A classification of Arizona man-made lakes with applications to the prediction of water quality
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Authors Kessler, Steven Jack.
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Link to Item http://hdl.handle.net/10150/191672 A CLASSIFICATION OF ARIZONA MAN-MADE LAKES WITH APPLICATIONS
TO THE PREDICTION OF WATER QUALITY
by
Steven Jack Kessler
A Thesis Submitted to the Faculty of the
DEPARTMENT OF ECOLOGY AND EVOLUTIONARY BIOLOGY
In Partial Fulfillment of the Requirements For the Degree of MASTER OF SCIENCE
In the Graduate College THE UNIVERSITY OF ARIZONA
1978 STATEMENT BY AUTHOR
This thesis has been submitted in partial fulfillment of requirements for an advanced degree at The University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.
Brief quotations from this thesis are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his judg- ment the proposed use of the material is in the interests of scholar- ship. In all other instances, however, permission must be obtained from the author.
SIGNED: ,A4:..'- ,01-t,4
APPROVAL BY THESIS DIRECTOR
This thesis has been approved on the date shown below:
7 ,7L/7 Assistant Professor of Ecology and Evolutionary Biology ACKNOWLEDGMENTS
I wish to express my sincere gratitude to my major professor,
Dr. Elisabeth Ann Stull, who helped me with this project with equal zeal from start to finish. It was on a research grant to her, from the
United States Department of the Interior's Office of Water Resources and
Technology (as authorized under the Water Resources Research Act of
1964), that the impetus and funding for this study arose.
I also wish to thank the members of my committee, Dr. Robert
Hoshaw, Dr. Jerry Tash, and Dr. Donald Thomson, for their assistance and patience in the completion of the thesis.
For field sampling, I appreciate the assistance of Rick McCourt and John Kessler in the collection of samples during the initial survey of over 50 lakes and I would like to especially thank David Kreamer for making the two-week collecting period in July, 1976, most enjoyable.
Others assisting in often tedious data collection were Katrina
Mangan, Loretta Shelton, and a few high school students of Tucson
District Number One's Professional Internship Program.
Conversations with Carla Fisher and Tom Satterthwaite, students in the Cooperative Fishery Unit, were important to my understanding of the linnology of Arizona lakes.
Also, special thanks to Linda Dobbyn and Pat Price for their assistance in typing of the rough draft under extreme time pressures. i v Without the continued help of the staff of the University of
Arizona's Map Library and their excellent collections, many parts of this study could not have been completed.
I also thank Mr. Phil Steago of the White Mountain Apache Reser- vation for permission to include Reservation, Horseshoe Cienega,
Sunrise, and Cooley lakes in the study and the Arizona Game and Fish Department and their officers for their assistance.
Dr. Donald Thomson and his marine science program was my starting point into water-related research for which I have always been most grateful.
And, of course, very special thanks to my parents for always helping (and pushing) me in everything. TABLE OF CONTENTS
Page
LIST OF TABLES vii
. LIST OF ILLUSTRATIONS ix ABSTRACT
1. INTRODUCTION
2. LITERATURE REVIEW 2
Arizona Lakes 2 Classification 4
3. METHODS 8
Lake Locations 8 Sailing Periods 8 Field Procedures 13 Sampling Location 13 Water and Temperature Profiles 13 Secchi Disk, Sediments, Field Notes 16 Laboratory Procedures 17 Water 17 Sediments 19 Cartographic Procedures 20 Statistical Procedures 22 Multiple Regression 22 Factor Analysis 22 Dendrographs 24 Variable Transformations 24 Computer Techniques 25
4. CHARACTERISTICS OF ARIZONA LAKES 26 Data Presentation 26 Lake Morphometry Parameters 27 Watershed Variables 27 Conservative Water Quality Parameters 27 Non-Conservative Water Quality Parameters 28 Physical Lake Characteristics 28 Sediment Parameters 28 Geographical Distribution of Water Quality 28 vi
TABLE OF CONTENTS--Continued
Page
5. SELECTION OF SIGNIFICANT VARIABLES 36
Lake Morphometry Variables 36 Watershed Variables 38 Conservative Water Quality Parameters 39 Non-Conservative Water Quality Parameters 39 Physical Lake Characteristics 42 Sediment Parameters 42 Summary 45
6. CLASSIFICATION 48
Dendrograph 48 Factor Analysis 53 Comparison of Dendrograph and Factor Analysis Classifications 58 A Classification of Arizona Lakes 58
7. PREDICTION OF WATER QUALITY IN UNCONSTRUCTED IMPOUNDMENTS • • 63
What Governs Water Quality'? 63 Multiple Regression Predictions of Water Quality . . . . 65
8. CONCLUSIONS 71
APPENDIX A: RAW DATA FOR VARIABLES USED IN FACTOR ANALYSIS . 73
APPENDIX B: LIST OF VARIABLES AND THE MEAN AND STANDARD DEVIATION OF VALUES 81
APPENDIX C: Q MODE CORRELATION MATRIX OF THE 23 ARIZONA LAKES IN THE STUDY 83
LITERATURE CITED 85 LIST OF TABLES
Table Page
1. Types of classification systems 5
2. Location summary of the 23 lakes sampled in the study . . 10
3. Sampling dates and station depth 12
4. Summary of laboratory procedures used in lake water and sediment analyses 18
5. Categories of parameters measured 26
6. Correlation of specific conductance with other conservative water quality parameters 31
7. Correlation of altitude with variables of lake morphometry, watershed topography, physical lake characteristics and sediments 35
8. Factor analysis coefficients of lake morphometry variables 37
9. Factor analysis coefficients of watershed variables . . • • 38
10. Factor analysis coefficients of conservative water quality variables 40
11. Factor analysis coefficients of non-conservative water quality variables 41
12. Factor analysis coefficients of physical lake characteristics 43
13. Factor analysis coefficients of lake sediment variables . . 44
14. Summary table of factor analysis results 46
15. Q mode factor analysis classification of 23 Arizona lakes based on 15 variables 54
16. Lake groups formed by the overlap of dendrograph and factor analysis techniques and their constituents . • • 59
vii viii
LIST OF TABLES--Continued
Table Page
17. Results of multiple regression analysis by water quality variables on variables of pre-lake construction determinability 67 LIST OF ILLUSTRATIONS
Figure Page
1. Location of the 23 Arizona lakes used in the study . . • • 9
2. Continuous flow lake sampling device 14
3. Specific conductance of Arizona lakes in the study, presented geographically 30
4. Percent composition of cations of the 23 Arizona lakes in the study 32
5. Annual precipitation in Arizona 33
6. Dendrograph showing independence of R mode selected variables 47
7. Q mode dendrograph clustering of 23 Arizona lakes based on 15 variables 49
8. Lake groups formed by Q mode dendrograph clustering, presented geographically 50
9. Lake groups formed by Q mode factor analysis, presented geographically 56
10. Lake groups formed by the overlap of factor analysis and dendrograph clustering 60
ix ABSTRACT
Twenty-three small to large man-made lakes in Arizona (13 to 909 hectares) were surveyed for 44 variables of lake morphometry, watershed topography and climate, conservative and non-conservative water quality, and lake sediments. Water and sediment collections were made in two
14-day sampling tours during the summer of 1976.
R mode factor analysis technique reduced the original data set to 15 independent variables to be used in Q mode factor analysis and dendrograph lake classification.
On the basis of the two clustering methods, six classes of lakes are indicated: 1) White Mountain, 2) Southern Arizona, 3) Salt River-
Phoenix water supply, 4) Escudilla Mountain, 5) miscellaneous large irrigation, and 6) miscellaneous high altitude. Nine lakes are indepen- dent of all others.
Predictions of water and sediment quality in Arizona lakes, based upon variables of lake morphometry, watershed topography and climate as selected by the R mode factor analysis technique, were per- formed using multiple regression analysis. The significant relation- ships found may be useful in the tentative prediction of water quality and fishery potential in unconstructed water bodies. CHAPTER 1
INTRODUCTION
Man, in an effort to order natural occurrences on Earth, has attempted to classify lakes according to many different criteria on worldwide, regional and local levels. Potential effects of pollution, water level control, fishery harvest and natural eutrophication pro- cesses can be assessed with an adequate a priori classification. In the case of man-made lakes where preimpoundment data are available, classi- fication schemes may be used to predict effects of recreational use, fishery potential and water yield and quality. The interest in lake classification is old; but, in Arizona, expansive lake studies are nearly nonexistent.
This study examines 23 man-made impoundments in Arizona and con- siders two major topics. A classification based on variables of water quality, lake morphometry, watershed topography and lake sediments is presented, followed by a discussion of predictions of water quality where certain characteristics are known prior to lake construction. It is hoped that this information may be of some use to the lake planner as well as serve as a description of general characteristics of Arizona lakes.
1 CHAPTER 2
LITERATURE REVIEW
Arizona Lakes
Arizona lakes, as seen today, are limnologically recent. Most lakes are man-made and have been constructed within the last fifty years, while natural water bodies periodically go dry and might be better defined as ponds. The man-made impoundments of the state serve in one or more capacities: recreation, fisheries, hydroelectrical generation, flood control or agricultural, mining or municipal water storage.
Few general studies have been conducted on Arizona lakes; the reviews by Cole in the collections Limnology in North America (1966) and
Desert Biology (1968) are the most complete to date. His reviews con- centrate on generalities applicable to natural desert lakes and large storage reservoirs of the entire Southwest and Middle America, ignoring the small reservoirs which are the most numerous water bodies. There are many reports published on various individual lakes or groups of lakes, most specifically pertaining to fisheries and recreation (e.g., the fisheries theses of Glucksman, 1965; Stewart, 1967; Biggins, 1968;
Bergersen, 1969; Saiki, 1973; and the recreation theses of Utter, 1975;
Quimby, 1976). The Salt River lakes of central Arizona have been the subject of the most intense research on any group of impoundments,
2 3 excluding the Colorado River mainstem reservoirs (for example, Bersell,
1973; Rinne, 1973; Olsen and Sommerfeld, 1977). These studies post-date
Cole's reviews and have yet to be synthesized.
Several studies of the chemistry of inflow waters as it effects primary production have been conducted. Kemmerer et al. (1968) showed that alkalinity and total phosphate of interflood inflow were closely related to gross primary production in high to low elevation fishing impoundments in eastern Arizona. McConnell (1968) found that organic material inflow, mostly from oak litter, into Pena Blanca Lake was an important energy source for some microorganisms and that annual fish harvest may have been increased due to an increase in organic input.
