EPA-660/3-75-033
JUNE 1975
LAKE CLASSIFICATION—A TROPHIC CHARACTERIZATION
OF WISCONSIN LAKES
by
Paul D. Uttormark
J. Peter Wall
Water Resources Center
University of Wisconsin
Madison, Wisconsin 53706
Grant R-801363
Program Element 1BA031
ROAP 21 AIY, Task 16
Project Officer
Kenneth W. Malueg
Pacific Northwest Environmental Research Laboratory
National Environmental Research Center
Corvallis, Oregon 97330
NATIONAL ENVIRONMENTAL RESEARCH CENTER
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CORVALLIS, OREGON 97330
For sale by the Superintendent of Documents, U.S. Government
Printing Office Washington, D.C. 20402
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development,
U.S. Environmental Protection Agency, have been grouped into
five series. These five broad categories were established to
facilitate further development and application of environmental
technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in
related fields. The five series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
This report has been assigned to the ECOLOGICAL RESEARCH STUDIES
series. This series describes research on the effects of pollution
on humans, plant and animal species, and materials. Problems are
assessed for their long- and short-term influences. Investigations
include formation, transport, and pathway studies to determine the
fate of pollutants and their effects. This work provides the technical
basis for setting standards to minimize undesirable changes in living
organisms in the aquatic, terrestrial and atmospheric environments.
EPA REVIEW NOTICE
This report has been reviewed by the Office of Research and
Development, EPA, and approved for publication. Approval does
not signify that the contents necessarily reflect the views and
policies of the Environmental Protection Agency, nor does mention
of trade names or commercial products constitute endorsement or
recommendation for use.
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ABSTRACT
The design and application of the Lake Condition Index (LCI)
system of classifying lakes is described, and it is demon-
strated that lake classification can be employed as a useful
tool by resource managers for comparing the trophic condi-
tion of large numbers of lakes. The LCI system was gener-
ated when an evaluation of other systems revealed that most
are presently unsuitable for classifying the vast majority
of lakes because the analytical data required for their use
are lacking. Utilizing subjective information, the LCI
system was applied to the classification of more than 1100
large Wisconsin lakes. Checks of the results show that 86%
of the lakes were appropriately classified within the limits
of the system; 14% were misclassified, as judged by in-
dividuals familiar with the lakes in question. Most, but
not all, discrepancies were due to erroneous input data.
The LCI values obtained were coupled with nutrient-loading
considerations and shoreline density-development factors to
demonstrate that lake classification can serve as a workable
data base for lake renewal and management programs. The LCI
system is easily modified to incorporate additional data for
special purposes. The system could be used to classify an
estimated 70-80% of the larger lakes in the United States.
This report was submitted in fulfillment of Grant R-801363
by the University of Wisconsin under the sponsorship of the
Environmental Protection Agency. Work was completed as of
December, 1974.
11
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CONTENTS
Page
Abstract ii
List of Figures iv
List of Tables v
Acknowledgments vi
Sections
I Conclusions and Recommendations 1
II Introduction 3
III Lake Classification Methodology 7
IV Data Acquisition 23
V Trophic Characterization of Wisconsin
Lakes 37
VI Lake Classification for Decision Making . 57
VII Regional Application of the LCI System. . 72
VIII References 78
IX Appendices 81
111
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LIST OF FIGURES
Paqe
1. Frequency Distribution of Lakes by Condition
Index for Four Areas of Wisconsin 44
2. Frequency Distribution of Wisconsin Lakes
According to Condition Index 46
3. Distribution of Surface Area of Wisconsin
Lakes as a Function of Condition Index ... 47
4. Average Surface Area of Wisconsin Lakes
as a Function of Condition Index 48
5. Distribution and Average LCI of Wisconsin
Study Lakes - by County 49
6. Dissolved Oxygen Conditions in Wisconsin
Lakes 52
7. Calculated Condition Indexes for Three Types
of Wisconsin Lakes 53
8. Relationship between Nutrient Loading and
Lake Condition (Hypothetical Data) 62
IV
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LIST OF TABLES
Page
1. Water Quality Scores for 10 Michigan Lakes. 12
2. Fifty-Five Florida Lakes Ranked According
to Trophic State Index (TSI) 14
3. Composite Rating of 12 Wisconsin Lakes. . . 16
4. Classification of 7 New Zealand Lakes ... 19
5. Summary of Systems to Classify Lakes
According to Trophic Status 21
6. Summary of Lake Inventory Data 24-25
7. Availability of Water Quality Data
for Lakes 26-27
8. Point System for Lake Condition Index ... 37
9. Comparative Rank of 12 Wisconsin Lakes. . . 42
10. Summary of Classification System Review
by Wisconsin DNR Area Fish Managers .... 55
11. Specific Loading Levels for Lakes
Expressed as Total Nitrogen and
Total Phosphorus in g/m2/yr 60
12. Development Density of Wisconsin Lakes. . . 69
13. Applicability of Lake Classification System 75
v
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ACKNOWLEDGMENTS
The described project was possible only because
of the cooperation received from many individuals,
In particular, the contribution made by the staff
of the Wisconsin Department of Natural Resources
is singled out for special recognition. Although
the DNR received compensation for some services
provided to the project, many additional contri-
butions were made. These included computer
services, review and submission of data forms,
as well as evaluations of project results.
Considerable advice and input was received from
faculty members of the University of Wisconsin,
and a great deal of help was received from the
staff of the Water Resources Center in preparing
the manuscript.
VI
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SECTION I
CONCLUSIONS AND RECOMMENDATIONS
Based on a review of lake classification methodology, the
development of a technique for calculating Lake Condition
Index values, and the use of that technique to classify
more than 1100 lakes in Wisconsin, it is concluded that:
1) Lake classification is a valuable decision-making tool
that could be used to great advantage for lake manage-
ment purposes.
2) Sophisticated lake classification systems requiring
multiple water quality parameters cannot be used to
classify most lakes at the present time because the
necessary input data are lacking. Compilation of the
necessary input data is likely to be required as a
part of most large-scale classification efforts.
3) The methodology of calculating Lake Condition Index
values presented in this report provides a basis for
classifying lakes in the absence of detailed biological
and chemical data. Necessary input information can be
compiled without resorting to extensive field investi-
gations . The system is flexible and can be modified
to incorporate additional data input for special
purposes.
4) Computed LCI values are accurate to approximately
±2 units and give a workable basis for comparing water
quality in lakes as judged by individuals familiar
with subsets of the lakes classified.
5) Many of the benefits of lake classification as a
decision-making device could be achieved by the imple-
mentation of intrastate classification systems. Some
additional advantages would result from the use of a
single system on a regional or national basis.
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6) It is estimated that the LCI technique could be used
to classify about 70% of the larger lakes in this
country and, with some modification, could probably be
used to classify an additional 12% of the lakes larger
than 100 acres (40 ha).
7) Ultimately, it would be desirable to classify lakes on
the basis of repetitive field data. As an interim
step, classification systems of the type presented here
can and should be used to attain many of the practical
benefits of lake classification.
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SECTION II
INTRODUCTION
The literal definition (from Webster) of classification and
its processes refers to a systematic, orderly arrangement
of things in groups or categories according to established
criteria or presumed relationships. Classification is an
activity which has engaged man, consciously or subcon-
sciously, throughout his existence with systems ranging
from simple categorizations (edible vs inedible) to complex
academic exercises (taxonomy of the earth's flora and
fauna). The motivations for classifying a body of subject
matter can be numerous and varied, but during the course
of this project two general purposes appear behind most
classification schemes:
1) The size and scope of the subject matter preclude con-
sideration as a whole, and classification into subsets
is necessary to create comprehensible units,
2) The subject matter is poorly understood and classi-
fication is used as a dissection tray for examining
and evaluating the "working parts" of the unit.
In the first case, the classification serves as a tool,
but usually becomes an end in itself, with categories
undergoing relatively little change or modification while
serving to orient and guide the user through an otherwise
hopeless bulk of material. Again, the notable example
of this form of classification is the taxonomy of plants
and animals. With well over 650,000 species listed in the
class Inseota alone, the establishment of systematic
zoological categories is essential to the perspective
and understanding of the inquirer.
In the second case, however, the classification process
serves primarily as a tool and may only have incidental
value as an end product. As an example, we can use the
subject of this study, the classification of lakes. While
a definitive, comprehensive classification of waters would
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be desirable, there seems to be little immediate hope of
achieving such a goal because:
1) both the quantity and quality of pertinent, compre-
hensive data are lacking;
2) many of the physical, chemical, and biological
mechanisms of aquatic environs are but vaguely under-
stood, if at all; and
3) the diverse pressures and special interests competing
for water and its use dampen the probability that one
all-purpose classification system will satisfy all
points of view.
The fact that these difficulties exist, however, does
nothing to mollify the need for methods to assess a critical
and limited resource which is so easy to degrade and so
difficult to restore. Therefore, classification is needed
here, particularly as a tool or device for assessing unde-
sirable trends in the environment and as a platform for
administrative decision-making. These needs are specifi-
cally recognized in Section 314(a) of the 1972 Amendments
to the Water Quality Act, which states that,
"Each State shall prepare or establish, and submit
to the Administrator for his approval:
1) an identification and classification according
to eutrophic condition of all publicly owned
fresh water lakes in such State;
2) procedures, processes, and methods (including
land use requirements) to control sources of
pollution of such lakes; and
3) methods and procedures, in conjunction with
appropriate Federal agencies, to restore quality
of such lakes."
At the state level, Wisconsin has enacted the Public Inland
Lake Protection and Rehabilitation Act. This legislation,
which became effective in June, 1974, established a State-
local program to:
1) compile background information on Wisconsin lakes;
2) define problems where they exist and identify their
cause(s);
3) consider appropriate protective and remedial steps
to confront problems;
4) delineate feasible courses of action; and
5) implement the protective or corrective measure.
4
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In order to fulfill federal and state water quality require-
ments and enable agencies to manage lakes efficiently and
effectively, it is essential that methods be developed which
allow an overview of resource conditions and elucidate
appropriate management options. To this end, one of the
specific goals of the Lake Inventory Project was to compile
an information base for lakes and to develop a system for
classifying them—the information base as the start of a
flexible, multipurpose data core, and the classification
system as a contributor to management's overview. From the
outset, the lake classification objective was viewed from
a nationwide perspective, with the goal of developing
methodology for assessing the trophic character of the
nation's lakes. Thus, the efforts were shaped by the
practical realities and constraints of geographical di-
versity, data availability, as well as the vaguely-defined
classification parameter, trophic status.
This report provides an assessment of the country's lake
data base and a review of the published lake classifica-
tion systems which have been used to classify lakes
according to trophic conditions. Based on this informa-
tion, a lake classification method requiring minimal data
input was developed and tested by classifying all Wisconsin
lakes larger than 100 acres (40 ha). This latter activity
was undertaken as a pilot project to evaluate the benefits
that could be obtained from a broad-scale classification
effort. Other reports generated by this activity include:
Wall, P. J., M. J. Ketelle, and P. D. Uttormark.
Wisconsin Lakes Receiving Sewage Effluents. Tech-
nical Report 73-1, Water Resources Center, University
of Wisconsin, Madison, Wisconsin, 1973.
Ketelle, M. J. and P. D. Uttormark. Problem Lakes
in the United States. Project #16010 EHR report for
the Office of Research and Monitoring, Environmental
Protection Agency, 1971.
In developing the background information for this project,
one report, "Classifying Water Bodies: Feasibility and
Recommendations for Classifying Water" (Aukerman, R. and
G. I. Chesley, 1971), stood apart from the rest because
of its broad, philosophical approach to the topic.
Aukerman and Chesley addressed the questions, "Should
lakes be classified?," and if so, "How should this be
done?" It was concluded that lakes should definitely be
classified because the perceived advantages—as viewed by
individuals responsible for a broad spectrum of water-
related activities—clearly outweighed potential dis-
advantages. The ten advantages cited by the authors
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stressed the potential uses of lake classification in the
planning aspects of water resource management, namely:
designation for optimum use,
guidelines for allocation,
guidelines for orderly development,
guidelines for zoning and control,
protection of the physical resource,
environmental quality protection,
protection of fisheries and wildlife resources,
protection of human health and safety,
preservation of the resource, and
orderly growth and development of lands adjacent to
water.
The disadvantages associated with classification were not
as obvious as the advantages. The two limitations cited
were: 1) the misconceptions which may arise from a poorly
designed system, and 2) the static nature of predetermined
classes which are often inappropriate for evaluating
changing resources, technologies, and social demands.
Thus, potential disadvantages were envisioned if improperly
designed classification systems were used; however, with
careful planning and design these shortcomings can be
avoided. The desirability of maintaining flexibility
within classification systems appears to be particularly
well taken. Classification is likely to be most useful
if it is viewed as a decision-making tool which is subject
to continual update and revision to incorporate new infor-
mation or to address changing needs.
Aukerman and Chesley did not present specific methodology
for classifying lakes; however, their presentation of
classification concepts and their discussion of the in-
adequacies of static classification systems were useful in
designing and carrying out this project. Throughout this
report, attempts are made to demonstrate the advantages
gained when lake classification is viewed as a flexible,
dynamic aid for evaluation, rather than a static, pre-set
method for making small groups out of large ones.
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SECTION III
LAKE CLASSIFICATION METHODOLOGY
Classification as an aid for establishing broad-based pri-
orities is not only advantageous, it is an essential
undertaking. The concept that "each lake has its own
unique personality" has its place, but it provides little
help to the administrator who must allocate funds and man-
power to areas of critical need. Some method of "lumping"
lake characteristics must be used. If an administrator
had to read a detailed description of each and every lake
in his district before making a decision--and in many
cases there are hundreds of lakes—he would probably resort
to a dart board as his prime decision-making tool.
For purposes of this report, lake classification is viewed
as an activity consisting of three components—input,
processing and output. Or, in slightly more detail, lake
classification may be described by a flow diagram such as,
Application of a
classification system
which defines how the _
Data or ^ input is to be ^ Grouping or
information handled—the rules, rj i £g
criteria, equations, ° laKes
etc., by which
input is "sorted"
This provides an operational definition which is used in
this report, i.e., classification systems are rules,
criteria, guidelines or equations by which lake informa-
tion or data can be manipulated to yield a grouping or
ranking of lakes. Obviously lakes can be classified in an
almost endless variety of ways, depending on specific
interests or concerns.
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Primary emphasis here is placed on systems which relate to
the trophic character of lakes, particularly with regard
to the need, priority, and potential for rehabilitation or
the need and urgency for protective action to prevent
imminent degradation.
The definition of lake classification given above provides
one method of comparing and analyzing classification
systems. The following terminology is used:
Types of data - Objective input, numbers or values
input: determined by measurement.
information - Subjective input, descriptions
or assessments of conditions which may or
may not be based on documented evidence.
Types of grouped output - Lakes having similar charac-
output: teristics are organized in distinct groups.
There is no hierarchy of lakes within groups;
they are simply different from one another.
(Example: Output consisting of wild lakes,
general recreation lakes, development lakes,
and urban lakes.)
ranked output - Lakes are ordered relative
to one another with respect to some scale
of reference.
Types of relative systems - Lakes are classed only with
systems: respect to one another, either by similarities
or differences. Ranks and groups are defined
by the input set, and the classification of a
particular lake may change if additional lakes
are classified.
independent systems - Lakes are classified
according to criteria which are not dependent7
on the classification of other lakes in the
data set. It is possible to select a single
lake and classify it with systems of this type.
analytic systems - Combinations of parameters
and the formation of groups are based on
statistical or other numerical methods
(i.e., correlations, cluster analysis, etc.).
empirical systems - Parameters are combined
on an intuitive basis or boundaries of groups
are defined arbitrarily.
pre-set systems - Number and characteristics
of groups are specified from the outset, and
8
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classification is accomplished by matching the
characteristics of a given lake to those of
the predefined groups.
post-set systems - Groups or ranks are formed
from analysis or treatments of lake data.
The above definitions are useful for analyzing classifica-
tion systems, but it should be recognized that the defini-
tions are not mutually exclusive—a particular system may
involve a combination of the identified input, output and
processing types. Also, in some cases the distinction of
one type over another is not clear-cut. For example, a
system may be designed to classify several hundred lakes
according to their potential for additional recreational
development based on an integer scale from one to ten.
Since many lakes would have the same scale-value, and
these subgroups would have no internal hierarchy, this
could be viewed as a grouping system. However, based on
the gradation of groups with respect to a common scale of
reference, this could properly be considered to be a
ranking system. This latter view is most consistent with
the philosophy of this report.
PRE-SET GROUPING SYSTEMS
In systems of this type, the number of groups to be formed
and the characteristics of each group are specified at the
outset. Classification is accomplished by comparing the
characteristics of each lake to those of the predefined
groups. Thus, an original set of lakes is divided into
"homogeneous" subsets.
Simplicity is both the strength and weakness of pre-set
grouping systems. Useful systems can be devised which
require minimal data and, in some instances, subjective
information can be used when data are lacking. However,
the objective for classification must also be simple so
that logical groups can be defined which 1) include all
the combinations represented by the set of lakes to be
classified, or 2) are mutually exclusive so that a specific
lake "fits" into only one group.
Pre-set grouping systems can be used effectively for some
types of regulatory lake management. For example, dif-
ferent boating regulations could apply to lakes depending
on their classification based on size and depth. Outboard
motors could be prohibited on all small and/or shallow
lakes; an intermediate group could be defined on which
motors would be permitted, but at controlled speed or
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horsepower; high-speed activities could be permitted only
on large, deep lakes. The terms "large," "small," "deep"
and "shallow" must be quantified to apply such a system,
but this could most likely be done without difficulty.
The example illustrates the type of situation for which
pre-set grouping systems are most applicable:
1) Classification is done for a single purpose (boating
regulation).
2) The number of groups to be formed is readily
established in advance.
3) Each group is defined in a mutually exclusive manner
so that a given lake would fit in only one group.
4) There is a logical, though perhaps arbitrary, dis-
tinction between groups that is consistent with the
overall objective of classification.
For the most part, pre-set grouping systems are not readily
applicable to classifying lakes according to trophic status
or water quality consideration because it is extremely
difficult to define distinct groups. Trophic status is
generally viewed as a continuum extending from oligotrophy
to eutrophy with no interim breakpoints. One exception
to this generality is presented by Zafar (1959). As part
of a general system designed to provide a worldwide, taxo-
nomic description of lakes, he expanded on the earlier
work of Strom (1930) and suggested that Pearsall's basic
ratio, (Na+K)/(Ca+Mg), could be used to separate lakes
into three basic trophic groups (ratio is based on equiva-
lents) .
Trophic state Pearsall's ratio
Basically eutrophic Less than 1.2
" mesotrophic Between 1.2 & 2.0
" oligotrophic Greater than 2.0
These three groups were then further subdivided by con-
sidering the concentrations of N, P, and humus. The
subdivision of "basically eutrophic" is given below where
(+) and (-) refer to rich and poor, respectively.
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Basically Eutrophic
a-eutrophic + N, + P, + humus
g-eutrophic + N, - P, + humus
y-eutrophic + N, + P, - humus
6-eutrophic + N, - P, - humus
v-eutrophic - N, + P, + humus
X-eutrophic - N, - P, + humus
y-eutrophic - N, + P, - humus
ir-eutrophic - N, - P, - humus
from Zafar (1959)
An identical breakdown is given for basically mesotrophic
and oligotrophic lakes. The terms "rich" and "poor" were
not quantified by Zafar, and the system has not been tested
to establish its general validity or usefulness. It is
interesting to note that eight subgroups were defined to
cover all possible combinations of the three parameters
without regard to whether or not all combinations exist
in nature.
The worldwide taxonomy of lakes presented by Zafar was
intended as the basis for a standard vocabulary among
scientists. The intent was to give lakes descriptive
labels which depict their trophic character. Because of
differences in purpose and objectives, this system is not
directly applicable to assessments of lake protection and
renewal. However, if utilized in the manner intended by
Zafar, the system could help to clarify scientific com-
munication dealing with limnology.
POST-SET SYSTEMS
Techniques for calculating indices of trophic status from
mathematical equations are reported by Newton and
Fetterolf (1966) and Shannon and Brezonik (1972). In each
case, the objective was to develop an equation which com-
bined several indicators of trophic condition to yield a
composite "score" for any given lake. Parameters (inde-
pendent variables in the equations) were selected which,
based on the judgment of the investigators, were indicative
of general water quality conditions in the lakes.
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Newton and Fetterolf computed "chemical water quality
evaluation scores" using the equation,
Score = 2.(OP) + 0.1(COD) + (ON) + (NH3)
where OP = soluble orthophosphate concentration
in mg POi^/l
COD = chemical oxygen demand in mg 02/1
ON = organic nitrogen in mg N/l
NHs = free ammonia in mg N/l
For all parameters, the values inserted in the equation
are the sum of surface concentrations and hypolimnetic
concentrations (upper hypolimnion for lakes deeper than
40', lower hypolimnion for shallower lakes).
No information is given regarding the rationale for select-
ing the coefficients in the equation, except that "each
factor was weighted to adjust for its relative importance
in lake water qualtiy."
Data were collected for 10 lakes in Michigan and the lakes
were ranked as shown below.
Table 1. WATER QUALITY SCORES
FOR 10 MICHIGAN LAKES
Lake
Chemung
Crooked
Fenton
Lob dell
Ore
Ponemah
Potter
Silver
Squaw
Woodland
Score
8.7
8.7
4.5
4.7
12.3
21.0
22.2
4.8
13.3
7.4
Rank
5
6
1
2
7
9
10
3
8
4
from Newton and FetterQlf (1966)
The results were judged by the authors to give a reasonable
portrayal of the relative water quality in the 10 lakes.
12
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A somewhat different approach was used by Shannon and
Brezonik (1972). They approached the idea of "trophic
state" as a multidimensional concept that cannot be
measured directly or considered realistically on the
basis of individual trophic indicators. In this case,
indicators of trophic state are combined through treatment
with multivariate statistical procedures to yield alge-
braic expressions for computing "trophic status indexes"
for lakes. Separate expressions were developed for clear
and colored lakes, as given below.
For
clear
lakes:
TSI = 0.936|^ + 0.827(COND) + 0.907(TON)
0.748(TP) + 0.938(PP) + 0.892(CHA)
0.579 I Ad +4.76
For TSI = 0.848 g^ + 0.809(COND) + 0.887(TON)
colored *> '
lakes: + 0>768(TP) + o.930(PP) + 0.780(CHA)
+ 0.893 + 9.33
In these equations,the independent variables are stan-
dardized (i.e., raw value minus the mean for that
parameter and the difference is divided by the standard
deviation). The indicator parameters selected were:
SD = Secchi disk transparency in meters
COND = specific conductance in micromhos/cm
TON = total organic nitrogen in mg-N/1
TP = total phosphorus in mg-P/1
PP = primary productivity in mg-C/m3/hr
CHA = chlorophyll-a in mg/m3
CR = Pearson's cation ratio
Fifty-five lakes were sampled at 4-month intervals for one
year and average values were used as raw input for each
of the seven indicator parameters. The data were reduced
to standardized values and the method of principal com-
ponent analysis was used to generate the expressions for
computing TSI values. Importantly, the coefficients
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Table 2. FIFTY-FIVE FLORIDA LAKES
RANKED ACCORDING TO TROPHIC STATE INDEX (TSI)
Lake
Hypereutrophic group
Apopka
Twenty
Dora
Bivin's Arm
Griffin
Kanapaha
Alice
Eustis
Eutrophic group
Hawthorne
Clear
Burnt Pond
Wauberg
Newnan ' s
Mesotrophic group
Twenty- five
Harris
Twenty-s even
Cooter Pond
Lochloosa
Tuscawilla
Calf Pond
Orange
Mize
Watermelon Pond
Little Orange
Weir
Elizabeth
Ten
Palatka Pond
Seville's Pond
Meta
TSI
22.1
18.5
18.5
14.7
13.7
13.5
10.7
10.5
9.1
8.8
8.3
7.4
7.1
6.4
6.3
5.8
5.3
5.2
4.8
4.6
4.3
4.2
3.6
3.4
3.3
3.2
3.2
3.2
3.1
3.1
Lake
Oligotrophic group
Jeggord
Moss Lee
Long Pond
Clearwater
Altho
Hickory Pond
Santa Fe
Suggs
Little Santa Fe
Adaho
Wall
Winnott
Ultraoligotrophic group
Still Pond
Kingsley
Geneva
Gallilee
Swan
Anderson-Cue
McCloud
Brooklyn
Cowpen
Long
Sumter-Lowry
Magnolia
Santa Rosa
TSI
2.8
2.8
2.8
2.6
2.5
2.5
2.5
2.3
2.3
2.2
2.1
2.0
1.9
1.9
1.8
1.6
1.5
1.5
1.5
1.5
1.5
1.3
1.3
1.3
1.3
from Shannon & Brezonik (1972)
(weighting factors) in the TSI equations were not deter-
mined separately or arbitrarily, but were developed from
correlative relationships among the indicator parameters.
Thus, the approach is an attempt to deal directly with the
multidimensional nature of trophic status. The constants
14
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which appear in each of the TSI equations were introduced
to eliminate negative TSI values when zero raw data values
are used; if all indicator parameters are zero, the TSI
value is zero.
The system was judged to work very well for clear lakes;
less well, but satisfactorily, for colored lakes. The
composite ranking of the 55 lakes was divided into five
groups as follows:
hypereutrophic - TSI greater than 10
eutrophic - TSI between 7 & 10
mesotrophic - TSI between 3 & 7
oligotrophic - TSI between 2 & 3
ultraoligotrophic - TSI less than 2
The authors cite the major value of the TSI concept to be
the possibility of ranking lakes in a logical and objective
manner. They warn, however, that if the sample set is too
diverse, interpretation of results may be difficult or
impossible, and they did not suggest extending the TSI
concept to develop a single, universal index for all lakes.
A classification system based on a straight-forward ranking
approach is reported by Lueschow et al (1970) . In this
study, 12 relatively well-known lakes in Wisconsin were
selected for analysis. Care was taken in selecting the
lakes to assure that the set contained a variety of types
ranging from oligotrophic to eutrophic. Seven parameters
were selected for measurement: 1) hypolimnetic dissolved
oxygen, 2) plankton (No. 20 mesh net), 3) Secchi disk
transparency, 4) organic nitrogen, 5) total inorganic
nitrogen, 6) soluble phosphorus, and 7) total phosphorus;
and the study was then conducted to determine whether or
not these parameters would permit the test lakes to be
ranked in a manner consistent with their perceived trophic
condition.
The lakes were sampled at monthly intervals for one year,
and average values of each parameter, except dissolved
oxygen, were used for classification purposes. Minimum
dissolved oxygen concentrations, measured 1 meter off the
bottom, were used.
Classification was accomplished by selecting one parameter
and then ranking all 12 lakes according to that parameter
alone. This was done for each of the seven parameters
listed above. Difficulty was encountered with the total
15
-------
and soluble phosphorus data, however. Four of the 12 lakes
had received treatments with arsenic compounds for aquatic
nuisance control in the past. Consequently, arsenic inter-
ference with the phosphorus determinations precluded use of
the data for these lakes; therefore, only eight of the lakes
could be ranked with respect to the two phosphorus parameters,
A composite ranking of the 12 lakes was then prepared from
five of the individual rankings; phosphorus rankings were
ignored for the composite. Points were assigned to each
lake equivalent to its position in the rankings, i.e., one
point for first, two for second, and so forth. Points
were then totalled for the five lists. Possible scores
ranged from 5 to 60 with eutrophic lakes scoring highest.
