SEPA
United States
Environmental Protection
Agency
Municipal Environmental Research EPA 600 2 79
Laboratory Amjuv '
Cincinnati OH 45268
Research and Development
Level III: Receiving
Water Quality
Modeling for Urban
Stormwater
Management
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The nine series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
6. Scientific and Technical Assessment Reports (STAR)
7. Interagency Energy-Environment Research and Development
8. Special’ Reports
9. Miscellaneous Reports
This report has been assigned to the ENVIRONMENTAL PROTECTION TECH-
NOLOGY series. This series describes research performed to develop and dem-
onstrate instrumentation, equipment, and methodology to repair or prevent en-
vironmental degradation from point and non-point sources of pollution. This work
provides the new or improved technology required for the control and treatment
of pollution -sources to meet environmental quality standards.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/2-79-100
August 1979
LEVEL III: RECEIVING WATER QUALITY
MODELING FOR URBAN STORMWATER MANAGEMENT
by
Miguel A. Medina, Jr.
Department of Civil Engineering
Duke University
Durham, North Carolina 27706
Grant No. R-802411
Project Officers
Richard Field
Chi-Yuan Fan
Storm and Combined Sewer Section
Wastewater Research Division
Municipal Environmental Research Laboratory (Cincinnati)
Edison, New Jersey 08817
MUNICIPAL ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OHIO 45268
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DISCLAIMER
This report has been reviewed by the Municipal Environ-
mental Research Laboratory, U.S. Environmental Protection
Agency, and approved for publication. Approval does not signify
that the contents necessarily reflect the views and policies of
the U.S. Environmental Protection Agency, nor does mention of
trade names or commercial products constitute endorsement or
recommendation for use.
1 ]
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FOREWORD
The U.S. Environmental Protection Agency was created be-
cause 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 Environ-
mental 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 supplied and to minimize the
adverse economic, social, health, and aesthetic effects of pol-
lution. This publication is one of the products of that re-
search; a most vital communications link between the researcher
and the user community.
The deleterious effects of storm sewer discharges and com-
bined sewer overflows upon the nation’s waterways have become of
increasing concern in recent times. Efforts to alleviate the
problem depend in part upon the development of improved flow
attenuation and treatment devices.
Assessment of the magnitude and frequency of occurrence of
the aforementioned deleterious impacts derived from wet weather
flows constitutes one of the goals of urban water management
analysis. The mathematical model described hereafter is a user
assistance tool for preliminary screening of areawide wastewater
treatment strategies. It requires the availability of an elec-
tronic digital computer. The effectiveness of proposed control
measures may be compared in terms of the number of water quality
violations which may result from their implementation, or by
more traditional methods.
Francis T. Mayo
Director
Municipal Environmental Research
Laboratory
iii
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ABSTRACT
A simplified continuous receiving water quality model has
been developed as a planning guide to permit preliminary screen-
ing of areawide wastewater treatment strategies. The model sim-
ulates the hypothetical response of the stream or tidal river
system to the separate and combined effects of waste inputs
from: 1) upstream sources, 2) dry weather urban sources, and
3) wet weather urban sources. The total hours of runoff—produc-
ing rainfall throughout a year are separated into storm events
by defining a minimum interevent time. For a given storm event,
the runoff and pollutant loads are summed and critical dissolved
oxygen concentrations are estimated as a function of several
hydrodynamic and biochemical parameters. Alternative control
strategies are evaluated in terms of relative impacts by deter-
mining the probability of occurrence of water quality violations.
Model output includes the downstream dissolved oxygen sag
curves computed per each event, and the dissolved oxygen profile
computed at a user-specified location downstream for all simu—
lated events. An application to the Des Moines River at Des
Moines, Iowa, is presented.
This report is submitted in partial fulfillment of Grant
No. R-80241l to the University of Florida under sponsorship of
the U.S. Environmental Protection Agency. This study was under-
taken by Duke University under contract to the University of
Florida and completed in November, 1978.
iv
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CONTENT S
Disclaimer
Foreword
Abstract .
Figures
Tables
Acknowledgments
Conclusions and Recommendations
Conclusions
Recommendations
II Introduction and Model Overview
Problem Definition and Management Tools
Model Overview
Calculation of Urban Runoff Quantity and
Quality
Page
ii
• • iii
iv
• • vii
• . ix
• . xi
1
1
2
4
4
5
10
III Use of Frequency Curves in Hydrologic and Water
Quality Simulation
Hydrologic Frequency Studies
Water Quality Frequency Curves
IV Methodology
Event Definition
Separate Storm, Combined and Dry-Weather
Flows and Loadings
Effect on Receiving Waters
V Programming Considerations
Computer system core requirements
Program Compilation and Execution Times and
Costs
v
18
19
22
26
26
28
30
47
47
48
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CONTENTS (concluded)
Page
Job Control Language 48
Interfacing with Continuous Simulation
Models 56
VI Model Operation 59
Program Operation 59
Instructions for Data Preparation 62
Sample Application 66
Model Utility for Areawide Water Quality
Management Plans 126
VII Abbreviations and Symbols 153
viii References 159
IX Glossary 163
Appendix A 166
Appendix B 168
vi
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FIGURES
Number Page
1 1-1 Urban Wastewater Inputs to Receiving 8
Body of Water
11-2 Infiltration Relationships for 13
Continuous Hydrologic Simulation
11-3 Regeneration (Recovery) of Infiltration 16
Capacity During Dry Time Steps
111—1 The Probability Density Function and its 21
Cumulative Distribution Function
111-2 Flow-Duration Curve for Hypothetical 23
Watershed
111-3 Cumulative Water Quality Frequency Curves 24
for Hypothetical Watershed
IV-l Deoxygenation Rate as a. Function of 40
Stream Depth
VI-l Level Ill-Receiving Subprograms 60
VI-2 Map of Des Moines Area 67
VI—3 River Sampling Stations 68
vi-4 Point Rainfall for Des Moines, Iowa 71
VI-5 Application to Des Moines, Iowa. Measured 78
and computed values of DO at 5.6 mi
(9.0 km) downstream from confluence of
Raccoon and Des Moines Rivers
VI-6 Sample Correlogram of the Hydrologic Time 97
Series
vii
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FIGURES (Concluded)
Page
VI-7 Spatial Distribution of DO for Event No. 14 102
for Various Waste Inf low Combinations
VI-8 Sample Output of Critical DO Frequency 103
Histograms
VI-9 Sample Output of Critical DO Cumulative 104
Frequency Curves
VI-lO DO Distribution Per Event Number At a 106
Specified Location Downstream
VI-li Lag-k Autocorrelation Function of Des Moines, 108
Iowa, Hourly Rainfall, 1968
VI-12 Autocorrelation Function of Hourly Urban 110
Runoff for Des Moines, Iowa, 1968
VI-13 Minimum DO Frequency Curves for Existing 113
Conditions in the Des Moines River
VI-14 Minimum DO Frequency Curves for Varied 114
Percent of Combined Sewer Area
VI-l5 Minimum DO Frequency Curves for Varied 115
Percent of Actual Measured Upstream
River Flow
VI-16 Minimum DO Frequency Curves for Varied 117
DWF Treatment
VI-l7 Minimum DO Frequency Curves for Varied 118
WWF Treatment
VI-l8 Minimum DO Frequency Curves for Varied 119
Treatment Alternatives
VI-l9 Dry-Weather Minimum DO Frequency Curves 121
for Varied DWF Treatment Alternatives
VI-20 Annual Minimum DO Frequency Curves 122
VI-21 Spatial Distribution of Stream DO Concentra- 123
tions for Event No. 14
VI-22 Spatial Distribution of Stream DO Concentra- 124
tions for Event No. 31
VI-23 Input Data Card Deck for Level 111-Receiving 130
viii
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TABLES
Number Page
1 1-1 Levels of Urban Water Management Analysis 6
Developed by EPA Research &
Development Programs
V-l Compilation Times and Required Core 49
Capacity
V-2 Execution Times and Costs at TUCC 50
V-3 Compilation and Execution Times and Costs 51
of NERDC
VI-1 Representative Deoxygenation Rate and 65
Dispersion Coefficients
VI-2 Options Used for Des Moines Simulations 70
VI—3 Measured DO Concentrations Downstream 80
From Des Moines, Iowa
VI-4 Input Data for Level Ill-Receiving, 82
Des Moines Application
VI-5 Model Output Identification Banner 95
VI-6 Sample Computed Values of the Autocorrelation 96
Functión
VI-7 Sample Display of Subprogram Common Input 99
Data
VI-8 Typical Printed Output for Each Wet-Weather 100
Event
VI-9 Chronologically Sorted Wet and Dry Weather 105
Events
VI-lO Volume of DO Deficit 125
VI-li DWF Tertiary Treatment vs. WWF Control 126
ix
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TABLES (Concluded) Page
VI-12 Cost-Effectiveness of Control Options 128
VI-13 Instructions for Data Pretaration 131
x
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ACKNOWLEDGMENTS
Wayne C. Huber and James P. Heaney at the University of
Florida provided invaluable suggestions throughout the model
development stages. W. Alan Peltz was responsible for several
program improvements and made the necessary modifications to
interface the model with the continuous version of the Storm
Water Management Model’s Runoff Block. Tony Arroyo allowed the
author to modify a multiple-plot subroutine for use in
Level 111—Receiving.
Chi-Yuan Fan and Richard Field of the Environmental Protec-
tion Agency deserve appreciation for their guidance and critical
review of this manuscript.
Numerous persons at Duke University contributed to this
effort. Paul Spence was assigned the task of translating por-
tions of the author’s original program from PL/I to FORTRAN IV,
and assisted with instructions for data preparation. Mark
Flaherty and Barbara Ann Buzun tested the model at various
stages of development. Ms. Buzun performed all programming
tasks assigned to her extremely well, and also assisted with
instructions for data preparation. The typing was performed
with dedication by Judy Edwards and Kathy Worrell. Computations
were performed at the Triangle Universities Computation Center,
Research Triangle Park.
xi
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SECTION I
CONCLUSIONS AND RECOMMENDATIONS
CONCLUSIONS
A simplified continuous receiving water quality model has
been developed to permit preliminary planning and screening of
areawide urban wastewater treatment alternatives, in terms of
frequency of water quality violations and more traditional
approaches such as dissolved oxygen profiles. The model name is
Level Ill-Receiving.
Model Capabilities
1. Level Ill-Receiving may be interfaced through peripheral
storage devices with various hourly, continuous urban catch-
ment hydrologic simulation models, notably the Hydrologic
Engineering Center model STORM and the continuous version of
the EPA Storm Water Management Model (SWMN).
2. A large number of wastewater inflow combinations to the
receiving body of water, dry—weather flow and wet—weather
flow treatment rates, and upstream flow conditions may be
simulated. Thus, a comparative evaluation of many urban
pollution control alternatives is possible in terms of their
subsequent impact on receiving water quality.
3. Continuous analysis of receiving water quality allows repre-
sentation of the impacts due to the random occurrence and
probabilistic nature of hydrologic phenomena. Two methods
of evaluation of alternatives are provided by comparing:
(1) frequency histograms or normalized cumulative frequency
curves of critical dissolved oxygen concentrations (or
those at a specified location downstream); and
(ii) classical dissolved oxygen sag curves per event and a
composite dissolved oxygen profile for all events at a
specified location downstream from the point of waste
discharge.
4. Level Ill—Receiving computes a minimum interevent time to
define statistically independent storm events. However, an
1
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arbitrary or otherwise user—defined value may also be
supplied directly to the model as input data.
Model Applications
1. Level Ill-Receiving has been developed on a general basis
so that it may be applied to the surface drainage phase of
most urban catchments by simply changing the input data to
reflect the particular study area and hydrologic time
series. There is virtually no limitation to the size of
catchment modeled.
2. In theory, an unlimited number of storm events may be pro-
cessed; however, practical considerations such as computer
time and costs may be limiting to some users.
3. Data requirements are common to engineering analysis of
non—point source problems and complete instructions on data
preparation are provided.
4. Field measurements, quantitative and qualitative, are nec-
essary to adequately calibrate model parameters and verify
predicted values.
Model Limitations
1. The methodology is not applicable to stream and tidal river
systems of such geometry and hydrodynamic behavior as to
require multi—dimensional transient analysis.
2. Complex water quality conditions, such as eutrophication,
non—linear kinetic interactions, sedimentation and sediment
exchange are not accounted for by the mathematical repre-
sentation of the physical system.
RECOMNENDAT IONS
1. Because of the model’s virginity, it should be tested on
other urban watersheds by those who would ordinarily inter-
pret the results of simulation in a decision-making context.
2. The use and continuous evaluation of the program will un-
doubtedly result in improvements to fulfill more closely
its objectives.
3. Numerical predictions made by the model should serve
as guidelines throughout the planning process, and are not
intended to substitute for engineering experience and
j udginent.
4. Data availability and quality continued to be limiting
factors, even for less detailed models. Therefore,
2
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collection efforts should be expanded along with model
development.
5. The model should be expanded to characterize receiving
water response when storage of wet—weather wastewater
streams is considered in combination with treatment.
6. The inclusion of simplified sediment deposition and resus-
pension algorithms should be considered with any future
development.
7. Simplified techniques to approximate the complex mechanisms
of pollutant transport in lakes, bays, and estuaries should
also be developed and incorporated.
3
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SECTION II
INTRODUCTION AND MODEL OVERVIEW
PROBLEM DEFINITION AND MANAGEMENT TOOLS
In a 1.67 square mile (433 hectare) urban watershed in
Durham, North Carolina, it was found that the dissolved oxygen
content of the receiving watercourse was independent of the
degree of municipal waste treatment beyond secondary during
storm flows (Colston, 1974). Approximately one-half of the
stream miles in the United States are water quality limited and
30 percent of these stream segments are considered polluted to a
certain degree with urban stormwater runoff (Field, et al. ,
1977). The implication is that, generally, secondary treatment
of dry—weather wastewater flows is insufficient to meet desired
receiving water quality standards; therefore, control of runoff
pollution must be considered in areawide wastewater management
plans and abatement programs. The results of a nationwide
assessment of costs and related water quality impacts derived
from non—point sources (Heaney, et al. , 1977) were, among others,
that:
• wet-weather flows represent at least 50 percent of
the total wastewater flow from urban areas;
• a generalized optimization model, assuming linear
costs, predicted primary type facilities are prefer-
able only up to a 10 percent level of BOD removal
for wet-weather flows, with a secondary type facility
preferable for higher levels of control; and
• on a national average basis using BOD removal as the
effectiveness parameter, approximately 39 percent
of the combined sewer problem and 10 percent of
the wet-weather flows should be controlled before
initiating tertiary treatment of point sources.
The study also confirmed that gross inadequacies exist in our
present data base and conclusions are highly sensitive to simpli-
fying assumptions necessary for successful simulation of complex
physical processes occurring throughout our watersheds. Never-
theless, mathematical models are needed to predict variable
responses to stochastic hydrologic phenomena.
4
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The 208 planning effort (Section 208, PL 92—500) has estab-
lished the need for various levels of urban wastewater manage-
ment analysis (Field, et al. , 1977) to permit preliminary
screening of municipal treatment alternatives. Four distinct
levels of evaluation techniques, ranging from simple to complex
procedures which can be integrated with one another, are summar-
ized in Table 11-1. The first three levels essentially repre-
sent various degrees of planning detail with models running on
hourly time steps for long simulation periods (years). Mathe-
matical complexity and data requirements are kept at a minimum.
Of course, the original detailed (single-event) SWMM is typi-
cally used with short time steps (minutes) and short simulation
times (hours). Its data requirements are usually very substan-
tial. The approach guiding the development of Level 111-Receiv-
ing was that the cost—effectiveness of various treatment alter-
natives can be determined realistically only by a continuous
analysis of the frequency of violation of water quality
standards.
MODEL OVERVIEW
The essence of a rational water quality and quantity manage-
ment program is the decision making process. The high cost of
pollution control facilities, in terms of both energy utiliza-
tion and financial burden, obligates the planning agency to
select the optimal strategy for areawide wastewater management.
Such a process must focus on a systematic procedure that identi—
fies and defines: 1) the cause/effect relationships of the
physical environment; 2) the economic realities of control alter-
natives; and 3) the benefits to be derived from implementation
of these controls. A preliminary analysis that provides an
approximation of system responses to proposed treatment measures
should aid the selection of the best strategy for restoration of
water bodies to accepted water quality standards. Such an anal-
ysis must never be interpreted as other than a guide to be
tempered by professional judgment. The mathematical models
applied need not incorporate all phenomena but rather should be
relevant to the problem under consideration. The problem of
specific interest is to assess the separate and combined effects
of the major urban sources of water pollution upon the quality
of the receiving waters. Oxygen concentration is considered the
key to the quality of natural water bodies, although it certain-
ly is not the only water quality indicator. Thus, the relative
impact of these wastewater sources is appraised by their effect
on the dissolved oxygen concentrations downstream from the urban
area. It is of further interest to distinguish clearly between
the two types of urban stormwater runoff, separate sewer flow
and combined sewer overflow, and their relative pollutional
impacts. In essence, the mathematical model must be responsive
to the land use, hydrology, and climatology of the drainage area
while performing the following functions:
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Table 11-1. Levels of Urban Water Management
Analysis Developed by EPA Research
& Development Programs
(modified after Field, et al. , 1977)
Level I: a desktop calculator, statistical analysis proce-
dure, no electronic digital computer required.
• University of Florida Methodology - permits the
user to estimate the quantity and quality of
urban runoff in the combined, storm and Un—
sewered portions of each urban area.
• Hydroscience, Inc. Methodology - use of a
stormwater simulator and an analytical method
based on probability distribution functions
and statistical properties of rainfall, run-
off, treatment and receiving water impact.
Level II: a simplified continuous simulation model for plan-
ning and preliminary sizing of facilities, devel—
oped by Metcalf & Eddy, mc; or the computerized
optimization version of University of Florida
Methodology described above.
Level III: a refined continuous simulation model approach.
Continuous hydrologic simulation models (e.g.,
STORM or continuous SWMM) which generate urban
runoff hydrographs and pollutographs are followed
by continuous receiving water impact analyses
(Level 111—Receiving model).
• Continuous SWMM - University of Florida
• STORM — Corps of Engineers by Water Resources
Engineers, Inc.
Le.ve2 llI-Rece,Lv &ig -- Vuiae. Li vü’vI4 Lty
Level I V: a sophisticated single event simulation model, e.g.
EPA SWMM developed by Metcalf & Eddy, Inc.,
University of Florida, and Water Resources Engi-
neers, Inc.
6
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• generate stormwater runoff pollutant loads and dry—
weather sanitary flow pollutant loads;
• simulate the pollutant removal efficiency of various
treatment schemes;
• simulate the conveyance system, including mixing in
combined sewers of wet— and dry—weather pollutants;
• mix the various pollutant inflow combinations with
pollutants already in the receiving water (from up-
stream sources)
• predict the oxygen balance of the polluted waters
downstream from the waste sources; and
• predict the frequency with which wastewater inputs
result in dissolved oxygen levels in the receiving
body of water which exceed a wide range of DO values
extending throughout the possible spectrum (say, 0
to 15 mg/i in intervals of 0.5 mg/l).
Data for the study area are used to simulate the hypotheti-
cal response of the receiving water to the separate and combined
effects of BOD waste inputs from: 1) upstream sources, 2) dry-
weather urban sources, and 3) wet-weather urban sources. A
system schematic is presented in Figure lI-i. The urban comrnun—
ity served by a separate sewer system will convey stormwater
runoff and municipal sewage through conduits which are not
connected together. The BOD concentration of the storm sewer
runoff is mixed with the dry-weather flow (DWF) and the solids
accumulated in the combined sewer system. An interceptor
carries the sanitary design flow to the municipal sewage treat-
ment plant. The combined sewer overflow is either given treat-
ment or allowed to discharge directly to the receiving water.
Since complete mixing is assumed, the BOD concentrations of the
combined sewer overflow (Q ) and the flow (DWFCMB) intercepted
for treatment by the DWF facility are identical. Any degree of
treatment desired may be imposed at both the DWF and the wet-
weather flow (WWF) treatment plants. The concentration of the
combined BOD inputs in the receiving water is given by:
BOD Q + BOD Q + BOD Q
uu d ww
m -
where BODm = mixed BOD concentration in receiving water, mg/i,
BOD = mixed BOD concentration from sources upstream of
u urban area, mg/i,
BODd = BOD concentration of dry-weather flow treatment
plant effluent, mg/i,
7
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- _ - - - I
± I
URB AN RUNOFF _L T ! J
- I
URBAN _____
Figure 11—i. Urban Wastewater Inputs to Receiving Body of Water.
UPSTREAM
SOURCES
Q ,3OD
ATER BOOm
8
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BOD = BOD concentration of wet—weather flow treatment
w
facility effluent, mg/i,
= upstream flow, cfs,
= DWF treated effluent, cfs, and
= WWF treated effluent, cfs.
The technique for calculation of the quantity and quality of
stormwater and combined sewer overflows is discussed in further
detail subsequently. The BOD concentrations of the DWF and WWF
treated effluents are given by:
[ BODf • DWFSEP + BODc • DWFCNB] (l _ Rd )
BODd = DWFSEP + DWFCMB (11-2)
[ BOD • Q + BOD • Q I (l-R
S s c c w 113
Q +Q
5 C
where BODf = BOD concentration of municipal sewage, mg/l,
BOD = mixed BOD concentration in the combined sewer,
C
mg/i,
BOD = BOD concentration of urban stormwater runoff,
mg/i,
DWFSEP = DWF contribution from separate sewer area, cfs,
DWFCMB = DWF contribution from combined sewer area, cfs,
Q 5 = urban runoff carried by the separate storm sewer,
cfs,
Q = urban runoff carried by collection system of corn—
C bined sewer area, cfs,
Rd = fraction removal of BOD achieved by the DWF
treatment facility, and
R = fraction removal of BOD achieved by the WWF
W treatment facility.
The initial conditions of BOD in the river are defined by
equation 11-1, and the hypothetical impact on the oxygen balance
of the receiving stream is estimated by using simplified mathe-
matical modeling approaches. The total hours of runoff—produc-
ing rainfall throughout the year are separated into storm events
by defining a minimum interevent time. The procedure is dis-
cussed in detail subsequently. For a given storm event, the
runoff and pollutant loads are summed and the critical DO deficit
is estimated as a function of several stream parameters: temper-
ature, flow, oxygen concentration, deoxygenation and reaeration
rates, longitudinal dispersion, and BOD concentrations. The
9
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minimum DO is calculated subsequently and a frequency analysis
is performed. Stream velocity is computed as a function of the
discharge and the time and distance to each critical deficit
point are obtained for each event.
The options used for the simulations include:
1. five inflow combinations:
a. river flow + DWF
b river flow ÷ DWP + separate storm flow
c. river flow + DWF + combined storm flow
d. river flow + separate storm flow + combined
storm flow
e. river flow + separate storm flow + combined
storm flow + DWF,
2. four DWF treatment rates (variable),
3. three WWF treatment rates (variable), and
4. three fractions of measured upstream flow may be
investigated.
Item 4 is included as a model option to investigate whether the
relative impact of urban stormwater runoff is most significant
in the upstream portions of river basins. This effect may be
simulated by simply reducing the upstream flow to any desired
fraction of its actual measured value. Thus, discharge into a
dry river bed may be studied.
CALCULATION OF URBAN RUNOFF QUANTITY AND QUALITY
The methods used to generate storm runoff flows and pollu—
tant mass rates or concentrations depend upon the continuous
hydrologic simulation model selected by the user. Such tech-
niques are described here briefly for two models which may be
applied to both urban and non-urban watersheds: 1) STORM, the
H.E.C. Storage, Treatment, Overflow, Runoff Model (Hydrologic
Engineering Center, 1976), and 2) the U. S. Environmental Pro-
tection Agency’s Storm Water Management Model (SWMM), continuous
version of Runoff Block (Huber, et al, , 1977).
Urban Runoff Quantity -- STORM
STORM computes urban runoff as a function of land use and
rainfall/snowmelt losses (Hydrologic Engineering Center, 1976):
AR = CR - (11-4)
where ARu = urban area runoff, in/hr,
CR = composite runoff coefficient dependent on
U urban land use,
10
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P = hourly rainfall/snowmelt in inches over the
U urban area, and
= available urban depression storage, in.
The hourly urban runoff values, expressed in cfs, are saved in a
file for later recall by Level ITT-Receiving (this requires a
minor modification to STORM — see Section V). The composite
runoff coefficient accounts for losses due to infiltration and
is a function of runoff coefficients for pervious and impervious
surfaces, land use, and fraction of impervious surface area for
each land use.
Urban Runoff Quantity -- SWM.M
To use SWMM the drainage area is subdivided into subcatch—
ments, gutters, and pipes. Each catchment is divided into per—
vious areas and impervious areas, with and without surface
detention, for runoff computations from rainfall data in the
form of hyetographs read into the Runoff Block (Huber, et al. ,
1975). Subcatchments are defined by area, width, slope, and
ground cover, while gutters and pipes are described by slope,
length, and Manning’s roughness coefficient. Instant runoff is
assumed from impervious areas without surface detention, and no
infiltration is computed on impervious areas with detention. A
stepwise computation of runoff volume for pervious areas pro-
ceeds as follows (Metcalf and Eddy, Inc., et al. , 1971):
1. The water depth on the subcatchment is found from the
hyetograph from
= Dt + Rt t (11-5)
where D 1 = water depth after rainfall
Dt = water depth at time t
Rt = intensity of rain, time interval t t.
2. The integrated form of Eorton’s exponential function
(Huber, et al. , 1977) is used to account for infiltra-
tion loss by
M(t ) = cap dt = ft +
(11—6)
(f — f
0 —ctt
(l-e P)
where cap = infiltration capacity into soil ,
11
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f , = minimum or ultimate value of f (at
t 00),
f = maximum or initial value of f (at t = 0),
t = time from beginning of storm,
= decay coefficient,
M = cumulative infiltration at time t .
The above equation cannot be solved explicitly f or t ,
and thus must be computed iteratively. It should
be noted that tp < t which states that the time tp on
the cumulative Horton curve will be less than or equal
to actual elapsed time. Therefore, the available
infiltration capacity, f cap (tp) in Figure 11—2(b),
will be greater than or equal to that given by Horton’s
equation, shown in Figure 11-2(a). Thus, fcap will be
a function of actual water infiltrated and not a func-
tion of time only. At each time step, the value of
fcap depends upon M. Then, the average infiltration
capacity, fcapi available over the next time step is
computed by
M(t 1 ) — M(t
p (11—7)
cap
The actual infiltration is determined from
Y = mm [ ,I] ( 1 1-8)
t cap
where average actual infiltration over the
time step,
t average rainfall intensity over the time
step,
and equation (11—8) simply states that actual infiltra-
tion will be the lesser of actual rainfall and inf ii—
tration capacity.
3. Infiltration is subtracted from water depth according
to
= — Yt t (11—9)
where = depth after infiltration.
4.. The water depth D 2 is compared to the specified deten-
tion depth Dd and, if found greater, the outflow for
the subcatchment is found by first applying Manning’s
equation for velocity
12
-------
TYPICAL RAINFALL HYETOGRAPH
(values of 1)
RUNOFF
1 1
(a) Horton Infiltration Curve
Hyetograph
0 t t t, t
and Tvpica].
(b) Cumulative Infiltration, M, Integral
Form of Horton’s Equation
Figure 11-2.
Infiltration Relationships for Continuous
Hydrologic Simulation (modified after Huber,
et al. , 1977).
fo
-at
‘ 1 cap f€D + Cfo — f , )e
fcap
1a,
0
0 t
fo
cap( t )
fcap
op
M
0
13
-------
v = 1.49 (D 2 — Dd) 2 ” 3 S” 2 (11—10)
where V = velocity
n = Manning’s coefficient
= detention depth
S = ground slope
and then obtaining outflow, from
= VW (D - Dd) (1 1-11)
where W is the width of the area.
5. Water depths on the subcatchments are computed by the
continuity equation:
— Q t
Dt+L t — D 2 — A (11—12)
where A is the surface area of the subcatchment.
The preceding steps are repeated until computations
for all subcatchments are completed, then
6. Gutter inflow, is found by adding the outflow of
the subcatchments tributary to it and the flow from
all upstream gutters,
Q. = + (11-13)
in w,i g,i
where = sum of flow from subcatchments
g,i = sum of flow from upstream gutters.
7. Depth of flow in gutters is calculated as follows:
Q.
Yl = Yt + A (11-14)
S
where Y 11 = water depths in gutter
A = mean water surface area between
S and
8. Outflow from the gutters is computed from Manning’s
equation also,
14
-------
= 1.49 (R) 2 ’ 13 (S ) 1 ” 2 (11-15)
where B = hydraulic radius
Si = invert slope
Qg = VA (11—16)
where A is the cross—sectional area at Y
c 1
9. Water depth in gutters is found using the continuity
equation
(Q. — Q ) t
= , + in A : (11-17)
where all symbols are as defined previously.
10. Gutter computations are carried out for all gutters in
the system and summed to yield runoff. All of the
above processes are repeated for successive time
periods until the complete hydrograph is computed.
The algorithms used in Runoff for the continuous version
are almost identical to the single event model (Huber, et_al.,
1977), with the exception of snowmelt. The main difference is
basically that the continuous option uses data sets or off-line
storage (disk/drum/tape) to access precipitation and temperature
input data instead of dimensioned arrays. Also, for continuous
simulation, infiltration capacity will be regenerated during dry
weather time steps according to the hypothetical drying curve
(see Figure 11—3):
cap = — — f ) ex [ —ct (t — tv)] (11—16)
where ad = decay coefficient for the recovery curve
t = hypothetical projected time at which 1 ca =
on the recovery curve. p
Urban Runoff Quality
The techniques used by both STORM and the surface runoff
quality model in SWMM are essentially the same. It is assumed
that the amount of pollutant which can be removed during a
storm event is dependent on rainfall duration and initial quan-
tity of pollutant. The process can be modeled by a first-order
differential equation:
15
-------
fo
f ,
0
Figure 11-3.
Regeneration (Recovery) of Infiltration
Capacity During Dry Time Steps (modified
after Huber, et al. , 1977)
fo— (f 0 _f )ei(tt
0 tw t
16
-------
dP
which integrates to
p 0 — p = p 0 (1 - e_kt) (11-18)
where P 0 = pollutant originally on ground, mg
P = pollutant after time t, mg
k = constant, and is assumed to be directly propor-
tional to the rate of runoff.
The basic water quality parameters modeled by STORM and SWMM
are suspended and settleable solids, BOD, total nitrogen (N),
total phosphate (P0 4 ), and total coliforni. It is important to
emphasize that the BOD values are expressed in terms of the
standard BOD 5 test: incubation at a temperature of 20°C for
5 days. The BOD loading rates generated by STORM and SWMM are
based on land use and other factors such as number of dry days
without runoff since the last storm and the street sweeping
intervals. Other water quality parameters modeled by SWMM are
grease, oil and COD.
17
-------
SECTION III
USE OF FREQUENCY CURVES IN HYDROLOGIC
AND WATER QUALITY SIMULATION
Rational water resource management must account for hydro-
logic uncertainty and associated water quality variability.
The justification for continuous hydrologic simulation in
dealing with problems of urban stormwater runoff quantity and
quality is the probability of occurrence of events of various
magnitudes (Linsley and Crawford, 1974). The practice of per-
forming frequency analysis on historical data collected from
natural phenomena has been in existence for almost a century.
Frequency analysis of streamf low data is believed to have been
first applied to flood studies by Herschel and Freeman (Foster,
1934) . Today, modern electronic computers are used to generate
synthetic streamflows because in many cases existing records are
not sufficiently extensive to provide estimates of important
statistics. Such approximate models are sufficiently realistic
to improve the planning process significantly (Fiering and
Jackson, 1971).
The conventional approach of selecting single design events
during critical time periods (low—flow conditions) for water
resource management is inadequate for several important reasons:
• No reliable probability or frequency of occurrence can
be determined for the single event (Linsley and
Crawford, 1974).
• The most critical impact on receiving water quality
does not necessarily occur under low flow conditions,
because of intermittent urban runoff pollutant shock
loads (Heaney, et al. , 1977).
• Studies have demonstrated that high frequency storms
over urban catchments cause significantly greater total
annual pollutant loadings from combined sewer overflows
than low frequency storms associated with higher flows
(Vilaret and Pyne, 1971).
• No accepted design event condition exists which also
specifies a design antecedent dry-weather period (Heaney,
et al. , 1977)
18
-------
• Sizing wet—weather pollution control units for storm
intensities associated with the less—frequent events
(e.g., two year recurrence—one hour duration storms
versus two-week recurrence intervals) requires relative-
iy large storage/treatment capacities (Heaney, et al. ,
1977)
HYDROLOGIC FREQUENCY STUDIES
The traditional approach upon the problem of determining
theoretical probabilities of hydrologic events has been the
derivation of eqae icy cuiz.ue . These curves relate the magni-
tude of a variable to quevicj o oc.ca .nce, and are an esti-
mate of the cumulative distribution of the population of that
variable as prepared from a sample of data (Riggs, 1968). The
probability of a single event, say x 1 , is defined as the rela-
tive number of occurrences of the event after a long series of
trials or observations from a historical record:
P(X=x 1 ) = n 1 /N (111—1)
where X = denotes a hydrologic e ven.t, say streamflow
x = mctgni tade of that event
= number of occurrences of event of magnitude x 1
N = total number of observations of event X.
The number of occurrences i’ll is the frequency, whereas n 1 /N is
the ke!c t ive qae icy. When the number of values a random
variable can take on is restricted to an integer number (say 0,
1, 2, ...), the random variable is called di ci e te. and its
probability law is usually presented in the form of a probabil-
ity mass function (PMF):
Px(x.) = P(X=x ) (111—2)
and, by definition
Px(X.) = 1 (111-3)
allx.
1
where = discrete values of random variable X.
Equation (111-2) describes the probability or frequency distri-
bution of a random variable. An equivalent means is obtained
through the use of the cam ea t.Lve. d -I ba.t on urlc.ti on (CDF):
19
-------
Fx(Xi) = P(X 0 (111—6)
i:fxx = 1 (111-7)
and
f — lim AF(x ) — dF(x ) (111—8)
x AX÷0 Ax — dx
= probability density function (PDF).
The cumulative distribution function is defined in terms of the
PDF as
Fx(X) = P(X
-------
f X)
n
N x
Figure 111-i.
x
The Probability Density Function
and its Cumulative Distribution
Function.
F (x
F(x)
o F(x)
x
x
21
-------
The cumulative distribution function has been defined as
the expected number of occurrences less than a given value; how-
ever, it is also convenient to examine its complement —— the
expected number greater than or equal to the given magnitude:
Gx(X) 1 F (X) = P(X>X) (111-10)
It should be noted again that for hydrologic applications the
CDF or its complement may be referred to as frequency curves.
Earlier statisticians also used the term cumulative frequency
function (Burr, 1942). The area under either the CDF curve or
its complement is meaningless: expected frequencies in any
given range are found by simply taking the difference between
ordinates. For example, P(x 1
-------
99.9-
500,000
Figure 111-2. Flow-Duration Curve for Hypothetical
Watershed.
0
w
w
w
0
x
w
0
50-
0
w
-J
w
I—
20-
0
F-
2
w
0
w
0-
STREAMFLOW, cf s
23
-------
R Leve’ of Pollutant Removal
R 2 >R 1
Figure 111—3.
Cumulative Water Quality Frequency
Curves for Hypothetical Watershed.
Li
Li
U i
U
Li
0
Li
-J
4
a
Ui
Li
U-
0
2
Ui
U
Li
0.
DESIRED CONCENTRATION OF WATER
QUAUTY STANDARD, mgi !
24
-------
interpretation of such curves is the subject of further detail
in Section VI. Frequencies of dissolved oxygen concentrations
are computed in the model for class intervals of 0.5 mg/i, from
0.0 to 15.0 mg/i. The percent of time equaled or exceeded for a
given magnitude of the dissolved oxygen stream standard is com-
puted from:
% Time Equaled = 100 [ N _ n 1
or Exceeded L N
where n = cumulative frequency of occurrence (successive
partial sums) in class interval i
i = 1, 2, ... , 31
for various levels of pollutant removal and waste inflow com-
binations.
In contrast to the century—old practice of frequency analy-
sis for flood control, drought severity, and other quantitative
hydrologic applications, its use in water quality control has
developed within the last decade. Downstream damages, in terms
of water treatment costs at a point, have been related to
probability of occurrence or exceedence (Kneese and Bower, 1968)
The damages varied according to the dilution provided by stream-
flow. Cumulative frequency curves have been proposed to relate
probability to annual, stream waste-assimilative capacity (Velz,
1970) under natural hydrologic variations. In a study by
Hydrocomp International and Black & Veatch of the South Platte
River (where the modeling area was centered around Denver,
Colorado) minimum dissolved oxygen cumulative frequency curves
were compared for various dry—weather wastewater treatment plant
configurations (Denver Regional Council of Governments, 1974).
25
-------
SECTION IV
METHODOLOGY
This section describes the development of the water quality
model in terms of the basic mathematical formulations. The
limitations of a simplified approach to the modeling of receiv-
ing water quality are also discussed. As stated previously, the
results of an actual model application to Des Moines, Iowa, and
the Des Moines River are presented and interpreted in
Section VI.
EVENT DEFINITION
The continuous hydrologic simulation models, STORM and the
latest version of SWMN, operate on hourly time steps. Rainfall
inputs are used by these models to generate the corresponding
series of hourly urban runoff. The basic approach to define a
wet—weather event is to analyze the runoff time series and
establish the minimum number of consecutive dry—weather hours
(DWH) that separates independent storm events. The independence
of these events is not defined in a strictly climatologic sense,
it is in fact statistically derived. The DWH refer to periods
during which no runoff was produced. If STORM is selected to
generate the hydrologic time series, depression storage and
evaporation rates must be satisfied before any runoff is pre-
dicted but no runoff occurs during periods of no measurable
precipitation. The continuous version of SWMM allows runoff to
decay temporally beyond intervals with zero precipitation input.
Therefore, for an identical precipitation time series, runoff
events generated by SWMM will generally be of longer duration.
The runoff time series is subjected to autocorrelation
analysis. For hydrologic processes, it is practical to estimate
the autocorrelation coefficients by an open—series approach
(Yevjevich, 1972 and Fiering and Jackson, 1971):
X X - [ I x ] [ . xi] (Iv-l)
1=1 1=1 i=k+1
r (k) =
26
-------
where r 1 (k) = sample estimate of lag-k autocorrelation
coefficient for hydrologic process I,
x. discrete data series (observations) of
1 hydrologic process I, for i = 1, 2, ..., n,
n = total number of data points or observations,
and
k = number of hourly lags.
The tolerance limits for a normal random time series which is
circular and of lag 1, TL [ r 1 (1)], is given by (Anderson, 1942)
-l ± t
TL [ r 1 (l)] n—i (Iv—2)
where t = standardized normal variate corresponding to
probability level 1 -
A circular time series is defined as a series where the last
value is followed by the fi lst so that the time series repeats
itself. Equation (IV-2) has been extended for use with an open
series, for the general lag case (Yevjevich, 1972) . At a
95 percent probability level, the tolerance limits are given by:
TL [ r 1 (k)] = 1 ± (Iv-3)
A plot of the serial correlation coefficients, r(k) , against
the number of lags, k, is called a correlogram. The technique
of autocorrelation analysis is essentially a study of the behav-
ior of the correlogram of the process under investigation
(Quimpo, 1968). The model compares the value of r(k) obtained
from equation (IV-l) with TL [ r 1 (k)}, computed by equation (IV-3),
for the corresponding number of hourly lags k. The minimum
interevent time (MIT) which separates independent wet—weather
events is defined as the minimum value for k for which r(k) is
not significantly different from zero at a 95 percent probabil-
ity level.
Once the MIT has been determined by the mathematical model,
its value is compared with the number of DWH preceding each run-
off event. A wet—weather event and its duration are defined by
the model as follows:
(1) any runoff occurrence having a number of DWH preceding
it greater than or equal to the MIT denotes the
beginning of the event;
(2) subsequent runoff occurrences are considered part of
the event as long as the DWH immediately preceding
each occurrence are less than the MIT;
27
-------
(3) the event runoff duration (in hours) is equal to the
sum of all the runoff occurrences in (1) and (2); and
(4) the actual event duration (in hours) must be deter-
mined by examining the date and hour of the first
runoff value and the date and hour of occurrence of
the last runoff value within the event.
The hourly urban runoff and associated pollutant loads within
each event (including DWF pollutant loads during DWH periods
less than the MIT) are summed, average conditions are determined,
and the model proceeds with the receiving water analysis. Of
course, the user may impose a value for the MIT chosen arbitrar-
ily, from experience, or obtained from an alternate analysis
technique. For example, a MIT of zero suggests that all hourly
runoff occurrences are to be considered independent wet—weather
events.
SEPARATE STORM, COMBINED AND
DRY-WEATHER FLOWS AND LOADINGS
All of the following methodology can be used regardless of
the technique employed to generate storm runoff and quality, as
long as these values pertain to the entire area being modeled.
Separate Storm Flows and Loadings
Apportionment of the total flow and BOD loading is made on
the basis of the relative area served by separate and combined
sewers. Runoff from separate sewered areas is thus (refer to
Figure 11-1):
A
Q =— Q (IV-4)
s At t
where = stormwater flows from separate sewered areas, cfs,
A = area served by separate sewers, acres,
= total (storm plus combined) urban runoff, cfs, and
At = total area of catchment, acres.
Dry-Weather Flow and Loadings
Dry—weather flow and BOD loadings are assumed known from
data on point sources in the area. Thus, Qd represents the flow
(cfs) into receiving waters of treated wastewater, and BODd
represents the BOD concentration [ at 68°F (20°C) for 5 days,
mg/li. The amount of treatment can be varied in the analysis,
as stated in the overview.
28
-------
Combined Flows and Loadings
Dry—weather flow (DWF) is assumed to cause only a negli-
gible increase in flow in a combined sewer during a storm event.
However, two factors related to DWF may increase significantly
the BOD concentration of the combined sewer stormwater:
(1) the BOD strength of the municipal sewage with which
it mixes; and
(2) the BOD exerted by sediment accumulation in each sec-
tion of the sewer under DWF conditions which is
subject to the “first flush” effect induced by the
initial runoff.
To incorporate the “first flush” effect, it is assumed that the
hourly in—sewer sediment build—up is constant over consecutive
dry-weather hours. This assumption is reasonable although it
is evident that particle size and specific gravity, depth of
flow, and the slope of the conduit are important factors
affecting deposition.
Data collected at various combined sewer overflow stations
in Des Moines, Iowa, support the first flush theory (Davis and
Borchardt, 1974). BOD and total suspended solids (TSS) concen-
trations decreased with time with little or no relation to the
flow pattern. Furthermore, pollutographs (BOD and TSS concen-
trations versus time) for these stations seem to indicate that
the flushing occurs mostly during the first hour of runoff
generated by the storm event.
The sewer solids build—up that occurs during consecutive
DWH is computed, then the BOD load contribution from these
solids is lumped into the first hour of runoff. The first flush
BOD load is given by
FF = FFLBS • DWH (IV-5)
where FF = first flush BOD load, lbs/hour,
FFLBS = first flush factor, lbs/first flush hour
per DWH, and
DWH = number of dry-weather hours preceding each
runoff event.
The first flush factor, FFLBS, must be determined from
(1) the total flow generated by the combined sewer area
(including dry-weather flow contribution) during the
wet year;
29
-------
(2) the difference in annual average concentration between
BODc (excluding factor FF) and the measured annual
average value; and
(3) the total number of DWH for the entire year under
study.
An example of this calculation is presented in the application
of the model to Des Moines, Iowa.
Apportionment to the total flow on the basis of relative
area gives:
A
Q = Q (IV-6)
c At t
where = combined sewer overflow rate, cfs, and
A = area served by combined sewers, acres.
Finally, the mixed BOD concentration in the combined sewer, BODC
(mg/i), is computed by the following expression:
BODt Q ± BODf • d • (A /At) + FF . C 1
BODc = C + (A/A) (IV-7)
where C 1 = 4.45, a factor to convert FF from lb/hr to
cfs • mg/i.
EFFECT ON RECEIVING WATERS
A simplified mathematical modeling approach is used in
which deficits and resulting DO concentrations are determined
for a large number of waste input combinations, treatment
schemes, and receiving water conditions. The .development of a
detailed and sophisticated model is not justified for the
problem context: to provide adequate information on the rela-
tive effectiveness Of various pollutant control strategies in
achieving selected water quality standards. The basic theory
of mathematical modeling of one-dimensional bodies of water is
presented for the spectrum of natural systems from freshwater
streams to tidal rivers and estuaries. The approach is parti-
cularly advantageous for a limited data base on natural system
geometry, hydrodynamic variables, and discrete rather than
continuous water quality measurements.
Assumptions typical of models limited for interim planning
are made (Hydroscience, Inc. 1971):
(1) Temporal steady—state conditions prevail, where all
30
-------
system parameters and inputs are constant with respect
to time; however, a relatively short time step (1 hour)
is used for simulation in the wet-weather flow model.
(2) Natural system parameters (such as flow, velocity,
water depth, deoxygenation and reaeration rates, and
longitudinal dispersion) are spatially constant along
the flow axis throughout each time step.
(3) All waste inflows to the receiving body of water occur
at one point.
(4) The effects of various natural biological processes
(algal photosynthesis and respiration, benthal
stabilization) are incorporated into a background
quality which is reflected by DO deficit (if none, by
saturation) upstream from the waste inflow point. Any
benthic build-up is incorporated into the BOD decay
rate.
(5) Waste treatment facilities operate at constant eff i-
ciencies, independent of hydraulic and organic
loadings, for the entire period of simulation.
Initial Conditions
Initial conditions of BOD in the river or estuary are
defined by equation (11-1). Subsequently, the mixed BOD concen-
tration in the body of water will be denoted by L 0 . Thus,
L E BOO (IV-8)
0 m
The assumption that all waste inflows occur at one point is not
unreasonable since critical water quality conditions develop
relatively far downstream from urban waste sources, but in some
locations the distribution of inflows along the flow axis of the
natural system may need to be considered. All of the BOD con-
tributorsin equation (11-1) represent standard BOD 5 values.
Thus, BODm is also in terms of the standard BOD test. The ulti-
mate first—stage (carbonaceous) demand is computed from the BOD 5
value by:
BOD
(Lo)c = (IV-9)
i—e
where (L 0 ) = ultimate first-stage BOD demand, mg/i, and
1(1 = first-order BOD decay rate constant, day 1 .
The value of (L also varies with receiving water temperature,
so that: 0
31
-------
[ (LQ)c]T = [ (L 0 ) ] 2 oo [ 1 + 0.02 (T—20)} (IV—lO)
where [ (L 0 ) 1200 = ultimate first-stage BOD demand @ 20°C,
C mg/i, and
T = water temperature in °C.
To simplify notation, the temperature—corrected ultimate, car-
bonaceous BOD demand will hereafter be denoted simply L 0 .
The other initial condition required is the initial oxygen
deficit, D. It is assumed that all waste inflows will be at
saturation. Thus, the only contribution to the initial deficit
will be from the upstream portion of the body of water. Thus,
DQ
uu
— Q + Q + Q + Q
u d s c
where D 0 = initial DO deficit, mg/i, and
D = DO deficit in receiving waters upstream of inflow
U point, mg/i.
