Copyright

by

Bradley Donald Cey

2008

The Dissertation Committee for Bradley Donald Cey Certifies that this is the

approved version of the following dissertation:

Dissolved Noble Gases in Groundwater

Committee:

Bridget R. Scanlon, Supervisor

Philip C. Bennett

John M. Sharp

Zong-Liang Yang

G. Bryant Hudson

Dissolved Noble Gases in Groundwater

by

Bradley Donald Cey, B.Sc., M.Sc.

Dissertation

Presented to the Faculty of the Graduate School of

The University of Texas at Austin

in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

The University of Texas at Austin

May 2008

Acknowledgements

Financial assistance for my studies was provided by the Jackson School of

Geosciences, the Glenn T. Seaborg Institute at Lawrence Livermore National Laboratory,

California State Water Resources Control Board Groundwater Ambient Monitoring and

Assessment Program, and the Geological Society of America. The funding provided by

these organizations was critical to the completion of this degree and is gratefully

appreciated.

Thank you to the landowner of the Kings County field site for generously

allowing access for this study. I have enjoyed my studies at UT in large part because of

the people I have had the pleasure of meeting and working with. Thank you to my

dissertation committee members Phil Bennett, Jack Sharp, and Liang Yang, for their

insightful assistance. I am thankful for the opportunity to have worked on an interesting

and challenging research project with scientists from Lawrence Livermore National

Laboratory. I thank Jean Moran, Bryant Hudson, Brad Esser, Mike Singleton, and Steve

Carle for allowing me to join them in their research. Special thanks to Mike, Bryant, Gail

Eaton, Wayne Culham, and Bob Reedy for assistance in the lab. Thank you to Jean for

her support and encouragement throughout the course of my research. I am especially

grateful to Bryant for his willingness to serve on my dissertation committee and for

sharing his enthusiasm for science and knowledge of noble gases with me.

iv

Thank you to Gerald Flerchinger for allowing me to use the SHAW numerical

modeling code, and to Lindsey Gulden and Enrique Rosero for assistance with accessing

data and computer issues.

Thank you to Jack Sharp for his assistance during with my application to UT.

Without Jack’s persistence, it is unlikely that I would have come to study at UT.

I am grateful to Bridget Scanlon for supervising my dissertation. She believed in

me throughout—during the successes and during the challenges. Thank you so very

much for your enthusiastic encouragement.

Most importantly, thank you to my family. To Anna and Clare for their love and

their zest for life. To my wife Misty, for her trust, love, encouragement, and giving so

freely of herself to allow me to complete this work.

v

Dissolved Noble Gases in Groundwater

Publication No._____________

Bradley Donald Cey, Ph.D.

The University of Texas at Austin, 2008

Supervisor: Bridget R. Scanlon

Atmospheric noble gases (He, Ne, Ar, Kr, and Xe) dissolved in groundwater are a

valuable tool in hydrology. Numerous studies have relied on groundwater recharge

temperatures calculated from dissolved noble gas data (noble gas temperatures, NGT) to

infer paleoclimate conditions. This research investigated gas dissolution during

groundwater recharge and critically examined the use of dissolved noble gas data in

groundwater research. A detailed investigation of an agriculturally impacted shallow

aquifer allowed comparison of measured water table temperatures (WTT) with calculated

NGT. Results suggest that NGT calculated from widely used noble gas interpretive

models do reflect measured WTT, supporting the use of dissolved noble gases to deduce

recharge temperatures. Samples having dissolved gas concentrations below the

equilibrium concentration with respect to atmospheric pressure were attributed to

denitrification induced gas stripping in the saturated zone. Modeling indicated that minor

degassing (<10% ΔNe) may cause underestimation of groundwater recharge temperature

by up to 2 °C. In another study a large dissolved noble gas data set (905 samples) from

California was analyzed. Noble gas modeling using the same interpretive models

vi

indicates that multiple models may fit measured data within measurement uncertainty,

suggesting that goodness-of-fit is not a robust indicator of model appropriateness. A

unique aspect of this study was the high Ne and excess air concentrations associated with

surficial artificial recharge facilities. A final study examined whether climatic/hydrologic

changes occurring over glacial-interglacial time periods could impact the accuracy of

NGT used in paleoclimate studies. Numerical modeling experiments estimated WTT

sensitivity to changes in: 1) precipitation amount, 2) water table depth, and 3) air

temperature. Precipitation and water table depth had a minor impact on WTT (~0.2 °C).

In contrast, the impact of air temperature changes on WTT was more pronounced.

Results suggest that air temperatures inferred from NGT data may underestimate actual

air temperature change since the last glacial maximum by ~1 °C at sites having seasonal

snowcover. These results suggest despite uncertainty in the exact physical processes

controlling gas dissolution during groundwater recharge, NGT do reflect WTT.

However, inferring paleo-air temperatures from NGT are subject to error, especially

locations with seasonal snowcover.

vii

Table of Contents

List of Tables ...........................................................................................................x

List of Figures ........................................................................................................ xi

Chapter 1 : Introduction .........................................................................................1 Overview.........................................................................................................1 Research Questions.........................................................................................2 Hypotheses and Research Approach...............................................................3

Chapter 2 : Noble Gas Recharge Temperature Studies at an Agricultural Site in California ........................................................................................................5 Introduction.....................................................................................................6 Materials and Methods....................................................................................8

Study Site ...............................................................................................8 Noble Gas Modeling............................................................................11

Results and Discussion .................................................................................16 Soil Gas Pressure .................................................................................16 Temperature .........................................................................................16

Oxygen Isotope Data (δ18O) ................................................................17 Dissolved Noble Gases ........................................................................18

Undersaturation...........................................................................18 Noble Gas Temperatures.............................................................20

Modeling Degassing ............................................................................23 Conclusions...................................................................................................25

Chapter 3 : Impact of Artificial Recharge on Dissolved Noble Gases in Groundwater in California ..................................................................................................45 Introduction...................................................................................................46 Materials and Methods..................................................................................48

Overview of Study ...............................................................................48 Sampling and Analysis ........................................................................49 Modeling..............................................................................................50

viii

Results and Discussion .................................................................................51 Spatial Characterization .......................................................................51 Noble Gas Models................................................................................53 Excess Air ............................................................................................54 3H–3He Age-dating ..............................................................................56 Recharge Temperature .........................................................................57 Artificial Recharge...............................................................................58

Conclusions...................................................................................................60

Chapter 4 : On the Accuracy of Noble Gas Paleotemperatures over Glacial-Interglacial Periods .......................................................................................76 Introduction...................................................................................................77 Methods.........................................................................................................80

Model Description ...............................................................................80 Numerical Experiments .......................................................................82

Results...........................................................................................................83 Base Case .............................................................................................83 Precipitation .........................................................................................84 Water Table Depth...............................................................................85 Temperature .........................................................................................85

Discussion.....................................................................................................87 Conclusions...................................................................................................89

Chapter 5 : Conclusions and Outlook ................................................................105

Appendix A : Laboratory Soil Testing Results from Kings County Field Site .108

Appendix B : Field Measurements from Kings County Field Site....................118

Appendix C : Dissolved Noble Gas Data and NOBLE90 Modeling Results ....125

Appendix D : SHAW Model Input Files ...........................................................158

Bibliography ........................................................................................................166

Vita ....................................................................................................................179

ix

List of Tables

Table 2.1. Well depths, sample collection dates, and selected measured data (originally published in McNab et al., 2007; Singleton et al., 2007).................................................. 27

Table 2.2. Dissolved noble gas data (originally published in Singleton et al., 2007)...... 29

Table 2.3. Results of noble gas modeling. Modeling was done using He, Ne, Ar, Kr, and Xe for the UA, PR, and CE models. Additional modeling was done using only Ne, Ar, Kr, and Xe with the CE model. Results rejected because of poor fitting (i.e. p < 0.05) are not shown. ......................................................................................................................... 31

Table 3.1. Number of samples fit by each model for a given region according to criteria discussed in the Supporting Information. AR refers to artificial recharge impacted areas (i.e. SFBA, LAB, and Bakersfield)................................................................................... 62

Table 3.2. Statistical summary of modeled excess air concentrations (% ΔNe). AR refers to artificial recharge impacted areas (i.e. SFBA, LAB, and Bakersfield). ....................... 63

Table 3.3. Statistical summary of calculated recharge temperatures (°C). AR refers to artificial recharge impacted areas (i.e. SFBA, LAB, and Bakersfield)............................. 64

Table 4.1. Soil properties used in SHAW model (from Rawls and Brakensiek, 1989)... 90

Table 4.2. Soil thermal properties used in SHAW model................................................ 91

Table 4.3. Meteorological forcing data used in the Base Case scenario.......................... 92

x

List of Figures

Figure 2.1. Map of study site. Only the sampled irrigation wells are uniquely labeled. Irrigation wells owned by other landowners are not shown. ............................................ 35

Figure 2.2. Monitor well depths. Ground surface at each monitor well location shown as horizontal black line, screened intervals shown in gray. Elevation in meters above mean sea level............................................................................................................................. 36

Figure 2.3. Atmospheric pressure data from nearby National Climatic Data Center (NCDC) station showing a) seasonal fluctuations and (b) diurnal fluctuations. The dashed line in (b) is 3 day moving average. Panel (c) shows the impact of the May 22, 2005 irrigation event at 2S on soil gas pressure (site 2S sensors – gray, sites 3S and 5S – black)................................................................................................................................. 37

Figure 2.4. Subsurface temperature data from 3S location. Local water table fluctuated between 3.8 and 5.5 m below ground surface (BGS) during the study. Subsurface temperature data from the other instrumentation locations (2S and 5S) are similar. Air temperature data are from the nearby National Climatic Data Center (NCDC) station. .. 38

Figure 2.5. Water table temperatures for the three instrumented locations. Curves for 3S and 5S are data from the deepest heat dissipation sensor. The lower 2006 maximum temperature at 5S is attributed to increased vegetation cover in 2006. The curve for 2S is extrapolated from measured data assuming exponential decay of the seasonal temperature signal with depth (water table was ~1.1 m below the deepest 2S sensor). ....................... 39

Figure 2.6. Oxygen isotope (δ18O) data reported as parts per thousand (‰) relative to Vienna Standard Mean Ocean Water (VSMOW)............................................................. 40

Figure 2.7. Helium versus Ne concentrations of undersaturated samples. Equilibrium concentration given for T = 19 °C, S = 0, p = 0.991 atm. Lines show impact of degassing for each of the three degassing models. ............................................................................ 41

Figure 2.8. Ne and Xe concentrations of samples from the shallowest wells. Analytical uncertainties shown (Ne 2%, Xe 3%). Equilibrium solubilities from T = 17–20 °C (S = 0, p = 0.991 atm) are shown. Unfractionated excess air concentrations are also shown..... 42

Figure 2.9. Noble Gas Temperatures (NGTs) calculated for samples from the shallowest wells for four different models: Unfractionated Air (UA), Closed system Equilibrium (CE), Partial Re-equilibration (PR), and Diffusive Degassing (DD). The shaded region indicates the water table temperature (WTT) range for that location. For wells with multiple samples, the sample number is noted. ................................................................ 43

Figure 2.10. Impact of degassing on calculated NGTs. These synthetic samples had excess air added (ΔNe = 30%), some unfractionated and others fractionated according to the CE model (both F = 0.65 and 0.75). Samples were then degassed by various amounts

xi

according to the DS1 and DD models. The resultant gas concentrations were then modeled by the CE and UA models. Panels (a) and (b) show the difference between original recharge temperature (19 °C) and modeled recharge temperature after degassing. Panels (c) and (d) show model goodness-of-fit (probability of χ2 being greater than a given value obtained from the χ2 distribution for the appropriate number of degrees of freedom). Panel (e) shows the modeled CE fractionation parameter, F (note that the UA model is a limiting case of the CE model in which F = 0). .............................................. 44

Figure 3.1. Map of California showing sample locations in each of the six regions: Los Angeles Basin (LAB), Mojave Desert Basin (MDB), northern California (NC), northern portion of the Central Valley (NCV), southern portion of the Central Valley (SCV), and San Francisco Bay Area (SFBA). ..................................................................................... 65

Figure 3.2. Histogram of Ne concentrations for: literature data (Aeschbach-Hertig et al., 2002c; Andrews et al., 1991; Andrews et al., 1994; Beyerle et al., 1998; Beyerle et al., 2003; Clark et al., 1998; Clark et al., 1997; Dennis et al., 1997; Fontes et al., 1991; Hall et al., 2005; Kulongoski et al., 2004; Ma et al., 2004; Saar et al., 2005; Stute et al., 1995a; Stute and Deak, 1989; Stute et al., 1995b; Stute and Sonntag, 1992; Thomas et al., 2003; Weyhenmeyer et al., 2000; Wilson et al., 1990; Wilson et al., 1994; Zuber et al., 2004; Zuber et al., 2000), artificial recharge impacted areas (SFBA, LAB, and Bakersfield), and non-artificial recharge impacted areas. ............................................................................. 66

Figure 3.3. CE modeled versus measured gas concentrations. Note that sample 100706 plots off scale for Kr. ........................................................................................................ 67

Figure 3.4. PR modeled versus measured gas concentrations. Note that sample 100706 plots off scale for Kr. ........................................................................................................ 68

Figure 3.5. UA modeled versus measured gas concentrations. Note that sample 100706 plots off scale for Kr. ........................................................................................................ 69

Figure 3.6. Scatter plots of model error (relative difference between modeled and measured gas concentrations) for samples fit by the CE model. Results from the other models are comparable. Note that analytical uncertainties are approximately 2% for Ne and Ar, and 3% for Kr and Xe. ......................................................................................... 70

Figure 3.7. Element ratios for: 1) measured gas concentrations for artificial recharge impacted areas (AR) and non-AR areas, and 2) total model predicted gas concentrations (UA, CE, and PR). Measured ratios below the cluster of CE points can not be explained by the UA or CE models and are consistent with diffusive degassing or other fractionating processes. The UA model predicted ratios lie on the line shown. Not all data are shown at this scale............................................................................................... 71

Figure 3.8. Relationship between CE model parameters and excess air. As excess air increases, the degree of fractionation decreases. Note that the CE model reduces to the

xii

UA model when F = 0. One data point is not shown at the given scale (ΔNe = 737%, F = 0.031, q = 5.8). ........................................................................................................... 72

Figure 3.9. Histogram of calculated 3H–3He ages for each of three models. .................. 73

Figure 3.10. Modeled recharge temperatures for samples fit by all three excess air models. In general, the PR recharge temperature > CE recharge temperature > UA recharge temperature......................................................................................................... 74

Figure 3.11. Median calculated recharge temperatures from each of the three models. The measured data are mean annual air temperatures taken from the U.S. Historical Climatology Network (http://www.ncdc.noaa.gov/ol/climate/research/ushcn/ushcn.html) and are the medians of stations nearest each sample location. ......................................... 75

Figure 4.1. Schematic diagram showing processes modeled, input forcings, and boundary conditions.......................................................................................................... 93

Figure 4.2. Input forcing data from year 2004 at 38.5° N, 97° W of the North American Land Data Assimilation System (NLDAS) (Cosgrove et al., 2003). (a) Hourly temperature data, and (b) daily precipitation totals. ......................................................... 94

Figure 4.3. Profiles of mean annual soil temperature and saturation for the Base Case scenario (mean annual air temperature, MAAT = 13.68 °C; mean annual water table temperature, WTTloam = 14.66 °C; WTTsand = 15.40 °C). ................................................ 95

Figure 4.4. Water table fluctuations of the Base Case scenarios (mean annual water table depth of 3 m)..................................................................................................................... 96

Figure 4.5. Modeled time series of (a) temperature and (b) saturation at various depths for the loam soil Base Case scenario (mean annual air temperature, MAAT = 13.68 °C; mean annual water table temperature, WTTloam = 14.66 °C)............................................ 97

Figure 4.6. Response of water table temperature (WTT) relative to mean annual air temperature (MAAT) to changes in precipitation amount................................................ 98

Figure 4.7. Amount of seasonal decoupling of ground surface temperature (GST) from mean annual air temperature (MAAT) in response to changes in precipitation amount. . 99

Figure 4.8. Response of water table temperature (WTT) relative to mean annual air temperature (MAAT) to changes in mean annual depth of water table below ground surface. Mean annual water table depth was 3 m in the Base Case scenario. ............... 100

Figure 4.9. Mean annual soil temperature profiles for simulations in which the water table depth was varied from 2–5 m. Mean annual water table depth was 3 m in the Base Case scenario. ................................................................................................................. 101

xiii

Figure 4.10. Response of water table temperature (WTT) relative to mean annual air temperature (MAAT) to changes in MAAT. MAAT was 13.68 °C in the Base Case scenario. .......................................................................................................................... 102

Figure 4.11. Seasonal response of ground surface temperature (GST) relative to mean annual air temperature (MAAT) for loam. ..................................................................... 103

Figure 4.12. Response of water table temperature (WTT) relative to mean annual air temperature (MAAT) to changes in the annual amplitude of MAAT. ........................... 104

xiv

Chapter 1:

Introduction

OVERVIEW

Atmospheric noble gases (He, Ne, Ar, Kr, and Xe) dissolved in groundwater are a

valuable tool in hydrology. They have been used in hydrologic studies for over half a

century (Oana, 1957 as referenced in Kipfer et al., 2002). An early application was to

determine groundwater flow velocity based on seasonal variations in dissolved gas

concentration (Sugisaki, 1961). Mazor (1972) first recognized the potential to derive

paleotemperature records from dissolved noble gases in groundwater. Since then,

numerous studies have relied on groundwater recharge temperatures calculated from

dissolved noble gas data (noble gas temperatures, NGT) to infer paleoclimate conditions

(Kipfer et al., 2002; Stute and Schlosser, 1993). Dissolved noble gases in groundwater

are also important for determining the age of young groundwater (<50 yr) using the 3H–3He technique (Solomon and Cook, 2000).

The sources of dissolved noble gases in groundwater can be either atmospheric or

non-atmospheric. Atmospheric sources include equilibrium concentration with respect to

atmospheric pressure (governed by Henry’s Law) and “excess air” (Heaton and Vogel,

1981). The excess air component is the result of dissolution of air bubbles trapped in the

saturated zone (Kipfer et al., 2002). Non-atmospheric sources can be either radiogenic

(e.g. 3He from 3H decay), terrigenic (e.g. primordial 3He from the mantle), or both (e.g. 4He from U and Th decay in aquifer rocks). For many groundwater systems, the non-

atmospheric sources are much smaller than atmospheric sources (Lehmann et al., 1993).

1

If non-atmospheric components are insignificant, equilibrium concentration with

respect to atmospheric pressure and excess air components can be distinguished by

measuring multiple gases because of the differences in gas solubility. According to

Henry’s Law, equilibrium concentration is a function of temperature, gas partial pressure,

and water salinity. In most noble gas studies, the recharge elevation is assumed which

allows the calculation of gas partial pressure (Kipfer et al., 2002). Also, the recharging

water is commonly assumed to be fresh. Therefore, the recharge temperature (i.e. NGT)

can be calculated from the dissolved gas concentration.

NGT are commonly used in paleoclimate studies of the last glacial maximum

(LGM). NGT are important because they are few other quantitative temperature proxies

for low-elevation terrestrial sites (Farrera et al., 1999).

Despite the importance of dissolved noble gas data, questions about the

interpretation of these data remain (Hall et al., 2005). Most interpretations rely on simple

models of gas dissolution, with little experimental confirmation of these models.

Furthermore, the use of NGT in paleoclimatology requires multiple assumptions which

have received minimal attention to date (Castro et al., 2007).

RESEARCH QUESTIONS

The research questions addressed in this work are in the following areas:

1. Validity of NGT as a measure of water table temperature: Do

measured NGT accurately reflect water table temperature? What, if any,

processes affect dissolved gas concentrations after groundwater recharge?

If such processes occur, what is the impact on calculated NGT?

2. Noble gas interpretive models: How do noble gas interpretive models

differ? Which model best fits measured data? What are the implications

of using an “incorrect” model?

2

3. Noble gas paleothermometry: Do climatic/hydrological changes that

occur over glacial-interglacial periods affect paleo-air temperatures

inferred from NGT?

HYPOTHESES AND RESEARCH APPROACH

The aim of this dissertation is to improve our understanding of gas dissolution

during groundwater recharge and to critically examine the utility of dissolved noble gas

data in groundwater research. Multiple methods were used to achieve this goal.

Chapter 2. I hypothesized that calculated NGT reflect measured water table

temperatures. Additionally, I hypothesized that differing recharge regimes would affect

the concentrations of noble gases dissolved during recharge. To test these hypotheses a

shallow aquifer in Kings County, California was instrumented and monitored. This field

study also examined the impact of saturated zone degassing on interpreted NGT. This

work was an extension of a research program on denitrification conducted by Lawrence

Livermore National Laboratory (LLNL). This program involved multiple research sites

and focused on denitrification of nitrate discharged from agricultural operations in

California’s Central Valley. LLNL generated the geochemical and dissolved noble gas

data reported in Chapter 2.

Chapter 3. Multiple models exist to interpret dissolved noble gas data. I

hypothesized that differences in model formulation would lead to differences in model

output. This hypothesis was tested by comparing model results from a large dissolved

noble gas data set (905 samples). The data set included samples from across California—

including many samples impacted by artificial recharge. The impact of artificial recharge

on gas dissolution was examined. This study was an extension of work performed at

LLNL as part of the California Aquifer Susceptibility (CAS) Assessment of the

Groundwater Ambient Monitoring and Assessment (GAMA) program run by California’s

3

State Water Resources Control Board. CAS examined water quality and relative

susceptibility to contamination of groundwater that serves as a source for public drinking

water. The data set analyzed in Chapter 3 was generated by LLNL as part of CAS and

GAMA.

Chapter 4. Studies investigating paleoclimate are primarily concerned with air

temperatures rather than subsurface temperatures. The value of NGT data to

paleoclimate studies relies on the temporal constancy of the coupling between water table

temperature and air temperature. I hypothesized that water table temperatures and air

temperatures do not remain coupled over glacial-interglacial time scales. To test this

hypothesis, a series of numerical modeling experiments were completed.

Chapter 5. This chapter summarizes the key findings uncovered during the

course of this research. It also suggests areas of future work for hydrologic studies using

dissolved noble gas data.

4

Chapter 2:

Noble Gas Recharge Temperature Studies at an Agricultural Site in California

Abstract

Recharge temperature inferred from dissolved noble gas concentrations (noble gas

temperatures, NGT) are useful as a quantitative proxy for air temperature change since

the last glacial maximum. Despite their importance in paleoclimate research, few studies

have investigated the relationship between NGT and actual recharge temperatures in field

settings. This study presents dissolved noble gas data from a series of monitor wells in a

shallow unconfined aquifer in an area heavily impacted by agriculture. Multiple samples

had dissolved gas concentrations below the equilibrium concentration with respect to

atmospheric pressure, indicating degassing. The NGT calculated from common

physically based interpretive gas dissolution models on samples unaffected by degassing

do reflect the measured water table temperatures (WTT). The ability to fit the data to

multiple interpretive models indicates that model goodness-of-fit does not necessarily

mean that the model reflects the actual gas dissolution process. Although NGT are useful

in that they reflect WTT, caution is recommended when using these interpretive models.

There was no measurable difference in excess air characteristics (amount and degree of

fractionation) between the two recharge regimes studied. Geochemical and dissolved gas

data indicate that saturated zone denitrification caused degassing by gas stripping.

Modeling indicates that minor degassing (<10% ΔNe) may cause underestimation of

groundwater recharge temperature by 2 °C. Such errors are problematic because

degassing may not be apparent and degassed samples may be fit by a model with a high

5

degree of certainty. This source of error has implications for application of NGT data in

paleoclimate studies.

INTRODUCTION

Dissolved noble gases (He, Ne, Ar, Kr, and Xe) impart unique and valuable

information in hydrologic studies. The conservative behavior of noble gases allows

estimation of groundwater recharge temperatures (noble gas temperatures, NGT) as well

as groundwater ages. NGT are particularly important in paleoclimate research for

quantifying the temperature difference from the last glacial maximum (LGM) to present

(e.g. Farrera et al., 1999).

It is common for groundwater to contain dissolved gas concentrations greater than

equilibrium concentration with respect to atmospheric pressure. The additional dissolved

gas is termed excess air because of its compositional similarity to air (Heaton and Vogel,

1981). Accurate determination of excess air is necessary for groundwater age-dating

using the 3H–3He technique (Solomon and Cook, 2000). Some suggest that excess air

may itself be a valuable paleoclimate proxy (Aeschbach-Hertig et al., 2002b; Castro et

al., 2007).

NGT are much more sensitive to concentrations of heavier gases (e.g. Xe, Kr)

because the solubility of these gases have much greater temperature dependency. In

contrast, excess air is much more sensitive to concentrations of lighter gases (e.g. He,

Ne). It is common to measure multiple gases to calculate NGT and excess air

simultaneously using an error weighted inverse modeling procedure (Aeschbach-Hertig

et al., 1999; Aeschbach-Hertig et al., 2000).

Despite the importance of NGT in paleoclimate research, few studies have

attempted to experimentally confirm that NGT accurately reflect water table temperatures

(WTT). This deficiency is critical given recent work examining assumptions in NGT

6

calculations (Castro et al., 2007; Hall et al., 2005). Most noble gas studies report the

sampled water temperature and mean annual air temperature (MAAT); however, it is

extremely rare for researchers to directly measure WTT.

Holocher et al. (2002) completed a series of laboratory column experiments in

which excess air was generated. The NGT matched the column temperature within

measurement uncertainty for all samples. Stute and Sonntag (1992) investigated the

relationship between NGT and subsurface temperature. NGT at a site near Bocholt,

Germany showed evidence of recharge from two areas (i.e. forest and field/meadow)

having different soil thermal regimes. Subsurface temperature data from ~1 m above the

water table were available from a nearby meteorological station having field/meadow

vegetation. No temperature measurements for the forested area were reported. The NGT

of groundwater recharged in the field/meadow was the same as the measured soil

temperature.

In a regional study Castro et al. (2007) compared calculated NGT to recharge

zone WTT. Details of the WTT data (e.g. precise location, measurement date(s), etc.)

used in the study were not reported. While unable to connect the WTT to NGT using

common gas dissolution models, the NGT results matched WTT if subsurface noble gas

partial pressures were assumed to be greater than their respective atmospheric partial

pressures. Subsurface noble gas partial pressures could be elevated relative to

atmospheric conditions from O2 consumption by biological processes and subsequent

dissolution of the produced CO2 (Stute and Schlosser, 2000).

Klump et al. (2007) reported on field scale noble gas dissolution experiments

from two sites. In situ temperatures were not taken at the two study sites; however,

subsurface temperatures were inferred from either measuring samples of recently

7

recharged water or using data from a nearby (~20 km) meteorological station. They

concluded that calculated NGT accurately reflected in situ soil temperatures.

In each of these three field studies, subsurface temperature data were considered

in an attempt to compare NGT to WTT. However, none of these studies incorporated

direct measurements of subsurface temperature to examine the relationship between NGT

of very young (weeks to years) groundwater to WTT.

The objectives of the study were to: 1) compare the modeled NGT to the

measured WTT to evaluate potential bias in NGT, 2) compare differences in gas

dissolution occurring under different recharge regimes, and 3) examine the potential

impact of degassing on NGT. Improved understanding of gas dissolution processes

occurring during groundwater recharge will benefit groundwater age-dating and

paleoclimate studies. This study offers the following improvements over the noble gas

studies discussed above: 1) high frequency measurements of subsurface temperature

throughout the unsaturated zone at multiple locations, 2) noble gas concentrations

measured at multiple locations across the site, and 3) two different recharge regimes.

This study complements recent work from the same site by Singleton et al. (2007) that

focused on evidence for saturated zone denitrification, and by McNab et al. (2007) that

focused on the geochemistry of lagoon water. Singleton et al. (2007) present average

NGT and excess air for the site. All dissolved noble gas analyses presented here are

original—revised and expanded from the previous analyses by Singleton et al. (2007).

MATERIALS AND METHODS

Study Site

The study site includes a dairy farm and surrounding fields in Kings County, CA.

The climate is Mediterranean type with hot summers and mild winters

(MAAT = 16.6 °C). Mean annual precipitation is 170 mm, with 80% falling during the

8

coolest five months (November through March). Local meteorological data were

obtained from a nearby (~10 km) National Climatic Data Center (NCDC) station. The

site has minimal topographic relief and an elevation ~70 m above mean sea level. The

local geology consists of unconsolidated sediments (primarily sands and silts) that were

deposited in a series of alluvial fan systems originating where rivers exit the Sierra

Nevada mountains (Weissmann et al., 1999).

Cropland surrounding the dairy operation is flood irrigated with a combination of

groundwater and dairy wastewater (i.e. liquid manure). Occasionally water from the

Kings River is transported through unlined canals for irrigation. Groundwater used for

irrigation is drawn from both a shallow perched aquifer (<25 m below ground surface,

BGS) and a deeper aquifer (>40 m BGS). Water for domestic use is drawn from the deep

aquifer. The water table is ~5 m BGS across the site. Groundwater flow direction within

the perched aquifer is difficult to characterize because of the many irrigation wells that

pump intermittently and the seasonally filled irrigation canals. Deeper regional

groundwater flow is generally westward toward the center of the valley (Williamson et

al., 1989). Additional details of the study site are given elsewhere (McNab et al., 2007;

Singleton et al., 2007).

Five sets (locations 1S, 2S, 3S, 4S, and 6S) of small diameter multi-level wells

were previously installed at the site (locations given in Figure 2.1, depths given in Table

2.1 and Figure 2.2) as part of related studies (McNab et al., 2007; Singleton et al., 2007).

These multi-level well sites are all located on the edges of fields alternatingly planted

with corn and wheat, except 2S which is beside an alfalfa field and 6S which is between

cattle pens and manure lagoons. A sixth single completion well site (well 5S1) is located

in a field ~11 m from the study area’s main irrigation canal. This 14 m wide canal is

commonly full only during spring and summer months.

9

At three of the six well locations additional unsaturated zone instrumentation was

installed in February 2005. The three instrumented sites span the range of recharge

conditions at the site: near canal (5S), field away from irrigation wells (2S), and field

near irrigation wells (3S). The 3S wells are between two irrigations wells (~25 and

~40 m away). The instrumentation was placed at multiple depths in hand-augered

boreholes to span the entire unsaturated zone at each location. Disturbed (bag) and core

samples were recovered from the boreholes for laboratory analyses. Moisture content

(n = 41), particle size distribution (n = 44), particle density (n = 15), and soil water

characteristic curve analyses (n = 6) were all done according to American Society for

Testing and Materials (ASTM) standards (Appendix A). Each borehole was

instrumented with multiple sensors to take hourly measurements of soil temperature and

matric potential (heat dissipation sensor model 229-L, Campbell Scientific Inc., Logan,

UT) and soil gas pressure (Druck barometer model RPT410F, Campbell Scientific Inc.,

Logan, UT). Prior to installation, heat dissipation sensors were calibrated using both

pressure plate extractors and salt solutions (Scanlon et al., 2005).

Groundwater samples from multi-level wells were analyzed for pH in the field

using a Horiba U-22 water quality meter. Cation and anion concentrations were

measured by ion chromatography using a Dionex DX-600. Oxygen isotopic composition

of water was measured using the carbon dioxide equilibration method for 18O/16O

(Epstein and Mayeda, 1953) on a VG Prism II isotope mass spectrometer. Tritium

samples were collected in 1 L glass bottles. Tritium concentrations were determined on

500 g subsamples by the 3He in-growth method (approximately 15 day accumulation

time).

Dissolved noble gas samples were collected using standard sampling techniques.

Copper tubing was used as the sample vessel (8 mm inner diameter, 250 mm long). Steel

10

clamps pinched the copper tubing flat in two locations to secure the water sample.

Reactive gases were removed with multiple reactive metal getters. Known quantities of

isotopically enriched 22Ne, 86Kr and 136Xe were added to provide internal standards.

Noble gases were separated from one another using cryogenic adsorption. Helium was

analyzed using a VG-5400 noble gas mass spectrometer. Other noble gas isotopic

compositions were measured using a quadrupole mass spectrometer. The Ar abundance

was determined by measuring the total noble gas sample pressure using a high-sensitivity

capacitive manometer. The procedure was calibrated using water samples equilibrated

with the atmosphere at a known temperature and pressure. Analytical uncertainties are

approximately 2% for He, Ne, and Ar, and 3% for Kr and Xe.

All laboratory analyses of groundwater samples were completed at Lawrence

Livermore National Laboratory (LLNL). The pH, chloride, and 18O data presented here

were previously reported by McNab et al. (2007), and the nitrate and dissolved noble gas

data presented here were previously reported by Singleton et al. (2007).

Noble Gas Modeling

The equilibrium concentration, Ci,eq, of gas i is given by Henry’s Law as:

( )T,SHpC

i

ii,eq = (2.1)

where pi is partial pressure of gas i and Hi is Henry’s Law constant which is a function of

temperature T and salinity S. The total measured concentration, Ci, of dissolved gas i is

the sum of multiple components:

i,teri,radi,exci,eqi CCCCC +++= (2.2)

where subscripts exc, rad, and ter refer to excess air, radiogenic, and terrigenic

components, respectively.

Helium is commonly excluded from noble gas modeling because of complications

that arise due to the presence of radiogenic sources such as tritiogenic 3He (Solomon and

11

Cook, 2000) or 4He from U and Th decay (Solomon, 2000). Calculation of equilibrium

and excess air components of He are required to quantify tritiogenic 3He which is used to

calculate 3H–3He groundwater ages (Solomon and Cook, 2000). It was assumed that Ne,

Ar, Kr, and Xe did not have significant radiogenic or terrigenic components in the study

area (Lehmann et al., 1993).

Three physically based models are commonly used to interpret dissolved noble

gas concentration data in groundwater: 1) unfractionated air, UA, model (Heaton and

Vogel, 1981), 2) partial re-equilibration, PR, model (Stute et al., 1995b), and 3) closed

system equilibrium, CE, model (Aeschbach-Hertig et al., 2000). The UA model is the

simplest because it assumes the excess air component is atmospheric air resulting from

complete dissolution of entrapped air bubbles during recharge. The total concentration of

gas i as given by the UA model is:

idi,eqUAi zACC ⋅+= (2.3)

where Ad is concentration of dry air dissolved, and zi is volume fraction of gas i in dry air.

Stute et al. (1995b) found elemental fractionation in the excess air component (whereby

lighter gases are depleted relative to heavier gases) and suggested that this fractionation

was caused by complete bubble dissolution followed by diffusive degassing (PR model).

The total concentration of gas i as given by the PR model is:

( ) Ne

iPR

DDR

idi,eqPRi ezACC

⋅−

⋅⋅+= (2.4)

where Ad is initial concentration of dissolved excess air, RPR is degree of re-equilibration,

Di is molecular diffusivity of gas i, and DNe is molecular diffusivity of Ne. Kipfer et al.

(2002) extended the PR model to include multiple dissolution-degassing cycles.

Aeschbach-Hertig et al. (2000) suggested that fractionation of excess air results from

incomplete dissolution of entrapped air bubbles and fractionation is related to differing

gas solubilities (CE model). The total concentration of gas i as given by the CE model is:

12

( )

⎟⎟⎠

⎞⎜⎜⎝

⎛ ⋅⋅+

⋅⋅−+=

i,eq

ie

iei,eq

CEi

CzAFzAFCC

1

1 (2.5)

where F is a fractionation parameter and Ae is initial concentration of entrapped air

( )( )

0

sg

w

0g

e PeP

VT,SρV

A−

⋅⋅

= (2.6)

where Vg0 is initial volume of entrapped air, ρ is water density as a function of

temperature T and salinity S, Vw is volume of water, Pg is pressure of entrapped air, es is

saturation water vapor pressure, and P0 is standard pressure (1 atm). The fractionation

parameter is:

⎟⎟⎠

⎞⎜⎜⎝

⎛−

−

⎟⎟⎠

⎞⎜⎜⎝

⎛

==

satm

sg

g

g

ePeP

VV

qvF

0

(2.7)

where Vg is volume of entrapped air, Patm is atmospheric pressure, v is fraction of

entrapped air remaining, and q is the ratio of dry entrapped air pressure to dry

atmospheric pressure (which is approximately the pressure on the entrapped air).

The UA model is a limiting case for both the CE model (when F = 0) and the PR

model (when RPR = 0). CE and PR models can give similar results as underlying physical

processes for these two models vary similarly among gases. Peeters et al. (2002)

suggested that in addition to noble gas concentration data, isotopic data—especially Ne—

are helpful in distinguishing between diffusive degassing (PR model) and incomplete

bubble dissolution (CE model).

Addition of excess air has the greatest relative impact on He and Ne

concentrations because the equilibrium component is relatively small. A common way to

represent the amount of excess air is as percent Ne, ΔNe (Kipfer et al., 2002):

13

100%ΔNe ×=Ne,eq

Ne,exc

CC

(2.8)

Dissolved gas concentrations may be reduced by degassing after recharge. Just as

gas dissolution models are based on solubility (CE model) and diffusion (PR model),

degassing can be controlled by solubility or diffusion. Such degassing occurs as a result

of the formation of initially noble gas free gas bubbles (e.g. CO2, CH4, or N2). In the case

of solubility controlled degassing occurring as a single step (DS1 model), the final

degassed concentration of gas i is:

*i

i

*iDS1

i

CzB

CC⋅

+=

1 (2.9)

where Ci* is the initial (pre-degassing) concentration, B is a degassing parameter, and zi

is the concentration of gas i in air. This model is comparable to the CE model, except

that the “entrapped air” of the CE model is free of noble gases in this case. This model

was presented in Brennwald et al. (2003) as the “one-step degassing model”. It can be

extended to the case of repeated (continuous) gas bubble formation/equilibration (DSC

model). The final degassed concentration is (equation 7 in Brennwald et al., 2005):

⎟⎟⎠

⎞⎜⎜⎝

⎛ −

⋅=*i

i

CzB

*i

DSCi eCC (2.10)

Alternatively, degassing may be controlled by gas diffusion (DD model, Stute,

1989). The final degassed concentration is:

⎟⎟⎠

⎞⎜⎜⎝

⎛−

⋅= Ne

iDD

DD

R*i

DDi eCC (2.11)

Diffusion controlled degassing is similar to the PR model; however, in this case

the dissolved gas diffuses into a reservoir that is initially free of noble gases. The

limiting case of RDD → ∞ is therefore complete transfer of all noble gases from the water

to the gas phase. In contrast, the limiting case of the PR dissolution model (when

14

RPR → ∞) results in minimum gas concentrations equivalent to the equilibrium

concentration with respect to atmospheric pressure.

Measured dissolved noble gas concentrations were modeled using NOBLE90, an

error weighted, least-squares fitting, inverse modeling program (Aeschbach-Hertig et al.,

1999; Peeters et al., 2002). NOBLE90 solves for parameter combinations for the selected

interpretive model that match measured data within experimental error by minimizing χ2,

the sum of the weighted squared deviations between the modeled and measured

concentrations. The ability of the selected model to describe the observed data (i.e.

goodness-of-fit of the selected model) is judged on the probability of χ2 being greater

than a given value obtained from the χ2 distribution (for the appropriate number of

degrees of freedom). If this probability is lower than a predetermined cutoff value the

solution is rejected and it is concluded that the selected model is unable to describe the

measured data (Aeschbach-Hertig et al., 1999). This approach allows assessment of the

likelihood that differences between modeled and measured values result from

experimental error. In this study solutions with probabilities (p) <0.05 are rejected. The

gas solubility data used in the NOBLE90 calculations were from multiple sources

(Clever, 1979; Weiss, 1970; Weiss, 1971; Weiss and Kyser, 1978). Additional details of

NOBLE90 are given by Aeschbach-Hertig et al. (1999; 2000) and Peeters et al. (2002).

Measured He, Ne, Ar, Kr, and Xe concentrations were fitted by UA, PR, and CE models

using NOBLE90 to solve for excess air, degree of excess air fractionation (in CE and PR

models only), and recharge temperature. Additional modeling using only measured Ne,

Ar, Kr, and Xe concentrations was also done. For all modeling the recharging water was

assumed to be fresh (S = 0) and atmospheric pressure data from the local NCDC

meteorological station were used.

15

RESULTS AND DISCUSSION

Soil Gas Pressure

Soil gas pressure data show pronounced diurnal and seasonal fluctuations (Figure

2.3). Atmospheric pressure data from the nearby NCDC station closely track the

measured soil gas pressures, with few exceptions. Each instance of soil gas pressures

deviating from atmospheric pressures can be linked to irrigation events during which soil

gas pressure beneath the irrigated field increased ~0.1 kPa and subsequently dissipated in

<12 hr (Figure 2.3c). No measurable pressure gradient was found between the

atmosphere and the unsaturated zone except during irrigation events, and no measurable

vertical pressure gradient existed within the unsaturated zone during irrigation events.

Temperature

Measured subsurface temperature data show both the lag and damping of seasonal

temperature fluctuations with increasing depth in the soil column (Figure 2.4 and

Appendix B). These data were used to extrapolate the WTT at each instrumented site

(Figure 2.5). Variations in WTT across the site are likely related to differences in:

vegetation/crop type, irrigation (amount, timing, heating of irrigation water prior to

infiltration), and depth to water table. Both 2S and 3S are located on the edges of

irrigated fields. The 2S temperatures are higher than those at 3S and lag slightly

(seasonal peaks occur later at 2S). The small lag difference is attributed to the water

table at 2S being ~0.8 m BGS deeper than at 3S. Higher temperatures at 2S are likely the

result of greater heating of irrigation water prior to infiltration. Greater heating is caused

by irrigation practices (water standing in 2S field longer) and location (2S is midway

down the length of the field while 3S is at the end of the field from which water is

applied). Virtually all irrigation events occurred when air temperatures were greater than

WTT.

