An adaptive management approach to improve water quality at a model dairy farm in Vermont, USA

Ecological Engineering 40 (2012) 131–143

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An adaptive management approach to improve water quality at a model dairy farm in Vermont, USA Hisashi Kominami a , Sarah Taylor Lovell b,∗ a b

Vermont Association of Conservation Districts, United States Department of Crop Sciences, University of Illinois at Urbana-Champaign, United States

a r t i c l e

i n f o

Article history: Received 8 June 2011 Received in revised form 17 November 2011 Accepted 10 December 2011 Available online 11 January 2012 Keywords: Agroecology Buffer Constructed wetland Farm design Multifunctional landscape Phosphorus Stormwater runoff Water quality

a b s t r a c t Shelburne Farms is a non-profit environmental education center and working dairy farm located in Shelburne, Vermont, on the shores of Lake Champlain. Ecological and recreational functions of Lake Champlain have been increasingly threatened by high inputs of contaminants such as phosphorus, a high proportion of which is contributed by non-point sources including agriculture. Concerns about agricultural pollution motivated Shelburne Farms to set an example of good stewardship by applying an adaptive management process to investigate and improve water quality. The purpose of this paper is to describe both the process and outcomes as they evolved through a collaborative effort between Shelburne Farms, local stakeholders, and researchers at the University of Vermont. The adaptive management process included: (1) assessing conditions based on four years of water quality monitoring, (2) identifying and prioritizing potential solutions, (3) implementing a solution by installing a stormwater treatment system, (4) evaluating start-up performance of the treatment system, and (5) adjusting the strategy based on the results. Pre-implementation monitoring indicated that unacceptable levels of phosphorus and Escherichia coli entered the lake in runoff during storm events and that the dairy barnyard area was a significant contributor of pollutants. The stormwater treatment system, installed to treat runoff from the dairy barnyard area, reduced TSS and P concentrations within the first few months of operation on average by 25% and 43%, respectively, across all storm events. E. coli concentrations were highly variable with a mean reduction of 1% and median reduction of 78%. The adaptive management process was effective in engaging stakeholders, securing funds to implement solutions, and providing research results to inform future work. © 2011 Elsevier B.V. All rights reserved.

1. Introduction and background Declining water quality resulting from human activities is a serious problem for many freshwater resources worldwide. Lake Champlain is a surface water resource negatively impacted by inputs of nutrients from urbanization and agriculture that have accelerated eutrophication. The resulting oxygen depletion threatens the health of many aquatic organisms and the lake ecosystem overall. This 1100-km2 lake, which runs along the boundary between New York and Vermont in the US and reaches north into Quebec, Canada, is an important recreational resource, supporting an estimated $3.8 billion in tourism in the Lake Champlain Basin in 2000 (Lake Champlain Basin Steering Committee, 2003). In 1990, the Lake Champlain Special Designation Act was established to recognize the lake as a resource of national significance

∗ Corresponding author. Tel.: +1 217 244 3433. E-mail address: [email protected] (S.T. Lovell). 0925-8574/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoleng.2011.12.003

and to coordinate efforts between states and across the border into Canada, to prevent pollution and improve water quality. A specific plan to reduce pollution and restore the health of the lake was put in place in 1996 with endorsements from New York and Vermont, and a revised plan was endorsed by Quebec in 2003 (http://www.lcbp.org/impofa.htm). One of the primary goals of this effort was to “reduce phosphorus inputs to Lake Champlain to promote a healthy and diverse ecosystem and provide for sustainable human use and enjoyment of the Lake” (p. 8, Lake Champlain Basin Steering Committee, 2003). As with most large lake systems, water quality in Lake Champlain is largely determined by the quality of surface runoff from its contributing watersheds. Surface runoff from the basin, particularly urban and agricultural sources, has played a significant role in the eutrophication and toxic algae blooms found in Lake Champlain. In fact, nonpoint source pollution is estimated to account for 80% of the phosphorus entering the lake, with agriculture contributing up to 55% of this portion (Lake Champlain Basin Steering Committee, 2003). As a result of the decline in water quality,

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millions of dollars have been spent on the development and implementation of Lake Champlain total maximum daily loads (TMDLs) to reduce P inputs from contributing watersheds. In addition, pathogens carried in surface runoff, including bacteria, viruses, and parasites, pose a threat to public health as they can cause gastrointestinal illness if ingested. Livestock manure is a known source of these pathogens, so agricultural practices including grazing, manure storage and transfer, and manure application play a critical role in water quality (Fajardo et al., 2001). Shelburne Farms is a 530-ha non-profit environmental education center and working dairy farm with 250–270 cows including young stock, milking cows, and beef cows. The farm strives to demonstrate sustainable farming practices, while also representing the interests of other farms in the Lake Champlain watershed with a focus on sustainable local food production, agritourism, and environmental stewardship. The issue of water quality, however, is particularly critical since Shelburne Farms is located directly on shores of Lake Champlain. The implementation of best management practices including a grass-based system, large land base for manure spreading, and comprehensive Nutrient Management Plan, would suggest that Shelburne Farms has low potential for nutrient runoff and bacterial contamination from agricultural activities. Yet, some local stakeholders had raised concerns that surface runoff from the farm carried nutrients and pathogens from source areas on the farm directly to the lake. The Shelburne Farms administration was eager to collaborate with researchers at University of Vermont to investigate concerns and explore potential solutions, as such an initiative would support the farm’s mission of agricultural stewardship and environmental education. While Shelburne Farms is unique as an NGO, the administrators requested that the solutions be practical and cost-effective, so the technology could be transferable to private, commercial farms. An adaptive management approach was employed for this project because it provides a framework for making decisions based on available information and stakeholder input, even where uncertainty in future conditions exists. Johnson (1999) suggests that to deal with uncertainty, “the most effective way to learn is to view management actions as experiments and design them to produce critical information about the resource being managed” (p. 1). Adaptive management can be defined as a learningbased, decision-making process that allows flexibility to adjust as information from management interventions and other actions becomes available (National Research Council, 2004). Fig. 1 shows a diagram of the process with various steps that can run in a continuous circular pattern—a process relying on cooperation between managers, scientists, and stakeholders throughout. Gilmour et al. (1999) suggest that the effectiveness of adaptive management approaches might be improved if workshops are used to facilitate negotiations to try to reach consensus and projects include a commitment to community development. Adaptive management has been applied to numerous water resources projects (c.f., case studies from National Research Council, 2004), however, many of these projects are largely descriptive in nature and do not move beyond the conceptual phase in applying the approach. The Shelburne Farms project was operationalized and specifically designed to document a complete cycle of adaptive management, including stakeholder input and water quality monitoring. The stormwater treatment system implemented as a result of this project was an example of applying an innovative technology at the scale and funding level appropriate for a private, commercial farm, supporting an additional goal of knowledge transfer. The project at Shelburne Farms offers a model of adaptive management designed to integrate multiple goals for improving water quality on the farm, offering educational opportunities for visitors, and supporting the research mission of

