A Dissertation
Entitled
Coupling Ecosystem Rehabilitation to Water Quality Improvements in the Wolf Creek
Watershed
by
Ryan Walter Jackwood
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the
Doctor of Philosophy Degree in
Biology (Ecology Track)
______Dr. Daryl Dwyer, Committee Chair
______Dr. Richard Becker, Committee Member
______Dr. Johan Gottgens, Committee Member
______Dr. Helen Michaels, Committee Member
______Dr. Alison Spongberg, Committee Member
______Dr. Cyndee Gruden, Dean College of Graduate Studies
August 2019
Copyright 2019, Ryan Walter Jackwood
This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author.
An Abstract of
Coupling Ecosystem Rehabilitation to Water Quality Improvements in the Wolf Creek Watershed
by
Ryan Walter Jackwood
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Biology (Ecology Track)
The University of Toledo
May 2019
Eutrophication and fecal contamination contribute to poor water quality and affect
1.8 billion people. My research focus is on two pollutants in Lake Erie: phosphorus, which drives the annual occurrence of harmful algal blooms, and Escherichia coli, an indicator of fecal contamination. The Wolf Creek watershed (41 km2) is a contributor of phosphorus to the western basin of Lake Erie and is the proximal source of E. coli to the
Lake Erie beaches at Maumee Bay State Park. To lessen contaminant loadings from
Wolf Creek, two watershed rehabilitation projects were implemented. This dissertation outlines my efforts as three chapters. Chapter 1 presents the history and impact of phosphorus and fecal contamination in western Lake Erie. Chapter 2 reports on the collection and analysis of baseline water quality data that were used to design two rehabilitation projects. Chapter 3 describes the implementation of the projects, an analysis of their effect on water quality, and an attempt to model the resultant improvements to water quality.
iii
Notable conclusions from each chapter are as follows:
(1) Two means for improving water quality appropriate for Wolf Creek are a
rehabilitated floodplain attained by widening the stream channel to trap suspended
particles, attached E. coli and phosphorus and a rehabilitated subsurface flow
wetland to reduce dissolved phosphorus and suspended bacteria.
(2) Major discharge events and seasonal trends have a significant impact on loadings
of phosphorus, E. coli, and suspended solids in Wolf Creek. To increase capacity
during peak loading events a sedimentation basin was included in the rehabilitated
floodplain to slow discharge events and settle particulates. Limestone was
included in the rehabilitated wetland to increase adsorptive capacity of
phosphorus.
(3) Water samples indicated reductions in total suspended solids by 27.8% and
91.7%, total phosphorus by 16.6% and 74.1%, dissolved reactive phosphorus by
20.2% and 46.7%, and E. coli by 39.5% and 86.7% for the rehabilitated
floodplain and wetland, respectively. Results were scaled and modeled for the
Maumee River (important source of phosphorus to western Lake Erie) and
indicated that a 1000 ha wetland would be sufficient to reduce DRP loadings by
the IJC recommended 40%.
iv
I dedicate this dissertation to my parents without whom I would not be where I am today:
Dr. Judy Jackwood
Dr. Daral and Renee Jackwood
v
Acknowledgements
I would first like to thank my thesis advisor, Dr. Daryl Dwyer, for his patience, knowledge and friendship. He has been incredibly supportive and positive throughout my Ph.D. journey. I also want to thank my thesis committee, Dr. Richard Becker, Dr.
Johan Gottgens, Dr. Helen Michaels, and Dr. Alison Spongberg for their constructive criticism and patience during my dissertation research.
I am incredibly thankful for all of the people that have helped me with my dissertation research: Pamela Struffolino, Kris Barnswell, Matthew Mayher, Stephanie
Clendenen, Holly Hutchinson, Jeffrey Niedermeyer, Kevin Corbin, Sarah Carter, Tvisha
Martin, Austin Bartos, and all of the amazing people at the Lake Erie Center and in the
Department of Environmental Sciences.
I want to thank my loyal, supportive, and inspirational friends Matt Sewell,
Guhan Venguswamy, Brettlas Kramer, Ari Hadgis, Jordan Rofkar, Brian Lewis, and Ken
Gibbons. They keep me grounded and always lift my spirits when things get difficult.
Finally, I want to thank my family Dr. Daral and Renee Jackwood, Dr. Judy
Jackwood, Taylor Jackwood, Nick, Kristen, and Zoey Caulier, Niki Fisk, and Dallon and
Wyla Weathers, Janice Sheridan, and Audrey Jackwood. I could never ask for a more loving and encouraging family.
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Table of Contents
Abstract ...... iii
Acknowledgements ...... vi
Table of Contents ...... vii
List of Tables ...... xi
List of Figures ...... xii
List of Abbreviations ...... xiv
List of Symbols ...... xv
1 Introduction ...... 1
1.1 Introduction ...... 1
1.2 Harmful Algal Blooms in Lake Erie ...... 2
1.3 Fecal Contamination in Lake Erie ...... 6
1.4 Watershed Rehabilitation ...... 8
1.5 Data to Inform Watershed Rehabilitation ...... 11
1.6 Global Perspective of Harmful Algal Blooms and Mitigation Planning ...... 13
1.7 Global Perspective of Fecal Contamination and Mitigation Planning...... 13
1.8 Dissertation Goals ...... 14
Chapter References ...... 15
2 Evaluation of Wolf Creek watershed and Implications for Rehabilitation Projects
to Reduce Escherichia coli and Phosphorus Loadings ...... 35
vii
2.1 Abstract ...... 35
2.2 Introduction ...... 36
2.3 Materials and Methods ...... 39
2.3.1 Study Area ...... 39
2.3.2 Sample Collection ...... 40
2.3.3 Sample Analyses ...... 41
2.3.3 Analysis of Data ...... 41
2.3.3.1 Daily and Annual Discharge ...... 41
2.3.3.2 Daily and Annual Loadings ...... 42
2.3.3.2 Flashiness Index ...... 43
2.4 Results and Discussion ...... 43
2.4.1 Interannual Variability of TP, TSS, and E. coli Loadings ...... 43
2.4.2 Seasonal Variability of TP, TSS, and E. coli Loadings ...... 47
2.4.3 Concentration and Loading Trends during Major Discharge
Events ...... 52
2.4.4 Comparison between WCW and Regional Watersheds ...... 55
2.4.5 Turbidity as a Predictor of General Water Quality ...... 56
2.4.6 Estimating Water Treatment Capacity for Watershed
Rehabilitation ...... 57
2.5 Conclusion ...... 60
Chapter References ...... 61
3 Rehabilitation of Ecosystem Services to Improve Water Quality in a Lake Erie
Watershed ...... 68
viii
3.1 Abstract ...... 68
3.2 Introduction ...... 69
3.3 Materials and Methods ...... 72
3.3.1 Study Area ...... 72
3.3.2 Floodplain Rehabilitation...... 73
3.3.3 Wetland Rehabilitation ...... 75
3.3.4 Sample Collection ...... 79
3.3.4.1 Rehabilitated Floodplain ...... 79
3.3.4.2 Rehabilitated Wetland ...... 80
3.3.5 Sample Analyses ...... 80
3.3.5.1 Analysis of Water ...... 80
3.3.5.2 Analysis of Sediment ...... 80
3.3.6 Analysis of Data ...... 81
3.4 Results ...... 82
3.4.1 Rehabilitated Floodplain ...... 82
3.4.2 Rehabilitated Wetland ...... 86
3.5 Discussion ...... 91
3.5.1 Dissolved Reactive Phosphorus ...... 91
3.5.2 Total Phosphorus ...... 93
3.5.3 Escherichia coli ...... 95
3.5.4 Total Suspended Solids ...... 96
3.5.5 Implications and Treatment Scenarios in the Maumee River ...... 97
3.6 Conclusion ...... 105
ix
Chapter References ...... 105
All References ...... 121
A List and Ratios of Seeds Planted in the Rehabilitation Areas ...... 150
x
List of Tables
2.1 Summary of Calculated Discharge and Loading Data for the WCW...... 45
2.2 Percentage of annual loading that occurs during major discharge events for the
WCW and comparative watersheds...... 56
3.1 Mean values for densities of E. coli and concentrations of TSS, DRP, and TP in
the rehabilitated floodplain as a three-year mean and during base flow and
major discharge events ...... 82
3.2 Mean values and reduction percentages of E. coli, TSS, DRP, and TP observed
in the rehabilitated wetland ...... 87
3.4 A comparison of treatment scenarios to achieve DRP load reductions within the
Maumee River watershed ...... 101
A.1 List of species seeded in the rehabilitated floodplain and wetland...... 150
xi
List of Figures
1 – 1 Flow-weighted concentration of total phosphorus and dissolved reactive
phosphorus in the Maumee River...... 4
2 – 1 Site map of the Wolf Creek and watershed boundaries ...... 40
2 – 2 Correlations between annual discharge and annual precipitation, annual TP load,
annual E. coli load, and annual TSS load in the Wolf Creek watershed ...... 44
2 – 3 Mean daily discharge and water quality parameters measured in Wolf Creek in
2008...... 49
2 – 4 Seasonal loadings of total phosphorus, total suspended solids, and E. coli in Wolf
Creek, Maumee, and Blanchard Rivers ...... 50
2 – 5 Log densities of E. coli correlated to log daily discharge by season ...... 51
2 – 6 Mean daily discharge values measured in the Wolf Creek watershed ...... 54
2 – 7 Log discharge correlated to log total phosphorus, log total suspended solids, and
log E. coli in the Wolf Creek watershed ...... 55
2 – 8 Log turbidity correlated to log total phosphorus, log total suspended solids, and
log E. coli in the Wolf Creek watershed ...... 57
2 – 9 Discharge and theoretical discharge entering hypothetical wetland rehabilitation
project in 2011 ...... 59
2 – 10 Discharge and theoretical discharge entering hypothetical wetland rehabilitation
project in 2013 ...... 60
xii
3 – 1 Site map of the Wolf Creek and watershed boundaries ...... 73
3 – 2 Overhead view and cross-section of rehabilitated floodplain and sedimentation
basin ...... 75
3 – 3 Overhead view of the rehabilitated wetland along Wolf Creek ...... 77
3 – 4 Cross-section of the first wetland cell ...... 78
3 – 5 Photos during (May 2014) and after (August 2015) construction of the
rehabilitated wetland...... 79
3 – 6 Seasonal changes and reduction percentage in E. coli, dissolved reactive
phosphorus, total suspended solids, and total phosphorus in the rehabilitated
floodplain ...... 84
3 – 7 Depth of sediment in the sedimentation basin at the inlet, center, and outlet
locations through time...... 86
3 – 8 Linear regression for dissolved reactive phosphorus removal and initial dissolved
reactive phosphorus concentration in the rehabilitated wetland ...... 89
3 – 9 Linear models of water quality improvement based on concentration/density of
contaminant flowing in and out of the first wetland cell ...... 90
xiii
List of Abbreviations
AD ...... Annual Discharge
BMP ...... Best Management Practice
CD ...... Cumulative Discharge CFU ...... Colony Forming Unit
DRP ...... Dissolved Reactive Phosphorus
EPA ...... Environmental Protection Agency
FC ...... Fecal Contamination
HAB ...... Harmful Algal Bloom
IJC ...... International Joint Commission
MBSP ...... Maumee Bay State Park ML...... Monitored Load MT...... Metric Tons
NPDES ...... National Pollutant Discharge Elimination System
OD ...... Observed Discharge
PC ...... Phosphorus Contamination PP ...... Particulate Phosphorus
SD ...... Seasonal Discharge
TP ...... Total Phosphorus TSS ...... Total Suspended Solids
USGS ...... United States Geological Survey
WCW ...... Wolf Creek Watershed
xiv
List of Symbols
푞푖 ...... Mean daily discharge
C1...... Concentration of water quality parameter at inflow C2...... Concentration of water quality parameter at outflow C% ...... Percent change in water quality parameter between inflow and outflow
xv
Chapter 1
Introduction
1.1 Introduction
A multitude of environmental problems affect Lake Erie, including phosphorus contamination (PC), which drives the growth of harmful algal blooms (HABs; Michalak et al., 2013) and fecal contamination (FC), which directly impacts human health
(Marsalek & Rochfort, 2004; Wade et al., 2005). In the 1950s and 60s, PC and FC primarily arose from point sources such as industrial and municipal “end of pipe” discharge (IJC, 1970). Government regulations limiting the discharge of these wastes
(CWA, 1972) led to a decrease in the severity of HABs by the mid-1980s (Nicholls, et al., 1993) and FC by the mid-1970s (Konasewich et al., 1975). Unfortunately, PC and
FC are once again at levels that impair water quality (Kane et al., 2014; Maumee RAP,
2006) within Lake Erie. This begs the question: why are these water quality impairments again problematic if government regulations are still in place? The answer may be that
PC and FC now enter the watershed as non-point source contaminants (Byappanahalli et al., 2007; Maccoux et al., 2016), which are unaffected by these regulations. My research focusses on an approach capable of targeting and reducing these non-point contaminants - i.e. the rehabilitation of impaired watersheds to enhance ecosystem processes that reduce the loads of PC and FC entering Lake Erie. 1
This introduction (Chapter 1) presents a framework for my research, and includes the history and impact of PC (section 1-2) and FC (section 1-3) in the watershed of western Lake Erie, the criteria used to select and design the rehabilitation projects within the WCW (section 1-4 and 1-5), and the implications of my research for other locations in the world (section 1-6 and 1-7).
1.2 Harmful Algal Blooms in Lake Erie
The motivation to solve the problem of HABs in Lake Erie began with the signing of the Great Lakes Water Quality Agreement (IJC, 1972a) by the United States and
Canada and, later, the International Joint Commission’s report, which called for a reduction to Lake Erie in annual phosphorus loadings to below 11,000 metric tons (MT) from the original load of 25,000 MT (IJC, 1972). This goal was achieved in 1981 (De
Pinto et al., 1986; Richards et al., 1993) via a phosphorus abatement strategy (National
Pollutant Discharge Elimination System - NPDES) that targets point sources
(i.e. wastewater treatment plants, industrial discharge, etc.) (CWA, 1972). As a result,
HABs were effectively eliminated by the end of the decade, leading to headlines such as
“Lake Erie – A Better Place to Visit Now” (Lake Erie, 1981).
A reemergence of HABs occurred in the late 1990s culminating in 2011 with a
HAB three times larger than any previously noted (Michalak et al., 2013). In the same time period, an increase in dissolved reactive phosphorus (DRP) concentrations and a decrease in particulate phosphorus (PP) (Fig. 1-1) were noted in the Maumee River
(Baker et al., 2014; Kane et al., 2014). The observed shift may be due to the popularity of reduced tillage practices within the Maumee River watershed that prevent erosion of 2
agricultural soil (Jarvie et al, 2017). Whatever the cause, this shift in DRP load appears to contribute to HAB reemergence .(Kane et al., 2014).
