Fate and Transport of Nitrate in Agricultural Soils in Northwestern Whatcom County, Washington

Thesis Proposal for the Master of Science Degree, Department of Geology, Western Washington

University, Bellingham, Washington

Sarah Gregory August 2013

Approved by Advisory Committee Members:

Dr. Robert Mitchell, Thesis Committee Chair

Dr. Scott Linneman, Thesis Committee Advisor

Dr. Nichole Embertson, Thesis Committee Advisor

TABLE OF CONTENTS Page Number

1.0 Problem Statement……………………………………………………………………………………………………………………………1 2.0 Introduction………………………………………………………………………………………………………………………………………1 3.0 Background……………………………………………………………………………………………………………………………………….4

3.1 Geologic Setting………………………………………………………………………………………………………………......4 3.10 Glacial History……………………………………………………………………………………………………………….4 3.12 Hydrogeologic Units………………………………………………………………………………………………………4

3.2 Geographic Setting……………………………………………………………………………………………………………….5 3.3 Climate and Soils…………………………………………………………………………………………………………………..6 3.4 Land Use Above the Abbotsford-Sumas Aquifer……………………………………………………………………7 3.5 Nitrogen Cycle………………………………………………………………………………………………………………………8

3.51 Nitrogen in Manure in Fertilizer……………………………………………………………………………………8 3.52 Nitrate Leaching……………………………………………………………………………………………………………9

3.6 Nutrient Management…………………………………………………………………………………………………………11 3.61 WCD Nutrient Management Plan Requirements…………………………………………………..11 3.62 Seasonal Manure Application……………………………………………………………………………….12 3.7 The ARM Program conducted by the WCD and funded by the EPA……………………………………..12 3.71 Field Collection and Setup…………………………………………………………………………………….13 3.8 Nitrogen Modeling………………………………………………………………………………………………………………14 3.81 NLEAP and NLOS…………………………………………………………………………………………………..14 4.0 Proposed Research………………………………………………………………………………………………………………………….15 5.0 Methods………………………………………………………………………………………………………………………………………….15 5.1 Soil and Soil Water Collection…………………………………………………………………………………………..…15 5.2 Analysis of Soil and Soil Water Samples at Western Washington University………………………..16 5.3 NLOS Modeling……………………………………………………………………………………………………………………16 5.31 NLOS Inputs………………………………………………………………………………………………………….16 5.312 General Information………………………………………………………………………………17 5.313 Soil Properties……………………………………………………………………………………….17 5.314 Crop Information…………………………………………………………………………………..17 5.315 Management Information……………………………………………………………………..18 5.316 Field Information and Climate Data……………………………………………………….18 5.32 NLOS Outputs……………………………………………………………………………………………………………………18 5.4 Statistical Analysis……………………………………………………………………………………………………………….19 5.41 R Statistical Modeling……………………………………………………………………………………………19 5.42 Exploratory Statistics…………………………………………………………………………………………….19 5.43 Association Analysis…………………………………………………………………………………………..…20 5.5 Timeline………………………………………………………………………………………………………………………………20 6.0 Importance of Research…………………………………………………………………………………………………………………..21 7.0 References……………………………………………………………………………………………………………………………………….22 8.0 Figures……………………………………………………………………………………………………………………………………………..25

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1.0 Problem Statement

My objective is to assess the impact of nutrient loading practices on the fate and transport

of nitrate in agricultural fields overlying the Abbotsford-Sumas Aquifer in Washington State

(Figure 1). Nutrient application on fields above the Abbotsford-Sumas Aquifer has compromised

the health of the aquifer and has been documented since groundwater monitoring began in the

1970s (Wassenaar et al., 2006). In order to reduce the impact of manure and fertilizer application

on the aquifer, the Whatcom Conservation District (WCD) is conducting a study looking at

alternative manure application strategies and tools to reduce nutrient losses. My goal is to use

soil and pore-water data collected both by the WCD and myself to determine which

anthropogenic and environmental factors have the greatest effect on nitrate leaching to the

aquifer. Factors such as precipitation intensity, manure application rates, residual soil nitrogen

content, and soil type impact nitrate leaching rates. I will analyze the data using multivariate

statistics to determine which factors have a statistically significant correlation with nitrate

concentrations. I will then attempt to use the data to calibrate the nitrate modeling software

Nitrogen Loss and Environmental Assessment Package On Stella (NLOS) to field conditions in

northwest Washington to be used as a nitrate leaching predictive tool by planners and agencies

helping farmers develop better farming practices.

2.0 Introduction

The Abbotsford-Sumas Aquifer in Whatcom County, Washington and the District of

Abbotsford, British Columbia, Canada (British Columbia) is a shallow unconfined aquifer that

has a history of nitrate contamination (Wassenaar et al., 2006). Agriculture has fueled the

economy in the region since the 1950s (Cox and Kahle, 1999). The region’s long history of

agriculture has contributed to high nitrate concentrations within the Abbotsford-Sumas Aquifer,

which have been detected since the installation of monitoring wells in the early 1970s

(Wassenaar et al., 2006).

The United States Environmental Protection Agency (EPA) and Environment Canada

have established the maximum contaminant level (MCL) for nitrate at 10 milligrams per liter as

nitrogen (mg-N/L). The MCL represents the maximum safe human exposure level to potentially

toxic chemicals. Ingestion of nitrate at concentrations above the MCL for extended periods can

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lead to methemoglobinemia, which reduces the ability of red blood cells to transport oxygen.

Infants and the elderly are especially susceptible to the effects of this disease (EPA, 2009).

Multiple studies over the past few decades have shown that a significant percentage of

wells within the Abbotsford-Sumas Aquifer contain groundwater that exceeds the MCL for

nitrate (e.g., Erickson 1998; Zebarth et al., 1998; Cox and Kahle, 1999; Cox, S.E. and Liebscher,

H., 1999; Hii et al., 1999; Mitchell et al., 2003; Mitchell et al., 2005; Wassenaar et al., 2006;

Redding, 2011; Casey and Cummings, 2012). For example, Cox and Kahle (1999) with the

United States Geologic Survey (USGS) conducted a study within the Lynden, Everson,

Nooksack, and Sumas regions of the Abbotsford-Sumas Aquifer from 1990 to 1992 that found

15 percent of wells contained concentrations of nitrate greater than 10 mg-N/L. More recently, a

study assessing monitoring data from 1981 to 2010 by the Washington Department of Ecology

(Ecology) and the USGS found that 29 percent of 515 wells surveyed within the Abbotsford-

Sumas Aquifer contained nitrate concentrations at or above the MCL (Casey and Cummings,

2012). A study conducted by Turney in 1981 showed that on average only three percent of wells

throughout the entire Puget lowlands exceed the MCL for nitrate (Turney, 1986). Thus, the

Abbotsford-Sumas Aquifer is the exception, not the norm in western Washington.