Scores for the 12 lakes tested ranged from 8 to 52 as shown
below. Although consideration of phosphorus concentra-
tions was not entered into the composite rankings, the
authors state that including phosphorus would not alter
the results significantly. The final ranking produced by
Lueschow et al is generally regarded as a realistic one
by limnologists familiar with the 12 test lakes.
Table 3. COMPOSITE RATING OF 12 WISCONSIN LAKES
CJ
•H
O
-P
O
-P t7>
tn -H
O rH
sot
0 4-
•H
0,
O
-P >-i
03 -P
O 3
S OJ
Rank
1
2
3
4
5
6
7
8
9
10
11
12
Lake name
Crystal
Big Green
Geneva
Trout
Round
Pine
Middle
Oconomowoc
Mendota
Pewaukee
Delavan
Winnebago
Composite
County score
Vilas
Green Lake
Walworth
Vilas
Waupaca
Waukesha
Walworth
Waukesha
Dane
Waukesha
Walworth
Winnebago
8
17
17
19
31
33
33
34
45
49
52
52
from Lueschow et al (1970)
16
-------
A modification of the methodology presented by Lueschow et
al was used to classify 209 lakes and reservoirs as part
of the preliminary data analysis for the National Eutro-
phication Survey (U.S. Environmental Protection Agency,
1974). The basic approach is the same, but some changes
were made in the input parameters, and the absolute ranking
technique used by Lueschow et al was replaced with a per-
centile ranking procedure.
The six parameters selected for inclusion in the classi-
fication were: 1) total phosphorus, 2) dissolved phosphorus,
3) inorganic nitrogen, 4) Secchi depth, 5) minimum dissolved
oxygen (in hypolimnion if stratified), and 6) chlorophyll-a.
It is suggested that input values be means or medians for
the entire lake, but no information is given regarding the
time of sampling or if averages of several sampling dates
should be used.
To classify lakes, percentile rankings are determined in-
dividually for each of the six parameters (The values for
Secchi disk transparency and minimum DO were first subtracted
from fixed values, so that all parameters indicative of
decreasing water quality affect percentile rankings in the
same direction.), and composite values are determined by
summing each of the six percentile ranks. Composite ranking
values were termed, "Trophic Index Numbers," (TIN). Since
a percentile ranking system was used, the range of possible
TIN values depends on the number of lakes classified; how-
ever, if 99 or more lakes are classified, the -possible range
extends from 0 to 594 (6x99).
Results of the classification of more than 200 lakes in the
northeast and north-central parts of the country were judged
to be quite satisfactory. Some difficulties were encountered
with reservoirs having short hydraulic-residence-times and
with shallow lakes having extensive macrophyte growths, but
the problems were minor. For the data set analyzed, the
following lake type-TIN ranges were found to apply:
0-420 eutrophic
420-499 mesotrophic
500-594 oligotrophic
It should be noted, however, that these ranges are meaning-
ful only for the specific data set analyzed, because TIN
values (or index values from any relative classification
system) are indicative of a lake's rank relative to others
in the data set, but have no absolute meaning. Therefore,
TIN values cannot be used directly to compare two sets of
lakes classified separately by the same system.
17
-------
Statistically significant differences were used as a basis
for lake classification by McColl (1972). Surface and
bottom waters of 7 New Zealand lakes were sampled monthly
for one year, and 24 different chemical determinations
were made. Chemical analyses included dissolved oxygen,
major ions, silica, P, N, algal pigments and some trace
elements.
It was found that dissolved oxygen (hypolimnetic defi-
ciencies) , Secchi disk transparency, alkalinity differen-
tial between epilimnion and hypolimnion in summer, algal
pigment, P, N, Fe, and Mn were related to trophic status.
On the contrary, no relationship was found between trophic
status and pH, Ca, Mg, Na, K, SO 4, Cl, Cu, Zn, Si and total
dissolved solids. These parameters were then set aside,
and analyses of variance, t-tests, and Tukey's Studentized
range tests were used to classify the 7 lakes based on the
remaining parameters. Groups were formed by determining
whether means of given parameters diffused significantly
at the 1% level (P=0.01).
The statistical procedures resulted in the separation of
the 7 lakes into three subsets—two lakes were judged to
be eutrophic, two were mesotrophic and three were oligo-
trophic. The results were judged to be a realistic classi-
fication.
In France, Feuillade (1972) used factor analysis to compare
data from two lakes and reported that the technique may be
useful for lake classification on a much broader scale.
A thorough discussion of the philosophy, as well as the
techniques, of classification is reported by Sheldon
(1972) . After a comprehensive discussion of statistical
methods, he employs a combination of principal components
reduction, vector ordination, and the D measure of the
Gower method (Gower, 1966) to the physical, chemical, and
biological data collected from different regions of the
world by various investigators. Sets of lakes studied by
others are reclassified by Sheldon, but the rankings are
de-emphasized in favor of the general methodology. The
author's main purpose is to demonstrate that statistical
procedures can be used to delineate the same grouping of
lakes determined by other workers, but in a more orderly
and universal fashion. As examples, Sheldon ranked 121
lakes in North America and 15 in Sweden and showed that
numerical analysis can be used to form groups of lakes
that are limnologically similar, without resorting to
empirical classification systems designed to incorporate
some aspects of the eutrophication process. Other advan-
18
-------
Table 4. CLASSIFICATION OF 7 NEW ZEALAND LAKES
Lake
Eutrophic
Okaro
Ngapouri
Mesotrophic
Rotokakahi
Okareka
Oligotrophic
Okataina
Rotoma
Tikitapu
Mean (geoi
surface
Chlorophyll
17
8
3
2
1.2
1.7
1.3
Pi
netric)
water
Total P
69
50
25
13
14
9
5
arameter
Maximal summer
surface water
(Dec . , Jan . , Feb . )
Reactive POi»-P Nitrate-N
lir* /I
yg/i — —
11 8
17 3
5 2
5 3
5 3
5 2
4 1
after McColl (1972)
19
-------
tages of this general approach are 1) the rapid retrieval
of information, 2) the ability to organize large data sets,
and 3) the ready identification of unusual or unique
qualities.
Sheldon's philosophical commentary is at least as valuable
as his contribution to method. In a lucid discussion of
classification in general, he asserts that the resource
planning process neglects the small lakes in favor of the
"large, spectacular, and unusual" which will be managed,
abused or preserved quite independently of the rest. While
citing the value of simple, one-directional classification
systems, Sheldon warns against the use of classification
systems as a substitute for knowledge, and suggests that
special use or single purpose classification systems breed
conflict rather than resolve it.
Systems to classify lakes according to trophic status are
summarized in Table 5. This summary illustrates some of
the difficulties related to developing these systems.
There presently exists no generally-accepted definition
of the term, "trophic status." This is pointed out quite
clearly by the different parameters which were selected
as indicators, and the different ways in which these pa-
rameters were combined in the various systems. The number
of input parameters ranged from 4 to 16 and, in some cases,
single measurements were sufficient while repetitive deter-
minations were required in others.
Lack of a precise definition of "trophic status" also makes
it difficult to assess the "accuracy" of different systems.
When developing analytical procedures in chemistry, one can
prepare a set of standard solutions to test the procedure.
No analogous set of standards exists for measuring trophic
status of lakes. At best, one can evaluate the results
on a subjective basis to see if they appear to be reason-
able. All of the techniques used were judged by the
investigators to yield reasonable results. In most cases
it was felt that the technique might have broader applica-
tion, and it was suggested that the systems be applied
with caution elsewhere.
The techniques reported by EPA (1974) , Feuillade (1972) ,
Lueschow et al (1970), McColl (1972) and Sheldon (1972) are
all relative systems in which lakes are classified only
with respect to each other and not to some independent scale.
(To some extent, the system described by Shannon and Brezonik
(1972) is also the relative type because the coefficients
in the TSI equations are determined from the input data
set.) This is considered to be a disadvantage for most
20
-------
Table 5. SUMMARY OF SYSTEMS TO CLASSIFY LAKES ACCORDING TO TROPHIC STATUS
Lakes
Investigators classified
EPA 209
Feuillade 2-France
Lueschow .. _ TT.
, 1 12-Wisconsin
&t aZ-
McColl 7-N. Zealand
Newton and in ... ,
Fetterolf 10-Michigan
Shannon and 55_Florida
Brezonik
Sheldon 121-N. America
Sheldon 15-Sweden
Zafar
4J
c
0) rH
4J >C 01 U td
•U Ol (3 > iH CJ
O> (0 0) -H 4J -H
D- CO 1 PL. 4-J >s M
3 ^i 1 W 01 CO r-l -rl
ocoico'di-irtp.
j-injvioccudg
CJ5 p^ PH PH HH fv]
-------
broad-scale applications because the classification of
specific lakes may change if additional lakes are added
to the data set and index numbers have meaning only with
respect to the lakes in that siet. However, it is likely
that most of the systems could be modified to eliminate
or minimize the disadvantage.
From a practical standpoint, it is interesting to note that
data collection was an integral part of the classification
studies except for the work of Zafar (1959) and Sheldon
(1972). The other investigators found that sufficient
data were not available from other sources, even though
12 or fewer lakes were classified in four of the reported
studies. Lack of uniform data imposes a serious con-
straint on the immediate, broad-scale use of any of the
systems listed in Table 5.
22
-------
SECTION IV
DATA ACQUISITION
With the data requirements for the various lake classifica-
tion systems in mind, an assessment was made of the number
of lakes throughout the country which could potentially be
classified using one of the published techniques. Toward
this end, an attempt was made to determine whether suffi-
cient data had been compiled by various state agencies,
and whether this data might be made available for the
purpose of lake classification. Contacts were made with
conservation departments, fish and game commissions, pollu-
tion control agencies or natural resources departments in
each of the 50 states to determine: 1) the number of lakes
in each state, 2) the number of lakes larger than 100 surface
acres (40 ha), and 3) the extent to which water quality data
had been compiled for the lakes. (The selection of 100 acres
as a breakpoint was an arbitrary decision intended to form
a smaller, more workable subset for the initial analysis,
and to concentrate on the larger lakes which were judged
most likely to be of regional, rather than local, sig-
nificance. )
Results of our inquiries are summarized in Tables 6 and 7.
It was found that complete lake inventories have been con-
ducted in 34 of the 48 contiguous states; however, for
9 of these the data are unpublished (and may be located in
scattered office files without a summary compilation
available). By a complete inventory it is meant that lakes
are identified by name, location, size and sometimes depth,
and that the inventory covers the entire state and includes
all lakes with surface areas larger than 50 acres. An
additional 7 states have conducted partial inventories.
Major reservoirs only have been listed for Alabama, Kansas,
Texas and Virginia; a registry of dams is available for
California and North Carolina. Nebraska has conducted an
inventory which covers only the sandhills area of the
state, but includes most of the state's lakes.
23
-------
Table 6. SUMMARY OF LAKE INVENTORY DATA
(U
(d
rH
to
(U
•8
(U
•H
(U
N
•H
CO
to
Q) W
td u
i—i fd
w o
State
Alabama*
Arizona
Arkansas
California*
Colorado*
Connecticut
Delaware*
Florida
Georgia*
Idaho
Illinois*
Indiana
Iowa
Kansas*
Kentucky*
Louisiana*
Maine*
Maryland*
Massachusetts
Michigan*
Minnesota
Mississippi*
Missouri*
Montana*
O
W -P
3 C
-P Q)
m >
•P C
CO -H
c
a
a
c
d
a
b
a
d
b
a
a
a
c
a
d
b
d
a
b
a
d
b
d
o
< i i \
-------
Table 6. SUMMARY OF LAKE INVENTORY Con't
State
Nebraska*
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina*
North Dakota*
Ohio
Oklahoma
Oregon*
Pennsylvania*
Rhode Island
South Carolina
South Dakota
Tennessee
Texas*
Utah
Vermont
Virginia*
Washington
West Virginia*
Wisconsin*
Wyoming*
Totals
0)
x
fO
H
>W >i
o n
o
to -P
P C
-P 0)
rt >
4J fi
CO -H
c
a
a
a
a
a
c
d
b
a
b
a
a
a
a
a
c
a
a
c
a
b
a
b
co
0)
X
(0
•H -O
0)
m -H
0 H
o
H -P
Q) C
o
3 c
23 -H
1,885
329
845
1,727
200
4,155
928
(2,562)
3,017
1,777
6,435
2,403
175
193
488
552
152
256
535
435
7,894
(8)
5,553
2,261
94,877
a)
N
•H
CO
CO
e
-------
Table 7. AVAILABILITY OF WATER QUALITY DATA FOR LAKES
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana*
Iowa
Kansas
Kentucky
Louisiana
Maine*
Maryland
Massachusetts
Michigan
Minnesota*
Mississippi
Missouri
Montana
a
-P
(0
"0
H
(0
u
•H
W
>i
,C
PM
28
12
11
63
75
64
26
-
208
16
1
27
127
*
16
77
76
9
*
8
16
89 +
*
25
8
74
iH
(0
U W
•H -P
e c
0) 0)
x: 3
O -M
-H
n -p
O W
•r-> C
5 0
S o
3
7
5
4
18
23
6
_
163
2
1
12
11+
*
p
23
54
p
*
-
—
37
*
6
-
18
rH
(0
O W
•H 4J
e c
0) (U
43 3
0 -P
•H
M 4J
O W
G C
S8
2
2
—
1
4
1
1
_
?
_
1
4
_
*
—
—
-
_
*
—
—
4
*
—
—
2
rH
i
i— 1 rH
O (C
•H C
« ti
4
3
3
7
11
2
p
_
_
_
_
*
_
19
p
_
*
—
_
11
*
p
_
-
*This tabulation does not include the EPA National Lake
Survey data.
*Sizable monitoring programs, but summary information not
available.
26
-------
Table 7. AVAILABILITY OF WATER QUALITY DATA FOR LAKES Con't
State
Nebraska
Nevada
New Hampshire*
New Jersey*
New Mexico
New York*
North Carolina
North Dakota
Ohio
Oklahoma
Oregon*
Pennsylvania*
Rhode Island*
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin*
Wyoming
m
-P
(0
T3
rH
m
o
-H
05
!>i
£
50
*
*
4
65+*
71
189 +
82 +
42
*
*
*
149
38 +
145+
24
21
42
15+
37+
47
400 +
34
rH
m
o 05
-H -P
e c
a)
c*t j3
0 -P
-H
r-l -P
O 05
•n C
(0 O
S u
p
*
*
p
65+*
13
34 +
p
-
*
*
*
32
p
?
4
-
?
•p
25+
8
400+
12
rH
0 05
•rH -M
e c
i
rH rH
O (0
-H C
PQ 13
-
*
*
-
?*
7
p
12
—
*
*
18
-
-
-
—
-
-
?
-
-
"^This tabulation does not include the EPA National Lake
Survey data.
*Sizable monitoring programs, but summary information not
available.
27
-------
When lake inventory information was not available, an
attempt was made to estimate the number of lakes. In some
cases, estimates were provided by agency representatives
(Del, Ga, Kans, Ky, Me, Neb, N.C., Va, W.Va); and for eight
states, project personnel counted lakes which appear on
1:500,000 scale maps obtained from the U.S. Geological Survey
(Colo, La, Md, Miss, Mo, Mont, N.D., Wyo). Tabulations from
maps should be interpreted as minimum values because small
lakes (<40-50 acres) were not shown. Lake sizes were esti-
mated from the maps to determine the number of lakes with
surface areas larger than 100 acres.
It was found that at the time of this survey there were
11,490 lakes larger than 100 acres listed in inventory
records. It is estimated that there are an additional
2,109 of these lakes, making a total of 13,599 "large"
lakes. This is considered to be a good estimate which is
not likely to be in error by more than a few percent.
A total of 94,877 lakes are listed in Table 6 (87,557 from
inventories; 7,320 from maps). Although these numbers
represent the best data obtained in the survey, it is known
that these estimates are far less accurate than those given
for larger lakes. There are several reasons for this, but
probably the most important factor is the lack of a universal
definition of what constitutes a lake. The approach used
in compiling most state inventories was to specify a minimum
surface area which must be exceeded for a water body to be
included in the lake inventory. Values between 5 and 10
acres were used most often for the minimum size criterion.
Selection of minimum lake sizes drastically affects the
number of lakes that "exist" in any state. For example,
the lake inventory in Illinois lists 2,167 lakes which are
6 acres or larger (Lopinot, 1966). The same report states
that in 1965 there were 62,627 ponds in Illinois (mostly
impoundments) ranging in size from 0.1 to 5.9 acres, and
that an average of 1,590 impoundments were constructed
each year between 1963-65. Thus, lakes larger than 6 acres
accounted for only 3.3% of the total number of "lakes" but,
at the same time, 79.6% of the total surface acreage came
from the larger lakes.
This example illustrates the degree of variability in total
lake numbers which can be introduced by different minimum
size criteria. The numbers given in Table 6 have not been
adjusted to any standard definition of "lake," and this
should be taken into account when conclusions are drawn
from the data. In preparing the table, no distinction was
made between natural lakes and reservoirs; both types are
28
-------
included. However, farm ponds are not included, and inter-
mittent lakes were excluded wherever possible.
Hawaii and Alaska are not included in the lake summary.
Hawaii has 9 lakes—5 larger than 100 acres. The total
number of lakes in Alaska is not known; however, there are
94 lakes with surface areas in excess of 10 square miles,
and the total number may be greater than the combined total
in the remaining states.
An attempt was also made to determine the availability of
water quality data for lakes throughout the country—
particularly for the larger lakes. It was hoped that an
estimate could be made of the number of lakes which could
be classified using each of the systems described in the
preceding section. This effort was only partially suc-
cessful. It was not possible to obtain descriptions of
existing data holdings in sufficient detail to quantify the
degree to which each of the various systems could be used;
however, a general description of the information received
through our inquiry is summarized in Table 7. The number
of lake monitoring stations are listed by state and the type
of data collected is separated into four general categories:
1) field measurements and physical data - to include one
or more of the following: temperature, transparency,
specific conductance, dissolved oxygen, pH;
2) major chemical constituents - to include one or more
of the following: P, N (the various species), Cl,
SO it, Fe, Ca, Mg;
3) minor chemical constituents - such as trace metals
and miscellaneous organic compounds;
4) biological measurements - to include zooplankton and
phytoplankton measurements, bacterial analyses and/or
rooted aquatic plant surveys.
Generally, the figures may be described as sketchy at best.
The numbers listed are almost certainly underestimates (the
research literature of the past five to ten years suggests
more lake monitoring stations than are accounted for in
Table 7). Through the course of our inquiry it was noted,
ironically enough, that the ability to assess a state's
program varied inversely with the size of the program. That
is, those states with the most comprehensive monitoring
efforts were the least able to quantify the extent or char-
acter of those efforts. Nine of the states listed in Table 7
(those marked with an asterisk) are known to have sizable
water quality monitoring programs, but no accurate summary
information was available. For this reason no totals have
been calculated for the various data categories.
29
-------
Qualitatively, the figures may imply more data than
actually exist because many of the stations have been
monitored only once, and then for only one or two pa-
rameters. Very few of the stations have been sampled
on a regular basis (i.e., monthly or quarterly) or for
detailed, comprehensive analyses. Furthermore, the range
and variability of available lake data within a state are
often great. As an example, the state of Wisconsin has
two to three dozen lakes which have been the subjects of
fairly intensive investigation by educational institutions
and state agencies. The state's Department of Natural
Resources (DNR) is currently monitoring approximately
200 lakes on a quarterly basis for a wide spectrum of lim-
nological parameters. Similar data are available for an
additional 100 lakes which are not currently monitored.
The DNR "Surface Water Inventory Program" has been in
existence for over 15 years, and during its course vir-
tually all of Wisconsin's lakes have been surveyed at least
once, although some of this information is no longer current,
Finally, at the bottom of the scale there will be a few
lakes about which little or nothing is known.
While the results of this portion of our inquiry have
proved inadequate for determining the number of lakes
which could be classified with the systems described pre-
viously, the reasons for these vagaries are worth enumer-
ating for their own sake.
1) Some states have no formal monitoring program at all
and the data which exist have been generated through
university research programs or contracts with federal
agencies. To the extent that such information was
available from state officials it is included in Table
7, but our survey was limited to state agencies—no
inquiries were made directly to educational institu-
tions or federal agencies.
2) Other states have monitoring programs, but these are
often limited to a small number of sites for special
purposes. A number of states have programs which
emphasize river and stream surveys for water supply
(quantity rather than quality), flood control, or
public health purposes.
3) Still other states have programs for which several
agencies (including county and municipal as well as
state) share responsibility, but not necessarily
mutual interest or goals. Consequently, the infor-
mation available to any given agency may be limited
to the results of its own efforts.
30
-------
4) Finally, a few states do have monitoring programs of
significant size, but in such cases there is no "summary"
information available and it appeared that data handling
and exchange were noteworthy problems.
At this point, distinction should be made between the
existence and accessibility of data. In most states, the
responsibility for gathering lake data is split among
several agencies, and interagency coordination is sometimes
weak. Shared data banks are not common, and data exchange
between agencies is often cumbersome. Problems of acces-
sibility are further compounded by the lack of centralized
data storage within many agencies. A common response to
our inquiries was, "I believe the type of data you are
seeking has been collected for some of our lakes, but the
data would be located in the files of our district offices
and it would be a major undertaking for this information
to be pulled together." Concern was also expressed for the
quality, completeness and comparability of existing data,
but fragmentation of data holdings was clearly the factor
which most seriously limits data accessibility. This is not
to imply that the need for data consolidation was not recog-
nized; on the contrary, the need was widely recognized, but
practical constraints of staff limitations and fiscal
problems have hampered progress in this direction. However,
several states reported that coordinated data collection
and storage programs are in the planning stage, and ex-
pressed optimism that recent and future legislation would
encourage an expanded, coordinated approach with well-
defined long-term goals.
Efforts to assess the present lake data base led to the
following conclusions:
1) The number of lakes for which water quality data exist
is small, and the number for which it is accessible is
even smaller. Even when only those lakes larger than
100 acres are considered, data sufficient to utilize
reported lake classification systems are not available
for the vast majority of lakes. For smaller lakes,
the situation is even more bleak.
2) Consolidation of existing data within states would
permit the classification of more lakes, but the total
number would still be small. Accessibility of this
data to parties in other states is virtually impossible.
Thus, it became obvious quite early in the study that if
classification of any significant number of lakes was to
be accomplished, a system requiring very minimal data input
would have to be devised; insofar as possible, subjective
information would have to be used as a replacement for
31
-------
field data; and compilation of even this minimal information
would have to be undertaken as an integral part of the
classification effort. The necessity of compiling an
information base also led to consideration of perhaps
collecting auxiliary information which would facilitate
the use of lake classification in a management context.
It was felt that, if possible, the information collected
should not only be indicative of the trophic character of
lakes, but should also contribute to the development of
management strategies for lake renewal and protection.
It was concluded that a far better perspective of the
options and priorities for broad-scale lake management
would be attained if it were possible to identify three
general lake-quality categories:
1) those lakes which are presently of high quality and
are not expected to degrade in the near future;
2) those lakes which are of high quality, but are subject
to degradation and require protective action to main-
tain their present condition; and
3) those lakes which have deteriorated to the extent that
protective action is no longer sufficient and reha-
bilitation is necessary to restore satisfactory water
quality conditions.
Each of these categories implies a combined knowledge of
lake condition and nutrient loading rates. The ideal
approach of obtaining specific loading rates for nitrogen
and phosphorus, and lake residence-time data for conser-
vative and nonconservative substances was not a realistic
option for a project of this scope. However, it did appear
feasible to collect general land-use information for lake
basins so that nutrient loading rates could be approximated.
Another consideration, which was significant in shaping
the basic approach used, had to do with the use of frag-
mentary field data as a basis for classification. In
particular, questions arose regarding the use of nutrient
data when only a single measurement was available for a
lake. Is a single measurement of dissolved inorganic phos-
phorus a meaningful indicator of the trophic character of
a lake? It was decided that the use of numerical data of
this type would probably contribute little useful informa-
tion, and considering the time and space variations of many
chemical parameters (without regard to differing sampling
and analytical techniques), it was judged that reliance
on single-point values could easily lead to misleading or
erroneous results. This is not to say that fragmentary
32
-------
data should be totally ignored, but its use should be tem-
pered by judgment. The values should not be used blindly
by entering them into an equation for calculating a "trophic
score."
In accordance with the above information and philosophy, an
approach was developed in which lake information would be
sought from individuals who had personal knowledge of the
lakes in question, were familiar with basic limnological
principles, and had access to whatever data might exist.
Thus, a standard data form, or "questionnaire," was designed
with the intent that responses would be solicited from
resource managers or individuals in similar positions who
could provide the firsthand information we requested.
The data form finally selected (see appendix) was the product
of an iterative process which involved several generations
in its development. Initially, a small group of researchers
put together a "questionnaire" for use in obtaining the
desired information. Emphasis was directed not only to
"what is needed," but equally, or perhaps even more
strongly, to "what can we eliminate and still have valid
results." Draft forms underwent peer review and were also
submitted to several individuals who were representative
of the type of respondents likely to be asked later to
provide information. The latter group was asked to give an
assessment of the extent to which requested information
could be provided and to identify items which would be
difficult (or impossible) to provide. The development also
involved computer specialists to minimize potential dif-
ficulties in data processing and handling. Fortunately, it
was practical to design the input form primarily for ease
and continuity of thought for those who would be providing
the selected information—it was not necessary to impose a
computer format on the data form itself. This was viewed
as a distinct advantage by those asked to complete data
forms.
The input form selected does not completely ignore "data"
or hard numbers, but nearly so. Numbers are requested for
certain descriptors, such as area, depth, and location.
And, regarding shoreline development and drainage area land
use, "percentage" estimates are requested; but the remaining
questions are answered "yes" or "no," or by checking the
appropriate range or category. Basically, the questions
are in the following categories:
1) physical and morphometric characteristics;
2) water quality (dissolved oxygen, transparency, etc.);
33
-------
3) problems, impairment, and treatments;
4) availability of background information;
5) drainage area land use; and
6) potential sources of pollutants or nutrients.
It must be emphasized that the design of this data form to
"ignore data" is not intended to demean the value of de-
tailed studies which must ultimately be performed in any
serious attempt at lake renewal and management. Rather,
the purpose here is to gather a significant amount of scien-
tifically valid information which is realistically acquired
and computer compatible. With regard to the latter, arrange-
ments were made with the University of Wisconsin Data and
Computation Center for data processing and storage of in-
formation generated by the inventory. A program was written
which transfers coded data punched directly from the ques-
tionnaire to a data file on magnetic tape.
The questionnaire was designed for use throughout the
country, but a decision was reached to concentrate initial
efforts in Wisconsin to: 1) test the questionnaire, 2) gain
preliminary insight into the results, and 3) explore the
administrative arrangements necessary to procure informa-
tion. (One factor which contributed to this decision was
the passage of the 1972 Amendments to the Water Quality Act
which included the provision that " each State shall
classify its publicly owned lakes according to eutrophic
conditions." Thus, the responsibility for classifying lakes
was placed squarely on each state, and similar efforts by
outside groups were, in some instances, viewed as a dupli-
cation of effort.)
In Wisconsin, the primary responsibility for lake manage-
ment is vested with the Department of Natural Resources,
and arrangements were made with that agency to provide the
necessary lake information for the state. Some of the de-
sired information had already been compiled by the DNR and
was stored in a computer data file. A study of the file
contents showed that 23 items—about 30% of the needed in-
formation—were contained in the data file. A print-out
of this information was provided by the DNR and was trans-
ferred to the data forms by project personnel.