If the effluent temperatures are high, an adjustment can be
made by increasing Du•
Oxygen Balance of Polluted Streams and Estuaries
In view of the modeling objectives, pollutant transport
processes in these systems may be adequately approximated by the
one—dimensional version of the classical convective diffusion
equation. This partial differential equation is based on the
principle of conservation of mass (continuity) and is given by:
= [ E - - UC] ± S (IV—12)
where C = concentration of 3 water quality parameter
(pollutant), M/L
t = time, T,
—E = mass flux due to longitudinal dispersion along
the flow axis, the x direction, M/L 2 T,
UC = mass flux due to advection by the fluid contain-
ing the mass of pollutant, M/L 2 T,
S = sources or sinks of the substance C, M/L 3 T,
U = flow velocity, L/T, and
32
-------
E = longitudinal dispersion coefficient, L 2 /T.
The equation assumes no diffusion of pollutants through the
natural body of water boundaries (other than what may be inclu-
ded in the source—sink term) and is best suited to predict con-
centrations relatively far downstream from the point of waste
Injection. Since critical DO deficits usually occur some dis-
tance downstream from the waste source, equation (IV—l2) is
particularly well suited for such predictions. The main sources
of dissolved oxygen in stream or estuarine systems are atmos-
pheric reaeration and oxygen production by photosynthesis . The
major sinks include carbonaceous oxygen demand (CBOD)., nitro-
genous oxygen demand (NBOD), benthal demand , and respiration of
aquatic plants. All natural system parameters are assumed
spatially constant along the flow axis, and by substituting the
various sources and sinks of DO into equation (IV-12) the
following expression is obtained:
= E - + K 2 (C - C)
X (Iv—13)
-KL-KN+P-R -B
1 n e
where C = concentration of DO in the stream or estuary, mg/i,
B = longitudinal dispersion coefficient, ft 2 /sec,
U = freshwater stream or tidal river velocity, ft/sec,
K 2 = atmospheric reaeration coefficient, hours ,
C = dissolved oxygen saturation, mg/l,
C - C = dissolved oxygen deficit, mg/i = D,
K 1 = deoxygenation constant of carbonaceous BOD, hours 1 ,
L = remaining carbonaceous BOD concentration, mg/i,
Kn = oxidation coefficient of nitrogenous BOD, hours 1 ,
N = remaining nitrogenous BOD concentration, mg/i,
P oxygen production rate by algal photosynthesis,
mg/i-hour,
Re = algal respiration rate, mg/l-hour, and
B = benthal demand of bottom deposits, mg/l-hour.
33
-------
For freshwater streams, the advective flux is significantly
larger than the mass flux due to longitudinal dispersion. In a
tidal river, the advective and dispersive fluxes are both signi-
ficant. In an estuary, the dispersive component is usually pre-
dominant (Hydroscience, Inc., 1971). For steady-state analysis,
all system parameters are assumed time invariant, and since it
is desired to solve for the DO deficit and
ac_ D
— o , (IV 14)
equation (IV-l3) reduces to a second—order ordinary differential
equation:
2
O=EdD_U +KL+KN_KD_(P_R -B). (IV-l5)
2 dx 1 n 2
dx
Before equation (IV-15) can be integrated the spatial distribu-
tions of both the remaining carbonaceous BOD, L, and the
remaining nitrogenous BOD, N, must be determined. From mass
balances across a control volume, the following steady—state
relationships are obtained:
2
dx
2
(IV-l7)
2 dx n
dx
Applying boundary conditions, such that L = L 0 and N = N 0 at
x = 0 and L = N 0 at x = , equations (IV-l6) and (IV-17)
integrate to:
L = L 0 exp 1 (IVl8)
N = N 0 exp [ . (1 )] ( 1vl9)
for x > 0.
Substituting expressions (IV-l8) and (IV—19) into equation
(Iv-l5), the governing differential equation for dissolved oxy-
gen deficit becomes:
I 4KE
-V1+
U
34
-------
o = ___ - + K 1 L e + KNe - K 2 D - (P - Re - B) (IV-20)
where
u / 4K 1 E
m = - • ( i _V1 ÷
for x > 0.
Stoichiometrically, the magnitude of the nitrogenous demand may
be evaluated from
N = 4.57 TKN (Iv—21)
0
where TKN is the total Kjeldahl nitrogen, which is the total
oxidizable organic plus ammonia nitrogen. The oxidation coeffi-
cient of nitrogenous BOD, K , has been estimated to range from
0.1 to 0.6 per day at 20°C, for a first order decay assumption
(Thomann, 1972).
In the model, the effects of the biological processes
(P Re - B) are assumed to be incorporated into the measured
upstream DO deficit. Field measurements of organic and ammonia
nitrogen present in all wastewater inputs to receiving waters
are seldom available, much less during runoff events. Further-
more, Kn is usually unknown even if some total organic nitrogen
measurements were recorded. Thus, nitrogenous oxygen demand is
currently neglected, reducing equation (IV-20) to the expression:
E - U + K 1 L 0 e - K 2 D = 0 (IV-22)
Critical Deficit and DO Levels
The solution to equation (117-22) as a function of time
since release is given by
LK S
D=K 01 Jt i t (IV-23)
2 1
35
-------
where D = DO deficit, mg/i,
K 1 = deoxygenation coefficient, hours ,
K 2 = reaeration coefficient, hours 1 ,
t = = lapsed time, hours,
2 / 4KE 1
j = 1 -Vi + I hours 1 ,
Ui
g = [ + hours
/ 4K E
S 1 =\/ - + 2 dimensionless, and
V U
4K E
s 2 =Vl + , dimensionless.
U
To determine the time at which the critical (maximum) deficit
occurs the partial derivative of the DO deficit equation,
equation (IV—23), is taken with respect to time and set equal to
zero (BD/ t 0):
0 K 2 -K 1 [ jeltc - ge c] + g tc (1v24)
Solving for t , the following expression is obtained:
S D K -K
= (j g) [ in ( ) + in ( i — Ki 1 )J (IV—25)
Equation (IV-25) may be simplified for convenience by making
some substitutions,
t = 1 in 1 ( - fR + R (IV-26)
c (jg) ° °ii
where tc = elapsed time at which the critical deficit occurs,
hours,
36
-------
f = self-purification ratio
= and
R = ratio of the initial DO deficit, D 0 , to the initial
BOD, L 0 , dimensionless.
Finally, the critical deficit is found by substituting the value
of tcl given by equation (IV—26) , into equation (IV—23)
D = L 0 K 1 ejtc — gt + D gtc (IV-27)
c s j o
where D = critical (maximum) deficit, mg/l.
The minimum DO level is calculated as
C = C - D (IV-28)
mm s c
where Cmin = concentration of DO at maximum deficit, mg/i, and
C 5 = saturation concentration of DO, mg/i.
The saturation concentration is determined from the regression
relationship (American Society of Civil Engineers, 1960)
C = 14.652 - 0.41022T + O.O0799lOT 2 - O.000077774T 3 (IV-29)
where T = water temperature, °C.
If the model user wishes to neglect the dispersive flux in
a freshwater stream, a value of zero is specified for E. Then,
equation (IV—22) reduces to the expression:
0 = U + K 2 D - K 1 L 0 e t (IV-30)
The solution of equation (IV—30) constitutes the Streeter-Pheips
formulation in which the deficit as a function of time since
release is
D = K 2 -K 1 (e_Klt e_K2t) + De <2t (IV-31)
where D = DO deficit, mg/i,
K 1 = deoxygenation coefficient, hours 1 ,
37
-------
K 2 = reaeration coefficient, hours -, and
t = elapsed time, hours.
The critical (maximum) deficit is found through differentiation
to be
KL
D = 1 e tc (IV-32)
c K 2
Then the value of tc is given by:
tc = K( l ) in f [ l — fR + R] (IV—33)
Equations (IV-26), (IV-27), (IV-32) and (IV-33) are unde-
fined when: f = 1, L = 0, or R 0 > 1/f. These conditions may
arise when a large nunther of waste inflow and river flow com-
binations are simulated (e.g., dry watercourses in which waste
inputs constitute the only flow). For example,
1. the deficit load ratio, R 0 , is undefined if L 0 = 0 or
both D 0 and L 0 are zero, since R 0 =
2. the self-purification ratio, f, becomes equal to one
and j = g when the reaeration rate K 2 and the deoxy—
genation rate K 1 coincide in value; and
3. tc will be negative or undefined when f = 1 but R 0 is
not within the bounds 0 1/f, then
D = D . (IV-36)
c o
38
-------
Deoxygenation and Reaeration Rates
The deoxygenation coefficient, K 1 , represents the loss of
DO in the waterway due to reduction of BOD. It is expressed as
a constant fraction of the remaining unoxidized organic matter
in any arbitrary unit of time. The average domestic sewage
deoxygenates at about 0.23 per day at 20°C under standardized
laboratory conditions. In freshwater streams, the reaction
coefficient for BOD ranges from 0.2 to 2.0 per day for water
temperatures from 20°C to 25°C (Hydroscience, Inc., 1971).
There are at least four generally accepted methods to determine
the value of K 1 from the BOD curve, for a wastewater sample.
These include: (1) the least-squares technique, (2) the slope
method, (3) the moments method, and (4) the logarithmic method
(Nemerow, 1974).
The magnitude of K 1 in streams is related to the average
water depth. The explanation behind this correlation lies in
the fact that the smaller the depth the greater the contact
with biological film in the stream bed, one of the most impor-
tant factors in natural oxidative processes (Hydroscience, Inc.,
1971). From data reported in the literature, a straight-line
plot between the variables is obtained (within certain bounds)
as shown in Figure IV—l. A mathematical representation is given
by:
K 1 = H (IV—37)
—1
where K 1 = deoxygenation coefficient at 20 C, day
‘1’ ‘ ‘2 = regression coefficients
H = stream depth, ft.
The above relationship appears reasonable within a range of
depths from 1 foot to 10 feet. Thus, K 1 must be limited by
program variables (XK1MAX and XK1MIN) to upper and lower bounds,
respectively. These may be selected by the user, and supplied
to the model as input data, so as to further extend or restrict
the range of applicability of equation (IV-37) to suit local
stream conditions. A temperature correction yields:
K 1 (T) = K 1 (20°) 1 047 T20 (I V 38)
where T = water temperature, °C, and conversion is made to units
of hour 1 in the wet weather flow model. The magnitude of
XK1MAX, XK1MIN, Y and 2 may be adjusted during calibration
procedures.
39
-------
(Kj)max
I
—0.28
K 1 0.99 H
(Ki)min
I
I
I
I
I
I
I I I I I I III I
I I I I I iii I I
Figure IV-1.
1.0
STREAM
DEPTH, H, ft.
10.0
Deoxygenation Rate As A Function of Stream
Depth (modified after Hydroscience, Inc.
1971) • Ecuation shown is a specific function
which corresponds to = 0.99 and =-0.28.
0
0
0
(‘4
4-.
0
1> 1
0
•0
.0
0
I
z
0
I-
z
w
( .9
>-
><
001
w
c 0.1
-------
Numerous formulations exist for prediction of the reaera-
tion coefficient K 2 , almost all of which depend upon velocity,
U, and depth, H. The equation below (Langbein and Durum, 1967)
was chosen because it is most closely related to subsequent pro-
cedures applied to obtain U and H:
K 2 = 2.303 [ 3.3 HL33 ] -l
where K 2 = reaeration coefficient at 20 C, day
U = stream velocity, ft/sec, and
H = stream depth, ft.
The problem lies in obtaining values of U and H, since the
streamfiow varies with time. In the absence of measurements, or
if the data cannot be obtained in an expedient manner ( as in
the ensuing application to the Des Moines River) , an approxima-
tion can be made (Leopold and Maddock, 1953) which uses strong
correlations between velocity versus flow and depth versus flow,
namely:
U = c 1 Q 2 (IV—40)
H = Q 2 (Iv—41)
where Q = streamfiow, cfs, and
G 1 “2’ 2 = regression coefficients.
When equations (Iv-40) and (IV-41) are substituted into
(IV—39) and conversion is made to units of hour 1 , the reaera—
tion coefficient is established as a function of streamfiow, Q:
K 2 (20°) = 0.3167 Qa 2 — 1.33 2 (IV-42)
The reaeration rate is corrected for temperature by:
K 2 (T) = K 2 (20°) 1 024 T— 20 (Iv—43)
41
-------
Total Volume of DO Deficit
Another measure of the relative effect of one waste source
versus another on receiving water quality is the integral, or
summation, of the deficit equation over all time:
= [ D dt = J { K -K 1 [ e t - — + De t} dt (IV-44)
After integration, equation (IV-44) becomes
Co Co
L K 1 [ jtl s 1 [ gtl gtl
1 e i L 1 e +D e (IV-45)
K 2 —K 1 j j S 2 g j 0 g
As defined in equation (IV-23), the coefficients j and g apply
only to the solution of equation (IV-22) for the region down-
stream from the point of waste discharge (x > 0) . For nonzero
values of K 1 , K 2 and E
[ < 0
I 4KE 1
1 —\/l + < 0 (IV—47)
V uJ
and consequently j, g < 0. Thus, equation (IV-45) may be evalu-
ated between the limits shown to yield:
LK [ s 1 D
ol _ I_2 (IV-48)
K 2 —K 1 s 2 g jj g
where V may be interpreted as the total volume of deficit, with
units of mg-hours/i. Values of V are displayed by the model for
each inflow combination.
Again, if the longitudinal dispersion coefficient is equal
to zero, equation (IV-3l) is the appropriate expression for the
deficit. When integrated over all time, the relationship
obtained is
D +L
V = I Ddt = ° ° (IV-49)
Jo K 2
42
-------
The results of applying either equation (IV-48) or (Iv-49) may
be compared for each model option as another indication of rela-
tive impacts. The physical significance of is perhaps better
understood by dimensional analysis after the quantity is multi-
plied by stream velocity and cross-sectional area:
• (U • A) = • (Q) (IV—50)
mg—hours ft 3
1 sec
_MT L 3
- L 3 T
=M
= mass of DO under the
deficit curve.
Determination of the Longitudinal Dispersion Coefficient, E
The discharge of freshwater streams into tidal waters, cou-
pled with the intrusion of saline waters into tidal waters and
the simultaneous retreat of fresh waters, results in a complex
hydrodynamic situation of economic importance. Estuaries are
used for diverse purposes such as commercial fishing, shellfish
harvesting, navigation, recreation, water supply and waste dis-
posal. Thus, receiving water quality criteria must be estab-
lished to provide a cost—effective balance: economical waste—
water treatment before disposal and an acceptable degradation to
the receiving body of water. The geometry of tidal estuaries
may vary widely. For example, sections landward from the mouth
expand in San Francisco Bay whereas they contract in the
Delaware estuary (Ippen, 1966). The rise and fall of the tide
at the mouth is associated with an exchange of water masses:
temporary storage of large amounts of seawater in the estuary
during high tide and the drainage of this water seaward during
low tide (Ippen, 1966). The total volume of water exchanged is
referred to as the t-Lda p)t i.4m. The total volume of freshwater
inflow to the estuary from upland sources equals the discharge
rate totaled over the tidal period. The freshwater inflow rate,
Qf, is variable with time. Nevertheless, the ratio of fresh-
water volume to the tidal prism is useful for general classifi-
cation. The Delaware River estuary is characterized by a low
ratio of freshwater—to—seawater volume Ll:lOO) while by con-
trast the Mississippi River estuary exhibits a much higher ratio
(‘l:l) due to low Gulf tides and higher discharge rates (Ippen,
1966)
Of particular importance with respect to the relative
magnitude of the freshwater to salt-water prism ratio is the
43
-------
degree of mixing of the freshwater into the salt water. The
higher the value of this ratio the less diffusion takes place,
the estuary is termed 4ai Le d and a distinct av -Lt wedge.
exists (Ippen, 1966). As might be expected, a well-defined salt
water wedge underlays the fresh waters in the Mississippi and
the Amazon estuaries. A low ratio indicates an advanced state
of diffusion and the estuary is classified as we -m-ixed with
only small variations in the vertical salinity profiles (which
become more uniform with increased mixing through tidal action).
A gradual decrease in salinity is observed as one proceeds up-
stream in the Delaware River estuary, and may serve as an exam-
ple of the well-mixed state (Pyatt, 1964 and Ippen, 1966).
The equilibrium of an estuary can only be maintained if the
quantities of solids, freshwater and minerals in solution each
remain in balance: continuity of matter (McDowell and O’Connor,
1977). The salinity wedge discussed above derives its shape
primarily due to density gradients: the density difference be—
tween the water at the seaward end of an estuary and freshwater
entering from rivers causes net landward movement of water near
the estuary bed and a compensating seaward movement near the
water surface. If turbulent mixing is so intense that there is
only a small variation in density over depth at any point, a
horizontal density gradient must exist ranging from 1 g/cm 3 at
the upper tidal limit to the density of seawater ( 1.026 g/cm 3 )
at some distance offshore. The Mississippi River is tidal for
up to 265 miles (426 km) landward from its mouth. In the Amazon
River, densities do not approach typical ocean values for as far
as 622 miles (1000 km) seaward from its mouth (McDowell and
O’Connor, 1977).
One—dimensional mathematical models such as equation (IV-l3)
encompass a wide range of time—averaging concepts; therefore,
classifications such as “unsteady”, “quasi—steady state” or
“steady” are meaningless unless precisely defined with respect
to:
1. the duration of the time period to be used in
averaging , and
2. the quantity to which the descriptive term applies
(Harleman, 1971).
The length of the time-averaging period ( t) will, for example,
determine whether the
-------
3. the non-t-Ldczj. adut ve. oa t j — At .‘ 12 hours,
equal to the velocity due to freshwater inflow, Uf,
“steady” or “unsteady” depending on the time
variation in the magnitude of the freshwater inflow;
or
4. the mean, non-t-Lda- adueetLve ve! oe Lt j - At > 25 hours
(Harleman, 1971). —
The time of zero tidal velocity within the tidal cycle is the
time of slack water tide: high wate’L s1ac!z if the velocity
change is from flood to ebb and Low wctte ’t a c!? from ebb to
flood tide. Equation (IV—13) represents the real time mass
transfer equation in an idealized estuary of constant cross-
sectional area, where U is the tidal velocity as a function of
time only (independent of x) and the longitudinal dispersion
coefficient E is constant. By considering concentration distri-
butions only at times of slack tide, the tidal advective velo-
city disappears and U becomes the non-tidal advective velocity
due to freshwater inflow (Uf = Qf/A). Then, the solution to
equation (IV-22) constitutes the “quasi-steady state” spatial
concentration distribution for water quality conditions at
either high or low water slack.
As stated earlier, estuaries may exhibit either relatively
homogeneous salinity in a particular cross section or pronounced
vertical gradients in addition to their longitudinal salinity
characteristics. Application of the one—dimensional equations
to a fully stratified or saline-wedge estuary should be excluded.
The one—dimensional, quasi—steady state analysis is a compromise
between the desirability of a rigorous mathematical approach and
the practical necessity to achieve workable engineering solutions
and evaluations for planning purposes. A detailed assessment of
estuarine models and the tradeoffs involved in their application
is available elsewhere (TRACOR, Inc., 1971). It should also be
noted that analyses limited to any fixed fraction of the tidal
period are classified as quasi-steady state.
Various methods have been developed to determine the longi-
tudinal, lateral, and vertical diffusion coefficients that
govern dye dispersion rates (Diachishin, 1963). These procedures
are appropriate for a multi-dimensional approach. The longitu-
dinal dispersion coefficient, E, for an estuary or tidal river
may be evaluated empirically from observed quasi-steady state
chloride concentration profiles for a particular net advective
flow (}Tydroscience, Inc., 1972). The procedure assumes homo-
geneous mixing in the cross-section and constant E. Considering
chlorides as conservative substances, the underlying equation is:
c = c 0 eE (IV-5l)
45
-------
for x < 0
where C = chloride concentration, mg/i
C 0 = maximum concentration at x = 0, mg/i
U = net advective velocity, miles/day
E = dispersion coefficient, miles 2 /day
x = distance upstream, negative, miles
(usually x = 0 at mouth of estuary).
Taking the natural logarithm of equation (IV-51) yields:
1nC= x+lnC 0 (IV-52)
A semi-logarithmic plot of in chlorides versus distance x up-
stream should yield a straight line with slope (U/E) and inter—
cept at the ordinate given by in C 0 . The longitudinal dispersion
coefficient, E, is then computed by dividing the net advective
velocity by the slope. In the saline portion of an estuary,
dispersive flux of mass is usually predominant and determination
of E becomes important. In the tidal, but non—saline sections
of the river both advective and dispersive fluxes may be signi-
ficant. Upstream of the tidal influence the longitudinal mixing
decreases considerably and may be disregarded altogether in the
analysis.
46
-------
SECTION V
PROGRAMMING CONSIDERATIONS
User requirements for application of Level 111-Receiving
may be broadly classified into: (1) computer facilities and
programming considerations, (2) data requirements, and (3)
calibration and verification procedures. Items (2) and (3) are
discussed in detail in the next section; however, a few remarks
are in order at this point concerning input data. All program
subroutines may be executed during a single job submission, or
selectively as the data reduction process is completed for each
subprogram. Such flexibility is made available through the use
of control cards in the input stream. Collection of data from
municipal and other sources may be accomplished in a few days.
Reduction of the data to the appropriate format for program in-
put is estimated at one man—week for a one year period of
simulation, and is largely independent of the size of the urban
drainage area modeled. An increase in the length of the simula-
tion time period does not imply an increase in computer system
core requirements; however, it will obviously increase time of
execution.
COMPUTER SYSTEM CORE REQUIREMENTS
The program has been tested with two different Central
Processing Units (CPU) and comparable supporting hardware. The
Duke University Computation Center (DUCC) is connected by a
high-speed microwave link to a dual IBM 370/165 configuration
located at the Triangle Universities Computation Center (TUCC)
in the Research Triangle Park. The Northeast Regional Data
Center (NERDC), located at the University of Florida, is
equipped with an AMDAHL 470-V6/II.
Level 111—Receiving users have the option of running under
FORTRAN IV compilers comparable to either of the two standard
IBM compilers, G or H. IBM ’s minimum system requirements for
installation of the FORTRAN IV (G) compiler and the FORTRAN IV
(H) compiler are: respectively, 128K bytes and 256K bytes of
storage. Furthermore, the user may run Level Ill-Receiving from
the actual card version (program source and data decks) or from
a pre-compiled Load Module of the program stored on disk. All
of these options result in varying core storage capacity require-
ments for the program. At TUCC, the FORTRAN G compiler is much
47
-------
faster and is recommended for all debugging runs. However, the
FORTRAN H compiler has an optimization feature which results in
the compiled coding being “optimized” for faster running in the
execution step. Unless the user actually alters the program,
debugging should be unnecessary and most errors will be due to
incorrect formatting or sequencing of input data. Therefore,
the program should be compiled in H and the coding stored on
disk for future production runs. An additional feature is the
IBM OS Loader, which replaces the Link-Edit and Go steps with a
single, faster operation. Core storage capacity and average
compilation times are presented in Table V-l for Level III-
Receiving under TUCC’s IBM 370/165 system. By comparison, the
FORTRAN compiler of the AMDAHL 470-V6/II at NERDC required 110K
bytes of core storage for execution.
Additional computer system requirements include peripheral
storage devices which may consist of disk/tape/drum units
depending upon machine configuration and user—selected input
options (see Card Group I, Table VI-l3).
PROGRAM COMPILATION AND EXECUTION TIMES AND COSTS
The IBM 370/165 compilation and execution times with total
costs for various subprogram options are listed in Table V—2.
The savings incurred by storing the compiled subroutines of the
program in a permanent job library (Load Module) suggest that
the procedure is worthwhile if Level Ill-Receiving is going to
be used frequently. Of course, at most computer installations
there is a daily or monthly charge for storing Load Modules.
For example, at TUCC the charge for online disk space is $0.50
per track per month. Thus, $4 per month (8 tracks) is the
approximate charge for the Level 111-Receiving Load Module. The
total costs and total CPU time for four program options are
shown in Table V-3 for the AMDAHL system at NERDC. As noted,
commercial rates are higher.
JOB CONTROL LANGUAGE
The user must supply the necessary job control language
(JCL) which is compatible with the computer system and particu-
lar installation involved. The JOB card is highly installation-
dependent and often differs for identical CPU configurations at
different installations. However, other JCL is fairly standard
on IBM operating systems, and several examples are provided here
as typical JCL required for the model on systems running IBM
05/36 0.
1. To execute the program from card input only (IPROG =
0,ITSAG = 0, see Table VI-l3)—
48
-------
TABLE V-i. COMPILATION TIMES 1 AND REQUIRED CORE
CAPACITY 2
Compiler
Compile
Time,sec
Link-Edit
Time,sec
Compile
Core
bytes
Execute
Core
bytes
FORTRAN
G
29.4
10.3
146K
104K
FORTRAN
H
60.3
9.9
300K
lOOK
FORT RAN
G with IBM
OS Loader
28.6
none
146K
200K
1
Average values.
370/165 syStem, Triangle Universities Computation
Center, Research Triangle Park, North Carolina.
49
-------
TABLE V-2. EXECUTION TIMES AND COSTS AT TUCC 1
Program Options 2
Input/Output
Time,sec
Total
CPU
Time,
sec
Total
Cost
$
3 4 5 6
IPROG ICORR IWWFM IDWFM
o i 0 0
o o 1 0
o o 0 1
0 1 1 1
5.9
49.9
28.1
80.7
38.7
28.7
9.4
80.3
4.85
11.77
5.70
22.12
1.
Triangle Universities Computation Center, FORTRAN IV
(H) - compiled Load Module on disk, for one year period
of simulation, IBM 370/165 system. Commercial rates
are approximately three times the university rates at
TUCC.
2 See Card Group I, Table VI-13.
3 lnput data from cards only when = 0.
4
Autocorrelation analysis performed if = 1.
5 Wet-weather flow model
6 Dry-weather flow model
run if = 1.
run if = 1.
50
-------
TABLE V—3. COMPILATION AND EXECUTION TIMES AND
COSTS OF NERDC 1
Program Options 2
Total CPU
Time,sec
Total
Cost
$
IPROG 3 ICORR 4 IWWFM 5 IDWFM 6
o 1 0 0
o o 1 0
1 0 1 0
0 0 0 1
46.84
17.85
20.67
8.66
11.12
10.04
10.85
5.59
‘Northeast Regional Data Center, FORTRAN compiler
for AMDA}IL 470-V6/II, for one year period of
simulation. Commercial rates are approximately
twice those indicated at NERDC.
2 See Card Group I, Table VI-l3.
3 lnput data from cards only when = 0, and from
cards and SWMM tape when = 1.
4 Autocorrelation analysis performed if = 1.
5 Wet-weather flow model run if = 1.
6 Dry-weather flow model run if = 1.
51
-------
/1 ... JOB card
// EXEC FTHCLG
//C.SYSIN DD *
(the FORTRAN IV source deck)
/*
//G.SYSIN DD *
(your input data deck)
1*
1/
where above instructions apply to the FORTRAN H
compiler. To use the FORTRAN G compiler simply
replace the second card with /1 EXEC FTGCLG
2. To execute the program from a previously created Load
Module (IPROG = 0, ITSAG 0, see Table VI-13)—
//...JOB...
//G EXEC PGM=program name within load module data
set
//STEPLIB DD DSN=data set name,DISP=SHR
//FTO1FOO1 DD DDNAME=SYSIN
//FTO3FOO3 DD SYSOUT=A,DCB= (RECFM=FBA,
// BLKSIZE=133,LRECL=133)
//SYSIN DD *
(your input data deck)
/*
//
For an actual run at TUCC the first three cards
were replaced by
//LEVELIII JOB DU.D06.AT4119,MEDINA,M=l,T=3,R=150K,P=350
//G EXEC PGM=LEVELIII
//STEPLIB DD DSN=DU.D06.AT4119.MEDINA.LOAD,DISP=SHR
52
-------
(program source deck)
/*
//G.FTwwFOO1 DD DSN=data set name,DISP=NEW,
// SPACE=space , UNIT=unit ,VOL=SER=volume
number
//G.FTddFOO1 DD DSN=data set name,DISP=NEW,
7/ SPACE=space allocation, UNIT=unit ,VOL=SER=volume
number
//G.SYSIN DD *
(input data deck)
1*
/7
where in card //G.FTwwFOO1 DD DSN=..., ww refers
to a two-digit number (usually greater than 6) which
is equal to the value of IDISKW selected by the user
(see Card Group IV, Card Type 10, Table vI-13).
Similarly, dd above refers to a two-digit number
specified by IDISKD (Card Group V, Card Type 14,
Table VI-13). For example, where ITSAG=l, IDISKW=9
and IDISKD=10 an actual run at TUCC would use the
sequence
//G.FTO9FOO1 DD DSN=&&TEMP1,DISPNEW,
/1 SPACE=(TRK, (1,1)) ,UNIT=DISK,VOLSERDUK111
//G.FT1OFOO1 DD DSN= &&TEMP2 ,DISP=NEW,
/7 SPACE=(TRX, (1,1)) ,UNIT=DISK,VOLSERDUK111
ii) if the user wishes a plot of the DO concentrations
versus wet—weather events only, then ITSAG=1,
IDISKW=ww and IDISKDO. Thus, only the
//G.FTwwFOO1 DD DSN=... sequence need be
specified. Conversely, for DO versus dry-
weather events only, then ITSAG=l, IDISKW=0
and IDISKD=dd. Only the sequence
//G.FTddFOO1 DD DSN=... would be required.
The scratch data set(s) specified when ITSAG=l are
temporary, as the name implies, and should be deleted
after execution of the program is completed.
53
-------
3. To execute the program from both cards and a user—
created data set (which would contain the urban
runoff hydrographs and pollutographs, IPROG 2,
ITSAG = 0) —
1/... JOB...
1/ EXEC FTHCLG
//C.SYSIN DD *
(program source card deck)
1*
//G.FTxxFOO1 DD DSN=data set name,
II DISP=SHR,UNIT=unit type ,VOL=SER=volume number
//G.SYSIN DD *
(data which is on cards)
/*
//
In card //G.FrxxFOOl DD DSN=..., xx refers to a 2-digit
number (usually greater than 6) which is equal to the
value of IFILE (see Card Group I, Table VI-l3) selected
by the user. For example,
//G.FTO8FOO1 DD DSN=DtJ.D06.AT4119.MEDINA.DSTRM,DISP=SHR,
1/ UNIT=DISK ,VOL=SER=DUKAAA
includes, as part of the data set name: the valid
computer system account number, the name of the person
responsible for creating the data set, and an identif i-
cation name.
4. To execute the program and obtain a composite plot of
DO concentrations versus chronologically sorted wet—
weather and dry—weather events (ITSAG = 1) , two scratch
data sets must be specified in the job control
language
i) for card input of urban runoff hydrographs and
pollutographs (IPROG = 0)
1/... JOB...
/1 EXEC FTHCLG
//C.SYSIN DD *
54
-------
5. To create a Load Module for Level 111—Receiving —
/7.. .JOB...
//FORTRAN EXEC FTHCL,PAP M.C=’ OPT=2 ,NODECK’,
// REGION.C=300K
//C.SYSIN DD *
(program source deck)
/*
//L.SYSLMOD DD DSN-DU.D06.AT4119.MEDINA.LOAD(LEVELIII),
// DISP= (MOD,KEEP) ,VOL=SER=DUK222 ,UNIT=DISK,
// SPACE=(TRK, (15,10,2) ,RLSE)
//
6. In the event that the user might wish to alter the Level
III Receiving program and recompile the whole program
or just selected subroutines, the following JCL would
be in order for the recompile runs:
//. . .JOB...
//FORTRAN EXEC FTHCL ,PARM. C’ OPT=2 ,NODECK’,
/1 REGION.C=300K
//C.SYSIN DD *
(program source deck - entire program or
just one or more subroutines)
1*
//L.SYSLMOD DD DSN=DU.D06.AT4119.MEDINA.LOAD,
/7 DISP=OLD,VOL=SERDUK222 ,UNIT=DISK,
// SPACE=(TRK, (15,10,2))
//L.SYSIN DD *
INCLUDE SYSLMOD (LEVELIII)
ENTRY MAIN
NAME LEVELIII(R)
7*
//COMPRESS EXEC PGM IEBCOPY ,COND= (5 ,LT)
//SYSPRINT DD SYSOUT=A
//SYSUT3 DD SPACE=(TRK, (5,2)) ,UNIT=SYSDA
//SYSUT4 DD SPACE=(TRK, (5,2)) ,UNIT=SYSDA
//LOADMOD DD DSNDU.D06 .AT4119 .MEDINA.LOAD,
/7 DISPOLD,VOLSERDUK222 ,UNIT=DISK
//SYSIN DD *
COPY INDD=LOADMOD , OUTDD=LOADMOD
55
-------
/*
//
INTERFACING WITH CONTINUOUS SIMULATION MODELS
Urban runoff flows and pollutant concentrations and mass
rates, derived from storm events over the drainage area of
interest, must be generated by continuous models simulating the
washoff process. These concurrent time series are read by the
program through the standard card reader devices, or from
peripheral storage units (disk/tape/drum). Since the program
has the built-in capability of accessing a user-created data set,
any continuous hydrologic and water quality model may be con-
sidered. The interfacing of Level 111-Receiving with STORM and
continuous SWMM is discussed subsequently. Regardless of the
models used, it should be noted by the user that the urban run-
off quantity and quality time series must represent hourly
values.
Use of STORM Output
Version 2.0, L7520, of STORM (Hydrologic Engineering Center,
1976) does not store model output on peripheral devices. Thus,
the program itself must be modified to do so for the quantity
and quality variables of interest. For example, for the July
1976 version to store runoff in cfs and BOD 5 mass rate in lbs/
hour, two statements must be added to SUBROUTINE OUTPUT immedi-
ately above its RETURN and END statements:
C EVENT OUTPUT
SUBROUTINE OUTPUT
(‘ 607 FORTRP N statements)
WRITE(8,7150)QTOTCF,POLLRT (3)
7150 FORMAT(2F7.1)
RETURN
END
where POLLRT(3) would be replaced by POLCON(3) if BOD 5 concen-
trations are desired. The information will be transferred to
output device number 8. Thus, the type of file required to
interface Level 111-Receiving with STORM stores hourly values
of urban runoff in the sequence:
flow, BOD 5
flow, BOD 5
56
-------
flow, BOD 5
The typical Job Control Language specified to accomplish this
task follows:
//STORM JOB ...
// EXEC PGM=STORM
//STEPLIB DD PSN=DU.D06.AT4096.MEDINA.STORM1,DISP=SHR,
/1 UNIT=DISK ,VOL=SER=DUKBBB
//FTO6FOO1 DD SYSOUT=A
//FTO8FOO1 DD DSN=DU.D06.AT4119.MEDINA.DSTRM.DISP=(NEW,
KEEP),
/1 UNIT DISK,VOL=SER=DUKAAA,SPACE=(TRK, (3,1) ,RLSE),
// DCB= (RECFM=FB,LRECL=14 ,BLKSIZE=7294)
//FT11FOO1 DD UNIT=SYSDA,SPACE=(CYL, (2,1))
//FT12FOO1 DD UNIT=SYSDA,SPACE=(CYL, (2,1))
//FT13FOO1 DD SYSOUT=A,DCB=(PECFM=FA,BLKSIZE=133)
//FT14FOO1 DD SYSOUT=A,DCB=(PECFM=FA,BLKSIZE=133)
//FT15FOO1 DD SYSOUT=A,DCB=(BECFM=FA,BLKSIZE133)
//FTO5FOO1 DD *
(STORM input data cards)
/*
//
Once the data set has been created, the user simply follows
the instructions for data preparation provided in Section VI to
access the data set. For the example above, IPROG = 2 and IFILE
= 8 (see Card Group I, Table VI-13). The job is submitted in
accordance with the example provided under item (3), JOB CONTROL
LANGUAGE.
Use of Continuous SWMM Output
Level Ill—Receiving has been programmed to interface
directly with continuous SWMM output files. Thus, the user does
not modify SWMM to provide input to the simplified receiving
water quality model. The JCL required for a Level Ill-Receiving
job submission which accesses the SWMM tape storing the urban
runoff quantity and quality time series is, as follows:
//LEVELIII JOB DU.D06.AT4119,MEDINA,Ml,T3,R100K,P350
hG EXEC PGM=LEVELIII
//STEPLIB DD DSN=DU.D06 .AT4119 .MEDINA.LOAD,
// DISPSHR
57
-------
//FTO1FOO1 DD DSNAME=SYSIN
//FTO3FOO1 DD SYSOUT=A,DCB= (RECFM=FBA,
/1 BLKSIZE=133,LI ECL=l33)
//FTO9FOO1 DD DSN=MEDINA.SWMMF,DISP=OLD,
7/ VOL=SER=]DO6AO1 ,UNIT=TAPE
//SYSIN DD *
(Level Ill-Receiving input data card deck)
/*
//
The above instructions apply to the pre-compiled Load
Module of the program which was created previously. The user
specified IPROG = 1 and IFILE = 9 (see Card Group I, Table VI-13).
In addition, within the first card of the input deck is speci-
fied the value of JNS, the input junction number, which identi-
fies the inlet to the receiving waterway which is of interest
for the particular simulation.
The type of file created by SWMM stores hourly values in
the sequence:
time,flow,BOD 5 ,suspended solids,coliform bacteria
time,flow,BOD 5 ,suspended solids ,coliform bacteria
where flow is given in cfs and the pollutants in units of mass
rate (lbs/rain for BOD 5 and suspended solids, MPN/min for
coliform bacteria). Through the use of dummy variables, Level
Ill-Receiving reads from the SWMM tape all of the above values
but retains for further computation only flow and BOD 5 mass rate.
The mass rate is converted to units of lbs BOD 5 /hour.
It is important to reemphasize that Job Control Language
examples are useful, but actual instructions may vary to some
extent from installation to installation and machine configura-
tions. All of the above instructions are valid on the dual
IBM 370/165 system at TUCC, Research Triangle Park, North
Carolina.
58
-------
SECTION VI
MODEL OPERATION
This section describes the program operation, provides
instructions on data preparation and input data card formats,
defines key variables, shows sample runs, and presents the
results of calibration and verification. The complete FORTRAN
IV source program is listed in Appendix B.
PROGRAM OPERATION
The relationship among the main program and its subroutines
are shown in Figure VI-l. The main program (hereafter referred
to as subroutine MAIN) provides overall control and includes in
its entirety the wet-weather flow model (WWFM). The first input
data card to subroutine MAIN allows the user to select which
subprograms will be executed during simulation, and is discussed
at greater length in the next subsection. The WWFM is a mathe-
matical abstraction of the physical system depicted earlier in
Figure 11—1. The user is encouraged to review the referenced
schematic for better comprehension of model objectives. Storm—
water runoff flows and pollutant loads or concentrations gen-
erated by an urban, continuous hydrologic simulation model (e.g.
STORM or SWMN) are read either directly from card input or from
peripheral devices (tape/disk/drum) depending on the machine
configuration. The WWFM in subroutine MAIN simulates the
conveyance system, including mixing in combined sewers of wet-
weather and dry-weather pollutants during periods of runoff; the
pollutant removal efficiency of various treatment schemes; mix-
ing of the various pollutant inflow combinations with upstream
sources in the receiving waters to determine initial conditions
of BOD, DO, streamflow and other parameters; and computes the
oxygen balance of the polluted waters downstream from the waste
sources. The procedure continues for each independent storm
event as defined by the minimum interevent time (MIT). Pollutant
loadings and receiving water quality conditions are averaged over
each event ‘s total duration , which includes wet-weather and dry—
weather hours. The spatial distribution of dissolved oxygen
concentrations along the flow axis is computed for each event
for a distance downstream chosen by the model user. Frequency
analyses are performed on either the resultant DO concentrations
at a specified location downstream, or critical (minimum) DO
concentrations as predicted by the model for the entire period
59
-------
CORREL
_ MGRAPH__J _ [ ROUND1
Lf11
0 z
DWFM
PLOT
— CALLING
— — RETURNING
Figure VI-1. Level 111-Receiving Subprograms
-------
of simulation.
• Subroutine CORREL subjects the hydrologic time series
(either rainfall or runoff) to autocorrelation analysis. It
automatically defines the MIT used in the WWFM to separate
wet—weather events. The methodology has been discussed pre—
ylously in Section IV. This subroutine may be executed
independently of the WWFM, but may be accessed only through
subroutine MAIN. Subroutine MGRAPH, a single and multiple-curve
plotting subprogram, is called by CORREL to display the correlo-
gram of the time series. MGRAPH, in turn, calls subroutine
ROUND to set the appropriate scale on the coordinate axes from
examination of minimum and maximum values to be plotted.
Subroutine DWFM performs the same functions as the WWFM,
during periods of no urban runoff . Thus, a model assumption is
that no combined sewer overflows occur. Therefore, there is no
“first—flush” effect, and there are no storm events over which
to average pollutant loads. It may be executed independently of
all other subroutines, except MAIN. Subroutine PLOT is called
by the WWFM (contained in subroutine MAIN) and also by subrou-
tine DWFM to display frequency histograms of receiving water
DO concentrations, optionally. Likewise, subroutine MGRAPH
is called by both models to plot cumulative , multiple frequency
curves of DO concentrations.
Subroutine DOSAG is called from subroutine MAIN to display
in tabular form and chronological order: the computed dissolved
oxygen concentration at a user—specified location downstream,
for each event simulated by the WWFM only, the DWFM only, or
both . Thus, the listing may include DO concentrations resulting
from wet-weather event pollutant loadings as well as DWF pollu-
tant loadings during periods of no urban runoff. Consequently,
the subprogram sorts the values according to date of occurrence
in order to produce a composite chronological record. These
DO concentrations are read by DOSAG from scratch data sets
created automatically (ITSAG = 1) by the WWFM and the DWFM.
Subroutine MGRAPH is called by subroutine DOSAG to provide a
plot corresponding to the tabular listing. The plot ordinate
(dissolved oxygen concentration) is scaled according to
magnitude; however, the abscissa represents the total number
of simulated events, numbered sequentially and not scaled
according to their time of occurrence since the beginning of
simulation. Each event number is identified according to date
in a tabular format which precedes each plot. If the simulated
events are not distributed evenly throughout the period of
simulation, the DO profile may be distorted with respect to the
abscissa. Thus, the user is cautioned to scale the profile
appropriately by manual plotting with reference to the tabular
output. The utility of the model-generated DO-versus-event
plot is best appreciated during calibration procedures, when
61
-------
measured values may be quickly hand-plotted on the printed
output to visualize the degree of adjustment required of
model parameters.
INSTRUCTIONS FOR DATA PREPARATION
The input data cards should be sequenced as illustrated in
Figure vi—23 and prepared according to the format specifications
given in Table vi-13 , at the end of this section. The data
cards are organized into 5 major card groups and 15 card types.
Each card group may contain one or more card types. The total
number of cards depends on the length of the simulation period
and the number of computational options selected by the user.
Card group I consists of one card which controls the execution
of three major subprograms: 1) subroutine CORBEL, 2) the WWFM
in subroutine MAIN, and 3) the DWFM. It also specifies the
type of input device used to transfer information into program
storage (memory) for further processing. If input from other
than cards applies (IPROG = 1, or 2), the unit number (IFILE)
of the input device (tape/disk/drum) must be provided. Pro-
gramming considerations such as job control language (JCL) have
been discussed in detail in Section V. As stated previously,
the SWMM input junction number (JNS, required if IPROG = 1)
refers to the receiving water inlet number. If the number of
outlets where hydrographs and pollutographs are being stored
equals one (NOUTS = 1), then it follows that JNS = 1 also.
Subroutine CORBEL requires the preparation of Card Group
II. A maximum number of data points of N = 8760 may be analyzed
currently, as well as a maximum number of hourly lags of NLAGS =
800. For most applications, these size limitations are quite
adequate. Certainly, the minimum interevent time will be well
defined within that range. However, if cyclical aspects of
the time series are of interest, the user may easily increase
the size of the applicable arrays within the subroutine. Card
group II would be skipped if ICORR = 0, and card group III
would then follow directly behind card group I.
Card group III is required if either IWWFM or IDWFM = 1,
and consists of four cards. Card group IV is required if
IWWFM = 1. Card type 11 or 12, or 13 (whichever is applicable
depends on IPROG = 0, 1, or 2) is repeated for each hourly run-
off event. Similarly, the rest of the card groups and card types
are prepared in accordance with user—selected control options
and given format specifications.
Estimates of Model Coefficients
Good initial estimates of various model coefficients are
desirable to reduce the calibration process to a minimum. Even
62
-------
though there is no real substitute for the actual in situ field
measurement, it is helpful to examine ranges of reported values
when the former is not possible. The parameters of interest
are: the longitudinal dispersion coefficient, E; maximum and
minimum values of the deoxygenation rate constant, K 1 , of car-
bonaceous BOO 5 @ 20°C (XK1MAX and XK1MIN); regression coeffic-
ients ct 1 (ALPHA1) and a 2 (ALPHA2) which relate stream velocity
to discharge; regression coefficients i (BETA1) and 2 (BETA2)
which relate stream depth to discharge; and regression coeffic—
ients (GANMA1) and 2 (GANNA2), which relate stream depth to
deoxygenation rate of carbonaceous BOO.
A simplified technique for the determination of the longi-
tudinal dispersion coefficient from observed quasi-steady state
chloride concentration profiles (Hydroscience, Inc., 1972) has
been presented in Section IV. Similarly, first estimates of K 1
can be obtained from semi-logarithmic plots of observed long
term BOD 5 stream data as a function of distance downstream.
Neglecting longitudinal dispersion E in equation (IV-16) , the
simplified relationship
U = — K 1 L (VI-1)
is obtained, which integrates to
-K
L = L 0 e 1 U (VI-2)
where
L= BOD 5 concentration, mg/l
= BOD 5 concentration @ x = 0, mg/i
U = receiving water velocity, ft/hr
K 1 = deoxygenation rate constant, hour 1 , and
x = distance downstream, feet
Equation (VI-2), in natural logarithms, becomes
K 1
in L = - x + in L 0 (VI-3)
63
-------
From a straight—line fit, the slope of the curve can be
determined, and thus:
K 1 = (Slope) U (VI-4)
If model variables XK1MAX and XK1MIN are both assigned the
same numerical value (say, as obtained from above equation) ,
Level 111—Receiving will adjust K, only for stream temperature
and not stream depth (see Figure tv-i). When converted to
units of hour— 1 , the magnitudes of XK1NAX and XK1MIN in Figure
IV—l correspond to 0.0417 and 0.0220, respectively. The user
also has the flexibility of selecting limiting values which
bracket the estimates obtained from equation (VI-4), or have
been chosen from experience. Representative values of K 1 @
20°C and E (where applicable) are listed for selected streams,
tidal rivers and estuaries in Table VI—l.
In an estensive study of the hydraulic geometry of stream
channels (Leopold and Maddock, 1953) average values of 2 =
0.34 and 82 = 0.40 were found for 20 river cross sections
representing a large variety of rivers in the Great Plains and
the Southwest. These mean values provide an indication of
order of magnitude only. For example, from plots of depth
versus discharge for the Kansas River System in Kansas and
Nebraska, the coefficients were determined to be:
= 1.60
= 0.03
= 0.11
82 = 0.45
which can be used as initial estimates for a river system of
similar physiographic characteristics and mean annual discharge.