Before the health of the Abbotsford-Sumas Aquifer became a topic of public concern,

farmers stockpiled manure and applied an average of seven times the amount of manure and

fertilizer needed for their crops (Chesnaux et al., 2007). Not until the early 1990s did Best

Management Practices (BMPs) begin to be implemented in the region. BMPs were created in

order to reduce the impact of nutrient application on the health of the aquifer system.

Wassenaar et al. (2006) with Environment Canada conducted a decade long study from

1993 to 2004 to examine the impacts of BMPs on nitrate concentrations. The study utilized a

combination of geochemistry data, monitoring data, stable isotopes analysis, and 3H/3He age

dating to determine if nitrate concentrations decreased in the Abbotsford-Sumas Aquifer after the

implementation of BMPs. After a decade of monitoring, Wassenaar et al. (2006) found that the

BMPs did not significantly reduce nitrate concentrations. Wassenaar (2006) sampled 31

monitoring wells and 25 domestic wells for their decade long study on BMPs. When samples

taken in 1993 were compared to those taken in 2004 from identical wells during the same month,

64 percent of monitoring wells showed an increase in nitrate concentrations. The average

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increase in nitrate concentrations for monitoring wells were 2.8 mg-N/L. This large average

increase in nitrate concentrations might have been influenced by a very high increase in one

particular monitoring well of 6.7 mg-N/L.

Samples taken from domestic wells showed an opposite trend. Of the 25 domestic wells

sampled in both 1993 and 2004, 72 percent recorded a decrease in nitrate concentrations.

Decreases in the individual wells ranged from 0.24 to 13.9 mg-N/L. On average, the decrease in

all domestic wells was 1.2 mg-N/L over the decade. When monitoring and domestic wells were

combined, an overall average increase in the wells occurred. The average increase was 0.3 mg-

N/L when only wells that were installed and sampled in both 1993 and 2004 were used to

determine the average. The 0.3 mg-N/L increase was determined to not be statistically

significant. Wassenaar et al. (2006) stated that more intensive studies need to be conducted,

especially from wells within 4 meters of the subsurface, which is the depth at which he

calculated the residence time of the groundwater to be an average of five years.

Wassenaar et al. (2006) noted that the best way to test the effectiveness of a new BMP is

to intensively monitor field conditions before, during, and after the implementation of the BMP.

The WCD is currently conducting a four year study (2012-2014) to test the effectiveness of a

BMP for dairy farmers. In order to participate in the study, farmers are required to set aside 10

homogeneous acres on which they must grow grass for the duration of the study. Participating

farmers follow a specific manure application protocol outlined by WCD. Before farmers apply

manure, particularly in the high risk seasons (January, October), farmers must fill out an

Application Risk Management (ARM) worksheet. If the ARM worksheet indicates that

conditions are favorable, farmers may go ahead and apply manure. If the ARM worksheet

indicates that conditions are not favorable for manure application, farmers must wait until

another date. The WCD installed 12 pan lysimeters and the USGS installed eight shallow

groundwater-monitoring wells to analyze field conditions on each participating farm and assess

the effectiveness of ARM.

I am working along with the WCD in collecting and analyzing

soil and soil pore-water samples. I will combine the soil data along with groundwater data

collected from the USGS monitoring wells and use the statistical analysis software package R to

perform a multivariate statistical analysis to quantify nitrate leaching relationships. By clustering

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the data and then testing for statistically significant relationships I intend to determine the

environmental and anthropological factors that correlate most strongly with an increase and/or

decrease in nitrate concentrations within the aquifer.

3.0 Background

3.1 Geologic Setting

3.11 Glacial History

The Abbotsford-Sumas Aquifer is a shallow unconfined aquifer composed of medium to

coarse grained sands and gravels. The aquifer is glacially derived; formed during the Quaternary

period (Carey, 2002). Around 18,000 years ago, northwest Washington was covered by the

Puget lobe of the Cordilleran Ice Sheet. The Cordilleran Ice Sheet originated in British

Columbia and extended to cover northern Idaho, northern Montana, and northwestern

Washington at its maximum (Booth, et al., 2003). The Puget lobe of the Cordilleran Ice Sheet

reached its maximum and there after began to retreat around 14,000 years ago. As it retreated

into British Columbia, the ice thinned and sea level rose to cover the modern day Fraser River

lowlands. During this period, fine grained sediment was deposited on the valley floors. The fine

grained silts and clays deposited comprise the unit referred to as the Everson Glaciomarine Drift.

This glaciomarine drift serves as the confining unit below the Abbotsford-Sumas Aquifer (Booth

et al., 2003).

Approximately 12,000 years ago, after a short period of readvancement from British

Columbia, the terminus of the Puget lobe of the Cordilleran Ice Sheet resided along the edge of

modern day Sumas Mountain. As the Cordilleran Ice Sheet began to retreat from Sumas

Mountain around 11,000 years ago, glacial outwash rivers deposited sands and gravels in the

Fraser River lowlands (Porter and Swanson, 1998). These sands and gravels accumulated up to

100 feet in thickness throughout the Fraser River lowlands, forming the Abbotsford-Sumas

aquifer. In total, the aquifer spans an area of 161 square kilometers (Wassenaar et al., 2006).

3.12 Hydrogeological Units

Four hydrogeological units were identified by Cox and Kahle (1999) within northwestern

Whatcom County and the District of Abbotsford: the Sumas aquifer, the Everson-Vashon

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semiconfining unit, the Vashon semiconfining unit, and the bedrock semiconfining unit. All of

the units other than the bedrock semiconfining unit are glacially derived. The Sumas aquifer unit

contains the Abbotsford-Sumas Aquifer and serves as the primary water source for much of

northwestern Whatcom County and the District of Abbotsford in British Columbia. The other

three wells are not productive aquifers, but serve as the bottom boundary for the Abbotsford-

Sumas Aquifer (Cox and Kahle, 1999).

The Sumas aquifer unit consists of stratified sand and gravel outwash deposits. The

outwash deposits grade from pebble and cobble sized alluvium in the north to sand in the south.

Some clay lenses are present, especially in the southwestern portion of the aquifer. The hydraulic

conductivity of the Abbotsford-Sumas Aquifer is highly variable, due in part to the presence of

the clay lenses. The hydraulic conductivity ranges from 74 to 610 feet per day. The hydraulic

gradient of the aquifer is typically around 15 feet per mile, but can range between five and 100

feet per mile (Cox and Kahle, 1999).