Fig. 1. Adaptive management process.

funding agencies. Our specific objectives for this project were to (1) assess conditions based on water quality monitoring results from previous years, (2) identify and prioritize potential solutions collaboratively with stakeholders, (3) implement a solution by installing a stormwater treatment system to treat runoff from the dairy barnyard area, (4) evaluate start-up performance of the treatment system, and (5) adjust the strategy based on the results. The paper is divided into sections based on the adaptive management steps, demonstrating how outcomes from one step inform subsequent steps. 2. Assess conditions through water quality monitoring (2004–2008) 2.1. Water quality monitoring methods At the request of Shelburne Farms stakeholders, researchers from the University of Vermont began monitoring the water quality in 2004 at several drainage ditches and outflows across the farm. Water quality samples were collected by several UVM affiliates at various locations on the farm during summers between 2004 and 2008 (Fig. 2, Table 1). Samples were analyzed for constituents commonly associated with agricultural pollution and included total phosphorus (TP), dissolved reactive phosphorus (DRP), total suspended solids (TSS), and Escherichia coli. TP and DRP were determined colorimetrically using the stannous chloride method following persulfate digestion. Phosphorus export was estimated using the Simple Method, a method that relies on a modest amount of information (subwatershed drainage area and impervious cover, stormwater pollutant concentrations, and annual precipitation) to estimate annual stormwater pollutant exports. TSS was measured by weighing the dried residue on a glass-fiber filter disk following filtration and drying at 103–105 ◦ C. Shelburne Farms staff also collected E. coli samples at three swimming areas in the lake during summer 2008 because high levels of E. coli were observed at several watershed outfalls and at two in-lake sampling sites during

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Fig. 2. Locations of water quality monitoring sites at Shelburne Farms.

previous summers. All E. coli samples were stored at 4 ◦ C and analyzed within 24 hr of collection using the Quanti-Tray® method (Eaton et al., 1998). 2.2. Water quality monitoring results The results of water quality monitoring suggested that agricultural runoff posed a potential threat to human health at swimming areas following storm events. Storm event grab sampling demonstrated that the two monitored lake sites frequently exceeded the 77 E. coli/100 mL threshold criterion established as Vermont’s beach bathing water quality standard. E. coli concentrations exceeded the water quality standard 82% of the time at

Elm Swamp Lake (a site below a watershed outfall) and 75% of the time at Orchard Point Beach (a site below tile-drained hayfields) (Table 2). Furthermore, based on the high E. coli concentrations observed at other watershed outfall sites, it is reasonable to infer that in-lake E. coli concentrations below those sites were also likely to have exceeded Vermont’s water quality standard after storms. E. coli concentrations at the dairy barnyard were high, averaging 222,000 mpn/100 mL (n = 24). The ditch just downstream from the dairy barnyard was monitored in 2008, and it had the highest levels of E. coli, averaging 441,000 (n = 8). It is worth noting that data for E. coli were highly variable, with standard deviations often exceeding the mean. Photographs taken at Elm Swamp after other storm events document plumes of sediment entering the lake, providing

Table 1 Names, abbreviations, landscape positions, and descriptions of sites monitored for water quality from 2004 to 2008. Site code

Site name

Position

Sampling site description

BHS

Butternut Hill Stream

Stream

DBM

Dairy Barn Manure

Drainage ditch

DB

Dairy Barn

Drainage ditch

MGR

Market Garden Road

Drainage ditch

NPB

North Pasture Beach

Outfall

ES

Elm Swamp

Outfall

OCP

Orchard Cove/Compost

Outfall

SBN

South Beach North

Outfall

SBS

South Beach South

Outfall

OPB L

Orchard Point Beach

Lake

ES L

Elm Swamp

Lake

Within riparian area, drains intensively grazed pastures, receives surface runoff from roads Ditch closest to barnyard, receives runoff from silage storage area, barnyard, cow path, and pasture Ditch downstream from DBM and class III wetland, receives runoff from barnyard catchment and dirt road Ditch surrounded by shrub layer, receives runoff from woodlands, market garden, and adjacent dirt road Agricultural stream outlet into Lake Champlain from catchment dominated by woodlands and hayfields Lake outlet draining larges catchment at Shelburne Farms; upstream area frequently floods Lake outlet located downstream from MGR, tile-drained fields, and composting area Culvert draining some hay fields and partially vegetated dirt road Culvert draining some hay fields and partially vegetated dirt road In-lake sampling site at private beach, located below tile-drained fields In-lake sampling site below ES outfall, located near swimming area currently used by Shelburne Farms

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Table 2 Results of water quality monitoring at Shelburne Farms from 2004 to 2008. Site

Type

BHS DBM DB MGR NPB ES OCP SBN SBS OPB L ES L

Ditch Ditch Ditch Ditch Outfall Outfall Outfall Outfall Outfall Lake Lake

TP (mg L−1 )

E. coli (mpn/100 mL) Mean (n) 27,000 (12) 441,000 (7) 222,000 (24) 40,000 (15) 8000 (20) 7100 (19) 42,000 (20) 26,000 (16) 26,000 (7) 3000 (14) 3000 (13)

