Prepared in cooperation with the Silver Lake Improvement Board

Water Quality and Hydrology of Silver Lake, Oceana County, 

Michigan, with Emphasis on Lake Response to Nutrient 

Loading, 2012–14

Scientific Investigations Report 2015–5158

U.S. Department of the Interior

U.S. Geological Survey
Cover photograph. Silver Lake, Oceana County, Michigan, taken by Jim Nicholas.
Water Quality and Hydrology of Silver Lake, 

Oceana County, Michigan, with Emphasis 

on Lake Response to Nutrient Loading, 

2012–14

By Angela K. Brennan, Christopher J. Hoard, Joseph W. Duris, Mary E. Ogdahl, 

and Alan D. Steinman

Prepared in cooperation with the Silver Lake Improvement Board

Scientific Investigations Report 2015–5158

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia: 2016

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

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U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Brennan, A.K., Hoard, C.J., Duris, J.W., Ogdahl, M.E., and Steinman, A.D., 2015, Water quality and hydrology of 

Silver Lake, Oceana County, Michigan, with Emphasis on lake response to nutrient loading, 2012–14. U.S. Geological 

Survey Scientific Investigations Report 2015–5158, 75 p., http://dx.doi.org/10.3133/sir20155158.

ISSN 2328-031X (print)

ISSN 2328-0328 (online)
iii

Acknowledgments

The project partners on this study include the U.S. Geological Survey (USGS)-Michigan Water 

Science Center; the Annis Water Resources Institute-Grand Valley State University (AWRI-GVSU); 

and Progressive AE. The project coordinators were members of the Silver Lake Improvement 

Board. The authors thank all the following who worked hard to make the project successful. 

Silver Lake Project Steering Committee

Anthony Groves, Progressive AE, Grand Rapids, Michigan

Larry Byl, Oceana County Commissioner, Shelby, Mich.

William DeJong, Silver Lake property owner, Mears, Mich.

Bill Bryker, Silver Lake property owner, Mears, Mich.

Ed DeJong, Silver Lake property owner, Mears, Mich.

Marc DeMatt, Silver Lake property owner, Mears, Mich.

Project Volunteers

Marc DeMatt, Silver Lake property owner/Silver Creek Dam Operator, Mears, Mich.

Susan Sharp, Friends of Silver Lake/Silver Lake property owner, Mears, Mich.

Chuck Persenaire, Silver Lake property owner, Mears, Mich.

Silver Lake State Park, Michigan Department of Natural Resources, Mears, Mich.

Data Collection and Web site Design

Thomas Morgan, Hydrologic Technician, USGS, Lansing, Mich.

Charles Taricska, Hydrologic Technician, USGS, Lansing, Mich.

Joshua Loewel, Hydrologic Technician, USGS, Lansing, Mich.

Ryan Oster, Microbiologist, formerly with USGS, Lansing, Mich.

Julia Giesen, Hydrologist, USGS, Lansing, Mich.

Marie Reynolds, Information Technology Specialist, USGS, Lansing, Mich.

Maggie Weinert, AWRI-GVSU, Muskegon, Mich.

Kurt Thompson, AWRI-GVSU, Muskegon, Mich.

James Smit, AWRI-GVSU, Muskegon, Mich.

Brian Scull, AWRI-GVSU, Muskegon, Mich.

Technical Reviewers

Howard Reeves, Research Hydrologist, USGS, Lansing, Mich.

Angela Crain, Hydrologist, USGS, Louisville, Kentucky

v

Contents

Acknowledgments........................................................................................................................................iii

Executive Summary.......................................................................................................................................1

Introduction.....................................................................................................................................................2

Purpose and Scope ..............................................................................................................................3

Silver Lake and Surrounding Watershed..........................................................................................3

Land Use........................................................................................................................................6

Population .....................................................................................................................................6

Climate...........................................................................................................................................6

Permitted Discharges .................................................................................................................6

History of Monitoring on Silver Lake .................................................................................................8

Silver Lake–2011 Water Quality Monitoring Report, Progressive AE..................................8

A Pilot Study: Silver Lake Water Quality–Michigan State University .................................8

Silver Lake Water Quality and Watershed Management Study, 2001–Water Quality 

Investigators ...................................................................................................................9

Friends of Silver Lake Monitoring .............................................................................................9

Michigan Cooperative Lakes Monitoring Program................................................................9

Water Quality Characteristics of Michigan’s Inland Lakes, 2001–10–USGS/MDEQ.........9

Study Methods and Sampling Sites..........................................................................................................10

Silver Lake Study Overview...............................................................................................................10

Site Selection.......................................................................................................................................11

Water-Quality Data Collection..........................................................................................................11

A Summary of Nutrients ....................................................................................................................15

Phosphorus.................................................................................................................................15

Nitrogen.......................................................................................................................................16

Lake Monitoring .........................................................................................................................17

Internal Nutrient Loading ................................................................................................17

Diel Dissolved Oxygen Measurements .........................................................................18

Bioassay Study..................................................................................................................18

Tributary Monitoring and Streamgaging................................................................................19

Groundwater Monitoring and Flow.........................................................................................20

Septic Systems as Sources of Nutrients ......................................................................21

Meteorology................................................................................................................................24

Atmospheric Deposition Monitoring ......................................................................................24

Waterfowl....................................................................................................................................24

Lawn Runoff................................................................................................................................25

Water Budget ......................................................................................................................................25

Change in Storage .....................................................................................................................25

Precipitation................................................................................................................................25

Evaporation.................................................................................................................................25

Surface-Water Inflow and Outflow.........................................................................................26

Groundwater Inflow and Outflow (Flux).................................................................................26
vi

Appendix 1. Estimation of Streamflow at Silver Lake Dam ...............................................................62

References Cited..........................................................................................................................................65

Appendix 2. Additional Data Collected .................................................................................................66

Bioassay Study–Microcystin and Phytoplankton .........................................................................66

Laboratory Analysis...................................................................................................................66

Data Analysis..............................................................................................................................66

Bioassay Study Results–Microcystin and Phytoplankton ..................................................66

Laboratory Analysis............................................................................................................................28

U.S. Geological Survey National Water-Quality Laboratory/ U.S. Geological Survey 

Sediment Laboratory ..................................................................................................28

Non-U.S. Geological Survey Laboratories.............................................................................28

Internal Nutrient Loading ................................................................................................28

Sediment Metal Analysis.................................................................................................28

 Bioassay Study..........................................................................................................................28

Water-Quality Standards and Criteria.............................................................................................28

Data-Quality Assurance.....................................................................................................................28

Lake Water-Quality Characteristics..........................................................................................................30

Trophic Status and Lake Classification...........................................................................................33

Bioassay Study Results......................................................................................................................34

Diel Dissolved Oxygen........................................................................................................................34

Hydrology: Sources of Water and Nutrients ...........................................................................................37

Sources of Water–Water Budget ....................................................................................................37

Sources of Nutrients–Nutrient Budget ...........................................................................................38

Nutrient Concentrations ...........................................................................................................39

Surface-Water Inflow and Outflow................................................................................39

Precipitation and Dry Deposition ...................................................................................40

Groundwater Influence....................................................................................................40

Other Sources of Nutrients .............................................................................................40

Nutrient Loads............................................................................................................................40

Surface-Water Inflow and Outflow................................................................................40

Precipitation and Dry Deposition ...................................................................................42

Groundwater Influence....................................................................................................42

Internal Nutrient Loading ................................................................................................42

Lawn Runoff.......................................................................................................................46

Nutrient Budget Summary........................................................................................................46

Nutrient Load Modeling..............................................................................................................................48

BATHTUB Data Requirements and Limitations..............................................................................48

Algorithms and Calibration................................................................................................................49

Management Scenario Simulation Approach ...............................................................................50

BATHTUB Model Simulations ...........................................................................................................50

Nutrient Loading Scenarios .....................................................................................................50

Trophic Status....................................................................................................................50

Changes in Percentage of Days with Algal Blooms ...................................................52

Reduced Septic Input to Silver Lake .............................................................................52

Summary and Conclusions.........................................................................................................................55

References Cited .........................................................................................................................................57
vii

E. coli Monitoring................................................................................................................................69

Laboratory Analysis...................................................................................................................69

Results .........................................................................................................................................69

Suspended-Sediment Concentrations ............................................................................................69

References Cited..........................................................................................................................................69

Appendix 3. Quantification of Groundwater Flow to Silver Lake......................................................70

References Cited..........................................................................................................................................75

Figures

1. Map showing Silver Lake watershed delineation and land-use 

classifications, Oceana County, Michigan...............................................................................4

2. Map showing Silver Lake sampling locations, Oceana County, Michigan.........................5

3. Schematic overview of study in Silver Lake Oceana County, Michigan...........................10

4. Schematic diagram showing the phosphorus cycle in lakes .............................................15

5. Schematic diagram showing the nitrogen cycle in lakes....................................................16

6. Photographs of bioassay carboys (A) prior to final subsampling at the end 

of the incubation period, and bioassay incubation frame (B) at its deployment 

location at the north end of Silver Lake, Oceana County, Michigan..................................19

7. Graph showing the total nitrogen and total phosphorus concentrations 

measured in Silver Lake Oceana County, Michigan, 2012 to 2014......................................31

8. Graph showing nitrogen to phosphorus ratios in Silver Lake, Oceana 

County, Michigan, 2012 to 2014.................................................................................................32

9. Bar graph showing the trophic status of Silver Lake, Oceana County, 

Michigan, based on Carlson’s Trophic State Index...............................................................34

10. Bar graph showing mean (plus or minus standard deviation) chlorophyll a

concentrations from the four bioassay treatments ..............................................................35

11. Graph series showing diel dissolved oxygen (DO) and wind data from the 

south (L5) and middle (L2) monitoring stations on Silver Lake, Oceana 

County, Michigan, on four dates in 2013.................................................................................36

12. Pie diagrams showing the annual water budget for Silver Lake, 

Oceana County, Michigan.........................................................................................................37

13. Pie diagrams showing the water budget, by season, for Silver Lake, 

Oceana County, Michigan.........................................................................................................38

14. Pie diagrams showing mean nutrient loading, by season, to Silver Lake, 

Oceana County, Michigan.........................................................................................................41

15. Graphs showing percentage of modeled septic contribution to total nutrient 

load in Silver Lake, Oceana County, Michigan, estimated using BATHTUB.....................43

16. Graph showing total phosphorus concentrations released/retained from 

sediment into the water column for oxic (Ox) and anoxic (An) treatments ......................44

17. Graph showing ammonium concentrations released/retained from sediment 

into the water column for oxic (Ox) and anoxic (An) treatments........................................45

18. Bar graph showing total phosphorus concentrations in sediment 

(upper 5 centimeters) at the two internal loading study locations in Silver Lake, 

Oceana County, Michigan, on each core collection date....................................................46

19. Pie diagrams showing annual phosphorus and nitrogen budget 

summaries for Silver Lake, Oceana County, Michigan.........................................................47
viii

20. Bar graphs showing summary of nutrient loading scenarios using the 

BATHTUB model for Silver Lake, Oceana County, Michigan ..............................................51

21. Bar graphs showing the simulated effect of conversion from onsite 

septic-waste treatment (within 200 feet of shoreline) to central sewer 

system using BATHTUB.............................................................................................................54

1–1. Schematic diagram of the approximate dam configuration for the Silver Lake 

Dam at Silver Creek, Oceana County, Michigan....................................................................62

1–2. Graph showing the discharge rating for Silver Creek Dam, Oceana 

County, Michigan........................................................................................................................64

2–1. Barplots (mean plus 1 standard error) of phytoplankton biovolume in Silver Lake, 

Oceana County, Michigan, for five nutrient treatments.......................................................68

3–1. Graph showing continuous and discrete water-level elevations for the 

shallow wells around Silver Lake, Oceana County, Michigan, 

April 2013–December 2014........................................................................................................71

3–2. Map showing locations of piezometers, seepage meters, and temperature 

sensor array deployments around Silver Lake, Oceana County, Michigan......................72

3–3. Graph series showing the comparison of simulated to observed 

temperature values at 3.9 and 7.9 inches in depth for the east, north, and 

south temperature arrays deployed at Silver Lake, Oceana County, Michigan ..............74

Tables

1. Historical and current water-quality studies for Silver Lake, Oceana County, 

Michigan, and associated analytes...........................................................................................8

2. Nutrient study sampling locations and sampling strategy for Silver Lake, 

Oceana County, Michigan.........................................................................................................12

3. Analytes measured as part of the 2012–14 nutrient loading study for 

Silver Lake, Oceana County, Michigan ...................................................................................14

4. Septic contribution model input...............................................................................................23

5. References for Michigan water-quality nutrient criteria recommendations 

for lakes and rivers.....................................................................................................................29

6. Statistical summary of nutrient and chlorophyll a concentrations measured from 

2012–14 nutrient study in Silver Lake, Oceana County, Michigan......................................31

7. Lake Trophic State classification.............................................................................................32

8. Nutrient loading from groundwater to Silver Lake, Oceana County, Michigan ...............42

9. Mean and standard deviation (SD) of total phosphorus (TP) and ammonium (NH4+) 

flux, in milligrams per squaremeter per day, from sediment cores collected 

from Silver Lake, Oceana County, Michigan, and incubated for 28 days..........................44

10. Summary of septic-load estimates to Silver Lake, Oceana County, 

Michigan, using the septic model............................................................................................48

11. Model algorithms selected when running BATHTUB simulations for 

Silver Lake, Oceana County, Michigan ...................................................................................49

12. Nutrient concentration adjustment scenarios simulated using 

the BATHTUB model...................................................................................................................50

Figures—Continued
ix

13. Simulated algal bloom frequency for Silver Lake, Oceana County, Michigan, 

in response to changes in phosphorus and nitrogen loading scenarios ..........................53

1–1. List of adjustments made to dam configuration during this study .....................................63

2–1. Ten most abundant phytoplankton species based on frequency of occurrence ............67

Tables—Continued
x

Conversion Factors

[Inch/Pound to International System of Units[

Multiply By To obtain

Length

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

yard (yd) 0.9144 meter (m)

Area

acre 4,047 square meter (m2)

acre 0.004047 square kilometer (km2)

square foot (ft2) 0.09290 square meter (m2)

square mile (mi2) 259.0 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2) 

Volume

cubic inch (in3) 16.39 cubic centimeter (cm3) 

cubic inch (in3) 0.01639 liter (L)

cubic foot (ft3) 0.02832 cubic meter (m3) 

cubic yard (yd3) 0.7646 cubic meter (m3) 

cubic mile (mi3) 4.168 cubic kilometer (km3) 

acre-foot (acre-ft) 1,233 cubic meter (m3)

acre-foot (acre-ft) 0.001233 cubic hectometer (hm3) 

Flow rate

acre-foot per day (acre-ft/d) 0.01427 cubic meter per second (m3/s)

acre-foot per year (acre-ft/yr) 1,233 cubic meter per year (m3/yr)

foot per second (ft/s) 0.3048 meter per second (m/s)

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

cubic foot per second per square

mile [(ft3/s)/mi2]

0.01093 cubic meter per second per 

square kilometer [(m3/s)/km2]

cubic foot per day (ft3/d) 0.02832 cubic meter per day (m3/d)

gallon per day (gal/d) 0.003785 cubic meter per day (m3/d)

million gallons per day (Mgal/d) 0.04381 cubic meter per second (m3/s)

million gallons per day per square

mile [(Mgal/d)/mi2]

1,461 cubic meter per day per square

kilometer [(m3/d)/km2]

inch per year (in/yr) 25.4 millimeter per year (mm/yr)

Mass

ounce, avoirdupois (oz) 28.35 gram (g) 

pound, avoirdupois (lb) 0.4536 kilogram (kg) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as:

°F = (1.8 × °C) + 32.

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as:

°C = (°F – 32) / 1.8.

Micron is a unit of length equal to one millionth of a meter.
xi

Datum

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD88), and National Geodetic Vertical Datum of 1929 (NGVD29)

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD83), 

and North American Datum of 1927 (NAD27).

Altitude, as used in this report, refers to distance above the vertical datum.

Abbreviations—Continued

ADV acoustic doppler velocimeter

ADVM acoustic doppler velocity meter

ANOSIM analysis of similarity

ANOVA analysis of variance

AWRI Annis Water Resources Institute

BMPs best management practices

CLMP Cooperative Lakes Monitoring Program

DPP drive-point piezometer

E. coli Escherichia coli

EPA U.S. Environmental Protection Agency

EWI equal-width-increment

GPS global positioning system

hrt Hart

ldt Ludington

LC-MS liquid chromatography-mass spectrometry

MDEQ Michigan Department of Environmental Quality

MDNR Michigan Department of Natural Resources

MI-WSC Michigan Water Science Center

MJ megajoules

MSU Michigan State University

NCDC National Climatic Data Center
xii

Abbreviations—Continued

NLCD National Land Cover Database

NMDS nonmetric multidimensional scaling

NOAA National Oceanic and Atmospheric Administration

NWIS National Water Information System

NWQL National Water Quality Laboratory

ORV off-road vehicle

PAR photosynthetically-active radiation

PCU platinum cobalt units

ppm parts per million

PVC polyvinyl chloride

RMSE root mean square error

RPD relative percent difference

SD standard deviation

SLIB Silver Lake Improvement Board

SLSP Silver Lake State Park

SRP soluble reactive phosphorus

SSC suspended sediment concentration

TSI Trophic State Index

TDS total dissolved solids

TNTC too numerous to count

TSS total suspended solids

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey

UV ultraviolet

WHO World Health Organization

yr year
1 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Water Quality and Hydrology of Silver Lake, Oceana 

County, Michigan, with Emphasis on Lake Response to 

Nutrient Loading, 2012–14

By Angela K. Brennan,1 Christopher J. Hoard,1 Joseph W. Duris,1 Mary E. Ogdahl,2 and Alan D. Steinman2

Executive Summary

Silver Lake is a 672-acre inland lake located in Oceana 

County, Michigan, and is a major tourist destination due to 

its proximity to Lake Michigan and the surrounding outdoor recreational opportunities. In recent years, Silver Lake 

exhibited patterns of high phosphorus concentrations, elevated 

chlorophyll a concentrations, and nuisance algal blooms. 

The U.S. Geological Survey (USGS), in cooperation with the 

Silver Lake Improvement Board and in collaboration with 

the Annis Water Resources Institute (AWRI) of Grand Valley 

State University, designed a study to assess the hydrologic and 

nutrient inputs to Silver Lake in order to identify the events 

and conditions that affect the nutrient chemistry and production of algal blooms in the lake. This information can inform 

water-resource managers in developing various management 

strategies to prevent or reduce the occurrence of future algal 

blooms.

USGS and AWRI scientists collected data from November 2012 to December 2014 to provide information for future 

management decisions for Silver Lake. Silver Lake can be 

classified as a polymictic lake and has a residence time of 

approximately 223 days. Based on the mean lake Secchi depth, 

total phosphorus, and total nitrogen concentrations, Silver 

Lake is classified as a eutrophic lake. In-situ bioassay results 

indicate that algal growth in Silver Lake is colimited by both 

nitrogen and phosphorus. The nutrient budget for Silver Lake 

was calculated using the BATHTUB model based on 2 years 

of water-quality data collection. The BATHTUB model, developed by the U.S. Army Corps of Engineers, treats the lake as 

a well-mixed system with multiple inputs and outlets for both 

water and dissolved constituents, such as nutrients.

Based on results of the BATHTUB model, which were 

conditioned on observed concentrations and flows, the mean 

annual input of phosphorus to Silver Lake was approximately 

1,342 pounds (lb); the mean annual input of nitrogen to Silver 

Lake was approximately 51,998 lb. The major measured 

sources of phosphorus loading to Silver Lake were groundwater and Hunter Creek, whereas the major measured sources of 

nitrogen to Silver Lake were Hunter Creek, groundwater, and 

atmospheric deposition. The largest loading of phosphorus and 

nitrogen to Silver Lake occurred during the spring. Minimal phosphorus deposition (if any) occurred in the lakebed 

sediment; however, of the nitrogen that entered Silver Lake, 

approximately 42.2 percent was deposited in the lakebed sediment as simulated by the BATHTUB model. 

In addition to measured sources, a septic load model 

was used to estimate the likely range of septic contribution to groundwater and adjacent surface waters. The likely 

septic loading scenario estimates that septic systems contribute 47.8 percent of the phosphorus to groundwater and 

22.3 percent of phosphorus to Hunter Creek. These results 

indicate that septic systems are a major source of phosphorus 

loading to Silver Lake. The likely septic loading scenario 

indicated that septic systems account for 0.95 percent of the 

nitrogen load to Hunter Creek and 1.1 percent of the contribution of nitrogen to groundwater. 

The BATHTUB model was used to estimate future 

nutrient loading and eutrophication scenarios based on waterquality data collected from Silver Lake, groundwater, major 

tributaries, and atmospheric deposition. A separate septic load 

model was used to estimate the septic contribution to groundwater or directly to surface water, and the nutrient load estimates were modeled using the BATHTUB model to determine 

subsequent water-quality changes to Silver Lake.

• BATHTUB model scenarios based on measured data:

• The first BATHTUB scenario evaluated the condition of Silver Lake and the change to lake water 

quality (trophic status) as a result of changes in 

nutrient loading from different sources. Based on 

BATHTUB model simulations, if groundwater loading of phosphorus and nitrogen only were decreased 

by 75 percent, and all of the other nutrient inputs 

stayed the same, the future condition of Silver Lake 

would most likely remain highly mesotrophic to 

1

U.S. Geological Survey, Michigan Water Science Center, Lansing, Michigan

2

Annis Water Resources Institute, Grand Valley State University, Muskegon, 

Michigan
eutrophic (the current [2014] condition of Silver 

Lake). If nutrient loading continued to increase in 

groundwater, the lake would continue to remain 

eutrophic with more frequent algal blooms. If nutrient loading from Hunter Creek only decreased by 

50–75 percent, and all of the other nutrient inputs 

stayed the same as the baseline dataset, Silver Lake 

would remain eutrophic to highly mesotrophic. By 

reducing the input of manageable nutrient sources 

(Hunter Creek, groundwater, and lawn runoff) by 

75 percent, the BATHTUB model simulation indicates that Silver Lake would be classified as mesotrophic, which is indicative of improved water quality, water clarity, and reduced algal bloom frequency.

• Simulations also were run using the BATHTUB 

model to evaluate the number of days Silver Lake 

could experience algal blooms (algal blooms are 

defined as modeled chlorophyll a in excess of 

10 micrograms per liter [μg/L]) as a result of an 

increase/decrease in phosphorus and nitrogen 

loading from groundwater, Hunter Creek, and (or) 

a combination of sources. If the phosphorus and 

nitrogen loading from Hunter Creek is decreased 

(and all other sources are not altered), Silver Lake 

will continue to experience algal blooms, but less 

frequently than what is currently experienced. The 

same scenario holds true if the nutrient loading from 

groundwater is decreased. Another scenario was 

simulated using a combination of sources, which 

includes increases and decreases in phosphorus and 

nitrogen loading from sources that are the most 

likely to be managed, and includes groundwater (as 

a result of conversion of household septic to sewers), 

Hunter Creek (conversion of household septic to 

sewers), and lawn runoff. Results of the BATHTUB 

model indicated that a 50-percent reduction of phos

phorus and nitrogen from these sources would result 

in a considerable decrease in algal bloom frequency 

(from 231 to 132 days) and severity, and a 75-percent 

reduction would greatly reduce algal bloom occur

-

-

rence on Silver Lake (from 231 to 57 days).

• BATHTUB model scenarios based on septic load 

model:

• A scenario also was conducted using the BATHTUB model to simulate the conversion of septic to 

sewer and included a low, high, and medium (likely) 

scenario of nutrient loading to Silver Lake. Simulations of the BATHTUB model indicated that, under 

the likely scenario, the conversion of all onsite septic 

treatment to sewers would result in an overall change 

in lake trophic status from eutrophic to mesotrophic, 

thereby reducing the frequency of algal blooms and 

algal bloom intensity on Silver Lake (chlorophyll a

>10 μg/L, from 231 to 184 days per year, or chlorophyll a >20 μg/L, from 80 to 49 days per year).

Introduction 

“A lake is the landscape’s most beautiful and 

expressive feature. It is earth’s eye; looking into 

which the beholder measures the depth of his 

own nature.” ~Henry David Thoreau

2 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Silver Lake is a 672-acre inland lake located along the 

eastern shore of Lake Michigan in Oceana County, Michigan. The lake is separated from Lake Michigan by a large 

dune complex of rolling sand hills that measures about 

1.5 miles (mi) wide and about 3 mi long (http://www.thinkdunes.com). These dunes are considered one of the largest 

deposits of living dunes on the shores of Lake Michigan, as 

the dunes continue to grow and move, covering cabins and 

filling in approximately 18 acres of Silver Lake from 1950 

to 2012 (Groves and others, 2013). During May–September, 

almost 1 million people visit Silver Lake and the surrounding 

sand dunes for outdoor recreation, including the 450-acre offroad vehicle (ORV) area owned by the Silver Lake State Park 

(SLSP). The Silver Lake sand dunes are the only dunes east of 

the Mississippi River where people are allowed to drive ORVs 

(http://www.thinkdunes.com). The recreational opportunities 

and the water resources of Silver Lake are considered a highly 

valued asset to the surrounding community and tourists alike.
Introduction 3

Why Study Lakes in Michigan?

There are more than 11,000 inland 

lakes in the State of Michigan. Lakes have 

always been important natural resources 

and are highly valued for their recreational, 

aesthetic, and scenic qualities. In Michigan 

alone, the lake recreational industry is 

estimated to bring $15 billion per year 

to the State (Stynes, 2002).

In 1989, the Silver Lake Improvement Board (SLIB) 

initiated a program to control the growth of the exotic aquatic 

plant Eurasian milfoil (Myriophyllum spicatum) and nuisance 

algae blooms. In addition to plant control, water-quality 

monitoring was conducted to evaluate baseline water quality 

conditions in Silver Lake.

In 2011, Progressive AE published the “Silver Lake 2011 

Water Quality Monitoring Report,” which evaluated data collected from 1989 to 2011. In that report, there were several 

key conclusions made: 

1. Total phosphorus concentrations were relatively low 

(less than 0.01 milligrams per liter [mg/L]) in the spring 

and increased considerably in the summer months (from 

0.03 to 0.04 mg/L); this pattern has been observed 

repeatedly since 1989 (Progressive AE, 2011). 

2. Water transparency of Silver Lake was moderate (Secchi 

depth 8.5 feet [ft]) in the spring and low (Secchi depth 

2.8 ft) in the summer, likely as a result of algal growth in 

the lake. 

3. During 2010–11, there were elevated (9 and 6 micrograms per liter [μg/L], respectively) chlorophyll a

concentrations in the summer related to persistent algal 

blooms on the lake.

The 2011 Progressive AE study stated that, based on 

historical and recent data, Silver Lake appeared to be undergoing more accelerated eutrophication and if the trend continued, 

that there would be more frequent and prolonged algal blooms, 

reduced transparency, and a decline in overall water quality.

The SLIB concluded that a detailed interpretive study 

was necessary to help identify nutrient sources that could be 

causing algal blooms in the lake. The U.S. Geological Survey 

(USGS), in cooperation with the SLIB, and in collaboration 

with the Annis Water Resources Institute (AWRI) of Grand 

Valley State University, designed a study to assess the hydrologic and nutrient inputs to Silver Lake, as well as to identify 

the events and conditions that affect the nutrient chemistry and 

production of algal blooms in the lake. The primary purpose 

of this study was to help identify nutrient sources that could be 

causing algal blooms and eutrophication in Silver Lake in an 

effort to inform management decisions that could prevent or 

reduce the occurrence of future algal blooms.

Purpose and Scope

This report describes the hydrology and water quality of 

Silver Lake with emphasis on nutrient loading. The objectives 

of this report are to (1) describe the Silver Lake watershed 

and historical water-quality monitoring studies; (2) describe 

current water quality in the lake, groundwater, tributaries, and 

atmosphere; (3) quantify the water and nutrient budgets for 

Silver Lake and estimate the contribution of septic systems; 

(4) identify the nutrient(s) limiting algal growth in Silver 

Lake; and (5) present model scenarios of future lake conditions in response to changes in nutrient loading inputs. The 

BATHTUB model developed by the U.S. Army Corps of 

Engineers (Walker, 1996) was used to simulate current conditions in the lake and nutrient loading scenarios.

Silver Lake and Surrounding Watershed

Located in Oceana County, Michigan, Silver Lake is a 

672-acre natural inland lake with a volume of 9,823 acre-feet 

(acre-ft) and a maximum depth of 22 ft (Groves and others, 2013) (fig. 1). Silver Lake is located about 8 miles (mi) 

west of Hart, Mich., and about 31 mi south of Ludington, 

Mich. Silver Lake is situated along the eastern shore of Lake 

Michigan and is separated from the Great Lake by a large 

2,000-acre dune formation to its west and northwest. The 

Silver Lake shoreline is mostly developed on the east and 

north shores of the lake, with the remaining shoreline associated with the State of Michigan-owned Silver Lake State Park 

(SLSP). SLSP consists of a campground on the eastern shore 

of the lake, and almost 3,000 acres of dune and mature forest 

on the western and northern shores of the lake. Silver Lake is 

the last inland lake in a “chain-of-lakes,” including Upper Silver Lake, Lake Holiday, and Wyckoff Lake that are connected 

by a series of tributaries. Silver Lake empties through Silver 

Creek and ultimately drains into Lake Michigan (fig. 2). 

Hunter Creek is the main tributary to Silver Lake, which 

includes drainage from the Hunter Creek Basin as well as 

drainage from Upper Silver Lake (fig. 2). Two additional 

smaller tributaries that were monitored as part of this project 

included a small tributary near the SLSP campground and 

another small tributary located north of SLSP on North Shore 

Drive.

The soils near Silver Lake consist mainly of welldrained sandy deposits, including Grattan sand (rapid permeability), Epworth fine sand (rapid permeability), and Covert 

sand (moderate permeability) along with Pipestone fine sand 

(somewhat poor permeability) along the eastern perimeter 

of Silver Lake and dune land (rapid permeability) along the 

western side of the lake (U.S. Department of AgricultureNatural Resources Conservation Service, 2015).
4 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Figure 1. Silver Lake watershed delineation and land-use classifications, Oceana County, Michigan.

Silver Lake

Upper 

Silver Lake

Lake Holiday

Wyckoff Lake

Mears

US 31

43°40'

43°44'

86°30' 86°26'

Lake Michigan

SILVER 

LAKE 

STATE 

PARK

Silver Lake, 

Oceana County

Hart

Ludington

MICHIGAN

EXPLANATION

Subwatershed

1,640-foot buffer

Roads

Silver Lake State 

Park boundary

Grassland/

herbaceous

Water

Urban

Barren

Forest

Shrub

Agriculture

Wetlands

0 1 2 MILES

0 1 2 KILOMETERS

National Land Cover Data from Fry and others, 2011

Hunter Creek
Introduction 5

Figure 2. Silver Lake sampling locations, Oceana County, Michigan.

!

WD3

WD4

!

43°40'

86°31' 86°30'

43°41'

20

10

Lake Holiday

Upper Silver Lake

W1

T2

W2

!(

T6

T1

W4

!(

!(

Hunter Creek

Silver Creek

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

B D1

W3

L1

L2 L3

L4 L5

T5

T3

T4

A

!

WD1b

! WD1a

!WD2

Silver Lake

15

0

5

0 0.5 1 MILE

0 0.5 1 KILOMETER

Base from ESRI Imagery Basemaps

Lake, streams, and catchment from the National Hydrography Dataset Plus version 2 

Lake contours from Michigan Center for Geographic Information, 2005

Lake Michigan

NORTH SHORE DRIVE

EXPLANATION

Atmospheric deposition 

station (D)

Lake (L)

Tributary (T)

Well (W)

Wastewater discharge 

(WD)

Lakes

Streams

Silver Lake depth 

contours, in feet

Stage gage (B)

Streamgage (A)

5
6 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Land Use

The Silver Lake watershed drains approximately 

25 square miles (mi2), and the area is predominantly forest 

(38 percent) and agriculture (26 percent). Developed and 

grassland/herbaceous land each account for 9 percent of the 

watershed, and the remaining land use consists of 6 percent 

wetlands, 5 percent water, and 3 percent barren and shrub 

according to the 2006 National Land Cover Database (Fry and 

others, 2011) (fig. 1). The land use within 1,640 ft of Silver 

Lake is predominantly barren (dune) (33 percent), developed 

(29 percent), and forest (23 percent). The remaining land use 

including wetlands, grassland/herbaceous, shrub, and agriculture make up the remaining 15 percent of land use within 

1,640 ft of the lake. However, approximately 65 percent of the 

shoreline around Silver Lake has been developed, while the 

remaining shoreline is primarily dune with a small percentage 

of wetland and forest land cover.

In the mid- to late 1800s, the lumber industry was booming in the small towns of Hart and Mears near Silver Lake. 

When the lumbering era came to an end in the early 1900s, 

residents found the land to be a great location for orchards. 

Currently, Oceana County boasts to having the largest number 

of peach and pear trees, by acreage, in the State of Michigan. 

It is the largest supplier of asparagus in the State and is known 

as the Asparagus Capital of the World (Pure Michigan, 2015). 

