An Investigation of Hydrological Separation

Home , Xerophyte

Water cycling on cultivated land: an investigation of hydrological separation

in the vadose zone

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in

the Graduate School of The Ohio State University

Devin Foster Smith

Graduate Program in Earth Sciences

The Ohio State University

2019

Thesis Committee

Anne E. Carey

Thomas H. Darrah

Michael T. Durand

Rattan Lal

Copyrighted by

Devin Foster Smith

2019

Abstract

There is a need to improve our understanding of water cycling in the soil-plant- atmosphere-continuum and the degree of hydrological separation in the vadose zone.

Stable water isotopes, oxygen-18 and hydrogen-2, are natural tracers that effectively delineate water flow paths. Isotopic fingerprinting methods can be used to track hydrologic flow through a system after the water is extracted from the sample. However, the ecohydrologic community lacks a universally accepted method of water extraction from soils and plants. This study incorporated methods development, field work, and modeling to delineate water flow and determine the degree of hydrological separation in a Crosby silt loam field planted with Zea mays L. (maize) at the Waterman Agricultural and Natural Resources Laboratory on The Ohio State University campus from May to

September 2018. Stable water isotopes were used as natural tracers to delineate flow throughout the soil profile. Soil water samples were extracted via cryogenic vacuum distillation and analyzed for δ18O and δD using cavity ring-down spectroscopy. Field results showed vertically dominated flow regimes and low variation in soil water isotopic composition, which indicated limited mixing between precipitation event water and pre- existing soil water. The expected relationship between maize xylem tissue and soil water content was modeled using IsoSource.

Acknowledgments

I would like to thank my advisor, Anne Carey, for acting as my mentor and providing support throughout this project. She has been a phenomenal female role model in the Earth Sciences. I would also like to thank my committee members for providing me with guidance throughout the research process. In addition to providing intellectual guidance, Thomas Darrah contributed funding for field equipment and lab space. Rattan

Lal provided lab space and equipment as well. I greatly appreciate both contributions.

Additionally, I would like to thank Nall Moonihall for assisting with field samples and my peers in my lab group. I also like to acknowledge Sue Welch and Berry Lyons for providing guidance and assistance with my research over the past two years.

Finally, I extend a loving thank you to my family and friends who have always supported me and encouraged me to pursue my passion. Thank you to everyone who made this possible along the way. I feel incredibly lucky to have had this opportunity.

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Vita

May 2013 New Trier High School

May 2017 B.S. Environmental Science, B.A. Geography, Villanova University

August 2017 – Graduate Fellow, August 2018 School of Earth Sciences, The Ohio State University

August 2018 – Graduate Teaching Assistant, Present The Ohio State University

Fields of Study

Major Field: Earth Sciences

Table of Contents

Abstract ...... ii Acknowledgments...... iii Vita ...... iv List of Tables ...... vii List of Figures ...... viii Chapter 1. Introduction ...... 1 1.1 Stable Water Isotopes ...... 2 1.2 Global and Local Meteoric Water Lines ...... 4 1.3 The two water world hypothesis ...... 6 1.4 Transition into “n water worlds”...... 9 1.5 Hydrological Separation in Agriculture ...... 11 1.6 Soil Moisture Characteristics ...... 12 1.7 Mass Balance Mixing Models ...... 16 1.8 Objectives and Hypothesis ...... 19 Chapter 2. Methods ...... 21 2.1 Overview ...... 21 2.2 Site Location ...... 21 2.3 EC-5 Soil Moisture Sensor Calibration ...... 23 2.4 Field Placement of Sensors ...... 24 2.5 Soil Moisture Content Determination at Field Capacity and Permanent Wilting Point ...... 25 2.6 Local Meteoric Water Line (LMWL) ...... 25 2.7 Field Sampling Overview ...... 26 2.8 Soil Sampling ...... 26 2.9 Field Water Sampling ...... 26 v

2.10 Zea mays L. (Maize) Sampling ...... 27 2.11 Sample Water Analysis ...... 27 2.12 Cryogenic Vacuum Distillation: Literature Review ...... 28 2.13 Cryogenic Vacuum Distillation Methods ...... 33 Chapter 3. Results ...... 39 3.1 Establishing Cryogenic Vacuum Distillation Methods...... 39 3.2 Extraction Results of Standard Soil Samples...... 40 3.3 Climate Conditions in Ohio 2018 ...... 41 3.4 Soil Physical Properties ...... 42 3.5 Soil Water Characteristics...... 43 3.6 Analysis of Sample Contamination: ChemCorrectTM ...... 48 3.7 Soil Water and Precipitation Water Isotopic Composition ...... 51 3.8 Xylem Isotopic Compositions and Modeling Soil Water Proportion with IsoSource ...... 56 Chapter 4. Discussion ...... 64 4.1 Cryogenic Vacuum Distillation Methods Development ...... 64 4.2 Cryogenic Vacuum Distillation: Sample Water Extraction ...... 66 4.3 Soil Water Characteristics in the Soil Profile ...... 67 4.4 Insight from the Composition of Groundwater, Tile Water and Stream Water ...... 70 4.5 Soil Water Isotopic Composition ...... 71 4.6 Modeled Xylem Isotopic Compositions and Expected Outcomes ...... 75 4.7 Soil Water Source Proportion Scenarios...... 77 Chapter 5. Future Work ...... 79 Chapter 6. Conclusion ...... 81 Work Cited ...... 83 Appendix A. Soil Water Source Proportion Scenarios ...... 90 Appendix B. Cryogenic Vacuum Distillation Standards ...... 94

List of Tables

Table 1. 2018 Ohio Climate Conditions: Monthly precipitation totals, percent of annual precipitation, temperature and relative humidity for 2018. Mean monthly precipitation totals for 2014-2018...... 42

Table 2. Soil density, porosity and texture properties with soil moisture contents corresponding to field capacity and permanent wilting point measurements...... 43

Table 3. Soil water extraction data for δ18O, δD and lc-excess...... 49

Table 4. Calculated maize xylem isotopic compositions and the modeled values of soil water source contributing maize root water uptake by depth...... 56

Table 5. Mean δ18O and δD values for soils at the four sampling depths among sample sites for the six sampling dates...... 73

Table A1. Soil water proportion scenarios determined by the calculated root depth and density distribution at the four sampled depths for each sampling date...... 91

Table B1. Cryogenic vacuum distillation fractionation factors obtained from standard tests...... 95

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List of Figures

Figure 1. Field site map...... 21

Figure 2. Cryogenic distillation set up used to extract water from samples and standards...... 34

Figure 3. Average soil moisture content of the three site locations for the four sampling depths and precipitation values recorded at Waterman Farm...... 44

Figure 4. Average soil moisture content of the three site locations for the four sampling depths and precipitation values recorded at Waterman Farm...... 45

Figure 5. Soil water characteristic (pF) curves for each site and depth...... 47

Figure 6. Soil depth profiles of δ18O and δD for each sampling date...... 52

Figure 7. Soil water isotopic compositions, precipitation, groundwater and tile water samples plotted with the local meteoric water line (LMWL) and the evaporation line. .. 54

Figure 8. Regression analysis of modeled soil water proportions and calculated soil water proportions differentiated by depth...... 58

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Chapter 1. Introduction

The study of water cycling in the vadose zone is an inherently interdisciplinary subject that questions how water moves across the soil-plant-atmosphere continuum. Work in this field continues to promote scientific exploration of new water cycling hypotheses in unsaturated soils.

Recent studies suggest that there is a degree of hydrological separation of water within the vadose zone, which is a result of incoming precipitation water bypassing pre-existing soil water.

This idea diverges from the traditional translatory flow model because it assumes a highly limited mixing regime rather than a well-mixed soil water profile with high replacement rates of pre-existing soil water with event water (Hewlett and Hibbert, 1966). Water in the vadose zone is the primary reservoir of water that contributes to the evapotranspiration flux from the continents

(Jasechko et al., 2013). To advance knowledge of hydrologic flow patterns and water residence time of soil water will allow us to constrain the source and age of water exchanged in an evapotranspirative flux (Sprenger et al., 2017a; Penna et al., 2018).

While the concept of hydrological separation has primarily been researched in undisturbed catchments, the delineation of water cycling within agricultural soils is particularly important for agricultural management practices (Wu et al., 2016; Zhao et al., 2017; Penna et al., 2018).

Information gleamed from researching fine scale water cycling across the soil-plant-atmosphere continuum can be utilized to understand catchment scale hydrologic processes and improve

1 agricultural management practices. Scientific investigating of water cycling processes on developed lands will diversify the growing research focusing on hydrological separation.

1.1 Stable Water Isotopes

Ratios of oxygen isotopes (18O/16O) and hydrogen isotopes (2H/1H) are used as natural water tracers in atmospheric and hydrologic studies to delineate the path of water within natural systems. These stable water isotope ratios are expressed using the delta notation, δ18O and δD, respectively. The delta notation refers to the universal standard, the Vienna Standard Mean

Oceanic Water (VSMOW) (Clark and Fritz 1997; Craig, 1961a). Equation 1 demonstrates calculation of the delta notation for 18O/16O and 2H/1H ratios:

푅 − 푅 훿(‰) = 푠푎푚푝푙푒 푠푡푎푛푑푎푟푑 ∙ 1000 [1] 푅푠푡푎푛푑푎푟푑 where Rsample signifies the isotopic ratio of the sample while Rstandard signifies the isotopic composition of VSMOW. A positive delta demonstrates an enrichment of the less abundant (18O or 2H) to the more abundant (16O or 1H) isotope compared to VSMOW. A negative delta demonstrates a depletion of the less abundant relative to the more abundant standard compared to

VSMOW. The ratios of these isotopes are modified during mass dependent reactions, such as evaporation and condensation of water. Therefore, these isotopic ratios can be measured in reservoirs of water to determine whether the reservoir has been enriched or depleted following a mass dependent reaction.

Isotope fractionation is the process in which the relative abundance of isotopes in a reservoir is changed due to a physical or chemical process. Raleigh distillation is a theoretical principle that describes isotopic fractionation (partitioning of the two isotopes) in equilibrium

2 conditions, during which the fractionation factor remains constant (Dansgaard, 1964; Gat, 1996).

Kinetic fractionation describes non-equilibrium processes, in which the fractionation factor does not remain constant (Dansgaard, 1964; Gat, 1996). The partitioning of isotopes during Raleigh distillation and kinetic reactions results in changes in the relative proportions of the heavy and light isotopes between the reactants and products of a reaction and phase transfer. The reservoir with a higher abundance of light isotopes is considered depleted, and the reservoir with a higher abundance of heavier isotopes is considered enriched. For example, in an evaporative process where the partitioning of the water isotopes is kinetic, the initial reservoir will contain enriched water and the final reservoir will contain depleted water (Gat, 1996).

The Raleigh distillation model is used to describe differences in the isotopic composition of precipitation around the world with a series of equations that apply to a steady-state system

(Dansgaard, 1964). These equations generally apply to an open system but can also apply to a closed system, in which water molecules are continuously removed by undergoing a phase change (Gat, 1996). Raleigh equations also apply when there is a steady-state inflow and outflow of water to a system occurs via a phase change. The outflow of this system is fractionated and can be described by a fractionation factor in relation to the reservoir (Gat, 1996).

In the hydrologic cycle, Raleigh distillation can be used to describe the rainout concept of precipitation and the temperature relationship (Fröhlich et al., 2002; Aggarwal et al., 2016). In this research the Raleigh distillation concepts provide the theoretical basis for the methods used to extract water from soils for isotopic analysis (cryogenic vacuum distillation).

Slow reactions in the hydrologic cycle can be treated as equilibrium processes, however the speed at which condensation and evaporation occur implies a kinetic change to the molecule,

3 affecting the fractionation factor (Dansgaard, 1964). Kinetic fractionation principles dominate in non-steady-state systems in which reactions occur relatively quickly and more simply where products are physically separated from the reactants. Vapor pressure of water is changed during a kinetic reaction, creating different fractionation factors and falling below the isotopic composition of water molecules from the meteoric water line (Dansgaard, 1964). For example, the kinetic fractionation of evaporation of a falling raindrop is dependent on the isotopic composition of the atmospheric vapor, the relative humidity and the ambient temperature

(Stewart, 1975). Evaporation of soil water also results in kinetic fractionation (Gat, 1996).

Raleigh distillation and kinetic fractionation explain isotopic composition fluctuations in the hydrologic cycle and are also important for understanding precipitation patterns, condensation and evaporation trends applicable to this study.

1.2 Global and Local Meteoric Water Lines

Craig (1961a) was the first to describe the precipitation by the relationship between stable water isotopes δ18O and δD. A single linear equation, calculated from the δ18O and δD in precipitation samples around the world, is used to depict global precipitation (Craig, 1961a;

IAEA/WMO 2018). It describes the progressive rainout of a precipitation airmass based on a fixed fractionation factor, and it is titled the Global Meteoric Water Line (GMWL):

훿퐷 = 8 ∗ 훿18O + 10 [2] where δD and δ18O are relative to VSMOW and intercept of 10 is an indication of deuterium excess (Clark and Fritz 1997; Craig,1961a,1961b). Deuterium excess (d-excess) specifies the evaporation rate at the precipitation source (Dansgaard, 1964). This value provides information on changes in precipitation sources for a given region, making it useful in understanding 4 meteorological patterns. The GMWL is a function of the Raleigh distillation process, however kinetic fractionation processes create localized variations in precipitation isotopic composition that results in the isotopic composition of precipitation samples deviating from the GMWL.

Localized variations in the δ18O and δD linear relationship result in Local Meteoric Water

Lines (LMWLs). These variations are a function of the Raleigh distillation and kinetic effects previously discussed (Craig, 1961a; Dansgaard, 1964; Gat, 1996; Rozanski et al., 1999).

LMWLs show a range of slope and intercept deviation from the GMWL depending on geographic characteristics and the water source origin. These effects are the elevation, latitude, seasonal, amount, continental, and cloud dynamic effects (Craig, 1961a; Dansgaard, 1964; Gat,

1996; Rozanski et al., 1999; Aggarwal et al., 2016). The latitude and seasonal effects are interrelated because seasonal changes are more pronounced at higher latitudes. These two effects have a direct relation to temperature. The below-cloud temperature controls the equilibration of the falling precipitation with the atmospheric vapor, making it a primary control of isotopic composition (Rozanski et al., 1999; Aggarwal et al., 2016). Less exchange occurs between a raindrop and the atmospheric vapor at lower temperatures, or when the precipitation is solid

(Jouzel and Merlivat, 1984). A seasonal pattern is created when isotopically lighter precipitation falls without equilibrating with the surrounding vapor in cooler months. Seasonal variation is also related to the growing season through the influence of evapotranspiration and the seasonality of wind patterns driving different vapor sources (Rozanski et al., 1999).

The amount effect, continental effect, and altitude effect depend upon the degree of rainout, which is the amount of precipitation that has fallen from a precipitating airmass

(Dansgaard, 1964). Heavier isotopolouges will condense and precipitate first, making the

5 isotopic composition of precipitation at the beginning of a rainstorm heavier than the precipitation at the end of the storm. This phenomenon results in heavier isotopic signatures near coastlines and lighter isotopic signatures further inland, known as the continental effect

(Dansgaard, 1964). The amount effect is prominent in equatorial regions and becomes prominent with increasing latitudes where the temperature effect has a greater influence (Dansgaard, 1964).

Due to rainout occuring as the precipitating airmass travels to higher latitudes and lower temperatures at higher latitudes, the amount effect results in lighter precipitation at higher latitudes. Through these five effects the precipitation deviations from the GMWL can be explained, and LMWLs can be produced regionally. LMWLs are important components in hydrologic studies using stable water isotopes at hillslope and catchment scales because they describe the systems input (precipitation). As a result, a well-developed LMWL is essential in understanding hydrologic cycling across the soil-plant-atmosphere-continuum.

1.3 The two water world hypothesis

In 2009 Brooks et al. published a study indicating a distinct separation of water pools in the unsaturated portion of a soil profile. The discovery of two water pools inspired Brooks and colleagues to question the historical understanding of water flow in the vadose zone. This hypothesis describing the distinct separation of water pools within the soil reservoir was titled the two water worlds (TWW) hypothesis.

Historically, water cycling in the vadose zone has been described by translatory flow, which assumes that incoming water mixes with pre-existing soil water (Hewlett and Hibbert,

1966). The TWW hypothesis challenged this paradigm, stating that two distinct pools of water reside in the shallow subsurface. One pool, bulk water, is soil water that is stagnant in the vadose 6 zone. This water is held by matric suction and it contributes to plant water supply (Brooks et al.,

2009). The second pool, mobile water, primarily bypasses water held by matric suction and recharges groundwater and contributes to stream baseflow (Brooks et al., 2009; McDonnell,

2014; Oerter et al., 2014). Bulk water may imply the combination of the two water pools, however the pool stagnant in the vadose zone is termed bulk water instead of bound water because bound water implies water held by only adsorptive forces, rather than capillary and adsorptive forces (Oerter et al., 2014). This term indicates that mobile water could be mixed with this pool, however the findings from previous work show that the mixing regime between bulk and mobile water pools is extremely limited (Brooks et al., 2009; Oerter et al., 2014; Hervé-

Fernández et al., 2016). The separation of these two pools was titled ecohydrologic separation.

The TWW hypothesis theory is a hypothesis that juxtaposed scientific knowledge of water cycling in the vadose zone and the source of plant root water uptake. The suction gradient across plant roots is lower for bulk than mobile water because bulk water is held at a lower water potential (Brooks et al., 2009; McDonnell, 2014).

Once the TWW hypothesis was published, studies focusing on hydrologic cycling in the vadose zone began to focus on whether hydrological separation exists, and further, to investigate how these pools of water contribute to root water uptake or to groundwater and stream baseflow

(Goldsmith et al., 2012; Penna et al., 2013, 2018, Evaristo et al., 2015b, 2016; Bowling et al.,

2017). Hydrological separation was shown to exist in a range of biomes, including arid,

Mediterranean, temperate forest, temperate grassland and tropical biomes (Evaristo et al.,

2015b). The initial study providing evidence of hydrological separation was conducted in a

Mediterranean climate. Distinct wet and dry seasons associated with a Mediterranean climate

7 reduced the memory effect of precipitation from previous wetting events before the first seasonal rainfall event (Brooks et al., 2009). A flurry of research followed this study to investigate water cycling in the shallow subsurface and to derive a stronger connection between water stored in the vadose zone and the flux of evapotranspiration.

The bulk water and mobile water pools can be differentiated by their δ18O and δD values in the soil profile. Bulk water exhibits an enriched isotopic signature. Heavier isotopolouges remain as soil water as the lighter isotopolouges evaporate, differentiating the isotopic composition of soil bulk water from precipitation (Brooks et al., 2009; McDonnell, 2014).

However, the isotopic composition of mobile water closely matches that of incoming precipitation (Brooks et al., 2009; McDonnell, 2014). Plants, with few exceptions, do not fractionate water during root uptake (Dawson and Ehleringer, 1998; Ellsworth and Williams,

2007). However water in the leaves is enriched due to transpiration (Gonfiantini and Tongiorgi,

1965). Isotopic enrichment occurs during the transpiration process due to the (essentially) equilibrium evaporative process that occurs when the plant stomata are open (Gat, 1996). As a result, the lighter isotopolouges are preferentially evaporated and heavier isotopolouges are concentrated in the cell. Therefore, the source of plant water can be identified by sampling the water within a plant’s vascular system in the stem or trunk. Determination of the isotopic composition of infiltrating precipitation, soil water and plant water, the hydrologic flow within the system can be conceptualized using the isotopic fingerprinting technique. To delineate water flow through a system, water samples are isotopically analyzed from the precipitation input, at depths throughout the soil profile, from ground water source, a and the vascular system of plants

(Brooks et al., 2009; McDonnell, 2014; Goldsmith et al., 2012; Penna et al., 2018).

