Ecological Research Series
MODELING NONPOINT POLLUTION
FROM THE LAND SURFACE
Environmental Research Laooratory
Office of Research and Development
U.S. Environmental Protection Agency
Athens, Georgia 30601
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into five series. These five broad
categories were established to facilitate further development a,nd application of
environmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The five series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
This report has been assigned to the ECOLOGICAL RESEARCH series. This series
describes research on the effects of pollution on humans, plant and animal
species, and materials. Problems are assessed for their long- and short-term
influences. Investigations include formation, transport, and pathway studies to
determine the fate of pollutants and their effects. This work provides the technical
basis for setting standards to minimize undesirable changes in living organisms
in the aquatic, terrestrial, and atmospheric environments.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/3-76-083
July 1976
MODELING NONPOINT POLLUTION
FROM THE LAND SURFACE
by
Anthony S. Donigian, Jr.
Norman H. Crawford
Hydrocomp Inc.
Palo Alto, California 94304
Research Grant No. R803315-01-0
Project Officer
Lee A. Mulkey
Technology Development and Applications Branch
U.S. Environmental Protection Agency
Athens, Georgia 30601
U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
ENVIRONMENTAL RESEARCH LABORATORY
ATHENS, GEORGIA 30601
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DISCLAIMER
This report has been reviewed by the Athens Environmental.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 consti-
tute endorsement or recommendation for use.
n
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ABSTRACT
%
Development and initial testing of a mathematical model to continuously
simulate pollutant contributions to stream channels from nonpoint
sources is presented. The Nonpoint Source Pollutant Loading (NFS) Model
is comprised of subprograms to represent the hydrologic response of a
watershed, including snow accumulation and melt, and the processes of
pollutant accumulation, generation, and washoff from the land surface.
The hydrologic algorithms, derived from the Stanford Watershed Model and
the Hydrocomp Simulation Program, have been previously tested and
verified on numerous watersheds across the country. The simulation of
nonpoint pollutants is based on sediment as a pollutant indicator.
Daily accumulation of sediment, generation of sediment fines by raindrop
impact, and transport of available sediment material by overland flow is
simulated for both pervious and impervious areas. The calculated
sediment washoff in each simulation time interval is multiplied by
user-specified 'potency factors' (pollutant mass/sediment mass x 100
percent) that indicate the pollutant strength of the sediment for each
pollutant simulated.
The NPS Model can simulate nonpoint source pollution from a maximum of
five different land use categories in a single operation. In addition
to runoff, water temperature, dissolved oxygen, and sediment, the NPS
Model allows for simulation of up to five user-specified pollutants from
each land use category. Pollutant parameters are specified separately
for pervious and impervious areas within each land use and can vary with
the month of the year to represent seasonal pollution problems. Thus,
the methodology is sufficiently flexible to accommodate a variety of
land use and land surface conditions.
Initial testing of the NPS Model was performed on three urban watersheds
in Durham, North Carolina; Madison, Wisconsin; and Seattle, Washington.
The hydrologic simulation results were good while the simulation of
nonpoint pollutants was fair to good. Sediment, BOD, and SS were the
major pollutants investigated. The use of sediment as a pollutant
indicator appears to be acceptable for nonsoluble and partially soluble
pollutants; however, highly soluble pollutants may not be directly
related to sediment loss and may demonstrate significant deviation from
simulated values. The scarcity of adequate water quality data severely
hampered complete testing and verification of the NPS Model. In essence
the results indicate that the Model can be calibrated to provide
estimates of nonpoint pollutant loadings to stream channels. A detailed
user manual is provided in Appendix A to assist potential users.
Parameter definitions and guidelines for parameter evaluation and
calibration are included. Limitations of the Model and recommendations
i i i
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for future work and application are presented, and possible uses of the
NFS Model are discussed.
This report was submitted in fulfillment of Grant Number R803315-01-0 by
Hydrocomp Inc. under the sponsorship of the Environmental Protection
Agency. Work was completed as of February 1976.
iv
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CONTENTS
Page
Abstract i i i
List of Figures v1
List of Tables 1x
Acknowl edgments xi i
Sections
I Conclusions 1
II Recommendations 3
III Introduction 5
IV The Nonpoint Source Pollutant Loading (NPS) Model 14
V Hydrologic Process Simulation 18
VI Snow Accumulation and Melt Process Simulation 25
VII Nonpoint Pollution Process Simulation 33
VIII Model Testing and Simulation Results 55
IX Model Use and Recommendations 106
X References 109
XI Appendices 117
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FIGURES
No. Page
1 NFS Model Structure and Operation 15
2 The Hydro!ogic Cycle 19
3 LANDS Simulation , 21
4 Snow Accumulation and Melt Processes 27
5 Snow Simulation 30
6 Sources of Nonpoint Pollution 34
7 An Example of the Land Cover Function in the NPS,Model 45
8 Functional Flowchart of the QUAL Subroutine 51
9 Third Fork Creek, Durham, North Carolina 57
10 Monthly Simulation Results for Third Fork Creek
(October 1971-March 1973) 64
11 Runoff and Sediment Loss for Third Pork Creek for
the storm of January 10, 1972 68
12 BOD and SS Concentrations for Third Fork Creek for
the storm of January 10, 1972 69
13 Runoff and Sediment Loss for Third Fork Creek for the
storm of May 14, 1972 70
14 BOD and SS Concentrations for Third Fork Creek for the
storm of May 14, 1972 , 71
15 Runoff and Sediment Loss for Third Fork Creek for the
storm of June 20, 1972 72
16 BOD and SS Concentrations for Third Fork Creek for the
storm of June 20, 1972 73
17 Runoff and Sediment Loss for Third Creek for the
storm of October 5, 1972 74
vi
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No_._ Page
18 BOD and SS Concentrations for Third Fork Creek for the
storm of October 5, 1972 75
19 Pollutant Mass Transport for Third Fork Creek for the
storm of May 14, 1972 77
20 Manitou Way Storm Drain, Madison, Wisconsin 80
21 Monthly Simulation Results for the Manitou Way Watershed
(October 1970-March 1972) 83
22 Runoff, Sediment, and Phosphorus Loss for Manitou Way
for the storm of September 2, 1970 87
23 Runoff, Sediment, and Phosphorus Loss for Manitou Way
for the storm of November 9, 1970 88
24 South Seattle Watershed, Seattle, Washington 89
25 Monthly Simulation Results for the South Seattle Watershed
(January-September 1973) 93
26 Runoff and Sediment Loss for the South Seattle Watershed
for the storm of March 10, 1973 97
27 BOD and SS Concentrations for the South Seattle Watershed
for the storm of March 10, 1973 98
28 Water Temperature and DO for the South Seattle Watershed
for the storm of March 10, 1973 99
29 Runoff and Sediment Loss for the South Seattle Watershed
for the storm of March 16, 1973 100
30 BOD and SS Concentrations for the South Seattle Watershed
for the storm of March 16, 1973 101
31 Water Temperature and DO for the South Seattle Watershed
for the storm of March 16, 1973 102
32 NPS Model Structure and Operation 121
33 Nominal Lower Zone Soil Moisture (LZSN) Parameter Map 157
vn
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No..
34 Watershed Locations for Calibrated LANDS Parameters I58
35 Interflow (INTER) Parameter Map I63
36 Soil Erodibility Nomograph 169
37 Example of the Response of the INTER Parameter 181
38 Schematic Frequency Distribution of Infiltration Capacity
in a Watershed 192
39 Cumulative Frequency Distribution of Infiltration Capacity 192
40 Application of Cumulative Frequency Distribution of
Infiltration Capacity in HSP i94
41 Mean Watershed Infiltration as a Function of Soil Moisture 194
42 Cumulative Frequency Distribution of Infiltration Capacity
Showing Infiltrated Volumes, Interflow, and Surface
Detension I95
43 Interflow C as a function of LZS/LZSN 195
44 Components of HSP Response vs. Moisture Supply 197
45 Surface Detention Retained in the Upper Zone 197
46 HSP Overland'Flow Simulation 201
47 HSP Overland Flow Simulation 201
48 Hydrograph Simulation (0.26 square miles) 203
49 Hydrograph Simulation (18,5 square miles) 203
50 Infiltration Entering Groundwater Storage 204
51 Groundwater Flow 206
52 Potential and Actual Evapotranspiration 206
vm
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TABLES
No. Page
1 Characteristics of Nonpoint Pollution Compared with
Municipal Sewage 9
2 Hydro!ogic Model (LANDS) Parameters 22
3 Snowmelt Parameters "'"' 32
4 Sediment and Water Quality Parameters 53
5 Third Fork Creek Land Use Characterization by Sub-Basins 59
6 Data Summary for Third Fork Creek 60
7 Hydrologic Description of Selected Urban Runoff Events
on Third Fork Creek 61
8 Average and Standard Deviations of Solids and Organics
in Urban Runoff Events on Third Fork Creek 62
9 Monthly Simulation Results for Third Fork Creek
(October 1971-March 1973) 65
10 NPS Model Parameter Values for Third Fork Creek * 66
11 Simulated and Recorded Runoff Characteristics
for Selected Storm Events on Third Fork Creek 78
12 Data Summary for Manitou Way 81
13 Monthly Simulation Results for the Manitou Way Watershed
(October 1970-March 1972) 84
14 NPS Model Parameters for the Manitou Way Watershed 85
15 Data Summary for the South Seattle Watershed 91
16 Urban Runoff Characteristics for Selected Storms
on the South Seattle Watershed 92
17 Monthly Simulation Results for the South Seattle Watershed
(January-September 1973) 94
ix
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Np_._
QB
18 NFS Model Parameters for the South Seattle Watershed
19 Simulated and Recorded Runoff Characteristics 104
for Selected Storm NEvents on the South Seattle Watershed
20 Selected Meteorologic Data Published by the Environmental
Data Service ^b
127
21 Selected Federal Agencies as Possible Data Sources
22 Input Sequence for the NPS Model 13°
23 Sample Input and Format for Daily Meteorologic Data 131
24 Meteorologic Data Input Sequence and Attributes I32
25 NPS Model Precipitation Input Data Format 133
26 NPS Model Output Heading (Annual Water Quality Parameters) 135
27 NPS Model Output Heading (Monthly Water Quality Parameters) 137
28 Calibration Run Output for Storm Events (Sediment and
Water Quality Calibration, HYCAL=2) 13y
29 Production Run Output for Storm Events (HYCAL=3) 141
30 Daily Snowmelt Output (Calibration Run, English Units) 143
31 Daily Snowmelt Output Definitions
(Calibration Run, English Units) 144
32 Monthly Summary Output of the NPS Model 145
33 Annual Summary Output of the NPS Model 146
34 Sample Output and Format for Production Run Output
Directed to Unit 4 (HYCAL=4) 147
35 NPS Model Input Parameter Description
36 NPS Model Parameter Input Sequence and Attributes I52
37 Watersheds with Calibrated LANDS Parameters 159
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No. Page
38 Computed K Values for Soils on Erosion Research Stations 168
39 C Values for Permanent Pasture, Rangeland, and Idle Land 171
40 C Factors for Woodland 171
41 Representative Sediment Accumulation Rates for
Various Land Uses and Location 172
42 Representative Potency Factors for BOD , COD, and SS
for Various Land Uses and Locations 175
xi
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ACKNOWLEDGMENTS
Many individuals contributed directly and indirectly to the completion
of this research effort throughout the duration of the project. The
authors gratefully acknowledge the assistance and coordination provided
by Mr. Lee A. Mulkey, Project Officer, of the EPA Environmental
Research Laboratory in Athens, Georgia.
The following individuals and associated organizations were instrumental in
supplying data for the various test watersheds:
H. Curtis Gunter, U.S. Geological Survey, Raleigh, North Carolina
William S. Galler, Civil Engineering Department, North Carolina
State University, Raleigh, North Carolina
Dale D. Huff, Oak Ridge National Laboratory, Oak Ridge, Tennessee
Peter Weiler, University of Wisconsin, Madison, Wisconsin
John Buffo, Municipality of Metropolitan Seattle, Seattle, Washington
Harvey Duff, Seattle Engineering Department, Seattle, Washington
Their assistance is sincerely appreciated.
In addition to the principal investigator, Dr. Norman H. Crawford and
project manager, Mr. Anthony S. Donigian, Jr., numerous staff personnel
at Hydrocomp contributed to the research work. Dr. Yoram J. Li twin
assisted in preparation of the final report and was responsible for
software development and water quality calibration and testing. Mr.
James Hunt directed the test watershed selection and supervised the data
preparation and hydrologic calibrations as performed by Mr. Stan
Praisewater, Mr. Jack Kittle, and Mr. John C. Imhoff for the three test
watersheds. Dr. Alan M. Lumb and Dr. Thomas N. Debo developed
guidelines for estimation of hydrologic parameters while Mr. Malcolm
Leytham and Dr. George Fleming investigated the estimation of sediment
parameters. Drafting and graphical expertise was provided by Mrs.
Margaret Muller and Mr. Guy Funabiki.
XII
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SECTION I
CONCLUSIONS
(1) The Nonpoint Source Pollutant Loading (NFS) Model can simulate land
surface contributions of nonpoint pollutants from a variety of land
uses. Model testing on three urban watersheds, comprised of
residential, commercial, industrial, and open land, indicated good
agreement between recorded and simulated hydrology and pollutant
washoff.
(2) The NPS Model continuously simulates hydrologic processes,
including snow accumulation and melt, and the nonpoint pollutant
processes of accumulation, generation, and transport from the land
surface. The Model can accommodate up to five land use categories
and simulates water temperature, dissolved oxgygen, sediment, and
up to five user-specified nonpoint pollutants from each land use.
(3) Review of the literature has shown that existing nonpoint pollution
models do not consistently represent the physical processes of soil
erosion and pollutant transport from both pervious and impervious
land surfaces. The Universal Soil Loss Equation is not applicable
to the continuous simulation of soil erosion processes although it
has been used for this purpose. Thus, the NPS Model was developed
to provide a consistent method of simulating soil erosion and
nonpoint pollution transport from both pervious and impervious
areas. The Model is designed for immediate application by planning
agencies in the analysis of nonpoint pollution problems.
(4) The hydrologic methodology of the NPS Model has been extensively
applied, tested, and verified on numerous watersheds of varying
size across the country. Simulation results were good on the
watersheds tested in this study, and similar accuracy can be
generally expected in other areas.
(5) Sediment and sediment!ike material can be used as an indicator of
the land surface contributions of many nonpoint pollutants. Thus,
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specification of the pollutant strength, or potency, of sediment in
conjunction with the simulation of sediment yield from pervious ana
impervious areas provides a workable methodology for simulating
nonpoint pollution. The NPS Model algorithms are based on this
concept. Although the simulated pollutants in this study were
limited to sediment, biochemical oxygen demand, and suspended
solids, the methodology is applicable to most insoluble and
partially-soluble pollutants including many nutrient forms, heavy
metals, organic matter, etc. However, highly soluble pollutants
may demonstrate significant deviation from the simulated values.
(6) The NPS Model provides estimates of the total land surface loading
to water bodies for various nonpoint source pollutants. Since the
Model does not simulate channel processes, comparison of simulated
and recorded values should be performed on watersheds less than 250
to 500 hectares (1 to 2 square miles) in order to avoid the effects
of channel processes on the recorded flow and water quality. Size
limit will vary with climatic, topographic, and hydrologic
characteristics. Whenever channel processes appear to be
significant, the output from the NPS Model should be input to a
model that simulates stream processes before simulated and recorded
values are compared.
(7) Due to incomplete quantitative descriptions of the processes
controlling nonpoint pollution, calibration of certain Model
parameters by comparing simulated and recorded values is a
necessary step when applying the NPS Model to a watershed.
Although all parameters can be estimated from available physical,
topographic, hydrologic, and water quality information, calibration
is needed to insure representation of the processes occurring on
the particular watershed.
(8) The NPS Model can provide long-term continuous information on
nonpoint pollution that can be used to establish the probability
and frequency of occurrence of pollutant loadings under various
land use configurations. Thus, when properly calibrated, the NPS
Model can supplement available nonpoint pollution information and
provide a tool for evaluating the water quality impact of land use
and policy decisions.
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SECTION II
RECOMMENDATIONS
(1) Application of the NPS Model to watersheds across the country is
the primary need at this time. Although the Model has been tested
on three watersheds, further application is required before it will
be acceptable as a general and a reliable model. These
applications will provide additional information on parameter
evaluation under varying climatic, edaphic, hydro!ogic, and land use
conditions, and may expose areas requiring further development and
refinement in the simulation methodology.
(2) The application and use of the NPS Model as a tool for evaluating
the impact of land use policy on the generation of nonpoint
pollutants should be demonstrated. This could be done in
conjunction with local planning agencies who might assist in Model
application, benefit from simulation results, and have access to
the NPS Model for continuing use in the planning process. Such a
project would demonstrate the utility of the NPS Model in a
real-world setting.
(3) To promote use of the NPS Model, user workshops and seminars should
be held to acquaint potential users with the operation, application,
and data needs of the Model. In addition, a central users'
clearinghouse could be initiated to (a) provide assistance to users
with special problems, (b) recommend possible sources of data, (c)
categorize and collect parameter information on calibrated
watersheds, and (d) direct future improvements in the Model as
indicated by the needs and comments of the users. The availability
of these services would greatly facilitate, expand, and promote the
use of the NPS Model.
(4) Further research and development of the NPS Model should be
directed to the following topics:
(a) development of computer programs to further assist user
application, such as: plotting and statistical analyses
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routines; data handing and management programs; and
self-calibration and parameter optimization procedures.
(b) testing and application of the NFS Model on agricultural,
construction, and silvicultural areas to examine special
problems and pollutants associated with these land use
activities.
(c) development of a stream simulation model to accept output from
the NFS Model and perform the necessary flow and pollutant
simulation for in-stream processes. Such a model would help
eliminate the watershed size limitation of the NFS Model.
(d) continued research and refinement of the land surface
pollutant washoff algorithms with examination of the behavior
of highly soluble pollutants.
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SECTION III
INTRODUCTION
It is becoming increasingly evident that the water quality goals
established by the Federal Water Pollution Control Act Amendments
(FWPCAA) of 1972 cannot be attained by regulation of only point source
pollution. Indeed, in many areas pollutants emanating from nonpoint
sources comprise the major contribution to water quality degradation.
This is especially true for rural and agricultural lands. Even in urban
areas, where point source pollution is frequent, the importance of
nonpoint pollution in overall water quality management has been clearly
demonstrated (1, 2). The U.S. Environmental Protection Agency,
responsible for the administration of FWPCAA, has stated the following
reasons for the control of nonpoint source pollution (3):
(1) attainment and maintenance of water quality objectives may be
impossible using only the point source controls;
(2) inequity may result from imposition of point source controls
only;
(3) nonpoint source controls may be the most cost-effective.
.- /f
Before nonpoint sources can be adequately controlled, evaluation and
prediction of their extent and origin must be performed. This report
describes the development and initial testing of a tool, in the form of
a mathematical model, and a methodology for the evaluation of nonpoint
source pollution.
NONPOINT SOURCE POLLUTION
To fully realize the extent and nature of nonpoint source pollution, a
formal definition would be helpful. However, a clear precise definition
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is not presently available. The FWPCAA of 1972 do not specifically
define nonpoint pollutants. Section 208 requires that the responsible
agencies identify those nonpoint source pollution problems of concern in
the individual planning areas (4). Thus, the issue is side-stepped, and
the responsibility to define this problem is passed to the states and
planning agencies. As with many elusive concepts, nonpoint pollution's
specified in terms of its negative, i.e., what it isn't. Literally, it
is defined as pollutants that are not discharged from point sources.
However, this is not entirely satisfactory since nonpoint pollution
includes many small point sources (rural septic tanks, small animal
feedlots, combined sewer overflows, etc.) for which effluent permits are
not required under the National Pollution Discharge Elimination System (5),
In the absence of a precise-definition, the EPA has provided substantial
guidance for the understanding of nonpoint pollution problems by
specifying various categories and sources. The categories have been
enumerated as follows (6):
sediment
mineral pollutants (acid mine drainage, salinity,
heavy metals)
nutrients (especially nitrogen and phosphorus compounds)
pesticides
biodegradable pollutants
thermal pollution
radioactivity
microbial pollution
The first five categories are considered to be the major types of
nonpoint pollutants of immediate concern. Sediment is by far the
largest pollutant in terms of total annual volume. An often quoted
figure of 3.6 billion tonnes (4 billion tons) is considered to be the
total annual sediment production from the land surface of the United
States, 50 percent of which is estimated to reach lakes and streams (7).
In addition, sediment is a carrier for many other nonpoint pollutants.
Pesticides and mineral pollutants are important because of their
toxicity to various forms of plant and animal life. Nutrients
accelerate the eutrophication process and biodegradable pollutants
deplete the oxygen content of surface waters. Thus, all of these five
categories have considerable impact on water quality. Thermal pollution
and radioactivity are relatively minor nonpoint pollutants although they
can be associated with silviculture and mining activities, respectively.
Microbial pollution (pathogens and bacteria) can be a significant health
problem produced by livestock and rural human waste disposal. However,
these problems are highly individual in nature and are not well
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characterized or documented. Microbial pollution continues to be a
major research topic.
Sources of nonpoint pollution are most often discussed in terms of the
land use activities that produce the various pollutants. The major land
useiactivities contributing to nonpoint pollution include:
urban development
agriculture
urban and rural construction
silviculture
mining
Man is obviously the benefactor from these activities, but he is also
the culprit behind the generation of pollutants. The impact of these
activities on water quality is indicated by the types of substances
produced. Urban development contributes a wide variety of materials
from all five of the major pollutant categories. The relative mixture
of land uses in the urban area (residential, commerical, industrial,
open space, etc.) affects the relative quantities of the individual
pollutants. Agriculture, construction, and silvicultural operations
produce sediment, nutrients, and pesticides as nonpoint pollutants.
Mineral pollutants (dissolved salts) can be a product of agricultural
activities through irrigation return flows. Mining produces mineral
pollutants, such as acids, heavy metals, and dissolved salts. Although
certain localized investigations (8, 9) in urban areas have not
established the relationship between land use and water quality, more
general studies (2, 10, 11, 12) have clearly indicated the importance of
different types and concentrations of human activities on water quality.
Indeed, EPA Administrator, Russell E. Train, has advocated land
management techniques as a control method for nonpoint pollution (13).
General statements about the quantities of specific pollutants are
difficult to make because of the inherent complexities and variability
of nonpoint pollution. In addition to land use, pollutant quantities
are affected by hydrologic and topographic characteristics, vegetal
cover, season of the year, street cleaning, land management practices,
etc. In short, anything that influences the accumulation of pollutants
on the land surface or the mechanisms which transport pollutants from
the land surface has a direct impact on nonpoint source pollution.
The end result of all these factors is generally presented in the
literature as concentrations of various water quality pollutants
measured in the runoff. Unfortunately, literature values are often
sporadic or fragmentary. Differences in sampling procedures, analytical
methods, and measured parameters complicate comparisons of reported
data. In addition, mixed land uses in watersheds hide the effects of
specific land use activities.
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In spite of these problems a brief discussion of the relative magnitudes
of nonpoint pollution is in order. A conventional literature review, an
integral part of many reports on nonpoint pollution, will not be
presented here. Several excellent reviews are available (2, 6, 12, 14,
15). Table 1 has been abstracted from various sources to provide a
quantitative overview of nonpoint pollution and a comparison with
typical municipal sewage. Pollutant content of precipitation is
included in Table 1 to indicate the magnitude of contamination from this
relatively uncontrollable source. Urban runoff is generally considered
to have a BOD content similar to secondary municipal effluent, while
suspended solids and coliform numbers are significantly greater than
secondary effluent (10}. Agricultural cropland is considered to be a
major contributor of sediment and attached nutrients (6). Although the
average nutrient concentrations from agricultural land in Table 1 are
low to moderate, the resulting mass loading of nutrients to streams can
be large due to the high volume of runoff and the large acreage of
agricultural land. Approximately 60 percent of the nitrogen and 42
percent of the phosphorus input to water supplies each year is
attributed to agriculture (16). Unmanaged forest and rangeland
generally produce low pollutant concentrations; they are often
considered to be natural, or background conditions due to low human and
animal populations and relatively undisturbed acreage. Animal feedlots
produce high concentrations of nutrients and oxygen demanding material.
Construction areas produce extreme sediment loads, with attached
nutrients, pesticides, and other pollutants, during the period when land
surface disturbances are occurring and the land is subject to erosive
forces. Both animal feedlots and construction areas are localized
problems that produce intense nonpoint pollutant loads in the specific
area of concern.
Perhaps the most valid statement that can be made about nonpoint source
pollution is that it is extremely variable. The ranges of pollutant
concentrations in Table 1 are a partial indication of this variability.
Except for irrigation return flow and ground water contributions,
nonpoint pollution occurs exclusively durfng storm events. Pollutant
concentrations vary by orders of magnitude from one watershed to
another, from one storm to the next, and within a single storm event.
Thus, average pollutant concentrations have very little meaning in
quantifying the extent of specific nonpoint pollution problems. Total
pollutant mass loading, the product of pollutant concentration and flow,
is a better measure for evaluating these problems (2, 10, 12). The fact
that total mass is the product of concentration and flow indicates the
dual importance of water quality (pollutant concentration) and
hydro!ogic (flow) characteristics in the proper analysis of nonpoint
source pollution.
8
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«
Table 1. CHARACTERISTICS OF NONPOINT POLLUTION COMPARED WITH
MUNICIPAL SEWAGE3
(mg/1) '
Municipal sewage
typical untreated
typical treated
primary
secondary
General
characteristics
precipitation
forested land
agricultural
cropland
urban land
drainage
animal feedlot
runoff
Individual studies
Kansas
beef cattle
feedlot
Waynesboro, VA
forested (site 2)
Durham, N.C.
urban
(Bryan study)
Durham, N.C.
urban
(Colston study)
Cincinnati, Ohio
urban
Coshocton, Ohio
rural
Seattle, WA
urban industrial
SS3 site
Seattle, WA
urban commercial
CBD site
Tulsa, OKh
urban
mixed land uses
Madison, HI
urban residential
Eastern
South Dakota
agriculture runof!
cultivated (rain'
cultivated (snow!
pasture (snow)
grassland (snow)
Total solids
Mean"
>
2730
1440
140e
303e
545
280
1241
187
150
134
Range
194-8620
10000-
25000
274-13800
194-86201
199-2242
Susp solids
Meanb
200
80
15
1223
227
313
80
190
367
1021
51
18
42
Range
100-350
40-120
10-30
11-13
<„
5-7340
15-311
27-73401
5-1200
5-2074
84-2052
BOD
Meanb
200
135
25
,7
14.5
17 '
7
19
22
11.8
Range
100-300
70-200
15-45
12-13
12-160
1000-
11000
1000-
11000
2-232
1-173
.5-23
8-18
COD
Meanb
500
330
55
80
179
170
111
79
95
66
85.5
148
49
69
62
Range
250-750
165-500
25-80
9-16
85-110
3100-
41000
4000-
40000
24-52
40-600
20-1042
20-610
30-159
42-138
N03-N
Meanb
0.4
0.83
0.72
0.60
1.5
1.0
0.9
0.8
Range
0.14-1.1
0.1-1.3
10-23
Total N
Meanb
40
35
30
9
3
0.96C
.851
\f
4.55K
t
4.1^
3'^
4.2*
3.6k
tenge
1.2-1.3
0.3-1.8
920-2100
200-450C
1.05-1.68°
.1-11.6°
2.91f
2.82f
.36-1.481
Total P
leanb
10
7.5
5.0
.58d
.82
1.1"
1.71
.329
.879
1.151
.98
1.05
0.44
0.67
0.43
lange
.02-. 04
.01-. 11
.02-1.7
.2-1.1
290-360
0-0.33
.15-2.5d
.2-16
.02-7.3d
25-3. 3d
.
.54-3.491
Ref
10
10
10
12
12
12
12
12
12
14
68
8
2
2
72
72
10
71
60
60
60
a. Data presented here are for general comparison only. Since different sampling methods, number of samples, and other
procedures were used, the reader should consult the references before using the data for specific planning purposes.
b. Individual values may apply to average or median. Check cited reference for clarification.
c. Total Kjeldahl Nitrogen, mg/1 N
d. Total phosphate, mg/1 P
e. Suspended plus settleable solids.
f. Sum of organic, ammonia, nitrite, and nitrate as mg/1 N.
g. Hydrolyzable and ortho as mg/1 P.
h. Values refer to the mean and range of mean values for 15 test areas.
i. Organic Kjeldahl nitrogen.
j. Only soluble orthophosphate.
k. Sum of organic, ammonia, and nitrate as mg/1 N.
1. Range of values reported below.
-------
MODELING NONPOINT SOURCE POLLUTION
Because of the complex relationships and the limited field data,
statistical methods of analysis are not effective in the evaluation of
nonpoint pollution (17). Models based on such methods most often
utilize average conditions and characteristics (land use, climate,
hydrology, etc.) that cannot represent the inherent variability.
Moreover,= extrapolation to other geographic areas or conditions is often
impossible. The only viable method of modeling nonpoint pollution is to
represent in mathematical form, the physical processes that determine
the accumulation/deposition, attenuation, and transport of pollutants to
the aquatic environment. These water quality-related (chemical,
physical, biological) and hydrologic processes that occur on the land
surface and in the soil profile are continuous in nature; hence,
continuous simulation is critical to their accurate representation.
Although nonpoint source pollution from the land surface takes place
only during storm events, the status of the land cover, soil moisture,
and pollutant prior to the event is a major determinant of the volume of
runoff and mass of pollutants that can reach the stream during the
event. In turn, the land cover, soil moisture, and pollutant status
prior to the event is the result of processes that occur between events.
Street cleaning operations, urban and industrial activity, agricultural
operations, vegetal growth, and pollutant transformations all critically
affect the mass of pollutant that can enter the aquatic environment
during a storm event. Models that simulate only single storm events
cannot accurately evaluate nonpoint pollution since between event
porcesses are ignored.
When modeling nonpoint source pollution, the need for continuous
simulation is joined by the fact that the transport mechanisms of such
pollutants are universal. Whether the pollutants originate from
pervious or impervious lands, from urban or agricultural areas, or from
natural or developed lands, the major transport modes of surface runoff
and sediment loss are operative. (Wind transport may be significant in
some areas, but its importance relative to surface runoff and sediment
loss is usually small.) In this way, the simulation of nonpoint
pollution is analogous to a three-layered pyramid. The basic foundation
of the pyramid is the hydrology of the watershed. Without accurate
simulation of runoff, modeling nonpoint pollutants is practically
impossible. Indeed, models of nonpoint source pollution have been
referred to as "hydrologic transport" models (18, 19) to indicate the
importance of the hydrologic processes. Sediment loss simulation, the
second layer of the pyramid, follows the hydrologic modeling. Although
highly complex and variable in nature, sediment modeling provides the
other critical transport process that must be represented. The final
layer of the pyramid is the interaction or relationship of various
10
-------
pollutants with sediment loss and runoff, resulting in the overall
transport simulation of nonpoint source pollutants.
In the past decade, the engineering and scientific community has
witnessed a surge of modeling efforts related to water resource
evaluation ,and management. Modeling of nonpoint source pollution has
been a recent topic receiving considerable attention in the past five
years. This attention will likely continue and intensify as a result of
the impetus provided by the FWPCAA of 1972. Some of the available
models that consider various forms of nonpoint pollution include:
Agricultural Chemical Transport Model, ACTMO (20)
Agricultural Runoff Management Model, ARM Model (21)
Battelle Urban Wastewater Management Model, (22)
Hydrocomp Simulation Program, HSP (23)
Pesticide Transport and Runoff Model, PTR Model (24)
Storm Water Management Model, SWMM (25, 26)
Storage, Treatment, and Overflow Model, STORM (27)
Unified Transport Model, UTM (28)
Water-Sediment-Chemical Effluent Prediction, WASCH Model (29)
This list is by no means complete. It is representative of the types of
models pertinent to nonpoint pollution currently in the literature.
Many of these models are comprehensive and include simulation of lakes
and reservoirs, in-stream water quality, soil profile chemical and
biological reactions, wastewater collection systems, financial and
economic aspects, etc. The capabilities for simulating nonpoint
pollution are generally divided between urban and agricultural runoff
problems; few, if any, models include the capability of evaluating both.
In addition, few of the models, especially those for urban areas, are
based on the philosophy of continuous simulation that is critical to the
modeling of nonpoint pollution. In a recent review of urban runoff
models, only two out of 18 models combined the capabilities of
continuous simulation and modeling urban storm runoff (30). In the
agricultural realm, the importance of continuous simulation has been
more consistently recognized (ACTMO, UTM, PTR Model, ARM Model, etc.)
because of the obvious continuous nature of the land surface and soil
profile processes that determine the extent of nonpoint pollution.
Thus, in spite of the plethora of available models, sufficient gaps in
model capabilities and differences in methodology warrant further
research and development work. Development and refinement of models are
continuing processes that parallel the understanding of the important
physical processes and other advances in technology.
11
-------
OBJECTIVES AND SCOPE OF THIS STUDY
The overall objectives of this study were to (1) develop a simulation •
model to evaluate and quantify the contribution to watercourses from
nonpoint sources of pollution, and (2) develop a methodology using the
above model to allow preliminary estimates of nonpoint source pollution
by regional, state, and local planning agencies.
A model for nonpoint pollution requires mathematical expressions
(algorithms) to represent complex physical processes. This complexity
must not be reflected in the application of the Model if the Model is to
be widely used. Extensive expertise in model calibration and
application are not required. The Nonpoint Source Pollutant Loading
(NPS) Model utilizes the state of the art in modeling nonpoint pollution
in conjunction with a methodology of parameter evaluation to simplify
model use.
The scope of this work is limited in the sense that only land surface
contributions to nonpoint source pollution are evaluated. Subsurface
and ground water pollutants are not considered, and channel processes are
ignored. The NPS Model is concerned with the pollutant input to a
water body from surface nonpoint pollution. Thus, the NPS Model will
need to be interfaced with a stream model if overall water quality is to
be evaluated in watersheds where fn-stream water quality processes are
significant.
The study effort was generalized to consider nonpoint pollutants from
the major land use categories of urban, agriculture, forest, and
construction. Although the emphasis in model testing has been on urban
watersheds, the methodology is sufficiently flexible to allow
application to other land uses. The water quality constituents
considered in this study include temperature, dissolved oxygen (DO),
sediment, biochemical oxygen demand (BOD), and suspended solids (SS).
However, other constituents specified by the user can be evaluated.
Since this study is concerned solely with surface pollutants, all
constituents are assumed to be conservative. Efforts are underway to
include in the NPS Model the capability to simulate surface nutrient
contributions (nitrogen and phosphorus) from both urban and rural lands.
REPORT FORMAT
An overall description of the structure and operation of the NPS Model
is provided in Section IV. The hydrologic and snowmelt processes are
discussed in Sections V and VI, respectively. Detailed algorithm
12
-------
descriptions for these processes have been included in Appendices B and
C, respectively. Since the objective of this work is the modeling of
nonpoint pollution, Section VII describes the pollutant accumulation and
transport processes, and their representation (algorithms) in the NFS
Model. Model testing and simulation results for three urban watersheds
are presented in Section VIII. Section IX enumerates possible uses of
the NPS Model, its application to wastewater planning requirements of
the FWPCAA (Section 208}, and topics for future research and further
development.
The appendices include, in addition to the hydrologic and snowmelt
algorithm descriptions, the NPS Model User Manual (Appendix A), a sample
input sequence (Appendix D), and the NPS Model Source Listing (Appendix
E). The User Manual in Appendix A is intended to be a general handbook
for use and application of the NPS Model. Model operation is described;
data requirements and sources are listed; input format and output option
specifications are explained; and guidelines for parameter evaluation
and model calibration are provided. The potential user is advised to
develop a reasonable understanding of the NPS Model parameters and their
significance prior to attempting,,use of the NPS Model. Any model is
only a tool, and a tool used improperly can do more harm than good.
13
-------
SECTION IV
THE NONPOINT SOURCE POLLUTANT LOADING (NPS) MODEL
The Nonpoint Source Pollutant Loading (NPS) Model is a continuous
simulation model that represents the generation of nonpoint source
pollutants from the land surface. The Model continuously simulates
hydrologic processes (surface and subsurface), snow accumulation and
melt, sediment generation, pollutant accumulation, and pollutant
transport for any selected period of record of input meteorologic data.
The NPS Model is called a 'pollutant loading1 model because it estimates
the total transport of pollutants from the land surface to a
watercourse. It does not simulate channel processes that occur after
the pollutants are in the stream. Thus, to simulate in-stream water
quality in large watersheds, the NPS Model must be interfaced with a
stream simulation model that evaluates the impact of channel processes.
The Model uses mathematical equations, or algorithms, that represent the
physical processes important to nonpoint source pollution. Parameters
within the equations allow the user to adjust the Model to a
specific watershed. Thus, the NPS Model should be calibrated whenever
it is applied to a new watershed. Calibration is the process of
adjusting parameter values until a good agreement between simulated and
observed data is obtained. It allows the NPS Model to better represent
the peculiar characteristics of the watershed being simulated.
Fortunately, most of the NPS Model parameters are specified by physical
watershed characteristics and do not require calibration. However, the
importance of calibration should not be underestimated; it is a critical
step in applying and using the NPS Model. Guidelines and
recommendations for parameter evaluation and calibration are provided in
the User Manual, Appendix A.
MODEL STRUCTURE AND OPERATION
The NPS Model is composed of three major components: MAIN, LANDS, and
QUAL. Figure 1 is an operational flowchart of the NPS Model
14
-------
MAIN
1
1
(LANDS)
(QUAL)
(^sri
READ MODEL
wT^
PARAMETERS
^
(^ BEGIN YEARLY LOOP ^
<
READ METEOROLOGIC DATA
(^ BEGIN MONTHLY LOOP ^)
INITIALIZE MONTHLY VARIABLES
(^ BEGIN DAILY LOOP J^>
f- „_
INITIALIZE DAILY VARIABLES
•
-
READ PRECIPITATION
r
YES /fRRORINPRECIPITATION\
\ SEQUENCE? /
INO
BEGIN 15-MINUTE YES /
... tiuin imnu i e ITnRM
NO
/ LAST INTERVAL \
\ HF THF nAY? /
\ / /
/ LAST m
\MONTHORS
FVFMT7 \
f OF THE \.NO
IMULATION? y
YES
/LAST MONTH OF X, N0 |
\SIMULATION OR YEAR1 \/
YES
PRINT MONTHLY SUMMARY
/ LAST
YEAR \NO_
V OF SIMULATION? /
1
^YES
PRINT YEARLY SUMMARY
i
oT^>
g
(LANDS)
i
( QUAL )
1
OS
•a:
SUBROUTINE ( J
| DECISION POINT <^ )>
OPERATION
PATH DESIGNATION
Figure 1. N PS model structure and operation
15
-------
demonstrating the sequence of computation and the relationships between
the components. The Model operates sequentially reading parameter
values and meteorologic data, performing computations in LANDS and QUAL,
providing storm event information, and printing monthly and yearly
summaries as it steps through the entire simulation period. MAIN, the
master or executive routine, performs the tasks contained within the
dashed portion of Figure 1. It reads Model parameters and meteorologic
data, initializes variables, monitors the passage of time, calls the
LANDS and QUAL subprograms, and prints monthly and yearly output
summaries. LANDS simulates the hydro!ogic response of the watershed and
the processes of snow accumulation and melt. The QUAL subprogram
simulates erosion processes, sediment accumulation, and sediment and
pollutant washoff from the land surface. During storm events, LANDS and
QUAL operate on a 15-minute time interval. LANDS provides values of
runoff from pervious and impervious areas while QUAL uses the runoff
values and precipitation data to simulate the erosion and pollutant
washoff processes. For nonstorm periods, LANDS uses a combination of
15-minute, hourly, and daily time intervals to simulate the
evapotranspiration and percolation processes that determine the soil
moisture status of the watershed. Since nonpoint pollution from the
land surface occurs only during storms, QUAL operates on a daily
interval between storm events to estimate pollutant accumulations on the
land surface that will be available for transport at the next storm
event. Figure 1 indicates the individual operations of the MAIN program
that occur on 15-minute, daily, monthly, and yearly intervals; these
operations support the LANDS and QUAL simulation.
MODEL CAPABILITIES
The NFS Model can simulate nonpoint pollution from a maximum of five
different land uses in a single simulation run. The water quality
constituents simulated include water temperature, dissolved oxygen (DO),
sediment, and a maximum of five user-specified constituents. All are
considered to be conservative due to the short resident time on the land
surface that is characteristic of nonpoint pollution. Pollutant
accumulation and removal on both pervious and impervious areas is
simulated separately for each land use. The Model allows monthly
variations in land coyer, pollutant accumulation, and pollutant removal
to provide the flexibility of simulating seasonally dependent nonpoint
pollution problems, such as construction, winter street salting, leaf
fall, etc. Although separate land uses are considered in the QUAL
subprogram, LANDS combines all pervious and impervious areas into two
groups for the hydrologic simulation regardless of land use. Pervious
and impervious areas are simulated separately because of the differences
16
-------
in hydrologic response and because of the importance of impervious areas
to nonpoint pollution in the urban environment.
Output from the NPS Model Is available in various forms. During storm
events, flow, water temperature, dissolved oxygen, pollutant
concentration, and pollutant mass removal are printed for each 15-minute
interval. Storm summaries are provided at the end of each event, and
monthly and yearly summaries are printed. The yearly summaries include
the mean, maximum, minimum, and standard deviation of each variable. To
assist interfacing with other continuous models, the NPS Model includes
the option to write the 15-minute output without summaries to a separate
file (or output device) for later input to the stream model. In
general, the NPS Model output is provided in different forms so that the
information will be usable irrespective of the type of analysis being
performed. The User Manual, Appendix A, contains a full description of
the output and options of the NPS Model.
17
-------
SECTION V
HYDROLOGIC PROCESS SIMULATION
Since the hydrologic behavior of a watershed is a major determinant of
the extent of nonpoint source pollution, an understanding of hydrologic
processes is basic to the simulation of such pollutants. This section
will describe briefly the hydrologic processes simulated in the HPS
Model with particular emphasis on those mechanisms of importance for
nonpoint pollution. Hydrologic model parameters will be defined and
discussed in order to provide a sound basis for use and application of
the NFS Model.
THE HYDROLOGIC CYCLE
The science of hydrology deals with the overall occurrence and
distribution of water on land and in the atmosphere. The central
feature of hydrology is the hydrologic cycle, which can be defined as
fol1ows:
The circuit of water movement from the atmosphere to the earth and
return to the atmosphere through various stages or processes such
as precipitation, interception, runoff, infiltration, percolation,
storage, evaporation, and transpiration. Also called water cycle (31),
Figure 2 schematically portrays the processes and interactions that
comprise the hydrologic cycle. Entering the cycle in the precipitation
phase, interception by forests or crops, overland flow across the land
surface, infiltration through the soil profile, and movement through
rivers and streams are all possible components in the cycle.
Evaporation from water bodies and evapotranspi ration from vegetation
directly returns moisture to the atmosphere. Then condensation of
atmospheric moisture will result in precipitation returning to the land
surface to begin another cycle. The streamflow resulting from a
watershed is the end product of the variable time and areal distribution
of precipitation, evapotranspiration, soil moisture conditions, and
physical land characteristics.
18
-------
PRECIPITATION / / /
\
/ rn
/EVAPOTRANSPfRATION
/ / /
INTERCEPTION
iNTEgFLOW
GROUNOWAT6R
SURFACE'.RUNOFF
I/
Figure 2. The hydrologic cycle
19
-------
SIMULATION METHODOLOGY
The task of simulating the complex hydrologic processes described above
is performed in the NFS Model by the LANDS subprogram. LANDS simulates
the hydrologic response of the watershed to inputs of precipitation and
evaporation. If snowmelt simulation (described below) is to be
performed, additional meteorologic data is required. LANDS continuously
simulates runoff through a set of mathematical functions derived from
theoretical and empirical evidence. It is basically a moisture
accounting procedure for water in each major component of the hydrologic
cycle. Parameters within the functions are used to characterize the
land surface and soil profile characteristics of the watershed. These
parameters can be determined for a watershed from soil information,
topographic characteristics, meteorologic data, and comparision of
simulated and recorded streamflow.
A flowchart of the LANDS subprogram is shown in Figure 3. The
mathematical foundation of LANDS was originally derived from the
Stanford Watershed Model (32) and has been presented, with minor
variations, in subsequent publications (5, 6). The LANDS algorithms are
presented in Appendix B. The major parameters of the LANDS subprogram
are defined in Table 2 and in the User Manual (Appendix A). These
parameters are essentially identical to those in the corresponding
subprogram of the ARM Model (21) and in Hydrocomp Simulation
Programming, HSP (23). The only exceptions are the parameters
pertaining to the simulation of overland flow from impervious areas,
i.e., LI, SSI, and NNI. This modification will be described below.
The LANDS subprogram operates continuously on a 15-minute interval
throughout the simulation period. Daily potential evapotranspiration
and precipitation for 15-minute or hourly intervals are required inputs.
If snowmelt simulation is not performed, precipitation first encounters
the interception function. Interception is a storage function dependent
on vegetation and land cover. In many areas interception capacity will
vary with the season of the year. When interception storage is filled,
any remaining precipitation is added to the moisture supply of the
infiltration function, which performs the basic division of available
moisture into surface detention, interflow detention, and infiltration.
Surface detention includes overland flow and an increment to upper zone
soil moisture storage. Interflow detention is a delay mechanism
controlling the release of interflow to the stream. Infiltration and
percolation from the upper zone provide the means by which moisture
reaches lower zone storage. From lower zone storage, moisture moves to
active ground water storage from which the ground water component of
streamflow is derived.
20
-------
ACTUAL
T
POTENTIAL ET
PRECIPITATION
TEMPERATURE
RADIATION
WIND.DEWPOINT
SNOWMELTj>
FUNCTION
STORAGE
CT -.- INTERCEPTION -_
ET STORAGE
^1 4
CT -. LOWER ZONE -_
ET *" STORAGE
T
INTERCEPTION
i
i
IMPERVIOUS
AREA
i
INFILTRATION
\
LOWER ZONE
OR
GROUNDWATER
STORAGE
1
i
ACTIVE
OR DEEP
GROUNOWATER
STORAGE
EVAPOTRANSPIRATION-ET
SURFACE
RUNOFF
INTERFLOW
.
4g>
ET ~- —^
DEEP OR INACTIVE
GROUNDWATER
STORAGE
^~-
UPPER
\
ZONE
, \
1 UPPER ZONE
STORAGE
UPPER
DEPLf
'
GROUND
r
ZONE
:TION
WATER
STORAGE
\
OVERLAND
FLOW
OVERLAND
FLOW
INTERFLOW
\
1
\ STREAM /
Figure 3. LANDS simulation
-------
Table 2. HYDROLOGIC MODEL (LANDS) PARAMETERS
EPXM The interception storage parameter, related to vegetal cover
density.
UZSN The nominal upper zone soil moisture storage parameter.
LZSN The nominal lower zone soil moisture storage parameter.
K3 Index to actual evaporation (a function of vegetal cover).
Kl The precipitation adjustment factor.
PETMUL The potential evapotranspiration adjustment factor.
K24L The fraction of groundwater recharge that percolates to deep
groundwater.
INFIL A function of soil characteristics defining the infiltration
characteristics of the watershed.
INTER Defines the interflow characteristics of the watershed.
AREA The area of the watershed.
L, LI Length of overland flow plane (pervious and impervious).
SS, SSI Average overland flow slope (pervious and impervious).
NN, NNI Manning's "n" for overland flow (pervious and impervious).
IRC, KK24 The interflow and groundwater recession parameters.
22
-------
Other than stream-flow and losses to inactive ground water,
evapotranspiration is the only remaining component in the moisture
balance performed in LANDS. Evapotranspiration occurs at different
rates from each of the various moisture storages shown ,.in Figure 3.
Daily potential evapotranspiration values are input and transformed to
hourly values by an empirical diurnal variation. Actual
evapotranspiration is calculated on an hourly basis from interception,
upper zone, and lower zone storages, and on a daily basis from
ground water storage. From interception storage, evapotranspiration
occurs at the potential rate. Any remaining potential is satisfied
initially from the upper zone and then from the lower zone, depending on
existing moisture conditions.
OVERLAND FLOW SIMULATION
The process of overland flow is treated separately to emphasize its
importance'in the simulation of nonpoint source pollutants. Since the
NFS Model is concerned solely with the surface washoff of pollutants,
overland flow from pervious and impervious areas is the key transport
mechanism to be simulated. The contributions from pervious and
impervious areas are simulated separately but in an analogous fashion.
Separate parameters describing the pervious and impervious overland flow
planes (length, slope, roughness) are required for the NFS Model.
As described above, the infiltration function assigns a fraction of the
incoming moisture to surface detention, which in turn is divided into
upper zone soil moisture storage and overland flow. This division is
performed for pervious areas in each simulation interval. The fraction
of overland flow which will reach the stream channel in any interval is
determined by the characteristics of the pervious overland flow plane
and the routing procedure (described in Appendix B). The fraction of
overland flow that does not reach the stream is available for
infiltration in subsequent time intervals. This is the interaction
between the infiltration and overland flow mechanisms on pervious lands.
For impervious areas, infiltration does not occur. The overland flow
component is determined as the fraction of incoming rainfall that occurs
on impervious areas directly connected to the stream channel. As with
pervious flow, a fraction of the impervious flow component reaches the
stream during the current time interval. However, the water that does
not reach the stream remains on the impervious overland flow plane and
is added to the incoming rainfall in the subsequent time interval.
Thus, the distinction between pervious and impervious overland flow is
that all rainfall that occurs on the impervious overland flow plane will
eventually reach the stream channel as overland flow, whereas delayed
infiltration is possible on the pervious overland flow plane.
23
-------
CONCLUSION
As mentioned above, Appendix B presents the mathematical formulations of
the hydrologic processes shown as functions in Figure 3. A thorough
understanding of this material and the parameters described in Table 2
is necessary for a successful calibration and application of the NFS
Model. This methodology for hydrologic simulation has been successfully
applied to hundreds of watersheds in the U.S. and abroad. Modifications
of the algorithms have been employed in the National Weather Service
River Forecast System (33), the Kentucky Watershed Model (34), and the
Georgia Tech Watershed Simulation Model (35). The User Manual in
Appendix A provides guidelines for parameter evaluation and calibration
based on past experience.
24
-------
SECTION VI
SNOW ACCUMULATION AND MELT SIMULATION
In the simulation of water quality processes, the mechanisms of snow
accumulation and melt are often neglected. The stated reasons for this
omission generally pertain to an assumed minor influence on water
quality, the extensive data requirements, and the extreme complexity of
the component processes. Obviously, in the southern latitudes of the
United States and at many coastal locations, snow accumulation during
winter months is often negligible. However, considering its location in
a temperate climatic zone, over 50 percent of the continental United
States experiences significant snow accumulation. In many areas
streamflow contributions from melting snow continue through the
spring and early summer. For many urban areas, water supply during the
critical summer period is entirely a function of the extent of snow
accumulation during the previous winter. Section III stressed the
importance of continuous simulation in the modeling of nonpoint source
pollutants. Snow accumulation and melt is a major component in
continuous hydro!ogic simulation, and an important part of any
hydrologic model that is to provide a basis for the simulation of water
quality processes.
PHYSICAL PROCESS DESCRIPTION
Snow accumulation and melt are separate but often concurrent mechanisms.
The initial snow accumulation is largely a function of air (and
atmospheric) temperature at the time of precipitation; whereas, snowmelt
is an energy transfer process in the form of heat between the snowpack
and its environment. Eighty cal/cm2 of heat must be supplied to obtain
one centimeter of water from a snowpack at 0 °C (203 cal/cm2 or 750
Btu/ft2for one inch of melt at 32 °F). This heat or energy requirement
is derived from the following sources:
25
-------
(1) solar (shortwave) radiation
(2) terrestrial (longwave) radiation
(3) convective and advective transfer of sensible heat from
overlying air
(4) condensation of water vapor from the air
(5) heat conduction from soil and surroundings
(6) heat content of precipitation
The complexity of the snowmelt process is due to the many factors that
influence the contributions from each of the above energy sources.
Figure 4 conceptually indicates the factors and processes involved in
snow accumulation and melt on a watershed. The combination of
precipitation and near or below freezing temperatures results in the
initial accumulation of the snowpack. Although relative humidity and
air pressure influence the form of precipitation, temperature is the
major determining factor in the rain/snow division. The rain/snow
division is important to the hydrologic response of the watershed.
Precipitation in the form of rain can become surface runoff immediately
and will contain sufficient heat energy to melt a portion of the
snowpack. On the other hand, precipitation in the form of snow will
augment the snowpack and is more likely to contribute to soil moisture,
ground water, and subsurface flow as the snowpack melts. Just as the
snow begins to accumulate, the major melt processes are initiated.. Both
solar (shortwave) radiation and terrestrial (longwave) radiation are
contributors to the snowmelt process, although solar radiation provides
the major radiation melt component. The effective energy transfer to
the snowpack from solar radiation is modified by the albedo, or
reflectivity, of the snow surface and the forest canopy in watersheds
with forested land. Terrestrial radiation exchange occurs between the
atmosphere, clouds, trees, buildings, and even the snowpack itself.
Generally, solar radiation dominates the net radiation exchange during
daylight hours resulting in a heat gain to the snowpack. Terrestrial
radiation continues during the night causing a net heat loss from the
snowpack during the dark hours. The radiation balance, in addition to
the other heat exchange processes, allows melting of the pack during the
day and a refreezing during the night.
When air temperatures are above freezing, convective and advective heat
transfer to the snowpack produces another melt component. Condensation
of water vapor on the snowpack from the surrounding air and the opposing
mechanism of snow evaporation from the pack, respectively, add and
subtract a component in the snowpack heat balance. Wind movement is a
significant factor in all of these processes; its effect on heat
transfer is readily acknowledged by anyone who has experienced a
chilling northeaster. Depending on climatic conditions the condensation
and convection processes can contribute to a significant portion of the
snowmelt.
26
-------
ro
TERRESTRIAL
RADIATION
RAIN/SNO
DETERMINATION
Xv. •'
-// / DEWPOINT
" TEMPERATURE
/ ,'/ /a.
SNOW SURFACE x>*
CONVECn'VE
HEAT EXCHANGE
SNOW
OMPACTIO
AND DEPTH
LIQUID STORAGE
IN SNOWPACK
WIND
GROUNDMELT
LAND SURFACE
AREAL EXTENT
OF SNOW COVER
Figure 4. Snow accumulation and melt processes
-------
The remaining melt mechanisms include the ground melt resulting from
heat from the land surface and surroundings and rainmelt due to the heat
input of rain impinging on the snowpack. Ground melt is due to the
temperature difference between the snowpack and the land surface and
subsurface. Areas that experience relatively light snowfall and low
temperatures will have a small ground melt component due to the
insulating effects of frost and frozen ground conditions. On the other
hand, ground melt can be significant in areas with rapid accumulation
and deep snowpacks. Urban areas with heat input from roads, buildings,
and underground utilities, and special geologic areas (hot springs,
volcanoes, etc.) can cause an unusually high ground melt contribution.
Snowmelt caused by rain on a pack is usually quite small. Twenty-five
millimeters (1 inch) of rainfall at 10 °C (50 °F) will produce only 3.2
millimeters (0.125 inch) of melt. However, rain often occurs at high
atmospheric humidity when condensation of water vapor can take place.
Condensation of 25 millimeters (1 inch) of water vapor (water
equivalent) can produce 190 millimeters (7.5 inches) of melt. Thus,
water vapor condensation can cause rapid snowmelt and seems to be
responsible for the myth that rainfall causes rapid snowmelt.
The release of melt water from the snowpack is a function of the liquid
moisture holding capacity of the snowpack and does not necessarily occur
at the time of melt. The snowpack contains moisture in both frozen and
liquid form; spaces between snow crystals contain water molecules. As
melt occurs, more water molecules are added to the spaces in the
snowpack until the moisture holding capacity is reached. Additional
melt will reach the land surface and possibly result in runoff. As the
snowpack increases in depth over the season, compaction of the pack
results in a lower depth and a higher snow density. As density
increases the moisture holding capacity of the snowpack decreases due to
less pore space between snow crystals and a change in crystal structure.
Thus, the snowmelt reaching the land surface results from complex
interactions between the melt components, climatic conditions, and
snowpack characteristics. For the most part, the snowpack behaves like
a moisture reservoir gradually releasing its storage. However, the
combination of extreme climatic conditions and snowpack characteristics
can lead to abnormally high liquid moisture holding capacity and sudden
release of melt in relatively short time periods (36). The damage which
can occur during such events emphasizes the need to further study and
understand the snowmelt process.
28
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SNOWMELT SIMULATION
The objective of snow accumulation and melt simulation is to approximate
the physical processes (described above) and their interactions in order
to evaluate the timing and volume of melt water released from the
snowpack. The algorithms used in simulating the processes shown in
Figure 4 are based on extensive work by the Corps of Engineers (37),
Anderson and Crawford (38), and Anderson (39). Empirical relationships
are employed when quantitative descriptions of the process are not
available. An energy balance method of simulation is utilized in the
NFS Model in opposition to conventional temperature index methods in
general use. The energy balance method calculates the various melt
components according to the specific sources of energy in the form of
heat. Meteorologic data series for radiation, wind, and dewpoint are
generally required in addition to air temperature. On the other hand,
the temperature index method uses air temperature as the sole index for
the calculation of energy exchange and resulting snowmelt. In many
instances, the temperature index method has been shown to approach the
accuracy of the energy balance method (39, 40), especially when the
accuracy of the meteorologic data (radiation, wind, dewpoint) is
questionable. Moreover, the minimal data requirements further promotes
its use. However, the energy balance method is generally considered to
be more reliable and accurate if reliable meteorologic data is available
(38, 39, 40). The use of the additional meteorologic data series can
significantly improve snowmelt prediction (41). The energy balance
method provides a sound framework for incorporation of future advances
in the understanding of snowmelt simulation. In addition, short-time
interval simulation of these processes for nonpoint source pollution can
only be attempted in this manner. For these reasons, the energy balance
method was chosen for inclusion in the NPS Model.
A mathematical description of the snowmelt algorithms is presented in
Appendix C. They are identical to those employed in HSP and the ARM
Model and have demonstrated reasonably successful results on numerous
watersheds (42, 43, 44, 45). A flowchart of the snowmelt routine is
shown in Figure 5. The routine operates on an hourly basis.
Meteorologic data specifies the occurrence and amount of precipitation
during the hourly interval; the form of precipitation is determined as a
function of air temperature and dewpoint. The individual melt
components are evaluated; heat exchange calculations within the snowpack
are performed; and the resulting total melt is compared with the liquid
water storage within the snowpack. The end product of the calculations
is the total snowmelt released from the snowpack that reaches the land
surface. This water then enters the hydrologic simulation (Section V
and Appendix B) to participate in the generation of runoff. Since the
LANDS simulation is performed on 15-minute intervals, the hourly melt
29
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Co
o
IEY
HEAT
CD INPUT/OUTPUT
O FUNCTION
Q STORAGE
WATER RELEASED
FROM SNOWPACK
« —
;DK
SNOWPACK
1
f
V
SNOW
EVAPORATION
NEGATIVE
HEAT
STORAGE
GROUND MELT
Figure 5. Snowmelt simulation
-------
values are divided into the shorter time intervals to continue the
simulation. Since the snowmelt process is much slower than the runoff
process, the hourly time interval appears to be adequate.
In addition to precipitation and evaporation, the version of the
snowmelt routine in the NPS Model requires the continuous data of daily
max-min air temperature, daily wind movement, and daily solar radiation.
Because the routine operates on an hourly basis, hourly values for each
of these meteorologic values would be preferable. However, with the
exception of experimental watersheds, few locations would have such
detailed data. Consequently, the routine provides an empirical hourly
distribution for wind movement and solar radiation. The daily max-min
air temperature values are fitted to a sinusoidal distribution assuming
that minimum and maximum temperatures occur during the hours beginning
at 6:00 AM and 3:00 PM. Dewpoint temperature is not a required input
because of the general lack of such data. It is estimated as being
equal to the minimum daily temperature, a reasonable approximation in
many cases (46).
Table 3 defines the input snow parameters required for operation. These
parameters are used to (1) define the physical characteristics and snow
conditions of the watershed, (2) adjust input meteorological data series
to the specific location of the watershed, and (3) modify the
theoretical melt components to field conditions. Evaluation of the
snowmelt parameters is discussed in the User Manual (Appendix A). An
understanding of the physical processes and the algorithm approximations
is critical to the intelligent use of the snowmelt routine.
Consequently, the potential user is advised to read and study the
algorithm descriptions and parameter definitions prior to attempting
application of the snowmelt routine.
31
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Table 3. SNOWMELT PARAMETERS
RADCON Parameter to adjust theoretical solar radiation melt equations
to field conditions.
CCFAC Parameter, to adjust theoretical condensation and convection
melt equation to field conditions.
EVAPSN Parameter to adjust theoretical snow evaporation to field
conditions.
MELEV Mean elevation of the watershed.
ELDIF Elevation difference between the temperature station and the
midpoint of the watershed.
TSNOW Wet-bulb air temperature below which snowfall occurs.
MPACK Water equivalent of the snowpack required for complete coverage
of the watershed.
DGM Daily groundmelt.
WC Maximum water content of the snow.
IONS Index density of new snow at 0° F.
SCF Snow correction factor to compensate for deficiencies in the
gage during snowfall.
WMUL Wind multiplier to adjust observed daily wind values.
RMUL Solar radiation multiplier to adjust observed daily solar
radiation values.
F Fraction of watershed with forest cover.
KUGI Index to the extent of undergrowth in forested areas.