Std dev. 37,000 387,000 263,000 34,000 12,000 13,000 65,000 47,000 52,000 4800 5000

additional supporting evidence that agricultural stormwater could widely impact near-shore lake water quality (see Supplementary Data). Results also suggest that phosphorus export could have adverse environmental impacts, even though Shelburne Farms likely exports considerably less phosphorus in runoff compared to other dairies in Vermont. The range of TP export rates for Shelburne Farms’ monitored watersheds (0.1–0.4 kg ha−1 year−1 ), was still much lower than estimates developed by Troy et al. (2007) for two Vermont watersheds, the Missisquoi River (1.86 kg ha−1 year−1 ) and the Pike River (1.70 kg ha−1 year−1 ) watersheds, where dairy farming has for many years contributed significantly to phosphorus pollution. TP export rates at Shelburne Farms were also substantially lower than those reported by Meals and Hopkins (2002), who showed that TP export rates remained high (1.1–3.4 kg ha−1 year−1 ) within several agricultural catchments in the Missisquoi River watershed, even after best management practices (BMPs) had been implemented on dairy farms. Even though Shelburne Farms probably exports less phosphorus in runoff compared to other dairies, TP export rates and concentrations were still much greater than forested land, which suggests that diffuse phosphorus pollution is still an environmental problem. Median stormwater TP concentrations at outfalls (0.3–0.6 mg L−1 ) were many times greater than those reported by Bishop et al. (2003) for a forested watershed in the Northeast U.S. (0.036–0.101 mg L−1 ). Across four years of monitoring during rainfall events, TP from grab samples was highest from the ditch sites draining the dairy barnyard area (DBM and DB, Table 2). Concentrations of TSS were also high, particularly in samples obtained from drainage ditches. Concentrations at the ditches draining the dairy barnyard were 396 and 1080 mg L−1 for DBM and DB, respectively. Runoff from dirt roads in certain locations at Shelburne Farms likely contributed to this problem. Upstream from the Market Garden Road and Butternut Hill Stream sampling sites, for instance, runoff from roads frequently transported sediments to nearby drainage ditches during storm events, contributing to high TSS concentrations (658 and 1134 mg L−1 , respectively). The high TSS concentrations were generally comparable to concentrations observed at the dairy barn sampling sites, as well as those reported by Hively et al. (2005). TSS concentrations varied considerably between sites and sampling dates, but median concentrations from outfalls at North Pasture Beach, Elm Swamp, South Beach North, and South Beach South (93, 11, 55, and 30 mg L−1 , respectively) were still generally comparable to or slightly lower than those reported by Bishop et al. (2003) which ranged from 70 to 130 mg L−1 . The only site with a much higher median TSS concentration (287 mg L−1 ) was the Orchard Cove/Compost Pile sampling site.

TSS (mg L−1 )

Mean (n)

Std dev.

Mean (n)

Std dev.

0.7 (10) 1.8 (7) 2.0 (19) 1.2 (11) 0.4 (17) 0.3 (18) 0.7 (18) 0.9 (14) 0.3 (7) 0.05 (9) 0.11 (10)

0.4 0.4 1.3 0.9 0.2 0.2 0.6 1.4 0.2 0.1 0.1

1134 (12) 396 (7) 1080 (21) 658 (14) 159 (20) 45 (21) 289 (20) 242 (15) 44 (7) 3 (10) 19 (12)

869 250 1129 636 236 92 216 435 53 3 28

Though it is well documented that farmsteads can act as point sources of pollution (Hively et al., 2005; Jokela et al., 2004; Sims and Kleinman, 2005), the situation is made worse at the Shelburne Farms dairy because of a drainage ditch centrally located within the catchment that facilitates pollutant transport from the site. Runoff is rapidly conveyed either directly to the shallow drainage ditch or indirectly through subsurface drains, which then carries pollutants downstream to Lake Champlain. It is not surprising then that the two drainage ditch sampling sites below the dairy barnyard area consistently had some of the highest TP, DRP, TSS, and E. coli concentrations observed on the farm. However, it should be noted that while pollutant concentrations were high at the dairy barn sites, other researchers have reported much higher TP (13–18 mg L−1 ) and total dissolved phosphorus (11 mg L−1 ) concentrations in runoff from cow paths, barnyards, and barnyard filter strips (Hively et al., 2005; Schellinger and Clausen, 1992). As a critical step in the adaptive management process, summarizing the water quality monitoring results helped us to assess existing conditions, identify source areas, and understand better the relationship between farming practices and water quality. This in turn helped to guide the development of appropriate solutions and obtain funding to support implementation, which reduced the burden on Shelburne Farms for their proactive efforts. 3. Identify and prioritize potential solutions 3.1. Water quality working group As the trends emerged from the monitoring effort, Shelburne Farms stakeholders established a Water Quality Working Group (WQWG) in 2007 to review the results and begin considering alternatives that could be implemented to improve water quality. The group included representatives from Shelburne Farms, researchers from the University of Vermont, a private landowner with residence adjacent to Shelburne Farms, and environmental consultants. Meetings would often include guest presenters who helped inform the decision-making process by expounding on topics germane to our objectives (e.g., stormwater regulation, farm conservation policy, ecological engineering, etc.). The group met approximately once every two months and also worked on developing a Geographic Information System (GIS) database for the farm, exploring funding opportunities, and developing relationships with regulatory agencies. In April 2008, the WQWG organized a design charrette at the University of Vermont, drawing on the knowledge and expertise of academics and area professionals (e.g., engineers, water quality specialists, ecologists, etc.) to explore solutions. For the charrette, the entire group was presented with a summary of monitoring

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Fig. 3. Map of Shelburne Farms and barnyard site.

results from 2004 to 2007, a land use map (Fig. 3), and a set of design criteria aimed at promoting multifunctional solutions (see Table 3). Individuals were grouped into teams and explored design solutions that they later presented to the entire group for open discussion. The design charrette ultimately helped to guide the WQWG in identifying opportunities and prioritizing projects. 3.2. Potential solutions that were considered The next step in the adaptive management process was to develop a detailed strategy and timeline for implementing treatment solutions on the farm. Based on a review of the literature, along with input from the design charrette, a number of different solutions were considered. The goal was to design and implement a solution that employed several different technologies known to be ecological, practical, and cost-effective, in an effort to demonstrate opportunities to reduce pollution from agricultural sources. The solutions needed to be appropriate for implementation on most farms with livestock operations and rely on vegetative treatment system (VTS) technologies (Koelsch et al., 2006). Local agency representatives were consulted on the transferability of the technologies to private, commercial farms, considering cost-share programs that help to fund best management practices. For various areas throughout the farm, several different VTSs were considered including vegetative buffers, filtration systems, and bioretention systems. For Shelburne Farms, vegetative buffers were proposed for filtering runoff between source areas (e.g., manure-applied fields) and sensitive zones (e.g., beaches). Because of the significant role that dirt roads are likely to play in degrading water quality on the farm, vegetative buffers could also have been installed along roadways where there were clear connections to the hydrologic network. Riparian buffers were also considered as appropriate

Table 3 Criteria to guide design development for the Shelburne Farms charrette. Design criteria