1.4 Transition into “n water worlds”

Despite an observed isotopic separation among water pools, both bulk and mobile water are both available for plant uptake. Mobile water, since it has a higher water potential, requires less energy in the root water uptake process. Bulk water, which includes water held by matric suction, and is described by a low energy potential value, requires greater energy in the root water uptake process. Several studies have shown that the isotopic composition of mobile water matches that of precipitation and groundwater, and the isotopic composition of bulk water matches that of xylem water in different plant species, including both trees and grasses (Brooks et al., 2009; Evaristo et al., 2015b; Goldsmith et al., 2012; Oerter and Bowen, 2017). This finding indicates that there is a distinct separation of water pools in the vadose zone and that

plants preferentially take up bulk water over mobile water.

A stronger argument for soil water reservoir mixing of bulk and mobile water is called convective-dispersive flow (Vereecken et al., 2016; Berry et al., 2017). This model is sometimes called the mobile-immobile water model, and it provides evidence for partial mixing of pre- existing (bulk) water and event (mobile) water (Berry et al., 2017). Isotopolouge profiles (the variation of soil water isotopic signature in the vertical soil profile) are created by the combination of capillary rise of water that is fractionated during mixing with water that is enriched from an evaporative influence (Vereecken et al., 2016). Macropore flow can also cause infiltrating precipitation to bypass pre-existing water, resulting in a vertical flow dominated flow scheme, in which there is limited mixing of bulk and mobile water due to high flow rates (Berry et al., 2017). The division between the pools in this model is driven by the flow rate, which

9 accounts for partial mixing of pre-existing (bulk) and event (mobile) water in the vadose zone rather than a complete mixing of incoming and pre-existing water.

The validity of the TWW hypothesis (complete ecohydrologic separation) hypothesis has been questioned because of the formerly mentioned observations and the two following challenges. The first challenge questions the biophysical feasibility of the hypothesis. As formerly mentioned, the TWW hypothesis defies established physics for plants to preferentially take up bulk water, a process that is more energy intensive than uptake of mobile water. The second, as is discussed in section 2.12, laboratory methods in extracting the bulk water from soil and vegetation samples can possibly compromise the initial isotopic composition of the water sample.

A workshop titled “Isotope-based studies of water partitioning and plant-soil interactions in forested and agricultural catchments” took place in San Casciano, Italy, September 2017

(Penna et al., 2018). This workshop included research from twelve countries that discussed advancements, limitations and future challenges in the field, attempting to generate unity among labs (Penna et al., 2018). The conclusion was that the dichotomous idea of two water worlds should be swapped for a more fluid idea of “n water worlds” (Penna et al., 2018). In this conceptualization of unsaturated flow multiple water reservoirs exist. This idea switched the community research focus to determine the degree of hydrological separation rather than if complete hydrological separation exists.

Both bulk and mobile water contribute to the available soil water content for vegetation but the water available in a given system is contingent on the soil physical characteristics, vegetation cover, precipitation amount and intensity (Berry et al. 2017; Penna et al. 2018;

Sprenger et al. 2018). Furthermore, the separation of water pools within the vadose zone can also depend on the characteristics of the rainfall event. There are numerous complexities affecting the soil water mixing processes that are compounded by the uncertainty that scientists face during the water extraction process. These have made it hard to define the number of pools within the subsurface (Berry et al., 2017; Bowling et al., 2017; Penna et al., 2018). However, there is not abundant mixing of water pools in the subsurface, and the question has transformed to one that questions the degree of hydrological separation, not whether hydrological separation exists.

1.5 Hydrological Separation in Agriculture

Hydrological separation has been investigated primarily in natural environments, rather than in agricultural systems. Crops differ from natural vegetation due to shortened lifespan of the plant. Maize specifically is planted and harvested in the span of approximately 100–150 days, making the initial growth states vital in determining root length, root health, and as a result, the depth for plant water uptake. Maize physiological maturity is defined by a series of vegetation growth stages (VE-V12), which is based on the number of crop leaves. These growth stages are followed by reproductive stages (R1-R6). Vegetation growth stages occur from cultivation to approximately week 9 or 10. Reproductive growth stages begin at that point and the plant reaches full physiological maturity at approximately 60 (5) days after the silking (R1 stage)

(Channel Bio, 2013). Plant maturity will affect the source of root water uptake because as the plant grows deeper water resources will be available for root water uptake. Furthermore, the crop will need a greater volume of water during the late vegetation stages and early reproductive stages, which can affect the root water uptake patterns.

Root distribution and length greatly affect the depth at which water is taken up, and if crops received low amounts of water and nutrients at the beginning growing stages (Ma and

Song, 2016). Maize root systems have the greatest root density in the top soil layers, which affects root water uptake throughout the growing season (~0cm–20cm) (Ma and Song, 2016).

Nevertheless, maize roots can extract water from depths greater than their primary root distribution when necessary (Ma and Song, 2016; Wu et al., 2016). Therefore, in later stages of the growing season, especially in drier conditions, water extracted from maize xylem can reflect deeper sources (Wu et al., 2016; Zhao et al., 2017).

Multiple studies have investigated the source of maize water-uptake using stable water isotopes. These studies related results to the crop’s physiological maturity, the environmental conditions, the nitrogen input and slope gradient of the land (Ma and Song, 2016; Wu et al.,

2016; Zhao et al., 2016). Of these studies, Zhao et al. (2016) investigated soil water uptake in relation to mobile water, immobile water (held by matric suction) and a combination of the two sources. Findings showed that maize primarily used immobile water with minimal contribution from mobile water, rather than only mobile water. Results indicated mobile soil water, from event precipitation, reflected a distinct isotopic composition from pre-existing soil water, providing evidence of hydrological separation on agricultural landscapes. However, further research is required to determine the partitioning of soil water in agricultural landscapes to improve management practices.

1.6 Soil Moisture Characteristics

Soil water cycling cannot be traced isotopically without knowledge of soil hydrologic characteristics and the soil physical properties that influence them. The two soil moisture 12 components that directly relate to available water for plant uptake are soil moisture content and soil water potential. Knowledge of each of these variables throughout the soil profile provide information on water flow, water content and water energy.

Soil water can reside within the vadose zone as water held by capillary forces in open pore space between soil particles or by adsorptive forces, which is the adhesion of water to the surface of soil colloids. The combination of these forces is called matric potential (Hillel, 1980).

Water held by matric potential and free flowing (mobile) water in the soil profile represent the soil moisture content.

Soil moisture content commonly varies between the field moisture capacity and the permanent wilting point. Field moisture capacity (FC), which can also be referred to as the moisture-holding capacity, is the water content of soil after the soil is saturated and freely drained via gravity (Briggs and Shantz, 1912). In ideal conditions no evaporation occurs, and the water left in the soil is the maximum amount of water that the soil can hold without drainage.

The permanent wilting point (PWP) is a low soil moisture content in the soil when water is no longer available for plant root water uptake (Briggs and Shantz, 1912). This occurs when the potential energy of water is so low that plants cannot take it up via a pressure gradient. Strong adhesion and cohesion forces dominate the system when the soil water content falls below the

PWP. The difference in the FC and PWP is the available water content (AWC) for plant uptake.

Measurement of the AWC at different depths within the soil profile is important to understand the root water uptake process. Locations with higher soil moisture content will have more free water, which is less energy intensive for plant uptake. Quantification of the moisture content

13 throughout the soil profile is an important part of understanding water flow throughout the system.

Soil moisture content describes the quantity of water in the profile, however the soil water potential describes the energy. Therefore, soil water potential is an important variable in determining root water uptake. Furthermore, soil water movement will differ between soils based on soil physical characteristics and textural composition. Soil water potential is defined as the potential energy of water in reference to pure water (i.e. the standard reference state). This pure water is deionized, at atmospheric pressure and at the same temperature and level of the soil moisture (Lal and Shukla, 2004).

While soil water has kinetic energy, the velocity of water is slow, making this energy component negligible and therefore it can be ignored as part of water movement in the soil.

Water moves in the soil by a series of potential energy differentials (i.e., pressure differentials).

Total soil water potential energy (Φt ) includes pressure potential (Φp), matric potential (Φm), height potential (Φz), osmotic potential (Φπ), and overburden potential (Φo) (Lal and Shukla,

2004).

Φt = Φp + Φm + Φz + Φπ + Φo [3]

The pressure potential is determined by the water pressure from the saturated soil above the point of interest and is applicable only in saturated soils. The matric potential is a function of unsaturated soils, and it is determined by the forces of adhesion and cohesion between soil particles and water molecules. Gravitational potential is determined by the height of the liquid within the soil profile. These components are always determined relative to the reference level

(typically the soil surface). The osmotic potential is determined by the composition and amount

14 of solutes in the soil water. Higher concentration of solutes causes a decrease in the vapor pressure and an increase in the boiling point, both of which affect the water energy status. The osmotic pressure can also have implications for plant root water absorptions. Soil water with a greater concentration of dissolved solids will inhibit root water uptake. Finally, the overburden potential is an energy change in the soil water due to the unsupported material above the point of interest. Typically the total potential is viewed as a combination of osmotic and matric potential, as these potentials primarily determine the water content available for plants (Hillel, 1980).

Plants will take up water via an osmotic process. Roots, with a low pressure, take up water at a relatively higher pressure within the soil. The pressure at which root water uptake occurs correspond with available water content soil moisture.

The soil moisture characteristic curve plots the relationship between soil matric potential and soil moisture content and it creates a soil moisture characteristics curve, or pF curve. The pF is the measurement of -log10 (cm head). The relationship between these two variables is highly dependent on the soil structure, primarily on the porosity. For example, clayey soils generally have a stronger structure than sandy soils and a higher porosity (Lal and Shukla, 2004). Clayey soils also have a greater pore size distribution of smaller pores than sandy soils. This results in lower pF values in clay soils corresponding the same moisture content in sandy soils due to the difference in potential energy associated with water held in smaller versus larger pores.

Other soil characteristics can affect this relationship including the soil organic content

(Lal and Shukla, 2004). Soil temperature influences the soil moisture content and soil water potential relationship. High soil temperatures correspond to relatively lower pF values corresponding with soil moisture contents because more energy required to hold soil moisture.

The relationship between soil water potential and soil moisture content changes as soil wets and dries. This results in a hysteretic relationship that is dependent on the soil moisture content in the soil as wetting and drying occur (Topp, 1971). Not all soils are hysteretic, and those that are show varying degrees of hysteresis depending on soil texture and structural characteristics as well as the local climate. During the rewetting process, soil water potential will correspond to a lesser water content than that same water potential value corresponds to during the drying path in hysteretic soils (Likos et al., 2013). Shorter periods of soil rewetting and drying will result in scanning pF curves, which are signs of hysteretic activity residing between the major wetting and drying pF curves. The degree of hysteresis occurring in soil wetting and drying patterns is important for soil water redistribution throughout the profile and therefore can affect root water uptake patterns (Lal and Shukla, 2004).

Soil water characteristics must be considered when using the isotopic fingerprinting method to track water flow in the vadose zone. While isotopic fingerprinting is a powerful method of hydrologic delineation, knowledge of soil structure and water characteristics help to generate stronger conclusions of hydrologic patterns observed in the vadose zone.

1.7 Mass Balance Mixing Models

Modeling the proportion of water pools (e.g. soil water depth, precipitation, irrigation water) that contribute to plant water is an important component of understanding water flux across the soil-plant-atmosphere continuum. This modeling can be done by using the isotopic compositions of water pools as natural tracers. Stable isotope mass balance mixing models

(MBMM) have been used in ecologic, hydrologic, and biogeochemical research areas to determine the relative contribution of a series of sources to a mixture (Phillips and Gregg, 2003; 16

Davis et al., 2015). A single calculated solution that identifies the source proportion is possible when the number of sources (n) is less than or equal to one plus the number of tracers (d). The number of unknown values (fraction of each source) must be no more than the number of known variables (the number of tracers). When these conditions are met, the solution can be found using a series of linear mass balance equations (Phillips, 2001; Phillips and Gregg, 2003). For example, in a two source, one isotopic tracer system, the following equations can be used to solve for the fraction of each source (Phillips and Gregg, 2001).

훿푑푚𝑖푥푡푢푟푒 = 훿푑푎 ∗ 푓푎 + 훿푑푏 ∗ 푓푏 [4]

1 = 푓푡표푡푎푙 = 푓푎 + 푓푏 [5]

훿푑푚푖푥푡푢푟푒−훿푑푏 푓1 = [6] 훿푑푎−훿푑푏

In a dual isotope system, such as the system in this study, the proportion of source contribution to can be accurately calculated with three sources. However, additional sources increase the uncertainty in the calculation. Davis et al. (2015) highlights three primary issues in determining source proportions with MBMM:

1. Multiple sources where n ≥ d+1

2. Ambiguous sources of isotopic composition

3. Similar or identical ranges of isotopic compositions of multiple sources

Mixing models have been developed to minimize these errors, providing a solution with the greatest accuracy and precision. Mixing models vary in method, with some that use iterative analysis with linear mixing equations and others that use Bayesian statistics, using a statistical framework to determine the optimal proportion output and reducing error (Phillips et al., 2014;

Davis et al., 2015). A priori research and data analysis are essential when utilizing the 17 computation abilities of mixing models (Phillips et al., 2014). Driving questions and a strong understanding of the data are needed to validate the mixing model output.

IsoSource is an open source model published by the EPA that iteratively solves a series of mass balancing mixing equations to determine source proportions (Phillips and Gregg, 2003;

Phillips et al., 2005). All of the possible solutions where ftotal = 1 are sorted through in increments. The model sorts through these combinations to determine which fraction combinations result in a specific tolerance (provided by the user) of the known mixture value.

The data output provides descriptive statistics on the results (Phillips and Gregg, 2003). The number of accepted solutions is dependent on the number of sources and their isotopic compositions, the composition of the mixture and the increment and tolerance values set by the modeler (Wu et al., 2016). The number of accepted solutions can therefore be described as:

100 [( )+(푆−1)]! 푖 푁 = 100 [7] [( )!+(푆−1)!] 푖

Where N is equal to the number of possible combinations, S is the number of sources contributing to the mixture and i is the increment assigned by the user. The input mixture must fall within a polygon in an isospace plot that is created by the input source value for accurate determination of a source. In larger datasets it is acceptable to use larger increments of 2% to

2.5%. However, in smaller datasets, a 0.5% to 1% increment is more appropriate (Phillips and

Gregg, 2003). An iterative linear mass balance mixing model was used in this study because of the small number of sources (n=4) and the single isotopic value assigned to each source, rather than a population mean.

1.8 Objectives and Hypothesis

This research was driven by one overall goal with three supporting objectives. The overall goal of the research was to observe spatio-temporal fluctuations in available water content and to determine soil water isotopic composition to trace water cycling through an agricultural system and to determine whether hydrologic separation exists. Three objectives support this primary goal.

1) Develop soil water extraction methods that consistently achieve complete extraction of

soil water and generate reproducible fractionation factors for δ18O and δD.

2) Determine whether soil water exhibits distinct δ18O and δD ratios with increasing depth

in the soil profile.

3) Model root water uptake from soil profile depth with the IsoSource MBMM using

calculated maize xylem isotopic compositions and sampled soil water compositions to

determine the range of expected xylem compositions.

To accomplish these objectives the δ18O and δD values of water were obtained as water experiences phase changes and transport to and within the atmosphere, soil and vegetative reservoirs in the agricultural system. Soil moisture content (SMC), soil water potential (SWP) and soil temperature (ST) were continuously monitored at 10 cm, 20 cm, 40 cm, and 60 cm depths in three site locations to quantify the range of available water content in the field. Water was extracted from soil samples collected at depths corresponding to SMC, SWP and ST sensors. Root nodes, xylem and leaf water were extracted from maize stalk samples collected at each site location.

Cryogenic distillation techniques were used to withdraw water from soils. Extraction of water from maize tissue will be completed in future work. Expected maize xylem isotopic compositions were

19 calculated using expected root length and root density distribution corresponding to soil water isotopic compositions. Results were input to IsoSource to determine the accuracy of the model in predicting source proportion and investigate the range of feasible maize xylem water isotopic compositions.

The primary hypothesis for this research was that the δ18O and δD of bulk and mobile soil waters will express distinct isotopic signatures when soil water content is below field capacity and will express a lesser degree of isotopic differentiation when at or above field capacity. It is also predicted that the isotopic composition of deeper soil water will be heavier relative to the isotopic composition of soil layers that are shallower in the profile due to the influence of evaporation at shallow depths.

Chapter 2. Methods

2.1 Overview

Hydrologic cycling was delineated across the soil-plant-atmosphere continuum in an agricultural system in Columbus, Ohio. During the field study period soil moisture content, soil water potential, and soil temperature were continuously monitored. Stable water isotopes δ18O and δD were used as natural tracers to delineate the flow throughout the system. Soil, maize tissue, groundwater and surface water samples were taken after rainfall events and an extended dry period (>5 days) to characterize hydrologic patterns in an agricultural field planted with Zea mays L. (maize). After sample collection, water was extracted from soil using cryogenic vacuum distillation methods and analyzed for δ18O and δD ratios. A mass balance mixing model

(MBMM) equation was used to calculate expected maize xylem isotopic compositions based on the δ18O and δD of soil water samples. Xylem δ18O and δD values were input as mixtures in the

IsoSource model and soil water δ18O and δD values were input as sources to discover the accuracy at which the model could predict the correct values and the range of the feasible xylem compositions sourcing only from soil water.

2.2 Site Location

The field experiment occurred at the Waterman Agricultural and Natural Resources

Laboratory, hereafter referred to as the Waterman Farm (Figure 1). The experiment as conducted in a field of a pioneer brand hybrid P0157AM maize crop, which has a 101-day maturity. Site management followed conventional practices at the Waterman Farm. The field had been grid

21 sampled during autumn of 2016 and was determined at or above tri-state fertility guide critical levels. Therefore, no dry fertilizer was applied (i.e., no additions of phosphate or potassium). On

June 11, 2018, during the V-6 stage of the maize, approximately 101 kg/ha of nitrogen was applied to the field using 28% urea ammonium nitrate acting as a mid-season fertilization.

Figure 1. Field site map. The image depicts the soil and vegetation sampling site locations, organization of METER Environment EC-5 soil moisture content and TEROS 21 soil water potential sensors. Four EC-5 and four TEROS 21 were placed at each sampling site location. Outlets of Tile 1 and Tile 2 and the stream sampling site location are noted on the photograph.

Conventional practices included chisel plowing that penetrated the top soil to approximately 8 cm at the end of the previous harvest in autumn 2017. Spring planting occurred on May 1, 2018. Seeds were planted approximately 5 cm deep. Integrated pest management practices include tillage to reduce fungal inoculum and planting resistant hybrids rather than an herbicide application. Four herbicides were used: Caperno, Pmax, Atrazine, and Interlock. These were applied on May 14, 2018 and Pmax was applied again on June 7, 2018. The field was harvested October 2, 2018.