In addition, Oceana County also has the second largest amount 

of Christmas tree acreage in the State, dominated by Douglas 

Fir (Curtis and others, 2012).

Population

Silver Lake became a tourist destination in the early 

1920s and cottages quickly began to appear along the eastern 

edge of Silver Lake. As access became more readily available, 

more visitors began to flock to the area (Curtis and others, 

2012). Malcolm “Mac” Wood is credited for creating the 

area’s first “dune scooter” in 1927 (Curtis and others, 2012).

According to the 2010 U.S. Census, Golden Township 

had a population of 1,742 persons (U.S. Census Bureau, 

2010). However, the population data from the 2010 Census is 

an estimate of the permanent year-round population and those 

that are seasonal or visitors are not included in these estimates. 

Since the population is primarily seasonal, the estimated number of persons may range from less than 1,000 during the winter months to over 14,000 in the peak summer months. With 

the inclusion of single day visitors, the population can increase 

to over 20,000 in the summer months and nearly 25,000 on 

the 4th of July. An estimated 772,231 people visited SLSP in 

2014, and two-thirds of those were visitors to the ORV dune 

recreation area (DeJong, 2015). 

Based on a review of the Oceana County parcel maps, 

there are approximately 192 homes and cottages located on 

Silver Lake and approximately 610 homes and cottages in the 

Silver Lake area that are not located on the lake itself. The Silver Lake area (properties within approximately 1 mi of Silver 

Lake) is a seasonal community where the vast majority of the 

housing units are occupied during the summer. 

There are 9 campgrounds located within 1.5 mi of 

Silver Lake with a total of approximately 1,693 camp sites. 

The camping experience ranges from sites with no utilities 

to mobile and modular homes that typically remain in place. 

Campgrounds in the Silver Lake area are considered seasonal, and no campgrounds are open from November through 

March. In general, there are very few campers in April, May, 

September, and October while most usage of the sites occurs 

from the end of May through mid-September. In addition, 

there are 3 hotels in the Silver Lake area with approximately 

156 hotel rooms. All of the local hotels typically operate from 

April through October and are closed throughout the winter 

(DeJong, 2015).

Climate

The mean high air temperatures at Silver Lake typically occur in July and August, with the lowest temperatures 

experienced during January and February. The mean annual 

precipitation in rainfall is 32–34 inches (in.), and lake-effect 

snowfalls average from 100 to 140 in. The months with the 

greatest amount of precipitation tend to be May, August, and 

September, and the driest month is February. A lake-modified 

climate results in a long growing season of 140 to 150 days 

which, coupled with late spring warming, makes the climate 

suitable for commercial fruit production (Curtis and others, 

2012).

Permitted Discharges

Currently (2015), there are four approved wastewaterdischarge permits on file within the Silver Lake watershed and 

all are groundwater discharge permits. There are currently no 

surface-water discharge permits within the watershed. The 

Michigan Department of Environmental Quality (MDEQ) has 

issued the following discharge permits:

1. Silver Lake State Park (SLSP) / Michigan Department 

of Natural Resources (MDNR), effective February 1, 

2010, expired February 1, 2015, new permit re-issued 

and in effect March 1, 2015 through March 1, 2018, 

Permit #GW1810028 (fig. 2, sites WD1a and WD1b). 

The SLSP permit allows for discharge via spray irrigation on land surfaces (SLSP lagoon system and irrigation 
Introduction 7

field) and subsurface disposal through septic tanks and 

tile fields (ORV public toilets, manager’s residence, and 

SLSP office/garage) from April 15th through October 31st. The discharge permit that was in effect from 

February 1, 2010 through February 1, 2015, authorized 

the SLSP to discharge 66,000 gallons per day (gal/d) 

(2,700,000 gallons per year [gal/yr]) of sanitary wastewater to the groundwater. The re-issued permit allows 

for maximum daily surface discharge of 132,000 gal/d 

(spray irrigation), and subsurface discharge not to exceed 

264,000 gallons per week and 8,000 gal/d to the tile 

fields. The new permit allows for a maximum cumulative annual discharge (that is, surface and subsurface) 

of 4,806,915 gal/yr. This permit requires that discharge 

is recorded daily during discharge, and effluent waterquality testing is conducted weekly during discharge and 

analyzed for nutrients (total inorganic nitrogen, ammonia, nitrate, nitrite, and total phosphorus), pH, chloride, 

sodium, total suspended solids (TSS), and biochemical 

oxygen demand (BOD). Soil samples are collected annually (every August) from the land application site and 

monitored for total phosphorus, sodium, pH, and cation 

exchange capacity. Groundwater quality and groundwater elevations also are monitored upgradient and downgradient of the treatment lagoons on a quarterly basis 

(February, May, August, and November) and analyzed 

for nutrients, metals, chloride, and sodium. They are 

analyzed annually for calcium, magnesium, potassium, 

bicarbonate, and sulfate.

2. Sandy Shores Campground, effective August 1, 2014, 

expires August 1, 2016, Permit #GW1110131 (fig. 2, 

site WD2). This permit allows for discharge to groundwater via subsurface disposal through a septic tank and 

tile field. This permit authorizes discharge of less than 

10,000 gal/d and requires weekly flow measurement 

reporting; however, no water-quality monitoring is 

required under this permit. 

3. Silver Creek RV Resort LLC, effective April 1, 2011, 

expires April 1, 2016, Permit #GW1110253 (fig. 2, 

site WD3). The discharge permit requirements for Silver 

Creek RV Resort are the same as for Sandy Shores 

Campground permit; subsurface disposal to groundwater 

through a septic tank and tile field and authorized to discharge less than 10,000 gal/d with weekly flow monitoring and no water-quality requirements.

4. Grace Adventures Campground, effective January 1, 

2015, expires January 1, 2018, Permit #GW1110724 

(fig. 2, site WD4). This campground is not yet operational; however, the permit for groundwater discharge 

less than 10,000 gal/d with weekly flow monitoring will 

be required.
8 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

History of Monitoring on Silver Lake

There is a long history of water-quality monitoring on Silver Lake. The results of these studies were used in the development of the current study objectives and to support the findings discussed in this report. A brief summary of the monitoring 

studies are discussed in the following sections and summarized in table 1.

Table 1. Historical and current water-quality studies for Silver Lake, Oceana County, Michigan, and associated analytes.

[MSU, Michigan State University; WQI, Water Quality Investigators; USGS, U.S. Geological Survey; MDEQ, Michigan Department of Environmental Quality]

Study

Water 

transparency 

(Secchi depth)

Surfacewater 

nutrients

Chlorophyll a

Bacteriological 

data

Physical 

water-quality 

data

Groundwater

Atmospheric 

deposition

Internal 

nutrient 

loading

Microcystin 

and 

phytoplankton

2011 Water Quality 

Monitoring Report, 

Progressive AE

X X X X X

A Pilot Study: Silver Lake 

Water Quality-MSU

X X

Study of Silver Lake 

Water Quality-MSU

X X

A Water Quality & Watershed Management 

Study-WQI

X X X X X

Friends of Silver Lake 

Monitoring

X

Michigan Cooperative 

Lakes Monitoring 

Program

X X X X

Michigan Inland Lakes 

Report-USGS/MDEQ

X X X X

Silver Lake Nutrient 

Study, 2012–14 

(this report)

X X X X X X X X X

Silver Lake—2011 Water Quality Monitoring 

Report, Progressive AE

The 2011 Water Quality Monitoring Report summarized 

the key water-quality data that had been collected periodically since 1989 in an effort to evaluate baseline water-quality 

conditions in the lake. Monitoring results indicated that Silver 

Lake is isothermic and well-oxygenated throughout the water 

column; phosphorus levels have been increasing; water clarity has decreased; and while chlorophyll a levels have been 

decreasing slightly, high chlorophyll a concentrations in the 

summers of 2010 and 2011 (9 and 6 μg/L, respectively) coincided with significant algal blooms. Fecal coliform bacteria 

samples were collected from near-shore areas around the lake; 

sample results were low, indicating a minimal health threat. 

The study concluded that Silver Lake can currently be classified as a meso-eutrophic lake and that the lake is currently 

undergoing accelerated eutrophication (Progressive AE, 2011). 

A Pilot Study: Silver Lake Water Quality—

Michigan State University

In 2006, a study was conducted to evaluate the level of 

fecal pollution in Silver Lake, potentially associated with septic 

tanks in the area. Multiple microbial indicators were monitored 

as part of this pilot study, including Escherichia coli (E. coli), 

enterococci, and Clostridium perfringens (C. perfringens). In 

addition, coliphage, a type of virus that infects E. coli bacteria, 

was analyzed to determine if fecal contamination was caused 

from recent or past events. This study was conducted on August 

30, 2006, at 11 sites around the perimeter of the lake, including one site located on Lake Michigan near the mouth of Silver 

Creek. Results indicated that water quality at numerous sites 

was considered impaired based on the MDEQ recreational 

freshwater standards for total-body and partial-body contact for 

E. coli for a single day (less than or equal to 300 colony-forming 

units per 100 milliliters [CFU/100 mL)], less than or equal to 

1,000 CFU/100 mL, respectively) and the U.S. Environmental 
Introduction 9

Why are Nutrients Important?

Nutrients are essential for natural plant and animal growth; however, excessive concentrations of 

nutrients can adversely affect aquatic life and human health. Elevated nutrient concentrations in streams 

and lakes can trigger eutrophication, which results in excessive, often unsightly, growth of algae and 

aquatic plants. As the plants and algae decay, there are often foul odors produced as well as low dissolved 

oxygen in water (hypoxia), which can harm fish and shellfish. High nutrient concentrations also can cause 

growth of harmful algae, which can be potentially toxic to fish and other organisms, including humans

(U.S. Geological Survey, 2014).

Protection Agency (EPA) criterion for total-body contact for 

enterococci (less than or equal to 33 CFU/100 mL [single 

day], less than or equal to 151 CFU/100 mL (infrequently 

used]). Six of the 11 sites exceeded the MDEQ standards for 

E. coli (from 2,367 CFU/100 mL to too numerous to count

[TNTC]), and 2 of the 11 sites exceeded the EPA standards 

for enterococci (from 177 to 443 CFU/100 mL). The study 

concluded that Hunter Creek and a small ephemeral tributary 

near Floradale Resort (not monitored as part of this study due 

to minimal streamflow as well as the ephemeral nature of the 

tributary) were contributing to higher microbial concentrations, and that there also were higher concentrations found 

near the shoreline indicating that septic tanks also may be 

contributing to higher bacterial concentrations. Also, five sites 

had detectable coliphage, which was indicative of recent and 

widespread fecal pollution (Rose and Shibata, 2006).

A follow-up study was conducted by Michigan State 

University (MSU) in 2007. Study results indicated that recreational water quality was not impaired at Silver Lake during 

that period. However, Hunter Creek was still considered a 

significant source of pollution to the lake. Recommendations 

included adding more sampling events throughout the swimming season to further evaluate the risk associated with recreating in Silver Lake (Verhougstraete and others, 2007).

Silver Lake Water Quality and Watershed 

Management Study, 2001—Water Quality 

Investigators 

In 2000, Water Quality Investigators conducted a study 

to evaluate the water quality of Silver Lake and the watershed. 

Water samples were collected on two occasions in Silver Lake, 

April 26, 2000, and August 25, 2000, and analyzed for nutrients and chlorophyll a. In addition, Hunter Creek was sampled 

on nine occasions from October 24, 1999 through November 1, 2000. The results from this small study indicated that 

water transparency decreased from spring to summer and that 

surface to bottom phosphorus concentrations were essentially 

the same, demonstrating that the lake was well mixed (Fusilier 

and Fusilier, 2001). The study indicated that Silver Lake 

and Hunter Creek are receiving nutrients from on-site septic 

systems and that plant abundance and algae production will 

continue to increase if nothing is done to reduce the nutrient 

inputs (Fusilier and Fusilier, 2001).

Friends of Silver Lake Monitoring 

Since 2011, the Friends of Silver Lake have funded the 

nutrient analysis of samples collected by volunteers from 

Silver Lake, its tributaries, and other contributing waterbodies 

(www.friendsofsilverlake.org/reports-maps/).

Michigan Cooperative Lakes Monitoring 

Program

The Cooperative Lakes Monitoring Program (CLMP) 

has existed in Michigan for over 40 years with the purpose of 

helping citizen volunteers monitor indicators in water quality 

and document changes over time. From May 2012 through 

April 2013, a CLMP volunteer collected water-quality data 

on Silver Lake. These data include Secchi-disk depth, total 

phosphorus, chlorophyll a, and dissolved oxygen and temperature profiles (www.micorps.net/data). This information 

is used to provide baseline lake data for the State, to educate 

lake residents and citizens, and to build support for lake quality protection. 

Water Quality Characteristics of Michigan’s 

Inland Lakes, 2001–10—USGS/MDEQ

USGS and MDEQ conducted a joint study during 

2001–10 in which 729 lakes greater than 25 acres (with 

public boat-launch access) were monitored as part of Michigan’s Lake Water-Quality Assessment program. Silver Lake 

was included in the study and was monitored in 2005 for 

nutrients, Secchi depth (transparency), chlorophyll a, major 

ions, and other physical properties including vertical-profile 

measurements. Based on 2005 data, the lake was classified 

as eutrophic. This study compared water-quality data results 

and lake trophic status with historical data collected during 

1974–84 by the MDEQ (Fuller and Taricska, 2011).
Study Methods and Sampling Sites

USGS and AWRI scientists collected data from November 2012 to December 2014 to inform future management 

decisions for Silver Lake. Volunteers also aided in the data 

collection effort for collection of atmospheric nutrient deposition data. Site information and water-quality data collected 

from previous studies aided in the overall study design for this 

project; however, the data used in the analyses for this study 

were collected from November 2012 to December 2014. The 

following sections detail the site selection criteria and methods 

used in this study. 

Silver Lake Study Overview 

This report includes several sections describing the study 

design and results. Figure 3 summarizes the numerous components 

of the project and some of the key questions related to the study.

Septic system

Modeled septic 

nutrients

River nutrients

Atmospheric 

deposition

sampling

Streamflow

monitoring

Groundwater

Monitoring

Internal

nutrient

load

Lake sampling

Which nutrient 

limits algae growth?

Do lake sediments 

contribute nutrients?

N P

+

Groundwater

flow to lake 

Sediment 

sampling

Evaporation

Is the lake mixed

 at all times of day?

Waterfowl

contribution

Lawn

runoff

How many days will have algal

blooms, how will the trophic

state of the lake change?

Precipitation

10 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Lake Status.........................................................................33

Water Budget.............................................................37–38

Lake Mixing.................................................................34–35

Sediment Sampling and Internal Nutrient Load....42–45

Nutrient Sampling.......................................................39–40

Modeled Septic Input................................................52–54

Nutrient Budget..........................................................38–47

Limiting Nutrients........................................................34, 37

Algal Bloom Frequency and Lake Trophic State....50–52

Figure 3. Overview of study in Silver Lake Oceana County, Michigan.
Study Methods and Sampling Sites 11

Site Selection

Silver Lake was divided into five sections (north, middle, 

east, southeast, and south) and monitoring locations were 

established in each section. The middle section includes the 

historic deep basin location sampled in previous studies (L2; 

fig. 2). After the first year of lake monitoring (2013), the lake 

water-quality data were evaluated to determine if there was 

spatial variation between lake measurement locations. Using 

the Wilcoxon signed-rank test (Helsel and Hirsch, 2002), 

results indicated that there was no significant difference 

(p-value >0.05) in the water-quality data collected from the 

five lake sampling locations for that year. As a result, the three 

lake sites that showed the most variation were kept in the sampling design (north (L1), middle (L2), and south (L5) sites), 

which were sampled through 2014 (fig. 2). Sampling location 

information is presented in table 2. 

All major inflows and outflow were monitored as part of 

this study. These included Hunter Creek (T1), the unnamed 

tributary to Silver Lake at North Shore Drive (tributary at 

North Shore Drive) (T2), the unnamed tributary to Silver 

Lake at the North End State Park (tributary at the State Park) 

(T3), and Silver Creek (outlet) (T4) (fig. 2, table 2). Some of 

the smaller ephemeral tributaries and drains were not directly 

measured as part of this study due to the minimal flow contributions to the lake. A continuous streamgage was installed 

at Hunter Creek (A) to measure streamflow into the lake, and 

(B) to continuously monitor the stream stage at Silver Creek 

dam to use in the estimation of streamflow leaving the lake 

(fig. 2, table 2).

Groundwater-monitoring locations were determined 

based on reconnaissance synoptic groundwater sampling that 

was conducted in October 2012 using a temporary drive-point 

piezometer (DPP) at 17 locations in the nearshore/beach environment around the perimeter of the lake (see Groundwater 

Monitoring and Flow section of this report). As a result of the 

groundwater reconnaissance, four locations [north (W1), south 

(W4), east (W2), and west (W3)] were identified for installation of monitoring wells for longer term groundwater-level 

measurements and groundwater sampling (fig. 2, table 2). The 

piezometer installation locations also were chosen to represent 

expected variable inputs from septic systems around the lake. 

Direct groundwater discharge to the lake was measured using 

seepage meters (Rosenberry and LaBaugh, 2008) that were 

temporarily installed at 18 locations around the perimeter of the 

lake. Lastly, an array of discrete temperature sensors (Briggs 

and others, 2013) was deployed at five locations around the 

lake to estimate groundwater discharge rates to the lake.

Sediment cores were collected for internal nutrient loading analysis from the middle (L2) and south (L5) lake sites 

(fig. 2) on four separate dates in 2013 and 2014. Diel dissolved oxygen measurements were made at the near-surface 

and near-bottom of the lake at the middle and south lake sites 

on four separate dates in 2013. Water for an algal bioassay 

was collected at the middle lake monitoring site (L2) and then 

transported to the shore near the north groundwater monitoring 

well (W1) (fig. 2) to determine if nitrogen or phosphorus were 

limiting phytoplankton growth in Silver Lake.

The atmospheric deposition monitoring station was 

installed in December 2013 near the south end of Silver Lake 

(D1) (fig. 2). The location for the deposition station was 

carefully selected based on the proximity to Silver Lake, the 

absence of overhead and nearby vegetation, and easy accessibility for the volunteer conducting the sampling. 

Water-Quality Data; Collection

A detailed summary of the water-quality data collection 

effort is presented in table 2. Data collected by the USGS as 

part of this study are publicly available and can be accessed 

via the USGS National Water Information System (NWIS) 

database (waterdata.usgs.gov/nwis) through use of the station number (table 2) to identify the sampling locations. All 

samples were collected according to the methods described 

in the USGS National Field Manual (U.S. Geological Survey, 

2006) unless otherwise described. The constituents measured 

by USGS and AWRI in this study are presented in table 3.
12 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14 Table 2. Nutrient study sampling locations and sampling strategy for Silver Lake, Oceana County, Michigan. [AWRI, Annis Water Resources Institute; MI, Michigan; na, not applicable; QC, quality control; USGS, U.S. Geological Survey; *, streamgage or stage recorder; N/A, no samples collected; NAD27, North American Vertical Datum of 1927; NAD83, North American Datum of 1983] USGS station number Map label (see fig. 2) Site identification Latitude (degrees, minutes, seconds) Longitude (degrees, minutes, seconds) Datum Location Total number of samples collected Total number of QC samples collected Frequency of sample collection Partner (USGS/ AWRI) 0412220999 T1, A HUNTER CREEK AT NORTH SHORE DRIVE NEAR MEARS, MI* 43°40’14” 86°29’26” NAD 27 Tributary, streamgage 12 5 Quarterly, 2–3 storm events annually USGS 041222099902 T2 TRIB TO SILVER LAKE AT NORTH SHORE DR NR MEARS, MI 43°39’58” 86°29’30” NAD 83 Tributary 6 0 Quarterly, 2–3 storm events annually USGS 041222099901 T3 TRIB TO SILVER LAKE AT NORTH END ST PK NR MEARS, MI 43°39’51” 86°29’38” NAD 83 Tributary 11 1 Quarterly, 2–3 storm events annually USGS 04122210 T4 SILVER CREEK AT SILVER LAKE OUTLET NEAR MEARS, MI* 43°39’34” 86°31’05” NAD 27 Tributary 12 3 Quarterly, 2–3 storm events annually USGS 04122209972 T5 HUNTER CR US OF UPPER SILVER LK OUTLET NR MEARS, MI 43°40’25” 86°28’56” NAD 83 Tributary 3 1 3 sampling events USGS 04122209986 T6 UPPER SILVER LK OUTLET AT W TAYLOR RD NR MEARS, MI 43°40’26” 86°28’57” NAD 83 Tributary 3 1 3 sampling events USGS 04122211

B SILVER CREEK AT SILVER LAKE 

DAM NEAR MEARS, MI*

43°39’25” 86°31’43” NAD 

27

Tributary, 

stage gage

na na na USGS

434025086300305 L1 SILVER LAKE NEAR MEARS, MI 

(NORTH, 3 FT BL LK SRF)

43°40’25” 86°30’04” NAD 

83

Lake 8 2 Quarterly USGS

434025086300303 L1 SILVER LAKE NR MEARS, MI 

(NORTH, PHOTIC ZONE)

43°40’25” 86°30’04” NAD 

83

Lake 8 2 Quarterly USGS

434025086300302 L1 SILVER LAKE NEAR MEARS, MI 

(NORTH, 3 FT AB LK BOT)

43°40’25” 86°30’04” NAD 

83

Lake 8 2 Quarterly USGS

1Sites discontinued after 1 year of data collection (USGS only).
Study Methods and Sampling Sites 13 Table 2. Nutrient study sampling locations and sampling strategy for Silver Lake, Oceana County, Michigan. [AWRI, Annis Water Resources Institute; MI, Michigan; na, not applicable; QC, quality control; USGS, U.S. Geological Survey; *, streamgage or stage recorder; N/A, no samples collected; NAD27, North American Vertical Datum of 1927; NAD83, North American Datum of 1983] USGS station number Map label (see fig. 2) Site identification Latitude (degrees, minutes, seconds) Longitude (degrees, minutes, seconds) Datum Location Total number of samples collected Total number of QC samples collected Frequency of sample collection Partner (USGS/ AWRI) 434006086301105 L2 SILVER LAKE NR MEARS, MI (MIDDLE, 3 FT BL LK SRF) 43°40’06” 86°30’11” NAD 83 Lake 8 3 Quarterly USGS 434006086301103 L2 SILVER LAKE NEAR MEARS, MI (MIDDLE, PHOTIC ZONE) 43°40’06” 86°30’11” NAD 83 Lake 8 3 Quarterly USGS 2013, 2014 AWRI 434006086301102 L2 SILVER LAKE NR MEARS, MI (MIDDLE, 3 FT AB LK BOT) 43°40’06” 86°30’11” NAD 83 Lake 8 2 Quarterly USGS 2013, 2014 AWRI 434012086294105 L3 SILVER LAKE NEAR MEARS, MI (EAST, 3 FT BL LK SRF)1 43°40’12” 86°29’41” NAD 83 Lake 5 1 Quarterly USGS 434012086294103 L3 SILVER LAKE NEAR MEARS, MI (EAST, PHOTIC ZONE)1 43°40’12” 86°29’41” NAD 83 Lake 5 1 Quarterly USGS 434012086294102 L3 SILVER LAKE NEAR MEARS, MI (EAST, 3 FT AB LK BOT)1 43°40’12” 86°29’41” NAD 83 Lake 5 1 Quarterly USGS 433952086300805 L4 SILVER LAKE NR MEARS, MI (SOUTHEAST, 3FT BL LK SF)1 43°39’52” 86°30’08” NAD 83 Lake 5 1 Quarterly USGS 433952086300803 L4 SILVER LAKE NR MEARS, MI (SOUTHEAST, PHOTIC ZONE)1 43°39’52” 86°30’08” NAD 83 Lake 5 1 Quarterly USGS 433952086300802 L4 SILVER LAKE NR MEARS, MI (SOUTHEAST, 3FT AB LK BT)1 43°39’52” 86°30’08” NAD 83 Lake 5 1 Quarterly USGS 433946086304805 L5 SILVER LAKE NEAR MEARS, MI (SOUTH, 3 FT BL LK SRF) 43°39’46” 86°30’48” NAD 83 Lake 8 3 Quarterly USGS 2013, 2014 AWRI 433946086304803 L5 SILVER LAKE NEAR MEARS, MI (SOUTH, PHOTIC ZONE) 43°39’46” 86°30’48” NAD 83 Lake 8 3 Quarterly USGS 2013, 2014 AWRI 433946086304802 L5 SILVER LAKE NEAR MEARS, MI (SOUTH, 3 FT AB LK BOT) 43°39’46” 86°30’48” NAD 83 Lake 8 2 Quarterly USGS 2013, 2014 AWRI 434041086295802 W1 OCEANA 15 18 SL NORTH 43°40’41” 86°29’58” NAD 83 Well 8 2 Quarterly USGS 2013 AWRI 434014086292702 W2 OCEANA 15 18 SL EAST 43°40’14” 86°29’27” NAD 83 Well 8 3 Quarterly USGS 434020086302801 W3 OCEANA 15 18 SL WEST 43°40’20” 86°30’28” NAD 83 Well 8 3 Quarterly USGS 1Sites discontinued after 1 year of data collection (USGS only).
14 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14 Table 2. Nutrient study sampling locations and sampling strategy for Silver Lake, Oceana County, Michigan. [AWRI, Annis Water Resources Institute; MI, Michigan; na, not applicable; QC, quality control; USGS, U.S. Geological Survey; *, streamgage or stage recorder; N/A, no samples collected; NAD27, North American Vertical Datum of 1927; NAD83, North American Datum of 1983] USGS station number Map label (see fig. 2) Site identification Latitude (degrees, minutes, seconds) Longitude (degrees, minutes, seconds) Datum Location Total number of samples collected Total number of QC samples collected Frequency of sample collection Partner (USGS/ AWRI) 433936086304302 W4 OCEANA 15 18 SL SOUTH 43°39’36” 86°30’43” NAD 83 Well 8 2 Quarterly USGS 433936086305201 D1 ATMOSPHERIC DEPOSITION STATION NEAR MEARS, MI 43°39’36” 86°30’52” NAD 83 Atmospheric deposition station

9 1 2014 Volunteer

1Sites discontinued after 1 year of data collection (USGS only).

Table 3. Analytes measured as part of the 2012–14 nutrient loading study for Silver Lake, Oceana County, Michigan.[SSC, suspended sediment concentration]

Location

Water 

transparency 

(Secchi depth)

Nutrients Chlorophyll a Bacteriological data Physical water-quality data

Sediment

Total 

dissolved 

solids

Microcystin Phytoplankton

Common ions, 

color, 

alkalinity, 

total dissolved 

solids, and 

silica

SSC Sediment 

cores

Tributaries

X

X

X

X

X

X

Lake

X

X

X

X

X

X

X

X

Groundwater

X

X

X

Atmospheric 

deposition station

X

X

Lake sediment

X

X

X
Study Methods and Sampling Sites 15

A Summary of Nutrients

Nutrients can come from natural sources such as eroding soils, decomposing plant material, and wildlife wastes; 

however, excess nutrients enter surface waters from pointsource discharges (a discrete pipe) as well as nonpoint sources 

(overland runoff). 

“In Michigan, nutrient control has focused on 

phosphorus since the majority of surface waters are 

limited in this nutrient. Nitrogen reductions have 

been necessary when this nutrient was considered the 

limiting factor for plant productivity or has been the 

direct cause of water quality impairment”(Michigan 

Department of Environmental Quality, 2013).

Phosphorus

Phosphorus plays a major role in biological metabolism, 

and is an essential nutrient used by all organisms for the basic 

processes of life. Its natural source is the weathering and 

leaching of phosphate-rich geological formations. However, 

phosphorus is used extensively in agricultural and residential 

fertilizers, and is a component of domestic sewage (wastewater discharge, failing septic systems). Phosphorus also can 

enter the system through wet and dry atmospheric deposition, waterfowl feces, and plant decomposition. Phosphorus 

is considered a limiting nutrient in most Midwestern lakes, as 

phosphorus typically “limits” macrophyte and algal growth 

because it is less available for uptake than other nutrients, 

such as nitrogen (Wetzel, 1983). Aquatic plants may struggle 

to grow in lakes with little to no phosphorus, but may grow 

rapidly in systems with excessive amounts of phosphorus.

Nutrient loading in lakes can come from outside the lake 

(for example, atmospheric, tributaries, waterfowl), which is 

referred to as external loading, or from the sediments within 

the lake, which is referred to as internal loading. In the case of 

phosphorus, internal loading is often a function of sediment 

redox state, as anoxic conditions will cause the reduction of 

ferric iron (Fe3+) to ferrous iron (Fe2+), liberating phosphorus 

that is bound to ferric oxyhydroxides, resulting in phosphorus 

flux from the sediment to the overlying water column (fig. 4).

Figure 4. Phosphorus cycle in lakes. (Used with permission of the Queensland Government, Department of Environment and Heritage 

Protection.) 

Death

Decomposition

Decomposition

P

P

Consumption

Resuspension of

bottom sediment

Death

Death

Consumption

Consumption

Organic phosphorus

enters the water 

as detritus

Inorganic phosphorus enters 

the water from rock 

weathering in the catchment

EXPLANATION

Phosphorus Cycle

Phosphorus can be found in 

the sediment or in the 

water column

Movement of phosphorus

Bacteria mediated process

Phytoplankton and other 

aquatic plants consume 

inorganic phsphorus

Phosphorus is transferred 

through the food chain 

through consumption

Detritus is composed of 

dead organisms and 

excretions and dead 

material from organisms

P
16 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Nitrogen

All organisms require the nutrient nitrogen to live and 

grow; it is a building block of cellular proteins. Many of the 

same sources that contribute phosphorus to the system also 

contribute nitrogen, and nitrogen is a major nutrient that 

affects the productivity of fresh waters. The nitrogen cycle is 

a complex biochemical process in which nitrogen in various 

forms is altered by nitrogen fixation, assimilation, and denitrification (Wetzel, 1983) (fig. 5).

“Although nitrogen is abundant naturally 

in the environment, it is also introduced through 

sewage and fertilizers. Some nitrate enters water 

from the atmosphere, which carries nitrogencontaining compounds derived from automobiles 

and other sources. Ammonia and organic nitrogen 

can enter water through sewage effluent and runoff 

from land where manure has been applied or 

stored” (U.S. Geological Survey, 2015b).

Figure 5. Nitrogen cycle in lakes. (Used with permission of the Queensland Government, Department of Environment and Heritage 

Protection.)

N2

NH4

+ NO2- NO3-

Death

Decomposition

Nitrogen fixation

Death

Death

Denitrification

Consumption

Consumption

Detritus entering the water

Nitrification Nitrification

EXPLANATION

Nitrogen Cycle

Nitrogen gas in the N2 atmosphere

NO2

-

NH4

+

NO3

-

Bacteria mediated process

Nitrate

Nitrite

Ammonium

Movement of nitrogen

Detritus is composed of 

dead organisms and 

excretions and dead 

material from organisms

Phytoplankton and other 

aquatic plants consume 

nitrogen fixed by bacteria

Nitrogen is transferred 

through the food chain 

through consumption
Study Methods and Sampling Sites 17

Lake Monitoring 

Silver Lake monitoring was conducted on a quarterly 

basis by USGS personnel for a total of eight routine visits 

during November 2012–September 2014. During each visit, 

physical properties such as water temperature, dissolved oxygen, specific conductance, and pH were recorded using a YSI 

ProPlus multiparameter water-quality sonde. Turbidity was 

measured using a portable Hach Series 2100P Turbidimeter. 

A Secchi disk was used to record Secchi depth (water clarity) at all locations and to determine the photic zone required 

for the collection of chlorophyll a, phytoplankton, and E. coli

samples. 

Water samples were collected near surface (3 ft below 

the lake surface) and near bottom (3 ft above lake bottom) in 

order to describe the distribution of constituents in the water 

column. Both the near-surface and near-bottom samples 

were analyzed for nitrogen (total nitrogen, ammonia, organic 

nitrogen, nitrite, and nitrate) and phosphorus (total phosphorus and orthophosphate). Chlorophyll a, phytoplankton, and 

E. coli samples were collected in the photic zone (twice the 

Secchi depth) of the water column (U.S. Geological Survey, 

2006). Additional water samples were collected each spring 

(2013 and 2014) at the middle lake site (L2; deepest basin) 

and analyzed for common ions, color, turbidity, alkalinity, total 

dissolved solids (TDS), and silica.

Near-surface and near-bottom lake samples were collected by lowering weighted 3/16 in. peristaltic tubing to the 

appropriate depth in the water column. Lake water was then 

pumped through the tubing using a peristaltic pump. After 

field rinsing, water samples were collected from the end of the 

tubing, and filters (0.45 micron [μm]) necessary for dissolved 

nutrient analysis were connected directly to the tubing. Clean 

tubing was used at each lake sampling location. The chlorophyll a, phytoplankton, and E. coli samples were collected 

using a weighted-bottle sampler that was manually lowered 

through the photic zone at a steady rate so as not to overfill the 

sample bottle.