32
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SECTION VII
NONPOINT POLLUTION PROCESS SIMULATION
Section III briefly discusses the nature of nonpoint pollution. Figure
6 schematically demonstrates the contributions of urban, agriculture,
silviculture, and construction activities to the nonpoint pollutant load
entering a water body. Mining activities are not included in Figure 6
because they are highly localized and specific in nature. The total
nonpoint pollution problem is comprised of both the sources of
pollutants, indicated in Figure 6, and the mechanism that moves the
pollutants to the aquatic environment. In other words, the two
processes of concern are:
(1) the accumulation and/or generation of pollutants, and
(2) the transport mechanisms that move pollutants to a
water body.
The transport mechanisms, runoff and sediment loss, are universal
whereas accumulation processes are entirely site specific. The range of
activities in Figure 6 indicates the variable manner in which pollutants
accumulate and become available for transport. Even within a single
land use category, characteristics of the activities will vary with
differences in socioeconomic levels, geographic regions, climate, etc.
A methodology to evaluate nonpoint pollution must include an accurate
representation of the transport mechanisms on the watershed and a
flexible representation of the accumulation processes to allow
adaptation to the specific site and land use.
PAST WORK
Section III notes the recent emphasis on the simulation of nonpoint
pollution and lists various models that have been developed. Urban
areas have received the major attention in terms of application of
33
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Figure 6. Sources of nonpoint pollution
34
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available models. At the present time, nonpoint pollution models that
can be applied to non-urban areas (mostly agricultural and rural land)
are just beginning to emerge from the research community. The next few
years will witness greater application and testing of both urban and
non-urban models as a result of the impetus provided by the FWPCAA of
1972. The urban models most widely used presently include the Storm
Water Management Model - SWMM (25, 26), the Storage, Treatment, and
Overflow Model - STORM (27), and the water quality section of the
Hydrocomp Simulation Program - HSP QUALITY (23, 47). All these models
contain capabilities in addition to the simulation of nonpoint source
pollutants generated from the land surface. However, only the portions
related to nonpoint pollution were evaluated in this work. The methods
of simulating accumulation and transport, or washoff, of nonpoint
pollutants were reviewed and evaluated for possible inclusion in the NPS
Model.
SWMM is an event-oriented model while STORM and HSP QUALITY are
continuous simulation models. The accumulation functions for SWMM and
STORM are essentially identical and can be stated as follows:
(DD * DRDRY, for DRDAY < CLFREQ
TOTDD = (1)
-------
must be specified for each pollutant to be simulated. The formulation
is as follows:
L (T) = L (T-l) * (1-R) + Y (2)
where L (T) = pollutant accumulation at time, T
L (T-l) = pollutant accumulation at time, T-l
R = general removal rate
Y = pollutant accumulation
The general removal rate, R, is a function of street cleaning
(efficiency and frequency), wind, and biochemical processes and can be
evaluated as
R = P * E/D + W + K
(3)
where
P
E
D
W
K
fraction impervious area
efficiency of street cleaning on the impervious area
frequency of street cleaning
pollutant removal by wind
pollutant decay by biochemical processes
The above equation yields an approximate value of R that can be modified
in the calibration process. R is considered a calibration parameter
because of inaccuracies in attempting to quantify all removal processes.
A study by Sartor and Boyd (50) indicates that stated efficiencies of
street cleaning practices are inaccurate with respect to the small
particle size fractions which are highly polluting. Also, the frequency
of street cleaning is often inconsistent and other removal processes are
ignored in the SWMM and STORM formulations. Thus, an accurate
deterministic evaluation of the effects of removal processes is highly
questionable. Calibration is a logical alternative in such situations.
Nonpoint pollutant transport is calculated separately for both
impervious and pervious areas in all three models. In addition, SWMM
and STORM simulate sediment transport, or erosion, from pervious lands
by an entirely separate methodology. The method of calculating
pollutant washoff from impervious areas is basically identical in SWMM,
STORM, and HSP QUALITY. The premise is that the amount of pollutant
washed off is proportional to the amount remaining
dt ~ "Nr
where P = amount of pollutant on the land surface
K = proportionality constant
(4)
36
-------
Rearranging and integrating leads to the basic form of the washoff
function
AP = P. - P = p.d - e"Kt) (5)
o o'
where P0 = pollutant initially on the land surface
P = pollutant on the land surface after time
interval t
AP = Pollutant washed off during time interval t
With the assumptions that K is directly proportional to overland flow and 90
percent of P0 is washed off during one hour at a runoff rate, r, of 0.5
inches per hour, K is evaluated as 4.6r and the equation becomes
AP = P0(l - e~') (6)
This is the basic form of the washoff equation used in all three models
although there are other differences in the overall formulations. The
SWMM and STORM functions adjust P0 by an availability factor, A, which
is also a function of runoff, r. Separate relationships were developed
for suspended and settleable solids (STORM only) due to lack of
agreement with observed values. The relationship for suspended solids
is
A = .057 + 1.4 r 1-1 (7)
As runoff increases larger particles become
more "available" for transport. Without additional verification, the
availability factors appear to be specific to the watershed and
the observed data from which they were developed, thereby reducing the
general applicability of the models.
HSP QUALITY does not include availability factors. However,
accumulation and washoff are calculated separately for each pollutant;
hence, the P0 values are not tied directly to D&D accumulation.
Calibration is used to modify pollutant accumulation and removal rates
to best match the observed data.
The above discussions have been concerned only with impervious areas.
Except for the process of soil erosion, pollutant washoff from pervious
areas is handled in the same fashion. SWMM and STORM use the same value
of K (4.6r) for both impervious and pervious areas. However, STORM and
HSP QUALITY allow the user to specify the K value as an input parameter.
The default value in HSP QUALITY for pervious areas assumes 50 percent
pollutant washoff at a runoff rate of 0.5 inches per hour, resulting in
K = 1.4r.
37
-------
For pervious areas, sediment is a major pollutant for which simulation
is a complex problem. At the present time, MSP QUALITY does not
simulate the soil erosion process; the washoff function described
previously is used for all nonpoint pollutants on both impervious and
pervious areas. SWMM (University of Florida version) and STORM employ
the Universal Soil Loss Equation (USLE) to simulate soil erosion from
pervious areas (51). The USLE is
A = R*K*L*S*C*P (8)
where A = annual soil loss per unit area
R = rainfall factor
K = soil-erodibility factor
L - slope-length factor
S = slope-gradient factor
C = cropping management factor
P = erosion control practice factor
The USLE was developed from statistical analyses of historical soil loss
and associated data on numerous erosion plots at research stations
across the country operated by the Agricultural Research Service.
Guidelines for evaluating the various factors for specific geographic
areas, soil conditions, and agricultural management practices are
provided in the original publication (51). The rainfall factor, R, is
the number of erosion index units in a normal year's rainfall, evaluated
as the sum of the product of storm kinetic energy, E, and maximum
30-minute rainfall intensity (El). The credibility factor, K, is the
erosion rate (soil loss per unit area) per erosion index unit, evaluated
by R, for a specific soil under base conditions. The base conditions
were defined as a cultivated continuous fallow plot with a 9 percent
slope 72.6 feet long. The combined term RK corresponds to the potential
erosion rate from the watershed under base conditions. The remaining
factors (L, S, C, P) in the equation are evaluated as the rates of soil
loss on the watershed to soil loss under the base conditions stated
above; thus, L, S, C, and P adjust the potential rate for effects of
slope length, land slope, cropping and management characteristics, and
erosion control practices, respectively.
i
In the absence of greater understanding of the soil erosion process, the
USLE is a tool for estimating average annual soil erosion and the impact
of land management practices. During the past ten years, a vast amount
of experience with the USLE and with evaluation of its factors has
evolved. This experience has provided a valuable basis for more
accurate quantification of the individual processes controlling soil
erosion. However, the USLE has been modified numerous times to overcome
inherent weaknesses in its1 formulation and to adapt the equation to
localized conditions. Although each of the USLE factors can be
38
-------
evaluated on a storm event basis, the entire equation was "particularly
designed to predict average annual soil loss for any specific field over
an extended period" (51, p. 39). When used for this purpose, the USLE
can provide estimates of average annual soil loss when more accurate
methods are unavailable.
SWMM and STORM use the USLE methodology for simulation of soil
erosion from pervious areas during storm events. This use of the USLE
was rejected in the evaluation of algorithms for the NFS Model for the
following reasons:
(1) The USLE methodology does not account for the effects of
antecedent soil moisture or availability of detached soil
particles. These conditions are critical to the accurate
representation of runoff and sediment loss.
(2) The USLE contains no term to specifically account for the
effects of overland flow, the major transport mechanism by
which soil erosion occurs. Research has shown that runoff is
the best single indicator of sediment yield from small
watersheds (52, 53). This is reflected in recent
modifications of the USLE to specifically include the effects
of runoff (54, 55).
(3) Although the factors in the USLE are directly relevant to the
soil erosion process (especially K, C, P), the formulation of
the USLE does not specifically evaluate the mechanisms of soil
detachment and transport; these are the major determinants of
erosion during storm events.
(4) The USLE was originally developed for estimates of average
annual soil loss from croplands east of the Rocky Mountains. It
has had limited success in other areas and has been modified
numerous times to adapt to local conditions.
In summary, SWMM, STORM, and HSP QUALITY were reviewed to investigate
methods of representing the pollutant accumulation and transport
functions important to nonpoint pollution. The pollutant accumulation
functions of these models are based on daily accumulation as a function
of land use, street cleaning practices, and season of the year. SWMM
and STORM simulate pollutant accumulation solely as a function of D&D
accumulation, while HSP QUALITY provides for independent accumulation'of
each water quality constituent. None of the models consistently
represent the physical processes involved in both soil erosion and
pollutant transport from impervious and pervious areas. The USLE as
used in SWMM and STORM is not considered applicable to short-time
interval simulation of the soil erosion process for the reasons stated
39
-------
above. In fact, a basic contradiction exists in the use of both the
USLE and the pollutant washoff functions in S'WMM and STORM. The effects
of overland flow are specifically included in the "availability factors"
and the pollutant washoff equation in both models. On the other hand,
no factor for overland flow is in the USLE although pollutant transport
is being simulated in both instances. The physical processes governing
pollutant transport from the land surface are the same whether the
phenomenon occurs on pervious areas, impervious areas, cropland, or
forests. The magnitude of the relevant factors may vary, but the
controlling processes are identical. Thus, a consistent approach is
needed to represent the universal mechanisms involved in the transport
and movement of all land surface nonpoint source pollutants.
NONPOINT POLLUTION SIMULATION BY THE QUAL SUBROUTINE
In light of the goals and scope of this project, and within the setting
of existing simulation methods, the development of the QUAL subroutine
was guided by the following criteria:
(1) Individual processes controlling nonpoint source pollution
from the land surface should be represented as accurately as
possible within the current state of technology.
(2) Nonpoint pollution from both pervious and impervious areas
should be simulated in a consistent manner to emphasize the
universal nature of the controlling transport processes.
(3) The methodology should be sufficiently flexible to accommodate
all major land use categories and activities and should be
applicable to the largest possible number of nonpoint
pollutants.
(4) To the extent possible, the methodology should minimize the
number of water quality parameters that must be evaluated
through calibration with observed data in order to simplify
application.
Obviously the criterion for simplification is somewhat contradictory to
the development of a general model with technical algorithms based on
the current state-of-the-art. The present understanding of pollutant
accumulation, generation, and transport processes cannot provide an
exact quantitative description; hence, the need for empirical parameters
evaluated through calibration. In any case, the attempt to meet the
above criteria in the development of the QUAL subroutine produced the
following nonpoint pollutant simulation methodology:
40
-------
(1) The transport, or washoff, process of pollutants from the land
surface is simulated in the same manner on both pervious and
impervious areas.
(2) Sediment is used as the indicator of nonpoint pollutants.
Sediment accumulation and washoff is simulated for both
pervious and impervious areas, and a user-input 'potency
factor1 specifies the pollutant content of the washed-off
sediment. All water quality constituents except water
temperature and dissolved oxygen content are simulated in this
manner.
(3) Sediment from impervious areas will include both suspended and
settleable solids measured in the surface runoff. In urban
areas, sediment is often referred to as 'Total Solids';
sediment and total solids (suspended and settleable) are
assumed to be identical in the NPS Model.
(4) Pollutant accumulation is simulated in terms of sediment
accumulation on impervious areas and sediment accumulation
and generation on pervious areas. Thus, impervious areas
receive pollutants almost entirely from human activity,
whereas pervious areas can generate sediment particles
by the force of raindrop impact on the land surface.
Sediment and sedimentlike material was chosen as the indicator for
nonpoint pollutants because it is the major constituent of nonpoint
pollution from the land surface. This is the central theme throughout
much of the present literature on nonpoint source pollution. From
agriculture and construction areas, sediment loss is the primary concern
as a pollutant itself and as a carrier for other pollutants (6, 56, 57).
The same is true for silvicultural activities (6, 56, 58). In urban
areas one would not expect sediment to be a major pollution problem.
However, numerous studies have variously characterized the major
pollutant in urban runoff as 'dust and dirt' (11), 'sediments' (50),
'suspended and settleable solids' (10), etc. In other words, sediment
and sedimentlike material is the most common constituent in nonpoint
pollution from urban, agriculture, silviculture, and- construction areas.
Thus, a method of representing soil erosion, sediment or pollutant
accumulation, and sediment transport from both pervious and impervious
land surfaces would be applicable to all the above land uses.
Application of the QUAL subroutine and the NPS Model is greatly
simplified by using sediment as a pollutant indicator. Accumulation,
detachment, and washoff parameters need to be evaluated only for
sediment on pervious areas and impervious areas. On the other hand, if
each pollutant was simulated separately, all parameters (accumulation,
41
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detachment, washoff) would need to be evaluated separately. Although
individual simulation of each pollutant would likely produce more
accurate results, the number of parameters and the required effort for
parameter evaluation would increase substantially. Moreover, the
available data on nonpoint source pollution is insufficient to warrant
such a detailed approach. Thus, the use of sediment as a pollutant
indicator satisfies the need for a consistent, flexible method for
representing nonpoint pollution from various land uses and provides a
reasonable compromise between algorithm complexity and simplicity of
application.
QUAL Subroutine Algorithms^
As indicated above, simulation of nonpoint source pollution from
pervious and impervious areas is performed separately in the QUAL
subroutine; hence, the component processes on pervious and impervious
areas are discussed separately below.
Pervious Areas-
The processes on pervious areas simulated in the QUAL subroutine include
(1) net daily accumulation of sediment by dustfall and human activities,
(2) detachment of particles by raindrop impact into fine sediment
material, and (3) transport of sediment fines by overland flow. On
pervious areas detachment heavily outweighs dustfall and accumulation
from land surface activities; hence, the accumulation algorithm will be
discussed in the section on impervious areas where it is the sole source
of surface sediments. However, accumulation is also simulated on
pervious areas.
Pervious area simulation is presented first because research on the
mechanisms involved in soil erosion provided the basis for the
detachment and transport algorithms in the QUAL subroutine. These
algorithms were initially derived from work by Negev at Stanford
University (59) and have been subsequently influenced by the work of
Meyer and Wischmeier (60) and Onstad and Foster (61). Although Meyer
and Wischmeier enumerated four mechanisms, detachment and transport by
rainfall and detachment and transport by runoff, only the two major
mechanisms of detachment by rainfall and transport by overland flow are
included in the QUAL subroutine. The algorithms for these two processes
are identical to those in the ARM Model (21) and are as follows:
soil fines detachment:
RER(t) = (1 - COVER(T))*KRER*PR(t)JRER (9)
42
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SRER(t) = SRER(t - 1) + RER(t)
soil fines transport:
SER(t) = KSER*OVQ(t)JSER for SER(t)< SRER(t)
SER(t) = SRER(t)
ERSN(t) = SER(t)*F
for SER(t)>SRER(t)
(10)
(11)
(12)
(13)
where RER(t) = soil fines detached during time
interval t, tonnes/ha
COVER(T) = fraction of land cover as a function
of time, T, during the year
KRER =' detachment coefficient for soil
properties
PR(t) = precipitation during the time interval, mm
JRER = exponent for soil detachment
SER(t) = transport of fines by overland flow,
tonnes/ha
KSER = coefficient of transport
JSER = exponent for fines transport by overland flow
SRER(t) = reservoir of soil fines at the beginning
of time interval, t, tonnes/ha
OVQ(t) = total overland flow occurring during the
time interval, t, mm
F = fraction of overland flow reaching the
stream during the time interval, t
ERSN(t) = sediment loss to the stream during the time
interval, t, tonnes/ha
In the operation of the algorithms, the soil fines detachment (RER)
during each 15-minute interval is calculated by Equation 9 and added to
the total fines storage (SRER) in Equation 10. Next, the total
transport capacity of the overland flow (SER) is determined by Equation
11. Sediment is assumed to be transported at capacity if sufficient
fines are available, otherwise the amount of fines in transport is
limited by the fines storage, SRER (Equation 12). The sediment entering
the waterway in the time interval is calculated in Equation 13 by the
fraction of total overland flow that reaches the stream. A land surface
flow-routing technique described in Appendix B determines the overland
flow contribution to the stream in each time interval. After the fines
storage (SRER) is reduced by the actual sediment entering the stream
(ERSN), the algorithms are ready for simulation of the next time
interval. Thus, the sediment that does not reach the stream is returned
to the fines storage and is available for transport in the next
interval.
43
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The land cover variable in Equation 9, COVER(T), represents the fraction
of the land surface effectively protected from the kinetic energy and
detachment capability of rainfall. Mean monthly values are specified by
the user. The NPS Model interpolates linearly between the monthly values
to evaluate land cover on each day. Figure 7 demonstrates the land
cover function in the NPS Model. In essence, the land cover function is
the key to differentiating erosion rates on different land uses.
Agricultural, silvicultural, and construction areas will have highly
variable land cover with portions of the land surface completely exposed
during certain seasons of the year. The land cover function in Figure 7
is typical for an agricultural watershed. Storm events occurring when
the land is exposed can produce extreme sediment loss. On the other
hand, the pervious portion of urban areas will include lawns, parks,
golf courses, etc., that have a reasonably constant and complete
vegetal cover. The kinetic energy of rainfall is effectively dissipated
by the land cover with values of 90 to 95 percent of the area. Thus,
judicious use of the land cover function in the NPS Model will allow
simulation of various land surface conditions. Monthly cover factors
can be estimated in terms of the 'C1 factor in the USLE as COVER(month)
= 1 - C(month) when the 'C1 factor is evaluated on a monthly basis.
Additional guidelines for evaluating the cover factors are provided in
the User Manual, Appendix A.
Impervious Areas-
The processes of importance on impervious areas are the accumulation of
pollutants on the land surface and transport of pollutants by overland
flow. Accumulation of dust, dirt, debris, and other contaminants from
streets, roads, and parking lots is the major source of nonpoint
pollutants on impervious areas. As indicated previously, the
composition of these pollutants is similar to sediment and is often
measured as Total Solids (suspended and settleable). Thus, these
pollutants are simulated as sediment on impervious areas.
Rates of sediment accumulation on impervious areas are a function of
land use, street cleaning practices, and climatic factors such as wind
and rainfall. Much of the research on accumulation rates has involved
grab-sampling of runoff in urban areas; few data from continuous
monitoring of storm runoff are available. Extrapolation of grab-sample
data can be highly erroneous. Moreover, sampling of storm runoff may
indicate actual pollutant loads but provides little information on
accumulation rates which represent the potential pollutant load. The
actual sediment washoff during a storm event depends on the amount of
accumulated sediment prior to the event and the overland flow occurring
during the event.
The most relevant studies on accumulation rates have been performed by
the APWA (48) in Chicago and Sartor and Boyd (50) in 12 cities across
44
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USER INPUT COVER VALUES
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Figure 7. An example of the land cover function in the NPS model
45
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the country. The APWA study of 1969 was one of the few attempts to
directly measure the accumulation of urban nonpoint source pollutants.
At various test sites throughout the city, the accumulation of the dust
and dirt (D&D) fraction of street litter was determined. Then
concentrations of various pollutants were related to the D&D fraction.
In addition, the effects of street cleaning methods, catch basins, air
pollution, and the chemicals from urban activities were investigated.
The study by Sartor and Boyd in 1972 included as one of its major goals
the evaluation of the amounts and types of materials that accumulate on
street surfaces and contribute to urban storm runoff pollution. Ten
land use categories in twelve cities were included in the intensive
sampling program. Numerous water quality indices were analyzed at each
sampling point and accumulation rates between storms were evaluated as a
function of total solids (TS) content. This study is the most complete
survey of accumulation rates, urban pollutant composition, and street
cleaning practices in urban areas.
V
These two studies provide the basis for evaluation of sediment
accumulation rates in the NPS Model. Greater emphasis is placed on the
study by Sartor and Boyd because of the comprehensive nature of the
study and the emphasis on TS, or sediment, accumulation.
To evaluate the amount of sediment on the watershed prior to each event,
the effects of non-runoff removal processes must be determined and
incorporated into the accumulation function. The accumulation function
simulates the net accumulation of sediment, i.e., the difference between
accumulation and removal by mechanisms other than runoff. The major
removal processes of concern are street cleaning and entrainment and
transport by wind. The accumulation function in the QUAL subroutine is
TS(T) = TS(T-1)*(1-R) + ACCI (14)
where TS(T) = sediment on the impervious land
surface at time T
TS(T-l) = sediment on the impervious land
surface at time T-l
R = fraction of sediment removed daily
ACCI = daily accumulation rate of sediment
R and ACCI are dependent on land use and season of the year. The
formulation for pervious areas is identical to that above with separate
accumulation and removal rates and separate sediment storage.
In the operation of the QUAL subroutine, the accumulation function is
performed each day that a storm does not occur. Thus, as time between
46
-------
storm events increases, the accumulated sediment approaches a limiting
value. From Equation 14
ATS = - TS(T)*R + ACCI (15)
and at equlibrium ATS = 0
TS(T) = ACCI/R (16)
This shows that the limiting value of TS(T) is simply the daily
accumulation rate divided by the daily removal rate. Also, the maximum
accumulation would be 1/R in terms of days of accumulation. Limiting
accumulation of 8 to 10 days were found for BOD in a study at Oxney,
England (62) and Sartor and Boyd (50) reported values of 10 to 12 days
for accumulated total solids for all land uses combined.
Sediment transport from impervious areas is analogous to the same
process on pervious areas. It is represented as follows:
TSS(t) = KEIM*OVQI(t)JEIM for TSS(t) < TS(t) (17)
TSS(t) = TS(t) for TSS(t) > TS.(t) (18)
EIM(t) = TSS(t)*F (19)
where TSS(t) = sediment transport during time interval t
OVQI(t) = impervious area overland flow occurring in time
interval t
KEIM = impervious area coefficient of transport
JEIM = impervious area exponent of transport
TS(t) = reservoir of deposited sediment on impervious areas
F = fraction of impervious overland flow reaching the
stream in time interval t
EIM(t) = sediment loss to the stream from impervious
area in time interval t
As with pervious areas, sediment transport is limited in each time
interval by the availability of deposited sediment. Thus, Equation 17
prevails if the movement of sediment is limited by the transport
capacity of overland flow, and Equation 18 is applied if deposited
sediment limits sediment washoff. Total sediment input to the stream
(per unit impervious area) is proportional to the fraction of total
overland flow entering the stream during the time interval, as indicated
in Equation 19.
Quality of Overland Flow-
The pollutant content of overland flow is specified by user-input
'potency factors' that indicate the pollutant strength of the sediment
47
-------
for each pollutant being simulated, i.e., potency factor = (pollutant
mass/sediment mass) x 100 %. In each time interval, the mass of
sediment washed off the land surface is multiplied by the appropriate
potency factor to obtain the mass of pollutant transported to the
stream. As indicated earlier, this methodology was chosen because of
the prevalence of sediment as a pollutant and as a carrier for other
pollutants, and because of the simplicity of the approach. It is
recognized that a simple linear relationship between pollutants and
sediment is not applicable to all pollutants. Indeed, highly soluble
pollutants may demonstrate little or no relationship to sediment loss.
However, various water quality studies have shown a striking similarity
in the behavior of sediment and pollutant washoff during storm events
(8, 15, 24). This is especially true on relatively small watersheds
(less than 200 hectares), where channel processes are less important,
and when the data is plotted in terms of mass removal instead of
concentration. The majority of non-soluble pollutants will demonstrate
washoff and transport behavior similar to sediment. Many soluble
pollutants will be transported like particulate matter during the short
residence time on the land surface. Thus, the use of potency factors is
applicable to a large number of pollutants. The results of this study
(Section VIII) indicate that a reasonable simulation can be obtained
with the use of potency factors varying with land use and season of the
year. Obviously, the availability and strength of pollutants on the
land surface is a function of a large number of variables; quantitative
descriptions of these functional relationships are not available at the
present time. Further, research is needed to define and describe these
relationships that the potency factors attempt to represent.
The use of potency factors to indicate the pollutant content of overland
flow can be represented as follows:
pervious areas:
POLP(t)pJ = ERSN(t) *PMPp5ljm (20)
impervious areas:
POLI(t) 1 = EIM(t) *PMI 1 m (21)
where
POLP(t)pj = mass of pollutant p transported from pervious
areas in land use 1 during time interval t
POLI(t)pj-| = mass of pollutant p transported from impervious
areas in land use 1 during time interval t
ERSN(t)i = sediment loss from pervious areas in land use 1
during time interval t
48
-------
EIM(t)] = sediment loss from impervious areas in land use 1
during time interval t
PMPp,l,m = Potency factor for pollutant p on pervious
areas in land use 1 for month m
PMIp,l,m = Potency factor for pollutant p on impervious
areas in land use 1 for month m
The subscripts in Equations 20 and 21 indicate the variations allowed in
the QUAL subroutine; thus, the potency factors (PMP, PMI) for each
pollutant (p) can vary according to land use (1) and month of the year (m).
In each time interval the sediment loss from pervious (ERSN) and impervious
(EIM) areas is calculated by Equations 13 and 19, respectively. Then
Equations 20 and 21, using the appropriate potency factor, calculate the
mass of pollutant transported to the stream. Pollutant concentrations can
then be specified from the pollutant mass and the volume of flow during the
time interval.
Overland Flow Temperature and Dissolved Oxygen-
The temperature and dissolved oxygen content of overland flow are
generated by the QUAL subroutine for each simulation time interval.
Each of these variables represent an important water quality
constituent, almost always used to characterize the quality of receiving
waters. Their inclusion in the NFS Model provides greater flexibility
to interface with existing stream quality models.
-The information available on water temperature and its driving forces is
voluminous. It represents the importance associated with temperature as
one of the major factors affecting aquatic and biochemical activities in
water. Several attempts have been reported in the literature (63, 64,
65) to estimate in-stream water temperature from empirical relationships
with various climatological factors such as radiation, wind velocity,
cloudiness, and air temperature. Modeling in-stream water temperatures
with an energy-balance approach involving these climatologic factors is
a well established procedure (47, 66). However, simulation of overland
flow temperature is a substantially different problem and has received
little attention in the past. The temperature of overland flow depends
on land surface characteristics, such as vegetation, impervious area,
soil characteristics, etc., in addition to climatic conditions. Direct
measurement of overland flow temperature is difficult, and research on
the subject is scarce. Consequently, quantitative relationships for
predicting overland flow temperature are not available.
Because of the short residence time on the land surface, a common
assumption is that overland flow temperature equals the air temperature
at the time of precipitation. The QUAL subprogram assumes a direct
relationship between air temperature and overland flow temperature
represented by a seasonal temperature correction factor as follows:
49
-------
T = T *TCF (22)
w a
where T = overland flow temperature for time interval t
= air temperature for time interval t
TCP = temperature correction factor
As discussed in Section VI, Ta is calculated each hour from input
max-min air temperature, and an assumed 24-hour distribution internal to
the NFS Model. It is assumed that the value of the TCP will vary with
factors such as watershed characteristics, time of the year, etc. and
can be estimated by calibration. Results of testing have shown that a
reasonable representation of overland flow temperature is produced with
this methodology in many situations. As more information and experience
with application of the NFS Model becomes available, this simplistic
approach can be easily replaced by a more reliable scheme of modeling
overland flow temperature.
The dissolved oxygen concentration of the overland flow is a direct
function of its temperature. Since the NPS Model was developed for
small watersheds where flow times are insufficient for any degrading
processes to become significant, it is possible to assume that the
concentration of DO in the overland flow is close to the saturation
level. Therefore, the QUAL subroutine uses the following empirical
non-linear equation relating DO at saturation to water temperature (67):
DO = 14.652 - 0.41022TW+ 0.00791T^ - 0.00077774T^ (23)
where DO = dissolved oxygen in ppm
Tw = overland flow temperature in degrees C
QUAL Subroutine Operation-^
The operation of the QUAL subroutine for simulating nonpoint pollutant
accumulation and transport is illustrated in Figure 8. Operation is
controlled directly by the MAIN subprogram. The algorithm consists of
two alternate loops, each one iterated with different frequency,
depending on the rainfall and runoff conditions as they are transferred
from the LANDS subprogram. At the beginning of each simulation day, the
MAIN subprogram determines whether or not a storm has occurred on that
day; daily rainfall and/or the occurrence of overland flow indicate a
'storm1 day. Whenever a 'storm1 day occurs both the LANDS and QUAL
subprograms are sequentially iterated throughout the whole day at
15-minute intervals (96 times). Otherwise the non-storm path is
activated resulting in only one call to the LANDS and QUAL subprograms.
In this case the role of the QUAL algorithm is limited to the evaluation
of the daily increment of the sediment available for transport from the
pervious (SRER) and impervious (TS) lands. The calculations are carried
out iteratively for each of the land uses defined by the input data.
50
-------
CALL FROM
YES/ ANOTHER
LAND USE?
V
TOTAL SEDIMENT
AND POLLUTANT
JfASHOFL
/
PRINT INTERVAL
OUTPUT
SEDIMENT
ACCUMULATION
SEDIMENT
ACCUMULATION
(MAIN )
Figure 8. Functional flowchart of the QIIAL subroutine
SUBROUTINE \~___)
DECISION POINT ( )
PATH DESIGNATION
OPERATION
51
-------
The factors considered are the daily accumulation rate in mass per unit
area (Ib/acre, kg/ha), and the removal effect R representing the percent
of sediment loss due to wind and other factors not related to storm
runoff. Both accumulation and removal rates must be specified
separately for the pervious and impervious areas. An option to allow
monthly variations in the accumulation and removal rates is included in
the NPS Model.
The major portion of the QUAL algorithm pertains to the 'storm day1
path. The key portions of this loop are the analytical representations
of sediment fines generation, sediment washoff, and pollutant washoff
from pervious and impervious areas. Simulation of these processes is
carried out for each land use within the watershed. The aggregate
quantities of the washed-off sediments and pollutants are summed to
yield the total mass and the equivalent concentration of pollutants in
the overland flow. The values of the water quality constituents
selected for analysis are accompanied by the simulated values of runoff,
water temperature, and dissolved oxygen. The results are printed each
time interval and in monthly and yearly summaries. The User Manual
describes the output options available in the NPS Model. Table 4
defines the input parameters of the QUAL subroutine, many of which have
been discussed above. Section VIII demonstrates the application of the
NPS Model to three urban watersheds and presents simulation results.
52
-------
Table 4. QUAL SUBPROGRAM PARAMETERS
Sediment Generation and Washoff
COVVEC fraction land cover of pervious surfaces within a given
land use (monthly basis - 12 values)
JRER exponent of rainfall intensity in soil splash equation
KRER coefficient in soil splash equation
JSER exponent of overland flow in sediment washoff equation
from pervious areas
KSER coefficient in sediment washoff equation from pervious
areas
JEIM exponent of overland flow in sediment washoff equation
for impervious areas
KEIM coefficient in sediment washoff equation for impervious
areas
Sediment Accumulation and Removal
ACUP daily accumulation rates on pervious surfaces
ACUPV daily accumulation rates on pervious surface (monthly
basis, 12 values), optional
ACUI daily accumulation rates on impervious surfaces
ACUIV daily accumulation rates on impervious surface
(monthly basis, 12 values), optional
REPER daily, non-runoff sediment removal rate from pervious
surfaces
REPERV daily non-runoff sediment removal from pervious
surfaces (monthly basis, 12 values), optional
REIMP daily non-runoff sediment removal from impervious
surfaces
REIMPV daily non-runoff sediment removal from impervious
surfaces (monthly basis, 12 values), optional
53
-------
Table 4 (continued). QUAL SUBPROGRAM PARAMETERS
Potency Factors
PMPVEC potency factors for simulated water quality constituents
washed off pervious surfaces
PMPMAT potency factors for the simulated water quality
constituents washed off pervious surfaces (monthly basis,
12 values for each constituent), optional
PMIVEC potency factors for the simulated water quality
constituents washed off impervious surfaces
PMIMAT potency factors for the simulated water quality
constituents washed off impervious surfaces (monthly basis,
12 values for each constituent), optional
Miscellaneous
TCP
SRERI
TSI
temperature correction factor relating runoff and air
temperatures (monthly basis, 12 values),
initial deposit of sediment on pervious surfaces within
a given land use
initial deposit of sediments on impervious surfaces within
a given land use
54
-------
SECTION VIII
MODEL TESTING AND SIMULATION RESULTS
The NFS Model was tested on three urban watersheds to evaluate the
validity of the nonpoint pollution simulation methodology. The choice
of watersheds was governed by the availability of meteorologic,
hydrologic, and water quality data in a form that did not require
extensive data reduction and analysis. Urban watersheds were chosen in
response to the growing emphasis on the evaluation and control of
nonpoint pollution in urban areas. Also, the size of the test
watersheds was limited to a maximum of one to two square miles in order
to minimize the influence of channel processes on the recorded data. In
this way simulation results of the NPS Model, which does not simulate
channel processes, could be realistically compared with the recorded
observations.
The goals of the testing were to demonstrate the ability to (1)
calibrate the NPS Model on representative watersheds, and (2) provide
continuous information for the evaluation of nonpoint pollution
problems. Since the hydrologic methodology has been extensively tested
and verified in past work, the emphasis in this study was on the
evaluation of the water quality simulation methodology. Because of
time, financial constraints, and the scarcity of data, the degree of
testing on each of the watersheds was not as extensive as would be
recommended in an actual application of the Model. However, sufficient
results were obtained to establish the capabilities and weaknesses of
the NPS Model.
Although calibration is fully discussed in the User Manual, a brief
explanation is necessary to evaluate the simulation results presented
below. As mentioned previously, calibration is the adjustment of model
parameters to improve agreement between simulated and recorded values.
In the NPS Model, calibration is performed for hydrology, sediment, and
water quality parameters in that order. Sediment calibration cannot be
initiated until the hydrologic calibration is completed; likewise, water
quality calibration follows sediment calibration. In each step,
55
-------
recorded and simulated values are compared for both monthly volumes (or
mass) and individual storm events if the data is available. Lack of
data for any particular comparison severely hampers the entire
calibration.
The three test watersheds are: the Third Fork. Creek, Durham, North
Carolina; the Manitou Way Storm Drain, Madison, Wisconsin; and the South
Seattle watershed, Seattle, Washington. Each of these watersheds and
the available data is described. Simulation results are presented and
discussed, followed by the general conclusions obtained from the NPS
Model testing.
THIRD FORK CREEK: DURHAM, NORTH CAROLINA
Watershed and Data Description
The Third Fork Creek basin is located in the eastern section of the
North Carolina Piedmont, a region of gently rolling hills. The stream
flows in a southerly direction and is tributary to the New Hope, Haw,
and Cape Fear River systems. Upper Third Fork Creek, simulated in this
study, drains an area of 433 hectares (1,069 acres) located within the
city limits of Durham, North Carolina. As shown in Figure 9, the
drainage basin is primarily composed of two shallow valleys with
relatively minor flood plains along the lower reaches of the streams.
Surface runoff generally follows natural drainage paths with a few
man-made channels. No storm sewer system exists; storm runoff
occurs largely as surface runoff in street gutters, small pipes, and
culverts under roads.
The region experiences a moderate climate without distinct wet and dry
seasons. Summer storms tend to be brief, high intensity thunderstorms
and convective showers. Winter and spring storms are of longer duration
and lower intensity and are caused by migratory low pressure weather
systems. Mean annual precipitation is approximately 1,200 millimeters
(47 inches) of which less than 25 millimeters (1 inch water equivalent)
occurs as snowfall with little significant accumulation.
The Third Fork Creek basin represents a typical urbanized area in the
Piedmont region of the Southeastern United States. The basin
encompasses a variety of land uses, including:
high and low density housing units of varying quality
undeveloped land
56
-------
DURHAM, N»C.
UNHTS
MAIN GAGING STATION
PPT. GAGE NO. 1 O
PPT. GAGE NO. 2 •
BASIN BOUNDARY —
SUB-BASIN BOUNDARY
Figure 9. Third Fork Creek, Durham, North Carolina
57
-------
shopping centers
portion of the central business district
institutional buildings (churches and schools) among
scattered, small businesses
an urban redevelopment section
a tobacco processing plant
a completed section of expressway
a cemetery
slums
railroad yard
a flood plain utilized mainly as a city park
Table 5 summarizes the major land use characteristics for the subbasins
shown in Figure 9.
The data available for applying the NFS Model to Third Fork Creek were
the most extensive of any of the test watersheds. Previous water
quality studies on the basin by Colston (8) and Bryan (68) provided land
use and topographic information. The report by Colston (8) supplied
necessary hydrology and water quality data for calibration of the NFS
Model. Table 6 summarizes the data used in simulation of Third Fork
Creek. Continuous meteorologic data series were developed to coincide
with the period of record of available water quality data, i.e., October
1971 to March 1973. A continuous record of 15-minute precipitation was
developed from the Blue Cross gage (ppt. no. 2 tn Figure 9) and
augmented for missing periods with recorded hourly data published for
the Raleigh Airport gage. The resulting data series was checked for
consistency with precipitation data at the basin outlet (ppt. no. 1 in
Figure 9) and daily published values for Durham. Daily pan evaporation
data was obtained from published data at Chapel Hill (16 kilometers
southwest of Durham). Maximum and minimum air temperature data from
Durham completed the meteorologic data requirements. Continuous daily
streamflow and monthly runoff volumes were obtained from U.S. Geological
Survey records for the upper Third Fork Creek basin. The Colston report
(8) provided detailed storm hydrographs and water quality constituent
concentrations for 36 storm events throughout the 18-month sampling
period. Although the water quality measurements were not continuous
(i.e., available for every storm event), they represent an extensive
compilation of data on urban runoff quality. Tables 7 and 8 summarize a
portion of the data contained in the Colston report.
58
-------
Table 5. THIRD FORK CREEK LAND USE CHARACTERIZATION BY SUB-BASINS
Sub-
basin
E-l
E-2
N-l
N-2
W-l
W-2
Total
B«8ln
Area
Acres
56
263
163
191
169
207
1069
Z of
Total
5.2
24.6
17.1
17.9
15.8
19.4
100Z
Population
Density
Fer Acre
13.5
6.9
3.8
1.5
3.5
10.8
6.0
Physical Features
Stream
Length
Feet
1312
3221
3350
3484
3282
2610
-
Stream
Slope
%
3
1.4
1.0
2.1
0.9
1.8
-
Mean
Land
Slop*
Z
9.2
5.2
7.4
8.1
8.4
9.1
-
Z of Residential
Dwellings of
Low
Quality
100
100
6
62
0
62
24
Med.
Quality
0
0
52
31
30
38
27
High
Quality
0
0
42
7
70
0
49
Percent Land
Resi-
dent.
100
50
63
18
85
73
59
Comm.
li
Indus.
0
36
8
44
0
4
19
Pub.
&
Inst.
0
9
19
13
15
9
12
Unused
0
5
10
25
0
14
10
Sub-basin Surface Characteristics
Z of Sub-basin
Paved
5
27
16
33
16
11
20
Roof-
tops
7
13
5
12
5
9
9
Unpaved
Streets
12
3
1
1
3
6
3
Vegetation
76
57
78
54
77
74
68
cn
Source: Colston (8), p. 13
-------
Table 6. DATA SUMMARY FOR THIRD FORK CREEK
Type
Precipitation
Evaporation
Max-Mi n
Air Temperature
Streamflow
Water Quality
Station
Number
7069
251503
167703
251503
02097243
02097293
02097293
Location
Synthesis
Blue Cross
Gaging Station
Raleigh AP
(20 SE)
Durham
Chapel Hill
(12 SW)
Lumberton
(95 S)
Durham
Third Fork Creek
Third Fork Creek
Third Fork Creek
Period of Record
10/71-3/73
in/71-3/73
10/71-3/73
10/71-3/73
10/71-2/73
10/71-2/73
3/73
10/71-3/73
10/71-3/73
10/71-3/73
1-/71-3/73
Time
Interval
15 min
5 min
5 min
hourly
daily
daily
daily
daily
dai ly
i rregul ar
intervals less
than 30 minute
irregular
intervals
less than 30
minutes
Comments
See note a
Selected storms within basin
Selected storms within basin
Adjusted with monthly pan
coefficients
Adjusted with monthly pan
coefficients
Units - cfs
36 selected storm events
Colston report (8)
36 selected storm events
Colston report (8)
en
o
Precipitation record used is a synthesis using the Blue Cross gage where available, and filled
in by the Raleigh A.P. gage. The resulting 15-minute record was checked against the gage at
the strearnflow gaging station and the daily Durham gage.
-------
Table 7. HYDROLOGIC DESCRIPTION OF SELECTED
URBAN RUNOFF EVENTS ON THIRD FORK CREEK
Dace
10/23/71
11/24/71
12/16/71
12/20/71
1/4/72
1/10/72
2/1-2/72
2/12-13/72
2/18/72
2/23/72
2/26/72
3/8/72
3/16/72
3/31/72
4/12/72
5/3/72
5/14/72
5/22/72
5/30-31/72
6/20/72
6/28/72
7/11/72
7/12/72
7/17/72
7/31/72
8/28/72
9/17/72
9/21/72
10/5/72
10/19/72
11/14/72
11/19/72
11/30/72
1/19/73
2/26/73
3/21/73
SCOT*
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Rainfall
Inches
1.55
0.05
0.43
0.2
0.55
1.19
0.96
0.44
0.13
0.19
0.04
0.6
0.46
0.33
1.14
0.71
0.92
0.25
0.24
1.78
0.1
0.33
0.26
0.38
0.06
1.51
0.5
2.36
0.74
0.79
0.5
0.11
0.4
0.25
Duration
Hours
32.5
NO PREI
0.5
19.5
2.5
12.0
10
10
8
0.5
0.5
0.083
10.33
11.33
2.17
7.25
8.0
15.5
10.0
6.5
2.13
0.5
3.83
1.0
2.5
2.1
3.3
9.0
26.0
3.63
4.0
14
1.25
1.6
5.0
Intensity
In/hr
0.047
IPITATION
0.1
0.022
0.08
0.046
0.119
0.096
0.049
0.26
0.38
0.48
0.058
0.04
0.15
0.15
0.089
0.059
0.025
0.037
0.83
0.2
0.086
0.26
0.15
0.028
0.45
0.055
0.34
* ]
0.2
0.19
0.04
0.09
0.25
0.05
Runoff
Inches
0.88
RECORD!
0.003
0.15
0.04
0.19
0.84
0.54
0.2
0.04
0.03
0.01
0.36
0.15
0.12
0.47
0.29
0.513
0.03
0.07
1.55
0.005
0.083
0.15
0.34
0.004
0.7
0.083
2.07
ZCOEDEI
0.25
0.48
0.09
0.03
0.09
0.05
Runoff
Coefficient
0.54
.*•
AVAILABLE
0.0061
0.34
0.19
0.34
0.7
0.56
0.45
0.29
0.18
0.25
0.59
0.33
0.35
0.41
0.41
0.56
0.12
0.29
0.87
0.054
0.25
0.57
0.9
0.066
0.46
0.16
0.88
£ INOPERABU
0.34
0.61
0.18
0.30
0.24
0.23
Peak
Discharge
CFS
33.2
-^
2.5
31.3
22.6
63.0
138.4
126.6
32.0
22.0
19.0
4.3
51.8
40.6
73.0
135.7
109.0
349.0
29.9
75.4
1740
2.25
36.2
125.0
152.0
2.58
700.0
4i.4
872.0
-»
120.8
106.0
57
27.8
83
38
Days Since
Last Storm
3.25
34.0
4.0
0.5
4.75
1.0
11.5
9.0
5.5
5.5
2v83
4.88
7.25
9.5
4.25
21.0
5.5
5.0
5.62
20.5
7.17
6.54
7.33
5.25
0.75
6.54
11.3
3.5
5.0
6.0
2.45
4.6
4.2
12.1
4.1
No. Samples
Taken
15
13
10
16
9
19
27
20
27
a
23
15
23
23
17
21
24
9
8
16
5
4
15
7
20
3
10
10
7
11
9
20
12
26
3
16
Source: Colston (8), p. 37
61
-------
Table 8. AVERAGE AND STANDARD DEVIATION OF SOLIDS AND ORGANICS FROM THIRD FORK CREEK
IX)
Stan
Nuabar
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Total Solids
««/!
Avg
226
538
571
520
676
1675
1423
982
1169
391
913
1124
960
1932
1583
1215
991
871
2460
3940
682
3570
3080
5423
3300
1147
1487
1050
1144
1497
1822
1234
719
a
27
143
186
264
294
492
874
384
453
63
574
435
412
1273
506
1197
426
324
467
2820
319
908
1117
2597
3076
343
664
588
913
542
941
258
152 j
Volatile Solids
ng/1
Avg
78
215
147
148
182
133
107
110
145
288
500
168
485
224
323
283
147
186
242
138
260
285
284
177
o
18
84
39
29
65
44
17
51
40
88
452
29
102
123
127
182
38
60
56
43
41
135
45
30
Total Suspended
ms 1
Avit
89
274
163
346
474
1459
1233
1754
572
990
146
687
1087
843
2596
1525
849
899
895
2732
2332
554
2889
3913
2522
1024
1326
1340
83
777
1246
1463
1029
643
Q
38
164
86
272
249
535
949
1194
421
733
58
472
492
429
2107
655
1117
576
789
725
1090
290
1266
2204
2434
376
624
1100
62
788
550
923
288
202
Volatile Suspended
mg/1
Avg
75
15
119
92
121
152
132
76
82
129
240
380
40
318
136
152
221
71
105
147
14
120
145
188
136
104
0
91
8
68
36
40
102
208
15
74
101
67
395
27
129
93
101
149
25
49
24
7
53
40
97
10
17
COI
na
Avit
25
259
111
171
146
141
195
143
149
125
171
82
176
123
89
257
150
41
144
220
271
402
96
348
187
184
253
140
142
157
132
110
93
374
289
92
>
i
a
14
62
21
45
89
60
103
104
116
96
146
39
144
73
49
190
175
J
106
135
130
430
52
198
79
80
232
60
59
69
83
77
28
103
101
31
IOC
mg
Avg
30
36
35
25
36
33
24
36
36
36
44
46
36|"
17
15
16
41
39
73
165
26
94
48
50
51
21
38
44
49
34
38
105
An
31
<
a
7
7
34
11
41
16
17
27
25
10
30
20
12
12
8
5
25
18
30
148
9
41
14
18
41
11
16
13
15
10
14
35
19
14
BOD
n/1
Ayi
18
18
17
6
2
IS
20
18
42
5
55
105
73
100
80
16
220
41
138
182
80
49
50
100
a
14
13
12
6
.4
11
12
9
11
3
14
23
10
5
19
2
10
24
15
60
74
20
12
20
Source: Colston (8), pp. 38 and 44
-------
Calibration and Simulation Results
The calibration of the NPS Model on Third Fork Creek was performed on 18
months of data in order to coincide with the period of record of
available water quality data. The monthly simulation results are
presented in Figure 10 and Table 9, and the final Model parameter values
are listed in Table 10. Comparison of monthly recorded and simulated
values was possible only for runoff since continuous water quality «••-<-
observations were not available. Because of time and financial
considerations, water quality calibration and simulation was limited to
three constituents: sediment (reported as total solids by Colston),
BOD, and SS.
•p.
As shown in Figure 10, simulated and recorded monthly runoff volumes
agree quite well. In the initial trials, simulated monthly runoff and
storm flows were consistently and uniformly low throughout the
calibration period. Analysis of the annual water balance indicated a
possible bias in the synthesized input rainfall derived from hourly
rainfall at Raleigh Airport, 32 kilometers southeast of the watershed. "
Consequently, the precipitation adjustment factor, Kl (see User Manual,
Appendix A), was set at 1.4 to increase the precipitation by 40 percent.
This correction substantially improved the simulation of both monthly
runoff volumes and storm flows. Ideally, hydrologic calibration should
be performed for a minimum of two to three years to obtain parameters
evaluated over a range of hydrologic conditions. Thus, the calibration
of Third Creek was based on a shorter period than would be normally
recommended. Since the emphasis of this study is on nonpoint pollution,
the hydrologic calibration is sufficiently accurate to demonstrate the
NPS Model capabilities. Further calibration efforts would be
recommended if the NPS Model is used for evaluation of nonpoint
pollution control plans in the Third Fork Creek Basin.
The lack of continuous water quality observations prevented comparison
of simulated and recorded monthly pollutant loading. Consequently,
water quality calibration was based on the simulation of individual
storm events. The simulated monthly pollutant loading values shown in
Figure 10 demonstrate the dependence of BOD and SS on sediment loading,
as represented in the NPS Model. Observed monthly values would likely
show a similar relationship especially for SS. Since BOD may include a
soluble component, some deviation may be expected. However, overall BOD
washoff from the land surface will be closely related to sediment
washoff in most cases.
Although observed monthly pollutant loadings were not available, Colston
did estimate the total 1972 loadings for various pollutants. The
63
-------
u.
U-
o
o:
-------
Table 9. MONTHLY SIMULATION RESULTS FOR THIRD FORK CREEK
(October 1971-March 1973)
Month
1971,
October
November
December
1972
January
February
March
April
May
June
July
August
September
October
November
December
1973
January
February
March
Total
Total for
1972
Runoff
Recorded
(mm)
131
33
21
28
75
24
31
100
109
83
18
82
68
102
154
41
129
77
1306
874
Simulated
(mm)
119
38
25
35
88
35
25
91
108
77
32
74
100
141
138
67
148
78
1419
944
Simulated Quality Constituents
Sediment
(tonnes/ha)
t
0.47
0.35
0.10
0.13
0.51
0.35
0.15
0.60
1.32
0.86
0.09
0.69
0.86
1.00
0.86
0.31
1.73
0.44
10.83
7.43
BOD
(kg/ha)
18.9
14.1
,4-2'
5.3
20.5
14:1
6.2
23.9
53.0
34.5
3.6
27.4
34.3
40.1
34.4
12.5
69.2
17.6
433.8
297.3
!SS
(kg/ha)
336
250
74
95
364
250
109
424
942
611
64
487
609
711
611
223
1229
311
7700
5277
65
-------
Table 10. NFS MODEL PARAMETERS FOR THE THIRD FORK CREEK WATERSHED
(English units)
HYDROLOGY
UZSN
LZSN
INFIL
INTER
IRC
AREA
0.4
6.0
0.04
2.0
0.5
1069
NN
L
SS
NNI
LI
SSI
0.30
300
0.10
0.15
600
0.10
Kl
PETMUL
K3
EXPM
K24L
KK24
1.4
1.0
0.25
0.15
0.0
0.99
Initial Conditions:
UZS 0.0
October 1, 1971
LZS 2.25
SEDIMENT AND WATER QUALITY
JRER 2.2 JEIM 1.8
KRER 1.5 KEIM 0.3
JSER 1.8 TCF 12*1.0
KSER 0.3
Open Land
Residential Commercial
ARFRAC
IMPKO
COVVEC
PMPVEC:
PMIVEC:
ACUP
ACUI
REPER
RE IMP
Initial
SRERI
TSI
BOD
SS
BOD
SS
0.10
0.05
12*0.90
4
71
4
71
30
30
0.05
0.08
0.60 0.17
0.18 0.55
12*0.95 12*0.90
4 4
71 71
4 4
71 71
70 75
70 75
0.05 0.05
0.08 0.08
Conditions: October 1, 1971
1758 1880 2658
106 246 266
Industrial
0.13
0.75
12*0.90
4
71
4
71
80
80
0.05
0.08
2758
284
66
-------
estimates were based on regression equations developed from data on the
36 water quality events sampled and extended to all 66 events that
occurred on the Third Fork Creek watershed tn 1972. The predicted
loadings were then adjusted to correct a bias in the automatic sampling
technique and to subtract the estimated pollutant content of the base
flow. The resulting estimates for sediment and SS are shown below with
simulated loadings from the NPS Model. Colston did not estimate BOD
loadings due to questions on the reliability of the measured BOD values.
1972 Annual Pollutant Loadings in Urban Runoff
from Third Fork Creek
(kg/ha)
Estimated by ' NPS Model
Colston (8) Simulation
Sediment (or TS) 7952 7430
BOD - 297
SS 7411 . 5277
The simulated values are reasonably close to Colston's estimates.
Moreover,;the effects of channel processes and the bias noted by Colston
in his automatic sampling procedure would tend to produce measured
pollutant loadings higher than the real nonpoint contributions.
Simulation of storm events indicated the importance of channel processes
in the Third Fork Creek watershed. Recorded and simulated flow and
sediment concentrations are presented in Figures 11, 13, 15, and 17 for
selected storms, whereas recorded and simulated BOD and SS
concentrations are provided in Figures 12, 14, 16, and 18. These
results must be evaluated in light of the effects of channel processes
on both flow and quality. With an area of 433 hectares, the Third Fork
Creek basin approaches the upper limit of applicability for the NPS
Model. A significant baseflow component and well-defined natural
drainage channels indicate the occurrence of channel processes. Colston
provides numerous photographs of the stream channel system; in many
places it was clogged with trash, natural debris, and deposited
sediment.
One of the hydrologic impacts of a defined channel system is to decrease
the time variation of runoff as a result of channel storage. Thus,
natural channel systems generally decrease peak flows from immediate
67
-------
I I I I I I I I
I I
I I
o
C£.
CO
CO
o
UJ
I—I
o
UJ
CO
2.5
2.0
1.5
1.0
0.5
2000
1500
1000
500
I I I I
RECORDED
SIMULATED
i t t
I
J I
I
1200
Figure 11.
1300 1400 1500 1600
TIME, hours
1700
1800 1900
Runoff and sediment loss for Third Fork Creek for
the storm of January 10, 1972.
68
-------
o
o
CQ
60
50
40
30
20
10
0>
CO
1000
750
500
250
T 1 1 1 1 T
1 1 1 \ 1 1 T
I I
T
I
RECORDED
SIMULATED
I
I
I
I
I
_L
I
J_
I
1200 1300
1400 1500 1600
TIME, hours
1700 1800
Figure 12. BOD and SS concentrations for Third Fork Creek
for the storm of January 10, 1972.
69
-------
in
u
O
2:
,ID
o;
OO
00
O
LJ
O
UJ
oo
3.0
2.5
2.0
1.5
1.0
0.5
g> 4000
3000
2000
1000
T
T
T
I
RECORDED
_„__ SIMULATED
500
700 900
TIME, hours
1100
1300
Figure 13. Runoff and sediment loss for Third Fork Creek
for the storm of May 14, 1972.
70
-------
en
o
o
CO
100 _
80
60
40
20
3000
2500
2000
1500
1000
500
I I T
_ RECORDED
_ SIMULATED
J.
_L
500
700 900
TIME, hours
1100
1300
Figure 14. BOD and SS concentrations for Third Fork Creek
for the storm of May 14, 1972.
71
-------
o
3.0 -
2.5
2.0
1.5
02 1.0
0.5
2500
Si 2000
•*
% 1500
o
£ 1000
o
LU
to
500
i i I r i i i I I r
i I I I
iii r i r ir r
i
i r
RECORDED
SIMULATED
i i i i I I i I I I I I I I i
630 s 700 730 800 330 900 930 1000
TIME, hours
Figure 15. Runoff and sediment loss for Third Fork Creek
for the storm of June 20, 1972
72
-------
o
o
CO
80
60
40
20
f 1 1 1 T
i—i—r
I I I I I I I I I I I I I i
co
3000
2500
2000
1500
1000
500
I I I I 1
I I T 1 T
I I _
RECORDED
SIMULATED
i I I i I I i I
630 700 730 800 830 900
TIME, hours
930
Figure 16. BOD and SS concentrations for Third Ford
Creek for the storm of June 20, 1972.
73
-------
15
12
u_
o
3000
2500
2000
1500
i—
UJ
~ 1000
RECORDED
SIMULATED
CO
CO
o
co
500
_L
I
JL
830
900
930 1000
TIME, hours
1030
Figure 17. Runoff and sediment loss for Third Fork Creek for
the storm of October 5, 1972.
74
-------
T 1 \ T
Q
O
CD
200
150
100
50/
J I
I I I I
T I
250C-
200C
150C
GO
GO
I I
I
RECORDED
SIMULATED
50C
J L
I L
J L
830
Figure 18.
900
930
1000
1030
TIME, hours
BOD and SS concentrations for Third Fork Creek
for the storm of October 5, 1972.
75
-------
surface runoff and increase low flows with little effect on total storm
runoff volume. The hydrographs in Figures 11, 13, 15, and 17 partially
demonstrate these effects. During initial calibration trials, the
channel effects were more dramatic with extreme time variations in the
flow rate. To partially compensate for these channel effects, the
length of overland flow on impervious areas was increased because the
impervious flow component provides the greatest time variation in
runoff. In effect, this change delayed the impervious flow on the land
surface and thus decreased the variability in the simulated hydrograph,
improving the overall simulation. The results presented here are
generally representative of the hydrologic simulation throughout the
18-month period and provide a reasonable basis for evaluating the
nonpoint pollutant simulation methodology of the NFS Model.
Channel processes will also affect the time-variability of measured
pollutant concentrations. In addition, erosion and deposition in the
channel and accumulation of trash and debris can provide an additional
source of pollutants within the channel system itself. The high
sediment content of the runoff from Third Fork Creek and the pictures of
debris cluttered channels indicate that the channel itself is a likely
source of pollutants. The results of the sediment simulation (Figures
11, 12, 15, and 17) and the BOD and SS simulation (Ftgures 12, 14, 16,
and 18) show greater pollutant concentration variability than in the
measured values. Also, the recorded pollutant concentrations do not
demonstrate the dependence on flow that is characteristic of nonpoint
pollution. This is likely caused by dilution from baseflow and by
mixing in the channel system. Although dilution would tend to decrease
concentrations, erosion of channel sediment that likely occurs in Third
Fork Creek could more than compensate for the dilution and results in
high pollutant concentrations with less variability.
Pollutant mass removal in terms of mass per time interval is often more
representative of nonpoint pollution than instantaneous concentrations.
Mass removal of sediment, BOD, and SS is shown in Figure 19 for the
storm of May 14, 1972. Since mass is obtained from the product of flow
and concentration, the mass removal curves in Figure 19 clearly
demonstrate the dependence on flow as a transport medium. For this
reason, comparison of mass curves is the best method of evaluating
simulated and recorded pollutant transport.
Despite the effects of channel processes discussed above, the results
presented here indicate that estimates of nonpoint pollution from Third
Fork Creek can be obtained with the NPS Model. Table 11 summarizes
average simulated and recorded concentrations of sediment, BOD, and SS
for selected storms on Third Fork. Creek. Although discrepancies do
exist, the overall agreement is sufficient to justify the use of the NPS
Model as a tool for evaluation of nonpoint pollution problems.
76
-------
6000
%
•| 5000
LO
D>
co
et
4000
,3000
2000
S 100°
co
250
200
150
100
50
CD
CO
co
o
o
CO
LO
CO
CO
4000
3000
2000
1000
I
I
I
. RECORDED .
. SIMULATED
500 700 900
TIME, hours
1100
1300
Figure 19. Pollutant mass transport for Third Fork
Creek for the storm of May 14, 1972.
77
-------
Table 11. SIMULATED AND RECORDED RUNOFF CHARACTERISTICS
FOR SELECTED STORM EVENTS ON THIRD FORK CREEK3
CXI
Storm
Date
10/23/71
1/10/72
2/1/72
2/12/72
2/18/72
3/16/72
3/31/72
4/12/72
5/22/72
6/29/72
6/28/72
7/17/72
7/31/72
9/17/72
10/5/72
Runoff Average Water Quality
Mean Flow
( cms )
Rec
1.63
0.81
2.51
0.94
0.58
0.68
0.52
1.17
3.97
0.54
26.83
2.27
2.29
6.63
6.96
Sim
1.06
0.78
2.49
1.25
0.67
0.75
0.65
1.38
3.04
0.83
17.26
2.06
0.78
9.32
7.46
Peak Flow
(cms)
Rec
3.14
2.35
3.91
2.15
0.82
1.25
1.16
2.07
9.77
2.12
49.28
3.54
3.94
10.34
12.89
Sim
3.79
2.02
4.56
2.13
1.05
1.77
1.47
2.79
6.92
2.43
67.06
4.76
1.59
28.49
12.94
Sediment
(mg/1 )
Rec
226
716
1676
1435
NA
1042
1020
1407
1583
871
2460
3570
2821
2322
1487
Sim
247
856
1020
1059
850
940
820
1260
640
960
1400
1570
700
1303
946
BOD
(mg/1 )
Rec
18.2
18.0
7.0
NA
NA
30.5
NA
22.7
NA
55.0
95.0
80.5
15.3
37.9
128.6
Sim
10.0
34.2
41.7
30.0
34.0
38.0
33.0
50.0
10.0
38.0
56.0
63.0
28.0
49.1
37.9
SS
(mg/1 )
Rec Sim
89
510
1459
1396
1337
826
945
1213
997
875
2397
2886
NA
NA
1326
178
608
724
752
602
670
583
891
456
681
991
1116
495
445
790
NA - Not Available
a. Recorded values may not equal those in Tables 7 and 8 because the
comparisons were made on identical time periods that may or may
not include the entire storm. Also, certain discrepancies were found
between the storm event data and Colston's tables.
-------
MANITOU WAY STORM DRAIN: MADISON, WISCONSIN
Watershed and Data Description
The Manitou Way Storm Drain is located in Madison, Wisconsin in the
south central portion of the state. The 60-hectare watershed (147
acres) is contained within the Lake Wingra drainage basin as shown in
Figure 20. Located on a low ridge with a northern exposure, the
watershed drains in a northeasterly direction to Lake Wingra.