The field is tile drained (at approximately 75 cm deep) by two drainage systems

(Sullivan, 1997). Tile 1 drains into a small creek running through the research facility and into the Olentangy River, while Tile 2 joins a larger tiling system, flowing directly into the Olentangy

River (Figure 1). Field dimensions were 143 m on the north side, 66 m on the east side, 165 m on the south side and 40 m on the west side. The plot was split between two soil types: a Crobsy Silt

Loam (0.30 ha) and a Miamian Silt Loam (.51 ha). Maize was planting in 90.4 cm rows. Field sampling and moisture monitoring took place in a Crosby Silt Loam soil field with a slope between 2% and 6%. Entering the Miamian Silt Loam portion of the field and nearing the creek, the slope increased, ranging between 6% and 12%.

2.3 EC-5 Soil Moisture Sensor Calibration

Soil moisture content, soil water potential and soil temperature were monitored at three locations and at four depths within the soil profile. METER Group EC-5 soil moisture sensors and TEROS-21 soil water potential and temperature sensors were used. Soil-specific calibrations were conducted for the METER EC-5 Sensors based on METER Environment’s soil specific calibration method A (METER Group Inc., 2017). Bulk and particle density were measured and porosity was calculated for the soil at each depth (Topp et al., 1993; Grossman and Reinsch,

2002; Topp and Ferre, 2002). With the measured bulk density, volume and mass of soil samples from the four sampling depths (10 cm, 20 cm, 40 cm, 60 cm) were packed to a calculated height in a 1-L Mason jar to mimic approximate field bulk density conditions at depth for calibration purposes. The four samples were wetted with an initial 100 mL and the addition of 50mL of water six times to reach saturation. After each additional wetting, a soil moisture sensor was placed into each the Mason jar and readings recorded for five minutes. A soil sample was collected from every Mason jar after each additional wetting and weighed on an aluminum weighing dish before being placed the oven at 105°C for 24 hours. Following the 24 hour drying period the sample was weighed again to identify the gravimetric moisture content (GMC). The volumetric moisture content (VMC) was calculated from the GMC values. The VMC values were then compared to the EC-5 logger dielectric constant measurements to calibrate the sensor.

2.4 Field Placement of Sensors

Two posts supporting a total of five ECH2O Em50 data loggers were placed 1.5 m apart study field. Three sampling locations were selected within a 4 m diameter of each post, creating a triangle to capture the attributes of soil wetness on a highly localized spatial scale (Figure 1). A hole with an approximate diameter of 40 cm and depth of 60 cm, was dug at each site. Sensors were placed in the soil at each site according to METER Environment guidelines. Five data loggers, each with five logging ports, were attached to two posts in the field. Soil moisture content, water potential and temperature were measured at 10 cm, 20 cm, 40 cm and 60 cm depths in the soil profile at each location. Monitoring was continuous from May 16, 2018 to

September 16, 2018. Data were recorded every hour and downloaded every week for correction based on calibration for further analysis (METER Environment., 2017).

2.5 Soil Moisture Content Determination at Field Capacity and Permanent Wilting Point

Field moisture capacity (FC) was determined at each depth based on the definition that states the FC is the soil moisture content 2 days following a precipitation event (Lal and Shukla,

2004). Six dates during the field season (6/7, 6/15, 6/29, 7/4, 8/14, 9/3) following a single precipitation event larger than 1.5 cm or cumulative precipitation events larger than 1.5 cm were chosen to determine field capacity for each sensor. These values were averaged to determine the field capacity at each site and depth. Field capacity for each sensor was considered separately due to sensitivity differences that could have resulted from installation.

The soil moisture corresponding to PWP was only determined for 10 cm and 20 cm depth. The soil water potential fell below -1500kPa (the suction associated with PWP) at these two depths on 7/18 and 7/19 (Lal and Shukla, 2004). The soil moisture content was averaged at the 10 cm and 20 cm depths over this 48-hour period.

2.6 Local Meteoric Water Line (LMWL)

A local meteoric water line created by the Carey Lab Group for central Ohio was used to classify precipitation throughout the system. Rain water was collected in an open bucket collector from October 2014 to October 2018 in four central Ohio locations. Most samples were collected from Clintonville, Ohio (77%) with the remainder at the Ohio State campus, Mansfield,

Ohio and Galena, Ohio. Samples were collected immediately following the end of a precipitation event and placed in 20 mL glass scintillation vials capped with polyethylene cone-shaped liners, with no head space, and refrigerated until analysis. All precipitation samples collected prior to

2018 were analyzed on Picarro Wavelength Scanned-Cavity Ring Down Spectroscopy Analyzer for Isotopic Water-Model L1102-i for δ18O and δD and all samples collected in 2018 were analyzed on Picarro Wavelength Scanned-Cavity Ring Down Spectroscopy Analyzer for Isotopic

Water-Model L2130-i for δ18O and δD. Precipitation samples were continuously collected in

Clintonville, Columbus Ohio throughout the study period to contribute to the generation of a central Ohio LWML as well as to match precipitation events to sampling events (Smith et al., in preparation).

2.7 Field Sampling Overview

Sampling began at the V6 stage of maize growth and continued through R5. All sampling events occurred between the hours of 7:00 to 13:00. Soil, maize tissue, and water sampling were conducted concurrently. All solid samples were frozen until cryogenic vacuum distillation and water samples were refrigerated until isotopic analysis via cavity ring down spectroscopy.

2.8 Soil Sampling

Soil samples were collected at each site location and at four depths (10, 20, 40, 60 cm) that corresponded to depths of soil moisture and soil water potential monitoring. Sampling locations were within 1 m of the site sensors. Holes were dug between crop rows with a 6.5 cm diameter auger and soil samples were collected from desired depths. Soil samples were placed into Labco Exetainer® 12 mL flat bottom vials that were immediately capped and placed on ice.

Samples were then brought back to the laboratory and placed in the freezer until the extraction process.

2.9 Field Water Sampling

Water was sampled from two agricultural tile drain pipes during the sampling season.

Tile 1 drained the study field and flowed into the creek running through Waterman Farm. Tile 2 drained the study field and the field south of the to the study field. The outflow of this tile drain is to the Olentangy River, which lies east of the field site (Figure 1). Tile 1 samples were

26 collected at the tile drain outlet, and tile 2 samples were collected from an access well southeast of the sample field. Creek samples were collected in 20 mL glass vials. Water from drainage tiles was collected in either 2 mL of 20 mL vials. A 2 mL vial was used to collect water from Tile 1 because of the low flow and the 20 mL vial was used to collect water from Tile 2.

2.10 Zea mays L. (Maize) Sampling

Maize sampling included collection of maize leaf, xylem and root node of three plants at each sampling site. The maize plants chosen for sampling were collected from the rows directly on each side of the sensor site location, which is less than half a meter from the soil site. Leaf sampling consisted of a single full leaf that was cut in half and then rolled and placed into an

Exetainer® vial and then frozen prior to water extraction. The major vein of the leaf was discarded so that enrichment from transpiration could be observed without contaminating the sample.

For the first month of the growing season the xylem and root crown were indistinguishable and thus sampled as one. After the V-3 stage, the root crown and the xylem were sampled separately. Leaves were stripped from the edges of the xylem prior to cutting the xylem and placing it into Exetainer® vials. Root crown samples were cleared of soil and then cut before being placed in Exetainer® vials and frozen prior to extraction.

2.11 Sample Water Analysis

The isotopic composition analysis of samples collected during the field season of 2018 were conducted on the Picarro Wavelength Scanned-Cavity Ring Down Spectroscopy Analyzer for Isotopic Water-Model L2130-i for δ18O and δD, hereon referred to as the Picarro 2130-i. Soil sample waters were run on the Picaro 2130-i immediately following the extraction process. The precision of each of these runs was determined by including two duplicate analyses. The average

27 precision for the Picarro 2130-i was 0.106‰ and 0.852‰ for δ18O and δD respectively. Soil samples were analyzed using the ChemCorrectTM software (Picarro Inc., Santa Clara, CA, USA), which is a post-processing software used to check if interference occurred during analysis.

Samples are denoted as having (a) no detectable contamination influencing the δ18O and δD readings, (b) containing trace values of contamination that could slightly shift the δ18O and δD values, and (c) containing detectable contamination influencing the δ18O and δD readings. Seven of the nine extractions were run through ChemCorrectTM.

2.12 Cryogenic Vacuum Distillation: Literature Review

Stable water isotopes δ18O and δD have become important natural hydrologic tracers to track water across the soil-plant-atmosphere continuum (Penna et al., 2018). Technical improvements in mass spectroscopic methods have facilitated the use of these isotopes to trace water pathways throughout natural and anthropogenically altered environments. However, extraction of a sufficient amount of water from soils and plant tissue for isotopic sampling has proven difficult without causing fractionation of water in the sample (Koeniger et al., 2011;

Orlowski et al., 2016a; Berry et al., 2017; Newberry et al., 2017). Numerous methods have been tested to successfully extract water from soils and plant tissue both in the field and laboratory

(Orlowski et al., 2016b). Laboratory methods include cryogenic vacuum distillation, squeezing, direct vapor equilibrium, CO2/hydrogen equilibration, azeotropic distillation, microdistillation, microwave extraction, He-purging distillation, accelerated solvent extraction, and centrifugation

(Sprenger et al., 2015; Orlowski et al., 2016b). Field sampling techniques include zero-tension and suction lysimeters and wick samplers (Sprenger et al., 2017b). Laboratory methods of extraction are more commonly used than field methods, with cryogenic vacuum distillation as

28 the most commonly cited method of extracting water from soils and plant tissue (Koeniger et al.,

2011; Orlowski et al., 2016a, 2016b).

Orlowski et al. (2016b) tested six laboratory extraction techniques to compare the accuracy and precision of each technique. In their review, the isotopic results of cryogenic vacuum distillation, hereon shortened to cryogenic distillation, demonstrated a variability that fell within the middle of other extraction methods (Orlowski et al., 2016b). Despite the inconsistency of isotopic composition found by that study, cryogenic distillation has consistently been used because of its reliability in extracting adequate amounts of liquid from small volumes of soil and vegetation for analysis, especially in low soil moisture conditions (Sprenger et al.,

2015; Orlowski et al., 2016b). As a result, cryogenic distillation extraction methods were selected for this study despite challenges associated with the methods, which are derived from both the extraction method and the characteristics of the sample itself.

Cryogenic distillation is a phase-change method for retrieving water within a soil or plant tissue sample. The method relies on evacuating the sample and collection vials and creating a temperature gradient to cause a phase change in the sample water from ice to vapor. The water is then completely transferred from its initial reservoir, the sample, to its final reservoir, the collection vial. It is essential that all water is transferred from one reservoir to the other to prevent fractionation of the collected liquid. If the collection of water is incomplete the collected water sample will be too light. When a fraction of the soil water is evaporated, the higher ratio of lighter isotopolouges will be collected leaving the heavier isotopolouges behind, resulting in a compromised isotopic composition of the soil water (Ingraham and Shadel, 1992; Araguás-

Araguás et al., 1995).

Simplified and inexpensive extraction set-ups that maintain the integrity of the distillation process have been successfully adopted to extract water from both soils and plant tissue (West, et al., 2006; Koeniger et al., 2011). All cryogenic vacuum distillation methods require a closed system so that no water is lost during the extraction process. The theory behind the extraction process is based on Raleigh distillation, and while the idealized Raleigh distillation system is an open system, the equations apply to this closed model because of the separation of one reservoir from another (Gat, 1996).

Methods of cryogenic distillation differ between laboratories, however follow a similar procedure. The system is put under vacuum to expedite the distillation process. Next, an extreme temperature gradient is applied to the system. The sample is heated to a desired temperature within the range of 100°C to 200°C. The desired temperature is dependent on the interlaboratory methods for the extraction procedure. The collection vial is cooled to approximately -195°C in a bath of liquid nitrogen (West et al., 2006; Koeniger et al., 2011; Orlowski et al., 2013, 2016a,

2017, 2018). Appropriate heating extraction time and temperature are important factors in the extraction process. Temperatures that are too low or time periods that are too short will hinder complete water collection, causing fractionation of the collected sample.

The method set-up used in this experiment was modeled after systems used by Koeniger et al. (2011) and by West et al. (2006). The method was altered according to laboratory resources available and to recent literature that provided insight on how to reduce fractionation in the samples (Orlowski, et al., 2016a, 2016b, 2017; Penna et al., 2018). A successful cryogenic vacuum distillation is dependent on two controlled variables: the time of sample heating and the temperature at which the sample is heated. Some studies have investigated the optimal time and temperature to heat soil and plant samples in order to extract water with minimal fractionation

(West et al., 2006; Koeniger et al., 2011; Orlowski et al., 2013; Gaj et al., 2017). Extraction time and temperature further depend on soil physical, geochemical, and water characteristics, and the type of plant tissue (West et al., 2006; Koeniger et al., 2011; Orlowski et al., 2013, 2017). For example, soil water can be retrieved without fractionation at lower extraction temperatures and shorter times from soil samples with high sand content (West et al., 2006; Koeniger et al., 2011;

Orlowski et al., 2013, 2017). It is essential to conduct spike tests with soils to develop extraction timing curves to development of cryogenic methods and progress using δ18O and δD as hydrologic tracers (Orlowski et al., 2017, 2018; Penna et al., 2018).

Studies that have conducted spike tests developed extraction timing curves that have shown longer extraction times at higher temperaures extract more water from soils, as a higher fraction of bulk water is evaporated during the process (Gaj et al., 2017; Koeniger et al., 2011;

Orlowski et al., 2013, 2016a, 2016b, 2017, 2018; West et al., 2006). Spiking experiments that used soils with a high clay content showed that fractionation commonly occurs in samples

(Oerter et al., 2014; Gaj et al., 2017). Even with experiments where all water was successfully extracted, clay water samples were found to be fractionated, which indicates that another variable is causing fractionation apart from the incomplete Raleigh process (Gaj et al., 2017).

Fractionation was attrributed to grain size distribution and clay mineral characteristics (Gaj et al.,

2017).

Soil texture, mineral composition, cation exchange capacity (CEC), organic, clay, and carbon content all can cause fractionation of water during the extraction process (Gaj et al.,

2017; Meißner, et al., 2014; Oerter et al., 2014; Orlowski et al., 2018, 2013). Previous work has shown that the δ18O composition of water molecules bound to the surface of phyosilicates is fractionated. These bound water molecules create a hydrated sphere around cations in the

31 electrical double layer, resulting in a 18O:16O fractionation effect (Oerter et al., 2014). Due to fractionation occuring when water is extracted from these clayey soils, the collected water is depleted relative to the intial soil water contnet, however the degree fractionation is reduced with increased temperatures during extraction (Oerter et al., 2014; Gaj et al., 2017; Newberry et al.,

2017). The fractionation is a result of residual water, which is interlayer water adsorped to negatively charged phylosilicate layers and on the surface of minerals. In ambient conditions interlayer water readily exchanges with the soil water vapor, altering the isotopic composition of the water (Sayin and Epstein, 1970; Meißner et al., 2014). For this reason, the soil mineral composition and charge can afftect the degree of fractionation occuring during the extraction process. The presence of carbonates in soil will deplete δ18O from the original soil water, but that process does not change the isotopic compsiton of δD (Meißner et al., 2014). Stable water isotope δ18O will exchange with oxygen in the soil carbonate, depleting the ratio of 18O:16O in the soil water (Meißner et al., 2014).

Multiple factors faciliate fractionation in soil samples and vegetation samples. Orlowski et al. (2016b) conducted an inter-laboratory comparison of cyrogenic extraction methods and showed that the extraction signature differed greatly depending upon the soil type and soil water content (refer to Orlowski et al. 2016b for methods and participating laboratories). Their study showed that generating an inter-laboratory standard for cyrogenic extraction technique would be difficult, as different soils, lab instruemnts, and sample conditions all affect the extraction process. Therefore it is imporant to develop intralaboratory standards and spiked tests to accurately quantify and correct for the fractionation occuring during soil water extraction process. The cryogenic distillation methods used in this study were modeled using the preivously mentioned studies. Results from Orlowski et al. (2017) guided the temperature and duration of

32 heating decisions. The extraction set-up was determined from several studies (West et al., 2006;

Koeniger et al., 2011; Orlowski et al., 2017).

2.13 Cryogenic Vacuum Distillation Methods

Soil and maize tissue samples were collected in Exetainer vials and placed on ice immediately after collection. Upon return to the laboratory samples were transferred to a freezer where they were kept until undergoing cryogenic vacuum distillation to extract water. The cryogenic vacuum distillation set-up was modeled after Koeniger et al. (2011) and West et al.

(2006). Extraction times were experimentally determined and based on extraction times of

Orlowski et al. (2017).

To complete the extraction process with minimal fractionation a closed system under vacuum was designed and built prior to the distillation process. The system included two

Exetainer® vials, one holding the frozen sample, and the other vial empty. A 7-inch connection line was created between the two Exetainer® vials using 0.125 inch capillary tubing connected to

0.25 inch Ultra Torr fittings (SS-4-UT-6-200). PrecisionGlide 20ga regular wall, regular bevel needles were placed inside each fitting and secured using an O-ring. Needles on both ends of the connection line were used to pierce the Exetainer® vial septum, creating a closed system between the Exetainer® vial holding the frozen sample and the empty Exetainer® vial (Figure

2). This allowed the sample water to be fully transferred from its initial reservoir (the sample) to its final reservoir (the collection vial).

Figure 2. Cryogenic distillation set up used to extract water from samples and standards. Vials holding soil samples in the heating block were wrapped in aluminum foil to evenly distribute heat (not shown). A total of ten extractions were conducted each experiment (only six shown).

The extraction process took three hours per sample. A single extraction experiment consisted of eight samples with two standards per set. All samples were weighed prior to the extraction process and following the extraction process to quantify the amount of water extracted per sample.

Soil standards were created by spiking air-dried soil with deionized water, hereafter called reference water. Soil standards had a gravimetric water content of approximately 0.35

Mg/Mg. The reference water used to spike the soils was pipetted into a 2mL vial to compare extracted soil water isotopic value to actual isotopic value of the water. Dried soils were Crosby

Silt Loam soils collected from the field site at 10cm depth. Standards were created the day before an extraction and frozen overnight to represent the process that the samples would go through.

Samples were removed from the freezer in two sets of three and one set of four to reduce the potential for melting before the sample was pumped down to a low pressure. The sample was placed in liquid nitrogen for 30 to 45 seconds prior to being pumped down to a pressure of 100 mTorr using an MKS Type PDR 2000 Dual Capacitance Diaphragm Gauge Controller (MKS

Instruments Inc., 2001). This was done to prevent unintentional extraction of liquid or water vapor while the system was being evacuated. Once frozen, the sample Exetainer® vial was connected to the empty Exetainer® vial via the connection set up to create a closed system at atmospheric pressure. Once a needle had pierced the septum of each respective Exetainer® vial, the needle was not removed. The double Exetainer® vial system was connected to a vacuum line by piercing the Exetainer® vial holding the frozen sample a second time with a needle

(PrecisionGlide 20ga regular wall, regular bevel needle) attached to the vacuum line. Once the system reached a pressure of 100 mTorr, the setup was removed from the vacuum line and transferred to the cryogenic distillation set up. The cryogenic distillation set up was composed of a heating block at 180°C and a liquid nitrogen trap in a Dewar flask. The Exetainer® vial holding the frozen sample was placed in the heating block holding space, lined with aluminum foil to distribute heat evenly around the vial. The corresponding collection Exetainer® vial was immersed half way in a liquid nitrogen bath (Figure 2). The connection between the two

Exetainer® vials was supported using a metal rod. The samples were left for three hours and the liquid nitrogen was refilled periodically to keep a constant volume in the Dewar.