Seawell, Adams and McConnell's (1969) study on Rose Canyon Lake (Santa
Catalina Mountains, Pima County) indicated that Arizona Ponderosa Pine litter has no conspicuous beneficial or detrimental effect on the biotic communities of small impoundments. An in-depth study of Canyon Lake by
Olsen and Sommerfeld (1977) showed that temporally heterogeneous inflow of nutrients from the watershed was a significant determinant of Changes in lake water quality and, by inference, productivity.
Arizona is located in a region of high annual temperature, high solar insolation and seasonal, biennial rainfall patterns (Sellers and
Hill, 1974). In some lakes, such as Pena Blanca Lake, this leads to temperature and oxygen stratification extending from March to November
(Ziebell, 1969) and has a corresponding effect on the fisheries by limiting water strata where fish can survive in the summer time. Often, fish kills (Ziebell, 1969) as well as large blue-green algae blooms result (Schwartz, 1976; pers. observ.). 4 Classification
Why is it necessary to classify lakes? Is it based only on man's desire to make order of occurrences in nature where there seems to be little (and hence the science of taxonomy and systematics)? Or is there always some useful function involved? In the case of lakes,
Uttormark and Wall (1975) state that its purpose is to enable allocation of funds and man power to areas of critical needs. They further suggest that the concept of each lake having its own "unique personality" has its place; however, without lake grouping, whether in our minds or on paper, we have no way to manage them. Hence, it is my primary purpose to propose classifications of Arizona lakes using a variety of tech- niques. Furthermore, without the evidence that Arizona lakes can be classified, which implies predictability, it would be unreasonable to try to predict water quality based on preimpoundment characteristics.
Uttormark and Wall (1975) discuss in detail types of classifica- tions that can be made; and these are summarized in Table 1. Whereas many classification schemes are based on the independent or empirical systems, the methods used in this research are of the relative and analytical styles where the actual data collected on the lakes are used in statistical classifications. This is a valid approach when a new classification method is being designed and where the range of lake types is unknown. The major disadvantage of these systems is that the inclusion or omission of a lake or a piece of datum can drastically change the classification.
Many published lake classification schemes have had a single direction -- the prediction of fishery potential in a lake or a series 5
Table 1. Types of classification systems. -- From Uttormark and Wall (1975).
System Description
1. Relative 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.
2. Independent 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.
3. Analytic Combinations of parameters and the formation of groups are based on statistical or other numerical methods (i.e., correlations, cluster analysis, etc.).
4. Empirical Parameters are combined on an intuitive basis or boundaries of groups are defined arbitrarily.
5. Pre-set Number and characteristics of groups are specified from the outset, and classification is accomplished by matching the characteristics of a given lake to those of predefined groups.
6. Post-set Groups or ranks are formed from analysis or treat- ments of lake data. 6 of lakes. To do this, data on various variables and sets of variables are collected and, following regression analyses against fishery produc- tion, the fishery potential of as yet uncensused lakes is predicted and the predictions tested.
Ryder et al. (1974) reviewed the morphoedaphic factor index, a combination of a water quality variable (total dissolved solids) and a morphological variable (mean lake depth), and they produced compelling evidence for its use as a fish yield forecaster. But, in Arizona, where summer temperature maximums and oxygen minimums are often the limiting factor of fish yield (Ziebell, 1969), this index is of little utility.
Other proposed classifications may simply correlate surface area with fish yield per unit area (Rounsefell, 1946), mean depth of a lake to fish production (Rawson, 1952, 1955) and total dissolved solids with lake productivity (Northcote and Larkin, 1956) or more complex methods combining energy related variables (air temperature, day length), mixing type, latitude and Chemical factors in a factor analysis scheme on lakes of greatly varying location (Brylinsky and Mann, 1973). A combination of easily measured variables such as alkalinity, depth and lake area
(Hayes and Anthony, 1964) or outlet depth, shore development and total dissolved solids of reservoirs (Jenkins and Morais, 1971) may also be used to predict fishery potential.
A host of other schemes have been proposed but lend little light to the Chaotic classification problem. Many of the schemes (e.g.,
Jenkins and Morais, 1971; Brylinsky and Mann, 1973) are appropriate for the specific systems studied and are likely transferable to "maximum potential yield" under ideal circumstances in Arizona lakes and 7 reservoirs but not to "actual fish yield." Even Hutchinson's (1957) use of oxygen deficits as predictors of biological production cannot be interpolated to fishery yield but must remain as an indication of poten- tial only.
Most of the classification schemes depend on a gradient of eutrophic to oligotrophic lake types but in Arizona essentially all water bodies are eutrophic. An Arizona lake classification, then, must
select lakes within the boundaries of the eutrophic category.
In this study, a large set of variables is presented and through
statistical methods a reduction of variables occurs which are used in an
analytical classification system (Table 1) based on those selected vari-
ables alone. Lakes are classified with respect to one another (the
relative system, Table 1) and are displayed as clusters of related
lakes. No a priori judgments are made. CHAPTER 3
METHODS
Lake Locations
Twenty-three impoundments located in both the Basin and Range and Colorado Plateau provinces of Arizona (Fig. 1) were chosen for study following a preliminary survey of over fifty lakes. The three primary criteria used for the final selection were geographic location, varying morphology and ease of access. An effort was made to select groups of lakes geographically distinct and with the lakes of each group morpho- metrically dissimilar. Table 2 locates each lake by county, latitude, longitude, altitude and predominant vegetation of the surrounding watershed.
Sampling Periods
All 23 lakes (except Parker Canyon Lake which was sampled only in July) were sampled twice during the summer of 1976, once in May or
June and during the month of July (Table 3). The sampling schedule, two periods spaced six weeks apart, enabled calculation of rate of change for oxygen, pH and temperature.
8
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Becker Lake May 24 4.25 July 8 5.25 Big Lake May 25 4.25 July 9 7.0 Canyon Lake June 7 22. July 22 23. Cooley Lake May 27 8.75 July 12 8.5 Fools Hollow Lake May 31 10.5 July 15 10.25 Horseshoe Cienega May 29 9.25 July 11 9. Knoll Lake June 2 14.5 July 17 12.5 Lake Patagonia May 19 20. July 5 20. Lee Valley Reservoir May 26 6. July 9 5. Luna Lake May 23 6.5 July 7 4.75 Lyman Lake May 24 10.75 July 7 9.5 Lynx Lake June 5 20. July 19 18. Nelson Reservoir May 23 6.75 July 7 6.25 Parker Canyon Lake not sampled July 6 7.5 Pena Blanca Lake May 18 15. July 5 16.75 Reservation Lake May 25 10.5 July 9 11. River Reservoir May 27 11.75 July 8 11.0 Sunrise Lake May 26 8.25 July 8 6.0 Upper Lake Mary June 3 7.5 July 18 7.0 Upper Lake Pleasant June 5 17.75 July 21 16.75 Willow Springs Res. June 1 18.5 July 16 18. 13 Field Procedures Sampling Location Due to the large number of lakes sampled, collections were made at only one station. For uniformity, this site was chosen at the deepest location in the lake, unless either it was too deep for the anchor and sample gear to reach bottom or the location was improperly selected (Fools Hollow and Parker Canyon lakes). In Canyon Lake and Bartlett Reservoir, the point where the anchor would first hold, upstream from the damsite, was chosen (ca. 20 meters depth). The mean sample depth of the two collecting periods, not maximum depth, is used in all calculations. Water and Temperature Profiles A water sampler, consisting of a variation of a submersible pump type (Whaley, 1958) and a narrow level collector (Parker, Leeper and Hurni, 1968), was specifically designed for this project. The device allowed rapid collection of water at every meter of depth and of inte- gral samples representing the entire water column from lake surface to sediment surface. The continuous flow lake sampler (Figure 2) works with positive pressure exerted by a small submersible, 12-volt bilge R pump (Sears R brand) connected to the end of 15 meters of Nalgene tubing (5/8-inch inside diameter). The pump, powered by two standard, six-volt lantern batteries wired in series with an in-line on/off switch, is con- nected to the surface by a double strand of 16 gauge pliable extension wire. One set of batteries is serviceable for about four hours of 14 Rolling pin The rmisto r bearing readout 4- Crank Hose winder Water Skif f +-- Cable connectors A Water flow CONTINUOUS FLOW LAKE SAMPLING DEVICE -4 44- 5 1 8 - I.D. tubing intake U U 12 Volt power input 8 cm Bilge pump /1 4, -"CP Thermistor •490 /intake Figure 2. Continuous flow lake sampling device. -- Not to scale. In use, a five-pound weight is attached inferior to the bilge pump, assuring wire angles of near zero degrees. 