SYSTEMS PROGRAMS FOR THE ANALYSIS OF NONURBAN, NONPOINT
SOURCE POLLUTANTS IN THE MISSOURI
BASIN REGION
by
A. Eugene Vandegrift
Shen-yann Chiu
Midwest Research Institute
425 Volker Boulevard
Kansas City, Missouri 64110
for the
Office of Water Programs
U.S. Environmental Protection Agency
Suite 906, Lincoln Towers
1860 Lincoln Street
Denver, Colorado
Attn: Mr. Keith Snyder
EPA Contract No. 68-01-0159
MRI Project No. 3572-C
3 May 1973
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DISCLAIMER
This report has been reviewed by the Office of Water Programs
and approved for publication. Approval does not signify that
the contents necessarily reflect the views and policies of
the Office of Water Programs, nor does mention of trade names
or commercial products constitute endorsement or recommenda-
tion for use.
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PREFACE
This report was prepared and submitted in partial fulfillment of EPA
Contract No. 68-01-0159 which was monitored by Mr. Keith Snyder.
In the program, data related to nonurban, nonpoint sources of pollu-
tants were collected, evaluated, compiled, and organized. The data
are now available on computer tape and also in a data bank.
In addition to this report, a User’s Manual has been prepared (and is
being submitted as a separate document) for the computer programs which
were developed to create, update, and use the data storage bank devel-
oped during the project. Complete printouts of the data bank (COUNTY
MASTER FILE and STATION MASTER FILE) have been forwarded to Mr. Snyder,
Mr. Robert V. Brown, and Dr. Marshall Rose. Also, magnetic tapes of
the COUNTY MASTER FILE and STATION MASTER FILE were sent to Mr. Snyder.
The work was conducted in the Environmental Sciences Section of the
Physical Sciences Division. The report was written by Dr. A. E.
Vandegrift and Dr. S. Y. chiu with assistance from Mr. Richard Salmon,
Mr. Duane Dieckman, and Mr. Evan Stewart,
Approved for:
MII 7EST RESEARCH INSTITUTE
H. M. Hubbard, Director
P ysical Sciences Division
3 May 1973
iii
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No Page iv
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TABLE OF CONTENTS
Page
Summary. . . . . . . . 1
Introduction 5
RelatedStudies 5
Physical Characteristics of the Missouri River Basin. 6
Approachtothe Study.. . . . . . . . . , . . . . . . . 8
ReportOrganization. 10
Identification of Data Sources and Collection of Data. 11
Evaluationof Data. . . . . . . . . . . . . . . . . . . . . . . 17
Organizationof Data 21
Data Files . . . 25
Co mnputerSoftware.......... 26
Methods for Updating the Data System 39
Identification of Data by Source 40
Conclusions 43
Recommendations . . . 45
Appendix A - Data Sources Investigated . . . . . . . . . . . . . 47
Appendix B - Individual Contacts . . . . . . . . . . 77
Appendix C - Water Quality Stations Selected . 85
Appendix D - Selection of Important Parameters . . . 93
Appendix E - MRI Station and River Codes. . 101
Appendix F - Maps Showing County-Watershed Relationships for
Subbasins 31 through 42 . . . . . . . 111
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SUMMARY
A program for the analysis of nonurban, nonpoint source pollutants in
the Missouri River Basin was conducted at Midwest Research Institute
under the sponsorship of the EPA. The ultimate goal of the program is
to develop system models that can be used by EPA to assess the impact
of nonpoint source pollutants on the pollution of surface water, and
to facilitate long-range planning for pollution control.
EPA is deeply concerned about (a) the influence on water quality of
• substances introduced into surface water courses from the agricultural
sector, and (b) the associated policy decisions about their abatement.
Specifically, questions are frequently asked about the contribution of
fertilizers to nutrient level in waterways, and the value of treatment
of waste from point sources for improving water quality if agricultural
• pollution continues unabated; in both cases careful studies have to be
conducted-before final answers can be given.
MRI’s program has three phases. Phase I (the work conducted to date)
involved efforts to (1) provide physical data in order to establish
relationships that can be used to predict and assess water quality prob-
lems, establish abatement priorities, and determine effects under various
conditions; ‘and (2) provide economic base data that can be used for cost
projections, economic analyses, incentive development, and EPA policy
determination for the various components of nonpoint source pollution.
Phase I and Phase III involve development of models, analytical tools,
and computer programs that EPA can use in planning and implementing ef-
fective pollution-control programs. These analytical tools will allow
EPA to choose between various alternatives in carrying out its mandate
to improve the water quarity in the basin.
This report describes the results accomplished in Phase I of this pro-
gram. The principal results are highlighted in the following paragraphs.
Determination of Data Needs and Data Sources
An extensive study was conducted to evaluate the types and sources of
data affecting the quaiity of surface waters in the Missouri River Basin.
The sources were eva?.uated with respect to the quantity and quality of
the data as well as their accessibility. As a result of our information
search, we concluded that data categories should include: water quality,
hydrology, climatology, topography, soil classification, land use, live-
stock distribution, fertilizer use, pesticide use, and demography.
1
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Personal communications were established with national, regional, and
local agencies which compiled and/or analyzed these data. The sources
explored by MRI include: EPA, USGS, Bureau of Census, USDA, NOAA,
Corps of Engineers, and State Pollution, Health and Agriculture Agencies.
We found that the majority of data needed are available in a computer-
processible form, such as STORET (water quality and stream flow data),
Census of Agriculture (land use and livestock data), Conservation Needs
Inventory (land use and soil classification), and NOAA (precipitation
data). Other necessary data not available on tapes were also collected,
or estimated. Pesticide data were obtained through work with the county
agents.
Preliminary Data Evaluation
This task was conducted concurrently with the collection of data, to
establish our confidence that the collected data would be useful for
model development. Several two-parameter correlations were prepared
for this purpose. We also evaluated the various data parameters when-
ever possible by comparing the same piece of data from different data
sources.
Data Compilation
The significant achievement of this study has been the compilation of
all pertinent data in a logical fashion, which allows easy access for
whatever analysis might be desired.
Data are collected and classified on the basis of three separate systems:
data on water quality and hydrology come from the stream stations at
which they are monitored; data for slope, soil classification, land use,
livestock distribution, fertilizer use, and pesticides use are available
by counties; and precipitation data are from each weather station. We
related all data to a coon base--the watershed . To achieve this, the
county-watershed relationships and county-weather station relationships
were determined and utilized to transform data from counties or weather
stations into the watershed. The data systems developed directly re-
lates water quality parameters to all physical, economical and climato-
logical parameters. The present file has water quality data and hydro-
logical data for 1968, 1969, and 1970, and land use data for 1969. How-
ever, provisions were made for updating when new data become available.
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Two major computer files were produced for this program. The COIJNTY
MASTER FILE has one record for each of the 438 counties in the Missc’uri
River Basin. Records f or each county consist of data in their land use,
fertilizer and pesticide application, soil classification, livestock
distribution, topography, and climatology. The STATION MASTER FILE
contains information unique to an individual station and the watershed
which impacts that station. In this file, the station location, the
water quality and hydrological data, the area and physical character-
istics of the watershed, and all information available for counties in
the watershed as recorded in the COUNTY MASTER FILE are compiled, Two
hundred thirty-three water quality stations were selected in this pro-
gram. These stations were chosen on the basis of their locations, and
the type and frequency of their measurements.
Report Organization
The document submitted herein consists of two parts: the Final Report
and the Data Bank. The Final Report describes the accomplishment of
Phase I efforts, including the result of literature search and personal
contact, and types and characters of data collected, evaluation of data,
the methods of data compilation, and use of these data for development
of analytical tools. The Data Bank consists of COUNTY MASTER FILE and
STATION MASTER FILE, magnetic tapes containing the information of these
two files, and the User’s Manual. Also included are the more extensive
raw data files (magnetic tapes) from which the Master Files were com-
piled.
Conclusions and Recommendations
Principal conclusions of Phase I are:
- -Methodology developed to treat nonurban situations is unique and use-
ful for similar studies in other areas of the nation.
--Preliminary evaluation indicates that data collected are sufficient
in their quality and quantity for the development of system models.
--Available data are sufficient for economic project ons.
On the basis of these conclusions, we recommend tI at Phases II and III
of this program should be initiated to further evaluate the data and to
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develop system models which relate water quality and hydrologic parain-
eters to physical, climatic, and economic factors. These models will
allow EPA to make economic and planning decisions concerning nonurban,
nonpoint pollution sources in the Missouri River Basin.
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INTRODUCTION
EPA is deeply concerned about the amount and proportion of agricultur-
ally generated pollution in running streams and rivers, and needs more
factual information on these matters as a basis for policy decisions
affecting, for example, the use of fertilizers or pesticides in a given
area. As a result of this concern, EPA has sponsored an investigation
by Midwest Research Institute (MRI) to determine if data exist which
will supply the factual information EPA needs, and then (assuming the
data exist) to compile and organize these data into a computer data
bank. This report describes HRI’s effort to collect, analyze, and or-
ganize data regarding nonurban nonpoint sources of pollutants in the
Missouri Basin region. Background on why the study is needed, where
it was done, and how it was done, as well as, how the report is organ-
ized are presented in the remainder of the Introduction.
Related Studies
State and local officials as well as the GAO* have contended for some
time that agriculture-related pollutants from natural runoff affect the
water quality of the Missouri River to a far greater extent than do ef-
fluents from primary-treated municipal and industrial waste along the
main stem. Any expected enhancement in water quality from secondary
treatment, they say, will be obscured by the magnitude of pollutants
from agricultural sources.
EPA has, therefore, initiated this study to obtain data that will allow
it to relate, water quality to Land use practices or other economic and
physical parameters. The objectives of the program discussed in this
report were:
1. To provide economic base data that could be used for cost projections,
economic analyses, incentives development, and Environmental Protection
Agency policy determination of the various components of nonpoint source
pollution.
2. To provide physical data regarding climate, topography, and land
use practices in order to establish relationships that can be used to
predict and assess water quality problems, establish abatement priorities,
and determine effects under various conditions.
* “Alternatives to Secondary Sewage Treatment Offer Greater Improvement
in Missouri River Water Quality,” GAO Report B-l25042, 6 January 1972.
.5
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3. To combine (1) and (2) to provide guides regarding the likely ef-
fects of changes in economic structure or land use practices on water
quality.
Several earlier studies were conducted elsewhere to develop models for
urban runoff. However, only one or two attempts have been made to in-
vestigate nonpoint, nonurban pollutant sources.* Studies have also been
conducted to collect and organize certain of the data that were needed
for the present study: for example, the Type I Comprehensive Framework
Study of the Missouri River Basin conducted by the Missouri Basin Inter-
Agency Committee (MBIAC). However, these data collections have not been
extensive enough for the purposes of the present program, and have been
used mainly as the basis of projections of water- and land-related re-
source needs. EPA has also recently initiated a number of studies to
apply existing detailed water quality models to Important stretches of
various river basins around the country. Although such studies are
being initiated for the upper South Platte River, the upper Mississippi
River, the Iowa-Cedar River Basin, etc., they will not emphasize non-
urban, noripoint pollution problems. We therefore believe that this
program is the first step in the development of a methodology which
will facilitate the evaluation of water quality management alternatives
in cases where nonurban, nonpoint factors are significant.
Physical Characteristics of the Missouri River Basin
The land area which is drained by the Missouri River and its tributaries
consists of approximately 530,000 sq miles with all except about 10,000
sq miles in the United States. This land area covers approximately one-
sixth of the contiguous United States, and includes all or parts of 10
different states. The basin contains about 113 million acres of tillable
cropland, about 175 million acres of grassland, approximately 45 million
acres of woodlands and forest, and approximately 7 million acres which
have been utilized for urban or nonurban development purposes.
There are several different ways of subdividing the Missouri Basin, and
we have chosen that used by the Office of Water Data Coordination (OWDC).
The OWDC divides the basin into 13 subbasins designated subbasins 31 to
43. •These subbasins cover the northwestern portion of Missouri, the
* “Agricultural Pollution of the Great Lakes Basin,” EPA/WQO, Report
No. 13020-07/71. “DeterminatIon of Land Runoff Effects on Dissolved
Oxygen by Mathematical Modeling,” “Douglass-Wallace, Ph.D. Thesis, Uni-
versity of Iowa, August 1971.
6
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northern portion of Kansas, all of Nebraska, the western half of Iowa,
the southwestern tip of Minnesota, most of North and South Dakota, the
eastern portion of Montana, and the northwestern portions of Colorado
and Wyoming.
The Missouri River itself flows 2,315 miles from its formation at the
junction of the Jefferson, Gallatin, and Madison rivers in Montana to
its mouth just above St. Louis, Missouri. Seven major tributaries flow
into the Missouri from the south and west. These are the Yellowstone,
the Cheyenne, the Niobrara, the Platte, the Kansas, the Osage and the
Gasconade rivers. Five tributaries flow in from the north and east.
These are the Milk River, the James, the Big Sioux, the Grand and the
Chariton. The Missouri, discharges an average of 83,000 cfs at its
mouth, but its maximum record flow has been as great as 900,000 cfs
and the minimum flow as low as 4,000 cfs.
There are three major physiographic divisions within the Missouri River--
the interior highlands, the interior plains, and the Rocky Mounta Ln Sys-
tem. Within the basin, the Rocky Mountain System division includes
parts of the Northern Rocky Mountains, Middle Rocky Mountains, Wyoming
Basin, and Southern Rocky Mountains provinces. The Interior Plains
division includes parts of the Great Plains and the Central Lowlands.
Distinct topographic features within the Great Plains provinces are
the Black Hills of South Dakota and Wyoming and the Sand Hills of
Nebraska. That portion of the Interior Highlands division within the
basin is characterized by its Ozark Plateau provinces.
In the Missouri River Basin, climate has a great influence on the liv-
ing habits and socioeconomic structure of the people. Obviously, it
has a strong influence upon the basic agricultural industry, primarily
because of the seasonal and variable regimen of precipitation, temper-
ature and wind.
The cl.fmate within the, basin is determined largely by the interaction
of three great air masses which have their origins over the Gulf of
Mexico, the northern Pacific Ocean, and the northern polar regions.
They regularly invade and pass over the basin throughout the year, with
Gulf air tending to dominate the weather in the sunmier and polar air
dominating 1.n the winter. This seasonal domination of the air masses,
and the frontal activity caused by their colliding with each other,
produce the general weather regimens found within the basin. Primar-
ily because of its midcontinental location, the basin experiences
weather that is known for its fluctuations and extremes. Also, there
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are variations between areas within the basin. Winters are relatively
long and cold over much of the basin, while sununers are fair and hot.
Spring is cool, moist and windy; autumn is cool, dry and sunny. Aver-
ages are misleading, for seldom does “average weather” actually occur.
Instead, weather tends to fluctuate widely around the annual averages,
with the occurrences and degrees of fluctuation being unpredictable.
The average annual precipitation varies from about 40 in. in parts of
the Rocky Mountains and in southeastern parts of the basin, to as low
as 6-12 in. inunediately east of the Rocky Mountains . Complicating the
annual variations, there is a wide variation in the basin pattern of
monthly precipitation.
Winds in the basin are the rule rather than the exception, particularly
in the plains area. Average wind velocities at 10 miles/hr are preva-
lent over much of the basin. In the plains area, strong winds accom-
panied by snow sometimes create blizzard conditions, dangerous to both
man and livestock. High winds that occasionally prevail during periods
of high temperature, decrease the moisture, destroy crops, and desiccate
range land within a few days. Parts of the basin, particularly the
southeast, are subject to cyclonic and tornadic winds that occasionally
do considerable damage.
Approach to the Study
To meet the program objectives, the following data categories were
investigated: water quality, climatology, hydrology, land use, topog-
raphy, soil classification, livestock distribution, pesticide use , and
fertilizer use . (We also have demographic data available if they are
needed.) In developing a logical plan of attack, this program was orig-
inally divided into five tasks:
1. Determination of available data sources;
2. Collection of available data;
3. Compilation and cross-correlation of data;
4. Evaluation of data; and
5. Preparation of data bank.
The steps necessary to accomplish these tasks are presented in Figure 1.
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county—
Weather Station
Relation Matrix
Figure 1 - Phase 1 Program Flow Sheet
9
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After the first 8 weeks of the program, it was apparent that substan-
tially more data existed on magnetic tapes than had previously been
supposed. Therefore, in conference with the Project Officer, it was
decided that the most effective use of available resources would be
to concentrate on development of the data base and a data storage for-
mating system. Using this approach, it was felt that we would have
substantial quantities and types of data on automatic data processing
tapes in an easily accessible format that could then be analyzed with
ADP systems on a much more sophisticated scale than the originally
anticipated two-parameter correlations.
The effect of this action was to set Tasks 3 and 4 at a later point in
the system following Task 5. Although the ultimate impact on resources
in terms of this contract was not known at that time, it was decided
that if parts of Tasks 3 and 4 could not be completed with available
resources, the resulting ability to upgrade and extend the analyses
more than offset any reductions in these two tasks.
One of the significant problems with the data that we found available
was that physical data, such as water quality parameters, were measured
on the basis of the water quality station location, land use parameters
such as livestock distribution, fertilizer use, and soil types, are all
on the county basis; while climatological data, such as rainfall amount,
are available on the basis of weather stations. For the purpose of data
correlation it was necessary to relate all data to a single base . The
data storage system we developed is based on watersheds . This system
will be explained more fully in “Organization of Data” of this report.
Report Organization
In the subsequent sections of this report, we discuss how the needed
data sources were identified and the methods of data collection that
we used; the method of organizing the data; the data-storage techniques;
the conclusions that we have reached; and our recommendations.
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IDENTIFICATION OF DATA SOURCES AND COLLECTION OF DATA
The data categories selected for this program, as given in the Intro-
duction, were selected on the basis of needs for the development of
empirical models or correlations. Such correlations can be used to
develop a systematic methodology, which, in turn, will facilitate the
evaluation of water quality management alternatives in cases where non-
urban, nonpoint factors are significant. In other words, the data
needs were based on requirements to develop appropriate models, analyt-
ical tools, and computer programs to be used by EPA in planning and
implementing effective pollution control programs.
Information was gathered from a search of literature, both published
and unpublished, and by telephone contact, and personal visits with
knowledgeable persons in government agencies including EPA, USGS, NOAA,
USDA (SCS, ASCS), OWDC, Corps of Engineers, Bureau of Reclamation,
Bureau of Census, Missouri Basin Inter-Agency Committee (MBIAC), and
state and local health, pollution, and agriculture agencies, and in
universities. From this extensive search, we found a large number of
data sources including inventories which contained data pertinent to
this program. For each inventory or other information source, we con-
ducted a thorough investigation into the types of data available, method
of data storage, techniques that could be used to retrieve the data,
and, if possible, methods by which the data were obtained (i.e., sam-
pling and analysis techniques). Information resulting from these in-
vestigations was subsequently used in selecting the data sources for
input to our data system.
