HYDROLOGIC SIMULATION ON SOLID WASTE DISPOSAL SITES
by
U.
Eugene R. Perrier and Anthony C. Gibson
Water Resources pngineering Group
Environmental Laboratory
S. Army Engineer Waterways Experiment Station
Vicksburg, Miss. 39180
Contract No. EPA-LAG-D7-01097
Project Officer
Robert E. Landreth
Solid and Hazardous Waste Research Division
Municipal Environmental Research Laboratory
Cincinnati, Ohio 45268
MUNICIPAL ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U. S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OHIO 45268
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DISCLAIMER
This report 'has been reviewed by the Municipal Environmental Research
Laboratory, U. S. Environmental Protection Agency, and approved for publica-
tion. Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
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PREFACE
The land disposal of hazardous waste is subject to the requirements
of Subtitle C of the Resource Conservation and Recovery Act of 1976. This
Act requires that the treatment, storage, or disposal of hazardous wastes
after November 19, 1980 be carried out in accordance with a permit. The
one exception to this rule is that facilities in existence as of November
19, 1980 may continue operations until final administrative disposition is
made of the permit application (providing that the facility complies with
the Interim Status Standards for disposers of hazardous waste in 40 CFR
Part 265). Owners or operators of new facilities must apply for and receive
a permit before beginning operation of such a facility.
The Interim Status Standards (40 CFR Part 265) and some of the adminis-
trative portions of the Permit Standards (40 CFR Part 264) were published
by the Environmental Protection Agency in the Federal Register on May 19,
1980. The Environmental Protection Agency published interim final rules
in Part 264 for hazardous waste disposal facilities on July 26, 1982.
These regulations consist primarily of two sets of performance standards.
One is a set of design and operating standards separately tailored to each
of the four types of facilities covered by the regulations. The other
(Subpart F) is a single set of ground-water monitoring and response require-
ments applicable to each of these facilities. The permit official must
review and evaluate permit applications to determine whether the proposed
objectives, design, and operation of a land disposal facility will comply
with all applicable provisions of the regulations (40 CFR 264).
The Environmental Protection Agency is preparing two types of documents
for permit officials responsible for hazardous waste landfills, surface
impoundments, land treatment facilities and piles: Draft RCRA Guidance
Documents and Technical Resource Documents. The draft RCRA guidance
documents present design and operating specifications which the Agency
believes comply with the requirements of Part 264, for the Design and
Operating Requirements and the Closure and Post-Closure Requirements
contained in these regulations. The Technical Resource Documents support
the RCRA Guidance Documents in certain areas (i.e., liners, leachate
management, closure, covers, water balance) by describing current techno-
logies and methods for evaluating the performance of the applicant's design.
The information and guidance presented in these manuals constitute a
suggested approach for review and evaluation based on good engineering
practices. There may be alternative and equivalent methods for conducting
the review and evaluation. However, if the results of these methods differ
from those of the Environmental Protection Agency method, they may have to
be validated by the applicant.
iii
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In reviewing and evaluating the permit application, the permit official
must make all decisions in a well defined and well documented manner. Once
an initial decision is made to issue or deny the permit, the Subtitle C
regulations (40 CFR 124.6, 124.7 and 124.8) require preparation of either a
statement of basis or a fact sheet that discusises the reasons behind the
decision. The statement of basis or fact sheet then becomes part of the
permit review process specified in 40 CFR 124.6-124.20.
These manuals are intended to assist the permit official in arriving
at a logical, well-defined, and well-documented decision. Checklists and
logic flow diagrams are provided throughout the manuals to ensure that
necessary factors are considered in the decision process. Technical data
are presented to enable the permit official to identify proposed designs
that may require more detailed analysis because of a deviation from suggested
practices. The technical data are not meant to provide rigid guidelines
for arriving at a decision. The references are cited throughout the manuals
to provide further guidance for the permit officials when necessary.
There was a previous version of this document dated September 1980.
The new version supercedes the September 1980 version.
iv
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FOREWORD
The Environmental Protection Agency was created because of increasing
public and governmental concern about the dangers of pollution to the health
and welfare of the American people. Noxious air, foul water, and spoiled land
are tragic testimony to the deterioration of our natural environment. The
complexity of the environment and the interplay between its components require
a concentrated and integrated attack on the problem.
Research and development is the first necessary step in problem solution;
it involves defining the problem, measuring its impact, and searching for
solutions. The Municipal Environmental Research Laboratory develops new and
improved technology and systems to prevent, treat, and manage wastewater and
the solid and hazardous waste pollutant discharges from municipal and
community sources; to preserve and treat public drinking water supplies; and
to minimize the adverse economic, social, health and aesthetic effects of
pollution. This publications is one of the products of that research—a vital
communications link between the researcher and the user community.
The Hydrologic Simulation Model on Solid Waste Disposal Sites was developed
to help landfill designers and permit officials estimate the amount of moisture
percolation through different typep of Ia,n4fiil covers.
Francis T. Mayo, Director
Municipal Environmental Research
Laboratory
v
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ABSTRACT
The Hydrologic Simulation Model on Solid Waste Disposal Sites was devel-
oped to help landfill designers and evaluators estimate the amount of moisture
percolation through different types of landfill covers. This one-dimensional
deterministic, computer-based, water budget model was developed and adapted
from the U. S. Department of Agriculture CREAMS hydrologic model and uses the
Soil Conservation Service curve method for calculating runoff. The model
takes engineering, hydrologic, and climatologic input data in the form of
rainfall, average temperatures, solar radiation, and leaf area indices, and
characteristics of cover material and performs a sequential analysis to
derive a water budget including the runoff, percolation, and \
evapotranspiration.
The user can specify up to three soil layers and may also specify a
membrane liner at the base of the cover. The decreasing effectiveness of the
liner is simulated. Five years of climatological default data are on files
accessible to the program user. If no climatic data are available for a
specific site, data from the nearest site where weather records are available
can be substituted. The model also stores logical hydrological default
values for the minimum infiltration rate, the porosity, the hydraulic conduc-
tivity, the available water capacity, and the evaporation coefficient where
measurements or estimates are not available.
The model is ordinarily used in the conversational mode, which enables
the user to interact directly with the program and receive output through
the terminal immediately. No prior experience with computer programming is
required. The model can also be run in the batch mode, which requires more
computer programming experience.
VI
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CONTENTS
Preface ill
Foreword v
Abstract vi
Figures ix
Tables xi
Acknowledgements .xiii
1. Introduction 1
Background ............. 1
Purpose . 1
Scope 2
Assumptions and Limitations 2
2. General Description of HSSWDS Program , . 3
3. HSSWDS User's Manual ..... 5
Model operation using default data 5
Steps to log on and off NCC ........ . 8
Worksheet for default data 15
Entry of default data ...... ....... 15
Model operation using manual input data files 22
Manual data entry for the climatological module 22
Data entry for the hydrological module 38
Interact between the default and manual
input options 44
Output for program 45
Hydrologic output 45
4. Loading Precipitation Data from Off-Line Media 51
Saving precipitation data files .... 52
5. Batch Operations 53
Example 1 (Batch Default Input Option) 56
Example 2 (Batch Manual Input Option) 58
References 67
Appendices
A. Hydrologic Simulation 69
Runoff 69
Evapotranspiration 74
Percolation 76
vii
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B. Cost Analysis for the National Computer Center (NCC)
Time-Sharing Operation . . . 73
C. NCC Access Numbers and Terminal Identifiers 79
D. Sensitivity Analysis 87
Impermeable liner 87
SCS curve number 96
Winter cover factor 102
Thickness of soil layer 2 104
Thickness of vegetative soil . . 108
Leaf area index (LAI) i 109
Soil layer 2 compaction 117
Soil texture , 123
Summary of sensitivity study . 125
viii
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FIGURES
Number —°—
1 Generalized flowchart for the hydrologic simulation
Model HSSWDS
2 Schematic diagram of the hydrologic cycle on a solid
waste disposal site
3 General relation between soil-water, soil texture,
and hydraulic conductivity
4 Default data worksheet
5 Example of initial program heading 16
6 Example of power law relations used to estimate the effective
aging of an impermeable liner
7 Work sheets for manual data input (no defaults) 23
8 SCS curve number for several vegetative covers in relation
to the minimum infiltration rate (MIR) 42
A-l Relation between the fraction of runoff and the fraction
of retention
A-2 SCS rainfall-runoff relation standardized on retention
parameter S • •
D-l Annual Cincinnati, Ohio, precipitation from 1974 to 1978 ... 94
D-2 Annual percolation as related to the impermeable liner .... 97
D-3 Annual soil drainage as related to the impermeable liner ... 98
D-4 Annual runoff as related to the SCS curve number 99
D-5 Annual evapotranspiration as related to SCS curve number ... 100
D-6 Annual percolation as related to the SCS curve number .... 101
D-7 Average monthly evapotranspiration as related to
winter cover factor
ix
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FIGURES (continued)
Number
D-8
D-9
D-10
D-ll
D-12
D-13
D-14
D-15
D-16
D-17
D-18
D-19
D-20
D-21
D-22
D-23
D-24
D-25
Page
Average monthly percolation as related to the winter
cover factor
103
Average monthly runoff as related to the winter cover factor . . 104
Annual surface runoff as related to thickness of
soil layer 2
105
Annual percolation as related to thickness of soil layer 2 ... 106
.... 107
Average monthly percolation as related to thickness of
soil layer 2
Average monthly surface runoff as related to thickness of
soil layer 2 .......................... 108
Annual percolation as related to the thickness of
vegetative soil ........................ HQ
Average monthly soil water for the 5-year data set and
for January 1976 through February 1977 with vegetative
soil thickness as the parameter ................
Average annual soil-water as related to the vegetative
soil depth ........................... H2
Average monthly evapotranspiration as related to the LAI .... 114
Average monthly percolation as related to the LAI ....... 115
Average monthly surface runoff as related to the LAI ...... 116
Average monthly soil-water as related to the LAI ........ 117
Annual surface runoff as related to soil layer 2 compaction . . 119
Annual percolation as related to soil layer 2 compaction .... 120
Comparison of average monthly precipitation to 1976
precipitation „
121
Percolation and precipitation during the month of
occurrence for 1978 122
Percolation as related to time in days for various soil
textures (V = vegetative soil, BS = soil layer 2,
comp s compacted) 128
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TABLES
Number
1
2
3
4
5
C-l
C-2
D-l
D-2
D-3
D-4
D-5
D-6
D-7
D-8
D-9
Cover Soil Characteristics Used as Default Values
Typical Leaf Area Index Distributions for Various
SCS Curve Numbers for Non-eroded Soil-Cover Complexes ....
Listing of Cities and States
Identifiers, by Terminal Make and Model
Daily Precipitation
Mean Monthly Temperatures and Isolation
Hydrological Input for Fictitious Solid Waste Site near
Parameters Varied for Sensitivity Analysis
Surface Runoff, Percolation, and Evapotranspiration as
Percentages of Annual Precipitation for Various SCS
Surface Runoff, Percolation, and Evapotranspiration as
Percentages of Average Annual Precipitation .for Various
Thicknesses of Vegetative Soil
Surface Runoff, Percolation, and Evapotranspiration as Per-
centages of Average Annual Precipitation for Various LAI . . .
Hydraulic Conductivity, Available Water Content, and
Pnrns-itv Values Used to Evaluate Soil Layer 2 Compaction ...
Page
9
, 10
36
. 43
66
, 79
85
88
93
. 93
96
, 101
. 113
. 118
xi
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TABLES (continued)
Number
D-10
D-ll
D-12
D-13
D-14
D-15
Surface Runoff, Percolation, and Evapotranspiration as
Percentages of the Average Annual Precipitation for Soil
Layer 2 Compaction
118
Soil Hydrological Data Used in the Sensitivity Study 123
Combinations of Vegetative and Soil Layer 2 Soil Textures :
for the Sensitivity Study 124
Surface Runoff, Percolation, and Evapotranspiration as
Percentages of the Average Annual Precipitation for
Various Soil Textures „
Amount of Percolation and Precipitation as a Function of
Time and Soil Texture
126
Summary of Sensitivity Study Results . 129
xii
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ACKNOWLEDGEMENTS
The authors gratefully acknowledge the contributions of several individ-
uals. Dr. Arlin D. Nicks of the U. S. Department of Agriculture provided
a copy of the CREAMS model and made helpful criticisms and suggestions.
Mr. James H. Terry III of the Waterways Experiment Station (WES), Environ-
mental Laboratory (EL), developed the city/state climatological tape.
Mr. Robert J. Wills, Jr., made constructive suggestions and prepared Appendix
D of. this report. Mr. Paul R. Schroeder of EL also made significant contri-
butions. Ms. Jane Harris, S.E. Huey Co., Monroe, La., assisted throughout
the project. Dr. Richard Lutton, of the WES Geotechnical Laboratory, was
principal investigator for the study entitled "Cover for Solid and Hazardous
Wastes." Dr. Lutton and Dr. Philip G. Malone, of EL, updated sections of
the manual. Mr. James Jefferson, of the WES Computer Systems Division, con-
tributed to the batch mode development.
The model development was conducted under the general supervision of
Dr. John Harrison, Chief, EL, and Mr. Andrew J. Green, Chief, Environmental
Engineering Division (EED). Direct supervision was provided by Mr. Michael R.
Palermo, Chief, Water Resources Engineering Group (EED). Mr. Douglas Ammon,
Mr. Robert E. Landreth, Mr. Dirk Brunner, Dr. Mike Roulier, and Mr. Bryan
Young, Solid and Hazardous Waste Research Division, EPA, Cincinnati, Ohio,
followed progress in the interest of the sponsor.
xiii
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SECTION 1
INTRODUCTION
BACKGROUND
Current requirements for landfill design call for minimizing the dis-
charge of contaminated percolating water (leachate) associated with landfills.
Landfill leachate control begins with proper siting of a landfill and con-
tinues with the design of liners, covers, and collection and treatment systems.
In a completed landfill that has been graded to eliminate or minimize runon of
surface water, the major source of moisture that produces leachate is the
percolation of precipitation through cover materials. In a modern, lined
landfill, the percolation rate will determine when and in what volumes
leachate will be produced for treatment and discharge. Predicting percola-
tion rates in cover materials is vital to evaluating a cover design and also
to establishing design criteria for collection and/or treatment systems. A
recent report on the subjects of design and construction of cover identified
several useful quantitative methods for estimating percolation through cover
for the purpose of checking cover designs (1).
PURPOSE
This report describes the use of a computer-based model for simulating
the percolation of precipitation through cover material at a solid waste
disposal site. The model, referred to as the HSSWDS model (for hydrologic
simulation at solid waste disposal sites) is a modification and adaptation
of a soil percolation model developed by the U. S. Department of Agriculture
(USDA).* The HSSWDS can be employed in the evaluation of present cover
materials at a landfill or in the design of new or improved landfill covers.
Of course, covers do more than limit moisture movement; they are also impor-
tant in the control of disease vectors and landfill gases and in fire pro-
tection. Since cover design involves much more than estimating percolation,
the evaluator should also review the companion technical resource document (2),
which summarizes steps in evaluating cover designs, plans, construction, and
maintenance.
* The USDA model is entitled "Chemical Runoff and Erosion from Agricultural
Management Systems" and is also identified as the CREAMS model (3).
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SCOPE
The HSSWDS model is presented as a communication-type computer package
that permits rapid evaluation of landfill, cover designs, and soil materials.
This format makes the relatively sophisticated computer analysis available
even to evaluators with little computer experience. Any mathematical model
that is used in engineering design should be ussed with careful consideration
of the assumptions that go into the calculation of design parameters and the
nature of the input data.
ASSUMPTIONS AND LIMITATIONS
The HSSWDS model, in its present configuration, is a deterministic, one-
dimensional model that develops a long-term water balance based on historical
or simulated daily rainfall records. Infiltration of moisture through the
soil surface is calculated using the SCS curve number technique. The SCS :
curve number technique relates runoff to soil type, land use, and management
practices and uses daily rainfall records. The actual rainfall intensity, :
duration, and distribution are not considered.
Factors such as slope and surface roughness, which would be important if
individual rainfall events (storms) were input, are considered in the context
of the land use/land management factors used in the selection of the SCS curve
number. Average daily temperatures, average daily solar radiations, and
average leaf area indices are used to estimate water loss by evaporation or
transpiration. The model is no more complex than a manual tabulation of mois-
ture balance (4), but HSSWDS makes available a more complete data base and a
state-of-the-art system for obtaining an accurate water budget over a wide
variety of climatic, soil, and vegetative conditions.
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SECTION 2
GENERAL DESCRIPTION OF PROGRAM
The HSSWDS program consists of a set of computer-based modules that per-
form daily water balance calculations on the input cover design. The water
balance method can be used to estimate percolation through cover by computer
analysis and by manual tabulation as well.(1,4) The HSSWDS program has
been written for users who may have no background in computer programming.
The only equipment required to run the program is a small computer terminal
and a telephone. The input and output is interactive, so the user obtains
results quickly. To reduce costs, a batch session of the program is avail-
able, but it requires additional computer programming knowledge.
The hydrologic portion of the CREAMS model (3) has been modified to con-
form to the configuration of cover over solid waste. Those important parts
of the CREAMS model that are basic to the HSSWDS model are reviewed in
Appendix A. The flow chart for HSSWDS is shown in Figure 1 for daily time
steps. From minimal input data, the model will simulate daily, monthly, and
annual values for runoff, percolation,* temperature, soil-water, and
evapotranspiration.
To expedite its use, the model stores many default values for various
parameters * These values are to be used when measured and existing data are
not available—for example, soil-water characterization, precipitation, mean
monthly temperatures, mean monthly solar radiation, and vegetative charac-
teristics. Five years' worth of climatic records from many weather stations
within the United States are on tape for use in lieu of onsite measurements.
From 2 to 20 years' worth of climatic data can be input if the user wishes to
do so manually. The user must supply the title and geographical location and
the characteristics of the soil and vegeative cover. A sensitivity study of
the model is given in Appendix D.
* Percolation quantities may be interpreted directly as leachate quantities
only by making the major simplying assumption that water content of solid
waste below the cover is at field capacity so that percolation moves instan-
taneously through the waste cell.
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ENTER CLIMATOLOGICAL
PATA; RAIN, TEMP, RAD,. LAI.
ENTER HYDROLOGICAL DATA-
SOLID WASTE PARAMETERS '
COMPUTE DAILY TEMPERATURE
RADIATION AND LEAF AREA INDEX
(
READ ONE YEAR'S ,_
DAILY PRECIPITATION )~"*~
1=1
COMPUTE
SNOWMELT
COMPUTE
RUNOFF
COMPUTE
EVAPOTRANSPIRATIOM
AND SOIL WATER MOVEMENT
CALCULATE OVERALL
STATISTICS
OUTPUT
TABLES
Figure 1. Generalized flowchart for the hydrologic simulation
Model HSSWDS.
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SECTION 3
HSSWDS USER'S MANUAL
All major hydrologic processes that occur during a rainstorm (rainfall,
infiltration, soil-water movement, deep drainage, and surface water flow, for
example) can be simulated at various levels of detail. HSSWDS is a con-
tinuous model that uses 1 day as the time step for evapotranspiration, soil-
water movement, and percolation. This section is presented to help the
planner and technician to develop climatological input and site parameter
information and, if necessary, to set up data files for running the model.
The hydrologic processes that the model addresses are shown in Figure 2
for a solid waste disposal site. A portion of the precipitation in the form
of rain or melted snow that infiltrates the soil cover percolates across the
interface of the soil cover and solid waste. The model limits the user to
three layers in the final cover soil—a vegetative soil, a soil layer 2, and
a soil layer 3. At the interface of the cover soil and the solid waste, the
user may specify an impermeable liner, usually of a polymeric material. The
model will evaluate the effect of the finite life of the liner using the age
equations (power law). The model permits an examination of the soil cover
system to produce a better design under specified climatic conditions.
A conceptual understanding of soil water contents and movement is neces-
sary to model operation (Figure 3). The terminology (5) used in the model is
defined as follows:
a) Field capacity is the water content that a soil retains after
drainage ceases (due to the forces of gravity).
b) Wilting point is the water content a soil retains after plants
cannot extract any more soil water and remain wilted.
c) Available water capacity is the difference between the soil water
at field capacity and the wilting point.
d) Hydraulic conductivity is the rate of soil-water movement (because
of the forces of gravity) between the soil-water contents at satura-
tion and field capacity.
MODEL OPERATION USING DEFAULT DATA
To expedite model usage, the default option provides for input of spe-
cific default values of evapotranspiration, evaporation, and soil/water
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PRECIPITATION
EVAPOTRANSPIRATION
VEGETATIVE
SOIL LAYER
SOIL LAYER
COVER DRAINAGE
(with liner only)
LEACHATE
Figure 2. Schematic diagram of the hydrologic cycle on a solid waste
disposal site.
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0.30
IUNAVAILABLE WATER}?',,',"':
3.60
2.88
2.16
« 1.44
0.72
O
to
SAND
SANDY
LOAM
LOAM
SILT
LOAM
CLAY
LOAM
CLAY
DECREASING HYDRAULIC CONDUCTIVITY
Figure 3. General relation between soil water, soil texture, and
hydraulic conductivity.
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characteristics. Examples of these values are shown in Table 1. Refer-
ences 1 and 2 describe and compare the USDA aad USCS soil classes that are;
used in the table. In addition, numerous stations within the United States
that contain 5 years' worth of climatic records are on disk for easy access
to the geographical location of interest.
default data are presented in Table 2.
The locations available for using
STEPS TO LOG ON AND OFF NCC
The 10 steps required to log on and the one step required to log off the
National Computer Center (NCC)* IBM Computer System are given as follows:
1. Turn on data terminal.
2. Dial appropriate telephone number given in Appendix C.
3. Put telephone handle in handset muff (or depress telephone line
button).
4. The computer system types:
PLEASE TYPE YOUR TERMINAL IDENTIFIER (see Appendix C).
You typef on the same line:
A :
5. The computer system types:
-1310-046-
PLEASE LOG IN:
You type on the same line:
IBMEPA1;NCC
Press RETURN key
6. The computer system types:
IBM3 IS ON LINE
You type:
TSO
Press RETURN key
7. The computer system types:
ENTER LOGON
You type:
LOGON
Press RETURN key
* To obtain cost information for the NCC Computer System, see Appendix B.
t To correct typing errors, use the BACKSPACE key.
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TABLE 1. COVER SOIL CHARACTERISTICS USED AS DEFAULT VALUES*t
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Soil
USDA
CoS
CoSL
S
FS
LS
LFS
LVFS
SL
FSL
VFSL
L
SIL
- SCL
CL
SICL
SC
SIC
C
class
uses
GW
GP
sw
SM
SM
SM
SM
SM
SM
MH
ML
ML
SC
CL
CL
CH
CH
CH
MIR
(in./hr)
0.50
0.45
0.40
0.39
0.38
0.34
0.32
0.30
0.25
0.25
0.20
0.17
0.11
0.09
0.07
0.06
0.02
0.01
Porosity
(vol/vol)
0.351
0.376
0.389
0.371
0.330
0.401
0.390
0.442
0.458
0.511
0.521
0.535
0.453
0.582
0.588
0.572
0.592
0.680
Ksat
(in./hr)
11.950
7.090
6.620
5.400
2.780
1.000
0.910
0.670
0.550
0.330
0.210
0.110
0.084
0.065
0.041
0.065
0.033
0.022
AWC
(vol/vol)
0.067
0.087
0.133
0.122
0.101
0.054
0.086
0.123
0.131
0.117
0.156
0.199
0.119
0.127
0. 149
0.078
0.123
0.115
Evaporation
coefficient
3.3
3.3
3.3
3.3
3.4
3.3
3.4
3.8
4.5
5.0
4.5
5.0
4.7
3.9
4.2
3.6
3.8
3.5
* USDA = USDA Soil Classification System, Co = coarse, C = clay,
SI »= silt, S = sand, L = loam, F = fine, V = very;
USCS = Unified Soil Classification System, S = sand, M = silt,
L = low liquid limit, H = high liquid limit, W = well graded;
MIR = Minimum Infiltration Rate;
Ksat = Hydraulic Conductivity; and
AWC « Available Water Capacity.
tWhen soil layer 2 or 3 is compacted, the values for porosity, Ksat, and
AWC are changed to account for compaction. The Ksat values are also
changed in the vegetative part of the soil cover as a function of
vegetation type. The AWC values listed in the table are used to compute
the field capacity and wilting point.