After the three-hour distillation period, the syringes were removed from the Exetainer® vials. Immediately after removal of the needle from the sample Exetainer® vial, a 2 mL vial with

35 a 0.4 mL aliquot holder was placed under the needle to catch any water still in the connection line that might be released after disconnecting the two vials. The sample and collection vials were removed from the heating block liquid nitrogen bath, respectively. The collected ice melted at room temperature before it was transferred to a 2 mL vial with a 0.4 mL aliquot holder for isotopic analysis on a Picarro Wavelength Scanned-Cavity Ring Down Spectroscopy Analyzer for Isotopic Water-Model L2130-i for δ18O and δD. The connect lines were heated at 80°C for 48 hours to dry any remaining water inside the connection.

Nine extraction experiments were conducted. Each experiment contained eight samples and two standards. Extracted water was analyzed immediately following extraction. Duplicate analyses were conducted during sample analysis on the Picarro L2130-i. The largest measurement difference between duplicates was used to determine the precision of the instrument for the analyses. The average precision for the Picarro L2130-i for the nine extraction experiments was 0.11‰ (σ =0.09) and 0.85‰ (σ =0.46) for δ18O and δD.

Not all soil water was collected into the collection Exetainer® vial during the extraction process.

During some extractions a portion of the soil remained wet after the three-hour extraction period.

If the septum seal was broken and the system was no longer kept under vacuum no water would be collected even if the soil was dry. Remnant water at the top of the Exetainer® vial was sometimes found even when the soil was dry. Water was found in the needles and set-up connection of every sample. This excess water could compromise the integrity of the collected water’s isotopic composition. Samples that experienced these issues were noted.

2.14 Modeling Xylem Isotopic Composition and Soil Water Fraction

Predicted xylem isotopic compositions were calculated using a simple mass balance mixing model (MBMM). The fraction of water adsorbed by maize roots at each sample depth

36 was determined by calculating the expected root depths and density distributions at sampling dates:

18 18 18 18 18 δ 푂푥푦푙푒푚 = δ 푂10 ∗ 푓10 + δ 푂20 ∗ 푓20 + δ 푂40 ∗ 푓40 + δ 푂60 ∗ 푓60 [8]

δ퐷푥푦푙푒푚 = δ퐷10 ∗ 푓10 + δ퐷20 ∗ 푓20 + δ퐷40 ∗ 푓40 + δ퐷60 ∗ 푓60 [9]

1 = 푓10 + 푓20 + 푓40 + 푓60 [10]

Where f10 cm, f20 cm, f40 cm, f60 cm represent the relative soil water fraction of root water uptake.

Soil water characteristics were considered for each sampling date when determining the fraction of soil water adsorbed by roots. During all sampling dates soil water potential was above PWP for all depths. Since soil water characteristics were not limiting factors in water availability, the fractionations were calculated using root length and density distribution. Rooting depth was observed during the sampling season, but it was not closely measured. Therefore, root depth throughout the growing season was modeled using field observation and an equation developed by Borg and Grimes (1986):

푅퐷 = 푅퐷푚[0.5 + 0.5 ∗ sin(3.03 ∗ 푡푟 − 1.47)] [11]

Where RD is the root depth on day of sampling, RDm is the expected maximum rooting depth and tr is the relative time calculated as:

퐷푎푦푠 푠𝑖푛푐푒 푠표푤𝑖푛𝑔 푡푟 = [12] 퐷푎푦푠 푡표 푝ℎ푠푦𝑖푐푎푙 푚푎푡푢푟𝑖푡푦

Three maximum root depths at physiological maturity were estimated (40 cm, 50 cm, and 60 cm). These estimations were set using field observations and knowledge of the soil profile at the field site. After maximum root depths were modeled for the respective sampling dates, the root density at, 10 cm, 20 cm, 40 cm, and 60 cm was calculated corresponding to rooting depths. This was conducted using the following linear equation that provided a ratio of root mass to root length distribution (Fan et al., 2016):

푅표표푡 푀푎푠푠 = 1.102 − 0.001 ∗ 푥 [13] 푅표표푡 퐿푒푛𝑔푡ℎ 퐷𝑖푠푡푟𝑖푏푢푡𝑖표푛

Where root length distribution is the soil depth over which the roots are distributed, root mass is the total mass of roots over that distribution and x is soil depth (cm). The total root mass was obtained from the calculated ratio by multiplying the ratio by the maximum root length at the sample date, which was determined by Equation 9. The density distribution of root mass was calculated for every 5 cm from 10 to 60 cm in the soil profile by determining the root mass at that depth and dividing the value by the total root mass. Fifty-one scenarios were created with rooting depth and density distribution varying based on maximum estimated length and point of maturity during the sampling date (Appendix A, Table 9). Scenarios were applied to every site, which generated 153 xylem δ18O and δD signatures. These xylem values and the corresponding soil water isotopic compositions were the inputs into the IsoSource Model. The goal of the

IsoSource Model was to calculate the contribution of each source (soil water samples) of the xylem using the isotopic compositions of each.

The goal of inputting a calculated xylem isotopic composition with the isotopic composition of the sampled soil was to determine the accuracy and precision of the IsoSource model in addition to determining the validity of the equations used to describe the root length and density distributions. The mean values and standard deviations provide insight for the range of isotopic compositions that are expected for the sampled xylem. The model ran at an increment of 1% and a tolerance of 0.1, which are the advised values (Phillips and Gregg, 2003).

Chapter 3. Results

3.1 Establishing Cryogenic Vacuum Distillation Methods

Three cryogenic distillation methods were tested during methods development to determine the best method for extracting soil water from samples. Each method was evaluated on its efficiency of soil water recovery and degree of soil water δ18O and δD fractionation during the extraction process. The vacuum line used to evacuate the samples, and the method in which the samples were evacuated were consistent for all three distillation methods tested. The heating element and duration of heating were modified during the methods development phase to design the optimal extraction procedure. All extraction methods were tested using soil standards created in the laboratory, as described in section 2.13.

The first tested extraction method used heating process modeled after Koeniger et al.

(2011). In this method, boiling water served as the heat source and extraction times ranged from

30 minutes to 90 minutes. Only a portion of the water was successfully extracted when testing this method. Therefore, the heating element was switched to heating tapes (fluctuating between

175°C to 190°C) to achieve higher temperatures. Aluminum foil was used to evenly distribute heat around the sample vials. The heating duration was also modified to include longer extraction times, ranging from 90 minutes to 120 minutes. Water recovery and calculated fractionation factors were inconsistent when using this method. The variation in water recovery was likely due to the uneven heat distribution during extraction. While the aluminum foil assisted in distributing heat around the sample vials, heat circulation was not uniform. It was also

39 observed that samples heated for a longer period had better water recovery results and showed less fractionation.

Therefore, methods were modified to increase the duration of extraction to 180 minutes to ensure consistent water recovery results (Orlowski et al., 2017). The heating element was also changed to a heating block to maintain a consistent 180°C for the entire extraction period.

Aluminum foil was still wrapped around the vials to evenly disperse heat.

3.2 Extraction Results of Standard Soil Samples

After the methods were finalized, multiple extraction experiments were conducted with air-dried soils and oven-dried soils to establish a standard fractionation factor. Refer to Methods section 2.13 for standard preparation details. Recovered water from all standards was fractionated. The average isotopic fractionation values of δ18O and δD of air-dried soil samples

(n=14) were 1.21 (σ18O= 0.06) and 1.24 (σD= 0.04), respectively. The average fractionation factor

18 of oven-dried soils (n=10), was 1.28 for δ O and δD (σ18O = 0.04, σD= 0.04) (Appendix B, Table

B1).

Nine cryogenic distillation experiments were run to extract water from the 67 soil samples. Each of these experiments had ten distillation setups (Figure 2). The ten distillation setups were divided into samples and standards, with eight samples and two standards analyzed during each extraction experiment. The average fractionation factors of standards corresponding with sample extractions (n=19) were 1.21 (σ18O = 0.094) and 1.24 (σD=0.084).

This cryogenic distillation method was applied to ten samples of liquid, deionized water.

The average fractionation factors for water were 1.25 (σ18O = 0.098) and 1.25 (σD=0.104) for

δ18O and δD (Table B1). A t-test assuming unequal variances (α=0.05) upheld the hypothesis

40 that there was no significant difference between the fractionation factors determined from these four standard datasets.

The total water recovery was measured for the oven dried soil standards. Water recovery was measured by mass difference from the initial input of 2g of water, assuming the density of water was 1 g/cm3. The soil sample vial and the collection vial were weighed before and after extraction to calculate the percent water recovery. This comparison demonstrated that the percent of water extracted from the soil and the percent of water collected were not equivalent. A student t-test assuming unequal variances (α = 0.05) was used to confirm this finding. Percent of water extracted ranged from 99.6% to 102.2%, while the percent of water collected ranged from 87.7% to 93.0%. The data confirmed soil water was successfully extracted but not successfully collected for analysis. Water recovery was also measured in the deionized water extraction experiment. The percent extracted ranged between 93.41% and 101.37%. However, the percent recovery was lower, ranging from 75.76% to 84.09%. The percentage of water collected in air- dried soil experiments ranged from 68.52% to 96.15%.

3.3 Climate Conditions in Ohio 2018

In 2018, the Columbus, Ohio area experienced ~15 cm of precipitation more than the

2014 to 2017 average (Table 1). Of the 119 samples of precipitation collected in 2018, 43% of the rainfall occurred from May to August, which was similar to the average of 2014 to 2017. The total preseason precipitation (January–April) was 34.5 cm, 5.6 cm more than the 2014 to 2017 average during those four months. However, the percentage of total precipitation during the preseason months was consistent for all five years (Table 1). The average relative humidity dropped slightly in July compared to June and August, corresponding to the lower rainfall amount. The average temperature was consistent during the three sampling months. Overall, the

41 precipitation conditions in Columbus, Ohio in 2018 were a consistent representation of conditions from 2014-2017. This validates the use of the LMWL for comparison of soil water samples.

Table 1. 2018 Ohio Climate Conditions. Monthly precipitation totals, percent of annual precipitation, temperature and relative humidity for 2018. Mean monthly precipitation totals for 2014-2018 (OARDC, 2018).

2014-2017 2018 Monthly 2018 Monthly Average Average Average Precipitation Percent Monthly Relative Month Monthly Totals of Total Average Humidity Precipitation (cm) Precipitation 2018 (°F) 2018 (cm) January 3.71 3.12% 5.02 27.80 77.73 February 12.62 10.61% 4.76 39.33 79.14 March 6.30 5.29% 8.58 38.61 67.42 April 11.86 9.97% 9.15 47.78 65.60 May 12.04 10.12% 9.35 71.45 70.19 June 17.93 15.07% 15.05 73.85 77.23 July 7.47 6.28% 12.80 75.12 70.48 August 13.74 11.55% 8.43 74.71 78.84 September 6.91 5.81% 6.71 70.59 81.63 October 5.26 4.42% 6.95 56.11 77.07 November 12.50 10.50% 5.70 37.42 81.04 December 8.66 7.28% 7.14 37.15 82.84 Annual 119 99.63

3.4 Soil Physical Properties

Soil bulk density, particle density and porosity were similar throughout the soil profile.

The porosity was greatest at the 10 cm (0.48) and 60 cm (0.45) depths (Table 2). The bulk density was lowest at the 10 cm (1.21 Mg/m3) layer and it was consistent across the other sampling depths cm (1.40 Mg/m3). Particle density progressively increased throughout the soil profile (Table 2). The texture at each sampling depth matched the published classification of a

Crosby Silt Loam soil. Due to its measured clay content (23%), the soil at 40 cm depth was

42 better classified as a loam, trending toward clay loam (NRCS Soil Survey: https://websoilsurvey.sc.egov.usda.gov). Soil texture was not measured at the 60 cm depth.

Table 2. Soil density, porosity and texture properties with soil moisture contents corresponding to field capacity and permanent wilting point measurements.

Permanent Bulk Particle Field Field Depth Sand Clay Silt Wilting Density Density Porosity Capacity Capacity (cm) (%) (%) (%) Point (Mg/m3) (Mg/m3) (m3m-3) (pF) (m3m-3)

10 1.21 2.31 0.48 30.8 19.5 49.7 0.26 2.12 0.22 20 1.40 2.34 0.41 28.5 20.3 51.3 0.41 2.17 0.33 40 1.40 2.39 0.41 30.9 23.1 46.0 0.35 2.06 0.28 60 1.40 2.52 0.45 0.22 2.02 0.14

3.5 Soil Water Characteristics

The soil water content was greatest at the 20 cm depth and lowest at the 60 cm depth

(Figure 3). Soil moisture content at 20 cm also showed the greatest variability in water content and soil water potential over the field season (Figures 3, 4). Moisture content was the least variable at the 10 cm depth throughout the season. Water content at 20 cm, 40 cm and 60 cm demonstrated greater variability following precipitation events. High intensity precipitation events did not always lead to an increase in soil water content of corresponding magnitude.

Frequent, low intensity precipitation events resulted in increased soil moisture content close to or at field capacity (Figure 3).

Figure 3. Average soil moisture content of the three site locations for the four sampling depths and precipitation values recorded at Waterman Farm. Field Capacity (FC) for each depth was determined separately due to sensitivities in the EC-5 METER Environment sensors.

Figure 4. Average soil moisture content of the three site locations for the four sampling depths and precipitation values recorded at Waterman Farm. Permanent wilting point (PWP) is shown at -1500kPa and field capacity (FC) is shown at -10.5 kPa. FC measurements for the four depths ranged from -10.3 kPa and -11.1 kPa.

Low soil moisture content values corresponded with the low soil water potential in July, which limited the available water content for maize (Figures 3, 4). The total precipitation for the study period (May through August) was 51.18 cm, with only 7.47 cm of the total precipitation falling during July (Table 1) (OARDC Weather System http://www.oardc.ohio-state.edu).

During this period the soil water potential declined, with the average soil water potential content at 10 cm and 20 cm depths, both falling below the PWP (-1500 kPa). The average soil water

45 potential at 40 cm depth was just above PWP during the dry July period. The falling soil water potential varied by site, however the soil water potential was consistently lowest for all three sites at 10 cm and 20 cm depths. The difference between site soil water characteristics was indicated on the soil water characteristics (pF) curves (Figure 5). Nevertheless, these curves established that soil water characteristics at the three sites were consistent at corresponding depths. The pF curves also showed hysteresis occurring at all three depths at the three sites.

Differences in hysteric curves demonstrated heterogeneity in wetting and drying behavior and depths.

Figure 5. Soil water characteristic (pF) curves for each site and depth. Field capacity (FC) and permanent wilting point (PWP) are noted. The pF value corresponding to PWP is (4.18) FC was determined for each depth, ranging from 2.02-2.06.

3.6 Analysis of Sample Contamination: ChemCorrectTM

The Picarro L2310-i precision was calculated for every set of samples analyzed. The

ChemCorrectTM post processing software was used confirm measurement accuracy and to account for any uncertainty during analysis. Two samples were denoted as having detectable contamination influencing the δ18O and δD readings and ten of the samples were denoted as containing trace values of contamination that could shift the δ18O and δD values slightly. There was no spectral interreference for the remainder of the samples processed using ChemCorrectTM

(Table 3).

Table 3. Soil water extraction data for δ18O, δD and lc-excess. Color denotes results using the ChemCorrectTM software. Green signifies no detectable contamination influencing analysis, yellow signifies trace values of contamination possibly influencing analysis and red signifying detectable contamination influencing analysis. Samples highlighted in gray were not analyzed using the ChemCorrectTM software.

Site Sample Date (Depth) δ18O (‰) δD (‰) lc-excess 1(20) -7.90 -57.86 -2.48 1(40) -7.04 -50.73 -1.92 1(60) -7.91 -56.19 -0.74 2(10) -7.90 -57.03 -1.61 2(40) -3.11 -44.10 -25.51 6/14/2018 2(60) -7.67 -57.16 -3.50 3(10) -8.03 -59.20 -2.84 3(20) -7.16 -52.07 -2.36 3(40) -8.02 -57.44 -1.16 3(60) -7.87 -57.16 -1.99 1(10) -6.61 -45.56 -0.08 1(20) -3.17 -44.32 -25.24 1(40) -7.67 -57.31 -3.70 2(10) -7.56 -59.50 -6.74 2(20) -7.81 -60.69 -5.96 7/2/2018 2(40) -7.62 -57.91 -4.65 2(60) -10.99 -77.63 1.51 3(10) -10.67 -77.81 -1.11 3(20) -5.82 -54.55 -15.15 3(40) -8.02 -60.90 -4.60 3(60) -10.84 -77.77 0.18 1(10) -10.06 -76.45 -4.45 1(20) -8.35 -66.97 -8.15 1(40) -9.50 -70.20 -2.54 1(60) -7.47 -56.17 -4.08 2(10) -6.75 -56.35 -9.80 7/11/2018 2(20) -8.80 -67.14 -4.85 2(40) -9.06 -66.77 -2.46 2(60) -10.65 -79.25 -2.73 3(10) -9.07 -71.41 -7.00 3(20) -9.65 -74.70 -5.86 3(40) -8.24 -63.42 -5.42 Continued

Continued Sample Site δ18O lc- Date (Depth) (‰) δD (‰) excess 1(10) -9.65 -67.06 1.81 1(20) -7.51 -55.14 -2.77 1(40) -8.31 -65.41 -6.87 1(60) -9.61 -65.84 2.66 2(10) -8.75 -66.27 -4.31 8/2/2018 2(20) -8.54 -66.89 -6.57 2(40) -6.06 -50.04 -8.80 3(10) -8.86 -63.39 -0.61 3(20) -5.55 -53.05 -15.71 3(40) -9.92 -68.32 2.60 3(60) -9.30 -64.69 1.49 1(10) -7.82 -55.97 -1.17 1(20) -9.15 -64.13 0.85 1(40) -6.60 -56.48 -11.10 1(60) -10.12 -71.01 1.48 2(10) -8.45 -62.03 -2.41 2(20) -5.55 -51.97 -14.67 8/9/2018 2(40) -6.49 -54.33 -9.74 2(60) -8.84 -61.30 1.33 3(10) -8.89 -66.78 -3.76 3(20) -8.18 -62.22 -4.66 3(40) -10.09 -68.13 4.08 3(60) -8.03 -59.27 -2.87 1(10) -8.02 -60.69 -4.34 1(20) -6.31 -49.53 -6.35 1(40) -8.24 -61.80 -3.79 1(60) -7.17 -53.21 -3.42 2(10) -8.67 -65.85 -4.58 2(20) -9.29 -66.38 -0.30 8/28/2018 2(40) -8.39 -60.84 -1.65 2(60) -8.53 -60.51 -0.29 3(10) -3.00 -42.67 -24.89 3(20) -7.40 -56.96 -5.40 3(40) -8.88 -64.06 -1.17 3(60) -8.54 -60.39 -0.05

3.7 Soil Water and Precipitation Water Isotopic Composition

Corresponding δ18O and δD of soil water varied similarly by depth in the soil profile, and the d-excess values ranged from 1.76‰ to 0.84‰ with an average of 0.99‰ (σd-excess = 0.18).