16

Oxygen Isotope Data (δ18O)

The δ18O data cover a wide range, -14.2 to -9.9‰ (parts per thousand relative to

Vienna Standard Mean Ocean Water, VSMOW) (Table 2.1 and Figure 2.6). The 1S

wells and 5S1 are unique in having relatively depleted 18O values compared to other

locations. Imported irrigation water is strongly depleted compared to local precipitation

because imported irrigation water is from the Kings River which is fed by Sierra Nevada

snowmelt. The δ18O range measured at 5S1 (adjacent canal) is -14.2 to -12.6‰, which is

essentially the same as that of Kings River water reported by Coplen and Kendall (2000)

over a four year period (-14.55 to -12.50‰). The presence of depleted 18O water at 5S1

confirms the importance of irrigation canal leakage as a recharge source.

The depleted 18O at 1S wells may indicate a distinct recharge source, as these

wells have consistently low δ18O despite having a location that is not immediately

adjacent to an irrigation canal. The lack of variability of these samples may indicate a

relative lack of mixing and therefore less impact by agricultural activity.

The most 18O enriched monitoring well sample was 6S3 (-11.0‰), the shallowest

monitoring well within the dairy farm operations area. The enrichment at 6S3 is likely

caused by evaporative enrichment of nearby manure lagoon water (range: -10.2 to -9.9‰,

n = 4). Evaporative enrichment likely occurs at manure lagoons as well as in the flood

irrigated fields.

Repeated application of various different water types—shallow groundwater,

imported Kings River water, and liquid manure—to fields limits the use of 18O in this

study. However, the 18O data verify the importance of irrigation canal leakage as a

source of recharge. These data also suggest that imported water may be a significant

contributor to 1S recharge.

17

Dissolved Noble Gases

Dissolved noble gas data were collected between August 2003 and August 2005

(Table 2.2). Analyses were completed on a number of duplicate samples. Some

duplicate analyses did not reproduce initial results; however, results from individual wells

were generally comparable, if not within the stated analytical uncertainty.

Undersaturation

Several samples have gas concentrations below equilibrium gas solubility (e.g.

samples from 1S2 and 2S4), indicative of degassing caused by gas stripping (i.e. removal

of dissolved noble gases from solution by partitioning into an initially noble gas free

bubble). The low measured gas concentrations are inconsistent with decreased

equilibrium concentrations caused by high salinity, high temperature, or low pressure.

Many scenarios can result in groundwater degassing by gas stripping.

Denitrification produced N2 is reported to cause groundwater degassing in various studies

(Blicher-Mathiesen et al., 1998; Dunkle et al., 1993; Mookherji et al., 2003; Visser et al.,

2007). Strongly anoxic conditions can lead to gas stripping by exsolution of methane

(Fortuin and Willemsen, 2005; Puckett et al., 2002). Methane production at hydrocarbon

contaminated sites (Amos et al., 2005) and landfills (Solomon et al., 1992) can also cause

degassing. Recent laboratory studies confirm the ability of biogenic gases to strip other

gases from solution (Amos and Mayer, 2006; Istok et al., 2007). Klump et al. (2006)

reported slight undersaturation of dissolved gases in groundwater and attributed it to gas

stripping by CO2 and/or CH4.

The location of subsurface gas production affects whether or not degassing

occurs. If gas production occurs deep in the saturated zone, degassing is less likely to

occur because increased hydrostatic pressure at greater depths force the produced gas to

remain in solution rather than form bubbles. However, if gas production occurs within a

18

few meters of the water table, gas bubble formation is more likely. Because the main

sources of gas production tend to be reactions in anoxic conditions (e.g. denitrification

and methanogenesis), the shallower the redox cline, the more favorable it is for degassing

to occur. The redox cline at this site is ~11 m BGS which is ~6 m below the water table

(Singleton et al., 2007).

There is no evidence for methanogenesis occurring in the shallow groundwater;

however, lagoon waters are methanogenic (McNab et al., 2007). McNab et al. (2007)

suggested that observed Ar undersaturation in 2S samples is caused by CO2 and/or CH4

bubbles stripping previously dissolved gases within lagoon water prior to its infiltration.

However, this explanation cannot account for the undersaturated samples at wells further

from the lagoon (e.g. samples from 3S4 and 5S1).

Ample evidence for denitrification at the site exists, but the observed N2 excess

caused by denitrification is less than expected for the observed nitrate concentration

declines (Singleton et al., 2007). As Singleton et al. (2007) suggest, the lack of mass

balance may be the result of N2 loss from the saturated zone which would strip noble

gases from the groundwater. Therefore, all samples below the zone of denitrification

(generally 11–12 m BGS) may have undergone some degree of degassing. Such

degassing may not be immediately noticeable if the initial excess air dissolved during

recharge is greater than the amount of gas lost during degassing, but the interpreted

recharge conditions could be inaccurate if degassing is not taken into account. Degassing

by denitrification may help explain the measured undersaturation at 1S and 2S.

Gas stripping may be controlled by gas solubility (equations 2.9 and 2.10) or by

diffusion (equation 2.11). The difference in gas concentrations between the processes is

most relevant for light gases (i.e. He, Ne). Data indicate solubility controlled degassing

(DS1 and DSC models) occurs at 2S4 (Figure 2.7). There is also other evidence (stable

19

isotopes of dissolved inorganic carbon) indicating that 2S is impacted by lagoon recharge

(McNab et al., 2007), suggesting that some 2S4 degassing occurred within the lagoons

prior to infiltration. There may be some diffusive degassing at other locations (e.g. 1S2);

however, definitive determination of the process causing degassing would require

isotopic analyses (Peeters et al., 2002).

The two undersaturated samples from 5S1 occurred during a time of little or no

recharge (i.e. no irrigation in the nearby field and the canal had been empty for months).

Samples from 5S1 taken when the canal was full were not undersaturated. There are no

indications of reducing conditions at 5S1. Low nitrate concentrations are associated with

low chloride (Table 2.1) indicating the dominance of low salinity recharge from the

irrigation canal. Much of the land in the area is subject to intensive agriculture, including

the application of cattle manure as fertilizer which provides organic carbon.

Undersaturation at 5S1 may be a result of gas stripping by CO2; however, more detailed

analyses are required to conclusively establish the cause of the undersaturation at this

location.

Noble Gas Temperatures

The subsurface temperature data allow a direct comparison of modeled NGT to

WTT. Groundwater flows along complex and shifting paths because of intense but

ephemeral pumping from relatively shallow irrigation wells in and around the study site.

Therefore, it is particularly difficult to identify the recharge area for anything but the

shallowest groundwater. Furthermore, even if the recharge area of a deeper well was

clearly identifiable, possible degassing would affect the calculation of NGT. For these

reasons, samples from the shallowest well at each field location (wells 2S1, 3S1, 4S1, and

5S1) were considered separately.

20

The water table at the shallow wells was generally ≤2 m above the screens. There

were no temperature sensors at 4S but it is similar to locations 2S and 3S. Of the nine

shallow well samples, two—both of which are from 5S1 when the canal was dry—are

slightly undersaturated (Figure 2.8) and therefore are not discussed here. When modeled

with all five gases (He, Ne, Ar, Kr, and Xe), all remaining seven samples were fit by the

PR model, but only five of the seven samples were fit by the CE model (Figure 2.9). The

superior fit of the PR model to the shallow well data suggests that some diffusive

degassing may occur; however, isotope data are necessary to confirm this (Peeters et al.,

2002). Most studies report that the CE model gives a superior fit to the PR model (Kipfer

et al., 2002). For those samples fit by both the CE and PR models, differences in NGT

are slight. The UA model produces systematically lower NGT than the CE or PR models

(as discussed by Cey et al., 2008). The DD model produces even lower NGT which tend

to be lower than measured temperatures, indicating its inappropriateness for modeling

these wells (Figure 2.9). It is clear that the ability of a given model to adequately fit

measured data is not evidence that the model reflects the actual gas dissolution process

(Aeschbach-Hertig et al., 2000; Cey et al., 2008).

The NGT of all shallow well samples are generally mid-range of their WTT at

each location, with the exception of 5S1 (Figure 2.9). Well 5S1 NGT are at the low end

of the range of WTT which is consistent with recharge occurring only during summer

months when WTT are coolest (Figure 2.5). These results confirm the ability of NGT to

match actual recharge temperatures.

Localized recharge conditions—such as strong seasonal variations in recharge as

at the irrigation canal (i.e. 5S1)—are distinguishable based on NGT. The recharge

regime at the canal is also unique as it consists of extended periods of either no recharge

or consistent, relatively high head recharge. In contrast, the field sites have relatively

21

uniform recharge flux as a result of many short term recharge pulses (primarily irrigation

events). At the field locations, matric potential measurements did not show short term

fluctuations below 2 m. The difference in recharge regimes was not apparent from the

amount of excess air or the degree of fractionation.

There is uncertainty regarding the recharge location of the deeper wells. It is

expected that groundwater sampled in this study was recharged at or near the site under

conditions similar to those measured at the site because land use in the vicinity of the site

is representative of regional land use. The range of all WTT at the site is from ~17 to

~21 °C (Figure 2.5). The NGT are consistently mid-range with the exception of 1S wells

(Table 2.3). The average CE model NGT of 1S samples is 14.8 °C, compared with an

average of 18.7 °C for all other samples. Despite the difference in NGT, 1S samples are

not distinctive in either their amount of excess air or in their excess air fractionation.

There is no evidence of unique agricultural practices near the 1S site nor is there any

evidence of pronounced differences in geology, but the possibility of localized spatial

differences cannot be completely ruled out. Samples from 1S are more depleted in 18O

than are other samples (Figure 2.6). The low NGT and depleted 18O—similar to well

5S1—suggest that the recharge source may be imported irrigation water. However, the

NGT are still less than any WTT measured at the site. Irrigation lowers summertime

ground temperature (Barnston and Schickedanz, 1984; Bonfils and Lobell, 2007), so it is

possible that areas which recharge 1S wells could have different irrigation and/or

cropping histories which depress WTT locally. Additionally, the modeled NGT may be

erroneously low owing to partial degassing of the groundwater after recharge (discussed

in following section). The exact cause of the low NGT is unclear, but it is clear based on

dissolved noble gases and 18O that 1S groundwater is distinct from the groundwater

produced at the other well sites.

22

The ability of NGT from the CE and PR models to match measured WTT

conditions at all but 1S confirms that the models are useful for the purposes of NGT

calculation. The results of the PR and CE models are comparable, but additional data are

required to establish the process(es) controlling gas dissolution. Caution is necessary

when using such models to interpret dissolved gas data because goodness-of-fit is not an

appropriate indicator of model validity.

Modeling Degassing

Degassing of groundwater after recharge impacts interpreted (modeled) values of

recharge temperature and excess air. Visser et al. (2007) examined the impact of

saturated zone degassing on calculated 3H–3He ages, but the impact on modeled NGT has

not been examined in the literature. To explore the impact of degassing on NGT, a

modeling study was conducted.

Hypothetical/synthetic dissolved noble gas data representative of site groundwater

conditions (T = 19.0 °C, p = 0.991 atm, S = 0, ΔNe = 30%) were generated—both

unfractionated (UA model) and fractionated according to the CE model (F = 0.65 and

0.75). The representative gas concentrations were subsequently degassed by both the

DS1 and DD models. The degree of degassing ranged from 0 to -10% ΔNe. The

maximum degassing of -10% ΔNe is comparable to that associated with denitrification at

the site based on the mass balance calculations of Singleton et al. (2007) (i.e. degassing

caused by ~100 mg L-1 of nitrate denitrified ~6 m below the water table). The degassed

samples were then modeled using the CE and UA models.

Results indicate that recharge temperature as fitted by the CE model can deviate

appreciably from the original recharge temperature at modest levels of degassing (Figure

2.10a). The largest deviations are associated with: 1) strong excess air fractionation, and

2) diffusive degassing. The CE modeled recharge temperatures of the degassed samples

23

underestimate the original recharge temperature for initially strongly fractionated excess

air (F = 0.75), but not for less fractionated excess air (F = 0.65). The degree of

fractionation is critical to the bias in modeled recharge temperature. Excess air

fractionation during groundwater recharge is common, with multiple studies reporting

strongly fractionated excess air (F >0.7) (Aeschbach-Hertig et al., 2000; Aeschbach-

Hertig et al., 2002c; Hall et al., 2005). The errors in interpreted recharge temperature are

slightly greater when degassing is controlled by diffusion rather than solubility. The

prevalence of diffusive degassing is uncertain, as most studies indicate that gas solubility

rather than diffusion controls both dissolution (Kipfer et al., 2002) and degassing (Visser

et al., 2007). However, there is some evidence that diffusive degassing may occur at this

site based on elemental ratios (1S2, Figure 2.7).

As shown in this study and in previous work (Blicher-Mathiesen et al., 1998;

Dunkle et al., 1993; Fortuin and Willemsen, 2005; Klump et al., 2006; Mookherji et al.,

2003; Puckett et al., 2002; Visser et al., 2007), groundwater degassing can occur in a

variety of settings. Minor degassing in the saturated zone after groundwater recharge

may help explain the NGT biases reported by Castro et al. (2007) and Hall et al. (2005).

Application of an incorrect interpretive model can result in substantial error in the

modeled recharge temperature, especially for samples with strongly fractionated excess

air (Figure 2.10b). The UA model systematically calculates lower recharge temperatures

than the CE model (Aeschbach-Hertig et al., 2000; Cey et al., 2008), therefore modeling

samples with fractionated excess air using the UA model results in underestimation of

recharge temperature. This is true regardless of the amount of degassing, including the

case of no degassing.

Caution is necessary when applying interpretive models to deduce recharge

temperature from dissolved noble gas data. Application of a model that is not

24

representative of the actual gas dissolution process may yield erroneous results. The

occurrence of degassing after recharge may also result in erroneous recharge

temperatures. Model goodness-of-fit is not necessarily indicative of model

appropriateness (Figure 2.10c and Figure 2.10d). Even models that match measured data

well may be seriously biased.

CONCLUSIONS

The study examined dissolved noble gases in shallow groundwater at an

agricultural site. The local hydrologic regime is heavily impacted by both pumping of

groundwater from a shallow unconfined aquifer as well as by importation of surface

water for irrigation. Measurements of soil gas pressure and subsurface temperature

defined conditions under which recharge occurred.

Samples from multiple wells had dissolved gas concentrations below equilibrium

concentration with respect to atmospheric pressure. The most plausible explanation for

the undersaturated samples is degassing caused by gas stripping. Multiple gas stripping

processes occur at the site: 1) within methanogenic liquid manure lagoons which

subsequently recharge, and 2) in the saturated zone because of denitrification and

possibly also CO2 exsolution. The degassing observed has the potential to bias NGT.

Synthetic samples were modeled to explore the possible impact of degassing on

interpreted NGT. Results indicate that relatively minor degassing (<10% ΔNe) may

cause bias of 2 °C. Such errors are problematic because the degassing may be masked by

excess air and the degassed samples may be fit by a model with a high degree of

certainty. These findings have implications for paleoclimate research because the errors

are large considering that the temperature difference based on NGT data since the LGM

is 5–7 °C (Kipfer et al., 2002). Therefore, caution is necessary when using NGT in

paleoclimate work.

25

The role of recharge regime on dissolved noble gases was also examined. There

are two recharge regimes occurring at the study site: 1) irrigation canal leakage during

spring and summer only (little to no recharge in fall and winter), and 2) relatively

uniform recharge caused by intensive flood irrigation. There was no measurable

difference in excess air characteristics (amount and degree of fractionation) between the

two recharge regimes studied.

The study examined the relationship between calculated NGT based on dissolved

noble gases and directly measured WTT. The complexity of groundwater flow and the

potential for degassing to impact NGT required that only shallow wells be used to

compare NGT to WTT. The NGT from both the CE and PR models reflect the measured

WTT conditions. This finding supports the use of dissolved noble gases to deduce

recharge temperatures. Prior to this study, field based experimental confirmation was

lacking despite decades of NGT applications.

Although NGT reflect WTT, careful application of interpretive models is required

as multiple gas dissolution/exsolution processes may contribute to measured dissolved

gas concentrations in groundwater. A particular model may be fit to measured data, but

this does not necessarily mean that the physical gas dissolution process is accurately

represented or that the model results accurately represent recharge conditions. Therefore,

caution is necessary when interpreting dissolved noble gas data.

26

Table 2.1. Well depths, sample collection dates, and selected measured data (originally published in McNab et al., 2007; Singleton et al., 2007).

Well Depth (m)a Sample Collection

Date pH

(field) Nitrate (mg/L)b

Chloride (mg/L)

δ18O (‰)c

3H (pCi/L)d

3H +/-(pCi/L)

1S2 11.0 2250 12/6/04 7.05 0.3 46.3 -12.9 29.1 1.5 2634 2/15/05 7.14 16.8 53.9 -12.8 37.6 1.9 3065 7/11/05 - 16.1 57.3 -12.6 29.3 1.5

1S3 14.6 1864 1/10/04 6.65 0.1 23.4 -13.1 32.7 1.7 1862 1/30/04 - 0.2 28.3 -13.0 33.7 1.8 1871 2/13/04 6.77 0.3 31.3 -13.0 29.9 1.6 1874 3/16/04 7.00 0.1 31.3 -12.9 28.2 1.5 1880 4/20/04 7.10 0.2 41.0 -12.9 28.8 1.5 1883 7/2/04 7.16 <0.07 46.1 -12.9 26.7 1.4 2253 12/6/04 6.81 <0.07 41.7 -12.9 29.5 1.6 2632 2/15/05 7.00 2.2 44.7 -12.9 42.0 2.2

1S4 19.8 1863 1/16/04 7.01 0.1 11.0 -13.3 28.6 1.5 1866 1/30/04 - 1.5 9.6 -13.4 30.0 1.6 1869 3/16/04 7.50 0.1 5.7 -13.3 27.2 1.4 1873 4/20/04 7.50 0.2 5.9 -13.4 24.4 1.3 1884 7/2/04 7.43 <0.07 8.2 -13.2 27.2 1.4 2252 12/6/04 7.03 0.7 15.1 -13.3 25.4 1.4 2631 2/15/05 7.30 0.4 13.4 -13.4 35.4 1.8

1S5 54.3 1865 1/16/04 9.50 0.2 2.0 -13.7 0.2 0.5 1870 2/13/04 9.42 0.3 1.6 -13.8 0.0 0.5 1868 3/16/04 9.30 0.1 1.9 -13.7 0.1 0.5 2251 12/6/04 9.04 0.4 2.2 -13.7 0.1 0.5

2S1 5.5 3352 8/25/05 - 146.7 113.1 -12.4 21.9 1.2 2S2 9.5 2123 10/4/04 - 164.3 78.7 -12.1 18.7 1.1

2259 12/7/04 6.50 201.4 120.8 -12.2 17.6 1.0 2627 2/16/05 6.64 195.9 85.6 -12.2 22.3 1.2

2S3 11.1 2124 10/4/04 - 169.1 80.9 -12.2 17.8 1.0 2628 2/15/05 - 199.7 90.1 -12.0 22.9 1.3

2S4 12.8 2125 10/4/04 - 4.7 72.4 -12.5 19.3 1.1 2261 12/7/04 - 1.0 89.8 -12.4 17.7 1.0 2633 2/15/05 6.97 15.5 55.9 -12.4 22.5 1.2

3S1 6.1 2258 12/7/04 6.60 163.9 116.4 -12.1 10.1 0.7 2623 2/16/05 6.64 225.9 190.0 -11.7 20.2 1.1 3070 7/11/05 - 219.6 204.7 -11.4 23.2 1.3

3S2 10.1 2257 12/7/04 6.58 253.9 257.1 -11.2 19.2 1.1 3071 7/11/05 - 240.0 230.6 -11.1 0.0 0.5

3S3 12.3 2256 12/7/04 6.59 135.1 89.1 -12.3 10.7 0.7 3S4 14.4 2255 12/7/04 - - - -11.7 15.9 0.9 4S1 6.4 2670 2/16/05 - 83.3 127.0 - 35.6 1.8 4S2 9.8 2264 12/8/04 6.93 122.0 32.4 -11.8 18.3 1.0

2625 2/16/05 - 128.9 31.8 -11.8 22.3 1.2 4S3 10.8 2262 12/7/04 7.12 91.2 42.2 -12.0 19.5 1.1

2636 2/17/05 7.27 62.9 42.4 -12.0 25.8 1.4 4S4 16.0 2263 12/8/04 6.98 <0.07 32.4 -13.0 44.0 2.3 5S1 4.9 2254 12/6/04 6.30 9.0 4.8 -14.2 13.4 0.8

27

Well Depth (m)a Sample Collection

Date pH

(field) Nitrate (mg/L)b

Chloride (mg/L)

δ18O (‰)c

3H (pCi/L)d

3H +/-(pCi/L)

2626 2/17/05 6.32 43.9 17.2 -14.0 18.0 1.0 2849 4/26/05 - 50.5 16.7 -14.0 16.6 1.0 3068 7/11/05 - 38.4 19.4 -12.6 2.2 0.5

6S1 12.9 3348 8/25/05 - 0.3 152.6 - 27.8 1.5 6S2 11.0 3349 8/25/05 - 0.2 164.2 - 33.1 1.7 6S3 7.6 3350 8/25/05 - 141.9 154.0 -11.0 0.0 0.5 I1 9.1 1889 5/28/04 6.83 161.7 113.4 -12.0 0.0 0.5 I2 9.1 1890 5/28/04 6.73 68.3 127.7 -12.1 17.5 1.0 I3 1771 8/21/03 - <0.07 1.0 -13.7 0.2 0.5 I4 9.1 1888 5/28/04 7.00 107.0 168.6 -9.9 20.2 1.1 I5 10.7 1886 5/28/04 6.89 29.2 45.1 -12.4 30.4 1.6

a depth to top of screen b mg/L as NO3

- , previously c parts per thousand relative to Vienna Standard Ocean Water, VSMOW d 3H concentration as of the sample collection date

28

Table 2.2. Dissolved noble gas data (originally published in Singleton et al., 2007).

Well Collection Date

Sample Number

Analysis Date

Hea (× 10-8)

Nea (× 10-7)

Ara (× 10-4)

Kra (× 10-8)

Xea (× 10-8)

1S2 12/6/04 2250A 4/6/05 2.46 1.22 3.18 7.54 1.02 2250B 4/19/05 2.41 1.19 3.18 7.46 1.08 2/15/05 2634 4/6/05 2.32 1.18 3.05 7.35 0.994 7/11/05 3065 9/30/05 2.52 1.32 3.18 7.40 1.00

1S3 1/16/04 1864A 2/27/04 7.57 2.96 4.13 8.86 1.17 1864B 6/25/04 8.21 3.24 4.22 9.02 1.22 1/30/04 1862A 2/27/04 5.89 2.48 3.99 8.44 1.15 1862B 6/25/04 5.75 2.43 3.96 8.55 1.12 2/13/04 1871A 3/27/04 5.26 2.25 3.81 8.27 1.17 1871B 6/25/04 8.60 3.18 4.19 8.86 1.18 3/16/04 1874A 3/24/04 5.44 2.27 3.92 8.52 1.16 1874B 6/26/04 5.18 2.25 3.80 8.47 1.16 4/20/04 1880A 4/23/04 5.04 2.21 3.89 8.62 1.15 1880B 6/26/04 5.15 2.26 3.93 8.51 1.16 7/2/04 1883 8/19/04 3.10 1.48 3.50 7.86 1.05 12/6/04 2253 4/5/05 4.91 2.17 3.81 8.65 1.16 2/15/05 2632 4/6/05 4.79 2.14 3.87 8.58 1.17

1S4 1/16/04 1863A 2/27/04 5.49 2.35 3.92 8.61 1.17 1863B 6/25/04 7.33 3.00 4.08 8.72 1.16 1/30/04 1866A 2/28/04 7.37 2.81 4.02 8.60 1.19 1866B 6/25/04 7.35 2.82 4.02 8.75 1.19 3/16/04 1869A 3/24/04 6.76 2.65 3.95 8.42 1.23 1869B 6/26/04 6.64 2.67 3.86 8.39 1.12 4/20/04 1873A 4/23/04 6.72 2.61 3.88 8.69 1.15 1873B 6/26/04 6.68 2.62 3.88 8.41 1.17 7/2/04 1884 8/19/04 6.52 2.46 3.79 8.33 1.09 12/6/04 2252 4/6/05 6.42 2.49 3.75 8.34 1.13 2/15/05 2631 3/25/05 6.25 2.47 3.83 8.41 1.11

1S5 1/16/04 1865 2/28/04 5.38 2.39 3.87 8.47 1.15 2/13/04 1870A 2/27/04 12.16 3.43 3.85 8.45 1.17 1870B 3/27/04 11.91 3.31 3.86 8.45 1.22 3/16/04 1868 3/25/04 8.00 2.18 3.59 8.35 1.19 12/6/04 2251 4/7/05 6.74 1.85 3.36 7.80 1.09

2S1 8/25/05 3352 9/8/05 4.79 2.24 3.34 7.48 0.990 2S2 10/4/04 2123 10/14/04 5.49 2.39 3.14 6.88 0.916

12/7/04 2259A 2/10/05 5.25 2.26 3.13 6.88 0.923 2259B 4/13/05 5.19 2.27 3.12 6.87 0.899 2/16/05 2627 4/13/05 5.07 2.24 3.12 6.94 0.917

2S3 10/4/04 2124 10/14/04 3.65 1.78 2.99 6.77 0.879 2/15/05 2628A 4/6/05 3.80 1.77 2.94 6.61 0.853 2628B 4/13/05 2.69 1.36 2.68 6.29 0.869

2S4 10/4/04 2125 10/14/04 1.85 0.638 1.99 5.39 0.792 12/7/04 2261 4/6/05 0.735 0.314 1.76 4.90 0.730 2/15/05 2633A 4/6/05 0.864 0.375 1.82 5.11 0.735 2633B 4/13/05 0.775 0.329 1.80 5.09 0.757

3S1 12/7/04 2258 2/9/05 4.62 2.08 3.38 7.54 0.984 2/16/05 2623 3/25/05 4.93 2.12 3.45 7.60 0.983

29

Well Collection Date

Sample Number

Analysis Date

Hea (× 10-8)

Nea (× 10-7)

Ara (× 10-4)

Kra (× 10-8)

Xea (× 10-8)

7/11/05 3070 9/30/05 5.14 2.37 3.46 7.67 1.01 3S2 12/7/04 2257 2/9/05 4.07 1.77 3.16 7.27 0.975

7/11/05 3071 9/30/05 4.55 2.31 3.28 7.28 0.907 3S3 12/7/04 2256 2/9/05 4.99 2.16 3.54 7.91 1.03 3S4 12/7/04 2255 2/9/05 4.09 1.80 3.16 7.24 0.955 4S1 2/16/05 2670 4/7/05 4.61 1.99 3.25 7.23 0.947 4S2 12/8/04 2264 2/10/05 6.62 2.85 3.83 8.30 1.06

2/16/05 2625 4/6/05 6.85 2.85 3.84 8.12 1.07 4S3 12/7/04 2262A 2/9/05 6.30 2.57 3.67 7.97 1.04

2262B 4/19/05 6.77 2.72 3.72 8.12 1.03 2/17/05 2636A 4/7/05 6.22 2.58 3.60 7.72 1.01 2636B 4/7/05 6.41 2.70 3.65 7.77 1.01

4S4 12/8/04 2263A 2/9/05 4.42 2.36 3.66 7.59 1.00 2263B 4/19/05 5.33 2.11 3.34 7.56 1.02

5S1 12/6/04 2254 4/5/05 3.82 1.68 3.19 7.48 1.02 2/17/05 2626 4/6/05 4.03 1.74 3.09 7.23 0.990 4/26/05 2849 5/17/05 4.38 1.89 3.32 7.49 1.00 7/11/05 3068 9/30/05 5.69 2.44 3.62 7.93 1.05

I1 5/28/04 1889 6/29/04 9.30 3.75 4.36 8.76 1.08 I2 5/28/04 1890 6/29/04 7.73 2.92 3.79 8.14 0.993 I3 8/21/03 1771 11/26/03 6.42 2.10 3.64 8.66 1.21 I4 5/28/04 1888 6/29/04 13.4 5.07 5.03 9.57 1.14 I5 5/28/04 1886 6/29/04 10.4 4.02 4.40 8.76 1.15

ASWb - - - 4.45 1.85 3.15 7.08 0.97 a values given in cm3 at standard temperature and pressure per gram water (cm3 STP g-1) b Air Saturated Water (i.e. equilibrium concentration) at T=19.0 °C and p=0.991 atm

30

Table 2.3. Results of noble gas modeling. Modeling was done using He, Ne, Ar, Kr, and Xe for the UA, PR, and CE models. Additional modeling was done using only Ne, Ar, Kr, and Xe with the CE model. Results rejected because of poor fitting (i.e. p < 0.05) are not shown.

UA PR

Well Sample p T (°C)

+/- (°C)

ΔNe (%) p T

(°C) +/-

(°C) ΔNe (%) R

1S2 2250A 0.00 - - - 0.00 - - - - 2250B 0.00 - - - 0.00 - - - - 2634 0.00 - - - 0.00 - - - - 3065 0.00 - - - 0.00 - - - -

1S3 1864A 0.06 13.1 0.6 26 0.10 13.7 0.7 30 0.24 1864B 0.02 - - - 0.03 - - - - 1862A 0.65 13.7 0.6 54 0.44 13.7 0.7 54 0.00 1862B 0.99 13.4 0.7 66 1.00 13.5 0.8 67 0.03 1871A 0.31 13.0 0.6 14 0.40 13.4 0.7 17 0.31 1871B 0.33 14.4 0.7 70 0.09 14.2 0.8 67 0.00 1874A 0.14 12.6 0.6 16 0.08 12.8 0.7 18 0.15 1874B 0.23 12.8 0.6 13 0.58 13.4 0.7 17 0.44 1880A 0.02 - - - 0.08 13.0 0.7 15 0.65 1880B 0.01 - - - 0.07 13.1 0.7 18 0.59 1883 0.00 - - - 0.00 - - - - 2253 0.08 12.3 0.6 8 0.31 13.0 0.7 13 0.78 2632 0.02 - - - 0.17 12.9 0.8 11 1.20

1S4 1863A 0.13 12.4 0.6 18 0.23 13.0 0.7 22 0.31 1863B 0.42 14.0 0.6 53 0.80 14.6 0.8 57 0.13 1866A 0.54 13.9 0.6 49 0.21 13.7 0.7 47 0.00 1866B 0.69 13.7 0.6 49 0.34 13.6 0.7 47 0.00 1869A 0.49 13.3 0.6 38 0.24 13.2 0.7 37 0.00 1869B 0.83 14.9 0.6 39 0.73 15.1 0.7 40 0.05 1873A 0.55 14.1 0.6 38 0.27 14.0 0.7 37 0.00 1873B 0.86 14.2 0.6 38 0.60 14.1 0.7 37 0.00 1884 0.08 15.4 0.6 34 0.02 - - - - 2252 0.64 14.9 0.6 33 0.28 14.6 0.7 31 0.00 2631 0.43 14.6 0.6 31 0.24 14.6 0.7 30 0.00

1S5 1865 0.01 - - - 0.48 14.0 0.7 25 0.56 1870A 0.00 - - - 0.00 - - - - 1870B 0.00 - - - 0.00 - - - - 1868 0.00 - - - 0.00 - - - - 2251 0.00 - - - 0.00 - - - -

2S1 3352 0.00 - - - 0.59 20.2 1.1 22 1.33 2S2 2123 0.00 - - - 0.12 23.5 0.9 32 0.47

2259A 0.05 - - - 0.34 22.6 0.9 24 0.47 2259B 0.01 - - - 0.37 23.4 0.9 26 0.60 2627 0.00 - - - 0.25 22.8 0.9 23 0.69

2S3 2124 0.00 - - - 0.00 - - - - 2628A 0.00 - - - 0.00 - - - - 2628B 0.00 - - - 0.00 - - - -

2S4 2125 0.00 - - - 0.00 - - - -

31

UA PR

Well Sample p T (°C)

+/- (°C)

ΔNe (%) p T

(°C) +/-

(°C) ΔNe (%) R

2261 0.00 - - - 0.00 - - - - 2633A 0.00 - - - 0.00 - - - - 2633B 0.00 - - - 0.00 - - - -

3S1 2258 0.01 - - - 0.73 19.4 1.2 13 1.79 2623 0.06 17.4 0.6 11 0.26 18.2 0.8 15 0.68 3070 0.00 - - - 0.78 19.3 0.9 28 0.89

3S2 2257 0.00 - - - 0.00 - - - - 3071 0.00 - - - 0.30 26.8 2.5 31 2.45

3S3 2256 0.08 16.0 0.6 11 0.33 16.8 0.8 16 0.62 3S4 2255 0.00 - - - 0.00 - - - - 4S1 2670 0.20 18.9 0.6 5 0.70 19.9 1.0 9 1.28 4S2 2264 0.01 - - - 0.62 17.4 0.8 53 0.33

2625 0.17 16.4 0.7 47 0.86 17.2 0.8 53 0.21 4S3 2262A 0.53 16.9 0.7 35 0.64 17.3 0.8 38 0.13

2262B 0.45 17.3 0.7 44 0.42 17.7 0.8 46 0.10 2636A 0.30 17.9 0.7 35 0.84 18.7 0.8 40 0.22 2636B 0.08 18.0 0.7 40 0.89 19.0 0.8 46 0.27

4S4 2263A 0.00 - - - 0.17 27.1 4.0 35 3.15 2263B 0.73 17.9 0.6 15 0.47 17.8 0.7 14 0.00

5S1 2254 0.00 - - - 0.00 - - - - 2626 0.00 - - - 0.00 - - - - 2849 0.39 17.2 0.6 0 0.22 17.2 ∞ 0 0.00 3068 0.12 16.3 0.6 25 0.85 17.2 0.8 30 0.35

I1 1889 0.03 - - - 0.31 17.8 0.9 103 0.17 I2 1890 0.20 18.8 0.7 60 0.09 18.7 0.8 59 0.00 I3 1771 0.00 - - - 0.00 - - - - I4 1888 0.24 17.1 0.8 169 0.32 17.8 1.0 175 0.07 I5 1886 0.87 16.6 0.7 111 0.93 16.9 0.9 114 0.04

32

Table 2.3. Continued.

CE CE (excluding He)

Well Sample p T (°C)

+/- (°C)

ΔNe (%) F p T

(°C) +/-

(°C) ΔNe (%) F

1S2 2250A 0.00 - - - - 0.00 - - - - 2250B 0.00 - - - - 0.00 - - - - 2634 0.00 - - - - 0.00 - - - - 3065 0.00 - - - - 0.00 - - - -

1S3 1864A 0.77 16.3 2.5 31 0.71 0.56 16.3 2.4 31 0.70 1864B 0.76 17.8 5.6 29 0.75 0.90 19.5 22.8 32 0.74 1862A 0.62 14.6 1.2 56 0.27 0.71 15.0 1.4 55 0.38 1862B 0.98 13.6 1.1 67 0.09 0.95 13.5 1.2 67 0.04 1871A 0.62 14.6 2.0 16 0.82 0.43 14.6 1.8 17 0.80 1871B 0.15 14.3 1.1 69 0.00 0.93 14.9 1.3 66 0.24 1874A 0.85 16.5 7.5 21 0.81 0.57 16.4 11.9 21 0.81 1874B 0.58 14.7 2.4 16 0.83 0.94 14.7 2.0 17 0.80 1880A 0.18 14.7 6.2 14 0.87 0.38 16.1 20.0 18 0.84 1880B 0.17 14.9 5.1 16 0.84 0.40 16.3 13.3 20 0.82 1883 0.00 - - - - 0.00 - - - - 2253 0.23 14.1 5.4 11 0.89 0.58 15.7 14.9 15 0.87 2632 0.07 13.4 5.6 8 0.92 0.31 14.8 17.5 13 0.88

1S4 1863A 0.80 15.2 2.9 22 0.78 0.93 15.3 2.6 23 0.77 1863B 0.80 15.4 1.3 56 0.36 0.83 15.2 1.3 57 0.29 1866A 0.29 13.8 1.0 48 0.00 0.68 14.2 1.3 46 0.30 1866B 0.42 13.7 1.0 48 0.00 0.93 14.0 1.2 46 0.28 1869A 0.23 13.3 1.0 37 0.00 0.19 13.2 1.2 36 0.00 1869B 0.94 15.7 1.3 41 0.39 0.77 15.8 1.4 41 0.42 1873A 0.33 14.1 1.0 37 0.00 0.42 14.6 1.3 36 0.45 1873B 0.66 14.1 1.0 37 0.00 0.70 14.4 1.2 36 0.32 1884 0.03 - - - - 0.55 17.0 1.8 31 0.67 2252 0.34 14.9 1.0 32 0.00 0.75 15.0 1.2 30 0.37 2631 0.35 15.7 1.3 33 0.54 0.62 16.4 1.7 31 0.66

1S5 1865 0.13 15.3 2.4 22 0.78 0.99 15.3 1.8 25 0.72 1870A 0.00 - - - - 0.24 13.3 1.1 11 0.00 1870B 0.00 - - - - 0.00 - - - - 1868 0.00 - - - - 0.00 - - - - 2251 0.00 - - - - 0.14 15.5 ∞ 0 0.00

2S1 3352 0.00 - - - - 0.49 18.8 1.3 21 0.002S2 2123 0.00 - - - - 0.05 - - - -

2259A 0.02 - - - - 0.15 21.7 1.4 23 0.00 2259B 0.01 - - - - 0.17 22.3 1.4 23 0.00 2627 0.00 - - - - 0.13 21.7 1.3 22 0.00

2S3 2124 0.00 - - - - 0.20 21.7 ∞ 0 0.00 2628A 0.00 - - - - 0.20 22.7 ∞ 0 0.00 2628B 0.00 - - - - 0.00 - - - -

2S4 2125 0.00 - - - - 0.00 - - - - 2261 0.00 - - - - 0.00 - - - - 2633A 0.00 - - - - 0.00 - - - - 2633B 0.00 - - - - 0.00 - - - -

3S1 2258 0.01 - - - - 0.57 21.9 7.1 15 0.86 2623 0.23 19.7 5.7 13 0.87 0.56 21.6 17.3 17 0.84

33

CE CE (excluding He)

Well Sample p T (°C)

+/- (°C)

ΔNe (%) F p T

(°C) +/-

(°C) ΔNe (%) F

3070 0.00 - - - - 0.52 18.4 1.3 28 0.263S2 2257 0.00 - - - - 0.02 - - - -

3071 0.00 - - - - 0.14 21.9 1.5 28 0.523S3 2256 0.25 18.3 4.8 14 0.86 0.53 21.0 7.4 19 0.843S4 2255 0.00 - - - - 0.10 19.0 ∞ 0 0.004S1 2670 0.21 20.0 4.8 6 0.93 0.61 21.8 10.5 10 0.904S2 2264 0.05 - - - - 0.34 17.7 1.4 53 0.33

2625 0.43 18.1 1.5 50 0.44 0.88 17.7 1.4 53 0.314S3 2262A 0.82 18.3 1.5 38 0.51 0.58 18.2 1.5 38 0.48

2262B 0.53 18.5 1.4 46 0.40 0.27 18.5 1.4 46 0.38 2636A 0.53 19.5 1.5 38 0.52 0.71 19.1 1.4 40 0.40 2636B 0.20 19.8 1.6 43 0.50 0.68 19.3 1.4 46 0.32

4S4 2263A 0.00 - - - - 0.57 21.9 4.8 31 0.73 2263B 0.47 17.8 1.1 14 0.00 0.59 17.7 1.2 12 0.00

5S1 2254 0.00 - - - - 0.00 - - - - 2626 0.00 - - - - 0.00 - - - - 2849 0.22 17.2 ∞ 0 0.00 0.30 17.9 21.1 2 0.98 3068 0.25 17.9 1.6 28 0.66 0.65 17.5 1.4 30 0.52

I1 1889 0.49 19.7 1.7 101 0.27 0.54 19.3 1.6 103 0.23I2 1890 0.12 19.7 1.4 62 0.23 0.14 20.4 1.6 60 0.39I3 1771 0.00 - - - - 0.25 12.3 1.1 6 0.00I4 1888 0.85 19.4 1.6 175 0.10 0.57 19.5 1.7 175 0.10I5 1886 0.77 17.0 1.3 112 0.05 0.73 16.8 1.4 113 0.00

34

Figure 2.1. Map of study site. Only the sampled irrigation wells are uniquely labeled. Irrigation wells owned by other landowners are not shown.

35

Figure 2.2. Monitor well depths. Ground surface at each monitor well location shown as horizontal black line, screened intervals shown in gray. Elevation in meters above mean sea level.

36

0.975

0.980

0.985

0.990

0.995

1.000

1.005

Year

Pres

sure

(atm

)

2004 2005 2006

(a)

0.984

0.986

0.988

0.990

0.992

0.994

5/21/05 5/22/05 5/23/05 5/24/05

Date

Pre

ssur

e (a

tm) (b)

0.986

0.988

0.990

0.992

6:00 12:00 18:00 0:00 6:00Time

(c)

Figure 2.3. Atmospheric pressure data from nearby National Climatic Data Center (NCDC) station showing a) seasonal fluctuations and (b) diurnal fluctuations. The dashed line in (b) is 3 day moving average. Panel (c) shows the impact of the May 22, 2005 irrigation event at 2S on soil gas pressure (site 2S sensors – gray, sites 3S and 5S – black).

37

Figure 2.4. Subsurface temperature data from 3S location. Local water table fluctuated between 3.8 and 5.5 m below ground surface (BGS) during the study. Subsurface temperature data from the other instrumentation locations (2S and 5S) are similar. Air temperature data are from the nearby National Climatic Data Center (NCDC) station.

38

Figure 2.5. Water table temperatures for the three instrumented locations. Curves for 3S and 5S are data from the deepest heat dissipation sensor. The lower 2006 maximum temperature at 5S is attributed to increased vegetation cover in 2006. The curve for 2S is extrapolated from measured data assuming exponential decay of the seasonal temperature signal with depth (water table was ~1.1 m below the deepest 2S sensor).

