Nutrient and Water Quality Analysis of a Lake Erie

NUTRIENT AND WATER QUALITY ANALYSIS OF A

LAKE ERIE HEADWATER TRIBUTARY

A Thesis

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

of the Requirements for the Degree

Master of Science

MaryAnne Hejna

August 2020

NUTRIENT AND WATER QUALITY ANALYSIS OF A

LAKE ERIE HEADWATER TRIBUTARY

MaryAnne Hejna

Thesis

Approved: Accepted:

______Advisor Department Chair Dr. Teresa J. Cutright Dr. Wieslaw K. Binienda

______Committee Member Interim Dean of the College Dr. Stephen E. Duirk Dr. Craig Menzemer

______Committee Member Acting Dean of the Graduate School Dr. Richard L. Einsporn Dr. Marnie M. Saunders

______Date

ABSTRACT

Lake Erie is a drinking water source for millions of people and therefore requires protection from anthropogenic impacts. Nine percent of Lake Erie’s freshwater comes from its tributaries. These sources should deliver clean water to the lake and thus warrant stewardship. Today, nonpoint sources emanating from agricultural and urbanized tributary watersheds are responsible for nutrient pollution loads to the lake and its tributaries.

This thesis focused on the existing water quality parameters (nutrients and water chemistry) throughout the Euclid Creek watershed, an urbanized Lake Erie headwater tributary east of the Cuyahoga River. Field sampling was conducted from

March 2019 to March 2020 at 14 sites with 23 dry weather collections and 11 wet weather collections. Results suggest that the 2019 annual phosphorus load entering Lake

Erie was 22,600 pounds, over four times the target of 5000 pounds.

Multiple upstream sites were the major nonpoint sources of nutrient pollution.

Four locations averaged phosphorus levels 12 to 15 times the target of 0.05 mg/L, with two in the East Branch and two in the Main Branch. The main cause of the pollution pointed to leaky sanitary sewers.

Like many urbanized areas throughout the United States, the original headwaters have been replaced by underground stormwater infrastructure. Due to the high level of

iii connectivity between the creek and the storm sewer network, Euclid Creek responds rapidly to rainfall. There was evidence of Combined Sewer Overflow (CSO) and Sanitary

Sewer Overflow (SSO) activations during storm events downstream of the confluence of the two branches and in the East Branch. Seasonally, spring storms contributed the most pollution during the monitoring period.

The presence of the Cleveland Metroparks significantly reduced [푝 < 0.05] nutrients during dry weather. Residential areas contributed more pollution than the three golf courses and the regional airport located within the watershed. The East Branch has little protection from urban run-off. This research suggests that water quality improvements are needed in both upstream branches. Autosamplers should be installed for future water quality monitoring at the two upstream existing US Geological Survey stations to gather data during wet weather events and baseflow conditions. Fish rocks, protective cave-like features, should be installed at upstream sites to protect aquatic life from storm-induced currents. If possible, storage for wet weather flows should be provided for both branches.

ACKNOWLEDGEMENTS

I would like to express my deepest gratitude to Dr. Teresa J. Cutright, my advisor, for her unwavering guidance and support throughout this research work. Her encouragement and helpful critiques were invaluable. I am grateful for everything: laboratory and field equipment use, weekly update meetings, abundant reference material, expert chemical advice, and the pivotal opportunity to expand my engineering knowledge. Additionally, I would like to acknowledge the help provided me by Dr.

Richard L. Einsporn. His statistical expertise and advice were greatly appreciated throughout my research process. I would also like to express my appreciation to Dr.

Stephen E. Duirk, for his wisdom, time, and review of this work.

My special thanks are extended to Michael Spade, who generously helped with a plethora of laboratory analysis and George Carleton, who skillfully trained me in proper lab procedures and assisted with my initial field collection. I would also like to thank

Elizabeth Hiser, the Euclid Creek Watershed Program Manager, for her time and communication. I wish to thank everyone who helped me in the field collecting data for this research, during all types of weather. Thanks to Caroline Kelemen, my sons,

Cameron and Ethan, and Anne Wiles. Thanks to Elena Stachew, who donated the turbidity tube and to Patricia Eaglewolf, who was always available for help in the Civil

Engineering office. Finally, I wish to thank my husband, Tony, for all his support.

TABLE OF CONTENTS

LIST OF TABLES……………………………………………………………………………………………………………………...…..xi

LIST OF FIGURES……………………………………………………………………………………………………………………….xiii

CHAPTER

I. INTRODUCTION………………………………………………………………………………………………………………….…….1

1.1 Water Pollution Background……………………………………………………………………………………….1

1.2 Euclid Creek Watershed……………………………………………………………………………………………..4

1.3 Objectives………………………………………………………………………………………………………………………..5

II. LITERATURE REVIEW…………………………………………………………………………………………………………..7

2.1 Lake Eutrophication……………………………………………………………………………………………………..7

2.2 Harmful Algal Blooms………………………………………………………………………………………………….9

2.3 Lake Erie………………………………………………………………………………………………………………………..10

2.4 Euclid Creek……………………………………………………………………………………………………………..….13

2.5 Dry Weather & Wet Weather Definitions……………………………………………………………14

III. HISTORICAL WATER QUALITY SAMPLING & RAINFALL…………………………….…….16

3.1 Rainfall Event Summary for the 2019-2020 Monitoring Period…….…………………..16 vi

3.2 Water Quality Monitoring & Assessment Reporting………………………………………..25

3.3 NEORSD Sampling…………………………………………………………………………………………………….29

3.4 The Euclid Creek Watershed Program……………………………………………………………….….31

3.5 Historical Rainfall Data Exploration………………………………………………………………………32

IV. EXPERIMENTAL METHODS……………………………………………………………………………………………..38

4.1 Overview of Site Selection Process………………………………………………………………………….38

4.2 Sampling Site Descriptions……………………………………………………………………………………...40

4.2.1 Acacia…………………………………………………………………………………………………………...40

4.2.2 Telling Mansion………………………………………………………………………………………...43

4.2.3 Schaefer Park………………………………………………………………………………………………45

4.2.4 Spencer Road……………………………………………………………………………………………..46

4.2.5 Harris Road…………………………………………………………………………………………………48

4.2.6 Community Center……………………………………………………………………………………50

4.2.7 U/S Stonewater……………………………………………………………………………………………51

4.2.8 Rockefeller Road………………………………………………………………………………………..52

4.2.9 Bishop Road………………………………………………………………………………………………..54

4.2.10 Richmond White……………………………………………………………………………………..55

4.2.11 Highland Main…………………………………………………………………………………………..56 vii

4.2.12 Highland East……………………………………………………………………………………………58

4.2.13 Villaview……………………………………………………………………………………………….……59

4.2.14 Wildwood……………………………………………………………………………………………….…61

4.3 Field Equipment………………………………………………………………………………………………………….62

4.4 Lab Analyses…………………………………………………………………………………………………………..……64

4.5 Statistical Methods…………………………………………………………………………………………………....65

V. DRY WEATHER RESULTS & DISCUSSION……………………………………………………………..……67

5.1 Dry Weather Flow Overview……………………………………………………………………………………67

5.2 Dry Weather Flow Conditions at Acacia……………………………………………………………...68

5.3 East & Main Branch Comparison……………………………………………………………………………78

5.4 Tributary Impact on East & Main Branches………………………………………………...……...81

5.5 Active Storm Sewer Collections During Dry Weather……………………….…….…….….89

VI. WET WEATHER RESULTS & DISCUSSION……………………………………………………………..…91

6.1 Wet Weather Overview………………………….…………………………………………………………….…...91

6.2 Wet Weather Flow Conditions at Acacia…………………………………………………………….92

6.3 Rainfall Characteristics of Wet Weather Collection Events……………………..…….101

6.4 East & Main Branch Comparison During Wet Weather for Upper and Lower Reaches………………………………………………...…………….……106

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6.5 Tributary Impact on Nutrient Concentrations of the Two Branches During Wet Weather…………………………………………………………………..……..109

6.6 Wet Weather Collections at Single Sites……………………………………………………………..112

VII. TOTAL PHOSPHORUS LOADING ANALYSIS……………………………………………..……………128

7.1 USGS Stations……………………………………………………………………………………………………….……128

7.2 Dry Weather Phosphorus Loading Calculations…………………………………………...……131

7.3 Wet Weather Phosphorus Loading Calculations………………………………………….…..133

7.4 Total Annual Phosphorus Loading Conclusion……………………………………………….…135

VIII. CONCLUSIONS AND RECOMMENDATIONS………………………………………………………..136

8.1 Conclusions………………………………………………………………………………………………………….……..136

8.2 Recommendations………………………………………………………………………………………..…………..141

REFERENCES…………………………………………………………………………………………………………………….…………145

APPENDICES………………………………………………………………………………………………………………………………..158

APPENDIX A: Dry Weather Results (Individual Sites) ………………………………..………159

APPENDIX B: Dry Weather East and Main Branch Comparison……………………..….186

APPENDIX C: Dry Weather ANOVA and Tukey Comparisons of Upstream Tributary Impact……………………………………………………………………………..…...…196

APPENDIX D: Wet Weather Results (Individual Sites)…………………………….……….…201

APPENDIX E: Wet Weather Collection Events……………………………………………………...228 ix

APPENDIX F: Phosphorus and Rainfall Characteristic Comparison………………………………………………………………………………………………………………………237

APPENDIX G: Wet Weather East and Main Branch Comparison……………………………………………………………………………………………………………………..243

APPENDIX H: Wet Weather ANOVA and Tukey Comparisons of Upstream Tributary Impact…………………………………………………………..………………………..249

LIST OF TABLES

Table Page

2.1 Trophic States for Water Bodies……………………………………………………………………………….…..…7

2.2 US Lake Survey for Cyanobacteria…………………………………………………………………………….…..10

2.3 Target Nutrient Concentration Goals for US Streams…………………………….………………..13

2.4 2006 Existing TMDL Calculations for Euclid Creek Watershed…………………….……..14

3.1 Summary of Rain Events During 2019-2020 Monitoring Period……………………………..17

3.2 Summary of Rainfall Duration and Depth for 2019-2020 Monitoring Period…………………………………………………………………………………………………………....22

3.3 Comparison of 2019-2020 Monitoring Period Rainfall to Historical Data………………………………………………………………………………………………………………22

3.4 NEORSD Beachwood Rain Gauge Data (2012-2020)……………………………..………………..24

3.5 Monthly Rainfall Characteristics for the 2019-2020 Monitoring Period………………………………………………………………………………………………………..…..25

3.6 2018 NEORSD Nutrient Results for Euclid Creek…………………………………………..………..30

3.7 Summary Statistics for Matched-Pairs t-test Between Rain Gauges (2012-2019)…………………………………………………………………………………………..…….34

3.8 Difference in Monthly Rain Gauge Totals (Beachwood – CLE Airport)………………………………………………………………………………………….34

5.1 Dry Weather Phosphorus Comparison for Upper Reaches of East & Main Branches……………………………………………………………………..………………………….78

5.2 Dry Weather Phosphorus Comparison for Lower Reaches of East & Main Branches…………………………………………………………………………………………….…..79

5.3 Summary Statistics for East & Main Branches During Dry Weather……………………………………………………………………………………………………………………….81

5.4 Site Descriptions for Adjacent Phosphorus Drop Comparison…………………………..…..87

5.5 Active Dry Weather Storm Sewer Nutrient Concentrations…………………………………..90

6.1 Peak Flow Differences in Branches During Wet Weather Events…………………….……………………………………………………………………..………..104

6.2 Summary Statistics for East & Main Branches During Wet Weather…………………………………………………………………………………………………………...... 108

6.3 Site Descriptions for Adjacent Phosphorus Drop Comparison……………………………………………………………………………………………………….……………...110

7.1 2019 Dry Weather Annual Predicted Phosphorus Loads for Euclid Creek……………………………………………………………………………………………………..……………...133

7.2 2019 Wet Weather Annual Predicted Phosphorus Loads for Euclid Creek……………………………………………………………………………………………………………..………135 .

7.3 2019 Total Annual Projected Phosphorus Loads for Euclid Creek…………………..………………………………………………………………………………………………...135

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LIST OF FIGURES

Figure Page

2.1 Satellite image of 2011 Lake Erie algal bloom…………………………………………………….………....11

2.2 Average Annual Total Phosphorus Inputs to Lake Erie

(2003 – 2011)……………………………………………………………………………………………………………………....12

3.1 Summary of 2019-2020 Rainfall Events: Depth and Duration………………………….……..23

3.2 Summary of 2019-2020 Rainfall Events: Antecedent and Rainfall Intensity………………………………………………………………………………………………………….……23

3.3 Impairment Causes for US Waters………………………………………………………………………….……26

3.4 Aquatic Life Attainment for Ohio’s Wading & Principal Streams………………………………………………………………………………………………………………………….....…27

3.5 Priority Ranking of Ohio Watersheds………………………………………………………….………………28

3.6 Euclid Creek Volunteer Phosphorus Monitoring Data (2006-2019)………………………………………………………………………………………………………………………..32

3.7 NOAA and NEORSD Rain Gauge Locations……………………………………………………….………33

3.8 Historical Monthly Trends at Cleveland Hopkins Airport (1939-2019)………………………………………………………………………………………………………….………………35

3.9 Beachwood Rain Gauge Monthly Rainfall Totals (2012 – 2019)………………………………………………………………………………………………………………………36

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3.10 Annual Means at Cleveland Hopkins Airport and Mean of all NEORSD Rain Gauge Data (2013-2019)………………………………………………………………….……37

4.1 Sampling sites used to assess water quality in Euclid Creek Watershed……….………………………………………………………………………………………………………….……..40

4.2 Acacia Site Views: (a) looking downstream from pedestrian bridge and (b) looking at sampling site from downstream location……………………………………………………………………………………………….……….42

4.3 Storm drains at Acacia on Eastern Embankment……………………………………….….….………42

4.4 Telling Mansion Site Views: (a) looking downstream from pedestrian bridge and (b) looking at sampling site from downstream location…………………………………………………………………………………………….………...44

4.5 Possible iron deposit area downstream of Telling Mansion Site…………………………….44

4.6 Land use surrounding unnamed tributary to Main Branch…………………………..…………45

4.7 Schaefer Park Site Views: (a) looking at sampling site from downstream location and adjacent storm sewer and (b) second storm sewer on downstream opposite embankment…………………………………………..……46

4.8 Schaefer Park Investigation Locations………………………………………………………………….………47

4.9 Downstream View at Spencer Road Site…………………………………………………………….……….48

4.10 East Branch Investigation Sample Locations………………………………………………………….…..49

4.11 Harris Road Site Looking Upstream…………………………………………………………………………….49

4.12 Community Center Site looking upstream………………………………………………………………….50

4.13 Community Center Site Views: (a) looking at storm sewer at site and (b) looking at second storm sewer downstream of site…………………………………………………………………………………………..…………….….51

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4.14 U/S Stonewater Site Views: (a) looking upstream from sampling site with storm drains further upstream and (b) looking upstream from site with storm sewer at sampling site………………………………………………………………………………………………………………………………………..52

4.15 Upstream view of Rockefeller Road Site…………………………………………….……………………….53

4.16 Bishop Road Site Views: (a) upstream view of tributary and (b) storm sewer at sampling site, downstream side of bridge………………….……….…….55

4.17 Richmond White Site Views: (a) upstream of East Branch at sampling site and (b) looking downstream at the sampling site from upstream location……………………………………………………………………………………….……56

4.18 Highland Main Site Views: (a) looking at sampling site and (b) looking upstream from sampling site………………………………………………………….…………58

4.19 Highland East Site Views: (a) downstream view of the confluence of the two branches and (b) looking upstream from site………………………………………………………………………………………………………………………………59

4.20 Villaview Site Views: (a) looking at sampling site from downstream side and (b) looking downstream from sampling site………………………………………………………………………………………………….……………..……60

4.21 Wildwood Site Views: (a) looking upstream from sampling site and (b) storm sewer at sampling site, upstream side………………………………………..………61

4.22 Turbidity tube used to determine stream characteristics…………….………………………….62

4.23 Measuring turbidity: (a) looking into the tube to view Secchi disk for unclear water (b) Looking at the tube filled with clear water……………………………………………………………………………………………………………………………….……63

4.24 Multiparameter Probe used to determine stream characteristics: (a) Typical reading in bucket completed at all site locations (b) Back-up stream reading completed at sites where access to direct stream sampling possible………………………………………….….64

4.25 Typical glass vial used for laboratory testing…………………………………………………….…..……65

5.1 Acacia: Dry Weather Phosphorus Mean and Standard Deviation Levels over Time Based on Triplicate Samples……………………………………………………………68

5.2 Dry Weather Nitrate Mean and Standard Deviation Levels over Time Based on Triplicate Samples Comparison for Old and New Colorimeter……………………………………………………………………………………….………69

5.3 Acacia: Dry Weather Nitrate Mean and Standard Deviation Levels over Time Based on Triplicate Samples………………….……………………..70

5.4 Acacia: Dry Weather pH levels over time…………………………………………………..………………..72

5.5 Acacia: Dry Weather Conductivity levels over time……………………………………..…………..73

5.6 Dry Weather Conductivity vs. Water Temperature at Acacia………………………….…….74

5.7 Acacia: Dry Weather Turbidity levels over time………………………………………………….……..76

5.8 Acacia: Dry Weather Water temperature levels over time…………………………….………..76

5.9 Acacia: Relationship between Water and Air Temperature During Dry Weather………………………………………………………………………………………………………...77

5.10 Dry Weather Phosphorus Level Comparison Between Original Nine Sites…………………………………………………………………………………………………………...82

5.11 Existing Sanitary Sewer Line at Schaefer Park Site…………………………………………………..83

5.12 Schaefer Park Investigation Phosphorus Results………………………………………………….……84

5.13 East Branch Tributary Investigation Phosphorus Results……………….………………………85

5.14 Location of Three Upstream Tributary Sites for East Branch……………….………………..85

5.15 Adjacent Site Phosphorus Drop Comparison…………………………………………………….……….87

5.16 Mapping of Change in Phosphorus Concentrations for Adjacent Sites During Dry Weather Conditions………………………………………….…………….88

5.17 Active Storm Sewer During Dry Weather at Telling Mansion………..…………..…………90

6.1 Acacia: Phosphorus concentrations during wet weather events. Standard deviation based on triplicate samples………………………………….……………………..92

6.2 Acacia: Nitrate concentrations during wet weather events. Standard deviation based on triplicate samples…………………………………………….…..………94

xvi

6.3 Acacia: pH levels over time during wet weather events……………………………………………96

6.4 Acacia: Conductivity levels over time during wet and dry weather……………………….97

6.5 Looking downstream from Acacia sampling site (a) during wet weather conditions on June 20, 2019 and (b) stone debris during September 15, 2019 collection…………………………………..……………….…….……99

6.6 Acacia: Turbidity levels over time during wet weather………………………………………..…100

6.7 Acacia: Water temperature levels over time during wet weather…………………………………………………………………………………………………………………………….…101

6.8 All wet weather events for the monitoring period (March 2019 – March 2020)…………………………………………………………………………………….……102

6.9 Wet Weather Collection 1: Main Branch at Telling Mansion (April 26, 2019)………………………………………………………………………………………..……..103

6.10 Wet Weather Collection 1: East Branch at Richmond White (April 26, 2019)……………………………………………………………………………………………………104

6.11 Wet Weather Conditions for East Branch at Richmond White: Phosphorus Concentration vs Rainfall based on triplicate samples…………………………………………………………………………………………………..………..106

6.12 Adjacent Site Phosphorus Drop Comparison During Wet Weather………………………………………………………………………………………………………………………..……110

6.13 Mapping of Change in Phosphorus Concentrations for Adjacent Sites During Wet Weather Conditions…………………………………………….……..…111

6.14 Rainfall and Discharge During the Monitoring Period for the July 16-17th storm event for the (a) Main Branch and (b) Main Branch and East Branch…………………………………………………………………………………114

6.15 Changes in (a) Mean Phosphorus Concentration and (b) Mean Nitrate Concentration at Highland Main During July 17th Rain Event. Standard deviations based on triplicate sampling………………………………………………………………………………………………………………………..……115

6.16 Changes in (a) Mean Phosphorus Concentration and (b) Mean Nitrate Concentration at Highland Main and Highland East (c) Turbidity at Highland Main and Highland East During July 17th Rain Event. Standard deviations based on triplicate sampling…………………………………………………………………………………………….….……117

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6.17. Monitoring period within the July 22nd rain event at Schaefer Park………………..…………………………………………………………………………………….………….…119

6.18 Water quality concentrations over time at Schaefer Park during July 22nd rain event: (a) Mean Phosphorus Levels (b) Mean Nitrate Levels. Standard deviations based on triplicate sampling (c) Turbidity and Conductivity (d) pH and Temperature……………………………………………………………………………………….….……120

6.19 July 22, 2019 Rain Event at Schaefer Park Illustrating (a) Rainfall Intensity (b) Main Branch Discharge at USGS Station 04208677…………………………………………………………………………………………………..…………122

6.20 October 30th Rain Event at Schaefer Park with (a) Total 2-Day Storm Discharge (b) Discharge and Rainfall Intensity for the monitoring period…………………………………………………………………………………….……..…124

6.21 Changes in (a) Mean Phosphorus and (b) Mean Nitrate Concentrations at Schaefer Park during October 30th rain event. Standard deviations based on triplicate sampling…………………………………..……125

6.22 Changes in (a) pH Levels, (b) Conductivity and (c) Temperature during October 30th Rain Event at Schaefer Park…………………………………………………………………………………………………………..……127

7.1 March 2019 – March 2020 Annual Discharge for Euclid Creek’s (a) East Branch (b) Main Branch, and (c) Downstream after Confluence of Two Branches…………………………………………………………………………….…130

7.2 Monitoring Stations used for pollutant loads…………………………………..…….…………….……131

7.3 Linear Regression Equations for Dry Weather Phosphorus Loads for (a) East Branch, (b) Main Branch, and (c) Downstream after the Confluence of the Two Branches……………………..……..……132

7.4 Linear Regression Equations for Wet Weather Phosphorus Loads for (a) East Branch, (b) Main Branch, and (c) Downstream after the Confluence of the Two Branches……………………..………..…134

8.1 Fish rocks designed by Professors Vogl and Benitez to protect fish from heavy storm flows…………………………………………………………………….…..…144

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CHAPTER 1

INTRODUCTION

1.1 Water Pollution Background

Chronic pollution in surface waters continues to plague the planet and threaten human health. Current stormwater and wastewater practices allow contaminants to freely enter drinking water sources (Holt, 2000). Headwater streams tributary to drinking water sources require protection. Since the Industrial Revolution, the manufacturing of goods alongside human migration to cities has caused widespread environmental pollution to waterways. Environmental public policy is headed toward more sustainable practices, balancing economic, social, and environmental principles

(Haque and Ntim, 2018; Lu et al., 2015; Costanza et al., 2016). Governments focused on intergenerational environmental ethics is necessary; without it, the planet’s natural resources are expended until extinction (Lockwood, 1874).

People have long suspected that urban run-off from storm events causes damage to the environment. During the 1800’s, surface run-off from the railroads was observed in local rivers and thought to cause harm to aquatic life (Lockwood, 1874). Similarly, human waste was known to contaminate drinking water, causing cholera, dysentery, typhoid fever and diarrhea (Vuorinen et al., 2007). By 1900, water closets were deemed necessary to protect public health in urban households, but there was still no thought

1 given to improve the sanitation process for domestic waste removal (Vuorinen et al.,

2007). American cities followed sanitation practices already developed throughout much of Europe. Sanitary sewers provided direct transportation routes for waste from individual city households to nearby rivers. Given these infrastructure origins, today’s stormwater and sanitary sewer systems have improved, but still negatively impact the environment.

Stormwater pipes continue to act as direct conduits between urban impervious surface areas and streams. Although today’s sanitary sewers now act as connections between individual homes and wastewater treatment plants instead of nearby rivers, the aging infrastructure steadily leaks into the surrounding environment (Lee et al., 2015;

Sercu et al, 2011; Kaushal & Belt, 2012). Additionally, and just as problematic, sanitary sewer flow volumes and rates have increased due to high infiltration and inflows. As a result, large interceptor tunnels have been constructed worldwide to capture and hold these vast quantities until processed at wastewater treatment facilities. Even with this supplementary infrastructure, exfiltration to nearby rivers continues to occur.

According to the United States Environmental Protection Agency (US EPA,

2020a), stormwater run-off can contain phosphorus and nitrogen, pathogens, petroleum hydrocarbons, metals, sediment, pesticides, herbicides, and organics. Sewage generally consists of organic matter, inorganic salts, heavy metals, bacteria, viruses, phosphorus and nitrogen. These contaminants can escape either through pipe leaks or by intentional overflow designs. Excess amounts of phosphorus and nitrogen are key contributors to harmful algal blooms (HABs). An added concern is that excess amounts of phosphorus and nitrogen increase biological activity which eventually leads to eutrophic conditions

2 in waterbodies. The excessive algae growth triggered by these nutrients can cause HABs.

It is difficult and costly to remove the toxins from HABs. In 2013, the Ohio EPA decreed

“do not drink” advisories due to increased cyanobacterial HABs in Lake Erie. In 2014,

Toledo, Ohio had to issue a ban on drinking and cooking with tap water (Fitzsimmons,

2014). Microcystins are a type of cyanotoxin from HABs that can cause illness or death if ingested (US EPA, 2020b). Lake Erie HABs usually contain Microcystis colonies (National

Oceanic and Atmospheric Administration (NOAA), 2020a). Heavy rains and sewer overflows push excess nutrients into the lake. HABs occur throughout the Great Lakes, from the western edge of Lake Superior to the eastern portion of Lake Ontario. This past year, 2019, was one of the strongest HAB activity in the Western Basin (Briscoe, 2019;

NOAA, 2020b). Cyanobacteria have been tested and confirmed at Cleveland beaches during this time (Johnston, 2019; Justice, 2019).

