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1979 Prediction of total phosphorus concentrations and trophic states in natural and artificial lakes: the importance of phosphorus sedimentation Daniel Evans Canfield rJ . Iowa State University

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CANFIELD, DANIEL EVANS, JR. PREDICTION OF TOTAL PHOSPHORUS COKCEMTRATlOMS AND TROPHIC STATES IN NATURAL AND ARTIFICIAL LAKES: THE IMPORTANCE OF PHOSPHORUS SEDIMENTATION.

IOWA STATE UNIVERSITY, PH.D., 1979

Universi^ Microfilms International soon, zeeb road, ann arbor, mi 48io6 Prediction of total phosphorus concentrations and

trophic states in natural and artificial lakes:

The importance of phosphorus sedimentation

Daniel Evans Canfield Jr.

A Dissertation Submitted to the

Graduate Faculty in Partial Fulfillment of the

Requirements for the Degree of

DOCTOR OF PHILOSOPHY

Department: Zoology Major: Zoology (Limnology)

Approved:

Signature was redacted for privacy.

Signature was redacted for privacy.

For the Major Department

Signature was redacted for privacy.

For the Graduate College

Iowa State University Ames, Iowa

1979 ii

TABLE OF CONTENTS

Page

INTRODUCTION 1

PART I. PHOSPHORUS SEDIMENTATION IN SOME NATURAL AND ARTIFICIAL IOWA LAKES 4

INTRODUCTION 5

METHODS 7

RESULTS AND DISCUSSION 15

CONCLUSIONS 37

PART II. PREDICTION OF TOTAL PHOSPHORUS CONCENTRATIONS AND TROPHIC STATES IN NATURAL AND ARTIFICIAL LAKES 40

INTRODUCTION 41

DATA BASE 45

PHOSPHORUS SEDIMENTATION RATES 49

MODEL VERIFICATION 55

PREDICTION OF TROPHIC STATES 75

DISCUSSION 81

SUMMARY 85

LITERATURE CITED 88

ACKNOWLEDGMENTS 91 1

INTRODUCTION

Many natural and artificial lakes throughout the world have undergone rapid eutrophication due to human activities. Because high densities of plankton algae can seriously degrade water quality, lake management pro­ grams often seek to prevent the development of excess growths of algae in lakes that have low algal populations and reduce algal levels in lakes that have high densities of plankton algae. Studies on natural lakes have demonstrated a strong correlation between algal population densities as measured by Chlorophyll ^concentrations and total phosphorus concentra­ tions (Sakamoto, 1966; Dillon and Rigler, 1974a; Jones and Bachmann,

1976). These findings suggest that phosphorus is the element controlling algal biomass in a broad range of lakes. For this reason, most lake management programs attempt to reduce and control the amounts of phospho­ rus in a lake. Because lake managers must weigh the cost of nutrient control against the potential benefits, quantitative relationships are needed to predict the response of lakes to changes in nutrient concentra­ tions.

In response to this need, considerable research effort has been de­ voted to understanding the process of eutrophication (Edmondson, 1961;

Shannon and Brezonik, 1972; Schindler et al., 1973; Jones, 1974; Dillon and Rigler, 1974a; Jones and Bachmann, 1976). An important finding of this research was the strong correlation between algal population densi­ ties as measured by chlorophyll ^ concentrations and total phosphorus con­ centrations over a broad range of lakes. The phosphorus-chlorophyll ^ relationship provided a means for understanding the differences in the 2 algal densities among lakes and a means of estimating the expected effects of changes in phosphorus concentrations on algal biomass within a lake. A strong relationship was also shown between water clarity as measured by use of a Secchi disc and chlorophyll à concentrations (Bachmann and Jones,

1974; Dillon and Rigler, 1975). Because the general public often equates water quality with water clarity, the chlorophyll ^a-Secchi relationship provided a means of estimating the public response to reductions in algal levels. However, effective use of these tools required the development of a method which could predict phosphorus concentrations in lakes.

Recently, simple empirical phosphorus loading models have been de­ veloped to predict the total phosphorus concentration in a lake from data on phosphorus inputs as modified by losses of phosphorus through the out­ let and to the sediments (Vollenweider, 1969; Dillon and Rigler, 1974b;

Vollenweider, 1975; Kirchner and Dillon, 1975; Chapra, 1975; Jones and

Bachmann, 1976; Larsen and Mercer, 1976). Because these models require a minimum of data, lake managers and researchers are using them to predict the response of lakes to changes in nutrient inputs (Chapra and Robertson,

1977).

Though the phosphorus-chlorophyll ^ and chlorophyll ^-Secchi rela­ tionships and the empirical phosphorus loading models have been used with success on selected lakes (Jones and Bachmann, 1976; Chapra and Robertson,

1977), the general applicability and potential limitations of these quantitative relationships have not been addressed. An obvious deficiency of these models is that because phosphorus losses to the sediments are difficult to measure directly, they are estimated by using empirical equa­ 3 tions developed from small data bases. Thus, the models may not be appli­ cable to both natural and artificial lakes over a broad trophic range.

This study was designed to determine the limnological factors that influence phosphorus sedimentation rates in natural and artificial lakes in order to determine the general applicability of empirical phosphorus models. The study was divided into two major parts. Part one involved the use of sediment traps to directly measure phosphorus sedimentation in four natural and four artificial lakes. The objectives were to determine the factors that influence sedimentation and whether these factors are different in natural and artificial lakes. Part two was based on an analysis of phosphorus budget data from 290 natural and 433 artificial lakes. The objectives were to determine which limnological factors could be statistically related to calculated phosphorus sedimentation rates in a large sample of natural and artificial lakes and to use these factors to develop a better predictive empirical phosphorus model for these lake types. The predictive abilities of these new models and previously pub­ lished empirical phosphorus models were tested. In addition, the degree to which total phosphorus concentrations can be used to predict chloro­ phyll ^ concentrations and Secchi disc depths in natural and artificial lakes was also examined. 4

PART I. PHOSPHORUS SEDIMENTATION IN SOME

NATURAL AND ARTIFICIAL IOWA LAKES 5

INTRODUCTION

Vollenweider (1969) proposed that the concentration of phosphorus in lakes is determined largely by the rate of phosphorus supply, but is modi­ fied by losses of phosphorus through the outlet and to the sediments. The steady-state solution for his general model is given by:

where 3 TP = concentration of total phosphorus in the lake water, mg/m

L = annual phosphorus loading per unit of lake surface area, 2 mg/m /yr

z = mean depth of the lake, m

a = phosphorus sedimentation rate (coefficient), yr"^

p = hydraulic flushing rate, yr~^

Because direct measurements of phosphorus sedimentation rates require in­ tensive sampling and are often difficult, other authors (Dillon and

Rigler, 1974b; Vollenweider, 1975; Kirchner and Dillon, 1975; Chapra,

1975; Jones and Bachmann, 1976; Larsen and Mercer, 1976) have used data from natural lakes to develop empirical phosphorus loading models that do not require direct measurements.

Though these models have been used with reasonable success to predict total phosphorus concentrations in natural lakes (Jones and Bachmann,

1976; Chapra and Robertson, 1977), studies by Jones and Bachmann (1978) on several central-Iowa artificial lakes have shown that these models over­ estimate summer phosphorus concentrations by 3 to 10 times. They suggest 6

this overestimation results because the models inadequately estimated the greater phosphorus sedimentation rates in the artificial lakes. Because sedimentary losses of phosphorus can be an important determinant of lake phosphorus concentrations, sediment traps were used to directly measure phosphorus sedimentation in some natural and artificial Iowa lakes. My objectives were to quantify the sedimentary loss of phosphorus in these lakes and to determine the limnological factors influencing their phospho­ rus sedimentation rates. I also wished to compare the measured phosphorus sedimentation rates to sedimentation rates estimated from lake phosphorus budgets to determine how well sediment traps estimate net sedimentation rates. 7

METHODS

Between June 1976 and September 1978, sediment traps were used to directly measure phosphorus sedimentation in 4 natural and 4 artificial

Iowa lakes (Table 1). (To obtain a microfiche copy of the data contact the Photo Service, Iowa State University, Ames, Iowa 50011, requesting

Supplement to Publication No. 6/7/79 Canfield Ph.D. Thesis and submitting

$1.00 in the form of check, cash, or money order. Give your name and complete address for mailing.) Morphometric and average chemical and hydrologie data for the lakes are summarized in Table 2. Preliminary studies were made between June and September 1976 to determine if the location of a sediment trap in a lake could significantly influence esti­ mates of lake sedimentation rates. Paired sediment traps were placed at three locations along the long axis of West Okoboji, East Okoboji, Big

Creek, and Don Williams lakes. Traps were also placed at various depths in the deep areas of Big Creek and West Okoboji lakes. The traps were retrieved approximately every 2 weeks.

Because differences in sedimentation rates were greater among lakes than between locations within a lake, beginning in January 1977, paired sediment traps were placed only in the deep areas of the eight study lakes. Winter phosphorus sedimentation rates were measured between

January and March 1977. These traps were retrieved after approximately 40 days. Because winter sedimentation rates were extremely low, measurements were limited to the open-water period. Sediment traps were retrieved approximately every 2 weeks during the summer and every 15 to 30 days during spring and fall. 8

Table 1. Location and origin of the Iowa lakes

Lake Location Origin

West Okoboji Dickinson County Natural East Okoboji Dickinson County Natural Spirit Dickinson County Natural Lost Island Clay and Palo Alto Counties Natural Big Creek Polk County Artificial Don Williams Boone County Artificial Beeds Franklin County Artificial Pine Hardin County Artificial

Sediment traps were similar in design to those used by Dudley (1976).

Collecting tubes consisted of four PVC cylinders (5.1 cm x 25.4 cm) closed at one end with a rubber stopper. No preservatives were used in the cylinders and two cylinders were inverted in the holding platform to cor­ rect for attached growth (White, 1974). The holding platforms were clamped 1.5 m above the bottom sediments to a nylon rope held in place by an anchor and subsurface float suspended at 2 m. Sediment traps were re­ trieved by using a dragline. After the float was snagged, the sediment trap was carefully raised to the surface. Cylinders were capped with rubber stoppers and removed. After clean cylinders were placed in the holding platform, the sediment trap was returned to its original location.