The relationship between deoxygenation rate, K 1 , and average
stream depth, H, is influenced by factors such as stream bed
conditions (stable, rocky versus unstable, sandy channel)
density of benthal communities, and the nature of the residual
organic matter transported by the waterway. All of these
factors are incorporated into the regression coefficients 11
and y 2 . In the absence of sufficient field data to yield a
logarithmic plot such as depicted in Figure IV-l, the user
should simply adopt the values shown (y] = 0.99 and -0.28)
as initial estimates subject to further adjustment by subse-
quent calibration procedures.
64
-------
Lfl
TABLE VI-l.
REPRESENTATIVE DEOXYGENATION RATE AND DISPERSION COEFFICIENTS
(MODIFIED AFTER HYDROSCIENCE, INC., 1972)
Receiving Watercourse Category
Discharge, cfs
(cu rn/sec)
K 1
@ 20°C,
hour 1
E, ft 2 /hour
(sq rn/hour)
A. Rivers
Clinton River, Michigan Shallow
33 (0.93)
0.140
—
N. Branch Potomac River, Shallow
Maryland and West Vir-
ginia
100 (2.83)
0.017
-
N. Branch Susquehanna Medium
River, New York
1000 (28.3)
0.015
-
Ohio River Deep
6000 (170)
0.010
—
Lower Sacramento Deep
River, California
10,000 (283)
0.017
—
Depth, ft
(m)
Net Non-Tidal
Flow, cfs
(cu m/sec)
K 1
@ 20°C,
hour 1
E, ft 2 /hour
(sq rn/hour)
B. Estuaries
Delaware River 25 (7.62)
2500 (71)
0.013
5,800,000
(540 ,000)
Savannah River 10 to 28
(3.05 to 8.53)
7000 (198)
0.013
1.16 x i0 7
(1.08 x 106)
Cape Fear River 9.7 to 20
North Carolina (2.96 to 6.1)
1000 (28.3)
0.010
2,300,000
(216,000)
Wappinger Creek, 9 (2.7)
New York
2 (0.06)
0.013
581,000
(54,000)
-------
SAMPLE APPLICATION
An example of an application of Level Ill-Receiving to the
city of Des Moines, Iowa, is presented in this subsection. It
has already been established that there is a need for continuous
hydrologic simulation to assess the frequency with which runoff
events cause adverse effects in the receiving waters. It would
be quite difficult to generate a realistic pollutant distribu—
tion for a long sequence of synthetic flows. There are other
important reasons that justify selection of a real study area.
The only way to establish the necessary validity which renders
a mathematical model such as Level 111—Receiving useful for
planning purposes is to conduct verification procedures and
calibrate against field-measured data. Furthermore, when eval-
uating the effectiveness of proposed control measures it is
important to base comparisons against existing conditions in the
study area.
Selection of the study area was based primarily on data
availability. Davis and Borchardt, of Henninqson, Durham &
Richardson, Inc., Omaha, Nebraska, conducted an extensive sampl-
ing program of combined sewer overflows, stormwater discharges,
and surface waters in the Des Moines, Iowa Metropolitan Area for
the U. S. Environmental Protection Agency. The objective was a
combined sewer overflow abatement plan for the metropolitan
area. The sampling program was conducted from March 1968 to
October 1969. Other considerations revolved around the fact
that Des Moines, Iowa is somewhat typical of many urban centers
throughout the country.
(1) it has a medium-sized population;
(2) its domestic and industrial dry weather flows receive
secondary treatment;
(3) its wastewaters are discharged into a non—tidal
receiving stream; and
(4) the urban area receives a mean annual precipitation
approximately equal to the national average, 31.27
inches (795 mm)
The city is located near the confluence of the Des Moines
River and the Raccoon River as shown in Figure VI-2. It has
a population of approximately 200,000 out of a total of 288,000
for the metropolitan area (Davis and Borchardt, 1974). River
sampling stations are shown in Figure VI-3. As stated earlier,
the relative impact of urban sources of water pollution is
expected to increase as the size of the upstream drainage area
decreases. To simulate this effect the model application to
Des Moines investigates the response of the receiving water when
66
-------
(
Figure VI-2. Map of Des Moines Area (Davis & Borchardt, 1974)
MI N
MO
AP45AS CITY
\ ‘
‘4.
/
67
-------
* Tr..s Pr. 1 .e?
o 3 f N, .*iC L.ab.’sS.’,
• USGS S?,.. .lI•w S .ii..
Figure VI-3.
River Sampling Stations (Davis and
Borchardt, 1974)
68
4. EOE 4O
A . S,. . I.M,r•r14,
R.UWC
-------
upstream river flow, Q , is reduced to various fractions of
measured flow. This option and others used for the Des Moines
simulations are summarized in Table VI-2. Note that although
the model allows for three DWF treatment rates (J = 1, 2, 3) to
be set per run, the user may vary the rates so that an unlimited
number of combinations may be investigated.
Data Sources
Data requirements may be broken into categories describing
needs for the hydrologic simulation, to obtain urban runoff
hydrographs and pollutographs, and those for Level Ill-Receiving
input. All land use, population density, areas, curb lengths,
etc., were obtained from data prepared by the American Public
Works Association (APWA) for STORM simulations (Manning, et al. ,
1977). Hourly rainfall measurements for the year 1968 were
obtained from the National Oceanic and Atmospheric Administra-
tion’s Environmental Data Service, National Climatic Center,
Asheville, North Carolina. The precipitation time series is
presented in Figure VI-4. The abscissa represents the 10-month
period, in hours from March 1 to December 30, 1968. An examina-
tion of the rainfall record provides considerable insight as to
storm groupings, their intensity and duration, frequency of
occurrence, and the extreme temporal variability. The broken
line on the abscissa indicates dry—weather periods at least 9
hours in length. Figure VI—4 provides the necessary information
to define a minimum interevent time.
The area served by combined sewers was obtained from a
combined sewer overflow abatement study for Des Moines, Iowa
(Davis and Borchardt, 1974) as well as: dry-weather flow values,
receiving water upstream flows, temperatures, and BUD and DO
levels in the Des Moines River. Total urban runoff (Q ) and its
BUD concentrations are obtained from the STORM simulat on on an
hourly basis. A value for the longitudinal dispersion coeffic-
ient of E= 180,000 ft 2 /hour was assumed since the river reach of
interest is not tidal or estuarine.
The first flush factor, FFLBS, was determined as follows:
DWH/year = 6,993 hour
Total flow combined = 1.55 x 108 cf/yr
(4.39 x io 6 cu m/yr).
Mixed concentration of storm water (from STORM) plus DWF = BODC =
62 mg/l. The annual average BUD concentration in the combined
sewer was measured to be 72 mg/i (Davis and Borchardt, 1974)
BOD difference = 10 mg/i
= 0.0006243 lbs/ft 3
69
-------
TABLE VI-2. OPTIONS USED FOR DES MOINES SIMULATIONS
The following five inflow combinations (M) 1 are used:
1. River flow + DWF
2. River flow + DWF + separate storm flow
3. River flow + DWF + combined storm flow
4. River flow + separate storm flow + combined storm
flow
5. River flow + DWF + separate storm flow + combined
storm flow
and the following four DWF treatment rates (J) are used:
1. 0% (no treatment)
2. 30% (primary)
3. 85% (secondary)
4. 95% (tertiary)
and the following three WWF treatment rates (L) are used:
1. 0% (no treatment)
2. 25%
3. 75%
and the following three river flows (K) are used:
1. 0% of measured flow
2. 25% of measured flow
3. 100% of measured flow
and the fraction of combined area is varied four times:
1. 0% of total urban area
2. 8.16% of total urban area (existing conditions)
3. 50% of total urban area
4. 100% of total urban area
1 Paraxneters J, K, L and M were used as subscripts in the
computer output and serve to aid in labeling the various
combinations.
70
-------
(:5
I.0
C
z
0
I—
I —
H 0
w
0
0 .0r
0.0 195
YEAR OF RECORD = (968
0.5
3.0
2.0
• 1.0
ib 1 . . .
445 831 (059 (089
. . ii .Lh 1 i 11 1 .
1144
1156 -
11871218 (284 (296 (650 18(1
TIME,
hours
Figure VI—4. Point Rainfall for Des Moines, Iowa
-------
P3,0
.1.0 -
z
0
I— Z,OE
C.)
I .-
cL
0
w
0.5
l.0
I . ‘. ii J i ll 1 . , 1 ,
l8 6 1927 2027 2080 2168 2233 24Th 2559 2797 2813 2842 2924
TiME, hours
Figure VI-4. (Continued)
-------
1.5
•I.o.
z
0
F- 2.OE
U
I —
1.0
0,0• I . 1 .. J L I
3104 3157 3336 3356 3509 3595 3669 3791 3663 3885 4048 4067 4121 4307 4402
TIME, hours
Figure VI-4. (Continued)
-------
Figure VI-4.
(Continued)
I0
C
z
0
I —
4
0
0
w
1 Ø5
E
U
0.0
4564 4830 4854 4927 5099 5122 5279
TIME, hours
-------
•3.0
I.o
C
z
0
H
4
H
01 0
Li
0
0.5
1.0
0.0• — 1 1 .— I 1 _ . __ 1 l.dlidIIIh _ 4 . ___ . , s... , . H o
6137 6251 6287 7073 7153 7176 7292 7366
TIME, hours
Figure VI-4. (Continued)
-------
BOD load = (0.0006243 lbs/cf) (1.55 x io8 cf/yr)
= 96,766.50 lbs/yr (43,892.50 kg/yr)
that can be attributed to first flush
effects
FFLBS = 96,766.50 lbs/yr
(6993 DWH/yr)
= 13.84 lbs/DWH
This factor as demonstrated earlier, is then used in equation
(IV—5) to estimate the first flush BOD load, FF, during the
first hour of runoff generated by each storm event.
Calibration and Verification
An important element of the total effort required to develop
a mathematical model of receiving water quality is devoted to
calibration and verification the improvement of model accuracy.
The procedure recommended for steady-state water quality models
includes:
1. examination of model output using preliminary
coefficients on a diverse set of data (different
waste loads and temperatures under conditions of
high and low flow, and variable initial stream
quality);
2. assessment of the closeness of fit of observed field
data to computed values;
3. adjustment of the model coefficients until the desired
accuracy is obtained; and
4. achievement of a mathematical abstraction that reasona-
bly reproduces observed stream response and establishes
the necessary validity for planning purposes.
The verification procedure was preceded by calibration of the
urban runoff BOD 5 loading rates for Des Moines, Iowa, as computed
by STORM. The dust and dirt surface loading factors were adjust-
ed to obtain an annual average BOD 5 concentration of 53 mg/i for
urban stormwater runoff. The above concentration was the average
value determined by the field monitoring program in the separate
sewer system (Davis and Borchardt, 1974). Level Ill-Receiving,
as discussed in the methodology, simulates the mixing of storm-
water runoff and sanitary sewage in the combined sewer system.
76
-------
The annual average BOD concentration of combined sewer over-
flows was computed to e 75 mg/i, including the effects of first
flush. The average value determined by the field monitoring pro-
gram in the combined sewer system was determined to be 72 mg/i.
Upper and lower bounds of the carbonaceous BOD deoxygenation
rate coefficient, K 1 , were set to 0.0417 and 0.022 hour- 1 at
20°C in accordance with results of field data surveys (Hydro-
science, Inc., 1971). These values (program variables XK1MAX
and XK1MIN) should be adjusted to local conditions if such a
range has been measured. Initial estimates of the regression
coefficients relating deoxygenation rate to stream depth were
refined during calibration to final values of: = 0.99 and
= —0.28. The model, of course, adjusts for temperature
variation through equation (IV-38). From initial estimates
obtained for the Kansas River System in Kansas and Nebraska,
the regression coefficients relating velocity to streamfiow
and depth to streamf low were adjusted to : a 1 = 1.300,
a 2 = 0.060, = 0.200, and 2 = 0.450. The atmospheric
reaeration coefficient, K.), is calculated internally as a
function of streamfiow, t ie above regression coefficients, and
temperature through equations (IV-42) and (IV-43). Therefore,
no adjustment of this model coefficient is necessary. Measured
and computed values of DO at a distance of 5.6 miles (9.0 km)
downstream from the confluence of the Raccoon and Des Moines
Rivers are compared in Figure VI—5. All model coefficients were
calibrated to obtain a satisfactory fit between the computed
profile and the individual DO measurements listed in Table VI-3
(Davis and Borchardt, 1974). The DO measurements in Figure VI-5
have been connected by a dashed line to represent a profile;
however, it should be noted that gaps in the time history
suggest that such a profile is only an approximate representa-
tion.
Depending on planning objectives and desired model
accuracy, further verification may be accomplished by simulating
the stream response to hydrologic and waste inputs for another
year if field measurements are available. Included in Figure
VI—5 are average total streamfiow values for each wet—weather
event (as defined by the minimum intervent time) . Differences
between measured and computed DO concentrations may be attributed
to such factors as: (1) the time of day during which the sample
was taken; (2) the lag time between sampling and laboratory
analysis and the temperature variations in the receiving water
during the day; and (3) a lack of data on photosynthesis, algal
respiration, and benthic demand. The time scale in days
represents the wet year beginning on March 8 and ending
December 30, 1968. Again, it should be reemphasized that these
DO values are not the minimum DO’s resulting from maximum defi-
cits. The maximum deficits occur much further downstream (18-40
miles or 29-64 km) and water quality standards are violated much
77
-------
MEASURED FLOW
URBAN RUNOFF
.0
200
‘300
PUT ED
14
12’
lO
N
d
d4 -
2-
0’
21
MARCH 6, 1968
II I
N
-0 .;
I,
.2 ‘n,
.-
0
-4 0
0
0
-J
c i :
w
Li:
-12
Application- to Des Moines, Iowa. Measured and
computed values of DO at 5.6 mi (9.0 km)
downstream from confluence of Raccoon and
Des Moines Rivers.
41
I 11111 1_
I0
Figure VI-5.
141
DAYS JULY 24, 1968
I IL _ I ____ 1 Ill II III I II II
EVENTS 20 0 I I J
-------
14
2
I0
E
d4
2
0•
AUGUST 3, 1968
liii I I
0 J
8 tL
w
.10?
I2
291
DEC. 31, 1968
I I I II
MEASURED
URBAN
-4
FLOW AND
RUNOFF
. 100
U
t;i’
E
•200
30
J41I II 11111 li ii
40 EVENTS 54
Figure VI-5. (Continued)
-------
TABLE VI-3. MEASURED DO CONCENTRATIONS 1 DOWNSTREAM
FROM DES MOINES, IOWA 2
Date DO Concentration
(Month/Day/Year) (mg/i)
Date DO Concentration
(Month/Day/Year) (mg/i)
3/06/68
10.3
6/19/68
5.0
3/12/68
12.2
6/25/68
3.8
3/15/68
12.2
7/02/68
7.4
3/19/68
8.1
7/09/68
6.5
3/28/68
4.8
7/16/68
2.9
4/10/68
2.8
7/23/68
6.7
4/12/68
0.8
7/30/68
6.6
4/17/68
2.0
8/06/68
7.4
4/24/68
8.1
8/21/68
3.0
5/01/68
7.3
8/27/68
5.9
5/07/68
5.2
9/03/68
7.0
5/14/68
2.5
9/10/68
7.3
5/21/58
4.8
9/17/68
5.1
5/28/68
3.9
9/24/68
7.6
6/04/68
0.3
11/05/68
11.0
6/12/68
3.4
12/03/68
12.7
1 Henningson, Durham & Richardson, Inc. , Omaha, Nebraska
(Davis and Borchardt, 1974)
2 Distance of 5.6 mi (9.0 km) downstream from the
confluence of the Raccoon and Des Moines Rivers,
sampling location No. 6 (see Figure VI-3).
80
-------
more frequently.
Sample Input Data and Printed Output
A map of the study area and its point rainfall history for
the year 1968 have been presented in Figures VI-2, VI-3, and
VI-4. Input data for Des Moines, Iowa (including the required
receiving water parameters for the Des Moines River) are shown
in Table VI-4. Card Groups and card types are identified also,
in accordance with Table VI- 13.
The ensuing tables and figures are examples of printed
output. Due to the volume of such output, only a partial
listing of the typical information displayed by the model is
provided. Table VI-5 illustrates the first page of output,
which identifies the model name and personnel to contact for
information and assistance. Computed values of the auto—
correlation function are shown in Table VI-6 and the correlogram
of the hydrologic times series is plotted in Figure VI-6. Input
data common to the wet weather flow and dry weather flow sub-
programs are displayed in Table VI-7, as well as the study area
identification label and the values of key parameters and con-
trol variables of the WWFM. Output typical of each wet-weather
event simulated is presented in Table VI-8. Figure VI-7 dis-
plays the spatial distribution of DO downstream from the point
of waste discharge for a distance of 150 miles (240 kin) , for
various waste inflow combinations (M = 1,..., 5). This distance
corresponds to 100 segments of length DX = 1.50 miles, a user-
selected parameter. Figures VI—8 and VI-9 represent, respec-
tively, plots of critical DO frequency histogram and cumulative
frequency curves. Table VI-9 consists of a chronologically
sorted listing of composite wet and dry weather events and
their corresponding DO concentration values, at a distance 5.61
miles (9.0 km) downstream from the point of waste discharge.
The distance for which the event DO profile is desired is
selected by the user (program variable X). Figure VI-lO is
a plot of the DO concentrations in Table VI-9 versus the event
number . Thus, the abscissa of the plot is not truly time-
scaled. However, since each table is followed by a plot
(repeated until the entire period of simulation is included),
a time-scaled profile can be easily obtained manually.
Interpretation of Output - Results
A detailed economic analysis of urban water pollution
control alternatives in Des Moines, Iowa and its integration
with an evaluation of receiving water quality impacts has been
presented elsewhere as part of a more comprehensive nationwide
assessment (Heaney, et al. , 1977) . Some of the results of the
cost—effectiveness approach are summarized throughout this sub—
section to further aid the potential user in the interpretation
81
-------
TABLE VI-4. INPUT DATA FOR LEVEL 1 11-RECEIVING,
DES MOINES APPLICATION ___________
Data Card Group
Number
1 1 1 0 0 0
73 4 800
2 170.
0.02 0.06 3.37 0.29 Q• 0 g 0.12 0.06 3.02
1 23’.
0.3 3.33 3.31
3 3°2.
3.10 0.14
-A
0.31
3.34
1
3.22
I
4 3.37 0.12 3.36 3.06 3.02
4 3.32
L I.
0.32 0.01 0.32
I 2’ .
0.28 3.08 0.22 0.34
7 27.
3.34 0.07
3.13 0.03 3.35 3.33 3.33 0.36 0.12 3 .2 0.01 0.35 3.36 0.01
I 1.
3.16 3.02
3 10.
4 0.31 3.31 3.31 0.34 3.31 C. 13 0.20 0.14 3.14 0.11 3.3 3.32 0.09 0.04 3.03
4 30.37 0.10 0.38 0.08 )•37 3.02 0.04
7 ‘2’.
4 0.32 0.04 3.02 3.01 0.32
1 2.
3.17
O 1.5.
4 0.17 3.29
3 4.
2.2 0.32
-, 3 .
•3.04
7
4 3.02 3.01
3
4 ).)l
3.32
1
- 4 3.32 3.02
I 91.
(Continued)
82
-------
TABLE VI-4. (Continued)
w 0.01.
1..
3.34
0 3.
3.06
0 1..
4 0.3 1 .
1 5.
0. 1 .5
0 3.
3.33
58.
9 3.06 3.13
O 53.
4 D . .L 0.21
1
0.03 3. 1 .1.
7 231.
9 0.36
3 1.
0.04
3 72.
4
1 ‘36.
3.02 0.06
1-..
9 0.31
2.
0.5
I 25.
4 0.03
7 1.
“4 0.1.8
7 5.
‘4 0.31.
fl
4 0.01
1 4.
‘4 3.31.
7 5 .
4 0.46
‘1 l 6.
‘9 0.32
I SO.
9 3.20
0 L 6.
9 0.31.
1 1..
‘ 4 0.13
3 1’.
Data
0.01 C.06
0.31.
0.02
0.02
0.1.2
Card Group
Number
3.03 0.06 0.02
0.06
0.39 0.07 3.02
0.01
0.31 3.50 3.13 0.1.5 0.37
3.33 0.01. 0.02 3.31. 0.02
0 .35
0 .01.
‘).12 3.16 0.15
0.28 0.21
0.38 0.47
3.20 0.07 0.03 0.01
(Continued)
83
-------
TABLE VI—4.
(Continued)
0.32 0.04
15 1..
1.33 3.32
- 2.
W 0.02 0.32
0 7 ,.
4 0.1.3 0.31.
91.
4 0.1é 0.35
)
3.31
- 2.
4 3.37
0 6 3.
0.03 0.IL
0 1.9.
0. 4 1.16
0 1.
I •J.u1 0.32
0 16..
0.1.6 0.05
3 1 .’.
4 0.32 3.32
0 1..
0.20 0.12
0 49.
4 0.32 0.31
0 18g.
4 1.12 0.11
O
3.01 0.06
0 2.
0.Jo 0.09
0.05 0.06
0 23.
0.35
0
4 3.34
0.1’
0 1..
4 3.1. .
3.
4
4 0.31
0 z .
.4 0.03
0 ,‘4.
(Continued)
Card Group
Number
Data
0.35
0.01 0.21
0.02.
3.31
0.33
0.13 0.01
0 .35
3.31 0.31 0.02 0.08
0.22 0.16 0.13 0.03 0.01
0.01 3.32
3.06 0.02
0.35
3.43 0.13 0.04 J.1 0.02
It
0.02
84
-------
TABLE VI-4.
(Continued)
0.01
C. 20
0.20
0.33
0.14
0.04
0.36
0.01
0.34
0.31
0.02
0.02
0.06
0.04
0.04
3.33
0.02
0.32
(Continued)
Data
0.08
0.0 1
0 .3 3
3.01
3.06
0.33
0.03
0.03
0 .3 1
0.31
Card Group
Nunthe
. 4 0.01
4.
. 4 0.10
I
0.01
3 1.77.
.4 0.02
20.
0.19
1
.4 3.31.
1 1.
0.31
0 7.
. 4 0.31
O 60.
. 4 3.01
0 4.
0.31
O
. 4 0.32
I 1.37.
0.13
O 470.
.4 0.31.
0 L.
.4 0.31.
O 2.
4 0.01.
0 5.
.4 0.31
O 33.
.4 0.36
.4 3.34
I 90.
0.01
O 3?.
.4 3.32
1 2.
.4 3.31
O 762.
.4 0.31
O 5.
.4 0.12
1 5.
.4 0.31
3 1.
. 4 0.01
O 55.
0.32
0.04 0.01.
0.09
0.02
0.13 0.34
0.05 0.03 0.03 0.06 0.07 0.06 0.04 0.33 0.04
0.05 0.09 0.1.8 0.03
3.02 0.01 3.02
0.06 3.07 3.36 3.12 0.12 0.12 0.12 0.12 0.11 0.0’ 0.04 3.34
3.01 0.01. 3.01 0.31. 0.01. 0.01
0.05
0.05 0.03 3.01
3.29 3.33 0.11
0.01 0.02 0.02 0.02
0.01. 0.02 0.02 0.01 0.01 0.02 0.33 0.03 0.32 0.32 3.01
85
-------
TABLE VI-4.
(Continued)
Data
0.02 j.oz o.oi O.O 1 0.31
0.01 0.01 0.01 0.02
0 9.
0.01
0.01
102.
0.01
3.
0 • 02
1.
3.31 0.01
1..
3.01 0.31 3.02
52.
0.31 3.01 0.31
,.
3.02 0.02
1 2 3
4 00O.00 5.o l l 0000.0 54.62 325.06
C.
0417
0.30 0.35 0.95 0.0 0.25 1.00
1.300 0.060 0.200 0.’5 3.990 -0.29
4LVSIS OF CRtT1C 1L CtSSCLVED CXVGE LEVELS tN THE
DES M0T IES 1VER..0 7W JST0E M..FDCi..JES NES, 7H
45000.00 4000.00 3a97•5Q 13.34 0.3 3.25 3.75
3 1 1. 1 2 9 5
30963 3 202 133.9 10S20 . s 80.0 9.0
9.5
15.0
308o8 0 1133.4 57619.3 630.0 .3
9.3
15.0
30363 5 3 1322.3 535 )8.0 680.0 9.0
30868 6 3 5477.9 163339.3 630.3 9..)
30863 7 0 1700.1 21015.5 680.3 9.0
°.5
15.0
0363 3 0 2256.’ 23274.6 60.0 9.0
9•5
15.0
30868 9 3 1133.. 7388.4 680.0 9.0
30868 10 0 377.3 2026.5 630.0 1.3
9.3
15.)
31868 5 234 944.5 24529.1 510.0 12.0
.9.6
1.5.2
313 8 6 3 566.7 12414.4 510.3 12.0
9.6
13.2
0 189.9 3719.2 510.0 12.3
9.6
13.2
40368 6 382 1700.1 ‘744 .9 390.0 13. )
5.3
40368 7 0 2644.5 102168.0 390.0 13.0
.3
.9
40365 13 2 94.4 2239.3 380.3 13.0
5.3
.0368 15 5 566.7 13731.3 380.3 1.3.0
5.
9.9
41265 19 21 366.3 168627.0 243.0 15.3
11. .5
10.
41.463 1 29 1133.4 2 392. 280.3 ls.S
16.5
13.1
13.1
9.5
41468 2 0 2266.7 511’7.3 380.0
41.469 3 0 1133.4 17342.3 380.0 16.5
13.1
9.5
41468 4 0 1133.4 15304.3 580.0 16.5
13.1
3•5
41468 5 ) 377.3 38 5.R 380.0 16.5
13.1
41668 3 50 188.9 7543.3 310.0 16.3
1 .6
.2
41 68 20 11 158.9 2762.2 31.0.0 16.8
13.c
41668 21 0 l 3.9 26 8.3 313.0 16.9
13.6
Continued
Card Group
Number
0
w
-3
.3
w
0
0
‘I
-3
‘I
I
86
-------
(Continued)
TABLE VI-4.
Data
(Continued)
41668
22
0
377.3
5377.4
313.0
16.8
13.6
9.2
418 9
3
23
(322.3
23751.4
430.0
14.5
13.0
9•5
‘s1968
4
0
15(1.2
23900.8
480.0
14.
13.0
9.5
41868
5
0
4155.7
76938.2
480.0
14.5
13.0
9.
41869
6
0
755.6
5796.8
480.0
14.3
13.0
C•5
4 1963
10
27
566.7
5245.9
600.0
13.5
12.0
‘ .5
:. 1 968
11
o
122.3
13594.0
600.0
13.5
12.0
4.5
( ) 9
13
1
2408.—
27422.
600.0
13.5
12.0
.5
4 1°68
14
3
566.7
3980.2
600.0
13.5
(2.0
4.5
41968
15
0
944.5
6464.1
600.0
13.5
12.0
9.5
41 68
16
0
566.7
3252.2
600.0
13.5
12.0
9•5
4(968
17
0
566.7
3152.4
600.0
13.5
12.0
4.5
4 (968
18
0
1133.4
387.3
600.0
13.5
(2.0
°. s
41 6S
19
0
2266.7
13691.0
600.0
13.5
12.’)
‘.5
4(963
23
0
4533.5
48(09.3
600.0
13.5
12.0
‘.5
41963
21
0
138.9
560.6
6C0.0
13.5
12.0
.5
41968
22
0
944.5
2941.6
600.3
13.5
(2.0
9.5
41969
23
3
1133.4
‘810.8
600.3
13.5
12.0
4.5
419 58
2’
0
188.9
526.5
600.0
(3.5
12.3
. 5
42258
51
2833.4
29285.3
780.3
11.0
9.3
9.5
42263
5
0
377.3
2006.4
790.0
11.3
9.3
9•6
42253
17
11
138.9
1094.7
780.0
11.0
9.0
9.5
2268
13
0
139.9
1070.3
780.0
11.3
.0
9.5
42263
19
0
755.6
4688.
‘90.0
(1.3
.0
9.5
42268
20
3
1888.9
(3445.4
780.3
11.0
9.0
Q•5
42268
21
3
3400.1
26451.5
790.0
11.3
.3
.5
4226
22
0
37’7.9
253°3.3
780.0
11.0
.0
.5
42268
23
0
2644.5
12328.4
780.0
11.0
9.0
9 5
4Z2 63
24
3
2644.5
1(104.8
730.0
11.)
9.3
° .
42358
1
0
2077.8
‘024.S
1600.0
9.3
3.0
9.5
421 s9
2
0
944.5
2156.7
1600.0
.3
5.0
9.5
.2363
3
0
377.3
675.9
1600.0
9.8
9.0
Q•5
42369
4
0
1700.1
4769.1
(600.0
9.3
9.3
.5
42368
5
3
755.5
(493.7
1600.0
9.3
‘.3
9.5
42368
6
3
5o6.7
1024.5
(600.0
9.9
9.0
Q 5
42368
7
0
1322.3
3101.7
1600.0
9.3
9.3
9.5
42368
8
3
1888.9
5044.7
1600.0
9.8
‘.3
9.9
42368
9
0
1511.2
3465.1
(600.0
9 .
.)
9.5
42353
1)
3
(511.2
3346.1
ISCO.0
9.5
3.3
.5
42369
(1
3
1322.3
2672.5
1600.0
9.9
9. J
42369
12
0
377.8
524.9
1600.’)
9.9
8.3
.5
42158
13
3
755.5
1215.7
600.U
.5
3.0
9.5
50763
10
332
183.9
5908.6
766.0
13.0
13.5
7 .
90763
11
3
795.5
22321.3
766.0
12.0
13.5
7.8
50768
12
3
377•3
9766.9
766.0
13.3
13.5
.9
50768
13
0
138.°
4565.4
766.0
13.0
13.5
7.8
53769
14
0
377.8
37 2.4
766.0
13.0
13.5
7.9
50769
17
2
3022.3
59573.(
766.0
13.3
13.5
‘.
51369
(4
145
3022.3
77081.3
5 0.3
11.4
19.1
.3
51368
20
0
5477.9
18496.3
543.3
11.’
13.1
7.3
Card Group
Number
87
-------
TABLE VI—4. (Continued) Card Group
Number
Data
1468 1 4 2077.8 17081.1 672.0 11.0 19.3 7.2
51468 2 0 377.3 1903.1 672.0 11.0 19.0 7.2
51568 16 37 566.’ .703. 2 660.3 11.3 13.1 7.4
51563 19 2 138.9 1423.1 660.0 11.0 19.1 7.4
51568 20 0 188.9 1384.7 660.0 11.0 18.1 7•4
51863 17 53 283.3 3751.1 600.0 10.4 15.3 2.6
51363 22 4 198.9 2443.4 600.0 10. - 15.3 3.6
¶1363 23 ‘0 377.8 4774.4 500.0 10.4 IS.? 3.6
522 8 20 92 198.9 1776.1 500.0 9. 14.1 .5
52258 21 0 1133.4 22268.6 500.0 9.’? 14.1 ?•5
52658 1 51 566.7 11623.3 473.3 3. 14.6 9.5
52563 2 0 j93 Q 3481.1 70.0 8.2 14.6 9.3
92563 11 8 944.5 1 924.9 470.3 3.2 14.6 9.5
52563 12 ‘3 377 3 5994.9 470.0 3.? 14.6 0 5
52568 13 0 566.7 85 7.8 47Q 3 S.’ 14.6 0.5
52563 1’ 3 1133.4 16335.5 470.0 3.? l .o 9.5
525o8 15 0 377.3 4319.5 70.0 3.2 14.6 9.6
52568 17 1 94.4 1001.1 4’3 .3 3.9 14.6 9.5
¶2568 18 0 377.3 066.9 470.0 3•9 14.6 9.5
52668 24 5 2933.4 377 .3 470.0 3•? 14.6 .S
¶2668 1 0 2266.7 22265.0 20.3 8.6 t4. 9.6
52563 2 3 1133.4 ‘573.4 520.3 9.5 14.7 9.5
526o 8 6 3 377.9 2005.1 520.0 9.3 14.7 9.6
52963 1? 53 944.5 10359.2 530.0 3.0 15.0 9.4
52968 18 3 2455.6 29333.3 530.0 3.) 15.0 ?.
53163 10 83 1388.9 25016.0 540.3 7•0 20.1 3.3
53168 11 3 3966.8 52347.3 540.0 7.9 20.1 3.8
53168 12 0 1700.1 12037.3 ¶40.0 23.1 .8
¶3168 13 0 1322.3 7643.3 54Q Q 7. 20.1 8.3
53168 14 C) 377.8 1553. 540.0 7.9 20.1 3.3
53163 19 377.8 1643.9 540.0 7.9 20.1 •3 8
53168 20 ‘3 2077.3 13098.5 540.3 7.? 20.1 3.3
53 168 21 0 188.9 636.4 540.0 7.? 23.1 9.8
61063 15 233 044.5 23044.2 420.0 7.2 24.0 6.4
61063 16 0 1700.1 36941.3 420.0 7.2 24.0 6.4
61068 17 0 5355.7 112762.0 420.3 ‘.2 24.0 6.4
5 1063 18 0 9444.7 122066.0 420.0 7.2 24.0 6.4
61068 19 3 2455.6 7681.2 420.0 7.2 24.0 6.4
61163 23 ‘3 2933.4 3521.3 420.0 .2 24.0 6.4
61368 21 0 1322.3 2526.0 2O.0 7.2 24.0 5.4
81068 23 1 621.8 965.9 420.0 7. 24.0 6.-
61358 24 72 1511.2 12134.0 980.0 6.7 23.3 5.1
62163 21 236 188.9 5168.3 360.0 5.0 25.0 6.1
52368 22 0 1133.4 290l .5 860.3 5.0 25.0 6.3
62468 iS 17 10955.9 230164.0 1100.0 5.0 25.0 5.1
62568 19 25 377.8 1974.4 1890.0 5.0 25.0 5.0
62569 19 0 566.7 2811.7 1990.0 5.0 25.3 6.0
62568 20 0 193.9 8 0.8 1390.0 5.0 75•Q .)
625o8 21 0 3’7.9 1650.0 1990.0 5.0 25.0 . ‘3
62563 22 0 138.0 769.’ 1990.0 5.0 3 .o 5.0
(Continued)
88
-------
TABLE VI—4. (Continued) Card Group
Data Number
62568 23 0 377.8 1515.3 1890.0 5.0 25.0 6.0
52 1 1 3266.0 16100.0 3500.0 4.9 25.1 6.1
62668 2 0 1133.4 3154.7 3500.0 4.9 25.1 6.1
62668 10 7 55.1 143.6 3500.0 4.9 25.1 6.1
62663 16 5 188.9 564.8 3500.0 4. 25.1 6.1
62968 4 59 8500.3 79205.1 7900.0 “.4 24.3 6.8
62968 5 0 2266.7 5834.3 7900.0 7•4 24.3 6.5
62968 6 0 3022.3 719Q.4 7900.0 •. 24.3
62968 7 0 3022.3 5981.2 7 OO.0 .4 24.3 6.5
70668 16 176 8S5.7 86070.0 3430.3 5.5 24.5 7.3
70668 17 0 529 .0 3576 .1 3400.0 5.5 24.5 7.3
73668 18 0 3966.8 14 42.6 3400.0 5.5 24.5 7.2
73868 21 50 3589.0 23320.2 2500.0 5. 25. “.9
70863 22 0 7178.0 3o535.4 2500.0 6.4 25.9 7.9
70868 23 0 3878.0 28169.6 2500.0 6.4 25.9 ‘.9
71668 10 178 1723.7 26632.5 11 0.0 13.3 25.0 6.5
71.668 11 0 3777.9 4 1a87.2 1190.0 13.0 25.0 5.8
71668 12 3 1322.3 7880.8 1190.0 13.) 25.0 6.3
71.668 13 0 566.7 2651.0 L1 0.0 13.0 25.0 6..
71668 14 0 158.9 789.5 1190.3 13.0 25.0 6.8
71.753 * 13 188.9 988.3 950.0 9.4 25.1 6.
71.768 5 0 755.6 3940.2 950.0 9.4 25.1 6.
71768 6 0 944.5 4453.1 950.3 9.4 25.1 5.9
72368 14 151 24934.1 254336.0 2420.3 2.3 25.0 7.5
72369 15 0 377.8 27.9 2420.0 2.0 25.0 7.5
7368 16 0 188.9 13.0 2420.0 2.3 25.0 7.5
72368 1.7 0 3Q55 3 256.6 2420.0 2.0 25.0 ‘.5
72368 20 2 188.9 40.2 2420.0 2.) 25.0 7.5
72368 21 0 377.8 76.7 2420.0 2.3 25.0 7.5
72365 22 0 377.8 71.3 2420.0 2.0 25.0 •5
72768 3 75 2256.7 1- 329.3 1400.0 .3 26.0 9.3
72768 ‘ 0 158.9 539.9 1400.0 6.3 25.0 9.3
72768 5 0 188.9 809.0 1400.0 6.0 25.0 9•3
73168 1 91 2333.4 31335.5 1700.0 12.3 26.3 11.3
73163 2 0 944.5 6548.5 L ’OO.O 12.0 26.0 11.)
711.68 3 0 566.7 331 ’.7 1700.0 12.0 25.0 11.0
50468 7 99 188.9 2585.7 1600.0 9.6 26.3 12.3
33468 10 2 1322.3 17781.2 1600.0 9.6 .26.3 1.2.)
80768 7 68 566.7 9449.8 1180.0 9.5 26.1 11.0
3 0 2077.5 30471.0 11.30.3 9•5 26.1 11.0
9 0 2455.6 25863.9 1180.0 °.5 26.1 Ii. )
50768 10 3 158.9 1231.5 1180.0 9.5 25.1 11.)
50568 6 19 10200.3 102666.0 1650.0 10.2 26.3 10.2
80868 7 0 21911.8 62372.7 1650.0 1.0.2 26.3 13.2
8 0 344.5 12.6 1650.0 10.2 25.3 13.2
80865 10 1 158.9 19.0 1650.0 10.2 25.3 10.2
80863 11 0 377.3 36.5 1650.0 10.2 26.3 10.?
81563 3 164 3022.3 39035.9 910.0 10.0 25.9 5.4
9 0 44.5 7478.4 930.0 1.0.0 5•C 6.4
3 17 377.8 3104.9 880.3 3.) 26.5 5.3
(Continued)
89
-------
(Continued)
TABLE VI-4.
Data
(Continued)
31668
4
0
377.9
2388.9
990.0
9.0 26.9 5 •q
91668
6
1
377.8
2723.7
380.0
8.0 26.8 5.8
91669
7
0
2266.7
15264.5
390.0
8.3 26.3
81968
9
49
377.8
3332.6
780.0
5.0 26.7 5.3
31868
[ 0
0
188.9
1555.1
750.0
5.0 26.7 5.0
91868
11
0
188.9
1500.7
780.0
5.0 26.7 5.0
31868
12
0
183.9
1448.5
780.0
5.0 26.7 5.0
81888
13
0
377.8
2799. 1
790.0
5.3 26.’ c.)
81969
14
0
1511.2
106 5.2
780.0
5.0 Z5. 5.0
92668
11
188
21156.2
270266.0
600.0
4.6 21.9 7•5
32668
12
0
20 7.3
424.5
600.0
4.6 21.9 “.5
82668
13
0
4155.7
598.6
600.0
4.6 21.9 7•5
326 3
1’
0
3022.3
2 2.7
600.0
4.6 21.9 .5
26o8
15
0
2455.6
135.1
600.0
4.6 21.9 7•5
92868
16
0
566.7
13.1
600.0
‘.6 71. 7•5
82668
17
0
5.2
500.0
4.5 21. ‘.7
33068
3
31
138.9
1388.6
800.0
5.5 21.4
33068
4
0
1133.’
6l6.1
300.0
5.6 21.4 4.8
83068
5
0
139.9
1)60.9
900.0
5.6 21.4 .3
83068
6
3
377.8
2025.’
900.0
5.5 21.4 9.9
930t,8
9
2
1133.’
5689.5
300 .0
5.6 21.4 .8
33068
10
0
1700.1
6855.7
900.0
5.6 21.4 0•9
83068
ii
0
1123.4
3407.2
900.0
5.5 21.4 .3
063
12
0
377.8
920.1
3C0.0
5.6 21.4 .8
83088
17
4
944.5
2523.6
900.0
5.6 21.4 .8
83068
13
0
1133.4
2615.3
800.0
5.6 21.4 9.8
83068
19
0
944.5
1811.8
300.0
5.6 21.4 .R
93168
19
23
944.5
3458.3
710.0
6.0 21.6 10.3
90363
17
69
755.o
5903.6
544.3
7.0 22.0 12.1
90369
22
4
2544.5
21334.8
544.0
7.0 22.3 12.1
90368
2’
1
2644.5
14373.3
544.0
7.0 22.0 12.1
90463
1
0
8122.5
37000.7
1000.0
7.1 21.3 12.0
90463
2
0
2455.6
3384.3
1300.C
7.1 21.8 12.0
°04 ’3
3
0
755.6
610.4
1000.0
7.1 21.3 12.3
90468
4
0
3022.3
3721.3
1300.0
7.1 21.8 12.0
9046 ?
5
0
377.?
203.3
1000.3
7.1 21. 12.)
90468
9
3
138.9
144.1
1000.0
7.1 21.8 12.3
90469
11
1
139.
153.3
1200.0
7. 21.8 12.3
0 568
[ 3
25
566.7
1755.4
1280.0
7.2 21.6 12.)
90 63
14
0
377.3
1056.2
1280.0
7.2 21.6 12.3
91668
15
264
198.9
001.3
460.
7.1 1 .2 7.6
8156.9
16
0
1S8.
‘813.9
460.0
7.1 19.2 7.6
91688
21
4
1889.9
43919.1
4 0.0
7.1 19.2 7.c
91568
22
0
3777.9
61350.1
460.0
.L 1 .2 7.6
91868
23
0
755.6
6441.3
460.0
7.1 1.2 7.6
91668
24
0
138.9
1391.9
460.0
7.1 19.2 7.6
82068
16
87
2824.5
720.0
.4 19.4 3.6
92068
17
0
3777.9
c5833.6
720.3
7.4 l .4 5.)
92068
[ 8
0
1700.1
13 07.
720.0
7.4 19.4 5.6
82963
176
377.9
,111L.a
1200.0
7.5 [ 5.5 Q•9
Card Group
Number
90
-------
TABLE VI-4.
(Continued)
Card Group
Data
Number
c2 8s8
5
0
566.7
11326.6
l200.0
7.5 15.5 4.4
92968
6
0
377.8
6307.5
1200.0
7.5 15.5 .3
92468
3
20
35 9.o
9974.6
1350.0
7.4 16.3 9.9
92968
4
0
2644.5
30502.1
1350.0
7.4 16.2 9 .
2 4 8
5
0
3300.1
31192.9
1350.0
7.-p 16.3 4.4
92 8
6
0
755.6
3443.8
1350.0
7.4 16.3 9.9
100568
S
145
188.0
3173.3
1800.0
6.8 16.0 10.2
100568
10
1
188.9
3083.1
1500.0
5.3 16.0 10.2
lO O5o3
13
2
188.9
3013.6
1300.0
6.3 16.0 10.2
1 30 68
14
0
755.6
11309.4
1300.0
5.3 16.0 10.2
130568
15
0
1511.2
21543.7
1800.0
6.8 16.0 10.2
130568
16
3
944.s
10400.4
1900.0
6.8 16.0 10.2
100568
L
0
566.7
5254.5
1300.0
5.8 16.0 10.2
100563
18
0
566.7
4865.6
1830.0
5.3 16.0 10.2
130568
19
3
1133.4
3643.6
1900.3
6.3 16.0 13.2
1005o8
20
0
1322.3
10133.2
1900.0
6.8 16.0 10.2
1 60568
21
0
1133.4
7346.9
1800.0
6.3 16.3 10.2
100563
22
3
755.6
9065.3
1500.0
6.2 15.0 10.2
103589
23
0
566.7
2715.7
1300.0
6.3 16.0 10.2
100563
24
0
755.6
3617.5
1300.0
6•3 16.0 10.2
L008 3
13
60
188.9
1747.4
1’OO.O
6.5 14.5 10.3
100868
14
3
1133.4
11173.3
1700.0
5.5 14.5 10.9
100868
15
0
138.9
1427.5
1’OO.J
6.5 14.5 10.3
1C0R 9
20
138.9
1457.5
1700.0
5.5 14.5 10.3
tCO 6R
1
4
3777.9
42336.2
2000.0
6.- . 13.0 10.4
130968
2
0
198.9
870.7
2000.0
5.4 13.0 10.4
100Q68
3
0
944•5
5039.4
2000 3
6.4 13.0 10.4
100969
4
0
1700.1
4736.4
2000.3
6.4 12.0 10.4
100968
5
0
3400.1
22705.4
2000.3
6.4 13.0 10.4
100°68
6
0
566.7
1653.4
2000.0
6.4 13.3 13.4
131758
2
187
3400.1
67355.7
3500.0
5.8 12.0 10.7
131768
3
0
755.6
3192.7
3500.0
5.4 12.0 10.7
110568
19
73
188.9
9772.6
5600.0
4.5 4.4 11.5
110568
21
1
47.2
2371.9
5600.0
4.5 11.5
110568
22
0
374.4
19576.1
5600.0
4.5 •4 11.5
110568
23
0
566.7
257 l.4
5600.0
4.5 ‘. 11.5
113568
24
0
374.3
15501.9
5600.0
4.5 4.4 11.5
110668
1
0
188.4
237.7
5400.0
‘. ‘.0 11.6
110668
2
0
377.3
13939.5
5400.3
4. .3 11.6
11 36o 3
6
3
377.8
13066.7
5400.0
‘. .3 11.6
113668
7
0
198.9
6094.2
5400.0
4.— .0 11.o
111368
1
29
944.5
36883.7
4500.0
‘.‘ 4.3 11.3
111363
2
0
1133.4
37740.0
4500.0
4.2 4.0 11.3
111368
3
0
1133.4
31414.9
4500.3
4.2 4.0 11.3
111068
4
0
1133.4
26434.1
4500.3
4.2 4.0 11.9
111068
5
0
1322.3
26695.5
4500.0
4.2 4.0 11.3
111068
6
0
1133.4
18921.3
4500.3
4.2 4.0 11.3
111062
7
0
2266.7
39246.1
‘500.0
4.2 4.0 11.3
111083
8
3
2266.7
31267.4
4500.0
4.? 4.0 11.9
111)58
9
0
2256.7
25496.0
4500.0
.2 4.0 11.3
(Continued)
91
-------
TABLE VI-4.
(Continued)
Card Group
(Continued)
Data
122768
6 0
377.9
6026.3
1200.0
5.4
4.0 12.3
122768
7 0
188.9
2798.0
1200.0
5.4
4.0 12.3
122768
8 0
138.9
2717.8
1200.0
5.+
4.0 12.3
1227o8
Q 0
188.9
2640.6
1200.0
5.4
4.0 12.1
L22 68
13 3
188.9
2617.8
1200.0
5.4
4.0 12.3
122768
14 C)
138.9
2544.7
1200.0
5.