At this point it was possible to define precisely the addi-
tional information that was required to complete the forms,
and a meeting was held with administrative representatives
of DNR to explore the possibility that district personnel
could provide the needed data. The representatives were
extremely hesitant to commit the necessary manpower to this
34
-------
effort because their staffs were already overtaxed and would
not have time to take on the added task. However, an agree-
ment was reached with the approval of the granting agency
whereby the DNR would be compensated for the time spent by
its personnel in providing the lake data.
Because the cost of obtaining data is an important factor
in any effort, the project's expenditures are outlined
below:
District
Southern
West Central
Northwestern
North Central
Lake Michigan
Southeastern
Number
of lakes
44
46
422
512
60
65
1149
Total cost
($112.00)
(288.75)
(1113.00)
(560.00)
($2073.75)
Cost per
questionnaire
$2.55
6.28
2.64
1.09
$2.02
The total cost for the 1024 lake forms completed by the DNR
was $2,073.75, or an average of $2.02 per questionnaire.
However, the cost per questionnaire varied substantially
among districts, a factor to be considered in any large-
scale or nationwide effort. If the North Central District,
with 512 lakes, had furnished the information at the same
unit cost as the West Central, the total cost for the
former would have been $3221 rather than $560. The North
Central District, with the lowest unit cost, nevertheless
furnished the most complete data forms and the highest
quality information.
It should be noted that the majority of the lake data for
Wisconsin was provided by a relatively small number of
people. Only about a dozen individuals were involved
directly in completing the data forms, although they did
contact additional people for specific items. This pro-
vided a degree of uniformity to the data base—an important
factor when subjective judgments are involved—and also
permitted project personnel to meet with each respondee
to explain project objectives and answer questions. This
latter aspect was significant in expediting compilation
of the information base. It is doubtful that the data
base could have been developed if the questionnaires were
mailed to a large number of potential respondents.
35
-------
In spite of the care exercised in specifying the information
to be collected, the arrangements which were made to procure
the data, and follow-up efforts by project staff, it was not
possible to obtain all the requested information for all
lakes. The most troublesome items were related to the
direct drainage areas for lakes and land use character-
istics within these drainage basins. Drainage areas have
not been delineated for all lakes--topographic quadrangles
have not been prepared for some portions of Wisconsin, and
information regarding land use within drainage basins is
only approximate. However, those items necessary for char-
acterizing the trophic nature of lakes were obtained for
virtually all lakes in the survey.
36
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SECTION V
TROPHIC CHARACTERIZATION OF WISCONSIN LAKES
Because of constraints imposed by the lack of chemical and
biological data for most lakes, a classification system
was developed which is based on some of the more readily
observable indicators of eutrophication. A technique was
devised in which "penalty points" were assigned to lakes
depending upon the degree to which they exhibited unde-
sirable symptoms of water quality. Four parameters were
selected for analysis, and ranges of values for each
parameter were specified which depicted lake conditions
ranging from desirable to undesirable. The parameters
used and the range of possible points assigned are listed
below.
Table 8. POINT SYSTEM
FOR LAKE CONDITION INDEX
Parameter Points
Dissolved oxygen 0-6
Transparency 0-4
Fishkills 0,4
Use impairment 0-9
Total 0-23
The parameters were treated independently, and composite
lake ratings were determined by summing the number of
points assigned in each of the four categories. The sum
is termed a "Lake Condition Index" (LCI).
It is important to recognize that although the system is
designed to segregate lakes according to the extent to
which they display undesirable characteristics normally
associated with eutrophication, the LCI is not necessarily
37
-------
synonymous with "trophic status" or "productivity." Two
equally productive lakes could have different LCI values,
depending on the manner in which this productivity in-
fluences the oxygen regimen, transparency, fish survival
and recreational uses of the lake. For example, two lakes
of varying depth may be very similar in productivity. The
deeper lake, with its larger hypolimnetic volume fore-
stalling oxygen depletion, would receive few penalty points
for this condition and, as a result, have a low LCI. On
the other hand, hypolimnetic oxygen depletion would likely
be a common occurrence in the shallower lake, and its LCI
could be substantially higher due to the penalty points
it received as a consequence.
Dissolved oxygen in the hypolimnion was selected as one
parameter for consideration because depletion of hypolim-
netic oxygen supplies reflects the integral effect of many
lake processes. The rate of hypolimnetic oxygen depletion,
normalized to account for lake morphometry, would likely
be an excellent parameter for classifying lakes. However,
even though DO profiles are one of the more common measure-
ments made on lakes, the frequency of measurement is often
insufficient to permit calculation of depletion rates.
Therefore, as a compromise, the classification methodology
for DO was based on the minimum conditions which were
expected to occur in the hypolimnion during the stratified
period. Points were assigned in the following manner:
Penalty points
Max depth Max depth
Dissolved oxygen conditions <30' >30'
Dissolved oxygen in hypolimnion
greater than 5 ppm at virtually 0
all times
Concentrations in hypolimnion
less than 5 ppm but greater 1
than 0 ppm
Portions of hypolimnion void
of oxygen at times
Entire hypolimnion void of
oxygen at times
As noted in the tabulation above, lake morphometry was
taken into account in an approximate way by assigning more
points to the deeper lakes. The breakpoint of 30 ft (10 m)
38
-------
maximum depth was selected arbitrarily as an indicator of
lake basin geometry, which separates lakes with "large"
or "small" hypolimnetic volumes as compared to the volume
of the epilimnion.
Lakes which do not stratify can receive few or no penalty
points for dissolved oxygen conditions. This affects the
LCI values of reservoirs with short hydraulic retention
times as well as shallow lakes which undergo continual
overturn.
Secchi disk transparency was incorporated into the system
by using typical annual maximum and minimum Secchi depths.
Ranges rather than specific values were used.
Range
1)
2)
3)
4)
Typical Secchi depth
0 - 1.5 ft
1.5 - 10 ft
10 - 23 ft
>23 ft
( 0 - 0.5 m)
(0.5 -
( 3 -
3
7
m)
m)
>7 m)
The first range represents a condition in which light
penetration would be severely limited. Within the second
range, the depth of the photic zone is likely to be less
than the depth of the epilimnion. Conversely, Secchi depths
within the third range are indicative of a photic zone which
extends below the epilimnion except for large lakes.
Points were assigned according to the combination of depth
ranges which encompass the typical maximum and minimum
Secchi depths. In the tabulation below, the above-listed
range numbers of 1-4 are used: (Note: A provision is also
included to cover the possibility that only one range of
Secchi depths would be given.)
Transparency conditions
if both ranges are given
Minimum
range
1
1
1
1
2
2
2
3
3
Maximum
range
1
2
3
4
2
3
4
3
4
Penalty
points
4
3
2
2
2
1
0
0
0
Transparency conditions
if only one range is given
Secchi
depth range
1
2
3
4
Penalty
points
4
2
1
0
39
-------
The occurrence of fishkills was considered in the classi-
fication system, but no attempt was made to stipulate fre-
quency or severity. Lake depth was taken into account
however, and 30 ft (10 m) was again used as the breakpoint,
Penalty
History of fishkills points
None 0
Yes, max depth <30' 3
Yes, max depth >30' 4
The presence of algal blooms and excessive rooted aquatic
vegetation was approached indirectly through information
describing the severity of recreational use impairment due
to the overabundance of these aquatic plants.
Recreational use impairment
Penalty points
Weeds Algae Weeds &
only only algae
No impairment of use
Very few algae present, no "bloom"
conditions n n n
AND/OR 0 U U
Very few weeds in littoral zone
Slight impairment of use
Occasional "blooms," primarily green
species of algae 9 •> 9
AND/OR ^
Moderate weed growth in the littoral
zone
Periodic impairment of use
Occasional "blooms," predominantly
bluegreen species 74 =;
AND/OR J °
Heavy weed growth in littoral zone
Severe impairment of use
Heavy "blooms" and mats occur fre-
quently, bluegreen species dominate
AND/OR 679
Excessive weed growth over entire
littoral zone
40
-------
Lakes were penalized least heavily for problems resulting
from "weed" growths; lakes having both "weed" and algae
problems were penalized most severely. This was based on
the rationale that algal blooms often affect an entire lake
whereas the effect of rooted aquatic vegetation is normally
restricted to the periphery. Also, rooted vegetation is
sometimes more indicative of lake morphometry than water
quality conditions.
Lake Condition Indexes were calculated by summing the points
received in each of the four categories. Thus, if a lake
exhibits none of the specified undesirable symptoms of
eutrophication, it would receive no points (LCI = 0). Con-
versely, for a lake to receive an LCI of 23 it would have
to have all the undesirable characteristics in the most
severe degree.
Initially, information regarding specific conductance and
alkalinity was included in trial computations of LCI values,
but these parameters were subsequently omitted when it was
determined that they had very little, if any, effect on the
results.
Prior to general use of the classification system for
Wisconsin lakes, tests were conducted to determine the
"reasonableness" of results generated by the technique.
One test involved the computation of LCI values for the
same 12 lakes classified earlier by Lueschow et al (1970) .
Results of this comparison are given in Table 9. Both
systems resulted in the formation of four subgroups of
lakes. In addition, the general makeup of each of the sub-
groups was identical; however, there were some differences
in the ordering of lakes within the subgroups. It was con-
cluded that the LCI system worked very well in this test.
The results presented by Lueschow et al were based on
annual averages of monthly determinations of five parameters.
Results equally as reasonable were achieved with much less
input data using the LCI approach. This was encouraging
because it would not be possible to rank many lakes if ex-
tensive data were required.
Another test was conducted by ranking subsets of lakes from
four different areas of the state. Each subset consisted
of approximately 40 lakes. The purpose of the test was to
determine if the classification system would differentiate
between lakes within a subset and, also, differentiate
between the average conditions of lakes in the four subsets.
In this case, less emphasis was placed on the classification
of specific lakes because there was no quantified baseline
41
-------
Table 9.
COMPARATIVE RANK OF 12 WISCONSIN LAKES
Lues chow et al
o
•H
a.
o
-P
o
en
-H
o
4J
M
o
s
t
t
4-
o
X!
O
0)
-p
to
£
Score
8
17
17
19
31
33
33
34
45
49
52
52
Lake name
Crystal
Big Green
Geneva
Trout
Round
Pine
Middle
Oconomowoc
Mendota
Pewaukee
Del a van
Winnebago
Lake inventory
Lake name
Crystal
Trout
Big Green
Geneva
Round
Pine
Middle
Oconomowoc
Winnebago
Del a van
Pewaukee
Mendota
LCI
0
2
4
5
7
7
7
8
13
14
15
17
from Lueschow et al (1970)
42
-------
for comparison. However, it was known that, in general,
lake quality was highest in the northern part of the state
with a gradual decline in quality toward the south.
Results of this test are shown in Figure 1. The progres-
sive decline in general lake conditions from north to
south is shown by the data plot, and the classification
methodology was judged to work reasonably well.
In compiling the information necessary for classification,
partial input forms were submitted independently from two
sources for 89 lakes. (DO information was duplicated for
all 89 lakes; transparency and use impairment were dupli-
cated for 21 and 53 of the lakes, respectively.) LCI
values were computed for each of the duplicate data forms,
and the results were compared to determine the degree of
variability that could result from differing data sources.
Comparisons of this type were of particular interest because
several judgmental decisions are required in preparing input
forms, and individual preferences or biases could influence
the results. A summary of these comparisons is given below.
Dual input - Effect on LCI values
Same LCI values 57
LCI differs by 1 10
LCI differs by 2 12
LCI differs by >2 lp_
Total lakes 89
Of the 89 duplicate forms, 57 (64%) resulted in the same
LCI values and only 10 (11%) resulted in LCI values that
differed by more than 2.
This analysis was carried one step further to determine the
source of this variation.
Dual input - Source of differences
Parameter Same Different Total
Dissolved oxygen 76 13 89
Transparency 15 6 21
Use impairment 38 15 53
Total comparison 129 34 163
It was found that if differences occur, the most likely
cause is a difference in perception of recreational use
impairment. For that parameter, 30% of the answers were
43
-------
w
O
0)
15 -
10-
5
0
Southern
(40 lakes)
10-
5-
o-
15-
10-
5-
West Central
(37 lakes)
Northwest
(40 lakes)
Highest population,
intensive agriculture,
moderate industry
L^
Moderate population,
some agriculture
and industry
Low population,
little industry
or agriculture
a 15-
10-
5-
o-
North Central
(46 lakes)
Low population,
little industry
or agriculture
1
0
24 6 8 10 12 14 16 18 20
Lake Condition Index
Figure 1. Frequency distribution of lakes by Condition
Index for four areas of Wisconsin.
44
-------
different, perhaps due partly to individual interests and
aesthetic values. A fisherman, for example, may not view
a weed bed with the same degree of disdain as would a
swimmer or pleasure boater. These results led to the
"rule of thumb" that LCI values for specific lakes should
be viewed as having a possible range of ±2 points.
Based on the generally positive results of these pre-
liminary tests, the classification methodology was left
intact, and LCI values were calculated for all lakes in the
state larger than 100 surface acres (40 ha). These LCI
values are listed in an appendix. Summary results based on
1129 LCI determinations are presented in Figures 2 through
5. Omissions in the data file prevented the calculation
of LCI values for 20 lakes. Probable values for 18 of these
20 are given in the appendix, but these values are not
included in the summary figures. Whereas inclusion of these
data would change the plots slightly, omission of these data
has no effect whatsoever on the conclusions.
A frequency distribution based on the numbers of lakes
having specific LCI values is shown in Figure 2. The dis-
tribution is very much skewed to the left, with more than
50% of the lakes having an LCI of 6 or less. A frequency
distribution was also plotted as a function of surface area
as shown in Figure 3. Data for Lake Winnebago (LCI = 13,
area = 137,708 acres) are not included in this plot because
the lake is so large. However, even without the inclusion
of these data, there is a shift in the distribution toward
the higher LCI values. Whereas only 20% of the lakes had
LCI values of 10 or greater, these lakes include 31% of
the total area (43% if Winnebago is included). Similar
results are shown in Figure 4 in which average lake size
is plotted against LCI. As shown on this plot, average
lake sizes are much larger at the higher LCI values.
Geographical distribution by county of the study lakes is
shown in Figure 5. The average LCI for each county is also
presented here. The greatest number of lakes, as well as
the higher quality lakes (as indicated by average LCI
value), are found in the northern counties. LCI averages
tend to increase in the southern part of the state and
in those counties adjacent to the Wisconsin River. There
are 11 counties with no lakes having surface areas greater
than 100 acres; in fact, Crawford County in the south-
western part of the state has only one lake, a four-acre
pond, listed in the Wisconsin inventory. Vilas County in
northern Wisconsin has the largest number (173) of study
lakes of any county, as well as one of the lowest LCI
averages (3.9). Several southern counties have LCI averages
of 13 or greater.
45
-------
200 -
150 -
w
-------
Cfl
o
o
ns
to
'O
c
w
O
-P
c
•H
(0
o
4-1
5-1
10
-P
O
EH
0
CO
o
in
o
n
0
-------
00
o
o
o
Cfi
0)
o
(0
C
•H
0)
N
•H
W
Q)
QJ
cn
rd
i-i
QJ
o
o,
o
ro
O
O1
O
CM
O
o
O-J
Data for Lake Winnebago
(LCI = 13; area = 137,708 acres)
not included.
/o
Statewide
averaae
0
1
2
4
i
6
i
8
i
10
i
12
14
i
16
18
20 2:
o
o
U)
0)
-P
u
a)
CM
o
o
oo
N
-H
to
0)
tr
0)
o >
•o <
- o
Lake Condition Index
Fiaure 4. Average surface area of Wisconsin lakes as a function of Condition Index.
-------
Figure 5. Distribution and average LCI
of Wisconsin study lakes - by county.
n = number of lakes in county with areas > 100 acres
n.n = average Lake Condition Index value for county
* = counties with no lakes with surface areas > than 100 acres
49
-------
Although LCI values are not synonymous with trophic status,
additional perception of the condition of Wisconsin's lakes
is attained by relating these values to the traditional
limnological classifications. With qualification, the
following comparisons apply:
Number
LCI Trophic classification of lakes
0-1 Very oligotrophic 28
2-4 Oligotrophic 308
5-9 Mesotrophic 586
10-12 Eutrophic 125
13- Very eutrophic 82
1129
Thus, approximately 30% of the lakes might be considered
oligotrophic; 50%, mesotrophic; and 20%, eutrophic.
By comparison, results obtained by the National Eutrophica-
tion Survey (U.S. Environmental Protection Agency, 1974)
suggest that perhaps a larger portion of the lakes in
Wisconsin are eutrophic. As part of the national survey,
data were collected from 42 of the same lakes classified
here, and these lakes were also classified according to a
system developed by EPA. Although quantitative comparisons
are difficult because of the relative nature of this latter
system, qualitative comparisons are possible by using the
relationship between index numbers and approximate trophic
status defined for the two systems. On that basis, classi-
fications of the 42 lakes compare as follows:
Lake type LCI EPA
Oligotrophic 5 2
Mesotrophic 16 5
Eutrophic 2JL 35
42 42
Assessing the adequacy of classification systems is a dif-
ficult task because universal definitions for these trophic
categories are lacking. In view of this, the LCI approach
was subjected to a number of retrospective tests to check
the validity of the technique.
One potential problem with the LCI approach, or any tech-
nique involving the sum of several numbers, relates to the
fact that it is possible to reach the same endpoint classi-
fication by many pathways. That is, a given LCI can result
from many combinations of penalty points among the four
50
-------
categories. Consequently, it is theoretically possible
that very different lakes could have the same LCI value and,
if the diversity of lakes within one rank is too large, it
negates the value of classification. To check on this poten-
tial problem, a graph was prepared in which LCI was plotted
against the number of lakes having each of the four DO con-
ditions included in the classification scheme, as shown in
Figure 6. (It should be noted that smooth curves were
drawn through the data points in this figure; the raw data
were more erratic than shown on this plot.) The graph
displays the diversity of DO conditions that occur within
each of the LCI ranks.
It was found that the four DO conditions were separated
reasonably well, although the ranges overlapped as ex-
pected. Most lakes with DO condition I had LCI values
between 0 and 4, with the peak number having an LCI of 2.
This condition describes hypolimnetic oxygen concentrations
in excess of 5 ppm and is indicative of oligotrophic lakes.
Conditions II and III represent partial oxygen depletion and
suggest mesotrophic lakes. These conditions were found
to occur most frequently at LCI values of 4 and 6, respec-
tively. Beyond an LCI value of 10, DO condition IV was
by far the most common, which suggests that most of these
lakes would be eutrophic.
Based on these results, it was concluded that diversity
within LCI ranks was not excessive. At most LCI values,
a single DO condition can be identified as being the most
probable contributor of penalty points. Only in the LCI
range of 8-10 is there a significant probability that any
of three conditions could be contributors to the LCI.
For another check, the data set was divided into three
subsets. First, natural lakes and natural lakes with level
controls were separated from impoundments. (Bog and marsh
lakes were excluded from this analysis.) Then the natural
lakes group was further subdivided according to the type
of fishery in the lakes. Two subsets were formed—warm
water lakes and warm/cold water lakes. (Only one lake in
Wisconsin was listed as a cold water fishery only.) The
three subsets were then plotted against the LCI as shown
in Figure 7.
The warm/cold water lakes spanned the LCI range from 0 to 8,
with the maximum number occurring at LCI = 4. Warm water
lakes spanned virtually the entire LCI range, but the
majority had LCI values between 2 and 9. LCI values for
reservoirs (warm water fisheries, also) covered most of
the total range, as well. It was thought that perhaps
51
-------
Ol
to
150 -
en 100 -
0)
(0
H
O
M
5 ppm
II DO in hypolimnion <5 ppm but >(
III DO in parts of hypolimnion = 0
IV DO in entire hypolimnion = 0
X-^TII
. \
/ \
I \
/ N \
/ "-11 *
' ' \ \ ./-N.IV
\ N / \
,-•-•-, i V--xx \
"'••• r ^ ^-^ %i^^.-
0 2 4 6 8 10 12 14 16 18 20 22
Lake Condition Index
Figure 6. Dissolved oxygen conditions in Wisconsin lakes.
-------
ui
to
120-
w
cu
o
h
CD
80-
40-
Warm water lakes
-o O Warm/cold water lakes
D Q D Reservoirs
P-
0
I
2
8 10 12 14 16
Lake Condition Index
18
20
22
24
Figure 7. Calculated Condition Indexes for three types of Wisconsin lakes,
-------
impoundments would have lower LCI values than other warm
water lakes because flow through the reservoirs often
improves DO conditions. This effect is not evident from
Figure 7.
Average LCI values for various types of lakes in Wisconsin
are given below:
Number Avg
Lake type of lakes LCI
Warm/cold water fishery 173 3.8
Warm water fishery 739 6.6
Reservoirs 191 7.8
Bog lakes 8 8.0
Marsh lakes 18_ 12.5
Total 1129 6.5
As a further check, the classification results were dis-
tributed to DNR area fish managers. They received a
description of the system and LCI scores for the lakes in
their area of responsibility. After reviewing the material
the managers were asked:
1) to identify those lakes with inaccurate scores;
2) to assign an alternate score if possible; and
3) to furnish information or reasons for any changes.
Nine of the area managers were able to furnish detailed
information; seven others were either unable to review the
information due to time constraints or reluctance to comment
in more than general terms.
The results of their critique are presented below. Table 10
gives an arithmetical breakdown for the nine areas where
detailed checks of the LCI results were provided. Total
lakes for each area are listed as well as the number of
lakes where the fish manager:
1) left the LCI unchanged;
2) changed the LCI by 2 points or less;
3) changed the LCI by 3 points or more.
54
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Table 10. SUMMARY OF CLASSIFICATION SYSTEM REVIEW
BY WISCONSIN DNR AREA FISH MANAGERS
Area
number
1
2
3
4
5
6
7
8
9
Totals
Total
lakes
9
42
32
62
84
16
21
23
14
303(100%)
LCI number
unchanged
2
32
20
52
63
11
5
8
9
202(66%)
LCI number
changed by
2 or less
6
4
6
7
11
4
10
9
3
60(20%)
LCI number
changed by
3 or more
1
6
6
3
10
1
6
6
2
41(14%)
The totals show that of the 303 LCI values reviewed in
detail, two-thirds (202) of the values were unchanged.
Sixty scores (20%) were changed by two or fewer points
which, as discussed previously, is within the estimated
limits of the classification accuracy. Forty-one (14%)
of the scores were changed by 3 or more points. About 60%
of the changes resulted in higher LCI values. In most
instances it was found that out-dated or inaccurate input
data caused these large discrepancies in LCI values. In
eight cases, however, the system itself was responsible
for distorted lake scores. The system does not take
residence time into consideration when awarding penalty
points for dissolved oxygen conditions. Consequently, a
few reservoirs of poor water quality, but with high flow
rates, received lower LCI scores which placed them in the
same category as lakes judged to have much higher water
quality.
Some of the area managers who did not have time to review
the classification results in detail nevertheless furnished
some subjective commentary. Most thought the system was
generally satisfactory. Three cited the tendency of reser-
voirs with high flow rates (as discussed above) to be
"forgiven" their poor water quality. Four managers suggested
55
-------
that methyl-purple alkalinity values should be included
in the system as an indicator of fertility or production
potential. Two others felt the use impairment parameter
was too subjective as it is presently phrased. These man-
agers said the wide range of individual interests clouded
the value of this particular bit of information, and sug-
gested that the question pertaining to nuisance algae and
excess rooted aquatics be reworked to make it more quanti-
tative. While the project staff would have preferred
specific, detailed reviews from all the area fish managers,
it is estimated that the number and extent of changes in
LCI scores would have been close to the percentage sum-
marized in Table 10.
In summary, it appears that the results of the LCI deter-
minations for the more than 1100 lakes in the study set
provide a good perspective of the water quality conditions
in these lakes.
56
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SECTION VI
LAKE CLASSIFICATION FOR DECISION MAKING
From the outset in this report, lake classification was
viewed as a dynamic rather than static concept. Lake classi-
fication is not an activity which should be undertaken with
the idea that after some finite period of time the job will
be done and all the lakes will be classified. Far greater
benefit can be attained by repetitive classifications de-
signed to address specific management needs. In this chap-
ter an attempt is made to illustrate the utility of this
philosophy of approach by citing specific examples in which
dynamic classification can be used to advantage. The ex-
amples given below are not abstract ideas; most are the out-
come of discussions with resource managers concerned with how
lake classification could be of value to them in solving
problems or identifying opportunities for improved management.
Two fundamental concepts underlie each of the examples given
below.
1) A single comprehensive classification system, capable of
taking into account all the diverse management interests
and objectives of today or of the future, does not exist
now, nor is it likely to exist at a later time. However,
it is possible to substitute for this, at least in part,
by utilizing cross-comparisons of several single-purpose
classification systems which are presently operational
or can be developed without undue difficulty.
2) Lake classification systems need not consist of rules,
guidelines or equations which are so broadly defined
that they are applicable to all lakes in all settings.
An equally valid, but often more advantageous, classi-
fication approach is to define those lake characteris-
tics or properties which are of interest for the specific
purpose at hand, and then segregate that subset of lakes
which possesses the desired traits. The concept of
57
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delineating special-purpose subsets greatly enhances
the usefulness of lake classification.
The above approach envisons the existence of an informa-
tion base from which one can retrieve selected information.
By analogy, this is quite similar to the approach presently
being used in the area of literature retrieval. Subject
indexes (static classification) have proven to be inade-
quate tools for searching and retrieving selected topics
from the literature because it has not been possible to
define a suitable indexing system which segregated entries
in accord with all the diverse interests of user groups.
Thus an approach has been developed in which "key words" or
"descriptors" are assigned to all entries in the data base,
and custom searches are then performed by selecting that
subset of items from the data base which possesses all the
predetermined key words deemed to be significant for the
purpose at hand. Thus a new "classification system" is
devised for each user (dynamic classification). In a
sense, this approach is the simplest type of classifica-
tion—pre-set grouping which has only two groups: those
items which are of interest, and those which are not. The
key to success is a flexible data base which contains items
of interest and a mechanism by which selected items can be
retrieved. In the examples below, the lake data for
Wisconsin are available either from the information com-
piled as part of this classification effort or are avail-
able from a lake information base maintained by the
Wisconsin Department of Natural Resources. In each case,
the data are stored on magnetic tape, and computer programs
can be written to retrieve selected information.
As suggested earlier in this report, an improved perspec-
tive of lake management priorities results when lake
condition and nutrient loading (the latter as a measure
of likelihood for change, either natural or man-made,
desirable or undesirable) are viewed simultaneously. In
its simplest form this may be viewed as a 2x2 matrix con-
sisting of four different lake sets.
58
-------
Specific General water quality
Set loading LCI management grouping
a low low No present danger
Renewal desirable; long-term
b low high benefits may be possible without
extensive nutrient abatement
Prompt protection needed;
c high low degradation may be imminent
Problem lakes; renovation desirable
d high high but lasting improvement may require
extensive nutrient abatement
Set a) These lakes of high quality would be identified as
unendangered. The management approach for these lakes_might
be to maintain the status quo, i.e., protection to avoid
degradation. The condition of these lakes would be expected
to be quite stable; thus some of them might be considered
for inclusion in monitoring programs designed to measure
"background levels" of chemical constituents. Also, by the
addition of information relating to public access, shoreline
development, public ownership, etc., some of these lakes
could be selected for special purpose management, such as
"wilderness" recreation areas.
Set b) Lakes in this group would be prime candidates for
lake renewal efforts because of the possibility for lasting
improvement. In-lake renewal techniques, such as aeration,
sediment manipulation, etc., could yield long-term benefits
in this type of situation because the influx of nutrients
from external sources is small. This might be a particu-
larly advantageous approach if there is reason to believe
that prior actions (such as the improvement of upstream
waste treatment facilities) have reduced present levels of
nutrient influx below levels which occurred previously.