Description

Performance

The primary objective of the system is to provide efficient treatment and uptake of phosphorus, as well as retention of E. coli The design should consider the latest innovations for improving water quality, including more aggressive approaches that go beyond conventional BMPs already established on the farm The design supports an economically viable and productive agricultural model The system must be practical and cost-effective to represent a solution that is transferable to small- and medium-sized dairy farms throughout Vermont The design minimizes use of energy and other natural resources, and opportunities to recycle resources will be considered. The system should be established to require minimal maintenance in the long term Potentially harmful impacts to human health and the environment must be avoided Opportunities to integrate additional functions such as biomass harvest as a fuel source should be explored The design must provide an educational opportunity by revealing the ecological function of a bioretention system (Ecorevelatory design) The system must be designed to fulfill the research objectives (performance evaluation) set forth in the research proposal The design should maximize the aesthetic quality of the site and consider the historic patterns of landscape stewardship

Innovation

Agricultural viability Practical

Resource efficient

Low-maintenance Minimize impacts Additional functions

Education

Research

Aesthetics

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Fig. 4. Schematic diagram of stormwater treatment system design at Shelburne Farms, not to scale.

solutions for reducing soil erosion and enhancing wildlife habitat. Filtration systems were considered for retrofitting existing ditches. However, the WQWG decided to hold off on installing a filtration system pending further investigation into the environmental impacts of certain filtration materials. For Shelburne Farms, bioretention systems were considered for areas that met several specific criteria: (1) stormwater runoff from the area was known to contain relatively high nutrient concentrations; (2) contamination was a threat to downstream ecosystems, including Lake Champlain; (3) stormwater was easily captured in a single drainage ditch; (4) the system would complement and not detract from the aesthetic of the farm; and (5) the location would be appropriate for other functions including research and education. 3.3. Prioritizing solutions While many of the proposed solutions had the potential to improve water quality at Shelburne Farms, the WQWG recognized the need to prioritize projects based on the relative threat of the source area, cost-effectiveness of the solution, and compatibility with goals of funding agencies. The results of water quality monitoring suggested that a system to treat runoff from the dairy barnyard would be an appropriate first step. The sampling sites

closest to the barnyard (DBM) and downstream from the barnyard (DB) had the highest levels of E. coli and total phosphorus, when averaged across the four years of the monitoring study. The DB location also had the highest concentration of total suspended solids. A treatment system located downstream from the dairy barnyard was ideal because it would help to reduce nutrient and pathogen loading to Lake Champlain. Treatment in this location made sense from a research and funding perspective, as well. The site all drained into a single ditch, so a system could be designed to capture and treat nearly all of the runoff, while also allowing the measurement of water flow and constituents at the inlet and outlet (Fig. 4). A portion of the funding was coming from the Vermont Water Resources and Lake Studies Center, so a rigorous assessment of system performance was critical to meet their research goals. Outreach was another important consideration, and the dairy barnyard is located in a highly accessible and visible space appropriate for demonstrating an innovative treatment system that might be implemented on farms throughout Vermont to improve water quality. After evaluating various treatment alternatives for the dairy barnyard runoff, we determined that a stormwater treatment system consisting of two ponds and a subsurface flow gravel wetland was an ideal choice, as it met treatment standards and design

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criteria set forth in the Vermont Stormwater Management Manual. Constructed wetlands and ponds, both individually, and as components of integrated stormwater treatment systems, have been shown to be effective at reducing nutrients, suspended solids, and bacteria in agricultural runoff (Cronk, 1996; Kadlec and Wallace, 2008; Knight et al., 2000; Tanner et al., 2005). Davis et al. (2006), for example, found removal rates of 70–85% for phosphorus and 55–65% for total Kjeldahl nitrogen in constructed wetlands. These treatment systems take advantage of the natural ability of plants to uptake nutrients and pollutants, increase infiltration by improving soil structure, and encourage evapotranspiration of stormwater runoff (Barrett et al., 1998a,b). They depend on microbial activity for the removal of many pollutants (especially carbon, nitrogen, and sulfur), and plants have been shown to enhance the establishment of some microorganisms (Faulwetter et al., 2009) and usually improve overall efficiency (Vymazal, 2011). Some studies have reported on the performance of constructed wetlands specifically designed for treating dairy or other agricultural runoff. In a review of the performance constructed wetlands with horizontal sub-surface flow for treating agricultural wastewaters, Vymazal (2009) reported average treatment efficiencies of 77%, 51%, and 54% for TSS, TN, and TP, respectively. Constructed wetlands designed to treat dairy soiled water in Ireland demonstrated average removal efficiencies of 88% for NH4 + nitrogen and 80% for ortho-P (PO4 3− phosphorus) (Healy and O’Flynn, 2011). In addition to reduction efficiencies of 95% for P and 93% for N, Forbes et al. (2011) reported a constructed wetland designed to treat agricultural effluents also reduced coliform to natural levels. Boutilier et al. (2009) determined inactivation to be the primary mechanism of E. coli removal by constructed wetlands, and the rates varied depending on environmental conditions (e.g., temperature). While several opportunities for improving water quality at Shelburne Farms were identified, limited funding resources required stakeholders to prioritize the solutions and agree on a system that would meet multiple goals. This approach was consistent with the adaptive management process, because each step toward water quality improvement could be evaluated and adjusted as necessary. The stormwater treatment system would serve as the first iteration of what promised to be a long-term commitment to water quality improvement.

4. Implement a solution: stormwater treatment system The stormwater treatment system was designed to capture and treat the runoff from the dairy barnyard area, located within a 5-ha catchment at Shelburne Farms. The small catchment is the principal site of the dairy farm’s operations and is comprised of a silage storage area, pasture, open areas, exercise yard, wooded area, impervious surfaces (e.g., cow path, farm roads, and barnyard), and several farm buildings (e.g., milking parlor, free stall barn, heifer barns, and refrigerated storage units). Soils are derived from glaciolacustrine deposits and coarse loamy till. Soil types include Palatine (PaB) – a well-drained silt loam, Vergennes (VeB) – a moderately well drained clay, and Covington (Cv) – a poorly drained clay. The catchment has little topographic relief and is characterized by gentle slopes between 2 and 8%. The site’s low elevations and proximity to Lake Champlain contribute to a climate regime that is comparatively warmer and drier than more upland areas in the Green Mountains. Mean temperatures in January and July are −8 ◦ C and 21 ◦ C, respectively. Annually, the study area receives 92 cm of rainfall and 208 cm of snowfall. Surface runoff in the catchment flows either directly or is conveyed through subsurface drainages to a shallow agricultural drainage ditch that bisects the catchment into east and west parts. The drainage ditch is vegetated and