The United Nations estimates approximately 80% of global wastewater is released without proper treatment (United Nations Educational, Scientific and Cultural

Organization (UNESCO), 2017). Water pollution originates at point and nonpoint sources. In the United States, point sources are regulated by the US EPA through the

Clean Water Act. Nonpoint sources are comprised of at least one unknown point source.

The EPA states that nonpoint sources are the most significant source of water pollution.

Although there are no directly stipulated regulations, cities and states are required to develop nonpoint source pollution management programs to obtain any federal funding

(US EPA, 2020c).

In local urban environments, water pollution occurs every day. Pollution amounts are dependent on weather conditions. During rain events, pollution enters surface waters

3 from direct connections with stormwater infrastructure and combined sewer overflow

(CSO) and sanitary sewer overflow (SSO) activations. During dry weather, pollution enters surface waters from National Pollutant Discharge Elimination System (NPDES) connections, illicit discharges, and leaky sanitary sewers.

1.2 Euclid Creek Watershed

According to the US EPA, Lake Erie provides drinking water for approximately 11 million people with most of the inflow for Lake Erie (~80%) coming from the Detroit

River (US EPA, 2020d). Of the remaining 20%, about half, 11%, comes from precipitation and 9% comes from tributaries (US EPA, 2020d). The Lake Erie Watershed has a total drainage area of 30,140 square miles (Augustyn, 2019).

In total, there are 34 watersheds tributary to Lake Erie, 26 in the United States and 8 in Canada. Euclid Creek lies within the Ashtabula-Chagrin Watershed Region

(Hydrological Unit Code 04110003). It is one of the most urbanized of the Lake Erie sub watersheds. The majority of Euclid Creek lies within Cuyahoga County, Ohio. It is approximately 43 miles long and drains approximately 24 square miles (Euclid Creek

Watershed Program, 2018). The Main Branch of the creek extends from its headwater location in the community of Beachwood to its downstream location at Wildwood Park, where the mouth of the creek meets Lake Erie in Cleveland. Between these two endpoints, the Main Branch travels through two additional communities, Lyndhurst and

South Euclid. A substantial portion of the watershed flows into the East Branch of the creek. This branch has its headwater location in Highland Heights and Willoughby Hills

(Lake County). The East Branch travels through the town of Richmond Heights and

Euclid before its confluence with the Main Branch.

Euclid Creek is in an Area of Concern for Lake Erie. The Great Lakes Water

Quality Agreement outlined fourteen beneficial uses to restore tributary rivers of the

Great Lakes back to good ecological health. Although not tributary to the Cuyahoga

River, it is listed as part of the Cuyahoga River Area of Concern by the International

Joint Commission (Cuyahoga River Restoration, 2020; International Joint Commission,

2020). The main water quality impairments of Euclid Creek are high nutrients, low fish populations, embankment erosion and illicit discharges (Ohio EPA, 2005).

There is evidence of continued pollution via sewer leakage through the water quality efforts at downstream beaches by the USGS Great Lakes NowCast Status. Villa

Angela Beach conditions were tested daily for Escherichia coli (E. coli) counts by the

Northeast Ohio Regional Sewer District (NEORSD). During the summer of 2019, 39% of the days that reported counts exceeded the beach action value of 235 most-probable number/100 ml (USGS, 2020a). These high-count days are not all associated with rainfall, suggesting that pollution occurs during dry weather as well as wet weather.

1.3 Objectives

The overall intent of this research was to gather reliable water chemistry, analyze the results, and make recommendations to reduce the pollution of a tributary to Lake

Erie, a local drinking water source. This research focused on gathering detailed information on nutrient levels. Hypothesis: The nutrient levels for Euclid Creek will be

5 highest downstream during the warmest part of the summer. There are three additional sub-objectives of this work:

1. Collect water quality data at the established volunteer Euclid Creek

Watershed monitoring locations in order to compare results with historical

data. Hypothesis: Nutrient levels relative to the spatial orientation within the

watershed will be akin to past conditions. New patterns are expected to

emerge since this is the first-time data has been collected at all monitoring

locations consecutively within the same day.

2. Monitor water quality during wet and dry conditions to establish baseline

conditions for wet weather impacts. Hypothesis: Nutrient levels during dry

weather will be low. Wet weather collections during storm events will be

compared to dry, baseline conditions. Collections will be made for wet

weather samples at all locations within a day as well as monitoring a wet

weather event at a single location. Wet weather nutrient levels will be

dependent upon the rainfall amount, rainfall intensity, and time of collection

within the storm event. Nutrient levels are expected to rise after the initial

rainfall, then dilute after the peak flow.

3. Determine the importance of land use as a factor on nutrient levels. Hypothesis:

The highest nutrient levels will occur downstream of golf courses and the

airport. The lowest nutrient levels will occur at upstream residential

headwater locations.

CHAPTER 2

LITERATURE REVIEW

2.1 Lake Eutrophication

Lakes naturally age over time, typically living hundreds, if not thousands, of years. Over time, a lake’s basin fills with sediment and nutrients (Dunn, 1989).

Ultimately, plants and algae proliferate, depleting dissolved oxygen levels and ending the lake’s life. This process is called natural eutrophication and occurs slowly, over a long period of time. Anthropogenic eutrophication is an artificial increase in the supply rate of organic matter to an ecosystem (Nixon, 1995). This increased organic carbon supply can be caused by increased inorganic nutrients, decreased water turbidity, change in hydraulic residence time, change in land use, and increased organic matter (Hinga et al.,

1995). The acceleration of an organic carbon supply can change the trophic state of the water body (Hinga et al., 1995). There are four basic trophic states as shown in Table 2.1.

Table 2.1. Trophic States for Water Bodies Trophic State Organic Carbon Supply (gC / m2/yr) Oligotrophic ≤100 Mesotrophic 101 – 300 Eutrophic 301 – 500 Hypertrophic › 500 Source: Hinga et al., 1995

Lake longevity varies due to differences in surface area, depth, stored volume, shoreline length, temperature, rainfall, and river inflow (Messager et al., 2016; Scavia et al., 2014). Lake maturity is based on its physical and chemical properties. The main physical characteristics are temperature, light, rainfall and wind, while the main chemical characteristics are biological, geological, and human processes (US EPA, 2020e;

National Geographic, 2020). Pollution, caused by human processes, can shorten a lake’s life dramatically. Key sources of pollution are acid rain, nutrient pollution, fish stocking, pesticide poisoning, invasive species, soil erosion, road salt, and climate change (Stager,

2018). Anthropogenic eutrophication can occur over a very short period, ending a lake’s life in decades.

A healthy lake ecosystem uses nitrogen and phosphorus as a food source for nutrient-rich plants and algae. In turn, these plants and algae become the food source for other organisms living in lakes. This natural food chain can become disrupted when a lake’s chemistry is artificially changed by humans. Sewage, storm run-off, and fertilizers can enter surface waters from nonpoint sources like lawn applications, accidentally from leaks. This additional volume of flow contains nutrients that can be easily consumed by microscopic algae as well as deposited in sediments. These organisms can multiple and create large algal blooms, a major environmental problem in all 50 US states (US EPA,

2020f). Algal blooms can deplete a lake’s oxygen level, block sunlight, and clog fish gills

(NOAA, 2020a). A small percentage of these blooms, less than one percent, release toxins that are harmful to the environment and humans (NOAA, 2020c).

2.2 Harmful Algal Blooms

Most algal blooms are beneficial, providing a food source for other organisms like bivalves and fish. They also provide almost half of the world’s photosynthesis (Rossini,

2014). There are 5,000 species of planktonic algae. About 300 species can cause discoloration of surface water and 80 species can produce toxins (Rossini, 2014). In freshwater, cyanobacteria are usually responsible for large blooms (CDC, 2020). When environmentally stressed, these blue-green algae can produce microcystin, a toxin that cause a variety of health problems to humans, from dermatitis to nerve damage (Rossini,

2014; USGS, 2016). As stated in Section 1.1, when a bloom contains cyanobacteria, it is designated as a HAB. HABs can also cause other problems including unpleasant taste and odor, a blue-green, yellow, red or brown water discoloration, anoxic conditions for other aquatic life, and unfavorable economic impacts (USGS, 2016).

Nutrient loading distribution is a key factor in forming HABs, as the cyanobacteria use phosphorus and nitrogen as a food source (Gilbert et al., 2005).

Nutrients can be delivered intermittently and intensely by heavy rainfall or continuously and slowly by sewer discharge (Gilbert et al., 2005). Other factors include time of year and the presence of competitor or consuming species. Predicting HAB occurrence is a current goal for the science community (USGS, 2016). The US EPA prioritizes watersheds within each state to target nutrient load reductions (US EPA, 2020g).

In 2007, the US EPA completed a lake assessment for all 50 contiguous states to help understand the number of lakes impacted by HABs. According to the World Health

Organization (WHO), when a water quality sample exceeds 20,000 cells per milliliter

(mL) of cyanobacteria, the water becomes a health risk. Scum formation occurs at

100,000 cells/mL (WHO, 2003). Table 2.2 details the ten states in the 2007 US EPA survey with the highest percentage of positive lake tests, a lake sample in excess of

20,000 cells/mL (US EPA, 2009). Samples were taken at the deepest part of each lake in an order to minimize false positives. The number of lakes tested in each state varied from

7 lakes to 66 lakes. Positive cyanobacteria tests occurred in 79% of states tested. Of the states that had positive lake results, over half had cyanobacteria in at least 20% of lakes tested. Ohio ranked 19th with 27% positive (6 out of 22 lakes).

Table 2.2. US Lake Survey for Cyanobacteria Percent of Lakes Number of Lakes Total Number of State Tested Positive Tested Positive Lakes Tested South Dakota 71% 29 41 Illinois 62% 13 21 North Carolina 52% 11 21 North Dakota 52% 23 44 Missouri 46% 13 28 Texas 46% 26 56 Delaware 44% 4 9 Virginia 42% 11 26 Indiana 39% 22 56 Idaho 37% 13 35 Source: US EPA, 2009

2.3 Lake Erie

Algal blooms were a common occurrence in Lake Erie during the 1950’s and

1960’s. Phosphorus loading from farms and sewage plants permeating into Lake Erie’s tributaries was the main factor adversely impacting Lake Erie during that time

(European Space Agency (ESA), 2020). In 1972, The Great Lakes Water Quality

Agreement implemented phosphorus limits and water quality improved. Unfortunately, by the mid-1990’s, nutrient enrichment conditions returned, especially noted by the

10 increased presence of cyanobacteria (Scavia et al., 2014). Figure 2.1 shows an ESA (2011) satellite image of a 2011 bloom. The re-eutrophication of Lake Erie in recent times has worsened in three ways. The Western Basin has seen the most frequent cyanobacteria blooms, with the Maumee River contributing the highest phosphorus loads. The Central

Basin experiences hypoxia and the Eastern Basin experiences nuisance blooms (Annex 4,

2015).

Figure 2.1. Satellite image of 2011 Lake Erie algal bloom Source: Envistat, ESA, 2011

Regulations effectively decreased point source total phosphorus (TP) loads, leaving nonpoint sources as the primary phosphorus source (Scavia et al., 2014). As

Figure 2.2 shows, during the first part of the 2000’s, nonpoint phosphorus inputs tripled point sources, the second leading major nutrient source. Lake Erie receives the most TP

11 from the Western Basin (60%). The Central Basin contributes about 30%, with the remaining 10% coming from the Eastern Basin (Scavia et al., 2014).

Upstream (Lake Huron) 4%

Atmospheric 6% Nonpoint 69%

Point 21%

Figure 2.2. Average Annual Total Phosphorus Inputs to Lake Erie (2003 – 2011) Source: Dolan and Chapra, 2012

Recently, dissolved reactive phosphorus (DRP) has been studied due to its bioavailability to cyanobacteria (Scavia et al., 2014). In the 1990’s, DRP represented about 11% of incoming TP loads from contributing watersheds to Lake Erie. A decade later, the level of DRP increased to 24% of incoming TP loads. It is now estimated that

DRP has increased 150% from the mid-1990’s levels (Annex 4, 2015). Although phytoplankton biomass decreased from 1970 to the mid 1980’s, the increased DRP loading has led to high abundances of cyanobacteria (predominantly Microcystis) in Lake

Erie. In order to reach improved water quality conditions, phosphorus levels need to be cut in half (Scavia et al., 2014). Modeling suggests that driving phosphorus loading rates

(i.e., the load associated with biomass increases) differs between the Western and

Central Basins. Driving phosphorus loading occurs in the spring for the Western Basin, but annual loading is the driver for the Central Basin (Annex 4, 2015).

2.4 Euclid Creek

Euclid Creek is one of nearly 100 headwater streams tributary to Lake Erie

(Euclid Creek Watershed Council et al., 2006). The Euclid Creek Watershed is on

Ohio’s list of impaired waters (Ohio EPA, 2005). The Ohio EPA identified the pollution causes as nutrient enrichment, sedimentation, and stream habitat degradation (Euclid

Creek Watershed Council et al., 2006). The corresponding sources were CSOs, failing septic systems, stormwater run-off, and nonpoint sources (Ohio EPA, 2005). Since

Euclid Creek is classified as an impaired water, the Clean Water Act requires that pollutant limits must be developed and enforced. Total maximum daily loads (TMDLs) for impaired streams are designed to restore Euclid Creek by full attainment of water quality standards (US EPA, 1991). US stream target concentrations for phosphorus and nitrate-nitrite are listed in Table 2.3. (Euclid Creek Watershed Council et al., 2006).

Table 2.3. Target Nutrient Concentration Goals for US Streams Stream Type Watershed Area Phosphorus Nitrate-nitrite (mi2) Concentration Concentration (mg/L) (mg/L) Headwaters < 20 0.05 1.00 Wadable 20 – 200 0.07 1.05 Source: Ohio EPA, 2005

States are responsible for submitting TMDL calculations to the US EPA for all waters classified as impaired (US EPA, 2020h). The Ohio EPA used the following equation to calculate target phosphorus loads (Ohio EPA, 2005):

푇푀퐷퐿 = ∑ 푊퐿퐴 + ∑ 퐿퐴 + 푀푂푆 Equation (1) where: WLA = waste load allocations (point sources)

LA = load allocations (nonpoint sources and background)

MOS = margin of safety (10%)

Using this approach, the approved WLA, LA, MOS, and TMDL were 0 lbs/yr, 4990.62 lb/yr, 554.41 lb/yr, and 5545.12 lb/yr, respectively (Ohio EPA, 2005). As of 2006, Euclid

Creek existing conditions exceeded target levels. Table 2.4 reveals that nonpoint source phosphorus levels were ten times as high as point sources. The phosphorus target reduction goal is 3450 pounds per year. In addition, the watershed implemented best management practices like obtaining conservation easements, establishing riparian setbacks, dam removals, and sustainable site design (Euclid Creek Watershed Council et al., 2006).

Table 2.4. 2006 Existing TMDL Watershed Calculations for Euclid Creek Watershed WLA LA LA Breakdown TMDL (lb/yr) (lb/yr) Surface Run- Baseflow (lb/yr) off 730.58 8439 8034.5 404.5 9169.58 Source: Euclid Creek Watershed Council et al., 2006

2.5 Dry Weather & Wet Weather Definitions

The Clean Water Act of 1972 requires all point sources discharging to US waters to have a permit through the NPDES. In 1990, the US EPA required water sampling as part of the permit application process (US EPA, 1992). During sampling it is important

14 to denote climate conditions (temperature, cloud cover, barometric pressure and amount/type of precipitation) as they can impact parameter levels.

To ensure dry weather conditions, sampling should be avoided during and immediately after a storm event. In most urban environments, stormwater run-off ends within 12 hours following the storm (Pitt, 2001). However, local knowledge and experience should be used to capture dry weather conditions as the time could extend past 12 hours due to upstream conditions (Pitt, 2001).

The US EPA defines a wet weather event as having a total depth of at least 0.10 inches of rainfall and an antecedent dry period of at least 72 hours (US EPA, 1992). The

US EPA states that whenever possible, representative rain events should not vary by more than 50 percent in terms of event duration and depth. This criterion was established in order to have measurable flow, a build-up of pollutants, and representative annual conditions (US EPA, 1992). Reliable local rainfall statistics should be used to characterize storm events throughout the year. Using these local statistics as well as annual NOAA statistics for US rain zones help define the representativeness of storm events (US EPA, 1992).

The US EPA discovered from past projects that dry weather flow conditions can contribute significant pollution loads to receiving waters (Pitt, 2001). The US EPA’s

Nationwide Urban Runoff Program (NURP) dry weather pollutant flows could be a result of directly connected illicit discharges or indirectly connected discharges like leaky sanitary sewer infiltration (US EPA, 1983). Conditions can also be quite different for warm and cold weather.

CHAPTER 3

HISTORICAL WATER QUALITY SAMPLING & RAINFALL

3.1 Rainfall Event Summary for the 2019-2020 Monitoring Period

During this research collection period (March 2019 – March 2020), there were 71 events with at least 0.10 inches of rainfall. Table 3.1 describes each rain event. Rainfall data was acquired from the Northeast Ohio Regional Sewer District’s (NEORSD)

Beachwood Rain Gauge located at 2670 Richmond Road. A rain event begins with the initial storm onset, recording a rainfall depth of at least 0.01 inches in a 5-minute interval, and continues until the end of the event. A rain event ends when there is no rain recorded for twelve consecutive hours of time.

The NEORSD Rain Gauge is a tipping bucket, typically mounted on a building’s roof, that records depth in 5-minute intervals. Events highlighted in Table 3.1 were wet weather collection samples used for this research. Events highlighted in yellow were collections conducted at all sites and events highlighted in green were collections conducted at one site. The antecedent dry period denotes the time between rain events.

Table 3.1. Summary of Rain Events During 2019-2020 Monitoring Period Event Date Start – End of Rainfall Storm Rainfall Ante- Event Depth Duration Intensity cedent (in) (hrs) (in/hr) Dry Period (hrs) 1 March 9 – 10 Mar 9 @ 7:10pm – Mar 10 @5:35 0.72 10.42 0.18 404.42 am 2 March 14 10:00am – 8:15 0.35 10.25 0.24 100.42 pm 3 March 22 7:00 am – 3:15 0.10 8.25 0.08 178.75 pm 4 March 29 3:25 am – 10:50 0.32 7.42 0.09 152.75 am 5 March 30 – 31 Mar 30 @ 1:45 1.47 31.5 0.26 14.92 am – Mar 31 @ 9:15 am 6 April 12 9:40 am -11:20 0.23 1.67 0.13 288.42 am 7 April 14 – 15 Apr 14 @ 4:10 0.70 26.83 0.25 16.84 am – Apr 15 @ 7:00 am 8 April 19 – 20 Apr 19 @ 5:55 1.29 27.42 0.26 123.42 am – Apr 20 @ 9:20 am 9 April 25 – 26 Apr 25 @ 5:25 1.02 24.58 0.21 128.08 pm – Apr 26 @ 6:00 pm 10 April 27 – 28 Apr 27 @ 8:20 0.40 10.17 0.10 14.33 pm – Apr 28 @ 6:30 am 11 April 29 2:50 pm – 7:55 0.49 5.08 0.21 32.33 pm 12 April 30 12:45 pm – 1:25 0.11 0.67 0.11 16.83 pm 13 May 1 – 2 May 1 @ 4:35 0.94 13.08 0.21 27.17 pm – May 2 @ 5:40 am 14 May 3 12:50 am – 1:20 0.10 12.50 0.03 19.17 pm 15 May 9 – 10 May 9 @ 1:50 0.51 15.25 0.13 144.50 pm – May 10 @ 5:05 am

Event Date Start – End of Rainfall Storm Rainfall Ante- Event Depth Duration Intensity cedent (in) (hrs) (in/hr) Dry Period (hrs) 16 May 11 – 12 May 11 @ 10:30 0.30 5.33 0.12 40.58 pm - May 12 @ 3:50 am 17 May 12 – 13 May 12 @ 5:45 0.36 17.92 0.11 13.92 pm – May 13 @ 11:40 am 18 May 19 5:15 pm – 7:05 0.15 1.83 0.09 149.58 pm 19 May 26 8:05 am – 7:00 0.38 10.92 0.19 157.00 pm 20 May 27 – 29 May 27 @ 10:20 1.19 28.42 0.71 27.33 pm – May 29 @ 2:45 am 21 May 30 7:00 am – 5:00 0.59 10.00 0.41 28.25 pm 22 June 1 – 2 June 1 @ 8:15 0.43 6.25 0.16 27.25 pm – June 2 @ 2:30 am 23 June 4 – 5 June 4 @ 10:45 1.47 12.67 0.41 47.25 pm – June 5 @ 11.25 pm 24 June 10 1:20 am – 6:00 1.08 16.67 0.34 97.92 pm 25 June 12 – 14 June 12 @ 10:40 1.84 26.50 0.51 52.67 pm – June 14 @ 1:10 am 26 June 15 – 16 June 15 @ 1:55 1.45 26.75 0.40 36.75 pm – June 16 @ 4:40 pm 27 June 20 12:20 am – 11:30 1.84 23.17 0.86 79.67 pm 28 June 24 4:35 pm – 8:50 2.03 4.25 1.35 89.08 pm 29 June 28 6:50 pm – 7:15 0.26 0.42 0.26 94.00 pm 30 July 2 – 3 July 2 @ 9:50 0.58 6.5 0.48 98.58 pm – July 3 @ 4:20 am 31 July 4 12:05 pm – 5:50 0.15 5.75 0.13 31.75 pm

Event Date Start – End of Rainfall Storm Rainfall Ante- Event Depth Duration Intensity cedent (in) (hrs) (in/hr) Dry Period (hrs) 32 July 16 – 17 July 16 @ 2:30 1.17 22.42 0.48 284.67 pm – July 17 @ 12:55 pm 33 July 20 4:35 am – 6:25 0.26 1.83 0.25 64.50 am 34 July 21 – 22 July 21 @ 8:30 0.40 13.42 0.26 38.08 pm – July 22 @ 9:55 am 35 July 30 12:00 am – 10:05 0.46 10.08 0.22 182.08 am 36 July 31 4:15 am – 4:25 0.19 0.17 0.19 18.17 am 37 August 6 – 7 August 6 @ 1.53 11.50 0.70 152.33 12:45 pm – August 7 @ 12:15 am 38 August 15 2:05 pm – 2:45 0.58 0.67 0.58 205.83 pm 39 August 18 5:15 am – 11:15 0.71 6.00 0.68 62.40 am 40 August 18 – 19 August 18 @ 0.31 2.00 0.26 12.25 11:30 pm – August 19 @ 1:30 am 41 August 22 1:20 am – 11:35 0.41 10.25 0.24 71.83 am 42 August 27 1:15 pm – 4:05 0.16 2.83 0.12 121.67 pm 43 September 1 6:10 am – 8:55 0.18 14.75 0.07 110.08 pm 44 Sept 11 – 12 Sept 11 @ 1:25 1.47 20.42 0.62 232.50 pm – Sept 12 @ 9:50 am 45 September 13 7:35 pm – 11:45 2.42 4.17 2.12 33.75 pm 46 October 2 – 3 Oct 2 @ 2:55 pm 0.32 14.17 0.11 423.17 – Oct 3 @ 5:05 am 47 October 6 – 7 Oct 6 @ 9:05 0.10 4.25 0.06 88.00 pm – Oct 7 @ 1:20 am

Event Date Start – End of Rainfall Storm Rainfall Ante- Event Depth Duration Intensity cedent (in) (hrs) (in/hr) Dry Period (hrs) 48 Oct 11 – 12 Oct 11 @ 10:15 0.42 7.83 0.11 116.92 pm – Oct 12 @ 6:05 am 49 Oct 16 – 17 Oct 16 @ 5:20 1.11 21.33 0.22 95.25 am – Oct 17 @ 3:00 am 50 Oct 21 -22 Oct 21 @ 10:50 0.13 14.42 0.05 115.83 pm – Oct 22 @ 1:15 pm 51 Oct 26 – 27 Oct 26 @ 11:55 0.78 20.58 0.13 94.67 am – Oct 27 @ 8:30 am 52 Oct 30 – Nov Oct 30 @ 2:10 1.83 49.33 0.21 77.67 1 pm – Nov 1 @ 3:30 pm 53 November 7 4:05 am – 1:45 0.21 9.67 0.06 132.58 pm 54 Nov 11 – 12 Nov 11 @ 12:50 0.75 13.83 0.13 95.08 pm – Nov 12 @ 2:40 pm 55 November 27 2:10 am – 9:55 0.17 7.75 0.09 347.50 am 56 December 1 5:25 am – 5:50 0.82 12.42 0.29 91.50 pm 57 Dec 2 – 3 Dec 2 @ 6:55 am 0.24 22.00 0.08 13.08 – Dec 3 @ 4:55 am 58 December 4 10:50 am – 9:55 0.13 11.08 0.06 17.92 pm 59 December 9 8:15 am – 9:35 0.55 13.33 0.10 106.33 pm 60 Dec 14 – 15 Dec 14 @ 6:10 0.49 19.50 0.09 104.58 am – Dec 15 @ 1:40 am 61 Dec 29 - 31 Dec 29 @ 5:45 0.70 55.17 0.13 340.08 am – Dec 31 @ 12:55 pm 62 January 3 – 5 Jan 3 @ 6:55 am 0.50 43.25 0.06 66.00 – Jan 5 @ 2:10 am

Event Date Start – End of Rainfall Storm Rainfall Ante- Event Depth Duration Intensity cedent (in) (hrs) (in/hr) Dry Period (hrs) 63 Jan 10 – 12 Jan 10 @ 12:25 1.32 41.17 0.13 118.25 am – Jan 12 @ 5:35 am 64 Jan 18 – 19 Jan 18 @ 2:00 0.78 25.17 0.19 140.42 am – Jan 19 @ 3:10 am 65 Jan 24 - 26 Jan 24 @9:25 0.58 39.50 0.12 126.25 am – Jan 26 @ 12:55 am 66 February 5 8:00 pm – 11:05 0.24 3.08 0.12 259.08 pm 67 February 6 – 7 Feb 6 @ 11:05 0.36 28.92 0.13 12.00 am – Feb 7 @ 4:00 pm 68 Feb 9 – 10 Feb 9 @ 5:50 0.34 21.08 0.09 49.83 pm – Feb 10 @ 8:55 am 69 Feb 12 – 13 Feb 12 @ 8:00 0.37 23.25 0.08 59.08 pm – Feb 13 @ 7:15 pm 70 Feb 17 – 18 Feb 17 @ 11:40 0.27 7.00 0.07 100.42 pm – Feb 18 @ 6:40 am 71 Feb 24 – 25 Feb 24 @ 9:00 0.15 3.92 0.06 158.33 pm – Feb 25 @ 12:55 am

Table 3.2 summarizes the average duration and depth of the 71 storms listed in

Table 3.1. The US EPA collected annual storm event statistics for 15 rain zones throughout the United States using NOAA precipitation data. The historical data

(created about 30 years ago) is compared to the 2019-2020 rain events in Table 3.3. The

2019-2020 monitoring period has higher rainfall statistics than the historical data. There are more storms that are longer, carry more volume, and have stronger intensity.