Set c) The condition of lakes in this group would be ex-
pected to be unstable and progressing toward further
degradation. Based on the general consensus that eutro-
phication prevention is better understood than eutrophica-
tion reversal, and that preventive management may be more
economical than restorative measures, these lakes require
protective action with some degree of urgency. Nutrient
removal from wastewaters in the drainage basin could be a
high priority consideration.
59
-------
Set d) The lakes in this group are not only fertile, but
also receive high inputs of nutrients. Extensive nutrient
abatement may be required before long-lasting lake reha-
bilitation could be anticipated. Perhaps the immediate
focus of management for these lakes should be to ease the
symptoms of excess fertility and to direct use toward those
activities compatible with fertile waters until renewal
techniques are more refined and related costs and benefits
are better defined. Another option would be to manage the
lake and shorelands as fertile areas, emphasizing environ-
mental diversity and high productivity as positive attri-
butes.
By noting the number of lakes in each set, their size, and
perhaps auxiliary parameters, such as the proximity to
population centers, some general management priorities can
be ascertained. For example, if the majority of a state's
lakes fall into sets c and d, a concerted program of nutrient
abatement would be of high priority. If the majority of
lakes fall into group b, the development and refinement of
in-lake renewal techniques might be a high priority objec-
tive. Of course, a clean distinction between "high" and
"low" levels of the parameters used in developing the 2x2
matrix may be difficult to establish, and selected levels
would certainly be open to debate. Some provisional guide-
lines were presented by Vollenweider (1968). However, these
guidelines are based on data from 30 large lakes (12 from
central Europe, 10 from North America, and 8 from northern
Europe), and their direct applicability to smaller lakes
or specific geographic areas has not been established.
Table 11. SPECIFIC LOADING LEVELS FOR LAKES EXPRESSED
AS TOTAL NITROGEN AND TOTAL PHOSPHORUS IN g/m2/yra
Permissible Dangerous loading,
loading, up to: in excess of:
Mean depth
up to: N P N
5
10
50
100
150
200
m
m
m
m
m
m
1
1
4
6
7
9
.0
.5
.0
.0
.5
.0
0
0
0
0
0
0
.07
.10
.25
.40
.50
.60
2
3
8
12
15
18
.0
.0
.0
.0
.0
.0
0
0
0
0
1
1
.13
.20
.50
.80
.00
.20
afrom Vollenweider (1968)
60
-------
The same general approach described above can also be
developed by considering the relationship between nutrient
loading and lake condition as a continuous function rather
than one which can be split neatly at predetermined break-
points. This approach avoids the necessity of specifying
high and low levels for nutrient loading rates and lake
condition values. In this case the 2x2 matrix is replaced
by a plot of nutrient loading versus lake condition as
shown in Figure 8.
Abscissa values shown in Figure 8 are LCI numbers. The
ordinate values shown are
P-loading
[ Mean depth 1 ^
[Hyd res timej
The selection of this parameter is based on some of the
more recent work of Vollenweider (in press). "Normalizing"
the loading rates to account for depth and hydraulic resi-
dence time would be expected to reduce data-scatter and
make it easier to define an empirical relationship between
the selected parameters.
Although LCI values have been calculated for many Wisconsin
lakes, nutrient loading data are, for the most part, lack-
ing. Therefore, Figure 8 is a hypothetical sketch which is
included to illustrate the general approach; it is not a
plot of actual data.
One of the principal advantages of this approach is the
development of loading-condition relationships which are
applicable in specific geographic regions. Then, by select-
ing LCI values which represent satisfactory or unsatis-
factory lake quality, permissible or dangerous loading rates
can be specified. An analysis very similar to this was con-
ducted by Shannon and Brezonik (1972). Based on data from
55 lakes in Florida, they reported permissible areal loadings
of 2.0 and 0.28 g/m2/yr and critical loadings of 3.4 and
0.49 g/m2/yr for nitrogen and phosphorus, respectively.
These values are considerably higher than the provisional
values reported earlier by Vollenweider (1968) and indicate
that, for conditions in Florida, somewhat higher levels of
nutrient influx can be tolerated without detrimental effects.
The four lake subsets described earlier for the 2x2 matrix
can also be identified from a plot such as that given by
Figure 8. Data points on the left side of the plot represent
lakes which are presently of high quality; those on the lower-
left would appear to be unendangered; whereas those in the
61
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O
O
(U
ft-p
QJ
•O W
-------
upper-left might be subject to imminent degradation and in
need of prompt protective action. Lakes in need of renewal
are on the right side of the plot, and the prime candidates
would be those with the lower nutrient loadings.
Nutrient abatement is a logical and necessary part of any
lake renewal effort. To assess the cost-effectiveness of
abatement measures it is necessary to specify the degree of
water quality improvement corresponding to achievable levels
of nutrient reduction. The establishment of loading-
condition relationships provides a basis for quantifying the
degree of anticipated improvement (or conversely, the degree
of degradation which could occur if abatement is not under-
taken or if nutrient loading would increase).
The dashed line in Figure 8 represents the stable or equi-
librium relationship between nutrient loading ("normalized")
and the lake condition index. Obviously, the process of
eutrophication is far too complex to be expressed uniquely
by these two parameters, and thus some degree of data-
scatter would be expected. However, as a first approxima-
tion, one would expect a change in nutrient loading to shift
the position of a data point along a path parallel to the
dashed line. Since the abscissa values are a measure of
readily-interpretable lake conditions, Figure 8 provides a
conceptual framework for estimating the extent of nutrient
abatement necessary to achieve desirable lake quality or
the degree of improvement which corresponds to a given level
of abatement.
At the present time, lake renewal can be described as an
emerging science consisting of two major aspects. One
aspect relates to the nutrient supply of lakes, with reno-
vation techniques being aimed at reducing the influx of
nutrients from external sources. The other aspect of lake
renewal relates to in-lake treatment or manipulation for the
purpose of minimizing or eliminating the undesirable effects
of excess fertility. These latter approaches are sometimes
considered to be palliative because they are aimed at treat-
ing symptoms and do not eliminate the source of the problem—
excess nutrient influx. Nevertheless, there are many situa-
tions in which in-lake renewal schemes can be used to
advantage for hastening the recovery of a lake where nutrient
abatement has been accomplished, or for improving the quality
of those lakes where the desirable reduction in nutrient
influx is not feasible. Also, long-lasting effects of in-
lake techniques, such as hypolimnetic aeration and nutrient
precipitation/inactivation, are possible in those situations
where repetitive internal nutrient cycling contributes
significantly to a lake's nutrient status. Because of these
possibilities, research and demonstration projects are being
63
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conducted on a number of lakes throughout the world (Dunst
et al, 1974) to evaluate various in-lake renewal schemes.
One measure of the need and priority for developing in-lake
renewal techniques relates to the potential applicability
of any scheme: that is, how many lakes or how many acres of
water could potentially be restored or improved by the use
of a particular technique? For the most part, assessments
of this type have not been made on either a state or national
level. Lake classification can be used to advantage in
making these assessments. The logical groups of lakes to be
considered in this type of analysis are lake sets b and d
defined earlier in this section.
Fortunately, the potential applicability of a number of
in-lake renewal techniques—namely, dredging, hypolimnetic
aeration, nutrient inactivation/precipitation, and selective
discharge—can be defined in terms of the physical setting
or characteristics of lakes. Classification techniques using
a relatively modest data base can thus be used to "sort out"
candidate lakes whose characteristics match the prerequisite
conditions of the various renewal schemes.
Hypolimnetic aeration is a technique which provides for the
maintenance of dissolved oxygen in the bottom waters of
lakes during periods of stratification. Thermal strati-
fication is maintained, making it possible for cold-water
fish species to survive. At the same time, aerobic condi-
tions in the hypolimnion tend to reduce the transfer of
nutrients and other dissolved substances from the sediments
to the overlying water and, therefore, some water quality
improvement can result because of the disruption of internal
chemical cycles. Lake characteristics necessary for the
successful use of hypolimnetic aeration include:
1) maximum depth in excess of 25 feet (8m);
2) strong, stable stratification during summer months;
3) oxygen-void hypolimnion; and
4) area of less than 1000 acres (400 ha).
The first three items are necessary conditions, whereas
item 4 is a practical constraint that is subject to change
depending on the type of equipment available. By "sorting"
the characteristics of lake sets b and d to determine how
many are smaller than 1000 acres, deeper than 25 feet,
stratified during the summer and experience oxygen deple-
tion in the hypolimnion, one obtains a measure of the
potential applicability of hypolimnetic aeration as a lake
64
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renewal technique. The same approach can also be used for
other renovation methods.
Precipitation processes using iron, aluminum and calcium
compounds to remove phosphorus from wastewater have become
standard practice. Adaptations of this technology to lake
renewal have been evaluated in laboratory studies (Peterson
et al, 1974) and field demonstrations (Dunst et at, 1974).
Experience to date indicates that the technique is most
successful on lakes which are stably stratified during the
summer growing period—intermittent summer stratification
appears to lessen the effect of treatment by permitting
nutrient recycling. However, phosphorus reduction in the
hypolimnion of Horseshoe Lake (Wis) was sustained for three
years following treatment (Born et al, 1974) even though the
lake underwent complete overturn several times during that
period.
Criteria for nutrient inactivation/precipitation include:
1) maximum depth in excess of 15 ft (5 m);
2) stratification occurring throughout the growing season;
3) present lake problems relating to algal growths, not
rooted vegetation; and
4) hydraulic residence time of one year or longer. (This
criterion could be related to drainage basin/lake area
ratios if flow data are lacking.)
Dredging to remove nutrient-rich sediments (as opposed to
dredging simply to make a portion of a lake deeper) is
generally considered to be a viable lake renewal technique.
It is known that lake sediments often contain large quan-
tities of nitrogen and phosphorus and, in some cases, the
recycling of these nutrients to overlying waters can have
a significant impact on the nutrient status of lakes.
Dredging could reduce nutrient recycling if sediments were
removed down to mineral soil, or if sediment characteristics
vary with depth such that, by skimming off the upper layers,
nutrient transfer to overlying waters is lessened. However,
except for results attained at Lake Trummen in Sweden
(Bjork,1972), studies documenting the effect of dredging
on the nutrient status of lakes are lacking.
Dredging would be expected to be most successful in lakes
which have accumulated layers of organic sediments as a
remnant of past production in the lake proper. Lakes which
receive large sediment loads from land runoff may be
benefitted by dredging because it provides increased depth.
However, dredging may have a minimal influence on the
65
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nutrient status of these lakes/ and thus the value of dredging
as a lake restoration technique is not likely to be great.
Classification criteria for evaluating the potential appli-
cability of dredging as a lake restoration measure include:
1) lakes in which 50% or more of the area has a depth
less than 8 ft (2.5 m) ;
2) landlocked lakes, or lakes with outlets only;
3) lakes with bottom materials consisting primarily of mud
or silt; and
4) lakes with problems related to excessive rooted aquatic
vegetation, not algae.
Criteria for other in-lake renewal techniques could be
developed as well; however, the examples given above are
sufficient to illustrate the usefulness of lake classifica-
tion as an assessment tool. Each of the criteria sets
listed above defines a pre-set, grouping classification
system that can be used to sort the information base for
those lakes judged to be in need of restoration (i.e., sets
b and d defined previously). Those lakes which satisfy the
criteria for each of the renewal techniques are identified,
and a comparison of the results provides a measure of the
applicability of the different techniques. (Note: A lake
may be identified as having characteristics which meet the
criteria for more than one renewal technique; the criteria
are not mutually exclusive.)
Of course, the lists of lakes generated by this approach are
only a crude measure of the applicability of lake restora-
tion methods. Virtually all of the techniques have site-
specific requirements which must be evaluated before the
applicability of a technique can be established. For
example, the settling characteristics of sediments from a
given lake and the availability of a sediment disposal site
must be known before dredging could be seriously considered
as a renewal measure. Nevertheless, lake classification
provides a start toward evaluating the applicability of
dredging and, of equal importance, eliminates those lakes
for which dredging is inappropriate and acquisition of
site-specific information is unnecessary.
This latter type of classification in which a data base is
sorted to identify potential candidates (and, of equal
importance, to eliminate unlikely candidates) can be used
to advantage in resource planning. A good example of this
arose in connection with the designation of scientific areas
in Wisconsin. A program has been initiated with the general
66
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purpose of identifying, designating, and preserving a
variety of "undisturbed areas" which could serve as base-
lines or control areas for environmental studies. Emphasis
is placed on areas which are sheltered from human influence
and exhibit a high degree of diversity in both plant and
animal populations, including rare or endangered species.
Although criteria for the selection of scientific areas have
been worked out in some detail, selection of areas is dif-
ficult because there exists no data base which includes the
specialized information and the degree of detail necessary
to apply the criteria directly. The necessity of collecting
field data cannot be avoided entirely, but lake classifica-
tion can be used to select a set of candidate lakes which
includes possible scientific areas.
In this case, a logical starting point is to conduct cross-
comparisons of lake condition, nutrient loading, and density
of shoreline development. The first two comparisons have
already been discussed, and likely candidates for scientific
areas would be found in set a (low LCI, low loading rates).
This group can then be further reduced by reclassifying the
lakes according to development density—dwellings/mile of
shoreline, or acres of water/dwelling. This latter step
could also include public ownership as part of the classi-
fication criteria. The end product of such an effort is a
list of potential scientific areas. Further reduction of
this list would require additional data; quite possibly field
examinations of each site would be necessary. However, by
utilizing a more general data base and applying lake classi-
fication concepts, it is possible to expedite the selection
of lakes for highly-specialized purposes, even though the
data bank may not contain all the information necessary for
the final decision.
Parameters relating to the extent of shoreline development—
either actual or potential—are likely to be included in
many lake management decisions. In some cases, such as the
example of selecting scientific areas, development density
may be a prime consideration. This is likely to be true in
only a minority of situations, however. Development density
is likely to be more important for establishing priorities
or ranking lakes within a group which has been selected on
the basis of other parameters. For example, if a field
demonstration of nutrient inactivation/precipitation were
to be undertaken, the selection of candidate lakes could be
accomplished with the use of the classification criteria
discussed previously. Then, the selection of high-priority
candidates could be based on development density, to intro-
duce the dimension of benefitting the maximum number of
people.
67
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Because of the potential usefulness of development density
as a lake classification parameter, a classification of
Wisconsin lakes according to development density was under-
taken. The analyses were conducted by the Wisconsin
Department of Natural Resources and are based on data
collected by that agency.
For purposes of this study, it was decided that development
density would be defined in two ways, as 1) dwellings per
mile of shoreline and 2) surface acres of water per dwelling.
The DNR lake inventory file contains survey data which were
compiled over the period 1959-1972. This computerized file
contains an entry for the number of cottages and the number
of resorts (up to a maximum of 9 resorts) on each given lake.
In this analysis, a resort was defined to be equivalent to
5 cottages, so the total number of dwellings was equal to
the number of cottages plus 5 times the number of resorts.
A summary of the results of this study is given in Table 12.
The data set used in this analysis included all lakes in
Wisconsin with surface areas of 20 acres (8 ha) or larger.
As shown in the right-hand column of the table, this in-
cluded 2790 lakes having a total shoreline of 8797 miles
(14,160 km) and a total area of 761,000 acres (308,000 ha).
Eleven different ranges of development density are shown
in Table 12. With respect to length of shoreline, the
lowest range given is 0-5 dwellings/mile; the highest range
includes all density values in excess of 50 dwellings/mile.
If it is assumed that most dwellings occupy about 100 feet
(30 m) of shoreline, then the lowest range given would
include those situations in which the total shoreline is
about 10% "developed"; likewise, the highest range is
equivalent to 100% development. Of the 2790 lakes in the
sample, 1926, or 69%, had 5 or less dwellings per mile of
shoreline. These lakes have a total shoreline of 5411 miles
(8712 km) which is equivalent to 61.5% of the total shore-
line in the sample lakes. Based on these results it would
appear that the lakes in Wisconsin are relatively undeveloped,
and the potential for additional development may be quite
high. However, the perspective is changed radically when
viewed from the standpoint of lake area.
With respect to lake area, the highest density range given
is 0-10 acres/dwelling, and the lowest includes all values
in excess of 100 acres/dwelling. The analysis showed that
1881 lakes (67.3% of the total number) had 10 or less
surface acres per dwelling, and that lakes developed to this
density account for more than half of the total acreage of
the study lakes (409,000 acres out of a total of 761,000).
68
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Table 12. DEVELOPMENT DENSITY OF WISCONSIN LAKES
Number of lakes
Percent of total
Miles of shoreline
Percent of total
0-5 6-10
1926 348
69.0 12.5
5411 1368
61.5 15.5
Development density in dwellings /mile of shoreline
11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 Over 50
183
6.3
906
10.3
112
4.0
369
4.2
38
2.1
232
2.6
32
1.1
139
1.6
25
0.9
96
1.1
24
0
0.9
61
0.7
14
0.5
46
0.5
16
0.5
50
0.6
52
1.9
119
1.4
Total*
2790
100
8797
100
Number of lakes
Percent of total
Area (thousands of acres)
Percent of total
0-10 11-20
1881 343
67.3 12.3
409 131
53.8 17.2
Development density in
21-30 31-40 41-50 51-60
203
7.3
38
5.0
122
4.4
55
7.2
72
2.6
16
2.1
47
1.7
7
0.9
surface acres /dwelling
61-70 71-80 81-90 91-100
31
1.1
6
0.8
16
0.6
16
2.1
16
0.6
22
2.9
14
0.5
4
0.6
Over 100
45
1.6
56
7.4
Total*
2790
100
761
100
*Wisconsin lakes 20 acres or larger
-------
The meaning of this development density becomes more clear
when it is compared to criteria used to estimate the
"recreational carrying capacity" of lakes.
To minimize use-conflicts and provide for high-quality
recreational experiences, resource managers have attempted
to specify minimum space requirements for the different
recreational uses of lakes. The values listed below are
perhaps somewhat arbitrary and subject to debate; however,
they are based on logical rationale and, when applied with
judgment, they can be used to calculate the carrying ca-
pacity of a lake for various uses.
Activity Space requirement
Swimming 185 swimmers/acre
Fishing 8 acres/boat
Boating 15 acres/boat
Water skiing 20 acres/boat
after Wisconsin Department
of Natural Resources (1968)
Some assumptions regarding use-frequency must be made before
these tabulated values can be compared to the development
density data, but it would appear that riparians alone could
exceed the recreational use capacity for boating and water
skiing on those lakes where the development density is in
the range of 0-10 acres/dwelling. And, this accounts for
more than 50% of the lakes in the study group.
Based on these data, it appears that most Wisconsin lakes
are developed to the point that additional recreational
development could place stress on users and, possibly, on
the resource itself. On the other hand, there are 566
lakes in the sample which have more than 20 acres/dwelling,
and 169 of those have more than 50 acres/dwelling. If
recreational home development is to continue, it might be
advantageous to direct future development toward these
lakes. Alternatively, a policy could be adopted which
would direct future development to lakes which are already
"crowded" on the basis that some measures to protect water
quality, such as wastewater collection and treatment facili-
ties, are more easily implemented in densely populated areas;
and it may be better to "sacrifice" the quality of recrea-
tional experiences on some lakes in order to retain it on
others. Other variations are also possible; the point to
be made is that lake classification can be used in a variety
70
-------
of ways to provide an improved perspective of management
issues and to lend a degree of quantification to the
decision-making process. Significant lake classification
can be accomplished with a modest information base, and it
is a technique which can, and should, be used more fre-
quently as a tool for improved resource management.
71
-------
SECTION VII
REGIONAL APPLICATION OF THE LCI SYSTEM
This project was a test of the paired hypotheses that:
1) a practical classification system could be developed by
which lakes could be ranked according to general water
quality characteristics; and
2) sufficient data and information could be compiled to
permit classification of large numbers of lakes.
These hypotheses are strongly interrelated: data availability
placed a major constraint on the type of classification system
that was developed; and, conversely, the need to describe
lake conditions encompassing both time and space variables
dictated minimum data requirements.
A methodology for lake classification was developed and used
to classify all lakes in Wisconsin having surface areas in
excess of 100 acres. Based on these efforts, it was con-
cluded that the hypotheses were affirmed; however, conclusive
"proof" could not be obtained in the strict scientific sense
because there is no solid baseline for assessing the "accu-
racy" of a lake classification system. As a substitute,
the adequacy of the system was checked by using several
subjective tests. Based on these tests it was concluded
that: 1) the results compare favorably to those of more
complicated systems which require considerably more input
data; 2) the system yields nearly the same results even
though different individuals provide input information; and
3) the data requirements are sufficiently minimal that costs
of compiling the necessary input are not prohibitive. In
addition, the relative position of ranked lakes was judged
to be reasonable when viewed by area resource managers, and
a breakdown of lakes according to fishery types proved to
be consistent with computed condition index values. In
short, the general approach and the classification scheme
worked well for Wisconsin lakes larger than 100 acres.
72
-------
The potential applicability of this classification-scheme to
other parts of the country can only be approximated, but an
estimate can be made by examining the individual parameters
included in the system.
1) dissolved oxygen in the hypolimnion - Stratification is
implicit in the definition of this part of the classi-
fication system. Thus, shallow lakes which undergo
frequent or continuous overturn and reservoirs in which
stratification is dominated by flow-through character-
istics receive few "penalty points" or none at all.
Therefore, these lakes are not separated by dissolved
oxygen considerations, and greater reliance is placed
on the other parameters in the classification scheme.
2) fishkills - This is another consideration of dissolved
oxygen conditions but, unlike the one above, this refers
to a depletion of oxygen throughout an entire lake. In
north-temperate lakes this condition occurs under ice
cover, and the occurrence of winterkill conditions in a
lake is often indicated by the fish species present,
i.e., a fishery dominated by those species which can
tolerate low oxygen concentrations. Thus, winterkill
conditions can be documented after the fact—a distinct
advantage for data acquisition. Although winterkill is
a useful parameter only in regions where lakes are ice-
covered, the analogous condition of "summerkill" would
probably be a useful parameter in more southerly loca-
tions. In this case, decay and respiration following
periods of intense organic production can reduce
dissolved oxygen concentrations to levels lethal to
fish. Like winterkill, lake morphology also plays an
important role in summerkill by influencing the degree
of oxygen depletion resulting from a given level of
oxygen demand, and some measure of morphology (e.g.,
mean depth) should be included in the classification
routine.
3) typical maximum and minimum Secchi depths - Secchi depth
information is, to a large extent, universally useful;
however, interpretation is different if changes in
measured water clarity are due to inorganic rather than
organic particulate matter. Water color also influences
Secchi depths somewhat, but it is expected that the
usefulness of this parameter is not restricted to any
particular region of the country.
4) impairment of use (extent to which algal blooms and
rooted aquatic vegetation interfere with uses of a lake)-
The conditions described for this parameter are suf-
ficiently general that they would probably apply to most
73
-------
regions of the country. However, variations in public
perception of water quality are likely to introduce
regional variations. For example, a "weed-choked" lake
in one place may be a relatively "weed-free" lake in
another, depending on the local norm.
Considering the type of regional differences that are likely
to affect the use of the classification system reported here,
it appears that the country may be divided into three general
zones: 1) states where the system might be applied without
modification, 2) states where minor changes may be desirable,
and 3) states where major changes might be needed. It is
estimated that the classification system reported here could
be applied directly in the northern tier of states extending
eastward from the Dakotas to the Atlantic Coast. The system
may need some modification in the mountain states of the
West and the band of states extending eastward from Kansas.
The system is likely to be least applicable in the warm
South and Southwest.
Extending this analysis a bit further, it is possible to
estimate the number of lakes that lie in each of the three
regions described above. (See Table 13.) Based on a com-
pilation of lake inventory data from each of the states, it
is estimated that 9503 lakes larger than 100 acres could be
classified by the technique reported here, and an additional
2073 large lakes could probably be classified if the system
were modified somewhat. Although there is no national lake
inventory, a summation of the state inventories shows that
there are at least 94,877 lakes in the country, and, of
these, 13,599 are larger than 100 acres in size. Therefore,
about 70% of the larger lakes could potentially be classi-
fied by the technique reported here, and it is estimated
that an additional 12% of the total could be classified if
the system were adjusted. Considering all lakes, 56% of
the total lie in the 18 states in which the system is thought
to be directly applicable, and an additional 32% lie in the
20 states where some modification of the system may be
desirable. Thus, even though the approach used here may
have regional restrictions, it is still likely to be
applicable to most lakes in the country.
If the classification system developed here is, in fact,
applicable to a distinct majority of the nation's lakes,
would it be sufficient to meet all classification needs?
The answer clearly is no. Systems based on subjective
information cannot be used as permanent replacements for
data-based systems. The ultimate necessity of obtaining
field data, quite possibly on a repetitive basis, cannot
be avoided. However, by using a subjective approach as
74
-------
Table 13. APPLICABILITY OF LAKE CLASSIFICATION SYSTEMa
Large lakes'3 Total lakes
Direct applicability -
Conn, 111, Ind, la, Me, Mass,
Mich, Minn, Neb, NH, NY, ND, 9,503 52,739
Ohio, Penn, RI, SD, Vt, Wis
Some modification -
Calif, Colo, Del, Ida, Kan,
Ky, Md, Mo, Mont, Nev, NJ, „ n^_ _„_
NC, Okla, Ore, Tenn, Utah, 2'073 30,087
Va, Wash, WVa, Wy
Major changes -
Ala, Ariz, Ark, Fla, Ga, La, ,, .„_ ., ._.
Miss, NM, SC, Tex 2'023 12'051
Totals 13,599 94,877
aBased on a summary of lake inventory data compiled by
individual states. Many inventories are incomplete,
so lake numbers given are minimum values.
larger than 100 surface acres.
75
-------
the initial step, it is possible to gain considerable manage-
ment insight and to establish priorities for data collection
programs which are needed to permit the use of more scien-
tific classification systems.
From a national point of view, it would be desirable to have
a uniform classfication system that could be applied through-
out the country to yield a broad-scale perspective of the
condition of all the nation's lakes. This would provide a
factual basis for federal lake improvement programs and would
permit priorities to be established consistent with national
objectives. Although several advantages could accrue from
the use of a single classification system applied nationwide,
it was, nevertheless, concluded that greater overall benefit
would result from the implementation of workable intrastate
systems. Comparisons among states and the integration of a
national picture should not be ignored; steps should be taken
to accommodate these goals, but they should be treated as
secondary objectives, not overriding considerations. Several
points contributed to this conclusion.
1) The primary benefits of lake classification relate to
its use as a decision-making tool, not to a static end
product. Lake condition is one variable that should
be considered as part of many management decisions.
Since management responsibilities are vested primarily
with agencies having state or local jurisdiction, it
is most important that systems with a corresponding
geographical base be implemented.
2) It would be extremely difficult, if not impossible, to
design a classification system that is sufficiently
broad to encompass the total range of diversity found
in lakes throughout the country, which, at the same
time, is sufficiently detailed to separate a less
diverse set of lakes within a single state. A scaled-
down version of this shortcoming was noted with the
classification of lakes in Wisconsin. From a statewide
point of view, the classification was judged to provide
a useful and meaningful perspective; however, at the
county level, classifications tended to be rather uni-
form and, consequently, the information may be of
minimal value for local decisions.