The reemergence of HABs was accompanied by the occasional inclusion of
Microcystis aeruginosa (Brittain et al., 2000; Rinta-Kanto et al., 2005), a prokaryotic bacterium able to produce the hepatotoxin, microcystin. Microcystin is linked to human deaths (e.g. observed in a Brazilian haemodialysis unit; Pouria et al., 1998), which underscore the potential harm of HABs. Concern for human health led to a “do not use” advisory for tap water during a HAB on Aug. 2nd, 2014 for 500,000 Northwest Ohio residents due to unacceptable levels of microcystin (Wilson, 2014; Wines, 2014).
To address this issue head-on and reduce the occurrences of HABs, we first need to ask: by how much do loadings of phosphorus need to be reduced and how do we accomplish this? The IJC recommended that the frequency and severity of HABs could be returned to acceptable levels by achieving a 39% reduction in annual TP loadings and a 41% reduction in spring time DRP loadings (IJC, 2014). These recommendations were based on the correlation between phosphorus loads from the Maumee River (NCWQR,
2017) and Lake Erie algal bloom index (Cyanobacterial Index; Stumpf et al., 2012), which revealed that HABs were mild in years when phosphorus loadings were below
1,600 MT of annual TP and 150 MT of spring DRP load. These values became the target loads; reduction percentages were obtained by comparing the target loads to the observed annual DRP and TP loads from 2007 through 2012 (IJC, 2014).
3
Figure 1-1: Flow-weighted concentration of total phosphorus (TP) and dissolved reactive phosphorus (DRP) in the Maumee River, NW Ohio, from 1976 through 2013. Data points indicate calculated average concentration for each year. Line indicates the 5-year moving average concentration. Graphic from Baker et al. (2014).
The answer to the second part of the question, “how do we accomplish this?” is the basis of my research. Starting in the 1970s, PC was reduced by targeting point sources; currently, nonpoint sources are responsible for 89% of all DRP in the Maumee
River (Maccoux et al., 2016). Agriculture is the primary contributor (Richards et al.,
2013) of total phosphorus from “new” sources derived from recent fertilizer application and stored “legacy” sources derived from past fertilization (Sharpley et al., 2013). A review of the literature reveals that phosphorus management strategies in agricultural landscapes generally focus on the mitigation of this “new” phosphorus (Bishop et al.,
2005; Geohring et al., 2001; Rice et al., 2002; Sharpley et al., 1994; Sharpley et al.,
2006) by: (1) limiting and managing the application of phosphorus to agricultural fields,
4
(2) reducing phosphorus export via Best Management Practices, and (3) capturing phosphorus in surface waters. Both (1) and (2), in my opinion, are less suitable (as I will explain in the following two sections) than strategy (3), which I will address in the section Watershed Rehabilitation.
(1) The 4R (Right Time, Right Place, Right Amount, Right Type) Nutrient Stewardship
Program disseminates information concerning the importance of managing fertilizer applications to local farmers (Mikkelsen, 2011). Over 4,000 farmers with 730,000 ha in production in the western Lake Erie watershed (for reference, the entire Maumee River watershed contains 2.1 million ha) participated in the 4R program since 2014 (Vollmer-
Sanders et al., 2016), but this has not been sufficient to reduce DRP loadings to the IJC recommended targets (NCWQR, 2017). Even if we were able to manage phosphorus applications to the point where export was negligible, legacy phosphorus, which may amount to a total of 200,000 metric tons (MT; or 0.1 MT/ha) or more in the Maumee
River watershed (Powers, et al., 2016), could still constitute a problem by remobilizing into surface waters for years or decades (Carpenter, 2005; King et al, 2017; Knox, 2006;
Sharpley et al., 2013).
(2) Best management practices (BMPs) are designed to prevent phosphorus from leaving agricultural fields. Although widely adopted in the Maumee River watershed, DRP loadings remain largely unchanged (NCWQR, 2017). In a series of experiments by Pease et al. (2018) DRP loss from fields positively correlated with precipitation and fertilizer application rates and not with BMP practices. On a larger scale, i.e. the Maumee River watershed, on-field BMPs (drainage management, conservation tillage, cover crops, etc.) increased from 45 to 60% of agricultural lands and edge of field practices that reduce 5
phosphorus runoff increased from 18 to 31% from 2006 through 2012. Unfortunately, the average loss of DRP from agricultural lands within the Maumee River watershed remained constant at 1.5 kg/ha/yr (USDA, 2016).
4R and BMP practices are designed to limit the amount of phosphorus loss from agricultural fields but additional management strategies such as ecosystem rehabilitation will provide further reductions on phosphorus transport as discussed in the section Watershed Rehabilitation.
1.3 Fecal Contamination in Lake Erie
The recreational waters of Lake Erie have long been affected by FC, which poses a threat to human health via the presence of pathogens (Cabelli et al., 1979) that are associated with gastrointestinal and respiratory illnesses (Pruss, 1998; Wade et al., 2006;
Zmirou et al., 2003, Soller et al., 2010). The IJC identified FC as a “pollution problem” that enters Lake Erie from industrialized areas and recommended infrastructure improvement of point sources to improve the quality of discharge water (IJC, 1970). In
1972, the NPDES regulated discharge of FC from industrial and municipal sources; as a result of these actions, FC at Lake Erie beaches trended downward (Konasewich et al.,
1975).
The reduction of FC at Lake Erie beaches and elsewhere did not persist, which prompted the IJC to recommend further water improvement strategies in the form of the
Remedial Action Plan (RAP; IJC, 1985). According to the RAP, pollution abatement strategies (i.e. NPDES) were not successful in achieving unrestricted use of recreational beaches due to continued occurrence of FC and resultant “swim advisories” (densities of 6
Escherichia coli exceeded the EPA’s Beach Action Value single sample maxima of 235 colony forming units (CFU) per 100 mL of water). These swim advisories indicate a risk of gastrointestinal illness to beachgoers (Wade et al., 2005). Maumee Bay State Park
(MBSP), the only public access beach on the U.S. side of the western Lake Erie basin, exhibited an average of 20 or more annual swim advisories over a 5-year mean and was designated with the “Beach Closing” BUI (Maumee RAP, 2006).
In an effort to identify and eventually mitigate E. coli transport to the MBSP beach, our research group performed an E. coli source tracking study in the western Lake
Erie basin (Francy et al., 2005). The Wolf Creek watershed (WCW) was identified as the proximate source of FC (Francy et al., 2005). Agricultural practices and septic tank seepage were considered as nonpoint sources of E. coli within the WCW – sources that have been noted in other Great Lakes watersheds (Byappanahalli et al., 2007).
Watershed rehabilitation offers a solution that can effectively reduce nonpoint source E. coli as well as phosphorus and provide ancillary benefits such as flood mitigation (Hey &
Philippi, 1995; Mitsch & Gosselink, 2000), carbon sequestration (Badiou et al., 2011), high quality habitat for native fauna (Crail et al., 2011), and improved environmental value for the local community (Mitsch & Gosselink, 2000b). In an economic study
(Awondo et al., 2011), the public users of MBSP valued the elimination of swim advisories and the added ancillary benefits of watershed rehabilitation at over $6 million per year. This stimulated efforts to start a rehabilitation project to mitigate E. coli loadings to MBSP. In the next section, Watershed Rehabilitation, I discuss strategies to address nonpoint sources of E. coli, specifically those that could be effective in the
WCW. 7
1.4 Watershed Rehabilitation
The primary goal was to assess the degree with which rehabilitation (i.e. enhancing previously degraded biological, chemical, and/or physical processes) within a channelized stream could be used to reduce loadings of phosphorus and E. coli. A search of the literature identified numerous ways by which rehabilitation could be implemented: sedimentation ponds (Gannon et al., 2005, Hupp and Morris, 1990; Johnston, 1991;
Mitsch et al., 2001), wetlands (Jordan et al., 2003; Kadlec & Wallace, 2008; Vymazal,
2007; Wang & Mitsch, 1998), bioretention units (Davis et al., 2006; Hunt et al., 2008;
Roy-Poirier et al., 2010; Zhang et al., 2008), and floodplain improvements (Davis et al.,
2015; Kiedrzyńska et al., 2008; McKergow et al., 2016; Roley et al., 2012). To choose the most appropriate approach for the WCW, I decided to focus on four factors within the
Lake Erie watershed as outlined below.
(1) Land Availability – The Maumee River watershed is 72% agriculture (Cousino et al., 2015). One recent suggestion to reduce phosphorus loadings to Lake Erie (Mitsch,
2017) was to follow the approach used for the restoration of the Florida Everglades and convert 16,200 ha of agricultural land into treatment wetlands. Another suggestion was to convert 50% of agricultural land to grassland (Scavia, et al., 2016). However, flooding agricultural soils that have been saturated with phosphorus from decades of fertilization to create wetlands may result in short-term sources rather than sinks of phosphorus
(Kinsman-Costello et al., 2016).
This suggests: An alternative approach for the western Lake Erie watershed is to utilize enhanced ecosystem services (i.e. adsorption) specifically designed for phosphorus 8
retention (e.g. concentrated water quality improvements). This could be achieved by utilizing adsorption media within subsurface flow wetlands (Grüneberg & Kern, 2001;
Vohla et al., 2011) to increase phosphorus retention compared to traditional in situ soils
(Arias et al., 2003; Brix et al., 2001; Kadlec & Wallace, 2008; Sakadevan & Bavor,
1998; Seo et al., 2005). For example, Sakadevan & Bavor (1998) determined that blast furnace slag adsorbed phosphorus by 44.2 g P/kg compared to wetland soils from
Richmond, Australia, which retained phosphorus by 934 - 1153 mg P/kg.
(2) Hydrology - Channelized watersheds and the widespread occurrence of impervious surfaces in our region result in flashy discharge events after storms, which is when a majority of phosphorus transports to Lake Erie (Baker et al., 2014) occurs and densities of E. coli in regional watersheds increase (Whitman et al., 2006).
This suggests: Rehabilitation efforts may need to improve water quality primarily during major discharge events, potentially by slowing water velocity via the reestablishment of floodplains.
(3) Seasonal Trends - In general, phosphorus transport primarily occurs in the spring months correlating with spring discharge events (Baker et al., 2014) while E. coli transport occurs primarily during summer months based on evidence from tributaries of
Lake Michigan (Whitman et al., 2006).
This suggests: We need to identify whether these trends exist within the WCW to determine the appropriate means to reduce loadings of phosphorus and E. coli.
(4) Erosion - Agricultural land use and drainage ditches created stream channels that are susceptible to erosion and contribute to elevated sediment loadings. Conservation tillage appears to be one of the driving factors that reduced overall sediment loads within the 9
Maumee River by 46% between 1985 and 2009 (Richards et al., 2009). However, 40% of phosphorus (Baker et al., 2014) and 70% of E. coli loads (Francy et al., 2005) are attached to sediment.
This suggests: Further reductions in sediment loads should also be a goal of watershed rehabilitation to significantly reduce phosphorus and E. coli loads to Lake Erie.
This intellectual exercise led me to consider the following rehabilitation projects that may reduce phosphorus (both DRP and TP), E. coli, and sediment on a small parcel of land, across seasonal variations in loading trends and during major discharge events.
Rehabilitated Floodplain: Many first order streams in the watershed of western Lake
Erie have been channelized to increase drainage from agricultural land. This reduced floodplains and increased water velocity effectively eliminating floodplain function
(ODNR, 2008). Rehabilitation generally involved widening the channel and creating a vegetated floodplain bench (i.e. two-stage ditch) on either side of the channel. This impact, which improves water quality by slowing water velocity, promoting sedimentation (Davis et al., 2015) and removing attached phosphorus (Hodaj et al., 2017;
Mahl et al., 2015) and E. coli (Whitman et al., 2008). Resuspension of sediment during major discharge events may counteract this process (Filoso, 2015), a problem that may be addressed by creating a sedimentation basin (deepened section of streambed) to accumulate sediment with less resuspension (Chrétien et al., 2016). A rehabilitated floodplain also provides relief during major discharge events and promotes sediment entrainment in the vegetation (Needelman et al., 2007).
10
Rehabilitated Wetland: A subsurface flow wetland was selected as the second rehabilitation approach to retain contaminants that would be unaffected by sedimentation in the rehabilitated floodplain such as DRP and suspended E. coli (Arias et al., 2003;
Garcia et al., 2010; Vacca et al., 2005; Vymazal, 2005). Phosphorus retention within subsurface flow wetlands can be unreliable due to seasonal variations in plant uptake of phosphorus (Esser et al., 2004; Rousseau et al., 2004b; Verhoeven & Meuleman, 1999), which suggests the need for a sorbent containing calcium, iron, or aluminum (Garcia et al., 2010; Vohla et al., 2011; Vymazal, 2007). These sorbents are effective throughout the year accumulating a pool of DRP for bioassimilation during the growing season that may otherwise be limited to seasonal DRP inputs (Kadlec & Wallace, 2008). Finally, the incorporation of a sorbent will enhance filtration function for a given wetland size (c.f.
Vohla et al., 2011), which may provide an opportunity to create smaller wetland systems with equal filtration capacity.
1.5 Data to Inform Watershed Rehabilitation
Together, a floodplain and wetland offer a conceptual watershed rehabilitation design to reduce both suspended (i.e. sediment and particulate phosphorus) and dissolved contaminants (i.e. DRP and E. coli) throughout the year and during base flow and discharge event scenarios. The capacity of the combined systems ideally should be in alignment with the treatment goals identified for the Maumee watershed (a 39% reduction in annual TP loadings and a 41% reduction in springtime DRP loadings and reduction in E. coli densities to below 235 CFUs/100 mL). Sufficient field data within four categories are required to calculate the treatment goals: (1) annual and (2) seasonal 11
loads of TP and DRP while taking into account variations in year-to-year and seasonal changes in loadings, (3) event-based loads that occur during major discharge events, and
(4) stream hydrology to indicate the quantity of water (i.e. discharge) the rehabilitation projects need to treat throughout each year, season, and event. The analysis of the data could be used to improve rehabilitation size or design according to the specific hydrologic and loading trends within the WCW. For example, perhaps reduction goals can be attained by treating only periods of high discharge (e.g. rain events, ice melt) when concentrations of phosphorus and densities of E. coli are at their highest levels
(Baker et al., 2014; Whitman et al., 2008).
I emulated the approach by Johnes (2007) in the WCW to understand how regional watersheds behave with respect to the above factors. Johnes (2007) describes the uncertainties in measuring riverine phosphorus loads and concludes in catchments with clay soils and low baseflow index, which is indicative of watersheds that experience major discharge events, similar to the WCW. Johnes (2007) concluded that a stratified approach (regular weekly or semi-weekly samples paired with targeted sampling of precipitation events) yields the most accurate assessment of annual phosphorus loads.