Table 3.2 Summary of Rainfall Duration and Depth for 2019-2020 Monitoring Period Event Type Duration (hrs) Depth (in) Average Event 15 0.66 50% average event 7.5 0.33 150% average event 22.5 0.99

Table 3.3. Comparison of 2019-2020 Monitoring Period to Historical Data Annual Statistics Independent Storm Event Statistics No. of Precipitation Avg Avg Avg Storms (in) Duration Intensity Depth (hr) (in/hr) (in) Historical NOAA Data 55 34.6 9.5 0.087 0.55 2019 – 2020 Monitoring 71 46.8 15 0.26 0.66 Period

Overall, the rain events used to collect wet weather samples across the watershed seem representative. Figure 3.1 compares rainfall depth and storm duration for all 71 rain events. The eleven wet weather collections are delineated, highlighted in red.

Rainfall depth and duration for the eleven collections fell within 50% - 150% of the average depth except for June, which was a particularly wet month, and the last wet weather collection in January. The October 30th storm was sampled at the onset of the storm to obtain urban run-off nutrient concentrations, so duration and depth were not influential. Figure 3.2 shows the antecedent dry period for each event and rainfall intensity. Events are shown relative to the minimum desired 72 hours preceding a wet weather collection and the average 0.26 inches/hour storm event. All summer storms sampled met or exceeded the average rainfall intensity with the exception of the July 30th collection (0.22 in/hr). Three of the sampled events had antecedent dry periods less than

72 hours which may impact results.

Figure 3.1. Summary of 2019-2020 Rainfall Events: Depth and Duration

Figure 3.2. Summary of 2019-2020 Rainfall Events: Antecedent and Rainfall Intensity

The NEORSD Beachwood Rain Gauge began reporting data for the public on

February 28, 2012 (NEORSD, 2020). Average annual rainfall for this 8-year period was about 45 inches. In terms of annual rainfall, the monitoring period seems representative at 49 inches (March 2019 – March 2020). Table 3.4 shows the NEORSD Beachwood

Rain Gauge Data from 2012 until March 2020.

Table 3.4. NEORSD Beachwood Rain Gauge Data (2012-2020) 2012 2013 2014 2015 2016 2017 2018 2019 2020 Rainfall (in) January 1.92 1.09 2.18 1.00 5.92 2.86 3.26 3.53 February 1.53 1.79 1.60 2.65 3.46 3.62 2.44 2.37 March 3.42 2.15 1.67 1.52 4.00 5.22 4.66 3.18 6.68 April 1.83 3.41 5.91 3.31 3.22 5.78 5.06 4.55 3.95 May 1.19 3.67 3.02 4.75 2.95 7.90 4.24 4.75 5.44 June 1.40 3.65 6.41 9.45 0.79 5.30 4.19 10.47 July 3.16 6.78 7.05 3.34 2.92 3.07 3.98 3.36 August 2.93 2.55 4.66 1.83 2.83 6.08 3.72 3.83 September 8.18 3.37 4.99 2.93 3.04 1.49 5.52 4.26 October 12.16 5.94 5.27 2.61 4.31 4.00 5.11 4.16 November 1.13 3.63 3.21 2.13 3.16 6.81 5.21 1.88 December 4.78 3.29 2.14 2.82 2.47 1.50 2.95 3.05 TOTAL 41.89 47.21 38.47 33.34 56.53 51.12 49.19

In terms of a monthly comparison, 2019 was representative of recent years. June stands out as a wetter than normal month, but monthly precipitation of 10 inches does occur. Although unusual, both October 2012 and June 2015 posted similar volumes of rainfall as was shown in Table 3.4. Table 3.5 displays monthly statistics for the monitoring period. September 2019 was unusual in that it had a stronger rainfall intensity and higher average storm depth than recent years. Overall, September and

November had the least number of rain events. There was a strong storm recorded on

September 13th, which was preceded by another significant storm (See events 44 and 45 on Table 3.1).

Table 3.5. Monthly Rainfall Characteristics for the 2019-2020 Monitoring Period 2012-2020 Avg Avg Avg Avg Ant Number Total Beachwood Month Depth Duration Intensity Dry of Depth Average (in) (hrs) (in/hr) Period Rain (in) Total (hrs) Events Depth (in) Mar 0.59 13.57 0.17 170 5 3.18 3.61 Apr 0.61 13.77 0.18 89 7 4.55 4.11 May 0.50 12.81 0.22 68 9 4.75 4.21 Jun 1.30 14.59 0.54 66 8 10.47 5.21 Jul 0.46 8.60 0.29 103 7 3.36 4.21 Aug 0.62 5.54 0.43 104 6 3.83 3.55 Sept 1.36 13.11 0.94 125 3 4.26 4.22 Oct 0.67 18.84 0.13 145 7 4.16 5.45 Nov 0.38 10.42 0.09 192 3 1.88 3.40 Dec 0.49 22.25 0.13 112 6 3.05 2.88 Jan 0.80 37.27 0.13 113 4 3.53 2.72 Feb 0.29 14.54 0.09 106 6 2.37 2.43 AVG 0.67 15.44 0.28 116 6 4.12 3.83 TOTAL 71 49.39 45.39

3.2 Water Quality Monitoring & Assessment Reporting

Biennially, each state is required to submit a list (i.e., 303(d) list) of all impaired and threatened streams and lakes located within its borders to the US EPA, as required by the federal Clean Water Act (US EPA, 2020i). The law requires states to rank and develop TMDLs for these impaired waters (USEPA Office of Water, 2009). A TMDL is the maximum pollution tolerance a water body can tolerate while still meeting water quality standards (US EPA, 2020j). States submit long-term plans to reduce pollution loads for these surface waters and the US EPA assembles these individual state lists into one national tracking system (US EPA Office of Water, 2009).

This collaboration is called the EPA’s Assessment and TMDLs Tracking and

Implementation System (ATTAINS). According to the US EPA’s Office of Water, there are different reasons for stream impairment. Nutrients are responsible for approximately

10 percent of all reported impairments. Figure 3.3 displays the top 15 causes cited by the

US EPA. Causes shown on Figure 3.3 that account for no more than 5 percent of all impairments are (in descending order): polychlorinated biphenyls (PCBs), unknown impaired biota, turbidity, temperature, pesticides, salinity, unknown causes, and noxious aquatic plants. The top five sources account for the majority (58%) of all surface water impairments.

2% 2% 2% 5% 2% 15% 5% Pathogens (15%)

5% Mercury (12%) 12% Other Metals (11%) 5% Nutrients (10%)

Sediment (10%) 6% 11% 9% Organic Enrichment (9%)

10% pH (6%) 10%

Figure 3.3. Impairment Causes for US Waters Source: US EPA Office of Water, 2009

In 2018, the Ohio EPA reported stream assessments (biological and chemical data) on a total of 1,538 watershed units (Ohio EPA, 2018). Streams were assessed on four major uses: human health, recreation, aquatic life, and public drinking water

26 supplies. Human health impairment was based on the evaluation of fish tissue contamination. Recreation attainment was based on the number of bacteria in the water.

Ohio used surveys of fish and aquatic insects to assess aquatic life. Public drinking water supplies focused on testing of nitrate, pesticides, and cyanotoxins (Ohio EPA, 2018).

Ohio set a 2020 goal of 80 percent attainment of aquatic life for all its wading and principal streams and rivers (Ohio EPA, 2018). The overall trend since 2010, the baseline year, has been positive, increasing by almost 8 percent as shown in Figure 3.4.

Figure 3.4. Aquatic Life Attainment for Ohio’s Wading & Principal Streams Source: Ohio EPA, 2018

Euclid Creek was listed as impaired for aquatic life. Both the East and Main

Branch are in non-attainment status. The causes were listed as flow regime modification, pollutants in urban stormwater, habitat alterations, and unknown. The sources listed were channelization, municipal urbanized high-density area, contaminated sediment resuspension, unknown sources and urban run-off caused by wet weather storm sewers

27 and CSO activations. Fish were found to be more impacted than macroinvertebrates

(Euclid Creek Watershed Program, 2018). Overall, the upper watershed suffered from urban land use development and loss of riparian vegetation while hydromodification compromised the lower watershed (Euclid Creek Watershed Program, 2018).

Euclid Creek was also listed as impaired for recreation use, although this outcome relied on historical data. Attainment for human health was unknown and it is not currently used for public drinking water (Ohio EPA, 2018). The Ohio EPA submitted a priority ranking list for all 1536 reported watersheds. The highest priority areas were given the most points. The scale ranged from a minimum priority of zero to the highest priority of seventeen. Euclid Creek ranked in the 80th percentile with 5 priority points.

The state distribution was heavily right skewed with a median of 2 points. A histogram of the data is shown in Figure 3.5.

Figure 3.5. Priority Ranking of Ohio Watersheds Source: Ohio EPA, 2018

Sampling is an important strategic method to identify water quality problems in the watershed (Cuyahoga Soil & Water Conservation District, 2020). Water chemistry data has been collected by the Ohio EPA and The Northeast Ohio Regional Sewer

District (NEORSD) annually since 1989. The Euclid Creek TMDL used sampling conducted by Ohio EPA, NEORSD, Cuyahoga County Board of Health, and John Carroll

University (Euclid Creek Watershed Program, 2018).

Recent sampling by NEORSD and The Euclid Creek Watershed Program was reviewed for this thesis. NEORSD’s water quality data was conducted by its

Environmental Assessment Group in the Water Quality and Industrial Surveillance

Division (WQIS). WQIS are Level 3 Qualified Data Collectors certified by the Ohio EPA.

The Euclid Creek Watershed Monitoring Program began in 2005 and was conducted by trained volunteers.

3.3 NEORSD Sampling

Since 2006, WQIS has collected water quality data on Euclid Creek and its tributaries. The number of sites and sample size have varied each year. NEORSD recommends an annual monitoring plan that is subject to approval from the Ohio EPA.

The most recent water quality data publicly available was collected in 2018, downstream of the confluence of the two branches. Three sites were investigated at River Miles

(RMs) 0.40, 0.55 and 1.65, which were labeled according to their respective distance from the mouth of Euclid Creek. The sites were monitored to collect post restoration water chemistry data and for NPDES permit regulations (NEORSD, 2019a). Stream restoration and sewer infrastructure projects were completed before and during the

29 collection period (NEORSD, 2019a). The NEORSD projects involved interceptor tunnels designed to reduce CSO activations from 60 to two events per year and have been in service since July 2018. The 2012 – 2013 restoration project goal was to improve the downstream ecology of Euclid Creek (NEORSD, 2019a). NEORSD’s Analytical Services

Division analyzed water samples from the three sites for total phosphorus, DRP, nitrite, nitrate nitrite, ammonia, alkalinity, turbidity and suspended solids. Collection techniques followed procedures outlined in the Surface Water Field Sampling Manual

(Ohio EPA, 2015). WQIS collected water samples at each site on five occasions. Field measurements were also taken for dissolved oxygen, pH, temperature, conductivity and turbidity (NEORSD, 2019a). WQIS monitoring resulted in relatively low nutrient levels as shown in Table 3.6.

Table 3.6. 2018 NEORSD Nutrient Results for Euclid Creek River Sample Date Total DRP Dissolved Mile Phosphorus (mg/L) Inorganic (mg/L) Nitrogen (mg/L) 6/19/2018 0.087 0.039 0.636 6/26/2019 0.037 0.022 0.322 0.40 7/2/2019 0.081 0.023 0.139 7/10/2019 0.038 0.022 0.314 7/17/2019 0.072 0.031 0.542 GeoMean 0.059 0.027 0.344 6/19/2018 0.079 0.044 0.587 6/26/2019 0.04 0.028 0.294 0.55 7/2/2019 0.032 0.018 0.263 7/10/2019 0.037 0.025 0.270 7/17/2019 0.067 0.032 0.500 GeoMean 6/19/2018 0.0695 0.04 0.632 6/26/2019 0.04 0.03 0.431 1.65 7/2/2019 0.044 0.026 0.400 7/10/2019 0.038 0.028 0.458 7/17/2019 0.068 0.036 0.476 GeoMean 0.050 0.032 0.473 Source: NEORSD, 2019b

3.4 The Euclid Creek Watershed Program

The Euclid Creek Volunteer Monitoring Program (ECVMP) aimed to collect water chemistry data on a monthly basis at seven sites. The sites were located throughout the watershed and are detailed in Section 4.2. The purpose of the monitoring data was for public awareness and education (Cuyahoga Soil & Water Conservation

District, 2020). Any critical observations were reported to the Ohio EPA. ECVMP collectors conducted on-site analyses for turbidity, ammonia, reactive phosphate, dissolved oxygen, temperature, conductivity, total dissolved solids, salinity and pH.

Typically, ECVMP volunteers select one site to collect data at monthly. The monitoring program began in 2006 with five original sites. A sixth site was added in 2011 and the seventh in 2016. Weather and water level conditions were noted during collections. Historically, the water sampling collected by ECVMP volunteers was comparable to the data collected for the Euclid Creek TMDL (Cuyahoga Soil & Water

Conservation District, 2020).

Figure 3.6 summarizes the reactive phosphate levels from collections spanning from 2006 to 2019 for each site. Target levels were exceeded at all sites at least 75 percent of the time. Unlike phosphorus, ammonia levels (chosen to represent nitrogen), were consistently below target levels. Phosphorus levels throughout the watershed have remained above the target and continue to be problematic.

Figure 3.6. Euclid Creek Volunteer Phosphorus Monitoring Data (2006-2019) Source: Euclid Creek Watershed Program, 2020

3.5 Historical Rainfall Data Exploration

Historical rainfall patterns were explored using NOAA’s National Centers for

Environmental Information website and the NEORSD’s Rainfall Dashboard website.

Daily precipitation summaries were compiled from 1939 to the present for the Cleveland

(CLE) Hopkins Airport, the closest NOAA rain gauge to the monitoring site. Five- minute interval rainfall summaries were compiled from 2012 to the present for the

Beachwood Rain Gauge (RBH), the closest NEORSD rain gauge to the monitoring site.

The Euclid Creek Watershed and the Cleveland Hopkins Airport are approximately 20 miles apart. Figure 3.7 highlights the Airport’s Watershed location in yellow and the

Euclid Creek Watershed in red. The NEORSD’s rain gauges are shown in blue. The

Beachwood Rain Gauge (RBH) which was used for this study, is shown at the upstream portion of the Euclid Creek Watershed.

Figure 3.7. NOAA and NEORSD Rain Gauge Locations Sources: NOAA, 2020; NEORSD, 2020

Daily precipitation values were compared for the two rain gauges from 2012 to

2019. Results of a matched-pairs t-test shown in Table 3.7 show that there was a significant difference [ p < 0.05 ] between the two gauges. On average, the daily precipitation difference was 0.06 inches.

Table 3.7. Summary Statistics for Matched-Pairs t-test Between Rain Gauges (2012-2019) Beachwood – CLE Hopkins Airport Rain Gauge Statistical Measure Mean of Daily Precipitation 0.06 inches (Absolute Value of Differences) Standard Deviation of Daily Precipitation 0.17 inches (Absolute Value of Differences) Number of Days Compared 2863 t 18.9 P-value < 0.00001

When looking at monthly trends for these same years, at least two months each year differed by more than one inch. Table 3.8 shows monthly differences between the rain gauges for the 94 consecutive months. Most importantly, there was substantial variation during the summer and early fall, when most of the sampling occurred. During these months (May – October), over half exceeded differences of at least one inch of rainfall. On average, the monthly precipitation difference was 0.89 inches. [t=10.03, p<0.00001].

Table 3.8. Difference in Monthly Rain Gauge Totals (Beachwood – CLE Airport) 2012 2013 2014 2015 2016 2017 2018 2019 January -0.25 -0.91 -0.76 -0.37 1.14 0.59 0.08 February -0.77 -1.24 -0.26 -0.57 0.78 0.37 -0.04 March -0.46 -0.10 -0.53 -0.45 -0.17 1.20 0.65 0.09 April -0.14 -0.09 0.95 0.54 -0.64 1.29 0.68 0.54 May -0.25 1.30 -1.06 0.66 -0.36 1.81 -1.48 0.61 June -0.64 -4.25 0.15 0.93 -1.33 -0.55 0.36 2.39 July -1.16 1.87 3.06 0.62 1.10 0.60 -2.70 0.74 August 0.08 -0.26 0.07 -1.02 -0.67 4.83 -1.17 0.60 September 0.62 1.41 0.52 -1.98 -2.34 0.68 1.18 2.98 October 1.76 1.24 2.43 0.44 1.35 0.32 1.33 0.84 November 0.37 0.75 -0.47 -0.18 1.05 0.86 -0.26 0.18 December 0.85 -0.81 0.19 -0.14 -0.18 -0.23 0.10 0.20

Although the rain gauges recorded different amounts from 2012 to 2019, the monthly 2019 Beachwood precipitation was compared to the historical Cleveland data

(1939-2019) to obtain a sense of historical trends for the area prior to 2012. The monthly precipitation amounts this year in Beachwood seem typical when compared to past monthly Cleveland area weather trends, except for June. As Figure 3.8 shows, only two months since 1939 accumulated more than 10 inches of precipitation in the Cleveland area, September 1996 (11.05 inches) and October 2012 (10.40 inches). Like the historical

CLE data, the Beachwood Rain Gauge recorded two months exceeding ten inches of rainfall (See Figure 3.9).

Figure 3.8. Historical Monthly Trends at Cleveland Hopkins Airport (1939-2019) Source: NOAA, 2020

Figure 3.9. Beachwood Rain Gauge Monthly Rainfall Totals (2012 – 2019) Source: NEORSD, 2020

Historical data points to a plausible increase in precipitation. Both data from

Cleveland Airport and all NEORSD rain gauges show a slightly positive trend. Figure

3.10 shows annual precipitation amounts for the CLE Airport at about 0.46 inches of additional rain per year (r = 0.21) and annual mean precipitation amounts for the 26

NEORSD rain gauges at about 1.57 inches of additional rain per year (r = 0.65).

Figure 3.10. Annual Means at Cleveland Hopkins Airport and Mean of all NEORSD Rain Gauge Data (2013-2019) Sources: NOAA, 2020; NEORSD, 2020

The strongest rainfall event during the monitoring period fell on September 13,

2019. This storm had a rainfall depth of 2.42 inches over the course of 4.17 hours. The rainfall intensity was 2.12 inches/hour. In comparison to historical data, daily rainfall exceeded two inches at the Cleveland Hopkins Airport 53 times since 1939. In summary, the 2019-2020 monitoring period seems representative of rainfall in the area, noting that

June 2019 was an excessively wet month.

CHAPTER 4

EXPERIMENTAL METHODS

4.1 Overview of Site Selection Process

Sites were visited in the same order, upstream to downstream, throughout the monitoring period. During each site visit, observations were recorded, field measurements were taken, water samples were collected for subsequent lab analysis, and statistics of these samples were later compiled and calculated.

The first site visited for each collection was Acacia, the furthest upstream,

“headwaters” of the Main Branch. The next two sites, in order, were Telling Mansion and

Schaefer Park. After mid-summer investigations, four upstream tributary locations were added and sampled next in the order of: Spencer Road, Harris Road, Community Center, and U/S Stonewater. Collections then proceeded to the East Branch. The most upstream location for the East Branch, Rockefeller Road, was sampled next. Following this site were Bishop Road and Richmond White. The next site visited was the confluence of the two branches. Independent samples were taken immediately prior to the mixing of the branches, at Highland East and Highland Main. Downstream samples were taken last at

Villaview and Wildwood.

Field samples at each site were taken at the same location unless conditions were unsafe or difficult due to summer low flow. Samples were typically taken midstream,

38 using a bucket dropped from the center of a bridge for all locations except the Highland

Site. Here, samples were taken by wading out to the middle of the stream. During a few extreme wet weather events, samples were drawn from the embankment.

Sampling began in March 2019 with nine sites: the seven volunteer monitoring sites used for the 2019 Euclid Creek Watershed Monitoring Program, and two additional sites in the upper reaches of the East Branch. The initial seven volunteer monitoring sites were Acacia, Telling Mansion, Schafer Park, Richmond White, Highland East, Highland

Main and Wildwood. The two additional East Branch upstream locations were

Rockefeller Road and Bishop Road. Due to the influence of Lake Erie, a sampling location upstream of the Wildwood location was established on July 13, 2019. Based on the elevated nutrient levels at the Schaefer Park location, an investigation was conducted on

July 31, 2019 along the tributary incorporating Schaefer Park. As a result, sampling at the

Spencer Road site began on August 6, 2019. The differences in nutrient levels between the East and Main Branches led to an investigation that was carried out upstream of the

Richmond White site on August 29, 2019. The last three sites, Harris Road, Community

Center and U/S Stonewater were added on September 4, 2019.

Sites are discussed in order of collection, from upstream to downstream. Each sampling event was completed in a day, within an approximate 5-hour timeframe. The fourteen site locations are shown below on Figure 4.1.

Figure 4.1. Sampling sites used to assess water quality in Euclid Creek Watershed

4.2 Sampling Site Descriptions

4.2.1 Acacia

The Acacia site is the furthest upstream location in the Main Branch of Euclid

Creek. The Conservation Fund sold the 155-acre former golf course to the Cleveland

Metroparks in 2012 for $14 million (Ewinger, 2013). Future use of the site is heavily restricted, as it is intended to become a fully forested nature preserve.

The restoration project doubled the size of public park land within the watershed. There are nine other public parks in the watershed with a combined area of

141 acres (Ewinger, 2016). These 10 parks account for 2% of the total land use within the watershed. The Donald Ross-designed golf course had extensive underground drain tiles which transferred water off-site as quickly as possible. The Metroparks planted over

6,000 trees and restored Euclid Creek to its natural banks (Frolik, 2018).

Richmond Road and Cedar Road, both heavily used 4-lane carriageways, border

Acacia on the west and south sides, respectively. Condominiums and single-family homes border the park on its remaining sides. Immediately upstream of Acacia is the

Beachwood Place shopping mall. The mall has 137 stores and services totaling almost 1 million square feet of retail area. There are over 4,200 parking spaces (Beachwoodplace,

2019). The impervious surface area for this parking may be a pollution source during wet weather events.

The sampling site was located at the bridge as the creek enters the park, at Cedar

Road. Figure 4.2a shows a downstream view of the creek from the eastern approach to the bridge. Samples were taken on the downstream side, at the center of the bridge.

There were multiple storm sewers at this location. Two sewers were set parallel to the stream channel and the remaining sewers ran perpendicular to the stream channel. As

Figure 4.2b shows, the parallel storm sewers were culverted in separate channels adjacent to the stream, one on each side. The smaller perpendicular storm pipes were located on the downstream right side of the stream, as seen in Figure 4.3.

Figure 4.2 Acacia Site Views: (a) looking downstream from pedestrian bridge and (b) looking at sampling site from downstream location

Figure 4.3 Storm drains at Acacia on Eastern Embankment

4.2.2 Telling Mansion

The second monitoring site was Telling Mansion, located where the creek crosses

Mayfield Road. Mayfield Road is another busy, 4-lane carriageway. This site is named for the adjacent property, the Telling Mansion Museum of American Porcelain Art. Between

Acacia and the Telling Mansion sites, the creek passes The Legacy Village. This mall, opened in 2003, consists of about 50 stores, a 135-room Hyatt Hotel, and a 355-space parking garage (Jarboe, 2015). In addition to The Legacy Village, the creek passes by two schools, The Hawken Lower School and the Julie Billiart School, and a private 36-hole golf club, The Mayfield Sand Ridge Club.

The sampling site was located at the pedestrian bridge along Mayfield Road.

Samples were taken on the downstream side, at the center of the bridge. Figure 4.4a shows the downstream view of the creek from the sampling bridge site. There was a storm sewer located parallel to the creek. Looking upstream, as in Figure 4.4b, the storm sewer is to the right of the creek. During the summer, there was a distinct orange colored water area downstream approximately 30 feet from the storm sewer. Figure 4.5 shows this stagnant, discolored water area.