Elevations in the upper portions are nearly constant at 300 meters (1000
feet) from which the land slopes steeply at approximately nine percent
and then levels again near the basin outlet. As indicated by its name,
the watershed is drained by storm sewers except for a few streets in the
upper portions.
The continental climate of the region is only mildly affected by the
proximity of the Great Lakes. Cold air masses descending from Canada
keep winter temperatures quite low with frequent readings of -20 to
-25 °C (-4 to -13 °F). An annual snowfall of 900 to 1000 millimeters
(35 to 50 inches) results in snow-covered ground throughout most winters.
Summers are moderate with temperatures reaching 32 °C (90 °F) six to
eight days per year. Mean annual precipitation is approximately 760
millimeters (30 inches). The wettest period is generally in the spring
when rainfall and snowmelt combine to produce frequent flooding.
Thunderstorms are prevalent during the summer with an average of 40
storms per year.
The Manitou Way Storm Drain watershed is primarily a residential area of
upper and middle class homes. The area is well-established with some
houses more than 20 years old; there is little new construction in the
watershed. Small portions of the eastern topographic divide are
contained within an arboretum. The impervious portion of the watershed,
including streets, rooftops, driveways and sidewalks, is about 27
percent of the watershed area.
Table 12 summarizes the data used in applying and calibrating the NPS
Model to the Manitou Way Storm Drain watershed. The major source of
meteorologic data was published records for the Class A weather station
at Madison Airport (Truax Field) located 13.5 kilometers (8.4 miles)
northeast of Manitou Way. The only continuous precipitation record
available was an hourly record at Madison Airport. These data were
supplemented with a sporadic record from a gage (Nakoma) located
adjacent to Manitou Way in order to obtain a more precise time
definition of rainfall for events for which water quality data was
79
-------
LOCATION MAP
MADISON,
WISCONSIN
LAKE WINGRA
DRAINAGE BASIN
LAKE
WtNGRA
MANITOU WAY
STORM DRAIN
WATERSHED
NAKOMA
COUNTRY
CLUB
— WATERSHED BOUNDARY
STREETS
— CONTOUR LINES, FT.
Ilillll1 STORM DRAINS
• STORM SEWER GAGE
O WATER TANK
Figure 20. Manitou Way storm drain, Madison, Wisconsin
80
-------
Table 12. DATA SUMMARY FOR MANITOU WAY
Type
Precipitation
Evaporation
Hax-min Air
Temperature
Solar Radiation
Wind
Streamf 1 CM
Streauf 1 ow
Water Quality
Station Number
_
4961
C165
4961
4961
4961
4961
05429040
05429040
05429040
Location
Synthesis
f'adison Airport
Nakoma
Madison Airport
Madison Airport
Madison Airport
Madison Airport
Manitou Way
f'anitou Way
Manitou Way
Period of Record
9/70-9/72
4 storms
9/70-9/72
9/70-9/72
9/70-9/72
9/70-9/72
10/70-9/72
selected storm
events
selected storm
events
Time
Interval
15 min
hourly
5 minute
daily
daily
daily
daily
daily
irregular
intervals
irregular
intervals
Comment
see note a
These data were used for
storms on which quality
data were available.
Computed from dewpoint
temperature, wind,
radiation, and air
temperature by the
Wisconsin Hydrologic
Transport Model (70)
see above
Obtained from study by
Kluesener (69)
Obtained from study by
Kluesener (69)
CO
a. Precipitation record was synthesized from hourly data at Madison Airport and supplemented for
selected storm events with data from the Nakoma gage located adjacent to Manitou Way.
-------
available. Storm hydrographs and water quality concentrations were
obtained from a study by Kluesener (69, 71) on nutrient loadings to Lake
Wingra. Although that study emphasized nutrient data, total solids
measurements were included.
Calibration and Simulation Results
Monthly simulation results for Manitou Way are shown in Figure 21 and
listed in Table 13. The final Model parameters are presented in Table
14. As with the Durham watershed, monthly runoff values were the only
recorded continuous data available for comparison with simulation
results. The Kluesener study (69), which provided the water quality
data for Manitou Way, concentrated on the evaluation of nutrient runoff
into Lake Wingra. Consequently, total phosphorus was chosen for
simulation with the NFS Model in addition to sediment. The monthly
simulated values for these constituents are contained in Figure 21 and
Table 13. Simulation of nutrient runoff from both urban and
agricultural lands with the NFS Model is presently underway in a
continuing development effort.
Hydro!ogic calibration was initially performed for two years of data
(October 1969-September 1971) to obtain a general water balance.
Subsequent calibration efforts concentrated on the period from September
1970 to June 1971 to provide a sound basis for water quality
calibration. The agreement between simulated and recorded monthly
runoff values for this watershed is fair. The major discrepancies shown
in Figure 21 are due to either unrepresentative rainfall or the effects
of frozen ground conditions on infiltration. Since the only continuous
precipitation record available was at Madison Airport, numerous
instances occur where the airport gage does not record substantial
thunderstorms occurring on the watershed. Obviously, runoff cannot be
adequately simulated if the recorded rainfall does not indicate that
which fell on the watershed. This is a common problem that is
frequently important for small watersheds in thunderstorm-prone areas.
Frozen ground conditions tend to decrease infiltration and increase
surface runoff from that which would be expected under unfrozen
conditions. The snowmelt routine of the NPS Model attempts to decrease
infiltration when frozen ground conditions occur, but the accurate
quantitative representation of these effects is a research topic of
current interest. Thus, winter runoff volumes tend to be undersimulated
in areas where frozen ground occurs. The simulation results in Figure
21 partially indicate this effect. In areas with substantial and
continuous snow accumulations, frozen ground is less significant and
snow simulation is generally more accurate.
82
-------
o
ID
Q£
ro
Ol
*
CO
O
Q
UJ
CO
(O
CO
CO
o
CO
o:
o
Q.
CO
o
30
25
20
15
10
0
3.5
3.0
2.5
2.0
1.5
1.0
.5
0.06
0.04
0.02
H 1 M I i 1 1 1 1 1 1 1 1 I 1 I ITTTT
— RECORDED
— ; — SIMULATED
/' |
n 1 -
J \ A /\ /
1 NL_/ i i i /i 1YV"f i i V-i'' i i i i i
1 M 1 1 1 1 1 I I 1 1 1 | 1 II |"( | | | 1
_
A
\
l\
-
1 I ^
1 | / \
' 1 » / \ '
" \ ,N ! \ ' \ '
-\ ,'\' ',/' ^\ .'
» ' V \i
* , \ i
• i ^ '
~ . I • ' ~
- \ / \ /
\ i i i
i i VJ i i i i i i i i i i i V / i i i i i i
1 1 1 1 1 1 1 1 1 ! 1 I 1 1 1 i 1 i t 1 ! 1 I
1
M
'1
!\ /\
1 " / \
1 ,\ ' » ' * '
• i \ ! \ i ^"^ '
1 ' \' 1 / \ 1
1 • ^ . 1
II \ 1
_ • \ .
- \ i \
\ 1 * •'
^ • \ ;
1 ! \-l 1 1 1 1 1 1 I 1 1 1 I VJ 1. 1 1 1 1 I.
ONDJFMAMJJASONDJ FMAMJJA
1970 1971 1972
Figure 21. Monthly simulation results for the
Manitou Way Watershed (October
1970 - March 1972)
83
-------
Table 13. MONTHLY SIMULATION RESULTS FOR MANITOU WAY
(October 1970-March 1972)
Month
1970
October
November
December
1971
January
February
March
April
May
June3
July
August
September
October
November
December
1972
January
February
March
Total
Total for 1971
Runoff
Recorded
(mm)
6.1
2.3
0.2
0.0
8.6
8.9
19.0
3.8
4.8
5.6
1.8
7.1
3.8
9.9
13.2
0.8
1.0
20.6
117.5
86.5
Simulated
(mm)
10.7
2.3
0.0
0.0
7.9
7.1
25.6
1.0
4.6
2.3
9.1
2.3
1.8
7.9
13.2
0.3
0.3
3.6
100.0
82.8
Simulated Quality Parameters
Sediments
(kg/ha)
2.20
0.73
0.00
0.00
2.03
1.52
2.63
1.47
1.68
2.10
2.45
2.32
1.75
1.68
1.12
0.00
0.00
2.22
25.92
20.75
Total Phosphorus
(kg/ha)
0.047
0.016
0.000
0.000
0.044
0.032
0.057
0.031
0.036
0.045
0.053
0.050
0.038
0.036
0.025
0.000
0.000
0.048
0.558
0.447
a. The recorded runoff for June 1971 was modified to account for a
storm that was not recorded at the rain gage.
84
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Table 14. NFS MODEL PARAMETERS FOR THE MANITOU WAY WATERSHED
(English units)
HYDROLOGY
UZSN
LZSN
INFIL
INTER
IRC
AREA
Initial
UZS
PACK
SNOW
RADCON
CCFAC
EVAPSN
ELDIF
0.75
6.00
0.10
3.5
0.1
147.2
Conditions
0.75
0.0
0.25
0.25
0.60
0.0
NN
L
SS
NNI
KI
SSI
0.04
150
0.01
0.15
700
0.01
September 2, 1970
LZS 6.00
DEPTH 0.0
SEDIMENT AND WATER QUALITY
JRER 3.0
KRER 0.09
JSER 1.90
KSER 0.30
RESIDENTIAL LAND
TSNOW
MPACK
DGM
I DNS
JEIM
KEIM
TCF
33.0
0.10
0.001
0.10
2.0
0.35
12*1.0
ARFRAC
IMPKO
COVVEC
PMPVEC
PMIVEC
ACUP
ACUI
1.0
0.1
= Jan
Feb
Mar
Apr
May
Jan
0.60
0.63
0.65
0.67
0.70
0.73
(total phosphorus)
(total phosphorus)
1.20
1.20
Jul
Aug
Sep
Oct
Nov
Dec
2.15
2.15
REPER
RE IMP
0.75
0.80
0.76
0.71
0.68
0.64
0.05
0.08
Initial Conditions:
SRERI 35
TSI 45
September 2, 1970
Kl
PETMUL
K3
EXPM
K24L
KK24
1.05
0.93
0.40
0.15
1.0
0.99
SGW
SCF
WMUL
F
KUGI
0.50
1.10
1.00
0.50
8.0
85
-------
In spite of these problems, the monthly runoff volumes are reasonably
simulated. When more accurate rainfall records are available, the
simulation results are generally improved. Fifteen-minute rainfall
values were available at the Nakoma gage adjacent to the Manitou Way
watershed for the storm events of September 2, 1970 and November 9, 1970
shown in Figures 22 and 23, respectively. Except for timing variations,
the simulated storm hydrographs accurately represent peak flows with
some discrepancy in total storm volume. The simulated sediment and
phosphorus storm concentrations reflect the deviations in simulated
runoff, but they adequately approximate the recorded values. Further
calibration efforts on additional data for both hydrology and water
quality are recommended for this watershed. However, the close
correlation between sediment and phosphorus concentrations indicates
that sediment is an important indicator of nonpoint phosphorus
pollution, verifying the general methodology in the NPS Model. Based on
these calibrated results, it appears that the NPS Model can represent
the nonpoint pollution characteristics of the Manitou Way watershed for
the purpose of obtaining estimates of nonpoint pollutants. Additional
calibration for other pollutants and verification through split-sample
testing would be desirable when sufficient data are available.
SOUTH SEATTLE WATERSHED: SEATTLE, WASHINGTON
Watershed and Data Description
The South Seattle watershed contains the Benaroya Industrial Park and is
located in the southern portion of the City of Seattle, Washington (see
Figure 24). The drainage area is relatively flat (approximately 2
percent slopes) and covers 11.1 hectares (27.5 acres). A separate storm
sewer system drains the watershed in a south-southwesterly direction.
There are no known industrial discharges to the sewer system, and most
of the roads are paved and include catch basins. However, only 50
percent of the roads have curbs to contain and direct the street surface
runoff.
The Seattle area is subject to broad Pacific storm fronts approaching
from the south and southwest during the wet winter-spring season and
from the northwest during the summer. The climate is moderate due to
the area's coastal location. Average daily temperatures are 3.3 °C
(38 °F) and 18.3 °C (65 °F) in January and July, respectively. Mean
annual precipitation is 990 millimeters (39 inches) at the Seattle-Tacoma
Airport 11.5 kilometers (7.2 miles) south of the South Seattle
watershed. However, topographic characteristics of the region cause a
high degree of area! variability in the form and amount of
86
-------
u_
o
cc
CO
CO
o
z
LLJ
O
UJ
CO
0>
GO
C£.
O
3=
a.
CO
0.3 -
o.i
1000
500
1.5
1.0
0.5
i
i
i
i
\
i
^K..
I \^_L T'T'
T—i—i—i—i—i—r
(Ww-u.
1 1
! 1
1
1
i
RECORDED
SIMULATED
1
1400 1600
1800 2000 2200
TIME, hours
000
Lrr
200
Figure 22. Runoff, sediment and phosphorus loss for the
Manitou Way for the storm of September 2, 1970.
87
-------
GO
(/)
o
Ul
oo
CT>
on
Di
O
Q-
CO
O
O-
0.05
0.04
0.03
0.02
0.01
400
300
200
100
6.0
5-0
4.0
3.0
2.0
1.0
i i i i I i i i i i
\ \ I I I I I I I
RECORDED
SIMULATED
700 800 900 1000 1100 1200
TIME, hours
Figure 23. Runoff, sediment and phosphorus loss
for Manitou Way for storm of November 9,
1970.
88
-------
• SAMPLE SITE
• RAIN GAGE
U DRAINAGE AREA
A METEORLOGICAL DATA SITE
Figure 24. South Seattle watershed, Seattle, Washington
89
-------
precipitation. At the South Seattle site snowfall averages less than
300 millimeters (12 inches) per year with little prolonged accumulation.
The land use of the South Seattle watershed is classified as light
industrial. The area contains 30 to 35 manufacturing establishments
ranging from a large foundry to a clothing factory, and including
several freight handling companies. The industrial park was initiated
in the late 1950's, but was not fully developed until the late 1960's.
Approximately 60 percent of the watershed is impervious.
Table 15 summarizes the data used in the simulation of the South Seattle
watershed. Precipitation data were obtained from two rain gages
operated by the City of Seattle. One gage was located 1.6 kilometers
northwest of the watershed at the Diagonal Avenue pump station, and the
other 1.6 kilometers southwest of the basin at the East Marginal Way
pump station (see Figure 24). Due to the small size of the drainage
area and the area! variability in rainfall, it was necessary to combine
data from the two stations into a single record. For each rainstorm the
rainfall characteristics were chosen from one of the two stations
depending on the magnitude and direction of travel of the storm. In
each case, the rainfall record chosen logically appeared to produce the
recorded flow at the watershed outlet.
Evaporation data were obtained from the Seattle Maple Leaf reservoir
located 16 kilometers (10 miles) north of the watershed and were
adjusted by monthly pan coefficients. Maximum and minimum daily air
temperatures were obtained from the Seattle-Tacoma airport. This
completed the required meteorologic data series since snow simulation
was not performed.
Recorded streamflow and water quality data was available only for
selected storm events in a nine-month period from a study sponsored by
the Municipality of Metropolitan Seattle (METRO) (72). Flow data for 17
events and water quality data for five events from January to September
1973 was obtained for calibration purposes. Table 16 summarizes the
extensive water quality measurements made on each of five storms on the
South Seattle watershed.
Calibration and Simulation Results
Monthly simulation results are shown in Figure 25 and Table 17. The
final NFS Model parameters are listed in Table 18. Unfortunately, no
continuous recorded data for runoff or water quality were available for
comparison with simulated values. This severely hampered both
hydrologic and water quality calibration; thus, individual storm events
90
-------
Table 15. DATA SUMMARY FOR THE SOUTH SEATTLE WATERSHED
Type
Precipitation
Evaporation
Max-min air
temperature
Streamf 1 ow
Uater quality
Station
Number
7473
Location
Synthesis
Diagonal Ave
East Marginal
Way
Seattle Maple
Leaf Reservoir
Seattle-Tacoma
Airport
South Seattle
watershed
South Seattle
Period of Record
1/73-9/73
1/73-9/73
1/73-9/73
1/73-12/73
1/73-9/73
1/73-9/73
3/73-9/73
Time -
Interval
15-minute
5-minute
5-minute
semi-monthly
daily
5-minute
15-minute
Comments
see note a
see Figure 24
see Figure 24
for selected
storms only
for 5 selected
storms
a. Because of the area! variability in precipitation, the synthesized record was obtained
from either the East Marginal Way or Diagonal Avenue gages, depending on the direction
of travel of storm events.
-------
Table 16. URBAN RUNOFF CHARACTERISTICS FOR SELECTED
STORM EVENTS ON THE SOUTH SEATTLE WATERSHED
Parameter
Temp. C°
PH
Cond . umho/cm
Turbidity, JTU
DO, mg/1
BOD, mg/1
COD, rag/1
Hexane Ext. rag/1
Chloride, mg/1
Sulfate, mg/1
Organic N, mg/1
Ammonia N, mg/1
Nitrite N, mg/1
Nitrate N, mg/1
Hydrolyzable P, mg/1
Ortho P, mg/1
Copper, mg/1
Lead, mg/1
Iron, mg/1
Mercury, mg/1
Chromium, mg/1
Cadmium, mg/1
Zinc, mg/1
Sett. Solids, mg/1
Susp. Solids, mg/1
TDS, mg/1
Total Coliforma
org/100 mis
Fecal Coliform8
org/100 mis
Mean Concentrations
Mar 10
8.1
7.2
20
35
1.1.7
2.9
7.0
8.0
1.2
3.6
0.55
0.12
0.02
0.24
0.18
0.03
0.043
0.10
0.39
0.0004
0.010
0.005
0.08
41
63
179
1000
360
Mar 16
9.4
7.7
89
42
11.0
5.1
56
12
5.3
12
0.90
0.24
0.07
0.29
0.19
0.05
0.052
0.27
2.7
0.0002
0.010
0.005
0.30
52
91
181
3CO
20
June 6
18.0
6.7
169
40
6.4
38
147
12
28
30
1.8
0.25
0.06
0.90
0.28
0.10
0.076
0.13
0.90
0.0006
0.009
0.004
0.53
89
100
150
5300
20
Auq 16
20.1
6.7
243
81
5.6
36
156
27
24
41
2.9
0.57
0.07,
1.6
0.43
0.14
0.10
0.50
5.6
0.0003
0.009
0.006
0.70
78
109
233
4200
30
Sept 19
18.2
6.2
150
36
7.6
14
111
11
2.5
44
2.5
0.42
0.06
1.1
0.12
0.08
0.24
0.27
1.1
0.0003
0.010
0.004
0.53
39
39
138
14000
180
Mean
14.8
-
134
47
8.5
19
95
14
12.2
26.1
1.7
0.32
0.06
0.83
0.24
0.08
0.10
0.25
2.1
0.0004
0.010
0.005
0.43
60
80
176
4200
30
'Medians
Source: Municipality of Metropolitan Seattle (81), p. 80
92
-------
100
80
60
ii
uZ
i 40
•^
OS
20
.016
---f' i i
i i i i i i i i i i
A *
. \ / x
/ * / %-
\ ^ %X
^ •
/ \ ' \ 1
/ \ / » 1 ~
/ V \ i
- \ / \ / -
N* \ 1 _
t 1
t •
t t
"
1 '
1 1
t 1
- II —
1 1
t 1
1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 i 1
A ^
//**N '/^XTx '
" ^. / ^ \ / -
^^ \ '
__ BOD \ /
- ss v /
\ y
i i i i i i i _i — i — 1_
M
M J
A
6.0
4.0
2.0
ta
CO
CO
Figure 25.
Monthly simulation results for the
South Seattle Watershed (January -
September 1973)
93
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Table 17. MONTHLY SIMULATION RESULTS FOR SOUTH SEATTLE WATERSHED
(January 1973-September 1973)
Month
January
February
March
April
May
June
July
August
September
Total
Runoff
(mm)
87
8
23
22
21
33
4
4
19
221
Sediment
(kg/ha)
10.6
9.4
15.2
11.4
15.3
13.7
1.7
2.7
14.4
94.4
BOD
(kg/ha)
0.38
0.34
0.55
0.41
0.55
0.49
0.06
0.10
0.52
3.40
SS
(kg/ha)
4.02
3.57
5.77
4.33
5.81
5.22
0.63
1.03
5.47
35.85
94
-------
Table 18. NPS MODEL PARAMETER VALUES FOR THE SOUTH SEATTLE WATERSHED
(English units)
HYDROLOGY
UZSN
LZSN
INFIL
INTER
IRC'
AREA
0.90
9.00
0.04
3.00
0.50
27.5
NN
L
SS
NNI
LI
SSI
0.25
400
0.02
0.15
600
0.02
Initial Conditions: January 1, 1973
UZS_, 1.24 LZS 12.44
SEDIMENT AND WATER QUALITY
JRER 2.0 JEIM 1.80
KRER 0.09 KEIM 0.27
JSER* 1.80 TCF 12*1.15
KSER 0.27
Kl
PETMUL
K3
EPXM
K24L
KK24
SGW
1.0
1.0
0.30
0.017
0.0
0.99
0.0
INDUSTRIAL LAND
ARFRAC
IMPKO
COVVEC
PMPVEC:
PMIVEC:
1.00
0.60
12*0.90
BOD 3.6
SS 38.0
BOD 3.6
SS 38.0
Initial Conditions:
SRERI 0.0
ACUP
ACUI
REPER
REIMP
1.5
1.5
0.05
0.08
January 1, 1973
TSI 0.0
95
-------
were the only basis for calibration. Figures 26 through 31 present the
simulation results for two storms on the South Seattle watershed
occurring on March 10 and 16, 1973. Figures 26 and 29 show the
runoff and sediment simulation for each storm, while Figures 27 and 30
present the BOD and SS results, and,Figures 28 and 31 show the water
temperature and DO simulation.
The simulated and recorded runoff agree quite well. However, the
calibration should be considered tentative since continuous runoff data
were not available to check the simulation of the monthly and annual
water balance. Generally, large storm events are simulated considerably
better than small events due to more uniform meteorologic conditions
producing less areal variability in precipitation. Because of the high
fraction of impervious area, the watershed is extremely responsive to
rainfall. The data for simulation were obtained from gages 1.6
kilometers (1 mile) away from the watershed as described above.
Consequently, differences in rainfall between the gage and the watershed
are reflected in the simulation results. Moreover, the runoff
simulation presented here is for the period of measured water quality
data which were generally collected on the small events subject to
greater areal variations. In spite of these problems, the
simulated storm hydrographs shown in Figures 26 and 29 adequately represent
the recorded data. The responsiveness of the watershed required that a
small interception storage value (EPXM in Table 18) be used to
accurately simulate small events. This is probably true for small
watersheds with a high percentage of impervious area as often occurs in
commercial and industrial areas. The water quality constituents,
sediment, BOD, and SS are reasonably well simulated as shown in Figures
26, 27, 29, and 30 for the individual storm events. Sediment is more
accurately reproduced due to the number of calibration parameters
available to represent the sediment producing characteristics of the
watershed. However, the simulations of BOD and SS are quite good; thus,
validating the use of sediment as a pollutant indicator. Calibration of
the sediment accumulation rates and the pollutant potency factors for
impervious areas were of prime importance on the South Seattle watershed
because of the predominance of impervious areas as pollutant sources in
this watershed.
The South Seattle watershed provided an opportunity to evaluate the
simulation of water temperature and dissolved oxygen. Initial trials
indicated that the temperature of surface runoff can vary considerably
from the existing air temperature at the time of runoff. Consequently,
monthly temperature correction factors were introduced to allow
adjustment of the simulated water temperature to account for special
characteristics of the watershed. Dissolved oxygen is simulated by
assuming saturation at the simulated water temperature. The results
shown in Figures 28 and 31 indicate that the use of temperature
96
-------
I/I
o
u_
o
800
600
CO
O
UJ
Q
UJ
CO
400
200
I T
RECORDED
SIMULATED
000 100 200 300 400 500 600 700 800
TIME, hours
Figure 26. Runoff and sediment loss for the
South Seattle Watershed for the
storm of March 10,. 1973.
97
-------
28.0
24.0
20.0
- 16.0
o>
§ 12-°
CO
8.0
4.0
300
260
220
180
140
in
100
60
20
1 V ' ' '
I I I I I I
I I
i i r~r i n i
liiiiiiiiiiir T
RECORDED
SIMULATED
000 100 200 300 400 500 600 700 800
TIME, hours
Figure 27. BOD and SS concentrations for the South Seattle
Watershed for the storm of March 10, 1973.
98
-------
i—i—r
T i i TTT—r~i—i—i—i—i—r
o
o
X
O
LU
O
CO
CO
10.0
9.6
9.2
i 8.8
UJ
Q_
s;
UJ
i- 8.4
8.0
I i i I I t i i i
i I I i i i i
II r r i r i i r
i i i i i i i r
12.0
ll.O
10.0
9.0
8.0
I I I I I I
RECORDED
SIMULATED
I i I I I I I I I
000 100 200 300 400 500 600 700 800
TIME, hours
Figure 28. Water temperature and dissolved oxygen for
the South Seattle Watershed for the storm
of March 10, 1973.
99
-------
CO
8
o
ZD
o:
oo
•CO
O
LU
CO
.008
1 I T 1 I I I I I 1 I I I I I if I
.006
.004
.002
I—/ I I
I I I I I I I I I I I I I
1 I i I r r I \ i I I i
T r
500
400
300
200
100
I I I I I I I I I I I I I I
RECORDED
SIMULATED
I i i
800 900 1000 1100 1200 1300
TIME, hours
Figure 29. Runoff and sediment loss for the South Seattle
Watershed for the storm of March 16, 1973.
100
-------
a
o
CO
to
24.0
20.0
16.0
12.0
8.0
4.0
I I I I I I I I I I
r i i r r
i i i r r
200
160
120
80
40
I I I I r
RECORDED
SIMULATED
900
1000
1100
TIME, hours
1200
1300
Figure 30. BOD and SS concentrations for the South
Seattle Watershed for the storm of March 16,
1973.
101
-------
13.0
IIrnIIIIIIIIIIIFiIIiir
o
o
o:
0>
X
O
O
IS)
12.0
11.0
10.0
9.0
8.0
7.0
16.0
14.0
r
I I I I I I I I
J I
I I I I I I I
I I 1 I I I I
I I
12.0
10.0
RECORDED
SIMULATED
8.0
i i i i i i i I i I i I i i i i i i i I ii i
800 900 1000 1100 1200 1300 1400 1500
TIMf, hours
I
i
Figure 31. Water temperature and dissolved oxygen for
the South Seattle Watershed for the Storm
of March 16, 1973.
102
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correction factors and the assumption of DO saturation can be used to
estimate these water quality constituents in surface runoff from a
watershed. However, significant variations are possible and calibration
of the correction factors is mandatory.
Table 19 lists the mean simulated and recorded values of the water
quality constituents for the events on tKe ,South Seattle watershed..
Except for discrepancies in certain storms, results are relatively good;
they indicate that the NPS Model can be calibrated to represent nonpoint
pollutant production from this watershed.
CONCLUSIONS
This section has presented the results of testing the NPS Model on
watersheds in Durham,, North Carolina; Madison, Wisconsin; and Seattle,
Washington. The emphasis has been on the demonstration of the ability to
sufficiently calibrate the Model to represent the nonpoint pollutant
characteristics of the'watersheds.: Total verification of the NPS Model
could not be performed because of insufficient water quality data.
Verification refers to the ability of a model to represent data other than
that on which'the model is calibrated,.. However?, the hydrologic methodology
of the NPS Model has been verified in past studies. The sediment and
nonpoint pollutant simulation'methodology is partially verified by the
results on tHe Durham;watershed; not all storms were used in calibration
yet the NPS Model adequately, represented the recorded'data throughout the
period of record4 In continuous simulation parameters are not modified ,to
simulate each .storm separately; a single set of parameters is used for the
entire simulation period.; Also, the entire flexibility of the NPS Model
was not. completely utilized due to,the -lack of time and funds "for extensive
calibration efforts on each of,the watersheds. Further .work would have
employed the feature of monthly variations in accumulation rates, removal
rates,1 and potency factors to more accurately represent seasonal
characteristics of nonpoint pollution.,;:The results presented here were
obtained from preliminary calibration using only annual values for these
parameters.
In summary, the following conclusions are derived from the simulation
experience with the NPS Model and the results presented here:
(1) The Nonpoint Source Pollutant Loading (NPS) Model can simulate land
surface contributions of nonpoint pollutants from a variety of land
uses. Model testing on three urban watersheds,:comprised of
residential, commercial, industrial, and open land, indicated good
agreement between recorded and simulated hydrology and pollutant
washoff.
103
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Table 19. SIMULATED AND RECORDED URBAN RUNOFF CHARACTERISTICS FOR SELECTED STORMS
ON THE SOUTH SEATTLE WATERSHED3
Storm
Date
3/10/73
3/16/73
3/18-19/73
4/13/73
6/6/73
6/12/73
6/25-26/73
8/16/73
9/13/73
Runoff Characteristics
Mean Flow Peak Flow
(cms x 10 )
Rec
3.20
0.34
1.42
0.88
0.48
0.74
0.99
0.06
4.50
Sim
2.24
0.34
1.47
0.91
0.76
1.13
1.53
0.14
0.79
Rec
8.07
0.74
1.25
5.15
1.44
3.79
7.42
0.14
6.32
Sim
5.72
0.68
7.45
4.56
1.70
6.23
5.21
0.20
1.93
Average Water Quality Characteristics
Temperature
(°C)
Rec
8.5
9.3
18.0
20.2
18.0
Sim
8.7
10.2
20.3
19.9
19.7
DO
(me
Rec
11.0
11.3
6.5
5.9
7.3
/I)
Sim
11.6
11.3
9.0
8.8
8.8
Sediment
(mg/D
Rec
165
238
344
380
280
Sim
128
278
413
220
438
BOD
(mg/1)
Rec
5.4
6.4
34.0
31.5
15.6
Sim
6.2
9.2
15.0
7.8
15.4
SS
(mg/1)
Rec
60.0
81.3
94.6
94.9
80.7
Sim
46.0
98.8
157.1
83.6
165.7
a. Recorded average water quality concentrations may not equal those in Table 16 because comparisons were
made on identical tir,ie periods that may or may not include the entire storm.
-------
(2) The hydro!ogic methodology of the NPS Model has been extensively
applied, tested, and verified on numerous watersheds of varying
size across the country. Simulation results were good on the
watersheds tested in this study, and similar accuracy can be
generally expected in other areas.
(3) Sediment and sediment!ike material can be used as an indicator of
the land surface contributions of many nonpoint pollutants. Thus,
specification of the pollutant strength, or potency, of sediment in
conjunction with the simulation of sediment yield from pervious and
impervious areas provides a workable methodology for simulating
nonpoint pollution. The NPS Model algorithms are based on this
concept. Although the simulated pollutants in this study were
limited to sediment, biochemical oxygen demand, and suspended
solids, the methodology is applicable to most insoluble and
partially-soluble pollutants including many nutrient forms, heavy
metals, organic matter, etc. However, highly soluble pollutants
may demonstrate significant deviation from the simulated values.
(4) The NPS Model provides estimates of the total land surface loading
to water bodies for various nonpoint source pollutants. Since the
Model does not simulate channel processes, comparison of simulated
and recorded values should be performed on watersheds less than 250
to 500 hectares (1 to 2 square miles) in order to avoid the effects
of channel processes on the recorded flow and water quality. Size
limit will vary with climatic, topographic, and hydrologic
characteristics. Whenever channel processes appear to be
significant, the output from the NPS Model should be input to a
model that simulates stream processes before simulated and recorded
values are compared.
(5) Due to incomplete quantitative descriptions of the processes
controlling nonpoint pollution, calibration of certain Model
parameters by comparing simulated and recorded values is a
necessary step when applying the NPS Model to a watershed.
Although all parameters can be estimated from available physical,
topographic, hydrologic, and water quality information, calibration
is needed to insure representation of the processes occurring on
the particular watershed.
(6) The NPS Model can provide long-term continuous information on
nonpoint pollution that can be used to establish the probability
and frequency of occurrence of pollutant loadings under various
land use configurations. Thus, when properly calibrated, the NPS
Model can supplement available nonpoint pollution information and
provide a tool for evaluating the water quality impact of land use
and policy decisions.
105
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SECTION IX
MODEL USE AND RECOMMENDATIONS
With adequate calibration and verification, the NPS Model can be used
effectively in the analysis of nonpoint source pollution
problems in both urban and rural areas. Typical problems for which the NPS
Model may be applied include:
1) expected changes in pollutant loadings from urbanization
2) long-range pollutant loadings to water bodies under existing
conditions
(3) the effects of construction activities on nonpoint pollution
general impact of land use changes on nonpotnt pollution
evaluation of mulching, netting, and other land cover methods
to reduce surface erosion and nonpoint pollution
w/
81
Perhaps the most contemporary issue of concern for which the NPS Model can
be utilized is the evaluation of nonpoint pollution problems as required by
the Federal Water Pollution Control Act Amendments of 1972. The guidelines
issued by the U.S. Environmental Protection Agency (6) for nonpoint
pollution evaluation include the following formula:
N = (Q + S + D) - (P + I) (24)
where N = Quantity (mass) of nonpoint source pollutants in terms of a
given parameter, under a given design flow condition
Q = Quantity of pollutants in the water leaving the test area
S = Quantity of settlement and precipitation of pollutants
D <= Quantity of decay of nonconservattve pollutants
P = Quantity of pollutants discharged fay point sources (assumed
to be constant under a given design flow condition)
I - Quantity of pollutants in the water entering the test area
This formula calculates the total nonpoint pollutant loading
under the design conditions. Although this study makes no statements
106
-------
concerning the validity or usability of Equation 24, the NPS Model can be
used directly to estimate values of N, the nonpoint pollutant loading. Of
course, the Model must be employed with the knowledge that the effects,
either positive or negative, of stream channel processes are ignored.
However, once calibration and verification have been completed, the
Model can be reasonably applied to larger areas surrounding the
calibrated watershed. The simulated values will be estimates of the
nonpoint pollutant loadings from the various land uses in the larger
area. In many situations, the NPS Model can be applied to watersheds
that have hydro!ogic, topographic, climatic, and land use
characteristics similar to the calibrated watershed. When used with
caution, the Model can provide estimates in this manner for nonpoint
pollutant loadings from similar areas.
The basic advantage of the NPS Model is the ability to provide continuous
and long-term estimate of surface nonpoint pollution from various land
uses. The manner in which this information is utilized depends on the
specific problem and the proposed method of analysis. The validity of the
information provided by the Model is a direct function of the extent of
calibration and verification efforts on the particular watershed. If no
calibration is performed, the best that can be expected is
'order-of-magnitude' estimates of annual or seasonal pollutant loadings.
On the other hand, calibration and veriftcation of the NPS Model can result
in relatively reliable loading values on both a short-term and long-term
basis.
In summary, wise use of the NPS Model requires an understanding of the
processes being simulated, their representation in the Model, and the
effects of certain important Model parameters. Study of the algorithm
descriptions and the User Manual in Appendix A will provide the potential
user with sufficient background to develop proficiency with the Model. To
promote the use, application, and further refinement of the NPS Model, the
following recommendations are extended:
(1) Application of the NPS Model to watersheds across the country is
the primary need at this time. Although the Model has been tested
on three watersheds, further application is required before it will
be acceptable as a general and a reliable model. These
applications will provide additional information on parameter
evaluation under varying climatic, edaphic, hydrologic, and land use
conditions, and may expose areas requiring further development and
^refinement in the simulation methodology.
(2) The application and use of the NPS Model as a tool for evaluating
the impact of land use policy on the generation of nonpoint
pollutants should be demonstrated. This could be done in
conjunction with local planning agencies who might assist in Model
107
-------
application, benefit from simulation results, and have access to
the NFS Model for continuing use in the planning process. Such a
project would demonstrate the utility of the NFS Model in a
real-world setting.
(3) To promote use of the NFS Model, user workshops and seminars should
be held to acquaint potential users with the operation, application,
and data needs of the Model. In addition, a central users'
clearinghouse could be initiated to (a) provide assistance to users
with special problems, (b) recommend possible sources of data, (c)
categorize and collect parameter information on calibrated
watersheds, and (d) direct future improvements in the Model as
indicated by the needs and comments of the users. The availability
of these services would greatly facilitate, expand, and promote the
use of the NFS Model.
(4) Further research and development of the NFS Model should be
directed to the following topics:
(a) development of computer programs to further assist user
application, such as: plotting and statistical analyses
routines; data handing and management programs; and
self-calibration and parameter optimization procedures.
(b) testing and application of the NFS Model on agricultural,
construction, and silvicultural areas to examine special
problems and pollutants associated with these land use
activities.
(c) development of a stream simulation model to accept output from
the NFS Model and perform the necessary flow and pollutant
simulation for in-stream processes. Such a model would help
eliminate the watershed size limitation of the NFS Model.
(d) continued research and refinement of the land surface
pollutant washoff algorithms with examination of the behavior
of highly soluble pollutants.
108
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SECTION X
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116
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SECTION XI
APPENDICES
Page
A. NPS Model User Manual 118
B. Hydrologic (LANDS) Simulation Algorithms 188
C. Snowmelt Simulation Algorithms 209
D. NPS Model Sample Input Listing 216
E. NPS Model Source Listing 226
117
-------
APPENDIX A
NPS MODEL USER MANUAL
CONTENTS
Section Page
Al. Introduction 119
A2. Model Structure and Operation 120
A3. Data Requirements and Sources 123
A4. Model Input and Output (I/O) 129
A5. Model Parameters and Parameter Eval uation 148
A6. Calibration Procedures and Guidelines 176
A7. Representative Costs and Computer Requirements 186
118
-------
Al. INTRODUCTION
The purpose of this User Manual is to provide a detailed description of
the method of operation, application, and use of the Nonpoint Source
Pollutant Loading (NPS) Model. Data requirements and sources, Model
input and output, parameter definition and evaluation, and calibration
procedures are discussed. This manual is not intended to replace the
discussion of the modeling philosophy and algorithms presented in the
body of this report. An understanding of the mechanisms of nonpoint
pollution and the method of representation in the NPS Model is critical
to successful application.
In general, the major steps involved in using the NPS Model are:
(1) data collection and analysis
(2) preparation of meteorologic data and Model input sequence
(3) parameter evaluation
(4) calibration
(5) production of needed information on nonpoint pollution.
The first three steps will often overlap as the input sequence of
parameters and meteorologic data is being prepared for calibration
trials. Section A2 describes the overall structure and operation of the
NPS Model and was reproduced from Section IV of this report. The
remaining sections provide the necessary information and guidelines for
performing the steps in the application process. The final portion of
this User Manual briefly discusses expected application and operation
costs and computer requirements for the NPS Model.
119
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A2. NPS MODEL STRUCTURE AND OPERATION
The NPS Model is a continuous simulation model that represents the
generation of nonpoint pollutants from the land surface. The Model
continuously simulates hydrologic processes (surface and subsurface),
snow accumulation and melt, sediment generation, pollutant accumulation,
and pollutant transport for any selected period of input meteorologic
data. The NPS Model is called a 'pollutant loading1 model because it
estimates the total transport of pollutants from the land surface to a
watercourse. It does not simulate channel processes that occur after
the pollutants are in the stream. Thus, to simulate in-stream water
quality in large watersheds, the NPS Model must interface with a stream
simulation model that evaluates the impact of channel processes. The
Model uses mathematical equations, or algorithms, that represent the
physical processes important to nonpoint source pollution. Parameters
within the algorithms allow the user to adjust the behavior of the Model
to a specific watershed. Thus, the NPS Model should be calibrated
whenever it is applied to a new watershed. Calibration is the process
of adjusting parameter values until a good agreement between simulated
and observed data is obtained. It allows the NPS Model to better
represent the peculiar characteristics of the watershed being simulated.
Fortunately, most of the NPS Model parameters are specified by physical
watershed characteristics and do not require calibration. However, the
importance of calibration should not be underestimated; it is a critical
step in applying and using the NPS Model.
The NPS Model is composed of three major components: MAIN, LANDS, and
QUAL. Figure 32 is an operational flowchart of the NPS Model
demonstrating the sequence of computation and the relationships between
the components. The Model operates sequentially reading parameter
values and meteorologic data, performing computations in LANDS and QUAL,
providing storm event information, and printing monthly and yearly
summaries as it steps through the entire simulation period. MAIN, the
master or executive routine, performs the tasks contained within the
dashed portion of Figure 32. It reads Model parameters and meteorologic
data, initializes variables, monitors the passage of time, calls the
LANDS and QUAL subprograms, and prints monthly and yearly output
summaries. LANDS simulates the hydrologic response of the watershed and
the processes of snow accumulation and melt. The QUAL subprogram
simulates erosion processes, sediment accumulation, and sediment and
pollutant washoff from the land surface. During storm events, LANDS and
QUAL operate on a 15-minute time interval. LANDS provides values of
runoff from pervious and impervious areas while QUAL uses the runoff
values and precipitation data to simulate the erosion and pollutant
washoff processes. For nonstorm periods, LANDS uses a combination of
15-minute, hourly, and daily time intervals to simulate the
120
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MAIN
1
(LANDS)
(QUAL )
C^ART)
READ MODEL PARAMETERS
1
<^_ BEGIN YEARLY LOOP ^>
1^
READ METEOROLOGIC DATA
1
<^_ BEGIN MONTHLY LOOP _^>
INITIALIZE MONTHLY VARIABLES
1
(^ BEGIN DAILY LOOP _^>
I*
INITIALIZE DAILY VARIABLES
*
READ PRECIPITATION
*
YE$ /fRRORINPRECIPITATION\
\ SEQUENCE? /
INO
BEGIN 15-MINUTE YES / \ NO
, tmiii iTinu - ' .— f STORM EVENT? \
x /
NO
/ LAST INTERVAL \
~~\. OF THE DAY' f \
x 7 S x un
/ LAST DAY OF THE \NO
\MONTH OR SIMULATION'./
YES
< LAST MONTH OF \HO
SIMULATION OR YEAR?/
YES
PRINT MONTHLY SUMMARY
/ LAST YEAR \ NO
\ OF SIMULATION? /
*YES
PRINT YEARLY SUMMARY
i 1
i
(LANDS)
( QUAL )
LlJ
SUBROUTINE ( J
| DECISION POINT <. . . ^
OPERATION | 1
PATH DESIGNATION C ")
Figure 32. NFS model structure and operation
121
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evapotranspiration and percolation processes that determine the soil
moisture status of the watershed. Since nonpoint pollution from the
land surface occurs only during storms, QUAL operates on a daily
interval between storm events to estimate pollutant accumulations on the
land surface that will be available for transport at the next storm
event. Figure 32 indicates the individual operations of the MAIM program
that occur on 15-minute, daily, monthly, and yearly intervals; these
operations support the LANDS and QUAL simulation.
The NPS Model can simulate nonpoint pollution from a maximum of five
different land uses in a single simulation run. The water quality
constituents simulated include water temperature, dissolved oxygen (DO),
sediment, and a maximum of five user-specified constituents. All are
considered to be conservative due to the short resident time on the land
surface that is characteristic of nonpoint pollution. Pollutant
accumulation and removal on both pervious and impervious areas is
simulated separately for each land use. The Model allows monthly
variations in land cover, pollutant accumulation, and pollutant removal
to provide the flexibility of simulating seasonally dependent nonpoint
pollution problems, such as construction, winter street salting, leaf
fall, etc. Although separate land uses are considered in the QUAL
subprogram, LANDS combines all pervious and impervious areas into two
groups for the hydro! ogic simulation regardless of land use. Pervious
and impervious areas are simulated separately because of the differences
in hydro!ogic response and because of the importance of impervious areas
to nonpoint pollution in the urban environment.
Output from the NPS Model is available in various forms. During storm
events, flow, water temperature, dissolved oxygen, pollutant
concentration, and pollutant mass removal are printed for each 15-minute
interval. Storm summaries are provided at the end of each event, and
monthly and yearly summaries are printed. The yearly summaries include
the mean, maximum, minimum, and standard deviation of each variable. To
assist interfacing with other continuous models, the NPS Model includes
the option to write the 15-minute output without summaries to a separate
file (or output device) for later input to the stream model. In
general, the NPS Model output is provided in different forms so that the
information will be usable irrespective of the type of analysis being
performed. Section A4 contains a full description of the output and
options of the NPS Model.
122
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A3. DATA REQUIREMENTS AND SOURCES
Data requirements for use on the NPS Model include those related to
Model operation, parameter evaluation, and calibration. These
requirements and possible sources of data are briefly discussed below.
The input format and sequence of the meteorologic data are presented
with Model I/O in Section A4.
Model Operation Data
The basic data for Model operation is the input meteorologic data
series. Normal operation requires 15-minute or hourly precipitation,
daily potential evapotranspiration, and daily maximum and minimum air
temperature. If snowmelt simulation is performed, daily solar radiation
and daily wind movement are also required. Since the NFS Model is a
continuous simulation model, the period of record needed for each of
these data series corresponds to the length of time for which simulation
will be performed. To overcome the impact of initial hydrologic
conditions (see Section A6) a minimum of one year should be simulated.
The actual time period of simulation will depend on the information
needed and the type of analysis being performed. There are no inherent
limitations in the NPS Model on the length of the simulation period.
Frequency analysis of the long-term output would provide valuable
information on the probability of nonpoint pollution.
Parameter Evaluation Data
Data requirements for parameter evaluation pertain to NPS Model
parameters that are evaluated largely from physical watershed
characteristics. These include parameters related to topography, soil
characteristics, land surface conditions, hydrologic characteristics,
climate, land use, etc. The section on model parameters will describe
each parameter individually and indicate methods of evaluation,
references, and specific data sources. In general, the types of
information needed for parameter evaluation include
topographic maps
soil maps and investigations
hydrologic/meteorologic studies
water quality studies
land use maps and studies
123
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Any investigations related to the above topics for the watershed to be
simulated should be collected and analyzed as a source of information for
parameter evaluation.
Calibration Data
Calibration involves the adjustment of parameters to improve agreement
between recorded and simulated information. For the NFS Model observed
runoff and water quality data are required. In addition, if snow
simulation is performed, recorded snow depth and water equivalent
information are needed to evaluate the accuracy of the simulated values.
Ideally, the observed data should be continuous to allow an .accurate
assessment of the continuous simulation produced by the NPS Model. In
addition, the continuous data should extend for three years to obtain an
adequate calibration of the parameters. However, data availability on
most watersheds seldom approaches the ideal, especially for water
quality. In such circumstances, calibration will be limited to
comparisons with whatever data can be obtained.
Hydrologic calibration involves comparison of simulated and recorded
runoff volumes and individual storm hydrographs for a calibration period
of one to three years. The volume comparison can be made on a storm,
daily, monthly, or yearly basis depending on the watershed area, the
length of the calibration period, and the available data. Since the NPS
Model simulates on 15-minute intervals, comparison of simulated and
recorded storm hydrographs can be performed for intervals greater than
15 minutes; minor storms with durations less than 15 minutes would not
provide sufficient hydrograph definition for a valid comparison. Thus,
data for hydrologic calibration includes both continuous runoff volumes
and selected storm hydrographs throughout the calibration period.
Water quality calibration for nonpoint pollution is analogous to
hydrologic calibration; simulated pollutant mass removal on a storm,
daily, monthly, or yearly basis, and individual storm pollutant graphs
for selected storms are compared with recorded data. Since nonpoint
pollution data is scarce, calibration is often reduced to comparison of
grab-sample measurements or selected storm pollutant graphs with the
simulated values. Actual data requirements for water quality
calibration in the NPS Model are thus reduced to obtaining whatever
water quality data are available for the watershed. Since the NPS Model
simulates nonpoint pollution in terms of sediment, information on
sediment (or Total Solids) yield and on the relationship between the
individual pollutants and sediment would be the most pertinent.
124
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Data Sources
To satisfy the data requirements of the NFS Model, a thorough search of
all possible data sources is a necessary task in the initial phase of
application. Many agencies at all governmental levels are involved in
the collection and analysis of data relevant to nonpoint source
pollution. Numerous federal agencies are active in monitoring and
collection of environmental data. With regard to meteorologic data, the
Environmental Date Service (formerly the Weather Bureau) provides a
comprehensive network of meteorologic stations and regularly publishes
the collected data. Table 20 lists publications of the Environmental
Data Service where selected meteorologic data can be found. Most of
these publications can be found in the libraries of colleges and
universities, or regional offices of the Environmental Data Service.
The EPA STORET and the USGS NASQAN data systems may be consulted for
stream related water quality data. Table 21 presents a brief summary of
selected federal agencies and data categories related to nonpoint
pollution that may be available. Regional offices of the agencies
listed in Table 21 should be contacted during the initial data
collection phase in order to uncover any data available for the specific
watershed being simulated.
Unfortunately, the large jurisdiction of federal agencies precludes data
collection and monitoring on many small watersheds where the NPS Model
would be applicable. Also, the emphasis of the federal agencies has
been directed to major streams and river basins where water quality
measurements include the effects of nonpoint pollution, point pollutant
discharges, in-stream water use, and channel processes. Consequently,
much of the available water quality data may not be directly comparable
with the NPS Model simulation results; joint use of the NPS Model and a
stream model may be needed.
Local, regional, and state agencies and possibly private firms located
in the subject watershed may be the most important sources of pertinent
data. Local agencies will often exhibit great interest in water quality
because of direct and indirect impacts of pollution on their activities.
The types of agencies that should be contacted include:
planning commissions
public works departments
public utilities
flood control districts
water conservancy districts
water resource and environmental agencies
125
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Table 20. SELECTED METEOROLOGIC DATA PUBLISHED BY THE
ENVIRONMENTAL DATA SERVICE3
Data Type
Publication1
Precipitation: Daily
Hourly
Evaporation
Max-min Air Temperature
Wind
Solar Radiation
Snowfall and Snow Depth
Climatological Data
Hourly Precipitation Data
Hourly Precipitation Data
Local Climatological Data
(for selected cities)
Climatological Data
Climatological Data
Local Climatological Data
(for selected cities)
Climatological Data
Local Climatological Data
Climatological Data-National
Summary
Climatological Data
a. formerly the Weather Bureau
b. The National Climatic Data Center, Asheville, North Carolina
can be contacted for assistance in locating published data
and can provide data on magnetic tapes or punched cards.
126
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Table 21. SELECTED FEDERAL AGENCIES AS POSSIBLE DATA SOURCES
INS
Agency
Environmental
Protection
Agency
U.S. Geological b
Survey
Forest Service
Bureau of
Land Management
Soil Conservation
Service
Bureau of
Mines
Bureau of
Reel arnati on
Census Bureau
National Park
Service
Data Category
Climatologic3
*
*
*
Hydrologic
*
**
*
••• .-.
*
*
Water Quality
**
*
* •
*
*
*
Land Use
*
*
*
*
*
Soil & Geology
**
*
-
**
*
Topographic
•
**
*
*
'
*
*additional source
**major involvement
a. Publications of the Environmental Data Service listed in Table 20 are a major
source of cliniatological data.
b. "Water Resources Data" is an annual publication of the USGS for each state.
It provides data streamflow values at all USGS sites in the state. Also,
regional offices of the USGS can often provide bi-hourly storm hydrographs
for selected events.
-------
Planning commissions and public works departments can be a source of
land use, soils, and topographic data. Public utilities, flood control
districts, and water conservancy districts will often establish
meteorologic stations and monitor streamflow and water quality. State
water resource and environmental departments are usually active in
projects and investigations of water resources and water quality in the
state. All agencies similar to those listed above should be consulted
for data, special watershed studies, and other information to provide a
sound base for application of the NPS Model.
128
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A4. MODEL INPUT AND OUTPUT (I/O)
Model Input
The NPS Model accepts input of parameters and meteorologic data on a
sequential basis in either English or metric units. Table 22
demonstrates the sequence of input data; a sample input listing is
included in Appendix D. Input of the NPS Model parameters begins the
sequence. Section A5 entitled "Model Parameters and Parameter
Evaluations" defines and describes the parameter input sequence.
The NPS Model parameters are followed by the meteorologic data. All
meteorologic data are input on a daily basis as a block of 31 lines (or
cards) with 12 values in each line. Thus, the resulting 31 x 12 matrix
corresponds to the 12 months of the year with a maximum of 31 days each.
Table 23 demonstrates the format for the daily meteorologic data and
Table 24 describes units and attributes. The only modification to the
format in Table 23 is for daily max-min air temperature since two values
are input for each day. In this case, the six spaces allowed for each
daily value are divided in half. The first three spaces contain the
maximum, and the second three spaces contain the minimum air temperature
for the day. Table 25 indicates the format for precipitation data input
on 15-minute or hourly intervals. For further clarification of these
formats, see the sample input listing in Appendix D.
The Model operates continuously from the beginning to the end of the
simulation period. To simplify input procedures and reduce computer
storage requirements, the meteorologic data are input on a calendar year
basis. Each block of meteorologic data indicated in Table 22 must
contain all daily values for the portion of the calendar year to be
simulated. Thus if the simulation period is July to February, the Model
reads and stores all the daily meteorologic data for the July to
December period. The Model then reads the precipitation data, on the
15-minute or hourly intervals, and performs the simulation day-by-day
from July to December. When the month of December is completed, the
Model reads the daily meteorologic data for January and February, and
then continues stepping through the simulation period by reading the
precipitation and performing the simulation day-by-day for the months of
January and February. Thus the input data must be ordered on a calendar
year basis to conform with the desired simulation period.
Model Output
The output obtained from the NPS Model includes the following:
129
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Table 22. INPUT SEQUENCE FOR THE NFS MODEL
NFS Model Parameters
Potential Evapotranspiration
Max-Min Air Temperature
Wind Movement > 1st Year
Solar Radiation
Precipitation
Potential Evapotranspiration
Max-Hin Air Temperature
Wind Movement
Solar Radiation
Precipitation
2nd Year
etc.
130
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Table 23. SAMPLE INPUT AND FORMAT FOR DAILY METEOROLOGIC DATA
Month
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
i
18 74 60 29 13
18 90 170 29 13
18 60 43 30 14
0 61 43 60 4
35 61 43 112 202
28 32 71 15 99
28 121 4 15 100
28 69 41 15 34
28 7 35 15 135
28 20 20 15 210
28 21 20 16 202
28 21 21 16 219
28 16 123 113 145
28 54 123 113 176
27 46 132 113 192
33 47 103 113 222
13 45 61 ]
L 171
41 45 61 88 173
41 46 61 88 159
54 46 61 88 72
54 81 112 88 103
55 83 44 88 198
118 101 104 88 154
32 45 87 13 232
24 46 87 13 153
24 46 87 19 114
24 28 72 332 90
25 60 86 58 152
25
91
17
50 58 3
31 58 153
31
I
198
•
266
70
65
70
171
8
72
70
37
108
63
142
132
90
156
121
160
70
72
161
84
149
183
C2
262
109
126
59
137
213
1
I
131
163
140
156
145
185
87
145
62
185
175
133
185
154
246
140
89
58
80
46
168
129
136
141
71
65
27
43
148
155
103
1
103
96
53
162
34
122
65
105
130
36
139
162
4
72
208
115
123
92
72
130
205
178
143
122
112
136
52
170
37
249
38
t
19
63
189
124
115
24
161
92
145
218
185
145
99
211
125
15C
191
139
112
119
73
79
132
152
112
92
33
66
79
165
1
I
41 90 68
69 72 68
97 48 47
104 48 52
117 114 47
138 54 42
124 12 31
90 0 57
117 78 36
159 72 10
76 60 57
34 48 36
110 48 57
117 5^
76 2.t
36
I 36
83 24 104
90 60 73
110 120 47
117 66 57
104 24 73
83 48 104
83 36 109
83 66 99
77 36 83
71 30 10
65 48 42
59 24 68
53 78 36
48 54 16
69 204 47
14
1
68
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
r*
Day
t
14
20 26
32
38 44
50
56 62
68
74 80
Col umn Number
Notes: 1. Columns 1-7 are ignored. They can be used to identify the data.
2. All data are input in integer form.
3. Identical fortrat for evaporation, wind, and solar radiation.
4. For I1ax-Hin air temperature data, the six spaces allowed for each
daily value (above) are divided in half; the first three spaces
contain the maximum temperature, and the second three spaces contain
the minimum temperature. See listing in Appendix D.
131
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Table 24. METEORQLOGIC DATA INPUT SEQUENCE AND ATTRIBUTESa
Data
Potential
Evapotranspi ration
Max-Mi n
Air Temperature
Wind
Solar Radiation
Precipitation
Interval
Daily
Daily
Daily
Daily
Hourly
15 minutes
Units
English
in x 100
degrees F
miles /day
langleys/
day
in x 100
in x 100
Metric
mm
degrees C
km/ day
langleys/
day
mm
mm
Comments
Assumed equal to lake evaporation and
lake evaporation = pan evaporation x pan
coefficient
1. Caution: Time of observation
determines whether the recorded values
refer to the day of observation or the
previous day.
Required only for snow simulation.
1. Total incident solar radiation.
2. Required only for snow simulation.
3. 1 langley = 1 calorie/cm2
co
ro
a. All meteorologic data is input in integer form. Format specifications are described in
Table 23.
-------
Table 25. NFS MODEL PRECIPITATION INPUT DATA FORMAT
Column No.
Description and Format
1
2-7
8
9-80
Blank
Year, Month, Day (e.g. January 1, 1940 is 400101).
Card Number:
15 minute data- each card represents a 3-hour period
Card #1 Midnight to 3:00 AM
#2 3:00 AM to 6:00 AM
#3 6:00 AM to 9:00 AM
#8 9:00 PM to Midnight
All eight cards are required if rain occurred any
time during the day. A card number of 9 signifies
that no rain occurred during the entire day, and
no other rainfall cards are required for that day.
Hourly data—Each card represents a 12-hour
period; thus, two (2) cards are required for
each day when precipitation occurs. Card #1
is for the 12 AM hours and Card #2 is for
the 12 PM hours. As with 15-minute, a card
#9 indicates no precipitation occurred in
that day.
Precipitation data (millimeters(00's of
i nches)).
15-minute intervals:
6 column per each 15-minutes in the 3-hour period
of each card. Number must be right justified,
i.e. number must end in the 6th column for the
15-minute period.
Hourly intervals:
6 columns per each hourly interval, i.e. the
hourly period still occupies 6 columns, but
only two cards are needed for the entire day.
Number must be right-adjusted.
Notes:
1.
2.
3.
4.
Appendix D contains a sample of input data.
At least one precipitation card is required for each day
of simulation.
Blanks are interpreted as zeros by the Model: consequently,
zeros do not need to be input.
Only integer values are allowed.
133
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(1) output heading
(2) time interval output and storm summaries
(3) monthly and yearly summaries
(4) output to interface with other models (optional)
The heading of the NFS Model output provides a summary of the watershed
characteristics, simulation run characteristics, and input parameters.
Analysis of this information will uncover errors in specification of the
input parameter values. Table 26 is an example of the output heading
when average yearly values are used for the sediment accumulation and
removal rates, and potency factors. Table 27 displays the output
heading when monthly variations in these parameters are employed.
The time interval and storm summary output constitute the major portion
of the output obtained from the NPS Model. Since the Model operates
continuously on a 15-minute time step throughout the simulation period,
output could be printed for every 15-minute interval. To prevent such
voluminous output, an input parameter (HYMIN) allows the user to specify
a minimum flow above which output is printed. Thus, output can be
limited to only the major storms or the most significant portions of
storm events. The type of output provided in each time interval depends
on the mode of operation as specified by the input parameter, HYCAL.
The modes of operation in the NPS Model are 'Calibration1 (HYCAL=1,2)
and 'Production' (HYCAL=3,4). The calibration mode can pertain to
either hydrologic calibration (HYCAL=1) or sediment and water quality
calibration (HYCAL=2). Table 28 provides an example of storm output for
sediment and water quality calibration; hydrologic calibration output is
identical except that the sediment and water quality constituent columns
are blank because the quality computations are bypassed to save computer
cost. The goal of the calibration output is to provide information
on the sources of flow and pollutants within the watershed. Thus,
calibration output indicates the contributions (flow and quality) from
both pervious and impervious areas for each land use in the watershed;
this information is valuable in the calibration process. At the end of
each storm event, a storm summary is printed including the length and
time of the storm, total and peak flow, and pollutant washoff
characteristics as shown in Table 28.
Production run storm output (HYCAL=3) is presented in Table 29. Only
total values of flow and quality from the entire watershed are printed.
The individual storm summaries printed at the end of each storm event,
and the sediment accumulation printed at the beginning of each storm are
identical for both modes of operation, as shown in Tables 28 and 29.
Since snowmelt simulation is performed hourly, output is provided only
during hydrologic calibration runs for each hour whenever snowmelt
calculations are performed. Table 30 presents an example of daily
134
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Table 26. NFS MODEL OUTPUT HEADING - ANNUAL WATER QUALITY PARAMETERS
NONPCINT SOURCE POLLUTANT LOADING MODEL
CHARACTERISTICS :
NAME
SAMPLE INCUT DATA
NFS MODEL
TCTAL AREA (ACRE)
1069.00
LAND USE
OPEN APS A
RES 10.AREA
COMMERCIAL
INDUSTRIAL
* OF TOTAL
10.0
60.0
17.0
13.0
AREA (ACRES)
106.90
641.4C
181.73
138.97
PERVIOUS (AtRES* IMPERVIOUS (ACRES) IMPERVIOUS III
101 .55
525.95
81.78
34. 74
5.34
115.45
99.95
134.23
5.00
18.00
55.00
75.00
FRACTION OF IMPERVIOUS AREA 0.30
00
01
SIMULATION CHARACTERISTICS :
TYPE OF RUN PRODUCTION (PHINTER OUTPUT ONLY)
DATE SIMULATION BtGI US NOVEMBER 15t 1970
DATE SIMULATION ENDS M-ARCH 31, 1971
INPUT PRECIPITATION TIME INTERVAL 60 MINUTES
SIMULATION TIME INTERVAL
IS SNOHMELT CONSIDERED ?
