
41-50 Residuals stream flow at the above depth, SQQRS(I.MM) None
ft /sec [m /sec]. Occurs only when IDRAW(I)
(card HI) permits.
*** Follow the last card (maximum depth) with ***
a blank card.
H4
Depth-treated outflow power equation card (equation 7-3).
Required only if IOUT(I) = 1 (card HI). Coefficients
must be consistent with the units used (see METRIC, card
Bl).
2X 1-2 Card group identifier = H4.
F8.0 3-10 Depth-treated outflow equation coeffi-, Cl
Blank
0.0
cient, C .
2F10.0 11-20 Depth below which no treated outflow DO
occurs, D-.
21-30 Depth-treated outflow equation coeffi- C2
cient, C_
0.0
0.0
Treated outflow pumping card. Required
only if IOUT(I) = 2 (card HI).
H5
2X
F8.0
1-2
3-10
4F10.0 11-20
21-30
Card identifier = H5.
Depth at which pumping rate QPUMP(I.l) DSTART(I,2)
begins, ft [m].
Depth at which pumping rate QPUMP(I,2) DSTARTU,?)
begins, ft [m]. Must be greater than or
equal to DSTART(I.l).
Pumping rate when depth is greater than QPUMP(I.l)
or equal to DSTART(I,1), ft /sec [m /sec|.
Blank
None
None
None
324
7-33
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card
Group
Format
Card
Column
Description
Variable
Name
Default
Value
31-40
41-50
Pumping rate when depth is greater than QPUMP(I,2)
or equal to DSTART(I,2), ft /sec [m /sec].
Depth below which all pumping stops,
ft [m]. Must be less than or equal to
DSTART(I,1).
DSTOP(I)
None
None
Depth-residual flow power equation card
(equation 7-4). Required only if IRES(I)
= 1 (card HI). Coefficients must be
consistent with the units used (see METRIC,
card Bl).
H6 2X
F8.0 3-10
2F10.0 11-20
21-30
Card group identifier = H6.
Depth-residual flow equation coefficient, C3
C3"
Depth below which no residual flow occurs, Dl
V
Depth-residual flow equation coefficient, C4
0 (card Bl).
H7 2X 1-2 Card group identifier = H7.
18 3-10 Pollutant responsible for sludge
generation. Required only if a
sludge depth warning message is
desired.
= 0, Not' used.
= 1, 2, or 3, Pollutant used to
generate sludge volume (must
correspond to the position on
card El).
2F10.0 11-20 Concentration of pollutant NPSL(I)
in sludge. Required only if NPSL(I)
5 1. The dimensions used must be
consistent with those indicated by
NDIM(I) (card El).
NPSL(I)
SLDE.'HI)
Blank
0
None
325
7-34
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card
Group
Format
Card
Column
Description
Variable
Name
Default
Value
21-30 Maximum sludge depth, ft [mj.
A warning message is printed
if this depth is exceeded by
the accumulated sludge. Required
only if NPSL(I) S 1.
SLDMAX(I)
None
H8
2X
F8.0
1-2
3-10
Initial conditions in detention
unit I.
Card identifier = H8.
Total volume of water in unit at the
Blank
WARN(I) 0.0
start of the simulation, ft [m ].
The following concentrations must be given
with dimensions consistent with those entered
on card E1(NDIM(IP)) if NOTAPE = l(card Bl)
or on the external input file if NOTAPE = 2.
3F10.0 11-20 Concentration of pollutant 1 in the PCO(I,1) 0.0
unit at the start of the simulation.
Required only if NP £ 1 (card Bl) and
WARN(I) > 0.0.
21-30 Concentration of pollutant 2 in the PCO(I,2) 0.0
unit volume at the start of the
simulation. Required only if NP g 2
(card Bl) and WARN(I) > 0.0.
31-40 Concentration of pollutant 3 in the PCO(I,3) 0.0
unit volume at the start of the
simulation. Required only if NP = 3
(card Bl) and WARN(I) > 0.0.
CARD GROUP II, COST DATA
REQUIRED ONLY IF ICOST = 1 (CARD Bl).
II
2X
1-2
Cost data card. The coefficients must be
consistent with the units used (see
METRIC, card Bl).
Card group identifier = II.
Blank
326
7-35
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card Card Variable Default
Group Format Column Description Name Value
18 3-10 Type of cost variable used in calculat- KPC(I.l) 0
ing initial capital cost.
= 0, Not used.
= 1, Maximum allowable inflow, QMAX(I),
ft /sec [m /sec), is used.
= 2, Maximum inflow observed during simula-
tion, QMAXS(I), ftJ/sec (ni /sec), is used.
= 3, Maximum allowable storage, VMAX(I),
ft [m ], is used (not applicable if
IDENT(I) = 0, card F2).
= 4, Maximum storage observed during simu-
lation, VMAXS(I),ftJ [m ), is used (not
applicable if IDENT(I) = 0, card F2).
2F10.0 11-20 Initial capital cost equation CC(I,1) 0.0
coefficient, a.
21-30 Initial capital cost equation CC(I,2) 0.0
coefficient, b.
110 31-40 Type of cost variable used in KPC(I,2) 0
calculating operation and maintenance
costs. See list for initial capital
cost (above).
3F10.0 41-50 Operation and maintenance costs equation CC(I,3) 0.0
coefficient, d.
51-60 Operation and maintenance costs equation CC(I,4) 0.0
coefficient, f.
61-70 Operation and maintenance costs equation CC(I,5) 0.0
coefficient, h.
CARD GROUP Jl; FLOW AND POLLUTANT DATA.
REQUIRED ONLY IF NOTAPE = 1 OR 2 (CARD Bl).
Flow and pollutant data cards. Requires
one card for each time step. All flows
and concentrations are instantaneous
values at the end of the time step. The
dimensions for concentration must be
identical to those on card El (NDIM(IP))
if NOTAPE = 1 (card Bl) or on the external
input file if NOTAPE = 2 (card Bl).
327 7-36
-------
Table 7-3 (continued). Storage/Treatment Block Card Data
Card
Group
Jl
Format
2X
F8.0
3F10.0
Card
Column
1-2
3-10
11-20
Description
Card group identifier = Jl.
Flow entering S/T plant (at unit 1),
ftj/sec [in /sec].
Concentration of pollutant 1 entering S/T
Variable
Name
QCAR
PCAR(l)
Default
Value
Blank
0.0
0.0
plant (at unit 1). Required only if NP
5 1 (card Bl) and QCAR > 0.0.
21-30 Concentration of pollutant 2 entering S/T PCAR(2)
plant (at unit 1). Required only if NP .
£ 2 (card 81) and QCAR > 0.0.
31-40 Concentration of pollutant 3 entering S/T PCAR(3)
plant (at unit 1). Required only if NP =
3 (card Bl) and QCAR > 0.0.
0.0
0.0
END OF STORAGE/TREATMENT BLOCK DATA
At this point the program seeks new input from the
Executive Block.
328
7-37
-------
SECTION 8
RECEIVING WATER BLOCK
The Receiving Water Model (Receive) has been a block of SWMM since
the original SWMM development. Receive is a dynamic, branching one-
dimensional network model that is often used as a pseudo-two dimensional
model when the links and nodes are arranged in a triangular or other
grid pattern. Based upon the EPA Dynamic Estuary Model (DEM), it is one
of several variants of the DEM, and in turn, has had several other
models developed based upon it. Due to the proliferation of link-node
models that are direct descendants of DEM or Receive, the EPA Athens,
Georgia Laboratory decided in 1980 to combine the best features of the
several variants into one new Receive/DEM-type model. It was also
decided that this would be the appropriate model for use with SWMM when
completed. As of this writing (May 1981), work is still underway on the
model and its User's Manual. Rather than publish outdated information
on Receive, it was decided to use this documentation in the form of an
addendum to this SWMM Version III User's Manual when it became available.
Hence, no details on Receive are included herein.
Current (May 1981) Receive capabilities are relatively unchanged
since the Version II SWMM (Huber et al., 1975), although a few minor
changes were included with an interim SWMM release in 1977. Persons
needing the Receive model should contact Mr. Tom Barnwell or Robert
Ambrose of the EPA Athens lab for information on the best available
version since it will not be included with the rest of the SWMM Ver-
sion III release.
329 8-1
-------
SECTION 9
STATISTICS BLOCK
BLOCK DESCRIPTION
Introduction
The Statistics Block has been developed to provide the added capability
within SWUM to perform simple statistical analyses on continuous event data.
Both quantity and quality parameters may be analyzed. The options available
include a table depicting the sequential series of events, a table of magni-
f.ade, return period and frequency of events, a graph of magnitude vs. retur:i
period, a graph of magnitude vs. frequency, and the first three moments of
the event data.
Statistical analyses are performed on data read from an interface file
arranged in the standardized SWMM format (refer to Section 2). The Statis-
tics Block may be called after any block that generates such a file. In
addition, the user may create an interface file of rainfall or other data
and, through an understanding and alteration of various conversion factors,
use the Statistics Block to analyze rainfall, rather than stormwater events.
Separation of the data into events depends on the unique series of zero
and non-zero instantaneous flow values found at each location within the
system being simulated. The results of the analyses would be expected to
vary from location to location. The Statistics Block handles only one
location at a time.
This section describes the program operation of the Statistics Block,
identifies output options, provides instructions for preparing input data
cards, defines program variables, presents the equations utilized within the
block and explains the messages and labels that may be printed.
Program Operation
The Statistics Block is a Fortran program of approximately 1500 state-
ments in length and consists of six subroutines. The relationships among
the Statistics Block, the rest of SWMM and the various subroutines are shown
in Figure 9-1.
The subroutine STATS comprises the major portion of the block. Input
data and data from the interface file are read in STATS. Descriptive infor-
mation from the file header is printed, followed by a summary of the input
data. STATS separates the flow/pollutant data into events and writes this
330 _
-------
EXECUTIVE
BLOCK
STATS
LABELS
(BLOCK
DATA)
Figure 9-1. Structure of the Statistics Block Subroutines.
331
9-2
-------
new data set on an off-line file. The write statement contains 561 bytes.
Using disk storage with 19500 bytes per track, data for 34 events can be
stored on one track. With an internal program limit of 3750 events, 111
tracks of disk storage are required for the Statistics Block. The limit of
3750 can be changed, resulting in a change in off-line storage requirements.
After the entire simulation period has been examined, a table of the
sequential series of events is printed (if requested). If a table of return
period and frequency or a graph of either of these is requested, SORT is
called to sort the series into descending order. SBTABL is utilized to
generate and print the table of magnitude, return period and frequency.
POINTS is called prior to CURVE. POINTS generates an array of (X,Y) pairs
to be plotted as points on either the return period or frequency graph.
CURVE and PPLOT are part of the Executive Block and are used to print the
graphs. MOMENT calculates and prints the mean, variance, standard devia-
tion, coefficient of variation and coefficient of skewness of the event data.
LABELS is a Block Data subroutine utilized for initializing constants in
labeled common blocks that are used for labeling graphs and other output.
Output Options
The table of sequential series of events depicts the original time
series of flow data after the time steps have been grouped into events. The
table includes the date and time of day that each event began, flow volume
of each event, duration of each event and interevent durations. The table
of magnitude, return period and frequency is a rank order table showing the
date and time that each event began, the magnitude of the event being ana-
lyzed, the return period (in months) of that magnitude and the percent of
occurrences that are less than or equal to the given magnitude. The graph
of magnitude vs. return period is a plot of two columns of the table, except
that return period is presented as the base ten logarithm. The graph of
magnitude vs. frequency is a similar plot with frequency presented as a per-
cent. Although the graphing routines plot information centered in the table,
it is not necessary to select the table option in order to select the graph
options. Any of these may be printed independently of the others. The last
option available is a calculation of a number of sample statistics of the
event data (enumerated above).
The table of sequential series pertains only to the volume of the flow
events. The remaining options can be requested for flow or pollutants. Any
(or all) of these can be selected for any (or all) of the five flow parame-
ters and for any (or all) of the five pollutant parameters. Different pol-
lutant parameters can be analyzed for different pollutants. Events are
identified on the basis of zero and nonzero flow values so that the duration
of events and interevents will be identical for flow and any of the pollu-.
tants selected.
Potential for Output
Sequential Series of Events --
This option prints a table of the original series of events before any
sorting has taken place. Printing of the table, which contains 120 events
332 9-3
-------
per page, may be accessed in several ways. First, the table may be printed
directly as an option under normal program execution. Second, when the num-
ber of events in the time series exceeds the designated limit and termination
of the program has not been requested, the table may be printed (ignoring
the rest of the series). Third, in the case where termination has been
selected, the option remains to print a table for that portion of the series
that has been separated into events.
Table of Magnitude, Return Period and Frequency --
For those parameters where this option is requested, one table will be
printed for each parameter chosen for each constitutent chosen. For example,
if, for the constitutent 'FLOW, two parameters are chosen (e.g., total flow
and event duration), and for each of two pollutants two parameters are chosen
(e.g., total load and peak concentration), then six separate tables are
printed, each containing magnitude, return period and frequency for the
appropriate parameter. Therefore, although it is unlikely that one would
have reason to do so, up to 55 tables can be printed in one run (five flow
parameters, and five pollutant parameters for ten pollutants). The length
of each table depends on the number of events within the period of analysis
(180 events are printed per page).
Graph of Magnitude vs. Return Period or Graph of Magnitude vs. Frequency
The follov/iug discussion refers to either type of graph. As with the
tables above, one graph is printed for each parameter chosen of each constit-
uent for which a graph is requested. Each graph comprises one page of output.
Again, up to 55 graphs can be printed, although this would be an unlikely
choice.
Moments --
This option calculates and prints unbiased estimates for the mean, vari-
ance, standard deviation, coefficient of variation and coefficient of skew-
ness. These values will be printed for each parameter chosen for each con-
stituent chosen. The output incorporates approximately 15 lines and will
appear in sequence where space is available (i.e., a new page is not printed
for each set of moments).
PREPARATION OF INPUT DATA
Extent of Data
The Statistics Block requires a minimal amount of input data under nor-
mal use. The flow/pollutant data to be analyzed will be read from interface
files generated by other blocks of SWMM. The input data required simply
indicate what type of analysis should be performed on the interface data.
Use of the block for rainfall data will be discussed in a later subsection.
Card by card instructions for preparing the input data cards will be
presented. The user is referred to Table 9-2 at the end of this section for
333 9 _4
-------
input format and order. With regard to integer formats, it is imperative
that all values be right-justified within the format field. All card groups
are required. Figure 9-2 shows the general structure of the data deck.
Card Group A1
The variable ISTART and TSTART indicate the date and time, respec-
tively, at which the program should begin searching for events. The vari-
able TEND and TEND indicate the last point on the file that should be read.
In this manner, the user can choose any period within the record (e.g., one
particular year, five sequential years, etc) on which to perform statistical
analyses. Default values of zero for both date and time can be chosen for
starting and/or ending. Zero starting values indicate that analyses should
commence with the first value on the interface file. Zero ending values
indicate that analyses should continue to the end of the available record.
Formats for date and time correspond to the standardized interface format.
Card Group B1
The minimum interevent time (MIT) indicates the minimum number of dry
time steps (time steps with zero flow) that will constitute an interevent.
In other words, the number of consecutive dry time steps encountered in the
search must be equal to or greater than MIT in order that the preceding wet
period (made up of at least one nonzero flow value) be considered a separate
event. Dry periods of duration less than MIT may exist within an event
preceded and followed by wet time steps. The number of events in a given
period of analysis is directly dependent on the value of MIT. A value of
zero may be chosen for MIT; in this manner every wet time step will be
viewed as a separate event. No "correct" value of MIT can be suggested,
although a value of 6 to 22 hrs is often used to separate rainfall events
(Heaney et al., 1977). The value utilized depends on the criteria employed
by the user in defining an event.
The variable LOCRQ indicates at which location, e.g., inlet or manhole
number, within the system the analyses are being performed. The interface
file may contain data for up to 200 locations, each identified in the array
LOCNOS(K). The variable LOCRQ should have the same value as one of the
elements of the array LOCNOS(K).
The variable NPR indicates the number of pollutants requested for
statistical analysis. NPR must be less than or equal to NPOLL (the number
of pollutants on the interface file).
Card Group B2
The variable array IPOLRQ will contain up to ten elements, corres-
ponding to the maximum value of NPR (and NPOLL). The pollutants requested
for analysis must be identified by their position on the interface file (not
by name). Therefore, the elements of IPOLRQ will contain integer values
from 1 to 10. For example, if the first pollutant to be analyzed is BOD,
and BOD is the third pollutant on the interface file, then IPOLRQO) would
have the value 3. Similarly, if the second pollutant to be analyzed is TSS,
and TSS is the fifth pollutant on the interface file, then IPOLRQ(2) would
have the value 5.
334 9-5
-------
GROUPS G1-G2; Codes for Termination
fCARD GROUP Fl; Code for Units
fCARD GROUP El; Code for Sequential Series
GROUPS D1-D4; Stat Options for Pollutants
GROUPS CI-C4; Stat Options for Flow
r"CARD GROUP B1-B2; General Data
fCARD GROUP Al; Starting and Ending Date/Time
STATS
Figure 9-2. Structure of the Data Deck.
335 9-6
-------
Card Groups C1-C4
The four "C" cards indicate the statistical options requested for flow.
One card is used for each type of statistical option. Card Cl is for a table
of event magnitude, return period and frequency. Card C2 is for a graph of
magnitude vs. return period. Card C3 is for a graph of magnitude vs. fre-
quency. Card C4 is for a table of the first three moments of the event data.
There are five fields on each card, one field for each of the five flow
parameters. The five parameters are (1) total flow for the event, measured
as a volume and reported as inches [mm], (2) average flow for the event,
measured as a rate and reported as inches/hour [mm/hr], (3) peak flow for
the event, measured as an instantaneous rate and reported as inches/hour
[mm/hr], (4) duration of the event, measured as the number of time steps
making up the event and reported as hours, and (5) duration of the inter-
event, measured as the number of dry time steps preceding the event and
reported as hours. For a given card, those fields containing a 1 indicate
the flow parameters for which that option has been selected. Values of zero
indicate that the analyses should not be performed. '
Card Groups D1-D4
The four "D" cards are identical in format to the "C" cards. The four
statistical options available for flow are also available for pollutants.
Again, one card is used for each type of statistical option. An entire set
of "D" cards must be included for each pollutant requested, indicating which
options for which parameters should be performed for each pollutant. The
"D" cards should be arranged as a group (D1-D4), with groups of cards in a
sequence corresponding to the order in which the pollutants were requested.
Again, there are five fields on each card, one field for each of the five
pollutant parameters. The five parameters are (1) total load, measured as a
sum of the concentration times the flow rate and reported as pounds [kg],
(2) average load, measured as a rate of pollutant loading and reported as
Ibs/hour [kg/hr], (3) peak load, measured as an instantaneous rate of pollu-
tant loading and reported as Ibs/hour [kg/hr], (4) flow weighted average
concentration, reported as mg/1, and (5) peak concentration, reported as
mg/1. As noted in Table 9-2, at least one set of D1-D4 cards must be
included. If NPR = 0, all "D" cards would contain zero values (or be left
blank).
Card Group El
The variable KSEQ indicates whether or not a table of the sequential
series of events should be printed.
Card Group Fl
The variable KENGSI indicates the system of units in which output should
be reported. To implement U.S. customary units, use KENGSI = 0. For metric
units, use KENGSI = 1.
336
-------
Card Groups Gl .md G2
The variable KTERM indicates whether or not to terminate analyses in
the case where the number of events exceeds the allowable computer memory
space. The number of events that can be sorted and analyzed has been set
within the program at 3750. This value corresponds to 150 events per year
for a 25 year period. As noted at the beginning of the STATS subroutine,
this value may be altered by the user. If the number of events exceeds the
limit set, the program will either (a) perform the analyses on the events
already identified, ignoring the remainder of the record (KTERM =0), or (b)
terminate execution of the block, performing no event analysis (KTERM = 1).
If the analyses are being performed, a table of the sequential series will
be printed if KSEQ =1. If the analyses are not performed, the option still
exists to print the table of sequential series before termination. The
variable KTSEQS indicates that the table should or should not be printed in
this case.
COMPUTATIONS
Return Period and Frequency
The logic of the program is described by comment statements at the
beginning of each subroutine and throughout the program listing. Computa-
tions that require further explanation will be discussed.
In subroutine STATS, the variables Tl and T2 indicate the beginning and
end, respectively, of the period of analysis. The values are reported as
elapsed time from the beginning of the simulation, measured in hours. The
return period of an event of a given magnitude is reported as months so that
a calculation of the number of months (NOMOS) within the period of analysis
is required. The average number of hours per month in a year of 365.25 days
is 730.5. This value is used to find a value for NOMOS, rounded to the
nearest month, by the equation
NOMOS = Integer 730*5 + 0.5 (9-1)
For short periods of analysis (e.g., on the order of one year) NOMOS may be in
error by one month depending on which months of the year are included in the
period. This should pose little difficulty as a return period analysis for
such a short period is generally not undertaken (or at best is of questionable
worth).
In subroutine TABLES, calculations are made of the return period and
frequency of events. The program is set up in such a way that the equation
used depends on the column of the table in which the values will be printed.
These calculations are summarized in the general forms
337
9-8
-------
Return Period (months) = NOH0^ * 1 (9_2)
where NOMOS = number of months within the period of analysis, and
M = r.ank of the given event (ranked in descending order), and
Frequency = I1 »" x 100 (9-3)
where Frequency = percent of occurrences less than or equal to the given
magnitude ,
M = rank of the event, and
N = total number of events within the period of analysis.
Moments
In subroutine MOMENT, calculations are made of estimates for the mean
(X), variance (S ), standard deviation (S) , coefficient of variation (CV)
and coefficient of skewness (C ). The equations utilized for these calcula-
tions
(9'4)
2 _ IX2 - IN) (X)2
S = (S2)'5 , (9-6)
CV = - , and (9-7)
X
- 3 IX2. (X) + 2(X)3
.
N _ U (N-D
/ O 1\1C
IX2. - (X)2!1-5
Equation 9-8 is equivalent to the more usual form
I(X-X)3
c -
~
IN (M-
s ~ I - 2 \ 1 S N-2
j-jQl I
N
338
9-9
-------
where X = magnitude of the event parameter, and
N = total number of events within the period of analysis.
2
S and C are unbiased estimates. All summations are from 1 to N.
s
Messages/Labels
The majority of the messages printed as part of the program execution
are self-explanatory and do not require discussion in the text. A notable
exception to this involves the units printed for pollutants. As a prelude
to this discussion, an explanation of the units provided in the tables and
graphs is called for. Table 9-1 summarizes the units printed for flow and
three types of pollutants. The labels printed for the ordinate of the
graphs are also presented.
All flow parameters are normalized to depth or depth/hr (i.e., in or mm
or in/hr or mm/hr). Should true volumes be desired, they may be obtained by
multiplying by the catchment area, printed after reading the interface file.
When NDIM = 2, .1 special message is printed on the graph or table.
Rather than printing the units described in Table 9-2, the output contains
"SEE NOTE" and the note "Magnitude has units of .... See user manual for
explanation." The explanation referred to is included in the following
discussion.
The user is referred to Sections 2 and A for an introduction to the
variable NDIM. For NDIM = 0, pollutant concentration is given in mg/1. In
this case, a direct conversion is possible for loading rates and concentra-
tions. For NDIM = 1, pollutant concentration is given in "other quantity"
per liter (e.g., MPN/1). Here, no conversion is possible to mass loading or
mass per unit volume. "Mass" must be presented as "quantity" and the user
must be aware of what "quantity" refers to for the pollutant involved. The
units printed for flow weighted average concentration and peak concentration
will correspond to the variable PUNIT found on the interface file for the
particular pollutant. For NDIM = 2, pollutant concentration is given in
some other units, not on a "per liter" basis (e.g., JTU). Therefore, no
units conversion can be made. Magnitudes reported for total load will have
units of a volume multiplied by the appropriate PUNIT. The magnitude is
obtained by summing the pollutant values found on the interface file (which
are in units of an instantaneous flow rate multiplied by a concentration)
and multiplying this value by the time step size (DTSEC) of the event.
Interpretation of the significance of these magnitudes is left strictly to
the user, who should exercise caution in selecting this statistical option.
A similar caution applies to average load and peak load. These magnitudes
will have units of a flow rate multiplied by the appropriate PUNIT. The
average load is the mean of the values found on the interface file for a
given event, with a units conversion for flow rate. The peak load is the
largest of the values within an event, with a similar units conversion for
flow rate. Flow weighted average concentration and peak concentration will
have units corresponding to PUNIT for the particular pollutant and an inter-
pretation of these magnitudes may be simpler than the above parameters. The
calculation of these two parameters is self-evident.
339
9-10
-------
Table 9-1. Labels and Units.
Flow
Pollutant with
NDItt=0
Pollutant with
NDIM=1
Pollutant with
NDIM=2
Parameter
Total Flow
Average Flow
Peak Flow
Event Duration
Interevent
Duration
Total Load
Average Load
Peak Load
Flow Weighted
Average
Concentration
Peak Concen-
tration
Total Load
Average Load
Peak Load
Flow Weighted
Ordinate
Label
Total Q
Aver Q
Peak Q
Duration
Interevt
Tot Load
Ave Load
PeakLoad
Ave Cone
PeakConc
Tot Load
Ave Load
PeakLoad
Ave Cone
U.S.
Customary Units
inches
inches/hr
inches/hr
hours
hours
pounds
Ibs/hr
Ibs/hr
mg/1
mg/1
Quantity
Quan/hr
Quan/hr
PUNIT
Average
Concentration
Peak Concen- PeakConc
tration
Total Load
Average Load
Peak Load
Flow Weighted
Average
Concentration
Peak Concen-
tration
Tot Load
Ave Load
PeakLoad
Ave Cone
PeakConc
PUNIT
ft -PUNIT
cfs PUNIT
cfs PUNIT
PUNIT
PUNIT
Metric Units
nun
mm/hr
mm/hr
hours
hours
kilogram
kg/hr
mg/1
mg/1
Quantity
Quan/hr
Quan/hr
PUNIT
PUNIT
Liter*PUNIT
Liter/S-PUNIT
Liter/S PUNIT
PUNIT
PUNIT
340
9-11
-------
ANALYSIS OF RAINFALL DATA
A series of rainfall measurements can be viewed as analogous to a
series of flow values and can be analyzed using the statistical options for
flow. The following discussion assumes that the rainfall record is in
hourly time steps. The user can make similar adjustments for other types of
rainfall records.
An interface file of the standardized SWMM format must be generated by
the user. This will contain rainfall data only, no pollutants. The file
may contain data for a number of locations, but only one location may be
analyzed during a run.
The event parameters will be viewed as follows: Values reported as
"total flow" will be total depth of the storm, in inches; values reported as
"average flow" will be average intensity during the event, in inches/hour,
and calculated as the total depth divided by the duration of the event;
"peak flow" will be peak intensity, in inches/hour, and will be the largest
single hourly value recorded for the storm; event duration and interevent
duration are identical in concept to stormwater events.
Variables that require the assignment of particular values are:
For the interface file:
- DTSEC = 3600
- TRIBA = 1.000
43560 ft/ac
- QCONV = 1.00833 =
12 in/ft x 3600 sec/hr
For the input data:
- NPR = 0
- KENGSI = 0
The input data also require one set of "D" cards, all blank.
As regards labels for the output, the term "FLOW" will remain. The user
must think of this in the context of the options selected (i.e., depth or
intensity). The remainder of the program operation will be identical to
stormwater events.
341
9-12
-------
Table 9-2. Statistics Block Card Data
Card
Group
Al
Format Card
Columns
CARD GROUP Al
2X 1-2
16 3-8
4X 9-12
F5.2 13-17
5X 18-22
16 23-28
4X 29-32
F5.2 33-37
CAW)
Description Variable
Name
; Starting and Ending Date/Time
Card identifier =
Al.
Starting date, ISTART
yr/mo/day.
Skip
Starting time, TSTART
decimal hours.
Skip
Ending date, IEND
yr/tno/day .
Skip
Ending time, TEND
decimal hours.
GROUP Rl; General l)nl.:i
Default
Value
Blank
000000
Blank
00.00
Blank
000000
Blank
00.00
Bl
2X
110
110
110
F10.0
1-2
3-12
13-22
23-32
33-42
Card identifier =
Bl.
Minimum interevent MIT
time, no. of time
steps.
Location requested. LOCRQ
Number of pollu- NPR
tants requested.
Base flow co
determine end of
event, in/hr [mm/hr].
Only flows >_ RASE
(and their
associated pollutant
loads) arc analyzed.
Others are created
as zeroes.
RASE
Blank
00.00
B2 2X 1-2 Card identifier -
B2.
IOF5 3-7 First pollutant IPOLKQ(l)
requested, identi-
fied by position on
interface file.
B1.1 nk
342
9-13
-------
Table 9-2. (continued). Statistics Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
8-12
48-52
Second pollutant
requested, identi-
fied by position on
interface file.
IPOLRQC2) none
Tenth pollutant IPOLRQ(IO)
requested, identi-
fied by position on
interface file.
CARD GROUPS C1-C4; Stat Options for Flow
Requests to print
table of magnitude,
return period and
frequency for each
of five parameters.