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I
TABLE 2. MEAN DAILY SOLAR RADIATION (LANGLEYS)*
States and cities
Alaska
Annette
Bethel
Fairbanks
Arizona
Flagstaff
Phoenix
Tucson
Arkansas
Little Rock
California
Sacramento
Fresno
Inyokern (China Lake)
San Diego
Los Angeles WBAS
Santa Maria
Colorado
Denver
Grand Junction
Florida
Tallahassee
W. Palm Beach
Jacksonville
Miami Airport
Tampa
Orlando
Jan
63
38
16
300
301
315
188
174
184
306
244
248
263
201
227
298
297
267
2,49
327
307
Feb
115
108
71
382
409
391
260
257
289
412
302
331
346
268
324
367
330
343
415
391
370
Mar
236
282
213
526
526
540
353
390
427
562
397
470
482
401
434
441
412
427
489
474
470
Apr
364
444
376
618
638
655
446
528
552
683
457
515
552
460
546
535
463
517
540
539
550
May
437
457
461
695
724
729
523
625
647
772
506
572
635
460
615
603
483
579
553
596
607
Jun
438
454
504
707
739
699
559
694
702
819
487
596
694
525
708
578
464
521
532
574
591
Jul
438
376
434
680
658
626
556
682
682
772
497
641
680
520
676
529
488
488
532
534
548
Aug
341
252
317
596
613
588
518
612
621
729
464
581
613
439
595
511
461
483
505
494
511
Sep
258
202
180
516
566
570
439
493
510
635
389
503
524
412
514
456
400
418
440
452
456
Oct
•122
115
82
402
449
442
343
347
376
467
320
373
419
310
373
413
366
347
384
400
396
Nov Dec
59 41
44 22
26 6
310 243
344 281
356 305
244 187
222 148
250 161
363 300
277 221
289 241
313 252
222 182
260 212
332 262
313 291
300 233
353 316
356 300
360 292
(Continued)
* Source: Reference 3.
-------
TABLE 2 (CONTINUED)
States and cities
Georgia
Atlanta
Watkinsville
Hawaii
Honolulu
Idaho
Boise
Pocatello
Lewiston
Illinois
Chicago
East St. Louis
Indiana
Indianapolis
Iowa
Des Moines
Kansas
Dodge City
Topeka
Kentucky
Lexington
Louisiana
Lake Charles
New Orleans
Shreveport
Maine
Caribou
Portland
Jan
218
236
363
138
163
121
96
170
144
174
255
192
172
245
214
232
133
152
Feb
290
292
422
236
240
205
147
242
213
253
316
264
263
306
259
292
231
235
Mar
380
375
516
342
355
304
227
340
316
326
418
345
357
397
335
384
364
352
Apr
488
464
559
485
462
462
331
402
396
403
528
433
480
481
412
446
400
409
May
533
535
617
585
552
558
424
506
488
480
568
527
581
555
449
558
476
514
Jun
562
568
615
636
592
653
458
553
543
541
650
551
628
591
443
557
470
539
Jul
532
555
615
670
602
699
473
540
541
436
642
531
617
526
417
578
508
561
Aug
508
500
612
576
540
562
403
498
490
460
592
526
563
511
416
528
448
488
Sep
416
417
573
460
432
410
313
398
405
367
493
410
494
449
383
414
336
383
Oct
344
328
507
301
286
245
207
275
293
274
380
492
357
402
357
354
212
278
Nov Dec
268 211
257 224
426 371
182 124
176 131
146 96
120 76
165 138
177 132
187 143
285 234
227 156
245 174
300 250
278 198
254 205
111 107
157 137
(Continued)
-------
States and cities
Massachusetts
Boston
Michigan
East lansing
Sault Ste. Marie
Minnesota
St. Cloud
Missouri
Columbia
Montana
Glasgow
Great Falls
Nebraska
Grand Island
North Omaha
Nevada
Ely
Las Vegas
New Jersey
Seabrook
Tfl -t •
Edison
New Mexico
Albuquerque
New York
Syracuse
Central Park
Ithaca
Schenectady
New York City (JFK)
Jan
129
121
130
168
173
154
140
188
193
236
277
157
150
303
116
130
160
130
155
Feb
194
210
225
260
251
258
232
259
299
339
384
227
232
386
194
199
249
200
232
Mar
290
309
356
368
340
385
366
350
365
468
519
318
339
511
272
290
335
273
339
Apr
350
359
416
426
434
466
434
416
463
563
621
403
403
618
334
369
415
338
428
May
445
483
523
496
530
568
528
494
516
625
702
482
482
686
440
432
494
413
502
Jun
483
547
557
535
574
605
583
544
546
712
748
527
527
726
501 .
470
565
448
573
Jul
486
540
573
557
574
645
639
568
568
647
675
509
509
683
515
459
543
441
543
Aug
411
466
472
486
522
531
532
484
519
618
627
455
455
626
453
389
462
397
475
Sep
334
.373
322
366
453
410
407
396
410
518
551
385
385
554
346
331
385
299
391
Oct
235
255
216
237
322
267
264
296
298
394
429
278
278
438
231
242
289
218
293
Nov Dec
136 115
136 108
105 96
146 124
225 158
154 116
154 112
199 159
204 170
289 218
318 258
192 140
182 140
334 276
120 96
147 115
186 142
128 104
182 146
(Continued)
-------
TABLE 2 (CONTINUED)
States and cities
North. Carolina
Greensboro
Jacksonville
North Dakota
Bismarck
Ohio
Cleveland
Columbus
Put- in-Bay
Cincinnati
Oklahoma
Oklahoma City
Tulsa
Oregon
Portland
Medford
Astoria
Pennsylvania
Pittsburgh
Philadelphia
Rhode Island
Providence
South Carolina
Charleston
South Dakota
Rapid City
Tennessee
Nashville
Knoxville
Jan
200
238
157
125
128
126
128
251
205
89
116
90
94
157
155
252
183
149
161
Feb
276
317
250
183
200
204
200
319
289
160
215
162
169
227
232
314
277
228
239
Mar
354
426
356
303
297
302
297
409
390
287
336
270
216
318
334
388
400
322
331
Apr
469
569
447
286
391
386
391
494
454
406
482
375
317
403
405
512
482
432
450
May
531
635
550
502
471
468
471
536
504
517
592
492
429
482
477
551
532
503
518
Jun
564
652
590
562
562
544
562
615
600
570
652
469
491
527
527
564
585
551
551
Jul
544
625
617
562
542
561
542
610
596
676
698
539
497
509
513
520
590
530
526
Aug
485
562
516
494
477
487
477
593
545
558
605
461
409
455
455
501
541
473
478
Sep
406
471
390
278
422
382
422
487
455
397
447
354
339
385
377
404
435
403
416
Oct
322
358
272
289
286
275
286
377
354
235
279
209
207
278
271
338
315
308
318
Nov Dec
243 197
282 214
161 124
141 115
176 129
144 109
176 129
291 240
269 209
144 80
149 93
111 79
118 77
192 140
176 139
286 225
204 158
208 150
213 163
(Continued)
-------
TABLE 2 (CONCLUDED)
States and cities
Texas
Brownsville
El Paso
Dallas
Midland
San Antonio
Utah
Cedar City
Salt Lake City
Virginia
Lynchburg
Norfolk
Washington
Yakima
Pullman
Seattle-Tacoma
Wisconsin
Madison
Wyoming
Lander
Cheyenne
Puerto Rico
San Juan
Jan
297
333
250
283
279
238
163
172
87
117
121
75
148
226
216
404
Feb
341
430
320
358
347
298
256
274
157
222
205
139
220
324
295
481
Mar
402
547
427
476
417
443
354
338
274
351
304
265
313
452
424
580
Apr
456
654
488
550
445
522
479
414
418
521
462
403
394
548
508
622
May
564
714
562
611
541
565
570
508
514
616
558
503
466
587
554
519
Jun
610
729
651
617
612
650
621
525
578
680
653
511
514
678
643
536
Jul
627
666
613
608
639
599
620
510
586
707
699
566
531
651
606
639
Aug
568
640
593
574
585
538
551
430
507
604
562
452
452
586
536
549
Sep
475
576
503
522
493
425
446
375
351
458
410
324
348
472
438
531
Oct
411
460
403
396
398
352
316
281
194
274
245
188
241
354
324
460
Nov
296
372
306
325
295
262
204
202
102
136
146
104
145
239
229
411
Dec
263
313
245
275
256
215
146
168
75
100
96
64
115
196
186
411
-------
8. The computer system types:
IKJ56700A ENTER USERID -
You type:
Identification number/password
Press RETURN key
9. The computer system types:
ENTER AC GUID
You type:
(account-VID-M)*
Press RETURN key
10. The computer system types:
READY
You type:
RUNHYDRO
Press RETURN key
11. When the program is finished, you type:
LOGOFF
Press RETURN key or repeat step 10 for reruns.
WORKSHEETS FOR DEFAULT DATA
A worksheet is presented in Figure 4 for the entry of site and soil
characteristics data necessary to run the model. Most computer input requests
are self-explanatory. The computer terminal that the user is operating should
be set to enter information using all capital letters.
ENTRY OF DEFAULT DATA
Initially, the program prints a heading (Figure 5) that details the
title, name, and address of the authors, and the telephone number to call
for information about the program and for clarification of problems if and
when they arise.
The following example illustrates the interaction that occurs between
the program and the user to obtain 5 years' worth of default data for Los
Angeles, California. To use default data, the city specified must be listed
in Table 2. After the heading, the computer will ask:
DO YOU WANT TO USE DEFAULT CLIMATOLOGIC DATA?
ENTER YES OR NO
TES
* Enter the account, the utilization identifer, and the letter P with no
separators or blanks.
15
-------
STATE:
CITY:
STUDY TITLE:
AREA LOCATION:
YEARS OF INTEREST:
Thickness of soil cover
Thickness of vegetative layer .,
Thickness of soil layer 2 ...
Thickness of soil layer 3 ...
_inches
_inches
_inches
inches
Figure 4. Default data worksheet.
**^^
*#***#*#**##########****#^S****^^
*
*
*
#
*
*
HYDROLOGIC SIMULATION ON SOLID WASTE DISPOSAL SITES
WRITTEN BY
EUGENE R. PERPIER AND ANTHONY C. GIBSON
OP THE
WATER RESOURCES ENGINEERING GROUP
ENVIRONMENTAL LABORATORY
USAE, WATERWAYS EXPERIMENT STATION
P.O. BOX 631
VICKSBURG, MS 39180
*
*
*
*
*
*
*
*
###**####*####*##:Mc##****##^^
USER'S MANUAL AVAILABLE UPON REQUEST
FOR CONSULTATION CONTACT AUTHORS AT
(601) 634-3710
*
*
*
###*############################^
#######################*##^^
Figure 5. Example of initial program heading.
16
-------
If the user enters NO, the program assumes that the method of input will be
manual. (See manual input option).
DO YOU WANT TO USE CLIMATOLOGIC DATA
FROM THE PBEVIOUS BUN?
ENTER YES OR NO
NO
The computer will type a table of the cities and states from which the clima-
tological default data is available. If YES is entered, the model will print
the name of the city in which the climatologic data are stored, and the user
should enter the number 2 for hydrologic input.
ENTER NAME OF STATE OF INTEREST
CALIFORNIA
ENTER NAME OF CITY OF INTEREST
LOS ANGELES
Note: The user must enter a word or value for each input, and after the
word or value has been entered, the user must press the RETURN key.
In the event that a typing error is committed, use the following proce-
dures. If, for example, CAILFORNIA was typed, press and hold the CONTROL
(CTRL) key, and press the H key 8 times (8 backspaces).* Then type LIFORNIA
to correct the spelling, and press the RETURN key as shown.
ENTER NAME OF STATE OF INTEREST
CAILFOENIALITOBNIA ,
To correct an entire line error, the user may press the BREAK key and the
computer will type *DEL*. Then the user should type in the correct message,
as shown.
ENTER NAME OF CITY OF INTEREST
LOO ANGELES '*DEL* LOS ANGELIS
* Some computer terminals use a different backspacing method.
17
-------
For the city requested, the program retrieves the climatological data
on precipitation, temperatures, solar radiation, and two types of leaf area
index (LAI) values (one for the row crop and the other for grass). Once this
has been done, the user should enter a 2 for the input of hydrologic
characteristics.
DO YOU WANT CLIMATOLOGY, HYDROLOGY OB: OUTPUT?
ENTER 1 FOR CLIMATOLOGICAL INPUT,
2 FOR HYDROLOGICAL INPUT,
3 FOR OUTPUT OR
4 TO STOP PROGRAM.
**^*#*****#*#*#*#**#*#^^
USE ONLY ENGLISH UNITS OF INCHES AND DAYS
UNLESS OTHERWISE INDICATED
########################ENTIR ALL ZEROS############*########***!
***********iMe************^^
A VALUE **MUST** BE ENTERED FOB EACH COMMAND
###**###*#*****^#***#***********#^^
The program requests the following for the user's information only, and this
information is printed twice in the output to label the job output. The. study
title could include site and vegetation information. ,
ENTER TITLE ON LINE 1,
LOCATION OF SOLID WASTE SITE ON LINE 2
AND TODAY)S DATE ON LINE 3.
HYDRCLOGY OF A SOLID WASTE DISPOSAL -SITS "
TEN MILES SOUTH OF TOWN
1 FEBUARY 1982
At this point, the user has the option of designing the final cover soil
with a vegetative layer, a soil layer"2, and/or a soil layer 3, or with a
uniform cover soil. Three layers are the most permitted. If the user desires
a two- or three-layer system, the following commands are answered.
18
-------
ENTER NUMBER OF LAYERS IN SOIL COVER
The user should also enter the total thickness of the soil cover when queried.
ENTER TOTAL THICKNESS OF SOIL COVER (INCHES)
36
Now the user must select the general texture class of vegetative soil from
the classes shown in Table 1. For example, the user inputs the number nine
in the example problem, which is the code for fine sandy loam. The vegeta-
tive soil cover is assumed to be spread uniformly. Any grass or row crop is
assumed to have had appropriate cultivation and seedbed preparation.
ENTER SOIL TEXTURE OF VEGETATIVE SOIL
ENTER A NUMBER (1 THROUGH 18) FOR TEXTURE CLASS OF SOIL MATERIAL.
**CHECK USER MANUAL FOR NUMBER CORRESPONDING TO SOIL TYPE**
The user must enter the thickness of soil layer 2 along with its texture
code and must answer whether or not soil layer 2 was compacted. If soil
layer 2 was compacted, the value of hydraulic conductivity is reduced by a
factor of 20, and the values of available water capacity and porosity are
multiplied by a factor of 0.75.
ENTER THICKNESS OF SOIL LAYER 2 (INCHES)
12
ENTER SOIL TEXTURE OF SOIL LAYER 2
ENTER A NUMBER (1 THROUGH 18) FOR TEXTURE CLASS OF SOIL MATERIAL
**CHECK USER MANUAL FOR NUMBER CORRESPONDING TO SOIL TYPE**
14
The user must also enter the thickness of soil layer 3 along with its
texture and must answer whether or not soil layer 3 was compacted. The
compaction effects are the same as those applied to soil layer 2.
19
-------
ENTER THICKNESS OF SOIL LAYER 3
10
EN"TER"Sirnr TEXTURE OY £OIIT EATER 3
ENTER A NUMBER (1 THROUGH 18) FOE TEXTURE CLASS OF SOIL MATERIAL.
**CHECK USER MANUAL FOR NUMBER CORRESPONDING TO.SOIL TYPE**
DID YOU COMPACT SOIL LAYER 2?
ENTER YES OR NO
YES
DID YOU COMPACT SOIL LAYER 3?
ENTER YES OR NO
NO
If the user is analyzing a unilayered cover, he selects the single soil
texture and enters the total thickness of the soil in inches. The computer
responds with: :
SELECT THE TYPE OF VEGETATIVE COVER ;
ENTER NUMBER
(1) BARE GROUNI
(2) GRASS (EXCELLENT)
(3) GRASS (GOOD)
(4) GRASS (FAIR)
(5) GRASS (POOR)
(6) ROW CROP (GOOD)
(7) ROW CROP (FAIR)
An explanation of the terms relating to vegetation is given in Appen-
dix D. The two sets of leaf area index (LAI) values are stored in the :
default climatologic data file—one is for excellent grass cover, and the
other is for good row crops. The program uses only one of these two sets of
values, which is determined by the user specifying grass or row crop. If
the user specifies excellent, good, fair, or poor grass, the LAI values are
multiplied by 1.0, 0.67, 0.33, or 0.17, respectively. On the other hand, if
the user specifies a good or fair row crop, the LAI values are multiplied by
20
-------
1.0 or 0.5. Neither IAI set is used if the user selects bare ground. Ex-
cellent grass implies that the soil cover will be planted with a grass which
has excellent production. This selection assumes that the vegetative layer
is well managed (that is, that fertilizer, weed control, and harvesting (not
grazing) operations are performed to maintain maximum production). Obviously,
such a vegetative system is the best available, but realistically, it is dif-
ficult to achieve. The designation "row crop" assumes that some type of
cultivation will be maintained throughout the season, and it is assumed the
crop will produce well. Loam is the ideal soil texture to maximize vegeta-
tive production, and soil textures other than loam will have lower production.
Of course, good management may circumvent some of the production loss, but a
clay or sand cannot maintain even a fair grass cover without management
difficulties.
Some solid waste sites (Figure 2) may be designed with an impermeable
liner separating the final soil cover from the waste cells (1). Since most
impermeable liners age and eventually deteriorate, a power law was used for
functional age relations (see Figure 6). The maximum life of a liner is
limited to 100 years. The computer asks the following questions:
IS THERE AN IMPERMEABLE LINER AT THE INTERFACE?
ENTER YES OR NO
YES
WHAT IS THE EXPECTED LIFE OF
(100 YEARS IS MAXIMUM LIFE)
THE LINER (YEARS)?-
For an answer of 5 years, the initial flow of water is totally impeded. As
a function of time, the volume of water percolating through the cover in-
creases, and in 5 years, the impermeable liner has no effect on the volume
of water percolating into the solid waste cells.
At this point, all necessary input data have been entered for clima-
tology and hydrology when using the default mode, and the user is ready
for output. The user must still specify the number of years of output and
whether or not daily, monthly, or annual summaries are required. Since out-
puts for both the default and manual input options have the same form, the
discussion of output follows later.
21
-------
0.001
10.
100. 1,000
EFFECTIVE LIFE, DAYS
10,000
100,000
Figure 6. Examples of the power law relations used to estimate the
effective aging of an impermeable liner.
MODEL OPERATION USING MANUAL INPUT DATA FILES :
When default data are not used, the worksheets for manual input data
(Figure 7) are required. The most difficult part of manual input is entering
the precipitation data. Daily precipitation data are available from local
libraries or from the National Weather Service* climatological data records.
When the precipitation data are to be entered, if the entire field of ten
values is zero, only one zero needs to be entered before the RETURN key is
pressed (right justified). If a line is partially filled with precipitation
data and the remainder is to be filled with zeros, only a RETURN is entered
after typing the precipitation data. Each year requires 10 values per line
and 37 lines of input. The model, as written, accepts a record ranging from
a minimum of 2 to a maximum of 20 years' worth of data. For best results, at
least 5 years' worth of precipitation data should be used.
MANUAL DATA ENTRY FOR THE CLIMATOLOGICAL MODULE •'.
When the user enters the Program, the following commands are given for
entry of data files.
* Director, National Climatic Center, NOAA, Federal Building, Asheville, N.C.
28801
22
-------
MANUAL CLIMATOLOGIC INPUT
DAILY PRECIPITATION (INCHES)
1 YEAR (10 VALUES/LINE, 37 LINES)
YEAR:
_8
_9
1()
11_
11
13
14
ii
16
17.
18
UL
20
21_
22
23_
24
25_
26
27.
28
29
30_
31
32
33_
3j4_
35
36
37
(continued)
Figure 7. Work sheets for manual data input (no defaults)
23
-------
YEAR:
1
2
3
4
b
6
/
8
9
10
11
12
13
14
Ib
16
I/
18
19
20
21
22
23
24
2b
26
2/
28
29
30
31
32
33
34
35
36
3/
i
!
1
i
'
(Continued)
Figure 7. (continued)
24
-------
Mean Monthly
Temperature
Month
Mean Monthly
Insolation
(Langleys/Day)
January
February
March
April
May
June
July
August
September
October
November
December
Leaf Area Index Values
Day Area
1
366
(Continued)
Figure 7. (continued)
25
-------
Manual hydrological input
Study title:
Area location:
Today's date:
Date of first storm event (Julian date):
(example = 73038, 1973 and 38 Julian day)
Hydraulic conductivity of vegetative soil .
Hydraulic conductivity of soil layer 2 . .
Hydraulic conductivity of soil layer 3 . .
Thickness of soil cover
Thickness of vegetative layer .
Thickness of soil layer 2
Thickness of soil layer 3
Soil porosity of vegetative soil
Soil porosity of soil layer 2
Soil porosity of soil layer 3 .......
SCS curve number
Field capacity of vegetative soil
Field capacity of soil layer 2
Field capacity of soil layer 3
Winter cover factor* .
Evaporation coefficient of vegetative soil
Evaporation coefficient of soil layer 2 . .
Evaporation coefficient of soil
-------
DO YOU WANT TO USE DEFAULT CLIMATOLO&IC DATA?
ENTER YES OR NO
NO
\
DO YOU WANT CLIMATOLOGY, HYDROLOGY OR OUTPUT?
ENTER 1 FOR CLIMATOLOGICAL INPUT,
2 FOR HYDROLOGICAL INPUT,
3 FOR OUTPUT OR
4 TO STOP PROGRAM.
The manual climatological module input data include the precipitation, mean
monthly temperature and solar radiation, winter cover factor, and the growth
characteristics of the vegetative cover in terms of the LAI. The manual
hydrologic module input data include site, soil-water, and evaporation
characteristics. The output module prints tables of the input and simulated
data.
DO YOU WANT TO ENTER PRECIPITATION DATA?
ANSWER YES OR NO
YES
NOT ICE ########tt#######1t#1HHW#'1Ht####
PRECIPITATION INPUT WILL ACCEPT **TWENTY** (20) YEARS MAXIMUM
AND ONLY #*TWO** (2) YEARS MINIMUM
DO YOU WANT TO ADD TO EXISTING PRECIPITATION DATA?
ENTER YES OR NO
NO
The user has the option of continuing the input of precipitation data
by typing YES or beginning a new precipitation data file by. typing NO.
For each year of input, the following commands are printed, and for this
example, the year of the data to be input is 74.
27
-------
ENTER DAILY RAINFALL .
BAINFALL ('EXAMPLE 76 LAST 2 DIGIT ONLY)
END RAINFALL INPUT.
n™
OR ZERO (0)
74
WHEN PRECIPITATION DATA ARE TO BE INPUT
IF THE ENTIRE FIILE OF TEN (10) VALUES '
ARE ZERO (0) ONLY ONE NEED BE ENTERED
BEFORE CARRIAGE RETURN (RIGHT JUSTIFIED)
IF YOU HAVE A LINE PARTIALLY FILLED WITH
PRECIPITATION DATA AND THE REMAINDER IS TO
BE FILLED WITE ZEROS *ONLT* A CARRIAGE
RETURN IS REQUIRED
**#********^^
At this point, 37 lines of data, with 10 values per line, are entered in
the following manner:
ENTER RAINFALL DATA OF 10 VALUES PER LINE
WITH 37 LINES PER YEAR.