We identified numerous sources of water quality data for the Missouri
River Basin. Among these were the EPA, the MBIAC, USGS, the Army Corps
of Engineers, the Bureau of Reclamation, state water pollution control
agencies, water- and waste-treatment plantS, etc. At each of these
sources, we interviewed persons in charge, studied their data, and
evaluated its utility and availability for this program. We found,
for example, that treatment plant data are generally stored in state
files or by the treatment plants themselves, and that these data are
generally not published and must be copied manually from their files.
On the other hand, the EPA’s STORET system is in computer-processible
form and collects data from many (although not all) of these sources
and is the single most comprehensive source of water quality data avail-
able. We, therefore, elected to use STORET as the primary source of
water quality data for this program.
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An extensive investigation was also made of land-use data sources in-
cluding the Bureau of Census, the Bureau of Reclamation, U.S. Depart-
merit of Agriculture--the Statistical Research Service, the Agricul-
tural Stabilization and Conservation Service, the Forest Service and
the Soil Conservation Service, and state departments of agriculture.
Again appropriate persons in each agency were contacted and samples
of data from each of these sources were investigated. Based on the
accessibility, estimated accuracy, and completeness of the data, we
selected the Census of Agriculture (1969) (the Bureau of Census), and
Conservation Needs Inventory (1958 and 1967) (the Soil Conservation
Service), as the primary sources of land-use data for this program.
We found that some data needed in this study had not been collected or
were still in the form of maps, tables, graphs, etc. In these cases,
special efforts were made: (1) to contact people who might have, or
be able to estimate, the needed data; and (2) to search the literature
for information from which the needed data could be generated using
various estimating techniques.
From the type of storage and availability, we classified data avail-
ability into three different categories. The first category includes
those which are available ‘on magnetic tapes, such as NOAA (precipita-
tion), Conservation Needs Inventory (1958 and 1967), Census of Agri-
culture (1969), and STORET. Magnetic tapes of these inventories were
obtained, from which, the needed data retrieved. Data in the second
category are those available in published literature in the form of
tables, graphs, maps, etc., from which the needed data were tabulated
and transformed into computer processible form. Data included in this
category are stream channel slope--from topographic maps and river
profiles; rainfall intensity--from NOAA’s Rainfall Frequency Atlas;
Soil Infiltration Rate--from soil maps; and county fertilizer use data
(for the states: Colorado, Iowa, Kansas, Missouri, and Nebraska)--
from materials published by each state department of agriculture. The
third category of data includes thosewhich did not exist in any reposi-
tory, such as county pesticide use data, and fertilizer use data for
counties in North and South Dakota, Wyoming, Montana, and Minnesota.
In these cases, we contacted county agents and other knowledgeable
persons to obtain their estimates of pesticide or fertilizer usage.
We also developed methods to estimate these data to check results ob-
tained from county agents, etc., or to use when no other estimate was
available.
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Data from all sources were retrieved, transformed, developed, or esti-
mated, and subsequently organized and stored in a data bank which con-
sists, on one hand, of information on natural factors and man’s activ-
ities which contribute nonpoint source pollution, and, on the other
hand, of water quality and hydrologic information, of the entire 10-
state Missouri River Basin. This data bank which will facilities the
analysis of cause-and-effect relationship is described in detail in-
cluding its organization and format in the section, “Organization of
Data.”
Table I presents a list of the major sources from which we obtained
the data. This table also indicates which parameters were obtained
from which data source.
Details describing the literature we searched, people and agencies that
we contacted, data inventories that we have investigated, contacting
methods for county agents, and methods that we developed to estimate
data are given in Appendices A and B. Of the several data sources listed
in Appendix A, we used those where the information was most accessible,
complete, accurate or was the only source available.
Important points concerning the data collected in this program are:
1. The data collected, including water quality, stream flow, land use,
precipitation, etc., are primarily for the year of 1969. Selection of
this year was made based on the fact that:
(a) considerably more water quality data are available for this year
than any of the previous years, and (b) it is the year when the most
recent Census of Agriculture was made.
2. We selected a total of 233 water quality stations which collected
suitable data for this program. The majority of these stations mea-
sured streatnf low in addition to the water quality.
3. There are 438 counties in the basin. For each of them we were able
to collect or estimate data for land use, soil type, ground slope, rain-
fall intensity, livestock distribution, fertilizer use, and pesticides
use.
4. There are 2,448 climatological stations in the 10 states in the
basin which have precipitation data available for this program.
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TABLE I -
TYPES AND MAJOR SOURCES OF DATA
Data Source
Date Category Agency Format Parameters
Water Quality EPA STORETA’ Sediment, Nutrient, Organic, Pesticides,
and Microorganism Concentrations
Hydrology EPA STORET. ’ Streainf low Rate
Climatology NOAA Precipitation J Seasonal and Yearly Precipitation
Data Tape
NOAA Rainfall Frequency Rainfall Intensity
Atlas of the U.S.
Topography SCS Conservation Needs Land Slope Percent Class
Inventory, l967. i
USGS Topographic Map Main channel Slope
Corps of River Profile Main Channel Slope
Engineers
Soil Classification SCS Conservation Needs Soil Capability Class, Soil Erosion
Inventory, l967 ’ Class, and Soil Name
SCS Hydrologic Soil Group Soil Index
Missouri Basin Infiltration Data Soil Infiltration Rate
Inter-Agency
Commit tee
Land Use Bureau of Census Census of Agriculture, Crop Acreage
l 969 .W
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TABLE I (Concluded)
Data Source
Data Category Agency Format Parameters
Livestock Bureau of Census Census of Agriculture, Cattle, Hogs, Sheep, Horses, Chickens,
l969. 1 and Commercial Feedlots
Fertilizer Use Bureau of Census Census of Agriculture, Lime Usage
l969. ’
State Fertilizer Tonnage Nitrogen, Phosphoric Pentoxide, and
Department of Report, 1969 Potash Usages
Agriculture
Pesticides Use County Agent NRI Questionnaire Insecticides and HerbLctdes Usages
a/ Available in computer-processible form.
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No Page 16
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EVALUATION OF DATh
The data obtained in this program were evaluated by two methods: (1) a
comparative evaluation of the same data from different data sources;
and (2) an evaluation based on simple, two-parameter correlations of
data collected.
We evaluated the various data parameters whenever possible by comparing
the same piece of data from different data sources. The crop acreage
and number of livestock, which constitutes one of the most important
data categories in this program, were repotted independently by the
state departments of Agriculture and the Bureau of Census. Data from
these two sources were compiled and compared, and did not show signifi-
cant deviation. This comparison increases our confidence in the relia-
bility of the land use data available. We have randomly selected data
from nine counties in the basin, which are shown in Table II.
For those data which could not be obtained from multiple sources, re-
gression methods combined with physical judgment were used to assess
the quality of available data.
As an example of those regression techniques, correlation between BOD
in the streams (lb/day) and number of cattle per county were success-
fully developed. The equation for this relation is:
BOD 4.19 x iO ’ (Number of Cattle)L 45
The coefficient of correlation for this relation is quite high at 0.86.
Several other correlations were also satisfactorily developed:
Nitrate Concentration versus Fertilizer Use
Sediment Yield versus Land Use
Fertilizer Use versus Crop Value
Pasture Land versus Herbicide Use
Pesticide Use versus Fertilizer Use
Details of these correlations have been presented in the Phase IA report
of this program.
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TABLE II
County
State
Logan
Colorado
Source
State
(1966)
Census
Corn
51
54
Sorghuns
13
6
Wheat
244
210
Soybean
NA
0
Hay
Cattle
93
162.800
86
185,955
flogs
7,200
12,482
chickens
NA
1.4,859
Sheep
4,400
5,355
Buena Vista
Iowa
State
( 1969) Census
194 207
0.3 0
0 0
144 142
20 19
NA 67,635
NA 162,768
NA 96,364
NA 1.3,318
AP ACRICULTURE (1969) DATh
OF StATE DATA WITH
Clay Vernon Powder River
Kanass Missouri Montana
State State State
( 1970) Census ( 1968) Census ( 1969) Census
26 21 61 72 0.5 0
101 91. 28 29 NA 0
125 129 50 35 41 35
8 8 97 85 NA 0
45 35 57 48 87 82
58,000 52,202 62,800 63,527 70,000 73,842
39,000 33,713 22,800 31,097 400 471.
166,000 124,054 NA 24,468 6,500 3,027
1,500 1,922 NA 1,826 56,000 53,1.22
Antelope Enz ns
Nebraska N Dakota
State State
( 1969) Census ( 1970) Census
227 207 44 34
11 8 NA 1
2 2 167 169
23 19 0 0
105 80 190 1.35
90,000 94,560 69,000 66,834
82,970 78,510 12,000 6,358
105,000 61,571. 38,000 35,540
5,300 5,080 1,020 1,710
Kingsbury
( 1970 )
153
3.5 5
34 29
NA 0
83 74
80,000 67,629
44,300 38,802
75,000 40,104
32,400 29,990
Niobrara
Wyoeing
State
( 1968J Census
0.8 1
NA 0
14 13 -
NA 0
63 54
56, 827
1,478
2,279
61,058
Unit. Crop - square mile,
NA. Not Available
NA
NA
livestock - head.
-------
Pertinent correlations developed by other researchers were also located.
Streamf low equations* developed by USGS for each state in the basin have
been obtained. These equations are the result of multiregression analy-
sis of the type required to develop models relating water quality to
other physical and economic parameters. The existence of these equations,
plus the correlations obtained in the program, raises the probability that
we will be able to use available data to develop meaningful models for
the Missouri River Basin.
* See Appendix D.
19
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No Page 20
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ORGANIZATION OF DATA
Data collected in this program can be classified on the basis of three
separate systems: data on water quality, and hydrology come from the
stream station at which they were monitored; data for slope, soil clas-
sification, land use, livestock distribution, fertilizer use, and pesti-
cides use are available by counties ; and precipitation data are from
each weather station . For the purpose of data correlation, it is neces-
sary to relate all data to a conunon base. The base we chose was the
watershed. Consequently, data which are based on county or weather sta-
tion must be transformed into watershed data.
The data transformation was accomplished in six steps:
1. Identification of the weather stations in each county.
2. Selection of the water quality stations.
3. Definition of the boundary and area of watersheds which directly
influence the measurement of the selected water quality stations.
4. Location of the counties and the portions of the counties covered
by the watershed.
5. Allocation of the county data into the county-watershed intersections
(hereafter called “zones”), and
6. Summation or averaging of the data for each zone in the watershed
to form the data base for that watershed.
Step 1 was done by referring to the NOAA’s publications* which show the
locations of all weather stations in the basin. Information obtained
at this step was subsequently used to determine the county precipitation
data by averaging those from the stations in the county.
Steps 2, 3, and 4 were carried out as follows:
An inventory of water quality stations in the basin was retrieved from
the STORET. Selection of stations was made based on their locations,
and the type and frequency of their measurements. As a result, 233 sta-
tions were selected.
* For example, “Climatological Data” Annual Summary for Colorado, 1969.
U.S. Department of Commerce, National Oceanic and Atmospheric Administra-
tion, Environmental Data Service.
21
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Each of these selected stations was located on the maps prepared by the
OWDC.* From the maps, the boundary of watershed which related to the
selected station, and the boundaries of zone in the watershed, were
determined.
The OWDC divides the Missouri River Basin into 13 subbasins and desi-
nates them 31 to 43. Naps were available for each of these subbasins.
Figure 2 presents the map for subbasin 43, which was subdivided as de-
scribed. The dots indicate the particular water quality stations. The
watershed boundaries and county lines are also indicated. Maps for sub-
basins 31 to 42 are presented in Appendix F.
The watershed and zone areas were determined by tracing a planimeter on
the OWDC watershed maps. In addition to the area, average distances
from centroid of zone to the respective monitoring station were also
determined. The distance information will be used when the effect of
river self-purification is to be investigated . For example, the BOD
monitored at a water quality station is more strongly influenced by the
zones that are closer to the station.
For each of these stations, the identification code given by MRI (de-
tails of which are discussed in Appendix E) and codes used by different
agencies, the location, the area, and the composition of the watershed
in terms of zones, have been determined and recorded. Table III shows
an example of these records.
The selected station, 5720006 , is in subbasin 32 and is on the Smokey
Hill River near Russell, Kansas. The watershed for this station includes
100 square miles of Russell County, 330 square miles of Ellis County,
200 square miles of Rush County, and 57 square miles of Ness County. The
drainage basin for this station has an area of 6945 square miles which
includes the above described watershed and the watersheds for the im-
mediate upstream stations 5720001 (on the Smokey Hill River) and 5723001
(on the Big Creek), respectively. The code and the distance to the next
downstream station (5720011) are also recorded. All this information
was stored in computer and subsequently used to construct our data system.
The approach we took to allocate county data into the zones (Step 5) is
based on the assumption that these data are uniformly distributed in
* “Catalog of Information on Water Data - Naps Showing Locations of
- Water Quality Stations,” Edition 1970, U.S. Department of Interi9r,
Geological Survey, Office of Water Data Coordination.
22
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1%
ii
l’
IJ
Figure 2 - Station-Watershed-County Relationships for Subbasin 43
ii
/
-1
I
\
)
)
iç’
S
/
23
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TABLE III
STATION INFORNAT ION RECORD
County-Basin Records
MRI Station Code: 5720006
Tributary Rank: 3
STORET Station No.: 06864000
OWDC W.Q.S. No.: 50232
X4DC S.W.S. No.: 5770
S.W.S. No.: 0686400
Sub-Subbasin: 32-C
Total Tributary Area: 6.965
Stream: Smoky Hill River
Location: Russell, Kansas
Lat. -Long.: 38 46 36 - 98 51 16
• Agency Code: L12WRD
• Agency Code: 112WRD
Square Miles (including immediate water-
shed area and watershed
area of next upstream sta-
tions)
Immediate Watershed Area:
County Stats
Cods Zone Area (ss milss Distance (mi1es
tussell
Kansas
20167
100
8
llis
Kansas
20051
330
30
tush
Kansas
20165
200
36
‘ess Kansas 20135 57
The Next Upstream Selected Stations
•
62
MRI Code Agency Code and Number Stream
Distance
(miles)
5720001
112WRD 06862700 Smoky Hill
River
46
5723001 1I2WRD 06863500 Big Creek
he Next Downstream Selected Station
34
MRI Code Agency Code and Number Stream
Distance
(miles)
5720011
112wRD 06864500 Smoky Hill
River
36
24
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each county.* Data such as crop acreage, number of cattle, fertilizer
use, etc., for each zone were calculated through multiplication of each
parameter value by the percentage of zone area in the county. Data for
watershed were then obtained by summing zone data of all zones located
in the watershed (Step 6). For data such as ground slope, soil erosion
class, precipitation amount, etc., we assumed that each zone in the
same county has the same data value. A weighted average (with respect
to area) of these data types for all zones in the watershed was then
determined to arrive at the data for the watershed (Step 6).
Data Files
Based on the need for development of a data system in this phase, and
for use in subsequent statistical and system analysis, two major com-
puter files were produced, and these constitute our data report.
COUNTY MASTER FILE : The first file, called the COUNTY MASTER FILE,
consists of 728 characters per county record, with one record for each
county in the Missouri Basin. The information in the county record
includes location, land characteristics, land use (crops, fertilizer,
livestock and poultry, etc.). For this report, the COUNTY MASTER FILE
will contain data for 1 year, 1969. It is anticipated that county
records for years other than 1969 could be developed in the same format.
There are four major sources for the data in the COUNTY MASTER FILE.
The first data source is from manual input. Information relating to
location, identification, fertilizer use, herbicide-insecticide-fungi-
cide use, for example, was obtained from various sources, coded on the
data sheets, and keypunched into cards for use in generating those data
elements in the county record.
The second data source is the Conservation Needs Inventory tapes. These
tapes were carefully reviewed to identify their unique data contents,
and selected data elements will be extracted from them to provide county
* We felt that for overall planning purposes on a large segment of the
country such as the Missouri River Basin, the accuracy of this apportion-
ment technique may be sufficient. As future work on correlations proceeds,
this assumption should be critically evaluated. In localized areas it
can have substantial impact and should be corrected before decisions are
made on that local area. Adequacy of the apportionment technique and
correction can be assessed and made by consulting individuals in each
county who have the best knowledge of their county in this regard; e.g.,
the county agent or county assessor.
25
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record information. Examples of these data are soil classes, land re-
source area and region, soil names, watershed, and slope and erosion
classes.
The third data source is the Census of Agriculture tapes. These files
provided data relating to land use such as crops grown and livestock.
The last data source is the NOAA tapes, providing precipitatiQn data.
STATION MASTER FILE : The second file to be developed for this project
is the STATION MASTER FILE. This file, consisting of 1,196 characters
per station record, contains information unique to an individual station
and the area which impacts that station.
There are three data sources for this file. The first, manual input,
consists of data elements relating to station identification and loca-
tion, area, channel slope, etc. Data sheets were designed, data ex-
tracted from various sources, keypunched, and a card file containing
these intermediate data developed.
The second data source is the STORET system. This source is used to
provide water quality data, hydrologic data, and statistics relative
to these data, for each of the stations which have been selected.
The third data source is the COUNTY MASTER FILE described previously.
The STATION MASTER FILE will contain data for each of the COUNTY MASTER
FILE records which impact the station area, and in proportion to the
percentage of the county area within the watershed.
Both of these master files are developed by means of COBOL routines, due
to the data manipulation and sorting required in file construction.
However, the data element values are expressed in formats which will
permit their use in FORTRAN routines. This capability will enable a
much broader use of these files for future correlation and other statisti-
cal activity.
The complete software developed in this program to generate the two
master files is discussed in the next section.
Computer Software
The computer programs required for this study consist largely of pro-
grants designed to create a unique data bank from various data sources.
For the preparation of reports from the created data bank, only two
26
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programs are used: (1) NPSPSTMD, which writes the station master re-
port, and (2) NPSPCTYD, which writes the county master report.
The programs used with appropriate data sources are as follows:
Manual Input: NPSPaIF1
NPSPCAL1
NP SPGSM1
NP SPG M1
NPSPFPC1
Census of Agriculture: NPSP OA1
Conservation Needs Inventory: NPSP NI2
NOAA: NPSPNOA1
STORF : NPSPSTR1
Utility Programs: NPSPCOA3
NPSPCNI4
NPSPGAL4
NP SPGAL6
NPSPC&LI
NP SPGSM3
NPSPCTS2
NPSPCrS3
NP SPNOA3
NPSPFPC3
NPSPSTR3
NPSPCOA4
Sort Programs: NPSPCOA2
NP SPCNI1
NPSPCNI3
NP SPNOA2
NPSPFPC2
NP SPGAL2
NP SPGAL3
NPSPGAL5
NPSPGAL7
NP SPGSM2
NPSPSTR2
NPSPCTS1
27
-------
An abstract of each program is presented in the following sections.