The Everson-Vashon semiconfining unit is composed of the aforementioned

glaciomarine drift. The unit consists of pebbly clay and sandy silt. Some coarse grained lenses

are present, which can reach 30 feet in thickness. The Vashon semiconfined unit is composed

primarily of glacial till with some gravel. The bedrock semiconfing unit consists of a variety of

rock types. Sandstone, mudstone, and conglomerate are all present. Coal seam may be found

throughout the bedrock units. The hydraulic conductivities for each of the semiconfining units is

much smaller than the hydraulic conductivity of the Sumas aquifer unit. While the coarse grained

sections of the Everson-Vashon unit can range from 19 to 87 feet per day, the fine grained

sections and the other semiconfining units are 0.027 feet per day or smaller (Cox and Kahle,

1999).

3.2 Geographic Setting

The Abbotsford-Sumas Aquifer is a transboundary aquifer located in the Fraser River and

Nooksack River lowlands of northwestern Washington and southwestern British Columbia. The

Canadian-American border virtually divides the aquifer in half. However, this boundary line is

merely a political boundary (Cox and Liebcher, 1999). Groundwater flows through the aquifer in

a general north to south trend; uninfluenced by the international border. Due to this north-to-

south flow path, pollution originating in British Columbia impacts groundwater quality in

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Washington. The aquifer extends from Abbotsford, British Columbia to Lynden, WA, providing

drinking water to both of these cities. In total, 100,000 people in British Columbia and 10,000

people in Washington rely on the aquifer as a source of water (Chesnaux et al., 2007). Public

water systems must be tested for nitrate; however, no laws require private wells to test for nitrate

(Environmental Protection Agency, 2010). These private well owners are highly vulnerable to

unknown nitrate contamination.

3.3 Climate and Soils

Western Washington and southwestern British Columbia experience a mild maritime

climate. The average annual temperature is 49 degrees Fahrenheit. The summer months in the

region are warm and dry, while the winter months are wet and cool (Mitchell et al., 2005).

During the winter months, low-pressure systems from the Pacific Ocean carry clouds and rain

into the region. Rainfall intensity is typically light, but can last for days at a time. Rainfall over

the Abbotsford-Sumas Aquifer ranges from 32 to 60 inches per year. The heaviest rainfall is on

the eastern region of the aquifer and the lightest on the western portion. Most of the rain fall

occurs between October and April (Mitchell et al., 2005).

The soils that cover the Abbotsford-Sumas Aquifer formed primarily on outwash terraces

and floodplains. The interior of the Abbotsford-Sumas Aquifer is dominated by outwash

terraces, while the boundaries are on more recent flood plains. The floodplain soils are especially

common on the southwestern boundary along the Nooksack River (Gouldin, 1992).

The soils formed on outwash deposits within the Abbotsford-Sumas Aquifer are

composed of two main soil types: the Kickerville and Hale soils (USDA, 2013). Both of these

soil units have a moderate permeability within the surface units. Gouldin (1992) defines soils of

moderate permeability as having a permeability between 0.6 and two inches per hour. These

permeability rates exceed the typical rainfall intensity within Whatcom County, Washington.

Thus, ponding does not typically occur on the surface (Gouldin, 1992).

The Kickerville soil is very well drained that extends to approximately 60 inches. The

surface and upper layers of the Kickerville soil layer are a silt loam. The bottom portion of the

Kickerville soil layer is a gravelly loam, and the subsoil is a gravelly sand. Hale soils are

partially poorly drained. Throughout much of the region, the Hale soils have been drained

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artificially. The surface layer of the Hale soil is a silt loam and the subsoil is a mottled loam. The

upper portion of the substratum is composed of a loamy fine sand while the lower portion is

sand.

The remaining soils formed on outwash terraces within the Abbotsford-Sumas Aquifer

are not expansive in extent. Glacial till is found in some regions of the outwash terraces. When

present, the glacial till is typically found at depths of 40 to 60 inches. Glacial till is poorly

drained. Also present are the Winston, Clipper, and Barnhardt soils. The Clipper soil, which is

found in low points on the outwash terraces is the only poorly drained of the three

aforementioned soils (Gouldin, 1992).

Three dominant main soil units formed on the floodplains within the Abbotsford-Sumas

Aquifer: the Briscot, Mt. Vernon, and Sumas soils (USDA, 2013). These soils are typically

naturally poorly drained; however, they have been artificially drained. While poorly drained,

they are moderately permeable (Gouldin, 1992).

The surface layer of the Briscot soil is a silt loam, which reaches a thickness of nine

inches. The subsoils are diverse, including mottled, stratified silt loam, very fine sandy loam,

fine sand, and fine sandy loam. The soil has a moderate permeability and high water capacity.

The surface layer of the Mt. Vernon soil series is a fine sandy loam. Unlike the Briscot and

Sumas soils, the Mt. Vernon soil is moderately well drained. The subsoils consist of mottled fine

sandy loam, very fine sandy loam, and sand. The Sumas soil consist of a surface layer is a silt

loam. The top 18 inches of the subsoil is a mottled silt loam, while the lower portion is a sand.

The permeability of the silt loam is moderate, while the permeability of the sand is very great.

The water capacity for the unit is moderate (Gouldin, 1992).

3.4 Land Use Above the Abbotsford-Sumas Aquifer

The predominant land use in the Fraser River lowlands overlying the Abbotsford-Sumas

aquifer is agriculture. In British Columbia, poultry and raspberry farms dominate the industry. In

Washington, dairy and berry farms are most common (Casey and Cummings, 2012). The fertile

soils overlying the outwash deposits along with the wet climate provide ideal farming conditions.

Sixty-five percent of raspberries produced in the United States are grown in Whatcom County,

Washington. Whatcom County has the second highest number of dairy farms of any county in

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the state of Washington (Whatcom Farm Friends, 2011). In order to maximize crop growth on

both berry and dairy farms, farmers supplement their fields with nitrogen-rich manure and

fertilizers (Johnson et al., 2005).

3.5 Nitrogen Cycle

The nitrogen cycle describes the transfer of nitrogen (N) through the environment (Figure

2). The nitrogen cycle consists of five major chemical reactions that cycle organic and inorganic

nitrogen through the atmosphere, biosphere, and geosphere: nitrogen fixation, nitrification,

ammonification, denitrification, and annamox (Berhard, 2012).

Nitrogen is a critical nutrient needed by living organisms. It is especially important in

crop growth. While nitrogen is abundant in the atmosphere, it is only available in the largely

unusable form of dinitrogen gas (N2). Nitrogen fixation, which is mediated naturally,

biologically, and industrially, converts dinitrogen gas to ammonia (NH3). Ammonia is also

introduced into the geosphere through ammonification, which converts organic nitrogen into

ammonia. The two-step process of nitrification converts ammonia first to nitrite (NO2-) and then

to nitrate (NO3-). Before some of the nitrite can be converted to nitrate, it is transformed into

dinitrogen gas through the process known as annamox. Annamox occurs when anaerobic

bacteria convert nitrite directly back into atmospheric nitrogen. Nitrate is converted back to

dinitrogen gas through the process of denitrification (Bernhard, 2012).