3) Some practical difficulties in the development of a
nationwide classification system have been imposed by
Section 314 of the 1972 Amendments to the Water Quality
Act (P.L. 92-500). This act places the responsibility
for lake classification on each of the individual
76
-------
states. This could expedite the use of classification
for decision-making; however, it could also result in
50 different classification schemes with no common basis
for comparison. This would be unfortunate and unneces-
sary. There is no need for that degree of specialization.
To address both state and federal needs, it would be ad-
vantageous to encourage a minimum number of classification
methodologies and, at the same time, foster the development
of techniques (such as remote sensing methods) for cor-
relating the different systems to achieve a better national
perspective. Systems based on subjective information can
be useful initially, but these should be replaced by more
sophisticated methods as data become available. Lake
classification should be viewed as an iterative process
which undergoes continual revision and refinement. The
approach presented in this report appears to be a reasonable
and practical start toward the implementation of broad-scale
lake classification.
77
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SECTION VIII
REFERENCES
Aukerman, R., and G. I. Chesley. 1971. Classifying Water
Bodies: Feasibility and Recommendations for Classifying
Water. Colorado State University. National Technical
Information Service, Springfield, Va. Publication No.
PB 208 667. 115 p.
Bjork, S. 1972. Swedish Lake Restoration Program Gets
Results. Ambio 1(5):153-165.
Born, S. 1974. Inland Lake Demonstration Project. Uni-
versity of Wisconsin-Extension and Wisconsin Department
of Natural Resources, Madison. 31 p.
Dunst, R. C., S. M. Born, P. D. Uttormark, S. A. Smith,
S. A. Nichols, J. O. Peterson, D. R. Knauer, S. L. Serns,
D. R. Winter, and T. L. Wirth. 1974. Survey of Lake
Rehabilitation Techniques and Experiences. Wisconsin
Department of Natural Resources and the University of
Wisconsin, Madison. Technical Bulletin No. 75. 179 p.
Feuillade, J. 1972. Application de la Methode de
1'Analyse Factorielle des Correspondances a la Classifi-
cation des Lacs en Fonction de Leur Degre d'Eutrophie.
Chemosphere (Pergamon Press, Great Britain) 2^:95-100.
Gower, J. C. 1967. A Comparison of Some Methods of
Cluster Analysis. Biometrics 23;623-637.
Gower, J. C. 1966. Some Distance Properties of Latent
Root and Vector Methods Used in Multivariate Analysis.
Biometrika 5_3_:325-338.
Ketelle, M. J., and P. D. Uttormark. 1971. Problem Lakes
in the United States. Water Resources Center, University
of Wisconsin, Madison, and U.S. Environmental Protection
Agency, Washington, D.C. Technical Report 16010 EHR 12/71.
282 p.
78
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Lueschow, L. A.f J. M. Helm, D. R. Winter, and G. W. Karl.
1970. Trophic Nature of Selected Wisconsin Lakes. Wisconsin
Academy of Sciences, Arts and Letters 58:237-264.
Lopinot, A. C. 1966. Illinois Surface Water Inventory,
1965 Supplement. Illinois Department of Conservation,
Division of Fisheries, Springfield. Special Fisheries
Report No. 1.
McColl, R. H. S. 1972. Chemistry and Trophic Status of
Seven New Zealand Lakes. New Zealand Journal of Marine and
Freshwater Research 6_(4) : 399-447.
Newton, M. E., and C. M. Fetterolf, Jr. 1966. Limnological
Data from Ten Lakes, Genesee and Livingston Counties,
Michigan, September 1965. Water Resources Commission,
Bureau of Water Management, Michigan Department of Natural
Resources, Lansing. 16 p., 40 appendices.
Peterson, S. A., W. D. Sanville, F. S. Stay, and C. F.
Powers. 1974. Nutrient Inactivation as a Lake Restoration
Procedure—Laboratory Investigations. U.S. Environmental
Protection Agency, Pacific Northwest Environmental Research
Laboratory, Corvallis, Oregon. Report No. EPA-660/3-74-032.
118 p.
Shannon, E. E., and P. L. Brezonik. 1972. Eutrophication
Analysis: A Multivariate Approach. J. of Sanitary Engineering
Division, ASCE 9_8(SA1, Proc. Paper 8735):37-57.
Sheldon, A. L. 1972. A Quantitative Approach to the Clas-
sification of Inland Waters. In: Natural Environments,
Krutilla, J. V. (ed.). Johns Hopkins University Press,
Baltimore, Md. p. 205-261.
Strom, K. M. 1930. Limnological Observations on Norwegian
Lakes. Arch. f. Hydrobiol. 21;97-124.
U.S. Environmental Protection Agency. 1974. An Approach
to a Relative Trophic Index System for Classifying Lakes
and Reservoirs. National Eutrophication Survey, Pacific
Northwest Environmental Research Laboratory, Corvallis,
Oregon. Working Paper No. 24. 36 p.
Vollenweider, R. A. In press. Input-Output Models. Canada
Centre for Inland Waters, Burlington, Ontario. 48 p.
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Vollenweider, R. A. 1968. Scientific Fundamentals of the
Eutrophication of Lakes and Flowing Waters, with Particular
Reference to Nitrogen and Phosphorus as Factors in Eutro-
phication. Organisation for Economic Co-operation and
Development, Directorate of Scientific Affairs, Paris,
France. Report No. DAS/CSI/68.27. 159 p., and appendices.
Wall, J. P., M. J. Ketelle, and P. D. Uttormark. 1973.
Wisconsin Lakes Receiving Sewage Effluents. Water Resources
Center, University of Wisconsin, Madison. Technical Report
No. 73-1.
Wisconsin Department of Natural Resources. 1968. Wisconsin
Outdoor Recreation Plan. Madison. Publication No. 802.
397 p.
Zafar, A. R. 1959. Taxonomy of Lakes. Hydrobiologia 13:
287-299.
80
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SECTION IX
APPENDICES
Page
Classification of Wisconsin Lakes 82
Notes Regarding State Lake Inventories . . 174
Lake Inventory Questionnaire 177
81
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APPENDIX I
CLASSIFICATION OF WISCONSIN LAKES
The following information is included in the appendix:
County Number
County Name
Lake Number (LKNO) as assigned by Dept. of Natural
Resources
Lake Name
Area (surface area in acres)
Maximum Depth (MXD) in feet
Outlet - Presence/absence denoted by "yes" or "no" answer.
Type of Lake:
Natural (NAT)
Natural Lake with Level Control (NLLC)
Impoundment (IMP)
Breakdown of Lake Classification scores:
D.O. = number of penalty points received for hypolim-
netic dissolved oxygen conditions
TENS = number of penalty points received for water
transparency (Secchi disk measurements)
FSKL = number of penalty points received for history
of fishkills
IMPR = number of penalty points received for impairment
of lake-use due to nuisance algal blooms or ex-
cess rooted aquatic plants
LCI = Lake Condition Index: the lake's score achieved
with the classification system (The score is a
total of the points received for D.O., TENS,
FSKL, and IMPR.)
There are 1149 lakes listed in the appendix, and Lake Condi-
tion Index (LCI) scores are presented for 1147 of these.
LCI values could not be calculated for the remaining 2
because of insufficient data.
Of the 1147 scores listed, 18 are marked with an asterisk
because some facet of the data input appeared questionable.
Therefore, the values for these 18 should be regarded as
probable.
Because the Wisconsin Department of Natural Resources main-
tains an active lake mapping program, certain measurements,
such as surface area and depth, will be subject to periodic
change. Generally, such changes will be minor (less than
1% overall), but this point should be kept in mind by the
reader.
82
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Notes for NLI Data Appendix:
The Wisconsin lakes which comprised the subject matter of this study
are listed alphabetically by county in the following appendix. There
are 72 counties (including Menominee) in the state; 61 have lakes with
surface areas of 100 acres or greater. The counties, with the number
of project lakes in each, are listed below.
County
number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
County name
Adams
Ashland
Barren
Bayfield
Brown
Buffalo
Burnett
Calumet
Chippewa
Clark
Columbia
Crawford
Dane
Dodge
Door
Douglas
Dunn
Eau Claire
Florence
Fond du Lac
Forest
Grant
Green
Green Lake
Iowa
Iron
Jackson
Jefferson
Juneau
Kenosha
Kewaunee
La Crosse
Lafayette
Langlade
Lincoln
Manitowoc
Number
of lakes
6
17
31
41
0
0
71
0*
13
4
5
0
12
7
5
21
3
4
16
4
35
5
2
5
2
32
7
9
13
8
0
1
1
15
19
4
County
number
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
County name
Marathon
Marinette
Marquette
Menominee
Milwaukee
Monroe
Oconto
Oneida
Outagamie
Ozaukee
Pep in
Pierce
Polk
Portage
Price
Racine
Rich land
Rock
Rusk
St. Croix
Sauk
Sawyer
Shawano
Sheboygan
Taylor
Trempealeau
Vernon
Vilas
Walworth
Washburn
Washington
Waukesha
Waupaca
Waushara
Winnebago
Wood
Number
of lakes
8
20
7
6
0
9
30
136
0
1
0
0
42
5
33
9
2
o**
17
8
4
58
10
5
4
1
0
173
19
55
7
22
13
12
6
9
*Lake Winnebago is listed under Winnebago County.
**Lake Koshkonong is listed under Jefferson County.
83
-------
COUNTY #1 - ADAMS
CO
LKNO
3
9
11
12
47
48
LAKE NAME
Big Roche-a-Cri
Friendship
Jordan
Mason
Sherwood
Camelot
AREA
122
115
213
857
250
415
MXD
20
16
79
10
30
30
INLET
Yes
Yes
No
Yes
Yes
Yes
OUTLT
Yes
Yes
No
Yes
Yes
Yes
TYPE
IMP
IMP
NAT
IMP
IMP
IMP
D.O.
3
3
4
3
3
3
TRNS
1
2
1
2
2
2
FSKL
0
0
0
0
0
0
IMPR
2
3
0
2
2
2
LCI
6
8
5
7
7
7
-------
COUNTY #2 - ASHLAND
oo
LKNO
1
2
5
9
14
23
24
26
29
33
39
41
46
48
62
71
153
LAKE NAME
Augustine
Bad River
Bear
Beaver Dam
Caroline
East Twin
English
Galillee
Gordon
Kakagon
Little Clam
Long
Meder
Mineral
Spider
Upper Clam
Madeline Island
AREA
166
185
220
118
130
110
244
212
142
766
144
111
135
225
103
165
105
MXD
10
7
8
19
8
15
40
23
28
26
11
15
10
29
20
20
10
INLET
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
OUTLT
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NAT
NLLC
NLLC
NAT
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
D.O.
1
0
0
1
3
1
2
1
1
1
1
3
3
1
1
1
3
TRNS
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
FSKL
0
0
0
0
3
0
0
0
0
0
0
3
0
0
0
0
3
IMPR
0
0
0
0
0
0
0
2
0
0
2
0
0
0
2
0
2
LCI
3
2
2
3
8
3
4
5
3
3
5
8
5
3
5
3
10
-------
COUNTY #3 - BARRON
CO
LKNO
8
11
12
13
14
19
21
34
35
40
42
44
50
59
64
66
70
77
84
85
87
LAKE NAME
Bass
Bear
Beaver Dam
Big Dummy
Big Moon
Butternut
Chain
Duck
Echo
Granite
Hemlock
Horseshoe
Lake Chetek
Little Sand
Lower Devil's
Lower Turtle
Montanis
Mud
Pokegama
Poskin
Prairie
AREA
118
1358
1112
135
191
141
107
100
161
154
357
115
770
101
162
276
200
578
506
150
1534
MXD
14
87
106
58
48
15
19
26
41
34
21
19
22
36
26
24
14
15
19
30
16
INLET
No
Yes
No
No
Yes
No
No
Yes
No
Yes
Yes
No
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
No
Yes
Yes
No
Yes
No
No
Yes
No
Yes
Yes
No
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
NAT
NLLC
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
NLLC
NLLC
NAT
NAT
IMP
IMP
NLLC
IMP
D.O.
0
0
0
2
0
3
3
1
2
2
1
3
0
2
3
3
0
0
1
1
1
TRNS
1
1
4
3
1
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
IMPR
0
2
2
0
2
2
0
0
2
0
2
0
9
0
0
0
2
4
7
4
7
LCI
1
3
6
5
3
7
5
3
6
4
5
5
11
4
7
5
4
6
10
7
10
-------
COUNTY #3 - BARRON Con't
LKNO
88
89
93
95
99
100
103
104
107
109
LAKE NAME
Red Cedar
Rice
Sand
Silver
Staples
Stump
Tenmile
Tuscobta
Upper Turtle
Vermillion
AREA
1841
939
322
337
305
129
376
157
440
208
MXD
53
19
57
91
17
7
12
27
25
55
INLET
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
NLLC
IMP
NLLC
NAT
NAT
IMP
IMP
NLLC
NAT
NAT
D.O.
0
3
2
0
0
5
0
3
3
2
TRNS
2
1
1
2
2
2
2
2
2
2
FSKL
0
0
0
0
3
3
0
0
0
0
IMPR
0
2
2
0
9
6
9
6
2
0
LCI
2
6
5
2
14
16
11
11
7
4
-------
COUNTY #4 - BAYFIEID
LKNO
8
11
20
31
38
41
46
47
co 60
CO
68
72
78
80
81
83
85
90
96
114
116
119
LAKE NAME
Atkins
Bark Bay
Basswood
Birch
Bony
Buffalo
Buskey Bay
Cable
Chippewa
Cranberry
Crystal
Deep
Dells
Delta
Diamond
Drummond
Eagle
Ellison
Ghost
Half Moon
Hart
AREA
190
116
119
129
200
190
100
166
319
131
122
125
103
180
331
130
159
110
142
106
259
MXD
81
8
9
8
52
25
51
44
11
12
30
29
42
30
83
44
55
18
30
10
54
INLET
Yes
Yes
No
Yes
No
No
Yes
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
No
No
No
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
No
Yes
No
Yes
TYPE
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NAT
D.O.
0
1
3
0
0
1
0
2
1
1
1
1
2
0
0
2
2
1
1
1
4
TRNS
1
2
2
2
1
1
1
1
2
2
1
1
2
1
2
1
1
1
2
2
1
FSKL
0
0
0
0
0
0
0
0
3
3
0
0
0
0
0
0
0
0
0
3
0
IMPR
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
LCI
1
3
7
2
1
2
1
3
6
6
2
2
4
1
2
5
3
2
3
6
5
-------
COUNTY #4 - BAYFIELD Con't
CO
LKNO
141
146
169
179
180
188
196
205
208
219
250
259
266
272
278
284
287
302
304
312
LAKE NAME
Iron
Jackson
Long
Middle Eau Claire
Millicent
Mud
Namekagon
Oriental
Owen
Pigeon
Sand Bar
Siskiwit
Spider
Star
Tahkodah
Tomahawk
Totagatic
Twin Bear
Upper Eau Claire
White Bass
AREA
248
142
263
902
184
178
3208
144
1323
213
114
330
124
201
152
134
537
160
1030
116
MXD
13
13
23
66
56
10
46
32
95
21
51
13
20
52
18
42
8
59
84
30
INLET
Yes
Yes
No
Yes
Yes
No
Yes
Yes
No
No
No
Yes
No
No
No
No
Yes
Yes
Yes
No
OUTLT
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No
No
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NAT
NLLC
NAT
NLLC
IMP
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
D.O.
3
1
1
2
2
3
2
2
2
1
0
1
1
0
1
0
3
0
2
1
TRNS
2
2
2
0
0
2
2
3
0
1
1
2
3
1
2
2
2
1
0
1
FSKL
3
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
IMPR
0
2
2
2
0
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
LCI
8
5
5
4
2
10
6
5
2
2
1
3
4
1
3
2
5
1
2
2
-------
COUNTY #7 - BURNETT
LKNO
8
10
13
15
20
23
24
25
26
27
39
41
43
45
51
52
56
58
59
60
64
LAKE NAME
Bashaw
Bass
Bass
Bass
Benoit
Big Bear
Big Doctor
Big McKenzie
Big Sand
Birch Island
Cadotte
Clam River
Clear
Conners
Crooked
Crooked
Danbury
Deer
Des Moines
Devils
Dunham
AREA
171
110
226
207
279
189
222
1142
1400
838
127
359
115
109
180
247
256
157
229
1001
243
MXD
15
18
24
6
40
17
6
71
55
13
18
29
55
13
10
13
10
18
37
24
63
INLET
Yes
No
No
No
No
No
No
Yes
No
No
Yes
No
No
No
No
Yes
No
No
Yes
OUTLT
Yes
No
No
No
Yes
No
No
Yes
No
No
Yes
Yes
No
No
No
No
Yes
No
No
Yes
TYPE
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
IMP
NAT
NAT
NAT
NAT
IMP
NAT
NAT
NAT
NAT
D.O.
0
5
1
5
2
1
5
0
0
5
1
1
0
1
5
5
1
3
2
1
2
TRNS
2
2
2
2
2
1
2
0
1
1
1
2
2
2
2
3
2
2
2
2
1
FSKL
0
3
0
3
0
0
3
0
0
3
0
0
0
0
3
3
0
0
0
0
0
IMPR
3
0
0
2
2
0
2
0
2
2
2
0
0
2
2
2
3
2
0
2
0
LCI
5
10
3
12
6
2
12
0
3
11
4
3
2
5
12
13
6
7
4
5
3
-------
COUNTY #7 - BURNETT Con't
LKNO
69
75
79
83
85
86
87
94
102
105
108
114
115
116
118
119
121
125
126
127
LAKE NAME
Elbow
Fish
Gaslyn
Green
Gull
Ham
Hanscom
Johnson
Lily
Lipsett
Little Bear
Little Long
Little Wood
Little Yellow
Long
Long
Loon
Lost Lakes
Love
Lower Clam
AREA
248
356
164
279
197
324
127
397
176
393
128
100
207
285
318
251
189
248
253
337
MXD
8
29
12
5
18
29
7
23
18
24
54
23
23
19
13
41
24
4
63
14
INLET
No
No
No
No
Yes
No
No
No
No
Yes
No
No
Yes
Yes
Yes
No
Yes
Yes
No
Yes
OUTLT
No
No
Yes
No
Yes
No
No
No
No
Yes
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NLLC
NLLC
NLLC
D.O.
5
0
5
5
1
1
5
5
3
3
4
1
1
1
1
2
1
5
0
1
TRNS
2
1
2
2
2
0
2
2
4
2
1
2
2
2
2
1
2
2
1
2
FSKL
3
0
3
3
0
0
3
3
3
0
0
0
0
0
0
0
0
3
0
0
IMPR
2
2
2
2
0
2
2
2
0
2
0
0
2
2
2
0
0
0
0
3
LCI
12
3
12
12
3
3
12
12
10
7
5
3
5
5
5
3
3
10
1
6
-------
COUNTY #7 - BURNETT Con't
to
LKNO
128
130
132
135
137
143
145
146
149
153
155
165
166
172
176
177
182
183
188
189
197
LAKE NAME
Lower Twin
Mallard
McGraw
Middle McKenzie
Minerva
Mud
Mud Hen
Myre
Nicaboyne
Oak
Owl
Point
Pokegama
Rice
Rooney
Round
Sand
Shoal
Spencer
Spirit
Tabor
AREA
123
113
135
530
222
163
563
128
291
227
127
144
223
311
322
204
962
247
188
593
163
MXD
9
35
25
45
22
3
66
20
34
19
27
7
56
10
30
27
73
4
19
27
25
INLET
No
No
Yes
Yes
Yes
No
Yes
No
No
No
No
No
Yes
No
No
Yes
No
No
No
Yes
No
OUTLT
No
No
Yes
Yes
Yes
No
Yes
No
No
No
No
No
Yes
No
No
Yes
No
No
No
Yes
Yes
TYPE
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
D.O.
5
0
3
4
1
5
0
1
4
3
1
5
4
1
3
3
0
5
5
3
3
TRNS
2
1
2
2
2
2
1
2
1
0
2
2
2
2
1
2
0
2
2
2
2
FSKL
3
0
0
0
0
3
0
0
0
0
0
3
0
0
0
3
0
3
3
0
0
IMPR
2
0
0
0
0
0
0
0
2
0
0
0
0
2
0
7
0
2
2
2
2
LCI
12
1
5
6
3
10
1
3
7
3
3
10
6
5
4
15
0
12
12
7
7
-------
COUNTY #7 - BURNETT Con't
U)
LKNO
204
206
208
209
210
211
212
214
215
LAKE NAME
Trade
Twenty-Six
Upper Clam
Upper Twin
Viola
Warner
Webb
Wood
Yellow
AREA
426
230
1207
163
262
176
759
521
2287
MXD
39
45
11
17
33
75
27
35
32
INLET
Yes
Yes
Yes
No
No
No
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
D.O.
4
0
1
5
2
0
3
4
2
TRNS
4
0
2
2
1
2
2
2
2
FSKL
0
0
0
3
0
0
0
0
0
IMPR
2
0
3
2
0
0
0
2
2
LCI
10
0
6
12
3
2
5
8
6
-------
COUNTY #9 - CHIPPEWA
LKNO
35
38
40
41
71
S 85
98
99
102
123
134
135
152
LAKE NAME
Chain
Chippewa Falls Flow
Cornell Flowage
Cornell
Holcombe Flowage
Lake Wissota
Long
Loon
Marsh Miller
Old Abe
Pike
Pine
Round
AREA
510
282
577
194
3890
5588
1060
125
436
646
173
262
216
MXD
78
29
54
39
61
72
96
5
14
36
32
115
18
INLET
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
No
No
No
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
TYPE
NAT
IMP
IMP
IMP
IMP
IMP
NAT
NAT
NLLC
IMP
NAT
NAT
NAT
D.O.
4
3
2
4
2
2
2
5
0
2
2
2
3
TRNS
0
2
2
2
2
2
1
2
2
2
2
0
2
FSKL
0
3
0
0
0
0
0
3
0
0
0
0
0
IMPR
0
0
0
2
0
2
0
0
3
0
2
0
2
LCI
4
8
4
8
4
6
3
10
5
4
6
2
7
-------
COUNTY #10 - CLARK
vo
Ul
LKNO
1
3
4
5
COUNTY
LKNO
9
13
17
21
50
LAKE NAME
Arbutus
Rock Dam
Mead
Sherwood
#11 - COLUMBIA
LAKE NAME
Lazy
Park
Swan
Wisconsin
French Creek
AREA
821
118
320
117
AREA
174
219
419
9000
655
MXD
56
10
16
9
MXD
8
27
82
24
5
INLET
Yes
Yes
Yes
Yes
INLET
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
TYPE
IMP
IMP
IMP
IMP
TYPE
IMP
IMP
NAT
IMP
IMP
D.O.
4
3
3
5
D.O.
5
5
2
5
3
TRNS
2
2
2
2
TRNS
3
2
2
3
3
FSKL
0
0
0
0
FSKL
3
3
0
3
0
IMPR
2
4
2
2
IMPR
6
9
2
5
2
LCI
8
9
7
9
LCI
17
19
6
16
8
-------
COUNTY #13 - DANE
LKNO
4
5
6
10
11
13
14
16
17
19
25
27
LAKE NAME
Cherokee
Crystal
Fish
Kegonsa
Marshall
Mendota
Monona
Mud-Lower
Mud- Upper
Rockdale
Waubesa
Wingra
AREA
335
571
252
2716
194
9730
3335
195
223
104
2113
345
MXD
5
9
62
31
5
82
64
15
8
5
34
21
INLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NAT
NLLC
IMP
NLLC
NAT
NAT
NAT
IMP
NLLC
NLLC
D.O.
3
5
2
4
3
2
2
1
3
3
2
1
TRNS
3
3
1
3
3
2
1
3
3
3
3
3
FSKL
0
3
0
4
0
4
4
0
3
3
4
0
IMPR
2
3
3
9
9
9
9
5
9
4
9
5
LCI
8
14
6
20
15
17
16
9
18
13
18
9
-------
COUNTY #14 - DODGE
LKNO
2
3
7
8
11
16
17
COUNTY
LKNO
4
6
10
15
17
LAKE NAME
Beaver Dam
Chub
Emily
Fox
Lost Lake
Neosho
Sinnissippi
#15 - DOOR
LAKE NAME
Clark
Europe
Kangaroo
MacKaysee
Mud
AREA
5540
120
268
2120
256
146
2300
AREA
778
290
1099
324
155
MXD
8
2
12
19
5
6
5
MXD
22
8
13
26
5
INLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
INLET
Yes
No
Yes
No
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
No
Yes
No
Yes
TYPE
IMP
NAT
NLLC
IMP
NAT
IMP
IMP
TYPE
NLLC
NAT
NLLC
NAT
NAT
D.O.
3
5
3
3
5
3
5
D.O.
1
1
1
1
5
TRNS
3
4
3
3
3
3
4
TRNS
1
2
2
1
2
FSKL
3
3
3
3
3
3
3
FSKL
0
0
0
0
3
IMPR
7
2
9
9
7
7
2
IMPR
0
0
0
0
2
LCI
16
14
18
18
18
16
14
LCI
2
3
3
2
12
-------
COUNTY #16 - DOUGLAS
LKNO
2
5
6
21
34
40
45
66
74
vo
°° 75
80
85
98
104
112
118
121
127
133
141
147
LAKE NAME
Amnicon
Bardon
Bass
Bond
Cranberry
Crystal
Bowling
Leader
Loon
Lower Eau Claire
Lyman
Minnesuing
Nebagamon
Person
Red
St. Croix
Sauntry
Simms
Steele
Twin
Upper St. Croix
AREA
426
832
126
292
172
293
154
165
109
792
403
432
914
172
252
1913
110
152
157
113
855
MXD
31
102
26
64
19
21
13
56
20
41
15
43
56
10
39
28
9
41
8
5
22
INLET
Yes
No
Yes
No
Yes
No
Yes
No
No
Yes
Yes
Yes
Yes
No
No
Yes
No
No
Yes
No
Yes
OUTLT
Yes
No
Yes
No
Yes
No
No
No
No
Yes
Yes
Yes
Yes
No
No
Yes
No
No
No
No
Yes
TYPE
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NLLC
NAT
NAT
NAT
NAT
IMP
NAT
NAT
NAT
NAT
NAT
D.O.
2
0
1
0
1
0
1
0
5
2
1
2
0
5
2
1
5
2
3
5
1
TRNS
2
1
2
1
2
1
2
1
2
1
2
2
1
2
1
2
2
1
2
2
2
FSKL
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
IMPR
2
0
0
0
0
0
0
0
0
0
2
0
0
2
0
3
0
0
2
0
0
LCI
6
1
3
1
3
1
3
1
10
3
5
4
1
9
3
6
7
3
7
7
3
-------
COUNTY #17 - DUNN
LKNO
10
11
18
COUNTY
LKNO
1
4
5
8
LAKE NAME
Eau Galle
Menomin
Tainter
#18 - EAU CLAIRE
LAKE NAME
Altoona
Dells Pond
Eau Claire
Halfmoon
AREA
351
1405
1752
AREA
840
739
1118
132
MXD
18
34
37
MXD
25
30
25
9
INLET
Yes
Yes
Yes
INLET
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
TYPE
IMP
IMP
IMP
TYPE
IMP
IMP
IMP
NAT
D.O.
1
0
4
D.O.
1
1
1
5
TRNS
2
2
2
TRNS
2
2
3
2
FSKL
0
0
0
FSKL
3
0
0
0
IMPR
0
2
7
IMPR
4
2
7
7
LCI
3
4
13
LCI
10
5
11
14
-------
COUNTY #19 - FLORENCE
LKNO
4
19
20
21
30
37
44
66
0 71
o /a-
77
79
82
10
39
69
89
LAKE NAME
Bass
Elwood
Emily
Fay
Halsey
Keyes
Long
Patten
Price
Savage
Sealion
Shadow
Brule River
Kings ford
Pine River
Twin Falls
AREA
102
135
197
247
512
202
340
256
107
150
122
109
197
415
145
682
MXD
68
25
43
13
10
77
23
52
8
10
82
30
70
33
38
15
INLET
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
No
Yes
Yes
Yes
No
Yes
Yes
No
No
Yes
No
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NAT
IMP
IMP
IMP
IMP
D.O.