ENTER LINE 1
.24 0 .25 1.7 .47 1.07 1.67 .06 .02
ENTER LINE 2
0 0 0 0 0 .11 .1 0 0 .11
ENTER LINE 3
ENTER LINE 4
000 .05
ENTER LINE 5
IT.'04 000 .85 .26
ENTER LINE 6
1.0 .04 000 .85 .26
ENTER LINE 7
1.0 .04 000 .85 .06
28
-------
ENTER LINE 31
0
ENTER LINE 32
0
ENTER LINE 33
0
ENTER LINE 34
ENTER LINE 35
0
ENTER LINE 36
0 0 0 0 0 .1
ENTER LINE 37
.99 .99 .99 .99 .99
After each year's entry, the heading is printed; however, when all the
precipitation data have been entered (2-year minimum and 20-year maximum) , a
zero is entered and the model asks whether precipitation values should be
checked. Each time this question is asked, the user should input the year
to be checked.
ENTER DAILY RAINFALL . •.-.-•
ENTER YEAR OF RAINFALL (EIAMPLI 76 LAST 2 DIGIT ONLY)
OR ZERO (0) TO END RAINFALL INPUT.
0
DO YOU WANT TO CHECK PRECIPITATION VALUES ENTERED?
ENTER YES CR NO
IES
ENTER YEAR
74 0.24 0.0 0.25 1.70 0.47 1.07 1.67 0.06 0.02 0.0
0.11 0 .10 0.0
0.0 0.0 0.0
0.0 0.0 0.0
0.85 0.26 0.0
0.85 0.26 0.0
0.85 0 .06 0.0
0.0 0.0 0.0
74
74
74
74
74
74
74
0.
0.
0.
0.
1.
1.
0.
0
0
0
0
00
00
0
0
0
0
0
0
0
0
.0
.0
.0
.04
.04
.04
.0
0
0
0
0
0
0
0
.0
.0
.0
.0
.0
.0
.0
0
0
0
0
0
0
0
.0
.0
.05
.0
.0
.0
.0
0.
0.
0.
0.
0.
0.
0.
29
0
0
0
0
0
0
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.11;
0.0
0.0
0.0
0.0
0.0
0.0
1
2
3
4
5
6
7
8
-------
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
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
0.99
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
0.99
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
0.99
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
.99
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
0.99
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
0.10
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
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
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
0.0
0.0
0.0
0.0
0.0
0.0
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
If an error has been made, as in the following example (year 74 and on
line 37, where five 0.99's were incorrectly entered), the following questions
would have to be answered and the corrected precipitation values entered:
ARE THESE VALUES CORRECT?
DO TOU WANT TO USE THEM?
ANSWER YES OR NO '.
NO
ENTER YEAR OF INTEREST
74
ENTER LINE OF INTEREST
37
ENTER 10 CORRECTED PRECIPITATION VALUES
0000 .01
30
-------
ARE THERE 'ANY MORE ERRORS?
ANSWER YES OR NO
NO
The precipitation tables are reprinted and the question as to their correct-
ness is asked before proceeding to the entry of mean monthly temperature data.
After the entry of data files for daily precipitation, mean monthly tem-
perature, mean monthly solar radiation, and LAI (see Figure 7), the program
reprints the input data and asks whether changes are required. The user has
the option of changing any of the data entered before advancing to the next
data entry. The following commands are used to enter mean monthly temperature
data:
DO YOU WANT TO ENTER TEMPERATURE DATA?
ANSWER YES OR NO
YES
If NO is entered, the program will print a set of default temperature
values and ask if you want to use them. The program will perform the same
operation for solar radiation and LAI values.
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX2XXXXXXXXXXXXXXX
XX XX
XX DO YOU WANT TO ENTER FOR EACH YEAR OF PRECIPITATION XX
XX A DIFFERENT SET OF MONTHLY TEMPERATURES, SOLAR XX
XX RADIATION, WINTER COVER FACTORS AND LEAF AREA INDEX XX
XX VALUES? XX
XX XX
XX ENTER YES OR NO XX
XX XX
XX (IF NO IS ENTERED THE PROGRAM WILL USE THE SAME SET XX .• . -
XX OF MONTHLY TEMPETATURES, SOLAR RADIATION AND LEAF XX
XX AREA INDEX VALUES (WINTER COVER FACTOR IS NOT XX
XX REQUESTED) OVER THE ENTIRE YEARS OF PRECIPITATION XX
XX SIMULATED.) XX
XX XX
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxx
YES
31
-------
If NO is entered, the program will request 12 mean monthly temperatures,
12 mean monthly solar radiation values, and 13 LAI values if there is
vegetation on the soil cover. Otherwise, bare ground is assumed, and LAI
values are omitted. Again, if NO is entered, the temperature, solar :
radiation, and LAI values are constant over all the years for which
precipitation data are supplied. ,
Mean monthly air temperature and mean monthly solar radiation
(insolation) data are required inputs (12 values each) that are used to
compute the daily evapotranspiration. Temperature data are regularly
published by the National Weather Service. Solar radiation data in Langleys/
day can be obtained from the Climatic Atlas of the United States or from
Table 2 for specific locations. :
If YES is entered for the above question asked by the program, mean
monthly temperature, solar radiation, winter factor, and LAI values are
requested for each year of precipitation data that is available.
ENTEB TEMPEBATUBE VALUES FOB THE YEAR 1974
ARE THEY THE SAME AS PBEVICUS YEAB?
ENTER YES OR NO
NO
The above question is not printed if the user entered NO to the question in
the X-bounded block. ;
ENTEB 6 TEMPEBATUBE VALUES
JAN.-JUNE (DEGREES J.)
62.7
61
68.7
59.6
69.6
70.7
32
-------
ENTER 6 TEMPERATURE VALUES
JULY-DEC. (DEGREES F.)
66.9
70
78.5
71.4
57.5
52.6
THESE ARE THE INPUT TEMPERATURE VALUES
JAN.-JUNE ' JULY-DEC.
62.7
61.0
68.7
59.6
69.6
70.7
66.9
70.0
78.5
71.4
57.5
52.6
DO YOU WANT TO CHANGE THEM?
ENTER YES O'R NO
NO
To enter solar radiation (6) data, the following commands are used (the
city of Los Angeles, California, is the example):
ENTER SOLAR RADIATION VALUES EOR THE YEAR 1974
ARE THEY THE SAME AS PREVIOUS YEAR?
ENTER YES OR NO
NO
33
-------
The above question is not requested if the user entered NO to the question in
the X-bounded block.
ENTER 6 SOLAR RADIATION VALUES
JAN.-JUNE (LANGLEYS/DAY)
248
331
397
4:57
506
486
ENTER 6 SOLAR RADIATION VALUES
JULY-DEC. (LANGLEYS/DAY)
497
464
389
320
277
221
THESE ARE THE INPUT RADIATION VALUES
JAN.-JUNE JULY-DEC.
248,0
331.0
397.0
457.0
506.0
486.0
497.0
464.0
389.0
320.0
277.0
221.0
DO YOU WANT TO CHANGE THEM?
ENTER YES OR NO
NO
Omit the following questions and answers concerning winter cover factor
if the user has entered NO for the answer to the question in the X-bounded
block.
34
-------
ENTIR WINTER COVER FACTOR FOR THE YEAR 1974
IS IT THE SAME AS PREVIOUS YEAR?
ENTER YES OR NO
NO
ENTER WINTER COVER FACTOR FOR THE YEAR 1974
.6
THE WINTER COVER FACTOR ENTERED IS0.60
DO YOU WANT TO CHANGE IT?
ENTER YES OR NO
NO
The winter cover factor is used to reduce soil evaporation as a result
of ground cover, for example, dormant grass, or a heavy crop residue (mulch).
The value of the winter cover factor usually varies from 0.5 for an excellent
grass cover to 1.0 for bare ground or harvested row crop (3). The value must
be estimated for each type of vegetative cover. The winter cover factor is
maximum when the surface area is bare ground.
The LAI is used to estimate the amount of vegetative ground cover of a
particular crop and is an effective partition of the rates of plant tran-
spiration to soil evaporation that is used in both model options. For 2
example, a conceptual understanding of LAI is made by considering a 1-ft
area of a soil surface with no vegetation (bare ground) on the 5th of January.
But 100 days later on the 15th of April, vegetation has grown on the example
area. Viewed from above, the vegetation now appears to cover 50 percent of
the surface area, which gives an LAI value of 1.50. Table 3 gives some leaf
area index distributions for normalized times through a growing season for
several crops. These values must be apportioned between actual local planting
and harvesting dates.* Points for day 1 and day 366 are necessary for model
operation. Exactly 13 LAI values must be entered .for a specific vegetative
ground cover. The program interpolates between the LAI values for daily
estimates.
* USDA, 1941, "Climate and Man, Yearbook of Agriculture," U. S. Govt,
Printing Office, Washington, D. C.
35
-------
TABLE 3. TYPICAL LEAF AREA INDEX DISTRIBUTIONS FOR VARIOUS
VEGETATIVE COVERS (3)
Portion of "
growing
season
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Corn
0.00
0.09
.0.19
0.23
0.49
1.16
2.97
3.00
2.72
1.83
0.00
Oats
0.00
0.42
0.84
0.90
0.90
0.98
2.62
3.00
3.00
3.00
0.00
LAlf
Wheat
0.00
0.47
0.90
0.90
0.90
0.90
1.62
3.00
3.00
0.96
0.00
Grass ^
0.00
1.84
3.00
3.00
3.00
3.00
3.00
2.70
1.96
0.96
0.50
Soybeans
0.00
0.15
0.40
2.18
2.97
3.00
2.96
2.92
2.30
1.15
0.50
t Good production assumed for all crops. LAI should be lowered for
poor production.
^No grazing assumed. LAI must be lowered if grazed or not managed.
To enter the data in the model, the following approach is required;
DOES TEE SOIL SURFACE HAVE VEGETATION EOE THE YEAR 1974
ENTER YES OR NO
YES
ENTER LEAF ARIA INDEX VALUES FOR THE YEAR 1974
ARE THEY THE SAME- AS PREVIOUS YEAR?
ENTER YES OR NO
NO
The above question is not requested if the user entered NO to the ques-
tion in the X-bounded block.
The condition for bare ground is entered automatically if no vegetation
is specified. Some of the input and inspection of the input follows.
36
-------
YOU MUST ENTER EXACTLY 13 VALUES FOR LAI
** REMEMBER TO START AT DAY 1 AND END AT DAY 366.**
ENTER TWO VALUES
. ONE FOR DAY OF MEASUREMENT (JULIAN DAY)
AND ONE FOR LEAF AREA INDEX.
(EXAMPLE, 100 1.65)
1 0
ENTER ANOTHER SET OF VALUES
41 0
ENTEP ANOTHER SET OF VALUES
59 .61
ENTER ANOTHER SET OF VALUES
77 1
ENTER ANOTHER SET OF VALUES
95 1
ENTER ANOTHER SET OF VALUES
113 1
ENTER ANOTHER SET OF VALUES
131 1
ENTER ANOTHER SET OF VALUES
149 1
ENTER ANOTHER SET OF VALUES
167 .9
ENTEP ANOTHER SET OF VALUES
185 .71
ENTER ANOTHER SET OF VALUES
203 .65
ENTER ANOTHER SET OF VALUES
221 0
ENTEP ANOTHER SET OF VALUES
366 0
37
-------
THESE ARE THE DAYS AND LAI VALUES INPUT
DAYS LAI
1
41
59
77
95
113
131
149
167
185
203
221
366
0.0
0.0
0.61
.00
.00
,00
.00
.00
1.00
0.71
0.65
0.0
0.0
1
1
1,
1
1
DO YOU WANT TO CHANGE THEM?
ENTER YES OR NO
NO
At this point, the user can make appropriate corrections to the data set if
so required.
If the user had entered NO to the question in the X-bounded block, at
this point the program would type END OF CLIMA.TOLOGICAL INPUT. Since YES was
input, the program will increase the year by 1 and follow the same procedure
until all years of precipitation data entered have associated years of mean
monthly temperature, solar radiation, winter cover factors, and IAI values.
Then the program will type END OF CLIMATOLOGICAL INPUT.
DATA ENTRY FOR THE HYDROLOGICAL MODULE
Data should now be entered in the manual hydrological module as
requested:
DO YOU WANT CLIMATOLOGY, HYDROLOGY OR OUTPUT?
ENTER 1 FOR CLIMATOLOGICAL INPUT,
2 FOR HYDROLOGICAL INPUT.
3 FOR OUTPUT
4 TO STOP PROGRAM
38
-------
The program user now enters the study title, site location, and the day's date,
This information is used for table headings in the output only and is not used
in the model operations.
ENTER TITLE ON LINE 1
LOCATION OF SOLID WASTE SITE ON LINE 2
AND TODAY'S DATE ON LINE 3.
HYDROLOGY OF A SOLID WASTE DISPOSAL SITE
TEN MILES SOUTH OF TOWN
1 FEB. 1982
The user must now enter the year and Julian date of the day before the
first storm event. Thus if the first year's data are only a partial set with,
for example, the.first 138 days set to zerp for 1973 data, this entry would
follow as 73138. But for the Los Angeles data set, it rained on 1 January 1974,
and the entry appears as:
ENTER YEAR AND DATE OF FIRST STORM EVENT (JULIAN DATE)
(EIAMPLE=?3138, 1973 AND 138 JULIAN DAYS)
74000
If the soil cover has a vegetative layer plus soil layers 2 and 3, this
information is entered here:
ENTER NUMBER OF LAYERS IN SOIL COVER (INCHES)
INTER TOTAL THICKNESS OF SOIL COVER (INCHES)
36
ENTER VALUES FOR VEGETATIVE SOIL
ENTER 5 VALUES, HYDRAULIC CONDUCTIVITY,(IN/HR)
SOIL POROSITY, (VOL/VOL)
EVAPORATION COEFFICIENT,
EVAPORATION COEFFICIENT, AND
FIELD WILTING POINT (VOL/VOL)
39
-------
.51
.41
4.5
.29
.16
ENTER THICKNESS OF SOIL LAYER 2 (INCHES)
12
ENTER VALUES FOR SOIL LAYER 2
ENTER 4 VALUES, HYDRAULIC CONDUCTIVITY,(IN/HR)
SOIL POROSITY,(VOL/VOL)
EVAPORATION COEFFICIENT AND
FIELD CAPACITY (VOL/VOL)
.004
.29
3.1
.14
ENTER THICKNESS OF SOIL LAYER 3 (INCHES)
10
IfNTEP VALUES FOR SOIL LAYER 3
ENTER 4 VALUES, HYDRAULIC CONDUCTIVITY, (IN/HR)
SOIL POROSITY, (VOL/VOL)
EVAPORATION COEFFICIENT AND
FIELD CAPACITY (VOL/VOL)
.52
.41
4.5
.29
40
-------
The effective hydraulic conductivity (7,8) of the vegetative layer, soil
layer 2, and soil layer 3 must be entered at this point. Experiments and
theory suggest that approximations of the variation of this parameter can
also be related to soil conditions (3). Thus the relative value entered
for the effective hydraulic conductivity should reflect the conditions of
the cover materials. If compaction of soil layer 2 and/or soil layer 3 is
requested, its effect on the hydraulic conductivity should be estimated.
The actual value of the hydraulic conductivity to estimate the runoff that
would be predicted by the Soil Conservation Service (SCS) curve number
method (9) depends largely on the storm depth and duration. Thus for
daily values, the hydraulic conductivity is moderately sensitive and the
quality of the input is generally only fair to good. Should measured
values from laboratory or field data be available, they can be used to
develop better parameter estimates.
The SCS curve number technique is the method used for predicting runoff
from daily rainfall. Figure 8 shows a graphic example of estimating the
curve number from the minimum infiltration rate (MIR) if it is not known from
other sources. Table 4 gives examples of SCS curve numbers for a different
soil cover condition. The evaporation coefficient (3) is a cover soil
evaporation parameter dependent on soil water transmission characteristics
and is used to fraction the evapotranspiratipn rate (ranges from about 3.3
to 5.5 mm/d1'2). A value of 4.5 is suggested for loamy soils, 3.5 for clays,
and 3.3 for sands; however, it cannot be less than 3.0.
The next question the program asks is whether or not an impermeable liner
was used. A discussion of the use of an impermeable liner was presented under
the default data option.
IS THERE AN IMPERMEABLE LINER AT THE INTERFACE?
ENTER YES OR NO
NO
ENTER SCS CURVE NUMBER.
79.3
At this point the program asks the following question if the winter
factor has not been entered with climatologic data:
ENTER WINTER COVER FACTOR
.6
HYDROLOGICAL INPUT IS COMPLETE
*#
41
-------
100 r-
V)
oc
Ul
Ui
flC
3 40-
BAREGROUND
ROW CROP (FAIR)
GRASS (POOR)
0.2 0.3
MIR, IN./HR
Figure 8. SCS curve number for several vegetative covers in
relation to the minimum infiltration rate (MIR).
42
-------
TABLE 4. SCS CURVE NUMBERS FOR NON-ERODED SOIL-COVER COMPLEXES*
Land Use
Cover Treatment
or Practice
SCS Curve Number for
Hydrologic Soil Groupsf
A B C D
Fallow
Planted in row
crops
Straight row
Straight row
Contoured
Contoured and terraced
77
67
65
62
86
87
75
71
91
85
82
78
94
89
86
81
Planted by
grasses and grain
Straight row
Contoured
Contoured and terraced
63
61
59
75
73
70
83
81
78
87
84
81
* After Table 9.1, Ref. 7. Assumes antecedent moisture condition II (AMCII).
t Hydrologic soil groups are:
Group A. (Low runoff potential). Soils having high infiltration rates
even when thoroughly wetted and consisting chiefly of deep, well
drained to excessively drained sands or gravels. These soils
have a high rate of water transmission.
Group B. Soils having moderate infiltration rates when thoroughly wetted
and consisting chiefly of moderately deep to deep, moderately
well to well drained soils with moderately fine to moderately
coarse textures. These soils have a moderate rate of water
transmission.
Group C. Soils having slow infiltration rates when thoroughly wetted and
consisting chiefly of soils with a layer that impedes downward
movement of water, or soils with moderately fine to fine texture.
These soils have a slow rate of water transmission.
Group D. (High runoff potential). Soils having slow infiltration rates
when thoroughly wetted and consisting chiefly of clay soils with
a high swelling potential, soils with a permanent high water
table, soils with a claypan or clay layer at or near the surface,
and shallow soils over nearly impervious material. These soils
have a slow rate of water transmission.
This step signals the end of the manual hydrological input option. The sec-
tion on output is to be entered after hydrologic input is completed.
43
-------
INTERACT BETWEEN THE DEFAULT AND MANUAL INPUT OPTIONS
Occasionally the user wants to use the default input option for one set
of data and the manual input option for another set. This mixing of options
can be done by following the example given below. Here the user wants to use
default climatological data and manual input of hydrological data.
The computer system types:
DO YOU WANT TO USE DEFAULT CLIMATOLOGIC DATA?
ENTER YES OR NO
The user types:
YES
The program types:
DO YOU WANT TO USE CLIMATOLOGIC DATA FROM THE PREVIOUS RUN?
ENTER YES OR NO
The user types:
NO ;
The system then types the listing of available cities and the user should
enter one. For example: :
CALIFORNIA
LOS ANGELES
The program types:
DO YOU WANT CLIMATOLOGY, HYDROLOGY, OR OUTPUT?
ENTER 1 FOR CLIMATOLOGICAL INPUT,
2 FOR HYDROLOGICAL INPUT,
3 FOR OUTPUT OR
4 TO STOP PROGRAM.
The user types: >
1 ;
The system types:
CLIMATOLOGICAL DATA ARE CURRENTLY ON FILE
DO YOU WANT MANUAL HYDROLOGICAL INPUT OPTION? ENTER YES OR NO
YES •.-.-.
44
-------
At this point the user should begin to enter values for the manual hydro-
logical input option.
OUTPUT FOR PROGRAM
The printing of the output starts with the first year entered. For
example, if climatological data were entered for 1974, 1975, 1976, 1977, and
1978, but only 2 years of printed output were requested, the program would
print only the 1974 and 1975 data sets. At this time, the consecutive output
dates cannot be user specified. In addition, any manually input or default
data files that have been entered will remain on line indefinitely or until
the user changes the files. The output for both the default and manual input
data options are the same, and questions about output follow:
DO YOU WANT CLIMATOLOGY, HYDROLOGY OB OUTPUT?
ENTER 1 EOR CLIMATOLOGICAL INPUT,
2 FOR HYDROLOGICAL INPUT,
3 FOR OUTPUT OR
4 TO STOP PROGRAM.
HOW MANY YEARS OF OUTPUT DO YOU WANT?
TWO (2) YEARS MINIKU'M AND
TWENTY (20) YEARS OF PRECIPITATION ARE MAXIMUM
#*ONLY FIVE (5) YEAR MAXIMUM FOR DEFAULT OPTION**
DO YOU WANT DAILY PRECIPITATION OUTPUT?
(NO PRINTS THE ANNUAL SUMMARIES)
ANSWER YES OR NO
YES
HYDROLOGIC OUTPUT
Hydrologic output is composed of input information and calculated values.
Daily and annual summaries of simulated output data are available for both op-
tions. Output for the simulation period includes monthly totals and means of
rainfall, runoff, evapotranspiration, cover, drainage percolation, and average
i
45
-------
soil-water content. The data include annual totals for each component.
For the hydrologic output, the program first prints the title of the
project, the location, and the current date of the run. Then for refer-
ence purposes, the program prints the input values. The input of the
climatological module is printed first, followed by the input of the hydro-
logical module. LAI-DAYS is an indicator of the potential growth index.
This figure is obtained by integrating the IAI versus time (days) data and
is used to check the model.
HYDROLOGIC OUTPUT
(DAILY PRECIPITATION VALUES)
HYDROLOGY OF A SOLID WASTE DISPOSAL SITE
TEN MILES SOUTH OF TOWN
1 FEB. 1982
JAN/JUL
54.98
68.38
JAN 'JUL
267.51
625.82
MONTHLY MEAN TEMPERATURES, DEGREES FAHRENHEIT
FEB/AUG MAR/SEP APR/OCT MAY/NOV JUN/DEC
54.34
69.02
55.67
67.70
58.61
64.76
62.37
61.00
65.95
57.42
MONTHLY MEAN RADIATION, LANGLEYS PEP, DAY
FEB/AUG MAR/SEP APR/OCT MAY/NOV JUN/DEC
325.76
567.57
416.41
476.92
515.17
378,16
595.57
297.76
636.07
257.26
LEAF AREA INDEX TABLE
DATE
1
30
50
70
90
110
130
150
170
190
210
230
366
LAI
0.0
0.0
0.61
1.00
1.00
1.00
1.00
1.00
0.90
•0.65
0.32
0.17
0.0
WINTER C FACTOR = 0.70
LAI-DAYS = 162.86
46
-------
Next is an input summary of the hydrologic characteristics.