4.0 12.3
122768
15 3
188.9
2474.5
1200.0
5.4
4.0 12.3
122768
16 0
188.9
2407.1
1200.0
5.4
4.0 32.3
122768
17 0
377.8
4794.1
1200.0
5..
.0 12.3
122788
19 1
Q4 4
1105.8
1200.0
5.4
4.0 12.3
122763
20 0
133.9
2207.7
1200.0
5.’
4.3 12.3
122763
22 1.
4.4
1070.9
1200.0
5.-.
4.0 12.3
122768
23 0
188.9
213 ’.9
1200.0
5.
4.0 12.3
122768
24 0
377.8
4299.0
1200.3
5.
.0 12.3
123068
6 53
188.9
2971.2
1150.0
5.8
-+.0 12.2
123069
7 0
138.9
2789.3
U50.O
5.3
4.0 12.2
123068
10 2
138.9
3744.3
1150.3
5.3
4.012.2
123068
11 0
377.8
5443.1
1150.0
5.8
4.3 12.2
5Q9000
80
1
1 2
3
13
30668
257.0
13.0
13.0
16.1
33768
300.0
10.0
11.3
15.0
31063
800.0
6.0
6.0
14.0
31268
621.0
6.0
2.5
13.7
31568
523.0
10.3
7.0
15.6
31968
516.0
13.0
10.5
12.7
32868
383.0
10.0
11.0
10.7
40168
340.0
11.0
9.0
10.0
3568
410.0
14.0
7.0
10.0
41068
264.0
15.0
10.0
10.7
41568
320.0
17.)
13.0
9.0
41768
355.0
17.0
14.3
9.5
42063
700.0
12.0
13.0
9.6
42468
2520.3
9.0
7.0
9.4
42588
2200.0
9.3
7.0
9.6
50168
1340.0
14.0
17.0
10.9
50568
900.0
13.0
13.7
8.8
50663
330.0
13.0
13.0
.6
50863
740.0
12.0
14.0
7.6
51068
703.3
12.0
16.0
7.6
51168
660.0
11.8
16.5
7.4
¶1263
600.0
11.5
17.0
7.3
51768
600.3
10.8
16.6
8.3
52068
560.0
10.0
15.0
.0
52168
523.0
10.3
14.3
9.5
‘2368
430.0
9.6
14.2
9.6
52768
525.0
7.8
14.8
.6
52968
540.0
7.8
15.0
9.0
53063
560.0
7.8
16.0
9.6
63468
‘88.3
8.0
26.5
7.8
Number
E
92
-------
TABLE VI-4. (Continued)
Card Group
Data Number
111063 10 0 2266.7 230 6.2 4500.0 4.2 4.0 11.3
111.068 11 0 2268.7 20571.4 4500.0 4.2 4.0 11.8
111368 12 0 2077.8 1.6379.7 4500.0 4.2 4.0 11.3
1.11.068 13 0 755.6 3712.7 4500.0 4.2 4.0 11.3
111068 14 0 755.3 3624.3 4500.0 4.2 4.0 11.3
111068 1.5 0 755.6 3543.2 4500.0 ‘.2 ‘.0 1.1.3
1.11063 1.6 0 755.6 3468.2 4500.0 4.2 ..0 11.3
111068 17 0 755.6 3398.5 4500.0 4.2 4.0 11.3
1.11069 18 0 566.7 2311.4 4500.0 4.2 ‘.0 11.3
1.11.063 19 3 188.3 639.3 4500.0 4.2 4.0 11.3
111368 20 0 188.3 635.7 4500.0 .2 4.0 11.3
11.1068 21 0 1 9.9 633.1 4500.0 4.2 4.0 U.S
1.11068 22 0 188.9 520.5 500.0 4.2 4.0 11.8
111368 23 0 138.9 628.0 4500.0 4.2 4.0 11.3
111)69 24 3 138.3 625.6 4500.0 4.2 4.) 1.1.3
1.11468 20 91 755.6 91.65.6 3300.0 3.9 4.0 12.)
1114 8 21 0 566.7 6064.2 3800.0 3.9 ‘.3 12.0
111468 22 0 344.5 3887.0 3800.0 3.9 ‘.0 12.0
111& & 7 32 158.9 2109.5 000.0 3.3 ‘.3 12.3
111638 8 0 566.7 6428.7 4000.0 3.3 4.0 12.3
111668 9 0 566.7 5954.9 4000.0 3.8 4.0 12.0
111668 10 0 944.5 °752.6 4000.0 3.8 4.3 12.3
111668 11 3 566.7 4926.5 4000.0 3.3 4.0 12.0
1.11 668 12 0 188.9 1431.1 4000.0 3.3 .0 12.3
121868 1. 771 2077.8 L51 R1.0 1200.0 4.3 4.0 11.6
121.868 17 0 5477.9 235417.0 1200.0 4.3 4.0 11.6
121868 19 0 6233.5 175859.3 1200.0 4.3 4.0 11.6
121368 19 0 2077.8 22928.4 1200.0 ‘..3 4.0 11.6
1.21.963 3 7 94.4 610.1. 1700.3 + .. 4.0 1.2.5
121968 4 0 377.8 2603.6 1’OO.O 4..— 4.3 12.5
121 6R 5 0 188.9 1135.3 1700.0 4.4 ‘.3 12.5
121968 6 0 138.9 1175.9 1700.0 4.’ 4.0 12.5
121968 7 ) 377.8 2441.5 1700.0 4.4 4.0 1.2.5
1.21.968 3 0 377.8 2363.7 1700.0 4.’ 4.0 1.2.5
121968 9 0 377.3 2300.9 1700.3 .4 4.3 12.5
1221.68 17 55 183.9 1993.8 2300.0 4.6 4.0 12.5
1.72163 18 3 377.3 3995.6 2300.0 4.6 ‘.3 1.2.5
1.22168 1.9 3 138.9 1347.8 2300.0 4.6 4.3 12.
1.22158 20 0 188.9 1803.0 2300.0 4.6 4.0 12.
122163 21 0 377.3 3634.3 2300.0 .3 4.0 1.2.5
122168 22 0 377.3 3473 . 2300.0 ‘.6 4.3 12.5
122168 23 0 1603.0 2300.0 4.6 4.0 1.2.5
1.22163 24 0 188.9 1567.7 2300.0 4.6 4.0 12.5
1222 ’ .S 1 0 377•3 3188.0 1300.3 4.8 4.0 12.4
1.22268 2 0 566.7 4782.6 1900.0 4.3 4.0 12.4
122268 3 0 566.7 4522.8 1900.0 4.3 .0 12.4
1.22268 4 0 377.8 2729.6 1. 00.b 4.3 4.0 12.4
1.22263 5 0 377.8 2635.8 1900.0 ‘.3 4.0 12.4
122268 6 0 188.9 1210.9 1900.0 4.8 4.0 12.4
122 69 5 118 377.8 6331.0 1200.0 5.4 4.3 12.3
(Continued)
93
-------
TABLE VI-4. (Continued) Card Group
Number
Data
60568 460.0 25.3 7.4
61263 1210.0 7.0 23.0 5.9
61948 1280.0 5.0 24.0 6.8
62368 1290.0 5.0 24.0 6.6
627 58 7000.0 ‘.6 24.0 6.6
63068 7400.0 4.2 22.0 7.3
7D2 3 5990.0 4.3 21.0 7.5
3563 3800.0 5.0 24.6 7.8
2290.0 7.3 26.0 8.0
71568 1300.0 12.3 25.0
72053 2000.3 5.2 24.0 7.2
72 6 .3 1300.3 2.3 26.3 5.0
1..iJ O.D 9.0 25.0 10.0
0563 L’ . 30.0 9.0 26.0 12.0
2 50. 3 9.0 26.0 12.
31058 1600.0 11.0 24.5 3.8
31368 1370.0 13.) 27.3 11.3
31763 860.3 5.3 26.0 5.0
2048 680.0 3. 26.0 4.2
82148 50.3 3.0 26.6 4.3
32563 500.0 ‘.0 22.3 4.3
32’53 593.0 5.3 23.9 8.3
90288 600.0 6. — 22.0 11.0
91 068 80.0 3.0 20.0 11.5
91568 343.3 7.0 30.3 8.2
902.0 3.3 20.0 9.8
°2563 p 30.0 .8 17.3
91053 1 ’0O.0 7.3 16.5 10.3
1 .03468 1300.3 6.3 16.8 10.3
100748 1650.3 4.4 15.6 1 .0.3
13136 ? 2300.0 6.3 l4 . 10.5
131558 33400.0 6.0 12.3 10.5
102368 13000.3 5.6 10.3 13.8
102568 : :-ooo.o 5.2 8.3 11.0
7200.3 4.8 6.0 1.1.1
113483 6000.0 4.6 4.4 11.5
5000.0 .4 4.0 11.7
11 12 5 9 —000.3 4.0 4.0 12.0
111555 3900.0 .O 4.3 12.0
00.3 3.6 .0 12.0
12558 3900.0 3.3 4.0 12.5
1 .130 5 8 3 1 .00.3 3.1 4.3 13.0
1203 58 3979.3 3.3 . ) l2.
129 56a 2900.0 3.1 4.3 1.3.0
121363 1150.0 3.5 4.0 12.3
121951 1104.0 4.0 4.0 12.5
122063 2204.3 4.4 4.) 12.5
22568 1304.3 5.0 4.0 12.4
122’) 8 1104.0 5.8 ..0 12.3
‘.211.58 11.34.3 5.9 4.0 12.2
94
-------
TABLE VI-5. MODEL OUTPUT IDENTIFICATION BANNER
* $
.5 5* 5 45 *5$
5S* *4* 5$5
* * I
• * * 45$
5$ * $ $ * *
* *44*5 $tI$S 4 *4 4* 5 4*4
• * 4$ * • $ ss *, S 5 * * it t * + * * $4 $ * t * S * $ S * $ $ $ $ * * S $ S S S S * S $ 454 $
5* F l iP INFORMAl ION AN!) ASSISTANCE: 5*
* 4 OR. MIGUE l A. frFOINA,Jfl.
DEPT. OF CIVil ENGINEFRING, UUXF (INIVEPSITY 4 4
5. D(jgIIAj1,N.C • TElEP hONE: 919—684—2414, EXT • 46 •
+ 5 —OR— A.
DR • WAYNE C • (( hIRER 4*
44 OEP’T. OF ENV. ENG. SCIENCES, UNIV. OF FlORIDA 4*
* 5 GAINESvIItI: , FLA. T I IEP I IO IIE : 904—392—0846 *
S 5 5*
5* VERSION: SEPTEMPER, 1918
$5 5$
* 5 5* $ 5$ * 5* $ 5$ * $ * * * $444 * $4 * $ *5
$ *5*4* $ 5 5*44* $
$ * $ * * $
* *4* * $ *4* *
01
*
*
$
5
5 * * *
5
$ *
*
4
4
4
$
*4* $
5*
*44*
*4*
*
5
$
*
$
* 5* *
S
4 $
*
*
-------
TABLE VI-6. SAMPLE COMPUTED VALUES OF THE
AUTOCOREELATION FUNCTION
• ••.••.4•••
•4I•S•$ ø PTSCU?I I LUP3
0,01 O .g2 g
iit1 44
: : : : : :%
—0.00 0 ,0 0.0 0.01 0.00 •0.flO —0.00 —
—0 ,t)fl (1 flO ..o.ol —0.01 —0.61 0.01 0.
O.nt O.of o.oO —0,00 —0.00 .0,00 —0.01 0.
—0.0$ —o.o —0.01 0.0$ 0.00 0.0j 0.0 0.00
0.00 —0,00 —0,01 —0.01 —0.01 —0.01 —0.0 —0.0
—0.00 —0.0 —0.01 —0.0 —0.00 —0.00 —0,0 —0.0
—0.01 —0.0 0.06 _fl.0 —0.01 —A. 01 —0.0 —0.
—0.01 —0.0 —D .01 —0.0 —0.01 —0.01 —0.0 0.
0.02 0.0 0.01 —0,0 —0.00 —0.00 0.0 0.0
0. ’$ 0 .fl 0,1 11 —0.0 —0.01 —0.0 —0.
—0.00 0.0 $ .Ô3 —0.0 —0.00 0.0 0. —
—0,00 —0.00 0.01 0.0 0,0s 0.0 —0. (1 0.
—0.00 —0.00 .0.06 —0,00 *0,01 —0.0 —0. 1 —0.
—0.01 —0.01 .0,01 —0,00 —0.01 0, Al 0. 1 0.
—0.01 —0.00 0.01 0. ’) —0.00 0.00 0. 1 —0.0
0.00 —0.00 0.01 0,0) 0.02 0,0.1 0. 2 0.00
0.00 —0.01 —o.o 0.00 0.01 .0.00 0. 0.0
—0.01 —0.0$ .0.01 —0.01 —0.00 0.0 —0. —0.0
—0.00 —0.01 —0.01 —0.0$ —0.01 —0.0 —0.0 —0.0
—o.o —o.os •o ,ol —0.0$ —0,01 —0.0 0.0 0.0
0.0 —0.00 0.01 —0.00 —0.01 —0.0 —0.0$ —0.0
—0.0 —0.01 —0.01 0. 1 —0.00 —0,00 0.0 0.
0,1 —0.f t) 0.00 —0.00 —0,00 —0.0 —0.0 .0.
—0.01 —0.01 —0.01 —0.01 —0,01 —0.0 —0.0 —0.0
—0,0 —0.01 0,00 0.01 0.00 .0.0 —0.01 .0.01
—0.01 0.01 0.0 —0,01 —0,01 ‘.0,01 “0.01 .0.00
—0.00 0.0? 0.0 0.0? 0.00 —0.00 0.00 0.00
•0.110 —0,0 —0.00 —0.00 0.0$ 0.00 —0.00
—2.60 —0.01 —0.0 Q.flfl 0,09 0 ,j9 0.01 —0.
1J.01 —0.01 —0.0 — .01 —0.01 .0.i 0 —0.Ou —0.
).Ou —0.0 I —0.0 u.00 —0.Q —0.uA —0.00 —0.00
-------
COIR!LOGIAM OP TIN1 5ER1!
:‘:::
[ _1 1. . L.._.._J
0
O.J500
N O. OO0
1 1 1
0 11500 S
1. 1 I I 1 1 1 1 1
:::::
o o —. . ...•.•• $—•+•e.$• •.I$*,..* •s •$ *•e .$ .e.*Ss ••s....•• •s•. —.s s ,* p —.,.e *•,e..•..
-O 1500 1 -I_I_i. iii .) .] I_I
0 0 20.0 110.0 60.0 •0.0 100
10.0 30.0 50.0 7 .0 O.O
NUflOEI OP IlOU LT LAOS
Figure VI-6. Sample Correlogram of the Hydrologic Time
Series
-------
j4jj _ r””””
E
i::: x:i: :: j ::::::: :
.1_I_!.!.
160. lao.. 200.
170. 190.
I
U
I
C
0
p
A
1
1•
I
0
C
0
I
F
V
I
E
H
CORIELOGRIN or tini SERIES
0.N2001—01 — — — —
0.3600 1-01 - -
0,30001-01 1.1. _ _
.1 _1 .1_I_I.
0.10001-01 1_ _! - - --— —-- - I_i
0,12001-01 I i I_I .
1 1 1
0.60001—02 —•
L: L . J .J. I
1 .s • .1. •j.
—0.60001—02 1 — — — —• $ — •‘•
•i • S S 55$
$4 5 S S S
II . 5 5
—0.1200 1—01 —
-0.1fl00E . I .1 .1 --- - .- . -1
110. 130. 150.
1U11019 or hOURLY LAOS
Figure VI—6. (Continued)
-------
TABLE VI-7. SAMPLE DISPLAY OF SUBPROGRAM COMMON
INPUT DATA
4*4*40 444*4 * 44*44fl4 444444*4** **4’*4 4e*t*+** i*** , *4****i#*+**tt44*** 4 *4 *4*44*4*4*444
cOMMON INPUT DAT A FOP. WE I —wrATs iro n ow ANt) DRY—wEATHE r R OW MODE lS * 4
4*4 * * * * *4*44*44 * 4*4 * * *4 * * *4 +4 * 4*444*A*’*i4 *44*44* 444*4* * 4 * ** b+** it*4* * * 4 * * 4*4*4*4*4*4* *
*4 IWWFM=l IDWFM l
4* 6101= ‘*9000.0 ACRES X= 29620.8 FEET F 180000.0 FT**2/I-1OIIR ‘
4* I)X 1.50 ITSAG= I IPRI= 2 IPP?= 3 +4
4* OWl X= 54.62 £35 flW or 325.06 G/l XKIMAXO .0417 1/HOUR *4
4* PCT1P 1 0.10 0.89 0.9S XKLMIN—0.0001 1/FlOOR 1*
to r 0.0 0.25 1.00 CAMMA L 11.990 GAMMA? 0. 7R0 * 4
4* Al ‘1161=1 • 300 AIPIIA2=0. 060 OF 161 —0. 200 OETA? 0 .4 0
\0
¼0
4*44*4 4* ANA lYSIS OF GRIT ICAI DlSSflIVEO OXYGEN IEVF IS IN THE
* 4*44*4* DES MOINES PIVEP ..DOWFISI R E AM. • FROM.. DES MOINES, ICWA *4*4*4* ’
4* WF 1—WEA TIlER FUN NOnE I I MOOT DAT A *4
* * *444 4 * 4 * * * 4* * * *4* * 44*44*4* * * + * * * * * * * * * 4*44+ * * * * * * 44*44*4 * * * * * 4 * * * *4 * * *
* 4 IPRnG • 0 IEIIE — 0 JNS = 0
4* ASlP= 45000.0 ACRES 4* AEON ’- 4000.0 ACRES *
4* l)WOflI)X 3907.50 InS/ROD/lIP *4 FF1.05’ 13. P4 LAS 000—SIDWII’’
*‘ pET ’ 0.0 0.29 0.79 *4
4* ICALC’ 0 1CALCI ’ 1 ICAIC2 1 ICAICX= 2
4 (flR3= I 1 0 15 KW’ 9 IPR4= 5 4*
-------
TABLE VI-8. TYPICAL PRINTED OUTPUT FOR EACH
WET-WEATHER EVENT
‘Cee DATE 5/15/68 HOUR 16 OWH 11 •***
S. .’ RUNOFF DURATION • 3 HOURS • ‘
c C IS ‘CS 5 5 5 * se, S 5 5 5* 5* 5 *5
usc EVENT NI).— 14 •S*
S Cii SC C S* C* c c sss S * * 5 *5*
* ‘UPSTREAM RIVER FLO’ 6 0.00 CFS ** RID 11.00 MG/I. Ci TEMP.— 113.5 DF.r,.CEHT. S* p.r). 7.40 M( /L
5* AVE. URRAN RUNOFF. 314.83 crs *** nor) (OAO • 7505.3 InS/HOUR *SS non CONC. 5•4 MG/I.
•,CI)iIRINATICN— 1 PCTTRT—0.85 RIVER FLOW FRACTN —1.00 PCTTPT PU )IOFF —0.0 •.. AVE. RIVER 1 10W- 714.6 FF5 55*
SCAVE. UPSTREAM PIVERFIOW— 660.0 CFS ‘ 5* ULTIMATE 000— 14.0 MG/I SI. DEPTh — 3.85 FEET *5*
• CR!TlC’tt OEFICIT. 2.71 MG/I OOCONC— 6.67 MG/I. SAT DO ’ 9.38MG/I. INTEGRAL OFF EON- 162.72 MG—HOUR/I *
‘*9.9. DFFICIT— 2.41 MG/I * ‘ 0.)). X 6.913 MU/I ‘**
ii TO 12.26 HOLES $0 5 XC — 16.13 MI. **$ TX 4.27 HOURS * 5 . 0 1ST X — 5.61 3 . 4 5 V 1.93 fp 5 CCC
‘SRfAfRATI’ N cOEFFICIFNT(xK2T)—O.972031.—O1 1/140(115 DEOXYGFNAT1’)N C0FFFICIEMT(XktT 10.25919 O I 1/HOUR
$$C0I4RIN tTI0N 2 PCTTRTO.85 RIVER F lOW FRACTN —1.00 PCTIRT RUNOFF —0.0 • . AVE. RIVER FIIJW 131313.1 irs *5*
C*A UPSTREAM RIVERROW- 660.0 OTS ULTIMATE 000— 18.3 MG/I. • * DEPTH — ‘.24 FEET *55
o **CPITICAL DEFICIT— 3.52 MG/I 000t)NC— 5.87 MG/I SAT 00— 9.38MG/I INTEGRAL DEE EON’ 229.22 MG—HOUR/I *‘*
O w5fl•9• 0FF!C.IT— 2.57 MG/I. 5.5 9.0. X — 6.62 MG/I. 55*
*S Ti — 16.513 hOURS 5* 5 XC — 22.09 341. *5* IX — 4.25 hflh)RS *5 * 0151 X — 5.61 MI. •.e V — 1.95 FPS ‘
**RFAERATI’)N COEFFICIENT(XK2T)—0.136468E—O1 1/140013 OEDXYGFNATICN CflEFFICIENTtXK1T10.252t9 OI 1/HOUR
**CO3iOINATICN— 3 PCTTRT—0.85 RIVER FLOW FRACTN —1.00 PCTTRI RUNOFF ‘0.0 i.e AVE. RIVER FLOW- 730.0 FF5 5*5
**AVF. UPSTREAM RIVERFI.’W 60.0 CFS ** ULTIMATE 1300 16.2 MG/I ** DEPTH — 3.139 FFET *5*
S*CRITICAI DEFICIT— 3.07 MG/I DOCONC— 6.32 MG/I SAT DO— 9.30MG/I INIFURAL DEE EON- 187.137 MG—hOoP/I * S
‘$0.0. I)EFICIT 2.57 MG/I iii 0.0. iJ X ‘ 6.131 MG/i 5*5
•‘ Ti — 13 .67 HOURS 5’’ XC — 17.93 MI. * ‘ TX — 4.26 HOURS .5* N ST X — 5 .61 MI • V = I .91 Fr ’S 5 s
**FFAERATION CIJFFFICIFNT(XK2T)0.96092F—01 1/HOUR OEOXYGENATIT1N CPEFFICIFNTIXK1T)0.25flSO OI 1/Hfl(JR
eiCO ’IIIINATION — 4 PCTTPT—0.85 RIVER FLOW FPACTN —1.00 PCTTRT PUNUFF —o.o *‘* AVE. RIVER FLOW’ 13413.9 CIS $**
**AVE. UPSTREAM RIVERF1OW— 60.0 iFS ii ’ ULTIMATE 1300— 113.2 HG/ I *‘‘ DEPTH — 4.16 FEET
CSORITICAL UFEICIT. 3.48 MG/I 0000NI. — 3.90 MG/I SAT DO— 9.TOMf,/L INTEGRAL DEE iON. 224.06 MGIIOoIP/I
5’D.I). OrFICIT — 2.60 MG/I * 5* 0.0. .4 X — 6.78 MG I *5*
*5 If. — 16.*)6 hOURS iCC XC — 21.34 MI. ‘$* IX — 4.22 HOURS si— 01ST X • 5.61 MI. S* V — 1.95 FPS ‘ ‘S
e*RiAEflATI )N COEFFICIflITIXK2T)O.13059 5E—0 1 1/1 101113 DEOXYGFNATION rMEFEICIFPITI XKI TIO. 25 1 63 EO 1 1/310(115
$*C)MRINATION 3 PCTTPTO.85 RIVER FLOW FRACIN ‘3.00 0011151 RUNflFF =0.0 $* AVE. RIVER 110W- O3.5 CFS *
‘ *AVF. UPSTREAM P1VERFIOW’ 660.0 Cf-S $ 55 UlTIMATE 01)0- 70.0 MG L *.$ DEPTH — 4.213 FEET ‘ ‘
*,CKIIICAI DEFICII 3.133 MG/I DI)CUNC. ’ 3.56 MG/I. SAT DO— 9.38MG/I INTEGRAL OFF FQN= 251.25 MG—HOUR/I. S *
ser).r ). OFF lOll. 7.69 MG/I. 5*5 9,0. X • 6.69 MG/I *5*
Ti 17.11 IIGIIRS *5* XC — 22.61 MI. * 5* TX — 4.21 I$flh)P. 5 *5* 9151 X • 5.1 ,1 MI. * ‘ ‘ V = L.0( F l ’S 5*4
SiRE 4ERATI IN COFFFICIFNTIXK2T) ’O. 13567()F—OI I/HOUR PEDXYGFNAT I ON CHEFF ICIFNTIXKIT ) O.?5 1651—01 1/001115
-------
TABLE VI-8. (Continued)
*$ flIs5fl 1VF( ) OXYGEN I’IlOFILFS rr ’p AU. WASTE INFtCW COMOUJATIflNS IM 1,S) *s
OISIANCE ——> 100 INtERVAlS. EACh INTERVA l • 1.50 MIIFS
EVENT NO. 14 UNITS IN MG/I **
•$*lt+ $*$$*St**$t•****$** ***+ .,.... a.*.***$***,***$*$**$$**** $***•* $$***
C0’IMNAIIflM I:
7.55 7.36 7.1 7.O(i 6.75 6.87 6.00 6.75 6.71 6.69 6.67 6.67 6.68 6.60 6.71 ( .14 6.77 6.00 6.33’. 6.PF
6.9? 6.97 7.01 7.06 7.11 1.16 1.21 7.26 1.31 7.36 7.41 7.46 1.50 1.55 7.60 7.64 7.69 1.74 7. IA 7.33?
7.fl .S 7.91 7.75 1.99 8.02 8.06 8.10 33.13 0.17 13.20 8.24 8.27 8.30 13.33 11.16 8.3 ’ 33.4? 33.45 (3,47 “.50
31.53 33.835 13.51 8.60 13.62 13.64 33.66 8.68 13.70 83.72 8.74 8.76 (3.711 (1.310 8.01 8.83 8.85 8.836 8.P 8.13
33.91 (3.9? 13.93 33.95 (3.76 33.91 (3.933 9.00 9.01 9.02 9.03 9.06 ‘7.05 ‘7.06 9.01 °.O ’ 9.09 7. 10 ‘3.10 7.11
9.1?
CCMI1INP .T 1fl9 7:
7.91 1.56 7.25 6.99 6.76 6.57 6.41 6.27 6.16 6.07 6.00 5.95 5.91 5.833 5.837 5.07 5.33 ( 3 5.A 5.91 5.9’.
5.90 6.02 6.06 6.11 6.16 6.21 6.26 6.32 6.31 6.43 6.49 6.55 6.61 6.67 6.73 6.79 6.05 6.01 6.97 7.03
7.0(3 7.14 7.19 7.25 7.30 7.36 1.41 7.46 7.51 1.56 7.60 7.65 7.70 1.7’. 7.78 7.83 7.3)7 7.01 7.”5 7. ’ 0
8.03 8.06 (3.10 0.13 (3.11 8.20 0.23 8.26 11.30 8.32 31.35 8.113 0.41 8.64 3.46 83.49 8.51 (3.54 83.36 83.511
(3.61 11.63 (1.65 8.317 8.69 8.71 (3.73 11.7’. 3.16 0.78 8.80 8.81 8.03 8.84 (3.06 (3.87 8.339 8.90 33.92 (3.51
3 —’ 0. 94
0
I—.
CCI4AINATI0N 3:
7.59 7.31 7.11 6.93 6.17 6.65 6.55 6.47 6.41 6.37 6.34 6.32 6.32 6.72 6.3’. 6.36 6.39 6.42 6.46 6.50
6.54 6.59 6.6’. 6.69 6.74 6.80 6.85 6.91 6.96 7.02 7.07 7.13 7.113 7.26 7.29 7.36 7.40 7.45 7.50 7.55
1.60 1.64 7.61 7.74 7.733 7.132 7.87 7.91 7.95 7.99 8.03 8.01 8.10 8.14 3.17 8.21 8.24 8.27 8.71 31.3’,
8.37 6. 39 8.42 8. 45 8.48 8.50 13.53 0.55 (3.511 8.60 0.62 8.64 8.66 8.69 8.71 (3.73 P. 7’. (3.76 8. 18 (3.110
33.81 0.113 8.115 8.86 (3.083 8.139 (3.91 8.92 8.93 0.09 ‘3.06 0.97 8.98 31.90 9.01 9.02 0.03 0.04 ‘7.05 9.06
9.07
C9M3)INATIO 3 4:
7.134 7.50 7.20 6.94 6.73 6.51 6.39 6.26 6.16 6.07 6.01 5.96 5.03 5.91 5.00 5.91 5.92 5.94 5.96 6.00
6.03 6.08 6.12 6.11 6.22 6.233 6.33 6.3 6.45 6.51 (‘.57 6.63 6.69 6.75 6.131 6.137 6.93 6.°8 1.94 7.10
1.16 1.21 1.27 7.32 1.31 7.47 7.613 7.51 7.57 7.62 7.67 7.71 7.76 7.130 7.114 7.89 7.93 7.97 11.01 8.04
8.08 (3.12 8.15 8.113 8.22 (3.25 0.28 ( 3.31 8.34 ‘3.37 ‘3.40 8.43 33.45 8.413 33.50 8.53 33.55 0. 7 33.60 33.6?
8.64 8.66 8.(.A 31.70 (3.72 (3.76 8.76 8.77 8.79 8.81 8.832 8.84 (3.83 , 8.337 ‘3.083 0.07) 83.91 8.03 (3.34 13.05
8.96
CCM13l IATl) I 5:
7•03 7.53 7.10 6.138 6.63 6.40 6.22 6.06 5.03 5.82 5.73 5.67 5.62 5.59 5.57 5.56 5.56 5.57 5.59 5.62
5.66 5.70 5.74 5. 79 5.834 5.91) 5.95 6.01 6.07 6.14 6.20 6.26 6.33 6.30 6.46 ( 157 6.59 6.65 6. 72 6.76
6.334 6.90 6.96 7.02 7.00 7.14 7.20 7.75 7.31 7.13, 7.41 1.46 7.51 7.56 7.61 7.66 7.70 7.15 1.19 7.133
7.330 7.97 7.96 7.99 8.03 8.07 8.10 33.16 8.17 1.21 (3.26 8.27 8.30 8.13 13.36 33.19 8.41 8.64 (3.41 33.49
8.5? 33.54 (3.56 8.59 0.61 8.63 0.65 3.61 (3.69 83.71 8.73 (3.15 11.71, 13.713 13.130 13.331 (3.333 ‘3.135 33.136 8.11 (3
8.119
-------
OISS ’1LVFI) OXYGEN PP )CItFS J”? (=3 1=1
I) 9.200 4—4—————’— — - --——-—— ‘————4— — —4-—•
I I I I I I I I I I I 1111
S I I I I I I I I I 111111333333
S I I I I I I I I I 111111333333 4’
1 I I I I I I I I 11111 73113 I 44464455
(D I R..’330 4- 4- 4- + 4——till l—3333 4444235555—4
V I I I I I I I 1111 3313 I 444442 555c I
C I I I I I I 111 I 133 14’.42?55 5 3I I
1) I I I I I I 1111 3333 44442 595 I
I I I I I I 111 333 I 44422 c55 I
IJ 9.460 4- + 4- 4- 4- + l1— .——13 444422—555+
X I I I I I I iii 333 4422 555 f I
V I I I I I 11 331 44421 35 I I I
H, C l) C, I p t i lii 313 I 4422 555 I I I
F I I I I I 11 I 13 I 442255 I I I I
8.090 + + tj———#33 442——55——— . 4 4 +
I I I I Ii i 33 44233 I I I
C I I I Ill III 442155 I I I I
6 I I I 1 3 I 44255 I I I I
N I I I I lii 33 1462251 I I I I
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Ut t IS I I II 3? 442I 5 I I I I I
P I I I 11 331 4225 I I I I
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15.0 75.0 105. 135.
0! S1A ICF !)OWNSTEF.A1I, NI IF 5
-------
MAIIM(JfI — 16.00000
flI$ HUM — 0.0
(OUTPUT BC LEO D l i 1.0 01
0 4
0.0 0.2 0.5 0.7 1.0 1.2 1.5 1.7 2.0 2.2 2.5
DO COlIC ?UQ.* 4—4 I —, 4—t 4—, CUM. TBEQ.
0.0 1.600 I.,I,*s,.II,**.* *I,,.,*ee4*4*.,*,.S.., i. oo
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1.50 0.500 I*• $* ’ 4’e I I I I I I I 2.700
2.00 0.400 1’**•*$*4 I $ I I I I I I 3.100
2.50 0.200 I”’I I I I I
3.00 0.500 je’**.**.’.s. I i I
3.50 0.1001’ ’ 3 I I I I I I I I 3.900
4.00 0.200 I”**I I I I I I I 4.100
‘4.50 0.500 II’ • ’4’ *4 I 3 I I 1.600
3.00 0.100 1*4 I I I I I I 3 4.700
5.50 O . 1 00 [ * 4 I I I I I I I 4.000
6.00 0.200 X” ’4I I I I I I I I I 5.000
6.50 0.100 Z ’4 I I I I I I 5.100
7.00 0.0 ‘ I I I I I I I I I 5.100
1.50 0.0 4 I I I I I I I I I I 5.100
0.00 0.100 1* ’ I I I I I I I I I 5.200
0.50 0.1001*4 I I I I I I I I I 5.300
I—. 9.00 0.200 IS”*•I I I I I I I I I 5.500
9.50 0.0 4 I I I I I I I I I 5.500
10.0 0.100 1*4 I I I I I I I I I I 5.600
10.3 0.100 1 ’ 3 I I I I I I I I 5.700
11.0 0.1001*4 I I 5.000
11.5 0.0 • i i 5.000
12.0 0.0 • I I I I I I I 5.000
12.3 0.0 I I I I I I I 5.000
13.0 0.0 ‘ 3 I I I I 5.000
13.5 0.0 • I I I I 5.000
14.0 0.0 • I I I I I I I I I 5.000
14.5 0.0 • I I I I I I I I I 5.000
15.0 0.0 • I I I I I I I I I I 5.U00
•‘ C4ITICAL 0.0. MT3T00 AH ••S
‘.• ,i—2 •*‘ .l—l •*4K ..1 • ‘eL—l
N0RlI LIZI$U PACTOR SB 111)415
Figure VI-8. Sample Output of Critical DO Frequency Histograms
-------
O8M*LXZE0 CIJI4ULLTIY8 J2 k ) L1
100.0 l 1 1111*1 1i 4—• • —, _ e
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0 $ I I I I I I I I I
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0 I I $ I 4 I ii I I I I
() V I $ $ I I I I I I I I
A I I I I 5 $3 I I I I I
1. $ I I I $ I 3$ I I
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0 I I I IS 5$ 31 I
I I I I I S I $ I I
I I I I I IS $3 3$ I I
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I I I I I I I 54 II $
I I I I I I I 55 I I I
I I I I I I $ I S $ I
I I I I I I I I I
0.0 I 4 4 4 + 4 I
0.0 3.00 6.00 9.00 12.4 15.0
1.50 4.50 1.50 10.5 13.5
DO CONCOHTAA?X0II• N0/L 0< > 15.0; 0.5 S?2P9
-------
TABLE VI-9. CHRONOLOGICALLY SORTED WET AND DRY WEATHER EVENTS
LISTING (if COMP SJTF IIET AND tJ1 Y WCAT8EI4 EVENTS AND C0 RESP0ND1NG 1J.U. VALUES AT X 5.61 MILES D0WNSTJ EAM
it4LJiii J3AIE 1i .O
1. 3/ 6/68 9. 19
2. 3/ 1/68 9.93
3. 3/ 8/60 7.33
4. 3/10/68 12.C8
5. 3/12/68 13.31
3/15/68 11.41
7. 3/10/68 7.66
8. 3/19/68 10.16
9. 3/28/68 9 C5
10. 4/ 1/68 9.62
1 1. 4/ 3/68 5.82
12. 4/ 5/68 10.03
13. 4/10/68 9.00
14. 4/12/66 1.40
15. 4/14/68 5.66
16. ‘ . 11 5/ 68 8.32
17. 4/16/68 1.10
18. 4/16/68 7.20
19. 4/17/68 8.36
20. 4/18/68 6.52
2!. 4/19/68 0.61
H 2?. 4/20/ 8 8.98
o 23. 4/22/68 9.12
U, 24. 4/23/68 10.09
25. 4/24/68 9.43
26. 4/25/68 9.61
27. 5/ 1/68 8.42
28. 5/ 5/68 8.22
29. 5/ 6/68 8.21
30. 5/ 1/68 5.65
31. 5/ 8/60 7.53
3?. 5/10/68 7.32
33. 5/11/68 7.12
34. 5/12/68 7.02
35. 5/14/68 3.94
36. 5/15/68 6.69
37. 5/11/ . 8 7.57
5/18/68 7.52
39. 5/20/68 8.44
40. 5/21/68 8.84
41. 5/22/68 6.16
4?. 5/23/68
43. 5/26/60 7.61
44. 5/27/68 8.95
45. 5/2t l/ 8 1.00
46. 5/29/68 8.59
47. 5/30/68 8.22
48. 5/31/68 6.02
49. 6/ 4/68 1.25
50. 6/ 5/68 6.30
51. ( /I0/68 2.69
-------
0! SSOIVFD XVGEN PPIF-It E. J=2 k=3 L=
15.00 • — — — —‘—# • —4_, —‘
I I I I I I I I I I
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I I I I I I I I I I I
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12.00 1— *———+ —4— + — 4—4 —4—ê_ _
I I I I I I I I I I I
I * I I I I I I I I
I I I I I I I I
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10.50 + + +
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I 1 I I $ I I I $1 *
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4.500 4 4 4 4 4
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1.500 + *———1. 4 4 *
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0.0 + + 4- + 4- 4 4 +
1.00 11.0 21.0 31.0 41.0 ci.o
1rj 0
I- .. 0
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0
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6.00 16.0
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SI IftATFD EVENTS .NOT TI 4- ’E —SCAIEO (N C,RAPH (SEE TAOEE ‘i 0VE)
-------
of Level 111—Receiving output.
A review of Table VI-2 is in order at this point. An
analysis of the precipitation time series, depicted in Figure
VI-4 earlier, results in the curve shown in Figure VI-il, which
corresponds to the computer printout of Figure VI-6. At an
hourly lag of zero, the correlation of the discrete open series
is unity because this point on the curve represents the linear
dependence of the data series on itself. The number of obser-
vations (including zero values) totals 7,344 consecutive values,
and lags up to 800 hours were investigated. The first minimum
of the autocorrelation function may be considered to occur at
a lag of 10 hours, where the value of the function is signifi-
cantly equal to zero at a 95 percent probability level. The
physical interpretation is that periods without rainfall for at
least 10 hours separate uncorrelated, and therefore independent,
storm events. Actually any point of the autocorrelation func-
tion which lies outside of the 95 percent tolerance limits
indicated suggests a significantly non—zero correlation between
storm events at that particular time lag. The Des Moines
rainfall record obviously exhibits nonrandom behavior at lags
of 377 hours (‘ 16 days) and 421 hours. ( 18 days) in parti-
cular. Values of the autocorrelation function between lags of
100 to 300 hours and 500 to 720 hours fell between the 95 per-
cent tolerance limits and are not shown.
Similarly, autocorrelation analysis was performed on the
sequence of hourly runoff values generated by STORM from the
rainfall input. The lag—k serial correlation coefficients, r (k),
are plotted against the number of lags in Figure VI-12. The
analytic technique established that the minimum interevent time
of consecutive DWH that separate independent runoff events is
9 hours. The runoff time series is not purely random either.
Linear dependeice is observed at time lags of 377 hours (“-‘ 16
days) and 436 hours ( 18 days), as expected, because of the
high correlation between rainfall and runoff processes. Thus,
only one of either time series need be analyzed to determine a
reasonable minimum interevent time. Based on NOAA records
(Asheville, North Carolina, the total precipitation that fell
over Des Moines, Iowa, during 1968 was 27.59 inches (701 mm).
STORM computed a total runoff of 10.28 inches (261 mm) over a
watershed area of 49,000 acres (19,600 ha) , for an overall urban
area runoff coefficient of 0.37. There were 65 days in the year
during which rainfall was recorded, from which 58 wet-weather
events were defined.
As a basis for comparison, it is appropriate to examine
first the model estimates of DO concentration in the Des Moines
River for conditions assumed to exist in 1968 during periods of
urban runoff :
1. combination M = 5, all waste inputs;
107
-------
Figure VI-il.
00
Lag—k Autocorrelation Function
of Des oines, Iowa, Hourly Rainfall,
1968.
1
LAG K, hours
108
-------
— A - — - — —
-\ N \L_
95Y. T:L
iiTIi Mui: —
3
I i
)0 310 320 330 340 350 360 370 360 390
hours
Figure VI-il.
(Continued)
109
0.2
0 . I
0.0
-0.1
-0.2
400
LAG K, hours
500
-------
hours
Figure VI-12. Autocorrelation Function of Hourly
Urban Runoff for Des Moines, Iowa,
1968.
0.8
MINIMUM INTEREVENT TIME:
9 HOURS OF
DRY WEATHER
0.4
- o z.
9 °! TL
0.0
20
LAG K,
40 50 60 70 80 90 100
110
-------
0.2
0.I i
95% T.L ____ ____ I! ________
0.0 __ jJ J \ 7
-(it.
—0.2—
300 310 320 330 340 350 350 370 380 390 4
hours
0.2-
0.1 k _____________ ________
______________________ 95% T.L
0.0- i-i V’ _ i
95°! T.L
-0.I-
400 410 420 430 440 450 480 470 430 490 500
LAG K, hours
Figure VI—12. (Continued)
111
-------
2. secondary treatment (85 percent BOD removal) of DWF,
J = 2;
3. no stormwater treatment, L = 1;
4. river flow 100 percent of measured flow, K = 3; and
5. the fraction of combined area is 8.16 percent of the
total urban area.
Figure VI—l3 illustrates all waste inflow combinations. The
curves indicate clearly that all combinations including a
substantial amount of wet-weather flow (WWF) result in a drastic
decrease in river minimum DO concentrations. For example, 42
percent of all the wet—weather events throughout the year pro-
duced conditions in the receiving water that caused minimum DO
levels below 4.0 mg/l. Combined sewers contributed WWF from
only 8 percent of the total urban area modeled, yet the BODC
concentration was sufficiently high to inflict an appreciable
reduction in DO levels when compared to DWF sources during
periods of runoff.
Similar cumulative DO frequency curves are computed, for
all possible combinations listed in Table VI-2, by the mathe-
matical model. Figures VI-14 through VI-18 represent the
results obtained by considering all waste inputs (combination
M = 5, Table VI-2) while varying the other parameters. Figure
VI-14 displays the minimum DO frequency curves obtained by vary-
ing the percent of the total urban area served by combined
sewers. There is a substantial, but not drastic, decrease in
water quality when the extreme conditions are compared: an
area served only by separate sewers (0 percent combined) versus
an area served exclusively by combined sewers. The curves
support the theory that total separation of sewers is not the
answer to the control of urban runoff pollution. The curves in
this figure all represent secondary treatment of DWF, no urban
runoff treatment, and full river flow. Figure VI-15 shows the
relative effect of urban stormwater runoff in the upstream
portions of the drainage basin. As explained earlier, this
effect is modeled by reducing upstream river flow to three
different fractions of its actual measured value. DWF is given
secondary treatment (85 percent BOD removal), while WWF is
untreated. Thus, the only flow in the river consists of DWF and
urban runoff when modeling discharge into a dry river bed (K=1,
Table VI-2). Variation of upstream river flow does not reveal
large differences in receiving water quality, as might be
expected, because of the relatively large volumes of stormwater
runoff discharged by the urban area into the river:
1. for all of the precipitation events defined by the
model, upstream river flow was on the average 50
percent of the total river flow; and
112
-------
\.
\ •\
. \\
\\‘\ ..•..• .
‘ •\“\
:
10-
0— • - I - I
0.0 12.0 14.0
PRECIPITATION YEAR OF RECORD’ 1968
DWF TREATMENT RATES 85%(SECONDARY)
WWF TREATMENT RATE’ 0% (NO TREATMENT)
RIVER FLOW’lOO% (OF MEASURED FLOW)
COMBINED SEWER AREA’ 8.16% (OF TOTAL URBAN AREA)
INFLOW COMBINATION
—S—’ 1 IVER FLOW + DWF
RIVER FLOW + DWF + SEPARATE FLOW
RIVER FLOW + DWF COMBINED FLOW
RIVER FLOW + SEPARATE FLOW 4- COMBINED FLOW
RIVER FLOW + DWF + SEPARATE FLOW + COMBINED FLOW
INDICATES EVENTS EXCEEDING DESIRED DO. LEVEL
DISSOLVED OXYGEN CONCENTRATION, mg/I
Minimum DO Frequency Curves for Existing Conditions
in the Des Moines River
100 -P
90
70
d
d
80
Ui
>
0
0
2:
6O
U- i.
C-)
x
Ui 50-
C I )
I —
z
lii
>
tu
n:: 30-
U -s
=
I —
w 20
I —
IL l
H
H
I - . - )
0
2.0
4.0
6.0
8,0
Figure VI-13.
-------
too
PRECIPITATION YEAR OF RECORD’ 1968
\ -s”
‘- -- \\
N. \ \\
\\ \ . .
t S
\
0 2.0
WF TREATMENT RATE 85 % (SECONDARY)
WWF TREATMENT RATE’ 0% (NO TREATMENT)
RIVER FLOW 100°!. (OF MEASURED FLOW)
INFLOW COMBINATION
RIVER FLOW ÷ DWF + COMBINED FLOW + SEPARATE FLOW
COMBINED AREA’
— - — 0°!. (OF TOTAL URBAN AREA)
8.16% (OF TOTAL URBAN AREA)
50°!. (OF TOTAL URBAN AREA)
10004 (OF TOTAL URBAN AREA)
I I
4.0 6.0
I ‘ I ‘ I
10.0 12.0 14.0
8.0
DISSOLVED OXYGEN CONCENTRATION, mg/I
Figure vI—14. Nini urn DO Frequency Curves for Varied
Percent of Cc bined Sewer Area
a 90
z
uj 80
>
( 70
z
0
w
60
C -,
Lu
C l , 50
Lu
4O
cr
Lu
30
F-
Lu
20
I-
I ii
I0
0
‘
114
-------
00 PRECIPITATION YEAR OF RECORD’ 968
DWF TREATMENT RATE’ 85%(SECONDARY)
90 WWF TREATMENT RATE I 0% (NO TREATMENT)
0 . COMBINED AREA I 8.I6% (OF TOTAL URBAN AREA)
N.
w 80 INFLOW COMBINATIONS’
\ \ RIVER FLOW DWF + SEPARATE FLOW # COMBINED FLOW
Z
( 70 ‘ RIVER FLOW.’
0 (OF MEASURED FLOW)
60 - 25 0/ (OF MEASURED FLOW)
X . \----- 100 % (OF MEASURED FLOW)
w
50
I-
w
H 4O- .‘., i
H
uJ
U i
3O
Lu
20
Lu
0
0 I I I
0 2.0 4.0 6.0 8.0 10.0 12.0 14.0
DISSOLVED OXYGEN CONCENTRATION, mg/I
Figure VI—15. Minimum DO Frequency Curves for Varied Percent
of Actual Measured Upstream River Flow
-------
2. this percentage ranged from as low as 6 percent to as
high as 97 percent of total river flow.