3.51 Nitrate in Manure and Fertilizer

Manure and fertilizer contain two forms of nitrogen: organic nitrogen and ammonium.

Ammonium must be incorporated into the soil in order to be of any use to crops and plants. The

ammonium that is incorporated into the soil is converted by bacteria to nitrate through

nitrification. If ammonium is not incorporated into the soil, it may volatilize Johnson et al.,

2005).

The more stable form of nitrogen present in manure is organic nitrogen. For plants to

make use of organic nitrogen, it must eventually be converted to nitrate (NO3-), which is the form

of nitrogen most easily processed by plants (Clark et al., 2011). The conversion of organic

nitrogen to nitrate is a multi-step process that begins with the conversion of organic nitrogen to

ammonium, which is referred to as mineralization. Once the organic nitrogen is mineralized, the

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resulting ammonium can either volatilize to ammonia or be converted to nitrate. The conversion

of ammonium to nitrate is termed nitrification. The bacteria that mediate nitrification thrive at

temperatures between 67 and 86 degrees Fahrenheit; if the soil temperature drops below 41

degrees Fahrenheit nitrification ceases altogether (Johnson et al., 2005)

Once nitrate is produced, it can follow many different paths (Figure 2). It may be taken

up by crops, which will use it to support crop growth. It might be converted to N2 gas and

returned to the atmosphere. Bacteria can immobilize nitrate in the soil. Nitrate can enter surface

water streams and lakes. Excess nitrate can leach into the groundwater system. If enough nitrate

leaches into the groundwater system, concentrations can accumulate to dangerous levels

(Johnson et al, 2005).

3.52 Nitrate Leaching

When manure is applied at agronomic rates, most of the nitrate produced from the

nitrogen in the manure is taken up by crops. When crops cannot process all of the nitrate in the

soil, the nitrate resides in the soil until precipitation infiltrates and transports the nitrate further

through the soil column. Carey (2002), with Ecology, conducted a two year study from 1997 to

1999 in which she analyzed the effects of manure and fertilizer application on groundwater, soil,

and soil water. Two fields were used in the study, in which unique amounts of manure and

fertilizer were applied. The amount of nutrient applied to the first field was double that

recommended by the farmer’s nutrient management plans. The amount of nutrient applied to the

second field ranged from 35 percent above the recommended application rate to slightly less than

the farmer’s recommended application rate. The first field received 1000 lb/acre/year in 1997,

900 lb/acre/year in 1998, and 400 lb/acre/year in 1999. The second field received 350

lb/acre/year nitrogen in 1998 and 500 lb/acre/year in 1999 (Carey, 2002).

Groundwater beneath the first field was found to contain statistically significant higher

nitrate + nitrite-N concentrations than upgradient groundwater. The groundwater nitrate+nitrite-

N concentrations were 15.4 and 19.6 mg-N/L under the field as opposed to 0.75 mg-N/L

upgradient. For field two, the groundwater nitrate+nitrite-N concentrations were not statistically

different upgradient than downgradient from the field. Residual soil nitrate concentrations within

the first two feet of soil at field one were 25 to 240 percent higher than 160 lbs/acre, which is

considered very high. Residual soil nitrate levels at field two were “high” in 1998 and “medium”

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in 1999. The results from this study demonstrate that nutrient application rates have a significant

effect on groundwater, soil, and soil water nitrogen concentrations (Carey, 2002).

Carey et al. (2009) conducted a second study from 2004 to 2008 in which she sampled

monitoring wells in the Sumas-Blaine Aquifer on a weekly basis in the fall, monthly basis in the

winter and spring, and every six weeks in the summer. She found that nitrate concentrations in

wells averaged much closer to the MCL in years in which manure was applied at the rate crops

could process the nutrients in it. In years in which excess nitrogen was applied, nitrate

concentrations averaged much higher than the MCL, reaching as high as 43mg-N/L (Carey et al.,

2009). The final report for this study is due to be published at the end of 2013.

Organic nitrogen may remain in the soil for extended periods before mineralizing.

Nitrate transport rates through the unsaturated zone to the groundwater table depends on a

variety of factors: the soil type (specifically its hydraulic conductivity and porosity), the

magnitude of recharge, fertilizer type, and the depth to the water table. The shorter the distance

to the water table, the more easily the aquifer is contaminated by nitrate.

During the drier months, nitrate accumulates in the soil and remains in the unsaturated

zone due to lack of precipitation. Then, during the rainy months, precipitation infiltrates into the

soil and transports the excess nitrate through the subsurface. Moreover, during the rainy months

of the year, October to April, the groundwater table rises closer to the surface (Figure 4),

reducing the distance nitrate must travel before accumulating in the aquifer system. Recharge to

the aquifer ranges from 11 to 45 inches each year. The water level of the aquifer fluctuates

between 2 and 8 feet per year, with the average fluctuation being 5 feet per year. As such, high

levels of precipitation during the winter months have led to the months of October to February

deemed as high risk periods, meaning fertilizer and manure must be applied with consideration

(Erwin and Tesoriero, 1997).

Carey (2002) demonstrated that residual organic nitrogen impacts nitrate and total

nitrogen concentrations for at least a year after application. Chesnaux et al. (2006) conducted a

study on 72 farms within the Abbotsford-Sumas Aquifer, and found that when 80 pounds of

nitrogen per acre is applied on fields in April it takes an average of seven months for the nitrate

front to reach the water table. Synthetic fertilizer was found to leach more quickly than manure.

They found that in ideal conditions, the leach rate of nitrogen is 14 lb/ac per 41 lb/ac applied to

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fields. The 46 lb/ac application rate represents the concentration of nitrogen in fertilizer that will

lead to pollution of the groundwater above the MCL, assuming optimum crop uptake.

3.6 Nutrient Management

The Dairy Nutrient Management Act of 1998 requires all dairy farms in Washington

State to develop and implement a nutrient management plan within the first eighteen months of

registering their dairy farm (KauzLoric, 1999). The nutrient management plan developed must

meet the standards set forth by the conservation commission. Each regional Conservation

District is responsible for approving local dairy farms nutrient management plans (KauzLoric,

1999).

Dairy farmers in northwestern Whatcom County, Washington fall under the jurisdiction

of the WCD. Farmers must consult with the WCD to develop their dairy nutrient management

plan (DNMP). Once the WCD supplies farmers with a DNMP, they must agree to follow it. The

WCD has set forth certain guidelines that must be included in all DNMPs (Whatcom

Conservation District, 2012).

3.61 WCD Nutrient Management Plans Requirements

Each farmer’s nutrient management plan must fulfill the criteria specified by the WCD.