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approximately 260 m in length from the northern part of the catchment to the inlet of the stormwater treatment system. Downstream from the treatment system, the drainage ditch passes through a small class III wetland dominated by cattails, then underneath a dirt road through a 0.9-m diameter culvert, and eventually discharges into Lake Champlain. The stormwater treatment system was designed by Ecosolutions LLC (Westford, VT) and constructed between 1 June and 5 June 2009. Based on criteria in The Vermont Stormwater Management Manual, the system was designed to treat roughly 90% of annual runoff-producing rainfall events (up to 2.3 cm for the state of Vermont) and was expected to reduce average annual TP and TSS loads by 40 and 80%, respectively. Based on previous studies of these systems, substantial reduction in E. coli was also anticipated, although specific performance would be difficult to predict because inactivation of coliform is highly dependent on temperature and other seasonal conditions (Boutilier et al., 2009; Forbes et al., 2011; McCarthy et al., 2011). The treatment system consisted of an inlet pond (170 m3 ), a rectangular subsurface flow gravel wetland (120 m3 ), a vegetated area adjacent the gravel wetland, and an outlet pond (90 m3 ) (Fig. 4). The storage capacity of the entire treatment system was designed to be approximately 460 m3 . Flows through the treatment system are predominantly driven by storm events and controlled by water level control structures that connect the gravel wetland to the inlet and outlet ponds. Subsurface flow into the gravel wetland is distributed through 10-cm PVC tubing. A typical sequence of events involves stormwater flowing into and filling each compartment sequentially, followed by discharge from the outlet pond to the drainage ditch via an overflow. The area adjacent the gravel wetland provides additional storage and detention for greater runoff volumes. Flows exceeding the storage capacity of the treatment system bypass treatment in the gravel wetland via a 1.2-m wide overflow that connects the inlet and outlet ponds. The gravel wetland, which has a surface area of 111 m2 and a depth of 1.1 m, consists of a 30-cm bottom layer of coarse gravel (5–10 cm diameter), a 61-cm middle layer of medium-sized gravel (2 cm diameter), and a 15-cm top layer of smaller gravel (0.6 cm diameter). A synthetic liner was used in the gravel wetland to prevent seepage to groundwater. The gravel wetland was also equipped with an aeration system used to flush sediments during cleaning to increase the longevity of the system, but was not used during the study period. The gravel wetland was planted in late June 2009 with 50 of each of Scirpus atroviren, Carex lacustris, Iris versicolor, Verbena hastate, Eleocharis palustris, Lysimachia ciliata, and Glyceria striata, and 100 of each of Asclepias incarnata, Calamagrostis canadensis, and Schoenoplectus tabernaemontani. Just as earlier phases of the adaptive management process required some adjustment and compromise on the part of stakeholders, so did the implementation stage of the project. An early design for a constructed wetland system, which would have treated a greater volume of water and provided longer retention times, had to be modified in order to fit within the level of funding that was designated for the project. The final design, as described above, was developed as an alternative that would require much less labor and modification of the landscape, although the treatment performance would be reduced slightly. The WQWG agreed that it was best to move forward with the alternative system, recognizing that the design would probably be more appropriate as a model for other dairy farms, where funding and space limitations would also be an issue. The installation of the system was accomplished with equipment that is typically available on dairy farms, and the cost was similar to other types of water quality projects that are supported, at least in part, by government programs.

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5. Evaluate start-up performance of the stormwater treatment system In this study, which was both resource- and time-limited, a mixed approach combining both simple, inexpensive grab sampling and more resource-intensive automatic sampling was recommended for assessing start-up performance of the stormwater treatment system. Start-up performance was evaluated by: (1) comparing storm flow and non-event flow pollutant concentrations at the treatment system’s inlet and outlet over a five-month period, and (2) determining pollutant removal efficiencies of the gravel wetland during two storms. 5.1. Sampling methodology Grab samples were collected during storm flows and non-event flows at the entry and exit points of the stormwater treatment system. Storm flows were sampled at least once per storm event, but multiple samples were often collected so that data were representative of different stages of flow through the treatment system. When multiple samples were collected, results were averaged to provide a storm average for each measured parameter. During nonevent flows, there was usually very low flow into the inlet pond and gravel wetland, but never flow through the entire system. Non-event samples were usually collected weekly, though gravel wetland maintenance and storm flows sometimes prevented sample collection. Rainfall was measured using a RG3-M HOBO Data Logging Rain Gauge (Onset Computer Corporation, Pocasset, MA). Temperature was measured at 30-min intervals using the same data logger used for measuring rainfall, which was housed in a RS1 Solar Radiation Shield (Onset Computer Corporation, Pocasset, MA). The rain gauge was mounted on a wooden post within the catchment in an open area free from obstructions. Gravel wetland inflows were measured in the inlet water level control structure using a sharp-crested 30◦ V-notch weir and a 6712 automatic sampler with an Isco 750 Area Velocity Flow Module (Teledyne Isco Inc., Lincoln, NE). The flow module was placed in a stilling well and recorded the height of water passing through the V-notch weir at 2-min intervals. During storm flows, a water level rise of 2.5 cm above the lowest point in the V-notch weir triggered the automatic sampler to collect up to 24 discrete 1000 mL samples. Gravel wetland outflows were measured in the outlet water level control structure using a 6712 automatic sampler with an Isco 720 Submerged Probe Flow Module (Teledyne Isco Inc., Lincoln, NE). The flow module was placed in a stilling well and recorded water level at 2-min intervals. Water levels were then converted to flow rates using Manning’s equation for a smooth PVC pipe with a 99-mm interior diameter and a slope of 0.03. A higher Manning’s roughness coefficient (0.037) than what is typically used for smooth PVC (0.011–0.017) was used because it resulted in predicted flow rates that best approximated empirical flow rates measured on two separate occasions. Flow rates were determined empirically during the falling limb of two storm flows by recording the time it took to fill a specified volume in the cylindrical outlet water level control structure and dividing the volume by the corresponding time. Flow rate was measured four times during each storm flow, averaged, and then compared to predicted flow rates using a range of different roughness coefficients. During storm flows, a water level rise of 1.5 cm above the bottom lip of the outflow pipe triggered sampling to begin. Once triggered, both automatic samplers collected samples hourly until all 24 samples were collected. For both intensively sampled storm flows, sampling programs were run more than once to capture the rising and falling limb of inflow and outflow storm hydrographs. Soon after the automatic samplers finished sampling