Figure 4.4. Telling Mansion Site Views: (a) looking downstream from pedestrian bridge and (b) looking at sampling site from downstream location

Figure 4.5. Possible iron deposit area downstream of Telling Mansion Site

4.2.3 Schaefer Park

The third site was located along an unnamed tributary of the Main Branch that connects downstream of Telling Mansion. This is the major tributary to the Main

Branch. The headwaters of this creek begin at Lyndhurst Park, immediately downstream of the Lyndhurst Municipal Court. This tributary creek meanders through residential homes and a second park (Roland Park) until it reaches Schaefer Park. The three town parks, Schaefer Park, Roland Park and Lyndhurst Park, are all smaller town parks, each about 10 acres of green space. Both Schaefer Park and Roland Park contain two baseball fields apiece and general green space. Lyndhurst Park has a community pool, four tennis courts, a community center, and additional green space. The approximate length of the tributary is one mile. As Figure 4.6 shows, Roland Park lies approximately halfway between Lyndhurst and Schaefer Parks.

Figure 4.6. Land use surrounding unnamed tributary to Main Branch

The sampling site was located at the pedestrian bridge between Edenwood Road and Ridgebury Boulevard. Samples were taken on the downstream side, at the center of the bridge. Figure 4.7a is a view of the sampling bridge site and the large storm sewer located on the left embankment looking downstream from the bridge site. There were two storm sewers located at this site, one on each side of the stream. Figure 4.7b shows the second storm sewer (located on the right embankment looking downstream from site).

Figure 4.7. Schaefer Park Site Views: (a) looking at sampling site from downstream location and adjacent storm sewer and (b) second storm sewer on downstream opposite embankment

4.2.4 Spencer Road

On July 31, an investigation was carried out upstream of Schaefer Park. Schaefer

Park nutrient levels were consistently the highest in the watershed throughout June

2019. The goal of the investigation was to sample throughout the entire tributary to determine if other areas along this creek yielded similar results. Six sampling locations were selected as shown on Figure 4.8: (1) Spencer Road, (2) Edenhurst Road, (3) Roland

Park, (4) Roland Park Storm Sewer, (5) Ridgebury Road, and (6) Schaefer Park. As a result of this investigation, the site at Spencer Road was added as another location due to high nutrient concentrations at the headwater of this tributary. (See Section 5.4 for results of the investigation.)

Figure 4.8. Schaefer Park Investigation Locations

Spencer Road is the upstream location of the Schaefer Park tributary. It is located in between Alvey Road and Roland Road. The closest residence is 5216 Spencer Road.

The location is downstream of Lyndhurst Park, in a residential area. Samples were taken on the downstream side of the stream, at the center of the stream, immediately after

47 being culverted under Spencer Road. Figure 4.9 shows the downstream view of the stream from the sampling site.

Figure 4.9. Downstream View at Spencer Road Site

4.2.5 Harris Road

On August 29th, an investigation was carried out upstream of Richmond White.

There are multiple tributaries that deposit their water into the East Branch. Eight sites were selected to gain further insight on upstream conditions as shown on Figure 4.10.

Sites sampled on August 29th were: (1) Rockefeller Road, (2) Bishop Road, (3) Golf View

Drive, (4) Route 175 (downstream of Cuyahoga County Airport), (5) Community Center on Highland Road, (6) Richmond White, (7) Downstream of Bishop Road (on White

Road), and (8) Downstream of Rockefeller Road (on Bishop Road). As a result of this investigation, three additional sites were monitored: Harris Road, Community Center and U/S Stonewater (See Section 5.4 for results of the investigation.).

Figure 4.10. East Branch Investigation Sample Locations

The Harris Road sampling site was located on Harris Road, where the tributary crosses the road, just south of the intersection with Highland Road. The stream is culverted under the residential road. Samples were taken at the middle of the stream, on the upstream side. This site monitored upstream conditions for

Redstone Run, a tributary to the East

Branch. Figure 4.11 shows the upstream view of the tributary from the sampling bridge location.

Figure 4.11. Harris Road Site Looking Upstream

4.2.6 Community Center

This site was located along Highland Road, at the Richmond Heights

Community Park. This small, 9-acre park is located east of the intersection of Richmond

Road and Highland Road. The park has three baseball fields, four tennis courts and a pool. This location was used to monitor upstream conditions of Claribel Creek, a tributary to the East Branch.

Samples were taken in the creek, immediately downstream of Park Avenue, at the entrance to the Community Park. Highland Road is a busy 2-lane thoroughfare for local traffic. Upstream of this sampling location, the stream travels through residential areas.

Figure 4.12 shows the upstream view of the creek at the entrance of the Community

Park. There was one storm sewer located at the sampling location as shown in Figure 4.13a. The storm sewer is located on the left side of the creek looking upstream. There was an additional storm sewer pipe draining into the creek visible further downstream from the sampling site.

Figure 4.13b shows this storm sewer pipe, located on the right side of the creek looking downstream.

Figure 4.12. Community Center Site looking upstream

Figure 4.13. Community Center Site Views: (a) looking at storm sewer at site and (b) looking at second storm sewer downstream of site

4.2.7 U/S Stonewater

This site was located along Highland Road, east of the intersection of Bishop

Road and Highland Road. This site was located at the upstream reaches of another tributary to the East Branch. Samples were taken on the upstream side of the stream, at the center of the stream. Samples were taken at the center of the roadway bridge, immediately prior to the culverted stream section on Highland Road. The land use upstream of the sampling location is largely residential homes. Downstream of the sampling location, the stream crosses Highland Road, traversing parallel and east of

Bishop Road. It travels through StoneWater Golf Club and under the Cuyahoga County

Airport. It then passes under White Road, where it joins the East Branch. Figure 4.14a shows an upstream view of this tributary from the center of the bridge. Looking

51 upstream, the left embankment has natural vegetation while the right embankment is supported by a double layer of gabion baskets. Two smaller storm sewer pipes are located within the gabion baskets and are visible in the background of Figure 4.14a.

These two pipes were located upstream of the sampling site. Figure 4.14b shows another storm sewer that is located at the base of the gabion baskets, a few feet upstream of the sampling location.

Figure 4.14. U/S Stonewater Site Views: (a) looking upstream from sampling site with storm drains further upstream and (b) looking upstream from site with storm sewer at sampling site

4.2.8 Rockefeller Road

This site was the furthest upstream sampling location along the East Branch of

Euclid Creek. Rockefeller Road parallels I-271, connecting White and Chardon Roads. It

52 is a 2-lane carriageway in a predominantly residential area. There is one school located upstream, the Willoughby-Eastlake School of Innovation.

The sampling site was located along the east shoulder of Rockefeller Road, just south of a residential home located at 2901 Rockefeller Road. Samples were taken on the upstream side of the stream, at the center of the bridge above the stream. The stream was culverted under the roadway. Figure 4.15 shows the upstream view of the East Branch.

On the left embankment, there is a storm sewer draining into the creek immediately upstream of the sampling location.

Figure 4.15. Upstream view of Rockefeller Road Site

4.2.9 Bishop Road

This site is directly downstream of the Airport Greens Golf Course. This small tributary drains an interior pond of the golf course, northeast of the Cuyahoga County

Airport. The tributary runs under Bishop Road, the location site for sampling. The tributary is east of Bishop Road, while the airport is west of Bishop Road. This location was selected to monitor any potential water quality impacts associated with the golf course.

The sampling site was located on the east shoulder of Bishop Road, just south of the intersection of White Road and Bishop Road. Bishop Road is a busy 2-lane carriageway. Samples were initially taken on the downstream side of the stream; however, the stream level was very low at this location during the early summer, making it difficult to sample. Starting in July 2019, sampling was continued at the same roadway location, but on the opposite (west) shoulder of Bishop Road. Samples were taken on the upstream side of the stream, at the center of the roadway bridge. Figure 4.16a shows the upstream view of the tributary as seen from the sampling site bridge. There is a storm sewer draining into the stream under the bridge abutment. Figure 4.16b shows this storm sewer. The photo was taken on the downstream side of the bridge, looking at the right abutment when facing upstream.

Figure 4.16. Bishop Road Site Views: (a) upstream view of tributary and (b) storm sewer at sampling site, downstream side of bridge

4.2.10. Richmond White

This site was downstream of the confluence of the Bishop Road tributary with the East Branch. The site is located on Richmond Road, at the intersection of Richmond and White Roads. Richmond Road is another busy 2-lane thoroughfare. Land use surrounding the site was a mixture of businesses and residential homes. This location was downstream of the Cuyahoga County Airport.

The sampling site was located at the pedestrian bridge along the east side of

Richmond Road. Samples were taken on the upstream side, at the center of the bridge.

Figure 4.17a shows the upstream view of the East Branch. On occasion, due to inclement weather or low flow conditions, samples were taken directly in the stream, upstream of the sampling site. These samples were accessed by climbing down the embankment

55 located alongside White Road. Figure 4.17b was taken at this location, looking downstream at the routine bridge sampling site.

Figure 4.17. Richmond White Site Views: (a) upstream of East Branch at sampling site and (b) looking downstream at the sampling site from upstream location

4.2.11. Highland Main

The East Branch of Euclid Creek converges with the Main Branch approximately

3 miles downstream of the Richmond White site. The confluence of the two branches occurs at the Highland Picnic area, within the Euclid Creek Reservation. This picnic area is located close to the intersection of Highland Road and Euclid Creek Parkway. The 3- mile stretch of the East Branch between Richmond White and the confluence of the branches is in a predominantly residential area. The East Branch loosely parallels Route

6, Chardon Road.

Unlike the residential land use of the East Branch, the Main Branch lies within the protected natural setting of the Cleveland Metropark. The Main Branch of Euclid

Creek parallels Euclid Creek Parkway until the Parkway terminates at the southern entrance of the Euclid Creek Reservation, at the intersection of East Green Road and

Euclid Creek Parkway. The Main Branch is protected for about 2.5 miles upstream of the stream confluence location. The park has wooded, canopied recreational trails. The Main

Branch meanders through the park, which is full of lush vegetation and steep hillsides.

Samples were taken directly in the stream, along the left embankment (looking upstream), just upstream of the confluence of the East and Main Branches. Access to the site was through the park, past the basketball courts and bridge. Figure 4.18a shows a downstream view of the Main Branch. The photo was taken on the bridge, looking at the confluence of the two branches. Figure 4.18b was taken at the approximate sampling site, looking upstream at the bridge.

Figure 4.18. Highland Main Site Views: (a) looking at sampling site and (b) looking upstream from sampling site

4.2.12. Highland East

This sampling site was adjacent to the Highland Main sampling site, in the interior of the Euclid Creek Reservation, near the intersection of Highland Road and

Euclid Creek Boulevard. Samples were taken in the middle of the stream, just upstream of the confluence of the streams. During inclement weather, care was taken to obtain independent samples as close to the site as possible.

Highland Road was closed due to construction during much of the monitoring period. Figure 4.19a shows construction equipment in the Main Branch, downstream of the confluence of the two branches. Ten years ago, a small dam was removed under this high bridge at Highland Road. The 6-foot high, 40-foot wide concrete dam was originally constructed to pond water for wading at a YMCA camp (Scott, 2010). After 77 years,

58 construction commenced in order to improve water quality issues like downstream flooding, erosion, sediment build-up and better fish migration (Scott, 2010). Figure 4.19b shows the upstream view of the East Branch.

Figure 4.19. Highland East Site Views: (a) downstream view of the confluence of the two branches and (b) looking upstream from site

4.2.13. Villaview

Collection began at this location on July 13, 2019. Due to Lake Erie’s record- setting mean monthly water levels, the direction of water flow was questionable at the

Wildwood sampling location, the most downstream sampling location. Beginning in

May 2019, the Great Lakes recorded 100-year water level records (Army Corps of

Engineers, 2020). High lake levels continued throughout the monitoring period, and into

2020. Thus, the Villaview site provided additional water chemistry testing downstream of the confluence of the two branches.

Euclid Creek is culverted under I-90 and is daylighted at Villaview Road.

Villaview Road is a busy 4-lane thoroughfare. The creek is channeled into three large parallel concrete culverts. The stream daylights west of the intersection of Villaview

Road and East 185th Street. The sampling site was located at the top of the headwall along the north side of Villaview Road. Samples were taken from this downstream side of the stream. Since the creek travels through three independent chambers, samples were taken in the western and central channel, whichever had the strongest moving current.

The eastern channel typically was stagnant. Figure 4.20a shows the downstream side of the culverts and the sampling location situated above. The downstream view of the creek is shown in Figure 4.20b, which was taken from the sampling location.

Figure 4.20 Villaview Site Views: (a) looking at sampling site from downstream side and (b) looking downstream from sampling site

4.2.14. Wildwood

This sampling site was in the Cleveland Metroparks, at Euclid Creek

Reservation, Wildwood Park. The sampling occurred along Villa Angela Drive. The sampling location is approximately 1000 feet from Lake Erie, where Euclid Creek empties into Lake Erie. The creek enters an oxbow at this location, where a general mixing pattern of water flow was frequently observed. The lakefront park consists of a mix of wooded land, walking trails, wetlands, a small boat marina, and beaches

(Cleveland Metroparks, 2014).

The sampling site was located at the pedestrian/roadway bridge prior to the parking lot for the boat launching ramp area. Samples were taken on the upstream side, at the center of the bridge. Figure 4.21a shows the upstream view of Euclid Creek taken from the left embankment of the bridge (looking upstream). Figure 4.21b shows a storm sewer along the upstream side of the right embankment.

Figure 21. Wildwood Site Views: (a) looking upstream from sampling site and (b) storm sewer at sampling site, upstream side

4.3 Field Equipment

Stream characteristics were observed and recorded for each site visit. The following parameters and units of measure were logged: turbidity (cm), conductivity

(µS/cm), water temperature (oC), pH, dissolved oxygen (% and mg/L), air temperature

(oC), time of day, and stream velocity (fps). Triplicate water samples were collected and stored on ice for lab analysis of reactive phosphate and nitrates within 24 hours.

A clear, 120 cm long tube was used to measure turbidity as shown in Figure 4.22.

Water was filled to the top of the transparent tube then drained through the attached hose until the 4.5 Secchi disk located on the tube bottom was visible. A value of 120 cm meant clear water, or no turbidity. If the visibility was less than 120 cm, two independent measurements were taken and averaged. For example, a value of 5 cm meant the Secchi disk was just visible, but any additional water eliminated the visibility of the disk. Figure 4.23 shows typical views used for recording different turbidity depths.

Figure 4.22 Turbidity tube used to determine stream characteristics

Figure 4.23. Measuring turbidity: (a) looking into the tube to view Secchi disk for unclear water (b) Looking at the tube filled with clear water.

Conductivity, water temperature, pH, and dissolved oxygen field measurements were taken with a Hanna HI 9829 Multiparameter Probe. The probe was immersed into a bucket containing stream water. Where feasible, the probe was also immersed into the stream to verify readings as shown in Figure 4.24.

Water velocity was initially recorded using a Geopacks Flowmeter. Flow could not be measured at the strongest current location for many of the sites. Approximate flow values were recorded using general observation methods. An object, such as the turbidity tube, was used to measure a distance along the stream. The time an object took to traverse this distance was recorded and repeated several times. The current was

63 approximated using the basic linear equation (푑 = 푣푡). If the stream flow was no stronger than a light wind current, no observation was recorded.

Figure 4.24. Multiparameter Probe used to determine stream characteristics: (a) Typical reading in bucket completed at all site locations (b) Back-up stream reading completed at sites where access to direct stream sampling possible.

4.4 Lab Analyses

As mentioned above, triplicate samples were collected during each site visit for nutrient analysis. All samples were analyzed in a lab within 24 hours. Each vial contained approximately 50 milliliters (mL) of stream water. Twenty mL was used for a reactive phosphate test and another 20 mL was used for a nitrate test.

Nutrient levels were tested with a Hach DR900 Colorimeter. For the reactive phosphorus test, Method 8048 (power pillows) was followed (HACH manual, Reactive

Phosphorus). For each water sample, two clear glass vials were filled with 10 mL of

64 stormwater per vial. One glass vial was used as the blank cell and the other as the sample cell. One packet of the phosphate reagent was added to the sample cell and shaken for 15 seconds. The sample cell was set aside for two minutes of reaction time. The blank cell was placed into the colorimeter, covered and zeroed. It was removed and the sample cell was read for its phosphorus (PO4) level in milligram per liter (mg/L).

A similar procedure was followed for nitrate testing. For each water sample, Method 8039 was followed using powder pillows(HACH manual, Nitrate). Once the nitrate reagent was added to the sample cell, it was shaken for one full minute. The sample cell was then set aside for five minutes then placed one at a time into the colorimeter and read for nitrate levels in mg/L nitrate as nitrogen. Figure 4.25 shows a typical vial used for testing. Figure 4.25. Typical glass vial

used for laboratory testing

4.5 Statistical Methods

Two-sample t-tests, matched paired t-tests, analysis of variance (ANOVA) and

Tukey comparison tests were performed for univariate data. A significance level of 0.05 and two-sided alternate hypotheses were used for all tests. These statistical methods were used to find any appreciable differences in the parameters collected for the 14 sites during wet and dry conditions.

For the investigation of conditions at an individual site, two-sample t-tests were used for comparing wet and dry conditions. Comparisons for multiple sites were

65 completed using matched paired t-tests. Statistics for these multiple site comparisons were analyzed using the same condition (wet versus dry) and the same collection day.

ANOVA and Tukey comparison tests were completed to analyze the difference in parameters for adjacent sites using Minitab software.

Linear regression was used to analyze bivariate data. A significance level of 0.05 and two-sided alternate hypotheses were used for tests for slope. Individual parameters collected versus time were analyzed. The strength of the relationship, coefficient of determination, and residual plots were evaluated. Additional bivariate analysis was completed for some individual sites using two of the collected parameters (regressions completed in Excel and Minitab software).

CHAPTER 5

DRY WEATHER RESULTS & DISCUSSION

5.1 Dry Weather Flow Overview

This section details the results of the parameters collected during dry weather conditions. Bivariate data was compiled for each site to determine if any appreciable changes in nutrient level, pH, conductivity, turbidity, and water temperature occurred over time. One of these sites, Acacia, will be described in detail below. This statistical process was repeated for each of the remaining 13 sites and can be found in the Appendix

A: Dry Weather Results. Parameters were also compared between the two branches of

Euclid Creek, the East and Main Branch. The statistical differences for each branch were compiled for all 23 dry weather collection days and tested for significance at 0.05 level.

This comparison was calculated for both the upper and lower reaches of the two branches and is discussed in this chapter. The upstream tributary impacts to the branches were investigated and compared by evaluating the differences in nutrient concentrations between adjacent sites for all dry weather collection dates.

5.2 Dry Weather Flow Conditions at Acacia

There was considerable variability in the phosphorus concentration at Acacia, ranging from a minimum 0.14 mg/L to a maximum of 0.79 mg/L. The lowest phosphorus level was the last collection on March 8, 2020 while the highest level was recorded on

September 11,, 2019. Phosphorus concentrations were observed to have some rapid changes during the summer. On June 30, the average concentration was 0.25 mg/L. Two days later, on July 2, the concentration more than doubled to 0.54 mg/L. There was no wet weather between the two collection dates. On July 2, there was active construction upstream of the collection site on the adjacent roadway, Cedar Road. Although construction on Cedar Road was present throughout the summer, the activities that day appeared to involve placement of new catch basins nearby, possibly influencing results.

Figure 5.1 shows the phosphorus concentrations over time for Acacia. The range of values

(0.65 mg/L) is almost four times as wide as the interquartile range (IQR) of 0.17 mg/L.

Figure 5.1. Acacia: Dry Weather Phosphorus Mean and Standard Deviation Levels over Time Based on Triplicate Samples

Two colorimeters were used for analysis. Since there was a discrepancy in nitrate readings between the meters, testing was carried out with standard solution. Results of testing showed that the new meter provided accurate readings and the old meter was reporting inflated nitrate values. Figure 5.2 shows the results of 81 water samples tested for nitrate level using both colorimeters. Lower nitrate readings (0.0 – 0.5 mg/L) tended to result in closer outputs between the two meters than higher readings. Due to the discrepancy in readings, all subsequent readings were conducted with the new meter.

Figure 5.2. Dry Weather Nitrate Mean and Standard Deviation Levels over Time Based on Triplicate Samples Comparison for Old and New Colorimeter

Phosphorus comparisons were also conducted for the two meters. There was no significant difference between the reported phosphorus levels [p > 0.05] and therefore no accuracy issues. In general, all nitrate readings were below 2.0 mg/L. The higher nitrate readings shown below in Figure 5.3 were performed with the older Hach meter.

Excluding the old meter readings, dry weather nitrate levels at Acacia ranged from 0.1 to

0.5 mg/L. There was little variation in nitrate readings (IQR = 0.2 mg/L). There may be a seasonal trend, with nitrate readings slightly increasing with warmer weather and declining with cooler temperatures.

Figure 5.3. Acacia: Dry Weather Nitrate Mean and Standard Deviation Levels over Time Based on Triplicate Samples

Nutrient levels peaked during the summer months. Mulholland and Hill (1977) studied weekly nutrient concentrations for two first-order streams in eastern Tennessee for seven years. They reported annual maximum concentrations of nitrates and soluble reactive phosphorus in both streams sampled during the summer and biannual minimum values in the spring and fall. Variability in phosphorus and nitrogen may be related to the connectivity of storm drains to upstream watersheds; which promotes rapid drainage and connectivity to diverse surface and groundwater sources (Janke et al., 2014).

Elevated concentrations of dissolved nutrients from these sources during dry weather baseflow can be substantial contributions to overall nutrient yields (Janke et al., 2014). 70

Headwater alterations, presence of buried sanitary sewers, potable water pipes, and storm drains can contribute to pulses of nutrients into streams (Kaushal et al., 2014; Paul and Meyer, 2001; Walsh et al., 2005; Allan et al., 2008).

The most common water quality problem in the United States is elevated levels of phosphorus in urban streams caused by nonpoint sources (US EPA, 1996). Urbanization may limit the natural process of phosphorus sorption by soil and biological uptake of phosphorus within riparian buffer zones (Sonoda and Yeakley, 2007). Soil saturated with phosphorus year-round may be an additional source for urban streams (Sonoda and

Yeakley, 2007). Landscape irrigation during dry weather conditions were shown to make large contributions to nutrient loads in residential catchments in a coastal residential community in Orange County, California (Toor et al., 2017). The highest daily nutrient concentrations for this study were found to occur from 6 pm to midnight. Pet waste nitrogen contribution exceeded lawn fertilizer contribution in a suburban Baltimore watershed and pet waste represented 84% of all phosphorus input in a Minneapolis-

Saint Paul watershed (Carey et al., 2013).

All efforts were made to minimize error for nutrient analysis. For small watersheds, Harmel et al. (2006) reports that there are four error sources leading up to nutrient analysis used for TMDL calculations: streamflow measurement, sample collection, sample preservation/storage, and laboratory analysis. The probable range error formula shown in Equation 2 below, the root mean square error propagation method, was used to estimate the cumulative water quality data uncertainty (Topping,

1972).

퐸 = ∑ (퐸 +퐸 +퐸 +퐸 ) Equation (2)

71 where E1 , E2, E3 , and E4 are potential sources of error (Harmel et al, 2006). Since a USGS gauge was used for streamflow conditions, the estimated E1 value was the best-case scenario of 3%. For E2, the median sample collection uncertainty of 3% was used for handling minimum flow conditions. Since samples were kept on ice and in the dark prior to lab analysis within 24 hours, E3 was estimated at 3%. Last, E4, uncertainty in lab analysis, was 8% based on the use of a colorimeter (Harmel et al., 2006).Using the 퐸 formula and aforementioned error values, this research would expect a range of error of

±9.54% for dry weather baseline conditions. This error is within the 10% margin of safety the Ohio EPA uses to calculate target phosphorus loads (Ohio EPA, 2005) and is therefore acceptable for pollutant load estimations.

Observed pH values for the monitoring period ranged from 7.70 to 8.52. The minimum pH level was recorded on June 30th and the maximum pH was recorded on

August 8th. Consecutive observations of pH varied, from no change to 0.53. Figure 5.4 shows the pH at Acacia versus time. The IQR for pH is 0.23 (8.04-8.27).

Figure 5.4. Acacia: Dry Weather pH levels over time

Pollution can change a water’s pH, potentially harming aquatic life (USGS,

2020b). Chronic (30-day) pH tests to determine the long-term effects on aquatic insects showed that lower pH has an adverse impact on aquatic insects (Bell, 1971). Although safe pH values vary for various aquatic insect families, a range of 6.5 to 9.0 is considered safe, while maximum productivity occurs between 6.5 and 8.5 (US EPA(b), 2020;

European Inland Fisheries Advisory Commission, 1969). The US EPA states that when pH is maintained within the 6.5 to 8.5 range, rapid pH changes do not cause adverse impacts to aquatic life (US EPA, 2020b; Robertson-Bryan, Inc, 2004).

Conductivity values for this site were also elevated. The minimum conductivity measured during dry weather was 1215 µS/cm on September 11, 2019. The highest reading was the last, on March 8, 2020 at 5680 µS/cm. Similar high conductivity readings were taken in March and April of 2019. Conductivity steadily decreased over time throughout the summer and fall months. Into the winter, conductivity increased again, most likely due to the use of road salt. Figure 5.5 displays all conductivity readings over the monitoring period.