Interannual variability in loading values can be prevalent in watersheds and driven by annual precipitation. For example, annual TP loads in the Maumee River can fluctuate from 1000 MT to 3000 MT depending on the year and amount of precipitation (Baker et al., 2014).
12
1.6 Global Perspective of Harmful Algal Blooms and Mitigation
The ecology of HABs is region or even lake-specific thus requiring a thorough understanding of regional factors to implement appropriate solutions. The negative impact of HABs on freshwater environments is not limited to Lake Erie and occurs in most regions throughout the world, including Africa (Kotut et al., 2006; Okello et al.,
2010; Thornton, 1980), Australia (Davis, 1997), United Kingdom (Rodger et al., 1994), and China (Duan et al., 2009). Phosphorus and nitrogen are the primary nutrients that contribute to HAB growth (Paerl et al., 2001; Smith, 2003). However, phosphorus can limit HAB growth in some estuarine and marine environments (Rudek et al., 1991; Fisher et al. 1992) whereas nitrogen can limit HAB growth in temperate and polar environments
(Dugdale & Goering, 1967; White, 1988). These generalizations help us understand that
HAB growth can rely on different resources depending on the specific region or environment but the true nature of HAB development is more complex. For example, in
Lake Taihu, China the HAB is phosphorus-limited in the winter and spring months and nitrogen-limited in the summer and fall months (Xu et al., 2010) requiring a dual nutrient management strategy. Regionally specific factors dictate the solutions to reduce HAB growth and need to be evaluated before management strategies can be implemented.
1.7 Global Perspective of Fecal Contamination and Mitigation
In the past two decades, swim advisories have increased in frequency along Lake
Erie (ODH, 2017). FC in surface waters is not exclusive to Lake Erie and occurs more frequently in developing regions of the world where the impact on humans and environmental health can be devastating (Bain et al., 2014). Poor water quality, hygiene, 13
and sanitation account for 1.7 million deaths a year primarily due to enteric pathogens
(Ashbolt, 2004). Most of these deaths occur in developing regions but pathogenic risks from FC are also present in the U.S. FC sources vary depending on regional-specific factors and have been tracked to sources such as wastewater treatment plant discharge
(Esseili et al., 2008), septic tank leakage (Harwood et al., 2000), sewage disposal (Lipp et al., 2001), and animal manure (Hagedorn et al., 1999). Given the region-specific sources of microbial contamination throughout the world, regionally appropriate solutions need to be developed based on hydrologic and water quality data sets. For example, three surface-flow and two subsurface-flow wetlands in Queensland, Australia reduce fecal coliforms in secondary effluent treatment system by 82 – 99.9% (Greenway, 2005).
Additionally, reedbeds are used in Latin America to reduce fecal pathogens from untreated greywater (Dallas et al., 2004). Many solutions rely on reducing fecal coliforms from point sources such as waste water treatment plants or industrial discharge.
For my project, I will demonstrate the use of ecosystem rehabilitation to reduce fecal contamination within a watershed that is dominated by nonpoint source pollution.
1.8 Dissertation Goals
Throughout the rest of my dissertation, I will address three goals: (1) Determine the type and extent of baseline water quality data necessary to inform rehabilitation design within the WCW. (2) Design and implement two rehabilitation projects within the
WCW and evaluate their ability to reduce nonpoint source TP, DRP, TSS, and E. coli.
(3) Construct and scale a model for the Maumee River watershed to determine the size of
14
a similarly designed rehabilitation project to reduce DRP to meet goals established by the
IJC.
These goals are addressed in the two additional chapters: Chapter 2 titled,
“Evaluation of a Lake Erie Watershed and the Conceptual Design of Rehabilitation
Projects to Reduce Escherichia coli and Phosphorus Loadings” reports on the accumulation and analysis of multiple years of baseline water quality data in the WCW
(goal 1), and Chapter 3 titled, “Rehabilitation of Ecosystem Services to Improve Water
Quality in a Lake Erie Watershed” details the design, implementation, and impact of a rehabilitated wetland and floodplain (goal 2) followed and an attempt to construct and scale a model for the Maumee River watershed to determine the size of a similarly designed rehabilitation project to reduce DRP to meet goals established by the IJC (goal
3).
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Chapter 2
Evaluation of a Lake Erie Watershed and Implications for Watershed Rehabilitation Projects to Reduce Escherichia coli and Phosphorus Loadings
2.1 Abstract
The western Lake Erie basin has recently witnessed a number of harmful algal blooms attributed in part to excessive loadings of phosphorus. Moreover, lakeshore beaches are posted with “do not swim” advisories due to high levels of Escherichia coli.
Rehabilitation of ecosystem services is a common strategy to reduce both phosphorus and
E. coli in impaired watersheds. As a prelude to two rehabilitation projects, I accumulated seven years of water quality data within the impaired Wolf Creek watershed using an auto-sampler and an acoustic Doppler velocity meter at a location 1.0 km upstream of the
Lake Erie shoreline. Water velocity (m/s) and stage-height (m) were assessed every 15- min. Samples of water were collected Monday through Friday at 1:00 am, 7:00 am, and
1:00 pm. Water samples were analyzed for total phosphorus (TP, mg/L), E. coli (colony forming units (CFUs)/100 mL), total suspended sediment (TSS, mg/L) and the data were used to calculate daily, seasonal, and annual loadings. This study suggested that several
35
years of data are needed to obtain an accurate assessment of the variability in annual, seasonal and rain-event based loadings: (1) Annual loadings over a seven-year period ranged from 14.6 MT/km2 to 87.7 MT/km2 for TSS, 0.071 MT/km2 to 0.434 MT/km2 for
TP, and 1.5 x 1012 CFUs/km2 to 2.0 x 1013 CFUs/km2 for E. coli. (2) Within each year, the majority of the loadings of TSS (58%), TP (60%) and E. coli (85%) occurred when discharge was > 2.1 m3/sec, accounting for 36 days per year (90th percentile); (3) Winter and spring accounted for most of the loadings of TP (81%) and TSS (83%), with summer accounting for the majority of E. coli (66%). Thus, data from a year with the highest loadings and periods of high discharge are both needed to calculate the treatment capacity of a rehabilitation project. Recent suggested target values for reductions in the loadings of TP of 40% could be achieved in this watershed by removing TP from discharge values between 0 and 2.1 m3/s of discharge effectively limiting the size of a rehabilitation project.
2.2 Introduction
The western Lake Erie basin has recently witnessed a number of harmful algal blooms (HABs) attributed in part to excessive loadings of phosphorus (Michalak et al.,
2013; Bingham et al., 2015) primarily from the Maumee River (Chaffin et al., 2014;
Stumpf et al., 2012), which represents 20-25% of the lake’s annual phosphorus load
(Baker et al., 2014). Phosphorus also is linked to increased densities of Microcystis aeruginosa (Baldia et al., 2007). The presence of this cyanobacterium resulted in a three- day “Do Not Drink” advisory for tap water obtained from Lake Erie during a recent HAB in 2014 (Wilson, 2014), which was centered on the City of Toledo, OH. 36
The western basin also witnessed numerous postings of “do not swim advisories” in recent years due to elevated densities (> 235 colony forming units (CFU)/100 ml;
USEPA, 1986) of Escherichia coli, which occurred on an average of 22 days of each recreational season for the public beach of Maumee Bay State Park (MBSP) from 2010 through 2018 (https://ny.water.usgs.gov/maps/nowcast). Both the excessive loadings of phosphorus (Michalak et al., 2013) and the elevated densities of E. coli (Francy et al.,
2005) derive from nonpoint sources within the Lake Erie watershed, which suggests that an alternative method to the point source mitigation strategies that worked in the past is required.
Scientists (Mitsch, 2017; Scavia et al., 2016) and government agencies (IJC,
2014; OEPA 2013, 2018) suggest that rehabilitation of the once ubiquitous wetlands and floodplains of the Maumee River watershed and other tributaries to Lake Erie could be the alternative method to improve surface water quality. Rehabilitation enhances biological, chemical, and physical processes that reduce levels of phosphorus (Vymazal,
2007) and E. coli (Mitsch & Gosselink, 2007; Vymazal, 2005). These ecosystem processes include sedimentation, filtration, adsorption, and biomass assimilation within wetlands (Garcia et al., 2010; Kadlec & Wallace, 2008; Sholtz & Lee, 2005; Vohla et al.,
2011) and floodplains (Ahilan et al., 2016; Davis et al., 2015).
The need for an extensive, rigorous sampling regime for data collection was based on my assumption that regional watersheds have four features that should be considered before deciding on the type of rehabilitation project and its design.
(1) Data for the loadings of phosphorus in the Maumee River vary widely from year-to-year; e.g. from 940 metric tons in 2001 to 3,500 MT in 2008 (NCWQR, 2017). 37
Thus, data from any single year might underestimate or overestimate the loading values used to calculate a target value for water quality improvement and thus the extent of rehabilitation needed to obtain reduction goals.
(2) Phosphorus loadings also vary seasonally; approximately 50% of inputs to
Lake Erie occur in the spring (Baker et al., 2014). Although data for loadings of E. coli to Lake Erie are not available, seasonal variations are evident for nearby Lake Michigan in which the summer months demonstrate the highest densities of E. coli (Whitman et al.,
2006).
(3) Regional watersheds connected to Lake Erie are primarily channelized
(ODNR, 2008) and drained efficiently with tile systems. Both result in flashy conditions during major discharge events when a majority of the loadings of phosphorus occur
(Baker et al., 2014), which suggests that data collection should emphasize observations during major discharge events.
(4) More than 40% of the annual phosphorus load (Baker et al., 2014) and 70% of
E. coli in water samples (Francy et al., 2005) are attached to sediment, suggesting that sediment loadings are important in the overall scope and design of a rehabilitation strategy.
In this paper, I report on the accumulation and analysis of the water quality data with respect to annual and seasonal variations in loadings of phosphorus, E. coli, and suspended sediment as well as hydrologic trends. My objective is to identify and model loading trends within the WCW in order to calculate water quality improvement goals and calculate flow rates necessary for watershed rehabilitation to achieve those goals.
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2.3 Materials and Methods
2.3.1 Study Area
The WCW (41 km2) is located in Lucas County, OH, (Hydraulic Unit Code:
041000100705) and discharges into Lake Erie at the MBSP marina, 70 m east of the park’s swimming beaches (Fig 2-1). In the 19th century, the region in which the WCW exists was known as the Great Black Swamp, which covered a 40 x 160 km area in northwest Ohio and northeast Indiana (Mitsch & Gosselink, 2007). To create suitable agricultural lands, the regional watershed was altered using ditched waterways that now receive input from agricultural drainage tiles and storm drains (ODNR, 2008). Land use within the WCW is primarily agricultural (72%; Cousino et al., 2015) with the remainder in urban and forested lands.
39
N
Figure 2-1: Site map of the Wolf Creek (blue lines) and watershed boundries (red lines).
Project locations are identified by red dots.
2.3.2 Sample Collection
A USGS operated stream gauge station (site no. 04194085) with an auto-sampler
(Hach Sigma 900 Max, Loveland, CO) and an acoustic Doppler velocity meter (ADVM,
SonTek Argonaut SW, San Diego, Ca) was installed in the WCW in 2007 at a location
1.0 km upstream of the Lake Erie shoreline. Water velocity (m/s) and stage-height (m) were assessed every 15-min. Discharge (m3/s) was calculated using the cross-sectional width of the channel; data were uploaded to a USGS website for storage and access.
40
Samples of water (1 L in sterile polypropylene bottles) were collected Monday through
Friday at 1:00 am, 7:00 am, and 1:00 pm between July 2007 and June 2014. The
Numeric Integration Approach (Richards, 1998) was used to change the frequency of sampling to once per hour during “major” discharge events (> 42 ft3/s =1.2 m3/s). Water samples were retrieved each morning, transported to the laboratory on ice, and analyzed for total phosphorus (TP, mg/L), total suspended sediment (TSS, mg/L), turbidity (NTU), and densities of E. coli (CFUs/100 mL). Data were reported as mean daily values.
2.3.3 Sample Analyses
Water samples were analyzed as follows: TP - USEPA method 365.2; E. coli -
USEPA Method 1603; TSS - Standard Method 2540-D (Rice et al. 2012); Turbidity -
Hach 2100P Portable Turbidimeter. Quality assurance included replicate samples for
10% of all analyses and sample blanks. Our laboratory participates in the USGS sponsored Phosphorus Data and Quality Control Program (https://bqs.usgs.gov/srs/) and received an acceptable performance designation by the USGS during this study.
2.3.4 Analysis of Data
2.3.4.1 Daily and Annual Discharge
The number of days (min of 160 days in 2014 – max of 362 days in 2013) for which discharge was measured varied from year-to-year primarily due to stream gauge maintenance. Estimates of annual and seasonal discharge were calculated by extrapolation from the days with data using standard procedures (Richards, 1998): (1) discharge values (m3, every 15 min) were multiplied by 900 s (i.e. 15 min) and the
3 products summed to obtain the observed daily discharge (ODD, i.e. m /d). (2) ODD values were summed for a given year, divided by the number of observation days and 41
then multiplied by 365 days to obtain an estimated annual discharge (AD). The same process was used to obtain values for seasonal discharge (SD), using 90 days for each season.
2.3.4.2 Daily and Annual Loadings
Daily measured loadings (MLD) for TP, TSS and E. coli were calculated by multiplying the mean daily values of TP, TSS, and E. coli by ODD. MLD values were summed annually (MLA) and seasonally (MLS) for each year. Adjusted annual (ALA) and seasonal (ALS) loads were obtained by multiplying the respective MLA and MLS values by a corresponding discharge correction term (AD/CDA and SD/CDS), respectively (Richards, 1989):
퐴퐷 퐴퐿퐴 = 푀퐿퐴 × (1) 퐶퐷퐴
푆퐷 퐴퐿푆 = 푀퐿푆 × (2) 퐶퐷푆 where CDA and CDS are the cumulative ODD that occurred on days in which TP, TSS, and E. coli were sampled in a given year and a given season, respectively. The number of days in a given year that were monitored for discharge were normally more than the number of days that were monitored for TSS, TP, and E. coli (Table 2.1). By utilizing a discharge correction term, the MLA value is adjusted proportionally with observed discharge (ODA), which yields a more accurate estimation of AL as opposed to simple summation of the load based on the limited number of days when MLD occurred.
Loading values were then divided by watershed area (41 km2) and reported as load per unit area.
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2.3.4.3 Flashiness Index
The Richards-Baker Index (R-B Index) was used to quantify “flashiness” (i.e. the tendency for discharge rates to change from one day to the next) using Eq. 3:
푛 ∑푖=1|푞푖−푞푖−1| 푅 − 퐵 퐼푛푑푒푥 = 푛 (3) ∑푖=1 푞푖 where qi is the mean daily discharge (Baker et al. 2004). “Flashiness” for the WCW was compared to those values reported for similarly sized tributaries in the region.