VEGETATIVE SOIL
EFFECTIVE HYDRAULIC CONDUCTIVITY
POHOSITY
EVAPORATION COEFFICIENT
AVAILABLE WATER CAPACITY
SOIL LAYEB 2
EFFECTIVE HYDRAULIC CONDUCTIVITY
POROSITY
EVAPORATION COEFFICIENT
AVAILABLE WATER CAPACITY
SOIL LAYER 3
EFFECTIVE HYDRAULIC CONDUCTIVITY
POROSITY
EVAPORATION COEFFICIENT
AVAILABLE^WATER CAPACITY
0.41350 IN/HE
0.34350 VOL/VOL
4.E0000
0.13100 VOL/VOL
0.00325 IN/HR
0.19400 VOL/VOL
3.10000
0.04230 VOL/VOL
7.08700 IN/ER
0.37600 VOL/VOL
3.30000
0.08700 VOL/VOL
FAIR GRASS
SCS CURVE NUMBER
UPPER LIMIT OF S.TO.RAGE
INITIAL SOIL WATER S-TOBAGE
76.20940
3.81800 IN
1.90900 IN
SOIL COVER THICKNESS (INj
TOTAL 36.0
VEGETATIVE 14.0
SOIL LAYER 2 12.0
SOIL LAYER 3 10.0
SOIL LAYER 2 COMPACTED
DESIGN LINER LIJE 5.0 YEARS
UPPER LIMIT OF STORAGES IN COVER (INCHES)
THICKNESS 0.875 3.500 7.000 10.500 14.000 26.000
0.086 0.256 0.344 0.244 0.344 1.572
INITIAL SOIL WATER STORAGE IN COVER (INCHES)
THICKNESS 0.875 3.500 7.000 10.500 14.080 26.000
0.043 0.129 0.172 0.172 0.172 0.786
36.000
0.870
36.000
0.435
47
-------
r
Daily output is printed only for days when precipitation occurred or on
a day when the temperature was above freezing and runoff occurred. The cover
drainage is only that liquid which flows out of the cover and does not perco-
late into the solid waste cells. The average temperature is that predicted
by the model. The accumulative evapotranspiration is carried through the
model in relation to the potential evapotranspiration and the available water
capacity. The average soil water is the depth-weighted fractional water con-
tent (volume basis) of the final soil cover—an average of each of seven soil
storages permitted by the CREAMS model for the final soil cover. The CREAMS
model (3) permits the top storage depth to equal 1/36 of the final soil cover
depth, the second storage depth to equal 5/36 of the final soil cover depth,
and the other storage depths to equal 1/6 of the final soil cover depth. For
example, if the final soil cover had a depth of 24 in. (60 cm), then the
7 depths for computational purposes would be 0.67, 3.33, 4, 4, 4, 4, 4 in.
(1.68, 8.33, 10, 10, 10, 10, 10 cm), respectively. The program apportions
these fractions, which are printed in the initial input data along with the
depth considered.
EATE RAIKTALL
JULIAN INCHES
76084
78005
7see.7
76010
78011'
78015
78316
78017
760 18
78020
76037
76038
76B39
78040
78041
76042
78044
78045
78058
78059
78060
78061
78062
76063
76064
78065
78069
78071
78081
76082
78090
78091
78095
78097
78106
78116
78248
78249
78294
78315
78316
78318
78326
78327
76336
78351
783 £2
78353
783S4
0.21
0.76
1.02
1.45
1.09
1.51
0.13
1.09
0.02
0.20
1.42
0.05
0.89
0.70
0.92
0.82;
0.75
0.23
0.20
0.07
1.61
1.4S
0.42
0.19
2.27
0.02
0.13
0.24
0.06
0.53
0.28
0.28
0.23
0.27
0.69
0.0.4
0.03
0.36
0.04
0.10
0.26
0.32
0.40
0.12
0.01
0.06
i.10
0.61
a. 05
EUNOFF
INCHES
0.0
0.20
0.78
1.C9
i.ez
0.94
0.06
0.P5
0.0
0.0
0.0
0.0
0.58
0.60
0.63
0.72
0.29
0.12
0.?.
0.0
e.e
1.21
0.28
0.e
i.es
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
COVER
TRAIN
INCHES
0.0
0.0
0.0
0.0
0.0
0.BC33
0.0061
0.0108
0.0061
0.0132
0.0616
0.0052
0.0097
0.0057
0.0097
0.0057
0.0139
0.0056
0.0450
0.0 01P
0.0052
' 0.0087
0.0046
0.0046
0.0114
0.0046
0.0167
0.0071
0.0135
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
e.e
0.0
0.0
e.e
PEKCOl.
INCHES
0.0
0.0
0.0
0.e
0.0
0.015E
0.0285
0.050'!
0.0286
0.0531
0.3094
0.0264
0.0491?
0.0289
0.0493
0.029B
0.0715
0.0290
C.2447
'0.0097
0.0287
0.047S
0.0267
0.0254
0.0635
0.0267
0.094?
0.0407
0.0806
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
AVFRAGE
TTMP.
DEC. P.
££ .81
55.' 63
55 . 52
55.35
55:23
£5.06
54.. 94
54. SS
E4.6E
54.78
54.46
54.32
54.31
54.31
54.31
54.31
£4.32
54.34
54.47
54.69
54.73
£4.77
54.82
54.66
£4 . SI
54.96
55.10
55.28
55.70
56.12
56.51
56.93
57.18
57.48
58.08
59.17
66.19
68.35
66.51
62. £1
61.53
61 .34
60.71
60.15
59.54
58.15
57.29
57.19
:;7.e9
AVERA8E
SCIL W.
. VOL/VCI,
0.27
0.28
0.23
0.29
0.29
PI. 29
0.29
0.30
0.29
0.29
0.27
0.29
0.30
0.29
0.30
0.29
0.29
0.29
0.27
g.25
'0.29
0.30
0.29
0.29
0.30
0.29
0.28
0.'27
0.25
0.25
0.24
0.23
0.23
0.22
0.21
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.21
0.22
0.21
0.21
0.21
0.23
0.23
ACCUK.
ET
INCHES
0.28
0.35
0.50
0.71
0.78
1.07
1.15
1.22
1.29
1.44
2.46
2.55
2.65
2.74
2.64
2. £4
3.15
3.26
4.49
4-.es
4.73
4.87
5.01
5.16
5.30
5.45
6.01
6.32
7.29
7.43
8.44
8.63
9.20
9.52
10.49
11.21
11.24
11.41
11.64
11.73
11.82
11.92
12.07
12.15
12.26
12.44
12.51
12.59
12.63
48
-------
The annual totals for the particular year in question are then
printed, and the water budget balance is presented. The latter shows whether
or not the parameters were properly computed and.the time changes correctly
evaluated. If the parameter for unmelted snow is not equal to zero, this
amount is carried over into the next year and is added to runoff and
infiltration when the temperature is above freezing. The water budget
balance should be off by the amount of unmelted snow. Otherwise, the water
budget balance is about zero.
ANNUAL TOTALS FOR 1978 (INCHES)
PRECIPITATION = 24.58
PREDICTED RUNOFI = 11.24
TOT COVER DRAIN = 0.2783
TOT PERCOLATION = 1.4580"
TOTAL ET = 12.76
UNMELTED SNOW = 0.0
BEGIN SOIL WATER = 3.06
FINAL SOIL WATER = 1.90
WATER BUDGET BAL. = 0.0
Next, the average annual values are printed for a quick glimpse at
the model output, in this case, 5-year averages.
AVERAGE ANNUAL VALUES
PRECIPITATION
PREDICTED RUNOFF =
TOT COVER DRAIN
TOT PERCOLATION
TOTAL ET
(INCHES)
13.52
.45
,2159
,3213
3,
0,
0,
9.53
Again, cover drainage refers to lateral transfer of moisture above a liner
(synthetic membrane) in the cover. Cover drainage appears only if a liner
is specified.
For the second phase of the data output, the heading is reprinted, the
following question is asked, and monthly averages for each year and for
monthly annual averages are printed as shown for 1978 and 5-year annual
averages.
49
-------
DO YOU WANT MONTHLY HYDROLOGY SUMMARY?
ENTER YES OR NO
YES
1978
MONTH
JAN
FEE
MAB
APE
MAT
JUN
JUL
AUG
SEP
OCT
NOV
DEC
RAIN
7.48
6.05
7.08
1.51
0.0
0.0
0.0
0.0
0.39
0.04
1.20
0.83
RUNOFF
4.93
2.93
3.38
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
ET
2.17
2.42
3.85
2.77
0.0
0.0
0.0
0.0
0.39
0.04
0.58
0.53
COVER
ERAIN
0.0376
0 .1639
0 .0768
0.0
0 .0
0.0
0 .0
0 .0
0.0
0.0
0.0
0.0
PERCCL.
0.1761
0.8469
0.4349
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
AVG SW
3.09
2.82
2.30
0.45
0.0
0.0
0.0
0.0
0.07
0.00
0.31
0.72
TOT/AVE
24.58
11.24
12.76
0.28
1.46
0.81
TOT/AVI
13.52
ANNUAK, AVERAGES
MONTH
JAN
FEE
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DIG
RAIN
3.30
2.36
2.92
0.63
0.52
0.06
0.00
0.50
0.45
0.46
0.42
1.88
RUNOFF
1.87
0.68
0.68
0.0'
0.01
0.0
0.0
0.03
0.0
0.00
0.0
0.18
ET
1.32
1.64
2.93
1.19
•* 0.51
0.06
0.00
0.28
0.37
0.28
0.39
0.54
COVER
TRAIN
0.0673
0 .0931
0.0555
0.0
0 .0
0.0
0.0
0.0
0.0
0 .0
0.0
0.0
PERCOL.
0.0360
0.1839
0.1014
0.0
0.0
0.0
0.0
0.0
0.-0
0.0
0.0
0.0
AVG SV
2.04
1.92
1.32
0.16
0.06
0.00
0.00
0.12
0.28
0.27
0.47
0.85
3.45
9.51
0.22
0.32
0.62
ENTER RUNHYDRO TO RERUN PROGRAM CR
ENTER LOGOFF TO LOGOFF COMPUTER SYSTEM
50
-------
The programming session is completed. The logoff command (LOGOFF) is
typed at the next READY prompt. But if the user would like to reenter the
hydrologic model, he should enter RUNHYDRO. At this point, the program
heading would be reprinted, and the initial questions asked.
LOADING PRECIPITATION DATA FROM OFF-LINE MEDIA
The user has the option of using manual input for 2 to 20 years of cli-
matic data, which consist of daily precipitation, mean monthly temperatures,
solar radiation, and LAI values. But this process can be very costly and
time consuming when more than 5 years' worth of precipitation data are used.
Thus, the user should consider input of daily precipitation values from an
off-line medium. The off-line medium can be a deck of cards, magnetic tape,
floppy disk, etc. Whatever off-line medium the user prefers, the user should
build a file with 37 records per year and each record should consist of a
field of 12 variables. The first variable has the format 110 and should con-
tain the last 2 digits of the year. The next 10 variables have the format
F5.2 which contains the daily precipitation values. The last variable has
the format 110 which contains the number of the record. Moreover, the first
line or record of the off-line medium must consist of the year, the daily
precipitation values for January 1 to January 10, and the number 1 to indicate
the first record. The following is an example of how the first record should
look.
The second record should consist of the year,
from January 11 to January 20, and the number 2 to
This procedure should continue as shown below.
daily precipitation values
indicate the second record.
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
74
0.0
0.0
0.0
0.0
1.00
1.00
0.0
0.0
0.12
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
0.04
0.04
0.04
0.0
0.0
0.02
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
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
0.05
0.0
0.0
0.0
0.0
0.0
0.01
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
2.0
0.0
0.0
0.0
0.01
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
0.11
0.0
0.0
0.85
0.85
0.85
0.0
0.26
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
0
0
0
0
0
.10
.0
.0
.26
.26
.06
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
0.0
0V 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
0.0
0.0
0.0
0.0
0.0
0.02
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
0.11
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
2
3
4
P
w
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
51
-------
74 0.
74 0.
74 0.
74 0.
74 0.
74 0.
74 0.
74 0.
74 0.
74 0.
74 0.
74 0.
74 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
0.00
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.01
0
0
0
0
0
0
0
0
0
0
0
0
0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.10
.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
0.0
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
0.0
0.0
0.0
0.0
0.0
25
26
27
28
29
30
31
32
33
34
35
36
37
Note that on line 37, the daily precipitation values are input for December 27
to December 31, and the last 5 values are zeros. If 74 were a leap year the
last 4 values would be zero. However, the user must start the next year with
a value of 75 for the year, 10 daily precipitation values, and the number 1
to indicate the first record.
Once the user has built the off-line precipitation data file according to
the format given, the user should log on the NCC's computer system, read the
off-line precipitation data file on the computer system, and save the data under
the file name of PRECIP.
The user must run the HSSWDS model for manual input of mean monthly
temperatures, solar radiation, and LAI values. Once the command RUNHYDRO is
issued, the computer system will ask the following question and the user
should enter the following commands. The computer system types:
ARE YOU USING DEFAULT CLIMATOLOGICAL INPUT
The user enters:
NO
Next, the user should respond by entering the number 1 to enter manual
climatological input. The computer system types:
DO YOU WANT TO INPUT PRECIPITATION DATA
The user enters:
NO
The computer system requests the input of temperature, solar radiation, and
LAI values. At this point, the model functions according to the information
given in the section for manual input of climatologic data.
SAVING PRECIPITATION DATA FILES
The HSSWDS model is constructed so that the precipitation data are
stored permanently. Once the user enters a new precipitation data file, the
old file is replaced in permanent storage by the new one. If the user wants
52
-------
to save a precipitation data file, he must save it under another file name.
If the user wants to retain a precipitation data file that is stored on the
user's account, he should replace the old file with the file name PRECIP. •
The file name for all precipitation data files created and run by the HSSWDS
model is PRECIP. So the user can conclude that the model will run only the
precipitation data that are stored under the file name of PRECIP.
For example, assume that the user has manually input 20 years of precip-
itation data for Vicksburg, Mississippi. Also assume that the rest of the
climatologic and hydrologic data have been input and that the output has been
printed. At this point, the user should save the precipitation data stored
on the file PRECIP under another name by using the EDIT, the SAVE, and the
END commands as follows:
EDIT PRECIP
SAVE VICKS
END
The 20 years of precipitation for Vicksburg, Mississippi, is now stored on the
permanent file named VICKS. The user can now run the model with another set
of precipitation data, without losing the precipitation data for Vicksburg,
Mississippi. To retrieve the precipitation data file for Vicksburg, Missis-
sippi, that is stored under the file name of VICKS, the user must enter the
following commands:
EDIT VICKS
SAVE PRECIP
END
The precipitation data for Vicksburg, Mississippi, has replaced the old pre-
cipitation data, and the model will run using the Vicksburg precipitation
data.
BATCH.OPERATIONS
The HSSWDS model can be run in the batch environment to reduce computer
costs. The batch procedures are for running the HSSWDS model on NCC's IBM
360/370 computer system. Instructions consist of punched cards of two forms-
control cards and sequential input cards. An example of the two forms of
instructions are given as follows. Note that the control card instructions
are separated into two steps.
The control card instructions of Step 1 that need to be changed or
explained are as follows:
53
-------
Explanation of Parameters
Example
EPAWRC
MAWCHSSWP
RMT129
EPARAC
TERL
EARL
USERID = user identification
ACCOUID = the user's account plus utiliza-
tion identifier plus the letter
P
RMTXXX = the remote number of output
routing
MASTID = contact authors for master
USERID
ACCOUNT = the user's account
MASTACC s contact authors for master
account
PRTY = priority of job where
1 = overnight turnaround time
2 = 4 hours turnaround time
3=2 hours turnaround time
4-1/2 hour turnaround time
TIME = time in minutes where
4 minutes are maximum amount .
needed
All other control cards for Steps 1 and 2 should be punched as shown below.
Be sure to insert your user identification and account. The job control
card instructions are the same for every run. Once the job control cards
are punched, they can be used continuously.
The sequential input cards are the answers to the interactive questions
prompted by the HSSWDS model. The user must refer to the interactive question
to build the sequential card deck or to use examples 1 and 2 which follow.
54
-------
Control Cards Step 1
//USERID JOB (ACCOUID), 'LAST NAME', PRTY=1, TIME=4
/-•ROUTE PRINT RMTXXX
//STEP1 EXEC PGM=IEBGENER
//SYSIN DD DUMMY
//SYSPRINT DD SYSOUT=A
//SYSUT2 DD DSN=CN.USERID.ACCOUNT.AFILE,
// DISP=(NEW,PASS,DELETE),SPACE=(TRK,(5,5)),
// DCB=(LRECL=80,RECFM=FB,BLKSIZE=3120),
// UNIT=DISK
//SYSUT1 DD *
Sequential Input Cards
YES
NO
CALIFORNIA
LOS ANGELES
2
TEST
TEST
TEST
1
24
10
7
NO
3
4
NO
YES
Control Cards Step 2
/*
//STEP2 EXEC PGM=HYDR02
//STEPLIB DD DSN=CN.MASTID.MASTACC.HYDRO.LOAD,DISP=SHR
//FT10F001 DD DSN=CN.USERID.ACCOUNT.AFILE,UNIT=DISK,
// DISP=(OLD,DELETE,DELETE),
// SPACE=(CYL,(5,2)),DCB=(RECFM=FB,LRECL=80,BLKSIZE=3120)
//FT06F001 DD SYSOUT=A
//FT04F001 DD DSN=CN.USERID.ACCOUNT.PRECIP,DISP=SHR
//FT05F001 DD DSN=CN.USERID.ACCOUNT.INPUT1,DISP=SHR
//FT07F001 DD DSN=CN.USERID.ACCOUNT.WEATHR,DISP=SHR
//FT08F001 DD DSN=CN.MASTID.MASTACC.CITIES,DISP=SHR
//FT09F001 DD DSN=CN.MASTID.MASTACC.PRE3,DISP=SHR
//FT11F001 DD DSN=CN.USERID.ACCOUNT.HSTATE,DISP=SHR
//FT12F001 DD DSN=CN.MASTID.MASTACC.DEDATA,DISP=SHR
//FT14F001 DD DSN=CN.USERID.ACCOUNT.WTHYR,DISP=SHR
55
-------
Card
No.
CD
Example 1 (Batch Default Input Option)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Example Entry
YES
NO
CALIFORNIA
LOS ANGELES
2
TEST
FOUR MILES FROM
TOWN
Interactive Question
DO YOU WANT TO USE DEFAULT
CLIMATOLOGIC DATA? (ENTER
YES OR NO.)
DO YOU WANT TO USE CLI--
MATOLOGIC DATA FROM THE
PREVIOUS RUN?
(ENTER YES OR NO.)
ENTER NAME OF STATE.
ENTER NAME OF CITY.
ENTER
1 FOR CLIMATOLOGICAL INPUT,
2 FOR HYDROLOGICAL INPUT,
3 FOR OUTPUT OR
4 TO STOP PROGRAM.
ENTER TITLE.
ENTER LOCATION.
Explanation
22 JANUARY 1982 ENTER TODAY'S DATE.
3 ENTER NUMBER OF LAYERS IN
SOIL COVER.
(10)
(11)
(12)
(13)
36
9
24
16
ENTER TOTAL THICKNESS OF
COVER SOIL.
ENTER A NUMBER (1 THROUGH
18) FOR TEXTURE CLASS OF
VEGETATIVE SOIL TYPE.
ENTER THICKNESS OF SOIL
LAYER 2.
ENTER A NUMBER (1 THROUGH
18) FOR TEXTURE CLASS OF
SOIL LAYER 2.
If YES is entered, the;
default input option is
initiated. If NO is
entered, go to example 2
for manual input option
instructions.
If NO is entered, con-
tinue to the next card.
If YES is entered, skip
to card number (5).
Enter a state located in
Table 5.
Enter a city located iii
Table 5.
Enter the number 2 for
the default hydrological
input section.
Enter the name of the
site.
Enter the location of
the site.
Enter the date of the
computer run.
If 1 is entered, skip
all cards referring to
soil layers 2 and 3. If
2 is entered, skip all
cards referring to soil
layer 3.
The units for the cover
thickness are inches.
See Table 1 for correct
soil texture that refers
to associated number.
The units for the thick-
ness of soil layer 2 are
in inches. :
See Table 1 for correct
soil texture that refers
to associated number.
56
-------
Example 1 (Continued)
Card
No.
(14)
(15)
(16)
Example Entry
6
13
YES
(17)
NO
(18)
(19)
(20)
YES
50
Interactive Question
ENTER THICKNESS OF SOIL
LAYER 3.
ENTER A NUMBER (1 THROUGH
18) FOR TEXTURE CLASS OF
SOIL LAYER 3.
DID YOU COMPACT THE SOIL
LAYER 2? (ENTER YES OR
NO.)
Explanation
DID YOU COMPACT THE SOIL
LAYER 3?
(ENTER YES OR NO.)
SELECT THE TYPE OF VEGETA-
TIVE COVER BY ENTERING A
NUMBER (1 THROUGH 7) WHERE
1 = BAREGROUND
2 = GRASS (EXCELLENT)
3 = GRASS (GOOD) ,
4 = GRASS (FAIR)
5 = GRASS (POOR)
6 = ROW CROP (GOOD)
7 = ROW CROP (FAIR)
IS THERE AN IMPERMEABLE
LINER AT THE INTERFACE?
(ENTER YES OR NO.)
WHAT IS THE EXPECTED LIFE
OF THE LINER (YEARS)?
The units for the thick-
ness of soil layer 3 are
in inches.
See Table 1 for correct
texture class that re-
fers to associated
number.
If YES is entered, the
hydraulic conductivity
is reduced by a factor
of 20, the available
water capacity and
porosity are reduced by
a factor of 3.
If YES is entered, the
hydraulic conductivity
is reduced by a factor
of 20, the available
water capacity and
porosity are reduced by
a factor of 3.
If grass,excellent,
good, fair, or poor is
selected the stored sur-
face area of the leaf
area indices (LAI) are
multiplied by 1.0, 0.66,
0.33, or 0.17, respec-
tively. If row crop
good or fair is selected
the stored surface area
of the LAI is multiplied
by 1.0 or 0.50, respec-
tively. If bare ground
is selected the surface
area of the LAI is mul-
tiplied by 0.0.
If NO is entered, skip
to card number (21).
The years range from 1
to 100.
57
-------
Example 1 (Concluded)
Card
No.
(21)
Example Entry
3
(22)
(23)
(24)
YES
YES
Interactive Question
ENTER
1 FOR CLIMATOLOGICAL INPUT,
2 FOR HYDROLOGICAL INPUT,
3 FOR OUTPUT OR
4 TO STOP PROGRAM.
HOW MANY YEARS OF OUTPUT
DO YOU WANT?
DO YOU WANT A DAILY
SUMMARY?
(ENTER YES OR NO.)
DO YOU WANT MONTHLY
SUMMARIES?
(ENTER YES OR NO)
Explanation
Enter the number 3 for
output.
You must enter a number
from 2 to 5.
If NO is entered, only
the yearly summaries are
printed.
If NO is entered, the
monthly summaries are ',
not printed.
Example 2 (Batch Manual Input Option)
Card
No.
(1)
Example Entry
NO
(2)
(3)
YES
(4)
NO
Interactive Question
DO YOU WANT TO USE DEFAULT
CLIMATOLOGIC DATA?
(ENTER YES OR NO.)
ENTER
1 FOR CLIMATOLOGICAL INPUT,
2 FOR HYDROLOGICAL INPUT,
3 FOR OUTPUT OR
4 TO STOP PROGRAM.
DO YOU WANT TO ENTER PRE-
CIPITATION DATA?
(ENTER YES OR NO.)
DO YOU WANT TO ADD TO EX-
ISTING PRECIPITATION DATA?
(ENTER YES OR NO.)
Explanation
If NO is entered the
manual input option is
initated. In YES is ;
entered, go to example;1
for default input option
instructions.
Enter 1 for climatolog-
ical input.
If NO is entered, pre-
cipitation data are used
from the previous run;;
skip to the card number
(47). If YES is
entered, continue.
If YES is entered, the!
program will attach the
existing precipitation
data to the precipita-
tion data to be entered.
58
-------
Example 2 (Continued)
Card
No.
(5)
(6)
(7)
(8)
(43)
Example Entry
74
Interactive Question
ENTER YEAR OF
PRECIPITATION.
(10 values)
ENTER 10 VALUES OF DAILY
PRECIPITATION DATA.