In all cases, No = 0,
Yes = 1.
Cl 2X 1-2 Card identifier =
Cl.
5110 3-12 Request table for ISFLOW(1,1)
total flow.
13-22 Request table for ISFLOW(1,2)
average flow.
23-32 Request table for ISFLOW(1,3)
peak flow.
33-42 Request table for ISFLOW(1,4)
event duration.
43-52 Request table for ISFLOW(1,5)
interevent duration.
Blank
0
0
0
0
0
Requests to print graph
of magnitude vs. return
period for each of five
parameters. In all cases,
No = 0, Yes = 1.
343
9-14
-------
Table 9-2. (continued). Statistics Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
C2 2X 1-2 Card identifier =
C2.
5110 3-12 Request graph for ISFLOW(2,1)
total flow.
13-22 Request graph for ISFLOW(2,2)
average flow.
23-32 Request graph for ISFLOW(2,3)
peak flow.
33-42 Request graph for ISFLOW(2,4)
event duration.
43-52 Request graph for ISFLOW(2,5)
interevent duration.
Blank
C4
2X
5110
Requests to print graph
of magnitude vs. frequency
for each of five parameters.
In all cases, No = 0,
Yes = 1.
C3
2X
5110
1-2
3-12
etc.
Card identifier =
C2.
Card similar to Cl
and C2.
Blank
1-2
3-12
etc.
Requests to print
moments for each of
five flow parameters.
In all cases, No = 0,
Yes = 1.
Card identifier =
C4.
Card similar to Cl
and C2.
Blank
344
9-15
-------
Table 9-2. (continued). Statistics Block Card Data
Card Format Card Description Variable Default
Group Columns Name Value
CARD GROUPS D1-D4; Stat Options for Pollutants
MUST INCLUDE AT LEAST ONE SET OF D1-D4 CARDS, EVEN IF NPR = 0. IF NPR = 0,
USE FOUR BLANK CARDS FOR D1-D4. IF NPR>1, USE ONE SET OF D1-D4 CARDS FOR
EACH POLLUTANT REQUESTED, UP TO TEN SETS OF CARDS, IN THE ORDER DEFINED BY
CARD B2. THE FIRST INDEX OF ISPOLL (K, I, J) IDENTIFIES THE POLLUTANT.
Requests to print table
of magnitude, return
period and frequency
for each of five
parameters. In all
cases, No = 0,
Yes = 1.
Dl 2X 1-2 Card identifier = -- Blank
Dl.
5110 3-12 Request table for ISPOLLd, 1,1) 0
total load.
13-22 Request table for ISPOU.( 1,1,2) 0
average load.
23-32 Request table for ISPOLLd, 1,3) 0
peak load.
33-42 Request table for ISPOLLd, 1,4) 0
flow weighted
average concentration.
43-52 Request table for ISPOLL(1,1,5) 0
peak concentration.
Requests to print graph
of magnitude vs. return
period for each of five
parameters. In all cases.
No = 0, Yes = 1.
D2 2X 1-2 Card identifier = -- Blank
D2.
345 9-16
-------
Table 9-2. (continued). Statistics Block Card Data
Card Format Card Description
Group Columns
5110 3-12 Request graph for
total load.
13-22 Request graph for
average load.
23-32 Request graph for
peak load.
Variable
Name
ISPOLLO
ISPOLLU
ISPOLLU
33-42 Request graph for ISPOLL(1
flow weighted
average concentration.
43-52 Request graph for
peak concentration.
ISPOLLU
Default
Value
,2,1)
,2,2)
,2,3)
,2,4)
,2,5)
0
0
0
0
0
D3
2X
5110
1-2
3-12
etc.
Requests to print graph
of magnitude vs. frequency
for each of five parameters.
In all cases, No = 0,
Yes = 1.
Card identifier =
D3.
Card similar to Dl
and D2.
Blank
2X
5110
1-2
3-12
etc.
Requests to print
moments for each of
five parameters. In
all cases, No = 0,
Yes = 1.
Card identifier =
D4.
Card similar to Dl
and D2.
Blank
346
9-17
-------
Table 9-2. (continued). Statistics Block Card Data
Card
Group
Format
Card
Columns
Description
Variable
Name
Default
Value
CARD GROUP El; Code for Sequential Series
£1 2X 1-2 Card identifier =
Fl.
110 3-12 Request to print
sequential series
of flow events.
No = 0, Yes = 1.
01
KSEQ
2X
110
In the case where
the number of events
exceeds allowable
memory space:
1-2 Card identifier =
Gl.
3-12 Code for terminal- KTERM
ing program.
= 0, Do not terminate
(perform analyses on
those events already
identified).
= 1, Terminate program
(no event analysis
performed).
Blank
0
CARD GROUP Fl; Code for Units
Fl 2X 1-2 Card identifier =
Fl.
110 3-12 Request type of KENGSI
units for output.
= 0, U.S. customary.
= 1, Metric.
Blank
0
CARD GROUPS Gl AMD 02; Codes for Termination
Blank
347
9-18
-------
Table 9-2. (continued). Statistics Block Card Data
Card Format
Group
Card
Columns
Description Variable
Name
Default
Value
G2 2X 1-2 Card identifier = Blank
G2.
110 3-12 Code for printing KTSEQS 0
sequential series
if termination occurs
(where number of events
exceeds limit and KTERM
= IK
= 0, Do not print
sequential series.
= 1, Print sequential
series of those events
already identified.
END OF STATISTICS BLOCK DATA. AT THIS POINT, PROGRAM EXECUTION
COMMENCES WITH READING OF INTERFACE FILE HEADER.
DATA CARDS FOLLOWING THESE WILL BE READ BY THE EXECUTIVE BLOCK.
348 9-19
-------
SECTION 10
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350 10-2
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351 10-3
-------
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352 10-4
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353 10-5
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354 10-6
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APPENDIX I
CONTINUOUS SIMULATION
CONTINUOUS AND SINGLE EVENT SIMULATION
The original Storm Water Management Model, designed for single event
simulation, produced detailed (i.e., short time increment) hydrographs and
pollutographs for individual storm events. Although this capability remains,
the model has now been altered so that it may be run for an unlimited number
of time steps, for multiple events. In this mode it may be used in a plan-
ning context, that is, for an overall assessment of urban runoff problems
and estimates cf the effectiveness and costs of abatement procedures. Trade-
offs among various control options, such as storage, treatment and street
sweeping, may be evaluated. Complex interactions between the meteorology,
e.g., precipitation patterns, and the hydrology of an area may be simulated
without resorting to average values or very simplified methods. In this
manner, critical events from the long period of simulation may be selected
for detailed analysis. In addition, return periods for intensity, duration
and volume (mass) of runoff (pollutant loads) may be assigned on the basis
of the simulated record instead of incorrectly equating them to the same
statistics of the rainfall record. In this manner, the critical events
chosen for study may be substituted for hypothetical "design storms", the
latter often being synthesized from intensity-duration-frequency curves on
the basis of questionable statistical assumptions. Linsley and Crawford
(1974) present a useful discussion of continuous simulation in urban
hydrology.
Several continuous simulation models are available for urban runoff
analysis. Among the earliest was the Stanford Watershed Model (Crawford and
Linsley, 1966), out of which evolved the HSPF Model (Johanson et al., 1980),
a versatile program for natural and agricultural as well as urban areas. It
uses a 15 minute time step whereas a 5 minute time step is used by the Dorsch
QQS model (Geiger et al., 1974, Geiger and Dorsch, 1980). Probably the most
widely used continuous simulation model for urban areas is STORM (Hydrologic
Engineering Center, 1977, Rosener et al., 1974), developed by Water Resources
Engineers, the City of San Francisco and the Hydrologic Engineering Center
of the Corps of Engineers. It utilizes one hour time steps coupled with
simplified runoff and pollutant estimation procedures and has been extensively
used for planning (Roesner et al., 1974) and overall urban runoff evaluation
(Heaney et al., 1977). A similar, but even simpler model, still producing
useful statistics of long-term urban runoff, is the Simplified Storm Water
Management Model developed by Metcalf and Eddy, Inc. (Lager et al., 1976).
Finally, several "first cut" procedures have been developed, based in part
upon continuous simulation, but avoiding any computer usage at all (Hydro-
science, Inc., 1976, Howard, 1976, Heaney et al., 1976, Chan and Bras, 1979).
367 1-1
-------
CONTINUOUS SWMM OVERVIEW
SWMM is run continuously using only the Runoff and Storage/Treatment
(S/T) blocks. Routing in Transport, WRE Transport (EXTRAN) or Receiving is
avoided and is unnecessary for the planning purposes to which the model is
applied. (However, there is no limitation on the number of time steps for
either Extran, Transport or Receive). A "Level III" receiving water model
that will couple with either continuous SWMM or STORM has been developed
based upon earlier work (Heaney et al., 1977) and is documented (Medina,
1979). The algorithms used in Runoff and S/T are almost identical when run
continuously or as a single event model, the only differences occuring in a
minor way in the snowmelt routines. A one-hour time step is required when
the model receives input from National Weather Service (NWS) precipitation
and temperature tapes, which will be true for most applications. Although
other time steps may be used, the output is generally formated for an assumed
24 time steps per day. In fact, inclusion of daily, monthly and annual
totals along with a few other I/O features forms about the only distinction
between the continuous and single event mode.
It is anticipated that continuous, long-term simulation will only be
used with a very coarse, "lumped" or aggregated catchment schematization in
order to minimize computer costs. For example, only one subcatchment and no
gutter/pipes will often suffice.
SNOWMELT
Following the earlier work of the Canadian SWMM study by Proctor and
Redfern and James F. MacLaren (1976a, 1976b) snowmelt simulation has been
added for both single event and continuous simulation. Since snowmelt com-
putations are explained in detail in Appendix II, only an outline is given
here. Most techniques are drawn from Anderson's (1973) work for the National
Weather Service (NWS). For continuous simulation, daily max-min temperatures
from the NWS "W3AN Summary of the Day, Deck 345" are converted to hourly
values by sinusoidal interpolation.
Urban snow removal practices may be simulated through "redistribution
fractions" input for each subcatchment (see Figure II-6 in Appendix II),
through alteration of the melt coefficients and base temperatures for the
regions of each subcatchment, and through the areal depletion curves-used
for continuous simulation. Anderson's temperature-index and heat balance
melt equations ;ire used for melt computations during dry and rainy periods,
respectively. For continous simulation, the "cold content" of the pack is
maintained in order to "ripen" the snow before melting. Routing of melt
water through the snow pack is performed as a simple reservoir routing pro-
cedure, as in the Canadian study.
The presence of a snow pack is assumed to have no effect on overland
flow processes beneath it. Melt is routed in the same manner as rainfall.
368 1-2
-------
INPUT DATA
Continuous SWMM requires all data entries previously required except
that a coarse schematization greatly reduces the amount of entries required
for subcatchments and gutter/pipes (see below). The key data need is a long-
term precipitation record for the area. SWMM is keyed to the use of magnetic
tapes available from the National Weather Records Center of the NWS at Ashe-
ville, North Carolina. These tapes contain card images of "NWS Card Deck
488, USWB Hourly Precipitation" and cost approximately $200 per station for
a 25 year record of hourly precipitation totals. (Similar tapes are supplied
in Canada by the Atmospheric Environment Service.) When snowmelt is simu-
lated, a record of daily temperature data is also required; see the snowmelt
documentation ia Appendix II. These data are processed in subroutine CTRAIN
in Runoff for Later use by the other routines in the block. Optionally, the
processed data, including a tabulation of the 50 highest values, may be exam-
ined prior to proceeding with the remainder of the simulation. When snowraelt
is simulated, rainfall or snowfall is determined from hourly air temperatures
synthesized from the daily max-min values for the station. Snowfall values
are keyed as negative precipitation for internal use in the program.
Other input data unique to continuous simulation consist mainly of dates
for starting and stopping, printing, etc. In addition, NWS Station ID num-
bers must be known for the precipitation and temperature tapes.
CATCHMENT SCHEMATIZATION
Guidelines for subcatchment "lumping" or aggregation are given by Smith
(1975) and Proctor and Redfern, Ltd. aad James F. MacLaren, Ltd. (I976a,
1976b). In general, almost identical outlet hydrographs may be produced
using only one subcatchment and one or no gutter/pipes as for a detailed
schematization, using several subcatchraents and gutter/pipes. A key param-
eter to be adjusted is the subcatchment width. Quality comparisons may be
more variable depending upon how the several land uses and/or pollutant
loading rates are aggregated.
OUTPUT
Runoff Block
Output from single event simulation consists basically of hydrographs
and pollutographs printed over the whole event at a specified interval of
time steps (e.g., every time step). Continuous SWMM retains this option for
up to five user-specified date intervals. In addition, daily, monthly,
annual and grand total values for runoff, precipitation and pollutant loads
are provided. Daily totals are printed whenever there is runoff and/or pre-
cipitation.
In addition, the 50 highest hourly totals are listed, by both runoff
volume and BOD load. These may be compared to the 50 highest hourly rainfall
depths and used in selecting critical time periods for more detailed study.
369 1-3
-------
For example, a two-year simulation of a 312 ac (126 ha) catchment tributary
to Lake Calhoun in Minneapolis was made, and the ten highest rainfall, runoff
and BOD loads (from the output of the 50 highest) are shown in Table 1-1.
The comparisons indicate that the rankings differ according to antecedent
conditions, etc. affecting each parameter. For example, the highest rainfall
depth corresponds to the third highest runoff depth and second highest BOD
load. The table adds further justification to the contention that it is
necessary to treat rainfall, runoff and pollutant loads separately in terms
of statistical analyses.
The most useful review of continuous SWMM output is probably accomplished
using the Statistics Block. Therein a frequency analysis of many storm quan-
tity and quality parameters (e.g., depth, duration, interevent time, load,
peak concentration, etc.) may be performed. Output is available in both
tabular and graphical forms. Analysis by the Statistics Block may follow
any other block (except Receive).
Storage/Treatment Block
There is no distinction made between output for a single event or con-
tinuous simulation in the Storage/Treatment Block. Regardless of the number
of time steps specified, the user has the option of printing a detailed
report for each time step during eight different intervals (or less), a
monthly summary, an annual summary, and/or a final summary. Of course, the
monthly and annual summaries have little value in a single event simulation
and should only be used for long-term runs.
DRY-PERIOD REGENERATION
Quantity
Infiltration capacity is regenerated during dry periods assuming an ex-
ponential "drying curve" analogous to the "wetting curve" of Morton's equa-
tion (see Appendix V). Monthly evaporation totals are used to regenerate
depression storage on both pervious and impervious areas and are also con-
sidered an initial "loss" for each time step with rainfall. Computations
are bypassed during dry periods if infiltration and depression storage re-
generation is complete.
Quality
Pollutant loadings on the subcatchment surfaces are regenerated during
dry time steps (i.e, no runoff) depending upon how they are input initially.
Linear or non-linear buildup may be used, with or without an upper limit.
If desired, a rating curve (load versus flow) may be used instead of a
washoff equation.
Street sweeping occurs at intervals specified for each land use. The
intervals are computed on the basis of intervening dry time steps. A dry
time step is one in which the subcatchment receives no precipitation and has
no water remaining in impervious area depression storage or as snow. When
siiowwell is simulated, street sweeping may be bypassed for a specified
interval of the year (e.g., the winter months). j_4
-------
Table 1-1. Hourly Event Ranking by Rain Flow and BOD for Two Year Simulation of Lake Calhoun
Catchment, Minneapolis.
Ten highest values are taken from the tabulated output of 50 highest given by SWUM.
Rank Date
Hr Rain (in./hr)
Date
Hr Flow (jn./lir)
Date
IIr BOD Clb/min)
1
2
3
4
5
6
7
8
9
10
7/20/51
7/21/51
7/22/50
7/30/51
5/15/51
7/21/51
9/11/51
8/07/51
5/05/50
6/25/51
22
1
15
8
21
2
23
18
10
24
0.98
0.80
0.79
0.65
0.63
0.56
0.55
0.54
0.49
0.49
7/21/51
7/20/51
7/20/51
5/15/51
7/21/51
7/30/51
7/16/51
7/22/51
5/18/51
7/22/50
2
23
22
22
1
8
2
16
16
15
0.543
0.429
0.392
0.383
0.320
0.295
0.254
0.253
0.238
0.221
5/15/51
7/20/51
7/16/51
7/20/51
5/15/51
9/08/51
7/22/51
7/30/51
5/05/50
7/22/50
22
22
2
23
21
20
15
8
10
16
16.78
12.88
9.62
7.64
6.19
5.70
5.43
5.42
5.25
5.11
-------
Runoff simulates any ten quality parameters with arbitrary units, plus,
optionally, erosion using the Universal Soil Loss Equation. As a user op-
tion, regeneration of selected constituents (e.g, chlorides) during dry
periods will occur only when snow is present.
CONTINUOUS SWMM COMPARED TO STORM
Preliminary comparisons of SWMM and STORM, without S/T simulation, in-
dicate that the two outputs are comparable and STORM is approximately 50
percent cheaper (see Appendix II). Why, then, might SWMM be used over STORM
or other existing continuous models? When just the Runoff Block is required,
STORM could be the choice because of its simplicity, good documentation,
useful output and inclusion of the SCS method for rural runoff generation.
SWMM might be preferred if flow routing in gutter/pipes were desired or par-
ticular features of runoff or quality generation were needed. In addition,
SWMM now couples both the single event and continuous simulation capability
into one model.
The principal advantage of continuous SWMM lies in its Storage/Treat-
ment block described in Appendix IV. Several pathways among and through the
various storage and/or treatment devices may be utilized instead of one
fixed configuration. Most importantly, the treatment that occurs in storage
as well as sludge generation by all control options, may be simulated by
SWMM. Finally, SWMM S/T will compute operation and maintenance costs on the
basis of actual hours of operation of wet-weather treatment devices,
providing more realistic cost data.
1-6
372
-------
APPENDIX II
SWMM SNOWMELI ROUTINES
INTRODUCTION
Snowmelt is an additional mechanism by which urban runoff may be gener-
ated. Although flow rates are typically low, they may be sustained over
several days and remove a significant fraction of pollutants deposited dur-
ing the winter. Rainfall events superimposed upon snowmelt baseflow may
produce higher runoff peaks and volumes as well as add to the melt rate of
the snow.
In the context of long term, continuous simulation, runoff and pollu-
tant loads are distributed quite differently in time between the cases when
snowmelt is and is not simulated. The water and pollutant storage that
occurs during winter months in colder climates cannot be simulated without
including snowmelt.
Several hydrologic models include snowmelt computations, e.g., Stanford
Watershed Model (Crawford and Linsley, 1966), HSPF (Johanson et al., 1980),
NWS (Anderson, L973, 1976), STORM (Hydrologic Engineering Center, 1977,
Roesner et al., 1974) and SSARR (Corps of Engineers, 1971). Of these exam-
ples, only HSPF and STORM include pollutant routing options. Useful summa-
ries of snowmelt modeling techniques are available in texts by Fleming
(1975), Eagleson (1970), Linsley et al. (1975), Viessraan et al. (1977), and
Gray (1970). All of these draw upon the classic work, Snow Hydrology, of
the Corps of Engineers (1956).
As part of a broad program of testing and adaptation to Canadian condi-
tions, a snowmelt routine was placed in. SWMM for single event simulation by
Proctor and Redfern, Ltd. and James F. MacLaren, Ltd., abbreviated PR-JFM
(1976a, 1976b, 1977), during 1974-1976. The basic melt computations were
based on routines developed by the U. 3. National Weather Service, NWS
(Anderson, 1973). The work reported herein has utilized the Canadian SWMM
snowmelt routines as a starting point and has considerably augmented their
capabilities as well as added the facility for snowmelt computations while
running continuous SWMM. In addition, features have been added which aid in
adapting the snowraelt process to urban conditions since most efforts in the
past, except for STORM, have been aimed at simulation of spring melt in large
river basins. The work of the National Weather Service (Anderson, 1973) has
also been heavily utilized, especially for the extension to continuous simu-
lation and the resulting inclusion of cold content, variable melt coefficients
and areal depletion.
373
-------
The following sections describe the methodology presently programmed in
the SWMM Runoff Block. It is intended to aid in understanding the various
input parameters required, computations performed, and output produced.
OVERVIEW
Throughout the program, all snow depths are treated as "depth of water
equivalent" to avoid specification of the specific gravity of the snow pack
which is highly variable with time. The specific gravity of new snow is on
the order of 0.09; an 11:1 or 10:1 ratio of snow pack depth to water equiva-
lent depth is often used as a rule of thumb. With time, the pack compresses
until the specific gravity can be considerably greater, to 0.5 and above.
In urban areas, lingering snow piles may resemble ice more than snow., with
specific gravities approaching 1.0. Although snow pack heat conduction and
storage depend on specific gravity, sufficient accuracy may be obtained
without using it. It is adequate to maintain continuity through the use of
depth of water equivalent.
Most input parameters are in units of inches of water equivalent (in.
w.e.). For all computations, conversions are made to feet of water equiva-
lent.
Single Event Simulation
For single event simulation, it is unnecessary to generate a long rec-
ord of precipitation and temperature data. Snow quantities are input as
initial depths (water equivalent) on subcatchments and as negative rainfall
intensities on rainfall input cards. Snowfall is generally keyed as nega-
tive precipitation on input files. Temperature data are read for each time
step from card input. (Other parameters are explained subsequently.)
During the simulation, melt is generated at each time step using a
degree-day type equation during dry weather and Anderson's NWS equation
(1973) during rainfall periods. Specified, constant areas of each sub-
catchment are designated as snow covered. Melt, after routing through the
remaining snow pack, is combined with rainfall to form the spatially
weighted "effective rainfall" for overland flow routing in the same manner
as in the past.
Continuous Simulation
For continuous simulation, hourly precipitation depths from NWS magnet-
ic tapes are utilized along with daily raax-min temperatures from other NWS
tapes. The latter are interpolated sinusoidally to produce hourly tempera-
ture values, as explained in detail in the next subsection. If temperatures
are below a dividing value (e.g., 32°F) , precipitation values are treated as
snow and keyed with a negative sign. The hourly temperatures are also used
in the melt computations.
374 II-2
-------
Melt is again generated using a degree-day type equation during dry
weather and Anderson's NWS equation during rainfall periods. In addition, a
record of the cold content of the snow is maintained. Thus, before melt can
occur, the pack must be "ripened", that is, heated to a specified base tem-
perature.
One partition of the urban subcatchment is the "normally bare impervi-
ous area." This is intended tc represent surfaces such as streets, parking
lots and sidewalks which are subject to plowing or snow "redistribution".
The program includes this feature.
Following the practice of melt computations in natural basins, "areal
depletion curves" describe the spatial extent of snow cover as the pack
melts. For instance, shaded areas would be expected to retain a snow cover
longer than exposed areas. Thus, the snow covered area of each subcatchment
changes with time during continuous simulation.
Melt computations themselves proceed as in the single event simulation,
except that the degree-day melt coefficients vary sinusoidally, from a maxi-
mum on June 21 to a miniaum on December 21.
Pollutant Simulation
Pollutant washoff is simulated as in the past using combined runoff
from snowmelt and/or rainfall. For continuous SWMM, regeneration of any
pollutant may depend upon whether snow cover is present if, for example,
chlorides are to be simulated.
SNOW AND TEMPERATURE GENERATION FROM NWS TAPES
National Weather Service (NWS) Data
As explained in the description, continuous SWMM and other continuous
simulation models utilize long-term precipitation and temperature data ob-
tained from the National Weather Records Center (NWRC) at Asheville, North
Carolina for the nearest NWS or airport weather station of record. If snow-
melt is not simulated only the precipitation tape is needed; hourly precipi-
tation totals are included on it for every day with measureable precipita-
tion, plus for the first day of each month whether or not there is precipi-
tation. For continuous SWMM without snowmelt, all such hourly values are
treated as rainfall.
Maximum and minimum temperatures as well as several other meteorologi-
cal parameters are given for every day of the year on the NWRC's "WBAN
Summary of Day, Deck 345". A card is illustrated in Figure II-l. Only the
ID number, date and raax-min temperatures are used although other data may be
useful for other purposes. Note that the five-digit ID number is not neces-
sarily the same as for the hourly precipitation data. The program, in sub-
routine CTRAIN, accesses a magnetic tape containing card images of data
shown in Figure II-l. The unit number of the tape is input in the executive
Block as NSCRAT(3). As explained in the description of continuous SWMM, a
375 n_3
-------
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used by SWMM are Station number, date, maximum temperature and minimum
temperature. One card is read for each day of the simulation.
-------
magnetic tape containing card images of hourly precipitation values is
accessed similarly using unit number JIN(l).
Temperature data are input, and processed, for every day of the year,
including summer months. Should an entry (date) be missing, the raax-min
values for the previous day are used.
Cr°3tion of Hourlv Temperatures
The -"WBAN Summary of Day" or temperature tape does not list the time of
day at which the minimum and maximum temperatures occur. Hence, the minimum
temperature is assumed to occur at sunrise each day, and the maximum is
assumed to occur three hours prior to sunset. All times are rounded to the
nearest hour. This scheme obviously cannot account for many meteorological
phenomena that would create other temperature-time distributions but is
apparently the most appropriate one under the circumstances. Given the
max-min temperatures and their assumed hours of occurrence, the other 22
hourly temperatures are readily created by sinusoidal interpolation, as
sketched in Figure II-2. The interpolation is performed, using three differ-
ent periods, 1) between the maximum of the previous day and the minimum of
the present, 2) between the minimum and maximum of the present, and 3)
between the maximum of the present and minimum of the following.
The time of day of sunrise and sunset are easily obtained as a function
of latitude and longitude of the catchment and the date. Techniques for
these computations are explained, for example, by List (1966) and by the TVA
(1972). The program, in subroutine CTRAIN, utilizes approximate (but suffi-
ciently accurate) formulas given in the latter reference. Their use is ex-
plained briefly below.
The hour angle of the sun, h, is the angular distance between the in-
stantaneous meridian of the sun (i.e., the meridian through which passes a
line from the center of the earth to the sun) and the meridian of the obser-
ver (i.e., the meridian of the catchment). It may be measured in degrees or
radians or readily converted to hours, since 24 hours is equivalent to 360
degrees or 2n radians. The hour angle is a function of latitude, declination
of the earth and time of day and is zero at noon, true solar time, and posi-
tive in the afternoon. However, at sunrise and sunset, the solar altitude
of the sun (vertical angle of the sun measured from the earth's surface) is
zero, and the hour angle is computed only as a function of latitude and dec-
lination,
cos h = -tan 8 tan $ (II-l)
where h = hour angle, radians,
8 = earth's declination, a function of season (date),
radians, and
= latitude of observer, radians.
The earth's declination is provided in tables (e.g., List, 1966), but for
programming purposes, an approximate formula is used (TVA, 1972):
377
-------
Tmax
CJ
^J
00
Tmin
SUNRISE
SINUSOIDAL INTERPOLATION
SUNSET
MIDNIGHT
NOON
MIDNIGHT
Figure II-2. Sinusoidal Interpolation of Hourly Temperatures.
-------
6=
where D is number of the day of the year (no leap year correction is war-
ranted) and 6 is in radians. Having the latitude as an input parameter, the
hour angle is thus computed in hours, positive for sunset, negative for
sunrise, as
1? -1
K ±£ . r~~ * f_«-~n * . *,- AN /'TT_0'\
&A C\s*9 ^ Can w c.uC yy v. *» ->y
The computation is valid for any latitude between the arctic and antarctic
circles, and no correction is made for obstruction of the horizon.
The hour of sunrise and sunset is symmetric about noon, true solar
time. True solar noon occurs when the sun is at its highest elevation for
the day. It differs from standard zone time, (i.e., the time on clocks)
because of a longitude effect and because of the "equation of time". The
latter is of astronomical origin and causes a correction that varies season-
ally between approximately ± 15 minutes. It is neglected here. The longi-
tude correction accounts for the time difference due to the separation of
r.he meridian of the observer and the meridian of the standard time zone.
These are listed in Table II-l. It is readily computed as
DTLONG = 4 r°iautes x (9 - SM) (II-4)
degree
where DTLONG = longitude correction, minutes (of time),
9 = longitude of the observer, degrees, and
SM = standard meridian of the time zone, degrees, from Table
II-l.
Note that DTLONG can be either positive or negative, and the sign should be
retained. For instance, Boston at approximately 71°W has DTLONG = -16 min-
utes, meaning that mean solar noon precedes EST noon by 16 minutes. (Mean
solar time differs from true solar time by the neglected "equation of time".)
The time of day of sunrise is then
HSR = 12 - h + DTLONG/60. (II-5)
and the time of day of sunset is
HSS = 12 + h + DTLONG/60. (II-6)
These times are rounded to the nearest hour for use in continuous SWMM since
hourly time steps are used. If shorter time steps are allowed in the future,
such rounding could be removed. As stated earlier, the maximum temperature
is assumed to occur at hour HSS - 3.
Standard time is used in all calculations and in NWS tapes. There is
no input or output that includes allowance for daylight savings time.