Visual examination of trap contents indicated that no resuspension of de­ posited materials occurred during trap retrieval.

Surface water samples were collected at four midlake stations near the location of the paired sediment traps. All water samples were col- Table 2. Morphometric data and average hydrologie and chemical condi­ tions for the surface waters of the natural and artificial Iowa lakes during the open-water seasons of 1977 and 1978; S indicates surnner stratification and NS indicates the ab­ sence of permanent summer stratification

Hydraulic Water­ flushing shed Surface Mean rate (yr-1) area^ area Depth Strati­ Lake (ha) (ha) (m) fication 1977 1978

West Okoboji 7698 1540 11.9 S .008 .07

East Okoboji 5903 764 2.8 NS .05 .44

Spi ri t 9962 2168 5.2 NS .02 .14

Lost Island 5180 465 3.1 NS .06 .56

Big Creek 19453 358 5.4 S 1.8 1.9

Don Williams 7995 65 5.1 S 4.4 4.6

Beeds 8236 43 2.9 s 5.6 21.7

Pine 4118 25 2.8 NS 2.6 14.6

^Drainage areas for West Okoboji, East Okoboji, and Spirit Lakes are assumed to be independent (Jones, 1974). 10

Phosphorus Chloro- Total Calcium loading Total P phyll a_ hardness hardness (g/m2/yr) (mg/m^) (mg/m3) (mg/1 CaCOg) (mg/1 CaCOg) 1977 1978 1977 1978 1977 1978 1977 1978 1977 1978

.06 .29 20.8 23.5 3.8 6.6 219 219 75 78

.08 .44 194.2 121.3 38.5 83.6 239 217 96 87

.05 .27 40.0 40.1 24.6 24.0 240 233 72 74

.10 .61 115.6 98.5 92.3 61.6 210 217 71 80

3.3 3.4 30.3 40.7 22.1 25.9 203 249 119 155

7.4 7.7 57.8 43.3 43.7 25.7 221 283 120 166

3.6 21.0 110.9 72.8 75.0 44.4 223 271 118 165

2.4 13.6 79.2 75.5 70.2 58.0 149 180 73 95 11 lected in acid-cleaned nalgene bottles and transported in insulated boxes to the laboratory for chemical analysis. Water clarity was measured by using a Secchi disc and water temperature was measured by using a re­ sistance thermometer.

For chlorophyll à analysis, a measured volume of lake water was filtered through a Gelman Type A-E glass fiber filter. Chlorophyll a^ con­ centrations were determined by using the methods of Richards with Thompson

(1952) and Yentsch and Menzel (1963). Chlorophyll a^values were calcu­ lated by using the equations of Parsons and Strickland (1963). These values were not corrected for pheophytin.

Total phosphorus concentrations were determined by using the proce­ dures of Murphy and Riley (1962) with a persulfate oxidation described by

Menzel and Corwin (1965). After oxidation, samples were centrifuged to remove suspended particles. Estimates of dissolved phosphorus were deter­ mined by filtering lake water through a 0.45 ym, presoaked Millipore mem­ brane filter and analyzing the filtrate for total phosphorus. Particulate phosphorus values were determined by difference.

Total iron concentrations were determined by using the ferrozine method of Hach Chemical Company (1975). Sample volumes were 50 ml and all samples were gently boiled for 20 min to insure that all the iron had re­ acted. After cooling, sample volumes were returned to 50 ml with dis­ tilled water and centrifuged to remove particulate matter.

Concentrations of total, organic, and inorganic suspended matter were determined on measured volumes of lake water filtered through precombusted

(550 C), preweighed Gelman type A-E glass fiber filters. The total weight 12 of particulate matter on a filter was determined after drying the filters at 103 C for 1 hr. The quantity of inorganic matter was determined after combustion at 550 C for 1 hr. Organic weights were estimated by differ­ ence. All filters were cooled under desiccation and weighed to ±0.01 mg with a Cahn Model 6 Electrobalance (Cahn Instrument Company, Paramount,

California). Frequent filter blanks were processed and all filters were corrected for losses during handling.

Contents of each PVC cylinder from a sediment trap were emptied into acid-cleaned, 1-liter glass jars. Samples were preserved by adding 10 ml of formaldehyde and refrigerating at 2 C. Prior to chemical analysis, the sample was poured into a 1-liter graduated cylinder and brought to a vol­ ume of 600 ml with distilled water. Sediment samples were mixed by using a magnetic stirrer and subsamples (the volume being dependent upon the concentration of sediments) were withdrawn by using a large bore 10 ml pipette for analysis of total phosphorus, total iron, and total, organic, and inorganic particulate matter. Procedures used for chemical analyses of surface waters were also used to analyze the sediment samples. Because large quantities of blue-green algae were found to float up into the in­ verted cylinders, no corrections for attached growth were made. Visual examination of the collecting cylinders, however, indicated attached growth was minimal compared to the quantity of sedimented material.

To determine the quantity of potentially settleable phosphorus in each lake, four 1-1iter samples of surface water were returned to the laboratory and allowed to settle in 1-1iter graduated cylinders for 24 hr.

Samples were kept in the dark and near the temperature recorded at the 13 time cf collection. After 24 hr, approximately 950+ ml of sample were siphoned off. The sedimented materials were resuspended, poured into a

50-ml graduated cylinder where the sample volume was recorded before being analyzed for total phosphorus. Results were expressed as the quantity of phosphorus lost from a cubic meter of lake water.

To determine how well sediment traps estimate net annual phosphorus sedimentation rates, these rates were estimated from lake phosphorus budgets by rearranging the terms of Equation 1:

(7 ~ - P (2)

Water inputs were calculated from watershed areas and runoff from the nearest United States Geological Survey Gauging Station (Table 3). Lake evaporation was assumed to equal precipitation and groundwater influences were ignored. Water inputs were divided by lake volumes to determine hydraulic flushing rates for each lake. Phosphorus loadings to the lakes were estimated by multiplying water inputs by an average total phosphorus 3 concentration of 332 mg/m . This value is an average of 288 samples of

Des Moines River water taken between 1972 and 1978. Because the Des

Moines River is the largest river draining the geologic area in which the lakes are located, this concentration should provide a reasonable estimate of the inflow phosphorus concentrations. Annual phosphorus inputs from precipitation were taken as 0.32 kg/ha (Jones and Bachmann, 1976). Lake total phosphorus concentrations were measured directly. Calculations were made for the ice free period but expressed on a yearly basis.

To compare phosphorus sedimentation rates measured by using sediment traps with the net annual phosphorus sedimentation rates estimated from Table 3. Location of United States Geological Survey Gauging Stations and estimates of water and phosphorus inputs to the Iowa lakes

yggg Water inputs (lO^m^) Total phosphorus inputs (lO^mg) Lake station Mar-Oct, 1977 Feb-Aug, 1978 Mar-Oct, 1977 Feb-Aug, 1978

West Okoboji 06605850 .92 7,1 634 2660

East Okoboji 06605850 .71 5.5 394 1957

Spirit 06605850 1.19 9.2 850 3470

Lost Island 06605850 .62 47.0 303 1676

Big Creek 05470000 24.0 21.0 7884 7237

Don Williams 05470000 9.7 8.9 3224 2958

Beeds 05458900 3.1 16.0 1044 5307

Pine 05463000 1.2 6.0 408 1994 15 lake phosphorus budgets, sedimentation coefficients were calculated by

Equation 3:

2 2 0 = P sedimented (mg/m /day)x lake surface area(m ) x 365 ^2) mean P concentration(mg/m^) x lake volume(m^) 16

RESULTS AND DISCUSSION

Studies on natural lakes have shown that sedimentation rates measured by using sediment traps may vary significantly between locations in some lakes (Birch, 1976; Serruya, 1977) while in other lakes there may be only slight differences between locations (Lawacz, 1969). Other studies have shown that even within a location sedimentation rates may change with depth (Toyoda et al., 1968; White, 1974; Birch, 1976; Hakala, 1977).

Similar spatial variations in sedimentation were found in the natural and artificial Iowa lakes (Table 4; Figure 1). Sedimentation rates in East

Okoboji Lake varied only slightly between locations, but the locations had similar water depths. In the other lakes, sedimentation rates generally increased significantly at shallow water locations, but phosphorus sedi­ mentation rates in Don Williams Lake were greatest at deep water loca­ tions. Phosphorus sedimentation rates generally increased with depth in the deep area of Big Creek Lake, but the highest phosphorus sedimentation rates in West Okoboji Lake were found at intermediate depths. From these data, it is obvious that unless the factors that determine spatial sedi­ mentation patterns in lakes are known, sediment traps should be placed at many different locations and depths to accurately measure sedimentation in a single lake.