Internal Nutrient Loading

Estimates of internal nitrogen and phosphorus loading 

from the sediments were made by incubating sediment cores 

in a climate-controlled growth chamber housed at the AWRI 

laboratory. Sediment cores were collected from the middle 

(L2) and south (L5) lake monitoring sites (fig. 2) on four 

separate dates over the project period: May 29, 2013; August 

20, 2013; June 19, 2014; and August 20, 2014. Sediment core 

sampling and laboratory incubation followed the procedures 

of Steinman and others (2004). Six sediment cores were collected from each site using a piston corer (Fisher and others, 

1992; Steinman and others, 2004). The corer was constructed 

of a graduated 0.6-meter (m) long polycarbonate core tube 

(7-centimeter [cm] inner diameter) and a polyvinyl chloride 

(PVC) attachment assembly for coupling to aluminum drive 

rods. The piston was retracted 20 to 25 cm prior to deployment 

to maintain a water layer on top of the core during collection. 

The corer was positioned vertically at the sediment-water 

interface and pushed downward with the piston cable remaining stationary. After collection, the core was brought to the 

surface and the bottom was sealed with a rubber stopper prior 

to removal from the water, resulting in an intact sediment 

core that was approximately 20 cm in length, with a 25-cm 

overlying water column. The piston was then bolted to the 

top of the core tube to keep it stationary during transit. Core 

tubes were placed in a vertical rack and maintained at ambient 

near-bottom water temperature during transit. An additional 

sediment core was collected from each site and the top 5 cm 

was removed for analysis of sediment total phosphorus and 

sediment elements (August 20, 2014 only) in the laboratory.

Water quality was measured at both coring locations 

at the time of core collection. Dissolved oxygen, pH, temperature, specific conductance, turbidity, chlorophyll a, and 

TDS were measured at the surface, middle, and bottom of the 

water column using a YSI 6600 multiparameter water-quality 

sonde. Photosynthetically-active radiation (PAR) profiles were 

measured using a LiCor Li-193SA spherical quantum sensor 

and used to calculate the light extinction coefficient (Kd). Secchi depth was measured at both sites to estimate water clarity. 

Water samples for phosphorus analysis were collected at the 

During the winter, the ice was augered in a clover-leaf 

fashion to allow the Secchi-disk to pass through the ice. 
18 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

near-surface and near-bottom with a Niskin bottle; samples 

for soluble reactive phosphorus (SRP) and total phosphorus 

were processed as described in the following paragraphs. 

Samples were stored on ice until transported to the laboratory, 

within 5 hours of collection. Water-quality sampling occurred 

between 0930 and 1330 during each sampling event.

The 12 sediment cores (6 per site) were incubated at 

AWRI in an environmental growth chamber, with the temperature maintained to match ambient near-bottom conditions 

in Silver Lake at the time of collection. The water column in 

three of the cores from each site was bubbled with nitrogen 

(with 5 percent carbon dioxide) to create buffered anoxic 

conditions, while the remaining three were bubbled with air to 

create oxic conditions.

Phosphorus release rate estimates were made using the 

methods outlined in Moore and others (1998), with minor 

modifications (Steinman and others, 2004, 2009). Briefly, 

a 60 milliliter (mL) water sample was removed by syringe 

through the sampling port of each core tube at 0 hours (h), 

12 h, 1 day (d), 2 d, 4 d, 6 d, 8 d, 12 d, 16 d, 20 d, 24 d, and 28 d. 

Immediately after removal, a 20 mL subsample was refrigerated for analysis of total phosphorus, a 20 mL subsample was 

filtered through a 0.45 μm membrane filter and frozen for 

analysis of SRP, and a 20 mL subsample was frozen for analysis of ammonium (NH4

+

). The 60 mL subsample was replaced 

with filtered water collected from the corresponding site in the 

lake; this maintained the original volume in the core tubes (at 

the beginning of the incubation).

Flux (phosphorus and nitrogen release rate) calculations 

were based on the change in water column total phosphorus or 

ammonium using the following equation (Steinman and others, 2004, 2009): 

(1)

where

RR is the net total phosphorus or ammonium 

release rate (positive values) or 

retention rate (negative values) per unit 

surface area of sediments, 

Ct is the total phosphorus or ammonium 

concentration in the water column at 

time t, 

 C0 is the total phosphorus or ammonium 

concentration in the water column at 

time 0, 

V is the volume of water in the water column, 

A is the planar surface area of the sediment 

cores. 

Release rates were calculated over the time period that 

resulted in the maximum apparent release rate, with the caveat 

that the initial and final samplings could not be consecutive sampling dates to avoid potential short-term bias. SRP 

RR C C V

A = −t ( )0

concentration was nearly always below detection, precluding 

SRP flux calculations. Sediment elements (aluminum [Al], 

calcium [Ca], iron [Fe], magnesium [Mg]) were measured on 

cores collected on August 20, 2014, and not used for nutrient 

release experiments. 

Diel Dissolved Oxygen Measurements

Measurements of diel dissolved oxygen concentration 

(that is, daily variation in dissolved oxygen concentration) 

provided information on the redox status of Silver Lake, which 

can influence sediment phosphorus biogeochemistry (Boström 

and others, 1982). Specifically, hypoxic conditions (dissolved 

oxygen concentrations less than 2 mg/L) can facilitate the 

release of phosphorus from sediments into the water column, 

as iron-bound phosphorus is dissociated under reducing conditions. Water column dissolved-oxygen concentrations were 

measured overnight, from late afternoon until mid-morning, 

to characterize in situ diel fluctuations in dissolved oxygen. 

Multiparameter water-quality sondes were suspended from 

an anchored buoy at the near-surface and near-bottom of the 

lake at the middle (L2) and south (L5) lake monitoring locations (fig. 2). The sondes were programmed to record data 

every 10 minutes. Diel dissolved oxygen concentrations were 

measured on four separate occasions over the study period: 

July 9, 2013; July 22, 2013; August 26, 2013; and August 

29, 2013. Hourly wind data measured at the Mason County 

Airport (located approximately 21 mi north from Silver Lake) 

were downloaded from the National Oceanic and Atmospheric 

Administration (NOAA) National Climatic Data Center 

(National Oceanic and Atmospheric Administration, 2013) for 

all diel dissolved oxygen events.

Bioassay Study

A nutrient bioassay was conducted July 22–25, 2013, to 

determine if nitrogen and/or phosphorus were limiting phytoplankton growth in Silver Lake. Sixteen 10-liter (L) carboys 

were filled with surface water at the middle lake monitoring 

site (L2) (fig. 2). Grab samples (n=1 unless otherwise noted) 

were taken from the water surface at the time of carboy filling 

for initial analysis of SRP, nitrate (NO3

), chlorophyll a (n=3), 

microcystin content, and phytoplankton community composition (n=3). The carboys were transported to the shore near the 

north groundwater monitoring well (W1) (fig. 2), and exposed 

to four nutrient treatments, each replicated four times: nitrogen 

addition (potassium nitrate [KNO3], 10× ambient concentration); phosphorus addition (monopotassium phosphate 

[KH2PO4], 10× ambient concentration); nitrogen plus phosphorus addition (KNO3

 + KH2PO4, each 10× ambient concentration); and a control (no nutrient additions). A PVC frame was 

anchored in the nearshore zone (approximately 3.3 ft water 

depth) and the carboys were secured to the frame in a way that 

allowed them to float just below the water surface. The carboys were randomly assigned positions on the frame (fig. 6).
Study Methods and Sampling Sites 19

Figure 6. Bioassay carboys (A) prior to final subsampling at the end of the incubation period, and bioassay incubation frame 

(B) at its deployment location at the north end of Silver Lake, Oceana County, Michigan. (note submerged white caps from top of 

carboys). (Photo by Mary Ogdahl, Grand Valley State University-Annis Water Resources Institute, Muskegon, Michigan).

A B

General water-quality parameters (dissolved oxygen, 

temperature, pH, specific conductance, turbidity, TDS, and 

chlorophyll a) were measured at the water surface using a 

multiparameter water-quality sonde at the middle lake site 

at the time of collection and at the incubation location at the 

beginning and end of the bioassay. 

After 72 hours, the carboys were retrieved and subsampled for analysis of phytoplankton taxonomic composition, 

chlorophyll a, SRP, nitrate, and microcystin content. Carboys 

were inverted 30 times prior to subsampling to ensure wellmixed subsamples. Subsamples for SRP were processed as 

described here. Subsamples for chlorophyll a were filtered 

through glass-fiber filters, which were frozen until analysis 

(within 28 days). Nitrate (NO3) subsamples were filtered 

through 0.45-μm membrane filters and frozen until analysis. Subsamples for phytoplankton identification (1 L) were 

preserved with formalin to a final concentration of 4 percent. 

Phytoplankton subsamples were then allowed to settle for at 

least 48 hours in glass cylinders before the supernatant was 

removed and the concentrated phytoplankton volume (less 

than 125 mL) was quantitatively transferred to an opaque 

sample bottle. An additional 2 mL of formalin was added to 

each sample upon transfer and samples were stored in opaque 

bottles until processing for microscopic analysis (see Laboratory Analysis, Bioassay Study section for more details).

Tributary Monitoring and Streamgaging

Three tributaries were routinely monitored to evaluate the 

surface water-quality and discharge from the watershed and 

upstream lakes. These include Hunter Creek (T1) (the largest 

tributary) and two unnamed smaller tributaries: the tributary 

at the State Park (T3) and the tributary at North Shore Drive 

(T2), located at North Shore Drive just north of the State Park 

campground (fig. 2, table 2). In addition, Silver Creek at the 

outlet of Silver Lake (T4) was routinely monitored to evaluate the surface-water quality and discharge leaving the lake 

(fig. 2). Water-quality samples were collected quarterly from 

November 2012 through September 2014, as well as during storm events, targeting 2–3 storm events annually. Two 

additional stream locations were monitored on three occasions 

in August and September 2014; these sites included Hunter 

Creek upstream from the Upper Silver Lake outlet (T5) and 

the Upper Silver Lake outlet at West Taylor Road (T6).

Streamflow, or discharge, was continuously measured and 

recorded at Hunter Creek at 5-minute intervals from October 

1, 2012 through September 30, 2014 using a SonTek SL1200 

acoustic doppler velocity meter (ADVM). This streamgage, 

Hunter Creek at North Shore Drive near Mears, MI. 

0412220999, was located downstream from the North Shore 

Drive road crossing and near the mouth of the creek (A; fig. 2) 

as the upstream portions of the tributary were altered by a 

series of lagoons used for docking boats. Due to the proximity 

of the streamgage to the lake, an ADVM that is able to measure forward and reverse flow direction was installed. These 

reverse flow conditions occurred at Silver Lake when there 

were strong winds from the west. 

To measure the amount of water leaving the lake, a 

continuous stage recorder was installed just upstream from the 

Silver Creek channel dam (B; fig. 2) that continuously measured and reported water stage at 5-minute intervals (Silver 

Creek at Silver Lake Dam near Mears, MI, 04122211). Given 

the known dimensions of the dam and the stage of the water, a 

discharge rating was developed that was then used to calculate 

discharge for Silver Creek (appendix 1). Discharge was measured at the smaller tributaries [tributary at the State Park (T3) 

and the tributary at North Shore Drive (T2)] during sample 

collection using a FlowTracker® acoustic doppler velocimeter 

(ADV) and established USGS protocols (fig. 2) (Turnipseed 

and Sauer, 2010). 
20 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Water samples were collected from each of the tributaries 

following the USGS equal-width-increment (EWI) method or 

the grab sample method (U.S. Geological Survey, 2006) and 

were analyzed for nitrogen and phosphorus species, chlorophyll a, phytoplankton, turbidity, E. coli, and suspended-sediment concentrations (SSC) as well as pH, dissolved oxygen, 

temperature, specific conductance, and turbidity. Suspendedsediment concentrations and E. coli data, collected as part of 

the tributary monitoring, are included in appendix 2.

Average annual mean nutrient concentrations for the 

tributaries were calculated based on 2 years of data collection 

by summing the nutrient concentrations and dividing by the 

total number of samples. These concentration data were not 

flow-adjusted as only one site (Hunter Creek) had adequate 

discharge records for that calculation.

Groundwater Monitoring and Flow

An initial synoptic groundwater sampling was conducted 

using a temporary drive-point piezometer (DPP) at 17 locations in the near shore/beach environment circling the lake. 

The DPP was driven into the saturated zone beneath the lake 

and was then purged of three casing volumes. After purging, the shallow groundwater was sampled for basic physical 

water quality properties (pH, specific conductance, dissolved 

oxygen, and temperature) and groundwater was field tested for 

orthophosphorus, nitrate, nitrite, ammonia, and chloride using 

Hach water-quality test strips. Groundwater collected using a 

DPP was also tested for E. coli. The results of the groundwater 

reconnaissance were then used to identify four locations for 

installation of monitoring wells for groundwater-level measurements and groundwater sampling. 

Synoptic groundwater sampling on the west side of Silver Lake.
Study Methods and Sampling Sites 21

The results of the synoptic groundwater monitoring indicated likely locations around the lake where shallow groundwater was being influenced by septic system discharge. The relative rank of each synoptic-sampling site was computed by first 

determining the mean, and 1 and 2 times the standard deviation 

(SD) values for each of the measured property or constituent 

(nitrate, nitrite, ammonia, chloride, phosphate, pH, dissolved 

oxygen, specific conductance, temperature, and E. coli bacteria). The relative percent difference from the mean for each 

property or constituent was then determined by subtracting 

the mean value from the individual observed value and then 

dividing by the mean value for that analyte. The values for sites 

were then scored for each parameter, with those less than 1 SD 

from the mean receiving a zero, those from 1 to 2 SDs from the 

mean receiving a score of 1, and those greater than 2 SDs from 

the mean receiving a score of 2. The scores for all parameters 

were then added for each site and ranked. It was anticipated that 

higher-ranked sites or those with higher constituent concentrations and lower dissolved oxygen (when compared to the other 

groundwater samples collected) could indicate the likely presence of septic influence. The west and east wells were placed 

at sites that ranked in the bottom 25 percent of sites sampled 

for measurements indicating less potential for septic influence, 

while the north and south wells were placed at sites in the top 

25 percent of sites where measurements indicated a likely septic 

influence. 

As a result of the synoptic sampling, four 1.25-in. diameter 

monitoring wells were installed on the north (T1), south (T4), 

east (T2), and west (T3) shorelines of Silver Lake (fig. 2). These 

wells were installed near the edge of the lake to depths 6.42 ft 

(west well), 6.75 ft (north well), 9.6 ft (south well), and 10.2 ft 

(east well). Groundwater pumped from these four shallow wells 

was monitored quarterly for 2 years, and water samples were 

analyzed for nitrogen and phosphorus species, turbidity, and 

E. coli, as well as pH, dissolved oxygen, temperature, and specific conductance. The four wells were installed to characterize 

local groundwater flow by determining measured gradients of 

groundwater from recorded continuous water levels at 15-minute intervals at the east and west side of the lake, modeled after 

Garn and others (2006). Groundwater levels at all four wells 

were recorded pre-sampling and post sampling during each 

quarterly sampling event. E. coli data collected as part of the 

groundwater monitoring are included in appendix 2.

In addition, groundwater-flow data were supplemented by 

the installation of seepage meters, which were used to measure 

the flux of water across the sediment-water interface following 

methods described by Rosenberry (2008). On July 22 and 23, 

2014, seepage meters were temporarily installed at 18 locations 

around the perimeter of Silver Lake. 

In the northern section of the lake, several properties have 

tile drainage systems installed to assist in lowering groundwater levels. These drains remove groundwater that would have 

normally discharged to the lake through groundwater seepage 

and convey the groundwater directly to the lake. There were 38 

visible drain tile outlets to the lake. Samples from five drain tile 

outlets were collected four times over the course of a year. 

Septic Systems as Sources of Nutrients

Since lakeshore septic systems could be an important 

source of the phosphorus and nitrogen observed in shallow groundwater, the likely contribution of phosphorus and 

nitrogen from septic systems was computed using a model for 

septic transport originally published by Reckhow and others 

(1980) and adapted by Garn and others (1996). This model 

estimated phosphorus and nitrogen loading to Silver Lake 

from septic systems based on published literature values and 

by applying per-capita occupancy and likely soil-retention 

coefficients. Phosphorus and nitrogen loads were estimated 

based on the sum of all contributions from systems that were 

likely to impact the groundwater monitoring wells, as well as 

the flow from septic systems that was likely intercepted by 

surface water between the septic sources and the lake.

The following is the general septic mass expression (Garn 

and others, 1996; Reckhow and others, 1980) used to compute 

the mass of both phosphorus and nitrogen that directly load 

the lake or that load discrete surface-water bodies:

M = Es (number of per capita years) (1–SR) (2)

where

M is the annual mass (load) of phosphorus or 

nitrogen entering the lake from septic 

systems, likely draining toward a 

groundwater-monitoring well (pounds 

per year),

Es is the export coefficient to each household 

septic tank system (per capita export 

from a household to a septic tank) 

(pounds per capita year), 

number of per

 capita years is the number of people using septic 

systems within 200 ft of the lake, or 

surface-water body that drains to the 

lake, multiplied by the fraction of the 

year that the residence is occupied, 

SR is the soil-retention coefficient representing 

the fraction of phosphorus or nitrogen 

retained between the septic-treatment 

system and the lake.

This septic model considers the number of residences 

and facilities within 200 ft of the shore of the lake or surface 

water that drain to the lake. Septic systems further than 200 ft 

from the lake still have an expected impact, but because of the 

longer travel distance and larger area of diffusion, the uncertainty in the estimation of load from these systems is too large 

to produce a reliable number. Furthermore, the model included 

only personal residences in the septic model estimates, not 

commercial facilities such as campgrounds and hotels within 

200 ft of the shoreline and Hunter Creek, as the septic model 

was not designed to accurately represent commercial facilities. 

If commercial facilities were included, the estimated nutrient loading from septic sources to the lake and Hunter Creek 
22 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

would be higher. However, it should be noted that commercial 

and residential septic sources further than 200 ft from the lake 

are still captured in the nutrient budget from the groundwater 

well monitoring that includes nearby (within 200 ft) and diffuse (further than 200 ft) septic sources. As a result of these 

constraints, the amount of phosphorus and nitrogen being 

contributed directly to the lake or tributaries to the lake, as 

computed using this model, are likely a conservative estimation (an underestimate) of the actual phosphorus load coming 

from all septic tanks around the lake. 

The number of facilities with septic systems was estimated by partitioning the area within 200 ft of the lakeshore 

into drainage areas that likely impacted a groundwater monitoring point or a surface water body (based on groundwater 

gradient and flow direction) and then overlaying a parcel map 

(written communication Oceana County Equalization Department, 2012). For the south portion of Silver Lake there are 

109 parcels, in the east there are 75 parcels, in the north there 

are 49 parcels, and in the west there are 0 parcels assumed 

to be impacting Silver Lake. Along the canals and directly 

adjacent to Hunter Creek there are 70 parcels, the tributary 

at North Shore Drive has 7 parcels. The drainage area of the 

tributary at the State Park was uncertain because it is routed 

underground. Based on an estimated drainage area, it was 

uncertain if there were any full parcels directly influencing the 

tributary at the State Park; therefore, an estimate of 0.5 parcels 

was used. Some of the parcels contributing to each area were 

not counted because aerial photographs (U.S. Department of 

Agriculture, 2014) ) indicated that they were empty lots likely 

purchased for lake access by residences that are not directly 

connected to the lake. Therefore, the number of household or 

hotel units used in the analysis can be different from the number of parcels identified (table 4). 

Data on monthly occupancy were obtained from the 

Silver Lake Area Demographic Analysis (DeJong, 2015). 

Mean monthly occupancy rates were calculated by dividing 

the reported number of persons by the number of homes and 

cottages in the Silver Lake area. This number was multiplied 

by the expected number of units contributing to each area 

and the fraction of the year represented by each month to 

obtain capita-months. These monthly values were summed 

to produce a capita-year value for each contributing area. 

There were no parcels on the west side of Silver Lake in the 

Silver Lake dunes therefore the contribution from septic was 

assumed to be zero.

“Es,” the per-capita export of phosphorus and nitrogen 

from a household to an onsite septic system, was estimated 

using previously published values. The median, 25th percentile, and 75th percentile of phosphorus were calculated as 

reported in Reckhow and others (1980), 3.2, 2.6, and 3.3 lb/

capita-yr, respectively. Due to the variation in nitrogen monitoring data, the mean, maximum, and minimum values were 

calculated as reported in Reckhow and others (1980), 10.5, 

4.7, and 18.1 lb/capita-yr, respectively. 

There are several other factors that influence if phosphorus and nitrogen from septic systems can reach Silver Lake. 

These factors are typically related to the physical characteristics of the sites (groundwater gradient, depth to water table, 

and distance to surface water), properties of the soil (phosphorus and nitrogen adsorption capacity), and properties of the 

septic system (age of the system, designed use of the system, 

maintenance of the system). Properly working septic systems 

can remove from 23 to 95 percent of phosphorus and an average of 30 percent of nitrogen from wastewater (Robertson 

and others, 1998; Canter and Knox, 1986). However, soil has 

finite adsorptive abilities. The age of the system is directly 

related to the nutrient saturation of the soil that is intended to 

treat the waste, and therefore is directly related to treatment 

efficiency, with older systems becoming less efficient. In addition, older or poorly maintained septic systems may build up 

solids overtime, which can then migrate to the drain field and 

further negatively influence treatment efficiency by reducing 

the amount of time that wastewater is in the septic treatment 

process (Canter and Knox, 1986; Garn and others 1996). Inefficient settings for septic systems to remove phosphorus and 

nitrogen include settings where water tables are high, systems 

are placed on steep slopes, systems are placed in very coarse 

soil with little organic content, or systems are placed very 

close to surface water features, and some of these settings are 

applicable to Silver Lake. 

To account for soil retention, the soil retention coefficient 

(SR; fraction of phosphorus or nitrogen retained in the soil), 

was estimated based on the described influencing factors. The 

SR can range from 0 to 1.0. If 100 percent of phosphorus and 

nitrogen reach surface water, the SR = 0; if there is no phosphorus and nitrogen that reaches the surface water, then the 

SR = 1.0. In this study, only parcels within 200 ft of surface 

water (lake or stream tributary to the lake) were considered. 

The soil type within 200 ft of Silver Lake is dominated by 

pipestone fine sand on a 0 to 4 percent grade (U.S. Department of Agriculture, 2015). This sand is defined by the USDA 

as “very limited” for use as a septic tank absorption field. 

This rating indicates that the soil may have a high saturated 

hydraulic conductivity (Ksat) and a low depth to water table, 

which can affect the absorption of the effluent. The soil could 

also be underlain by loose sand and gravel at a depth of less 

than 4 feet below the septic system. These factors all indicate 

that pipestone fine sand may not adequately filter the effluent 

(U.S. Department of Agriculture, 2015). SR coefficients for 

these poor soils have been reported to be 0.2 to 0.6, whereas 

failing septic systems have been reported using an SR of 0.25 

(Garn and others, 1996). In an effort to be as conservative as 

possible when estimating septic contributions, and to match 

the observed data from groundwater monitoring, a range of 

SR values were used (table 4). Due to the uncertainty of septic 

function, age, and soil saturation, SR values were adjusted. 

Low values of SR were increased by 0.15 units to indicate the 

potential for better filtration (less phosphorus and nitrogen 

reaching the lake), while high SR values were decreased by 

0.15 to indicate the potential for poorer filtration (more phosphorus and nitrogen reaching the lake). 
Study Methods and Sampling Sites 23 Table 4. Septic contribution model input. [Units, household or hotel; number of per-capita years, number of people using septic systems multiplied by the fraction of the year that the residence is occupied; Es, per-capita export of phosphorus and nitrogen from a household to septic system; lb, pounds; SR, soil-retention coefficient; TP, total phosphorus; TN, total nitrogen; na, not applicable as no units influence this well] Input area Units Per-capita years Export coefficient (Es) in lb/capita-year Soil-retention coefficient Likely Es Low Es High Es Likely SR Low SR High SR TP TN TP TN TP TN Hunter Creek 70 156.9 3.22 10.47 2.56 4.73 3.34 18.08 0.83 0.98 0.68 Tributary at State Park 0.5 1.1 3.22 10.47 2.56 4.73 3.34 18.08 0.83 0.98 0.68 Tributary at North Shore Drive 7 15.7 3.22 10.47 2.56 4.73 3.34 18.08 0.83 0.98 0.68 Groundwater north 20 44.8 3.22 10.47 2.56 4.73 3.34 18.08 0.71 0.86 0.56 Groundwater south 109 244.3 3.22 10.47 2.56 4.73 3.34 18.08 0.68 0.83 0.53 Groundwater east 6 13.4 3.22 10.47 2.56 4.73 3.34 18.08 0.83 0.98 0.68 Groundwater west 0 na 3.22 10.47 2.56 4.73 3.34 18.08 na na na
24 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Meteorology

Meteorological data including precipitation, air temperature, soil moisture, relative humidity, soil temperature, total 

solar flux, wind direction, and wind speed were obtained from 

the Enviro-weather Network (Enviro-weather, 2015) Hart (hrt) 

and Ludington (ldt) stations. The Hart station was the primary 

station used for data analysis and is located at Asparagus 

Research Farm in Hart, Michigan, approximately 8.5 mi 

northeast of Silver Lake (http://www.agweather.geo.msu.edu/

mawn/station.asp?id=hrt&rt=60). In the event of data interruption at the Hart weather station, the data from the Ludington, 

Michigan (http://www.agweather.geo.msu.edu/mawn/station.

asp?id=ldt&rt=60) weather station were used, which is located 

at Vanortwick Farms, approximately 17 mi northeast of Silver 

Lake. Data at both stations are recorded and reported hourly. 

There were 603 hourly measurements (of 19,704 total hourly 

measurements throughout the study period) when the Hart 

station was not operational due to maintenance issues and data 

were substituted with the Ludington weather station. During the period October 1, 2012–September 30, 2014, Silver 

Lake received an average of 31.9 in. of precipitation per year. 

The minimum, maximum, and mean temperatures during 

that time period were 24.3 degrees Celsius (°C), 33.2 °C and 

7.26 °C (−11.7 degrees Fahrenheit (°F), 91.8 °F, and 45.1 °F), 

respectively.

Atmospheric Deposition Monitoring

Atmospheric deposition can contribute both phosphorus 

and nitrogen to a system. Atmospheric nutrient deposition to 

Silver Lake was determined by the analysis of wet and dry 

deposition samples. During February 2014–September 2014, 

wet samples were collected after a precipitation event (rain or 

snow) and dry samples were collected after several days (from 

5 to 7 days) following no precipitation. Samples were collected according to methods described in Robertson and others 

(2009). The atmospheric deposition station was constructed by 

securing a 2 1⁄2 × 2 1⁄2 ft table into the ground and attaching a 

stainless steel bowl on top of the table to which a clean polycarbonate sample bowl was attached prior to a sampling event. 

A wire mesh screen was secured over the sample bowl during 

periods of dry deposition sampling to minimize contamination from birds, leaves, and so forth. For wet deposition, 

samples were collected as soon as possible after the event in 

an effort to minimize evaporation and prevent contamination. 

A clean cover was placed on the sample collection bowl and 

the sample bowl was shaken until the sample was well mixed. 

Then, using a funnel, the sample water was decanted into a 

125-mL sample bottle using clean sampling techniques and 

preserved for shipment to the National Water Quality Laboratory (NWQL). Prior to sample collection, sample bottles were 

rinsed three times with sample water.

For dry deposition, a 250-mL bottle of deionized water 

was poured into the sample collection bowl, a clean cover was 

placed on the sample collection bowl, and the contents were 

shaken until the sample was well mixed. A small amount of 

sample water was poured into the sample bottle (3 times) in 

order to rinse the bottles. Sample water was then decanted into 

a 125-mL sample bottle using clean sampling techniques and 

preserved for shipment to NWQL.

Total precipitation (inches) was aggregated by season: 

winter (December, January, and February); spring (March, 

April, and May); summer (June, July, and August); and fall 

(September, October, and November). The seasonal total precipitation volume was estimated and multiplied by the nutrient 

concentration for the respective season. If there was a single 

measurement during that season, then the observed concentration was used. If there were multiple measurements during a 

season, the mean concentration was used for the season. This 

value was then used with a unit conversion factor to compute 

the pounds of phosphorus and nitrogen from wet deposition 

that fell on Silver Lake during each season. Annual wet deposition was then computed by summing the seasonal deposition. 

Phosphorus and nitrogen mass from dry deposition were 

computed based on the measured concentrations, the volume 

used to suspend the mass (0.25 L), and the open area of the 

collection bowl. Mass per unit area of the collection bowl was 

extrapolated to the area of Silver Lake, and mass per area was 

divided by the time to obtain the rate in pounds per day of dry 

deposition on the lake. Seasonal dry deposition (pounds per 

season) was computed by multiplying the number of dry days 

(to the nearest hour) in the season by the daily deposition rate 

for each nutrient. No dry deposition samples were collected 

during the winter; therefore, winter dry deposition rates were 

estimated by applying the ratio of mean wet to mean dry 

deposition in spring, summer, and fall to the mean wet deposition that was measured in the winter. The resultant rate was 

used for winter dry deposition. If only one measurement was 

made, then that concentration was used to compute seasonal 

dry deposition rates. If multiple concentrations of dry deposition were measured during a season, the mean concentration 

was used to compute seasonal dry deposition rates. Annual 

dry deposition was computed by summing the seasonal dry 

deposition rates. 

Waterfowl

The mass of nitrogen and phosphorus deposited by waterfowl also was estimated. Weekly average counts of Canada 

geese and mallards for Oceana County were obtained from 

eBird (eBird, 2014). The mean count is the mean count of 

birds from reported observations within Oceana County. This 

number indicates the mean number of birds expected to be 

observed on any day during that week, wherever the species 

is encountered in the county, although the mean is likely an 

underestimate because bird densities will typically be higher 

near water. Each weekly number was multiplied by the number of days attributed that week in the eBird data. The resultant values for bird counts were multiplied by published values 

for deposition of phosphorus and nitrogen by each bird species 

(Manny and others, 1994). Resultant loads were determined 
Study Methods and Sampling Sites 25

in pounds of phosphorus and nitrogen per year. Because it 

represents a solid mass (not a mass delivered in water), this 

load was input into the BATHTUB model as an addition to the 

atmospheric deposition value.

Lawn Runoff

The mass of nitrogen and phosphorus from lawn runoff was estimated using previously measured values from 

lake studies conducted in Wisconsin (Garn, 2002). Published mean runoff nutrient concentrations were weighted 

based on estimated fertilization patterns which were based 

on existing land use and on observed new lawns/construction around the lake where phosphorus fertilizer could likely 

be applied. The fertilizer utilization patterns for Silver Lake 

were estimated as 10 percent of fertilizer from unfertilized 

forest (representative of the SLSP), 50 percent unfertilized 

lawn (representing lawns around the lake that do not fertilize), 35 percent nonphosphorus fertilized lawns, (representing 

lawns that fertilize with regulated nonphosphorus fertilizer), 

and 5 percent regular phosphorus fertilized lawn (representing 

new lawns legally able to use standard phosphorus-containing 

fertilizer). Runoff rain events were those where greater than 

0.3 in. of rain fell continuously. The data presented here 

assume that 25 percent of the rain that fell moved to the lake 

as runoff. This value falls in the middle of minimum and 

maximum percentages of runoff from precipitation that has 

been previously reported (Hunt and others, 2006; Garn, 2002). 

Water Budget

A water budget was developed for Silver Lake to evaluate 

the various components of the hydrologic cycle and their relation to the lake. In addition to improving our understanding 

of the water cycle in Silver Lake, the information is important 

to identifying the key pathways that deliver nutrients to Silver 

Lake. In its simplest form, the water budget equation is a balance between the flow into and the flow out of a study unit. In 

this case the study unit was Silver Lake and the specific water 

balance equation used for Silver Lake was as follows:

 ET+∆S+Qout

SW+ Qout

GW=P+Qin

SW+Qin

GW (3)

where

ET is the evaporation from the lake,

∆S is the change in storage of the lake,

Qout

SW is the loss of lake water to surface water,

Qout

GW is the loss of lake water to the groundwater,

P is precipitation on the lake,

Qin

SW is the gain from surface water tributaries 

into the lake,

Qin

GW is the gain of groundwater into the lake

Each of these components was measured during the 

course of the study. For this analysis, a time period of October 

1, 2012–September 30, 2014, was examined. 

Change in Storage

The change in storage for the lake accounts for changes 

in the volume of the lake during the study. The lake volume 

change was estimated using the mean monthly gage height 

for the beginning and end months of the study period from 

the Hunter Creek gage. This was because the gage height was 

collected right at the river mouth confluence with the lake. The 

mean monthly lake stage was 586.5 ft above North American 

Vertical Datum of 1988 (NAVD 88) in October 2012 and 

also in September 2014. As a result, there was no net storage 

change in the lake for that time period. This was expected as 

the lake stage is controlled through the operation of the Silver 

Lake Dam located 0.75 mi downstream from Silver Lake on 

Silver Creek. Seasonally, there are storage changes that occur 

as the dam that controls the lake level is adjusted to lower the 

water level in the late fall and winter and then raise the water 

level in the late spring and summer. This equates to a storage 

loss in the fall and winter and a storage gain in the spring and 

summer.