NPSPSTMD : This program reads the STATION MASTER FILE and creates a
report for each MRI station in the Missouri River Basin. The report
reflects the pertinent data within the following major categories:
I. Location
2. Land characteristics
3. Land use (acres)
4. Water quality data (averages)
5. Crop data
6. Fertilizer and pesticide data (tons)
7. Livestock and poultry
8. Contributing counties (up to 100)
The data furnished within each of the above categories are as follows:
1. Location
a. Stream name
b. Location
c. County
d. State
e. Latitude
f. Longitude
g. Downstream station
h. Distance to downstream station
i. MRI station number
j. STORET station and agency number
k. USAS station and agency number
1. 4DC WQ station number
m. OWDC SW station number
ii. OWDC sub-subbasin number
o. Total watershed area
28
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2. Land characteristics
a. Main channel slope
b. Slope percent class
c. Soil erosion class
d. Soil infilatration
e. Soil index
f. Rainfall intensity
g. Total precipitation in inches for each season and the year
h. Soil capability classes for the eight types
3. Land use (square miles)
a. Land area
b. Total cropland
c. Pasture/grazing
d. Woodlands
e. Irrigated
4. Water quality data (averages) for each season and the year
a. Temperature (°C)
b. Dissolved oxygen (mg/liter)
c. Biochemical oxygen demand
d. Nitrogen
e. Dissolved solids
f. Nitrate
g. Phosphate
h. Suspended solids
i Sulfate
j. Chloride
k. Herbicides
1. Insecticides
m. Fungicides
n. Streamf low
o. Total coliform
p. Fecal coliforni
q. Dissolved oxygen (percent saturation)
r. Temperature (°F)
a. Turbidity (Jackson units)
t. Turbidity (ppm as silicon dioxide)
u. Phosphoru8, wet method (mg/Liter as P)
(for details, see Table A-i in Appendix A).
29
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5. Crop data
a. Corn
b. Sorghums
c, Wheat
d. Other grains
e. Soybeans
f. Hay
g. Cotton
h. Peanuts
i. Tobacco
j. Potatoes
k. Vegetables
1. Berries
m. Orchards
n. Other crops
o. Greenhouses
6. Chemical fertilizer data (tons) - by half year and
pesticide data (tons) - by year
a. Total nitrogen
b. Total P 2 0 5
c. Total 1(20
d. Lime
e. Herbicides
f. Insecticides
g. Fungicides
7. Livestock and poultry
a. Cattle and calves - total - feedlots - stock in lots
b. Hogs - total - hog lots - hogs in lots
c. Chickens - total - chicken houses - chickens in houses
d. Sheep
e. Horses and ponies
8. Contributing counties
a. Each county that shares in the watershed of this station is
listed, showing the county area, and percent of the county area in the
watershed, and the distance to the station.
30
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NPSPCTYD : This program reads the COUNTY MASTER FILE and creates a re-
port for each county in each state within the Missouri River Basin. Th
report reflects the pertinent data within the following major categories:
1. Location
2. Land characteristics
3. Land use
4. Crop data
5. Fertilizer data and pesticide data
6. Livestock and poultry
The data furnished within each of the above categories are as follow:
1. Location
a. County
b. State
c. Watershed
d. Land resource area
e. Land resource region
2. Land characteristics
a. Slope percent class
b. Soil erosion class
c. Rainfall intensity
d. Total precipitation (all seasons and the year)
e. Soil name
f. Soil capability classes
3. Land use
a. Land area
b. Total cropland
c. Pasture and grazing
d. Woodlands
e. Irrigated
31
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4. Crop data
a. Corn
b. Sorghums
c. Wheat
d. Other grain
e. Soybeans
f. Hay
g. Cotton
h. Peanuts
i. Tobacco
J. Potatoes
k. Vegetables
1. Berries
m. Orchards
n. Other crops
o. Greenhouses
5. Fertilizer data by half year and pesticide data by year
a. Total nitrogen
b. Total P 2 0 5
c. Total K 2 0
d. Lime
e. Herbicides
f. Insecticides
g. Fungicides
6. Livestock and poultry
a. Cattle and calves - total - feedlots - stock in lots
b. Hogs - total - hog lots - hogs in lots
c. Chickens - total - chicken houses - chickens in houses
d. Sheep
e. Horses and ponies
NPSPQff 1 : This program will read the various cards created manually
and write them onto a magnetic tape. This provides data to the STATION
MASTER FILE and the COUNTY MASTER FILE that are not available from other
sources. The purpose is to provide all card input for the system from
a central file in an easily accessible form, There are 10 different
input types defined as follows:
32
-------
Type Number Description
1. State name and state number
2. County data
a. State and county codes
b. Maximum 30 mm rainfalls
c. Soil type
d. County area
e. County name
3. Station location and identification
a. MRI station number
b. State and county code
c. Latitude of station
d. Longitude of station
e. Stream name
f. Location
4. Station cross-reference list
a. MRI station number
b, STORE station number
c. STORET agency number
d. OWDC water quality station num-
ber
e. OWDC surface water station
number
f. USGS station number
g. USGS agency number
5. Zone definition
a. MRI station number
b. County - (up to 100 entries)
1. State and county code
2. County area
3. County - distance to
station
33
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Type Number Description
6. Hydrological sequence
a. MRI station number
b. Downstream MRI station number
c. Mileage to downstream station
7. Watershed slope and soil characteris-
tics
a. MRI station number
b. Main channel slope
c. Soil infiltration rate
d. Soil index
e. Map number and location
8. Applied fertilizer by county by half
year
a. State and county code
b. Half year code
c Total nitrogen
d. Total P 2 0 5
e. Total K 2 0
9. Pesticide data
a. State and county code
b, Insecticide
c. Herbicides
d. Fungicides
10. NOAA station - state county cross-
ref erence
a. State and county code
b. NOAA stations (up to 30)
NPSPGAL1 : This program reads the tape created in NPSP F1 and extracts
the data Types 2 and 5 onto intermediate data files for sorting. The
sorted files will be used by NPSPGAL4 for county allocation data.
NPSPGSM2 will sort Type 5 records and NPSPGAL3 will sort Type 2 records.
34
-------
NPSPGSM1 : This program reads the tape created in NPSPQIP1 and ex-
tracts the data Types 3, 4, 5, 6, and 7 onto intermediate data files for
sorting. The sorted files are used by NPSPGSM3 in the creation of the
STATION MASTER FILE. NPSPGAL2 sorts the selected records into station
sequence.
NPSPGcM1 : This program reads the tape created in NPSPG F1 and selects
data Types 1 and 2 for the creation of the COUNTY MASTER FILE.
NPSPFPC1 : This program reads the tape created in NPSPGMF1 and extracts
the data Type 8 onto an intermediate data file for sorting. The sorted
file is based in NPSPFPC3 to add the fertilizer data into the COUNTY
MASTER FILE. NPSPFPC2 sorts the selected tecords into state and county
sequence.
NPSPCOA1 : This program reads the Census of Agriculture tape (1969)
and selects the following data which will be sorted by NPSPCOA2 for
inclusion onto the COUNTY MASTER FILE in NPSPCOA3:
1. State
2. County
3. Data year
4. Land area
5. Total crop area
6. Pasture grazing
7. Woodlands
8. Irrigated
9. Total cattle (including dairy cattle)
10. Total hogs
11. Corn
12. Sorghum
13. Ray
14. Cotton
15. Peanuts
16. Tobacco
17. Potatoes
18. Sheep
19. Horses
20. Total chickens
21. Chicken houses
22. Chickens in houses
23. Wheat
35
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24. Other grain
25. Soybeans
26. Greenhouses
27. Vegetables
28. Berries
29. Orchards
30. Other crops
31. Cattle feedlots
32. Cattle in feedlots
33. Hog lots
34. Hogs in lots
NPSPCNI2 : This program reads the Conservation Needs Inventory file
sorted by NPSPCNI2 and selects the following data which will be sorted
in NPSPCNI3 for inclusion with the COUNTY MASTER FILE in NPSPCNS4:
1. Land resource area
2. Land resource region
3. Soil name
4. Watershed
5. Soil classes
6. Slope percent class
7. Soil erosion class
NPSPNOA1 : This program reads the NOAA. tape and selects the following
data which will be sorted by NPSPNOA2 and added to the COUNTY MASTER
in NPSPNOA3:
Precipitation data for each season and the total for the years 1968, 1969,
and 1970.
NPSPSTR1 : This program will read a print image tape created by STORET
and reformats the data onto an intermediate data file for sorting by
NPSPSTR2. The sorted file will be read by NPSP1 R3 for updating the
STATION MASTER FILE. The following data and others will be selected
from the STORET print image tape:
36
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1. Temperature °C and °F
2. Dissolved oxygen (mg/liter) and (percent saturation)
3. Biochemical oxygen demand (5 days, mg/liter)
4. Total dissolved solids
5. Total suspended solids
6. Turbidity (Jackson units) and (ppm as silicon dioxide)
7. Total nitrogen
8. Nitrate
9. Total phosphate
10. Total phosphorus
11. Chloride
12. Total coliform
13. Fecal coliforin
14. Insecticides
15. Herbicides
16. Fungicides
17. Streamf low
(for details see Table A-i in Appendix A)
NPSPCOA3 : This program reads the file created by NPSPCOA1, sorted by
NPSPCOA2, and updates the COUNTY MASTER FILE. The fields specified under
NPSPCOA1 are the only fields affected. For consistency of units and be-
cause the large areas involved lead to extremely large numbers if the
areas are given in acres, the data reported on the Census of Agriculture
file in acres were converted to square miles in NPSPCOAI.
37
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NPSPCNI4 : This program reads the files created in NPSPCNI2, sorted by
NPSPcNI3, and updates the COUNTY MASTER FILE with the apposite data. The
data fields affected are defined in NPSPcNI2. An error report will be
printed to reflect c NI data for counties which do not exist on the COUNTY
MASTER FILE.
NPSPGAL4 : This program reads the files created by NPSPCAL1, sorted by
NPSPGAL2, NPSPGAL3 and allocates county area, county area for the station
and percent of county area for the station and writes the allocation data
onto a magnetic tape which is sorted by NPSPGAL5 and used by NPSPGAL6.
A report will be written reflecting the data generated with an “*“ flagging
any fields in error.
NPSPGAL6 : This program reads the zone master created in NPSPGAL4 sorted
into station sequence by NPSPGAL5 and accumulates the zone areas in each
record. The sum of the zone areas by station will be the station area
which will be written into each record. The output file will be a disk
file to be sorted by NPSPGAL7 into county, state sequence.
NPSPGAL1 : This program reads the allocation file created by NPSPGAL6,
sorted by NPSPGAL7, and the COUNTY MASTER FILE. The allocation data for
each county are used in generating the zone master file. An error report
is written reflecting:
1. No allocations for county records and/or
2. No counties for allocation records.
NPSPGSM3 : This program reads the file created in NPSP F1 and the sorted
file created by, NPSPGSM2 and generates the STATION MASTER FILE. An error
report is written for each station which does not have the necessary data.
NPSPCJ S2 : This program reads the zone master file created by NPSPCAL1,
sorted by NPSPCTS1, and accumulates the zone records by station and writes
the apposite county data to an intermediate data file used in NPSPCTS3.
NPSP TS3 : This program reads the intermediate file created in NPSPCTS2
and updates the STATION MASTER FILE created in NPSPGSM3. An error report
is written, reflecting: (a) data for which a station master record does
not exist; and (b) station master records without county data.
NPSPNOA3 : This program reads the intermediate file created by NOSPNOA1,
sorted by NPSPNOA2, and updates the COUNTY MASTER FILE. An error re-
port is written to show records selected for which a county master record
does not exist.
38
-------
NPSPFPC3 : This program reads the intermediate file created in NPSPFPC1,
sorted by NPSPFPC2, and updates the COUNTY MASTER FILE. An error report
is written reflecting the data selected for which a county master record
does not exist.
NPSPSTR3 : This program reads the intermediate file created by NPSPSTR1,
sorted by NPSPSTR2 and updates the STATION MASTER FILE. An error report
is written for the data records for which no station master record exists.
NPSPCOA4 : This program reads the Census of Agriculture tape and creates
a report showing the amount of fertilizer (in tons) used by county in
Minnesota and Montana (while fertilizer use data for counties in other
states were obtained from other data sources, see Appendix A). This will
be used to manually input these data with the fertilizer data.
Methods for Updating the Data System
To insure the utility of the data system which has been developed during
this program, it has been designed to facilitate the updating of the many
parameters in the system. A description of the step-by-step procedure to
be followed in updating the system is given below. The detailed operating
procedures for this updating will accompany the computer cards and tapes
when they are submitted to EPA.
The 4ata necessary to complete the COUNTY MASTER FILE come from four
unique sources. The data necessary to complete the STATION MASTER FILE
come from one unique source in addition to the complete COUNTY MASTER.
Creation of the COUNTY MASTER FILE:
Manual data : The initial phase of creating the COUNTY MASTER FILE is
done by one program which reads manually prepared data writing onto a
magnetic tape which is the original County Master. The information loaded
during this step is fixed and normally will never change.
Conservation Needs Inventory : The data rea4 from the Conservation Needs
Inventory files normally will remain static and therefore they are not kept
by year. The data from CNI are merged with the COUNTY MASTER.
Census of Agriculture : The Census of Agriculture’ files provide data to
the COUNTY MASTER for the year 1969. Space is available so that when
data become available up to five census from the Census of Agriculture
data may be saved. The data read from COA files aremerged into the
COUNTY MASTER for the appropriate year.
39
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NOAA : The NOAA provides data to the COUNTY MASTER for the years 1968,
1969, and 1970. Space is available for two more years of the NOAA data.
The data read from the NOAA file are merged into the COUNTY MASTER for
the appropriate year.
Updating : Steps for creation of COUNTY MASTER FILE may be run any
time new data become available and the program will update the most
recent COUNTY MASTER FILE.
Creation of the STATION MASTER FILE:
Manual data : The original STATION MASTER is created from manually pre-
pared data most of which are constant and will be permanent.
STORET : These data are in the same form as a report using STOR.ET except
that they are available on magnetic tape. The tape is converted back
into valid data which are added to the STATION MASTER FILE for the cor-
rect year. The present file has data for 1968, 1969, and 1970 by season
and year.
COUNTY MASTER : The data available on the COUNTY MASTER FILE are spread
to each watershed region based on the proportion of the county allocated
to the region. With the exception of rainfall data, slope percent class
and soil erosion class, all data are spread to the watershed based on
a county-to-zone area ratio.
Updating : If data in the COUNTY MASTER or STORET have been updated,
it must be assured that a proper dispersion of the data to each
watershed is maintained. Each change in data for the COUNTY MASTER
will necessitate the updating of the STATION MASTER.
Identification of Data by Source
Source Field Name Description
Manual Data MEl Station Code’ Card Type 3
OWDC Map and Basin card Type 7
State and County Card Type 3
State Name Card Type 1
County Name Card Type 2
Latitude and Longitude Card Type 3
Stream Name Card Type 3
Location Card Type 3
STORET Station and Agency Codes Card Type 4
JDC Water QuaLity Station Number Card Type 4
OWDC Surface Water Station Number Card Type 4
USGS Station Code Card Type 4
40
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Source
Field Name
Description
Census of Agriculture
(1969)
Downstream Station
Distance to Downstream Station
Main Channel Slope
Soil Infiltration Rate
Soil Index
County Code-Each Couri
in Watershed
County Total Area-Each County
in Watershed
County Distance to Station-Each
County in Watershed
Rainfall Intensity
Land Area
Total Farmland
Total Crop Area
Total Other Land
Pasture Grazing
Harvested Cropland
Wood lands
Other Crop land
Irrigated Other Farmland
Corn
Sorghum
Wheat
Other Grain
Soybeans
Hay
Cotton
Peanuts
Tobacco
Potatoes
Vegetables
Berries
Orchards
Other Crops
Card Type 6
Card Type 6
Card Type 7
Card Type 7
Card Type 7
Card Type 5
Card Type 5
Card Type 5
Card Type 2
41
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Source Field Name Description
Greenhouses
Total Cattle
Cattle Feedlots
Cattle in Feedlots
Total Hogs
Hog Lots
Hogs in Lots
Sheep
Horses
Total Chickens
Chicken Houses
Chickens in Houses
Lime
Conservation Needs
Inventory (1969) Soil Name
Slope Percent Class
Erosion Class
Soil Class Area by Class and Subclass
Watershed
Resource Area
Resource Region
STORET Parameters Listed in Table A-l of Appendix A
Manual Data Nitrogen
P205
1 (20
Herbicides
Insecticides
Fungicides
NOAA (1968, 1969, Rainfall Amount for quar-
and 1970) ters and year
42
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CONCLUSIONS
The objectives of Phase I of this study (that portion of the overall
program covered by this report) were to collect, evaluate, and compile
the physical and economic base data for the analysis of nonurban, non-
point, source pollutants in the Missouri River Basin. The results of
our study lead to the following major conclusions: (1) we have developed
a unique and extremely useful data allocation methodology that is par-
ticularly useful for nonurban situations in all parts of the country.
We have also catalogued the types and sources of relevant data as well
as èompiling the data in a form which will be amenable to analysis of
all types; (2) the amount of data available for the Missouri River Basin
on physical and economic parameters that we investigated are sufficiently
large that it should be possible to develop systems models relating non-
urban, nonpoint source pollutants to water quality parameters; however,
additional evaluation of the collected data should be conducted.
In addition to these major conclusions from the study, there are several
other conclusions that can be drawn: (1) the computerized data-storage
system developed during this program logically relates county segments
to watersheds and should be useful to other government ageiicies such
as USDA and USD1. (2) It is possible to correlate average water quality
parameters with land use and other nonwater quality parameters. (This
was demonstrated by several two-parameter correlations which were de-
veloped in the early stages of the program.) (3) Streamf low equations
for all the states in the basin developed by the USGS as a result of
multiregression analysis can serve as the basis for the development of
basin runoff models. Furthermore, the existence of these equations and
of the two-parameter correlations developed in this program, raise the
probability that the available data can be used to build useful water
quality models for the Missouri River Basin. (4) Relationships between
nonwater quality parameters can be developed with the data compiled in
this program to make economic projections. For example, fertilizer and
pesticide use can be related to land use, agricultural practices to
soil type, rainfall amount, and ground slope, etc. (5) For the purpose
of obtaining more useful information for improving water quality manage-
ment planning in the basin, further development of the water quality
monitoring network--in terms of station distribution, and types and
frequency of measurements at the stations--is needed. The data collected
during the present program definitely reflect this need.
43
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No Page 44
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RECO1 ,1ENDATIONS
On the basis of the conclusions presented in the previous section, several
recommendations can be developed. The major recommendation, based on
the two major conclusions, is that Phases II and III of this program
should be initiated to carry out an in-depth evaluation of both the quan-
tity and quality of data available in the existing data bank. The fol-
lowing analyses are recommended:
1. Assessment of the quality of land use and soil data, e.g., by com-
paring data from independent sources, and by consulting with knowledge-
able individuals (e.g., county agents);
2. Assessment of the quality of hydrological and water quality data,
e.g., by statistical comparison among related parameters, and by corn-
parison of existing STORET data with other data which were not available
in STORET (e.g., from water and wastewater treatment plants);
3. Identification of interrelationships among different types of vari-
ables, e.g., correlation of water quality data with land use data; and
4. Assessment of significance of missing data and use appropriate
methods for obtaining or estimating such.