15 discontinuous operation. A thermister probe placed adjacent to one incurrent port of the pump and connected on the surface to a calibrated YSI model 46-TUC thermister readout gives simultaneous temperature records, enabling accurate calculation of percent saturation of oxygen corrected for temperature (Wetzel, 1975). The entire unit is mounted on a garden hose reel, as a winch system, and attached to the transom of a 16-foot skiff. A five-pound weight attached below the pump assures a wire angle of near zero degrees. It was possible to operate the device efficiently with accuracy comparable to a Van Dorn-type sampling bottle. To avoid contamination, the entire tubing system was allowed to flush twice with water of the desired location prior to sample collection. Using the continuous flow lake sampler, oxygen samples were taken every meter, fixed immediately and titrated onshore within two hours of collection using micro-Winkler technique with the azide modifi- cation (Carpenter, 1965). Alkalinity and pH samples were obtained at the surface, approximately half the depth of the lake and at the bottom. An integral sample, containing the entire water column from the surface to the lake bottom, was collected by emptying the sampling device, dropping the pump to the bottom of the lake, turning the pump on and receiving only as much water as the calcùlated volume of the hose to the lake bottom. The first sediment particles appearing in the hose could be used as indicators of the lake bottom to assure accurate sampling. All samples were analyzed for alkalinity onshore within two hours of collection by titration with .20 N HC1 delivered with a two- milliliter capacity, micro-burette to a potentiometrically measured end point of pH 4.5 using a Beckman Electromate R pH meter (American Public 16 Health Association, 1971). A pH end point of 4.5 is suggested for values of over 500 mg/1 alkalinity as CaCO 3 , while a somewhat higher pH values (4.8 for 150 mg/1; 5.1 for 30 mg/1) is recommended for lower alkalinities. A pH of 4.5 was selected because of its accuracy at the higher alkalinities while only minor errors are introduced in the lower ranges. Surface and integral lake waters were collected concurrently in acid soaked, one-liter polyethylene bottles and frozen immediately on dry ice or in brine solution for later laboratory chemical assay. Samples that burst during freezing or were suspected of brine contamina- tion from the freezing process were eliminated from statistical analysis of the data. Water was stored up to six months in cold storage at -40 ° C and thawed just prior to analysis. Acidification of samples, although recommended for major Chemical constituents (United States Environmental Protection Agency, 1976), was not used because of its interference with other tests (silica, nitrogen and phosphorus) on the same samples. Secchi Disk, Sediments, Field Notes Secchi disk, a measure of transparency and an indicator of tur- bidity, was measured at each sample station. This measurement, although rarely precisely reproducible due to changing light and surface effects, is a good approximation of water clarity and is sometimes used as a correlated measure of lake productivity (Wetzel, 1975). Sediments were collected by gravity corer and stored in air- tight ointment jars. An effort was made to fill the bottles completely so that sediments would remain anaerobic as, in most cases, they were at 17 the lake bottom. Following collection, the jars were stored at near 0 ° C until analysis. Samples could not be collected from all lakes during both sampling periods because of a) rocky substrate, b) high winds or c) unconsolidated sediments draining from the corer on its way to the surface. At Reservation Lake, a sample was taken from the sediments scooped up by the mushroom-type anchor. At each site, obvious lake use, climatic conditions and notable peculiarities were recorded. The lake and surrounding terrestrial vege- tation were photographed as well as described in field notes. Laboratory Procedures Water Samples of frozen lake water were thawed and divided into two containers. One was sent to the University of Arizona's Soils, Water and Plant Tissue Testing Laboratory for atomic absorption analysis of calcium, magnesium, potassium and sodium, and digestion for Kjeldahl nitrogen on unfiltered water (Table 4), while the other was refrozen until the other analyses could be performed. The second bottle was rethawed (up to six months following col- lection) and split into a part for filtration through prewashed HA (4.5 micron nominal pore size) Millipore R fiber filters for nitrate nitrogen, ammonia nitrogen, orthophosphate, iron, Chloride and sulfate measurement and an unfiltered component for total phosphorus and specific conduc- tance analyses. Filtration removes particulate material (mostly living) so that the nutrients in the samples represent those imnediately avail- able for primary production uptake. Total phosphorus and specific 18 Table 4. Summary of laboratory procedures used in lake water and sediment analyses. Test Titration Method Water Kjelddhl nitrogen no Kjeldahl digestion a Nitrate nitrogen yes Cadmium reduction -- low rangeb Ammonia nitrogen yes Nessler's Reagentb Total phosphorus no Oxidation to orthophosphateb Orthophosphate yes Ascorbic acie Iron yes 1,10-Phenanthrolineb Chloride yes Mercuric nitrate fil trati onb Sulfate yes Turbidometricb Specific conductance no Conductance probeb Sodium no Atomic absorption a Calcium no Atomic absorptiona Magnesium no Atomic absorption a Potassium no Atomic dbsorption a Sediments Kjeldahl nitrogen Kjeldahl digestion a,c Total phosphorus Perdhloric acid digestiona , c Weight loss on ignition (LOI) Ignition at 500 ° C in a muffle furnace Percent sand, silt, clay Mechanical analysis (Bouyoucos, 1927, 1936) a By the University of Arizona Soils, Water and Plant Tissue Testing Laboratory. bWith HachR DR-EL/2 spectrophotometer apparatus. cSamples dried and ground prior to analysis. 19 conductance samples were not filtered so that overall water quality, indicated by total phosphorus present and total ionic activity, could be determined. Measurement of water samples for nitrate nitrogen, ammonia nitro- gen, total phosphorus, iron, orthophosphate, sulfate, silica and specific conductance was done using techniques of the Hach Chemical Company and a R Hach DR-EL/2 spectrophotometer apparatus. Cadmium reduction (for nitrate nitrogen), Nessler's reagent (for ammonia nitrogen), oxidation to orthophosphate (for total phosphorus), ascorbic acid (for orthophos- phate), 1,10-Phenanthroline (for iron) and turbidometric (for sulfate) colorometric techniques were used as well as mercuric nitrate titration (for chloride) and conductance probe (for specific conductance) (Table 4). All chemical analyses were standardized with known solutions and performed at room temperature (20-25 ° C). Sediments Kjeldahl nitrogen and total phosphorus analyses on sediments were also performed by the University of Arizona's Soils, Water and Plant Tissue Testing Laboratory (Table 4). These findings are condi- tional since values represent one unreplicated sample. Percent weight loss on ignition (LOI) was obtained by first drying the sediments for 24 hours in porcelain crucibles at 100 ° C followed by ignition at 500 ° C in a muffle furnace (as in Dean and Gorham, 1976). At this temperature, the organic carbon in the samples will ignite and vaporize, while most of the inorganic carbon compounds remain stable. An attempt was made to heat the crucibles to 1000 ° C to quantify percent calcium carbonate (Dean 20 and Gorham, 1976); however, this permanently damaged the crucibles and made no discernible difference in weight of the samples tested. LOI results are for the average of at least three replicates from a given R sample. Prior to weighing on a Danforth Type 10 N analytical balance, R all samples were cooled in a desiccator filled with Drierlte . A mechanical analysis for size distribution of sediment particles into percent sand, silt and clay was performed using the techniques developed by Bouyoucos (1927, 1936). The analysis measures specific density changes of a column of water and sediment, completely mixed by agitation, following a settling period of 40 seconds for sand and 2 hours for silt. Results were corrected for temperature and are reported at a standard 25 ° C. Cartographic Procedures Lake and watershed areas for each lake were calculated, using 7 1/2 or 15 minute United States Geological Survey topographical maps, with polar planimetry. For exceptionally large watersheds (e.g., Canyon Lake and Bartlett Reservoir), United States Army series 1:250,000 scale maps were more practical although less accurate. Lake area, as used in this study, is defined as topographic map size which is assumed to be maximum potential area of the lake if it were at spillway level. Con- tour lines on the topographic maps were analyzed to find maximum water- shed area from which water drains and enters into the lake. Maximum effective length, maximum length, mean width and shore development for each lake were determined according to the definitions of Welch (1948). Watershed slope was defined as relief from the lake to 21 the high point on the watershed divided by the distance from the dam to that point (i.e., watershed length). Climate was described by annual lake evaporation and quantity of average annual precipitation falling on the entire watershed. Also, rain/hectare for the watershed was derived by dividing the precipitation value by the area of the watershed. Lake evaporation was obtained by interpolating points between isolines of a map produced by the Arizona Resources Information System (1975) and quantity of precipitation from the average annual Arizona precipitation map (scale 1:500,000) (Univer- sity of Arizona, 1965). The latter quantity was found by summing polar planimetered values of the area between isohyetals of rainfall mapped for each lake's watershed. Lull and Sopper (1966) showed, in north- eastern United States watersheds, that average annual runoff from a watershed correlated better with isohyetal precipitation than values obtained from rain gauge stations near the watershed. The values obtained may accurately predict the expected average annual rainfall for Arizona watersheds but cannot be expected to be a true value of any given year's precipitation. The basin shape factor, defined as the square of the maximum straight-line length of the watershed basin divided by the total area, has been found to be one of the best geomorphic parameters for predicting rise time, peak discharge and storm volume (Murphey, Wallace and Lane, 1977) of streams draining watersheds. The combination of total rainfall from isohyetal precipitation and the basin shape factor, a climatic- geomorphic variable, are used to estimate the runoff from the watershed. 22 Statistical Procedures Two major types of statistical analyses were performed, that of R mode and Q mode forms. In R mode, either multiple regression between variables is performed or a correlation matrix between variables is made using multiple cases (objects); in this case, lakes. This correlation matrix is then used in factor analysis and dendrograph formation. Similarly, in Q mode, a correlation matrix is also made; however, this time the objects become the variables, that is, the lakes become the variables and the variables become the cases (Nie et al., 1975; Jareskog, Klovan and Reyment, 1976). To clarify: in Q mode, each lake is corre- lated against every other lake using as many variables as there are in a given analysis, and the correlations are then analyzed with factor analysis and dendrographic techniques. Therefore, where R mode studies the relation between variables, Q mode is used for the grouping and classification of lakes. Multiple Regression The relationship between one dependent variable and two or more independent variables is examined by multiple regression statistics. In this study, multiple regression is used in R mode to elucidate how one variable is governed by the action of others. Unless otherwise indi- cated, all correlation coefficients shown are highly significant (p < .01, n = 23). Factor Analysis Factor analysis, using an array of correlation coefficients between variables, searches for the existence of "some underlying pattern 2 3 or relationship . . . such that the data may be 'rearranged' or 'reduced' to a smaller set of factors or components that may be taken as source variables accounting for the observed interrelations in the data" (Nie et al., 1975). Factor analysis does this by extracting the most important correlations from a correlation matrix (the initial factor) followed by extraction of other factors, unrelated and independent of (orthogonal to) the first. After removing these new, hypothetical vari- ables, the remaining partial correlations between the original variables become zero (Nie et al., 1975; Jiireskog et al., 1976). With this technique, in Q mode, a classification of lakes can be made. Factor analysis weights each lake in each factor according to its degree of