While the location of a sediment trap may significantly influence estimates of sedimentation rates within a lake, there are greater differ­ ences in sedimentation rates among lakes (Table 4). Variance components calculated from a standard analysis of variance on the 1976 phosphorus sedimentation data showed that 65% of the total observed variance could be Table 4. Mean values and standard error of the mean for total dry matter (g/m /day), organic matter (g/m2/day), inorganic matter (g/m2/day), phosphorus (mg/m^/day), and iron (mg/m^/day) sedimentation rates at 3 locations; the number in parentheses indicates the number of sam­ ples in the means

Average depth of Loca­ Strati­ sediment Lake tion fication traps (m) Total Organic Inorganic Phosphorus Iron

West Okoboji 1 S 27.5 5.29+0.59 1.61±0.19 3.68+0.42 8.56+2.88 32±13 (2) (2) (2) (2) (2) 2 S 17.0 10.78±2.59 3.02+0.64 7.76+1.95 15.48+2.40 78±14 (3) (3) (3) (3) (3) 3 NS 10.5 20.44+5.33 4.92+1.12 15.52±4.22 18.44±3.67 98±28 (3) (3) (3) (3) (3)

East Okoboji 1 NS 4.5 56.47+16.18 12.35+3.38 44.12+13.27 60.18+7.80 126±39 (3) (3) (3) (3) (3) 2 NS 4.5 67.46+19.13 15.15+3.48 52.31±16.11 65.82±12.59 167±54 (3) (3) (3) (3) (3) 3 NS 4.5 53.11±13.03 11.42±2.35 41.69+11.59 67.87+9.08 139+40 (3) (3) (3) (3) (3)

Big Creek 1 S 13.0 19.23+1.78 3.50+0.22 15.73+1.60 17.40±1.67 209+18 (3) (3) (3) (3) (3) 2 S? 8.0 26.55+0.28 5.21±0.26 21.32+0.45 16.69+1.36 313+21 (3) (3) (3) (3) (3) 3 NS 5.0 54.30+2.79 9.60+0.86 44.70+1.98 36.90+6.91 570+49 (3) (3) (3) (3) (3) Table 4. (Continued)

Average depth of Loca- Strati­ sediment Lake tion fication traps (m) Total Organic Inorganic Phosphorus Iron

Don Williams 1 S 11.0 9.64±2.68 2.67+0.47 6.97±2.23 24.78+2.45 85±17 (3) (3) (3) (3) (3) 2 S? 7.5 10.73±2.10 2.70+0.14 8.03+1.96 16.39±2.32 98+18 (3) (3) (3) (3) (3) 3 NS 3.5 16.97±2.08 3.68±0.18 13.29±2.09 11.69±0.98 159+9 (3) (3) (3) (3) (3) 2 Figure 1. Variations in phosphorus sedimentation rates (mg/m /day) with depth in Big Creek and West Okoboji; bars represent the standard error of the mean (n = 4). 20

TOTAL P SEDIMENTATION RATE MG/M< /DAY 0 12 3 4 5 6 7 1 1 1 1 1 1 WEST OKOBOJI

10 - V)(r w wt- IS - Z

-

a 1 1 25

30 JUL 20-AUG 6

AUG 6-18

35

TOTAL P SEDIMENTATION RATE MG/M^ /DAY 20 25 30 BIG CREEK

4

6

8

10

12

4 JUL 13-30

-- AUG 1-12 21

attributed to differences among lakes while only 13% of the total vari­

ance was attributed to the effect of location within individual lakes

(Table 5). For this reason, sediment traps were placed only in the deep­

est area of each lake and two natural and two artificial lakes were added

to the study (Table 1) to determine those limnological factors that in­

fluence sedimentation rates in a range of natural and artificial lakes.

Table 5. Percent contribution of various sources to the total variance of phosphorus sedimentation rates

Variance source Percent

Lake 65 Trap location 13 Traps within location 9 Sampling 1 Error 12

Studies on natural lakes have shown that sedimentation rates may vary

considerably over an annual cycle. Lawacz (1969), White (1974), Lastein

(1976), and others have shown that sedimentation rates vary in a bimodal pattern with sedimentation being highest in the spring and fall and lowest

during the summer and winter. However, Toyoda et al. (1968) and Gasith

(1976) have shown that unimodal sedimentation patterns may occur also.

Toyoda et al. (1968) showed that sedimentation rates in Lake Biwa were

highest during the spring, but Gasith (1976) showed that rates in Lake

Wingra were highest during the summer. In the Iowa lakes, phosphorus

sedimentation rates were highly variable over an annual cycle both within 22 and among lakes (Figure 2). Rates were lowest when the lakes were cov­ ered by ice. After the ice cover was lost, sedimentation rates increased measurably in all lakes. Though there seemed to be distinct seasonal sedimentation patterns during the open-water period in some lakes, other lakes showed no distinct patterns, thus suggesting that generalizations about seasonal sedimentation patterns should be made with caution.

The considerable variability in sedimentation rates over time suggest that many factors may be important determinants of sedimentation rates in lakes. Studies on natural lakes have shown that inputs of allochthonous sediments (Toyoda et al., 1968; Hakala, 1977), phytoplankton production

(Thomas, 1955; Lawacz, 1959; Lastein, 1976; Gasith, 1976; Birch, 1976;

Hakala, 1977) and resuspension and resedimentation of bottom sediments

(Tutin, 1955; Davis, 1968; Pennington, 1974; Lastein, 1976) may be impor­ tant determinants of sedimentation rates. In the Iowa lakes, all these mechanisms seem to influence sedimentation rates. Inputs of allochthonous sediments substantially increased sedimentation rates in the artificial lakes (Figure 2) as seen by the peaks in phosphorus sedimentation rates following runoff periods in the spring of 1978. High summer phosphorus sedimentation rates in some lakes coincided with high algal levels, thus suggesting phytoplankton production might be an important determinant of sedimentation rates. Resuspension and resedimentation of bottom sediments also seemed very important because rates were generally greater in shallow water locations (Table 4), increased substantially immediately after the loss of ice cover (Figure 2), and increased with the loss of thermal stratification in the fall (Figure 2). ? Figure 2. Seasonal variations in phosphorus sedimentation rates (mg/m /day) in the Iowa lakes; note scale change for Lost Island Lake. TOTAL P SEDMENTATION RATE MC/wZ/DAY TOTAL P SEDIMENTATION RATE MG/M' /DAY

TOTAL P SEOWENTATION RATE UG/M' /DAY TOTAL P SEDIMENTATION MG/W^ /DAY

tz -n TOTAL P SEOMENTATKW RATE HG/M'- /DAY total p SEDMENTATKW RATE MG/M^ /DAY O

m c

I I

g

*0

o o

TOTAL P SEO(MENTATK>N RATE MG/M^ /DAY TOTAL P SEDWENTATION RATE MG/M^ /DAY O

> z

o S

s s i

> > 3 3 I I

i

> > S g

g d

o o o o -4

SZ 26

Despite the many mechanisms that could influence sedimentation rates within a lake, there are significant average differences among the lakes

(Table 6). Because rates are not distinctly different in the natural and artificial lakes, these differences do not seem to be due to the origin of the lakes. Mean phosphorus sedimentation rates, however, were directly correlated to mean inorganic suspended matter concentrations (Table 7; r = 0.89; p = 0.01) and mean inorganic matter sedimentation rates (r =

0.97; p = 0.01) thus suggesting inorganic sediments may be an important determinant of phosphorus sedimentation in the Iowa lakes. Though this mechanism may be important, other factors also correlate significantly with mean phosphorus sedimentation rates (Table 7).

To determine which factors control sedimentation rates over a broad range of lakes, the sample was expanded by using data from the published literature (Table 8). Because different methods and collecting times were used, the quality of the data is variable. However, even occasional large errors should not obscure general relationships because the total range of the measured parameters extends over orders of magnitude. A comparison of lake sedimentation rates shows that the Iowa lakes have some of the high­ est values yet reported, but the Iowa lakes are also relatively shallow and very productive. Because resuspension and resedimentation of bottom sediments should be greater in shallow lakes, the relationship between sedimentation rates and lake mean depth was examined. Total dry weight sedimentation rates and lake mean depth were weakly correlated (r = -0.61; p = 0,01). Though these data suggest traps may catch more material in shallow lakes because of resuspension and resedimentation of bottom Table 6. Averages of 1imnological measurements made on some natural and artificial Iowa lakes during the ice free periods between June, 1976 and September, 1973

West East Lost Big Don Parameter Okoboji Spirit Okoboji Island Creek Williams Beeds Pine 3 Total phosphorus mg/m 18.7 40.0 139.4 109.5 30.7 42.0 96.2 77.8 Soluable phosphorus mg/m^ 12.1 13.9 90.9 32.0 13.5 20.3 34.1 22.3 Particulate phosphorus mg/m^ 6.6 26.1 48.5 77.5 17.2 21.7 62.1 55.5 Total iron mg/m^ 18 37 38 43 64 58 135 195 Total suspended matter g/m^ 2.2 9.6 13.2 33.9 6.5 7.4 16.4 13.7 Organic suspended matter g/m^ 1.4 6.6 8.2 17.6 3.5 4.5 8.3 8.9 Inorganic suspended matter g/m3 0.8 3.0 5.0 16.3 3.0 2.9 8.0 4.8 Chlorophyll ^ mg/m^ 4 24 66 81 27 31 63 66 Secchi disc transparency m 3.7 1.3 1.0 0.4 1.6 1.6 0.8 0.7 Total dry matter sedimented g/mZ/day 11.6 16.9 65.8 202.8 32.0 23.8 72.4 19.5 Organic matter sedimented g/m^/day 3.0 7.0 14.8 57.3 5.2 4.7 12.4 4.1 Inorganic matter sedimented g/m2/day 8.6 9.9 51.0 145.5 26.8 19.1 60.0 15.4 Total iron sedimented mg/m2/day 54 53 119 130 351 215 717 281 Total phosphorus sedimented mg/m^/day 13.3 20.8 60.3 218.1 28.8 29.1 62.9 21.1 Total phosphorus sedimented in laboratory mg/m^ 2.0 7.4 7.9 29.0 4.5 5.9 9.4 5.6 28

Table 7. Correlations between mean phosphorus sedimentation rates and other measured parameters

Parameter r

Total phosphorus .75 Particulate phosphorus .74 Total suspended matter .84 Organic suspended matter .78 Inorganic suspended matter .89 Chlorophyll a .70

Total dry matter sedimentation rates .99 Organic matter sedimentation rates .97 Inorganic matter sedimentation rates .97

sediments, the relationship is not very strong, indicating other factors may be important. Total dry weight sedimentation rates, however, were strongly correlated to chlorophyll a concentrations (r = 0.94; p = 0.01) and phosphorus sedimentation rates were also directly correlated to chlorophyll ^concentrations (r = 0.94; p = 0.01). These data strongly suggest that sedimentation rates as measured by sediment traps are in­ fluenced greatly by the trophic status of a lake.