The first criterion is that the rate and timing of organic nutrient application must follow

agronomic application guidelines (Whatcom Conservation District, 2012). Secondly, soil and

manure tests must be taken prior to nutrient application to determine the concentration of

nutrients, such as nitrogen, already present in the soil. Before applying manure to their fields,

farmers must check the Manure Spreading Advisory, which gives a real-time risk rating for

runoff based on the current 72 hour precipitation forecast. In the winter, farmers must check

their Applied Risk Management map in order to determine if it is safe to apply manure. Manure

applied must also follow the seasonal manure application set-back distances from sensitive areas

such as streams and ponds (Whatcom Conservation District, 2012).

Farmers must record and keep nutrient records for the manure, fertilizer, or other

nutrients applied on file for five years. Farmers need to conduct soil tests to measure for pH, EC,

OM, nitrate-N, ammonium-N, P, and K every three years. A Pre-side-dress Nitrate Test (PSNT)

is recommended on corn ground prior to application. Farmers are required to take annual post-

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harvest soil nitrate tests in the fall in each field or management unit. Manure should be tested at

least three times annually, during the spring, summer, and fall. If the soil in a farmer’s field is

frozen, snow-covered, ponded, or saturated on the day of application he may not apply manure.

If heavy or prolonged rain or flooding is expected to occur during or immediately following

application and risk for runoff exists, farmers are not allowed to apply manure to their fields

(Whatcom Conservation District, 2012).

3.62 Seasonal Manure Application

Traditionally, nutrient application has not been allowed in Whatcom County, Washington

between mid-October and mid-February. However, the Whatcom Conservation District is

conducting a study in which participating farmers are allowed to apply manure to their fields in

the months of January, February, and October. This study is coined Applied Risk Management

(ARM).The ARM program is available to interested dairy farmers in Whatcom County. The

ARM program requires participating farms to fill out an ARM worksheet that factors in various

parameters and field conditions to determine when nutrient application is most appropriate for a

specific field (Whatcom Conservation District, 2012). If the ARM worksheet indicates it is

unsafe to apply manure, the farmer must wait until the worksheet indicates it is safe to apply.

3.7 The Applied Risk Management Program conducted by the WCD and funded by the EPA

The WCD has received a grant from the EPA to fund a better farming practices study

from 2010 to 2014. Two farms in northern Whatcom County, Washington are currently

participating in the study. Each farmer granted the WCD access to 10 acres of their farm. Their

10 acre plots were split in half into control and treatment plots. The crop grown on all fields is

grass. The difference between the treatment and control plots is the manure application strategy.

For the control fields, farmers apply the conventional nutrient application strategy. This

“standard” nutrient application strategy follows the standard dates and application practices

employed throughout the county. On the treatment plots, farmers apply an “alternative”

treatment, in which manure is applied according to the ARM guidelines. The standard and

alternative treatment plots are rotated on an annual basis to account for plot/site effects.

When farmers adhere to the alternative treatment, they apply manure to their fields based

on soil type, weather, and current field conditions. For two of the fields tested in this study, this

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is from January to late August. When farmers follow the standard treatment, they apply manure

based on dates from February through October. Before farmers apply manure to their fields in

January they must fill out an ARM worksheet. If the ARM worksheet indicates that climate and

field conditions are acceptable for application, the farmers may apply the manure to their fields.

If not, they must wait to apply until a later date.

3.71 Field Setup and Sample Collection

Pan lysimeters were constructed and randomly placed at six locations on both the control

and treatment plots (Figure 5). Shallow pan lysimeter were placed at depths of 12, 24, and 36

inches at each location. The pan lysimeters are designed to collect pore water that percolates

through the soil. The gravitational soil water comes in contact with the lid of the lysimeter which

is composed of a perforated plastic top with 50 micron felt and mesh on top. Once water

infiltrates into the back it rests until it is pumped into plastic containers at the surface via inert

plastic tubing under suction. Soil pore-water samples are being collected on a monthly basis

from each lysimeter. More frequent sampling will occur around large storm or irrigation events.

Samples are analyzed for total nitrogen, ammonia-nitrogen, nitrate, total phosphorous, and pH by

Exact Scientific laboratory.

Groundwater samples are being collected from groundwater monitoring wells installed

by the USGS. Four groundwater monitoring wells are located on both the control and treatment

fields. These monitoring wells are positioned near pan lysimeters (Figure 5). The USGS samples

from their groundwater monitoring wells once per month at both the water table and shallow

depth zones down to 12 inches. USGS is conducting all sample analysis for nitrate, chloride,

specific conductance, pH, total nitrogen, and depth.

Soils samples are collected from each of the six lysimeter locations within each plot on a

monthly basis (12 sample locations per field site). Three soil samples are taken from both the

control and treatment fields at depths of 12, 24, and 36 inches for each location. The samples are

analyzed for, total-Nitrogen, nitrate, pH, electrical conductivity, ammonium, organic matter, and

total-Phosphorous by Exact Scientific laboratory. Soil moisture is also being measured by the

WCD. Soil temperature is measured in the field. In addition to monthly samples, soil samples

may be taken before manure application, after manure application, during high rainfall events,

and during high risk months for additional analysis.

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Additional data be measured by the project includes manure, forage, and meteorological

conditions (precipitation, temperature).

3.8 Nitrogen Modeling

Nitrogen modeling software can help farmers and regulatory agencies predict and

determine the fate of the nitrogen from the manure they apply to their fields. Nitrogen modeling

systems have the potential to aid farmers and regulatory agencies in determining the best timing

for nutrient application. Before a nitrogen modeling program is accepted for use by regulatory

agencies, the software must be validated and calibrated to local conditions (Clark et al., 2011).

3.81 NLEAP and NLOS

The United States Department of Agriculture (USDA) developed the nitrogen modeling

software program Nitrogen Leaching and Economic Analysis Package (NLEAP) to aid farmers

in their nutrient application decisions (Shaffer et al., 1991). While developed in Colorado,

NLEAP has primarily been calibrated to Midwestern soils and climates (Delgado, et al., 2010). It

has not yet been calibrated to soils in the Pacific Northwest. NLEAP is a simplified nitrate

modeling program that ignores transient flow. Instead, it assumes steady-state conditions for

flow in the subsurface.

Shabtai Bittman and Derek Hunt of Agri- Food Canada have translated NLEAP to the

STELLA platform, creating NLEAP On STELLA, or NLOS. (Bittman et al., 2001). NLOS is

designed to function in the climate of British Columbia. The software incorporates climate data,

plant growth, soil characteristics, and fertilizer application timing to predict water and nitrate

movement in soils. Recently, Bittman and Hunt have developed a web-based version of the

model (Hunt and Bittman, 2010; www.nlos.ca). Ideally they would like to have regulatory

agencies implement their models into their management decision making strategies. As of yet,

the model has not been proven to be fully calibrated to the soils and climate of the Pacific

Northwest. Hirsch (2007) examined the applicability of the model by calibrating it to an

agricultural field plot in southern British Columbia having a soil and climate similar to Whatcom

County. Although the model performed reasonably well, Hirsch suggested more frequent field

data collection to better calibrate and validate the model.

