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


OeT

COUNTY #44 - ONEIDA Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
64 Clear 1049 100 No No NAT 4 0 0 0 4
66 Clearwater 343 29 No Yes NAT 3 2 (0) 0 5
67 Columbus 670 26 Yes Yes NAT 3 2 0 0 5
73° Crescent Lake 612 37 No Yes NAT 4 2 0 2 8
75 Crooked 176 17 No Yes NAT 3 2 3 3 11
77 ~~ “Crystal 112 7 Yes Yes NAT 5 2 0 2 9
82 Dam 716 30 Yes Yes NLLC 3 2 i?) 2 7
84 Deer 175 17 Yes Yes NLLC 3 2 0 0 5
88 Diamond 124 17 Yes Yes NAT 3 2 0 2 7
89 Dog 241 10 Yes Yes NAT 3 2 0 (0) 5
99 East Horse Head 184 26 No Yes NAT 3 2 0 0 5

106 = Emma 223 17 No No NAT 3 2 0 2 7

109 Fifth 240 9 Yes Yes NLLC 3 2 0 0 5

113. Flannery 112 33 No No NAT 4 0 0 2 6

116 Four Mile 252 18 Yes Yes NAT 3 2 0 0 5

117. Fourth 258 9 Yes Yes NLLC 3 2 0 0 5

119 Franklin 161 25 No No NAT 3 2 0 0 5

121 Fuller 101 4 No Yes MAT 5 2 3 3 13*
123. Garth 114 22 No No NAT 3 2 0 4 9

125 George 435 26 Yes Yes NAT 3 2 0 2

127. Gilmore 301 24 Yes Yes NLLC 3 2 0 2 7


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 set. 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


TST

COUNTY #66 — WASHBURN Con't

LENO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
197 Shell 2580 36 No No NAT 2 1 0 2 5
200 Silver 188 28 No No NAT 1 1 0 0 2
202 Slim 224 42 Yes Yes NAT 2 1 0 0 3
203 Slim Creek 101 27 Yes Yes IMP 1 1 0 3 5
205 South Twin 115 29 No No NAT 1 1 0 0 2
210 Spider No. 5 177 49 No No NAT 2 2 0 0 4
211 Spooner 1092 17 Yes Yes IMP 5 2 0 6 13
216 Spring 211 24 No No NAT 1 1 0 2 4
225 Stone 523 49 No No NAT 2 0 0 0 2
236 Tranus 175 12 Yes Yes NAT 5 1 3 2 11
237 Trego 451 36 Yes Yes IMP 2 2 0 2
257 Yellow River Flowage 344 17 Yes Yes IMP 1 2 0 3

Unnamed 201 29 Yes Yes IMP 3 2 0 2 7

COUNTY #67 - WASHINGTON

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI

5 Big Cedar 932 105 Yes Yes NLLC 2 1 0 2 5
7 Druid 124 40 Yes Yes NAT 2 2 0 2 6
10 Five 102 23 No No NAT 5 1 3 3 12
11 Friess 131 51 Yes Yes NAT 2 1 0 0 3
26 Little Cedar 259 55 Yes Yes NLLC 4 1 0 0 5
42 Pike 522 45 Yes Yes NLLC 2 1 0 0 3
47 Silver 119 45 No Yes NLLC 2 1 0 0) 3



9bT

COUNTY #64 - VILAS Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
478 Stone, Crawling 1460 80 Yes Yes NAT 4 0 0 0) 4
479 Stone, Little Crawl 116 48 No Yes NAT 4 0 0 0 4
483 Stormy 525 61 No No NAT 2 0 0 0 2
488 Sugarbush, Lower 180 40 Yes Yes NAT 4 0 te) 2 6
489 Sugarbush, Middle 265 55 Yes Yes NAT 4 i) 0 2 6
490 Sugarbush, Upper 142 30 Yes Yes NAT 3 0 0 2 5
493 Sunset 185 43 No No NAT 4 te) te) i) 4
495 Tamarack, Little 200 16 Yes Yes TMP 3 2 0 0 5
496 Tambling 169 11 No Yes NAT 5 2 3 2 12
500 Tenderfoot 461 36 Yes Yes NAT 4 2 0 2 8
503 Tippecanoe 155 34 No No NAT 4 0 0 0 4
506 Towanda 132 27 No No NAT 3 2 0 2 7
509 Tree, Lone 121 16 No No NAT 3 2 0 0 5
512 Trout 3870 9=-115 Yes Yes NAT 2 0 0 0 2
513 Trout, Little 982 91 No No NAT 4 1 0 2 7
514 Turtle, North 369 58 Yes Yes NAT 4 2 0 2 8
515 Turtle, South 454 23 Yes Yes NAT 3 2 0 2 7
516 ‘Twin, North 2782 45 Yes Yes NLLC 4 0 0 0 4
517. Twin, South 630 42 Yes Yes NLLC 4 0 0 0 4
520 Twin Island 205 14 Yes Yes NAT 3 2 0 2 7
523. Van Vliet 220 13 No Yes NAT 3 2 0 2 7


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:

Environmental Health Effects Research
Environmental Protection Technology
Ecological Research

Environmental Monitoring
Socioeconomic Environmental Studies

APWNa

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.


9TT

COUNTY #42 - MONROE

LKNO LAKE NAME AREA MXD INLET OUTLIT TYPE D.O. TRNS FSKL IMPR LCI

Lake Tomah 225 19 Yes Yes IMP 3 3 0 6 12

8 Monroe Co. Flowage 263 8 Yes Yes IMP 3 3 3 0 9

52 14/16 118 12 Yes IMP 5 2 1) ) 7
56 23/4 120. <30 Yes NLLC 3 2 0 2 7*

71 12/14 100 13 Yes Yes IMP 3 2 0 0 5

80 14/13 101 10 Yes Yes IMP 5 2 0 2 9

96 12/1 173 17 Yes Yes IMP 3 2 (0) 0 5

107. 7/8 201 8 Yes Yes IMP 5 2 i) 0 7

115 31/14 162 Yes Yes IMP 5 2 ) 0 7



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 al, 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 aZ, 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


6PT

COUNTY #66 - WASHBURN

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
4 Baker 114 21 No No NAT 1 2 3 0 6
5 Balsam 295 49 Yes Yes NAT 2 2 0 0 4
8 Bass 128 66 No No NAT 2 1 0 0 3
9 Bass 188 35 No No NAT 2 1 0 0 3

10 Bass 144 31 No No NAT 2 1 0 0) 3
12 Bean 100 35 No Yes NAT 2 2 0 2 6
18 Big Bass 203 27 No No NAT 1 2 0 2 5
19 Big Casey 247 27 Yes Yes NAT 1 1 0 2 4
20 Big Devil's 162 75 Yes Yes NAT 2 1 0 0 3
21 Big Ripley 190 27 No No NAT 1 1 0 0 2
22 = =Birch 368 73 Yes Yes NLLC 2 1 0 ie) 3
30 Cable 200 24 No No NAT 3 2 3 0 8
36 Chicog 125 25 Yes Yes NAT 1 1 0 0 2
49 Deer 102 19 Yes Yes NAT 1 2 0 2 5
51 ‘De Rosier 109 11 No No NAT 5 2 3 te) 10
56 Dunn 193 39 Yes Yes NAT 2 2 0 0 4
61 Ellsworth 174 6 No No NAT 5 2 3 2 12
64 Fenton 139 52 No No NAT 2 1 0 0 3
68 Gilmore 389 36 No Yes NAT 4 1 0 2 7
76 «Gull 511 19 Yes Yes NLLC 1 1 0 2 4
85 Horseshoe 194 21 No No NAT 1 1 0 0 2


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


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 POu/1
COD = chemical oxygen demand in mg 02/1
ON = organic nitrogen in mg N/1
NH3 = free ammonia in mg N/1

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 Score Rank
Chemung 8.7 5
Crooked 8.7 6
Fenton 4.5 1
Lobdell 4.7 2
Ore 12.3 7
Ponemah 21.0 9
Potter 22.2 10
Silver 4.8 3
Squaw 13.3
Woodland 7.4

from Newton and Fetterolf (1966)

The results were judged by the authors to give a reasonable
portrayal of the relative water quality in the 10 lakes.

12


Set

COUNTY #44 - ONEIDA Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
372 Tom Doyle 108 27 Yes Yes NAT 3 2 0 2 7
374 Town Line 150 30 Yes Yes NLLC 3 2 0 2 7
376 Two Sisters 705 64 No Yes NAT 4 0 0 0 4
378 Upper Kaubashine 183 57 No Yes NAT 4 0 0 0 4
383 Virgin 266 20 Yes Yes NLLC 3 2 0 2 7
387 West Horsehead 145 26 Yes Yes NAT 3 2 0 2 7
391 Whitefish 185 31 Yes Yes NLLC 4 2 0 2 8
395 Willow 395 6 Yes Yes NLLC 3 2 0 2 7
396 Willow Flowage 5135 22 Yes Yes IMP 3 2 0 0 5
398 Wind Pudding 196 34 No No NAT 4 10) 0 2 6

COUNTY #46 - OZAUKEE

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
17 Mud Lake 245 4 Yes Yes NAT 5 2 3 10) 10



TeT

COUNTY #52 - RACINE

D.O. TRNS FSKL IMPR LCI

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE
2 Bohners 124 30 Yes Yes NLLC 3 1 0 2
4 Browns 396 44 Yes Yes NLLC 4 1 0 3
5 Buena 241 8 Yes Yes IMP 1 2 0 3
7 Eagle 520 15 Yes Yes NLLC 5 3 3 9 20
8 Echo 71 Yes Yes IMP 1 3 0 2 6
11 Long 123 Yes Yes NAT 5 3 3 6 17
16 Tichigan 268 63 Yes Yes NLLC 6 2 4 9 21
17 Waubeesee 129 73 Yes Yes NLLC 4 1 0 2 7
18 Wind 936 47 Yes Yes NLLC 4 1 0 2 7
COUNTY #53 - RICHLAND
LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
8 Lawson Pond 153 Yes Yes IMP 11
10 Sand Prairie Slough 100 5 Yes Yes NAT 2 11



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


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,, 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


oot

COUNTY #19 - FLORENCE

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
4 Bass 102 68 Yes Yes NAT 4 0 0 0 4
19 Elwood 135 25 No No NAT 3 0 0 0 3
20 =Emily 197 43 Yes Yes NAT 2 (0) 0 0 2
21 ~~ ~Fay 247 13 Yes Yes NAT 3 2 0 2 7
30 Halsey 512 10 Yes Yes NAT 3 2 0 2 7
37. Keyes 202 77 Yes No NAT 2 0 ) 0 2
44 Long 340 23 Yes Yes NLLC 5 2 3 2 12
66 Patten 256 52 Yes Yes NAT 4 2 0 0 6
71 = Price 107 8 Yes No NAT 3 2 3 2 10
77 “Savage 150 10 Yes No NAT 3 2 3 0 8
79  Sealion 122 82 Yes Yes NAT 4 1 0 2 7
82 Shadow 109 30 Yes No NAT 3 1 (0) 0 4
10 Brule River 197 70 Yes Yes IMP 4 2 0 0 6
39 Kingsford 415 33 Yes Yes IMP 4 2 0 0 6
69 Pine River 145 38 Yes Yes IMP 4 2 0 0 6
89 ‘Twin Falls 682 15 Yes Yes IMP 3 2 0 0 5



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


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... ... cece cece ee eee eee e ee eee eae ecae

Concentrations in hypolimnion less than 5 ppm
but greater than 0 ppm... ce cece cece cece ce cene

Portions of hypolimnion void of oxygen
At tiMeS.... cece eee cece cee teen eee teeeeee

Entire hypolimnion void of oxygen at times....

DO NOt KNOW... eee eee eee eee e rere cece renee cene
Alkalinity:

Low: O - 50 ppm CaCOg... cece eeeeevene

Medium: 50 - 100 ppm CaCO3...........-

High: > 100 ppm Caco;.

DO NOt KNOW... ee eee cece eee eee cece eee eeeee

Specific conductance:

Low: 0 - 75 wmhos @ 25°C......... cease
Medium: 75 - 200 umhos @ 25°C. ..... cece eee
High: > 200 umhos @ 25°C...

Do not kKnOW...... eee eee eeeeee

Trans parency:

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 - 0.5 m)..........
1.5 - 10 ft (0.5 - 3 m)..--eeeeee
10 - 23 ft ( 3- Tom) ..eeeeeeee
> 23 ft ( > Tom) eee eeeeee

DO NOt KNOW... eee cece cece reece cccecce

161


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 a
Hyd res time

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/m*/yr and critical loadings of 3.4 and

0.49 g/m?2/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


StT

COUNTY #64 - VILAS Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
427 Round 116 25 Yes Yes NAT 3 0 0 0) 3
432 St. Germain, Big 1463 35 Yes Yes NLLC 4 1 0 2 7
433 St. Germain, Little 956 56 Yes Yes NLLC 4 2 0 2 8
435 Sanborn 253 13 Yes NAT 3 2 0 2 7
436 Sand, Big 1408 35 Yes Yes NAT 4 0 0 0 4
437 Sand, Little 107 32 No Yes NAT 4 0 0 2 6
438 Sand, White 728 68 Yes Yes NAT 4 0 0 0 4
439 Sand, White 1195 50 Yes Yes NLLC 4 0 0 0 4
446 Sherman 123 19 No No NAT 3 2 0 2 7
450 Snipe 223 15 No No NAT 3 2 0 2 7
456 Sparkling 127 64 No No NAT 2 0 0 0 2
457 Spectacle 160 37 No No NAT 4 0 0 0 4
458 Spider 256 41 Yes Yes NLLC 4 1 0 2 7
459 Spider, Little 225 28 No Yes NAT 3 0 0 0 3
462 Spring 205 11 Yes Yes NAT 3 2 te) 0 5
467 Star 1150 67 Yes Yes NLLC 4 0 0 0 4
468 Star, Little 244 63 Yes Yes NLLC 4 0 0 0 4
472 Stateline 199 67 No Yes NAT 4 (0) 0 0 4
473 Statenaker 202 50 NAT 4 0 0 0 4
474 Stearns 217 16 No No NAT 3 0 0 10) 3
477 Stone 134 21 Yes Yes NLLC 3 2 0 2 7


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


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 (8 m);

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


TOT

COUNTY #20 - FOND DU LAC

LKNO = LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
1 Auburn 107 29 Yes Yes NAT 3 1 0 2 6

12 Kettle Morraine 240 30 Yes Yes NAT 5 2 3 3 13
13 Long 409 47 Yes Yes NLLC 4 1 1) 2 7
19 Mullet 200 7 Yes Yes NAT 5 2 3 3 13



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
MEASULEMENCS .. eee ee ere eee ere reecees

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... cece eee cece eee eeeccee

Is there a nutrient budget available for the lake?
M@ASULEK 2... ee ee eee eee ccc ec eee cee cece cence eee
EStimated..... cece eee cece ccc e reece nce eceneecce

DO NOt KNOW... cece cece eee cere nec ce nee cerreceees

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......6.