Figure 5.5. Acacia: Dry Weather Conductivity levels over time

Natural conductivity in streams is primary caused by the geology in the surrounding upstream soil (USEPA, 2020k). Streams tend to have a relatively constant range of conductivity, and conductivity will increase with warmer water temperatures

(Tyler et al, 2017; US EPA, 2020k). Since the data in Figure 5.6 suggests the opposite trend of higher conductivity associated with colder water temperature, there are likely anthropogenic causes present. The US EPA (2020k) states that significant changes may indicate pollution entering the stream and a possible cause for elevated conductivity is failing sewage systems; with 150-500 µS/cm being unsuitable for some fish and macroinvertebrates. The conductivity of US freshwater rivers generally ranged from 50-

1500 µS/cm while the volumetrically weighted mean conductivity of the global ocean is

33,100 µS/cm (Tyler et al, 2017; US EPA, 2020k).

Figure 5.6. Dry Weather Conductivity vs. Water Temperature at Acacia

It is not unusual to have high winter conductivity readings in an urban stream.

The US Army Corps of Engineers reported when deicing salts were used during the winter season in a small urban watershed in Pittsburgh, the receiving waters

74 experienced shock loads of conductivity exceeding 30,000 µS/cm while base flow dry weather conductivity values averaged 1,232 µS/cm during the two-year study period

(Koryak et al., 2001). The flashiness of smaller urban streams combined with the irregularity of deicing winter runoff made it challenging to fully capture conductivity values (Koryak et al., 2001).

Weekly specific conductance measurements in an urban Maryland stream from

2005 to 2008 indicated baseline conditions of 551 µS/cm with winter spikes reaching a maximum of 16,030 µS/cm (Cooper et al., 2014). In another study, roads were the most important contribution to high stream water conductivity (Wu et al., 2015). The study of

20 urban watersheds in Iowa, it was concluded that minimizing road surface area proximity to streams, building treatment trains to filter salts, and finding alternatives to the application of road salt were best mitigation practices (Wu et al., 2015).

During dry weather, the water was clear indicating no turbidity issues during dry weather for this site. The Secchi disk was easily visible through the 120 cm depth of water sampled. There were only two collection days with visibility less than 120 cm.

Figure 5.7 shows the near constant readings. Incoming water from storm sewers may have influenced the two days in which the Secchi disk was visible at lower depths. July

2nd and August 13th recorded values of 97 cm and 81 cm, respectively. As noted earlier, upstream construction activity was present on July 2nd. On August 13th, although there was no appreciable rainfall recorded, both storm sewers were active.

Figure 5.7. Acacia: Dry Weather Turbidity levels over time

Water temperature increased during the monitoring period from the onset until mid-August. As shown in Figure 5.8, temperatures ranged from a low of 6.54 °C (44 °F) to a high of 24.47 °C (76°F). Temperature is a fundamental regulator of nutrients in watersheds (Kaushal et al., 2014). Decreased shading due to urbanization and clearing of riparian zones can increase water temperatures by approximately 4-5 0C (Kaushal et al,

2014; Burton and Likens, 1973; Beschta, 1997).

Figure 5.8. Acacia: Dry Weather Water temperature levels over time

Thermal pollution sources and impervious surface can transfer heat to streams during summer months (Kaushal et al., 2014). In turn, elevated water temperature may increase rates of bacterial mineralization and production of greenhouse gas emissions

(Kaushal et al., 2014). Figure 5.9 shows the strong relationship between air temperature and water temperature. A long-term study of US streams and rivers found that the annual mean water temperature warming rates were most rapid in urban areas and were correlated to increases in air temperature (Kaushal et al., 2010). If stream temperatures increase, it could have adverse impact on stream metabolism, loss of aquatic biodiversity, and eutrophication (Kaushal et al., 2010; USGS, 2020a).

Figure 5.9. Acacia: Relationship between Water and Air Temperature During Dry Weather

Studies have shown that increased temperatures positively influence phosphorus availability on growth rates of algae (Cross et al., 2015; Persson et al., 2011; Wojewodzic et al., 2011). A study on the influence of temperature and dissolved nitrates on phosphates on microbial activity in four headwater streams in the southern Appalachian

Mountains found that temperature changes of 5°C induced significant changes in glucose mineralization and thymidine incorporation (Peters et al., 1987). The pronounced 77 seasonal changes in soluble reactive phosphorus concentration in two headwater streams with highest concentrations in summer and lowest in winter suggest that nutrient concentration may be driven by changes in stream temperature (Duan et al.,

2012). Incubation experiments suggest that phosphorus release from soil and other temperature dependent processes may influence base flow nutrient concentrations

(Duan et al., 2012).

5.3 East & Main Branch Comparison

For same day collections, there was a significant difference between phosphorus levels in the East and Main Branches at both the upper and lower reaches [t = -8.09, p<0.001]. Table 5.1 shows the average concentration of triplicate samples for each date and site. On average, phosphorus levels at the upper reach of the Main Branch were three times higher than the East Branch. There was a significant difference of 0.27 mg/L between the upper branches (p < 0.05). During dry conditions, Acacia averaged 0.40 mg/L of phosphorus while Rockefeller averaged 0.14 mg/L. There was twice as much variation at Acacia, with a standard deviation of 0.15 mg/L, compared to 0.07 mg/L at Rockefeller

(p<0.05).

Table 5.1. Dry Weather Phosphorus Comparison for Upper Reaches of East & Main Branches East Branch Main Branch Rockefeller Acacia Difference Date Sample (Dry) (Dry) (East – Main) 3/28 1 0.15 0.46 -0.31 4/12 2 0.15 0.40 -0.25 5/14 3 0.10 0.31 -0.21 5/19 4 0.00 0.28 -0.28 6/24 5 0.10 0.30 -0.20 6/30 6 0.06 0.25 -0.19

East Branch Main Branch Rockefeller Acacia Difference Date Sample (Dry) (Dry) (East – Main) 7/2 7 0.09 0.54 -0.45 7/11 8 0.19 0.32 -0.13 7/13 9 0.06 0.37 -0.31 7/25 10 0.21 0.45 -0.24 8/6 11 0.11 0.40 -0.29 8/8 12 0.16 0.32 -0.16 8/13 13 0.19 0.45 -0.26 8/20 14 0.25 0.21 0.04 8/27 15 0.20 0.37 -0.17 9/4 16 0.28 0.41 -0.13 9/6 17 0.19 0.49 -0.30 9/11 18 0.11 0.79 -0.68 9/15 19 0.20 0.31 -0.11 9/23 20 0.12 0.48 -0.36 10/6 21 0.11 0.59 -0.48 11/24 22 0.06 0.63 -0.57 3/8 23 0.03 0.14 -0.11 Mean 0.14 0.40 -0.27 Standard Deviation 0.07 0.15 0.16

The matched pairs t-test for the lower reaches also showed a significant difference between the two branches. However, this time it was the East Branch with higher phosphorus levels, on average with a test statistic of t = 10.39 and a p < 0.001. Table

5.2 shows that on average, the East Branch was twice as high as the Main Branch at its lower reaches, approximately 0.14 mg/L higher than the Main Branch during dry weather.

Table 5.2. Dry Weather Phosphorus Comparison for Lower Reaches of East & Main Branches East Branch Main Branch Highland East Highland Main Date Sample (Dry) (Dry) Difference 3/28 1 0.40 0.33 0.07 4/12 2 0.27 0.19 0.08 5/14 3 0.24 0.17 0.07

East Branch Main Branch Highland East Highland Main Date Sample (Dry) (Dry) Difference 5/19 4 0.17 0.01 0.16 6/24 5 0.20 0.17 0.03 6/30 6 0.18 0.08 0.10 7/2 7 0.20 0.13 0.07 7/11 8 0.29 0.09 0.20 7/13 9 0.17 0.07 0.10 7/25 10 0.42 0.20 0.22 8/6 11 0.24 0.09 0.15 8/8 12 0.27 0.10 0.17 8/13 13 0.26 0.14 0.12 8/20 14 0.22 0.13 0.09 8/27 15 0.28 0.09 0.19 9/4 16 0.23 0.10 0.13 9/6 17 0.29 0.11 0.18 9/11 18 0.28 0.10 0.18 9/15 19 0.28 0.11 0.17 9/23 20 0.29 0.08 0.21 10/6 21 0.27 0.09 0.18 11/24 22 0.21 0.17 0.04 3/8 23 0.21 0.07 0.14 Mean 0.26 0.12 0.13 Standard Deviation 0.06 0.06 0.06

Matched pairs t-tests were carried out for branch comparisons of nitrates, pH, conductivity, turbidity, and water temperature. Complete tables are provided in

Appendix B: Dry Weather East and Main Branch Comparison. Table 5.3 details the summary statistics of all test results. Note, the green highlight indicates significant results.

Table 5.3. Summary Statistics for East & Main Branches During Dry Weather Location Parameter Mean Standard Test P-value Difference Deviation of Statistic (East – Difference (t) Main) (East – Main) Phosphorus -0.27 mg/L 0.16 mg/L -8.09 < 0.001 Nitrate -0.3 mg/L 0.3 mg/L -4.80 < 0.001

Location Parameter Mean Standard Test P-value Difference Deviation of Statistic (East – Difference (t) Main) (East – Main) Upper pH 0.09 0.27 1.60 0.124 Conductivity -1095 µS/cm 843 µS/cm -6.23 < 0.001 Turbidity 2 cm 9 cm 1.07 0.298 Water 0.70 °C 1.52 °C 2.2 0.038 Temperature Phosphorus 0.13 mg/L 0.06 mg/L 10.39 < 0.001 Nitrate 0 mg/L 0.2 mg/L 0 1 Lower pH 0.08 0.14 2.74 0.012 Conductivity -460 µS/cm 396 µS/cm -5.57 < 0.001 Turbidity 0 0 0 1 Water -0.10 °C 0.99 °C -0.48 0.633 Temperature |Water Temp| 0.67 0.42 7.65 < 0.001

In summary, the East and Main Branch differ in water chemistry. At the upper reaches, the Main Branch tested on average 0.3 mg/L higher in nutrients than the East

Branch. The Main Branch was also higher in conductivity on average by 1095 µS/cm. The

East Branch was slightly warmer at 0.70 °C (1.26 °F). At the lower reaches, differences in nutrient levels were reversed. The East Branch tested higher in phosphorus, on average

0.13 mg/L more than the Main Branch and there was no noticeable difference in nitrates.

The pH of the East Branch was higher on average by 0.08 and the Main Branch continued to have higher conductivity, albeit not as much at 460 µS/cm. The two branches at the lower reaches differed in temperature by 0.67 °C (1.2 °F), on average.

5.4 Tributary Impact on East & Main Branches

After the first ten dry weather collection days, the nutrient levels along the tributary for the Main Branch (Schaefer Park site) were reviewed and found considerably higher than the other eight original collection sites. From March 28th to July 25th,

Schaefer Park had tested between 1.5 and 5 times higher than the other sites. Figure 5.10 81 displays the cumulative phosphorus levels for each of the nine original monitoring sites for these 10 dry weather collections up to and including July 25, 2019.

An investigation was carried out on July 31st to see if a nutrient source could be located to explain the higher values in the Schaefer Park sampling location. There was a large existing sanitary sewer line near the tributary, as shown in Figure 5.11. It was unclear if high nutrient levels were the result of a leaky nearby sewer line or from unknown upstream conditions. Samples were taken at six locations throughout the length of the tributary.

Figure 5.10. Dry Weather Phosphorus Level Comparison Between Original Nine Sites

The most upstream sample location was Spencer Road. This site is downstream of Lyndhurst Park, the first accessible headwater location. The second site was at

Edenhurst Road, upstream of Roland Park. The next two samples were at Ridgebury

Boulevard, immediately downstream of Roland Park and east of Richmond Road (Route

175). The fifth location was at Ridgebury Boulevard, upstream of Schaefer Park and west of Richmond Road. The last location was Schaefer Park.

Two samples were taken at the location downstream of Roland Park due to a six- foot diameter active storm sewer draining into the tributary. The storm sewer tested at

1.19 mg/L phosphorus and was thought to be the possible nutrient source. However, both sites upstream of Roland Park tested as high as the three sites downstream of the storm sewer discharge. The tributary had consistent nutrient levels throughout its length as shown on Figure 5.12. It was suspected that aged sewer infrastructure upstream of the tributary could be the nutrient source. The upstream location,

Spencer Road, was added as an additional monitoring site.

Figure 5.11 Existing Sanitary Sewer Line at Schaefer Park Site

Figure 5.12. Schaefer Park Investigation Phosphorus Results

A second tributary investigation was carried out on August 29th during dry weather along the East Branch as the Richmond White site had higher nutrient levels than the combined testing of upstream sites Bishop Road and Rockefeller Road. The three existing sites were tested, Rockefeller Road, Bishop Road, and Richmond White.

Five new sites were tested. A site was added on the East Branch between Rockefeller

Road and Richmond White (D/S Rock Rd). Two locations were added along the tributary that Bishop Road feeds into prior to its discharge to the East Branch. One new location was downstream of the StoneWater Golf Club on Golf View Drive and the second was downstream of the airport (D/S Bishop). The last new site was located along

Richmond Road (Route 175), immediately west of the Cuyahoga County Airport.

Like the Schaefer Park investigation, the highest nutrient level was the furthest upstream location (Figure 5.13). The Community Center on Highland Road had the highest level at 0.82 mg/L with Golf View Drive the next highest at 0.45 mg/L. As a result

84 of this investigation, three additional sites were added as monitoring sites. All three were headwater locations for three separate tributaries to the East Branch as shown in Figure

5.14.

Figure 5.13. East Branch Tributary Investigation Phosphorus Results

Figure 5.14. Location of Three Upstream Tributary Sites for East Branch

To determine the nutrient impact of the tributaries, One-factor ANOVA was conducted between all adjacent sites with the data blocked by sampling date. For each 85 date, the difference in phosphorus concentrations were compared (upstream station – downstream station). Figure 5.15 shows that five groupings had no statistically significant drop in nutrients (p > 0.05): Telling Mansion/Highland Main, Highland

Main/Villaview, Villaview/Wildwood, and Spencer Road/Schaefer Park. Results indicate that there were six significant drops in phosphorus between monitoring stations. Two had significant drops of 0.01 to 0.17 mg/L (Richmond White/ Highland East and

Highland East/ Villaview, p < 0.05). Four had even more substantial significant drops that ranged from drops of 0.25 to 0.58 mg/L (p < 0.05). These locations were: Acacia/Telling

Mansion, Schaefer Park/Highland Main, U/S Stonewater/Richmond White, and

Community Center/Highland East. It is important to note that three out of four of these drops were in the tributaries to the East and Main Branches. Thus, the tributaries contributed significant nutrients to Euclid Creek during dry weather conditions. It should also be noted that the remaining two drops in Figure 5.15 have significant negative drops, meaning the upstream station nutrient level was less than the downstream station (p < 0.05). These negative drops occurred at Rockefeller

Road/Richmond White and Bishop Road/Richmond White. These two drops suggest that nutrient levels increased from 0.13 to 0.25 mg/L and these two headwater locations were not significant sources of nutrient concentrations (p > 0.05, Table 5.4 details the specific sites for each drop). Figure 5.16 shows these results on the watershed map.

Stream stretches between sites that have no significant change in phosphorus loads are shown in blue. Areas of concern are shown in red, and nonpollution sources are shown in green. Supplementary information for this analysis is located in Appendix C: Dry

Weather ANOVA and Tukey Comparisons of Upstream Tributary Impact.

Figure 5.15. Adjacent Site Phosphorus Drop Comparison

Table 5.4 Site Descriptions for Adjacent Phosphorus Drop Comparison Drop Number Upstream Station – Downstream Station 1 Acacia – Telling Mansion 2 Telling Mansion – Highland Main 3 Highland Main – Villaview (Wildwood) 4 Villaview – Wildwood 5 Spencer Road – Schaefer Park 6 Schaefer Park – Highland Main 7 Rockefeller Road – Richmond White 8 Bishop Road – Richmond White 9 U/S Stonewater – Richmond White 10 Community Center – Highland East 11 Harris Road – Highland East 12 Richmond White – Highland East 13 Highland East – Villaview (Wildwood)

Figure 5.16. Mapping of Change in Phosphorus Concentrations for Adjacent Sites During Dry Weather Conditions

Research was conducted to examine spatial and temporal patterns in nutrient concentrations in ten small watershed streams in South Carolina (Tufford et al., 2003).

The study found that urban streams had greater nutrient concentrations than forested streams. Phosphorus levels were highest in summer months, but nitrates were highest in winter (Tufford et al., 2003).

The National Science Foundation funded the Baltimore urban Long-Term

Ecological Research project to explore the impact of urbanization on phosphorus export to streams reported that soluble reactive phosphorus and total phosphorus were highest

88 in small urban watersheds (Duan et al., 2012). The study also showed that nutrient concentrations during baseflow conditions were significantly higher in summer than those in winter. It was suggested that the strong correlation of phosphorus export with impervious surface cover within watersheds may be related to leaky sewers, a common phosphorus source in urban areas and that phosphorus concentrations increased longitudinally whereas nitrate concentrations decreased longitudinally with increased urbanization (Duan et al., 2012). This corroborates that the increased phosphorus at the upstream tributary and headwater locations in Euclid Creek was due to leaky sewers and higher urbanization.

Nationwide, substantial resources have been targeted to improve water quality from nonpoint source pollution. A study from three separate sources of stream water chemistry across five watersheds in Arkansas found a positive correlation between nutrient concentrations in streams during dry weather and wet weather conditions.

High base flow nutrient concentration sites may be used to best target nonpoint source pollution within watersheds at or near the hydrologic unit code (HUC 12) level instead of annual TMDL calculations (McCarty and Haggard, 2016).

5.5 Active Storm Sewer Collections During Dry Weather

Dry weather flows from storm sewers typically contribute considerable contaminants to collecting waters and usually come from sanitary wastewater, industrial discharges, failing septic systems, and vehicle-maintenance activities (Field et al., 1994).

Any inappropriate dry weather flow was sampled and reported to the Euclid Creek

Watershed Program Manager. Active dry weather storm sewers were observed and mean nutrient values based on triplicate samples are documented in Table 5.5.

Table 5.5 Active Dry Weather Storm Sewer Nutrient Concentrations Nitrate Old Date Location Phosphorus meter (mg/L) (mg/L) July 13, 2019 Telling Mansion 0.87 2.7 July 31, 2019 Roland Park 1.19 1.6 August 8, 2019 Acacia 0.01 1.1 (right embankment looking downstream) August 13, 2019 Acacia 0.91 1.1 (Left side of stream looking downstream) August 20, 2019 Acacia 0.22 1.7 (Right side of stream looking downstream)

The Telling Mansion site had an active storm sewer during dry conditions from

May to late August as shown in Figure 5.17. A strong sewer smell was present during these collections. Although reported and stopped, subsequent activation began again in mid-October and continued throughout the remainder of the monitoring period. A large storm sewer directly discharging into the tributary upstream of Schaefer Park was discovered and reported during the Schaefer

Park investigation. The only other site location with any dry weather storm sewer activation was Acacia, where observed flow Figure 5.17. Active Storm Sewer During Dry Weather at Telling was less frequent than Telling Mansion. Mansion

CHAPTER 6

WET WEATHER RESULTS & DISCUSSION

6.1 Wet Weather Flow Overview

This section details the results of the parameters collected during wet weather conditions. There were eleven wet weather collection days. Eight of these collections were identical to the dry weather procedure, with all sites monitored in a day. The remaining three wet weather collections consisted of sampling throughout a rain event at one site. The first eight collections gained insight into general watershed behavior during wet weather; whereas, the last three wet weather collections were designed to understand specific site performance during a wet weather event.

For the first eight wet weather events, bivariate data was compiled for each site to compare changes in nutrient level, pH, conductivity, turbidity, and water temperature over time. As was done for the dry weather analysis, wet weather conditions for the

Acacia site will be described in detail below. Dry and wet conditions were compared for the Acacia site. This statistical process was repeated for each of the remaining 13 sites and can be found in the Appendix D: Wet Weather Results. Rainfall intensity, rainfall depth, time of collection within the wet weather event, storm duration, and antecedent

91 dry period are important factors that directly impact collection results. Therefore, rain event characteristics were also summarized and discussed for the eight collections at sites in close proximity to the USGS gauge stations in the watershed. Next, wet weather conditions were compared for the upper and lower reaches of the East and Main branches for the eight weather collection days. Upstream tributary impacts to the branches were investigated and compared by evaluating the differences in nutrient concentrations between adjacent sites for the eight wet weather collection dates. The last analyses were the three wet weather collections carried out at specific site locations.

6.2 Wet Weather Flow Conditions at Acacia

Mean phosphorus levels during wet weather ranged from 0.07 mg/L to 0.31 mg/L as shown in Figure 6.1. These minimum and maximum concentrations were recorded on

August 18th and July 30th, respectively. Standard deviation ranged from 0.1 mg/L to 0.07 mg/L. The lower mean phosphorus concentrations had the most variability in sampling.

There was more variability in the summer months than the spring, fall, and winter months.

Figure 6.1. Acacia: Phosphorus concentrations during wet weather events. Standard deviation based on triplicate samples

During rain events, phosphorus concentrations varied due to surface run-off and soil erosion (Stutter et al., 2008). Understanding the phosphorus transport process from land to streams is important in determining pollutant loads (Stutter et al., 2008). In an east-central Illinois study on the transport mode of phosphorus from fields to streams in tile-drained agricultural watersheds, phosphorus stream concentrations during storm events were greater than 0.2 mg/L compared to baseflow levels which were less than 0.05 mg/L (Gentry et al., 2007). Extreme discharge events were identified with large phosphorus exports while moderate precipitation events replicated similar phosphorus concentrations at peak discharges (Gentry et al., 2007). Similarly, a study in southeast

China saw total phosphorus load increase 3 to 16 times higher than baseflow conditions

(Chen et al., 2015). The highest change in phosphorus concentrations was observed in the areas of the watershed with most human activity and the highest fluctuations were exhibited during extreme rainfall events (Chen et al, 2015).

Another study noted that urban streams had as much as five times as much variability in water quality parameters than rural streams during rain events

(Hasenmueller et al., 2017). For instance, the stream’s discharge in the rural watershed was not responsive for rain events up to 0.3 inches of rainfall and intensities up to 0.16 in/hr, while the urban watersheds saw discharge responses as little as 0.02 inches and intensities as low as 0.007 in/hr (Hasenmueller et al., 2017).

Wet weather results for the current study were diluted, and more variable compared to dry weather (Chapter 5). On average, mean concentration wet weather conditions at Acacia were 40% lower than those exhibited during dry weather. It is important to note that sampling during the initial stages of wet weather were not

93 captured for this study. It is unknown if nutrient concentrations increased prior to peak flow.

Average nitrate levels ranged from below detection limits (BDL) to 1.9 mg/L

(Figure 6.2). Although the highest nitrate levels were recorded during the summer months, higher nitrate levels were detected at both colder (6.5°C) and higher (24°C) water temperatures. With the exception of June 25th, there was little variability in the samples (± 0.1 mg/L). In addition, Euclid Creek Watershed nitrate levels did not see increases during rain events. Other studies were found to have conflicting results between dry and wet conditions. For example, a 13-year study in a predominantly agricultural area in Australia reported nitrate levels increased by 16% in baseflow conditions after high discharge rainfall events (Mitchell et al., 2005). Conversely, in a study of the Wei River basin in China, the mean concentrations of nitrates were lower during the rainy season compared to the dry season (Yu et al., 2016).

Figure 6.2. Acacia: Nitrate concentrations during wet weather events. Standard deviation based on triplicate samples

In a central Pennsylvania study, nitrate and phosphorus levels generally increased during the early stages of increased stream discharge, implying surface run-off as the nutrient source (McDiffett et al., 1989). The highest nutrient concentrations occurred very early in the rain event while diluted concentrations were found at higher rates of discharge that returned to pre-storm levels as discharge returned to baseflow conditions

(McDiffett et al., 1989). The authors point out that different types of watersheds, longer duration rain events, and seasonal fluctuations are likely to present different outcomes.

At Acacia, nitrate levels were compared for wet and dry conditions. There was no noticeable difference between conditions. Factoring in the colorimeter did not make any difference in results. Using the EP formula (Harmel et al, 2006), Euclid Creek Watershed would expect a range of error of ±26.6% for wet weather flow conditions. Since a USGS gauge was used for streamflow conditions, the estimated E1 value was the best-case scenario of 3%. For E2, using a grab sampling method during a storm event results in an uncertainty of 25%. As samples were kept on ice and in the dark prior to lab analysis within 24 hours, E3 was estimated at 3%. Last, E4, uncertainty in lab analysis, was 8% based on colorimetric procedures (Harmel et al., 2006). Typical cumulative probable uncertainty for measured storm loads range from 11% to 104% for dissolved phosphorus

(Harmel et al., 2006). This study’s expected uncertainty of 26.6% for wet weather collections yields useful information for the watershed.

In general, the average pH level during wet weather was 7.74. The maximum pH reading during wet weather was the last, January 12, 2020 at 8.34. The lowest pH was recorded on July 30, 2019 at 7.51. When only looking at wet weather collections over time, there appears to be a moderately strong, positive, linear relationship between pH

95 and time as shown in Figure 6.3. All observed readings remain in the maximum productivity range of 6.5 to 8.5 for aquatic life (US EPA(b), 2020).

Figure 6.3. Acacia: pH levels over time during wet weather events

When compared to dry weather, the pH readings for wet weather were significantly lower [p < 0.05]. This was expected as typical rainwater pH is 5.6 (Smith et al., 1984; Khemani et al., 1985; Peart, 2000). Although wet weather pH was lower, it was still at acceptable, safe levels. This is important as changes in pH can negatively impact aquatic life. Lower pH decreased survival rate and size at metamorphosis for a study on tadpole survival (Warner et al., 1991). Fish populations like fathead minnows were absent in a study of North American waters below a pH of 6.5 (Magnuson et al., 1984).