Based on these data, sedimentation rates should be greater in pro­ ductive lakes, but it is difficult to believe the high rates measured in the Iowa lakes (Table 5: average daily phosphorus loss rates range from 5 to 60% of the total quantity of phosphorus in the lakes) represent the net loss of materials to the bottom sediments. If the materials caught in the sediment traps were permanently incorporated into the bottom sediments, such high sedimentation rates should rapidly deplete the Iowa lakes of Table 8. Total dry weight and total phosphorus sedimentation rates measured by use of sediment traps reported in the published literature and in this study

Mean Chlorophyll Dry weight Phosophorus depth ^ sedimentation sedimentation Reference Lake (m) (mg/m^) (g/m^/day) (mg/mVday)

Toyoda et al. (1968) Biwa 41.2 1.7 0.57 0.79

Lawacz (1969) Mikolajki 11.0 - 4.10 -

Brunskill (1969) Fayetteville 28.0 - 0.81 0.40 Green

Johnson & Brinkhurst (1971) Ontario 91 - 0.66 -

Hayashi et al. (1972) Suwa 5.0 - 9.0 - Pennington (1974) Blelham Tarn 6.8 2.45 Windermere 21 0.50 - Ennerdale 17.8 - 0.56 Wastwater 39.7 0.45 Bloesch (1974) Rotsee 9 2.4 5.8 Lake of Lucerne 42.6 3.5 3.8 (Horw Bay) :

White (1974) Lawrence 5.9 - .14 -

Charlton (1975) Char Lake 10.2 - 1.0 - Birch (1976) Findley 8.2 0.88 0.21 0.37 Chestermorse 19 1.4 0.45 0.83 Sammamish 17 5.9 1.8 4.2 Washington 33 6.3 2.4 7.6 Dudley (1976) Big Creek 5.4 17.4 34.7 34.8 Don Williams 5.1 30.8 15.6 28.0

Gasith (1976) Wingra 2.4 - 13.2 -

Lastein (1976) Esrom 12.3 - 1.7 - Table 8. (Continued)

Mean Chlorophyll Dry weight Phosphorus depth ^ sedimentation sedimentation Reference Lake (m) (mg/m^) (g/m^/day) (mg/m2/day)

Hakala (1977) Paajarvi 14.4 0.4 -

Kajak & Lawacz (1977) Smolak 2.4 •- 1.0 - Piecek 3.4 " 5.7 - Dgal Maly 4.3 - 7.8 - Czarna Kuta 1.3 - 30.0 -

Ravera & Viola (1977) Lugona 130 - 1.74 -

Serruya (1977) Kinneret 25 - 3.6 8.4 This study West Okoboji 11.9 4.2 11.6 13.3 East Okoboji 2.8 66.2 65.7 60.3 Spirit 5.2 24.3 16.9 20.7 Lost Island 3.1 81.3 203 218 Big Creek 5.4 27.1 32.0 28.8 Don Williams 5.1 30.8 23.8 29.1 Beeds 2.9 63.3 72.4 62.9 Pine 2.8 65.7 19.5 21.1 31 particulate materials. This, however, is not observed. I, therefore, hypothesize that either the sediment traps are artificially increasing sedimentation rates by their presence in the lakes, or there are mecha­ nisms like resuspension and resedimentation of bottom sediments that sus­ tain the high sedimentation rates.

Kleerekoper (1952) and Golterman (1973) have suggested sediment traps increase sedimentation rates by decreasing turbulence in the vicinity of the trap. However, Kirchner (1975), by evaluating the performance of different sized sediment traps, has provided convincing data suggesting otherwise. Studies by Pennington (1974) have also shown that in some lakes sediment accumulations in traps were similar to those expected from radioactive dating of bottom sediments.

I did not experimentally determine if sediment traps were increasing sedimentation rates by decreasing turbulence in the vicinity of the trap, but laboratory settling experiments were conducted to determine the poten­ tial quantity of settleable phosphorus in the lakes. The experiments showed large quantities of potentially settleable phosphorus are suspended in the surface waters of the Iowa lakes (Table 6). The average quantity O of phosphorus sedimented ranged from 2.0 to 29,0 mg/m , which represents from 5 to 26% of the phosphorus suspended in the surface waters. Because these estimates are based on grab samples of surface waters, they are not directly comparable to the sediment trap data, but the sedimentation rates measured by the traps parallel these laboratory values. Whereas these data do not refute the hypothesis of Kleerekoper and Golterman, the data 32 suggest sediment traps are measuring the actual downward movement of par­ ticulate matter as suggested by Kirchner.

If sediment traps are measuring the actual downward movement of particulate materials, there must be mechanisms that support the high sedimentation rates found in productive lakes. Pennington (1974) showed that in some lakes sediment accumulations in traps were greater than those expected from radioactive dating of bottom sediments because of resuspen- sion and resedimentation of bottom sediments. Other mechanisms, however, may also cause sediment traps to overestimate the net accumulation of materials in bottom sediments. Overestimations might result because plant nutrients are rapidly recycled. Water circulation patterns might also recirculate particulate materials before they reach the bottom sediments.

Obviously, the intensity of these processes must influence the inter­ pretation of sediment trap data. Where these processes are quantitatively important, sediment traps are measuring the gross downward movement of particulate materials (gross sedimentation). However, where these proc­ esses are not quantitatively important, sediment traps will estimate the net accumulation of materials in the bottom sediments (net sedimentation).

Because nutrient recycling, water circulation, and resuspension and re- sedimentation of bottom sediments occur to some extent in all lakes, I suggest sedimentation rates measured by using sediment traps should be considered as gross sedimentation rates.

To determine how well sediment traps estimate net phosphorus sedimen­ tation rates in the Iowa lakes, sedimentation rates measured by traps

(expressed as a sedimentation coefficient. Equation 3) were compared to 33 the net phosphorus sedimentation rates estimated from lake phosphorus budgets. This comparison showed the sediment traps overestimated phos­ phorus budget sedimentation rates by approximately 200x in the natural lakes, but by less than 4x in the artificial lakes (Table 9). To explain this difference, I initially hypothesized that resuspension and resedi- mentation of bottom sediments might be significantly greater in the natural lakes. Conditions for resuspension of sediments seemed to be less favorable in the artificial lakes because these lakes have smaller surface areas (Table 2), channel-type morphometry, and are better protected from the wind by surrounding hills. However, with the loss of ice cover, sedi­ mentation rates increased by a similar magnitude in both the natural and artificial lakes (Figure 2). If these increases are used as an index of potential resuspension of sediments, this suggests both types of lakes have large quantities of materials resuspended from the bottom sediments.

If resuspension and resedimentation of bottom sediments are of a similar magnitude in the natural and artificial lakes, some other quanti­ tatively important mechanism must influence net sedimentation in the artificial lakes to explain why sediment traps only slightly overestimate net phosphorus sedimentation rates in these lakes. Algal production might be a possible mechanism, but algal population densities as measured by chlorophyll ^ concentrations are similar in the natural and artificial lakes (Table 6). However, Jones and Bachmann (1978) suggested inputs and sedimentation of inorganic particulate materials might be an important phosphorus removal mechanism in the artificial Iowa lakes. My data also support this hypothesis. 34

Table 9. Mean phosphorus sedimentation coefficients calculated from sedi­ ment trap data and lake phosphorus budgets for the Iowa lakes during 1977 and 1978

a from sedi­ a from lake Lake ment trap data phosphorus budgets

West Okoboji 22 .61 East Okoboji 56 .47 Spirit 36 .72 Lost Island 235 .86

Big Creek 63 16 Don Williams 49 26 Beeds 82 43 Pine 35 29

The timing of the highest phosphorus sedimentation rates measured by sediment traps in the artificial lakes {Figure 2) coincide with large in­ puts of allochthonous particulate materials during storm events. The only important exceptions are the peak sedimentation rates associated with the loss of thermal stratification in the fall. After the surface runoff sub­ sides, phosphorus sedimentation rates drop rapidly (Figure 2). This sug­ gests the allochthonous materials are rapidly sedimented to the bottom and relatively little of this material is resuspended and resedimented over time. Because these events are generally greater than those events caused by other processes, the sedimentation of allochthonous sediments dominates not only gross sedimentation, but also net sedimentation. This explains why the sediment traps only slightly overestimate net phosphorus sedimen­ tation in the artificial lakes. 35

Though the sediment trap data cannot be used directly to explain what factor controls net phosphorus sedimentation in natural lakes, inputs and sedimentation of allochthonous inorganic sediments may also control net phosphorus sedimentation in these lakes by two possible mechanisms.

First, the tributary streams of the natural lakes may carry smaller loads of inorganic particulate materials relative to their phosphorus loads than artificial lakes, thus a smaller fraction of the phosphorus inputs to natural lakes would sediment. This would cause natural lakes to have smaller net phosphorus sedimentation rates than artificial lakes. This idea is supported by the fact that the natural Iowa lakes lie in glaciated depressional topography whereas the artificial Iowa lakes are built in valleys of erosional topography (Jones and Bachmann, 1978). However, I have no field data to verify this hypothesis. Second, if the tributary streams of the natural and artificial lakes carry similar quantities of inorganic sediments relative to their phosphorus loads, the phosphorus removal capacity of the sediment may depend upon the concentration of these sediments in the lakes. Because the artificial lakes have greater hydraulic flushing rates than the natural lakes (Table 2), the artificial lakes become extremely turbid with inorganic sediments during storm events. At these high concentrations, the clays, iron, aluminum, and other possible phosphorus absorbents may effectively bind phosphorus and remove phosphorus to the sediments. This would be similar to the action of fly ash, iron, and alum used in nutrient inactivation experiments. In the natural lakes, however, the inflowing sediment concentrations are diluted by the large volumes of lake water. At a lower concentration the sediments would be much less effective at removing phosphorus from the 36 water. This mechanism could also explain why the artificial lakes have higher net phosphorus sedimentation rates than the natural lakes.

The higher net phosphorus sedimentation rates in the artificial lakes might also result because these lakes have higher average iron concentra- O tions (58 to 195 mg/m ) and iron sedimentation rates as measured by using 2 sediment traps (215 to 717 mg/m /day) than the natural lakes (18 to 78 3 2 mg/m ; 53 to 130 mg/m /day: Table 6). Because iron is known to bind phosphorus, the sedimentation of iron-containing particles may remove phosphorus and effectively retain phosphorus in the bottom sediments.