Agricultural...
Rural non-agricultural....
Other (specify)

164


€@T

COUNTY #44 - ONEIDA Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
268 Oscar Jennie 104 24 No No NAT 3 0 0 (0) 3
271 Pelican 3585 39 Yes Yes NLLC 4 2 0) 2 8
279 =Pickerel 477 17 Yes Yes NLLC 3 2 0 2 7
280 Pier 257 12 Yes Yes NLLC 3 2 0 0 5
282 =Pine 240 32 Yes Yes NAT 4 2 0 0 6
283 Pine 203 23 No No NAT 3 2 (0) 0 5
284 Planting Ground 1015 18 Yes Yes NLLC 3 2 0 2 7
289 Rainbow Flowage 2035 28 Yes Yes IMP 3 2 0 2 7
290 Range Line 130 21 Yes Yes NLLC 3 2 0 2 7
291 Rhinelander Flowage 1326 12 Yes Yes IMP 3 2 0 2 7
292 Rice 118 3 Yes Yes NLLC 5 2 0 2 9x
293 Rice River Flowage 1150 17 Yes Yes IMP 3 2 0 2 7
300 = =Round 152 11 Yes Yes NLLC 3 2 0 2 7
305 Sand 544 20 Yes Yes NLLC 3 2 0 0 5
307 Second i111 11 Yes Yes NLLC 3 2 10) 0 5
309 Seven Mile 240 36 Yes Yes NLLC 4 2 (0) 2 8
310 Seventeen 172 33 No No NAT 4 1 i} 0 5
313 Shepard 179 18 Yes Yes NAT 3 2 0 0 5
315 Shisheogama 716 42 Yes Yes NAT 4 2 i?) 2 8
322 = Skunk 130 7 No Yes NLLC 5 2 3 2 12
324 Snowden 135 12 No No NAT 5 0 3 0 8


Table 9.
COMPARATIVE RANK OF 12 WISCONSIN LAKES

Lueschow et al Lake inventory

Score Lake name Lake name LcI
(3)
a 8 Crystal Crystal 0
°
a
of 17 Big Green Trout 2
a
a 17 Geneva Big Green 4
° 19 Trout Geneva 5
a
ke}
: 31 Round Round 7
4 33 Pine Pine 7
| 33. Middle Middle 7
3 34 Oconomowoc Oconomowoc 8
3
3 45 Mendota Winnebago 13
3 49 Pewaukee Delavan 14
4 52 Delavan Pewaukee 15
g 52 Winnebago Mendota 17

from Lueschow et aZ (1970)

42


8

COUNTY #1 - ADAMS

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Big Roche-a-Cri 122 20 Yes Yes IMP 3 1 0 2 6
Friendship 115 16 Yes Yes IMP 3 2 0 3 8

11 Jordan 213 79 No No NAT 4 1 0) 0 5
12 Mason 857 10 Yes Yes IMP 3 2 0 2 7
47. Sherwood 250 30 Yes Yes IMP 3 2 0 2 7
48 Camelot 415 30 Yes Yes IMP 3 2 0 2 7



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

5


LIST OF FIGURES
Frequency Distribution of Lakes by Condition
Index for Four Areas of Wisconsin. .... .

Frequency Distribution of Wisconsin Lakes
According to Condition Index ........

Distribution of Surface Area of Wisconsin
Lakes as a Function of Condition Index...

Average Surface Area of Wisconsin Lakes
as a Function of Condition Index ......

Distribution and Average LCI of Wisconsin
Study Lakes - by County. .......+e.e.-s

Dissolved Oxygen Conditions in Wisconsin
Lakes. 2. 6. 6 6 ee ee ee ee eee ee

Calculated Condition Indexes for Three Types
of Wisconsin Lakes... .. +--+ eee eee

Relationship between Nutrient Loading and
Lake Condition (Hypothetical Data) .....

iv

47

48

49

52

53

62


Let

COUNTY #64 - VILAS Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCL
530 Watersmeet 100 12 Yes Yes NLLC 3 2 (0) 2
534 Whitefish 196 40 Yes NAT 4 0 0 0 4
535 Whitney 102 8 No Yes NAT 5 2 3 2 12
536 Wildcat 316 35 Yes Yes NAT 4 2 0 2 8
544 Wolf 393 28 Yes Yes NAT 3 2 0 2



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 Typical Secchi depth
1) 0- 1.5 ft ( 0- 0.5 m)
2) 1.5 - 10 ft (0.5 - 3 m)
3) 10 - 23 ft ( 3 - 7 m)
4) >23 ft ( >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 Transparency conditions
if both ranges are given if only one range is given
Minimum Maximum Penalty Secchi Penalty
range range points depth range points
1 1 4 1 4
1 2 3 2 2
1 3 2 3 1
1 4 2 4 0
2 2 2
2 3 1
2 4 0
3 3 0
3 4 0

39


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


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


Lueschow, L. A., 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 98(SAl, 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. £. 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.

79


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 Documenta, U.S. Government
Printing Office Washington, D.C. 20402


SOT

COUNTY #24 - GREEN LAKE

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
2 Grand 234 7 Yes Yes IMP 3 0 2
3 Green 7325 = 229 Yes Yes NAT 2 (0) 0 2 4
4 Little Green 465 24 Yes Yes NAT 3 1 3 5 12
7 Maria 596 No No NAT 5 2 3 2 12
8 Puckaway 5433 Yes Yes NLLC 1 3 1) 3 7
COUNTY #25 - IOWA
LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
10 Twin Valley 145 36 Yes Yes IMP 6 1 0 5 12
15 Blackhawk 220 40 Yes Yes IMP 4 1 0 2 7



8eT

COUNTY #50 - PORTAGE

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE -O. TRNS FSKL IMPR LCI
15 Emily 96 35 Yes Yes NAT 4 1 2 7
30 = McDill Pond 261 15 Yes Yes IMP 3 3 0 4 10
69 Wisconsin River 220 25 Yes Yes IMP 3 3 (0) 4 10
70 Wisconsin River 2093 25 Yes Yes IMP 3 3 0 5 11

Little Eau Pleine 600 2 Yes Yes IMP i} 3 0 0



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


Abstract

CONTENTS

List of Figures. . 2... 6 6 2 ee ee ew ee

List of Tables . . . 1 6 1 ee ee eee eee

Acknowledgments. 2. 2. 6. 6. 6 ee ee ee ew te we

Sections

I

II

III

Iv

vI

viIIr

Ix

Conclusions and Recommendations .. .
Introduction. . . 2... 1. e+e ee ee
Lake Classification Methodology .. .
Data Acquisition. .......+-e+e-.

Trophic Characterization of Wisconsin
Lakes 2. 6 ee ee ee ee ee ee

Lake Classification for Decision Making

Regional Application of the LCI System.

References. . . 2. 2 6 2 2 se ee eee

Appendices. . . 2... 2 eee ee ee

Page
ii

iv

vi

23

37
57
72
78
81


90T

COUNTY #26 - IRON

LKNO = LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
13. Big Pine 632 22 Yes Yes NAT 3 2 0 2 7
17 Boot 180 16 No No NAT 3 2 i) 2 7
23 Catherine 118 11 Yes Yes NAT 3 2 0 2 7
24 Cedar 193 21 Yes Yes NAT 3 2 0 0 5
47 Echo 220 25 Yes Yes NAT 3 2 0 0 5
57. Fisher 452 25 Yes Yes NLLC 3 2 0 2 7
58 Flambeau 13545 50 Yes Yes IMP 4 2 1) 0 6
64 Gile 3384 25 Yes Yes IMP 3 2 0) 0 5
66 Grand Portage 144 31 Yes Yes NAT 4 2 0 2 8
67 = Grant 107 10 Yes Yes NAT 3 2 0 2 7
80 Island 352 7 Yes Yes NAT 3 2 0 0 5
95 Lake of the Falls 338 23 Yes Yes NLLC 3 2 (0) 1) 5
96 Lake Six 147 11 No No NAT 5 2 3 0 10

109 Little Pike 100 19 Yes No NAT 5 2 3 2 12
111 Long 373 30 Yes Yes NAT 3 2 0 2 7
117. Martha 146 55 Yes Yes NAT 4 2 (0) 0 6
122 = Mercer 184 24 Yes Yes NAT 3 2 0 2 7
125 Moose 269 12 Yes Yes NAT 3 2 0 0 5
135 North Bass 180 9 No No NAT 3 2 0 (0) 5
149 Owl 126 38 No No NAT 4 2 0 2 8
151 Pardee 206 27 Yes Yes NAT 3 2 0 0 5


+

Table 7. AVAILABILITY OF WATER QUALITY DATA FOR LAKES Con't
ad ad

© © 9

v On ON

© Pres) “qv

ue} ea Bs al

oo ao ®

a as as On

© OL op Pn)

16 od cd on

ret) yy Ob

a On ou dra

B 26 gE ce
State ay =O =o ad
Nebraska
Nevada 50 ? - -
New Hampshire* * * * *
New Jersey* * * * *
New Mexico 4 ? - -
New York* 65+* 65+%* -* ?*
North Carolina 71 13 3 7
North Dakota 189+ 34+ - ?
Ohio 82+ ? - 12
Oklahoma 42 - - -
Oregon* * * * *
Pennsylvania* * * * *
Rhode Island* * *
South Carolina 149 32 8 18
South Dakota 38+ 2 - -
Tennessee 145+ ? - -
Texas 24 4 - -
Utah 21 - -
Vermont 42 ? - -
Virginia 15+ ? - -
Washington 37+ 25+ ? ?
West Virginia 47 8 2 -
Wisconsin* 400+ 400+
Wyoming 34 12 - -

trhis tabulation does not include the EPA National Lake

Survey data.

*Sizable monitoring programs, but summary information not

available.

27


98

COUNTY #3 - BARRON

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCL
8 Bass 118 14 No No NAT 0 1 0 (0) 1
11 Bear 1358 87 Yes Yes NLLC 0 1 0 2 3
12 Beaver Dam 1112-106 No Yes NLLC 0 4 0 2 6
13 Big Dummy 13558 No No NAT 2 3 0 0 5
14 Big Moon 191 48 Yes Yes NAT 0 1 0 2 3
19 Butternut 141 15 No No NAT 3 2 0 2 7
21 Chain 107 19 No No NAT 3 2 0 0 5
34 Duck 100 26 Yes Yes NAT 1 2 0 0 3
35 Echo 161 41 No No NAT 2 2 0 2 6
40 Granite 154 34 Yes Yes NAT 2 2 0 0 4
42 Hemlock 357 21 Yes Yes NLLC 1 2 0 2 5
44 Horseshoe 115 19 No No NAT 3 2 i) 0 5
50 Lake Chetek 770 22 Yes Yes NLLC 0 2 0 9 11
59 Little Sand 101 36 No No NLLC 2 2 0 0 4
64 Lower Devil's 162 26 No No NAT 3 1 3 0 7
66 Lower Turtle 276 24 Yes Yes 3 2 0 10) 5
70 Montanis 200 14 Yes Yes NAT 0 2 0 2 4
77° Mud 578 15 Yes Yes IMP 0 2 0 4 6
84 Pokegama 506 19 Yes Yes IMP 1 2 0 7 10
85 Poskin 150 30 Yes Yes NLLC 1 2 0 4 7
87 Prairie 1534 16 Yes Yes IMP 1 2 0 7 10


SECTION Ix

APPENDICES
Page
Classification of Wisconsin Lakes. ... . 82
Notes Regarding State Lake Inventories . . 174
177

Lake Inventory Questionnaire...

81


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

9


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 andIII 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


£6

COUNTY #7 - BURNETT Con't

LKNO =LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
204 Trade 426 39 Yes Yes NAT 4 4 0 2 10
206 ‘Twenty-Six 230 45 Yes Yes NAT (0) (0) te) 0
208 Upper Clam 1207 11 Yes Yes NLLC 1 2 0 3 6
209° =Upper Twin 163 17 No No NAT 5 2 3 2 12
210 Viola 262 33 No No NAT 2 1 0 0 3
211 Warner 176 75 No Yes NAT 0 2 0 0 2
212 = Webb 759 27 Yes Yes NAT 3 2 0 0) 5
214 Wood 521 35 Yes Yes NAT 4 2 0 2 8
215 Yellow 2287 32 Yes Yes NAT 2 2 0 2 6



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


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


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


vTT

COUNTY #38 - MARINETTE

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
6 Bagley 281 20 Yes Yes IMP 3 2 0 2 7
29 Caldron Falls 1180 40 Yes Yes IMP 4 1 i) (0) 5
35 Chalk Hills 300 35 Yes Yes IMP 4 2 0 0 6
42 Coleman 246 67 Yes Yes NLLC 4 1 0 2 7
67 Gilas 125 84 No No NAT 4 (0) 0 0 4
72 Grand Rapids 202 23 Yes Yes IMP 3 2 0 0 5
84 High Falls 1498 54 Yes Yes IMP 4 1 0 0 5
85 Hilbert 247 38 No No NAT 4 0 0 i) 4
96 Johnson Falls 68 40 Yes Yes IMP 4 2 0 0 6
133° Mary 167 20 Yes Yes NAT 3 2 0 2 7
147 Montana 135 28 Yes Yes NAT 3 2 0 v0) 5
120 Lake Quinnesee Flowage 67 40 Yes Yes IMP 4 2 0 0 6
163  Noquebay 2162 54 Yes Yes NLLC 4 2 0 2 8
172. Peshtigo 232 15 Yes Yes IMP 3 2 0 2 7
196 Sandstone 153 39 Yes Yes IMP 4 2 0 2 8
213 Sturgeon Falls 200 40 Yes Yes IMP 4 2 0 0 6
216 Thunder 135 62 Yes Yes NAT 4 0) 0 0 4
218 Town Corner 175 12 No Yes NAT 5 1 3 2 11
225 Upper Scott 283 17 Yes Yes IMP 3 2 0 2 7
230 White Rapids 449 32 Yes Yes IMP 4 2 (0) 0 6



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 LCL EPA
Oligotrophic 5 2
Mesotrophic 16 5
Eutrophic 21 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


GET

COUNTY #58 - SAWYER Con't

LKNO = =LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
114 Lac Court Oreilles 5039 90 Yes Yes NAT 0 1 0 2 3
175 Lake of the Pines 223 39 Yes Yes NAT 2 2 0 (0) 4
116 Little Court Oreilles 240 46 Yes Yes NAT 10) 1 0 2 3
120 Little Round 174 38 Yes Yes NLLC 2 1 10) 2 5
123. Little Sissabagama 299 75 Yes Yes NAT 0 1 0 0 1
127. Lost Land 1304 21 Yes Yes NAT 1 2 0 0 3
129 Lower Clam 229 22 Yes Yes NLLC 1 2 0) 2 5
134 Lower Twin 247 30 Yes Yes NLLC 3 1 0 2 6
137 Mason 190 39 Yes Yes NAT 4 2 0) 2 8
145 Moose 1670 21 Yes Yes IMP (0) 2 0 0 2
153° Mud 480 15 Yes Yes NLLC 1 2 0) 2 5
158 Nelson 2503 33 Yes Yes IMP 4 2 0 2 8
160 North 129 30 Yes NAT 3 2 3 0 8
164 Pacwawong 187 8 Yes Yes IMP 1 2 1) 3 6
169 Perch 129 24 No No NAT 0 2 0 0 2
179 ~Placid 160 30 No Yes NLLC 3 1 0 2 6
182 Price 715 14 Yes Yes IMP 1 2 9 0 3
183 Radisson 255 12 Yes Yes IMP 0 2 0 0 2
188 Round 2784 70 Yes Yes NLLC (0) 1 0 0 1
193 Sand 928 50 Yes Yes NLLC 2 1 0 2 5
197 Sissabagama 719 48 Yes NLLC 2 1 0 0 3


90

roo

150 +

100 4

Number of lakes

50

od

T T U T T ' q
8 10 12 14 16 18 20 22

Lake Condition Index

q
0

no
—
a

Figure 2, Frequency distribution of Wisconsin lakes

according to Condition Index.