4
3
2
3
3
2
5
4
3
3
4
3
4
4
4
3
TRNS
0
0
0
2
2
0
2
2
2
2
1
1
2
2
2
2
FSKL
0
0
0
0
0
0
3
0
3
3
0
0
0
0
0
0
IMPR
0
0
0
2
2
0
2
0
2
0
2
0
0
0
0
0
LCI
4
3
2
7
7
2
12
6
10
8
7
4
6
6
6
5
-------
COUNTY #20 - FOND DU LAC
LKNO
1
12
13
19
LAKE NAME
Auburn
Kettle Morraine
Long
Mullet
AREA
107
240
409
200
MXD
29
30
47
7
INLET
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NLLC
NAT
D.O.
3
5
4
5
TRNS
1
2
1
2
FSKL
0
3
0
3
IMPR
2
3
2
3
LCI
6
13
7
13
-------
COUNTY #21 - FOREST
LKNO
1
2
8
9
13
20
32
52
o 64
to
65
66
69
75
76
84
85
90
94
96
104
LAKE NAME
Arbutus
Atkins
Birch
Bishop
Bog Brook
Butternut
Crane
Franklin
Hay Meadow
Hiles
Himley
Howell
Julia
Jungle
Laura
Lily
Little Long
Little Rice
Little Sand
Lucerne
AREA
161
151
468
288
490
1292
337
892
241
713
149
177
401
182
110
212
102
1219
248
1026
MXD
35
4
72
12
6
42
26
53
9
5
8
15
47
12
21
25
28
10
21
68
INLET
No
No
No
Yes
Yes
No
No
No
Yes
Yes
No
No
Yes
Yes
No
Yes
No
Yes
No
No
OUTLT
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
No
Yes
TYPE
NAT
NAT
NAT
NLLC
IMP
NAT
NAT
NAT
IMP
IMP
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
IMP
NAT
NAT
D.O.
4
5
4
3
5
4
3
4
5
5
3
3
4
3
3
3
3
5
3
2
TRNS
2
2
2
2
2
0
2
0
2
2
2
2
2
2
2
2
0
2
0
0
FSKL
0
0
0
0
0
0
0
0
3
3
0
0
0
0
0
0
0
3
0
0
IMPR
0
0
0
5
0
0
0
0
0
2
0
0
0
0
0
0
0
2
0
0
LCI
6
7
6
10
7
4
5
4
10
12
5
5
6
5
5
5
3
12
3
2
-------
COUNTY #21 - FOREST Con't
LKNO
114
128
130
131
133
142
144
147
168
152
159
160
167
175
179
LAKE NAME
Metonga
Pat Shay
Peshtigo
Pickerel
Pine
Rice
Riley
Roberts
St. Johns
Scattered Rice
Shoe
Silver
Stevens
Trump
Wabicon
AREA
2157
120
156
1299
1670
208
213
452
104
486
168
334
295
172
594
MXD
74
5
4
14
14
6
12
32
21
10
7
20
10
20
15
INLET
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No
No
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
NLLC
NAT
NAT
NLLC
NLLC
NAT
NAT
NAT
NAT
IMP
NAT
NAT
NAT
NAT
D.O.
2
5
5
5
5
5
3
4
3
3
5
3
3
5
3
TRNS
0
2
4
2
2
2
2
2
0
2
2
0
2
2
2
FSKL
0
0
0
0
3
0
0
0
0
0
3
0
0
0
0
IMPR
0
2
7
5
2
0
0
2
0
2
0
0
0
2
2
LCI
2
9
16
12
12
7
5
8
3
7
10
3
5
9
7
-------
COUNTY #22 - GRANT
LKNO
1
5
16
20
29
COUNTY
LKNO
1
2
LAKE NAME
Bertom Lake
Cassville Slough
Jack Oak Slough
McCartney Lake
State Line Slough
#23 - GREEN
LAKE NAME
Albany
Decatur
AREA
126
674
145
924
310
AREA
102
151
MXD
8
30
16
10
30
MXD
8
10
INLET
Yes
Yes
Yes
Yes
Yes
INLET
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
TYPE D.O.
5
2
1
5
2
TYPE D.O.
IMP 1
IMP 3
TRNS
3
3
3
3
3
TRNS
3
3
FSKL
3
0
0
3
0
FSKL
0
0
IMPR
2
2
2
2
2
IMPR
5
5
LCI
13
7
6
13
7
LCI
9
11
-------
COUNTY #24 - GREEN LAKE
LKNO
2
3
4
7
8
M
O
Ul
COUNTY
LKNO
10
15
LAKE NAME
Grand
Green
Little Green
Maria
Puckaway
#25 - IOWA
LAKE NAME
Twin Valley
Blackhawk
AREA
234
7325
465
596
5433
AREA
145
220
MXD
7
229
24
6
5
MXD
36
40
INLET
Yes
Yes
Yes
No
Yes
INLET
Yes
Yes
OUTLT
Yes
Yes
Yes
No
Yes
OUTLT
Yes
Yes
TYPE
IMP
NAT
NAT
NAT
NLLC
TYPE
IMP
IMP
D.O.
2
3
5
1
D.O.
6
4
TRNS
3
0
1
2
3
TRNS
1
1
FSKL
0
0
3
3
0
FSKL
0
0
IMPR
2
2
5
2
3
IMPR
5
2
LCI
4
12
12
7
LCI
12
7
-------
COUNTY #26 - IRON
LKNO
13
17
23
24
47
57
58
64
66
67
80
95
96
109
111
117
122
125
135
149
151
LAKE NAME
Big Pine
Boot
Catherine
Cedar
Echo
Fisher
Flambeau
Gile
Grand Portage
Grant
Island
Lake of the Falls
Lake Six
Little Pike
Long
Martha
Mercer
Moose
North Bass
Owl
Pardee
AREA
632
180
118
193
220
452
13545
3384
144
107
352
338
147
100
373
146
184
269
180
126
206
MXD
22
16
11
21
25
25
50
25
31
10
17
23
11
19
30
55
24
12
9
38
27
INLET
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
No
Yes
OUTLT
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
No
No
Yes
TYPE
NAT
NAT
NAT
NAT
NAT
NLLC
IMP
IMP
NAT
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
D.O.
3
3
3
3
3
3
4
3
4
3
3
3
5
5
3
4
3
3
3
4
3
TRNS
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
3
3
0
0
0
0
0
0
0
IMPR
2
2
2
0
0
2
0
0
2
2
0
0
0
2
2
0
2
0
0
2
0
LCI
7
7
7
5
5
7
6
5
8
7
5
5
10
12
7
6
7
5
5
8
5
-------
COUNTY #26 - IRON Con't
LKNO
154
155
160
163
168
170
184
197
199
203
211
LAKE NAME
Pike
Pine
Randall
Rice
Sand
Sandy Beach
Spider
Trude
Upper Springstead
Virgin
Wilson
AREA
194
312
115
125
101
112
361
754
126
119
155
MXD
80
34
10
20
35
7
41
48
23
45
21
INLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
No
No
Yes
No
Yes
Yes
No
TYPE
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
D.O.
4
4
3
3
4
3
4
4
3
4
3
TRNS
2
2
2
2
0
2
2
2
2
2
2
FSKL
0
0
0
0
0
0
0
0
0
0
0
IMPR
0
0
0
2
0
2
0
2
0
0
0
LCI
6
6
5
7
4
7
6
8
5
6
5
-------
COUNTY #27 - JACKSON
00
LKNO
14
16
34
42
60
61
110
COUNTY
LKNO
2
5
9
10
12
15
16
17
18
LAKE NAME
Potter
Seventeen
Black River
13/8
31/2
31/5
16/14
#28 - JEFFERSON
LAKE NAME
Blue Spring
Goose
Hope
Koshkonong
Spring-Lower
Red Cedar
Rip ley
Rock
Rome
AREA
348
178
198
110
182
110
170
AREA
136
144
142
10480
106
370
433
1371
477
MXD
24
4
34
4
5
7
7
MXD
12
4
24
6
14
6
50
56
12
INLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
INLET
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
IMP
IMP
IMP
IMP
IMP
IMP
IMP
TYPE
NLLC
NAT
NAT
NLLC
IMP
NAT
NAT
NLLC
IMP
D.O.
5
5
2
3
3
3
2
D.O.
1
5
5
5
3
5
2
2
5
TRNS
2
2
2
4
2
2
2
TRNS
2
3
1
4
3
3
2
2
3
FSKL
0
3
0
0
0
0
0
FSKL
0
3
3
3
0
3
0
0
3
IMPR
0
0
2
3
0
0
6
IMPR
5
5
5
2
5
9
5
5
9
LCI
7
10
6
10
5
5
10
LCI
8
16
14
14
11
20
9
9
20
-------
COUNTY #29 - JUNEAU
LKNO
2
3
4
5
6
7
8
16
23
37
44
48
50
LAKE NAME
Castle Rock Flowage
Meadow Valley
Petenwell
Potters
Ryne arson
Rynearson
Sprague
8/8
19/8
19/9
12/15
33/15
13/13
AREA
16440
1190
22218
299
570
493
1930
249
180
136
125
1868
300
MXD
36
8
44
15
14
10
9
4
8
8
15
35
13
INLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
IMP
IMP
IMP
IMP
IMP
IMP
IMP
IMP
IMP
IMP
IMP
IMP
IMP
D.O.
6
5
6
3
5
5
5
5
5
5
3
4
3
TRNS
3
3
3
3
3
3
3
3
3
3
3
3
3
FSKL
4
3
4
3
3
3
3
3
3
3
0
0
0
IMPR
2
2
2
2
2
2
2
2
2
2
2
2
2
LCI
15
13
15
11
13
13
13
13
13
13
8
9
8
-------
COUNTY #30 - KENOSHA
LKNO
3
4
5
8
15
19
21
23
COUNTY
LKNO
2
COUNTY
LAKE NAME
Benet and Shangrlla
Camp
Center
Elizabeth
Marie
Paddock
Powers
Silver
#32 - LA CROSSE
LAKE NAME
Neshonoc
#33 - LAFAYETTE
AREA
154
482
106
622
297
112
418
499
AREA
687
MXD
24
17
30
30
38
32
34
43
MXD
11
INLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
INLET
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
TYPE
NLLC
NLLC
NLLC
NLLC
NLLC
NLLC
NLLC
NLLC
TYPE
IMP
D.O.
5
5
3
3
4
.4
4
4
D.O.
3
TRNS
2
2
1
1
2
2
2
2
TRNS
2
FSKL
3
3
0
0
0
0
0
0
FSKL
0
IMPR
3
4
2
2
2
3
2
2
IMPR
0
LCI
13
14
6
6
8
9
8
8
LCI
5
LKNO LAKE NAME
AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Yellowstone
455 21
Yes
Yes
IMP
18
-------
COUNTY #34 - LANGLADE
LKNO
15
29
31
40
56
III
127
129
111
216
170
171
176
196
224
LAKE NAME
Black Oak
Duck
Dynamite
Enterprise
Greater Bass
Mary
Moccasin
Moose
Post, Lower
Post, Upper
Rolling Stone
Rose
Sawyer
Summit
White
AREA
59
125
97
502
270
156
110
105
377
758
688
109
169
282
166
MXD
32
19
22
28
28
9
38
20
8
14
12
24
30
26
44
INLET
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
No
OUTLT
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
TYPE
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
IMP
IMP
NLLC
NAT
NAT
NLLC
NLLC
D.O.
4
3
3
3
3
3
4
3
3
3
3
3
3
3
4
TRNS
2
2
2
2
2
2
2
0
2
2
2
1
0
2
0
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
IMPR
0
0
0
0
0
0
0
0
2
2
2
0
0
0
0
LCI
6
5
5
5
5
5
6
3
7
7
7
4
3
5
4
-------
COUNTY #35 - LINCOLN
LKNO
3
4
20
33
37
38
54
55
71
85
98
102
110
115
122
128
130
142
144
LAKE NAME
Alexander
Alice
Bridge
Clear
Crystal
Deer
Grandfather
Grandmother
Jersey City
Long
Mohawks in
Muskellunge
Pesobic
Pine
Seven Island
Somo, Big
Spirit River
Tug
Ward
AREA
750
1491
444
295
104
137
223
199
349
132
1898
159
146
145
136
345
1239
154
108
MXD
36
32
17
45
22
62
32
22
17
64
26
23
11
15
33
25
22
21
13
INLET
Yes
Yes
Yes
No
No
No
Yes
Yes
Yes
No
Yes
No
No
No
No
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
TYPE
IMP
IMP
NLLC
NAT
NAT
NLLC
IMP
IMP
IMP
NAT
IMP
NAT
NAT
NAT
NAT
NAT
IMP
NLLC
IMP
D.O.
4
4
3
4
3
4
4
3
3
4
3
3
3
1
4
3
3
3
3
TRNS
2
1
2
2
0
0
2
2
2
0
2
1
2
2
1
2
2
2
2
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
IMPR
2
0
2
2
0
0
0
0
2
0
2
0
2
2
0
0
0
0
2
LCI
8
5
7
8
3
4
6
5
7
4
7
4
10
5
5
5
5
5
7
-------
COUNTY #36 - MANITOWOC
U)
LKNO
4
22
33
46
COUNTY
LKNO
4
5
15
24
27
37
57
LAKE NAME
Cedar
Long
Rockville
Wilke
#37 - MARATHON
LAKE NAME
Big Bass
Big Eau Pleine
Du Bay
Half Moon
Mayflower
Mosinee
Pike
Wausau
AREA
139
120
137
107
AREA
174
6830
6700
800
100
200
208
1900
MXD
26
37
7
22
MXD
6
46
<30
12
14
<30
33
<30
INLET
No
No
Yes
Yes
INLET
No
Yes
Yes
Yes
No
Yes
Yes
Yes
OUTLT
No
No
Yes
Yes
OUTLT
No
Yes
Yes
Yes
No
Yes
Yes
Yes
TYPE
NAT
NAT
IMP
NAT
TYPE
NAT
IMP
IMP
IMP
NAT
IMP
NLLC
IMP
D.O.
3
4
5
3
D.O.
5
6
3
3
5
3
6
3
TRNS
1
1
3
1
TRNS
2
3
2
2
2
2
2
2
FSKL
0
0
0
0
FSKL
0
4
0
0
3
0
4
0
IMPR
2
4
0
3
IMPR
2
7
2
2
2
4
2
3
LCI
6
9
8
7
LCI
9
20
7*
7
12
9*
14
8*
-------
COUNTY #38 - MARINETTE
LKNO
6
29
35
42
67
72
84
85
96
133
147
120
163
172
196
213
216
218
225
230
LAKE NAME
Bagley
Caldron Falls
Chalk Hills
Coleman
Gilas
Grand Rapids
High Falls
Hilbert
Johnson Falls
Mary
Montana
Lake Quinnesee Flowage
Noquebay
Peshtigo
Sandstone
Sturgeon Falls
Thunder
Town Corner
Upper Scott
White Rapids
AREA
281
1180
300
246
125
202
1498
247
68
167
135
67
2162
232
153
200
135
175
283
449
MXD
20
40
35
67
84
23
54
38
40
20
28
40
54
15
39
40
62
12
17
32
INLET
Yes
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
IMP
IMP
IMP
NLLC
NAT
IMP
IMP
NAT
IMP
NAT
NAT
IMP
NLLC
IMP
IMP
IMP
NAT
NAT
IMP
IMP
D.O.
3
4
4
4
4
3
4
4
4
3
3
4
4
3
4
4
4
5
3
4
TRNS
2
1
2
1
0
2
1
0
2
2
2
2
2
2
2
2
0
1
2
2
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
IMPR
2
0
0
2
0
0
0
0
0
2
0
0
2
2
2
0
0
2
2
0
LCI
7
5
6
7
4
5
5
4
6
7
5
6
8
7
8
6
4
11
7
6
-------
COUNTY
LKNO
4
10
17
22
26
33
54
COUNTY
LKNO
2
3
19
27
29
33
#39 - MARQUETTE
LAKE NAME
Buffalo
Crystal
Harris Pond
Lawrence Pond
Montello
Neshkoro
Tuttle
#40 - MENOMINEE
LAKE NAME
Bass (Lower)
Bass (Upper)
Lamotte
Moshawquit
Neopit
Pine
AREA
2447
124
245
231
286
192
167
AREA
106
120
185
296
202
152
MXD
15
60
10
12
17
12
36
MXD
35
47
71
30
10
3
INLET
Yes
Yes
Yes
Yes
Yes
Yes
No
INLET
No
Yes
No
Yes
Yes
No
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
No
OUTLT
No
Yes
No
Yes
Yes
Yes
TYPE
IMP
NAT
IMP
IMP
IMP
IMP
NAT
TYPE
NAT
NAT
NAT
NAT
IMP
NAT
D.O.
1
2
1
3
1
3
4
D.O.
2
0
2
1
0
3
TRNS
3
1
2
1
1
3
1
TRNS
1
1
0
2
2
4
FSKL
0
0
0
0
0
0
0
FSKL
0
0
0
0
0
3
IMPR
2
0
6
2
3
9
0
IMPR
0
0
0
0
0
4
LCI
6
3
9
6
5
15
5
LCI
3
1
2
3
2
14
-------
COUNTY #42 - MONROE
LKNO
7
8
52
56
71
80
96
107
115
LAKE NAME
Lake Tomah
Monroe Co. Flowage
14/16
23/4
12/14
14/13
12/1
7/8
31/14
AREA
225
263
118
120
100
101
173
201
162
MXD
19
8
12
<30
13
10
17
8
9
INLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TYPE D.O.
IMP
IMP
IMP
NLLC
IMP
IMP
IMP
IMP
IMP
3
3
5
3
3
5
3
5
5
TRNS
3
3
2
2
2
2
2
2
2
FSKL
0
3
0
0
0
0
0
0
0
IMPR
6
0
0
2
0
2
0
0
0
LCI
12
9
7
7*
5
9
5
7
7
-------
COUNTY #43 - OCONTO
LKNO
1
2
7
13
17
18
28
31
32
36
69
71
81
75
84
106
108
121
124
131
140
LAKE NAME
Anderson
Archiband
Bass
Berry
Boot
Boulder
Chain
Christy
Chute Pond
Crooked
Horn
Impassable
John
Kelly
Lee
Machickanee
Maiden
Oconto Falls Pond
Paya
Pickerel
Reservoir Pond
AREA
171
430
149
143
263
362
31
387
417
143
132
84
103
361
91
435
290
123
121
127
409
MXD
40
58
50
20
40
11
50
10
18
43
13
5
26
41
40
23
60
29
40
15
13
INLET
Yes
Yes
Yes
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
OUTLT
Yes
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
TYPE
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
IMP
NLLC
NAT
NLLC
NAT
NAT
NLLC
IMP
NAT
IMP
NAT
NLLC
IMP
D.O.
4
4
4
3
4
3
4
5
3
4
3
5
3
2
4
5
4
3
4
3
3
TRNS
2
0
0
2
0
0
0
2
2
1
2
2
1
0
0
2
0
2
0
2
2
FSKL
0
0
0
0
0
0
0
3
0
0
0
3
0
0
0
3
0
0
0
0
0
IMPR
0
0
0
0
0
0
0
2
2
0
0
2
2
0
0
5
0
0
0
0
2
LCI
6
4
4
5
4
3
4
12
7
5
5
12
6
2
4
15
4
5
4
5
7
-------
COUNTY #43 - OCONTO Con't
LKNO
142
149
160
175
178
181
171
LAKE NAME
Rost
Shay
Surprise
Townsend Flowage
Waubee
Wescott
Wheeler
Upper Wheeler Pond
AREA
86
50
70
303
137
38
305
140
MXD
24
36
30
27
20
29
40
10
INLET
No
No
No
Yes
No
No
No
Yes
OUTLT
No
No
No
Yes
No
Yes
No
Yes
TYPE
NAT
NAT
NAT
IMP
NAT
NAT
NAT
IMP
D.O.
3
4
3
3
3
3
4
3
TRNS
0
0
0
2
0
0
0
2
FSKL
0
0
0
0
0
0
0
0
IMPR
0
0
0
2
0
2
0
0
LCI
3
4
3
7
3
5
4
5
oo
183 White Potato 1011 11 No No NAT 5 1 3 2 11
-------
COUNTY #44 - ONEIDA
vo
LKNO
3
7
15
20
21
26
27
28
29
30
31
32
37
38
39
46
47
52
55
56
63
LAKE NAME
Aldridge
Alva
Bass
Bear
Bearskin
Big
Big Carr
Big Fork
Big Stone
Birch
Bird
Blue
Bolger
Boom
Booth
Buckskin
Buffalo
Burrows
Carrol
Chain
Clear Lake
AREA
134
201
124
675
384
867
213
624
567
180
103
433
119
437
207
634
104
156
335
213
212
MXD
12
36
20
29
26
30
71
22
57
24
40
56
39
30
34
22
27
26
31
18
21
INLET
No
No
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No
No
No
Yes
No
No
No
No
Yes
Yes
No
OUTLT
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No
No
No
Yes
Yes
Yes
No
No
Yes
Yes
No
TYPE
NAT
NAT
IMP
NAT
NAT
NLLC
NAT
NLLC
NLLC
NAT
NAT
NAT
NAT
IMP
NAT
NAT
NAT
NAT
NLLC
NLLC
NAT
D.O.
3
4
3
3
3
3
0
3
4
3
4
4
4
3
4
3
3
3
4
3
3
TRNS
2
1
2
2
2
2
0
2
2
2
0
0
0
2
2
2
2
2
2
2
2
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
IMPR
2
2
0
2
2
0
0
0
0
2
0
0
0
0
2
2
0
2
2
2
2
LCI
7
7
5
7
7
5
0
5
6
7
4
4
4
5
8
10
5
7
8
7
7
-------
COUNTY #44 - ONEIDA Con't
LKNO
64
66
67
73
75
77
82
84
88
89
99
106
109
113
116
117
119
121
123
125
127
LAKE NAME
Clear
Clearwater
Columbus
Crescent Lake
Crooked
Crystal
Dam
Deer
Diamond
Dog
East Horse Head
Emma
Fifth
Flannery
Four Mile
Fourth
Franklin
Fuller
Garth
George
Gilmore
AREA
1049
343
670
612
176
112
716
175
124
241
184
223
240
112
252
258
161
101
114
435
301
MXD
100
29
26
37
17
7
30
17
17
10
26
17
9
33
18
9
25
4
22
26
24
INLET
No
No
Yes
No
No
Yes
Yes
Yes
Yes
Yes
No
No
Yes
No
Yes
Yes
No
No
No
Yes
Yes
OUTLT
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes
No
Yes
No
Yes
Yes
TYPE
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NLLC
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NLLC
D.O.
4
3
3
4
3
5
3
3
3
3
3
3
3
4
3
3
3
5
3
3
3
TRNS
0
2
2
2
2
2
2
2
2
2
2
2
2
0
2
2
2
2
2
2
2
FSKL
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
IMPR
0
0
0
2
3
2
2
0
2
0
0
2
0
2
0
0
0
3
4
2
2
LCI
4
5
5
8
11
9
7
5
7
5
5
7
5
6
5
5
5
13*
9
7
7
-------
COUNTY #44 - ONEIDA Con't
i-o
LKNO
128
132
136
138
148
151
155
158
163
166
169
170
171
172
173
178
187
190
198
199
202
LAKE NAME
Ginty
Great Bass
Hancock
Hash rook
Hodstradt
Horsehead
Indian
Island
Jennie Weber
Julia
Kathan
Katherine
Kawaguesaga
Killarney
Lake Creek
Laurel
Little Bearskin
Little Fork
Little Tomahawk
Lone Stone
Long
AREA
131
118
259
302
126
356
397
297
237
238
189
555
801
421
172
149
174
336
163
172
113
MXD
12
15
22
50
37
12
26
22
9
19
15
32
41
8
12
11
23
30
50
29
58
INLET
Yes
No
Yes
No
No
No
No
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
OUTLT
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
TYPE
NAT
NAT
NLLC
NAT
NAT
NLLC
NAT
NLLC
NAT
NAT
NAT
NLLC
NLLC
IMP
IMP
NLLC
NAT
NLLC
NLLC
NAT
NAT
D.O.
3
3
3
4
4
5
3
3
3
3
3
4
4
5
3
3
3
3
4
3
4
TRNS
2
2
2
2
0
2
2
2
2
2
2
2
2
2
2
2
2
2
1
2
0
FSKL
0
0
0
0
0
3
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
IMPR
2
2
5
0
0
9
2
2
2
2
2
0
2
0
0
0
2
2
0
2
0
LCI
7
7
10
6
4
19
7
7
7
7
7
6
8
10
5
5
7
7
5
7
4
-------
COUNTY #44 - ONEIDA Con't
LKNO
203
205
209
211
215
217
218
226
M 228
NJ
10 229
230
232
234
241
244
251
254
257
258
261
266
LAKE NAME
Long
Long
Lost
Lower Kaubashine
Madeline
Mans on
Maple
McCormick
McNaughton
Medicine
Mercer
Mid
Mildred
Minoqua
Mo en
Muskellunge
Neptune
Nokomis
North Nokomis
North Two
Oneida
AREA
115
588
155
187
154
236
142
110
120
411
254
215
191
1285
460
283
126
1950
468
150
255
MXD
31
22
14
36
25
54
15
8
7
42
23
13
45
60
11
23
7
18
73
43
34
INLET
No
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes
No
No
Yes
Yes
No
Yes
Yes
No
No
Yes
OUTLT
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
TYPE
NAT
NLLC
NAT
NAT
NLLC
NAT
NLLC
NAT
NAT
NLLC
NAT
NLLC
NAT
NLLC
NLLC
NAT
NAT
IMP
NAT
NAT
NLLC
D.O.
4
3
5
4
3
4
5
5
5
4
3
5
4
4
3
3
5
3
4
4
4
TENS
0
2
2
2
2
0
2
2
2
2
2
2
0
1
2
2
2
2
0
0
2
FSKL
0
0
0
0
0
0
3
3
3
0
0
3
0
0
0
0
0
0
0
0
0
IMPR
0
2
2
0
2
0
2
0
2
2
2
2
0
2
0
0
2
2
0
0
0
LCI
4
7
9
6
7
4
12
10
12
8
7
12
4
7
5
5
9
7
4
4
6
-------
COUNTY #44 - ONEIDA Con't
U)
LKNO
268
271
279
280
282
283
284
289
290
291
292
293
300
305
307
309
310
313
315
322
324
LAKE NAME
Oscar Jennie
Pelican
Pickerel
Pier
Pine
Pine
Planting Ground
Rainbow Flowage
Range Line
Rhine lander Flowage
Rice
Rice River Flowage
Round
Sand
Second
Seven Mile
Seventeen
Shepard
Shisheogama
Skunk
Snowden
AREA
104
3585
477
257
240
203
1015
2035
130
1326
118
1150
152
544
111
240
172
179
716
130
135
MXD
24
39
17
12
32
23
18
28
21
12
3
17
11
20
11
36
33
18
42
7
12
INLET
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
No
No
OUTLT
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No
TYPE
NAT
NLLC
NLLC
NLLC
NAT
NAT
NLLC
IMP
NLLC
IMP
NLLC
IMP
NLLC
NLLC
NLLC
NLLC
NAT
NAT
NAT
NLLC
NAT
D.O.