(usually within 24 h), samples were brought to the Agricultural and Environmental Testing Laboratory at the University of Vermont and stored at 4 ◦ C until processed. Because of time constraints, not all hourly samples collected during the second storm event were analyzed for TP and TSS. Instead, TP and TSS concentration data from samples analyzed within close proximity (2–5 h) to unanalyzed samples were used as estimates. Concentration data from additional grab samples collected after the last sampling programs were run were also used as estimates for unsampled very low flow periods. Phosphorus and TSS samples were collected in 1000 mL polyethylene bottles washed in a 10% HCl solution and triple-rinsed in distilled water. E. coli samples were collected in 100 mL bottles purchased from IDEXX Laboratories Inc. (Westbrook, ME) that had been acid-washed, soaked in a 10% bleach solution, and triplerinsed in distilled water. Collected samples were transported on ice to the Agricultural and Environmental Testing Laboratory at the University of Vermont and stored at 4 ◦ C until further processed. Within 24 h, total dissolved phosphorus (TDP) and DRP samples were filtered using pre-washed 45 ␮m membrane filters and were either stored at −20 ◦ C until analyzed or analyzed immediately. DRP concentrations were determined colorimetrically using the stannous chloride method. TP samples were either stored at −20 ◦ C until analyzed or were stored for less than a month at 4 ◦ C until analyzed. TP and TDP concentrations were determined colorimetrically using the stannous chloride method following digestion with persulfate. Particulate phosphorus (PP) was determined by subtracting TDP values from TP values. TSS was measured by weighing the dried residue on a glass-fiber filter disk following filtration and drying at 103–105 ◦ C. All E. coli samples were analyzed within 24 h of collection using the Quanti-Tray® method (Eaton et al., 1998). All statistical tests for phosphorus, TSS, and E. coli were performed using JMP software version 8.0.1 (SAS Institute) at ˛ = 0.05. Paired t tests were performed for parameters meeting the assumption of normality, while Wilcoxon signed-rank tests were performed when the distribution of differences were non-normal. 5.2. Load estimation and efficiency calculations Storm event loads were calculated by multiplying each hourly incremental flow volume by the corresponding sample concentration for the specified period to get an hourly load and summing up all hourly loads for the storm event. Hourly incremental flow volumes were calculated by averaging all flow rates that were recorded during the hour interval. The equation used to determine gravel wetland inflow and outflow storm event loads was adapted from Rekolainen et al. (1991) and is defined by: L=

N−1 

ci · qi

i=1

where L is the storm flow load, ci is the sample concentration for the specified hour interval i, qi is the average flow rate for the specified hour interval i, and N is the total number of hourly incremental storm flow loads estimated. When TP and TSS concentration data were not available for the specified hour, concentrations from the nearest sample in time were used. Concentration data from additional grab samples collected during very low storm flows after sampling programs had ended were also used for calculating loads when concentration data were not available for the specified hour. Pollutant removal efficiencies were determined using the following equation: 1−

Loutlet Linlet

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139

Table 4 Grab sample summary statistics at the stormwater treatment system. Parameter

n

Inlet Min

Storm events TP PP TDP DRP TSS E. coli (1000×) Non-flow events TP PP TDP DRP TSS E. coli (1000×) a

Outlet Max

Mean

Med

Min

Mean decline (%) Max

Mean

Med decline (%)

p-Value

Med

12 10 10 10 12 11

0.1 0.1 0.0 0.1 14 0.8

2.1 1.0 1.1 1.1 108 937

1.4 0.6 0.7 0.8 50 117

1.5 0.6 0.9 0.8 36 24

0.1 0.1 ∼ ∼ 10 0.1

2.1 1.1 1.1 1.2 117 613

0.8 0.3 0.5 0.4 37 116

0.7 0.2 0.2 0.2 24 5.2

43 50 29 50 26 1

55 67 78 75 33 78

0.0003 0.0036 0.0289 0.0015 0.0252 0.2065a

7 7 7 7 7 7

0.6 0.2 0.4 0.4 7 0.1

1.9 1.1 1.2 1.5 45 19.9

1.5 0.7 0.8 0.8 26 7.0

1.6 0.8 0.8 0.9 26 0.5

0.1 0.1 ∼ 0.1 8 0

1.2 0.6 0.9 0.7 56 41

0.7 0.3 0.4 0.4 23 7.8

0.6 0.3 0.3 0.4 17 1.0

53 57 50 50 12 −11

63 63 63 56 35 −100

0.0016 0.0142 0.0005 0.0006 0.1484a 0.3438a

Indicates a Wilcoxon signed rank test.

where Linlet and Loutlet were, respectively, the summation of all hourly storm flow loads at the gravel wetland’s inlet and outlet. In calculating storm event loads, several assumptions were made. Because of the low ambient temperatures at the time of sampling and the gravel wetland’s relatively small surface area and minimal vegetative cover, evapotranspiration was assumed to have a negligible impact on the gravel wetland’s overall water mass balance. Rainfall was also assumed to contribute a negligible volume of water to the gravel wetland, and was estimated to be less than 1% of storm inflows. Because a synthetic liner had been installed in the gravel wetland, lateral flows were assumed to be negligible. However, this assumption may not have been valid because water level decreases were observed in the gravel wetland after storm flows had ended that could not have been attributable to evapotranspiration. Therefore, lateral flows could have accounted for some water losses from the gravel wetland during storm flows. There also may have been some seepage to ground water in the additional storage area adjacent to the gravel wetland that was unaccounted for. Although V-notch weirs are commonly used in watershed research and typically have very good accuracy, the Vnotch weir installed in the inlet water level control structure did not meet some standard practice requirements because of space limitations within the structure and therefore may have reduced the accuracy of flow measurement. Additionally, partial weir submergence was observed during several high throughput events and could have reduced the accuracy of flow rate and load calculations. 5.3. Results of grab samples Between 10 July and 10 December 2009, twelve storm flows were sampled, representing approximately half of all runoffproducing storm events during the study period. Storm event characteristics such as rainfall amount, precipitation intensity, and duration varied considerably for the sampled storm flows. Of the 12 storm flows sampled, half were grab sampled either two or three times, and two were intensively sampled with automatic samplers. Because multiple grab samples were often collected during each storm flow, samples represented various stages of flow through the treatment system including the rising limb, peak, and falling limb of storm flows. Storm flows exceeded the treatment system’s capacity on at least three dates and could also have occurred on three other dates. Though overflow is expected when rainfall is greater than 23 mm, the results demonstrated that when soils are saturated, substantially smaller rainfall amounts (15.0 mm) could also generate flows that exceed the treatment system’s capacity. During