correlation with that hypothetical factor formed. Those numbers near one indicate high positive correlation, those near negative one indicate high negative correlation. Therefore, two lakes highly positively correlated with a factor are themselves very similar. R mode factor analysis serves a very different function. Where data on many variables have been collected and many show strong inter- dependence, the number of variables can be greatly reduced for further analysis by selecting only one from each orthogonal factor formed. If conductance, sodium and chloride weight heavily on a single factor, then it is possible to only use one of these variables, such as the easily obtained parameter conductivity, in further analyses. In this way, a few independent variables can be chosen and used in subsequent statistical work (Shannon, 1969). In this study, R mode selected 15 variables from the original 44, and it is those variables that are then 24 used in Q mode lake classification. With this data reduction scheme, the dominance of a few highly intercorrelated variables is removed. Dendrographs A dendrograph is a two-dimensional diagram depicting relation- ship among objects (for instance, lakes) based on a measure of similarity between them. An arrangement showing both within group and between group similarity using an unweighted pair group method developed by McCammon and Wenninger (1970) clusters lakes into a hierarchial struc- ture. In the analyses presented in this inquiry, a simple correlation matrix for both R and Q mode studies is inputted. This type of multi- variate classification of lake data has been reviewed by Sheldon (1969). Variable Transformations Calcium, magnesium, sodium, chloride, sulfate, alkalinity, iron, silica and potassium data were transformed from milligrams per liter (mg/1) to milligram equivalents per liter (meq/l) prior to incorporation in the statistical analyses. Equivalents are the amounts of substances that are equivalent to each other in chemical reactions and therefore more accurately represent chemical processes in lake water (Hem, 1959). All variables were Z transformed to standardize scale length. Using the original variable, a new variable with mean of zero and stan- dard deviation of one is generated by subtracting the original mean of the variable and dividing by the standard deviation (Nie et al., 1975). Therefore, each case is now represented by how many standard deviation units above or below the mean it is. Comparison of variables with greatly different means and variances becomes possible. 25 Computer Techniques The multivariate factor analysis and regression programs are "canned programs" from SPSS -- Statistical Package for the Social Sciences (Nie et al., 1975). Default options, except use of pairwise deletion of missing data, were used in both programs. The dendrograph program was written by McCammon and Wenninger (1970) of The University of Kansas while simple statistical, matrix transformations and listing programs were written in FORTRAN by the author. All programs were run on the University of Arizona's Cyber 170 computer and plots were done by Calconp plotter. CHAPTER 4 CHARACTERISTICS OF ARIZONA LAKES Data Presentation Forty-four parameters measured for each lake are divided into six categories of 5 to 12 variables each in Table 5. Appendix A lists the data by category in tabular form for each lake, while Appendix B lists the untransformed means and standard deviations of each variable. The following criteria were used to group variables. Table 5. Categories of parameters measured. -- Raw data presented in Appendix A. Number of Variables Data Found in Category in the Category Appendix A 7Lake morphometry parameters Table Al Watershed parameters 7 Table A2 Conservative water quality parameters 12 Table A3 Non-conservative water quality parameters 5 Table A4 Physical lake characteristics 6 Table AS Sediment parameters 7 Table A6 26 27 Lake Morphometry Parameters Area, shore development, maximum length, maximum effective length, and mean width are variables of lake shape. Shape factor, obtained by dividing area by mean sample depth, was included to repre- sent in one dimension the morphological shape of a three-dimensional space. Watershed Variables Area, length, rainfall and slope are variables of watershed size, topography and climate. Altitude, due to its climatic influence on the watershed, was included in this category. The basin shape factor, a dimensionaless measure of a two-dimensional projection of the water- shed shape (Murphey et al., 1977), is derived by dividing the square of the length of the watershed by the area. Conservative Water Quality Parameters Conservative water quality parameters would be expected to change little on a daily basis due to biological demands. Calcium, magnesium, sodium, chloride, sulfate, alkalinity, iron, silica and potassium as well as specific conductance, a measure of the net ionic content, were included in this category. Daily rate of pH increase of the entire lake water column, a measure of the long-term (weeks) accumulative effect of photosynthesis on hydrogen ion concentration, was included because of its conservative nature. Maximum pH of the lake changes hourly based on continually changing rates of production and respiration (Wetzel, 1975) and is included in the next section on non-conservative water quality. 2 8 Non-Conservative Water Quality Parameters A non-conservative water quality variable is defined as a vari- able that may fluctuate due to biological activity. These variables are often closely tied with rapid changes in productivity of the euphotic zone. Phosphate, nitrogen and surface pH measures were included in this category. Physical Lake Characteristics These parameters are peculiar to each lake and are often governed by a combination of variables from other categories. For instance, tem- perature is partially influenced by solar insolation (altitude con- trolled) and lake and watershed morphometries due to turnover character- istics and stream inputs warming or cooling the lake. Oxygen content, similarly, is based on productivity which is dependent on many combined variables. Sediment Parameters Seven qualities of sediments were analyzed from core samples. They were included in the analyses because Dean and Gorham (1976) showed that sediments could be used in classification of Minnesota lakes since they represent the results of sediment input to the lake and biological, physical and Chemical cycling within the lake. Geographical Distribution of Water Quality Lake waters were found to be chemically diverse, exhibiting a range in specific conductance of 24 pmhos/cm to 983 pmhos/cm. Other Chemical qualities were equally variable in concentration as shown in 29 Appendix A (Tables A3 and A4) and Appendix B. The distribution of specific conductance of the 23 lakes, geographically presented in Figure 3, shows that conductance is at a minimum along a highland axis from Flagstaff to the Mogollon Rim and the White Mountains. Extending northeast and southwest from this line, specific conductance increases, reaching a maximum in Canyon Lake and Lake Patagonia. Other major cations and ions are positively correlated with specific conductance and follow a similar geographic pattern (Table 6). Cationic composition may be measured by percentage of equiva- lents of sodium, calcium and magnesium. Figure 4, a three-dimensional plot where the apex of the triangle represents 100 percent of an ion and the opposite base represents zero percent, shows that two lakes are dominated by sodium, three lakes by magnesium and ten lakes by calcium. A similar triangular plot of the anionic composition would show that 18 lakes are dominated by carbonates, one by chloride and one by sulfate. Distribution of ions in lakes corresponds well with average annual precipitation. The highest rainfall in the state, in the Mogollon Rim and White Mountains regions, matches the lowest specific conductance (compare Figure 3 with Figure 5), while the highest specific conductance occurs in regions of low rainfall. The relation of aridity and ion concentration in Arizona lake waters is further discussed in Stull and Kessler (In Press). It is shown that the chemical relations of sodium and calcium with specific conductance in Arizona man-made lake bodies, in fact, follow global trends. 30 i- ._...... , 1 1-1 A L_ % • i . -- 7\ w i 1 i% 0i ii""131111 L. i a.1 • tti I .s. nn . ..,.. s. s. L.. . .—‘. -'-- 0 (0/2. I i 04.11P – - I .."C . s..% ' I V-. i 1 i . •.,--; i V • .\ • '16 • 141 • 392 u m hosic m 835 study, presented Figure 3. Specific conductance of Arizona lakes in the geographically. -- Values are in pmhos/cm. The line A-B-C indicates the highland axis from Flagstaff (A) to the Mogollon Rim (B) and the White Mountains (C). 31 Table 6. Correlation of specific conductance with other conservative water quality parameters. -- Variables have been Z transformed and converted to milligram equivalents per liter prior to correlation analysis. All correlation coeffi- cients, except for silica (which is insignifi- cant), are significant at better than the .01 level. Variable Correlation Coefficient Calcium .60 Magnesium .77 Sodium .85 Chloride .74 Sulfate .66 Alkalinity .60 Iron a Silica .31 Potassium .86 a Quantities too low for calculation. 32 Mg N0+ Ca Figure 4. Percent composition of cations of the 23 Arizona lakes in the study. -- Values have been transformed to milligram equiva- lents per liter. Numbers refer to lakes as listed in Table 2. The apices of the triangle represent 100% composi- tion of the indicated cation while the opposite base repre- sents 0%. Redrawn from Stull and Kessler (In Press). 33 Figure 5. Annual precipitation in Arizona. -- Isohyetals are in inches. Large dots indicate the location of lakes used in the study. 34 The values of many other variables measured seem to be closely tied with altitudinal effects. Table 7 lists the simple correlation coefficients of parameters significantly correlating with altitude. The two most significant relationships are negative correlates of altitude, annual lake evaporation (-.86) and mean July epilimnetic temperature (-.98). These variables are both climate dependent physical parameters and should be related to altitude. The next highest correlates are shore development (-.85) and lake depth (-.75), two lake morphometric variables. These may be arti- factual, a function of lake location and type of construction. At the lower elevations, a lake must be deep because of seasonal inflow, high downstream water demands and high evaporation levels. These lakes are usually built in narrow canyons, with small side canyons entering, leading to high shore development figures. Other variables significant at better than the .01 level are in the watershed topography (length, size, rainfall), sediments (loss on ignition) and lake morphometry (maximum length) categories. 35 Table 7. Correlation of altitude with variables of lake morphometry, watershed topography, physical lake characteristics and sediments. -- Variables have been Z transformed prior to correlation analysis. Correlation coefficients marked "a" are significant at better than the .01 level, while those marked "b" are significant at better than the .05 level. Correlation Category Variable Coefficient Lake morphometry Area -.58 Shore development -.85a a Depth - .75 Maximum length -.70a Watershed topography Area -.70a Length Total rainfall -.69a Mean rainfall/hectare .74a Physical lake Lake evaporation -.86a characteristics Mean July epilimnetic temperature -.98a Sediments Loss on ignition (LOI) .62a b Kjeldahl nitrogen . 