3
4
3
3
4
3
3
3
3
3
5
3
3
3
3
4
4
3
4
5
5
TRNS
0
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
2
2
2
0
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
3
IMPR
0
2
2
0
0
0
2
2
2
2
2
2
2
0
0
2
0
0
2
2
0
LCI
3
8
7
5
6
5
7
7
7
7
9*
7
7
5
5
8
5
5
8
12
8
-------
COUNTY #44 - ONEIDA Con't
to
LKNO
325
330
331
332
336
337
339
340
342
344
346
347
351
355
357
359
363
364
366
367
371
LAKE NAME
Soo
South Two
Spider
Spirit
Spur
Squash
Squaw
Squirrel
Starks Flowage
Stella
Stone
Stone
Sugar Camp
Swamp
Swampsauger
Sweeney
Third
Thompson
Thunder
Thunder
Tomahawk
AREA
135
223
125
342
113
392
785
1352
120
425
189
248
545
296
141
187
103
382
172
1768
3627
MXD
13
70
27
7
3
81
22
45
9
15
12
10
38
8
12
18
14
35
12
9
79
INLET
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
OUTLT
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NAT
NLLC
NAT
NAT
NLLC
NLLC
IMP
NAT
NLLC
NAT
NAT
NLLC
NAT
NLLC
NLLC
NAT
IMP
NLLC
NLLC
D.O.
3
4
3
5
5
4
3
4
3
5
3
3
4
3
3
5
3
4
3
3
4
TRNS
2
0
0
2
2
0
2
2
2
2
2
2
0
2
2
2
2
2
2
2
0
FSKL
0
0
0
0
3
0
0
0
0
3
0
0
0
0
0
3
0
0
0
0
0
IMPR
0
0
0
2
2
0
2
2
2
2
0
0
0
2
0
2
0
0
2
2
0
LCI
5
4
3
9*
12
4
7
8
7
12
5
5
4
7
5
12
5
6
7
7
4
-------
COUNTY #44 - ONEIDA Con't
LKNO
372
374
376
378
383
387
£ 391
Ul
395
396
398
LAKE NAME
Tom Doyle
Town Line
Two Sisters
Upper Kaubashine
Virgin
West Horsehead
Whitefish
Willow
Willow Flowage
Wind Pudding
AREA
108
150
705
183
266
145
185
395
5135
196
MXD
27
30
64
57
20
26
31
6
22
34
INLET
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
No
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
TYPE
NAT
NLLC
NAT
NAT
NLLC
NAT
NLLC
NLLC
IMP
NAT
D.O.
3
3
4
4
3
3
4
3
3
4
TRNS
2
2
0
0
2
2
2
2
2
0
FSKL
0
0
0
0
0
0
0
0
0
0
IMPR
2
2
0
0
2
2
2
2
0
2
LCI
7
7
4
4
7
7
8
7
5
6
COUNTY #46 - OZAUKEE
LKNO
LAKE NAME
AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
17
Mud Lake
245
Yes
Yes
NAT
0 10
-------
COUNTY #49 - POLK
to
LKNO
5
6
15
20
22
25
26
35
36
37
70
72
73
110
113
116
337
170
182
187
196
LAKE NAME
Antler
Apple River Flowage
Balsam
Bass
Beartrap
Big
Big Butternut
Blake
Blom
Bone
Cedar
Church Pine
Clam Falls Flowage
Deer
Diamond
East
Freedom #2
Garfield
Glenton
Half Moon
Horse
AREA
101
639
2054
138
241
259
378
302
210
1781
1100
107
127
807
126
228
106
120
128
579
228
MXD
22
18
37
19
25
24
19
14
13
43
32
45
14
46
15
15
4
8
10
60
11
INLET
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No
No
Yes
No
No
Yes
Yes
Yes
OUTLT
No
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Yes
No
No
Yes
Yes
Yes
TYPE
NAT
IMP
NLLC
NAT
NAT
NLLC
NLLC
NLLC
NAT
NAT
NLLC
NAT
IMP
NLLC
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
D.O.
5
1
2
3
1
1
5
5
3
2
2
2
1
2
5
3
5
5
5
2
5
TRNS
2
2
2
2
1
1
2
2
1
1
2
1
2
1
2
2
2
2
2
2
2
FSKL
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
3
3
3
3
0
0
IMPR
0
3
5
2
4
3
5
3
3
2
2
0
2
2
2
2
2
2
3
2
3
LCI
7
6
9
10
6
5
12
10
7
5
6
3
5
5
9
10
12*
12*
13
6
10
-------
COUNTY #49 - POLK
LKNO
197
200
252
257
264
265
274
292
339
341
350
351
353
355
358
365
371
389
401
425
426
LAKE NAME
Horseshoe
Indian Head Flow
Largon
Little Butternut
Long
Long Trade
Loveless
Magnor
North
North Twin
Pike
Pine
Pipe
Poplar
Round
Sand
Somers
Straight
Wapogasset
White Ash
Wild Goose
AREA
377
776
129
189
272
153
141
231
119
135
159
153
281
125
1015
187
101
107
1186
153
181
MXD
57
57
10
23
17
13
20
26
9
27
33
53
68
34
17
58
12
12
32
9
12
INLET
No
Yes
Yes
Yes
No
Yes
No
Yes
No
Yes
No
No
No
Yes
No
Yes
No
Yes
Yes
No
OUTLT
No
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No
No
No
Yes
No
Yes
Yes
Yes
Yes
No
TYPE
NAT
IMP
NLLC
NAT
NAT
IMP
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
D.O.
2
2
1
1
3
5
1
3
5
3
4
2
0
4
5
2
1
1
2
5
5
TRNS
1
2
2
2
2
2
2
2
2
2
2
1
1
1
2
2
2
2
2
2
2
FSKL
0
0
3
0
0
0
3
0
3
0
0
0
0
0
0
0
0
0
0
0
3
IMPR
2
0
2
2
2
2
2
0
3
0
2
2
0
0
3
2
3
0
5
5
2
LCI
5
4
8
5
7
9
8
5
13
5
8
5
1
5
10
6
6
3
9
12
12
-------
H1
M
00
COUNTY #50 - PORTAGE
LKNO
15
30
69
70
LAKE NAME
Emily
McDill Pond
Wisconsin River
Wisconsin River
Little Eau Pleine
AREA
96
261
220
2093
600
MXD
35
15
25
25
?
INLET
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
TYPE
NAT
IMP
IMP
IMP
IMP
D.O.
4
3
3
3
0
TRNS
1
3
3
3
3
FSKL
0
0
0
0
0
IMPR
2
4
4
5
0
LCI
7
10
10
11
-------
COUNTY #51 - PRICE
VD
LKNO
15
16
19
25
29
31
36
40
52
54
62
66
70
71
90
96
108
109
117
119
122
IAKE NAME
Big Dardis
Blockhouse
Butternut
Cochram
Cranberry
Crowley Flowage
Deer
Duroy
Hay
Hultman
Lac Sault Dore
Le Tourneau
Long
Long
Musser
North Spirit
Pike
Pixley Flowage
Riley
Round
Sailor
AREA
144
242
1006
111
512
422
145
338
101
181
561
124
418
240
563
213
848
193
182
718
165
MXD
23
12
32
16
18
23
18
16
6
14
21
16
54
49
15
22
18
23
8
24
6
INLET
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NLLC
NAT
NLLC
IMP
NAT
NLLC
NAT
NAT
IMP
NAT
NLLC
NAT
IMP
NAT
NAT
IMP
NAT
NLLC
NAT
D.O.
0
0
2
1
5
1
0
1
1
1
3
1
2
4
1
1
1
3
5
1
5
TRNS
2
2
2
2
2
2
2
2
4
2
2
2
2
1
2
2
2
2
3
2
2
FSKL
0
0
0
0
3
0
0
0
0
0
0
3
0
0
0
0
0
0
3
0
3
IMPR
0
2
7
0
2
2
0
2
0
2
0
0
0
0
0
0
0
3
0
0
2
LCI
2
4
11
3
12
5
2
5
5
5
5
6
4
5
3
3
3
8
11
3
12
-------
U)
o
COUNTY #51 - PRICE Con't
LKNO
123
125
127
128
129
131
139
145
146
148
154
156
LAKE NAME
Sailor Creek Flowage
Schnur
Sixteen
Solberg
Spirit
Squaw Creek Flowage
Thompson
Tucker
Turner
Upper Park Falls
Wilson
Worcester
AREA
155
158
119
859
126
150
111
118
203
431
351
100
MXD
10
27
5
16
9
7
25
32
15
17
11
37
INLET
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
IMP
NAT
NAT
IMP
NLLC
IMP
NAT
NAT
NAT
IMP
IMP
NAT
D.O.
1
0
5
0
3
0
3
4
5
0
0
6
TRNS
2
2
2
2
2
2
2
2
2
2
2
2
FSKL
0
0
3
0
3
0
0
0
0
0
0
0
IMPR
3
4
0
0
0
0
0
0
0
0
0
0
LCI
6
6
10
2
8
2
5
6
7
2
2
8
-------
COUNTY #52 - RACINE
LKNO
2
4
5
7
8
11
16
17
18
COUNTY
LKNO
8
10
LAKE NAME
Bohners
Browns
Buena
Eagle
Echo
Long
Tichigan
Waubeesee
Wind
#53 - RICHIAND
LAKE NAME
Laws on Pond
Sand Prairie Slough
AREA
124
396
241
520
71
123
268
129
936
AREA
153
100
MXD
30
44
8
15
9
5
63
73
47
MXD
8
5
INLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
INLET
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
TYPE
NLLC
NLLC
IMP
NLLC
IMP
NAT
NLLC
NLLC
NLLC
TYPE
IMP
NAT
D.O.
3
4
1
5
1
5
6
4
4
D.O.
3
3
TRNS
1
1
2
3
3
3
2
1
1
TRNS
3
3
FSKL
0
0
0
3
0
3
4
0
0
FSKL
0
3
IMPR
2
3
3
9
2
6
9
2
2
IMPR
5
2
LCI
6
8
6
20
6
17
21
7
7
LCI
11
11
-------
COUNTY #55 - RUSK
U)
LKNO
1
3
9
23
25
26
34
36
37
43
55
56
57
58
51
70
75
LAKE NAME
Amacoy
Audie
Big Falls
Dairyland Reservoir
Fireside
Fish
Island
Ladysmith
Lea
McCann
Potato Creek
Potato
Pulaski
Round
Sand
Thornapple
Washington Creek
AREA
278
128
369
1745
302
115
526
288
232
133
174
534
126
105
262
268
165
MXD
20
32
45
70
30
40
54
19
9
38
5
40
40
5
100
19
5
INLET
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
Yes
Yes
OUTLT
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
Yes
Yes
TYPE
NAT
NAT
IMP
IMP
NAT
NAT
NAT
IMP
IMP
NAT
IMP
NAT
NAT
NAT
NAT
IMP
IMP
D.O.
1
2
2
2
1
4
0
0
5
2
1
4
2
5
0
0
3
TRNS
2
2
2
2
2
1
1
2
2
2
2
2
2
2
1
2
2
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
IMPR
2
0
0
0
2
0
2
0
2
2
4
2
0
2
0
2
2
LCI
5
4
4
4
5
5
3
2
9
6
7
8
4
9
1
4
7
-------
COUNTY #56 - ST. CROIX
H
CO
CO
LKNO
3
28
30
31
34
36
49
62
LAKE NAME
Bass
St. Croix
Little Falls
Mallalieu
New Richmond
Pine
Squaw
Spring Valley
AREA
301
4668
172
282
142
107
129
126
MXD
33
60
18
17
12
21
32
29
INLET
No
Yes
Yes
Yes
Yes
Yes
OUTLT
No
Yes
Yes
Yes
Yes
Yes
TYPE
NAT
NLLC
IMP
IMP
IMP
IMP
D.O.
6
0
5
1
1
5
6
5
TRNS
0
2
2
2
0
3
3
3
FSKL
0
0
0
0
0
3
4
0
IMPR
0
0
0
0
6
2
2
2
LCI
6
2
7
3
7
13*
15*
10*
COUNTY #57 - SAUK
LKNO
5
6
19
24
LAKE NAME
Delton
Devil's
Redstone
White Pond
AREA
254
357
600
104
MXD
16
44
40
23
INLET
Yes
Yes
Yes
Yes
OUTLT
Yes
No
Yes
Yes
TYPE
IMP
NAT
IMP
IMP
D.O.
3
0
6
3
TRNS
2
0
3
3
FSKL
0
0
4
0
IMPR
5
2
9
5
LCI
10
2
22
11
-------
COUNTY #58 - SAWYER
co
LKNO
3
4
15
16
17
19
23
33
41
43
48
55
57
58
64
68
73
83
87
91
101
LAKE NAME
Barber
Barker
Black
Black Dan
Blaisdell
Blueberry
Brunet
Callahan
Lake Chetac
Chippewa
Connor
Deer
Devils
Durphee
Evergreen
Fishtrap
Ghost
Grindstone
Ham
Hayward
Hunter
AREA
238
238
129
128
370
280
126
106
2149
15300
408
423
188
193
200
197
372
3111
100
247
126
MXD
21
12
15
37
15
29
12
18
30
82
87
18
6
16
25
8
12
59
28
17
11
INLET
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
TYPE
NAT
NAT
NLLC
NAT
NAT
NAT
IMP
NLLC
NAT
IMP
NLLC
NLLC
NAT
NLLC
NAT
NLLC
IMP
NAT
NAT
IMP
NAT
D.O.
1
0
1
2
0
1
1
1
1
2
2
5
5
1
1
1
1
0
1
0
0
TRNS
2
2
2
1
2
1
2
2
2
2
1
2
2
2
2
2
2
1
2
1
2
FSKL
0
0
0
0
0
0
0
0
0
4
0
3
3
3
0
0
0
0
0
0
0
IMPR
2
0
2
2
2
0
2
0
2
0
0
0
3
7
0
0
2
0
0
2
0
LCI
5
2
5
5
4
2
5
3
5
8
3
10
13
13
3
3
5
1
3
3
2
-------
COUNTY #58 - SAWYER Con't
LKNO
114
175
116
120
123
127
129
134
i-' 137
u>
145
153
158
160
164
169
179
182
183
188
193
197
LAKE NAME
Lac Court Oreilles
Lake of the Pines
Little Court Oreilles
Little Round
Little Sissabagama
Lost Land
Lower Clam
Lower Twin
Mason
Moose
Mud
Nelson
North
Pacwawong
Perch
Placid
Price
Radisson
Round
Sand
Sissabagama
AREA
5039
223
240
174
299
1304
229
247
190
1670
480
2503
129
187
129
160
715
255
2784
928
719
MXD
90
39
46
38
75
21
22
30
39
21
15
33
30
8
24
30
14
12
70
50
48
INLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NAT
NLLC
NAT
NAT
NLLC
NLLC
NAT
IMP
NLLC
IMP
NAT
IMP
NAT
NLLC
IMP
IMP
NLLC
NLLC
NLLC
D.O.
0
2
0
2
0
1
1
3
4
0
I
4
3
1
0
3
1
0
0
2
2
TRNS
1
2
1
1
1
2
2
1
2
2
2
2
2
2
2
1
2
2
1
1
1
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
IMPR
2
0
2
2
0
0
2
2
2
0
2
2
0
3
0
2
0
0
0
2
0
LCI
3
4
3
5
1
3
5
6
8
2
5
8
8
6
2
6
3
2
1
5
3
-------
COUNTY #58 - SAWYER Con't
CJ
LKNO
198
203
206
208
209
213
215
219
220
227
231
239
241
242
243
434
LAKE NAME
Smith
Spider
Spring
Squaw
Star
Swamp
Teal
Tiger Cat
Totogatic
Two Boys
Upper Twin
Whitefish
Wilson
Windfall
Windigo
10/9
AREA
323
1454
220
208
104
258
1049
224
243
117
299
785
103
102
522
248
MXD
29
64
18
32
4
9
31
11
17
37
27
105
14
16
51
7
INLET
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
TYPE
NAT
NLLC
NLLC
NAT
NAT
NAT
NAT
IMP
IMP
NAT
NAT
NAT
NAT
NAT
NAT
IMP
D.O.
1
2
5
2
3
5
2
1
1
4
1
0
3
1
2
5
TRNS
2
1
2
1
2
2
2
2
2
1
2
0
,2
2
2
2
FSKL
0
0
3
0
3
3
0
0
0
0
0
0
0
0
0
3
IMPR
0
0
0
2
0
0
0
6
2
0
2
0
3
0
2
2
LCI
3
3
10
5
8
10
4
9
5
5
5
0
8
3
6
12
-------
COUNTY #59 - SHAWANO
LKNO
24
36
37
38
42
46
H 50
U)
51
52
54
LAKE NAME
Loon
Pella Pond
Pensaukee
Pine
Shawano
Tigerton
Weed Dam
White
White Clay
Wolf River
AREA
278
115
104
209
6178
167
207
181
256
223
MXD
23
14
26
35
42
14
25
12
45
10
INLET
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
TYPE
NAT
IMP
NAT
NLLC
NAT
IMP
IMP
NAT
NAT
IMP
D.O.
1
0
1
2
2
0
1
5
2
0
TRNS
2
2
2
2
2
2
2
2
2
2
FSKL
0
0
3
0
0
0
0
3
0
0
IMPR
0
2
2
0
5
2
0
3
0
0
LCI
3
4
8
4
9
4
3
13*
4
2
-------
COUNTY #60 - SHEBOYGAN
00
LKNO
8
10
11
34
38
COUNTY
LKNO
10
48
69
81
COUNTY
LKNO
9
LAKE NAME
Crystal
Big Elkhart
Ellen
Random
Sheboygan Marsh
#61 - TAYLOR
LAKE NAME
Chequamegon Waters
Mondeaux
Rib
Steve Creek
#62 - TREMPEALEAU
LAKE NAME
Marinuka
AREA
114
300
110
209
674
AREA
2730
416
320
140
AREA
107
MXD
61
113
48
19
4
MXD
22
10
9
8
MXD
9
INLET
Yes
Yes
Yes
Yes
Yes
INLET
Yes
Yes
Yes
Yes
INLET
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
OUTLT
Yes
TYPE
NAT
NAT
NAT
NAT
NAT
TYPE
IMP
IMP
NLLC
IMP
TYPE
IMP
D.O.
4
4
4
5
5
D.O.
1
3
3
5
D.O.
1
TRNS
1
1
1
1
3
TRNS
2
2
3
2
TRNS
3
FSKL
0
0
0
3
3
FSKL
3
0
3
3
FSKL
0
IMPR
5
5
4
2
2
IMPR
0
2
5
0
IMPR
9
LCI
10
10
9
11
13
LCI
6
7
14
10
LCI
13
-------
CJ
COUNTY #64 - VILAS
LKNO
6
7
11
14
17
18
19
20
21
28
32
36
39
52
53
55
56
56
58
65
66
LAKE NAME
Alder
Allequash
Amlk
Annabelle
Anvil
Apeekwa
Arbor Vitae, Big
Arbor Vltae, Little
Armour
Ballard
Bass
Bateau, Big
Bay, West
Big
Big
Birch
White Birch
Birch, Yellow
Bittersweet
Bolton
Boot
AREA
274
405
187
186
380
188
1065
533
319
505
266
230
368
850
723
528
117
185
104
138
284
MXD
33
33
10
30
40
10
36
32
53
25
15
>30
31
65
30
35
27
14
31
36
14
INLET
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
TYPE
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NLLC
NAT
NLLC
NAT
NAT
NLLC
NAT
NAT
NAT
D.O.
4
4
3
3
4
3
4
4
4
3
3
4
4
4
3
4
3
3
4
4
3
TRNS
2
1
2
2
0
2
2
2
0
2
2
1
2
2
2
2
2
2
1
2
2
FSKL
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
IMPR
2
2
0
0
0
2
2
2
0
0
2
0
2
0
2
2
0
0
0
0
2
LCI
8
7
5
5
4
7
8
8
4
5
10
5*
8
6
7
8
5
5
5
6
7
-------
COUNTY #64 - VILAS Con't
LKNO
69
72
74
76
77
88
90
91
93
102
104
108
111
116
120
123
124
125
133
143
148
LAKE NAME
Boulder
Brandy
Broken Bow
Buckatabon, Lower
Buckatabon, Upper
Canoe, (Lost)
Carl in
Carpenter
Catfish
Circle Lily
Clear
Cochran
Content
Crab
Cranberry
Crooked, Big
Crooked, Big
Crooked, Little
Day
Deerskin
Diamond
AREA
525
110
134
352
477
249
153
333
991
223
518
126
244
920
940
383
682
153
117
282
122
MXD
23
44
23
16
30
41
36
16
26
35
39
12
14
56
11
50
34
20
48
24
40
INLET
Yes
Yes
No
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
OUTLT
Yes
Yes
No
Yes
Yes
Yes
No
No
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
No
No
No
TYPE
NAT
NAT
NAT
NLLC
NLLC
NAT
NAT
NAT
NLLC
NAT
NAT
NAT
NLLC
NAT
NLLC
NAT
NAT
NAT
NAT
NLLC
NAT
D.O.
3
4
3
3
3
4
4
3
3
4
4
5
5
4
3
2
4
3
4
3
2
TRNS
2
2
2
2
2
1
2
2
2
2
1
2
2
0
2
0
0
2
0
2
0
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
IMPR
0
2
0
2
2
0
0
0
2
0
0
2
2
0
2
0
0
2
0
2
0
LCI
5
8
5
7
7
5
6
5
7
6
5
9
12
4
7
2
4
7
4
7
2
-------
COUNTY #64 - VIIAS Con't
LKNO
151
160
162
169
177
179
187
190
192
193
194
195
196
199
208
213
214
216
219
224
231
LAKE NAME
Dollar
Duck
Eagle
Ellerson, East
Erickson
Escanaba
Fence
Finley
Fishtrap
Flanibeau
Flora
Forest
Found
Frank
Gibson, Big
Grassy
Gresham, Lower
Gresham, Upper
Gunlock
Harris
Helen
AREA
105
109
591
136
106
288
3340
107
329
1145
100
466
326
141
116
220
149
375
267
523
111
MXD
15
18
27
26
18
25
86
26
41
49
29
60
20
24
15
4
12
20
25
48
19
INLET
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
No
Yes
Yes
No
No
Yes
No
Yes
Yes
Yes
No
Yes
No
No
Yes
Yes
No
Yes
Yes
TYPE
NAT
NLLC
NLLC
NAT
NAT
NAT
NAT
NAT
NLLC
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
D.O.
3
3
3
3
3
3
4
3
4
4
3
4
3
3
3
5
3
3
3
4
3
TRNS
2
2
2
1
2
2
0
0
2
0
2
0
2
0
1
2
2
2
1
0
2
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
IMPR
0
2
2
0
0
0
0
0
0
0
0
0
2
0
0
2
2
2
2
0
2
LCI
5
7
7
4
5
5
4
3
6
4
5
4
7
3
4
12
7
7
6
4
7
-------
COUNTY #64 - VILAS Con't
LKNO
236
241
245
249
254
255
256
257
262
268
271
273
282
283
287
289
293
295
299
301
303
LAKE NAME
High
Horsehead
Hunter
Ike Walton
Interlaken, Long
Irving
Island
Jag
John, Little
Jute
Katinka
Kentuck
Lac Du Lune
Lac Vifux Desert
Landing
Laura
Long
Lost
Lynx
Mamie
Manitowish
AREA
734
229
148
1439
368
403
757
158
166
194
172
995
407
4280
220
599
872
541
295
360
506
MXD
31
50
50
63
65
8
33
14
19
25
60
40
56
38
11
43
95
25
42
18
58
INLET
Yes
Yes
No
No
Yes
Yes
Yes
No
Yes
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
No
No
Yes
Yes
Yes
No
No
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
TYPE
NLLC
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
NLLC
NLLC
NAT
NLLC
NLLC
D.O.
4
4
4
4
4
5
4
3
3
3
4
0
2
4
5
4
4
3
4
3
4
TRNS
1
0
0
0
0
2
2
0
2
0
0
2
0
2
2
0
0
2
0
2
1
FSKL
0
0
0
0
0
3
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
IMPR
0
0
0
0
0
2
0
0
2
0
0
2
0
2
2
0
0
2
0
2
0
LCI
5
4
4
4
4
12
6
3
7
3
4
4*
2
8
12
4
4
7
4
7
5
-------
COUNTY #64 - VILAS Con't
LKNO
304
307
315
321
324
329
332
334
335
341
342
343
350
353
356
357
358
362
366
367
368
LAKE NAME
Mann
Maple, Sugar
McCullough
Meta
Mill
Mitien
Moon
Morton
Moss
Muskellunge
Muskellunge, Big
Muskesin
Nelson
Ninemile, Lower
Nixon
No Mans
Norwood
Oak, Black
Otter
Oxbow
Palletie
AREA
250
129
216
175
131
140
124
163
196
266
923
115
104
646
110
225
125
549
199
515
173
MXD
22
27
27
25
9
23
40
29
29
18
65
22
50
5
5
31
89
63
20
42
65
INLET
No
No
Yes
No
Yes
No
No
Yes
Yes
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
OUTLT
Yes
No
Yes
No
Yes
No
No
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
TYPE
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
IMP
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
D.O.
5
3
3
3
5
3
4
3
3
5
4
3
4
3
3
4
4
2
3
4
2
TRNS
2
0
2
1
2
2
0
2
2
2
0
2
0
2
2
2
0
0
2
1
0
FSKL
3
0
0
0
3
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
IMPR
2
0
2
0
2
0
0
2
2
2
0
0
0
2
2
2
0
0
2
0
0
LCI
12
3
7
4
12
5
4
7
7
12
4
5
4
7
7
8
4
2
7
5
2
-------
COUNTY #64 - VILAS Con't
LKNO
369
370
372
379
381
386
388
390
391
393
395
396
399
404
408
412
416
417
418
421
425
LAKE NAME
Palmer
Papoose
Partridge
Pickerel
Pike, Dead
Pine, Lone
Pioneer
Plum
Plummer
Pokegama
Portage, Big
Portage, Little
Presque Isle
Rainbow
Razorback
Rest
Rice, Scattering
Rice, Wild
Roach
Rock
Ross
AREA
640
428
228
273
317
142
415
938
211
1052
601
170
1280
146
372
640
280
365
125
122
146
MXD
13
65
14
24
59
41
27
48
55
65
38
11
80
36
28
49
17
26
37
18
14
INLET
Yes
No
No
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
TYPE
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NLLC
NAT
NAT
NAT
NAT
D.O.
3
4
3
3
2
4
3
4
4
4
4
3
4
4
3
4
3
3
4
3
5
TRNS
2
0
2
2
2
2
2
0
0
0
0
2
0
1
0
2
2
2
0
2
2
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
IMPR
2
0
2
2
0
0
2
0
0
2
0
2
0
2
0
2
2
2
0
0
2
LCI
7
4
7
7
4
6
7
4
4
6
4
7
4
7
3
8
7
7
4
5
12
-------
COUNTY #64 - VILAS Con't
Ul
LKNO
427
432
433
435
436
437
438
439
446
450
456
457
458
459
462
467
468
472
473
474
477
LAKE NAME
Round
St. Germain, Big
St. Germain, Little
Sanborn
Sand, Big
Sand, Little
Sand, White
Sand, White
Sherman
Snipe
Sparkling
Spectacle
Spider
Spider, Little
Spring
Star
Star, Little
Stateline
Statenaker
Stearns
Stone
AREA
116
1463
956
253
1408
107
728
1195
123
223
127
160
256
225
205
1150
244
199
202
217
134
MXD
25
35
56
13
35
32
68
50
19
15
64
37
41
28
11
67
63
67
50
16
21
INLET
Yes
Yes
Yes
Yes
No
Yes
Yes
No
No
No
No
Yes
No
Yes
Yes
Yes
No
No
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
TYPE
NAT
NLLC
NLLC
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
NLLC
NLLC
NAT
NAT
NAT
NLLC
D.O.