the study period, seven non-event flows were sampled and were characterized by little or no flow into the gravel wetland. Non-event flows were sampled between one and ten days after storm events. Storm flow and non-event flow grab sampling data are provided in Table 4. Results suggest that the stormwater treatment system reduced pollutant concentrations for most measured parameters during the study period. Storm flow mean outlet pond concentrations were significantly lower (p < 0.05) than at the inlet pond for TP, PP, TDP, DRP, and TSS (Table 4). Mean and median concentration reductions between the inlet and outlet ponds were generally comparable, but median concentration reductions were always greater because phosphorus and TSS distributions tended to be left-skewed for the inlet pond and right-skewed for the outlet pond. TP concentration reductions observed in this study were comparable to mean and median TP reductions (49 and 48%) for 21 pond-wetland systems reported by Kadlec and Wallace (2008). However, they reported much higher mean and median TSS concentration reductions for the pond-wetland systems than in this study. The results also suggest that the treatment system may not have been as effective at reducing E. coli concentrations. Storm flow outlet E. coli concentrations, for instance, were not significantly lower (p = 0.2065) than at the inlet. Inlet and outlet pond E. coli concentrations were extremely variable spanning three orders of magnitude and had coefficients of variation that were 2.37 and 1.89, respectively. In addition, storm flow mean and median concentration reductions for E. coli were 1 and 78%, respectively. However, closer inspection of the data revealed that outlet E. coli concentrations were lower than at the inlet for eight of eleven sampled storm events, suggesting that the treatment system may have contributed to some reductions in E. coli concentrations during the study period. Storm flow sampling also revealed that pollutant concentrations were occasionally elevated or higher in the outlet pond when overflow occurred or was suspected to have occurred. Outlet pond concentrations were higher than at the inlet pond, for instance, for most parameters on 18 July 2009. Though overflow was not observed at the time of sampling, the high rainfall amount (17.0 mm) and several rain events earlier in the week suggest that overflow could have occurred. In addition, outlet pond concentrations were either higher or the same as inlet pond concentrations for most parameters on 2 August 2009 when overflow was observed. Field observations during the study period confirmed that overflows from the inlet pond were eroding parts of the overflow berm and creating an incised channel. Therefore,

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Table 5 Storm event flow and phosphorus and suspended solids load data for the gravel wetland during two intensively sampled storms. Storm event 14-November Rainfall amount (mm) Rainfall duration (h) Flow (m3 ) In Out P load (g) In Out P retained (g) TSS load (kg) In Out TSS retained (kg) P removal efficiency (%) TSS removal efficiency (%)

25.4 13

03-December 15.0 11

360 350

330 310

330 200 130

600 520 80

17.1 9.9 7.2 39 42

24.2 15.0 9.2 13 38

overflows could potentially explain why outlet pond pollutant concentrations were sometimes as high or higher than inlet pond concentration. Non-event flow grab sampling data are summarized in Table 4 (above). Mean outlet pond concentrations were significantly lower (p < 0.05) than at the inlet pond for TP, PP, TDP, and DRP. Mean, median, and concentration ranges for TP, PP, TDP, and DRP during non-event flows were generally comparable to results reported for storm flows in this study at the inlet and outlet ponds. This similarity could be explained because of the short interevent period during which some non-event flows were sampled. Because some samples were collected within close proximity to storm events (1–2 days), non-event flow pollutant concentrations may have been similar to concentrations characteristic of late stages of flow through the treatment system. Clay particles, dissolved and particulate forms of phosphorus, and E. coli may have been slow to settle out and could have remained suspended for a few days within both inlet and outlet ponds. In contrast, inlet and outlet pond concentrations were nearly identical and were relatively low for all parameters except TSS on 10 September 2009, because the extended dry period prior to sampling probably allowed for pollutants in the inlet and outlet ponds to settle out. TSS concentrations were not significantly lower (p = 0.1484) than at the inlet pond for non-event flows, but outlet concentrations were numerically lower for six of seven paired samples. Similarly, outlet pond E. coli concentrations were also not significantly lower (p = 0.3438) than at the inlet pond, but outlet concentrations were numerically lower for four of seven paired samples. Inlet and outlet pond E. coli concentrations spanned several orders of magnitude and had coefficients of variation that were 1.26 and 1.94, respectively. Both mean and median outlet E. coli concentrations were greater than at the inlet, possibly because of the small sample size of non-event flow grab samples. Had more non-event grab samples been collected, it is possible that a single high outlet pond data point would not have had as much of an impact on the mean and median and could have resulted in similar trends observed for E. coli during storm flows. 5.4. Results of automatic sampling Data for the two intensively sampled storms during the study period are summarized in Table 5 and shown in Figs. 5 and 6. Automatic sampling revealed that for storm events on 14 November and

Fig. 5. Results from automatic sampling for 14-November-09 storm event for: (a) total phosphorus, (b) total suspended solids flux, (c) rainfall, and (d) gravel wetland flow.

3 December 2009, the gravel wetland retained, respectively, 130 and 80 g of P and 7.2 and 9.2 kg of TSS, which represented P removal efficiencies of 39 and 13% and TSS removal efficiencies of 42 and 38%. Results for P were generally comparable to those of Raisin et al. (1997), who reported P removal efficiencies between 0 and 63% for a small storm event driven constructed wetland in an agricultural watershed. The gravel wetland’s treatment performance for TSS was also comparable to reported values in the literature. Average outflow TSS concentrations for the first and second storm flows were 28.0 and 40.6 mg L−1 , respectively. Kadlec and Wallace (2008) reported that for 26 horizontal subsurface flow wetlands spanning 130 years of system operation, the average effluent TSS concentration was 22.5 mg L−1 and that the 90th percentile limit was 42 mg L−1 . For both storm flows, gravel wetland outlet TP and TSS concentrations were frequently lower than at the inlet. TP and TSS concentrations were generally higher during peak inflows and outflows during the second storm. As a result, both inflow and outflow TP and TSS loads were greater during the second storm than during the first storm. A possible explanation for the higher inflow concentrations and loads observed during the second storm is that there was a significant rainfall event (18.4 mm) four days earlier, which likely caused elevated TP and TSS concentrations in the inlet pond. Results from both storms suggest that inflow TP and TSS concentrations can remain high well after storm flows have subsided. Grab samples collected during flows less than 1 m3 h−1 after sampling programs had ended also seemed to suggest that concentrations could remain elevated for some time. Furthermore, data from roughly synchronous sampling of treatment system and