Continuous monitoring of pH for two Southeastern Massachusetts ponds from 1990 to

1993 found seasonal variation in pH as well as temporary decreases in stream pH due to rain events (Hagar et al., 2000).

Conductivity levels were highest during the winter with the highest wet weather reading of 2640 µS/cm recorded on January 12, 2020. During the spring and early

96 summer, conductivity measurements averaged 985 µS/cm. Wet weather conductivity was lower in the later summer months and into fall (July 30 – October 16) with an average conductivity reading of 590 µS/cm. Conductivity on July 30th was the lowest recording measurement at 282 µS/cm. Figure 6.4 shows the general declining readings over the course of the monitoring period. Compared to dry weather, wet weather readings showed a similar pattern over time but at diluted levels. The rate of change for dry weather was about four times higher than wet weather, decreasing at a rate of about

100 and 20 µS/cm per week, respectively.

In an urban Pennsylvania stream study, pH and conductivity levels were elevated during dry weather but decreased during wet weather (Koryak et al., 2001). This pattern changed in the winter during wet weather events. The flushing of road salt contributed to pronounced conductivity values; the highest levels were always recorded at the upstream culvert and decreased with each consecutive downstream monitoring location

(Koryak et al., 2001).

Figure 6.4. Acacia: Conductivity levels over time during wet and dry weather 97

As expected, there was less visibility in the turbidity tube during wet weather.

The color of the water was brown, and it was cloudy in appearance. There was usually debris floating in the creek. After the heavy rains from September 11 -13th, a substantial amount of large stone material was swept downstream of the Acacia site as shown on

Figure 6.5. The first photo was taken on June 20, 2019, the third wet weather collection.

Turbidity measured 20.5 cm on this date. The second photo was taken during dry weather on September 15, 2019. Turbidity measured 120 cm, but much of the channel was blocked by the stone debris that was eventually removed by construction equipment.

July 30th had the most turbid conditions, with visibility a mere 13 cm. As Figure 6.6 shows, there was evidence of sediment transport during rain due to the reduced water clarity. During dry weather, water was clear. But, during wet weather the water turns cloudy and has a brownish hue. The majority of wet weather events had water clarity levels reduced by over 50%. A study in southeastern North Carolina noted that turbidity and phosphorus were significantly higher during rain events compared to dry weather conditions (Mallin et al., 2009).

A Seattle study found that sediment particle size varied in streams due to seasonal wet weather events and that early spring sedimentation from storms decreased stream suitability for benthic colonization (Perkins, 1982). Suspended sediment adversely impacts aquatic life in many ways like damaging fish gills, scouring plants from rocks, reduced light penetration, and reduced fish feeding efficiency (Schueler et al.,

1997). Turbidity exceeding 25 nephelometric turbidity units (NTUs) caused loss of sensitive fish species and monthly turbidity exceeding 100 NTUs caused decline in sunfish, bass, chub, and catfish populations (Schueler et al., 1997). The overall effects of urban runoff on urban streams is different for individual watersheds (Pitt, 2003). In

98 addition, individual rain events dictate unique particulate wash off, transport, and dilution (Pitt, 2003).

Figure 6.5. Looking downstream from Acacia sampling site (a) during wet weather conditions on June 20, 2019 and (b) stone debris during September 15, 2019 collection.

Figure 6.6. Acacia: Turbidity levels over time during wet weather

As expected, water temperature gradually rose from March 2019 until the late summer (Figure 6.7). The highest water temperature during wet weather was taken on

August 18th (23.78°C). The overall pattern is similar between dry and wet conditions.

Like dry weather, there was a strong relationship between air temperature and water temperature. Studies have shown that impervious areas draining into receiving waters can act as heat collectors and serve to increase stream water temperatures by as much as

5 to 10°C during rain events (Galli, 1990). However, a similar temperature increase can occur during dry weather due to the loss of the protective riparian tree canopy (Schueler,

1991).

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Figure 6.7. Acacia: Water temperature levels over time during wet weather

6.3 Rainfall Characteristics of Wet Weather Collection Events

Land use, soil conditions, antecedent conditions and rain event characteristics all play an important role in a catchment’s dynamic phosphorus response (Stutter et al.,

2008). Single events sampled in a year cannot be considered representative of the range of events possible (Mitchell et al., 2005). Rainfall can be localized within a watershed due to areas of varying land use and nutrient concentrations will depend on the location of the most intense rainfall and run-off (Mitchell et al., 2005). Figure 6.8 shows all 79 rainfall events for the 2019-2020 monitoring period, with the eight (all sites sampled) wet weather collection events highlighted. There were five wet weather collections during the summer, one in the spring, one in the fall, and one in the winter.

Of the eight collection days, the July 30th rain event had the least amount of rainfall at 0.46 inches and the June 25th rain event recorded the most rainfall at 2.03 inches. The June 25th event also had the strongest rainfall intensity at 1.35 in/hr. The

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January 12th rain event was the least intense at 0.13 in/hr rainfall intensity but recorded

1.32 inches of rain.

Figure 6.8. All wet weather events for the monitoring period (March 2019 – March 2020) Source: NEORSD, 2020

The July 30th rain event had a rainfall intensity of 0.21 in/hr. As shown in the figure below, this storm had a rainfall intensity and amount representative of this monitoring period. Two other events were nearly identical in intensity but recorded more rainfall: April 26th recorded 0.21 in/hr with 1.02 inches of rain and October 16th had

0.22 in/hr and 1.11 inches of rain. The summer storms were the strongest which is typical for the Cleveland area (Andresen et al., 2012; Huff and Changnon, 1973; Niyogi et al.,

2017). Prior to the June 25th storm, the June 20th storm had an intensity of 0.86 in/hr with

1.84 inches of rain. Prior to that, the June 16th storm had 0.40 in/hr rainfall intensity and 102

1.45 inches of rain. The remaining event occurred on August 18th, had a rainfall intensity of 0.68 in/hr and 0.71 inches of rain.

Tracking the collection time within the rain event for both branches as well as after the confluence of the branches determined that the wet weather collection typically occurred after peak discharge. The following graphs detail the rainfall distribution and subsequent discharge for each event as well as the collection time for each of the independent branches. Euclid Creek is quite flashy, responding to rainfall quickly. As shown in Figure 6.9, discharge response was typically within about an hour and peak discharge was within 2 to 3 hours, depending on rainfall intensity. The Beachwood

NEORSD rain gauge and USGS discharge station data was used for analysis. Figures 6.9 and 6.10 display representative collection events for the two branches. Graphs of the remaining wet weather collection events are located in Appendix E: Wet Weather

Collection Events.

Figure 6.9. Wet Weather Collection 1: Main Branch at Telling Mansion (April 26, 2019)

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Figure 6.10. Wet Weather Collection 1: East Branch at Richmond White (April 26, 2019)

The Main Branch reached peak discharge before the East Branch and transferred more water in all eight events. Rainfall volumes and intensities likely varied across the watershed, as the difference in peak volumetric flows during storms were not consistent.

Table 6.1 shows percent peak flow changes between the two branches varied from 1% to

94%.

Table 6.1. Peak Flow Differences in Branches During Wet Weather Events Sampling Date Peak Peak Flow Peak Flow Storm Discharge Main Branch East Branch Intensity Difference (cfs) (cfs) (in/hr) (%) April 26, 2019 20 111 89 0.21 June 16, 2019 1 167 165 0.40 June 20, 2019 62 363 139 0.86 June 25, 2019 52 431 206 1.35 July 30, 2019 79 177 37 0.22 August 8, 2019 94 171 11 0.68 October 16, 2019 72 65 18 0.22 January 12, 2020 24 147 112 0.13

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The Main Branch showed a repeated pattern of rapid response to rainfall for each event. Nitrate readings for both branches at these two locations were very similar. The first three events had nitrate readings of below detection and each of the last five events had measurements within 0.2 mg/L. There was some evidence indicating there may be a difference in phosphorus concentrations. A matched pairs t-test for the difference in phosphorus, although not significant [p > 0.05], suggests that further wet weather testing at these locations may be of interest [t = -2.32, p = 0.053]. During the July 30th event, sampling occurred just after peak discharge for both branches, but the East Branch had a much higher concentration [ East Branch = 0.68 mg/L, Main Branch = 0.19 mg/L PO4]. In the August 18th event, the East Branch experienced several discharge increases of unknown origins. There was no recorded rainfall for the area that would cause such a response [East Branch = 0.35 mg/L, Main Branch = 0.05 mg/L PO4]. For all eight wet weather events, phosphorus levels were higher in the East Branch than the Main Branch.

Rainfall depth, discharge, antecedent dry period, and storm duration for the eight events were examined for the two USGS stations in the branches and the USGS station after the confluence of the two branches. An example of phosphorus concentrations for one of the three USGS gauge stations are compared below in Figure 6.11. (Additional graphs are provided in the Appendix F: Phosphorus and Rainfall Characteristic

Comparison.) The East Branch had higher concentrations for lower rainfall depths and shorter storms while the Main Branch had higher concentrations for higher discharges.

The data documents that antecedent dry periods matter for both branches. The longer the dry period prior to rainfall, the higher the phosphorus export for both branches. Other researchers have found similar findings. For instance, in their study of five storm events for agricultural catchments in northeast Scotland, Stutter et al. (2007) 105 concluded that the most important water quality impact to streams are isolated summer storms that mobilize large amounts material from antecedent dry periods. In a coastal southeast Australian study, the largest storm event produced the largest peak flow and phosphorus concentrations and was preceded by drought conditions (Drewry et al.,

2009).

Figure 6.11. Wet Weather Conditions for East Branch at Richmond White: Phosphorus Concentration vs Rainfall based on triplicate samples

6.4 East & Main Branch Comparison During Wet Weather for Upper and Lower Reaches

Unlike dry weather conditions, the East and Main Branch appear more similar than different in water chemistry composition during wet weather. In the upper reaches, there was little difference in wet weather nutrient levels. The wet weather means for phosphorus were 0.18 mg/L and 0.17 mg/L for the upper reaches of the East and Main

Branches, respectively [p > 0.05]. The Rockefeller site [upper reach of the East Branch] experienced no change between wet and dry conditions [p > 0.05]; however, Acacia

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[upper reach of the Main Branch] saw dilution of phosphorus levels during wet weather events [ p < 0.05 ]. Acacia exhibited a reduction of more than 50% of mean phosphorus levels during the wet weather sampling.

In the lower reaches of the two branches, wet weather phosphorus concentrations for the East Branch were higher than the Main Branch [ p < 0.05 ]. The wet weather means for phosphorus were 0.25 mg/L and 0.16 mg/L for the lower reaches of the East and Main Branches, respectively. Average phosphorus levels seemed to remain relatively constant in the Main Branch, but there was more variability in the East Branch.

Water was more turbid in the lower reach of the Main Branch. Under dry conditions, the Secchi disk was visible in 120 cm of water. During wet conditions, visibility is reduced by 50% at Highland East. Highland Main’s water clarity was impacted even more. On average, the Secchi disk was only visible at 40 cm, a reduction of

67% at Highland Main.

Although pH levels were about the same in the branches during wet weather, they were lower than dry conditions. Regardless of weather during sampling, pH saw an increase over time. In the upper reaches, pH increased about 0.1 every 100 days in the

Main Branch while pH increased about 0.1 in 39 days in the East Branch.

Conductivity was similar during wet weather in the branches, but both exhibited lower readings than dry weather (i.e., approximately 50% less) than dry weather values.

Although there was not a significant difference between the branches [p > 0.05], the lower reach of the Main Branch saw substantial increases during the last two observations in October and January.

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During wet weather, the two branches yield similar temperature and did not appear to be different than dry weather collections [p > 0.05]. The warmest temperature during a rain event was in the lower reach of the East Branch on August 18th (24.2°C).

The peak water temperature, regardless of the weather condition, was measured on July

2nd. From April to July, water temperature rose about 21°C, or roughly 7°C per month.

From July to September, temperature decreased about 10.5°C, about 3.5 per month. There was a daily difference in water temperature between the branches, both at the upper and lower reaches, with a more noticeable differentiation at the upper reaches (2 °F). Table

6.2 provides summary statistics for the two branches. Note, the green highlight indicates significant results. Complete tables are provided in Appendix G: Wet Weather East and

Main Branch Comparison.

Table 6.2. Summary Statistics for East & Main Branches During Wet Weather Location Parameter Mean Standard Test P-value Difference Deviation of Statistic (East – Main) Difference (t) (East – Main) Phosphorus 0 mg/L 0.08 mg/L 0 1 Nitrate -0.2 mg/L 0.3 mg/L -1.89 0.101 Upper pH 0.11 0.21 1.48 0.182 Conductivity -239 µS/cm 595 µS/cm -1.14 0.293 Turbidity -1 cm 37 cm -0.08 0.941 Water 0.37 °C 1.45 °C 0.72 0.494 Temperature |Water Temp| 1.13 °C 0.9 °C 3.55 0.009 Phosphorus 0.09 mg/L 0.10 mg/L 2.55 0.038 Nitrate 0 mg/L 0.1 mg/L 0 1 Lower pH 0.04 0.12 0.94 0.377 Conductivity -137 µS/cm 245 µS/cm -1.58 0.158 Turbidity 23 cm 24 cm 2.71 0.030 Water 0.14 °C 0.51 °C 0.78 0.463 Temperature |Water Temp| 0.41 °C 0.30 °C 3.87 0.006

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As expected, water velocity increased in both branches during wet weather events. The velocity was approximated by timing the flow of an object over a known distance [ d = v t ] due to safety reasons. In the upper reaches, water velocity reached about 2.5 feet per second (fps) for both branches. In the lower reaches, the Main Branch saw its average velocity almost triple from 1.7 fps to 4.9 fps. During dry weather, the East

Branch was about 1.5 times faster than the Main Branch, traveling at about 2.5 fps.

During wet weather, the East Branch increased by a factor of 1.6 to an average velocity of

4.1 fps.

6.5 Tributary Impact on Nutrient Concentrations of the Two Branches during Wet Weather

One-factor ANOVA analysis was conducted between all adjacent sites to determine any differences in phosphorus concentrations. Figure 6.12 shows the wet weather results for the 13-comparison groupings. Seven groupings had no statistical significance [ p > 0.05] and six showed significance [ p < 0.05]. Like dry weather, the stations represent the change from upstream to downstream as detailed in Table 6.3.

Supplementary information for this analysis is located in Appendix H: Wet Weather

ANOVA and Tukey Comparisons of Upstream Tributary Impact.

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Figure 6.12. Adjacent Site Phosphorus Drop Comparison During Wet Weather

Table 6.3 Site Descriptions for Adjacent Phosphorus Drop Comparison Drop Number Upstream Station – Downstream Station 1 Acacia – Telling Mansion 2 Telling Mansion – Highland Main 3 Highland Main – Villaview (Wildwood) 4 Villaview – Wildwood 5 Spencer Road – Schaefer Park 6 Schaefer Park – Highland Main 7 Rockefeller Road – Richmond White 8 Bishop Road – Richmond White 9 U/S Stonewater – Richmond White 10 Community Center – Highland East 11 Harris Road – Highland East 12 Richmond White – Highland East 13 Highland East – Villaview (Wildwood)

During wet weather, Schaefer Park, U/S Stonewater, Community Center and

Highland East had significantly higher phosphorus levels then their downstream site

[p<0.05]. These areas remain problematic pollution sources during wet weather. Like dry weather, Rockefeller Road and Bishop Road had significantly lower phosphorus

110 concentration then their adjacent downstream site, Richmond White [p<0.05]. This gives further evidence that these two tributaries to the East Branch contribute significant pollution to Euclid Creek. Figure 6.13 shows these results on the watershed map. Stream stretches between sites that have no significant change in phosphorus loads are shown in blue. Areas of concern are shown in red, and nonpollution sources are shown in green.

Figure 6.13. Mapping of Change in Phosphorus Concentrations for Adjacent Sites During Wet Weather Conditions

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6.6 Wet Weather Collections at Single Sites

There were three wet weather collections completed at a single site. The first collection occurred on July 17th at the confluence of the two branches. Starting at 9 am, after 0.56 inches of rainfall, triplicate water samples were taken for both streams every

30 minutes. Monitoring ended at 11:30 am, at which time there was 0.63 inches of cumulative rainfall. Figure 6.14 shows that the collection occurred several hours after peak flow. The Main Branch responded quickly to rainfall and collected a larger quantity of flow during rain compared to the East Branch. Upon site arrival, the Main Branch was moving slower [Main Branch stream velocity ≈ 3 fps, East Branch stream velocity ≈

4 fps] and water was more turbid [Main Branch turbidity = 19 cm, East Branch turbidity

= 58 cm]. About 0.1 inch of rain fell during the monitoring period, from about 10:30 am to

11:15 am. The Main Branch was observed to already begin a response, starting to surge about noon. The “flashy” hydrology observed in Euclid Creek is a symptom of “urban stream syndrome”, the observed ecological degradation of streams receiving urban run- off (Walsh et al., 2005). Strong hydraulic efficiency of stormwater drainage prevents nutrient uptake (Walsh et al., 2005). Frequent “flashy” storm events of highly impervious areas impaired the biotic stream community (Walsh et al., 2005).

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Figure 6.14. Rainfall and Discharge During the Monitoring Period for the July 16-17th storm event for the (a) Main Branch and (b) Main Branch and East Branch.

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Nutrient levels are shown in Figure 6.15 for Highland Main. At the lower reach of the Main Branch, phosphorus and nitrate levels did not depict a discernable trend over the three-hour monitoring period. Phosphorus level was ~0.15 mg/L PO4, about the same as the eight previous wet weather collections and the nitrate level was about 1.2 mg/L

NO3.

Figure 6.15. Changes in (a) Mean Phosphorus Concentration and (b) Mean Nitrate Concentration at Highland Main During July 17th Rain Event. Standard deviations based on triplicate sampling.

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Stutter et al. (2008) observed that peak nutrient concentration occurred preceding or at maximum discharge with nutrient concentration levels returning to baseflow levels as discharge levels off. Unlike the Main Branch, the East Branch had a clear decreasing trend for both nutrients. Figure 6.16 compares the nutrient concentrations changing over time.

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Figure 6.16. Changes in (a) Mean Phosphorus Concentration and (b) Mean Nitrate Concentration at Highland Main and Highland East (c) Turbidity at Highland Main and Highland East During July 17th Rain Event. Standard deviations based on triplicate sampling.

The Main Branch reacted quickly to rainfall. Upon site arrival, the Main Branch appeared more noticeably past peak conditions then the East Branch as it was moving slower [Main Branch stream velocity ≈ 3 fps, East Branch stream velocity ≈ 4 fps] and the water was more turbid [Main Branch turbidity = 19 cm, East Branch turbidity = 58 cm]. Water quality response differed between the two branches as observed in the turbidity measurements over time. During collection, the water column in the Main

Branch gradually improved until additional rainfall at 10:30, while the East Branch was experiencing increased delayed turbidity. If collection had continued, it is anticipated that the energetic nature of the Main Branch would result in further increased turbidity from the new rainfall, while East Branch response would be delayed.

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The next wet weather event site investigation was at Schaefer Park on July 22nd.

Schaefer Park was selected since it consistently reported high nutrient levels compared to the remaining sites. After approximately 0.32 inches of rainfall (as shown on Figure

6.17), sampling began at 9:30 and continued every 30 minutes for 3 hours.

Figure 6.17. Monitoring period within the July 22nd rain event at Schaefer Park

Immediately downstream of the site, two storm sewers directly connect to the tributary: a larger storm sewer located along the left embankment and a smaller storm sewer located along the right embankment. Both were sampled at 10:00 am and repeated again at 11:00 am. Nutrient levels did not rise as expected during the monitoring period.

The storm sewer phosphorus levels were below that of the stream while the nitrates were at or higher than stream concentration (See Figure 6.18).

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Figure 6.18. Water quality concentrations over time at Schaefer Park during July 22nd rain event: (a) Mean Phosphorus Levels (b) Mean Nitrate Levels. Standard deviations based on triplicate sampling (c) Turbidity and Conductivity (d) pH and Temperature

Although collection commenced prior to peak flow, and concentration levels were expected to increase, there are several reasons cited by studies as to why there was a decreasing concentration trend. First, the peak concentration may have already passed.

Verhoff et al. (1982) found that the peak phosphorus concentration almost always leads the peak discharge. Additionally, there was a short antecedent dry period [38.08 hours] resulting in plausible lower nutrient transfer from the surrounding catchment area.

Stutter et al. (2008) found drying-rewetting cycles impact concentrations and fluxes in phosphorus. Additional studies found depleted phosphorus sources during consecutive storms events, weakened nutrient flushing, and maximum phosphorus concentrations after peak discharge (Bowes et al., 2005; Jordan et al., 2005). Evans and Davies (1998) determined that a given discharge preceding peak discharge has a different nutrient concentration than the same discharge after peak. Further studies indicate that general nonlinear concentration-discharge patterns are useful in understanding nutrient

120 transport, but due to the irregular nature of rainfall events, seasonal variability, and antecedent conditions, nutrient concentration response within an individual watershed will likely vary between storms (Aguilera and Melack, 2018).

There were additional water quality parameter trends during the July 22nd collection. Similar to phosphorus concentration, conductivity decreased over the 3-hour monitoring period. Turbidity increased over time, matching conductivity rates of change over time. Likewise, temperature and pH exhibited an inverse relationship. As Figure

6.18 shows, all four water quality parameters change behavior after 11:00 am, which coincides with the time of peak discharge, 11:10 am.

These parameter changes may be related to rainfall intensity. Upon arrival at

Schaefer Park, it was raining and both storm sewers were active. Rain began at 7:30 am and continued until 9:00 am. Intermittent rain fell until 10:00 am. At this time, the storm sewers were flowing faster than the creek [storm sewer velocity ≈ 2.6 fps, creek velocity

≈ 0.5 fps]. At 10:30, there was a considerable depth and speed change in the creek, with little discharge from the storm sewers [creek velocity ≈ 1.5 fps]. The strongest rainfall intensity, 0.27 in/hr occurred at 8:40 am.

Rainfall intensity for this storm was approximately normally distributed over time (Figure 6.19). Changes in pH and temperature appear to coincide with this pattern.

The decreased temperature over 1.5 hours may be influenced by the rising limb of rainfall intensity. After peak rainfall intensity, water temperature began to rise. Water temperature initially decreased 2.3%, then increased by 4.4%. Likewise, pH increased

0.57 units in 1.5 hours until peak discharge (8.1% increase), then decreased 0.21 in the subsequent 1.5 hours (3.9% decrease).

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Figure 6.19. July 22, 2019 Rain Event at Schaefer Park Illustrating (a) Rainfall Intensity (b) Main Branch Discharge at USGS Station 04208677.

Some studies noted substantial decreases in pH with dilution of urban runoff

(Dempsey et al., 1993; Peart, 2000). Similar observations were made in this study when comparing dry and wet pH levels for individual sites. As stated in Section 6.2, the eight wet weather collections throughout the watershed monitored conditions after peak discharge; whereas, this collection monitored conditions prior to peak discharge.

A study completed in the United Kingdom with similar highly responsive and flashiness to rainfall events found peak flows lagged peak rainfall intensity by 1-2 hours

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(Lawler et al., 2006). This urbanized watershed study found substantial increases in turbidity and increases in pH (Lawler et al., 2006). The authors state that initial turbidity response was slow (no “first flush”) but increased strongly and lagged behind peak flow.

The last wet weather collection for a single site was carried out on October 30th at Schaefer Park. The goal was to collect samples at the very onset of rainfall. Collection began at 3:00 pm and samples were taken every 20 minutes until 6:00 pm. The storm sewers were also sampled again. The small storm sewer activated first, so it was sampled at 3:45pm and 4:45 pm. The large storm sewer was sampled at 4:50 pm and 5:30 pm.

Cumulative rainfall at the start of the collection was 0.03 inches. At the end of sampling, cumulative rainfall was at 0.13 inches (Figure 6.20).

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Figure 6.20. October 30th Rain Event at Schaefer Park with (a) Total 2-Day Storm Discharge (b) Discharge and Rainfall Intensity for the monitoring period

There was no observable change in nutrient concentrations for the stream.

Phosphorus levels in the stream remained stable at about 0.55 mg/L and nitrate levels

nd were low (i.e., ≤0.3 mg/L NO3). Unlike the July 22 event, only moderate changes were observed in water velocity and discharge. At this initial stage of the rain event, the maximum rainfall intensity was 0.05 in/hr, an 80% reduction from the July 22nd event.

Phosphorus levels in the storm sewer had a noticeable trend. The first collection, taken after 0.04 inches of rainfall at 3:45 pm was 0.72 mg/L PO4, which was 0.20 mg/L higher than the stream concentration at that time. Since these downstream pipes did not influence stream collection samples and there were no other visible direct connections upstream, any increase in nutrient load was likely to happen beyond the three-hour monitoring period (Figure 6.21). The storm sewer nutrient concentrations capture the

“first pollutant flush” into the stream.

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Figure 6.21. Changes in (a) Mean Phosphorus and (b) Mean Nitrate Concentrations at Schaefer Park during October 30th rain event. Standard deviations based on triplicate sampling.

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Other notable water quality parameter trends were pH and conductivity (Figure

6.22). During the monitoring period, pH increased 0.36 units (4.5%). The trend was nonlinear in nature, increasing from peak rainfall intensity until reaching steady state as rainfall intensity reached zero. After 5:00 pm, pH levels began to decrease. Conductivity decreased 9.5% from 3:00 pm to 3:20 pm, when it appeared to reach steady state. After

5:00 pm, conductivity levels began to decrease like pH. Water temperature increased

2.0% with the most noticeable increase There was no noticeable change in turbidity. The increase in pH and lack of “first flush” for turbidity corroborate the UK study findings

(Lawler et al., 2006).