If the results of these analyses show that the amount and quality of
both physical and economic data are sufficient, analytical tools should
be developed which will allow EPA to make economic and planning deci-
sions concerning nonurban, nonpoint pollutant sources.
In addition to the major recommendation, other recommendations that can
be made based on the previous conclusions are: (1) the present data file
should be updated whenever new data become available. Updating of the
data will provide a base for time series analysis of nonpoint source
pollution, as well as allow EPA to update their economic and planning
decisions concerning nonurban, nonpoint pollutant sources. (2) The
water quality monitoring network in the basin should be improved- - for
example, the location of stations should be changed so that they are
more uniformly distributed in the basin and the types of water quality
parameter measured at each station should be reevaluated so that all
important data such as pesticide concentrations are measured. This will
require a joint effort on national, regional and local pollution control
agencies, but this effort should lead to the availability of better in-
formation for basin-wide water quality management planning.
45
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No Page 46
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APPENDIX A
DATA SOURCES INVESTIGATED
47
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This Appendix presents a discussion of the major data sources that were
investigated during the course of this program. The data sources that
were actually used in compiling our data bank are presented in “Iden-
tification of Data and Collection of Data” of this report.
Tables A-2 through A-il list all organizations that were contacted to
obtain information. Personal contacts that were made are presented in
Appendix B.
Water Quality Data
We identified numerous sources of water qualtty data for the Missouri
River Basin. Among these were the Environmental Protection Agency, the
U.S. Geological Survey, the Army Corps of Engineers, the Bureau of Recla-
mation, State Water Pollution Control Agencies, Water-Treatment and Sewage-
Treatment plants, etc. Most of the information collected by the treat-
ment plants are stored in state files or by the treatment plants them-
selves, and not in the computer-processibie form. Furthermore, these
data are generally not published and must be copied manually from their
files. Because we found applicable information from other sources, we
did not obtain data from these plants.
The EPA’s STORET system collects data from many agencies. It is the
most comprehensive source of water quality data and, furthermore, it is
in computer-processible form which permits easy access. We, therefore,
elected to use it as the primary source of water quality data for this
program.
The STORET system has the data for approximately 1,800 stations in the
Missouri River Basin and we originally selected, based on the type,
frequency, and duration of their measurements, a total of 520 stations.
After locating the watershed for these stations from the maps prepared
by the OWDC, it was found that some stations are located in close proximity
to one another. Also, a number of the stations either do not have sur-
face water stations nearby or do not collect streamflow data themselves.
By picking one station when several are located together, and eliminating
those stations that do not have flow information (because the stream-
flow data are essential in our future modeling effort), we reduced the
number of the selected stations to 233. Locations of these stations
are presented in Appendix C.
In this program, the water quality data retrieved from the STORET for
these 233 water quality stations are for those parameters which are
most strongly influenced by the nonpoint sources. These parameters
48
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include sediment, nutrients, pesticide level, organic concentration
and bacterial density. Table A-i presents the specifics of parameters
retrieved from the STORET system, and their abbreviations appeared in
the data report.* We found that most water quality statj.ons measure
different sets of water quality parameters. The set of parameters pre-
sented in Table A-i are not necessarily collected by all the stations
we selected. We, therefore, elected to have printed, those parameters
which are available from the ST0R for each station in our data re-
port. Furthermore, we used the frequency of sampling for the majority
of stations, and also the needs of our future modeling efforts as a
basis for choosing only the seasonal and yearly averages for the years
of 1968, 1969, and 1970. Periods of seasons and year are defined as
follows:
First season: December (previous year), January, February
Second season: March, April, Nay
Third season: June, July, August
Fourth season: September, October, November
Year: December (previous year) to November
* STATION MASTER FILE.
49
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TABLE A-i
SELECTED WATER QUALITY PARAMETERS
Code Parameter Description Parameter Abbrev .ation
00010 TEMP CENT
00011 TEMP FAHN
00060 FLOW CFS
00070 JURB, J.U.
00075 TURB PPMSIO2
00095 CNDUTVY MO
00300 DO /L
00301 DO SAT PCT
00310 BOD5 MG/L
O03 5 COD r /L
00400 PH SU
00500 T RSD MC/L
00505 TV RSD MG/L
00510 TFX RSD li/L
00515 TF RSD MC/L
00520 SIP RSD MC/L
bo525 FF RSD MG/L
00530 TNF RSD MG/L
00535 VNF RSD MC/L
00540 FNF RSD MG/L
00545 S RSD ML/L
00600 T N MG/L
00605 OG N MG/L
00610 AM N MC/L
00615 NO2 MG/L N
00620 N03 MG/L N
00630 NO2&3 /L N
00635
AM&OG N MG/L
00650 T P04 /L
00655 FL P04 MC/L
100660 OT P04 MC/L
00665 T P MC/L
00666 D P MG/L
00667 I P MG/L
00670 T OG P MC/L
00930 D NA MG/L
00931 NA ADS RATIO
00935 D K MG/L
00936 SUS K MG/L
Water Temperature (degree Centigrade)
Water Temperature (degree Fahrenheit)
Stream Flow (cubic feet per second)
Turbidity, Jackson Candle (Jackson Unit)
Turbidity, Ilellige (ppm as silicon dioxide)
Conductivity (micromhos at 25°C)
Dissolved Oxygen (mg/i)
Dissolved Oxygen (percent of saturation)
Biochemical Oxygen Demand (mg/i, 5-day-20?C)
Chemical Oxygen Demand, 0.025N K 2 CrO 7 (mg/i)
pH (standard unit)
Total Residue (mg/i)
Total Volatile Residue (mg/i)
Total Fixed Residue (mg/i)
Total Filtrable Residue (mg/i)
Volatile Filtrabie Residue (mg/i)
Fixed Filtrabie Residue (mg/i)
Total Nonfiltrabie Residue (mg/i)
Volatile Nonfiltrable Residue (mg/i)
Fixed Nonfiltrabie Residue (mg/I)
Settlable Residue (mi/I)
Total Nitrogen (mg/i as N)
Organic Nitrogen (mg/i as N)
Ammonia Nitrogen (mg/i as N)
Nitrite (mg/i as N)
Nitrate (mg/I as N)
Nitrite Plus Nitrate, One Determ. (mg/i as N)
Ammonia & Organic Nitrogen, One Determ.
(mg/i as N)
Total Phosphate (mg/i as P0 4 )
Poly Phosphate (mg/i as P0 4 )
Ortiho Phosphate (mg/i as P0 4 )
Total Phosphorus, Wet Method (mg/i as P)
Diss. Phosphorus, Wet Method (mg/i as P)
Insoluble Phosphorus (mg/l as P)
Total Organic Phosphorus (mg/i as P)
Dissolved Sodium (mg/i as N 8 )
Sodium Adsorption Ratio
Dissolved Potassium (mg/i as K)
Suspended Potassium (mg/i as K)
50
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TABLE A-i (Concluded)
Code Parameter Description Parameter Abbreviation
00937 T K MG/L
00940 CL MG/L
31501
T CLF1/100 ML
31503
T CLF2/l00 ML
31504
T CLF3/100 ML
31615
F CLF1/100 ML
31616
F CLF2/i00 ML
3 16 73
F STC1/l00 ML
31679
F STC2/l00 ML
38260 MBAS t’ /L
70299
SS MG/L
70300 TF RSD MG/L
70302 DS TONS/DAY
70303 DS TONS/A-FT
70324 SUS SED MG/L
71845 AM MC/L Nl- 14
71850 N03 /L
71855 NO2 MC/L
71886 TP MG/L P04
71887 TN MG/L N03
71888 TSP MG/L P04
71889 so P04 MC/L
Total Potassium (mg/i as K)
Chloride (mg/i as Ci)
Tot3L Coliform (membrane filter, imined.,
M-Endo Med, 35°C, /100 ml)
Total Coliform (membrane filter, delayed,
M-Endo Med, 35°C, /100 ml)
Total Coliform (membrane filter, immed.,
Lcs Endo Agar, 35°C, /100 ml)
Fecal Coliform (MPN, EC Med., 44.5°C, tube.
31614, /100 ml)
Fecai Coliform (membrane filter, M-FC
Broth, 44.5°C, /100 ml)
Fecal Streptoccoci (membrane filter, KF Agar,
35°C, 48 hr, /100 ml)
Fecal Streptoccoci (MF M-Enderoccocus Agar,
35 C, 48 hr, /100 ml)
Methylene Blue Active Substance
Suspended Solids (Residue on Evap. at
180 C, mg/I)
‘Total Filtrable Residue (dried at 180 C, mg/l)
Dissolved Solids - Tons per Day
Dissolved Solids - Tons per Ac-Ft
Suspended Sediment (mg/l)
Ammonia Nitrogen (mg/i as N H 4 )
Nitrate (mg/i as N03)
Nitrite (mg/l as NO 2 )
Total Phosphorus (mg/I as P0 4 )
Total Nitrogen (mg/i as N03)
Total Soluble Phosphorus (mg/i as P04)
Soluble Orthophosphate (mg/i as P0 4 )
51
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The concentration of insecticides, as denoted by INSECTICIDES in the
data report, was taken as the average concentration in whole water sani-
pie (jig/liter) of the following parameters:
Code - - - Parameter
39330 A ldrin
39340 BHC
39350 Chiorodane
39360 DDD
39365 DDE
39370 DDT
39380 Dieldrin
39388 Endosul fan
39390 Endrin
39398 Ethion
39400 Toxphene
39410 Heptachior
39420 Heptachior Epoxide
39430 Isodrin
39440 NUX
39450 NHP
39460 Ch lorobenzilate
39470 Dilan
39480 Methoxychior
39530 Malathion
39540 Parathion
39550 Chiorothion
39560 Demeton
39570 Diazinon
39580 Guthion
39590 Hept
39600 Methyl Parathion
39610 Phosdrin
39620 Tepp
39750 Sevin
39780 Dicofol
39782 Lindane
39786 Trithion
39790 Methyl Trithion
39800 Imidan
39900 Al lethriri
39910 Cinerin
39930 Pyrethrins
52
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The concentration of herbicides, as denoted by HERBICIDES in the data
report, was the average concentration of the following parameters in
the whole water saniple, with unit pg/i:
Code Parameter
39730 2,4-D
39740 2,4,5-T
39760 Silvex
39770 Dacthal (DCPA)
39920 DNOC
Hydrological Data
The principal agencies operating hydrologic stations in the basin are
the USGS and the Corps of Engineers. The USGS is the more complete
source of hydrologic information. It publishes hydrologic data each
year for each state in the basin. Besides being published in tabular
form, these data are available in computer-processible form. The USGS
operates more than 2,900 surface water stations in the basin with at
least 40 stations in every basin state; the single exception is Minnesota.
Of these 2,900 stations, over 1,300 report daily discharge and other
data less frequently. These data are, for example, stage, peak stage,
low flow, cross section, flow duration, flood frequency, coeffieient of
roughness, time of travel and surface inflow-outflow.
The USGS office in each basin state has developed a streamfiow model
for that state. These models are based on a regression analysis of all
pertinent data, and relate such parameters as rainfall, soil class,
drainage area, etc., to the s treamf low rate. The number, frequency and
location of hydrologic measurenents have been sufficient to generate
multiple-regression correlations that can be used to predict streamflow,
The results of these analyses will be extrenely useful to this program.
Further details of these are presented in Appendix D.
The Corps of Engineers takes ‘hydrologic data for use in its reservoir
and channel straightening work. Most of the information obtained is
put into the USGS computer system. The Corps also investigates flood
plain for specific watersheds. The results of these investigations
are published in reports.
The less important source of hydrologic information for the Missouri
River Basin is the River Forecasting Center of National Oceanic and
Atmospheric Administration (NOAA) in Kansas City. It has developed
long-term hydrographs for 800 subbasins which encompass the entire
53
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Missouri Basin plus an additional quarter milliop square miles. These
subbasins average about 1,000 square miles, but may be as small as 200
to 400 square miles. With these hydrographs and computer models for
runpff, NOAA can successfully predict river stages along the Missouri
River.
The major concern of this program is the analysis of pollutants from non-
point sources. The pertinent hydrologi c data are obviously the stream-
flow rate. Among 233 selected water quality stations, 183 stations col-
lect streamfiow data. For 50 other stations, we have identified the
nearest gauge stations which have streaniflow information to supplement
our need. We were able to retrieve these data for 24 of these 50 sta-
tions from EPA’s STORET system. Data for the re aining 26 gauge sta-
tions can be obtained from their respective agencies.
In our data report, the streamf low daia are presented in terms of their
seasonal and yearly averages for 1968, .969, and 1970.
Climatological Data
Climatological factors of most importance to our study are rainfall
intensity and amount.* Because of the frequez cy with which these data
are taken, much data are available. The principal sources of these
data are the U.S. Department of Commerce, National Oceanic and Atmospheric
Administrations, Environmental Data Service, Asheville, North Carolina.
Climatological information, such as temperature, precipitation, wind,
humidity, etc., for various locales in the country are stored by the
NOAA in computer-processible form. From this storage, in addition to
the raw data, information resulting from variots types of statistical
manipulation can also be retrieved, e.g., the total precipitation for
a certain geographical area in a defined period of time, or probability
that a given rainfall will occur at a certain location for a certain
number of times per year, etc.
In the data report of this program, the precipitation data are presented
on the seasonal and yearly basis. These data were originallyprocessed
by the NOAA. from its tapes, and were available from each weather st i •.
in the basin. NRI carried out further statistical manipula i n on these
data to obtain county-precipitation data, which were subsequently pre-
sented in the Data Report.
* Wischmeier, W. IL, and D. D. Smith, “Ratnfall Energy and Its Rela-
tionship to Soil Loss,” Transaction 1 Amer. Geoph. Union , 39 (2), 851
(1958).
54
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For the information about the rainfall intensity, we elected to use
statistical values of maximum 30-mm rainfall that will occur once per
year. Intensity data for each county in the basin were also obtained
from the NOAA.*
Soil-classification data
The types of soil directly influence the quality and quantity of pol-
lutant in the surface runoff and the agricultural practices on the lands.
Furthermore, the agricultural practices greatly affect the water quality
of the streams. Soil-classification data, therefore, are extremely im-
portant to the development of models re3ating water quality to land use.
There are several ways in which soil can be classified. One method is
to classify soil by the crops or plants that it will support. There are
eight such classifications.
Class 1, few limitations that restrict its use;
Class 2, some limitations that reduce the choice of crops or require
moderate conservation practices;
Class 3, severe limitations that reduce the choice of crops or require
special conservation practices or both;
Class 4, severe limitations that restrict choice of crops and require
cAreful management;
Class 5, little or no erosion hazard but limitations that are impracti-
cal to remove--that limit use largely to pasture, range, woodland, or
wildlife food and cover;
Class 6, severe limitations that make it unsuited for cultivation and
limit use to pasture, range, woodland, or wildlife;
Class 7, limitations that make it unsuited for cultivation or restrict
itsuse to grazing woodland or wildlife; and
Class 8, limitations that preclude their use for commercial plant produc-
tion or restrict their use to recreation, wildlife, water supply, and
aesthetic purpose.
* “Rainfall Frequency Atlas of the U.S.,tt U.S. Department of Commerce,
Washington, D.C. (1961).
• 55
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There are subclasses for each of these classes with the exception of
Class 1:
Subclass e, erosion--made up of soils where susceptibility to erosion
is the dominant hazard and problem in its use;
Subclass w, wetness--made up of soils where excess water is the dominant
hazard to limitations in its use;
Subicass s, soil--made up of soils with characteristics in the root zone
area which are dominant limitations in its use;
Subclass c, climate--made up of soils where temperature and lack of
moisture are limitations in its use.
Information on the preceding type of classification is available from
the Conservation Needs Inventory (1967). This inventory classifies
the soil on a county-by-county- basis for each state in he basin. These
data are stored in computer accessible form and we have included them
in the data report.
The 1967 Conservation Needs Inventory has data of “Soil Erosion Class”
for each county. The Inventory classifies the soils into: little ero-
sion; moderate erosion; severe erosion and gullies; and denoted them
with 1, 2, 3, and 4, respectively. Whenever there is no information for
a county, a number 0 is shown. We have these data compiled in the data
report.
The other method of classifying soil is based on the rate of infiltra-
tion of water and takes into account water intake, surface texture, soil
permeability, water-holding capacity, and tendency to seal under rain-
drop action. The effects of land use, management, slope, vegetation
cover, etc., are not considered. The result of this classification is
the following six hydrologic soil groupings:
Group 1 is barren rock, shale, and badlands--included are barren and
seini-barren badlands, high Alpine mountain areas, and impermeable saline
alkaline soil areas. The rate of infiltration is less than 0.05 in/hr.
Group 2 is clay, and claypans, which are slowly permeable and have a
slow intake rate of 0.05 to 0.2 in/hr.
Group 3 is moderately fine textured, and has moderately slow permeability
and intake of 0.2 to 0.6 in/hr.
56
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Group 4 is medium textured, moderately permeable, and has a moderate
intake of 0.6 to 2 ] n/hr.
Group 5 is moderately sandy, has moderately rapid permeability and an
intake of 2 to 6.3 in/hr.
Group 6 is sand and gravel, rapidly permeable, and has a very rapid
intake--over 6.3 in/hr.
These types of data are available from the U.S. Department of Agriculture,
Soil and Water Conservation Research Division, The Forest Service and
the Soil Conservation Service. They are also available from the U.S.
Army Corps of Engineers and the U.S. Department of Interior, Bureau of
Reclamation. Data from these sources have been summarized in the Missouri
Basin Inter-Agency Committee report, and are presented in our data re-
port.
Another type of hydrologic soil grouping which has also been developed
by SCS is the soil infiltration index. The computed data (in inches)
were based on information on soil type, cover, and agricultural practices,
and have been obtained for most parts of the basin. These values were
used in system studies of streamf low characteristics of various drainage
areas by the USGS. These data also appear in our Data Report.
Land use data :
Land use in the Missouri River Basin can be divided into the following:
agriculture, recreation, urban, military, grassland, wildlife areas,
forest areas, and water areas. The sources of land use data include the
Bureau of Census, the various branches of the U.S. Department of Agri-
oilture-- the Statistical Research Service, the Agricultural Stabilization
and Conservation Service, the Forest Service and the Soil Conservation
Service. Each state also has a epartment of Agriculture which collects
land-use data. The Department of Interior has land use data in several
branches, such as Bureau of Reclamation, etc.