INPUT UNITS
OUTPUT UNITS
MINIMUM FLOW FOR OUTPUT PER UTERVAL (CFS
NUMBER OF QUALITY INDICATORS ANALYZED
THF ACMLYZEt QUALITY INDICATORS
15 MINUTES
YES
ENGLISH
ENGLISH
) 10.0000
5
SEDIMENTS, DO .TEMP,
BOD ,
ss .
SUMMARY OF INPUT PARAMETERS :
LANDS
SNOh
INTER =
NN
NNI
Kl
EPXM
LZSN
RADCON
ȣLEV
MPACK
IONS
RMUL
> 2 . 000
0.3CC
0. HO
1.400
•J.15C
J.4JO
0.250
800.000
3.100
3.100
l.COO
IRC
L
LI
PETMUL
K24L
USIS
CCFAC
UOIF
OGM
SCF
F
0.500
300.000
60C.OOO
1.000
0.9
6. COO
0.250
0.0
0.001
I. 100
O.E01
IW=IL
SS
SSI
K3
KK24
EVAPSN'
TSUOW *
we *
fcMUL *
KUil '
0.040
o.iao
O.lfaO
0.250
U.990
0.600
33.000
U.JiO
1.UOO
-------
Table 26 (continued). NPS MODEL OUTPUT HEADING - ANNUAL WATER QUALITY PARAMETERS
GO
cn
CUAI.
PCTENOi FACTORS
POTENCY FJCTQRS
JR6R, = 2.200
JSER. = i.soo
JEIM * 1.830
CFKN AREA
RES 10 .AREA
COMMERCIAL
INDUSTRIAL
OPEN AREA
RSSIO.AREA
COMMERCIAL
INDUSTRIAL
FOR P£RVIO'JS AREAS
eoo
SS
FOR IMPERVIOUS APEHS
BOD
SS
KRER = 1. 5M
KSgR - C.300
KEIM =• 0.303
ACUP = 3C.COO
«CLP = 70.000
ACUP = 75.000
ACUP = 80.013
RPER = 0.050
RPSR = 0.050
RP6R » 0.050
RPf-R » 0.050
OPEN ARtA
4.090
71. COO
OPEN AREA
4.000
71.000
CCNThLY DISTRIBJTION JAN FE8R MAR
TEMP CORRECTION
FACTOR 1.00 1
.00 1.00
ACUI <* 30.000
ACUI * 70.000
ACUI • 75.000
ACUI = ao.aoo
RIMP - 0.080
PIMP = 0.080
Rinp - o.oac
RI,«|P a 0.060
RLSiO.A.ttA COMMERCIAL
4.000 4. CCO
7l.)Ju 11. COO
R£SIO.AK£A CCMMJRCIAL
4.000 4.000
71 . 300 7 1 .030
APR PAY JW JUL
1.00 1.03 1.10 1.00
INDUSTRIAL
7l'.000
INDUSTRIAL
4.000
71.030
AUG SEPT
l.OC l.OC
OCT MOVE OECE
1.00 1.00 l.OO
- PERVICUS LAKOJ -
LANO COVER- OPEN AREA C.932 0
RKSIC. /<*?/» 0.950 C
CCMMrRCIAL 0.900 0
IiMOJSTRIAL C.900 0
.901 O.SJO
.950 0.950
.900 0.90C
.900 0.900
9.900 0.990 0.9CC 0.900
0.950 0.950 0.950 0.950
0.900 0.900 0.903 0.900
0.900 U»00 0.900 0.900
0.930 0.900
0.950 C.950
0.900 0.930
0.900 0.900
3.990 0.900 0.900
0.950 0.953 0.950
0.9CO 0.939 0.900
0.990 0.903 0.9JO
IHTIAL CONDITIONS
LANGS
SNOW
QUA I
UZS
PACK
0.0
0.0
OPtN AREA
PESIO.AREA
CCMKERCI AL
INDUSTRIAL
LZS - 2.250 SOW * 1.000
OEPTH . 0.0
15
TS
TS
TS
106.000
248.000
266.000
284. DJO
SR£R
SRSR
rR:K
SRER
* 1758.000
* 1380.000
=. 2658.000
= 275a.QOC
-------
Table 27. NPS MODEL OUTPUT HEADING - MONTHLY WATER QUALITY PARAMETERS
NONPOINT SOURCE POLLUTANT LOADING MODEL
WATERSHED CHARACTERISTICS :
NAME
MONITQU WAY STORM DRAIN
MADISON, WISCONSIN
TOTAL AREA (ACREI 147.20
LAND USE % OF TOTAL AREA (ACRES)
RfrSIO.AREA 100.0 147.20
FRACTION OF IMPtf VldUS AREA 0.10
PERVIOUS (ACRES I IMPERVIOUS (ACRES) IMPERVIOUS (tl
132.48 14.72 10.00
co
SIMULATION CHARACTERISTICS !
TYPE OF RUN PRODUCTION (POINTER OUTPUT ONLY I
OATF SIMULATION BEGINS SEPT'lBtK Z, 1970
DATE SIMULATION ENDS MARCH 31, 1972
INPUT PRECIPITATION TIMF INTERVAL 6j MINUTES
SIMULATION TIME INT^SViL 15 rtlNUTcS
IS SNOWMELT CONSIDERED ? YES
INPUT UNITS ENGLISH
OUTPUT UNITS ENGLISH
MU4IMUM FLOW FOR OUTPUT PER INTERVAL (CFS I J.0500
NUMBER OF CUALITY INDICATORS ANALYZED 4
THE ANALYZED QUALITY INCICATCRS S EOIM£I«TS, OJ ,
T3TAL-P
SUMMARY CF INPUT PARAMETERS
LANDS
INTEK
NN
NN1
Kl
EPXM
UZSlv
3.
C.
C.
1.
0.
C.
5JJ
400
150
05O
150
750
IRC
I
LI
PETMUL
K2>tL
LiSH
S!
*
C
S
—
X
0. 100
lij.OOj
700.0'JO
U. VJO
l.UJJ
D*OJO
INFIL
SS
SSI
K3
KK24
X
X
,
s
s
a.
0.
0.
0.
i.
100
010
nio
400
000
-------
Table 27 (continued). NPS MODEL OUTPUT HEADING - MONTHLY WATER QUALITY PARAMETERS
SNOW RADCCN» 0.250 CCFAC = 0.250 EVAPSN* 0.603
"ELEV *8CC.OQt> EL3IF « 0.0 TSNCIW * 33.000
CPtCK = C.100 DiiM « 0. uul <
-------
Table 28. CALIBRATION RUN OUTPUT FOR STORM EVENTS
(Sediment and water quality calibration, HYCAL=2)
OUTPUT FOR STORM NO. 13 - OCTOBER 1971
ACCUMULATION OF DEPOSITS ON GPOUND AT THE BEGINNING OF STORM,TONS/ACRE
LAND USE WEIGHTED ME UN PERVIOUS IMPERVIOUS
flG
-------
Table 28 (continued). CALIBRATION RUN OUTPUT FOR STORM EVENTS
(Sediment and water quality calibration, HYCAL=2)
OCT 23 5:30 12.759 65.00 9.34 313.77 0.438 12.55 0.018 222.78 0.311
TOTAl FLOW 12.759. CFS
IMPERV. FLOW 10.064. CFS
PRECIPITATION 0.0 , IN
AGRIC.APEA
COVER* 0.90 PERV.
IMPERV.
RFSID.AREA
CCVER= 0.95 PERV.
IMPEPV-
CQ^ERCIAL
COVER* 0.90 PEPV.
COVER* 0.90
INDUSTRIAL
PERV.
IMPERV.
O.?0
0.0
0.30
113.23
0.0
113.23
9S.03
0.0
98.03
102.22
0.0
102.22
0.012
0.0
0.012
4.529
C.O
4.529
3.921
0.0
3.921
4.089
0.0
4.069
0.210
0.0
0.210
80.393
0.0
80.393
69.599
0.0
69.599
72.577
0.0
72.577
SUMMARY FOP STORM * 11
NUMBER OF TIME INTERVALS 3
STORM BEGINS OCT 23 5: 0
STOPM ENDS OCT 23 5:45
TOTAl PLOW ( IN } O.OOS
FLOW ICFS > 14.034
TOTAL WASHOFF (TONS!
MAX WASHOFF ( LB /15M1N)
MEAN CONCENTRATION (GM/L)
WAX CONCENTRATION
-------
Table 29. PRODUCTION RUN OUTPUT FOR STORM EVENTS (HYCAL = 3)
OUTPUT FOR STORM NO. 7 - JANUARY 1972
ACCUMULATION OP DEPOSITS ON GROUND AT THE BEGINNING OP STORM,TONS/ACBE
LAND USE WEIGHTED MMN PERVIOUS IMPERVIOUS
OPEN AREA
FESIE. AREA
COMMERCIAL
INDUSTRIAL
WEIGHTED BEAN
0.388
0.662
0.553
0.479
0.592
0.406
0.7U7
0.856
0.906
0.720
0.045
0.275
0.306
0.337
0.300
QUALITY CONSTITUENTS
DATE
JAN 13
JAN 13
JAN 13
JAN 13
JAN 13
TIME
4:30
4:45
5: 0
5:15
5:30
FLOW
CFS
TEMP
(F)
12. 343 45.22
44. 323 45.22
24.012 45.22
18.373 45.00
12.121 45wOO
DO(PPH) S ED IN PUTS
(LB)
12.04
12.04
12.04
12.08
12.08
575
2171
1091
633
305
.07
.29
.90
.72
.36
(GH/L)
0.
0.
0.
0.
0.
830
873
810
614
449
BOD
(LB) (GB/L)
23.
86.
43.
25.
12.
00
85
68
35
21
0.
0.
0.
0.
0.
033
035
032
025
018
55
(IB)
408.
1541.
775.
449.
216.
30
62
25
94
80
(GM/L)
0.589
P. 620
0.575
0.436
0.319
SUMMARY FOR STORK f 7
NUMBER OP TIME INTERVALS 5
STORM BEGINS JAN 13 4:30
S10FM ENDS JAN 13 5:45
TOTAL FLOW { IN ) 0.026
PEAK PLOW (GPS ) 44.323
TOTAL WASHOPP (TONS)
MAX WASHOFF ( LB /15MI1?)
MEAN CONCENTRATION (GH/L)
MAX CONCENTRATION (GM/L)
SEDIMENTS
2.39
2171.29
0.72
0.87
BOD
0.09555
86.85162
0.02860
0.03190
SS
1.69595
1541.61621
0.50773
0*61954
-------
snowmelt output that is printed in the last hour of each snow simulation
day when the calibration option is specified. Table 31 defines the
snowmelt output values shown in Table 30.
The monthly and yearly summaries are shown in Tables 32 and 33,
respectively. These summaries are identical for both calibration and
production modes of operation. The information provided in the monthly
summary includes total values for hydrologic information; soil moisture
storages and sediment accumulation at the end of the month; total
sediment and pollutant washoff for pervious and impervious areas in each
land use; and average storm values for temperature, dissolved oxygen, and
concentration of simulated pollutants. The yearly summary in Table 33
contains analogous values for the entire year, in addition to the
average, standard deviation, maximum, minimum, and range of values for
storm events for the following information:
total runoff
peak flow
total pollutant washoff
maximum pollutant mass washoff
mean pollutant concentration
maximum pollutant concentration
Although mean or average conditions have little meaning in the
evaluation of nonpoint pollution, these values are provided by the NPS
Model in order to supply the information in a form useful to the user.
Obviously, many users of the NPS Model may be forced by financial or
time considerations to employ analysis techniques requiring only mean
daily, monthly, or yearly pollutant loadings on a per acre or per
stream-mile basis. In such situations, the necessary information can be
obtained directly from the NPS Model output. However, for users
requiring complete definition of the hydrograph and pollutant graph, the
standard output for each storm event is provided. The NPS Model also
includes the option (HYCAL=4) to write output to a separate file, or
output device, for later input to a continuous or event stream
simulation model. The output is essentially identical to production run
output (Table 29) except that headings, titles and all summaries are
excluded. A row of dashes (—) separate the information for different
storm events. The format for the output is shown in Table 34. The
intent of this option is to allow users to simulate larger watersheds if
a suitable stream simulation model is available to accept the land
surface simulation output from the NPS Model. In addition, statistical
analysis of the output could provide probabilities of nonpoint pollution
for use in the evaluation of alternate management policies.
142
-------
Table 30. DAILY SNOWMELT OUTPUT
(Calibration run, English units)
SNOHMELT OUTPUT FOR
DECEMBER
CO
HOUR
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
PACK
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
DEPTH
3.0
3.0
3.0
3.0
3.0
3.0
3.0
3.0
3.0
3.0
3.0
3.0
2.9
2.9
2.9
2.9
2.8
2.8
2.8
2.8
2.8
2.8
2.3
2.8
SO EN
0.204
0.204
0.204
0.205
0.205
0.205
0.204
0.204
0.204
0.2C4
0.203
0.202
0.203
0.204
0.205
0.206
0.205
0.204
0.204
0. 203
0.203
0.202
0.202
0.202
ALBEDO
0.735
0.734
0.733
0.732
C.731
C.730
0.730
C.729
C.72B
0.727
0.726
0.725
0.725
0.724
C.723
0.722
0.721
0.721
0.720
C.719
C.718
C.717
0.717
0.716
CLDF
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
I. 000
1.000
1.000
1.000
N6GMELT
0.013
0.017
0.021
0.023
0.024
0.025
0.024
0.022
0.016
0.009
0.001
0.001
0.0
0.0
0.0
0.0
0.0
0.0
0.0
o.o
0.002
0.005
0.009
0.014
LIUW
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.017
0.017
0.017
0.017
0.017
0.017
0.017
0.017
0.017
0.017
TX
23.77
22.61
21.74
21.16
20.58
20.00
20.38
21.52
24.18
27.60
31.40
34.63
37.10
38.24
38.81
39.00
38.05
36.72
34.82
32.35
29.50
27.03
24.75
23.23
RA
0.
0.
0.
0.
0.
0.
1.
2.
3.
4.
5.
5.
5.
5.
5.
5.
4.
3.
1.
0.
0.
0.
0.
0.
LW
-8.
-8.
-8.
-8.
-9.
-9.
-9.
-8.
-8.
-7.
-7.
-6.
-6.
-5.
-5.
-5.
-5.
-6.
-6.
-7.
-7.
-7.
-8.
-8.
PX
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
MEtT
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.003
0.006
0.007
0.005
0.002
0.0
0.0
0.0
0.0
0.0
0.0
0.0
CONV
0.0
0.0
0.0
0.0
o.c
0.0
0.0
0.0
0.0
0.0
0.0
0.002
0.005
0.006
0.007
0.007
0.006
0.004
0.002
0.000
0.0
0.0
0.0
0.0
RAINM
0.0
0.0
0.0
0.0
0.0
0.0
0.0
o.b
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
CONOS
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
ICE
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
-------
Table 31. DAILY SNOWICLT OUTPUT DEFINITIONS
(Calibration run, English units)
HOUR: Hour of the day, numbered 1 to 24
PACK: Water equivalent of the snowpack, inches
DEPTH: Snow depth, inches
SDEN: Snow density in inches of water per inch of snow
ALBEDO: Albedo, or snow reflectivity, percent
CLDF: Fraction of sky that is cloudless
NEGMELT: Heat loss from the snowpack, equivalent inches of melt
LIQVJ: Liquid water content of the snowpack, inches
TX: Hourly air temperature, degrees Fahrenheit
RA: Incident solar radiation, langleys
LW: Net terrestrial radiation, langleys (negative value indicates
outgoing radiation from the pack)
PX: Total snowmelt reaching the land surface, inches
MELT: Total melt, inches
CONV: Convection melt, inches
RAINM: Rain melt, inches
CONDS: Condensation melt, inches
ICE: Ice formation at the land surface, inches
144
-------
Table 32. MONTHLY SUMMARY OUTPUT OF THE NPS MODEL
SUMMARY FOR MONTH OF JANUARY 1972
WiTfR,
IN
RUNOFF
OVERLAND FLOW
INTERFLOW
IMPERVIOUS
RASE FLOW
TOTAL
GRDWATER RECHARGE
PRECIPITATION
EVAFOTR&NSPIRATION
POTENIAL
NET
STORAGES
UPPER /ONE
LOWER ZONE
GROUNDWATER
INTERCEPTION
OVERLAND FLOW
INTERFLOW
MATER BALANCE
TOTAL
0.014
0.303
0.590
0.4*9
1.356
0.0
2.576
1.180
0.997
0.553
7.033
1.403
0.0
0.0
0.0
0.0
SEDIMENTS ACCUMULATION iTONS/ACRE
OPEN AREA
RESID.AREA
COMMERCIAL
INDUSTRIAL
WEIGHTED VEAN
SEDIMENTS LOSS,
OPEN AREA
RESJO.AREA
COMMERCIAL
INDUSTRIAL
TOTAL LOSS
POLLUTANT WASHOFF,
TOTAL
1.091
22.7*0
19.472
20.275
63.307
(TONS)
WEIGHTfcO MEAN
J.351
0.660
0.574
0.523
0.597
TOTAL ( L8 /ACRE)
20.416
70.933
214.2S6
291. 7bB
lib.965
TOTAL ( LB I TOTAL I LB /ACRE)
WASHOFF OF
•OPEN AREA
RESIO.AREA
COMMERCIAL
INDUSTRIAL
TCTAL WASHOFF
WASHOFF OF
CPEto AREA
RESIO.AReA
COMP6FCIAL
INDUSTRIAL
TOTAL
eoo
ss
87.307
1619.914
1557.759
1621.995
5086.573
1549.709
32303.512
27650.270
28790.434
90293.87$
J.S17
2.837
8. 572
11.672
4.759
14.-.97
50.364
152.150
207.170
84.466
STORM WATER QUALITY - AVERAGES
TEMPFftATURE (Fl 48.45
OISSCLVED OXYGEN (PPMI 11.057
SEDIfEWTS
BCC
SS
NO. OF STORfS
(GM/L)
(GM/LI
0.093
O.OU4
O.O66
11
PERVIOUS
0.364
0.729
0.814
0.864
0.695
PERVIOUS (?
5.546
1.378
0.250
0.102
(1.697
PERVIOUS 1*
5.546
1.378
0.253
0.102
0.697
5.546
1.378
0.250
0.102
0.697
IMPERVIOUS
0.104
0.347
0.378
0.410
0.373
IMPERVIOUS (*)
94.454
98.622
99.750
99.898
99.303
IMPERVIOUS (S>
94.454
98.622
99.750
99.898
99 .303
94.454
98.622
99.750
99.898
99.303
145
-------
Table 33. ANNUAL SUMMARY OUTPUT OF THE NPS MODEL
SUMMARY FOR 1972
WATER,
IN
RUNCFF
OVERLAND FLOh
INTERFLOW
IMPERVIOUS
BASE FLOW
TOTAL
GROHATEP RECHARGE
PRECIPITATION
EVAPOTRANSPIRATION
POTENIAL
NET
STORAGES
UPPER ZONE
LOMER ZONE
GRUUMDNATcR
INTERCEPTION
OVERLAND FLOW
INTERFLOW
MATER BALANCE
SEDIMENTS LOSS,
OPEN AREA
F.ESIC.JREA
COMMERCIAL
INDUSTRIAL
TOTAL LOSS
POLLUTANT WASHOFF,
TOTAL
6.961
8.309
16.055
5.631
37.lt*
O.U
64.688
39.993
25.150
0.953
7.9*5
2.447
0.084
0.0
0.056
0.0
TOTAL (TONS)
239.148
1809.324
772.302
725.830
3546.604
TOTAL (TUNS/ACRE!
2.237
2.821
4.250
5.223
3.318
PERVIOUS <*l
93.919
64.291
23.419
10.586
46.398
TOTAL ( LB I TOTAL ( LB /ACRE) PERVIOUS (XI
WASHCFF OF
CPEN APEA
RES1D.AREA
COMMERCIAL
INOLSTRIAL
TOTAL WASHOFF
ViASHOFF OF
OPEN AKEA
F.ESID.AF.EA
COMMERCIAL
INDUSTRIAL
TOTAL WASHOFF
ecc
ss
19131.941
144744.875
61783.578
56065.547
263725.875
32S592.000
256S219.00J
10S6658.000
1030661.250
5036129.000
178.971
225.670
334.975
417.828
20S.412
3170.727
*>0o5.643
6034.547
74i6;430
4711.063
STORM fcATER QUALITY - AVERAGE*
TEMPEPATURE (F) 56.28
DISSOLVED OXYGEN (PPMI 10.742
SEDIMENTS
BOC
SS
NC. OF STORMS
(GM/ll
(GM/LI
(GM/LI
0.103
0.004
0.073
115
93.919
64.291
23.420
10.587
46.398
93.919
64.291
23.420
10.587
46.398
IMPERVIOUS (*l
6.081
35.709
76.581
89.414
53.602
IMPERVIOUS (XI
6.081
35.709
76.580
89.413
S3 .602
6.081
35.709
76.580
89.413
53.602
SUMMARY OF STORMS* CHARACTERISTICS
AVERAGE
ST.DEV.
MAXIMA
MINIMA
RANGE
SEDIMENTS LOSS
TOTAL WASHOFF (TONS I
MAX WASHOFF ( LB /15MINJ
MEAN CONCENTRATION (GM/LI
MAX COHCEflTRATICI* (GWLI
V.ASHOFF OF
BOO
TOTAL HAShOFF (TCNSt
MAX MASHOFF ( LB /15«INI
MFAN COMCENTJUTICN (G1/LI
MAX C3NC=NTRATIOrt (GM/LI
WASHOFF OF
SS
TOTAL WASHOFF (TONS)
MAX MASHGFF ( LB /15MINI
MEAN CONCENTRATION (GM/LI
MAX CONCENTRATION (CM/LI
30.^30
18831.J63
0.64i
1.267
1.221
753.254
J.026
J.J51
21.676
13370.21)0
0.»S»
0.899
57.332
47604.699
0.445
0.995
2.293
1904.178
0.018
0.040
40.706
33799.184
0.316
0.707
298.848
411374.438
1.823
3.917
11.954
16454.969
0.073
0.157
212.183
292375.750
1.294
2.781
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
298.848
411374.438
1.823
3.917
11.954
16454.969
0.073
0.157
212.183
292075.750
1.294
2.781
146
-------
Table 34. SAMPLE OUTPUT AND FORMAT FOR PRODUCTION RUN OUTPUT
DIRECTED TO UNIT 4 (HYCAL=4)
For Each Pollutant Simulated
(maximum of 5)
/A
Data type Ye
Format I
English units
Metric units
\
Sample
Output
ar Date
4 A4 IX 12
J\
Time
IX 12 1A 12
/
Fl ow Temp
F8.3 F5.2
cfs °F
m3 I °C
/y.
M \ I ( f f
1972 JAN 11 11: 0 25.54154.8010.58
1972 JAN 11 11:15 32.50656.1610.40
1972 JAN 11 11:30 25.19056.1610.40
1972 JAN 11 11:45 19. 28156.1610. 40
1972 JAN 11 12: 0 12.S0856.1610.40
1972 JAN 11 21:45 10.04848.5511.50
1972 JAN 11 22: 0 10.69246.5511.50
1972 JAN 11 22:15 11.21746.7511.76
1972 JAN 11 23: 0 10.51746.7511.78
1972 JAN 11 23:45 10.29945.5511.98
1972 JAN 11 24: 0 10.89745.5511.98
1972 JAN 12 0:15 11.38644.9512.09
1972 JAN 13 4:30 12.34345.2212.04
1972 JAN 13 4:45 44.32345.22.12.04
1972 JAN 13 5: 0 24.01245.2212.04
1972 JAN 13 5:15 18.37345.0012.08
1972 JAN 13 5:30 12.12145.0012.08
DO
F5.2
ppm
PPm
Sediment
F9.2 1 F8.3
Ib gm/1
kg 1 gm/l
Pollutant #1
F8.2
Ib
kg
F8.3
gm/l
gm/1
Pollutant #2
F8.2
Ib
kg
F8.3
gm/l
gm/l
; r r ( ( f
1300.07 0.907 52.00 0.036 923.05 0.644
1578.87 0.865 63.15 C.035 1121.00 0.614
1126.56
0.797 45. Cf> C.032 799.85 0.566
646.56 0.597 25.86 0.024 459.06 0.424
310.10 0.428 12. 4C 0.017 220.17 0.304
189.11
0.335 7.56 0.013 134.27 0.238
254.09 0.423 10.16 0.017 180.41
235.29 0.374 S.<]
C.C15 167.06
0.301
0.265
205.19 0.348 8.21 0.014 145.68 0.247
195.39 0.338 7.82 0.014 138.73
259.27 C.424 10.37 0.017 184.08
238.64
575.07
0.373 9.55 C.015 169.43
O.?40
0.301
0.265
G.630 23.00 0.033 408.30 0.589
2171.29 0.873 86.85 0.035 1541.62
1091.90
633.72
305.36
0.810 43.6? 0.032 775.25
0.614 25.35 0.025 449.94
0.449 12.21 0.018 216.80
0.620
0.575
0.436
0.319
Note: The format for reading output data from unit 4 is
FORMAT (14, A4, 2(1X,I2), 1A, 12, F8.3, 2(F5.2), F9.2, F8.3, 5(F8.2, F8.3)).
-------
A5. MODEL PARAMETERS AND PARAMETER EVALUATION
The NPS Model includes parameters that must be evaluated whenever the
Model is applied to a specific watershed. Since the Model is designed
to be applicable to watersheds across the county, the parameters provide
the mechanism to adjust the simulation for the specific topographic,
hydro!ogic, edaphic, and land use conditions of the watershed. The
large majority of the parameters are easily evaluated from known
watershed characteristics. Parameters that cannot be precisely
determined in this manner must be evaluated through calibration with
recorded data. This section discusses and defines the NPS Model
parameters, the parameter input sequence, and methods of parameter
evaluation. Section A6 provides calibration procedures and guidelines.
Table 35 lists and briefly defines the NPS Model parameters while Table
36 describes the parameter input sequence and attributes (units,
type, and options, etc.). The major parameters will be further
discussed with methods of evaluation. Parameter input is accomplished
in the FORTRAN 'namelist1 format except for alphanumeric variables which
are input under a fixed format. The parameters are divided into the
categories of simulation control, hydrology, snow, and water quality.
As indicated in Table 36, the control parameters begin the parameter
input sequence. The first two lines provide space for the watershed
name and identification of the specific simulation run. This
information is followed by the control namelists (ROPT, DTYP, STRT,
ENDD) that include parameters specifying units, run options, and the
beginning and ending dates of the simulation period. The hydrology
namelists (LND1, LND2, LND3, LND4) are next in sequence. If snow
simulation is to be performed, the snow namelists (SN01, SN02, SN03,
SN04, SN05) follow; otherwise the water quality parameters and namelists
begin. As indicated in Table 36, the water quality information begins
with the specification of the washoff 'namelist' (WASH) followed by the
names of the nonpoint pollutants to be simulated (one name per line).
Each pollutant name is followed (column #15) by the concentration units
to be used. Either gm/1 or mg/1 can be specified, and gm/1 is the
default specification.
Next, a block of information for each land use follows the pollutant
names and units. This block of information contains the land use name
followed by the water quality namelists (WSCH, MPTM or YPTM, MACR or
YACR, MRMR or YRMR, INAC) with the parameter values for the specific
land use. These parameters specify land cover, impervious area, land
use area, potency factors, accumulation, and removal rates; all these
parameters are specific to each land use. (Note that different input
namelist names are used to indicate average annual and monthly
variations for potency factors, sediment accumulation, and sediment
148
-------
Table 35. NFS MODEL INPUT PARAMETER DESCRIPTION
Type
Control
"".
Hydrology
Name
HYCAL
HYMIN
HLAND
NQUAL
SNOW
UNIT
PINT
I-1NVAR
BGNDAY
BGMMON
BGlNYR
ENDDAY
EHDMON
ENDYR
UZSN
LZSN
INFIL
INTER
IRC
AREA
NN
SS
L
NNI
SSI
LI
Kl
PETMUL
Description
Type of simulation run desired:
(1) hydrologic calibration (HYCAL=1)
(2) sediments and quality calibration (HYCAL=2)
(3) production run— printer output only (HYCAL=3)
(4) production run— printer and unit 4 output (HYCAL=4)
Minimum flow for output during a time interval
Number of land type uses within watershed (up to five)
Number of optional quality constituents simulated (up to 5)
Controls snowmelt simulation:
(1) snowmelt performed (SNOW=1)
(2) snowmelt not performed (SNOW=0)
Specifies units of input and output:
(1) English units (UNIT=-1)
(2) metric units (UNIT=1)
Specifies type of input precipitation data:
(1) 15 minute intervals (PINT=0)
(2) hourly intervals (PINT=1)
Specifies type of input quality data
(1) mean monthly accumulation and removal data (f-"NVAR=l)
(2) mean annual accumulation and removal rates (!f!VAR=0)
Date simulation begins: day, month, year
Date simulation ends: day, month, year
Nominal upper zone storage
Nominal lower zone storage
Mean infiltration rate
Interflow parameter, alters runoff timing
Interflow recession rate
Watershed area
Manning's "n" for overland flow on pervious areas
Average slope of overland flow on pervious areas
Length of overland pervious flow to channel
Manning's "n" for overland flow on impervious areas
Average slope of overland flow on impervious areas
Length of overland impervious flow to channel
Ratio of spatial average rainfall to gage rainfall
Potential evapotranspi ration data correction factor
149
-------
Table 35 (continued). NPS MODEL INPUT PARAMETER DESCRIPTION
Type
Snow
Quality
Name
K3
EXPM
K24L
KK24
UZS
LZS
SGU
RADCON
CCFAC
EVAPSN
MELEV
ELDIF
TSNOW
MPACK
DGM
we
I DNS
SCF
WMUL
RMUL
F
KUG1
PACK
DEPTH
JRER
KRER
JSER
KSER
JEIM
KEIM
TCF
Description
Index to actual evapotranspi ration
Maximum interception storage
Fraction of groundwater recharge percolating to deep
groundwater
Ground recession rate
Initial upper zone storage
Initial lower zone storage
Initial groundwater storage
Correction factor for radiation melt
Correction factor for condensation and convection melt
Correction factor for snow evaporation
Mean elevation of watershed
Elevation difference from temperature station to mean
watershed elevation
Temperature below which precipitation occurs as snow
Water equivalent of snowpack for complete watershed
coverage
Daily groundmelt
Water content of snowpack by height
Initial density of new snow
Snow correction factor for raingage catch deficiency
Wind data correction factor
Radiation data correction factor
Fraction of watershed with complete forest cover
Index to forest density and undergrowth
Initial water equivalent of snowpack
Initial depth of snowpack
Exponent of rainfall intensity in soil splash equation
Coefficient in soil splash equation
Exponent of overland flow in sediment washoff equation
from pervious areas
Coefficient in sediment washoff equation from pervious
areas
Exponent of overland flow in sediment washoff equation for
impervious areas
Coefficient in sediment washoff equation for impervious
areas
Monthly water temperature correction factors
The following parameters are required for each land use simulated:
ARFRAC
IMPKO
Fraction of the total watershed area with this
land use
Impervious fraction of the land use area
150
-------
Table 35 (continued). NPS MODEL INPUT PARAMETER DESCRIPTION
Type ,,
Name
Description
COVVEC
PMPVEC
PMIVEC
PKPMAT
PHIHAT
ACUP
ACUI
ACUPV
ACUIV
RE PER
RE IMP
REPERV
REIKPV
SRERI
TSI
tfean monthly land cover factors for pervious areas
Mean annual potency factors for pervious areas
Mean annual potency factors for impervious areas
Mean monthly potency factors for pervious areas
(optional)
Mean monthly potency factors for impervious areas
(optional)
Daily accumulation rates of deposits on pervious areas
mean annual values
Daily accumulation rates of deposits on impervious areas
mean annual values
Daily accumulation rates of deposits on pervious areas
mean monthly values (optional)
Daily accumulation rates of deposits on impervious areas
mean monthly values (optimal)
Daily removal rates of sediments from pervious areas
mean annual values
Daily removal rates of sediments from impervious areas
mean annual values
Daily removal rates of sediments from pervious areas
mean monthly values (optional)
Daily removal rates of sediments from impervious areas
mean monthly values (optional)
Initial accumulation of sediments on pervious areas
Initial accumulation of sediments on impervious areas
151
-------
Table 36. NFS MODEL PARAMETER INPUT SEQUENCE AND ATTRIBUTES
Name! 1st
Nai.ie
ROPT
DTYP
STRT
ENDD
LND1
LND2
LIJD3
Parameter
Name
ilatershed [lame
Computer Run
Information
HYCAL
HYMIN
NLAND
I1QUAL
SNOW
UillT
PINT
MNVAR
BGNDAY
BGNMOH
BGNYR
EIIDDAY
EUDKON
ENDYR
UZSN
LZSN
INFIL
INTER
IRC
AREA
NN
L
SS
Ml
LI
SSI
Kl
PETMUL
K3
EXPI1
K24L
KK24
Type
character
character
integer
real
integer
integer
integer
integer
integer
integer
integer
integer
integer
1 nteger
integer
integer
real
real
real
real
real
real
real
real
real
real
real
real
real
real
real
real
real
real
English
Units
ft3/sec
inches
inches
in/hr
acres
feet
feet
inches
Metric
Units
A
m°/sec
millimeters
millimeters
mm/hr
hectares
meters
meters
mill i meters
Comment
up to 8 characters
up to 8 characters
1, 2, 3, or 4
up to 5 land uses
up to 5 pollutants
0 or 1
0 or 1
0 or 1
0 or 1
'
152
-------
Table 36 (continued). NFS MODEL PARAMETER INPUT SEQUENCE AND ATTRIBUTES
Namelist
Hame
LND4
SfiOl
-
SN02
SH03
SN04
SN05
WASH
Parameter
Name
UZS
LZS
SGW
RADCON
CCFAC
EVAPSN
MELEV
EL DIP
TSNOW
1-iPACK
DGH
we
I DNS
SCF
WMUL
RMUL
F
KUGI
PACK
DEPTH
JKER
KRER
JSER
KSER
JEII1
KEIM
TCF
Pollutant name
Type
real
real
real
real
real
real
real
real
real
real
real
real
real
real
real
real
real
integer
real
real
real
real
real
real
real
real
real
character
English
Units
inches
inches
inches
feet
1000 feet
degrees F
inches
in/ day
inches
inches
Metric
Units
millimeters
millimeters
millimeters
meters
ki 1 ometers
degrees C
millimeters
mm/ day
millimeters
millimeters
Comment
12 values
up to 8 characters3
repeat for each
pollutant
REPEAT THE FOLLOWING INFORMATION FOR EACH LAND USE
WSCH
Land Use Type
ARFRAC
I11PKO
COVVEC
character
real
real
real
up to 12 characters
12 values
a.
Each pollutant name is followed by the concentration units to
be used, either 'MG/L1 or 'GM/L,1 beginning in column no. 15
(see Appendix D). 'GM/L1 is the default value.
153
-------
Table 36 (continued). NPS MODEL PARAMETER INPUT SEQUENCE AND ATTRIBUTES
Name! 1st
llame
YPTM
MPTM
YACR
MACR
YRHR
MRMR
I MAC
Parameter
Name
PflPVEC
PMIVEC
PMPMAT
PMIMAT
ACUP
ACUI
ACUPV
ACUP I
REPER
RE I HP
REPERV
P.EIMPV
SRERI
TSI
Type
real
real
real
real
real
real
real
real
real
real
real
real
real
real
English
Units
percent
percent
percent
percent
Ib/ac/day
Ib/ac/day
Ib/ac/day
Ib/ac/day
dayl]
day
dayl!
day '
Ib/ac
Ib/ac
Metric
Units
percent
percent
percent
percent
km/ha/day
km/ha/day
km/ha/day
km/ha/day
day"!
day '
day!1,
day
kg/ha
kg/ha
Comment
1 value per pollutant
include if MI1VAR=0
12 values per pollutant
include if MNVAR=1
1 value per pollutant
include if !1IVAR=0
12 values per pollutant
include is UWR=1
1 value per pollutant
include if MNVAR=0
12 values per pollutant
include if KNVAR=1
154
-------
removal rates.) The block of land use information is repeated for each
land use in the watershed. The last land use information completes the
parameter input sequence. Reference to Table 36 and the sample input
listing in Appendix D should clarify the parameter input sequence of the
NFS Model. .
P a rameter Eva!uatjon
Guidelines for evaluating the NFS Model parameters relating to
hydrology, snowmelt, and nonpoint pollutant simulation are provided
below. The simulation control parameters are self-explanatory by their
definitions in Table 35 and are not discussed. Also, guidelines are
provided below for obtaining initial values of the calibration
parameters. However, precise evaluation of these parameters can only be
obtained through calibration as discussed in Section A6.
Hydrology Parameters-
HYMIN: Although HYMIN is a control parameter representing the minimum
flow above which storm output is printed, it also has a direct
impact on the storm summary characteristics printed at the end
of each storm. A storm is defined to begin when the flow
exceeds HYMIN, and ends when the flow falls below HYMIN The
storm summary characteristics pertain to the intervening
period. Thus HYMIN should be chosen to include the
significant portion of the hydrograph and pollutant graph
within the defined storm period. Investigation of recorded
storm hydrographs and pollutant graphs will indicate an
appropriate value for HYMIN.
EPXM: This interception storage parameter is a function of cover
density. The following values are expected:
urban areas with average
imperviousness 0.05 in.
grassland 0.10 in.
forest cover (light) 0.15 in.
forest cover (heavy) 0.20 in.
Since EPXM applies to the entire watershed, areas with much
imperviousness may require values in the lower end of the
above range, e.g., 0.01-0.05 in.
UZSN: The nominal storage in the upper zone is generally relatPH tn
LZSN and watershed topography. However, agriculturally
managed watersheds may deviate significantly from the
following guidelines:
155
-------
Low depression storage,
steep slopes, limited
vegetation 0.06*LZSN
Moderate depression storage
slopes and vegetation 0.08*LZSN
High depression storage,
soil fissures, flat slopes,
heavy vegetation
LZSN: The nominal lower zone soil moisture storage parameter is
related to the annual cycle of rainfall and
evapotranspiration. Approximate values range from 5.0 to 20.0
inches for most of the continental United States depending on
soil properties. Figure 33 presents an approximate mapping of
LZSN values for the United States. This map was obtained by
overlaying climatic, topographic, physiographic, and soils
information with LZSN values for watersheds calibrated with
various versions of the Stanford Watershed Model hydrologic
algorithms. The watershed locations are shown in Figure 34
and listed in Table 37 with various watershed characteristics
and calibrated parameter values. Since Figure 34 shows that
many areas of the country have few calibrated watersheds,
Figure 37 and Table 37 should be used with caution. Initial
values of LZSN can be obtained from this information, but the
proper value will need to be checked by calibration.
K3: As an index to actual evapotranspiration, K3 affects
evapotranspiration from the lower soil moisture zone. The
area covered by forest or deep rooted vegetation as a fraction
of total watershed area is an estimate of K3. Values
generally range from 0.25 for open land and grassland to
0.7-0.9 for heavy forest.
K24L: This parameter controls the loss of water from the near
surface or active ground water storage to deep percolation.
K24L is the fraction of the ground water recharge that
percolates to the deep ground water table. Thus a value of 1.0
for K24L would preclude any ground water contribution to
streamflow and is used on small watersheds without a base flow
component from ground water.
INFIL: This parameter is an index to the mean infiltration rate on
the watershed and is generally a function of soil
characteristics. INFIL can range from 0.01 to 1.0 in./hr
depending on the cohesiveness and permeability of the soil.
156
-------
in
•vl
LZSN
(INCHES)
5.0
6.0
4 o. 6 o 4.0 • ELEVATIONS ABOVE 1000- 2000 FT.
6.0-LOWER ELEVATIONS
7.0
80-140 8.0-LOWER ELEVATIONS
14.0- HIGHER ELEVATIONS
Figure 33. Nominal lower zone soil moisture (LZSN) parameter map
-------
en
oo
12
13
Figure 34. Watershed locations for calibrated LANDS parameters
-------
Table 37. WATERSHEDS WITH CALIBRATED LANDS PARAMETERS
Watershed Information
No.
1
2
f\
o
4
5
6
7
3
9
10
11
12
13
14
15
16
17
It!
19
General Location
Seattle, Washington
Spokane, WA
Aschoft, Oregon
Whites on, Oregon
Central Sierra
Snowlab, CA
between Chico and
Flemniing, CA
Cloverdale, CA
Napa, CA
Lurlingame, CA
Santa Cruz, CA
San .'lateo Co, CA
Santa Ynez, CA
Santa Iteria, CA
Goleta, CA
Santa Ynez, CA
Los Angeles, CA
Pasadena, CA
Upper Col unti a
Snowlab, ITT
Denver, CO
30 mi. south of
Denver, CO
Name
Lower Green R
liiddle Green R
Upper Green R
Lake Washington
Little Spokane R
bull Run
South Yamhill R
Upper Castle Creek
N Fork Feather R
Dry Creek
Dry Creek
Col ma Creek
Branci forte Creek
Denniston Creek
Sisquoc River
Santa Maria River
San Jose Creek
Santa Ynez River
Echo Park
Arroyo Seco
Sky land Creek
South -Platte R
Cherry Creek
Area
(sq mi)
107
502
3.96
300
878
14.4
10.8
17.3
3.6
281
2.38
5.5
895
0.4
16
8.1
69
Type
plains, rural
rural , steep
forest
rural , rocky
forest
rural , steep
forest
rural , moderate
si ope, chaparral
rural , moderate
slope, chaparral
urban, moderate
slopes
rural
rural , steep
chaparral
rural , steep
light chaparral
urban, flat
slopes
rural , steep
rural , steep
urban, steep
residential
urban, steep
rural , steep
rural , moderate
slope, grasses
rural , moderate
LANDS Parameters
Model a
HSP
HSP
HSP
HSP
HSP
HSP
NUS
NWS
HSP
SUM V
HSP
HSP
HSP
SWM IV
HSP
HSP
HSP
HSP
HSP
HSP
NUS
HSP
HSP
UZSN
3.0
1.15
0.9
0.5
0.56
0.75
1.20
0.70
0.8
0.8
0.8
0.25
1.0
0.95
0.7
0.3
0.5
0.74
0.04
0.20
1.83
0.1
0.8
LZSN
12.0
9.5
14.0
8.0
7.0
14.0
5.3
9.0
12.0
15.0
12.0
12.0
16.0
12.7
8.5
5.0
10.0
8.3
5.0
7.0
10.7
0.7
7.0
INFIL
0.06
0.10
0.05
0.05
0.20
0.08
0.24
0.08
0.12
0.03
0.025
'-:>
0.07
0.04
1.35
0.18
0.02
0.03
0.035
0.03
0.05
0.071
0.03
0.005
INTER
10.0
3.0
11.5
10.0
15
3.5
0.5
0.67
2.5
1.8
2.5
2.0
2.5
2.0
1.5
1.4
3.5
1.5
0
1.2
5.6
1.0
3.0
Comments1*
POWER=0.37
POWER=1.5
POWER=0.83
en
-------
Table 37 (continued). WATERSHEDS WITH CALIBRATED LANDS PARAMETERS
Watershed Information
No.
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
General Location
Sperry, OK
Austin, TX
bryon, TX
Lannesboro, UN
Rock Rapids, I A
Iowa City, IA
St. James, HO
Steel vi lie, fiO
;iottleton, 110
Collins, MI
Chicago, IL
florthbrook, IL
Champaign/Urbana, IL
Selkirk, MI
Springfield, 01!
Green Lick
Reservoir, PA
Frederic, ID
E of Washington D.C.
in HD
Rosman, NC
Swannanoa, NC
Blairsville, GA
Fayetteville, GA
Alca, GA
Danville, VT
Passumpic, VT
ilame
Bird Creek
Ualler Creek
Burton Creek
Root River
Rock River
Rapid Creek
Bourbeuse River
Fteramec River
Town Creek
Leaf River
North Branch,
Chicago River
W Fork N Branch
Chicago River
Boneyard Creek
S Branch Shepards
Creek
I'ad River
Green Lick Run
Monocacy River
W Branch of
Patuxent River
French Broad R
Seetree Creek
Nottely River
Camp Creek
Hurricane Creek
Sleepers River
Passumpsic River
Area
(sq mi)
905
6.5
1.3
625
788
25.3
21.3
781
617
752
IOC
11.5
3.6
1.2
490
3.1
817
30.2
67.9
5.5
74.8
17.2
150
3.2
436
Type
slope, grassland
urban, moderate
urban, flat
urban, flat,
rural
urban, flat
slope
rural, flat
rural, limestone
forest
rural
rural , forest
mountains
urban, hilly
forests
rural , forested
rural
rural
LANDS Parameters
Model a
NWS
HSP
HSP'
NWS
NisIS
HSP
HSP
NWS
NWS
NWS
HSP
HSP '
HSP
HSP
NUS
HSP
NWS
HSP
NWS
HSP
NWS
NWS
NWS
NWS
NWS
UZSI1
1.38
1.0
0.3
0.2
0.75
0.5
0.75
1.2
0.44
0.05
1-4
1.40
0.80
1.0
0.41
1.0
1.2
1.2
0.01
0.30
0.02
0.5
0.2
0.25
0.15
r LZSN
10.0
8.0
5.0
5.0
4.0
7.0
5.0
12.7
7.35
7.5
7.5
7.5
7.5
5.0
4.1
8.0
1.75
7.0
5.38
3.0
3.4
5.0
2.0
4.55
5.0
INFIL
0.048
0.04
0.02
0.08
0.02
0.035
0.02
0.043
0.066
0.33
0.18
0.18
0.05
0.04
0.125
0.007
0.058
0.02
0.8
0.10
0.45
0.16
0.13
0.40
0.33
INTER
0.67
1.25
1.5
0.5
1.4
3.5
1.0
1.05
0.89
0.37
3.5 ,
3.0
2.0
1.0
0.83
1.0
1.0
2.0
0.25
30
2.5
0.75
2.6
0.25
0.9
Comments"
POWER=0.78
POk'ER=2.0
POWER=2.5
POWER=1.56
POWER=2.6
POWER=2.85
POWER=0.40
POWER=0.30
POWER=0.36
POHER=2.0
POWER=2.0
POWER=2.0
POWER=3.0
POUER=3.0
-------
Table 37 (continued). WATERSHEDS WITH CALIBRATED LANDS PARAMETERS
Watershed Information
No.
46
47
48
50
51
General Location
West Hartford, VT
Grafton, VT
Bath, IJH
Plymouth, IJH
Knights ville Darn, MA
others
52
53
54
55
56
57
Fairbanks, AK
Seattle, WA
Spokane, WA
Santa Cruz, CA
Ingham, Co. MI
Athens, GA
Name
White River
Saxton River
Ammonoosuc River
Pemigewasset River
Sykes Brook
Chena River,
Issaquah Creek
Hangman Creek
Neary's Lagoon
Deer Creek
Southern Piedmont
Area
(sq mi)
690
72.2
395
622
1.6
1980
55
54
1.0.
16.3
0.01
Type
rural
rural
rural
rural , steep
heavy forest
agriculture
urban, steep
rural , f 1 at
agriculture
small plot
watersheds
RANGES
LANDS Parameters
Model3
NWS
SUM V
MWS
NWS
HSP
NWS
HSP
HSP
HSP
HSP
PTP
'.'
UZSN
0.25
0.8
, 0.3
0.25
1.2
0.05
1.12
0.50
0.80
1.5
,
0.05
0.01-3.0
LZSN
5.0
8.0
5.0
5.0,
8.0
5.0
14.0
7.0
n.o
.5.0
18.0.
1.75-18
INFIL
0.15
0.05
0.12
0.22
0.03
0.08
0.03
0.02
0.04
0.05
0.5
.005-
1.35
INTER
1.3
2.0
0.65
0.53
1.0
0.25
7.0
3.5
2.5
2.0
0.7
0-30
Comments0
POUER=0.95
POWER* 1.50
POWER=2.08
POWER=1.0
en
a. HSP Hydrocomp Simulation Program
SUM IV Stanford Watershed flodel IV
SWH V Stanford Watershed Model V
NWS National Weather Service Hodel
PTR Pesticide Transport and Runoff Model
b. HSP and the SWH Models use a value of 2.0 in the infiltration function (see Appendix 8),
while the NUS Model allows the user to specify this value with the POUER parameter. The
values of POWER are indicated in the comments column.
-------
Initial values for INFIL can be obtained by reference to the
hydrologic soil groups of the Soil Conservation Service (73)
in the following manner:
SCS Hydrologic INFIL Runoff
Soil Group Estimate (in./hr) Potential
A 0.4-1.0 low
B 0.1-0.4 moderate
C 0.05-0.1 moderate-to-high
D 0.01-0.05 high
The SCS has specified the hydrologic soil group for various soil
classifications across the country (73). As for LZSN, the values
of INFIL obtained above should be used with caution and only as
initial values to be checked by calibration.
INTER: This parameter refers to the interflow component of runoff and
generally alters runoff timing. It is closely related to
INFIL and LZSN and values generally range from 0.5 to 5.0.
Figure 39 provides an approximate mapping of the INTER
parameter for the United States. This map was obtained as
described for the LZSN parameter. In addition, INTER values
in Table 37 provide an indication of representative values.
This information should be used only to obtain initial values
that need to be checked by calibration.
L, LI: Length of overland flow for pervious and impervious areas is
obtained from topographic maps and approximates the length to
a stream channel. The value for pervious areas can be
approximated by dividing the entire watershed area by twice
the length of the drainage path or channel. Values for
impervious areas can be obtained by estimating the average
width of impervious areas surrounding the drainage path or
channel.
NN, NNI: Manning's n for overland flow will vary considerably from
published channel values because of the extremely small depths of
overland flow. Approximate values are:
smooth, packed surface 0.05
normal roads and parking lots 0.10
disturbed land surfaces 0.15
turf 0.25
heavy turf and forest litter 0.35
SS, SSI: Average overland flow slope (pervious and impervious) is also
obtained from topographic maps. The average slope can be
162
-------
CTl
CO
30-50 3.0-LOWER ELEVATIONS
5.0-HIGHER ELEVATIONS
Figure 35. Interflow (INTER) parameter map
-------
estimated by superimposing a grid pattern on the watershed,
estimating the land slope at each point of the grid on pervious
and impervious areas, and obtaining the average of all values
measured in each category. Slopes of impervious areas will often
be less than pervious slopes due to construction practices and
specifications.
PETMUL: PETMUL adjusts the input potential evapotranspiration data to
expected conditions on the watershed. Values near 1.0 are
used if the input data has been collected on or near the
watershed to be simulated.
IRC,
KK24: These parameters are the interflow and ground water recession
rates. They can be estimated graphically by hydrograph
separation techniques (74), or found by trial from simulation
runs. Since these parameters are defined below on a daily
basis, they are generally close to 0.0 for small "watersheds
that only experience runoff during or immediately following
storm events.
IRC _ Interflow discharge on any day
Interflow discharge 24 hours earlier (25)
= Groundwater discharge on any day
Groundwater discharge 24 hours earlier (26)
UZS, LZS
SGW: These parameters are the initial soil moisture conditions for
the upper zone, lower zone, and ground water zone, respectively
at the beginning of the simulation period. SGW is the
component of ground water storage that contributes to
streamflow. It is usually set to 0.0 for initial calibration
runs. The factor (1.0-K24L) specifies the fraction of the
total ground water component added to SGW, while the outflow
from active ground water is determined by the recession rate,
KK24 (see Appendix B). UZS and LZS are generally specified
relative to their nominal storages, UZSN and LZSN. If
simulation begins in a dry period, UZS and LZS should be less
than their nominal values; whereas values greater than nominal
should be employed if simulation begins in a wet period of the
year. UZS, LZS, and SGW should be reset after a few
calibration runs according to the guidelines provided in
Section A6.
164
-------
Snowmelt Parameters-
RADCON,
CCFAC:, These parameters adjust the 'theoretical melt1 equations for
solar radiation and condensation/convection melt to actual
field conditions. Values near 1.0 are to be expected,
although past experience indicates a range of 0.5 to 2.0.
RADCON is sensitive to watershed slopes and exposure, while
CCFAC is a function of climatic conditions.
SCF: The snow correction factor is used to compensate for catch
deficiency in rain gages when precipitation occurs as snow.
Precipitation times (SCF-1.0) is the added catch. Values are
generally greater than 1.0 and usually are in the range of 1.0
to 1.5.
ELDIF: This parameter is the elevation difference from the temperature
station to the mean elevation in the watershed in thousands of
feet (or kilometers). It is used to correct the observed air
temperatures for the watershed using a lapse rate of 3 degrees
F per 1,000 feet elevation change.
IDNS: This parameter is the density of new snow at 0 degrees F. The
expected values are from 0.10 to 0.20 with 0.15 a common
value. Appendix C provides a relationship for the variation
in snow density with temperature.
F: This'parameter is the fraction of the watershed that has
complete forest cover. Areal photographs are the best basis
for estimates.
DGM: DGM is the daily groundmelt. Values of 0.01 in/day or less are
usual. Areas with deep frost penetration may have little
groundmelt with DGM values approaching 0.0.
WC: This parameter is the maximum water content of the snowpack by
weight. Experimental values range from O.Q1 to 0.05 with 0.03
a common value.
i-:1,-
MPACK: MPACK is the estimated water equivalent'of the snowpack for
complete area! coverage in a watershed. Values 6f 1.0 to 6.0
inches are generally employed. MPACK is a-function of
topography and climatic conditions. Mountainous watersheds
will generally have MPACK values near the high end of the
range.
165
-------
EVAPSN: -Adjusts the amounts of snow evaporation given by an analytic
equation. Values near 0.1 are expected.
MELEV: The mean elevation of the watershed in feet (meters).
TSNOW: Temperature below which snow is assumed to occur. Values of 31
degrees to 33 degrees F are often used. Comparing the
recorded form of precipitation and the simulated form for a
number of years will indicate needed modifications to TSNOW.
WMUL,
RMUL: These parameters are used to adjust input wind movement and
solar radiation, respectively, for expected conditions on the
watershed. Values of 1.0 are used if the input meteorologic
data is observed on or near the watershed to be simulated.
KUGI: KUGI is an integer index to forest density and undergrowth for
the reduction of wind in forested areas. Values range from 0
to 10; for KUGI = 0, wind in the forested area is 35 percent
of the input wind value, and for KUGI = 10 the corresponding
value is 5 percent. For medium undergrowth and forest density
a value of 5 is generally used.
Water Quality Parameters-
JRER: JRER is the exponent in the soil splash equation (Equation 9) and
thus approximates the relationship between rainfall intensity and
incident energy to the land surface for the production of soil
fines. Wischmeier and Smith (75) have proposed the following
relationship for the kinetic energy produced by natural rainfall;
Y = 916 + 331 log X (27)
i
where Y = kinetic energy, foot-tons per acre-in.
X = rainfall intensity, in./hr
Using this relationship, various investigations have also shown
that soil splash is proportional to the square of the rainfall
intensity (60, 76). Thus, a. value of about 2.0 for JRER is
predicted from these studies. In general, values in the range of
2.0 to 3.0 have demonstrated reasonable results on the limited
number of watersheds tested. The best value will need to be
checked through calibration.
KRER: This parameter is the coefficient of the soil splash equation
and is related to the erodibility or detachability of the
specific soil type. KRER is directly related to the 'K1
factor in the Universal Soil Loss Equation (54). Initially
166
-------
KRER can be set equal to the corresponding K factor for the
watershed. K values can be obtained with techniques published
in the literature (51, 77) or from soil scientists familiar
with local soil conditions. Table 38 provides a sample list
of estimated K values for various soils, and Figure 36 is a
nomograph for general estimation of K from soil properties.
Other available information on K factors for the specific
watershed should be consulted. However, this initial value
will need to be checked through calibration trials.
OSER,
JEIM: These parameters are the exponents in the sediment washoff, or
transport, equations for pervious and impervious areas, and
thus approximate the relationship between overland flow
intensity and sediment transport capacity. Values in the
range of 1.0 to 2.5 have been used on the limited number of
watersheds tested to date. The most common values are between
1.6 and 2.0 but initial values should be checked through
calibration.
KSER,
KEIM: These parameters are the coefficients in the sediment washoff,
or transport, equation. They represent an attempt to combine
the effects of (1) slope, (2) overland flow length, (3)
sediment particle size, and (4) surface roughness on sediment
transport capacity of overland flow into a single calibration
parameter. Consequently, at the present time calibration is
the major method of evaluating both KSER and KEIM. Land
surface conditions will have a significant effect on KSER.
Limited experience to date has indicated a possible range of
values of 0.01 to 5.0. However, significant variations from
this can be expected.
SRERI,
TSI: These parameters indicate the amount of detached soil fines
(sediment) on the land surface of pervious (SRERI) and
impervious (TSI) areas at the beginning of the simulation
period. Very little research or experience relates to the
estimation of these parameters especially on pervious areas.
Estimation of these parameters is closely tied to the
calibration process discussed in Section'A6.
167
-------
Table 38. COMPUTED K VALUES FOR SOILS ON EROSION-RESEARCH STATIONS
Soil
Dunkirk silt loam
Keene silt loam
Shelby loam
Lodi loam
Fayette silt loam ••;.
Cecil sandy clay loam
Marshall silt loam
Ida silt loam
Mans ic clay loam
Hagerstown silty clay loam
Austin clay
Mexico silt loam
Honeoye silt loam
Cecil sandy loam
Ontario loam
Cecil clay loam
Boswell fine sandy loam
Cecil sandy loam
Zaneis fine sandy loam
Tifton loamy sand
Freehold loamy sand
Bath flaggy silt loam with
surface stones 2 inches
removed .
Albia gravelly loam
Source of data
Geneva, N.Y.
Zanesville, Ohio
Bethany, Mo
Blacksburg, Va
LaCrosse, Wis
Watkinsville, Ga
Clarinda, Iowa
Castana, Iowa
Hays, Kans
State College, Pa
Temple, Tex
McCredie, Mo
Marcel! us, N.Y.
Clemson, S.C.
Geneva, N.Y.
Watkinsville, Ga
Tyler, Tex
Watkinsville, Ga
Guthrie Okla
Tifton, Ga
Marlboro, N.J.
Arnot, N.Y.
Beemerville, N.J.
Computed K
0.69a
-48
.41
•39a
.38a
.36
.33
.33
•32a
.31a
.29
•28a
.28a
.28a
.27a
.26
.25
.23
.22
.10
.08,
.05a
.03
Evaluated from continuous fallow. All others were computed from row-
crop data.
Source: Wischmeier and Smith (54), p. 5
168
-------
vo
Figure 36. Soil Credibility Nomograph
Source: Wischmeier, Johnson, and Cross (74), p. 190
-------
COVVEC: This parameter is the percent land cover on pervious areas of
the watershed, and is used to decrease the fraction of the
land surface that is susceptible to soil fines detachment by
raindrop impact. Twelve monthly values for the mid-point of
each month are input to the Model, and the cover on any day is
determined by linear interpolation. COVVEC values can be
evaluated as one minus the C factor in the Universal Soil Loss
Equation, i.e., COVVEC = 1 - C, when C is a monthly value.
Evaluation methods for the C factor have been published in the
literature (51, 78). Tables 39 and 40 pertain to the
evaluation of C on undisturbed lands and have been reproduced
from the paper by Wischmeier (78). C factors for disturbed
lands (cropland, agriculture, and construction areas) have
been published in the USLE Report (51). The user should refer
to both of these cited references for an understanding of the
factors considered in the evaluation of land cover.
ARFRAC,
IMPKO: These parameters are evaluated for each land use. They
represent the fraction of the total watershed in a particular
land use (ARFRAC) and the impervious fraction of that land use
(IMPKO). The impervious area fraction includes only
impervious areas directly connected to a drainage path or
channel. Land use and topographic maps are the major source
of information for evaluating ARFRAC and IMPKO. Correlation
equations for estimating imperviousness, curb length, and
other land use factors from socioeconomic data have been
published (79, 80). However, the general reliability of these
correlations is unknown. They should be used with caution and
only if no relevant data is available for the watershed.
ACUP, ACUPV, ACUI,
ACUIV: These parameters represent the daily sediment accumulation
rates from land use activities on pervious (ACUP, ACUPV) and
impervious (ACUI, ACUIV) areas. If monthly variations are
specified, 12 values must be input for both ACUPV (pervious)
and ACUIV (impervious). On the other hand, only single values
for ACUP (pervious) and ACUI (impervious) are required if
average annual accumulation rates are used. Table 41
summarizes the available data on sediment (or Total Solids)
accumulation rates for various cities across the country. The
data in Table 41 pertains to impervious areas since it was
collected on street surfaces. Logically, one would expect
impervious areas to experience larger accumulation rates than
pervious areas because of the predominant concentration of
pollutant-generating activities around impervious surfaces
(streets, parking lots, buildings, etc.). However very little
170
-------
Table 39. C VALUES FOR PERMANENT PASTURE, RANGELAND, AND IDLE LAND6
Canopy
Type and Pet Type d
height b cover c
(D (2) (3)
(G
Weeds or
short brush «
(0.5m).
25 IW
J G
50 |w
/_
G
[75 \ W
/G
r« tw
Brush or f G
bushes ^50 \ W
JG
[75 t W
f G
Trees, no
low brush <
(4m).
25 ^W
/ G
50 |w
G
W
Ground cover
Pet cover
0
(4)
0.45
.45
.36
.36
.26
.26
.17
.17
.40
.40
.34
.34
.28
.28
.42
.42
.39
.39
.36
.36
20
(5)
0.20
.24
.17
.20
.13
.16
.10
.12
.18
.22
.16
.19
.14
.17
.19
.23
.18
.21
.17
.20
40
(6)
0.10
.15
.09
.13
.07
.11
.06
.09
.09
.14
.08
.13
.08
.12
.10
.14
.09
.U
.09
.13
60
(7)
0.042
.091
.038
.083
.035
.076
.032
.068
.040
.087
.038
.082
.036
.078
.041
.089
.040
.087
.039
.084
80
(8)
0.012
.043
.013
.041
.012
.039
.011
.038
.013
.042
.012
.041
.012
.040
.013
.042
.013
.042
.013
.041
95-100
0)
0.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
a All values assume (1) random distribution of mulch or vegetation, and (2) mulch of substantial depth where
credited.
b Classified by average fall height of waterdrops from canopy to soil surface, in meters.
c" Percentage of total-area surface that would be hidden from view by canopy in a vertical projection.