2.4 Results and Discussion
2.4.1 Interannual Variability of TP, TSS, and E. coli Loadings
As expected, the adjusted annual loads for each of the studied constituents varied from year-to-year with ranges of 600.3 to 3594.0 MT (14.6 MT/km2 to 87.7 MT/km2) for
TSS, 2.9 to 17.8 MT (0.071 MT/km2 to 0.434 MT/km2) for TP, and 6.3 x 1013 to 8.2 x
1014 CFUs (1.5 x 1012 CFUs/km2 to 2.0 x 1013 CFUs/km2) for E. coli (Table 2.1).
Precipitation was a main driver for loadings as evidenced by a linear correlation of discharge with TSS (p-value = 0.0062), TP (p-value = 0.014), and E. coli (p-value =
0.012). The variation in annual loadings from year-to-year may have an impact on planning watershed rehabilitation if only a single year of data is used for TP or E. coli.
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Figure 2-2: Correlations and resulting regressions between annual discharge and annual precipitation, annual TP load, annual E. coli load, and annual TSS load in the Wolf Creek watershed between 2007 and 2014.
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Table 2.1: Summary of calculated discharge, loading, standard deviation (SD) and coefficient of variation (CV) for TSS, TP, and E. coli as well as the percentage of the annual load contributed during major discharge events for each parameter in the WCW.
Discharge Total Suspended Solids Total Phosphorus E. coli Contribution Contribution Contribution of High Flow of High Flow of High Flow 3 Precipitation Days AD (m ) x ALA Load to ALA ALA Load to ALA ALA Load to ALA Year (cm) Sampled 1,000,000 (MT) (%) (MT) (%) (CFUs) (%) 2007 87.1 106 14.67 2424 74.5 7.33 55.8 8.24E+14 80.1 2008 93.5 248 29.39 3594 63.2 12.18 59.5 8.12E+14 83.3 2010 84.1 149 5.90 2046 94.9 2.95 86.7 1.20E+14 90.9 2011 111.3 321 21.96 1464 82.4 17.85 54.4 4.78E+14 71.6 2012 74.8 337 5.46 713 93.1 NA NA 6.34E+13 65.7 2013 81.7 362 8.40 600 92.6 2.92 86.5 3.95E+14 91.9 2014 69.2 160 13.51 966 59.3 2.92 40.8 2.11E+14 58.1 Avg. 86.0 240 14.18 1687 80.0 7.69 63.9 4.15E+14 77.3 SD 12.7 - 8.20 1001 - 5.64 - 2.88E+14 - CV 0.15 - 0.58 0.59 - 0.73 - 0.70 -
The importance of accurate annual loadings, particularly for TP, in the western Lake Erie watershed is evidenced by the recommendations issued by the International Joint Commission (IJC) that state a 40% reduction of annual TP load is necessary to eliminate HABs in Lake Erie. Therefore, one must first determine the value for annual
TP load and then apply a 40% reduction target to establish a load reduction for use in mitigation planning, which in my case is the use of watershed rehabilitation (IJC, 2014). The coefficient of variation (CV) for TP (0.73) is the highest among the parameters indicating that annual TP load exhibits the highest internal variability and will be the limiting factor to determine how many years are necessary to accurately quantify mean annual load. The standard deviation (SD) for TP after the first four years of sampling is 5.55 MT, which is similar to the SD for the seven-year data set (5.64). This indicates that
I have captured the annual internal variability within the first four years and thus is sufficient to determine annual means. However, four years of data collection would not be sufficient if the first four years exhibited similar precipitation patterns (i.e. drought conditions) followed by years with relatively higher or lower precipitation values. The
45
ranges in the data also exemplify the necessity of multiple year data sets to accurately determine this annual load value: a rehabilitation project that is designed with data from 2010 (2.95 MT of TP) could be woefully short in treating the TP loading (17.85 MT of TP) from 2011 where a 7.14 MT reduction of TP is needed to achieve a value along the lines of the targets recommended by the IJC. Similarly, TSS should also be considered to help achieve TP reduction targets since we have established that 40% of TP is attached to sediment particles within the water column. For example, reducing annual TSS loadings from the WCW by 50% would also yield a 20% reduction in TP load. An accurate valuation of annual TSS load may only be calculated after multiple years of data collection given the degree of variation that can occur from year to year. This is helpful information when planning mitigation strategies because, in general, TSS is a more efficient contaminant to remove from surface water than TP (Weiss et al., 2007). The degree of variability in annual loadings of E. coli is also important to consider for the purpose of establishing reduction targets in an attempt to reduce contamination of downstream recreational beaches. The Ohio EPA recommends a goal of reducing the number of exceedances of E. coli at Maumee Bay State Park beach to less than 19 days in each recreational season for three of five consecutive years (Maumee RAP, 2006). Therefore, year-to-year variability in E. coli loadings from the WCW, a proximal source of E. coli to the beach, is important to consider in order to achieve this goal. For example, watershed rehabilitation size or design may be inadequate in the WCW if data from 2012 (Table 2.1) were used to plan the rehabilitation because less than half of the annual E. coli load occurred in 2012 compared to the seven year average. To ensure that E. coli reductions are sufficient for 3 out of 5 consecutive years we must target annual loadings from multi-year averages instead of a single year. Similar to targeted removal of TP, multiple years of TSS data can be used to help establish
46
rehabilitation strategies that will target sufficient TSS removal as a surrogate to reduce E. coli loads of which 70% are attached to sediment.
Annual variability similarly impacts the Maumee River watershed, which is much larger but contains similar land use characteristics to Wolf Creek. However, the relative variability in TP loadings in the Maumee may be less than in the WCW. For example,
TP loadings in the Maumee in 2010 were 1,530 MT (0.071 MT/km2) and 2,780 (0.13
MT/km2) in 2011 indicating almost a two-fold increase (OEPA, 2013) whereas the WCW
(0.072 MT/km2 in 2010 and 0.43 MT/km2 in 2011) experienced a six-fold increase in TP loads by comparing the same years (Table 2.1). Kronvang et al. (2007) also noted a difference in the variability of annual TP loadings between 35 European micro- catchments (<30 km2) and 10 European river basins (>50,000 km2). This difference in variability may be attributed to longer flow pathways leading to additional phosphorus cycling and delayed TP loadings within the larger watersheds. Thus, multiple year datasets may be more important for smaller watersheds.
2.4.2 Seasonal Variability of TP, TSS, and E. coli Loadings
Loadings and concentration of each water quality parameter were evaluated based on season to investigate how variability throughout the year may impact potential watershed rehabilitation. Peak concentrations of TP (Fig. 2-3-B) and TSS (Fig. 2-3-D) occurred primarily from January through April with corresponding peaks in discharge
(Fig. 2-3-A) and turbidity (Fig. 2-3-E) (data from 2008 are provided). Increases in the densities of E. coli occurred only in the summer months corresponding to peaks in discharge (Fig. 2-3-C). As a result, seasonal loadings (Eq. 2) of TSS and TP were
47
elevated in the winter and spring months whereas E. coli loadings primarily occur in the summer months (Fig. 2-2). Interestingly, E. coli densities seemed to increase with discharge during the summer months while remaining relatively low throughout the rest of the year (Fig. 2-3-C). Thus, an additional evaluation of these data was performed by grouping all seven years of data by season to observe a possible correlation between densities of E. coli and discharge, which suggested that densities correlated with discharge only during the summer and fall months (Fig. 2-5). The seasonality of the loadings for TP, TSS, and E. coli within the WCW were also analyzed as means of seven years of data (Fig. 2-4). TP loadings in the WCW averaged 7.7 MT per year and of that
6.2 MT (80.5% of annual mean) occurred during the winter and spring months; TSS loadings averaged 1,687 MT per year with 1,383 MT (82.0% of annual mean) during the winter and spring months. However, as predicted in Fig. 2-3-C, loadings of E. coli primarily occurred during the summer months (Fig. 2-4).
48
Figure 2-3: Mean daily discharge and water quality parameters measured in Wolf Creek in 2008. Spring rain events (discharge peaks in gray box between Jan. and Apr.) cause increase in TP, TSS, and turbidity compared to summer rain events (gray box in Jul.), which in addition to TP, TSS, and turbidity also corresponds to an increase in densities of
E. coli.
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Figure 2-4: Winter (Dec. through Feb.), spring (Mar. through Apr.), summer (May through Aug.), and fall (Sep. through Nov.) loadings of TP and TSS between 2007 and
2014 in the WCW, Maumee River, and Blanchard River. E. coli data are not available for the Maumee and Blanchard Rivers.
50
Figure 2-5: Log densities of E. coli correlated to log daily discharge (m3/s) by season:
Spring (green, r = 0.05, p-value = 0.223), summer (orange, r = 0.64, p-value = 2E-16), fall (red, r = 0.55, p-value = 5E-09), and winter (blue, r = 0.37, p-value = 0.0049). The correlation between daily discharge and E. coli densities is strongest during the summer with a significant but weaker correlation in the fall and winter and negligible in the spring.
Seasonal variability in loadings of TP, TSS, and E. coli may be an important factor when considering watershed rehabilitation design in the WCW. This is evident when we explore the effectiveness of individual ecosystem processes to improve water quality and if they are affected by seasonal changes (i.e. temperature, growing season,
51
etc.). Biological processes (i.e. biomass assimilation) are most effective at retaining phosphorus during the growing season (May - August) when vegetation and microorganisms have an increased growth rate (Kadlec and Wallace, 2008). Sedimentation and sediment entrainment is also more effective during the summer months when vegetation in surface flow wetlands or floodplains help interrupt water flow pathways allowing sedimentation to occur. Given these limitations, restored wetlands in temperate regions traditionally are more efficient at reducing phosphorus and E. coli loadings during the summer months (growing season) compared to the rest of the year. Alternatively, physical processes (i.e. sedimentation and filtration) and chemical processes (i.e. adsorption) are effective year round.
Similar to the WCW, a majority of the annual load of TP and TSS in the Maumee
(82% for TP and 83% for TSS of annual mean) and Blanchard (72% for TP and 79% for
TSS of annual mean) rivers occurred during the winter/spring months between the same time period 2007 through 2014 (NCWQR, 2017; Fig. 2-4). The importance of winter and spring loadings of TP and TSS may be enhanced in the future due to climate change and the likely increase in seasonal storms (Cousino et al., 2015). This emphasis on seasonal loadings in the western Lake Erie watershed indicate that TP and TSS reduction efforts need to target the winter and spring months, which can be problematic if rehabilitation targets ecosystem processes that rely on vegetation growth such as biomass assimilation for phosphorus or entrainment for TSS (Kadlec and Wallace, 2008).
2.4.3 Concentration and Loading Trends during Major Discharge Events
Daily loadings of TP, TSS, and E. coli are driven by daily discharge values such that 10% of those days within a year with discharge >1.2 m3/s (approximately 36 days) 52
(Fig. 2-6) accounted for 58% of the annual TSS load, 60% of the annual TP load, and
85% of the annual E. coli load. Precipitation, which drives discharge, is the primary driver for these loading values (Table 2.1) and the events often last less than a day. For example, on 07/03/08, one 24-hr period accounted for 5% of the annual loading for TP
(0.55 MT) and 23% of the annual loading for E. coli (1.87E+14 CFUs). The WCW provides a good example for consideration. In 2010, 86.7%% of the annual TP load and
90.9% of the annual E. coli load occurred during major discharge events whereas in
2014, 40.8% of the annual TP load and 58.1% of the annual E. coli load occurred during major discharge events (Table 2.1). However, annual loadings for TP, TSS, and E. coli in 2014 ranked among the lowest of the years sampled in the WCW (Table 2.1) further indicating the importance of major discharge when a year has relatively high annual loads. These values are consistent with similar watersheds for E. coli (Jamieson et al.,
2003; Reeves et al., 2004; Simon & Makarewicz, 2009), TP (Banner et al., 2009;
Sharpley et al., 2008), and TSS (Gonzalez-Hidalgo et al., 2010). Additionally, mean daily discharge values correlated with concentrations of TP and TSS (Fig. 2-7), which indicates that reductions of TP and TSS would be more efficient (i.e. greater mass reduction per unit volume) during periods of maximum discharge.
Although the major portion of E. coli loadings occurred during major discharge events, this was observed only in the summer (Fig. 2-4), which was also noted by
Whitman et al. (2006) and Ishii et al. (2007) for other watersheds. This observation may be attributable to the effect of cooler temperatures during non-summer months causing reductions in the densities of E. coli (Byappanahalli et al., 2006; Whitman et al., 2008) in surface waters. Unfortunately annual E. coli loadings may be inaccurate if the critical 53
time periods (i.e. discharge events during summer months) are not captured within the sampling program, for example Gentry et al. (2006) focused water sampling during base flow conditions and likely missed these periods.
Figure 2-6: Mean daily discharge values measured in the WCW. Data were collected at the USGS operated stream gauge station (site no. 04194085). The horizontal solid black line represents the threshold for major discharge events, which was quantified based on the highest 10% of measured discharge values.
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Figure 2-7: Log discharge correlated to log TP, log TSS, and log E. coli in the WCW.
Concentrations of TP and TSS increase with discharge throughout the year however densities of E. coli exhibit a weaker correlation with discharge.
2.4.4 Comparison between WCW and Regional Watersheds
From my point of view, it was important to demonstrate whether this study in the
WCW would provide information that was relevant to rehabilitation efforts for other regional watersheds by comparing data for TP, TSS, E. coli and hydrology: (1) The majority of the annual loadings of TP, TSS, and E. coli occurred during the upper 10% of rates for daily discharge in the WCW and in other watersheds for which data exist (Table
2.2); (2) The majority of the annual loadings of TP and TSS for the WCW as well as the
55
Maumee and the Blanchard Rivers occurred during the winter and spring months (Fig. 2-
4); and (3) The calculated R-B Index value of 0.794 for the WCW indicated that discharge values change abruptly, which also happens in similarly channelized systems
(Baker et al., 2004) such as Rock Creek (0.803, USGS gauge no. 04197170), Mill Creek
(0.651, USGS gauge no. 03220000), McDonald Creek (0.667, USGS gauge no.
05529500), and White Oak Creek (0.954, USGS gauge no. 03238500).
Table 2.2. Percentage of annual loading that occurs during major discharge eventsa for the WCW and comparative watersheds.
TP Watershed E. coli (%) TSS (%) Reference (%) 25 Midwest Streams 66-99 Banner et al., 2009 3 Illinois Rivers >40 Gentry et al., 2007 FD-36, PA 80 Sharpley et al., 2008 Gonzalez-Hidalgo et al., 155 watersheds in OH 55 2010 Graywood Gully, NYb 92 Simon & Makarewicz, 2009 Wolf Creek, OH 60 85 58 This Study aMajor discharge events are defined as the upper 10% of daily discharge rates (~36 days per year). bAlthough not necessarily within our region, Graywood Gully was the most proximal watershed with relevant E. coli data.