39

0

10

20

30

40

50

60-14.5 -13.5 -12.5 -11.5 -10.5

δ18OVSMOW (‰)

Dep

th (m

BG

S)

1S 2S 3S

4S 5S 6S

Figure 2.6. Oxygen isotope (δ18O) data reported as parts per thousand (‰) relative to Vienna Standard Mean Ocean Water (VSMOW).

40

0

1

2

3

4

0.0 0.5 1.0 1.5 2.0

Ne (x 10-7 cm3 STP g-1)

He

(x 1

0-8 c

m3 S

TP g

-1)

Equil. conc.

DS1 model

DSC model

DD model

1S2

2S3

2S4

5S1

Figure 2.7. Helium versus Ne concentrations of undersaturated samples. Equilibrium concentration given for T = 19 °C, S = 0, p = 0.991 atm. Lines show impact of degassing for each of the three degassing models.

41

1.5

2.0

2.5

0.90 0.95 1.00 1.05 1.10

Xe (x 10-8 cm3 STP g-1)

Ne

(x 1

0-7 c

m3 S

TP g

-1)

Equil. sol.

Excess air

2S1

3S1

4S1

5S1

30%

20%

ΔNe=10%

T =20°C 19°C 18°C 17°C

Figure 2.8. Ne and Xe concentrations of samples from the shallowest wells. Analytical uncertainties shown (Ne 2%, Xe 3%). Equilibrium solubilities from T = 17–20 °C (S = 0, p = 0.991 atm) are shown. Unfractionated excess air concentrations are also shown.

42

Figure 2.9. Noble Gas Temperatures (NGTs) calculated for samples from the shallowest wells for four different models: Unfractionated Air (UA), Closed system Equilibrium (CE), Partial Re-equilibration (PR), and Diffusive Degassing (DD). The shaded region indicates the water table temperature (WTT) range for that location. For wells with multiple samples, the sample number is noted.

43

15

16

17

18

19

20

21

22

Model

Tem

pera

ture

(°C

)

15

16

17

18

19

20

21

22

Model

28493068

5S14S1

UA CE PR DD UA CE PR DD

15

16

17

18

19

20

21

22225826233070

3S1

UA CE PR DD 15

16

17

18

19

20

21

22Te

mpe

ratu

re (°

C)

2S1

UA CE PR DD

-6

-5

-4

-3

-2

-1

0

1C

alcu

late

d R

echa

rge

Tem

pera

ture

Diff

eren

ce (°

C)

-6

-5

-4

-3

-2

-1

0

1

0

20

40

60

80

100

Prob

abili

ty (%

)

0

20

40

60

80

100

-10-8-6-4-20

Amount of Degassing (ΔNe %)

DS1 DD

DS1 DD

DS1 DD

F = 0

F = 0.65

F = 0.75

-0.10.00.10.20.30.40.50.60.70.80.9

-10-8-6-4-20

Amount of Degassing (ΔNe %)

Frac

tiona

tion

Para

met

er, F

UA ModelCE Model

(b)

(d)

(e)

(c)

(a)

Figure 2.10. Impact of degassing on calculated NGTs. These synthetic samples had excess air added (ΔNe = 30%), some unfractionated and others fractionated according to the CE model (both F = 0.65 and 0.75). Samples were then degassed by various amounts according to the DS1 and DD models. The resultant gas concentrations were then modeled by the CE and UA models. Panels (a) and (b) show the difference between original recharge temperature (19 °C) and modeled recharge temperature after degassing. Panels (c) and (d) show model goodness-of-fit (probability of χ2 being greater than a given value obtained from the χ2 distribution for the appropriate number of degrees of freedom). Panel (e) shows the modeled CE fractionation parameter, F (note that the UA model is a limiting case of the CE model in which F = 0).

44

Chapter 3:

Impact of Artificial Recharge on Dissolved Noble Gases in Groundwater in California

Abstract1

Dissolved noble gas concentrations in groundwater can provide valuable

information on recharge temperatures and enable 3H–3He age-dating with the use of

physically based interpretive models. This study presents a large (905 samples) data set

of dissolved noble gas concentrations from drinking water supply wells throughout

California, representing a range of physiographic, climatic, and water management

conditions. Three common interpretive models (unfractionated air, UA; partial re-

equilibration, PR; and closed system equilibrium, CE) produce systematically different

recharge temperatures or ages; however, the ability of the different models to fit

measured data within measurement uncertainty indicates that goodness-of-fit is not a

robust indicator for model appropriateness. Therefore caution is necessary when

interpreting model results. Samples from multiple locations contained significantly

higher Ne and excess air concentrations than reported in the literature, with maximum

excess air tending toward 0.05 cm3 STP g-1 (ΔNe ~400%). Artificial recharge is the most

plausible cause of the high excess air concentrations. The ability of artificial recharge to

dissolve greater amounts of atmospheric gases has important implications for oxidation-

reduction dependent chemical reactions. Measured gas concentration ratios suggest that

diffusive degassing may have occurred. Understanding the physical processes

1 Reproduced with permission from Cey, B.D., Hudson, G.B., Moran, J.E. and Scanlon, B.R., 2008. Impact of Artificial Recharge on Dissolved Noble Gases in Groundwater in California. Environmental Science and Technology, 42(4): 1017-1023. Copyright 2008 American Chemical Society. 45

controlling gas dissolution during groundwater recharge is critical for optimal

management of artificial recharge and for predicting changes in water quality that can

occur following artificial recharge.

INTRODUCTION

Concentrations of atmospherically derived noble gases dissolved in groundwater

are used to determine recharge temperatures which are useful for paleoclimatic studies of

glacial-interglacial temperature variation (Aeschbach-Hertig et al., 2002c; Andrews et al.,

1994; Clark et al., 1998; Stute et al., 1995b; Weyhenmeyer et al., 2000). Concentrations

of dissolved noble gases in groundwater are virtually always greater than equilibrium

concentration with respect to atmospheric pressure. The portion of gas in excess of

equilibrium concentration with respect to atmospheric pressure is referred to as “excess

air” because of its compositional similarity to air, although it is commonly fractionated

relative to air (heavy gases more enriched than light gases) (Kipfer et al., 2002). Because

determinations of recharge temperature and 3H–3He age are based on equilibrium gas

solubility, total measured gas must be corrected for excess air. Entrapped soil air near the

water table is generally considered to be the source of excess air (Kipfer et al., 2002).

These bubbles dissolve during downward water flux and during water table fluctuations.

Early interpretations of dissolved gas data assumed excess air resulted from complete

dissolution of entrapped air (Andrews et al., 1994; Heaton and Vogel, 1981). Subsequent

research suggested that the commonly observed elemental fractionation of excess air was

caused by complete dissolution of entrapped air and subsequent diffusive degassing

(Stute et al., 1995b) or by incomplete dissolution of entrapped air (Aeschbach-Hertig et

al., 2000).

Three physically based models are used to interpret dissolved noble gas

concentration data in groundwater: 1) unfractionated air, UA, model based on complete

46

dissolution of entrapped air (Heaton and Vogel, 1981), 2) partial re-equilibration, PR,

model based on complete dissolution of entrapped air bubbles followed by diffusive

degassing (Stute et al., 1995b), and 3) closed system equilibrium, CE, model based on

incomplete dissolution of entrapped air (Aeschbach-Hertig et al., 2000). The PR and CE

models both simulate excess air elemental fractionation, but PR fractionation is

controlled by diffusivity whereas CE fractionation is controlled by gas solubility.

It is generally believed that the CE model best represents physical processes

occurring during recharge (Kipfer et al., 2002), although few studies have systematically

compared all three models. Aeschbach-Hertig et al. (2000) showed that the CE model

provided the best fit when comparing models using dissolved noble gas data from four

diverse sites. Peeters et al. (2002) used data from an aquifer in Niger to compare the

three models and found that the CE and PR models both adequately fit elemental data;

however, only the CE model fit isotopic data.

The process interpreted to control gas dissolution during groundwater recharge

can have a significant impact on calculated recharge temperatures and 3H–3He

groundwater ages (Aeschbach-Hertig et al., 2000; Kipfer et al., 2002; Peeters et al.,

2002). For example, incorrectly assuming complete dissolution of entrapped air

(unfractionated excess air) results in erroneously low 3H–3He ages. Accurate

determination of 3H–3He ages require an understanding of the processes affecting gas

dissolution during groundwater recharge.

Understanding gas dissolution during groundwater recharge has broad

implications for water resources management. Regions suffering from water shortages

and poor water quality increasingly rely on: 1) reclaimed municipal wastewater as a

source of water, and 2) artificial recharge to store surface water for later reuse and to

improve water quality (Asano and Cotruvo, 2004) (herein we limit the definition of

47

artificial recharge to recharge from surface sources actively managed to maximize

recharge). Water quality improvement by artificial recharge largely results from

microbially mediated, oxidation-reduction (redox) dependent degradation of organic

compounds (e.g. trihalomethanes and endocrine-disrupting compounds) (Greskowiak et

al., 2005). Because of the importance of dissolved oxygen as a terminal electron

acceptor, understanding gas dissolution processes is especially important for artificially

recharged treated wastewater.

Objectives of this study were to: 1) quantify variability in observed dissolved

noble gas concentrations across the study area, 2) compare measured data to previously

published data, 3) examine possible causes of observed variability in dissolved noble gas

concentrations, 4) evaluate ability of interpretive models to fit measured gas data, and 5)

assess implications of these results for groundwater age-dating and groundwater

management. This study is based on dissolved noble gas data from groundwater basins

throughout California. Unique aspects of the study include: 1) the size of study area

which is nearly twice that of any previous study of dissolved noble gases in groundwater,

2) the large range of physiographic (Coastal Plain, Central Valley, Sierra Nevada) and

climatic (arid to humid) regimes, 3) the range of water management regimes (artificial

recharge versus natural recharge), and 4) application of various interpretive models to a

large and highly variable data set. The size of this study provides a unique opportunity to

assess factors controlling excess air generation during groundwater recharge.

MATERIALS AND METHODS

Overview of Study

Samples evaluated in this study were collected as part of a statewide,

comprehensive assessment of groundwater contamination vulnerability in California

(www.swrcb.ca.gov/gama). 3H–3He groundwater age was used in combination with

48

analyses of ubiquitous contaminants at ultralow levels to assess the relative probability

that drinking water aquifers will become contaminated with anthropogenic pollutants.

Dissolved noble gas analyses were used to calculate 3H–3He groundwater ages (Solomon

and Cook, 2000).

Nearly all of the wells in this study are public drinking water supply wells. There

are ~16,000 public supply wells in California; wells sampled for this study are a subset

from large metropolitan areas (Los Angeles, San Jose, and Sacramento), as well as

several smaller population centers (e.g. Bakersfield, Chico, Stockton) and rural areas that

rely on groundwater for drinking water supply (see Supporting Information for sample

locations). Wells were selected according to the following criteria: 1) spatial distribution

for the groundwater basin, including depth distribution that represents the exploited

aquifers, 2) availability of well information such as total depth, open intervals, typical

pumping rate, and year of construction, and 3) availability for sampling during the study

period. The sample set almost exclusively comprises deep (>50 m total depth below

ground surface), long-screened/perforated (>50 m open interval), high production (>10-

2 m3 s-1) wells actively used for drinking water supply.

Sampling and Analysis

Dissolved noble gas samples were collected using standard sampling techniques.

Samples were collected by connecting the sample vessel (8 mm inner diameter copper

tubing, 250 mm long) to the wellhead of operating (pumping) wells with clear tygon

tubing at full wellhead pressure. Water flowed for several minutes to purge air bubbles.

The copper tubing was tapped lightly to dislodge bubbles and a visual inspection for

bubbles was made. Steel clamps pinched the copper tubing flat in two locations to secure

the water sample. Tritium samples were collected in 1 L glass bottles.

49

Sample analyses were performed at Lawrence Livermore National Laboratory

(LLNL). Reactive gases were removed with multiple reactive metal getters. Known

quantities of isotopically enriched 22Ne, 86Kr and 136Xe were added to provide internal

standards. The isotope dilution protocol used for measuring noble gas concentrations is

insensitive to potential isotopic composition variation in dissolved gases (especially Ne)

from diffusive gas exchange. Noble gases were separated from one another using

cryogenic adsorption. Helium was analyzed using a VG-5400 noble gas mass

spectrometer. Other noble gas isotopic compositions were measured using a quadrupole

mass spectrometer. The Ar abundance was determined by measuring the total noble gas

sample pressure using a high-sensitivity capacitive manometer. The procedure was

calibrated using water samples equilibrated with the atmosphere at a known temperature

and pressure. Tritium concentrations were determined on 500 g subsamples by the 3He

in-growth method (approximately 15 day accumulation time). Analytical uncertainties

are approximately 1% for 3He/4He, 2% for He, Ne, and Ar, and 3% for Kr and Xe.

Modeling

The total measured concentration of dissolved noble gas is the sum of

equilibrium, excess air, and radiogenic components (Kipfer et al., 2002). Helium may

have an additional radiogenic component, tritiogenic 3He (Kipfer et al., 2002; Solomon

and Cook, 2000). Addition of excess air has the greatest relative impact on He and Ne

concentrations because the equilibrium component is relatively small. A common way to

represent the amount of excess air is as percent Ne, ΔNe (excess Ne relative to

equilibrium component) (Kipfer et al., 2002).

Helium is commonly excluded from noble gas modeling because of the presence

of radiogenic or tritiogenic sources. Calculation of equilibrium and excess air

components of He are required to quantify tritiogenic 3He which is used to calculate 3H–

50

3He groundwater ages (Solomon and Cook, 2000). Helium concentrations are impacted

more by excess air than Ne; therefore, the model used to determine excess air can have a

significant impact on calculated 3H–3He ages (Kipfer et al., 2002).

Measured Ne, Ar, Kr, and Xe concentrations were fitted by UA, PR, and CE

models using NOBLE90 to solve for excess air, degree of excess air fractionation (in CE

and PR models only), and recharge temperature. For all modeling the recharging water

was assumed to be fresh (S = 0) and recharge elevation was assumed to be the wellhead

elevation. It is common to assume the recharging water is fresh in noble gas studies

(Aeschbach-Hertig et al., 2000; Aeschbach-Hertig et al., 2002c). The assumption that the

recharge elevation is the wellhead elevation is less accurate and may introduce error into

the analysis.

RESULTS AND DISCUSSION

Dissolved Ne, Ar, Kr, and Xe were measured on 905 samples (Figure 3.1 and

Appendix C). Dissolved He and 3He/4He were measured in all but one of the samples,

and 3H was measured in all but six samples.

Three measures were used to evaluate NOBLE90 output; 1) χ2 test (exclude if

p <0.05), 2) physically reasonable A values (exclude if A >1 cm3 STP g-1), and 3)

physically reasonable recharge temperatures (exclude if modeled recharge temperature is

either greater than 2 °C above the greatest average monthly air temperature or less than

2 °C below the lowest average monthly air temperature). Aeschbach-Hertig et al.

(2002a) note that samples that fit according to χ2 but with large A are likely an artifact of

the fitting procedure, because of correlation between A and T.

Spatial Characterization

To spatially characterize the data, samples were grouped into six geographic

regions; 1) San Francisco Bay Area (SFBA), 2) Los Angeles Basin (LAB), 3)

51

igneous/volcanic aquifers of northern California (NC), 4) Mojave Desert Basin (MDB),

5) northern portion of the Central Valley (NCV), and 6) southern portion of the Central

Valley (SCV) (Figure 3.1). All of the regions are alluvial basins with the exception of

NC. Seasonal timing of precipitation is similar for all regions, with ≥80% of

precipitation occurring from November through April. The total amount of precipitation

varies greatly among the six regions (~150 mm yr-1 in MDB to ~1000 mm yr-1 for some

NC sample locations).

Dense population and large-scale irrigated agriculture have led to significant

modification of the natural hydrologic cycle in California. Water is transferred from

northern and eastern portions of California to heavily populated coastal areas (e.g. SFBA

and LAB). Irrigated agriculture has considerably altered the natural groundwater and

surface water flow regimes throughout the Central Valley, where many areas are severely

overdrafted and recharge is dominated by net irrigation return flow (Bertoldi et al., 1991).

The current situation masks natural environmental forcing such as precipitation,

temperature, and vegetative cover. Furthermore, because sampled wells all have long

open intervals, samples are composites of waters recharged under various conditions.

Detailed spatial characterization is therefore unrealistic, yet regional differences in

dissolved gas concentrations are apparent.

High Ne concentrations are most commonly associated with locations having

long-term artificial recharge. Major artificial recharge facilities in LAB, SFBA, and

Bakersfield (which is in the SCV region) have been operational for decades (Anders and

Schroeder, 2003; City of Bakersfield, 2005; Mills, 2002; Santa Clara Valley Water

District, 2001). High Ne concentrations in areas impacted by artificial recharge contrast

sharply with both the remainder of this data set and also literature data (Figure 3.2).

Literature data are from studies in a wide range of climatic and hydrogeologic settings

52

(463 samples from 23 different studies: Aeschbach-Hertig et al., 2002c; Andrews et al.,

1991; Andrews et al., 1994; Beyerle et al., 1998; Beyerle et al., 2003; Clark et al., 1998;

Clark et al., 1997; Dennis et al., 1997; Fontes et al., 1991; Hall et al., 2005; Kulongoski et

al., 2004; Ma et al., 2004; Saar et al., 2005; Stute et al., 1995a; Stute and Deak, 1989;

Stute et al., 1995b; Stute and Sonntag, 1992; Thomas et al., 2003; Weyhenmeyer et al.,

2000; Wilson et al., 1990; Wilson et al., 1994; Zuber et al., 2004; Zuber et al., 2000).

Median Ne concentration in areas impacted by artificial recharge is 50% greater than that

in the literature evaluated, while in areas not impacted by artificial recharge it is 3% less

than the literature value. In contrast to Ne, all Xe concentrations fall in a relatively

narrow range. Spatial differences in gas concentrations decrease with increasing

molecular weight.

Data from the SCV region point to artificial recharge as the cause of high Ne

concentrations. The SCV region is relatively uniform in terms of land use (irrigated

agriculture), geology, and climate. However, Bakersfield is the only SCV area in our

study that has a history of intensive artificial recharge. The mean Ne concentration of

samples from Bakersfield (n = 43) is 49% greater than samples from the remainder of

SCV (n = 182).

As noted earlier, excess air has the greatest relative impact on the lightest gases.

The unique data from areas impacted by artificial recharge indicate large amounts of

excess air that cannot be attributed to sampling (e.g. trapped bubbles in sampling device)

or analytical problems because many of the samples with high gas concentrations were

reanalyzed and showed good replication.

Noble Gas Models

The UA model is a limiting case of both models incorporating fractionation;

therefore, fractionation models always fit more samples than the UA model. The

53

fractionation models fit slightly more samples than the UA model for all regions;

however, the greatest difference is for areas impacted by artificial recharge (Table 3.1).

Correlation between measured and modeled gas concentrations for samples fit by

fractionation models was greatest for Ne ( = 0.9998, = 0.9997) and least for Kr

( = 0.982, = 0.984) (

2CER 2

PRR2CER 2

PRR Figure 3.3, Figure 3.4, and Figure 3.5). Model errors

(relative difference between measured and modeled gas concentrations) between the

gases provide additional insight. Visual inspection of model error scatter plots reveals

correlations (Figure 3.6). Negative correlation exists between gases with similar

molecular weights (e.g. Kr–Xe), which is consistent with model errors produced when

modeling synthetic data.

Excess Air

Of samples fit by the CE model, those in artificially recharging areas had much

higher median excess air (89% ΔNe) than samples from non-artificial recharging areas

(28% ΔNe) (results of the other models are comparable, see Table 3.2). Samples not fit

by the models tended to have greater average Ne concentrations (by a factor of 1.6).

Inability of the models to fit samples with large Ne concentrations is reflected in the

lower proportion of samples from artificial recharge areas fit by the models (Table 3.1).

The reason that models do not fit samples with large Ne concentrations may be non-

equilibrium effects (e.g. gas dissolution stopping prior to attaining equilibrium between

entrapped gas and dissolved gas). Kinetic factors may control dissolved gas

concentrations (Holocher et al., 2003), especially in locations with high recharge flux.

Neon, Ar, Kr, and Xe concentrations do not allow one to clearly differentiate

between the CE and PR models. Peeters et al. (2002) explain the benefit of isotopic data

for distinguishing between the CE and PR models; however, isotopic data are not

available in this study. In the absence of isotopic data, an examination of model-

54

predicted He concentrations can help differentiate the models. Helium data were not

used for modeling but NOBLE90 outputs predicted He concentrations. The range of

predicted He:Ne ratios from the CE model is limited because He:Ne in the excess air

component can only range from that of air (0.288) to that of air saturated water (0.230 at

10 °C), whereas the PR model has a theoretical lower limit of zero. Over 160 samples–

the majority of which are fit by the PR model–have low measured He:Ne ratios that

cannot be readily explained by either the UA or CE models (Figure 3.7). Approximately

two-thirds of these samples are from areas impacted by artificial recharge. The 160

samples represent a minimum number as some samples may have had low He:Ne ratios

immediately after recharge followed by addition of radiogenic He. These results are

consistent with diffusive degassing; however, elemental ratios alone can not be

considered conclusive evidence of diffusive degassing. Although groundwater studies

have shown degassing caused by CO2/CH4 gas stripping (Klump et al., 2006; Thomas et

al., 2003), no studies are known that present evidence of dissolved noble gas fractionation

by diffusive degassing.

It is clear that for high excess air concentrations, excess air is less fractionated

(e.g. lower CE model fractionation factor F values, where F = v/q, v is the ratio of

entrapped gas volumes in final and initial states, and q is the ratio of dry gas pressures in

entrapped air to that in the atmosphere (Aeschbach-Hertig et al., 2000)) (Figure 3.8).

This is expected because pore spaces hold a finite amount of entrapped air in bubbles and

as dissolution of entrapped air continues the excess air component becomes decreasingly

fractionated until the entrapped air completely dissolves. The upper bound of excess air

observed in this study is 400–500% ΔNe or ~0.05 cm3 at standard temperature and

pressure (STP) per gram of water, cm3 STP g-1 (as air; Figure 3.8). This is reasonable

given that the amount of entrapped air in quasi-saturated porous media is

55

>0.05 cm3 STP g-1, as reported from laboratory pycnometer measurements (Stonestrom

and Rubin, 1989) and field experiments using a neutron probe (Fayer and Hillel, 1986).

Stonestrom and Rubin (1989) measured entrapped air in alluvium (i.e. Oakley sand) from

the NCV region and reported a range of 0.09–0.14 cm3 g-1 under capillary saturation at

21 °C (STP values would be even greater because the pressure of entrapped air was

unknown).

Excess air concentrations are strongly correlated with the CE model parameter q

(Figure 3.8), consistent with results of Aeschbach-Hertig et al. (2002a). Aeschbach-

Hertig et al. (2002a) and Kipfer et al. (2002) assert that q is a semi-quantitative measure

of hydrostatic pressure exerted on entrapped air and therefore of water table fluctuations.

The upper range of these data would then indicate water table fluctuations of >20 m

assuming all the pressure was generated hydrostatically (interfacial tension only produces

such high pressures for bubble radii <0.001 mm). It is unlikely that such large water

table fluctuations naturally occur in any of the study regions. An alternative mechanism

that could generate large q values (large hydrostatic pressures) is entrainment of

entrapped bubbles in areas of high recharge. Bubbles could be transported to sufficient

depths to cause dissolution of large amounts of excess air.

3H–3He Age-dating

The choice of excess air model has an impact on interpreted 3H–3He groundwater

ages, especially for the youngest waters (Solomon and Cook, 2000). Normally the excess

air component of He is determined based on Ne data alone and assumes no gas

fractionation (Kipfer et al., 2002; Solomon and Cook, 2000). Kipfer et al. (2002) and

Aeschbach-Hertig et al. (2000) demonstrate that the assumption of no gas fractionation

may give unreasonably low ages (even negative values of tritiogenic He).

56

The interpreted ages resulting from each of the three excess air models vary

systematically as discussed by Peeters et al. (2002). The oldest age is calculated by PR,

the youngest age is calculated by UA, while the CE model yields ages between the PR

and UA models (Figure 3.9). The reason is that the interpreted excess air component of

He is maximized for unfractionated conditions. A larger excess air component of He

results in a lower interpreted tritiogenic He component for a given total measured amount

of He, and a younger interpreted age. As expected the difference between PR and CE

calculated ages is more pronounced for samples having higher excess air, for artificial

recharge areas the mean difference is 4.1 yr (n = 170) versus 3.6 yr for non-artificial

recharge areas (n = 144). Samples with 3H < 3 pCi L-1, negative calculated tritiogenic 3He, or calculated ages >55 yr were excluded. Age-dating calculations assumed

radiogenic 3He/4He = 2.0 × 10-8 and no mantle derived He.

The differences in interpreted groundwater ages were greatest for: 1) younger

samples, and 2) samples with fractionated excess air. These results are consistent with

the literature (Kipfer et al., 2002) and suggest calculating groundwater age without

considering gas fractionation will underestimate groundwater age, especially if diffusive

degassing occurs.

Recharge Temperature

Model recharge temperatures generally range from lowest to highest in the

following order; UA, CE, PR (Table 3.3, Figure 3.10, and Figure 3.11). Because Xe

solubility increases substantially with decreasing temperature, calculated recharge

temperature (which is based on the equilibrium concentration component) tends to be

higher for fractionation models than for the UA model. In other words, fractionation

models tend to produce higher calculated recharge temperatures than the UA model

because more of the total Xe is attributed to the excess air component (and less to the

57

equilibrium component) in fractionation models. An evaluation of the relationship

between model recharge temperature differences and amount of excess air did not reveal

a clear correlation.

The mean calculated recharge temperature of each region broadly tracks the

measured mean annual air temperature (MAAT) for each of the three models (Figure

3.11). In virtually all cases soil temperatures are slightly greater (1–3 °C) than MAAT;

therefore, it is common for NGTs to be greater than MAAT (Kipfer et al., 2002). These

data show the opposite relationship, which is unusual but not unprecedented (e.g. Hall et

al., 2005). There are possible reasons that NGTs are less than MAAT. Groundwater

recharge may reflect winter temperatures rather than MAAT because of focused seasonal

recharge. Spring snowmelt runoff from uplands (e.g. Sierra Nevada) into the alluvial

basins causes focused recharge from surface channels (rivers, streams, canals). Also,

MAAT data used in this analysis are referenced to well location rather than recharge

location. Detailed analyses of recharge areas for each well are beyond the scope of this

study; however, it is likely that some groundwater recharged near basin edges at higher

elevations and cooler temperatures than the wellhead (especially MDB). Furthermore,

the assumption that the recharge elevation is the wellhead elevation may yield incorrect

NGTs.

Artificial Recharge

The most plausible reason for the large excess air concentrations is artificial

recharge. Presently LAB and SFBA artificially recharge ~4.0 × 108 and

>1.0 × 108 m3 yr-1, respectively; which is greater than half of groundwater pumping for

each area (Clark et al., 2004; Reichard et al., 2003; Santa Clara Valley Water District,

2001). Operational procedures of large-capacity artificial recharge facilities in LAB

provide the necessary conditions to dissolve large amounts of air (Mills, 2002).

58

Specifically Reichard et al. (2003) present data from shallow wells near artificial

recharge facilities in LAB with annual head fluctuations >10 m (well 2S/12W-14J1

1590F) and Mills (2002) notes a sustained percolation rate of 1.2 m s-1 at an artificial

recharge facility in LAB. Furthermore, Anders and Schroeder (2003) suggest that a

measured increase in redox potential of an artificially recharged groundwater plume in

LAB may be caused by large amounts of excess air.

Management of artificial recharge facilities can have a major impact on the

amount of dissolved gas in recharging water. Continual buildup of clogging layers on the

bottom of spreading basins necessitates periodic drying to allow degradation and/or

removal of clogging layers (Mills, 2002). Optimal management balances the effects of

increased recharge flux caused by removal of low permeability clogging layers against

the interruption to recharge caused by basin drying/cleaning. An additional operational

consideration is reduced hydraulic conductivity following basin drying caused by the

presence of newly entrapped air. Therefore, frequency and duration of basin drying

strongly influence the amount of excess air in groundwater recharged from artificial

recharge facilities.

The majority of samples not fit by the models (75–82% depending on model) are

from areas impacted by artificial recharge (Table 3.1). Using such models to determine

recharge parameters of samples in areas impacted by artificial recharge is challenging.

Artificial recharge may occur at temperatures different from the local average water table

temperature due to seasonal recharging activities and heating of water in spreading

basins. For example, temperatures of surface water used for artificial recharge in LAB

can vary from 10 to 35 °C annually (Davisson et al., 2004). It is also likely that many

well water samples are mixtures of naturally recharged and artificially recharged waters.

Furthermore, it is unlikely that gas dissolution occurs under equilibrium conditions under

59

very high recharge fluxes. Non-equilibrium gas dissolution during groundwater recharge

has received relatively little attention (Holocher et al., 2003), but is likely to occur during

artificial recharge and other rapid infiltration scenarios (e.g. highly conductive river

beds).

California, like many other regions with water supply challenges, is increasingly

using reclaimed wastewater as a source for artificial recharge (Anders and Schroeder,

2003; Reichard et al., 2003). Fate and transport of organic compounds such as

disinfection byproducts (e.g. trihalomethanes) and endocrine-disrupting compounds in

artificially recharged wastewater is a major water quality concern in California (Anders

and Schroeder, 2003; Asano and Cotruvo, 2004) and in many other areas (Greskowiak et

al., 2005). California has proposed that artificially recharged wastewater have a

minimum subsurface residence time of 6 months to allow degradation of contaminants

(Asano and Cotruvo, 2004). The contribution of dissolved O2 in excess air plays a

critical role in the redox chemistry of groundwater and therefore the fate of many organic

pollutants (Greskowiak et al., 2005). The proportion of dissolved O2 added by excess air

relative to equilibrium concentration with respect to atmospheric pressure, ΔO2, is less

than ΔNe (ΔO2:ΔNe is ~0.3 for unfractionated air, but can exceed 0.5 for fractionated

conditions). Therefore artificially recharged groundwater could have a dissolved O2

concentration up to twice that of equilibrium concentration with respect to atmospheric

pressure.

CONCLUSIONS

Improved understanding of excess air generation is key for both managing the

physical process of artificial recharge and predicting water quality changes related to

artificial recharge. Few studies have addressed the issue of gas dissolution during

artificial recharge (Clark et al., 2005; Clark et al., 2004). Long-term field studies in well

60

characterized media would be particularly helpful in assessing the impact of dissolved

gases on artificially recharged groundwater.

Three main noble gas models exist to interpret dissolved gas concentrations.

Parameters deduced from model output (recharge temperatures or 3H–3He ages)

systematically differ among models because of the different physical processes upon

which the models are based. As demonstrated in this study, all three models may

adequately fit measured data. This suggests that goodness-of-fit is not a robust indicator

for model appropriateness and caution is necessary when interpreting results from noble

gas modeling. Furthermore, gas concentration ratios suggest that diffusive degassing

may have occurred. Further research aimed at understanding the physical processes

controlling gas dissolution during groundwater recharge is warranted. For example,

analyses of dissolved gas isotopic ratios would provide information on the possible

occurrence of diffusive degassing at artificial recharge sites.

61

Table 3.1. Number of samples fit by each model for a given region according to criteria discussed in the Supporting Information. AR refers to artificial recharge impacted areas (i.e. SFBA, LAB, and Bakersfield).

Number Samples Fit

Area of samples UA CE PR SFBA 221 147 148 147 LAB 170 129 147 149 NC 31 27 31 31 MDB 64 57 63 63 NCV 194 179 182 182 SCV 225 195 216 211 AR 434 305 337 338 non-AR 471 429 450 445 Totals 905 734 787 783

62

Table 3.2. Statistical summary of modeled excess air concentrations (% ΔNe). AR refers to artificial recharge impacted areas (i.e. SFBA, LAB, and Bakersfield).

Model Area Mean Std. dev. Median Lower quartile

Upper quartile

UA Model SFBA 150 86 132 87 203 LAB 66 48 57 31 85 NC 20 14 16 11 26 MDB 47 29 45 31 60 NCV 25 25 19 12 32 SCV 46 55 33 23 49 AR 108 83 85 46 147 non-AR 33 35 26 16 40 All 64 70 38 21 77 CE Model SFBA 146 82 124 87 194 LAB 80 64 66 33 94 NC 26 21 19 12 35 MDB 63 82 47 33 64 NCV 26 28 19 12 33 SCV 58 82 35 25 55 AR 113 84 89 50 154 non-AR 38 57 28 17 45 All 70 79 42 22 85 PR Model SFBA 146 83 126 86 195 LAB 79 63 67 34 94 NC 27 23 19 13 35 MDB 64 84 49 33 66 NCV 27 28 19 12 33 SCV 60 85 36 25 57 AR 113 84 89 51 152 non-AR 39 59 28 17 45 All 71 80 42 22 86

63

Table 3.3. Statistical summary of calculated recharge temperatures (°C). AR refers to artificial recharge impacted areas (i.e. SFBA, LAB, and Bakersfield).

Model Area Mean Std. dev. Median Lower

quartile Upper

quartile UA Model SFBA 12.9 2.0 13.1 11.4 14.3 LAB 15.4 1.6 15.4 14.2 16.4 NC 10.4 3.7 10.4 7.2 11.8 MDB 12.9 2.6 12.6 11.8 14.4 NCV 14.9 2.1 14.8 13.4 16.3 SCV 14.5 2.4 14.7 12.7 16.4 AR 14.2 2.2 14.3 12.9 15.8 non-AR 14.1 2.7 14.4 12.3 16.1 All 14.1 2.5 14.4 12.5 15.9 CE Model SFBA 13.7 2.0 13.6 12.5 14.8 LAB 16.7 2.0 16.6 15.3 18.1 NC 10.2 3.6 10.4 7.2 11.7 MDB 13.7 2.3 13.8 11.8 14.9 NCV 15.1 2.2 15.1 13.8 16.5 SCV 15.5 2.6 15.6 13.8 17.4 AR 15.4 2.4 15.4 13.7 17.1 non-AR 14.6 2.9 14.8 12.7 16.7 All 14.9 2.7 15.0 13.2 16.9 PR Model SFBA 14.2 2.1 14.1 12.8 15.3 LAB 17.5 2.4 17.5 15.8 18.9 NC 11.0 3.6 10.7 7.9 12.8 MDB 14.4 2.4 14.7 12.5 15.7 NCV 15.4 2.5 15.4 13.9 16.9 SCV 16.2 3.0 16.1 14.2 18.2 AR 16.1 2.8 15.8 14.1 18.1 non-AR 15.1 3.0 15.2 13.0 17.0 All 15.5 3.0 15.4 13.6 17.6

64

120°W

N 40°N

Figure 3.1. Map of California showing sample locations in each of the six regions: Los Angeles Basin (LAB), Mojave Desert Basin (MDB), northern California (NC), northern portion of the Central Valley (NCV), southern portion of the Central Valley (SCV), and San Francisco Bay Area (SFBA).

65

0

50

100

150

200250

300

350

400

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

(x 10-7 cm3 STP g-1)Dissolved Ne Concentration

Freq

uenc

y

Literature dataNon-Artificial RechargeArtificial Recharge

Figure 3.2. Histogram of Ne concentrations for: literature data (Aeschbach-Hertig et al., 2002c; Andrews et al., 1991; Andrews et al., 1994; Beyerle et al., 1998; Beyerle et al., 2003; Clark et al., 1998; Clark et al., 1997; Dennis et al., 1997; Fontes et al., 1991; Hall et al., 2005; Kulongoski et al., 2004; Ma et al., 2004; Saar et al., 2005; Stute et al., 1995a; Stute and Deak, 1989; Stute et al., 1995b; Stute and Sonntag, 1992; Thomas et al., 2003; Weyhenmeyer et al., 2000; Wilson et al., 1990; Wilson et al., 1994; Zuber et al., 2004; Zuber et al., 2000), artificial recharge impacted areas (SFBA, LAB, and Bakersfield), and non-artificial recharge impacted areas.

66

0.0E+00

4.0E-07

8.0E-07

1.2E-06

1.6E-06

0.0E

+00

4.0E

-07

8.0E

-07

1.2E

-06

1.6E

-06

2.0E

-06

2.4E

-06

Ne fit

Ne non-fit

2.0E-04

4.0E-04

6.0E-04

8.0E-04

1.0E-03

1.2E-03

2.0E

-04

4.0E

-04

6.0E

-04

8.0E

-04

1.0E

-03

1.2E

-03

Ar fit

Ar non-fit

0.0E+00

1.0E-07

2.0E-07

0.

) S

TP g

-13

4.0E-09

8.0E-09

1.2E-08

1.6E-08

2.0E-08

2.4E-084.

0E-0

9

8.0E

-09

1.2E

-08

1.6E

-08

2.0E

-08

2.4E

-08

2.8E

-08

Xe fitKr fit

Kr non-fit

Figure 3.3. CE modeled versus measured gas concentrations. Note that sample 100706 plots off scale for Kr.

0E+0

0

1.0E

-07

2.0E

-07

3.0E

-07

4.0E

-07

5.0E

-07

6.0E

-07

Xe non-fit

Measured Gas Concentration (cm3 STP g-1)

Mod

eled

Gas

Con

cent

ratio

n (c

m

67

Measured Gas Concentration (cm3 STP g-1)

0.0E+00

4.0E-07

8.0E-07

1.2E-06

1.6E-060.

0E+0

0

4.0E

-07

8.0E

-07

1.2E

-06

1.6E

-06

2.0E

-06

2.4E

-06

Ne fit

Ne non-fit

2.0E-04

4.0E-04

6.0E-04

8.0E-04

1.0E-03

1.2E-03

2.0E

-04

4.0E

-04

6.0E

-04

8.0E

-04

1.0E

-03

1.2E

-03

Ar fit

Ar non-fit

0.0E+00

1.0E-07

2.0E-07

0.0E

+00

1.0E

-07

2.0E

-07

3.0E

-07

4.0E

-07

5.0E

-07

6.0E

-07

Kr fit

Kr non-fit

4.0E-09

8.0E-09

1.2E-08

1.6E-08

2.0E-08

2.4E-08

4.0E

-09

8.0E

-09

1.2E

-08

1.6E

-08

2.0E

-08

2.4E

-08

2.8E

-08

Xe fit

Xe non-fit

Mod

eled

Gas

Con

cent

ratio

n (c

m3 S

TP g

-1)

Figure 3.4. PR modeled versus measured gas concentrations. Note that sample 100706 plots off scale for Kr.

68

Measured Gas Concentration (cm3 STP g-1)

Mod

eled

Gas

Con

cent

ratio

n (c

m3 S

TP g

-1)

0.0E+00

4.0E-07

8.0E-07

1.2E-06

1.6E-060.

0E+0

0

4.0E

-07

8.0E

-07

1.2E

-06

1.6E

-06

2.0E

-06

2.4E

-06

Ne fit

Ne non-fit

2.0E-04

4.0E-04

6.0E-04

8.0E-04

1.0E-03

1.2E-03

2.0E

-04

4.0E

-04

6.0E

-04

8.0E

-04

1.0E

-03

1.2E

-03

Ar fit

Ar non-fit

0.0E+00

1.0E-07

2.0E-07

0.0E

+00

1.0E

-07

2.0E

-07

3.0E

-07

4.0E

-07

5.0E

-07

6.0E

-07

Kr fit

Kr non-fit

4.0E-09

8.0E-09

1.2E-08

1.6E-08

2.0E-08

2.4E-08

4.0E

-09

8.0E

-09

1.2E

-08

1.6E

-08

2.0E

-08

2.4E

-08

2.8E

-08

Xe fit

Xe non-fit

Figure 3.5. UA modeled versus measured gas concentrations. Note that sample 100706 plots off scale for Kr.

69

-3

-2

-1

4

3

Figure 3.6. Scatter plots of model error (relative difference between modeled and measured gas concentrations) for samples fit by the CE model. Results from the other models are comparable. Note that analytical uncertainties are approximately 2% for Ne and Ar, and 3% for Kr and Xe.

0

1

2

-4 -3 -2 -1 0 1 2 3Ar Model Error (%)

Ne

Mod

el E

rror

(%)

-3

-2

-1

4

2

1

0

3

-5 -4 -3 -2 -1 0 1 2 3 4 5

Kr Model Error (%)

Xe

Mod

el E

rror

(%)

70

0.15

0.20

0.25

0.30

0.35

0.40

500 750 1000 1250 1500 1750 2000

Ar:Ne

He:

Ne

Data (AR) PRData (non-AR) CEUA

Figure 3.7. Element ratios for: 1) measured gas concentrations for artificial recharge impacted areas (AR) and non-AR areas, and 2) total model predicted gas concentrations (UA, CE, and PR). Measured ratios below the cluster of CE points can not be explained by the UA or CE models and are consistent with diffusive degassing or other fractionating processes. The UA model predicted ratios lie on the line shown. Not all data are shown at this scale.

71

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.00% 100% 200% 300% 400% 500% 600%

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

F - AR

F - non-AR

q - AR

q - non-AR

CE

mod

el F

val

ue

CE

mod

el q

val

ue

Excess Air (ΔNe)

Figure 3.8. Relationship between CE model parameters and excess air. As excess air increases, the degree of fractionation decreases. Note that the CE model reduces to the UA model when F = 0. One data point is not shown at the given scale (ΔNe = 737%, F = 0.031, q = 5.8).

72

0

20

40

60

80

100

4 12 20 28 36 44 52

Calculated Age (years)

Freq

uenc

y

UA

CE

PR

Figure 3.9. Histogram of calculated 3H–3He ages for each of three models.

73

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

5 10 15 20

CE model recharge temperature (C)

Cal

cula

ted

rech

arge

tem

pera

ture

min

us

the

CE

rech

arge

tem

pera

ture

(C)

UA - artificial recharge area

UA - non-artificial recharge area

PR - artificial recharge area

PR - non-artificial recharge area

Figure 3.10. Modeled recharge temperatures for samples fit by all three excess air models. In general, the PR recharge temperature > CE recharge temperature > UA recharge temperature.