379
II-7
-------
Table II-l. Time Zones and Standard Meridians
CO
CD
o
Time Zone
Newfoundland Std. Time
Atlantic Std. Time
Eastern Std. Time
Central Std. Time
Mountain Std. Time
Pacific Std. Time
Yukon Std. Time
Alaska Std. Time
Hawaiian Std. Time
Bering Std. Time
Example Cities
St. John's, Newfoundland
Halifax, Nova Scotia
San Juan, Puerto Rico
New York, New York
Chicago, Illinois
Denver, Colorado
San Francisco, California
Yakutat, Alaska
Anchorage, Alaska
Honolulu, Hawaii
Nome, Alaska
Standard Meridian
(degrees west longitude)
52.5a
60
75
90
105
120
135
150
165
i
CO
The time zone of the island of Newfoundland is offset one half hour from
other zones.
All of the Yukon Territory is on Pacific Standard Time.
-------
Generation of Snowfall Intensities
The estimated hourly temperatures, T, in °F, are compared to a dividing
temperature, SNOTMP, for each hour with precipitation. Then if
T > SNOTMP, precipitation = rain;
T < SNOTMP, precipitation = snow.
Snowfall depths are tagged as negative quantities for identification by
later components of the program.
Gage Catch Deficiency Correction
Precipitation gages tend to produce inaccurate snowfall measurements
because of the complicated aerodynamics of snow flakes falling into the gage.
Snowfall totals are generally underestimated as a result, by a factor that
varies considerably depending upon gage exposure, wind velocity and whether
or not the gage has a wind shield. The program includes a parameter, SCF,
which multiplies snow depths only, as computed using equation II-7. Although
it will vary considerably from storm to storm, it acts as a mean correction
factor over a season in the model. Anderson (1973) provides typical values
of SCF as a function of wind speed, as shown in Figure II-3, which may be
helpful in establishing an initial estimate. The value of SCF can also be
used to account for other factors, such as losses of snow due to interception
and sublimation not accounted for in the model. Anderson (1973) states that
both losses are usually small compared to the gage catch deficiency.
Structure of Precipitation - Temperature Data Set
Output from subroutine CTRAIN is placed as a file on off-line storage
unit number NSCRAT(2) as diagrammed in Figure II-4. If snowmelt is not sim-
ulated, only hourly rainfall intensities (in feet/second) are placed on the
unit, in blocks of eight 24 hour days, for 192 values. No other parameters
(e.g., dates) are included.
When snowmelt is run, the subroutine first makes all the temperature
calculations, and stores these temporarily on unit NSCRAT(l). (This volum-
inous file may optionally be printed for error checking.) These values are
then retrieved for utilization with the precipitation tape. The final file
is stored on unit NSCRAT(2) in the form day number/hour, temperature, pre-
cipitation, day number/hour, temperature, precipitation, etc., to 192 groups
of three, as diagrammed in Figure II-4. The day number/hour parameter is
IDTHR where the first three digits are the day of the year and the last two
are the hour of the day, (e.g., for noon on February 3, IDTHR = 03412). The
year is not included and must be calculated knowing the date of the initial
entry on the file. This file may also be printed for debugging purposes but
will produce voluminous output.
For most purposes, it will be best to use appropriate job control
language (JCL) to save permanently the file on NSCRAT(2) for future repeated
use. This avoids reprocessing the NWS tapes; reuse is available as a pro-
gram option by setting ICRAIN = 2. The JCL required to do this is highly
381 _
-------
-I
<
g
3.0
oe
o
U
2 2.5
O
H
U
oe
o
u
o
UJ
u
UL
UJ
O
o
Ul
0
t
u.
u
1.0
I ' I
8 12 16 20
WIND SPEED (MPH) AT THE GAGE
24
Figure II-3. Typical Gage Catch Deficiency Correction (Anderson, 1973,
p. 5-20).
382
11-10
-------
ISNOW= 0
DAY I
DAY 2
OJ
00
CO
NSCRAT(2)
HOUR 1
HOUR 2
HOUR 3
HOUR 24
HOUR 1
HOUR 2
HOUR 1
HOUR 2
HOUR 23
HOUR 24
RAIN 0)
RAIN (2)
RAIN (3)
RAIN (24)
RAIN (25)
RAIN (26)
RAIN (169)
RAIN (170)
RAIN (191)
RAIN (192)
DAY I HOUR i
HOUR 2
DAYS HOUR i
HOUR 24
ISNOW=2 or 4
NSCRAT(I)
IDTHR(I)
TEMP (I)
IDTHR12)
TEMP (2)
IOTHR (169)
TEMP (169)
IDTHR !I92)
TEMP(I92)
DAY I HOUR I
HOUR 2
DAYS HOUR i
NSCRAT (2)
HOUR 24
IDTHR (I)
TEMP (I)
RAIN (I)
IDTHR (2)
TEMP (2)
RAIN (2)
IDTHR (I6S)
TEMP069)
RAIN (169)
IDTHR (192)
TEMP (192)
RAIN (192)
Figure II-4.
Structure of Precipitation-Temperature Data Set Used Internally in Runoff for
Continuous Simulation.
-------
dependent upon ihe type of machine and upon the invididual computer
installation. If it is not done, however, the file will be "lost" after the
program has executed.
Output Options
Depending upon the value of IRPRNT, all non-zero, hourly precipitation
values ma'" be "Tinted alon<> with either all generated hourly temperatures
for all days, or only the max-min temperatures for all days. In addition,
the 50 highest hourly precipitation intensities are printed to aid in choos-
ing critical events for detailed simulation. At the option of the user, the
program will stop at this point, by setting parameter ICRAIN = 4, so that
this output may be reviewed prior to the actual continuous SWMM simulation.
Of course, in this case file NSCRAT(2) should be saved so that it can be
retrieved and used later for a continuous SWMM simulation when ICRAIN is set
equal to 2.
Single Event SWMM
NWS tapes .ire not used, nor is subroutine CTRAIN called for single evert
simulation. Precipitation is entered on cards as in the past. However,
snowfall can be included, if desired, as a negative precipitation value at
any time step.
SUBCATCHMENT SCHEMATIZATION
Land Surface - Snow Cover Combinations
In order to have flexibility in treating different combinations of snow
cover and ground surface types, four such combinations are provided, as
described in Table II-2 and illustrated in Figure II-5. When snowmelt is
not simulated, only the first three ars used, as in the past. (Type 3,
impervious area with no depression storage, is specified in Runoff by the
parameter PCTZER, percent of impervious area with immediate runoff.) Snow
cover is treated identically on types 1 and 3 since these surfaces are likely
to be of a similar nature, e.g., streets, sidewalks, parking lots, etc. For
continuous simulation, these surfaces are considered "normally bare" because
of probable plowing, salting or other rapid snow removal, but are subject to
snow cover also, as described subsequently. For single event simulation,
these surfaces are always bare; all snow on impervious areas is handled in
type 4.
In Runoff, especially subroutine WSHED, the "types" are subscripts for
the parameter WDEPTH, the water depth on each surface type. Since snow
cover is the same for types 1 and 3, snow depths, WSNOW, are only triply
subscripted.
For single event simulation, the fraction of snow covered pervious area
is constant; for continuous simulation the fraction varies according to an
areal depletion curve (as for type 4 impervious). The depletion curves are
explained in a following subsection.
384 11-12
-------
Apportionment of impervious area is different when simulating with and
without snowmelt. For the latter situation, the area with zero depression
storage (type 3) is taken as a percentage, PCTZER, of the total impervious
area. For the former situation (with snowmelt), it is taken as a percentage,
the same PCTZER, of the bare impervious area (single event simulation) or of
the "normally bare" impervious area (continuous simulation). Thus, the type
3 area will vary according to whether snowmelt is simulated or not, as showu
in Figure II-5. The effect on outflow is very minor. The fraction of imper-
vious area with 100 percent snow cover (single event) or subject to an areal
depletion curve (continuous) is an input parameter, SNN1, for each subcatch-
ment.
Table II-2. Subcatchment Surface Classification
Depression Snow Cover and Extent
Type Perviousness Storage Single Event Continuous
1 Impervious Yes Bare Normally bare, but
may have snow cover
over 100% of type 1
plus type 3 area.
2 Pervious Yes Constant frac- Snow covered subject
tion, SNCP, of to areal depletion
area is snow curve.
covered.
3 Impervious No Bare Same as type 1.
4 Impervious Yes 100% covered Snow covered subject
to areal depletion
curve.
Redistribution and Simulation of Snow Removal
Snow removal practices form a major difference between the snow hydrol-
ogy of urban and rural areas. Much of the snow cover may be completely
removed from heavily urbanized areas, or plowed into windrows or piles, with
melt characteristics that differ markedly from those of undisturbed snow.
Management practices in cities vary according to location, climate, topogra-
phy and the storm itself; they are summarized in a study by APWA (1974). It
is probably not possible to treat them all in a simulation model. However,
in continuous S'WMM, provision is made for approximate simulation of some
practices.
It is assumed that all snow subject to "redistribution", (e.g. plowing)
resides on the "normally bare" category, type 1 plus 3 above, (see Figure
II-5), which might consist of streets, sidewalks, parking lots, etc. (The
desired degree of definition may be obtained by using several subcatchments,
although a coarse schematization, e.g., one or two subcatchments, is likely
to be all that is required for continuous simulation.) For each subcatch-
385 H_13
-------
WITH SNOWMELT
SUBCATCHMENT
WIDTH
SNOW COVERED
(Single Event)
AREAL DEPLETION CURVE
FOR A2.A4
(Continuous)
NORMALLY
BARE
WITHOUT SNOWMELT
PCTZER = A3/(AH-A3)
TO INLET OR GUTTER/PIPE
Figure II-5. Subcatchment Schematization With and Without Snowmelt
Simulation. See also Table II-2.
386
11-14
-------
ment, a depth of snow, WEPLOW, is input for this area, above which redistri-
bution occurs as indicated in Figure II-6. All snow in excess of this depth,
say 0.1 - 0.2 in. water equivalent (2.5 - 5.1 mm), is redistributed to other
areas according to five fractions, SFRAC, input for each subcatchment. These
are described on Figure II-6. For instance, if snow is usually windrowed
onto adjacent impervious or pervious areas, SFRAC(l) or SFRAC(2) may be used.
If it is trucked to another subcatchment, (the last one input is used for
this purpose), a fraction SFRAC(3) will so indicate, or SFRAC(4) if the snow
is removed entirely from the simulated watershed. In the latter case, such
removals are tabulated and included in the final continuity check. Finally,
excess snow may be immediately "melted", (i.e., treated as rainfall), using
SFRAC(5). The transfers are area weighted, of course, and the five fractions
should sum to 1.0. A depth of snow WEPLOW remains on the normally bare area
and is subject to melting as on the other areas. See Table II-3 for guide-
lines as to typical levels of service for snow and ice control (Richardson
et al., 1974).
No pollutants are transferred with the snow. The transfers are assumed
to have no effect on pollutant washoff and regeneration. In addition, all
the parameters of thin process remain constant throughout the simulation and
can only represent averages over a snow season.
The redistribution simulation does not account for snow management
practices using chemicals, e.g., roadway salting. This is handled using the
melt equations, as described subsequently.
Array Restrictions
In an attempt to maintain a manageable size of the Runoff Block, con-
tinuous SWMM is limited to 30 subcatchments and 30 inlets because several
additional parameters are needed. However, this should be more than ade-
quate for continuous simulation, with and without snowmelt, since only a
coarse catchment discretization should be sufficient. Limitations for single
event SWMM remain at 200 subcatchments and 200 gutter/pipes plus inlets.
MELT CALCULATIONS
Theory of Snowmelt
Introduction
Excellent descriptions of the processes of snowraelt and accumulation
are available in several texts and simulation model reports and in the well-
known 1956 Snow Hydrology report by the Corps of Engineers (1956). The impor-
tant heat budget and melt components are first mentioned briefly here; any
of the above sources may be consulted for detailed explanations. A brief
justification for the techniques adopted for snowmelt calculations in SWMM
is presented below.
387 11-15
-------
Al = IMPERVIOUS AREA WITH DEPRESSION STORAGE
A2= PERVIOUS AREA
A3= IMPERVIOUS AREA WITH ZERO DEPRESSION STORAGE
A4= SNOW COVERED IMPERVIOUS AREA
Al +A3= NORMALLY BARE
SFRAC (5)
SFRAC (3)
PERVIOUS IN
LAST SUBCATCHMENT
SFRAC(4)
OUT OF SIMULATION
AMOUNT TRANSFERRED
IS FRACTION OF SNOW
ABOVE WEPLOW INCHES
WATER EQUIVALENT
Figure II-6. Redistribution of Snow During Continuous Simulation.
388
11-16
-------
Table II-3.
Guidelines for Levels of Service in Snow and Ice Control (Richardson et
al., 1974).
CO
oo
Road Classification
}. Low Speed Multiline
Urban Expressway
2. High-Speed
4-Lane Divided Highways
Interstate System
ACT greater than 10,000a
3. Primary Highways
Undivided 2 and 3 lanes
ADT 500 5000a
4. Secondary Roads
ADT less than 500'
Level of Service
Full Pave- Full Pavement
Snow Depth to Max. Snow Depth meet Clear of Clear of Ice
Start Plowing on Pavement Snow After After Storm
(Inches) (Inches) Storm (Hours) Hours
Roadway routinely patrolled during storms 0.5 to ]
All traffic lanes treated with chemicals
All lanes (including breakdown lanes) operable
at all times but at reduced speeds
Occasional patches of well-sanded snow pack
Roadway repeatedly cleared by echelons
of plows to minimize traffic disruption
Clear pavement obtained as soon as possible
Roadway routinely patrolled during storms l
Driving and passing lanes treated with chemicals
Driving lane operable at all times at reduced
speeds
Passing lane operable depending on equipment
availability
Clear pavement obtained as soon as possible
Roadway is routinely patrolled during storms I
Mostly clear pavement after storm stops
Hazardous areas receive treatment of chemicals
or abrasive
Remaining snow and ice removed when thawing occurs
Roadway is patrolled at least once during a storm 2.
Bare left-wheel track with intermittent snow cover
Hazardous areas are plowed and treated with chemicals
or abrasives as a first order of work
Full width of road is cleared as equipment becomes
available
12
1.5
12
2.5
ADT - average daily traffic
-------
Snowpack Heat Budget
Heat may be added or removed from a snowpack by the following
processes:
1. Absorbed solar radiation (addition).
2. Nat longwave radiation exchange with the surrounding environment
(addition or removal).
3. Convective transfer of sensible heat from air (addition or
removal).
4. Release of latent heat of vaporization by condensate (addition)
or, the opposite, its removal by sublimation (removing the latent
heat of vaporization plus the latent heat of fusion).
5. Advection of heat by rain (addition) plus addition of the heat of
fusion if the rain freezes.
6. Conduction of heat from underlying ground (removal or addition).
The terms -nay be summed, with appropriate signs, and equated to the
change of heat stored in the snowpack to form a conservation of heat equa-
tion. Refer to Appendix III for further detail. All of the processes listed
above vary in relative importance with topography, season, climate, local
meteorological conditions, etc., but items 1-4 are the most important. Item
5 is of less importance on a seasonal basis, and item 6 is often neglected.
A snowpack is termed "ripe" when any additional heat will produce liquid
runoff. Rainfall (item 5) will rapidly ripen a snowpack by release of its
latent heat of fusion as it freezes in subfreezing snow, followed by quickly
filling the free water holding capacity of the snow.
Melt Prediction Techniques
Prediction of melt follows from prediction of the heat storage of the
snow pack. "Energy budget" techniques are the most exact formulation since
they evaluate each of the heat budget terms individually, requiring as mete-
orologic input quantities such as solar radiation, air temperature, dew point
or relative humidity, wind speed, and precipitation. Assumptions must be
made about the density, surface roughness and heat and water storage (mass
balance) of the snow pack as well as on related topographical and vegetative
parameters. Further complications arise in dealing with heat conduction and
roughness of tha underlying ground and whether or not it is permeable.
Several models treat individually some or all of these effects. One of
the more recent was developed for the NWS river forecast system by Anderson
(1976). Interestingly, under many conditions, he found that results obtained
using his energy balance model were not significantly better than those ob-
tained using simpler (e.g., degree-day or temperature-index) techniques in
his earlier model (1973). The more open and variable the conditions, the
390 11-18
-------
better is the energy balance technique. Closest agreement between his two
models was for heavily forested watersheds.
Minimal data needed to apply an energy balance model are a good esti-
mate of incoming solar radiation, plus measurements of air temperature, vapor
pressure (or dew point or relative humidity) and wind speed. All of these
data, except possibly solar radiation, are available at at least one location.
(e.g.; the airport) for almost all reasonably sized cities. Even solar radi-
ation measurements are taken at several locations in most states. Predictive
techniques are also available, for solar radiation and other parameters,
based on available measurements (TVA, 1972, Franz, 1974).
Choice of Predictive Method
Two major reasons suggest that simpler, e.g., temperature-index, tech-
niques should be used for simulation of snowmelt and accumulation in urban
areas. First, =ven though required meteorologic data for energy balance
models are likely to be available, there is a large local variation in the
magnitude of thase parameters due to the urbanization itself. For example,
radiation melt will be influenced heavily by shading of buildings and
reduced albedo caused by urban pollutants. In view of the many unknown pro-
perties of the snowpack itself in urban areas, it may be overly ambitious to
attempt to predict melt at all! But at the least, simpler techniques are
probably all that are warranted. They have the added advantage of consider-
ably reducing the already extensive input data to a model such as SWMM.
Second, the objective of the modeling should be examined. Although it
may contribute, snowmelt seldom causes flooding or hydrologic extremes in an
urban area itself. Hence, exact prediction of flow magnitudes does not as-
sume nearly the importance it has in the models of, say, the NWS, in which
river flood forecasting is of paramount importance. For continuous simula-
tion and planning purposes in urban areas, exact quantity (or quality) pre-
diction is not the objective in any event; rather, these efforts produce a
statistical evaluation of a complex system and help identify critical time
periods for more detailed analysis.
For these and other reasons, simple snowmelt prediction techniques have
been incorporated into SWMM. In their literature review preparatory to sim-
ilar earlier work, Proctor and Redfern and James F. MacLaren (1976a, 1976b)
felt that Anderson's NWS (1973) temperature-index method was most appropriate
for SWMM. It is also well documented and tested, and has been incorporated
into the SWMM version described herein. As described subsequently, the snow-
melt modeling follows Anderson's work in several areas, not just in the melt
equations. The energy budget technique is illustrated in this report, how-
ever, in Appendix III, in order to show how it reduces to a temperature-index
equation under certain assumptions. It may be noted that the STORM model
(Hydrologic Engineering Center, 1977, Roesner et al., 1974) also uses the
temperature-index method for snowmelt prediction, in a considerably less
complex manner than is now programmed in SWMM.
391 n_19
-------
SWMM Melt Equations
Anderson's NWS model (1973) treats two different melt situations: with
and without rainfall. When there is rainfall (greater than 0.1 in./6 hr or
2.5 mm/6 hr in the NWS model, greater than 0.02 in./hr or 0.51 mm/hr in SWMM),
accurate assumptions may be made about several energy budget terms. These
are: zero solar radiation, incoming longwave radiation equals blackbody
radiation at the ambient air temperature, the snow surface temperature is
32°F (0°C), and the dew point and rain water temperatures equal the ambient
air temperature. Anderson combines the appropriate terms for each heat
budget component into one equation for the melt rate. As used in subroutine
MELT in SWMM, it is
SMELT = (TA - 32) (0.001167 + SGAMMA-UADJ +
0.007-PREC) + 8.5 UADJ (EA - 0.18)
where SMELT = melt rate, in./hr,
TA = air temprature, °F,
SGAMMA = 7.5-y, in. Hg/°F,
y = psychometric constant, in. Hg/°F,
UADJ = wind speed function, in./in. Hg - hr,
PREC = rainfall intensity, in./hr, and
EA = saturation vapor pressure at air temperature, in. Hg.
The psychometric constant, y, is calculated as
Y = 0.000359 PA Ul-9)
where PA = atmospheric pressure, in. Hg.
Average atmospheric pressure is, in turn, calculated as a function of eleva-
tion, z,
PA = 29.9 - 1.02 (z/1000) + 0.0032 (z/1000)2'4 (11-10)
where z = average catchment elevation, ft.
The elevation, z, is an input parameter, ELEV. The wind function, UADJ,
accounts for turbulent transport of sensible heat and water vapor. Anderson
(1973) gives
UADJ = 0.006 u (II-ll)
where UADJ = wind speed function, in./in. Hg - hr, and
u = average wind speed 1.64 ft (0.5 m) above the snow surface,
mi/hr.
In practice, available wind data are used and are seldom corrected for the
actual elevation of the anemometer. For SWMM, average wind speeds are input
for each month. Finally, the saturation vapor pressure, EA, is given
accurately by the convenient exponential approximation,
392 11-20
-------
3A = 8.1175 x 106 exp[-7701.544/(TA + 405.0265)] (11-12)
where EA = saturation vapor pressure, in. Hg, and
TA = air temperature, °F.
The origin of numerical constants found in equation II-8 is given by
Anderson (1973), and reflects units conversions as well as US customary
units for physical properties. Note that aquation 111-13 of Appendix III
may be reduced to equation II-8.
During non-rain periods, melt is calculated as a linear function of the
difference between the air temperature, TA, and a base temperature, TBASE,
using a degree-day or temperature-index type equation,
SMELT = DHM (TA - TBASE) (11-13)
where SMELT = snowmelt, in./hr or ft/sec,
TA = air temperature, °F,
TBASE = base melt temperature, °F, and
DHM = melt factor, in./hr-°F or ft/sec-°F.
Different values of TBASE and DHM may be input for three area classifications
for each subcatchment (see Table II-2 and Figure II-5). For instance, these
parameters may be used to account for street salting which lowers the base
melt temperature. If desired, rooftops could be simulated as a separate
subcatchment using a lower value of TBASE to reflect heat transfer vertically
through the roof. Values of TBASE will probably range between 25 and 32 °F
(-4 and 0°C). Unfortunately, few urban area data exist to define adequately
appropriate modified values for TBASE and DHM, and they may be considered
calibration parameters.
In rural areas, the melt coefficient ranges from 0.03 - 0.15 in./day-°F
(1.4 - 6.9 mm/day-°C) or from 0.001 - 0.006 in./hr-°F (0.057 - 0.29 mm/hr-
°C); the latter are units used for SWMM input. In urban areas, values may
tend toward the higher part of the range due to compression of the pack by
vehicles, pedestrians, windrows, etc. Again, there appear to be few data
available to produce accurate estimates. However, Bengston (1981) and
Westerstrom (1931) do describe preliminary results of urban snowmelt studies
in Sweden, including degree-day coefficients which range from 3 to 8 mm/°C-
day (0.007 - 0.17 in./°F-day).
It is important to realize that a degree-day equation may be derived
from the complete energy budget equation if parameters other than air tem-
perature are held constant. The equation is simply linearized about a
desired air temperature range, and numerical values for DHM and TBASE com-
puted. The values are accurate for the assumed values of other parameters,
but may not appear to make sense physically, e.g., it is not difficult to
use parameters that produce negative values of TBASE. An example of this
procedure is given in Appendix III. It also serves to illustrate the energy
budget computation method.
393 n-21
-------
For single event SWMM, parameters DHM and TBASE are constant throughout
the simulation. For continuous SWMM, TBASE remains constant, but DHM is
allowed a seasonal variation, as illustrated in Figure II-7. Following
Anderson (1973), the minimum melt coefficient is assumed to occur on Decem-
ber 21 and the maximum on June 21. Parameters DHMIN and DHMAX are input for
the three areas of each subcatchment, and sinusoidal interpolation is used
to produce a value of DHM, constant over each day,
DHM .__ (DHMAX + DHMIN) + flfflAX - DHMIN) . ^ [J)] (II.14)
where DHMIN = minimum melt coefficient, occurring Dec. 21, in./hr-°F,
DHMAX = maximum melt coefficient, occurring June 21, in./hr-°F, and
D = number of the day of the year.
No special allowance is made for leap year in any seasonal computations of
the type of equation 11-14. However, the correct date (and day number, D)
is maintained.
Heat Exchange Daring Nou-Melt Periods
During subfreezing weather, the snow pack does not melt, and heat ex-
change with the atmosphere can either warm or cool the pack. The difference
between the heat content of the subfreezing pack and the (higher) base melt
temperature is taken as positive and termed the "cold content" of the pack.
No melt will occur until this quantity, COLDC in SWMM, is reduced to zero.
It is maintained in inches (or feet) of water equivalent. That is, a cold
content of 0.1 in. (2.5 mm) is equivalent to the heat required to melt 0.1
in. (2.5 mm) of snow. Following Anderson (1973), the heat exchange altering
the cold content is proportional to the difference between the air tempera-
ture, TA, and an antecedent temperature index, ATI, indicative of the temper-
ature of the surface layer of the snow pack. The revised value of ATI at
time step 2 is calculated as
ATI2 = ATI: + TIPM (TA2 - ATlj) (H-15)
where ATI = antecedent temperature index, °F,
TA = air temperature, °F,
TIPM = antecedent temperature index parameter,
0 < TIPM < 1.0, and
subscripts 1 and 2 refer to time steps 1 and 2, respectively. The value of
ATI is not allowed to exceed TBASE, and when snowfall is occuring, ATI takes
on the current air temperature.
The weighting factor, TIPM, is an indication of the thickness of the
"surface" layer of snow. Values of TIPM less than 0.1 give significant
weight to temperatures over the past week or more and would thus indicate a
deeper layer than TIPM values greater than, say, 0.5 which would essentially
only give weight to temperatures during the past day. In other words, the
pack will both warm and cool more slowly with low values of TIPM. Anderson
states that TIPM =0.5 has given reasonable results in natural watersheds,
394 _
-------
DHMAX
DHMIN
u>
VO
Ul
250
Dec. 21
DAY NUMBER
200
June 21
Figure II-7. Seasonal Variation of Melt Coefficients.
i
to
U)
-------
although there is some evidence that a lower value may be more appropriate.
No calibration has been attempted on urban watersheds.
Following computation of the antecedent temperature index, the cold
content is changed by an amount
ACOLDC = RNM DHM (ATI - TA) At (11-16)
where ACOLDC = change in cold content, ft water equiv.,
RNM = ratio of negative melt coefficient to melt
coefficient,
DHM = melt coefficient, ft/sec-°F,
TA = air temperature, °F,
ATI = antecedent temperature index, °F, and
At = time step, sec.
Note that the cold content is increased, (ACOLDC is positive) when the air
temperature is less (colder) than the antecedent temperature index. Since
heat transfer during non-melt periods is less than during melt periods,
Anderson uses a "negative melt coefficient" in the heat exchange computation.
SWMM computes this simply as a fraction, RNM, of the melt coefficient, DHM.
Hence, the negative melt coefficient, i.e., the product RHM DHM, also
varies seasonally. A typical value of RNM is 0.6.
When heat is added to a snow pack with zero cold content, liquid melt
is produced, but runoff does not occur, until the "free water holding capa-
city" of the snow pack is filled. This is discussed subsequently. For
single event SWMM no cold content calculations are performed; values of
COLDC are assumed to equal zero throughout the simulation. The value of
COLDC is in units of feet of water equivalent over the area in question.
The cold content "volume", equivalent to calories or BTU's is obtained by
multiplying by the area. Finally, an adjustment is made to equation 11-16
depending on the areal extent of snow cover. This is discussed below.
Areal Extent of Snow Cover
Introduction --
The snow pack on a catchment rarely melts uniformly over the total area.
Rather, due to shading, drifting, topography, etc., certain portions of the
catchment will become bare before others, and only a fraction, ASC, will be
snow covered. This fraction must be known in order to compute the snow
covered area available for heat exchange and melt, and to know how much rain
falls on bare ground. Because of year to year similarities in topography,
vegetation, drift patterns, etc., the fraction, ASC, is primarily only a
function of the amount of snow on the catchment at a given time; this func-
tion, called an "areal depletion curve", is discussed below. These functions
are used only for continuous SWMM to describe the seasonal growth and reces-
sion of the snow pack. For single event simulation, fractions of snow
covered area are fixed for the pervious and impervious areas of each
subcatchment.
396 H-24
-------
Areal Depletion Curves
The functional dependence of the areal depletion curve, ADC, has just
been described. As used in most snowmelt models, it is assumed that there
is a depth, SI, above which there will always be 100 percent cover. In some
models, the value of SI is adjusted during the simulation; in SWMM it remains
constant. The amount of snow present at any time is indicated by the param-
eter WSNGW, which is Lue depth (water equivalent) over each of the three
possible snow covered areas of each subcatchment, (see Figure II-5). This
depth is nondimensionalized by SI for use in calculating ASC. Thus, an areal
depletion curve is a plot of WSNOW/SI versus ASC; a typical ADC for a natural
catchment is shown in Figure II-8. For values of the ratio AWESI = WSNOW/SI
greater than 1.0, ASC = 1.0, that is, the area is 100 percent snow covered.
Some of the implications of different functional forms of the ADC may
be seen in Figure II-9. Since the program maintains snow quantities, WSNOW,
as the depth over the total area, AT, the actual snow depth, WS, and actual
area covered, AS, are related by continuity,
WSNGW AT = WS AS (H-17)
where WSNOW = depth of snow over total area AT, ft water equivalent,
2
AT = total area, ft ,
WS = actual snow depth, ft water equivalent, and
2
AS = snow covered area, ft .