Additional field studies are needed to clarify what mechanism or mecha­ nisms actually dominate the removal of phosphorus from the water columns of natural and artificial lakes. 37

CONCLUSIONS

Sediment traps have been used by many investigators to estimate loss of materials from the water column or accumulations of materials in the bottom sediments. In this study, sedimentation traps were used to measure sedimentation in some natural and artificial lakes. As found by previous studies on natural lakes, sedimentation rates were highly variable both within and among the Iowa lakes. Rates were found to vary significantly at different locations within some lakes, with depth at a single location, and over time. However, despite the high variance within lakes a sig­ nificant average difference was found among the Iowa lakes. By combining the data on the Iowa lakes with published data from other lakes, I found that mean total dry weight sedimentation rates and mean phosphorus sedi­ mentation rates are highly correlated with mean chlorophyll a concentra­ tions. These data strongly suggest that the trophic status of a lake is a major determinant of lake sedimentation rates measured by sediment traps.

Though sedimentation rates measured by traps are higher in productive lakes, these high rates cannot represent the net accumulations of materi­ als in the bottom sediments. Sustained sedimentation rates as measured by the sediment traps would rapidly deplete lakes of particulate matter. Be­ cause this is not observed, there must be mechanisms that recycle materi­ als in the lakes. One mechanism suggested by this study and others

(Pennington, 1974; Lastein, 1975; Gasith, 1976) to sustain these high sedimentation rates is the resuspension and resedimentation of bottom sediments. However, nutrient recycling and recirculation of particulate materials before they reach the bottom sediments could also contribute to 38 high sedimentation rates. Because these processes can occur to some ex­ tent in all lakes, I suggest sediment traps are measuring the gross down­ ward movement of materials in lakes. Under certain conditions, sediment traps may accurately estimate the net loss of materials from the water column into the sediments (Pennington, 1974). In the Iowa lakes, however,

I found that sediment traps overestimated net phosphorus sedimentation rates as measured by nutrient budgets by approximately 200x in the natural lakes, but by less than 4x in the artificial lakes.

Large quantities of materials move through the water columns of the

Iowa lakes. Resuspension of bottom sediments has been suggested as an important source of this material. Resuspension should be greater in shallow lakes, but likely occurs to some degree in all lakes, particular­ ly in shallow water areas. However, there have been no studies that di­ rectly measure this process and there is very little information on the limnological significance of this process. Valid questions are: How often are the sediments mixed into the water column and how long do they remain there? What factors determine this mixing process in a broad range of lakes? Does this process release nutrients to the water column or are nutrients absorbed by the sediments? Additional work is strongly sug­

gested in this area.

Though I could not use sediment traps to directly measure net phos­ phorus sedimentation in the natural and artificial Iowa lakes, my studies

suggest that inputs and sedimentation of inorganic sediments or perhaps

iron concentrations are important determinants of net phosphorus sedimen­

tation in these lakes. These materials seem to remove phosphorus from the 39 water column and effectively retain phosphorus in the sediments. My studies suggest lakes with high flushing rates and large inputs of allochthonous sediments, such as the artificial lakes in Iowa, should have higher net phosphorus sedimentation rates. I suggest studies are needed on a broad range of natural and artificial lakes to quantify the relation­ ship between sediment and phosphorus inputs and the subsequent loss of these materials to the bottom sediments. The interaction of phosphorus and sediments may be an important determinant of lake phosphorus concen­ trations and hence the biological productivity of a lake. The study of sediment dynamics in a broad range of lakes may provide information that will be useful in understanding phosphorus dynamics in lakes. 40

PART II. PREDICTION OF TOTAL PHOSPHORUS CONCENTRATIONS AND

TROPHIC STATES IN NATURAL AND ARTIFICIAL LAKES 41

INTRODUCTION

Studies have demonstrated that the concentration of total phosphorus in natural lakes can be an important indicator of lake trophic state

(Vollenweider, 1968; Dillon, 1975), algal population densities as meas­ ured by chlorophyll ^ concentrations (Dillon and Rigler, 1974a; Jones and

Bachmann, 1976), and water clarity (Bachmann and Jones, 1974; Dillon and

Rigler, 1975). Recently, simple empirical models have been developed to predict lake total phosphorus concentrations from data on annual phospho­ rus inputs, hydraulic flushing rates, and lake morphometry (Vollenweider,

1975; Kirchner and Dillon, 1975; Chapra, 1975; Jones and Bachmann, 1976;

Larsen and Mercer, 1976). Because these models require a minimum of data and the predicted phosphorus values can be used to estimate other accepted limnological measures of trophic state, lake managers and researchers are using them to predict the response of natural and artificial lakes to changes in phosphorus inputs (Chapra and Robertson, 1977). However, the general application and potential limitations of these models and the re­ lationship between total phosphorus concentrations and lake trophic state, chlorophyll a concentrations and water clarity have not been thoroughly addressed.

The empirical phosphorus loading models have been developed from data on selected natural lakes in Canada, northern Europe and the northern

United States. Though the models seem to be different, they are based upon the general model proposed by Vollenweider (1969): 42 where 3 TP = concentration of total phosphorus in the lake water, mg/m

L = annual phosphorus loading per unit of lake surface area, 2 mg/m /yr

z = mean depth of the lake, m

a = phosphorus sedimentation rate (coefficient), yr"^

p = hydraulic flushing rate, yr~^

The only substantial difference between the models is the empirical equa­ tions used to estimate phosphorus sedimentation rates, but these equations are critical to the successful prediction of lake phosphorus concentra­ tions. Using a small sample of lakes, Vollenweider (1975) proposed that lake phosphorus sedimentation rates could be estimated by dividing 10 by lake mean depth. Jones and Bachmann (1976) showed that a constant phos­ phorus sedimentation rate of 0.65 yr~^ worked well for 51 natural lakes.

Other authors have chosen to reformulate the Vollenweider equation and work with the phosphorus retention coefficient of a lake (Dillon and

Rigler, 1974b) rather than with the phosphorus sedimentation coefficient:

P. (2, ^s where 3 P = concentration of total phosphorus in the lake water, mg/m 2 L = annual areal phosphorus loading, mg/m /yr

Qg = annual areal water loading (lake outflow volume/lake surface

area), m/yr 43

R = phosphorus retention coefficient (difference between annual

phosphorus inputs and phosphorus outputs divided by the annual

phosphorus input)

Kirchner and Dillon (1975) developed a relationship between the phospho­ rus retention capacity and area! water loading for 15 southern Ontario lakes. Chapra (1975) subsequently reformulated and modified this rela­ tionship. Larsen and Mercer (1976) by working with data from 20 lakes suggested that phosphorus retention coefficients could be better estimated by the inverse of 1 plus the square root of the hydraulic flushing rate.

Successful application of these empirical models is greatly dependent upon how well the models estimate phosphorus losses to the sediments.

Though these models have been used with reasonable success on selected natural lakes (Jones and Bachmann, 1976; Chapra and Robertson, 1977), studies by Jones and Bachmann (1978) on several central-Iowa artificial lakes have shown the direct use of these models resulted in a 3 to 10 times overestimation of summer phosphorus concentrations. They suggest this overestimation resulted because phosphorus sedimentation rates are greater in artificial lakes. By using sedimentation coefficients two orders of magnitude greater than those used for natural lakes, Jones and

Bachmann were able to use the Vollenweider model (Equation 1) to predict summer phosphorus concentrations.

The importance of quantifying those factors controlling phosphorus sedimentation in natural and artificial lakes is evident (Equation 1).

This study was designed to determine the general applicability of em­ pirical phosphorus loading models. My approach was to examine the phos­ 44

phorus input-output relationships of a large number of natural and arti­

ficial lakes to determine the general limnological factors that influence

phosphorus sedimentation. By using these factors, I wished to determine

if empirical models could be developed to predict total phosphorus concen­

trations in both natural and artificial lakes or if these two lake types

needed to be treated differently. I also wished to test the predictive

abilities of the new and published empirical phosphorus models and deter­ mine the degree to which total phosphorus concentrations can be used to

predict chlorophyll ^ concentrations and Secchi disc depths in natural and

artificial lakes. 45

DATA BASE

Data from a broad range of natural and artificial lakes were gathered from lakes cited in the published literature (Jones and Bachmann, 1976;

Larsen and Mercer, 1976), the Environmental Protection Agency National

Eutrophication Survey (EPA-NES), and our unpublished studies in Iowa. (To obtain a microfiche copy of the data contact the Photo Service, Iowa State

University, Ames, Iowa 50011, requesting Supplement to Publication No.

6/7/79 Canfield Ph.D. Thesis and submitting $1.00 in the form of check, cash, or money order. Give your name and complete address for mailing.)

Data on annual areal phosphorus loading rates, lake mean depths, hydraulic flushing rates and total phosphorus concentrations (mean total phosphorus concentrations for the literature lakes; median total phosphorus concen­ trations for the EPA-NES lakes) were tabulated for all lakes. Chlorophyll a concentrations, Secchi disc transparencies and total alkalinity values were tabulated for the EPA-NES lakes. Phosphorus sedimentation rates for each lake were estimated from the data by rearranging the terms in Equa­ tion 1 (Jones and Bachmann, 1976):

- P (3)

The published literature provided data from 77 natural lakes located in Canada, northern Europe, and the northern united States. Our studies provided data from an additional 20 natural lakes. The EPA-NES provided data from 193 natural lakes and 433 artificial lakes located throughout the United States. Because some natural lakes had more than 1 year of data, there are 19 lakes represented more than once in the data set. One 46 lake is represented 4 times, 7 lakes are represented 3 times, and 11 lakes are represented twice. This large sample includes a wide range of limno- logical conditions ranging from oligotrophic to eutrophic (Table 10).

Some lakes are stratified and some are not. Areal phosphorus loading o rates range from 30 to 820,000 mg/m /yr. Measured total phosphorus concen- 3 trations range from 4 to 2600 mg/m . Lake mean depths range from 0.2 to

307 m. Hydraulic flushing rates range from 0.001 to 1800 yr~^ and calcu­ lated phosphorus sedimentation rates range from -290 to 490 yr~^.