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4.0 Proposed Research

My first research objective is to determine which environmental and anthropological

factors correlate significantly with increases and/or decreases in nitrate concentrations within the

soil, soil pore-water, and groundwater samples collected from two dairy farms in Whatcom

County, Washington. For privacy reasons the exact locations of the farms are not provided. Soil,

soil pore-water, and groundwater samples are collected on a monthly basis for the study period

2010 to 2014. Soil and soil pore-water samples are analyzed both by myself and Exact Scientific

Laboratories. Groundwater samples are analyzed the USGS.

I will cluster all the data collected in the study using R Statistical Software Package to

determine if any correlations exist between nitrate concentrations, total nitrogen, total

phosphorous, crop yield, pH, soil moisture and environmental and/or anthropogenic factors.

Factors I plan to analyze include application date, application method, weather conditions, and

irrigation patterns. If I find any correlations I will test the significance of the relationship using

association analysis.

My other major research objective is to validate the nitrate modeling software NLOS in

order to implement it into the ARM program. I will calibrate and use NLOS to predict nitrate

concentrations in the unsaturated zone, saturated zone, and atmosphere resulting from the

manure applied to each dairy field. The concentrations predicted by NLOS will be compared to

those determined by Exact Scientific Laboratories, as well as samples I analyze in-house at

Western Washington University (WWU). If the datasets demonstrate a statistically significant

relationship, the NLOS modeling software may be implemented into the ARM worksheet to

predict nitrate loss before farmers apply manure to their fields.

5.0 Methods

5.1 Soil and Soil Pore-Water Collection

Beginning in the summer of 2013 I will start collecting my own soil and soil pore-water

samples at each farm on a monthly basis. To collect the soil samples, I will travel to each farm

along with technicians from the WCD. Together we will collect soil samples from twelve

different lysimeters locations per site. The soil samples are collected using a metal soil auger. A

core of soil is collected for the first 12 inches. The core is then broken up and mixed together in a

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bucket to gather a composite sample of the first foot of soil for analysis. I collect 30 grams of the

composite soil samples for analysis at WWU. After the twelve inch core has been collected, a

core of the next twelve inches is taken. The same procedure is followed for this core, and then

again for the core taken at 36 inches. In total, I will collect 18 soil samples from each farm.

I will collect soil pore-water samples on the same days that I collect soil samples. To

collect soil pore-water samples I attach a hand vacuum pump to the plastic tubes connected to

pan lysimeters buried at depths of 12, 24, and 36 inches. I collect from each pan lysimeter

individually. To collect from the pan lysimeters, I pump for at least 30 seconds to determine if

any soil pore-water is present. If water is present, I pump until no more water is left in the

lysimeter. The first 10 to 100 mL of pore-soil water are sent to Exact Scientific for analysis. If I

am able to collect more than 100 mL of water, I take the additional water back to WWU for

analysis.

5.2 Analysis of Soil and Soil Water at Western Washington University

After collecting soil pore-water samples I freeze them until I am able to analyze the

samples. I refrigerate the soil samples immediately after collection. Freezing the soil pore-water

and refrigerating the soil samples preserves them until I am able to analyze them at the

Washington State certified water-quality lab in the Institute for Watershed Studies (IWS) at

WWU. Using the water-quality lab, I will analyze the samples for nitrate, total nitrogen, and total

phosphorous.

5.3 NLOS Modeling

5.31 NLOS Inputs

I will use NLOS and the pore water, soil, and loading data results to calibrate and model

nitrate movement through the subsurface of farms participating in the ARM program. The NLOS

model requires many inputs, which are broken down into general information, soil properties,

crop information, management information, field information, and climate data. I will compile

the data required for the model and input it in the appropriate location.

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5.312 General Information

General information required by the NLOS model encompasses all of the basic

geographic and physical information for the farmer’s field. The general information tab is also

where I enter the farmer’s personal information. The first step is to select a province and

municipality. Currently, the NLOS model has only been calibrated for Canadian provinces. This

is one of the major differences between NLOS and NLEAP. NLEAP is calibrated for American

states, while NLOS is calibrated to Canadian provinces. While NLEAP is the version used most

commonly in the United States, NLOS might prove better suited for residents of the Pacific

Northwest whose climate resembles the climate of British Columbia more than any other region

in America.

5.313 Soil Properties

The soil properties inputs include the initial state of the soil, the physical soil

characteristics, and organic matter characteristics. The subsurface of each location is broken

down into the surface layer, layer one, layer two, and layer tree. The surface layer refers to the

first inch of soil where soil microbial activity flourishes. Layer one represents the next twelve

inches of soil. Layer two represents the thickness of the crop rooting depth and is given a

maximum of sixty inches. Layer three is the soil beginning at the end of the rooting system and

extending to a maximum of sixty inches.

Each dairy nutrient management plan must include a soil nitrate test once annually. I will

enter the results of these tests into the initial state categories for each layer. Soil ammonium,

nitrate, and water content must be recorded. Under the physical soil characteristics tab the bulk

density, permanent wilting point, permeability, salinity, clay percent, plant available water

holding capacity, and pH are recorded. The thickness for each soil layer is also included under

physical soil characteristics. The amount of soil organic matter in the first layer and the surface

layer are recorded under “organic matter content.”

5.314 Crop Information

The crop type grown on each field is grass. The planting date, harvest date, nitrogen

content, crop yield, and moisture content should be recorded. I will enter these data into the crop

information section of NLOS. I will also record the percentage and physical characteristics of the

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crop residue under crop type. The germination, development, canopy cover, and maximum for

each crop are also recorded here (Hunt and Bittman, 2012).

5.315 Management Information

The management information includes tillage information, fertilizer information, manure

information, and amendment information for each farm. Although my project focuses on

manure, the option does exist for those farmers who use synthetic fertilizer instead of manure to

use NLOS. Tillage information includes the date and method of tillage. The manure information

includes the type of manure, application method, the application date, and the amount of manure

applied. The concentration of ammonium, organic nitrogen, and total nitrogen present in the

manure must also be recorded (Hunt and Bittman, 2012).

5.316 Field Information and Climate Data

The field information includes the tillage classification, contour planting, terraced field,

tile drainage, and runoff adjustment factor. The climate data allows users to either select weather

data from a database or upload a file with their own weather data. The climate data tab also

requires users to enter their general climate class and effective precipitation (Hunt and Bittman,

2012). I will use climate data from Washington State University’s weather station in Lynden,

Washington.