In terms of parameters shown on the ADC, equation 11-17 may be rearranged to
read
AWES! - _ WS . AS _ WS
AWESI - SI - SI AT - SI AbL
Equation 11-18 can be used to compute the actual snow depth, WS, from known
ADC parameters, if desired. It is unnecessary to do this in the program,
but it is helpful in understanding the curves of Figure II-9. Thus,
WS = ~ SI (11-19)
Consider the three ADC curves, B, C and D. For curve B, AWESI is always less
than ASC; hence, WS is always less than SI as shown in Figure II-9d. For
curve C, AWESI = ASC, hence WS = SI, as shown in Figure II-9e. Finally, for
curve D, AWESI is always greater than ASC; hence, WS is always greater than
SI, as shown in Figure II-9f. Constant values of ASC at 100 percent cover
and 40 percent cover are illustrated in Figures II-9c, curve A, and Figure
II-9g, curve E, respectively. At a given time (e.g., t.. in Figure II-9),
the area of each snow depth versus area curve is the same and equal to AWESI
SI, (e.g., 0.8 SI for time tj).
Curve B on Figure II-9a is the most common type of ADC occurring in
nature, as shown in Figure II-8. The convex curve D requires some mech-
anism for raising snow levels above their original depth, SI. In nature,
397 _
-------
1.0
AWESI =
WSNOW/SI
0.5
0
I I I I
i i r
TYPICAL
ADC FOR
NATURAL AREA
TEMPORARY ADC.
FOR NEW SNOW
+ SNEW
SBWS
-------
AREAL DEPLETION CURVES
9a 1.0
AWESI=
WSNOW/SI
0.5
9b DEFINITION SKETCH
I 3,
SNOW
DEPTH
SNOW PACK BARE
" CATCHMENT
ws
AS AT
AREA
WSNOW-AT = WS-AS
0 0.5 1.0
ASC - AS/AT
INITIAL MELT PROGRESSING
» fl
AWESI= 1.0 0.8
WSNOW WS AS
SI SI ' AT
9c A
9d B
9e C
9f D
0.6
0.3
Figure II-9. Effect on Snow Cover of Areal Depletion Curves.
399
11-27
-------
drifting might provide such a mechanism; in urban areas, plowing and wind-
rowing could cause a similar effect. A complex curve could be generated to
represent specific snow removal practices in a city. However, the program
utilizes only one ADC curve for all impervious areas (e.g., area A4 of Figure
II-5 for all subcatchments) and only one ADC curve for all pervious areas
(e.g., area A2 of Figure II-5 for all subcatchments). This limitation should
not hinder an adequate simulation since the effects of variations in individ-
ual locations ara averaged out in the city-wide scope of most continuous
simulations.
The two ADC curves for pervious and impervious areas are input by the
user, as are values of SI for each subcatchment. The program does not
require the ADC curves to pass through the origin, AWESI=ASC=0; they may
intersect the abscissa at a value of ASC > 0 in order to maintain some snow
covered area up until the instant that all snow disappears (see Figure II-8).
However, the curves may not intersect the ordinate, AWESI > 0 when ASC = 0.
The preceding paragraphs have centered on the situation where a depth
of snow greater than or equal to SI has fallen and is melting. (The ADC
curves are not employed until WSNOW becomes less than SI.) The situation
when there is new snow needs to be discussed, starting from both zero or
non-zero initial cover. The SWMM procedure again follows Anderson's NWS
method (1973).
When there is new snow and WSNOW is already greater than or equal to
SI, then ASC remains unchanged at 1.0. However, when there is new snow on
bare or partially bare ground, it is assumed that the total area is 100 per-
cent covered for a period of time, and a "temporary" ADC is established as
shown in Figure II-8. This temporary curve returns to the same point on the
ADC as the snow melts. Let the depth of new snow be SNO, measured in equi-
valent feet of water. Then the value of AWESI will be changed from an ini-
tial value of AWE to a new value of SNEW by
SNEW = AWE + SNO/SI (11-20)
It is assumed that the areal snow cover remains at 100 percent until 25 per-
cent of the new snow melts. This defines the value of SBWS of Figure II-8
as
SBWS = AWE + 0.75 ANO/SI (11-21)
Anderson (1973) reports low sensitivity of model results to the arbitrary 25
percent assumption. When melt produces a value of AWESI between SBWS and
AWE, linear interpolation of the temporary curve is used to find ASC until
the actual ADC curve is again reached. When new snow has fallen, the pro-
gram thus maintains values of AWE, SBA and SBWS.
The interactive nature of melt and fraction of snow cover is not
accounted for during each time step. It is sufficient to use the value of
ASC at the beginning of each time step, especially with the short one-hour
time step used for the simulation.
400 n-28
-------
Use of Value of ASC --
The fraction of area that is snow covered, ASC, is used to adjust 1)
the volume of melt that occurs, and 2) the "volume" of cold content change,
since it is assumed that heat transfer occurs only over the snow covered
area. The melt rate is computed from either equation II-8 or equation 11-13.
The snow depth is then reduced from its value at time step 1 to time step 2
33
WSNOW2 = WSNOWj - SMELT ASC (11-22)
with variables as defined previously and including appropriate continuity
checks in the program to avoid melting more snow than is there, etc.
Cold content changes are also adjusted by the value of ASC. Thus,
using equation 11-16, cold content at time step 2 is computed from the value
at time step 1 by
COLDC2 = COLDC1 + RNM DHM (ATI-TA) At ASC (11-23)
where variables are as previously defined. Again there are program checks
for negative values of COLDC, etc.
Liquid Water Routing in Snow Pack
Production of melt does not necessarily mean that there will be liquid
runoff at a given time step since a snow pack, acting as a porous medium
with a "porosity", has a certain "free water holding capacity" at a given
instant in time. Following PR-JFM (1976a, 1976b), this capacity is taken as
a constant fraction, FWFRAC, of the variable snow depth, WSNOW, at each time
step. This volume (depth) must be filled before runoff from the snow pack
occurs. The program maintains the depth of free water, FW, ft of water, for
use in these computations. When FW = FWFRAC WSNOW, the snow pack is fully
ripe. The procedure is sketched in Figure 11-10.
The inclusion of the free water holding capacity via this simple reser-
voir-type routing delays and somewhat attenuates the appearance of liquid
runoff. The value of FWFRAC will normally be less than 0.10 and usually
between 0.02 - 0.05 for deep snow packs (WSNOW > 10 in. or 254 mm water
equivalent). However, Anderson (1973) reports that a value of 0.25 is not
unreasonable for shallow snow packs that may form a slush layer. When rain-
fall occurs, it is added to the melt rate entering storage as free water.
No free water is released when melt does not occur, but remains in storage,
available for release when the pack is again ripe. This re-frozen free water
is not included in subsequent cold content or melt computations.
Net Runoff
Melt from snow covered areas and rainfall on bare surfaces are area
weighted and combined to produce net runoff onto the surface as follows,
RI = ASC SMELT + (1.0-ASC) RINE (11-24)
401 n_29
-------
SNOW
PACK
FREE
WATER
WSNOW
FWFRAC-WSNOW
EXCESS= RUNOFF
Figure 11-10. Schematic of Liquid Water Routing Through Snow Pack.
402
11-30
-------
where RI = net omoff onto surface, ft/sec,
ASC = fraction of area that is snow covered,
SMELT = melt rate, including effect of attenuation due to free water
holding capacity, ft/sec, and
RINE = rainfall intensity, ft/sec.
Tiius, the net runoff acts just as rainfall would act alone in subsequent
r'VVTOV.I'***'^ £1 S*TJ m^ «4 *«*£* lfew«^~*»r* -.<^1f*'ll«fe^'«.««.
W» C A. .Lt»4A\* A.-IWV UAAU It* J. 4. J. U A. (1 W J. Utl t_ U X C l4 X O k. X WHO .
If immediate melt is produced through the use of the snow redistribu-
tion fraction SJRAC(5), discussed earlier (see Figure II-6), it is added to
equation 11-24. Furthermore, all melt calculations are ended when the depth
of snow water equivalent becomes less than 0.001 in. (0.025 mm), and remain-
ing snow and free water are converted to immediate melt and added to equa-
tion 11-24.
Effect of Snow on Infiltration and Surface Parameters
A snow pack tends to insulate the surface beneath it. If ground has
frozen prior to snowfall, it will tend to remain so, even as the snow begins
to melt. Conversely, unfrozen ground is generally not frozen by subsequent
snowfall. The infiltration characteristics of frozen versus unfrozen ground
are not well understood and depend upon the moisture content at the time of
freezing. For these and other reasons, SWUM assumes that snow has no effect
on infiltration or other parameters, such as surface roughness or detention
storage (although the latter is altered in a sense through the use of the
free water holding capacity of the snow). In addition, all heat transfer
calculations cease when the water becomes "net runoff". Thus, water in tem-
porary surface storage during the overland flow routing will not refreeze as
the temperature drops and is also subject to evaporation beneath the snow
pack.
QUALITY INTERACTIONS
Pollutant Accumulation
Snowmelt Quality
A detailed review of literature related to snowmelt quality is given by
PR-JFM (1976a, 1976b). Among the various contaminants found in deposited
snow and melt water, chlorides and lead appear to be the most serious and
potentially hazardous. Chloride concentrations in runoff along major high-
ways can be higher than 20,000 mg/1, with typical values of from 1,000 to
10,000 mg/1. Several other studies also document chloride contamination and
discuss street salting practices (Field et al., 1973, Richardson et al.,
1974, Ontario Ministry of the Environment, 1974). Lead concentrations in
snow windrows have been as high as 100 mg/1 with typical values of from 1 to
10 mg/1. However, most deposited lead results from automobile combustion
and is insoluble. Hence, melt runoff concentrations are lower than snow
pack values and are mostly associated with suspended solids.
403 n_31
-------
Table II-4. Guidelines for Chemical Application Rates (Richardson et al., 1974).
WEATHER CONDITIONS
Sleet or Freezing Haln 200 salt
APPLICATION RATE (Pounds of material per mile of 2-lane road or 2-lane a of divldedj
INSTRUCTIONS
- wait mt least O.S
hour before plowing
- reapply aa necessary
Temperature
30* F and
above
Pavement
Conditions
Wet
Precipitation
Snow
Lou-and High-Speed
Hultllane Divided
300 salt
Two and Three-Lane
Primary
300 salt
Two-Lane
Secondary
300 salt
200 salt
200 salt
25-30'F
20-25*F
P-
o
15-20*F
below IS'F
Wet Snow or Sleet
Freezing Rain
Wet Snow or Sleet
Freezing Rain
Dry Dry Snow
W.t
Dry
Wet Snow or Sleet
Dry Snow
Initial at 400 salt initial at 400 salt
repeat at 200 salt repeat at 200 salt
initial at 300 aalt Initial at 300 salt
repeat.at 200 salt repeat at 200 salt
Initial at 500 salt Initial at 500 salt
repeat at 250 salt repeat at 250 salt
initial at 400 salt Initial at 400 salt
repeat at 300 aalt repeat at 300 salt
plow
500 of 3:1 Salt/
Calcium Chloride
plow
plow
500 of 3:1 Salt/
Calcium Chloride
plow
initial at 400 aalt
repeat at 200 salt
initial at 300 salt
repeat at 200 aalt
1200 of 5:1
Sand/Salt; repeat
same
plow
1200 of 5:1 Sand
plow
wait at least 0.5
hour before plowing;
repeat
repeat as necessary
- wait about 0.75 hour
before plowing;
repeat
- repeat as necessary
treat hazardous
areas with 1200 of
20:1 Sand/Salt
wait about one hour
before plowing;
continue plowing
until atom ends;
then repeat
application
treat hazardous area
with 1200 of 20:1
Sand/Salt
I
OJ
tv>
-------
Pollutant Loadings
Mechanisms and modeling alternatives for pollutant buildup and washoff
are described extensively in Section 4 (Runoff Block). Any parameter
related to snowmelt may be generated using linear or non-linear buildup, or
else a rating curve (load proportional to flow). Specifically, street salt-
ing chemicals may be simulated, such as sodium chloride or calcium chloride.
Adjustments for Presence of Snow
As a user option, regeneration of any quality constituent may be per-
formed only when snow is present. This; option is indicated by parameter
LINKUP. Thus, if chlorides are simulated, for example, they will not be re-
generated from bare ground, during the summer months for instance. However,
regeneration whe-n it does occur is a function only ^f_ the snow is present,
not the actual amount (depth).
Possible Loading Rates
Pollutant loading rates are best determined from local data. The liter-
ature review of PR-JFM (1976a, 1976b) may also be consulted for tables that
may be used to estimate loading rate parameters for snow-associated pollu-
tants. Other references will also be useful (e.g., Field et al., 1973,
Richardson et a].., 1974, Ontario Ministry of the Environment, 1974).
Table II-4 (Richardson et al, 197*0 lists recommended deicing chemical
application rates for roadways. In general, PR-JFM show that observed load-
ing rates are functions of population density with suburban rates lower than
arterial highway rates, as indicated iit Table II-5. This is also true for
other pollutants.
Table II-5. Salting Rates Used in Ontario
(Proctor and Redfern Ltd. and James F. MacLaren, Ltd., Vol. II, 1976b)
Population Density Salting Rate per Application
(pop, per sq mile) (Ib per lane-mile)
Less than 1,000 75-800
1,000 to 5,000 350-1,800
More than 5,000 400-1,200
Street Sweeping
Simulation of street sweeping in ftWMM is performed as in the past, except.
for slight modifications as described in Section 4. The effect of snow is
included in two minor ways. First, beginning and ending dates, parameters
KLNBGN and KLNEND respectively, may be input for continuous SWMM to indicate
405 H-33
-------
the interval during the year subject to street sweeping. If sweeping
normally is not done between, say, December 1 and March 1, because of high
snow volumes, this may be so indicated.
Second, the presence of snow can alter the street sweeping interval.
These intervals are specified for each of the five land uses. Each sub-
catchment is swept when the number of dry time steps for that subcatchment
exceeds the interval for the given land use. A dry tise step, in subroutine
QSHED, is one in which there is no precipitation and no water or snow on
areas Al and A3 (Figure II-5). Thus, subcatchments will not be swept until
there is no snow or water on "normally bare" impervious areas.
Other Considerations
The snow itself is assumed to be "pure" and contain no pollutants.
Thus, the redistribution or transfers of snow described earlier (Figure II-6)
will not remove accumulated pollutants. This is partially justified on the
basis of the assumption that such transfers would occur soon after fresh snow
has fallen. Ttnay occur during the same time step in the model.
Although not well tested, it is assumed that the principal effect of
inclusion of snowmelt upon runoff quality predictions of continuous SWMM will
be to shift the season and magnitude of pollutant washoff. There will tend
to be fewer periods of washoff during the winter. As snowmelt, equivalent
melt rates are likely to be less than the usual magnitude of rainfall inten-
sities experienced. Hence, concentrations may tend to be more uniform during
the melt washoff events.
DATA REQUIREMENTS
New Parameters
The revised Runoff Block input data formats should be examined for the
new snowmelt variables. For single event simulation these include watershed
elevation, free water holding capacities, air temperatures and wind speeds,
and for each subcatchment, snow covered fractions, initial snow and free
water, melt coefficients and base temperatures. Continuous simulation
requires the same data as above, except that air temperatures are computed
using other input parameters. In addition, it requires the snow gage cor-
rection factor, negative heat exchange parameter, areal depletion curves,
and, for each subcatchment, the redistribution parameters. Of course, for
continuous simulation, the required parameters can be kept to a minimum by
keeping the number of subcatchments used to a minimum (e.g., one). Also
required are pollutant loading data that may or may not be related to snow.
Sensitivity
The melt routines have not been sufficiently tested to date to quantify
the sensitivity of results to various input parameters. It is expected that
melt volumes will be most related to the precipitation record, of course,
and to the gage correction factor, which influences the amount of snow that
406
11-34
-------
falls. Melt rates will be influenced by the melt coefficients and base tem-
peratures, and, to some degree, by the areal depletion curves which simulate
the relative "piling" or "stacking" of the snow.
OUTPUT
Temperature and Snowfall Generation
Output from subroutine CTRAIN has been described earlier and consists
of temperatures synthesized from daily max-min values, and hourly precipita-
tion totals, in which snowfall is tagged as a negative value.
Runoff Simulation Output
Snowmelt events are not tagged for output by continuous SWMM. If. daily
output is used, snowmelt may be disceraed to some degree by observingvwhether
precipitation accompanies the runoff for that day. Snowfall and initral snow
depths are identified as separate items in the final continuity check-for
the total watershed.
PROGRAMMING NOTES
Subroutines
Hourly temperatures are synthesized in subroutine CTRAIN, called from
subroutine RHYDRO. Hourly precipitation intensities (rain and snow) are
also computed therein. All input data other than NWS tapes are read in sub-
routine RHYDRO, as in the past.
The bulk of the melt computations are performed in subroutine WSHED,
used to generate overland flow from rainfall, and now, from snowmelt as
well. Subroutine WSHED calls subroutine MELT to compute the melt rate and to
perform computations related to the areal depletion curves. Subroutine
AREAL calls subroutine FINDSC for linear interpolation along the curves.
Minor changes related to snowmelt have also been made in subroutines
HYDRO and QSHED. Changes in the former relate primarily to initialization
and the continuity check output. Changes in the latter account for the
minor interactions of snow and quality predictions.
Variable Names
Variable names for input snowmelt parameters are included in the Runoff
Block input data format table. Other key parameters have been mentioned in
the preceding text and used in the equations.
Computer Time
Although not tested extensively, an approximate idea of computer costs
for various conditions may be seen in Table II-6. STORM results are shown
407 H_35
-------
Table II-6. Comparative Computer Runs of Continuous
SWMM with Snowmelt (Runoff Block only).
Run
1. Generation of temperature
and precipitation file
ICRAIN = 4, ISNOW =2)
Simulation
Time, months
13
25
63
121
2. Use of generated file 13
(above) for continuous 25
simulation with snowmelt. 63
One subcatchment, no gutter/ 121
pipes. (ICRAIN = 2,
ISNOW = 2)
3. Generation of precipitation 25
file only. (ICRAIN = 4,
ISNOW = 0).
4. Use of generated precipitation 25
for continuous simulation
without snowmelt. One sub-
catchment, no gutter/pipes.
(ICRAIN = 2, ISNOW = 0)
5. STORM run comparable to 25
combined runs 1 plus 2
above.
CPU Time,
sec
11.01
12.89
19.68
33.65
12.80
22.80
49.39
96.46
8.00
19.43
9.86
Cost ,
_J
5.49
6.27
8.91
13.88
4.15
6.29
12.31
22.64
3.96
5.74
5.12
University of Florida Amdahl 470. Appears to user similar to IBM 370/165
but is approximately four times as fast.
'includes costs for CPU (link edit and execute), cards read, lines and pages
printed, off-line I/O, etc. Rate is approximately half the commercial rate.
408
11-36
-------
for comparison. The latter runs prove to be faster and cheaper, reflecting
the considerably simpler formulations used in STORM. Thus, the choice of
STORM or continuous SWMM for runoff generation will depend upon costs, model
formulations, I/O, availability, etc.
Computer Size Requirements
Every effort was aade during the snowmelt programming to minimize the
use of new arrays, although several were necessary. However, during the
course of the programming it was possible to eliminate several unneeded
arrays and overlay others. As a result, the present size of the Runoff
Block plus the Executive Block is a bit less than 360K bytes or 90K words.
409 11-37
-------
APPENDIX III
REDUCTION OF ENERGY BALANCE EQUATION TO DEGREE-DAY EQUATION
PURPOSE
This appendix presents equations that can be used for each term in the
energy balance equation discussed in Appendix II. The equation is then lin-
earized and typical numerical values are used to reduce it to a degree-day
or temperature-index type equation. The energy budget method will thus be
better understood, and a physical basis for the simple prediction equations
will be seen. Notation and equations used will follow Eagleson (1970), al-
though an identical development could be based on several, other references.
ENERGY BUDGET
The energy budget given in Appendix II is repeated and symbols are
assigned to each term. Units for each energy budget term will be energy/
area-time, e.g., ly/day (one langley = one cal/cm ). However, within this
scope, there will be mixtures of units used as convenient, e.g., minutes and
days, °C and °F. The equation will ultimately be converted to U.S. custom-
ary units.
The snow pack energy budget is (e.g., units of ly/day)
AH=H +H+H,+H+H+H (III-l)
rs g rl c e p
where AH = change in heat storage in snow pack,
H = net short wave radiation entering the snow pack,
H = conduction of heat to snow pack from underlying ground,
H , = net (incoming minus outgoing) long wave radiation entering
the snow pack,
H = convective transport of sensible heat from air to snow pack,
H = release of latent heat of vaporization by condensation of
atmospheric water vapor, and
H = advection of heat to snow pack by rain.
All terms can be positive or negative except for H (sublimation will not be
considered since it also involves the heat of fusion), H and H (heat can-
not be removed by rain).
It will be assumed that the snow pack is ripe, and all heat added will
product liquid melt. Since inches of melt are desired, and it requires
410
III-l
-------
about 80 cal to melt one gram of water (the latent heat of fusion) or 80 ly
per cm, it requires 2.54 cm/in, x 80 ly/cm = 203.2 ly per inch of melt.
Thus, equation III-l will eventually be linearized and put in the form of
equation 11-13 in Appendix II,
SMELT = AH/203.2 = DHM (T - T. ) (III-2)
a D
where SMELT = melt rate, in./day,
AH = change in heat storage, ly/day,
DHM = melt coefficient, in./day-°F,
T = air temperature, °F, and
T, = base melt temperature, °F.
Other terms will be defined where introduced. Caution should be used to in-
sure that all terms eventually have the same units.
SHORT WAVE RADIATION, H
' rs
Measured values from NWS stations are ordinarily used. The albedo
(reflection coefficient) of new snow can be as high as 0.80 and is seldom
lower than 0.4 in natural areas. Albedos of dirty urban snow surfaces are
not documented, but probably lower than 0.4. Net shortwave radiation, H ,
is incoming minus reflected. If measurements of incoming radiation are
unavailable, predictive techniques may be used (TVA, 1972, Franz, 1974).
HEAT CONDUCTION THROUGH GROUND, H
o
Few data are available to quantify this term, and it is often deter-
mined as a residual in the energy budget equation. For urban areas, the
intriguing possibility exists of predicting heat transfer through roofs
based upon assumed temperature differences across the roof surface and
thermal properties of the roofing material. In most cases, however, such
calculations will be inaccurate and/or infeasible. Hence, this term is
usually neglected.
NET LONG WAVE RADIATION, Hfl
Incoming minus outgoing long wave radiation is given by the Stefan-
Boltzman law,
H n = 0.97 e a T4 - 0.97 a T4 (III-3)
rl a a s
where e = atmospheric emissivity, a function of_water vapor content,
a3 = Stefan-Boltzman constant = 0.826 x 10~ ly/rain-°K ,
T = air temperature at specified elevation, °K,
T = snow surface temperature, °K.
411 IH-2
-------
The first factoc of 0.97 accounts for three percent reflection of incoming
long wave radiation, and the second factor of 0.97 is the emissivity of the
snow surface.
The key unknown is the atmospheric emissivity, for which several em-
pirical formulas are available and in which the effect of clouds may also
be included (TVA, 1972). For example, a simple formula due to Anderson
^ for clear skies is
e = 0.74 + 0.0049e (III-4)
a
where e = ground level atmospheric vapor pressure, mb.
Clouds may be assumed to radiate with an emissivity of 0.97 at the cloud
base temperature, if known.
The snow surface temperature may be taken as 0°C. Hence, it is neces-
sary to linearize only the air temperature term. This may be done by means
of a Taylor series, under the assumption
T = T + AT (III-5)
a o
The fourth-power term is then linearized about the reference temperature,
V
T4 = (T + AT)4 = T4 + AT 4 T3 + ... = T3 (4T - 3T ) (III-6)
a o o o o a o
The reference temperature, T , will be a constant in the equation and is
chosen near the midpoint of the expected temperature range at the time of
evaluation of the heat budget. Equation III-6 may be substituted into equa-
tion III-3,
H . = 0.97 e a T3 (4T - 3T ) - 0.97 a T4 (III-7)
rl a o a o s
Which is linear in T , in °K. Later, temperatures must be converted to °F
for consistency.
CONVECTIVE HEAT TRANSFER, HC
Equations for this process (and for condensation melt) vary according
to the asumptions made about surface roughness, wind speed profiles and tur-
bulent transfer coefficients. A common equation is (Eagleson, 1970)
H = 203.2 k p /p (z zh)"1/6 u, (T - T ) (III-8)
C CSOat? UaS
where p = surface atmospheric pressure (consistent units with p ) ,
p = sea level atmospheric pressure (consistent units with p ) ,
z° = height above surface of air temperature (and humidity)
measurement, ft,
412 m_3
-------
z, = height above surface of wind speed measurement, ft,
u, = wind speed at height z, , mph,
T = air temperature, °F,
T = snow surface temperature, °F, and
H = heat transfer in ly/day.
The factor 203.2 converts inches to langleys and the coefficient k has
been aseasursd for the Sierra Nevada mountains as
kc = 0.00629 in. ft1/3 hr/day-°F-mi (III-9)
CONDENSATION HEAT TRANSFER, H
Since both this and convective heat transfer are diffusive type pro-
cesses, the same introductory remarks hold as for the latter. A common
equation is (Eagleson, 1970)
H = 203.2 8.5 k (z z, )"1/6 u. (e - e ) (111-10)
e e a o b a s
where e = vapor pressure of atmosphere at temperature and relative
humidity at height z , mb,
d
e = saturation vapor pressure at the snow surface temperature, mb,
S
and other variables are defined as for equation III-8. The coefficient k
has been measured for the Sierras as
kg = 0.00635 in. ft1/3 hr/day-mb-mile. (III-ll)
The factor of 8.5 in equation 111-10 accounts for the fact that when the
snow pack is ripe, the latent heat of condensation will supply the latent
heat of fusion to melt the snow. Because of the ratio of these latent
heats, 600/80 =7.5, each inch of condensate will cause 7.5 + 1 = 8.5
inches of "melt".
HEAT ADVECTION BY RAIN, H
Heat is advected by rain in proportion to the rainfall depth and tem-
perature of the rain (assumed to be equal to the air temperature). Then
H = 1.41 d (T - T ) (111-12)
p 3 s
where H = heat advected in ly/day, and
d = daily rainfall depth, in./day, and
the temperatures are in °F.
413 III-4
-------
COMBINED EQUATIONS
When equations for each component are substituted into equation III-l,
and using equation III-2 to generate inches of melt, all equations may be
combined into
Atl 0.97 e a 4 T3
SMELT =
i.\i J . i-
(265.2 + 5/9 T )
3
H +H ~ 0.97 a T
rs g - __ s
203.2
203.2
°K
(2
0.97 e a 3 T
a c
203.2
V8'5 ' ke ^
/>,
6
'l.4l
(T -
' k
V
203.2
(111-13)
Ts]
where terms have been defined previously, and temperature units are:
T = reference temperature, °K,
T = snow surface temperature, °F, (except °K in term 4), and
s
T = air temperature, °F.
3
The units of SMELT are inches/day. The equation is linear in the air tem-
perature, T , which will be the only variable when the others are assigned
numerical values. Note also the conversion from °K to °F in term 1. A
further refinement would make saturation atmospheric vapor pressure a
linear function of air temperature, which is valid over say, 10°F ranges.
Then,
e = r e = r
f(T )
(111-14)
where r = relative humidity, fraction, and
e = saturation vapor pressure at air temperature, T .
3 3
s
This modification would then add another term in T to equation 111-13; it
is pursued no further here. Note that equation II-8 in Appendix II is only
a simplification of equation 111-13 under suitable assumptions for rainfall
conditions and with units conversions.
414
III-5
-------
NUMERICAL EXAMPLE
The following meteorological parameters are assumed,
Hrg = 288 ly/day,
T = 35°F = 274.7°K
o
T = 32°F = 273°K
s
2 = 6 ft
a
zv = 20 ft
u, = 9 mph
e = e (32°F) = 6.11 mb
S S
r = 0.6
e = 0.6 e (35°F) = 0.6 6.87 = 4.12 mb
3 S
£ = 0.90
a
PQ = 1013.2 mb
p = 950 mb (about 2000 ft elevation),
S
rainfall = zero
H = zero
§
Each of the terms in equation 111-13 is now evaluated, with units of
inches/day.
Term Constant Temperature Term
1 +11.24 + 0.0235 T
3
2 +1.24 + 0.0239 T
3
3 + 1.417
4 - 3.154
5 - 1.200
6 - 8.729
- 0.426 + 0.0474 T
3
m_6
-------
Then the degree-day or temperature-index equation becomes, with T in °F,
3
SMELT = DHM (T - T, ) (111-15)
3 O
= 0.0474 T - 0.426 in./day
<1
= 0.0474 (T - 8.99) in./day
3
= 0.00198 (T - 8.99) in./hr
cl
The low value of T, of about 9°F implies sufficient energy input (via
solar radiation and condensation) to cause melt even at low temperatures.