Samples showed little spatial or temporal variation with no significant difference (α=0.05) among the isotopic compositions of the depth profiles or sample sites (Table 3, Figure 6).

With few exceptions, the isotopic composition of soil water at all depths was consistently lighter than the value of the event precipitation collected prior to sampling (Figures 6, 7). The isotopic compositions reported in Figures 6 and 7 were raw values, meaning they were not corrected for potential fractionation during the extraction process. In a separate analysis the same soil water isotopic compositions were corrected for fractionation to confirm that the samples were consistently lighter than precipitation samples. The fractionation factors used in this correction were the average fractionation values for δ18O and δD derived from extraction standards (1.204 and 1.236). Sample isotopic composition was still consistently lighter than precipitation, indicating the finding was not an artifact of fractionation during the extraction process.

Figure 6. Soil water isotopic composition depth profiles of δ18O and δD for six sampling dates. Groundwater samples were not collected for 6/14 and 7/2. Values shown in the figure are an average of the groundwater composition from 7/11 to 8/28. Samples flagged by the ChemCorrectTM and samples compromised during the extraction processes are noted. 52

Isotopic composition of soil water samples typically fell below the LMWL, which suggested an evaporative effect for all samples (Figure 7). Soil water at the 60 cm depth deviated the least from the LMWL. The magnitude of deviation was measured by calculating the line conditioned excess (lc-excess) (Landwehr and Coplen, 2006). The lc-excess provides a relative measurement of the degree of evaporation occurring in the soil water from evaporation. The lc- excess was calculated using the following equation: lc-excess = [δD − a ∗ δ18푂 − 푏] [14] where a is the slope of the LMWL and b is the intercept of the LMWL (Table 3). This equation does not take the standard deviation associated with uncertainty of measurement for the samples

(Landwehr and Coplen, 2006). This is because the use of lc-excess in this study is to evaluate the relative distance from the LMLW, rather than conduct a statistical test for deviation from the

LMWL measurement accuracy (Evaristo et al., 2015a; Hervé-Fernández et al., 2016).

Figure 7. Soil water isotopic compositions, precipitation, groundwater and tile water samples plotted with the local meteoric water line (LMWL) and the evaporation line. 2014-2018 LMWL: δD = 7.81*δ18O + 7.54 (R2 = 0.98). Evaporation line: δD = 4.61*δ18O - 24 (R2 = 0.84)

An evaporation trend line describing soil water isotopic composition was plotted to demonstrate the deviation from the LMWL due to evaporation in the soil water samples (Figure

7). The evaporation line intersected the LWML at (-9.82‰, -66.00‰), with a slope of 4.61. The point of intersection was lighter than the average isotopic composition of precipitation over the

54 study period (-5.84‰, -37.89‰). This intersection value is close to the average value of precipitation from 6 months prior to the growing season (-11.38‰, -79.51‰). Snowfall accounts for 26% of those 2017 and 2018 samples.

The isotopic composition of the stream water was heavier than the precipitation, save for

June 14 and August 28, when the precipitation values were exceptionally heavy for the region.

The stream water samples from the six sample dates were also heavier than ground water and agricultural tile water (Figure 6). Groundwater and tile water samples fell upon the LMWL. The groundwater and tile water samples were also lighter than all antecedent precipitation samples and the average precipitation sample for May to August (Figure 7). Groundwater sample signatures stayed consistent throughout the field season, with averages of -7.64‰, -51.51‰ for

δ18O and δD. The isotopic values of groundwater fluctuated within the precision of the Picarro

L2130-i. Groundwater samples were only collected for the sampling dates after July 2. Due to the consistent isotopic composition, the average value of the four samples was taken and applied to June 14 and July 2 dates (Figure 6).

The tile water isotopic composition was also consistent throughout the season, with averages of -6.93‰, -46.58‰ for δ18O and δD (Figure 6). The tile directly draining the field site

(Tile 1) produced water flow only during the June 14 sampling date. All tile water collected after the June 14 sampling date was collected from the main tile (Tile 2). Irrespective of the collection location, the isotopic composition of the tile water was consistent throughout the field season, with any isotopic variation falling within the precision of the Picarro L2130-i. The soil water signatures were similar to the tile and groundwater signature, particularly for the δ18O values.

However, the soil water plotted below the LMWL due to 18O/16O enrichment (Figure 7).

3.8 Xylem Isotopic Compositions and Modeling Soil Water Proportion with IsoSource

All calculated xylem compositions fell into the region between the LMWL and evaporation line termed the soil-xylem mixing space because they were derived from the isotopic compositions of the sampled soil water (Figure 7) (Evaristo et al., 2017). Fifty-one scenarios were created to determine the possible isotopic composition of maize xylem water throughout the growing season (Appendix A, Table 1). The isotopic composition of maize water was dependent on the measured soil water isotopic composition on that sample date. Therefore, each scenario was applicable to a specific sample date and maximum rooting depth at physiological maturity. The 51 scenarios were applied to all three sites resulting in a total of 151 calculated xylem values (Table 4). See section 2.14 for a description of how the scenarios were calculated.

Xylem isotopic compositions were not calculated for sites 1 and 2 on June 14 because there were no water samples for site 1, depth 10 cm and site 2, depth 20 cm. Calculated xylem compositions were similar for the 151 scenarios due to the similarity in isotopic composition throughout the profiles for the season.

Of the 151 xylem values calculated, 143 were then used in modeling the proportion of soil water absorbed at 10 cm, 20 cm, 40 cm and 60 cm depths. August 2, site 2, scenarios 27 and 28 could not be modeled (Table 4).

When the modeled soil water proportions were organized by depth, the intercepts values showed that over-estimations of soil water proportions were made at lower soil water fractions

(Figure 8). The R2 values revealed a high variance in predicted scenario accuracy, which can also be seen in the standard deviations of the means. While the positive intercepts for all regression lines showed an overestimate of results at lower source proportions, the slopes (< 1) demonstrated that underestimates were common for the higher source water proportion

56 scenarios. The correlation coefficient was particularly poor for 60 cm due to the frequent assumption that water from the 60 cm depth was contributing to the xylem isotopic composition when the appointed proportion was zero (Figure 8).

Figure 8. Regression analysis of modeled soil water proportions and calculated soil water proportions differentiated by depth. Data points represent the mean soil water proportion determined by Isosource. Gray bars represent the standard deviation of feasible proportions for each estimate.

The standard deviations for the depth datasets were 0.287 (10 cm), 0.231 (20 cm), 0.232 (40 cm) and 0.242 (60 cm), showing a range of approximately 0.5 in the fraction of soil water predicted as a source of xylem water (Table 4, Figure 8). More than 50% of the source predictions were made with σ ≤ 0.05 for 10 cm, 20 cm, and 40 cm depths and more than 50% of source predictions were made with an σ ≤ 0.1 for the 60 cm depth. Twenty of the total 143 scenarios modeled were estimated perfectly (Table 4). Specific characteristics of these 20 scenarios were as follows:

1) Soil water fraction was from one depth

2) Soil water fraction was from two depths

3) Three soil water fraction sources were included, rather than four.

There was no observable or statistical pattern (α =0.05) between sites, depths of scenarios to determining model accuracy. It was expected that the model would have the most difficulty predicting scenarios with root lengths that fell between two sampling depths. In these scenarios a floating root density (i.e., a portion of the root mass) was not associated with any sample depth and therefore not associated with any isotopic input for the xylem composition. However, the floating root density had to be associated with an isotopic composition, and so multiple scenarios were calculated when a fraction of the root density was split between two sample depth in the soil profile. The purpose of generating multiple scenarios associated with one maximum rooting length (at physiological maturity) and one sample date was to account for variation in the source of water uptake between the two depths. Of these multiple scenarios, the first assumed the floating root density would represent the shallower depth’s isotopic composition of the split depths. The second scenario assumed the floating root density would represent the deeper depth’s isotopic composition of the split depths. The third and fourth scenarios split the fraction

58 of the floating root density between the two depths. These divisions were always conducted in multiples of 25%, for example scenarios 16–19 (Table A1). The scenarios previously described were entitled floating root density scenarios and scenarios that determined root water uptake fractions based on root density at depth were titled root density driven scenarios.

A total of 24 scenarios split the floating root density source fraction between two sample depth isotopic compositions. A student t-test assuming unequal variances (α=0.05) was used to determine if the model was less accurate in predicting the soil water proportions for floating root density scenarios. The accuracy was calculated by subtracting the modeled source fractions from known source fractions. The same statistical test was conducted for the standard deviation of the datasets to examine any difference in scenario precision. No significant difference was found between the floating root density and root density driven scenarios for 10 cm, 40 cm and 60 cm depths. However, due to the frequent underestimation at the 20 cm depth at higher soil water proportions, a significant difference was found in both the accuracy and precision tests for the 20 cm scenario (Figure 8).

Table 4. Calculated maize xylem isotopic compositions and the modeled values of soil water source contributing maize root water uptake by depth. Results are organized by sampling date, sampling site and scenario number. Soil water proportions are reported in fractions summing to a value of one.

Sample Scenario Calculated Xylem IsoSource Mean Soil Water Source Site Date Number Composition Proportions

δ18O (‰) δD(‰) 10 - 20 20 - 40 40 - 60 0 - 10 cm cm cm cm 3 1 -8.03 -59.20 1.00 0.00 0.00 0.00 3 2 -7.77 -57.06 0.67 0.28 0.02 0.03 6/14/2018 3 3 -7.67 -56.28 0.56 0.39 0.02 0.03 3 4 -8.64 -63.55 0.84 0.11 0.02 0.03 1 5 -4.96 -44.97 0.52 0.48 0.00 0.00 1 6 -6.04 -48.08 0.55 0.23 0.12 0.10 1 7 -4.62 -44.84 0.42 0.58 0.00 0.00 1 8 -4.82 -45.43 0.43 0.53 0.02 0.02 1 9 -5.77 -48.78 0.38 0.33 0.19 0.10 1 10 -5.07 -46.77 0.37 0.48 0.10 0.05 2 5 -7.68 -60.07 0.71 0.25 0.02 0.02 2 6 -7.63 -59.40 0.57 0.17 0.25 0.01 2 7 -7.71 -60.19 0.67 0.28 0.03 0.02 7/2/2018 2 8 -7.70 -60.06 0.60 0.32 0.06 0.02 2 9 -7.66 -59.41 0.46 0.21 0.32 0.01 2 10 -7.69 -59.84 0.52 0.29 0.17 0.02 3 5 -8.34 -66.65 0.49 0.48 0.00 0.03 3 6 -8.87 -68.17 0.30 0.27 0.20 0.23 3 7 -7.86 -64.32 0.39 0.58 0.00 0.03 3 8 -7.96 -64.61 0.26 0.56 0.01 0.18 3 9 -8.20 -64.66 0.16 0.36 0.27 0.20 3 10 -7.86 -63.68 0.18 0.52 0.12 0.18 1 11 -9.26 -71.53 0.50 0.39 0.07 0.04 1 12 -9.17 -71.28 0.47 0.48 0.03 0.02 1 13 -9.08 -71.04 0.43 0.57 0.00 0.00 1 14 -9.30 -71.22 0.48 0.27 0.17 0.08 7/11/2018 1 15 -9.11 -70.71 0.41 0.47 0.08 0.04 1 16 -9.34 -71.11 0.49 0.19 0.21 0.11 1 17 -9.09 -70.41 0.40 0.44 0.10 0.06 1 18 -8.97 -70.06 0.34 0.58 0.06 0.02 1 19 -9.21 -70.76 0.42 0.32 0.18 0.08 Continued

Continued 2 11 -7.96 -62.44 0.48 0.26 0.21 0.05 2 12 -7.94 -62.47 0.51 0.24 0.18 0.07 2 13 -7.92 -62.50 0.52 0.28 0.12 0.08 2 14 -8.18 -63.35 0.34 0.31 0.34 0.01 2 15 -8.14 -63.41 0.42 0.23 0.27 0.08 2 16 -8.32 -63.85 0.30 0.24 0.45 0.01 2 17 -8.26 -63.93 0.37 0.25 0.31 0.07 2 18 -8.23 -63.97 0.37 0.34 0.21 0.08 2 19 -8.29 -63.89 0.32 0.30 0.36 0.03 7/11/2018 3 11 -9.19 -71.60 0.43 0.41 0.15 0.00 3 12 -9.30 -72.44 0.43 0.50 0.08 0.00 3 13 -9.40 -73.29 0.43 0.57 0.00 0.00 3 14 -9.00 -69.97 0.34 0.34 0.32 0.00 3 15 -9.23 -71.78 0.34 0.50 0.16 0.00 3 16 -8.88 -68.90 0.29 0.28 0.43 0.00 3 17 -9.18 -71.32 0.29 0.49 0.22 0.00 3 18 -9.33 -72.54 0.27 0.61 0.12 0.00 3 19 -9.03 -70.11 0.29 0.39 0.32 0.00 1 20 -8.17 -58.83 0.19 0.67 0.02 0.12 1 21 -8.45 -62.38 0.17 0.32 0.37 0.14 1 22 -9.65 -67.06 1.00 0.00 0.00 0.00 1 23 -8.44 -63.25 0.12 0.24 0.52 0.12 1 24 -8.24 -60.68 0.12 0.49 0.27 0.12 1 25 -8.14 -59.40 0.12 0.60 0.15 0.13 1 26 -8.55 -64.54 0.12 0.10 0.65 0.13 1 27 -8.66 -63.67 0.21 0.21 0.40 0.18 1 28 -8.53 -63.63 0.14 0.21 0.51 0.14 1 29 -8.42 -63.60 0.10 0.20 0.60 0.10 8/2/2018 2 20 -8.61 -66.70 0.31 0.69 0.00 0.00 2 21 -7.75 -60.88 0.31 0.34 0.35 0.00 2 22 -8.76 -66.27 1.00 0.00 0.00 0.00 2 23 -7.35 -58.31 0.25 0.25 0.50 0.00 2 24 -7.97 -62.52 0.25 0.50 0.25 0.00 2 25 -8.28 -64.63 0.29 0.59 0.12 0.00 2 26 -7.04 -56.20 0.19 0.18 0.63 0.00 2 27 2 28 2 29 -7.146 -56.99 0.21 0.21 0.58 0 Continued

Continued 3 20 -6.58 -56.26 0.134 0.72 0.01 0.14 3 21 -8.09 -61.53 0.17 0.36 0.35 0.12 3 22 -8.86 -63.39 0.46 0.06 0.03 0.45 3 23 -8.56 -63.27 0.12 0.27 0.51 0.10 3 24 -7.47 -59.46 0.12 0.52 0.26 0.10 8/2/2018 3 25 -6.93 -57.55 0.12 0.64 0.13 0.11 3 26 -9.11 -65.18 0.12 0.14 0.63 0.11 3 27 -8.67 -63.43 0.21 0.21 0.40 0.18 3 28 -8.73 -63.79 0.15 0.22 0.50 0.13 3 29 -8.78 -64.08 0.10 0.22 0.59 0.09 1 30 -7.69 -58.55 0.37 0.14 0.41 0.08 1 31 -8.23 -60.16 0.42 0.23 0.20 0.15 1 32 -8.76 -61.76 0.32 0.65 0 0.03 1 33 -7.49 -58.20 0.31 0.09 0.52 0.08 1 34 -8.17 -60.23 0.36 0.24 0.25 0.15 1 35 -8.51 -61.24 0.38 0.34 0.12 0.16 1 36 -7.83 -59.21 0.33 0.18 0.38 0.11 1 37 -8.14 -61.17 0.13 0.32 0.39 0.16 1 38 -7.73 -59.45 0.21 0.19 0.49 0.11 1 39 -7.33 -57.83 0.23 0.12 0.61 0.04 2 30 -6.79 -55.88 0.25 0.44 0.22 0.09 2 31 -6.59 -55.38 0.27 0.56 0.13 0.04 2 32 -6.39 -54.89 0.28 0.70 0.01 0.01 2 33 -6.72 -55.54 0.19 0.42 0.28 0.11 8/9/2018 2 34 -6.47 -54.91 0.20 0.61 0.12 0.07 2 35 -6.34 -54.60 0.22 0.68 0.07 0.03 2 36 -6.59 -55.22 0.20 0.51 0.20 0.09 2 37 -7.25 -57.03 0.19 0.23 0.32 0.26 2 38 -6.97 -56.17 0.16 0.28 0.38 0.18 2 39 -6.71 -55.42 0.15 0.41 0.31 0.13 3 30 -9.19 -66.03 0.37 0.15 0.40 0.08 3 31 -8.79 -64.79 0.44 0.25 0.17 0.14 3 32 -8.39 -63.55 0.30 0.69 0 0.01 3 33 -9.36 -66.40 0.30 0.13 0.51 0.06 3 34 -8.85 -64.84 0.38 0.24 0.22 0.15 3 35 -8.60 -64.06 0.41 0.33 0.08 0.18 3 36 -9.10 -65.62 0.34 0.20 0.37 0.10 3 37 -9.01 -64.70 0.16 0.26 0.39 0.19 Continued

Continued 3 38 -9.24 -65.65 0.19 0.20 0.49 0.12 8/9/2018 3 39 -9.49 -66.74 0.25 0.11 0.59 0.05 1 40 -7.68 -58.31 0.51 0.21 0.24 0.04 1 41 -7.23 -55.43 0.34 0.48 0.16 0.02 1 42 -6.77 -52.55 0.19 0.73 0.07 0.01 1 43 -7.79 -58.99 0.51 0.14 0.30 0.05 1 44 -7.23 -55.43 0.33 0.47 0.17 0.03 1 45 -6.95 -53.65 0.23 0.63 0.12 0.02 1 46 -7.51 -57.21 0.40 0.31 0.26 0.03 1 47 -7.61 -57.53 0.33 0.18 0.30 0.19 1 48 -7.70 -58.26 0.36 0.17 0.36 0.11 1 49 -7.53 -56.81 0.27 0.16 0.26 0.31 1 50 -7.69 -58.10 0.36 0.14 0.32 0.18 1 51 -7.85 -59.39 0.51 0.12 0.32 0.05 2 40 -8.70 -63.63 0.35 0.20 0.23 0.22 2 41 -8.91 -64.94 0.35 0.20 0.23 0.22 2 42 -9.12 -66.24 0.27 0.73 0 0 2 43 -8.64 -63.06 0.30 0.14 0.30 0.26 2 44 -8.90 -64.66 0.25 0.47 0.15 0.13 2 45 -9.03 -65.47 0.23 0.63 0.08 0.06 8/28/2018 2 46 -8.77 -63.86 0.28 0.30 0.20 0.23 2 47 -8.66 -63.00 0.25 0.18 0.29 0.28 2 48 -8.65 -63.03 0.27 0.16 0.26 0.30 2 49 -8.65 -62.64 0.18 0.18 0.33 0.31 2 50 -8.62 -62.69 0.24 0.14 0.31 0.31 2 51 -8.60 -62.74 0.28 0.11 0.32 0.29 3 40 -6.91 -56.44 0.28 0.22 0.47 0.03 3 41 -6.56 -54.77 0.32 0.24 0.26 0.18 3 42 -6.21 -53.10 0.36 0.31 0.04 0.29 3 43 -7.33 -58.08 0.22 0.17 0.58 0.03 3 44 -6.91 -56.02 0.26 0.26 0.31 0.17 3 45 -6.69 -54.99 0.28 0.33 0.17 0.22 3 46 -7.12 -57.05 0.25 0.16 0.45 0.14 3 47 -7.28 -57.45 0.21 0.21 0.41 0.17 3 48 -7.31 -57.76 0.23 0.13 0.50 0.14 3 49 -7.45 -57.83 0.15 0.33 0.33 0.19 3 50 -7.51 -58.38 0.18 0.18 0.49 0.15 3 51 -7.55 -58.93 0.19 0.14 0.64 0.03

Chapter 4. Discussion

4.1 Cryogenic Vacuum Distillation Methods Development

Standards testing for the cryogenic distillation methods confirmed that a fractionation factor could be obtained if extractions were performed at a consistent temperature and pressure of 180°C and 100 mTorr for 180 minutes. The oven-dried soil dataset had the highest mean fractionation factor and the smallest standard deviation of the four datasets. However, the lack of significant difference among all four datasets indicated that the primary source of fractionation was water loss in the connection, and not water remaining in the soil.