2.4.5 Turbidity as a Predictor of General Water Quality
Turbidity values reasonably correlated with E. coli densities (p-value = < 2.2E-
16) and TSS concentrations (p-value = < 2.2E-16), but not with TP concentrations (p- value = 2.86E-08) (Fig. 2-8). The data suggest that turbidity may be a cost-effective substitute to quantify E. coli densities and TSS concentrations. This indicates that future collection of baseline data from other regional watersheds may be able to reduce costs by
56
substituting E. coli and/or TSS analyzes with a turbidity analysis to establish annual, seasonal, and hydrologic trends that are relevant to rehabilitation planning.
Figure 2-8: Log turbidity correlated to log TP, log TSS, and log E. coli. Turbidity has a moderate correlation with TSS and E. coli densities.
3.6 Estimating Water Treatment Capacity for Watershed Rehabilitation
Background water quality data can inform decisions about size and design of watershed rehabilitation projects based on historic conditions within the watershed and recommended targets for load reductions. For example, estimates of discharge and water volume that should be treated within watershed rehabilitation projects can be calculated based on the IJC recommendations of a 40% reduction in annual TP load coupled with
57
loading trends for daily discharge and TP load. A year with elevated precipitation (2011) and a year with drought conditions (2013) were used to estimate water treatment capacity for future rehabilitation (Fig. 2-9 & 2-10). A greater amount of water (16,000,000 m3) carries 39% of the TP in a year with elevated precipitation compared to a year with drought conditions (3,800,000 m3). To treat 16,000,000 m3 of water, a rehabilitation project would need have a large enough capacity to treat discharge values from base flow conditions to 2.1 m3/s compared to a drought year that only requires maximum treated discharge to be 0.3 m3/s. Conceptually these values could be used to design a wetland, for example, that is either fed by pump with a 2.1 m3/s capacity or is driven by pulse-flow with overflow channel set at a height where discharge above 2.1 m3/s bypasses the system. These estimations can help build watershed rehabilitation projects that are cost- effective while also achieving water quality improvement goals. Finally, these estimations are another example of why multiyear data sets are critically important prior to rehabilitation because data from a drought year may underestimate the true size of a rehabilitation project required to achieve water quality improvement goals during a year with elevated discharge. In this case, my estimates from 2013 data indicate a volume of water seven-times less than what would be required for 2011.
58
Figure 2-9: Discharge (black), theoretical discharge (blue) entering a hypothetical watershed rehabilitation project, and TP concentration (green) in 2011. All discharge values at or below the black line (major discharge threshold of 2.1 m3/s) represent 7.3
MT of the year’s total 17.85 MT of load. To treat 39% of TP we would need to treat water from baseflow to 2.1 m3/s or 16,000,000 m3 of water throughout the year.
59
Figure 2-10: Discharge (black) and theoretical discharge entering wetland rehabilitation project (blue) with TP concentration (green) in 2013. All discharge values at or below black line (major discharge threshold of 2.1 m3/s) represent 2.8 MT of the year’s total
2.92 MT load. To treat 39% of TP we would need to treat water from base flow to 0.3 m3/s or 3,800,000 m3 of water throughout the year.
2.5 Conclusion
This paper highlights the importance of preliminary water quality and hydrologic data within a watershed to help inform the size and design of rehabilitation projects.
Specifically, the variations in annual, seasonal, and event-based loadings of TP, TSS, and
E. coli were utilized to identify three major conclusions: (1) Four years of data collection
60
are required to accurately assess the annual load of TP in the WCW; (2) Data collection throughout the year is necessary to identify seasonal loading trends. A majority of the annual TP and TSS load occurred during the winter and spring months and a majority of annual load occurred in the summer months; (3) Major discharge events cause the majority of loading for TP, TSS, and E. coli in a given year and should be a focus for any data collection program and design of a treatment wetland.
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Chapter 3
Rehabilitation of Ecosystem Processes to Improve Water Quality in a Lake Erie Watershed
3.1 Abstract
Poor water quality is a global issue that affects 1.8 billion people and may result from eutrophication and fecal contamination. My focus has been on two nonpoint source pollutants in Lake Erie: phosphorus and Escherichia coli. Phosphorus contributes to the growth and proliferation of harmful algal blooms. E. coli is an indicator of fecal contamination, particularly at Maumee Bay State Park beach in Oregon, OH. The Wolf
Creek watershed (41 km2) is a contributor of E. coli and phosphorus to the western basin of Lake Erie. To lessen contaminant loadings from Wolf Creek, two rehabilitation projects were implemented within the watershed: a floodplain with in-stream sedimentation basin and an off-stream, subsurface flow wetland with a limestone filtration media. Improvements in water quality were assessed by measuring total suspended sediment (TSS), total phosphorus (TP), dissolved reactive phosphorus (DRP), and E. coli in the water column at the inflow and outflow of each project. Water samples
(collected between July 2014 and September 2017) from the rehabilitated floodplain indicated reductions of TSS from 60.5 to 43.6 mg/L (27.8 %), TP from 0.161 to 0.134 mg/L (16.6 %), DRP from 0.099 to 0.079 mg/L (20.2 %), and E. coli from 1879 to 1136 68
CFUs/100 mL (39.5 %). In addition, sediment cores (collected between November 2014 and November 2015; N = 48) were used to track the accumulation rate of sediment (54.5 m3/yr or 76.8 MT dry weight/yr). Water samples (collected between July 2015 and
September 2017; N = 225) from the inflow and outflow of the rehabilitated wetland indicated further improvements to water quality with reductions of TSS from 103.8 to
8.60 mg/L (91.7 % reduction), TP from 0.112 to 0.029 mg/L (74.1 % reduction), DRP from 0.043 to 0.016 mg/L (46.7 % reduction), and E. coli from 302.6 to 40.1 CFUs
100m/L (86.7 % reduction). Targeted implementation of similar systems in the western
Lake Erie watershed can be designed to achieve International Joint Commission reduction goal of a 40% reduction in the DRP load from the Maumee River.
3.2 Introduction
More than four billion people experience a water shortage for at least one month each year (Mekonnen & Hoekstra, 2016) due in part to changes in global climate
(Gosling & Arnell, 2016), over-utilization of water resources (Liu et al., 2017), and impaired water quality. Human activities are often the source of water quality impairments such as eutrophication (Anderson et al., 2002) and fecal contamination
(Bain et al., 2014). The solutions to impaired water quality require consideration of local factors (Liu et al., 2017), in other words – there is not a one size fits all solution due to variations in local climate and weather (Mimikou et al., 2000), land use (Banner et al.,
2009), contaminant sources and types (Gleick, 1993), soil composition (Hinojosa et al.,
2004), and watershed size and conditions (Mitsch & Gosselink, 2000). Thus, solutions are as varied as agricultural best management practices (Lam et al., 2011), restoration and 69
rehabilitation of degraded ecosystems (Land et al., 2016; Richardson et al., 2011), green
(Yang and Li, 2013) and gray (Reisinger et al., 2019) infrastructure improvements in urban areas, and point source control (Nicholls et al., 1993).
My research focuses on using ecosystem processes to improve water quality in the western basin of Lake Erie, a source of drinking water for millions of people in Ohio
(Bingham et al., 2015) and the basis of a multibillion-dollar tourist industry (GLC, 2014).
The water quality impairments targeted by me are phosphorus levels that result in harmful algal blooms (HABs) (Michalak et al., 2013; Bingham et al., 2015) and densities of Escherichia coli that exceed state regulations (i.e. > 235 colony forming units (CFUs) per 100 mL), specifically at the swimming beaches of Maumee Bay State Park (MBSP)
(Francy, et al., 2005).
Increases in the frequency and severity of HABs in the western basin have been linked to loadings from the Maumee River of dissolved reactive phosphorus (DRP)
(Chaffin et al., 2014; Stumpf et al., 2012), which contributes 20-25% of the annual total phosphorus load to Lake Erie (Baker et al., 2014). The presence of microcystin, a toxin produced by Microcystis aeruginosa in recent HABs, resulted in a three-day, “Do Not
Drink” advisory for tap water in 2014 (Wilson, 2014) for the City of Toledo, which increased water treatment costs by $3,000 per day (Lake Erie Improvement Association,
2012), and caused millions of dollars in economic losses from reduced recreational activities (Bingham et al., 2015). The International Joint Commission (IJC) suggested that the frequency and severity of HABs could be returned to acceptable levels by a 39% reduction in annual total phosphorus (TP) loadings and a 41% reduction in spring time
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DRP loadings. These values were based on observations of phosphorus loadings and concomitant algal blooms from 2007 through 2012 (IJC, 2014).
The elevated densities of E. coli at the beaches of MBSP required postings of “do not swim” advisories for an average of 22 days in each recreational season from 2000 through 2010 (ODH, 2017). The proximate source of E. coli is the Wolf Creek watershed
(WCW) (Francy et al., 2005), which discharges into the marina of MBSP 70 m east of the beaches. The Ohio Environmental Protection Agency identified the Maumee River watershed, which includes the WCW, as an area of concern with beneficial use impairments (BUIs) and associated goals for water quality improvement (Maumee RAP,
2006). The WCW was given a “Beach Closings” BUI with a goal of reducing the number of exceedances of E. coli to less than 19 days in each recreational season for three of five consecutive years. A reduction of E. coli loadings from the WCW could potentially reduce the number of “do not swim” advisories.
Phosphorus that is attached to suspended sediment (i.e. particulate phosphorus;
PP) represents as much as 77% of the annual load of TP in the Maumee River (Baker et al., 2014). Densities of E. coli that are attached to sediment can be 10 to 10,000 times greater than densities of E. coli that are suspended in the water column (Irvine and
Pettibone, 1993; Davies and Bavor, 2000). Reductions of TSS in surface waters are an efficient and cost effective means of removal of both TP (Weiss et al., 2007) and E. coli (Díaz et al., 2012).
Both the “Beach Closings” BUI and recommended phosphorus reduction targets by the IJC provide measurable goals to improve water quality. I propose that these goals can be partially attained via environmental rehabilitation of the region’s channelized 71
watersheds, which would reduce the transport of large quantities of phosphorus (Banner et al., 2009; Baker et al., 2014; Gentry et al., 2007; Sharpley et al., 2008), sediment
(Gonzalez-Hidalgo et al., 2010) and E. coli (Simon & Makarewicz, 2009) to Lake Erie.
I hypothesize that environmental rehabilitation in the WCW will significantly reduce TP, DRP, TSS, and E. coli concentrations in waters flowing through these rehabilitated sections. To test this hypothesis, two rehabilitation projects were installed in the WCW. (1) Rehabilitation of the floodplain and installation of a sediment collection basin were used to increase hydraulic retention time, slow water velocity, and encourage deposition of sediment, particulate phosphorus, and E. coli (Powell et al.,
2007; Davis et al., 2015). (2) Downstream of this area, a subsurface-flow wetland was constructed using limestone as a sorbent for DRP (Vohla, et al., 2011; Vymazal, 2007) and E. coli (Decamp and Warren, 2000; Mantovi et al., 2003; Reinoso et al., 2008). The conceptual designs of both projects were based on data for water quality and stream discharge that were collected during the prior seven-years. The design and construction of the rehabilitation projects and a multi-year evaluation of their performance are included in this chapter.
3.3 Materials and Methods
3.3.1 Study Area
The WCW (41 km2) is located in Lucas County, OH, (Hydraulic Unit Code:
041000100705) and discharges into Lake Erie at the MBSP marina, 70 m east of the park’s swimming beaches (Fig. 3-1). In the 19th century, the region in which the WCW exists was known as the Great Black Swamp, which covered a 40 km x 160 km area in 72
northwest Ohio and northeast Indiana (Mitsch & Gosselink, 2007). The regional watershed now contains numerous ditched waterways with input from agricultural drainage tiles and storm drains (ODNR, 2008) that were created to produce suitable agricultural lands.
N
Figure 3-1: Site map of the Wolf Creek (blue lines) and watershed boundries (red lines).
Project locations are identified by red dots.
3.3.2 Floodplain Rehabilitation
Both sides of a 240-m stretch of Wolf Creek, located approximately 2.4 km upstream of the shoreline of Lake Erie (Fig. 3-2), were expanded in June/July of 2014
73
from a 3.5 m-wide channel to a 20-m wide floodplain. A 150-m long sediment collection basin was installed in the center of the floodplain to a depth of 3-m below the former channel bed. The alterations to the channel’s width and depth increased the volume of the channel from 47,200 m3 to 283,000 m3 during periods of base flow (includes the sedimentation basin) and from 201,000 m3 to 702,000 m3 during periods of high flow – defined as when discharge >2.1 m3/s, which corresponded to the 90th percentile of discharge. Creek banks and floodplain were seeded in July 2014 with an emergent wetland seed mix (Appendix A) purchased from Cardno Native Plant Nursery
(Walkerton, Indiana) and applied at a rate of 22 kg/ha with a cover crop of grain rye at 35 kg/ha. Silt fences were installed between the stream channel and the new floodplain to prevent bank erosion until vegetation was established.
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N
Figure 3-2: Overhead and cross-section of rehabilitated floodplain and sedimentation basin. The Wolf Creek channel (3.5 m width) was expanded with a 10 m floodplain on either side along a 240 m section. The sedimentation basin was excavated 3 m below the previous creek bed for a 150 m stretch of the rehabilitated area.
3.3.3 Wetland Rehabilitation
A degraded wetland located next to Wolf Creek and 1.0 km upstream of the Lake
Erie shoreline was rehabilitated in June 2014. Soil was excavated to establish an off- channel pond (3,000 m2 x 3 m deep) that was hydraulically connected to Wolf Creek
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(Fig. 3-3). The pond serves as a source for water pumped into a cascading system of three wetland cells (each 2,800 m2) that were created by excavating soil to a depth of 0.9 m and backfilling with 0.6 m of washed limestone gravel (2 – 2.5 cm diameter) that was overlain with 0.1 m of course aggregate (0.2 – 0.5 cm diameter) and topped with 0.2 m of on-site soil. The top soil layer was shallow to allow plant roots to penetrate the washed limestone gravel and allow for biomass accumulation of phosphorus. Water distribution and collection pipes (Fig. 3-4) were installed along the length of each wetland cell prior to the addition of the limestone. Erosion control was achieved with matting, compost socks, and the addition of three seed mixtures: (1) an emergent wetland mixture was used around the off-channel pond and the lowest, third wetland cell; (2) a wet meadow seed mixture was used for the elevated first and second wetland cells; and (3) a dry shortgrass prairie mix was used on the observational hill containing the remaining soil excavated from the off-channel pond (Appendix A).