This is not really true, however, since the melt equation was linearized
about a temperature of 35°F and should only be used in that range. The
exercise serves to indicate the range of values that may be found when sub-
stituting actual meteorological data iato the equations. Although seem-
ingly wrong values may result, e.g., base melt temperatures less than zero,
the equation with such values is still valid for the input parameters used
and over the range of the linearization
416
III-7
-------
APPENDIX IV
STORAGE/TREATMENT SIMULATION
OBJECTIVES
The primary objectives of the Storage/Treatment Block are to:
1. provide the capability of modeling a larger number of processes in both
the single event and continuous modes;
2. simulate the quality improvement provided by each process;
3. simulate the handling of sludges; and
^. provide estimates of capital, operation and maintenance costs.
Although the objectives of the Storage/Treatment Block have not changed
appreciably from earlier versions (Metcalf and Eddy et al., 1971a), the model
has been virtually rewritten. The earlier versions were more limited in use
and scope. This version is much more flexible in terms of the control units
available, pollutant routing and cost estimating. However, the user is
advised that increased flexibility implies increased user input and knowledge
of the processes to be modeled. In other words, the model does not provide
several dozen specialized designs, but provides the tools necessary to simu-
late the desired processes. Naturally, flexibility precludes ultrasophisti-
cation.
Several precautions should be notsd before setting up the S/T Block.
1. Local waste characterization data are essential to appraise realisti-
cally the performance of treatment units.
2. Lab or pilot plant performance data should be used whenever possible to
derive performance functions.
3. Dry-weather treatment performance functions should be applied cautious-
ly to wet-weather units.
PROGRAM DEVELOPMENT AND OVERVIEW
Development
Past versions of the Storage/Treatment Block simulated various proces-
ses on the basis of limited empirical data and operating experience. Often
the data were localized and/or specialized. Thus, they were of questionable
applicability to a wide variety of situations. Additionally, the model did
not account for the physical characteristics of the incoming waste stream or
the handling of residuals (sludges).
417 IV-1
-------
To improve the storage/treatment modeling capabilities of SWMM the fol-
lowing considerations were instrumental in creating a new model.
1. There should be a high degree of flexibility in the simulation of in-
dividual units and the interaction among units.
2. In addition to simulating the mass of pollutants, it is important to
account for the physical characteristics (i.e., particle size and spe-
cific gravity distribution) of each pollutant.
3. Residual (sludge) handling is an important part of any wastewater
treatment scheme and should be simulated.
4. All costing routines should be as flexible as the performance
algorithms.
5. The model should be capable of modeling wet- and dry-weather
facilities.
Overview
The present Storage/Treatment Block is approximately 2000 Fortran state-
ments in length and consists of eight subroutines. The routing of flow and
pollutants through the entire block is controlled by subroutine STRT which
is called from the Executive Block. STRT also provides the main driving loop
for the model and generally acts as the central coordinating subroutine.
Subroutine STRDAT is called in STRT and is responsible for reading the input
data provided by the user. Subroutine CONTRL is called each time-step from
the main driving loop in STRT. CONTRL directs flow and pollutants from one
unit to another as prescribed by the desired scheme and coordinates the
majority of the printed output. Subroutine UNIT is called from CONTRL for
each unit modeled and is the heart of the Storage/Treatment Block. It con-
tains the necessary flexibility and capability to model most storage/treat-
ment processes (units). Subroutine EQUATE is used by UNIT to provide several
forms of pollutant removal equations. Subroutine INTERP is employed by UNIT
for linear interpolation. Subroutine PLUGS is used by UNIT to model perfect
plug flow through a detention unit. Subroutine STCOST is called from STRT
to determine capital and operation and maintenance costs.
The model has become user-intensive rather than program-intensive. The
user is responsible for providing the program with the desired storage/treat-
ment scheme and operating characteristics of each unit (along with other
information). However, input guidelines are provided in the User's Manual
for several types of units. Again, the strength of this approach is to
maximize flexibility and applicability to local conditions and design
criteria.
SIMULATION TECHNIQUES
Introduction
Flow and pollutants are routed through one or more storage/treatment
units by several techniques. The flows into, through and out of a unit are
shown in Figure IV-1. The units may be arranged in any fashion, restricted
only by the requirements that inflow to the plant enters at only one unit
418 IV-2
-------
'tot
BYPASS EXCESS
(YES)
Q
V
by
LEGEND
Qtot
Q
res
= TOTAL INFLOW, ft3/sec
= MAXIMUM ALLOWABLE INFLOW,
ft3/sec
Qb = BYPASSED FLOW, ft3/sec
Qjn = DIRECT INFLOW TO UNIT, ft3/sec
Qout = TREATED OUTFLOW, ft3/sec
Qres = RESIDUAL STREAM, ft3/S8C
Figure IV-1. Flows Into, Through, and Out of a Storage/Treatment Unit.
419
IV-3
-------
and that the products (treated outflow, residuals, and bypass flow) from each
unit not be directed to more than three units. Treatment and sludge handling
units are modeled by the same subroutine (UNIT). Additionally, both wet-
and dry-weather facilities may be simulated by the proper selection of unit
arrangement and characteristics. Unita may be modeled as having a detention
capability or instantaneous throughflow. Pollutants or sludges may be repre-
sented as mass only or further characterized by a particle size or settling
velocity distribution. A unit may remove pollutants (or concentrate sludges)
as a function of particle size and specific gravity, detention time, incoming
concentration, the removal rate of another pollutant, or a constant percentage.
The S/T Block can receive the flow and any three pollutants from any one outlet
in any other block of SWMM. Also, flows and pollutants may be provided by
the user and fed directly to the S/T Block. If both sources are present they
are combined and treated as one input. For example, the user may enter
directly dry-weather flows and enter wet-weather flows from the Runoff Block.
All flows and pollutant concentrations reported by the S/T Block are average
values over each time step. This is necessary for some of the algorithms in
the S/T Block (in particular, the plug flow routines); it does not signifi-
cantly affect the results.
The following sections describe the techniques available for flow and
pollutant routing which allow the user to model several types of storage/
treatment units.
Flow Routing
Detention vs. Instantaneous Throughflow
A unit may be modeled to handle flow in one of two ways; as a detention
unit (reservoir) or a unit instantaneously passing all flow. The idea of a
detention unit is not limited to storage basins and sedimentation tanks but
also includes such processes as dissolved air flotation, activated sludge,
and chlorination. Processes that may be modeled as having instantaneous
throughflow include microscreens, fine screens and other forms of screening.
Detention Units
The rate of change of storage in a detention unit or reservoir is found
by writing a mass balance equation for the system shown in Figure IV-2.
V
0
Figure IV-2. Time Varying "Inflow and Outflow Rates for a Reservoir.
420
IV-4
-------
The rate of change of storage equals inflow minus outflow, or
AV/At =1-0 (IV-1)
_ 3
where I = average inflow rate during At, ft /sec,
_ 3
0 = average outflow rate during At, ft /sec,
3
V = reservoir volume, ft , and
At = time step, sec.
Let subscripts 1 and 2 denote the beginning and end of the time step,
respectively. Then, the average inflow rate I, is
I = (I1 + I2)/2 (IV-2)
The average outflow rate, 0, is
0 = (01 + 02)/2 (IV-3)
Also, the change in reservoir volume is
AV = V2 - VT (IV-4)
Substituting equations IV-2, IV-3, and IV-4 into equation IV-1 and multiplying
through by At yields the desired expression for the change in volume, i.e.,
I + I 0 + 0
V - V = At - At (IV-Si
" r\ * 1 ?\ tit. /* tJU ^ A V J y
212 2
For a given time step, I., I~, 0.., and V.. are known and CL and V2 need to be
determined. Grouping the unRnowns on the left hand side of the equation and
rearranging yields one of two required equations:
0.502At + V2 = 0.5(I1 + I2)At + (V: - O.SC^At)
The second required equation is found by relating CL and V2, each of which
is a function of reservoir depth. The procedure is illustrated in the fol-
lowing example.
Table IV-1 presents geometric and routing data for a hypothetical reser
voir with a base elevation of 343.0 ft and a maximum pool elevation of 353.0
ft. The corresponding depths are shown in column 3. Surface area, as a
function of depth, is presented in column 4. If the reservoir has an irreg-
ular geometry, the surface area is measured from a topographic map. The
deptharea data pairs shown in columns 3 and 4 of Table IV-1 are required
input data. If the user desires, the depth-discharge relationship may be
input directly by assigning values of CL to each depth or generated by a
user-supplied depth-discharge equation fe.g., weir equation). Similarly,
the user may specify the volume, V., associated with each depth or allow
421 IV~5
-------
Table IV-1. Geometric and Hydraulic Data for Hypothetical Reservoir
n
(1)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Co lumn
(1)
(2)
(3)
(4)
(5)
(6)
17)
(8)
Elevation
h
ft
(2)
343.0
344.0
345.0
346.0
347.0
348.0
349.0
350.0
351.0
351.5
352.0
352.2
352.4
352.6
352.8
353.0
Counter
Elevation
Dc-ptli = h
Mujsured
Measured
Measured
Surface
Depth Area Discharge Volume
y .A o2 v2
ft 1000 ft2 ft3/sec 1000 ft3
(3) (4) (5) (6)
0.0 0.
1.0 3.
2.0 15.
3.0 45.
4.0 121.
5.0 225.
'6.0 365.
7.0 550.
8.0 790.
8.5 910.
9.0 1080.
9.2 1130.
9.4 1190.
9:6 1270.
9.8 1350.
10.0 1440.
from topographic map
- 343.0
from topographic map or
data or calculated from
iluta or calculated
Calculated from column 5, At = 21
0.
0.
0.
0.
0.
0.
0.
0.
0.
30.
65.
80.
105.
130.
165.
200.
0.
2.
10.
40.
120.
300.
590.
1050.
1720.
2140.
2650.
2900.
3100.
3400.
3700.
3900.
may be calculated
discharge
,600 sec.
formulas
02DT2
0.502At
1000 ft3
(7)
0.
0.
0.
0.
0.
0.
0.
0.
0.
324.
702.
864.
1134.
1404.
1782.
2160.
(by user)
SATERM
0.502At+V2 Remarks
1000 ft3
(8) (9)
0. Base of reservoir
2.
10.
40.
120.
300.
590.
1050.
1720. Weir elevation
2464.
3352.
3764.
4234.
4804.
5482.
6060. Maximum pool
if geometry is regular
Calculated from columns 6 and 7
422
IV-6
-------
the model to calculate the depth-volume relationship. This is accomplished
by averaging the surface area between adjacent values of depth, multiplying
by the difference in depth, and adding the incremental volume to the accumu-
lated total. The depth-area data pairs are also used to estimate the volume
lost from the reservoir due to evaporation.
Recalling equation IV-6, the objective is to find
0.502At = f(0.502At + V2). (IV-7)
The data in Table IV-1 give 0. and V_ as functions of depth. In this case,
discharge or outflow occurs only if the reservoir depth exceeds 8.0 ft. The
model uses these data to calculate the values of 0.50-At (column 7) and
0.50-At + V2 (column 8) for each depth (defined in the model as 02DT2 and
SATERM, respectively). Thus, the relationship required by equation IV-7 is
indirectly generated. During the simulation, the value of O.SCLAt + V_ is
calculated by equation IV-6 and the corresponding value of 0.50-At found by
linear interpolation through the previously generated set of 02DT2 - SATERM
values. The values of CL and V? are subsequently calculated. This proce-
dure is repeated each time step with the value of CL and V~ becoming the
values of 0- and V.. for use in equation IV-6 during the next time step. In
a normal simulation the outflow, 0-, represents treated outflow, residual
flow, and evaporation.
The computational procedure is summarized as follows:
1. Known values of I.. , I~, CL , At, and V.. are substituted into the right
hand side of equation IV-6. The result is the first value of O.SCLAt +
V
2. Knowing (O.SCLAt + V~) the value of O.SCLAt is obtained by interpola-
ting between adjacent values of 02DT2 and SATERM.
3. The values of V~ and CL are determined and become the values of V and
0 , respectively, in the next time step.
4. Add O.Sdj + I2) At to the new value of VJ - O.SCLAt to get the new
value of O.SCLAt + V2-
5. Continue this process until all inflows have been routed.
To summarize the input alternatives, the earlier version of the storage
model permitted the user to read in depth-area data and an outflow condition
of a weir, orifice, or pumping. It could not handle the case of a natural
reservoir with an irregular stage-discharge relationship. The updated model
allows the user to input the required relationship between depth-surface
area, treated outflow, residual flow, and storage volume through as many as
423 IV-7
-------
sixteen data se!:s. This approach permits the user to select the data points
which best approximate the desired functional relationships. This approach
is felt to be preferable to adding more complexity to the model to analyze
automatically tae wide variety of reservoir geometries and operating policies
encountered in practice.
An excellent description of this level-surface routing procedure (the
Puls Method) is presented in Viessman et al. (1977). Sound engineering
judgement is essential in setting up this routing procedure. The input data
and associated assumptions should be checked carefully.
Residual Flow --
Residual flows occur only during dry periods (i.e. no inflow or treated
outflow), and thus, serve to drain the detention unit between storms. The
user can direct the unit to be drained after a specified number of dry time
steps or on a scheduled basis (every i time, depending on the inflow/out-
flow status). Residual flows are handled in the same manner as the outflow
in the routing procedure outlined previously. These flows contain a mixture
of the stored wasf.ew.9ter and removed pollutant quantities (see later discus-
sion). The manner in which pollutants are removed and accumulated is dis-
cussed later. In detention units, the residual flow is suspended when wet
weather occurs.
Evaporation
Evaporation losses are also accounted for in detention units. The loss
rate is computed by
ev = A ed /k (IV-8)
3
where e = evaporation loss rate, ft /sec,
2
A = surface area at the water level in the unit, ft ,
e, = evaporation rate, in./day, and
k = 1036800.0, conversion factor, in./day per ft/sec.
The user must supply the values of e, for each month of the simulation period.
Instantaneous Throughflow
If the unit is specified to have no detention capability, then the model
assumes that what arrives during a time step leaves as treated outflow that
same time step less the residual flow. The residual flow is calculated as a
constant fraction of the inflow.
424 iv-8
-------
Pollutant Routing
Complete Mixing
Pollutants are routed through a detention unit by one of two modes:
Complete mixing or plug flow. For complete mixing, the concentration of the
pollutant in the unit is assumed to be equal to the effluent concentration.
The Cuss balance equation, for the assumed weil-oixcu, variable-volume reset'-
voir shown in Figure IV-3 is (Medina, 1976):
C:(t) - 0(t) C(t) - K C(t) V(t)
3
where V = reservoir volume, ft
C = influent pollutant concentration, mg/1,
C = effluent and reservoir pollutant concentration, mg/1,
3
I = inflow rate, ft /sec,
3
0 = outflow rate, ft /sec,
t = time, sec, and
K = decay coefficient, sec
Equation IV-9 is very difficult to work with directly. It may be approxi-
mated by writing the mass balance equation for the pollutant over the inter-
val, At:
Change in Mass entering Mass leaving Decay during
mass in basin = during At - during At - At
during At
Cl ll + C2 T2 Cl°l + C2°2 C1V1 + C2V2
C2V2 " C1V1 = 2 At " 2 At " K 2
where subscripts 1 and 2 refer to the beginning and end of the time step,
respectively.
425 iv-9
-------
0(t),C (t)
V
V(t),C(t)
Figure IV-3. Well-Mixed, Variable-Volume Reservoir (Rich, 1973).
From the flow routing procedure discussed earlier, I-, I~, CL , 0_, V..,
and V. are known. The concentration in the reservoirTat theTbeginning of
the time step, C , and the influent concentrations, C. and C? are also known
as are the decay rate, K, and the time step, At. Thus, the only unknown,
the end of time step concentration, C_, can be found directly by rearranging
equation IV-10 to yield
C2 =
(C1 Tl + C2I
1 1 2
At- -.
/it -
0 K C V
At- At-
iit « iit
(IV-11)
At
Equation IV-11 is the basis for the complete mixing model of pollutant
routing through a detention unit.
Equations IV-9, IV-10, and IV-11 assume that pollutants are removed at
a rate proportional to the concentration present in the unit. In other
words, a first-order reaction is assumed. The coefficient K is the rate
constant. The product of K and At is represented by the value of R in a
user-supplied removal equation (See Equation IV-14 and accompanying
discussion).
Removed pollutant quantities are not allowed to accumulate in a
completely-mixed detention unit. Strictly, pollutants cannot settle under
such conditions. Therefore, the residual stream is effectively another
route for treated outflow. All pollutant removal is assumed to occur by
non-physical means (e.g., biological decomposition). Several processes such
as flocculation and rapid-mix chlorination are essentially completely-mixed
detention units.
426
IV-10
-------
Plug Flow
If the user selects the plug flow option, the inflow during each time
step, herein called a plug, is labeled and queued through the detention unit.
Transfer of pollutants between plugs is not permitted. The outflow for any
time step is comprised of the oldest plugs, and/or fractions thereof, present
in the unit. This is accomplished by satisfying continuity for the present
outflow volume (which was calculated earlier):
LP
Z V. f. = V (IV-12)
j=JP J J °
where V = volume leaving unit during the present time step, ft ,
t" h *}
V. = volume entering unit during j time step (plug j), ft ,
f. = fraction of plug j that must be removed to satisfy
J continuity with V , 0 S f. £ 1,
JP = time step number of the oldest plug in the unit, and
LP = time step number of the youngest plug required to
satisfy continuity with V .
As in a completely-mixed detention unit, detention time is the most impor-
tant indicator of pollutant removal ability. Removal equations are speci-
fied by the user (see later discussion) and, in this case, should be written
as a function of detention time (along with other possible parameters). The
detention time for each plug j is calculated as
(td)j = (KKDT - j)At (IV-13)
where KKDT = present time step number. The detention time is calculated in
the same manner during dry- and wet-weather periods because the plugs always
maintain their identity.
Removed pollutant quantities accumulate in a plug-flow unit until they
are drawn off by residual flow. The accumulated pollutants do not affect
the amount of available storage and are assumed to be conservative (i:e., no
decay). When residual flow occurs the entire unit contents (including the
removed pollutant quantities) are mixed and drawn off until the unit is empty
or wet weather continues. If wet weather (i.e., inflow) occurs before the
unit is empty, the contents are placed into one plug for further routing.
Instantaneous Throughflow --
Pollutants are routed instantaneously through units modeled as having
no detention capability. In other words, the pollutants arriving during a
time step leave the same time step less the removed portion. The amount of
removed pollutants is determined by user-supplied removal equations (see
later discussion). The removed pollutants are routed with the residuals
stireani.
427 IV-H
-------
Pollutant Characterization
Pollutants are characterized by their magnitude (i.e., mass flow and
concentration) and, if the user desires, by particle size/specific gravity
or settling velocity distributions. Describing pollutants by their particle
size distribution is especially appropriate where small or large particles
dominate or where several storage/treatment units are operated in series.
For example, if the influent is primarily sand and grit, then a sedimentation
unit would be very effective; if clay and silt predominate, sedimentation
may be of little use. Also, if several units are operated in series, the
first units will remove a certain range: of particle sizes thus affecting the
performance of downstream units. Therefore, the need for describing pollu-
tants in more detail is obvious for modeling purposes. The pollutant removal
mechanism peculiar to each characterization is discussed below.
Pollutant Removal
Characterized by Magnitude
II pollutants are characterized only by their magnitude then the model
improves the quality of the waste stream by removal equations. Removal of a
pollutant may be simulated as a function of (1) detention time (detention
units only), (2) time step size, (3) its influent concentration, (4) inflow
rate, (5) the removal fractions of pollutants, and/or (6) the influent con-
centrations of other pollutants. This selection is left to the user but
there are some restrictions (depending on the unit type). A single flexible
equation is provided by the program to construct the desired removal equa-
tion:
a.x a ax a. a XQ a,
R = a12e l l x/ + a13e 3 J x,4 + a^e 5 5 xfi6
+ a!5e X9 X10 Xll /
where x. = removal equation variables,
a. = coefficients, and
R = removal fraction, 0 ^ R i 1-0
The user assigns the removal equation variables, x. to specific program
variables (detention time, flow rate, «;tc.). If an equation variable is not
assigned it is set equal to 1.0 for the duration of the simulation. The
values of the coefficients, a., are directly specified by the users. There
is considerable flexibility contained in equation IV-14 and, with a judicious
selection of coefficients and assignment of variables, the user probably can
create the desired equation. Three examples are given below.
An earlier version of the Storage/Treatment Block employed the following
removal equation for suspended solids in a sedimentation tank (Huber et al.,
1975):
428 IV_12
-------
_
RSS = < l - e d)
where Roc = suspended solids removal fraction, 0 ^ Rcc S R ,
SS So max
R = maximum removal fraction,
ID3X
t, = detention time, sec, and
K = first order decay coefficient, sec
This same equation could be built from equation IV-14 by setting a-_ = R ,
a,, = -R 1 a_ = -K, a,, = 1.0, and letting x_ = detention time, t,.
13 max 3 16 3 d
All other coefficients, a., would equal zero.
Another example is taken from a study by Lager et al. (1977a). Several
curves for suspended solids removal from microstrainers with a variety of
aperture sizes were derived. Fitting a power function to the curve repre-
senting a 35-micron microstrainer yields
Roc = 0.0963 SS°'286 (IV-16)
oo
where R,,_ = suspended solids removal fraction, and 0 i R<,<, i 1.0, and,
SS = influent suspended solids concentration, mg/1.
Equation IV-14 can be used to duplicate this removal equation by setting a-.
= 0.0963, a~ = 0.286, a.., = 1.0, and x~ = influent suspended solids concen-
tration, SS. All other a. are zero.
Sludge handling may also be modeled with equation IV-14. Figure IV-4
shows the reduction in volatile solids in raw sludge (suspended solids see
earlier discussion) by a digester as a function of percent volatile solids
and detention time (Rich, 1973). These curves can be approximated by
'
where R.-s = volatile solids reduction, 0 ^ RVS ^ 1.0
t, = detention time, sec,
PU{, = percent volatile solids in raw sludge,
wq
Pvg = 100 If (IV-18)
where VS = influent volatile solids concentration, mg/1, and
SS = influent suspended solids (raw sludge) concentration, mg/1.
429
-------
10 20 30 40 50 60 70
RETENTION BASED ON RAW SLUDGE
FEED, DAYS
Figure IV-4. Reduction in Volatile Solids in Raw Sludge (Rich, 1973).
430 IV-14
-------
Equation IVrl4 can be used to construct equation IV-17 by setting a _ =
(1.31 x 10"^)(1440)"U"3-:i(100)i'b/, ag = 0.33, alfl = 1.67, an = -1.67,
a1fi = 1.0, xq = detention time, t,, x-_ = influent volatile solids concen-
tration, VS, and x11 = influent suspended solids (raw sludge) concentration,
SS. A current description of sludge handling can be found in several ref-
erences (Gupta et al., 1977, Huibregtse, 1977, Osantowski et al., 1977).
Characterized by Particle Size Specific Gravity or Settling Velocity
Distribution
Particle Sizes and Specific Gravities If a pollutant is characterized by
its particle size/specific gravity or settling velocity distribution, then
it is removed from the waste stream by particle settling or obstruction.
Many storage/treatment processes use these physical methods to treat waste-
water; sedimentation and screening are among the most obvious examples.
In this mode, the pollutant is apportioned over several (up to 10) par-
ticle size/specific gravity ranges (e.g., ten percent of the BOD is found
in the range from 10 to 50 microns) or settling velocities. Each of the
ranges is preset by the user and assigned an upper and lower bound on the
particle diametar and a value for specific gravity. If a size/specific
gravity distribution is specified the model estimates the average settling
velocity for each range. Alternatively, the user may specify a set of set-
tling velocity ranges. The user also specifies the apportionment of the
pollutant over the various ranges as it enters the first unit. This distri-
bution is modified as it passes through, the storage/treatment plant. Un-
fortunately, the distribution entered at the first unit must remain constant
over time since the other blocks of SWMM do not provide a time-varying par-
ticle size or settling velocity distribution.
Each unit removes all or some portion of the particles in each range
or velocity; the associated removal of the pollutant is easily determined.
For example, if a sedimentation unit removes 50 percent of the particles in
the 50 to 100 micron range and ten percent of the pollutant in question is
found in this range, then five percent of the total pollutant load is re-
moved. The total removal is determined by summing the effects of the sev-
eral ranges or settling velocities passing through this unit. Once certain
particles are removed, the distribution of particle sizes or settling ve-
locities for the outflow can be determined and passed on to the next unit
or receiving water. The removed particles constitute the size or settling
velocity distribution for the sludge volume. The next several paragraphs
describe the two mechanisms available to the user for pollutant removal
when a pollutant is characterized by particle size or settling velocity.
Particle Settling -- There are several forms of settling: unhindered set-
tling by discrete particles, settling by flocculating particles, and hind-
ered settling by closely spaced particles (Fair et al., 1968). For sim-
plicity, the unhindered settling of discrete particles will be the removal
mechanism simulated in this model. This procedure is only applicable to
detention basins modeled as plug-flow reactors.
431 iv-15
-------
Discrete particles settling in a quiescent fluid accelerate to the
point where the drag force exerted by the suspending fluid reaches equilib-
rium with the gravitational force exerted on the particle (Fair et al.,
1968). At this point, the particle settles at a constant velocity known as
the terminal velocity. By equating the forces acting on such a particle,
the equation for the terminal or settling velocity of the particle is de-
rived and approximated by
where v = terminal velocity of particle, ft/sec,
S
2
g = gravitational constant, 32 ft/sec ,
CL = drag coefficient,
S = specific gravity of particle, and
d = diameter of particle, ft.
Additionally,
Cn = §^, if N- < 0.5, or (IV-20)
D NR R
Cr> = %r + KT- + 0.34, if 0.5 ^ NB < 104, or (IV-21)
D NR VNR R
= 0.4, if ND 2 :t04. (IV-22)
n
U
where N = Reynolds number, dimensionless,
K
N = v d/v (IV-23)
R S
2
andv= kinematic viscosity, ft /sec. Kinematic viscosity is a function of
temperature and is approximated by (Fair et al., 1969)
v Z 8.46 x 10~4/(T + 10) (IV-24)
where T = water temperature, °F.
The procedure for finding v under any of the above conditions is demon-
strated by Sonnen (1977). fhe average of the high and low ends of each
particle size range is used as the representative particle size for use in
the above calculations. If a settling velocity distribution is provided
by the user these calculations are omitted.
A range of conditions may exist in an actual detention unit, from very
quiescent, to highly turbulent and nonquiescent. Camp's (1946) ideal removal
efficiency, E , will be used for quiescent conditions, and an adaptation of
432 IV-16
-------
his sedimentation trap efficiency curves (Camp, 1946, Dobbins, 1944, Brown,
1950) as described by Chen (1975) will be used to make the extension to
nonquiescent conditions, as described below.
For quiescent conditions,
En = min ; (IV-25)
v /v
s u
where EQ = particle removal efficiency as a fraction, 0 < E-. £ 1,
v = terminal velocity of particle, ft/sec, and
v = overflow velocity, ft/sec.
Additionally,
v = Q/A = Ay/td = y/t. (IV-26)
u d
3
where Q = flow rate, ft /sec,
2
A = surface area of detention unit, ft ,
y = depth of water in unit, ft, and
t, = detention time, sec.
Equation IV-26 .assumes a rectangular detention unit with vertical sides.
However, a circular unit (with vertical sides) may also be modeled when
characterizing pollutants by particle size. In other words, equation IV-26
is restricted to units that allow the surface area to remain constant at any
depth. Applying this equation (and, thus, the entire methodology) to other
unit types should only be done when the surface area is independent of depth.
Equation IV-25 represents an ideal quiescent basin in which all -parti-
cles with settling velocities greater than v will be removed. Deviations
from quiescent conditions can be handled explicitly based on Camp's (1946)
sedimentation trap efficiency curves, which were developed as a complex
function of particle settling velocity and several basin parameters, i.e.,
v y v A v 2 v
E = f( . - = = >
where E = particle removal efficiency, 0 ^ E ^ 1,
e = vertical turbulent diffusivity or mixing coefficient, ft /sec,
v = flow through velocity of detention unit, ft/sec,
S. = travel length of detention unit, ft, and
other terms are defined previously.
433
-------
Camp (1946) solves for the functional form of equation IV-27 assuming
a uniform horizontal velocity distribution and constant diffusivity, e. A
form of the advective-diffusion equation then results in which local changes
in concentration at any vertical elevation are equal to the net effect of
settling from above and diffusion from below. The diffusivity will be con-
stant if the horizontal velocity is assumed to have a parabolic distribution,
(although this -assumption is clearly at variance with the uniform velocity
H1* C t T*-i Kilt" -I nri ^ *? ciinvot" -i r\ri oHr^T«»^ T«*- *-^»o r» n *
^.~^V«_l~~-_ _ v*^ wv .* ^ «».*. v.»L M .* I* C / *. o *. WAA*. £*bl4
found from
e = 0.075 yVFTp (IV-28)
2
where T = boundary shear stress, Ib/ft and
3 3
p = density of water =1.94 slug/ft (1.00 g/cm ).
The term Vl/p is known as the shear velocity, u.,., and can be evaluated
Manning's equation for open channel flow (Brown, 1950),
Wg
where n = Manning's roughness coefficient.
The flow through ("horizontal") velocity, v , is also given by
vt = £/td (IV-30)
where £ = traval length of detention unit, ft, and
t, = detention time, sec.