Precipitation

During October 1, 2012–September 30, 2014, a total of 

64.1 in. of precipitation fell in Hart, Mich., and it was assumed 

that the same amount fell on Silver Lake. The 1981–2010 

mean annual precipitation for Hart, Mich., was 36.54 in. 

(National Oceanic and Atmospheric Administration, 2013) 

or would be 73.08 in. for 2 years. The precipitation measured 

during our study was below the long-term mean, indicating drier conditions than normal. Assuming a surface area 

of 672 acres, the total volume of precipitation that the lake 

received is estimated to be 1.56 × 108 cubic feet (ft3).

Evaporation

Estimates of evaporation from the lake were derived 

from the parameters measured at the climate stations in Hart 

and Ludington. Previous studies indicate that when using data 

from a climate station not located on the lake (Winter and 

others, 1995, Rosenberry and others, 2004) the Jensen-Haise 

method (Jensen and Haise, 1963) produces results that match 

favorably with energy budget estimates of evaporation. The 

data required for developing an evaporation estimate were the 

daily maximum temperature, daily minimum temperature, and 

solar radiation. The evapotranspiration package for R (Guo 

and Westra, 2015) was used to process the climate data and 

return an estimated daily evaporation in millimeters. These 

daily estimates were summed and reported as total evaporation in inches. In addition to the climate data, some equation 

constants were required to estimate evapotranspiration, which 
26 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

included elevation of the lake (586.5 ft), latitude of the lake 

in radians (0.7621), latent heat of vaporization (2.45 ), and 

the solar constant (0.0820 ), where MJ equals megajoules, kg equals kilogram, m2

 equals square meter and min 

equals minute. The lake was observed to be ice-covered from 

December through March during the study, so lake evaporation estimates were assumed to be zero during those months. 

The total evaporative loss estimated for the period October 1, 

2012–September 30, 2014, was 50.7 in. from the lake. Assuming a surface area of 672 acres for the lake, the evaporative 

loss is estimated to be 1.24 × 108 ft3. 

Surface-Water Inflow and Outflow

Surface water serves as a pathway to both remove and 

deliver water to Silver Lake. For the removal of water from 

the lake there was one outflow, Silver Creek, at the southern 

end of the lake that connects Silver Lake to Lake Michigan 

(fig. 2, table 2). The Silver Lake Dam is located approximately 

0.75 mi downstream from Silver Lake and is used to regulate 

the lake level on Silver Lake. To estimate flow leaving the 

outlet, the reservoir upstream from the control structure was 

instrumented to measure the gage height of the water. 

A rating for the dam that relates gage height to flow was 

developed using the gage height information and the geometry 

of the structure. A description of the process for developing 

the rating for the dam can be found in appendix 1. Using that 

rating, the estimated total volume of water that left Silver Lake 

through Silver Creek from October 1, 2012 to September 30, 

2014, was estimated to be 1.39×109 ft3.

Three surface-water tributaries that contributed flow to 

Silver Lake included Hunter Creek, the tributary at the State 

Park, and the tributary at North Shore Drive (fig. 2, table 2). 

For the period October 1, 2012–September 30, 2014, an estimated total of 7.92×108

 ft3 of water entered Silver Lake from 

Hunter Creek based on information from the streamgage. The 

two other tributaries were monitored at various times throughout the study but were not continuously monitored. The tributary at North Shore Drive was measured 5 times and the tributary at the State Park was measured 10 times. The estimates of 

streamflow at the tributary at North Shore Drive ranged from 

0.26 to 1.68 cubic feet per second (ft3

/s) with a mean flow of 

0.662 ft3/s. Using the mean streamflow and applying that to 

the 2-year period of study, results in an estimated contribution of 4.18×107

 ft3. Similarly, estimates of streamflow at the 

tributary at the State Park ranged from 0.79 to 1.54 ft3/s with 

a mean streamflow of 0.993 ft3/s. Using the mean streamflow 

and applying that to the 2-year period of study results in an 

estimated contribution of 6.27×107 ft3.

Groundwater Inflow and Outflow (Flux)

Groundwater also may serve dual roles as a pathway 

to remove or contribute water to Silver Lake. Several methods were used to understand the role of groundwater in 

Silver Lake. Based on the water levels collected at shallow 

MJ

kg MJ

m × min 2

piezometers and estimates of flux from seepage meters and 

discrete temperature sensors, the lake did not appear to be losing 

water to the aquifer. Rather, groundwater seemed to be contributing water to the lake. 

An attempt was made to quantify the flux to the lake using 

the water-level data collected at the four monitoring wells 

around the lake (see appendix 3). Complexities associated with 

incomplete water-level data for part of the study period and the 

heterogeneity in groundwater hydraulic properties resulted in 

flux values that were not consistent with the other components of 

the water budget. Therefore, to maintain a balance with the other 

variables in the water budget equation, the groundwater inflow 

was set to the difference between the sum of the loss terms (ET, 

Qout

GW ) and the sum of the remaining input terms (P, Qin

SW ). The 

result was a net contribution of 4.65×108 ft3 for the period from 

October 1, 2012 to September 30, 2014. 

For the purposes of the BATHTUB model, the groundwater 

flux was refined into different input sources. In the northern section of the lake, five drain tiles were measured (out of 38 visible 

drain tiles). A mean flow rate was estimated from these sampling 

events and used to estimate a mean annual flow of 5.04×106 ft3. 

Extrapolating that value to the 2-year period of study yields a 

total flow of 1.01×107 ft3.

The groundwater component of the water budget was further refined by quadrant of the lake. Silver Lake was divided into 

four quadrants on the basis of the four shallow wells installed 

to monitor groundwater-nutrient concentrations (fig. 3-2). To 

distribute groundwater flux by quadrant, the mean seepage 

per quadrant was multiplied by the length of shoreline in that 

quadrant to get a perimeter-weighted mean flux. Each quadrants’ 

perimeter-weighted mean flux was divided by the sum of the 

perimeter-weighted mean fluxes to get a relative percentage of 

how much water is contributed by each quadrant. This percentage was then multiplied by the total groundwater contribution 

to obtain the estimated flux by quadrant. When broken down by 

quadrant, the estimated total groundwater flow for the 2-year 

study period was 1.21×107 ft3 for the north quadrant, 4.98×107

ft3 for the east quadrant, 1.14×108 ft3 for the south quadrant, and 

2.79×108 ft3 for the west quadrant (appendix 3).

Rates of groundwater flow around the lake were estimated 

using discrete seepage meters. On the basis of seepage measurements collected (appendix 3), specific discharge, or the apparent 

velocity of water moving through a section of aquifer ranged 

from 0.0042 to 1.3 feet per day (ft/d). The average linear velocity, defined as the specific discharge divided by the effective 

porosity of the medium, represents the velocity that water is 

actually moving through the porous medium. The average linear 

velocity was computed assuming an effective porosity of 20 

percent (Fetter, 1994) and results in a range of groundwater flow 

rates of 0.014 to 4.3 ft/d. It should be noted that the effective 

porosity represents the estimate of the connected pore space in 

the aquifer. This is different from the total porosity which was an 

estimate of the total pore space in a porous medium. A total porosity of 33 percent was used for the lakebed sediment when doing 

the temperature modeling of groundwater flux in appendix 3.
Study Methods and Sampling Sites 27

USGS staff measuring groundwater flux using seepage meters in Silver Lake.
28 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Laboratory Analysis

U.S. Geological Survey National Water-Quality 

Laboratory/ U.S. Geological Survey Sediment 

Laboratory 

All water samples collected by the USGS were analyzed 

by the NWQL located in Denver, Colorado, in accordance 

with analytical procedures, holding time requirements, and 

quality assurance procedures as described in the USGS methods of analysis by the U.S. Geological Survey National Water 

Quality Laboratory (Fishman, 1993); determination of inorganic and organic constituents in water and fluvial sediments ). 

All sediment samples collected by the USGS were 

analyzed for SSC by the USGS Kentucky Sediment Laboratory located at the USGS Kentucky Water Science Center in 

Louisville. SSC analysis was conducted according to USGS 

methods and procedures, as described in the Quality Assurance Plan for the Kentucky Water Science Center Sediment 

Laboratory (Shreve and Downs, 2005; Guy, 1969).

Non-U.S. Geological Survey Laboratories

Internal Nutrient Loading 

Water samples from internal nutrient loading sediment 

core incubations were analyzed in the AWRI laboratory for 

soluble reactive phosphorus (SRP), total phosphorus, and 

ammonium on a SEAL AQ2 discrete automated analyzer (U.S. 

Environmental Protection Agency, 1983). Values below detection were calculated as half the detection limit (SRP: 5 μg/L, 

total phosphorus: 10 μg/L, ammonium: 20 μg/L).

Sediment Metal Analysis

Sediment elements were analyzed using inductively 

coupled plasma-atomic emission spectrometry according to 

EPA method 6010B (U.S. Environmental Protection Agency, 

2007) at Trace Analytical Laboratory, Muskegon, Michigan. 

Sediment iron:phosphorus ratios were determined by weight 

using dry weight-normalized total phosphorus concentrations.

 Bioassay Study

Nitrate (NO3) subsamples were filtered through 0.45-μm 

membrane filters and frozen until analysis. SRP was analyzed 

in the AWRI laboratory as described in Study Methods and 

Sampling Sites, Bioassay Study section. Chlorophyll a was 

extracted using a combination of an organic solvent (90 percent acetone with saturated magnesium carbonate) and tissue 

grinding. Chlorophyll a was analyzed using the phaeophytin 

correction method on a Shimadzu UV-1601 spectrophotometer 

following standard methods (American Public Health Association, 1992). Nitrate was analyzed by ion chromatography on a 

Dionex DX500 (American Public Health Association, 1998).

Water-Quality Standards and Criteria

The development of nutrient criteria for water quality is an 

important, albeit complicated, process. Several references for 

nutrient-related criteria have been compiled for Michigan inland 

lakes, as the MDEQ has yet to adopt numeric nutrient criteria 

standards for Michigan (table 5). In 2012, the MDEQ awarded 

a grant to Michigan State University to update the methods used 

to develop expected nutrient concentrations for inland lakes in 

an effort to further the development of numeric criteria (Michigan Department of Environmental Quality, 2013). 

In 2000, the EPA published the recommended water-quality criteria for nutrients (nitrogen and phosphorus) for different 

ecoregions around the country (U.S. Environmental Protection 

Agency, 2000a, b). The criteria were established as guidelines 

for each State to use as a starting point in the development 

of their own nutrient criteria. According to the EPA ambient 

water quality criteria recommendations for lakes in Ecoregion 

VII, total nitrogen values greater than 0.66 mg/L and total 

phosphorus values greater than 0.015 mg/L would indicate 

eutrophic conditions in the lake. The EPA ambient waterquality criteria recommendations for tributaries in Ecoregion 

VII for total nitrogen and total phosphorus are 0.54 and 0.033 

mg/L, respectively (table 5). Since Michigan has yet to adopt 

numeric nutrient criteria, the EPA nutrient criteria for Ecoregion VII for lakes and rivers/streams was used in this report.

Data-Quality Assurance

Sample field blanks and field replicate samples were 

collected to assess for bias and precision, following methods 

described in the USGS National Field Manual (U.S. Geological Survey, 2006). Inorganic-grade reagent water was used as 

the sample medium for field blanks. Replicate and field blank 

samples were collected as part of the lake, tributary, groundwater, and atmospheric deposition monitoring. 

For all field replicates, the relative percent difference 

(RPD) was calculated between the environmental and replicate 

samples for all major constituents using the formula below: 

(4)

where

 is absolute value of the difference between 

concentrations of the analyte in the 

primary environmental samples and the 

replicate sample, 

 is mean concentration of the analyte in the 

primary environmental and replicate 

sample.

RPD

C C

Mean

env rep = −

( ) × :

100

C C env rep

C C env− rep

Mean( ) C C: env rep
Study Methods and Sampling Sites 29

Table 5. References for Michigan water-quality nutrient criteria recommendations for lakes and rivers.

[*, indicates nutrient criteria used in this report; mg/L, milligrams per liter; μg/L, micrograms per liter; TP, total phosphorus]

Reference

Waterbody 

type

Total nitrogen 

(mg/L)

Total 

phosphorus 

(mg/L)

Chlorophyll a

(μg/L)

(flourometric)

Secchi 

depth 

(feet)

Notes

Michigan Department of 

Natural Resources (1982)

Lake Not available 0.021–0.050 

mg/L

Eutrophic trophic state 

classification.

Robertson and others (2003) Lake Not available 0.017–0.020 

mg/L

Eutrophic trophic classification, U.S. Geological 

Survey, Wisconsin.

Carlson (1977) Lake Not available 0.024 mg/L Concentrations of nearsurface total phosphorus 

greater than 0.024 mg/L 

indicate eutrophic conditions. 

U.S. Environmental 

Protection Agency (EPA) 

(2000a)*

Lake 0.66 mg/L* 

(reported)

0.015 mg/L* 2.63 10.93 Based on 25th percentile 

(EPA Ecoregion 7 criteria).

Michigan Department of 

Environmental QualitySurface Water Quality 

Division (2001)

Lake Not available 0.020–0.030 

mg/L

Goal of 0.02 to 0.03 mg/L (TP) 

has been set for more 

productive lakes classified as 

eutrophic in Michigan.

U.S. Environmental 

Protection Agency (2000b)*

River 0.54 mg/L* 0.033 mg/L* 1.54 Based on 25th percentile 

(EPA Ecoregion 7 criteria).

For this study, RPD values greater than 20 percent were 

considered indicative of high variability of sample results. 

Overall, for all of the major chemical and sediment parameters 

analyzed by USGS as part of this study, the RPD values were 

less than 10.5 percent (ammonia, 0.3 percent; nitrate plus 

nitrite, 2.8 percent; total nitrogen, 5.9 percent; orthophosphate, 

2.5 percent; total phosphorus, 7.5 percent; chlorophyll a, 

10.5 percent; and SSC, 7.3 percent). Field replicates accounted 

for approximately 17 percent of all samples collected by the 

USGS as part of this study. 

Field blank sample results indicate that there was no 

contamination that could be attributed to equipment cleaning, sample collection and handling, and laboratory handling. 

Almost all field blanks were at or below the detection limits 

for the major chemical and sediment parameters except for 

one total nitrogen concentration that exceeded the detection 

limit by 0.01 mg/L, which is considered negligible for this 

study. Field blanks accounted for approximately 3 percent of 

all samples.
30 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Lake Water-Quality Characteristics

This section summarizes the key water quality findings from 

Silver Lake and presents data collected by AWRI and the USGS. 

Silver Lake can be classified as a polymictic lake, 

meaning that the lake has frequent circulation and remains 

well mixed throughout the open-water period. The lake has a 

residence time (the amount of time it takes for water to replace 

the lake volume estimated by dividing the volume of the lake 

by the flow rate of water through the lake) of approximately 

223 days. The temperature profiles collected throughout the 

five lake sampling sites confirmed that the lake did not exhibit 

thermal stratification in the open-water period or during iceon. During the winter months, the lake temperature varied 

minimally (within 2 °C) and the lake did not appear to become 

anoxic during the day. 

AWRI conducted a diel dissolved oxygen study to determine if, during the summer months, the lake turns hypoxic 

(dissolved oxygen less than 2 mg/L) during the night but then 

mixes the next day due to wind-driven mixing or boat activity (see Diel Dissolved Oxygen section). Based on the USGS 

dataset, dissolved oxygen concentrations (measured during the day) in Silver Lake never decreased below 5.5 mg/L 

(59 percent saturation) during the study. The lowest dissolved oxygen concentrations typically were measured during 

summer months (June–August) and early fall (September). 

Supersaturated conditions (dissolved oxygen concentrations 

greater than 100 percent, which can indicate the presence of 

oxygen-producing algae in the water column) were commonly 

measured at all sampling sites, most frequently 3 ft below the 

lake surface and throughout the photic zone; however, supersaturation infrequently occurred at the lake bottom.

The specific conductance of lake water is a measure of 

the ability of water to conduct an electrical current and is 

closely related to concentrations of the major ions (calcium, 

magnesium, sodium, and so forth), but not proportional to 

minor constituents of lake water such as iron, strontium, 

and manganese (Wetzel, 1983). Specific conductance values 

remained fairly consistent throughout the lake, ranging from 

292 to 335 microsiemens per centimeter (μS/cm), with a mean 

lake conductivity value of 304 μS/cm. The lowest specific 

conductance values were measured in June 2013 at all lake 

sites, and the highest values were measured during the March 

2014 sampling event. Values greater than 600 μS/cm are 

generally considered indicative of water-quality impairment in 

lakes (Steinman and others 2011). 

The pH in Silver Lake ranged from 7.8 to 9.5 standard 

units which is consistent with the range of pH observed in 

most open lakes that are well buffered (6 to 9 standard units) 

(Wetzel, 1983). The lowest pH values were measured during 

summer (July 2014) and spring (March 2013 and 2014), 

whereas the highest pH values were measured during the summer (July 2013) and early fall (September 2014) when productivity was the highest. The lowest pH values were measured 

at the near surface, photic zone, and near bottom sampling 

locations, whereas the highest pH values were measured only 

at the near surface sampling location.

During spring 2013 and 2014, water samples were collected from the middle deep basin (L2; fig. 2) and analyzed 

for common ions, color, hardness, TDS, and silica. The mean 

concentrations of calcium, magnesium, chloride, and hardness 

in Silver Lake were 37, 14, 8, and 151 mg/L, respectively. 

These values indicate that Silver Lake is classified as having hard water, which is comparable to other lakes along the 

western coast and southern regions of Michigan (Fuller and 

Taricska, 2012). The water color in Silver Lake ranged from 

5 to 15 platinum cobalt units (PCU) in the spring, which is on 

the lower end of the PCU color scale (laboratory test range 

from 0 to 500). Highly colored water has significant effects 

on aquatic plants and algal growth as colored water can limit 

the penetration of light which is very critical for the growth 

of aquatic plants and algae (U.S. Geological Survey, 2015a). 

Total dissolved solids and silica concentrations averaged 168 

and 2 mg/L, respectively. 

The water clarity (transparency) was measured using a 

Secchi disk in Silver Lake and ranged from 2.5 to 16 ft. The 

lowest water clarity (Secchi depth of 2.5 ft) was measured in 

mid-November 2012 on the tail end of a summer with excessive algal blooms (prior to the start of this study). The highest 

water clarity (Secchi depth of 16 ft) was measured during the 

winter sampling event in March 2014 under approximately 

2.5 ft of ice. The mean water clarity for Silver Lake during the 

study period was a Secchi depth of 6.2 ft. 

The minimum, maximum, and average nutrient concentrations measured in Silver Lake are presented in table 6. The 

lowest total phosphorus concentrations, 0.009 mg/L, were 

measured during the winter sampling event in March 2014, 

and the highest total phosphorus concentrations, 0.053 mg/L, 

were measured in mid-November 2012. Average total phosphorus concentrations collected from 3 ft below lake surface 

(0.03 mg/L) and 3 ft above lake bottom (0.03 mg/L) exhibited 

no variation throughout the water column. Silver Lake exhibited seasonal variation in phosphorus concentrations, however nitrogen did not exhibit the same pattern (fig. 7). Total 

phosphorus concentrations were lowest during the late winter/

early spring and increased until mid-summer when concentrations peaked and then continued to decrease through the fall 

and winter months. Silver Lake is classified as a eutrophic lake 

based on the average total phosphorus concentrations (table 7) 

and the phosphorus nutrient criteria recommendations listed in 

table 6.
Lake Water-Quality Characteristics 31

Table 6. Statistical summary of nutrient and chlorophyll a concentrations measured from 2012–14 nutrient study in Silver Lake, 

Oceana County, Michigan.

[mg/L, milligrams per liter; μg/L, micrograms per liter; min, minimum; max, maximum; *bold typeface, identifies average concentrations that are in exceedance of the U.S. Environmental Protection Agency (EPA) ambient nutrient water-quality criteria recommendations for lakes and streams; na, not applicable]

Total phosphorus (mg/L) Total nitrogen (mg/L) Chlorophyll a (μg/L)

Min Max Average Min Max Average Min Max Average

Silver Lake 0.009 0.053 *0.031 0.44 0.98 *0.69 0.7 38.9 *15

Hunter Creek 0.016 0.034 0.022 0.91 1.47 *1.07 0.9 7.2 *3.6

Tributary at North End State Park 0.008 0.041 0.022 0.73 1.23 *0.93 0.2 1.1 0.6

Tributary at North Shore Drive 0.031 0.063 *0.041 0.65 1.35 *0.95 1.9 7.7 *4.2

Silver Creek (lake outlet) 0.014 0.037 *0.027 0.56 0.8 0.66 1.2 27.9 *13.4

Wet deposition 0.011 0.065 0.030 0.99 1.64 1.27 na na na

Dry deposition 0.009 0.124 0.048 0.22 0.98 0.54 na na na

Groundwater wells North 0.012 0.018 0.016 0.05 0.17 0.1 na na na

South 0.014 0.118 0.059 0.13 4.84 1.85 na na na

East 0.004 0.008 0.005 0.06 0.14 0.11 na na na

West 0.004 0.011 0.007 0.10 0.63 0.32 na na na

North end drain tiles 0.063 0.165 0.114 0.77 1.22 0.97 na na na

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

0.01

0.02

0.03

0.04

0.05

0.06

Jul-12 Oct-12 Jan-13 May-13 Aug-13 Nov-13 Mar-14 Jun-14 Sep-14 Dec-14

Total nitrogen, in milligrams per liter

Total phosphorus, in milligrams per liter

Mean phosphorus (surface and bottom) Mean nitrogen (surface and bottom)

Mean N = 

0.69 mg/L

Mean P = 

0.031 mg/L

EXPLANATION

Figure 7. Total nitrogen and total phosphorus concentrations measured in Silver Lake Oceana County, Michigan, 2012 to 2014. (Mean 

nutrient concentration from samples collected near the surface of the lake and near the bottom; mg/L, milligrams per liter; Mean N, 

mean nitrogen concentration; Mean P, mean phosphorus concentration).
32 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Table 7. Lake Trophic State classification.

[Based on Robert Carlson’s Trophic State Index (Carlson, 1977); TSI, Trophic 

State Index; <, less than]

Lake 

trophic state

Carlson’s 

TSI Description

Oligotrophic <40 Limited supply of nutrients, very high 

clarity and low algal densities, well 

oxygenated in the deepest zones.

Mesotrophic 40–50 Moderate supply of nutrients, moderate clarity and algal densities, 

oxygen can become depleted in 

deepest zone.s

Eutrophic 50–70 Large supply of nutrients, low water 

clarity and frequent algal blooms, 

oxygen depletion in deeper zones.

Hypereutrophic 70–100+ Excessive nutrients, more frequent 

algal blooms, fish kills.

Total nitrogen concentrations continually decreased after 

the initial sampling event on November 13, 2012, with the 

lowest total nitrogen concentrations, 0.44 mg/L, recorded during the November 5, 2013, sampling event at all five lake locations. Average total nitrogen concentrations increased through 

the remainder of the study period. The highest total nitrogen 

concentrations, 0.98 mg/L, were measured at the beginning 

of the study and during the March 2013 and 2014 sampling 

events. On average, total nitrogen concentrations collected 

from 3 ft below lake surface (0.67 mg/L) and 3 ft above lake 

bottom (0.71 mg/L) exhibited minimal variation throughout 

the water column. Total nitrogen concentrations in Silver Lake 

did not vary seasonally (fig. 7). It should be noted that even 

though total nitrogen concentrations did not exhibit seasonal 

variability in concentrations, both ammonia and nitrate concentrations peaked during the late winter sampling events in 

March, which is a common occurrence in eutrophic lakes in 

the Midwest (Wetzel, 1983). Also, during the late fall, organic 

nitrogen made up the bulk of total nitrogen in Silver Lake. 

Based on the EPA nutrient criteria recommendation for nitrogen 

in Ecoregion VII, the lake would be classified as eutrophic.

The ratio of nitrogen to phosphorus (N:P ratio) is related 

to the amount of primary production in the system (as measured by chrorophyll a), and when the N:P value falls below 

14 or above 17, the system will typically be limited by nitrogen or phosphorus respectively (Conley and others, 2009). 

This is because as the seasonal phosphorus and nitrogen 

concentrations in Silver Lake increase and decrease, the N:P 

ratio therefore varies seasonally throughout the study period. 

The N:P ratio for Silver Lake ranges from a low of 17:1 in the 

summer and fall, to a high of 56:1 in early spring (March), 

with a study-period mean N:P ratio of 22:1. The seasonal fluctuation in the N:P ratio likely influences nutrient limitation and 

primary productivity (as indicated by chlorophyll a concentrations) throughout the study period (fig. 8).

Chlorophyll a, the major pigment involved in photosynthesis in plants and eukaryotic algae, is an indicator of water 

quality and is frequently used as a proxy for algal biomass. 

Chlorophyll a is often used to determine the trophic status 

0

10

20

30

40

50

60

–75

–50

–25

0

25

50

75

100

125

150

175

200

225

Nitrogen:phosphorus ratio and mean chlorophyll 

a

concentration, in milligrams per liter

Change in total nitrogen and total phosphorus, 

in percent

Total phosphorus 

 percent change

Total nitrogen 

 percent change

EXPLANATION

Chlorophyll a Nitrogen:phosphorus

11/13/2012

3/6/2013

6/11/2013

7/30/2013

11/5/2013

3/11/2014

7/8/2014

9/16/2014

Figure 8. Nitrogen to phosphorus ratios in Silver Lake, Oceana County, Michigan, 2012 to 2014.
Lake Water-Quality Characteristics 33

of a lake, along with water transparency and total phosphorus. During this study, chlorophyll a concentrations peaked 

in late summer and early fall (maximum chlorophyll a value 

38.9 μg/L, at the north lake location) and were lowest (minimum chlorophyll a value 0.7 μg/L, at the south lake location) 

during the late winter and early spring months. 

Trophic Status and Lake Classification

Lakes are often classified by their trophic status, which is

an indicator of the amount of biomass or biological productivity in the lake. The trophic status of lakes can be compared 

and tracked over time to help evaluate lake response to factors

such as nutrient enrichment, age, or the implementation of bes

management practices (BMPs). In 1977, Dr. Robert Carlson 

published one of the most frequently used biomass-related 

trophic state indices (TSI) that remains widely used today to 

classify lakes. 

According to Carlson’s TSI, values of less than 40 represent oligotrophic conditions (low productivity, low nutrient 

content, clear water); 40–50, mesotrophic conditions (moderate productivity with medium level of nutrients, commonly 

clear); and greater than 50 represent eutrophic conditions (hig

productivity [abundance of aquatic plants and (or) algae], 

and low transparency) (table 7). Although the concept of TSI 

ranges is simple, the interpretation can be complex because 

of the interdependency of chlorophyll a, total phosphorus, 

and Secchi-disk transparency, thus the TSI values computed 

from any of these three indicators may not necessarily be the 

same (Fuller and Taricska, 2012). The chemical and physical environment can influence particular indicators and the 

interrelation with one another (Wetzel, 2001). For this study 

chlorophyll a, Secchi depth, and the mean of near-surface and

near-bottom total phosphorus concentrations were used to 

calculate TSI using the following equations (Carlson, 1977):

TSIsecchi = 60−14.41 * ln (Secchi-disk transparency, in meters) (5

TSIchlor-a = 9.81 * ln (chlorophyll a, in micrograms per liter) + 30.6 (6

TSIphos = 14.42 * ln (total phosphorus, in micrograms per liter) + 4.15 (7

Mean TSI values were calculated for Silver Lake for eac

of the sampling events using Secchi depth, total phosphorus 

(mean value from near surface and near bottom samples, as 

well as a mean value among all lake sites), and chlorophyll a

and are presented in figure 9. All three TSI indexes in this stu

classified Silver Lake as eutrophic approximately 63 percent 

of the time (five sampling events); mesotrophic to slightly 

eutrophic approximately 25 percent of the time (two sampling events); and oligotrophic to slightly mesotrophic about 

13 percent of the time (one sampling event). In March 2013, 

the TSI calculations using chlorophyll a indicated that the lak

was eutrophic when the other two indices indicated the lake 

was mesotrophic. Also in March 2014, the TSI calculations 

t 

h 

using total phosphorus indicated the lake was mesotrophic 

when chlorophyll a and Secchi disk depth indicated the lake 

was oligotrophic.

“I suggest that for purposes of classification 

priority be given to the biological parameters, 

especially the chlorophyll index, during 

summer, and perhaps to the phosphorus 

values in spring, fall, and winter, when the 

algae may be limited by factors other than 

phosphorus. These priorities would result in 

about the same index value during any season 

of the year (Carlson, 1977).”

Based on Carlson’s recommendations (Carlson, 1977), phosphorus may be the better indicator to use during the spring 

sampling events, thus indicating that the lake was mesotrophic 

in March 2013 and 2014.

 USGS staff sample water quality in Silver Lake.

)

)

)

h 

dy 

e 
34 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Figure 9. Trophic status of Silver Lake, Oceana County, Michigan, based on Carlson’s Trophic State Index. (TSI, trophic state 

index; less than 40 represents oligotrophic conditions, 40-50 mesotrophic, greater than 50 represents eutrophic conditions).

0

10

20

30

40

50

60

70

Trophic state index values

TSI - Total phosphorus TSI - Secchi depth TSI - Chlorophyl a

Eutrophic

Mesotrophic

Oligotrophic

EXPLANATION

11/13/2012

3/6/2013

6/11/2013

7/30/2013

11/5/2013

3/11/2014

7/8/2014

9/16/2014

Bioassay Study Results 

Bioassay results indicated that algal growth in Silver 

Lake is colimited by nitrogen and phosphorus. Chlorophyll a

concentrations were statistically different among nutrient 

treatments (p-value <0.001). Algal growth was stimulated 

by the nitrogen plus phosphorus treatment, which had the 

highest mean chlorophyll a concentration (32 μg/L; fig. 10). 

The nitrogen treatment had the second highest mean chlorophyll a concentration (15 μg/L), which was less than half of 

the nitrogen plus phosphorus treatment mean concentration. 

Mean chlorophyll a concentration in the phosphorus treatment 

was not significantly different from the control concentration. 

Only the nitrogen plus phosphorus treatment resulted in a net 

increase in chlorophyll a concentration relative to initial concentrations. These results indicate that algal growth in Silver 

Lake is colimited by nitrogen and phosphorus. 

General water-quality conditions were similar among the 

recorded measurements taken at the time/location of water 

collection (that is, mid-lake) and at the incubation location 

(that is, north end of lake), confirming that incubation location conditions simulated the natural environment in the open 

lake. Values for water-quality variables were similar to those 

recorded during the August 20, 2013, core collection. 

Results of the microcystin analysis and phytoplankton 

identification and enumeration are included in appendix 2.

Diel Dissolved Oxygen

Diel fluctuations in dissolved oxygen concentrations were 

evident in the near-bottom waters of the south (L5) and middle 

(L2) sites during the study period, although periods of hypoxia 

(dissolved oxygen less than 2 mg/L) were rare (fig. 11). When 

reliable data were recorded, dissolved oxygen concentrations 

in near-bottom water were generally greater than 6 mg/L 

at both sites outside of the brief diel low dissolved oxygen 

period. The exception was the middle site (L2) on August 

29–30, 2013, when dissolved oxygen concentrations were less 

than 5 mg/L during nearly the entire recording period (fig. 

11C). Dissolved oxygen concentrations at the middle site fell 

below 2 mg/L, at the near-bottom location, during the evening/

night hours of the three monitoring events; sensor malfunction 

resulted in missing data for the September 26–27, 2013, monitoring event (fig. 11D). A strong diel signal was recorded at the 

near-bottom of the south site during only one monitoring event 

(July 9–10, 2013), when concentrations fell below 3 mg/L for 

approximately 2.5 hours (fig. 11A). The near-bottom dissolved 

oxygen sensor at the south site made intermittent contact with 

the bottom sediments during the July 22–23, 2013, event rendering the data unreliable, and therefore the data are not shown 

in figure 11B. Near-surface dissolved oxygen concentrations 

were greater than 7 mg/L at both sites; the near-surface sensor 

at the south site malfunctioned on July 9–10, 2013, so data are 
Lake Water-Quality Characteristics 35

Figure 10. Mean (plus or minus standard deviation) chlorophyll a concentrations from the four bioassay treatments.

EXPLANATION

Micrograms per liter

Statistically significant 

contrasts (p <0.05)

Statistical result of the 

analysis of variance 

test

Nitrogen

Phosphorus

40

30

20

10

0

Control N P

a

a

b

c

N+P

Chlorophyll a, in micrograms per liter

17μg/L

p <0.001

Mean initial chlorophyll a

concentration

μg/L 

P value

a, b, c

P 

N 

not shown in figure 11A. However, based on the other monitoring events, it is suspected that concentrations were similar 

to those recorded at the near-surface of the middle site. 

Wind speed was about 10–20 miles per hour (mi/h) 

during the first two diel dissolved oxygen monitoring events 

(fig. 11A, B) and less than 5 mi/h during the last two events 

(fig. 11B, C). No clear relation was evident between dissolved 

oxygen fluctuation and wind speed or direction. 