54 Percent sand . 56b Percent clay -.54 CHAPTERS SELECTION OF SIGNIFICANT VARIABLES The R mode factor analysis technique, as described in the methods section, was performed on each category of variables. These calculations served two purposes: to succinctly show variable inter- correlation and to pick those parameters that, alone, can best be used in lake classification. Since an artificial partitioning results when classification includes many highly intercorrelated variables, the factor analysis approach is useful because it selects only a few related variables for use in further statistics. Lake Morphometry Variables Factor analysis formed two factors from the lake morphometry variables (Table 8). Shore development loaded highest (.86) on factor one followed by maximum length (.84), mean sample depth (.73) and the shape factor (.72). The results are not surprising since, by definition, shore development increases with increased length and the shape factor is calculated by division of the extracted variable mean depth and lake area. The second factor is best described by mean width (.95) followed by lake area (.92); a larger lake has a greater width! Shore development and mean width, alone, will be used in further analysis of lake morphometry variables. 36 37 Table 8. Factor analysis coefficients of lake morphometry variables. -- The coefficient underlined indicates the variable chosen for further analysis. Factor Parameter 1 2 Area .39 .92 Shore development .86 .30 Mean (June and July) sample depth .73 .06 Maximum length .84 .42 Maximum effective length .29 .78 Mean width -.07 .95 Shape factor .72 .03 38 Watershed Variables Area, length and total rainfall load equally (.95) on factor number one in an analysis of watershed variables (Table 9). Intuitively, and as statistically shown, these three variables should be highly correlated. Table 9. Factor analysis coefficients of water- shed variables. -- The coefficient underlined indicates the variable chosen for further analysis. Factor Parameter 1 2 Altitude (at lake) -.52 .84 Area .95 -.24 Length .95 -.30 Slope -.40 .05 Total rainfall .95 -.23 Average rainfall/hectare -.21 .74 Shape factor -.01 .23 Factor two, described by altitude (.84) of the lake and average rainfall per hectare (.74), reflects the climatological correspondence of higher altitude with higher rainfall (Strahler, 1972) in Arizona. In subsequent analyses, only area and altitude will be used to represent this category. 39 Conservative Water Quality Parameters Four factors were formed by factor analysis of 12 conservative water quality variables (Table 10). The first factor, best described by chloride (.98), also highly loads sodium (.97) and conductance (.75). These results are predictable (Hem, 1959) since the salt, sodium chloride, is highly soluble in water in comparison to the salts of cal- cium sulfate and calcium carbonate. Sulfate (.98) and calcium (.97) load highest on factor two -- a chemical relation previously reported in the literature (Hem, 1959). Most often, this is due to their common source, evaporates of gypsum or anhydrite. Factor three loads alkalinity (.94) and magnesium (.76), while factor four loads most heavily on silica (.77). Summer rate of pH increase is uncorrelated with all the factors, indicating its indepen- dence from other conservative qualitities. In summary, factor analysis has chosen chloride, sulfate, alkalinity and silica for further analysis. Non-Conservative Water Quality Parameters Two factors were formed from five variables (Table 11), the first comprised of phosphates and the second of nitrogen. Maximum sur- face pH correlates best with the nitrogen factor. Orthophosphate and the sum of integral ammonia, nitrate and Kjeldahl nitrogen will represent the non-conservative water quality category in further analysis. 40 Table 10. Factor analysis coefficients of conservative water quality variables. -- The coefficient underlined indicates the variable chosen for further analysis. Factor Parameter 1 2 3 4 Conductance .75 .54 .33 .17 Calcium .06 .97 .11 -.05 Magnesium .30 .50 .76 .12 Sodium .97 .06 .22 .08 Chloride .98 .00 -.07 .13 Sulfate .12 .98 .08 .11 Alkalinity .26 .14 .94 .11 Iron a Silica .19 -.13 .34 .77 Potassium .65 .48 .39 -.11 Summer rate of pH increase -.01 -.02 .00 -.36 Sodium + potassium .97 .08 .23 .07 aQuantity too small for calculations. 41 Table 11. Factor analysis coefficients of non-conservative water quality variables. -- The coefficient underlined indicates the variable chosen for further analysis. Factor Parameter 1 2 Orthophosphate a .97 .06 Total phosphate a .92 .25 Ammonia, nitrate, and Kjeldahl nitrogenb .14 .56 Ammonia and nitrate nitrogen' -.02 .53 Maximum surface pHa .27 .52 a Includes surface and integral samples. b Integral samples only. cSurface samples only. 42 Physical Lake Characteristics Mean July epilimnetic temperature (.94) followed by annual lake evaporation (.87), due to the dependence of evaporation on temperature, weight highest on factor one of the physical lake characteristics category (Table 12). Factor two is best described by mean July oxygen saturation taken every meter of depth of the lake (.77). This variable and mean July epilimnetic temperature will be used in further analyses. Sediment Parameters The three factors formed by R mode factor analysis of lake sedi- ment variables are best described by the three variables: percent sand, percent silt and total phosphorus gable 13). Percent sand (.89) was highly correlated with weight loss on ignition (.83) and Kjeldahl nitro- gen (.74), while negatively correlated with percent clay (-.76). Per- cent silt (factor two) and total phosphorus (factor three) were uncorrelated or only slightly correlated (percent silt, .85, with per- cent clay, -.61) with other variables. Dean and Gorham (1976), in their study of Minnesota lake sedi- ments, found no correlation of percent sand with carbon loss on ignition or nitrogen but did find a very heavy correlation (.96 with an n of 46) between weight loss on ignition and nitrogen content. This relation- ship, as also found in Arizona lakes, is expected because of the rela- tively constant relationship of carbon and nitrogen in similar lake types (Wetzel, 1975). The relationship of sand to nitrogen and carbon 43 Table 12. Factor analysis coefficients of physical lake characteristics. -- The coefficient underlined indicates the variable chosen for further analysis. Factor Parameter 1 2 Annual lake evaporation .87 -.17 Mean July epilimnetic temperature .94 .00 Standard deviation of July temperature (taken every meter of depth) .45 .25 Mean July oxygen saturation (taken every meter of depth) .22 .77 Summer deoxygenation rate -.51 -.15 Mean Secchi disk depth (June and July) -.04 .42 44 Table 13. Factor analysis coefficients of lake sediment variables. -- The coefficient underlined indi- cates the variable chosen for further analysis. Factor Parameter 1 2 3 Weight loss on ignition .83 -.02 .26 Kjeldahl nitrogen .74 .33 .25 Total phosphorus .33 -.04 .78 Percent gravel -.07 .08 .56 Percent sand .89 -.02 -.16 Percent silt .05 .85 .06 Percent clay -.76 -.61 .00 45 is not easily decipherable but may be due to the correlation of these variables with altitude (Table 7). Summary Table 14 is a summary of the variables chosen for further use by R mode factor analysis. It presents the variable, range of untrans- formed data and percent of variance explained in each category by the factor of which it is the highest correlating parameter. R mode factor analysis succeeded in discarding 29 variables that were highly correlated with other variables. Figure 6, a dendrograph analysis using an unweighted pair group method of clustering on the 15 selected variables, shows a marked absence of significant groups. Except for mean July epilimnetic temperature clustering with shore development at the .8 level, no other two variable groups were formed more significant than the .6 level. The goal of independence of vari- ables has been attained. In future lake studies, the results of factor analysis could be used to reduce the number of variables to be sampled. Since only the 15 variables will be included in further analysis, only data for these parameters need to be collected. 46 Table 14. Summary table of factor analysis results. -- Shows highest correlating variable in each factor, percent of variance within each category explained by each factor and range of untransformed data for the selected variable. Percent of Variance Parameter Range Explained Lake morphometry variables Shore development 1.1-6.1 a 72 Lake mean width 138-1,222 m 28 Watershed variables Watershed area 462-1,580,000 ha 84 Lake altitude 478-2,871 m 16 Conservative water quality variables Chloride 2-241 mg/1 60 Sulfate 1.4-279 mg/1 21 Alkalinity 6.1-270b 13 Silica .14-13 mg/1 7 Non-conservative water quality variables Orthophosphate .09-.55 mg/1 74 Ammonia, nitrate and Kjeldahl nitrogen .18-3.1 mg/1 26 Physical lake characteristics Mean July epilimnetic temperature 16.2-29.9°C 72 Mean July oxygen saturation 48-129% 28 Sediment variables Percent silt 5-52% 62 Percent sand 2-73% 20 Total phosphorus 540-4,120 mg/1 18 aDimensionless. bAs mg/1 CaCO3 . 47 I s-1 Al titude tr) Percent sand Total phosphorus 4-) in sediments o Percent silt 1 1 • 0 Ammonia, nitrate and , Kjeldahl nitrogen in sediments -o 4-) , Orthophosphate • • 0 .4 En PL., cts 0 "cf Mean July oxygen o 0 saturation • H cc -c) LH-o o Lake mean width 4..) c4-+ -o o 0 ,Sulfate O W E bO ,Alkalinity 3 -T-4 • H cn Silica P4 cd 4-) • cd O 'CS Chloride H 0 "0 4..) g ci) O .H Watershed area co Mean July epilimnetic 0 temperature H Shore development Lc. CHAPTER 6 CLASSIFICATION Classification of the 23 Arizona lakes in this study is per- formed using two techniques, an unweighted pair group method of clustering (illustrated in dendrograph form) and factor analysis similarly based on the correlation coefficients between lakes. This chapter presents the results of the classifications, compares the tech- niques and discusses the significance of the results. Dendrograph Presented in Figure 7 are the results of the unweighted pair group method of clustering computed for the 23 lakes using the 15 R mode factor analysis selected variables. The dendrograph is based on the Q mode correlation matrix between lakes presented in Appendix C (Table A3). Depending on the correlation stringency, a variety of classifications can be made. For instance, at the extremes, at the zero level all lakes are members of one cluster while at the 1.0 level all lakes are dissimi- lar and independent. In this discussion, two arbitrarily selected levels are discussed, .5 and .6. At the .5 level, four groups of two lakes each, one group of three lakes and one group of seven lakes can be identified as well as lakes that are independent of all others. The results are cartographi- cally presented in Figure 8. 48 • 49 Lr) oo • 1—I 1 1 1 1 1 I a) 4-) Lee Valley Reservoir 44.) 4 m River Reservoir .i-i o Reservation Lake a) (,)a) 4-)-. , Horseshoe Cienega = 4-) I M 'm....f.-14r"...= -o Willow Springs Res. • a) U) O V/ Big Lake v--4• H ______4 a) Knoll Lake $-1 $.