(10 values)
(10 values)
ENTER 10 VALUES OF DAILY
PRECIPITATION DATA.
ENTER 10 VALUES OF DAILY
PRECIPITATION DATA.
(•) (10 values)
(•) (10 values)
(•) (10 values)
(42) (5 or 6 values)
0
ENTER YEAR OF
PRECIPITATION.
(44)
(45)
YES
74
DO YOU WANT A LISTING OF
PRECIPITATION VALUES?
(ENTER YES OR NO.)
ENTER YEAR YOU WANT LISTED.
Explanation
Enter last two digits of
the year only. If pre-
cipitation data entry is
completed do not enter
a year and skip to card
number (43).
These 10 values repre-
sent the amount of pre-
cipitation (IN/HR) for
January 1 through
January 10. Separate
each value with a blank.
These 10 values repre-
sent the amount of pre-
cipitation (IN/HR) for
January 11 through
January 20.
These 10 values repre-
sent the amount of pre-
cipitation (IN/HR) for
January 21 through
January 30.
The program will request
that 10 values of daily
precipitation be entered
until 37 cards are
punched. This is enough
space for 365 or 366
(leap year) days of the
year and the last 5 or
4 values are left blank.
Last 2 digits only; if a
year is entered, repeat
cards 6 through 42.
Enter zero when all
years of precipitation
data are entered.
If NO is entered, skip
to card number (47).
The program print pre-
cipitation data of the
given year.
59
-------
Example 2 (Continued)
(46)
(47)
(48)
(49a)
(50a)
(5 la)
Example Entry
YES
YES
NO
40.1
50.8
60.
70.
80.
85.0
90.2
94.4
93.4
60.3
40.0
31.0
NO
YES
Interactive Question
DO YOU WANT TO USE THEM
(ENTER YES ONLY).
DO YOU WANT TO ENTER
TEMPERATURE DATA?
(ENTER YES OR NO.)
DO YOU WANT TO ENTER
YEARLY CLIMATOLOGICAL
DATA?
(ENTER YES OR NO.)
Explanation
ENTER 12 MONTHLY TEMPERA-
TURE VALUES.
DO YOU WANT TO CHANGE
TEMPERATURE LISTED?
(ENTER NO ONLY.)
DO YOU WANT TO ENTER
SOLAR RADIATION DATA?
(ENTER YES OR NO.)
THE
Repeat card numbers 44
through 46 until all
years of precipitation
data are listed.
If NO is entered, the
program assumes temper-
ature data are to be
used from previous run.
If NO is entered, temf
peratures, solar radia-
tion, and LAI values
are to be constant over
entire years of precip-
itation. Insert only;
cards with the label a.
If YES is entered,
yearly associated tem-
peratures, solar radia-
tion, winter cover
factors, and LAI values
for each year of pre-
cipitation must be
entered. Insert only
card numbers with the
label b and skip to
card number 49b.
Enter 12 values with :
one value per card.
The user should enter
NO only.
If NO is entered, the
program assumes solar
radiation data are to
be used from previous
run.
60
-------
Example 2 (Continued)
(52a)
(53a)
(54a)
Example Entry
230
260
290
310
320
350
370
400
355
325
250
225
YES
YES
(55a)
1
90
144
160
190
200
215
230
280
300
315
325
366
0.0
0.0
0.9
1.0
2.0
3.0
3.0
3.0
3.0
2.9
2.4
1.0
0.0
(56a)
(49b)
(50b)
NO
NO
30.1
40.2
50.0
55.0
65.4
Interactive Question
ENTER 12 MONTHLY SOLAR
RADIATION VALUES.
Explanation
Enter 12 values with
one value per card.
DOES THE SOIL SURFACE HAVE
VEGETATION?
(ENTER YES OR NO.)
DO YOU WANT TO ENTER THE
LEAF AREA INDEX?
(ENTER YES OR NO.)
ENTER LAI ON 13 CARDS WITH
2 VALUES PER CARD.
DO YOU WANT TO CHANGE
THEM?
(ENTER NO ONLY.)
ARE THE TEMPERATURES THE
SAME AS THE PREVIOUS YEAR?
(ENTER YES OR NO.)
ENTER 12 MONTHLY TEMPERA-
TURES FOR THE YEAR 1974.
If NO is entered skip
to card number 62.
If NO is entered the
program assumes the
leaf area index data
are to be used from the
previous run.
The first value on the
card is the day of
measurement (Julian
Day) and the second
value is the surface
area of the leaf area
index.
Enter NO only and skip
to card number (62).
If NO is entered con-
tinue. If YES is
entered, skip to card
number 52b.
Enter 12 values on
separate cards for the
first year of precipi-
tation data entered.
If this is the second,
61
-------
Example 2 (Continued)
(50b)
(51b)
(52b)
Example Entry
73.3
79.1
75.5
62.4
61.2
31.1
29.0
NO
NO
Interactive Question
Explanation
(53b)
203
244
264
284
300
315
318
350
348
300
255
225
(54b)
(55b)
(56b)
(57b)
(58b)
NO
NO
0.9
NO
YES
DO YOU WANT TO CHANGE
THEM?
(ENTER NO ONLY.)
ARE THE SOLAR RADIATION
VALUES THE SAME AS THE
PREVIOUS YEAR?
(ENTER YES OR NO..)
ENTER 12 MONTHLY SOLAR
RADIATION VALUES.
DO YOU WANT TO CHANGE
THEM?
(ENTER NO ONLY.)
IS THE WINTER COVER FACTOR
THE SAME AS THE PREVIOUS
YEAR?
(ENTER YES OR NO.)
ENTER WINTER COVER FACTOR.
DO YOU WANT TO CHANGE IT?
(ENTER NO ONLY.)
DOES THE SOIL SURFACE HAVE
VEGETATION?
(ENTER YES OR NO.)
third, fourth ., ., .,
or twentieth time j
through this loop the
year (1974) is incre-
ment by 1, 2, 3, ., i,
., or nineteen,
respectively.
Enter NO only and
continue.
NO is entered for the
first time through the
loop. If YES is
entered, skip to card
number 55b.
Enter 12 values on
separate cards.
Enter NO only and
continue.
NO is entered the first
time through the loop;
If YES is entered, skip
to card number 58b. ;
The number range from
0.5 to 1.0, where 1.0
is for bare ground.
Enter NO only and
continue.
If NO is entered and it
is not the last year in
which precipitation
data were entered, go
to card number 49b; but
62
-------
Example 2 (Continued)
Card
No. Example Entry
(5815) (Continued)
(59b)
NO
(60b) 1
90
144
160
190
200
215
230
280
300
315
325
366
0.0
0.0
0.9
1.0
2.0
3.0
3.0
3.0
3.0
2.9
2.4
1.0
0.0
(61b)
NO
(62)
Interactive Question
ARE LAI's THE SAME AS THE
PREVIOUS YEAR?
(ENTER YES OR NO.)
ENTER LAI ON 13 CARDS WITH
2 VALUES PER CARD.
DO YOU WANT TO CHANGE
THEM?
(ENTER NO ONLY.)
ENTER
1 FOR CLIMATOLOGICAL INPUT,
2 FOR HYDRO-LOGICAL INPUT,
3 FOR OUTPUT OR
4 TO STOP PROGRAM.
Explanation
if it is the last
year, skip to card
number 62. The pro-
gram will insert
the LAI value if the
soil surface does not
have vegetation.
Enter NO only if it is
the first time through
the loop. If YES is
entered skip to card
number 49b. If NO is
entered and the sur-
face does not have
vegetation, the pro-
gram will automati-
cally insert the LAI
values and skip to
card number 49b. If
this is the last year
entered, skip to card
number 62.
The first value on the
card is the day of
measurement (Julian
Day) and the second
value is the surface
area of the leaf area
index.
Enter NO only. If
this is the last year
of precipitation data
entered, go to the
next cards; otherwise
go to card 49b.
Enter 2 only for input
of hydrological data.
63
-------
Example 2 (Continued)
Card
No. Example Entry
(63)
TEST
(64) FOUR MILES
FROM TOWN
(65) 22 JANUARY 1982
(66) 74003
(67)
(68)
(69)
(70)
(71)
(72)
(74)
(75)
(76)
(77)
36
0.550
0.458
4.5
0.287
(73) 0.156
24
0.022
0.680
3.5
Interactive Question
ENTER TITLE.
ENTER LOCATION OF SOLID
WASTE SITE.
ENTER TODAY'S DATE.
ENTER YEAR AND DATE OF
FIRST STORM EVENT (JULIAN
DATE).
ENTER NUMBER OF LAYERS IN
SOIL COVER.
Explanation
ENTER TOTAL THICKNESS OF
SOIL COVER.
ENTER HYDROLOGIC CONDUC-
TIVITY OF VEGETATIVE SOIL.
ENTER SOIL POROSITY OF
VEGETATIVE SOIL.
ENTER EVAPORATION COEFFI-
CIENT OF VEGETATIVE SOIL.
ENTER FIELD CAPACITY OF
VEGETATIVE SOIL.
ENTER WILTING POINT OF
VEGETATIVE SOIL.
ENTER THICKNESS OF iSOIL
LAYER 2 (in.).
ENTER HYDRAULIC CONDUC-
TIVITY OF SOIL LAYER 2
(in./hr).
ENTER SOIL POROSITY OF
SOIL LAYER 2 (vol/vol).
ENTER EVAPORATION COEFFI-
CIENT OF SOIL LAYER 2
(vol/vol).
Enter the name of the
site. !
Enter the location of
the site. ;
Enter the date of the
computer run.
The first rain storm
occurred on January 3,
1974.
If layer is 1, skip
all cards referring;to
layers 2 and 3, if ;
layer is 2, skip all
cards referring to ,
layer 3.
The units for the
cover thickness are in
inches.
The units for the hy-
drologic conductivity
are in inches per hour.
The units for the po-
rosity are in volume
per volume.
This value has no
units.
The field capacity has
the units of volume
per volume.
The wilting point has
units of volume per
volume.
64
-------
Example 2 (Concluded)
Card
No. Example Entry
(78) 0.252
(79) 6
(80) 6.620
(81) 0.389
(82) 3.3
(83) 0.248
(84) YES
(85)
(86)
(87)
(88)
(89)
(90)
(91)
50
79
0.9
20
YES
YES
Interactive Question
ENTER FIELD CAPACITY OF
SOIL LAYER 2 (vol/vol).
ENTER THICKNESS OF .SOIL
LAYER 3 (in.).
ENTER HYDRAULIC CONDUC-
TIVITY OF SOIL LAYER 3
(in./hr).
ENTER SOIL POROSITY OF
SOIL LAYER 3 (vol/vol).
ENTER EVAPORATION COEF-
FICIENT OF SOIL LAYER 3
(vol/vol).
ENTER FIELD CAPACITY OF
SOIL LAYER 3 (vol/vol).
IS THERE AN IMPERMEABLE
LINER AT THE INTERFACE?
(ENTER YES OR NO).
WHAT IS THE EXPECTED LIFE
OF THE LINER (YEARS)?
ENTER SCS CURVE NUMBER.
ENTER WINTER COVER FACTOR
ONLY IF THE SAME SET OF
MONTHLY TEMPERATURES,
SOLAR RADIATION, AND LAI
VALUES ARE USED FOR THE
ENTIRE YEARS OF
PRECIPITATION.
ENTER
1 FOR CLIMATOLOGICAL INPUT,
2 FOR HYDROLOGICAL INPUT,
3 FOR OUTPUT OR
4 TO STOP PROGRAM.
HOW MANY YEARS OF OUTPUT
DO YOU WANT?
DO YOU WANT A DAILY
SUMMARY?
(ENTER YES OR NO.)
DO YOU WANT MONTHLY
SUMMARIES?
(ENTER YES OR NO.)
Explanation
If NO is entered, skip
to card number (85).
The years range from
1 to 100.
Enter 3 only for
instruction on how to
control the output.
Do not input a number
greater than the maxi-
mum number of precipi-
tation years entered.
If NO is entered, only
the yearly summaries
are printed.
If NO is entered, the
monthly summaries are
not printed.
65
-------
TABLE 5. LISTING OF CITIES AND STATES
Alaska
Annette
Bethel
Fairbanks
Arizona
Flagstaff
Phoenix
Tucson
Arkansas
Little Rock
California
Sacramento
Fresno
San Diego
Los Angeles
Santa Maria
Colorado
Denver
Grand Junction
Florida
Tallahassee
W. Palm Beach
Jacksonville
Miami Airport
Tampa
Orlando
Georgia
Atlanta
Watkinsville
Hawaii
Honolulu
Idaho
Boise
Pocatello
Illinois
Chicago
E. St. Louis
Indiana
Indianapolis
Iowa
Des Moines
Kansas
Dodge City
Topeka
Kentucky
Lexington
Louisiana
Lake Charles
New Orleans
Shreveport
Maine
Caribou
Portland
Massachusetts
Boston
Michigan
E. Lansing
Sault Ste. Marie
Minnesota
St. Cloud
Missouri
Columbia
Montana
Glasgow
Great Falls
Nebraska
Grand Island
North Omaha
Nevada
Ely
Las Vegas
New Jersey
Edison
Seabrook
New Mexico
Albuquerque
New York
Syracuse
Central Park
Ithaca
Schenectady
New York City
North Carolina
Greensboro
North Dakota
Bismark
Ohio
Cleveland
Columbus
Cincinnati
Put- in-Bay
Oklahoma
Oklahoma City
Tulsa
Oregon
Portland
Medford
Astoria
Pennsylvania
Pittsburgh
Philadelphia
Rhode Island
Providence
South Carolina
Charleston
South Dakota
Rapid City.,
Tennessee
Nashville
Knoxville
Texas '.
Brownsville
El Paso |
Dallas
Midland
San Antonio
Utah
Cedar City
Salt Lake City
Virginia
Lynchburg
Norfolk
Washington .
Yakima
Pullman
Seattle ;
Wisconsin
Madison
Wyoming
Lander
Cheyenne
Puerto Rico \
San Juan
66
-------
REFERENCES
1. Lutton, R. J., G. L. Regan, and L. W. Jones, "Design and Construction of
Covers for Solid Waste Landfills," Aug 1979, EPA-600/2-79-165, Environ-
mental Protection Agency, Cincinnati, Ohio.
2. Lutton, R. J., "Evaluating Cover Systems for Solid and Hazardous Waste,"
Sept. 1980, EPA-IAG-D7-01097, Environmental Protection Agency,
Cincinnati, Ohio. SW 867.
3. Knisel, W. J., Jr., Editor, "CREAMS, A Field Scale Model for Chemical
Runoff and Erosion from Agricultural Management Systems," Vols. I, II,
and III, Cons. Res. Report 24, U. S. Department of Agriculture, 1980
(draft copy).
4. Fenn, D. G., K. J. Hanley, and T. V. DeGeare, "Use of the Water Balance
Method for Predicting Leachate Generation from Solid Waste Disposal
Sites," EPA/630/SW-168, 1975, US-EPA, Cincinnati, Ohio.
5. Baver, L. D., W. H. Gardner, and W. R. Gardner, "Soil Physics," 1972,
John Wiley & Sons, Inc., New York.
6. Robinson, N., "Solar Radiation," 1966, Elsevier Publishing Co.,
New York, 1966.
7. USDA, Soil Conservation Service, "National Engineering Handbook,
Section 4, Hydrology," 1972, U. S. Government Printing Office,
Washington, D.C.
8. Holtan, H. N., G. J. Stilner, W. H. Hensen, and N. C. Lopez, "USDAHL-74,
Revised Model For Watershed Hydrology," USDA, Agr. Res. Service, Tech.
Bulletin No. 1518, December 1975, Washington, D. C.
9. Hjelmfelt, A. T., Jr., and J. J. Cassidy, "Hydrology for Engineers and
Planners," 1975, Iowa State University Press, Ames, Iowa.
10. Williams, J. R., and W. V. LaSeur, "Water Yield Model Using SCS Curve
Numbers," Jour, of the Hydraulics Div., ASCE, Vol. 102, No. HY9, 1976,
pp. 1241-1253.
11. Hawkins, R. H., "Runoff Curve Numbers with Varying Site Moisture," Jour.
of the Irrig. and Drain. Div., ASCE, vol. 104, No. IR4, 1978, pp. 389-
398.
67
-------
12.
Hawkins, R. H., "Runoff Curve Numbers from Partial Area Watersheds,"
Jour, of the Irrig. and Drain. Div., ASCE, Vol. 105, No. IR4, 1979,
pp. 375-389.
68
-------
APPENDIX A
HYDROLOGIC SIMULATION
Model development will be presented in this section for daily water
movement on the surface and through the final cover soil. The following de-
scription of the principles on which the model was developed is from Knisel
(3), SCS-NEH (7), and Hjelmfelt and Cassidy (9). In the model, precipita-
tion is separated into runoff, evapotranspiration, and subsurface drainage to
maintain a continuous water balance.
Mathematical modeling concepts deal with deterministic and stochastic
variables. A deterministic variable is one whose temporal and spatial prop-
erties are known (i.e., it is assumed that the behavior of a hydrologic vari-
able is definite and its characteristics can be predicted without uncer-
tainty) . A stochastic variable is one whose properties are governed by purely
random-time events, sequential relations, and functional relations with other
hydrologic variables. The HSSWDS model is deterministic in its modeling con-
cepts. A general weakness with most research efforts employing deterministic
models is that they focus on obtaining "best" estimates of runoff and p^rcola-
tion parameters, which are then used as the "true" values of the process.
RUNOFF
During a given rainfall, water is continually being intercepted by trees,
plants, root surfaces, etc. Transport and evapotranspiration are also
occurring simultaneously throughout the period. Once rain begins to fall and
the initial requirements of infiltration are fulfilled, natural depressions
collect the excess rain to form small puddles. In addition, minute depths of
water begin to build up on permeable and impermeable surfaces within the waste
disposal site. This stored water collects in small rivulets, conveying the
water into small channels (i.e., overland flow or surface runoff).
The SCS curve number technique (7) was selected (3) for the runoff pro-
cess (10) because the method:
a) Is a well established, reliable procedure,
b) is computationally efficient,
c) requires inputs that are available, and
d) permit the use of estimated data for soil types, land use, and
management.
A plot of the accumulative rainfall versus the accumulative runoff can be
used to develop the relation (7) between rainfall, runoff, and retention
69
-------
(the rainfall not converted to runoff). Although rainfall and runoff do
not start at the same time (because of initial abstraction, I ), this •
relation as shown in Figure A-l can be expressed as: a
F 0
— ps —*•
S P1
potential maximum retention, exclusive of I (S > F)
SL ~~
where F = actual retention
S
Q
P'
actual or direct runoff
potential maximum runoff (PT £_ Q)
initial abstraction
The retention S is a constant for a particular storm because it is the
maximum that can occur under the existing conditions if the. storm continues
without limit. The time delay I between rainfall and runoff consists
mainly of interception and surface storage, all of which occur before
runoff begins. Therefore, the initial abstraction I is brought into
the relation by subtracting it from the rainfall, T§us;
- I
where
the daily rainfall
The retention F varies because it is the difference between P' and Q at
any point along the plotted curve. Thus:
where
Now combining terms, it follows:
F *_ S
Ql (I
- q
After algebraic manipulation this expression becomes:
70
-------
Q =
- V
(P -
i ) + s
9
Figure A-l,
1.0
Relation between the fraction of runoff and the
fraction of retention.
•V - \ N
Rainfall and runoff data from a large number of small watersheds showed the
relation between I and S (which includes I ) as:
I = 0.2S
a
Thus the runoff is predicted for daily rainfall for hazardous and solid
waste disposal sites using:
(P - 0.2S)'
g P + 0.8S
where Q = the daily runoff
P = the daily rainfall
S = the retention parameter
71
-------
all having the dimensions of length. This equation represents a family of
curves of Q on P for a range of values of S from zero to °° .
Expanding the numerator, applying polynomial division, and dividing
through by S yields (11,12):
s \
+ 0.8S/
where the term in the brackets is the remainder from division that approaches
zero as P approaches » . This relation can be seen in Figure A-2 and
shows that the maximum possible amount that can be stored or infiltrated is:
P - Q = 1.2S
(2)
or
where P approaches °°
and rearranging gives:
Rewriting equation 1 by dividing through by
..(§-•».•)'
" 1+0.8
for all P/S > 0.2 . This relation is also illustrated in Figure A-2, which
shows that the value of Q/S approaches P/S - 1.2 asymptotically.
A convenient method was selected to transform the site storage S into
curve numbers CN that had a range of 0 to 100 (7).
CN =
1000
10 + S
(3)
As stated, the system is in inches and must be converted to use metric units.
The potential site retention parameter S is related to the soil water
content (3) by the expression:
S = S
mx
where Smx = maximum value of S
SM = soil-water content in the final soil cover
UL = upper limit of soil-water storage
72
(4)
-------
The maximum value of S is estimated with initial moisture condition I for
the curve number CNL by combining equations 2 and 3 as:
2.0
O/S 1.0 —
[g/S = 0.2
Figure A-2. SCS rainfall-runoff relation standardized
on retention parameter S
O — 1 O
mx ~ X
/lOOO
\ CNI"
10'
(5)
In this model, moisture condition II was related to CN_ using the
polynomial:
CNj. = -16.91 + 1.348(CNI]:) - 0.01379(CNI;[) + 0.0001177(CNI;[) (6)
Hydrologic condition II can be estimated using text Figure 6 or the de-
tailed listings in the SCS-HEC (7) manual for the specified final soil cover
complex.
To assist in uniformly distributing the soil water in the profile, a
weighting technique was developed that divided the soil profile into seven
layers and weighting factors with the equation:
73
-------
mx
1 -
(7)
where W. = weighting factor at depth i .
The weighting factors decrease with depth according to the default values:
0.115, 0.405, 0.266, 0.139, 0.075, 0.000, and 0.000. With this procedure,
runoff is predicted for the solid waste disposal site.
Generally, each solid waste disposal site is thought to be unique; but
uniqueness suggests a lack of information as well as a limitation in data-
gathering capabilities. Proper perspective must be given to the role that
such items as rainfall intensity, storm duration, interception, site slope,
shape, size, and roughness play on the time distribution of runoff. Between
storms, however, water within the soil also moves upward (capillary rise)
because of the flux of water from soil to atmosphere. The vaporization of
rainfall or snow resting on the outer plant surfaces is also gained by thei
atmosphere. These processes are usually called evaporation.
I
EVAPOTRANSPIRATION
The major portion of solar radiation is used in the process of evapo-
transpiration, or the amount of water lost by evaporation from the soil and
transpiration from a plant surface. For example, if thermal energy is added
to a body of water with a free surface, the kinetic energy of the molecules
is increased to the extent that some of the water molecules at the surface
can overcome their surrounding cohesive bonds and are able to escape across
the air/water interface (9). As the molecule of water passes from the liquid
to the vapor state, it absorbs heat energy, thus cooling the water left behind.
As water enters the soil it becomes either evapotranspiration, storage, or
drainage below the final soil cover. In this simulation model a daily time
interval is used to evaluate the components of the water balance equation such
as,
SM. = SM. . + FR - ET - DR + M
where SM. = soil water storage on day i
FR. = water entering the soil on day i
ET. s evapotranspiration on day i
DR. = drainage below the final soil cover on day i
(8)
M, = amount of snowmelt on day i :
When precipitation occurs and the temperature is below freezing (32°F or
0°C), that precipitation is stored in the form of snow. When snow storage
exists and the temperature T is above freezing, snowmelt M. occurs by the
following equation as:
74
-------
M± = 0.10T
(9)
This relation is used unless M. is greater than the amount of surface snow.