These results point to (1) a lack of continuous hypoxic 

conditions at the times and locations of the two monitoring 

sites but (2) occasional periods of intermittent hypoxia (less 

than 2 mg/L) during evening hours, especially at the middle 

site, from early July through late August. Although nearbottom data were missing at the middle site from the late 

September monitoring event, the near-bottom data from the 

south site indicate well-oxygenated conditions throughout the 

water column during that time (fig. 11D). Hence, the hypoxic 

or anoxic conditions necessary to support internal phosphorus 

loading in Silver Lake are rare. As shown by the sediment core 

incubation results (see Nutrient Loads, Internal Nutrient Loading section), Silver Lake sediments are expected to release 

very little or no phosphorus, regardless of redox state. 
36 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14 Figure 11. Diel dissolved oxygen (DO) and wind data from the south (L5) and middle (L2) monitoring stations on Silver Lake, Oceana County, Michigan, on four dates in 2013. (Sensor malfunction resulted in missing data on July 9–10 [south surface] and September 26–27 [middle bottom]; data recorded from south bottom were unusable on July 22–23 due to intermittent sensor contact with the bottom sediments.) 3025201510

50

8

6420

1210

18:00

20:00

22:00

0:00

2:00

4:00

6:00

8:00

10:00

Wind, in miles per hour

Dissolved oxygen concentration, 

in milligrams per liter 

Time

D. September 26–27, 2013

3025201510

50

86420

1210

15:00

17:00

19:00

21:00

23:00

1:00

3:00

5:00

7:00

9:00

11:00

Wind, in miles per hour

Dissolved oxygen concentration, 

in milligrams per liter 

Time

C. August 29–30, 2013

30

2520

1510

5

0

86420

1210

14:30

16:30

18:30

20:30

22:30

0:30

2:30

4:30

6:30

8:30

10:30

Wind, in miles per hour

Dissolved oxygen concentration, 

in milligrams per liter 

Time

B. July 22–23, 2013

302520

1510

5

0 08642

1210

15:00

17:00

19:00

21:00

23:00

1:00

3:00

5:00

7:00

9:00

11:00

Wind, in miles per hour

Dissolved oxygen concentration, 

in milligramsper liter 

A. July 9–10, 2013

Time

NightSouth topSouth bottomMiddle topMiddle bottomWind

EXPLANATION

Figure 11. Diel dissolved oxygen (DO) and wind data from the south and middle monitoring stations on Silver Lake, Oceana County, Michigan, on four dates in 2013. (Sensor malfunction resulted in missing data on July 9–10 (south top) and September 26–27 (middle bottom); data recorded from south bottom were unusable on July 22–23 due to intermittent sensor contact with the bottom sediments.)
Hydrology: Sources of Water and Nutrients 37

Hydrology: Sources of Water and 

Nutrients

Study results indicate that Silver Lake is colimited by 

both phosphorus and nitrogen, at the time when the bioassay 

was conducted. The average N:P ratio was found to be 22:1; 

however, it varied seasonally as phosphorus and nitrogen 

concentrations in the lake changed (fig. 8). As a result, both 

phosphorus and nitrogen are deemed critical nutrients in the 

development of algal blooms and lake eutrophication in Silver 

Lake. While this colimitation indicates that both nitrogen and 

phosphorus are needed to produce excessive algal growth during the summer months, it does not indicate that both nutrients 

need to be reduced to control algal blooms. Based on the bioassay study, management practices that reduce either nitrogen 

or phosphorus should be enough to limit excessive primary 

production in the summer months, and a reduction in both 

nutrients would likely impact magnitude (Harpole and others, 

2011; Conley and others, 2009). To determine the sources 

of nutrients to Silver Lake, contributions from surface water 

(tributary inputs, including septic contribution to tributaries), 

groundwater (including septic), atmospheric deposition (wet 

and dry), waterfowl, lawn runoff, and internal loading from 

lake sediments were evaluated as described in the following 

sections.

Sources of Water–Water Budget

The water budget for Silver Lake can be visualized as a 

pie graph with individual components represented as a slice 

of the overall budget. Figure 12 illustrates the various components of the water budget by percentage of outflow and inflow 

over the 2-year study period. Water loss from Silver Lake is 

primarily the outflow of water from Silver Lake through Silver 

Creek which accounts for 91.9 percent of all water leaving 

Silver Lake. The remaining 8.1 percent is removed through 

evaporation. The primary contribution of water to Silver Lake 

is from Hunter Creek at 52.2 percent. Groundwater sources 

contribute 30.6 percent of inflow, precipitation contributes 

10.3 percent, the tributary at the State Park contributes 

4.1 percent, and the tributary at North Shore Drive contributes 

2.8 percent. Runoff of precipitation from the land directly to 

the lake is assumed to be small and is considered as part of the 

groundwater input to the lake.

Further refining the water budget by season for the 2-year 

study period provides insight on the relative contribution of 

the various components at different times of year (fig. 13). 

The three largest components of the water budget remain the 

outflow of water from Silver Lake, the inflow of water from 

Hunter Creek, and inflow of water from groundwater sources. 

However, the relative amounts of each component vary by 

Figure 12. Annual water budget for Silver Lake, Oceana County, Michigan.

EXPLANATION

Surface outflow

Evaporation

Hunter Creek

Groundwater

Precipitation

Tributary at North Shore Drive

Tributary at State Park

91.9 percent

8.1 percent

52.2 percent

30.6 percent

10.3 percent

2.8 percent

4.1 percent

Water out Water in

Volume 1.52 x 109 cubic feet Volume 1.52 x 109

 cubic feet

Figure 12. Annual water budget for Silver Lake, Oceana County, Michigan.
38 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Figure 13. Water budget, by season, for Silver Lake, Oceana County, Michigan.

EXPLANATION

Surface outflow

Evaporation

Hunter Creek

Groundwater

Precipitation

Tributary at North Shore Drive

Tributary at State Park

Change in storage

96.2 percent

3.8 percent

Spring outflow

62.6 percent

18.7 percent

9.3 percent

3.3 percent

5 percent

1.1 percent

Spring inflow

Volume 5.41 x 108

cubic feet

Volume 5.41 x 108

cubic feet

76.6 percent

23.4 percent

Summer outflow

40.9 percent

43.7

percent

10 percent

2.2 percent

3.3 percent

Summer inflow

Volume 3.39 x 108

cubic feet

Volume 3.39 x 108

cubic feet

91.6 percent

7.5 percent

0.9 percent

Fall outflow Fall inflow

Volume 3.14 x 108

cubic feet

Volume 3.14 x 108

cubic feet

42.8 percent

36.5

percent

15.1 percent

2.3 percent

3.4 percent

99.1 percent

0.9 percent

Winter outflow

54.9 percent

30.4

percent 7.6 percent

2.9 percent

4.3 percent

Winter inflow

Volume 3.24 x 108

cubic feet

Volume 3.24 x 108

cubic feet

season. During the spring season (March—May), Hunter 

Creek is the dominant source (62.6 percent) of water to the 

lake. In summer (June—August), the primary contributor of 

water to the lake becomes groundwater (43.7 percent). The 

fall season (September—November) shows both Hunter Creek 

and groundwater contributing similar proportions of the overall water budget (42.8 and 36.5 percent, respectively), with 

Hunter Creek being slightly more. During winter (December 

through February), Hunter Creek becomes the dominant 

source (54.9 percent) of water to the lake again. It should be 

noted that there is a very small change in storage for the lake 

by season. During the fall and winter, the lake level drops so 

there is a loss of water in storage of about 0.9 percent of the 

outflow volume in each season. In the spring there is a gain 

in storage of 1.1 percent of the inflow volume, which over the 

2-year study balances to no net change in storage.

Sources of Nutrients—Nutrient Budget

A nutrient budget was calculated to (1) determine the 

sources of phosphorus and nitrogen to Silver Lake and to 

identify the contribution from each of these sources, and (2) 

calculate the nutrient output. The annual and seasonal nutrient 

budgets were computed using the BATHTUB model (Walker, 

1996) based on 2 years of data collection. The BATHTUB 

model was used to simulate the current-conditions in the lake 

based on the data collected. The model is used to predict 

eutrophication-related water-quality conditions in a lake using 

empirical relationships that have been previously developed 

for lake applications (Walker, 1985). Loads estimated using 

the BATHTUB model are discussed in this section; details of 

the model data requirements, limitations, and calibration are 

given in the Nutrient Load Modeling section.
Hydrology: Sources of Water and Nutrients 39

Nutrient Concentrations

Nutrient data collected from Hunter Creek, the tributary 

at the State Park, and the tributary at North Shore Drive were 

analyzed and compared to the EPA ambient water-quality criteria 

recommendations (EPA nutrient criteria) for rivers and streams 

for Ecoregion VII (table 5). The seasonal nutrient loading during 

the winter months was not modeled due to minimal data collection during the winter season. 

Surface-Water Inflow and Outflow

Hunter Creek is the largest tributary entering Silver Lake 

and contributes the highest inputs of nutrients compared to the 

other surface water inflows to the lake. Average total phosphorus 

concentrations fell below the EPA nutrient criteria recommendation of 0.033 mg/L (tables 5 and 6). The highest total phosphorus 

concentrations were typically measured during storm events 

(except for the April 15, 2013 event) and total phosphorus 

concentrations peaked during late summer and early fall months 

with lower concentrations recorded during the April sampling 

events in 2013 and 2014. Orthophosphate concentrations for 

Hunter Creek were typically at or below the NWQL detection 

limit of 0.004 mg/L and, therefore, orthophosphate is a minimal component of the phosphorus contribution from Hunter 

Creek. The average total nitrogen concentrations in Hunter 

Creek exceeded the EPA nutrient criterion recommendation of 

0.54 mg/L (table 6). The highest total nitrogen concentrations 

were measured during the spring storm events in 2013–14, 

with the lowest concentrations measured in mid to late summer. Nitrate typically accounted for half of the total nitrogen in 

Hunter Creek, averaging 0.63 mg/L during the study. 

The tributaries at the State Park and at North Shore Drive 

also were monitored to assess the remaining surface water inputs 

to Silver Lake. The average total phosphorus and total nitrogen 

concentrations are presented in table 6. Total phosphorus concentrations were below or slightly above the EPA nutrient criteria for 

both small tributaries (tables 5 and 6). There was no correlation 

between total phosphorus and storm events at the tributary at the 

State Park; however, it appears there is a seasonal distribution 

where total phosphorus concentrations are lowest in late winter/

early spring and increase through the summer and peak in late 

summer/early fall. Almost all samples collected at the tributary at 

North Shore Drive were collected during storm event conditions 

due to the low flows and minimal input during base-flow periods. 

Orthophosphate concentrations for both small tributaries were at 

or below the minimum detection limit of 0.004 mg/L.

The average total nitrogen concentrations were consistent 

for both tributaries (table 6). At both sites, total nitrogen concentrations exceeded the EPA nutrient criterion of 0.54 mg/L (table 

5, table 6). Nitrate and organic nitrogen made up the bulk of the 

total nitrogen at both tributaries. At the tributary at the State Park, 

nitrate accounted for 71 percent of total nitrogen, and organic 

nitrogen accounted for about 28 percent. At the tributary at North 

Shore Drive, nitrate made up approximately 27 percent of total 

nitrogen, while organic nitrogen accounted for 69 percent. 

Hunter Creek upstream from the Upper 

Silver Lake outlet and the Upper Silver Lake outlet at 

West Taylor Road (fig. 2, table 2) were monitored on three 

occasions in August and September 2014; twice during storm events and once during base-flow conditions. 

Nutrient loads at these two sites represent approximately 

1 month (29 days) of the year and were calculated from 

the date the first sample was collected until the last 

sample was collected. Upstream Hunter Creek total phosphorus concentrations ranged from 0.02 to 0.07 mg/L, 

with an average concentration of 0.04 mg/L. The Upper 

Silver Lake outlet total phosphorus concentrations ranged 

from 0.02 to 0.03 mg/L, with an average concentration of 

0.02 mg/L. Total phosphorus concentrations either slightly 

exceeded or fell below the EPA nutrient criterion recommendation of 0.033 mg/L (table 5). Total nitrogen concentrations at the upstream Hunter Creek tributary ranged 

from 1.08 to 1.45 mg/L and averaged 1.22 mg/L, and total 

nitrogen concentrations at Upper Silver lake outlet ranged 

from 0.67 to 0.77 mg/L and averaged 0.72 mg/L. Both 

sites exceeded the EPA nutrient criterion for total nitrogen 

(table 5). From August 19, 2014 through September 17, 

2014, approximately 53 lb of phosphorus and 1,331 lb of 

nitrogen were delivered from Upper Hunter Creek and 

approximately 13 lb of phosphorus and 439 lb of nitrogen 

from the Upper Silver Lake outlet. 

Additional monitoring and discharge measurements will be necessary to determine the annual nutrient 

loading from Upper Silver Lake and upstream Hunter 

Creek and to determine if nutrient contributions vary 

seasonally from each of these tributaries. Some potential 

sources contributing to elevated nitrogen concentrations 

in these tributaries include agricultural fertilizers and 

manure, illicit wastewater discharge, septic system input 

(including septic systems further than 200 ft from Hunter 

Creek), precipitation and dry atmospheric deposition, and 

natural organic decomposition. E. coli concentrations 

at upper Hunter Creek exceeded the recreational criteria during two of the three sampling events (340, 1,700, 

and 140 MPN/100 mL). Upper Silver Lake outlet E. coli 

concentrations exceeded the recreational criteria once 

(58, 440, and 6 MPN/100mL). 
40 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

The Silver Lake outlet (Silver Creek) also was 

monitored to determine the nutrient concentrations leaving 

Silver Lake. Those data are presented in table 6. 

Precipitation and Dry Deposition

Phosphorus and nitrogen were measured in dry deposition and wet deposition on four occasions from February 

2014 to September 2014, and the average concentrations are 

presented in table 6. Concentrations of nutrients in wet and 

dry deposition displayed seasonal variability with the highest 

concentrations of nitrogen being observed in wet deposition 

in the winter, while the highest concentrations of phosphorus 

were observed in dry deposition in the summer. Total nitrogen 

concentrations were always higher in wet deposition when 

compared to dry deposition, while total phosphorus concentrations were higher in wet deposition in winter and fall, but 

higher in dry deposition in spring and summer. Spring and 

summer peaks in phosphorus are typically related to increased 

pollen deposition that occurs during these seasons (Lewis and 

others, 1985).

Groundwater Influence

Nutrient loading from groundwater to Silver Lake was 

monitored as described in the Water Quality Data CollectionGroundwater Monitoring and Flow section of this report. The 

influence of groundwater on Silver Lake was unknown prior to 

this study as previous studies never accounted for that component in the nutrient and water budgets. 

To accurately determine the nutrient loading from 

groundwater, phosphorus and nitrogen concentrations were 

analyzed at the four wells installed on the east, west, north, 

and south sides of Silver Lake. Groundwater nutrient concentrations varied amongst the four wells (table 6) and both total 

phosphorus and total nitrogen concentrations were consistently 

highest at the south well. Orthophosphate accounted for the 

majority of phosphorus found in groundwater with orthophosphate concentrations ranging from 83 percent (south well) 

to 100 percent (west well). In addition, nutrient data from a 

subset of five drain tiles at the north end of Silver Lake are 

presented in table 6. Orthophosphate concentrations accounted 

for approximately 81 percent of the total phosphorus concentrations in the drain tile discharge. 

Other Sources of Nutrients

This study describes 2 years of water-quality monitoring 

in Silver Lake (including internal loading), as well as monitoring of nutrients contributed by groundwater, tributaries, and 

atmospheric deposition. Other potential sources of nutrients 

are not individually identified in this report, as the nutrient 

inputs from these other sources are ultimately captured in 

the overall water-quality monitoring design of the study. For 

example, the human impacts on water quality as a result of the 

recreational use of the sand dunes and Silver Lake were not 

independently measured; however, the lake and groundwater 

monitoring in this study accounted for these specific inputs. 

Nutrient Loads

Continuous flow data and discrete water-quality data 

were used to estimate nutrient loads of total phosphorus and 

total nitrogen from Hunter Creek using the BATHTUB model 

(Walker, 1996). The BATHTUB model also was used to calculate a nutrient budget for Silver Lake using water-quality data 

from this study.

Surface-Water Inflow and Outflow

Based on 2 years of monitoring, the mean annual load 

of phosphorus from Hunter Creek was 384.5 lb, and the 

mean annual load of nitrogen was 29,277 lb. Of the estimated 

loads from Hunter Creek, the septic loading model (septic 

systems as sources of nutrients) indicated that septic systems 

likely contributed 22.3 percent of phosphorus (85.9 lb) and 

0.95 percent of nitrogen (279.3 lb). The seasonal contributions 

of phosphorus and nitrogen from Hunter Creek are presented 

in figure 14. Based on the BATHTUB model output, Hunter 

Creek accounted for approximately 25 percent of the phosphorus load to Silver Lake in the fall (73 lb), 33 percent in the 

spring (149 lb), and 20 percent in the summer (65 lb). Hunter 

Creek was the largest contributing source of nitrogen during 

the fall (51 percent, 8,280 lb) and spring seasons (80 percent, 

or 27,825 lb), respectively. During the summer, Hunter Creek 

accounted for 38 percent (7,895 lb) of the nitrogen budget to 

Silver Lake (fig. 14).

Discharge was measured concurrently with sample collection at the two small tributaries and the data were entered 

into the BATHTUB model, along with nutrient concentrations, to calculate phosphorus and nitrogen loading. Based 

on 2 years of monitoring, the mean annual load of phosphorus from the tributary at the State Park was 37 lb, and 

the mean annual load of nitrogen was 2,111 lb. The mean 

annual load of phosphorus from the tributary at North Shore 

Drive was 29.6 lb, and the mean annual load of nitrogen was 

1,032 lb. The small tributaries collectively accounted for about 

5 percent of the phosphorus load during the fall and 4 percent 

of the phosphorus load to Silver Lake during the spring and 

summer. The nitrogen loading from the two small tributaries 

collectively made up approximately 6 percent of the nitrogen 

budget in the fall, 7 percent in the spring, and 5 percent in the 

summer (fig. 14). 

The total annual output of phosphorus and nitrogen leaving the lake was approximately 1,340 lb (99.8 percent of total 

phosphorus load to the lake) and 30,071 lb (57.8 percent of 

total nitrogen load to the lake), respectively. Even though the 

majority of phosphorus leaves Silver Lake, the phosphorus 

remains in the lake for the duration of the lake residence time 

(223 days), contributing to algal blooms and eutrophication.
Hydrology: Sources of Water and Nutrients 41

Figure 14. Mean nutrient loading, by season, to Silver Lake, Oceana County, Michigan. (Internal load of phosphorus and nitrogen 

was measured only during the summer and assumed to be negligible during the remainder of the year. Internal phosphorus loading 

during the summer was less than 1 percent of the total phosphorus load.).

Hunter Creek

EXPLANATION

Tributary at State Park

Tributary at North Shore Drive

Groundwater

Lawn runoff

Atmospheric deposition

25 percent

3 percent

2 percent 52 percent

15 percent

3 percent

Mean phosphorus loading for fall (percent)

Fall phosphorus load = 287 (pounds) Fall nitrogen load = 8,921 (pounds) 

Spring phosphorus load = 459 (pounds) Spring nitrogen load = 19,180 (pounds) 

Summer phosphorus load = 324 (pounds) Summer nitrogen load = 11,356 (pounds) 

51 percent

4 percent 2 percent

37 percent

2 percent 4 percent

Mean nitrogen loading for fall (percent)

33 percent

2 percent

2 percent

52 percent

9 percent

2 percent

Mean phosphorus loading for spring (percent) 

80 percent

4 percent

3 percent 10

percent

1 percent 2 percent

Mean nitrogen loading for spring (percent)

20 percent 2 percent

2 percent

61 percent

14

percent

1 percent

Mean phosphorus loading for summer (percent) 

38 percent

4 percent

1 percent

41 percent

2 percent

2 percent 12

percent

Mean nitrogen loading for summer (percent)
42 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Precipitation and Dry Deposition

The mean annual atmospheric load of phosphorus to 

Silver Lake was 87 lbs, and the mean annual load of nitrogen 

was 4,586 lb. The seasonal phosphorus loading from atmospheric deposition was approximately 3 percent of the phosphorus budget in the fall (7 lb), 2 percent in the spring (9 lb), 

and 1 percent in the summer (4.5 lb). Nitrogen loading from 

atmospheric deposition was highest in the fall (4 percent), and 

made up 2 percent of the nitrogen budget during the spring 

and summer (fig. 14). Loads of both phosphorus and nitrogen 

from dry deposition were 10 to 100 times smaller than measured wet deposition loads.

The nutrient load of phosphorus and nitrogen contributed 

from waterfowl represents a solid mass (not a mass delivered 

in water) and was included in the BATHTUB model as an 

addition to the atmospheric deposition value. Loads attributed 

to waterfowl account for 0.99 percent of total phosphorus 

deposition and 0.11 percent of total nitrogen deposition to 

Silver Lake. In total, atmospheric and waterfowl loads were 

computed to be approximately 6 percent of the total phosphorus annual load and 9 percent of the total nitrogen annual load 

to Silver Lake.

Groundwater Influence

The groundwater component of the nutrient budget was 

calculated by dividing Silver Lake into four quadrants as 

described in appendix 3. The drain tiles at the north end of 

Silver Lake were evaluated separately and are included in the 

groundwater nutrient budget. Based on 2 years of monitoring, 

the mean annual load of phosphorus and nitrogen from the 

four groundwater quadrants and the drain tiles are described 

in table 8. The mean annual load of phosphorus and nitrogen 

from the north end drain tiles is 68 and 340 lb, respectively. 

Results from the BATHTUB model showed the estimated contribution of phosphorus via groundwater to Silver 

Lake was highest in the summer and made up approximately 

61 percent of the summer phosphorus budget (198 lb). The 

phosphorus loading from groundwater accounted for approximately 52 percent of the phosphorus budget during both the 

fall and spring (149 and 237 lb, respectively). Study findings 

indicate that much of the phosphorus contribution to Silver 

Lake via groundwater is orthophosphate; a form of phosphorus 

that can readily stimulate algae growth. Based on the BATHTUB model output, groundwater accounted for 80 percent of 

the nitrogen budget during the spring (1,945 lb), 51 percent 

during the fall (3,290 lb), and 38 percent during the summer 

(4,702 lb) (fig. 14). The mean annual load from all monitored 

groundwater sources was 630 lb of phosphorus and 12,951 lb 

of nitrogen (table 8). 

The septic model (Garn and others, 1996; Reckhow and 

others, 1980) estimated that septic systems likely contributed 47.8 percent (300.9 lb) of phosphorus and 1.1 percent 

(136.1 lb) of nitrogen to groundwater annually (fig. 15). 

This septic model is designed to calculate both phosphorus 

and nitrogen loading, however has mostly been used for the 

estimation of phosphorus from septic sources as phosphorus is 

often times the nutrient of concern. The soil retention coefficient (SR), which represents the fraction of phosphorus or 

nitrogen retained between the septic system and the lake, was 

held constant for both phosphorus and nitrogen. The modeled 

septic and nonseptic (plus septic input greater than 200 ft from 

shoreline) contributions to the Silver Lake nutrient budget are 

illustrated in figure 15.

Internal Nutrient Loading

The results of the sediment core incubations experiments 

showed that internal loading is not a significant source of 

phosphorus in Silver Lake. Concentrations of total phosphorus in the water column overlying sediment cores tended to 

remain stable or slightly decline over time, and were generally 

less than 0.02 mg/L during the core incubations regardless of 

site, sampling date, or redox treatment (fig. 16). Ninety-seven 

to 100 percent of SRP concentrations were below the detection limit, so those data are not shown. Mean total phosphorus flux was negative and similar between redox treatments, 

ranging from −0.23 to −0.02 milligrams per square meter per 

day (mg/m2

/d) (table 9). In lakes where internal phosphorus 

loading is problematic, release rates are usually much higher 

in anoxic than oxic conditions because a lack of dissolved 

oxygen leads to the reduction of ferric oxyhydroxides, thereby 

resulting in the dissociation of phosphorus from its bound 

form to iron, and a diffusion of phosphorus into the water 

column (Boström and others, 1982). This was not observed in 

the Silver Lake sediment incubations in this study, indicating 

that sediment release is not a significant source of phosphorus; indeed, the data indicate that sediments may actually be a 

minor sink for phosphorus in Silver Lake, given the negative 

release rates. 

Table 8. Nutrient loading from groundwater to Silver Lake, 

Oceana County, Michigan.

[lb/yr; pounds per year]

Groundwater contribution

Total 

phosphorus

 (lb/yr)

Total 

nitrogen

 (lb/yr)

North quadrant 12.4 39.7

South quadrant 403 8,811

East quadrant 14.8 108

West quadrant 132 3,653

North drain tiles 67.6 340

Total groundwater nutrient load 630 12,951
Hydrology: Sources of Water and Nutrients 43

0

10

20

30

40

50

0

10

20

30

40

50

Phosphorus Nitrogen

Atmospheric 

deposition

Groundwater

Hunter Creek

Internal load

Lawn runoff

Contributing source

Total estimated load to Silver Lake, in percent

Component

EXPLANATION

Tributary at 

North Shore Drive

Tributary at 

State Park

Nonseptic sources plus 

septic input greater than 

200 feet from shoreline

Modeled septic contribution

Figure 15. Percentage of modeled septic 

contribution to total nutrient load in Silver 

Lake, Oceana County, Michigan, estimated 

using BATHTUB. (Septic component 

estimated using the septic model [modeled 

200 feet from shoreline])

To place these results in a regional context, total phosphorus sediment release rates in Silver Lake were compared to 

that of Spring Lake, a west Michigan Lake that was experiencing significant internal phosphorus loading. This lake has 

undergone an alum treatment to control internal phosphorus 

release. In anoxic treatments, mean total phosphorus flux in 

Spring Lake was 18 mg/m2/d prior to the alum treatment, but 

after treatment that rate was reduced to only 0.4 mg/m2/d (1 

year after alum) and 2 mg/m2/d (5 years after alum) (table 

9) (Steinman and others, 2004; Steinman and Ogdahl, 2008, 

2012). Even in oxic treatments, when phosphorus is expected 

to be bound to iron, and therefore not prone to diffusion into 

the water column, total phosphorus flux from Spring Lake 

sediments was positive, albeit much lower than in anoxic treatments (table 9). Phosphorus release from sediments in Silver 

Lake is much lower than that in Spring Lake, irrespective of 

oxic or anoxic conditions.

Unlike phosphorus, the sediment core experiments 

revealed that internal loading is contributing nitrogen to the 

water column of Silver Lake. There was a distinct difference 

in ammonium concentrations between the redox treatments 

throughout the core incubation period. Initial ammonium 

concentrations were approximately 0.1 mg/L and increased to 

as much as 1.5 mg/L in anoxic treatments by day 28 (fig. 17, 

illustrates data from middle site only, L2). 
44 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Figure 16. Total phosphorus concentrations released/retained from sediment into the water column for oxic (Ox) and 

anoxic (An) treatments. (These data are from the middle site [L2], August 2014, and phosphorus release/retention patterns 

are representative of all release experiments.)

Ox-1

Ox-2

Ox-3

An-1

An-2

An-3

EXPLANATION

Days

4

0

0 8 12 16 20 24

Total phosphorus concentration, in milligrams per liter

0.02

0.08

28

0.06

0.04

Figure 16. Total phosphorus concentrations released/retained from sediment into the water column for oxic (Ox) 

and anoxic (An) treatments. (These data are from the middle site, August 2014, and phosphorus release/retention 

patterns are representative of all release experiments.)

Table 9. Mean and standard deviation (SD) of total phosphorus (TP) and ammonium (NH4

+

) flux, in milligrams per squaremeter per 

day, from sediment cores collected from Silver Lake, Oceana County, Michigan, and incubated for 28 days. 

[Total phosphorus flux measured in cores from Spring Lake, Michigan, are presented for comparison: pre-alum treatment (Steinman and others, 2004), 

1 year after alum treatment (Steinman and Ogdahl, 2008), and 5 years after alum treatment (Steinman and Ogdahl, 2012); mg/m2/d, milligrams per square 

meter per day; SD, standard deviation; —, no data]

Total phosphorus flux (mg/m2/d) Ammonium flux (mg/m2/d)

Oxic Anoxic Oxic Anoxic

Mean SD Mean SD Mean SD Mean SD

Silver Lake May 2013 −0.16 0.12 −0.16 0.06 −1.10 0.00 4.53 1.92

August 2013 −0.08 0.03 −0.09 0.00 4.33 5.04 9.01 2.72

June 2014 −0.11 0.07 −0.02 0.07 4.53 5.79 8.91 2.08

August 2014 −0.23 0.00 −0.23 0.00 2.21 0.08 9.89 2.92

Silver Lake (before alum) June/July 2003 0.03 0.44 17.97 8.07 — — — —

Silver Lake (1 year after alum) July 2006 0.20 0.08 0.41 0.38 — — — —

Silver Lake (5 years after alum) September 2010 1.14 1.24 2.27 0.46 — — — —
Hydrology: Sources of Water and Nutrients 45

Figure 17. Ammonium concentrations released/retained from sediment into the water column for oxic (Ox) and anoxic (An) 

treatments. (These data are from the middle site [L2], August 2014, and release/retention patterns are representative of all 

release experiments.)

Ox-1

Ox-2

Ox-3

An-1

An-2

An-3

EXPLANATION

Ammonium concentration, in milligrams per liter

Days

4

0

0 8 12 16 20 24 28

0.5

1.0

1.5

Figure 17. Ammonium concentrations released/retained from sediment into the water column for oxic (Ox) and 

anoxic (An) treatments. (These data are from the middle site, August 2014, and release/retention patterns are 

Ammonium concentrations in oxic treatments tended representative of all release experiments.)

to have a slight increase for 8–12 days before decreasing to 

below the detection limit (fig. 17). Mean ammonium flux 

ranged from −1.1 to 4.5 mg/m2/d in oxic treatments and 4.5 

to 9.9 mg/m2/d in anoxic treatments (table 9). There was no 

clear relation between ammonium flux and site or sampling 

date. Given that anoxic conditions appear to be rare in Silver 

Lake, the lower release rates in the oxic treatments are probably more representative of what is occurring in the lake than 

the rates under anoxic conditions. Nonetheless, if the mean 

oxic ammonium release rate of 2.54 mg/m2

/d is representative of the entire lake, and it is assumed this rate is constant 

but occurs only during summer months, the overall mass of 

ammonium potentially released from Silver Lake sediments is 

622 kg (2,719,488 m2 [672 acres] × 2.54 mg/m2/d × 90 d) or 

approximately 1,371 lb, or about 2.6 percent of the estimated 

annual nitrogen mass in Silver Lake (see Nutrient Budget section). Based on the BATHTUB model simulations, the internal 

loading of nitrogen to Silver Lake makes up 12 percent of 

the nitrogen load during the summer months (fig. 14). Internal loading of nitrogen from sediments is a modest source in 

Silver Lake; however, these rates may be overestimates given 

that they are gross inputs and do not account for potential 

nitrogen losses such as denitrification. 

Sediment total phosphorus concentrations (upper 5 cm) 

were less than 1,000 milligrams per kilogram (mg/kg) at both 

sites during all coring events, and less than 500 mg/kg at the 

middle site with the exception of August 2014 (fig. 18). Compared to other west Michigan lakes, sediment total phosphorus concentrations in Silver Lake are low, particularly at the 

middle site (fig. 18). 

The concentrations of four key sediment metals (calcium, 

iron, phosphorus, and aluminum) were higher at the south 

site than at the middle site. Calcium was the most abundant 

element, followed by iron. Iron:total phosphorus ratios (by 

weight) were 23 and 12 at the south and middle sites, respectively. Lakes with iron:total phosphorus ratios greater than 15 

tend to have low SRP flux under oxic conditions (Jensen and 

others, 1992), such as those observed in Silver Lake. This is 

because the ferric oxyhydroxides bind to phosphorus, preventing its diffusion into the water column above (Boström and 

others, 1982). In general, it appears there is sufficient iron 

to bind the phosphorus that is present in Silver Lake sediments, and prevent diffusion under oxic conditions. However, 

the iron:total phosphorus ratio should be applied with care 

because the ratio uses total phosphorus, which includes forms 

that may not be available for biotic uptake (Rydin and others, 

2000). Phosphorus fractionation would be necessary to determine how much of the total phosphorus is in the bioavailable 

phosphorus fraction in these sediments.
46 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Figure 18. Total phosphorus concentrations in sediment (upper 5 centimeters) at the two internal loading study locations 

in Silver Lake, Oceana County, Michigan, on each core collection date. (Mean [plus or minus standard error] total 

phosphorus in sediment for other lakes in west Michigan are provided for comparison [gray bars]. Sources: Bear Lake, 

Steinman and Ogdahl, 2015; Mona Lake, Steinman and others, 2009; White Lake, Steinman and others, 2008; Spring Lake 

before alum treatment, Steinman and others, 2004; Spring Lake after alum treatment, Steinman and Ogdahl, 2008) Total phosphorus concentration in sediment, in milligrams per kilogram of dry weight

3,000

2,000

1,000

0

4,000

5/29/2013 8/20/2013 6/19/2014 8/20/2014 Bear

Lake

Mona

Lake

White

Lake

Spring Lake

pre-alum

Spring Lake

post-alum

Silver Lake

Silver Lake: south

Silver Lake: middle

Other area lakes

EXPLANATION

Figure 18. Total phosphorus concentrations in sediment (upper 5 centimeters) at the two internal loading study 

locations in Silver Lake, Oceana County, Michigan, on each core collection date. (Mean [plus or minus standard error] 

total phosphorus in sediment for other lakes in west Michigan are provided for comparison [gray bars]. Sources: Bear 

Lake, Steinman and Ogdahl, 2015; Mona Lake, Steinman and others, 2009; White Lake, Steinman and others, 2008; 

Spring Lake pre-alum, Steinman and others, 2004; Spring Lake post-alum, Steinman and Ogdahl, 2008).