-4 cd Cooley Lake Lynx Lake Sunrise Lake Upper Lake Mary Cd • g O g Bartlett Reservoir N•H •H • 0 •c4 Canyon Lake m tf) 4-4 o Lyman Lake O 4-1 o Upper Lake Pleasant •H 0 ;•••I ..g • Fools Hollow Lake coz E cd • ol) • 0 Parker Canyon Lake 0 ;-1 4 F-4 '73 P4 td) g cd Peña Blanca Lake 1:4 •H O t S-1 P4 Lake Patagonia -0 4-) g Q) a) 4-I "c:$ 4-) 0 • b.0 •ri Becker Lake O 0 0.0 E Ashurst Lake 0' t-- Nelson Reservoir Luna Lake 50 ( iv I . L.. 1 I. I , 4 Li • 1 + , • • N • ..<1 I • 1• ) • .. . 5 1+ %.... 1 1 2 6 • — •...... • - • I • + I— N• .. •% • --: • 1 3 ? I I 1 • L.\ Li "/ i r/ * .., • -; N • ...._.__.rt k. I 1. j - 2 i . I • I 1 . .1. I I ) 4n1= nn I-- . • ••....i1• • • •• • .1nI•M • . ''.. 1 ' i • Figure 8. Lake groups formed by Q mode dendrograph clustering, presented geographically. -- The numbers represent the sig- nificant clusters in Figure 7. Those lakes marked "+" are independent. 51 The large group, made up of Mogollon Rim lakes (Knoll Lake and Willow Springs Reservoir) and White Mountain lakes (Big Lake, Horseshoe Cienega, Reservation Lake, River Reservoir and Lee Valley Reservoir), represents the majority of high altitude, high rainfall lakes in the study. Additionally, all of these lakes have small watersheds (less than 14,200 hectares, mean of 3,700 hectares). Sunrise Lake, the one high altitude, White Mountain lake not included in the seven lake cluster, grouped with Upper Lake Mary, located southeast of Flagstaff. These lakes, although of dissimilar lake and watershed morphometrics, have similar water quality problems. Upper Lake Mary is mechanically aerated during the summer, apparently due to previous summer deoxygenation problems, while Sunrise exhibits severe summer deoxygenation (to 48 percent total mean oxygen concentra- tion in this study). The group formed by Upper Lake Pleasant and Lyman Lake, the largest lakes in this study, correlates at approximately the .35 level with the cluster of the second two largest lakes, Canyon Lake and Bartlett Reservoir. The latter two impoundments, on the Salt and Verde rivers, respectively, are located in the Lower Sonoran Desert Scrub vegetation zone (Table 2) and are used for hydroelectrical generation and water supply to the Phoenix area. Chemically, these lakes appear quite dissimilar because Canyon Lake has a very high chloride concentra- tion due to the solution of salt deposits in the Salt River's watershed. However, the lakes aie similar in other characters, such as alkalinity, silica and factors of size and watershed topography. 52 Upper Lake Pleasant and Lyman Lake are also of irrigation water storage importance and, as such, tend to be reduced to very low levels every summer. These lakes, at very different altitudes, are located in separate geographic and vegetational regions of the state (Figure 1, Table 2). Upper Lake Pleasant is in the Sonoran Desert south of the Mogollon Rim at an elevation of 478 meters, while Lyman Reservoir is north of the Mbgollon Rim in high altitude grasslands. These lakes, however, exhibit similar water quality (sulfate, alkalinity and silica) and sediment characteristics (phosphorus, clays and loss on ignition). The group formed of Pena Blanca, Parker Canyon and Fools Hollow lakes is significant at the .52 level; at the .6 level, only Parker Canyon and Fools Hollow lakes cluster. All three lakes are located in similar vegetation zones, that of Oak-juniper woodland (see Table 2). Fools Hollow Lake occurs near the edge of the Mogollon Rim in the vicinity of Show Low at the boundary of the Ponderosa pine and Oak- juniper woodland vegetation zones, while Parker Canyon and Pena Blanca lakes are located in southern Arizona at the base of the Huachuca and Atascosa Mountains, respectively. Chemically, all three lakes are simi- lar although Fools Hollow has twice the sodium and chlorides and about ten percent less specific conductance. Luna Lake and Nelson Reservoir, the last lake group, are of geo- graphic proximity, both on the foothills of Escudilla Mountain on the Central eastern boundary of Arizona. Becker Lake, which clusters with Luna and Nelson at approximately the .35 level, is the only other lake from this drainage included in the study. Chemically, Luna and Nelson are very similar while Becker exhibits approximately double the 53 alkalinity concentration of the other two lakes. Becker Lake, built in 1880, is one of the oldest lakes in the state. It is supplied with water only by aqueduct connection to the Little Colorado River and is an evaporation basin. At the more stringent .6 level of clustering, Upper Lake Pleasant, Lyman Reservoir, Canyon Lake, Bartlett Reservoir and Pena Blanca Lake become independent of all others, while the cluster formed by Knoll and Big lakes is removed from the large White Mountain-Mogollon Rim lake group. The results are surprising since Knoll and Big lakes are dissimilar in most aspects of water quality (for example, specific conductance, sodium, chloride, sulfate and alkalinity). However, they are similar in aspects of watershed topography (area, slope and mean rainfall per hectare). Furthermore, Knoll is apparently a very unpro- ductive lake of dystrophic nature (it has a brown color) with a very limited fish population (many dead flies were observed on the lake sur- face in July, 1976, indicating a meager insectivorous fish population), while Big Lake is considered one of the better White Mountain fishing lakes. Factor Analysis Results of Q mode factor analysis of the 23 lakes using the 15 R mode selected variables are presented in Table 15. In this analysis, one can define each factor, seven of them, as groups of lakes or clus- ters. A high positive number indicates high positive correlation with that factor while a negative number (near negative one) indicates a high negative correlation with that factor or cluster. - 54 Lie) CD CO CD CO r- Ln CD Cn un VD 00 et un 1--( 00 an VD rn Cn rq Cn t..") 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I:li 0' .1..) ..1 0 .4 .4 o o cd 4-) .--i ,.. m x 1=4 E...) no .4) 0 cd cd un 4-) 4-) .. = 4= .] cd ,--i cd .- cd ,--1 m o4 a) .4 4 En G) /..1 cd g >, En a., cd .4 .4 g H oc) ? En 3 H .--1 a) 4 0 a) En o ,-1 ›. g oc H H •H H H 0 4-) ... , 1-1 1-1 V) ,-4 (1) cd cd En x cd a.) a) HI o o ,--i -gI-4() EuDg00H40Xa)gaP ,-IHI?gEn I:)...a4,-E cn cd 0 •H cd 000 g cc a) ›, ›, a) cd a) a) •.-4 .2 on an ta c...)L) a-, X .4 4 4 4 .4 Z a. a, r:4 a4 cn cd CNI r"... 00 CA CD eq "r un VD r- 00 Cn C) IN V) ri ri ri ri ri ri ri ri ri ri NI Cg rq (N.1 55 Unfortunately, the presence of a high negative correlation, indicating a lake being very different from other lakes in the cluster, makes interpretation of the results harder than in the dendrograph tech- nique. In fact, the negative correlates and the positive correlates are distinct groups of lakes, thus increasing the net group number formed by factor analysis classification. In Table 15, the highest correlation number, whether positive or negative, for each lake has been underlined to show the primary location of that lake into a factor (= group = cluster). The results of factor analysis are further displayed to show geographical location and relation in Figure 9. In this illustration, each lake's factor number and whether the relation is positive or nega- tive is indicated. The first factor selects four White Mountain lakes (Horseshoe Cienega, Lee Valley Reservoir, Reservation Lake and River Reservoir) as high positive correlates and two southern Arizona lakes, (Parker Canyon Lake and Pena Blanca Lake) as negative correlates. In the dendrograph (Figure 7), the same four White Mountain lakes cluster very signifi- cantly at the .75 level, while Pena Blanca Lake and Parker Canyon Lake cluster in a group related to the White Mountain lakes only through the zero correlation level of the tree. The factor analysis results in the first cluster, then, are in accordance with the dendrographic technique. The reasons behind the extreme separation in the two lake groups stem from differences in altitude and specific conductance and their corre- lates (see Chapter 4 for a geographical analysis of water quality distribution). 56 Figure 9. Lake groups formed by Q mode factor analysis, presented geographically. -- The numbers represent the factors in Table 15 and whether the correlation is positive or negative. Circled numbers indicate the independent lakes. 57 The second factor analysis cluster is loaded positively by Lynx Lake (near Prescott) and Willow Springs Reservoir (near Heber) and nega- tively by Lyman Lake and to some degree (only -.47) by Upper Lake Pleasant. The relationship of Willow Springs Reservoir and Lynx Lake was not significant in the dendrograph analysis, while the latter rela- tion between Lyman Lake and Upper Lake Pleasant was. Again, these two sets of lakes are found in clusters separated by great distance on the dendrograph tree. Bartlett Reservoir and Canyon Lake load negatively on factor three, while only Big Lake loads positively. The same results for Bartlett Reservoir and Canyon Lake were found in the dendrograph analysis, while Big Lake, a White Mountain lake, clustered with Knoll Lake, a Mogollon Rim lake. The last factor, number four, making up greater than ten percent of the variance in Q mode factor analysis, is negatively weighted by Sunrise Lake and Upper Lake Mary (a relationship significant at the .6 level in the dendrograph) and positively by Becker Lake. Factors, five, six and seven, with three, two and one lake, respectively, make up less than ten percent of the variance each. The combination of Ashurst Lake, Luna Lake and Nelson Reservoir in factor five is significant as a cluster at about the .48 level in the dendro- graph, while factor six constituents Fools Hollow and Knoll lakes are very far apart in the tree. Cooley Lake, the lone member of factor seven, clusters with the White Mountain and Mogollon Rim cluster in the dendrograph. 5 8 Comparison of Dendrograph and Factor Analysis Classifications The results of dendrograph and factor analysis classifications differ in one major way -- the handling of the Mogollon Rim lakes. Where the dendrograph placed the Mogollon Rim lakes Knoll Lake and Willow Springs Reservoir with the White Mountain lakes, and Fools Hollow Lake with Parker Canyon Lake (southern Arizona), factor analysis placed the Rim lakes in their own categories. Factor two, positively loaded by Lynx Lake and Willow Springs Reservoir, contains two lakes geographically distant but on the same geological formation; whereas the dendrograph elucidated Lynx as an independent lake, only related to others at the .35 level. Factor six, Fools Hollow and Knoll lakes, are classified in unrelated clusters in the dendrograph but in factor analysis there is evidence of similarity. Reviewing, the results of the two types of analyses are similar except for a few differences, most notably that of the handling of the Mogollon Rim lakes. Additionally, in the dendrograph, but not in factor analysis, Ashurst Lake, Lynx Lake and Lake Patagonia were independent of all other lakes, while factor analysis removed only Big Lake as indepen- dent (with this result not present in the dendrograph). A Classification of Arizona Lakes When the results of the two analyses, dendrograph and factor analysis, are compared, six groups of lakes overlapping in the two classifications can be formed (Table 16). Figure 10 geographically illustrates this grouping. The new classification, formed by the inter- section of the two other systems, identifies the lake groups as: 59 Table 16. Lake groups formed by the overlap of dendrograph and factor analysis techniques and their constituents. -- Group numbers are used in referring to geographi- cal location (Figure 10). Lake Group Constituents 1. White Mountain Horseshoe Cienega Lee Valley Reservoir Reservation Lake River Reservoir 2. Southern Arizona Parker Canyon Lake Pena Blanca Lake 3. Salt River-Phoenix water Bartlett Reservoir supply Canyon Lake 4. Escudilla Mountain Luna Lake Nelson Reservoir 5. Miscellaneous large irrigation Lyman Lake Upper Lake Pleasant 6. Miscellaneous high altitude Sunrise Lake Upper Lake Mary Independents Ashurst Lake Becker Lake Big Lake Cooley Lake Fools Hollow Lake Knoll Lake Lake Patagonia Lynx Lake Willow Springs Reservoir 60 i / i #• ....