Table 13. APPLICABILITY OF LAKE CLASSIFICATION SYSTEM@

Large lakes Total lakes

Direct applicability -
Conn, Ill, Ind, Ia, Me, Mass,
Mich, Minn, Neb, NH, NY, ND, 9,503
Ohio, Penn, RI, SD, Vt, Wis

Some modification -

Calif, Colo, Del, Ida, Kan,
Ky, Md, Mo, Mont, Nev, NJ,

NC, Okla, Ore, Tenn, Utah, 2,073
Va, Wash, WVa, Wy
Major changes -
Ala, Ariz, Ark, Fla, Ga, La, 2,023
Miss, NM, SC, Tex '
Totals 13,599

52,739

30,087

12,051

94,877

4Based on a summary of lake inventory data compiled by
individual states. Many inventories are incomplete,

so lake numbers given are minimum values.
brakes larger than 100 surface acres.

75


Ss

COUNTY #2 - ASHLAND

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
1 = Augustine 166 10 No No NAT 1 2 0 0 3
Bad River 185 7 No Yes NAT 0 2 0 0 2

Bear 220 8 Yes Yes NAT 0 2 (e) 0 2
Beaver Dam 118 19 Yes Yes NLLC 1 2 0 0 3

14 Caroline 130 8 Yes Yes NLLC 3 2 3 1) 8
23. East Twin 110 15 Yes Yes NAT 1 2 0 0 3
24 English 244 40 Yes Yes NAT 2 2 0 0 4
26 Galillee 212 23 Yes Yes NAT 1 2 (0) 2 5
29 Gordon 142 28 Yes Yes NLLC 1 2 0 le) 3
33 Kakagon 766 26 Yes Yes NAT 1 2 0 0 3
39 Little Clam 144 11 No No NAT 1 2 te) 2 5
41 Long 111 15 No Yes NAT 3 2 3 (0) 8
46 Meder 135 10 Yes Yes NAT 3 2 0 0) 5
48 Mineral 225 29 Yes Yes NAT 1 2 0 0 3
62 Spider 103 20 Yes Yes NAT 1 2 0 2 5
71 Upper Clam 165 20 Yes Yes NLLC 1 2 0 0 3
153 Madeline Island 105 10 Yes NAT 3 2 3 2 10



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 0 0 0
AND/OR

Very few weeds in littoral zone

Slight impairment of use

Occasional "blooms," primarily green

species of algae 2 2 2
AND/OR

Moderate weed growth in the littoral

zone

Periodic impairment of use

Occasional "blooms," predominantly

bluegreen species 3 4 5
AND/OR

Heavy weed growth in littoral zone

Severe impairment of use

Heavy "blooms" and mats occur fre-
quently, bluegreen species dominate
AND/OR 6 7 9
Excessive weed growth over entire
littoral zone

40


92T

COUNTY #49 - POLK

LKNO = LAKE _ NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
5 Antler 101 22 No No NAT 5 2 te) 0
6 Apple River Flowage 639 18 Yes Yes IMP 1 2 0 3
15 Balsam 2054 37 Yes Yes NLLC 2 2 0 5 9
20 Bass 138 19 No No NAT 3 2 3 2 10
22  Beartrap 241 25 Yes NAT 1 1 0 4 6
25 Big 259 24 Yes NLLC 1 1 0 3 5
26 «Big Butternut 378 19 Yes Yes NLLC 5 2 i) 5 12
35 Blake 302 14 Yes Yes NLLC 5 2 0 3 10
36 = Blom 210 13 No No NAT 3 1 1) 3 7
37 Bone 1781 43 Yes Yes NAT 2 1 0 2 5
70 Cedar 1100 32 Yes Yes NLLC 2 2 0 2 6
72 Church Pine 107 45 Yes NAT 2 1 (0) 0 3
73 Clam Falls Flowage 127 14 Yes Yes IMP 1 2 0 2 5
110 Deer 807 46 No Yes NLLC 2 1 0 2 5
113. Diamond 126 15 No No NAT 5 2 0 2 9
116 East 228 15 Yes Yes NAT 3 2 3 2 10
337. Freedom #2 106 4 No No NAT 5 2 3 2 12%
170 Garfield 120 8 No No NAT 5 2 3 2 12*
182. Glenton 128 10 Yes Yes NAT 5 2 3 3 13
187 Half Moon 579 60 Yes Yes NLLC 2 2 0 2 6
196 Horse 228 11 Yes Yes NAT 5 2 0 3 10


OTT

COUNTY #30 - KENOSHA

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI

3 Benet and Shangrila 154 24 Yes Yes NLLC 5 2 3 3 13

4 Camp 482 17 Yes Yes NLLC 5 2 3 4 14

5 Center 106 30 Yes Yes NLLC 3 1 (0) 2 6

8 Elizabeth 622 30 Yes Yes NLLC 3 1 0 2 6

15 Marie 297 38 Yes Yes NLLC 4 2 0 2 8

19 Paddock 112 32 Yes Yes NLLC 4 2 0 3 9

21 Powers 418 34 Yes Yes NLLC 4 2 0 2 8

23 Silver 499 43 Yes Yes NLLC 4 2 i) 2 8
COUNTY #32 - LA CROSSE

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI

2 Neshonoc 687 11 Yes Yes IMP 3 2 (0) 0 5
COUNTY #33 - LAFAYETTE

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI

4 Yellowstone 455 21 Yes Yes IMP 3 3 3 9 18



6TT

COUNTY #44 - ONEIDA

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
3 Aldridge 134 12 No Yes NAT 3 2 0 2 7
7 Alva 201 36 No No NAT 4 1 0 2 7

15 Bass 124 20 Yes Yes IMP 3 2 0 0 5
20 Bear 675 29 No Yes NAT 3 2 0 2 7
21 Bearskin 384 26 Yes Yes NAT 3 2 0 2 7
26 «Big 867 30 Yes Yes NLLC 3 2 i) 0 5
27° ~Big Carr 213 71 No No NAT 0 0 0 0 0
28 Big Fork 624 22 Yes Yes NLLC 3 2 0 0 5
29 Big Stone 567 57 Yes Yes NLLC 4 2 0 0 6
30 =Birch 180 24 Yes Yes NAT 3 2 0 2 7
31 —- Bird 103 40 No No NAT 4 0 0 0 4
32 Blue 433 56 No No NAT 4 0 (0) 0 4
37 —- Bolger 119 39 No No NAT 4 0 0 10) 4
38 Boom 437 30 Yes Yes IMP 3 2 0 (0) 5
39 = Booth 207 34 No Yes NAT 4 2 0 2 8
46 Buckskin 634 22 No Yes NAT 3 2 3 2 10
47 Buffalo 104 27 No No NAT 3 2 0 0 5
52 Burrows 156 26 No No NAT 3 2 0 2 7
55 Carrol 335 31 Yes Yes NLLC 4 2 0 2 8
56 Chain 213 18 Yes Yes NLLC 3 2 0 2 7
63 Clear Lake 212 21 No No NAT 3 2 0 2 7


vel

COUNTY #44 - ONEIDA Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
325 Soo 135 13 No No NAT 3 2 0 0 5
330 = =South Two 223 70 No No NAT 4 (0) 0 te)

331 Spider 125 27 No Yes NAT 3 0 0 (0)

332 Spirit 342 7 Yes Yes NLLC 5 2 0 2 ox
336 4 8=©6Spur 113 3 Yes Yes NAT 5 2 3 2 12
337. Squash 392 81 No No NAT 4 0 0) ie}

339 Squaw 785 22 Yes Yes NLLC 3 2 0 2

340 Squirrel 1352 45 Yes Yes NLLC 4 2 (0) 2 8
342 = Starks Flowage 120 9 Yes Yes IMP 3 2 0 2 7
344 Stella 425 15 Yes NAT 5 2 3 2 12
346 Stone 189 12 Yes Yes NLLC 3 2 0 0 5
347 Stone 248 10 Yes Yes NAT 3 2 0 0 5
351 Sugar Camp 545 38 No No NAT 4 i} i} 0 4
355 Swamp 296 8 Yes Yes NLLC 3 2 0 2 7
357 +Swampsauger 141 12 Yes Yes NAT 3 2 0 0 5
359 Sweeney 187 18 Yes Yes NLLC 5 2 3 2 12
363 Third 103 14 Yes Yes NLLC 3 2 0 0) 5
364 Thompson 382 35 Yes Yes NAT 4 2 1) 0 6
366 Thunder 172 12 Yes Yes IMP 3 2 0 2 7
367 Thunder 1768 9 No Yes NLLC 3 2 0 2 7
371 Tomahawk 3627 79 Yes Yes NLLC 4 0 0 0 4


8PT

COUNTY #65 - WALWORTH

LKNO LAKE NAME AREA MXD INLET OUTLET TYPE D.O. TRNS FSKL IMPR LCI
2 Beulah 712 58 Yes Yes NLLC 4 1 0 2 7
3 Booth 108 24 Yes Yes NAT 3 1 0 2
5 Como 1058 Yes Yes NLLC 5 2 3 3 13
6  Comus 117 Yes Yes IMP 5 3 3 4 15
8 Delavan 2072 56 Yes Yes NLLC 6 (0) 4 4 14

10 Geneva 5104 135 Yes Yes NLLC 2 1 0 2 5
11 Green 292 57 Yes Yes NLLC 4 1 0 4 9
16 Lorraine 133 8 Yes Yes NAT 5 2 3 2 2
17 Lower Whitewater 137 10 Yes NAT 5 2 3 3 13
20 Middle 256 42 Yes Yes NLLC 4 1 0 2

21 Mill 261 44 Yes Yes NLLC 4 1 0 3 8
23° North 191 11 NAT 5 2 3 3 13
25° Pell 107 13 Yes Yes NAT 5 2 3 2 12
28 Pleasant 138 29 Yes Yes NAT 3 1 10) (0) 4
29 Potters 157 26 Yes Yes NAT 5 2 te) 5 12
33. Trapp 115 6 Yes Yes IMP 1 3 0 2 6
34 Turtle 140 35 Yes Yes NLLC 4 1 0 0)

35  Wandawega 119 9 Yes Yes NAT 5 2 3 3 13
36 Whitewater 640 38 Yes Yes NLLC 4 1 0 2 7



est

COUNTY #68 - WAUKESHA

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Beaver 316 49 Yes Yes NLLC 4 1 0) 2 7
Big Muskego 2260 26 Yes Yes NLLC 5 1 3 3 12
10  Denoon 162 60 Yes Yes NLLC 4 1 0 3
13 Eagle Spring 227 12 Yes Yes IMP 1 1 0 3
22 Golden 250 46 No Yes NAT 4 1 0 3
26 Keesus 237 42 No Yes NAT 4 1 0 3
27 Lac Labelle 1117 47 Yes Yes NLLC 4 2 0 4 10
32 Little Muskego 518 65 Yes Yes NLLC 6 1 v) 5 12
36 Lower Nemahbin 271 36 Yes Yes NLLC 4 1 0 0
37 Lower Phantom 433 12 Yes Yes NLLC 3 1 0 5
39 Middle Genesse 102 38 No No NAT 2 1 (0) 10) 3
45 Nagawicka 917 90 Yes Yes NLLC 4 2 0 7 13
47 North 437 78 Yes Yes NAT 4 1 (¢) 0
48 Oconomowoc 767 62 Yes Yes NLLC 4 1 0 3
49 Okauchee 1187 94 Yes Yes NLLC 2 0 0 2
51 Pewaukee 2359 45 Yes Yes NLLC 4 2 0 9 15
52 Pine 703 85 Yes Yes NAT 4 1 0 2
61 Silver 222 44 Yes Yes NAT 4 1 0 0
63 Spring 100 20 Yes Yes NAT 3 1 0 0 4


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.

B
Be


eet

COUNTY #64 - VILAS Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
236 «High 734 31 Yes Yes NLLC 4 1 1) 0 5
241 Horsehead 229 50 Yes Yes NAT 4 0 0 0 4
245 Hunter 148 50 No No NAT 4 0 0 0 4
249 Ike Walton 1439 63 No No NAT 4 0 0 0 4
254 Interlaken, Long 368 65 Yes Yes NAT 4 0 0 0) 4
255 Irving 403 8 Yes Yes NAT 5 2 3 2 12
256 Island 757 33 Yes Yes NLLC 4 2 te) 0) 6
257) Jag 158 14 No No NAT 3 0 i} 0 3
262 John, Little 166 19 Yes No NAT 3 2 0 2 7
268 Jute 194 25 No No NAT 3 0 te) i} 3
271 ~=Katinka 172 60 Yes NAT 4 0 0 0 4
273 Kentuck 995 40 Yes NAT 0 2 0 2 4*
282 Lac Du Lune 407 56 No No NAT 2 0 0 0 2
283 Lac Vifux Desert 4280 38 Yes Yes NLLC 4 2 0 2 8
287 ~=Landing 220 11 Yes Yes NAT 5 2 3 2 12
289 =Laura 599 43 No No NAT 4 0 0 10) 4
293 = Long 872 95 Yes Yes NLLC 4 1} 0 0 4
295 Lost 541 25 Yes Yes NLLC 3 2 0 2 7
299° Lynx 295 42 Yes Yes NAT 4 0 0 0 4
301 Mamie 360 18 Yes Yes NLLC 3 2 (0) 2 7
303 Manitowish 506 58 Yes Yes NLLC 4 1 0 0 5


£oTt

COUNTY #21 - FOREST Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
114 Metonga 2157 74 Yes Yes NLLC 2 0 0 0 2
128 Pat Shay 120 5 No Yes NAT 5 2 0 2 9
130 Peshtigo 156 4 Yes Yes NAT 5 4 0 7 16
131 Pickerel 1299 14 Yes Yes NLLC 5 2 0 5 12
133 Pine 1670 14 Yes Yes NLLC 5 2 3 2 12
142.) Rice 208 6 Yes Yes NAT 5 2 0 0 7
144 Riley 213 12 Yes Yes NAT 3 2 0 0 5
147 Roberts 452 32 Yes Yes NAT 4 2 (e) 2 8
168 St. Johns 104 21 No No NAT 3 0 0 0 3
152 Scattered Rice 486 10 Yes Yes IMP 3 2 0 2 7
159 = Shoe 168 7 Yes 5 2 3 0 10
160 Silver 334 20 Yes Yes NAT 3 te) 0 (0) 3
167 Stevens 295 10 Yes Yes NAT 3 2 0 0) 5
175 Trump 172 20 No Yes NAT 5 2 0 2 9
179 Wabicon 594 15 No Yes NAT 3 2 0 2 7



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


Figure 5. Distribution and average LCI
of Wisconsin study lakes - by county.

number of lakes in county with areas > 100 acres
n.n = average Lake Condition Index value for county
*

i]
1

counties with no lakes with surface areas > than 100 acres

49


86

COUNTY #16 - DOUGLAS

LENO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Amnicon 426 31 Yes Yes NAT 2 2 0 2 6
Bardon 832 102 No No NAT 0 1 0 0 1
Bass 126 26 Yes Yes NAT 1 2 0 0 3