Lawn Runoff

Based on the BATHTUB model output, the contribution 

of phosphorus via lawn runoff to Silver Lake was highest in 

the summer and fall and made up approximately 14 and 15 

percent of the summer and fall phosphorus budgets, respectively (estimated 44 lb per season) (fig. 14). The phosphorus 

loading from lawn runoff accounted for approximately 9 percent of the phosphorus budget during the spring even though 

the lawn-runoff estimates considered the predominant use of 

phosphate-free fertilizer. Lawn runoff accounted for a small 

component of the nitrogen budget during the spring, summer, 

and fall (1 percent during the spring, and 2 percent during the 

summer and fall) (163 lb per season) (fig. 14). Overall, lawn 

runoff accounted for approximately 13 percent of the annual 

phosphorus budget and 1 percent of the annual nitrogen budget (fig. 19).

Nutrient Budget Summary

The nutrient budget for Silver Lake was calculated using 

the BATHTUB model based on 2 years of water quality data 

collection. A mean annual phosphorus and nitrogen budget summary were calculated for Silver Lake and are presented in figure 

19. A seasonal breakdown on the phosphorus and nitrogen 

budgets was also summarized and is presented in figure 14.

The mean annual input of phosphorus to Silver Lake was 

approximately 1,342 lb. The major sources of phosphorus to Silver Lake were groundwater (630 lb, or 46.9 percent) followed 

by Hunter Creek (385 lb, or 28.7 percent) (fig. 19). A large 

proportion of the phosphorus load being delivered to the lake by 

groundwater and Hunter Creek is likely originating from septic 

systems. Septic model loading scenarios indicate that 19.5 to 

73.7 percent of phosphorus in groundwater and 2.1 to 43.6 

percent of phosphorus in Hunter Creek is likely derived from 

septic loading. The most likely septic loading scenario estimates 

that septic systems contribute 47.8 percent of the phosphorus 

to groundwater and 22.3 percent of phosphorus to Hunter 

Creek (table 10). These results indicate that septic systems 

are a major source of phosphorus loading to Silver Lake. The 
Hydrology: Sources of Water and Nutrients 47

Figure 19. Annual phosphorus and nitrogen budget summaries for Silver Lake, Oceana County, Michigan. (Internal load 

of phosphorus and nitrogen was measured only during the summer and assumed to be negligible during the remainder of 

the year. Internal phosphorus loading during the summer was less than 1 percent of the total phosphorous load.)

28.7 percent

2.8 percent

2.2 percent 

46.9 percent

13.0 percent

6.5 percent

56.3 percent

4 percent

2.0 percent

24.9 percent

1.3 percent

4.1 percent

Average annual phosphorus loading

(in percent)

Average annual nitrogen loading 

(in percent)

Annual phosphorus load=1,342 pounds Annual nitrogen load=51,998 pounds

8.8 percent 2.7 percent

Hunter Creek

Tributary at State Park

Tributary at North 

Shore Drive

Groundwater

Lawn runoff

Atmospheric deposition

Internal load

EXPLANATION

largest loading of phosphorus to Silver Lake occurred in the 

spring months, followed by the summer and fall (fig. 14). Of 

the total 1,342 lb of phosphorus that entered Silver Lake annually (mean), almost all of the phosphorus was exported out of 

the lake (1,340 lb), with minimal to no phosphorus deposition 

occurring in the lake-bed sediment, which is consistent with 

the estimated analysis of internal phosphorus loading from 

the sediment cores. Although the majority of phosphorus is 

exported out of the lake with minimal internal loading, the 

residence time (223 days) is long enough for phosphorus to 

contribute to algal blooms and lake eutrophication in Silver 

Lake.

The mean annual input of nitrogen to Silver Lake was 

approximately 51,998 lb. The major source of nitrogen to 

Silver Lake was Hunter Creek (29,277 lb, or 56.3 percent 

of the nitrogen budget). Other major sources of nitrogen to 

Silver Lake include groundwater (12,951 lb, or 24.9 percent 

of the nitrogen budget) and atmospheric deposition (4,586 lb 

or 8.8 percent) (fig. 19). The septic model loading scenarios 

indicate that septic systems account for 0.05 to 3.1 percent 

of the load to Hunter Creek, and 0.23 to 2.8 percent of the 

contribution of nitrogen to groundwater (table 10). The most 

likely septic loading scenario indicates that septic systems 

account for 0.95 percent of the nitrogen load to Hunter Creek 

and 1.1 percent of the contribution of nitrogen to groundwater. 

Other potential sources of nitrogen to groundwater include 

animal manure and nitrogen fertilizers used for agriculture, 

animal pasture, food processing discharge, leaching from 

fertilizers from private lawns and golf courses, and the ongoing decay of organic matter in the soil (Oregon Department of 

Environmental Quality, 2011). The largest nitrogen loading to 

Silver Lake also was observed during the spring, followed by 

the summer and fall months. About twice as much nitrogen 

entered Silver Lake during the spring (19,180 lb) compared to 

the fall months (8,921 lb) (fig. 14). Of the 51,998 lb of nitrogen that entered Silver Lake annually (mean), approximately 

42.2 percent (or 21,928 lb) of nitrogen was deposited in the 

lake bed sediment as simulated by the BATHTUB model. 
48 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Table 10. Summary of septic-load estimates to Silver Lake, Oceana County, Michigan, using the septic model.

[lb/yr; pounds per year]

Total phosphorus

Septic load 

scenario

Phosphorus load 

from septic 

(lb/yr)

Septic percentage of 

average annual Load 

(percent)

Septic contribution 

to groundwater 

(percent)

Septic contribution 

to Hunter Creek 

(percent)

Most likely 396 29.5 47.8 22.3

Low 132 9.8 19.5 2.1

High 650 48.4 73.7 43.6

Total nitrogen

Septic load 

scenario

Nitrogen load 

from septic 

(lb/yr)

Septic percentage of 

average annual Load 

(percent)

Septic contribution 

to groundwater 

(percent)

Septic contribution 

to Hunter Creek 

(percent)

Most likely 445 0.86 1.1 0.95

Low 46 0.09 0.23 0.05

High 1,362 2.62 2.8 3.10

Nutrient Load Modeling

Nutrient concentration data and flow data collected from 

all major potential nutrient pathways were used to estimate the 

trophic status of Silver Lake. The BATHTUB model (Walker, 

1996) was used to simulate the current conditions in the lake 

based on the data collected. In addition, the BATHTUB model 

was used to estimate the effect on the trophic status of Silver 

Lake as a result of changes to various nutrient inputs to the 

lake.

BATHTUB Data Requirements and Limitations

Multiple datasets were compiled and evaluated to determine the appropriate input data for the BATHTUB model: (1) 

an annual dataset consisting of data collected October 1, 2013–

September 30, 2014 (water year 2014), (2) a complete dataset 

consisting of all data collected throughout the study period (2 

years), and (3) a categorization of seasonal and specified storm 

events. For this dataset, the nutrient turnover ratio (Nutrient 

Turnover Ratio = Length of Averaging Period for Mass Balances [years] / Mass Residence Time [years]), which approximates the number of times that the nutrient mass in the lake 

is displaced during the averaging period, was approximately 

or greater than 2.0 (turnover ratio for phosphorus and nitrogen, 1.6 and 2.8, respectively) as recommended in the model 

(Walker, 1996), and therefore was chosen as the final dataset 

for the model. Note that the seasonal datasets (spring, summer, 

and fall) were evaluated and used in the calculation of seasonal 

nutrient loading as described in the Nutrient Budget Summary 

section.

Even though the loading data are summarized for the 

entire study, the BATHTUB model predicts the effects of nutrient loading on an annual basis. The idea of partitioning out the 

storm event datasets in an effort to evaluate these events separately from the annual loading proved to be difficult. The storm 

event datasets were limited and because of insufficient data 

from which to accurately describe the different input sources 

without excessive model calibration, which fell outside the 

recommended calibration criteria (Walker, 1996). Thus, storm 

events were not examined separately but were evaluated as a 

whole with the rest of the nutrient dataset.

The BATHTUB model required specified input data 

including Silver Lake morphometric data, hydrologic data, and 

nutrient concentration data for each source. Silver Lake was 

treated as one segment, and observed nutrient concentrations 

and flow data were entered in the model based on an annual 

mean using 2 years of data for each of the tributaries, the outlet, 

the four groundwater quadrants, and the tile drains. The atmospheric deposition component of the BATHTUB model was 

based on approximately 1 year of data collection. The internal 

nutrient loading was calculated based on two sampling events. 

The model input for lawn runoff was calculated (as described 

in Study Methods and Sampling Sites, Lawn Runoff section of 

this report), and identified in the model as a point source. 
Nutrient Load Modeling 49

Algorithms and Calibration

Prior to running the BATHTUB model, the model algorithms must be defined. The model algorithms selected for this 

study were based on site characteristics, available data, and for 

diagnostic and predictive purposes. The model algorithms are 

described in table 11.

Calibration factors provide means for adjusting model 

predictions to account for site-specific conditions in order to 

match observed conditions. The need for site-specific calibration is indicated when significant differences between observed 

and predicted concentrations are found during initial model runs 

(Walker, 1996). The nutrient model algorithms chosen for this 

study (table 11) have been empirically calibrated and tested for 

predicting lake-mean conditions (Walker, 1996). The BATHTUB 

error analysis calculations indicate that sedimentation rates predicted by these models are generally accurate to within a factor of 

2 for phosphorus and a factor of 3 for nitrogen (Walker, 1996). To 

account for this error, nutrient calibration factors can be adjusted 

within the nominal ranges of 0.5 to 2.0 and 0.33 to 3 for phosphorus and nitrogen, respectively. For this study, calibration coefficients for each water-quality constituent were applied globally 

to the base model prior to running diagnostic and predictive scenarios. The calibration factors are, by model default, set to 1. The 

model calibration factors used to adjust the predicted water-quality 

conditions for this study ranged from 1 to 1.7 and fell within the 

acceptable ranges for each water-quality constituent (total phosphorus, 1.67; total nitrogen 1.09; and chlorophyll a, 1.23).

Table 11. Model algorithms selected when running BATHTUB simulations for Silver Lake, Oceana County, Michigan.

Select model Options Model process Description (excerpt from Walker, 1996)

Conservative 

substance

0 Not computed Conservative substance concentration is user-defined, typically chloride; 

can be used for verification of water budgets or calibration of longitudinal 

dispersion rates.

Phosphorus balance 1 Second order, available 

phosphorus

Performs mass balance calculations on available phosphorus, a weighted 

sum of orthophosphorus and non-orthophosphorus which places a heavier 

emphasis on the orthophosphorus (more biologically available) component.

Nitrogen balance 1 Second order, available 

nitrogen

The nitrogen models are structured similarly, although nitrogen balances are 

much less sensitive to inflow nutrient partitioning than are phosphorus 

balances, probably because inflow nitrogen tends to be less strongly 

associated with suspended sediments.

Chlorophyll a 1 Function of phosphorus, 

nitrogen, light, and 

temperature

Eutrophication response models relate observed or predicted pool nutrient 

levels to measures of algal density and related water quality conditions.

Secchi depth 1 Function of chlorophyll a

and turbidity

Model default, secchi reported as a function of chlorophyll a and turbidity.

Dispersion 1 Fischer numeric For a given segment width, mean depth, and outflow, numeric dispersion is 

proportional to segment length. By selecting segment lengths to keep 

numeric dispersion rates less than the estimated values, the effects of 

numeric dispersion on the calculations can be approximately controlled.

Phosphorus 

calibration

2 Concentrations The empirical models implemented in BATHTUB are generalizations about 

reservoir behavior. When applied to data from a particular reservoir without 

site-specific calibration, observations may differ from predictions by a factor of two or more. Such differences reflect data limitations (measurement 

or estimation errors in the average inflow and outflow concentrations), as 

well as unique features of the particular reservoir. A procedure to calibrate 

the model to match observed reservoir conditions is provided in BATHTUB.

Nitrogen calibration 2 Concentrations Same as above.

Error analysis 1 Model and data The first-order error analysis procedure implemented by BATHTUB can be 

used to estimate the uncertainty in model predictions derived from uncertainty in model inputs and uncertainty inherent in the empirical models.

Availability factors 1 Use for model 1 only Selected as orthophosphorus and organic nitrogen values were specified; 

calculates nutrient balances based upon available nutrient loads.

Mass-balance tables 0 Use observed 

concentrations

Use observed segment concentrations to calculate outflow and storage terms.
50 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Management Scenario Simulation Approach

Several nutrient modification scenarios were modeled to 

illustrate the effect of changing input nutrient concentrations on 

the trophic status of Silver Lake as well as algal bloom occurrence. Only nutrient inputs that could be adjusted by employing 

a nutrient management practice around the lake were tested. 

Thus, only groundwater and surface-water pathways of nutrients to the lake were adjusted. The atmospheric pathway would 

likely not be affected by any management practice employed by 

the community around Silver Lake. Likewise, the groundwater 

on the west side of Silver Lake was not adjusted, because no 

controllable source of nutrients was identified for that side of 

the lake. Thirty separate scenarios were developed to assess 

the effects of nutrient input adjustment on the trophic status of 

the lake. The calibration coefficients determined for the base 

scenario were held constant for all the management scenarios. 

Results from those scenarios are provided in table 12. The 

results of these scenarios provide a basis to assess the amount 

nutrient inputs may need to change to have an effect on the trophic status of Silver Lake. Also, the nutrient input adjustments 

listed in table 12 were modeled to predict the number of days 

that Silver Lake would likely experience algal blooms. Finally, 

three scenarios were modeled to examine the potential effect 

on the trophic status of Silver Lake from converting residences 

around the lake from on-site septic waste disposal to a centralized sewage system. 

BATHTUB Model Simulations

This section describes the BATHTUB model simulations 

that were processed in order to predict future lake responses 

as a result of nutrient reductions or increases and other sitespecific scenarios. 

Nutrient Loading Scenarios 

Nutrient adjustment scenarios of phosphorus and nitrogen 

to Silver Lake were processed using the BATHTUB model. 

Results of the simulations for groundwater, Hunter Creek, as 

well as a combination of sources are presented in table 12. 

Trophic Status

The trophic status of Silver Lake is directly affected by 

changes in nutrient loading; thus, the predicted changes in 

nutrient loading and associated trophic status were examined. 

Carlson’s TSI was calculated to identify changes in trophic 

status using the equations for Secchi depth, phosphorus, and 

chlorophyll a (Carlson, 1977) (also see, Trophic Status and 

Lake Classification section).

The groundwater, Hunter Creek, and combination of 

sources (groundwater + Hunter Creek + lawn runoff) nutrient 

loading scenarios for phosphorus and nitrogen are presented 

in figure 20. Results from the baseline, or base model, are 

highlighted in pink and indicate the calibrated dataset. Based 

on Carlson’s TSI, the baseline calibrated dataset indicates that 

Silver Lake is eutrophic. Thus, all nutrient loading scenarios 

begin with the lake being eutrophic. All scenarios that involve 

an increase in nutrient loading result in an increase in lake 

eutrophication.

Changes in groundwater nutrient loading of phosphorus 

and nitrogen were simulated by adjusting groundwater contributions from the north, east, and south quadrants of Silver Lake 

as well as groundwater contribution from the drain tiles at the 

north end of the lake. A reduction or increase in groundwater 

nutrient loading from the west quadrant of Silver Lake is not 

likely to change from the current conditions. Thus the nutrient 

loading for the west quadrant of Silver Lake stayed at baseline 

conditions for all scenario testing. According to the BATHTUB 

model, if groundwater loading of phosphorus and nitrogen were 

decreased by 75 percent, and all of the other nutrient inputs 

stayed the same, the condition of Silver Lake would most likely 

range from highly mesotrophic to eutrophic (the current condition of Silver Lake). If nutrient loading continued to increase in 

groundwater, for instance as a result of aging septic fields or the 

introduction of more homes on the lake, the lake would continue to remain eutrophic with more frequent algal blooms.

BATHTUB model simulations of changes in nutrient loading of phosphorus and nitrogen in Hunter Creek are presented 

in figure 20. If nutrient loading from Hunter Creek decreased 

by 50 percent or 75 percent, and all of the other nutrient inputs 

stayed the same as the baseline dataset, Silver Lake would 

remain eutrophic based on TSI calculations using phosphorus 

and chlorophyll a. Only under the TSI scenario based on Secchi 

depth would the trophic status of the lake develop into highly 

mesotrophic. Therefore, the reduction of nutrients from Hunter 

Creek alone may not be enough to effectively improve the 

trophic status and related water quality issues in Silver Lake as 

simulated by the BATHTUB model.

Table 12. Nutrient concentration adjustment scenarios 

simulated using the BATHTUB model.

Adjusted source, in percent

Groundwater1 Hunter Creek

Combination of 

groundwater1

, 

Hunter Creek, and 

lawn runoff

Phosphorus 

and nitrogen 

concentration 

adjustment

−75 −75 −75

−50 −50 −50

−25 −25 −25

25 25 25

50 50 50

75 75 75

100 100 100

200 200 200

300 300 300

400 400 400

1

 Groundwater inputs were only adjusted for north, east, and south quadrants and the drain tile component in the north quadrant.
Nutrient Load Modeling 51

Figure 20. Summary of nutrient loading scenarios using the BATHTUB model for Silver Lake, Oceana County, Michigan. (Based 

on Carlson’s Trophic State Index (TSI) values, less than 40 represents oligotrophic conditions, 40–50 mesotrophic, and greater 

than 50 represents eutrophic conditions.)

40

50

60

70

40

50

60

70

−75 −50 −25 0 25 50 75 100 200 300 400

−75 −50 −25 0 25 50 75 100 200 300 400

−75 −50 −25 0 25 50 75 100 200 300 400

40

50

60

70

Base

Scenarios

Combination

Groundwater

Hunter Creek

EXPLANATION

TSI Trophic state index

Eutrophic

Eutrophic

Eutrophic

Mesotrophic

Mesotrophic

Mesotrophic

Oligotrophic

Oligotrophic

Oligotrophic

Carlson TSI−Secchi depth Carlson TSI−phosphorus

Percent Nutrient Adjustment

Carlson TSI−chlorophyll a

Concentration adjustment scenario

Figure 21. Summary of nutrient loading scenarios using the BATHTUB model for Silver Lake, Oceana County, Michigan. (Based on 

Carlson’s TSI values, less than 40 represents oligotrophic conditions, 40–50 mesotrophic, and greater than 50 represents eutrophic 

conditions.)
52 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

By reducing the input of multiple manageable nutrient 

sources, the BATHTUB model indicates that there is a better 

likelihood of positively impacting the trophic status of Silver 

Lake and reducing algal bloom frequency. Multiple nutrient 

sources, including groundwater contribution from the north, 

east, and south quadrants, Hunter Creek, and lawn runoff were 

evaluated as these sources offer the best opportunity for future 

nutrient management. Both phosphorus and nitrogen scenarios 

were run for each of these three sources, collectively referred 

to in figure 20 as Combination. Increases in nutrients continue to push the lake into a higher eutrophic state; however, a 

reduction of nutrients indicates a move towards mesotrophy. 

Using the TSI equation for Secchi depth, a combination of 

source reduction of only 25 percent would be sufficient to 

classify Silver Lake as mesotrophic. A reduction of phosphorus and nitrogen by 75 percent would classify Silver Lake as 

mesotrophic using all three of Carlson’s TSI equations which 

is indicative of improved water quality, water clarity, and 

reduced algal bloom frequency. Similarly, an annual phosphorus loading reduction from the current condition (1,342 lb) 

to 549 lb and a change in the annual nitrogen loading to 

22,580 lb would be necessary to classify Silver Lake as mesotrophic based on all three TSI equations.

Changes in Percentage of Days with Algal Blooms

Algal blooms can negatively impact the recreational use 

of a lake and also have an impact on lake aesthetics. Some 

algal blooms can also be potentially hazardous to human and 

pet health. Silver Lake has a history of algal bloom occurrence 

during the summer and fall months, and in October 2012, an 

algal bloom was reported in Hunter Creek. Chlorophyll a is 

used to measure algal productivity in the water and the higher 

the chlorophyll concentrations, the more severe the algal 

bloom intensities. Carlson describes that the range of chlorophyll a concentrations indicating eutrophic conditions range 

from 7.3 to 20 μg/L (Carlson, 1977), thus for this study it was 

determined that chlorophyll a concentrations greater than 

10 μg/L may indicate that an algal bloom is present. Simulations were run using the BATHTUB model to evaluate the 

number of days Silver Lake could experience algal blooms 

as a result of an increase and decrease in phosphorus and 

nitrogen loading from groundwater, Hunter Creek, as well as a 

combination of sources (table 13).

During the course of this study, measured chlorophyll a

concentrations ranged from 0.7 to 39 μg/L and averaged 

approximately 15 μg/L. According to the BATHTUB model, 

Silver Lake currently experiences about 231 days of the year 

where chlorophyll a concentrations are greater than 10 μg/L 

and about 80 days of the year where chlorophyll a concentrations are greater than 20 μg/L (conditions may be appropriate 

for algal blooms). The model estimates approximately 28 days 

where chlorophyll a concentrations exceed 30 μg/L (severe 

algal blooms are likely), and about 11 days where chlorophyll a concentrations exceed 40 μg/L (severe algal blooms) 

(table 13). 

However, if phosphorus and nitrogen loading to Silver 

Lake decreases, the number of days that algal blooms may 

be present also would decrease. If the phosphorus and nitrogen loading from Hunter Creek is decreased (and all other 

sources are not altered), Silver Lake will continue to experience algal blooms but less frequently that what is experienced 

currently. On the other hand, if nutrient loads increase from 

Hunter Creek, Silver Lake will experience more frequent and 

more intense algal blooms (table 13). If the nutrient loading 

from groundwater alone was decreased, the overall impact on 

Silver Lake would not be as effective at reducing algal bloom 

frequency as the reduction of nutrient loading from Hunter 

Creek. The same scenario applies if phosphorus and nitrogen 

loading from groundwater were increased. 

The third scenario includes an increase and decrease in 

phosphorus and nitrogen loading from nutrient sources that 

are the most likely to be managed and include groundwater, 

Hunter Creek, and lawn runoff. According to the BATHTUB 

model, a 50-percent reduction of phosphorus and nitrogen 

from all three of these sources would result in a considerable 

decrease in algal bloom frequency (from 231 to 132 days) 

and severity, and a 75-percent reduction would greatly 

reduce algal bloom occurrence on Silver Lake (from 231 

to 57 days) with severe algal blooms essentially eliminated 

(chlorophyll a >30 μg/L, from 231 days to 1 day) (table 13).

Reduced Septic Input to Silver Lake

By using the range of SR values and the range of potential Es values, load scenarios from septic systems to Silver 

Lake and to nearby tributaries to Silver Lake were estimated. 

A summary of the medium (most likely), low, and high estimates for septic loads to Silver Lake are presented in table 10. 

The observed load of phosphorus and nitrogen from both 

groundwater and streams to Silver Lake is likely conservative. 

As indicated in the Study Methods and Sampling Sites section, two of the monitoring wells (east and west) were placed 

in zones of low expected septic contamination, while two 

wells (north and south) were placed in areas of high expected 

septic contamination. Septic loading models from the south, 

east, and north wells’ groundwater contribution area were all 

calibrated to observed data by adjusting the SR value until the 

model predicted an observed background value for each well. 

By placing wells in zones of high and low expected loads, our 

monitoring program likely provides a robust description of the 

median septic contributions to shallow groundwater adjacent 

to Silver Lake.

Scenarios were also run using the BATHTUB model to 

predict the effect of eliminated septic load to Silver Lake as a 

result of sewer installation. These scenarios were based on the 

low, medium (most likely), and high estimates for septic loads 

from both groundwater and surface water sources, including 

Hunter Creek, as summarized in table 10. Figure 21 illustrates 

the modeled impact of the conversion of septic systems to 

sewer on the trophic status of Silver Lake using the BATHTUB model.
Nutrient Load Modeling 53

Table 13. Simulated algal bloom frequency for Silver Lake, 

Oceana County, Michigan, in response to changes in phosphorus and 

nitrogen loading scenarios.

[μg/L, micrograms per liter; >, greater than]

Change in 

nutrient 

concentration,

in percent 

Number of days per year with algal blooms 

by load adjustment scenario

Groundwater Hunter Creek

Combination of 

groundwater, 

Hunter Creek, 

and lawn runoff

Chlorophyll a, >10 μg/L

−75 181 152 57.3

−50 200 187 132

−25 217 212 190

0 231 231 231

25 243 246 260

50 254 259 280

75 264 269 295

100 272 278 307

Chlorophyll a, >20 μg/L

−75 47.1 33.6 6.2

−50 58.4 50.4 25.6

−25 69.4 65.7 52.2

0 79.6 79.6 79.6

25 89.8 92.3 105

50 99.6 104 128

75 109 115 147

100 118 125 164

Chlorophyll a, >30 μg/L

−75 13.5 8.8 1.1

−50 17.9 14.9 6.2

−25 23.0 21.2 15.7

0 27.7 27.7 27.7

25 32.9 34.3 41.2

50 38.0 40.5 54.4

75 43.1 46.7 67.5

100 48.2 52.9 79.6

Chlorophyll a, >40 μg/L

−75 4.4 2.6 0.4

−50 6.2 5.1 1.8

−25 8.4 7.7 5.5

0 10.6 10.6 10.6

25 13.1 13.5 17.2

50 15.7 16.8 24.1

75 18.3 20.1 31.8

100 20.8 23.4 39.1
54 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Figure 21. Simulated effect of conversion from onsite septic-waste treatment (within 200 feet of shoreline) 

to central sewer system using BATHTUB. (Based on Carlson’s Trophic State Index (TSI) values, less than 40 

represents oligotrophic conditions, 40–50 mesotrophic, and greater than 50 represents eutrophic conditions.)

Base 

Scenario

Sewer high reduction

Sewer low reduction

Sewer medium reduction

EXPLANATION

TSI Trophic state index

40

50

60

70

40

50

60

70

High Medium Low Base

40

50

60

70

High Medium Low Base

High Medium Low Base

Carlson TSI−phosphorus Carlson TSI−chlorophyll a

Eutrophic

Mesotrophic

Oligotrophic

Eutrophic

Mesotrophic

Oligotrophic

Eutrophic

Mesotrophic

Carlson TSI−Secchi depth

Oligotrophic

Simple septic models have demonstrated that a large 

proportion of the phosphorus load in groundwater and Hunter 

Creek is likely contributed by septic systems. The septic 

model provides a possible range of septic loading based on 

the uncertainty in the load going to septic tanks and the variability of soil retention (based on septic system function). The 

BATHTUB model was used to evaluate the elimination of 

septic as a source to both groundwater and all tributaries based 

on the low, medium (most likely), and high loading scenarios. 

The BATHTUB model predicted reductions in the total phosphorus load to the lake of 9.8–48.4 percent, and simultaneous 

reductions in nitrogen of 0.2–2.8 percent (fig. 21). The 

septic-removal simulation using the likely septic-load scenario 

indicates that a complete removal of septic systems would 

improve the lake to an average mesotrophic condition. The 

high-septic-load scenario shows a greater improvement in 

trophic status, while the low-septic-load scenario indicates an 

improvement in lake trophic status that ranges from mesotrophic to eutrophic (fig. 21). Therefore, based on the previous 

BATHTUB scenarios, it is likely that the elimination of septic 

influence to the lake would improve the average trophic status 

of Silver Lake to mesotrophic. 
Summary and Conclusions 55

Based on the BATHTUB model, the number of days 

that Silver Lake would experience algal blooms (under the 

medium, or most likely, septic scenario), would decrease from 

the current estimated 231 days (chlorophyll a >10 μg/L) to 

184 days, or from 80 days (chlorophyll a >20 μg/L) to 49 

days. According to the high septic efficiency model, algal 

bloom occurrence (chlorophyll a >10 μg/L) would decrease 

from the current status to 132 days, and based on the low septic efficiency model, algal bloom occurrence would decrease 

to 219 days. Using the nutrient source loading information 

provided in table 10, the BATHTUB model indicates that the 

conversion of onsite septic treatment to sewers would result 

in an overall improvement in lake trophic status and greatly 

reduce the frequency of algal blooms and algal bloom intensity on Silver Lake.

Summary and Conclusions

Silver Lake is a 672-acre inland lake located in Oceana 

County, Michigan, and is a major tourist destination due to its 

proximity to Lake Michigan and the surrounding outdoor recreational opportunities. In recent years, Silver Lake exhibited 

patterns of high phosphorus concentrations, elevated chlorophyll a concentrations, and nuisance algal blooms. Previous 

studies conducted indicated that Silver Lake was experiencing 

advanced eutrophication and more frequent algal blooms in 

recent years. As a result, the Silver Lake Improvement Board 

(SLIB) concluded that a detailed interpretive study was necessary. The U.S. Geological Survey (USGS), in cooperation 

with the Silver Lake Improvement Board and in collaboration 

with the Annis Water Resources Institute (AWRI) of Grand 

Valley State University, designed a study to assess the hydrologic and nutrient inputs to Silver Lake, as well as identify 

the conditions that affect the nutrient chemistry and production of algal blooms in the lake. This information can inform 

water-resource managers in developing various management 

strategies to prevent or reduce the occurrence of future algal 

blooms.

USGS and AWRI scientists collected data from November 2012 to December 2014 to provide information for future 

management decisions for Silver Lake. Silver Lake can be 

classified as a polymictic (well mixed) lake with a residence 

time of approximately 223 days.

On an annual basis, the primary contributor of water 

to Silver Lake is Hunter Creek, at 52.2 percent. Other water 

sources include groundwater (30.6 percent), direct precipitation (10.3 percent), the tributary at the State Park (4.1 percent), 

and the tributary at North Shore Drive (2.8 percent). During the spring season, Hunter Creek is the dominant source 

(62.6 percent) of water to the lake; however, in the summer the 

primary contributor of water to the lake becomes groundwater 

(43.7 percent). In the fall, both Hunter Creek and groundwater 

contribute similar proportions (42.8 and 36.5 percent, respectively) of the overall water budget and during winter Hunter 

Creek again becomes the dominant source (54.9 percent) of 

water to the lake. The primary component of water loss from 

Silver Lake is the outflow of water from Silver Lake via Silver 

Creek, which accounts for 91.9 percent of all water leaving Silver Lake. The remaining 8.1 percent is lost through 

evaporation.

Study results indicated that Silver Lake is colimited by 

both phosphorus and nitrogen, based on both the bioassay 

results and the nitrogen:phosphorus ratios in the lake. As a 

result, both phosphorus and nitrogen are deemed critical nutrients in the development of algal blooms and lake eutrophication. Although this colimitation indicates that both nitrogen 

and phosphorus are needed to produce excessive algal growth 

during the summer months, it does not indicate that both nutrients must be reduced to control algal blooms. Study results 

indicate that the largest controllable nutrient to Silver Lake is 

phosphorus.

Based on the average total phosphorus and total nitrogen 

concentrations in Silver Lake and the U.S. Environmental 

Protection Agency (EPA) nutrient criteria recommendation for 

phosphorus and nitrogen, the lake is classified as eutrophic. 

Silver Lake also was classified as eutrophic using Carlson’s 

TSI approximately 63 percent of the time (five sampling 

events), mesotrophic to slightly eutrophic approximately 

25 percent of the time (two sampling events), and oligotrophic to slightly mesotrophic about 13 percent of the time (one 

sampling event). 

The likely contribution of phosphorus and nitrogen from 

septic systems was computed by using a model for septic 

transport, because septic systems sited on the lakeshore could 

be important sources of the phosphorus and nitrogen observed 

in shallow groundwater. This septic model considers the 

number of residences within 200 feet (ft) of the shore of the 

lake or surface waters that drain to the lake. Septic systems 

further than 200 ft from the lake still have an expected impact, 

but because of the longer travel distance and larger area of diffusion from these systems, the uncertainty in the estimation of 

their loads is too large to produce a reliable number. Further, 

the model included only personal residences in the septic 

model estimates, not commercial facilities such as campgrounds and hotels within 200 ft of the shoreline and Hunter 

Creek, because the septic model was not designed to accurately represent commercial facilities. If commercial facilities were included, the estimated impact (nutrient loading) of 

septic systems to the lake and Hunter Creek would be higher. 