74

8

10

12

14

16

18S

FBA

LAB

NC

MD

B

NC

V

SC

V

Region

Tem

pera

ture

(C)

Measured

PR CE

UA

Figure 3.11. Median calculated recharge temperatures from each of the three models. The measured data are mean annual air temperatures taken from the U.S. Historical Climatology Network (http://www.ncdc.noaa.gov/ol/climate/research/ushcn/ushcn.html) and are the medians of stations nearest each sample location.

75

Chapter 4:

On the Accuracy of Noble Gas Paleotemperatures over Glacial-Interglacial Periods

Abstract

Dissolved noble gases in groundwater are an important terrestrial temperature

proxy for the last glacial maximum (LGM). Noble gas temperatures (NGT) are a record

of long term mean water table temperature (WTT) during groundwater recharge. For

NGT to accurately represent surface air temperatures (SAT), the difference between

mean annual air temperature (MAAT) and WTT must be known through time. Many

paleoclimate studies reference NGT without articulating the potential difference between

NGT and air temperature. Recognizing the vast array of climatic changes that have

occurred since the LGM, it is possible some of these changes have altered the

relationship between WTT and MAAT in groundwater recharge zones. The coupling of

WTT and MAAT was evaluated in a series of numerical modeling experiments that

examined WTT sensitivity to changes in: 1) precipitation amount, 2) water table depth,

and 3) air temperature. Moderate changes in precipitation amount (±20%) and water

table depth (1–2 m) caused WTT-MAAT decoupling of ~0.2 °C. Varying air

temperature—either MAAT or annual amplitude—changed the duration of snowcover

which caused a seasonal decoupling of WTT from SAT. Assuming SAT was actually 5–

7 °C cooler at the LGM than at present, these modeling experiments suggest that errors

associated with WTT-MAAT decoupling at snow-free sites are insignificant given the

precision of NGT. However, results indicate that WTT-MAAT decoupling could cause

an underestimation of the actual SAT change by ~1 °C at sites having seasonal

76

snowcover. Therefore, caution is necessary when inferring atmospheric temperature

changes from dissolved noble gas data in regions with seasonal snowcover.

INTRODUCTION

Anthropogenic induced climate change has stimulated paleoclimate research,

especially the climate of the last glacial maximum (LGM). Insight into past climate is

drawn from various different proxies (e.g. alpine snow lines, δ18O, pollen, coral,

foraminifera Mg:Ca, alkenones). Noble gas temperatures (NGT) are another common

paleotemperature proxy for the LGM (Stute and Schlosser, 1993).

The NGT technique is based on the temperature dependence of noble gases

dissolved in groundwater during recharge (Kipfer et al., 2002). The heaviest noble gases

(i.e. Xe, Kr) provide the most information about recharge temperature because their

solubility is strongly temperature dependent. After correcting for gas dissolved from

entrapped bubbles of soil gas (termed “excess air”), the recharge temperature is

calculated from the equilibrium dissolved gas concentration with respect to atmospheric

pressure (using Henry’s Law). It is generally accepted that NGT are an accurate

indicator of long term mean water table temperature (WTT) (e.g. Kipfer et al., 2002; see

discussion in Chapter 2). The groundwater is most commonly dated using 14C (Stute and

Schlosser, 2000). Dispersion restricts the ability of NGT to determine high frequency

changes; however, NGT response to the LGM temperature signal is normally easily

identifiable (Stute and Schlosser, 1993).

NGT are especially significant because there are few other quantitative, terrestrial,

low elevation paleotemperature proxies (Farrera et al., 1999). Stute et al. (1995b) found a

5 °C NGT cooling in tropical Brazil during the LGM. These results are important for

unraveling tropical LGM conditions, especially considering the seemingly contradictory

results from marine proxy data (coral ~5 °C cooling (Guilderson et al., 1994); alkenone

77

~2 °C (Rosell-Melé et al., 2004)). Many studies have used NGT at mid-latitudes as well

(see references in Kipfer et al., 2002); however, the absence of groundwater recharge

beneath continental ice limits NGT applicability at high latitudes (Beyerle et al., 1998).

Studies investigating paleoclimate are primarily concerned with surface air

temperatures (SAT) rather than subsurface temperatures. Unfortunately, in

communicating NGT data the important distinction between WTT as measured by NGT

and mean annual air temperature (MAAT) is not always clearly articulated (e.g.

Condesso de Melo et al., 2001; Kim et al., 2003). The value of NGT data to paleoclimate

studies relies on the temporal constancy of the coupling between WTT and MAAT. The

use of NGT as a proxy of paleo-air temperature is compromised when WTT does not

track MAAT.

Subsurface temperature is affected by numerous inter-related processes near the

land-atmosphere interface. Decoupling of WTT-MAAT could result from changes in:

climate seasonality, snowcover, soil moisture, groundwater recharge, water table depth,

vegetation, or any other factor that is involved in heat or mass transfer near the land-

atmosphere interface. Arguably the most important of these for noble gas

paleothermometry is snowcover (Farrera et al., 1999). Snow insulates the subsurface

from the coldest temperatures, therefore increased snowcover (amount and/or duration)

will result in warming of the subsurface relative to air (Zhang, 2005). Not accounting for

WTT-MAAT decoupling caused by changes in snowcover since the LGM would result in

underestimation of atmospheric warming since the LGM.

Despite obvious major climatic/hydrologic changes since the LGM, little attention

has been given to WTT-MAAT (de)coupling. Stute and Sonntag (1992) discuss the

relationship between MAAT and subsurface temperature and note the importance of

vegetation on subsurface temperature. Recent work by Beyerle et al. (2003) provide

78

evidence that WTT-MAAT coupling may not exist over glacial-interglacial periods.

They measured NGT from Niger, west Africa and suggest that the majority of observed

NGT warming since the late glacial/early Holocene (10–14 kyr before present) is the

result of changes in WTT-MAAT difference rather than changes in atmospheric warming

(3.5 °C attributed to WTT-MAAT difference versus 2 °C attributed to atmospheric

warming). Edmunds et al. (2006) also suggested that WTT-MAAT decoupling over

glacial-interglacial climate changes may explain observed NGT.

Many studies have addressed the coupling of ground surface temperature (GST)

to MAAT by researchers using borehole temperature profiles to deduce past air

temperatures (e.g. Beltrami and Kellman, 2003; González-Rouco et al., 2006; Smerdon et

al., 2004). Field evidence exists to support the coupling of GST-MAAT since the

beginning of the air temperature observational record (Baker and Ruschy, 1993), while

synthetic subsurface temperature profiles generated from air temperature data over the

last century compare favorably with measured borehole temperature profiles (Chapman et

al., 1992; Harris and Gosnold, 1999). However, the validity of GST-MAAT coupling

over centuries remains unclear (Mann and Schmidt, 2003).

Studies on GST-MAAT coupling are clearly relevant to the question of WTT-

MAAT coupling. For example, Beltrami and Kellman (2003) present data from multiple

sites showing a correlation between soil-air temperature difference and snowcover

duration. Smerdon et al. (2006) examine data from three sites to identify the main

meteorological changes that lead to differences between SAT and GST. They associate

measured GST-SAT differences to differences in annual amplitudes caused by

evapotranspiration differences in summer and snowcover differences in winter. Lin et al.

(2003) use numerical modeling experiments to explore the possibility of GST-MAAT

decoupling as a result of changes in the amount, intensity, and timing of precipitation.

79

While these studies are germane, they do not specifically discuss WTT under recharging

conditions. This study builds on these related studies by specifically addressing WTT

while considering multiple conditions that may affect WTT-MAAT coupling.

The goal of this study was to critically evaluate the precision and applicability of

NGT records spanning glacial-interglacial cycles. The specific objectives of the study

were to evaluate WTT-MAAT coupling as impacted by changes in 1) precipitation, 2)

water table depth, and 3) air temperature. This study was not meant to be an exhaustive

exploration of the precision of NGT. However, it was designed to evaluate some of the

potential uncertainties associated with NGT use in paleoclimatology.

METHODS

Model Description

The impact of climate variables on WTT-MAAT coupling was evaluated through

a series of numerical modeling experiments using the Simultaneous Heat and Water

(SHAW) model (Flerchinger, 2000; Flerchinger and Saxton, 1989a). SHAW can

simulate heat, water, and solute transport through a one dimensional profile containing

plant cover, snow, residue, and soil (Figure 4.1). SHAW is well suited for this study

because it can simulate snow accumulation and melt, soil freezing and thawing including

freezing-induced moisture migration, and frozen soil related runoff. SHAW has been

used in a variety of studies examining coupled heat and water flow in the unsaturated

zone (Flerchinger and Cooley, 2000; Flerchinger et al., 1996; Flerchinger and Saxton,

1989b). The plant canopy, snowpack, and soil are each discretized into multiple layers.

Energy and moisture fluxes are computed between layers for each time step, and balance

equations for each layer are written in implicit finite-difference form (Flerchinger, 2000).

SHAW uses the water retention function of Campbell (1974), which is a

modification of the Brooks and Corey (1966) function in which the residual water content

80

is zero. The Burdine (1953) hydraulic conductivity function is used to calculate

unsaturated hydraulic conductivity. Soil thermal conductivity, λ, is calculated as follows:

∑∑=

jj

jjj

mm

θθλ

λ (4.1)

where mj is the weighting factor, λj is the thermal conductivity, and θj is the volumetric

fraction of the jth soil constituent (i.e. sand, silt, clay, water, ice, or air). The weighting

factor is calculated using the method detailed by de Vries (1963). A detailed description

of SHAW is given by Flerchinger (2000).

The system considered in this study consisted of a 0.02 m residue layer overlying

20 m of soil. The soil profile was divided into 47 layers ranging in thickness from 0.010

to 2.0 m. Two soil types were considered; loam and sand. Soil properties were uniform

with depth for both soil types considered (Table 4.1 and Table 4.2). Soil hydraulic

properties were taken from Rawls and Brakensiek (1989). The response of vegetation to

climatic variations is very complex. In an attempt to isolate the impacts of individual

parameter variability, a simplified system exclusive of vegetation was used.

Modifications were made to SHAW to allow zero heat flux as a lower boundary

condition. Lower boundary conditions were constant head and zero heat flux for all

simulations. Mean annual water table depth in the Base Case scenarios was 3 m. The

soil zone was made sufficiently deep to ensure that the lower thermal boundary condition

did not affect seasonal temperature fluctuations (Smerdon and Stieglitz, 2006). The 20 m

soil depth was sufficient to meet this requirement, consistent with the findings of Lin et

al. (2003).

A zero heat flux lower boundary condition is physically unrealistic. The impact

of using a zero heat flux condition versus a (non-zero) specified heat flux condition at the

lower boundary was tested for the Base Case scenarios. At present, SHAW does not

81

allow a non-zero specified heat flux boundary condition. For these comparison

simulations, a specified temperature lower boundary condition was used that resulted in

an upward heat flux of 50 mW/m2. The difference in the range of annual water table

temperatures between the zero heat flux boundary condition and the 50 mW/m2 boundary

condition was <0.01 °C for both loam and sand. These results suggest that the

simplification of using a zero heat flux lower boundary condition is appropriate for this

sensitivity study.

The region represented in this study is mid-continent North America (the Great

Plains). Reasons for simulating conditions representative of this region are: 1) the region

has seasonal snowcover which would have increased during the LGM, 2) a strong east-

west precipitation gradient presently exists in the region, making local precipitation

changes more likely during periods of climate change, and 3) a study examining NGT

changes between the LGM and present was recently conducted in the region (McMahon

et al., 2004). Although conditions representative of the Great Plains region were used,

this study did not attempt to simulate local conditions of any particular site. Hourly input

data used for the Base Case were taken from the North American Land Data Assimilation

System (NLDAS; year 2004 at 38.5° N, 97° W) (Cosgrove et al., 2003) (Figure 4.2 and

Table 4.3). Data from 2004 were chosen because 2004 precipitation data (710 mm) are

representative of recent precipitation in the area (30 year mean of 757 mm) (Figure 4.2

and Table 4.3).

Numerical Experiments

Three parameters were varied in this study: 1) precipitation amount, 2) water table

depth, and 3) air temperature. Precipitation was varied by uniformly increasing or

decreasing each day’s precipitation by a scaling factor. The precipitation scaling factor

range was 0.6–1.4 that of the Base Case scenario. Mean annual water table depth was

82

varied from 2–5 m. Input air temperature was varied in two different ways; i) varying

MAAT from 13 °C below to 7 °C above the Base Case scenario (i.e. 0.68–20.68 °C), and

ii) the amplitude of the annual temperature fluctuation was varied (+/-4 °C) while

keeping MAAT constant (amplitude was varied by adding or subtracting temperatures

taken from a sinusoidal curve fit to the full year of hourly temperature data).

The duration of all model simulations was 40 years, with each simulation using

one year of input data repeatedly for 40 consecutive years. Example input files are

presented in Appendix D. In every case, model simulation results were checked to

confirm that equilibrium was achieved. Reported output is from the last full year

simulated.

RESULTS

Base Case

Both loam and sand had GST and WTT greater than MAAT for the Base Case

scenario (Figure 4.3). Mean annual WTT for loam and sand were 14.66 °C and 15.40 °C,

respectively. Differences in water retention functions between the two soils caused the

loam to have a higher average saturation (Figure 4.3). The greater soil moisture of the

loam resulted in higher latent heat flux (and lower sensible heat flux) than in the sand.

The lower hydraulic conductivity of the loam results in more pronounced water table

fluctuations (Figure 4.4).

Soil temperatures simulated in the Base Case showed increasing damping and lag

with depth (Figure 4.5a). Precipitation events caused high frequency perturbations of

both temperature and water content (Figure 4.5). For example, the day of greatest

precipitation (59 mm on day 64) caused a pronounced increase in saturation and led to

peak annual recharge flux on day 86 in loam and on day 71 in sand. Short term (daily to

weekly) temperature and water content fluctuations caused by precipitation were largely

83

removed within the upper meter of soil (Figure 4.5). The seasonal temperature cycle

caused temperature fluctuations much deeper into the soil profile—well below the water

table. The seasonal range of WTT was 7.5 °C for loam and 6.8 °C for sand.

Precipitation

Precipitation was varied by scaling the daily precipitation amount relative to the

Base Case. An increase in precipitation caused a cooling of the water table relative to the

MAAT for both soil types (Figure 4.6). Summer (June, July, August) is the season of

greatest precipitation (41%) and therefore precipitation changes had the greatest impact

on soil moisture conditions during summer. Increased soil moisture associated with

greater precipitation resulted in an increase in latent heat flux and a decrease in sensible

heat flux. Increased precipitation caused a small increase in snowcover days which

warmed the soil in winter; however, this effect was minor relative to summertime

changes (Figure 4.7). The primary reason for soil cooling (warming) associated with

increased (decreased) precipitation was increased (decreased) evaporation from the

wetter (drier) soil. The greatest impact on temperature occurred during summer/fall and

the least impact occurred in winter (December, January, February) (Figure 4.7).

Loam has a greater available water capacity (i.e. the difference in water content

between field capacity and permanent wilting point), which leads to larger changes in soil

moisture for a given change in precipitation. Therefore, the impact of precipitation on

soil temperatures was much more pronounced for loam (Figure 4.6). For loam,

decreasing precipitation by 40% caused a warming of the water table relative to MAAT

of 0.48 °C while an increase of 40% caused a cooling of 0.19 °C. For sand, a 40%

precipitation reduction caused a warming of 0.36 °C and a 40% precipitation increase

caused a cooling of 0.11 °C.

84

Lin et al. (2003) conducted a study similar to the precipitation experiments

reported here, except that their model included grassy vegetation. Their results for

scaling precipitation amount show a similar trend and magnitude of soil temperature

change results presented here.

Water Table Depth

The second series of experiments involved varying the mean annual water table

depth. Water table depth was not fixed in any simulation; rather the specified head

condition at the lower boundary was adjusted to achieve the desired mean water table

depth (Figure 4.4).

For both loam and sand, increasing water table depth caused an increase in WTT

relative to MAAT (Figure 4.8). Decreasing the water table depth from 3 m to 2 m

resulted in a cooling of the water table relative to MAAT of 0.04 °C for loam and 0.08 °C

for sand. Increasing the water table depth from 3 m to 5 m caused a water table warming

relative to MAAT of 0.05 °C for loam and 0.15 °C for sand. The impact of water table

depth on WTT was greater for sand than for loam (Figure 4.8) in part because of the

steeper temperature gradient in sand’s relatively drier unsaturated zone (Figure 4.9).

Temperature

The last two series of experiments investigated the impact of air temperature

changes on WTT-MAAT coupling. In the first of these experiments, MAAT was varied

while maintaining all other inputs constant. When MAAT was increased, the difference

between WTT and MAAT decreased (Figure 4.10). The change in WTT-MAAT was

similar for both soil types and strongly correlated to snowcover duration. As MAAT

decreased, more precipitation occurred as snow rather than rain and the duration of the

resulting snowpack increased. Snow insulates soil from the coldest temperatures

resulting in wintertime soil warming relative to air temperature. The importance of

85

snowcover is shown by the seasonal differences in GST. Relative to MAAT, winter GST

was strongly affected whereas summer GST change was minimal (Figure 4.11).

The impact of changing MAAT on WTT-MAAT coupling was much greater than

the impact of changes to either precipitation amount or water table depth. When

considering a MAAT cooling of 7 °C from the Base Case, WTT warms 1.38 °C for loam

and 1.42 °C for sand relative to MAAT. For a unit change in MAAT, the corresponding

change in WTT-MAAT ranged from -0.37 to -0.02 °C for loam, and from -0.36 to

-0.03 °C for sand, with the larger changes occurring at lower MAAT (Figure 4.10).

The impact of changing annual air temperature amplitude on WTT-MAAT

coupling was also examined. Increases in annual air temperature amplitude caused

warming of the water table (Figure 4.12). The cause of this result is similar to that of

changes in MAAT discussed previously. Increased amplitude means warmer summers

and colder winters, and colder winters produce more snow and a longer period of

snowcover. Snowcover driven soil warming was the major process causing changes in

WTT-MAAT as shown by the strong correlation between snowcover and WTT-MAAT

(Figure 4.12). For a unit change in temperature amplitude, the corresponding change in

WTT-MAAT was 0.09–0.20 °C for loam, and 0.09–0.16 °C for sand, with the larger

changes occurring at larger amplitudes.

Both types of air temperature variation examined here, MAAT and annual

amplitude, caused decoupling of WTT from MAAT. There was little difference between

loam and sand in the magnitude of WTT-MAAT decoupling caused by air temperature

changes. This differs from precipitation and water table depth experiments in which the

magnitude of WTT-MAAT decoupling varied with soil type.

86

DISCUSSION

Climate simulations and, where available, proxy data rarely give a consistent

picture of paleoclimate conditions. Therefore, the sensitivity study presented here

isolated one variable per experiment to identify the impact of individual

climatic/hydrologic variables. Precipitation amount was examined and its impact on

WTT-MAAT coupling was only a few tenths of a degree or less for moderate

precipitation changes (~20%), which is consistent with the results of Lin et al. (2003).

Varying mean annual water table depth by 1–2 m also caused changes to WTT-MAAT of

only a few tenths of a degree or less. Impacts of both precipitation and water table depth

on WTT differed between the two soil textures, with loam impacted more than sand.

NGT data from the LGM commonly indicate a cooling of 5–7 °C (Kipfer et al., 2002),

therefore these results suggest that moderate precipitation or water table changes are not

sufficient to introduce significant error in paleo-SAT inferred from NGT.

Results indicate that air temperature changes have the potential to cause WTT-

MAAT decoupling, which causes errors in paleotemperature interpretations based on

NGT data. Assuming the LGM SAT was actually 5–7 °C cooler than present, these

results suggest that decoupling of WTT from MAAT causes an underestimation of the

actual SAT shift by ~1.4 °C. The primary cause of this effect is the presence of snow

acting as a seasonally selective insulator. Therefore, it is reasonable to expect the largest

decoupling related errors to be in regions that have seasonal snowcover, which is

common in the mid-latitudes. Many studies suggest the temperature difference between

the LGM and present was larger during winter than summer (e.g. Denton et al., 2005;

Wright et al., 1993). Such an increase in annual temperature amplitude would increase

the error of underestimating the SAT shift since the LGM.

87

The results presented here are consistent with the results of Smerdon et al. (2006).

In evaluating data from multiple sites, they found that either summer or winter

decoupling of GST from SAT could largely explain differences between mean annual

GST and MAAT. Furthermore, they note the importance of snowcover in accounting for

differences between GST and MAAT at a site in the Great Plains.

These results suggest that only minor decoupling would occur at sites without

snowcover. The resulting error in glacial-interglacial atmospheric temperature change

inferred from NGT is less than NGT uncertainty itself (Stute et al., 1995b).

This study is a significant contribution to our understanding of factors affecting

WTT-MAAT (de)coupling; however, it does have several limitations. The site

considered, while based on actual data from the Great Plains, was highly idealized. For

example, soil properties were constant throughout the soil profile. Potentially more

significant was the absence of vegetation from the models. Vegetation clearly influences

water and heat flow across the land-atmosphere boundary, and vegetation changes in

response to climatic changes are well documented (e.g. Williams, 2003). Excluding

vegetation is more likely to affect the outcome of experiments involving precipitation and

water table depth because in these experiments the soil moisture/soil textural differences

were more critical. In contrast, soil texture was relatively unimportant in air temperature

experiments because the dominant change was heat conduction as affected by snowcover.

Modeling necessarily requires simplifications due to the complexity of land-atmosphere

processes; however, simplifications made in this study do not negate the usefulness of

these results in quantitatively examining WTT-MAAT decoupling.

This study examined several key parameters that can affect WTT-MAAT

coupling. However, further investigation into WTT-MAAT decoupling would be

beneficial. Recommendations for further work are: 1) include vegetation in the model, 2)

88

examine additional parameters (e.g. precipitation intensity and timing), 3) simultaneously

examine multiple parameters (e.g. precipitation and MAAT), and 4) perform sensitivity

analyses on specific sites.

CONCLUSIONS

Numerical modeling experiments suggest only modest WTT changes (tenths of a

degree) in response to moderate changes in precipitation amount (~20%) and water table

depth (1–2 m). Soil texture differences were more significant for experiments involving

precipitation amount and water table depth than for those involving air temperature.

Results of simulations varying air temperature suggest that changes since the LGM can

lead to ~1 °C decoupling of WTT from MAAT. This decoupling is primarily the result

of changes in snowcover. The decoupling causes an underestimation of the atmospheric

warming since the LGM as inferred from dissolved noble gas data.

The usefulness of noble gas paleothermometry lies in its ability to quantify low

frequency changes in mean annual (water table) temperature. This study suggests that

snowcover changes caused by glacial-interglacial atmospheric temperature change results

in an underestimation of that atmospheric temperature change as inferred from dissolved

noble gas data. The complexities of land-atmosphere interactions and site specific

heterogeneities preclude a simple correction to account for WTT-MAAT decoupling.

Therefore, caution is necessary when deducing atmospheric temperature changes from

dissolved noble gas data, especially in areas with seasonal snowcover.

89

Table 4.1. Soil properties used in SHAW model (from Rawls and Brakensiek, 1989).

SoilaSaturated Volumetric

Water Content (m3/m3)

Saturated Hydraulic

Conductivity (m/s)

Bulk Density (kg/m3)

Air Entry Potential

(m)

Campbell’s pore-size

distribution index

Sand (%)

Silt (%)

Clay(%)

Loam 0.43 1.9×10-6 1500 -0.11 4.5 40 40 20 Sand 0.42 5.8×10-5 1500 -0.07 1.7 90 5 5 a United States Department of Agriculture soil texture class

90

Table 4.2. Soil thermal properties used in SHAW model.

SoilaSaturated Volumetric

Water Content (m3/m3)

Saturated Thermal

Conductivity (W/m/K)

Sandb

(%) Siltc

(%) Clayc

(%)

Loam 0.43 2.40 40 40 20 Sand 0.42 1.87 90 5 5

a United States Department of Agriculture soil texture class b thermal conductivity = 8.80 W/m/K c thermal conductivity = 2.92 W/m/K

91

Table 4.3. Meteorological forcing data used in the Base Case scenario.

Input Parameter Value

Mean Annual Air Temperature, MAAT (°C) 13.68 Total Annual Precipitation (mm of rain equivalent) 710

Median Wind Speed (m/s) 4.6 Median Relative Humidity (%) 71.0

92

Figure 4.1. Schematic diagram showing processes modeled, input forcings, and boundary conditions.

93

Julian Day0 50 100 150 200 250 300 350

Pre

cipi

tatio

n (m

m)

0

10

20

30

40

50

60

Tem

pera

ture

(°C

)

-20

-10

0

10

20

30

40(a)

(b)

Figure 4.2. Input forcing data from year 2004 at 38.5° N, 97° W of the North American Land Data Assimilation System (NLDAS) (Cosgrove et al., 2003). (a) Hourly temperature data, and (b) daily precipitation totals.

94

Saturation0.2 0.4 0.6 0.8 1.0

Temperature (°C)14.2 14.6 15.0 15.4

Dep

th (m

)

0

4

8

12

16

20

LoamSand

Figure 4.3. Profiles of mean annual soil temperature and saturation for the Base Case scenario (mean annual air temperature, MAAT = 13.68 °C; mean annual water table temperature, WTTloam = 14.66 °C; WTTsand = 15.40 °C).

95

Julian Day

0 50 100 150 200 250 300 350

Dep

th to

wat

er ta

ble

(m) 2.8

2.9

3.0

3.1

3.2

Loam Sand

Figure 4.4. Water table fluctuations of the Base Case scenarios (mean annual water table depth of 3 m).

96

Tem

pera

ture

(°C

)

-5

0

5

10

15

20

25

30

35

0.0 m

6.0 m 20 m3.0 m1.4 m

Julian Day0 50 100 150 200 250 300 350

Sat

urat

ion

3.0 m

0.01 m

(a)

(b)0.2

0.4

0.6

0.8

1.0

2.6 m

0.7 m

Figure 4.5. Modeled time series of (a) temperature and (b) saturation at various depths for the loam soil Base Case scenario (mean annual air temperature, MAAT = 13.68 °C; mean annual water table temperature, WTTloam = 14.66 °C).

97

Precipitation Scaling Factor0.6 0.8 1.0 1.2 1.4

WTT

- M

AA

T (°

C)

0.6

1.0

1.4

1.8

2.2

Loam Sand

Figure 4.6. Response of water table temperature (WTT) relative to mean annual air temperature (MAAT) to changes in precipitation amount.

98

Precipitation Scaling Factor0.6 0.8 1.0 1.2 1.4

(GS

T - M

AA

T) -

(GST

- M

AAT

) Base

Cas

e (°C

)

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8 Spring Summer Fall Winter

Figure 4.7. Amount of seasonal decoupling of ground surface temperature (GST) from mean annual air temperature (MAAT) in response to changes in precipitation amount.

99

Water Table Depth (m)2 3 4 5

WTT

- M

AA

T (°

C)

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Loam Sand

Figure 4.8. Response of water table temperature (WTT) relative to mean annual air temperature (MAAT) to changes in mean annual depth of water table below ground surface. Mean annual water table depth was 3 m in the Base Case scenario.

100

Temperature (°C)14.4 14.7 15.0 15.3 15.6

Dep

th (m

)

0

2

4

6

8

10

12

14

2 m3 m4 m5 m

Loam Sand

Figure 4.9. Mean annual soil temperature profiles for simulations in which the water table depth was varied from 2–5 m. Mean annual water table depth was 3 m in the Base Case scenario.

101

MAAT (°C)0 4 8 12 16 20

WTT

- M

AAT

(°C

)

1

2

3

4

5WTT - MAAT; LoamWTT - MAAT; Sand

Sno

wco

ver (

days

)

0

25

50

75

100

125

150

175

Snowcover

Figure 4.10. Response of water table temperature (WTT) relative to mean annual air temperature (MAAT) to changes in MAAT. MAAT was 13.68 °C in the Base Case scenario.

102

MAAT (°C)0 4 8 12 16 20

GS

T - M

AA

T (°

C)

-16

-12

-8

-4

0

4

8

12Summer

Spring

Winter

Fall

Figure 4.11. Seasonal response of ground surface temperature (GST) relative to mean annual air temperature (MAAT) for loam.

103

Change in Annual Temperature Amplitude (°C)-4 -2 0 2 4

WTT

- M

AA

T (°

C)

0.4

0.8

1.2

1.6

2.0

2.4 WTT - MAAT; LoamWTT - MAAT; Sand

Sno

wco

ver (

days

)

0

10

20

30

40

50Snowcover

Figure 4.12. Response of water table temperature (WTT) relative to mean annual air temperature (MAAT) to changes in the annual amplitude of MAAT.

104

Chapter 5:

Conclusions and Outlook

The aim of this dissertation was to improve our understanding of gas dissolution

during groundwater recharge and to critically examine the utility of dissolved noble gas

data in groundwater research. Three studies were completed to achieve that goal.

The first of these was a field study in California of a shallow aquifer heavily

impacted by agriculture. This study was unique because the measurements of subsurface

temperature allowed direct comparison with calculated NGT. Results suggest that NGT

from both the CE and PR models reflect the measured WTT conditions, which supports

the use of dissolved noble gases to deduce recharge temperatures. There was no

measurable difference in excess air characteristics (amount and degree of fractionation)

between the two recharge regimes studied at this site. Multiple samples had dissolved

gas concentrations below equilibrium concentration with respect to atmospheric pressure,

indicating degassing. Geochemical and dissolved gas data indicate that saturated zone

denitrification caused degassing by gas stripping. Modeling was done to examine the

impact of degassing on calculated NGT. Results indicate that minor degassing

(<10% ΔNe) may cause underestimation of groundwater recharge temperature by up to

2 °C.

The second study was an analysis of a large (905 samples) dissolved noble gas

data set from drinking water supply wells throughout California. The three most

common dissolved noble gas interpretive models were compared. The models produce

systematically different recharge temperatures or ages; however, the ability of the

different models to fit measured data within measurement uncertainty indicates that

105

goodness-of-fit is not a robust indicator for model appropriateness. A unique aspect of

this study was the high Ne and excess air concentrations associated with surficial

artificial recharge facilities. The ability of artificial recharge to dissolve greater amounts

of air has important implications for predicting water quality changes occurring following

artificial recharge.

The final study examined whether climatic/hydrological changes occurring over

glacial-interglacial time periods could impact the accuracy of NGT used in paleoclimate

studies. To answer that question, a series of numerical modeling experiments were done

that measured WTT sensitivity to changes in: 1) precipitation amount, 2) water table

depth, and 3) air temperature. Precipitation and water table depth had only a minor

impact on WTT-MAAT coupling (~0.2 °C) and therefore such changes would not

compromise the use of NGT to infer paleo-air temperature. Varying air temperature—

either MAAT or annual amplitude—changes the duration of snowcover which causes a

seasonal decoupling of WTT from SAT. This WTT-MAAT decoupling could cause an

underestimation of the actual SAT change by ~1 °C at sites having seasonal snowcover.

Each of these studies brought new insights into the application of dissolved noble

gases in hydrologic research. Some of these insights confirmed previous assumptions,

while others challenged current assumptions. This research has improved our

understanding of gas dissolution during groundwater recharge, which will assist future

research in applying dissolved noble gas data to hydrologic problems. However,

questions remain. Therefore, I recommend four specific topics for future research:

1. Pore scale study investigating the entrapment and dissolution of air

bubbles near the water table during groundwater recharge.

106

2. Detailed field study of recharge area(s) having very high recharge flux

(e.g. artificial recharge spreading basins) to examine non-equilibrium gas

dissolution processes.

3. Site specific modeling experiments on the coupling of WTT-MAAT at

locations where NGT were used to infer paleo-air temperatures.

4. Investigation of the impact of relative sea-level changes since the LGM on

NGT.

I believe that these four topics provide the best opportunity to continue to improve

our understanding of the physical processes controlling gas dissolution during recharge as

well as quantifying uncertainties associated with noble gas paleothermometry.

107

Appendix A:

Laboratory Soil Testing Results from Kings County Field Site

108

Table A1. Particle size analyses from 5S location. Sample depth

(m)

Particle dia.

(mm)

Percent finer than

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

0.21a 4.75 100.0% 1.22a 4.75 100.0% 2.04a 4.75 100.0% 2.00 100.0% 2.00 100.0% 2.00 100.0% 0.71 99.8% 0.71 99.9% 0.71 97.1% 0.42 98.8% 0.42 98.6% 0.42 90.7% 0.25 90.9% 0.25 71.6% 0.25 86.4% 0.149 71.9% 0.149 23.4% 0.149 80.5% 0.074 49.6% 0.074 6.0% 0.074 64.1% 0.034 20.4% 0.036 2.4% 0.031 44.4% 0.022 13.1% 0.023 1.3% 0.020 36.2% 0.0130 8.4% 0.0132 1.3% 0.0122 28.3% 0.0092 5.7% 0.0094 0.7% 0.0088 24.5% 0.0066 4.8% 0.0066 0.6% 0.0063 20.9% 0.0032 3.7% 0.0033 0.4% 0.0031 16.2% 0.00143 0.9% 0.00142 0.0% 0.00135 10.7%

0.49a 4.75 100.0% 1.46a 4.75 100.0% 2.35a 4.75 100.0% 2.00 100.0% 2.00 100.0% 2.00 100.0% 0.71 99.8% 0.71 99.9% 0.71 97.2% 0.42 97.9% 0.42 99.8% 0.42 90.8% 0.25 73.9% 0.25 99.4% 0.25 86.2% 0.149 42.0% 0.149 98.8% 0.149 79.3% 0.074 17.3% 0.074 96.7% 0.074 61.3% 0.035 6.4% 0.029 67.4% 0.032 38.3% 0.022 4.6% 0.0195 54.8% 0.021 29.3% 0.0131 2.8% 0.0119 37.1% 0.0126 19.9% 0.0093 2.0% 0.0087 27.5% 0.0091 15.7% 0.0065 2.0% 0.0063 20.0% 0.0065 12.7% 0.0033 1.2% 0.0032 11.3% 0.0032 8.5% 0.00143 0.0% 0.00140 6.0% 0.00137 5.7%

0.79a 4.75 100.0% 1.74a 4.75 100.0% 2.65a 4.75 100.0% 2.00 100.0% 2.00 100.0% 2.00 100.0% 0.71 99.9% 0.71 98.9% 0.71 96.3% 0.42 99.1% 0.42 96.6% 0.42 88.3% 0.25 90.6% 0.25 94.9% 0.25 83.3% 0.149 62.5% 0.149 92.6% 0.149 76.8% 0.074 21.3% 0.074 85.5% 0.074 59.3% 0.035 6.5% 0.030 59.6% 0.032 36.8% 0.023 4.4% 0.0195 50.8% 0.021 27.2% 0.0131 3.1% 0.0117 41.7% 0.0126 19.4% 0.0093 2.3% 0.0085 35.6% 0.0090 15.4% 0.0066 1.4% 0.0061 30.6% 0.0064 12.4% 0.0033 1.2% 0.0031 21.9% 0.0032 8.3% 0.00142 0.0% 0.00133 14.3% 0.00135 6.1%

109

Table A1. Continued Sample depth

(m)

Particle dia.

(mm)

Percent finer than

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

3.05a 4.75 100.0% 3.72a 4.75 100.0% 1.5b 4.75 100.0% 2.00 100.0% 2.00 100.0% 2.00 100.0% 0.71 96.9% 0.71 97.5% 0.71 99.6% 0.42 89.2% 0.42 90.9% 0.42 98.6% 0.25 84.0% 0.25 86.5% 0.25 97.6% 0.149 76.4% 0.149 80.7% 0.149 96.2% 0.074 53.4% 0.074 56.0% 0.074 92.3% 0.033 29.2% 0.033 26.1% 0.029 60.6% 0.022 21.5% 0.022 18.1% 0.0192 52.8% 0.0128 14.5% 0.0127 12.1% 0.0115 42.1% 0.0091 10.7% 0.0091 9.0% 0.0083 35.3% 0.0065 8.6% 0.0064 7.1% 0.0060 28.5% 0.0032 6.2% 0.0031 4.2% 0.0031 17.8% 0.00136 4.2% 0.00133 2.9% 0.00132 9.7%

3.35a 4.75 100.0% 0.6b 4.75 100.0% 2.7b 4.75 100.0% 2.00 100.0% 2.00 100.0% 2.00 100.0% 0.71 97.0% 0.71 99.8% 0.71 96.5% 0.42 89.7% 0.42 97.0% 0.42 87.5% 0.25 84.9% 0.25 76.0% 0.25 82.1% 0.149 78.0% 0.149 49.9% 0.149 75.0% 0.074 58.3% 0.074 18.3% 0.074 55.7% 0.033 28.6% 0.035 6.7% 0.032 29.7% 0.021 20.8% 0.022 5.1% 0.021 21.7% 0.0127 12.8% 0.0130 3.3% 0.0126 12.9% 0.0091 8.9% 0.0092 2.5% 0.0090 8.9% 0.0064 7.0% 0.0065 1.8% 0.0064 7.3% 0.0030 5.2% 0.0031 1.5% 0.0032 4.2% 0.00134 3.1% 0.00134 0.7% 0.00135 1.5%

a Disturbed (bag) sample b Core sample

110

Table A2. Particle size analyses from 3S location.

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

0.15 4.75 100.0% 0.76 4.75 100.0% 1.52 4.75 100.0% 2.00 100.0% 2.00 100.0% 2.00 100.0% 0.42 81.8% 0.42 91.1% 0.42 84.1% 0.25 67.6% 0.25 82.2% 0.25 67.3% 0.149 57.0% 0.149 74.6% 0.149 53.5% 0.074 44.1% 0.074 64.2% 0.074 34.3% 0.033 29.0% 0.032 35.6% 0.035 14.9% 0.021 23.5% 0.020 28.7% 0.022 10.8% 0.013 17.7% 0.012 24.6% 0.013 7.7% 0.0092 14.0% 0.0088 20.9% 0.0092 6.5% 0.0065 12.3% 0.0063 17.8% 0.0066 4.3% 0.0032 8.6% 0.0031 11.9% 0.0032 2.7% 0.0013 4.5% 0.0014 6.3% 0.0014 1.6%

0.30 4.75 100.0% 0.91 4.75 100.0% 1.83 4.75 100.0% 2.00 100.0% 2.00 100.0% 2.00 100.0% 0.42 81.8% 0.42 88.9% 0.42 84.4% 0.25 70.6% 0.25 76.8% 0.25 70.0% 0.149 61.6% 0.149 67.1% 0.149 58.9% 0.074 49.7% 0.074 54.4% 0.074 42.8% 0.033 26.2% 0.035 26.9% 0.034 18.6% 0.021 19.9% 0.022 22.8% 0.022 12.4% 0.012 13.8% 0.0126 17.9% 0.013 8.3% 0.0091 11.6% 0.0090 14.6% 0.0092 6.2% 0.0065 9.5% 0.0064 12.7% 0.0066 4.2% 0.0031 5.6% 0.0032 7.6% 0.0032 3.0% 0.0013 3.3% 0.0014 3.2% 0.0014 1.5%

0.46 4.75 100.0% 1.22 4.75 100.0% 1.98 4.75 100.0% 2.00 100.0% 2.00 100.0% 2.00 100.0% 0.42 76.6% 0.42 84.8% 0.42 91.7% 0.25 66.0% 0.25 68.9% 0.25 79.2% 0.149 57.8% 0.149 56.0% 0.149 70.8% 0.074 46.6% 0.074 38.2% 0.074 56.7% 0.033 27.1% 0.034 18.5% 0.032 32.0% 0.021 19.6% 0.022 13.5% 0.021 25.6% 0.013 15.5% 0.0131 10.4% 0.012 20.8% 0.0091 12.4% 0.0092 8.3% 0.0089 17.3% 0.0065 10.3% 0.0065 6.6% 0.0063 15.0% 0.0032 7.3% 0.0032 4.0% 0.0032 9.7% 0.0014 3.7% 0.0014 2.0% 0.0014 5.0%

111

Table A2. Continued

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

2.13 4.75 100.0% 2.74 4.75 100.0% 3.35 4.75 100.0% 2.00 100.0% 2.00 100.0% 2.00 100.0% 0.42 91.8% 0.42 97.8% 0.42 99.3% 0.25 77.5% 0.25 95.6% 0.25 98.5% 0.149 68.3% 0.149 93.7% 0.149 96.7% 0.074 52.5% 0.074 87.3% 0.074 69.1% 0.033 29.6% 0.030 62.5% 0.033 33.0% 0.021 23.8% 0.019 52.4% 0.021 22.1% 0.013 18.0% 0.012 40.1% 0.0126 13.5% 0.0090 15.7% 0.0084 34.0% 0.0087 9.8% 0.0064 13.2% 0.0060 28.9% 0.0064 7.9% 0.0032 9.7% 0.0031 20.5% 0.0031 4.6% 0.0013 6.3% 0.0013 14.4% 0.0014 2.5%

2.29 4.75 100.0% 2.90 4.75 100.0% 2.00 100.0% 2.00 100.0% 0.42 90.2% 0.42 98.6% 0.25 75.5% 0.25 97.2% 0.149 66.6% 0.149 95.4% 0.074 52.7% 0.074 83.1% 0.031 28.9% 0.031 51.9% 0.022 23.5% 0.020 39.5% 0.012 17.2% 0.0121 28.2% 0.0089 14.2% 0.0087 23.0% 0.0063 11.9% 0.0062 18.9% 0.0031 8.1% 0.0031 12.4% 0.0013 6.0% 0.0013 7.4%

2.44 4.75 100.0% 3.05 4.75 100.0% 2.00 100.0% 2.00 100.0% 0.42 91.5% 0.42 99.4% 0.25 81.0% 0.25 98.6% 0.149 73.9% 0.149 97.4% 0.074 60.7% 0.074 86.8% 0.032 36.2% 0.030 54.3% 0.021 28.4% 0.020 43.3% 0.012 20.7% 0.012 29.4% 0.0089 16.8% 0.0087 23.4% 0.0063 13.9% 0.0062 17.3% 0.0031 9.8% 0.0030 10.9% 0.0013 6.0% 0.0013 5.6%

112

Table A3. Particle size analyses from 2S location. Sample depth

(m)

Particle dia.