To compute the potential evaporation, a modification of the Penman method
that uses energy balance principles is used in the model as:
1.28AH
c
A +
(10)
where E = potential evaporation
A = slope of the saturation vapor pressure curve at the mean air
temperature
H = net solar radiation
o
y = the psychrometric constant, i.e., 0.68
The slope A of the saturation vapor pressure curve for water at the mean
air temperature is computed from:
A _ 5304 (21.255-5304/T)
A - —- e
(11)
where T is the daily temperature in degrees Kelvin.
tion H is computed from the equation:
(1 -
58.3
The net solar radia-
(12)
where \ = albedo for solar radiation, i.e., 0.23
R = daily solar radiation
When the potential evaporation E is known, the potential soil evapora-
tion E at the soil surface is predicted by:
so v j
E = E e
so o
-0.4 LAI
(13)
where LAI is the leaf area index defined as the area of plant leaves relative
to the soil surface (i.e., a ground cover component). The actual soil evapo-
ration is computed in two stages. In the first stage, soil evaporation is
limited only by the energy available at the soil surface and therefore is
equal to the potential soil evaporation. When the accumulated soil evapora-
tion exceeds the stage one upper limit, the stage two evaporative process
begins. The stage one upper limit U is estimated by:
U = 9 (« - 3)
0.42
(14)
75
-------
where « is a soil evaporation parameter whose values are given in Table 1
for various soil types and water transmission characteristics. Stage two
daily soil evaporation is predicted by:
1/2 1
- (t- I)
where E = soil evaporation for day t
S
t = number of days since stage two evaporation began
Plant transpiration E is computed by the equations:
(LAI)
E =
p
, 0 < LAI < 3
— —
(15)
(16)
In general, this relation requires LAI to be on a scale of 0 to 3, where 3 is
a complete ground cover (i.e., when LAI = 3, then E = E ). Occasionally,
LAI values found in the literature are determined on different scales, but it
is a simple matter to recompute them on the required 0-3 scale.
If soil moisture is limiting plant growth, plant transpiration, E , ,
is calculated by the equation:
E SM
E , = -^
Pi 6.25 FC
SM < 0.25 FC
(17)
where E = normal plant transpiration
FC = field capacity of the soil
Evapotranspiration (the sum of plant and soil evaporation) cannot exceed the
potential evaporation E
When the soil water falls below the wilting
point of plants, plant growth is stopped by holding the LAI constant until
soil water becomes available to the plants.
PERCOLATION
The model uses a soil storage routing technique to predict flow through
the final soil cover (3). The soil cover is divided into seven layers for
routing as follows:
(18)
where a = the storage coefficient
F = the inflow rate
76
-------
ST = the storage volume
At = the routing interval (24 hours)
If the inflow plus the storage does not exceed the field capacity FC ,
drainage cannot occur. The storage coefficient a is a function of the
travel time t through the storage and is expressed by the equation:
a =
2At
2t + At
The travel time t is estimated by the equation:
SM - FC
t =
K
sat
where SM = soil water storage
K = hydraulic conductivity
S3. U
Each soil storage layer is subject to evapotranspiration, ET , losses
in addition to those due to deep drainage. The water use rate, U , as a
function of final cover depth, D , is given by:
where U is the water use rate at the surface and U is the water use rate
by the crop at depth D . / The evapotranspiration ET for any depth can be
obtained by integrating the above equation:
ET -
ET ~
_
4.16
i _
The value of U is determined for the depth D each day.
Percolation from the final soil cover occurs when the saturated volume
of the soil exceeds the field capacity. The total soil water storage, UL ,
is equal to the porosity, $ , times the final soil cover depth D as:
UL = *D
77
-------
1.
APPENDIX B
COST ANALYSIS FOR THE NATIONAL COMPUTER CENTER
TIME-SHARING OPERATION
Three cost parameters are associated with the National Computer System
(NCC) time-sharing operation (TSO). These are storage charges, central
computer processing costs and connection costs.
2. Three types of data storage exist on NCC/TSO. The public online disk
storage charge is $.035 per track per week. Private online disk cost
is $4250.00 per pack per month, and private mountable disk cost is :
$125.00 per pack per month. No charge is made for private disk pack
mounts.
3. NCC time-sharing charges are computed by the TSO Utilization Unit (TUU)
algorithm. The TUU costs are $0.56 per TUU.
4. The connection cost is $6.00 per hour.
78
-------
APPENDIX C
NCC ACCESS NUMBERS AND TERMINAL IDENTIFIERS
The following list contains current NCC access numbers for 300 and 1200
band rates.* These numbers are to be used to access the TSO computer in
Research Triangle Park, NC. A user should locate his city of interest on the
list and dial the appropriate number for access to TSO. Users who fail to
find their city of interest on the list should dial the WATS number
800-334-8581 for the 300 or 1200 BAUD rate.
TABLE C-l. ACCESS TELEPHONE NUMBERS
CITIES
BIRMINGHAM
BIRMINGHAM
HUNTSVILLE
MOBILE
MONTGOMERY
PHOENIX
PHOENIX
TUCSON
TUCSON
FT . SMITH
JONESBORO
LITTLE ROCK
LITTLE ROCK
SPRINGDALE
ALHAMBRA
ANTIOCH
ARCADIA
BURLINGAME
EL SEGUNDO
EL SEGUNDO
FRESNO
HAYWARD
LONG BEACH
LOS ANGELES
LOS ANGELES
LOS ANGELES
LOS ANGELES
STATES
ALABAMA
ALABAMA
ALABAMA
ALABAMA
ALABAMA
ARIZONA
ARIZONA
ARIZONA
ARIZONA
ARKANSAS
ARKANSAS
ARKANSAS
ARKANSAS
ARKANSAS
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
LOCAL
NUMBERS
205/942-4141
205/942-1015
205/533-5137
205/432-3382
205/834-3410
602/249-3862
602/249-9261
602/747-4097
602/790-0764
501/782-3210
501/932-6886
501/376-3768
501/372-5780
59*/756-2201
213/572-0999
415/757-6855
213/574-7636
415/348-4992
213/640-1281
213/640-1570
209/445-0911
415/785-3431
213/435-7088
213/683-0451
213/626-0365
213/629-1561
213/629-3451
BAUD
RATES
(300)
(1200)
(300)
(1200)
(300)
(1200)
(300)
(1200)
(300)
(1200)
(1200)
(1200)
(300)
(1200)
(300)
(300)
(300)
(300)
(1200)
(300)
(300)
(300)
(1200)
(300)
(1200)
(300)
(300)
* The band rate is the speed that the terminal prints output.
79
-------
TABLE C-l. (CONTINUED)
CITIES
LOS ANGELES
MARINA DEL REY
MISSION HILLS
MODESTO
MOUNTAIN VIEW
MOUNTAIN VIEW
MOUNTAIN VIEW
NEWPORT BEACH
NEWPORT BEACH
NORTHRIDGE
NORWALK
OAKLAND
OAKLAND
PALO ALTO
PASADENA
RIVERSIDE/ COLTON
RIVERSIDE/COLTON
SACRAMENTO
SACRAMENTO
SALINAS
SAN CLEMENTS
SAN DIEGO
SAN DIEGO
SAN FRANCISCO
SAN FRANCISCO
SAN FRANCISCO
SAN JOSE/CUPERTINO
SAN JOSE/CUPERTINO
SAN JOSE/CUPERTINO
SAN PEDRO
SANTA BARBARA
SANTA CRUZ
SANTA ROSA
SANTA ROSA
VAN NUYS
VENTURA/OXNARD
VENTURA/ OXNARD
VISTA
WEST COVINA
COLORADO SPRINGS
COLORADO SPRINGS
DENVER
DENVER
DENVER
DENVER
BRIDGEPORT
DANBURY
DANBURY
DARIEN
STATES
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFRONIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
CALIFORNIA
COLORADO
COLORADO
COLORADO
COLORADO
COLORADO
COLORADO
CONNECTICUT
CONNECTICUT
CONNECTICUT
CONNECTICUT
LOCAL
NUMBERS
213/623-8500
213/821-2257
213/365-2013
209/578-4236
415/961-7971
415/941-8450
415/949-0330
714/540-0951
714/540-9560
213/865-2066
213/865-2066
415/836-8900
415/836-8700
415/856-9080
213/577-8722
714/825-9372
714/824-8170
916/448-8151
916/441-6550
408/443-4333
714/498-3130
714/291-8700
714/293-3590
415/986-8200
415/397-4300
415/788-7955
408/446-7001
408/446-7309
408/446-1470
213/830-0775
805/687-6119
408/429-9572
707/546-6776
707/546-1050
213/986-9503
805/486-4536
805/487-0482
714/727-6011
213/331-3954
•303/633-9599
303/475-2121
303/572-1107
303/825-0635
303/573-0177
303/573-9981
203/579-7820
203/743-1340
203/743-1650
203/655-7951
BAUD
RATES
(1200)
(300)
(300)
(300)
(300)
(300)
(1200)
(1200)
(300)
(1200)
(1200)
(1200)
(300)
(300)
(1200)
(300)
(1200)
(1200)
(300)
(300)
(300)
(300)
(1200)
(300)
(1200)
(1200)
(1200)
(1200)
(300)
(30Q)
(300)
(1200)
(1200)
(300)
(300)
(1200)
(300)
(300)
(300)
(1200)
(300)
(1200)
(1200)
(300)
(300)
(300)
(300)
(1200)
(1200)
80
-------
TABLE C-l. (CONTINUED)
CITIES
DARIEN
FAIRFIELD /BRIDGE PORT
HARTFORD
HARTFORD
NEW HAVEN
NEW HAVEN
WATERBURY
WASHINGTON
WASHINGTON
WASHINGTON
WASHINGTON
WILMINGTON
WILMINGTON
DAYTONA BEACH
FT LAUDERDALE
FT LAUDERDALE
JACKSONVILLE
MIAMI
MIAMI
ORLANDO
ORLANDO
PENSACOLA
SARASOTA
ST PETERSBURG
TAMPA
TAMPA
W. PALM BEACH
ATLANTA
ATLANTA
ATLANTA
SAVANNAH
BOISE
BOISE
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
FREEPORT
JOLIET
PEORIA
ROCKFORD
SPRINGFIELD
SPRINGFIELD
EVANSVILLE
FT WAYNE
HIGHLAND
INDIANAPOLIS
INDIANAPOLIS
STATES
CONNECTICUT
CONNECTICUT
CONNECTICUT
CONNECTICUT
CONNECTICUT
CONNECTICUT
CONNECTICUT
DC
DC
DC
DC
DELAWARE
DELAWARE
FLORIDA
FLORIDA
FLORIDA
FLORIDA
FLORIDA
FLORIDA
FLORIDA
FLORIDA
FLORIDA
FLORIDA
FLORIDA
FLORIDA
FLORIDA
FLORIDA
GEORGIA
GEORGIA
GEORGIA
GEORGIA
IDAHO
IDAHO
ILLINOIS
ILLINOIS
ILLINOIS
ILLINOIS
ILLINOIS
ILLINOIS
ILLINOIS
ILLINOIS
ILLINOIS
ILLINOIS
ILLINOIS
INDIANA
INDIANA
INDIANA
INDIANA
INDIANA
LOCAL
NUMBERS
203/655-8931
203/333-4926
203/568-2610
203/569-3643
203/787-1702
203/789-0579
203/755-1153
703/841-9330
703/841-0200
•703/841-9560
703/734-8370
302/658-5261
302/658-8611
904/252-4481
305/467-7550
305/467-3807
904/721-8100
305/374-7120
305/358-7271
305/859-7670
305/851-3530
904/434-0134
813/365-3526
813/535-6441
813/977-8032
813/977-3891
305/622-2871
404/659-6670
404/581-0619
404/659-2910
912/352-7259
208/344-4311
208/343-4851
312/368-4700
312/641-1630
312/372-0391
312/368-4607
312/346-4961
815/233-5585
815/723-9854
309/673-2156
815/398-6090
217/753-7900
217/753-7905
812/423-6885
219/424-5162
219/836-5452
317/926-1253
317/257-3461
BAUD
RATES
(300)
(1200)
(300)
(1200)
(1200)
(300)
(300)
(1200)
(300)
(300)
(1200)
(300)
(1200)
' (300)
(300)
(1200)
(300)
(300)
(1200)
(1200)
(300)
(300)
(1200)
(300)
(300)
(1200)
(300)
(300)
(1200)
(300)
(300)
(1200)
(300)
(300)
(1200)
(1200)
(300)
(300)
(300)
(1200)
(300)
(300)
(1200)
(300)
(300)
(300)
(300)
(1200)
(300)
81
-------
TABLE C-l. (CONTINUED)
CITIES
MARION
MERRILLVILLE
SOUTH BEND
CEDAR RAPIDS
DES MOINES
IOWA CITY
WATERLOO
SHAWNEE MISSION
SHAWNEE MISSION
TOPEKA
WICHITA
WICHITA
LEXINGTON
LEXINGTON
LOUISVILLE
LOUISVILLE
BATON ROUGE
BATON ROUGE
LAFAYETTE
NEW ORLEANS
NEW ORLEANS
SHREVEPORT
BALTIMORE ,
BALTIMORE
BOSTON
BOSTON
BOSTON
BOSTON
SPRINGFIELD
SPRINGFIELD
WORCESTER
WORCESTER
ANN ARBOR
ANN ARBOR
DETROIT
DETROIT
DETROIT
FLINT
GRAND RAPIDS
GRAND RAPIDS
JACKSON
KALAMAZOO
LANSING
MANISTEE
PLYMOUTH
PLYMOUTH
SOUTHFIELD
ST JOSEPH
TRAVERSE CITY
STATES
INDIANA
INDIANA
INDIANA
IOWA
IOWA
IOWA
IOWA
KANSAS
KANSAS
KANSAS
KANSAS
KANSAS
KENTUCKY
KENTUCKY
KENTUCKY
KENTUCKY
LOUISIANA
LOUISIANA
LOUISIANA
LOUISIANA
LOUISIANA
LOUISIANA
MARYLAND
MARYLAND
MASSACHUSETTS
MASSACHUSETTS
MASSACHUSETTS
MASSACHUSETTS
MASSACHUSETTS
MASSACHUSETTS
MASSACHUSETTS
MASSACHUSETTS
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
MICHIGAN
LOCAL
NUMBERS
317/662-0091
219/769-7254
219/233-4163
319/363-2482
515/288-6640
319/354-7371
319/233-0227
913/677-2833
913/677-0707
913/233-0690
316/265-1241
316/264-7386
606/253-3463
606/253-3498
502/361-2645
502/361-3881
504/292-4050
504/292-2650
318/235-3501
504/586-1071
504/524-4371
318/688-4666
301/547-8100
301/244-8959
617/482-1854
617/482-5622
617/482-4677
617/482-3386
413/781-6830
413/781-0145
617/755-5601
617/754-9451
313/662-8282
313/665-2627
313/963-3388
313/963-8880
313/963-2353
313/732-7303
616/456-9092
616/459-5069
517/787-9461
616/385-3150
517/487-2040
616/723-8760
313/459-8100
313/459-8900
313/569-8350
616/429-2568
616/946-0002
BAUD
RATES
(300)
(300)
(300)
(300)
(300)
(300)
(1200)
(300)
(1200)
(300)
(300)
(1200)
(300)
(1200)
(1200)
(300)
(300)
(1200)
(300)
(300)
(1200)
(1200)
(300)
(1200)
(1200)
(300)
(300)
(1200)
(300)
(1200)
(1200)
(300)
(1200)
(300)
(300)
(1200)
(1200)
(1200)
(1200)
(300)
(300)
(300)
(300)
(300)
(1200)
(300)
(300)
(300)
(300)
82
-------
TABLE C-l. (CONTINUED)
CITIES
MANKATO
WHITE PLAINS
WHITE PLAINS
ASHEVILLE
CHARLOTTE
CHARLOTTE
DURHAM
DURHAM
GREENSBORO
GREENSBORO
RALEIGH
RESEARCH TRIANGLE PARK
RESEARCH TRIANGLE PARK
RESEARCH TRIANGLE PARK
WINSTON-SALEM
WINSTON-SALEM
AKRON
CINCINNATI
CINCINNATI
CLEVELAND
CLEVELAND
COLUMBUS
COLUMBUS
DAYTON
DAYTON
MARYSVILLE
TOLEDO
TOLEDO
YOUNGS TOWN
OKLAHOMA CITY
OKLAHOMA CITY
TULSA
TULSA
PORTLAND
PORTLAND
ALLENTOWN
ALLENTOWN
ALTOONA
ERIE
HARRISBURG
PHILADELPHIA
PHILADELPHIA
PITTSBURGH
PITTSBURGH
VALLEY FORGE
VALLEY FORGE
YORK
PROVIDENCE
PROVIDENCE
STATES
MINNESOTA
NEW YORK
NEW YORK
NORTH CAROLINA
NORTH CAROLINA
NORTH CAROLINA
NORTH CAROLINA
NORTH CAROLINA
NORTH CAROLINA
NORTH CAROLINA
NORTH CAROLINA
NORTH CAROLINA
NORTH CAROLINA
NORTH CAROLINA
NORTH CAROLINA
NORTH CAROLINA
OHIO
OHIO
OHIO
OHIO
OHIO
OHIO
OHIO
OHIO
OHIO
OHIO
OHIO
OHIO
OHIO
OKLAHOMA
OKLAHOMA
OKLAHOMA
OKLAHOMA
OREGON
OREGON
PENNSYLVANIA
PENNSYLVANIA
PENNSYLVANIA
PENNSYLVANIA
PENNSYLVANIA
PENNSYLVANIA
PENNSYLVANIA
PENNSYLVANIA
PENNSYLVANIA
PENNSYLVANIA
PENNSYLVANIA
PENNSYLVANIA
RHODE ISLAND
RHODE ISLAND
LOCAL
NUMBERS
507/625-1684
914/694-8960
914/694-9361
704/255-0021
704/376-2544
704/376-2545
919/549-8910
919/549-0441
919/275-8231
919/379-0034
919/832-6592
919/549-9100
919/549-9100
919/549-9100
919/725-1414
919/725-9252
216/535-1861
513/791-5311
513/891-7211
216/781-7050
216/861-5383
614/421-1650
614/421-7270
513/223-3847
513/461-6400
513/642-2015
419/255-2946
419/243-3144
216/744-5326
405/847-0561
405/949-0125
918/663-2220
918/665-2750
503/231-4050
503/231-4077
215/433-6131
215/432-5926
814/946-8888
814/453-7161
717/236-1190
215/567-1381
215/561-6120
412/261-4151
412/765-1320
215/666-0930
215/666-9190
717/846-3900
401/274-5783
401/831-5566
BAUD
RATES
(1200)
(1200)
(300)
(1200)
(1200)
(300)
(1200)
(300)
(1200)
(300)
(1200)
(300)
(1200)
(1200)
(300)
(1200)
(300)
(1200)
(300)
(300)
(1200)
(1200)
(300)
(300)
(1200)
(1200)
(1200)
(300)
(1200)
(300)
(1200)
(300)
(1200)
(300)
(1200)
(300)
(1200)
(1200)
(300)
(300)
(1200)
(300)
(1200)
(300)
(1200)
(300)
(300)
(300)
(1200)
83
-------
TABLE C-l. (CONTINUED)
CITIES
COLUMBIA
GREENVILLE
CHATTANOOGA
CHATTANOOGA
KNOXVILLE
KNOXVILLE
MEMPHIS
MEMPHIS
NASHVILLE
NASHVILLE
AUSTIN
BAYTOWN
BEAUMONT
CORPUS CHRISTI
DALLAS
DALLAS
EL PASO
EL PASO
FT WORTH
FT WORTH
HOUSTON
HOUSTON
HOUSTON
HOUSTON
HOUSTON
LONGVIEW
LUBBOCK
MIDLAND
MIDLAND
ODESSA
SAN ANTONIO
SAN ANTONIO
SALT LAKE CITY
SALT LAKE CITY
BURLINGTON
NEWPORT NEWS
NORFOLK
RICHMOND
RICHMOND
ENUMCLAW
OLYMPIA
HIGHLAND
RICHLAND
SEATTLE
SEATTLE
SPOKANE
TACOMA
CHARLESTON
HUNTINGTON
STATES
SOUTH CAROLINA
SOUTH CAROLINA
TENNESSEE
TENNESSEE
TENNESSEE
TENNESSEE
TENNESSEE
TENNESSEE
TENNESSEE
TENNESSEE
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
; TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
TEXAS
UTAH
UTAH
VERMONT
VIRGINIA
VIRGINIA
VIRGINIA
VIRGINIA
WASHINGTON
WASHINGTON
WASHINGTON
WASHINGTON'
WASHINGTON
WASHINGTON
WASHINGTON
WASHINGTON
WEST VIRGINIA
WEST VIRGINIA
LOCAL
NUMBERS
803/252-0840
803/271-2418
615/756-0561
615/756-5856
615/637-3118
615/523-7458
901/529-0183
901/529-0170
615/361-7566
615/367-9382
512/444-5800
713/427-5856
713/832-2589
512/882-3641
214/638-8888
214/688-1444
915/544-9590
915/532-1936
214/263-4581
214/263-0278
713/977-4080
..;. 713/785-4411
713/780-7496
713/977-7671
713/780-7390
214/758-1756
806/762-0136
915/683-9833
915/683-5645
915/563-3745
512/699-9627
512/696-4002
801/582-8972
801/582-6060
802/864-0054
804/596-5754
804/625-8301
804/788-4604
804/649-3050
206/825-6909
206/943-4190
509/375-3367
509/375-1975
206/625-9937
206/625-9900
509/838-8226
: 206/952-6800
304/345-2908
304/522-6261
BAUD
RATES
(300)
(300)
(1200)
(300)
(300)
(1200)
\ •*•,*- W J
(1200)
(300)
(1200)
(300)
(1200)
(300)
(300)
(300)
(300)
(1200)
(300)
(1200)
(300)
\ T* v* v /
(1200)
(300)
(300)
\ «.» W \J J
(1200)
(1200)
\ * ~ *•* w /
(300)
(300)
(300)
(1200)
(300)
(300)
(1200)
(300)
(300)
(1200)
(1200)
(300)
(1200)
(1200)
(300)
(300)
\ -~f\J \S J
(300)
(1200)
(300)
(1200)
(300)
(1200)
(300)
\ <~r \s \s /
(1200)
(300)
84
-------
TABLE C-l. (CONCLUDED)
CITIES
APPLETON
EAU CLAIRE
GREEN BAY
MADISON
MADISON
MILWAUKEE
MILWAUKEE
NEENAH
OSHKOSH
STATES
WISCONSIN
WISCONSIN
WISCONSIN
WISCONSIN
WISCONSIN
WISCONSIN
WISCONSIN
WISCONSIN
WISCONSIN
LOCAL
NUMBERS
414/734-9940
715/834-7863
414/468-6808
608/221-0891
608/221-4211
414/257-3482
414/257-1703
414/722-5580
414/235-4594
BAUD
RATES
(300)
(300)
(1200)
(1200)
(300)
(300)
(1200)
(300)
(300)
NCC TERMINAL IDENTIFIERS
The NCC terminal identifiers (Table C-2) are user-entered characters
that identify terminal speeds, carriage-return delay times, and codes to
NCC.
If you are in doubt as to which NCC terminal identifier to use, contact
Anthony Gibson at (601) 634-3710 (FTS 542-3710).
TABLE C-2. IDENTIFIERS, BY TERMINAL MAKE AND MODEL
TERMINAL
ID*
TERMINAL
ID*
ADDS
580, 620, 680, 880, 980 A
Anderson Jacobson ^
330
830, 832 A
630 E
860t A
Ann Arbor Terminals
Design III, 200 A
Beehive Medical Electronics
Mini Bee 1, 2, 4 A
Super Bee 2, 3 A
1-211, M-501, R-211 A
Bell System
Dataspeed 40/2
KD A
KDP G
Computer Devices
1030 E
1132, 1201, 1202, 1203
1204, 1205, 1206
Computek
200, 300
Conrac
401, 480
Control Data
713
Computer Transceiver
Systems
Execuport
DEC
GT40, LA34, LA36, LA38,
LA120,t LS120,t VT05,
VT50, VT100, VT132
Datamedia
1500, 2000, 2100, 2500
Datapoint
1100, 3000, 3300
A
A
A
A
A
A
A
* The symbol ^) represents a carriage return.
t During log in, enter Control R immediately before typing your user name.