It should be noted, however, that commercial and residential 

septic sources further than 200 ft from the lake are still captured in the nutrient budget from the groundwater well monitoring that includes nearby (within 200 ft) and diffuse (further 

than 200 ft) septic sources. As a result of these constraints, the 

amounts of phosphorus and nitrogen being contributed directly 

to the lake or tributaries to the lake, as computed using this 

model, are likely a conservative estimation (an underestimation) of the actual phosphorus and nitrogen loads contributed 

by all of the septic systems around the lake. The septic model 

estimated that septic systems likely contributed 47.8 percent 
56 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

(300.9 pounds [lbs]) of phosphorus and 1.1 percent (136.1 lb) 

of nitrogen to groundwater annually. Study findings indicate 

that much of the phosphorus contribution to Silver Lake via 

groundwater is orthophosphate, a form of phosphorus that can 

readily stimulate algae growth. 

The mean annual input of phosphorus to Silver Lake was 

approximately 1,342 lb. The major pathways of phosphorus 

to Silver Lake were groundwater (46.9 percent), followed by 

Hunter Creek (28.7 percent). However, 19.5 to 73.7 percent of 

phosphorus in groundwater and 2.1 to 43.6 percent of phosphorus in Hunter Creek is likely derived from septic loading, 

indicating that septic loading is the largest potentially controllable nutrient load modeled in this study. The largest loading 

of phosphorus to Silver Lake occurred in the spring months, 

followed by the summer and fall. Of the total 1,342 lb of 

phosphorus that entered Silver Lake annually (mean), almost 

all of the phosphorus was exported out of the lake (1,340 lb), 

with minimal phosphorus deposition occurring in the lakebed 

sediment. 

The mean annual input of nitrogen to Silver Lake was 

approximately 51,998 lb. The major sources of nitrogen to 

Silver Lake were Hunter Creek (56.3 percent), groundwater 

(24.9 percent), and atmospheric deposition (8.8 percent). The 

septic model loading scenarios indicate that septic systems 

account for 0.05 to 3.1 percent of the load to Hunter Creek, 

and 0.23 to 2.8 percent of the contribution of nitrogen to 

groundwater. The largest nitrogen loading to Silver Lake was 

observed during the spring, followed by the summer and fall 

months. About twice as much nitrogen entered Silver Lake 

during the spring (19,180 lb) as compared to the fall months 

(8,921 lb). Of the 51,998 lb of nitrogen that entered Silver 

Lake annually (mean), approximately 42.2 percent (21,928 lb) 

of nitrogen was deposited in the lakebed sediment according 

to the BATHTUB model.

The data collected as part of this study were used to 

model future nutrient loading and eutrophication scenarios 

using the BATHTUB model developed by the U.S. Army 

Corps of Engineers. Both phosphorus and nitrogen constituents were altered in the various nutrient-loading scenarios. 

Based on Carlson’s trophic state index (TSI), the baseline 

calibrated dataset for the model indicated that Silver Lake was 

eutrophic, and thus all of the nutrient loading scenarios began 

with the lake being eutrophic. BATHTUB model simulations indicate that if phosphorus and nitrogen loading from 

groundwater were decreased by 75 percent, and all of the other 

nutrient inputs stayed the same, the condition of Silver Lake 

would most likely range from highly mesotrophic to eutrophic 

(the current [2014] condition of Silver Lake). This result also 

applies to the same scenario of nutrient loading from Hunter 

Creek. If nutrient loading continued to increase in groundwater or Hunter Creek, the lake would continue to remain eutrophic with more frequent algal blooms. Therefore, BATHTUB 

model simulations indicate that the reduction of nutrients 

from either Hunter Creek or groundwater alone may not be 

enough to effectively improve the trophic status and related 

water-quality issues in Silver Lake. However, if phosphorus 

and nitrogen from a combination of sources (groundwater, 

Hunter Creek, and lawn runoff) were reduced by 75 percent, 

then Silver Lake could be classified as mesotrophic based on 

Carlson’s three TSI equations. 

The reduction of phosphorus and nitrogen from either 

of the small tributaries was not significant enough to modify 

the trophic status of the lake. The model output indicated that 

Silver Lake would remain eutrophic, even with a 75-percent 

reduction of phosphorus and nitrogen from these sources. 

If the phosphorus and nitrogen loading from Hunter 

Creek is decreased (and all other sources are not altered), 

Silver Lake will continue to experience algal blooms but less 

frequently than what is currently experienced. On the other 

hand, if nutrient loads increase from Hunter Creek, Silver 

Lake will experience more frequent and more intense algal 

blooms. If the nutrient loading from groundwater alone were 

decreased, the overall impact on Silver Lake would not be as 

effective at reducing algal bloom frequency as the reduction 

of nutrient loading from Hunter Creek alone. A third scenario 

includes an increase and decrease in phosphorus and nitrogen 

loading from nutrient sources that are the most likely to be 

managed and include groundwater (sewer installation), Hunter 

Creek (sewer installation and riparian best management 

practices [BMPs]), and lawn runoff (fertilizer BMPs). BATHTUB model simulations indicate that a 50-percent reduction of 

phosphorus and nitrogen from these sources would result in a 

considerable decrease in algal bloom frequency and severity, 

and a 75-percent reduction would greatly reduce algal bloom 

occurrence on Silver Lake.

A scenario also was conducted using the BATHTUB 

model to simulate the conversion of septic to sewer and 

included a high, low, and medium (most likely) scenario for 

both phosphorus and nitrogen loading to Silver Lake. The 

BATHTUB model simulation indicated that the conversion 

of onsite septic treatment to sewers would result in an overall 

change in lake trophic status from eutrophic to mesotrophic, 

and greatly reduce the frequency of algal blooms and algal 

bloom intensity on Silver Lake. According to the BATHTUB model, the installation of a sewer system may eliminate 

the largest phosphorus load modeled in the study and thus 

improve the trophic status of Silver Lake.

It is important to note that the reduction of the productivity in lakes by decreasing nutrient loading can be very 

effective; however, the rate at which the productivity of the 

lake will revert toward conditions existing prior to increased 

loading is variable. In other words, it took time for Silver Lake 

to become eutrophic, and it will take time for the lake to begin 

to recover after phosphorus and nitrogen loading from the 

lake’s major nutrient sources is addressed. The local residents 

and businesses, as well as local government agencies, will 

need to determine the best management strategy or strategies 

to address the major nutrient contributions to Silver Lake.
References Cited 57

References Cited 

Boström, B., Jansson, M., and Forsberg, C., 1982, Phosphorus 

release from lake sediments: Archiv fur HydrobiologieBeiheft Ergebnisse der Limnologie, v. 18, p. 5–59.

Briggs, M.A., Voytek, E.B., Day-Lewis, F.D., Rosenberry, 

D.O., and Lane, J.W., 2013, Understanding water column 

and streambed thermal refugia for endangered mussels in 

the Delaware River: Environmental Science and Technology, v. 47, no. 20, p. 11423–11431.

Canter, L.W., and Knox, R.C, 1985, Septic tank system effects 

on ground water quality: Lewis Publishers Inc., 360 p. 

Carlson, R.E., 1977, A trophic state index for lakes: Limnology and Oceanography, v. 22, no. 2, p. 361–369.

Clescerl, L.S., Greenberg, A.E., and Eaton, A.D., eds. 1999, 

Standard methods for the examination of water and wastewater (20th ed.): Washington, D.C., American Public Health 

Association, 1,325 p.

Conley, D.J., Paerl, H.W., Howarth, R.W., Boesch, D.F., Seitzinger, S.P., Havens, K.E., Lancelot, Christiane, and Likens, 

G.E., 2009, Controlling eutrophication—Nitrogen and phosphorus: Science, v. 323, no. 5917, p. 1014–1015.

Curtis, P.N.; Rife, Troy; Lambert, Scott; and Pellow, Josh, 

2012, Silver Lake State Park—General management 

plan: Lansing, Mich., Michigan Department of Natural 

Resources, 91 p.

DeJong, W.S., 2015, Silver Lake area demographic analysis—

Golden Township, Oceana County, Michigan: Mears, Mich., 

Friends of Silver Lake, 77 p.

eBird, 2015, eBird—An online database of bird distribution 

and abundance: Ithaca, New York, accessed February 12, 

2015, at http://www.ebird.org. 

Fetter, C.W., 1994, Applied Hydrogeology (3d ed.): Englewood Cliffs, N.J., Prentice-Hall, 691 p.

Fisher, M.M., Brenner, Mark, and Reddy, K.R., 1992, A 

simple, inexpensive piston corer for collecting undisturbed 

sediment/water interface profiles: Journal of Paleolimnology, v. 7, p. 157–161.

Fishman, M.J., ed., 1993, Methods of analysis by the U.S. 

Geological Survey National Water Quality Laboratory—

Determination of inorganic and organic constituents in 

water and fluvial sediments: U.S. Geological Survey OpenFile Report 1993–125, 217 p. 

Fry, J.A., Xian, G., Jin, S., Dewitz, J.A., Homer, C.G., Yang, 

L., Barnes, C.A., Herold, N.D., and Wickham, J.D., 2011, 

Completion of the 2006 National Land Cover Database for 

the conterminous United States: Photogrammetric Engineering and Remote Sensing, v. 77, no. 9, p. 858–864.

Fuller, L.M., and Taricska, C.K., 2012, Water-quality characteristics of Michigan’s inland lakes, 2001–10: U.S. Geological Survey Scientific Investigations Report 2011–5233, 53 

p., plus CD–ROM.

Fusilier, W., and Fusilier, B., 2001, Silver Lake—A water 

quality and watershed management study: Water Quality 

Investigators, Dexter, Mich., 267 p.

Garn, H.S., 2002, Effects of lawn fertilizer on nutrient concentration in runoff from lakeshore lawns, Lauderdale Lakes, 

Wisconsin: U.S. Geological Survey Water-Resources Investigation Report 2002–4130, 6 p.

Garn H.S., Robertson, D.M., Rose, W.J., Goddard, G.L., 

and Horwatich, J.A., 2006, Water quality, hydrology, and 

response to changes in phosphorus loading of Nagawicka 

Lake, a calcareous lake in Waukesha County, Wisconsin: 

U.S. Geological Survey Scientific Investigations Report 

2006–5273, 41 p.

Garn, H.S., Seidel, T.L., and Rose, W.J., 1996, Hydrology and 

water quality of Lauderdale Lakes, Walworth County, Wisconsin, 1993–94: U.S. Geological Survey Water-Resources 

Investigations Report 96–4235, 29 p.

Greenberg, A.E., 1992, Standard methods for the examination of water and wastewater (18th ed.): Washington, D.C., 

American Public Health Association, 1,100 p.

Groves, Tony; Tyning, Pam; and Meyers, Tory, 2013, Lake 

mapping—An overview of methods and potential uses: 

Water Resources Group, Progressive AE, 4 p., accessed 

August 14, 2015, at http://michiganlakeinfo.com/Lake_

Mapping.html. 

Guo, Danlu, and Westra, Seth, 2015, Evapotranspiration—

Estimating potential and actual evapotranspiration with 

multiple models (R package ver. 1.5), accessed August 14, 

2015, at https://cran.r-project.org/web/packages/Evapotranspiration/index.html.

Guy, H.P., 1969, Laboratory theory and methods for sediment 

analysis: U.S. Geological Survey Techniques of WaterResources Investigations, book 5, chap. C1, 58 p.

Harpole, W.S., Ngai, J.T., Cleland, E.E., Seabloom, E.W., 

Borer, E.T., Bracken, M.E.S., Elser, J.J., Gruner, D.S., Hilliebrand, Helmut, Shurin, J.B., and Smith J.E., 2011, Nutrient 

co-limitation of primary producer communities: Ecology 

Letters, v. 14, no. 9, p. 852–862.
58 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Helsel, D.R. and Hirsch, R.M., 2002, Statistical methods 

in water resources: U.S. Geological Survey Techniques 

of Water-Resources Investigations, book 4, chapter A3, 

522 pages.

Hunt, R.J., Greb, S.R., and Graczyk, D.J., 2006, Evaluating 

the effects of nearshore development on Wisconsin Lakes: 

U.S. Geological Survey Fact Sheet 2006–3033, 4 p.

Jensen, H.S., Kristensen, P., Jeppesen, E., and Skytthe, A., 

1992, Iron:phosphorus ratio in surface sediment as an indicator of phosphate release from aerobic sediments in shallow lakes: Hydrobiologia, v. 235–236, no. 1, p. 731–743. 

Jensen, M.E., and Haise, H.R., 1963, Estimating evapotranspiration from solar radiation: Proceedings of the American 

Society of Civil Engineers, Journal of the Irrigation and 

Drainage Division, v. 89, p. 15–41.

Lewis, W.M., Jr., Grant, M.C., and Hamilton, S.K., 1985, 

Evidence that filterable phosphorus is a significant atmospheric link in the phosphorus cycle: Oikos, v. 45, no. 3, 

p. 428–432.

Maloney, T.J., ed., 2005, Quality management system, U.S. 

Geological Survey National Water Quality Laboratory: U.S. 

Geological Survey Open-File Report 2005–1263, version 

1.3, 9 November 2005, chapters and appendixes variously 

paged.

Manny, B.A., Johnson, W.C., and Wetzel, R.G., 1994, Nutrient 

additions by waterfowl to lakes and reservoirs—Predicting 

their effects on productivity and water quality: Hydrobiologia, v. 279–280, p. 121–132.

Michigan Department of Environmental Quality, 2013, Nutrient criteria—Nutrient framework to reduce phosphorus 

and nitrogen pollution, accessed June 2013 at http://www.

michigan.gov/documents/deq/wrd-nutrient-frameworkcriteria_429128_7.pdf.

Michigan Department of Environmental Quality-Surface 

Water Quality Division, 2001, Total Maximum Daily 

Load (TMDL) for total phosphorus in Lake Allegan, 9 p., 

accessed August 17, 2015, at http://www.michigan.gov/

documents/deq/wrd-swas-tmdl-allegan_450908_7.pdf.

Michigan Department of Natural Resources, 1982, Michigan 

Inland Lake Project—Identification, survey, and classification: Clean Lakes Agreement No. S 005511–01, 20 p., plus 

apps.

Michigan State University, 2015, Enviro-weather—Weatherbased pest, natural resources, and production management 

tools, accessed March 2015 at http://www.enviro-weather.

msu.edu/homeMap.php.

Moore, P.A., Reddy, K.R., and Fisher, M.M., 1998. Phosphorus flux between sediment and overlying water in Lake 

Okeechobee, Florida: spatial and temporal variations. 

Journal of Environmental Quality, 27(6), 1428–1439. 

National Oceanic and Atmospheric Administration, 2013, 

Climate data online, accessed August 17, 2015, at 

http://www.ncdc.noaa.gov/cdo-web/.

Oregon Department of Environmental Quality, 2011, Estimation of nitrogen sources, nitrogen applied, and nitrogen 

leached to groundwater in the lower Umatilla Basin groundwater management area, 21 p., accessed August 17, 2015, at 

http://www.deq.state.or.us/er/reports/11er001.pdf.

Progressive AE, 2011, Silver Lake 2011 water quality 

monitoring report: Grand Rapids, Mich., Project no. 

50740101, 34 p., accessed August 17, 2015, at 

http://www.friendsofsilverlake.org/reports-maps/.

R Development Core Team, 2012, R—A language and 

environment for statistical computing: Vienna, Austria, R 

Foundation for Statistical Computing, accessed August 17, 

2015, at http://www.R-project.org.

Reckhow, K.H., Beaulac, M.N., and Simpson, J.T., 1980, 

Modeling phosphorus loading and lake response under 

uncertainty—A manual and compilation of export coefficients: Washington, D.C., U.S. Environmental Protection 

Agency, EPA–440/5–80–011, 214 p.

Robertson, D.M., Rose, W.J., and Garn, H.S., 2003, Water 

quality and the effects of changes in phosphorus loading, 

Red Cedar Lakes, Barron and Washburn Counties, Wisconsin: U.S. Geological Survey Water-Resources Investigations 

Report 2003–4238, 42 p.

Robertson, D.M., Rose, W.J., and Juckem, P.F., 2009, Water 

quality and hydrology of Whitefish (Bardon) Lake, Douglas 

County, Wisconsin, with special emphasis on responses 

of an oligotrophic seepage lake to changes in phosphorus 

loading and water level: U.S. Geological Survey Scientific 

Investigations Report 2009–5089, 41 p.

Robertson, W.D., Schiff, S.L., and Ptacek, C.J., 1998, Review 

of phosphate mobility and persistence in 10 septic system 

plumes: Ground Water, v. 36, no. 6, p. 1000–1010.

Rose, J.B., and Shibata, T., 2006, A pilot study—Silver Lake 

water quality: Lansing, Mich., Michigan State University, 

Department of Fisheries & Wildlife, Microbiological Water 

& Health Laboratory, 13 p.

Rosenberry, D.O., and LaBaugh, J.W., 2008, Field techniques 

for estimating water fluxes between surface water and 

ground water: U.S. Geological Survey Techniques and 

Methods 4–D2, 128 p.
References Cited 59

Rosenberry, D.O., Stannard, D.I., Winter, T.C., and Martinez, 

M.L., 2004, Comparison of 13 equations for determining 

evapotranspiration from a prairie wetland, Cottonwood 

Lake area, North Dakota, USA: Wetlands, v. 24, no. 3, 

p. 483–497.

Rydin, Emil, Huser, B.J., and Welch, E.B., 2000, Amount 

of phosphorus inactivated by alum treatments in Washington lakes: Limnology and Oceanography, v. 45, no. 1, 

p. 226–230.

Shreve, E.A., and Downs, A.C., 2005, Quality-assurance plan 

for the analysis of fluvial sediment by the U.S. Geological 

Survey Kentucky Water Science Center Sediment Laboratory: U.S. Geological Survey Open-File Report 2005–1230, 

28 p.

Steinman, A.D., Chu, Xuefeng, and Ogdahl, M.E., 2009, 

Spatial and temporal variability of internal and external 

phosphorus loads in Mona Lake, Michigan: Aquatic 

Ecology, v. 43, no. 1, p. 1–18.

Steinman, A.D., and Ogdahl, M.E., 2008, Ecological effects 

after an alum treatment in Spring Lake, Michigan: Journal 

of Environmental Quality, v. 37, no. 1, p. 22–29. 

Steinman, A.D., and Ogdahl, M.E., 2012, Macroinvertebrate 

response and internal phosphorus loading in a Michigan 

Lake after alum treatment: Journal of Environmental 

Quality, v. 41, no. 5, p. 1540–1548. 

Steinman, A.D., and Ogdahl, M.E., 2015, TMDL reevaluation—Reconciling internal phosphorus load reductions in 

a eutrophic lake: Lake and Reservoir Management, v. 31, 

no. 2, p. 115–126. 

Steinman, A.D., Ogdahl, M.E., and Ruetz, C.R., III, 2011, 

An environmental assessment of a small, shallow lake 

(Little Black Lake, MI) threatened by urbanization: Environmental Monitoring and Assessment, v. 173, p. 193–209.

Steinman, A.D.; Rediske, Rick; Denning, Rod; Nemeth, Lori; 

Chu, Xuefeng; Uzarski, Don; Biddanda, Bopi; and Luttenton, Mark, 2006, An environmental assessment of an 

impacted, urbanized watershed—The Mona Lake Watershed, Michigan: Archiv für Hydrobiologie, v. 166, no. 1, 

p. 117–144.

Steinman, A.D., Rediske, Rick, and Reddy, K.R., 2004, The 

reduction of internal phosphorus loading using alum in 

Spring Lake, Michigan: Journal of Environmental Quality, 

v. 33, no. 6, p. 2040–2048.

Stynes, D.J., 2002, Michigan statewide tourism spending and 

economic impact estimates, 1998–2000, accessed August 

21, 2015, at http://www.tourismcenter.msu.edu/VirtualTourism/MichiganSatExec.pdf. 

Turnipseed, D.P., and Sauer, V.B, 2010, Discharge measurements at gaging stations: U.S. Geological Survey Techniques and Methods, book 3, chap. A8, 87 p. (Also available 

at http://pubs.usgs.gov/tm/tm3-a8/.)

U.S. Census Bureau, 2011, Age and sex composition—2010, 

accessed August 17, 2015, at http://www.census.gov/prod/

cen2010/briefs/c2010br-03.pdf.

U.S. Department of Agriculture National Agriculture Imagery Program, 2014, accessed as ArcGIS Image Service at http://gis.apfo.usda.gov/arcgis/services/NAIP/

Michigan_2014_1m/ImageServer.

U.S. Department of Agriculture-Natural Resources Conservation Service, 2015, Web soil survey for Oceana County, 

Michigan, accessed February 26, 2015, at http://websoilsurvey.sc.egov.usda.gov. 

U.S. Environmental Protection Agency, 1983, Methods for 

chemical analysis of water and wastes: Washington, D.C., 

Office of Research and Development, EPA/600/4–79/020, 

491 p.

U.S. Environmental Protection Agency, 2000a, Ambient water 

quality criteria recommendations—Information supporting the development of State and Tribal nutrient criteria—

Lakes and reservoirs in nutrient ecoregion VII: Office of 

Water, EPA 822–B–00–009, accessed August 21, 2015, at 

http://water.epa.gov/scitech/swguidance/standards/criteria/

library_index.cfm.

U.S. Environmental Protection Agency, 2000b, Ambient water 

quality criteria recommendations—Information supporting 

the development of State and Tribal nutrient criteria—Rivers and streams in nutrient ecoregion VII: Office of Water, 

EPA 822–B–00–018, 93 p, accessed August 21, 2015, at 

http://water.epa.gov/scitech/swguidance/standards/criteria/

nutrients/upload/2007_09_27_criteria_nutrient_ecoregions_

rivers_rivers_7.pdf.

U.S. Environmental Protection Agency, 2007, Test methods 

for evaluating solid waste, physical/chemical methods 

(SW–846) (rev. 2), accessed August 17, 2015, at http://

www.epa.gov/epawaste/hazard/testmethods/sw846/

pdfs/7000b.pdf.

U.S. Geological Survey, 2006, Collection of water samples 

(ver. 2.0): U.S. Geological Survey Techniques of WaterResources Investigations, book 9, chap. A4, September 

2006, accessed February 4, 2015, at http://pubs.water.usgs.

gov/twri9A4/. 

U.S. Geological Survey, 2014, USGS Circular 1350—Nutrients in the Nation’s streams and groundwater—Frequently 

asked questions, accessed February 4, 2015, at http://water.

usgs.gov/nawqa/nutrients/pubs/circ1350/faq.html.
60 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

U.S. Geological Survey, 2015a, The USGS water science 

school—Water color, accessed March 10, 2015, at 

http://water.usgs.gov/edu/color.html.

U.S. Geological Survey, 2015b, The USGS water science 

school—Nitrogen and water, accessed February 4, 2015, at 

http://water.usgs.gov/edu/nitrogen.html.

Verhougstraete, Marc; Singh, Shikha; Shibata, Tomoyuki; and 

Rose, J.B., 2007, Study of Silver Lake water quality: Lansing, Mich., Michigan State University, The Water Quality, 

Environmental, and Molecular Microbiological Laboratory, 

Department of Fisheries & Wildlife, 14 p.

Walker, W.W., 1985, Empirical methods for predicting eutrophication in impoundments—Report 3—Model refinements: prepared for Office, Chief of Engineers, U.S. Army, 

Washington, D.C., Technical Report E–81–9, Vicksburg, 

Miss., U.S. Army Corps of Engineers, Waterways Experiment Station.

Walker, W.W., 1996, Simplified procedures for eutrophication 

assessment and prediction: Vicksburg, Miss., U.S. Army 

Corps of Engineers, Waterways Experiment Station, User 

Manual Instruction Report W–96–2 [variously paged].

Wetzel, R.G, 1983, Limnology (2d ed.): Philadelphia, 

Saunders College Publishing, 858 p. 

Winter, T.C., Rosenberry, D.O., and Sturrock, A.M., 1995, 

Evaluation of 11 equations for determining evaporation 

for a small lake in the north central United States: Water 

Resources Research, v. 31, no. 4, p. 983–993, http://dx.doi.

org/10.1029/94WR02537.
Appendixes 1–3
62 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Appendix 1. Estimation of Streamflow at Silver Lake Dam

Water leaving Silver Lake through Silver Creek had to 

be accounted for in the water budget. To do this, the water 

level in the impoundment upstream from the Silver Lake 

Dam (fig. 2, table 2 in the main report) was monitored using 

a water-level sensor to measure changes in the water level 

above the dam. In addition, estimating streamflow required 

knowledge of the geometry of the dam, records of how the 

dam was operated, and manual measurements of streamflow 

downstream from the dam.

Water-level measurements in the Silver Lake Dam 

impoundment were collected from September 26, 2012, to 

October 1, 2014. The water levels were collected with reference to a gage datum of 579.85 feet (ft) above North American 

Vertical Datum of 1988 (NAVD 88). To obtain the elevation 

of the water in the impoundment in feet above NAVD 88, the 

gage height reported at the sensor is added to the reference 

value of 579.85 ft above NAVD 88. The gage height values for 

the impoundment are served online at http://waterdata.usgs.

gov/mi/nwis/uv/?site_no=04122211&agency_cd=USGS.

The next component needed to estimate flow was 

the geometry of the dam. The dam consists of four gates 

(fig. 1–1): two outer gates that are sluice gates and two inner 

gates that are overflow gates. The two inner gates may be 

manipulated by removing metal control boards that are 0.5 ft 

in height. Engineering plans for the dam’s construction were 

provided by Progressive AE (Tony Groves, Progressive AE, 

written commun., 2013). The engineering plans provided were 

referenced to an older vertical datum of National Geodetic 

Vertical Datum of 1929 (NGVD 29). Because the other gages 

used in this study were referenced to NAVD 88, the elevations 

of the dam were converted to NAVD 88 by applying a correction of −0.328 ft to the NGVD 29 elevations. This correction factor was obtained from the National Geodetic Survey 

VERTCON site for orthometric height conversion (National 

Geodetic Survey, 2003). In addition, the elevation of the dam 

gates and the crest of the dam were surveyed using surveygrade global positioning system (GPS) to better correlate the 

elevation of the dam to the gage height elevation of water 

measured at the gage (U.S. Geological Survey (USGS) station 

number 04122211).

The operation of the dam was obtained through personal 

communication with the drain commissioner and a volunteer 

who assisted with maintenance of the dam. In the fall, two 

boards were removed from one of the inner gates to reduce 

water levels in the lake to their legal winter levels (Fusilier 

and Fusilier, 2001). In the spring, when high flows are common, there are periods where the sluice gates as well as the 

overflow gates are adjusted to help move water faster to Lake 

Michigan to prevent high water from damaging property in 

and around Silver Lake. Table 1–1 illustrates how the dam was 

adjusted during the period of study.

Figure 1–1. Approximate dam configuration for the Silver Lake Dam at Silver Creek, Oceana County, Michigan.

Gate 1 Gate 4

Gate 2 Gate 3

Left bank Right bank

Crest 5

Crest 1 Crest 2 Crest 3 Crest 4

Figure 22. Approximate dam configuration for the Silver Lake Dam at Silver Creek, Oceana County, Michigan.
Appendix 1 63

Table 1–1. List of adjustments made to dam configuration during this study.

[—, no time recorded]

Date

(month/day/year)

Approximate time

(24-hour clock) Adjustment made

2012

9/26/2012 — All boards in at left and right sills, side gates completely closed.

10/10/2012 — Two boards in overflow gate closest to left bank were removed.

2013

4/15/2013 12:00 Open both sluice gates, 2-1/2 inches each.

4/17/2013 12:00 Open both sluice gates to 6 inches each.

4/18/2013 12:00 Open both sluice gates to 11 inches each.

4/21–4/23/2013 — Closed all gates, no time recorded.

5/6/2013 11:45 Put first board in for summer level in left overflow gate.

5/10/2013 15:50 Put in last board left overflow gate for summer lake level.

10/18/2013 15:15 Two boards in overflow gate closest to left bank were removed.

2014

4/14/2014 8:45 Opened left and right sluice gates, about 1 foot each.

4/18/2014 13:00 Drain commissioner closed gates before the weekend.

4/21/2014 15:20 Drain commissioner opened sluice gates 1 foot.

4/22/2014 15:00 Sluice gates lowered 6-inch opening.

4/26/2014 12:58 Sluice gates were completely closed.

4/28/2014 13:50 Put first board in for summer level in left overflow gate.

5/9/2014 17:45 Opened the sluice gates, both sides, about 6–7 inches each.

5/12/2014 11:00 Pulled out one board on the left overflow gate, and raised the left sluice 

gate an additional 2 inches.

5/19/2014 16:40 Closed both sluice gates.

5/25/2014 16:35 Put first board in left overflow gate.

6/18/2014 20:00 Pulled out one board on the left overflow gate.

6/22/2014 17:00 Put first board in left overflow gate.

7/14/2014 12:00 Put last board in left overflow gate for summer lake level.
64 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Figure 1–2. Discharge rating for Silver Creek Dam, Oceana County, Michigan.

r2 = 0.969

0

5

10

15

20

25

30

35

40

0 10 15 20 25 30 35 40

Measured discharge, in cubic feet per second

Rated discharge, in cubic feet per second

5

r2

Figure 23. Discharge rating for Silver Creek Dam, Oceana County, Michigan.

EXPLANATION

Discharge measurement

Linear fit of measured 

and rated discharge

1:1 line of identity

Coefficient of 

determination
Appendix 1 65

To develop a rating, the dam was divided into the nine 

sections that can convey water. Those nine sections include 

four different gates and any overflow that goes over the five 

different crest sections of the dam (fig. 1–1). Each section was 

solved by an appropriate weir or sluice gate equation using the 

geometry of the dam, and the elevation of the water surface 

in the impoundment upstream from the dam. The results of 

each section were then summed to get the total estimated 

streamflow.

For the sluice gates (gates 1 and 4), the equation below 

described by Rouse and Howe (1953) was used to estimate the 

flow:

(8)

where 

Q is streamflow, 

Cc is the coefficient of contraction, 

b is the height of gate opening, 

h is the head (water level) measured above the 

opening, 

L is the width of the gate, and

g is the gravitational constant in square feet per 

second

Of those coefficients, L, g and Cc are held constant at 5 ft, 

32.17 square feet per second (ft2/s), and 0.61, respectively. 

The water level (h) varied through time as did the height of the 

opening, b, as noted in table 1–1 and measured by the water 

level sensor. During the periods when gates 1 and 4 were not 

opened flow was not estimated through those gates. 

For the overflow gates (gates 2 and 3), the flow was estimated using a sharp-crested weir equation (Rouse and Howe, 

1953) of the form:

(9)

where 

Q is streamflow, 

Cd is the discharge coefficient, 

g is the gravitational constant in square feet per 

second, 

L is the width of the gate, and 

h is the head (water level) measured above top 

of the weir. 

The coefficients Cd, L, and g are held constant at 0.652, 5 ft, 

and 32.17 ft2/s, respectively. Only the water level above the 

weir, h, varied through time. Any additional flow that may go 

over the crest of the dam, if the water level in the impoundment should rise high enough, was estimated using the same 

weir equation only using the width from the various five different crest segments (Crests 1-5; fig. 1–1). 

2

1

c

c

C Q bL gh C bh = +

2 3 2

3 2 Q C gLh = d

Lastly, the streamflow going past the Silver Lake dam 

was measured 15 times during this study to assist in calibrating the developed rating. The individual streamflow measurements can be obtained from http://waterdata.usgs.gov/mi/

nwis/measurements/?site_no=04122211&agency_cd=USGS. 

Streamflow measurements were only made during periods 

where the overflow gate boards were manipulated, so events 

where sluice gates were manipulated were not measured. 

Initially, the comparison of the rated discharge to the measured 

discharge was poor. However the discharge coefficient, Cd, 

was adjusted to find the best fit between the rated and measured streamflow (fig. 1–2). The discharge coefficient that 

provided the best fit between the rated and measured streamflow was 0.652. The linear best-fit line produced an R-squared 

of 0.969, which indicates good correlation between the rated 

and measured streamflow estimate. 

References Cited

National Geodetic Survey, 2003, Orthometric Height Conversion Tool, accessed March 17, 2015, at http://www.ngs.

noaa.gov/cgi-bin/VERTCON/vert_con.prl.

Rouse, Hunter, and Howe, J.W., 1953, Basic mechanics of 

fluids: New York, Wiley, 245 p.
66 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Appendix 2. Additional Data Collected

Bioassay Study—Microcystin and Phytoplankton

Laboratory Analysis 

Intercellular microcystin concentrations were determined 

by liquid chromatography-mass spectrometry (LC-MS) after 

an extraction process involving both a freeze-thaw step and 

lyophilization to lyse the algal cells (Fastner and others, 1998). 

A five-point calibration curve with internal standard addition 

was used to determine final concentrations of the two primary 

forms of microcystin: microcystin LR and microcystin RR; 

these were summed to estimate total microcystin.

Identification and enumeration of phytoplankton samples 

were done in a Palmer-Maloney counting chamber (0.1 milliliter [mL]) at 400 × magnification with a Leica DM LB2 compound microscope. A total of 300 algal units (cells, filaments, 

or colonies) were counted and identified to the lowest possible 

taxonomic level. The biovolume of dominant taxa (that is, 

all taxa that composed greater than 5 percent of the biovolume in an individual sample) was estimated by measuring 10 

individuals and applying the mathematical formulae for their 

appropriate geometric shape (Hillebrand and others, 1999). 