G—Cover at surface is grass or decaying, compacted duff of substantial depth. W—Cover at surface is weeds
(plants with little lateral-root network near the surface) or undecayed residue.
Table 40. C FACTORS FOR WOODLAND
Stand
condition
Wall «toplroH
: Tree canopy
(pet of area) a
inn_7K
>7K An
40 90
Forest
litter
(pet of area)
ion QO
qn 7^
70 40
Undergrowth0
C-Factor
0.001
.003-0.011
002- .004
01- .04
003- .009
02- .09e
a Area with tree canopy over less than 20 pet will be considered grassland or cropland for estimating soil loss (ta-
ble2).
b Forest litter is assumed to be of substantial depth over the percent of the area on which it is credited.
c Undergrowth is defined as shrubs, weeds, grasses, vines, etc. on the surface area not protected by forest litter.
U sually found under canopy openings.
d Managed—Grazing and fires are controlled. Unmanaged—Stands that are overgrazed or subjected to repeated
burning.
e For unmanaged woodland with litter cover of less than 75 pet, C-values should be derived by taking 0.7 of the
appropriate values in table 2. The factor of 0.7 adjusts for the much higher soil organic matter on permanent wood-
land" Source: Wischmeier and Smith (74), pp. 123-24
171
-------
Table 41. REPRESENTATIVE SEDIMENT ACCUMULATION,RATES FOR VARIOUS LAND USES AND LOCATIONS
a, c
Land Use
Residential13:
low/ old/single
lod/old/multi
niedium/new/single
median/old/multi
Industrial :
light
medium
heavy
Commercial :
suburban shopping
central business
Weighted Mean
Sampling Time
Sediment Accumulation
(Ib/acre/day)
S Jose I
23
29
8
-
44
20
-
8
7
29
12/70
S Jose II
51
5
4
-
68
7
-
9
1
8
6/71
Phoenix I
21
51
5
9
12
36
-
17
6
31
1/71
Phoenix II
16
14
4
5
3
15
-
2
2
9
6/71
Tulsa
1
58
14
63
21
-
16
10
22
6/71
Seattle
32
25
11
54
-
-
13
15
23
7/71
Baltimore
23
23
-
27
18
5
1
2
18
5/71
Atlanta
187
98
13
-
_
124
22
159
102
6/71
Notes:
a. These values should be used only as guidelines. They are based on a single sampling period in
each of the locations and are derived from loading intensities published in Table 3 of Sartor and
Boyd (GO) divided by the estimated time since the last storm as shown in Appendix B of that report.
b. For residential land: low or medium density/old or new area/single or multi housing
c. For comparison purposes, the values used in the UPS t'odel testing were as follows:
Durham, ilorth Carolina (nixed urban land use): 30 -80 Ib/ac/day
I'ladison, Uisconsin (residential): 1.2 Ib/ac/day
Seattle, Uashington (commercial): 1.5 Ib/ac/day
-------
information is presently available to quantify the difference
in accumulation rates between pervious and impervious areas.
If data on accumulation rates are available for the watershed,
they should be used in place of the values shown in Table 41.
Differences in socioeconomic factors, types of activities in
each land use, and climate influence accumulation rates; thus,
data for the specific site or in a nearby area should be used
to the extent possible. Often accumulation rates are
presented in terms of pounds per day per mile of curb length.
Curb length per acre must be estimated to convert these rates
to the units required by the NFS Model (Ib/day/acre). The
correlation equations mentioned above (79, 80) may be used to
estimate the conversion factor if no other data is available.
Values of accumulation rates estimated from Table 41 or from
specific watershed data will need to be verified through
calibration.
REFER, REPERV,
REIMP, REIMPV: These parameters refer to the removal of sediment from
pervious (REPER, REPERV) and impervious (REIMP, REIMPV) areas
by processes other than runoff. As with accumulation rates
either monthly variations (REPERV, REIMPV) or average annual
values (REPER, REIMP) can be specified. On pervious areas
these removal processes will include wind, air currents from
traffic, and possibly consolidation/aggregation of sediments
to,larger particles less susceptible to transport by overland
flow. On impervious areas street cleaning activities must be
included in the above list. The removal rates are expressed
as the fraction of sediment (or Total Solids) removed per day.
Very little information is available for evaluation of removal
rates. Values for removal rates from pervious areas may range
from 0.01 to 0.10 largely as a function of wind and associated
air currents. For impervious areas, the effects of street
cleaning should be added to the wind component and can be
estimated as
R = P*(E/D) (28)
where R = sediment removal from impervious areas by street
cleaning
P = fraction of impervious area on which street
cleaning is performed
E = efficiency of street cleaning
D = frequency of street cleaning
173
-------
Thus, if street cleaning is performed every five days on 40 percent
of the impervious area with an efficiency of 80 percent, then
R = (.40)(.80)/(5) = 0.0512 (32)
If wind removal is estimated as 0.02, then REIMP would be
approximately 0.07 and REPER would be 0.02. In essence the
removal rates are evaluated in conjunction with accumulation
rates to establish a limit to the total sediment accumulation
that can occur. As indicated in Section VII, this limit for
impervious areas would be 1/REIMP days of accumulation.
Consequently, joint calibration of accumulation and removal
rates is required.
PMPVEC, PMPMAT
PMIVEC, PMIMAT: These parameters are the potency factors specifying the
pollutant content of sediment washed from pervious (PMPVEC,
PMPMAT) and impervious (PMIVEC, PMIMAT) areas. As with
accumulation and removal rates, the user can specify 12 monthly
potency factors (PMPMAT, PMIMAT) for each pollutant simulated or
use an average annual potency factor (PMPVEC, PMIVEC) for each
pollutant. Table 42 summarizes the most relevant available data
for the evaluation of potency factors for various pollutants and
land uses. Obviously, any available water quality data on the
watershed should be used to evaluate and adjust the potency
factors obtained from Table 42. Pollutant concentrations divided
by sediment (or TS) concentrations, on a storm or single sample
basis, will provide estimates of potency factors. Although large
variations may exist in potency factors obtained from recorded
data, relatively stable relationships can be found when the
recorded data is categorized by land use and season (or time) of
the year.
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Table 42. REPRESENTATIVE POTENCY FACTORS FOR BOD
FOR VARIOUS LAND USES AND LOCATIONS
COD, AND SS
Land Use/Location
Residential :
Industrial:
Commercial :
Sites Sampled
Low/old/single
Low/old/multi
Medium/new/single
Medi um/ol d/rnul ti
Light
Medi urn
Heavy
Suburban shopping
Central business
by Sartor and Boyd (50):
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
numerical mean
average deviation
NPS Model Test Sites:
Durham, North Carolina
Seattle, Washington
Potency Factors (% of sediment)
BOD
0.86
2.00
1.06
0.77
L70
1.11
0.33
0.86
0.86
1.70
1.00
0.44
0.21
6.10
0.89
0.45
4.30
1.10
1.00
1.70
1.30
4.0
3.6
COD
2.70
2.30
3.54
2.62
8.26
5.89
1.49
2.07
3.11
34.00
4.60
1.80
2.10
2.00
6.80
3.00
9.10
5.80
3.80
7.30
6.80
SS
15
20
25
20
20
30
40
20
30
9.2
46.2
29.5
18.2
14.7
71.0
38.0
Motes:
1. For residential land use: low or median density/old or new
area/single or multi housing
2. These values should be used only as guidelines for estimation of
initial values of potency factors. Water quality data on the
watershed should pre-empt the table values.
3. The 300 and COD potency factors for the individual land uses and
cities were obtained from Tables 7 and C-7 in Sartor and Boyd
(50).
4. The SS potency factors for the individual cities were obtained
from Table 5 in Sartor and Boyd (50) assuming SS are particle
sizes less than 104 microns, while those for the separate land
uses are gross estimates based on the judgment of the authors.
Specific sites may vary significantly from the above values.
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A6. CALIBRATION PROCEDURES AND GUIDELINES
Calibration has been repeatedly mentioned throughout this report and
user manual; this indicates the importance of the calibration process in
application of the NFS Model. At the risk of further repetition, the
calibration process will be defined and described in this section and
recommended procedures and guidelines will be presented. The goal is to
provide a general calibration methodology for potential users of the NPS
Model. As one gains experience in calibration, the methodology will
become second-nature and individual methods and guidelines will evolve.
Calibration is an iterative procedure of parameter evaluation and
refinement by comparing simulated and observed values of interest. It
is required for parameters that cannot be deterministically evaluated
from topographic, climatic, edaphic, or physical/chemical
characteristics. Fortunately, the large majority of NPS parameters do
not fall in this category. Calibration should be based on several years
of simulation (3 to 5 years is optimal) in order to evaluate parameters
under a variety of climatic, soil moisture, and water quality
conditions. The areal variability of meteorologic data series,
especially precipitation and air temperature, may cause additional
uncertain!ty in the simulation. Years with heavy precipitation are
often better simulated because of the relative uniformity of large
events over a watershed. In contrast low annual runoff may be caused by
a single or a series of small events that did not have a uniform areal
coverage. Parameters calibrated on a dry period of record may not
adequately represent the processes occurring during wet periods. Also,
the effects of initial conditons of soil moisture and pollutant
accumulation can extend for several months resulting in biased
parameter values calibrated on short simulation periods. Calibration
should result in parameter values that produce the best overall
agreement between simulated and observed values throughout the
calibration period.
Calibration includes the comparison of both monthly and annual values
and individual storm events. Both comparisons should be performed for a
proper calibration of hydrology and water quality parameters.
Hydro!ogic calibration must preceed sediment and water quality
calibration since runoff is the transport mechanism by which nonpoint
pollution occurs. The steps in the overall calibration process for the
NPS Model are:
(1) Estimate initial values for all parameters from the guidelines
provided.
(2) Perform hydrologic calibration run (HYCAL=1).
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(3) Compare simulated monthly and annual runoff volumes with recorded
data.
(4) Adjust hydrologic calibration parameters, and initial conditions if
necessary, to improve agreement between simulated monthly and annual
runoff and recorded values.
•;- >t -.
(5) Repeat steps 2, 3, and 4 until satisfactory agreement is obtained.
\
(6) Compare simulated and recorded hydrographs for selected storm events.
t,
(7) Adjust hydrologic calibration parameters to improve storm hydrograph
simulation.
- '•• •.. \
(8) Perform additional calibration runs and repeat step 7 until
satisfactory storm simulation is obtained while maintaining agreement
in the monthly and annual runoff simulation.
^
(9) Perform calibration run for sediment parameters (HYCAL=2).
(10) Compare simulated monthly and annual sediment loss with recorded
values, if available.
(11) Compare simulated storm sediment graphs with recorded values for
selected events.
(12) Adjust sediment calibration parameters to improve the simulation of
monthly and annual values and storm sediment graphs.
(13) Repeat steps 9, 10, 11, and 12 until satisfactory sediment simulation
is obtained.
(14) Compare simulated monthly and annual pollutant loss with recorded
values, if available.
(15) Compare simulated and recorded pollutant graphs (concentration and/or
mass removal) with recorded data for selected events.
(16) Adjust pollutant potency factors and perform additional pollutant
calibration trials until satisfactory agreement is obtained.
At the completion of the above steps, the NPS Model is calibrated to the
watershed being simulated under the land use conditions in effect during
the calibration period. Production runs can be performed (HYCAL=3 or 4)
for existing conditions or projected future conditions for evaluation of
nonpoint pollution problems. Often times, sufficient data will not be
available to complete all steps in the calibration process. For
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example, monthly and annual values of sediment or pollutants will not be
available for comparison with simulated results. In these
circumstances, the user may omit the corresponding steps in calibration;
however, simulated values should be analyzed and evaluated with respect
to data from similar watersheds, personal experience, and guidelines
provided below.
Hydro!ogic Calibration
Hydrologic simulation combines the physical characteristics of the
watershed geometry and the observed meteorologic data series to produce
the simulated hydrologic response. All watersheds have similar
hydrologic components, but they are generally present in different
combinations; thus different hydrologic responses occur on individual
watersheds. The NFS Model simulates runoff from four components:
surface runoff from impervious areas directly connected to the channel
network, surface runoff from pervious areas, interflow from pervious
areas, and ground water flow. Since the historic streamflow is not
divided into these four units, the relative relationship among these
components must be inferred from the examination of many events over
several years of continuous simulation. Periods of record with a
predominance of one component (e.g., surface runoff during storm periods,
or ground water flow after extended dry periods) can be studied to
evaluate the simulation of the individual runoff components.
The first task in hydrologic calibration is to establish a water balance
on an annual basis. This balance specifies the ultimate destination of
incoming precipitation and is indicated as
Precipitation - Actual Evapotranspiration - Deep percolation
- ASoil Moisture Storage = Runoff (30)
In addition to the input meteorologic data series, the parameters that
govern this balance are LZSN, INFIL, and K3 (evapotranspiration index
parameter). Thus, if precipitation is measured on the watershed and if
deep percolation to ground water is small, actual evapotranspiration must
be adjusted to cause a change in the long-term runoff component of the
water balance. LZSN and INFIL have a major impact on percolation and
are important in obtaining an annual water balance. In addition, on
extremely small watersheds (less than 100-200 hectares) that contribute
runoff only during and immediately following storm events, the UZSN
parameter can also affect annual runoff volumes because of its impact on
individual storm events (described below).
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Recommendations for obtaining an annual water balance are as follows:
(I); Annual precipitation should be greater than or equal to the sum of
.annual evaporation plus annual runoff if ground water recharge
through deep percolation is not significant in the watershed. If
this does not occur the Kl parameter should be re-evaluated (see
Section A5) and adjusted to insure that the input precipitation is
indicative of that occurring on the watershed.
(2) Since the major portion of actual evapotranspiration occurs from
the lower soil moisture zone, increasing LZSN will increase actual
evapotranspiration and decrease annual,runoff. Also, decreasing
LZSN will reduce actual evapotranspiration and increase annual
runoff. Thus, LZSN is the major parameter for deriving an annual
water balance.
(3) Actual evapotranspiration is extremely sensitive to K3. Since K3
, is evaluated as the fraction of the watershed with deep rooted
vegetation, increasing K3 will increase actual evapotranspiration
and vice versa. Thus, minor adjustments in K3 may be used to
effect changes in annual runoff if actual evapotranspiration is a
significant hydrologic component in the watershed.
(4) The INFIL parameter can also assist in deriving an annual water
balance although its main effect is to adjust the seasonal, or
monthly runoff distribution described below. Since INFIL governs
the division of precipitation into various components, increasing
INFIL will decrease surface runoff and increase the transfer of
water to lower zone and ground water. The resulting increase in
water in the lower zone will produce higher actual
evapotranspiration. Decreasing INFIL will generally reduce actual
evapotranspiration and increase surface runoff. In watersheds with
no baseflow component (from ground water), INFIL can be used in
conjuction with LZSN to establish the annual water balance.
When an annual water balance is obtained, the seasonal or monthly
distribution of runoff can be adjusted with use of the INFIL parameter.
INFIL, the infiltration parameter, accomplishes this seasonal
distribution by dividing the incoming moisture among surface runoff,
interflow, upper zone soil moisture storage, percolation to lower
zone soil moisture, and ground water storage. Of the various hydrologic
components, ground water is often the easiest to identify. In watersheds
with a continuous baseflow, or ground water component, increasing INFIL
will reduce immediate surface runoff (including interflow) and increase
the ground water component. In this way, runoff is delayed and occurs
later in the season as an increased ground water, or base flow.
Decreasing INFIL will produce the opposite result. Although INFIL and
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LZSN control the volume of runoff from ground water, the KK24 parameter
controls the rate of outflow from the ground water storage.
In watersheds with no ground water component, the K24L parameter is used
to direct the ground water contributions to deep inactive ground water
storage that does not contribute to runoff (K24L = 1.0 in this case).
For these watersheds, runoff cannot be transferred from one season or
month to another, and the INFIL parameter is used in conjunction with LZSN
to obtain the annual and individual monthly water balance.
Continuous simulation is a prerequisite for correct modeling of
individual events. The initial conditions that influence the magnitude
and character of events are the result of hydro!ogic processes occurring
between events. Thus, the choice of initial conditions for the first
year of simulation is an important consideration and can be misleading
if not properly selected. The initial values for UZS, LZS, and SGW
should be chosen according to the guidelines in Section A5 and
readjusted after the first calibration run. UZS, LZS, and SGW for the
starting day of simulation should be reset approximately to the values
for the corresponding day in subsequent years of simulation. Thus, if
simulation begins in October, the soil moisture conditions in subsequent
Octobers in the calibration period can usually be used as likely initial
conditions for the simulation. Meteorologic conditions proceeding each
October should also be examined to insure that the assumption of similar
soil moisture conditions is realistic.
When annual and monthly runoff volumes are adequately simulated,
hydrographs for selected storm events can be effectively altered with
the UZSN and INTER parameters to better agree with observed values.
Also, minor adjustments to the INFIL parameter can be used to improve
simulated hydrographs; however, adjustments to INFIL should be minimal
to prevent disruption of the established annual and monthly water
balance. Parameter adjustment should be concluded when changes do not
produce an overall improvement in the simulation. One event should not
be matched at the expense of other events in the calibration period.
Recommended guidelines for adjustment of hydrograph shape are as
follows:
(1) The interflow parameter, INTER, can be used effectively to alter
hydrograph shape after storm runoff volumes have been correctly
adjusted. INTER has a minimal effect on runoff volumes. As shown
in Figure 37 where the values of INTER were (a) 1.4, (b) 1.8, and
(c) 1.0, increasing INTER will reduce peak flows and prolong
recession of the hydrograph. Decreasing INTER has the opposite
effect. On large watersheds where storm events extend over a
number of days, the IRC parameter (see Section A5) can be used to
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adjust the recession of the interflow portion of the hydrograph to
further improve the simulation.
0
O)
i_
co
.c
O
CO
T
T
Time
Figure 37. Example of the response to the INTER parameter
(2) The UZSN parameter also affects hydrograph shape. Decreasing UZSN
will generally increase flows especially during the initial
portions, or rising limb, of the hydrograph. Low UZSN values are
indicative of highly responsive watersheds where the surface runoff
component is dominant. Increasing UZSN will have the opposite
effect, and high UZSN values are common on watersheds with
significant subsurface flow arid interflow components. Caution
should be exercised when adjusting hydrograph shape with the UZSN
parameters to insure that the overall water balance is not
significantly affected.
(3) The INFIL parameter can be used for minor adjustments to storm
runoff volumes and distribution. Its effects have been discussed
above. As with UZSN, changes to INFIL can affect the water
balance; thus, modifications should be minor.
When the calibration of storm hydrographs is completed, the entire
hydrologic calibration is finished, and sediment and water quality
calibration can be initiated.
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Sediment and Water Quality Calibration
As indicated in the description of the calibration process, sediment
calibration follows the hydrologic calibration and must preceed the
adjustment of the pollutant potency factors in water quality
calibration.
Sediment parameter calibration is more uncertain than hydrologic
calibration due to less experience with sediment simulation in different
regions of the country. The process is analogous; the major sediment
parameters are modified to increase agreement between simulated and
recorded monthly sediment loss and storm event sediment removal.
However, observed monthly sediment loss is often not available, and the
sediment calibration parameters are not as distinctly separated between
those that affect monthly sediment and those that control storm sediment
loss. In general, sediment calibration involves the development of an
approximate equilibrium or balance between the accumulation and
generation of sediment particles on one hand and the washoff or
transport of sediment on the other hand. Thus, the accumulated sediment
on the land surface should not be continually increasing or decreasing
throughout the calibration period. Extended dry periods will produce
increases in surface pollutants, and extended wet periods will produce
decreases. However, the overall trend should be relatively stable.
This equilibrium must be developed on both pervious and impervious
surfaces, and must exist in conjuction with the accurate simulation of
monthly and storm event sediment loss. The accumulated sediment on
pervious and impervious areas is printed in the monthly and annual
summaries and at the beginning of each storm event (for HYCAL=2). To
assist in sediment calibration, the following guidelines are extended:
(1) On pervious areas, KRER, ACUP, and REPER are the major parameters
that control the availability of sediment on the land surface,
while KSER and JSER control the sediment washoff. The daily
accumulation and removal of sediments by ACUP and REPER will
dominate sediment availability for land surfaces with high cover
factors (COVVEC). On exposed land surfaces, sediment generation by
soil splash is important and is controlled largely by the KRER
parameter. To offset the sediment availability on pervious areas,
the KSER and JSER parameters control sediment washoff to prevent
continually increasing or decreasing sediment on the land surface.
Thus, a balance must be established between the KRER, ACUP, and
REPER parameters and the KSER and JSER parameters to develop the
equilibrium described above.
(2) On impervious areas, soil splash is not significant. The major
sediment accumulation and removal parameters are ACUI and REIMP,
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and the sediment washoff parameters are KEIM and JEIM. These two
parameter sets must be adjusted to maintain a relatively stable
amount of sediment on impervious surfaces throughout the
calibration period.
(3) The calibration output indicates the flow contributions from
pervious and impervious surfaces and pollutant contributions from
pervious and impervious surfaces in each land use simulated (see
Section A4). In urban areas, the majority of nonpoint pollutants
will emanate from impervious land surfaces especially during small
storm events and in the early portion of extended events. Pervious
land surfaces in urban areas will generally contribute a
significant amount of pollutants only during large storm events and
the latter portion of extended events. The user should note this
behavior from the output provided during calibration runs.
(4) The output from the NFS Model indicates the accumulated sediment on
pervious and impervious surfaces in each land use. This
information is provided at the beginning of each storm event (for
HYCAL=2 or 3) and in the monthly and annual summaries to assist in
the development of the sediment balance.
(5) The daily removal factors, REFER and REIMP, are usually assumed to
be relatively constant and fixed. Also, the exponents of soil
splash (JRER) and sediment washoff (JSER, JEIM) are reasonably well
defined. Thus, the parameters that receive major consideration
during sediment calibration are: the accumulation rates, ACUP and
ACUI; the coefficient of soil splash, KRER (especially for exposed
land surfaces); and the coefficients of sediment washoff, KSER and
KEIM.
(6) In general, an increasing sediment storage throughout the
calibration period indicates that either accumulation and soil
fines generation is too high, or sediment washoff is too low.
Examination of individual events will confirm whether or not
sediment washoff is under-simulated. Also, the relative
contributions of pervious and impervious surfaces will help to
determine whether the pervious or impervious washoff parameters
should be modified. A continually decreasing sediment storage can
be analyzed in an analogous manner.
(7) The sediment washoff during each simulation interval is equal to
the smaller of two values; the transport capacity of overland flow
or the sediment available for transport from pervious or impervious
surfaces in each land use. To indicate which condition is
occurring, an asterisk (*) is printed in the calibration output
whenever sediment washoff is limited by the accumulated sediment
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(see Table 28). Thus, when no asterisks are printed washoff is
occurring at the estimated transport capacity of overland flow.
Generally, washoff will be at capacity (no asterisks) during the<
beginning intervals of a significant storm event; this simulates
the "first flush" effect observed in many nonpoint pollution
studies. As the surface sediment storage is reduced, washoff will
be limited by the sediment storage during the latter part of storm
events. However, for very small events overland flow will be quite
small and washoff can occur at capacity throughout. Also, on
agricultural and construction areas washoff will likely occur at
capacity for an extended period of time due to the large amount of
sediment available for transport.
(8) Using the information provided by the asterisks (described above)
minor adjustments in JRER, JSER, and JEIM can be used to alter the
shape of the sediment graph for storm events. For pervious areas
when available sediment is limiting (asterisks printed),
increasing JRER will tend to increase peak values and decrease low
values in the sediment graph. Decreasing JRER will have the
opposite effect tending to decrease the variability of simulated
values. When sediment is not limiting (no asterisks printed),
the JSER parameter will produce the same effect. Increasing JSER
will increase variability while decreasing it will decrease
vari ability.
For impervious areas, the JEIM parameter will produce the effects
described above when sediment washoff from impervious areas is
occurring at the transport capacity. All these parameters will
also influence the overall sediment balance, but if parameter
adjustments are minor, the impact should not be significant.
Both sediment and water quality calibration should be performed on a
single land use at a time, if possible, in order to correctly evaluate
contributions from individual land uses. However, the calibration
output does indicate the individual land use contributions so that the
user can implicitly evaluate the distribution for reasonableness.
When the sediment calibration is completed, adjustments in the pollutant
potency factors can be performed. Generally, monthly and annual
pollutant loss will not be available, so the potency factors will be
adjusted by comparing simulated and recorded pollutant concentrations,
or mass removal, for selected storm events. For nonpoint pollution,
mass removal in terms of pollutant mass per unit time (e.g., gm/min) is
often more indicative of the washoff mechanism than instantaneous
observed pollutant concentrations. However, the available data will
often govern the type of comparison performed.
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Storms that are well simulated for both flow and sediment should be used
for calibrating the potency factors. The initial values of potency
factors should be .increased if pollutant graphs are uniformly low and
decreased if the graphs are uniformly high. Monthly variations in
potency factors can be used for finer adjustments of simulation in
different seasons if sufficient evidence and information is available to
indicate variations for the specific pollutant. However, individual
storms should not be closely matched at the expense of the other storms
in the season. Also, consistency between the sediment and pollutant
simulation is important; tf sediment is under-simulated then the
pollutant should be under-simulated, and vice versa. Inconsistent
simulations can indicate that sediment is not a transport mechanism for
the particular pollutant or that the potency factors have been
incorrectly applied. Also, if there is no similarity between the shapes
of the recorded sediment and pollutant graphs, then pollutant transport
is not directly related to sediment transport and no amount of
adjustment will allow an effective simulation of that pollutant.
- r-^
Conclusion
The use of a continuous simulation model provides insight into the
relationships among the various components in the hydrologic cycle and
nonpoint source pollution. A model cannot be applied without
understanding these relationships, yet the process of modeling itself is
instructive in developing this understanding. The calibration process
described above requires such an understanding of the physical process
being simulated, the method of representation, and the impact of
critical NPS Model parameters. It is not a simple procedure. However,
study of the parameter definitions, the algorithm formulation, and the
above guidelines should allow the user to become reasonably effective in
calibrating and applying the NPS Model.
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A7. COMPUTER AND MANPOWER REQUIREMENTS
The NPS Model is written in the IBM FORTRAN IV language and was developed
and run on the Stanford University IBM 360/67 and 370/168 computers.
The 'handy minimal language1 concept (81) was adopted to the extent possible
to produce a reasonably compatible computer code for at least the following
computer systems: IBM 360, UNIVAC 1108, CDC 6000, and Honeywell Series. 32.
However, at the present time, Model operation has been limited to the
Stanford IBM systems. The NPS Model operates most efficiently in a two-
step procedure. The first step involves the compilation of the program and the
storage of the compiled version on disk or magnetic tape. In step two the
compiled Model is provided the necessary input data and is executed. Thus,
the Model can operate a number of types of different input data with a
single compilation.
Representative time and core requirements for compilation and execution
of the NPS Model on the Stanford systems (FORTRAN G Compiler) are shown below.
Compilation
IBM 360/67
IBM 370/168
Execution
Hydrologic Calibration (HYCAL=1)
IBM 360/67
IBM 370/168
Sediment & Water Quality
Calibration (HYCAL=2)
IBM 360/67
IBM 370/168
Production Run (HYCAL=4)
IBM 360/67
IBM 370/168
Central Processor
Unit Time
(minutes)
2.5
0.5
2.0/year
0.5/year
4.7/year
0.5/year
2.8/year
0.6/year
Computer Core
Requirements
(bytes)
194 K
124 K
128 K
136 K
128 K
136 K
142 K
144 K
Execution time requirements are based on simulation runs for the Durham,
North Carolina watershed including simulation of four land use
categories and two water quality constituents, in addition to water
temperature and dissolved oxygen. Substantial time reductions occur
when sediment and water quality simulation is performed for fewer land
uses and/or constituents. Also, simulation of snow accumulation and
melt will increase computer time approximately 20 to 30 percent.
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The manpower effort required to use and apply the NPS Model will vary
considerably with the level of technical personnel, the data
availability, and the length of the simulation period. Considerable
economies of scale are introduced in personnel requirements when longer
simulation periods are utilized. The estimates below for the necessary
tasks in applying the NPS Model assume an individual with a bachelor's
degree in a technical field with 2 to 3 years experience in water resource
and water quality related work. These estimates further assume a
reasonable level of technician support.
Task Estimated Person-Weeks
(1) Familiarization with NPS Model
report and user manual 2.5
(2) Data collection and analysis 1.0/year of simulation
(3) Preparation of Model input sequence
of meteorologic data 1.5/year of simulation
(4) Parameter evaluation 1.0
(5) Calibration (hydrology, sediment,
and water quality) 3.0/year of calibration
These values should be used only as approximate guidelines; extended
simulation periods will allow reductions in the above "per year"
estimates. On the other hand peculiar problems in data availability and
calibration could expand the required effort. Personnel requirements
for production runs and simulation of various land use alternatives need
to be added to the above values. In essence, these estimates only
indicate that the NPS Model cannot be adequately applied in a short time
span of 2 to 3 weeks; however, application does not require an extensive
1 to 1.5 year effort.
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APPENDIX B
HYDROLOGIC (LANDS) SIMULATION ALGORITHMS
This appendix reviews the equations or algorithms used in the simulation
of hydrologic processes in the LANDS subprogram of the NPS Model.
Except for the numbering of equations and figures, the following
discussion is abstracted directly from the corresponding sections of the
Hydrocomp Simulation Programming (HSP) Operations Manual (23). The
potential user of the NPS Model should thoroughly understand the Model
representation of the hydrologic processes and the importance of Model
parameters prior to attempting application and calibration of the NPS
Model. The flowchart of the LANDS subprogram was shown in Figure 3 of
the report, and the LANDS parameters are shown in capital letters in the
algorithm descriptions below.
INTERCEPTION
The first loss to which falling precipitation is subjected is
interception or retention on leaves, branches, and stems of vegetation.
Interception in any single storm is small in amount and is not important
in flood-producing storms. However, in the aggregate interception may
have a significant effect on annual runoff volumes.
In nature, interception is a function of the type and extent of
vegetation and, for deciduous vegetation, the season of the year. In
the NPS Model interception is modeled by defining an interception
storage capacity EPXM as an input parameter. All precipitation is
assumed to enter interception storage until it is filled to capacity.
Water is removed from interception storage by evapotranspiration at the
potential rate. Evapotranspiration may occur even during rain so that
after the storage is filled there is a continuing interception equal to
the potential evapotranspiration.
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IMPERVIOUS AREA
Precipitation on impervious areas that are adjacent to or connected with
stream channels will contribute directly to surface runoff. The
"impervious" fraction of the total watershed area is calculated in the
NPS Model from the impervious fraction of each land use and the land use
area. Precipitation minus interception is multiplied by the impervious
area fraction to determine the impervious area contribution to
streamflow. In simulating the effects, of impervious areas, small losses
result from the film of water retained on the impervious surface after a
rain, and the continuing exposure of water on the impervious area to
evapotranspiration. Rock outcrops, buildings, or roads that are so
located that runoff from them must flow over soil before reaching a
channel should not be counted in the impervious area. Such runoff is
represented by the direct infiltration functions in the model.'
The impervious area is usually a very small percentage of the total
watershed except in urban areas where the impervious area term becomes
very important. In rural watersheds impervious area does not contribute
large amounts of runoff. However, for light rains with relatively dry
soil, the impervious area may be the sole contributor to runoff to the
stream. During the calibration phase the impervious area term is useful
in reproducing these small runoff events and enhances the detailed
understanding of the hydrologic process during simulation.
Calculations in the LANDS subprogram are carried in terms of water depth
(inches or millimeters) over a unit area. When concentrations of
quality constituents and flow rates are required, these depths are
multiplied by the area and divided by the time interval to derive actual
volumes and rates of runoff.
INFILTRATION
The process of infiltration is essential and basic to simulation of the
hydrologic cycle. Infiltration is the movement of water through the
soil surface into the soil profile. Infiltration rates are highly
variable and change with the moisture content of the soil profile.
Infiltration is the largest single process diverting precipitation from
immediate streamflow. Usually more than half of the water which
infiltrates is retained in the soil until it is returned to the
atmosphere by evapotranspiration. However, not all infiltrated water is
permanently diverted from streamflow. Some infiltrated water may move
laterally through the upper soil to the stream channel as interflow,
189
-------
and some may enter temporary storages and later discharge into the
stream channel as base or ground water flow.
Water which does not infiltrate directly into the soil moves over the
land surface and is subject to delayed infiltration and retention in
surface depressions. The delayed infiltration is introduced by the
upper zone function.
The infiltration capacity, the maximum rate at which soil will accept
infiltration, is a function of fixed characteristics of the watershed,
e.g., soil type, permeability, land slopes, and vegetal cover; and of
variable characteristics, primarily soil moisture content. Soils
containing clay colloids may expand as moisture content increases, thus
reducing pore space and infiltration capacity. The actual rate at any
time is equal to the infiltration capacity or the supply rate
(precipitation minus interception plus surface detention), whichever is
less.
Traditionally, infiltration has been represented by an infiltration
capacity curve in which the capacity is an exponential function of time.
This is in accord with experimental evidence provided the supply rate
always exceeds the capacity. Since supply rates are frequently less
than infiltration capacity, the variation of infiltration capacity is
controlled by accumulation of soil moisture and may not be described by
any smooth function of time.
Infiltration relationships used for continuous simulation must:
(1) Represent mean infiltration rates continuously.
Since variable moisture supply rates preclude continuous
functions of time, expressions for infiltration as a
function of soil moisture content are used.
(2) Represent the areal variation in infiltration, i.e.,
infiltration capacities at any time are distributed
about the watershed mean value of infiltration.
To meet the first requirement the LANDS subprogram uses a method based
on infiltration equations developed by Philips (82).
F = st + at (31)
f = st"% + a (32)
190
-------
Where F = cumulative infiltration, f = infiltration rate, t = time, a
and s = constants that depend on soil properties.
If the constant a is small, Equations 31 and 32 can be written:
fF = I' (33)
Since s2/2 is constant, Equation 33 continuously relates infiltration
rate to cumulative infiltration or infiltrated volume. This is the type
of relationship needed in hydrologic simulation.
Equation 33 will apply approximately to intermittent infiltration when
the moisture distribution in the soil profile adjusts between rains.
Homogeneous soil is also assumed, but a decrease in permeability as
depth increases is more common. Therefore, Equation 33 is modified to:
fFb = constant (34)
where b = a constant greater than one. Numerous trials have resulted in
adoption of b = 2 as a standard value.
The second requirement listed above, representation of areal variations
in infiltration capacity, has not normally been considered in
applications of the infiltration concepts. Areal variation results from
differences in soil type and permeability and from differences in soil
moisture, which in turn result from differing vegetal cover,
precipitation, and exposure to evaporation. It can be expected that the
infiltration capacities that exist from point to point in a watershed at
a given time will have some distribution about a mean value (Figure 38).
The corresponding cumulative infiltration capacity curve (Figure 39) is
of interest as a basis for runoff volume calculations. The solid line
sketched in Figure 39 is plotted from the example of an actual frequency
distribution sketched in Figure 38.
The shape of the cumulative frequency distribution that will apply in a
watershed at any time is impractical to determine, and for mathematical
simplification the dashed line in Figure 39, corresponding to the dashed
frequency distribution in Figure 38, is assumed in LANDS. The
assumption of a linear variation is reasonably well verified by the
limited experimental data that is available, and experience indicates
that the assumption yields satisfactory results.
191
-------
Relative Infiltration Capacity
Figure 38. Schematic frequency distribution of infiltration capacity
in a watershed
Assumed in HSR
0 20 40 60 80 100
Percent of Area with Infiltration Capacity equal to
or less than Indicated Value
Figure 39. Cumulative frequency distribution of infiltration capacity
192
-------
The results of the assumptions developed above are illustrated in Figure
40. Rainfall or snowmelt gives a moisture supply of x inches in a
certain time interval. The cross hatched area in Figure 40 represents
the infiltration that is added to soil moisture or ground water storage
in the time interval.
The mean infiltration capacity f is time Variable, decreasing as
infiltration increases the soil moisture content. The value of f is
calculated based on Equation 34
f = INFIL/(LZS/LZSN)2 (35)
LZS/LZSN is a dimensionless soil moisture storage ratio, LZS is the
current storage in the soil profile, and LZSN (an input parameter) is an
index level for moisture storage. INFIL is an input parameter that
establishes an index infiltration level, and is equal to f when LZS/LZSN = 1.
Numeric values of LZSN and INFIL are discussed in the User Manual, Appendix A.
To illustrate the sequence of calculations for time dependence of
infiltration consider that rainfall produces the moisture supply x in
Figure 40 in a given time interval. Infiltration occurs and the
variable soil moisture storage LZS increases. In the next time interval
f will decrease since LZS/LZSN in Figure 41 has increased. The
combination of functions represented by Figures 40 and 41 simulates the
complex time and areal variation of infiltration over a watershed.
Simulation algorithms make infiltration a function of the supply rate
and vary continuously the area contributing to runoff.
INTERFLOW
Infiltration may lead to interflow, runoff that moves laterally in the
soil for some part of its path toward a stream channel. Interflow is
encouraged by relatively impermeable soil layers and has been observed
to follow roots and animal burrows in the soil. Interflow may come to
the surface to join overland flow if its flow path intersects the
surface. Figure 40 is extended (Figure 42) to infiltration for the
interflow process.
The variable c is defined by
c = INTER * 20-ZS/LZSN> (36)
193
-------
Moisture
Supply
( Inches )_
x
Infiltration
Capacity
(Inches)
Percent of Area
Figure 40. Application of cumulative frequency distribution
of infiltration capacity in HSP
Iw-
0.5 1.0 1.5 2.0 2.5
Soil Moisture Ratio ( LZS/LZSN)
Figure 41. Mean watershed infiltration as a function of soil moisture
194
-------
Moisture
Supply
( Inches);
Surface
"Detention
Interflow
Detention
Infiltration
Capacity
(Inches)
25 50 75
Percent of Area
Figure 42. Cumulative frequency distribution of infiltration capacity
showing infiltrated volumes, interflow and surface dentention
4.0
3.0
2.0
1.0
0
c vs LZS/LZSN
for INTER =1.0
J_
0 0.5 1.0 1.5 2.0
Lower Zone Soil Moisture Ratio ( LZS/LZSN )
Figure 43. Interflow c as a function of LZS/LZSN
195
-------
an empirical equation that results in the variation with soil moisture
sketched in Figure 43. INTER is an input parameter that governs the
volume assigned to interflow.
This simulation scheme makes interflow a function of the local
infiltration rate and of soil moisture, i.e., the higher the soil
moisture, the greater the fraction of infiltration which becomes
interflow. The combination of interflow and infiltration functions
yields a smooth response to variations in moisture supply in any time
interval. Figure 44 illustrates this response.
UPPER ZONE
Moisture that is not infiltrated directly will increase surface
detention storage. The increment to surface detention calculated from
Figure 42 will either contribute to overland flow or enter upper zone
storage. Depression storage and storage in highly permeable surface
soils are modeled by the upper zone. The upper zone inflow percentage P
is independent of rainfall intensity, but upper zone storage capacity is
low. Moisture is lost from the upper zone by evaporation and
percolation to the lower zone and ground water storages.
The following expressions are used to calculate the response of the
upper zone storage. The upper zone has a nominal capacity given by the
input parameter UZSN. The percentage P of a potential addition to
overland flow surface detention that is held in the upper zone is a
function of the upper zone storage UZS and the nominal capacity UZSN
(Figure 45). When the ratio UZS/UZSN is less than two,
P- 100
where = 2.0
{1.0 - (2*uzSN)*(i n' + k )ki| (37)
V 1 J
, uzs
) - 1.0 + 1.0 (38)
When UZS/UZSN is greater than 2.0 the percentage is given by
196
-------
Increase in Overland
Flow Surface Detention
Increase in Interflow Detention
Net Infiltration
0.8 1.2 1.6
Moisture Supply
Figure 44. Components of HSP response vs. moisture supply
d>
a>
S
M—
0
c
(1)
u
i_
0
O.
0,100
c
§^ 80
1 a
4-> Q.
d) —•%
Q .
o
(D
<0
c
(0
60
40
20
« o
cr
0 1.0 2.0 3.0
Upper Zone Soil Moisture Ratio ( UZS/UZSN )
Figure 45. Surface detention retained in the upper zone
197
-------
(39)
where k2 is
k9 = 2.0
(UZS/UZSN) - 2.0
+ 1.0 (40)
The upper zone storage prevents overland flow from a portion of the
watershed depending on the value of the ratio UZS/UZSN, but since the
nominal capacity UZSN is small, the upper zone retention percentage
decreases rapidly with increments of accretion of water early in the
storm.
Percolation (PERC) occurs from the upper zone to the ground water and
lower zone storages when the upper zone storage ratio UZS/UZSN exceeds
the lower zone storage ratio LZS/LZSN. This is calculated as
PERC = 0.1*INFIL*UZSN*{(UZS/UZSN) - (LZS/LZSN)! 3 (41)
where INFIL is the infiltration level input parameter and PERC is the
percolation rate in inches/hour. Evapotranspi ration occurs from the
upper zone storage at the potential rate if UZS/UZSN is greater than
2.0. If UZS/UZSN is less than 2.0 the portion of the potential
evapotranspiration (PET) that is satisfied by upper zone is given by
ET (actual) = 0.5*(UZS/UZSN)* PET (42)
j
Potential evapotranspiration that is not assigned to the upper zone is
passed to the lower zone. Equation 42 models direct evaporation from
near-surface soil. Moisture loss from the lower zone models
transpiration by vegetation.
The use of a nominal rather than an absolute capacity for the upper zone
storage permits a smooth increase in overland flow rates as upper zone
storage increases. If an absolute capacity were used, there would be an
abrupt increase in overland flow when the capacity was attained. Such
an abrupt change is not consistent with experience nor with the
198
-------
observation that a truly "saturated" state is rarely, if ever, observed.
Because of the use of a nominal capacity, it is not possible to define
upper zone storage in any rigorous physical sense. It is best viewed as
an input parameter representing moisture retention at and near the soil
surface.
OVERLAND FLOW
The movement of water in surface or overland flow is an important land
surface process. Interactions between overland flow and infiltration
need to be considered since both processes occur simultaneously. The
variations in rates of infiltration described above allow overland flow
in areas with low infiltration while preventing overland flow in other
areas. During overland flow, water held in detention storage remains
available for infiltration. Surface conditions such as heavy turf or
very mild slopes that restrict the velocity of overland flow terfd to
reduce the total quantity of runoff by allowing more time for >
infiltration. Short, high intensity rainfall bursts are attenuated by
surface detention storage reducing the maximum outflow rate from
overland flow.
A wide range of methods for the calculation of unsteady overland flow
was considered. The only rigorous general methods for simulating
unsteady overland flow are finite difference techniques for the
numerical solution of the partial differential equations of continuity
and momentum. These methods have a major disadvantage for continuous
simulation since substantial amounts of computer time are needed. In a
natural watershed there are areal variations in the amount of runoff
moving in overland flow because of areal variations in infiltration.
Average values must be used in the calculations for the length, slope,
and roughness of overland flow. Hence, the accuracy gained by using ?
finite difference methods for overland flow is subject to question
because of the limitations on the input data.
In LANDS, overland flow is treated as a turbulent flow process. Since
continuous surface detention storage is computed, the volume of surface
detention was chosen as the parameter to be related to overland flow
discharge. Using the Chezy-Manning equation, the relationship between
surface detention storage at equilibrium D , the supply rate to overland
flow i, Manning's n and the length L and slope S of the flow plane "is
0,6 0.6, 1.6
n = 0-000818 i n L (43)
e so.3 •
199
-------
Using the ratio of detention depth at any instant D to detention depth
at equilibrium De as an index of the distribution of flow over the
overland plane, an empirical expression relating outflow depth and
detention storage which fits experimental data quite well is
(44)
Substituting Equation 44 in the Chezy-Manning Equation the rate of
discharge from overland flow in ft3/sec/ft is
- (45)
q = > >
where De is a function of the current supply rate to overland flow and
is calculated from Equation 46. During recession flow when De is less
than D the ratio D/D is assumed to be one. LANDS continuously solves a
continuity equation e
D2 = DI + AD - q At (46)
where At is the time interval used, D2 is the surface detention at the end
of the current time interval, DI is the surface detention at the end of
the previous time interval, AD is the increment added to surface detention
in the time interval, and q is the overland flow into the stream channel
during the time interval. The discharge q is a function of the moisture
supply rate and of (Dx + D2)/2, the average detention storage during the
time interval (D in Equation 45).
The system of equations can be solved numerically with good accuracy if the
time interval of the calculation is sufficiently small so that the value of
discharge in any time interval remains a small fraction of the volume of
surface detention. In the NPS Model calculations of discharge from
overland flow are made on a 15-minute time interval.
The overland flow calculations enter the delayed infiltration process
through the fact that any water remaining in detention at the end of an
interval is added to the rainfall minus interception of the next period to
give the supply rate for the infiltration calculations. Overland flow
detention is an important part of the total delay time in runoff on small
watersheds. Figures 46 and 47 illustrate the "fit" of the LANDS simulation
of overland flow to experimental data. Figure 48 shows that on a watershed
200
-------
0)
3 0
3-u
2.0
1.0
I I 1 1 1 1 1 T
Simulated overland flow (Watershed Model)
Simulated overland flow (Morgali)
Observed overland flow
Surface-Turf
Rainfall -3.60 in/hr
S-0.04
1=72 ft
6
12
14
5.0
Time (minutes )
Figure 46. HSP overland flow simulation
iiiiiiiiir~riir
Simulated overland flow (Watershed Model)
— Observed overland flow
Surface-crushed
slate
Rainfall-3.68 in/hr
S=0.04 ft/ft
L= 72 ft
Figure 47.
12 IB 20
Time (minutes)
HSP overland flow simulation
201
-------
of 0.26 square miles, overland flow simulation closely approximates the
actual outflow hydrograph indicating that overland flow delay is much more
important than channel storage in controlling hydrograph shape. Figure 49
shows a similar comparison for a watershed of 18.5 square miles which is
partly urbanized. Here, the overland flow effects on hydrograph shape are
relatively small although the effect through delayed infiltration is still
present.
INTERFLOW
The calculation of an increment to interflow detention storage SRGX was
described above. Outflow from this storage to the stream is calculated
on a 15-minute time interval by the equation
INTF = LIRC4 * SRGX (47)
where
LIRC4 = 1.0 - (IRC)1/96 (48)
IRC, an input parameter, is the daily recession constant for the
interflow component calculated as the ratio of the interflow discharge
at any instant to the interflow discharge 24 hours earlier.
LOWER ZONE AND GROUND WATER STORAGE FUNCTION
This function operates on the direct or immediate infiltration (Figure
42) and the percolation from upper zone storage (PERC in Equation 41).
The available water is divided between the lower zone soil moisture
storage and the ground water storage. The division is based on the lower
zone storage ratio LZS/LZSN where LZSN is the lower zone nominal
capacity. The percentage of the infiltration plus percolation that
enters ground water storage (Figure 50) is given by
202
-------
0.6
CO
CD
CO
CD
O)
CO
C
CO
_c
o
c
0.5
0.4
0.3
o> 0.2
CO
£1
0
CO
0
"•
San Francisquito Creek tributary
March 5, 1962
Drainage area 0.26 sq. miles
Simulated overland flow
Observed outflow hydrograph
Watershed Model
0.0
456789
Time (hours)
Figure 48. Hydrograph simulation (0.26 square miles)
0.5
CO
CO
CD
CO
c
CO
T!
(0s
CD
O
C
0.3
CO
o
CO
Q 0.1
0.0
1
1
Beargrass Creek, Kentucky
.January 20-22,1959
Drainage area 18.5 sq. miles
— Simulated overland flow
--Simulated outflow hydrograph
^Watershed Model
M N M N M N
20 21 22
Time (days)
Figure 49. Hydrograph simulation (18.5 square miles)
203
-------
when LZS/LZSN is less than 1.0 and by
(50)
when LZS/LZSN is greater than 1.0, z is defined by
z = 1.5*
LZS
LZSN
- 1.0
+ 1.0
(51)
These relationships are clotted in Figure 50.
c
o
•4-»
CO
100
0)
£80
0 o
— -*= Z en
.- j_, m 60
y_
£ C9 !_
t ^ « 40
c
Q)
o
L~
0)
Q.
c -o
LJJ c 20
o
5 0
0.5 1.0 1.5 2.0 2.5
Soil Moisture Ratio (LZS/LZSN)
Figure 50. Infiltration entering groundwater storage
LOWER ZONE STORAGE
The lower zone storage is the main moisture storage for the land
surface. Like the upper zone storage, it is defined in terms of a
204
-------
nominal capacity LZSN, the storage level at which half of the incoming
infiltration enters the lower zone and half moves to ground water. This
use of a nominal rather than an absolute capacity serves the same
purpose as for the upper zone, i.e., it avoids the abrupt change which
would occur if an absolute capacity were reached and permits a smooth
transition in hydrologic performance as the lower zone storage
increases.
Physically the lower zone may be viewed as the entire soil from just
below the surface down to the capillary fringe above the water table.
In practice we are concerned only with the transient portion of this
storage, i.e., the volume which is emptied by evapotranspiration and
refilled by infiltration. Consequently, numerical values of the input
parameter LZSN do not necessarily reflect the total moisture storage
capacity of the lower zone.
GROUND WATER
Equations 49 and 50 determine the accretion to ground water in each time
increment. If some part of this water is believed to percolate to deep
ground water storage, this is modeled by allowing a fixed percentage of
the inflow to ground water to bypass the active ground water storage and
proceed directly to the deep or inactive storage. This portion is
assigned by the input parameter K24L. Water assigned to deep ground
water is lost from the surface phase of the hydrologic cycle of the
watershed. It may leave the basin as subsurface flow, but it does not
contribute to streamflow.
The outflow from active ground water storage at any time is based on the
simplified model in Figure 51. The discharge of an aquifer is
proportional to the product of the cross sectional area and the energy
gradient of the flow. A representative cross sectional area of flow is
assumed proportional to the ground water storage level computed by
LANDS.
Groundwater outflow (GWF) is calculated on 15-minute intervals as a
function of ground water storage (SGW) as follows:
GWF = LKK4 * SGW (52)
1 /Qfi
where LKK4 = 1.0 - (KK24) (53)
205
-------
Land Surface
Groundwater
Level
Impervious Layer
Figure 51. Groundwater flow
CO
0)
-C
u
C
CO
C (0 &p
0) >-
II
CD
o
a
(0
>
0)
!iii; Evapotranspiration
jiiiiiiiiiiiiiiljjiiiiiiiiiiiiiiiiijjtiiiJiiiiiJiJiiiJiiiiiJiiiiiiiii
o
•o
° <
<•* fl)
I ?
*^ "^
Q)
3 "5.
O -I
^r Q)
w 5
0 25 50 75 100
Percent of area with a daily evapotranspiration
opportunity equal to or less than the indicated value
Figure 52. Potential and actual evapotranspiration
206
-------
KK24 is the minumum observed daily recession constant of ground water
flow, the ratio of current ground water discharge to the ground water
discharge twenty-four hours earlier. Equation 52 reproduces the
commonly used logarithmic depletion curve, i.e., the flow after a period
of n days decreases by (KK24)n, and a semi-logarithmic plot of
discharge vs. time is a straight line.
EVAPOTRANSPIRATION
The volume of water that leaves a watershed as evaporation and
transpiration exceeds the total volume of streamflow in most hydrologic
regimes. Continuous estimates of actual evapotranspiration must
therefore be made by LANDS. There are two components involved in
estimating actual evapotranspiration. Measured potential
evapotranspiration and calculated soil moisture conditions are used to
estimate actual evapotranspiration.
Potential evapotranspiration is assumed to be equal to lake evaporation
estimated from Weather Bureau Class A pan records (74). This
procedure is more convenient than an approach based on meteorological
data since input requirements are less stringent. A single variable,
adjusted pan evaporation data, serves a purpose that would otherwise
require input of several variables. If pan evaporation data are not
available, the input data for potential evapotranspiration may be
estimated by any appropriate method.
The relationship of actual evapotranspiration to potential
evapotranspiration over large areas should logically be a function of
moisture conditions. Even if transpiration from vegetation is
independent of soil moisture until the wilting point is reached,
variable soil moisture will cause wilting in some parts of a watershed
but not in others. Evaporation from soil, a component of the total
process, is dependent on moisture conditions.
When near surface storage is depleted, the concept of evapotranspiration
opportunity is defined as the maximum quantity of water accessible for
evapotranspiration in a time interval at a point in the watershed. It
is analogous to infiltration capacity and would have a cumulative
distribution similar to that in Figure 39. The cumulative
evapotranspiration opportunity curve will be a function of watershed
soil moisture conditions. This curve estimates actual
evapotranspiration for any quantity of potential evapotranspiration just
as the cumulative infiltration capacity curve estimates net infiltration
for any moisture supply.
207
-------
Evapotranspi ration occurs from interception storage at the potential
rate. Evapotranspi ration opportunity controls evapotranspi ration from
the lower zone storage. Daily lake evaporation, daily potential
evapotranspi ration data, or average daily rates for semi-monthly periods
are used as input. LANDS computes hourly values from the daily totals
using an empirical diurnal variation.
Potential evapotranspi ration will result in a water loss or actual
evapotranspi ration only if water is available. LANDS first attempts to
satisfy the potential from interception storage and from the upper zone
in that order. The contribution to actual evapotranspi ration of the
upper zone is limited if UZS/UZSN is less than 2.0 (Equation 42). Any
remaining potential enters as Ep in Figure 52. Since evapotranspi rat ion
opportunity in a watershed on a given day may be expected to vary
through a considerable range, a cumulative frequency distribution
similar to those found for infiltration capacity in Figure 40 might be
reasonable.
Following the assumption made for infiltration capacity the cumulative
frequency distribution of evapotranspi ration opportunity is assumed to
be linear (Figure 52). The quantity of water lost by evapotranspi ration
from the lower zone when En is less than r is given by the cross-hatched
trapezoid of Figure 52. The variable r is an index given by
= / _ 0.25 x
U.O - K3;
Evapotranspiration is further limited when K3 is less than 0.5. A
fraction of the watershed area given by 1.0-2*K3 is considered devoid of
vegetation that can draw from the lower zone storage. K3 is an input
parameter that is an index to vegetation density.
208
-------
APPENDIX C
SNOWMELT SIMULATION ALGORITHMS
As stated in Section VI, the objective of snow accumulation and melt
simulation is to approximate the physical processes (and their
interactions) in order to evaluate the timing and volume of melt water
released from the snowpack. The algorithms used in the NPS Model are
based on extensive work by the Corps of Engineers (37), Anderson and
Crawford (38), and Anderson (39). In addition, empirical relationships
are employed when quantitative descriptions of the process are not
available. The algorithms presented below are identical to those
employed in HSP (23) and the ARM Model (21). The flowchart of the
snowmelt routine was shown in Figure 4 of this report. The major
simulated processes can be divided into the two general categories of
melt components and snowpack characteristics. The algorithms for the
individual processes within each of these categories are briefly
presented below in computer format and English units to promote
recognition of the equations in the Model source code. Refer to the
original source materials for a more in-depth explanation.
MELT COMPONENTS
Radiation Melt
The total melt component in each hour due to incident radiation energy
is calculated as
RM = (RA + LW)/203.2 (55)
where RM = radiation melt, in./hr
RA = net solar radiation, langleys/hr
209
-------
LW = net terrestrial radiation, langleys/hr
203.2 = langleys required to produce 1 inch of melt from snow at 32 °F
The effects of solar and terrestrial radiation are evaluated separately.
An input parameter, RADCON, allows the user to adjust the solar
radiation melt component to the conditions of the particular watershed.
Daily solar radiation is required input data for the present version of
the snowmelt routine. Hourly values are derived from a fixed 24-hour
distribution and are modified by the watershed forest cover (input
parameter F) and the effective albedo (calculations described under
'snowpack characteristics').
Terrestrial radiation is not generally measured; hence, an estimate must
be obtained from theoretical considerations and modified by
environmental factors (e.g., cloud cover, forest canopy, etc.). The
following relationship for terrestrial radiation based on Stefan's Law
of Black Body Radiation (37).
R = oTA'KF + (1-F)0.757} - aTS4 (56)
where R = net terrestrial radiation, langleys/min
F = fraction forest cover
TA = air temperature, °K
TS = snow temperature, °K
cr = Stefan's constant, 0.826 x 10 "10, langleys/min/°K
The snowmelt routine employs a linear approximation to the above
relationship and modifies the resulting hourly terrestrial radiation for
cloud cover effects. Back radiation from clouds can partially offset
terrestrial radiation losses from the snowpack. Since cloud cover data
information is not generally available and transposition of data from
the closest observation point can be highly inaccurate, a daily cloud
cover correction factor is estimated to reduce this radiation loss from
the pack. For days when precipitation occurs, terrestrial radiation
loss from the pack is reduced by 85 percent to account for the effects
of complete cloud cover; this reduction factor decreases to zero in the
days following the storm event.
Condensation-Convection Melt
The melt resulting from the heat exchange due to condensation and
convection is often combined in a single equation. A constant ratio
between the coefficients of convection and condensation (Bowen's ratio)
is generally assumed. Since the two mechanisms are operative under
different climatic situations, the algorithms are presented here
210
-------
separately. Condensation only occurs when the vapor pressure of the air
is greater than saturation, whereas convection melt only occurs when the
air temperature is greater than freezing. The algorithms are
CONV = CCFAC*.00026*WIN*(TX-32)*(1.0-0.3*(MELEV/10000)) (57)
CONDS = CCFAC*.00026*WIN*8.59*(VAPP-6.108)
(58)
where CONV
CONDS
CCFAC
WIN
TX
MELEV
VAPP
6.108
0.00026,
8.59
convection melt, in./hr
condensation melt, in./hr
input correction factor to adjust melt values to
field conditions
wind movement, mi/hr
air temperature, °F
mean elevation of the watershed, ft
Note: the expression 1.0-0.3*(MELEV/10000) is a
linear approximation of the relative change in air
pressure with elevation, and corresponds to P/P0
in "Snow Hydrology" (37).
vapor pressure of the air, millibars
saturation vapor pressure over ice at 32 °F, millibars
constants in the analogous expression in "Snow Hydrology"
(Note: 0.00026 corresponds to the daily coefficient,
0.00629, adjusted to an hourly basis.)
Rain Helt
Whenever rain occurs on a snowpack, heat is transmitted to the snowpack,
and melt is likely to occur. The quantity of snowmelt from this
component is calculated as follows, assuming the temperature of the rain
equals air temperature:
where RAINM
PX
TX
144
RAINM = ((TX-32)*PX)/144
rain melt, in./hr
rain, in./hr
air temperature, °F
units conversion factor, °F
(59)
Ground Melt
As mentioned previously, melt due to heat supplied from the land surface
and subsurface can be significant in the overall water balance. Since
211
-------
this component is relatively constant, an input parameter specifies the
daily contribution from this component. Heat loss from the snowpack can
result in snowpack temperatures less than 32 °F. When this occurs, the
groundmelt component is reduced 3 percent for each degree below 32 °F.
SNOWPACK CHARACTERISTICS
Rain/Snow Determination
The form of precipitation is critical to the reliable simulation of
runoff and snowmelt. The following empirical expression based on work
by Anderson (39) is employed to calculate the effective air temperature
below which snow occurs:
SNTEMP = TSNOW + (TX-DEWX)*(0.12 + 0.008*TX) (60)
where SNTEMP = temperature below which snow occurs, °F
TSNOW = input parameter, °F
TX = air temperature, °F
DEWX = dewpoint temperature, °F
Variable meteorologic conditions and the relatively imprecise estimates
of hourly temperature derived from maximum and minimum daily values can
cause some discrepancies in this determination. For this reason, the
use of TSNOW as an input parameter allows the user flexibility in
specifying the form of precipitation recorded in meteorologic
observations. The above expression allows snow to occur at air
temperatures above TSNOW if the dewpoint temperature is sufficiently
depressed. However, a maximum variation of one Fahrenheit degree is
specified resulting in a maximum value for SNTEMP = TSNOW + 1.
Snow Density and Compaction
The variation of the density of new snow with air temperature is
obtained from "Snow Hydrology" (37) in the following form:
DNS = IDNS + (TX/100)2 (61)
where DNS = density of new snow
IDNS = density of new snow at an air temperature of 0 °F
TX = air temperature, °F
212
-------
Snow density is expressed in inches of water equivalent for each inch of
snow. With snow fall and melt processes occurring continuously, the
snow density is evaluated each hour. If the snow density is less than
0.55, compaction of the pack is assumed to occur. The new value for
snow depth is calculated by the empirical expression:
DEPTH2 = DEPTH1*(1.0-0.00002*(DEPTH1*(.55-SDEN))) (62)
where DEPTH2 = new snow depth, in.
DEPTH1 = old snow depth, in.
SDEN = snow density
Area! Snow Coverage
The areal snow coverage of a watershed is highly variable. Watershed
response differs depending on whether the precipitation, especially in
the form of rain, is falling on bare ground or snow covered land. The
area! snow coverage is modeled in the snowmelt routine by specifying
that the water equivalent of the existing snowpack, PACK, must exceed
the variable IPACK for complete coverage. IPACK is initially set to a
low value to insure complete coverage for the initial events of the
season and is reset to the maximum value of PACK attained to date in
each snowmelt season. Since the ratio PACK/IPACK indicates the fraction
of the watershed with snow coverage, less than complete coverage results
as the melt process reduces the value of PACK. An input parameter,
MPACK, allows the user to specify the water equivalent required for
complete snow coverage. Thus MPACK is the maximum value of IPACK,
resulting in complete coverage when PACK is greater than MPACK, and less
than complete coverage (PACK/MPACK) when PACK decreases to values less
than MPACK.