This finding was discovered because water was fully recovered from the soil during extraction standard experiments, but it was not fully recovered in the collection vial. The >100% water recovery from soil can be attributed to balance error from initial to final masses or the removal of tightly bulk soil water, held by low matric pressure, during the extraction process.

Oven-dried and air-dried soils experienced >100% recoveries, suggesting that the initial mass included water held in microscopic soil pores (Newberry et al., 2017; Orlowski et al., 2017). The water lost in transport was found by visual inspection in the connection line between the two vials. Approximately 10% to 20% of the water was consistently lost in transport, resulting in fractionation of the final isotopic composition. No relationship between volume of water lost and fractionation factor magnitude was observed due to the small standard deviations of both datasets. The hypothesis that water volume was lost in the connection line was further investigated by conducting extraction experiments with liquid water. These samples were heated for 30 minutes, rather than 180 minutes due to the rapid evaporation of liquid water (not in soil) 64 under high heat and low pressure. Results indicated larger standard deviation in this experiment but still produced fractionation factors that were consistent with the soil standards test. The relatively large standard deviation was likely due to the shorter extraction period, which resulted in less time for water in the connection line to pass into the collection vial.

Similar fractionation factors for the water and soil standards confirmed the hypothesis that the fractionation factor was primarily derived from water stuck in the set-up connection.

Suggestions to vaporize the water in the connection include encircling the connection line with heating tapes and utilizing a butane blowtorch to heat the connection at the end of the extraction period. Longer extraction time are not likely to have a significant impact on the extraction isotopic composition. After a 180-minute period, 100% of the soil water was recovered but the water condensed in the connection because the connection was at room temperature. However, previous work has also revealed that soil organic and mineral composition may modify the δ18O isotopic composition of adsorbed water thereby affecting the standard results. (Meißner et al.,

2014; Oerter et al., 2014; Gaj et al., 2017). Interlayer water can be evaporated from soils under pressure at a temperarture of 200°C for four hours to improve water spiking methods for soil water extraction tests (Vandevelde and Bowen, 2013). Nevertheless Newberry et al. (2017) noted that complete drying of soil standards before testing (i.e., drying out bound water at high temperatures) is not representative of wetting and drying processes in nature. Therefore, complete drying of a standard before spiking the soil with water lead to biased results when determining the degree of fractionation during soil water extraction. The impact of soil organic and mineral characteristics can be identified with further standards testing.

Future work to improve methods should include a heating element around the connection to keep the water in a gaseous phase prior to condensation in the liquid nitrogen trap during

65 extraction. Work should also be done to quantify the impact of soil organic and mineral composition may modify the fractionation factor during extraction.

4.2 Cryogenic Vacuum Distillation: Sample Water Extraction

Unexpected heavy or light soil water isotopic signature may have been a result of issues during the extraction process. Samples with moisture remaining in the sample Exetainer® vial demonstrated lighter isotopic signatures (Figure 6). The light isotopic signature associated with these samples was a result of an incomplete distillation process, where the remaining soil water contained heavier isotopologues and the condensed water in the collection vial contained the lighter isotopologues (Dansgaard, 1964; Gat, 1996). Water that was not fully transferred to the collection vial, but was collected from the connection line, compromised the isotopic composition of soil water sample analyzed. The samples with these issues were identified using notes from extraction experiments and analysis of isotopic signature in reference to the average signature for that depth and the standard deviation. These samples were not removed from the dataset, but the associated extraction issues were considered when looking at δ18O and δD patterns throughout the soil profile. This finding exhibits the importance of transferring the entire water sample from the soil reservoir to the collection vial to reduce fractionation of the sample.

While the extraction procedure used in this study produced a fractionated water sample, the methods were consistent and deemed reliable for the intra-comparison of soil water samples.

All samples experienced a significantly similar fractionation factor during the extraction procedure, as confirmed by the standard tests. The method development process demonstrated that a consistent fractionation factor can be determined via cryogenic distillation for soil water extraction. However, the extraction procedure still requires rigorous methods testing and pure water extractions to identify mechanisms causing the fractionation process. New fractionation

66 factors must be tested with different sample sets to account for soil characteristics (i.e., soil mineral composition, organic matter and texture) (Meißner et al., 2014; Gaj et al., 2017). There is a need for consistent inter-laboratory methods development to extract water from soils and plant tissues (McDonnell, 2014; Berry et al., 2017; Penna et al., 2018). The current work has demonstrated that with more testing a consistent method can be identified, and approximations of pressure, temperature and time can be determined within the scientific community. However, the choices of pressure, temperature and length of extraction must be confirmed by intra-laboratory testing according to the specific sample characteristics.

4.3 Soil Water Characteristics in the Soil Profile

Similar drainage patterns and isotopic compositions at depth suggested an efficient, vertically-dominated water flow in the system (Figures 5, 6). Water characteristics were similar for all three sites at the corresponding depths (Figure 6). The parallel soil moisture contents and soil water potentials at each site confirmed the accuracy of EC-5 and TEROS-21 sensors. This also validated the differences in soil hydrologic activity stratified in the soil profile (Figures 3,

4). The variation in soil moisture content at the four depths was a result of soil physical properties. Lower bulk density and porosity at the 10 cm depth was the result of conventional tillage, a practice which loosened the top 5 cm of the soil. The 20 cm, 40 cm and 60 cm soil layers had similar bulk density values. However, the 60 cm depth also expressed a greater porosity, which allowed for increased drainage and therefore facilitated lower soil moisture content (Table 2, Figure 3). The soil profile extends to approximately 60–70 cm before becoming increasingly rocky and hitting glacial till. This composition change and the presence of the tile drainage at approximately 75cm deep in the profile resulted in reduced soil moisture content

(Sullivan, 1997). The presence of the drainage tile increased drainage of bulk soil water,

67 reducing the soil water content throughout the season (Abid and Lal, 2009). The soil moisture content was greatest at 20 cm and 40 cm, depths which had lower porosity than the 10 cm and 60 cm layers (Table 2, Figure 3).

The soil water potential was most variable at the 10 cm and 20 cm depths, as these two layers expressed the greatest fluctuations in soil moisture content (Figures 3, 4). The soil at 10 cm depth experienced the greatest evaporative influence due to its proximity to the surface. The evaporation was also intensified by loosening of the top soil (Lal and Shukla, 2004). However, the lc-excess values for soil water at 10 cm did not differ significantly from the lc-excess values deeper in the profile (α=0.05) (Table 3). There was no strong evaporative influence at the 10 cm depth, likely a result of the high relative humidity throughout the summer and also influenced by a dense canopy cover (Dansgaard, 1964; Sprenger et al., 2017b) (Table 1) The low soil water content and fluctuating soil water potential were likely influenced by maize root density and the relatively high porosity due to tillage at that depth (Pires et al., 2017). Maize roots have been shown to obtain the highest proportion of water from the 10 cm and 20 cm depths, affecting the fluctuation of soil water content and potential at these depths (Asbjornsen et al., 2007; Djaman and Irmak, 2012; Ma and Song, 2016; Zhao et al., 2016). Maize utilizes water from deeper water sources during periods of reduced water content in the soil (Ma and Song, 2016; Wu et al.,

2016b). This statement is complicated if there is physical heterogeneity within the soil profile.

For example, a soil layer with high clay content reduces root growth and has a lower available water content at that layer (Rudnick and Irmak, 2014b). Tillage can also cause a reduction in porosity at soil layers below the tillage zone due to compaction from heavy machinery (Lal and

Shukla, 2004; Pires et al., 2017). High water content at the 20 cm depth is likely a result of compaction during plowing and planting.

Depths at which maize concentrates its water uptake vary based on the current stage of development. As maize matures physiologically, the proportion of water source shifts to lower points in the soil profile (Djaman and Irmak, 2012). However, it has been observed that maize primary water source gradually increased with depth until the reproductive physiological stages, when the plant then obtains its water from the upper layers in the profile, ranging from 0–20 cm

(Ma and Song, 2016) to 0–30 cm (Rudnick and Irmak, 2014b). These contrasting findings are likely due to a combination of climate conditions, soil water availability, and root length. Root length has been directly related to source of water in roots (Leitner et al., 2013).

All sites and depths showed signs of hysteresis due to the rewetting and drying throughout the summer (Figure 5). Summer precipitation was frequent in the months of May,

June, and August but lower frequency of precipitation in July resulted in the water content at 10 cm and 20 cm depths falling below the PWP (-1500 kPa). On July 18 and 19, the average soil water potential for 40 cm depth was above PWP at -1249 kPa, indicating water stress (Figure 4).

All four depths at site 2 experienced enough water loss to show soil water potential readings at a greater suction than PWP (4.18 pF), however for site 1 and site 3, only soil water potential at 10 cm and 20 cm fell below the PWP (Figure 5). This suggested that there was a higher probability of roots sourcing water from deeper within the profile, reflecting an isotopic signature of 40 cm or 60 cm depths (Ma and Song, 2016; Wu et al., 2016). Crops receiving a consistent and plentiful water source absorb a relatively higher volume of water extracted from shallower profile depths than in rain fed crops receiving an inconsistent water input (Djaman and Irmak, 2012). During the mid-July moisture-stress period, bulk soil water at the 10 cm and 20 cm depths could have acted as a primary source of maize root water uptake. Roots were unlikely to draw water from

69 deeper than ~30 cm in the profile at this point due to the observed rooting depth during the July

11th sampling date.

4.4 Insight from the Composition of Groundwater, Tile Water and Stream Water

The isotopic signatures of the small stream reflected the current precipitation isotopic composition with an evaporative influence (Figure 6). The relatively heavy stream water composition was an indication of evaporation. Groundwater and tile water sample isotopic compositions plotted on the LMWL. The consistent isotopic signature of bulk soil water throughout the profile indicated that bulk soil water was not displaced by seasonal precipitation events. The isotopic composition of groundwater, collected from an onsite well (>200 ft), closely matched the average isotopic composition of central Ohio precipitation from 2014 to 2018.

Jasechko et al. (2014) showed that groundwater in the United States temperate zone experiences a winter-biased recharge signal. Groundwater obtained from the onsite well at Waterman Farm represents an aquifer that showed a biased recharged by winter precipitation. Although the volume of precipitation in Columbus is weighted toward summer months, the isotopic composition is weighted toward average values of winter precipitation indicating a biased winter recharge for the groundwater sampled (Table 1, Figure 7). Therefore the reflection of precipitation water in groundwater coincides with previous work showing groundwater isotopic signature is analogous to the precipitation signature (Brooks et al., 2009; Evaristo et al., 2015a).

The tile water isotopic composition was heavier than the four-year precipitation average, however it did not reflect the isotopic composition of the precipitation during the growing season. This water source was influenced by the current precipitation, as the tile drain assists in field drainage, and also carries older waters. Tile 1 had a consistent drip at the outflow during the

June 14 sampling event but no flow during the following sampling events. After the June 14

70 sampling event, the Tile 2 tile was sampled from a shallow well. This water was visually determined as stagnant. Flow from the field tiles occurs primarily in the spring and it is a rare occurrence during the growing season (Lal, personal communication). The tile water isotopic composition reflected a mix of winter precipitation and summer and early spring precipitation, including rain and melting snow and spring precipitation. The combination of lighter winter precipitation and heavier precipitation resulted in a mixture that represented the annual precipitation isotopic composition.

4.5 Soil Water Isotopic Composition

Soil samples showed little variation in isotopic composition temporally and spatially. The isotopic similarity observed among sites and depths indicated parallel water cycle regimes at each site. This finding was confirmed by the minimal variation observed in soil texture observed throughout the soil profile (Table 2). A larger temporal variation in soil water isotopic composition at the 10 cm depth indicated an evaporative effect at this top layer (Brooks et al.,

2009; Hervé-Fernández et al., 2016; Geris et al., 2017; Sprenger et al., 2017a). With slight temporal and spatial variation, the mean isotopic compositions for all 4 depths resembled a mix of the precipitation’s mean isotopic composition for the region from 2014 to 2018 and the preseason average (Table 5, Figure 7). Soil water did not strongly resemble the antecedent precipitation nor the average seasonal precipitation (Figure 7). These findings confirmed that soil water was an amalgamation of historical precipitation, influenced by annual precipitation signatures rather than seasonal signatures.

Table 5. Mean δ18O and δD values for soils at the four sampling depths among sample sites for the six sampling dates. All samples, including samples flagged by the ChemCorrectTM software and samples with issues noted during the extraction processes were included.

Standard Standard Depth Mean δ18O δD Mean Deviation Deviation (cm) (‰) (‰) (‰) (‰) 10 -8.11 1.65 -61.41 9.02 20 -7.28 1.63 -58.12 7.71 40 -7.82 1.58 -59.69 6.86 60 -8.90 1.24 -63.84 8.36

Unlike findings from previous work which showed that the soil water isotopic influence was confined to the previous seasons, the present work utilized a long-term LMWL to show that soil water is a mixture of annual precipitation (Brooks et al., 2009; Goldsmith et al., 2012;

Sprenger et al., 2017a). Seasonal precipitation isotopic signatures in soil water have been indistinguishable in other climates without distinct wet and dry seasons, with complete soil water turnover occurring within 1000 days (Geris et al., 2017; Sprenger et al., 2017a, 2018). The lack of seasonal signature in soil water may be confined to temperate climates and to other regions that do not experience a distinct wet and dry season, as regional climate has been shown to be strong control of soil water content and residence time (Sprenger et al., 2018). Mixing of event and pre-existing water in the soil matrix is also affected by soil water residence time, as residence time varies with soil storage capacity (Sprenger et al., 2018). Studies focusing on catchments with distinct dry and wet seasons have indicated that the soil water resembles the initial precipitation event following the wet season (Brooks et al., 2009; Goldsmith et al., 2012;

Oerter and Bowen, 2017; Sprenger et al., 2017a).

Ohio experiences stratiform precipitation events from October to March and convective events from April to September. The convective storms produce heavier isotopic signatures that are counteracted by stratiform events, with integrated isotopic compositions of snowfall and

72 rainfall, to create the average isotopic composition for the region (Figure 7) (Climate Services

Branch). The isotopic signature of the soil water can be interpreted as expressing a memory effect because it reflects stratiform precipitation, including snow melt, and convective precipitation. Stable water isotope studies in catchments influenced by snowmelt and early spring precipitation found similar results (Hervé-Fernández et al., 2016; Mccutcheon et al., 2017;

Sprenger et al., 2017a). The isotopic composition at 60 cm depth was lighter and had a relatively smaller lc-excess values compared to samples at the 10 cm, 20 cm and 40 cm depths. These observations indicated that the isotopic composition at the 60 cm depth showed less evaporation and corresponded to an isotopic signature resembling a stratiform precipitation event. Soil water at this depth was less influenced by the seasonal precipitation isotopic composition possibly due to root water uptake, and the combination of a relatively higher porosity supplemented by tile drainage at 75 cm (Sullivan, 1997).

No significant relationship was found between isotopic composition and soil water content, indicating that the heavier and lighter isotopic signatures may be a function of pore size of the sample collected. Smaller pores have tighter matric suction, reducing the interaction of bulk soil water with recently infiltrated, while pores with larger radii act as transmission pores

(Lal and Shukla, 2004). Sprenger et al. (2018) discovered variation in water age based on pore size in a modeling study that aimed at quantifying soil water travel times in a catchment.

Therefore, samples with a greater ratio of transmission pores or macropores were more likely to express isotopic signatures mixing with antecedent rainfall rather than reflect a predominantly stratiform signature.

This study showed that hydrological separation was evident in a Crosby Silt Loam soil under conventional tillage practices. The differentiation between infiltrating precipitation and

73 pre-existing soil water in the vadose zone indicated that there was an elongated water residence time of bulk soil water. The distinctly different soil water, growing season precipitation, and preseason precipitation isotopic signatures demonstrate limited mixing of infiltrating precipitation event water and water held by high matric suction.

This work also showed that there was mixing of water in the soil matrix, however this mixing was likely limited by pore site and matric suction. In conditions near, at, or above field capacity, soil water flow is conducted through larger water-filled pore spaces. However, as the soil dries these large pore spaces with less matric suction drain first. This elongates the tortuosity

(actual path of water / straight path) of the soil water’s flow and thus the residence time of soil water in the vadose zone. Water transportation processes vary based on soil type, which is a function of texture and bulk density, and consequently porosity. There is often heterogeneity of soil physical characteristics within the soil profile, causing soil water characteristics to vary spatially on a fine scale (Sprenger et al., 2016). The similar isotopic composition and soil physical properties throughout the soil profile in this work indicated that small scale lateral and vertical heterogeneity in the profile was insufficiently diverse to generate distinct patterns or differences in water cycling processes (Figure 7). Conservation of preseason water in micropores with high matric suction is dependent on soil type, determining saturated and unsaturated hydraulic conductivity of the soil. Soil physical properties control the residence time and mixing of precipitation water in the soil profile (Sprenger et al., 2016). Therefore, soil type must be considered when investigating water flow within the vadose zone.

Previous studies indicating the existence of hydrological separation in the vadose zone have not aimed to dichotomize a soil water pool and a mobile water pool within the profile

(Brooks et al., 2009; Goldsmith et al., 2012). Instead, these studies highlight a distinction

74 between bulk soil water and mobile water, stating that the entire system is not mixed, but that infiltrating precipitation takes preferential flow paths through the soil matrix, reducing interaction with water held by matric suction in smaller pore spaces (Sprenger et al., 2017a). The result is a consistent stable water isotope profile in the soil despite the fluctuating isotopic composition of precipitation associated with the growing season.

4.6 Modeled Xylem Isotopic Compositions and Expected Outcomes

The calculated maize root length and density distributions were validated by a previous study at Waterman Farm that recorded maximum maize root length at the 60 cm depth with 6% of the root density distribution (Nakajima et al., 2015). The derived scenarios were chosen to represent a diversity of root depths at different physiological phases because the length and density distribution of root samples differ based on plant heat and highly localized soil physical characteristics and hydrology (Table A1) (Tron et al., 2015). Deeper rooting scenarios may better represent sampled xylem values during the July 11th sampling due to the dry conditions causing roots to source water from deeper within the soil profile (Tron et al., 2015; Ma and Song, 2016;

Wu et al., 2016). The 40 cm maximum rooting conditions may better represent soil water uptake during the August sampling dates because maize has been shown to source water primarily from upper portions of the soil profile during late physiological maturity.