21 ‘Bond 292 64 No No NAT 0 1 0 0 1
34 Cranberry 172 19 Yes Yes NAT 1 2 0 0 3
40 Crystal 293 21 No No NAT 0 1 0 0 1
45 Dowling 154 13 Yes No NAT 1 2 0 td) 3
66 Leader 165 56 No No NAT 0 1 0 0 1
74 Loon 109 20 No No NAT 5 2 3 0 10
75 Lower Eau Claire 792 41 Yes Yes NLLC 2 1 0 0 3
80 Lyman 403 15 Yes Yes NLLC 1 2 0 2 5
85 Minnesuing 432 43 Yes Yes NAT 2 2 0 0 4
98 Nebagamon 914 56 Yes Yes NAT 0 1 0 0 1
104 Person 172 10 No No NAT 5 2 0 2 9
112) Red 252 39 No No NAT 2 1 0 10) 3
118 St. Croix 1913 28 Yes Yes TMP 1 2 i) 3 6
121 Sauntry 110 9 No No NAT 5 2 0 0 7
127. Simms 152 41 No No NAT 2 1 (0) 0 3
133. Steele 157 Yes No NAT 3 2 0 2 7
141 Twin 113 No No NAT 5 2 0) (0) 7
147 Upper St. Croix 855 22 Yes Yes NAT 1 2 0 i} 3



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 4. CLASSIFICATION OF 7 NEW ZEALAND LAKES

Parameter
Maximal summer
Mean (geometric) surface water
surface water (Dec., Jan., Feb.)
Lake Chlorophyll Total P Reactive POy-P Nitrate-N
motte nnn ug/1 -----------------------
Eutrophic
Okaro 17 69 11
Ngapouri 8 50 17
Mesotrophic
Rotokakahi 3 25 5 2
Okareka 2 13 5 3
Oligotrophic
Okataina 1.2 14
Rotoma 1.7 9 5
Tikitapu 1.3

after McColl (1972)

19


Zot

COUNTY #21 - FOREST

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
1 Arbutus 161 35 No No NAT 4 2 10) i?) 6
2 Atkins 151 4 No Yes NAT 5 2 0 0)

8 Birch 468 72 No Yes NAT 4 2 0 0

9 Bishop 288 12 Yes Yes NLLC 3 2 0 5 10
13. Bog Brook 490 6 Yes Yes IMP 5 2 0 0

20 Butternut 1292 42 No Yes NAT 4 0 0 0

32 Crane 337 26 No Yes NAT 3 2 0 0

52. Franklin 892 53 No Yes NAT 4 0 0 0

64 Hay Meadow 241 9 Yes Yes IMP 5 2 3 0 10
65 Hiles 713 5 Yes Yes IMP 5 2 3 2 12
66 Himley 149 8 No Yes NAT 3 2 0 10) 5
69 Howell 177 15 No Yes NAT 3 2 te) 0 5
75 Julia 401 47 Yes Yes NAT 4 2 0 0 6
76 Jungle 182 12 Yes Yes NAT 3 2 0 0 5
84 Laura 110 21 No No NAT 3 2 0 0 5
85 Lily 212 25 Yes Yes NLLC 3 2 0 ie) 5
90 Little Long 102 28 No No NAT 3 0 10) 0 3
94 Little Rice 1219 10 Yes Yes IMP 5 2 3 2 12
96 Little Sand 248 21 No No NAT 3 0 0 te) 3
104 Lucerne 1026 68 No Yes NAT 2 0) 0 0 2


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


SHORELINE DEVELOPMENT:

What is the length of the lakes's shoreline?

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.... cece cece cece cece
Agricultural water supply...........seeeeeeeee
F1lOOd CONtLOL. . cee eee eee eee eee een e cece cree nee
Low-flow augmentation. ......ceeecc cere eee ccere
Power...

Recreation

Other (specify)

miles

oe

oP 0?

LAKE PROBLEM:
Mark the appropriate problem(s) which exist(s)
in the lake.
Excessive aquatic weed growth...........eeeeee
Nuisance algal growth.........ceseeeeeeeeeceee
FAShHkKil1S... ccc cece cee cece ee eee e ee eeeee

Bacterial contamination. se eeeeee
Unstable water levelS..... cece eee cece cree cece
Excessive sediment accumulation...........----
Excessive dissolved solids........e.eeeeeeeeee
Taste and/or odor in water supply..........---
NO problem... .. cece cece cece e cere eee ec eeececes

Other (specify)

TTT T TL

162


TECHNICAL REPORT DATA

(Pease read Instructions on the reverse before completing)

TALPORT NO. z
|___EPA-660/3-75-033

3. RECIPIENT'S ACCESSION NO.

Ja, TITLE AND SUBTITLE

LAKE CLASSIFICATION--A TROPHIC CHARACTERIZATION
OF WISCONSIN LAKES

6, REPORT DATE
6/75 (Preparation Date)

6. PERFORMING ORGANIZATION CODE

7, AUTHOAIS)
Paul D. Uttormark, J. Peter Wall

Js. PERFORMING ORGANIZATION REPORT NO.

9. PERFORMING ORO \NIZATION NAME AND AOORESS
Water Resources Center
University of Wisconsin

Madison, Wisconsin 53706

70. 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

numbers of lakes.

than 1100 large Wisconsin lakes.

for lake renewal. and management programs.
incorporate additional data for special purposes.

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
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.
subjective information, the LCI system was applied to the classification of more
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.
all, discrepancies were due to erroneous input data.
coupled with nutrient-loading considerations and shoreline density-development
factors to demonstrate that lake classification can serve as a workable data base
The LCI system is easily modified to
The system could be used to
classify an estimated 70-80% of the larger lakes in the United States.

Utilizing

Most, but not
The LCI values obtained were

Ika KEY WORDS AND DOCUMENT ANALYSIS

Lb. “DESCRIPTORS

lb, IDENTIFIERS/OPEN ENDED TEAMS |c.

COSATI Ficld/Group

*Lakes, *Trophic level, *Classification,
Water quality, Secchi disks, Dissolved
oxygen, Fishkill, Wisconsin

*Lake Condition Index

7B. DISTRIBUTION STATEMENT

Release unlimited. None

19. SECURITY CLASS (This Report)

21. NO. OF PAGES
165

None

20. SECURITY CLASS (This page)

(22. PRICE

EPA Form 2220-1 (9-73)

fe U.S, GOVERNMENT PRINTING OFFICE: 1975~699-027 JI2_ REGION 10



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


ans

COUNTY #35 ~ LINCOLN

LKNO- LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Alexander 750 36 Yes Yes IMP 4 2 0 2 8
Alice 1491 32 Yes Yes IMP 4 1 (e) 0 5

20 ~=Bridge 444 17 Yes Yes NLLC 3 2 0 2 7
33. Clear 295 45 No Yes NAT 4 2 0 2 8
37 Crystal 104 22 No No NAT 3 0 ) 0 3
38 Deer 137 62 No Yes NLLC 4 0 0 0 4
54 Grandfather 223 32 Yes Yes IMP 4 2 (0) 0 6
55 Grandmother 199 22 Yes Yes IMP 3 2 0 0 5
71 Jersey City 349 17 Yes Yes IMP 3 2 ) 2 7
85 Long 132 64 No Yes NAT 4 0 0 0 4
98 Mohawksin 1898 26 Yes Yes IMP 3 2 0 2 7
102  Muskellunge 159 23 No Yes NAT 3 1 0 0) 4
110 Pesobic 146 11 No Yes NAT 3 2 3 2 10
115 Pine 145 15 No No NAT 1 2 0 2 5
122 Seven Island 136 33 No No NAT 4 1 0 10) 5
128 Somo, Big 345 25 Yes Yes NAT 3 2 0 0 5
130 Spirit River 1239 22 Yes Yes IMP 3 2 0 0 5
142 Tug 154 21 Yes Yes NLLC 3 2 0 0 5
144 Ward 108 13 Yes Yes IMP 3 2 10) 2 7



eet

COUNTY #56 - ST. CROIX

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
3 Bass 301 33 No No NAT 6 0 0 0 6
28 St. Croix 4668 60 Yes Yes NLLC 0 2 0 0 2
30 Little Falls 172 18 Yes Yes IMP 5 2 0 0 7
31 Mallalieu 282 17 Yes Yes IMP 1 2 le) 0 3
34 New Richmond 142 12 Yes Yes IMP 1 0 0 6 7
36 Pine 107 21 5 3 3 2 13*
49 Squaw 129 32 6 3 4 2 15*
62 Spring Valley 126 29 Yes Yes IMP 5 3 0 2 10*
COUNTY #57 - SAUK
LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Delton 254 16 Yes Yes IMP 3 2 0 5 10
Devil's 357 44 Yes No NAT 0 0 0 2 2
19 Redstone 600 40 Yes Yes IMP 6 3 4 9 22
24 White Pond 104 23 Yes Yes IMP 3 3 te) 5 11



Table 10, SUMMARY OF CLASSIFICATION SYSTEM REVIEW
BY WISCONSIN DNR AREA FISH MANAGERS

LCI number LCI number

Area Total LCI number changed by changed by
number lakes unchanged 2 or less 3 or more

1 9 2 6 1

2 42 32 4 6

3 32 20 6 6

4 62 52 7 3

5 84 63 11 10

6 16 11 4 1

7 21 5 10 6

8 23 8 9 6

9 14 9 3 2
Totals 303(100%) 202 (66%) 60 (208%) 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


60T

COUNTY #29 - JUNEAU

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
2 Castle Rock Flowage 16440 36 Yes Yes IMP 6 3 4 2 15
3 Meadow Valley 1190 8 Yes Yes IMP 5 3 3 2 13
4  Petenwell 22218 44 Yes Yes IMP 6 3 4 2 15
5 Potters 299 15 Yes Yes IMP 3 3 3 2 11
6  Rynearson 570 14 Yes Yes IMP 5 3 3 2 13
7 Rynearson 493 10 Yes Yes IMP 5 3 3 2 13
8 Sprague 1930 Yes Yes IMP 5 3 3 2 13

16 8/8 249 No Yes IMP 5 3 3 2 13
23 19/8 180 Yes Yes IMP 5 3 3 2 13
37 19/9 136 Yes Yes IMP 5 3 3 2 13
44 12/15 125 15 Yes Yes IMP 3 3 0 2
48 33/15 1868 35 Yes Yes IMP 4 3 0 2
50 13/13 300 13 Yes Yes IMP 3 3 0 2



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)

2)

3)

4)

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.

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.

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.

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


Table 6.

SUMMARY OF LAKE INVENTORY Con't

® 3 3
avd avd ao
© Ci 0) aH
a Av N Ces)
o a AG

oD Wed a

On ou n no
° ° £0 ao
av Hp 34 WM

Be og BO od
ae ee 0s a7 8
state $8 B58 ds B35
Nebraska* c 1,885 (193)
Nevada a 329 - 52
New Hampshire a 845 - 183
New Jersey a 1,727 - 89
New Mexico a 200 - 32
New York a 4,155 6.4 654
North Carolina* c 928 (48)
North Dakota* d (2,562) (265)
Ohio b 3,017 2 127
Oklahoma a 1,777 10 113
Oregon* b 6,435 1 190
Pennsylvania* a 2,403 - 200
Rhode Island a 175 - 46
South Carolina a 193 50 86
South Dakota a 488 - 231
Tennessee a 552 5 65
Texas* c 152 152
Utah a 256 - 24
Vermont a 535 - 98
Virginia* c 435 (21)
Washington a 7,894 1 300
West Virginia* b (8) (7)
Wisconsin* a 5,553 1,147
Wyoming* b 2,261 - (107)
Totals 94,877 13,599

*
()
Statu

init tau ud

aa ow

25

Additional commentary given in appendix
Estimated values (probably minimum)

No minimum size specified

of lake inventory:

Complete inventory, published

Complete inventory, unpublished

Partial inventory

No inventory


T6

COUNTY #7 - BURNETT Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
69 Elbow 248 8 No No NAT 5 2 3 2 12
75 Fish 356 29 No No NAT 0 1 0 2 3
79 = Gaslyn 164 12 No Yes NAT 5 2 3 2 12
83 Green 279 5 No No NAT 5 2 3 2 12
85 Gull 197 18 Yes Yes NAT 1 2 0 0 3
86 Ham 324 29 No No NAT 1 le) 0 2 3
87 Hanscom 127 7 No No NAT 5 2 3 2 12
94 Johnson 397 23 No No NAT 5 2 3 2 12

102 Lily 176 18 No No NAT 3 4 3 0 10
105 Lipsett 393 24 Yes Yes NAT 3 2 0 2 7
108 Little Bear 128 54 No No NAT 4 1 0 (0) 5
114 Little Long 100 23 No No NAT 1 2 0 0 3
115 Little Wood 207 23 Yes Yes NAT 1 2 0 2 5
116 Little Yellow 285 19 Yes Yes NAT 1 2 0 2 5
118 Long 318 13 Yes Yes NAT 1 2 0 2 5
119 Long 251 4l No No NAT 2 1 i) 0 3
121 Loon 189 24 Yes Yes NLLC 1 2 0 0 3
125 Lost Lakes 248 4 Yes Yes NLLC 5 2 3 0 10
126 Love 253 63 No Yes NLLC 0 1 0 0 1
127. Lower Clam 337 14 Yes Yes NLLC 1 2 0 3 6


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,

pretection 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.


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


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


6)

7)

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).

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.


e
°
fos)

COUNTY #27 - JACKSON

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
14 Potter 348 24 Yes Yes IMP 5 2 0 0) 7
16 Seventeen 178 4 Yes Yes IMP 5 2 3 (0) 10
34 Black River 198 34 Yes Yes IMP 2 2 (0) 2 6
42 13/8 110 4 Yes Yes IMP 3 4 0 3 10
60 31/2 182 5 Yes Yes IMP 3 2 0 0
61 31/5 110 7 Yes Yes IMP 3 2 t¢) 0)

110 16/14 170 7 Yes Yes IMP 2 2 0 6 10

COUNTY #28 - JEFFERSON

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI

Blue Spring 136 12 Yes Yes NLLC 1 2 0 5 8

Goose 144 4 Yes Yes NAT 5 3 3 5 16

9 Hope 142 24 No Yes NAT 5 1 3 5 14

10 Koshkonong 10480 6 Yes Yes NLLC 5 4 3 2 14

12 Spring-Lower 106 14 Yes Yes IMP 3 3 0 5 11

15 Red Cedar 370 6 No NAT 5 3 3 9 20
16 Ripley 433 50 Yes Yes NAT 2 2 0 5

17 Rock 1371 56 Yes Yes NLLC 2 2 iv) 5 9

18 Rome 477 12 Yes Yes IMP 5 3 3 9 20



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
l'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 53:325-338.