Albedo
The albedo or reflectivity of the snowpack is a function of the
condition of the snow surface and the time since the last snow event.
During the snow season, the maximum and minimum values for albedo are
specified as 0.85 and 0.60, respectively. It is reset to approximately
the maximum value with each major snow event and decreases gradually as
the snowpack ages.
213
-------
Snow Evaporation
Evaporation from the snow surface is usually quite small, but its inclusion
in snowmelt calculations is necessary to complete the overall water balance
of the snowpack. The physical process is the opposite of condensation
occurring only when the vapor pressure of the air is less than the
saturation vapor pressure over snow. The following empirical relationship
is used to calculate hourly snow evaporation:
SEVAP = EVAPSN*0.0002*WIN*(VAPP-SATVAP)*PACKRA (63)
where SEVAP = snow evaporation, in./hr
EVAPSN = correction factor to adjust to field conditions
WIN = wind movement, mi/hr
VAPP = vapor pressure of the air, millibars
SATVAP = saturation vapor pressure over snow, millibars
PACKRA = fraction of watershed covered with snow
Snowpack Heat Loss
Heat loss from the snowpack can occur if terrestrial back radiation from
the pack is large, or if air temperatures are very low. Since this heat
is emitted by the pack, it is simulated as a negative heat storage,
NEGMLT, which must be satisfied before melt can occur. Any heat
available to the snowpack first offsets NEGMLT before melting can occur.
The hourly increment to NEGMLT is calculated from the following
empirical relationship whenever the air temperature is less than the
temperature of the pack:
GM = 0.0007*(TP-TX) (64)
where GM = hourly increment to negative heat storage, in.
TP = temperature of the pack, °F
TX = air temperature, °F
NEGMLT and GM are calculated in terms of inches of melt corresponding to
the heat loss from the pack. The current value of NEGMLT is used to
calculate the temperature of the pack simulating the drop in temperature
as heat loss from the pack continues. A maximum value of NEGMLT is
calculated as a function of air temperature and the water equivalent of
the pack by assuming that the temperature in the pack varies linearly from
ambient air temperature at the snow surface to 32 °F at the soil surface.
This maximum negative heat storage is calculated as follows:
214
-------
NEGMM = 0.00695*(PACK/2.0)*(32.0-TX) (65)
where NEGMM = maximum negative heat storage, in.
PACK = water equivalent of the snowpack, in.
TX = air temperature, °F (<32 F)
0.00695 = conversion factor, °F"1
Snowpack Liquid Water Storage
Liquid water storage within the snowpack is limited by a user input
parameter, WC, which specifies the maximum allowable water content per
inch of snowpack water equivalent. Thus, the maximum liquid water
storage is calculated as WC x PACK. However, this value is reduced if
high snow density values are attained.
215
-------
APPENDIX D
NPS MODEL SAMPLE INPUT LISTING
216
-------
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17-
18.
19.
20.
21.
22.
23.
24.
25.
2G.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53 *
54.
55.
//YJL7412 JOB (A12$X2,510,1,90),'J7412LITHIN1
/*OOBPARI1 COPIES=2
//JOBLIB DD DSNAriE=WYL.X2.A12.YJL.J7412.!101,DISP=(OLD,KEEP),
// UNIT=DISK,VOL=SER=PUB003
//STEP1 EXEC PGH=ilPS
//SYSPRINT DD SYSOUT=A
//FTOGF001 DD SYSOUT=A
//FT05F001 DD *
HPS MODEL
SAMPLE INPUT SEQUENCE-HOURLY INPUT PRECIPITATION
&ROPT HYCAL=3, HYMIN=10.0 ,NLAND=4, NQUAL=2, SNOH=1
8DTYP UHIT=-1, PINT-1, I1!IVAR=0
&STRT BGUDAY=15, BGNHON=11, BGNYR=1970
&ENDD ENDDAY=31, ENDHOM=3, ENDYR=1971
&LHD1 UZSiH).40,LZSH=6.0,INFIL=0.04,INTEP.=2.0,IRC=0.5,AREA=1069.
&LND2 NN=0.30, L=300., SS=0.10, HHI=0.15, LI=600., SSI=0.10
6LND3 Kl=1.4, PETMUL=1.0, K3=0.25, EPXM.=0.15, K24L=0.0, KK24=0.99
&LND4 UZS=0.000, LZS=2.250, S6W=1.00
&SN01 RADCOI1=.25, CCFAC=.25, EVAPS!1=0.6
&SN02 I€LEV=800.00,ELDIF=0.0,TSNOU=33.0
&SH03 11PACK=0.1, D6H=0.0010, UC=0.05, IDNS=0.1
&SN04 SCF=1.10, WHUL=1.0, RMUL=1.0, F=0.5, KUGI=8.0
&SN05 PACK=0., DEPTH=0.
&WASH JRER=2.2>KRER=1.50,JSER=1.8,KSER=0.30,JEI(!=1.8,KEIH=0.305TCF=12*1.0
BOD GM/L
SS GK/L
OPEN AREA
&HSCH ARFRAC=0.10, IMPKO=0.05, COVVEC=12*0.90
&YPTI1 PHPVEC=4. ,71.,3*0.0, PHIVEC=4.0,71. ,3*0.0
&YACR ACUP=30., ACUI=30.
&YRMR REPER=0.05, REIMP=0.08
&INAC SRERI=1758., TSI=106.
-RESID.AREA
&USCH ARFRAC=0.60, IHPKO=0.18, COVVEC=12*0.95
&YPTH Pt'.PVEC=4. ,71. ,3*0.0, PMIVEC=4.0,71. ,3*0.0
&YACR ACUP=70., ACUI=70.
&YRI1R REPER=0.05, REIMP=0.08
&INAC SRERI=1880., TSI=248.
COMMERCIAL
SWSCH ARFRAC=0.17, IMPKO=0.55, COVVEC=12*0.90
&YPTI1 P[1PVEC=4.,71. ,3*0.0,
&YACR ACUP=75., ACUI=75.
&YRMR REPER=0.05, REIHP=0.08
&II1AC SRERI=2658., TSI=266.
INDUSTRIAL
&USCH ARFRAC=0.13, IMPKO=0.75, COVVEC=12*0.90
&YPTM PHPVEC=4.,71.,3*0.0,
&YACR ACUP=80., ACUI=80.
&YRMR REPER=0.05, REIMP=0.08
&IIIAC SRERI=2753., TSI=284.
14 27
PHIVEC=4.0,71.,3*0.0
PMIVEC=4.0,71.,3*0.0
EVAP70
EVAP70
EVAP70
EVAP70
EVAP7Q
4
1
1
3
3
32
24
10
13
25
7
6
65
59
129
61
126
86
80
103
179
205
169
40
77
215
154
179
165
210
210
91
231
213
187
212
94
144
87
113
72
127
141
71
105
85
81
101
24
13-
7
34
51
&EF1D
&EHD
REND
SEND
&EMD
SEND
&END
&EHD
&EMD
SEND
&EIID
&END
&END
SEND
&END
&EMD
&END
&Ef!0
SEND
&END
&END
&END
SEND
&END
&EMD
&EMD
SEND-
SEND
&END
6EHD
&END
&EMD
&EMD
&EKD
31
35
21
22
29
217
-------
56.
57.
08.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80. '
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
104.
105.
106.
107-
108.
109.
110.
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
EVAP70
TEHP70
TEMP70
TEHP70
TEI1P70
TEI1P70
TEI1P70
TEMP70
TEHP70
TEMP70
TEHP70 <
TEMP70
TEMP70
TEMP70
TEMP70
TEMP70
TEHP70
TEHP70
TEMP70
TEHP70
TEHP70
TEHP70
TEMP70
TEI1P70
TEMP70
TEI1P70
TEHP70
TEHP70
TEMP70
TEMP70
4
4
4
10
4
4
10
2
8
12
3
4
5
3
8
6
1
10
12
3
8
16
2
24
20
34
26 8
20 5
16 -1
22 -7
5-12
2-15
3-11
-2-12
13 -4
16-13
22 16
24 0
14 -7
23 6
28 22
32 9
9 -1
0-16
-4-21
-1-14
0-21
13-14
14 0
30 11
34 25
33 16
32 18
38 30
36 14
24
6
3
34
25
13
9
14
11
22
20
22
8
29
33
43
65
52
65
52
32
28
25
43 23
37-11
-6-15
29 -7
32 3
39 13
35 32
34 22
29 19
33 22
30 14
18 4
11-11
12 -9
25 4
27 -5
39 15
40 11
18 4
21 -1
42 21
47 30
42 21
47 26
26 8
42 9
30 16
35 10
73
57
20
37
41
54
69
30
76
66
69
71
86
40
24
98
69
65
83
28
78
58
78
80
88
60
35 27
36 31
47 36
46 25
45 21
44 31
45 22
24 18
30 7
32 16
38 12
41 15
28 16
35 16
32 9
44 10
46 14
48 25
47 21
33 32
54 24
52 28
48 22
50 27
44 29
47 28
44 20
32 7
38 14
40
119
148
176
129
113
87
21
138
114
153
154
26
16
34
40
88
178
175
167
171
208
117
130
50
42 26
50 27
46 28
50 25
54 26
50 25
66 21
70 42
60 33
55 19
51 32
51 33
43 35
62 31
C8 30
71 48
57 33
43 33
42 38
44 38
50 32
68 28
62 37
68 36
77 42
80 50
83 55
82 56
85 51
171 197
195 222
207 241
132 229
211 209
27' 231
97 153
26 88
6 113
15 79
70 181
195 157
222 171
188 221
214 21
230 198
150 218
27 239
129 197
165 168
207 49
29 175
155 172
99 251
55 248
109
68 39 73 57
53 34 65 50
74 36 67 41
67 41 74 39
60 33 78 50
58 25 80 46
81 34 85 53
84 53 82 60
81 57 83 62
76 56 85 63
59 47 85 66
78 46 85 64
54 43 74 60
50 43 82 57
54 45 76 60
56 40 84 67
69 35 G5 62
82 46 76 52
82 53 70 44
84 50 61 46
87 62 75 45
85 57 80 49
64 54 79 58
80 56 82 51
69 50 71 41
62 42 69 49
54 38 77 45
80 40 81 62
76 60 91 70
197
209
155
111
221
223
221
145
161
120
232
239
47
31
200
199
204
201
154
172
193
132
129
94
232
188
89 70
90 65
81 58
69 51
80 43
81 46
85 65
76 60
78 54
87 52
89 56
90 61
90 67
88 69
77 60
83 58
86 65
71 61
72 50
72 45
76 44
78 49
78 46
83 52
89 63
90 64
87 70
90 67
83 68
170
128
110
184
167
163
153
175
192
148
206
144
37
109
189
165
94
140
170
142
192
161
108
81
131
90
81 58
87 55
76 53
71 52
81 49
82 52
82 50
80 58
84 57
85 59
85 56
86 52
85 59
89 63
87 66
83 55
83 46
74 60
90 60
79 49
80 43
76 54
76 48
86 45
90 51
91 63
90 59
84 58
79 59
16
141
157
125
143
137
28
11
2
33
102
10
114
115-
84
39
30
11
22
98
57
82
86
91
88
73 40
88 54
80 64
83 58
86 53
73 62
86 65
78 57
80 52
66 44
72 43
64 48
50 45
52 45
68 50
66 43
59 52
72 51
78 47
84 56
79 62
65 55
61 48
64 57
74 48
59 39
58 38
57 34
70 34
70
85
16
25
72
51
26
19
73
56
55
73
75
59
17
20
11
19
33
18
39
21
8
38
19
25
67 36 45
68 47 43
54 36 43
64 30 50
76 49 56
72 59 55
77 58 50
63 61 47
67 39 54
52 36 55
57 33 48
60 46 45
63 50 41
54 37 33
49 27 34
52 25 45
59 32 50
64 36 45
65 29 48
58 42 44
56 42 48
57 41 44
60 53 19
69 49 25
62 38 38
66 55 38
66 56 33
56 33 44
54 33 41
26 15
27 24
4 25
7 15
34 9
10 7
4 5
16 12
24 6
34 0
32 1
31 2
9 1
7 0
8 8
12 3
31 6
22 7
23 0
42 10
12 7
9 3
27 7
6 1
16 5
5
36 62 39
37 49 29
36 56 34
33 35 27
33 36 9
31 25 5
27 33 19
28 46 27
47 41 22
36 32 17
35 35 30
38 31 26
33 29 22
28 22-10
24 36-12
23 36 24
32 36 27
30 35 34
26 34 5
29 15 0
25 26 1
9 33 26
7 32 -1
10 16 -4
24 15 1
31 36 -2
28 20 -7
20 19-12
36 23 -8
218
-------
111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126.
127-
128.
129.
130.
131.
132.
133.
134.
135.
136.
137.
138.
139.
140.
141.
142.
143.
144.
145.
146.
147.
148.
149.
150.
151.
152.
153.
154.
155.
156.
157.
158.
159.
160.
161.
162.
163.
164.
165.
TEMP70
TEI1P70
WIND70
WIMD70
WIND70
HIND70
UIND70
UIND70
WIHD70
WIHD70
WIND70
WIHD70
WIND70
WIHD70
WIND70
U1HD70
UII1D70
WIND70
WIU070
WIMD70
WIND70
WIND70
WIND70
WIIID70
UIHD70
W1HD70
WIHD70
WIND70
WIND70
WIND70
WIHD70
WIND70
WIHD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
RAD70
30 16
38 21
72
60
211
156
144
132
274
394
230
127
163
197
79
230
218
238
293
214
151
322
206
298
163
269
214
166
235
211
329
226
250
177
194
217
200
250
231
196
224
233
201
103
251
172
150
161
151
138
269
280
265
274
100
329
410
283
420
221
125
149
293
266
228
206
190
110
94
149
238
211
338
305
300
238
218
214
293
370
362
230
120
256
288
324
118
311
302
79
101
332
249
181
203
370
302
335
345
284
112
373
398
362
385
48 20
46 22
218
300
228
322
170
254
259
317
254
226
115
166
300
360
281
62
139
242
281
307
142
89
194
175
211
434
298
230
190
125
115
152
36
44
432
423
415
353
166
419
376
446
477
287
495
499
499
474
461
267
215
527
411
72 60
175
336
322
211
338
331
269
391
415
187
346
401
415
252
204
199
262
324
415
379
298
211
355
197
262
250
262
254
250
221
173
586
347
600
425
279
552
464
612
620
507
408
55
599
525
550
652
149
40
190
212
434
74 64
78 65
418
242
269
238
262
180
300
300
348
290
259
211
362
281
156
259
118
331
194
211
322
245
166
115
300
310
175
190
293
221
281
369
485
635
710
684
727
653
629
429
663
202
419
166
81
145
369
749
696
625
669
651
490
93 73
175
331
276
96
94
101
180
230
290
262
173
242
274
173
221
242
170
336
127
134
214
151
250
286
175
228
86
310
338
218
228
359
775
619
649
691
706
726
671
621
688
475
386
430
344
572
543
604
775
147
697
734
88 68
88 64
206
142
281
310
120
194
281
305
199
41
46
101
199
211
324
170
245
166
262
266
62
120
182
120
158
139
134
110
158
238
108
513
646
651
375
762
668
607
527
429
714
696
671
440
501
430
709
686
232
207
700
715
700
85 58
70 44
163
151
190
156
55
115
144
190
180
166
101
65
142
254
238
242
62
180
84
194
65
211
206
156
72
206
274
108
290
314
84
680
622
700
407
549
600
490
403
595
550
557
550
570
567
459
637
540
162
387
632
608
382
67 40
108
194
238
166
89
305
338
187
235
346
156
250
173
230
245
118
115
120
120
226
298
77
226
274
197
293
204
108
228
139
397
389
287
444
497
75
460
537
483
565
538
156
88
70
155
485
85
485'
475
310
181
147
49 37
46 36
223
276
322
199
295
372
348
350
490
240
168
55
125
281
132
110
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0
1
0
0
0
0
0
0
329
242
424
339
198
421
278
255
401
412
263
335
361
341
252
281
229
70
187
104
159
43
48
132
179
144
310
163
194
313
0
0
3
1
0
0
0
0
0
0
140
197
278
136
301
297
158
169
255
201
162
174
236
241
145
186
41
105
110
184
226
84
78
37
42
118
112
129
224
0
0
6
4
0
0
0
0
1
0
182
147
91
76
74
51
74
105
39
181
131
86
28
37
197
201
68
89
120
193
176
88
180
28
32
26
207
122
165
206
0
0
10
9
0
0
0
0
4
0
223
-------
386.
387.
388.
389.
390.
391.
392.
393.
394.
395.
396.
397.
398.
399.
400.
401.
402.
403.
404.
405.
406.
407.
408.
409.
410.
411.
412.
413.
414.
415.
416.
417.
418.
419.
420.
421.
422.
423.
424.
425.
426.
427.
428.
429.
430.
431.
432.
433.
434.
435.
436.
437.
438.
439.
440.
71 1219
71 1229
71 1239
71 1249
71 1251
71 1252
71 1261
71 1262
71 1271
71 1272
71 1289
71 1291
71 1292
71 1309
71 1319
71 2 19
71 2 21
71 2 22
71 2 39
71 2 41
71 2 42
71 2 51
71 2 52
71 2 69
71 2 79
71 2 89
71 2 99
71 2109
71 2111
71 2112
71 2129
71 2139
71 2149
71 2159
71 2161
71 2162
71 2179
71 2181
71 2182
71 2191
71 2192
71 2201
71 2202
71 2219
71 2221
71 2222
71 2231
71 2232
71 2249
71 2259
71 2261
71 2262
71 2279
71 2289
71 3 19
0
0
0
0
0
0
0
0
0
1
0
0
3
0
0
0
0
0
0
4
3
1
3
0
0
3
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
Q
0
0
0
0
0
7
0
0
2
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
1
0
0
0
0
0
0
4
0
0
6
0
0
0
1
0
0
3
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
9
1
0
8
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
3
1
7
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
1
4
0
0
0
0
0
0
0
12
6
5
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
6
0
0
0
0
0
0
0
1
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
5
0
0
0
0
0
1
0
4
15
3
0
0
2
0
0
0
2
0
0
0
0
0
0
1
7
0
0
0
0
5
0
0
0
1
0
2
0
2
3
4
0
0
5
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
7
0
0
0
0
0
0
0
8
5
5
0
0
11
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
5
.13
7
0
0
14
1
0
0
0
0
224
-------
441.
442.
443.
444.
445.
446.
447.
448.
449.
450.
451.
452.
453.
454.
455.
456.
457.
458.
459.
460.
461.
462.
463.
464.
465.
466.
467.
468.
4G9.
470.
471.
472.
473.
474.
475.
476.
477.
478.
479.
479.1
71 3 29
71 3 39
71 3 49
71 3 51
71 3 52
71 3 61
71 3 62
71 3 71
71 3 72
71 3 89
71 3 99
71 3109
71 3119
71 3121
71 3122
71 3139
71 3141
71 3142
71 3151
71 3152
71 3169
71 3179
71 3181
71 3182
71 3191
71 3192
71 3209
71 3219
71 2229
71 3239
71 3249
71 3259
71 3269
71 3271
71 3272
71 3289
71 3299
71 3309
71 3319
/*
0
0
1
0
0
0
0
0
0
0
0
0
0
4
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
4
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
3
1
0
0
0
0
2
0
0
0
1
0
0
0
0
0
0
0
4
3
0
0
0
0
8
0
0
0
0
0
0
0
1
0
0
0
8
1
0
0
0
0
6
0
0
0
0
2
0
0
0
0
0
0
10
1
0
0
0
0
4
0
0
0
0
2
0
0
15
0
0
3
8
0
0
0
0
0
1
0
0
0
0
1
0
0
1
0
0
4
3
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
3
8
0
0
2
0
0
2
0
0
0
0
0
0
0
0
0
0
2
8
1
0
0
0
225
-------
APPENDIX E
NPS MODEL SOURCE LISTING
226
-------
1. //A12YOL JOB C * NONPOINT SOURCE POLL3TAHT LOADING (NPS) MODEL *
12. C * »
13. C ***************************************************************
14. C
1£* C DEVELOPED BY: flYDROCOMP, INCORPORATED
'6> C 1502 PAGE HILL ROAD
17< c PALO ALTO, CA. 94304
18' c 415-493-5522
19. C
20. C FOR: U.S. ENVIRONMENTAL
21. C PROTECTION AGENCY
22. C OFFICE OF RESEARCH
23. C AND DEVELOPMENT
24. C SOUTHEAST ENVIROSHEHTAL
25. C RESEARCH LABORATORY
26. C ATHENS, GA, 30601
27. C 404-546-358-i
28. C
29. C
30. C HPS - 8AIH PROGRAM
31. C
32. IMPLICIT BEAL(L)
33. C
34. DIMENSION HNAB(24) ,PAD(24),TESPX(24J ,WINDX(24),RftIN(96) ,
35. 1 IRAINfS'e) ,IBAD(12,31) f IE? AP (12, 31) , IBIN D (12, 31) ,
36. 2 ITEHP(12,31,2),3RAD(2») ,RADDIS(24),HIHDIS{24),
37. 3 AR10UT(28) , AR20(!T{28) ,CDVVEC(12) , REPERM (5,12) ,TCF(12) ,
38. 4 TOrAL(24) ,VMIS{24) ,V«AX(24) ,SD(24) ,P.AKGE(2«) ,AVER(24) ,
39. 5 REIMP«(5,12), ACUIR(5,12| ,PHPTA3(5,5, 12) ,PKITAB (5,5,12),
40. 6 ACOPH(5,12)
41. C
42. COHHDN /ALL/ Z5U,HY«IN,HYCAL, DPST, UNIT,TIHFAC,LZS, AREA ,RESB, SFLAG,
43. 1 RESB1 ,80SB,SRGK,INTF,RGX,RU7,B,UZSB,PERCB,RI3,P3,TF,
44. 2 KSPLB,LASr,PP.EV,rE1PX,IHR,IHRR,PR,RHI,A,PA,GWF,NOSY,
45. 3 SRER(5) ,TS(5) , LUDDS E(3, 5) , AR (5) ,QUALIN(3,5) ,NOSI,N05,
46. 4 KOSIM,UFL,UT«P,UST1(2,2),UNT2(2,2),OKT3(2,2) ,RH3T,
47. 5 HHT,DEPB,BOSBI, RES BI, P.ESBI1, ARUS,LKTS (5) ,IHPK(5) ,
48. 6 NLAHD,NQOAL,SI!ICH(20D,24) ,RECOUT (5) , FLOBT,SCALEF (5) ,
49. 7 SNOW,PACK,IPACK
50. C
51. COHHDN /LAND/DAY, PRTK,IBIK, IX, TWBAL, SGVf, GWS, K7,LIRC4,LKK4 ,ALTR (9) ,
52. 1 UZS, 17,, OZ5H,LZSM, ISFIL, INTER, SGB1 , DEC, DECI,riT(13) ,
53. 2 K24L,KK24,K24SL,EP,rFS,K3,EPXM,RESS1,RESS,SCEP,IRC,
5«. 3 SRGXn,KHPIN,KGPHA,HErDPT,CCFAC,SCEP1,SR3XT,RAIN,SRC,
227
-------
55. 4 SCF,IDMS,F,D3M,HC,HPACK,EVAPSN,HELEV,TSNOB,PET!1IN,
56. 5 DEHX,DEPTH, MONTH,THIS,PETHAX,ELDIF,SDEN,HINDX,INFT05,
57. 6 TSNBAL,ROBrOM,ROBror,RXB,ROITOH,ROITOT,YEAR,CONIT(7> ,
58. 7 IHFTOT,MNAM,RAD,SPCI,FORM (42).
59. C
60. CDMKDN /QLS/ WSNAHE(6),KRER,JEER, KSER,JSBR,TEMPCF,COVHAT(5,12),
61. 1 KEIK, JPIM, NDSR, ARP<5( , ARI (5) ,ACCP (5) ,ACCI(5) ,RPER(5) ,
62. 2 PHP(5,5) ,PNI(5,5| ,2SNOH,SNOWY,SEDTM,SEDTY,S5DTCA,
63. 3 ACFOLP(5,5) ,ACER5N(5| ,APOLP(5,5) ,AERSN(5) ,C3VER{5) ,
64. 4 APOLT(5,5) , ACEIH{5) ,AEIH(5) ,POLTM(5) ,POLTY(5),
65. 5 TEHPA,DOA,POLTCA(5) ,AERSNY(5),AEIKY(5),APOLPY(5,5),
66. 6 APOLIY(5,5) ,POLTC(5) ,PLTCAY(5) , ACPOLI (5, 5) , RIMP (^)
67. C
68. COMMON /LNDOOT/ EOSTOM, RINTDS, PITD«,RnTOH,EASTOH,RCHXOfl,PRTOM,
69. 1 SOHSN«,PXSNN,MELRAM,RADHEMrCOSMEK,CDR«KM,
70. 2 :RAIHM,S6HH,SSEG!l«,PACKOT,SEVAP«,EPTOM,SSPrO!1,
7t. 3
72. 4
73. 5 PETOT,SUHSN!f,PX5R!f,«ELRAY,RADMEY,CO!)MEy,CDRMET,
74. 6 BRAINY,SGNY,SSEGHY,PACK1,SEV&PY,EPTOT,NEPTOT,
75. 7 OZSMT,LZSMT,S3»Hr,SCEPT,PESSr,SRGXTT,TWBtHT
76. C
77. COSHOS /IHTR/ RTYPE {«,«) ,DTYPE (2) , GR AD,R» DDIS,TJINDrS,ICS,OPS,
7ft. 1 rEHPAY,DDAY,NDSIY, IHTRVL,WBWI,K»fL,SS,WNI,LI,SSI,
79. 2 EHUI,,KUGI,SEDrCY,P.SPERV (12) ,REIRPV(12) ,ACtIPV{12) ,
80. 3 ACUIV(12| tPNP(!Ar(12,5) ,PHI«AT (12, 5) ,PHPVEC(5) ,
81. H PHIVEC(5) ,ACOI,ACaP,REIHP,5EPER,PRINTR
82. C
83. EQUIVALENCE (ROSTOH,AR1OUT(1)),(T3NBOL,AR200r(1))
84. C
85. LOGICAL LAST, PREV
86. C
87. IKTEGEE 8GNPAY, BGHMON, BGHYR, ENDDAY, ERDMOK, ENDYR,
88. 1 DYSTHT, DYEND, YEAR, DAY, H, HYCAL, TIHE, PINT, PRIHTR,
89. 2 YR, CN, TF, DA, DY, BNIT, SNOW, LBTS, RECOOT, SPLAG
90. C
91. REAL IRC, KH, H»I, KV, K24L, KK24, IHPIL, INTER,
92. 1 IPS, ICS, K24EL, K3, NEPTOS, HEPTOT, IDNS, MPACK,
93. 2 JRER, KRER, JSEF, KSER,KEIH,JEIM, NELEV, KOGI,
94. 3 K1, KK4, IR=«, MELRAH, HELRAY, IPACK, IHPKO,
95. 4 INFTOM, INFTOT, IHPK, HKPIN, METOPT,
96. 5 KGPLB,KGPHA
97. C
98. REAL*8 BSNAME,RtYPE,UTYPE
99. C
100. C NAHELIST INPUT VARIABLES
101. C
102. NAMELIST /ROPT/ HYCAL, HYMIS, NLARD, HQUAL, SNOW
103. NAKELIST /DTYP/ OEIT, PINT, HNVAR
104. KAMFLIST /STRTX BGKDAY, BGH13N, BGNYR
105. NAMELIST /ENDD/ EKDDAY, ENDMON, ENDYR
106. NAHELIST /LND1/ HZSN, LZSN, INPIL, INTER ,IRC, AREA
107. KAHELIST /LND2/ NN, L, SS, HNI, LI, SSI
108. NAKELIST /LND3/ K1, PETMOL, K3 ,EPXH, K24L, KK24
109. KSKHLIST /LND4/ UZS, LZS, SG»
228
-------
110.
111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126.
127.
128.
129.
130.
131.
132.
133.
134.
135.
136.
137.
138.
139.
140.
111.
142.
143.
144.
145.
146.
147.
148.
149.
150.
151.
152.
153.
154.
155.
156.
157.
158.
159.
160.
161.
162.
163.
164.
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
.C
C
C
C
C
C
C
C
C
C
C
C
C
KAEE
NABS
KANE
NAME
SAME
NAME
NAME
NAME
NAME
NAME
KAKE
SAMS
NAME
NAHE
N
HYCAL
HYMIN
UNIT
NLAND
NQUAL
SHOW
MSVAR
PINT
BGNDAY
ENDDAY
UZSN
LZSN
INFIL
INTER
I EC
AREA
NN
NNI
L
LI
SS
SSI
K1
K3
PEIMUL
K24L
KK24
UZS
LZS
saw
RADCON
CCFAC
SCF
r /SN01/ P.ADCOK, CCFAC, EVAPSN
P /SK02/ MELEV, ELDIF, r5NO»
P /SN03/ MPACK, DGM, HCf IONS
P /SN04/ SCF, SMUL, RMUL, F, KUGI
C /SN05/ PACK, DEPTH
T /WSCH/ ARFRAC ,IMPKO, CDVVEC
P /TPTM/ PKPVEC, PSIVEC
T /MPTB/ PMP«AI,PHIHAT
P /WASH/ JREB, KEER, JSE8 ,SSER, JEIM, KEIH, TCP
I /YACR/ ACOP,ACOI
T /MACR/ ACOPV,ACniV
T /YRHR/ HFPEB, REIMP
T /MRMR/ REPEPV.KEISPV
T /INAC/ SEFRI, TSI
NAHELIST INPOT PARAMETER DESCRIPTION
INDICATES TYPE OF SIKULAriDN RUN
1 HYDEOLOGIC CALIBRATION
2 SEDIMENTS ASD QUALITY CALIBRATION
3 PRODUCTION HUN (PRINTER 30TPUT)
4 PRODUCTION RON (PRIHTER > W/0 HEADINGS OUTPUT ON UNIT 4)
MINIMUM FLOW FOP OUTPUT DURING A TIME INTERVAL (CFS, CHS)
ENGLISH(-I), METRIC(1)
EUHBER OF LAND TYPE USES IN THE WATERSHED
NUMBER OF QUALITY CORSriTUENTS SIMULATED
(0) SNOWMELT EOT PERFORMED, (1) SNOWRELT CALC'S PERFORMED
HONTHLY VARIATION IN ACCUHULATIOH RATES, REMOVAL RATES,
AND POTENCY FACTORS USED (1), OH NOT OSED (0)
INPUT PRECIPITATION IN INTERVALS OF 15 HIN. (0), OR HOURLY (1)
BGNKON, BGNYR : DATE SIMULATION BEGINS
ENDMON, ENDYK : DATE SIMULATION ENDS
NOMIMAL UPPER 7,ONE STORAGE (IN, MM)
NOMINAL LOWSE ZOKE STORAGE (IN, MM)
INFILTRATION BATE (IN/HR, KS/HR)
INTERFLOW PARAMETER, ALTERS RUNOFF TIRING
INTERFLOW RECESSION RATE
WATERSHED AREA IN ACRES
MANNING'S N FOR OVERLAND PERVIOUS FLOW
MANNING'S N FOP OVERLAND IMPERVIOUS FLOW
LENGTH OF OVEELAKD PEKVIOUS FLOW TO CHANNEL (FT, M)
LENGTH OF OVERLAND ISPERVI3U3 FLOW TO CHANNEL (FT, M)
AVERAGE OVERLAND PERVIOUS FLOH SLOPE
AVERAGE OVERLAND ISPEEVIDUS FLDW SLOPE
RATIO OF SPATIAL AVERAGE RAINFALL TO GAGE RAINFALL
INDEX TO ACTUAL EVAPORATION
POTENTIAL EVAPOTPANSPIRATION MULTIPLICATION FACTOP
FRACTION OF 3ROUNDWATER RECHARGE PERCOLATING TO DEEP
GROUNDKATER
GROUNDKATER SECESSION RATE
INITIAL UPPER ZONE STORAGE (IN, SM)
INITIAL LOWER ZONE STORAGE (IN, MM)
INITIAL GROUNDWATFR STORAGE (IS, MM)
CORRECTION FACTOR FOR RADIATION
CORRECTION FACTOR FOR CONDENSATION AND CONVECTION
SNOW CORRECTION FACTOR FDR RAISGAGE CATCH DEFICIENCY
229
-------
165.
166.
167.
168.
169.
170.
171.
172.
173.
17«.
175.
176.
177.
178.
179.
180.
181.
182.
183.
184.
185.
186.
187.
188.
189.
190.
191.
192.
193.
191*.
195.
196.
197.
198.
199.
200.
201.
202.
203.
201.
205.
206.
207.
208.
209.
210.
211.
212.
213.
21«.
215.
216.
217.
218.
219.
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
ELDIF
IDNS
F
DGM
KC
MPACK
EVAPSN
MSLEV
TSNOW
PACK
DEPTH
ARFRAC
IMPKO
COVVEC
PMPVEC
PMIVEC
TCF
JRER
KRFR
JSER
KSER
3EIH
KEIK
ACUI
ACUP
REIHP
REPER
SRERI
TSI
REAE
REAr
REAP
READ
RE AC
REAE
READ
READ
REAP
IF <
QSNO
EEAE
REAE
PEAD
READ
REAP
20 READ
DO 3
30 READ
DO 1
READ
READ
AR (I
ELEVATION DIFFERENCE FROM IE«P. STATION TO KEAH SEGHEHT ElEVA
(1000 FT, Kf!)
DENSITY OF NEH SNOW AT 0 DEGREES F.
FRACTION OF SEGMENT WITH COMPLETE FOREST COVEB
DAILY GROUND MELT (IN/DAY, MS/DAY)
MAXIMUM WATER CONTENT OF- SHOWPACK BY WEIGHT
ESTIMATED WATER EQUIVALENT OF SNOWPACK FOR COMPLETE COVEPAGE
CORRECTION FACTOR FOR SNOW EVAPORATION
MEAN ELEVATION OF WATERSHED (FT, H)
TEMPERATURE BELOW WHICH SNOW FALLS (F, C)
INITIAL WATER EQUIVALENT OF 5NOSPACK (IB, HH)
INITIAL DEPTH OF SNOHPACK (IS, MH)
PERCENT OF A GIVEN LAND TYPE OSE
PERCENTAGE OF IMPERVIOUS AREA FOR A GIVEN LAUD TYPE USE
MONTHLY COVER COEFF. FOR A 3IVEN LAND TYPE OSE
POTENCY VECTOR FOR A GIVEN LAND TYPE - PERVIOUS AREAS
POTENCY VECTOR FOR A GIVEN LAND TYPE - IMPERVIOUS AREAS
TEMPERATURE CORRECTION FACTOR RELATING RUHOFF AND
AIR TEMPERATURES
EXPONENT IN RAINDROP SOIL SPLASH EQUATION
COEF. IN RAINDROP SOIL SPLASH EQUATION
EXPONENT IN KA?H OFF FUNCTIOS FOR PERVIOUS AREAS
COEF. IN HASH OFF FUNCTION FOR PERVIOUS AREAS
EXPONENT IN BASH OFF FUNCTION FOR IMPERVIOUS AREAS
COEF. IN BASH OFF FUNCTION FOR IMPERVIOUS AREAS
ACCUMULATION EATES - IMPERVIOUS AREAS
ACCUMULATION RATES - PERVIOUS AREAS
REMOVAL COEF. -IMPERVIOUS AREAS
REMOVAL COEF. - PERVIOUS AREAS
INITIAL AMOUNT OF FINES AVAILABLE FOR TRANSPORT
INITIAL AMOUNT OF SOLIDS AVAILABLE FOR TRANSPORT
(5,U520) (WSNAME(I) ,1=1,6)
(5,HOPT)
(5,DTYP)
(5,STRT)
(5,EHDD)
(5,LND1)
(5,LND2)
(5,LND3)
(5,LNDH)
IF (SHO» .LT. 1) GO TO 20
J=1,NQUAL
30 READ (5,«060) (QUALIN (I,J),1 = 1,3| ,CUNIT(J)
DO 100 II=1,NLAND
READ (5,i»060) (LNDUSE (K,II) ,K=1, 3|
230
-------
220. IHPK(II) *IHPKO
221. DO «0 IJ=1,12
222. HO COV«fcT(II,IJ)=EOVVEC(IJ)
223. IF, (RNVAP.EQ.1) GO TO 60
22U. C
225. C READ INPUT DATA OF ACCHMOlAriOH RATES, REMOVAL RATES, AND
226. C POTENCY MATRICES HITHOUT BSSrifLY VARIATION
227. C
228. RFAD (5.YPTB)
229. READ (S,YACR) ,
230. BEAD (5,YB«R)
231. DO 50 IJ=1,KQUAL
232. PMPTAB(IJ,II,BGNHON)=PKPVEC(IJ)
233. 50 PHITAB(IJ,JI,BGSBO»1) = PHIVEC(IJ)
23«. ACUPB{II,BGNMON)=ACnP
235. ACOIH(II,BGN«ON)=ACOI
236. , a RSPSRH(II,B3fIKOR) =PEPER
237. , REIHPH(II,BGHHON)=REIHP
238. 60 TO 90
239. C
2<»0. C 'READ IHPOT DATA OF ACCOKULAriOH RATES, REKO'/Ai RATES, AND
2*1. C POTENCY HftTRICES HITH HOKtHLY VARIATION
242. C
2H3. 60 READ (5,HPT«)
2«U. READ (5,HACR)
2«5. READ (5,BRKR)
2U6. DO 70 IJ=1,NQOAL
247. DO 70 HN=1,12
248. PHPTAB(IJ,II,BH)=P«rBAT(BN,IJ)
219. 70 PHITAB(IJ,II,BN)=PKIHAX(K»,IJ|
250. DO 80 BN=1,1-2
251. ACUPB(II,HK)=ACOPV(MK)
252. ACUIB(II,HN)=ACUIV(f!N)
253. REPERH(II,HK)=RBPERV(HN)
25U. 80 REIMPR(II,HN)=REIHPV(KH|
255. 90 CONTINUE
256. READ (5,INAC)
257, SRES(II)=SRERI
258. TS(II)=TSI
259. 100 CONTINUE
260. IF (ONIT .EQ. -1J GO TO 120
261. DEPK=UNT1(2,1)
262. WHGT=ONT1{1,1)
263. WHT=UNT2{1,1)
261. OFt=UNT2(2,1)
265. OTHP=ONT3(1,1)
266. ARUN=ONT3(2,1)
267. KOHT=1
268. GO TO 130
269. 12C DEPH=ONT1(2,2)
270. «HGT=ONT1(1,2)
271. KHT=Ut!T2(1,2)
272. OP1=ONT2(2,2)
273. OTBP=ONT3(1,2)
274. ARON=ONT3(2,2)
231
-------
275. KUNT=2
276. C
277. C PRINTING OF TITLE PAGE AND INPBI PARAMETERS
278. C
279. 130 WRITE (6,4070)
280. WRITE (6,1080) (HSNAHB(I),1=1,6),ARON,APEA
.281. WRITE (6,4090) ARUN, ARON, ABDK
282. AflPT=0.0
283, ARIT=0.0
284. DO 140 I=1,NLAND
285. TEH=ARES*AR (I)
286. ARP(I)=TE«*(1.-IMPK(I)|
287. ARPT=ARPT+ARP(I)
288. ARI(I)=TEK*IMPK(I)
289. ARIT=ARIT*AF,I(I)
290. AR(I)=AR(I)*100.
291. PER=IHFK(I)*100.
292. WRITE (6,4100) (LNDUSE(KK, I) ,KK=1,3| ,AR(I),TKM,RRP(I),ARI (I), PER
293. AR(I)=TEK
29ft. 140 CONTINUE
295. A=ARIT/AREA
296. WRITE (6,«110) A
297. IF (ABS((ARIT+ARPT-AREA)/AREA) .LE.0.001) GO TO 150
298. WRITE (6,4120)
299. GO TO 1600
300. C
301. C PRINTING OF SIHBLAriON CHAPACTERISTICS
302. C
303. 150 IZ=BSNHON*2-1
304. IX=IZ+1
305. IP=EKDMOK*2-1
306. IQ=IP*1
307. NQI=NQUALO I f
308. IF (PINT.EQ.1) PRINTR=60
309. WRITE (6,4130) (RTYPE (HlfCAL,!) ,1 = 1 , 4) , MNAB (IZ) ,HN AH(IX) ,BGNDAY,
310. * BSNYR,KNAR(IP),NNAS(IQ),ENDDAY,ENDYS.PRINTR,IBTRVL,
311. * QSNOW,nTYPS(KOND ,arYPE(KONT} ,OFL,
312. * RY«IN,NQI, (OUALIH(I,J) ,1=1,3) ,J=1,NQOAL)
313. WRITE (6,4140) INTER, IRC,INPII,,NH, L, SS, NKI,LI,SSI,K1,
314. * PETMOL,K3,EPXH,K24L,KK24,UZSN,LZSN
315. IF (SNOW.EQ.1) WRITS (6,4150) FADCON,CCFAC,EVAPSK,HELEV,
316. * EI.DIF,TSNOH,!1PACK,DGH,HC,IDNS,SCF,
317. * »BOL,RMOL,F,KOGI
318. WRITE (6,4160) JRER,KRER,JSER, KSER,JEIH,KEIH
319. IF (BNVAR.EQ.1) GO TO 200
320. C
321. C PRINTING OF ACCOHOLATIOS RAIE3, EEHOVAL P.AIES,
322. C AND POTENCY FACTORS WITHOUT MONTHLY VARIATION
323. C
324. DO 160 I=1,NLAND
325. 160 WRITE (6,4230) (LNDOSE(K,I) ,K = 1,3) ,ACOPM(I.BGNHON) ,ACWIR(I,BGNHON)
326. WRITE (6,4010)
327. DO 170 1=1,KLAND
328. 170 WRITE (6,4240) (LNDOSE(KK,I),KK=1,3),REPERM(I.BGNSON),
329. * REIMPH(I,BGN«ON)
232
-------
330. WRITE (6,4250) ( (LNDDSE (KK,I) ,KK = 1 ,3) ,1=1 ,'NLAND)
331. DO 180 1=1 ,NQUAL
332. 180 WRITS (6,4260) (Qt)ALI.H( J,I) , J=1, 3) ,
-------
385. LZSK = LZSN/KMPIN
386. INFIL= INFIt/BMPIH
387. i = L*3.281
388. II = LI*3.281
389. UZS = UZS/MKPIN
390. LZS = LZS/HEPIN
391. SGW = SGW/MKPIN
392. ICS = ICS/MBPIN
393. OPS = OFS/MMPIN
394. IPS = IFS/HMPIN
395. EPXK = EPXB/MMPIK
396. AREA = AREA*2.471
397. DO 3HO 1=1,NLAND
398. >R (I)=A8(I) *2.471
399. ARP(I)=ARP(I)*2.it71
«00. AEI(I)=ABI{I) *2.«71
i»01. SREP(I)=SRER(I)*KGPHA
l»02. TS(I)=T3(I) *K6PHA
403. IP (KNVRR.6T. 0) GO TO 320
«0«. RCUPH(T,3GNMON)=ACHPM(I,BGNKDH)*KGPHA
405. ACOr«(I,BSNHON)=&CUIM(I,BGNHONJ *K3PHA
406. 60 TO 340
407. 320 DO 330 J=1,12
408. ACUPfH!,^)=ACOPB(I,J) *KGPHA
409. 330 ACOIM (I, J) =ACUIK(I, J) *KGPHA
410. 340 CONTINUE
411. DO 345 1=7,37,6
412. 345 FOPH(I)=ALTR{2)
413. IF (SHOW. LT.1) GO TO 350
414. ELDIF = ELDIF/0.3C48
415. DGK = DGfl/MMPIN
416. BELEV = HELEV/0.3048
417. TSMOH = 1.8*TSROH + 32.0
418. PACK = PACK/MPIH
419. DEPTH = DEPTH/MMPIN
420. C
421. C ADJUSTMENT OF CONSTANTS
422. C
423. 350 H = 60/INTRVL
424. TIMPAC = INTBVt
425. INTRVL = 24*H
426. ARIT=0.0
427. PRER=KPES*H**(JRER-1.0)
428. KSER=KSER*H**(JSES-1.0>
429. KEIH=KEIM*R**(JEIM-1.0)
430. DO 355 I*1,MQOAI.
431. IF (COKIT(I).EQ.TIT(I)) CONIT(I) =COHIT (7)
432. 355 IF (CUNIT(I).EQ.CUNIT (6)) SCALEF(I)=1000.
433. IF (NQU&L.EQ.5) GO TO 357
434. II=11 + NQUAI.*6
435. DO 356 1 = 11,40
436. 356 FORM(I)=ftLTR(1)
437. 357 !=NQUAL+4
438.
439. J=0
234
-------
440. DO 358 1=15,39,6
441. 0=J+1
442. IF(SCALEF(J).LE.2.) GO TO 358
443. FORH(I)=ALTR(3)
444. 358 CONTINUE
445. C
446. C CONVERT ACCUMULATION RATES INTO TOVS/ACRE/DAY
447. C
448. DO 380 1=1,ELAND
449, TS(I)=TS(I)/2000.
450. SRER{I)=SRER(I)/200C.
451. IF (MNVAP.GT.0) GO TO 360
452, ACIJPH(I,BGNMON) =ACUPK (I, BGNKDS) /2DDO.
453. ACUIM(I,BGNKON)=ACUIK(I,BSNKON)/2000.
454. GO TO 380
455. 360 DO 370 J=1 ,12
456. ACUIK(I.,J)=ACOIM(I,>1) /200C.
457. ACUPH (I, J)=ACUPH(I,J)/2000.
458. 370 CONTINUE
459. 380 CONTINUE
460. PA=1.0-A
461. ISC4=IRC**(1.0/96.0)
462. L3RC4=1.Q-IP.C<4
463. KK4=KK24**(1.0/96.0)
464. LKK4= 1.0 - KK4
465. C
466. IF ((24.*60./TTKFAC) .3T. 100.) GO TO 390
467. GO TC UOO
468. 390 LIRC4 = LlRC'l/3.0
469. LKK4 = LKK4/3.0
470. 400 DEC= 0.00982*((NN*L/SQRT(SS))**0.5)
-------
495. C BEGIN YEARLY LOOP
496. DO 1590 YEAR=BGNYRiENDYS
497. MNSTRT = 1
498. MNEND = 12
499. IF (YEAR ,EQ. BGNYF) HNSTRr = BGNMON
500. IF (YEAR .EQ. EKDYR) HNEN-D = ENDMON
501. C
502. C
503. C EVAP, TEMP(HAX-MIN), BAD, AND HIND DAIA IHPUT
504. C
505. C
506. C
507. DO «10 DA = 1,31
508. 410 READ (5,4050) (IEVAP(MN,DA), HN =1,12)
509. C
510. C
511. DO 420 DA = 1,31 {
512. 420 READ(5,4040) ((ITEMP{HN,DA,IT> , IT=1,2),HN=1,12)
513. C
51ft, IF (SNOW . LT. 1) GO TO 450
515. DO 430 DA = 1,31
516. 430 READ(5,4050) (IHIND (UN,DA) , HK=1,12)
517. C
518. DO H«0 DA =1,31
519. HHO READ (5,tC50) (IRAD (PIN,DA) , HH=1,12)
520. C
521. t50 IF (UNIT .EQ. -1) GO TO «90
522. DO 480 DR=1,31
523. DO i»70 HN=1,12
521. IEVAP(HH,DA) = IEVAP («N, DA) *3, 937
525. IF (SNOW.EQ.1) IWIND(HH,DA) = IWIND(HK, DA) *0.6214
526. DO «60 IT=1,2
527. U60 ITEBP(MK,DA,IT) = 1. 8*irEMP (MN, DA, IT) + 32.5
528. U70 CONTINUE
529. 180 CONTINUE
530. C SAV THIN OF JAN 1 ON 11/31
531. tt90 ITEHP(11,31,2), = ITEMP(1,1,2)
532* C
533. C
53U. C
535. C BEGIN BDNTHtY 100P
536. 500 DO 12«0 HONTH=MNSTRT,HNEND
537. C
538. C ASSIGN CURRENT MONTHLY VALUES OF ACCUMULATION RATES,
539. C REMOVAL RATES, AND POTENCY FACTORS
5<»0. C
541. IF '(HYCAL. EQ. 1) GO TO 530
542. IF (KNVAR.EQ.0.AND.MONTH.NE.BGKSON* GO TO 530
543. DO 520 I=1,KLAND
544. DO 510 J=1,NQUAL
545. PHP (J,I)=PMPTAR(J,I,MONTH)
546. 510 PHI(J,I)=PBITAB(J,I,HONTH)
547. ACCP(I)=ACUPH(I,MONTH)
548. ACCI(I)=ACUIK(I,I50NTH)
549. RPER(I)=REPEFB(I,HONTH)
236
-------
550. 520 PIHP(I)=REIMPM(I,MONTH)
551. 530 CONTINUE
552. TEHPCF=TCF(MONTH)
553. C
551. C ZEROING OF VARIABLES
555. C
556. DO 540 1=1,28
557. C ZFROING OF THE FIBST 28 VARIABLES CONTAINED IN COMSDN/LHDOtir/
558. 540 AR10UT(I)=0.0
559. PRTM=0.
560. ROBTOH=0.
561. INFTOH=0.
562. DO 560 J=1,NQUAL
563. DO 550 1=1,KLAND
564. *POLP(I,J)=C.O
565. APOLI (I,J)=0,0
566. 550 CONTINUE
567. POLTCA(J)=0.0
568. 560 POLTC(J)=0.0
569. DO 570 1=1,NLAND
570. AERSN(I)=0.0
571. AEIM(I)=0.0
572. 570 CONTINUE
573. NOSIM=0
574. NOS=0
575. TEHPA=0.0
576. DOA=0.0
577. SEDTCA=0.0
578. IX=2*MONTH
579. IZ=IX-1
580. RECOUT(1)=YEAR
581. DYST3T = 1
582. { IF (MOD(YEAR,4)) 590, 580, 530
583. 580 GO TO {630,610,630,620,630,620,630,630,620,630,620,630},
584. *HOSTH
585. 590 GO TO (630,600,630,620,630,620,630,630,620,630,620,630),
586. *KONTH
587. 600 DYEND = 28
588. GO TO 640
589. 610 DYEND = 29
590. SO TO 640
591. 620 DYEND = 30
592. GO TO 640
593. 630 DYEND =31
594. C
595. 640 IMDENJ)=DYEND
596. IF (YEAR .HE. BGNYR) GO TD 650
597. IF (MONTH . NE. BSNMON) GO TO 650
598. DYSTRT = BSNDAY /
599. C
600. 650 IF (YEAR .NE. ENDYE) GD TD 660
601. IF (MONTH . NE. ENDfiON) GD TO 660
602. DYEND = ENDDAY
603< c BEGIR DAILY LOOP
604! 660 DO 990 DAY=DYSTRT,DYEHD
237
-------
605. TIH3 = 0
606. RAINT * 0.0
607. EP = PBTMUL*IEVAP(MONTH,DA?)/1000.
608. DO 670 I=1,INTRVL
609. IRAIN(I) = 0
610. RAIK(I) = 0.0
611. 670 CONTINUE
612. C
613. C
611. C CHECK TO SEE IP SNOHMELT CALC'S WILL BE DONE - IF fES THEN
615. C CALCULATE CONTINUOUS TEMP, HIND, HAD AND APPLY CORSES HtJlT
616. C FACTORS
617. C
618. IF (SNOH.LT.1) GO TO 790
619. HINF=(1.0-F) + F*(.35-.03*KUGI)
620. C WIHF REDUCES WIND FOR FORESTED AREAS
621. C
622. C /* KUGI IS INDEX TO DNDERGSDSTH AMD FOREST DENSITY,*/
623. C /* WITH VALUES 0 70 10 - HIND IK FOREST IS 35* OF */
621. C /* BIND IN OPEN WHEN KUGI=0, AND 5* WHEN KOGI=10 - */
625, C /* WIND IS ASSUMED MEASURED AT 1-5 FT ABOVE GROUND */
626. C /* OR SNOW SURFACE */
627. C
628. WIND = IWIND(MONTH,DAY)
629. THIN = ITEHP(MONTH,DAY,2)
630. DEWX = THIN - 1.0*ELDIF
631. ER = IRAD(HONTH,DAY)
632. C PEWPr ASSUMED TO BE MIK TEMP AND OSES
633. C LAPSE RATE OF 1 DESREE/1000 FT
634. C
635, C CALCULATE CONTINUOUS TEMP, BIND, AND RAD
636. 680 CONTINUE
637. TGRAD = 0.0
638. DO 780 1=1,24
639. IF (1-7) 740, 690, 700
640. 690 CHANGE = ITEMP(MONTH,DAY,1) - TEMPI
641. 700 IF (1-17) 740, 710, 740
642. C IHDEND IS LAST DAY OF PRESENT MONTH
6«3. 710 IF (DAY .NE. IMDEND) CHANGE = ITEMP (MONTH, DAY + 1 , 2) - TEMPI
644. IF (KONTH-12) 730, 720, 730
645. 720 IF (DAY .EQ. IMDEND) CHANGE = ITEMP (11,31,2) - TE8PI
646. GO TO 740
647. 730 IF (DAY .EQ. IM0END) CHANGE = ITESP(KONTB+1,1,2) - TEMPI
648. C
649. 740 IF (ABS(CHASGE)-O.OOI) 750, 750, 760
650. 750 TGRAD = 0.0
651. GO TO 770
652. 760 TGRAD = GRAD (I) *CHANGE
653. 770 TEKPX(I) = TEMPI + TGRAD
654. TEMPI = TEMPI * TGRAD
655. IP (SNOH.LT.1) GO TO 780
656. WINDX(I) = WMUL*WIND*HINF*WINDIS(I)
657. RAD (I) = RMUL*RR*KADCON*RADDI5(I)
658. 780 CONTINUE
659. IF (SNOW. LT. 1) GO TO 950
238
-------
ion ,.„ 15-HIH PPECIP INP0T
79° IF (PINT.FQ.1) GO TO 850
662. j=0
663. 800 J=J+1
664. JK = J*12
665. JJ * JK - 11
***• RERD I5'"020) *R» HO, DY, CN, (IRAIK(I), I=JJ,JK)
°67' IF (UNIT .EQ. -1) GO TO 820
668« DO 810 I=JJ,JK
f6,9/ o A IRAIN(I) = IRAIN(I|*3.937 *0.5
670. 810 CONTINUE
671. 820 IF (CN .EQ. 9) J=9
6/2. YR = YB + 1900
673< IT * (YEAR-TS) t (BDKrH-HO) * (DAY-DY) * (J-CN)
6""*. IF (IT .EQ. 0) GD T3 830
675' BRITS (6,4000) J, BOSTH, DAY, YEAR, CH, «0, DY, YR
676. SO TO 1600
677. 830 IP (J.IT. 8) GO TO BOO
678. DO 840 I*1,IKTP.VL
679. BAIN(I) = IRAI» =IRAIH (!•> *3. 937 * 0. 5
693. 870 CONTINUE
694. 880 IF (CN .EQ. 9) J=9
695. YR = YR * 1900
696. IT = (YEAR-YF) * (HORTH-flO) + (DAY-DY) * (J-CN)
697. IF (IT .EQ. 0) GO T3 890
698. KRITB (6,4000) J, HOSTH, DAY^ YEAR, CN, BO, DX, YR
699. GO TO 1600
700. 890 IF(J.LT.2) GO TD 860
701. C
702. DO 910 I=1,INTRVL,4
703. TEH = IRAIN(I) *(K1/100.|/4.
704. DO 900 K=1,4
705. 900 RAIN(I+4-K)=TEH
706. RAINT = RAINT + PAIN(I)
707. 910 CONTINUE
708. C
709. 920 IF (RAINT) 930, 930, 940
710. C
711. C USE PAIK LOOP IF HOISTURE STORAGES ARE NOT EHPTY
712 C
733! 930 IF «RBSS .LT. 0.001). OR. (SR3XT .tT. 0.001)) GO TO 980
714. C
239
-------
715. C RAIN LOOP
716. C
717. C CONDITIONAL BRANCHING TO CALCULATE HOURLT TEMPERATURES
718. C
719. 940 IP (SNOW. IT. 1) GO TO 680
720. 950 CONTINUE
721. C
722. C CALCULATE COVEE FUNCTION FOR THE PERVIOUS
723. C AREAS HITHIH EACH LAND TIPE OSE
724. C
725. HTX=MONTH
726. NTX=MONTH+1
727. IF (NTX. GT.12) KTX=1
728. DO 960 I=1,NLAND
729. COVER(I) =COVMAT(I, MIX) + (FLOAT(DAVI /FLOAT (DYESD))*
730. 1 (COVHAT(I,KTX) -COVMAT(I,HIX| )
731. 960 CONTINUE
732. DO 970 I=1,INTBVL
733. TIHE = TIKE + 1
73<». TF = 1
735. PR - BAIN(I)
736. C
737. IHIN = MOD(TIME,H)
738. IHH = .(TIMS - IMIN)/H
739. IMIN = TIHFAC*IMIN
740. IX = 2*KONTH
741. 12 = IX - 1
7U2. CALt LANDS
7«3. IF (HYCAL.EQ. 1) GO TO 970
7U4. CALL QUAL
7«5. 970 CONTINUE
7«6. NDSR=0
7«7. C
7'I8. GO TO 990
7«9. C
750. C NO BAIN LOOP
751. C
752. 980 TF = I8TFVL
753. PR = 0.0
754. P3 = 0.0
755. RESB1 = 0.0
756. IHIN = 00
757. IHR = 24
758. IX = 2*MONTH
759. 12 = IX - 1
760. NDSB=NDSR+1
761. CALL LANDS
762. IF (HYCAL.EQ. 1) SO TO 990
763. CALL QUAL
764. C END DAILY LOOP
765. 990 CONTINUE
766. C
767. C «ONTHLr SUMMARY
768. C
769. WRITE (6,4320) UNAK(IZ),HNAH(IX),fEAR
240
-------
770. UZSOT=UZS
771. tZSOT=l,ZS
772. SGHOT=SGH
773. SCEPOT=SCEP
774. 8ESSOT=RESS
775. SRGXTO=SPGXT
776. TWBALO=T«BAL
777. TSNBOL=TSNBAL
778. PACKOT=PACK
779. IP (UNIT.EQ.-1) GO TO 1010
780. DO 1000 1=1,28
781. C CONVERSION TO METRIC UNITS OF THE FIRST 28 VARIABLES
782, C CONTAINED IS COHHON/LND30I/
783. AB100T(I)=AR10WT(I)*HMPIN
784. 1000 CONTINUE
785. 1010 RRITB (6,4330) I>EPB, ROSTOPI, RINTOB, RITOW, BASrOK,RUTOM,RCHTOH,PHTOH
786. IF (SNOB.IT.1) GO TO 1030
787. COVR=100.
788. IF (fACK.LT.IPACK) COVE= (PACK/IPACK) *100.
789. IF (PACK.GT.0.01) GO TO 1020
790. COVR=0.0
791. SD2N=0.0
792. 1020 WHITE (6,4340) SfJMSKM,PXSN«, KELRAM,R ADMEH,CDNMEH, CDRREH,CRAIHM,
793. * SGMM,SNESHM,PACKOr,SDEN,COVR,SEVRPH
794. 1030 HRITE (6,4350) EPTG«, NEPTOM,UZS3r,LZSOT,SGHDT,SCEPOT,RESSOT,
795. * SRGXTO,TWBA10
796. IF (SNOH.GT.O) HRITE (6,4363) TSNBOI.
797. IF (HYCAL.EQ.1) GO TO 1230
798. C
799. C OUTPUT OF SEDIKEKTS DEPOSIT ON GROUND AT HONTH'S END
800. C
801. URITE (6,4370) WHT,ARUN
802. TEH1=0. 0
803. TEM2=0.0
804. TEM3=0.0
805. TEH4=C.O
806. DO 1050 I=1,HLAND
807. TEH=SRER(I)*(1-IHPK(I)) +TS (I) *IKPK (I)
808. BHFUR1 = (AR(I)/AREA)*(1-IMPK(I))
809. KHFUN2=(AR(I)/AREA)*IBPK(I)
810. TEM1=TEH1+SRER(I)*WHFUN1
811. TEH2=TEM2*TS(!)*WHFUN2
812. TEM3=TE«3*HHFUN1
813. TEH4=TE«4»»HFDN2
814. IF (UNIT.GT.-1) 30 TO 1040
815. HRITE (6,4390) (LNDUSE(IK,I) ,IK=1,3) ,TEB,SRER(I) ,TS(I)
816. GO TO 1050
817. 1040 TEH5=SEEP(I)*2.2«
818. TF«6=TS(I)*2.24
819. TEH=TEH*2.24
820. WRITE (6,4390) (tKDUSE(IK,I),IK=1,3),TEM,TEM5,TEH6
821. 1050 CONTINUE
822. IF (NIKND. EQ. 1) SO TO 1070
823* IF (TEM3.GT.O.O) TFK1=TEMl/rE!l3
824. IF (TEH3.I.E. 0.0) TEM1=0.0
241
-------
825. IF (TEM4.GT.O.O) TEH2=TEM2/TEP14
826. IF (TSM4.LE.O.C) TEM2=0.0
827. TEM=TEK1*(1-A)+TSM2*A
828. IF (UNTT.LT.1) GO TO 1060
829. TEM=TE?i*2.24
830. TEH1=TEM1*2.24
831. TEM2=TEH2*2.24
832. 1060 WRITE (6,4380) TEM,TEH1,TEH2
833. C
834. C OUTPUT MONTHLY SEDIMENTS LOSS FOR EACH LAND TYPE OSB
835. C
836. 1070 WRITE (6,4400) WHT,HHGT,ARUN
837. AERSHT=0.0
838. AEIMT=0.0
839. DO 1100 1 = 1,BLAND
840. TEM=AEIH(I)+AE5SN(I)
841. IF (TEM.GT.0.0) GO TO 1080
812. TEH1=0.0
843. TEK2=0.0
8«t«. TEH3=0.0
845. GO TO 1090
8«6. 1080 TEH1=TEM*2000./AR(I)
847. TEH2=100.*AEESN(I)/TEH
848. TEH3=100.*AEIH(I)/TEH
849. IF (ONIT.LT.1) SO TO 1090
850. TEH=TEH*.9072
851. TEH1=TEH1*1.12
852. 1090 WRIT? (6,4410) (LKDOSE (IK,I) , IK=1 , 3) ,TEH,TEM1,TEK2,TF,M3
853. AERSNT=AERSNT+AERSK(I)
854. AEIMT=AEIMT+AEIH(I)
855. 1100 CONTINUE
856. C
857. C OUTPUT HONTHLY SEDIMENTS LOSS FOR THE ENTIRE WATERSHED
858. C
859. TEM=AERSNT+AEIMT
860. IF (TEM.GT.0.0) 30 TO 1110
861. TEM1=0.0
862. TEM2=0.0
863. TEH3=0.0
864. GO TO 1120
865. 1110 TEM1=TEM*2000./AREA
866. TEM2=100.*AERSNT/TEM
867. TEH3=100.*AEIMT/TSM
868. IF (ONIT.LT.1) GO TO 1120
869. TER=TEM*.9072
870. TEM1=TEM1*1.12
871. 1120 HRITE (6,4470) TEH,TEB1,TEM2,TEH3
872. WRITE (6,4420) HH3T ,HHST,ARUN
873. C
874. C DDTPOT MONTHLY WAS30FF FDR EACH OF THE ANALYZED POLLUTANTS
875. C
876. DO 1180 J=1,NQOAL
877, »RITE (6,4430) (QOALIN(I,J),1=1,3|
878. APOLFT=0.0
879. APOLIT=0.0
242
-------
880.