Further, maize maximum rooting depth and density are dependent on nitrogen inputs, which can alter soil water regimes. When nitrogen fertilizer is applied maize has been shown to obtain a larger proportion of its water from 20 cm depth in the soil and also utilizes a larger total volume of water soil water (Rudnick and Irmak, 2014a; Ma and Song, 2016). Approximately 101 kg/ha of N was applied to the field site during the V6 stage. The fertilizer input could change

75 expected rooting depths. The multiple rooting depth scenarios account for any variation as a result of fertilizer input.

Despite the number of scenarios calculated, the variances for the 151 calculated xylem compositions were 1.02 and 33.52 for δ18O and δD. Low variation in the dataset was a function of the consistency of isotopic composition throughout the profile (Table 3). The antecedent precipitation signature was not considered when modeling the xylem isotopic compositions because of the uncertainty in root absorption of precipitation. Factors affecting the rate of absorption included the residence time of precipitation water in the soil profile, plant anatomy, and climatic factors (Berry et al., 2017). Due to the variability in these factors, precipitation signatures were not incorporated in xylem modeling. Nevertheless, this modeling endeavor remains of great value in understanding water cycling for this study. If field-sampled xylem isotopic signatures are outside of the range of the modeled xylem signatures it will indicate that the maize roots are absorbing a larger proportion of antecedent precipitation than pre-existing soil water, and the results will improve generation of accurate proportions of pre-existing soil water and antecedent precipitation water.

If the sampled xylem values plot below the evaporation line this will indicate a fractionation effect during root absorption or during the extraction process. It has been previously established that the isotopic composition of water is not fractionated or enriched during plant water absorption (Dawson and Ehleringer, 1998). However, work by Ellsworth and

Williams (2007) has shown that 2H/1H fractionation occurs during water absorption in xerophyte and halophytes. In recent work, Evaristo et al. (2017) found 2H/1H fraction in numerous tree species (not classified as xerophytes and halophytes) and authors cautioned against using these results in MBMM to determine proportion of water sources. Similar work also showed an

76 increased 2H/1H fractionation in vegetation samples, indicating fractionation during uptake

(Brooks et al., 2009; Geris et al., 2017). While a relatively lighter δD signature in xylem than soil water could indicate fractionation during root absorption in this study, it could also be due to fractionation during the extraction process.

4.7 Soil Water Source Proportion Scenarios

Two of the formerly mentioned issues with MBMMs, discussed in section 1.7, were applicable to this study. The number of pools acting as sources was greater than the number of tracers (n ≥ d+1), and the soil water isotopic compositions were significantly similar (Table 3), making it more difficult to identify the soil layer from which the maize was sourcing its water.

The latter issue was the greatest source of uncertainty when modeling the soil water isotopic compositions. Comparable soil water isotopic compositions throughout the soil profile made it difficult for the IsoSource model to predict the soil water source proportion with sufficient accuracy and precision despite the few input sources (Figure 8). The model output was particularly poor for the 60 cm fraction. The predicted fraction of soil water contribution from this depth was most commonly zero, however the isotopic composition was included as an input because it is part of the soil water profile. Soil water at the 60 cm depth was modeled as having a greater contribution than zero in many scenarios due to the similar isotopic composition throughout the profile, especially between 40 cm and 60 cm depths (Figure 8). The incorrect predictions of soil water contribution for the 60 cm depth suggest that strong consideration should be taken before including sources that will potentially have little contribution.

As observed in previous work, the precision of the model estimates varied inversely with the difference in source signature throughout the profile (Phillips and Gregg, 2001). The only way to solve this issue is to increase the sample size which is not applicable in this research. It

77 has been suggested that the first meter of the soil profile can be modeled as one isotopic component because of similarities in the isotopic composition of soil water stratified throughout this profile (Phillips et al., 2005). Aggregation of isotopic sources is valid when the sources are not significantly different (Philips et al. 2005). This statement is directed at larger studies utilizing an abundance of sources and data within each source. In this study the only 4 sources were directly known, therefore aggregation is not applicable. Therefore, the uncertainty generated from similarity in source isotopic compositions cannot be resolved.

This modeling endeavor preemptively highlights the difficulty in predicting depth of root water uptake based on differences in soil water isotopic composition in a temperate humid environment. While the consistency of the isotopic composition makes this a difficult tracer to determine the isotopic composition at depth, it will allow for a strong distinction to be made between pre-existing soil water and antecedent precipitation water in the xylem, as was discussed previously.

Chapter 5. Future Work

Future work for this study will continue to focus on quantifying the hydrological separation occurring in the maize field studies during the 2018 growing season. This work will include calculating the water mass balance in the system, improving the precision during soil water extraction experiments and extracting water from maize xylem samples collected during the 2018 field season.

Soil standard extraction experiments will be conducted in order to constrain the fractionation factor during extraction and apply corrections to the soil data. Correction of the isotopic composition of soil water by an empirically defined fractionation factor will allow for a direct comparison between soil water and maize xylem water. The future soil standard extraction experiments will mimic the ones discussed in this study in order to increase the accuracy of the fractionation occurring during extraction.

Next steps in extracting xylem water from maize samples collected during the 2018 field season include identifying any fractionation occurring during the xylem extraction process and modeling the source proportion of soil water and antecedent precipitation water. The xylem water extraction process will be conducted using the methods process discussed in this study.

Extraction times and temperatures will be determined based on Orlowski et al. (2017). Maize root nodes and xylem samples were collected during the 2018 field season. Since water does not fractionate when absorbed by plants, the root nodes will be used as standards in the extraction

79 process to ensure accuracy and precision in the extraction process (Gonfiantini and Tongiorgi,

1965).

The IsoSource model will then be used to determine the relative source of soil water and antecedent precipitation water in maize tissue. A series of scenarios will be tested in an attempt to optimize model accuracy and precision (Phillips et al., 2005). For example, soil water will be considered by depth and as a bulk sample to determine the difference in proportional source outcome. This study will contribute to growing research investigating hydrological separation that is now being conducted on developed lands (Zhao et al., 2016; Oerter and Bowen, 2017).

Chapter 6. Conclusion

This study investigated the degree of hydrological separation within the vadose zone by combining field experiments with water extraction methods development. This research would not have been accomplished without rigorous methods testing and development for soil water extraction via cryogenic distillation. There is a need to determine a consistent inter-laboratory procedure that involves developing and testing standards for soil and vegetation samples. A consistent standard testing method will allow for better comparison of ecohydrologic and soil studies in the scientific community.

Soil water cycling regimes are highly dependent on the climate and soil physical and biochemical characteristics. The diversity in soil characteristics and seasonal variations in climate make it difficult to quantify the degree of hydrological separation within the vadose zone. Results from this study demonstrate that there is an incomplete mixing of precipitation event water and pre-existing soil water due to the variation in matric suction associated with different pore sizes. Evidence of hydrological separation was confirmed by the consistent soil water profile isotopic composition representing the annual and preseason precipitation signatures. The hydrological separation within the soil profile shows that an annual mixing regime is dominant within the system.

Knowledge of the degree of soil water mixing has important implications for modeling hydrologic cycling at the catchment scale and to understand the flux of water via evapotranspiration as climate shifts. The focus on agricultural systems in this study shows that hydrological separation is observable in soils on developed lands. The degree of hydrological separation will be better understood by an investigation of the maize xylem isotopic composition collected during the study and a comparison of isotopic values to those that were modeled in this research

Work Cited

Abid, M., and Lal, R., 2009, Tillage and drainage impact on soil quality: II. Tensile strength of aggregates, moisture retention and water infiltration: Soil and Tillage Research, v. 103, p. 364–372, doi:10.1016/j.still.2008.11.004. Aggarwal, P.K., Romatschke, U., Araguas-Sraguas, L., Belachew, D., Longsta, F.J., Berg, P., Schumacher, C., and Funk, A., 2016, Proportions of convective and stratiform precipitation revealed in water isotope ratios: Nature Geoscience, v. 9, p. 624–629, doi:10.1038/NGEO2739. Araguás-Araguás, L., Rozanski, K., Gonfiantini, R., and Louvat, D., 1995, Isotope effects accompanying vacuum extraction of soil water for stable isotope analyses: Journal of Hydrology, v. 168, p. 159–171, https://ac.els-cdn.com/002216949402636P/1- s2.0-002216949402636P-main.pdf?_tid=aed8b7ab-97ed-47d6-b150- 7a6313be88da&acdnat=1546371229_bb3819fcaca833e2b2543cef6fec45a0 (accessed January 2019). Asbjornsen, H., Mora, G., and Helmers, M.J., 2007, Variation in water uptake dynamics among contrasting agricultural and native plant communities in the Midwestern U.S.: Agriculture, Ecosystems and Environment, v. 121, p. 343–356, doi:10.1016/j.agee.2006.11.009. Berry, Z.C., Evaristo, J., Moore, G., Poca, M., Steppe, K., Verrot, L., Asbjornsen, H., Borma, L. S., Bretfeld, M., Hervé-Fernández, P., Seyfried, M., Schwendenmann, L., Sinacore, K., De Wispelaere, L., McDonnell, J., 2017, The two water worlds hypothesis: Addressing multiple working hypotheses and proposing a way forward: Ecohydrology, v. 11, doi:10.1002/eco.1843. Bowling, D.R., Schulze, E.S., and Hall, S.J., 2017, Revisiting streamside trees that do not use stream water: can the two water worlds hypothesis and snowpack isotopic effects explain a missing water source? Ecohydrology, v. 10, p. 1–13, doi:10.1002/eco.1771. Branch, C.S. Climate of Ohio:, https://www.ncdc.noaa.gov/climatenormals/clim60/states/Clim_OH_01.pdf. Briggs, L.J., and Shantz, H.L., 1912, The wilting coefficient for different plants: and its indirect determination: Brooks, J.R., Barnard, H.R., Coulombe, R., and McDonnell, J.J., 2009, Ecohydrologic separation of water between trees and streams in a Mediterranean climate: Nature Geoscience, v. 3, p. 100–104, doi:10.1038/ngeo722. Channel Bio, L., 2013, Corn Growth Stages: Monsanto Company, http://www.channel.com/agronomics/Documents/AgronomicContentPDF/GrowthSt ages GuidesChannel.pdf (accessed February 2019). Clark, I.D., and Fritz, P., 1997, Environmental isotopes in hydrology: CRC press. Craig, H., 1961a, Isotopic variations in meteoric waters: Science, v. 133, p. 1702–1703. Craig, H. 1961b, Standard for reporting concentrations of deuterium and oxygen-18 in natural waters: Science, v. 133, p. 1833–1834. Dansgaard, W., 1964, Stable isotopes in precipitation: Tellus, v. 2826, p. 436–468, 83

doi:10.3402/tellusa.v16i4.8993. Davis, P., Syme, J., Heikoop, J., Fessenden-rahn, J., Perkins, G., Newman, B., Chrystal, A.E., and Hagerty, S.B., 2015, Quantifying uncertainty in stable isotope mixing models: , p. 903–923, doi:10.1002/2014JG002839.Received. Dawson, T.E., and Ehleringer, J.R., 1998, Plants, isotopes and water use: a catchment- scale perspective, in Isotope Tracers in Catchment Hydrology, p. 165–202. Djaman, K., and Irmak, S., 2012, Soil water extraction patterns and crop, irrigation and evapotranspiration water use efficiency of maize under full and limited irrigation and rainfed settings: American Society of Agricultural and Biological Engineers, v. 55, p. 1223–1238. Ellsworth, P.Z., and Williams, D.G., 2007, Hydrogen isotope fractionation during water uptake by woody xerophytes: Plant and Soil, v. 291, p. 93–107, doi:10.1007/s11104- 006-9177-1. Evaristo, J., Jasechko, S., and McDonnell, J.J., 2015a, Global separation of plant transpiration from groundwater and streamflow:, doi:10.1038/nature14983. Evaristo, J., Jasechko, S., and McDonnell, J.J., 2015b, Global separation of plant transpiration from groundwater and streamflow: Nature, v. 525, p. 91–94, doi:10.1038/nature14983. Evaristo, J., McDonnell, J.J., and Clemens, J., 2017, Plant source water apportionment using stable isotopes : A comparison of simple linear , two ‐ compartment mixing model approaches: , p. 3750–3758, doi:10.1002/hyp.11233. Evaristo, J., McDonnell, J.J., Scholl, M.A., Bruijnzeel, L.A., and Chun, K.P., 2016, Insights into plant water uptake from xylem-water isotope measurements in two tropical catchments with contrasting moisture conditions: v. 3227, p. 3210–3227, doi:10.1002/hyp.10841. Fan, J., Mcconkey, B., Wang, H., and Janzen, H., 2016, Root distribution by depth for temperate agricultural crops: Field Crops Research, v. 189, p. 68–74, doi:10.1016/j.fcr.2016.02.013. Fröhlich, K., Gibson, J.J., and Aggarwal, P.K., 2002, Deuterium excess in precipitation and its clomatological signficance. Gaj, M., Kaufhold, S., Koeniger, P., Beyer, M., Weiler, M., and Himmelsbach, T., 2017, Mineral mediated isotope fractionation of soil water: Rapid Communications in Mass Spectrometry, . 31, p. 269–280, doi:10.1002/rcm.7787. Gat, J.R., 1996, Oxygen and hydrogen isotopes in the hydrologic cycle.: Annual Review of Earth and Planetary Sciences, v. 24, p. 225–262, www.annualreviews.org (accessed December 2018). Geris, J., Tetzlaff, D., McDonnell, J.J., and Soulsby, C., 2017, Spatial and temporal patterns of soil water storage and vegetation water use in humid northern catchments: Science of the Total Environment, v. 595, p. 486–493, doi:10.1016/j.scitotenv.2017.03.275. Goldsmith, G.R., Muñoz‐Villers, L.E., Holwerda, F., McDonnell, J.J., Asbjornsen, H., and Dawson, T.E., 2012, Stable isotopes reveal linkages among ecohydrological processes in a seasonally dry tropical montane cloud forest: Ecohydrology, v. 5, p. 779–790. 84

Gonfiantini, R., and Tongiorgi, E., 1965, Oxygen isotopic composition of water in leaves, in Isotopes and Radiation in Soil-Plant Nutrition Studies, p. 405–410. Grossman, R.B., and Reinsch, T.G., 2002, Bulk Density and Linear Extensibility, in Dane, J.H., Topp, G.C., and Campbell, G.S. eds., Methods of Soil Analysis: Part 4 Physical Methods, Madison, WI, Soil Science Society of America Inc., p. 201–208. Hervé-Fernández, P., Oyarzún, C., Brumbt, C., Huygens, D., Bodé, S., Verhoest, N.E.C., and Boeckx, P., 2016, Assessing the ‘ two water worlds ’ hypothesis and water sources for native and exotic evergreen species in south-central Chile: Hydrological Processes, v. 30, p. 4227–4241, doi:10.1002/hyp.10984. Hewlett, J.D., and Hibbert, A.R., 1966, Factors affecting the repsonse of small watersheds to precipitation in humid areas: Forest Hydrology, v.1, p. 275-290. http://coweeta.uga.edu/publications/851.pdf (accessed December 2018). Hillel, D., 1980, Fundamentals of Soil Physics: New York, Academic Press, 123-165 p. IAEA/WMO, 2018, lobal Network of Isotopes in Precipitation. The GNIP Database:, http://www.iaea.org/water. Ingraham, N.L., and Shadel, C., 1992, A comparison of the toluene distillation and vacuum/heat methods for extracting soil water for stable isotopic analysis: Journal of Hydrology, v. 140, p. 371-387. Jasechko, S., Sharp, Z.D., Gibson, J.J., Birks, S.J., Yi, Y., and Fawcett, P.J., 2013, Terrestrial water fluxes dominated by transpiration: Nature, v. 496, p. 347–350, doi:10.1038/nature11983. Jouzel, J., and Merlivat, L., 1984, Deuterium and oxygen 18 in precipitation: Modeling of the isotopic effects during snow formation: Journal of Geophysical Research: Atmospheres, v. 89, p. 11748–11757, doi:https://doi.org/10.1029/JD089iD07p11749. Koeniger, P., Marshall, J.D., Link, T., and Mulch, A., 2011, An inexpensive , fast , and reliable method for vacuum extraction of soil and plant water for stable isotope analyses by mass spectrometry: , p. 3041–3048, doi:10.1002/rcm.5198. Lal, R., and Shukla, M.K., 2004, Principles of Soil Physics: New York, CRC Press, p. 285-347 Landwehr, J.M., and Coplen, T.B., 2006, Line-conditioned excess: a new method for characterizing stable hydrogen and oxygen isotope ratios in hydrologic systems.: Leitner, D., Kaul, H.-P., Sobotik, M., Nakhforoosh, A., Bodner, G., and Moder, K., 2013, A statistical approach to root system classification: Frontiers in Plant Science, v. 4, p. 1–15, doi:10.3389/fpls.2013.00292. Likos, W.J., Asce, M., Lu, N., Asce, F., and Godt, J.W., 2013, Hysteresis and Uncertainty in Soil Water-Retention Curve Parameters:, doi:10.1061/(ASCE)GT.1943- 5606.0001071. Ma, Y., and Song, X., 2016, Using stable isotopes to determine seasonal variations in water uptake of summer maize under different fertilization treatments:, doi:10.1016/j.scitotenv.2016.01.148. Mccutcheon, R.J., Mcnamara, J.P., Kohn, M.J., and Evans, S.L., 2017, An evaluation of the ecohydrological separation hypothesis in a semiarid catchment: , p. 783–799, doi:10.1002/hyp.11052. 85