76
N
Figure 3-3: Overhead view of the rehabilitated wetland and off-channel pond along Wolf
Creek. The off-channel pond has a surface area of 3,000 m2 with a 3 m depth. Each wetland cell is ~0.2 ha with the entire project area occupying 4.0 ha of land. Water is pumped from the off-channel pond to water level control structure (WLCS)-1 and flows through the three wetland cells before discharging back into Wolf Creek at WLCS-4. A parking lot and observation hill allow visitors to tour the site.
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Figure 3-4: Cross-section of the first wetland cell. Water flows from water level control structure (WLCS) 1 through the subsurface medium of wetland cell 1 and into WLCS-2.
Wetland cell 2 and 3 are essentially identical to cell 1 in hydrology and design with the exception of decreased elevations from cell 1 to 3.
Starting in August of 2015, after seed germination and growth (Fig. 3-5), water was pumped at a rate of 190 L/min (Sulzer Ltd., ABS Submersible Sewage Pump,
XFP80C-201G, 230V at 1740 RPM) to the first of four water level control structures
(WLCS) used to maintain water levels within each wetland cell (Fig. 3-4). Within
WLCS-1, water flows over a riser at an elevation of 175.5 m above sea level (MASL), through the distribution pipes, into the limestone bed of Cell-1 and then passes to a collection pipe that enters WLCS-2 (175.0 MASL). Water then passes through Cell-2, enters WLCS-3 (174.7 MASL), passes through Cell-3, enters WLCS-4 (174.4 MASL) and finally re-enters Wolf Creek.
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Figure 3-5: The (top) photo was taken in May 2014 near WLCS-1 and depicts the three unfinished wetland cells filled with washed limestone gravel and course aggregate. The
(bottom) photo taken in August 2015 depicts the wetland cells one year after construction was completed.
3.3.4 Sample Collection
3.3.4.1 Rehabilitated Floodplain
Water samples (500 mL sterile polypropylene bottles; 15 cm below water surface) were collected weekly from July 2014 through September 2017 from the center of the stream channel immediately upstream and downstream of the rehabilitated floodplain
(Fig. 3-2) using a swing sampler (Nasco Swing Sampler, Fort Atkinson, WI). Triplicate sediment cores from six locations in the sedimentation bed (Fig. 3-2) were collected using a gravity corer (Aquatic Research Instruments, Hope, ID) on six dates from
79
11/2014 through 11/2015. Sediment cores were collected by lowering the sediment corer until the clay layer at the bottom of the sedimentation basin halted vertical movement.
The corer length (0.61 m) was sufficient to collect a full sediment profile because sediment depth did not exceed 12 cm during my study period. After collection, cores were transported on ice to the Lake Erie Center and analyzed for depth, TP, and E. coli.
3.3.4.2 Rehabilitated Wetland
Water samples were collected weekly from each of the four WLCS from July
2015 through September 2017, transported to the laboratory on ice, and analyzed for
DRP, TP, TSS, and E. coli. Soil erosion between wetland cell 3 and the off-channel pond in August 2015 made water samples collected from WLCS-4 unusable thereafter.
3.3.5 Sample Analyses
3.3.5.1 Analysis of Water
Water samples were analyzed as follows: DRP (mg/L) and TP (mg/L) - USEPA method 365.2; E. coli (CFUs/100 mL) - USEPA Method 1603; TSS (mg/L) - Standard
Method 2540-D (Rice et al. 2012). Quality assurance included replicate samples for 10% of all analyses and sample blanks. Our laboratory participates in the U.S. Geological
Survey sponsored Phosphorus Data and Quality Control Program
(https://bqs.usgs.gov/srs/) and received an acceptable performance evaluation during this study.
3.3.5.2 Analysis of Sediment
Sediment cores were measured for depth of sediment (cm) using a ruler to determine distance between clay layer and top of sediment, TP (mg/g) using triplicate 80
subsamples (10 mg each) with the persulfate method (Ostrofsky, 2012), and E. coli
(CFUs/gram dry weight (gdw)) using triplicate subsamples (20 g each) with the bed- sediment method (Myers, 2007).
3.3.6 Analysis of data
The mean values for TP, TSS, and E. coli were calculated for the duration of the sampling period for each sampling location in the rehabilitated floodplain and wetland to determine the effects of rehabilitation as the percent change (C%) of TP, TSS, DRP, and
E. coli within Wetland Cell 1 (i.e. between WLCS-1 and 2), Wetland Cell 2 (i.e. between
WLCS-2 and 3) and the floodplain (i.e. between upstream and downstream measurements) using Eq. (3):
(퐶1−퐶2) 퐶% = × 100 (1) 퐶1 where C1 is the concentration at the inflow, and C2 is the concentration at the outflow.
An analysis of the effects of Wetland Cell 3 was not possible, due to the aforementioned erosion. A paired ANOVA was used to determine if the mean water quality values were statistically different (α = 0.05) between upstream and downstream locations of the floodplain, between WLCS-1 and WLCS-2, and between WLCS-2 and WLCS-3 of the wetland. All calculations and statistical analyses were performed with R Project software
(R Core Team 2018; Version 1.0.153).
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3.4 Results
3.4.1 Rehabilitated Floodplain
Results were evaluated based on discharge conditions and seasonal trends to determine the ability of the floodplain to improve water quality during periods when loadings in WCW are elevated. Rehabilitation of the floodplain increased theoretical hydraulic retention time from 4.9 to 29.1 h (hydraulic retention time = basin volume
(m3)/discharge (m3/s)) during base flow conditions (discharge = 0.08 m3/s) and from 1.3 to 4.6 h during major discharge events (discharge = 1.2 m3/s). The changes in these hydraulic retention times resulted in significant changes to mean densities of E. coli and concentrations of TSS, DRP, and TP at the inlet and outlet of the floodplain throughout the sampling period (Table 3.1).
Table 3.1. Mean values for densities of E. coli and concentrations of TSS, DRP, and TP at upstream and downstream locations in rehabilitated floodplain as a three-year mean and during base flow and major discharge events. (N=147).a
Three-Year Means Parameters Upstream Downstream Reduction (%) p-value E. coli (CFUs/100 mL) 1842 1100 40.3 1.80x10-06 TSS (mg/L) 55 40.4 26.5 6.65x10-03 DRP (mg/L) 0.097 0.078 19.5 1.08x10-04 TP (mg/L) 0.149 0.121 19.3 9.67x10-05 Base Flow (<1.2 m3/s) Major Discharge Events (>1.2 m3/s) Upstream Downstream Difference Upstream Downstream Difference E. coli (CFUs/100 mL) 1841 1086 755 3140 2760 380 TSS (mg/L) 54.4 38.9 15.5 259.1 201.8 57.3 DRP (mg/L) 0.097 0.077 0.02 0.146 0.144 0.002 TP (mg/L) 0.153 0.127 0.026 0.419 0.371 0.048 a Data collected from July 2014 through September 2017.
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A comparison of water quality at the inlet and outlet of the floodplain during base flow conditions and major discharge events (Table 3.1) indicates that the increase in hydraulic retention time results in statistically significant reductions (p < 0.05) of E. coli,
TSS, and TP during both base flow conditions and major discharge events whereas DRP is only significantly reduced during base flow conditions. This observation is reasonable considering DRP is not attached to sediment and thus is not influenced by sedimentation.
During base flow conditions, DRP is likely being retained via biomass assimilation
(Davis et al., 2015), which may not be a reliable source for DRP retention when flow rates increase during major discharge events.
Seasonal changes in discharge, concentrations of TP, DRP, and TSS, and densities of E. coli led to variations in water quality improvements throughout the year
(Fig. 3-6). TP, DRP, TSS, and E. coli experience greater reductions during the summer months when discharge is relatively low. In general, reduction percentages increased with concentration of TP, DRP, and TSS and densities of E. coli all of which were elevated in July, Aug., and Sept. Whitman et al. (2008) observed a similar trend in which
E. coli reductions in a recently ponded stream were greatest during the late-summer.
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A B 3500 80 0.2 60 3000 60 40 0.15 2500 40 2000 20 20 0.1 1500 0 0
1000 DRP (mg/L) Reduction(%) Reduction(%) 0.05 -20
500 -20 E. E. (CFUs/100 coli mL) 0 -40 0 -40 1 3 4 5 6 7 8 9 101112 1 3 4 5 6 7 8 9 101112
C D 120 80 0.3 50 100 60 0.25 40 80 40 0.2 30 20 60 20 0.15 10
40 0 0.1 TP (mg/L) TP
TSS TSS (mg/L) 0 Reduction(%) 20 -20 0.05 -10 Reduction(%) 0 -40 0 -20 1 3 4 5 6 7 8 9 101112 1 3 4 5 6 7 8 9 101112 Month Month
Figure 3-6. Seasonal changes and reduction percentage (dark gray line) in E. coli (A),
DRP (B), TSS (C), and TP (D) between the inlet (light gray) and outlet (dark gray) of the rehabilitated floodplain by month. Mean values for each month were calculated from the three year data set between 2014 and 2017. No samples were collected in the month of
Feb. because Wolf Creek is generally frozen at that time.
Evidence of sediment deposition was observed from sediment cores, which indicated a mean sediment accumulation of 12.9 cm +/- 4.4 of depth (volume of 54.5 m3
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and mass of 76.8 MT) in the sedimentation basin (1.08 cm/month) between Nov 2014 and Nov 2015 (Fig. 3-7). Sediment depth increased between Nov 2014 and Jun 2015 with the depth of sediment greatest at the basin inlet. Accumulation was negligible between Jun 2015 and Sep 2015, which corresponds with reduced TSS transport in Wolf
Creek during the summer months. During that time the depth of sediment equalized between the three locations. Sediment depth increased from Sep 2015 to Nov 2015 and once again the inlet location contained the largest depth of sediment. Among all locations and all sampling dates, total phosphorus existed within sediment at a range of
0.65 – 1.24 mg/g sediment (average of 1.03 mg/g sediment). By contrast, densities of E. coli in accumulated sediment were greatest on 7/2015 (6025 CFUs/GDW) and 9/2015
(7048 CFUs/GDW) with the fewest densities observed on 11/2015 (1103 CFUs/GDW).
This observation corresponds with water quality data in that E. coli densities were elevated during the summer months compared to the fall, winter, or spring.
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20 18 16 14 12 Inlet 10 Center 8 Outlet 6 Average
Depthof Sediment (cm) 4 2 0 Nov-14 Jun-15 Jul-15 Aug-15 Sep-15 Nov-15
Figure 3-7. Depth of sediment (cm) in the sedimentation basin at the inlet, center, and outlet locations through time. Only center location was collected on 11/2014. Black triangles indicate the average of all three locations for each sampling date. Error bars represent +/- one standard deviation.
3.4.2 Rehabilitated Wetland
Statistically significant reductions of TP, TSS, E. coli, and DRP were observed between WLCS-1 and WLCS-3 in the first two years within the wetland ranging from a
62.8 % reduction to a 91.7 % reduction (Table 3.2). Within the first wetland cell
(WLCS-1 to WLCS-2) all water quality parameters were significantly reduced. Within the second wetland cell (WLCS-2 to WLCS-3) TSS and DRP were significantly reduced further. Reductions of TSS, TP, DRP, and E. coli primarily occurred in the first wetland cell.
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Table 3.2. The rehabilitated wetland improved water quality. Mean values of E. coli,
TSS, DRP, and TP observed from within WLCS-1, 2, and 3 and total reduction percentage for each water quality parameter between WLCS-1 and WLCS-3 (N=225).
Location E. coli (CFUs/100 mL) TSS (mg/L) DRP (mg/L) TP (mg/L) WLCS-1 302.6 103.8 0.043 0.112 WLCS-2 39.8a 12.2a 0.024a 0.038a WLCS-3 40.1 8.60a 0.016a 0.029 Total Reductions (%) 86.7 91.7 62.8 74.1 a Indicates a statistically significant (p < 0.05) reduction from the previous sampling location
One mechanism for DRP retention within the wetland is adsorption to the limestone media, which depends on DRP concentrations related to the equilibrium phosphorus concentration (EPC0) – theoretical concentration of dissolved phosphorus in water when limestone no longer adsorbs or desorbs phosphorus as shown by an adsorption isotherm (Reddy et al., 1999; Fig. 3-8). The EPC0 value of 0.026 mg/L indicates that when inflow concentrations of DRP are below 0.026 mg/L then limestone within the constructed wetland no longer adsorbs DRP. However, the higher the concentration of phosphorus that enters the wetland the more adsorption that will occur per until volume until the EPC0 value is reached. Thus, during periods of elevated discharge when phosphorus concentrations increase, the wetland’s ability to adsorb phosphorus also increases. This interaction will occur until the limestone becomes saturated with phosphorus, which is equivalent to the adsorption maxima, which was estimated to be 1.1 mg P/g limestone (Vohla et al., 2011).
TP, TSS, and E. coli are also removed within the wetland with a similar trend
(Fig. 3-9). I chose to focus on removal trends within the first wetland cell because water
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quality improvement was so successful within the first wetland cell (Table 3.3) that measurements for TP, DRP, TSS, and E. coli were often close to zero with little or no availability for water quality to improve further. TSS, TP, and E. coli removal within the first wetland cell improved per unit volume with initial concentration but did not exhibit an equilibrium threshold and thus these models (Fig. 3-9) trended toward zero as the final outflow concentration. These removal trends were utilized to evaluate the annual retention of TP, DRP, TSS, and E. coli by the rehabilitated wetland based on water quality in Wolf Creek. For example, the wetland improved water quality between
WLCS-1 and WLCS-3 during a precipitation event (3/28/2016 - 3/29/2016): Mean E. coli densities reduced from 657 to 79 CFUs/ 100 mL (88 % reduction, p-value = 1.78E-05),
TSS concentrations reduced from 396 to 11.4 mg/L (97 % reduction, p-value = 1.28E-
07), TP reduced from 0.50 to 0.03 mg/L (94 % reduction, p-value = 3.56E-06), and DRP reduced from 0.16 to 0.02 mg/L (88 % reduction, p-value = 8.88E-04).
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Figure 3-8. Linear regression for DRP removal and initial DRP concentration that occurred in the rehabilitated wetland. The point which zero DRP removal occurs (0 mg/L on y-axis) indicates the equilibrium point (EPC0) where adsorption and desorption do not occur.
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Figure 3-9. Linear models of water quality improvement based on concentration/density of contaminant flowing in and out of the first wetland cell.