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


vOT

COUNTY #22 - GRANT

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Bertom Lake 126 8 Yes Yes 5 3 3 2 13
Cassville Slough 674 30 Yes Yes 2 3 0 2 7

16 Jack Oak Slough 145 16 Yes Yes 1 3 0 2
20 McCartney Lake 924 10 Yes Yes 5 3 3 2 13
29 State Line Slough 310 30 Yes Yes 2 3 0 2 7
COUNTY #23 - GREEN
LKNO LAKE NAME AREA MXD INLET QUTLT TYPE D.O. TRNS FSKL IMPR LCI
1 Albany 102 8 Yes Yes IMP 0 5 9
2 Decatur 151 10 Yes Yes IMP 3 11



68

COUNTY #4 - BAYFIELD Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
141 Iron 248 13 Yes Yes NAT 3 2 3 0) 8
146 Jackson 142 13 Yes Yes NAT 1 2 0 2 5
169 Long 263 23 No No NAT 1 2 0 2 5
179 Middle Eau Claire 902 66 Yes Yes NLLC 2 10) 0 2 4
180 Millicent 184 56 Yes Yes NAT 2 0 0 0 2
188 Mud 178 10 No No 3 2 3 2 10
196 Namekagon 3208 46 Yes Yes NLLC 2 2 i) 2 6
205 Oriental 144 32 Yes Yes IMP 2 3 10) 0 5
208 Owen 1323 95 No Yes NLLC 2 (0) 0 ie) 2
219 Pigeon 213 21 No No NAT 1 1 0 0 2
250 Sand Bar 114 51 No No NAT 0 1 0 0 1
259 Siskiwit 330 13 Yes Yes NAT 1 2 0 0 3
266 Spider 124 20 No Yes NAT 1 3 0 0 4
272 = Star 201 52 No Yes NAT 0 1 0 0 1
278  Tahkodah 152 18 No No NAT 1 2 0 0) 3
284 Tomahawk 134 42 No No NAT 0 2 0 0 2
287 + =Totagatic 537 8 Yes Yes NAT 3 2 0 0 5
302 Twin Bear 160 59 Yes Yes NAT 0 1 0 0 1
304 Upper Eau Claire 1030 84 Yes Yes NLLC 2 0 0 0 2
312 White Bass 116 30 No Yes NAT 1 1 0 10) 2



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


eS

_

120-4
@——_®——® Warm water lakes
Hy O-----0----O Warm/cold water lakes
3 Dee O.------0 Reservoirs
a
8 807
My
2
Ean |
40
| ’
aa
fo}
CT T
0 2 4 6 8 10 12 14 16 18 20 22 24

Lake Condition Index

Figure 7. Calculated Condition Indexes for three types of Wisconsin lakes.


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 ai (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


15 Southern Highest population,
(40 lakes) intensive agriculture,
Lo moderate industry
54
o 0 oO om l
o
1574 West Central Moderate population,
M (37 lakes) some agriculture
and industry
*® 104
ad
55
“od Ol mm
°
1579 Northwest Low population,
(40 lakes) little industry
4 or agriculture
10-4
(0)
a S54
&
o4
3
z 154 North Central Low population,
(46 lakes) little industry
or agriculture
104
5-4
o- m i )

t T T T T T T T T 7
0 2 4 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


80

Average lake size in acres
2000

1000

4000
1

3000
1

Figure 4.

Data for Lake Winnebago
(LCI = 13; area = 137,708 acres)
not included.

T
1200 1600

T
800

Average lake size in hectares

Statewide
average

T
400

f T T T T T T T
0 2 4 6 8 10 12 14
Lake Condition Index

Average surface area of Wisconsin lakes as a function of Condition Index.


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


6eT

COUNTY #64 - VILAS

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Alder 274 33 Yes Yes NLLC 4 2 0 2 8
Allequash 405 33 Yes Yes NAT 4 1 i) 2 7

11 Amik 187 10 Yes Yes NAT 3 2 0 0 5
14 Annabelle 186 30 Yes Yes NAT 3 2 0 0 5
17s Anvil 380 40 No No NAT 4 0 0 0 4
18 Apeekwa 188 10 Yes Yes NAT 3 2 0 2 7
19 Arbor Vitae, Big 1065 36 Yes Yes NAT 4 2 0 2 8
20 Arbor Vitae, Little 533 32 Yes Yes NLLC 4 2 0 2 8
21 Armour 319 53 Yes Yes NAT 4 0 ie) 0 4
28 Ballard 505 25 Yes Yes NAT 3 2 0) 0 5
32 Bass 266 15 Yes No NAT 3 2 3 2 10
36 ©Bateau, Big 230 = >30 No No NAT 4 1 0 0 5*
39 Bay, West 368 31 Yes Yes NLLC 4 2 0 2 8
52 Big 850 65 Yes Yes NAT 4 2 0 0 6
53 Big 723 30. Yes Yes NLLC 3 2 0 2 7
55 Birch 528 35 Yes Yes NAT 4 2 0 2 8
56 White Birch 117 27 Yes Yes NAT 3 2 0 0 5
56 Birch, Yellow 185 14 Yes Yes NLLC 3 2 0 0 5
58 Bittersweet 104 31 No No NAT 4 1 0 0 5
65 Bolton 138 36 Yes No NAT 4 2 0 0 6
66 Boot 284 14 Yes Yes NAT 3 2 te) 2 7


cet

COUNTY #55 - RUSK

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Amacoy 278 20 Yes Yes NAT 1 2 i) 2 5
Audie 128 32 No No NAT 2 2 0 0 4

9 Big Falls 369 45 Yes Yes IMP 2 2 0 0 4
23 Dairyland Reservoir 1745 70 Yes Yes IMP 2 2 0 0 4
25 Fireside 302 30 Yes Yes NAT 1 2 0 2 5
26 Fish 115 40 No No NAT 4 1 0 0 5
34 Island 526 54 Yes Yes NAT 0 1 10) 2 3
36 =©Ladysmith 288 19 Yes Yes IMP 0 2 0 0 2
37 Lea 232 9 Yes Yes IMP 5 2 0 2 9
43 McCann 133 38 Yes Yes NAT 2 2 0 2 6
55 Potato Creek 174 5 Yes Yes IMP 1 2 0) 4 7
56 Potato 534 40 Yes Yes NAT 4 2 0 2 8
57. Pulaski 126 40 No No NAT 2 2 0 0 4
58 Round 105 5 No No NAT 5 2 0 2 9
51 Sand 262 100 No No NAT 0 1 0 0 1
70 Thornapple 268 19 Yes Yes IMP 0 2 0 2 4
75 Washington Creek 165 5 Yes Yes IMP 3 2 0 2 7



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, (Nat+K)/(Ca+tMg), 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.

10


vet

COUNTY #58 - SAWYER

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Barber 238 21 Yes Yes NAT 1 2 0 2 5

4 Barker 238 12 Yes Yes NAT (0) 2 0 0 2
15 Black 129 15 Yes Yes NLLC 1 2 0 2 5
16 Black Dan 128 37 Yes NAT 2 1 0 2 5
17_——«~Blaisdell 370 15 Yes Yes NAT 0 2 0) 2 4
19 Blueberry 280 29 No No NAT 1 1 0 0 2
23 Brunet 126 12 Yes Yes IMP 1 2 0 2 5
33 Callahan 106 18 Yes Yes NLLC 1 2 0 0 3
41 Lake Chetac 2149 30 Yes Yes NAT 1 2 0 2 5
43 Chippewa 15300 82 Yes Yes IMP 2 2 4 0 8
48 Connor 408 87 Yes Yes NLLC 2 1 0 0 3
55 Deer 423 18 Yes Yes NLLC 5 2 3 (0) 10
57. Devils 188 6 Yes NAT 5 2 3 3 13
58 Durphee 193 16 No No NLLC 1 2 3 7 13
64 Evergreen 200 25 Yes Yes NAT 1 2 0 0 3
68 Fishtrap 197 8 Yes Yes NLLC 1 2 i) i) 3
73° Ghost 372 12 Yes Yes IMP 1 2 0 2 5
83 Grindstone 3111 59 Yes Yes NAT 0 1 0 10) 1
87 Ham 100 28 No No NAT 1 2 0 0 3
91 Hayward 247 17 Yes Yes TMP 0 1 0 2 3
101 Hunter 126 11 Yes Yes NAT 0 2 0 0 2


es

Number of lakes

150 4 I DO in hypolimnion >5 ppm

II DO in hypolimnion <5 ppm but >0
III DO in parts of hypolimnion = 0
IV DO in entire hypolimnion = 0
~s
100 4 / CII
50 4 vhs,
/ NII
/ \ --
\ 7 Niv
\ / N
ste, Ny -
«1 xX \,
| : * MIT N
. The, ee Tt ~—
0 7 v T = T —> : —

T
0 2 4 6 8 10 12 14 16 18 20

Lake Condition Index

Figure 6. Dissolved oxygen conditions in Wisconsin lakes.


8.
9.

10.

ll.

12.
13.

LIST OF TABLES

Water Quality Scores for 10 Michigan Lakes.

Fifty-Five Florida Lakes Ranked According
to Trophic State Index (TSI). ......-.

Composite Rating of 12 Wisconsin Lakes. . .
Classification of 7 New Zealand Lakes .. .

Summary of Systems to Classify Lakes
According to Trophic Status ........

Summary of Lake Inventory Data. ......

Availability of Water Quality Data
for Lakes . 1. 6 6 1 6 ee ee ee ee ee

Point System for Lake Condition Index .. .
Comparative Rank of 12 Wisconsin Lakes. . .

Summary of Classification System Review
by Wisconsin DNR Area Fish Managers ....

Specific Loading Levels for Lakes
Expressed as Total Nitrogen and

Total Phosphorus in g/m’/yr .......-
Development Density of Wisconsin Lakes. . .

Applicability of Lake Classification System

14
16
19

21

24-25

26-27
37
42

55

60

69
75


LAKE CLASS:

Check one of the following classes if it applies to
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.. ccc ccccec cece ccac cen ccecscncccseeesccees

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. ......ceccccec cence ccceeccee

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... .. cece eee cece eee e eee nee

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

the


69

Table 12,

DEVELOPMENT DENSITY OF WISCONSIN LAKES

Development density in dwellings/mile of shoreline
0-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 Ov.

er 50 Total*

Number of lakes 1926
Percent of total 69.0
Miles of shoreline 5411
Percent of total 61.5

348 =183 112 38 32 25 24 14
12.5 6.3 4.0 2.1 1.1

°
0.9 0.9 0.5

1368 906 369 232 139 96 61 46
15.5

10.3 4.2 2.6 1.6 1.1 0.7 0.5

16

0.5

50

0.6

52 2790
1.9 100
119 8797
1.4 100

0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 Ov

Development density in surface acres/dwelling

er 100 Total*

Number of lakes
Percent of total

1881
67.3

Area (thousands of acres) 409

Percent of total 53.8

343 203 122 72 47 31 16 16
12.3. 7.3 4.4 2.6 1.7 1.1 0.6 0.6

131 38 «55 16 7 6 16 22
17.20 5.0 7.2 2.1 0.9 0.8 2.1 2.9

14
0.5

45 2790
1.6 100
56 761
7.4 100

*Wisconsin lakes 20 acres or larger


Table 6. SUMMARY OF LAKE INVENTORY DATA

4 & ao
o a4
d AD N Cas)
Woy aed ‘a a8
O° 8 O° 8 9 nO
av Hp En Hao
Be og £0 o's
en Lee Bg
State an Bed a5 BOs
Alabama* c 35 500 35
Arizona a 451 - 90
Arkansas a 2,334 5 361
California* c 1,050 311
Colorado* d (763) (163)
Connecticut a 493 5 121
Delaware* b 55 - (20)
Florida a 7,712 10 916
Georgia* da (45) (15)
Idaho b 2,178 - 83
Illinois* a 2,167 6 192
Indiana a 547 - 131
Iowa a 311 - 85
Kansas* c 141 (47)
Kentucky* a 285 10 (21)
Louisiana* d (713) (262)
Maine* b 3,094 - (454)
Maryland* d (47) (6)
Massachusetts a 1,557 10 294
Michigan* b 7,661 5 1,070
Minnesota a 15,291 10 4,012
Mississippi* d (216) (74
Missouri* b (1,254) (98)
Montana* d (1,712) (308)
* = Additional commentary given in appendix
( ) = Estimated values (probably minimum)
- = No minimum size specified
Status of lake inventory:
a = Complete inventory, published
b = Complete inventory, unpublished
c = Partial inventory
d = No inventory

24


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


€vT

COUNTY #64 - VILAS Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
304 Mann 250 22 No Yes NLLC 5 2 3 2 12
307 Maple, Sugar 129 27 No No NAT 3 0 0 iv) 3
315 McCullough 216 27 Yes Yes NAT 3 2 0 2
321 Meta 175 25 No No NAT 3 1 0 0 4
324 Mill 131 9 Yes Yes NAT 5 2 3 2 12
329° Mitien 140 23 No No NAT 3 2 0 0
332 Moon 124 40 No No NAT 4 0 0 (0)

334 Morton 163 29 Yes Yes NAT 3 2 0 2 7
335 Moss 196 29 Yes NAT 3 2 0 2 7
341 + Muskellunge 266 18 Yes Yes NAT 5 2 3 2 12
342 Muskellunge, Big 923 65 Yes NAT 4 0 0) 0 4
343 Muskesin 115 22 No No NAT 3 2 0) 0 5
350 Nelson 104 50 No No NAT 4 0 0 ie) 4
353 Ninemile, Lower 646 5 Yes Yes IMP 3 2 0 2 7
356 Nixon 110 5 Yes Yes NAT 3 2 0 2 7
357 No Mans 225 31 Yes Yes NAT 4 2 0 2 8
358 Norwood 125 89 No No NAT 4 0 0 e) 4
362 Oak, Black 549 63 Yes Yes NAT 2 (0) 0 0 2
366 ©Otter 199 20 Yes Yes NLLC 3 2 0 2 7
367 Oxbow 515 42 Yes Yes NAT 4 1 0 0) 5
368 Palletie 173 65 No No NAT 2 0 0 0 2


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/m*/yr@

Permissible Dangerous loading,
loading, up to: in excess of:
Mean depth
up to: N P N P
5 m 1.0 0.07 2.0 0.13
10 m 1.5 0.10 3.0 0.20
50 m 4.0 0.25 8.0 0.50
100 m . 0.40 12.0 0.80
150 m 7.5 0.50 15.0 1.00
200 m 9.0 0.60 18.0 1.20

8from Vollenweider (1968)

60


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 aZ 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


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


PST

COUNTY #70 - WAUSHARA

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
1 = Auroraville Millpond 209 6 Yes Yes IMP 5 2 3 3 13
22 Fish (Wautoma) 289 5 No Yes NLLC 5 2 3 3 13
23 Fish (Hancock) 177 42 No No NAT 2 1 0 0 3
26 Gilbert 141 65 No No NAT 2 1 0 0 3
27 +Hills, Big 135 20 No No NAT 3 1 1) 2 6
39 Long (Saxeville) 272 71 No No NAT 0 1 0 (0) 1
47 Mount Morris 163 40 Yes Yes IMP 2 1 0 6 9
56 Pine (Hancock) Pike 163 15 No No NAT 5 1 3 9 18
57. Pine (Springwater) 143 48 No No NAT 2 1 0 0 3
60 Pleasant 127 30 No No NAT 1 1 0 0 2
70 «Silver 328 46 No No NAT 2 1 0 0 3
86 White River (Lower) 110 22 Yes Yes IMP 1 1 0 2 4



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


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


Tet

COUNTY #64 - VILAS Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
151 Dollar 105 15 No No NAT 3 2 0 0 5
160 Duck 109 18 Yes Yes NLLC 3 2 0 2 7
162 Eagle 591 27 Yes Yes NLLC 3 2 0 2 7
169 Ellerson, East 136 26 No No NAT 3 1 0 0 4
177.‘ Erickson 106 18 Yes No NAT 3 2 0 0) 5
179 ~~ Escanaba 288 25 NAT 3 2 0 0 5
187 + =Fence 3340 86 Yes Yes NAT 4 0) 0 0 4
190 Finley 107 26 No No NAT 3 0 0) 0 3
192 Fishtrap 329 41 Yes Yes NLLC 4 2 0 0 6
193 Flambeau 1145 49 Yes Yes NLLC 4 0 0 0 4
194 Flora 100 29 Yes Yes NAT 3 2 0 0 5
195 Forest 466 60 No No NAT 4 0 0 0 4
196 Found 326 20 Yes Yes NAT 3 2 0 2 7
199 Frank 141 24 No No NAT 3 0 (0) 0 3
208 Gibson, Big 116 15 No No NAT 3 1 0 0 4
213 Grassy 220 4 Yes Yes NAT 5 2 3 2 12
214 Gresham, Lower 149 12 Yes Yes NAT 3 2 0 2 7
216 Gresham, Upper 375 20 Yes NAT 3 2 0 2 7
219 Gunlock 267 25 Yes No NAT 3 1 0 2 6
224 Harris 523 48 Yes Yes NAT 4 0 (0) i) 4
231 Helen 111 19 Yes Yes NAT 3 2 0 2 7


L8

COUNTY #3 - BARRON

Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
88 Red Cedar 1841 53 Yes Yes NLLC ie) 2 0 0 2
89 Rice 939 19 Yes Yes IMP 3 1 0 2 6
93 Sand 322 57 Yes Yes NLLC 2 1 0 2 5
95 Silver 337 91 No No NAT 0 2 0 0 2
99 Staples 305 17 Yes Yes NAT 0 2 3 9 14

100 Stump 129 7 Yes Yes TMP 5 2 3 6 16
103 Tenmile 376 12 Yes Yes IMP 0 2 0 9 11
104 Tuscobta 157 27 Yes Yes NLLC 3 2 0 6 11
107. Upper Turtle 440 25 Yes Yes NAT 3 2 0 2
109 = =Vermillion 208 55 Yes Yes NAT 2 2 0 0



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 aZ 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

Al


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
onrte _ 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
TRNS = 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., TRNS,
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


Specific General water quality

Set loading LCL 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;

e high low degradation may be imminent

Problem lakes; renovation desirable
a 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


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.

of project lakes in each, are listed below.