• ../. I 1 I f i i_ i-%. 1 • N.. I ...... 1 i ; n 6 4 i I L. - ^ • I i I i . j 1 ' 5 I ee e•• i .0.- • ..\ 1 f -. e ie t 1...._•_•. •-... e(!1 s 4 ••n • n •-••-1 %. ...• 5 3 1• ! i 1 1 tt 4 I 1 . .f.... . 1 3/.‘ sk /.T . .e•-• ' • ./ • 1 e I I.- - --r i I . ,•••-n I. • I • I %. I . -1 L. . s . i 1 *) • _ . _ • s . _• _ . _ . ..I.. Figure 10. Lake groups formed by the overlap of factor analysis and dendrograph clustering. -- Those lakes marked "*" are independent of all others. Group numbers refer to those lakes listed in Table 16. 61 1) White Mountain lakes, 2) southern Arizona lakes, 3) Salt River- Phoenix water supply lakes, 4) Escudilla Mountain lakes, 5) miscellaneous large irrigation lakes and 6) miscellaneous high altitude lakes. These groupings and their constituent lakes are summarized in Table 16. As observed by comparison of the lake classification map (Figure 10) with the maps of dendrograph clustering (Figure 8) and factor analysis (Figure 9), Lake Patagonia fits into the southern Arizona lake class and Ashurst Lake into the Escudilla Mountain classifi- cation (which now should be renamed) and Fools Hollow Lake clusters with the southern Arizona lakes (and this category could now be renamed the Oak-juniper woodland group) and Knoll Lake and Willow Springs Reservoir join the White Mountain lakes and this category would be renamed the White Mountain-Mogollon Rim group. A number of lakes (Becker Lake, Big Lake, Cooley Lake) still remain independent of all others. The ability to classify Arizona man-made impoundments is an important result of this study. Classifications are usually performed on lakes of great age [although Wetzel (1975) considers less than 25,000 years very young], primarily those in North America formed during the Pleistocene glaciations, on relatively uniform geological substrate and within regions of uniform climate (Frey, 1966). Furthermore, classifi- cation is usually made with only one or a few variables. By comparison, Arizona lakes are young, Becker Lake constructed in 1880 being the oldest of those studied, of greatly varying geographical location (Figure 1) and in very different climatic regions (Figure 5). However, as discussed in Chapter 4, the lakes show geographical water quality 62 trends correlated with climatic factors and in accordance with global trends. The lake groups formed are, in many ways, intuitively pleasing. Generally high altitude lakes, of similar climatic regimes and vegeta- tional assemblages grouped together. The Mogollon Rim lakes, primarily due to their low productivity (pers. observ.) possibly due to a sand- stone (quartz) substrate of very slow chemical leaching quality (Pettijohn, 1975), also clustered together. Factor analysis showed that Lynx Lake, near Prescott on the western part of the Mogollon Rim, clustered with other Rim lakes. Lynx Lake's management plans probably should be of Mogollon Rim lake association rather than that of a highly productive lake. CHAPTER 7 PREDICTION OF WATER QUALITY IN UNCONSTRUCTED IMPOUNDMENTS Classification of Arizona lakes implies that water quality, here defined as chemical, physical and biological aspects of water, ought to be predictable. If this were not the case, classes of similar lakes would not have been found. This section begins with a discussion of the relationships of a watershed to lake water quality to show that a basis for water chemistry prediction based on parameters obtainable prior to lake impoundment exists. What Governs Water Quality? A model showing a lake's location in an ecosystem has been sug- gested by Likens and Bormann (1975). They characterize three major components: 1) inputs from the watershed; 2) biological, Chemical and physical phenomena within the lake and 3) stream output and sediment deposition. In this section, these subjects will be studied, in partic- ular as they apply to Arizona impoundments. Water enters a lake in two ways, by watershed runoff (including underground flow) and by precipitation directly on the lake surface. In all areas of Arizona annual lake evaporation greatly exceeds annual pre- cipitation to the lake surface (Arizona Resources Information System, 63 64 (1975); therefore, in order to maintain water in a lake, the majority of input must be contributed by the watershed. For that reason, in this discussion, only watershed inputs will be considered. Precipitation incorporates other atmospheric materials derived from the sea, land surface, volcanoes, air pollution and organic debris. These may also be transported in dry form by wind or in a gaseous form (Gorham, 1961). Likens et al. (1977) found that bulk precipitation and its gaseous inputs were the major sources of sulfur, nitrogen, chloride and phosphorus in an eastern forested watershed. In many cases, the supply of ions by soil and rock weathering (solution, oxidation-reduction reactions, activity of hydrogen ions and complex formation) is more important than atmospheric sources (Gorham, 1961). Likens et al. (1977) found this to be true for their fifteen- year study of the Hubbard Brook Ecosystem in New Hampshire. They found, as Gorham (1961) suggested, that the watershed is the major factor influencing stream water quality. Size of the watershed, holding time of water within its boundaries for chemical exchange (Brady, 1974), vegetation of the watershed for nutrient storage and resupply in plant litter (Likens et al., 1977), geological substrate available for chemical weathering (Miller, 1961) and initial chemical content of precipitation (Likens et al., 1977) interact to influence the final water quality inputted from the watershed to the lake. In mesic areas, the influences of a watershed on lake water quality is consistent from year to year where rainfall and climatic con- ditions do not vary. However, in arid regions, including Arizona, where inputs of organic and inorganic nutrients vary with the highly seasonal 65 and unpredictable rainfall (Sellers and Hill, 1974; pers. observ.), nutrients supplied from watersheds may change annually. This was shown by Gregory (1976) in his study of water flowing from watersheds covered with Ponderosa pine and discussed by Olsen and Sommerfeld (1977). Hem (1948) found that fluctuations in the concentration of dissolved solids in water of streams of the Southwestern United States fluctuates exten- sively as the stream discharge changes with varying precipitation in a time span of a few days or even a few hours. In light of the unpredictable inputs that may be generated from watersheds in areas of irregular rainfall, how can one suggest that Arizona lake water quality might be predicted? A basic premise of this section is that in some way the lake, as a water storage basin, should be an indicator of processes of the watershed. Biological, chemical and physical cycling within the lake as well as stream and sediments outputs may substantially modify the water quality [see Wetzel (1975) for dis- cussions], but there is evidence from Kemmerer (1965), Kemmerer et al. (1968) and Stewart (1967) that inflow waters to Arizona lakes are corre- lated with water quality and organic productivity. Multiple Regression Predictions of Water Quality In Arizona, high temperature and low oxygen content of waters result in summer fish kills (Ziebell, 1969). Water quality variables such as chloride, alkalinity, silica, sulfate, nitrogen and phosphate as well as the sediment variables sand, silt and phosphorus govern what animals and plants can live in the water column and bottom sediments. The lake planner with knowledge of water and sediment quality, 66 forecasted prior to lake impoundment, might predict the potential fishery, whether it be cold or warm water and whether to expect fish kills. He also could attempt prediction of water quality of waters for municipal, irrigation, and recreational use. Based on variables selected by R mode factor analysis, four parameters obtainable prior to lake construction (altitude of the lake, shore development, lake mean width and watershed area) are used as pre- dictors of water quality using multiple regression analysis. Table 17 shows that 95 percent of the variance in mean July epilimnetic temperature is predictable using data available prior to construction (altitude of the lake). This relationship is not surprising and is attributable to the climatological relationship of increasing temperatures at lower altitudes (Strahler, 1972). Mean July oxygen saturation, the other variable important in predicting summer fish kills, is only 26 percent explained by the pre- lake construction determinable parameter, mean lake width. Generally, decreased mean width indicates placement in a narrow canyon where lakes tend to be deep. A basic assumption of Ryder et al. (1974), in their review of the morphoedaphic factor as a predictor of fishery potential, is that the deeper lake is relatively more oligotrophic and exhibits less of an oxygen deficit. The negative relationships, where as lake mean width decreases mean July oxygen saturation increases (indicated by the -.46 slope of the regression), reinforces this point. Hutchinson (1975) discusses the chemical ecology of plant life in lakes and shows that chloride, alkalinity, silica, sulfate, nitrogen and phosphorus, among others, are important for plant growth. Multiple 67 Table 17. Results of multiple regression analysis by water quality variables on variables of pre-lake construc- tion determinability. -- Independent variables marked "a" are significant at the .05 level, while those marked "b" are significant at the .10 level. Vari- ables in the equation have been Z transformed. Multiple R squared is the proportion of variability explained by the independent variables. Multiple Dependent Variable Independent Variable (s) R Squared Mean July epilimnetic -1.09 altitude a .95 temperature Silica .84 watershed areaa .60 + .42 mean lake widtha Chloride .55 watershed areaa .50 Percent sand in 1.06 altitudea .44 sediments + .45 watershed areab Mean July oxygen -.46 lake mean width a .26 saturation Alkalinity -1.10 altitude a .23 -.96 shore developmentb Sulfate -.47 altitude b .20 Percent silt in -.46 lake mean widthb .17 sediments Total phosphorus in .48 altitudeb .17 sediments + .42 lake mean widthb Ammonia, nitrate and -.82 shore development" .06 Kjeldahl nitrogen Orthophosphate none 68 regression analysis results using these chemical substances as the independent variables indicate that all, except orthophosphate, are to some degree predictable by variables obtainable prior to impoundment. The prediction of 60 percent of the variability in silica is based on the positive relationships with watershed area and lake mean width (Table 17). Prediction of chloride (50 percent of the variance is explained) as well as silica is probably attributable to the long time available for solution, evaporation and concentration while waters move through the larger watershed area (Stull and Kessler, In Press; Livingstone, 1963; Gibbs, 1970). This is the basis for the finding of higher salinities at lower elevations, especially in arid regions. The remaining variables of water quality (alkalinity, sulfate, nitrogen and orthophosphate) are each less than 25 percent explained by the independent variables (Table 17). The reasons for this is probably the annually and seasonally changing input of organic matter to the lake bodies. Predictions are difficult when samples at only two points in a year have been taken. Alkalinity (23 percent of the variance explained) and nitrogen (6 percent of the variance explained) prediction on the basis of shore development may be valid, however, because of the interaction of the shore and lake water. Shore development reflects the potential littoral community development in relation to lake volume. A larger littoral community collects more nutrients, removing these chemical substances from the lake waters tHutchinson, 1975). Although the relationship to shore development is not highly significant, prediction of alkalinity and nitrogen levels by shore development may be useful. 