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3.5 Discussion
3.5.1 Dissolved Reactive Phosphorus
The expansion of modern stream rehabilitation in the mid-1980s provided an opportunity for watershed managers to utilize ecosystem processes in order to address phosphorus contamination (Lave, 2012). The major DRP removal processes within a watershed are adsorption (Litaor et al., 2004, 2005; Sade et al., 2010) and biomass assimilation (Hoffman et al., 2009; Kronvang et al., 2009; Vymazal, 2007). These processes drive the reduction of DRP within wetland and floodplain rehabilitation projects. For example, average DRP retention within rehabilitated wetlands can range from -19 to 89% (Braskerud et al., 2005) whereas floodplain rehabilitation reduces DRP by 11% (Hodaj et al., 2017) and 23% (Mahl et al., 2015). The degree of variability for
DRP reduction among rehabilitated wetlands is attributed to several factors including inlet concentration, retention time, sorptive media, and wetland age (Land et al., 2016;
Vymazal, 2007). Under conditions when inlet concentrations and retention times are relatively low without an appropriate sorptive media, a newly created wetland will likely have difficulty reducing significant amounts of DRP as evidenced by a constructed wetland treating agricultural drainage in Sweden that reduced DRP by 2% during a 2- year study (Reinhardt et al., 2005). Few wetlands have reported utilization of a sorbent material to enhance reduction of DRP. Gravel yielded a 4.33% reduction (Korkusuz et al., 2005), wollastonite tailings yielded a 27.5% reduction (Hill et al., 2000), and iron oxide yielded a 75.2% reduction (Martin et al., 2013). Iron oxide appears to yield the most efficient DRP removal in a full-scale setting however, the inlet concentration of
DRP was 0.521 mg P/L (Martin et al., 2013), which is more than ten times that of the 91
WCW and likely inflates the reduction percentage (Kadlec, 1999). The WCW wetland appears to be a novel combination of a full-scale wetland exposed to relatively low inlet concentrations of DRP combined with a phosphorus sorbent that yielded substantial removal percentages.
DRP removal within a wetland via adsorption is restricted by the EPC0 of the sorbent material limiting the minimum DRP concentration that can occur within outlet flow. The rehabilitated wetland in WCW achieved a DRP concentration of 0.016 mg P/L at the outlet (Table 3.2). This concentration value is similar to the prolonged levels of
0.01 mg P/L of DRP recommended for Lake Erie that would limit the growth of HABs
(OEPA, 2013). Other studies indicate that similarly low concentration values at wetland outlets are difficult to achieve (Tanner et al., 2005; Vellidis et al., 2003). Unsurprisingly, reduction of DRP per liter is higher when inlet concentrations are elevated (Fig. 3-7), which correlate to discharge (i.e. precipitation events) within the WCW. Thus, DRP reductions in the Wolf Creek wetland are most efficient (highest DRP reduction per unit volume water) during major discharge events. The DRP concentration is statistically significant between WLCS-1 and WLCS-2 as well as WLCS-2 and WLCS-3 indicating that wetland cell 1 and 2 provide valuable treatment capacity compared to removal of
TSS, TP, and E. coli all of which primarily occurs in wetland cell 1 (Table 3.2). An extrapolation of the limestone adsorption maxima of 1.1 mg P/g to the total mass of limestone gravel within wetland cell-1 and 2 (4,700 MT) yields the potential for the wetland to retain 5.6 MT (14 MT/ha) of DRP by adsorption compared to the range of 10
– 100 kg/ha that Kronvang et al. (2009) estimates for restored wetlands without adsorption media. The depth of limestone layer and efficiency to adsorb phosphorus in 92
the Wolf Creek wetland drastically increase the efficiency to reduce phosphorus per area of land.
Rehabilitated floodplains have limited ability to reduce DRP and sometimes become a source (Davis et al., 2015). This limited potential is likely dependent on the presence of fine sediments, which increase the ability of soils to adsorb DRP (Meyer,
1979; Newbold et al., 1983a, b), and rate of biomass assimilation (Roley et al., 2014).
Sediment within the WCW is composed of 42% sand, 55% silt, and 3% clay (Francy et al., 2005) and may contribute to the 19.5% reduction of DRP observed in the rehabilitated floodplain. Interestingly, Davis et al. (2015) determined that a lower floodplain height resulted in more frequent inundation events (10.6 events/yr) throughout the year leads to an increase in the amount of DRP reduction that occurred. The WCW experienced 31 major discharge events per year, which may contribute to DRP reduction within the rehabilitated floodplain; however, DRP reductions during major discharge events were negligible (Table 3.1) indicating that DRP reductions were primarily occurring during base flow conditions. Biomass assimilation of DRP is the most likely cause for DRP reductions evidenced by the increase in reduction to 40% during the summer months corresponding to the growing season compared to reductions between -
30 and 5% during all other times of the year (Fig. 3-6).
3.5.2 Total Phosphorus
TP is composed of both PP and DRP and is thus impacted by adsorption and biomass assimilation similar to DRP as well as sedimentation (Litaor et al., 2004, 2005;
Sade et al., 2010; Schoumans et al., 2014). Sedimentation is promoted by reducing water velocity or inversely increasing water retention time, which De Klein and Koelmans 93
(2011) estimate would reduce 15 - 20% of TP if retention time was increased by 50% within a watershed. Both floodplain and wetland rehabilitation reduce water flow and are thus logical strategies to reduce TP.
The Wolf Creek wetland reduced TP by a mean of 74.1% compared to the 41.1% mean reduction value for 149 subsurface flow wetlands in a review by Vymazal (2007).
The elevated reduction value is likely attributed to age of the wetland and the presence of fresh limestone media (Vohla et al., 2011) that improved adsorption. Additionally, the consistent inflow rate also increases sedimentation and filtration as opposed to pulse inflow conditions (Land et al., 2016). TP reductions primarily occurred within Wetland
Cell 1 compared to DRP reductions, which exhibited statistically significant reductions in
Wetland Cell 1 and 2 (Table 3.2). Reductions of PP and by extension TP within subsurface wetland systems are more reliable than DRP reductions given that sorption is a reversible process whereas sedimentation and filtration are semi-permanent (Hoffman et al., 2009). TP reductions increase with inflow TP concentrations (Fig. 3-9), which correlate with discharge in the WCW. Thus, wetlands with a similar design will be more efficient at reducing TP per unit volume of water during major discharge events or in environments that inherently have elevated TP concentrations.
The rehabilitated floodplain reduced TP by 19.3 % compared to the 12.6 %, -8.2
%, -1.7 %, and 8.2% reductions observed in four floodplain rehabilitation projects in northern Indiana (Davis et al., 2015). This discrepancy between reduction percentages can partially be attributed to floodplain age (Indiana floodplains were between 4 and 8 years old compared to the newly created Wolf Creek floodplain) however the Indiana floodplains did not contain a sedimentation basin, which will increase the longevity of 94
sedimentation due to the larger capacity to store sediment. The inclusion of a sedimentation basin is a novel aspect of floodplain rehabilitation and is one of the drivers for improved TP reductions. TP reductions increase during the summer months (Fig. 3-
6), which indicates that sediment entrainment or biomass assimilation promoted by vegetation also contributes to the reduction of TP (Powell et al., 2007). The sedimentation basin accumulated 79.1 kg of PP within accumulated sediment (76.8 MT) between Nov. 2014 and Nov. 2015. By extrapolating the sediment accumulation rate, I anticipate the PP mass will steadily increase within the sedimentation basin contributing to overall TP reductions for 20 years but accumulation rates will likely decrease as the sedimentation basin approaches capacity in later years.
3.5.3 Escherichia coli
E. coli preferentially attaches to sediment within surface waters but may also exist suspended within the water column (Boutilier et al., 2009; Jeng et al., 2005).
Sedimentation, filtration, and adsorption are the primary ecosystem processes that promote the retention of E. coli (Boutilier et al., 2009). The Wolf Creek wetland promotes filtration and adsorption whereas the rehabilitated floodplain promotes sedimentation.
Reductions of E. coli within the Wolf Creek wetland (86.7 %) were comparable to other wetlands (Knox et al., 2008; Molleda et al., 2008; Ulrich et al., 2005). E. coli densities decreased, on average, from 302.6 to 39.8 CFUs/100 mL (Table 3.2) within wetland cell 1 achieving the 235 CFUs/100 mL threshold recommended by the EPA for primary contact. Reduction values also increase with inflow densities (Fig. 3-9) limiting
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contamination of downstream water bodies traditionally observed during major discharge events in summer months (Ishii et al., 2007; Whitman et al., 2006).
The rehabilitated floodplain reduced E. coli densities by 40.3 % (Table 3.1) similar to the 46 % reduction by an artificially ponded stream in in northwest Indiana
(Whitman et al., 2008). Major discharge events corresponded to elevated densities of E. coli entering the rehabilitated floodplain, which is observed in other Midwest watersheds
(Olyphant et al., 2003). These discharge events and subsequent increase in velocity and decrease in retention time within the rehabilitated floodplain cause a decrease in E. coli reductions compared to base flow (Table 3.1) indicating sedimentation is the major ecosystem process affecting E. coli reductions. E. coli densities and reductions appear to exhibit seasonal trends with relatively low values in the winter months and elevated values in the summer and fall months (Fig. 3-6). This trend is observed by Whitman et al. (2008) and driven by the increased availability of E. coli in the summer and fall months due to warm weather conditions leading to greater reduction potential.
3.5.4 Total Suspended Solids
Suspended solids are removed from the water column via sedimentation and filtration. The rehabilitated floodplain promotes sedimentation whereas the wetland promotes filtration thus the two systems complement each other to maximize sediment reductions.
Mean sediment reductions (91.7 %) within the wetland are higher than the 17 - 60
% reduction values observed in surface flow wetlands (Ockenden et al., 2012) and reduction values of 46 - 88 % observed in subsurface flow wetlands (Karathanasis et al.,
2003). These higher values may be due to the young age of the wetland and have been 96
shown to decline over time (citations). TSS removal rate remained consistent throughout the year and was independent of inflow concentration (Fig. 3-9) indicating that filtration was the primary mechanism of removal compared to other biological processes (i.e. microbial metabolism of organics).
The rehabilitated floodplain reduced TSS by an average of 26.5% (Table 3.1), which is higher compared to other rehabilitated floodplain projects (sampled between 4 and 8 years after construction) that exhibited negligible reductions in TSS (Davis et al.,
2015). The efficiency of the rehabilitated floodplain in the WCW is likely attributable to the inclusion of a sedimentation basin, which traps TSS by preventing resuspension of particles during major discharge events, a common occurrence in surface flow wetlands and floodplain rehabilitation projects (Braskerud, 2002). Resuspension did not appear to influence accumulation of sediment within the sedimentation basin evidenced by the positive accumulation of sediment between Nov. 2014 and Nov. 2015 (Fig. 3-7).
3.5.5 Implications and Treatment Scenarios in the Maumee River
The rehabilitated floodplain and wetland each provide ecosystem services that benefit water quality. A combination of these two systems help address water quality impairments during base flow and major discharge events. In general, contaminant reductions are greater during base flow for the rehabilitated floodplain due to an increase in water velocity during major discharge events, which reduce the potential for sedimentation to occur (Table 3.1). Contaminant reductions increase within the wetland system during major discharge events compared to base flow because concentrations of each parameter increase resulting in greater reduction potential via adsorption and filtration within the subsurface (Fig. 3-9). The Maumee River exhibits similar loading 97
dynamics as the WCW in that major discharge events result in elevated contaminant concentrations and ultimately contribute the most to annual loadings. I used the DRP adsorption model for the rehabilitated wetland (Fig. 3-8) to estimate the load reductions experienced by the Maumee River if this system were scaled for a larger watershed. The
Wolf Creek wetland is a model system for our region and offers more realistic estimations of DRP retention compared to results from laboratory or microcosm studies
(Prochaska & Zouboulis, 2006). Load reductions were modeled for three scenarios: (1) continuous treatment throughout the year, (2) targeted treatment during major discharge events when DRP concentrations are elevated, and (3) continuous treatment only in the spring months (Table 3.4): Scenario (1) and (2) utilized the 39% reduction target for annual DRP load and scenario (3) utilized the 41% reduction target for springtime DRP load both recommended by the IJC (IJC, 2014). Discharge and concentration data from
2011 and 2012 collected in the Maumee were used to represent a year with a relatively large DRP load and corresponding HAB (2011) compared to a year with relatively small
DRP load and limited to no HAB growth (2012) (NCWQR, 2017).
Scenario (1) utilizes a bottom-up approach for discharge in that water from the
Maumee River will continuously flow into a theoretical wetland 365 days a year and to achieve a 39% DRP reduction this flow rate is increased until sufficient DRP mass enters the wetland and is retained based on the Wolf Creek wetland reduction potential (Fig. 3-
7). This inflow rate was calculated to be 368 m3/s and 198 m3/s for 2011 and 2012 respectively. The inflow rate is considered a maximum value because the inflow rate was set to the Maumee River discharge on days when river discharge was lower than 368 m3/s or 198 m3/s for 2011 and 2012, respectively. Inflow DRP load was calculated based on 98
the inflow rate and DRP concentration in the Maumee for each day and totaled for the year. Outflow DRP load was determined based on the DRP adsorption isotherm from the
Wolf Creek wetland (Fig. 3-7), inflow concentration and rate for each day, and totaled for the year. Ultimately to achieve a 39% DRP reduction in 2011 (retention of 353 MT of
DRP), 465 of the 904 MT of annual DRP must enter the wetland for treatment, which can be achieved if the maximum inflow rate is set at 368 m3/s resulting in an outflow load of
112 MT (mean annual reduction of 72 mg of P/m3 of water). Based on the adsorption capacity for limestone gravel (1.2 mg P/g limestone) within the Wolf Creek wetland,
290,000 MT of limestone is needed per year to adsorb the 353 MT of DRP for 2011. By comparison, treatment requirements for 2012 (retention of 198 MT of DRP) are considerably less given annual DRP load is only 199 MT thus a 39% reduction can be achieved with an inflow rate of 198 m3/s resulting in a reduction of 78 MT of DRP.
Scenario (2) targets major discharge events when DRP concentrations are elevated in an attempt to increase DRP adsorption per volume of water treated effectively improving the efficiency of the wetland. In this scenario the 39% reduction can be achieved during a 39 and 24 day period throughout the year when major discharge events occur. During these times the mean concentration of DRP (0.10 mg/L for 2011 and 0.09 mg/L for 2012) increases compared to the annual mean (0.081 mg/L for 2011 and 0.048 mg/L for 2012) resulting in a decrease of total volume required to be treated by the wetland (4.5 x 109 m3 for 2011 and 1.1 x 109 for 2012) but an increase in the maximum inflow rate (2,300 m3/s for 2011 and 890 m3/s for 2012). The mass of limestone required for this scenario is equivalent to scenario (1) given that the same DRP reduction target
(353 MT for 2012 and 198 MT for 2012) is required for a 39% reduction in both 99
scenarios but treatment efficiency (DRP adsorption per volume of water - 78 mg/m3 for
2012 and 71 mg/m3 for 2012) is increased for scenario (2).