The counties, with the number

County Number County Number
number County name of _lakes number County name __of_lakes
1 Adams 6 37 Marathon 8
2 Ashland 17 38 Marinette 20
3 Barron 31 39 Marquette 7
4 Bayfield 41 40 Menominee 6
5 Brown 0 41 Milwaukee 0
6 Buffalo 0 42 Monroe 9
7 Burnett 71 43 Oconto 30
8 Calumet o* 44 Oneida 136
9 Chippewa 13 45 Outagamie i)
10 Clark 4 46 Ozaukee 1
11 Columbia 5 47 Pepin 0
12 Crawford 0 48 Pierce 0)
13 Dane 12 49 Polk 42
14 Dodge 7 50 Portage 5
15 Door 5 51 Price 33
16 Douglas 21 52 Racine 9
7 Dunn 3 53 Richland 2

18 Eau Claire 4 54 Rock Ox*
19 Florence 16 55 Rusk 17
20 Fond du Lac 4 56 St. Croix 8
21 Forest 35 57 Sauk 4
22 Grant 5 58 Sawyer 58
23 Green 2 59 Shawano 10
24 Green Lake 5 60 Sheboygan 5
25 Iowa 2 61 Taylor 4
26 Iron 32 62 Trempealeau 1
27 Jackson 7 63 Vernon 0
28 Jefferson 9 64 Vilas 173
29 Juneau 13 65 Walworth 19
30 Kenosha 8 66 Washburn 55
31 Kewaunee 0 67 Washington 7
32 La Crosse 1 68 Waukesha 22
33 Lafayette 1 69 Waupaca 13
34 Langlade 15 70 Waushara 12
35 Lincoln 19 71 Winnebago 6
36 Manitowoc 4 72 Wood 9

*Lake Winnebago is listed under Winnebago County.
*kLake Koshkonong is listed under Jefferson County.

83


8TT

COUNTY #43 - OCONTO Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
142 Rost 86 24 No No NAT 3 (0) 0 0 3
149 Shay 50 36 No No NAT 4 0 0 0 4
160 Surprise 70 30 No No NAT 3 0 0 0 3

Townsend Flowage 303 27 Yes Yes IMP 3 2 0 2 7
175 Waubee 137 20 No No NAT 3 0 (0) 0 3
178 Wescott 38 29 No Yes NAT 3 0 0 2 5
181 Wheeler 305 40 No No NAT 4 0 0 0 4
171 Upper Wheeler Pond 140 10 Yes Yes IMP 3 2 0 0 5
183 White Potato 1011 iz No No NAT 5 1 3 2 1



Tet

COUNTY #44 - ONEIDA Con't

LKNO =LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
128 Ginty 131 12 Yes Yes NAT 3 2 0 2 7
132 Great Bass 118 15 No No NAT 3 2 0 2 7
136 Hancock 259 22 Yes Yes NLLC 3 2 0 5 10
138 Hasbrook 302 50 No Yes NAT 4 2 0 0
148 Hodstradt 126 37 No No NAT 4 0 0 0 4
151  Horsehead 356 12 No Yes NLLC 5 2 3 9 19
155 Indian 397 26 No Yes NAT 3 2 0 2 7
158 Island 297 22 Yes Yes NLLC 3 2 te) 2 7
163 Jennie Weber 237 9 Yes Yes NAT 3 2 0 2 7
166 = Julia 238 19 No No NAT 3 2 9 2 7
169 Kathan 189 15 Yes Yes NAT 3 2 0 2 7
170 Katherine 555 32 No Yes NLLC 4 2 0) 0 6
171 Kawaguesaga 801 41 Yes Yes NLLC 4 2 0 2 8
172 Killarney 421 8 Yes Yes IMP 5 2 3 le) 10
173. Lake Creek 172 12 Yes Yes IMP 3 2 0 0 5
178 Laurel 149 11 Yes Yes NLLC 3 2 0 0 5
187 Little Bearskin 174 23 Yes Yes NAT 3 2 10) 2 7
190 Little Fork 336 30 Yes Yes NLLC 3 2 0 2 7
198 Little Tomahawk 163 50 No Yes NLLC 4 1 0 0 5
199 Lone Stone 172 29 No Yes NAT 3 2 0 2 7
202 = Long 113 58 No No NAT 4 0 0 0 4


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


€St

COUNTY #68 - WAUKESHA Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
70 Upper Nashotah 133 53 No Yes NLLC 0 0
71 Upper Nemahbin 283 61 Yes Yes NAT
73 Upper Phantom 111 29 Yes Yes NLLC 6
COUNTY #69 - WAUPACA
LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
4 Bear 194 62 Yes Yes NAT 2 1 0 2 5
22 Long (Chain) 104 75 Yes Yes NAT 4 1 0 0
32 Rainbow (Chain) 116 95 Yes Yes NAT 4 1 0 0
38 Cinco Bayou 169 4 Yes Yes NAT 5 3 3 3 14
55 Hatch 112 13 Yes Yes NAT 5 2 3 0 10
60 Iola Millpond 206 11 Yes Yes IMP 3 3 0 3
79 Manawa 195 12 Yes Yes IMP 3 3 0 3
80 Marion Millpond 109 il Yes Yes IMP 5 4 3 5 17
104 Partridge 1124 Yes Yes NAT 3 3 0 2 8
105 Partridge Crop Bayou 238 Yes Yes NAT 3 3 0 2 8x
109 Pigeon-Clintonville 218 12 Yes Yes IMP 5 3 3 5 16*
142 Weyauwega 274 11 Yes Yes IMP 3 3 0 4 10*
143 White 1026 11 No No NLLC 5 3 3 3 14



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


9ET

COUNTY #58 — SAWYER Con't

LKNO = =LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
198 Smith 323 29 Yes Yes NAT 1 2 0 0
203 Spider 1454 64 Yes Yes NLLC 2 1 0 0
206 Spring 220 18 Yes Yes NLLC 5 2 3 0 10
208 Squaw 208 32 Yes Yes NAT 2 1 0 2
209 = Star 104 4 Yes Yes NAT 3 2 3 0 8
213 Swamp 258 9 No Yes NAT 5 2 3 0 10
215 Teal 1049 31 Yes Yes NAT 2 2 0 0 4
219 Tiger Cat 224 11 Yes Yes IMP 1 2 0 6 9
220 Totogatic 243 17 Yes Yes IMP 1 2 0 2 5
227. Two Boys 117 37 Yes NAT 4 1 0 0 5
231 Upper Twin 299 27 Yes Yes NAT 1 2 0 2 5
239 Whitefish 785 105 Yes Yes NAT 0 0 0 0 0
241 Wilson 103 14 Yes Yes NAT 3 2 0 3 8
242 Windfall 102 16 No No NAT 1 2 0 0 3
243 Windigo 522 51 No No NAT 2 2 0 2 6
434 10/9 248 7 No Yes IMP 5 2 3 2 12



Laas

COUNTY #64 - VILAS Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
369 Palmer 640 13 Yes Yes NAT 3 2 0 2 7
370 Papoose 428 65 No Yes NAT 4 0 9 0 4
372 Partridge 228 14 No Yes NAT 3 2 0 2 7
379 = Pickerel 273 24 Yes Yes NAT 3 2 0 2 7
381 Pike, Dead 317 59 Yes Yes NAT 2 2 0 0) 4
386 Pine, Lone 142 41 Yes Yes NAT 4 2 0 0 6
388 Pioneer 415 27 Yes Yes NLLC 3 2 0 2 7
390 = Plum 938 48 Yes Yes NAT 4 0 0 0 4
391 Plummer 211 55 No No NAT 4 0 0 (0) 4
393 Pokegama 1052 65 Yes Yes NAT 4 0 0 2 6
395 Portage, Big 601 38 No No NAT 4 0 0 (0) 4
396 Portage, Little 170 11 Yes Yes NAT 3 2 0 2 7
399 Presque Isle 1280 80 Yes Yes NAT 4 0 0 0 4
404 Rainbow 146 36 Yes Yes NAT 4 1 0 2 7
408 Razorback 372 28 No No NAT 3 0 0 0 3
412 Rest 640 49 Yes Yes NLLC 4 2 te) 2 8
416 Rice, Scattering 280 17 Yes Yes NLLC 3 2 0 2 7
417 Rice, Wild 365 26 Yes Yes NAT 3 2 0 2 7
418 Roach 125 37 No No NAT 4 0 0 0 4
421 Rock 122 18 Yes Yes NAT 3 2 0 0 5
425 Ross 146 14 Yes Yes NAT 5 2 3 2 12


OST

COUNTY #66 - WASHBURN Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
86 Island 276 44 No No NAT 2 1 0 2 5
89 Kekegama 109 24 Yes Yes NAT 3 2 0 0 5
97  Leesome 146 53 No No NAT 2 1 0 0) 3

100 = Lincoln 101 27 Yes Yes NAT 1 2 0 2 5
116 = Long 3290 74 Yes Yes NLLC 0 2 0 0 2
124 Lower Kimball 129 6 Yes Yes NLLC 1 2 0 2 5
125 Lower McKenzie 185 17 Yes Yes NLLC 1 2 0 2 5
130 Macrae 124 45 No No NAT 2 1 te) 0 3
133. Matthews 263 26 No No NAT 1 1 0 it) 2
136 = McKinley 105 23 Yes Yes NAT 1 2 0) 2 5
137. McLain 150 30 No No NAT 1 1 0) 0 2
143 Minong Flowage 1564 21 Yes Yes IMP 1 2 i} 0 3
147) Mud 103 13 Yes Yes NAT 1 2 0) 2 5
151 Nancy 772 39 Yes Yes NAT 2 2 0 2 6
152 Nice 138 11 No No NAT 1 1 (0) 2 4
155 North Twin 113 20 No No NAT 1 2 0 0 3
171 ~=Pokegama 453 23 Yes Yes NAT 3 2 (0) 5 10
173 Potato 222 20 No Yes NAT 5 2 3 2 12
177 Rice 132 11 Yes Yes NAT 3 2 0 4 9
187 Sand 198 9 No No NAT 1 2 0 10) 3
195 Shallow 137 10 No No NAT 5 2 3 2 12


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 lo
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


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 Insecta 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


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


LTt

COUNTY #43 - OCONTO

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Anderson 171 40 Yes Yes NLLC 4 2 te) 0 6
Archiband 430 58 Yes No NAT 4 0 10) 0 4
Bass 149 50 Yes No 4 0 0 0 4

13. Berry 143 20 No No NAT 3 2 0 0 5
17 Boot 263 40 No No NAT 4 id) 0 0 4

18 Boulder 362 11 No Yes NAT 3 0 0 0 3

28 Chain 31 50 No Yes NAT 4 0 le) 0) 4

31 Christy 387 10 No Yes NAT 5 2 3 2 12

32 Chute Pond 417 18 Yes Yes IMP 3 2 0) 2 7

36 Crooked 143 43 Yes Yes NLLC 4 1 0 0

69 Horn 132 13 No Yes NAT 3 2 0 0

71 Impassable 84 5 Yes Yes NLLC 5 2 3 2 12

81 John 103 26 Yes Yes NAT 3 1 0 2

75 Kelly 361 4l Yes Yes NAT 2 0 0 0

84 Lee 91 40 Yes NLLC 4 0 0 0 4
106 Machickanee 435 23 Yes Yes IMP 5 2 3 5 15
108 Maiden 290 60 Yes Yes NAT 4 0 i) 0 4
121 Oconto Falls Pond 123 29 Yes Yes IMP 3 2 0 0 5
124 Paya 121 40 No No NAT 4 0 0) 0 4
131 Pickerel 127 15 Yes Yes NLLC 3 2 0 0 5
140 Reservoir Pond 409 13 Yes Yes IMP 3 2 0 2 7


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

Composite
Rank Lake name County score

a 1 Crystal Vilas 8
8 2 Big Green Green Lake 17
3 3 Geneva Walworth 17
eS 4 Trout Vilas 19
23 + 5 Round Waupaca 31
6 Pine Waukesha 33

7 Middle Walworth 33

8 Oconomowoc Waukesha 34

oy 9 Mendota Dane 45
q 10 Pewaukee Waukesha 49
» 5 il Delavan Walworth 52
g 3 12 Winnebago Winnebago 52

from Lueschow et al (1970)

16


Basically Eutrophic

a-eutrophic +N, + P, + humus
8-eutrophic +N, - P, + humus
y-eutrophic +N, + P, - humus
6-eutrophic +N, - P, - humus
v-eutrophic - N, + P, + humus
A-eutrophic - N, - P, + humus
p-eutrophic - N, + P, - humus
t-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.