The data were examined for obvious errors, but I made no judgments on the quality of the data. In fact, this sample includes some lakes that other authors excluded when formulating their models. Obviously, the quality of the data is variable. Investigators used different methods to collect data and some lakes were studied intensively while other lakes were sampled only a few times during extensive lake surveys. In the pro­ cedure to estimate phosphorus sedimentation rates, all the errors involved in estimating phosphorus loading, lake mean depth, hydraulic flushing rate, and total phorphorus concentration are incorporated into the sedi­ mentation coefficient. Negative phosphorus sedimentation coefficients might result from the combined effects of the errors of estimation or indicate lakes that are not in a steady state. Because the data range over orders of magnitude, I expected that even occasional large errors would not obscure general relationship. In fact, empirical phosphorus loading models that can predict total phosphorus concentrations in this large sample of lakes would be of great practical value in applied prob­ lems where less than ideal data are often available. 2 Table 10. Mean values and related statistics for areal phosphorus loading rates (mg/m /yr), total phosphorus concentrations (mg/m^), mean depths (m), hydraulic flushing rates (yr-1), and calculated sedimentation coefficients (yr-T) for 723 natural and artificial lakes

Range No. in Standard Variable Lake type sample Mean deviation Minimum Maximum

Areal phosphorus natural 290 2800 8500 30 76000 loading (L) artificial 433 15000 62000 40 820000

Total phosphorus natural 290 120 250 4 2600 (TP) artificial 433 78 117 6 1500

Mean depth (z) natural 290 13 31 0.2 307 artificial 433 9 8 0.6 59

Hydraulic flushing natural 290 5 14 0.001 183 rate (p) artificial 433 35 130 0.019 1800

Sedimentation co­ natural 290 24 63 -26 45 efficient (a) artificial 433 145 55 -290 490 48

To avoid the problems associated with developing and testing a model with the same data, I randomly sorted the lakes into two data sets. One data set (model development), which included 151 natural and 210 artifi­ cial lakes, was used to determine the limnological factors that influence phosphorus sedimentation rates. The other data set (model verification), which included 139 natural and 233 artificial lakes, was used to test the predictive abilities of the empirical models. Prior to all statistical analyses, the data values were transformed to their natural logarithms. 49

PHOSPHORUS SEDIMENTATION RATES

Using the data set for model development, I examined several factors to determine if they could be statistically related to the calculated phosphorus sedimentation coefficients (Figure 3). No significant correla­ tions were found with algal densities as measured by chlorophyll a_ concen­ trations, total phosphorus concentrations, or alkalinity. Phosphorus sedimentation coefficients were significantly correlated with lake mean depth (r = -0.44; p = 0.01), and areal water loading (r = 0.65; p = 0.01) as suggested by the studies of Vollenweider (1975), Kirchner and Dillon

(1975), and Chapra (1975). A stronger correlation was found with hydraul­ ic flushing rates (r = 0.76; p = 0.01) in agreement with Larsen and Mercer

(1976). I also found equally good correlations for areal phosphorus load­ ing rates (r = 0.76; p = 0.01) and the volumetric phosphorus loading rates

(r = 0.78; p = 0.01) where the volumetric phosphorus loading was calcu­ lated as the annual phosphorus input divided by the lake volume.

While this type of statistical analysis indicates that phosphorus sedimentation coefficients are closely correlated with some measure of water or phosphorus loading rates, it does not directly indicate a cause and effect relationship or which variable should be used in an empirical model. Because the water and phosphorus loading variables are closely correlated with each other (Table 11), either one could influence phospho­ rus sedimentation or there could be an important unmeasured variable that is also correlated with either water or phosphorus inputs.

The positive relationships between phosphorus sedimentation and hydraulic flushing rates found in this and other studies is puzzling as it Figure 3. Relationships between phosphorus sedimentation rates (a) and various limnological parameters; lines represent best fit linear regression lines. 1000 o NATURAL LAKES ARTIFICIAL RESERVOIRS r = 0.05

• • 100 e •

«• •• g «• g 10 •• •• •ééScT'** o s» *• «4* • _ «y. pP *0 o cr c» o o"' • a» &) # ° *0. .o • • _ o • o o .g o* * 0.1

0.01

0.001 _L 10 100 CHLOROPHYLL MG/M- 1000 NATURAL LAKES • ARTIFICIAL RESERVOIRS r=O.I9

100 oc>-

e o UJz •o* «• o 10 - e o# u. s u e z I - ro g 00'•S'-'îr-l;;-' o poo . e peo • I o o o K< o • o 00' V y- s • ° *6 ao °o z °*o o o° ° CO o • •

o ' • : K 0.1 o w

3 O 0.01 - < o

0.001 _L X 10 100 1000 MEASURED TOTAL PHOSPHORUS MG/M» Figure 3. (Continued) 1000 o NATURAL LAKES ARTIFICIAL RESERVOIRS r = -O.I4

or>- 100 -

h- Z » •* •• u . "f. ^m8 • 10 - 'JF°° * o & o CL^O e #0_ o • 0» * o»1 8 i z ® O (jr\ o OJ i - O • « z # cfo t UJ O o o « ° 5 ^ V Q o o o o ^ G 0.1 e

3 U S 0.01

0.001 100 1000 TOTAL ALKALINITY MG/L as C0CO3 Figure 3. (Continued) 1000 o NATURAL LAKES ARTIFICIAL RESERVOIRS

r = -0.44 .

'a: '00

1r 10 y ... • •••• • oi i I- < h- i § 0.1 S o 3 O o < 0.01 u

0.001 _L 0.1 10 100 MEAN DEPTH M Figure 3. (Continued) 1000 o NATURAL LAKES • ARTIFICIAL RESERVOIRS

r = 0.65

100 - >-o:

o • ilj u u. 10 li. *o

cn B cn V o# o 5 Z I 0.1 > o uj

3V 5 0.01

0.001 10-I lO*- 10' I0< 10- 10 Figure 3. (Continued) AREAL WATER LOADING M/YR 1000 o NATURAL LAKES • ARTIFICIAL RESERVOIRS

r=0.76

a 100 >•

o u. 10 iLl o

z U1 g cn »- t- .°o%° z w O 8 00 1 o oo o o o • œ «0 UJ V) oo 0.1 Q uj

D O g 0.01

0.001 _L -2 10" 10 10" 10^ lO' lO'^ 10" 10^ RATE YR~' Figure 3. (Continued) FLUSHING 1000

o NATURAL LAKES • ARTIFICIAL RESERVOIRS

r = 0.76 K 100 >•

wz u IL u. 10 o

z o o o o* o z O CD 8 P LU 1 o o

0.1 UiQ

3 o o o o< 0.01

0.001 10 10" 10" 10 10" l( AREAL PHOSPHORUS LOADING MG/MVYR Figure 3. (Continued) 1000 o NATURAL LAKES • ARTIFICIAL RESERVOIRS r = 0.78

100 o: >-

z w » 4

ow H < o _J 0.01 - < o

0.001 _L _L 10 10® lo' 10' 10- 10 10" 10* Figure 3. (Continued) VOLUMETRIC PHOSPHORUS LOADING MG/M^/YR 59

Table 11. Correlations between the variables used in the phosphorus load­ ing models

L L/z P a

L 1.0 0.91 0.82 0.76 L/z 1.00 0.86 0.78 P 1.00 0.76 a 1.00

is difficult to envision how a higher hydraulic flushing rate could in­ crease the loss of phosphorus to the sediments. In fact, one might expect that a higher hydraulic flushing rate would reduce the opportunity for phosphorus to be removed by sedimentation rather than enhance it. I am led to believe that some material brought in with the water rather than water loading itself is responsible.

Phosphorus itself could be that material. A higher phosphorus load­ ing rate leads to higher phosphorus concentrations in the lake water.

This could promote algal growth, which would provide a greater supply of sedimenting particles to remove phosphorus to the sediments. However, the lack of a significant correlation between phosphorus sedimentation coeffi­ cients and chlorophyll levels would suggest that this hypothesis should be rejected. Because algal growth can cause calcium carbonate precipitation and the coprecipitation of phosphorus with calcium carbonate has been sug­ gested as an important mechanism for the removal of phosphorus from some lakes (Otsuki and Wetzel, 1972; White, 1974), I had expected a positive relationship with alkalinity as it might be an index of calcium carbonate 60 precipitation. This mechanism, however, also does not seem to be an im­ portant determinant of phosphorus sedimentation over a broad range of lakes (Figure 3). In fact, the lack of a significant correlation between total phosphorus concentrations and phosphorus sedimentation coefficients suggests that phosphorus is not the material controlling the loss of phos­ phorus to the sediments either directly or indirectly.

Jones and Bachmann (1978) suggested that allochthonous inorganic particulate materials brought in by tributary streams could remove phos­ phorus to the sediments. Because I lack data on sediment inputs, I cannot directly test this hypothesis. However, phosphorus tends to be associated with particulate materials in aquatic environments; therefore, it is possible that phosphorus loading rates are proportional to the loading rates of particulate materials. To test this idea, data were obtained on mean annual total phosphorus concentrations and mean annual suspended sediment concentrations from 301 rivers located throughout the United

States (U.S. Geological Survey, 1977). These data covered all the major drainages and were based in most instances on 10 grab samples from each site. A significant correlation was found between mean total phosphorus concentrations and mean suspended sediment concentrations (r = 0.67; p =

0.01), thus suggesting that phosphorus loading rates are correlated with sediment loading rates.

If phosphorus loading can be used as an index of the quantity of in­ coming sedimentsi volumetric phosphorus loading rates may be a useful index of the potential concentration of sedimenting allochthonous parti­ cles in a lake. Because phosphorus loading rates are correlated to 61 hydraulic flushing rates, greater quantities of sediments would be ex­ pected to enter lakes with higher flushing rates. Sedimentation of in­ flowing sediments would seem to explain the observed correlations and provide the physical mechanism for removal of phosphorus to the sediments.

However, I have no field data to verify these hypotheses.

A laboratory experiment was conducted to simulate this process.

Turbid water from the Des Moines River was collected during September,

1978 and mixed in different proportions with water collected on the same date from Big Creek Lake, Iowa. The mixtures were placed in 1-1iter graduated cylinders and allowed to settle for 24 hr at a constant tempera­ ture. I found that the greater the proportion of river water the greater the percent of phosphorus sedimented to the bottom of the cylinders

(Figure 4). This is consistent with my hypothesis that sediment concen­ trations are important determinants of phosphorus sedimentation rates.