(mm)

Percent finer than

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

0.15 4.75 100.0% 0.66 4.75 100.0% 1.52 4.75 100.0% 2.00 100.0% 2.00 100.0% 2.00 100.0% 0.71 91.3% 0.71 94.2% 0.42 99.0% 0.42 72.1% 0.42 73.0% 0.25 98.1% 0.25 58.0% 0.25 36.8% 0.149 95.1% 0.149 47.6% 0.149 16.3% 0.074 74.8% 0.074 25.7% 0.074 6.2% 0.032 39.6% 0.035 11.0% 0.036 2.5% 0.021 25.5% 0.022 7.2% 0.023 1.5% 0.0126 15.2% 0.0130 5.1% 0.0133 1.1% 0.0093 11.8% 0.0093 4.1% 0.0094 0.8% 0.0065 8.9% 0.0066 3.2% 0.0067 0.8% 0.0033 6.1% 0.0032 2.8% 0.0032 0.6% 0.00138 4.2% 0.00140 1.8% 0.00141 0.4%

0.30 4.75 100.0% 0.91 4.75 100.0% 1.83 4.75 100.0% 2.00 100.0% 2.00 99.9% 2.00 100.0% 0.71 86.5% 0.71 83.5% 0.42 91.5% 0.42 60.5% 0.42 60.8% 0.25 83.9% 0.25 44.0% 0.25 42.1% 0.149 75.4% 0.149 34.9% 0.149 30.8% 0.074 54.9% 0.074 20.0% 0.074 19.3% 0.031 28.5% 0.035 8.1% 0.035 7.6% 0.021 22.8% 0.023 5.1% 0.023 5.5% 0.0127 16.1% 0.0131 3.9% 0.0132 4.1% 0.0091 12.7% 0.0093 2.9% 0.0092 2.5% 0.0065 10.6% 0.0066 2.3% 0.0067 1.7% 0.0032 7.5% 0.0033 1.6% 0.0033 1.7% 0.00137 5.4% 0.00140 1.3% 0.00139 0.5%

0.46 4.75 100.0% 1.22 4.75 100.0% 2.13 4.75 100.0% 2.00 99.9% 2.00 100.0% 2.00 100.0% 0.71 88.4% 0.42 88.9% 0.42 88.4% 0.42 76.0% 0.25 79.2% 0.25 79.4% 0.25 59.9% 0.149 53.9% 0.149 72.3% 0.149 38.2% 0.074 17.0% 0.074 58.6% 0.074 16.2% 0.035 6.3% 0.033 33.4% 0.035 6.4% 0.023 4.3% 0.021 24.6% 0.022 4.8% 0.0132 3.0% 0.0127 16.5% 0.0131 3.1% 0.0086 2.2% 0.0091 12.9% 0.0094 2.2% 0.0067 2.0% 0.0065 10.6% 0.0067 2.0% 0.0032 1.6% 0.0033 7.1% 0.0032 1.3% 0.00138 1.4% 0.00138 4.8% 0.00140 1.1%

113

Table A3. Continued Sample depth

(m)

Particle dia.

(mm)

Percent finer than

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

Sample depth

(m)

Particle dia.

(mm)

Percent finer than

2.44 4.75 100.0% 3.10 4.75 100.0% 3.35 4.75 100.0% 2.00 100.0% 2.00 100.0% 2.00 100.0% 0.42 98.3% 0.71 94.6% 0.71 94.2% 0.25 96.8% 0.42 75.4% 0.42 72.8% 0.149 95.5% 0.25 59.8% 0.25 49.9% 0.074 93.2% 0.149 50.3% 0.149 35.2% 0.027 73.2% 0.074 38.3% 0.074 23.3% 0.0187 62.8% 0.033 25.0% 0.033 13.1% 0.0115 47.2% 0.021 20.0% 0.021 10.6% 0.0085 36.1% 0.0124 17.1% 0.0126 7.6% 0.0058 26.7% 0.0088 14.6% 0.0089 6.6% 0.0032 18.9% 0.0063 12.9% 0.0062 5.6% 0.00136 12.6% 0.0032 10.0% 0.0033 3.9% 0.00135 6.4% 0.00134 2.3%

2.74 4.75 100.0% 2.00 100.0% 0.71 94.6% 0.42 83.7% 0.25 55.6% 0.149 44.2% 0.074 32.9% 0.034 20.7% 0.022 17.2% 0.0122 14.8% 0.0089 13.4% 0.0063 12.0% 0.0031 9.1% 0.00134 6.9%

114

Table A4. Soil water characteristic curve data from 5S location.

Sample Depth

(m)

Suction (m of water)

Vol. water

content

Sample Depth

(m)

Suction (m of water)

Vol. water

content

Sample Depth

(m)

Suction (m of water)

Vol. water

content

0.6 0.056 0.48 1.5 0.084 0.48 2.7 0.043 0.36 0.111 0.46 0.21 0.46 0.094 0.35 0.199 0.46 0.38 0.45 0.161 0.34 0.27 0.44 0.64 0.45 0.26 0.32 0.35 0.39 1.05 0.44 0.46 0.30 0.57 0.28 1.08 0.44 0.60 0.29 0.84 0.18 1.71 0.43 1.03 0.28 1.14 0.15 2.1 0.43 1.22 0.27 1.53 0.13 2.2 0.43 1.53 0.26 2.1 0.06 4.1 0.40 1.98 0.25 2.3 0.05 8.7 0.40 1.92 0.25 2.2 0.07 24 0.38 2.2 0.25 4.1 0.05 4.1 0.20 8.7 0.05 8.7 0.19 24 0.03 24 0.15

115

Table A5. Soil water characteristic curve data from 3S location.

Sample Depth

(m)

Suction (m of water)

Vol. water

content

Sample Depth

(m)

Suction (m of water)

Vol. water

content

Sample Depth

(m)

Suction (m of water)

Vol. water

content

0.56 0.010 0.40 1.1 0.010 0.40 1.7 0.010 0.43 0.143 0.38 0.119 0.38 0.124 0.41 0.45 0.35 0.23 0.36 0.21 0.38 0.93 0.34 0.44 0.34 0.43 0.34 1.44 0.33 0.89 0.31 0.79 0.29 1.99 0.33 1.40 0.29 1.31 0.23 1.53 0.33 1.99 0.28 1.90 0.20 3.7 0.32 1.53 0.27 1.53 0.22 5.6 0.31 3.7 0.26 3.7 0.19 9.0 0.31 5.6 0.24 5.6 0.15 27 0.30 9.0 0.23 9.0 0.13 58 0.28 27 0.22 27 0.10 10000 0.08 58 0.19 58 0.06 10000 0.03 10000 0.01

2.6 0.010 0.30 0.149 0.29 0.52 0.28 0.95 0.27 1.47 0.27 2.0 0.27 1.5 0.27 3.7 0.26 5.6 0.25 9.0 0.25 27 0.24 58 0.21 10000 0.06

116

Table A6. Soil water characteristic curve data from 2S location.

Sample Depth

(m)

Suction (m of water)

Vol. water

content

Sample Depth

(m)

Suction (m of water)

Vol. water

content

1.1 0.010 0.56 3.0 0.010 0.40 0.088 0.52 0.073 0.38 0.2 0.50 0.196 0.34 0.5 0.48 0.34 0.29 0.8 0.43 0.55 0.24 1.3 0.36 1.21 0.17 1.8 0.31 1.91 0.15 1.5 0.32 1.53 0.15 3.7 0.30 3.7 0.12 5.6 0.25 5.6 0.09 9.0 0.24 9.0 0.09 27 0.22 27 0.07 58 0.12 58 0.05 10000 0.03 10000 0.03

117

Appendix B:

Field Measurements from Kings County Field Site

118

Figure B1. Soil temperature data from 2S site.

119

Figure B2. Soil temperature data from 3S site.

120

Figure B3. Soil temperature data from 5S site.

121

Figure B4. Matric potential data from 2S site.

122

Figure B5. Matric potential data from 3S site.

123

Figure B6. Matric potential data from 5S site.

124

Appendix C:

Dissolved Noble Gas Data and NOBLE90 Modeling Results

125

Table B1. Sample areas and measured data. Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

100595 LAB Y 40.8 0.773 3.00 3.45 3.53 8.22 0.970 100598 LAB Y 19.9 0.839 1.54 3.48 4.21 8.55 1.07 100599 LAB Y 40.3 0.809 2.57 3.34 4.18 8.81 1.15 100613 LAB Y 14.5 1.93 1.59 8.59 5.39 14.5 1.59 100618 LAB Y 25.9 0.836 1.91 3.38 4.24 9.07 1.13 100620 LAB Y 23.3 0.746 2.97 2.85 3.99 8.95 1.10 100624 LAB Y 19.9 0.621 1.61 2.55 3.67 8.27 1.05 100627 LAB Y 0.1 1.19 0.786 2.15 3.46 7.55 1.10 100628 LAB Y 0.4 0.621 1.25 2.36 3.60 8.15 1.17 100630 LAB Y 0.0 0.684 1.19 2.42 3.66 8.35 1.22 100634 LAB Y 30.1 0.871 2.33 3.61 4.49 9.53 1.20 100635 LAB Y 34.8 0.882 2.56 3.69 4.53 9.44 1.20 100636 LAB Y 0.2 0.618 1.30 2.47 3.64 8.22 1.10 100637 LAB Y 0.0 1.68 0.539 2.32 3.74 8.56 0.883 100638 LAB Y 0.4 1.03 1.36 2.15 3.47 7.63 1.01 100640 LAB Y 10.5 0.866 1.95 3.54 4.25 8.85 1.17 100641 LAB Y 2.0 0.757 1.56 3.00 4.15 8.47 1.16 100642 LAB Y 17.6 1.23 1.54 4.82 4.83 9.19 1.14 100643 LAB Y 0.7 0.655 1.41 2.57 3.80 8.02 1.17 100644 LAB Y 29.2 0.955 2.62 3.71 4.50 8.37 1.16 100645 LAB Y 16.1 1.40 1.49 5.59 5.43 11.2 1.13 100646 LAB Y 17.8 0.802 1.63 3.32 4.21 8.39 1.12 100647 LAB Y 25.1 0.872 2.28 3.68 4.48 9.63 1.18 100648 LAB Y 0.0 0.717 1.41 2.94 4.05 8.11 1.11 100649 LAB Y 24.5 0.899 2.02 3.75 4.40 8.61 1.14 100650 LAB Y 13.8 0.842 2.11 3.35 4.34 8.62 1.17 100651 LAB Y 13.2 2.01 1.76 6.98 5.01 10.3 1.26 100652 LAB Y 21.9 0.615 1.43 2.59 3.61 7.34 1.03 100654 LAB Y 3.9 0.744 1.58 3.16 4.21 8.96 1.14 100656 LAB Y 2.3 0.764 1.53 3.12 4.26 8.64 1.18 100659 LAB Y 6.9 0.844 1.72 3.42 4.57 9.42 1.22 100660 LAB Y 19.8 0.894 2.33 3.69 4.54 9.13 1.24 100661 LAB Y 18.4 0.749 1.61 3.07 4.00 8.06 1.07 100662 LAB Y 16.7 0.781 2.26 3.16 4.14 8.55 1.09 100663 LAB Y 0.1 0.597 1.30 2.31 3.58 7.73 1.05 100664 LAB Y 55.0 0.830 3.56 3.37 4.22 8.74 1.18 100665 LAB Y 23.8 0.593 1.80 2.55 3.65 8.20 1.05 100666 LAB Y 22.4 2.14 1.56 6.59 5.87 9.92 1.24 100667 LAB Y 0.8 0.644 1.40 2.80 3.96 9.13 1.14 100669 LAB Y 16.4 0.896 1.44 3.61 4.28 9.12 1.12 100670 LAB Y 37.1 1.00 2.66 4.01 4.55 9.04 1.16 100671 LAB Y 0.0 0.691 1.40 2.90 3.99 8.36 1.11 100672 LAB Y 0.1 0.740 1.24 2.30 3.59 7.84 1.11 100673 LAB Y 23.9 1.18 2.10 4.80 4.60 8.76 1.10 100674 LAB Y 30.2 0.802 1.86 3.13 4.09 8.31 1.11 100675 LAB Y 0.2 0.671 1.25 2.34 3.57 7.77 1.10 100676 LAB Y 23.8 0.661 1.69 2.39 3.54 8.01 1.05 100679 LAB Y 42.4 0.808 3.70 3.38 4.40 9.05 1.22 100680 LAB Y 12.6 0.638 2.23 2.67 3.97 8.68 1.18 100682 LAB Y 0.0 1.57 0.564 2.41 5.04 8.13 1.12 100683 LAB Y 41.1 0.842 3.05 3.40 4.42 8.94 1.16 100684 LAB Y 43.4 0.923 3.21 3.68 4.55 9.06 1.23 100685 LAB Y 9.3 0.752 1.61 2.14 4.64 7.48 1.03 100686 LAB Y 25.8 0.755 1.76 3.21 3.99 8.84 1.08 100688 LAB Y 19.9 0.708 2.43 2.91 4.04 8.25 1.14 100689 LAB Y 29.4 0.951 1.90 3.78 4.47 8.85 1.12 100690 LAB Y 39.7 0.782 2.55 3.29 4.17 8.35 1.14

126

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

100691 LAB Y 0.4 0.665 1.38 2.81 4.04 8.37 1.11 100692 LAB Y 0.2 0.549 1.30 2.12 3.48 8.33 1.19 100693 LAB Y 13.2 1.00 1.15 2.18 3.48 7.93 1.12 100694 LAB Y 10.3 0.698 1.52 3.19 3.64 8.23 1.01 100695 LAB Y 18.6 0.950 2.42 3.08 4.09 8.40 1.17 100696 LAB Y 61.7 0.600 5.43 2.11 3.42 7.68 1.05 100697 LAB Y 0.4 0.619 1.39 2.65 3.86 8.19 1.15 100698 LAB Y 0.3 0.708 1.18 2.54 3.75 8.11 1.11 100699 LAB Y 0.0 0.616 1.34 2.50 3.65 7.82 1.07 100700 LAB Y 0.0 0.535 1.38 2.46 3.71 8.19 1.11 100701 LAB Y 13.5 0.642 1.54 2.45 3.53 7.66 1.04 100702 LAB Y 30.8 0.768 2.88 2.94 4.12 8.60 1.12 100703 LAB Y 0.9 0.726 1.35 3.04 3.91 8.50 1.11 100704 LAB Y 0.2 0.609 1.39 2.38 3.78 8.20 1.09 100705 LAB Y 0.3 0.794 1.32 3.00 4.11 8.85 1.19 100706 LAB Y 31.6 0.662 3.46 2.79 3.79 116 1.04 100707 LAB Y 2.6 0.658 1.34 2.22 5.76 8.01 1.10 100708 LAB Y 3.2 0.979 1.50 3.62 4.20 8.74 1.10 100710 LAB Y 61.8 0.697 5.73 1.98 3.31 7.39 0.976 100719 LAB Y 12.9 2.50 1.51 8.48 5.05 10.7 1.23 100721 LAB Y 1.0 0.540 1.41 2.34 3.84 8.47 1.13 100722 LAB Y 0.1 0.808 1.15 2.87 4.08 8.57 1.09 100724 LAB Y 8.9 0.638 1.32 2.08 3.50 7.76 1.03 100726 LAB Y 9.2 0.625 1.86 2.66 4.02 8.84 1.18 100727 LAB Y 18.2 0.608 1.50 2.37 3.61 7.83 1.07 100728 LAB Y 18.5 2.20 1.81 4.76 5.29 9.82 1.21 100729 LAB Y 20.5 12.3 1.88 3.88 4.55 8.93 1.10 100730 LAB Y 30.9 0.961 1.74 4.11 4.61 9.52 1.08 100731 LAB Y 0.0 1.06 0.982 3.23 4.30 8.88 1.14 100732 LAB Y 32.0 1.00 2.96 4.11 4.80 9.73 1.22 100733 LAB Y 1.5 1.06 0.726 2.08 3.62 8.05 1.09 100734 LAB Y 2.0 1.64 2.11 2.45 3.75 7.93 1.04 100735 LAB Y 18.2 0.664 1.82 2.68 3.85 8.29 1.08 100736 LAB Y 0.8 0.460 1.38 2.06 3.55 8.13 1.12 100737 LAB Y 75.9 0.638 9.11 2.12 3.41 7.74 1.04 100738 LAB Y 48.7 0.944 2.90 3.61 4.46 8.86 1.11 100739 LAB Y 0.2 0.652 1.24 2.41 3.66 7.81 1.06 100740 LAB Y 0.0 0.702 1.14 2.43 3.71 7.96 1.09 100742 LAB Y 0.4 2.60 0.458 1.94 3.15 7.01 0.986 100743 LAB Y 67.5 0.988 3.66 2.16 4.02 7.96 1.14 100744 LAB Y 21.9 0.781 2.03 2.07 4.26 7.13 1.04 100745 LAB Y 41.9 1.11 2.90 4.04 4.61 9.58 1.22 100746 LAB Y 32.1 0.792 2.99 3.12 4.22 8.76 1.14 100747 LAB Y 35.9 0.797 2.22 3.18 4.24 8.36 1.08 100748 LAB Y 29.8 0.670 2.22 2.75 4.01 8.16 1.08 100749 LAB Y 0.0 0.681 1.14 2.40 3.74 8.28 1.14 100750 LAB Y 19.7 0.855 1.54 3.46 4.22 8.64 1.10 100751 LAB Y 16.4 1.13 1.63 4.60 4.76 9.31 1.22 100753 LAB Y 24.3 0.557 1.96 2.46 3.57 7.95 1.05 100755 LAB Y 23.4 0.892 1.58 3.99 4.43 8.90 1.07 100756 LAB Y 22.2 0.803 1.51 3.45 4.20 8.19 1.08 100757 LAB Y 24.3 0.616 1.84 2.70 3.76 7.92 1.01 100758 LAB Y - 0.759 1.47 3.34 4.12 8.34 1.06 100759 LAB Y 1.7 0.622 1.38 2.55 3.88 8.39 1.19 100760 LAB Y 0.3 0.615 1.33 2.57 3.88 8.45 1.17 100761 LAB Y 0.7 0.631 1.24 2.47 3.81 8.08 1.07 100762 LAB Y 18.2 0.695 2.61 2.85 4.10 8.73 1.14 100763 LAB Y 0.2 0.721 1.32 2.89 4.18 9.40 1.20

127

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

100764 LAB Y 0.0 0.700 1.16 2.42 3.87 8.56 1.14 100765 LAB Y 1.6 0.621 1.40 2.42 3.81 8.72 1.17 100767 LAB Y 17.1 0.701 2.19 2.85 4.08 8.58 1.14 100768 LAB Y 19.0 0.959 2.40 3.92 4.65 9.26 1.22 100771 LAB Y 22.3 1.45 1.95 5.59 5.02 10.5 1.25 100772 LAB Y 3.5 0.735 1.50 3.18 3.96 8.47 1.17 100773 LAB Y 24.8 1.13 2.21 4.42 4.75 9.75 1.25 100775 LAB Y 8.3 1.02 1.60 4.14 4.64 10.3 1.16 100776 LAB Y 16.4 1.15 1.78 4.55 4.63 9.72 1.19 100777 LAB Y 13.4 1.07 1.99 4.43 4.81 9.46 1.21 100778 LAB Y 17.4 0.816 1.53 3.33 4.21 8.60 1.15 100779 LAB Y 2.9 0.665 1.54 2.79 4.04 8.41 1.13 100780 LAB Y 1.1 0.685 1.44 2.97 4.09 8.27 1.12 100781 LAB Y 0.0 0.621 1.38 2.60 3.85 7.82 1.08 100782 LAB Y 10.0 1.20 1.58 4.80 4.98 9.21 1.16 100783 LAB Y 22.4 1.36 2.06 5.66 5.95 10.2 1.28 100784 LAB Y 3.2 3.39 1.84 6.70 5.98 10.9 1.30 100785 LAB Y 63.7 0.599 2.01 2.37 3.72 7.65 1.07 100786 LAB Y 5.7 1.23 1.46 4.87 5.01 9.70 1.13 100787 LAB Y 33.6 0.824 2.23 3.44 4.38 9.06 1.10 100825 SCV N 3.4 1.08 1.59 2.20 3.42 7.80 1.08 100827 SCV N 16.6 0.788 1.70 3.18 4.08 8.62 1.15 100828 SCV N 3.6 3.62 1.09 4.67 5.05 10.3 1.26 100830 SCV N 7.4 0.863 1.40 2.23 3.48 8.08 1.05 100831 SCV N 32.1 1.76 1.42 2.21 3.55 7.98 1.12 100832 SCV N 20.5 4.72 0.932 2.46 3.75 8.29 1.11 100833 SCV N 38.3 0.781 3.39 2.92 4.03 8.59 1.11 100834 SCV N 17.7 2.07 0.962 2.08 3.63 7.90 1.09 100835 SCV N 34.2 0.607 3.18 2.55 3.83 8.26 1.16 100836 SCV N 5.4 5.06 1.11 2.40 3.84 8.22 1.15 100837 SCV N 9.8 4.03 1.04 2.78 3.90 7.90 1.12 100838 SCV N 18.7 0.955 1.72 2.52 3.97 8.77 1.22 100839 SCV N 1.8 20.1 0.807 2.17 3.69 8.44 1.19 100840 SCV N 11.1 2.07 1.05 2.74 3.98 8.72 1.19 100841 SCV N 13.1 3.21 1.08 2.40 3.74 8.27 1.15 100842 SCV N 18.0 1.51 1.20 2.16 3.76 8.79 1.20 100843 SCV N 16.5 15.5 0.828 2.44 3.83 8.65 1.19 100844 SCV N 9.7 1.55 1.87 2.71 3.87 8.24 1.11 100845 SCV N 9.3 38.0 0.719 2.27 3.94 9.34 1.33 100846 SCV N 22.8 14.1 0.800 2.19 3.48 8.13 1.14 100847 SCV N 32.1 2.42 1.21 2.18 3.64 8.44 1.10 100848 SCV N 2.2 16.6 0.801 2.39 3.58 8.19 1.14 100849 SCV N 5.3 6.55 1.03 2.46 3.68 8.25 1.12 100850 SCV N 1.6 64.3 0.622 2.25 3.82 9.12 1.36 100851 SCV N 21.5 4.35 0.968 2.28 3.56 8.16 1.13 100853 SCV N 1.0 3.48 0.961 3.96 4.47 9.64 1.29 100854 SCV N 26.6 0.797 2.34 2.58 3.86 8.68 1.17 100855 SCV N 5.4 1.52 1.05 2.12 3.67 8.71 1.23 100856 SCV N 0.8 0.883 1.21 2.41 3.85 8.88 1.22 100857 SCV N 24.3 8.50 0.865 2.45 3.64 7.98 1.14 100858 SCV N 9.3 1.49 1.39 2.94 4.18 9.30 1.28 100859 SCV N 72.6 10.9 0.750 2.59 3.78 8.20 1.17 100860 SCV N 6.0 6.14 0.812 2.16 3.71 8.39 1.17 100861 SCV N 2.8 41.7 0.727 2.12 3.74 8.61 1.23 100862 SCV N 24.7 19.6 0.813 2.35 3.54 7.77 1.12 100863 SCV N 18.9 1.93 1.56 2.56 3.74 8.47 1.15 100864 SCV N 15.4 16.3 0.806 2.33 3.60 8.44 1.13 100865 SFBA Y 9.3 1.09 1.29 3.69 4.80 9.06 1.19

128

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

100866 SFBA Y 1.5 1.35 0.893 3.26 4.58 9.24 1.19 100867 SFBA Y 18.6 1.37 1.40 4.16 4.91 9.60 1.21 100868 SFBA Y 35.9 1.15 1.87 4.08 4.69 9.25 1.20 100869 SFBA Y 13.3 1.27 1.18 3.44 4.33 8.96 1.24 100875 SFBA Y 0.5 1.05 1.03 2.67 4.16 9.10 1.29 100876 SFBA Y 0.4 0.809 1.11 2.40 3.80 8.09 1.18 100877 SFBA Y 0.0 1.05 0.843 2.52 4.06 9.67 1.25 100878 SFBA Y 0.0 0.680 1.40 2.96 4.53 9.96 1.17 100880 SFBA Y 7.4 0.551 1.47 2.14 4.72 8.19 1.13 100881 SFBA Y 3.6 0.566 1.37 1.98 4.60 7.80 1.13 100882 SFBA Y 5.7 0.591 1.56 2.21 4.82 8.31 1.18 100883 SFBA Y 0.1 1.48 0.707 2.37 5.40 9.25 1.38 100884 SFBA Y 5.8 0.566 1.52 2.15 4.78 8.11 1.13 100885 SFBA Y 1.5 0.646 1.41 2.40 4.98 8.37 1.17 100886 SFBA Y 0.9 0.601 1.34 2.57 3.76 8.65 1.14 100888 SFBA Y 0.3 0.686 1.37 2.52 4.95 8.05 1.13 100891 SFBA Y 36.9 0.946 2.36 3.56 4.53 9.55 1.24 100892 SFBA Y 21.5 0.822 1.46 3.16 3.70 8.62 1.18 100893 SFBA Y 29.5 0.649 2.25 2.60 3.80 8.13 1.16 100894 SFBA Y 23.5 0.842 1.53 3.25 4.12 8.38 1.18 100895 SFBA Y 22.9 0.856 1.62 3.12 4.14 8.42 1.17 100896 SFBA Y 19.4 0.772 1.41 3.02 4.06 8.45 1.23 100897 SFBA Y 19.9 0.730 1.40 2.87 4.00 8.38 1.19 100898 SFBA Y 17.6 0.699 1.41 2.83 4.04 9.23 1.18 100899 SFBA Y 19.9 0.711 1.43 2.85 3.99 9.11 1.19 100900 SFBA Y 19.7 0.840 1.47 3.21 4.30 9.23 1.18 100901 SFBA Y 20.3 0.678 1.50 2.73 3.88 8.37 1.14 100902 SFBA Y 18.7 0.811 1.44 3.12 4.16 8.49 1.20 100903 SFBA Y 6.1 9.54 1.24 9.26 5.38 25.7 1.19 100904 SFBA Y 6.5 2.04 1.43 4.50 5.04 10.4 1.30 100905 SFBA Y 36.0 1.10 1.55 3.82 4.49 9.31 1.17 100906 SFBA Y 15.2 0.968 1.83 3.78 4.79 8.39 1.23 100907 SFBA Y 46.2 1.10 2.04 3.96 4.55 9.27 1.14 100908 SFBA Y 27.3 1.21 1.55 4.15 4.82 9.91 1.21 100909 SFBA Y 10.0 0.633 1.41 2.92 4.21 23.3 1.26 100910 SFBA Y 10.4 0.783 1.16 3.22 4.30 17.7 0.519 100911 SFBA Y 11.1 0.633 1.41 2.93 4.44 8.27 0.812 100912 SFBA Y 9.4 0.682 1.40 3.02 4.46 53.2 0.888 100915 SFBA Y 12.6 1.75 0.887 2.95 4.01 8.96 1.20 100916 SFBA Y 13.8 1.92 1.49 6.97 4.96 21.4 0.694 100918 SFBA Y 13.3 0.723 1.51 2.97 4.10 8.99 1.12 100919 SFBA Y 12.5 1.68 1.45 6.18 4.99 11.3 1.39 100920 SFBA Y 13.4 1.34 1.67 4.94 4.95 11.4 1.41 100923 SFBA Y 15.2 1.24 1.47 4.64 4.99 11.9 1.30 100924 SFBA Y 1.2 5.83 1.22 4.18 4.94 11.4 1.50 100925 SFBA Y 0.1 9.83 1.25 4.07 4.96 11.6 1.33 100926 SFBA Y 3.6 4.28 1.36 5.32 4.94 12.0 1.39 100927 SFBA Y 1.6 6.48 1.22 5.05 5.05 12.0 1.27 100928 NCV N 0.4 18.4 0.545 2.08 3.57 8.05 1.12 100929 NCV N 2.1 14.6 0.571 2.22 3.71 8.37 1.20 100930 NCV N 0.5 7.32 0.624 2.17 3.66 8.11 1.20 100931 NCV N 0.5 11.9 0.622 2.21 3.76 8.40 1.26 100932 NCV N 0.8 12.6 0.624 2.30 3.85 8.66 1.25 100933 NCV N 0.4 10.6 0.648 2.23 3.80 8.47 1.24 100934 NCV N 0.5 2.63 0.810 2.19 3.72 8.44 1.20 100935 NCV N 0.0 6.78 0.725 2.19 3.77 8.53 1.26 100936 NCV N 18.9 3.17 1.04 2.24 3.82 8.63 1.27 100937 NCV N 17.0 0.849 2.00 2.27 3.80 8.39 1.21

129

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

100938 NCV N 19.0 0.542 1.71 2.24 3.86 8.76 1.28 100939 NCV N 13.9 2.40 1.04 2.31 3.82 8.65 1.23 100940 NCV N 22.3 0.707 2.11 2.24 3.90 8.99 1.33 100941 NCV N 0.0 19.5 0.517 2.05 3.53 7.96 1.15 100943 NCV N 0.8 33.6 0.691 2.41 3.95 8.32 1.22 100944 NCV N 3.3 1.40 1.02 2.24 3.62 8.10 1.13 100945 NCV N 1.0 7.47 0.734 2.29 3.74 8.36 1.18 100946 NCV N 0.3 9.78 0.684 2.21 3.75 8.55 1.25 100947 NCV N 16.2 18.6 0.591 2.28 3.91 8.94 1.31 100948 NCV N 21.1 1.95 1.07 2.39 3.97 9.24 1.30 100949 NCV N 1.9 11.7 0.658 2.23 3.80 8.70 1.26 100950 NCV N 1.6 16.7 0.604 2.27 3.70 8.06 1.13 100951 NCV N 2.5 4.24 0.728 2.25 3.59 8.18 1.14 100952 NCV N 0.8 6.99 0.720 2.21 3.79 8.64 1.24 100953 NCV N 1.5 2.63 0.809 2.33 3.72 8.37 1.19 100954 NCV N 1.6 4.42 0.707 2.21 3.67 8.30 1.14 100955 NCV N 0.1 7.80 0.682 2.26 3.87 8.85 1.24 100956 NCV N 2.6 3.32 0.806 2.31 3.75 8.60 1.19 100957 SFBA Y 16.0 1.57 1.58 6.06 4.98 11.3 1.45 100958 SFBA Y 11.5 1.62 1.55 6.12 4.99 11.9 1.47 100961 SFBA Y 6.0 1.82 0.928 3.32 4.59 10.1 1.33 100963 SFBA Y 11.8 0.871 1.50 4.00 4.54 10.1 1.28 100965 SFBA Y 2.2 1.52 1.54 6.13 4.90 12.0 1.36 100966 SFBA Y 7.2 3.46 1.47 12.7 5.09 15.2 1.69 100967 SFBA Y 2.0 2.07 1.31 8.93 7.50 12.6 1.55 100968 SFBA Y 4.1 1.78 1.34 8.20 6.81 11.9 1.45 100969 SFBA Y 13.2 1.09 1.30 5.36 5.27 10.0 1.32 100970 SFBA Y 7.0 1.53 1.42 6.43 6.03 11.1 1.41 100973 LAB Y 11.2 2.11 1.43 7.48 8.69 11.9 1.39 100974 LAB Y 7.3 2.10 1.24 6.36 5.20 12.2 1.39 100975 LAB Y 0.4 1.21 0.891 2.94 4.30 9.40 1.20 100976 LAB Y 7.6 0.863 1.39 3.25 4.29 9.36 1.17 100977 SFBA Y 26.4 1.38 1.54 5.23 7.39 10.6 1.38 100978 SFBA Y 30.7 1.40 1.56 5.66 5.75 10.9 1.40 100979 NCV N 0.6 0.713 1.12 2.18 3.54 8.12 1.14 100980 NCV N 0.0 14.4 0.642 2.25 3.77 8.72 1.25 100981 NCV N 1.4 11.9 0.597 2.19 3.71 8.46 1.19 100982 NCV N 0.3 3.03 0.744 2.29 3.56 7.66 1.07 100983 NCV N 0.1 7.50 0.677 2.32 3.84 8.52 1.21 100984 NCV N 0.9 5.37 0.709 2.20 3.61 8.30 1.21 100985 NCV N 1.8 9.05 0.565 2.14 3.60 7.98 1.12 100986 NCV N 0.0 15.9 0.508 2.13 3.53 7.97 1.16 100987 NCV N 0.2 8.83 0.371 2.02 3.33 7.77 1.12 100988 NCV N 0.2 5.74 0.398 2.12 3.53 7.81 1.12 100989 NCV N 0.5 11.9 0.371 2.15 3.50 7.36 1.14 100990 NCV N 1.0 0.558 1.22 2.06 3.38 7.56 1.06 100991 NCV N 0.2 20.5 0.427 1.94 3.48 7.66 1.09 100992 NCV N 0.1 22.1 0.426 2.05 3.50 7.71 1.16 100993 NCV N 0.1 13.4 0.463 5.66 5.31 9.85 1.26 100994 NCV N 0.2 23.1 0.476 2.05 3.50 8.10 1.09 100995 NCV N 0.9 0.728 1.12 2.31 3.85 8.79 1.17 100996 NCV N 15.9 2.02 0.844 2.37 3.79 8.52 1.14 100997 NCV N 32.0 3.06 1.08 2.31 3.72 8.77 1.26 100998 NCV N 14.4 0.619 1.66 2.27 3.89 8.60 1.32 100999 SFBA Y 13.3 0.699 1.74 2.94 4.00 8.86 1.21 101000 SFBA Y 13.3 0.707 1.52 2.91 4.10 8.48 1.15 101001 SFBA Y 12.4 0.604 1.48 2.53 3.82 7.71 1.03 101002 SFBA Y 13.3 0.712 1.52 2.85 3.98 8.33 1.16

130

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

101003 SFBA Y 13.6 0.721 1.51 2.95 3.98 8.56 1.11 101004 SFBA Y 14.5 0.957 1.43 3.97 4.51 9.28 1.26 101005 SFBA Y 13.5 0.989 1.43 3.61 4.29 7.43 0.878 101006 SFBA Y 4.6 0.754 1.48 2.95 4.12 6.66 0.921 101007 SFBA Y 15.6 0.706 1.45 3.26 4.22 8.04 1.15 101008 SFBA Y 13.5 0.724 1.53 3.08 4.16 8.13 1.19 101009 SFBA Y 14.2 1.06 1.45 4.12 4.75 9.36 1.24 101010 SFBA Y 0.8 1.92 1.01 4.64 4.67 10.8 1.45 101011 SFBA Y 4.8 4.73 0.976 4.71 5.53 10.5 1.39 101012 SFBA Y 13.5 1.80 1.38 6.02 5.89 11.4 1.48 101013 SFBA Y 8.1 1.52 1.48 6.14 6.04 11.7 1.51 101014 SFBA Y 13.6 1.64 1.54 6.22 6.05 11.0 1.44 101015 SFBA Y 14.7 1.56 1.57 6.34 5.74 10.7 1.43 101016 SFBA Y 7.6 1.76 1.47 6.83 4.99 12.5 1.40 101017 SFBA Y 7.7 1.82 1.48 7.05 4.98 12.0 1.45 101018 SFBA Y 2.7 1.14 1.34 5.38 5.84 11.5 1.46 101019 SFBA Y 14.0 1.00 1.50 3.68 4.38 9.01 1.22 101020 SFBA Y 13.9 0.897 1.48 3.31 4.16 9.12 1.19 101021 SFBA Y 13.2 1.42 1.63 4.07 4.85 10.1 1.24 101022 SFBA Y 12.5 1.49 1.97 5.37 4.97 11.0 1.32 101023 SFBA Y 10.7 2.36 2.97 4.15 4.98 10.5 1.27 101024 SFBA Y 14.1 1.21 1.49 4.52 4.80 9.94 1.22 101025 SFBA Y 12.8 0.822 1.46 3.35 4.14 41.0 1.21 101026 SFBA Y 8.1 2.81 1.21 4.54 4.98 10.3 1.31 101027 SFBA Y 14.2 1.30 1.72 4.93 5.00 10.9 1.33 101028 SFBA Y 14.5 2.03 1.60 7.04 5.09 11.4 1.36 101029 SFBA Y 13.0 1.56 1.62 5.98 4.94 11.6 1.30 101030 SFBA Y 10.3 1.77 1.54 6.67 4.94 12.0 1.45 101031 SFBA Y 8.0 1.46 1.54 5.93 4.97 11.9 1.37 101032 SFBA Y 3.9 1.27 1.43 5.02 4.95 11.4 1.43 101033 SFBA Y 10.2 1.70 1.50 6.74 4.99 9.37 1.32 101034 SFBA Y 5.8 1.70 1.50 6.95 4.97 11.1 1.28 101035 SFBA Y 9.8 1.58 1.45 6.46 4.96 12.1 1.48 101036 SFBA Y 2.5 1.33 1.23 4.61 4.97 10.7 1.44 101037 SFBA Y 9.3 1.42 1.42 6.29 6.16 11.9 1.49 101038 SFBA Y 0.7 1.11 1.31 4.71 5.43 11.0 1.45 101039 SFBA Y 7.5 1.56 1.56 5.60 5.84 11.3 1.43 101040 SFBA Y 13.0 1.58 1.48 6.31 5.87 10.2 1.40 101041 SFBA Y 8.9 3.82 1.51 11.6 6.87 13.4 1.55 101042 SFBA Y 10.8 2.16 1.32 6.57 6.07 11.0 1.36 101043 SFBA Y 15.5 0.949 1.54 3.62 4.45 9.06 1.22 101044 SFBA Y 9.7 1.52 1.44 5.40 5.40 10.7 1.36 101045 SFBA Y 30.2 1.87 1.36 5.55 5.47 10.8 1.36 101046 SFBA Y 30.1 1.77 1.46 6.42 5.63 10.8 1.39 101048 SFBA Y 27.7 2.77 1.16 5.51 4.93 11.2 1.38 101049 SFBA Y 32.0 1.65 1.43 5.41 4.93 10.7 1.31 101050 SFBA Y 19.5 1.71 1.51 5.73 4.95 10.6 1.30 101051 SFBA Y 11.9 1.57 1.37 5.83 5.50 10.6 1.32 101052 SFBA Y 13.4 2.09 1.43 7.77 6.24 11.5 1.37 101053 SFBA Y 14.8 1.65 1.46 6.62 5.60 10.8 1.39 101054 SFBA Y 14.6 1.46 1.41 5.39 5.35 10.3 1.32 101055 SCV N 2.2 1.28 1.49 2.77 4.26 9.62 1.30 101056 SCV N 9.0 1.19 1.91 3.05 4.31 9.35 1.31 101057 SCV N 9.0 1.45 1.83 3.74 4.58 9.50 1.31 101064 SFBA Y 7.8 1.88 1.49 8.91 6.90 12.1 1.46 101065 SFBA Y 6.5 1.32 1.49 5.97 5.79 10.6 1.43 101066 SFBA Y - 0.897 1.49 4.29 5.01 9.78 1.28 101067 SFBA Y 3.8 31.9 1.17 5.67 6.04 11.2 1.47