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Figure 6.22. Changes in (a) pH Levels, (b) Conductivity and (c) Temperature during October 30th Rain Event at Schaefer Park.

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CHAPTER 7

TOTAL PHOSPHORUS LOADING ANALYSIS

7.1 USGS Stations

Euclid Creek’s streamflow was downloaded from the USGS National Water

Information System website and used for this thesis (USGS, 2020b). Discharge was collected from three field stations in five-minute intervals for the 2019 – 2020 collection period. Figure 7.1 shows Euclid Creek’s East Branch and Main Branch flow from March

24, 2019 – March 24, 2020 at the Richmond White location and Telling Mansion sites, respectively. In 2019, Euclid Creek’s baseflow was high during the spring months.

Baseflow decreased throughout the summer, until reaching low baseflow conditions in the subsequent fall. Baseflow gradually returned to higher conditions over the winter.

During the spring, the dry weather baseflow condition in the East Branch was higher than the Main Branch. Spring discharge in the East Branch was about 6 cubic feet per second (cfs), while the Main Branch flow was about 2 cfs. During the fall, baseflow dropped to about 1 cfs in the East Branch and about 0.2 cfs in the Main Branch. Levels in both branches returned to similar levels in the winter.

Conditions downstream after the confluence of the two branches were seasonally similar. Spring baseflow levels downstream were about 11 cfs. Fall baseflow levels were

128 about 3 cfs. Unlike the other two USGS gauges, much of downstream flow used estimated discharge values as shown in red on Figure 7.1.

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Figure 7.1. March 2019 – March 2020 Annual Discharge for Euclid Creek’s (a) East Branch (b) Main Branch, and (c) Downstream after Confluence of Two Branches Source: USGS, 2020b

Annual pollution loads were calculated using the discharge rates from the three

USGS gauge stations in conjunction with field sampling site concentration levels.

Calculations were carried out for the East Branch at Richmond & White (USGS Gauge

Station 04208684), the Main Branch at Telling Mansion (USGS Gauge Station

04208677) and downstream after the confluence of the two branches (USGS Gauge

Station 04208700). Figure 7.2 shows gauge locations and corresponding monitoring collection sites. The Wildwood site was used in conjunction with the Cleveland USGS

Station until July. At that point, the corresponding monitoring site was switched to

Villaview due to the high lake levels downstream.

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Figure 7.2. Monitoring Stations used for pollutant loads Sources: USGS, Esri, NEORSD, 2020

7.2 Dry Weather Phosphorus Loading Calculations

Dry weather sample collections times were matched with USGS discharges for each of the 23 collection days. Phosphorus loads were calculated and plotted as shown on Figure 7.3 for each gauge. Linear regressions were calculated and used to obtain daily average dry weather phosphorus loads. Using this approach, it was found that the East

Branch contributed over double the pollutant load than the Main Branch. Table 7.1 summarizes the loading by branch and season. Total dry weather phosphorus loading was estimated at 2450 pounds per year.

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Figure 7.3. Linear Regression Equations for Dry Weather Phosphorus Loads for (a) East Branch, (b) Main Branch, and (c) Downstream after the Confluence of the Two Branches

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Table 7.1. 2019 Dry Weather Annual Predicted Phosphorus Loads for Euclid Creek Season Main Branch @ East Branch @ Downstream Telling Mansion Richmond (lb/yr) (lb/yr) White(lb/yr) Spring 2019 139 330 710 Summer 2019 72 348 560 Fall 2019 171 456 510 Winter 2019 227 362 670 Total 609 1496 2450

7.3 Wet Weather Phosphorus Loading Calculations

Like dry weather conditions, wet weather sample collection times were also matched with USGS discharges. There were eight wet weather events. Figure 7.3 shows each sampling point with its corresponding discharge. The Main Branch wet weather collections resulted in a strong, positive, linear relationship. As discharge increased, pollution loading steadily increased. The East Branch also exhibited a positive relationship. However, one event exceeded the prediction, and appears as an outlier on the figure. This high pollutant loading may identify a possible sewer overflow.

Wet weather phosphorus loading was significantly higher [푝 < 0.05] in both branches. Seasonally, the highest loading occurred during the spring rains. Downstream levels were very high, most likely due to CSO activations. Table 7.2 summarizes wet weather phosphorus loading estimates.

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Figure 7.4. Linear Regression Equations for Wet Weather Phosphorus Loads for (a) East Branch, (b) Main Branch, and (c) Downstream after the Confluence of the Two Branches

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Table 7.2. 2019 Wet Weather Annual Predicted Phosphorus Loads for Euclid Creek Season Main Branch @ East Branch @ Downstream Telling Mansion Richmond (lb/yr) (lb/yr) White(lb/yr) Spring 2019 2517 3430 10,648 Summer 2019 1313 948 4357 Fall 2019 472 685 1516 Winter 2019 856 2130 3610 Total 5158 7193 20,131

7.4 Total Annual Phosphorus Loading Conclusion

The annual target phosphorus load for Euclid Creek was 5000 pounds per year.

The 31 sampling events indicated that existing conditions were four times higher than the EPA target nutrient limit. Both branches upstream of the confluence of the stream exceed the target. The excess phosphorus loading was induced by wet weather flows.

Unlike wet conditions, dry conditions were well below the target at an annual contribution of about 2450 pounds per year. The Main Branch dry weather flow phosphorus loads were consistently lower than the East Branch throughout the year.

Table 7.3. 2019 Total Annual Predicted Phosphorus Loads Season Main Branch @ East Branch @ Downstream Telling Mansion Richmond (lb/yr) (lb/yr) White(lb/yr) Spring 2019 2656 3760 11,358 Summer 2019 1385 1297 4917 Fall 2019 643 1142 2027 Winter 2019 1083 2492 4281 Total 5767 8690 22,583

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CHAPTER 8

CONCLUSIONS AND RECOMMENDATIONS

8.1 Conclusions

This study monitored water quality for one of Lake Erie’s tributaries, Euclid

Creek, over the course of a year, March 2019 to March 2020. The goal of this thesis was to estimate pollution by gathering reliable water chemistry to understand watershed behavior during dry and wet conditions. Overall, there were 36 collection days with the majority of sampling occurring from June to October. Sampling was used to understand baseflow dry conditions throughout the watershed and to quantify the impact of storm events. Water quality parameters were collected for each site and trends were studied.

Conductivity, pH, dissolved oxygen, turbidity, water temperature, and water velocity were recorded for each site visit. As an impaired stream, Euclid Creek has an annual

TMDL restriction of 5000 pounds of phosphorus . Investigations were carried out to determine if this target is being met. Additionally, analyses were conducted to determine potential water chemistry differences between wet and dry conditions, differences between the two branches, and upstream tributary impact to the branches. Synthesis of all sampling results and their associated site locations enabled the analysis of the impact of land use on contaminant levels.

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The year-long dry weather monitoring confirmed high nutrient levels at upstream locations in both branches. In the Main Branch, there were two problematic locations,

Acacia and the Schaefer Park tributary. These sites tested up to 16 times higher than the target. In the East Branch, the two upstream tributary locations on Highland Road, the

Community Center and U/S of Stonewater were of concern, testing 17 times higher than the target. Although these particular four areas resulted in the highest levels, the target phosphorus concentration level of 0.05 mg/L for the watershed was consistently exceeded throughout the watershed upstream of the confluence of the two branches. It was anticipated that land uses like golf courses and the regional airport would contribute more pollutant loads than residential areas. Contrary to this hypothesis, results ultimately showed that the highest nutrient nonpoint sources were leaky sanitary sewers from residential areas at multiple headwater locations.

This sampling carried out in this study was similar to historical sampling conducted by WQIS and the Euclid Creek Watershed Monitoring Program; however, new trends were discovered. In dry weather, the upper reach of the Main Branch, Acacia, had about three times the phosphorus level compared to the upper reach of the East

Branch, Rockefeller Road. Immediately prior to the mixing of the two branches, the East

Branch had about twice as much phosphorus as the Main Branch, on average. The branches had different reductions in phosphorus levels between their respective upper and lower reaches. The East Branch doubled its concentration along its reach while the

Main Branch reduced its concentration by one-third. The restoration of Acacia from a golf course to a Cleveland Metropark and the existing Euclid Creek Metropark provide impactful nutrient reduction for the Main Branch. The East Branch lacks protection.

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Like the Main Branch, it traverses through residential communities and golf courses and a regional airport. Unlike the Main Branch, it does not have a substantial park to mitigate nutrient pollution. Dry weather nonpoint pollution sources seem to be the old and presumably leaky upstream sanitary sewers.

Nitrate levels throughout the watershed were below the target concentration of

1. 0 mg/L. The highest levels were observed at the Community Center (mean = 0.5 mg/L) and the lowest levels were recorded at Telling Mansion and Rockefeller, (mean = 0.2 mg/L). Nitrate levels did have a seasonal trend. Nitrate levels increased throughout the monitoring period, reaching maximum levels in the fall, while variability between sites was similar.

Another noteworthy dry weather trend was elevated conductivity levels. The highest and most variable conductivity readings were observed during the winter months. There was substantial seasonal variation. Sites reached measurements of 5000

µS/cm and varied by as much as 4000 µS/cm during March collections. Readings declined and fluctuated less in the summer. Average site conductivity for winter collections was about 2000 µS/cm compared to 800 µS/cm in late summer. The upstream reaches of both branches maintained the highest readings, with Acacia averaging 1000

µS/cm higher than Rockefeller Road. Another conductivity “hot spot” was Harris Road.

Added in September, it was consistently the highest site throughout all of September and October.

Readings for pH during dry weather were stable, safely ranging from 7.05 to 9.24 within the monitoring period. Higher pH values across the watershed were observed from August 2019 – March 2020. The highest pH values were at Villaview, below the

138 confluence of the two branches. The lowest pH was observed at Bishop Road, the tributary downstream of Airport Greens Golf Course and Wildwood, the furthest downstream site. There was a seasonal trend. Levels decreased from March 2019 into the spring, increasing throughout summer and into late November. Levels appear to decrease over the winter. Variability between sites was similar, about 0.23. Wildwood had the most variability in pH, which was likely influenced by Lake Erie.

The two branches revealed different nutrient results during wet weather as well.

During the monitoring of a weather event at the confluence of the two branches, the

Main Branch appeared to have reached a steady-state phosphorus concentration of 0.15 mg/L while the East Branch had a higher concentration that decreased linearly with time.

Both branches experienced higher nutrient levels than the preceding dry weather collection, four days prior. Testing during a wet weather event at Schaefer Park showed a decrease over time in nutrient levels. Although testing occurred prior to peak flow, there was no noticeable spike in concentration. Nearby storm sewers were tested and found to have lower concentration levels than the stream flow at the same time. Another wet weather event at Schaefer Park showed no change in nutrient levels during collection for the stream flow, but storm sewers tested had higher nutrient levels at the onset of rain.

Over the course of the three hours, the phosphorus level in the storm sewer decreased at a steady rate.

Across the watershed, there was a difference in wet weather responses for the upper reaches of the two branches. Acacia experienced a significant drop in phosphorus concentration, but Rockefeller Road did not. Both of these locations experienced significant drops in pH and conductivity levels and significant increases in turbidity and

139 water velocity. At the lower reaches of the branches, the East Branch continued to have about twice the phosphorus concentration than that of the Main Branch. Both lower branches had significant drops in pH and conductivity and increased turbidity and water velocity. Telling Mansion, Bishop Road, Richmond White, Villaview, Community

Center, and Harris Road did not experience a change in phosphorus during wet weather.

Schaefer Park, U/S Stonewater, and Spencer Road did have significant drops in phosphorus during wet weather. Wildwood results showed a significant increase during wet weather.

Rainfall depth, storm discharge, antecedent dry period, and storm duration all are important variables for storm events and can influence nutrient results. Both branches were studied for any dependence with phosphorus concentrations during wet weather.

Results showed a negative relationship between the phosphorus level in the East Branch and rainfall depth. Smaller rainfall storm amounts produce larger phosphorus concentrations. There was a positive relationship between phosphorus levels in the Main

Branch and discharge. Both branches show a positive relationship between phosphorus level and antecedent dry period. Last, there was a negative relationship between phosphorus level in the East Branch and storm duration.

Nutrient levels were highest during the summer at upstream locations. As a Lake

Erie headwater tributary, Euclid Creek has an annual target phosphorus load of approximately 5000 pounds per year. Linear regressions were performed to estimate

2019 nutrient contribution. Even with high nutrient concentrations during dry weather, the annual dry weather load projections were estimated to be 2450 pounds, well below the target. Spring rains are problematic, with an estimated phosphorus load exceeding

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11,000 pounds due to the high volume of rain and subsequent urban run-off. Even if target levels were met across the watershed, the volume of flow entering Lake Erie at a minimum level would exceed target TMDL.

8.2 Recommendations

Currently, nonpoint pollution sources are the main cause of phosphorus loads into Lake Erie. The goal of this study was to understand dry and wet weather water quality conditions for the Euclid Creek Watershed. Collections throughout the watershed in the same day over the course of 23 collection days yielded meaningful results. Future monitoring of dry conditions can be handled similarly. For wet weather collections, there are a few recommended procedures. Euclid Creek has numerous direct storm connections, and as a result responds very quickly to rain events. This study provided information mainly after peak discharge, during dilution. Nutrient levels were higher than previous dry weather conditions. In order to more completely understand peak nutrient loads, auto-sampler installation is recommended at the two existing USGS gauge stations on the East and Main Branches. This year there were 79 rain events of at least 0.10 inches of rainfall. Seasonal wet weather events should be sampled. Due to the heavy impact of spring storms, sampling in the spring should be a priority. One March pre-spring storm should be sampled. Antecedent conditions vary and may make it challenging to successfully sample storms with an antecedent period of at least 72 dry weather hours. Three spring storms should be sampled. Sampling should commence at the start of the storm and continue every thirty minutes until the depth of Euclid Creek

141 returns to baseflow conditions. Four summer storms should be sampled, preferably with at least one storm per month. A final storm collection should take place in the fall.

Wet weather plays a key role in influencing nutrient loads to Lake Erie due to large urban runoff. Phosphorus levels during wet weather were tested above the target limit of 0.05 mg/L at all 14 sites throughout the Euclid Creek Watershed. The eight events were collected past peak discharge, during dilution. Phosphorus levels were likely higher in the early stages of each storm event. Euclid Creek is peppered with direct storm connections throughout the watershed. A reduction of direct connections would help reach the 40% phosphorus target reduction goal for the watershed. The two

Cleveland Metroparks on the Main Branch are effective in mitigating nutrients during dry weather. During rain events, it would be advantageous to build a new wet weather stream throughout Acacia park. A channel could be created parallel to the existing walking paths. When rain levels exceed baseflow conditions, water could be diverted to a new wet weather stream to dissipate volume and speed of flow. Water could meander around the park before preceding downstream toward Telling Mansion.

Mitigating phosphorus loads is more challenging for the East Branch, but opportunities do exist. Unlike the Main Branch, there is no existing Cleveland

Metropark along this branch. For wet weather, the goal is to slow down the flow. This can be accomplished in a variety of ways but will likely take community involvement.

One approach is to ask residents and businesses to install rain barrels, green roofs, or rain gardens on their property. Tax incentives could be used to encourage participation.

Another option is to collaborate with the two golf courses and airport. Large cisterns could be installed upstream of each facility to store water for their use. Both ideas keep

142 stormwater out of Euclid Creek. Alternately, if stormwater continues to head into the stream, buffering wetlands can be used throughout the tributary area. Additionally, small bioreactor trenches could be installed in multiple locations. Another opportunity is the installation of permanent gates within the streams to remove phosphorus using passive treatment. A series of gates could be installed at the Community Center, Rockefeller

Road, and U/S Stonewater locations. The gates would contain mesh bags filled with water treatment residual that would adsorb the incoming phosphorus.

Wet weather flows also adversely impact aquatic life. Small fish populations were seen throughout the watershed during dry weather collections. During wet weather, they disappear, it is assumed they are swept downstream by the strong current.

It is recommended that “fish rocks” be placed throughout the watershed to protect fish.

Professor Markus Vogl of the University of Akron and Professor Margarita Benitez of

Kent State University have collaborated and designed these rocks, a small-scale example is shown in Figure 8.1. These structures serve as safe spaces for fishes to wait out the heavy storm flow. Hydraulic testing will be performed in a laboratory setting this summer to ensure currents are minimized around the opening of the cave like structures.

They are intentionally colored brightly to attract peoples’ attention. Public attention is desired in order to educate. Through public education, more people can become stewards to protect the environment.

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Figure 8.1. Fish rocks designed by Professors Vogl and Benitez to protect fish from heavy storm flows

The fish rocks should be placed in locations visible to the public. Two ideal locations on the Main Branch are Acacia Park and Schaefer Park. The public does not have as much accessibility to the East Branch. Two locations along the East Branch could be within StoneWater Golf Club and the Richmond Heights Middle and High Schools.

Partnering with these two groups could successfully mitigate phosphorus loading to the

East Branch. The schools are located about 1000 feet downstream of the Community

Center site. The golf course is about 1200 feet downstream of the U/S Stonewater site.

Active community engagement is critical to helping solve urban runoff. Euclid Creek is one of the most urbanized watersheds of Lake Erie. Future remediation is imperative to ensure the health of Lake Erie for the next generation.

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APPENDICES

158

APPENDIX A: Dry Weather Results

Telling Mansion

Schaefer Park

Spencer Road

Community Center

U/S Stonewater

Bishop Road

Rockefeller Road

Richmond White

Harris Road

Highland East

Highland Main

Villaview

Wildwood

159

Telling Mansion – Dry Weather

160

Telling Mansion – Dry Weather

161

Schaefer Park – Dry Weather

162

Schaefer Park – Dry Weather

163

Spencer Road – Dry Weather

164

Spencer Road – Dry Weather

165

Community Center – Dry Weather

166

Community Center – Dry Weather

167

U/S Stonewater – Dry Weather

168

U/S Stonewater – Dry Weather

169

Bishop Road – Dry Weather

170

Bishop Road – Dry Weather

171

Rockefeller Road – Dry Weather

172

Rockefeller Road – Dry Weather

173

Richmond White – Dry Weather

174

Richmond White – Dry Weather

175

Harris Road – Dry Weather

176

Harris Road – Dry Weather

177

Highland East – Dry Weather

178

Highland East – Dry Weather

179

Highland Main – Dry Weather

180

Highland Main – Dry Weather

181

Villaview – Dry Weather

182

Villaview – Dry Weather

183

Wildwood – Dry Weather

184

Wildwood – Dry Weather

185

APPENDIX B: Dry Weather East and Main Branch Comparison

Upper Reaches:

Nitrate pH Conductivity Turbidity Water Temperature

Lower Reaches:

Nitrate pH Conductivity Turbidity Water Temperature

186

Dry Weather Nitrate Comparison for Upper Reaches of East & Main Branches

East Branch Main Branch Rockefeller Acacia Difference Date Sample (Dry) (Dry) (East – Main) 3/28 1 0.3 0.8 -0.5 4/12 2 0.1 0.7 -0.6 5/14 3 0 0.1 -0.1 5/19 4 0 0.3 -0.3 6/24 5 0.7 1.5 -0.8 6/30 6 0.3 0.5 -0.2 7/2 7 1.0 1.9 -0.9 7/11 8 0.6 1.3 -0.7 7/13 9 0.7 0.9 -0.2 7/25 10 0.9 1.9 -1.0 8/6 11 0.6 0.9 -0.3 8/8 12 0.7 1.0 -0.3 8/13 13 0.6 1.1 -0.5 8/20 14 0.3 0.4 -0.1 8/27 15 0.2 0.3 -0.1 9/4 16 0.3 0.4 -0.1 9/6 17 0.2 0.3 -0.1 9/11 18 0.2 0.5 -0.3 9/15 19 0.4 0.5 -0.1 9/23 20 0.3 0.4 -0.1 10/6 21 0.3 0.4 -0.1 11/24 22 0.3 0.5 -0.2 3/8 23 0.1 0.3 -0.2 Mean 0.4 0.7 -0.3 Standard Deviation 0.3 0.5 0.3

Dry Weather pH Comparison for Upper Reaches of East & Main Branches

East Branch Main Branch Rockefeller Acacia Difference Date Sample (Dry) (Dry) (East – Main) 3/28 1 8.09 8.47 -0.38 4/12 2 7.96 8.29 -0.33 5/14 3 7.8 8.04 -0.24 5/19 4 8.26 8.17 0.09 6/24 5 8.03 8.14 -0.11

187

East Branch Main Branch Rockefeller Acacia Difference Date Sample (Dry) (Dry) (East – Main) 6/30 6 7.9 7.7 0.2 7/2 7 8.12 8.04 0.08 7/11 8 8.32 8.39 -0.07 7/13 9 8.39 8.07 0.32 7/25 10 8.55 8.10 0.45 8/6 11 8.08 8.14 -0.06 8/8 12 8.37 8.52 -0.15 8/13 13 8.18 8.01 0.17 8/20 14 8.05 7.95 0.10 8/27 15 8.22 7.95 0.27 9/4 16 8.25 8.04 0.21 9/6 17 8.33 8.30 0.03 9/11 18 8.29 8.13 0.16 9/15 19 8.41 8.18 0.23 9/23 20 8.18 8.11 0.07 10/6 21 8.41 8.25 0.16 11/24 22 8.66 7.75 0.91 3/8 23 8.35 8.28 0.07 Mean 8.23 8.13 0.09 Standard Deviation 0.21 0.20 0.27

Dry Weather Conductivity Comparison for Upper Reaches of East & Main Branches

East Branch Main Branch Rockefeller Acacia Difference Date Sample (Dry) (Dry) (East – Main) 3/28 1 2650 4944 -2294 4/12 2 2708 4819 -2111 5/14 3 1106 2786 -1680 5/19 4 1436 3031 -1595 6/24 5 1163 2688 -1525 6/30 6 1030 2375 -1345 7/2 7 1196 2363 -1167 7/11 8 1311 3008 -1697 7/13 9 1304 2451 -1147 7/25 10 1236 2405 -1169 8/6 11 1091 2478 -1387 8/8 12 981 2076 -1095

188

East Branch Main Branch Rockefeller Acacia Difference Date Sample (Dry) (Dry) (East – Main) 8/13 13 1080 1505 -425 8/20 14 910 1755 -845 8/27 15 1140 2226 -1086 9/4 16 1062 1869 -807 9/6 17 1056 2107 -1051 9/11 18 1422 1215 207 9/15 19 1031 1563 -532 9/23 20 1531 1564 -33 10/6 21 1136 1642 -506 11/24 22 3174 2150 1024 3/8 23 2770 5680 -2910 Mean 1458 2552 -1095 Standard Deviation 665 1143 843

Dry Weather Turbidity Comparison for Upper Reaches of East & Main Branches

East Branch Main Branch Rockefeller Acacia Difference Date Sample (Dry) (Dry) (East – Main) 3/28 1 120 120 0 4/12 2 120 120 0 5/14 3 120 120 0 5/19 4 120 120 0 6/24 5 112.5 120 -7.5 6/30 6 120 120 0 7/2 7 120 97 23 7/11 8 120 120 0 7/13 9 120 120 0 7/25 10 120 120 0 8/6 11 120 120 0 8/8 12 120 120 0 8/13 13 120 81 39 8/20 14 120 120 0 8/27 15 120 120 0 9/4 16 120 120 0 9/6 17 120 120 0 9/11 18 120 120 0 9/15 19 120 120 0

189

East Branch Main Branch Rockefeller Acacia Difference Date Sample (Dry) (Dry) (East – Main) 9/23 20 120 120 0 10/6 21 119 120 -1 11/24 22 120 120 0 3/8 23 120 120 0 Mean 120 117 2 Standard Deviation 2 9 9

Dry Weather Water Temperature Comparison for Upper Reaches of East & Main Branches East Branch Main Branch Rockefeller Acacia Difference Date Sample (Dry) (Dry) (East – Main) 3/28 1 5.78 6.54 -0.76 4/12 2 12.60 10.28 2.32 5/14 3 10.65 11.56 -0.91 5/19 4 22.58 21.61 0.97 6/24 5 n/a n/a n/a 6/30 6 21.59 19.98 1.61 7/2 7 24.29 22.97 1.32 7/11 8 25.66 24.09 1.57 7/13 9 21.86 19.07 2.79 7/25 10 22.91 22.18 0.73 8/6 11 23.00 21.96 1.04 8/8 12 23.45 24.47 -1.02 8/13 13 22.25 22.08 0.17 8/20 14 23.13 21.45 1.68 8/27 15 19.79 19.99 -0.20 9/4 16 20.83 20.92 -0.09 9/6 17 17.17 19.17 -2.00 9/11 18 23.22 22.67 0.55 9/15 19 19.85 18.73 1.12 9/23 20 21.10 20.55 0.55 10/6 21 16.10 16.22 -0.12 11/24 22 5.62 6.53 -0.91 3/8 23 13.20 8.20 5.00 Mean 18.94 18.24 0.70 Standard Deviation 5.84 5.72 1.52