5.32 NLOS Outputs

After inputting all required fields into NLOS, I will run the NLOS model. NLOS will

provide me with graphs displaying the concentration of ammonia and nitrate in each soil layer.

NLOS will further break down how much ammonia and nitrate have been immobilized in each

soil layer. The model will show me how much of the organic nitrogen in the manure was

mineralized in the first layer (Hunt and Bittman, 2012).

NLOS predicts how much ammonia and nitrate from the manure applied to the field is in

all three soil layers, as well as how much has leached to the groundwater. The model also

predicts denitrification rates and nitrous oxide emissions. Other parameters are also displayed

graphically in the NLOS model including crop yield, the total amount of ammonia and nitrate

applied from the fertilizer, and climate data (Hunt and Bittman, 2012). I will compare the outputs

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from NLOS to the data collected by the WCD. If the results from each are statistically similar, I

will use the NLOS model to account for some of the parameters unmeasured by the WCD. The

major parameters predicted by NLOS but unmonitored by the WCD are the nitrous oxide

emissions and the amount of ammonia volatilized.

5.4 Statistical Analysis

5.41 R Statistical Modeling

R is a GNU project initiated to provide a high performing statistical analysis

environment. R is an open-source statistical analysis package that can be installed on Windows,

UNIX, or Mac operating systems. The R environment is built upon the R programming

language. R provides high performing statistical operations for free. Examples of statistical

operations that can be performed using R are clustering, linear modeling, nonlinear modeling,

classification, and ordination (Venables and Smith, 2013)

The WCD has provided me access to all records of data collected from each farm

participating in the ARM Study from 2010 to 2014. I will analyze the data using multivariate

statistics to determine differences or similarities between the control and experimental field

plots, and hence the applicability of the ARM practices. The first step in analyzing the data will

be to conduct exploratory statistics on the data.

5.42 Exploratory Statistics

One form of exploratory statistics I intend to incorporate is the use of box plots. I will

create box plots for all soil, soil pore-water, groundwater, and forage samples taken from each

plot. Creating boxplots allows me to determine if the data is hetersocedastic or homescedastic,

which determine the variance of the data. Homoscedastic variables have the same finite variance

as each other, while heteroscedastic variables do not. I can also determine if the dataset is

normalized (Teetor, 2011). Based off my observations I will better be able to determine the

multivariate statistical tests to conduct.

A second important form of exploratory statistics is clustering. My primary clustering

method will be hierarchical clustering. Hierarchical clustering is a form of agglomerative

clustering which divides data into similar groups based on the difference in distance between

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data points. The two closest data points are joined, then the next closest data point is joined.

Hierarchical clustering requires a distance and a clustering metric. The distance metric

determines how individual data points are grouped together (Kabacoff, 2012). The clustering

method determines how groups of points are connected. For example, when should a new group,

or cluster, be created. I will use the Euclidean, maximum, manhattan, canberra, and binary

distance metrics. I will use the average distance, unweighted distance, single linkage, complete

linkage, centroid, median, and minimum variance cluster methods (Crawley, 2007).

In addition to hierarchical clustering, I may also use two forms of divisive clustering:

kmeans and nonmetric clustering. Kmeans and nonmetric clustering are forms of divisive

clustering. Divisive clustering begins with one large group of data. Iterations are run to regroup

the data into smaller clusters (Crawley, 2007).

5.43 Association Analysis

After all clusters have been created I will use association analysis to determine if the

trends shown in the clusters are statistically significant. I intend to use the chi-squared test to

determine if the data in each cluster are related to each other in a statistically significant manner.

If the p-value from the chi-squared tests is greater than 0.05 the relationship between the data is

not statistically significant.

5.5 Timeline

Step_________________________________________ Planned Completion______

• Compile soil, soil pore-water, forage, and groundwater data April 2013-April 2014

• Conduct multivariate statistical analysis on data May 2013-May 2014

• Collect Soil and Soil Pore-Water Samples July 2013-May2014

• Analyze Soil and Soil Pore-Water Samples Aug. 2013-June 2014

• Calibrate NLOS model to field data Sept.2013- June 2014

• Thesis Writing Jan. 2014-August 2014

• Expected Defense Quarter Fall 2014

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6.0 Importance of Research

Nitrate concentrations have exceeded the EPA’s MCL of 10 mg-N/L for groundwater and

surface water in the Abbotsford-Sumas aquifer for decades due to extensive agricultural practices

(Casey and Cummings, 2012). As a BMP, the WCD intends to transition farmers away from

previous farming practices, in which manure is applied between fixed time periods (February-

October) during the year. The old system was implemented to satisfy both the Dairy Nutrient

Management Act of 1998 and the Whatcom Manure Spreading Ordinance 16.28. The current

nutrient management plan guidance created to satisfy this act have not yet been able to resolve

the nitrate contamination problems within Whatcom County (Wassenaar et al., 2006). The ARM

system will allow farmers a more dynamic system for manure application. Farmers will still be

required to follow their current nutrient management plans; the ARM system will be an addition

to the current plans. Instead of strictly prohibiting manure application during certain dates,

farmers will be required to complete an ARM worksheet to get the go ahead for applying

manure. Ideally, farmers will use the ARM worksheet throughout the year. This would provide

better protection in months such as March-July when rain still occurs fairly frequently, but

manure application is allowed. Moreover, an accurate model (e.g., NLOS) for the leaching of

nitrate through the soils to the Abbotsford-Sumas aquifer will help farmers and local authorities

determine when applying manure application will result in minimal nitrate contamination, hence

preventing excessive nitrate pollution within the aquifer.

Other areas in Western Washington suffer from nitrate contamination as well (Erwin and

Tesoriero, 1997). If the implementation of Application Risk Management proves successful for

Whatcom County, other areas throughout the Puget Sound with similar climate and soil types

should be able to implement the program as well.

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7.0 References

Bernhard, A. 2012. The nitrogen cycle: processes, players, and human impact. Nature Education Knowledge. 10:25.

Bittman, S., D.E. Hunt, and M.J. Shaffer. 2001. NLOS -A nitrogen cycling model with a graphical interface: implications for model developers and users. Ch.11 pp. 283-402. In M.J. Shaffer et al. (eds.) Modeling Carbon and Nitrogen Dynamics for Soil Management. CRC PRESS LLC, Boca Raton, FL.

Booth, D.B., K.G. Troost, J.J. Clague, and J.B. Waitt. 2003. The Cordilleran Ice Sheet. Development in Quaternary Science. 1:17-27. doi:10.1016/S1571-0866(03)01002-9.

Canfield, D.E., A.N. Glazer, and P.G. Falkowski. 2010. The evolution and future of earth’s nitrogen: Science. 336:192-196. Doi: 10.1126/science.1186120.