McDonnell, J.J., 2014, The two water worlds hypothesis : ecohydrological separation of water between streams: Wiely Interdisciplinary Reviews: Water, v. 1, p. 323–329, doi:10.1002/wat2.1027. Meißner, M., Köhler, M., Schwendenmann, L., Hölscher, D., and Dyckmans, J., 2014, Soil water uptake by trees using water stable isotopes ( δ 2 H and δ 18 O ) − a method test regarding soil moisture, texture and carbonate: , p. 327–335, doi:10.1007/s11104-013-1970-z. METER Group Inc., 2017, Soil-specific calibrations for METER soil moisture sensors: MKS Instruments, I., 2001, MKS Type PDR 2000 Dual Capacitance Diaphragm Gauge Controller Product Manual: http://orca.physics.unc.edu/~markhowe/RS232/MKS_PDR2000_files/pdr2000man.p df (accessed March 2019). Nakajima, T., Lal, R., and Jiang, S., 2015, Soil quality index of a crosby silt loam in central Ohio: Soil & Tillage Research, v. 146, p. 323–328, doi:10.1016/j.still.2014.10.001. Newberry, S.L., Nelson, D.B., and Kahmen, A., 2017, Cryogenic vacuum artifacts do not affect plant water ‐ uptake studies using stable isotope analysis: Ecohydrology, v. 10, p. e1892, doi:10.1002/eco.1892. Oerter, E.J., and Bowen, G., 2017, In situ monitoring of H and O stable isotopes in soil water reveals ecohydrologic dynamics in managed soil systems: Ecohydrology, v. 10, doi:10.1002/eco.1841. Oerter, E., Finstad, K., Schaefer, J., Goldsmith, G.R., Dawson, T., and Amundson, R., 2014, Oxygen isotope fractionation effects in soil water via interaction with cations ( Mg , Ca , K , Na ) adsorbed to phyllosilicate clay minerals: Journal of Hydrology, v. 515, p. 1–9, doi:10.1016/j.jhydrol.2014.04.029. Orlowski, N., Breuer, L. Angelo, N., Boeckx, P., Brumbt, C., Cook, C., Dubbert, M., Dyckmans, J., Gallagher, B., Gralher, B., Herbstritt, B., Hervé-Fernández, P., Hissler, C., Koeniger, P., Legout, A., Macdonald, C. J., Oyrzún, C., Redelstein, R., Seidler, C., Siegwold, R., Stumpp, C., Thomsen, S., Weiler, M., Werner, C., McDonnell, J.J., 2018, Inter-laboratory comparison of cryogenic water extraction systems for stable isotope analysis of soil water: Hydrology and Earth System Sciences, v. 22, p. 3619–3637, doi:10.5194/hess-22-3619-2018. Orlowski, N., Breuer, L., and Mcdonnell, J.J., 2016a, Invited Commentary Critical issues with cryogenic extraction of soil water for stable isotope analysis: v. 10, p. 3–10, doi:10.1002/eco.1722. Orlowski, N., Frede, H, G., Brüggenmann, and Breuer, L., 2013, Validation and application of a cryogenic vacuum extraction system for soil and plant water extraction for isotope analysis: , p. 179–193, doi:10.5194/jsss-2-179-2013. Orlowski, N., Pratt, D.L., and Mcdonnell, J.J., 2016b, Intercomparison of soil pore water extraction methods for stable isotope analysis: v. 3449, p. 3434–3449, doi:10.1002/hyp.10870. Orlowski, N., Winkler, A., Mcdonnell, J.J., and Breuer, L., 2017, A simple greenhouse experiment to explore the effect of cryogenic water extraction for tracing plant source water: Ecohydrology, p. e1967. 86

Penna, D., Hopp, L., Scandellari, F., Allen, S. T., Benettin, P., Beyer, M., Geris, J., Lkaus, J., Marshall, J. D., Schwendenmann, L., Volkmann, T. H. M., von Freyberg, J., Amin, A., Ceperley, N., Engel, M., Frentress, J., Giambastiani, Y., McDonnell, J. J., Zuecco, G., Llorens, P., Siegwold, R. T. W., Dawson, T. E., Kirchner, J. W., 2018, Ideas and perspectives: Tracing terrestrial ecosystem water fluxes using hydrogen and oxygen stable isotopes-challenges and opportunities from an interdisciplinary perspective: Biogeosciences, v. 15, p. 6399–6415, doi:10.5194/bg- 15-6399-2018. Penna, D., Oliviero, O., Assendelft, R., Zuecco, G., Ilja, H.J., Meerveld, V., Anfodillo, T., Carraro, V., and Borga, M., 2013, Tracing the water sources of trees and streams : isotopic analysis in a small pre-alpine catchment: Procedia Environmental Sciences, v. 19, p. 106–112, doi:10.1016/j.proenv.2013.06.012. Phillips, D.L., 2001, Mixing models in analyses of diet using multiple stable isotopes: A critique: Oecologia, v. 127, p. 166–170, doi:10.1007/s004420000571. Phillips, D.L., and Gregg, J.W., 2003, Source partitioning using stable isotopes: coping with too many sources: Oecologia, v. 136, p. 261–269, doi:10.1007/s00442-003- 1218-3. Phillips, D.L., and Gregg, J.W., 2001, Uncertainty in source partitioning using stable isotopes: Oecologia, v. 127, p. 171–179, doi:10.1007/s004420000578. Phillips, D.L., Inger, R., Bearhop, S., Jackson, A.L., Moore, J.W., Parnell, A.C., Semmens, B.X., and Ward, E.J., 2014, Best practices for use of stable isotope mixing models in food-web studies: Canadian Journal of Zoology, v. 92, p. 823– 835, doi:10.1139/cjz-2014-0127. Phillips, D.L., Newsome, S.D., and Gregg, J.W., 2005, Combining sources in stable isotope mixing models: Alternative methods: Oecologia, v. 144, p. 520–527, doi:10.1007/s00442-004-1816-8. Picarro Inc., 2019, ChemCorrectTM, Santa Clara, CA, USA. Pires, L.F., Borges, J.A.R., Rosa, J.A., Cooper, M., Heck, R.J., Passoni, S., and Roque, W.L., 2017, Soil structure changes induced by tillage systems: Soil and Tillage Research, v. 165, p. 66–79, doi:10.1016/j.still.2016.07.010. Rozanski, K., Araguas-araguas, L., and Gonfiantini, R., 1999, Isotopic patterns in modern global precipitation: Climate Change in continental isotopic records, v. 78, p. 1–36. Rudnick, D.R., and Irmak, S., 2014a, Spatial and temporal maize soil extraction (depletion) dynamics: Part I. development and evaluation of a soil water extraction model: American Society of Agricultural and Biological Engineers, v. 57, p. 431– 444, doi:10.13031/trans.57.10253. Rudnick, D.R., and Irmak, S., 2014b, Spatial and Temporal Maize Soil Water Extraction (Depletion) Dynamics: Part II. Impact of Water and Nitrogen Management Strategies on Soil Water Extraction: Transactions of the ASABE, v. 57, p. 445–462, doi:10.13031/trans.57.10254. Sayin, S.M., and Epstein, S., 1970, The oxygen and hydrogen isotope geochemistry of clay minerals: Pergamon Press. Printed in Northern Ireland, Geochemica et Comochimica Acta, v. 34, p. 25-42. Sprenger, M., Herbstritt, B., and Weiler, M., 2015, Established methods and new 87

opportunities for pore water stable isotope analysis: v. 5192, p. 5174–5192, doi:10.1002/hyp.10643. Sprenger, M., Seeger, S., Blume, T., and Weiler, M., 2016, Travel times in the vadose zone: Variability in space and time: Water Resources Research, v. 52, p. 5727–5754, doi:10.1002/2015WR018077. Sprenger, M., Tetzlaff, D., Buttle, J., Carey, S.K., McNamara, J.P., Laudon, H., Shatilla, N.J., and Soulsby, C., 2017a, Storage, mixing, and fluxes of water in the critical zone across northern environments inferred by stable isotopes of soil water: Hydrological Processes, v. 32, p. 1720–1737, doi:10.1002/hyp.13135. Sprenger, M., Tetzlaff, D., Buttle, J., Laudon, H., and Soulsby, C., 2018, Water ages in the critical zone of long-term experimental sites in northern latitudes: Hydrol. Earth Syst. Sci, v. 22, p. 3965–3981, doi:10.5194/hess-22-3965-2018. Sprenger, M., Tetzlaff, D., and Soulsby, C., 2017b, Soil water stable isotopes reveal evaporation dynamics at the soil – plant – atmosphere interface of the critical zone: , p. 3839–3858. Stewart, M.K., 1975, Stable Isotope Fractionation Due to Evaporation and Isotopic Exchange of Falling Waterdrops: Application to Atmospheric PRocesses and Evaporation of Lakes: v. 80, p. 1133–1146. Sullivan, M.D., 1997, Dissolved organic carbon and soil solution pH as affected by drainage, tillage, depth and season in an agricultural watershed.: The Ohio State University, 1-121 p. Topp, G.C., 1971, Soil-Water Hysteresis: the Domain Theory Extended to Pore Interaction Conditions 1: Soil Science Society of America Journal, v. 32, p. 219– 225. Topp, G.C., and Ferre, P.A., 2002, Gravimetric Using Microwave Oven-Drying, in Topp, G.C., Dane, J.H., and Campbell, G.S. eds., Methods of Soil Analysis: Part 4 Physical Methods, Madison, WI, p. 425–434. Topp, G.C., Galganov, Y.T., Ball, B.C., and Carter, M.R., 1993, Soil water desorption curves, in Carter, M.R. ed., Soil sampling and methods of analysis, Canadian Society of Soil Science, p. 569–579. Tron, S., Bodner, G., Laio, F., Ridolfi, L., and Leitner, D., 2015, Can diversity in root architecture explain plant water use efficiency? A modeling study: Ecological Modelling, v. 312, p. 200–210, doi:10.1016/j.ecolmodel.2015.05.028. Vandevelde, J.H., and Bowen, G.J., 2013, Effects of chemical pretreatments on the hydrogen isotope composition of 2:1 clay minerals:, doi:10.1002/rcm.6554. Vereecken, H., Schnepf, A., Hopmans, J. W., Javaux, M., Or, D., Roose, T., Vanderborght, J., Young, M.H., Amelung, W., Aitkenhead, M., Allison, S. D., Assouline, S., Baveye, P., Berli, M., Brüggemann, N., Finke, P., Flury, M., Gaiser, T., Govers, G., Ghezzehei, T., Hallett, P., Hendricks Franssen, H.J., Heppel, J., Horn, R., Huisman, J.A, Jacques, D., Jonard, F., Kollet, S., Lafolie, F., Lamorski, K., Leitner, D., McBratney,A., Minansy, B., Montzka, C., Nowak, W., Pachepsky, Y., Padarian, J., Romano, N., Roth, K., Rothfuss, Y., Rowe, E. C., Schwen, A., Šimůnek, J., Tikatak, A., Van Dam, J., van der Zee, S.E.A.T.N., Vogel, H. J., Vrugt, J.A., Wöhling, T., Young, I.M., 2016, Modeling Soil Processes: Review, Key 88

Challenges, and New Perspectives: Vadose Zone Journal, v. 15, doi:10.2136/vzj2015.09.0131. West, A.G., Patrickson, S.J., and Ehleringer, J.R., 2006, Water extraction times for plant and soil materials used in stable isotope analysis: , p. 1317–1321, doi:10.1002/rcm. Wu, Y., Du, T., Li, F., Li, S., Ding, R., and Tong, L., 2016, Quantification of maize water uptake from different layers and root zones under alternate furrow irrigation using stable oxygen isotope: Agricultural Water Management, v. 168, p. 35–44, doi:10.1016/j.agwat.2016.01.013. Zhao, X., Li, F., Ai, Z., Li, J., and Gu, C., 2017, Stable isotope evidences for identifying crop water uptake in a typical winter wheat-summer maize rotation field in the North China Plain: Science of the Total Environment, v. 618, p. 121–131, doi:10.1016/j.scitotenv.2017.10.315. Zhao, P., Tang, X., Zhao, P., and Tang, J., 2016, Dynamics of water uptake by maize on sloping farmland in a shallow Entisol in Southwest China: Catena, v. 147, p. 511– 521, doi:10.1016/j.catena.2016.08.001.

Appendix A. Soil Water Source Proportion Scenarios

Table A1. Soil water proportion scenarios determined by the calculated root depth and density distribution at the four sampled depths for each sampling date. Rooting depth at sample date was calculated using three theoretically maximum root lengths at plant maturity. Proportions of soil water uptake are equivalent to the root density distribution at depth, unless noted in column 6. Column 4 shows predicted root lengths at physiological maturity (40 cm, 50 cm or 60 cm). These values are inputs to the calculated root length at sample date (column 5). Column 6 explains the proportion of soil water associated with a floating root density where SWF means soil water traction and IC means isotopic composition. See section 3.8 for description of floating root density.

4. Root 1. 2. 5. Calculated 3. Proportion of Soil Water Length at 6. Soil Water Fraction (SWF) Uptake Sample Scenario Root Length at Uptake by Depth (cm) Physiological Determined by Density Distribution Date Number Sample Date Maturity

0-10 10-20 20-40 40-60 1 1 0 0 0 40 11.4 100% of the SWF = 10 cm IC 2 0.7 0.3 0 0 50 11.4 100% of the SWF <10cm = 20 cm IC 6/14/2018 3 0.59 0.41 0 0 60 11.4 100% of the SWF <10cm = 20 cm IC 4 0.88 0.12 0 0 60 11.4 100% of the SWF <10cm = 20 cm IC 5 0.52 0.48 0 0 40 19.5 100% of the SWF <10cm = 20 cm IC 6 0.52 0.24 0.24 0 40 19.5 50% of SWF <10 cm = 20 cm IC 7 0.42 0.58 0 0 50 24.27 100% of SWF <20 cm = 20 cm IC 7/2/2018 8 0.42 0.53 0.05 0 50 24.27 75% of the SWF <20 cm = 20 cm IC 9 0.35 0.34 0.31 0 60 30 100% of the SWF <20 cm = 40 cm IC 10 0.35 0.50 0.15 0 60 30 50% of the SWF <20 cm = 40 cm IC Continued

Continued 11 0.43 0.42 0.15 0 40 23.67 100% of the SWF 20cm = 40 cm IC 12 0.43 0.50 0.07 0 40 23.67 50% of the SWF <20 cm = 40 cm IC 13 0.43 0.57 0 0 40 23.67 100% of the SWF <20 cm = 20cm IC 14 0.34 0.34 0.32 0 50 31.47 100% of the SWF 20 cm = 40 cm IC 7/11/2018 15 0.34 0.5 0.16 0 50 31.47 50% of the SWF <20 cm = 40 cm IC 16 0.29 0.28 0.43 0 60 37.88 100% of the SWF <20 cm = 40 cm IC 17 0.29 0.50 0.21 0 60 37.88 50% of the SWF <20 cm = 40 cm IC 18 0.29 0.61 0.10 0 60 37.88 75% of the SWF <20 cm = 20 cm IC 19 0.29 0.39 0.32 0 60 37.88 75% of the SWF <20 cm = 40 cm IC 20 0.31 0.69 0 0 40 32.85 100% of the SWF <20 cm = 40 cm IC 21 0.31 0.35 0.34 0 40 32.85 50% of the SWF <20 cm = 40 cm IC 22 1 0 0 0 40 32.85 100% of the SWF <20 cm = 20 cm IC 23 0.25 0.25 0.5 0 50 41.06 100% of the SWF <20 cm = 40 cm IC 24 0.25 0.5 0.25 0 50 41.06 50% of the SWF <20 cm = 40 cm IC 8/2/2018 25 0.25 0.63 0.12 0 50 41.06 75% of the SWF <20 cm = 20 cm IC 26 0.25 0.13 0.62 0 50 41.06 75% of the SWF <20 cm = 40 cm IC 27 0.21 0.21 0.40 0.18 60 49.27 100% of the SWF <40 cm = 60 cm IC 28 0.21 0.21 0.50 0.08 60 49.27 50% of the SWF <40 cm = 60 cm IC 29 0.21 0.21 0.58 0 60 49.27 100% of the SWF <40 cm = 40 cm IC Continued

Continued 30 0.29 0.29 0.42 0 40 35.16 100% of the SWF <20 cm = 40 cm IC 31 0.29 0.5 0.21 0 40 35.16 50% of the SWF <20 cm = 20 cm IC 32 0.29 0.71 0 0 40 35.16 100% of the SWF <20 cm = 20cm IC 33 0.23 0.24 0.53 0 50 43.95 100% of the SWF <20 cm = 40 cm IC 34 0.23 0.50 0.27 0 50 43.95 50% of the SWF <20 cm = 20 cm IC 8/9/2018 35 0.23 0.64 0.13 0 50 43.95 75% of the SWF <20 cm = 20 cm IC 36 0.23 0.37 0.40 0 50 43.95 75% of the SWF <20 cm = 40 cm IC 37 0.2 0.19 0.38 0.23 60 52.75 100% of the SWF <40 cm = 60cm IC 38 0.2 0.19 0.49 0.12 60 52.75 50% of the SWF <40 cm = 60cm IC 39 0.2 0.19 0.61 0 60 52.75 100% of the SWF <40 cm = 40cm IC 40 0.27 0.26 0.47 0 40 38.72 100% of the SWF <20 cm = 40 cm IC 41 0.27 0.50 0.23 0 40 38.72 50% of the SWF <20 cm =h 20 cm IC 42 0.27 0.73 0 0 40 38.72 100% of the SWF <20 cm = 20 cm IC 43 0.21 0.21 0.58 0 50 48.39 100% of the SWF <20 cm = 40 cm IC 44 0.21 0.5 0.29 0 50 48.39 50% of the SWF <20 cm = 20 cm IC 45 0.21 0.65 0.14 0 50 48.39 75% of the SWF <20 cm = 20 cm IC 8/28/2018 46 0.21 0.36 0.43 0 50 48.39 75% of the SWF <20 cm = 40 cm IC 47 0.21 0.21 0.41 0.17 50 48.39 100% of the SWF <40 cm = 60 cm IC 48 0.21 0.21 0.50 0.08 50 48.39 50% of the SWF <40 cm = 60 cm IC 49 0.18 0.18 0.34 0.3 60 58.07 100% of the SWF <40 cm = 60 cm IC 50 0.18 0.18 0.49 0.15 60 58.07 50% of the SWF <40 cm = 60cm IC 51 0.18 0.18 0.64 0 60 58.07 100% of the SWF <40 cm = 40 cm IC

Appendix B. Cryogenic Vacuum Distillation Standards

Table B1. Cryogenic vacuum distillation fractionation factors obtained from standard tests. Extraction sets are numbered if standards were tested over multiple extraction experiments. Standard numbers are associated with the extraction set.

Standard Extraction Set Numbers δ18O δD Air-Dried 1 1 1.30 1.30 Air-Dried 1 2 1.22 1.24 Air-Dried 1 3 1.24 1.27 Air-Dried 1 4 1.22 1.22 Air-Dried 1 5 1.21 1.24 Air-Dried 1 6 1.29 1.29 Air-Dried 2 1 1.10 1.16 Air-Dried 2 2 1.26 1.28 Air-Dried 2 3 1.14 1.19 Air-Dried 2 4 1.20 1.24 Air-Dried 2 5 1.24 1.27 Air-Dried 2 6 1.11 1.16 Air-Dried 2 7 1.20 1.27 Air-Dried 2 8 1.28 1.28 Oven-Dried 1 1.33 1.35 Oven-Dried 2 1.36 1.36 Oven-Dried 3 1.30 1.30 Oven-Dried 4 1.27 1.28 Oven-Dried 5 1.22 1.24 Oven-Dried 6 1.30 1.29 Oven-Dried 7 1.21 1.24 Oven-Dried 8 1.27 1.27 Oven-Dried 9 1.31 1.33 Oven-Dried 10 1.21 1.22 Water 1 1.15 1.14 Water 2 1.16 1.15 Water 3 1.16 1.15 Water 4 1.29 1.28 Water 5 1.36 1.36 Water 6 1.38 1.40 Water 7 1.27 1.27 Water 8 1.09 1.08 Water 9 1.30 1.29 Water 10 1.36 1.36 Continued

Continued Samples 1 1 1.11 1.13 Samples 1 2 1.17 1.21 Samples 1 3 1.27 1.26 Samples 2 1 1.17 1.18 Samples 2 2 1.18 1.20 Samples 3 1 1.09 1.13 Samples 3 2 1.15 1.23 Samples 4 1 1.25 1.27 Samples 4 2 1.19 1.22 Samples 5 1 1.22 1.23 Samples 6 1 1.16 1.18 Samples 6 2 1.56 1.54 Samples 7 1 1.21 1.26 Samples 7 2 1.19 1.24 Samples 8 1 1.21 1.25 Samples 8 2 1.20 1.25 Samples 9 1 1.20 1.27 Samples 9 2 1.20 1.25