For the Des Moines application, and the particular rainfall year
selected (1968) , urban runoff seems to be the key factor in re-
ceiving water critical DO levels. However, an urban area
located very far upstream in a river basin would have a more
detrimental impact on water quality downstream from the urban
area than if the same urban area was located on a higher order
stream within the network.
Figure VI-16 shows the effect of varying the degree of treatment
of DWF while holding the other parameters constant. It can be
inferred that there is no significant improvement of stream water
quality (DO) by upgrading DWF treatment from secondary to
tertiary during periods of wet weather. However, it is clear
that the improvement in minimum DO levels by upgrading DWF treat-
ment from primary to secondary is probably worthwhile: 7 percent
more wet—weather events would exceed a DO value of 4.0 mg/i.
Examination of Figure VI-17 reveals that critical DO levels are
improved appreciably with 25 percent treatment of WWF and
markedly with 75 percent treatment of WWF, while providing
secondary treatment of DWF. The minimum DO frequency curves in
Figure VI—18 compare four treatment alternatives to reduce water
pollution during periods of urban runoff:
1. 95 percent treatment of DWF and no treatment of urban
runoff,
2. 85 percent treatment of DWF and 25 percent treatment
(BOD removal) of WWF,
3. 85 percent treatment of DWF and 75 percent treatment
of WWP, and
4. 85 percent treatment of DWF and no treatment or
urban runoff.
The zero treatment and primary treatment curves are also shown
for comparison, but are not considered acceptable alternatives.
It appears that options i and 4 above result in comparable
critical DO levels in the receiving stream. However, options 2
and 3 result in much more improved critical DO levels.
it is now appropriate to examine the results of applying the
model to periods throughout the year during which no urban runoff
was produced. Dry weather was experienced for approximately
300 days throughout 1968. The DWFM was applied to these periods,
using a daily time step. This is certainly justified since
conditions are more truly steady—state than during periods of
precipitation and subsequent runoff: for example, waste
116
-------
100
80
60
0 2.0
PRECIPITATION YEAR OF RECORD 1968
COMBINED AREA • 8.16% (OF TOTAL URBAN AREA)
WWF TREATMENT RATE 0 % (NO TREATMENT)
RIVER FLOW’ 100% (OF MEASURED FLOW)
INFLOW COMBINATION’
RIVER FLOW + DWF SEPARATE FLOW . COMBINED FLOW
DWF TREATh4ENT RATE
30 % (PRIMARY)
85 % (SECONDARY)
— — 95 ‘7. (TERTIARY)
INDICATES EVENTS EXCEEDING
DESIRED D.O. LEVEL
—T ‘ I ‘ I ‘ -I ‘
4.0 6.0 8.0 10.0 12.0 14.0
DISSOLVED OXYGEN CONCENTRATION, mg/I
Figure VI—16. Minimum DO Frequency Curves for Varied
DWF Treatment
70
0
>
tLI
‘ii
()
U i
U) 50
I—
z
U i
40-
Ui
3O -
U i
20
I—
Ui
10
F’
117
-------
DISSOLVED OXYGEU CONCENTRATiON, m /1
Figure VI—17. Ninirnui DO Frequency Curves for Varied
WWF Treatnent
.% \
••\ ..- —-—-
S.
‘
“
—
S
S.
S
S
S
too.
ogO
0
z
w 8O
C.,
c 7o
z
0
Li
6O
Li
U, 50
I .-
LU
4o .
o.
2O
I -
Li
10•
PRECIPITATION YEAR OF RECORD’ 1968
DWF TREATMENT RATE (SEC0 DARY)
COMSINED AREA 8. 16% (OF TOTAL URBAN AREA)
RIVER FL0W 100% (OF MEASURED FLOW)
INFLOW COU51UATtON
RIVER FLOW. DWF.e SEPARATE F1..OWe CO .iBtNED FLOW
\ WWF TREATMENT RATE’
0% (NO TREATMENT)
\—._-— INDICATES EVENTS EXCEEDING
‘•. DESIRED 0.0. LEVEL
I
\ ‘
S
S
\\ \
\\ \
I 1 I T T’ I
2.0 4.0 6.0 8.0 10.0 12.0 14.0
118
-------
PREC PITATI0N YEAR OF RECORD: 1968
INFLOW CCMBINATX)N’
RIVER FLOW CWF’COMBINED FLOW+SEPARATE FLGN
COMBINED AREAS’ 8.I6 ’. (OF TOTAL URBAN AREA)
RIVER FLOW’ (COT. (OF MEASURED FLoW)
IVF TREATMENT RATE’
WWF TREATMENT RATE’
— 95 T. (TERTiARY) 0 % (NO TREATMENT)
—---. 85 ¶. (SECONDARY) 75 .
85 Ta SECONDARY) 25 •1.
-— 85 T. (SECONDARY) 0 °I. (NO TREJ T) ENfl
O% (PRIMARY) 0 T. (NO TREATMENfl
0% (NO TREATMENT) O%(NO TREATMENT)
——INDICATES EVENTS EXcEEDING DESIRED 0.0. LEVEL
‘ I ‘ ‘ I • .
2.0 4.0 6.0 8.0 (0.0 12.0 14.0
DISSOLVED OXYGEN CONCENTRATION, mg/I
Figure VI-18. Ninimum DO Frequency Curves for Varied Treatrnent
Al ternatives
\
(00
0
90
z
80
0
70
Lu
L i )
60
L i i
- 50
L i i
>
40
Lu
=
Lu
20
I—
Lu
I0
0
0
119
-------
loadings (DWF treatment plant effluent) and river flow do not
vary as much during the day. For the dry-weather simulation
period, upstream river flow was on the average 94 percent of
total river flow, ranging from 82 percent to 99.6 percent. The
results are shown in Figure VI-19. A remarkable 97 percent of
the dry-weather days exceed a minimum DO concentration of 4.0
mg/i. Upgrading of DWF treatment becomes meaningful only if
stream DO standards are set higher than 4.0 mg/i. The Des
Moines River carries a high BOD load upstream of the Des Moines
urban area. This explains, why, even during dry—weather periods
only, a significant increase in the DWF treatment rate does
not result in a corresponding increase in the critical DO levels.
To maintain the proper perspective, it is desirable to view the
effects of urban runoff on an annual basis, not just during
periods of wet weather. The frequency curves shown in Figures
VI-18 and VI-19 are combined by weighting on the basis of the
number of rainfall days and dry-weather days in the year. The
composite totals are presented in Figure VI-20. For example,
a given stream standard of 4.0 mg/i is exceeded 90 percent of
the time for existing conditions in Des Moines, Iowa, throughout
the year 1968. A significant amount of treatment (75% BOD
removal) of WWF in addition to secondary treatment of DWF
results in critical DO levels such that the same stream standard
is exceeded 97 percent of the days in the year. Annual DO
duration curves tend to mask the impact of shock loads of
organic pollutants discharged during periods of urban runoff.
A few extended violations of stream DO standards may cause
anaerobic conditions resulting in fish kills and proliferation
of undesirable microorganisms.
The effect of individual storm events on receiving water
oualitv may be viewed in terms of the more traditional dissolved
oxygen sag curves. The spatial distributions of DO concentra-
tions for a distance of 150 miles (240 km) downstream from the
point of waste discharge are illustrated in Fiaures VI-21 and
VI-22 for wet weather events No. 14 and No. 31, respectively.
Each graph illustrates the curves which correspond to waste in-
f low combinations (M) described earlier in Table VI-2. Event
No. 31 exhibits a more drastic difference in DO sag for those
combinations which include significant amounts of urban runoff.
The integral of the DO deficit ecuation over all time,
ecruations (IV-48) and (IV-49), has been suggested as a measure
of the relative effect of one waste source versus another.
Denoted at V, the volume of DO deficit, this parameter is com-
puted for each treatment option during both wet- and dry-weather
periods. The average values obtained are given in Table VI—lO.
The results indicate the same ranking of the treatment alterna-
tives as suggested by the curves in Figure VI—20, from a water
quality viewroint. This implies that the integrated DO deficit
V, may provide a simple method of comparing the impact upon
120
-------
:00-
SIMULATION PERIOD’
DRY WEATHER DAYS OF 1968
WASTE INPUT’
URBAN DWF + UPSTREAM SOURCEST
RIVER FLOW 100% OF MEASURED FLOW
DWF TREATMENT RATE*
95 % (TERT1ARY)
- 85% (SECONDARY)
3O”J (PRIMARY)
0% (NO TREATMENT)
INDICATES EVENTS EXCEEDING
DESIRED D.O. LEVEL
\ _ xt
0
DISSOLVED OXYGEN CONCENTRATION, mg/I
Figure VI—19. Dry—Weather Minimum DO Frequency Curves for
Varied DWF Treatmeht Alternatives
I •
I
I —
I —
I -
I •
I
2.0
4.0
6.0
8.0
10.0
I20
14.0
ci 90
a
z
L i i 80
>
( 9
(9 70
0
L ii
u 60-
C-)
L i i
U) 50-
>-
0
40
Lu
•‘z ( 30-
Li
>- 20-
0
0
o o-
N-.
121
-------
100 — --
SIMULATION PERIOD’ 1968
90 ,-•-z WASTE INPUT’ UPSTREAM SOURCES • DWF.
SEPARATE SEWER FLOW. COMBINED SEWER FLOW
a
80 : \ RIVER FLOW - 100% OF MEASURED F W
COMBINED SEWER AREA: 8. 16/. OF URBAN AREA
>
70 DWF TREATMENT RATE’ WWF TREATMENT RATE’
-. 95 % (TERTIARY) 0 % (NO TREATMENT)
C,
z —— 85 % (SECONDARY) 75 %
85 ‘V. (SECONDARY) 25 °I.
tiJ
0 — 30 ‘ V. (PRIMARY) 0 °/.(NO TREATMENT)
L i i 5 . 0 ‘V. (NO TREATMENT) O%(N0 TREATMENT)
60 85 • (SECONDARY) 0 %(NO TREATMENT)
INDICATES EVENTS EXCEEDING DESIRED 0.0. LEVEl.
40
0
_i 20
0
I-
10
0 I
I I
I I
0 2.0 4.0 6.0 ao 10.0 12.0 14.0
DISSOLVED OXYGEN CONCENTRATION, mg/I
Figure VI-20. Annual MinLmu DO Frequency Curves
122
-------
85% DWF TREATMENT RATE, NO WWF TREATMENT, FULL RIVER FLOW
/
/
/
/
Figure VI-21.
Spatial Distribution of Stream DO
Concentrations for Event No. 14
/.
0
2
0
I—
8.O
I—
z
L i i
0
z
0
U
z 7 .O
w
0
>-
x
0
0
w
0
(I)
U)
/
/
/
/
/
,
/
/
,
,
/
/
/
/
IN F LOW
COMBIN AT IONS
MaI -___
Ma2 --___
M-3
M-4
M 5
/
/
5.0
0.0
15.0 30.0 45.0 60.0 75.0 900 105.0
DISTANCE DOWNSTREAM. MILES
120.0 135.0 150.0
123
-------
85 /. DWF TREATMENT RATE, NO WWF TREATMENT, FULL RIVER FLOW
Figure VI-22. Spatial Distribution of Stream DO
Concentrations for Event No. 31
-J
z
0
z
L*i
U
z
0
U
I ii
w
>-
x
0
0
U
0
U)
0
90.0 05.0 120.0 135.0 150.0
O STANCE DOWNSTREAM, MILES
124
-------
TABLE VI-lO. VOLUME OF DO DEFICIT
DWF Treatment Rate WWF Treatment Rate Average V for Average V for Average V
Option (% BOD Removal) (% BOD Removal) Wet Periods Dry Periods Year
(mg-day/i) (mg-day/i) (mg—day/i)
1 95 (Tertiary)
2 85 (Secondary)
3 85 (Secondary)
4 85 (Secondary)
5 30 (Primary)
6 0 (No Treatment)
o (No Treatment)
75 (Biological,
Physical,
Chemical)
25 (Physical)
o (No Treatment)
o (No Treatment)
o (No Treatment)
See equation (IV—48) when longitudinal dispersion coefficient E > 0,
and equation (IV-49) when E = 0.
H
(SI
30.3
6.1
10.4
12.1
7.0
7.9
24.6
7.0
10.1
30.9
7.0
11.3
34.1
12.3
16.2
35.9
15.4
19.1
-------
receiving waters of alternative inDut configurations.
MODEL UTILITY FOP. AREAWIDE WATER QUALITY MANAGEMENT PLANS
Municipal water pollution control alternatives should be
evaluated in terms of pollutant removal efficiency, receiving
water impacts, and associated costs. The true cost—effective-
ness of various treatment strategies can be determined realis-
tically only by a continuous analysis of the frequency of
violation of established water quality standards. Of course,
in the selection of the best control strategy other factors
may become important: 1) recovery of receiving waters from
shock loads generated by urban runoff, 2) local and regional
water quality goals in addition to established standards, 3)
public willingness to pay the additional costs associated with
increased control, and 4) dual use of WWF facilities, as DWF
treatment units during periods of no runoff.
The cost figures shown in Table VI-il represent the
additional expense incurred in providing storage/treatment
beyond that already available with secondary treatment of
DWF and no control of urban runoff (existing conditions for
Des Moines, Iowa). Details of the cost assessment for typical
wet-weather and dry-weather control facilities have been
presented in the nationwide study (Heaney, et al. , 1977). The
Des Moines River stretches for 200 mi (322 km) from the City
of Des Moines to its junction with the Mississippi River and
is generally wide and swift with a broad flood plain. Bottom
material is composed of silt deposits, sand, gravel and rubble
providing numerous habitats for fish and other aquatic life
(State Hygienic Laboratory, 1974). The entire reach is classi-
fied by the Iowa Water Quality Standards such that the absolute
minimum DO level must equal 4.0 mg/i, and 5.0mg/i during at
least 16 hours per day (State Hygienic Laboratory, 1970). The
effects of various control strategies upon the critical (minimum)
DO concentrations of the Des Moines River have been presented
in Figures VI-18, VI-19, and VI—20. Thus, taking 4.0 mg/l
as the standard or basis for water quality comparisons, the
different controloptions may be judged by the following
criteria:
1. total annual cost, and
2. violations of the minimum allowable dissolved oxygen
level.
Table VI-12 summarizes control costs versus DO standard
violations for two advanced waste treatment options, two wet—
weather control options, and existing DWF secondary treatment
facilities. For comparative purposes, two additional treatment
conditions which are not presently acceptable by government
126
-------
H
TABLE VI-li.
DWF TERITARY TREATMENT vs. WWF CONTROL 1
(HEANEY, ET AL. , 1977)
1 Based on 49,000 acres (19,600 ha) of developed urban area with population
approximately 200,000. The total annual cost includes amortized capital
cost (20 yrs, 8%) and operation and maintenance costs, 1975 dollars (ENR
2200); design flow of 35.3 mgd (134,000 cu rn/day) for the DWF facility.
O tions
p
$
Amortized Annual
Capital Cost
(20 yrs, 8%)
Operation
Maintenance Cost
($/yr)
Total Annual Cost
($/yr)
1.
DWF Complete Tertiary
Treatment, No WWF
Treatment
2,158,000
4,132,000
6,290,000
2.
DWF Activated Sludge—
Coagulation-Filtration,
No WWF Treatment
- — -
- - -
1,664,000
3.
WWF 75% BOD Removal,
DWF Secondary Treatment
— — —
— — —
9,293,000
4.
WWF 25% BOD Removal,
DWF Secondary Treatment
- - —
- - -
816,000
-------
TABLE VI-12.
COST-EFFECTIVENESS OF CONTROL
OPTIONS (HEANEY, ET AL , 1977)
H
% Wet—Weather
Options Events 1 Violating
Standard
% Dry Days in
Year Violating
Standard
Total Incremental
Annual Cost 2
($/yr)
Total No.
During Year
Standard is
of Days
that
Violated 3
1.
DWF Complete Tertiary 40
1.5
6,290,000
31
Treatment, No WWF
Treatment
2.
DWF Activated Sludge 40
1.5
1,664,000
31
Coagulation—Filtration
Treatment, No WWF
Treatment
3.
DWF Secondary Treatment 3
2.0
9,293,000
8
WWF 75% BOD Removal
4.
DWF Secondary Treatment, 30
2.0
816,000
26
WWF 25% BOD Removal
5.
DWF Secondary Treatment, 42
2.0
0
33
No WWF Treatment
6.
DWF Primary Treatment 4 , 50
3.0
—1,438,000
42
No WWF Treatment
7.
No DWF Treatment 5 , 53
7.0
—1,843,000
55
No WWF Treatment
1 Defined by a minimum interevent time of 9 DWH.
2 1fl addition to control costs for existing conditions (option 5).
3 Based on a minimum allowable DO concentration of 4.0 mg/i.
4 Savings incurred by reducing DWF treatment of trickling filter plant of 35.3 mgd (1.55 cu m/sec).
5 Savlngs by completely eliminating treatment.
-------
regulation are present. Detailed process flow charts for the
DWF facilities are included in the nationwide assessment
(Heaney, et al. , 1977). Results of the simulation and the
economic evaluation reveal that:
1. Since both types of tertiary treatment remove
essentially the same amount of BOD 51 option 1 is
justified over option 2 only when nutrient removal
is necessary;
2. option 4 is preferred over any form of advanced
waste treatment;
3. option 3 is attractive because it causes the least
amount of damage to the receiving stream, but it
is the most expensive alternative; and
4. any reduction in the degree of DWF treatment for
existing conditions, option 5, results in a sub-
stantial deterioration to receiving water dissolved
oxygen levels and must be weighed against the savings
incurred.
Furthermore, the issue of shock load prevention seems to favor
high levels of WWF control.
The user should be cautioned that the above results were
derived from application of the model to a specific urban area
for a historical record of storm events occurring during a
particular year. However, the usefulness of the methodology
applied should be clear. The success of its application still
relies heavily on the quality of the field data available.
Dissolved oxygen in the receiving water body has been used as
the key indicator of water quality, yet other parameters may
have to be considered depending upon water quality goals. Com-
plex hydrodynamic conditions will require additional, more
detailed modeling efforts. Engineering judgement must be
exercised carefully in the interpretation of model output and
the importance of verification procedures cannot be overempha-
sized. The model is a user assistance tool in the preparation
of water quality management plans, not a decision—maker by
itself.
129
-------
Figure VI—23.
Input Data Card Deck for
Level Ill-Receiving
CONTROL CARDS
(ICORR, IWWFM, IDWFM, IPROG, IFILE, JNS)
130
-------
TABLE VI—13. INSTRUCTIONS FOR DATA PREPARATION 1
Card
Card
Variable
Group Format Columns Description Name
Control card: one
card, which indi-
cates which sub-
routines are to
be called and also
specifies input
data options.
Card 1 615
1-5 Command to execute ICORR
autocorrelation
subroutine for
event definition
= 1; = 0, user
must specify mini-
mum interevent time
(see Card 10)
6-10 Command to execute IWWFM
wet—weather flow
model (main pro—
grain) = 1; other-
wise, = 0.
11-15 Command to execute IDWFM
the dry weather
flow model (sub-
routine DWFM) = 1;
otherwise, = 0.
1 Superscripts denote footnotes at the end of this table.
131
-------
TABLE vI-13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
16—20 Specifies input IPROG
data options.
= 0, input from
cards only;
= 1, input from
cards and
SWMM;
= 2, input from
cards and
user—created
data set, e.g.
from STORM
output.
21—25 Specifies data IFILE
set unit number.
= N, integer unit
number re-
quired if
IPROG > 0,
set by user
in accordance
with computer
system;
= 0, no input from
data set.
26-30 SWMN input junction JNS
number, required if
IPROG = 1 (see
Section V)
= 0, otherwise.
132
-------
TABLE VI-13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
II Autocorrelation
analysis of hydro-
logic time series,
skipped if ICORR=
0; variable number
of cards.
Card 2 215
1-5 Total number of N
data points, equal
to number of
consecutive hours
within specified
simulation time
period, maximum
value = 8760.
If > 8760, user
must re—dimension
array X in sub-
routine CORREL.
6-10 Number of hourly NLAGS
lags, usually 10%
of value for N
above, maximum
value = 800. The
MIT is well-defined
for this value even
if N is increased.
Card 3 Al 1 Key indicating IKEY
wet or dry weather
hours; = W if wet
weather data typed
on rest of card;
= D if dry weather
hours follow.
133
-------
TABLE VI-13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
15F5.0 6-10 If IKEY = W, type DUMMY(J)
11-15 up to 15 consecu-
16-20 tive hourly wet
weather rainfall or
76:80 runoff values. Units
may be arbitrarily
chosen by user; for
example, hundredths
of an inch.
If there are more
than 15 consecutive
wet weather hours,
repeat the wet-
weather format of
this card until all
hourly values have
been listed.
If IKEY D, then
type the number of
consecutive dry—
weather hours in
columns 6—10, leav-
ing the rest of the
card blank.
NOTE: Card 3 may be
repeated until all
dry and wet weather
events have been
listed chronologic-
ally, with each dry-
weather card being
separated by one or
more wet-weather
cards.
( Blank card is required to end Card Group II)
134
-------
TABLE VI-l3. (Continued)
Card
Card
Variable
Group Format Columns Description Name
III Input data common
to both wet-weather
and dry-weather flow
models; four cards.
Group is skipped if
both IWWFM and
IDWFM = 0.
Card 4 315 1-5 Command to plot ITSAG
composite event
DO profile at
end of program.
= 1, if plot de-
sired.
= 0, otherwise.
6-10 Command to select IPR1
DWF treatment rate
combination (sub-
script J in MAIN)
to be printed :
must be either 1,
2, or 3. Example:
= 1, primary;
= 2, secondary;
= 3, tertiary
treatment rate.
11-15 Command to select IPR2
upstream river flow
fraction combination
(subscript K in MAIN)
to be printed :
must be 1, 2, or 3.
Card 5 FlO.0
1-10 Total area of urban ATOT
catchment, acres.
135
-------
TABLE VI- 13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
F10.2 11-20 Any distance X
downstream for
which dissolved
oxygen concen-
trations are to
be computed, miles.
F15.0 21—35 Longitudinal dis— E
persion coefficient,
ft. 2 /hour. 2
4F10.0 36-45 Average wastewater DWFX
(DWF) treatment
plant flow rate,
cfs.
46—55 Wastewater treat- DWFLO
ment plant BOD
in fluent concen-
tration, mg/l.
56-65 Upper bound XK1MAX
for deoxygenation
rate constant of
carbonaceous BOD 5
@ 20°C, hour’.
66-75 Lower bound for XK1MIN
deoxygen ati on
rate constant of
carbonaceous BOD 5
@ 20°C, hour 1 .
F5.2 76-80 Spatial increment DX
(100 segments of
length DX) at which
DO concentrations
are computed in
136
-------
TABLE vi-13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
receiving body of
water per event,
miles.
NOTE: This parameter
should be used if
either or both
ICALCX on Card 10 or
ICALCD on Card 14 are
greater than 0. If
both of these para-
meters are 0, then
leave DX blank.
Card 6 6F5.0
1-5 Three values of TRTPCT
6-10 BOD 5 fraction
11-15 removed by DWF
wastewater treat-
ment facility,
dimensionless.
Example: 0.85
for 85% BOD
removal.
16-20 Three fractions RF
21—25 of measured
26-30 upstream flow,
dimensionless.
Example: 0.50
for upstream
flow which is
50% of actual
measured value.
137
-------
TABLE VI-13. (Continued)
Card
Groups
Format
Card
Columns
Description
Variable
Name
Card 7
6F6.
0
1-6
Regression
coefficient, 3
dimensionless.
ALPHA1
7-12
Regression
coefficient,
dimensionless.
ALPHA2
13-18
Regression
coefficient,
dimensionless.
BETA1
19-24
Regression
coefficient,
dimensionless.
BETA2
25-30
Regression
coefficient,
dimensionless.
GAMMA1
31-36
Regression
coefficient,
dimensionless.
GAMMA2
Iv Wet-weather flow
model data require-
ments; variable
number of cards.
Group is skipped
if IWWFM = 0.
138
-------
TABLE VI-13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
Card 8 20A4 1—80 Two title cards HEADNG
20A4 1-80 which print de-
sired heading,
such as urban
catchment iden—
tification, on
wet—weather flow
model output; if
none, insert two
blank cards.
Card 9
4FlO.O 1-10 Urban area served ASEP
by separate sewers,
acres.
11-20 Urban area served ACOM
by combined sewers,
acres.
21-30 Total hourly BOD DWBODX
load from munici-
pal (DWF) waste—
water, lbs BOD/
hour.
31—40 First flush factor, FFLBS
lbs BOD/DWH pre-
ceding first hour
of runoff of each
wet weather event. 6
3F5.O 41-45 Three fractions TPCT
46-50 of BOD 5 removed by
51-55 WWF treatment
facility, dimen-
sionless.
Example: 0.75
139
-------
TABLE VI-13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
for 75% removal
of BOD 5 .
Card 10 815 1-5 Minimum interevent MXT
time, hours.
(not required if
ICORR = 1)
6-10 Control option ICALC
command which
identifies
whether urban
runoff BOD
washoff values
are supplied to
the model as
concentration or
mass rate.
= 1, BOD
concentration.
mg/l
= 0, BOD mass
rate, lbs/hour.
11-15 Control option ICALC 1
command for DO
freauency histo—
riram (subroutine
PLOT).
= 0, no plot;
= 1, critical DO
frequency
histogram is
plotted;
= 2, DO frequency
histogram (at
distance X
downstream) is
140
-------
TABLE VI-13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
plotted.
16-20 Control option ICALC2
command to plot
cumulative DO
frequency curves.
= 1, subroutine
MGRAPH called;
= 0, no plot.
21-25 Command to select IPR3
WWF treatment
rate combination
(subscript L)
to be printed .
(value must be
either 1,2, or 3)
26-30 Command to print ICALCX
matrices and plot
DO concentration
for each wet—weather
event at 100 incre-
ments of DX miles
downstream (see
variable DX on Card
5):
= 0, no plot;
= 1, print matrices
of 100 DO values
for all 5 inflow
combinations
(subscript M)
= 2, print matrices
and plot profile
of 100 values
for all 5 inflow
combinations;
141
-------
TABLE VI-13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
= 3, print all
matrices, but
plot only DO
profile for
combination
M = 1;
= 4, print all
matrices, but
plot only DO
profile for
combination
M = 2;
= 5, print all
matrices, but
plot only DO
profile for
combination
M = 3;
= 6, print all
matrices, but
plot only DO
profile for
combination
M = 4;
= 7, print all
matrices, but
plot only DO
profile for
combination
M=5.
31-35 FORTRAN unit number IDISKW
for wet—weather
scratch data set,
needed only if
ITSAG = 1; must be
matched in the job
control cards by
142
-------
TABLE VI-13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
the corresponding
file specification
for the user’s
computer system.
36-40 Command to select IPR4
one of 5 inflow
combinations
(subscriptM) to be
printed out on
composite DO pro-
file; needed only
if ITSAG = 1; must
be 1, 2, 3, 4, or 5.
Card 11 This card is read
if IPROG = 0,
input from the
cards only. The
program inter-
polates linearly
between the values
on successive
cards of Card
Type 11. Repeat
Card 11, if data
is available, for
each hourly urban
runoff event.
16 1-6 Six-digit integer IDATE
number identifying
month/day/year
corresponding to
input data on card.
Example: 083168
refers to
August 31, 1968.
143
-------
TABLE VI-13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
215 11-15 Time of day IHOUR
(24 hour clock)
identi fying
hour of the day
corresponding
to input data
on card.
Example: 13
refers to 1:00 P.M.
16-20 Number of IDWHX
dry-weather
hours (DWH)
preceding an
event.
3F10.0 21-30 Stormwater runoff WWFX
from urban catch—
ment, cfs.
31-40 Urban runoff BOD 5 URBOD
IF ICALC = 1
(see Card 10),
expressed as
concentration,
mg/l. If ICALC = 0,
expressed as mass
rate, lbs/hour.
41-50 Receiving water QX
discharge (stream-
flow) , cfs.
3F5.0 51—55 Initial receiving BODX
water BOD 5
concentration,
mg/l.
144
-------
TABLE VI-13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
56-60 Receiving water T
temperature, °C.
61—65 Initial receiving C
water dissolved
oxygen concentration,
mg/i.
Card 12 This card is read if
IPROG = 1, input from
cards and SWMM tape. 7
16 1-6 Same as in Card 11. IDATE
215 11-15 Same as in Card 11. IHOUR
16-20 Dummy variable, the IDUMNY
dry weather hours
preceding an event
are determined from
continuous SWMM input
tape. (No card
input required for
this variable)
3F10.0 21-30 Dummy variable, DUM
stormwater runoff
if obtained directly
from SWMM input tape.
(No card input re-
quired for this
variable)
31-40 Dummy variable, DUM
BOD 5 mass rates or
concentrations are
read from SWMM input
tape. (No card
input required for
145
-------
TABLE VI-13.
(Continued)
Card
Card
Variable
Group Format Columns Description Name
this variable)
41-50 Same as in Card 11. QX
3F5.0 51-55 Same as in Card 11. BODX
56-60 Same as in Card 11. T
61-65 Same as in Card 11. C
Card 13 This card is read
if IPROG = 2, input
from cards and user—
created data set. 8
16 1-6 Same as in Card 11. IDATE
215 11—15 Same as in Card 11. IHOUR
16-20 Same as in Card 11. IDWHX
3F10.0 21-30 Dummy variable, DUM
stormwater runoff
read from user—
created data set.
(No card input
required for this
variable)
31-40 Dummy variable, DUM
BOD 5 mass rates
or concentrations
read from user—
created data set.
(No card input
recTuired for this
variable)
146
-------
TABLE vi-13. (Continued)
Card
Group
Format
Card
Columns
Description
Variable
Name
41-50
Same as
in Card
11.
QX
3F5.O
51-55
Same as
in Card
11.
BODX
56-60
Same as
in Card
11.
T
61-65
Same as
in Card
11.
C
16
1-6
Sentinel
required
card in
= 999999
value,
as last
group.
IDATEF
V Dry-weather flow
model data require-
ments: variable
number of cards.
Model operates on
a daily (24—hour)
time step.
Card 14 615
1-5 Number of dry ND
days to be
simulated.
6-10 Control option ICALC3
command for DO
frequency histo-
gram (subroutine
PLOT).
= 0, no plot;
= 1, critical DO
147
-------
TABLE VI— 13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
frequency histo-
gram plotted;
= 2, DO frequency
histogram (at
distance X
downs tre am)
plotted.
11-15 Control option ICALC4
command to plot
cumulative DO
frequency curves.
= 1, subroutine
MGRAPH execu-
ted;
= 0, no plot.
16-20 Command to print ICALCD
matrices and plot
DO concentration
for each dry—
weather event at
100 increments of
DX miles downstream
(see variable DX
on Card 5):
= 0, no plot;
= 1, print matrices
of 100 DO
values for all
3 DWF treatment
rates (subscript
J);
2, print matrices
and plot profile
of 100 values for
all 3 DWF treat-
ment rates;
148
-------
TABLE VI-13. (Continued)
Card
Card
Variable
Groups Format Columns Description Name
3, print all
matrices, but
plot only DO
profile for
treatment rate
J = 1;
= 4, print all
matrices, but
plot only DO
profile for
treatment rate
J = 2;
= 5, print all
matrices, but
plot only DO
profile for
treatment rate
J = 3;
21-25 Command to select IPRD
upstream river flow
fraction (subscript
K) to be printed with
D.O. profile down-
stream; needed only
if ICALCD 0.
(Value equals 1, 2,
or 3.
26—30 FORTRAN unit number IDISKD
for dry-weather
scratch data set;
needed only if
ITSAG = 1; must be
matched in the job
control cards by
the corresponding
file specification
149
-------
TABLE VI-13. (Continued)
Card
Card
Variable
Group Format Columns Description Name
for the user’s
computer system.
Card 15 Receiving water
quantity and quality
parameters.
16 1-6 Six-digit integer IDATE
number identifying
month/day/year
corresponding to
input data on card.
4F10.O 11-20 Receiving water RFLOW
flow (upstream),
cfs.
21-30 Initial receiving RBOD
water BOD 5
concentration,
mg/i.
31-40 Receiving water RTEMP
temperature, °C
41-50 Initial DO RDO
concentration
upstream from
waste source,
mg/i.
NOTE: Repeat Card
15 according to the
number of dry days
(ND) specified by
the user in Card
14.
150
-------
Table VI—13. (Continued)
2 See Table VI—2 for typical range of values, from streams
to tidal rivers and estuaries.
3 user may determine these coefficients from available data,
or assume reasonable values and adjust during the calibra-
tion process. The coefficients are related to velocity and
discharge by:
U = c 1 (IV—40)
4 Determined as above, these coefficients are related to
depth and discharge by:
H = i (IV—41)
5 Determined as above, these coefficients are related to
deoxygenation rate constant carbonaceous BOD 5 @ 20°C
and depth by:
K 1 = H 1 2 (IV—37)
6 example calculation for this factor is provided in
Section VI.
7 lnterfacing of this model with continuous SWMM is discussed
in further detail in Section V.
8 When IPROG > 0, IFILE is required and specifies the input
device number. The data read from the data set correspond
151
-------
Table VI-13. (Continued)
to WWFX (stormwater runoff, cf s) and URBOD (either BOD mass
rate in lbs/hour or concentration in mg/i) , respective y:
using a FORMAT (F8.1, 2X, FlO.1) specification. Refer
to program statements MA1N3390 and MAIN3400, Appendix B.
NOTE : The user must insure that program statements MAINO39O
and MAINO400 (corresponding to FORTRAN statement
numbers 1 and 2, respectively)identify the appropriate
reference numbers for card input (INPT) and printer
output (lOUT). For operation through the Triangle
Universities Computation Center (TUCC) and the Duke
University Computation Center (DUCC), they are:
INPT = 1
lOUT = 3
Other installations often use:
INPT = 5
lOUT = 6
152
-------
SECTION VII
ABBREVIATIONS AND SYMBOLS
Area served by combined sewers, acres
A Area served by separate sewers, acres
A Mean water surface area between Y 1 and
At Total area of catchment, acres
AR Urban area runoff, inches per hour
Infiltration decay coefficient
Infiltration decay coefficient for the recovery
curve
ct 1 ,ct 2 Regression coefficients
B Benthal demand of bottom deposits, ing per 1-hour
BOD Biochemical oxygen demand, mg/i
BOD Mixed BOD concentration in the combined sewer, mg/i
BODd BOD concentration of wet—weather flow treatment
facility effluent, mg/i
BODf DOD concentration of municipal sewage, mg/i
BODm Mixed BOD concentration in receiving water, mg/i
BOD BOD concentration of urban stormwater runoff, mg/i
BODt Hourly DOD concentration of total urban runoff, mg/i
DOD Mixed BOD concentration from sources upstream of
U urban area, mg/i
BOD BOD concentration of treated wet-weather effiuent,
W mg/i
153
-------
BOD 5 Standard BOD test, 5 days at 68°F (20°C), mg/i
Regression coefficients
C Concentration of water quality parameter, M/L 3
C Concentration of dissolved oxygen (DO) in the stream,
mg/i
C Concentration of DO at maximum deficit, mg/i
mm
C Saturation concentration of DO, mg/i
CBOD Carbonaceous biochemical oxygen demand
CRu Composite runoff coefficient dependent on urban land
use
C 1 Conversion factor, pounds per hour to mg/i cfs
D Dissolved oxygen deficit = C - C, mg/i
D Critical (maximum) deficit, mg/i
Dd Detention depth
D Initial DO deficit, mg/l
Dt Water depth at time t
D DO deficit in receiving waters upstream of inf low
U point, mg/i
DO Dissolved oxygen
DWF Dry-weather flow, cfs
DWFCMB DWF contribution from combined sewer area, cfs
DWFSEP Dry-weather flow contribution from separate sewer
area, cfs
DWH Number of dry—weather hours preceding each runoff
event
Water depth after rainfall
Depth after infiltration
Time interval
154
-------
E Longitudinal dispersion coefficient, feet 2 per
second
f Self-purification ratio, K 2 /K 1
cap Infiltration capacity into soil
cap Average infiltration capacity over time interval t
Minimum or ultimate value of infiltration capacity
(at t =
f Maximum or initial value of infiltration capacity
(at t = 0)
Available urban depression storage, inches
FF First flush BOD load, pounds per hour
FFLBS First flush factor, pounds/hour per DWH
Fx(x.) Cumulative distribution function (CDF)
Probability density function (PDF)
Gx(x) Complement of the cumulative distribution function,
expected number of occurrences greater than or
equal to given magnitude
11,12 Regression coefficients
H Stream depth, feet
T Average rainfall intensity over the time step
Average actual infiltration over the time step
k Number of hourly lags
k Pollutant decay constant, directly proportional to
the rate of runoff
Kn Oxidation coefficient of nitrogenous BOD, hours 1
—1
K 1 Deoxygenation constant of carbonaceous BOD, hours
155
-------
K 2 Atmospheric reaeration coefficient, hours’
L Remaining carbonaceous BOD concentration, mg/i
L 0 Mixed BOD concentration in the river, mg/i
(L 0 ) Ultimate first-stage BOD demand, mg/i
M(t ) Cumulative infiltration at time t
p p
n Total number of data points or observations of a
hydrologic process
n Manning’s Coefficient
N Remaining nitrogenous BOD concentration, mg/i
N Total number of observations of event X
Cumulative frequency of occurrence (successive
partial sums) in class interval i
Number of occurrences of event of magnitude x 1
N Magnitude of nitrogenous demand
NBOD Nitrogenous biochemical oxygen demand
P Pollutant after time t, mg
P Oxygen production rate by algal photosynthesis,
mg/i-hour
P Pollutant originally on ground, mg
Hourly rainfall—snowmelt in inches over the urban
area
Px(x ) Probability mass function (PMF)
Q Streamfiow, cfs
Combined sewer flow, cfs
DWF treated effluent, cfs
in Gutter inflow
156
-------
Q Urban runoff carried by the separate storm sewer, cfs
Total (storm plus combined) urban runoff, cfs
Upstream flow, cfs
Wet-weather flow (WWF) treated effluent, cfs
Q Subcatchment outflow
R Hydraulic radius
Rd Fraction removal of BOD achieved by the DWF treatment
facility
Re Algal respiration rate, mg/l-hour
r 1 (k) Sample estimate of lag-k autocorrelation coefficient
for rainfall
Deficit load ratio = DQ/L 0
rQ(k) Sample estimate of lag-k autocorrelation coefficient
for runoff
R Fraction removal of BOD achieved by the WWF treatment
w
facility
3
S Sources and sinks of the substance C, M/L T
S Ground slope
S Invert slope
T Stream temperature, °C
TKN Total Kjeldahl nitrogen
t Time, hours or days
t Elapsed time at which critical deficit occurs, hours
c or days
Hypothetical projected time at which cap = f on the
recovery curve
TL [ r 1 (k)] Tolerance limits at a 95 percent probability level
U Flow velocity in stream, feet per second
157
-------
u Dummy variable of integration
V Subcatchment velocity of flow
V Volume of DO deficit, mg-hours/i or mg-day/i
WWF Wet-weather flow, cfs
x Distance downstream, feet or miles
x Magnitude of hydrologic event
x Discrete data series (observations) of a hydrologic
process
X A hydrologic event
11 Water depths in gutter
158
-------
SECTION VIII
REFERENCES
1. ASCE, Committee on Sanitary Engineering Research, “Solu-
bility of Atmospheric Oxygen in Water,” Journal of Sani-
tary Engineering Division, Proc. ASCE, Vol. 86, No. SA4,
Pp. 41—53, July 1960.
2. Anderson, R. L., “Distribution of the Serial Correlation
Coefficients,” Annals of Mathematical Statistics, Vol. 13,
pp. 1—13, 1942.
3. Beard, L. R., “Statistical Analysis in Hydrology,” Trans .
Am. Soc . Civil Engrs. , pp. 1110—1160, Vol. 108, 1943.
4. Benjamin, J. R., and Cornell, C. A., “Probability, Statis-
tics, and Decision for Civil Engineers,” McGraw-Hill Book
Company, N. Y., 1970.
5. Burr, I. W., “Cumulative Frequency Functions,” Annals of
Mathematical Statistics, Vol. 13, pp. 215-232, 1942.
6. Coiston, N. V., “Characterization and Treatment of Urban
Land Runoff,” USEPA Report EPA-670/2-74-096, NTIS-PB 240
987, December 1974.
7. Davis, P. L. and Borchardt, F., “Combined Sewer Overflow
Abatement Plan,” USEPA Report EPA-R2-73--170, April, 1974.
8. Denver Regional Council of Governments, “Volume II Hydro
Quality Model Report,” Black & Veatch, Denver, Colorado,
1974.
9. Diachishin, A. N., “Dye Dispersion Studies,” Journal of
Sanitary Engineering Division, ASCE, Vol. 89, No. SAl,
PP. 29—49, January 1963.
10. Field, R., Tafuri, A. N., and Masters, H. E., “Urban Runoff
Pollution Control Technology Overview,” EPA-600/2-77-047,
March 1977.
11. Fiering, M. B. and Jackson, B. B., “Synthetic Streanif lows,”
Water Resources Monograph 1, American Geophysical Union,
Washington, D. C., 1971.
159
-------
12. Foster, H. Aidren, “Duration Curves,” Trans . Am. Soc. Civil
Engrs., Vol. 99, pp. 1213—1235, 1934. — ___ _____
13. Harleinan, D. R. F., “One Dimensional Models,” Chapter 3 of
Estuarine Modeling: An Assessment , edited by G. H. Ward
and W. H. Espey, TRACOR, Inc., Austin, Texas, 1971.
14. Heaney, J. P., Huber, W. C., Medina, M. A., Murphy, M. P.,
Nix, S. J., and Hasan, S. M., “Nationwide Evaluation of
Combined Sewer Overflows and Urban Stormwater Discharges,”
Volume II: Cost Assessment and Impacts, EPA-600/2-77-064,
March 1977.
15. Huber, W. C., Heaney, J. P., Medina, M. A., Peltz, W. A.,
Hasan, S. M., and Smith, G. F., “Storm Water Management
Model User’s Manual—Version II,” EPA-670/2-75-017, March
1975.
16. Huber, W. C., Heaney, J. P., Peltz, W. A., Nix, S. J., and
Smolenyak, K. J., “Interim Documentation, November 1977
Release of EPA SWMM,” University of Florida, Gainesville,
Florida, 1977.
17. Hydrologic Engineering Center, Corps of Engineers, “Urban
Stormwater Runoff: STORM,” Generalized Computer Program
723—S8—L7520, July 1976.
18. Hydroscience, Inc., “Simplified Mathematical Modeling of
Water Quality,” Environmental Protection Agency, March
1971.
19. Hydroscience, Inc., “Addendum to Simplified Mathematical
Modeling of Water Quality,” Environmental Protection
Agency, Washington, D. C., May 1972.
20. Ippen, A. T., “Estuary and Coastline Hydrodynamics,”
McGraw-Hill Book Company, N. Y., 1966.
21. Kneese, A. V., and Bower, B. T., “Managing Water Quality:
Economics, Technology, Institutions,” Resources for the
Future, Inc. , Johns Hopkins Press. Baltimore. 1968.
22. Lane, E. W., and Lei, K., “Stream Flow Variability,” Trans .
Am. Soc . of Civil Engrs., Vol. 115, pp. 1084—1134, 1950.
23. Langbein, W. B. and Durum, W. H., “The Aeration Capacity
of Streams,” USGS Circular 542, 1967.
24. Leopold, L. B. and Maddock, T., “The Hydraulic Geometry of
Stream Channels and Some Physiographic Implications,” USGS
Professional Paper 252, 1953.
160
-------
25. Linsley, R. and Crawford, N., “Continuous Simulation Models
in Hydrology,” Geophysical Research Letters, Vol. 1, No. 1,
pp. 59—62, 1974.
26. Manning, M. J., Sullivan, R. H. Kipp, T. M., “Nationwide
Evaluation of Combined Sewer Overflows and Urban Stormwater
Discharges,” Volume III: Characterization of Discharges,
EPA—600/2—77—064c, August 1977.
27. McDowell, D. M. and O’Connor, B. A., “Hydraulic Behavior
of Estuaries,” Haisted Press, Division of John Wiley &
Sons, Inc., N. Y., 1977.
28. Metcalf and Eddy, Inc., University of Florida, and Water
Resources Engineers, Inc., “Storm Water Management Model,
Volume I - Final Report,” USEPA Report 11024DOCO7/7l,
NTIS—PB 203 289, September 1971.
29. Nemerow, N. L., “Scientific Stream Pollution Analysis,”
McGraw Hill Book Co., N. Y., 1974.
30. Pyatt, E. E., “On Determining Pollutant Distribution in
Tidal Estuaries,” Geological Survey Water-Supply Paper
1586—F, 1964.
31. Quimpo, R. G., “Autocorrelation and Spectral Analyses in
Hydrology,” J. Hyd. Div., Proc. ASCE, Vol. 94, No. HY2,
pp. 363—373, March 1968.
32. Riggs, H. C., “Frequency Curves,” Chapter A2, Techniques
of Water-Resources Investigations of the United States
Geological Survey, Washington, D. C., 1968.
33. Searcy, James K., “Flow-Duration Curves,” Manual of Hydro-
logy: Part 2, Geological Survey Water-Supply Paper 1542-A,
Washington, D. C., 1959.
34. State Hygienic Laboratory, “Des Moines River — Limnology
Study,” Report submitted to the Department of Environmental
Quality and the Iowa Water Quality CommissiOn, April 1974.
35. Thomann, R. V., “Systems Analysis and Water Quality Manage-
ment,” Environmental Science Services, 1972.
36. TRACOR, Inc., “Estuarine Modeling: An Assessment,” edited
by G. H. Ward and W. H. Espey, Austin, Texas, 1971.
37. Velz, Clarence J., “Applied Stream Sanitation,” WIley-
Interscience, New York, 1970.
161
-------
38. Vilaret, M., and Pyne, R. D. G., “Storm and Combined Sewer
Pollution Sources and Abatement,” EPA Water Pollution
Control Research Series 11024 ELB 01/71, Black, Crow &
Eidsness, Inc., Atlanta, Georgia, 1971.
39. Yevjevich, V., “Stochastic Processes in Hydrology,” Water
Resources Publications, Fort Collins, Co., 1972.
162
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SECTION IX
GLOSSARY
Antecedent conditions: Initial conditions in catchment as
determined from hydrologic events prior to storm.
Biological treatment processes: Means of treatment in which
bacterial or biochemical action is intensified to stabi—
lize, oxidize, and nitrify the unstable organic matter
present. Trickling filters, activated sludge processes
and lagoons are examples.
Catchment: Surface drainage area.
Combined sewage: Sewage containing both domestic sewage and
surface water or stormwater, with or without industrial
wastes. Includes flow in heavily infiltrated sanitary
sewer systems as well as combined sewer systems.
Combined sewer: A sewer receiving both intercepted surface
runoff and municipal sewage.
Combined sewer overflow: Flow from a combined sewer in excess
of the interceptor capacity that is discharged into a
receiving water.
Conservative: Non—interacting substance, undergoing no
kinetic reaction; examples are salinity, total dissolved
solids, total nitrogen, total phosphorus.
Depression Storage: Anount of precipitation which can fall on
an area without causing runoff.