Equations IV-27 and IV-28 are then used to convert v y/2e to a more usable
form,
1/6
v y v v y
where a = turbulence factor, dimensionless when all parameters are in units
of feet and seconds.
Camp's sedimentation trap efficiency curves (Camp, 1946; Dobbins, 1944,
Brown, 1950, Chen, 1975) are the solution to the advective-dif fusion equa-
tion mentioned previously and are shown in Figure IV-5 as a function of Of.
Ideally, these curves could be included in the model in some manner, but
their representation is not straight forward from a programming standpoint.
Instead, a simplification is used, based on early work of Hazen (1904) and
the Bureau of Reclamation as described by Chen (1975).
434 IV_18
-------
1.00
UJ
o
z
UJ
o
UL
Ul
.60
UJ
3
Q
Ul
OT
.00
0.01 0.10 1.0 10.0
TURBULENCE FACTOR
or = v*y>/6 = _!_ v>y
vf nvT 10 26
Figure IV-5. Camp's Sediment Trap Efficiency Curves (Camp, 1946, Dobbins,
1944, Brown, 1950, Chen, 1975).
100
I III III
IDEAL QUIESCENT
CONDITIONS
v
TURBULENT FLOW
CONDITION (CC « 0.01)
Et
I 1LJ l ' I ' I ' ' ' ' « i it
ui
S i.o
w .1
.2 .3 .4 .6 .8 1.0 2.0
4.0
Figure IV-6. Limiting Cases in Sediment Trap Efficiency (Chen, 1975).
435 IV-19
-------
It is assusied that an upper limit on turbulent conditions is given by a
= 0.01. Removal efficiency under these conditions is accurately represented
by the function (fitted to the ordinate of Figure IV-5),
ET = (1 - e"vs/vu) ' (IV-32)
or
ET = (1 - e"Vs Vy) (IV-33)
where E_ = particle removal efficiency under turbulent conditions,
0*34*1.
Quiescent conditions are assumed to exist for a = 1.0 for which removal is
given by equation IV-25. Equations IV-25 and IV-32 are shown in Figure IV-6.
The parameter a may now be used as a weighting factor to obtain the overall
removal efficiency, E,
F - F + ln " ~ la °'01 (7 - F ^ '" ~
" ~ T 1 1 _ 1- A C\1 ^A ~ k-pj
In 1 - In 0.01 Q ~ Q 47605~ Q
Thus, a linear approximation (with respect to In a) is made of the curves
shown in Figure IV-5. Within the program, values of the turbulence factor
are limited to 0.01 ^ a i 1.0. If a value computed from equation IV-31 is
less than 0.01 it is set equal to 0.01 and similarly for the quiescent
boundary.
To summarize, the particle settling computations proceed as follows.
1. For each size and specific gravity range a settling velocity is com-
puted using equations IV-19 to IV-24 or a distribution of settling
velocities is provided by the user. If a settling velocity distri-
bution is used the end points of each range are averaged to estimate
the representative velocity. Then for each velocity (in each plug
leaving the unit) all steps below are performed.
2. The turbulence factor, a, is computed from equation IV-31.
3. EQ is computed using equation IV-25.
4. ET is computed using equation IV-32 or IV-33.
5. Finally, the removal efficiency for the particular particle
settling velocity is computed from equation IV-34.
In a normal simulation, several plugs leave the detention unit in any
given time step. The effluent is all. or part of a number of plugs depending
on the required outflow as determined by the storage routing techniques dis-
cussed earlier. Thus, the effluent particle size or settling velocity dis-
tribution is a composite of several plugs. This composite distribution is
436
IV-20
-------
determined by taking a weighted average (by pollutant weight in each plug)
over the effluent plugs. This distribution is then routed downstream for
release or further treatment. The particles that were removed from each
plug are also composited and are used to characterize the sludge volume.
Particle Obstruction The second removal mechanism used when a pollutant
is characterized by a particle size or settling velocity distribution is
obstruction. The most obvious example of a storage/treatment process using
this mechanism is screening. This mechanism is assumed by the model whenever
a non-detention unit is encountered (and a pollutant is characterized by a
size or settling velocity distribution). The user simply specifies a "criti-
cal" size or settling velocity and any particles with a greater size or
settling velocity are removed (and, in turn, so are the associated pollu-
tants). The program operation is simple but the interpretation of the
"critical" size or settling velocity is more complex.
The primary intent of including this mechanism in the model is to simu-
late screens. Pollutant removal by screens is a result of two actions: the
straining of ths screens, and the additional filtration provided by the mat
produced by the initial screening (Maher, 1974). Screens vary widely in the
size of the aperture and the manner in which the waste water flows through
them. To simplify the analysis, the removal of particles may be assumed to
be a function of screen size only; i.e., the filtration by the mat is
ignored. In other words, a particular screen size will remove only those
particles larger than that size. If a settling velocity distribution is
employed, the user must specify a settling velocity. This is not entirely
accurate, of course, but the result is a conservative removal estimate that
may be accurate in cases where backwashing is at a relatively high rate. In
fact, a study by Maher indicates that this simplifying assumption is reason-
able (Maher, 1974). In this case, a microstrainer with a Mark "0" screen
(aperture of 23 microns) was installed in a residential area of Philadelphia,
Pennsylvania. The analysis of the backwash material for two storms (one in
which a coagulant was used) revealed that, by particle count, 88 to 96 per-
cent of the particles were indeed smaller than 23 microns. However, by
weight, over 99 percent of the material was found in particles greater than
23 microns. Although Maher did not report the distribution in terms of
weight, it was a simple matter to convert by assuming a specific gravity.
During the simulation, a screen alters a particle size distribution for
a particular time step without detention time. Again, only the particles
larger than a specified or "critical" size are removed. The specified size
may or may not correspond to the screen aperture, but such an assumption is
probably valid given the preceeding discussion. If the specified size falls
between the high and low ends of any range, the pollutants are removed by
simple linear interpolation. For example, if 20 percent of the suspended
solids are found in the range from 10 to 50 microns and the "critical" size
is 20 microns, then 75 percent of the suspended solids in that range will be
removed or 15 percent of the total suspended solids load. Of course, if the
entire range is larger than the specified size, then all pollutants in that
range are removed. If a pollutant is characterized by a settling velocity
distribution (in lieu of a size distribution) the user specifies a "critical"
settling velocity. The portion associated with velocities greater than or
equal to the "critical" value is removed.
437 iv-21
-------
Comment on Characterization by Particle Size Distribution Pollutants
characterized by a particle size or settling velocity distribution are
restricted by the model to the two removal mechanisms discussed above. This
limits the user somewhat if this characterization is chosen. The types of
units that could be considered in this case would include sedimentation
tanks and storage basins (operating as plug-flow reactors), bar racks, fine
screens and microscreens. However, these units probably represent a large
portion of the processes applied to the problem of combined sewer overflow
and stormwater runoff. Thus, the limits of the applicability of the model
using this mode are probably not too severe.
Cost Calculations
Initial capital and operation and maintenance costs are calculated at
the end of a simulation. These costs are computed using only the informa-
tion processed for the simulation period. In other words, no attempt is
made to derive costs for particular time intervals (e.g., annual). It is
left for the user to interpret the results produced by the subroutine STCOST.
The capital cost for each unit is ccnrputed as a function of a design
flow or volume specified by the user or is calculated by the model as a
function of the maximum value recorded during the simulation.
C = a Qb (IV-35)
cap max
or C a(Q. )b (IV-36)
cap in max
or C = a Vb (IV-37)
cap max
or C a(V . )b (IV-38)
cap obs max
where C = initial capital cost, dollars,
cap r ' '
3
Q = maximum allowable inflow, ft /sec,
(Q. ) = maximum inflow encountered during the simulation, ft /sec,
vxin max 6
3
V = maximum allowable storage (detention units only), ft ,
max
(V , ) = maximum storage encountered during the simulation
(detention units only), ft , and
a, b = coefficients (specified by the user).
Power functions are frequently used in wastewater treatment cost estimations.
Therefore, the above equations should be widely applicable.
Operation and maintenance costs are calculated as functions of the
variables listed above and the total operating time (calculated as the
number of time steps with inflow to the unit).
438 IV-22
-------
C = d o + hD (IV-39)
om Tnax op v '
or C = d(Q. )f + hD (IV-40)
om vxm max op v '
or C = d Vf + hD (IV-41)
om max op
or C = d(V , )f * hD (IV-42)
om obs max op
where C = operation and maintenance costs, dollars,
D = total operating time during the simulation period, hours, and
d,f,h = coefficients (supplied by the user).
The user is cautioned not to misinterpret the cost calculated by the
model. For example, in a single event simulation the calculated capital
cost could only be considered an estimator of the true capital cost when the
e^ent simulated is a. design event. Likewise, when operating time is "a fac-
tor in computing operation and maintenance costs, the calculated costs can
be a valid estimator of the true costs only when a long term simulation is
performed. Recent EPA publications provide useful information for the proper
selection of the coefficients required in equations IV-35 through IV-42 (EPA,
1976, Benjes, 1976).
SUMMARY
A new Storage/Treatment Block has been developed that is somewhat dif-
ferent from its predecessor. The model requires greater user input and
knowledge of the processes being modeled. Storage/Treatment units may be
modeled as detention or non-detention units. Pollutants may be character-
ized by their magnitude alone or by magnitude and their particle size/
specific gravity distribution. Any three of the pollutants available from
other blocks may be routed through the S/T Block. A simple cost routine is
also included.
In summary, the Storage/Treatment Block offers the user a flexible tool
for modeling wet- and dry-weather facilities and evaluating their performance
and costs.
439 Iu-23
-------
APPENDIX V
OTHER RUNOFF BLOCK REVISIONS
INFILTRATION
Introduction
For pervious areas SWMM users now have the option of specifying one of
two alternative infiltration models: The Horton model or the modified
Green-Ampt model (Horton, 1940, Green and Ampt, 1911). Horton's model is
empirical and is perhaps the best known of the infiltration equations. Many
hydrologists have a "fet;l" for the best values of its three parameters dc~
spite the fact that little published information is available. In its usual
form it is applicable only to events for which the rainfall intensity always
exceeds the infiltration capacity, although the modified form used in SWMM
is intended to overcome this deficiency.
On the other hand the Green-Ampt equation is a physically based model
which can give a good description of the infiltration process. The Mein-
Larson (1973) formulation of it is applicable also for the case of rainfall
intensity being less than the infiltration capacity at the beginning of the
storm. Work is currently underway to help users evaluate the parameter val-
ues from available soil data. With results from these studies now being
published, use of the Green-Ampt model for estimating infiltration should
increase.
Evaporation
Evaporation is input for each month as parameter VAP in subroutine
RHYDRO and used in equations in subroutine WSHED as parameter EVAP. It is
considered as a loss "off the top". That is, evaporation is subtracted from
rainfall depths and/or ponded water prior to calculating infiltration. Thus,
subsequent use of the symbol i for "rainfall intensity" is really rainfall
intensity less avaporation rate. Although evaporation and infiltration are
summed to form one total loss (RLOSS in subroutine WSHED) for the subcatch-
ment runoff calculations, separate totals are maintained for the overall
continuity check.
Integrated Horton's Equation
Cumulative Infiltration --
SWMM and many other hydrologic analysis techniques have used Horton1s
equation (Horton, 1940) for prediction of infiltration capacity into the
440 V-l
-------
soil as a function of time,
where f = infiltration capacity into soil, ft/sec,
f^ = minimum or ultimate value of f (at t = ») , ft/sec,
f = maximum or initial value of f (at t = 0), ft/sec,
t = time from beginning of storm, sec, and
a = decay coefficient, sec
See Figure V-l for a sketch of equation V-l. Actual infiltration is
f(t) = min [fp(t), i(t)] (V-2)
where f = actual infiltration into the soil, ft/sec, and
i = rainfall intensity, ft/sec.
Equation V-2 simply states that actual infiltration will be the lesser of
actual rainfall and infiltration capacity.
Typical values for parameters f and fw are often greater than typical
rainfall intensities. Thus, when equation V-l is used such that f is a
function of time only, f will decrease even if rainfall intensities are
very light, as sketched In Figure V-l. This results in a reduction in in-
filtration capacity regardless of the actual amount of entry of water into
the soil.
To correct this problem, the integrated form of Horton's equation V-l
may be used,
t (f - f ) -at
F(v= V fP dt = f» v -v-^a -e P) (v-3)
where F = cumulative infiltration at time t , ft.
This is shown schematically in Figure V-2 and assumes that actual infiltra-
tion has been equal to f . In fact, this is seldom the case, as sketched
in Figure V-l. Thus, the true cumulative infiltration will be
F(t) = jjj f(t) dl (V-4)
where f is given by equation V-2.
441 V-2
-------
fp=VKf0-fJe
-at
TYPICAL RAINFALL HYETOGRAPH
(values of i )
RUNOFF (SHADED AREAS)
TIME
Figure V-l. Horton Infiltration Curve and Typical Hyetograph. For the case illustrated,
runoff would be intermittent.
-------
fp(tp)
f.
0 w
EQUIVALENT TIME
Figure V-2. Cumulative Infiltration, F, is the Integral of f, i.e.,
the Area Under the Curve.
443
V-4
-------
Equations 7-3 and V-4 may be used to define the time t . That is,
actual cumulative infiltration given by equation V-4 is equated to the area
under the Horton curve, given by equation V-3, and the resulting equation is
solved for t and serves as its definition. Unfortunately, the equation
(fo ' f«) -atp
F = f^ + -^ (1 - e P) (V-5)
cannot be solved explicity for t , and it must be done iteratively.
Note that p
t < t (V-6)
which states that the time t on the cumulative Horton curve will be less
than or equal to actual elapsed time. This also implies that available
infiltration capacity, f (t ) in Figure V-2, will be greater than or equal
to that given by equation* V^l. Thus, f will be a function of actual water
infiltrated and not just a function of time that ignores other effects.
Smnuiary of Procedure --
Use of the cumulative Horton function in SWMM may be summarized as fol-
lows. Note that average values over time intervals are used.
1. At each time step, the value of f depends upon F, the actual infil-
tration up to that time. This is known by maintaining the value of t . Then
the average infiltration capacity, f , available over the next time sEep is
tj=t +At
f =Jr J fP dt = F(V - F(V (V-7)
P At J p ft L
P-
2. Equation V-2 is then used.
f if i > f
f = P ~ p (V-8)
i if i < f
P
where f = average actual infiltration over the time step, ft/sec, and
i = average rainfall intensity over the time step, ft/sec.
3. Cumulative infiltration is then incremented.
F(t + At) = F(t) + AF = F(t) + f At (V-9)
where AF = f At = additional cumulative infiltration, ft, (see Figure V-2).
444 V-5
-------
4. A new value of t is then found, t , from equation V-5. If
AF = f At, t is found simply by t = t + At. However, it is
necessary to solve equation V-5 iteratively when the new t will be
less than t + At, as sketched in Figure V-2. This is done using the
P
Newton-Raphson procedure:
(f - f ) -at
FF = 0 = f^tp * ° g - (1 - e P) - F (V-10)
-at
FF' = fp(tp) = f. + (f0 - f J e P (V-ll)
An initial guess is made for t , say t (n) = t + At/2 (V-12)
where n refers to the number of the iteration. Then a correction is
made to t (n) using FF and FF' ,
t (n+1) = t (n) - FF/FF' (V-13)
P! ?!
The convergence criterion is
FF/FF' < 0.001 At (V-14)
and is achieved quite rapidly.
5. If t > 16/a, the Horton curve is essentially flat and f = f^.
Beyond this point there is no need to iterate since f will be constant at
f^ and independent of F.
Regeneration of Infiltration Capacity
For continuous simulation, infiltration capacity will be regenerated
(recovered) during dry weather. SWMM performs this function whenever there
are dry time steps - no precipitation or surface water - according to the
hypothetical dr/ing curve sketched in Figure V-3.
fp = fo - e
-------
f.
f.
f;
f,
f,
f.
0
wr
»k
EQUIVALENT TIME
Figure V-3. Regeneration (Recovery) of Infiltration Capacity During
Dry Time Steps.
446
V-7
-------
In the absence of better knowledge of Of,, it is taken to be a constant frac-
tion or multiple of a,
ad = R a (V-16)
where R = constant ratio, probably « 1.0, (implying a "longer" drying curve
than wetting curve).
New values of t are then generated as indicated in Figure V-3. Let
t = value of t at beginning of recovery, sec,
Pr P
f = corresponding value of f , ft/sec, and
T =t -t,T =t -t, etc.
W CJ Ufi7 W W
1 1 2 *2
Thus, along the recovery curve, for example,
-a- T
f. = f (t ) = f - (f - fj e d wl (V-17)
1 p w o o °°
Solving equation V-17 for the initial time difference, T ,
r
1 f ' foo
T = t -t=-ln ^ z- (V-18)
w p w a, f - f
r rr dor
Then T = T + At (V-19)
wl wr
and f in Figure V-3 is found from equation V-17. Finally t is found
from equation V-l,
The procedure may be summarized as follows:
1. Knowing t , find f from equation V-l.
2. Solve for T from equation V-18.
r
3. Increment T according to equation V-19.
r
4. Solve for f.. from equation V-17.
5. Solve for t^ from equation V-20.
447 v-8
pl
-------
All steps are combined in
-a,At
a
t = -i In [ 1 - e
-at
(1 - e Pr)]
(V-21)
On succeeding time steps, t may be substituted for t and t may be
substituted for t , etc. Note that f has reached it maximum value
pl P
of f when t =0.
o p
Program Variables
t
The infiltration computations are performed in subroutine WSHED in the
Runoff Block of SWMM. Correspondence of program variables to those of this
subsection is as follows:
At = BELT
= TP1
f = WLMAX
o
f = WLMIN
00
a = DECAY
R = REGEN
FF = FF
FF' = DFF
f = RLOSS (RLOSS is also the sum of
infiltration plus evaporation)
I = RI
= RLOSS1
t = TP ]
P
F = CUMINF = CUMI
Green-Ampt Equation
Infiltration During Rainfall Events
The Green-Ampt equation (Green and Ampt, 1911) has received consider-
able attention in recent years. The original equation, was for infiltration
with excess water at the surface at all times. Mein and Larson (1973)
showed how it could be adapted to a steady rainfall input and proposed a way
in which the capillary suction parameter could be determined. More recently
Chu (1978) has shown the applicability of the equation to the unsteady rain-
fall situation, using data for a field catchment.
The Mein-Larson formulation is a two-stage model. The first step pre-
dicts the volume of water which will infiltrate before the surface becomes
saturated. From this point onward, infiltration capacity is predicted by
the Green-Arapt equation. Thus,
448
V-9
-------
For F < F :
s
_, S IMP
s ~ i/K - 1
for i > K
f = i
and
No calculation of F
(V-22)
for i < KS
For F > F ;
s
f = f
and f = K (1 +
P sv
IMD,
"p "- ~p "sv" F
where f = infiltration rate, ft/sec,
infiltration capacity, ft/sec,
rainfall intensity, ft/sec,
cumulative infiltration volume, this event, ft,
(V-23)
f =
P
i
F =
F =
cumulative infiltration volume required to cause surface saturation,
ft,
S = average capillary suction at the wetting front, ft. water,
IMD = initial moisture deficit for this event, ft/ft, and
K = saturated hydraulic conductivity of soil, ft/sec.
S
Equation V-22 shows that the volume of rainfall required to saturate
the surface depends on the current value of the rainfall intensity. Hence,
at each time step for which i > K , the value of F is calculated and com-
s s
pared with the volume of rainfall already infiltrated for this event. Only
if F >_ F does the surface saturate, and further calculations for this con-
dition use equation V-23.
When rainfall occurs at an intensity less than or equal to K , all
rainfall infiltrates and is used only to update the initial moisture defi-
cit, IMD. (The mechanism for this is discussed in the next subsection with
reference to equation V-31.) The cumulative infiltration is not altered for
this case of low rainfall intensity (relative to the saturated hydraulic
conductivity, K ).
S
Equation V-23 shows that the infiltration capacity after surface satur-
ation depends on the infiltrated volume, which in turn depends on the infil-
tration rates in previous time steps. To avoid numerical errors over long
time steps, the integrated form of the Green-Ampt equation is more suitable.
That is, f is replaced by dF/dt and integrated to obtain
- C
ln(F2 + C) -
+ C
C)
(V-24)
449
V-10
-------
where C = IMD S, ft of water,
t = time, sec, and
1,2 = subscripts for start and end of time interval respectively.
Equation V-24 must be solved iteratively for F_, the cumulative infiltration
at the end of tie time step. A Newtoa-Raphsoa routine is used.
The infiltration volume during time step (t- - t.) is thus (t« - t ) i
if the surface does not saturate, and (F_ - F..) if saturation has previously
occurred and a sufficient water supply is at the surface. If saturation oc-
curs during the time interval, the infiltration volumes over each stage of
the process within the time steps are calculated and summed. When rainfall
ends (or falls below infiltration capacity) any water ponded on the surface
is allowed to infiltrate and added to the cumulative infiltration volume.
Recovery of Infiltration Capacity (Redistribution) ;
Evaporation, subsurface drainage, and moisture redistribution between
rainfall events decrease the soil moisture content in the upper soil zone
and increase the infiltration capacity of the soil. The processes involved
are complex and depend on many factors. In SWUM a simple empirical routine
is used as outline below; commonly used units are given in the equations to
make the description easier to understand.
Infiltration is usually dominated by conditions in the uppermost layer
of the soil. The thickness of this layer depends on the soil type; for a
sandy soil it could be several inches, for a heavy clay it would be less.
The equation used to determine the thickness of the layer is
L = 4 JK~ (V-25)
where L = thickness of layer, in, and
K = saturated hydraulic conductivity, in/hr.
Thus for a high K of 0.5 in/hr (12.7 mm/hr) the thickness computed by equa-
tion V-25 is 2.33Sinches (71.8 mm). For a soil with a low hydraulic conduc-
tivity, say K = 0.1 in/hr (2.5 mm), the computed thickness is 1.26 inches
(32.1 mm). S
A depletion factor is applied to the soil moisture during all time steps
for which there is no infiltration from rainfall or depression storage. This
factor is indirectly related again to the saturated hydraulic conductivity
of the soil and is calculated by
DF = L/300 (V-26)
where DF = depletion factor, hr , and
L = depth of upper zone, in.
450 V-ll
-------
Hence, for Kg = 0.5 in/hr (12.7 mm/hr), DF = 0.9 percent per hour; for K =
0.1 in/hr (2.5 mm/hr), DF = 0.4 percent per hour. The depletion volume fDV)
per time step is then
DV = DF FU -At (V-27)
where FU = L IMD = saturated moisture content of the upper
max .max **
zone , in ,
IMD = maximum initial moisture deficit, in/in, and
033 X
At = time step, hr.
The computations used are
FU = FU - DV for FU > 0 (V-28)
F = F - DV for F > 0 (V-29)
where FU = current moiszure content of upper zone, in, and
F = cumulative infiltration volume for this event, in.
To use the Green-Ampt infiltration model in continuous SWMM, it is
necessary to choose a time interval after which further rainfall will be
considered as an independent event. This time is computed as
T = 6/UOO-DF) (V-30)
where T = time interval for independent event, hr.
For example, when K =0.5 in/hr (12.7 mm/hr) the time between independent
events as given by equation V-30 is 6.4 hr; when K =0.1 in/hr (2.5 mm/hr)
the time is 14.3 hr. After time T has elapsed the variable F is set to zero,
ready for the next event. The moisture remaining in the upper zone of the
soil is then redistributed (diminished) at each time step by equation V-28
in order to update the current moisture deficit (IMD) . The deficit is
allowed to increase up to its maximum value (IMD , an input parameter)
over prolonged dry periods. The equation used is
FU - FU
IMD = _£*£ - f
max
When light rainfall (i £ K ) occurs during the redistribution period, the
upper zone moisture storage, FU, is increased by the infiltrated rainfall
volume and IMD is again updated using equation V-31.
Guidelines for estimating parameter values for the Green-Ampt model are
given in Section 4. As is also the case for the Horton equation, different
soil types can be modeled for different subcatchments .
451 V-12
-------
Program Variables --
The infiltration computations are performed in subroutines WSHED and
GAMP in the Runoff Block. Correspondence of program variables to those of
this subsection is as follows:
S = SUCT(J) L = UL(J)
IMD = SMDMAX (J) DF = DF(J)
K = HYDCON(J) i = RI
FU = FUMAX(J) t = TIME
FU = FU(J) At = DELT
IMD = SMD(J) DV = DEP
F = F(J) F = FS
S
SUBCATCHMENT RUNOFF CALCULATIONS
Overland Flow
The Runoff Block forms the origin of flow generation within SWMM, and
much of the emphasis in data preparation and user effort is aimed at success-
ful execution of this block. In order to understand better the conversion
of rainfall excess (rainfall and/or snowmelt less infiltration and/or evapor-
ation) into runoff (overland flow), this subsection briefly describes the
equations used for this purpose. It is intended to supplement the material
presented in the original SWMM documentation (Metcalf and Eddy et al., 1971a).
As discussed in Section 4, subcatchments are subdivided into three sub-
areas that simulate impervious area, with and without depression (detention)
storage, and pervious area (with depression storage). These are areas Al,
A3 and A2 respectively on Figure V-4 and are denoted in subroutine WSHED by
the subscript J, (J = 1, 2, 3, 4). When snowmelt is included, a fourth sub-
area is added to account for the presence or absence of snow cover, (see
Figure II-5 in Appendix II) but that case will not be considered further here.
The depth of depression storage is an input parameter (WSTORE) for the im-
pervious and pe.rvious areas of each catchment. The impervious area without
depression storage is specified for all subcatchments by parameter PCTZER
(as a percent),
A3 = log (Al + A3) (V-32)
Of course, any subcatchment may be assigned zero depression storage over its
entirety through the use of parameter WSTORE.
Overland flow is generated from each of the three subareas by approxi-
mating them as non-linear reservoirs, as sketched in Figure V-5. This is a
452 V-13
-------
/
>
PERVIOUS
IMPERVIOUS
PERVIOUS'
AREA FLOW^
IMPERVIOUS AREA FLOW
TOTAL SUBCATCHMENT FLOW- WFLOW
TO INLET OR GUTTER/PIPE
Figure V-4. Subcatchment Schematization for Overland Flow Calcu-
lations. Flow from each subarea is directly to an inlet
or gutter/pipe. Flow from one subarea is not routed over
another subarea.
453
V-14
-------
RAINFALL,
EVAPORATION SNOWMELT
Q
S
S/////////////SS S//
V
INFILTRATION
rt
Figure V-5. Non-linear Reservoir Model of Subcatchment
-------
spatially "lumped" .configuration and really assumes no special shape. How-
ever, if the subcatchment width, W, is assumed to represent a true prototype
width of overland flow, then the reservoir will behave as a rectangular
catchment, as sketched in Figure V-4. Otherwise, the width (and the slope
and roughness) may be considered calibration parameters and used to adjust
predicted to measured hydrographs.
The non-linear reservoir is established by coupling tlie continuity
equation with Manning's equation. Continuity may be written for a subarea
as
g A g = A i* - Q CV-33)
3
where V = A d = volume of water on the subarea, ft ,
d = water depth, ft,
t = time, sec,
2
A = surface area of subarea, ft ,
i* = rainfall excess = rainfall/snowmelt intensity minus evaporation/
infiltration rate, ft/sec, and
Q = outflow rate, cfs.
The outflow is .generated using Manning's equation
Q = w . i^i (d . d )5/3 gl/2 (v.34)
n p
where W = subcatchment width, ft.
n = Manning's roughness, coefficient,
d = depth of depression storage, ft, and
S = subcatchment slope, ft/ft.
Equations V-33 and V-34 may be combined into one non-linear differential
equation that may be solved for one unknown, the depth, d. This produces
the non-linear reservoir equation,
dt A n P
= i* + WCON (d - d )5/3
P
(d - d ) 1/2 (v.35)
where WCON= - *-49 ^ W ; (V-36)
Note the grouping of width, slope and roughness into only one parameter.
455
V-16
-------
Equation V-35 is solved at each time step by means of a simple finite
difference scheme. For this purpose, the net inflow and outflow on the right
hand side (RHS) of the equation must be averages over the time step. The
rainfall excess, i*, is given in the program as a time step average. The
average outflow is approximated by computing it using the average between
the old and new depths. That is, letting subscripts 1 and 2 denote the
beginning and the end of a time step, respectively, equation V-35 is
approximated by
- = i* + WCON (dl + j(d2 - dx) - d ] 5/3 (V-37)
r. J. nv-wii iu. 72 1 nJ
where At = time step, sec.
Equation V-37 is then solved for d« using a Newton-Raphson iteration; the
Fortran coding is located near the end of subroutine WSHED.
Given d-, the instantaneous outflow at the end of a time step, WFLOW,
is computed using equation V-34. Parameter WFLOW is used in runoff quality
calculations and is the flow value that is input to inlets and gutter/pipes.
The instantaneous outflow at a given time is also the flow value transfered
to subsequent SWMM blocks.