1l


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


6eT

COUNTY #51 - PRICE

LKNO =LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
15 Big Dardis 144 23 Yes Yes NAT 0 2 i) 0
16 Blockhouse 242 12 Yes Yes NAT 0 2 0 2 4
19 Butternut 1006 32 Yes Yes NLLC 2 2 0 7 11
25 Cochram 111 16 No Yes NAT 1 2 0 (0) 3
29 Cranberry 512 18 Yes Yes NLLC 5 2 3 2 12
31 Crowley Flowage 422 23 Yes Yes IMP 1 2 0 2 5
36 ©Deer 145 18 No Yes NAT 10) 2 (0) 0 2
40 Duroy 338 16 Yes Yes NLLC 1 2 0 2 5
52 Hay 101 6 Yes Yes NAT 1 4 0 0 5
54 Hultman 181 14 Yes Yes NAT 1 2 0 2 5
62 Lac Sault Dore 561 21 Yes Yes IMP 3 2 (0) 0 5
66 Le Tourneau 124 16 Yes Yes NAT 1 2 3 0 6
70 ~=Long 418 54 Yes Yes NLLC 2 2 0 0 4
71 Long 240 49 Yes Yes NAT 4 1 0 0 5
90 Musser 563 15 Yes Yes IMP 1 2 0 0) 3
96 North Spirit 213 22 Yes Yes NAT 1 2 0 1) 3
108 Pike 848 18 Yes Yes NAT 1 2 0 0 3
109 Pixley Flowage 193 23 Yes Yes IMP 3 2 0 3 8
117.‘ Riley 182 8 No Yes NAT 5 3 3 0 11
119 Round 718 24 Yes Yes NLLC 1 2 0 0 3
122 Sailor 165 6 Yes Yes NAT 5 2 3 2 12


LOT

COUNTY #26 - IRON Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
154 Pike 194 80 Yes Yes NAT 4 2 0 0 6
155 Pine 312 34 Yes Yes NAT 4 2 0 0 6
160 Randall 115 10 Yes Yes NAT 3 2 0 0 5
163 Rice 125 20 Yes Yes NAT 3 2 0 2 7
168 Sand 101 35 Yes No NAT 4 0 0 0 4
170 Sandy Beach 112 7 Yes No NAT 3 2 0 2 7
184 Spider 361 4l Yes Yes NAT 4 2 0 0 6
197 Trude 754 48 Yes No NAT 4 2 0 2 8
199 Upper Springstead 126 23 Yes Yes NAT 3 2 0 0 5
203 Virgin 119 45 Yes Yes NAT 4 2 0 0 6
211 Wilson 155 21 Yes No NAT 3 2 0 0 5



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


TZ

Table 5. SUMMARY OF SYSTEMS TO CLASSIFY LAKES ACCORDING TO TROPHIC STATUS

g
o o qd
» 3 2 8 2 8
a # # @ eg dg
Lakes 8 ¢ 2 a 3 a ‘a a
Investigators classified oO a a 4 A a < a Parameters used
DO, tot-P, sol-P, inorg-N,
EPA 209 x x x x Secchi, chlor-a
Temp, DO, cond, NH3-N, NO3-N,
Feuillade 2-France x x x x NO2-N, Ca, Mg, Cl, SO,, Si,
sol-P, K, Na, hardness, alk
DO, plankton wt, Secchi,
bueschow 12-Wisconsin x x x x org-N, inorg-N (avg monthly
values)
DO, Secchi, alk, chlor-a,
McColl 7-N. Zealand x x x x pigment, tot~P, sol-P, NO3-N,
NH3-N
Newton and 10-Mti chi, x x x X  Sol-P, COD, org-N, NHs-N
Fetterolf entgan oO > » org-N, 3
Secchi, cond, org-N, tot-P,
Shannon and _ prim. prod., chlor-a, Ca, Mg,
Brezonik 55-Florida x x x x Na, K (avg of 3-6 seasonal
values)
Sheldon 121-N.America  X x x Xx PH, trans, alk, temp, depth,
Sheldon 15-Sweden x x x Xx PH, trans, color, Kind, demand,
cond
Zafar - x x x x N, P, Ca, K, Na, Mg, humus



Ort

COUNTY #64 - VILAS Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
69 Boulder 525 23 Yes Yes NAT 3 2 0 0 5
72 ~+Brandy 110 44 Yes Yes NAT 4 2 0 2 8
74 Broken Bow 134 23 No No NAT 3 2 0 0 5
76 Buckatabon, Lower 352 16 Yes Yes NLLC 3 2 te) 2 7
77 ~Buckatabon, Upper 477 30 = =Yes Yes NLLC 3 2 0 2 7
88 Canoe, (Lost) 249 41 Yes Yes NAT 4 1 0 0 5
90 Carlin 153 36 No No NAT 4 2 (0) 0 6
91 Carpenter 333 16 No No NAT 3 2 0 0 5
93 Catfish 991 26 Yes Yes NLLC 3 2 0 2 7
102 Circle Lily 223 35 Yes Yes NAT 4 2 0 0 6
104 Clear 518 39 Yes No NAT 4 1 0) 0 5
108 Cochran 126 12 Yes No NAT 5 2 0 2 9
111 Content 244 14 NLLC 5 2 3 2 12
116 Crab 920 56 Yes Yes NAT 4 0 0 0) 4
120 Cranberry 940 11 Yes Yes NLLC 3 2 0 2 7
123. Crooked, Big 383 50 Yes Yes NAT 2 (0) 0 i) 2
124 Crooked, Big 682 34 Yes Yes NAT 4 0 0 i) 4
125 Crooked, Little 153 20 Yes Yes NAT 3 2 0 2 7
133 ~Day 117 48 No No NAT 4 0 0 0 4
143. Deerskin 282 24 Yes No NLLC 3 2 0 2 7
148 Diamond 122 40 No No NAT 2 0 0 0 2


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 =
information handled--the rules,
criteria, equations,
etc., by which
input is "sorted"

Grouping or
ranking
of lakes

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.


BET

COUNTY #60 - SHEBOYGAN

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
8 Crystal 114 61 Yes Yes NAT 4 1 0 5 10
10 Big Elkhart 300 «113 Yes Yes NAT 4 1 i) 5 10
11 Ellen 110 48 Yes Yes NAT 4 1 0 4 9
34 Random 209 19 Yes Yes NAT 5 1 3 2 11
38 Sheboygan Marsh 674 4 Yes Yes NAT 5 3 3 2 13

COUNTY #61 - TAYLOR

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
10 Chequamegon Waters 2730 22 Yes Yes IMP 1 2 3 0 6
48 Mondeaux 416 10 Yes Yes IMP 3 2 (0) 2
69 Rib 320 Yes Yes NLLC 3 3 3 5 14
81 Steve Creek 140 Yes Yes IMP 5 2 3 0 10

COUNTY #62 - TREMPEALEAU

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
9 Marinuka 107 9 Yes Yes IMP 1 3 0 9 13



Table 7. AVAILABILITY OF WATER QUALITY DATA FOR LAKES t
a ad

s 8 » 5 uw

% dv cs)

° a 8 & 3 ‘q

‘a oa 85 a0

is) 4 Do

al Hp ae Om

uv on on ad

al me as ow
State a £8 28 ad
Alabama 28 3 2
Alaska 12 7 2 4
Arizona 1l 5 - 3
Arkansas 63 4 1 3
California 75 18 4 7
Colorado 64 23 1 11
Connecticut 26 6 1 2
Delaware - - - -
Florida 208 163 ? ?
Georgia 16 2 - -
Hawaii 1 1 1
Idaho 27 12 4 -
Illinois 127 11+ - -
Indiana* * * * *
Iowa 16 ? - -
Kansas 77 23 - 19
Kentucky 76 54 - ?
Louisiana 9 ? - -
Maine* * * * *
Maryland 8 - -
Massachusetts 16 - - -
Michigan 89+ 37 4 11
Minnesota* * * * *
Mississippi 25 6 - ?
Missouri 8 - - -
Montana 74 18 2 -

tthis tabulation does not include the EPA National Lake

Survey data.

*Sizable monitoring programs, but summary information not

available.

26


IMPAIRMENT OF USE:

Check the category which most closely describes
the lake condition.

No impairment of use

Very few algae present, no "bloom" conditions
; AND/OR
Very few weeds in littoral zone...........-eeeee

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....... cece ence eee nnn eens wee e ee eeee
DKEdging. ... eee eee eee eee eee eee ence cece eces
Waste water diversion........csee reece cere eecees

Nutrient removal from waste waters...........---
Hypolimnetic withdrawal........ceecee eee ereecoee
Water level management...... eee cere eee cee cence
Nutrient immobilization or precipitation..

No treatment... cee cece ee eee eee e cece ee ceee

Other (specify)

| |

|

ELTTTT TT

163


COUNTY #10 - CLARK

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI

1 Arbutus 821 56 Yes Yes IMP 4 2 0 2 8

3 Rock Dam 118 10 Yes Yes IMP 3 2 0 4 9

4 Mead 320 16 Yes Yes IMP 3 2 0 2 7

5 Sherwood 117 9 Yes Yes IMP 5 2 1) 2 9
wo
uw

COUNTY #11 ~ COLUMBIA

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE -O. TRNS FSKL IMPR LCI

9 Lazy 174 8 Yes Yes IMP 5 3 3 6 17

13 Park 219 27 Yes Yes IMP 5 2 3 9 19

17 Swan 419 82 Yes Yes NAT 2 2 0 2 6

21 Wisconsin 9000 24 Yes Yes IMP 5 3 3 5 16

50 French Creek 655 5 Yes IMP 3 3 0 2 8



LET

COUNTY #59 - SHAWANO

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
24 Loon 278 23 Yes Yes NAT 1 2 (0) 0 3
36 Pella Pond 115 14 Yes Yes IMP 0 2 0) 2 4
37 ~~ Pensaukee 104 26 Yes NAT 1 2 3 2 8
38 Pine 209 35 Yes Yes NLLC 2 2 0 0 4
42 Shawano 6178 42 Yes Yes NAT 2 2 0 5 9
46 Tigerton 167 14 Yes Yes IMP 0) 2 0 2 4
50 Weed Dam 207 25 Yes Yes IMP 1 2 0) 0 3
51 White 181 12 No No NAT 5 2 3 3 13*
52 White Clay 256 45 Yes NAT 2 2 0 0 4
54 Wolf River 223 10 Yes Yes IMP 0 2 0 0 2



88

COUNTY #4 - BAYFIELD

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
8 Atkins 190 81 Yes Yes NAT i} 1 0 0 1
11 Bark Bay 116 8 Yes Yes NAT 1 2 0 0 3
20 Basswood 119 9 No Yes NAT 3 2 0 2 7
31 Birch 129 8 Yes Yes NAT i) 2 0 0 2
38 Bony 200 52 No Yes NAT ) 1 0 0 1
41 Buffalo 190 25 No No NAT 1 1 0) 0 2
46  Buskey Bay 100 51 Yes Yes NAT 0 1 0 0 1
47 Cable 166 44 Yes Yes NAT 2 1 0 0) 3
60 Chippewa 319 11 No Yes NAT 1 2 3 0 6
68 Cranberry 131 12 No Yes NAT 1 2 3 (0) 6
72 Crystal 122 30 No Yes NAT 1 1 0 0) 2
78 Deep 125 29 No No NAT 1 1 0 0 2
80 Dells 103 42 No No NAT 2 2 0 0) 4
81 Delta 180 30 Yes Yes NAT 0 1 0 0 1
83 Diamond 331 83 Yes Yes NAT 0 2 (0) 0 2
85 Drummond 130 44 Yes Yes NLLC 2 1 0 2 5
90 Eagle 159 55 Yes Yes NAT 2 1 (0) 0 3
96 Ellison 110 18 No No NAT 1 1 0 0 2
114 Ghost 142 30 No Yes NAT 1 2 0 0 3
116 Half Moon 106 10 No No NAT 1 2 3 0 6
119 «Hart 259 54 Yes Yes NAT 4 1 it) 0 5


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.

Types of
input:

Types of
output:

Types of
systems:

The following terminology is used:

data - Objective input, numbers or values
determined by measurement.

information - Subjective input, descriptions
or assessments of conditions which may or
may not be based on documented evidence.

grouped output - Lakes having similar charac-
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.

relative systems - Lakes are classed only with
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 dependent’
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


v6

COUNTY #9 - CHIPPEWA

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
35 Chain 510 78 Yes Yes NAT 4 0 0 0 4
38 Chippewa Falls Flow 282 29 Yes Yes IMP 3 2 3 0 8
40 Cornell Flowage 577 54 Yes Yes IMP 2 2 0 0 4
41 Cornell 194 39 Yes IMP 4 2 0 2 8
71 Holcombe Flowage 3890 61 Yes Yes IMP 2 2 0 10) 4
85 Lake Wissota 5588 72 Yes Yes IMP 2 2 0 2 6
98 Long 1060 96 Yes Yes NAT 2 1 0 0 3
99 Loon 125 5 No No NAT 5 2 3 0 10

102 Marsh Miller 436 14 Yes Yes NLLC O 2 0 3 5
123 Old Abe 646 36 Yes Yes IMP 2 2 0 0 4
134 Pike 173 32 No Yes NAT 2 2 0 2 6
135 Pine 262 «115 No Yes NAT 2 i} 0 0 2
152 Round 216 18 No Yes NAT 3 2 0 2 7



66

COUNTY #17 - DUNN

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
10 Eau Galle 351 18 Yes Yes IMP 2
11 Menomin 1405 34 Yes Yes IMP 2
18 Tainter 1752 37 Yes Yes IMP 2 13

COUNTY #18 - EAU CLAIRE

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
1 Altoona 840 25 Yes Yes IMP 1 2 3 4 10
4 Dells Pond 739 30 Yes Yes IMP 1 2 0 2 5
5 Eau Claire 1118 25 Yes Yes IMP 1 3 0 7 11
8 Halfmoon 132 9 Yes Yes NAT 5 2 0 7 14



STT

COUNTY #39 - MARQUETTE

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
4 Buffalo 2447 15 Yes Yes IMP 1 3 (0) 2 6
10 Crystal 124 60 Yes Yes NAT 2 1 0 0 3
17 Harris Pond 245 10 Yes Yes IMP 1 2 0 6 9
22 Lawrence Pond 231 12 Yes Yes IMP 3 1 0 2 6
26 Montello 286 17 Yes Yes IMP 1 1 0 3 5
33 Neshkoro 192 12 Yes Yes IMP 3 3 0 9 15
54 Tuttle 167 36 No No NAT 4 1 1) (0) 5
COUNTY #40 - MENOMINEE
LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Bass (Lower) 106 35 No No NAT 2 1 0 0 3
Bass (Upper) 120 47 Yes Yes NAT 0 1 0 0 1
19 Lamotte 185 71 No No NAT 2 0 0 0 2
27 Moshawquit 296 30 Yes Yes NAT 1 2 0 0 3
29 Neopit 202 10 Yes Yes IMP 0 2 0 0 2
33 Pine 152 3 No Yes NAT 3 4 3 4 14



(Ac)

}*

P-loading
Mean depth
Hyd res time

Normalized nutrient loading, aa

°
°
° °
°
° °

°
8
x?
o a

Lake Condition Index

Figure 8. Relationship between nutrient loading
and lake condition (hypothetical data).


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


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 TSI = 0.936(25] + 0.827(COND) + 0.907(TON)
clear
takes: + 0.748(TP) + 0.938(PP) + 0.892(CHA)
+ 0.579/4] + 4.76
579 | ae .
For TSI = 0.848 [35] + 0.809(COND) + 0.887(TON)
colored
lakes: + 0.768(TP) + 0.930(PP) + 0.780(CHA)
+ 0.893/4.| + 9.33
+893 | a5 .

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/m*/hr
CHA = chlorophyll-a in mg/m?
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

13


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


96

COUNTY #13 - DANE

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
Cherokee 335 5 Yes Yes NAT 3 3 0 2 8
Crystal 571 9 Yes Yes NAT 5 3 3 3 14

6 Fish 252 62 Yes Yes NAT 2 1 0 3 6
10 Kegonsa 2716 31 Yes Yes NLLC 4 3 4 9 20
11 Marshall 194 5 Yes Yes IMP 3 3 0 9 15
13. Mendota 9730 82 Yes Yes NLLC 2 2 4 9 17
14 Monona 3335 64 Yes Yes NAT 2 1 4 9 16
16 Mud-Lower 195 15 Yes Yes NAT 1 3 0 5 9
17. Mud-Upper 223 8 Yes Yes NAT 3 3 3 9 18
19 Rockdale 104 5 Yes Yes IMP 3 3 3 4 13
25  Waubesa 2113 34 Yes Yes NLLC 2 3 4 9 18
27° ~Wingra 345 21 Yes Yes NLLC 1 3 0) 5 9



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,, 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


Lo

Total surface area in thousands of acres

80

70
l

60

50
1

40

od

Data for Lake Winnebago
(LCI = 13; area = 137,708 acres)
not included.

q q T qT q 4
o 2 4 +6 8 10 412 414 #16 418 20 22

Lake Condition Index

Figure 3. Distribution of surface area of Wisconsin lakes
as a function of Condition Index.