Carey, B. 2002. Effects of land application of manure on groundwater at two dairies over the Sumas-Blaine Surficial Aquifer. Washington Department of Ecology Publication 02-03-007.

Carey, B., J. Harrison, and L. VanWieringen. 2009. Groundwater nitrate below a manured dairy field over the Sumas-Blaine Aquifer. Department of Ecology Publication No. 09-03-027.

Carey, C.M. 2004. Quality assurance project plan: groundwater, soil, and crop nitrogen at a field where dairy waste is used as fertilizer in Whatcom County. Washington State Department of Ecology Publication No. 04-03-112.

Carey, B. and R. Cummings. 2012. Sumas-Blaine Aquifer Nitrate contamination survey. Washington Department of Ecology Publication No. WA-01-1010.

Chesnaux, R., D.M. Allen, and G. Graham. 2007. Assessment of the impact of nutrient management practices on nitrate contamination in the Abbotsford-Sumas Aquifer. Environmental Science and Technology. 41:7229-7234. doi: 10.1007/s125-01300310-9.

Clark, C., D. Hunt, and S. Bittman, 2011. Interpreting soil N tests with the help of computer simulation. Whatcom Conservation District and Agriculture and Agri-Food Canada. http://www.farmwest.com/book/export/html/946.

Cox, S,E. and S. Kahle. 1999. Hydrogeology, ground-water quality, and sources of nitrate in lowland glacial aquifers of Whatcom County, Washington, and British Columbia, Canada. United States Geological Survey Water-Resources Investigations Report 98-4195.

Cox, S.E. and H. Liebscher. 1999. Ground-water quality data from the Abbotsford-Sumas aquifer of southwestern British Columbia and northwestern Washington State, February 1997: United States Geological Survey Open-File Report 99-244.

Crawley, M.J. 2007. The R Book. West Sussex, England: John Wiley & Sons Ltd. p. 749-787.

Delgado, J.A., P.M. Gagliardi, D. Neer, and M.J. Shaffer. 2010. Nitrogen Loss and Environmental Assessment Package with GIS capabilities (NLEAP-GIS 4.2): User guide. United States Department of Agriculture.

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Environmental Protection Agency (EPA). 2009. List of contaminants and their (MCL’s). http://water.epa.gov/drink/contaminants/index.cfm#List Erwin, M.L. and A.J. Tesoriero, editors. 1997. Predicting groundwater-vulnerability to nitrate in the Puget Sound basin. United States Geological Survey Fact Sheet FS-061-97.

Gouldin, A. 1992. Soil Survey report of Whatcom County Area, Washington.

Hirsch, H.R. 2007. Applicability of the NLOS model for predictions of soil water movement and nitrogen transport of soil water movement and nitrogen transport in an agricultural soil, Agassiz, BC. M.S. thesis. Western Washington University, Bellingham, Washington.

Hunt, D. and S. Bittman. 2012. NLOS Model Web Interface. www.nlos.ca.

Johnson, C., G. Albrecht, Q. Ketterings, J. Beckman, and K. Stocktin, editors. 2005. Nitrogen basics – the nitrogen cycle. Cornell University Cooperative Extension Agronomy Fact Sheet 2.

Kabacoff, R.I. 2012. Quick R: cluster analysis, http://www.statmethods.net/advstats/cluster.html.

KauzLoric, P. 1999. 1998 Department of Ecology report: Implementation of the Dairy Nutrient Management Act (Chapter 90.64 RCW). Washington State Department of Ecology water quality Program publication no. 98-38WQ.

Mansouri, A. and A.A. Lurie. 2006. Methemoglobinemia. American Journal of Hematology 42:7 -12.

Mitchell, R.J., R.S. Babcock, H. Hirsch, L. McKee, R.A. Matthews, and J. Vandersypen. 2005. Water Quality: Abbotsford-Sumas Final Report. Western Washington University.

Norman, E.S. and J. Melious. 2004. Transboundary environmental management: A study of the Abbotsford-Sumas aquifer in British Columbia and western Washington. Journal of Borderlands Studies. 19:101-110.

Porter, S.C. and T.W. Swanson. 1998. Radiocarbon age constraints on rates of advance and retreat of the Puget lobe of the Cordilleran Ice Sheet during the last glaciation. Quaternary Research, 50:205-213. doi: 0033-5894/98.

Scibek, J. and D.M. Allen. 2006. Comparing modelled responses of two high-permeability, unconfined aquifers to predicted climate change. Global and Planetary Change. 50:50-62.

Shaffer, M.J., A.D. Halvorson, and F.J. Pierce. 1991. Nitrate Leaching and Economic Analysis Package (NLEAP): Model description and application, in managing nitrogen for groundwater quality and farm profitability. Soil Science Society of America. 285-322 Shaffer, M.J., B.J. Newton, and C.M. Gross. 2001. An internet-based simulation model for nitrogen management in agricultural settings. The Scientific World.1:728-736. Teetor, P. 2011. R Cookbook: Sebastopol, Ca. O’Reilley Media Incorporated, p. 209-218.

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Turney, G.L.1986. Quality of groundwater in the Puget Sound region, Washington, 1981. U.S. Geological Survey Water-Resources Investigation Report 84-4258. United States Department of Agriculture (USDA). 2013. Web Soil Survey. http://websoilsurvey.nrcs.usda.gov/app/ Urban Design Tools. 2013. Nitrogen Cycle in Soil. http://www.lid-stormwater.net/greenroofs_benefits_ncycle.htm Wassenaar, L. I., M. J. Hendry, and N. Harrington. 2006. Decadal geochemical and isotopic trends for nitrate in a transboundary aquifer and implications for agricultural beneficial management practices. Environmental Science and Technology. 40:4626-4632. Whatcom Conservation District (WCD). 2012. Nutrient Management. http://www.whatcomcd.org/nutrient-management. Whatcom Farm Friends. 2011. Whatcom County Agricultural Facts. http://www.wcfarmfriends.com/go/doc/1579/181808/ Venables, W.N. and D.M. Smith. 2013. Notes on R: A programming environment for data analysis and graphics in An Introduction to R version 3.0.1.

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8.0 Figures

Figure 1. Location of the Abbotsford-Sumas Aquifer, which is outlined in white on the large map and black in the inset map. The orange color denotes areas of higher population (modified from Scibek and Allen, 2006).

26

Figure 2. Schematic representation of the nitrogen cycle (Urban Design Tools, 2013).

27

Figure 3. Areas within the Puget Sound that are susceptible to nitrate contamination (Mitchell et al., 2005)

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Figure 4. Depth to the groundwater table across the United States portion of the Abbotsford-Sumas Aquifer (Carey and Cummings, 2012).

29

99

Figure 5. General layout of fields participating in the WCD’s ARM study.