Although the solution of equation V-37 is straightforward and simple
(and in fact may be performed on programmable hand calculators), some pecu-
liarities exist in the way the parameters for individual subareas (Al, A2,
A3 in Figure V-4) are specified. In particular, only two values of WCON are
computed (equation V-36), one for the pervious and one for the total imper-
vious subareas. Thus, WCON is the same for calculating depths on subareas
Al and A3 and is computed from equation V-36 using the total impervious area,
Al plus A3, in the denominator. However, the instantaneous flow is computed
using the individual area of each subarea (e.g., Al or A3). The net effect
for subareas Al and A3 is approximately to reduce the subcatchment width by
the ratio A1/(A1 + A3) or A3/(A1 + A3) as implied in Figure V-4. Numerical
tests of this scheme versus one that uses the individual areas (and propor-
tional widths) in parameter WCON indicate only about a half percent differ-
ence between the two methods. Hence, it should be satisfactory.
Prior to performing these calculations, a check is made to see if losses
are greater than the rainfall depth plus ponded water. If so, the losses
(evaporation plus infiltration) absorb all water and outflow is zero.
Similarly, if losses alone would be sufficient to lower the depth below the
depression storage, the new depth is computed on this basis only and the
outflow is zero.
The computational scheme (equations V-37 and V-38) has proven quite
stable. The only instance for which non-convergence problems arise (or an
attempt to compute a negative depth) is when the subarea values are very
small (e.g., a few square feet) coupled with a large time step (e.g., ten
minutes). Should a non-convergence message be printed, the problem may
usually be cured by increasing the appropriate area or decreasing the time
step.
456 V-17
-------
Gutter/Pipes
Flow routing in gutter/pipes is also performed by coupling the continu-
ity equation with Manning's equation to produce a non-linear reservoir. The
solution technique is performed in subroutine GUTTER and is entirely analo-
gous to that just described for overland flow; no details will be given here.
However, a few comments are in order. Two cross sectional shapes are avail-
able for gutter/pipes: circular and trapezoidal, as shown in Figure V-6.
Parameters representing depth (e.g., GDEPTH, Dl, DO) are actual depths, in
feet, for trapezoidal gutters but not for circular pipes. Rather, for pipes
the "depth" parameters are half of the angle subtended by the wetted peri-
meter, in radians, as shown in Figure V-6. Knowledge of this fact aids in
understanding the Fortran coding in subroutine GUTTER.
Since a gutter/pipe acts as a reservoir with a water surface parallel
to the invert, inflows are automatically "distributed" along its length.
Hence, concentration of subcatchment inflows only at the upstream end of a
gutter/pipe may be reasonable. On the other hand, this leads to consider-
able dispersion or flattening of a hydrograph peak when it is routed through
a cascade of gutter/pipes. Of course; for this flow routing scheme, down-
stream changes are not "felt" upstream, and no backwater effects can be
simulated.
Non-convergence messages are encountered more frequently during gutter/
pipe routing than for subcatchment flow routing, due to the tendency to in-
clude short gutter/pipes of small dimensions in the simulation. Again, this
can usually be cured by increasing the dimensions (e.g., length and width/
diameter) or decreasing the time step.
457 v-18
-------
V
GDEPTH
TRAPEZOIDAL
GUTTER
GDEPTH
CIRCULAR
PIPE
Figure V-6. Depth Parameters for Trapezoidal Gutter and Circular
Pipe.
458
V-19
-------
APPENDIX VI
TRANSPORT BLOCK SCOUR AND DEPOSITION
INTRODUCTION
Deposition of solid material during dry-weather flow (DWF) in
combined sewers and subsequent scour during wet-weather flow has long
been assumed to form a significant contribution of solids to combined
sewer overflows. The deposition-scour phenomenon is also evident in the
"first flush" high solids concentrations at the beginning of a storm
event found in many sewer systems. Even storm sewer systems may show
a first flush if there is a base flow due to infiltration or illegal
connections.
Deposition and scour processes were included in the original SWMM
Transport Block as described in the documentation (Metcalf and Eddy et
al., 1971a). It simulated solids buildup during DWDAYS dry days prior
to the storm and scour during the storm, as velocities increased. A
constant horizontal approximation to the dimensionless shear stress on
Shields' curve (described subsequently) was used to determine incipient
motion, and one fixed particle size distribution (for suspended solids
only) and specific gravity of 2.7 were used to characterize the solids.
Several problems existed in the routine, perhaps unknown to most
SWMM users. The deposition-scour was dependent on the time step.
Buildup of solids would occur using a 1-hr time step for the dry days
prior to simulation, but scour would occur using, say, a 10-min simu-
lation time step with the same flow conditions. The particle size
distribution was unaffected by the amount scoured from the bottom or
deposited from the flow. Thus, there was no simulation of large particles
being deposited in upstream conduits (and thereby unavailable for deposi-
tion further downstream). It was not possible to calibrate the routine
or even "turn it off" since all constants were incorporated into the
program and were not input parameters. Finally, there were situations
in which conservation of solids mass was violated. Although the revised
routine still represents a gross approximation to the real sediment
transport processes at work in sewer systems, it is at least consistent
within itself, it conserves mass, and is both calibratable and avoidable.
There have been other recent investigations of solids deposition in
sewers, most notably the work of Sonnen (1977) and Pisano et al. (1979).
Sonnen's work is highly relevant to the modeling aspect since he developed
459 VI-1
-------
a deposition-scour routine to accompany the Extran Block of SWMM. This
model simulated both bed load and suspended load sediment transport and
characterized the sediment by up to ten particle size-specific gravity
ranges. Although his routines worked satisfactorily, they are not
compatible with the "old" Transport Block, and the "new" Extran Block no
longer routes quality parameters. In addition, they are perhaps overly
sophisticated for the present needs. Thus, the current programming
utilizes an approximate method that is not as sound as Sonnen's but does
have the attributes described earlier.
The best characterization of solids in real sewer systems is given
by Pisano et al. (1979) in their description of extensive field and
analytical work done in the Boston area. The many problems inherent in
dealing with real systems are amply demonstrated.
METHODOLOGY AND ASSUMPTIONS
Overview
Since the criterion for deposition and scour depends upon the sediment
characteristics (notably size and specific gravity), one option for
simulation of the range of characteristics found in real sewer sediment
is to carry along a group of different sizes and specific gravities and
route each range individually. This is done in the Storage/Treatment
Block of SWMM and was done by Sonnen (1977). This has the disadvantage
of requiring large array sizes since each range must be simulated for
each conduit and preferably for each pollutant.
As an alternative, the present methodology utilizes a fixed particle
size distribution and specific gravity (input by the user) for each
desired pollutant and maintains a time history for each conduit of the
maximum particle diameter (DS) in suspension (really, in motion via
bed or suspended load) and the minimum particle diameter (DB) in the
bed. Thus, the particle size distribution of particles in motion is the
input distribution truncated on the right at DS, and the particle size
distribution of deposited solids is the input distribution truncated on
the left at DB. Mass-weighted values of DS are routed downstream for
entry to subsequent conduits.
Assumptions
Several assumptions are inherent in the following development,
including the following:
1. Solids in sewer systems are assumed to behave like ideal non-
cohesive sediment described in various texts (e.g., Graf, 1971, Vanoni,
1975). Unfortunately, the work of Fisano et al. (1979) shows little
evidence of this, and in fact, it may be an impossible task to provide
an accurate theoretical description of transport of the highly hetero-
geneous material constituting "solids" in real sewer systems. The only
hope is that the theory will appear to behave in a "reasonable" manner.
460 VI-2
-------
2. No distinction is made between particle size distributions
resulting from different pollutant sources, e.g., dry-weather flow and
storms water. Only one distribution (and one average specific gravity)
is used for each pollutant.
3. Shields' criterion is used to determine the dividing particle
size between motion and no motion.
4. Once in motion, no distinction is made between bed and suspended
load. Particles in motion ("suspension") are routed downstream in each
conduit by complete mixing, the same as other quality parameters.
5. When a critical diameter (CRITD) is determined for scour, all
particles with diameter less than or equal to CRITD are eroded. There
is no effect simulated of armoring or of erosion of layers of the bed.
6. Scour-deposition is considered only in conduits. It is not
simulated in non-conduits, including storage elements.
7. The effect of deposited sediment on the bed geometry is not
considered. When the hydraulic radius (an important parameter) is
calculated to determine the critical diameter for motion, the bed is
assumed to have the geometry of the conduit. This leads to some under-
estimation of deposited material, mainly at low flows.
SHIELDS' CRITERION
Shields' diagram for the definition of incipient motion is shown in
Figure VI-1. It is widely accepted as a good definition of the beginning
of particle motion and describes the balance between the hydrodynamic
forces of drag and lift on a particle (tending to induce motion) and the
submerged weight of a particle (tending to resist motion). When hydro-
dynamic forces acting on a sediment particle reach a value such that if
increased even slightly will put the particle into motion, critical or
threshold conditions are said to have been reached. Dimensional analysis
of this condition leads to
(vi-i)
where T = critical shear stress required to induce particle
C motion, Ib/ft ,
Y = specific weight of the sediment, Ib/ft ,
Y = specific weight of water = 62.4 Ib/ft ,
d = sediment diameter, ft (a conversion is made from mm),
u^ = shear or friction velocity, ft/sec, and
2
v = kinematic viscosity of water, ft /sec.
461 VI-3
-------
to
08
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Fully developed
turbulent
velocity profile
Sym
o
o
d
t,
^
4-
Description
Amber |
Lignite 1
Qronite I
Borite J
Sand (Casey )
Sond (Kramer)
Sond (US WE. S)
Sond (Gilbert)
rt .g/cm1
1 06
127
27
4 25
2 65
2 65
265
2.65
Turbulent
boundory
layer
*-
-
a
CD
a
Sand (Vanoml
Glass beads (Vanoni)
Sond (White)
Sond in air (White)
Steel shot (White)
2.65
2 49
2 61
2. 1O
7.9
<
M
Figure VI-1. Sliields' Diagram for Definition of Incipient Motion. (After Graf, 1971,
p. 96)
-------
The equation may be stated in words that the dimensionless critical
shear stress is a function of the shear Reynolds number. The critical
shear stress and shear velocity are related to each other and to flow
properties by
u* = /T~/P = /g R S (VI-2)
*
3
where p = water density = 1.98 slug/ft ,
2
g = gravity =32.2 ft/sec ,
R = hydraulic radius, ft, and
S = slope of energy grade line (assumed equal to invert slope).
In addition, the specific weight difference may be related to the specific
gravity difference between sediment and water,
Yg - Y = Y(SPG - 1) (VI-3) ..:
where SPG = specific gravity of the sediment,
and the specific gravity of water is taken as 1.0.
Experiments on critical shear stress (e.g., see Graf, 1971 and
Vanoni, 1975) reveal the motion of sediment grains to be highly unsteady
and non-uniformly distributed. Near critical conditions, observations
of a large area of the sediment bed will show that the incidence of
sediment motion occurs as gusts and is random in both time and space.
Shields and others observed the process of initiation of motion to be
stochastic in nature, so that there is no true "critical condition" at
which motion suddenly begins. In fact, data on critical shear stress
are based upon arbitrary definitions of critical conditions by several
investigators. Shields himself determined T as the value for zero
sediment discharge obtained by extrapolation on a graph of observed
sediment discharge versus shear stress.
Although experiments have been performed incorporating various
materials, (e.g., sand, glass beads, steel shot, minerals), size range's
and specific gravities, the Shields criterion is generally not used for
cohesive sediment that may be more characteristic of sewer systems.
Nonetheless, it appears to be the only well documented criterion for
initiation of motion and is utilized in spite of its limitations.
In SWMM, the Shields diagram is used to determine the dividing
sediment diameter between motion and no motion. Thus, it is necessary
to solve the functional relationship for the critical diameter, d =
CRITD. For programming purposes, the diagram is approximated as shown
in Figure VI-2, where two straight line segments bound a central polyno-
mial approximation, all on a log-log plot. Letting the dimensionless
shear stress = Y, and the shear Reynolds number = R^, then the functional
forms and their best-fit parameters are as follows:
463 VI-5
-------
i.o
O.IO
008
006
0.03
"-R*
0.1166
0.977842
I
y
x =
aQ= -0.9078950
a = -1.2326090
a = 0.7298640
a = -0.0772426
a = 0.0227
b = 0.1568
Y = 0.06
O.I
1.0 147
10
100
40O
IOOO
Figure VI-2. Linear and Parabolic Approximation of Shields' Diagram
-------
< 1.47
Y = a R b (VI-4)
*
with a = 0.1166, and
b = -0.977842 » -1
1.47 < RA < 10
2 . 3
y
y
X
a.
= a_ + a *x + a
" 10g10 (ys-Y)d
= ^°^10 R*
, = -0.9078950
(VI-5)
where
and with
al = -1.2326090
a2 = 0.7298640
a3 = -0.0772426
10 1 R* £ 400
Y = a-R^ (VI-6)
with a = 0.0227, and
b = 0.1568
R* 1 40°
Y = 0.06 or
d * (SPG-1?'' 0.06
(VI'7>
The straight line segments may be solved directly for the critical
diameter from ,
u.d,
(Y -Y)d
S
and using the relationships of equations VI-2 and VI-3, resulting in
b-,
CRITD =
(SPG-l)-a-gb/2
1+b
(VI-9)
465 vi-7
-------
Equation VI-9 works well for the coefficients a and b of equation VI-6.
But for equation VI-4, b - -1 and the exponent approaches infinity. For
the region R^ <_ 1.47, all sediment particles are within the laminar
sublayer of the flow, and motion is independent of the diameter (Graf,
1971). For practical purposes, there is no apparent motion, and the
critical diameter is assumed to be the value at RA = 1.47 in the model,
. fc . . 1.47-j
that is d = .
The polynomial for the transition region, 1.47 <_ R^ <_ 10, is
rapidly solved using a Newton-Raphson iteration. In the program, equation
VI-9 is first solved using the a and b values for equation VI-6 (10 <_ R^
<_ 400). If the resulting value of RA is greater than 400, the critical
diameter is evaluated from equation VI-7. If RA is less 10, the polynomial
approximation is then solved. If the resulting value of R^ from polyno-
mial is greater than 10, the critical diameter is assumed to be the
value at R^ = 10, and if RA is less than 1.47, the value at RA= 1.47 is
used as a default.
Regarding the parameters of equation VI-9, the slope, S is taken as
the invert slope (SLOPE) for each conduit, used by the Transport Block.
The hydraulic radius is calculated at each time step, and the kinematic
viscosity, v, (GNU) is input for each run. (It incorporates any tempera-
ture effects.)- The specific gravity (SPG) of sewer particles ranges
from 1.1 for organic material to 2.7 for sand and grit. An average
value, based upon the rough composition of the sediment, must be used.
When quality parameters are input in card group Fl of the Transport
Block, if SPG <_ 1.0, the deposition-scour routine will not be used. It
may be seen that if SPG is greater than 1.0 but very close to it, the
value of CRITD in equation VI-9 becomes highly sensitive to it.
PARTICLE SIZE DISTRIBUTION
The particle size distribution for each pollutant for which it is
desired to simulate deposition and scour is input by up to four straight
line segments, as shown in Figure VI-3 (see also Figure 6-6). The
distribution may be based upon characteristics of surface sediment for
simulation of storm sewers, but should utilize sewer conduit samples for
combined sewers.
An example will best illustrate the use of the particle size distri-
bution. Consider first an example of scour. The distribution of Figure
VI-3 is sketched again in Figure VI-4a. At the beginning of the time
step, all particles in the bed are assumed to have diameters >_ DB = 0.6
mm in the example. If a new critical diameter, CRITD, is calculated
that is greater than DB, (CRITD = 1.5 mm in the example), the new bed
distribution will become as shown in Figure VI-4b. The percent of the
bed mass that is scoured is
^7p x 100 = 51%
466 VI-8
-------
100
ON
<
M
I
Figure
I 2
DIAMETER d, mm
VI-3. Particle Size Distribution for a Pollutant.
-------
00
o:
[iJ 100
uj
I 72
Q
50
^ 35
UJ
O
cr
UJ
°- o
a:
H 100
LJ
SCOUR
<
o
vy/
UJ
o
a:
UJ
a.
56
50
34
DIAMETER, mm
DEPOSITION
\
100
50
35
100
56
50
s
\
O I 2
DIAMETER, mm
I
0123
DIAMETER, mm
Figure VI-4. Truncation of Particle Size Distribution During Scour and Deposition.
01 2
DIAMETER, mm
_^i
3
-------
(Under the original methodology in the Transport Block, it would have
been assumed that 100-35 or 65 percent of the mass of the bed would be
scoured.)
A similar calculation applies to deposition. If the suspended
material (particles in motion) have the distribution shown in Figure VI-
4c, it becomes that of Figure VI-4d. The percent of the suspended load
that is deposited is
33%
(Under the original methodology in the Transport Block, it would have
been assumed that 56 percent of the suspended load would be deposited.)
When scoured material is added to suspended material, a new value of DS
is computed by mass-weighting:
DS -M + CRITD -M
DS
2 M +M
s e
where DS- = new value of DS , mm,
DS1 = old value of DS, mm,
M = original mass in suspension, mg, and
M = mass eroded from bed, mg.
Similarly, if suspended material is deposited,
DB, -M. + CRITD'M,
'
where DB2 = new value of DB, mm,
DB. = old value of DB, mm,
M, = original mass of bed material, mg, and
M, = mass deposited from flow, mg.
Due to this weighting, ordinarily it will not be true that DB=DS even
though the same critical diameter, CRITD, applies to both.
Another reason why DB will not necessarily equal DS results from
the condition in which CRITD < DB, for scour (or CRITD > DS^^ for deposi
tion) . In these cases, DB_ = DB^^ (or DS2 = DS. ) , prior to addition of
mass from the flow (or bed;, since no mass would be lost from the bed
(or from the suspended material) .
469 VI-11
-------
INFLOWS AND JUNCTIONS
To allow some difference between surface inflows to the sewer
system and dry-weather flow inflows (e.g., domestic sewage) a maximum
particle size, PSDWF, may be specified (in card group Fl) for the
pollutant found in DWF. This also applies to pollutants entering as
a base flow in manholes. Pollutants entering via infiltration are
assumed to be completely dissolved and have "zero particle sizes."
At junctions (manholes or other non-conduits), a new value of DS
is computed by mass weighting the merging values. For instance,
3
.E,. DS -Q -C + PSDWF-CL _._
i=l u. u. u. DWF DWF
DSm = (VI-12)
i£iQu.*Cu. + VF'CDWF + Qinf
1 1
where DS = value of DS of mixture, mm,
m
DS = DS value in upstream conduit i, mm,
Q = outflow from upstream conduit i, cfs,
i
C = concentration in upstream conduit i, mg/1, and
subscripts DWF and inf refer to dry-weather flow and infiltration
respectively.
470 VI-12
-------
APPENDIX VII
EXAMPLE ANALYSIS OF URBAN RUNOFF
QUALITY DATA FOR MODELING APPLICATIONS
Options for simulation of surface runoff quality in SWMM have been
described in Section 4 of this report. They are basically two: use of
a buildup-washoff equation or use of a rating curve. The former (equation
4-35) results in a characteristic loop effect when intra-storm loads are
plotted versus runoff rate (Figure 4-36) while the latter must result in
a single valued function. In this appendix, data from three separate
sewered catchments in Seattle and one combined sewered catchment in
Lancaster, PA are examined in order to see if SWMM has the ability to
mimic their load and concentration versus flow characteristics. The
data were taken from the EPA Urban Rainfall-Runoff-Quality Data Base
(Huber et al., 1979) and analyzed using library statistical packages.
Three parameters were chosen for analysis:
1) Five day biochemical oxygen demand, BOD,.,
2) Total suspended solids, TSS, and
3) NO +NO.-N for Seattle and NO -N for Lancaster.
The first parameter, BOD, is likely to be associated with solids, the
second parameter is a solid, and the nitrogen parameters are likely to
be dissolved. Hence, they may exhibit different characteristic relation-
ships.
Results are presented in the following figures. (See also Figure
4-37.) Loops are evident in the Lancaster data (Figures VII-17 to VII-
19) although eratic, since the runoff hydrographs are eratic. Loops are
also present for the Seattle data although harder to detect since suces-
sive points are not connected (Figures VII-4 to VII-6). As expected,
nitrate concentrations decrease with increasing flow rate (Figures VII-
16 and VII-20). Log-log plots tend to reduce the magnitude of the loops
and could form the basis of a rating curve approximation. When loads
for the three Seattle catchments are normalized by dividing by their
respective catchment areas the data are grouped with less scatter (Figure
VII-12) than when not normalized (Figure VII-11). The runoff rate in
inches/hr is already a normalized flow rate for these catchments.
471 VII-1
-------
SCATTUK VIEW flIOOl I (UAU)
CVCNT M 2/14/T3
CATCHMENT AREA 630 ACRES
9TOMCT COM
HOUR
0. 68*
0. 620
9. 333
0. 944
0. 60O
0. 461
0.384
0. 370
vira UKI i
EVENT » lO/:0/74
000
"*ft'a
I!
!:?
ftt
124.
34.
i
}J:
630
N03M03
jw-
t. 6O
0:55
S:«
§:£
ID
0. 67
8:2!
i:l!
IS
0. 679
0. 72*
0. 613
0. 143
0. 903
8:S8
8:!i3
iiif
SIATTU
EVENT »7
vin BIOCI i
11/17/7*
nUM XU^Uf UUU
ICF9> i :t*/M»l 0
11. 9BO
' 3SO
33O
TSS
!ii:
632.
O 3O3I
0 3361
0. 41 It i
0 «7«t <
0 429C '
0 306C '
0 3976 <
0 184C <
o taae '
O 397| <
O 193E <
0 1948
':il
UOAO
-------
SOUTH OATTU (U*i3)
CVfMT *3 4/4/TT
CATCMVNT MIA - 37. 9 ACKCI
?eSi
U. UIO
8:8ii
8: IS
0. 17<
tm
SURU ioun sumi
EVCMT M a/U/73
anjtrr cool
HOUI
lunu
Evmr o
soon iiuni
Figure VII-2.
Flow and Quality Data for Seattle, Washington
South Seattle Catchment (Industrial, Separate
Sewers), Event 3, Event 4, Event 5.
473
VII-3
-------
ATTIC SXTTHClHTtH 24 ACPTO
TBS Noa»«i3 BOO UMO
(HO/LI (HQ/ll (HO/SKI
s>. sris o.sw
J?: 8:8 !«?. HJ.
EVtXT «7 11/17/74
H
310
too
7*1
10.0
11
3*0
Figure VII-3.
Flow and Quality Data for Seattle, Washington
Southcenter Catchment (Shopping Center, Separate
Sewers), Event 2, Event 3, Event 7.
474
VII-4
-------
Figure VII-4.
BOD Load vs. Flow. Top: View Ridge 1 catchment - (*) Event 1,
(0) Event 6, (X) Event 7. Middle: South Seattle catchment -
(*) Event 3, (0) Event 4, (X) Event 5. Bottom: Southcenter
catchment - (*) Event 2, (0) Event 3, (X) Event 7.
o- o»or oo»
o- o»ar oaa
in oo- o>ar aa«B
-P*
*-4
P o S
§Q O
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O
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s
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at
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-------
Figure VII-5. TSS Load Vs. Flow. Same symbols as Figure VII-4.
oO- o»or u>w-i
.p-
,!
K'
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ft
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fl
8 -
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3 "
r
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-------
Figure VII-6. N02~N + NO -N Load Vs. Flow. Same symbols as Figure VII-4.
i»
J -
- »*
omwvoa o- o»or 5
S 2 S
O
o
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«
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u
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l" ~0
o
..
,
"
o
-------
Figure VII-7. Login BOD Load Vs. Log _ Flow. Same symbols as Figure VII-4
'10
10
o>or OOD oor
o»or oom oor
-P-
-~J
00
M
I
oo
9*
-
s
8 ^
i u
c
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5 i
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8
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g 0
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s i
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oo o
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1
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i
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1
1
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.
-
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s
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,°0
°°
0
0
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j
t g
I
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i
1
!
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t
!
' - i
"a 1
" M 1
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i
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-------
Figure VII-8. Log TSS Load Vs. Log Flow. Same symbols as Figure VII-4.
J10
10
o>or *><*-* ear
o»or inu-1 oar
§ I § §
SO
I
VO
B I
t
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> <
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o -
-
o
3 O
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-------
Figure VII-9. Log N02-N + NO--N Load Vs. Log _ Flow. Same symbols as Figure VII-4.
o»or S oor
o»or S oar
0
0
1
i
!
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HMO 1
1
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g
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r
i
o
0
o
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-------
Figure VII-10.
Log - Load Vs. Log,Q Flow for all Events. (1) View Ridge 1 -
Event 1, (2) View Ridge 1 - Event 6, (3) View Ridge - Event 7,
(4) South Seattle - Event 3, (5) South Seattle - Event 4, (6)
South Seattle - Event 5, (7) Southcenter - Event 2, (8) South-
center - Event 3, (9) Southcenter - Event 7.
o>or 1010-4 oar
00
i
8
M
x
u
8
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3 8
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Figure VII-11. L°8in Load Vs. Log ~ Runoff for All Events BOD, TSS and NO»-N +
NO ±H. Same notation as Figure VII-10.
5
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r-o
-------
Figure VII-12. L°glfl Load/Area Vs. Log - Runoff for All Events BOD, TSS, &
NO -N + NO--N. Same notation as Figure VII-10.
>m»->a»ar * oor
6
oom oor
~
%
t»
»
^
u
x
2 .
I*
1
«
M
M
9
tf
o
i
g
O
3
O
i !
I I
u
s i
M
U
3 "_3
U -
4
"
: i
i
i
i
.
M
LJ
(J
*
U
Js* *
u u
4 u U
u o *
U M
..i..ya^a.i
U J.J
m
4
(
g
X*.
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i
u
V
u
M
fi
«
o
8
i ;
i
o
-
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-
1
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u **
- u
5°"
! a -
!U UUW
u
u w
M
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U
u
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%
1 O
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]
M
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u
M
us *
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8
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11
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-------
uwCABTn. PA.
IVfNTtl 9/14/73
3
10
11
II
14
931
931
941
931
1001
1011
1041
LANCARTUt. PA.
n.oua iona TSK
11. 144
It. 109
13. 347
17. 066
9 .174
17 344
17. 473
36. 73
.
. 739
. 484
69. 339
'SI lit
13.313
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49976
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PLOW OO03 TM3
6. 31
3. u2
394
1
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60 13
eVSHTU 11/34/73
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15
11379
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3 tuia
6 1618
7 163U
8 1638
9 164B
UAMCARTEM. PA.
J87
31U
147
14/
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SIS
SVKT»4 11/39/7?
146446
133796
447O3
m
08107
76900
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18831
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it?: 2:
UANCAaTOI. PA.
EVCNTU ia/u/74
LOAOiooa LOAOTSM
Ill
30:
101371
47119
B
33MJ
18977
Figure VII-13. Flow and Quality Data for Stevens Avenue
Catchment (Single and Multi-Family Residential,
Combined Sewers), Lancaster, Pennsylvania.
Parameters are: Flow (cfs); BOD, TSS, NO.J-N
concentration (mg/1); BOD, TSS and NO.J-N loads
(mg/sec).
484
VII-14
-------
Figure VII-14. BOD Concentration Vs. Flow, Event 1, Event 2, Event 6. Sequence
of letters follows time history.
i II
i i < i i i-i S i i i i i i i I i I 1
-e-
oo
<
M
M
I
I''
-------
Figure VII-15. TSS Concentration Vs. Flow, Event 1, Event 2, Event 6. Sequence
of letters follows time history.
00
ON
I
M
O1
i i t i i i i i i I I I I i
-------
Figure VII-16. NO--N Concentration Vs. Flow, Event 1, Event 2, Event 6. Sequence
of letters follows time history.
00
<
M
V
-------
Figure VII-17. BOD Load Vs. Flow, Event 1, Event 2, Event 6. Sequence of letters
follows time history.
N 111 ii 11IM f i 11 i M 11 i M
M M i
i i M i i ti
in
Hi
. i l I i I i i Hi i M i M M
OO
oo
M
t->
co
i
-------
Figure VII-18. TSS Load Vs. Flow, Event 1, Event 2, Event 6. Sequence of letters
follows time history.
i!
.iiiiiimrmiimmil .mum INN
i i i i i I'M i i i i i
00
I
(-
X)
-------
Figure VII-19. NO -N Load Vs. Flow, Event 1, Event 2, Event 6. Sequence of letters
follows time history.
. i I I I I i
-------
Figure VII-20. Log -Concentration Vs. Log QFlow for All Events, BOD, TSS, NO--N.
(1) Event 1, (2) Event 2, 13) Event 3, (A) Event 4, (5) Event 6.
.p-
VO
I"
I:
Mi
-------
Figure VII-21. Log -Load Vs. Log nFlow for All Events, BOD, TSS, NO -N. (1)
Event 1, (2) Event 2, (3) Event 3, (4) Event 4, (5) Event 6.
M
M
N>
N>
I"
-------
APPENDIX VIII
MISCELLANEOUS TRANSPORT BLOCK TABLES
The following tables provide guidelines for input into the infil-
tration (subroutine INFIL) and dry-weather flow (subroutine FILTH)
routines of the Transport Block. Table VIII-1 gives average degree-days
(cumulative deviation below 65°F, summed for the days of each month) for
several U.S. cities. Current values are also tabulated in various
summary forms by the National Weather Service.