85
-------
TABLE C-2
TERMINAL
ID*
TERMINAL
ID*
Delta Data
5000, 5100, 5200
Digi-Log
33, 209, 300
General Electric
Terminet
300, 1200
Gen-Corn
300
Hazeltine
1200, 2000
Hewlett-Packard
2615, 2616, 262X Series,
263X Series, 264X
Series, 7220Af
Hydra
Model B
IBM
2741
Interdata
Carousel 300
Incoterm
SPD 10/20, 20/20, 900
Infoton
Vistar
ITT
3501 Asciscope
Lear Siegler
7700, ADM-1, ADM-2,
ADM-3, ADM-31
LogAbax Informatique
LX180
LXlOlOf
MI
2400f
Megadata
Memo rex
1240
NCR
260
796
Oraron
8525
Ontel
4000
A
A
A
A
A
I
3
E
A
A
A
I
A
I
A
A
G
E
A
A
A
Perkin-Elmer
1200, 1250 A
Research
Teleray 3300, 3311, 3712 A
Raytheon
PTS-100 A
Singer
30 £
Scientific Measurement
Systems
1440 A
Tally
16l2t A
Tec
400 Series, 1440 A
Tektronix
4012, 4013, 4014, 4023
4025 A
Teletype ;
33, 35 D
38 B
43 A
Texas Instruments
720, 725, 733, 735 E
743, 745, 763, 765, 771,f
820t , A
Texas Scientific
Entelkon 10 A
Typagraph
DP-30 c
Tymshare
100, 110, 212, 213 ; E
200 ; D
310, 311 C
125, 126, 225, 315, 316
325, 350,f 420, 425,t 430,
440W, 444,f 470,t
550,f llOOf A
Wang Laboratories
220 OB A
Westinghouse :
1600, 1620 A
Xerox
BC100, BC200 A
* The symbol !^> represents a carriage return.
t During log in, enter Control R immediately before typing your user name.
86
-------
APPENDIX D
SENSITIVITY ANALYSIS
by
R. J. Wills, Jr., E. R. Perrier, and A. C. Gibson
The default option of HSSWDS inputs climatological and hydrological data
from permanent data files stored in the computer, and the data input option
permits the user to enter all the necessary data from external or measured
sources. Both input options use the same output formats, however. To
facilitate data handling for the sensitivity analysis, only the complete data
input option was used; no defaults were requested.
Climatological and hydrological data were input for the Cincinnati,
Ohio, area, and the values used are shown in Tables D-l through D-3 and Fig-
ure D-l. The climatological data consist of 5 years worth of daily precipi-
tation values, the yearly means, and the mean monthly temperature, solar
radiation, and LAI values. In addition, Table D-4 presents hydrological data
for a fictitious solid waste site near Cincinnati.
Table D-5 presents the sensitivity runs made for each parameter, with
the other variables being fixed as shown in Table D-4. Thirty-six computer
runs were made to demonstrate the sensitivity of the selected variables to
changes in climatological and hydrological data of the solid waste site. The
discussion of each parameter will follow the organization presented in
Table D-5. Hydrological data used for the vegetative soil and soil layer 2
are generally representative of loamy and compacted clay soils, respectively.
In the interest of simplicity only two soil layers were considered. No liner
was used except when the effect of the liner was being investigated.
IMPERMEABLE LINER
As shown in Table D-5 the life of the impermeable liner (see Figure 2 of
main text) was varied for values of 5, 10, 15, and 20 years as well as an op-
tion of a maximum life of 100 years. As expected, the impermeable liner only
affects water that migrated beyond runoff and evapotranspiration. The effect
of the liner is to force some of the percolated water to drain from the site
as lateral drainage rather than percolation (see Figure 2 of main text). As
shown in Figure D-2, a liner with a 5-year life passed only 9.6 percent of
the total percolation the first year, but 89 percent by the 5th year. By
comparison, a 100-year-life liner passed 2.5 percent of total percolation
the first year and 13 percent by the 5th year. The final percentages of
87
-------
TABLE D-l. DAILY PRECIPITATION
CLIMATOLOGIC INPUT
DAILY PRECIPITATION (INCHES)
1 YEAR (10 VALUES/LINE, 37 LINES)
YEAR: 1974
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
37
0.02
0.38
0.53
1.03
0.18
0.12
0.02
0.20
0.41
0.55
0.45
0.30
0.71
0.04
0.37
0.21
0.63
1.03
0.09
0.14
0.13
0.05
0.04
2.03
0.05
0.49
0.09
0.03
0.02
0.23
0.62
0.01
0.07
0.01
0.46
0.03
1.55
0.45
0.05
0.21
0.03
0.01
0.39
0.01
0.17
0.41
0.20
0.57
0.34
0.10
0.16
0.64
1.02
0.06
0.63
0.21
0.20
0.05
0.22
0.03
0.32
0.57
0.02
0.19
0.72
0.14
0.03
0.03
0.42
0.42
0.29
0.05
0.15
0.30
0.03
0.11
0.75
1.81
0.58
0.17
0.04
0.42
0.20
0.73
0.03
0.57
0.61
0.08
0.02
0.03
0.16
0.03
0.32
0.02
0.52
0.78
0.19
0.31
0.04
0.54
0.06
0.26
0.60
0.68
0.05
0.67
0.36
0.10
0.09
0.15
0.20
0.80
0.42
0.26
0.15
0.34
0.80
0.41
0.05
0.02
0.36
0.11
0.64
0.33
0.43
0.46
1.11
0.34
0.54
0.81
0.02
2.09
0.16
88
(continued)
-------
TABLE D-l. (CONTINUED)
YEAR:
1^__
2
/
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
or
36
37
1975
0.02
0.46_
0.96
0.38
0.5Q_
1.12
0.05
0.25
0.09
0.03
0.71
1.18
0.38
0.19
0.02
0.04
0.01
0.07
0.02
0.01
0.01
0.98
0.27_
0.10
0.20
0.82
0.05
0.07
1.38
0.07
0.12
0.28
0.02__
0.21
0. 19
0.32
0.54
0.60
0.32
0 . 13_
0.08
0.14
0.23
0.02
0.77
0.14
0.08
2.32
0.22
1 .56
0.01
0.27
0.15
1.75
0.39
0.04
0.55
0.04
0.33
0.17
0.17
0.05
0.53
. Ul
0.02
1 £O '
1 .oz
0.21
0.43
0.54
0.27
0.18
0.02
0.96
0.03
0.01
0.03
0.23
0.07
0.22
0.35
0.33
0.09
n A9
0.02
"
0.88
"0.04
0.07
0.08
0.31
0.50
0.05
0.04
013
0.44
0.69
n c\L.
0.04
n n^
0.67
0.26
0.78
0.05
0.03
0.42
0.44
0.07
0 15
0.08
0.70
1.41
0.12
0.24
0.06
0.02
0.01
0.14
0.33
0.28
0.01
0.55
0.44
0.02
0.41
0.07
0.22
0.06
0.90
0.63
1.04
0.13
0.09
0.04
0.18
0.13
0.20
1.90
0.35
0.19
(continued)
89
-------
TABLE D-l. (CONTINUED)
YEAR: 1976
1
3
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
0.01
0.23
0.05
0.15
0.33
0.51
0.20
0.16
0.46
0.37
0.15
0.48
0.24
0 36
0.69
0.97
0.01
0.01
0.67
0.09
0.16
0.07
0.02
1 0.05
1.07
0.06
n n^?
0.04
0.48
0.01
0.53
0.09
0.16
0.02
0.08
0.01
0.26
0.05
0.33
0.70
0.02
0.65
_ . UO
0.14
0.21
0.04
0.01
0.16
~ 1 O
. J.O
0.05
0.13
0.05
0.46
0.23
0.20
0 . 06
0.20
0.19
0.12
0.74
1.04
0.03
0.43
0.01
0.18
0.12
0.01
0.10
0.15
0.41
0.04
1.00
0.02
0.01
0.01
0.05
0.59
0.92
0.18
0.41
0.72
0.10
0.70
0.07
0.73
0.19
0.12
0.95
0.18
2.40
0.22
0.48
0.07
0.04
^).01
•
0.78
0.05
0.60
0.21
0 09
0.41
0.49
0.03
(continued)
90
-------
YEAR: 1977
TABLE'D-1. (CONTINUED)
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
37
0.01
0.02
0.62
0.08
0.15
1.07
0.36
0.01
0.45
0.02
0.10
0.30
0.01
1.12
1.84
0.23
0.02
0.75
0.16
0.10
0.53
0.03
0.48
0.13
0.06
0.01
0.02
0.10
0.01
0.07
0.11
0.51
0.04
0.56
0.59
0.08
0.04
0.15
0.04
0.05
0.45
0.38
0.28
0.40
0.02
0.21
0.10
0.01
0.29
0.85
0.57
0.04
0.08
0.33
1.18
0.02
0.04
0.90
0.01
0.03
0.03
0.20
0.05
0.04
0.07
0.02
0.01
0.01
0.27
0.13
0.03
0.01
0.02
0.08
0.22
0.41
2.34
0.11
1.53
0.05
0.05
0.02
0.03
0.05
0.77
0.07
0.14
0.77
0.10
0.18
0.33
0.03
0.02
0.38
0.71
0.34
0.01
0.34
0.52
0.14
0.07
1.40
0.54
0.17
0.19
0.15
0.15
0.18
0.50
1.05
0.03
0.07
0.02
0.13
0.10
0.03
0.01
1.07
0.15
0.04
0.29
0.30
0.40
0.12
0.04
0.27
1.05
0.38
(continued)
91
-------
TABLE D-l. (CONCLUDED)
YEAR: 1978
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
37
0.03
0.01
0.01
0.01
0.17
0.26
0.05
0.02
0.26
0.01
0.35
0.03
0.02
0.62
0.58
0.02
0.08
0.02
0.11
0.05
0.11
0.61
0.11
1.12
0.11
1.05
1.80
0.06
0.02
0.49
0.57
0.01
0.40
0.75
0.94
0.01
0.16
1.41
0.60
0.32
0.09
0.03
0.02
0.22
0.13
0.02
0.06
0.20
0.46
0.37
0.66
0.13
1.02
0.60
0.46
0.28
0.45
0.02
0.09
0.04
0.07
0.02
0.02
0.02
0.37
1.91
0.49
1.32
0.09
1.02
0.88
0.35
0.31
0.04
0.08
0.34
0.05
1.31
0.03
0.66
0.49
1.45
0.09
0.22
0.10
0.03
0.43
0.31
0.03
0.24
0.07
0.11
0.6
0.6
0.66
0.19
1.45
1.32
0.01
1.03
0.68
0.61
0.02
0.56
0.06
0.19
0.09
0.16
0.04
0.03
0.03
0.20
0.01
0.04
0.14
0.26
1.08
0.35
0.15
0.12
0.02
0.56
0.48
0.51
0.18
0.02
0.19
0.08
0.06
0.22
0.01
0.02
0.01
0.16
0.06
92
-------
TABLE D-2. MEAN MONTHLY TEMPERATURES AND ISOLATION
Month
January
February
March
April
May
June
July
August
September
October
November
December
Mean monthly
temperature
(°F)
11.3
18.8
25.3
54.3
59.6
72.9
73.8
72.5
74.6
58.8
50.0
40.8
Mean monthly
insolation
(Langleys/day)
128
200
297
391
471
562
542
477
422
286
176
129
TABLE D-3. LEAF AREA INDEX VALUES
Day of year
1
92
104
116
128
140
152
164
176
188
200
213
366
Area
0
0
.61
.99
.99
.99
.99
.99
.89
.71
.65
.61
0
93
-------
60
50
40
30
20
10
74
75 76
YEAR
77 78
Figure D-l. Annual Cincinnati, Ohio, precipitation from 1974 to 1978.
94
-------
TABLE D-4. HYDROLOGICAL INPUT FOR FICTITIOUS SOLID
WASTE SITE NEAR CINCINNATI, OHIO
Item
Description
Study Title Sensitivity study
Area Location Cincinnati, Ohio
Today's Date 18 juiy jggQ
Date ,of first storm event (Julian date) 74003
(example = 73038, 1973 and 38 Julian day)
Hydraulic conductivity of vegetative soil 0.33 in./hr
Hydraulic conductivity of soil layer 2 .0011 in./hr
Total thickness of soil cover 24 inches
Thickness of vegetative layer 18 inches
Thickness of soil layer 2 6 inches
Soil porosity of vegetative soil .621 vol/vol
Soil porosity of soil layer 2 .226 vol/vol
SCS curve number 90
Available water capacity of vegetative soil .156 vol/vol
Available water capacity of soil layer 2 .038 vol/vol
Winter cover factor .8
Evaporation coefficient of vegetative soil 4.5
Evaporation coefficient of soil layer 2 3.1
95
-------
TABLE D-5. PARAMETERS VARIED FOR SENSITIVITY ANALYSIS
Number
of
runs
Parameters
Parameter variation
5
3
3
3
3
5
2
12
Impermeable liner
SCS curve number
Winter cover factor
Depth of barrier soil
Depth of vegetative soil
Leaf area index
Barrier soil compaction
Soil texturef
5, 10, 15, 20, Ind. (years)
81, 90, 99
0.5, 0.8, 1.0 !
6, 12, 18 (inches)
12, 24, 36 (inches)
Ex, Gd, Fr, Pr, Brgnd*
Compacted, not compacted
Soil layer 2
S, SL, L, SCL, C
L, SCL, C
SCL, C
C
C (compacted)
Vegetative soil
S
SL
L
SCL
SCL
* Excellent, good, fair, poor, bare ground.
t S = sand, L = loam, C = clay.
percolation passing through for the 10-, 15-, and 20-year options were 31,
38, and 50 percent, respectively.
Figures D-2 and D-3 show that the 10-, 15-, and 20-year life options
correlated with the 100-year life liner. Based on the 5-year data set,
percolation increased by 585 percent with the a 5-year liner life as compared
to 285 percent with a 100-year liner life. ;
SCS CURVE NUMBER
The results for SCS curve number are interpreted with respect to yearly
totals because the curve number is not time-dependent. As expected, the
curve number is a primary factor for surface runoff (Figure D-4) and a sec-
ondary factor for evapotranspiration (Figure D-5) and percolation (Fig-
ure D-6). As presented in Table D-6, the average annual totals for a curve
number of 81 show that surface runoff was 17 percent of the total precipita-
tion; whereas, for a curve number of 99, the surface runoff increased to ;
96
-------
YEAR
Figure D-2. Annual percolation as
related to the impermeable liner.
97
-------
78
Figure D-3. Annual soil drainage* as
related to the impermeable liner.
* Soil drainage is lateral drainage.
98
-------
30.0r-
20.0
O
D
tc
10.0
\
74
75
\
V
_L
76
YEAR
77
_l
78
Figure D-4. Annual runoff as related to'the SCS curve number.
99
-------
3.0
o
oc
I
en
I 2.0
1.0
99-
74
75
76
77
78
YEAR
Figure D-5. Annual evapotranijpiration as
related to SCS curve number.
100
-------
4.0 r-
3.0
z
o
se
2.0
1.0
81
90
,99
74
75
76
YEAR
77
78
Figure D-6. Annual percolation as related
to the SCS curve number.
52.2 percent, for an increase of 35 percentage points. Evapotranspiration
decreased by 26 percentage points—from 73.8 percent for a curve number of
81 to 47.5 percent for a curve number of 99. These differences in evapo-
transpiration accounted for most of the increase in surface runoff, with the
remainder (about 9 percentage points) being accounted for by decreases in
percolation and soil water.
Table D-6 shows that the percentages for the curve numbers of 81 and 90 were
more comparable than the percentages for the curve number of 99.
Figure D-6 shows that percolation decreased from an average of
2.87 in./year for a curve number of 81 to nearly zero (0.0549 in./year)
for a curve number of 99.
TABLE D-6. SURFACE RUNOFF, PERCOLATION, AND EVAPOTRANSPIRATION
AS PERCENTAGES OF THE ANNUAL PRECIPITATION* FOR VARIOUS
SCS CURVE NUMBERS
Variable
Surface runoff
Percolation
Evapotranspiration
81
17,0
7.1
73.8
SCS curve number
90
21.6
5.6
70.8
99
52.2
0.1
47.5
* Average annual precipitation =40.6 inches.
101
-------
WINTER COVER FACTOR :
The winter cover factor is seasonally dependent and directly affects the
process of evapotranspiration. Figures D-7 through D-9 demonstrate that the
effect is greatest from September through April and declines considerably
during the growing season. Since the winter cover factor is seasonably
dependent, monthly evaluation is preferable.
JAN FEB MAR APR MAY JUN JUL AUGSEPT OCT NOV DEC
Figure D-7. Average monthly evapotranspiration
as related to winter cover factor.
102
-------
0.5
JAM FEB MARAPR MAY JUN JUL AUG SEPT OCTNOV DEC
Figure D-8. Average monthly percolatioin as
related to winter cover factor.
103
-------
3. •-
JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC
MONTH
Figure D-9. Average monthly runoff as related
to the winter cover factor.
When each variable was expressed as a percentage of average annual pre-
cipitation, evapotranspiration was shown to increase by 10.3 percentage :
points as the winter cover factor went from 0.5 to 1.0. Over the same range
of winter cover factors, surface runoff and percolation decreased by 6.9 and
2.6 percentage points, respectively. The winter cover factor of 0.5 implies
an excellent grass cover, whereas the winter cover factor of 1.0 implies the
bare ground condition. In this study, however, these values were linked with
the LAI for a grass in fair condition. Although this contradiction was
necessary to protect the integrity of the study, it should be noted that i
these extreme conditions would rarely be found in a field situation. If the
user chooses the default option, the winter cover factor that corresponds to
the selected LAI is automatically assigned.
THICKNESS OF SOIL LAYER 2
To evaluate the effect of varying the thickness of soil layer 2 the
total soil thickness was set at 24 in. and soil layer 2 was assigned thick-
nesses of 6, 12, and 18 in. The thicknesses of vegetative soil computed by
the model therefore varied accordingly. Thus, in reality, two parameters
were varied simultaneously.
Figures D-10 and D-ll show the significance of soil layer 2 thicknesses
104
-------
IB.OI-
16.0 -
14.0 -
12.0 -
o
oe
10.0 -
Figure D-10. Annual surface runoff as related
to thickness of soil layer 2.
105
-------
4.or
3.0
2.0
1.0
12"
18
74
75
YEAR
76
77
78
Figure D-ll.
Annual percolation as related to thickness
of soil layer 2.
as related to annual percolation and surface runoff. As expected, runoff
varied directly with the thickness of soil layer 2 while percolation varied
inversely The effect of soil layer 2 thickness on the seasonal variability
of percolation and runoff is shown on Figures D-12 and D-13, respectively.
Expression of runoff, percolation, and evapotranspiration as a percentage
of the average annual precipitation showed that surface runoff increased by
12.5 percentage points from the 6- to the 18-in. soil layer 2 thickness. How-
ever, percolation and evapotranspiration decreased by 4.6 and 6.7 percentage
points, respectively. It should be noted that the selection of the 18-in.
thickness of soil layer 2 was for test purposes only. In most instances, a
106
-------
3.0i-
18"
I I 1 I 1 1
FEE MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC
Figure D-12. Average monthly percolation as
related to thickness of soil layer 2.
107
-------
1. 1-
MAY JUN JUL AUG SEPT OCT NOV DEC
Figure D-13. Average monthly surface runoff
as related to thickness of soil layer 2.
6-in. vegetative soil layer would not support an adequate plant growth, and
it is not recommended for field applications.
THICKNESS OF VEGETATIVE SOIL
For this part of the study the vegetative soil layer was assigned thick-
nesses of the vegetative soil layer 12, 24, and 36 in. and no soil layer 2
was used. Table D-7 compares surface runoff, percolation, and evapotranspi-
ration as percentages of the average annual precipitation for each thickness
of vegetative soil. Surface runoff showed the least change as soil thickness
was varied. The greatest difference was only 0.3 percentage point, and was
not considered significant.
108
-------
TABLE D-7 SURFACE RUNOFF, PERCOLATION, AND EVAPOTRANSPIRATION
AS PERCENTAGES OF AVERAGE ANNUAL PRECIPITATION* FOR -
Variable
Surface runoff
Percolation
Evapotranspiration
Vegetatd
12
15.1
16.7
67.9
.ve soil thickne
24
15.2
12.1
72.2
-Ss , in.
36
14.9
8.7
75.7
* Average annual precipitation - 40.6 in.
The initial soil-water storage and the upper limit of the soil-water storage
increased significantly with increased soil thickness. For a vegetative soil
thickness of 12 in., the initial soil water was 0.936 in and ^e upper
storage limit was 1.87 in.; however, for the 36-in vegetative soil ^ckness,
the initial soil water increased to 2.81 in. and the upper limit of storage
increased to 5.62 in. As the soil thickness increased, larger volumes of
water were available to the plants. This, in turn resulted in greased
evapotranspiration and decreased percolation. Table D-7 shows that evapo
transpiration increased by 7.8 percentage points and percolation decreased
by 8.0 percentage points as the vegetative soil thickness was increased from
12 to 36 in.
Figure D-14 shows the relation of annual percolation to the year of
occurrence with vegetative soil thickness as the parameter. The extreme
variation in percolation for 1976-77 is caused, in part, by differences in
Initial soil-water storage and the upper limit thereof for the various vege-
tative soil thicknesses. This is not surprising since stored soil-water is
subject to replenishment by precipitation and depletion by evapotranspiration
and percolation.
Figure D-15 shows average monthly values for the 5-year data set. The
1976 data set is an expansion of Figure D-16 for the average annual soil
water Vegetative soil thickness is the parameter for both figures.
Tab?e D-l fhows that 1976 was the driest year in the 5-year study period
with only 30.07 in. of precipitation during the year. The lack of precipi-
tation affected the 12-in. soil thickness percolation immediately (see the
36-in. soil thickness, where the volume of stored soil—tar was greater^
The effect of the lack of precipitation is dramatized since the drier months
occurred in the last quarter of the calendar year, when evapotranspiration
Trolly ^creases, and thus allowed for even greater Percolation than would
have otherwise occurred. The situation is reversed for the first half of
1977; the seasonal precipitation required considerable time to "fill the-
soil profile to the 36-in. depth, but percolation was possible at an earlier
time for the 12-in. thickness.
LEAF AREA INDEX (LAI)
The LAI is a measurable indicator of the amount of vegetative ground
109
-------
10. r-
78
Figure D-14. Annual percolation as related to
the thickness of vegetative soil.
110
-------
SOIL WATER, M
-------
Figure D-16. Average annual soil-water as related
to the vegetative soil depth.
cover that exists as a function of time, and directly affects the ratio of
plant transpiration to soil evaporation. This part of the sensitivity study
was designed to investigate changes resulting from the use of five different
htL n^ SrT aSuinPuts' Bare 8round conditions, as the name indicates,
have a 0.0 LAI for the entire year. An excellent crop condition is regarded
as the best possible condition, and an occurrence of good, fair, and poor
cropping conditions are designated as 66.6 percent, 33.3 percent, and
16.7 percent of the excellent crop value, respectively. For the Cincinnati,
Uhio, climatic condition, the growing season starts on day 92 (April 1st) and
continues until day 213 (July 31st).
112
-------
As expected, the variable most sensitive to change in LAI was evapo-
transpiration. Surface runoff, percolation, and evapotranspiration are
presented as percentages of average annual precipitation in Table D-8.
These figures show that evapotranspiration decreased by 14.5 percentage
points between the extreme values for an excellent crop and bare ground.