The major source of land use information for this program is the Census
of Agriculture for 1969. This census includes detailed land use data
by county for each state of the union. The 1969 Census for each state
in the basin has become available recently on tape and includes 1964
statistics for comparison with 1.969 figures. For each county, data in-
clude information about all farms, such as the number, the acreage in
the farms, and the land use. The census includes the size of the farms,
the farm operator tenure, the farm income and sales, farm production
expenses, machinery and equipment, amounts of livestock and poultry and
- 57
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crops harvested. It also contains some data on irrigation and artificial
drainage as well as the agricultural chemicals and commercial fertilizer
used. The crops are divided into corn, sorghums, hay, feed, field seeds,
strawberries, anall grains, soybeans, peanuts, potatoes, tobacco, cotton,
vegetables, tree fruit and grapes, nursery and greenhouse products, and
forest products. All farms with sales over $2,500 I year were polled,
and 507. of farms with sales less than $2,500/year were polled to obtain
this information. Over a 907. return was received.
The other important source of land use information is the Conservation
Needs Inventory. Two Conservation Needs Inventories have been published,
one for 1958 and the other for 1967. The 1958 inventory contains data
on land use and conservation treatment needs by county for the entire
United States. Statistics include cropping patterns with yield and pro-
jected yield by resource area, soil resource group, and subbasin. The
inventory information is correlated with the soil classification data
in the Missouri Basin and the correlations are on tapes. The inventory
includes all land use except built-up areas and land owned by the federal
government.
The 1967 inventory includes all acreage except urban and built-up acres,
and all land owned by the federal government other than cropland operated
under lease or permit. The inventory was developed from basic data re-
garding present acreage in major uses and acreage of each land use clas-
sified by physical problems affecting use. Estimates of needs for con-
servation treatment for each major land use were based on the acreages
and conditions of the land or the vegetation cover as of the crop year
1967, with due regard to locally applicable information and experience
in solving conservation problems.
The 1967 Conservation Needs Inventory is available on magnetic tape for
all states in the basin. On a county basis, the data include land
capability class and subclass, information on acreage of corn and sorghum,
other row crops, close grown crops, summer fallow, total field crops,
rotation hay and pasture, hay land, conservation use only, temporarily
idle cropland, total tillage rotation, orchards, vineyards and bush fruit,
open lands formally cropped and total cropland. Most of these data are
based on a 27. sample.
Still another source of information on crops and land use is the Agri-
cultural Stabilization and Conservation Service of the USDA. They have
a summary by county of all farms which participate in the ASCS program.
This summary represents over 75% of all farms. Through the year 1970,
the data cover wheat, cotton, and feed grains, but the 1971 report will
58
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be more extensive--going into more detail and including other grains.
The 1971 data will include some information on nonparticipating farms.
ASCS uses magnetic tapes for storing their data.
Another data source is the detailed data sheets compiled by counties
for the Land Resource Appendix of the Missouri Basin Comprehensive Frame-
work Study. These working papers include land use data for public lands
which are not available in the Conservation Needs Inventory. The papers
are of value because they present a single source of data on use of these
public lands. These data are located otherwise in several government
agencies having legal jurisdiction over these lands, i.e., Bureau of
Land Management, Forest Service, Bureau of Reclamation, Corps of Engineers,
etc. We have these papers in our file.
A final source of land use information is state cropland and livestock
data. All states in the Missouri Basin provide an annual or biannual
agricultural census and keep a record of major crop acreages and of the
number of livestock on farms on a county basis. For most Missouri Basin
states, these reports have been compiled for many years (10 to 40 years).
While these data are not as detailed as the Conservation Needs Inventory
or Census of Agriculture, they represent an annual data source.
Because of their easy accessibility, and nature of the data, the Census
of Agriculture (1969) was chosen as the primary source of land use data
reported in this program. Data extracted from this document are listed
below:
County land area
Total crop area
Pasture grazing
Woodlands
Irrigated
Corn
Sorghum
Wheat
Other grain
Soybeans
Hay
Cotton
Peanuts
Tobacco
Potatoes
Vegetables
Berries
Orchards
Other crops
Cr eenhous es
59
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From the data tapes of the Census of Agriculture, we also obtained data
on fertilizer use and livestock.
Topographic data :
The topographic information needed for this study includes items such
as tributary location, drainage area, river mileage, river channel slope,
drainage area slope, etc. For drainage area and river mileage, we were
able to obtain data from the following agencies:
MBIAC : It has published a report entitled ItCondensed Tabulation of River
Mileage and Drainage Areas,” in 1965, which lists for approximately 900
gauged tributaries: (1) gauge location, (2) mileage above the mouth,
(3) noncontributing area, and (4) drainage area. This publication is a
supplement to the 1949 edition of the Corps of Engineers’ “Missouri River
Basin - River Mileages and Drainage Areas.”
USGS : Gauge locations and drainage areas are always listed in its annually
published “Water Resources Data.”
The slope data are probably the most important topographic data for this
program. We found that river basin slopes are available from USGS for
a very small number of areas in the basin, and were restricted to small
tributary areas only. The amount of data was so small that it did not
warrant collection. The 1967 Conservation Needs Inventory has data of
“Slope Percent Classtt for each county, which relate to our needs. We
have these data compiled in our Data Report.
Ideally, we need drainage basin slopes for all 233 watersheds selected
in this program. However, basin slopes are not available in any publica-
tion or from any agency, and would require a tremendous effort if esti-
mated by ourselves.
However, our literature survey indicated that main channel slopes may
be used in place of basin slopes. Empirical evidence shows that steep
ground slopes are expected to correspond with steep channel gradients;
and shallow ground slopes with low stream channel gradients. This re-
lationship has been developed by Horton,* who used the definition:
Slope 1 atio = Channel Slope
Ground Slope
* Horton, R. E., “Erosional Development of Streams and Their Drainage
Basins; Ilydrophysical Approach to Quantitative Morphology,” Geol. Soc.
Amer. Bull. , 56, 275 (1945).
60
-------
Strahler* found that, in general, ground slope varies somewhat less than
the first power of channel slope, and that the slope ratio tends to
range from one-fifth or one-fourth for low values of slopes, up to about
one-half for higher values.
We have, therefore, decided to include main channel slope data in our
data report. Some of these data are available from the USGS and the
Corps of Engineers, and some of them have been estimated from the USGS’s
topographical maps.
Livestock Data
Data on livestock are available semiannually from the State Agriculture
Boards on a county basis. Similar data are also reported in the Census
of Agriculture (1969), although less frequently (at 5-year intervals).
Due to the computer accessibility, the latter was chosen as the primary
source of livestock data in this report. Types of data reported are:
Total cattle
Cattle feed].ots
Cattle in feedlots
Total hogs
Hog lots
Hogs in lots
Sheep
Horses and ponies
Total chickens
Chicken lots
Chickens in lots
Sizes of commercial feedlots have been arbitrarily defined as:
Cattle 500 +
Hog 200+
Chicken 1,600 +
Fertilizer Use Data
The Census of Agriculture reports the total tonnages of fertilizer and
lime on a county level. The lime data presented in our report were
* Strahier, A. N., “Equilibrium Theory of Erosional Slopes Approached
by Frequency Distribution Analysis,” Am. J. of Sci. , 248, 673 (1950).
61
-------
retrieved from this document. For informatioi about fertili er use, we
need more specific information than what is available from th Census,
We have beer able to obtain desirable data either directly frqm state
agriculture agencies, or from the combination of several information
sources.
The fertilizer-use-data compiled in this program are given as to total
nitrogen (N), phosphoric pentoxide (P 2 0 5 ), and potash (K 2 0) tonnages
for the first and second half of 1969. For Colorado 1 Iowa, Kansas,
Missouri , and Nebraska , whjch report their county iisage data semiannually,
transformation of their data into the desirabl format was straightfor-
ward. For Minnesota, Montana, Wyomi g , and North and South Dakota ,
which report data only on a statewide basis, two 4Ufe en’t methods we e
adopted to calculate N, P, K data for each county.
North and South Dakota : Fertili er usage in t rms oç N, P. iç or each
county were calculated based on the acreage of iajor crops in the county
and application rate of N, P, K to these crops. qounty crop çLata of
1969 were obtained from the reports of the State Department of 4gricul-
ture, while the fertilizer application rates to these crops were sup-
plied by persons who have the best know .e4ge ot their state i this regard.
They are:
Mr. E. H. Vasey
Extension Soils Specialist
Cooperative Extension Seri4ce
Department of Agriculture
North Dakota University
Fargo, North Dakota arid
Mr. Paul Carson
Soil Testing
Plant Science Department
College of Agriculture and Biological Sciences
South Dakota University
Brookings, South Dakota
Minnesota, Montana, and Wyoming : These three states h ve the tonnage
data of N, P, K sold for the first and second half o ] 969 on a state ’-
wide basis. To arrive at fertilizer usage jn each county, calculations
were carried out as follows:
I. County data from the 1969 Census of ‘Agr c ilture were used to deter-
mine the percentage o the state’s total fertilizer which wap applte in
each county.
62
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2. This percentage was used in conjunction with the state report of
fertilizer usage.
The 1969 Census reported total tons of material in some cases and tons
of plant nutrients (N, P, K) in others.* The method we adopted for these
three states may be more accurate because we know the tons of plant nu-
trient sold.
Pesticide Data
Pesticides are usually grouped into three major categories: herbicides,
fungicides, and insecticides.
Herbicides are used primarily for weed control on crop and pastureland
and for brush control on range and pastureland. The Conservation and
Land Use Division of ASCS reports the acreage of rangeland, cropland and
pastureland treated for control of brush and weeds in each state. This
report deals with cost-sharing programs, and is estimated to cover 90%
of all herbicide use. During 1969-- the last year for which data are
presently available--some 2.4 million acres were treated for weed and
brush control. Of this total, about 157 .were in the 10 states included
in the Missouri River Basin, with about 8.57. of the total U.S. Herbicide
consumption actually taking place within the basin.
Fungicides are used in substantial quantities for a variety of purposes,
although farmers are depending less on chemicals and more on resistant
varieties for control of plant pathogens. Some of the major uses of
fungicides are for algae control (copper sulfate) and as wood preserva-
tives (creosote and pentachlorophenol). The use of these chemicals is
not controlled, and reports of their application are not required. Con-
sequently, only the total quantity of various types of fungicides con-
sumed in the U.S. is available. If we allocate usage according to the
total land in farms, the Missouri River Basin is estimated to account
for some 23% of the total domestic use of fungicides.
Insecticides are the most controversial of the pesticides. As in the
case of herbicides and fungicides, the total U.S. production and con-
sumption are reported annually by ASCS, although the methods, extent
and timing of use are not known for any specific area. About 26.57. of
the total U.S. insecticide use is believed to occur in the Missouri River
Basin. This estimate is based on the area in crops and the insect con-
trol reconmiendations published by the various state agencies.
* Personal comnuinlcation, Mr. Larry Bourret, Resource Management Special-
ist, Wyoming Department of Agriculture, Cheyenne, Wyoming.
63
-------
No reliable data are available on a county basis regarding pesticide
applications, other than those published in the Census of Agriculture.
The Census reports expenditures of each farmer on a county basis for
pesticides for various uses, and the number of acres on which the pesti-
cides were used. However, these data do not constitute the information
directly related to our needs.
For the estimation of pesticide applicatiqns on a county level, two
different methods were considered.
1. A mail survey of county extension agents to determine local pesti-
cide application practices.
2. The following equation, developed from data on a national level and
based on typical farm practices for different types of crops:
P = 6.5 C + 2.0 S + 1.0 W + 4.0 B + 0.2 H + 8.0 M
where P is the total pesticide application for a county, state or
region, ecpressed in pounds; C represents the acres of corn harvested
within that area; S , the acres of sorghum harvested; W , the acres of
wheat harvested; B , the acres of soybeans harvested; H , the acres of
hay, grass and pasturelands; and M , the total acres of all other
farm crops harvested. This equation has been discussed in more detail
in the Phase IA Final Report of this project.
Though easy to handle, the pesticide equation described above does not
tak? into account the heterogeneity of economical, physical, and land
use characteristics of subdivisions in the basin. We, therefore, used
a uailed survey as the primary source of pesticide application data on
a county basis.
The survey questioanaire for this purpose was prepared and sent to all
county agents in the basin. The data surveyed include the quantity of
herbicides and insecticides used on crops and on pasture, the most used
pesticides in their county, the seasons those substances were applied,
and the extra usage of particular insecticides during the past 10 years.
Of the total 409 questionnaires mailed, 114 were returned with the re-
quested data, representing 30% coverage of counties within the basin.
Most indicated at least 307. accuracy in their estimations.
64
-------
These 114 counties (Figure A-i) which responded to our survey are spread
randomly but uniformly over the entire basin and should, therefore, be
representative of agricultural practices throughout the Missouri River
Basin.
For these 114 counties, the pesticide use data that appear in the data
report were directly obtained from the survey. For those counties
which did not answer the survey, usage data were estimated by using the
insecticide and herbicides application rate per acre of crop .and. The
pesticide application rates were calculated by averaging the application
rates of the counties in the same land resources subregions,* which re-
turned the survey. This figure was multiplied by the total cropland
acreage to obtain the amount of pesticides applied.
From the results of the county pesticide survey, we reached a conclusion
that 857, of the herbicides and 99.67, of the insecticides were applied
to the cropla.nd. We, therefore, chose to estimate pesticide use based
on crop acreage alone.
* U.S. Department of Agriculture, Soil Conservation Service, Agri-
culture Handbook 296 (1965).
65
-------
Figure A-i - The Result of Pesticides Use Data Survey. The dotted counties are those
that answered the questionnaires.
a’
0’
L
-------
t 5 ELE A-2
DArA Lt!t,Ofl 4flfl Zafl’Y
Infostatien
Water Quality Records,
Statistics
Ikily Records
Water Q ality Records,
Statistics
Statistics of Water Quality
Stations
Waste frealusont Pacilities In-
ventory and Facil ltiea Needs
Statistics of Jaier Quality
Stations
Population Service, &ipply Source
Type of eatnent, Design Ca-
I)acit3’, daate (PR;, Strews Clas-
s i t icatton
Daily Records of the azepended
Sediment l,osd, Statistics
Drainage Area, Aversge Sediment
for the Drainage Area and Per
Unit Area, Soil Classification
Daily Records of the Suspended
Sediment losd
A Suonisry of General Chemical
Quality of Surface Water snd
Relation of Quality Chsracteris-
tics with Geologic, TOpographic,
Influences
Water Quality Related to Fishery
Pollutin Data, Stream Water
Quality Data, Inventory of
Treatment Facilities
Stream Mater Duality Near
the Theaheent Plant
Moderate Light Project Reports
Moderate — Yes
Unknown Sample Reports from
K C fltada, Kansas.
Bi. Blue hver West-
side Mtss,,ar
Analysis r each
Snb-bas n
Format
0 ’
•- 1
Data Coverage
EPA
STORET —
Natoowide
S
“Water ftesources Data”
Pert h -
Nationwide
URG E
IJIGS Magnetic Storage
Nationwide
GWDC
Tables and Magnetic
Storage
Nationwide
EPA
S f 12’ ?
hattoawide
118 15
“National Reference List
of Water Quality Stations”
Nationwide
State Dovernts
“Inventory of Public Water
and Waste ‘freathient Pa-
c i ii ties’
Each State
Corps of Reginesra
“Suspended Sediment in
the Missouri River
Baainwide
MBIAC
“Sedimentation” (9 parts)
Basinwide
Corps of Engcneera
C,E. Magnetic Tapes
Baainwid.e
!Uascur dater
“Chemical Quality of
State of
Pollution Board
Missouri Surface Water”
Missouri
dtjo’sing iame and
Reports on Water
Some Streams
“ish Coneission
Quality Studies
and Lakes
in Wyoming
r tra :tate
“hater P—hutton in
Two Major
‘i
Montana’ (2 Parts)
l 5R-l9eO
Basins in
Montana
tier arc datte
“)peratin,r Records”
Basi’iaide
:reaiaent -e.nta
Aonunt
as ,fand Puma.rke
Extensive
All Stations in Basin,
Statistics
Extensive
All Basin States
Extensive
None
Extensive
Basin
Extenstve
Basin
Extensive
Water Tears 1 5 11-1 512
Extensive
Kansas, Missouri, So’..tn
Dakota, lortn Inkota
Wyoming, Montana, Iowa
Moderate
- Basin, 1949-1964
Modarata
Yes
Moderate
None
-------
ThBLS A—3
DATA CAflZOfl : MYDROSCG!C
Aeency Forent Data Coverage — — Information Awnnt On Hand Remarks
USGS “Water Resources Data,” Nati ide Drainage Area, Daily Dischsrge Extensive All Basin States
Pert I Statistics
USGS USGS Magnetic Tapes Nationwide Daily Discharge, Statistics Extensive None The Rest Hydrologic
Data Storage Avail-
able
EPA STORE? Nationwide Daily Discharge, Statistics Moderate All Stations in Basin.
Statistics
River FOrecast SF0 Magnetic Tapes Basin-wide Gage Bating Curve, I idrograph Extensive None
Center, W.B.
USGS “Travel of Salutes in the Prom Yankton, Time of’ Travel of Solutea, Moderate Yes
Lower Missouri River” South Dakota, Dispersion and Dilution of
to St. Louis, the River
Missouri
USGS ‘tow Flow Characteristics State of’ Statietical Study of tow-Flow Extensive Yes
of Missouri Streams” Missouri Information
C D
MBIAC “Stream Flow Character- Basinwide Stream Flow Duration, Mi Extensive Tee
istics in the MBR,” and Low-flow Variation Char—
9 Parts acteristics
USGS ‘ valuation of the Stream The Basin Stream Flow Characteristics, Extensive AU. the Basin States Except
flow Data Program” States Basin Characteristics, and Minnesota
Climatic Characteristics,
and Relation Aenng Thea, on
the State Basis
Bureau of Tables, a List of Water Basinwide The Stations for Which the Moderate Yes
Reclamation Stattons Data Have Not Been Published
USGS Tables Basuiwide River Depth, flow Velocity Extensive None Not Published
Tables, and Magnetic Stor- Nationwide Statistics of Surface Water Extensive -Basin
age, “Catalog of InfOrmation Stations
on Water Data”
Corps of Maps, “Flood Plain In- Nationwide Water Surface Profiles; Extensive Johnson County, llama
Engi-eers fornation” Flood Plain Cross Sections
-------
BLE A-4
MTA CAWLOFI; CLIM&TOLO IICAI
Data Coverage Information A m aunt On Hand Re 1rarks
RCAA “Cl lratological lhta” Nattons,tde (i) Hourly Precipitation end Extensive 1966-1571 Excellent Data
Tar. les Temperature for the First AU Basin States
Order localities;
(ii) Daily Precipitation, Daily
Mexiaiss and Mlnlrasn T era-
bite for all Stations, Prow
Fail end Daow on the Ground
for S Stations
(iii) Monthly S ry; Yearly
Busrj.