190

Dry Weather Nitrate Comparison for Lower Reaches of East & Main Branches

East Branch Main Branch Highland Highland Date Sample East Main Difference (Dry) (Dry) (East – Main) 3/28 1 0.6 0.5 0.1 4/12 2 0.5 0.5 0.0 5/14 3 0.2 0.1 0.1 5/19 4 0.2 0.1 0.1 6/24 5 1.5 1.8 -0.3 6/30 6 0.5 0.4 0.1 7/2 7 1.6 1.9 -0.3 7/11 8 1.5 1.2 0.3 7/13 9 0.8 1.0 -0.2 7/25 10 1.1 1.0 0.1 8/6 11 1.0 0.9 0.1 8/8 12 1.1 0.9 0.2 8/13 13 0.8 1.1 -0.3 8/20 14 0.3 0.4 -0.1 8/27 15 0.3 0.3 0.0 9/4 16 0.4 0.3 0.1 9/6 17 0.3 0.3 0.0 9/11 18 0.4 0.2 0.2 9/15 19 0.3 0.6 -0.3 9/23 20 0.3 0.3 0.0 10/6 21 0.4 0.3 0.1 11/24 22 0.4 0.4 0.0 3/8 23 0.1 0.5 -0.4 Mean 0.6 0.7 0.0 Standard Deviation 0.5 0.5 0.2

Dry Weather pH Comparison for Lower Reaches of East & Main Branches

East Branch Main Branch Highland Highland Date Sample East Main Difference (Dry) (Dry) (East – Main) 3/28 1 8.54 8.60 -0.06 4/12 2 8.19 8.16 0.03 5/14 3 8.01 7.77 0.24

191

East Branch Main Branch Highland Highland Date Sample East Main Difference (Dry) (Dry) (East – Main) 5/19 4 8.74 8.49 0.25 6/24 5 8.23 8.27 -0.04 6/30 6 8.25 8.12 0.13 7/2 7 8.55 8.25 0.30 7/11 8 8.27 8.13 0.14 7/13 9 8.76 8.47 0.29 7/25 10 8.58 8.54 0.04 8/6 11 8.46 8.36 0.10 8/8 12 8.27 8.26 0.01 8/13 13 8.62 8.71 -0.09 8/20 14 8.53 8.37 0.16 8/27 15 8.62 8.46 0.16 9/4 16 8.84 8.72 0.12 9/6 17 8.93 8.82 0.11 9/11 18 8.78 8.67 0.11 9/15 19 8.41 8.33 0.08 9/23 20 8.72 8.63 0.09 10/6 21 8.94 8.72 0.22 11/24 22 9.06 9.24 -0.18 3/8 23 8.58 8.84 -0.26 Mean 8.56 8.48 0.08 Standard Deviation 0.27 0.31 0.14

Dry Weather Conductivity Comparison for Lower Reaches of East & Main Branches

East Branch Main Branch Highland Highland Date Sample East Main Difference (Dry) (Dry) (East – Main) 3/28 1 987 2082 -1095 4/12 2 1042 2070 -1028 5/14 3 715 1104 -389 5/19 4 427 1625 -1198 6/24 5 881 1059 -178 6/30 6 769 902 -133 7/2 7 865 1183 -318 7/11 8 686 1161 -475 7/13 9 892 1094 -202 192

East Branch Main Branch Highland Highland Date Sample East Main Difference (Dry) (Dry) (East – Main) 7/25 10 724 1005 -281 8/6 11 804 943 -139 8/8 12 682 930 -248 8/13 13 692 972 -280 8/20 14 595 754 -159 8/27 15 655 1145 -490 9/4 16 654 920 -266 9/6 17 629 885 -256 9/11 18 635 1082 -447 9/15 19 555 806 -251 9/23 20 987 1189 -202 10/6 21 558 917 -359 11/24 22 1108 1664 -556 3/8 23 1375 3010 -1635 Mean 779 1239 -460 Standard Deviation 215 529 396

Dry Weather Turbidity Comparison for Lower Reaches of East & Main Branches

East Branch Main Branch Highland Highland Date Sample East Main Difference (Dry) (Dry) (East – Main) 3/28 1 120 120 0 4/12 2 120 120 0 5/14 3 120 120 0 5/19 4 120 120 0 6/24 5 120 120 0 6/30 6 120 120 0 7/2 7 120 120 0 7/11 8 120 120 0 7/13 9 120 120 0 7/25 10 120 120 0 8/6 11 120 120 0 8/8 12 120 120 0 8/13 13 120 120 0 8/20 14 120 120 0 8/27 15 120 120 0 193

East Branch Main Branch Highland Highland Date Sample East Main Difference (Dry) (Dry) (East – Main) 9/4 16 120 120 0 9/6 17 120 120 0 9/11 18 120 120 0 9/15 19 120 120 0 9/23 20 120 120 0 10/6 21 120 120 0 11/24 22 120 120 0 3/8 23 120 120 0 Mean 120 120 0 Standard Deviation 0 0 0

Dry Weather Water Temperature Comparison for Lower Reaches of East & Main

Branches

East Branch Main Branch Absolute Highland Highland Value of Date Sample East Main Difference Difference (Dry) (Dry) (East – Main) (East – Main) 3/28 1 5.62 6.01 -0.39 0.39 4/12 2 11.20 10.75 0.45 0.45 5/14 3 10.13 9.71 0.42 0.42 5/19 4 20.47 19.96 0.51 0.51 6/24 5 22.49 23.43 -0.94 0.94 6/30 6 23.65 22.48 1.17 1.17 7/2 7 26.33 27.20 -0.87 0.87 7/11 8 24.68 26.00 -1.32 1.32 7/13 9 24.72 25.63 -0.91 0.91 7/25 10 22.91 24.69 -1.78 1.78 8/6 11 22.93 24.04 -1.11 1.11 8/8 12 23.56 24.28 -0.72 0.72 8/13 13 22.77 23.14 -0.37 0.37 8/20 14 24.43 23.51 0.92 0.92 8/27 15 20.49 20.43 0.06 0.06 9/4 16 21.85 21.46 0.39 0.39 9/6 17 19.18 19.25 -0.07 0.07 9/11 18 22.75 23.45 -0.70 0.70 9/15 19 20.54 20.39 0.15 0.15

194

East Branch Main Branch Absolute Highland Highland Value of Date Sample East Main Difference Difference (Dry) (Dry) (East – Main) (East – Main) 9/23 20 21.09 21.61 -0.52 0.52 10/6 21 17.02 16.70 0.32 0.32 11/24 22 5.20 5.16 0.04 0.67 3/8 23 15.80 12.90 2.90 0.68 Mean 19.56 19.66 -0.10 0.67 Standard Deviation 6.02 6.40 0.99 0.42

195

APPENDIX C: Dry Weather ANOVA and Tukey Comparisons

of Upstream Tributary Impact

196

One-way ANOVA: PO4_Drop1, PO4_Drop2, PO4_Drop3, PO4_Drop4, PO4_Drop5, PO4_Drop6, PO4_Drop7, PO4_Drop8, PO4_Drop9, PO4_Drop10, PO4_Drop11, PO4_Drop12, PO4_Drop13 * NOTE * Cannot draw the interval plot for the Tukey procedure. Interval plots for comparisons are illegible with more than 45 intervals. Method Null All means hypothesis are equal Alternative Not all hypothesis means are equal Significance α = 0.05 level Rows 63 unused Equal variances were assumed for the analysis.

Factor Information Factor Levels Values Factor 13 PO4_Drop1, PO4_Drop2, PO4_Drop3, PO4_Drop4, PO4_Drop5, PO4_Drop6, PO4_Drop7, PO4_Drop8, PO4_Drop9, PO4_Drop10, PO4_Drop11, PO4_Drop12, PO4_Drop13 Analysis of Variance

F- P- Source DF Adj SS Adj MS Value Value Factor 12 10.197 0.84976 65.58 0.000 Error 223 2.889 0.01296 Total 235 13.086 Model Summary

S R-sq R-sq(adj) R-sq(pred) 0.113827 77.92% 76.73% 75.42%

Means Factor N Mean StDev 95% CI PO4_Drop1 23 0.3004 0.1531 (0.2537, 0.3472)

197

PO4_Drop2 23 -0.0348 0.0886 (-0.0816, 0.0120)

PO4_Drop3 23 0.0009 0.0646 (-0.0459, 0.0476)

PO4_Drop4 15 0.0380 0.0388 (-0.0199, 0.0959)

PO4_Drop5 13 0.0423 0.0856 (-0.0199, 0.1045)

PO4_Drop6 23 0.4187 0.1650 (0.3719, 0.4655)

PO4_Drop7 23 -0.1851 0.1429 (-0.2318, -0.1383)

PO4_Drop8 23 -0.2113 0.1338 (-0.2581, -0.1645)

PO4_Drop9 8 0.3900 0.1356 (0.3107, 0.4693)

PO4_Drop10 8 0.5075 0.1010 (0.4282, 0.5868)

PO4_Drop11 8 -0.0238 0.0699 (-0.1031, 0.0556)

PO4_Drop12 23 0.0565 0.1152 (0.0097, 0.1033)

PO4_Drop13 23 0.12739 0.03828 (0.08062, 0.17416)

Pooled StDev = 0.113827

Tukey Pairwise Comparisons Grouping Information Using the Tukey Method and 95% Confidence Factor N Mean Grouping PO4_Drop10 8 0.5075 A PO4_Drop6 23 0.4187 A PO4_Drop9 8 0.3900 A B PO4_Drop1 23 0.3004 B PO4_Drop13 23 0.12739 C PO4_Drop12 23 0.0565 C D PO4_Drop5 13 0.0423 C D PO4_Drop4 15 0.0380 C D PO4_Drop3 23 0.0009 D PO4_Drop11 8 -0.0238 C D

198

PO4_Drop2 23 -0.0348 D

PO4_Drop7 23 -0.1851 E PO4_Drop8 23 -0.2113 E Means that do not share a letter are significantly different.

199

200

APPENDIX D: Wet Weather Results

Telling Mansion

Schaefer Park

Spencer Road

Community Center

U/S Stonewater

Bishop Road

Rockefeller Road

Richmond White

Harris Road

Highland East

Highland Main

Villaview

Wildwood

201

Telling Mansion – Wet Weather

202

Telling Mansion – Wet Weather

203

Schaefer Park – Wet Weather

204

Schaefer Park – Wet Weather

205

Spencer Road – Wet Weather

206

Spencer Road – Wet Weather

207

Community Center – Wet Weather

208

Community Center – Wet Weather

209

U/S Stonewater – Wet Weather

210

U/S Stonewater – Wet Weather

211

Bishop Road – Wet Weather

212

Bishop Road – Wet Weather

213

Rockefeller Road – Wet Weather

214

Rockefeller Road – Wet Weather

215

Richmond White – Wet Weather

216

Richmond White – Wet Weather

217

Harris Road – Wet Weather

218

Harris Road – Wet Weather

219

Highland East – Wet Weather

220

Highland East – Wet Weather

221

Highland Main – Wet Weather

222

Highland Main – Wet Weather

223

Villaview – Wet Weather

224

Villaview – Wet Weather

225

Wildwood -Wet Weather

226

Wildwood -Wet Weather

227

APPENDIX E: Wet Weather Collection Events

Main Branch at Telling Mansion

East Branch at Richmond White

Downstream of Confluence of Two Branches at Villaview (or Wildwood)

228

Wet Weather Collection 2: Main Branch at Telling Mansion (June 16, 2019)

Wet Weather Collection 3: Main Branch at Telling Mansion (June 20, 2019)

Wet Weather Collection 4: Main Branch at Telling Mansion (June 25, 2019)

229

Wet Weather Collection 5: Main Branch at Telling Mansion (July 30, 2019)

Wet Weather Collection 6: Main Branch at Telling Mansion (August 18, 2019)

Wet Weather Collection 7: Main Branch at Telling Mansion (October 16, 2019)

230

Wet Weather Collection 8: Main Branch at Telling Mansion (January 12, 2020)

Wet Weather Collection 2: East Branch at Richmond White (June 16, 2019)

231

Wet Weather Collection 3: East Branch at Richmond White (June 20, 2019)

Wet Weather Collection 4: East Branch at Richmond White (June 25, 2019)

Wet Weather Collection 5: East Branch at Richmond White (July 30, 2019)

232

Wet Weather Collection 6: East Branch at Richmond White (August 18, 2019)

Wet Weather Collection 7: East Branch at Richmond White (October 16, 2019)

Wet Weather Collection 8: East Branch at Richmond White (January 12, 2019)

233

Wet Weather Collection 1: Downstream of the Confluence of the Two Branches at Wildwood (April 26, 2019)

Wet Weather Collection 2: Downstream of the Confluence of the Two Branches at Wildwood (June 16, 2019)

Wet Weather Collection 3: Downstream of the Confluence of the Two Branches at Wildwood (June 20, 2019)

234

Wet Weather Collection 4: Downstream of the Confluence of the Two Branches at Wildwood (June 25, 2019)

Wet Weather Collection 5: Downstream of the Confluence of the Two Branches at Villaview (July 30, 2019)

Wet Weather Collection 6: Downstream of the Confluence of the Two Branches at Villaview (August 18, 2019)

235

Wet Weather Collection 7: Downstream of the Confluence of the Two Branches at Villaview (October 16, 2019)

Wet Weather Collection 8: Downstream of the Confluence of the Two Branches at Villaview (January 12, 2019)

236

APPENDIX F: Phosphorus and Rainfall Characteristic Comparison

Phosphorus vs Rainfall

Phosphorus vs Discharge

Phosphorus vs Antecedent Dry Period

Phosphorus vs Storm Duration

Phosphorus vs Rainfall Intensity

237

Wet Weather Conditions for Main Branch at Telling Mansion: Phosphorus Concentration vs Rainfall based on triplicate samples

Wet Weather Conditions for East Branch at Richmond White: Phosphorus Concentration vs Rainfall based on triplicate samples

Wet Weather Conditions for Downstream of Branch Confluence at Villaview: Phosphorus Concentration vs Rainfall based on triplicate samples

238

Wet Weather Conditions for Main Branch at Telling Mansion: Phosphorus Concentration vs Discharge based on triplicate samples

Wet Weather Conditions for East Branch at Richmond White: Phosphorus Concentration vs Discharge based on triplicate samples

Wet Weather Conditions for Downstream of Branch Confluence at Villaview: Phosphorus Concentration vs Discharge based on triplicate samples

239

Wet Weather Conditions for Main Branch at Telling Mansion: Phosphorus Concentration vs Antecedent Dry Period based on triplicate samples

Wet Weather Conditions for East Branch at Richmond White: Phosphorus Concentration vs Antecedent Dry Period based on triplicate samples

Wet Weather Conditions for Downstream of Branch Confluence at Villaview: Phosphorus Concentration vs Antecedent Dry Period based on triplicate samples

240

Wet Weather Conditions for Main Branch at Telling Mansion: Phosphorus Concentration vs Storm Duration based on triplicate samples

Wet Weather Conditions for East Branch at Richmond White: Phosphorus Concentration vs Storm Duration based on triplicate samples

Wet Weather Conditions for Downstream of Branch Confluence at Villaview: Phosphorus Concentration vs Storm Duration based on triplicate samples

241

Wet Weather Conditions for Main Branch at Telling Mansion: Phosphorus Concentration vs Rainfall Intensity based on triplicate samples

Wet Weather Conditions for East Branch at Richmond White: Phosphorus Concentration vs Rainfall Intensity based on triplicate samples

Wet Weather Conditions for Downstream of Branch Confluence at Villaview: Phosphorus Concentration vs Rainfall Intensity based on triplicate samples

242

APPENDIX G: Wet Weather East and Main Branch Comparison

Upper Reaches:

Phosphorus Nitrate pH Conductivity Turbidity Water Temperature

Lower Reaches:

Phosphorus Nitrate pH Conductivity Turbidity Water Temperature

243

Wet Weather Phosphorus Comparison for Upper Reaches of East & Main Branches East Branch Main Branch Rockefeller Acacia Difference Date Sample (Wet) (Wet) (East – Main) 4/26 1 0.23 0.16 0.07 6/16 2 0.18 0.13 0.05 6/20 3 0.13 0.10 0.03 6/25 4 0.20 0.30 -0.10 7/30 5 0.27 0.31 -0.04 8/18 6 0.19 0.07 0.12 10/16 7 0.11 0.20 -0.09 1/12 8 0.09 0.12 -0.03 Mean 0.18 0.17 0.00 Standard Deviation 0.06 0.09 0.08

Wet Weather Nitrate Comparison for Upper Reaches of East & Main Branches East Branch Main Branch Rockefeller Acacia Difference Date Sample (Wet) (Wet) (East – Main) 4/26 1 0.0 0.0 0.0 6/16 2 0.0 0.0 0.0 6/20 3 0.0 0.0 0.0 6/25 4 1.1 1.5 -0.4 7/30 5 1.1 1.9 -0.8 8/18 6 0.2 0.4 -0.2 10/16 7 0.2 0.1 0.1 1/12 8 0.3 0.5 -0.2 Mean 0.3 0.5 -0.2 Standard Deviation 0.4 0.7 0.3

Wet Weather pH Comparison for Upper Reaches of East & Main Branches East Branch Main Branch Rockefeller Acacia Difference Date Sample (Wet) (Wet) (East – Main) 4/26 1 7.63 7.54 0.09 6/16 2 7.62 7.76 -0.14 6/20 3 7.62 7.58 0.04 6/25 4 7.87 7.66 0.21 7/30 5 7.98 7.51 0.47 8/18 6 7.96 7.83 0.13 10/16 7 7.99 7.73 0.26

244

East Branch Main Branch Rockefeller Acacia Difference Date Sample (Wet) (Wet) (East – Main) 1/12 8 8.16 8.34 -0.18 Mean 7.85 7.74 0.11 Standard Deviation 0.21 0.27 0.21

Wet Weather Conductivity Comparison for Upper Reaches of East & Main Branches East Branch Main Branch Rockefeller Acacia Difference Date Sample (Wet) (Wet) (East – Main) 4/26 1 685 1024 -339 6/16 2 345 1038 -693 6/20 3 459 629 -170 6/25 4 580 1250 -670 7/30 5 1102 282 820 8/18 6 962 856 106 10/16 7 743 631 112 1/12 8 1562 2640 -1078 Mean 805 1044 -239 Standard Deviation 394 712 595

Wet Weather Turbidity Comparison for Upper Reaches of East & Main Branches East Branch Main Branch Rockefeller Acacia Difference Date Sample (Wet) (Wet) (East – Main) 4/26 1 40 44 -4 6/16 2 10 51 -41 6/20 3 8 20.5 -13 6/25 4 48 44 4 7/30 5 80 13 67 8/18 6 59 28 31 10/16 7 33 36 -3 1/12 8 56 103 -47 Mean 42 42 -1 Standard Deviation 25 28 37

245

Wet Weather Water Temperature Comparison for Upper Reaches of East & Main Branches Absolute East Branch Main Branch Value of Rockefeller Acacia Difference Difference Date Sample (Wet) (Wet) (East – Main) (East – Main) 4/26 1 11.36 11.20 0.16 0.16 6/16 2 18.84 17.48 1.36 1.36 6/20 3 19.92 21.06 -1.14 1.14 6/25 4 21.22 20.79 0.43 0.43 7/30 5 23.38 22.45 0.93 0.93 8/18 6 23.06 23.78 -0.72 0.72 10/16 7 11.82 12.97 -1.15 1.15 1/12 8 9.61 6.50 3.11 3.11 Mean 17.40 17.03 0.37 1.13 Standard Deviation 5.59 6.17 1.45 0.90

Wet Weather Phosphorus Comparison for Lower Reaches of East & Main Branches East Branch Main Branch Highland Highland Date Sample East Main Difference (Wet) (Wet) (East – Main) 4/26 1 0.47 0.19 0.28 6/16 2 0.15 0.18 -0.03 6/20 3 0.10 0.06 0.04 6/25 4 0.16 0.14 0.02 7/30 5 0.38 0.32 0.06 8/18 6 0.29 0.10 0.19 10/16 7 0.25 0.17 0.08 1/12 8 0.18 0.13 0.05 Mean 0.25 0.16 0.09 Standard Deviation 0.13 0.08 0.10

Wet Weather Nitrate Comparison for Lower Reaches of East & Main Branches East Branch Main Branch Highland Highland Date Sample East Main Difference (Wet) (Wet) (East – Main) 4/26 1 0.0 0.1 -0.1 6/16 2 0.0 0.0 0.0 6/20 3 0.0 0.0 0.0 6/25 4 1.4 1.2 0.2

246

East Branch Main Branch Highland Highland Date Sample East Main Difference (Wet) (Wet) (East – Main) 7/30 5 1.7 1.9 -0.2 8/18 6 0.4 0.4 0.0 10/16 7 0.2 0.1 0.1 1/12 8 0.6 0.4 0.2 Mean 0.5 0.5 0.0 Standard Deviation 0.7 0.7 0.1

Wet Weather pH Comparison for Lower Reaches of East & Main Branches East Branch Main Branch Highland Highland Date Sample East Main Difference (Wet) (Wet) (East – Main) 4/26 1 7.87 8.00 -0.13 6/16 2 7.87 7.77 0.10 6/20 3 7.95 8.01 -0.06 6/25 4 8.10 8.13 -0.03 7/30 5 8.29 8.12 0.17 8/18 6 8.40 8.27 0.13 10/16 7 8.46 8.30 0.16 1/12 8 8.49 8.53 -0.04 Mean 8.18 8.14 0.04 Standard Deviation 0.26 0.23 0.12

Wet Weather Conductivity Comparison for Lower Reaches of East & Main Branches East Branch Main Branch Highland Highland Date Sample East Main Difference (Wet) (Wet) (East – Main) 4/26 1 503 671 -168 6/16 2 276 252 24 6/20 3 231 309 -78 6/25 4 443 581 -138 7/30 5 556 408 148 8/18 6 548 455 93 10/16 7 408 831 -423 1/12 8 991 1547 -556 Mean 495 632 -137 Standard Deviation 233 416 245

247

Wet Weather Turbidity Comparison for Lower Reaches of East & Main Branches East Branch Main Branch Highland Highland Date Sample East Main Difference (Wet) (Wet) (East – Main) 4/26 1 35 39 -4 6/16 2 17 13 4 6/20 3 15 10 5 6/25 4 109 89 20 7/30 5 41 20 20 8/18 6 95 22 73 10/16 7 73 37 36 1/12 8 120 94 26 Mean 63 40 23 Standard Deviation 42 33 24

Wet Weather Water Temperature Comparison for Lower Reaches of East & Main Branches East Branch Main Branch Absolute Highland Highland Value of Date Sample East Main Difference Difference (Wet) (Wet) (East – Main) (East – Main) 4/26 1 11.33 11.11 0.22 0.22 6/16 2 18.37 18.78 -0.41 0.41 6/20 3 19.65 20.17 -0.52 0.52 6/25 4 23.43 23.27 0.16 0.16 7/30 5 24.00 23.73 0.27 0.27 8/18 6 24.20 23.15 1.05 1.05 10/16 7 12.31 12.46 -0.15 0.15 1/12 8 5.80 5.30 0.50 0.50 Mean 17.39 17.25 0.14 0.41 Standard Deviation 6.86 6.84 0.51 0.30

248

APPENDIX H: Wet Weather ANOVA and Tukey Comparisons of Upstream Tributary Impact

249

One-way ANOVA: Station 1, Station 2, Station 3, Station 4, Station 5, Station 6, Station 7, Station 8, Station 9, Station 10, Station 11, Station 12, Station 13 * NOTE * Cannot draw the interval plot for the Tukey procedure. Interval plots for comparisons are illegible with more than 45 intervals. Method Null All means hypothesis are equal Alternative Not all hypothesis means are equal Significance α = 0.05 level Rows 27 unused Equal variances were assumed for the analysis.

Factor Information Factor Levels Values Factor 13 Station 1, Station 2, Station 3, Station 4, Station 5, Station 6, Station 7, Station 8, Station 9, Station 10, Station 11, Station 12, Station 13

Analysis of Variance

Source DF Adj SS Adj MS F-Value P-Value Factor 12 0.7101 0.05917 5.20 0.000 Error 64 0.7287 0.01139 Total 76 1.4388 Model Summary

S R-sq R-sq(adj) R-sq(pred) 0.106701 49.36% 39.86% 21.84% Means Factor N Mean StDev 95% CI Station 1 8 0.0200 0.0493 (-0.0554, 0.0954)

Station 2 8 -0.0212 0.0664 (-0.0966, 0.0541)

250

Station 3 8 -0.0063 0.0484 (-0.0816, 0.0691)

Station 4 4 -0.0200 0.0337 (-0.1266, 0.0866)

Station 5 3 -0.0467 0.0709 (-0.1697, 0.0764)

Station 6 8 0.1113 0.1067 (0.0359, 0.1866)

Station 7 8 -0.1017 0.1349 (-0.1770, -0.0263)

Station 8 8 -0.1825 0.1648 (-0.2579, -0.1071)

Station 9 2 0.1850 0.1061 (0.0343, 0.3357)

Station 10 2 0.240 0.141 (0.089, 0.391)

Station 11 2 0.050 0.170 (-0.101, 0.201)

Station 12 8 0.0300 0.1433 (-0.0454, 0.1054)

Station 13 8 0.0800 0.0843 (0.0046, 0.1554)

Pooled StDev = 0.106701

Tukey Pairwise Comparisons Grouping Information Using the Tukey Method and 95% Confidence Factor N Mean Grouping Station 10 2 0.240 A

Station 9 2 0.1850 A B

Station 6 8 0.1113 A

Station 13 8 0.0800 A B

Station 11 2 0.050 A B C

Station 12 8 0.0300 A B

Station 1 8 0.0200 A B

Station 3 8 -0.0063 A B C

251

Station 4 4 -0.0200 A B C

Station 2 8 -0.0212 A B C

Station 5 3 -0.0467 A B C

Station 7 8 -0.1017 B C

Station 8 8 -0.1825 C

Means that do not share a letter are significantly different.

252

253