Surface runoff increased by 7.6 percentage points, and percolation increased
by 6.1 percentage points. But the greater portion of the variation occurred
between the values for poor crops and bare ground. From excellent to poor
crop conditions, the increases for surface runoff and percolation were 3.1
and 1.2 percentage points, whereas evapotranspiration increased by 4.5
percentage points.
TABLE D-8. SURFACE RUNOFF, PERCOLATION, AND EVAPOTRANSPIRATION
AS PERCENTAGES OF AVERAGE ANNUAL PRECIPITATION*
FOR VARIOUS LAI
Leaf area index
Variable
Surface runoff
Percolation
Evapotranspiration
Excellent
19.9
5.1
73.1
Good
20.4
5.2
72.5
Fair
21.5
5.6
70.8
Poor
23.0
6.2
68.6
Bare ground
27.5
11.1
58.6
* Average annual precipitation =40.6 in.
Figures D-17 through D-19 show the large variation that occurred be-
tween the values for a poor crop condition and a bare ground condition. As
expected, Figures D-17 and D-19 demonstrate that the effect of LAI is sea-
sonal and for variables such as evapotranspiration and surface runoff, LAI
has little effect before the growing season begins. After the growing season
starts, differences between the variables affected by the LAI increase and
then subsequently decrease toward the end of the season.
Figure D-18 shows that percolation differences are evident early in the
year as a result of accumulated differentials in the soil-water parameter.
The effect of the soil-water condition is also shown in Figure D-19. When
the various LAI options for a vegetative cover are compared with that of bare
ground, the significant beneficial effect of the vegetative cover is to pro-
vide additional control of percolation. This effect is also noted in Fig-
ure D-18, which shows that even a poor crop condition decreases percolation
during the growing season to nearly zero.
An unusual result is shown in Figure D-17, where evapotranspiration is
related to time. For the month of April, the order of the cropping options
from the highest to the lowest evapotranspiration was excellent, good, tair,
poor, and bare ground. But for the month of May, the cropping order was
changed to fair, poor, good, excellent, and bare ground. Figure D-20 ex-
plains this apparent inconsistency by displaying the average soil-water stor-
age The higher LAI values for the good and excellent cropping options •
resulted in increased evapotranspiration that lowered the soil water in April
to a level where further evapotranspiration in May was limited. The increase
in evapotranspiration for the poor and fair cropping options was not large
113
-------
EXCELLENT
JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC
Figure D-17.
Average monthly evapotranspiration
as related to the LAI.
114
-------
0.6
0.5
0.4
0.3
•^
\V\
^
J L
^.
GOOD ~^lll
Mf
EXCELLENT
~T*'
$
JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC
MONTH
Figure D-18.
Average monthly percolation as
related to the LAI.
115
-------
2.0i-
iBAREGROUND
EXCELLENT
I I 1 I I
JAN FEE MAR APR MAY JUN JUL AUGSEPT OCT NOV DEC
MONTH
Figure D-19. Average monthly surface runoff as
related to the LAI.
116
-------
2.5r-
cc
111
1
JAN FEB MAR APR MAV
JUN JUL AUG SEPT OCT NOV DEC
MONT4H
Figure D-20. Average monthly soil-water as
related to the LAI.
enough to affect the soil-water. The difference between the extreme cropping
options, excellent and poor, was about 0.4 in. during May.
SOIL LAYER 2 COMPACTION
For this section of the sensitivity study the concern was whether soil
layer 2 was left as placed or compacted by some means. In the model, compac-
tion reduces the values for hydraulic conductivity, porosity, and available
water. When using the model default option, the hydraulic conductivity is
reduced by a factor of 20, and the values of available water content and
porosity are reduced by a factor of 2. The input values for these param-
eters in the sensitivity analysis are shown in Table D-9. These values re-
sulted in an upper limit for soil-water storage of 2.8 in. for the compacted
soil layer 2, as opposed to 3.1 in. for the noncompacted soil layer 2.
The variables most sensitive to the degree of compaction of soil layer 2
were surface runoff and percolation. Over the 5-year study period, percola-
tion averaged 13.2 percent of precipitation for the noncompacted soil layer 2
(Table D-10), and 5.6 percent for the compacted soil layer 2—a decrease of
7.6 percentage points. The surface runoff showed a decrease of 6.6 percentage
117
-------
TABLE D-9. HYDRAULIC CONDUCTIVITY, AVAILABLE WATER CONTENT, AND
Variable
Hydraulic conductivity (in./hr)
Available water content (vol/vol)
Porosity (vol/vol)
None omp acted
0.022
0.076
0.452
Compacted
0.0011
0.038
0.226
TABLE D-10. SURFACE RUNOFF, PERCOLATION, AND EVAPOTRANSPIRATION
AS PERCENTAGES OF THE AVERAGE ANNUAL PRECIPITATION*
FOR SOIL LAYER 2 COMPACTION
Variable
Surface runoff
Percolation
Evapotranspiration
Compacted
21.5
5.6
70.8
Noncompacted
15.0
13.2
71.0
* Average annual precipitation = 40.6 in. !
points between the compacted and noncompacted soil. The effect of soil
layer 2 compaction on evapotranspiration was negligible.
The relationship of soil layer 2 compaction to surface runoff and per-
colation need not be limited to analysis on a yearly basis, but it can
affect the parameters monthly and seasonally. Figures D-21 and D-22 show
that surface runoff is not as sensitive to compaction as is percolation.
Figure D-22 shows that percolation during 1976 and 1977 was affected by a
delay or time lag associated with the lower hydraulic conductivity of the
compacted soil layer 2. Also, it is evident that percolation is sensitive to
the delay in the downward water movement process.
As noted earlier, the Cincinnati, Ohio, growing season runs from April!
1st to July 31st. For most of this season, the increased evapotranspiration
resulting from increased LAI decreased the soil-water to a level where perco-
lation was zero. Later in the season, the precipitation restored the soil-
water, and the percolation continued to cycle through the winter and into
early spring. Since the precipitation cycle typically starts in the last
quarter of the year and continues to the first quarter of the next, yearly
totals can be deceptive, especially when the results of abnormal rainfall
are affected by time dependency.
In Figure D-22, for instance, the 1976 percolation for the noncompacted
soil decreased much more rapidly than that for the compacted soil layer 2.
But in 1977, percolation from the noncompacted soil increased and the per-
colation from the compacted soils continued to decrease. This apparent
118
-------
14.0.
12.0
10.0
8.0
u_
O
D
o:
6J
4.
COMPACTED
NONCOMPACTED
74 75
76
77
YEAR
Figure D-21. Annual surface runoff as related
to soil layer 2 compaction.
78
119
-------
8.0
7.0
6.0
5.0
4.0
3.0
2.0 -
1.0 -
NONCOMPACTED
COMPACTED
74 75 76 77 78
YIAR
Figure D-22. Annual percolation as related to
soil layer 2 compaction.
inconsistency is explained by the increased time lag associated with the
compacted soil layer 2.
Figure D-23 shows that the total precipitation for 1976 is significantly
less than the average (30.37 in. compared with 40.64 in.), with the largest
deficits occurring from March to May and late in the year from November
through December. The precipitation during the middle of the year (see
120
-------
e.or
z
Z 3.0
I
AVERAGE MONTHLY RAINFALL
ACTUAL J976 MONTHLY RAINFALL-
FEB MAR APR
MAY JUN JUL
MONTH
AUG SEPT OCT NOV OEC
Figure D-23. Comparison of average monthly
precipitation to 1976 precipitation.
Table D-5) was not much below average, but since it occurred during the time
of year when evapotranspiration was at a peak, percolation was negligible.
Later in 1976, when precipitation could have had a more direct effect on per-
colation, the lack of rainfall meant that soil-water remained depleted and
percolation was lowered. Had the precipitation reached normal levels during
this time period, the soil-water would have been replenished and some percola-
tion would have occurred. When soil layer 2 is not compacted, normal precip-
itation increases percolation during November and December. But the compacted
soil layer 2 permits less water to percolate and therefore some percolation
occurs in December, January, and February of 1977. With normal late-year pre-
cipitation, the percolation from uncompacted soil occurs soon after the rain-
fall. But for compacted soil layer 2, some of the percolation occurs during
the next year.
Figure D-24 compares the monthly percolation to the corresponding precip-
itation for 1978. Once again, the effect of the time lag is shown as the re-
sult of extremely low precipitation during February. The immediate effect
was to reduce percolation for the uncompacted soil. The compacted soil
layer 2 shows the time lag of the percolation for December 1977 and Jan-
uary 1978. Also, the percolation decreases to zero from May through Septem-
ber as precipitation increases (the effect of increased evapotranspiration).
Not until later in the year, when soil-water increases and evapotranspiration
decreases, does percolation occur again.
121
-------
7.0
6.0
z
"1 4.0
-1
< 3.0
1.0
DEC JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC
77 78
1.0 -
•A
\
\
\
\
''Nx
l^\x_
/
/
NONCOMPACTED^
1
1
COMPACTED-L^i
X L I i -r^r^.. -I
DEC JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC
MONTH
Figure D-24. Percolation and precipitation during
the month of occurrence for 1978.
122
-------
SOIL TEXTURE
The purpose of this section is to evaluate the sensitivity of the hydro-
logic modeling processes to changes in the soil texture of the two soil
layers. Varying the soil texture changes many of the other input data such
as hydraulic conductivity, soil porosity, evaporation coefficient, and avail-
able water capacity. Data that were used with the various soil textures^ are
presented in Table D-ll, and combinations of vegetative soil layer and soil
layer 2 are shown in Table D-12.
Changing the hydraulic conductivity, soil porosity, evaporation coeffi-
cient, and available water capacity for the vegetative soil and soil layer 2
resulted in small changes in upper storage limit and initial water storage.
Since these variables are used in computations of surface runoff, evapotrans-
piration, and percolation, these processes would be expected to reflect these
changes. They do not, however, show a uniform change with respect to a
single variable when evaluated on a yearly basis. Table D-13 presents each
variable for each parameter computed as a percentage of the average annual
precipitation. Percolation changed by 9.8 percentage points from one soil
texture extreme to the other, and evapotranspiration and surface runoff
changed by 3.5 and 8.5 percentage points, respectively. Most of the varia-
tion is attributable to case No. 12 (sandy clay loam/clay, compacted). If
the results of this soil texture are disregarded, percolation changes by only
2.3 percentage points, evapotranspiration by 3.5 percentage points, and sur-
face runoff by 2.1 percentage points. The variations resulting from changing
the soil texture are small compared with variations found with other param-
eters. Most of the changes caused by soil texture are a result of the pre-
viously mentioned variations in soil-water relationships which are compounded
by conditions in the late fall and winter. These conditions involve the
replenishment of the soil-water to levels approaching the storage limit by
the precipitation later in the year. Percolation,;which depends on the level
of soil-water, is sensitive to the precipitation r4te as well as the effect
of soil texture on percolation.
TABLE D-ll. SOIL HYDROLOGICAL DATA USED IN THE SENSITIVITY STUDY
Soil
texture
Sand
Sandy loam
Loam
Sandy clay loam
Clay (noncompacted)
Clay (compacted)
Hydraulic
conductivity
(in./hr)
5.4
0.67
0.21
0.084
0.022
0.0011
Soil
porosity
(vol/vol)
0.389
0.442
0.521
0.453
0.680
0.226
Evaporation
coefficient
3.3
3.8
4.5
4.7
3.5
3.1
Available
water
content
(vol/vol)
0.133
0.123
0.156
0.199
0.115
0 . 038
123
-------
TABLE D-12. COMBINATIONS OF VEGETATIVE AND SOIL LAYER 2
SOIL TEXTURES FOR THE SENSITIVITY STUDY
No. Vegetative soil
1 Sand
2 Sand
3 Sand
4 Sand
5 Sand
6 Sandy loam
7 Sandy loam
8 Sandy loam
9 Loam
10 Loam
11 Sandy clay loam
12 Sandy clay loam
Soil layer 2
Sand
Sandy loam
Loam
Sandy clay loam
Clay
Loam
Sandy clay loam
Clay
Sandy clay loam
Clay
Clay (noncompacted)
Clay (compacted)
TABLE D-13. SURFACE RUNOFF, PERCOLATION, AND EVAPOTRANSPIRATION '
AS PERCENTAGES OF AVERAGE ANNUAL PRECIPITATION FOR 1
VARIOUS SOIL TEXTURES
No.
1
2
3
4
5
6
7
•8
9
10
11
12
Surface
Vegetative/Soil layer 2 runoff
Sand/sand 16.7
Sand/sandy loam 16.7
Sand/loam 16.7
Sand/sandy clay loam 16.7
Sand/ clay 16.1
Sandy loam/loam 15.8
Sandy loam/sandy clay loam 15.8
Sandy loam/clay 15.2
Loam/sandy clay loam 15.3
Loam/clay 14.6
Sandy clay loam/clay (non) 14.5
Sandy clay loam/clay 23.1
Percolation Evapotranspiration
14.7
14.8
14.4
14.9
14.8
13.8
14.4
14.2
12.6
12.5
13.7
5.1
68.1
68.0
68.4 ;
68.0
67.9
69.9
69.4
69.3
71.6
71.6
70.6
69.8
124
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To illustrate relationships on a daily basis as well as to evaluate the
time lag of percolation for the different soil textures, the first 4 months
of 1978 were selected for detailed analysis. Table D-14 shows percolation as
a function of time and displays precipitation data for the first 114 days of
1978. Percolation was zero after 114 days (continuing through summer and
early fall) for all cases except the sandy clay loam vegetative soil and the
compacted clay soil layer 2 combination. This occurred because of increased
evapotranspiration following the start of the growing season on day 92.
The percolation output will be evaluated—first, for those conditions
without a soil layer 2 of clay, and second, for those conditions with a soil
layer 2 of clay. The percolation characteristics of soil layer 2 without
clay demonstrate that leachate production occurs on a day of heavy precipita-
tion. As these soil textures have relatively high hydraulic conductivities,
water percolates within the 24-hr period and rapidly appears as percolation.
Even though some of the hydraulic conductivities are 60 times as large as
others (sand equals 5.4 in./hr and sandy clay loam 0.084 in./hr), all percola-
tion is completed within the 24-hr time interval. During the early part of
the season, percolation is essentially the same for cases without a clay soil
layer 2. Some differences begin to show after the 72nd day as a result of
increased evapotranspiration as solar radiation and temperature increase.
After the growing season starts on the 92nd day, increased evapotranspiration
causes the percolation to go to zero, except for the sandy vegetative soil
layers, where the available water capacity has been reduced to a level
unavailable to plants.
Second to be considered is the output from those cases with a soil
layer 2 of clay. The low hydraulic conductivity (0.022 in./hr) results in
percolation that exceeds the 24 hr model time period. From days 5 through
36, percolation occurred continually for all clay soil layer 2 cases. In
comparison, nonclay soils experienced five events during the 31-day time
period when no percolation occurred. Also, the peak values of percolation
for clay soil layer 2 were not as high as for nonclay soil layer 2.
The percolation is virtually identical for all cases involving a clay
soil layer 2 (this was also true of the cases involving a nonclay soil
layer 2). When the clay soil layer 2 was compacted and the hydraulic con-
ductivity was lowered to 0.0011 in./hr, percolation continued through the
first 125 days although at a greatly reduced rate and magnitude (1.2452 in.
of percolation in 117 days for the compacted clay soil layer 2, and 2.5909 in.
in 117 days for the noncompacted clay soil layer 2). The time lag on percola-
tion was great enough to carry through the dry period—from day 36 through 72.
Figure D-25 shows the time lag for the three extreme soil texture com-
binations. Although some correlation of peak percolation is indicated, the
reduced magnitude and time lag effect is readily apparent.
SUMMARY OF SENSITIVITY STUDY
A summary of the sensitivity study is shown in Table D-15, which demon-
strates the relative effect of changes in the selected parameters on the more
salient features of the simulation. However, it should be noted that the
125
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TABIE D-14. AMOUNT OF PERCOIATION AND PRECIPITATION AS A FUNCTION OF TIME AND SOIL TEXTURE
Day Precipitation
1 0.03
2 0.02
5 0.45
7 0.43
8 1.32
12 0. 08
13 0.06
14 0. 02
M 15 0.02
J£ 16 0.35
17 0.31
19 0. 04
20 0.18
21 0.01
24 0.22
25 0.09
26 0.31
28 0.01
31 0.01
36 0.04
44 0.13
47 0.03
49 0.03
51 0. 01
52 0.02
53_ _. 0.02
VS = vegetative soil,
vs-s vs-s vs-s vs-s vs-s
BS-S BS-SL BS-L BS-SCL BS-C
0.1854 0.1854 0.1853 0.1853 0.1013
0.3103 0.3103 0.3103 0.3103 0.2426
0.5476 0.5476 0.5476 0.5476 0.3814
0.3052
0.0264 0.0264 0.0264 0.0264 0.0172
0.0086
0.0034
0.2733 0.2733 0.2733 0.2733 0.1466
0.2519 0.2519 0.2519 0.2519 0.2119
0.1448
0.1480 0.1480 0.1480 0.1480 0.0931
0.0474
0.1326 0.1326 0.1326 0.1326 0.0984
0.0656 0.0656 0.0656 0.0656 0.0738
0.2504 0.2504 0.2504 0.2504 0.1673
0.1191
0.0175
0.0006
BS = soil layer 2, coup = compacted.
VS-SL VS-SL VS-SL
BS-L BS-SCL BS-C
0.1851 0.1851 0.1012
0.3114 0.3114 0.2431
0.5476 0.5476 0.3.816
0.3503
0.0264 0.0264 0.0173
0.0086
0.0034
0.2735 0.2735 0.1467
0.2519 0.2519 0.2120
0.1448
0.1480 0.1480 0.0932
0.0475
0.1329 0.1329 0.0986
0.0656 0.0656 0.0739
0.2504 0.2504 0.1674
0.1191
0.0175
0.0006
VS-L VS-L
BS-SCL BS-C
0.1763 0.0965
0.3085 0.2380
0.5476 0.3805
0.3047
0.0264 0.0173
0.0086
0.0034
0.2729 0.1464
0.2519 0.2118
0.1447
0.1479 0.0931
0.0475
0.1320 0.0980
0.0656 0.0736
0.2504 0.1673
0.1190
0.0175
0. 0006
.
VS-SCL
BS-C
0. 1019
0.2439
0. 3818
0. 3054
0.0173
0. 0086
0. 0034
0. 1467
0.2120
0. 1448
0. 0932
0. 0475
0.0986
0. 0739
0.1674
0.1191
0.0175 .
0. 0006
VS-SCL
BS-C-Comp
0. 0631
0. 0115
0.0394
0. 0390
0. 0187
0. 0698
0.0170
0.0166
0. 0162
0. 0211
0. 0187
0. 0360
0. 0200
0. 0187
0. 0561
0. 0187
0. 0207
0. 0369
0. 0516
0. 0766
0. 1036
0. 0337
0.0211
0.0200
0. 0096
0.0094
(Continued)
-------
Day Precipitation
59 0.03
60 0.02
61 0.17
62 0.11
66 0.08
67 0.24
70 0.19
71 0.06
72 0.05
73 0.49
75 0.04
jo 79 0.20
81 0.05
82 0.11
83 0.57
84 0.02
91 0.02
93 0.01
94 0.06
96 0.34
99 0.01
101 0.26
108 1.03
109 0.04
110 0.08
113 0.40
114 0.20
TABUS U-lt. ^LUJNiJ-WUJuu;
VS-S VS-S VS-S VS-S VS-S VS-SL VS-SL VS-SL VS-L VS-L VS-SCL VS-SCL
BS-S BS-SL BS-L BS-SCL BS-C BS-L BS-SCL BS-C ' BS-SCL BS-C BS-C BS-C-Comp
0. 0516
0.0078
0.0076
0. 0074
0.0274
0. 0059
0. 0150
0. 0041
0.0037
0:1565 0.1565 0.1565 0.1565 0.0832 0.0377 0.0377 0.0201 0.0258 0.0137 0.0111 0.0045
0.0634 0.0153 0.0105 0.0085 0.0102
0.0098 0.0024 0.0016 0.0013 0.0198
0.001 0.0082
0. 0036
0.2845 0.2845 0.2845 0.2845 0.1513 0.2798 0.2798 0.1488 0.2689 0.1430 0.1496 0.0104
0.0819 0.0806 0.0774 0.0801 0.0127
0.0513 0.0506 0.0484 0.0507 0.0803
1st day of growing season 0- 020^
0. 0098
0. 0189
0.0266
0. 0166
0.0268 0.0268 0.0268 0.0268 0.0143 0.0516
0.0077 0.0063
0.0031 0.0058
0.0016 0.0147
0.0040
-------
at
i
0.6
i 0,4
0.3
^VS-SLC
BS-C tCOMPi
BAYS
Figure D-25. Percolation as related to time in days for
various soil textures (V = vegetative soil,
BS = soil layer 2, COMP = compacted).
128
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TABLE D-15. SUMMARY OF SENSITIVITY STUDY RESULTS
Change
Parameter
Impermeable liner
SCS curve number
Winter cover
factor
Thickness of
soil layer 2
Thickness of veg-
etative soil
Leaf area index
Soil layer 2
compaction
., Soil texture
(O Vegetative
•vo layer-S
Vegetative
layer-SL
Vegetative
layer-L
Vegetative
layer-SLC
From
5 yr
81
0.5
6 in.
12 in.
Excell
NCP
S
L
SCL
NCP
To
Ind.
99
1.0
18 in.
36 in.
Brgd
CP
C
C
C
CPD
Surface runoff
Sensitivity
NA**
tt ..
*
4=*
-t
§
*
t
t
t
§
Direction
NA
t
4
t
V**4
t
t
V4
4
4
t
Rank*
NA
1
2
1
3
2
2
. 1
1
1
1
Evapotranspiration
Sensitivity
NA
tt
tt
*
§
44
t
t
t
t
t
Direction
NA
4
t
4
t
4
4
V4
4
4
4
Rank
NA
2
1
2
2
1
3
2
2
3
3
Percolation
Sensitivity
t
*
t
t
§
*
§
t
t
t
§
Direction
t
4
4
4
4
t
4
Vt
Vt
4
4.
Rank
1
3
3
3
1
3
1
3
3
2
1
Cover drainage
Sensitivity
t
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Direction
t
NA
NA
NA
NA
NA
NA
NA
NA
.NA
NA
Rank
1
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Type of
Variable
Computed
Constant
Seasonal
Constant
Constant
Seasonal
Constant
Constant
NOTE: Arrow indicates direction of changes, (t increase and 4 decrease).
* Rank means the percentage change when the parameter is related to the average annual precipitation (1 = largest, 3 = least change).
** NA - Not affected, and V - Variable (arrow indicates general tendancy).
t Slightly.
tt Extremely.
4= Moderately.
44- Highly.
S Significantly.
-------
study was for a particular area in or near Cincinnati, Ohio. Therefore,
responses shown may change somewhat for hazardous and solid waste sites with
radically different climatological and hydrological data sets.
Conclusions drawn from the sensitivity study are as follows:
a) Percolation and evapotranspiration are significantly affected by
changes in soil-water storage and available water capacity.
b) The effect of the winter cover factor is seasonal and the variable
most affected is evaporation.
c) The SCS curve number primarily affects surface runoff and secondarily
affects both evapotranspiration and percolation.
d) The impermeable liner only affects water that has percolated past
the point at which it is controlled by evapotranspiration and sur-
face runoff.
e) Surface runoff was the variable most affected by the thickness of
soil layer 2.
f) The effects of the LAI are seasonally dependent and the variables
most sensitive to changes in LAI are evapotranspiration and
percolation.
g) The variables most affected by soil layer 2 compaction are percola-
tion and surface runoff.
h) Changes in soil texture result in highly time-dependent changes in
runoff and percolation and produced conditions under which other
variables become more sensitive.
•U.S. GO7IEHMINT PEINTIHG OFFICE: 1982-O-361-O82/319
130
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