131

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

101068 SFBA Y 11.3 1.00 1.54 4.20 4.71 8.65 1.23 101070 SFBA Y 12.5 0.748 1.47 3.50 4.11 8.56 1.19 101071 SFBA Y 4.4 2.30 1.46 9.31 7.51 13.6 1.58 101072 SFBA Y 4.5 2.70 1.36 11.2 7.83 14.1 1.60 101073 SFBA Y 3.4 50.1 0.936 5.06 5.65 10.7 1.53 101074 SFBA Y 11.6 1.68 1.78 6.41 6.07 10.7 1.42 101075 SFBA Y 4.5 6.56 0.808 6.72 6.19 11.0 1.46 101077 SFBA Y 9.5 1.59 1.54 6.30 6.25 11.1 1.51 101078 SFBA Y 1.4 1.23 1.32 5.65 5.53 11.0 1.31 101081 SFBA Y 5.6 1.14 1.43 5.11 5.78 11.3 1.35 101082 SFBA Y 6.8 2.40 1.43 9.27 7.38 12.7 1.59 101083 SFBA Y 5.3 0.682 1.37 3.70 4.77 10.1 1.29 101086 SFBA Y 15.5 2.62 1.54 9.17 6.99 12.1 1.34 101087 SFBA Y 15.3 1.55 1.52 5.95 5.65 10.7 1.26 101088 SFBA Y 11.0 2.75 1.84 8.18 6.80 12.3 1.45 101090 SFBA Y 15.8 1.51 1.46 5.35 5.35 10.4 1.29 101091 SFBA Y 12.7 0.792 1.48 3.16 4.05 8.14 1.15 101092 SFBA Y 14.6 3.41 1.39 12.1 7.94 13.7 1.60 101093 SFBA Y 8.8 1.46 1.54 5.83 5.55 10.9 1.36 101094 SFBA Y 12.2 1.05 1.37 4.48 4.75 9.76 1.25 101095 SFBA Y 16.0 2.16 1.22 5.38 5.21 10.3 1.35 101096 SFBA Y 2.2 2.54 0.954 4.27 5.18 10.3 1.37 101097 SFBA Y 11.9 1.81 1.83 5.39 5.49 10.6 1.38 101098 SFBA Y 10.3 1.48 1.40 5.72 5.24 10.1 1.33 101099 SFBA Y 13.8 1.16 1.60 4.64 4.97 10.0 1.31 101100 SFBA Y 14.2 3.41 1.62 12.3 7.93 13.5 1.57 101101 SFBA Y 14.2 2.62 1.38 8.16 6.30 11.3 1.41 101104 SFBA Y 14.9 1.26 1.56 5.01 5.02 10.0 1.31 101105 SFBA Y 17.1 5.46 1.53 23.5 7.95 26.9 2.56 101106 SFBA Y 3.1 2.15 3.18 3.91 4.90 10.2 1.36 101107 SFBA Y 11.2 1.46 1.49 5.56 5.70 11.0 1.37 101108 SFBA Y 7.9 3.14 2.10 6.87 6.35 11.6 1.41 101109 SFBA Y 16.0 0.874 1.58 3.54 4.37 9.30 1.25 101110 SFBA Y 1.1 3.01 0.807 4.36 5.06 9.91 1.37 101111 SFBA Y 14.9 1.55 1.63 5.99 5.67 10.8 1.38 101112 SFBA Y 3.3 1.81 1.04 4.89 5.29 10.8 1.38 101113 SFBA Y 14.4 1.66 1.37 6.06 5.57 10.7 1.30 101127 LAB Y 108.9 0.845 1.87 3.35 4.30 8.94 1.15 101128 SCV N 23.5 1.56 1.98 2.61 3.96 8.54 1.21 101129 SFBA Y 12.2 5.56 0.745 6.08 5.46 10.7 1.38 101130 SFBA Y 10.8 1.99 1.31 6.81 5.62 10.6 1.35 101131 SCV N 18.2 2.11 1.66 2.58 4.00 9.00 1.24 101132 SCV N 18.4 0.941 2.46 2.67 3.94 8.61 1.22 101133 SCV N 5.9 1.04 1.60 2.69 4.20 9.07 1.26 101134 SFBA Y 14.3 1.50 1.20 4.30 4.68 9.74 1.30 101136 SCV N 1.3 2.58 1.14 2.52 4.04 9.22 1.28 101137 SCV N 1.7 2.04 1.49 2.52 3.94 8.69 1.23 101138 SFBA Y 4.8 3.98 4.98 4.23 4.98 10.3 1.38 101139 SFBA Y 10.8 1.30 2.31 4.41 5.03 10.2 1.35 101140 SCV N 4.7 3.09 1.44 2.37 3.82 8.60 1.21 101141 SCV N 7.6 2.75 1.84 2.31 3.75 8.36 1.17 101142 SCV N 25.9 2.68 2.42 2.29 3.86 8.95 1.27 101143 SFBA Y 11.1 1.75 2.23 5.95 5.52 10.7 1.38 101145 SCV N 24.7 2.55 2.45 2.33 3.78 8.59 1.23 101146 SCV N 19.2 1.38 2.13 2.67 4.28 9.79 1.40 101147 SCV N 29.0 6.00 2.20 2.29 3.79 8.36 1.28 101148 SCV N 24.2 6.90 2.20 2.44 3.86 8.43 1.24 101149 SCV N 25.5 0.565 2.69 2.68 3.93 8.33 1.29

132

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

101150 SCV N 31.3 1.46 2.63 2.25 3.81 9.02 1.30 101151 SCV N 22.9 0.905 2.05 3.78 4.96 10.6 1.40 101152 SCV N 0.8 1.50 1.51 2.67 4.17 9.48 1.31 101153 SCV N 16.0 0.864 2.22 3.00 4.38 9.82 1.33 101154 SCV N 7.8 1.12 1.83 2.81 4.32 9.70 1.35 101156 SFBA Y 5.1 1.12 1.42 4.73 5.17 10.3 1.37 101157 SFBA Y 9.4 1.03 1.40 4.60 5.10 10.2 1.31 101160 SFBA Y 2.6 1.15 1.38 4.32 5.21 10.8 1.39 101161 SFBA Y 6.4 0.833 1.46 3.78 4.93 10.3 1.39 101162 SFBA Y 4.5 1.25 1.46 5.00 5.73 11.4 1.49 101163 SFBA Y 4.8 8.50 0.784 7.17 6.66 12.6 1.56 101170 SFBA Y 10.8 0.877 1.49 3.60 4.55 9.71 1.27 101171 SFBA Y 12.2 0.879 1.43 3.49 4.50 9.43 1.23 101172 SFBA Y 12.0 0.807 1.40 3.06 4.22 9.11 1.22 101173 SFBA Y 5.0 1.27 1.34 3.78 4.94 10.3 1.33 101174 SFBA Y 13.3 1.53 1.60 5.15 5.36 9.89 1.29 101175 SFBA Y 3.0 1.33 1.26 3.67 4.81 9.87 1.33 101176 SFBA Y 12.1 0.702 1.39 3.02 4.15 8.59 1.26 101177 SCV N 6.7 1.05 1.82 3.21 4.52 9.82 1.36 101178 SCV N 17.4 0.744 2.18 2.36 3.91 8.98 1.30 101179 SCV N 12.2 0.536 1.70 2.25 3.85 9.04 1.33 101182 SFBA Y 11.5 1.87 1.46 7.21 6.36 12.1 1.51 101183 SFBA Y 14.2 2.39 1.55 9.34 7.32 13.0 1.67 101184 SFBA Y 11.6 2.31 1.53 8.92 7.29 13.2 1.67 101185 SFBA Y 7.0 1.72 1.46 6.84 6.53 12.3 1.49 101186 SFBA Y 13.0 1.66 1.50 6.46 6.06 11.6 1.52 101187 SFBA Y 2.7 1.90 1.38 7.73 7.12 13.0 1.58 101188 SFBA Y 4.3 1.88 1.42 7.26 6.60 12.2 1.54 101198 LAB Y 0.4 0.891 1.34 3.63 4.67 10.3 1.36 101199 LAB Y 6.6 1.49 1.51 6.01 5.77 11.2 1.42 101200 LAB Y 0.0 0.966 1.36 3.97 4.87 10.4 1.39 101201 LAB Y 1.0 1.10 1.10 3.77 4.68 10.3 1.32 101202 SFBA Y 31.2 0.973 1.62 3.58 4.84 9.97 1.36 101203 SFBA Y 3.6 1.15 1.24 3.97 5.14 10.6 1.40 101204 SFBA Y 10.5 1.14 1.49 5.39 5.65 10.9 1.41 101205 SFBA Y 13.1 1.64 1.50 6.62 6.84 12.7 1.60 101208 NCV N 1.4 6.88 0.493 2.20 3.64 7.91 1.09 101209 NCV N 0.1 7.64 0.484 2.25 3.51 7.97 1.11 101210 NCV N 0.3 1.75 1.28 4.68 4.89 10.0 1.34 101211 NCV N 0.0 6.16 0.498 2.23 3.55 7.96 1.12 101212 NCV N 0.2 3.34 0.562 2.24 3.43 7.89 1.16 101213 NCV N 2.3 1.50 0.772 2.29 3.58 8.17 1.11 101214 NCV N 0.0 4.53 0.536 2.03 3.35 7.62 1.09 101215 NCV N 0.1 8.75 0.449 2.09 2.81 7.83 1.09 101216 NCV N 0.1 16.2 0.432 2.00 4.70 7.39 1.07 101217 NCV N 0.1 7.12 0.532 2.07 3.41 7.67 1.08 101218 NCV N 9.6 0.707 1.48 2.95 3.85 8.57 1.15 101219 NCV N 4.4 0.653 1.21 2.05 3.27 7.80 1.05 101220 NCV N 4.7 11.6 0.657 2.49 3.63 8.13 1.11 101221 NCV N 8.8 9.56 0.690 2.27 3.55 8.10 1.10 101222 NCV N 21.1 3.39 0.867 1.97 3.39 7.92 1.05 101223 NCV N 2.9 5.00 0.818 2.36 3.89 8.88 1.27 101224 NCV N 12.6 1.13 0.979 1.90 3.32 7.58 1.07 101225 NCV N 0.2 15.0 0.716 2.48 3.97 8.65 1.28 101226 NCV N 0.5 5.10 0.764 2.08 3.60 8.07 1.17 101227 NCV N 0.4 13.0 0.709 2.31 3.95 8.99 1.34 101228 NCV N 0.3 9.01 0.714 2.30 3.77 8.59 1.23 101229 NCV N 0.6 4.37 0.792 2.09 3.33 7.40 1.06

133

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

101230 NCV N 2.1 2.66 0.928 2.29 3.45 7.78 1.09 101231 NCV N 1.6 2.75 0.897 2.12 3.37 7.43 1.05 101232 NCV N 0.0 3.10 0.836 2.03 3.23 7.13 1.02 101234 NCV N 0.1 5.49 0.753 2.10 3.29 7.15 1.05 101235 NCV N 16.9 6.18 0.716 3.44 4.86 10.2 1.45 101236 NCV N 3.5 25.4 0.482 2.19 3.62 8.37 1.12 101237 NCV N 0.0 15.7 0.511 4.35 5.09 9.89 1.22 101238 NCV N 0.4 23.0 0.483 2.51 3.81 8.65 1.14 101239 NCV N 2.4 13.4 0.477 2.38 3.61 7.82 1.12 101240 NCV N 0.2 22.9 0.505 3.49 4.79 9.57 1.23 101241 NCV N 4.0 13.9 0.483 2.63 4.04 8.61 1.21 101242 NCV N 4.1 17.9 0.480 2.32 3.64 7.92 1.14 101243 NCV N 1.4 14.4 0.462 2.58 3.88 8.65 1.17 101248 NCV N 0.2 8.90 0.585 2.06 3.35 7.70 1.04 101249 NCV N 2.2 3.73 0.654 2.20 3.39 7.62 1.03 101250 NCV N 3.3 0.962 0.922 2.01 3.26 7.43 1.03 101251 NCV N 1.7 4.21 0.513 1.91 3.23 7.37 1.00 101252 NCV N 1.4 0.481 1.33 1.98 3.26 7.17 0.973 101253 NCV N 1.9 0.463 1.35 1.96 3.23 7.13 0.969 101254 NCV N 0.2 1.87 0.524 2.00 3.26 7.38 0.963 101255 NCV N 1.0 4.92 0.474 2.05 3.29 7.39 0.982 101256 NCV N 1.5 14.9 0.410 2.06 3.40 7.62 1.09 101257 NCV N 0.1 29.2 0.429 2.54 3.64 7.86 1.14 101258 SFBA Y 2.4 1.25 1.48 5.01 5.13 10.5 1.33 101259 SFBA Y 10.3 1.01 1.63 4.22 4.63 9.85 1.31 101260 SFBA Y 5.4 1.15 1.67 4.27 5.02 10.0 1.32 101261 SFBA Y 10.2 1.05 1.63 4.13 4.83 9.95 1.29 101262 SFBA Y 9.6 1.28 1.44 5.19 5.14 10.5 1.34 101263 SFBA Y 12.2 1.30 1.50 5.27 5.38 10.6 1.34 101264 SFBA Y 14.2 1.08 1.62 4.47 4.94 9.88 1.31 101265 SFBA Y 13.0 0.615 1.38 2.54 3.75 8.38 1.13 101266 SFBA Y 0.0 45.7 0.543 1.97 3.61 8.34 1.20 101267 SFBA Y 12.6 1.29 1.31 4.20 6.16 9.33 1.20 101268 SFBA Y 8.8 1.37 1.74 4.97 7.18 10.7 1.34 101269 SFBA Y 12.2 1.69 1.39 6.27 8.14 11.0 1.41 101271 SFBA Y 22.6 3.26 5.06 4.03 6.30 9.66 1.30 101272 SFBA Y 11.6 1.14 1.88 4.18 6.20 9.33 1.27 101274 NCV N 1.7 11.3 0.244 2.54 3.53 7.78 0.984 101275 NCV N 26.2 15.2 0.320 6.19 5.86 10.7 1.27 101277 NCV N 2.9 6.32 0.472 2.16 3.57 8.18 1.05 101278 NCV N 1.7 12.6 0.477 2.87 4.22 9.44 1.16 101279 NCV N 0.3 5.23 0.526 2.56 3.85 8.54 1.14 101280 NCV N 0.3 11.6 0.452 2.34 3.56 7.92 1.09 101281 NCV N 2.8 4.27 0.730 4.15 6.01 12.5 1.71 101282 NCV N 1.8 14.5 0.486 2.98 4.32 9.34 1.26 101283 NCV N 0.2 13.4 0.460 2.55 3.85 8.48 1.15 101284 NCV N 1.3 19.7 0.480 2.95 4.38 9.50 1.29 101285 NCV N 2.6 3.15 0.883 2.65 3.86 8.52 1.15 101286 NCV N 2.3 3.63 0.820 2.49 3.65 7.95 1.11 101287 SCV N 14.4 1.96 1.94 2.45 3.98 8.19 1.20 101288 SCV N 21.9 4.41 2.10 2.27 3.84 8.03 1.13 101289 SCV N 29.5 1.17 2.90 2.43 3.81 7.69 1.11 101290 SCV N 11.6 - - 2.39 3.84 7.77 1.11 101291 SCV N 26.0 1.14 2.49 2.53 3.93 8.01 1.14 101292 SCV N 16.6 1.25 1.95 2.41 3.94 8.10 1.17 101293 SCV N 4.0 1.70 1.70 2.55 4.13 8.84 1.19 101294 SCV N 1.6 1.10 1.57 2.45 4.01 8.15 1.20 101295 SCV N 1.0 3.88 0.918 2.52 3.98 8.63 1.19

134

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

101296 SCV N 7.4 0.770 1.73 3.13 4.45 9.33 1.28 101297 SCV N 18.7 0.844 2.18 3.44 4.80 9.78 1.32 101298 SCV N 13.2 0.836 1.87 3.24 4.66 9.40 1.28 101299 SCV N 3.2 0.915 1.64 2.72 4.16 8.68 1.20 101300 SCV N 2.9 0.837 1.53 2.79 4.31 9.00 1.23 101301 SCV N 9.8 1.27 2.04 2.94 4.40 9.11 1.27 101302 SCV N 2.1 0.823 1.93 3.38 4.71 9.77 1.30 101303 SCV N 1.0 0.868 1.44 2.81 4.29 9.11 1.23 101304 SCV N 19.7 3.54 1.47 11.4 8.76 13.7 1.58 101305 SCV N 0.3 4.67 1.56 2.29 3.67 8.05 1.10 101306 SCV N 0.8 2.87 1.56 2.99 4.22 9.06 1.18 101307 SCV N 0.4 9.32 0.906 7.02 6.20 11.3 1.31 101308 SCV N 0.2 17.4 0.690 2.15 3.39 7.65 1.02 101309 SCV N 0.1 3.94 0.924 2.22 3.62 7.79 1.05 101310 SCV N 0.5 16.8 0.672 2.34 3.55 8.04 1.02 101311 SCV N 0.0 6.95 0.863 2.18 3.61 8.00 1.05 101312 SCV N 0.7 15.6 0.673 2.25 3.57 7.87 1.05 101313 SCV N 0.2 14.2 0.702 2.23 3.55 7.79 1.07 101314 SCV N 15.8 3.71 1.40 2.29 3.67 7.96 1.06 101315 SCV N 0.8 10.1 1.06 2.66 4.10 8.54 1.12 101316 SCV N 27.0 3.27 1.49 2.24 3.63 7.67 1.04 101317 SCV N 4.8 7.45 1.18 1.81 3.15 6.96 0.965 101318 SCV N 16.9 1.47 1.98 2.18 3.52 7.48 1.01 101320 SCV N 22.7 4.25 1.54 2.43 3.70 7.91 1.09 101321 SCV N 23.9 1.68 2.18 2.42 3.72 8.42 1.13 101322 SCV N 16.7 2.82 1.74 2.33 3.66 8.48 1.14 101323 SCV N 28.6 1.18 2.71 2.58 3.75 8.21 1.07 101324 SCV N 36.7 0.872 2.86 2.51 3.71 8.38 1.10 101325 SCV N 5.9 2.28 1.59 2.23 3.54 8.04 1.05 101326 SCV N 48.2 1.19 3.17 2.42 3.68 8.34 1.11 101327 SCV N 13.0 2.18 1.72 2.44 3.70 8.57 1.11 101328 SCV N 3.2 3.13 0.912 2.46 3.81 8.65 1.18 101329 SCV N 3.2 1.75 1.05 2.93 4.16 9.17 1.19 101330 SCV N 3.1 2.22 1.01 3.06 4.26 9.37 1.28 101331 SCV N 2.1 4.44 0.803 2.85 3.96 8.68 1.17 101332 SCV N 12.1 20.6 0.719 2.48 3.88 8.53 1.19 101333 SCV N 13.8 5.79 0.906 2.41 3.81 8.48 1.15 101334 SCV N 20.3 0.981 2.52 2.92 4.27 9.28 1.27 101335 SCV N 2.8 0.858 1.61 2.83 4.23 9.48 1.32 101336 SCV N 0.5 6.94 0.829 2.63 4.14 9.60 1.32 101337 SCV N 9.8 0.716 2.26 2.73 4.19 9.68 1.31 101338 SCV N 1.8 0.729 1.47 2.99 4.39 9.93 1.34 101339 SCV N 12.3 0.877 1.71 3.66 4.81 10.5 1.38 101340 SCV N 32.0 0.783 3.54 3.12 4.37 9.61 1.30 101341 SCV N 1.1 3.94 1.59 2.51 3.98 9.30 1.24 101342 SCV N 0.4 3.36 1.19 2.61 4.11 9.36 1.26 101343 SCV N 0.2 0.942 1.42 2.75 4.22 9.68 1.28 101344 SCV N 1.0 3.67 1.01 2.59 4.12 9.43 1.27 101345 SCV N 0.9 1.16 1.37 2.77 4.23 9.62 1.29 101346 SCV N 6.9 3.17 0.942 2.62 4.12 9.31 1.28 101347 SCV N 15.6 1.55 1.50 2.69 4.05 9.18 1.17 101348 SCV N 19.2 0.704 2.65 3.04 4.29 9.46 1.20 101349 SCV N 43.9 0.810 4.51 3.30 4.40 9.66 1.24 101350 SCV N 31.3 0.796 3.39 3.21 4.44 9.76 1.26 101351 SCV N 25.8 0.842 3.18 3.42 4.57 9.85 1.25 101352 SCV N 14.5 0.722 2.01 2.97 4.20 9.29 1.20 101353 SCV N 0.1 4.77 1.00 2.62 4.05 9.26 1.27 101354 SCV N 32.5 1.70 2.32 2.50 3.79 8.68 1.13

135

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

101355 SCV N 21.9 1.65 2.00 2.46 3.75 8.45 1.13 101356 MDB N 0.4 2.98 0.454 2.96 4.13 8.98 1.20 101357 MDB N 0.1 1.94 0.582 2.91 4.08 8.77 1.18 101358 MDB N 0.1 6.42 0.269 2.66 3.76 8.14 1.09 101359 MDB N 0.2 2.47 0.498 2.99 4.14 9.08 1.21 101360 MDB N 0.2 5.58 0.308 2.95 4.13 8.91 1.20 101361 MDB N 0.2 5.88 1.26 17.8 9.07 15.3 1.64 101362 MDB N 1.3 17.7 0.184 2.05 3.39 7.42 1.05 101363 MDB N 0.3 149.2 0.156 2.42 3.66 8.20 1.08 101364 MDB N 1.4 6.75 0.297 2.91 4.16 9.20 1.21 101365 MDB N 0.3 3.70 0.345 2.54 3.66 7.96 1.10 101366 MDB N 12.9 0.737 1.59 2.86 4.05 8.38 1.20 101367 MDB N 5.9 0.890 1.37 3.74 4.81 9.33 1.23 101368 MDB N 0.7 4.37 0.375 3.07 4.30 9.22 1.24 101369 MDB N 0.3 2.06 0.474 2.36 3.46 7.63 1.01 101370 MDB N 1.7 0.133 3.31 2.49 3.78 8.28 1.14 101371 MDB N 1.5 1.18 0.763 2.27 3.51 7.76 1.05 101372 MDB N 0.4 31.3 0.185 2.51 3.66 7.83 1.04 101373 MDB N 0.2 0.916 1.46 3.77 4.80 9.69 1.33 101374 MDB N 0.2 148.6 0.191 2.36 3.64 8.01 1.12 101375 MDB N 4.7 0.935 1.21 2.90 4.18 9.13 1.23 101376 MDB N 0.0 0.914 1.13 2.98 4.23 9.23 1.22 101377 MDB N 0.2 1.41 0.637 2.51 3.52 7.50 1.05 101378 MDB N 0.5 1.22 0.774 2.58 3.82 8.15 1.10 101379 MDB N 10.1 0.689 1.42 2.96 4.30 9.50 1.26 101380 MDB N 1.2 0.672 1.29 2.58 3.79 8.33 1.10 101381 MDB N 6.2 0.797 1.72 3.30 4.52 9.71 1.33 101383 MDB N 0.2 0.713 1.33 2.89 3.87 8.25 1.11 101384 MDB N 0.3 22.9 0.191 1.91 2.93 6.50 0.872 101385 MDB N 0.9 0.875 1.11 2.88 3.91 8.28 1.11 101386 MDB N 0.1 0.791 1.16 2.64 3.62 7.91 1.04 101387 MDB N 0.4 0.717 1.32 2.90 3.95 8.42 1.10 101388 MDB N 0.1 0.933 0.782 2.23 3.27 7.08 0.973 101389 SCV N 3.1 9.70 1.08 2.28 3.60 7.46 1.09 101390 SCV N 0.0 14.3 0.904 2.31 3.66 7.62 1.14 101391 NCV N 1.7 0.477 1.47 2.02 3.79 8.00 1.20 101392 NCV N 6.2 0.695 1.47 2.31 3.42 7.20 1.03 101393 NCV N 9.2 0.571 1.62 2.28 3.43 7.21 1.05 101394 NCV N 12.2 0.541 1.86 2.31 3.48 7.17 1.05 101395 NCV N 9.2 0.670 1.52 2.49 3.59 7.30 1.03 101396 SFBA Y 0.7 1.79 0.929 4.37 5.40 10.8 1.41 101397 SCV N 19.8 1.03 2.35 2.58 3.73 7.71 1.06 101398 SCV N 4.7 0.677 1.42 2.48 3.69 7.80 1.05 101399 SCV N 2.4 0.775 1.20 2.53 3.67 7.56 1.01 101400 SCV N 2.4 0.771 1.19 2.35 3.40 7.22 0.955 101401 SCV N 12.3 0.919 1.48 3.06 3.92 7.94 1.08 101402 SCV N 2.4 0.599 1.44 2.50 3.61 7.83 1.02 101403 SCV N 0.1 1.31 0.812 2.32 3.48 7.57 1.00 101404 SCV N 6.1 0.694 1.60 2.69 3.73 7.98 1.05 101405 SCV N 1.8 0.884 1.06 2.60 3.68 8.02 1.03 101406 SCV N 4.1 0.679 1.52 2.61 3.68 8.06 1.05 101407 SCV N 19.3 0.618 2.42 2.47 3.65 8.04 1.05 101408 SCV N 18.2 0.589 1.75 2.27 3.43 7.52 1.01 101409 SCV N 14.8 0.890 1.69 2.51 3.86 8.34 1.11 101410 SCV N 10.6 2.19 0.883 2.38 3.52 7.76 1.02 101411 SCV N 0.0 3.23 0.634 2.17 3.29 7.11 0.946 101412 SCV N 2.1 1.31 0.881 2.31 3.40 7.36 0.985 101413 SCV N 22.2 0.747 2.08 2.79 3.89 8.29 1.05

136

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

101414 SCV N 3.8 4.74 0.635 2.38 3.41 7.33 1.01 101415 SCV N 16.7 0.583 1.84 2.16 3.58 7.82 1.11 101416 SCV N 22.3 0.624 3.28 2.54 3.87 8.46 1.11 101417 SCV N 5.2 0.623 1.61 2.51 3.77 7.95 1.08 101418 SCV N 6.9 0.753 1.61 2.74 4.07 8.87 1.11 101419 SCV N 17.1 0.625 2.35 2.55 3.64 8.19 1.03 101420 SCV N 2.7 0.848 1.25 2.71 3.98 8.52 1.11 101421 SCV N 11.2 0.637 2.25 2.55 3.76 8.07 1.07 101422 SCV N 19.3 0.558 2.24 2.27 3.54 7.89 1.07 101423 SCV N 11.0 0.676 1.88 2.71 4.34 8.55 1.13 101424 SCV N 8.5 0.648 1.64 2.55 3.85 8.30 1.11 101425 SCV N 4.1 0.708 1.60 2.77 3.95 8.70 1.17 101426 SCV N 0.1 3.98 0.603 2.15 3.32 7.25 0.940 101427 SCV N 16.4 0.717 1.52 2.17 3.60 8.22 1.13 101428 SCV N 46.3 0.631 4.19 2.46 3.78 8.30 1.06 101429 SCV N - 0.862 1.68 2.56 4.04 7.59 1.00 101430 SCV N 13.3 0.759 1.41 2.83 3.95 8.66 1.10 101431 SCV N 1.1 3.90 0.596 2.21 3.37 7.66 1.02 101432 SCV N 9.8 0.638 1.90 2.37 3.43 7.76 1.03 101433 SCV N 16.1 0.863 1.93 3.30 4.03 8.68 1.12 101434 SCV N 16.1 0.848 1.83 3.28 4.18 8.43 1.07 101435 SCV N 23.1 0.739 2.57 2.87 3.94 8.38 1.12 101436 SCV N 16.7 0.777 2.10 2.81 3.73 8.13 1.06 101437 SCV N 10.0 0.762 1.92 2.94 4.03 8.62 1.10 101438 SCV N 24.9 4.24 1.42 15.3 11.4 17.6 1.77 101439 SCV N 11.5 0.574 1.72 2.47 3.52 7.39 1.05 101440 SCV N 28.7 0.703 2.40 3.08 3.88 7.78 1.05 101441 SCV N 6.9 0.604 1.51 2.35 3.46 7.87 1.02 101442 SCV N 18.5 0.780 2.00 2.79 3.85 8.44 1.12 101443 SCV N 18.4 0.755 1.69 3.12 3.97 8.49 1.08 101444 SCV N 20.1 0.825 2.46 3.11 4.16 9.11 1.19 101445 SCV N 9.4 0.835 1.49 3.32 4.02 8.43 1.09 101446 SCV N 15.9 0.662 1.74 2.74 3.72 8.01 1.04 101447 NCV N 10.6 0.759 2.05 2.54 3.80 8.25 1.11 101448 NCV N 7.3 0.703 1.83 2.41 3.67 7.90 1.05 101449 NCV N 16.9 0.556 2.15 2.30 3.62 8.23 1.09 101451 NCV N 20.8 0.667 2.47 2.52 3.71 8.28 1.11 101452 NCV N 12.1 0.637 1.97 2.65 3.73 8.11 1.06 101453 NCV N 15.6 0.636 2.03 2.60 3.62 7.99 1.04 101454 NCV N 12.4 0.623 1.65 2.57 3.49 7.71 1.05 101455 NCV N 11.3 0.851 1.52 3.24 4.48 9.83 1.24 101456 NCV N 13.5 0.886 2.00 2.87 3.96 8.69 1.15 101457 NCV N 1.3 2.13 0.502 2.85 3.87 8.24 1.12 101458 NCV N 3.7 0.914 1.28 2.57 3.83 8.55 1.15 101459 NCV N 1.6 0.786 1.24 2.60 3.84 8.73 1.17 101460 NCV N 1.1 4.78 0.751 2.34 3.64 8.42 1.13 101461 NCV N 0.1 13.3 0.570 2.44 3.69 8.44 1.12 101462 MDB N 4.8 0.687 1.47 2.77 4.00 8.62 1.13 101463 MDB N 0.0 1.25 0.769 1.80 3.01 6.91 0.960 101464 MDB N 0.5 9.70 0.289 2.23 3.26 7.33 1.01 101465 MDB N 0.6 11.6 0.241 2.22 3.41 7.86 1.05 101466 MDB N 0.0 12.8 0.166 2.34 3.67 8.37 1.11 101467 MDB N 0.1 0.488 1.36 1.91 3.36 7.93 1.12 101468 MDB N 0.0 1.58 1.24 5.43 5.02 9.88 1.28 101469 MDB N 0.0 25.0 0.475 2.51 3.54 7.96 1.04 101470 MDB N 0.1 24.2 0.453 2.06 3.25 7.34 0.982 101471 MDB N 0.0 1.46 1.21 2.58 3.79 8.46 1.10 101472 MDB N 0.4 2.11 1.17 2.40 3.55 7.83 1.04

137

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

101473 MDB N 0.0 22.4 0.403 2.03 3.28 7.45 1.01 101474 MDB N 0.0 5.23 0.670 8.66 7.36 12.6 1.35 101475 MDB N 1.0 1.17 0.696 2.04 3.17 7.02 0.940 101476 MDB N 0.0 0.721 1.22 2.33 3.52 7.88 1.05 101477 MDB N 3.6 0.687 1.41 2.83 4.18 8.92 1.16 101478 MDB N 13.0 0.715 1.38 2.77 3.79 8.29 1.11 101479 MDB N 0.2 2.83 1.36 11.0 8.48 13.9 1.49 101480 MDB N 7.0 0.547 1.38 2.17 3.23 7.54 0.977 101481 NCV N 7.8 0.954 1.34 2.73 3.83 8.67 1.13 101482 NCV N 1.6 2.07 0.496 2.89 3.86 8.38 1.09 101484 NCV N 8.9 0.725 1.69 2.57 3.90 8.87 1.18 101485 NCV N 3.6 1.85 0.600 2.68 3.83 8.58 1.10 101486 NCV N 11.0 0.657 2.17 2.69 4.00 9.00 1.18 101487 NCV N 0.6 0.556 1.32 2.14 3.30 7.74 1.02 101488 NCV N 0.1 0.962 0.948 1.97 3.17 7.55 1.04 101489 NCV N 2.7 0.582 1.37 2.21 3.44 7.87 1.05 101490 NCV N 10.4 0.697 1.92 2.86 4.20 9.39 1.25 101491 NCV N 4.3 0.638 1.79 2.61 4.00 9.06 1.19 101492 NCV N 0.8 0.594 1.49 2.53 3.80 8.47 1.12 101493 NCV N 1.3 0.593 1.50 2.33 3.70 8.27 1.11 101494 NCV N 0.0 0.580 1.37 2.36 3.39 7.50 1.05 101495 NCV N 7.6 0.680 1.62 2.73 3.84 8.43 1.10 101496 NCV N 1.1 0.623 1.27 2.25 3.42 7.80 1.05 101497 NCV N 0.5 0.586 1.41 2.40 3.81 8.49 1.18 101498 NCV N 1.4 0.584 1.47 2.42 3.72 8.41 1.12 101499 NCV N 1.1 0.567 1.43 2.41 3.69 8.59 1.15 101500 NCV N 0.8 0.600 1.41 2.54 3.89 8.93 1.22 101501 NCV N 9.8 0.654 1.73 2.75 4.15 9.09 1.21 101502 NCV N 13.9 0.726 2.23 3.18 4.45 9.64 1.31 101503 NCV N 5.4 0.543 1.90 2.31 3.69 8.09 1.14 101504 NCV N 11.0 0.568 2.20 2.48 4.30 8.07 1.15 101505 NCV N 10.3 0.613 1.83 2.75 3.85 8.32 1.15 101506 NCV N 8.0 0.532 2.18 2.29 3.65 8.22 1.12 101507 NCV N 9.1 0.522 2.51 2.29 3.68 8.37 1.16 101508 NCV N 4.6 0.520 1.71 2.30 3.68 8.20 1.13 101509 NCV N 2.5 0.563 1.69 2.41 3.76 8.12 1.11 101510 NCV N 12.6 0.641 2.21 2.65 3.85 8.39 1.11 101511 NCV N 1.0 0.566 1.60 2.40 3.81 8.55 1.15 101512 NCV N 11.9 0.498 8.13 2.30 3.56 8.04 1.12 101513 NCV N 9.5 0.686 1.54 2.77 4.27 9.13 1.18 101514 NCV N 11.0 0.550 1.90 2.27 3.65 8.12 1.08 101515 NCV N 10.0 0.612 6.71 2.68 3.78 8.25 1.08 101516 NCV N 7.7 0.624 1.88 2.90 3.87 8.44 1.15 101517 NCV N 8.5 0.564 2.26 2.31 3.80 8.40 1.17 101518 NCV N 0.0 0.531 2.39 2.42 3.58 8.29 1.18 101519 NCV N 8.2 0.509 1.77 2.08 3.68 8.43 1.18 101520 NCV N 12.4 0.516 2.08 2.30 3.58 7.95 1.12 101521 NCV N 10.9 0.609 1.98 2.56 3.81 8.44 1.08 101522 NCV N 6.4 0.627 1.83 2.76 3.97 8.90 1.22 101523 NCV N 4.6 0.534 1.73 2.45 3.70 8.30 1.18 101524 NCV N 4.2 0.580 1.62 2.62 3.79 8.53 1.15 101525 NCV N 5.5 0.521 1.75 2.32 3.61 8.18 1.16 101526 NCV N 2.3 0.960 1.12 2.28 3.44 7.80 1.07 101527 NCV N 2.9 0.523 1.52 2.23 3.25 8.39 1.20 101528 NCV N 0.5 1.87 0.801 1.94 2.92 7.65 1.06 101529 NCV N 4.3 0.499 1.78 2.31 2.79 8.00 1.21 101530 NCV N 9.6 0.514 2.08 2.34 3.66 8.31 1.15 101551 MDB N 0.7 0.870 1.11 2.49 3.92 8.88 1.18

138

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

101552 MDB N 0.0 1.80 0.838 2.33 3.33 7.43 0.984 101553 MDB N 0.1 1.44 0.767 2.48 3.72 8.31 1.10 101554 MDB N 0.4 3.83 0.536 2.45 3.53 7.84 1.05 101555 MDB N 0.1 1.00 1.20 2.81 3.98 8.70 1.13 101556 MDB N 11.6 0.790 1.40 3.20 4.41 9.81 1.21 101557 MDB N 0.0 3.08 0.230 2.05 3.47 8.10 1.14 101558 MDB N 0.1 23.3 0.450 2.52 3.58 7.98 1.09 101559 MDB N 0.0 8.69 0.404 2.41 3.54 7.90 1.07 101560 MDB N 0.0 3.17 0.553 2.76 4.31 9.39 1.26 101561 MDB N 0.0 12.1 0.121 2.57 3.35 7.23 0.929 101562 MDB N 0.1 25.2 0.109 2.17 3.47 8.09 1.15 101563 MDB N 0.4 2.59 0.402 2.88 3.90 8.58 1.15 101567 SFBA Y 25.5 1.69 1.59 7.16 7.16 12.9 1.47 101573 NCV N 0.2 0.748 1.12 2.04 3.39 7.54 1.03 101576 SFBA Y 19.9 1.10 1.58 4.10 4.68 9.68 1.20 101579 NCV N 15.1 0.534 2.37 2.29 3.71 8.17 1.15 101582 NCV N 0.0 0.569 1.32 2.11 3.46 7.94 1.05 101583 NCV N 0.4 0.658 1.21 2.07 3.43 7.68 1.07 101584 NCV N 7.2 0.542 2.06 2.27 3.72 8.39 1.14 101585 NCV N 11.0 0.514 2.13 2.13 3.60 8.17 1.15 101586 NC N 10.6 9.05 0.391 2.18 3.76 8.70 1.22 101587 NC N 10.2 47.9 0.338 2.36 4.06 9.27 1.25 101588 NC N 7.0 0.401 1.39 1.71 3.58 8.76 1.26 101589 NC N 7.9 0.744 1.29 2.64 4.27 9.65 1.32 101590 NC N 10.3 0.777 1.71 3.08 4.77 10.7 1.33 101591 NC N 29.1 3.59 0.595 3.07 4.68 10.3 1.40 101594 NCV N 0.6 1.22 0.885 1.96 3.31 7.55 1.07 101595 NCV N 6.7 0.573 1.83 2.43 3.76 8.19 1.16 101596 NCV N 3.0 0.532 1.55 2.27 3.48 7.86 1.09 101597 NCV N 3.3 0.648 1.59 2.86 3.95 8.63 1.18 101598 NCV N 16.8 0.516 1.55 2.08 3.32 7.63 1.06 101600 NCV N 0.3 0.476 1.41 2.12 3.25 7.63 1.03 101601 NCV N 12.8 0.779 1.43 2.53 3.68 8.41 1.09 101602 NCV N 25.3 0.614 2.27 2.69 3.82 8.50 1.13 101603 NCV N 27.8 0.622 2.22 2.77 3.81 8.47 1.09 101604 NCV N 1.6 0.469 1.40 2.08 3.30 7.54 1.06 101605 NCV N 10.1 0.525 2.13 2.29 3.57 8.02 1.09 101606 NCV N 9.1 0.490 1.84 2.14 3.59 8.19 1.17 101607 NCV N 6.8 0.487 1.83 2.15 3.57 8.20 1.18 101608 NCV N 0.8 0.505 1.47 2.21 3.60 8.13 1.16 101610 NCV N 0.8 0.538 1.51 2.41 3.85 8.94 1.27 101620 SCV Y 20.1 2.01 1.55 7.42 7.25 13.3 1.47 101621 SCV Y 17.9 1.00 1.37 3.42 4.17 8.46 1.09 101622 SCV Y 19.2 1.31 1.70 4.64 4.76 9.00 1.21 101623 SCV Y 23.5 1.09 1.97 3.68 4.53 9.14 1.17 101624 SCV Y 30.0 0.938 2.79 3.46 4.29 8.61 1.18 101625 SCV Y 9.6 0.674 1.54 2.47 3.66 7.68 1.03 101626 SCV Y 11.1 0.683 1.56 2.56 3.68 7.90 1.06 101627 SCV Y 10.5 0.845 1.43 3.05 4.15 8.95 1.12 101628 SCV Y 38.1 1.14 3.19 4.62 5.39 10.1 1.26 101629 SCV Y 5.7 0.852 1.49 2.75 3.87 8.30 1.09 101630 SCV Y 13.9 0.577 2.10 2.25 3.52 7.57 1.05 101631 SCV Y 9.8 0.664 1.71 2.47 3.79 8.10 1.13 101632 SCV Y 9.7 0.645 1.55 2.57 3.66 8.27 1.11 101633 SCV Y 0.3 2.28 0.416 2.23 3.70 8.10 1.12 101634 SCV Y 8.2 0.918 1.74 3.24 4.29 8.89 1.12 101635 SCV Y 4.3 0.549 1.57 2.10 3.53 7.90 1.10 101636 SCV Y 22.5 0.662 2.34 2.57 3.78 8.07 1.07

139

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

101637 SCV Y 4.5 0.658 1.66 2.47 3.73 8.22 1.09 101638 SCV Y 27.3 0.568 2.23 2.13 3.41 7.37 0.981 101639 SCV Y 16.3 0.866 1.50 3.19 4.09 8.12 1.10 101640 SCV Y 21.0 3.36 1.44 9.74 8.04 13.2 1.45 101641 SCV Y 12.4 2.08 1.42 7.98 6.11 10.7 1.30 101642 SCV Y 31.7 1.56 2.22 6.14 6.22 11.3 1.31 101643 SCV Y 15.3 0.590 1.63 2.37 3.80 8.06 1.12 101644 SCV Y 14.4 1.01 1.35 3.59 4.13 8.30 1.06 101645 SCV Y 29.4 0.991 2.59 3.59 4.33 8.83 1.20 101646 SCV Y 20.1 1.19 1.95 4.70 5.37 9.86 1.25 101647 SCV Y 25.2 0.974 3.04 3.45 4.17 8.45 1.11 101648 SCV Y 17.1 0.786 1.57 2.96 3.85 8.30 1.14 101649 SCV Y 18.4 0.694 2.15 2.79 4.02 8.66 1.13 101660 SCV Y 19.1 1.04 1.56 3.56 4.26 8.79 1.17 101661 SCV Y 18.8 2.13 1.47 7.48 6.30 10.9 1.28 101662 SCV Y 23.8 1.31 1.87 4.75 5.63 10.9 1.26 101663 SCV Y 30.2 3.10 1.92 9.49 8.06 13.7 1.50 101664 SCV Y 11.5 0.711 1.94 2.71 4.03 8.65 1.10 101665 SFBA Y 14.9 0.859 1.61 3.96 4.41 9.11 1.22 101666 SFBA Y 11.1 1.08 1.49 4.95 5.01 9.67 1.29 101667 SFBA Y 11.1 1.35 1.50 6.40 5.84 10.8 1.34 101668 SFBA Y 2.2 1.28 1.45 5.83 5.49 10.6 1.30 101673 SFBA Y 15.6 67.6 0.176 4.76 4.91 9.48 1.27 101677 SCV Y 20.2 2.14 1.53 8.04 6.90 12.4 1.46 101678 SCV Y 28.5 1.32 2.41 5.42 5.84 10.8 1.34 101679 SCV Y 9.6 0.933 1.56 3.20 4.37 9.41 1.18 101680 SCV Y 11.3 0.761 2.08 3.12 4.34 9.03 1.18 101681 SCV Y 0.8 0.584 1.52 2.35 3.71 8.00 1.16 101682 SCV Y 28.4 1.30 2.11 5.04 5.54 10.4 1.33 101683 SCV Y 30.4 1.20 3.24 4.79 5.50 10.5 1.26 101684 SCV Y 9.7 1.98 1.23 6.00 6.03 11.3 1.33 101686 NC N 13.4 0.475 1.95 1.97 3.68 8.64 1.26 101687 NC N 0.4 3.58 7.00 2.08 3.69 8.89 1.27 101688 NC N 7.7 1.29 6.56 2.10 3.79 9.21 1.31 101690 NC N - 0.545 2.81 2.01 3.80 9.31 1.30 101691 NC N 1.3 2.31 3.17 1.93 3.42 8.14 1.10 101692 NC N 0.0 1.17 2.79 2.09 3.50 8.24 1.14 101693 NC N 15.9 1.29 1.30 2.80 4.12 9.31 1.21 101694 NC N 11.8 0.611 1.36 2.50 3.80 8.80 1.19 101695 NC N 13.2 0.726 1.37 2.60 3.86 8.50 1.18 101696 NC N 1.5 2.20 1.92 1.89 3.22 7.46 1.03 101697 NC N 1.4 1.98 1.68 1.93 3.37 7.78 1.08 101698 NC N 1.4 2.41 1.82 1.91 3.26 7.75 1.07 101699 NC N 2.2 2.47 1.77 1.89 3.29 7.75 1.06 101700 NC N 0.0 0.520 1.81 2.00 2.96 6.55 0.898 101701 NC N 0.2 0.521 1.81 1.92 2.97 6.54 0.919 101702 NC N 0.4 0.518 1.79 2.08 3.33 7.51 1.05 101703 NC N 0.3 0.490 1.49 1.88 2.89 6.42 0.903 101704 NC N 0.1 0.653 2.16 2.25 3.39 7.57 1.06 101705 NC N 1.2 0.529 1.48 2.07 3.11 6.85 0.958 101706 NC N 0.7 0.688 3.53 2.29 3.55 8.00 1.15 101707 NC N 1.4 0.770 2.71 2.12 3.64 8.32 1.20 101708 NC N 6.0 0.621 1.63 2.64 3.78 8.36 1.14 101709 NC N 8.5 0.441 1.47 1.98 3.78 9.14 1.37 101710 NC N 9.4 0.442 1.48 1.94 3.79 9.19 1.34 101711 NC N 9.5 0.437 1.48 1.98 3.74 9.11 1.35 101747 SFBA Y 25.5 1.04 1.52 4.54 4.65 9.11 1.12 101754 SFBA Y 0.9 1.38 0.870 4.01 4.76 9.66 1.21