T
100
Total surface area in km?



Z6

COUNTY #7 - BURNETT Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
128 Lower Twin 123 9 No No NAT 5 2 3 2 12
130 Mallard 113 35 No No NAT i) 1 0 0 1
132 McGraw 135 25 Yes Yes NAT 3 2 0 0
135 Middle McKenzie 530 45 Yes Yes NAT 4 2 0 0
137. Minerva 222 22 Yes Yes NLLC 1 2 0 0 3
143° Mud 163 3 No No NAT 5 2 3 0 10
145 Mud Hen 563 66 Yes Yes NAT 0 1 0 (0) 1
146 «Myre 128 20 No No NAT 1 2 0 0 3
149 =Nicaboyne 291 34 No No NAT 4 1 0 2 7
153 Oak 227 19 No No NAT 3 0 0 0 3
155 Owl 127 27 No No NAT 1 2 0 10) 3
165 Point 144 7 No No NAT 5 2 3 0 10
166 Pokegama 223 56 Yes Yes NLLC 4 2 te) 0 6
172 Rice 311 10 No No NAT 1 2 0 2
176 Rooney 322 30 No No NAT 3 1 0 0
177. Round 204 27 Yes Yes NAT 3 2 3 7 15
182 Sand 962 73 No No NAT 0 0 0 0 i)
183 Shoal 247 4 No No NAT 5 2 3 2 12
188 Spencer 188 19 No No NAT 5 2 3 2 12
189 Spirit 593 27 Yes Yes NLLC 3 2 0 2 7
197. Tabor 163 25 No Yes NAT 3 2 0 2 7


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


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 tanksS...... cece ccc e eee e cece cece eee eees
Municipal effluents: Untreated.............088
Primary treatment........
Secondary treatment......

Estimate the total population contributing
to each effluent type:

Untreated......-....
Primary treatment...
Secondary treatment.

Agricultural:
Cultivated land..... ce eee eee cece cee cee ee eeee _
PASCULE. . ce eee eee cee ee reer tence eee ween enees _
Animal feedlots..... ec eee eee eee reece eee eee _
Irrigation return f1lOWS......... cece ee ee erence _
Industrial:

Industrial effluents (treated or untreated)...
Urban runoff:

Storm drainage, Ctc. wo... cece eee e cece eee secnes
Other (specify) :

Form completed by:

(Initials)

165


OeT

COUNTY #51 - PRICE Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
123 Sailor Creek Flowage 155 10 Yes Yes IMP 1 2 0 3
125 = Schnur 158 27 No Yes NAT 10) 2 te) 4 6
127. Sixteen 119 5 No Yes NAT 5 2 3 0 10
128 Solberg 859 16 Yes Yes IMP 0 2 t¢) (0) 2
129) «Spirit 126 Yes Yes NLLC 3 2 3 0 8
131 Squaw Creek Flowage 150 Yes Yes IMP 0 2 0 0 2
139 Thompson 111 25 Yes Yes NAT 3 2 0 0 5
145 = Tucker 118 32 Yes Yes NAT 4 2 0 0 6
146 = Turner 203 15 Yes Yes NAT 5 2 0 0 7
148 Upper Park Falls 431 17 Yes Yes IMP Cv) 2 0 0 2
154 Wilson 351 11 Yes Yes IMP 0 2 0 0 2
156 Worcester 100 37 Yes Yes NAT 6 2 0 0 8



Table 2. FIFTY-FIVE FLORIDA LAKES
RANKED ACCORDING TO TROPHIC STATE INDEX (TSI)

Lake TSI Lake TSI

Hypereutrophic group Oligotrophic group

Apopka 22.1 Jeggord 2.8
Twenty 18.5 Moss Lee 2.8
Dora 18.5 Long Pond 2.8
Bivin's Arm 14.7 Clearwater 2.6
Griffin 13.7 Altho 2.5
Kanapaha 13.5 Hickory Pond 2.5
Alice 10.7 Santa Fe 2.5
Eustis 10.5 Suggs 2.3
. Little Santa Fe 2.3
Eutrophic group Adaho 2.2
Hawthorne 9.1 Wall 2.1
Clear 8.8 Winnott 2.0
Wauberg a3 Ultraoligotrophic group
Newnan's 7.1 Still Pond 1.9
: Kingsley 1.9
Mesotrophic group Geneva Lig
Twenty-five 6.4 Gallilee 1.6
Harris 6.3 Swan 1.5
Twenty-seven 5.8 Anderson-Cue 1.5
Cooter Pond 5.3 McCloud 1.5
Lochloosa 5.2 Brooklyn 1.5
Tuscawilla 4.8 Cowpen 1.5
Calf Pond 4.6 Long 1.3
Orange 4.3 Sumter-Lowry 1.3
Mize 4.2 Magnolia 1.3
Watermelon Pond 3.6 Santa Rosa 1.3
Little Orange 3.4
Weir 3.3
Elizabeth 3.2
Ten 3.2
Palatka Pond 3.2
Beville's Pond 3.1
Meta 3.1

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


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). Ina
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


Let

COUNTY #49 - POLK

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
197 Horseshoe 377 57 No No NAT 2 1 0 2 5
200 Indian Head Flow 776 57 Yes Yes IMP 2 2 0 10) 4
252 Largon 129 10 Yes Yes NLLC 1 2 3 2 8
257 Little Butternut 189 23 Yes Yes NAT 1 2 0 2 5
264 Long 272 17 No No NAT 3 2 ie) 2 7
265 Long Trade 153 13 Yes Yes IMP 5 2 0 2 9
274 Loveless 141 20 No Yes NLLC 1 2 3 2 8
292  Magnor 231 26 Yes No NAT 3 2 0) 0 5
339 =North 119 9 No Yes NAT 5 2 3 3 13
341 North Twin 135 27 Yes Yes NAT 3 2 0 0 5
350 =—~Pike 159 33 Yes NAT 4 2 0 2 8
351 Pine 153 53 No No NAT 2 1 0 2 5
353. Pipe 281 68 No No NAT (0) 1 0 0 1
355 Poplar 125 34 No No NAT 4 1 0 0 5
358 Round 1015 17 Yes Yes NAT 5 2 0 3 10
365 Sand 187 58 No No NAT 2 2 0 2 6
371 Somers 101 12 Yes Yes NAT 1 2 (0) 3
389 «Straight 107 12 No Yes NAT 1 2 i) 0
401 Wapogasset 1186 32 Yes Yes NLLC 2 2 0 5 9
425 White Ash 153 9 Yes Yes NAT 5 2 0 5 12
426 Wild Goose 181 12 No No NAT 5 2 3 2 12



TIT

COUNTY #34 - LANGLADE

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
15 Black Oak 59 32 Yes Yes NAT 4 2 0 0 6
29° ~Duck 125 19 No No NAT 3 2 0 0 5
31 Dynamite 97 22 Yes Yes NAT 3 2 0 0 5
40 Enterprise 502 28 Yes Yes NAT 3 2 0 0 5
56 Greater Bass 270 28 Yes Yes NAT 3 2 0 te) 5

117—— Mary 156 9 Yes Yes NAT 3 2 0 0 5
127. Moccasin 110 38 No No NAT 4 2 0 0 6
129° Moose 105 20 Yes Yes NAT 3 0 it) 0) 3
111 Post, Lower 377 8 Yes Yes IMP 3 2 0 2 7
216 Post, Upper 758 14 Yes Yes IMP 3 2 0 2 7
170 =Rolling Stone 688 12 Yes Yes NLLC 3 2 0 2 7
171 ~=Rose 109 24 No Yes NAT 3 1 0 0) 4
176 =Sawyer 169 30 Yes No NAT 3 i} 0 0 3
196 = Summit 282 26 Yes Yes NLLC 3 2 0 0 5
224 White 166 44 No Yes NLLC 4 0 0 te) 4



€1T

COUNTY #36 - MANITOWOC

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
4 Cedar 139 26 No No NAT 3 1 i} 2 6
22 Long 120 37 No No NAT 4 1 0 4 9
33 Rockville 137 7 Yes Yes IMP 5 3 0 (0) 8
46 Wilke 107 22 Yes Yes NAT 3 1 0 3 7
COUNTY #37 - MARATHON
LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
4 Big Bass 174 6 No No NAT 5 2 1} 2 9
Big Eau Pleine 6830 46 Yes Yes IMP 6 3 4 7 20
15 Du Bay 6700 <30 Yes Yes IMP 3 2 ie} 2 7*
Half Moon 800 12 Yes Yes IMP 3 2 0 2 7
24 Mayflower 100 14 No No NAT 5 2 3 2 12
27 Mosinee 200 = <30 Yes Yes IMP 3 2 0 4 9x
37 Pike 208 33 Yes Yes NLLC 6 2 4 2 14
57 Wausau 1900 =<30 Yes Yes IMP 3 2 0 3 8x



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)

2)

3)

4)

5)

Lake classification is a valuable decision-making tool
that could be used to great advantage for lake manage-
ment purposes.

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.

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.

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.

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.


06

COUNTY #7 - BURNETT

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
8 Bashaw 171 15 Yes Yes NAT 0 2 0 3 5
10 Bass 110 18 No No NAT 5 2 3 0 10
13. Bass 226 24 No No NAT 1 2 0 0 3
15 Bass 207 6 No No NAT 5 2 3 2 12
20 Benoit 279 40 No Yes NAT 2 2 (0) 2
23. Big Bear 189 17 No No NAT 1 1 0 0
24 Big Doctor 222 6 No No NAT 5 2 3 2 12
25 Big McKenzie 1142 71 Yes Yes NAT 0 0 0 0 0
26 Big Sand 1400 55 No No NAT 0 1 0 2
27 Birch Island 838 13 No No NAT 5 1 3 2 11
39 Cadotte 127 18 Yes NLLC 1 1 0 2
41 Clam River 359 29 Yes Yes IMP 1 2 i?) 0
43 Clear 115 55 No No NAT 0 2 0 0
45 Conners 109 13 No No NAT 1 2 0 2 5
51 Crooked 180 10 No No NAT 5 2 3 2 12
52 Crooked 247 13 No No NAT 5 3 3 2 13
56 Danbury 256 10 Yes Yes IMP 1 2 0 3 6
58 Deer 157 18 No No NAT 3 2 te) 2 7
59 Des Moines 229 37 NAT 2 2 (0) (0) 4
60 Devils 1001 24 No No NAT 1 2 0 2 5
64 Dunham 243 63 Yes Yes NAT 2 1 0 (0) 3


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:

Number Cost per

District of lakes Total cost questionnaire
Southern 44 ($112.00) $2.55
West Central 46 (288.75) 6.28
Northwestern 422 (1113.00) 2.64
North Central 512 (560.00) 1.09
Lake Michigan 60 * -
Southeastern 65 * -

1149 ($2073.75) $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


COUNTY #71 - WINNEBAGO

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
1 Butte Des Morts 8857 11 Yes Yes NAT 3 3 0 4 10
2 Little Butte Des Morts 1306 12 Yes Yes NAT 5 3 3 4 15
5 Poygan 14102 11 Yes Yes NAT 3 3 0 4 10
6 Rush 3070 16 No No NAT 5 2 3 3 13
7 Winnebago 137708 21 Yes Yes NLLC 3 3 0 7 13
8 Winneconne 4507 10 Yes Yes NAT 3 3 0 4 10
be COUNTY #72 - WOOD
wa
” LKNO LAKE NAME AREA MXD INLET OUTLET TYPE .O. TRNS FSKL IMPR LCI
1 Biron Flowage 2126 23 Yes Yes IMP 1 3 0 2 6
2 Centralia Flowage 231 19 Yes Yes IMP 1 3 0 2 6
3 Lake Dexter 300 14 Yes Yes IMP 1 2 0 0 3
4 Nekoosa Flowage 452 17 Yes Yes IMP 3 3 i} 2 8
5 Nepco 494 29 Yes Yes IMP 3 1 0 3 7
6 Port Edwards Flowage 117 16 Yes Yes IMP 1 3 0 2 6
8 Wazeecha 148 23 Yes Yes IMP 3 3 i) 3 9
9 Wisconsin Rapids Flowage 447 22 Yes Yes IMP 1 3 1) 2 6
24 South Gallagher Flowage 395 6 Yes Yes IMP 5 3 3 2 13



eet

COUNTY #44 - ONEIDA Con't

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI
203 Long 115 31 No No NAT 4 0 0) 0 4
205 = Long 588 22 Yes Yes NLLC 3 2 0 2 7
209° Lost 155 14 Yes Yes NAT 5 2 0 2 9
211 Lower Kaubashine 187 36 Yes Yes NAT 4 2 0 0 6
215 Madeline 154 25 Yes Yes NLLC 3 2 0 2 7
217 Manson 236 54 Yes Yes NAT 4 0 10) 0 4
218 Maple 142 15 No Yes NLLC 5 2 3 2 12
226 McCormick 110 8 Yes Yes NAT 5 2 3 0 10
228 McNaughton 120 7 No Yes NAT 5 2 3 2 12
229 Medicine 411 42 Yes Yes NLLC 4 2 0 2
230 Mercer 254 23 Yes Yes NAT 3 2 0 2 7
232 Mid 215 13 No Yes NLLC 5 2 3 2 12
234 Mildred 191 45 No No NAT 4 0 0 0) 4
241 Minoqua 1285 60 Yes Yes NLLC 4 1 0 2 7
244 Moen 460 11 Yes Yes NLLC 3 2 0 0 5
251 Muskellunge 283 23 No Yes NAT 3 2 0 0 5
254 Neptune 126 7 Yes Yes NAT 5 2 0 2 9
257 Nokomis 1950 18 Yes Yes IMP 3 2 0 2 7
258 North Nokomis 468 73 No Yes NAT 4 (0) 0 0 4
261 North Two 150 43 No No NAT 4 0 0 0) 4
266 Oneida 255 34 Yes Yes NLLC 4 2 0 0 6


L6

COUNTY #14 - DODGE

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI

2 Beaver Dam 5540 8 Yes Yes IMP 3 3 3 7 16

3 Chub 120 Yes Yes NAT 5 4 3 2 14

7 Emily 268 12 Yes Yes NLLC 3 3 3 9 18

8 Fox 2120 19 Yes Yes IMP 3 3 3 9 18

11 Lost Lake 256 5 Yes Yes NAT 5 3 3 7 18

16 Neosho 146 Yes Yes IMP 3 3 3 7 16

17 Sinnissippi 2300 Yes Yes IMP 5 4 3 2 14
COUNTY #15 - DOOR

LKNO LAKE NAME AREA MXD INLET OUTLT TYPE D.O. TRNS FSKL IMPR LCI

4 Clark 778 22 Yes Yes NLLC 1 1 0 0 2

6 Europe 290 8 No No NAT 1 2 0 0 3

10 Kangaroo 1099 13 Yes Yes NLLC 1 2 0 0 3

15 MacKaysee 324 26 No No NAT 1 1 0 O 2

17 Mud 155 5 Yes Yes NAT 5 2 3 2 12



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 0
all times

Concentrations in hypolimnion

less than 5 ppm but greater 1 2
than 0 ppm
Portions of hypolimnion void 3 4

of oxygen at times

Entire hypolimnion void of 5 6
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