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Table 6 Synchronous sampling at the inlet and outlet of both the treatment system and gravel wetland on 14-November-09. Sampling date

Sampling time Inlet pond inlet Gravel wetland inlet Gravel wetland outlet Outlet pond outlet

Total phosphorus (mg L−1 )

Total suspended solids (mg L−1 )

14-November

14-November

15-November

14-November

14-November

15-November

15:00 1.7 0.4 0.5 0.4

22:30 1.8 0.5 0.5 1.1

07:00 1.9 1.5 0.5 0.6

15:00 159 29 14 30

22:30 76 30 28 48

07:00 42 39 37 33

gravel wetland inflows from the storm event on 14 November clearly show that early on, TP and TSS concentrations were much higher at the inlet pond than at the gravel wetland inlet (Table 6). Because it had not rained in the two weeks before the storm event on 14 November, these results suggest that long inter-event periods can contribute to much higher levels of treatment than short inter-event periods. These results are consistent with other studies that have shown that long detention times in wetlands and ponds facilitate physical and chemical treatment processes such as sedimentation, filtration, and adsorption (Kadlec and Wallace, 2008). Based on the two intensively sampled storm flows, it is estimated that the nominal detention within the gravel wetland is between 2 and 4 h. The effort to evaluate the success of the system focused first on start-up performance, in order to provide early results and to fit within the timeframe of the funded research proposal. While it will be important to continue monitoring the system over the long term, some initial findings can help to guide future efforts.

Early results suggest that the stormwater treatment system helped mitigate agricultural pollution from the dairy barnyard catchment during its first five months in operation, but longer inter-event periods may improve water quality treatment and decrease pollutant loading into the gravel wetland. However, the results also indicated that treatment performance for E. coli was extremely variable and that storm flows resulting in overflow are likely to reduce the overall performance of the treatment system. Field observations suggest that minimizing post-construction erosion of treatment system structures is likely important for protecting downstream water quality and for maintaining proper flows through the treatment system. Because the suite of processes that comprise water quality treatment are not fully developed within the treatment system, it is not unreasonable to expect for treatment performance to improve in the years ahead. However, it is unclear whether the treatment system will achieve its design goals for reducing average annual TP and TSS loads by 40 and 80%, respectively. Therefore the need for longer term monitoring must be emphasized to develop appropriate responses to future performance. Limitations in funding and timing of this project required researchers to focus primarily on the water quality function, even though the system provides additional functions for the farm. The fact that the system was partially funded as a research project is evidence that the cultural function of “research” is already being supported. A strong case can also be made for the education function, as a number of classes and other groups visited the site during and following the installation to learn about water quality and the physical, chemical, and biological processes that comprise treatment. Stakeholders and visitors appreciated the visual quality that the wetland provided, although this was not quantified in the early phases. As the system matures, additional research should include the quantification of functions beyond water quality such as user perceptions, knowledge acquisition, wildlife habitat, carbon sequestration, and biomass production. 6. Adjust strategy and provide recommendations for future work

Fig. 6. Results from automatic sampling for 03-December-09 storm event for: (a) total phosphorus, (b) total suspended solids flux, (c) rainfall, and (d) gravel wetland flow.

Following the implementation and evaluation of the stormwater treatment system, some adjustments in the strategy are expected in order to continue improving water quality. Several of these adjustments relate to the stormwater treatment system itself. For example, stabilizing the edges and overflow areas with dense-rooted vegetation could reduce the input of sediment (often containing phosphorus) into the ponds. Encouraging the growth of plants and their root systems in the gravel wetland, and even harvesting above-ground biomass, could improve nutrient uptake by plants and facilitate long-term accretion (Davis et al., 2006). Treatment performance could also be improved by reducing the volume of stormwater that is treated and increasing retention time, which could be achieved by diverting “clean” water away from the system (e.g., roof runoff). Continuing to evaluate system performance over

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a longer period of time should be a priority (collecting samples monthly at the inlet and outlet of the system) in order to determine the impacts of plant growth, variation in storm flows, and other factors. Beyond the stormwater treatment system, additional strategies should be implemented at Shelburne Farms in the effort to improve water quality. Phosphorus stored in sediments throughout the farm could continue to be a source of contamination, despite the effectiveness of the constructed wetland. Continued monitoring of the outflows across the farm can help the Water Quality Working Group to make informed decisions in prioritizing projects. Results from past monitoring suggest several possible targets for future work. For example, in order to manage the observed movement of sediments from the roadways, vegetative buffers with stiff, dense grasses could be installed between roads and waterways. A site suitability analysis for the composting areas suggested that relocating two of the composting areas to sites with less potential for leaching and runoff (e.g., avoiding steep slopes, close proximity to waterways, or seasonally high water tables) or modifying the existing sites could reduce the threat to water quality. These and other strategies could be implemented at a relatively low cost as part of the adaptive management approach.

7. Conclusions Shelburne Farm’s water quality project serves as a model of adaptive management intended to meet the needs of multiple stakeholders in a complex agroecosystem. The process is really only in the early stages, as the first iteration/cycle is completed at this one site. While it is clear that improving the quality of Lake Champlain’s waters will require a long-term commitment to better farming practices across the entire basin, as well as a commitment to understanding the complex processes that impair watersheds, there is no doubt that many challenges remain. The adaptive management process provides opportunities for cooperation between landowners, residents, state and federal agencies, and researchers—all of whom have different objectives and goals. But, through creative solutions, all stakeholders could benefit from the outcomes. Funding is also a challenge, particularly to cover the costs of monitoring and evaluation following the implementation of new practices and design alternatives. This project demonstrates that designing the systems to support multiple functions could help to overcome the challenges. Creative solutions can be used to provide benefits beyond the reduction of phosphorus and other pollutants in stormwater runoff. Priority should be given to those alternatives that offer additional functions such as wildlife habitat, carbon sequestration, biomass production, visual quality, education, and research. The needs and perceptions of the landowner and other stakeholders should be used to help prioritize functions. Ultimately, the multifunctional solutions can improve landowner adoption and public perception.

Acknowledgements The authors would like to thank Dr. Alan McIntosh for sharing his expertise and time in developing the project concept, building on previous water quality monitoring efforts. We thank Dr. Alexandra Drizo and her lab group for assisting in sample analysis. We are very grateful to the stakeholders Alec Webb, Marshall Webb, Crea Lintilhac, Matt Kolan and others for their participation throughout the project.

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