140

Sample Number Areaa AR

areab3Hcd 4Hece 3He/4Hecf Nee Arg Krh Xeh

101755 SFBA Y 38.6 1.60 1.70 7.40 5.95 10.6 1.28 101756 SFBA Y 5.7 1.07 1.11 3.70 4.55 9.06 1.24 101757 SFBA Y 9.9 1.08 1.44 4.24 5.08 10.2 1.35 101758 SFBA Y 8.3 1.31 1.05 3.67 4.42 9.03 1.26 101759 SFBA Y 17.9 1.15 1.65 4.66 4.91 9.53 1.27 101760 SFBA Y 12.9 1.26 1.40 4.25 4.89 9.79 1.28 101761 SFBA Y 4.3 1.71 0.849 3.69 4.63 9.99 1.29 101763 SFBA Y 37.8 0.800 1.86 3.27 4.03 8.86 1.10 101764 SFBA Y 21.3 135.1 0.222 3.48 4.07 8.62 1.09 101765 SFBA Y 15.4 0.512 1.35 2.21 3.61 7.87 1.03 103118 LAB Y 3.8 3.00 0.728 4.28 4.83 9.61 1.19 103119 LAB Y 20.9 0.656 1.29 2.51 3.66 7.87 1.02 103120 LAB Y 7.7 1.32 1.29 4.84 5.60 10.7 1.34 103121 LAB Y 4.7 7.32 0.313 4.59 4.93 9.86 1.24 103122 LAB Y 16.2 0.763 1.28 2.37 3.55 7.66 1.01 103123 LAB Y 7.0 1.31 1.07 4.03 4.98 9.66 1.16 103124 LAB Y 16.4 0.853 1.11 2.37 3.61 8.00 1.07 103125 LAB Y 1.0 1.69 1.25 3.91 4.55 9.25 1.18 103126 LAB Y 21.4 0.708 1.64 2.83 3.83 8.08 1.04 103127 LAB Y - 0.667 1.52 2.67 3.75 8.27 1.04 103128 LAB Y 15.8 2.56 1.41 9.35 8.03 13.4 1.53 103129 LAB Y 15.8 1.97 1.42 7.55 6.97 12.4 1.44 103130 LAB Y 11.0 1.73 1.43 6.56 6.31 11.7 1.37 103132 LAB Y 16.1 1.57 1.37 6.55 6.65 12.1 1.43 103133 LAB Y 4.3 1.93 1.17 3.55 4.31 9.06 1.13 103134 LAB Y 1.1 1.08 1.06 3.29 4.27 8.97 1.12 103135 LAB Y 13.5 0.831 1.36 3.39 4.24 9.17 1.19 103136 LAB Y - 1.04 1.39 4.36 5.01 9.31 1.12 103137 LAB Y 9.1 2.43 0.527 3.07 3.94 8.31 1.10 103138 LAB Y 0.9 5.23 0.286 2.74 3.90 8.52 1.13 103139 LAB Y 1.1 5.31 0.292 2.83 3.90 8.41 1.11 103140 LAB Y 1.5 1.10 1.13 3.54 4.52 9.54 1.22 103141 LAB Y 12.8 1.17 1.42 4.69 5.00 10.1 1.25 103142 LAB Y 23.0 1.50 1.38 5.88 6.60 12.6 1.37 103143 LAB Y 14.6 1.72 1.40 6.61 6.05 11.4 1.38 103144 LAB Y 8.1 1.07 1.49 4.20 4.97 9.87 1.21

a San Francisco Bay Area (SFBA), Los Angeles Basin (LAB), Northern California (NC), Mojave Desert Basin (MDB), northern portion of the Central Valley (NCV), and southern portion of the Central Valley (SCV) b artificial recharge impacted area (Yes/No) c "-" indicates data unavailable d (pCi L-1) e (× 10-7 cm3 STP g-1) f (× 10-6) g (× 10-4 cm3 STP g-1) h (× 10-8 cm3 STP g-1)

141

Table B2. NOBLE90 modeling results. Sample

No. MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

100595 18.1 0.995 - - - - - - - - - - 100598 18.3 0.995 87 17.2 88 19.2 0.26 0.272 90 20.3 0.84 0.142 100599 18.1 0.995 75 15.2 75 15.7 0.13 0.281 76 16.0 0.32 0.218 100613 17.7 0.971 - - - - - - - - - - 100618 18.2 0.993 78 15.1 79 16.4 0.26 0.273 80 17.6 0.83 0.140 100620 18.2 0.993 - - - - - - 54 20.0 1.82 0.062 100624 18.2 0.993 35 16.6 36 17.3 0.43 0.273 37 18.4 1.10 0.112 100627 16.9 0.998 13 16.5 12 16.5 0.00 0.288 12 16.5 0.00 0.288 100628 18.1 0.995 22 14.8 21 14.6 0.00 0.288 21 14.6 0.00 0.288 100630 18.1 0.996 24 14.0 23 13.8 0.00 0.288 23 13.8 0.00 0.288 100634 18.1 0.996 87 13.6 88 15.1 0.24 0.272 90 16.3 0.82 0.140 100635 18.3 0.996 92 13.9 93 15.5 0.23 0.272 94 16.5 0.79 0.145 100636 18.1 0.995 29 15.8 29 15.7 0.00 0.288 29 15.7 0.00 0.288 100637 18.1 0.996 - - - - - - - - - - 100638 18.1 0.996 15 17.5 16 19.6 0.83 0.250 17 20.6 2.55 0.034 100640 18.1 0.995 85 15.4 85 15.5 0.03 0.287 86 15.6 0.07 0.270 100641 18.1 0.996 57 14.6 58 16.2 0.39 0.267 59 16.4 0.82 0.141 100642 18.1 0.995 158 17.7 159 18.6 0.06 0.282 160 19.0 0.24 0.235 100643 18.1 0.996 34 14.9 33 14.9 0.00 0.288 33 14.8 0.00 0.288 100644 18.1 0.996 - - - - - - - - - - 100645 17.6 0.993 - - - - - - - - - - 100646 18.1 0.995 76 16.2 78 17.9 0.29 0.271 78 18.4 0.73 0.154 100647 18.1 0.995 91 13.9 92 15.5 0.23 0.272 94 17.0 0.87 0.136 100648 18.1 0.995 56 15.9 57 17.8 0.43 0.265 58 18.1 0.95 0.128 100649 18.1 0.995 99 16.3 100 17.5 0.16 0.277 100 17.7 0.40 0.204 100650 18.1 0.995 76 14.8 78 16.7 0.31 0.268 78 17.1 0.78 0.147 100651 18.1 0.993 - - - - - - - - - - 100652 18.1 0.993 40 18.8 40 19.3 0.31 0.278 40 19.1 0.21 0.241 100654 18.1 0.996 65 14.6 67 16.7 0.38 0.265 69 18.2 1.22 0.101 100656 18.1 0.996 62 14.1 63 15.8 0.37 0.267 64 16.1 0.84 0.138 100659 18.1 0.996 76 12.5 79 15.2 0.37 0.262 81 16.6 1.25 0.097 100660 18.1 0.995 91 13.6 92 14.6 0.18 0.276 92 14.8 0.42 0.198 100661 17.6 0.986 65 16.9 66 18.7 0.35 0.269 67 19.2 0.83 0.142 100662 18.1 0.996 68 16.2 69 18.5 0.38 0.266 71 19.8 1.17 0.107 100663 18.1 0.995 23 16.9 23 18.0 0.68 0.261 24 18.4 1.34 0.091 100664 18.1 0.995 76 14.9 76 15.2 0.10 0.283 76 15.3 0.17 0.248 100665 18.2 0.995 35 16.8 36 17.2 0.34 0.278 36 17.9 0.76 0.150 100666 18.2 0.995 256 18.0 257 19.7 0.05 0.281 259 19.8 0.23 0.238 100667 18.1 0.995 45 14.0 - - - - 47 16.3 1.16 0.104 100669 17.6 0.995 91 15.9 91 16.8 0.16 0.278 92 17.8 0.58 0.175 100670 18.1 0.995 112 15.8 113 17.0 0.14 0.278 114 17.5 0.42 0.200 100671 18.1 0.995 53 15.6 54 17.1 0.41 0.268 55 17.7 0.94 0.128 100672 18.1 0.995 21 15.8 21 15.8 0.03 0.288 20 15.8 0.00 0.288 100673 18.1 0.995 160 19.9 158 19.8 0.00 0.288 158 19.8 0.00 0.288 100674 18.2 0.995 66 16.2 67 17.6 0.31 0.271 68 18.0 0.70 0.158 100675 18.1 0.996 23 16.4 22 16.3 0.00 0.288 22 16.3 0.00 0.288 100676 18.1 0.995 27 17.0 27 17.2 0.26 0.283 27 17.6 0.55 0.179 100679 18.1 0.996 74 13.4 75 14.6 0.25 0.273 76 14.9 0.58 0.173 100680 18.1 0.996 38 13.6 38 14.5 0.46 0.269 39 14.9 0.87 0.133 100682 18.1 0.995 - - - - - - - - - - 100683 18.3 0.996 78 14.4 80 17.0 0.35 0.264 82 18.2 1.12 0.110 100684 18.1 0.996 91 13.8 92 15.1 0.20 0.274 92 15.3 0.49 0.186 100685 16.9 0.998 - - - - - - - - - - 100686 18.1 0.995 70 16.7 - - - - 72 18.2 0.56 0.179 100688 18.1 0.996 52 15.2 53 16.4 0.38 0.270 54 16.6 0.66 0.162 100689 18.1 0.995 101 16.1 103 18.3 0.24 0.271 104 19.3 0.80 0.146

142

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

100690 18.1 0.995 74 15.9 74 16.8 0.21 0.277 75 16.8 0.37 0.208 100691 18.1 0.996 48 15.2 50 17.9 0.53 0.259 51 18.9 1.51 0.079 100692 18.1 0.996 9 14.1 - - - - - - - - 100693 16.2 0.998 14 15.8 13 15.7 0.00 0.288 13 15.7 0.00 0.288 100694 16.2 0.998 - - - - - - - - - - 100695 18.3 0.995 61 15.0 61 15.5 0.19 0.280 62 15.4 0.21 0.239 100696 17.1 0.998 11 17.0 11 17.1 0.55 0.280 11 17.2 0.45 0.194 100697 18.3 0.995 38 15.0 39 15.4 0.33 0.277 39 15.4 0.33 0.216 100698 18.3 0.995 33 15.6 34 16.2 0.43 0.273 34 16.4 0.60 0.171 100699 17.1 0.998 32 17.0 32 17.5 0.39 0.276 33 17.6 0.44 0.197 100700 18.1 0.996 29 15.4 29 15.6 0.33 0.280 29 15.8 0.38 0.206 100701 16.2 0.998 31 18.1 31 18.1 0.01 0.288 31 18.1 0.01 0.287 100702 18.3 0.995 55 14.8 56 17.4 0.48 0.260 58 18.7 1.44 0.084 100703 17.1 0.998 60 16.4 59 16.3 0.00 0.288 60 16.4 0.00 0.288 100704 18.3 0.995 25 15.1 27 17.5 0.72 0.251 28 18.6 2.12 0.047 100705 18.3 0.995 55 13.7 55 14.3 0.24 0.277 56 14.5 0.44 0.195 100706 17.1 0.998 - - - - - - - - - - 100707 17.1 0.998 - - - - - - - - - - 100708 18.1 0.993 93 17.1 94 17.7 0.11 0.282 94 18.2 0.35 0.214 100710 16.9 0.998 6 18.5 8 20.3 0.92 0.245 9 21.9 3.67 0.014 100719 18.1 0.993 - - - - - - - - - - 100721 18.1 0.995 22 13.8 24 16.3 0.77 0.248 25 17.5 2.39 0.036 100722 17.1 0.993 - - 54 18.5 0.53 0.257 57 20.6 1.83 0.062 100724 18.1 0.996 10 16.6 12 19.3 0.88 0.244 13 20.4 3.24 0.019 100726 18.1 0.995 37 13.1 38 14.5 0.54 0.262 39 15.4 1.35 0.087 100727 17.6 0.986 27 16.3 27 16.9 0.53 0.271 27 17.1 0.75 0.150 100728 17.1 0.993 - - 156 18.7 0.21 0.266 161 20.8 1.05 0.120 100729 17.1 0.993 - - 111 19.7 0.26 0.268 114 21.5 1.06 0.119 100730 18.1 0.995 - - - - - - 128 23.6 1.22 0.106 100731 17.1 0.993 70 14.5 72 17.1 0.39 0.263 74 18.6 1.27 0.097 100732 18.3 0.995 114 13.6 115 15.6 0.20 0.271 118 16.9 0.78 0.146 100733 18.1 0.996 9 14.9 11 17.1 0.90 0.242 11 17.7 3.10 0.020 100734 17.1 0.993 31 16.4 33 19.4 0.68 0.253 34 20.5 2.02 0.053 100735 17.6 0.986 43 15.7 44 17.5 0.52 0.262 45 18.7 1.43 0.085 100736 18.1 0.995 7 14.5 7 14.8 0.86 0.263 7 15.1 1.61 0.069 100737 16.9 0.998 12 17.1 12 17.3 0.60 0.278 12 17.6 0.90 0.132 100738 18.1 0.995 - - 95 19.2 0.32 0.265 98 21.0 1.21 0.105 100739 18.1 0.996 28 16.7 28 18.0 0.64 0.262 29 18.5 1.27 0.097 100740 18.1 0.996 28 15.9 29 16.9 0.58 0.266 29 17.1 0.96 0.125 100742 17.1 0.998 4 19.6 3 19.3 0.00 0.288 3 19.3 0.00 0.288 100743 17.1 0.998 - - - - - - - - - - 100744 17.1 0.998 - - - - - - - - - - 100745 18.3 0.996 110 14.2 110 14.7 0.08 0.283 111 15.1 0.27 0.227 100746 18.1 0.996 63 14.6 65 16.9 0.40 0.264 67 18.0 1.19 0.104 100747 18.1 0.995 - - 73 20.0 0.43 0.261 75 21.3 1.43 0.087 100748 18.2 0.995 - - 49 19.2 0.57 0.256 50 20.2 1.70 0.069 100749 18.1 0.996 25 14.6 25 14.8 0.42 0.277 25 15.0 0.43 0.196 100750 18.3 0.995 84 16.6 85 18.1 0.25 0.273 86 19.0 0.73 0.154 100751 18.2 0.995 141 15.7 140 15.6 0.00 0.288 139 15.6 0.00 0.288 100753 18.2 0.993 31 17.1 31 17.1 0.00 0.288 31 17.2 0.12 0.260 100755 18.2 0.995 115 17.9 116 19.9 0.18 0.274 119 21.3 0.74 0.155 100756 18.2 0.995 85 17.5 87 19.3 0.25 0.273 87 19.6 0.63 0.169 100757 18.2 0.987 46 17.7 48 19.9 0.51 0.263 49 21.3 1.46 0.085 100758 18.2 0.995 80 17.6 81 19.5 0.29 0.271 82 20.4 0.85 0.141 100759 18.1 0.996 32 13.8 32 14.1 0.31 0.279 32 14.0 0.20 0.241 100760 18.1 0.996 33 14.0 33 14.5 0.42 0.274 33 14.6 0.53 0.181 100761 18.1 0.996 30 15.8 33 18.9 0.69 0.252 34 20.0 2.09 0.049

143

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

100762 18.1 0.996 49 14.2 50 16.6 0.51 0.259 52 18.0 1.55 0.076 100763 18.1 0.996 48 12.4 49 13.8 0.45 0.264 51 15.4 1.39 0.084 100764 18.1 0.996 25 13.8 27 15.7 0.71 0.253 28 17.0 2.07 0.048 100765 18.3 0.995 25 13.4 25 13.7 0.46 0.275 25 14.2 0.84 0.137 100767 18.1 0.996 49 14.4 50 16.5 0.49 0.261 51 17.3 1.30 0.094 100768 18.1 0.993 105 14.0 106 15.5 0.19 0.274 107 16.1 0.57 0.176 100771 18.1 0.993 - - - - - - - - - - 100772 18.1 0.995 66 15.8 62 15.4 0.00 0.288 62 15.4 0.00 0.288 100773 18.1 0.993 129 14.1 128 14.0 0.00 0.288 129 14.1 0.00 0.288 100775 18.1 0.993 - - - - - - - - - - 100776 18.1 0.993 139 15.9 135 15.7 0.00 0.288 136 15.8 0.00 0.288 100777 18.1 0.993 133 15.0 134 16.1 0.10 0.279 135 16.5 0.35 0.212 100778 17.6 0.993 76 15.4 76 16.4 0.22 0.276 77 16.7 0.48 0.190 100779 18.1 0.995 46 14.7 48 16.7 0.51 0.261 49 17.4 1.26 0.097 100780 18.1 0.995 57 15.6 58 17.6 0.43 0.265 59 18.1 1.02 0.119 100781 18.1 0.996 38 16.4 40 18.6 0.58 0.259 40 18.9 1.29 0.096 100782 18.1 0.995 157 16.8 159 18.9 0.12 0.276 161 19.5 0.48 0.191 100783 18.1 0.995 - - 206 19.3 0.16 0.267 - - - - 100784 18.1 0.995 253 15.5 254 16.7 0.04 0.282 256 17.3 0.25 0.233 100785 18.1 0.996 26 16.4 27 18.3 0.70 0.256 27 18.3 1.41 0.086 100786 18.1 0.995 - - 165 20.0 0.15 0.273 169 22.1 0.80 0.149 100787 18.1 0.995 - - 85 18.8 0.37 0.263 89 21.5 1.48 0.084 100825 16.4 0.995 16 16.6 15 16.5 0.00 0.288 15 16.5 0.00 0.288 100827 16.4 0.995 66 15.3 66 15.5 0.08 0.285 67 15.6 0.14 0.254 100828 16.4 0.995 142 13.3 143 14.7 0.12 0.277 145 15.7 0.53 0.180 100830 16.4 0.995 18 16.5 17 16.5 0.00 0.288 18 16.9 0.62 0.169 100831 15.4 0.995 15 15.3 14 15.2 0.00 0.288 14 15.2 0.00 0.288 100832 16.4 0.995 29 15.0 29 15.8 0.55 0.268 30 16.3 1.03 0.117 100833 16.4 0.995 54 15.3 55 17.1 0.44 0.265 56 18.4 1.25 0.099 100834 16.4 0.995 9 14.9 11 17.0 0.89 0.243 11 16.8 2.56 0.031 100835 16.4 0.995 32 14.5 33 14.9 0.37 0.277 33 14.9 0.33 0.214 100836 16.4 0.995 25 14.0 26 15.2 0.67 0.259 26 15.3 1.18 0.102 100837 16.4 0.995 47 16.0 47 16.9 0.35 0.273 47 16.7 0.40 0.204 100838 16.4 0.995 29 12.5 30 13.2 0.54 0.267 30 13.5 0.88 0.131 100839 16.4 0.995 11 13.1 11 13.1 0.32 0.285 11 13.2 0.13 0.255 100840 16.4 0.995 42 13.6 42 14.0 0.30 0.277 42 14.3 0.47 0.190 100841 16.4 0.995 25 14.4 25 14.6 0.34 0.280 25 14.6 0.26 0.229 100842 16.4 0.995 10 12.2 10 12.7 0.83 0.257 12 14.1 2.59 0.028 100843 16.4 0.995 25 13.1 25 13.2 0.13 0.286 25 13.3 0.17 0.248 100844 16.4 0.995 43 15.6 43 16.6 0.43 0.270 44 17.0 0.81 0.142 100845 16.4 0.995 13 10.0 12 9.9 0.00 0.288 12 9.9 0.00 0.288 100846 16.4 0.995 14 15.1 13 15.0 0.00 0.288 13 15.0 0.00 0.288 100847 16.4 0.995 14 14.4 18 19.4 0.85 0.242 19 20.8 3.63 0.014 100848 16.4 0.995 24 15.3 23 15.2 0.00 0.288 23 15.2 0.00 0.288 100849 15.4 0.995 28 15.3 28 15.2 0.00 0.288 28 15.2 0.00 0.288 100850 16.4 0.995 12 10.3 - - - - - - - - 100851 16.4 0.995 19 15.2 18 15.1 0.00 0.288 18 15.1 0.00 0.288 100853 16.4 0.995 102 13.3 - - - - - - - - 100854 16.4 0.995 34 13.8 34 14.0 0.24 0.281 34 14.3 0.41 0.200 100855 16.4 0.995 8 12.3 7 12.1 0.00 0.288 7 12.1 0.00 0.288 100856 16.4 0.995 23 12.4 23 12.4 0.00 0.288 23 12.4 0.00 0.288 100857 16.4 0.995 28 15.5 26 15.4 0.00 0.288 26 15.4 0.00 0.288 100858 16.4 0.995 49 11.7 49 11.7 0.01 0.288 49 11.8 0.05 0.276 100859 16.4 0.995 34 14.7 33 14.7 0.00 0.288 33 14.6 0.00 0.288 100860 16.4 0.995 12 13.3 12 14.0 0.83 0.255 12 14.3 1.67 0.065 100861 16.4 0.995 8 12.0 8 12.0 0.00 0.288 7 12.0 0.00 0.288 100862 16.4 0.995 23 16.1 22 16.0 0.00 0.288 22 16.0 0.00 0.288

144

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

100863 15.4 0.995 33 14.7 31 14.6 0.00 0.288 31 14.6 0.00 0.288 100864 16.4 0.995 21 14.9 20 14.7 0.00 0.288 20 14.7 0.00 0.288 100865 15.2 0.979 - - 101 18.0 0.36 0.259 - - - - 100866 15.2 0.979 - - 76 17.1 0.46 0.254 80 19.3 1.84 0.060 100867 15.2 0.979 - - 123 16.3 0.24 0.266 126 17.8 0.99 0.123 100868 15.2 0.979 117 14.3 118 15.9 0.17 0.274 120 16.6 0.57 0.175 100869 15.2 0.979 79 13.1 80 13.3 0.06 0.285 80 13.2 0.05 0.277 100875 14.0 0.998 35 11.1 35 11.6 0.41 0.272 35 11.7 0.48 0.186 100876 14.0 0.998 24 14.2 24 14.6 0.53 0.271 24 14.5 0.33 0.214 100877 14.0 0.998 27 10.9 - - - - 31 15.8 2.60 0.029 100878 14.0 0.998 - - - - - - - - - - 100880 14.0 0.999 - - - - - - - - - - 100881 14.0 0.999 - - - - - - - - - - 100882 13.1 0.993 - - - - - - - - - - 100883 14.0 0.999 - - - - - - - - - - 100884 13.1 0.993 - - - - - - - - - - 100885 13.1 0.993 - - - - - - - - - - 100886 13.1 0.993 33 14.5 33 14.5 0.00 0.288 33 14.5 0.00 0.288 100888 13.1 0.993 - - - - - - - - - - 100891 15.1 0.998 82 12.7 83 13.9 0.23 0.273 84 14.7 0.68 0.157 100892 15.1 0.998 - - - - - - - - - - 100893 15.1 0.998 35 15.0 35 15.0 0.00 0.288 34 14.9 0.00 0.288 100894 15.1 0.998 70 15.5 69 15.5 0.00 0.288 68 15.4 0.00 0.288 100895 15.1 0.998 63 15.0 63 15.8 0.23 0.277 64 15.7 0.34 0.214 100896 15.1 0.998 55 14.2 54 14.1 0.00 0.288 53 14.0 0.00 0.288 100897 15.1 0.998 49 14.5 49 14.6 0.08 0.285 48 14.5 0.00 0.288 100898 15.1 0.998 45 13.3 46 14.1 0.37 0.272 47 15.3 1.06 0.113 100899 15.1 0.998 46 13.6 46 13.5 0.00 0.288 47 13.8 0.15 0.252 100900 15.1 0.998 66 13.6 67 15.4 0.36 0.267 69 16.8 1.12 0.108 100901 15.1 0.998 42 15.1 43 15.4 0.23 0.280 43 15.5 0.29 0.223 100902 15.1 0.998 62 14.4 62 14.8 0.14 0.282 62 14.6 0.09 0.266 100903 14.4 0.999 - - - - - - - - - - 100904 14.4 0.999 130 12.3 131 13.5 0.12 0.277 132 14.3 0.50 0.185 100905 15.2 0.979 102 14.3 103 15.5 0.16 0.276 104 16.3 0.56 0.176 100906 15.2 0.979 - - - - - - - - - - 100907 15.2 0.979 112 15.1 114 17.0 0.19 0.273 116 18.3 0.75 0.151 100908 15.2 0.979 119 13.1 121 15.5 0.21 0.269 124 17.2 0.92 0.130 100909 12.6 0.996 - - - - - - - - - - 100910 12.6 0.996 - - - - - - - - - - 100911 12.6 0.996 - - - - - - - - - - 100912 12.6 0.996 - - - - - - - - - - 100915 15.7 0.995 52 13.7 51 13.7 0.00 0.288 51 13.7 0.00 0.288 100916 15.0 0.995 - - - - - - - - - - 100918 12.3 0.995 55 14.5 57 16.6 0.45 0.262 59 18.9 1.56 0.076 100919 15.0 0.995 - - - - - - - - - - 100920 15.0 0.995 - - - - - - - - - - 100923 14.4 0.999 - - - - - - - - - - 100924 14.4 0.999 102 8.3 - - - - - - - - 100925 14.4 0.999 - - - - - - - - - - 100926 14.4 0.999 - - - - - - - - - - 100927 14.4 0.999 - - - - - - - - - - 100928 16.0 0.998 8 14.7 8 15.1 0.85 0.262 9 15.2 1.19 0.100 100929 16.0 0.998 13 13.2 13 13.1 0.00 0.288 13 13.1 0.00 0.288 100930 16.0 0.998 12 13.7 11 13.6 0.00 0.288 11 13.6 0.00 0.288 100931 16.0 0.998 12 12.4 12 12.3 0.00 0.288 12 12.3 0.00 0.288 100932 16.0 0.998 17 12.0 17 12.0 0.00 0.288 16 12.0 0.00 0.288 100933 16.0 0.998 13 12.4 13 12.4 0.00 0.288 13 12.3 0.00 0.288

145

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

100934 16.0 0.998 12 13.0 12 13.0 0.00 0.288 12 13.0 0.00 0.288 100935 16.0 0.998 11 12.1 10 12.0 0.00 0.288 10 12.0 0.00 0.288 100936 16.0 0.998 13 11.8 12 11.7 0.00 0.288 12 11.6 0.00 0.288 100937 16.0 0.998 16 12.9 16 13.1 0.55 0.276 16 13.0 0.17 0.246 100938 16.0 0.998 13 11.4 12 11.4 0.00 0.288 12 11.3 0.00 0.288 100939 16.0 0.998 17 12.4 17 12.4 0.00 0.288 17 12.4 0.00 0.288 100940 16.0 0.998 11 10.5 10 10.2 0.00 0.288 10 10.2 0.00 0.288 100941 16.0 0.998 6 14.5 6 14.5 0.00 0.288 6 14.5 0.00 0.288 100943 16.0 0.998 23 12.8 24 13.8 0.67 0.259 24 13.6 0.86 0.133 100944 16.0 0.998 16 15.0 16 15.1 0.42 0.281 16 15.1 0.26 0.229 100945 16.0 0.998 18 13.6 18 13.8 0.45 0.279 18 13.8 0.26 0.229 100946 16.0 0.998 12 12.3 12 12.1 0.00 0.288 12 12.1 0.00 0.288 100947 16.0 0.998 14 10.8 13 10.8 0.00 0.288 13 10.6 0.00 0.288 100948 16.0 0.998 20 10.7 19 10.7 0.00 0.288 19 10.7 0.00 0.288 100949 16.0 0.998 12 11.9 12 11.8 0.00 0.288 12 11.8 0.00 0.288 100950 16.0 0.998 18 14.8 19 15.7 0.73 0.259 19 15.8 1.19 0.101 100951 16.0 0.998 16 14.8 16 14.8 0.00 0.288 16 14.8 0.00 0.288 100952 16.0 0.998 12 12.1 12 12.1 0.00 0.288 12 12.1 0.00 0.288 100953 16.0 0.998 20 13.8 19 13.7 0.00 0.288 19 13.7 0.00 0.288 100954 16.0 0.998 14 14.3 15 14.8 0.75 0.263 15 15.2 1.38 0.085 100955 16.0 0.998 14 11.7 14 12.1 0.73 0.264 15 12.4 1.12 0.104 100956 16.0 0.998 18 13.2 18 13.2 0.00 0.288 18 13.2 0.00 0.288 100957 15.0 0.995 - - - - - - - - - - 100958 15.0 0.995 - - - - - - - - - - 100961 14.4 0.993 67 10.1 68 11.3 0.31 0.268 69 12.2 0.92 0.125 100963 14.4 0.993 104 12.8 - - - - - - - - 100965 14.4 0.993 - - - - - - - - - - 100966 15.0 0.993 - - - - - - - - - - 100967 14.4 0.993 359 12.0 361 13.6 0.03 0.281 363 13.8 0.20 0.241 100968 15.0 0.993 325 13.8 326 14.3 0.01 0.286 326 14.2 0.05 0.277 100969 15.0 0.993 178 14.1 177 14.1 0.00 0.288 176 14.1 0.00 0.288 100970 15.0 0.993 231 12.6 231 13.4 0.03 0.283 232 13.6 0.16 0.250 100973 18.1 0.947 - - - - - - - - - - 100974 18.1 0.947 - - - - - - - - - - 100975 18.0 0.947 - - 59 13.6 0.49 0.255 63 16.1 1.86 0.057 100976 18.1 0.947 75 12.2 77 14.2 0.34 0.265 79 16.1 1.24 0.097 100977 15.2 0.982 - - - - - - - - - - 100978 15.2 0.982 192 11.2 193 12.5 0.07 0.278 194 12.9 0.33 0.215 100979 16.0 0.998 13 14.9 12 14.9 0.00 0.288 12 14.9 0.00 0.288 100980 16.0 0.998 14 12.1 13 11.9 0.00 0.288 13 11.9 0.00 0.288 100981 16.0 0.998 12 13.2 12 13.3 0.36 0.284 12 13.3 0.20 0.241 100982 16.0 0.998 21 16.8 22 17.3 0.57 0.272 22 17.3 0.53 0.182 100983 16.0 0.998 19 12.7 19 13.1 0.59 0.271 19 13.1 0.50 0.183 100984 16.0 0.998 12 13.6 12 13.6 0.00 0.288 12 13.6 0.00 0.288 100985 16.0 0.998 11 14.9 11 15.4 0.80 0.262 12 15.4 1.00 0.119 100986 16.0 0.998 10 14.7 9 14.7 0.00 0.288 9 14.7 0.00 0.288 100987 16.0 0.998 5 16.1 5 16.0 0.00 0.288 5 16.0 0.00 0.288 100988 16.0 0.998 11 15.4 10 15.4 0.00 0.288 10 15.4 0.00 0.288 100989 16.5 0.998 - - - - - - - - - - 100990 16.5 0.998 9 17.0 8 16.9 0.00 0.288 8 16.9 0.00 0.288 100991 16.5 0.998 2 15.6 2 16.1 0.98 0.241 2 15.9 2.39 0.035 100992 16.5 0.998 6 15.0 6 15.0 0.00 0.288 6 15.0 0.00 0.288 100993 16.0 0.998 197 16.2 197 16.2 0.00 0.288 196 16.2 0.00 0.288 100994 16.0 0.998 7 15.3 8 15.9 0.90 0.252 10 19.5 3.86 0.011 100995 16.0 0.998 18 12.8 19 14.3 0.78 0.250 21 16.1 2.53 0.031 100996 16.0 0.998 23 14.1 23 15.2 0.69 0.258 25 16.2 1.75 0.062 100997 16.0 0.998 17 12.5 16 12.3 0.00 0.288 16 12.3 0.00 0.288

146

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

100998 16.0 0.998 14 11.3 13 11.1 0.00 0.288 13 11.0 0.00 0.288 100999 12.3 0.995 51 13.7 49 13.6 0.00 0.288 49 13.5 0.00 0.288 101000 12.3 0.995 52 14.5 54 16.2 0.43 0.265 54 16.7 0.98 0.122 101001 12.3 0.995 - - 38 20.8 0.65 0.254 39 21.3 1.89 0.060 101002 15.7 0.995 49 14.8 49 15.3 0.26 0.278 49 15.3 0.29 0.223 101003 15.7 0.995 55 15.6 56 16.7 0.33 0.272 57 17.5 0.82 0.141 101004 15.7 0.995 104 14.0 102 13.8 0.00 0.288 101 13.8 0.00 0.288 101005 15.7 0.995 - - - - - - - - - - 101006 15.7 0.995 - - - - - - - - - - 101007 15.7 0.995 - - - - - - - - - - 101008 15.7 0.995 - - - - - - - - - - 101009 12.3 0.995 114 13.9 115 14.9 0.13 0.278 115 15.2 0.35 0.211 101010 15.0 0.995 - - - - - - - - - - 101011 15.0 0.995 - - 142 13.3 0.21 0.266 145 14.3 0.87 0.133 101012 15.0 0.995 202 10.5 202 10.7 0.01 0.287 202 10.7 0.03 0.280 101013 15.0 0.995 206 9.7 206 10.1 0.02 0.284 207 10.3 0.12 0.258 101014 15.0 0.995 218 11.7 219 13.0 0.05 0.280 220 13.0 0.22 0.235 101015 15.0 0.995 223 13.2 216 12.8 0.00 0.288 - - - - 101016 15.0 0.995 - - - - - - - - - - 101017 15.0 0.995 - - - - - - - - - - 101018 15.0 0.995 170 9.2 171 11.2 0.12 0.272 174 12.3 0.61 0.165 101019 15.7 0.995 91 14.4 90 14.4 0.00 0.288 90 14.4 0.00 0.288 101020 15.7 0.995 71 14.3 71 14.3 0.00 0.288 71 14.3 0.00 0.288 101021 15.7 0.995 - - 112 15.0 0.23 0.268 115 16.8 0.98 0.122 101022 15.7 0.995 - - - - - - - - - - 101023 15.7 0.995 - - 115 14.3 0.24 0.266 119 16.5 1.11 0.110 101024 15.7 0.995 135 14.6 136 15.0 0.04 0.284 136 15.5 0.23 0.236 101025 15.7 0.995 - - - - - - - - - - 101026 15.7 0.995 132 12.3 132 12.7 0.04 0.284 133 13.1 0.20 0.241 101027 15.7 0.995 150 12.6 - - - - - - - - 101028 15.7 0.995 - - - - - - - - - - 101029 15.0 0.995 - - - - - - - - - - 101030 15.0 0.995 - - - - - - - - - - 101031 15.0 0.995 - - - - - - - - - - 101032 15.0 0.995 - - - - - - - - - - 101033 15.0 0.995 - - - - - - - - - - 101034 15.0 0.995 - - - - - - - - - - 101035 15.0 0.995 - - - - - - - - - - 101036 15.0 0.995 128 10.6 - - - - - - - - 101037 15.0 0.995 215 9.9 216 11.0 0.05 0.280 218 11.6 0.30 0.219 101038 15.0 0.995 135 9.1 136 10.4 0.12 0.275 137 10.9 0.46 0.189 101039 15.0 0.995 183 10.4 185 12.2 0.10 0.275 188 13.1 0.51 0.182 101040 15.0 0.995 - - - - - - - - - - 101041 15.1 0.995 - - - - - - - - - - 101042 15.1 0.995 242 13.7 243 15.0 0.05 0.281 244 15.4 0.26 0.230 101043 15.1 0.995 88 13.9 88 14.6 0.14 0.279 89 14.7 0.29 0.224 101044 15.1 0.995 175 12.4 175 12.5 0.01 0.287 175 12.6 0.05 0.275 101045 15.0 0.995 183 12.6 183 12.7 0.01 0.287 184 12.8 0.05 0.275 101046 15.0 0.995 228 14.1 - - - - - - - - 101048 15.0 0.995 - - - - - - - - - - 101049 15.0 0.995 - - - - - - - - - - 101050 15.0 0.995 - - - - - - - - - - 101051 15.0 0.995 202 14.1 202 14.2 0.00 0.288 202 14.3 0.03 0.282 101052 15.0 0.995 304 15.7 295 15.3 0.00 0.288 295 15.3 0.00 0.288 101053 15.0 0.995 - - - - - - - - - - 101054 15.7 0.995 178 13.6 179 14.1 0.03 0.285 179 14.2 0.12 0.259 101055 15.8 0.999 38 10.4 39 11.2 0.45 0.267 39 11.8 0.98 0.117

147

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

101056 15.8 0.999 53 11.3 53 11.5 0.12 0.283 53 11.5 0.12 0.259 101057 15.8 0.999 90 12.4 90 12.4 0.02 0.287 90 12.3 0.00 0.288 101064 14.4 0.995 362 15.1 353 14.7 0.00 0.288 351 14.7 0.00 0.288 101065 14.4 0.995 204 12.2 205 12.5 0.01 0.287 204 12.2 0.00 0.288 101066 14.4 0.995 122 12.6 123 14.7 0.19 0.271 125 15.4 0.67 0.159 101067 14.4 0.999 - - 187 12.3 0.12 0.272 190 12.9 0.55 0.175 101068 15.7 0.995 - - - - - - - - - - 101070 15.7 0.995 83 15.9 78 15.5 0.00 0.288 78 15.5 0.00 0.288 101071 14.4 0.995 372 11.4 372 11.9 0.01 0.286 374 12.4 0.12 0.259 101072 14.4 0.995 - - - - - - - - - - 101073 14.4 0.999 150 9.0 - - - - - - - - 101074 15.0 0.995 230 12.7 231 13.6 0.04 0.282 231 13.4 0.12 0.260 101075 14.4 0.995 244 12.4 244 12.8 0.01 0.286 244 12.4 0.00 0.288 101077 15.0 0.995 - - - - - - - - - - 101078 15.0 0.995 192 13.3 193 14.5 0.06 0.280 195 15.5 0.37 0.207 101081 14.4 0.995 - - 166 14.8 0.21 0.263 173 17.8 1.20 0.102 101082 14.4 0.995 370 12.3 366 12.1 0.00 0.288 363 12.0 0.00 0.288 101083 14.4 0.995 88 11.1 90 13.3 0.30 0.265 92 14.9 1.11 0.108 101086 15.0 0.995 391 18.0 392 18.3 0.01 0.287 393 18.7 0.07 0.272 101087 15.0 0.995 213 15.0 215 16.8 0.07 0.278 218 18.1 0.46 0.194 101088 15.7 0.995 322 13.4 322 13.9 0.01 0.286 324 14.2 0.11 0.262 101090 15.7 0.995 178 13.8 178 14.6 0.05 0.282 179 15.1 0.25 0.230 101091 15.7 0.995 66 16.1 66 16.3 0.07 0.285 66 16.1 0.00 0.288 101092 15.7 0.995 - - - - - - - - - - 101093 15.1 0.995 198 13.0 197 12.9 0.00 0.288 198 12.9 0.00 0.288 101094 15.0 0.995 132 14.4 130 14.2 0.00 0.288 130 14.3 0.00 0.288 101095 15.0 0.995 175 13.5 169 13.2 0.00 0.288 169 13.2 0.00 0.288 101096 15.7 0.995 116 10.3 118 12.4 0.21 0.268 119 13.2 0.75 0.146 101097 15.7 0.995 175 12.1 176 12.7 0.04 0.283 176 12.8 0.15 0.253 101098 15.0 0.995 195 15.0 - - - - - - - - 101099 15.7 0.995 138 12.9 138 13.0 0.01 0.288 138 13.0 0.02 0.282 101100 15.7 0.995 - - - - - - - - - - 101101 15.0 0.995 323 16.3 - - - - - - - - 101104 15.7 0.995 158 14.0 154 13.8 0.00 0.288 154 13.7 0.00 0.288 101105 15.7 0.995 - - - - - - - - - - 101106 15.7 0.995 97 10.2 98 11.4 0.18 0.274 99 11.9 0.54 0.176 101107 15.0 0.995 184 11.7 186 13.5 0.10 0.276 189 14.5 0.50 0.185 101108 15.7 0.995 254 12.4 256 14.2 0.06 0.279 259 15.0 0.35 0.210 101109 15.7 0.995 82 13.2 81 13.1 0.00 0.288 81 13.1 0.00 0.288 101110 15.0 0.995 121 11.3 122 12.1 0.10 0.279 122 12.1 0.23 0.234 101111 15.7 0.995 207 12.9 207 12.9 0.00 0.288 207 12.9 0.00 0.288 101112 15.0 0.995 147 11.0 148 11.6 0.06 0.281 148 12.1 0.28 0.224 101113 15.0 0.995 215 14.7 215 14.9 0.01 0.287 216 15.2 0.08 0.268 101127 18.1 0.996 75 14.8 77 16.6 0.31 0.269 78 17.6 0.92 0.130 101128 15.6 0.998 34 13.2 34 13.6 0.36 0.276 34 13.6 0.31 0.217 101129 15.0 0.995 210 13.8 - - - - - - - - 101130 15.1 0.995 252 16.1 - - - - - - - - 101131 15.6 0.998 31 12.1 31 12.3 0.30 0.279 31 12.5 0.37 0.205 101132 15.6 0.998 37 13.4 36 13.4 0.00 0.288 36 13.3 0.00 0.288 101133 15.6 0.998 36 11.4 37 12.8 0.56 0.259 38 13.3 1.21 0.096 101134 15.7 0.995 120 13.4 115 13.1 0.00 0.288 115 13.1 0.00 0.288 101136 15.6 0.998 27 11.1 27 11.3 0.33 0.279 27 11.4 0.42 0.197 101137 15.6 0.998 28 12.7 28 12.9 0.35 0.278 28 12.9 0.28 0.224 101138 15.7 0.995 113 10.7 113 11.1 0.08 0.282 113 11.3 0.21 0.239 101139 15.7 0.995 124 11.5 124 12.2 0.09 0.280 125 12.5 0.29 0.221 101140 15.6 0.998 21 13.0 21 13.0 0.28 0.283 21 13.1 0.17 0.248 101141 15.6 0.998 19 13.8 19 14.1 0.55 0.274 19 14.1 0.51 0.182