Because the calculated phosphorus sedimentation coefficients were most strongly correlated to volumetric phosphorus loading (r = 0.78; p =

0.01) and this parameter may be an index of the concentration of poten­ tially settleable sediments in a lake, I decided to use the best fit linear regression equation for this relationship to predict phosphorus sedimentation rates in natural and artificial lakes. The resulting equa­ tion is:

a = 0.129 (L/z)0'S49 (4)

However, significantly different regression equations could be calculated for the natural and artificial lakes. The resulting equations are:

a = 0.162(L/z)^'^^^ for natural lakes (5) Figure 4. Relationship between percent total phosphorus sedimented and percent river water. co Q: O X 30

CVJ

q lu _j

LLI (/)

CO =) a: o 20 X CL CO o ax _j < h- o M- z MIXTURE OF DES MOINES RIVER WATER u o WITH WATER FROM BIG CREEK RES q: UJ _l_ __L_ CL 10 50 100

PER CENT RIVER WATER 64

a = 0.n4(L/z)^'^^^ for artificial lakes (6)

By using Equation 4, or Equations 5 and 6, the general Vollenweider model

(Equation 1) should predict total phosphorus concentrations in natural and artificial lakes. 65

MODEL VERIFICATION

I tested the abilities of these models and the published empirical phosphorus loading models to predict the measured total phosphorus concen­ trations of lakes in the model verification data set. Correlation co­ efficients and best fit linear regression equations were calculated for the relationship between measured and calculated total phosphorus concen­ trations. I also determined an empirical 95% confidence interval for the calculated total phosphorus concentrations of each model by calculating the standard deviation of the mean difference between the measured total phosphorus concentrations and the calculated total phosphorus concentra­ tions. To determine which model best predicted the measured total phos­ phorus concentrations of the lakes in the verification data set, I estab­ lished 3 criteria:

1) The calculated total phosphorus concentrations should be highly correlated to the measured total phosphorus concentrations.

2) The slope of the best fit linear regression equation between the measured and calculated total phosphorus concentrations should not be significantly different from 1.0 thus indicating a 1:1 rela­ tionship between measured and calculated total phosphorus concen­ trations.

3) The 95% confidence interval for the calculated total phosphorus value should be as small as possible.

Figure 5 illustrates the ability of these models and the models pub­ lished in the literature to predict measured total phosphorus concentra­

tions of the lakes in the model verification data set. Chapra's model was the least accurate (r = 0.66; 95% confidence interval of 15 to 500% of the calculated total phosphorus value). The Vollenweider, Kirchner and

Dillon, Jones and Bachmann, and Larsen and Mercer models predicted the Figure 5. Relationship between measured total phosphorus values and total phosphorus values calculated by using empirical phosphorus models; lines represent the best fit linear regression lines. 67

MODEL VERIFICATION

o natural lakes • artificial reservoirs chapra (1975)

v=i2.4

00

_0D O 0< ° «a oo o •„ S o 000 o ° ®o " #o s t*'o an _ QQJ> • A. e ^ e V 8 •

S • M #

o* V* o ' • • o^bo o o o o o o o

r = 0.66 95% limit = 15 to 500% of calculated tp

j l 10 100 lopo 5000 calculated total phosphorus MG/M^ 68

MODEL VERIFICATION 5000 o natural lakes • artificial reservoirs

vollenweider (1975)

tp = l z(« +/0) lo/z 1000

100 ^ • <=•

"oJJoP,.-* - • • •• • • 8 10 £P° o 8 • • °&o* ° o o oo®.0

r = o.s8 95% limit = 18 of calculated tp

j _j_ I 10 100 1000 5000 calculated total phosphorus mg/m^

Figure 5. (Continued) 69

MODEL VERIFICATION

5000 o NATURAL LAKES ARTIFICIAL RESERVOIRS

KIRCHNER a DILLON (1975) R = 0.426 exp(-0.27lqg) + 0.574 e%p(-0.00949qs) L(l-R) 1000 - TP =

~ 0 o® 0» o ao ,Boao"«• o o ## s » °8°:9, 100 -

-•O • 6 • oo—.w • @ %«# • 10 ,oO 0^0»"

r=0.71 95% LIMIT = 19 TO 444% OF CALCULATED TP

J I I 10 100 1000 5000 CALCULATED TOTAL PHOSPHORUS MG/M^

Figure 5. (Continued) 70

MODEL VERIFICATION

O NATURAL LAKES • ARTIFICIAL RESERVOIRS

JONES a BACHMANN (1976)

tp: tf «0.65

• oo O • o, o o

A^.SP ? •o ° % ft • -^aV

!• • o oVoo % o ® °o ° oo o

r = 0.79 95% LIMIT = 34 TO 564% OF CALCULATED TP J I L 10 100 1000 5000 CALCULATED TOTAL PHOSPHORUS MG/M^

Figure 5. (Continued) 71

MODEL VERIFICATION

5000 o NATURAL LAKES • ARTIFICIAL RESERVOIRS LARSEN a MERCER (1976)

1000

CO

O 100

O 00

95% LIMIT = 3! TO 500% OF CALCULATED TP

10 100 1000 5000

CALCULATED TOTAL PHOSPHORUS MG/M^

Figure 5. (Continued) 72

MODEL VERIFICATION

5000 o NATURAL LAKES • ARTIFICIAL RESERVOIRS

THIS STUDY

TP = L Z(0.I29(L/Z)°-®'*® 4- p ) 1000

o e o o • 0 1 0_ CO °8 o • cffcxPo" aX 100 • o _ • d8î

m- 10 <3bA aoo

r =0.81 95% LIMIT = 29 TO 301% OF CALCULATED TP

I I I 10 100 1000 5000 CALCULATED TOTAL PHOSPHORUS MG/M^

Figure 5. (Continued) 73

MODEL VERIFICATION

5000 o NATURAL LAKES • ARTIFICIAL RESERVOIRS THIS STUDY

LAKES

COZ) a: 0 1 o 100

10

r = 0.83

95% LIMIT = 31 TO 288% OF CALCULATED TP

100 1000 5000 CALCULATED TOTAL PHOSPHORUS MG/M-

Figure 5. (Continued) 74 total phosphorus concentrations with greater accuracy, but even the best of the published models, that of Larsen and Mercer, had a 95% confidence interval of 31 to 500%. For all of the published empirical phosphorus loading models, the slopes of the regression lines shown in Figure 5 were significantly different from 1.0. These models will tend to underestimate the phosphorus concentrations of nutrient rich lakes, and overestimate the phosphorus concentrations in nutrient poor lakes. The poor performance of the published empirical models probably results from the fact that these models were developed from limited data on selected natural lakes.

The best empirical phosphorus loading model was formed by using

Equations 5 and 6 to predict phosphorus sedimentation rates separately in the natural and artificial lakes. This model had the smallest 95% confi­ dence interval (31 to 288% of the calculated total phosphorus value) and the highest correlation coefficient (r = 0.83). However, by use of Equa­ tion 4, which is based on a combined sedimentation rate for natural and artificial lakes, total phosphorus concentrations could be predicted al­ most as well (r = 0.81; 95% confidence interval of 29 to 301%). For these two models, the slopes of the regression lines are not significantly dif­ ferent from 1.0, indicating the models yield unbiased estimates of total phosphorus concentrations. These models can therefore be used to predict total phosphorus concentrations in a broad range of natural and artificial lakes. 75

PREDICTION OF TROPHIC STATES

If we assume these models can be used to provide a first approxima­ tion of the concentration of total phosphorus in natural and artificial lakes, can we predict lake trophic state? Chlorophyll ,a concentrations and Secchi disc transparencies are often used as indices of lake trophic state, therefore I examined the phosphorus-chlorophyll _a and chlorophyll

^-Secchi relationships for the EPA-NES natural and artificial lakes.

Figure 6 illustrates the phosphorus-chlorophyll a relationship for natural and artificial lakes. The relationship for natural lakes was not as strong as the relationships reported in the literature (Dillon and

Rigler, 1974a; Jones and Bachmann, 1976), but the EPA-NES chlorophylls were not peak summer chlorophyll values. Most of the scatter occurs at higher phosphorus concentrations, thus suggesting factors other than phosphorus are limiting algal levels in lakes with high phosphorus concen­ trations. Figure 6 clearly illustrates that the phosphorus-chlorophyll à relationship is much weaker (r = 0.57) in artificial lakes with many points falling below the line previously established for natural lakes.

Factors other than phosphorus would appear to be limiting algal levels in many artificial lakes, but many artificial lakes have algal levels in agreement with the phosphorus-chlorophyll a relationship.

Water clarity in natural lakes is often a function of algal levels

(Bachmann and Jones, 1974; Dillon and Rigler, 1975). In the EPA-NES lakes, I found a strong correlation between lake transparencies as meas­ ured by use of a Secchi disc and the chlorophyll ^concentrations (Figure

7; r = 0.83), but the relationship in the artificial lakes was much weaker Figure 6. Relationship between chlorophyll déconcentrations and measured total phosphorus concentration for the EPA-NES natural and artificial lakes. 77

NATURAL LAKES FROM EPA SURVEY

kx> Oo

3 3 i è g É

lme mom joncs UtO bachmamn (1*76) 0.1 10 100 1000 MEASURED TOTAL PHOSPMOfiUS MC/M^

artificial reservoirs from epa survey

100

"a 3

lim fkju jones and bacmmann (1*7*1 0.1 100 1000 measured total phosphorus mg/m^ Figure 7. Relationship between Secchi disc transparency and chloro­ phyll à concentrations for the EPA-NES natural and artifi­ cial lakes. stccmi depth m SCCCHI OCPTH M 80

(Figure 7; r = 0.44). This evidence suggests that r.ORalgal turbidities are more important as determinants of water clarity in many artificial lakes. From these results, it is evident that predictions of lake trophic states by using the phosphorus-chlorophyll ^ relationship or the chloro­ phyll ^-Secchi relationship developed from data on natural lakes will be less reliable in artificial lakes than natural lakes. 81

DISCUSSION

Simple empirical phosphorus loading models can be useful lake manage­ ment and research tools if the limitations of the models are recognized.