881.
882.
883.
884.
885.
886.
887.
888.
889.
890.
891.
892.
893.
891.
895.
896.
897.
898.
899.
900.
901.
902.
903.
90a.
905.
906.
907.
908.
909.
910.
C
C
C
DO 1150 I=1,NLAND
MONTHLY BASHOFF OP A GIVEN PDLLUTANT FROM EACH LAND TYPE USE
TEH=APOLP(I,J)+APOLI(I,J)
IF (IEK.GT.0.0) GO TO 1130
TEB1=0.0
TEB2=0.0
TE«3=0.0
GO TO 1140
1130 TEM1=TEM/AR(I)
TEM2=ieO.*APOLP{I,J)/TEM
TEH3 = 100.*APOLI (I,J)/TEM
IF (UNIT.LT.1) GO TO 1140
TEK=TEM*.454
TE»1=TEM1/KGPHA
1140 WRITE (6,4410) (LNDUSE(KK,I),KK=1,3),T£K,TEM1,TEK2,TFM3
APOLPT=APOLPT*APOLP(I,J)
APOLIT=APOLIT+APOLI(I,J)
1150 CONTINUE
TOTAL HONTHLY HASHOFF OF A SIVEH POLLUTANT
TEH=APOLPT+APOLIT
IF (TEH.GT.0.0) 30 TO 1160
TEH1=0.0
TEH2=0.0
C
C
C
912.
913.
914.
915.
916.
917.
918.
919.
920.
92t.
922.
923.
921.
925.
926.
927.
928.
929.
930.
931.
932.
933.
934.
GO TO 1170
1160 TE«1=TEM/AHEA
TEK2=100.*APOLPT/TEH
TEM3=100.*APOLIT/TEM
IF (UNIT.LT.1) GO TO 1170
TEB=TEH*.454
TEH1=TEH1/KGPHA
1170 WRITE (6,4440) TEK,TSB1,TEB2,rEM3
1180 CONTINUE
TEMPAY=TEHPAY+TE«PA
DOAY=DOAY+DOA
SEDTCY=SEDTCY+SEDTCA
CALCULATE AND PBINT MONTHLY AVERAGES OF TEMPERATURE,
DISSOLVED OXYGEN,AMD EACH 3F THE ANALYSED POLLUTANT
IF (KOSIK. LE.O) 30 TO 1190
TEHPft=TEBPA/NOSIH
DOA=DOA/NOSIH
SEDTCA=SEDTCA/NOSIM
1190 TEKPO=TEMPA
IF (UNIT.EQ.1) TEBPO=(TEKPO-32.| *5/9
WRITE (6,4450) UTKP,TE«PO,DDA, SED7CA
00 1210 J=1,NQUAL
PLTCRY(J)=PLTCAY(J)*POLTCA(J)
IF (HOSIM.LE.O) GO TO 1200
POLTCArJ)=POLTCA(J)/>«OSIfi
C
C
C
C
243
-------
935.
936.
937.
938.
939.
940.
941.
912,
943.
944.
945.
946.
947.
C
C
C
949.
950.
951.
952.
953.
954.
955.
956.
957.
958.
959.
960.
961.
962.
963.
964.
965.
966.
967.
968.
969.
970.
971.
972.
973.
974.
975.
976.
977.
978.
979.
980.
981.
982.
983.
984.
985.
986.
987.
988.
989.
1200 WRITE (6,4460) (QUALIN(I,J),1 = 1,3) ,CUNIT (J) ,POLTCS(J)
1210 CONTINUE
ACCUMULATION FOR YEARLY SOHHARIES
DO 1220 1=1,NLAND
AERSNY(I)=AERSNY(I)+AERSN(I)
AEIKY(I)=AEIMY(I) *AEIM(I)
DO 1220 J=1,NQUAL
APOLPY(I,J)=APOLPY(I, J) *APOLP(I, J(
APOLIY(I,J)=APOLIY(I, J) *APOLI (I, Jl
1220 CONTINUE
1230 CONTINUE
WRITE (6,4490) NOS
NOSIY=NOSIY+HOSI«
NOSY=NOSY+UOS
1240 CONTINUE
END BONTHLY LOOP
YEARLY SCTMHARIES
C
C
C
WRITS (6,4480) YEAR
UZSMT=UZS
LZSHT=LZS
SGWMT=S6W
SCEPT=SCEP
RESST=RESS
SRGXTT=SBGX
TWBLMT=TWBAL
TSNBOL=XSNB^L
IF (DNIT. EQ.-1) SO TO 1260
DO 1250 1 = 1 ,28
: CONVERSION TO HETEIC OMITS DP THE LAST 28 VARIABLES
: CONTAINED IN COHMON/LNDOOr/
1250 AR200T(I)=AR20UT(I)*HKPIN
1260 HPITE (6,4330) DEPW,ROSTOr,RINIOr,BITOI,BASrOT,ROTOTrRCHTOT,PRTOr
IF (SNOW.LT.1) GO TO 1280
COVR=100.
IF (PACK. LT. IPACK) COVR= (PACK/IPACK) *100.
IF (PACK.GT.0.01) GO TO 1270
COVR=0.0
SDEK=0.0
1270 HRITE (6,4340) SOHSNY,PXSNY,«ELRAY,8ADHEY,CONMEY,CDRHEY,CRAINY,
* S5MY,SKB3HY,PACKDr,SDKH,COV!»,SEVAPY
1280 KRITE (6,4350) EPTOT, UEPTOT, UZSHT ,LZSHT,SGWHT,SCEPT,RESST,
* SRGXTT,TKBL«r
IF (SNOW.GT.O) WRITE (6,4360) TSNBOL
IP (HYCAL.EQ.1) GO TO 1425
HRITE (6,4400) HHT,HKT,ARON
OUTPUT YEARLY SEDIHENTS LOSS FDR EACH LAND TYPE ffSB
AERSNT=0.0
AEIHT=0.0
DD 1310 I=1,NLAND
TEH=AEIHY(I) + AERSNY (I)
C
C
C
244
-------
990.
991.
992.
993.
994.
995.
996.
997.
998.
999.
1000.
1001.
1002.
1003.
1004.
1005.
1006.
1007.
1008.
1009.
1010.
1011.
1012.
1013.
1015!
1016.
1017.
1018.
1019.
1020.
1021.
1022.
1023.
1024.
1025.
1026.
1027.
1028.
1029.
1030.
1031.
1032.
1033.
1034.
1035.
1036.
1037.
1038.
1039.
1040.
1041.
1042.
1043.
1044.
1
C
C
c
c
c
c
c
c
c
IF (TEK.GT.0.0) 30 TO 1290
TEM1=0.0
TEH2=0.0
TEM3=0.0
GO TO 1300
1290 IEH1=TESl/iR(I)
TEM3=100.*AEIHY(I)/TEH
TEH2=100.*AERSNY(I)/TEK
IF (UNIT.LT.1) GO TO 1300
TEH=TEM*.9072
TEH1=TEM1/KGPHA
1300 WRITE <6,<4l»10) (LHDUSE(KK,I| ,KK = 1, 3) ,TEM,TEB 1,TEH 2.TEM3
AERSNT=AERSNT+AERSNY(I)
AEIHT=AEIMT+AEIHY(I)
1310 CONTINUE
OUTPUT YEARLY SEDIMENTS LOSS FOR THE ENTIRE WATERSHED
TEM=AERSNT*AEIMT
IF (TEM.GT.0.0) GO TO 1320
TEB1=0.0
TEM2=0.0
TEH3=0.0
GO TO 1330
1320 T5B1=TEH/AREA
TEM2=100.*AERSNT/TEM
TEM3=100.*AEIKT/TEK
IF (UNIT.LT.1) GO TO 1330
TEM=TEM*.9072
TEH1=TEH1*2.2H
1330 WRITE (6,1K»70) TSM,TE«1 ,TEM2, TES3
WRITE (S,«a20) WHGT,BHST,AS3N
OUTPUT YEARLY WASHOFP FOR EACH OF THE ANALYZED POLLUTANTS
DO 1390 J=1,NQUAL
WRITE (6,1*430) (QUALIN(I,a),I = 1,3»
APOLPT=0.0
APOLI7=0.0
DO 1360 1=1,NLAND
YEAIiLY HASHOFF OF A SIVEN POLLUTANT FROM EACH LAND TYPE OSE
TES=APOLPY{I,J)+?-POLIY(I,J)
IF (TEM.GT.0.0) 30 TO 13ttO
TEM1=0.0
TE«2=0.0
TEM3=0.0
GO TO 1350
1340 TEM1=TE«/&R(I)
TEH2=100..*APOLPY(I,J>/TEN
TEH3=100.*APOLIY(I,J)/TEH
IF (UNIT.LT.1) GO TO 1350
TEH=TE«*.'»5'»
TEM1=TEM1/KGPRA
245
-------
1045.
1046.
1047.
1048.
1049.
1050.
1051.
1052.
1053.
1054.
1055.
1056.
1057.
1058.
1059.
1060.
1061.
1062.
1063.
1064.
1065.
1066.
1067.
1068.
1069.
1070.
1071.
1072.
1073.
1074.
1075.
1076.
1077.
1078.
1079.
1080.
1081.
1082.
1083.
1084.
1085.
1086.
1087.
1088.
1089.
1090.
1091.
1092.
1093.
1094.
1095.
1096.
1097.
1098.
1099.
1350
1360
C
C
C
1370
1380
1390
C
C
C
C
1400
1410
1420
1425
C
C
C
C
1430
1440
1450
1460
KP.ITE (6,1*410) (LNDtISE(KK, I) , KK = 1, 3) , TEH,TEM 1 ,TEM2,TEK3
APOLPT=APOLPT + APOLPY(I, J)
APOLIT=APOLIT+APOLIY(I,J)
CONTINUE
TOTAL YEARLY HASHOFP OF A 'GIVEN POLLUTANT
TEH=APOLPT+APOLIT
IF (TEM.GT,0.0) GO TO 1370
TEH1=0.0
TEH2=0.0
TEH3=0.0
GO TD 1380
TEB1=TBM/AREA
TEM2=100.*APOLPT/TE«
TEM3=100.*APOLIT/TEH
IF (UNIT.LT.1) GO TO 1380
TEH=TEH*.454
TE«1=TEH1/KGPHA
WRITE (6,4440) TEH,TEM1,TEM2,PEH3
CONTINUE
CALCULATE AND PRINT YEARLY AVERAGES OF TEMPERATURE,
DISSOLVED OXYGEN,AND EACH 3F THE ANALYZED POLLUTANT
IF (NOSIY.LE.0) SO TO 1400
TEHPAY=TEHPAY/HOSIY
DOAY=DOAY/NOSIY
SEDTCY=SEDTCY/NOSIY
TEKPO=TEHPAY
IF (UNIT.EQ.1) TBMPO='(TBHPO-32.| *5/9
WRITE (6,4450) OT8P,TEMPO,DOAY,SEDTCY
DO 1420 J=1,NQUAL
IF (NOSIY.LE.0) SO TO 1410
PLTCAY(J)= PLTCAY(J)/NOSIY
HRITE (6,4460) (QDALIN(I,J) , 1=1, 3» ,CUHIT(J),PLTCAY(J)
CONTINUE
HRITE (6,4490) NOSY
ZEROINS OF VARIABLES
DO 1430 1=1,28
ZEROING OF THE LAST 28 VARIABLES CONTAINED IN COMMON/LNDOaT/
AR20DT(I)=0.0
DO 1450 J=1 ,NQOAL
DO 1440 1=1,5
APOLPY(I,J)=0.0
APOLIY(I,J)=0.0
PLTCAY(J)=0.0
DO 1460 1=1,5
AEBSNY(I)=0.0
AEIHY(I)=0.0
NOSIY=0
TEHPBY=0.0
DOAY=0.0
246
-------
1100.
1101.
1102.
1103.
1104.
1105.
1106.
1107.
1108.
1109.
1110.
1112!
1113.
1114.
1115.
1116.
1117.
1118.
1119.
1120.
1121.
1122.
1123.
1124.
1125.
1126.
1127.
1128.
1129.
1130.
1131.
1132.
1133.
1134.
1135.
1136.
1137.
1138.
1139.
1140.
1141.
1142.
1143.
1144.
1145.
1146.
1117.
1148.
1149.
1150.
1151.
1152.
1153.
1154.
C
C
C
C
C
C
C
C
C
C
C
C
SUHMARY OP STORMS' CHARACTERISTICS
IF (HYCAL. EQ. 1) NV=2
IF (NOSY. LT.2.0H. HOST. GT. 200) GO TO 1560
CLFAR OUTPUT VECTORS AND INITIALIZE VMIN ABD VHAX
DO 1470 K=1,NV
TOTAI.(K)=0.0
SD(K)=0.0
VHIN(K)=1.0E75
1470 V«AX{K)=-1,OE75
CALCULATE MEANS, ST.DEV S, HAXIHA, AND MINIMA
DO 1520 1=1, NOSY
DO 1520 K=1,SV
TOTAL (K)=TOTAL(K)+STKCH(I,K)
IF (STMCH(I,K)-VMIN(K)) 1480,11*90,1490
1480 VMIN(K)=STSCH(I,K)
1490 IF (STMCH(I,K)-VMAX(K)) 1510,1510,1500
1500 VMAX(K)=STMCH(I,K)
1510 SD (K)=SD(K) +STMCH(I,K)*STHCR(I,K)
1520 CONTINUE
DO 1530 K=1,NV
RANGE(K)=VMAX(K)-VMIN(K)
A VER (K) =TOTAL (K) /NOSY
SD (K)=SQRT(ABS((SD(K)-TOTAL(K) *TDt AL (K) /NOSY) /(KOSY-1)))
1530 CONTINUE
PBINT STORH CHARACTERISTICS
IF (HYCAL. NE. 1) 30 TO 1540
WRITE (6,4500):
BRITE (6,4580) DEPW, AVER (1 ), SD (1) , VSAX{ 1) ,VNIU(1) , RANGE (1)
WRITE (6,4590) UFL, AVER (2) ,SD(2) , VBAX (2) , VHIN (2) , RAMGE(2)
GO TO 1570
1540 WHITE (6,4500)
WRITE (6,4510)
BRITE (6,4530) HHT, AVER (1) ,SD{1) , VMAX (1) , V«IM (1) , RANGE (1)
WRITE (6,4540) WHST, AVER (2) , SD (2) , VH AX (2) >V«IN (2) , RANGE (2)
WRITE (6,4545) AVER (3) , SD(3) , VMAX (3) ,V«IN (3) , RAHGE(3)
WRITE (6,4555) A?ER(4) , SD(4) , VHAX (U) , VMIN (4) , RANGE (4)
DO 1550 J=1 ,NQOAL
WRITE (6,4430) (QO ALIN (I, J) ,1=1 , 3)
WRITE (6,4530) WHT, AVER (K) ,SD(K) ,VHAX(K) ,VHIN(K) , RANGE (K)
K=K+1
WRITE (6,4540) WHGT, AVER (K) , SD(K) , V« AX(K) ,VMIN(K) ,RANGE(K)
K=K+1
WRITE (6,4550) CUNIT(J) , AVER (K) , SD (K) ,VHAX(K) ,VBIN(K) , RANGE (1C)
K=R*1
WRITE (6,4560) CONIT (J) , AVER (K) , SD (K) ,V«AX(K) ,VKIN(K) , RANGE (K)
247
-------
1155.
1156.
1157.
1158.
1159.
1160.
1161.
1162.
1163.
1164.
1165.
1166.
1167.
1168.
1169.
1170.
1171.
1172.
1173.
1174.
1175.
1176.
1177.
1178.
1179.
1180.
1181'.
1182.
1183.
1184.
1185.
1186.
1187.
1188.
1189.
1190.
1191.
1192.
1193.
1194.
1195.
1196.
1197.
1198.
1199.
1200.
1201.
1202.
1203.
1204.
1205.
1206,
1207.
1208.
1209.
C
C
C
C
C
i
i
i
i
i
i
i
i
i
i
i
i
I
i
1550 CONTINUE
GO TO 1570
1560 WRITE (6,457C)
IP (NOSY.EQ.O) GO TO 1590
157.0 DO 1580 1 = 1 ,NOSY
DO 1580 K=1,NV
1580 STUCK (I,K)=0.0
NOSY=0
END OF YEARLY LOOP
1590
CONTINUE
1600 CONTINUE
FORMAT STATEMENTS
4000 FORMAT (' 1« , ' *****EREOR***** INC3REIECT INPUT DATA! DESIRED ',
* 'CARD ',11,' FOR ',I2,'/',I2,'/',I4,'; READ CARD ',11,' FOR ',
* 12, •/',12,'/',!<»)
4010 FORMAT ('0')
a020 FDHMAT (1X,312,11,1216)
4040 FORMAT (8X,24I3)
4050 FORMAT (8X,12I6)
4060 FORMAT (3A4,2X,A4 )
4070 F3RHAT («1',9(/),45X,'NONPOtNT SDURCE POLLUTANT LOADING HODEL',
1 /,44X,42(« = '),10(/))
4080 F3RKAT (' • ,1X,'WATERSHED CHARACTERISTICS :',///,6X,'NAME',8X,
1 3S8,/,18X,3A8,//,6X,'TOTAL AREA {•,AH,«) •,8X.F9. 2, /)
1090 FORMAT (9X,'LAND USE«,5X,'5E OF TDIAL ' ,6X,' AREA (' ,A4, «S) ' ,6X,
1 'PERVIOUS (',AH,«S)',3X,«IMPERVIOUS (',A«,•S)',3X,
2 'IMPERVIOUS (*)',/)
«100 FORMAT (' • ,7X,3A«,5X,r5.1,lt(10X,F9.2))
4110 FDRHAT (/,6X,'FRACTION OF IBPERVIDUS AREA',2X,F5.2)
412JO FORMAT (' O1 ,8X, «**WARNI NG**1 , 3X, ' CHECK IF THE LA»D TYPES AREAS ',
1 "ARE CORRECT')
4130 FORMAT (5(/),' ',1X,«SIHULATIDN CHARACTERISTICS :',///,6X,
1 'TYPE OF RUN',10X,4AS,/,5X,'DATE SIMULATION BESINS',
2 13X.2A4,2X,12,', ',14,/,6K,'DATE SIMULATION ENDS',15X,
3 2A4,2X,I2,', ',r4,/,6X,'INPUr PRECIPITATION TIKE INTERVAL',
4 9X,13,1X,'MINUTES',/,6X,'SIMULATION TIHE INTERVAL',19X,12,
5 IX,'MINUTES',/,6K,'IS SNDWMELT CONSIDERED ?',26XrA4,/,
6 6X,'INPUT UNITS',34X,1A8,/, 6X,'OUTPUT UNITS',33X,1A8,/,6X,
7 'MINIMUM FLOW FOR OUTPUT PER INTERVAL (',A4,')•,1K,F9.4,
8 /,6X,'NUMBER OF QUALITY INDICATOPS ANALYZED',14X,12,
9 /,6X,«THE ANALYZED QUALITY INDICATORS',4X,
X 'SEDIMENTS,DO,TEMP,«,/, 5(»6X,3A4,',',/))
4140 FDRHAT (5{/),2X,•SUMMARY OP INPUT PARAMETERS :«,///,6X,
1 'LANDS',13X,'INTER =', F7.3,4X,'IRC =',F7.3,4X,
2 'INFIL =',F7.3,/,24!t,'NN =',F7.3,4X,'L =',
3 F7.3,4X,'SS =',F7.3,/,24X,'NNI =',F7.3,4X,
4 'LI =',F7.3,4X,'SSI =•,F7.3,/,24X,'K1 =',F7.3,
5 4X,'PETMUL=',F7.3,4X,'K3 =•,F7«3, /,24X,«EPXH »•,
6 F7.3,4X,'K24L =«,F7.3,»X,'KK24 =•,F7.3,/,24X,
7 'UZSN =',F7.3,4X,'LZSN =',F7.3)
4150 FDRttAT (/,6X, 'SNOW , 14X, • RADON = « , F7. 3, 4X ,' CSFAC =',F7.3/4X,
1 'EYAPSN=',F7.3,/24X,«HELEY =',F7.3,4X,'ELDIF =',F7.3,4X,
248
-------
1212.
1213.
1214.
1215.
1216.
1217.
1218.
1219«
1220.
122.1.
1222.
1223.
122«.
1225.
1226.
1227.
1228.
1229.
1230.
1231.
1232.
1233.
1234.
1235.
1236.
1237.
1238.
1239.
12UO.
1241.
1242.
1243.
1244.
1245.
1246.
1247.
1248.
1249.
1250.
1251.
1252.
1253.
1254.
1255..
1256.
1257.
1258.
1259.
1260.
1261.
1262.
1263.
1264.
=',F7.3,
2 'TSHOW =',F7.3,/,24X,«HPACK =• ,F7. 3,4X, «DGM
3 4X,'WC = ',F7.3,/,24X,'rD!lS =' , F7. 3, 4X , • SCF
I* 4X,'HMUL = ' , F7. 3, /, 24X, ' RMUL = • ,F7. 3, 4X, 'P
5 P7.3,4X,'KUGI =«,F7.3,/)
4160 FORMAT (/,6X,'QtIAL «,12X,'JRER = • , F7. 3, 4X, • KRER = ',P7.3./,
1 24X,'JSER =',P7.3,4X,'KSER =',F7.3,/,
2 24X,'JEIM =. ,F7.3 ,'4X', • KEIN = ',F7.3,/)
417.0 FORMAT (//,f>X ,' MONTHLY DISTRIBUTION- ,7X, 11 (A4, 4X) , A4,//,6X,
1 'TEMP CORRECTION FACTOR1 , 1X, 12 (2X,F6. 2) ,///, 7X,
2 '- PERVIOUS LANDS -',///, 6X, ' LAND COVER-' , 3A4,1X,12( 1X,F7. 3))
4160 FORMAT <17X,3A4, 1X, 12 (1 X,F7. 3) )
4190 FORMAT (/,6X , • ACCU MtlLATI ON RATES'!
<»2CO FORMAT (/,6X, ' REMOVAL RATES')
U210 FORMAT (/, 6 X, ' POTENCY FACTORS FOR',1X,3A«)
«220 F3RMAT (//,6X, ' -I MPEBVI OOS LANDS-',/)
<»230 FORMAT (2« X,3A« ,6X, • ACUP =« , P7. 3, «X , ' ACUI =',F7.3)
tt2iJO FDSHAT (2i*X,3Aa ,6X, « RPER *• , F7. 3 ,UX ,'RISP =',F7.3)
4250 FORMAT ', 3X, « PERVIOUS (%) ' ,11, • IMPERVIOUS (%) ' )
4410 FORMAT (' ' ,8X,3A4, 9X,F11 . 3, 3 (5X, F15. 3) )
249
-------
1265.
1266.
1267.
1268.
1269.
1270.
1271.
1272.
1273.
1274.
1275.
1276.
1277.
1278.
1279.
1280.
1281.
1282.
1283.
1284.
1285.
1286.
1287.
1288.
1289.
1290.
1291.
1292.
1293.
1294.
1295.
1296.
1297.
1298.
2000.
2001.
2002.
2003.
2004.
2005.
2006.
2007.
2008.
2009.
2010.
2011.
2012.
2013.
2014.
2015.
2016.
2017.
2018.
2019.
2020.
AT {'0',8X,'POLLUTANT HASHOFF, ',8X,'TOTAL (',A4,•)',3X,
'TOTAL (',A4,'/', A4,«) ', 3X,' PEP VIOIIS (X) ' ,7X, 'IMPERVIOUS («
AT ('0', 9X,'WASHOFF OF ',3A4|
IAT (' ',10X,'TOTAL 'rf ASHOFP', 5X, F11. 3, 3(9X,F11. 3))
"" ('O1,8X,'STORM RATER QUALITY - AVERAGES',//,
11X,'TEKPBRATtIPE ', A4, 6X, F7 . 2 ,//, 11X,
'DISSOLVED OXYGEN (PPM| ',1X,F7.3,//,12X,
'SEDIMENTS (GK/L)',F11.3)
(' ' ,11X,3A4,'(',A4,')-,F11.3)
4470 FORMAT (' «,10X,'TOTAL LOSS',1 OX,F10.3,3(10X,F10. 3))
4480 FORMAT ('1' ,25X ,'SUMMARY FOR • , 14,/,25X,16(•_«) ,//,35X,'TOTAL•)
4490 FORMAT ('0',8X,'NO. OF STORMS',1UX,13)
4500 FORMAT ('0',8X,«SUBMARY OF STORMS'« CHARACTERISTICS',4X,
1 'AVERAGE'.8X,'ST.DEV. ' ,9X,» MAXIMA',9X,'MINIMA',
2 9X,'RANGE',//)
4510 FORMAT M1X .'SEDIMENTS LOSS'J
4420 FORMAT
1 'T
4430 FORMAT
4440 FORMAT
4450 FORMAT
1
2
3
4460 FORMAT
4470 FORMAT
4510 FORMAT (11X,'SEDIMENTS LOSS')
4520 FORMAT (3A8/3A8)
4530 FORMAT (/,14X,'TOTAL WASHOFF (',A4,•)',4X,5(5X,F10.3))
4540 FORMAT (14X,'MAX WASHOFF (',A4,'/153IN)•,5X,F10.3,
1 4(5X,F10.3))
4545 FORMAT (14X,'HSAN CONCENTRATION (3H/L) *,4X,F10.3,4 (5X,F10.3))
4550 FORMAT (14X,'MEAK CONCENTRATION («,84,')',4X,F10.3,4(5X,P10.3))
4555 FORMAT (14X,'MAX CONCENTRATION (SM/L)',5X,F10.3,4(5X,F10.3 ))
4560 FORMAT (14X,'(5AX CONCENTRATION (', A4 , •) • , 5X,F10. 3,4(5X,F1 0. 3 ))
4570 FORMAT (• 0',8X,'**WARNING**',3X,
1 'SUMMARY OF STORM CHARACTERISTICS NOT PRINTED',
2 /,22X,'NUMBER OF STORMS LESS THAN 2 OR MORE THAN 200',
3 ' - CHECK YOUR HYMIN PARAMETER')
4580 FORMAT (/, 1 4X,'TOTAL RUNOFF (', A4, •) ' ,5X, 5 (5X, F10 . 3) )
4590 FORMAT (14X,'BAX RUNOFF (',A4, •) ' ,12X,F10.3,
1 4(5X,F10.3))
C
STOP
END
BLOCK DATA
C
C
C
C
C
BLOCK DATA TO INITIALIZE VARIABLES
IMPLICIT REAL(L)
DIMENSION MNAM(24) ,RAD(24) ,TESPX(24) ,WIKDX(24) ,PA.IN(96) ,
1 GRAD(24),KADDIS(24),HINDIS(24)
COMMON /ALL/ RO ,HYHIN
«HT,DEPK,ROSB,RESBI,
NLAND,NQUAL,STMCH(200
250
-------
2021.
2C22.
2023.
2024.
2025.
2026.
2027.
2028.
2029.
2030.
2031.
2032.
2033.
2034.
2035.
2036.
2037.
2038.
2039.
2040.
2001.
2042.
2043,
2044.
2045.
2046.
2047.
2048.
2049.
2050.
2051.
2052.
2053.
2054.
2055.
2056.
2057.
2058.
2059.
2060.
2061.
2062.
2063.
2064.
2065.
2066.
2067.
2068.
2069.
2070.
2071.
2072.
2073.
2074.
2075.
COMMON /LAND/DAY,PR1B,I KIN,IX,THEM.,SGH,GWS,KV,LIRC4,LKK4,ALTR (9l v
1 OZS, 17, DZSN,LZSN,INF.II., INTER, SGH1, DEC, DECI,TIT (1 3) ,
K24L,KK24,K2«EL,EP,IFS.K3, EFX«,RESS1 , R£SS,SC:HP,IBC,
SRGXT1,M1!PIN, K3PHA,METDPT,CCFAC,SCEP1,SRGXT,RAIN,SR::,
SCF,IDNS,F,DGK,HC,RPACK,EVAPSK,HELEV,TSSOW,PETHIS,
D5HX,DEPTH, MONTH, TrTN,PETMAX,ELOIF,SDEN,WINDX.INFTOK,
TSNBA1, ROBTOM,RDBTar,RXB,ROITO«l,POITOT,YF.AB, CUNIT (7| ,
INFTOT,KNAM,RAD,SRCI,FOP.M(42)
COHMOM /QLS/ BSN1ME(6),KFER,JRER,KSE8,JSEF,,TF,HPCF,COVMAT(5, 12),
1 KEIH,JEIH,NDSP.,ARP(5) ,ARI(5) ,ACCP(5) ,ACCI (5) ,RPER(5l ,
2 PHP(5,5),P«I(5,5),QSfK>K,SNOHY,SEDTH,SEDTY,SEDTCA,
3 ACPOLP(5,5) ,ACEPSN(5),APOLP(5,5),AEBSN(5),COVEP(5),
4 APOLI(5,5) ,ACEIM(5) , AEIM(5) ,POI.m{5) ,POI.TY(5)r
5 TEMPA.DOA, POLTCA(5) , AES SNY (5) , AEIflY ( 5) ,APOLP* (5, 5) ,
6 APOLIY(5,5) ,POLTC{5» ,PLTCAY(5) ,ACPOLI(5,5) ,RXHP(5)
COMMON /LNDOOT/ ROSTOM,RIKTOH,RITOH,ROTOR,BASTOfi,ECHTOH,PRTOM,
1 SUHSKf!,PXSNH,aELBA!1,RADBEM,CONf5EK,CDRBER,
2 CRATKH, SGMM,SNEGf!M,PACKOT,SEVAPM,FPTOK,NEPTOH,
3 tr3SOI,LZSOT, SGW3r,SCEPOT,RESSOT,SEGXTO,TBBALO,
4 TSHBOL, ROSTOT, RINTOP, RITOT, RtTTOT, BASTOT,RCHTOT,
5 PRTOT,SDHSN!f,PXSSy,HELRAi,RADHEY,CONHEY,rDRHEY,
6 rRAINY,SGMY,SNEGJlY,PACK1,SEVAPY,EPTOT,HEPTOT,
7 UZSfiT,LZSHT, SG««T, SCErT,RESST,S8GXTT, TWBI.HT
COMKON /STS/ ACPOLT(5),PLTKX{5),PDLTSC(5),PLTHXC(5),
1 ACSEDT,SEDMX,SEDr5C,SED«XC,TOTRUNfPEAKRO
COHBON /INTH/ RTYPE(4,4) ,UTYPE(2) , GRAD,RADDIS,WINDIS,ICS,OFS,
1 TE«PAYfDOAY,HOSry,IHTRVL,WKOL,NK,L,SS,RNt,LI,SSr,
2 RHUL,KUGI,SEDrC¥,REPERV(12),REIMPV(12),ACOPV(12),
3 ACOIV(12) ,PKP«AT(12,5) ,PMIBAT(12, 5) ,PHPVEC(5) ,
4 PHIVEC(5) ,ACai,ACUP,REI!1P,REPER,PRrNTR
INTEGER UNIT , LHTS, RECOUT, SFLAG, PRINTR
LOGICAL LAST, PREV
KSNAHE,RTYPE,UTYPE
JRER, KPER, JSER, KSER,KEIH,JEIH
LZSR, IRC, N«, L, LZS, KV, K24L, KK24, INFIL, IKTEB
IFS, K24FL, K3, HEPTOM, NEPT3T, ICS; HNI, KHGI
INFTOH, INFTOT, INTF
MKPIN, KFTOPT, K3PLB, KGPHA
PEAL
REAL
REAL
FEAL
REAL
REAL
REAL
DATA
DATA
DATA
DATA
DATA
DATA
DATA
STU, STL ,IHPK
MELRAfl, HELRAY
LAST/.FALSE./,
PRTOT/0.0/
PHTO«,PSTM/2*0.0/
F.UTOH, FOSTOB, F.ITOM,
ROTOT, ROSTOT,
ROBTOK, ROBTOT,
PREV/.FALSE. /
RINPOM, NEPTOP!/5*0.0/
RITOT, RINIOr, NEPTOT/5*0. O/
IHFTOH, IKFIOr, ROITOH, ROITOT/6*0.O/
TWBAL, RESB, RESBI, ROSBI, RESBI1,SRGX, INTF/7*0.0/
251
-------
2076.
2077.
2078.
2079.
2080.
2081.
2082.
2083.
2081.
2085.
2086.
2087.
2088.
2089.
2090.
2091.
2092.
2093.
2094.
2095.
2096.
2097.
2098.
2099.
2100.
2101.
2102.
2103.
2104.
2105.
2106.
2107.
2108.
2109.
2110.
2111.
2112.
2113.
2114.
2115.
2116.
2117.
2118.
2119.
2120.
2121.
2122.
2123.
212«.
2125.
2126.
2127.
2128.
2129.
2130.
DATA SESB1, BASTOH, RCHTOM, BASTQT, RCHTOT/5*0.O/
DATA FPTOM, EPTOT/2*0.0/
DATA PR, P3, RXB, RGX, RUZB, OZ5B, PERCB, DPST/8*0.0/
DATA TIMFAC, (IZSH, LZSN, INFIL, INTER, IRC/6*0.0/
DATA A, OZS, IZS, SGW, GHS, KV, K24L, K24EL, KK24/9*0.0/
DATA IFS, K3, EPXK/3*0.0/
DATA PETHIN,PETKAX/35.,40./
DATA TOTRUN,PEAKRU,ACSEDT,SEDMX,SEDTSC,SEDMXC/6*0.O/
DATA ACPOLT,PLTKX,POLTSC, PLTHXC/20*0.O/
DATA HHAM/' JA N1,'UAF.Y',' FEBR',« UAS Y1 , ' nAR«,'CH
* 'IL ',« KAY',' ',' JOK',«E ',' JUL','Y ',
* 'OST ','SEPT'.'MBER1,' DCT' ,' OBEF,' , *NOVE« ,' HBER ', 'DECS' ,
* «MBER'/
DATA MHPIH/25.4/, BETOPT/0.9072/, KGPLB/0.4536/, KSPHA/0.892/
DATA SUMSNR, PXSNH, MELRAK, HAD«E«, CDRMEH, CRAINK,PACK,DEPTH,
* CONHEM, S3KI", SNEGMM, SEVAPM, SDHSHY, PXSNY, RELPAY,
* SADHEY, CDRREY, CONHEY, GRAINY, SGBY, SNEGMY, SE7APY,
* TSNBAL/23*0.0/
DATA INTRVL,PSINTR/15,15/, HH3L, RHUL, KUGI,SFtAG/1.0,1.0,0.0,O/
DATA ICS, OFS/2*0.0/
DATA GTAD/0.04,0.04,0.03,0.02,
*0. 02,0. 0 2, 0.0 2,0. 06, 0-.1 4, 0.1 8, 0.2 3, 0.17 ,0.13,0.06,0. 03,0. 01,0.05,
*0.07,0. 10,0.13,0.15,0.13,0.12,0.08/
DATA RADDTS/6*0.0,0.019, ;
*0. 041,0.067,0.088,0.102,0.110,0.110,0.110,0.105,0.095,0.081,0.055,
*0.017,5*0.O/
DATA HTHDIS/7*0.034,0.035,
*0. 037,0.041,0. 046, 0.050, 0.053, 0.05 4,0.058,0'. 057,0.056,0. 050,0.043,
*0.040,0.038,0.036,C.036,0.035/
DATA NN,L,SS/3*0.0/, N>?I ,LI, SSI/3*0. O/
DATA TEMPSY,DOAY,SEDTCA,SEDrCY/4*0.0/, KOSIY,NOSY/2*0/
DATA CUSIT/5*4HGH/L,4H«3/L,4HGH/L/
DATA FOP.*/4n(17X 4H,A4, , 4H4X,A , 4H4,7X
(GS
4!I« (GH
4PLB) '
* 4HLB) '
* 4HLB) '
* 4HLB) •
* 4HLB) '
* 4HLB) '
DATA ALTR/4H
* 4HM«G,
DATA TIT/4H ,
* 4H 0 N, 4H S T,
DATA RTYP3/8H
4H,2X, ,4H'(GS , 4H/L)
OH,2X, ,4!I» (GH , 4H/L)
4H,2X, ,4H« (GS , 4H/L)
4H,2X, ,4H"(G« , 4K/L)
4H.2X, ,4U'(GM , 4H/L)
4H,2X, ,4R«(GK , 4H/L)
4HK3)',
21,
4H ,4 , 4HX,'(
4H 4 4HX,
4H
4H
4H
4H
4H
4HX,
4HX,
4HX,
4HK,
4H/) )
27, 4H( 41, 4H{ 54, 4H( 63, 4H( 74 /
4H C.
4HX,'Q, 4H U A, 9H 1. I, 4H T Y,
H I T, «H 0 E, 4H N T, 4H S1 , ,«H / )/
,'SEDIMENT','PRODUCTI1 ,' PRODCI',' HYDROL1 ,
*' AND QUA','ON (PEIN','CTTON (0','OGIC CAI'.'tlTY CAL'.'TER OOTP',
*'UTPUT ON' ,'IBRATIOK' ,' I BRAriDN', • tit OBLY)',' ONIT 4) «/
DATA OTYPE/' METRIC»,' ERSLISH'/
DATA COVHAT/60*0.0/, COVER/5*0.0/
DATA I«PK,SCALEF/5*0-.,5*1./, KDSR, IHRR/2*0/
DATA FMP/25*0.0/f PMI/25*0.0/
DATA QOALIN/' BOD',2*4H ,« TD3',11*4H /
DATA QSKOW/« NO •/, SNOWY/'YES '/
DATA JEER/0.0/, KRFR/0.0/
DATA JSER/0.0/, KSEFI/0.0/
DATA JSIK/0.0/, KEIM/0.0/
252
-------
2131.
2132.
2133.
2134.
2135.
2136.
2137.
2138.
2139.
21 40.
2141.
2142.
2143.
2144.
2145.
2146.
2147.
3000.
3001.
3002.
3003.
3004.
3005.
3GC6.
30D7.
3008.
3C09.
3010.
3011.
3012.
3013.
3014.
3015.
3016.
3017.
3018.
3019.
3020.
3021.
3022.
3023.
3024.
3025.
3026.
3027.
3028.
3029.
3030.
3031.
3032.
3033.
3034.
3035.
3036.
3037.
C
C
C
C
C
C
C
C
C
C
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
UNT1/« KG «,« HM «,• IB ',' IN •/
STMCH/«ROO*0.0/
UNT2/' T «,'CHS ','TOMS',•CFS '/
UNT3/« (C) «,' HA ','(F) ','ACREV
AF.RSN/5*0.0/, AEIK/5*0.0/, APDLP/25*C. O/, APOLI/25*0. O/
ASRSNY/5*0.0/, AEI«Y/5*0.O/, APOLPY/25*0.0/, APOLIY/25*0.O/
TEHPi,DOA/2*0.0/ ,MOST,NDSR5,NDS/3*0/
POLTCA/5*0.0/,PLTCAY/5*0.0/
ACPOLP/25*0.0/, ACPOLI/25*0.0/
ACEIK,ACERSN/10*0.0/
ACCP/5*0./, ACCI/5*0./r FISP/5*0./, EPER/5*0./
EEEH/5*0./, TS/5*0./, L«TS/5*0/
PHPVEC,PKIVEC,P«PMAT,PilIMAT/5*0. ,5*0. ,60*0. ,60*0./
ACOP,ACOI,ACOPVfACUIV/0.,0.,12*0.,12*0./
EEPEfi,REI«P,REPESV,REIHPV/D.,0.,12*0.,12*0./
END
SU3FODTJNE LANDS
HSP LANDS
IHPLICIT REAL(L,K)
DIMENSION EVDIST(2«) ,LAPSE (2«( ,SVP(«0) ,SNCtjr (24,16) ,STRBGN (4) ,
1 M»RH'(24),'RAD(24),TE3PX(2I»') , WINDX(24) ,RAIN(96) , DHB1 (5) ,
2 DOB2(5)
COBHON
/A.LL/ RU,HyHIN,HYCAL,DPSr, OBIT, TI«?AC,!,ZS, AREA, RESB,SPLAG,
RESB1, ROSE, SRGX,ISrF,BGX,RUZB,UZSB,PERCB,RIB,P3,TF,
KGPLB,LAST, PREV , TEMPIC ,IHR, IHRR, PR,RtJI ,A ,Pfi ,GHF,NOSY,
SRER(5) ,TS{5) ,LNDUSE(3,5) ,AR (5) ,QO&LIN(3, 5) , NOSI,N03,
NOSIK,UFL,tJTHP,nHT1 (2,2) , OKT2 (2, 2) , UNT3(2,2) ,WHGT,
BHT,DEPK,ROSBI,RESBI,BESBI1,ARtJN,LMTS(5) ,IMTK{5) ,
KLAND,NQUAL,STHCtI(200,24) ,EECOOP (5) , FLOUT, 3CALEF (5) ,
SNOW, PACK, IPACK
COHHON /LAND/DA Y,PRTH, I HIN,IX, TKBAL, SGW ,GHS, KV, LI RC4, LKK4 , ALTR (9( ,
UZS,I7,OZSN,LZSN,IKFIL,IKTF,R,-SGW1,DEC,DECI,TIT(13} ,
K24L, KK24,K2ttEL,EP,IFS,K3,EPXH,RESS1,RESS,S^?;P,IRC,
SRG>rT1,MMPIK,K3PHA, METDPT, CCFAC,SCEP 1 ,SRGXT,RAIN,SP.:,
SCF,IDKS,F,D5H, KC,HPACK ,EVAPSN, HELEV ,TSNOH, PETHI N,
DEWX, DEPTH, BONr!!,TMIN,?ETKAX,ELDIF,5;DEK,WINDX,INFTOH,
TSKBAL,ROBTOtt,R3BTOr,RXB,ROITOf!,ROITOT,TEAR,CUNIT(7| ,
IKFTOT,HK\«,RAD,SRCI, FORK (42)
COSMOH /LNDOUT/ ROSTOH, RINTOtl, EITOH, RUTOH,BASrO«, RCHTOH,PRTOM,
SUMSNK,PXSNM,MELRr.M,RADMEM,COH«EM,CDEKEH,
1
2
3
4
5
6
7
2
3
U
5
6
7
UZSOT,LZSOT, SGWOT, SCEPOT, RESSOT, SRGXTO,T»BALO,
TSH30L, SOSTOT, EINI 01, RITOT, PUIOT, BASTOT,RCHTOT,
pRTOT,SOMSNy,PXSKY,MELRAY,RADPiEY,COKMEy,CDR«EY,
CRAINY,SGKY,SNE3M!f,PACKl,SEVAPY,EPTOT,HKPTOT,
OZSRT,LZS«T, SGBRT, SCEPT,RESST,SR5XTT, TWBLMT
253
-------
3038.
3039.
3040.
3041.
3012.
3043.
3044.
3015.
3016.
3047.
3048.
3049.
3050.
3051.
3052.
3053.
3054.
3055.
3056.
3057.
3058.
3059.
3060.
3061.
3062.
3063.
3064.
3065.
306t>.
3067.
3068.
3069.
3070.
3071.
3072.
3073.
3074.
3075.
3076.
3077.
3078.
3079.
3080.
3081.
3082.
3083.
3084.
3085.
3086,
3087.
3088.
3089.
3090.
3091.
3092.
C
C
C
C
C
COMMON /STS/ ACPOLT(5) , PLTHX (5) ,PDLTSC(5) ,PLTMXC(5),
ACS£DT,SEDKX,SEr>rSC,5EDKXC,IOTRUN,PEAKRU
LOGICAL LAST, PREV
INTEGER TF,HYCAL, RAY, CNII, SNDW, HRFLAG, H, SFLAG ,LMTS, STRBGN,
REC007.YEAR
C
C
C
1
PEAL INFIL, INTER, NN, INFLT, IRC, INTF, INFL
REAL IRC4, ICS, IFS, NEKTOM, NEPTDP
PEAL INFTOM, INFTOT, OHETRC ,IKPK
REAL MMPIN, METOPT, KGPLB
REAL OZSHF.T, LZSMET, SSWKEI, SCEPMT, RESSMT
P.SAL TWBLHT, SPGXTH, RESEMI, SHSXHT
F.EAL IDHS, HPACK, MELEV, KUGI, NEGMLT, KF.GHH
REAL MELT, IHI)T, KCLD, IPACK, BELRAK, MELRAt
DATA PERC, INFLT, SBA5, HRFLAG/0. 0, 0. 0,0*0, O/
DATA SNET1, SNKT, SRCH/3*0.0/, HUS1I/0/
DATA KOSINT, REPTN, EPTB1, AETH, KF/5*0.0/
DATA EVDIST/6*0. 0,0. 01 9, 0.01*1,0. 067, 0.03 8,0. 102, 3*0. 11,0.105,
C 0. 095, 0. OS 1,0. 055, 0.01 7, 5*0. O/
DATA SVP/1 0*1. 005, 1.01, 1.0 1,1. 01 5, 1.02,
*1. 03, 1.04, 1.06, 1.08, 1.1, 1.23, 1.66, 2. 13, 2. 74, 3. 19, 4. 40, 5. 5 5, 6. 87,
*8. 36, 1C. 09, 12. 19, 14. 6 3, 17. 51, 20. BS , 24. 79 , 29. 32,3U. 61 , 40. 67, 47.68,
*55.71 ,64. 88/
DATA LAPSE/6*3.5,3.7,4.0,4.1,
* 4. 3, 4. 6, 4. 7, 4. 8, 4. 9, 5. 0,5. 0,4. 8, 4. 6, 4. '4, H. 2, 4. 0,3. 8, 3. 7, 3. 6/
DATA APR, AEPIN/2*O.C/
DATA ASOSB, AINTF, AROSIT/3*0.0/
DATA ARU, ARUI, AEOS, ARGXT, ASNEr, ASBAS, ASRCH/7*0. O/
DATA SUMSN, INDT, KCLD, PXON5N, 5EVAPT, RADME, CDRHE, LIQW1,
* COKMS, GRAIN, NE3MLT, SHEGS, NEGMB, LIQS, LIQW, XICE,
* XLNKLT,SGH, SPX..WBAL, SF.V AP/2 1*0. 0/
DATA SNOOT/38H*0.0/
DATA CLDF/-1.C/
2T3ROIMG OF VARIABLES
LZS1 = LZS
UZS1 = DZS
KUBI = 0
DPST =0.0
PACK1 = PACK
LI.QW1 = LIQH
PSR = PR
LSRAT=LZS/LZSN
D3?V=(2.0*INFIL)/(LKRAT*LNRAT)
D4F= (TIMFAC/60.)*D3FV
IF (SNOW ,LT. 1} GO TO 20
REDUCE INFILTRATION IF ICE EXISTS
AT THE BOTTOM OF THE PACK -
ATTEMPT TO CORRECT FOR FROZEN LASD
254
-------
3093.
3094.
3095.
3096.
3097.
3098.
3099.
3100.
3101.
3102.
3103.
3104.
3105.
3106.
3107.
3108.
3109.
3110.
3111.
3112.
3113.
3114.
3115.
3116.
3117.
3118.
3119.
3120.
3121.
3122.
3123.
3124.
3125.
3126.
3127.
3128.
3129.
3130.
3131.
3132.
3133.
3134.
3135.
3136.
3137.
3138.
3139.
3140.
3141.
3142.
3143.
3144.
3145.
3146.
3147.
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
D4FX = (1.0 -XICE)
IF (D4FX . LT. 0.1) D4FX = 0.1
D4F = P4F*DUFX
20 RATIO= INTER*EXP(0.693147*LHEAT)
IF ( (RATIO).LT. (1.0)) RATIO=1.0
D4RA = D4F*RATIO
H = TF/2&
TF IS 1 FOB FAIN DAYS, AND 96
OR 288 FOB NON-BAIN DATS
IF (TF .GT. 2) IHP.R=0
DO 1480 111=1,TF
LNRAT = LZS/LZSN
IF (TF .LT. 2) GO TO 40
NUHT = NOMI * 1
IF (NOffI .EQ. H) GO TO 30
GO TO 40
30 NOHI = 0
40 SBAS =0.0
SRCH =0.0
EOS = 0.0
PO = 0.0
GWF =0.0
SGXT = 0.0
PERC = 0.0
INFLT =0.0
EESS =0.0
TIHFAC - TIME INTERVAL IK HIN3T2S
L - LENGTH OF OVERLAND SLOPE
KN - MANNING'S K FOB OVERLAND SLOPE
A - IHPEEVIOUP AREA
PA - PERVIOUS AREA
PR IS IHCOHISG HAIKFALI
P3 IS RAIN REACHING SURFACE(.OO'S INCHES)
P4 IS TOTAL BOISTOSE AVAILABLE ( IN.)
RESS IS OVHRLAND FLOW STORAGE( IN.»
D4F IS 'E' IN OP. HAKUAL
RATIO IS 'C1 IN OP. MANUAL
EP - DAILY EVAP ( IN.)
EPHR - HOOPLY E-VAP
EPIN - INTFSVAL EVAP
EPXX - FACTOR FOR REDOCING EVAP FOE 5NOH AND TEMP
255
-------
3148.
3149.
3150.
3151.
3152.
3153.
3154.
3155.
3156.
3157.
3158.
3159.
3160.
3161.
3162.
3163.
3164.
3165.
3166.
3167.
3168.
3169.
3170.
3171.
3172.
3173.
3174.
3175.
3176.
3177.
3178.
3179.
3180.
3181.
3182.
3183.
3184.
3185.
3186.
3187.
3188.
3189.
3190.
3191.
3192.
3193.
3194.
3195.
3196.
3197.
3198.
3199.
3200.
3201.
3202.
C
C
C
C
C
C
C
C
C
1
C
C
C
C
C
C
C
C
C
C
C
C
DETERMINE IF SNOWMELT IS TO BE DOME
50 HEFLAG=0
TEST = IMIN/TIMFAC
IP (NUNI .EQ. 1) HRFLAG = 1
IF {(TEST .LE. 1.0C1).AND. (TEST .GB. 0.999)) HRFLAG
HBFLAS=1 INDICATES BEGINNING OF THE HOUR
IF (HRFLAG) 770, 770, 60
60 ISHD = 0
IF (IHF-24) 70,60,70
70 IHRE = IHE + 1
GO TO 90
80 IHRK = IHRR * 1
90 EPHR = EVDIST(IHRR)*EP
IF (EPHR.LF.(0.0001)) EPHR=0.0
EPIN = 3PHR
KPIK1=EPIN
IF (SNOW ,EQ. 0) GO TO 770
IF ((PACK . LE
TSNOP1 = TSNOW
SNTESP = 32.
SEVAP =0.0
SFLAG = 0
PRHR=0.0
EPXX = 1.0
IKEND = 60./(TIKFAC)
IPT = (IHRR-1)*IKEHD
PX=0.0
DO 100 II = 1,IKEND
100 PEHR = PRHR * RAIS(IPT+II)
0.0) .AND. (THIN .GP. PBTHAX)) GO TO 770
***********************************
BEGIN SNOHHELT
***********************************
* 1.
SCJH PRECIP FOR THE HOUR
CORRECT TEMP FOR ELEVATION DIFF
USING LAPSE BATE OF 3.5 DDRI3G RAIB
PERIODS, AN0 AS HOUPLY VARIATIOK IH
LAPSE RATE (LAPSE (I)) FOR DfiY PERIOD
LAPS = LAPSE(IHRR)
IF (PRHR .GT. 0.05) LAPS = 3.5
TX = TEMPX(IHRR) - LAPS*ELDIF
8EDOCE REG EVAP FOR SKOHSELT
COKDiriOKS BASED ON PETMIN AND
PETMAX VALUES
IF (PACK-IPACK) 120,120,110
110 E1B=0.0
PACKRA = 1.0
GO TO 130
256
-------
3203.
3204.
3205.
3206.
3207.
3208.
3209.
3210.
3211.
3212.
3213.
3214.
3215.
3216.
3217.
3218.
3219.
3220.
3221.
3222.
3223.
322U.
3225.
3226.
3227.
3228.
3229.
3230.
3231.
3232.
3233.
323U.
3235.
3236.
3237.
3238.
3239.
3240.
3241.
3242.
3243.
3244.
3245.
3246.
3247.
3248.
3249.
3250.
3251.
3252. .
3253.
3254.
3255.
3256.
3257.
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
12C
130
140
150
160
170
180
19.0
200
210
220
PACKBA = PACK/IPACK
E1E=1.C - PACKRA
EPXX = (1.0-F)*E1E * P
IF (TX-PSTHAX) 140,170,170
IF (EPXX .GT. 0."5) EPXX=0.5
REDUCE EVAP BY SOS IF TX IS BETWEEN
PEIHIK AMD PETMAX
IF (TX-PETMIN) 160,170,170
EPXX=0.0
EPHH = SPKR*EPXX
EPIN=EPTN*EPXX
IEND=0
IF ((TX .GT. TSNOW) .AND. (PRHP. .GT. .02)) DEWX = TX
SET DEKPT TEMP EQUAL TO AIR TEHP BHEN RAINING
ON SNOW TO INCREASE SNOW KELT
IF (DESX .GT. TX) DESX = TX
SNTSKP = TSNOW + (TX-DEHX)*(0.12 * 0.008*TX)
RAIN/SKOW TEHP. DIVISION - SEE ANDERSON, WHR, VOL. 4, ND. 1,
FEB. 1968, P. 27, EG. 28
IF (SNTEMP .GT. TSNOW1) SNTEKP = PSNOS1
IF (TX -SHTEKP) 190, 1BO, 180
IF (PACK) 770, 770, 200
SFLAG = 1
IF ((PACK. LE. 0.0) .AND. (PRHR.LB. 0.3)) GO TO 770
SKIP SNOWKELT IF BOTH PACK AND PRECIP ARE ZERO
FOR THE HOOR
ISSD = 1
SROWHELT CALCULATIONS ARE DONE IF IT IS SNOWING, OR,
IF A SNOHPACK EXISTS
PX = PPHR
IF (PX) 250, 250, 210
KCLD = 35.
IF (SFLAG) 260, 260, 220
PX = PX*SCF
APR = APR*(SCF-1.0)*PRHE
PRHP. = PRHR*SCF
SOHSN = SUHSH * PX
DNS = IDNS
IF (TX .GT. 0.0) DNS = DNS
KCLD IS INDEX TO CLOUD COVER
SNOB IS FALLING
((rx/ioo.)**2)
SNOW DENSITY WITH TEMP. - APPRO* TO FIG. 0, PLATE B-1
SNOW HYDROLOGY SEE ALSO ANDERS3N, TR 36, P. 21
257
-------
3258. C
3259. PACK = PACK * PX
3260. C
3261. IF (PACK-IPACK) . 240,240,230
3262. 230 IPACK = PACK
3263. IF (IPACK .GT. BPACK) IPACK = HPACK
3264. C
3265. 240 DEPTH = DSPTR * (PX/DHS)
3266. IP (DEPTH .GT. O.C) SDFN = PACK/DEPTH
3267. INDT = INDT - 1000*PX
3268. IF (INDT . LT. 0.0) INDT = 0.0
3269. PX = 0.0
3270. GO TO 260
3271. 250 KCLD = KCLD - 1.
3272. 260 IF (KCLD . LT. 0.0) KCLD = 0.0
3273. PACKRA =- PACK/IPACK
3274. IF (PACK .GT. IPACK) PACKRA = 1.0
3275. C
3276. 270 IF (PACK - 0.005) 280, 300, 300
3277. C
3278. C IPACK IS AN INDF.X TO AREAL COVERAGE OF THE SNOSPACK
3279. C FOP INITIAL STORHS IPACK = ,1*HPACK SO TrtAT COMPLETE
3280. C AREAL COVERAGE RESULTS. IF EXISTING PACK > ,1 *«PACK THEN
3281. C IPACK IS SET EQUAL TO MPACK WHICH IS THE WATER EQUI. FOB
3282. C COBPI.STE AREAL COVFRAGE PACKRA IS THE FRACTION AREAL COVERAGE
3283. C AT ANY TIME.
3284. C
3285. 280 IPACK = 0.1*«PACK
3286. XICE = O.C
3287. XLNHLT =0.0
3288. NEGKLT = 0.0
3289. PX = PX * PACK t LIQW
3290. PACK = 0.0
3291. LIQH =0.0
3292. C
3293. C ZERO SNOWBELT OOTPOT ARRAY
3294. C
3295. DO 290 1=1,24
3296. DO 290 MM=1 ,16
3297. 290 SNOUT(I,KM)=0.0
3298. GO TO 760
3299. 300 PXONSN = PXONSK * PX
3300. IF (DEPTH .GT. 0.0) SDEN = PACK/DEPTH
3301. IF (INDT .LT, 800.) INDT = INDT * 1.
3302. C IHDI 13 INDEX TO ALBEDO
3303. BELT =0.0
3304. IF (SDEN .LT. 0.55) DEPTH=DEPTH*(1.0 - 0.00002*(DEPTH*(.55-SDEN) I )
3305. C
3306. C EMPIRICAL RELATIONSHIP FOR SNOW COMPACTION
3307. C
3308. IF (DEPTH .GT. 0.0)- SDEN = PACK/DEPTH
3309. BIN = WINDX(IHRR)
3310. C
3311. C HOURLY HIND VALUE
3312. C
258
-------
3313.
3314.
3315.
3316.
3317.
3318.
3319.
3320.
3321.
3322.
3323.
3324.
3325.
3326.
3327.
3328.
3329.
3330.
3331.
3332.
3333.
3334.
3335.
3336.
3337.
3338.
3339.
3 340.
3341.
3342.
3343.
3344.
3345.
3346.
3347.
3348.
3349.
3350.
3351,
3352.
3353.
3354.
3355.
3356.
3357.
3358.
3359.
3360.
3361.
3362.
3363.
3364.
3365.
3366.
3367.
C
c
C
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
LREF = (TX + 100. )/5
1REF = IFIX(LREF)
SVPP = SVP (LREF)
ITX = IFIX(TX)
SATVAP = SVPP *(MOD(ITX,5)/5)*(SVP(LSEF * 1) - SVPP)
IREF = (DEWX * 100.)/5
LEEF = IFIX (LREF)
SVPP = SVP (LREF)
IDSWX = IFIX(DEWX)
VAPP = SVPP «• (HOD(TDEHX,5)/5| *(SVP(LPEF .+' 1) - SVPP)
CALCULATION OF VAPOR PRESSURE AT AI8TEKP
AND DEKPOINr
IF (VAPP - 6.108) 320, 320, 310
310 CKM = 8.59* (VAPP - 6.108)
GO TO 330
320 CKR = 0.0 . l( '
DUHHY=(VAPP-SATVAP) *PACKRA
IF (VAPP .LT. SATVAP) SEVAP = EVAPSN*0.0002*WIK*DUNMY
t?.CK = PACK * SEVAP
SEVAPT = SEVAPT - SEVAP
CONDENSATION - CONVECTION MELr, EQ. T-296, P. 176, SNOW HYDROLOGY
COHV - CONVFCTIOH, COKDS - CON DENS ATIOM
SEVAP - EVAP FROM SNOW (NEGATIVE VALUE)
330 CNV =0.0 ". ' ".
IF (TX .GT. 32.) CKV = (TX-32.)*(1.0 - 0.3*(MELEV/10000.) )
CCXC = CCFAC*.00026*WIN
.C0026 = .00629/24, I.E. .00026 15 THE DAILY COEFFICIENT
(FROM SNOH HYDEOLOGY) REDUCED ID HOURLY VALUES,
COHV = CNV*CCXC
COSDS = CNB*CCXC
CLOUD COVER
CLDF IS FRACTION OPEK SKY - HINIHUM VALUE 0.15
IF ((IHRR.EQ.1) .OR.(CLDF.LT.O.0)) CLDF = (1.0 - 0.085*(KCLD/3.5) I
ALBSDO
IF (MONTH - 9) 340, 340, 360
340 IF (KONTH - 4) 360, 350, 350
350 ALBEDO = 0.8 - 0.1 * (SQRT (INDT/2U.) )
IF (ALBFDO .LT. 0.45) ALBEDO = 0.45
GO TO 370
360 ALBEDO = 0.85 - 0.C7* (RQRT(INDT/24.0) )
IF (ALPEDO.LT.0.6) ALBEDO=0.6
SROET BAVE RftDIAriON-RA - POSITIVE IHCORING
370 RA = RAD(IHER)*(1.0 - ALBEDO! * (1. 0-F)
LOKS WAVE RADIAriON - LW - POSITIVE INCORING
DEGHR = TX - 32.0
IF (DEGHR) -390, 390, 380
380 LW = F* 0.26*DEGHR * (1.0 - F) * (0. 2*DEGHR - 6.6)
GO TO 400
390 LP = F*0.2*DEGHR * (1 .0 - F) * (0.17*DEGHH - 6.6)
LW IS A LINEAR APPEOX. TO CURVES IN ,
259
-------
3368.