3
4
4
3
4
4
4
4
3
3
2
4
4
3
3
4
4
4
4
3
3
TRNS
0
1
2
2
0
0
0
0
2
2
0
0
1
0
2
0
0
0
0
0
2
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
IMPR
0
2
2
2
0
2
0
0
2
2
0
0
2
0
0
0
0
0
0
0
2
LCI
3
7
8
7
4
6
4
4
7
7
2
4
7
3
5
4
4
4
4
3
7
-------
COUNTY #64 - VI1AS Con't
LKNO
478
479
483
488
489
490
493
495
496
500
503
506
509
512
513
514
515
516
517
520
523
LAKE NAME
Stone, Crawling
Stone, Little Crawl
Stormy
Sugarbush, Lower
Sugarbush, Middle
Sugarbush, Upper
Sunset
Tamarack, Little
Tambling
Tenderfoot
Tippecanoe
Towanda
Tree, Lone
Trout
Trout, Little
Turtle, North
Turtle, South
Twin, North
Twin, South
Twin Island
Van Vliet
AREA
1460
116
525
180
265
142
185
200
169
461
155
132
121
3870
982
369
454
2782
630
205
220
MXD
80
48
61
40
55
30
43
16
11
36
34
27
16
115
91
58
23
45
42
14
13
INLET
Yes
No
No
Yes
Yes
Yes
No
Yes
No
Yes
No
No
No
Yes
No
Yes
Yes
Yes
Yes
Yes
No
OUTLT
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
No
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NAT
NAT
NAT
NAT
NAT
IMP
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NLLC
NAT
NAT
D.O.
4
4
2
4
4
3
4
3
5
4
4
3
3
2
4
4
3
4
4
3
3
TRNS
0
0
0
0
0
0
0
2
2
2
0
2
2
0
1
2
2
0
0
2
2
FSKL
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
IMPR
0
0
0
2
2
2
0
0
2
2
0
2
0
0
2
2
2
0
0
2
2
LCI
4
4
2
6
6
5
4
5
12
8
4
7
5
2
7
8
7
4
4
7
7
-------
COUNTY #64 - VILAS Con't
LKNO
530
534
535
536
544
LAKE NAME
Watersmeet
Whitefish
Whitney
Wildcat
Wolf
AREA
100
196
102
316
393
MXD
12
40
8
35
28
INLET
Yes
No
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
TYPE
NLLC
NAT
NAT
NAT
NAT
D.O.
3
4
5
4
3
TRNS
2
0
2
2
2
FSKL
0
0
3
0
0
IMPR
2
0
2
2
2
LCI
7
4
12
8
7
-------
COUNTY #65 - WALWORTH
00
LKNO
2
3
5
6
8
10
11
16
17
20
21
23
25
28
29
33
34
35
36
LAKE NAME
Beulah
Booth
Como
Comus
Delavan
Geneva
Green
Lorraine
Lower Whitewater
Middle
Mill
North
Pell
Pleasant
Potters
Trapp
Turtle
Wandawega
Whitewater
AREA
712
108
1058
117
2072
5104
292
133
137
256
261
191
107
138
157
115
140
119
640
MXD
58
24
8
8
56
135
57
8
10
42
44
11
13
29
26
6
35
9
38
INLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
OUTLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
NLLC
NAT
NLLC
IMP
NLLC
NLLC
NLLC
NAT
NAT
NLLC
NLLC
NAT
NAT
NAT
NAT
IMP
NLLC
NAT
NLLC
D.O.
4
3
5
5
6
2
4
5
5
4
4
5
5
3
5
1
4
5
4
TRNS
1
1
2
3
0
1
1
2
2
1
1
2
2
1
2
3
1
2
1
FSKL
0
0
3
3
4
0
0
3
3
0
0
3
3
0
0
0
0
3
0
IMPR
2
2
3
4
4
2
4
2
3
2
3
3
2
0
5
2
0
3
2
LCI
7
6
13
15
14
5
9
12
13
7
8
13
12
4
12
6
5
13
7
-------
COUNTY #66 - WASHBURN
LKNO
4
5
8
9
10
12
18
19
20
21
22
30
36
49
51
56
61
64
68
76
85
LAKE NAME
Baker
Balsam
Bass
Bass
Bass
Bean
Big Bass
Big Casey
Big Devil's
Big Ripley
Birch
Cable
Chicog
Deer
De Rosier
Dunn
Ellsworth
Fenton
Gilmore
Gull
Horseshoe
AREA
114
295
128
188
144
100
203
247
162
190
368
200
125
102
109
193
174
139
389
511
194
MXD
21
49
66
35
31
35
27
27
75
27
73
24
25
19
11
39
6
52
36
19
21
INLET
No
Yes
No
No
No
No
No
Yes
Yes
No
Yes
No
Yes
Yes
No
Yes
No
No
No
Yes
No
OUTLT
No
Yes
No
No
No
Yes
No
Yes
Yes
No
Yes
No
Yes
Yes
No
Yes
No
No
Yes
Yes
No
TYPE
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NLLC
NAT
D.O.
1
2
2
2
2
2
1
1
2
1
2
3
1
1
5
2
5
2
4
1
1
TRNS
2
2
1
1
1
2
2
1
1
1
1
2
1
2
2
2
2
1
1
1
1
FSKL
3
0
0
0
0
0
0
0
0
0
0
3
0
0
3
0
3
0
0
0
0
IMPR
0
0
0
0
0
2
2
2
0
0
0
0
0
2
0
0
2
0
2
2
0
LCI
6
4
3
3
3
6
5
4
3
2
3
8
2
5
10
4
12
3
7
4
2
-------
COUNTY #66 - WASHBURN Con't
ui
o
LKNO
86
89
97
100
116
124
125
130
133
136
137
143
147
151
152
155
171
173
177
187
195
LAKE NAME
Island
Kekegama
Leesome
Lincoln
Long
Lower Kimball
Lower McKenzle
Macrae
Matthews
McKinley
McLain
Minong Flowage
Mud
Nancy
Nice
North Twin
Pokegama
Potato
Rice
Sand
Shallow
AREA
276
109
146
101
3290
129
185
124
263
105
150
1564
103
772
138
113
453
222
132
198
137
MXD
44
24
53
27
74
6
17
45
26
23
30
21
13
39
11
20
23
20
11
9
10
INLET
No
Yes
No
Yes
Yes
Yes
Yes
No
No
Yes
No
Yes
Yes
Yes
No
No
Yes
No
Yes
No
No
OUTLT
No
Yes
No
Yes
Yes
Yes
Yes
No
No
Yes
No
Yes
Yes
Yes
No
No
Yes
Yes
Yes
No
No
TYPE
NAT
NAT
NAT
NAT
NLLC
NLLC
NLLC
NAT
NAT
NAT
NAT
IMP
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
NAT
D.O.
2
3
2
1
0
1
1
2
1
1
1
1
1
2
1
1
3
5
3
1
5
TRNS
1
2
1
2
2
2
2
1
1
2
1
2
2
2
1
2
2
2
2
2
2
FSKL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
3
IMPR
2
0
0
2
0
2
2
0
0
2
0
0
2
2
2
0
5
2
4
0
2
LCI
5
5
3
5
2
5
5
3
2
5
2
3
5
6
4
3
10
12
9
3
12
-------
COUNTY #66 - WASHBURN Con't
LKNO
197
200
202
203
205
210
211
216
225
236
237
257
COUNTY
LKNO
5
7
10
11
26
42
47
LAKE NAME
Shell
Silver
Slim
Slim Creek
South Twin
Spider No. 5
Spooner
Spring
Stone
Tranus
Trego
Yellow River Flowage
Unnamed
#67 - WASHINGTON
LAKE NAME
Big Cedar
Druid
Five
Friess
Little Cedar
Pike
Silver
AREA
2580
188
224
101
115
177
1092
211
523
175
451
344
201
AREA
932
124
102
131
259
522
119
MXD
36
28
42
27
29
49
17
24
49
12
36
17
29
MXD
105
40
23
51
55
45
45
INLET
No
No
Yes
Yes
No
No
Yes
No
No
Yes
Yes
Yes
Yes
INLET
Yes
Yes
No
Yes
Yes
Yes
No
OUTLT
No
No
Yes
Yes
No
No
Yes
No
No
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
No
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NAT
IMP
NAT
NAT
IMP
NAT
NAT
NAT
IMP
IMP
IMP
TYPE
NLLC
NAT
NAT
NAT
NLLC
NLLC
NLLC
D.O.
2
1
2
1
1
2
5
1
2
5
2
1
3
D.O.
2
2
5
2
4
2
2
TRNS
1
1
1
1
1
2
2
1
0
1
2
2
2
TRNS
1
2
1
1
1
1
1
FSKL
0
0
0
0
0
0
0
0
0
3
0
0
0
FSKL
0
0
3
0
0
0
0
IMPR
2
0
0
3
0
0
6
2
0
2
2
3
2
IMPR
2
2
3
0
0
0
0
LCI
5
2
3
5
2
4
13
4
2
11
6
6
7
LCI
5
6
12
3
5
3
3
-------
NJ
COUNTY #68 - WAUKESHA
LKNO
2
5
10
13
22
26
27
32
36
37
39
45
47
48
49
51
52
61
63
LAKE NAME
Beaver
Big Muskego
Denoon
Eagle Spring
Golden
Keesus
Lac Labelle
Little Muskego
Lower Nemahbin
Lower Phantom
Middle Genes se
Nagawicka
North
Oconomowoc
Okauchee
Pewaukee
Pine
Silver
Spring
AREA
316
2260
162
227
250
237
1117
518
271
433
102
917
437
767
1187
2359
703
222
100
MXD
49
26
60
12
46
42
47
65
36
12
38
90
78
62
94
45
85
44
20
INLET
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
NLLC
NLLC
NLLC
IMP
NAT
NAT
NLLC
NLLC
NLLC
NLLC
NAT
NLLC
NAT
NLLC
NLLC
NLLC
NAT
NAT
NAT
D.O.
4
5
4
1
4
4
4
6
4
3
2
4
4
4
2
4
4
4
3
TENS
1
1
1
1
1
1
2
1
1
1
1
2
1
1
0
2
1
1
1
FSKL
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
IMPR
2
3
3
3
3
3
4
5
0
5
0
7
0
3
2
9
2
0
0
LCI
7
12
8
5
8
8
10
12
5
9
3
13
5
8
4
15
7
5
4
-------
COUNTY #68 - WAUKESHA Con't
un
u>
LKNO
70
71
73
COUNTY
LKNO
4
22
32
38
55
60
79
80
104
105
109
142
143
LAKE NAME
Upper Nashotah
Upper Nemahbin
Upper Phantom
#69 - WAUPACA
LAKE NAME
Bear
Long (Chain)
Rainbow (Chain)
Cinco Bayou
Hatch
lola Millpond
Manawa
Marion Millpond
Partridge
Partridge Crop Bayou
Pigeon-Clintonville
Weyauwega
White
AREA
133
283
111
AREA
194
104
116
169
112
206
195
109
1124
238
218
274
1026
MXD
53
61
29
MXD
62
75
95
4
13
11
12
11
6
8
12
11
11
INLET
No
Yes
Yes
INLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
OUTLT
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
TYPE
NLLC
NAT
NLLC
TYPE
NAT
NAT
NAT
NAT
NAT
IMP
IMP
IMP
NAT
NAT
IMP
IMP
NLLC
D.O.
4
4
3
D.O.
2
4
4
5
5
3
3
5
3
3
5
3
5
TRNS
0
1
1
TRNS
1
1
1
3
2
3
3
4
3
3
3
3
3
FSKL
0
0
0
FSKL
0
0
0
3
3
0
0
3
0
0
3
0
3
IMPR
0
2
2
IMPR
2
0
0
3
0
3
3
5
2
2
5
4
3
LCI
4
7
6
LCI
5
5
5
14
10
9
9
17
8
8*
16*
10*
14
-------
COUNTY #70 - WAUSHARA
ui
LKNO
1
22
23
26
27
39
47
56
57
60
70
86
LAKE NAME
Auroraville Millpond
Fish (Wautoma)
Fish (Hancock)
Gilbert
Hills, Big
Long (Saxeville)
Mount Morris
Pine (Hancock) Pike
Pine (Springwater)
Pleasant
Silver
White River (Lower)
AREA
209
289
177
141
135
272
163
163
143
127
328
110
MXD
6
5
42
65
20
71
40
15
48
30
46
22
INLET
Yes
No
No
No
No
No
Yes
No
No
No
No
Yes
OUTLT
Yes
Yes
No
No
No
No
Yes
No
No
No
No
Yes
TYPE
IMP
NLLC
NAT
NAT
NAT
NAT
IMP
NAT
NAT
NAT
NAT
IMP
D.O.
5
5
2
2
3
0
2
5
2
1
2
1
TRNS
2
2
1
1
1
1
1
1
1
1
1
1
FSKL
3
3
0
0
0
0
0
3
0
0
0
0
IMPR
3
3
0
0
2
0
6
9
0
0
0
2
LCI
13
13
3
3
6
1
9
18
3
2
3
4
-------
COUNTY #71 - WINNEBAGO
LKNO
1
2
5
6
7
8
COUNTY
M
tn
Ul
LKNO
1
2
3
4
5
6
8
9
24
LAKE NAME
Butte Des Morts
Little Butte Des Morts
Poygan
Rush
AREA
8857
1306
14102
3070
Winnebago 137708
Winneconne
#72 - WOOD
LAKE NAME
Biron Flowage
Centralia Flowage
Lake Dexter
Nekoosa Flowage
Nepco
Port Edwards Flowage
Wazeecha
4507
AREA
2126
231
300
452
494
117
148
Wisconsin Rapids Flowage 447
South Gallagher Flowage
395
MXD
11
12
11
16
21
10
MXD
23
19
14
17
29
16
23
22
6
INLET
Yes .
Yes
Yes
No
Yes
Yes
INLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
OUTLT
Yes
Yes
Yes
No
Yes
Yes
OUTLET
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
TYPE
NAT
NAT
NAT
NAT
NLLC
NAT
TYPE
IMP
IMP
IMP
IMP
IMP
IMP
IMP
IMP
IMP
D.O.
3
5
3
5
3
3
D.O.
1
1
1
3
3
1
3
1
5
TRNS
3
3
3
2
3
3
TRNS
3
3
2
3
1
3
3
3
3
FSKL
0
3
0
3
0
0
FSKL
0
0
0
0
0
0
0
0
3
IMPR
4
4
4
3
7
4
IMPR
2
2
0
2
3
2
3
2
2
LCI
10
15
10
13
13
10
LCI
6
6
3
8
7
6
9
6
13
-------
APPENDIX II
NOTES REGARDING STATE LAKE INVENTORIES
ALABAMA:
CALIFORNIA:
COLORADO:
DELAWARE:
GEORGIA:
ILLINOIS:
KANSAS:
KENTUCKY:
LOUISIANA:
MAINE:
MARYLAND:
Reservoirs greater than 500 surface acres
only.
Dams within the jurisdiction of the state.
Estimate prepared by Water Resources Center
(WRC) staff from United States Geological
Survey (USGS) maps. Figures given do not
include intermittent lakes.
Estimate of number of lakes greater than
100 surface acres obtained from state
agency official.
Estimates obtained from state agency
official.
Lakes less than 6 acres have also been
inventoried and represent 96.7% of the
number of lakes in the state, but only
20.4% of the acreage.
Major reservoirs and selected city impound-
ments have been inventoried. Estimate of
number of lakes greater than 100 surface
acres obtained from state agency official.
Computerized inventory conducted; print-
outs are available. Estimate of number
of lakes greater than 100 surface acres
obtained from state agency official.
Estimate prepared by WRC staff from USGS
maps. Approximately 55-60% of the state's
lakes are located in the coastal marshes
of the Gulf of Mexico.
Estimate of the number of lakes greater
than 100 surface acres obtained from state
agency official.
Estimate prepared by WRC staff from USGS
maps. Of this number, approximately 50%
are located in coastal marshes.
156
-------
MICHIGAN:
MISSISSIPPI:
MISSOURI:
MONTANA:
NEBRASKA:
NORTH CAROLINA:
NORTH DAKOTA:
OREGON:
PENNSYLVANIA:
TEXAS:
VIRGINIA:
WEST VIRGINIA:
WISCONSIN:
Computerized inventory conducted; printouts
are not readily available.
Estimate prepared by WRC staff from USGS
maps.
State has information in map form only.
The estimate given in Table 6 was prepared
by WRC staff from USGS maps.
Estimate prepared by WRC staff from USGS
maps. Figures given do not include inter-
mittent lakes.
Only the sandhills area of the state has
been inventoried. Estimate of number of
lakes greater than 100 surface acres
obtained from state agency official.
State has published a register of dams and
dam sites. The estimate of the number of
lakes greater than 100 surface acres was
obtained from state agency official.
Estimate prepared by WRC staff from USGS
maps. Figures given do not include inter-
mittent lakes.
3,743 of the 6,435 lakes listed are unnamed.
Computerized inventory conducted; printouts
are available.
Reservoirs with greater than 5,000 acre-
feet storage capacity only.
Only major reservoirs have been inventoried.
Estimate of number of lakes greater than
100 surface acres obtained from state agency
official.
There appears to be only one natural lake
of appreciable size in the state; the
remainder are reservoirs. Estimates
obtained from state agency official.
The state's inventory includes all named
lakes regardless of size. A partial inven-
tory of unnamed lakes includes 4,803 addi-
tional entries.
157
-------
WYOMING: Computerized inventory conducted; printouts
not readily available. The estimate of
number of lakes greater than 100 surface
acres obtained from state agency official.
158
-------
APPENDIX III
LAKE INVENTORY QUESTIONNAIRE
STATE: SURFACE AREA: Acres
COUNTY: MAXIMUM DEPTH: Feet
LAKE NUMBER: *MEAN DEPTH: Feet
LAKE NAME: LOCATION:
Township Range Section
*Mean depth is defined as:
VOLUME/SURFACE AREA (ACRE FEET/AREA).
LAKE TYPE: Do not
Yes No know
Does the lake have an: Inflowing stream?
Outflowing stream?
Is the lake an:
Impoundment?
Natural lake?
Natural lake with level control?
Do not know.
THERMAL STRATIFICATION: Yes No
Is the lake ice-covered during the winter?
Which of the following best describes
the thermal stratification in the lake?
Lake is permanently stratified.
Lake undergoes one period of mixing annually.
Lake undergoes two periods of mixing annually.
Lake undergoes frequent mixing.
Lake undergoes continuous mixing.
Do not know.
159
-------
LAKE CLASS:
Check one of the following classes if it applies to the
lake:
Bog; Brown-stained water is the key character-
istic; dystrophic; acid, pH typically 6 or less;
encroaching leather-leaf bog, usually floating..
Marsh lake: Clear water; shallow, not strati-
fied;alkaline, pH typically 7 or more;
encroaching grassy marsh vegetation ,
Alpine lake; Lake occupying a depression in a
high mountainous region; rock or gravel bottom
usually; dissolved oxygen abundant at all times
in ice-free season, hypolimnion seldom falling
below 80% saturation; generally unproductive
lakes
Flow-through reservoir; An impoundment with a
hydraulic residence time of less than one year
(i.e., the ratio of the total volume to the
annual inflow is less than one)
If one of the foregoing classes is checked, skip
the following section and continue with LAKE
CONDITION.
If the lake is not described by one of the fore-
going classes, mark the appropriate class below:
Warmwater fisheries; If stratified, the lake
contains an insignificant amount of dissolved
oxygen in the hypolimnion (bottom waters) to
support fish life in late summer; major portion
of lake is open water; shoreline encroachment
is slight compared to open water; contains
typical warmwater fish populations, including
largemouth bass, bluegills, northern pike,
and/or yellow perch
Warm- and coldwater fisheries; Deeper,
stratified;at least 4 feet of water in the
hypolimnion which contains 4 ppm dissolved
oxygen in late summer; contains a population
of warmwater fish and may or may not contain
a population of trout
Coldwater fisheries; Deep, infertile; an
abundance of well oxygenated water in the hypo-
limnion; well suited to trout and cisco, may or
may not contain these species exclusively
160
-------
LAKE CONDITION:
Check the appropriate level of each of the following
parameters defining the lake's condition.
Dissolved oxygen:
Dissolved oxygen in hypolimnion, bottom
waters, greater than 5 ppm at virtually
all times
Concentrations in hypolimnion less than 5 ppm
but greater than 0 ppm
Portions of hypolimnion void of oxygen
at times ,
Entire hypolimnion void of oxygen at times....
Do not know
Alkalinity:
Low: 0-50 ppm CaCO 3
Medium: 50 - 100 ppm CaCO3
High: > 100 ppm CaCO3
Do not know
Specific conductance:
Low: 0-75 ymhos @ 25°C
Medium: 75 - 200 ymhos @ 25°C,
High: > 200 ymhos @ 25°C.
Do not know
Transparency;
Transparency, as indicated by the Secchi Disk
depth, should be expressed here as a typical
minimum and maximum range.
Minimum Maximum
0-1.5 ft ( 0-0.5m)
1.5 - 10 ft (0.5 - 3m)
10 - 23 ft ( 3 - 7m)
> 23 ft ( > 7m)
Do not know
161
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SHORELINE DEVELOPMENT:
What is the length of the lakes's shoreline? miles
Estimate the percent of shoreline which is
developed (occupied by improvements) %
Estimate the percent of shoreline which is
under the following ownership:
Public %
Private,
LAKE USE:
Mark the appropriate use(s) of the lake.
Municipal water supply
Industrial water supply
Agricultural water supply
Flood control
Low-flow augmentation
Power
Recreation
Other (specify)
LAKE PROBLEM:
Mark the appropriate problem(s) which exist(s)
in the lake.
Excessive aquatic weed growth
Nuisance algal growth
Fishkills
Bacterial contamination
Unstable water levels
Excessive sediment accumulation...
Excessive dissolved solids
Taste and/or odor in water supply,
No problem
Other (specify)
162
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IMPAIRMENT OF USE:
Check the category which most closely describes
the lake condition.
Mo impairment of use
Very few algae present, no "bloom" conditions
AND/OR
Very few weeds in littoral zone
Slight impairment of use
Occasional "blooms," primarily green species
of algae
AND/OR
Moderate weed growth in the littoral zone
Periodic impairment of use
Occasional "blooms," predominantly bluegreen
species
AND/OR
Heavy weed growth in littoral zone
Severe impairment of use
Heavy "blooms" and mats occur frequently,
bluegreen species dominate
AND/OR
Excessive weed growth over entire littoral zone.
PREVIOUS CORRECTIVE TREATMENTS:
Have any rehabilitative treatments been undertaken
in the past or at the present time to improve the
lake? If so, which of the following types?
Mechanical harvesting of plant growths
Chemical control of weeds or algae
Flushing
Dredging
Waste water diversion
Nutrient removal from waste waters
Hypolimnetic withdrawal
Water level management
Nutrient immobilization or precipitation
No treatment
Other (specify)
163
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BACKGROUND DATA:
For purposes of this inventory, three data categories have
been chosen: physical, chemical, and biological. To
account for variations in completeness of data among these
categories, the system of rating has been designed to
treat each of the three categories separately:
Phys Chem Biol
No record or only scattered
measurements
Partial data: continuous monitoring
on a limited scale; long-term sampling
program of low frequency; data prob-
ably not adequate to document a change
if renovation were undertaken
Extensive data: intensive monitoring
program covering a variety of loca-
tions and depths; frequent sampling
for a duration of two years or more;
sufficient to document a change if
renovation were undertaken
Do not know
Is there a nutrient budget available for the lake?
Measured
Es timated
Do not know
DRAINAGE AREA:
What is the size of the lake's direct drainage area?
The direct drainage area is defined as that
portion of the total drainage area which does
not drain to upstream lakes or impoundments.
square miles
Estimate the percent of the direct drainage area
devoted to the following uses.
Urban ,
Agricultural ,
Rural non-agricultural,
Other (specify)
164
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POTENTIAL SOURCES OF POLLUTANTS (NUTRIENTS):
Indicate below the potential sources of pollutants
(nutrients) within the direct drainage area which may
enter the lake. Check as many as are appropriate to
describe the potentially significant inputs to the lake,
Domestic:
Septic tanks
Municipal effluents: Untreated
Primary treatment
Secondary treatment
Estimate the total population contributing
to each effluent type:
Untreated
Primary treatment...
Secondary treatment.
Agricultural;
Cultivated land
Pasture
Animal feedlots
Irrigation return flows
Industrial;
Industrial effluents (treated or untreated)...
Urban runoff:
Storm drainage, etc.
Other (specify) ;
Form completed by:
(Initials)
165
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TECHNICAL REPORT DATA
(I'lcase read Instructions on the reverse before completing)
i. rui'om NO.
EPA-660/3 -75-033
4. TITLfc ANUS~UUl7TLE~
2.
LAKE CLASSIFICATION—A TROPHIC CHARACTERIZATION
OF WISCONSIN LAKES
6. REPORT DATE
6/75 (Preparation Date)
6. PERFORMING ORGANIZATION CODE
I. RECIPIENT'S ACCESSION-NO.
7. AUTHOH(S)
Paul D. Uttormark, J. Peter Wall
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING OHO -^NIZATION NAME AND ADDRESS
Water Resources Center
University of Wisconsin
Madison, Wisconsin 53706
10. PROGRAM ELEMENT NO.
1BA031
11. CONTRACT/GRANT NO.
R-801363
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. Environmental Protection Agency
Washington, D.C. 20460
13. TYPE OF REPORT AND PERIOD COVERED
Final (7/72 - 11/74)
14. SPONSORING AGENCY CODE
16. SUPPLEMENTARY NOTES
16. ABSTRACT
The design and application of the Lake Condition Index (LCI) system of classifying
lakes is described, and it is demonstrated that lake classification can be employed
as a useful tool by resource managers for comparing the trophic condition of large
numbers of lakes. The LCI system was generated when an evaluation of other systems
revealed that most are presently unsuitable for classifying the vast majority of
lakes because the analytical data required for their use are lacking. Utilizing
subjective information, the LCI system was applied to the classification of more
than 1100 large Wisconsin lakes. Checks of the results show that 86% of the lakes
were appropriately classified within the limits of the system; 14% were misclassi-
fied, as judged by individuals familiar with the lakes in question. Most, but not
all, discrepancies were due to erroneous input data. The LCI values obtained were
coupled with nutrient-loading considerations and shoreline density-development
factors to demonstrate that lake classification can serve as a workable data base
for lake renewal and management programs. The LCI system is easily modified to
incorporate additional data for special purposes. The system could be used to
classify an estimated 70-80% of the larger lakes in the United States.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b. IDENTIFIERS/OPEN ENDED TERMS C. COSATI Held/Group
*Lakes, *Trophic level, *Classification,
Water quality, Secchi disks, Dissolved
oxygen, Fishkill, Wisconsin
*Lake Condition Index
13. DISTRIBUTION STATEMENT
Release unlimited.
19. SECURITY CLASS (This Report)
None
21. NO. OF PAGES
165
20. SECURITY CLASS (This page i
None
22. PRICE
EPA Form 2220-1 (9-73)
ft U.S. GOVERNMENT PRINTING OFFICE: 1975-699-027 /I2 REGION 10
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