69 The results for percent sand in sediments are peculiar (17 per- cent of the variance explained) since the relationship includes a posi- tive value for both altitude and watershed area, which themselves are negatively correlated (Table 7). Altitude, significant in the equation at better than the .05 level, is the better predictor, however, and is easy to obtain. The higher altitude lake generally has the smaller watershed and is nearer the source of geological weathering than the lower elevation lake. Compounding the situation is the washing out of fine sediments in the high altitude lake. Percent silt in sediments, predicted by the inverse of lake mean width, is corroborated by analysis of particle size in relation to depth of a lake. Hutchinson (1975) indicates that higher silt concentrations are found in the deeper parts of a lake, while larger particles are generally found in the shallow regions. This implies that fine particles settle closer to the downstream end of the lake unless the lake is wide, in which case there will be settling of silt in a large area. Hence, the relative quantity of silt at any point is decreased in a wide lake. The results are an artifact of the sampling location which was always placed at the lower end of the lake, near the dam. The prediction of total phosphorus in the sediments (17 percent of the variance explained) shows a positive relationship to altitude and lake mean width. This may be partly due to alloChthonous sources of particulate matter directly entering the lake from the shore; at high elevations trees often grow directly over the lake surface (pers. observ.) and contribute large quantities of litter. The reason for the relationship of lake mean width and total phosphorus in sediments is not 70 Clear. Generally, the results of regression analysis for phosphorus (and probably nitrogen and carbon) are masked because of the annually inconsistent supply of organic matter from the watershed to the lake (McConnell, 1968; Rinne, 1973; pers. observ., 1975-1978; Olsen and Sommerfeld, 1977). Analysis shows that we can predict, prior to lake construction, many variables of post-impoundment water and sediment quality. This ability may enable the lake biologist to make general predictions of the faunal and floral make-up of a lake based on expected water and sediment quality. Obviously, the equations presented cannot be assured to work but need testing on lakes omitted from the study. The results are also noteworthy because, in spite of temporally heterogeneous inflow, greatly varying geomorphic and vegetational loca- tion and very young age, predictions are still possible. Further studies of Arizona lakes are suggested by this inquiry. These include relating biological characteristics (algae, zooplankton, fishes) of the lakes to the water quality parameters of this study, extending the study to include an entire year's sampling to elucidate seasonal effects and finally doing a similar type classification analysis in a year climatically very different to see how lake groups change. CHAPTER 8 CONCLUSIONS 1. Arizona man-made lakes can be classified into groups based on variables of water quality, lake and watershed morphometrics and sediments. 2. These variables can be reduced in number by studying the inter- correlation of variables with factor analysis. Henceforth, only a few of the most significant variables need be collected, saving time and money. Furthermore, use of uncorrelated variables reduces errors in analysis due to the dominance of a few highly intercorrelated variables. 3. Classification leads to associations of lakes which can be categorized into six major groups. They are: White Mountain, southern Arizona, Salt River-Phoenix water supply, Escudilla Mountain, miscellaneous large irrigation and miscellaneous high altitude lakes. 4. The finding that lakes within regions are similar shows that predictability of lakes occurs in spite of very young age. 5 Also, between regions, classification is possible so that lakes of similar location (geologically, vegetationally and climati- cally), although geographically distant, act in similar ways. This is exemplified by Lynx Lake clustering with the Mogollon Rim lakes located over 125 miles distant. 71 72 6. Since classification has shown that the lakes are "predictable," this means that aspects of water quality may also be predictable. Calculation of Chemical and physical water and sediment quality based on variables obtainable prior to impoundment should sig- nificantly help the fishery manager and lake planner in pre- dicting fishery potential and suitability for recreational needs in as yet unconstructed lakes. 7. Before all predictions are utilized, tests of the predictions should be corroborated by analysis with already constructed lakes not included in this study. APPENDIX A RAW DATA FOR VARIABLES USED IN FACTOR ANALYSIS 73 74 cD cn cy rO ch Ch Ch CD ro ch t rn ch Cn r, CD et Cv vp CD Ln oq ri Ni NI NI ri rq cg h) rn Cv cq et tn e-1 et co 1n1 r, cr Nn et Ch Un et et CD n-n un rl Cl 00 Ch r- CA 00 T7 01 t, es1 C71 cr vD Cn ct ct un 1-4 r- cV cv un cv cv rn ro ce) or) 1-1 Cq r...1 V) V) 00 V:, (N \XD CD CD Ln CV co et ct co CD CD Ln vD V; vD CD vD CD cr cv CD CD Cv uo r, un Nn 00 vp .0 cv r, un ro CD rn cr cv cn c0 vD r- C:) r- cv CA et Ln et te) NI N. cO cl' CD un C- CD 00 r- 03 r- • • • • • • • • • • • • • • • • • • V) 1••1 ri NI ri ri ri NI ri et ri ri ri ri (N ri te) Ln ri VD CD cr un CD 00 CD ..'2" CD CD CD Ul CD cr CD CD CD CD Cv o0 CD CD un CV cv eV C3 Nn CO VD VD CD NI eg et CD CD CV ct cv 00 et vD CD r, ev )-4 cv ct ct cr Ln Cn r- 00 r, rn 00 un cr cv 00 CD 00 et r- 0, • •S " • • • .1 • • • • • • • • as le, • .7r CNI V), ) te) r...! ri te) vD cv )-1 rn ct Nn N. 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Shore development 2.64 1.28 Mean sample depth 12.1 5.8 Maximum length 3,814. 3,814. Maximum effective length 2,090. 1,368. Mean width 383. 284. Lake shape factor 0.23 0.08 Altitude 1,959. 742. Watershed area 170,818. 450,976. Length 31.5 48.1 Slope 0.05 0.03 Total rainfall 79,153. 209,521. Mean rainfall per ha 0.61 0.16 Watershed shape factor 0.02 0.01 Conductivity 227. 232. Calcium 12.5 14.7 Magnesium 9.0 8.2 Sodium 18.5 30.5 Chloride 18.3 48.9 Sulfate 28.1 57.9 Alkalinity 90.6 69.2 Iron 0.04 0.02 Silica 3.73 3.16 Potassium 4.17 2.22 Summer rate of pH increase 0.29 0.98 Sum of sodium and potassium 0.90 1.35 Orthophosphate 0.16 0.10 Total phosphate 0.22 0.12 Ammonia, nitrate and Kjeldahl 0.81 0.61 Ammonia and nitrate 0.24 0.12 Maximum pH 8.45 0.82 Lake evaporation 157. 16. Mean epilimnetic temperature 21.8 3.8 Temperature, standard deviation 3.06 2.41 Mean 02 saturation 104.7 17.4 Deoxygenation rate .57 .53 Mean secchi disk 2.83 1.44 Loss on ignition 10.6 4.4 Kjeldahl nitrogen 2,832. 1,495. Total phosphorus 1,642. 944. Gravel 1. 3. Sand 26. 16. Silt 28. 12. Clay 44. 19. APPENDIX C Q MODE CORRELATION MATRIX OF THE 23 ARIZONA LAKES IN THE STUDY Analysis is based on 15 variables. Lake numbers correspond to the lake names in the left column. 83 84 O 0 0 n . . . . . O O o O NON, . • • • • ON .0 CNO ,r0 n n • • • • • • • • • 1.1 rel O h e VI • • • e .0 ,001,1000301 000R0Remr+ ...... 0 RCOONOCINNIA C el• NI C e. .• • • • • • • • • • ON '0e t n u N •m• • • • • • • • • • • . nw I 0 CA CC, er CO in el e ve tin 01 '0 •-• •-• 0 01 c. 0000,Lnaceo=0,Rtz, 0.-.6nHORP1INCIN0,0 • • • • • • • • • • • • • R 1 0 RI rUN N N CrIRORN,RMITOIRN C or eoeNoet--e'ONO uI- n 0 — rilri ri-0enne, 0n,...,• O raNraririn rieri el C noon ,g) n n 4.11 C • • • • • • • • • • • • • • • • AA v. .0 e ON CO .0. MCN0N en fee el 00 0 ^ O cesse Cli,v7,0010.0. N NCO,COION 001.nONCOWIR , VINt/IMINPI1 1,1 ,1 ;r:', " P. .1,7 e N O " COO n ‘.2 N n C 01 .0 n O e NON.. •WeINV•I0•Re0 R r ZNO0CRN,N,000 001011.1.1.0.erC.fOul 04 C RIIINNR0 • • • • • • • • • • • • • • • • • • • • • el cc) fei 1./1 0 el ef CO CT ca• C fel fcr 0=0.1.fler—ef — IA 01 n elf el C. el VI n ce el ori riN—eOOe,IPIRFROC I. .. -- 0 e O 0 , 01 g ..4 A . .000 . f. 0. 0 0 0 e." . -, .. ..a . I. ..1., 1 2 O 0 :0R• ). Z -' s ... f, > - -. -1 o v - :... ea . E > .. t e LITERATURE CITED American Public Health Association. 1971. Standard Methods for the Examination of Water and Wastewater, 13th Ed. Washington, D. C., 874 pp. Arizona Resources Information System. 1975. Average annual lake evapo- ration. Publication No. 5, prepared under the direction of the State Climatologist, Arizona State University, Tempe. Scale 1:1,000,000. Bergersen, E. P. 1969. Some factors affecting fish forage production in four Arizona lakes. M.S. Thesis, The University of Arizona, Tucson. 89 pp. Bersell, P. D. 1973. Vertical distribution of fishes relative to physical, chemical and biological features in two central Arizona reservoirs. M.S. Thesis, Arizona State University, Tempe. 64 pp. Biggins, R. G. 1968. Centrarchid feeding interactions in a small desert impoundment. M.S. Thesis, The University of Arizona, Tucson. 44 pp. Bouyoucos, G. J. 1927. The hydrometer as a method for the mechanical analyses of soils. Soil Sci. 23:343-353. Bouyoucos, G. J. 1936. Directions for making mechanical analyses of soils, the hydrometer method. Soil Sci. 32:225-228. Brady, N. C. 1974. The Nature and Properties of Soils, 8th Ed. MacMillan, New York, 639 pp. Brylinsky, M. and K. H. Mann. 1973. An analysis of factors governing productivity in lakes and reservoirs. Limnol. and Oceanogr. 18:1-14. Carpenter, J. H. 1965. The Chesapeake Bay Institute technique for Winkler dissolved oxygen method. Limnol. Oceanogr. 10:141-143. Cole, G. A. 1966. The American southwest and middle America. In D. G. Frey (Ed.), Limnology in North America. The University of Wisconsin Press, Madison, pp. 343-434. 85 86 Cole, G. A. 1968. Desert limnology. In G. W. Brown, Jr. (Ed.), Desert Biology, Vol. I. Academic Press, Inc., New York, pp. 423-486. Dean, W. E. and E. Gorham. 1976. Major chemical and mineral compounds of profundal surface sediments in Minnesota lakes. Limnol. and Oceanogr. 21:259-284. Frey, D. G. (Ed.). 1966. 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