5DM SCM Megnetic Tspea The Basin Precipitation Data Extensive Yea
States
MBIAC “Selected Climatic ltpa” The Baste (i) Normal Annual Total Moderate Ye,
States Precipitation
(ii) 2lrHr, 25-Year Rainfall
(iii) Monthly Distribution
of Precipitstion
(iv) Temperature Distribution
- ‘ by Months
0” (v) Prevailing Direction and
S O Stan Speed of Wind
(Monthly), etc.
MBIAC “Mclti-Annual Precipitation Sasiowide (i) Adjusted Average Annual Extensive Yes Analysis for Each
Probabilities” (S Parts) Precipitation Dab-basin
(ii) Multi-Annual Preciptta-
tion Probabilities
(iii) Annual Run-Off vs. An-
nual Precipitation, etc
I€IAC “Analjsis of Freeze-Thaw Basinwide (i) Preeze-Up and Break-Up Moderate Yes
Dates” Dates for )tjor lakes and
Reservoirs
(ii) Average Number of Days
Per Winter Season in a
Freeze Condition
(iii) Average Data of Com-
plete Break-Up, etc
‘Evaluation of Stream floe Bastowide (t) Mean Annual Precipitation Moderate All Basin States Br- Data were used t’ C,rrelats
Data Program” (ii) 2-Year, 24-dr, Precipt- cept Minnesota with Stream floe Data
tation Intensity
(iii) Mean Minimum Jsnua
Temperature etc
CiAA “ at’ Cs trequencj Nation.ide Rainfall Frequencj Moderste Tea
-tIe: c’ iou u.s.” and lotenait l
-------
rAPT -F -5---
DaTA CAThGORY: LARD USE
Pormat Data Coverage Information Amount On Rand markz
Soil Conservatior Tanles ‘Conservation Nationwide On t im County Basis - Land Use All Basin States
Service Needs Inientory,” 1958- and Conservation Trea nt
1360 Needs; Statistics of Cropping
- Patterns with Yield end Pro-
jected Yteld b7 Source Area.
- Soil Resource Groups, and
Sub-basins; Public Lande Are
Not Included.
IGIAC Magnetic Tapes Baainwide The Above Inventory Plus Soil £çs
Classification
Soil Conservation Tables. Magnetic Tapes Nationwide On the County Basis - Acreage Extensive All Basin States
Service “Conservation Needs In- — - in Major Uses end Acreage of
ventory” 1966-1967 Each Lend Use Classified by
Thysical Problems Affect ng
Its Use, Estimates of Needs
for Coneervation Treathent.
Public Lands are Not Included.
0 -
Bureau of tne Tablee, Magnetic Tapes, Nationwide FOr Each County, Number of Farm Extensive Magnetic Tapes, All
Census “Census of Agriculture” Land in Fkrms, the Lend Use; Basin States
1363 Livestock and Ibultry, Crops
Harvested, Agriculture Chemical
Used, Etc. Data Include 1964
Statistics for Comparison With
1969 Figures .
MBIAC Working Papers “Land Basinwide On tim Sub-Basin Basis - Areas Yes
Resource Appendix” of Land and Water, Land Owner-
ship, Land Uses
State Board of Tables. “State Crop- State Major Crop Acreage, Production Extensive All Basin States
Agriculture land and Livestock Data and Livestock Data, by Except Minnesota
Data” (Annual Publication) County
USDA (ABCS) Tables. Magnetic Tapes Nationwide By County and by State, Netail Extensive 1970, Tables by State
Feed Grain and Wheat Crop Acreage
Programs.” 1970, 1971
-------
______ TA CATEGOR - TOPCGRAFII ;
fl1E A.5
Toograph teal Maps
“Miso’jri River Basil
Condensed Tabulation or
River Mileage and Drain-
age Areas”
“Evaluation of Stream flow
Data Program”
_____________ Information
Elevatio’i Contour
Scale Interval
Natinr.i e 1:24,000 10 ft - -
1:250,000 50 ft
1.500,000 200 ft
—Sasnvite Listing 900 Tributaries for:
(a) Gage Locat:ons
(ii) Mileage Above the Mouth
(iii) Noncontributing Area
(iv) Drainage Area
Nationwide Basin Characteristics in Terms
of Drainage Area, Main Channel
Slope, Forest Cover, Soil In-
filtratio’i Index, Etc.
Mountainous
Area in
Colorado,
Wyoming and
Montana
Elevation vs. % of Area Below
or at Some Elevation
Moderate Al ], Basin States
Thccept Minnesota
Batensive Yellowstone River,
Big Horn River
Mire Letailed Information
Availanle from the State
Agencies
Correlation of Stressi
Flow Characteristics with
Basin and Climatic Charac-
teristics
USGS
u&;s
Table, “Slopes of Basil
Drainage Areas”
Tables
Basinvide For Drainage Area of Lees Than
10 Sq Mi
Basinwide Drainage Area, Cross-Sectional
Area of River Stream, River
Body Slope
Moderate None
Thctensive None
Information for Gaged and
iineaged Sites. Not Published
Soil Conservation
Service
!tgnetic Tapes “Conserva-
tion Needs Inventory”
1966-1967
Nationwide Slope Forcent Class of County bctensive Yes
Format : Eata Coverage
Agetcy
US GS
K IAC
USGS
River FOrecast
Center, NOAA
Remarks
Amount On Hard
Exteneive Clay County, Kansas
AU Basin States
Moderate Yes
“Area - Elevation Maps”
-------
TABLE P-i
______ Data Coverage Information
Generalized Soil Infiltration
Grounings, Infiltroseter
Field-Plot Data Infltrat :,r
Estimates from Rydrograph and
Storm Analyses, and Infil:rati:n
Values Used in Design Fltod
Studies
Basinwide Soil Classification CorreleLed
With the Land Use Survey Froa
the Conservation Needs Inventor’g
Nationwide Soil Maps, Soil Laboratory Data,
Land-Capability Interpretatz ons,
Soil-Woodland Interpretation
Engineering Use of Soil, eto
Tables, Maps Great Plains Soil Classification Based on
States Sediment Yield
Tables Basinwide Soil Index b,r Watershed
ttgnetic Tapes, “Conserva- Nationwide County Soil Erosion Class
tion Needs Inventory”
(1966-1967)
The Mist Cusprehensive
and Systnastic Soil Classi-
fication data Available
for MBR (According to
Mr Putman of MBIAC)
Extensive None (under Capable of Extend:ng to .it
preparation) Basin States
Extersiye AU Basin States
Extensive AU sin States
, Basinwjda
a,-
DATA CATh(YJRY: 50Th 2ASSIs IrrTo.,
A gency - i:rnat
MBIAC TaUss and ‘!ats
MBIAC Magnetic Tape
USDA (ASCS) “Soil Survey”
USDA (ASCS)
USGS
Soil Conservation
Service
1.)
Fusar ics
Amount c _ 1a’ -
Materste Yes
Extensive Yes
Extensive “Soil Sr sy” of
Shawnee County, Kansas
and “Published Soil
Survey of the U.S
-------
TABLE A-S
DATA C TROItf: LIVESTOCK
Pgency Format Data Coverage Information Ai unt On Hand Remarks
State Board of Tables, “State Cropl.and State Number of An imals (Cows, Beef Extensive All Basin States
A g rtcu lture and Livestock Data” Cattle, Hogs, Sheep, Pbultry)
of Each Type by Counties,
Published Annually
USDA “Agricultural Statistics” Nationwide Same, by Stetes Yes
Bureau of Census Tables, )tgnetic Tapes Nationwide Number of Animals, Inventory Extensive Sgnetic Tapes,
“Census of Agriculture” and Sales by County AU Basin States
1969
-------
TABLE A- S
DATA CA’ltGOfff’ PS3TICIJE3
Format Data Coverage Infcrmation Amcant On Hand Remarks
5:; ‘;s:s) Pest:cide Renew” ilstion’.ide Gross ran ai Prodactir o ’ Yes (1970)
Major Her :c des, ci es
and }bst c des; Some Data :n
State Level
Agr:czlr.ral Statistics Netionjide Total ?rod,.cticr. b T ’pe Yes
State Extezsiot ‘Livestock Ir’sect Co trc1 Basinwide Recon ended Applicaticri Rates Yes (Kansas)
Ser’nce Recommer,dations” ard Practices of I’secticides
on Livestock
State Extenz:c,’- F:eld Crop Thse:t Control
Service Reccendations” Stete Recou endedApplicattcn Rates Yes ( Kansas)
and Practices of Insectic:des
a - on Field Crops
State Exte sio’ ‘Chemical Weed Control State Recoinended Application Rates Yes (Kansas)
Service in Field Crops’ and Practices of Herbicides
on Field Crops
State Exte;s c-, “Chemical Weed Control State Recommended Application Rates Yes (Kansas)
Ser-nce in Horticult ural arid and Practices of Herbicides
Fcrestr 1 Plants” on Horticulture and B.brestr: ’
Plants
USDC, Bureau of ‘Census of Agriculture” Nationwide Cost of Chemicals Used on rtgnetic Tapes,
the Census Venous Crops end Acres Treated All Basin States
for Insect and Weed Control,
by Counties
Countj Extension “131 Qiestionneire” Bssinwide Qientity of I sticides Used in Extensive One third of
Service County, Names of the Most-Used Counties in the
&sticides, and Events of Heavy Basin
&st Infestations
-------
TABLE A- b
DnA ZEGCPY: FERTILI R
Forcat Data Coverage Inforaation Amturt On Rand
State 3c,ard :f “Fertilizer Tonnage Easinwide Consumption By County or State All Basin States
;ri:an.re Pepcrt” Spring and Fall .
“Agricultural Statistics” Nationwide Consumption by State of Yes
Four Major Categories of
Fertilizers
USDA CASTS) “The Fertilizer Supply” Nationwide Gross Production by ‘pe, Yes
of Major !L pes of Fertilizers
-4
t i ! USDC, ?ureau of Tables, gnatic Tapes Nationwide Cost and Ai unt of Corercial Magnetic Tapes,
the Census “Census of Agriculture” Fertilizers and Lime Used and All Basin Stetea
1969 Acres on Which Used, by County
-------
ThELE A-fl
; ci cLn::sI: : 2o;F ;.FE:
Fon at Data ovsra e Informat±on ______ - s r i Remarks
Bureau of tne Censt.s ttgnetic Tapes, Nationwide Population, Area, Income, Extensive Tatlas
Tat J.es end Other Economic Infor-
mat ion
U S Water ?esouces Tables, “Economic Nationwide Population, Income, Fhploy- Extensive Yes
Council Activity in the U.S. ment, and Other Economic
by Water Resources Information
Regions end Subareas,
Historical, and Pro-
jected 1929-2020.”
a ’
-------
APPENDIX B
INDIVI DUAL CONTACTS
77
-------
G.
R.
Coop
Philips
R. Johnson
Fl. F. Mondschein
V. Hagarty
W. D. Haggard
Balke
H. Gunewald
Walter Pool
R. W. Eikleberry
J. Karabatsos
E. Burnett
Organiz at ion
MBIAC
MBIAC
Missouri River Basin
Planning Office
Corps of Engineers
Corps of Engineers
Washington, D.C.
Lincoln, Nebraska
Lincoln, Nebraska
Name
Mr. N.
Mr. K.
Mr. P.
Barbarossa
F. Myers
L. Harley
City, State
Washington, D.C.
Lincoln, Nebraska
Omaha, Nebraska
Omaha, Nebraska
Kansas City, Missouri
Kansas City, Missouri
Asheville, North
Carolina
Kansas City, Missouri
Washington, D.C.
Kansas City, Missouri
Billings, Montana
Denver, Colorado
Washington, D.C.
Lincoln, Nebraska
Mr.
Mr.
Mr.
Mr.
Mr.
Mr.
Mr.
Mr.
Mr.
Mr.
Mr.
Mr.
Mr.
Mr.
Dr.
Mr.
Mr.
Mr.
Mr.
Mr.
Mr.
S. Conger
P. Linderstruth
D. B. Parke
J. Keyes
P. Gibbs
I. A. Watson
J. M. Ingles
R. D. Hockensmith
J. Putman
NOAA, River Forecast
Center
NOM, U.S. Department
of Commerce
NOAA, U.S. Department
of Commerce
EPA, STORET
EPA
Bureau of Reclamation
Bureau of Reclamation
USDA
Economic Research
Service
SCS
U.S. Forest Service
Scs
78
-------
Name
Mr. D. White
Mrs. M. Mason
Mr. J. Towler
Mr. J. T. Breen
Mr. E. A. Moulder
Mr. Grogil
Mr. S. W. Wiitala
Mr. q. W. Lane
Mr.,fIP. Jordan
Mr. A. M. Diaz
Mr. E. R. Hedman
Mr. C. R. Collier
Mr. A. Homyk
Mr Jeffrey
Mr. G. Pike
Mr. K. A. Mackich n
Mr.IR. C. Williams
Mr. J. E. Powell
Mr. 0. Larimar
Mr, R. Cushman
Mr.}1. D. Edwards
Mr. F. H. Pauszek
Mr. Leman
Mr. K. Webb
Organization
EPA
Iowa State University
ASCS
BMreau pf the Census
USGS
USGS
USGS
USGS
USGS
USGS
USGS
USGS
USGS
USGS
USGS
Division of Game,
Fish and Parks
Water Pollution
Control Commission
79
City, State
Arlington, Virginia
Ames, Iowa
Kansas City, Missouri
Suitland, Maryland
Denver, Colorado
Iowa City, Iowa
Lawrence, Kansas
St. Paul, Minnesota
Rolla, Missouri
Helena, Montana
Lincoln, Nebraska
Bismarck, North
Dakota
Huron, South
Dako t a
Cheyenne, Wyoming
Washington, D.C.
Denver, Colorado
Denver, Colorado
-------
Name Organization - - City, State
Mr. F. D. Rolf Colorado Crop and Livestock Denver, Colorado
Reporting Service
Dr. G. M. Van Dyne Colorado State University Fort Collins, Colorado
Colorado Soil Conservation Denver, Colorado
Service
Dr. Gakstatter The Stat;e Hygienic Des Moines, Iowa
Laboratory
Mr. R. Schliekelman Environmental Engineering Des Moines, Iowa
Service
Mr. R. H. Sutherland Iowa Crop and Livestock Des Moines, Iowa
Reporting Service
Iowa Soil Conservation Des Moines, Iowa
Service
Mr. 3. Burns Department of Health, Topeka, Kansas
Mr. J. Stoltenberg Sewage, and Water
Treatment
Mr. R. Schoonover Kansas Forestry, Fish and Pratt, Kansas
Came Commission
Mr. J. Reh State Soil Conservation Sauna, Kansas
Service
Mr. F. Mosier ASCS Manhattan, Kansas
Mr. F. Hoover
State Board of Agriculture Topeka, Kansas
Mr. J. E. Pallesen Kansas Crop and Livestock Topeka, Kansas
Reporting Service
Mr. Steps State Water Commission Topeka, Kansas
Dr. H. Jacob Kansas Water Resources Manhattan, Kansas
Research Institute
Mr. R. Hyde State Extension, KSU Manhattan, Kansas
(Agronomist)
80
-------
Organization
State Extension, KSU
(Agronomist)
Entomologist
Forester
Department of Agronomy,
KSU
Environmental Health
Program, KU
Clay County Extension
Johnson County Commission
Johnson County Extension
Miami County Extension
Water Department
Superintendent
Board of Public Utilities
Water Pollution Control
Plant
Water Department
Water Works Department
Water Department
Municipal Service
Soil Conservation Service
Missouri Water Pollution
Control Board
Missouri State Department
of Health
Missouri Crop and Livestock
Reporting Service
Manhattan, Kansas
Lawrence, Kansas
Clay Center, Kansas
Olathe, Kansas
Olathe, Kansas
Paola, Kansas
Parsons, Kansas
Kansas City, Kansas
Kansas City, Kansas
Ottawa, Kansas
Leavenworth, Kansas
Topeka, Kansas
Russell, Kansas
St. Paul, Minnesota
Jefferson City, Missouri
Jefferson City, Missouri
Name
Mr.
L.
Brooks
Mr.
J.
Geisler
Dr.
0.
V. Bidwell
City. State
Manhattan, Kansas
Mr. F. Bieverly
Mr. W.
Mr. R.
Mr. T.
Mr. E.
Mr. J.
Mr. A.
Mr. M.
Campbell
Davis
Hall
J. Sisk
N. Lackey
W. Rumsey
Cailtuex
Mr.C.C.
Mr. A. C.
Mr. C. E.
Mr. G. J.
Nut t
Lee
Reynolds
Boyd
Mr.
Mr.
Mr.
C. Stiefermann
J. K. Smith
D. Sayre
Mr. D. W. Barrowman
Columbia, Missouri
81
-------
C. H. Taylor
R. Jackson’
D. Taylor
Missouri Soil Conservation
Service
Capital City Water Company
Water Treatment Plant
Kansas City Water Works
Chillicothe Municipal
Utilities
Sedalia Water Department
Pollution Control Depart-
ment
Minnesota Crop and Live-
stock Reporting Service
Minnesota Department of
Agriculture
State Department of Health
State Statistical Office
Department of Game, Fish,
and Parks
Soil Conservation Service
Agricultural Experiment
Station, Montana State
University
Economic Research Service,
Montana State University
Nebraska Water Pollution
Control Council
Department of Game, Fish,
and Parks
82
City, State
Columbia, Missouri
Jefferson City, Missouri
Jefferson City, Missouri
Kansas City, Missouri
Chillico the, Missouri
Sedalia, Missouri
Kansas City, Missouri
St. Paul, Minnesota
St. Paul, Minnesota
Name
Mr. J. V. Martin
Organization
J. H. Fondreu
C. P. Wood
H. Marks
C. Bennett
Mr.
Mr.
Mr.
Mr.
Mr.
Mr.
Mr.
Helena,
Helena,
Helena,
Mr. L. M. Lehn
‘‘Mr. D. G. Williams
Mr. D. L. Herbert
Mr. Whitney
Dr. C. Smith
Dr. D. Larson
Mr. R. Langemeir
Mr. Foster
Montana
Montana
Montana
Bozeman, Montana
Bozeman, Montana
Bozeman, Montana
Lincoln, Nebraska
Lincoln, Nebraska
-------
Name
Mr. N. D. Belier
Mr. P. Carson
Williamson
Mr. D. Dexter
Mr. L. J. Hoffman
Mr. R. C. Kronenberger
Mr. L. Bourret
Professor H. W.