Scenario (3) utilizes the same treatment approach as scenario (1) but only for the spring months (Mar 1 to Jun 30) with a 41% DRP reduction target. In this scenario, DRP reduction mass (176 MT for 2011 and 25 MT for 2012) is achieved with an inflow rate of
510 m3/s and 255 m3/s for 2011 and 2012, respectively, resulting in treatment of 3.1 x 109 m3 and 7.8 x 108 m3 of water for 2011 and 2012, respectively. The amount of limestone required to adsorb DRP in this scenario is 147,000 MT and 21,000 MT for 2011 and
2012, respectively.
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Table 3.4. A comparison of treatment scenarios to achieve DRP load reductions within the Maumee River watershed based on recommendations from the International Joint
Commission. Each scenario is modeled based on the assumption that the Wolf Creek wetland could be scaled up for the Maumee River and retain similar capacity to reduce
DRP concentrations. Discharge and concentration data from the Maumee River were downloaded from the National Center for Water Quality Research (2017).Add data to show the kind of land requirement needed to treat Maumee discharge under these three scenarios.
2011a 2012 Duration of Treatment (days) 365 365 DRP Load (MT) 904 199 Mean DRP Concentration (mg/L) 0.081 0.048 Scenario 1: DRP Target Load (MT) 551 121 Continuous DRP Reduction Mass (MT) 353 78 treatment Maximum Inflow Rate (m3/s)b 368 198 throughout the 3 9 9 year to achieve Total Volume of Water to be Treated (m ) 4.9x10 1.9x10 39% reduction Inflow DRP Load (MT) 465 125 target Outflow DRP Load (MT) 112 47 DRP Load Reduction within Rehabilitation (MT) 353 78 Mass of Limestone for Adsorption (MT/yr) 290,000 65,000 DRP Reduction per m3 of Water (mg/m3) 72 41 Duration of Treatment (days) 39 24 DRP Load (MT) 447 105 Mean DRP Concentration (mg/L) 0.10 0.09 Scenario 2: DRP Target Load (MT) 94 27 Treatment only DRP Reduction Mass (MT) 353 78 during major Maximum Inflow Rate (m3/s) 2,300 890 discharge 3 9 9 events to Total Volume of Water to be Treated (m ) 4.5x10 1.1x10 achieve 39% Inflow DRP Load (MT) 447 105 reduction target Outflow DRP Load (MT) 94 27 DRP Load Reduction from treatment (MT) 353 78 Mass of Limestone for Adsorption (MT/yr) 290,000 65,000 DRP Reduction per m3 of Water (mg/m3) 78 71 101
2011a 2012 Duration of Treatment (days) 365 365 DRP Load (MT) 429 62 Mean DRP Concentration (mg/L) 0.070 0.040 Scenario 3: DRP Target Load (MT) 253 37 Treatment DRP Reduction Mass (MT) 176 25 during Spring Maximum Inflow Rate (m3/s) 510 255 (Mar. 1 to Jun. 3 9 8 30) months to Total Volume of Water to be Treated (m ) 3.1x10 7.8x10 achieve 41% Inflow DRP Load (MT) 247 44 reduction target Outflow DRP Load (MT) 71 19 DRP Load Reduction from treatment (MT) 176 25 Mass of Limestone for Adsorption (MT/spring) 147,000 21,000 DRP Reduction per m3 of Water (mg/m3) 57 32 a2011 and 2012 were used for this model to provide a year (2011) with relatively high DRP load and subsequent HAB size and a year (2012) with relatively low DRP load and limited HAB growth. bMaximum inflow rate refers to the maximum rate of flow entering a theoretical wetland.
Each scenario (Table 3.4) provides information to estimate the characteristics of a wetland or series of wetlands necessary to achieve DRP reductions recommended by the
IJC. The maximum inflow rate indicates the flow capacity required for a wetland, which will affect the overall wetland size as well as the type of filtration media - larger diameter gravel increases hydraulic conductivity and thus can handle greater flow rates.
Multiple wetlands throughout the Maumee could be created to additively achieve the maximum inflow rate (i.e. 10 wetlands would each need a 36.8 m3/s flow rate for scenario 1 in 2011). However, if wetlands are placed in series downstream of one another, then reduction efficiency of DRP will diminish due to the lower concentrations of DRP entering wetlands furthest downstream. Mass of limestone required for each scenario also impacts the size of a wetland or series of wetlands. A wetland’s capacity for limestone in the subsurface could increase by increasing the overall footprint of the wetland or increasing the depth of limestone layer. It is important to note that two meters 102
of depth is the limit of many wetland plants (Holm et al., 2005) thus biomass accumulation in vegetation will be limited if the limestone layer is below two meters.
Interannual variations for DRP load in the Maumee River are evident based on the comparison between 2011 and 2012 (Table 3.4). This variation directly affects the DRP reduction target since it is based on a 39% reduction as well as the size of a wetland that would achieve this reduction. A wetland designed based on data from 2012 would be inadequate in size and limestone mass to achieve the necessary reductions in 2011.
Therefore, wetlands should be designed based on data from years when DRP loads are elevated and HAB size is relatively large similar to conditions in 2011, which ranks among the highest annual DRP loads for the Maumee (Baker et al., 2014) and was a record-setting year for HAB size (Michalak et al., 2013).
A comparison between treatment scenarios (Table 3.4) is utilized to determine which scenario is more cost-effective or efficient in terms of achieving DRP reduction targets. Scenario (2) offers the most efficient (i.e. 78 mg P/m3 of water in 2011 and 72 mg P/m3 of water) means to reduce DRP but requires the largest inflow rate, which would increase the capacity and size required for the wetland. Scenario (1) requires the lowest inflow rate indicating a smaller wetland relative to the other two scenarios. Inflow rate for scenario (3) (510 m3/s and 255 m3/s for 2011 and 2012 respectively) is larger than scenario (1) (368 m3/s and 198 m3/s for 2011 and 2012 respectively), whereas mass of limestone required for scenario (1) (290,000 MT/yr and 65,000 MT/yr for 2011 and 2012 respectively) is greater than for scenario (3) (147,000 MT/yr and 21,000 MT/yr for 2011 and 2012 respectively). Scenario (3) may be the most cost-effective solution due to the decrease in mass of limestone required per year to achieve one of the IJC 103
recommendations. However, scenario (1) would be the best choice to satisfy my goals by achieving the 39% reduction target for annual DRP load and contribute to the 41% springtime reduction target. In order to include a more meaningful characteristic to
Scenario 1 in 2011, I used the values for mass of limestone and maximum inflow rate to calculate a hypothetical wetland size. In addition to these values, I conservatively estimated hydraulic conductivity of the limestone gravel to be 10 cm/s based on reported values for similarly sized pea gravel (Christianson et al., 2010). The cross sectional area of the wetland subsurface would need to be 3700 m2 to accommodate 368 m3/s at a hydraulic conductivity of 10 cm/s. This translates into a wetland with a 1 meter depth of limestone gravel and 3700 m width at the inflow point. The wetland would then need to be 54 meters in length to achieve the necessary amount of limestone gravel (290,000 MT at 1.45 g/cm3) to reduce DRP by 40% in a single year. The required limestone mass of
14,500,00 MT is necessary if I multiply the amount of limestone in a single year by 50 to estimate the amount of limestone necessary for 50 years of DRP removal at a 40% reduction rate per year. Thus, the wetland dimensions to contain enough limestone for 50 years of DRP removal would need to be 1 m deep, 3700 m in width and 2700 m in length. The footprint of this wetland would be 1000 ha (10 km2). This type of calculation is only relevant to add an additional characteristic to the Scenario 1 model and does not factor in processes that will likely impact or diminish DRP removal rates such as changes in hydraulic head, wetland clogging, changes in climate, etc.
In all three scenarios the required mass of limestone only has a lifespan of one- year and would need to be replaced or recharged in subsequent years. Conceptually this
104
can further inform wetland design to include vegetation that take up DRP and partially recharge limestone adsorption sites (Hylander et al., 2006; Kvarnström et al., 2004).
3.6 Conclusion
The rehabilitated floodplain and wetland yielded significant reductions in TSS, E. coli, DRP, and TP concentrations within the Wolf Creek watershed. Major discharge events caused diminished water quality improvements in the floodplain due to an increase in water velocity. However, these discharge events increased concentrations of TSS, TP, and DRP, which caused the wetland to improve water quality more efficiently (higher reduction per unit volume) compared to base flow conditions. Results were scaled and modeled using data from the Maumee River under three treatment scenarios to reduce loadings of DRP according to IJC recommendations. Model results indicate that multiyear data sets of water quality are essential to design effective rehabilitation projects for long-term success. Inflow rate and adsorptive material mass for a rehabilitation project within the Maumee River dictate that a 1000 ha wetland is necessary to achieve substantial annual and springtime DRP target loads.
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Appendix A
List and ratios of seeds planted in the rehabilitation areas
Table A.1. List and ratios of species seeded in the rehabilitated floodplain and wetland.
Dry Shortgrass Mix *Planted in upland areas Amount Species (Common Name - Scientific Name) (Ratio) 16 Sideoats Grama - Bouteloua curtipendula 8 Prairie Brome - Bromus kalmii 16 Wild Rye - Elymus canadensis 0.3 Junegrass - Koeleria cristata 1 Switch Grass - Panicum virgatum 30.5 Little Bluestem - Schizachyrium scoparium 0.2 Prairie Dropseed - Sporobolus heterolepis 0.1 Windflower - Anemone cylindrica 2 Butterfly Weed - Asclepias tuberosa 0.5 Smooth Aster - Aster laevis 2 Arrow-Leaved Aster - Aster sagittifolius 2 White Wild Indigo - Baptisia leucantha 1 Partridge Pea - Chamaecrista fasciculate 4 Lanceleaf Coreopsis - Coreopsis lanceolata 0.2 Tick Trefoil - Desmodium illinoense 4.3 Purple Coneflower - Echinacea purpurea 2 Rattlesnake-Master - Eryngium yuccifolium 0.2 Western Sunflower - Helianthus occidentalis 0.2 False Boneset - Kuhnia eupatoriodes 0.3 Ontario Blazing Star - Liatris cylindracea 150
3 Wild Lupine - Lupinus perennis 3 Wild Bergamot - Monarda fistulosa 0.1 Spotted Bee Balm - Monardra punctate 2.5 Foxglove - Penstemon digitalis 0.1 Hairy Penstemon - Penstemon hirsutus 4 Purple Prairie Clover - Dalea purpurea 1.3 Tall Cinquefoil - Potentilla argute 4 Grey-Head Coneflower - Ratibida pinnata 4 Black-Eyed Susan - Rudbeckia hirta 1.5 Stiff Goldenrod - Solidago rigida 0.2 Showy Goldenrod - Solidago speciosa 1 Ohio Spiderwort - Tradescantia ohiensis 4 Hoary Vervain - Verbana stricta 0.5 Heartleaf Golden Alexanders - Zizia aptera
Emergent Wetland Mix *Planted in lower wetland cell, pond area, and floodplain Amount Species (Common Name - Scientific Name) (Ratio) 4.5 American sloughgrass – Beckmannia syzigachne 4.6 Longhair Sedge – Carex comosa 3.1 Bottlebrush Sedge – Carex hystericina 8 Awlfruit Sedge – Carex stipata 2 Fox Sedge – Carex vulpinoidea 0.3 Common Spikerush – Eleocharis palustris 2.5 Rattlesnake Mannagrass – Glyceria grandis 4 American Mannagrass – Glyceria grandis 0.2 Common Rush – Juncus effusus 0.5 Rice Cutgrass – Leersia oryzoides 0.3 Hardstem Bulrush – Scirpus acutus 0.8 Green Bulrush – Scirpus atrovirens 7 River Bulrush – Scirpus fluviatilis 0.2 Woolgrass – Scirpus cyperinus 3 Softstem Bulrush – Scirpus validus 15 Wild Rice – Zizania aquatica 6 Sweet Flag – Acorus calamus 3 American Water Plantain – Alisma subcordatum 4 Pink Milkweed – Asclepias incarnate 0.6 Purple-Stem Aster – Aster puniceus
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Wet Mesic Prairie *Planted in lower wetland cell and pond area Amount Species (Common Name - Scientific Name) (Ratio) 3 American sloughgrass – Beckmannia syzigachne 6 Fringed Brome – Bromus ciliates 0.2 Bluejoint – Calamagrostis Canadensis 0.5 Bebb’s Sedge – Carex bebbii 2 Longhair Sedge – Carex comosa 2 Bottlebrush Sedge – Carex hystericina 1.2 Fox Sedge – Carex vulpinoidea 32 Virginia Wildrye – Elymus virginicus 0.3 Common Rush – Juncus effuses 0.1 Rice Cut Grass – Leersia oryzoides 3 Switchgrass – Panicum virgatum 0.5 Fowl Bluegrass – Poa palustris 0.8 Green Bulrush – Scirpus atrovirens 0.2 Woolgrass – Scirpus cyperinus 2.5 Prairie Cordgrass – Spartina pectinata 0.2 Wingstem – Verbesina alternifolia 4 Purplestem angelica – Angelica atropurpurea 1.5 Pink Milkweed – Asclepias incarnate 0.3 New England Aster – Symphyotrichum novae- angliae 0.5 Purplestem Aster – Symphyotrichum puniceum 0.2 Nodding Beggartick – Bidens cernua 0.2 Boltonia – Boltonia asteroids 6 American Senna – Senna hebecarpa 0.4 Spotted Joe-Pye-Weed – Eupatoriadelphus maculatus 0.2 Common Boneset – Eupatorium Perfoliatum 0.1 Closed Bottle Gentian – Gentiana Andrewsii 1 Common Sneezeweed – Helenium Autumnale 0.2 Sawtooth Sunflower – Helianthus grosseserratus 1.6 Great St. Johnswort – Hypericum pyramidatum 3 Virginia Iris – Iris virginica 2 Dense Blazing Star – Liatris spicata 0.5 Great Blue Lobelia – Lobelia siphilitica 0.1 Seedbox – Ludwigia alternifolia 0.2 Water Horehound – Lycopus americanus 0.1 Allegheny Monkeyflower – Mimulus ringens 152
0.2 Wild Bergamot – Monarda fistulosa 2 Foxglove Beardtongue – Penstemon digitalis 1 Obedient Plant – Physostegia virginiana 0.5 Virginia Mountain Mint – Pycnanthemum virginianum 2 Black-Eyed Susan – Rudbeckia hirta 0.2 Cutleaf Coneflower – Rudbeckia laciniata 0.2 Swamp Dock – Rumex verticillatus 4 Cup Plant – Silphium perfoliatum 1 Riddell’s Goldenrod – Solidago riddellii 0.2 King of the Meadow – Thalictrum pubescens 3 Blue Verbena – Verbena hastate 0.1 Tall Ironweed – Vernonia altissima 5 Golden Zizia – Zizia aurea
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