3369.
3370.
3371.
3372.
3373.
3374.
3375.
3376.
3377.
3378.
3379.
3380.
3381.
3382.
3383.
3384.
3385.
3386.
3387.
3388.
3389.
3390.
3391.
3392.
3393.
3394.
3395.
3396.
3397.
3398.
3399.
34CO.
3401.
3402.
3403.
3404.
3405.
3406.
3407.
3408.
3409.
3410.
3411.
3412.
3413.
3414.
3415.
3416.
3417.
3418.
3419.
3420.
3421.
3422.
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
400 IF (LH .LT. 0.0)
PAINM =0.0
FIG. 6, PL 5-3, IN SHOW HYDROLOGY. 6.6
IS ?VE BACK RADIATION LOST FROM THE SNOWPAZK
IN OPEN AREAS, IK LANGLEY3/HR.
CLOUD COVER CORRECTION
I.W = LW*CLDP
RAIN MELT
RAINHELT IS OPERATIVE IF IT IS
RAINING AND TEHP IS ABOVE 32 F
IF ((SFLAG .LT. 1J.AND.(TX . GI. 32.)) RAINH = DEGHR*PX/14«.
TOTAL KELP
RK = (LS * RAJ/203.2
203.2 LAN3LEYS REQUIRED TO PRODUCE I INCH
RUNOFF FROM S«IOW AT 32 DEGREES F
IF (PACK - IPACK ) 410, 430, 430
410 Rfi = RM*PACKRA
CONV = COKV*PACKFA
CONDS = C:ONDS*PACKBJ
RAINR = RAINH*PACKRA
IF (IHER - 6) 430, 420, 430
420 XLNEM = 0.01* (32.0 - TX)
IF (XLKSM .GT. XLHHLT) XLNHLT = XLNES
430 RADHF = RADSE * RH
CDKME = CDHKE * CONDS
COKME = CONKS + CONV
CHAIN = CRAIN * RAINK
KEL^f = SB * CONV »• CONDS + RAINH
IF (HFLT) 440, 470, 470
440 NEGHH = 0.0
IF (TX .LT. 32.) NESHH = 0.00695*(PACK/2.0)*(32.0 - TX)
HALF OF PACK IS USED TO CALCULATE
BAXIMUM NEGATIVE MELT
TP = 32.0 - (NEGMLT/(0.00695*PACM)
TP IS TEMP OF THE SK'OWPACK
0.00695 IS IN. MELT/IN. SNOW/DEGREE F
IF (TP - TX) 460, 460, 450
450 GM = 0.0007*(TP - TX)
NSGBLT = NEGKLT * GM
SHEGM = SNEGM + GH
460 IF (NEGMLT .GT. NEGHH) NEGULI = BEGUM
BELT =0.0
HELTING PROCESS BALANCE
470 PXBY = (1.0 - PACKHA)*PX
PX = PACKRA*PX
260
-------
3U23.
3424.
3U25.
3U26.
3427.
3428.
3429.
3430.
3431.
3432.
3433.
3434.
3435.
3436.
3437.
3438.
3439.
3440.
3441.
3442.
3443.
3444.
3445.
3446.
3447.
3448.-
3449.
3450.
3451.
3452.
3453.
3454.
3455.
3456.
3457.
3458.
3459.
3460.
3461.
3462.
3463.
3464.
3465.
3466.
3467.
3468.
3469.
3470.
3471.
3472.
3473.
3474.
3475.
3476.
3477.
C
- c
C
c
c
c
c
c
c
c
c
c
c
c
c
PXBY IS FRACTION OF PRECIP PULING ON BARE GROUND
IF (MELT + PX) 650,650,^80
SATISFY NEGHLT FROK PRECIP(PAIN) AND SNOWBELT
480 IF (MELT - NEGBLT) 490, 500, 500
l»90 NEGMLT = NEGMLT - KELT
KELT =0.0
GO TO 510
500 BELT = BELT - NE3BLT
NEGBLT = 0.0
510 IF (PX - NEGHLT) 520, 530, 530
520 NEGHLT = NEGHLT - PX
PACK = PACK * PX
PX = 0.0
GO TO 540
530 PX = PX - NEGBLT
PACK = PACK + NEGBLT
NEGBLT = 0.0
540 IF ((PX * BELT) ,EQ. 0.0} GO TO 550
COMPARE SNOHBELT TO EXISTING SNDWPACK AND VATER CONTENT OF
THE PACK
IF (KELT - PACK) 560, 560, 550
550 KELT = PACK * LIQH
DEPTH =0.0
PACK = 0.0
LIQK = 0.0
IKDT =0.0
GO TO 590
560 PACK = PACK - BELT
IF (SDEN .GT. 0.0) DEPTH = DEPTH - (BELT/SDEH)
IF (PACK .GE. (0.9*DEPTH)) DEPTH = 1.11*PACK
IF (PACK - 0.001) 570, 580, 580
570 LIQW = LIQH + PACK
PACK =0.0
580 LIQS = »C*PACK
IF (SDEN .GT. 0.6) LIQS = KC*(3.0 - (3. 33) *SDEN) *PACPC
IF (LIQS .LT. 0.0) LIQS =0.0
COMPARE AVAILABLE KOISTIJRE WITH AVAILABLE STORAGE IK SNOHPACK
-LIQS
590 IF ((LIQH + BELT + PX) - LIQS) 610, 610, 600
600 PX = KF.LT «• PX <• LIQB - LIQS
LIQH = LIQS
GO TO 620
610 LIQW = LIQW + BELT + PX
PX = 0.0
620 IF (FX - XLNHLT) 64C, 61)0, 630
630 PX = PX - XLNKLT
261
-------
3U78.
3U79.
31480.
3481.
31482.
3483.
3484.
3485.
3486.
3487.
3488.
3U89.
3490.
3491.
3492.
3493.
3494.
3495.
3496.
3497.
3498.
3499.
350C.
3501.
3502.
3503.
3504.'
3505.
3506.
3507.
3508.
3509.
3510.
3511.
3512.
3513.
3514.
3515.
3516.
3517.
3518.
3519.
3520.
3521.
3522.
3523.
3524.
3525.
3526.
3527.
3528.
3529.
3530.
3531.
3532.
PACK = PACK + XLNHLT
XICE = XICE + XLKHLT
XLNMLT = 0.0
GO TO 650
640 PACK = PACK + PX
XICE = XICE * PX
XLNKLT = XLNMLT - PX
PX = 0.0
650 IF (XICS .GT. PACK) XICE = PACK
C
C
C END MELTING PROCESS BALAKCE
C
660 IF (D^PTH .GT. 0.0) SDEN = PACK/DSPTH
IF (SDEN .LT. 0.1) SDEN = 0.1
C GROUNDHELP
IF (IHER - 12) 70C, 670, 700
670 PGBH = I>GM
IF (TP .LT. 5.0) TP = 5.0
IF (TP .LT. 32.) DSRH = DGd« - DGK*. 03* (32. 0 - TP)
IF (PACK - DGKM) 690, 690, 680
680 PX = PX * DGKM
PACK = PACK - DGMM
DEPTH = DEPTH - (DGHM/SDEU)
SGM = SGH + DGMK
GO TO 700
690 PX = PACK + PX + LIQW
SGB = SGH * PACK
PACK =0.0
DEPTH =0.0
LIQW = 0.0
NEGBLT =0.0
700 CONTINUE
PX = PX «• PXBY
SPX = SPX + PX
C
C HONTHLy SUHS
SOMSNH = SUKSNM * SUKSK
PXSNR = PXSNM *• PXONSN
MELEAM = MELEAM + SPX
RADBEH = HADMEM + RADKE
CDEWEK = CDRMEH * CDRHE
CONHEM = CONMSH * CONME
CBAINH = CRAINM * GRAIN
SGMH = SGHM + SG «
SNEGflH = SHEGHH + SNEGH
SEVAPK = S3VAPH + SEVAPT
C
C . YEARLY SONS
SUM5NY = SUMS NY * SUHSN
PXSNY = PXSNY * PXONSN
KELRAY = MEL2AY + SPX
RADKEY = RADSEY + RADBE
CDHBEY = CDRMEY + CDRBE
CONMEY = COKMEY •«• CONME
262
-------
3533.
3531.
3535.
3536.
3537.
3538.
3539.
35«0.
35U1.
35U2.
35«3.
35H5!
351*6.
3547.
3548.
35i*9.
355C.
3551.
3552.
3553.
3554.
3555.
3556.
3557.
3558.-
3559.
3560.
3561.
3562.
3563.
3564.
3565.
3566.
3567.
3568.
3569.
3570.
3571.
3572.
3573.
357«*.
3575.
3576.
3577.
3578.
3579.
3580.
3581.
3582.
3583.
3584.
3585.
3586.
3587.
C
C
c
c
c
c
c
c
c
c
GRAINY = GRAINY + GRAIN
SGMY = SGMY * SGK
SNEGMY = SNEGMY * SNEGM
SEVAPY = SEVAPY + SEVAPT
SUHSN =0.0
PXONSN =0.0
RAD.1E = 0.0
0.0
0.0
ZE8D HOURLY VALUES
CDRKE
CONKE
GRAIN
=0.0
SGM = 0.0
SNEGM =0.0
SEVAPT = 0.0
SPX =0.0
SNOOT (IHRR,1)
SNOUT{THRR,2)
SNOUT(IHRS,3)
SNOOT(IHRR,«)
SNOUT(IHRR,5)
SNOUT(IHHR,6)
SNOOT(IHPR,7)
SNODT(IHRR,8)
SNOOT{IHRR,9)
SNOUT(IRRR, 10)
SNOUT (IHP.R, 11)
SNOOT (IHRR, 12)
SNOOT(IHRR,13)
SNOUT(IHRR,ia)
SNOUT (IHRR, 15)
SNOUT(IKRR,16)
SNOWMSLT OUTPUT
PACK
DEPTH
SDEN
ALBEDO
CLDF
NEGHLT
TX
RA
LH
PX
HELT
CONV
.RtlNH
CONDS
XICE
IF (UNIT.LT.1.0R.HYCA1.GT.1) GO T3 730
CONV2RSIOS TO HETRIC SNOH OOTPOT
SNOOT (IHRR, 1) = PACK*KMPIN
StJOOT(I«RR,2) = DEPTH*f!HPIN
SNOUT(IHRR,6) = NEGMLT*MMPIN
SNOOT(IHRR,7) = !IQK*?inPIN
SNOUT(IHRH,8) = 0. 556* (TX-32.01
DO 720 ISNOUT=11,16
SKOUT(IHER,ISNOUT) = SNOOT (IHRR, ISNOUT) *H«PI»I
720 CONTIHOE
730 IF (HYCAL. G,T. 1) SO TO 760
IF (IHRR .HE. 21) GO TO 760
WHITE (6,U020) «SAH(IZ),KHA3(IX>,DAY
WRITE(6,«000)
DO 750 1=1 ,2t
WRITE (6,«010) I,(SNOUT(I,BH),HH=1,16)
263
-------
3588.
3589.
3590.
3591.
3592.
3593.
3594.
3595.
3596.
3597.
3598.
3599.
3600.
3601.
3602.
3603.
3604.
3605.
3606.
3607.
3608.
3609.
3610.
3611.
3612.
3613.-
3614.
3615.
3616.
3617.
3618.
3619.
3620.
362'1.
3622.
3623.
3624.
3625.
3626.
3627.
3628.
3629.
3630.
3631.
3632.
3633.
3634.
3635.
3636.
3637.
3638.
3639.
3640.
3641.
3642.
DO 740 MM=1,16
740 SNOUT(I,HM)=0.0
750 CONTINUE
C
C
4000 FORMAT('0','HOUR
* TX RA LW
PACK DEPTH SDEN ALBEDO CLDF KEGMBLT LIQif
PX MELT CONV RAIHM CORDS ICE1)
4010 FOPHATC ',I2,2X,F6.1,2X,F6.1,5(1X,F6.3) ,1X,F6. 2,2(1X,F4. 0)
*5(1X,F7.4) ,2X,F5.2)
4020 FORMAT ('0',25X,'SNOBMELT OUTPUT FOR',4X,A4,A4,2X,I2)
CORRECT HAPER BALANCE FOS SNOHHELT
PACK AND SNOW EVAP
PRR IS INCOMING PRECIP
PX IS MOISTURE TO THE LAND SURFACE
SEVAP IS SNOB EVAP - NEGATIVE
760 SNBAL = PRHRtSEVAP-PX-PACK+PACK1-LIO.K+LIQW1
IF ( (SNBAL.LT.O.OCC1) .AND. (SNBAL.GT.-0.0001) ) SNBAL=0.0
TSNBAL = TSNBAL + SNBAL
C
C
C
C
C
C
C
C
c
PACK1
LIQH1
PACK
LIQK
END SSOHKELT
0) PR=PX*TIHFAC/60.
INTERCEPTION FONC.
* * *
C
C
c
c
c
c
c
770 IF (IEND .GT.
C
C
C
c
c
c
c * * *
c
c
C EPXH - MAX. INTERCEPTION STORAGE
C SCEP - EXISTING INTSR. STORAGE
C EPX - AVAILABLE INTER. STORAGE
C RUI - IMPERVIOUS RUNOFF DURING INTERVAL
C .
C
EPX=EPXM-SCEP
IFJEPX.LT. (0.0001)) EPX=0.0
IF (PR-EPX) 790,780,780
780 P3= PS-EPX
SCEP = SCEP+EPX
GO TO 800
790 SCEP = SCEP+PR
P3=0.0
Py IS TOTAL HOISTORE INPOT T3
THE LAND SURFACE FROM PRECIP
AND SNOWBELT DURING THE HOUP
IEHD>C INDICATES SNOKKELT
OCCURRED DURING THE HOUR
264
-------
36«»3.
3644.
36H5.
3646.
3647.
3648.
3649.
3650.
3651.
3652.
3653.
3654.
3655.
3656.
3657.
3658.
3659.
3660.
3661.
3662.
3663.
3664.
3665.
3666.
3667.
3668.
3669,
367C.
3671.
3672.
3673.
3674.
3675.
3676.
3677.
3678.
3679,
3680.
3681.
3682.
3683.
3684.
3685.
3686.
3687.
3688.
3689.
3690.
3691.
3692.
3693.
3694.
3695.
3696.
3697.
C
c ***
C
c
C RXBI
C ROS
C RES
C
C
800
810
820
830
840
850
860
870
880
890
900
910
920
930
C
C
c
c *
c
c
940
C
950
960
970
980
990
Rt!=0.0
RUI<=0.0
OVERLAND IMPERVIOUS FLOW 80UIIHG ***
VOLUME OF IMPERVIOUS OVERLAND FLOW OS SURFACE
ROSBI = VOLUME OF OVERLAND IMPERVIOUS FLOH TO STREAH
RESBI = VOLUME OP OVERLAND IMPERVIOUS Q REMAINING OS SURFACE
IF (A) 810,810,820
RUI=0.0
GO TO 930
RXBI=P3+RESBI
IF (RXBI-0.001) 830,830,840
ROI=RXBI*A
RXBI=0.0
POSBI=RUI
GO TO 930
F1= RXBI- (RESBI)
F3 = (RESBI)* EXBI
IF (RXBI- (RESBI)) 860,060,850
DE= DECI*((F1)**3.6)
GO TO 870
DE = (F3)/2.0
IF (F3-(2.0*DE)) 890,890,880
DE=(F3)/2.0
IF ((F3)-(.C05)) 900,900,910
ROSBI= 0.0
GO TO 920
DUHV=(1.0 + 0.
(F3/(2.0*DE)) **3.) **1.67
ROSBI=(TIMFAC/60.) *SRCI* ( (F3/2, ) **1.67) *DUMV
IF ((ROSEI) .GT. (,95*RXBIJ) R03BI=. 95*RXBI
RESBI= RXBI-ROSBI
RUI=ROSBI*A
RU=RUI
* *
INTERCEPTION EVAP
* * *
IF ((NDMI .EQ. 0).A)«D. (IHIN .EQ. 0) )
GO TO 1000
IF (SCEP) 1000,1000,960
IF (SCEP-EPIK) 970,980,980
F.PIN = EPIN - SCEP
SUET = SKST * SCEP'
SCEP =0.0
GO TO 1000
SCEP=SCEP-EPIS
SNET=SNET*EPIN
EPIS =0.0
GO TO 950
265
-------
3698.
3699.
37CO.
3701.
3702.
3703.
3704.
3705.
37C6.
3707.
3708.
3709.
3710.
3711.
3712.
3713.
3714.
3715.
3716.
3717.
3718.
3719.
3720.
3721,
3722.
3723.'
3721,
3725,
3726.
3727.
3728.
3729.
3730.
3731.
3732.
3733.
3734.
3735.
3736.
3737.
3738.
3739.
3740.
3741.
3742.
3743.
3744.
3745.
3746.
3747.
3748.
3749.
3750.
3751.
3752.
C
C
C
C
C
C
C
C
C
C
i
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
*** INFILTRATION FOKC. ***
P4 IS TOTAL MOISTURE
SHSD = SURFACE DETENTION AND INTERFLOW
RXX = SURFACE DETENTION
SGXX = INTERFLOW COMPONENT
RGX = VOLUME TO INTEE. DEtEN STOR.
1000 P4 = P3 + RESB
PESB1 = RESB
IF (P4 - D4F) 1010,1010,1020
1010 SI!RD=(P4**2)/(2.0*D4F)
GO TO 1030
1020 SKRI>= P4 - 0.5*D4P
IF (P4 - D4RA) 1030,1030,1040
1030 RXX = (P4**2)/(2.0*D4Rft)
GO TO 1050
1040 RXX= P4 - 0.5*D4RA
1050 KGXX = SHRD-HXX
*** UPPER ZONE FUNCTION ***
PEE - * SURFACE DETENTION TO OVERLAND FLOW
D7.SB - UPPER ZONE STORAGE
U7.S - TOTAL UPPER ZONE STORAGE
8UZB - ADDITION TO U.Z. STORAGE DURING INTERVAL
IF (UZSB.LT.0.0) UZSB=0.0
UZRA= UZSB/UZSN
IF (UZRA.GT.6.0) GO TO 1060
IF (UZRA.GT.2.0) 50 TO 1070
UZI= 2.0*ABS((UZRA/2.0)-1.0) *1.0
PRE= (UZRA/2.0)*((1.0/(1,0 + UZI) ) **UZI)
GO TO 1080
1060 PRE =1.0
GO TO 1080
1070 UZI= (2.0*ABS (UZRA-2.0))*1.0
PRE= 1.0-((1.0/(1.0+UZI))**OZI)
1080 RXB= RXX* PRE
RGX=RGXX*PRS
RGXX=0.0
RUZB=SHRD-RGX-RXB
UZSB=UZSB+RUZB
RIB = P4 - RXB
* * *
UPPER ZONE EYAP
* * *
REPIN - ACCUH DAILY EVAP POT. FOR L.Z. AND GRDHATER, I.B
266
-------
3753.
375U.
3755.
3756.
3757.
3758.
3759.
3760.
3761.
3762.
3763.
3764.
3765.
3766.
3767.
3768.
3769.
3770.
3771.
3772.
3773.
3774.
3775.
3776.
3777.
3778.'
3779.
3780.
3781.
3782.
3783.
3784.
3785.
3786.
3787.
3788.
3789.
3790.
3791.
3792.
3793.
3794.
3795.
3796.
3797.
3798.
3799.
3800.
3801.
3802.
3803.
3804.
3805.
3806.
3807.
C
C
C
C
1090
,
1100
1110
1120
1130
1140
C
C
C
C
C
C
C
C
C
1150
C
c ***
C
c
C RXB
C KO
C FE
C
C
PORTION NOT SATISFIED FROH U.Z.
IF ((NUMI ,
GO TO 1150
EQ. 0).AND. (IHIN .EQ. 0)) GO TO 1090
50 TO 1150
IF (F.PIN. LE. (0.0))
EFFECT=1.0
IF(OZR£-2.0) 1120,1120,1100
IF (UZSB-EPIN) 1140,1140,1110
UZSB=UZSB-EPIN
RUZB= RUZB-EFIN
SNET=SKET+PA*EPIN
GO TO 1150
SFFF,CT= 0.5*OZRA
IF (EFFECT.LT. (0.02))
IF (OZSB-EPIN*EFFECr)
UZSE=UZSB - (EPIN*EFFECT)
RUZR= RUZB-(EPIN*EFFECT|
EDIFF= (1.0-EFFEC?)*EPIS
REPIN=REPIN * EDIFF
EDIFF=0.0
SRET= SUET + (PA*EPIN*EFFECT)
GO TO 1150
BDIFF= EPIN - UZSB
PEPIN= FSPIN + EDIFF
EDTFF=0.0
SBET= SNET
OZSB=0.0
ROZB=0.0
EFFECr=0.02
1140,1140,1130
* * * *
INTERFLOW FUNCTION * * *
SRGX - INTERFLOW DETENTION STDER.GE
INTF - IKT3PFLOW LEAVING STORAGE
SBGXT - TOTAL IHTEPFLOS STORAGE
PGXT - TOTAL IKTERFLOW LEAVING STORAGE DUBIKO IUTERVAL
INTF = LIEC4*SEGX
SRGX=SRGX*(BGX*PA)-IKTF
BO=EU «• INTF
SPGXT= SRGX? * (E3X*PA-INIF)
RGXT=EGXT + INTF
OVEPLAND
= VOLUHE TO OVERLAID SURFACE DETENTION
KOSB = VOLUME OF OVERLAND FLOW TO STREAM
RE3B = VOLUME OF OVERLAND Q REMAINING ON SURFACE
F1= P.XB-(RESB)
F3= (RFSB)+ RXB
OVEPLAND PERVIOUS FLOW ROOTING ***
267
-------
3808.
3809.
3810.
3811.
3812.
3813.
3814.
3815.
3816.
3817.
3818.
3819.
3820.
3821.
3822.
3823.
3824.
3825.
3826.
3827.
3828.
3829.
3830.
3831.
3832.
3833.-
3834.
3835.
3836.
3837.
3838.
3839.
3840.
3841.
3842.
3843.
3844.
3845.
3846.
3847.
3846.
3849.
3850.
3851.
3852.
3853.
3854.
3855.
3856.
3857.
3858.
3859.
386C.
3861.
3862.
IF
1160 DE
GO
1170 DE
1180 IF
1190 DZ
1200 IF
1210 P.O
GO
1220
RO
IF
1230 PE
C
C
C
C
C
C DEEPL
C PERCB
C . PEHC
C INFLT
C EOS
C
C
1240
1250
C
IF
C
1260
C
1270
1280
RE
U
RO
IF
C
C END OF
C
(RXB-(BESB)) 1170,1170,1160
DEC*((F1)**0.6)
TO 1180
(F3J/2.0
(F3-(2.0*DE)) 1200,1200,1190
(F3)/2.0
((?3)-(.005J) 1210,1210,1220
F.OSB= 0.0
TO 1230
DUHV=(1.0+0.6*{F3/(2.0*DE)) **3.)**1.67
ROSB=(TTMFAC/60.)*SP.C*{ (F3/2.) **1.fi7) *DUMV
IF ((P.OSB).GT. (,95*HXB) ) ROSB=0. 95*RXB
8= EXB-EOSB
ROSE = ROSB*PA
ROSINT = P.OSB * INTF
* * *
UPPER ZONE DEPLETION * * *
- DIFFERENCE IN UPPER AND LOHER ZONE RATIOS
- UPPER ZONE DEPLETION
- TOTAL U. Z. DEPLETION
- TOTAL INFILTFATION
- TOTAL OVERLAND FLOW TO THE STREAM
IF ((NUMI .EQ. 0).AND.(IHIN
PERCB =0.0
GO TO 1280
,EQ. 0)) GO TO 1240
DEEPL= ( (UZSB/UZSN)- (LZS/LZSNII
IF (DEEPL-.01) 1280,1280,1253
PERCB=0.1*INFIL*U7,SN*{DEEPL**3>
(SNOW .GT. 0) PERCB = PERCB*D»PX
IF (UZSB - PERCB)
PERCB = 0.0
GO TO 1280
1260,1260,1270
UZSB=UZSB-PERCB
PBRC=PERC+PERCB
RUZB = RUZB - PERCB
INFL= PH-SHRD
INFLT=INFLT *
S = RESS
S= UZS +
= EOS +
(UZS .LE
INFL
* RESB
RtJZB
ROSB
0.0001) UZS=0.0
BLOCK LOOP
RU=RU + EOS
IF ((RFSS).LT.(0.0001)) GO Tb 1290
GO TO 1300
268
-------
3863.
3864.
3865.
3866. •
3867.
3868.
3869.
3870.
3871.
3872.
3873.
3874.
3875.
3876.
3877.
3878.
3879.
3880.
3881.
3882.
3883.
3884.
3885.
3886.
3887.
3868r
3889.
3890.
3891.
3892.
3893.
3894.
3895.
3896.
3897.
3898.
3899.
3900.
3901.
3902.
3903.
3904.
3905.
3906.
3907.
3908.
3909.
3910.
3911.
3912.
3913.
3914.
3915.
3916.
3917.
1290 LZ
PB
RE
1300 IF
GO
1310 LZ
SR
C
C
c * * *
C
C SBAS -
C SRCH -
C PEEL - J
C F1A -
C
C K24L -
C
1320
1330
1340
F'
Gl
SI
Rl
SI
(
(
C
c * * *
c
c
c LOS - :
c
C NOT
C
i:
GI
1350 I
LI
S
G
S
B
I
C
c * * *
= LZS + RESS
PBSS =0.0
= 0.0
IF (SRGXT.LT.(0.0001)) 60101310
GO TO 1320
LZS + SRGXT/PA
SRGXT =0.0
SSGX = 0.0
* * * LOHEH ZONE AND GROONDHATER * * *
SRCH - SUM OF GF.DWATER RECHARGE
OF INFILTRATION AND fJ.Z. DEPLETION ENTERING L.Z
F1A - GPOUSDWATER RKCHAEGE - IE. PORTION OF INFIL.
AND U.Z. DEPLETION ENTERING GRDKATER
- FRACTION 07 F1A LOST TO DEEP GRDWATER
= 1.5*ABS((LZS/LZSH)-1.0) H.O
PREL=(1.0/{1.0*LZI))**LZI
IF (LZS.LT.LZSN) PREL=1.0-PREL*LNRAT
F3= PREL*(INFLT)
F^A = (1.0-PREL) *INFLT
IF ((NUMI .EQ. 0) .AND. (I!1IN , EQ. 0)) GO TO 1330
GO TO 13UO
F3 = F3 -•• PREL*PERC
F1A = F1A * (1.C-PREL)*PERC
LZS= LZS*F3
F1= F1A*(1.0 - K2i»L)*PA
GWF=SGK*LKKU*(1.0 + K¥*GWS)
GWF
RD=RO*GWF
SPCH= F1A*K2«L*PA
SG»=SGW - GKF * F1
GWS=G»S * F1
GROUNDHATER EVAP
* # *
NOTE: EVAP FROM GRDWATER AND LZ IS CALCULATED ONLY DML*
IF ((HRFLAG.EQ.1).AND. (IHRS.E3.21)) GO TO 1350
GO TO 1430
IF (GHS ,GT. 0.0001) GWS = 0.97*GWS
LOS= SGW*K24EL*REPIN*PA
SGW=SGW - LOS
GHS=GHS - LOS
SNST= SNET + LOS
ESPIK= FEPIN - LOS
IF (GWS.LT. (0.0)) GKS=0.0
LOWER ZONE EVAP
* * *
269
-------
3918.
3919.
3920.
3921.
3922,
3923.
3924.
3925.
3926.
3927.
3928.
3929.
3930.
3931.
3932.
3933.
3934.
3935.
3936.
3937.
3938.
3939.
3940.
3941.
3942.
3943.-
3944.
3945,
3946.
3947.
3948.
3949,
3950.
3951.
3952.
3953.
3954.
3955.
3956.
3957.
3958.
3959.
3960.
3961.
3962.
3963.
3964.
3965.
3966.
3967.
3968.
3969.
3970.
3971.
3972.
C
C ABTR - EVAP LOST FROM L.Z.
C
C
IF (REPIN.LT. (0.0001)) GOTO 1420
LSRAT = LZS/LZSH
IF (K3-1.0) 1370,1360,1360
1360 KF=50.0
GO TO 1380
1370 KF=0.25/(1.0-K3)
1380 IF (PSPIN - (KF*LNBAT)) 1390,1400,1400
1390 AETR = REPIN*(1.0-(REPrN/(2.0*KF*L8HAT)))
GO TO 1410
1400 AF.TR= 0.5*(KF*LNPAT)
1410 IF (K3.LT. (0.50) ) AETR= AETP*{2. 0*K3)
LZS=LZS - AETR
SNET= SNET + PA*AETR
ASNET = ASNET + LOS * Pftf&EIR
1420 REPIN = 0.0
1430 SNETI = SNET - SNET1
C
C
C
C HBAL - HATER BALANCE IN THE INTERVAL
C TBBAL - ACCUMULATED WATER BALANCE
C
C
1440 WBAL = (LZS-LZSI+nZS-OZSltPESS-RESSl) *P A+ (SNET-SNFT1+S3B-S3 W1+
X SCEP-SCF.FUSRCIH-SR3KT-SRGXTH-RU-PR) t (BESBI-RESSI1 )
1450 IF ((HBAL .LE. 0.0001) .AND. (BBAL . GE. -0,0001)) HBAL = 0.
THBAL=THBAL+WBAL
C
DPS = F1A*PA
DPST = DPSX + DPS
C
C
C RESETTINS VABIABLES
C
LZS1=LZS
UZS1=UZS
RESS1=RESS
SCEP1=SCEP
SRGXT1=SPGXT
SGH1=SGH
SNET1=SSET
RESBI1=RESBI
ASBAS = ASBAS * SBAS
ASRCH = ASRCB + SUCH
AP3 = APR + PfiR
ARfl = ARU + RU
ARUI = AROI * RUI
AROS = ARCS + ROS
ARGXT = ARGXT * RGXT
IF ((NUHI.EQ.O) .AND. (IHIM.EQ. 0>) GO TO 1460
GO TO 1470
*?.
0
270
-------
3973.
3974.
3975.
3976.
3977.
3978.
3979.
3980.
3981.
3982.
3983.
3984.
3985.
3986.
3987.
3988.
3989.
3990.
3991.
3992.
3993.
3994.
3995.
3996.
3997.
3998.-
3999.
4000.
4001.
4002.
4003.
4004.
4005.
4006.
4007.
4008.
4009.
4010.
4011.
4012.
4013.
4014.
4015.
4016.
4017.
4018.
4019.
4020.
4021.
4022.
4023.
4024.
4025.
4026.
4027.
1460 AEPIN = AEPIN + EPIN1
ASNET = ASNET + SNETI
1470 AROSE = AROSB * BOSB
ATJ5TF = AINTF * INTF
ASOSIT = AROSIT * ROSINT
1480 CONTINUE
C
C
C
C
C
C
C
C
C
C
C
CUMULATIVE RECORDS
PRTOM = PRTOM + APR
EPTOM = EPTOH + AEPIN
RUTOM = RUTOM * ABU
ROSTOM = HOSTOM * AROS
RITOM = RITOM * ARUI
RINTOM = RINTOM * ARGXT
NEPTOM = NEPTOM * ASNET
BASTOM = BASTOM + ASBAS
RCHTOM = RCHTOM + ASRCH
ROBTOM = ROBTOM + AROSE
ROBTOT = ROBTOT t AROSB
fNFTOM = INFTOM + AINTF
INFTOT = INFTOT + AINTF
ROITOM = ROITOM'+ AROSIT
ROITOT = ROITOT t AROSIT
PRTOT =
EPTOT =
ROTOT =
ROSTOT =
RITOT =
RINTOT =
NEPTOT =
BASTOT =
RCHTOT '
PRTOT * APE
EPTOT * AEPIN
HUTOT + ARU
• ROSTOT * AROS
RITOT + ARUI
'RINTOT * ARGXT
•• NEPTOT * ASNET
' BASTOT + ASBAS
•• RCHTOT * ASRCH
LOGICAL VARIABLES LAST AND P8EV ARE USED TO DETERMINE
BEGINNING AND END OF EACH STORM. STORtt BEGINS IF RU
IS LESS THAN HYMIN IN ONE TIRE INTERVAL, AND GRSATFR IN
THE FOLLOWING ONE (PHEV=.FALSE. , LAST=.TPUE.). STORM ENDS
IF THE OPPOSIT OCCURS (PREV=.TRUE. , LAST=. FALSE.)
FUINCM=RU
HU = (RU*AREA*4356C.)/(TIMFAC*720.)
IF ((RU.GE.HXHIN) .AND,(TF.LE.2)) G3 TO 1490
LAST=.FALSE.
GO TO 1570
1495 LAST=.TRUE.
IF (PREV) GO TO 1550
COUNT SUHBER OF STORKS AND RECORD TIME OF STORM BEGINNING
NOS=NOS+1
C
C
C
271
-------
4029.
4030.
4032!
4033.
4034.
4035.
4036.
4037.
4038.
4039.
4040.
4041.
4042.
4043.
4044.
4045.
4046.
4047.
4048.
4049.
4050.
4051.
4052.
4053.-
4054.
4055.
4056.
4057.
4058.
4059.
4060.
4061.
4062.
4063.
4064.
4065.
4066.
4067.
4068.
4069.
4070.
4071.
4072.
4073.
4074.
4075.
4076.
4077.
4078.
4079.
4080.
4081.
4082.
C
C
C
C
C
C
C
C
C
C
C
1
1
IF (NOS.EQ. 1) WKITF.(6,4045)
WRITE (6,4050) NOS,MNAH (IZ),MHAM(IX) ,YEA?
STEBGN(1)=MNAM(IZ)
STRBGN(2)=DAY
STRBGF(3)=IHE
STK3GN(4)=IHIN
INITIALIZATION OF VARIABLES F3R STOBM SOMHARY
NOSI=0
TOTRUN=0.
PEAKRU=0.
ACSEDT=0.
SEDMX=C.
SEDTSC=0.
SEPHXC=0.
DO 1495 1=1,5
ACPOLT(I)=0.
PLTHX(I)=0.
POLTSC(I)=0.
PLTMXC(T)=0.
1495 LHTS(I)=0
PEIHT INITIAL CONDITION FOR A NEH STOEH
IF (HYCAL. EQ. 1) (50 TO 1530
WRITE (6,1*060) WHT,ARUH^
CALCULATE ASD PKINT KEAN ACCOMUI.ATTOU F05 (1) EACH
LAND TYPE USE (WEIGHTED BY % OF PEEVIOOS AND IHPERVIOUS
AEEAS), (2) THE ENTIBE WATERSHED AND THE TOTAL PEP.VTOTJS
AND IMPEEVIODS AREAS (WEIGHTED BY *. 07 VARIOUS LAKD TYPE USE)
TEH1=C.O
TEM2=0.0
TEM3=D.O
TEM«=0,0
DO 1510 1=1,NLAND
TEB=SRER(I)*(1-IMPK(I))+TS (I)*IHPK(I)
WHFON1=(AE(I)/AREA)*(1-THPK(I) )
WHFUN2=(Afi(I)/AEEAJ *I«PK (I)
TEM1=TEM1+SEER(I)*WHFUN1
TEH2=TEK2+TS(T)*WHFON2
TEM3 = T£«3<-WHFHN1
TEH1=TEH«+KHFUN2
IF (UNIT. GT.-1): GO TO 1500
WEITE (6,4070) (LNDOSE (IK,I|,IK = 1,3) ,TEH,SRER(I),TS(I)
GO TO 1510
1500 TEM5=SKER(I)*2.2!*
TEM6=TS(I)*2.2«
TEM=TE«*2.2I»
WRITE (6,4070) (LHDUSE(IK,H ,IK=1,3),TEM,TEH5,TEM6
IF (LBTS(I).EQ.I) WRITE (6,!»0"*0(
1510 CONTINUE
IF (NLAND.EQ. 1) GO TO 1530
IF (TEM3.GT.O.O) TFfl1=TEH1/rEH3
272
-------
4083.
4084.
4085.
4086.
4087.
4088.
4089.
4090.
4091.
4092.
4093.
4094.
4095.
4096.
4097.
4C98.
4099.
4100.
4101.
4102.
4103.
4104.
4105.
4106.
41C7.
4108,
4109.
4110.
4111.
4112.
4113.
4114.
4115.
4116.
4117.
4118.
4119.
4120.
4121.
4122.
4123.
4124.
4125.
4126.
4127.
4128.
4129.
4130.
4131.
4132.
4133.
4134.
4135.
4136.
4137.
1520
1530
1540
1545
1550
C
C
C
i-
1560
1570
C
C
C
IP (TEM3.LE.O.O) TEM1=0.0
IF (TSM4.GT.O.O) TrN2=TEM2/rEH4
IP (TEM4.LE.O.O) TEM2=0.0
TEH=TEK1*(1-A)+TSM2*A
IP (UNIT.LT.1) GO TO 1520
TEM=TEM*2.24
TEM1=TEM1*2.24
TFH2=TEH2*2.2«
WRITS (6,4080) TEH,TEM1,TEH2
CONTINUE
WRITE (6,4090)
IF (HYCAL.GT.1) 60 TO 1540
WRITE (6,4110) UFL
GO TO 1550
WRITE (6,TIT)
WRITE (6,4100) ((QUALINJI,J) ,1=1,3) ,J=1,NQnAL)
IF (UNIT.EQ.-1) SO TO 1545
WRITE (6,FORK) UFL,UTHP
QHETRC=RU*.0283
PRINT DATE,TIME,AHD FLOW
WRITE (6,4130) HNAH(IZ) ,DAY, IHR, IHIH
NOSI=NOSI+1
FLOUT=EU
IF (UNIT, GT.O) FLOtIT=Q8ETRC
WRITE (6,4120) FLOUT
IF (HYCAL.NE.4) GO TO 1560
BECOOT(2)=KKAH(IZ)
RECOUT(3)=DAY
PECODT(4)=IHR
RECOOT(5)=IKIN
IF (RU.GT.PEAKEU) PEAKRO=RH
TOTRON=TOTEUN+RDINCH
APR = 0.0
AEPIN =0.0
ARU = 0,0
AH 01 = 0.0
AROS = 0.0
ARGXT =0.0
ASNET =0.0
ASBAS = 0.0
ASRCH =0.0
AROSE =0.0
AINTF =0.0
AROSIT = 0.0
IF (LAST.OS..NOT.PREV) GO TD 1640
STORM SUMMARY
IF (UNIT.LT.1) GO TO 1590
TOTRON=TOTRUN*25.4
PEAKR(J=PEAKRU*0.0283
ACS£DT=ACSSDT*0.9072
SEDHX=SEDBX*0.454
273
-------
4138.
M139.
4140.
4141.
4142.
4143.
4144.
4145.
4146.
4147.
4148.
4149,
4150.
4151.
4152.
4153.
4154.
4155.
4156.
4157.
4158.
4159.
4160.
4161.
4162.
4163..
4164.
4165.
4166.
4167.
4168.
4169.
4170.
4171.
4172.
4173.
4174.
4175.
4176.
4177.
4178.
4179.
4180.
4181.
4182.
4183.
4184.
4185.
4186.
4187.
4188.
4189.
4190.
4191.
4192.
DO 1580 I=1,NQUAL
ACPOLT(I)=ACPOLT(I) *0.454
1580 PLTKX(I)=PLTKX(I)*0.454
1590 WRITE (6,4150)
IP ("YCAL. EQ.4) WRITE (4,4150)
WRITE (6,4140) NOS
WRITE (6,4160) NOSI,(SrRBGN(I) ,1 = 1,4) ,MNAH(IZ) ,DAY,IHB,
*
* IMIN,DEPW,TOTR&N,nFL,PEAKS;J
IF (HYCAL.EQ.1) 30 TO 1610
VRITE (6,4170) ((QUA1XK(I,J| ,1 = 1^3) , J=1,NQUAL) !
HRITE (6,4180) WHT,ACSEDT, (RCPOLt(I) ,1=1,NQUAL)
WRITE (6,4190) WH«T,SEDMX, (PLTMX(I) ,I=1,NQUAL)
SEDTSC=SEDTSC/NOSI
DO 1600 1=1 ,5
1600 POLTSC(I)=POLTSC(I)/NOSI
DO 1605 1=1 ,NQUAL
DUH1 (I)=POLTSC(I)/SCALEF(I)
1605 DUS2 (I)=PLTMXC(I)/SCALEF(I)
WRITE (6,4200) SEDTSC,(DUM1(I) ,I = 1,NQOAL)
WEITE (6,4210) SED«XC,(DOM2(I),I=1,NQUAL)
1610 WRITE (6,4150)
C
C
C
ACCUMULATION FOR OVERAIL STORM SUMMARY
IF (HYCAL.NE.1) 30 TO 1620
STKCII (KOSY*HOS,1)=TOTRUN
STKCH(ROSY*NOS,2) =PEAKRO
GO TO 1640
1620 IF (HOSY+NOS.GT.200) GO TO 1640
STMCH(KOSY+HOS,1)=ACSEDT
ST«CH(NOSY+NOS,2)=SEDKX
STHCH(NOSY+NOS,3)=SEDTSC
STBCH(NOSY+NOS,4) =SEDHXC
DO 1630 I=1,NQOAL
KI=4*I
STHCH (NOSY*NOS,Kl4l) = ftCP01.T(I)
STHCH(HOSY + NOS,KI + 2) =PLT«X(I)
STHCH (KOSY*NOS,KI + 3)=POLTSC(I)
STHCH(NOSY + NOS,KI + 4) =PLTHXC(I)
1630 CONTINUE
WRITE (6,4030)
1640 COBTnsUE
PREV=LAST
FORHAT STATEHENIS
4030 FORMAT ('0')
4040 FORMAT ('+«,70X,«** LIMIT REACHED.**1)
4045 FORMAT (M1)
4050 FORMAT (3(/),130('*') ,2(/) ,55X,«OUTPUT POE SPORH NO.«,I3,
1 ' - ',A4,A4,1X,I4)
4060 FORMAT (//,1 X,'ACCUHtJLATION OF DEPOSITS ON GR00KD AI THE ',
1 'BEGINNIN3 OF STOBM,',A4,•/•,A4,
2 /,3X,'LAKD USE',8X,'»EIGHrED MEAN',9X,«PERVIOOS',8X,
C
C
C
274
-------
4193.
4194.
4195.
4197!
4198.
4199.
4200.
4201.
4202.
4203.
4204.
4205.
4206.
4207.
4208.
4209.
4210.
4211.
4212.
4213.
4214.
4215.
4216.
5000.
5001.
5002.
5003.
5004.
5005.
5006.
5007.
5008.
5009.
5010.
5011.
5012.
5013.
5014.
5015.
5016.
5017.
5018.
5019.
5020.
5021.
5022.
5023.
5024.
5025.
5026.
5027.
5028.
5029.
5030.
t
i
i
i
i
i
i
i
i
I
/
i
i
i
C
C
c
c
c
c
c
c
4 'IMPERVIOUS',/)
407.0 FORMAT (1 X.3A4 ,9X ,F7. 3, 2 (1 2X,F7. 3) )
4080 FORMAT (3X, 'WEIGHTED tIEAN' ,6X, F7. 3 , 2 (12X, F7. 3))
4090 FORMAT (//)
4100 FORMAT (2X,'DATE TIME FLOW TEMP DO(PPM)
1 5(4X,3A4))
4110 FORMAT (' DATA TIME FLOW («,A4 •)'»
4120 FORMAT (• *« ,1 4X ,F8. 3)
4130 FOBHRT (1X,A4,1X,12,1X,I2,•:' , 12)
4140 FORMAT (/,' SUMMARY FOR STORM # ',13)
4150 FORHAT (29 (' '))
4160 FORMAT
1
SEDIKENTS
(/,« NUMBER OF TIMK INPERVALS',14,/,
2
3
it
' STORM BEGINS',3X,A4,1X,12,1X,12,':',!?,/,
« STORM ENDS',5X,A4,1X,I2,1X,I2,':',I2,/,
' TOTAL FLOW (',A4,•)',1X,F13. 3,/,
1 PEAK FLOW (',A4,')',2X,F10.3)
4170 FORMAT (37X,1 SEDIMENTS ',4X,5 (»X,3A4))
4180 FORMAT (• TOTAL WASHOFF (',A4,«) «,1HX,F10*2,5X,5(F14. 5,2X))
4190 FORMAT (' MAX WASHOFF (•,A4,•/15BIN) «,10X,F10.2,5X,5(F14. 5, 2X) )
4200 FORFAT (« HEAN CONCENTRATION (GM/L) ',9X,F10.2,5X,5(F14. 5, 2X))
4210 FDRMAT (' MAX CONCENTRATION (GM/L) ',10X,F10.2,5X,5(F14.5, 2X))
RETURN
END
SUBROUTINE QUAL
POLP(5,5) ,POLI (5,5) ,EIM(5) ,POLTLO{5,5) ,POLT(5) ,
i TSS(5),RER(5),ERSS(5),SER(5),TEMPX(24)
COHROK /ALL/ RU.FYEIN.GYCAL,DPSI,UNIT,TIMFAC,LZS,AREA,RESB, SFLAG,
1 RESB1,ROSB,SRGX,IHrF,RGX,RUZB,UZSB,PERCB,RTB,P3,TF,
2 KGPLB,LAST,PREV,rEMPX,IHR,IHP.R,PR,RUI,ft,PA,SWF,NOSY,
3 SRER(5) ,TS{5) ,LNDUSE(3,5) , AR (5) , QOAt IN (3, 5) , N3SI ,N03 ,
4 NOSIM,UFL,UTHP, UNT1 (2,2) ,U1IT2(2,2) ,ONT3(2,2) ,HH3T,
5 tfHT,DEPK,ROSBI,RESBI,RESBI1, ARUN,L«TS(5) ,IKPK(5) ,
6 KLAND,NQUAL,STMCH(230,24) ,PECOUT(5) , FLOUT,SCALEF(5) ,
7 SNOW,PACK,I PACK
COMMON /QLS/ HSN4ME(6),KRER,JRER,KSER,JSER,rE«PCF,COVKAT(5,12) ,
1 KEIK,JEIM, KDSR,ARP(5) ,ARI(5) ,ACCP(5) ,ACCI (5) ,RPER(5l ,
2 ' P«P(5,5),PMI(5,5),Q5NOB,SNOKY,SEDIM,SEDTY,SEDTCA,
3 ACPOLP(5,5) ,ACERSN(5) ,APOLP(5,5) ,AERSN(5) ,:OV5R(5) ,
4 APOLI(5,5) ,ACEIM(5) ,AEIM(5) ,POLTH(5) ,POLTY(5),
5 TEM?A,DOA,POLTCA(5) , AESSN'Y (5) ,A2IMY( 5), APOLPY (5, 5) ,
6 APOLIY(5,5) ,POLTC(5) , PLTCAY (5) , ACPOLI (5, 5) ,SIMP (5)
COMMON /SIS/ ACPOLT(5),PLTMX(5) ,PDLTSC(5) ,PLTMXC(5),
1 ACSEDT,SEDMX,SEDrSC,SEDHXC,TOTRUNrPEAKRIJ
DIMENSION LIHP(5),LIBI(5)
REAL JBER, KRER, JSER, KSER,KEIM, JEIM
INTEGER HYCAL,TF,UNIT,LMTS , RECO.UT,SFLAG
BEAL*8 HSNAME
275
-------
5031.
5032.
5033.
5034.
5035.
5036.
5037.
5038.
5039.
5040.
5041.
5042.
5043.
5044.
5045.
5046.
5047,
5048.
5049.
5050.
5051.
5052.
5053.
5054.
5055.
5056.'
5057.
5058.
5059.
5060.
5061.
5062.
5063.
5064.
5065.
5066.
5067.
5068.
5069.
5070.
5071.
5072.
5073.
5074.
5075.
5076.
5077.
5078.
5079.
5080.
5081.
5082.
5083.
5084.
5085.
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
DO 10 1=1,5
LIMP(I)=.0
10 LIKI (I)=.0
IF (TF.GT. 2) GO TO 250
NOSIK=NOSim-1
CONVERT EOSB - VOLUME OF OVEPLAND FLOH REACHING STREW -
IN INCHES PER WHOLE WATERSHED TO INCHES
PER PEPVIDUS AREAS ONLY
IF ((I.-A).GT.O.OOCOI) GO TD 20
ROSBQ=0.0
GO TO 30
20 ROSBQ=POSB/(1.-A)
30 CONTINUE
DO 90 I=1rNLAND
IF RAIN ON SHOW, INCREASE COVER BY X OF SNOH COVER
IF (SNO«. EQ.O. OR. (PACK/I PACK) .LT. COVER (I)) GO TO 35
CR=COVFE(I)+(1-COVEP(I))* (PACK/IPACK)
IF (CR. LT.COVER(I) ) GO TO 35
IF (CR.LE. 1.0) COVER(I)=CR
35 CONTINUE x
RASHOFF FROM PERVIOUS AREAS
IF (SFLAG.EQ. 1) GO TO 40
IF SNOHS, BRANCH OVER FINES GENERATION
RER (T) = (1-COVER (I) ) *KRER*PB**JRER
SRER(I)=SRER(I)+REB(I)
40 IF (RU.LE.0.0) GO TO 270
IF((ROSBQ+RESB).GT.0.0) GO TO 63
ERSN (I) =0.0
DO 50 J=1,NQOAL
50 POLP(I,J)=0.0
GO TO 90
60 SER (I) =KSER* (ROSBQtBESB) **JSER
IF(SER (I).LE. SRER(I) ) GO TO 70
SER (I) =SRER (I)
LIMP(I)=1
70 F.PSR (I) =SBP (I)* (RCSBQ/(ROSBQ*RF5B| )
SRER(I)=SRER(I) -EESN(I)
EESK(I)=EHSM(I)*ARP(I)
IF(SRER{I).I,T.O.O) SREP(I)=0.0
MONTHLY ACCUMULATION OF WASHOFF FROM PERVIOUS AREAS
DO 80 J=1,NQUAL
POLP(I,J)=ERSN(I)*(PMP(J,I)/100.| *2000.
ACPOLP(I,J)=ACPOLP(I,J) +POLP(I,J)
80 APOLP(I,J)=APOLP(I,J)+POLP(I,J)
276
-------
5086.
5087.
5088.
5069.
5090.
5091.
5092.
5093.
509U.
5095.
5096.
5097.
5098.
5099.
5100.
5101.
5102.
5103.
5104.
5105.
5106.
5107.
5108.
5109.
5110.
5111.-
5112.
5113.
511K.
5115.
5116.
5117.
5118.
51 1 v.
5120.
5121.
5122.
5123,
512U.
5125.
5126.
5127.
5128.
5129.
5130.
5131.
5132.
5133.
513ft.
5135.
5136.
5137.
5138.
5139.
5140.
C
c
C
c
c
c
c
c
c
c
90
ACERSN(I)=ACERSN(I)+EPSN(I)
AERSH (I) = AEP.SK(I) +ERSN(I)
CONTINUE
100
110
120
130
150
160
170
HASHOPF FROH II PERVIOUS AREAS
CO 140 I=1,NLAND
IF ((PDSBI+RESBI).GT.O.) GO TO 110
EIB(I)=0.0
DO 10C J=1 ,NQUAL
POLT(I,J)=0.0
GO TO 1«0
TSS(I)=KEIH*((EOSBI + RESBI)
IF (TSS(I).LE. TS(I)) GO TO 120
TSS(I)=TS(I)
Fla(I)=TSS(I) *(ROSBI/(ROSBI*RESBI| )
TS (I)=TS(I)-EIK{!)
EIM(I)=EIH(I) *ARI (I)
DO 130 J=1 ,NOUAt
POLI(I,J)=EIH(I)*(rMI(J,I)/100.) *2000.
APOLI (I,J)=APOI,I(I,J) +POU (I, J)
ACPOLI (I, J) =ACPOLr (I, J) +POLI (I,J)
ACEIK (I) =ACEIH(I)
AKI«(I)=AEIM(I)
CONTINUE
STORNWATER TEHPERATOFE AND DISSOLVED
(ASCE,SEft(86) ,
TEMPC=(TEMPX(THRR) *TERPCF-32. > *5/9
IF (TEMPC.LT. 0.0) TEHPC=0.00
D0 = 14. 6 52-0. «1022*TEHPC + 0. 00799 1 * (IEHPC**2) -. 00007777
POLTCR (J)=POLTCA(J) +PCLTC(J)
CONTINUE
SEDT=0.000
DO 170 I=1,NLAND
SSDT=SSDT+ERSN(I)
CONTINUE
ACSEDT=ACSEDT*SECT
SEDT=SEDT*2000.
277
-------
5141.
5142.
5143.
5144.
5145.
5146.
5147.
5149.
5150.
5151.
5152.
5153.
5153.1
5154.
5155.
5156.
5157.
5158.
5159.
5160.
5161.
5162.
5163.
516ft.
5165.
5166..
5167.
5168.
5169.
5170.
5171.
5172.
5173.
517U.
5175.
5176.
5177.
5178.
5179.
5180.
51 81.
5182.
5183.
5184.
5185.
5186.
5187.
5188.
5189.
5190.
5191.
5192.
5193.
5194.
5195.
C
C
C
IF (SEDT.GT.SEDMX) SEDMX=SEDT
SEDTC=S2DT*454./(!>U*TIMFAC*60.0*23.32)
SEDTSC=S3DTSC+SEOTC
IF (SEDTC.GT.SEDMXC) SEDMXC=SEDrC
PRINTING OF DUrPUT FOR ONE TIKE INTERVAL
TEHP=TEHPX (THRR) *TEMPCF
IF (TEHP.LT.32.0) TEHP=32.00
DOA=DOA+DO
TEMPA=TEHPA-t-?EKP
SEDTCA=S£DTCA*SEDTC
IF (PO.LT. IIYMIN) GO TO 270
IF(UNIT. T.Q, -1) GO TO 190
TEHP=TEMPC
C
C
C
DO 130 J=1 ,KQUAL
180 POLT(J)=P01T(J) *O.U5H
190 CONTINUE
HEITE(6, 4000) TENP,DO,SBDT, SEDTC, (PDLT(J) ,POLTG(J) ,J=1,HQUAL|
IF (HYCAL. LT. «) RO TO 200
WRITE(4,i»100) (RECOUT{I) ,1=1,5) , FLOUT,
* TE«P,DO,SEDT,SERTC, (POLT(J) ,POLTC(J) ,J=1,NQUAL)
PRINT OF ADDITIONAL OUTPUT FDR CALIBRATION PUN
200 IF ((SEDT.LE. 0.001). OB.. (HYCAL.GT.2) | GO TO 270
POI=(RUI*AEEA*43560.)/(TIMFAC*720.)
TEH=RU
IF (UNIT, LT.1) GO TO 210
TEH=TEK*0.0283
RUI=RUI*0.0283
PR=PR*25.«
210 8BITE{6,4010) RO,DFL
WRITE (6, 4020) ROI,DFL
WRITE (6,4030) PR,DEPW
DO 240 1 = 1 ,NLAND
EPSH(I)=ERSN(I) *2000.
EIB(I)=Eia(I)*2000.
TEM=PRSN(I) +EIM(I)
IF (TEH. LE. 0.001) GO TO 240
IF (UNIT. LT.1) GO TO 230
TEH=TEK*0.45U
E!H(I)=E!M(I) *0.454
ERSN(I)=ERSN(I)*0.454
DO 220 J=1,NQUAL
POLTLO(I,J)=POLTLO(I,J) *0.«54
P01P(I,J)=POLP(I,J)*0.454
220 POLI (I,J)=POLI(T, J)*0.454
230 WRITE (6, 4040) (LNOUSE (KK,I) , KK=1 , 3) , TEH , (POLTLO (I ,J) , J = 1, KQUAL)
IF (LIKP(I) .EQ.O)
* HRITE(6,4050) CD VER (I) ,EBSS (T) , (POIP(I, J) , J=1 , NQUAL)
IF (LIMP(I) .EQ.1)
* HRITE(6,4060) COVER (I) ,ERSN (I) , (POLP(I, J) , J=1, NQU.AE.)
IF (LIBI(I).EQ.O) KRITE(6,4370) EI« (I) , (POLI (I, J) , J=1 ,NQtJAL)
278
-------
5196.
5197.
5198.
5199.
5200.
5201.
5202.
5203.
5204.
5205.
5206.
5207.
5208.
5209.
5210.
5211.
5212.
5213.
5214.
5215.
5216.
5217.
5218.
5219.
5220.
5221.
5222.
5223.
5224.
5225.
5226.
5227.
5228.
6000.
6001.
6002.
6003.
6004.
6005.
6006.
C
C
C
IF (LIMI(I).EQ. 1) «BITE(6,«080) EIH (I) , (POLI (I, J) , J= 1, NQJJ AL)
240 CONTINUE
WRITS (6,4090)
SO TO 270
ACCUMULATION OF DEPOSITS DURING THE NO RAIN DAYS
250 00 260 1=1,NLAND
TS(I)=TS(I) *(1.0-RIMP(I) )*ACCI (I)
SRER(I)=SRER(I)*(1.0-RPER(H J+ACCP(I)
IF (RIMP(I).LE.O.O) GO TO 250
TEM=ACCI(I)/RIMP(I)
IF (TS(I).LT.TBM) GO TO 260
TS (I)=TEM
260 CONTINUE
270 CONTINUE
C
4000 FOKKAT («+«,22X,F6.2,2X.F5.2,F9. 2, F3. 3,5(F8. 2,F8. 3))
4010 FORK RT (»0« ,3X,'TOTAL FLOW',F8.3,«, ' , A4)
4020 FORKAT (' ',1X,'IKPERV, FLOW«,F8.3,', *,A4)
4030 FORKAT (' PRECIPITATION ',F7.3,', ',A4)
4040 FORHAT {'0• ,21X ,3A4,1X,P10.2,8X,5(F10.3,6X))
4050 FORMAT (« •,8X,«COVER=',F5.2,7X,•PEHV.',3X,F10.2, 8X, 5("10.3,6X))
4060 FORMAT (9X,'COVER=«,P5.2,7X, • PERV. •,2X,'*',F10. 2, 8X, 5(F10.3,6X) )
4G70 FORHAT (27X,«IHPERV.«,1X,F10.2,8X, 5(F10.3,6X()
4080 FORHAT (27X,' IKPET!V. • ,' *• , F1 0. 2, 3X ,5 (F10. 3,6X) )
4090 FORHAT {/)
4100 FORMAT (I4,A4,1X,I2,1X,r2,':«,I2,FS.3,F5.2,F5.2,F9.2,
1 F8.3,5(F8.2,F8.3)|
C
RETURN
END
//LKED.SYSLMOD DD DSNAHE=WYL.X2.A12.YJL. J7412.N01,DISP=(NE»,KERP),
// SPACE=(TRK,(25,1,1) ,BtSE) ,UNIT-DISK,
// VOL=SER=PUB003
//LKED.SYSIN DO *
NAME NPS
/*
279
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
REPORT NO.
EPA-600/3-76-083
3. RECIPIENT'S ACCESSION-NO.
TITLE AND SUBTITLE
Modeling Nonpoint Pollution from the Land Surface
5. REPORT DATE
July 1976 (Issuing Date)
6. PERFORMING ORGANIZATION CODE
AUTHOR(S)
Anthony S. Donigian, Or. and Norman H. Crawford
8. PERFORMING ORGANIZATION REPORT NO.
PERFORMING ORGANIZATION NAME AND ADDRESS
Hydrocomp Inc.
1502 Page Mill Road
Palo Alto, Calif. 94304
10. PROGRAM ELEMENT NO.
1BA023
11. CONTRACT/GRANT NO.
R803315-01-0
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Research Laboratory
Office of Research and Development
US Environmental Protection Agency
Athens. Georgia 30601
13. TYPE OF REPORT AND PERIOD COVERED
FINAL
14. SPONSORING AGENCY CODE
EPA-ORD
15. SUPPLEMENTARY NOTES
16. ABSTRACT
Development and initial testing of a mathematical model to continuously simulate pol-
lutant contributions to stream channels from nonpoint sources is presented. The
Nonpoint Source Pollutant Loading (NPS) Model is comprised of subprograms to represent
the hydrologic response of a watershed, including snow accumulation and melt, and the
processes of pollutant accumulation, generation, and washoff from the land surface.
The simulation of nonpoint pollutants from both pervious and impervious areas is based
on sediment as a pollutant indicator. The calculated sediment washoff is multiplied
by user-specified 'potency factors' that indicate the pollutant strength of the sedi-
ment for each pollutant simulated. Both urban and rural areas can be simulated.
Initial testing of the NPS Model was performed on three urban watersheds in Durham,
North Carolina; Madison, Wisconsin; and Seattle, Washington. The hydrologic simulatior
results were good while the simulation of nonpoint pollutants was fair to good. Sedi-
ment, BOD, and SS were the major pollutants investiaged. A detailed user manual is
provided to assist potential users in application of the NPS Model. Parameter defini-
tions and guidelines for parameter evaluation and calibration are included. Possible
uses of the NPS Model for evaluation of nonpoint pollution problems are discussed.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS C. COS AT I Field/Group
Simulation, Runoff, Hydrology, Erosion,
Snowmelt, Water quality, Planning,
Land use
Nonpoint pollution,
Urban runoff, Model
studies
2A
2B
8H
8L
8M
13B
20D
18. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (ThisReport)
UNCLASSIFIED
21. NO. OF PAGES
292
20. SECURITY CLASS (Thispage)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (9-73)
280
*U.S. GOVERNMENT PRINTING OFFICE: 1976-657-695/6468
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