Tables VIII-2 and VIII-3 provide guidelines for relative water use
by various commercial establishments and industries. These may be
entered as process flows in card groups PI and Ql, for instance.
493 VIII-1
-------
Table VIII-1. Average Monthly Degree-days for Cities in the United
States (Base 65F).
State
Ala.
Ariz.
Ark.
Calif.
Colo. .
Conn.
0. C.
Fla.
Ga.
Idaho
111.
Ind.
Station
Anniston
Birmingham
Mobile
Montgomery
Flagstaff
Phoenix
Yuma
Bentonville
Fort Smith
Little Rock
Eureka
Fresno
Independence
Los Angeles
Needles
Point Reyes
Red Bluff
Sacramento
San Diego
San Francisco
San Jose
Denver
Durango
Grand Junction
Leadville
Pueblo
Hartford
New Haven
Washington
Apalachicola
Jacksonville
Key West
Miami
Pensacola
Tampa
Atlanta
Augusta
Macon
Savannah
Thomasville
Boise
Lewiston
Pocatello
Cairo
Chicago
Peoria
Springfield
Evans vi He
Fort Wayne
Indianapolis
Royal Center
Terre Haute
July
0
0
0
0
49
0
0
1
0
0
267
0
0
0
0
350
0
0
11
189
7
0
25
0
280
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
11
0
Aug
0
0
0
0
78
0
0
1
0
0
248
0
0
0
0
336
0
0
7
177
11
5
37
0
332
0
14
18
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
11
0
0
17
0
19
5
Sept
17
13
0
0
243
0
0
38
9
10
264
0
28
17
0
263
0
17
24
110
26
103
201
36
509
74
101
93
32
0
0
0
0
0
0
8
0
0
0
2
135
133
183
28
90
86
56
59
107
59
116
77
Oct
118
123
23
55
586
13
0
216
131
110
335
86
216
41
19
282
59
75
52
128
97
385
535
333
841
383
384
363
231
17
11
0
0
18
0
107
59
63
38
48
389
406
487
161
350
339
259
215
377
247
373
295
Mov Dec
438 614
396 598
198 357
267 458
876 1135
182 360
105 259
516 810
435 698
405 654
411 508
345 580
512 778
140 253
217 416
317 425
319 564
321 567
147 255
237 406
270 450
711 958
861 1204
792 1132
1139 1413
771 1051
699 1082
663 1026
510 831
154 304
129 276
0 18
5 48
177 334
60 163
387 611
282 494
280 481
225 412
208 361
762 1054
747 961
873 1184
492 784
765 1147
759 1128
666 1017
570 871
759 1122
642 986
740 1104
681 1023
Jan Feb
614 485
623 491
412 290
483 360
1231 1014
425 275
318 167
879 716
775 571
719 543
552 465
629 400
799 619
328 244
447 243
467 406
617 423
614 403.
317 247
462 336
487 342
1042 854
1271 1002
1271 924
1470 1285
1104 865
1178 1050
1113 1005
884 770
352 263
303 226
28 24
57 48
383 275
201 148
632 515
521 412
497 391
424 330
359 299
1169 868
1060 815
1333 1022
856 683
1243 1053
1240 1028
1116 907
939 770
1260 1036
1051 893
1239 976
1107 913
Mar
381
378
209
265
949
175
88
519
418
401
493
304
477
212
124
437
336
317
223
317
308
797
859
738
1245
775
871
865
606
184
154
7
15
203
102
392
308
275
238
178
719
663
880
523
868
828
713
589
874
725
860
715
April
128
128
40
66
687
62
14
247
127
122
432
145
267
129
26
413
117
196
151
279
229
492
615
402
990
456
528
567
314
33
14
0
0
45
0
135
62
62
43
52
453
408
561
182
507
435
350
251
516
375
502
371
May
25
30
0
0
465
0
0
86
24
18
375
43
120
68
3
415
51
85
97
248
137
266
394
145
740
203
201
261
80
0
0
0
0
0
0
24
0
0
0
5
249
222
317
47
229
192
127
90
226
140
245
145
June
0
0
0
0
212
0
0
7
0
0
282
0
18
19
0
363
0
5
43
180
46
60
139
23
434
27
31
52
0
0
0
0
0
0
0
0
0
0
0
1
92
68
136
0
58
41
14
6
53
16
54
24
494 VIII-2
-------
Table VIII-1. (continued)
State
Iowa
Kan.
Ky.
La.
Me.
Md.
Mass.
Mich.
Minn.
Miss.
Mo.
Mont.
Neb.
Station
Charles City
Davenport
Des Moines
Dubuque
Keokuk
Sioux City
Concord! a
"Dodge Sity
lola
Topeka
Wichita
Louisville
Lexington
New Orleans
Shreveport
Eastport
Greenville
Portland
Baltimore
Boston
Fitchburg
Nantucket
Alpena
Detroit-Willow Run
Detroit City
Escanaba
Grand Rapids
Houghton
Lans ing
Ludington
Marquette
Sault Ste. Marie
Duluth
Minneapolis
Moorhead
St. Paul
Corinth
Meridian
Vicksburg
Columbia
Hannibal
Kansas City
St. Louis
Springfield
Billings
Harve
Helena
Kalispell
Miles City
Kissoula
Drexel
Lincoln
North Platte
Omaha
Valentine
July
17
0
0
8
1
8
0
0
0
0
0
0
0
0
0
141
69
15
0
0
12
22
50
0
0
62
0
70
13
41
69
109
66
8
20
12
0
0
0
0
1
0
0
0
8
20
51
47
6
22
4
0
7
0
11
Aug.
30
7
6
28
3
17
0
0
1
0
0
0
0
0
0
136
113
56
0
7
29
34
85
10
8
95
20
94
33
55
87
126
91
17
47
21
1
0
0
6
3
0
0
8
20
38
78
83
11
57
6
7
11
5
10
Sept
151
79
89
149
71
128
55
40
40
42
32
41
56
0
0
261
315
199
29
77
144
111
215
96
96
247
105
268
140
182
236
298
277
157
240
154
13
0
0
62
66
44
38
61
194
270
359
326
187
292
95
79
120
88
145
Oct
444
320
346
444
303
405
277
262
236
242
219
206
259
5
53
521
642
515
207
315
432
372
530
393
381
555
394
582
455
472
543
639
614
459
607
459
142
90
51
262
288
240
202
249
497
564
598
639
525
623
405
310
425
331
461
Nov Dec
912 1352
756 1147
777 1178
882 1290
680 1077
885 1290
687 1029
669 980
579 930
630 977
597 915
549 849
636 933
141 283
305 490
798 1206
1012 1464
825 1238
489 812
618 998
774 1139
615 924
864 1218
759 1125
747 1101
933 1321
756 1107
965 1355
813 1175
794 1135
933 1299
1005 1398
1092 1550
960 1414
1105 1609
951 1401
418 669
338 528
268 456
654 989
652 1037
621 970
570 893
615 908
876 1172
1023 1383
969 1215
990 1249
966 1373
993 1233
788 1271
741 1113
846 1172
783 1166
891 1212
Jan Feb
1494 1240
1262 1044
1308 1072
1414 11Q7
1191 1025
1423 1170
1144 899
1076 840
1026 S17
1088 851
1023 778
911 762
1008 854
341 223
550 386
1333 1201
1625 1443
1373 1218
880 776
1113 1002
1240 1137
1020 949
1358 1263
1231 1089
1203 1972
1473 1327
1215 1086
1535 1421
1277 1142
1271 1183
1435 1291
1587 1442
1696 1448
1562 1310
1815 1555
1553 1305
.696 570
561 413
507 374
1091 876
1139 980
1085 851
983 792
1001 790
1305 1089
1513 1291
1438 1114
1386 1120
1516 1257
1414 1100
1353 1096
1240 1000
1271 1016
1302 1058
1361 1100
Mar
1001
834
849
983
761
930
725
694
590
669
619
605
710
163
272
1063
1251
1039
611
849
940
880
1156
915
927
1203
939
1251
986
1056
1181
1302
1252
1057
1225
1051
396
309
273
698
710
666
620
632
958
1076
992
970
1048
y39
843
794
887
831
970
April
537
432
425
543
397
474
341
347
232
295
280
270
368
19
61
774
842
693
326
534
572
642
762
552
558
804
546
820
591
698
739
846
801
570
679
564
149
85
71
326
374
292
270
295
564
597
660
639
570
609
493
377
489
389
543
May
256
175
183
267
136
228
146
135
no
;*u
112
101
86
140
0
0
524
468
394
73
236
254
394
437
244
251
471
248
474
287
418
477
499
487
259
327
256
32
9
0
135
128
111
94
118
304
313
427
391
285
365
219
172
243
175
288
June
70
35
41
76
18
54
20
15
3
13
7
0
15
0
0
288
194
117
0
42
70
139
135
55
60
166
58
195
70
153
189
224
200
80
98
77
1
0
0
14
15
3
7
16
119
125
225
215
106
176
38
32
59
32
83
495
VIII-3
-------
Table VIII-1. (continued)
State
»ev.
N.H.
N.J.
N.M.
N.Y.
N.C,
N.D.
Ohio
Okla.
Ore.
Pa.
R.I.
S.C.
Station
Reno
Tonopah
Winnemucca
Concord
Atlantic City
Cape May
Newark
Sandy Hook
Trenton
Albuquerque
Ro swell
Santa Fe
Albany
Binghamton
Buffalo
Canton
Ithaca
New York
Oswego
Rochester
Syracuse
Asheville
Charlotte
Hatter as
Manteo
Raleigh
Wilmington
Bismarck
Devils Lake
Grand Forks
Williston
Cincinnati
Cleveland
Columbus
Dayton
Sandusky
Toledo
Broken Arrow
Oklahoma City
Baker
Medford
Portland
Roseburg
Erie
Harrisburg
Philadelphia
Pittsburgh
Reading
Scranton
Block Island;
Narragansett Pier
Providence
Charleston
Columbia
Due West
Greenville
July^
27
0
0
11
0
1
0
1
0
0
0
12
0
0
16
27
17
0
20
9
0
0
0
0
0
0
0
29
47
32
29
0
0
0
0
0
0
0
0
25
0
13
14
0
0
0
0
0
0
6
1
0
0
0
0
0
Aug
61
5
17
57
0
2
0
2
0
0
0
15
6
36
30
61
40
0
39
34
29
0
0
0
0
0
0
37
61
60
42
0
9
0
5
0
12
0
0
47
0
14
10
17
0
0
0
5
18
21
26
7
0
0
0
0
Sept
165
96
180
192
29
38
47
40
55
10
8
129
98
141
122
219
156
31
139
133
117
50
7
0
7
10
0
227
276
274
261
42
60
59
74
66
102
28
12
255
77
85
98
76
69
33
56
57
115
88
121
68
0
0
9
10
Oct Nov
443 744
422 723
508 822
527 849
230 507
221 527
301 603
268 579
285 582
218 630
156 501
451 772
388 708
428 735
433 753
550 898
451 770
250 552
430 738
440 759
396 714
262 552
147 438
63 244
113 358
118 387
73 288
598 1098
654 1197
663 1160
605 1101
222 567
311 635
299 654
324 693
327 684
387 756
169 513
149 459
518 852
326 624
280 534
288 531
352 672
308 630
219 516
298 612
285 588
339 693
330 591
366 691
330 624
34 214
76 308
142 393
131 411
Dec Jan
986 1048
995 1082
1085 1153
1271 1392
831 905
852 936
961 1039
921 1016
930 1004
899 970
750 787
1071 1094
1113 1234
1113 1218
1116 1225
1368 1516
1129 1236
902 1001
1132 1249
1141 1249
1113 1225
769 794
682 704
481 527
595 642
651 691
508 533
1533 1730-
1558 1866
1631 1895
1528 1705
830 942
995 1101
933 1051
1032 1094
1039 1122
1119 1197
805 881
747 8^3
1138 1268
822 862
701 791
694 744
1020 1128
964 1051
856 933
924 992
936 1017
1057 1141
027 1026
1012 1113
986 1076
410 445
524 538
594 651
648 673
Feb
804
860
854
1226
829
876
932
973
904
714
566
892
1103
1100
1128
1385
1156
910
1134
1148
1117
678
577
487
594
577
463
1464
1576
1608
1442
812
977
907
941
997
1056
646
610
972
627
594
563
1039
921
837
879
902
1028
955
1074
972
363
443
491
552
Mar
756
763
794
1029
729
737
760
833
735
589
443
786
905
927
992
1139
978
747
995
992
955
572
449
394
469
440
347
1187
1314
1298
1194
645
846
741
781
853
905
506
47?.
837
552
515
508
911
750
667
735
725
849
865
916
809
260
318
411
442
April
519
504
546
660
468
459
450
499
429
289
185
544
531
570
636
695
606
435
654
615
570
285
172
171
249
172
104
657
750
718
663
314
510
408
435
513
555
212
169
591
381
347
366
573
423
369
402
411
516
603
622
507
43
77
158
161
May
318
272
299
316
189
188
148
206
133
70
28
297
202
240
315
340
292
130
355
289
247
105
29
25
75
29
7
355
394
359
360
103
223
153
179
217
245
61
38
384
207
199
223
273.
128
93
137
123
196
335
342
197
0
0
39
32
June
165
91
111
82
24
33
11
31
11
0
0
60
31
48
72
107
83
7
90
54
37
5
0
0
7
0
0
116
137
123
138
0
49
22
39
41
60
5
0
200
69
70
83
55
14
0
13
11
35
96
113
31
0
0
2
0
496 VIII-4
-------
Table VIII-1. (continued)
State
S.D.
Tenn.
Texas
Utah
Vt.
Va.
Wash.
W;Va.
Wls.
Wyo.
Station
Huron
Pierre
Rapid City
Chattanooga
Knoxville
Memphis
Nashville
Abilene
Amarillo
Austin
Brownsville
Corpus Christ!
Dallas
Del Rio
El Paso
Fort Worth
Calveston
Houston
Palestine
Port Arthur
San Antonio
Taylor >
I'iuueua
Salt Lake City
Burlington
Northfield
Cape Henry
Lynchburg
Norfolk
Richmond
Wytheville
North Head L.H.
Reservation
Seattle
Spokane
Tacoma
Tatoosh Island
Walla Walla
Yakima
Elkins
Parkersburg
Green Bay
La Crosse
Madison
Milwaukee
Wausau
Cheyenne
Lander
Yellowstone Park
July
10
4
32
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
£
V
0
19
62
0
0
0
0
7
239
49
17
66
295
0
0
9
0
32
11
10
11
26
33
7
125
Aug
16
11
24
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
11
0
47
112
0
0
0
0
13
205
45
28
62
288
0
7
31
0
58
20
30
24
58
39
23
173
Sept
149
136
193
24
33
13
22
5
37
0
0
0
0
0
0
0
0
0
0
0
0
2
155
61
172
283
0
49
5
31
82
234
134
205
177
315
93
150
122
56
183
152
137
112
216
241
244
424
Oct
472
438
500
169
179
98
154
98
240
30
0
0
53
26
70
58
0
0
45
8
25
56
499
330
521
602
120
236
118
181
352
341
329
508
375
406
308
446
412
272
515
447
419
397
568
577
632
759
Nov Dec
975 1407
887 1317
891 1218
477 710
498 744
392 639
471 725
350 595
594 859
214 402
59 159
113 252
299 518
188 371
390 626
299 533
131 271
162 303
260 440
170 315
201 374
234 462
332 1142
714 995
858 1308
947 1389
366 648
531 809
354 636
456 750
662 916
486 636
540 679
879 1113
579 719
528 648
675 890.
807 1066-
726 995
600 896
945 1392
921 1380
864 1287
795 1184
982 1427
897 1125
1050 1383
1079 1386
Jan Feb
1597 1327
1460 1253
1361 1151
725 588
760 630
716 574
778 636
673 479
921 711
484 322
219 106
330 192
607 432
419 235
670 445
622 446
356 247
378 240
531 368
381 258
462 293
494 375
1190 944
1119 857
1460 1313
1524 1384
698 636
846 722
679 602
787 695
945 836
704 585
753 602
1243 988
797 636
713 610
1023 748
1181 862
1017 910
949 826
1516 1336
1528 1280
1417 1207
1302 1117
1594 1381
1225 1044
1494 1179
1464 1252
Mar
1032
971
1045
467
500
423
498
344
586
211
74
118
288
147
330
308
176
166
265
181
190
214
816
701
1107
1176
512
584
464
529
677
598
558
834
595
629
564
660
797
672
1132
1035
1011
961
1147
1029
1045
1165
April
558
516
615
179
196
131
186
113
298
50
0
6
75
21
110
90
30
27
71
27
34
64
567
414
681
754
267
289
220
254
410
492
396
561
435
525
338 .
408
477
347
696
552
573
606
680
717
687
841
Uav
279
233
357
45
50
.20
43
0
99
0
0
0
0
0
0
5
0
0
0
0
0
8
338
208
307
405
60
82
41
57
168
406
246
330
282
437
171
205
224
119
347
250
266
335
315
315
396
603
Jung
80
52
148
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
97
64
72
166
0
5
0
0
35
285
107
146
143
330
38
53
53
13
107
74
79
100
100
100
163
334
Source: American Society of Heating and Air Conditioning Engineers,
"Heating, Ventilating, Air Conditioning Guide," Annual
Publication.
497 VIII-5
-------
Table VIII-2. Guide for Establishing Water Usage in Commercial
Subareas.
Commercial
category
Barber Shops
Beauty Shops
Bus-Pail Depots
Car Washes
Churches
Golf-rSwim Clubs
Bowling Alleys
Colleges Resid.
Hospitals
Hotels
Laundromats
Laundries
Medical Offices
Motels
Drive- In Movies
Nursing Homes
New Office Bldgs.
Old Office Bldgs.
Jails and Prisons
Restaurants
Drive-in Restaurants
Parameter
Barber Chair
Station
Sq ft
Inside Sq ft
Member
Member
Alley
Student
Bed
Sq ft
Sq ft
Sq ft
Sq ft
Sq ft unit
Car Stall
Bed
Sq ft
Sq ft
Occupant
Person
Seat
Car Stalls
Coefficients/ mean
annual water use,
gpd/unit of parameter
97.5
532.0
5.0
4.78
0.14
33.3-100.0
200.0
179.0
150.0-559.0
0.256
6.39
0.64
0.62
0.33
8.0
75.0-209.0
0.16
0.27
10.0-15.0
200.0
10.0-90.0
109.0
498
VIII-6
-------
Table VIII-2. (continued)
Commercial
category
Night Clubs
Retail Space
Schools , Elementary
Schools, High
YMCA-YWCA
Service Stations
Theaters
Apartments
Shopping Centers
Parameter
Person Served
Sale Sq ft
Student
Student
Person
Inside Sq ft
Employee
Seat
Dwelling Unit
Sq ft
Coefficients, mean
annual water use,
gpd/unit of parameter
2.0
0.16
6.0-15.0
10.0-19.9
50.0
0.49
30.0
5.0
50.0-195.0
0.20
Sources: Hittman Associates, Inc., "A System for Calculating and
Evaluating Municipal Water Requirements"; " and
F. P. Linaweaver and J. C. Geyer, "Commercial Water Use
Project," Johns Hopkins University, Baltimore, Maryland.
499
VIII-7
-------
Table VIII-3.
Guide for Establishing Water Usage in Industrial
Subareas.
Industrial
rAtsgory
Meat Products
Dairies
Can, Frozen Food
Grain Mills
Bakery Products
Sugar
Candy
Beverages
Miscellaneous Foods
Cigarettes
Weaving, Cotton
Weaving, Synthetics
Weaving, Wool
Knitting Mills
Textile Finish
Floor Covering
Yarn-Thread Mill
Miscellaneous Textile
Whl. Apparel Industry
Saw-Planning Mill
Millwork.
Wood Containers
Miscellaneous Wood
Home Furniture
Furniture Fixture
Pulp Mills
Paper Mills
Paperboard Mills
Paper Products
Paperboard Boxes
Building Paper Mills
Whl. Print Industry
Basic Chemicals
Fibers, Plastic
Drugs
Soap-Toilet Goods
Paint Allied Products
Gum-Wood Chemicals
Agricultural Chcm.
Miscellaneous Chemicals
Standard
Industrial
Classification Number
201
202
203
204
205
206
207
208
209
211
221
222
223
225
226
227
228
229
230
242
243
244
249
251
259
261
262
263
264
265
266
270
281
282
283
284
285
286
287
289
Mean Annual
Usage Coefficients
Bpd/employee
903.890
791.350
784.739
488.249
220.608
1433.611
244.306
1144.868
1077.360
193.613
171.434
344.259
464.439
273.429
810.741
297.392
63.558
346.976
20.000
223.822
316.420
238.000
144.745
122.178
122.178
13494.110
2433.856
2464.478
435.790
154.804
583.355
15.000
2744.401
864.892
457.356
672.043
845.725
332.895
449.836
984 . 415
500
VIII-8
-------
Table VIII-3. (continued)
Standard
Industrial Industrial
cateaorv Classification Number
Petroleum Refining
Paving-Roofing
Tires, Tubes
Rubber Footware
Reclaimed Rubber
Rubber Products
Plastic Products
Leather Tanning
Flat Glass
Pressed, Blown Glassware
Products of Purchased Glass
Cement, Hydraulic
Structural Clay
Pottery Products
Cement, Plaster
Cut Stone Products
Non-Metallic Mineral
Steel-Rolling
Iron, Steel Foundries
Prime Non-Ferrous
Secondary Non-Ferrous
Non-Ferrous Rolling
Non-Ferrous Foundries
Prime Metal Industries
Metal Cans
Cutlery, Hardware
Plumbing, Heating
Structure, Metal
Screw Machine
Metal Stamping
.Metal Service
Fabricated Wire
Fabricated Metal
Engines, Turbines
Farm Machinery
Construction Equipment
291
295
301
302
303
306
307
311
321
322
323
324
325
326
327
328
329
331
332
333
334
335
336
339
341
342
343
344
345
346
347
348
349
351
352
353
Mean Annual
Usage Coefficients
j»pd/employee
3141.100
829.592
375.211
82.592
1031.523
371.956
527,784
899.500
590.140
340.753
872.246
279.469
698.197
326.975
353.787
534.789
439.561
494.356
411.052
716.626
1016.596
675.475
969.586
498.331
162.547
459.300
411.576
319.875
433.193
463.209
1806.611
343.367
271.186
197.418
320.704
218.365
501
VIII-9
-------
Table VIII-3. (continued)
Standard
Industrial Industrial
category Classification Number
Metalwork, Machinery
Special Industry Machinery
General Industrial Machinery
Office Machines
Service Industrial Machine
Miscellaneous Machines
Electric Distribution Products
Electric Industrial Apparatus
Home Appliances
Light-Wiring Fixtures
Radio TV Receiving
Communication Equipment
Electronic Comp.
Electric Product
Motor Vehicles
Aircraft and Parts
Ship and Boat Building
Railroad Equipment
Motorcycle, Bike
Scientific Instruments
Mechanical Measure
Medical Instrument
Photo Equipment
Watches , Clocks
Jewelry, Silver
Toys, Sport Goods
Costume Jewelry
Miscellaneous Manufacturing
Miscellaneous Manufacturing
354
355
356
357
358
359
361
362
363
364
365
366
367
369
371
372
373
374
375
381
383
384
386
387
391
394
396
398
399
Mean Annual
Usage Coefficients
gpd/eraployee
196.255
290.494
246.689
138.025
334.203
238.839
272.001
336.016
411.914
369.592
235.763
86.270
203.289
393.272
318.233
154.769
-166.074
238.798
414.858
181.007
237.021
une. oic
^l/W . J t.J
120.253
164.815
306.491
213.907
423.124
258.270
258.270
Source: Hittman Associates, Inc., "A System for Calculating and
Evaluating Municipal Water Requirements".
502
VIII-10
-------
APPENDIX IX
INTEGRATED FORM OF COMPLETE MIXING
QUALITY ROUTING
Quality routing in the Transport and Runoff Blocks through conduit
segments has long been accomplished by assuming complete mixing within the
conduit in the manner of a continuously stirred tank reactor or "CSTR."
The procedure is described in the original SWMM documentation (Metcalf and
Eddy et al., 1971a, Appendix B) and was very similar to the complete mixing
formulation of the Storage/Treatment Block. See, for example, the discussion
of equations IV-9, IV-10 and IV-11 in Appendix IV. For the finite difference
scheme of equation IV-11, however, it may easily be shown that negative
concentrations may be predicted if
At > ^ (IX-1)
where At « time step, sec, .,
V = average volume in the conduit or storage unit, ft , and
Q = average flow through the conduit or storage unit, cfs.
This rarely causes a problem for storage unit simulation due to their large
volumes. But when long time steps (e.g., 1 hr) are used in Runoff or
Transport, instabilities in the predicted concentrations may arise.
These may readily be avoided with minimal loss of accuracy by using the
integrated form of the solution to the differential equation. The procedure
is described by Medina et al. (1981) and is outlined below as applied to the
Runoff and Transport Blocks.
The governing differential equation for a completely mixed volume
is
^oT ' Vf + Cf - 'iCi - QC - KCV + L (IX-2)
where C = concentration in effluent and in the mixed volume, e.g., mg/1,
V = volume, ft ,
Q. = inflow rate, cfs,
C. = concentration of influent, e.g., mg/1,
Q = outflow rate, cfs,
K = first order decay coefficient, I/sec, and
L = source (or sink) of pollutant to the mixed volume, mass/time,
e.g., cfs'mg/1.
503 IX-1
-------
An analytical solution of this equation is seldom possible when Q, Q , C ,
V and L vary arbitrarily with time, as in the usual routing through
conduits. However, a simple solution is available to the ordinary, first
order differential equation with constant coefficients if parameters Q,
QJ » C . , V, L and dV/dt are assumed to be constant over the solution time
interval, t to t + At. In practice, average values over the time interval
are used at each time step. Equation IX-2 is then readily integrated over
the time interval t to t + At with
C(0) = C(t) (IX-3)
to yield
C(0
where DENOM = Q/V + K + dV/dt (IX-5)
Thus, the concentration at the end of the time step is predicted as the
sum of a weighted inflow concentration and a decaying concentration from
the previous time step.
Equation IX-4 is used in both the Runoff and Transport Block and is
completely stable with respect to changes in At. It does not reflect rapid
changes in volume and flow as well as the finite difference solution (e.g.,
equation IV- 11) but it is updated at each time step. Given the many other
uncertainties of quality routing within the sewer system, it should be
adequate.
504 IX-2
-------
TECHNICAL REPORT DATA
(Please read liiunii'tiiins on the rcfcrsc be/ore c
I HEPORT NO.
3. RECIPIENT'S ACCESSION NO.
I. TITLE AND SUBTITLE
Storm Water Management Model User's Manual,
Version III
5. REPORT DATE
November 1981
6. PERFORMING ORGANIZATION COOE
7. AUTHOR(S)
S. PERFORMING ORGANIZATION REPORT NO.
Wayne C. Huber, James P. Heaney, Stephan J. Nix,
Robert E. Dickinson and Donald J. Polmann
y. PERFORMING ORGANIZATION NAME AND ADDRESS
10. PROGRAM ELEMENT NO.
Department of Environmental Engineering Sciences
University of Florida
Gainesville, Florida 32611
11. CONTRACT/GRANT NO.
CR-805664
12. SPONSORING AGENCY NAME AND ADDRESS
Municipal Environmental Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, Ohio 45268
13. TYPE OF REPORT AND PERIOD COVERED
5/75 - 11/81
14. SPONSORING AGENCY COOE
EPA/600/14
15. SUPPLEMENTARY NOTES
Douglas C. Ammon, EPA, Cincinnati, OH (513) 684-7635
Thomas Barnwell, EPA, Athens, GA (404) 546-3175
16. ABSTRACT
The EPA Storm Water Management Model (SWMM) is a comprehensive mathematical
model for simulation of urban runoff quantity and quality in storm and combined
sewer systems. All aspects of the urban hydrologic and quality cycles are simu-
lated, including surface runoff, transport through the drainage network, storage
and treatment, and receiving water effects. (The latter component is currently
under revision by the EPA.) This volume applies to Version III of SWMM and is an
update of two earlier User's Manuals issued in 1971 and 1975. It should be
coupled with;Addendum I in order to run the Extran Block (detailed hydraulic flow
routing) developed by Camp, Dresser and McKee.
Detailed descriptions are provided herein for all blocks (except the Receiving
Water Block): Runoff, Transport, Storage/Treatment, Combine, Statistics and
Graph (part of the Executive Block). The latter three blocks are "service" blocks
while the first three are the principal computational blocks. In addition,
extensive documentation of new procedures is provided in the text and in several
appendices.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
1>.IOENTIFIERS/OPEN ENDED TERMS
"Combined sewers, *Storm sewers, *Mathema-
tical models, *Water'quality, Sewers, Rain-
fall-runoff, Water storage, Waste treatment
Cost analysis, Hydraulics, Drainage,
Hydrology
*Urban runoff modeling,
*Combined sewer overflows
*Computer models, Water
pollution control, *Ur-
'ban hydrology, *Hydro-
logic models, Continuous
simulation, Flow routing,
c. COSATl Hcld/(jmii|>
13B
II. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (Tills Report)
UNCLASSIFIED
21. NO. OF PAGES
531
20. SECURITY CLASS (Thispage)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (9-73)
------- |