Detention: The slowing, dampening, or attenuating of flows
either entering the sewer system or within the sewer
system by temporarily holding the water on a surface
area, in a storage basin, or within the sewer itself.
Domestic sewage: Sewage derived principally from dwellings,
business buildings, institutions, and the like. It may
not contain groundwater.
163
-------
First flush: The condition, often occurring in storm sewer
discharges and combined sewer overflows, in which a
disproportionately high pollutional load is carried in
the first portion of the discharge or overflow.
Frequency diagram: Curve which relates the number of
occurrences of events to their magnitude.
Initial abstraction: Initial precipitation loss including
interception and depression storage.
In-system: Within the physical confines of the sewer pipe
network.
Interception: Initial loss of precipitation due to vegetation.
Non—conservative: Substance undergoing kinetic interaction,
assumed to be a first—order reaction; examples are
biochemical oxygen demand (BOD) , coliform bacteria,
dissolved oxygen (DO).
Physical—chemical treatment processes: Means of treatment in
which the removal of pollutants is brought about pri-
marily by chemical clarification in conjunction with
physical processes. The process string generally in-
cludes preliminary treatment, chemical clarification,
filtration, carbon adsorption, and disinfection.
Pollutant: Any harmful or objectionable material in, or
change in, physical characteristic of water or sewage.
Precipitation event: A precipitation event terminates if
zero rainfall has been recorded for the previous specified
time interval.
Primary treatment: Process which removes about 30% of the
biochemical oxygen demand of the waste.
Retention: The prevention of runoff from entering the sewer
system by storing on a surface area or in a storage basin.
Runoff coefficient: Fraction of rainfall that appears as
runoff after subtracting depression storage and inter-
ception. Typically accounts for infiltration into
ground and evaporation.
Sanitary sewer: A sewer that carries liquid and water—carried
wastes from residences, commercial buildings, industrial
plants, and institutions, together with relatively low
quantities of ground, storm, and surface waters that are
164
-------
not admitted intentionally.
Secondary treatment: Process which removes about 85% of the
biochemical oxygen demand of the waste.
Sewer: A pipe or conduit generally closed, but normally not
flowing full, for carrying sewage or other waste liquids.
Sewerage: System of piping, with appurtenances, for collecting
and conveying wastewaters from source to discharge.
Storm flow: Overland flow, sewer flow, or receiving stream
flow caused totally or partially by surface runoff or
snowmelt.
Storm sewer: A sewer that carries intercepted surface runoff,
street wash and other wash waters, or drainage, but
excludes domestic sewage and industrial wastes.
Storm sewer discharge: Flow from a storm sewer that is dis-
charged into a receiving water.
Stormwater: Water resulting from precipitation which either
percolates into the soil, runs off freely from the surface,
or is captured by storm sewer, combined sewer, and to a
limited degree, sanitary sewer facilities.
Surface runoff: Precipitation that falls onto the surfaces of
roofs, streets, ground, etc., and is not absorbed or
retained by that surface, thereby collecting and running
of f.
Tertiary treatment: Process which removes about 95% of the
biochemical oxygen demand of the waste.
Urbanized area: Central city, or cities, and surrounding
closely settled territory, which has a
population of 50,000 or more. Peripheral areas with
population density of 1,000 persons per acre or
more are included.
Urban runoff: Surface runoff from an urban drainage area
that reaches a stream or other body of water or a
sewer.
Wastewater: The spent water of a community.
165
-------
APPENDIX A
STORM Input Data and Load Module Job Control Language
//STOPN JOB DU.D0b.AT4119,DINA,T=t.P50M1.R300KtPRTY2
1/ EXEC ?GMSrO M
//STEPLIB DO DSNCU.D06.A14096.MED INA.STORMI,DLSPSHP,UNITOISK,
II VOL.SEROUI(9B8
//C.FTO6FOOI DO S SOUThA
,/G.FTI1FOOI DO UNIS1SOA,SPAC (CYt.(2,t),RLSE)
//G.FT12FOOI Or) UNIT=syS0A.Sp CE=(CYL..C2.t).Pl SE)
//C.FT 13FOO I DO SXSOUt,DC8:(f%ECF,4=VA.Lp.ECL=133.Bt-I(S1ZE:133)
//G.FT14FOOL Do s soI1r=.’,oc(8Ec M=,A.bREcL=t33.8LysrzE=t33)
i G.F1I5F001 Do sySaur .4,DcB=(RECFM=FA,LRECLs133,BLK51ZE133)
//C.1105F001 DO *
*1 STDPM
STORM RUN ON ENTIRE AREA OF £ ES MOINES
A3 DES MOINES,IQWA
81 1 0 0 1 1
82 42 3 1 680112
CI DES MOINES 5 680308
C2680308 2 6 7 29 9 12 6 2
C268 0318 6 3 1
C268 0403 10 14 1 4
C2690412 22
C2680414 7 12 6 6 2
C268041b 2 2 1 2
C26 0419 9 8 22 4
C2b804 19 4 7 13 3 5 3 3 6 12 24 1 6
C269 0422 lb 2 1 1 t 4 10 18 20 14 14
C268042311 5294371088724
C2690507 2 4 2 1 2 17
C2680 1 17 29
C2680514 12 2
C24805 15 4 2
C268051R 1 2 2 2
C2680522 1 1 6
C2680525 4 i 6 2 3. 6 2 1 2 16
C2680526 12 b 3
C2 68052 9 6 13
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C2b90610 6 9 31 50 13 15 7 4
C2680613 9
C2 680623 2 6
C2b80624 1 59
C2680625 3 3 1 2 1 2
C2680629 46 12 16 16
C2680706 32 28 21
C26H07 08 20 38 47
C2680716 1 10 20 7 3 1
C26 0717 2 4 5
C2e80723 133 2 1 21 2 2 2
C2 680727 13 1 1
C2b8073 1 16 5 3
C2b80804 1 7
C2680807 3 11 13 1
C26 Q8O3 54116 5 1 2
C2680815 1 5
C266o9 16 2 2 2 12
C268 08 18 2 1 I 1 2 9
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C2 O831
C2680903 4 14 14
C2690904 43 Ii 4 lb 2 1 1
C269O90 3 2
C2 dQ920
C2680928 2 3 2
C2b90929 19 14 1 4 4 • 4
C2b81005 1 1 1 4 6
C268 1008 1 6 1
C268t009 20 5 S 9 18 3
C268 1017 18 4
C26 8 1105 1 1
C2631106 1 2 1 2 1 1
166
-------
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C 268 1227
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-------
CflM 40N INPT.IOUT A1N01 1’)
COMMfl!J/SETI/ MIT M IN OO2’)
MATN0O3 )
I BET42,RF(3),DX,TPTPCT(3),ITSAG,IDISKW,!OISKr,,!PP1, 1 0 R2,p’Pi, IPP ,MA!N!fl4O
2 XKIMAX. XK1MIN,GAM? A1 ,GAMM4? TNO O SC)
CPMMON/SET3/ DATA1(31,5,3,3,3),HEADNG(40),PCT(3),FLfl(5, ),FL(5,U,MA!NOO O
I XK2T(5,3),C)U(3),DCJ(5 ,3) ,U(5,3),F(5,3),V(tj,3),TF(5,3),Tr)CT(3) , ATNOD7D
7 uLTL0(5,3,3,3),xG(5,31,POLL2(3,50),pCTn(5, 1),rG(3t ), MA1NOQ 30
I QQ?(50),JINNS(50),CCCJST(5),FI(5,3),F2(5,3),X(5,1),X 1?t ,1), MAIN000C)
4 xJ(5,3),xDG(101),XD,Tx,DTX,DTXCN,00xDx(5,10l),00XOX!(1O1 ), MAINOI Of)
5 XMl?(5,3),H(5,3),DENOM(5,3),XKlT(5,3),flhjp p4y(3971) MATN O11()
fljME ISj0N MONTH(12),JKLY(3),KKEY( 3),LKFY(T3) MAIN O I?fl
INTEGrR STITLE(20),COMKEY(5),TITIFI(2rfl,VT ITI I(5 1),IARFLI(0), MAfl\tfl1 f)
I HEADNG MATNOI4C) CD
DATA CCMKFY/’1’,’2’ .’3’,’4’.’5’/
f)ATA STITLE/’DISS’,’OLVE’,’D OX’ ,’YGF1 ’,’ PRD’,’FTLF’, MAT IO1 f) H
1’: ‘.‘J=I ‘.‘K=I’. ’L=I ‘‘‘ ‘,‘CflMB’,’INAT’,’TflN’, MATP fl)l70 H
7 ‘1 ‘,5*’ ‘I 1ATN0l H
DATA TITLEI/’D ISS’ .’OLVE’ ,’DOX’ ,’YGEN’,’ PP0’,’FIt ’,’S : ‘, MATNO!r40 I
I ‘J=l ‘,‘K=I 1,11=1 ‘.10*’ 1/ MAINO2rV) ‘
DATA YIITL1/’D’,’I’,’S’,’S’,’O’,’L’,’v’,’F’,’O’,’ ‘,‘fl’, MAINO’I’) 0
1 , X . , Y • . G , ‘ E , , N . , , C ‘ . 0 , , N 1 , c ‘ ‘ E , ‘N , ‘ 1’ , • R’ , MA T NO? 7r) CD d
7 ‘A’ ,‘T’.’ 1 , ‘0’, ‘N’ ,’,’ ,’ ,‘M’ .16 1 ,/ ,‘L’ , 1St’ ‘/ MATN02 0
DATA LABEL1/’D!ST’,’ANCE’ ,’ DOW’,’NSTR’,’FArJ,’,’ MIL’, MATN074 ) .
I ‘ES ‘.13*’ ‘/ MATNO7SO
INT GEk FREO(31),TITLE(20),YTITLE(51),1flpf1.(?O) MAYND7(,O ‘.Q H
DATA M0NTH/31,28.31,30,31,3O,31,31,30,31, o,11/ MATNO77()
DATA JKEV/’J=l ‘ ,‘J=2 ‘,‘J=3 ‘/.KKEY/’K=l ‘,‘K=7 ‘,‘K=3 ‘/, MA!NO? V) ‘
1 LK YI’L=1 ‘,‘L=2 ‘,‘L 3 ‘I M y g nr)
DATA TI1LE/’NORM ’,’ALJZ’,’EDC ’,IUMtJLl, ’ATIv ’, ’r: ‘, MT 1OlO’) ‘
I ‘J=t ‘.‘K=l 1,11=1 ‘.11*’ ‘I MAN l )
DATA YTITLE/ ’ ’,’ ‘,‘v ’,’F’,’T’,’ ‘,‘ ‘,‘E’,’A’,’T’,’H’, ’ ’, MA 4O 7’)
I ‘R ‘ . ‘ ‘ , ‘ [ ‘ , ‘V ‘ , ‘ E ‘ N’ . •1 , S , ‘ ‘ . F’ , , ‘C ‘ , ‘ F’ , , I N03 1)
2 D l,*J,INI,I I,I I,SGI,Ijl,IV l,lFl,I I,I e,I *,Ia*, t , L’
‘tJ’,’E’.9*’ MATN0 r) H
DATA LABEL/’D(i C’,’ONCE’ ,’NTPA’ ,’TIDN’,’, MG’,’/L ‘,‘ ‘ <—‘, MA!fl1’ ()
I ‘————‘ ,‘———>‘ ,‘15.O’,’; ‘,‘O.5’,’STFP’,’S ‘,e*’ ‘I TMO ,7O H.
C M INO’ R0
!NPT=l MAT !0 D
2 TflLIT=’
C MAI O4IO
IDISKW=O
IDISKD=0 1ATNO41)
WPITF( IIJUT,410) 1t NO461
410 FOPMAT(////////////) MA I 4 5r
WR1TF IfJUT,412)
412 FOPMAT(IHO,25(lb.), IOX,52H * ***** * * ***** c *MAI’ O47’)
** , IOX,25(1H.)/1X,25(1H.),1OX,5?H * ‘ * * ftAINO4R()
• * * * * ,l0X,?5(1H.)/1X,?5(lk.),lflX, )H * MA INO4flD
• *** * * *** * * * * ***,tflY , ?5(1H.)RX,?5(iMAT Ifl5 )’)
-------
.H.), 1OX,52H * * * * * * * * , 1 V AIN 5I )
.X,25(1H.)/ IX,25(1H•),1OX,52H ***** ***** * ***** ***** *MATND5’r)
•:** *** *** ,l0X,25(1H.)/1X,?5(1H,),72X,?5(1H.)/1X,2S(lH.),7X,5 3MAIN051O
.H **** ***** ***** ***** *** * * *** * * ****‘ ,7X,25(MA!r J054 )
.IH.)f ]X,25(1H.),7x,58H * * * * * * * MATNOS5()
** * * ,7X,25(IH.)/IX ,25( 1H.),7X,58H **** ***
* * * * * * * * **,7x,25f1H.)f1X,?6(lH.),7x, RH *MATNO57 )
• * * * * * * * * * ** * *,7X,25t1H. A!NOcqt)
.I’/1X.25(1H.),7X,58H * * ***** ***** ***** *** *** *MATNDc’)fl
• * *****,7X,25( IH.)//////) MAIN0fO
WRITE(JOuT ,415) M INO 10
415 FOPMAT(35X,55H *******************************************t*******MA1Nfl ?fl
.***f 5X,55H ** FUR INFORMATION AND ASSISTANCE:
./35X,55H ** DR. MIGUEL A. MFDINA,JR. **/15MATNOf 4()
.X,55H ** DEPT. CF CIViL ENGINEERING, DUKE UNIVERSITY
.SH ** DURHAM,N.C. TEIEPHCNF: 919—68 s—2434, EXT. 6f *‘ / 5X, MAiNO6’ O
•55H * —OR— / 5X,55HMAIN()(7n
** DR. WAYNE C. HUBEP *, I MAINO6P ()
.35X,55H ** DEP’T. OF ENV. [ NO. SCIENCES, UNIV. OF F1flRT )A **/ ¼1AJ q
.35X,55H ** GAINESVILLE, FLA. TELFPt-iCNE: 904 —3 2—O 4 6 **I MAINO7OD
•35X,5 H ******************************************************, MATNO71D
. 1//I/f) M A NO7?D
WPITE(Jfl(JT ,416) MATND7 ”)
416 FDPMAT(’ ‘,47X,30(’*’ /’ ‘,47X,’**’,26X,’**’/’ ‘,47X, MATNO74()
‘** VERSION: SEPTEMBER, 1978 **‘,/‘ ‘,47X,’**t,?6X,’**’/1 ‘, MAIN075 )
2 47X, 0(’*’)) Mt TND76D
C MA.TN077()
C MAINfYT9O
C IPROG =0= INPUT FROM CARDS ONLY. MATNO79O
C =1= INPUT FROM CARDS AND SWMM. M AJNQ8 00
C =2= INPUT FROM CARDS AND USER CREATED DATA SET. M.AINORIO
C MAIN0 O
C IFILE =0= NC INPUT FROM DATA SET. MAINOR3O
C =N= DATA SET UNIT NUMBER “N”. (REQUIRED IF T DG.GT.D). MAINOR4O
C MATNO85 O
C iNS = INPUT JUNCTION NUMBER. (PFQUIPED IF IOPOG 1). MAINO8f )
C MAT flR70
C
C>>>>>>>> READ CARD TYPE I <<(<((<( MATNO 9 )
c Mfl NO9O0
READ(TNPT ,420) ICORR ,IWWFM ,IDWFM,IPROG ,IFIIF,JNS MAIN0’ 1O
420 FORMAT(615) MATN09 D
c MAIND9I O
IF UCORR.EQ.1) CALL CORREL MA [ M0()4
C *****************************************s***************************** P40950
C MATN O96O
IF(ICDRR.E0.1.ANC.IWWFM.EQ.O.AND.IDWFM.FQ.0) GO TO c Q99 MATNO97 O
C M4f NO9RO
C MA!N0990
C>>>>>>>> READ CARD TYPE 4 <<<<<<<< MAINJD’) O
-------
C 4A1NLi1()
READ( INPT,422) ITSAG ,IPRI ,IPR2 MATN10 C)
42? FORMAT(315) M AIN1I)3()
14TNj040
C >> >) PEAD CARD TYPE 5 <<(((<(< MATN1 O5 O
C AIN10f0
READ(TNPT,4251 ATCI ,X,E,DWFX ,DWFLC,xKtr’AX,XKIMIN,OX MA 1N1070
4’S FORMAT(F10.O,F1C.2.F15.O,4F10.o,F5.7)
C
C CONVEkSION FROM MILES TC FEET MnhI\’llcrn
C MMN111 )
X=X*5280.0 1ATNU2()
c MAjNl1 ()
c lAT i114 )
C>>>>>>>> READ CARD TYPE 6 <<<<<<<< MATN115 )
C
READ INPT.410) IRTPCT,RF MIt.1N1170
410 FORMAT(6F5.0) ‘ MTNI180
flfl 411 J=1,3 MATNL 19 O
PCTTPT (J)=1.—TRTPCT(J) 4AY ’Jl2 0 )
43 C0NT N JE MAYN1?10
C MA1 41??r)
C MATNI?l0
(>>))>>)> READ CARD TYPE 7 <<<<<<(( MATN1 ?4fi
C MAIN1?50
PEAD(INPT,435,) ALPHA1,ALPHA2,BETA1,BFTA2,GM.4MA1,C MMA? MA!’ Jj7iy)
435 FflPMA’ (6F6.0) MAJN177()
C MATNt? i
WRITF(IQUT,440) MA!N1? ()
440 FORMAT(IHI) MAIN13 00
WRITF( IOUT ,443) 1A!NlT 1O
443 FOPMPtT(IH ,87(1H*)) A1 J1. 1 )
WRITF(IOUT,445) MA IN133O
445 FPRMAT(’ ** COMMON INPUT DATA FCP WET—WFATHF ‘, MAP4I I4()
I ‘FLOW AND DRY—WEATHER FLOW ‘ODELS **‘, MATNt I S’)
WR!TFf IflUT,447) MA!N 6()
447 FORMAT(IH ,87(1H*),//,IH ,87(1H*))
WRITF(IOUT,450) IWWFM,!OWFM A1 3crn
450 FORMAT( IOH ** IhhFM=,I1,1 1H IOWFrd ,!1,6?X,1H **) MA N I3q0
WRITF(JOUT ,455)ATOT,X,E ATNJ.40fi
455 FOR AT(9H ** ATCT=,F1O.1,12H ACRES X=,FlQ.1,11H FFFT F , MA!Ni4l )
.F20.!, IbH FT**2/HUIJR **) M AINI4’ O
WRITE(IUUT,457) CX,ITSAG,IPR1, IPR2 MA!N 1410
457 FORMATI’ ** DX ’,F10.2.5X,’ ITSAG=’,!5,5X, ‘IPRL=’,!5,5X,’ TPQ7 ’, MATN144I)
1 15,TR7,’**’) MATN14 SO
WR!TF(IOUT,46O)CWFX,OWFLO ,XK1MAX ‘i4INI46 ()
460 FORMATI9H ** OWFX=,F15.2,14H CFS DWFLC=,Fl0.7,16H M t /L XK1MA 1ATN1470
.X=,F6.4,18H 1/HOUR iA1N14Rfl
W ITF(1OUT,4 5)TRTP T,XKIMIN MAJN I4qç)
465 FORMATC ILH ** PCTTRT=,3F8.2, ?IX,8H XK1 1r =,F6.4,7H 1/HflIJR,QX,?H**)N ATNl500
-------
I-J
WRITF(LOUT,470)RF,GAMMA I,GAMMA2
470 FORMAT(7H ** RF=,3F8.25X,8H GAMMA1=,F .3,11H
13H **3
MATN151 O
GAMMA? ,F6.3,1 X, A!N1570
MAINIS3 0
WPITE(IUUT,477)AIPHA1,ALPHA2,BETA1,BETA?
MATNI54O
477 FORMAT( IIH ** ALPHAL=,F5.3,IOH ALPHA2=,F’5.1,9H
BETA2=,F5.3,26X,3H **)
WRITF(IOUT.443)
BET 1=,r5.3,9H MATNI55()
MA!N1560
MAINI57I)
C
M T s15R0
IF(IWWFM.NE.1) GO 10750
ATN SQ0
r
MA1N1 00
C******** WET WEATHER FLOW MODEL
AIN1610
C
MAIN167()
C
MATN 1 10
C>>>>>>>> READ CARD TYPE 8 <<<<<<<<
C
MA!N164 )
TN1650
READ(INPT,500) (HEAONG(fl,I=I,40
500 FOPMAT(20A4)
A!N1660
ATN1670
.
MA1N1 R0
C
M4IN1 9O
C>>>>>>>> READ CARD TYPE 9 <<<<<(<<
MAYN I70 0
C
READ(!NPT,505) ASEP,ACOM,DWE 0DX,FFLBS,TPCT
505 F0R AT(4F10.0 3F5.0)
MAT’ 17tfl
MAIN 172Q
1N17 r)
DC 506 L=1,3
PCT(L)=1.—TPCT(L)
ATT !17 )
506 CONTINUE
MA!NI7M)
C
ATN1770
C
M4TNI7R()
C>>>>>>>> READ CARD TYPE 10 <<<<<<<<
C
READ(INPT,51C) MXT,ICALC,ICALCI ,ICALC?,IPR ,ICALCX,I01SKW, IPR( MAI 1 10
510 FORMAT(8 15) MArN1 ’ 0
IF IflISKW.GT.0) REWIND 1 015KW
M4T I1 i
TF(LCORR.NE.11 MIT = MXT
MAINIA4O
TTTIF 1(8)=JKEYUPR1)
AIN1 0
STITE.F(S)=JKEY( IPR1)
TITLEL(9)=KKEY( IPR2)
MA!N1R fl
MATN1 70
STITIE(9) =KKEY(IPR2)
MAIN1RP()
T!TLEL(10)=LKEY(IPR3)
STITLE(10)=LKEY(IPR3)
AIN1 ”)0
MAYN 19 0 0
MA N1 1 0
DO 560 M=I.5
M A IN I9’ O
DCDIST(M)0.0
AiNi030
560 CONTINUE
MA!N 1Q4Q
On 66 11=1,31
MATN19cO
On 5 6 M1.5
MAIN1 6O
00 566 J=1,3
MAT?n97 0
DO 566 K=1,3
‘ AIN1980
On 566 L=1.3
MAT !t9Q0
OATAL(I1,M,J,K,L) 0.0
C
-------
566 CONTINUE MAtN7O1O
C
JNSS = JNS MAIN ’ 30
c MAIN7O4 )
!F(IPROG.NE.1) GO TO 568 MMN2r)5O
‘bIATN?r)6()
C******** SWMM INPUT MA!N?07 0
C
IF(IF!LE.EQ.0) GO TO 9999
JNS = 0 MAIN2 11()
REWIND IF ILE MAIN?1t1
RE4O( TFILF ) (HEADNG( I), 1=1,40) IN21 70
READ(IFILF) NDT,NOUTS,NPOLL,DTT,TZERC,TpTpA MA!N?11
PFAD( IFILE) (JINNS( I) ,I=1.NOUTS) ATN? l4fl
00 567 1 = 1 ,NUUTS MAIN215f)
IF(JMSS.NE.JINNS(1)) GO TO 567 MAIN2I6 D
JNS = I MATN2!.7 0
GO TO 568
567 CONTINUE MAIN21 O
C MA IN?700
IF(JNS.EQ.O) GO TO 9999 MA!N271O
C
568 WRITE(IOtJT,600) MA 1N223 0
600 FORMAT(//////////,1H ,lO0(IH*)) MAIN?240
WRITE(IOUT.6103 (HEADNG(I),I=1,40) MA!N?’ SO
610 rOPMP T(9H ********,2X,20A4,IO’4 ****s***)
WRITF(JOIIT,615) M AIN’?7 0
615 FORMAT( IH ,100(1H*J) MAIN?? 3()
WRLTEtIOUT,635) MAJN2 qO
635 FORMAT(////,1H .72(1H*)) MATN? O()
WRITE( IOUT ,640) MAT ’I? )
640 FORMAT(37H ** WET—WEATHER FLCw MODEL INPUT DATA,33X,3H *) M AI 23?O
WR ITE(IQUT ,643) MA) N71l()
643 FflRMP T(1H ,72(1h*1,// ,IH ,7?(LH*)) MATN?140
WRITE (LOUT ,.44) IPRCG, IF ILE,JNSS MA 1N235 0
644 FPRMAT(11H ** IPROG , 12 ,3X ,RH IFIIF =,13, X,6H iNS =,T5,)X,?H**)MAI’ ?16c)
WR!TF(IOUT ,645) SEP,4CflM MATN?37 0
645 F-OPMAT(9H ** ASEP=,F20.1,1.5H ACRES ** ACC? =,F2O.l,9H AC7 ) S **) MA1 !21 r)
WRITF(IOUT,65 01 0W8 00X,F FLBS MA ) N?19()
650 FORMAT(11H ** DWBODX= ,F15,2,?IH LBS/BOC/HP ** FFL 3S=,F10.2,16H LF SMA!N?4(Y)
• BOD—5/DWH**) MATN74Tf)
WRITF(IOUT,652,) TPCT
657 FORMAT(8H * PCT=,3F8.2,38X,3H **) MA1N243’)
IF(TCOPP.NE.l) WRITE(JOUT,656) MIT MAr 246r)
656 FORMAT(35H ** MIT OR MINIMUM TNTEPEVENT TI ’E=,I4,6H HGUQS,75X,3H *MA!N7450
MAT ,46r,
WPITF (JQUT,66O)ICALC,ICALC I, ICAtC2, ICAICX MAIN 47O
660 FflRMATI1OH ** ICALC , 15,8H ICALC 1=,15 ,8H ICAIC?=,15,8H ICALCX=, M AIN?4 ()
1 15,17X,2H**) M4T J749(
WPITE(IOUT ,665) IPR3, IDISKW,IPR4 MATN’ SO ()
-------
I C ()
I D =0
I L=0
IOWH = 0
IDWHX = 0
I OWHI = 0
IJJMP 0
[ TIME = 0
IT [ MFX = 0
TOATER = 0
IDATF9 = 0
I0WHX 0
IDWH! S = 0
IDWHXP = 0
IHU’JPF = 0
IMOURX 0
ISTAPT = 0
0=0.0
pc= 0.0
PT=0. ()
0 WF=0 .0
WWF 0. 0
RRrm=0.o
TIME 0.0
ASUM1O.0
ASUM7=0.0
ASUM7 =0.0
OWBOD=0. 0
URt BS=0.0
AB O OCB=0.0
AB ODSP0.0
AFICMB=0.0
AFLSEP=0.0
AN AVFL=0.0
SUMCMB=0.0
CKO 0.0
CKRC 0.0
CKP T 0.0
( K0WB 0.0
CKOWF = 0.0
CKWWF = 0.0
rK9. 80D = 0.0
= 0.0
Z = 600. 0
D =X/5280.
MA 12510
MAIN?5?()
M4 1N2510
MA 1N2540
MA 1N25 50
MI TN2560
MAT N? 570
MA I N 2580
‘ATN2590
MAT N? l0
MA IN?6 ‘0
MAT P42610
MA [ P426 40
MAT N’6 50
MAT P42f Y)
MA! N26 70
MA TN?6 8’)
MA TN?69r)
MATN27 0 0
‘iA TN27T O
MAT N?7 20
MA I N27 1f)
MA I P42740
MA I N 2750
MA I P 4?7
MA 1 P427 70
MAT P42780
MAT N?790
MA I P420 00
MAT P42810
MAT P42820
MAIN 2830
MA iN2840
MA 1 P428 50
MATN?A’0
MA I P42870
M4 IN?880
MAT N 2 891)
MAT P42900
MAT P42910
MA ! P429 3()
MAT P42040
MAT N?9 50
MAT P42960
MAT P47970
‘ IA! P47980
MAT
MA T N”) 00
MA I P431) 10
C
C
665 FORMAT(’ ** IPR3=’, 15,’ IDISKW=’, 15,’ IPR4=’,I5,T72,’**’)
WR lift IOUT,670)
670 FURMAT(1H .72(lh*),////)
(-i)
-------
C
C
C>))>>))> PEAD CARD TYPE 11 <<<(<<<<
r.
C
C
C>>>>>>>> READ CARD TYPE 12 <<<<<<<<
r
C
TF(IPPcJG.NE.1) GO TO 14
C
C******** SWMM INPUT
C
C
(T>>>>>>>> RFAD CARD TYPE 13 <<<<<<<<
C
1
C
C
IT IMF = IHOUR
!HOURP = IHOUR
IDATEF = (DATE
IDATEP IDATE
TDATF9 = JOATE
14 IF UPRUG.EQ.2) READ(IFILE,571) WWFX,UPROC
571 FORMAT(F8.1,2X,Fl0. 1)
NFVT= 999999
LEND = 0
ISKIP = 0
** ** $ $**$$* $ ** * $**
MAJOR EVENT LOOP
MAYN3 O ?0
MATNICY )
MATN I )40
MATN3OS O
MA I NIOM)
MAT N 1 070
MA I NIORO
MAIN30 0
MA I N31 00
MAT N ’l. 1 O
MATNIT 20
MA T NIl I
TMAINI 1 40
MAT N’I 50
MATNI t 0
MATN3I 70
MATN3I 90
MA! N31. 90
MAT Nil o
MA I NI? 1.0
MAT N’ ‘30
MAT NI? 40
MA 13250
MATN 1? 0
MAT NI? 70
MA! ’3? 90
MAINI 90
Of)
MA!N33 1.0
MA I NIl 20
MATN33 II
MA TN3140
MA T Nil ¶ 0
MA TNII6O
MA I N33 70
TMA I N33 qf)
MAIN 1IOO
MA I N 14 00
MA IN 34 1. 0
MATN347O
MA TNI4 0
MAT N144()
MA I N 145 0
MAT N346()
MA! N74 70
MAT N 148()
MA 1N3490
MATN I5 0 0
MATN I5 I.O
IF(IPROG.E0.0) READ(INPT,515) TDATE,IHCLJP,IDWHX,WWFX,UPOI)D,0x,
I 800X,T,C
515 FORMAT (16, 4X, 21 5,3F 10.0, 3F5.O)
!F(IPROG.EQ.21 READ(INPT,515.) 1DATE,IHCUR,1DWHX,OUM,OUM,) ,
I BODX,T,C
H
READ(INPT,515) IDATE , IHOUR, IDtJi ’MV,DUN,f)uM,CX, RCDX,T, C
C
RFAD(TFILE,END=999g) TIME.(0C2(J) .J=1,t flUTS),((POLL7tK,J),
1K=1 ,NPOLL) ,J=1,NOUTS3
WWFX = 002(JNS)
URBOD = PULL2(1,JNS) * 60.0
TF(WWFX.EQ.0.0I LOWHRS = 1
IDWHX° = LDWHPS
TDWH I = IDWHXP
C
C
C
I
C
C
C
-------
MA 1N 35 20
MA I N35 30
MAY N3540
MATNISSO
MA I N35 60
MA I N’ 5 70
MA T N 3 5 80
MAT 90
MA! N36 00
MA psJ36 10
MAT N36 ‘0
MAT N3610
MAT N3f 40
MA 1N36 5 0
MA I N1 60
MAT 1f, 70
MA TN1680
MAINI69 0
MAT N3700
MA I N37 TO
MAT N3 20
MA 1N37 10
M4 I N3740
MATN 175 O
MA 1N3760
MA I N37 70
MATN378 )
VAT NI? 90
MA I N39 00
MA 10
MAT N38 70
MATN 1(
MAT N18 40
MA 1380
MA I N1960
MA 1N3870
MAIN3RP,()
M AT N 3890
MAT
MA I N39 1 0
MA 1N39?0
MA 1N3930
MATN194 )
MAT N.950
MA IN39 0
MATNI97 O
MA! NV) 80
MAT NV) 9 ’)
MA!N4000
MA! N40 1 0
- ‘4
u - I
O 9 9 I 1,NEVT
15 IF(IFND.EQ.1) GO 10 9998
C
IL = IL + 1
C
IF(ISK(P.EQ.0) GO TO 39
ITIME ITIMEX
I ’ )ATFR = IDATE9
WWFX WWFXF
URBOD = URBOOF
C
IF(!PROG.EQ.1.AND.(IDATEF.NE.ICATE8.OR.IHCURF.GT.!TTME)) Of) TO 19
IF(IPROG.EO.1) GO 11) 38
C
IHOUR = IHOURE
IDATE = IDATEF
TDWHX = IDWHXF
18 C = CF
I TF
OX = OXF
BOOX = 8ODXF
C
39 ISKIP = 1
DCNCTX 0.0
TF(ICALC.E0.1) DCNCTX = UR80 0
IF(!CALC.EO.0.AND. WFX.NE.0.O) DCNCTX = URBOT) * 4.4491 / WWFX
DURBOD = URBOD
IFITCALC.EO.1) DURBOD = URBOD * W FX / 4.L,491
C
C
C>>>>>>>> READ CARD TVPE 11 <<<<<<<<
C
IF(IPROG.EQ.0)REAC(INPT,515,ENO45)
1 IJRF OflF,QXF,BODXF,TF,CF
C
IF(TPRUG.EO.01 GO 10 47
C
C
C>>>>>>>> READ CARC TVPE 12 <<<<<<<<
C
IF( IPROG .EQ.1. AMJ. 1041FF. EQ. IDAT ER .A \O. IHCUPF .LE • ITIME)
1RFA ’ )(INPT,515.Er\c=45) IDATEF.THOURF,IDUMMY,flUMF,OUMF,0XF,BOr XF,
? TF,CF
C
C
C>>>>>>>> RFAD CARD fl’PE 13 <<<<<<<<
C
IFIIPROG.E0.2) READ(INPT,515) IDATEF,IhOUPF,IDWHXF.IIUM,OUM,QXF,
1 BOf)XF,TF,CF
C
-------
LF(IDATEF.E0.9 9999) GO 10 45
C
!F(IPROG.NE.1) GC 10 43
C
C******** SWMM INPUT
TTIMFX = ITIME + 1
104 1F9 IDATER
IFUTIMEX.LE.24) GO TO 42
ITIMFX = 1
MON IDATE9 / 10000
IDAY = (IDATE9 — MON * 10000) / 100
IY = (IDATE9 — MON * 10000 — IDAY * 10 )
TOM ’ = 1DM + 1
MONTH(2) = 28
LEAP = TYR / 4 * 4
IF(LFAP.EQ.IYP) MONTH(2)
jF(IflAY.LE.MONTh(MOf )) GO T040
TDAV = I
MON = MON + 1
IF(MON.LE.12) GC IC 40
MON = 1
TYR = JYR + 1
40 1DATF ) = MON * 10000 + IDAY * 100 + TYR
C
47 PEAD(IFILE,END=45) TAME, (002(J),J=1,NCtJTS),((POLL2(K,J),
IK=1,NPCIL) ,J=1 , CUTS)
WWFXF = 002(JNS)
URBO OF = P0112(1 ,JNS) * 60.0
TF(ISTART.EQ.0.AND.ri FXF.GT.0.0) GC TC 7OCO
TF(WWFXF.E0.0SO) GC TO 2004
GO TO 2002
2000 IDATEP = IDATE9
!DWH1 = IDWHRS
TOWHXP = 1D HRS
IH OIJPP ITIMEX
IC = 0
1STAP T = 1
200? !DWHRS = 0
GO TO 47
2004 TDWHPS = IDWHPS + 1
IF(IDWHRS.LE.MIT.OR.ISTART.EQ.0) GO 1047
2006 TFUEND.NE.1) IC = IC — MIT
TJUMP = 1
0 0 — CKO
RC PC — CKRC
PT = PT — CKPT
DWF = tDWF — CKD F
WWF = WWF — CKWWF
PBO1) RBfJD — CKRBGD
I -I
C . ’
MA I N40 70
MAIN4 O3 O
MA! N40 40
MA !N40c’)
MAT N406 )
MAT N40 70
MA I N40 80
MA’ N40Q()
MAT N4 1 oo
MA ! N41 1 1)
MA T 41 21)
MAT N41. 10
MAIN4 I4 1 )
MAT N61 50
MATN4I F”)
MMN4I 70
MATN4J 80
MA I N41 00
MAT!. I4700
MA ! N4? 1 1)
MA I !4? 1)
MA I N4?30
AI”44240
MA!N4?50
MA! N42 F”)
MA I N4? 70
M4 1N4780
MA! N42 90
MA T 4l 00
MA 1N41 10
MAI’ ’4320
MA I N4 1
MA I N4140
MA I N41 0
MAT N4 60
MA 1 N4170
MAT 80
MA 1N439’)
Mfl I N44 01)
MATN44 1O
MAT N44 20
M4!N44’3 ()
MA I 4440
MA TN4450
MA 1N446 1)
MA I N44 70
MAT N44 .00
MAT N4490
MA T N45 00
MA I N45 1 0
-------
C
C
DWBIJD D BOD — CKOWB
IJR1E S = URLBS — CKURLB
IF(IENDeEQ.1) CC TO 50
rp TO 47
45 lEND = 1
IF(IPROG.E0.1.) GO TO 9998
GO TO 50
47 IF(EDATEF.E0.999999) GO TO 45
IF(IPPUG.NE.1) GO TO 7000
/ 100
/ 100
IDAYI * 100)
IOAY2 $ 100)
MA 1N452 0
MA TN4511
MA 1N4540
MA! N’+S 50
MA N4560
YA 1N45 70
MA I N45
MA TN4590
MA1N4( )O
MAT N46 ‘0
MA !N4 O
MA 1N46 40
MA!N4f S0
MAT N4 60
MAT N46 70
MATN46 3()
MA I N4f 9O
MA1N4700
MA 1N47 TO
MArN47?r)
MAT J471 fl
MA I N4740
MA I N 47 SO
MA 1N4760
MATN4TT O
MAT 47R0
MA 1N4790
MA IN4R O O
MAYN4R 10
MATN4R’ O
MA 1N4 3 30
MATN4R4O
MAT N4 3 O
MA1N4 60
MA! N48 70
MAT N48 0
MAT N4R90
MATN49 0 0
MA !N4° 10
MA 1N49?O
MAfN4g r)
MAT N4Q40
MA I N49 50
MA 1N4060
MA I N49 70
MAT 4q80
MA 1N4990
MA T J5O 00
MAT N5 010
MA!
C
C
41 IF(IPROG.E0.2) REAO(IFILE.571,FND=45) WF F,UPBCDF
GO TO 47
H
-J
3000
4000
THOI.JRX IHOURF
TFUDATE8.E0.IOATEF) GO TO 6000
MON 1 = IDATE8 / 10000
M JN? IDATEF I 10000
!DAY 1 IIOATE8 — MONt * 10000)
TDAY2 (IDATEF — MCN2 * 10000)
TYRL = (IOATE8 — MONI * 10000 —
TYR? = (IDATEF MON2 * 10000 —
IF(IYRI.NE.IYR2) GO TO 5000
IF(MON1.NE.MON2) GO TO 4000
IDAY3 IDAY2 — IOAVI — 1
IH OtJRX = 24 — IHOUR + IHOURF + TDAY3 * 24
GO TO 6000
NFMON = MONt + 1
NIMON = M ON2 — 1
IDAY3 = 0
4200 LEAP = IYR1 / 4 * 4
MONTH (2) 28
lF(LFAP.EQ lYR1) MONTH(2) 29
D I) 4400 NN = NFMON, LMON
4400 IDAY3 IDAY3 + MOt TH(NN1
IOAV3 104Y3 + MGNTH(MONI) — IDAVT + IDAY? —
GO TO 3000
5000 NLMON = 12
N MON = MON1 + 1
IDAY3 = 0
IYR4 = LYR1
TYPI IYR2 — 1 YR l
DO 5500 NN = 1,IVR3
TYR4 IYR4 + 1
LEAP = IYR4 / 4 * 4
MONTH(21 = 28
IF(LEAP.EQ.IYR4) MONTH(2) 29
TF(NN.E0.IYR3) GO TO 5300
00 5200 INN = 1,12
-------
C
5200 TDAY3 = [ OAV3 + MONTH(INNI
GO 10 500
5 00 IF(MON2.EQ.1) GC IC 4200
NILMON = M UN2 — 1
00 5400 INN = 1,NLLMON
5400 TDAY = 104Y3 + MONTH(INN)
5500 CONTINUE
GO 10 4200
MA I NSf) 30
MA INS () 40
MAIN5 O S O
MAIN 060
MA 1N5070
MAT N Sf) 80
MflJP J 5’)9f)
MA INSt 00
MAIM5I 10
MAT N51 70
MA T N5 1 30
M4 INSt 40
MA INS 1 50
MA!N5 16()
IHOIJR) MAINS 17t)
MA INSt 80
MAINS I9!)
MAINS? 00
MA I N57 1 )
MATNS2’ t )
MA INS? 3 )
MA TN S?40
MATN 5 )50
MA I N57 70
MAIN52F 0
MATNS?9()
MA I NS 00
MAINSITO
N!4 1N5320
MA I N51 30
MA IN SI4f)
MA I N51 5()
INS 360
MAI N53 70
MA TNSI 0
MA I N51 90
MA 1N5400
MA I N54 1 0
MA I NSA 20
MA I N54 1 f )
MA IN544t)
MA 1N5450
MA IN 54 6 0
MAIN541()
MA 1 N54 80
MA 1N5490
MAT N55 00
MA!N55 10
MA I N55 ‘0
MA 1N553()
C
6000 IHOUR = ITU4EX — 1
TrIIHOUR.Eo.IHOURX.AND.IDAY1.Eo.IDAY2) IHCURX IHOt)RX + 1
IF( 1HOUR.FQ.IHOURX.AND.IDAY I.NF.IOAY2) IbCURX = IHOIJQX + 74
IF(IHOUR.GT.IHCURXI IHOURX IHOUPX + 74
OX Ox + (ITIMEX— IHOUR) * (OXF — OX) / (IHOURX — 1HOUR)
BOOX = BOOX + (ITIMEX— IHOUR) * (RCOXF — POOX) / (THO’JRY —
I = I (ITIMEX— IHCUR) * (IF — I) / (IHOURX — IHfltJ )
C = C + (ITEMEX— IHOUR) * (CF — C) / ( IHOURX — IHflUP)
IHOUR = ITIMEX
10A 1E8 = IDATE9
IF(IL.EO.1.AND.ID HXF.LT.MIT) GO 1050
IFI IPROG.EO.1) GO 10 50
7000 IF(MIT.EO.0) GC TO 99
IF(IOWHX.GT.MIT.AND.IOWHXF.GT.1’TT) oc ir c9
50 TC=IC+1
TF(IPROG.EO.1) GO IC 62
IOWH= IDWH+IDWHX
IFUC.NE.1) GO TO 60
!HOIJRP = IHCUR
IDWHXP = IDhHX
IOWHT=IOWHX
IDWHX 0
60 ID=ID+IDWHX
62 TF(TPROG.EQ.1.AND.ISTART.EQ.0) GO 1066
0 = 0 + OX * (1 + IDWHX)
PC = PC + C * OX * (1 + IDWI4X)
RI = PT + I * (1 + IDWHX)
DWF=DWF+DWFX*( 1+IC HX)
WWF=WWF+WWF x
P800 = P800 + BCDX * OX * (1 + I1) HX)
DWB OD=DWBCD+DWBCDX*1 1 +IDWHX)
IJP L BS=URLBS+DUR BOO
IF(IPROG.NE.1) GO 10 66
IF(WWFX.EO.0.0) GO TO 64
CKQ = 0.0
CKRC = 0.0
CKRT = 0.0
CK OWB = 0.0
CKI)WF = 0.0
CKW’ F = 0.0
-------
CKRROD = 0.0
CKURLF = 0.0
64 CKO CKO + QX * (1 IDWHX)
CKPC CKRC + C * OX * (1 + IDWHX)
C (P,T = CKRT + T 4’ Li + IOWHX)
CKDWB = CKDWB + OWBCDX * (1 ‘ IDWf X)
CK OWF = CKD .F + D FX * (1 + IDWHX)
CKWWF = CKW IF + W FX
CK R0fl CKRBOD + BODX * OX 4’ (1 + IOWI-X)
CKURLB = CKUPLB OURBOD
66 CONCT = 0.0
TF(WWF.NE.0.0) CONCT = URLf3S * 4.4491
IF(IFND.EQ.1) GC TO 100
!F(TJUMP.EO.1) GO TO 100
TF(TP OG.EO.t) GD TO 15
IF(IC.E0.1) 1DW ’X IDWH 1
TF( 1flWHX.LT.M1T.AND.IDWHXF.GT. 1T) SC
GO TO 15
C
C******** COMPUTE OUTPUT FOR ONE EVENT
C
C
99 THOUPP = IHOUR
IDWHXP = IDWHX
IC =1
I 0=0
o=ox
OWF=OWFX
WW = WFX
IDWH =IDWHX
DW O0=D BU0 X
URIBS = DURBOD
CONCT OCNCTX
PT =
RC =
RBOD
100 C = RC / 0
BOOX RBOD / 0
T = RT / (IC i ID)
DFSED=ASEP*DWf/ ATOT
DFCOMR=ACOM*DWF /ATOT
QSEP= *SEP*WWFIATCT
0 COMB=ACOM* WwF/ATOT
C SFIO=OCOMB4-OFCCMB
F300SFP= ASEP*URLBS/ATGT
SEPLO=CONCT
IF(ASEP.E0.O.O) SEPLO=0.O
B ODCOM=CONCT* 00 0M8*O.2248+ACOM*DWBOD/ATOT
C
C
MA 1N5 5 40
ATN555()
MA TN’5560
MAIN 5570
MA 15580
MA 1N559()
MATN S6 0 0
MAIN96 10
MAT N56 20
I N56 0
MA TN5640
MAITNS65’)
MA TN5660
MAT N56 70
MAIN S6R O
M A I N 569 ()
MMN S7 0 0
ATN5710
MAIN57 ()
‘ A IN674 )
MAT N 5750
MAIN S76O
MAT N57 70
MATN S7B()
MA1N5790
TMA I N5R Q()
MA!N58 10
I N 5 20
M IN5 1 0
MAT* !5R4O
MA!N5850
MA 1N5 8 60
MAT N58 70
MATN5B 0
MA 1M5890
MA I’159 00
MATN59 1 0
MAT N59 80
MA 1N5930
MA I N59 40
MA 1N59 50
MA I
MA !N5970
MAIN5 ()
MATN5990
MA 1N6000
M4 I N60 10
MA 1N6 0 70
MA 1N6010
MA TN6040
—4
/ W F
TO 1CO
1
C * OX
BODX * OX
-------
FF=0.0
CCOMR=0.0
TF(CS LO.NE.0.0.AND.IPR0G.NE.1) FF FFIBS * If)WH
IF(CSFIO.NF.0.0) CCOMB=(B0OCOM+FF)/((0CCMB+OFCflM! fl*36O0.0)
COMB LO=CCOMB*16C16.60
CS= 14.652—O.41022 * I • 0.0079910 * (T**2.)— 0.000077774 *(T
/* *3,,)
Co=Cs—C
IF(CS.IT.C) Co = 0.0
W WFA=WWF/ IC
URI RA=URIRS/ IC
XNUM=7.5999*(1.024**(T — ?0.0))* (ALPHA) /BETA1**l.33)/ ’4.O
C
DO 110 K1 ,3
QtJ(K)=Q*RF(K)/( IC+.ID)
FLO(1,K)=Q*RF(K)+C F
Flfl( ?,K)=Q$RF(K )+CWf+QSEP
F10 3 ,K)=Q*RF(K )+CWF+OCOMB
Ft0(4,K)=Q *RF(K)+QSEP+QC OMB
FLO(5 ,K)=Q*RF(K )+C f+QSEP+QCQMB
C.