Hough
Organization
State-Federal Division of
Agricultural Statistics
Soil Conservation Service
North Dakota State
Department of Health
North Dakota Crop and
Livestock Reporting
Service
Soil Conservation Service
State University Station
North Dakota State University
State Department of Health
South Dakota Crop and
Livestock Reporting
Service
Soil Conservation Service
Plant Science Department
South Dakota State
University
State Office of Health,
Education and Welfare
State Game and Fish Office
Wyoming Cooperative Crop
and Livestock Reporting
Service
Soil Conservation Service
Wyoming Department of
Agriculture
Plant Science Division
The University of Wyoming
83
Lincoln, Nebraska
Bismarck, North
Dakota
Fargo, North Dakota
Bismarck, North Dakota
Fargo, North Dakota
Pierre, South Dakota
Sioux Falls, South
Dakota
Huron, South Dakota
Brookings, South Dakota
Cheyenne, Wyoming
Cheyenne, Wyoming
Cheyenne, Wyoming
Casper, Wyoming
Cheyenne, Wyoming
Laramie, Wyoming
City,
Lincoln,
State
Nebraska
Mr. V. Heavelen
Mr. N. Peterson
Mr. J. R. Price
Professor E. H.
Vasey
Mr. M. Alluin
Mr. J. C . Ranok
-------
No Page 84
-------
APPENDIX C
WATER QUALITY STATIONS SELECTED
85
-------
MRI STOR I’T ‘ STORET
( LLion Station Agency
Code Code Stream Name Station Location
1000001 06058502 II2WRD Missouri Canyon Ferry Dam, M,intan,i
10(10006 00370033 I11ONET Missouri Bismarck, North Dakota
1000011 00300031 1I1ONET Missouri Onaha, Nebraska
1000016 00190029 111ONET Missouri Kansas City, Kansas
1000021 06934500 112WRD Missouri Hermann, Missouri
1100001 06026500 I12WRD Jefferson Twin Bridge, Montana
1120001 06016000 112WRD Beaverhead Barretts, Montana
1120006 06018500 112WRD Beaverhead Twin Bridge, Montana
1400001 06089000 112WRD Sun Vaughn, Montana
2100001 06140500 I12WRD Milk Havre, Montana
2100006 06154100 1I2WRD Milk Harlem, Montana
2100011 06172000 112WRD Milk Vandalia, Montana
2100016 06174200 112WRD Milk Glasgow, Montana
2500001 00571004 1117MBR Yellowstone Fishing Bridge, Wyoming
2500006 06191500 112WRD Yellowstone Corwin Springs, Montana
2500011 06192500 112WRD Yellowstone Livingstone, Montana
2500016 06214100 I12WRD Yellowstone Laurel, Montana
2500021 06218000 112WRD Yellowstone Custer, Montana
2500026 06296120 112WRI) Yellowstone Miles City, Montana
2500031 00290055 I11ONET Yellowstone Sidney, Montana
2520001 06202610 112WRD Stiliwater Beehive, Montana
2530001 06207500 112WRD Clarks Fk. Yellow Belfry, Montana
2550001 06264700 112WRD Bighorri Lucerne, Wyoming
2550006 06268600 I12WRD Bighorn Worland, Wyoming
2550011 06279500 112WRD Bighorn Kane, Wyoming
2550016 06287000 112WRD Bighorn St. Xavier, Montana
2550021 06288500 112WRD Bighorn Hardin, Montana
2550026 06294701 I12WRD Bighorn Bighorn, Montana
2551001 06228000 112WRD Wind Riverton, Wyoming
2551006 06259000 112WRD Wind Boysen Res., Wyoming
2551101 06233600 112WRD Popo Agie Hudson, Wyoming
2551106 06235500 112WRD Little Wind Riverton, Wyoming
2554001 06270000 112WRD Nowood Tens leep, Wyoming
2554006 06274220 112WRD Nowood Manderson, Wyoming
2555001 06277500 I12WRD Greybull Basin, Wyoming
2556001 06282000 112WRD Shoshone Buffalo Bill Res., Wyoming
2556006 06284400 112WRD Shoshone Garland, Wyoming
2556011 06286200 I12WRD Shoshone Kane, Wyoming
86
-------
MR. [ sToREr STORLI
Stattim Station Agency
(oth. Code Code Stream Name Stat LcflL Locat lult
.!557001 06294000 II2WRD Little Bighorn Ilardin, MonLaiia
2 70001 06306300 1I2WRD Tongue Montana-Wyoming Line
5700O6 06308501 I12WRD Tongue Miles City, Montana
2530001 0631250() I12WRI) Powder Kaycce, Wyoming
2530006 0631700() U2WRD Powder Arvada, Wyoming
2580011 06324500 LI2WRJ) Powder Moorhead, Montana
2581001 06313000 I12WRD S. Fk Powder Kaycce, Wyoming
2583001 06313400 112WRI) Salt Creek Sussex, Wyoming
2584001 06316400 I12WRD Crazy Woman Cr. Upper Station, Wyoming
2585001 06326000 1 I2WRD Clear Creek Aryada, Wyoming
2700001 00400955 21S1)AKO1 Li LtIL’ Missouri Camp Crook. South Dakota
2800001 06340500 II2WRD Knife ll,izen, North Dakota
2810001 06340000 II2WRD Spring Creek Zap. North Dakota
3400001 06357500 I12WRD Grand River Sh .uhIitLL. South Dakota
3600001 06395000 1 IZWRD Cheyenne Edgc’nlont, South Dakota
3600006 06401500 112WRD Cheyenne Ango tura Dam, South Dakota
3600011 06423500 112WRD Cheyenne Wasta, South Dakota
3630001 06394000 112WRD Beaver Creek Newcastle, Wyoming
3650001 06427850 1I2WRD Belle Fourche Devils Tower, Wyoming
3650006 06428500 112WRD Belle Fourche Wyoming-South Dakota Line
3650011 06437000 112WRD Belle Fourche Sturgis, South Dakota
3650016 06438000 11ZWRD Belle Fourche Elm Springs, South Dakota
3651001 00460895 21SDAKO1 Redwater Belle Fourche, South Dakota
3800001 06445000 112WRD White Whitney, Nebraska
3800006 06447000 112WRD White Kadoka, South Dakota
3800011 00460003 I2IMBRCE White State Highway 47, South Dakota
3900001 00300917 21NEBOO1 Ponca Creek Verdel, Nebraska
4100001 06455900 112WRD Niobrara Dunlap, Nebraska
4100006 06457000 I12WRD Niobrara Coic. lesser, Nebraska
4100011 00300963 21NE13001 Niobrara RI ’c rview, Nehr akr
4100016 00300901 2 NEB00l Niobrara Niobrara, Nebraska
4110001 00300916 21NEBOO1 Keya Paha Napcr, Nebraska
4200001 06469000 I12WRD Jamestown Res. Jamestown, North Dakota
4200006 06470500 112WRD James l Moure, North Dakota
4200011 06471000 ]I2WRD James Cohm bia, South Dakota
4200016 00460041 I117MBR James Redfield, South Dakota
4200021 00460038 1117MBR James Huron, South Dakota
4200026 00460770 21SDAKOI James Mitchell, South Dakota
4200031 06478500 I12WRD James Scotland, South Dakota
4260001 00460052 I1L7MBR Redstone Creek Forestburg, South Dakota
4300001 06479000 1I2WRD Vemillion Wakoiila, SouLh lakota
440q0oL 00460740 2 ISDAKO1 Big Sioux W3ICL ’LOWII. South Dakota
87
-------
Stream Name
Big Sioux
Big Sioux
Big Sioux
Big Sioux
Rock
Floyd
Little Sioux
Maple
Soldier
Big Papillion Cr.
L’la I t ’
Platte
Platte
Platte
Platte
Platte
Medicine Bow
Rock Creek
Little Medicine
Sweetwater
Laramic
Laramie
Little Laramie
Rawhide Creek
Horse Creek
Horse Creek
Pumpkin Creek
South Platte
South Platte
South Platte
South Platte
Station Location
BrookingS, South Dakota
Dell Rapids, Iowa
Sioux Falls, South Dakota
Sioux City, Iowa
Minnesota-IOWa Line
Sioux City, Iowa
River Sioux, Iowa
Frederick, South Dakota
Mondamin, Iowa
1li1ha, Nebraska
Brady, Nebraska
Overton, Nebraska
Grand Island, Nebraska
Duncan, Nebraska
Schuyler, Nebraska
Plattsmouth, Nebraska
Northgate, Colorado
Seminoe Rca., Wyoming
Alcova, Wyoming
Casper, Wyoming
Glenrock, Wyoming
Orin, Wyoming
Glendo Rca., Wyoming
Guernsey Res., Wyoming
Wyoming-Nebraska Line
Lisco, Nebraska
Seminoc Res, Wyoming
Rock River, Wyoming
Bow Medicine Bow, Wyoming
Alcova, Wyoming
Laramie, Wyoming
Fort Laramie, Wyoming
Two Rivers, Wyoming
Lingle, Wyoming
Wyocross Ranch, Wyoming
Yodel, Wyoming
Bridgeport, Nebraska
South Platte, Colorado
Littleton, Colorado
Kersey, Colorado
Weldona, Colorado
MRI
STORET
SLati on
Station
Agency
Code
Code
Code
4400006
06480000
11 2WRD
44001)1 I
0o48 1000
1 1.!WRD
/440()0 H,
01)460081)
1 I 1 ONET
/i40 00 2 I
064859,1)
I I 2WRD
44 1000 I
0C8 1 5 E64
2 I Ml MN
/4500001
06600520
I I2WRD
5600001
06607513
[ I2WRD
46 I 00(11
00460810
.1 1SDAKO1
4/00001
0b( 0850”
I 2WRD
4800001.
00300205
2 INI:lioOl
4900001
06765991
I 12WRD
4900006
06767999
I I2WRD
4900011
06770499
I 12WRJ)
4900016
0674000
1L2WRD
4900021
06794700
1 I2WRD
4900026
00300086
I11ONET
4910001
06620000
I12WRD
4910006
06630000
1 12WRD
4910011
01,642000
I I2WRD
40 10() 16
0(m45000
1 I2WRD
4910021
06646800
IL2WRI)
4910026
0665200()
II2WRD
4010031
06652800
1 I2WRD
49W036
06656001
II2WRD
491004 I
066 74500
1 12WRD
/i910046
(16686000
I L2WRD
4911001
06635001)
ll2W IU)
491111)1
0663350(1
I12WR1)
4q [ 1 ‘Ill
06634601)
I I2WRI)
4912001
061,39000
I I2WRB
491’300l
06660000
1I2WRD
6913006
06656000
ILZWRD
4913101
06661500
I I2WRD
4914001
06671000
I12WRD
4915001
06676550
I 12WRD
4915006
06677010
II2WRD
491.6001
06685000
112WRD
4920001
00000025
21COLOO1
4920(106
00000024
2ICOLOOL
492001 1
06754000
I1ZWRD
North
North
North
North
North
North
North
North
North
North
Platte
Platte
Platte
Platte
Platte
Platte
Platte
Platte
Platte
Platte
88
-------
MR I ST ORE’l STORET
L.ILhon Station Agency
Code Code Code Stream Name Station Location
4t)20021 000O0O. 1 2ICOLOO1 South Platte lloliac, Colorado
4’) 0().!6 06764001 II2WRI) South Platte Julesburg, Nebraska
4’)20tfl 1 00300912 2INEBOOI South Platte Hershey, Nebraska
4 ’L 200L o000003 21c:oLOOi Bear Creek Jcff-Arapahoe Line, Colorado
4)2J001 00000346 2ICODIIDP Cherry Creek Denver, Colorado
4974001 00Q0003 2 1c:OLOOl Clear Creek Golden, Colorado
4924006 00000034 21c0L001 Clear Creek Derby, Colorado
4925001 06731000 1I2WRD St. Vram Creek Platteville, Colorado
4925101 00000033 21COLOOI Boulder Creek Boulder-Weld Line, Colorado
4926001 06744000 II2WRD Big Thompson La Salle, Colorado
4927001 00000027 2lCOLOOl Lache La Poudre Greeley, Colorado
4928001 00300931 2INEBOO1 Lodgepole Creek Chappell, Nebraska
4930001 06772500 112WRD Wood Chapman, Nebraska
4940001 00300914 21NE 13001 Loup Monroe, Nebraska
4941001 00300922 21NEBOO1 South Loup St. Michael, Nebraska
4942001 00300990 21NEBOO I Middle Loup Dunning, Nebraska
4942006 06778500 l].2WRD Middle Loup Comstock, Nebraska
4’)42011 00300921 21NEBOO1 Middle Loup Boelus, Nebraska
4942016 06785000 112WRD Middle Loup St. Paul, Nebraskd
4942101 00300993 21NEBOO1 Dismal Dunning, Nebraska
4943001 00300983 21NEBOO1 North Loup Taylor, Nebraska
4943006 00300920 21NEBOO1 North Loup St. Paul, Nebraska
4943101 00300986 21NEBOO1 Calamus Burwell, Nebraska
4944001 00300935 2INEBOOI Cedar Fullerton, Nebraska
4950001 06799000 112WRD Elkhorn Norfolk, Nebraska
4950006 06799300 112WRD Elkhorn Stanton, Nebraska
4950011 06800500 112WRD Elkhorn Waterloo, Nebraska
4951001 06799450 I12WRD Logan Creek Pender, Nebraska
4951006 06799500 112WRD Logan Creek Uehling, Nebraska
4952001 06800000 I12WRD Maple Creek Nickerson, Nebraska
4960001 06803500 112WRD Salt Creek Lincoln, Nebraska
4960006 00300930 21NEBOO1 Salt Creek Ashland, Nebraska
4961001 06804000 II2WRD Wahoo Creek Ithaca, Nebraska
5100001 06810000 1I2WRD Nishnabotna Hamburg, Iowa
5200001 00301140 21NEBOO1 Little Nemaha Auburn, Nebraska
5300001 00301136 21NEBOO1 N. Fork Big Nemaha Dawson, Nebraska
5300006 00300904 21NEBOO1 Big Nemaha Preston, Nebraska
5400001 06813000 112WRD Tarkio Fairfax, Missouri
5500001 06817500 112WRD Nodaway Burlington Junction, Missouri
5500006 06817800 112WRD Nodaway Oregon, Missouri
5600001 06821200 112WRD Platte Platte City, Missouri
5610001 00280158 12INBRCE 102 River Avenue City, Missouri
89
-------
MR I STORET STORET
tat1or Station Agency
Code Code Code Stream Name Station Location
570000] 06889000 112WRD Kansas Topeka, Kansas
0000ô 06892500 1 L2IJRD Kansas Bonner Springs, Kai’s;Lr
7 [ 0001 00000070 ICOLOO 1 Republican Bonny Res. , Kansas
7 [ 0006 06824500 II2WRD Republican Benkelman, Nebraska
‘ 7l0011 06829500 II2WRD Republican Trenton, Nebraska
5710016 00300913 2INEROOI Republican McCook, Nebraska
5710021 06844500 I12WRD Republican Orleans, Nebraska
5710026 06849500 I I2WRD Republican harlan County Dais, Nebraska
5710031 06853000 1I2WRD Republican Guide Rock, Nebraska
3710036 06853400 112WRD Republican Superior, Nebraska
5710041 00190175 21NEBOO1 Republican Hardy, Kansas
5710046 06854500 112WRD Republican Scandia, Kansas
5710051 06856000 112WRD Republican Concordia, Kansas
5710056 06857100 112WRD Republican Milford Darn, Kansas
5713001 06835500 112WRD French Creek Culburtsori, Ncbraska
5714001 06837900 I12WRD Red Willow Cr. Red Willow, Nebraska
5716001 06845000 1 I2WRD Sappa Creek Oberlin, Kansas
5716201 06844900 112WRD S. Fk. Sappa Creek Achilles, Kansas
5716301 06846500 112WRD Beaver Creek Cedar Bluffs, Kansas
5717001 06847900 112WRD Prairie Dog Cr. Norton, Kansas
5717006 06848500 I12WRD Prairie Dog Cr. Woodruff, Kansas
5718001 06855800 1I2WRD Buffalo Creek Jamestown, Kansas
5720001 06862700 1I2WRD Smoky Hill Schoenchen, Kansas
5720006 06864000 IL2WRD Smoky Hill Russell, Kansas
5720011 06864500 112WRD Smoky Hill Ellsworth, Kansas
5720016 06865500 112WRD Smoky Hill Langley, Kansas
5720021 06866500 1I2WRD Smoky Hill Mentor, Kansas
5720026 06870200 I12WRD Smoky Hill New Cambria, Kansas
5720031 06877600 112WRD Smoky Hill Enterprise, Kansas
5723001 06863500 1I2WRD Big Creek Hays, Kansas
5724001 06867000 I12WRD Saline Russell, Kansas
5724006 06868200 II2WRD Saline Wilson Dam, Kansas
5724011 06869500 I12WRD Saline Tescott, Kansas
5724016 06870100 112WRD Saline New Cambria, Kansas
5724101 06867500 1I2WRD Paradise Creek Paradise, Kansas
5725001 0687590() I12WRD Solomon Glen Elder, Kansas
5725006 06876900 I12WRD Solomon Niles, Kansas
5725101 06871000 1I2WRD N. Fk. Solomon Glade, Kansas
5725106 06872500 112WRD N. Fk. Solomon Portis, Kansas
5725201 06873000 112WRD S. Fk. Solomon Webster Res., Kansas
5725206 06874000 1L2WRD S. Fk. Solomon Osborne, Kansas
90
-------
N IH STORET STORET
Station Station Agency
Code Code Code Stream Name Station Location
5726001 06878500 I I2WRD Lyons Creek Woodbine, Kansas
5730001 06879900 I I2WRD Big Blue Surprise, Nebraska
730006 00300924 21NEBOO1 Big Blue Crete, Nebraska
S730011 068824(K) II2WRD Big Blue Okcto, Kansas
5730016 0688700() II2WRD Big Blue Manhattan, Kansas
5731001 06880000 1I2t4RD Lincoln Creek Seward, Nebraska
5732001 06880800 112WRD W. Fk. Big Blue Dorchester, Nebraska
5733001 06881200 1I2WRD Turkey Creek Wilber, Nebraska
5734001 06883000 1I2WRD Little Blue Deweese, Nebraska
5734006 00301107 2INEBOO I Little Blue GiLead, Nebrdska
5734011 00301104 2JNEBOO1 Little Blue Fairbury, Nebraska
57i40 16 06884400 111LJRD Little Blue Barnes, Kansas
57 i)00I 06890400 112.tJRD De1 iwarc Arrtngton, Kansas
5740006 00190191 UIMBRCE Delaware Perry Res. Kansas
57 500() I 06891500 1 12WRD Wakarusa Lawrence, Kansas
5800001 06902000 [ 12WRD Grand Sumner, Missouri
5810001 06899620 I12WRD Thompson Chillicothe, Missouri
5900001 06905500 112WRD Chariton Prairie Hill, Missouri
6100001 00280955 2lM0000l Lamine Pilot Grove, Missouri
6200001 00280943 211400001 Moreau Jefferson City, MissQuri
6300001 06916600 I12WRD Marais Des Cygnes Kansas-Missouri Line
Osage
6300006 0028095C) 211400001 Lake Ozark Warsaw, Missouri
6300011 00280957 211100001 Osage St. Thomas, Missouri
6310001 06917000 112WRD Little Osage Fulton, Kansas
6311001 06917500 112WRD Marmaton Fort Scott, Kansas
6320001 00280963 211400001 Sac Stockton, Missouri
6330001 00190206 12IMBRCE Poimne de Terre R. Hermitage, Missouri
6340001 00280965 21M00001 South Grand Urich, Missouri
6400001 00280967 211400001 Gasconade Jerome, Missouri
91
-------
No Page 92
-------
APPENDIX D
SELECTION OF IMPORTANT PARAMETERS
93
-------
For the purpose of providing adequate streamfiow information at any
point on any stream, the U.S. Geological Survey has set up a nationwide
program to study the streainflow characteristics by means of systems
model approach. Both methodology adopted and result obtained in this
program are published and are extremely useful to this project as an
indication of important data parameters.