Because the empirical models in this study were developed from data on lakes covering a broad geographic area and a wide range of limnological conditions, the models should be applicable to most natural and artificial lakes within the United States. However, the models should be applied with caution to lakes in closed basins, bays within lakes, or any lake that has characteristics different from the lakes used to develop these models. The models will probably work equally well throughout the tem­ perate region, but they should be carefully tested before being used in other areas of the world.

I have shown that empirical phosphorus models from the literature may yield biased estimates of total phosphorus concentrations in nutrient rich and nutrient poor lakes. These models also have large confidence inter­

vals associated with the predicted phosphorus concentrations. The models developed in this study yield unbiased estimates of phosphorus concentra­

tions particularly at the extremes of a broad range of nutrient condi­

tions. My models also have much smaller confidence intervals. However,

the confidence interval for the best model ranges from 31 to 288% of the

calculated total phosphorus value. Regardless of the specific model, the

empirical phosphorus models should be used only for making order of magnitude estimates of phosphorus concentrations in lakes because of the uncertainty associated with the predictions. 82

As with any type of model, the predicted results should be evaluated carefully. Special caution is warranted when the empirical models are used to predict the response of lakes to nutrient reductions. Though the models may predict a significant drop in lake phosphorus concentrations with a reduction in phosphorus loading, observed phosphorus concentrations may not drop. Previous studies have shown that lakes which were eutrophic for only a few years recovered nearly immediately with reductions in phosphorus inputs (Michalski and Conroy, 1973; Schindler, 1975). However, lakes with a long history of high phosphorus loadings recovered very slowly (Ahlgren, 1972; Bjork, 1972; Larsen et al., 1975). Schindler

(1976) suggested this difference in response time may be related to the degree to which sediments are saturated with nutrients. He suggested that in lakes receiving many years of high phosphorus loading, the sediments may release phosphorus to the water for long periods thus delaying re­ covery. Until research clarifies the role of bottom sediments, caution must be used when predicting the response of lakes to reductions in his­

toric phosphorus loading levels.

I believe the empirical models developed in this study represent the limit of the predictive abilities of the simple empirical phosphorus models. The addition of more lakes to my large sample probably will not

significantly alter my results. I recognize many of the assumptions of

the simple input-output models do not always hold and there are errors, occasionally large errors, associated with the measurements of the basic

data, but significant reductions in the remaining error term probably will

be accomplished only when additional variables are added to the models. 83

While increasing the complexity of the models will be necessary if we are to model lake systems accurately, we should try to choose variables that reduce the remaining error term yet preserve the generality of the models.

Although each lake probably is different, I believe there are other gener­ al relationships that will permit us to model a wide range of lakes with reasonable accuracy.

My studies suggest that information on sediment dynamics in a broad range of natural and artificial lakes may provide useful information for the development of better predictive models. Though phosphorus sedimenta­ tion rates could not be statistically related to total phosphorus concen­ trations, alkalinity, or chlorophyll ^ concentrations, my studies suggest­ ed that inputs of allochthonous sediments may be an important determinant of sedimentation rates. The inflowing sediments may absorb phosphorus from the water, remove the phosphorus to the sediments, and retain the phosphorus in the sediments similar to alum treatments used in nutrient inactivation experiments. Even if the sediments do not settle to the bot­ tom, they can reduce light availability to algae, thus preventing the de­ velopment of high densities of plankton algae. Studies on the effects of sediments on phosphorus sedimentation rates, nutrient and light availa­ bility are needed.

Natural and artificial lakes are often considered as two distinct lake types. There are of course many obvious differences between them, but my studies indicate that these waters represent a range of limnologi- cal conditions and should not be treated as distinctly different lake types. I was able to show that my phosphorus models could work equally 84 well in natural or artificial lakes. Although I did find a slight im­ provement when I used separate equations to estimate phosphorus sedimenta­ tion rates in natural and artificial lakes, this difference may reflect some qualitative differences in the phosphorus inputs related to geo­ graphical location. The natural and artificial lakes are not randomly distributed. Many of the natural lakes in this study are located in glaciated regions while the artificial lakes are located basically in older geological formations. Differences in sediment inputs may account for the observed differences. However, I suggest that natural and artifi­ cial lakes represent a continuum of limnological conditions that should be investigated thoroughly.

Though we can predict total phosphorus concentrations equally well in natural and artificial lakes, predictions of algal population densities as measured by chlorophyll a concentrations and Secchi disc transparency are less reliable in the artificial lakes. However, many artificial lakes do follow the relationships established for natural lakes. Those artificial lakes that deviate may have high levels of nonalgal turbidities. Until these problems are quantified, caution should be used when predicting the trophic status of artificial lakes. 85

SUMMARY

Studies on natural lakes have demonstrated strong relationships be­ tween total phosphorus concentrations and algal population densities as measured by chlorophyll ^ concentrations. With the development of simple empirical models that can predict lake total phosphorus concentrations from data on phosphorus loading as modified by outflow and loss to the sediment, lake managers and researchers are provided with a general approach for predicting the response of lakes to changes in nutrient in­ puts. However, the general applicability and potential limitations of this approach have not been thoroughly addressed. An obvious deficiency of these models is the usage of equations developed from rather small data bases to predict phosphorus losses to the sediments. Studies have recent­ ly shown these models overestimate summer phosphorus concentrations in some artificial Iowa lakes by 3 to 10 times. Because accurate estimation of phosphorus losses is necessary if the models are to successfully pre­ dict total phosphorus concentrations, this study was designed to determine the limnological factors that influence phosphorus sedimentation rates in natural and artificial lakes. First, sediment traps were used to measure phosphorus sedimentation in four natural and four artificial Iowa lakes.

Second, nutrient budget data from 290 natural and 433 artificial lakes were used to examine phosphorus input-cutput relationships, to develop an empirical phosphorus model, and to test the predictive ability of this new model and previously published models.

Between June 1976 and August 1978, sediment traps were used to meas­ ure phosphorus sedimentation in the Iowa lakes. Phosphorus sedimentation 86 rates measured by traps were found to be highly variable both within and among the natural and artificial lakes. No significant differences in sedimentation rates were found between the lake types despite the fact nutrient budgets had shown the artificial lakes had greater phosphorus losses to the sediments. However, mean phosphorus sedimentation rates for the Iowa lakes were directly and significantly correlated with mean in­ organic suspended matter concentrations and inorganic matter sedimentation rates indicating inorganic sediments influence phosphorus sedimentation in these lakes. Using sedimentation data from published studies, a strong correlation between mean phosphorus sedimentation rates as measured by using traps and mean chlorophyll ^ concentrations was found thus suggest­ ing lake trophic state determines phosphorus sedimentation rates over a broad range of lakes. However, the phosphorus loss rates measured in the

Iowa lakes indicated these lakes were losing from 5 to 60% of their total phosphorus content daily. Because these lakes were not rapidly depleted of phosphorus, it was suggested recycling processes like resuspension and resedimentation of bottom sediments causes sediment traps to overestimate the net accumulation of phosphorus in the sediments.

By using nutrient budget data from a broad range of lakes, a simple empirical phosphorus loading model has been developed and tested that can predict lake total phosphorus concentrations equally well in natural and artificial lakes. The model is based on the Vollenweider equation:

~ z(a + p) where TP = concentration of total phosphorus in the lake water, mg/m^; L = annual phosphorus input per unit of lake surface area, mg/m^/yr; z = lake mean depth, m; p = hydraulic flushing rate, yr"^; a = phosphorus sedimen­ tation rate, yr~^. Using regression analysis, I found that a = 0.162

for natural lakes and a = 0.114(L/z)^*^^^ for artificial lakes.

When the predictive ability of previously published empirical phosphorus models and this model are compared, this model yielded unbiased estimates of total phosphorus concentrations over a wide range of nutrient condi­ tions and had a 95% confidence interval of 31 to 288% of the calculated total phosphorus value. The best published model had a 95% confidence interval of 31 to 500%. Although the confidence interval is large, the model should be of practical value for making first approximations of lake total phosphorus concentrations.

While total phosphorus concentrations can be predicted equally well in natural and artificial lakes, prediction of lake trophic state will be less reliable in artificial lakes. Relationships between total phosphorus concentrations and chlorophyll ^ concentrations and chlorophyll ^ concen­ trations and Secchi disc transparency, which are often used as indices of lake trophic state, are less precise in artificial lakes than natural lakes. This difference between natural and artificial lakes seems to be related to nonalgal turbidities in the artificial lakes. 88

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ACKNOWLEDGMENTS

This research was supported through a grant from the Office of Water

Research and Technology (project no. A-063-IA), U.S. Department of the

Interior, Washington, D.C., as authorized by the Water Research and De­ velopment Act of 1978. The grant was administered as a project (2051) of the Iowa Agriculture and Home Economics Experiment Station, which also contributed financial support.

I am extremely grateful to Dr. Roger W. Bachmann, my major professor, for his advice and encouragement throughout this study. My appreciation is extended to the members of my committee. Dr. Kenneth D. Carlander, Dr.

David F. Cox, Dr. John D. Dodd, Dr. Merwin D. Dougal, Dr. Ellis A. Hicks, and Dr. John A. Mutchmor for their help and interest.

Sincere thanks go to Dr. Jack Gakstatter, U.S. EPA Corvallis Labora­ tory, Oregon, for providing data from the Environmental Protection Agency

National Eutrophication Survey.

I want to thank Dr. Richard Bovbjerg, Director of Iowa Lakeside

Laboratory, for oroviding laboratory facilities; Mr. and Mrs. Robert

Benson, managers of the laboratory for their help and friendship through­ out this study; Carol and David Bachmann, Mark Johnson, Sarah Lindequist,

Steve Parris and Pat Nagy for their help in collecting the data.

Sincere thanks go to Mark Hoyer, Jackie LaPerriere, and Terry Noonan who spent considerable time helping collect the data.

I would like to sincerely thank my colleagues. Jack Jones and Dave

Zimmer, for their advice and guidance throughout this study.

I am most grateful to my parents, for their support and encouragement.