DO 110 P4=1.5
FL(P4 , Y)=FLO (M,K)/ I IC+ID)
H Vl,K) =f ETA j*FL ( M,K) **B FT A?
XK 1=GAMMAI*H(M, K)**GAMMA2/24.0
IFCXK I.GT.XKIMAX) XKI=XK1MAX
TF(XK).LT.XK1MU) XK1=XK1MIN
XK!T(M,K)=XK I*1.047**(T—20.O)
OENOM(M,K)=( 1.—EXP(--5.*24.*XKIT(M,K) ))/(0.0?*T+0.6)
XK?T(M,K)=XNUM*(F1(M,K)**(41PHA2—1.33*t FTA2))
DO(M,K)= OU(K)*CC/FL(M,K)
U(M,K)=(ALPHA1*Z )*(FL(M,KI)**AL”H 42
Ft M.K1=XK2T (M, K)/XK 1T(Ni, K)
V (M,Ic)=U(M, K)/3600.0
Fl(M,K)=F(M ,K)—1.
F?( ’ 4,K)=XK2T(M,K)—XKLT(M,K)
TEl ‘4,K)=0.O
IFUJ(M,K).GT.0.C) TE(M,K)=X/U(M,K)
XML (M,K)=S ORT(1.0+(4.O*XK IT(M,K)*E)/U(M,K)**2.)
XM2(M,K)=SQRT(1.0+(4.0*XK2TLi ,K)*F)fU(P ,K)**?.)
XM 12(M ,K)=XM1(M ,K)—XM2(M.K)
XJ(M,K)=0.0
XGIM,K )=0.0
IF(F.NE.0.0) XJ(M,K)=U(M,K)**2.*(1.C—XMl(M,K))/(7.fl*E)
IF (F.ME.0.0) xG(M,K)=U(M,K)**?.*(1.O_XM2(M,K))/(7.0*F)
110 CONTINUE
C
DO 120 J=1 3
DO 170 (=1,3
00 120 1=1,3
0
MA I Nf ) 50
MAT N6060
M4 TN6070
MAT
M4 1N6090
MAT N6 1 00
MATN6I 10
MAIN6T 70
MA 1N61 If)
M4 I PSJE)j 40
Mfl iN 1 50
M4 I N6 1. M)
MAIN6T 70
M4 T NE) I 5 0
MA 1 N61 0
MA TN(7 00
MAINE)? 10
MA I NE)? 7 C)
MAINE)? 10
M ATN6?4r)
MAINE)? 50
MAT NE)? E)C)
MAj N62 70
MATN67RC )
MA I NE)? 90
MA TNE)1 00
M4 TNE)3 10
ATN63?O
MA I N6 1 1 0
MA I N6 140
MA I N6 50
MA I N6160
M4 I N61 70
YAINE)I 0
M 4 1N6 190
TM AIN64O()
MA1N64 1’)
‘ A 1N64?0
MA 1N64 10
MA TN6440
MAT N6450
‘1A 1N646C)
MA I N64 70
MA 1N64 RI
A I NE) 49’)
M4 T N65 00
MA I N65 1 0
MA 1N65?()
MA !N6SIC)
MA TN6540
-------
ULTLO(1,J,K,t)=((Q*RF(KJ * HOOX + flWF*PCTT T(J)*DwFLO)/FLr)(1,K))/ MAIN65Sr)
/DENflM(1,K ) UAJNÔrÔt)
ULTLO(2,J,K,LI=((C*RF(K) * B DX + DWF*PCTIRT(J)*DWFLO+QSFP*SEPLfl* A1N6S70
/PCT(1))/FLO(2 KI)/DENOM(2,K) MAIN65R O
ULTLO(3,J,K,Lj=UC*RF(K) * 8UDX + DFSEP*PCTTRT(J) )WFLO+DFr0MP* MA1 tc590
/PCTTRT(J)*CCMBLC÷0COMB*COMBLO*PCT(L))/FiCL ,K))/ ENflM( ,K) MAIN66 0 0
ULTLO(4,J,K,L} j(C*RF(K) * BOOX + C)SEP*SEPI..fJ*øCTft)+0C0W3*CflMR1O* MA1’ !66l0
/PCT(1))/FLO(4,K))/ OENtJM(4,K) MA1N( .620
tJLTLC(5,J,K,L)=U0*RF(K) * BOOX + DFSEP*PCTTPT(J)*OWFIn+OFCOMB* MATNf 63()
/PCTTRTfJ)*CCMBLG+0SEP*SEPLO*PCT(1)+QCC?l R*CCMFW0*PCTçL))/Ftfl(5,K))/MATN6640
/DEN OM(5,K) MAIN665t)
120 CONTiNUE M5IN6 )
C MA1N667’)
!F(IPRIJG.NE.1) IDATEP = IDATE
IDATF 1 = IDATEP/ 10000 MAIN6 90
IOAT 2.= (IDATEP— IDATE1 * 10000) / 100 MATNf7O()
TDATFI = (IDATEP— IDATEL * 10000 — IDATF? * tOO) r .AJN67l0
C Mfl.1N67?r)
WRITEfIOUT,19 0) M4 Nf 73O
190 FORMAT4IHI) M4IN 740
WRITE(I OUT,200) MA7N675 0
200 FOPMAT(1H ,40(1 *)J MA1N676()
WRITF(1OUT,210)IDATEl,IDATE2,1DATE3,fl- flURP,1DWHXP MA. N677O
H 210 FORMAT(I IH **** DATE ,12,LH/,!2, 1H/,I’,6H HCUP ,I7,SH DWH ,14,5H *MAIN67RO
/***I ATN67°0
H WRITE(I OUT,200)
RITF(I0UT,230) IC MA!N 1()
230 FORMAT(?3H **** RUNOFF DURATION ,14,IAH HOUQS ****) MATN R?O
WRITE(IcjUT,200) MATN6R”)
WR!TE(IOUT,250) 1ATN6840
250 FORMATI1H ,24(1H*)) MATN6 0
WRITE(IOUT,?60) j MATN6 ’ D
260 FORMAT(16H **** EVENT NO.=,13,6H *****) MA N6870
WPITEUOUT,25 0) MATN6R ()
WRITFI IQUT,280) CX,BODX,T,C TN Ø39O
280 FOR 4ftT(///22H **UPSTREAM RIVER FLO=,F1C.2, 13H CFS ** 801) =,F7.2, MATN6qOO
/15H MG/L ** TEMP.=,F5.1,19H DEG.CENT. ** C.0.=,F6.2,9H MG/L ***) MAr ’469l0
WRITF( lOUT, 29 0)WWFA,URLBA,CONCT MA I N69’ O
290 FO MAT(22H ** AVE. URBAN R1JNOFF=,F9.2,20H CFS *** 81)1) LOA) , MAIN Q30
/F9.1,25H L8S/HCUR *** BOD CONC.=,F9.1,10F- M /t. ***) MAT ’ 1 694()
C MA N 95O
00 400 M=l,5 MA 1N696()
00 400 J=1,3 MATN697 O
I ITLEI(8)=JKEY( IPR1) YA IN6c BO
1)0 400 K=1,3 MA!N 99O
DO 400 L=1.,3 MATN7 0 0 0
IC = o.o M AJN7 O I O
XC 0.0
IF (UITLO(M ,J ,K,L).NE.O.01 RO=DO(M,K) /ULT1C( ,J ,K,L) ‘IAIN7O1 O
IF(FI(M,K).NE.O.O.AND.XM12(M,K).NE.0.O) GC TO 310
-------
DC = ULTLO(M,J,K,L)*FXP(Ofl(M, K)/ULTLC(M,J,K,1)—t.01 MA1N7O 0
CO TO 350 MAIN7 O6O
110 IF (UITIO(M,J,K,1).NE.0.0.) CC 10320 M TN7O7O
DC = flO(M.K) MATN7O ()
GO TO 350 P ATN7O O
3?O fF tRfl.GE.0.0.ANO.RO.LE.(I./F(M,Kfl) C,fl Tfl 110 MAIN7!O0
DC = flO(M,K) M4TP J7I1()
r,O TO 350 MA!N71?()
310 IC = (ALOG(F(M,K)*(1.—F1(M,K)*flC(M,K)/ULTLfl( ,J,K,1))))/(xKMA1N7j3 )
.iT(M,K)*F1(M,K)) MAYN714O
Or =(XK1T(M.K)*ULTLO(M,J,K,L)/XK2T( ,g))sF p(_XKjT( , )*TC ) MAIN7 t3
IF(E.F0.0.) GO TO 340 MA!N7161)
IC ALCG( ((XMUM,K)/XM2(M,K) )—Pfl*F(r’,K)+R0)*XC(M,K)/XJ(M,KMATN7 17O
.) )/(XJ(M,K)—XG(M,X) ) MATN7I RO
DC (ULTLC(M,J,K,L)*XK1T( M,K ))/F2(M,K)*(FXP(XJ(M,K 1*10) MATN’lOO
1 —(XM I(M,K)/xM2(M,K)J*EXp(XG(M,K)*TC)) + Dfl( 1,K)*EXP(XC(M,KMi !N7?O0
?)*TC) M AIN72 IO
340 XC = tJ(M,K) * IC / 5280.0 MA!N7’?’)
150 DOCONC = CS — DC M AIN72 IO
IF(DC.GT.CS) DOCCNC = 0.0 MA!N724()
IF tDC.GT.CS) DC=CS M41N7?5O
!F(F7(M .K).NE.O.O) GO TO 360 MAIN7?6()
DT=XK1T(M,K)*ULTLC M,J,K,L)*TE(M,K)*FXP(—XKjT(M,K)*TE(M,K))+Dfl(M,KMAyN7?7O
1)*EXP(—XK2T(M,K)*TE(M,K)) MAIN72 30
cc GO 10 370 MA IN7?9()
360 0T=(FXP(—XK1T(M,K)*TE(M,K))—EXP( —XK2T( ,K)*TF(M,K)))*XK1T(M,K)*U1TMAIN1 00
MAIN7 I 1 O
IF(iE.E0.0.) GO TO 370 MAIN73 0
= (ULTLO(M,J,K,L)*XK1T(t ,K))/F7( d,K)*(EXP(XJ(M,K)*TE(M,K)) —(XMAIN71 1o
.M1(M,K)/XM?(M,K)J*EXP(XG(M,K)*TE(M,K ))+n (M,K)*Exp(xC M,K)*TF(M,KMnTN7 4o
M.AiN71 5O
370 OXCONC CS — D I M4TN716 0
!F(DT.GT.CS) OXCONC = 0.0 MAlN717
IF (PT.GT.CSI oT=cs MMN7 R0
C METHOD OF INTEGRATING DEFICIT EQUATION MA!N7190
XIOE = (ULTLO(M,J,K,1)+DO(M,K))/XK2T(M,K) M41N7400
IF (E.NE.O.O1 XIOE=ULTLO(M,J,K,1)*XK11(M,K)/(XK?T(M,K)—XK1T(M,K))*MATN74 IO
1 (XM1(M,K)/jXM2(M,K)*XG(M,K))—1./xJ( l,K) I DO(M,K)/XG(M,k!) MA!N7420
c MATN74 IO
MG! = 1 MA 1N7440
IF(ICALC1.Ec .1 .OR. 1CALC2.E0.1) MGI = 1.0 + DOCONC * ?.O AIN745O
IF(ICALC1.EQ.2 .GR. ICALC2.FQ.?) NC! = 1.0 + OXCONC * 7.0 MAiN7460
IF(NG 1.LT.1) NGL = 1 MA1N747f)
IF(NGI.GT.31) NG I = 31 MA 1N748()
04T41(NGL,M,J,K,L) = DATA I(NG1,M,J,K,L) + 1.0 MATN74 O
C MATN7c O O
IF(J.EQ.IPP1.ANC.K.EQ.IPP2.AND.L.EC.1PR3) GO TO 380 4AIN’5I0
GO TO 400 M IN757O
C MAIN7 S Ir)
380 WRITE(IOtJT,382) M,TRTPCT(J),RF(K),TPCT(1),FL(M,K) M4 1N7540
-------
382 FflPMAT(//15H **CCMBINATIUN=,12,9H PCTTPT=,F4.2.21H PIVFP FLOW FRMAIN75SO
/ACTN = ,F4.2,17H PCTTRT RUNOFF =,F4.2,?3H *** AVE. RIVER F OW MAINTS6O
/,F9.1,8HCFS ***) MATN757O
WRITE(I OUT,384)CU(K),ULTIO(M,J,K,1),H(fr,K) MATN7C5F3O
384 FORMAT(27F1 **AVF. UPSTREAM PIVERF ICW=,F9.1,2?H C S *** ULTIMATE flMAIN7590
/D=,FlO.1,18H MG/I *** DEPTH =,F6.2.I0I- FFFT ***) MAtN7 Y)0
WRITF(UJUT.386) DC,DOCONC,CS ,XIDE ATN76l0
386 FORMAT(20H **CRITICAL DEFICIT= ,F6.2,16H M It.. DOCONC=,F6.7, MAIN76?()
/16H MG/L SAT 0O=.F6.2,24HMG/L [ NTFGP L OFF DN=,FR.2,15H MG—MAIN763O
/HOUR/L ***) M4TN764 0
WRITE( 1001,388) DT,CXCONC MA!N7 50
38R FflRMAT(16H **O . . DEFLC [ T=,F6 .2,21H P’G/L *** 0.0. X MAIN76 0
/1OH MG/I ***) MA7N7f 7 )
WRITE IOUT.390) TC,XC,TE(M,K),D,V(M,K) MAIN7F,P,O
390 FORMAT(8H ** IC =,F7.2 ,15H HOURS *** XC =,F6.2,15H M I. *4* IX =,MAINTh’)t)
/F7.7,20H HOURS *** DIST x =,F6.2,14H MT. *** V 77t)()
/ 9 H FPS ***) MATN77 I O
WRTTE(1OUT 392) XK2T(.M ,K) , XK1T(M, <) MAIN77 ’0
39? FORMA.T(32H **REAERATION COEFFICIENT(XK2T)=,El1.5,44H 1/HOUR DE ’lATN77 ’)
/flXVGFNATION COEFFICJENT(XKIT)=,F11.5, H 1/HOUR) MA 1N774()
C MATN17S’)
DCD!ST(M)DCDIST(M) +XC MATr j77Y )
IF (ITSAG.E0.1.AND.J.EQ.IPRI.AND.K.EC.IPR?.AND.L.FQ.IPP’. MATN7771
1 &ND.M.EQ.IPR4) RITE( IDISKW) IDATFI ,IDATF?,I DATF ,DXCflNC MA1N7780
c MA1N7790
400 CONTINUE MATN7R00
C MATN7R ID
C <<<<(<<<<< D.C. PROFILE CALCULATIONS MATM7R?0
C Mt YN7A O
IF tICALCX.E0.0) GO TO 9050 MA 1N784()
DO 8040 iDX 1,l01 MATN7RS O
XDG(IDX.) (1DX—1)*DX MATN78 ()
Xfl=XDG(IOX)*5280.0 MA!N7R70
00 8040 M 1,5 MATNTBRO
TX=XD/U(M, IPR2) MA1N7890
iF IF2(M,lPR2).NE.0.0. ND.XM12(M,iPR2).NE.0.O) GO TO ROflO MATN7900
DTX XK 1T(M,IPR2)*ULTLC(M,lPR1,IPP?,I0P3 )*TX* MAINT9 I O
1 EXP(—XK1T(M,IPR2)*TXI+DO(M,IPR?)*EXP(—XK?TTM, IPPfl*TX) MAIN7 ’0
GO TO 8010 MA!N79’ 0
8000 0TX=XK1T(M,1PR21*ULTLO(M,IPR1,tPP2,TPR3)*(EXP(—XK1T(M,IPR7) A I N7940
1 TX)—EXP(--XK2T(M,IPR2)*TX))/F2(M,IPR2)4-Dfl(M,I ’R2)* MA1N7c S ’
2 EXP(—XK2ILM,IPR2)*TX) MATN79 fl
If (C.EO.0.0) GO TO 8010 MATN797O
DTX=(ULTLGIM,JPRI, IPP2,IPR3)*XK1T(M,IPR2))/F2tM, 12R2)* M T 79 Q
1 (EXP1XJ(M,1PR2)*TX)— XMl(M,IPR2)/XM2 M,TPR?))* 4TN799O
2 EXP(XG(M, IPR?)*TX))÷CO(M, 1PP2)*FXP(XG(M,IPR2)*TX) MATN8 000
8010 DTXCN=CS—DTX MATN 0l0
IF LDTX.GT.CS) DTXCN 0.0 MATNRO2O
iF (DTX.GT.CS) olx=cs MAINR O3 O
DOXDX(M,IOX)=DTXCN MA!N8040
-------
8040 CONTINUE MATNRO5 )
MA ’NRO50
C <<<<<<<<<< END CF 0.0. PROFILE CALCULATIONS >)>>>))))> M j R070
C MAINR0 0
C MAINROQ O
C <(<((<(((< PRINT CUT MATRIX OF 0.0. PRCFILF VALUES >>>>>>)>>) MA!N8100
C 1ATN8lU)
WPITFIIOUT.8050) DX,I ATNP12O
8050 FORMAT(’L’,///,78(’*’), ATNR11’)
1 1’ ** DISSCLVED OXYGEN PROFILES FCP I1 WASTE TNFIflW ‘, MATN8I4O
2 ICflM UNATIONS (M 1,5) **‘,/‘ *$I,7x,’OISTANC ——> 01 ‘, MAIF R 5O
3 ‘TNTFRVALS, EACH INTERVAL ‘,FIO.2,’ MILFS’,6X,’**’,/ M41NF3 16 0
4 ‘ **‘,lOx,’EvENT NO. ‘, 13,28X,’UNITS IN /L’,9X,’**’f 1ATNR170
5’ ‘,17(’*’)) AIN818O
00 8060 M1,5 M pq 9r)
WPITE( IOUT,8C52) M,(DOXDX(M,IDX),TflX l,10l)
8052 FORMAT(///’ COMBINATION ‘,Ii,’:’,6(/IOX,20F6.’)) MATN 710
8060 CONTINUE ‘1ATN 7?O
IF (ICALCX.E0.1) GO 10 9050
IF (ICALCX.EQ.?) CALL MGRAPH(XOG,00XDX,1fl1,5,TITLFI,VTITII, LA8EI1)MN8 )4r)
IF (ICAICX.LT.3) GO TO 9050 MATN82 SO
ICOMB=ICALCX—2 MAIN8?
00 8070 IDX I.ICt MAy77()
DOXDX I( ICX)=COXOX(ICOMB,IDX) M ATN82B O
STTTLE(15)=CCMKEY(ICCMB) MATNP?01
8070 CONTINUE MATN91O )
CALL GRAPH(XDG,COXCX1, l0I,1,STITLE,YTITLI,IAI3ELT)
9050 CONTINUE MATNP 70
AN? VFL=ANAVF1fFLt5,3) ATN83lO
ASUMt=ASUM1+SEPLC*WWF MA1N8340
ASUM?=ASUM2+WWF MATN 50
ABt’JDSP=ABOOSP+BCDSEP MATNP16O
AR O OCB=JSBODCB+BCCCOM+FF
SUMCMP SUMCMB+CC ’RLC*CSFLO N8 80
ASUM7=ASUM7+CSFLO MAIN8 39O
AFLSE AFL SEP fQSEP*3600.0 ATNP400
AF1CMP AFLCMB+CSFLO*3600.0 ‘ ATNR4lfl
IC= ’) MAINB47O
I0 ) MATNR4V’
TI = 0 MATN844O
IDWH O ATN 8450
IJUMP = 0 MATNS46 O
ISTAQI = 0 MAINR47O
0=0.0 MAYN84R O
C=0.O MATNR40 0
P 1=0.0 MAINRS OI
flWF=O.0 MAT 5 1()
WWF=0.0 MA!N 20
RB0D=0.0
flWRflO=0.0 MAT ’JR540
-------
M41N8550
MAT N8S 60
MAIN 5 70
MA I N8 80
MAT N8c90
MAT N86 On
MA I NR6 1 0
MAINR6?’)
MAT NP6 0
MAT N8640
MA 1N86
MA TN8660
MAINRE ,70
M41N868()
MAiN8 .’ 90
MAINB7 0 0
MAIN 8710
ATN87’0
) MA 1N8730
MA TN8740
MA IN 7c0
MAT N 8761
MA 1N9770
WRTTE(IOUT,905) M4 1N8780
905 FORMATUH1) MMN8790
WPITE(I OUT,910) MATNSRO O
910 FORMAT(///,LH ,54(1H*)) MA1N8 10
WRITF(IOUT,915) MATN88?()
915 FORMAT(55H ************ DURING WET WEATHER PERIODS *** ******)MATN8 0
WRJTE(IOUT,920) ANAVFL MAINR84 O
920 FORMAT(37H ** ANNUAL AVE. E’dNT TOT.RIVER. FICW =,F7.1,1 1H CFS ***MA1N8 S0
f*) MA!N 8960
WP ITE(IOUT.925) MA N8970
FORMt&T(LH ,54(1H*)) ATN8AR()
WPITF(IOUT,930) AFLSEP MAIN880 0
FORMAT(21H ** ANNUAL FLOW SEP =,E9.2,12H CF/VP ***) “A INR9 00
WRTTE 0UT,935) MATNB91 O
FORMAT(IH ,41(1I- *)) MATNR9 ,?0
WPITE(IOUr.9401 AFLCMB MATN89 0
FORMAT(21H ** ANNUAL FLOW COMB .E9.2,I2H CF/VP ***) MAIN8O4()
WRITEUOUT.935) MATNP 50
WRITF( IOUT.620) MA1N8945()
FOPMAT(/////.1H ,32(IH*)) M TN8 70
WRITF(I0UT 625) NEVNTS MA INR9AO
FORMAT(25H *** ET WEATHER EVENTS =,I4,4H ***) MATN8990
WRITEUOUT,630) MA IN9 O(Y)
FORMAT(1H ,32(1H*fl MATN9010
WRTTE IOUT,950) UA1N fl20
FORMAT(1H1) MAIN9OI O
WRITE(IOUT,955) M ATN O O4 O
URI.BS=0.0
CKQ = 0.0
CK C = 0.0
CKRT 0.0
CKDWB = 0.0
CK OWF = 0.0
CKWWF = 0.0
CKRBUD = 0.0
CKURLB = 0.0
NEV’ITS = I
999 CONTINUE
C
C *********************************************
C
C *** END OF MAJOR EVENT LOCP
C ***
C ************** ************************** *****
C
9998 ANAVFL=ANAVFL/FICAT(NEVNTS
4COMF L=0.O
I F( ASUM7.NE.O.C) ACCMBL=SUMCMB/ASUM7
ASEPLO=ASUM 1/ASUM2
C
cx
01
925
930
935
940
620
625
630
950
-------
955 FORMAT(IH ,44(IH*)) MATN9O SO
WRITE( IOUT ,960) MA!N906 3
960 FORMAT(45H ** ANN. 800 LOADS AND AVE. Cfl CENTRAT!Oi’ ’ **) MATN9 O7 O
WPITE(IOUT.955) MA IN9 OR()
WR1T (IOUT,970) ABGCSP,ASFPLC A1N90fl()
970 FQR¼1A1(////19H *** ANN.BOD SEP.=,F9.O,??H LF S/YR *** NN./ VF.=,FMAINQl00
/6.1, 9H MG/I ***1
WRITE(IOUT ,975) ABCOC8 ,ACOMI L MA 1N912 0
975 FORMAT(19H *** ANN.BOD COMB.=,F9.0,?7H IPS/YR *** ‘ NN, VE.=,F6,1,? ,ATM9l10
/ 9H MG/L ***) MATN9 14’)
C MATN915()
00 990 M=1.5 MAjN9 1()0
DCDIST(M) DCDIST(M) / FLCAT(NEVNTS AIN9170
WPITF( IIJUT ,985) •M,DCDIST (M) MA1N9!’ )
985 FflR lAT(///42H *** AVERAGE 01ST. 10 CRITICAL D.C. FOP M, [ ,5H IS =MATN919O
f ,F6.1 ,IOH MILES ***) MAINP2 0 0
990 CONTINUE MA!N9210
C MMN97?’)
C**** *** FPEOUENCY ANALYSiS OF DISSOLVED CXY(EF CONCENTRATIONS MAIN92I O
C. MMN924 ()
DO 700 1=1,31 iAtNQ?5O
700 CG(I)=(I—1)*0.5 MAYNQ25 O
C MATN9?70
DO 770 J=1,3 1ATN9?P,0
TITLF(7)JKEY(J) 1.ATN92qo
00 770 K=1,3 M TN fl0O
TITLE(8)=KKEY(K) MATN fl 10
DC 770 L=1,3 MATN9I’O
T ITLF(9J=LKEY(I) 1AIN033O
00 716 M=I.5 MA N 40
DO 105 1=1,31 MA IN91S O
705 FPEO(I) •= DATA III ,M,J,K,L) MA!NO 0
C 1ATN9 70
C MATN9 80
IFIICALC1.EC.0) GO TO 715 MAiN 0
C A 1N9400
CALL PLOT(CG.FREC ,31.NEVNTS) ‘AIN 4l0
C MATN94 0
IF(ICALC1.EC.1) WRITF(IOUT ,711) MA 1N9440
711 FORM TI//32H *** CRITICAL D.C. HISTOGRAM ***) MATNO4S O
IF(ICALC1.EO.2) WRITE(IOUT,712) MA 1N9460
712 FOP’IAT(//33H *** CRIT. 0.0. HISTOGRAM & X *** ATN9470
WRITE(IOUT,713) M,J,K,1 MA N94.3()
713 FO1 M T(6H ***M=,f1,6H ***J=,jI,oH ***K=,I1,6H ***L=,!I) M A 1N9490
WRITF(IOUT ,714) NEVNTS MA IN9S DD
714 FflRMAT(/////20H NORMAUZING FACTOR=,I4,7H EVENTS) MATN9 S 1 O
C MA1N0’ ’0
715 NI = 0 M TN953()
DO 716 1=1,31 MA1N9540
-------
N1=N1÷FREQLI) MA 1N9550
716 PCTG(M,I) = (100.0 * (FLCAT(NEVNTS) — Ni)) / FIOAT(NEVNTS) MA INQ 60
c MATN9 70
IF (ICALC2.GE.l) CALL MGRAPH(CG,PCTG. 1,5,TITLE,YTITLE,1ARFL) MA!N9cR0
N9 0
c M IN9 00
720 CONTINUE MA N96l0
C MATN9f,2 ()
750 IF ( IDWFM.EC.1) CALL DWFM(ND) MA1N96 O
C***************$****** *******t**** *** ****** cMA! N96 1 .f)
IF (TDWFM.NE.1) ND=0 MA!N9 50
IF (IWWFM.NE.1) NEVNTS=0 MATN966 O
C MATN96 7 O
IF (1TSAG.NE.1.CR.(IW FM.NE.I.AND.ID F .1\E.1)) GC TP 999q MATN9 6 0
CALL DOSAG(NEVNTS,ND) ATN9 90
C MA!N970 ()
999 STOP MA!N97 1’J
END MAI i7?0
SUBROUTINE CORREL CORLOD IO
C C ORLO O’()
C******** AUTOCORRELATICN ANALYSIS OF TIME SERIES CO”L0030
C CORLO O4O
DIMENSIQN OUMMY(15) CORLOOSO
COMMON INPT,IOUT CORLOO6O
COMMON/SETh MIT rflRL O O7r)
COMMON/SET3/ X(8760),RK(800),RKI(I01),XAGL(10l) C0 L0080
INTFGER TITLEt2O),YTITLE(51),LABEL(20), 2 OTNTS CORLOO9O
DATA TITLE/’CORR’,’ELOG’,’RAJ’ ‘,‘OF T’,’IMF ‘,‘SERI’, CORLOTO O
I ‘ES ‘.13*’ ‘1 COP.10hl0
DATA VTITLE/’A’,’U’ ,‘T’,’O’ ,‘C’,’C’,’R’,’P’,’E’,’L’,’A’ ,‘T’, CNt 0120
I ‘I’,’U’, ’N’, 5, ‘C’. Q , ‘E’,’F’.’F’ 5 I •I •, E , CORIO1. 30
2 ‘T’,’S’,23*’ ‘/ CORLQ14D
DATA LABEI/’NUMB’,’ER O’,’F HD’,’URLY’,’ LAG’,’S ‘, flRLOI50
1 14*’ ‘I C0R1O 1 O
DATA IW,IBLANK/’ ‘,‘ ‘I rORLOI7 O
C CORLOIBO
C******** READ NUMBER OF DATA POiNTS AND NUMBER OF LAGS. CORL OI9 O
C CORLO2 0 0
C COPLO21O
C>>)>>>>> READ CARD TYPE 2 <<<(<<<< CORLO?20
C C ORLO2IO
READ(INPT,99) N,NLAGS CORIO?40
99 FORMAT(215) C 0RL 025()
IF (N.GE.NIAGS) GO TO 75 COPLO?60
WPITE(IOUT,71) N,NLAGS C 0R10270
71 FORMAT(’ VALUE N = ‘.15. ,’ IS LESS TI4AN NIAGS ‘.15) rDRLO?q O
GO TO 999 C0RL030 0
75 IF (N.LE.8760) GO TO 80 CORLO3 I O
WRITE(IOUT,76j N rfl 7()
76 FORMAT(’ VALUE N ‘.15,’ IS GREATER ThAN 8760. ‘, CORI O3V)
-------
404
405
407
408
400
410
411
C0R10340
CORI OVO
ropioi si
r.ORL O3c2
r flRLO3S4
C” L0V55
CPPL OI6 O
C. 0 t 101 70
C112L03 80
COP 10 1 0
C OP 10410
1 ORL 04 10
rORL O6’ O
C ’ RL041O
rflRL O44f)
CPRLO4S O
Cflq L O4 50
C O°.L047r)
rfl p r)4q()
r OP. 104 00
NIRLOS I t,
C ORL OS7O
CflR1(V 10
CORL OS4’)
C0 10550
C OP 105 60
C ORL OS7 O
CORIO5 80
C OR 10590
CORLO6 10
COR LO6?0
rOPLO6 10
C ORL O64()
10R1065 1
rflRLo6 4 co
C QP 106 70
C 0 81 06 80
CORL O6 0 0
CORL O7 0 0
C 02107 tO
C ORL O7?0
C 0810710
C 0210 740
C 08107 50
C OP.L07 f)
COR L O77O
C 0Q 1078 0
‘—I
03
03
1 ‘REDIMENSIGN ARRAY X IN SUBROUTINE CflRRFI’)
GO TO 999
80 IF (NIAGS.LE.8001 GD TO 90
WRITE(IOUT,81) NIAGS
81 FORMAT(’ NIAGS ‘,15,’ IS GREATER THAN THE UPPFR LIMIT OF 800.’,
1 • JT WILL BE SET EQUAL TO 800.’)
NI AGS= 800
C
C
C>>>>))>> PEAD CARD TYPE 3 <<<<<<(<
C
90 1=0
00 400 12=1 ,N
RFAD(INPT,401) IKEY.DUMt4Y
401 FORMATLA4, IX, [ 5F5.0)
IF (IKEY.EQ.IBLANK) GO TO 410
IF (IKEY.NE.IWI GO 10 407
00 ‘.04 J=t ,15
IF (DLJMMY(J).EQ.0.0) GO TO 405
L=1+1
XI I )=DUMMY(J)
CONTI NUE
GO TO 400
‘c IFIXIDUMMY(1))
00 408 J=1,K
1=1+1
X( 1 )= 0.0
CONTINUE
CONTINUE
IF (I.EQ.N) GO 10 412
WRITE(IOUT,411) I,N
FORMAT(//’ TOTAL NUMBER OF DATA PCINTS TNPUT’,IS,
1 ‘ iS NUT EQUAL TO THE VALUE INPUT FCR THE VARIABLE ‘‘N’’=’,15)
STOP
412 XNN
NLAGS=NLAGS+1
xR=O.
X TL=O.
MI T=0
MPK=0
CURRF LUGRAM
00 15 J=1 ,NLAGS
SUMT=O.
SUM7=0.
SUM I=0.
SU 4=0.
SUM =0.
C
C
C
C
-------
M=N—J—1
XDIF=M
00 5 JJ=J,N
SUM 1=SUM1+X(JJ)
5 SUM4=SUM4+X(JJ)*X(JJ)
Of) 10 I=1,M
SUM?= SUM2+X (I)
SUM3=SUM3+X(I)*X(I+J-l)
10 SUMS=SUM5+X(1)*X(I)
SS1=SUM5—(SUM2**2 ) /XDIF
SS2=SUM4—( SUM I**2)/XDIF
RK( J)=0.
TIP = 0.0
IF(SSI.E0.0.0.GR.SS2.EQ.0.0) GO TC 12
RK(J)=(SUM3—SUM2*SUM I/XDIF)/(S ORT(SS1)*S OPT(SS2))
12 TIA.=1.645*((N—J—1)**0.51
TLP (—1.+TLAI/(N—J)
IF(XTL.NE.0.0.OR.RK(J).GT.TIP) GO If) 14
MIT = J
XTL=1 .0
14 IF(XR.NE.0.0.OR.RK(J).GT.0.0) GO 1015
MRK J
XP= I • 0
15 CONTINUE
WR TIE ( LOUT, 9999)
FORMAT( 1H1)
WPITE( lOUT. 101)
F0RM T( /1/// ,IH ,38( 1H*I I
WPITE( lOUT. 102)
102 FORMAT(39H ***** CURRELOGRA ’ Of T1? E SERIES *****)
WRITEt I [ iUT.103)
103 FOPMAT(1H ,38(JJ-*)I
WRITF IOUT,9999)
WRITF(IOUT.11O)
FORMAT( IH ,37(lh*))
WRITE( lOUT. 111)
FORMAT(38H ********** DISCRETE VALUES *******‘ )
WRITE( lOUT, 110)
N I. AGS 1= NLAG S/2
N LAGS 2=NLAGS—NL AGSL
WRITE(IOUT ,120) (RK(ihI=1 ,NLAGSI)
120 FORMAT(10F8.2 )
WPTTF( LOUT.9999)
WRITF(ICUT ,l25) (RK(j),I=1,t’ LAGS2)
125 FORMAT(10F8.2)
WRI TE(IOUT ,9999)
WRITE (lOUT. 53)
Si FORMAT(lOX,31(1t-*))
WRTTFtLOUT.54) 1T
54 FORMAT(1Qx,23H***** MIT ,I?, H *****)
OD
9999
101
110
111
CURLO79 O
C0RL0 00
C OR IJ)Ri0
C OQLOR?0
CORLO 30
10’ I .0 340
CORLOR 0
COQL0 60
CORL OR7 O
C OPL O9S O
C OP I. 08 90
CUR t. 09 0 1 )
Cfl L0 l 0
CflRLO9’O
COPL O9V)
CO P. L 0940
f’O LO950
CUR 100 60
Oq70
C ORLOO qo
Cn1 1O99r)
CORLI 01)0
C OR L 11) 1 0
CURL 102 1)
i f)
C 0R1104D
C. 0 R L 10 50
rflRL l O6’)
CflRLI 70
C OPL10 0
COR 110 00
CURL U 00
CUR I11 i )
CURI.i 120
CURL1 I3 O
CURL U 40
C 0 RLII 50
CURL 1160
COPLI1 70
CORLI1 0
CflRL 1IQO
CURL 1210
CURL i’! 0
r 0R 112?()
CORII? 30
C DPI 1? 40
CORLI2 SO
CURL 1261)
CURL 17 70
CURL? 280
-------
W jTF(LOIJT,57) frRK CORL I29O
57 FORMAT(1OX,23H***** RK =0 LAG = ,!?, H *****) C ORL1I O O
WRITF IOUT.53) rflRL l llr)
N —101 CIJRL!170
NN = —100 COPL133O
POTNTS=101 CORL 1331
t. IM=(NLAGS—2)/100fl CflQIl ll?
00 140 J = L,LIM CORL 1 I4 O
N N + 100 rflRLllc()
NN = NN + 100 COql l360
00 130 I = 1,101 C ORL1I7 O
XAGI(I) = I + N CflRL1 3B0
PKI(I) = RK(I+NN) CORL13 9 0
110 CONTINUE CORL I4 00
C COPLL4 1 O
iF (J.EQ.LLM) PCINTSNLAGS—((LIM--1)*100) CORL 14I.l
CALL MGRAPH(XAGI ,RK I. POINTS, 1,TITIE,YTITIE, LABEL) flRLl4?0
C CORLI44O
140 CONTINUE CflRt 1450
999 RETUPN CflRL14’ O
END C OPLL4T’)
SUBROUTINE PLOT(T,F,N,NTOT) P LOTODIO
C PL OT O O7 O
C******** PLOTTING FREQUEr CY HISTCGRAMS f LflTO010
Q C PIIJTO O4 O
COMMON IREAD.IWPITE PlJ)T00Y
DIMENSION 1(N) ,Fc ),OISP(3),DY(1l),SCA1F(1),SFRCT( ) PLflT00t 0
!NTFGFR PLINE(61),ASTRX,AX!S,BLANK,PLUS,VOASH,F PL OT O O’()
DATA ASTRX,AXIS,BLANK,PLUS,VDASH/’*’, ’I’,’ ‘,‘+,,, ‘/ PL11TO0 )
0AT SFRCT/1,,2.5,5.,7.5,1O./,DISP/1.5,61.5,31.5/,SCALF/7*f0. , 30./PLflT0090
PLI1TO1 00
C NOPMALIZATION SCI EME. PLOTO 11’ )
C PlflT Ot2 O
FMAX=F(1) PLOT0J 0
FMTN=F(1) 1flTO14fl
DO 4 I=t,N PLOTO I5O
IF(F(Iè—FMAX)2 ,2 ,1 PLOTOI6 O
. FMAX=F(I) P1 T0FT0
? IF( (l)—FMINI3 ,4 ,4 PLOT OU3 O
F fl =F(I) PLflT0l ()
4 CONTINUE L0TO2fl0
IF(ABS(FMAX)—ABS(FM 1N))6,5,5 PLOTO?I”)
5 DIV=MIS(FMAX) PLOTO2’ O
GO TO 7 PLOT O23()
DIV=ABS(FMIN) P1OT0?40
7 NEXP=TFIX(AIOG(OIV)/ALOG(10.J) PL OT O?50
LF(DIV.LT.I..)NEXPNEXP—1 Pt. 0T0260
PP=1O.**NEXP P1 0T0270
P=10.**(—NEXP) PI OT O?A0
-------
C
C
C
F R AC T= D I V*P
DO 8 1=1,5
DIV=SFRCT( I 3*PP
IF(FR. CT—SFRCT( 1fl9,9,8
8 CONTINUE
9 WPITE(IWRITE, 1O.)FMAX.FMIN
10 FORMAT(’l’/’ MAXIMUM = ‘ ,G15.7/’ MINIMUM = ‘,G15.7/)
INDEX THE APPROPRIATE PLOTTER SUBSECTICf .
WRITE(IWRITE, 11) PP
11 FCRMAT(138,’(OUTPUT SCALED BY: ‘,1PF7.I,’)’)
I F(FMIN) 12,20,20
12 IF MAX) 25, 25, 3C
20 INDFX=I
D V (11=0.
YINC=DIV/1O.
WRITF IIWRITE,21)
21 FORMAT(T21 ,’O + =====>•)
GO TO 100
25 INDFX=2
DV( 1 )=-D 1V*P
V INC=DIV/10.
WRITF( IWRITE,26)
26 FflRMAT(T72,’<== — 0’)
GO TO 100
30 INDEX=3
flY( l)=—OIV*P
V INC=f) [ V/5.
WRITE( IWRITE,31)
31 FORM.AT(T42 ,’< — 0 + = =>‘)
100 DO 110 1=2,11
110 DY(fl=DY(I —I)+YINC*P
WRITFI IWR lIE, 120) DY
120 FORMAT(16X , 11F6.1)
WR !TE( IWRITEI 125)
125 FORMAT(’ DO CGNC
1 2X ,’ EXCEEDING’)
XFP=0.0
XNTOT=FLOAT (NTGT)*P
D C 190 J=1,N
I=IFIX(IF(J )/DIV)*SCALE(INOEX)+D!SP( INDEX))
DO 135 L=l,60,6
D I) 120 K=1 ,5
130 PIINE(L+K)BLANK
135 PIINE(L)=VDASH
P1 1N 6 (61 )=VDASH
C
C
P10T0790
P101030 ’)
P1010110
P IOTO I2O
PL 0103 30
P 1010140
P 10 TO 50
PLO 10160
21010370
PL0T03 O
PLOTO I9 O
21010400
L ’ T04I ()
P101042 ’)
P1010410
P1010440
2 1010460
Pt flTf )46()
D L 0104 70
P10104 RO
PLOT O4O()
1 0105 Of)
PLOTOSID
PL OTO S’ O
PLCT05 O
P IOTO S4O
PLOT O SS O
P1 OTOS 60
PLOTO5 70
r’LOTOS RO
PL UT 05 90
PL 0106 00
PLflTO6 10
OL OT O6? ’)
Pt. 0T06’O
PLIITO64O
, ‘LUT069O
0 L 010655
21 0T 06 60
P1010665
“LOTO6 70
P 1OT0 c8O
PLOT 06 O
P1010700
21010710
°LflTO72O
°LO107’ ’)
P1 0T07 40
Pt T07 O
P1010760
FRE0.*’,T?1,’#’,lC ’—————4’),2X,’CLIM. FRFO.’
BRANCH TO APPROPRIATE SUBSECTION.
-------
167
168
169
110
P1 0T07 70
21. flT07’) ’)
PLITOA ’ )()
P1010 810
t OTf ? )
‘LflT081t)
PLOT 9 40
P10108 5 0
DLnTOR ()
21010870
2 10 10880
P10T0 890
2 190
PLOTO9 I’)
PLOT O9 2 ’ )
P1.-nIoglo
PLOTOO 4’)
21OT09 ()
°LflT OO S5
P1. OTO’) 6’)
D L 91 09 0
P IOT OQ 8 O
° LOT 09 ) 0
PLOT 10 0f)
P1011010
Pt.flTI O7 O
MC,RP001()
MGQ P0020
M(R P OQ I,
MGRP OO4()
1GRP0050
P OC) 60
MGP DO’) 70
MGP 20080
20 ” ) 90
MOPPO 100
Mr P01 1’)
MGRP )1 20
GRP0l 10
MGP P01 40
MGRD0 50
MGQP0 t )
MGRPOI 7”)
MGQPOI 00
MGRPO1. 90
MGR O? 00
GP 202 10
M( P0’ 7’)
M(209? 0
‘ -I
GO TO (140,15C,16C),INDEX
140 00 145 1=1,1
[ 45 PITNF(L)=ASTRX
PL INF( I. =4X1S
GO 10 170
150 90 155 1=1,60
155 PLINF(L)=ASTRX
PLINF(61 )=AXI S
GO TO 170
160 IF( 1—31)165,169,167
165 90 166 1=1,30
166 PLINF(L)=ASTRX
GO Tn 169
90 168 1=32,1
P IT NF(L)=ASTRX
PLINF( 31 ). AX IS
P1 ¶NF (I )PLIiS
F P= F ( J )
XFP=XFP+FP
PX=100.O*( I .0—XFP/XNTOT)
WRITEC IWR IT F, 180 )T( J) ,FP, P1 INE ,XFP,Px
180 FIJRMAT(’ ‘,G10.3, IX,F7.3, IX ,61A 1 ,1X,F10.3,5X,F6.2)
190 CONTINUE
WRITF( IWRITE, 195)
195 FORM T(20X, • I ‘ , 10( __I •
RETURN
END
SUF ROUTINE MGRAFH(X ,Y ,NPTS, NPL ,TITLF ,YT! TIE,IABEL)
C
C******** PLOTTING OF CUMULATIVE FREOUF? CIES,P.C. DROFILES
C
COMMON INPT ,IOUT
DIMFNSION X(NPTS) ,Y(NPL,NPTS),xSC(11
TNTEG R TITLEL?O).YTIILE(51) 1 LABtEL(?O)
(OGICAL*1 SP,SPACE ,UIV ,BAR ,MIMkJS ,PIUS,SPT,L1NF(1 ’ )l ),
• SYMB(c) , SYMB1
1 )ATA SPACE/’ ‘/,BAR/’I’/,MINUS/’—’/,PLUS/’+’/,
• S YMB / • 1 • 2 , 1 3 , 1 4 • •5 I • 6 , • 7 • , ‘ P ‘ , 19I / ,
• SYMBL/’*’/
C
XMAX = X(1)
XMIN = X(1)
YMAX1 = Y(1.11
YMTN1 = Y(1 ,l)
00 10 J = L ,NPL
DO tO I = 1,NPTS
XMAX = AMAX1(X(1),XMAX)
XM IN = AMIN1(X(1) ,XMIN)
YMAXI = AMAX L(VCJ,H ,YMAX1)
YMIN 1 = AMIN 1(Y(J,I) ,YMIN1)
-------
10 CONTINUE MGRPO24()
C GRP0250
CALL ROUND( YMAXL,VM INL,VMAX ,YMIN) MflRP0 6()
C********************************************************************** GPPfl270
C M(RPO2R O
C 4Gi P0290
C MGRP OI O’)
WRITtE(10UT 1 120) (TITLE(1I) ,1I=1 ,20) MGQP0 31.()
120 FORMAT(’1’/32X ,20A4//) Mr,RPO3?0
XP XMAX — XMU 1G P0310
VP YMAX — YMIN MflP 034r)
PYS 0.0 GRP0 50
IYT 52 MflPOf)16()
00 70 13 = 1,11 M(RP(fl70
00 70 14 = 1.5 1G P03 0
5P SPACE MGPP0’20
DIV = BAR MGRP04 ()
IYT = l vi — 1 MGRPO4 IO
IF (14 .NE. 1) GC TO 30 MGRP04 0
SP MINUS MGRPO4I O
DIV = PLUS G P044c)
30 K = 0 MGRP O4S O
00 40 15 1,11 *AGRPO4F,r)
K = K + 1 GP P0470
H LINE(K) = DIV GPP0480
If (15 .E0. 11) GO TO 40 M0Rp 0490
00 41 16 = 1,9 MG 0 00
K = K + 1 MGRP O S IO
41 LINE(K) SP G’ P057O
40 CONTINUE MGRPO510
IYTR = 52 — IYT PP0540
PT = .FALSE. MGRP O S5 O
IF (14 .NE. 1) GO TO 50 MGRP O5f D
$PT = .TRUE. MGRP O57 O
YSC = YMAX * RYS*YR/1O. MG P05 0
IF( BS(YSC) .LE. YR/200.) YSC 0.0 M( Pfl59 )
PYS RYS + 1.0 GR 0600
50 nfl 60 17 = 1,NPL MGRPO6 I O
On 60 hA = 1,NPTS MGq.P06
IX l00.*(X(17A)’-XMIN)/XR + 1.49999 GRP06 ()
IY 5Q.*(y(17,17A)—YMIN)/YR 1.4999S MflRPO64O
if (I V .NE. IYT) GO TO 60
IF((IX .LT. 1) .CR. (IX .GT. 101)) GO ID 60
IF(NPI.E0.1) L1NELIX)=SYMB1 M GRP Oo7O
IF(NPL.NE.1) LINE(IX)=SYMB(I7)
60 CONTINUE MGRPO69O
IF PT) WRITE(IOUT,1000) YTITLE(IYTR),YSC,(LINE( 11),1I=1,t01.) GQP0700
1000 FOPMAT(3X,A1,2X,G12.4,1X ,101A1) MGP ! 0710
!F(.NOT.$PTI WRITE(IOUT,1100) YTIILE(!YTP),(IINEUI) ,I1=l,101) MGPPO7 ?()
1100 FORMAT(3X,Al, 15X,IO IA1) MGRP073()
-------
IFU3 .E0. 11) GO IC 80
70 CONTINUE
Rr) RXS = 0.0
00 90 18 = 1.11
XSC(IRI XMIN + RX.S*XR/10.