The statistical characteristics of streamflow can be defined by stream
gauging, analytical methods of regionalization, systems studies, or any
combination of these. Stream gauging at every site where information
may be required is generally considered to be too expensive. However,
Cost of obtaining adequate information can be reduced by supplementing
the information with regionalization and system studies.
The method adopted to regionalize streamfiow characteristics for natural-
flow streams is to relate them to basin and climate characteristics in
equations developed to use multiple-regression techniques applied to
historic data. The standard error of estimate of regression equations
thus obtained can be used to judge the accuracy of estimation. The
equation and its constants having been defined, streamf low characteris-
tics for a specific site in a given basin can be computed by substituting
the appropriate values of the hydrologic variables in the equation.
Tables D-l, D-2, and D-3, respectively, list streamfiow parameters,
drainage-basin physical parameters and climatic parameters considered
in this program. Table D-4 presents regression equations for the basin
states.
94
-------
TABLE D-l
SUMMARY OF STREAMFLOW CHARACTERISTICS
Symbol Characteristics Definition
M 72 , M 7 , 10 , M 720 Low-flow Annual minimum 7-day flows at
2-year, 10-year, and 20-year
recurrence.
Q 2 Q 5 , etc. Flood-peak Discharge from the annual flood-
frequency curve at recurrence
- intervals of 2, 5, 10, 25 and
50 years. -
V 7 , 2 V 710 , V 750 Flood-volume Annual highest 7-day mean-flows
at recurrence intervals of 2,
10, and 50 years.
Q Mean-flow, annual Mean-flow of the annual means.
a
Q Mean-flow, monthly Mean-flow for each calendar month,
subscript “n” refers to the nu-
merical order of the month begin-
ning with January as 1.
SDa Flow-variability, Standard deviation of annual mean.
annual
SD Flow-variability, Standard deviation of monthly means,
monthly subscript “n” refers to the nu-
merical order of months with
January as 1.
-------
TABLE D-2
SUMMARY OF DRAINAGE BASIN PHYSICAL CHARACTERISTICS
Symbol Characteristics Unit Definition
A Drainage area Square mile Area measured in a horizontal plane, enclosed by
a topographic divide from which direct surface
runoff from precipitation normally drains by
gravity into the stream above the specific
point.
L Main-channel length Miles The distance from the gauging station to the basin
divide.
S Main-channel slope Ft/mile The river body slope determined from the elevatioz
at points 107. and 857. of the distance along the
channel from the gauging station to the divide.
E Mean-basin elevation Feet Mean elevation for the basin above mean sea level.
F Forest area Percent The percentage of forested area in total drainage
area.
S Area of lakes and ponds Percent The percentage of area of lakes and ponds in total
drainage area.
‘S 1 Soil infiltration index Inches The value of potential maximum infiltration during
an annual flood under average soil-moisture
conditions.
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TABLE D-3
SUMMARY OF DRAINAGE BASIN CLIMATIC CHARACTERISTICS
Symbol Characteristics Unit Definition
P Mean annual Inches
precipitation
P Mean monthly Inches Mean precipitation for each calendar month, sub-
precipitation scription “n ” refers to the numerical order of
months with January as 1.
P 24 2 Rainfall intensity I nches The maximum 24-hr rainfall having a recurrence
interval of 2 years.
P 24 , 50 Rainfall intensity Inches The maximum 24-hr rainfall having a recurrence
interval of 50 years.
E Evaporation Inches Mean annual evaporation in basin.
Snowfall index Inches The water equivalent of snow on ground 1-15 March
with 10% probability.
Mean minimum January
temperature
Mean maximum March
temperature
Mean maximum July
temperature
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TABLE D-4
SUNMARY OF REGRESSION ANALYSES OF ME&N ANNUAL FLOW
Standard
Error of
Region Equation Estimate, 7. Remarks
Colorado
0.189
-Mountains Region Qa = 0.00140 A° 956 S 0192 p 21 ° 1 T 1 47
Kansas Q = 0.106 A° 947 (P_14.O)L 613 E -0.771 28
a v
Missouri
-Plains Region a = 0.037 A° 983 P 242 2 . 7 O Si- 0 . 511 10.6
-Plateau Region 0.288 A 11 ° (F + 1)0.124 27.5
Montana
0.900 4.91 0.430
-Region A Qa = 0.000344 A (E/1000 F 31 E 6 , 000 = Area above 6,000 ft
P 0555 E UJ. 4 ft elevation, 7.
6,000
-Region B Q = 0.0522 L’’ 47 E° 613 53
a 6,000
0 937 0.406
-Region C Q 8 = 0.002 A S F° 581 P 0 . 889 27
pl. 61 E° -° 3
24,2 6,000
Nebraska
-Sand Hills Region a = 0.0175 LL 23 S °. 499 S 1 19 D ° 405 33 D = A special index developed
for Sand Hills Streams
-Outside the Sand Q = 0.0484 A ” 55 A ° 54 ° F° 97 P 1 . 65 31 A = Contributing drainage
Hills Region a E °• 954 S -1.70
n area, square miles
North Dakota a 0.54 A° 975 S° 36 ° (E - 12)1363 61 Sh = Basin shape factor, a
(T 3 - 26)] 176 h° 359 (F + 0.01)0084 ratio of length to the
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- TABLE D-4 (Concluded)
SUMMARY OF REGRESSION ANALYSES OF MEAN ANNUAL FLOW
Standard
Error of
Region Equation Estimate. % Remarks
0.951 4.342 1.283
South Dakota a = io 6 A s 66.2
-James River Basin
-the remaining portion Qa 6.11 X 1O ALOO 2 F° 224 1.916 0.624
: 1224 Sn 72.7
of the state
1.01 -0.123 0.051 0.788
Iowa = 0.00341 A S F (P-20) 15
0.261 -0.294 0.366
(T 7 -80) s
24.2
0.56 1.64 1.78 0.76
Wyoming Qa = 3.12 x iO A° 97 S E P Lat 27 Lat = Latitude of
the center of the
drainage basin, in
decimal form, less
40.00.
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No Page 100
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APPENDIX E
MRI STATION AND RIVER CODES
101
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The water quality stations selected in this program were operated by
10 different agencies, and each agency has its own coding system. To
facilitiate easy identification a special MRI coding system was designed.
The MRI station code has seven digits, the first five comprise the river
code and the last two are the station number. From the code, we can
tell on which river the station is located and its relative location to
other stations along that river.
The River Code
The five-digit river code (Table E-1) is a specific number assigned to
each stream for the identification of: (1) stream rank; and (2) stream
number. The stream rank is indicated by the number of digits in the
code, which are other than zero. These nonzero digits are assigned
from left to right, with higher nonzero digits for higher rank streams.
The Missouri River main stem is ranked one and is coded with one nonzero
digit (code 10000). Rivers flowing directly into the Missouri River are
ranked two and are coded with two nonzero digits (e.g., the Sun River:
14000). Rivers of other ranks are coded in the same manner.
The stream numbers are assigned consecutively in the downstream direction.
Based on the observer looking downstream, the first river encountered is
given a number 1 (the last nonzero digit). The next river encountered
is given a number 2, etc. In general, we have less than 10 rivers in a
single tributary. Therefore, a single digit has been sufficient to in-
dicate the stream number. The exception is the Missouri River, to which
more than 50 secondary streams are tributaries. The stream numbers for
these tributaries are given by the numbers 11 to 64 with deletion of the
numbers 20, 30, 40, 50, and 60, each of which would indicate a stream of
rank one. For example, the code for the Milk River is 21000 instead of
20000 which would indicate a stream rank equivalent to that of the Missouri
River.
Furthermore, MRI River Code 14000 given to the Sun River, indicates that:
(1) it is a second-rank stream (two nonzero digits); and (2) it is the
fourth tributary (from upstream) encountered by a river of the next higher
rank (the Missouri River).
102
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Station Number
The two-digit station number is assigned also in the downstream dircction.
The first station is given the number 01, the second station is given 06,
etc., each time with five higher than the previous number to leave space
for the addition of new stations.
The combination of River Code (at right) and Station Number constitutes
the MRI Station Code. For example, Station 2500006 is the second station
(06) on River 25000 (the Yellowstone River). Appendix C lists the codes
for all the selected stations.
103
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TABLE E-l
RIVER CODE
MISSOURI RIVER BASIN
River Code Tributary
Missouri River 10000
Jefferson River 11000 Montana
a. Bedrock River 11100 Montana
b. Beaverhead River 11200 Montana
c. Ruby River 11300 Montana
d. Big Hole River 11400 Montana
e. Boulder River 11500 Montana
f. Madison River 11600 Montana
Gallatin River 12000 Montana
Smith River 13000 Montana
Sun River 14000 Montana
Marias River 15000 Montana
a. Two Medicine Creek 15100 Montana
b. Outbank Creek 15200 Montana
c. Willow Creek 15300 Montana
d. Teton River 15400 Montana
Arrow River 16000 Montana
Judith River 17000 Montana
Husseishell River 18000 Montana
a. Boxelder Creek 18100 Montana
Dry Creek 19000 Montana
Milk River 21000 Montana
a. Big Sandy Creek 21100 Montana
1. Sago Creek 21110 Montana
b. Peoples Creek 21200 Montana
c. White Water Creek 21300 Montana
d. Frenchman River 21400 Montana
e. Beaver Creek 21500 Montana
f. Rock Creek 21600 Montana
Redwater Creek 22000 Montana
Poplar River 23000 Montana
Big Muddy Creek 24000 Montana
Yellowstone River 25000 Wyoming, Montana,
North Dakota
a. Shields River 25100 Montana
b. Stiliwater River 25200 Montana
c. Clarks Fork 25300 Wyoming, Montana
d. Pryor Creek 25400 Montana
104
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TABLE E-l (Continued)
River Code Tributary
e. Big Horn River 25500 Wyoming, Montana
1. Wind River 25510 Wyoming
(a) Popo Agie (Little
Wind) River 25511 Wyoming
2. Muskrat Creek 25520 Wyoming
3. Badwater Creek 25530 Wyoming
4. Nowood Creek 25540 Wyoming
5. Greybull River 25550 Wyoming
6. Shoshone River 25560 Wyoming
(a) North Fork Shoshone
River 25561 Wyoming
7. Little Big Horn River 25570 Montana
f. Rosebud Creek 25600 Montana
g. Tongue River 25700 Wyoming, Montana
h. Powder River 25800 Wyoming, Montana
1. South Fork Powder River 25810 Wyoming
2. Middle Fork Powder River 25820 Wyoming
3. Salt Creek 25830 Wyoming
4. Crazy Woman Creek 25840 Wyoming
5. Clear Creek 25850 Wyoming
6. Little Powder River 25860 Montana
i. O’Fallon Creek 25900 Montana
Little Muddy Creek 26000 North Dakota
Little Missouri River 27000 Montana, South Dakota,
North Dakota
a. Boxelder Creek 27100 Montana
Knife River 28000 North Dakota
a. Spring Creek 28100 North Dakota
Heart River 29000 North Dakota
Apple Creek 31000 North Dakota
Cannonball River 32000 North Dakota
a. Cedar Creek 32100 North Dakota
Beaver Creek 33000 North Dakota
Grand River 34000 North Dakota, South
Dakota
a. North Fork Grand River 34100 North Dakota
b. South Fork Grand River 34200 South Dakota
Moreau River 35000 South Dakota
105
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TABLE E-l (Continued)
River Code Tributary
Cheyenne 36000 Wyoming, South Dakota,
Nebraska
a. South Fork Cheyenne River 36100 Wyoming
b. Lance Creek 36200 Wyoming
c. Beaver Creek 36300 South Dakota, Wyoming
d. Hat Creek 36400 South Dakota, Nebraska
e. Belle Fourche River 36500 Wyoming, South Dakota
1. Redwater Creek 36510 South Dakota
f. Cherry Creek 36600 South Dakota
Bad River 37000 South Dakota
White River 38000 South Dakota, Nebraska
a. South Fork White River 38100 South Dakota
Ponca Creek 39000 South Dakota, Nebraska
Noibrara River 41000 Nebraska, South Dakota
a. Kayapaha River 41100 South Dakota, Nebraska
James River 42000 North Dakota, South
Dakota
a. Pepestem Creek 42100 North Dakota
b. Elm River 42200 South Dakota
c. Mud Creek 42300 South Dakota
d. Snake Creek 43400 South Dakota
e. Turtle Creek 42500 South Dakota
f. Redstone Creek 42600 South Dakota
Slermillion River 43000 South Dakota
Big Sioux River 44000 South Dakota, Iowa,
Minnesota
a. Rock River 44100 Iowa, Minnesota
Floyd River 45000 Iowa
Little Sioux River 46000 Iowa, Minnesota
a. Maple River 46100 Iowa
Soldier River 47000 Iowa
Papillion Creek 48000 Nebraska
Platte River 49000 Colorado, Wyoming,
Nebraska
a. North Platte River 49100 Colorado, Wyoming,
Nebraska
1. Medicine Bow River 49110 Wyoming
(a) Rock Creek 49111 Wyoming
(b) Little Medicine Bow
River 49112 Wyoming
l0
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TABLE E-1 (Continued)
River Code Tributary
2. Sweetwater River 49120 Wyoming
3. Larainie River 49130 Wyoming, Colorado
(a) Little Laramie River 49131 Wyoming
4. Rawhide Creek 49140 Wyoming
5. Horse Creek 49150 Wyoming, Nebraska
6. Pumpkin (Pumpkinseed)
Creek 49160 Nebraska
b. South Platte River 49200 Colorado, Nebraska,
Wyoming
1. South Fork South Platte
River 49210 Colorado
2. Bear Creek 49220 Colorado
3. Cherry Creek 49230 Coloardo
4. Clear Creek 49240 Colorado
5. St. Vram Creek 49250 Colorado
(a) Boulder Creek 49251 Colorado
6. Big Thompson River 49260 Colorado
7. Lathe la Poudre River 49270 Colorado
8. Lodgepole Creek 49280 Nebraska, Wyoming
c. Wood River 49300 Nebraska
d. Loup River 49400 Nebraska
1. South Loup River 49410 Nebraska
2. Middle Loup River 49420 Nebraska
(a) Dismal River 49421 Nebraska
3. North Loup River 49430 Nebraska
(a) Calanius River 49431. Nebraska
4. Cedar River 49440 Nebraska
a. Elkhorn River 49500 Nebraska
1. Logan Creek 49510 Nebraska
2. Maple Creek 49520 Nebraska
f. Salt Creek 49600 Nebraska
1. Wahoo Creek 49610 Nebraska
Nishnabotna River 51000 Iowa, Missouri
a. East Nishnabotna 51100 Iowa
Little Nemaha River 52000 Nebraska
Nemaha River 53000 Missouri, Kansas,
Nebraska
Tarkio River 54000 Iowa, Missouri
Nodaway River 55000 Iowa, Missouri
107
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TABLE E-1 (Continued)
River Code Tributary
Platte River 56000 Iowa, Missouri
a. One Hundred and Two River 56100 Iowa, Missouri
Kansas River 57000 Colorado, Nebraska,
Kansas
a. Republican River 57100 Colorado, Nebraska,
Kansas
1. North Republican River 57110 Colorado, Nebraska,
Kansas
(a) Arikaree River 57111 Colorado, Nebraska
2. South Republican River 57120 Colorado, Kansas
3. Frenchman Creek 57130 Colorado, Nebraska
(a) Stinking Water Creek 57131 Colorado, Nebraska
4. Redwillow Creek 57140 Nebraska
5. Medicine Creek 57150 Nebraska
6. Sappa River 57160 Colorado, Nebraska
Kansas
(a) North Fork Sappa
River 57161 Kansas
(b) South Fork Sappa
River 57162 Kansas
(c) Beaver Creek 57163 Colorado, Nebraska,
Kansas
7. Prairie Dog Creek 57170 Kansas, Nebraska
8. Buffalo Creek 57180 Kansas
b. Smoky Hill River 57200 Colorado, Kansas
1. Beaver (Ladder) Creek 57210 Coloardo, Kansas
2. Hockberry Creek 57220 Kansas
3. Big Creek 57230 Kansas
4. Saline River 57240 Kansas
(a) Paradise Creek 57241 Kansas
5. Soloinan River 57250 Kansas
(a) North Fork Soloman
River 57251 Kansas
(b) South Fork Soloman
River 57252 Kansas
6. Lyon Creek 57260 Kansas
c. Big Blue River 57300 Nebraska, Kansas
1. Lincoln Creek 57310 Nebraska
2. West Fork, Big Flue 57320 Nebraska
3. Turkey Creek 57330 Nebraska
4. Little Blue River 57340 Nebraska, Kansas
108
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TABLE E-l (Concluded)
River Code Tribut y
d. Delaware River 57400 Kansas
e. Wakarusa River 57500 Kansas
Grand River 58000 Iowa, Mis,souri
a. Thompson River 58100 Iowa, Missouri
Chariton River 59000 Iowa, Missouri
Lamine River 61000 Missouri
a. Blackwater River 61100 Missouri
Moreau River 62000 Missouri
Marias de Cygnes-Osage River 63000 Kansas, Missouri
a. Little Osage River 63100 Kansas, Missouri
1. Marmaton River 63110 Kansas, Missouri
b. Sac Riuer 63200 Missouri
c. Poitune de Terre River 63300 Missouri
d. South Grand River 63400 Missouri
e. Niangua River 63500 Missouri
Gasconade River 64000 Missouri
109
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No Page 110
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APPENDIX F
MAPS SHOW INC COUNTY - WATERSHED RELATIONSHIPS
FOR SUBBASINS 31 THROUGH 42
111
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Figure F-i - Subbasin 31
112
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NEBRASKA
KANSAS
1 -
ARK N A$-WHtTE-RED DRAIN.thF AflEA
• tE}!H Li -1 -
NEBRAS
KANSAS
YAP
Figure F-2 - Subbasin 32
-------
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Figure F-3 - Subbasin 33
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F 4 gure F-4 - Subbasin 34
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-------
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33
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Figure F-5 - Subbasin 35
-------
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Figure F-6 - Subbasin 36
117
MAP
UPPER M12 1SS1PPI
DRAINAGE AREA
YRLOW M DICIN(
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120
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122
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Figure F-12 - SubbaBin 42
123
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