The biovolume of less abundant taxa was estimated using data 

from Steinman and others (2008). Only organisms with intact 

cell contents were included in the biovolume estimation. The 

standard operating procedure was modified for phytoplankton analysis (U.S. Environmental Protection Agency, 2010) 

to increase the likelihood that species forming large floating colonies (for example, Gloeotrichia) would be detected. 

Each concentrated sample was settled for at least 24 hours 

before all visible colonies of Gloeotrichia were counted with a 

magnifying glass. The sample also was examined for individual Gloeotrichia filaments, but they were not encountered. 

Ten colonies were transferred to a Palmer-Maloney counting 

cell and measured under the microscope to estimate colony 

size, number of filaments, and biovolume. In only 1 instance 

(1 sample from initial treatment) were less than 10 colonies 

found. The Gloeotrichia biovolume was then included in total 

phytoplankton biovolume computations for each treatment.

Data Analysis

One-way analysis of variance (ANOVA) was used to 

assess differences in phytoplankton biovolume among the 

five treatments in the bioassay study (initial, control, nitrogen, 

phosphorus, and nitrogen plus phosphorus) with four replicates each (except initial, n = 3). Test assumptions were verified by examining biovolume data for normal distribution and 

equal variance among treatments. Statistically significant (α = 

0.05) results were further analyzed with Tukey Honest Significance Difference (HSD) test to identify significant differences 

between pairs of treatments (Tukey, 1949).

Nonmetric multidimensional scaling (NMDS) ordination 

was used to evaluate patterns in phytoplankton community 

structure due to nutrient treatment. NMDS used the BrayCurtis similarity coefficient as a distance measure (Bray and 

Curtis, 1957). To evaluate the goodness-of-fit between the 

calculated (plotted) and the actual (from the similarity matrix) 

distances among the samples, the stress value was estimated. 

A stress value less than 20 indicates a good ordination (Clarke, 

1993). The NMDS function was specified to run with 20 random starts in search of optimal solution with the lowest stress 

value. 

To test if treatments resulted in different species compositions, samples were analyzed with analysis of similarity 

(ANOSIM; Clarke, 1993). This method tests for significant 

differences between two or more groups using the rank order 

of the sample dissimilarity matrix. Dissimilarity values were 

calculated with the Bray-Curtis similarity coefficient. If two 

groups had very different species compositions, the dissimilarities between the groups would be larger than those within 

the groups. This was evaluated by the R statistic, which varies 

between −1 and 1, with values close to 0 indicating random 

grouping. The statistical significance (α = 0.05) of the R statistic was evaluated with 999 permutations. If there was a significant difference in the species composition among treatments, 

indicator species analysis (Dufrêne and Legendre, 1997) was 

performed to find the species most responsible for the differences. Indicator taxa were those that were more abundant and 

had a higher probability of occurrence in one particular treatment (α <0.05). These data analyses were performed in the R 

program for statistical computing (R Development Core Team, 

2012).

Other differences in algal response to nutrient treatments 

were analyzed using ANOVA. Differences in chlorophyll a 

concentration, microcystin concentration (RR, LR, total), and 

chlorophyll a-normalized microcystin concentration (RR, 

LR, total) were analyzed among treatments (control, nitrogen, 

phosphorus, nitrogen plus phosphorus). Chlorophyll a concentrations were ln-transformed prior to analysis to meet assumptions of normality and equal variance. Normality was tested 

using the Shapiro-Wilk test and multiple pairwise comparisons 

were tested using Holm-Sidak Method. Statistical significance 

was determined by p-values less than 0.05. Chlorophyll a and 

microcystin analyses were performed using SigmaPlot (version 12.5, Systat).

Bioassay Study Results—Microcystin and 

Phytoplankton

Microcystin concentrations were not significantly different among nutrient treatments and were similar to the initial 

values for all microcystin forms (RR, LR, total). The World 
Appendix 2 67

Health Organization (WHO) has issued guidelines for recreational exposure, including a low probability of acute health 

effects when microcystin-LR concentration is <10 μg/L, moderate risk at 10-20 μg/L, high risk at 20-2,000 μg/L, and very 

high risk at >2,000 μg/L (WHO, 2003).

In Silver Lake, all microcystin concentrations were well 

below the WHO low-risk guidance for recreational exposure 

and none exceeded the EPA drinking water advisory for adults. 

Microcystin-LR concentrations were >0.3 μg/L in early June 

and from mid-August through early October, though it is 

highly unlikely that any person could ingest a sufficient volume of lake water to cause adverse health effects from microcystin at such low concentrations (Steinman and others, 2015). 

There is no indication that increased nitrogen or phosphorus 

inputs will trigger greater toxin production, at least during the 

summer when these studies were conducted.

Mean total phytoplankton biovolume was 35.57 × 106

cubic micrograms per milliliter (μm3/mL) per sample. On 

average, diatoms accounted for the greatest biovolume 

(32.50 × 106 μm3/mL), followed by cyanobacteria 

(2.43 × 106 μm3/mL), and chlorophytes (0.34 × 106 μm3/mL). 

Diatoms were the most dominant group in all treatments; they 

reached their highest numbers in the nitrogen plus phosphorus 

treatment (56.34 × 106 μm3/mL) and lowest numbers in the 

initial samples (17.00 × 106 μm3/mL). Cyanobacteria were the 

second most dominant group in the control treatment 

(6.89 × 106 μm3/mL) and the second least dominant in the 

initial samples (0.21 × 106 μm3/mL). Similar to diatoms, chlorophytes were most abundant in the nitrogen plus phosphorus 

treatment (0.65 × 106 μm3/mL) but least abundant in the phosphorus treatment (0.19 × 106

 μm3/mL).

A total of 41 phytoplankton species was observed, which 

resulted in a sample richness of 15 species (range 10–19). 

The most diverse group were the chlorophytes (14 species), 

followed by diatoms and cyanobacteria (11 species each), 

dinoflagellates (2), chrysophytes (2), and 1 cryptophyte. On 

average, the control and phosphorus treatments had the least 

species richness (13 and 14, respectively), initial samples had 

15 taxa, while nitrogen and nitrogen plus phosphorus had 

16 taxa each. The control treatment had the highest Shannon 

and Simpson diversity indexes (4.0 and 3.2, respectively), 

while the nitrogen plus phosphorus treatment had the lowest 

diversity (2.5 and 1.9). Of the 10 most dominant taxa, 5 were 

diatoms, 4 were cyanobacteria, and 1 was a chlorophyte 

(table 3-1). Five phytoplankton species were found in all samples; they included two cyanobacteria (Anathece minutissima

[W. West] Komárek, Kastovsky & Jezberová and Gloeotrichia

echinulata Richter) and three diatoms (Aulacoseira granulata

[Ehrenberg] Simonsen, Aulacoseira islandica [O.Müller] 

Simonsen, and Fragilaria crotonensis Kitton). The three species with the highest mean biovolume were all chain-forming 

diatoms such as Aulacoseira granulata (Ehrenberg) Simonsen, 

Aulacoseira islandica (O.Müller) Simonsen, and Aulacoseira 

granulata var. angustissima (O.Müller) Simonsen (table 2–1). 

Although Gloeotrichia was observed in all samples, its overall 

biovolume was modest compared to the dominant phytoplankton in Silver Lake. 

Similar to the chlorophyll a results (fig. 10 in the main 

report), the nitrogen plus phosphorus treatment resulted in the 

greatest algal biovolume (fig. 2–1), although this difference 

was statistically different from only the initials (F-value 3.66, 

p-value <0.05); high variance precluded significant differences 

Table 2–1. Ten most abundant phytoplankton species based on frequency of occurrence.

[μm3/mL, cubic micrometers per milliliter]

Species Division

Taxon 

frequency 

(percent)

Biovolume 

(μm3

/mL)

Standard 

deviation 

(μm3 Minimum Median Maximum Mean /mL)

Aulacoseira granulata (Ehrenberg) 

Simonsen

Bacillariophyta 100 2,558,455 13,682,458 48,203,070 19,529,633 13,890,654

Aulacoseira islandica (O.Müller) Simonsen Bacillariophyta 100 3,209,901 9,672,541 28,240,490 10,566,959 5,933,022

Fragilaria crotonensis Kitton Bacillariophyta 100 10,570 298,069 865,670 382,200 244,195

Gloeotrichia echinulata Richter Cyanobacteria 100 1,585 38,957 104,528 42,520 29,189

Anathece minutissima (W. West) Komárek 

Kastovsky & Jezberová

Cyanobacteria 100 1,448 35,938 96,240 39,145 27,037

Aulacoseira granulata var. angustissima

(O.Müller) Simonsen

Bacillariophyta 89 198,147 986,254 2,511,333 979,394 650,360

Oocystis borgei Snow Chlorophyta 84 54,783 143,691 544,462 182,182 116,694

Chroococcus dispersus (V. Keiss) Lemm. Cyanobacteria 84 9,131 40,104 114,212 53,622 39,283

Pseudanabaena mucicola (Naumann & 

Huber-Pestalozzi) Schwabe

Cyanobacteria 84 129 743 1,628 802 488

Fragilaria capucina Desmazieres Bacillariophyta 68 2,715 12,446 72,938 25,873 24,424
68 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Figure 2–1. Barplots (mean plus 1 standard error) of phytoplankton biovolume in Silver Lake, Oceana County, Michigan, 

for five nutrient treatments.

Analysis of variance

p<0.05

Biovolume, 10^6 micrometers per millimeter

Treatment

I C N P NP

0

20

40

60

a ab ab ab bc

EXPLANATION

I

C

N

Initial

Control

Nitrogen

Different letters in the barplots (e.g., a versus bc) denote significant 

differences (p<0.05) based on Tukey’s Honest Significant 

Difference (HSD) test. Data bars with labels containing the same 

letter (e.g., ab versus bc) are not significant from each other 

(p>0.025).

P

NP

Phosphorus

Nitrogen and phosphorus

 (with four replicates 

each [except I, n=3])

between the nitrogen plus phosphorus versus the other treatments. Similarly, there were no significant differences among 

treatments in terms of Gloeotrichia biovolume, colony size 

and density, number of filaments per colony, filaments density, 

and biovolume. The same trend was observed for the dominant 

species. However, some of them exhibited differences among 

treatments and they included Aulacoseira granulata (significantly higher biovolume in the nitrogen plus phosphorus 

treatment), Chroococcus dispersus (significantly different 

biovolume between the control and phosphorus treatment), 

and Fragilaria capucina (significantly higher biovolume in 

nitrogen plus phosphorus treatment). Microcystis aeruginosa

also exhibited differences among treatments (significantly different biovolume between control and nitrogen plus phosphorus treatment).
Appendix 2 69

E. coli Monitoring

Laboratory Analysis

Water samples collected from the tributaries, lake, and 

groundwater sampling locations were tested for Escherichia 

coli (E. coli) concentrations using the IDEXX 24-hour Colilert 

most probable number (MPN) method (IDEXX, 2013). E. coli

samples were collected and stored away from sunlight and 

on ice for transport to the Michigan Bacteriological Research 

Laboratory at the U.S. Geological Survey (USGS), Michigan 

Water Science Center, Lansing, Michigan, where the samples 

were then analyzed. Samples were stored on ice for no longer 

than 60 hours prior to analysis. 

Results

E. coli concentrations (MPN/100 milliliters [mL]) analyzed at each of the lake sites ranged from less than 1 (nondetect) to 7 MPN/100 mL. The State of Michigan recreational 

criteria for E. coli is a daily geometric mean below 300 E. 

coli/100 mL for recreational activities such as swimming and 

snorkeling (total-body contact). The E. coli concentrations on 

Silver Lake measured as part of this study were far below the 

State’s recreational criteria guidelines; however, it should be 

noted that the holding time of 60 hours exceeded the regulatory holding time set by the State for E. coli (6 hours).

E. coli concentrations at Hunter Creek ranged from 

24 to 1,000 MPN/100 mL with a mean concentration of 

250 MPN/100 mL. E. coli concentrations at Hunter Creek 

exceeded the daily standard of 300 E. coli/100 mL on three 

occasions, two of which were during storm events. E. coli

concentrations at the tributary at State Park ranged from 6 

to 1,400 MPN/100 mL, with an average concentration of 

240 MPN/100 mL; the highest concentrations were recorded 

during storm events. For the tributary at North Shore Drive, 

E. coli concentrations ranged from 8 to 1,600 MPN/100 mL, 

with a mean of 352 MPN/100 mL, which is in exceedance of 

the State E. coli criterion of 300 E. coli/100 mL. 

E. coli concentrations were measured quarterly at the 

groundwater wells, and E. coli was not detected in any of the 

samples collected (indicated by less than 1 MPN/100 mL). 

The E. coli concentrations from the drain tiles at the north end 

of Silver Lake were consistently low, and averaged approximately 6 MPN/100 mL.

Suspended-Sediment Concentrations

Suspended-sediment concentrations (SSC) collected at 

Hunter Creek were typically low and ranged from 4 to 13 milligrams per liter (mg/L), and averaged 8 mg/L during the study 

period. SSC typically increased during storm events in Hunter 

Creek. SSC concentrations at the tributary at State Park and 

the tributary at North Shore Drive ranged from 5 to 155 mg/L 

and 8 to 33 mg/L, respectively.

References Cited

Bray, J.R., and Curtis, J.T., 1957, An ordination of the upland 

forest communities of southern Wisconsin: Ecological 

Monographs, v. 27, no. 4, p. 151–164.

Clarke, K.R., 1993, Non-parametric multivariate analysis of 

changes in community structure: Australian Journal of 

Ecology, v. 18, no. 1, p. 117–143. 

Dufrêne, Marc, and Legendre, Pierre, 1997, Species assemblages and indicator species—The need for a flexible asymmetrical approach: Ecological Monographs, v. 67, no. 3, 

p. 345–366.

Fastner, Jutta; Flieger, Inge; and Neumann, Uwe, 1998, 

Optimised extraction of microcystins from field samples—

A comparison of different solvents and procedures: 

Water Research, v. 32, no. 10, p. 3177–3181.

Hillebrand, Helmut; Dürselen, Claus-Dieter; Kirschtel, David; 

Pollingher, Utsa; and Zohary, Tamar, 1999, Biovolume 

calculation for pelagic and benthic microalgae: Journal of 

Phycology, v. 35, no. 2, p. 403–424.

IDEXX Laboratories, Inc., 2013, Instruction manuals for use 

of Colilert®, Quanti-Tray®/2000, and Quanti-Tray Sealer®, 

accessed August 6, 2015, at https://www.idexx.com/water/

water-testing-solutions.html.

R Development Core Team, 2012, R—A language and 

environment for statistical computing: Vienna, Austria, R 

Foundation for Statistical Computing, accessed August 6, 

2015, at http://www.R-project.org.

Steinman, A.D., Ogdahl, Mary, and Gillett, Nadia, 2015, An 

analysis of the cyanobacterium Gloeotrichia in Silver Lake: 

Grand Valley State University, Annis Water Resources 

Institute and Portland State University, 36 p.

Steinman, A.D., Ogdahl, Mary, Rediske, Richard, Ruetz, C.R., 

III, Biddanda, B.A., and Nemeth., Lori, 2008, Current status 

and trends in Muskegon Lake, Michigan: Journal of Great 

Lakes Research, v. 34, no. 1, p. 169–188.

Tukey, J.W., 1949, Comparing individual means in the 

analysis of variance: International Biometric Society , 

Biometrics, v. 5, no. 2, p. 99–114.

U.S. Environmental Protection Agency, 2010, Standard operating procedure for phytoplankton analysis (rev. 05): accessed 

August 6, 2015, at, http://www.epa.gov/greatlakes/monitoring/sop/chapter_4/lg401.pdf.

World Health Organization (WHO), 2003, Guidelines for safe 

recreational water environments, Volume 1, Coastal and 

fresh waters: Geneva, World Health Organization, 219 p. 
70 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Appendix 3. Quantification of Groundwater Flow to Silver Lake

The groundwater flow to Silver Lake was characterized 

on the basis of water levels measured at the four shallow wells 

around the lake, discrete seepage meters, and discrete groundwater temperature data. Water levels were monitored continuously in the wells on the east and west side of Silver Lake and 

discretely on the north and south side of Silver Lake (fig. 3–1).

These water levels were used in conjunction with the lake 

stage estimated near Hunter Creek (0412220999) to estimate 

groundwater flux using the Dupuit equation (Rosenberry and 

LaBaugh, 2008). The Dupuit equation for an unconfined aquifer is defined as

(10)

where 

K is horizontal hydraulic conductivity in meters 

per day,

m is length of shoreline in meters,

L is distance from piezometer to lake shore in 

meters,

h1 is saturated aquifer thickness at the 

piezometer, and

h2 is aquifer thickness at the edge of the surfacewater body.

The hydraulic conductivity for the unconfined aquifer 

adjacent to and below Silver Lake was estimated at 7 feet per 

day (ft/d) based on information from the Michigan Groundwater Mapping Project (2006) map viewer. The length of 

shoreline was split up into four quadrants around the lake as 

shown in figure 3–2 such that each quadrant corresponded to 

a piezometer. The length of shoreline by cardinal direction 

was 3,809, 5,485, 5,187, and 7,949 feet (ft) for the north, east, 

south, and west shorelines, respectively. The distance from 

the piezometer to the lakeshore varied as the water level in 

the lake varied, but, on average, the distance was 12, 38.4, 

8, and 24 ft, for the north, east, south, and west quadrants, 

respectively. 

The effective thickness of the aquifer was estimated 

assuming that the base of the aquifer contributing water was 

approximately the maximum depth of the lake which was 22 ft 

(Groves and others, 2013). Assuming a mean lake surface 

elevation of 586 ft, puts the base of the aquifer elevation 

at approximately 564 ft. The aquifer thickness, h1, for each 

piezometer was estimated by subtracting 564 ft from the water 

level elevation measured in the piezometer. The aquifer thickness at the edge of the surface water body, h2

 was estimated 

by subtracting 564 ft from the elevation for the lake stage 

estimated from the Hunter Creek streamgage. In order to get 

a daily estimate of flux for the piezometers with only discrete 

water-level elevation measurements, a correction factor was 

applied to the west piezometer water-level record to get the 

2 2

1 2 ( )

2

h h Q Km L− =

continuous water-level record to correspond to the discrete 

measurements at those wells. The factor applied was 0.2 ft 

for the north piezometer and 0.22 ft for the south piezometer. 

Solving the Dupuit equation for using the piezometer waterlevel data from October 1, 2013, to September 30, 2014, 

yields a total flux of 2.16 × 107 cubic feet (ft3). Comparison 

of the Dupuit groundwater-flux estimate to the groundwaterflux estimate from the water-balance compilation (Sources 

of Water–Water Budget section), derived as the difference 

between the inflows and outflows (2.27 × 108 ft3), indicated 

that some assumptions in the Dupuit estimate were likely 

wrong. Possibilities for error include, but are not limited to, 

using incorrect hydraulic conductivity estimates for the aquifer, inaccurate representation of the effective aquifer thickness, 

incomplete data for water year 2013 limiting the period for 

estimates to 2014, and an inability to represent water-level 

variability around the lake with the four water-level monitoring points available. A water year is the 12-month period 

October 1 through September 30 designated by the calendar 

year in which it ends.

As a result of the complexities with using the Dupuit 

estimate, a different approach to estimating the groundwater 

discharge to the lake was attempted. Discrete seepage meters 

(Rosenberry and LaBaugh, 2008) were deployed at 18 locations around Silver Lake (fig. 3–2). The seepage meter consists of a 2.69 square feet (ft2

) cutoff barrel that is attached to a 

bag containing a known volume of water sheltered in a plastic 

enclosure. The plastic enclosure helps protect the bag from 

effects of waves or flowing water from affecting the volume 

of water entering or leaving the bag. The procedure used for 

operating the seepage meters is outlined in Rosenberry and 

LaBaugh (2008). For this investigation, three individual seepage measurements were collected at each site and the mean 

value of those measurements was used as the seepage rate estimate for that site. Results of the seepage survey indicate that 

the seepage rates vary from 4.86 × 10-8 feet per second (ft/s) to 

1.52 × 10-5 ft/s with a mean value of 3.00 × 10-6 ft/s; note that 

seepage measured from the seepage meters was seepage from 

the groundwater to the lake. 

In addition to seepage meters, discrete temperature sensor 

arrays were deployed to estimate groundwater flux at several 

locations around Silver Lake. This was done to both confirm 

the seepage meter derived groundwater discharge estimate 

and identify any temporal variability in groundwater flow to 

the lake. The temperature array used for this investigation 

consisted of a wooden stake with four temperature sensors 

inserted in the stake at 3.9-inch (in.) intervals. The temperature sensors used in this study were iButton thermal data 

loggers (Maxim Integrated DS1922L), which were operated 

at a thermal precision of 0.0625 degrees Celsius (°C). The 

stake was then driven into the lakebed sediment such that a 

temperature sensor was located just below the sediment water 
Appendix 3 71

Figure 3–1. Continuous and discrete water-level elevations for the shallow wells around Silver Lake, Oceana County, 

Michigan, April 2013–December 2014.

434014086292702 (East)

434020086302801 (West)

434041086295802 (North)

433936086304302 (South)

EXPLANATION

Figure 24 Continuous and discrete water-level elevations for the shallow piezometers around Silver Lake, 

Oceana County, Michigan, April 2013–December 2014.

MayApr. June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. MayApr. June July Aug. Sept. Oct. Nov. Dec.

586.0

586.2

586.4

586.6

586.8

587.0

587.2

587.4

587.6

Water level, in feet above North American Vertical Datum of 1988

2013 2014

interface defined as 0 inches (in.), 3.9 in. below the lakebed, 

7.9 in. below the lakebed, and 11.8 in. below the lakebed. 

The temperature was then recorded every 20 minutes at each 

sensor while the array was deployed. The wooden stakes were 

then recovered and the data downloaded from each sensor for 

analysis. The temperature sensors were deployed at five locations around Silver Lake from July 23 to September 18, 2014. 

However, only two sensor arrays on the north and east sides of 

the lake were recovered. It is suspected that wave action and 

(or) anthropogenic activity removed the others. Two sensor 

arrays were deployed again in October 2014 at locations on 

the west side of the lake and the south side of the lake. Only 

the south sensor array was recovered in November, and again 

the west sensor array was not recovered due to suspected wave 

or anthropogenic activity.

Analysis of the temperature data was performed using 

1DTempPro (Voytek and others, 2013), which is a graphical 

user interface for VS2DH (Healy and Ronan, 1996). 1DTempPro formats the user-supplied groundwater temperature data 

for VS2DH to solve a one-dimensional finite-difference 

solution to the groundwater-flow and heat transport equation. The program then sets up a grid of 3 columns with 103 

rows, each 39.4 in. wide and 0.114 in. deep. The first and third 

columns establish no-flow boundaries and the central column 

represents a vertical section between 0 and 11.8 in. deep in 

the lakebed. Sensitivity of the groundwater flux estimates 

to the cells size was not determined for this study. Specified 

temperature boundaries were applied to the top and bottom 

cells of the model which correspond to the data collected at the 

top (0 in.) and bottom (11.8 in.) sensor of the temperature array. 
72 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14

Figure 3–2. Locations of wells, seepage meters, and temperature sensor array deployments around Silver Lake, Oceana County, 

Michigan.

!>

!>

!>

!>

T

T

T

434014086292702

434041086295802

434020086302801

West well

433936086304302

South quadrant

East quadrant

Source: Esri, Digital Globe GeoEye, i-cubed, USDA, USGS, AEX, Getmapping,

Aerogrid, IGNIGP, swisstopo, and the GIS User Community

43°40’0”

43°40’40”

86°30’40” 86°30’0” 86°29’20”

North quadrant

West quadrant

South well

East well

North well

Silver Lake

0

0

0.25 0.5 MILES

0.25 0.5 KILOMETERS

Monitoring well location

EXPLANATION

T Temperature sensor array 

deployment

1.31 x 10–8–1.52 x 10–5

1.09 x 10–5–1.30 x 10–5

8.69 x 10–6–1.08 x 10–5

6.53 x 10–6–8.68 x 10–6

4.37 x 10–6–6.52 x 10–6

2.22 x 10–6–4.36 x 10–6

4.86 x 10–8–2.21 x 10–6

!>

Quadrant divider

Seepage, in feet per second
Appendix 3 73

These temperature boundaries were time-varying as they represent the temperature over the duration of time that the array 

was deployed. There were no external stresses (for example, 

pumping) applied to the model. To analyze the groundwater 

flow to Silver Lake, temperature data from each sensor array 

was downloaded and merged into one input file for 1DTempPro. The data were then loaded into 1DTempPro and simulated 

using the following parameters for each site; porosity of 0.33, 

thermal conductivity of 2.3 W

m°C (where W is watts, m is meters 

and C is Celsius), dispersivity of 0.328 ft, and sediment heat 

capacity of 2.50 × 106 J

m3°C (where J is Joules, m is meters and 

C is Celsius). These parameters are within the range of values 

for coarse-grained sediment like the dune sands found at Silver 

Lake (Lapham, 1989). The model was calibrated by adjusting 

the estimated groundwater discharge until the simulated temperature signal matched the observed temperature signal. Figure 

2–3 indicates an accurate fit with high coefficient of determination (R2

) ranging from 0.971 to 0.998, and low root mean square 

error (RMSE) of estimated temperature ranging from 0.041 to 

0.231°C. Note that only the 3.9 and 7.9 in. depth temperature 

signals were calibrated to, as the 0 and 11.8 in. depth temperature signals serve as boundary conditions in the model. These 

boundary conditions are forced to match the observed temperature and are not appropriate to include in the simulated to 

observed value comparison. 

Limitations of using 1DTempPro for estimating groundwater fluxes to Silver Lake include but are not limited to: assuming that all flow to and from the lakebed is entirely vertical or 

1-dimensional, and that all hydraulic and thermal properties of 

the lakebed are homogenous and isotropic. If the flow over the 

length of lakebed studied was more 2-dimensional, the 1-dimensional approximation might not accurately represent the flux. 

Similarly, heterogeneity in the physical and thermal characteristics of the lake bed sediment may cause some uncertainty in the 

flux estimate. However, for the very short flow distance that was 

examined (11.8 in.), these assumptions appear to be valid given 

the low error between simulated and observed temperatures. 

The estimated groundwater discharge for the north array 

was very low, 1.8 × 10-6 ft/s and indicated that the lake was 

discharging water into the aquifer. The estimated groundwater 

discharge for the east array was also quite low, 1.46 ×10-6 ft/s, 

but indicated groundwater was discharging to Silver Lake. 

These low estimates can be classified as circum-neutral, less 

than 3.28 × 10-6 ft/s (Lautz, 2012), or essentially that they are 

so low that they cannot effectively be differentiated from a noflow condition using the temperature profiling approach. The 

estimate of groundwater discharge for the south array indicates 

discharge from the aquifer to the lake at a rate of 3.17 ×10-5 ft/s, 

which is slightly higher than that measured by the discrete 

seepage meter near that location (8.23 × 10-6 ft/s). Although 

the values are slightly different, they agree in general magnitude. Additionally, the rate of discharge for the period that was 

measured (October 29 to November 17, 2014) remained fairly 

constant which indicates that groundwater discharge to the lake 

did not vary greatly over short time scales. 

On the basis of the seepage meter estimates and temperature sensor data, a new approach was taken to allocating 

groundwater discharge to Silver Lake. Rather than independently estimate the total groundwater discharge to Silver Lake, 

the residual between the inflows and outflows of the water 

balance were used to estimate the total groundwater discharge 

to the lake. This total value was then proportionally divided 

amongst the four quadrants (fig. 3–2) using a proportion factor 

developed from the seepage meter estimates. This factor was 

defined as the mean seepage by each quadrant, multiplied by the 

length of shoreline in the quadrant and then divided by the sum 

of that product for all quadrants. The result was a proportion 

of flow by quadrant, which for Silver Lake was 2.7, 10.9, 25.0, 

and 61.4 percent for the north, east, south, and west quadrants, 

respectively. Those proportion factors were applied to the total 

volume of groundwater flow estimated from the water balance 

analysis to develop the flow estimates used in the BATHTUB 

model for estimating the nutrient load from groundwater 

sources. 

The flow estimates derived from the procedure outlined 

above were then compared to the discrete seepage measurements to ensure that the average value was a reasonable magnitude. This was done by dividing the volume of groundwater 

flow for each quadrant by the number of seconds in the study 

period (63,072,000), the length of shoreline for the respective 

quadrants, and a characteristic leakage length. The characteristic leakage length, termed lambda, is defined as = Tc, where 

T is the aquifer transmissivity and c is the resistance between 

the groundwater and surface water feature (Hunt and others, 

2003). It has been observed that approximately 95 percent of the 

leakage into a lake occurs within three times this characteristic 

leakage length lambda (Hunt and others, 2003). To estimate the 

characteristic leakage length λ, the transmissivity (T ), defined 

as the aquifer thickness multiplied by the hydraulic conductivity of the aquifer and the resistance (c), defined as thickness of 

the lakebed layer divided by the vertical k of the layer had to be 

estimated. The T estimate was based on an assumed thickness of 

22 ft based on the lake depth as well as hydraulic conductivity 

of 7 ft/d, which yields 154 square feet per day (ft2/d). The c estimate was based on an assumed lakebed thickness of 1.5 ft and 

a vertical hydraulic conductivity of 0.7 ft/d, which yields 2.14 

days. The square root of the product of T and c yields a λ of 

18.2 ft. Taking the volumetric flow of water by quadrant for the 

2-year study and dividing by the number of seconds in the study, 

the length of shoreline, and three times the characteristic length 

(λ) yields an average flux rate of 9.25 × 10-7 ft/s, 2.64 × 10-6 ft/s, 

6.38 × 10-6 ft/s, and 1.02 × 10-5 ft/s for the north, east, south and 

west quadrants, respectively. This compares well to the average rate of seepage by quadrant determined from the seepage 

meter survey 5.58 × 10-7 ft/s, 1.60 × 10-6 ft/s, 3.85 × 10-6 ft/s, 

6.20 × 10-6 ft/s for the north, east, south, and west quadrants, 

respectively. The agreement in these numbers further supports 

the estimates of groundwater flow to Silver Lake derived from 

the procedure outlined in this appendix (fig. 3–3).
74 Water Quality and Hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on Lake Response to Nutrient Loading, 2012–14 Figure 3–3. Comparison of simulated to observed temperature values at 3.9 and 7.9 inches in depth for the east, north, and south temperature arrays deployed at Silver Lake, Oceana County, Michigan. (r2, coefficient of determination; RMSE, root mean square error; °C, degrees Celsius)

18 20 22 24 26

Simulated temperature, in degrees Celsius 18 20 22 24 26

East array, 3.9 inch depth

24 26 18 20 22 18 20 22 24 26 East array, 7.9 inch depth

18 20 22 24 26 18 20 22 24 26

North array, 3.9 inch depth

26 24 22 18 20 18 20 22 24 26

North array, 7.9 inch depth

 9 10 11 12 13 14 9 10 11 12 13 14

South array, 3.9 inch depth

11 12 13 14 10 9 9 10 11 12 13 14 South array, 7.9 inch depth

Observed temperature, in degrees Celsius

r2=0.984

Root mean square error (RMSE) = 0.231°Cr2=0.971RMSE = 0.160°C

r

2=0.998

RMSE = 0.058°C

r

2=0.995

RMSE = 0.063°C

r2=0.996

rRMSE = 0.041°C

2=0.996

RMSE = 0.066°C

Figure 26. Comparison of simulated and observed temperature values at 3.9 and 7.9 inches in depth for the east, north, and south temperature arrays deployed at Silver Lake, Oceana County, Michigan. (°C, degrees Celsius)
Appendix 3 75

References Cited

Healy, R.W., and Ronan, A.D., 1996, Documentation of computer program VS2DH for simulation of energy transport 

in variably saturated porous media—Modification of the 

U.S. Geological Survey’s computer program VS2DT: U.S. 

Geological Survey Water-Resources Investigation Report 

96–4230, 36 p.

Hunt, R.J., Haitjema, H.M., Krohelski, J.T., and Feinstein, 

D.T., 2003, Simulating ground water-lake interactions: 

Approaches and insights: Ground Water, v. 41, no. 2, 

p. 227–237.

Lapham, W.W., 1989, Use of temperature profiles beneath 

streams to determine rates of vertical ground-water flow 

and vertical hydraulic conductivity: U.S. Geological Survey 

Water-Supply Paper 2337, 35 p.

Lautz, L.K., 2012, Observing temporal patterns of vertical flux 

through streambed sediments using time-series analysis of 

temperature records: Journal of Hydrology, v. 464–465, 

p. 199–215.

Michigan Groundwater Mapping Project Web Site, 2006, 

accessed October 31, 2014, at http://gwmap.rsgis.msu.edu/. 

Voytek, E.B., Drenkelfuss, Anja, Day-Lewis, F.D., Healy, 

Richard, Lane, J.W., and Werkema, Dale, 2013, 1DTempPro—Analyzing temperature profiles for groundwater/

surface-water exchange: Ground Water, v. 52, no. 2, 

p. 298–302.


Brennan and others—Water Quality and Hydrology of Silver Lake, Oceana County, Michigan 2012–14—Scientific Investigations Report 2015–5158

ISSN 2328-031X (print)

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20155158