IF(AOSIXSC(181) .LE. XR/200.1 XSC(I 3) = 0.0
RXS = RXS 4 1.0
WPITF(IUUT , 1200) (XSC(I1 ).1 1=1 .11 2) ,(XS1 11) ,11?,ll,?)
FORMAT(4X, 6G20. T3/14X , 5G20. 3)
WPITF( lOUT, 130) RABEI( 11), 11=1.20)
130 FCJPMAT(//30X,2044/)
P FT URN
END
SU!3POUTINE ROUND(YMAX I,YM IN 1,YMAX,YMIN)
DIMFNSION SEG (12)
DAT. SEGM/I. ,l.2,1.5.2. ,2.5,3. .4. .5. .6.,7.5,10..1?./
ISCAIE —1
IF(YMAX1—YMINL) 20,20.5
5 lF((YMAXI—YMINI I .LE. 101 GO IC 10
TSCAL.F = ISCALE + I
YMAXI YMAXL/lO.
YMINI = YMINI/lO.
GO TO 5
10 IF((YMAXL—YMIN I) .GE. 1.) GO TO 20
ISCALF = ISCALE 1
YMIN1 = YMINI * 10.
YMAX 1 = YMAXI * 10.
GO TO 10
20 IYMP’ YMINL * 10.
IVMAX = YMAXI 4’ 10.
IF(YMTN I .11. 0.) IYMIN = IYMIN — 1
JF(YMAX1 .G1. 0.) IYMAX = IYMAY + I
IFUYMAX .NE. IYMINI GO TO 30
IYMAX = IYMAX + 1
IYMIN = IYMIN — I
30 CENTER = ((1YMAX+IYMIN)*1./(lYMAX—LYMI )*1.)*l.
LEVEL = 6.50 — 4.51 * CENTER
IFREVEI .EC. 11) GO TO 40
IF(LEVEL .EQ. 1) GO 10 50
1F(A S(CENTER) .GT. 1.) GO IC 175
SEG AMAX1(( IYMAX/t11.—LEVEL)) . IYMIN/(1.—IEVEL))
On 35 K1=1 ,12
!F(SEGM(K1) .GE. SEG3 GO TO 36
35 CONTINUE
36 YMAX SEGM(KI)*(11—LEVEL)*10.**1SCAI E
YMIN = SEGM(kI)*(1 —LEVEL)*10.**TSCALE
GO TO 1000
40 YMAX = 0.0
Dr 45 KI=l.12
IF(1O.*SEGM(K1) .GE. (1—JYMIN)) GO TO ‘s6
90
1200
P07 40
‘AGPj 07 .0
MGRPO7 O
MG O77f)
M C P P07 01)
VRPO7 90
MG P POR O()
Y( T P1)1) 10
GRP0R 20
MGR POP 40
MGP POP 50
MGRPOR6 0
DON000I 0
NO 0 1) 7 1)
PDN 0 0 04 O
PON ) )OS0
P ON 000 60
‘flND0070
QON000RI
pON0 0 0q’)
t fl ’)fl 00
PONDO 1 )
PONtJQ1 20
RONDO1 30
‘O )01 40
PDNDO 15 O
flNDOI 60
PONDOI. 70
DONOOI RD
P ONDOI9O
PdND O ? Or
RONDO2IO
POND O??()
RONDO? 30
PO”400?4 0
RONDO? 50
RONDO? 6(1
P ON 00? 70
P UNDO? 0 0
P ONDO29r)
ON0030 ()
P flNflO3 10
P 0N 0 0 320
P ON DO 330
PO OO340
PONOO3 50
PQNDOI6O
PDNDO37 O
-------
45 CONTINUE PONDOIRO
46 YMIN = --SEGM(KI)*10.**(ISCALE+1) ND OI9D
GO 10 1000 R ONI)0400
50 YMIN 0.0 ‘ 0 ’JD041O
00 55 KI=1,12 RONfl04 )
IF(10.*SFGM(KI) .GE. (IYMAX—1)) GO TO P 0N00430
55 CONTINUE POND O44 O
56 YM *X = SEGM(KI)*lO.**(ISCALE+1) P ONDO4SO
GO TO 1000 P0N 00460
175 YMAX = 1.*IYMAX * 10.**ISCALE R0N 0 0470
YMIN 1.*IYMIN * 10.**ISCALE ROND O48O
KI = 12 P D049 )
1000 YMAX = YMAX * 1.00001 PONDOSDO
YMTN = YMIN * 1.00001 PONDOSIO
PETURN R OND O t 5?0
END PONDO S3O
SUBROUTINE DWFM(NC) r)WFMOOI O
C r)WFMO O7C)
C******** DPY WEATHER FLCW MODEL flWFM00 3’)
C OWFM OO4D
INTEGER IDATE1,IDATE2,IDATE3 DWFM O O5 O
COMMON INPT,IOUT DWFM00 0
COMMflN/SET2/ATOT,CWE.PCTTRT( ),DWFB0C,X,E,ALPHA1,ALPHA2,BETAl, WFM0070
t BFT4?,Rf(3 ),Dx,TRTPCTt3),1TSAG,IDISK% ,1DISK0,tPPl,IPR’,IPRl, 1PP4,OWFM00 ’)
2 XK IMAX,XKIMIN,GAMMAI,GAMMA2 OWFM0090
COMMON/SET3/XM2(3).DDATAL( 31,3,31,PFICW,RBOD, PWFMO IOO
1 RTEMP,RDO,Fl(3),F2(3),U(3) ,VLfl,XC(3,31, OWFM O I1O
2 TC13,3),Fj3),ULTLC(3,3),XK2T(1).0fl(1),FLC(3),DC(3, ),DflCONC(3, ),flWFM012°
3 X11)E(3,3),TE(3),0T(3,3),DXCNC(3,3),DPCT(3,31),CD 3l),XMit3), OWFMDII O
4 XG(3),XJ(3),XDG(101),XD,TX,CTX,DTXCt ,DOXDX(3,101),DflX ’1(1O1) , DWFMO14D
5 XM IZ(31,H(31,DENCM(.3),XK IT(3),DUMMY(8723) PWFMO 15O
INTEGER TITLE1(20),YT1TL1(51),LABELI 2O),5T1TLE(2O),JKEY(fl flWFM OI6O
INTEGER DFREQ(313,TITLE( 20) ,YTITLF(51) ,LARELt2O) ,KKEY( 3) wF 0I.7O
DATA TITLE1/’DISS’.’OIVE’,’COX’,’YGFN’,’ PPO’,’FILI’,’S : ‘, DWFM O1AO
I ‘KI ‘.12*’ ‘1 )WFMO I9O
DATA STLTLE/’OISS ’,’OLVE’,’O OX’,’YGEN’,’ PRC’,’FILF’,’ : ‘, 0WFM0200
1 ‘K=l ‘.‘ ‘, ‘PERC’ ,‘ENT • .‘TREA’, ‘TMEN’, ‘1 : ‘, ‘J=t ‘, OWFMO?1()
2 5*’ ‘./ OWFMO2?0
DATA YTITL I/’O’,’I ’,’S’,’S’,’O’,’L’,’V’,’E’,’fl’,’ ‘ ,‘fl’,’X’, DWFMO?3 0
1 ‘ V , ‘ G ‘ ‘ E l ‘N • • • , I I I , ‘ N , ‘C’ , ‘ F , ‘N’ , ‘1 ‘ , ‘R ‘ ‘ A’ , , OWE MO? 40
2 T I , ‘ o , • , I I I M , ‘ G’ / , 9.’ , 15* l • / OW FMO? 50
DATA LABELI/OIIST’ ,’ANCE’ ,’ D0W’, ’NSTR’,’EA ,’ ,’ MI1’,’ES ‘, DWFMO26 O
1 13*’ ‘ DWFMO?70
DATA JKEY/’J=I ‘,‘J=2 ‘,‘J=3 ‘I WFM0280
DATA KKEY/’K=l ‘,‘K=2 ‘,‘K=3 ‘I DWFMO q0
DATA TITLE/’NORM’,’ALIZ’,’EDC’,’UMUL’,’ATTV’,’E ‘,‘K= 1’, DWFM0 00
1 ‘; (C’,’URVE’,’ 1=P’,’RIMA’,’RY,?’,’SEC’,’ONOA’,’RY,3’ , f)WFMO31 ()
2 ‘=TFP’ ,’TIAR’,’Y) ‘.2*’ ‘/ DWFM O3?0
DATA YTITLE/’ ’, ’D’,’R’,’Y’, ‘ ‘,‘W’,’E’,’A’,’T’,’H’,’E’,’ ’ , PWFM03 ()
1 • ,D ,‘A’,’Y’,’S ’, I , , ‘C’ ,‘E’, ‘E’,’D’, ‘I’, ‘N’, PWFM OI4O
-------
2 ‘G’ , ,*GI, 1I ,$ I,s ,INI, s S,S ,IfJ , 4*I 1/
DATA LABEL/’DO C’.’ONCE’,’NTRA’ ,’TLON’.’. MG’,’/L ‘,
1 •____) , [ 5• ,* •, ‘0.5 • ,‘STFP’,’S
2 6*’ 1/
r)ATA IRLANK.IONE/’ ‘,I’/
C
00 950 1=1.31
00 950 J1 .3
00 950 K1 ,3
r )ATA1( 1 ,J ,K)=0.0
950 CONTINUE
C
C
C))>>>>>) READ CARD TYPE 14 <<<<<<<<
C
PE40(INPT,1000)t D.ICALC3,1CALC4,1CA1CC,TPPD,IDIS )
1000 FCRMAT(615)
IF (IDISKO.NE.0) REWIND ICISKD
C
WRITF( lOUT, 1070)
1070 FORMAT(32H1*******************************)
WRITE lOUT, 1080)
1080 FORYAT(32H ***** OXYGEN SAG ANALYSIS *$***)
WR!TF(IOUT, 1090)
1090 FOPM T(32H ***** CWF WASTES ONLY
WPITF (LOUT, 1100)
1100 FORMf T(32H ********************* *****$***)
WPITFI lOUT, 1110)
1110 FORMAT4////133H ********************************)
WR ITF( lOUT, 1120)ND
1120 FO MAT(23H ***** CAV SIMULATED *****)
WRTTE(IOUT.1123) ICALC3
1123 FflRMAT(’ ***** ICALC3 = ‘.15,’
WRITEI1OUT,1124) ICALC4
1124 FORMAT(’ ***** ICALC4 ‘.15,’
WRITE(IOUT ,1121) ICALCD
1121 FORMAT(’ ***** ICALCD = ‘.15,’
WPITF (IOUT,1172J !PPD
1122 FORMAT(’ ***** IPRD ‘,2X, 15 ,’
WRITF( lOUT. 1125) IDISKD
1125 FORMAT(’ ***** ICISKD = ‘.15,’
WPITE(1OUT.1126) IPR1
1126 FDRMAT( ’ ***** IPR I = ‘,2X,I5,’
WR!TF(IOUT,1127) IPR2
1127 FflRMAT(’ IPR2 = ‘,2X,15,’
WP 1TF( IOUT ,1130)
1130 FURM4T133H ********************************)
XND=ND
D=X /5 780.0
7=3600.*24.
H
! WFM015f)
r)WFM0 6()
WFM01 70
DWFMO’ 90
nWFM O4 00
OWFMO4 1 0
flWFMO4’ O
DWFMO4 f)
flWFM O44C)
DWFM O45 O
DWFM04 ()
,WFM04 70
OW F MO 4 R C)
C)WFM04 0
WFM0500
WFM05 l )
C ) W MO 5 ‘1
DW M053O
OWFMO54 O
flWrM OSSt)
!T)WFMOS ‘50
OWFMO5 7t)
DWFMOS RO
C )WFM O59O
DWFMO’5 rr
DWFMO6 10
flWFMO 20
DWFYC)610
OW FMOP 4O
!‘ WFM06 SC)
OWFMO6 ’ 5 C)
WFM06 70
90
OW F MO f, 90
C)WFMO700
0W 07 If)
P W F MO 720
DWFMO73 0
OW FMO7 40
OWFMO7
DWFMO7 SC)
DWFMO7 70
OWFMO78O
q
flWFMOR 00
WF MOP U)
C)WFMC) ?C)
OW FMOR U)
r’ WFM0R4O
-------
AN VF1=0.O DWFM O85 O
C MIXiNG AND RECEIVING STREAM YATHEMATIC 1 noa OWFMOP6 O
rio 000 I=1,ND OWFM OR7O
C f)WFM0 0
C
C>>>>>>)> PFAD CARD T ’PE 15 <<<<<<<<
C r)WFM OgF)
READ(TNPT,1050) IDATEI,IOATE2,IDATE3 ,pFt.C ,RBcO,RTE M P,RDO
1050 FIJPMAT(312,4X,4F1C.O) flWF i09 0
c flWFM0C 4 )
CrJ=CS—RDO
IF Vfl.LT.0.0) CC=0.0 flWFMO97 O
1)fl 2000 K1,3
FLrJ(K)=RFLOW*RFLK)÷DWF r IWFM O9Q O
H(K)=BETA1*F1O(K)**BETA2
XKl G.AMMAl*H(K)**GAMMA2 OWFM IOIO
IF(XKLIGT.XK1MAX*24.) XKL=XK1MAX*24.0 riWFM I O’()
1F(XK1.LT.XK1MIN*24.) XK1=XK1MIN*?4.0
XK T(K )=XK1*1.O47**(RTEMP—20.0 0WF 41040
DFNOM(K)=(1._EXP(_ -5.*XK1T(K)))/(0.02*PTFMP+C.6) 0WFM105
XN1JM 7.5999*(1.O24**(RTEMP_20.))*tALPt Al/9ETA1**1. ) !3WFM 1 OM)
) OWFM!070
D0(K) (RFLOW*RF(K)*CO)/FLO(K) r)WFM10 30
(J(K)=(ALPHAl*L)*Ft.G(K)**ALPH 2 DWFM10 10
V(K U(K)/Z riwF 11oo
F(K)=XK2T(Ki/XK1T(K) )WFM11 tO
F1(K)=FtK —1. OWFMU ?O
F2(K) XK2T(K)—XK1T(K) riWFM11 O
TE K)=0.O OWFM I 14O
!F(tJ(K).GT.0.0) TE(K)=X/U( K) OW M1I5O
XM1(K) SORT(1.O4(4.0*XK1T(K)*E)/U(K)**2.)
XM2(V)=SORT(1.0+(4.0*XK2T(K)*E)/U(KI**2.) OWFM1 I7O
XM12(K)=XM I (K )—XM2IK) DWFMI 1 O
XJ(K) 0.0 OWFM1 ()
XG(K)=O.0 DWFMI2 0 0
IF(E.NE.0.0) XJ(K)=(U(K)**2.)*(1.0—XM1(K))/(2.0* ) flWFM121()
IF(E.NE.0.0) XG(K) (U(K)**2.)*(1.0—XP 7(K))/(2.0*F) r iwFM12 O
2000 CONTINUE PWFMI23O
DO 2100 J=1,3 OWFM 124 O
00 2100 K1,3 DWFM12SO
ULTLO(J,K)(RFLCW*RF(K)*RBOD+DWF*PCTTRT(J )*OWFBOD)/FLO(K)/!JENOM(K)0wFM 1260
2100 CONTINUE )WFMt270
RTTF(IOUT ,22O0) DWFM12 O
2200 FflRMAT(///28H1$************************3 *) DWFM 120 0
WPTTE(IOUT,2300) IDATE1,IDATE2, !OATE3 0WFM1 O0
2300 FORMAT(14H ***** CATE ,I2, /’,I2,’/’,I2,6H *****) 0WFMI1 .O
WRITF(IOUT,2400) OWFM I3?0
2400 FORMAT(?BH *****s*********************) f. M 13 ()
WRITE(IOUT,2500) RFLOW,RBOD,RTFMP,ROC OWFMI14 0
-------
2500 FQRMAT(///24H ***UPSTREAM RIVER FLCW=,F9.2,12H CFS ** F OD=,F6.2, DWFM135O
/15H MG/L ** TEMP. ,F6.2,L9H OEG.CENT. ** C.O.=,F5.2,9H M /L ***) DWFM 13ÔO
C OXYGFN SAG EQUATIONS DWFM I37O
00 4500 J=1,3 0WFM13 O
DO 4500 K=1.3 0WFM13 )0
TC J,K)=0.O DWFM I40 0
XC(J.K)=0.0 flWFM I41O
IF(UITIO(J,K).NE.O.OI RO=DO(K)/U1T1O(J K) flWFM147O
IF(F1(K).NE.0.0.AND.XM12(K).NE.0.O) C, If’ 3310 DWFM 14IO
DC(J,K)=ULTI.G(J,K)*EXP(OO(K)/ULTLfJ(J,K)—l.) ‘)WFM I44O
GO TO 3350 DWFML45O
3310 IF(ULTIO(J,K).NE.O.O) GO TO 3320 DWFM I46O
DC(J,KJ=flO(K) DWFM I47O
GO TO 3350 flWFM 14 0
3320 IF(Rfl.GE.0.O.ANC.RO.LE.(1.0/F(KI))GO IC 3330 0WFM1490
DC(J,K)=D0(k) DWFML5O ()
GO 10 3350 OWFM I6LO
3330 TC(J,K)’=(ALOGIF(K)*(l.O—FI(K)*LJfl(K)/ijLyLO(J,K))) )/(XK1T(K)*FI(K) ) DWFMI52O
OC(J.K)=(XKIT(K)*ULTI OLJ,K)/XK2T(K))*EXp(_XK IT(K)*TC(J,K)) DWFM151O
IF(F.EQ.0.) GO IC 3340 OWFM I54O
TC(J,K)=ALOG(((XM I(K)/XM2(KJ)—RQ*F(K)4po)*XG(K)/XJ(K))/(XJ(K)—XG(K D WFM155O
DWFM I56O
DC(J,K)=(ULTLO(J,K)*XX1T(K))/(xx21(K) xKly(K))*(EXp(XJ(K)*Tc(J,K)) owFM1157O
1— IXM 1(K)/XM2(K))*EXP(XG(K)*TC(J,K)))+Dfl(K)*EXP(XG(K)*TC(J,K)) DWFM I5RO
3340 XC(J,Ki=Ij(K)*TClJ,K)/5280.O OWFM159O
3350 DOCONC(J,K)=CS-CC(J,K) DWFM 160 0
!F(DC(J,K).GT.CS) DOCUNC(J,KJ=0.0 flWFM 161O
IF (DC(J,K).GT.CS) OC(J,KI=CS DWFMI6 ’O
IF(F7(K).NE.0.0) GO TO 3360 DWFM I63O
DT J,K ) XK1T(K)*ULTL0(J,KI*TE(K)*EXP(—XK1T(K)*TE(K))+DO(K)*EXP(—xKflWFM164O
1?T(K)*TE(K)) wFMl65O
GO TO 3.370 0WFM1660
3360 DT(J,KI (EXP(—XK1T(K)*TE(K))—EXP(—XK2T(K)*TE(K)))*XK1T K)*ULTLO(J,flWFM167O
IK)1F2(K)+D0(K)*EXP(—XK2T(K)*TE(K)) !‘ WFM16 ()
IF(E.E0.0.) GO 10 3370 DWFM169 )
DT(J,K)=(ULTLO(J,K)*XK1T(K))/F?(K)*(Exp(xJ(K)*TF(K))—(xM 1(K)/xM2(KDwFMI7o
l))*EXP(XG(K)*TE(K)))+OO(K)*EXP(XG(K)*TF(K)) DWFM I7I.0
3370 DXCNI( J,K)=CS—DI( J,K) DWFM 172 O
EF(DT(J.K) .GT.CS) OXCNC( J,K)=O.0 WFM1.730
IF (DT(J,K).GT.CS) OT(J,K3=CS 0WFM174 0
IF (ITSAG.EQ.1.AND.J.EQ.IPR I.ANO.K.EC. IPR2) DWFM IT5O
1 WR!TE(IDISKO) IOAIE1,IDATE2,TDATE3,DXCNC(J,K) 0WFM1760
C METHOD OF INTEGRATING DEFICIT EQUATION flWFM 177O
XTDE(J,K)=(ULTIC(J,K)+DO(K))/XK2T(K) PWFM 17 3O
IF (E.NE.O.O) XIOE(J,K)=ULTLCIJ,K)*XK1flK)/UK2T(K)—XK1T(K))* PWFM179O
1 (XMI(K)/( XM2(K)*XG(K))—1./XJ(K))—DO(K)/XC(K) DWFM 1ROO
ICH?\R=IBLANK flWFM 18 I O
IF (J.EQ.3.AND.K,EQ.2) LCHAR IONE
WRITE(IfJUT,4000) LCHAR,TRTPCT(J),PF(K),FLC(K) DWFMI8V)
4000 FORMAT(//AI,IOH***PCTTRT=,F4.2,21f1 RIVER FLOW FRACTN=,F4.2, DWFMl 4O
-------
.?1H ***TOT.RIVER FLOW.F7.l,LOH CFS ***) DWFM IR5 O
WRITF(IOUT, 4100)DC(J,K),DOCONC J,K),CS,XIDE(J,K) 0WFM1860
4100 FORMAT(21H ***CRITICAL DEFICTT=,F6.2,18H T G/L ** Df)CONC=,F6.2,210WFM 1870
IH MG/I ** SAT. C.O.=,F6.2,23H MG/I ** INT.DEF.EON=,FR.?, DWFM 18RD
/15H MG—DAY/I ***) DWF 1990
WRITE(IOUT,4200)D1(J,K),DXCNC(J,X) D dFM1900
4200 FORMAT(17H ***D.O. DEFIC!T ,F6.2,18H MG/I ***0.O. X ,F6.?, DWFM I91 O
/11H MG/I *** 0 FM1 ?0
WRITE(IOUT,4300)TC(J,Ki,XC(J,K),TE(K),D,V(K) DWFM I’fl O
4300 FORMAT(8H *** Tr=,F7.2,15H DAYS *** XC=,F6.2,14H MI. *** 1X=, DWFMI94O
/F7.2,2OH DAYS • * DIST. X=,F6.2,16H P’!. *** VEI.=,F5.2, DWFM1 50
hUH FPS ***) OWFMI96 O
WPITE(IOUT.4400)XK1T(K),XK2T(K),ULTLC(J,K),H(K) DWFM1 70
4400 FUPMAT(9H *** Ki’N,E11.5,15H 1/DAY *** !<.2T=,E1 1.5,24H i/DAY *** UIDWFMI9RO
/TIMATE BOD=,F10.1,17’- MG/L *** DEPTH ,F6.2, 1OH FEET ***) OWFM 199O
NN1 1 DWFM?000
IF (ICALC3.EQ.1 .GR. ICALC4.EQ.1) NN1=I.0+DCCONC(J,K)*2.() OWFM?010
IF UCALC3.E0.2 .OR. ICALC4.EQ.2) NNl=I.O+DXCNC J,K)*2.0 DWFM2O2O
IF NN1.LT.1) NN1=1 r)WFM?0’ O
IF INN1.GT.31) NN1=31 9 WFM2 O4O
DDATA I(NN I,J,K)=DDATA ILNN1,J,K)+l.0 DWFM2 O5O
4500 CONTINUE rWFM2O6 O
C OWFM2 O7 ()
C (<<<<(<<<< D.O. PROFILE CALCULATIONS nWF M7 OR O
C PWFM2O9O
IF UCALCD.EQ.0) GO TO 7400 DWF 2100
TITLE 1(8)KKEY(IPRD) DWFM2 I I O
STITLE(8)KKEY( IPRO) DWFM2 12O
DO 7000 IDX=1,1O1 OWFM2 I3 O
XDG(1DX)=(IDX—1)*DX DWFM?140
XD=XDG(IDX)*5280.O DWFM2 15O
00 7000 J=1,3 DWFM2I6O
TX XD/U(IPRD1 DWFM?170
IF (F2(IPRD).NE.0.0.AND.XM12(IPRC).NF.0.0 GO TO 7100 PWFM7 I8 O
DTX=XK IT(IPRD)*UITLO(J,IPRD)*TX*EXP(_XK1T(IPRD)*TX)+DO(IPRD)’)WFM2 I90
1 *EXP(—XK2T(1PRD)*TX) !)WF M??0O
GO TO 7150 OWFM22I O
7100 DTX=XK1T(IPRD)*ULTLO(J,IPRD)*(EXP(—XK 1TtIPRE ))*TX) r) FM 22O
I EXP(—XK2T(IPRD)*TX))/ F2(IPRD)+OC(IPRO)*EXP(—XK2T(IPRO)*TX) DWFM2?30
IF (E.E0.O.0) GO TO 7150 PWFM2?40
DTX=1ULTLC(J,IPRD)*XKIT( PR0))/F2(IPRC)*(EXP(XJ(IPRD)*TX). OWFM?250
I (XM1IIPRD)/XM2(IPRO)*EXP(XG(lPRD)*TX))+D0(I RD)* DWFMZ?60
2 EXP(XG(IPRD)*TX) 0WFM2770
7150 DTXCN=CS—DTX DWFM??80
IF (DTX.GT.CS) DTXCN=0.0 OWFM229 O
IF (DTX.G1.CS) DTX=CS DWFM?30()
DOX0X(J,ICX) CTXCN DWFM23 I O
7000 CONTINUE 0WFM2320
C DWFM?310
C END OF 0.0. PROFILE CALCULATIONS 0WFM2340
-------
C DWFM235O
C 0WFM236 0
C <<<<(<<<<< PRINT OUT MATRIX CF D.C. VALUES >)>>>)>>>> r,WFM?370
C DWFM?380
WRITE(IOUT ,7050) DX DWFM23°0
7050 FORMAT(/////’ ,84(’*’),/’ ** DISSOLVED OXYGEN PROFILES FOR ‘, DWFM?400
1. ‘All.. DRY WEATHER FLOW TREATMENT RATES (J=I,3) **‘,/‘ **‘, DWFM24 IO
2 1OX,’OISTANCE —-) 100 INTERVALS, EACP INTERVAL = ‘,Fi0.2, flWF A2420
3 ‘ MILES’,LOX,’**’,/’ **‘,33X,’UNJTS ir p C,/1’,34X,’**’,/ DWFM24 0
4 ‘ ‘,84(’*’)) DWFM?44 0
DO 7200 J=1 ,3 DWFM245 O
WPITE(IOUT ,725CJ TRTPCT(J),(DOXDX(J,1DX),10X1,10t) DWFM246O
7250 FORMAT(///’ PERCENT TREATMENT = ‘,F4.2,’:’,6(/1OX,20F6.2)) !DWFM247O
7200 CONTINUE DWFM24RO
IF (TCALCO.EQ.L) GO TO 7400 OWFM?49 0
IF (ICALCO.EQ.21 CALL MGRAPH(XDG,flOXOX,lOl,3,TITLF1,YTITL 1,LABEL1)0WFM250’)
IF (TCALCD.LT.3) GO TO 7400 DWFM251D
ICOMB=ICALCD—2 t)WFM25?0
00 7300 IDX=1 ,101 0WFM253()
DflXDX1(IDX =OGXOX(ICOMB,IDX) DWFM2 S4 O
STTTLE(15)=JKEY(ICOMB) DWFM25 S O
7300 CONTINUE !)WFM2560
CALL MGRAPH(XDG,00XDXI,10l,1,STITIE,YTITI1,IABELI) DWFM?5T0
7400 CONTINUE OWFM25RO
ANAVFL=ANAVFL+FLO(3)/ND DWFM7S9O
5000 CONTINUE IDWFM26 0 0
WPITE(1OUT,5100) DWFM26 IO
5100 FORMAT( [ H1) ¶ WFM2620
WRITF( !OUT ,5200)ANAVFL “)WFM26 O
5200 FURMAT(/I/I///////32H *** ANNUAL AVE. TOT.RIVER FLO 4=,F7.1,I0H C! WF 7640
IFS ***) OWFM265 O
C )W M266O
C******** FREQUENCY ANALYSIS OF DISSOLVED CXYGE CONCENTRATIrNS WFM26T0
C DWF 268O
DO 6000 1=1,31
6000 C0(I)=(I—1J*O.5
DO 6050 K=1,3 ¶DWFM27IO
T1T IE(7)=KKEY(Ki DWFM272O
DO 6040 J1,3 DWFM?71C)
00 6010 1=1,31 DWFM?740
6010 DFREQ(IJ=DOATA1(I ,J ,Ki OW M?75()
IF( !CALC3.EO.0) GO TO 6035 0WFM2760
C DWFM?770
CALL PLOT(CD,DFREQ,31 ,ND) OWFM?780
C flWFM2 Y)0
WRITF(IOUT ,6030)J,K OWFM781O
6030 FORMAT(//bH ***J=,I1,bH ***K=,JI) WFM282’)
WPITFUCJUT,6032) ND )WF’ 0
6032 FORMAT(/////20H NORMALIZING FACTOR= ,14,7H O.DAYS) flWFM? 40
-------
6035 NN2 0 DWFM2R5O
00 6040 1=1,31 0WFM7860
NN2 = NN2 + DFPEQ(.1) DWFM2R7O
6040 0PCT(J,I)=100.*(Xf D—NN2)/XND OWFM?RRO
C DWFM2B9 O
IF( JCALC4.GE.1) CALL MGRAPH(CD,OPCT,31,3,T ITLE,YT!TLE,L.ABFL) DWFM29Or)
C ***************************************** ******************************DWFM?9 1’)
C nwF 2q20
6050 CONTINUE DWFM29 0
RETURN 0WF 2940
EN !) )WFM295O
SUBROUTINE DOSAGtNEVNTS ,ND) DOSGOO 1O
REAL 00(51) ,X,TCO ,TOOO ,EVENT( Si) c’c SG00?0
INTFGFR TITLE(20) ,LABEL(20),YTITLE(51),TDATEW(3), DOSGO O3 O
1 TDATED(3),NPKEY(1O1,PLANK DflSGOO4O
INTEGER DAIEt5I,3) ,N , 1DISKW,ID!SKC,D,W,NEVNTS,ND 1)flSG O O SO
INTEGER JKEY(3),KKEY(3),LKEY(3),MKEY(5) flflSG OO6O
COMMON LNPT,IUUT DOSG OO7O
COMMON/SET2/ATOT,OWF,PCTTRT(3),DWFBOC,X,E,ALPH A1,41P942,F3FTAI, OSG0Og()
1 BETA?,RF(3),DX,TRTPCT(3),ITSAG,IDISK ,1DiSKD,IPRl,1PR2,I 3,TPR4,D0SG00 0
2 XK1MAX,XKIMIN,GAMMAL,GAMMA2 c)OSC,O lflr)
DATA JKEY/’J=l ‘,‘J=2 ‘,‘J=3 ‘f,KKEY/ ’K=l ‘,‘K=2 ‘,‘K =3 ‘I, DDSG0I I.0
I LKEY/’L=1 ‘ ,‘L=2 ‘ ,‘L=3 ‘/,MKEY/’P =1 ‘,‘M 2 ‘,‘M=3 ‘, r)OSGO 17 O
2 ‘M=4 ‘ ,‘M=5 ‘I ODSGO1 O
o DATA TITIE/’DISS’,’CLVE’,’D CX’,’YGF , 1 PPO’,’FILE’, ‘ )OSGO14()
1 ‘, ‘,13*’ ‘1 fl 0SG 0150
DATA VTITLE/’D’,’O’,’ ‘,‘C’,’O’, ‘N’, ‘C’,’F’,’N’,’T’,’R’, flSGfl16fl
141 ,‘T •, q ‘,•C’.’N’ ,i I,tA• , ‘T ’ , ‘ ,‘x’,’ ‘, I , D0SG0I 0
2 3*1 I • M , ‘ I ‘ , 1.. ‘ , ‘ E ‘ , ‘ S ‘ ‘ ‘ , ‘ 1 , N , ‘ ‘ , • M’ , ‘ ‘ / , On SGO 1 RD
3 h 1 1,4* ‘I 0 0 5G019 0
DATA BLANK/’ ‘/,NRKEY/’0’,’1’,’2’,’3’,’4’ ,‘5’,’6’,’7’,’8’,’9’/ 0nSGO 0i
DATA LABEL/’SIMU’,’LATE’,’D EV’.’ENTS’ ,’ ,NOT’,’ TTM’,’E—SC’ , OOSGO2ID
I ‘ALED’,’ ON ‘,‘GRAP’.’H (S’,’EE T’,’Af3IE’,’ •ABO’, OOSGO27O
1 ‘VE) 1,5** ‘/ DOSGO?30
N=NEVNTS+ND ! ) SG0240
TIT1 t8)=JKEY (IPRl) OOSG O’ S O
TITLE(9)KKEY(1 PR2) 0flSG07( ()
X=X15280.O flflSGO2l O
T=X ¶3OSGO?RD
IFLAG =0 DflSG0? 0
00 20 1=1,9 DOSG(i )()
Y=T/(i0.0**( —l)/10O.0) DflSGO3IO
TNTGR= IFIX(Y)+1 r) SGO32()
IF (INTGR.GT.l) IFLAG 1 OOSGO3 O
J=1 D0 5G 034()
IF tI.GE.8) J=J+]. O OSGOISD
YT!TLE(23+J)= NRKEV(INTGR) DOSGO D
IF (IFLAG.EQ.O.AND.INTGR.E0.1 .Af D.J.1T.8 VT! TLE(?3÷J) F3LANK flSGO 7O
PEALNINTGR—1
T=T -(REALN*(10.O**(9—1)/I00.0)) OflSGO39 0
-------
604 00
00560410
00 560420
00560410
r) 0 56044r ,
D0SGO45 )
D0S60460
DOSGO4 70
D OS 604
D SG04 90
00560500
r)fl 5(()51j)
0 0S 605”°
1)0560510
00560540
1 )OSGO5 co
r)nSr,0560
00560570
00560500
00560590
00560600
DflSGO6 0
Dfl 560620
P OSG O6 I O
00560640
OflSGO6’50
1)0SG0660
DflSG0 70
D O S G06 80
D OSGO69O
DOS GO? 00
D SG01 1.0
1)0 5607 0
DOS 607 10
DO SG O7 40
DOSGO7 70
00560760
00560710
0 0 SG O7 80
00560790
DOS008 00
00560810
DO SG D8 20
OSGO8 I O
DUSGO8 40
DO SGOB 50
B OS GO 8 60
0 0 SG OR 70
DOS 608 80
DOSGO8 90
0
N)
C
C
C
C
C
C
20 CONTINUE
IF (IOISKW.GT.O1 REWIND LDISKW
IF (IDISKD.GT.0) RFW1ND IDISKO
I CNT=t)
IF tND.EQ.0.AND.NEVNTS.GT.0) GO TO ICC
IF (ND.GT.0.AND.lEVNTS.EQ.0) GO TO 200
IF (Nfl.GT.0.AND.NEVNTS.GT.0) GO IC 3CC
WRITF( IOUT,I0)
10 FORMAT(//’ NEVNTS AND ND VALUES ECUAt TO C CONFLICT WITH
1 ‘VAlUE OF ITSAG GREATER THAN 0’)
GO TO 999
WET WEATHER DO VALUES CNLY >>>>>>>>>>
100 TITIF(101=LKEY(IPR3)
TITIF(11)MKEY( IPR4)
00 150 I=1 ,N
ICNT=ICNT+1
EVFNT( ICNT )= I
R€AD(IOISKW) (DATE(ICNT , 12) ,12=1 ,3).DC(ICNT)
IF IICNT.IT.5fl GO TO 125
WRITE(IOUT,1lO) X
111) FORMAT(’l’,/’ LISTING OF WET WEATHFP EVENTS AND CORRESPOND’
I ,‘ING D.C. VALUES AT X = ‘,FlO.2,’ MILES DOWNSTPFAM’,
2 III ’ EVEt T NO.’.T17 ,
3 ‘DATE’ ,T3C ,’O.O.’/’+ ‘ ,T17 ,’ __‘,T3O,’.. . ._..._’/)
00 130 11=1,51
WP.ITE( IOUT ,115) EVENT(I1) ,(CATE( I1,12),1?=1,3),00( 11)
115 FORMAT(’ ‘.F8.0,T15,12 ,’/’ ,12 ,’ /‘,I?,T29,F6e?)
130 CONTINUE
CALL MGRAPH(EVENT,DO ,51, 1,TITIE,YTITLE,LABEL)
ICNT=0
GO TO 150
125 IF (I.IT.N) GO TO 150
WRITE(IOUT,110) x
DO 140 I1=l ,ICNT
WRITE(IOUT,115) EVENT(Il),(C TE(Il,I2),1? 1,3), )O(11
140 CONTINUE
13= ICNT+1
00 145 11=13,51
EVENT( I1)=N+l+1I— 13
DO(Ifl=0.O
145 CONTINUE
CALL MGRAPH(EVENT ,DO.51 ,1 ,T ITLE,YTITIE,LABELI
150 CONTINUE
6010999
DRY WEATHER 00 VALUES CNLY
200 00 250 I=1,N
-------
ICNT=ICNT+1 DDSGO90 0
EVENTUCNT)=I DOSGO91f)
READ (IDISKO) (OATE(ICNT,12),12=I ,3),DC(ICNT) DDSGO92O
IF (ICNT.LT.51) GO TO 225 DDSGO9V)
WRITE(IOUT,210) X fl SGO94O
210 FORMAT(’l’,/’ LISTING CF DRY WEATHER EVENTS AND CORRFSPOND’,Df)SG095f)
I ‘ING 0.0. VALUES AT X = ‘,FIO.?,’ ‘I1FS DOWNSTREAM’ ,/// ‘flSG096O
2 ‘ EVE1’ T NC.’, DOSG0 70
3 T1T,’DATE’,T3 0,’O.O.’/’+ — _‘,T17,’____’,T3O,’ __’/) DOSG09 0
DO 230 11=1,51 DflSGO9gO
WPITE( IOUT.115) EVENT(I1) ,(OATE( I1,12),12=1,3),DO( 11) DOSG 1000
230 CONTINUE DOSG 1O1D
CALL MGRAPH(EVENT,00 ,51 , l,TETIE,VT! 1LE,IABFL) DOSG 1 O2O
ICNT=O D0SG10 O
GO TO 250 DflSG 1 O4O
225 IF (I.LT.N) GO TO 250 D OSG1 O5c)
WRITE(IOUT,210) X DflSG lD6 O
00 240 I1= 1 ,ICNT flSG1070
WRITE( IOUT,115) EVENT(Il),(CATF(I1,12),12=1,3),00( 11) OOSG1O 30
240 CONTINUE •‘ flSr,1090
13=ICNT+1
DO 245 11=13,51 DOSGUIO
EVENT(Il)=N+1+11—13 DOSG 112O
DO(L1)=O.O OPSG I13O
245 CONTINUE OflS ,114O
CALL MGRAPH(EVENT,DQ,5l,1.,TITLE, TIT1F,1ABE1) )flSG11 0
250 CONTINUE O OSG I I6O
GO TO OflSG l l7O
C r)OSr,1180
C <<<<<<<<<< WET AND DRY WEATHER VALUES >>>>>>>>>> DflcGi Iqr)
C DOSGI200
300 TITLE(IO)=LKEYLIPR3) DOSG I2 IO
TITL 11)MKEY(1PR4) r)rSG l??r)
READ(IOLSKtIJ) (TCATEW (Il),I1=1 ,3) ,TDOW 0 0SG123()
W=0 D SGl240
P.EAD(IDISKD) tTIJATED(12),12=1,3).T000 DOSG17 SO
D 0
NMIN1=N—1 0fl5Gi270
DO 450 I=1 ,NMIN I
ICNT=ICNT*1 0 flSG l?00
EVFNT(ICNT)=I DOSG13 00
IF (W.EQ.NEVNTSI GO TO 320 flflSG1310
IF (D.EQ.ND) GO TO 310 DOSGY !70
IF (TDATEW(31.LT.TOATED(3)) GO TO 310 t)flSG1 3O
IF (TDATEW(3).GT.TDATED(3)) GO TO 320 0 0 SG1340
IF TDATEW(1).LT.TDATED(1)) CC TO 310 0 0SG 1350
IF (TOATEW(l).GT.TOATED(I)) GO TO 320 DOSG 136O
IF (TOATEW(2).LT.TOATEO(2)) GO TO 310 0 0SG1370
320 DO(ICNTI=T000 DOSG11 0
DO 330 11=1,3 0 0SG1390
-------
DATEI ICNT,I1)=TDATED(I1)
110 CONTINUE
0=0+1
IF ID.EQ.ND) GC TO 350
READ ( IDZSKO) (TOATEO( [ 2),12=1.3) .1000
GO TO 350
310 001 ICNT)=TDCb
00 340 11=1,3
DATE( ICNT.11ITDATEhi(I1)
140 CONTINUE
IF (W.E0.NEVNTS) GO 10 350
RFAD(IDISKW) (TOATEW(I1). Il=1.3),TDCW
150 I (ICNT.LT.51) GO TO 450
WRITE(IOUI ,410 1 X
410 FORMAT(’l’,/’ LISTING CF COMPOSITE WET AND DRY WEATHER ‘,
I ‘EVENTS AND CORRESPONDING D.C. VA1UES AT X =
2 ‘ MILES DCWNSTREAM’,///’ EVENT NC. ,Tt7,1DATF’,T1l,’D.O.’1’
3 ‘+ ‘ ,T17 ,’___’,T30.’ ‘I)
00 430 iT=i ,si
WPITE( IOUT,115) EVENTU I),(DATE(I1,12),!2l . 3 ) . 00 ( 11)
410 CONTINUE
CALL MGRAPH(EVENT,00,51, 1,TITLE,VTETIE,IABEL)
ICNTO
450 CONTINUE
!CNT= ICNT+1
FVENT ICNT)N
IF (W.LT.NEVNTSI GO TO 455
flD( ICNT)=TDCO
00 453 12=1,3
DATE( ICNT, 12)=TDATED( 17)
453 CONTINUE
GO TO 460
455 DO(ICNT)T0(1W
00 458 12=1.3
DATE(ICNT,!2ITOATEW( 12)
458 CONTINUE
460 WPITF(IOtJT .410) X
DO 440 Il=1.ICNT
WPITE( IOUT, 115) EVENT( II).IDATF(I1 ,12).I 2=1,3),00(II)
440 CONTINUE
13= ICNT+1
00 445 11=13.51
FVFNT( I1)=N+1+I1— 13
flfl ii )=O.0
445 CONTINUE
CALL MGRAPHIEVENT.OO,51 , 1,TITLE,YTITLE,LAPEI)
999 PFTUPN
FND
t \)
0
r- ,OSGL4 00
fl SG14I0
0flSG147 )
DPSG14 ID
On SG144 0
f)flSG I4 SD
DOSG I46O
0flSGl4 0
0flSG1’s V)
DOSG 1490
DPSG1 0O
00SG .S to
r)nsG l s2n
DOSGI5 10
flSG1,540
DOSGI 550
DOSG 15M)
r) SGt 570
riOSGI 5 qr
DOSG 1 590
On SGI6 0 0
r)r)sr,lo1
flflSGl ’7()
OOSG 16 I 0
flflSG1640
fl SG1 S0
fl OSG Ib6O
0OSG1 70
DOS G 1 6 0
flflSGl 90
DOSG1.700
POSG171 0
D OSG I7?0
fl SG17 30
OSG1740
DflSG 175O
00 561760
oosr, 177o
flflSGi78 O
00561790
flS 618 00
DflSG 18 10
DOSG 1R”()
DOS 61 30
DOSG1 R40
r)QS(fl U50
00561. R60
DOSG! 1 O
00561880
V
-------
TECHNICAL REPORT DATA
(Please read Instructions on th reoerse before corn pie ting
1 REPORT NO. 2.
EPA—600/2—79--100
3. RECIPIENT’S ACCESSION NO.
4. TITLE AND SUBTITLE
LEVEL III: RECEIVING WATER QUALITY MODELING
FOR URBAN STORMWATER MANAGEMENT
5. REPORT DATE
August 1979(Issuing Date)
6.PERFORMINGORGANIZATIONCODE
7. AUTHOR(S)
1iguel A. Medina, Jr.
8. PERFORMING ORGANIZATION REPORT NO.
3. PERFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELEMENT NO.
Department of Civil Engineering lBC822, SOS 2, Task 7
Duke University 11. CONTRACT/GRANT NO.
Durham, North Carolina 27706 Grant No. R—802411
12. SPONSORING AGENCY NAME AND ADDRESS Cm. OH
lunicipal Environmental Research Laboratory—-
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, Ohio 45268
13. TYPE OF REPORT AND PERIOD COVERED
Final 5/77—11/78
14.SPONSORINGAGENCVCO I3E
EPA/60 0/l4
15. SUPPLEMENTARY NOTES
Project Officers: Richard Field and Chi—Yuan Fan, Storm and Combined
Sewer Section, Edison, N.J., (201) 321—6674, FTS 340—6674
16. ABSTRACT
A simplified continuous receiving water quality model has been
developed as a planning guide to permit preliminary screening of areawide
wastewater treatment strategies. The model simulates the hypothetical
response of the stream or tidal river system to the separate and combined
effects of waste inputs from: 1) upstream sources, 2) dry weather urban
sources, and 3) wet weather urban sources. The total hours of runoff—
producing rainfall throughout a year are separated into storm events by
defining a minimum interevent time. For a given storm event, the runoff
and pollutant loads are summed and critical dissolved oxygen concentra-
tions are estimated as a function of several hydrodynamic and biochemical
?arameters. Alternative control strategies are evaluated in terms of
relative impacts by determining the probability of occurrence of water
quality violations. Model output includes the downstream dissolved
oxygen sag curves computed per each event, and the dissolved oxygen
)rofile computed at a user—specified location downstream for all simula-
ted events. An application to the Des Moines River at Des Moines, Iowa,
is presented.
7, KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
C. COSATI Field/Group
ombined sewers, Storm sewers,
athematical models, Surface water
runoff, Computer programs, Stream
)ollution, Water pollution
Urban runoff pollution
Des Moines (Iowa),
Combined sewer over—
flows, Water pollution
effects, Urban hydrol
ogy, Hydrologic models
ater pollution
l3B
18. DISTRIBUTION STATEMENT
.ELEASE TO PUBLIC
treatment
19. SECURITY CLASS (This Report)
UNCLASSIFIED
21. NO. OF PAGES
217
20. SECURITY CLASS (This page)
UNCLASSIFIED
22. PRICE
EPA Form 2220—1 (Rev. 4—77)
205
GOVERNMENT PRINTING OFFICE IT’S —o57—060 / 5450
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