148

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

101142 15.6 0.998 15 11.5 15 11.5 0.00 0.288 15 11.5 0.00 0.288 101143 15.7 0.995 204 13.3 198 13.1 0.00 0.288 198 13.1 0.00 0.288 101145 15.6 0.998 18 12.7 17 12.6 0.00 0.288 17 12.6 0.00 0.288 101146 15.6 0.998 31 8.9 31 8.9 0.00 0.288 31 8.9 0.00 0.288 101147 15.6 0.998 16 12.4 15 12.2 0.00 0.288 15 12.2 0.00 0.288 101148 15.6 0.998 25 12.9 24 12.9 0.00 0.288 23 12.8 0.00 0.288 101149 15.6 0.998 - - - - - - - - - - 101150 15.6 0.998 12 11.2 11 11.0 0.00 0.288 11 10.9 0.00 0.288 101151 15.8 0.999 88 9.1 89 10.7 0.26 0.268 91 11.7 0.87 0.130 101152 15.8 0.999 33 10.6 34 10.9 0.35 0.276 34 11.2 0.55 0.174 101153 15.8 0.999 50 10.2 50 10.8 0.30 0.274 50 11.2 0.63 0.162 101154 15.8 0.999 39 9.7 40 10.2 0.33 0.275 40 10.4 0.52 0.178 101156 15.0 0.993 140 11.5 140 11.9 0.04 0.284 140 11.9 0.12 0.258 101157 15.0 0.993 136 12.3 137 13.5 0.11 0.277 138 14.1 0.43 0.197 101160 14.4 0.995 117 9.6 118 11.5 0.20 0.270 120 12.6 0.78 0.142 101161 14.4 0.995 89 9.3 90 10.7 0.24 0.270 91 11.3 0.69 0.152 101162 14.4 0.995 148 8.3 150 10.3 0.15 0.270 152 11.1 0.65 0.158 101163 14.4 0.995 258 9.4 259 10.4 0.04 0.281 260 10.9 0.23 0.233 101170 15.6 0.991 84 12.0 85 13.0 0.19 0.276 86 13.6 0.55 0.177 101171 15.6 0.991 80 12.7 81 14.3 0.28 0.269 83 15.2 0.85 0.135 101172 15.6 0.991 58 12.8 58 13.5 0.27 0.275 59 13.9 0.57 0.173 101173 15.6 0.991 - - 94 12.6 0.31 0.263 97 14.1 1.16 0.102 101174 15.6 0.991 169 13.8 171 15.4 0.10 0.277 172 15.7 0.37 0.208 101175 15.6 0.991 86 10.4 87 12.2 0.28 0.267 89 12.8 0.80 0.140 101176 15.6 0.991 56 13.1 55 13.1 0.00 0.288 54 13.0 0.00 0.288 101177 15.8 0.999 60 10.0 60 10.6 0.24 0.276 60 10.8 0.43 0.193 101178 15.6 0.998 18 11.1 17 10.9 0.00 0.288 17 10.9 0.00 0.288 101179 15.6 0.998 12 10.6 11 10.4 0.00 0.288 11 10.4 0.00 0.288 101182 15.0 0.995 262 11.2 259 11.1 0.00 0.288 259 11.1 0.00 0.288 101183 15.0 0.995 364 11.0 - - - - - - - - 101184 14.4 0.995 342 10.0 334 9.7 0.00 0.288 333 9.7 0.00 0.288 101185 15.0 0.995 246 10.1 248 12.1 0.07 0.276 251 13.2 0.46 0.190 101186 15.0 0.995 223 10.5 220 10.4 0.00 0.288 220 10.4 0.00 0.288 101187 15.0 0.995 288 9.2 291 11.5 0.06 0.276 295 12.6 0.45 0.192 101188 15.0 0.995 264 10.2 264 10.8 0.02 0.284 265 11.0 0.11 0.260 101198 15.9 0.996 - - 81 10.6 0.11 0.281 82 11.0 0.33 0.212 101199 15.9 0.996 204 11.8 204 11.8 0.00 0.288 204 11.9 0.03 0.281 101200 15.9 0.996 - - 98 10.3 0.05 0.284 99 10.5 0.16 0.249 101201 15.9 0.996 90 11.0 90 11.4 0.09 0.282 91 11.9 0.35 0.209 101202 15.2 0.982 82 9.5 84 11.5 0.31 0.264 85 12.2 0.94 0.123 101203 15.2 0.982 100 8.6 102 11.0 0.27 0.264 105 12.0 0.98 0.117 101204 15.7 0.995 173 10.9 174 12.2 0.08 0.278 175 12.6 0.35 0.209 101205 15.7 0.995 - - 232 10.5 0.11 0.268 237 11.9 0.72 0.150 101208 16.0 0.998 15 15.5 16 17.0 0.81 0.251 17 17.3 1.92 0.055 101209 16.5 0.998 17 15.9 16 15.8 0.00 0.288 16 15.8 0.00 0.288 101210 16.0 0.998 137 13.0 131 12.7 0.00 0.288 131 12.7 0.00 0.288 101211 16.0 0.998 16 15.5 15 15.5 0.00 0.288 15 15.4 0.00 0.288 101212 16.0 0.998 - - - - - - - - - - 101213 16.0 0.998 19 15.4 18 15.3 0.00 0.288 18 15.3 0.00 0.288 101214 16.0 0.998 7 16.6 6 16.5 0.00 0.288 6 16.5 0.00 0.288 101215 16.0 0.998 - - - - - - - - - - 101216 16.0 0.998 - - - - - - - - - - 101217 16.0 0.998 9 16.5 8 16.4 0.00 0.288 8 16.4 0.00 0.288 101218 16.5 0.998 53 15.7 50 15.4 0.00 0.288 50 15.4 0.00 0.288 101219 16.5 0.998 8 17.3 7 17.2 0.00 0.288 7 17.2 0.00 0.288 101220 16.5 0.998 30 16.1 29 15.9 0.00 0.288 29 15.9 0.00 0.288 101221 16.5 0.998 18 15.7 18 15.6 0.00 0.288 18 15.6 0.00 0.288

149

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

101222 16.5 0.998 3 16.2 4 17.4 0.96 0.240 8 21.8 4.73 0.006 101223 16.5 0.998 19 11.6 18 11.6 0.00 0.288 18 11.5 0.00 0.288 101224 16.5 0.998 0 16.5 0 16.5 0.00 0.288 0 16.5 0.00 0.288 101225 16.5 0.998 26 11.9 25 11.9 0.00 0.288 25 11.9 0.00 0.288 101226 16.0 0.998 7 14.0 7 14.0 0.00 0.288 7 14.0 0.00 0.288 101227 16.5 0.998 15 10.5 14 10.4 0.00 0.288 14 10.3 0.00 0.288 101228 16.5 0.998 17 12.6 16 12.5 0.00 0.288 16 12.4 0.00 0.288 101229 16.0 0.998 11 17.6 10 17.4 0.00 0.288 10 17.4 0.00 0.288 101230 16.0 0.998 20 16.9 19 16.7 0.00 0.288 19 16.7 0.00 0.288 101231 16.0 0.998 12 17.6 11 17.4 0.00 0.288 11 17.3 0.00 0.288 101232 16.0 0.998 8 18.8 7 18.5 0.00 0.288 7 18.5 0.00 0.288 101234 16.0 0.998 12 18.5 11 18.1 0.00 0.288 11 18.1 0.00 0.288 101235 16.0 0.998 69 8.3 70 9.5 0.31 0.267 71 9.8 0.68 0.153 101236 16.0 0.998 13 14.5 14 15.0 0.76 0.263 15 16.4 2.21 0.042 101237 16.0 0.998 - - 130 17.3 0.25 0.265 135 19.4 1.14 0.109 101238 16.0 0.998 30 14.2 30 14.7 0.44 0.274 30 15.3 0.89 0.131 101239 16.0 0.998 25 16.0 23 15.9 0.00 0.288 23 15.8 0.00 0.288 101240 16.0 0.998 - - 84 16.7 0.42 0.255 88 18.9 1.71 0.067 101241 16.0 0.998 35 13.0 36 14.2 0.55 0.263 36 14.4 0.99 0.120 101242 16.0 0.998 21 15.3 20 15.3 0.00 0.288 20 15.2 0.00 0.288 101243 16.0 0.998 33 13.8 33 14.2 0.34 0.278 33 14.4 0.51 0.183 101248 16.0 0.998 9 17.2 8 17.2 0.00 0.288 9 17.2 0.00 0.288 101249 16.5 0.998 17 17.8 16 17.7 0.00 0.288 16 17.7 0.00 0.288 101250 16.5 0.998 7 18.0 6 17.9 0.00 0.288 6 17.9 0.00 0.288 101251 16.5 0.998 2 18.3 2 18.3 0.00 0.288 2 18.3 0.00 0.288 101252 16.5 0.998 6 19.1 7 19.9 0.92 0.251 7 20.1 2.15 0.047 101253 16.5 0.998 6 19.3 6 19.7 0.91 0.258 6 19.9 1.72 0.067 101254 16.5 0.998 8 19.0 10 22.0 0.91 0.245 12 24.3 3.91 0.012 101255 16.5 0.998 10 18.8 10 19.1 0.77 0.270 11 19.7 1.57 0.076 101256 16.5 0.998 8 16.4 8 16.4 0.00 0.288 8 16.4 0.00 0.288 101257 16.5 0.998 33 16.2 31 16.0 0.00 0.288 31 16.0 0.00 0.288 101258 15.6 0.989 157 12.6 156 12.6 0.00 0.288 156 12.6 0.00 0.288 101259 15.6 0.989 115 12.9 110 12.6 0.00 0.288 110 12.6 0.00 0.288 101260 15.6 0.989 119 11.4 120 13.0 0.17 0.273 121 13.6 0.57 0.173 101261 15.6 0.989 112 12.1 113 13.1 0.13 0.277 114 13.6 0.44 0.194 101262 15.6 0.989 166 13.0 161 12.7 0.00 0.288 162 12.8 0.00 0.288 101263 15.6 0.989 172 12.4 172 13.2 0.05 0.282 173 13.6 0.24 0.232 101264 15.6 0.989 130 12.7 130 13.1 0.05 0.284 131 13.2 0.15 0.252 101265 15.7 0.995 33 15.0 33 15.0 0.03 0.288 33 15.1 0.12 0.258 101266 15.7 0.995 1 12.8 1 12.8 0.00 0.288 1 12.8 0.00 0.288 101267 15.7 0.995 - - - - - - - - - - 101268 15.6 0.989 - - - - - - - - - - 101269 15.6 0.989 - - - - - - - - - - 101271 15.6 0.989 - - - - - - - - - - 101272 15.6 0.989 - - - - - - - - - - 101274 16.5 0.992 37 18.8 38 19.6 0.42 0.274 39 20.6 0.99 0.125 101275 16.5 0.992 229 15.0 231 17.7 0.09 0.276 235 19.1 0.54 0.182 101277 16.0 0.998 14 15.8 17 20.2 0.85 0.243 21 24.1 3.85 0.013 101278 16.0 0.998 - - 50 16.2 0.56 0.253 56 20.8 2.41 0.038 101279 16.0 0.998 33 14.4 33 15.3 0.51 0.268 34 15.9 1.06 0.113 101280 16.0 0.998 23 16.4 22 16.4 0.00 0.288 22 16.3 0.00 0.288 101281 16.0 0.998 - - 99 6.2 0.35 0.251 102 7.6 1.44 0.073 101282 16.0 0.998 51 11.6 52 13.0 0.42 0.265 53 13.7 1.03 0.114 101283 16.0 0.998 32 14.3 32 15.1 0.49 0.269 33 15.5 0.88 0.133 101284 16.0 0.998 48 10.8 49 12.2 0.46 0.262 50 12.9 1.13 0.104 101285 16.0 0.998 37 14.6 38 15.0 0.32 0.277 38 15.3 0.51 0.184 101286 16.0 0.998 31 16.3 30 16.2 0.00 0.288 30 16.2 0.00 0.288

150

UA ΔNe (%)c

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

101287 15.6 0.998 - - 28 14.9 0.68 0.255 - - - - 101288 15.6 0.998 - - 20 17.0 0.80 0.246 20 16.6 2.04 0.049 101289 15.6 0.998 - - - - - - - - - - 101290 15.6 0.998 - - - - - - - - - - 101291 15.6 0.998 32 14.7 33 16.6 0.64 0.257 - - - - 101292 15.6 0.998 25 13.7 26 15.5 0.71 0.253 - - - - 101293 15.8 0.999 - - 33 16.1 0.70 0.247 35 17.0 2.28 0.040 101294 15.8 0.999 - - - - - - - - - - 101295 15.8 0.999 30 13.1 30 14.5 0.63 0.258 31 15.0 1.36 0.087 101296 15.8 0.999 59 11.5 60 13.2 0.40 0.264 61 13.8 0.98 0.119 101297 15.8 0.999 - - 76 13.2 0.40 0.258 78 14.2 1.27 0.093 101298 15.8 0.999 - - 68 14.4 0.46 0.256 69 15.3 1.43 0.081 101299 15.8 0.999 40 12.9 41 14.9 0.57 0.257 42 15.3 1.34 0.088 101300 15.8 0.999 - - 45 14.9 0.59 0.252 46 15.7 1.72 0.063 101301 15.8 0.999 49 11.5 51 13.7 0.51 0.257 52 14.2 1.27 0.092 101302 15.8 0.999 - - 73 13.3 0.40 0.259 75 14.4 1.29 0.091 101303 15.8 0.999 43 11.9 45 14.5 0.58 0.254 47 15.5 1.67 0.066 101304 16.4 0.999 496 14.4 500 17.0 0.03 0.281 505 17.5 0.24 0.235 101305 16.4 0.999 20 15.4 20 16.5 0.73 0.258 21 17.0 1.50 0.078 101306 15.8 0.999 54 13.4 56 15.4 0.45 0.263 57 16.7 1.31 0.092 101307 16.4 0.999 269 15.4 271 17.2 0.05 0.280 274 18.2 0.35 0.213 101308 16.4 0.999 14 17.7 14 17.8 0.30 0.285 15 18.0 0.46 0.195 101309 16.4 0.999 18 16.5 19 19.1 0.81 0.248 20 19.5 2.33 0.040 101310 16.4 0.999 24 17.3 25 18.5 0.67 0.262 27 21.3 2.28 0.043 101311 16.4 0.999 15 16.0 18 20.0 0.84 0.244 18 20.4 3.01 0.023 101312 16.4 0.999 19 16.6 19 17.7 0.74 0.259 20 18.4 1.68 0.068 101313 16.4 0.999 17 16.5 18 17.1 0.71 0.264 18 17.4 1.10 0.111 101314 16.4 0.999 21 16.0 22 18.5 0.77 0.250 23 19.3 2.26 0.042 101315 16.4 0.999 - - 43 19.1 0.65 0.249 45 20.3 2.28 0.042 101316 16.4 0.999 19 16.7 21 19.2 0.79 0.249 21 19.3 2.09 0.049 101317 16.4 0.999 0 19.6 0 19.6 0.00 - 0 19.6 0.00 - 101318 16.4 0.999 17 17.8 19 20.6 0.82 0.249 19 20.7 2.30 0.042 101320 16.4 0.999 28 16.1 28 17.1 0.59 0.265 29 17.3 0.96 0.126 101321 16.4 0.999 25 14.7 25 14.9 0.30 0.281 26 15.1 0.46 0.192 101322 16.4 0.999 20 14.5 20 14.5 0.00 0.288 20 14.6 0.10 0.263 101323 16.4 0.999 36 16.3 36 17.3 0.50 0.268 37 18.2 1.14 0.108 101324 16.4 0.999 31 15.6 32 16.0 0.36 0.278 32 16.5 0.72 0.154 101325 16.4 0.999 17 16.4 18 17.1 0.72 0.263 19 18.6 1.99 0.052 101326 16.4 0.999 26 15.3 26 15.6 0.36 0.279 26 16.0 0.64 0.164 101327 16.4 0.999 27 15.0 27 15.3 0.40 0.277 27 16.2 1.02 0.118 101328 16.4 0.999 26 13.7 26 13.8 0.18 0.284 26 13.9 0.29 0.222 101329 16.4 0.999 50 13.1 51 14.5 0.42 0.266 53 15.6 1.15 0.104 101330 16.4 0.999 54 11.9 55 12.1 0.11 0.283 55 12.2 0.21 0.239 101331 16.4 0.999 47 14.4 47 14.4 0.00 0.288 47 14.5 0.04 0.277 101332 16.4 0.999 27 13.5 28 14.0 0.50 0.271 28 14.1 0.63 0.164 101333 16.4 0.999 24 14.0 25 14.7 0.62 0.265 25 15.2 1.15 0.104 101334 15.8 0.999 48 11.6 49 12.6 0.40 0.268 49 13.1 0.83 0.137 101335 15.8 0.999 42 10.7 42 10.9 0.17 0.282 42 11.0 0.23 0.233 101336 15.8 0.999 31 10.3 31 10.4 0.09 0.286 31 10.6 0.28 0.223 101337 15.8 0.999 36 10.4 36 10.5 0.21 0.281 36 10.9 0.44 0.192 101338 15.8 0.999 49 9.9 49 10.5 0.29 0.275 49 10.9 0.63 0.162 101339 15.8 0.999 82 9.5 83 10.5 0.22 0.272 84 11.4 0.70 0.152 101340 15.8 0.999 57 11.1 57 11.8 0.25 0.275 58 12.1 0.53 0.178 101341 15.8 0.999 27 11.7 27 12.0 0.46 0.274 27 12.8 1.05 0.112 101342 15.8 0.999 32 11.3 32 12.2 0.55 0.264 33 13.1 1.32 0.088 101343 15.8 0.999 38 10.7 39 11.7 0.49 0.264 40 12.8 1.32 0.087 101344 16.4 0.999 30 11.0 31 11.8 0.56 0.263 32 12.8 1.35 0.085

151

UA ΔNe (%)c

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

101345 15.8 0.999 38 10.6 39 11.3 0.43 0.269 40 12.0 0.95 0.121 101346 15.8 0.999 32 11.1 32 11.7 0.49 0.268 33 12.2 0.92 0.124 101347 15.8 0.999 38 13.0 39 14.8 0.58 0.258 42 17.4 2.00 0.051 101348 15.8 0.999 56 12.6 57 14.6 0.44 0.262 60 16.6 1.49 0.078 101349 15.8 0.999 68 12.3 69 13.5 0.29 0.271 70 14.6 0.87 0.132 101350 15.8 0.999 63 11.6 64 13.2 0.37 0.265 66 14.5 1.16 0.103 101351 15.8 0.999 74 11.8 76 14.0 0.35 0.264 78 15.8 1.26 0.095 101352 15.8 0.999 52 12.8 53 14.2 0.40 0.266 55 15.4 1.15 0.104 101353 15.8 0.999 32 11.5 32 11.6 0.22 0.282 32 11.8 0.36 0.208 101354 16.4 0.999 30 14.5 30 15.2 0.51 0.270 31 16.3 1.30 0.092 101355 16.4 0.999 28 14.7 28 15.0 0.34 0.279 28 15.3 0.54 0.178 101356 16.0 0.896 65 10.1 66 11.3 0.32 0.268 67 12.1 0.87 0.131 101357 16.0 0.896 63 10.6 64 12.1 0.36 0.265 66 12.9 0.98 0.119 101358 16.0 0.896 52 12.9 53 14.0 0.37 0.269 54 14.6 0.85 0.136 101359 16.0 0.896 66 9.8 67 10.9 0.30 0.269 68 11.6 0.81 0.137 101360 16.0 0.896 64 10.0 65 11.3 0.33 0.267 66 11.9 0.86 0.131 101361 16.0 0.896 - - - - - - - - - - 101362 16.0 0.896 18 13.7 18 14.6 0.74 0.258 19 14.7 1.20 0.099 101363 16.0 0.896 38 12.4 38 13.6 0.53 0.262 40 14.8 1.39 0.084 101364 16.0 0.898 61 9.5 62 11.0 0.38 0.264 64 12.1 1.15 0.101 101365 16.0 0.896 45 12.8 45 13.1 0.21 0.281 45 13.2 0.24 0.232 101366 16.0 0.898 60 10.8 61 11.8 0.31 0.270 61 11.8 0.51 0.181 101367 16.0 0.896 - - 114 13.6 0.30 0.259 118 15.1 1.25 0.095 101368 16.0 0.898 70 9.1 71 10.7 0.34 0.264 72 11.6 1.00 0.115 101369 16.0 0.896 37 14.8 37 15.6 0.44 0.271 38 16.2 0.91 0.130 101370 16.0 0.896 40 11.3 41 12.3 0.47 0.265 42 12.8 0.94 0.123 101371 16.0 0.896 31 13.5 31 14.5 0.57 0.264 32 15.1 1.20 0.100 101372 16.0 0.898 45 13.9 46 15.6 0.51 0.262 47 16.6 1.30 0.092 101373 16.0 0.898 106 8.1 108 10.0 0.23 0.267 110 10.5 0.74 0.145 101374 16.0 0.898 34 12.1 34 12.6 0.40 0.274 34 12.7 0.52 0.180 101375 16.0 0.896 60 9.2 62 10.6 0.38 0.264 63 11.5 1.06 0.109 101376 16.0 0.896 65 9.3 67 11.2 0.39 0.261 69 12.5 1.24 0.094 101377 16.0 0.896 45 14.7 45 14.7 0.00 0.288 45 14.7 0.00 0.288 101378 16.0 0.896 47 12.2 49 14.2 0.50 0.259 50 15.0 1.33 0.089 101379 16.0 0.896 62 8.2 64 10.1 0.41 0.259 66 11.5 1.33 0.085 101380 16.0 0.896 47 12.1 48 13.7 0.48 0.262 49 14.9 1.34 0.088 101381 16.0 0.896 79 7.4 80 8.6 0.26 0.269 81 9.1 0.69 0.150 101383 16.0 0.896 65 12.8 66 13.6 0.23 0.276 66 13.9 0.49 0.185 101384 16.0 0.898 15 19.7 14 19.6 0.00 0.288 15 19.6 0.00 0.288 101385 16.0 0.896 64 12.6 65 13.8 0.32 0.270 66 14.4 0.76 0.146 101386 16.0 0.896 53 14.4 53 14.9 0.25 0.277 53 15.4 0.54 0.179 101387 16.0 0.896 66 12.5 67 14.2 0.36 0.266 68 15.2 1.05 0.114 101388 16.0 0.896 31 16.6 31 16.6 0.08 0.287 31 16.6 0.01 0.287 101389 16.4 0.999 21 16.6 21 17.1 0.60 0.271 - - - - 101390 16.4 0.999 21 15.6 - - - - - - - - 101391 15.7 0.940 - - - - - - - - - - 101392 16.5 0.991 25 18.6 24 18.5 0.00 0.288 23 18.5 0.00 0.288 101393 16.5 0.991 22 18.1 21 18.0 0.00 0.288 21 17.8 0.00 0.288 101394 16.5 0.991 24 18.1 - - - - - - - - 101395 16.5 0.991 35 18.4 35 19.1 0.43 0.274 35 18.9 0.38 0.208 101396 15.2 0.982 - - 124 11.5 0.24 0.263 127 12.8 1.00 0.116 101397 17.4 0.987 38 16.9 39 18.1 0.50 0.267 39 18.2 0.82 0.143 101398 17.4 0.987 33 16.7 34 18.2 0.59 0.262 35 18.7 1.25 0.099 101399 17.4 0.987 38 18.0 39 20.1 0.58 0.261 40 20.7 1.36 0.092 101400 17.4 0.987 29 19.9 29 20.8 0.56 0.270 30 21.2 0.95 0.130 101401 17.4 0.987 65 17.4 65 18.1 0.20 0.279 65 18.1 0.30 0.223 101402 17.4 0.987 35 17.5 35 18.4 0.49 0.270 36 19.1 1.02 0.120

152

UA ΔNe (%)c

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

101403 17.4 0.987 25 18.1 26 19.1 0.63 0.266 27 19.8 1.30 0.096 101404 17.4 0.987 44 17.0 45 17.7 0.34 0.275 45 18.1 0.60 0.171 101405 17.4 0.987 40 17.2 40 18.4 0.49 0.268 41 19.5 1.22 0.103 101406 17.4 0.987 39 16.8 40 17.3 0.35 0.276 40 17.8 0.66 0.162 101407 17.4 0.987 32 16.4 32 17.5 0.55 0.267 33 18.3 1.24 0.099 101408 17.4 0.987 23 18.0 23 18.2 0.45 0.278 23 18.4 0.48 0.191 101409 17.4 0.987 33 14.5 34 16.5 0.64 0.256 35 17.6 1.72 0.065 101410 17.4 0.987 28 17.6 28 18.1 0.47 0.274 29 18.6 0.81 0.145 101411 17.4 0.987 19 19.9 19 20.7 0.69 0.267 20 21.1 1.16 0.109 101412 17.4 0.987 25 19.0 26 19.3 0.45 0.277 26 19.5 0.52 0.185 101413 17.4 0.987 50 16.5 51 18.9 0.50 0.262 54 20.7 1.58 0.077 101414 17.4 0.987 29 18.8 27 18.8 0.00 0.288 27 18.7 0.00 0.288 101415 17.4 0.987 14 15.1 14 15.7 0.75 0.263 14 15.6 0.85 0.136 101416 17.4 0.987 33 14.4 35 16.4 0.63 0.256 36 17.8 1.81 0.060 101417 17.4 0.987 34 15.8 35 17.6 0.60 0.260 35 18.1 1.34 0.091 101418 17.4 0.987 - - 47 17.5 0.59 0.253 51 20.7 2.29 0.042 101419 17.4 0.987 37 17.0 37 17.9 0.46 0.271 38 19.3 1.26 0.099 101420 17.4 0.987 43 14.5 45 16.8 0.55 0.258 47 18.2 1.62 0.072 101421 17.4 0.987 36 16.0 37 17.5 0.56 0.263 38 18.3 1.31 0.094 101422 17.4 0.987 20 16.1 21 16.5 0.53 0.275 21 16.7 0.68 0.159 101423 17.4 0.987 - - 50 20.6 0.63 0.248 - - - - 101424 17.4 0.987 35 14.8 36 16.5 0.59 0.260 37 17.4 1.48 0.080 101425 17.4 0.987 44 13.8 44 14.1 0.24 0.279 45 14.4 0.42 0.199 101426 17.4 0.987 18 19.6 19 21.3 0.78 0.256 20 22.8 2.29 0.044 101427 17.4 0.987 13 14.3 14 14.5 0.60 0.276 14 14.7 0.78 0.144 101428 17.4 0.987 31 15.4 33 18.4 0.69 0.252 35 20.9 2.42 0.038 101429 17.4 0.987 - - - - - - - - - - 101430 17.4 0.987 49 15.0 50 16.5 0.44 0.266 52 17.9 1.27 0.097 101431 17.4 0.987 18 17.7 18 17.7 0.00 0.288 18 17.7 0.00 0.288 101432 17.4 0.987 27 17.8 25 17.6 0.00 0.288 26 17.7 0.00 0.288 101433 17.4 0.987 75 16.1 74 16.0 0.00 0.288 75 16.1 0.00 0.288 101434 17.4 0.987 77 16.6 78 19.3 0.36 0.266 81 20.7 1.16 0.109 101435 17.4 0.987 52 15.3 52 16.2 0.33 0.273 53 16.6 0.67 0.161 101436 17.4 0.987 50 17.2 50 17.3 0.06 0.286 50 17.6 0.19 0.245 101437 17.4 0.987 56 15.1 57 17.0 0.43 0.265 58 18.3 1.26 0.098 101438 17.4 0.987 - - 737 19.7 0.03 0.276 770 24.5 0.57 0.182 101439 17.4 0.987 33 18.0 32 17.9 0.00 0.288 31 17.8 0.00 0.288 101440 17.4 0.987 67 18.2 67 18.9 0.19 0.280 67 18.8 0.26 0.232 101441 17.4 0.987 26 17.6 26 17.6 0.00 0.288 26 17.9 0.32 0.219 101442 17.4 0.987 47 15.2 47 15.4 0.14 0.284 47 15.7 0.25 0.230 101443 17.4 0.987 67 16.5 67 17.1 0.20 0.279 68 17.7 0.50 0.187 101444 17.4 0.987 62 13.4 62 13.9 0.18 0.279 62 14.3 0.45 0.193 101445 17.4 0.987 78 17.0 78 17.3 0.08 0.284 78 17.6 0.21 0.241 101446 17.4 0.987 47 17.4 48 18.1 0.31 0.276 48 18.6 0.63 0.168 101447 16.7 0.995 33 15.2 34 16.1 0.52 0.268 34 16.5 0.94 0.126 101448 16.7 0.995 28 16.7 29 18.3 0.66 0.259 30 19.2 1.62 0.073 101449 16.7 0.995 21 15.5 21 16.1 0.64 0.267 22 17.1 1.48 0.080 101451 16.7 0.995 32 15.5 32 15.5 0.07 0.287 32 15.7 0.17 0.247 101452 16.5 0.991 41 16.7 42 17.4 0.36 0.275 42 17.9 0.70 0.157 101453 16.5 0.991 39 17.4 39 17.5 0.14 0.284 39 17.8 0.30 0.222 101454 16.5 0.991 38 18.3 35 17.9 0.00 0.288 35 17.9 0.00 0.288 101455 16.1 0.997 - - 67 14.0 0.41 0.261 70 16.2 1.50 0.078 101456 16.7 0.995 49 14.7 50 15.1 0.21 0.280 50 15.4 0.42 0.199 101457 16.2 0.995 50 15.9 50 16.0 0.04 0.287 50 15.9 0.01 0.286 101458 16.2 0.995 34 14.3 34 14.7 0.34 0.277 34 15.0 0.56 0.176 101459 16.2 0.995 34 13.8 34 13.8 0.00 0.288 34 13.8 0.00 0.288 101460 16.7 0.995 21 14.7 21 14.7 0.00 0.288 21 14.7 0.00 0.288

153

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

101461 16.7 0.995 27 14.9 26 14.9 0.00 0.288 27 14.9 0.04 0.278 101462 16.7 0.916 54 11.9 56 14.2 0.48 0.259 58 15.7 1.50 0.077 101463 15.2 0.916 4 17.3 3 17.1 0.00 0.288 3 17.1 0.00 0.288 101464 15.2 0.916 27 16.4 26 16.1 0.00 0.288 26 16.1 0.00 0.288 101465 17.1 0.916 25 14.3 24 14.3 0.00 0.288 25 14.3 0.00 0.288 101466 17.1 0.916 30 12.2 30 13.2 0.58 0.262 32 14.4 1.52 0.074 101467 16.2 0.896 8 11.8 7 11.6 0.00 0.288 7 11.7 0.00 0.288 101468 15.2 0.884 209 12.1 201 11.7 0.00 0.288 201 11.7 0.00 0.288 101469 16.2 0.896 45 14.2 45 14.4 0.17 0.283 45 14.8 0.41 0.199 101470 16.2 0.896 20 15.4 20 15.9 0.60 0.271 21 16.4 1.08 0.112 101471 16.7 0.896 46 12.0 47 13.5 0.48 0.262 49 15.0 1.43 0.081 101472 16.7 0.916 36 14.6 37 15.5 0.47 0.269 37 16.1 0.99 0.121 101473 16.2 0.896 17 14.6 18 15.0 0.62 0.271 18 15.3 1.01 0.118 101474 17.1 0.916 - - 406 18.7 0.07 0.272 423 22.7 0.75 0.155 101475 16.2 0.896 21 16.9 21 17.5 0.63 0.269 21 17.9 1.07 0.115 101476 16.7 0.916 32 14.3 32 15.0 0.48 0.270 33 15.6 0.98 0.122 101477 16.7 0.916 - - 59 14.1 0.51 0.253 62 16.1 1.85 0.057 101478 16.7 0.916 55 13.4 55 13.8 0.19 0.280 56 14.1 0.39 0.204 101479 16.2 0.896 - - 544 16.0 0.03 0.278 556 18.2 0.43 0.200 101480 16.2 0.896 26 15.8 26 15.7 0.00 0.288 26 15.8 0.00 0.288 101481 16.2 0.995 42 15.0 42 15.0 0.00 0.288 42 15.2 0.14 0.255 101482 16.2 0.995 52 16.3 53 16.7 0.19 0.281 53 17.0 0.39 0.206 101484 16.2 0.995 32 13.3 33 13.7 0.40 0.274 33 14.3 0.82 0.138 101485 16.2 0.995 41 15.3 41 16.0 0.38 0.274 42 16.8 0.91 0.130 101486 16.1 0.993 39 13.0 39 13.9 0.46 0.268 40 14.8 1.11 0.108 101487 16.1 0.993 14 17.9 13 17.7 0.00 0.288 13 17.7 0.00 0.288 101488 16.1 0.993 4 17.9 - - - - - - - - 101489 16.1 0.993 17 16.8 17 16.8 0.00 0.288 17 16.8 0.00 0.288 101490 16.1 0.993 45 11.5 46 12.4 0.39 0.270 47 13.1 0.90 0.128 101491 16.1 0.993 34 12.6 35 13.7 0.55 0.263 36 14.9 1.45 0.079 101492 16.1 0.993 32 14.7 33 15.4 0.50 0.269 33 16.1 1.04 0.116 101493 16.1 0.993 22 14.8 22 15.6 0.67 0.263 23 16.4 1.45 0.081 101494 16.1 0.993 26 18.3 24 17.9 0.00 0.288 24 17.9 0.00 0.288 101495 16.1 0.993 44 15.6 44 16.4 0.36 0.274 45 17.0 0.78 0.146 101496 16.1 0.993 19 17.2 18 17.0 0.00 0.288 18 17.0 0.00 0.288 101497 16.1 0.993 24 13.4 24 13.9 0.52 0.272 24 14.0 0.66 0.159 101498 16.1 0.993 26 14.6 26 15.1 0.50 0.273 27 15.6 0.92 0.128 101499 16.1 0.993 25 14.2 23 14.0 0.00 0.288 24 14.1 0.00 0.288 101500 16.1 0.993 30 12.6 29 12.6 0.00 0.288 29 12.6 0.00 0.288 101501 16.1 0.993 41 12.2 42 13.8 0.54 0.260 43 14.8 1.42 0.082 101502 16.1 0.993 61 10.7 61 11.7 0.31 0.270 62 12.2 0.72 0.150 101503 16.1 0.993 20 14.6 21 15.0 0.54 0.274 21 15.0 0.45 0.194 101504 16.1 0.993 - - - - - - - - - - 101505 16.1 0.993 44 15.1 43 15.1 0.00 0.288 43 15.0 0.00 0.288 101506 16.1 0.993 19 14.8 20 15.1 0.55 0.274 20 15.4 0.78 0.144 101507 16.1 0.993 19 14.0 18 14.0 0.00 0.288 18 14.0 0.00 0.288 101508 16.1 0.993 20 14.5 20 14.9 0.58 0.272 20 15.1 0.69 0.157 101509 16.1 0.993 26 15.0 27 16.2 0.65 0.260 28 16.7 1.29 0.093 101510 16.1 0.993 39 15.1 40 16.2 0.47 0.268 41 16.9 1.04 0.117 101511 16.1 0.993 25 13.7 25 14.6 0.64 0.262 26 15.4 1.45 0.080 101512 16.1 0.993 20 15.4 19 15.3 0.00 0.288 19 15.3 0.00 0.288 101513 16.1 0.993 - - 47 16.4 0.61 0.249 50 18.5 2.26 0.042 101514 16.1 0.993 20 15.5 21 17.0 0.76 0.254 22 18.4 2.20 0.044 101515 16.1 0.993 42 16.1 42 16.9 0.38 0.273 43 17.5 0.77 0.147 101516 16.1 0.993 51 15.3 49 15.2 0.00 0.288 49 15.1 0.00 0.288 101517 16.1 0.993 20 13.4 20 14.2 0.71 0.260 20 14.4 1.22 0.097 101518 16.1 0.993 25 14.7 - - - - - - - -

154

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

101519 16.1 0.993 7 12.9 7 13.4 0.90 0.252 7 13.7 1.96 0.049 101520 16.1 0.993 20 15.5 19 15.4 0.00 0.288 19 15.4 0.00 0.288 101521 16.1 0.993 35 15.3 36 17.1 0.59 0.260 38 18.9 1.80 0.062 101522 16.1 0.993 41 13.0 40 12.9 0.00 0.288 40 12.9 0.00 0.288 101523 16.1 0.993 27 14.3 25 14.1 0.00 0.288 25 14.1 0.00 0.288 101524 16.1 0.993 36 14.6 35 14.5 0.00 0.288 36 14.5 0.00 0.288 101525 16.1 0.993 20 14.6 19 14.5 0.00 0.288 19 14.5 0.00 0.288 101526 16.1 0.993 21 17.0 19 16.8 0.00 0.288 19 16.8 0.00 0.288 101527 16.1 0.993 - - - - - - - - - - 101528 16.1 0.993 - - - - - - - - - - 101529 16.1 0.993 - - - - - - - - - - 101530 16.1 0.993 22 14.3 20 14.2 0.00 0.288 21 14.3 0.00 0.288 101551 16.7 0.916 36 10.3 37 11.8 0.58 0.257 39 13.2 1.67 0.064 101552 15.2 0.916 34 16.7 34 16.7 0.00 0.288 34 16.7 0.00 0.288 101553 16.2 0.916 38 12.8 39 13.9 0.51 0.264 40 15.0 1.30 0.091 101554 16.2 0.916 38 14.7 39 14.7 0.08 0.286 39 14.9 0.16 0.249 101555 16.7 0.916 56 12.0 58 13.7 0.42 0.263 59 15.1 1.26 0.094 101556 15.2 0.896 - - 80 11.6 0.38 0.258 84 14.6 1.65 0.066 101557 17.1 0.916 13 12.0 12 11.9 0.00 0.288 12 11.9 0.00 0.288 101558 16.2 0.916 41 14.0 40 13.9 0.00 0.288 40 13.9 0.00 0.288 101559 16.2 0.916 35 14.1 35 14.2 0.07 0.287 36 14.3 0.14 0.255 101560 16.2 0.916 - - 51 11.0 0.55 0.251 53 12.4 1.80 0.056 101561 17.1 0.916 51 18.9 51 19.0 0.05 0.287 51 19.3 0.17 0.249 101562 17.1 0.916 20 12.3 18 12.1 0.00 0.288 18 12.1 0.00 0.288 101563 16.2 0.916 59 12.3 60 12.4 0.05 0.286 60 12.6 0.14 0.254 101567 15.2 0.979 - - 281 15.5 0.13 0.265 295 19.7 1.09 0.114 101573 16.1 0.993 9 17.2 9 17.8 0.87 0.257 10 18.1 1.78 0.062 101576 15.2 0.979 116 13.7 117 15.0 0.15 0.276 119 16.0 0.58 0.174 101579 16.1 0.993 19 14.2 20 14.6 0.58 0.272 20 14.6 0.52 0.182 101582 16.1 0.993 11 16.3 12 17.0 0.83 0.256 15 20.3 3.25 0.019 101583 16.1 0.993 10 16.4 10 16.5 0.58 0.280 10 16.5 0.26 0.229 101584 16.1 0.993 18 13.9 18 14.6 0.73 0.260 19 15.2 1.51 0.076 101585 16.1 0.993 11 14.2 11 14.2 0.34 0.285 11 14.2 0.10 0.263 101586 7.8 0.836 26 6.9 27 8.0 0.67 0.252 28 8.8 1.70 0.058 101587 7.8 0.836 - - 38 8.5 0.66 0.241 41 10.5 2.43 0.030 101588 7.8 0.831 0 5.6 0 5.6 0.00 - 0 5.6 0.00 - 101589 7.8 0.812 - - 56 6.1 0.51 0.248 58 7.8 1.80 0.052 101590 7.8 0.809 - - 85 8.3 0.46 0.241 96 13.6 2.51 0.030 101591 7.8 0.807 - - 79 5.0 0.41 0.249 83 6.8 1.62 0.061 101594 16.1 0.993 4 16.7 4 16.7 0.00 0.288 4 16.7 0.00 0.288 101595 16.1 0.993 26 14.4 26 14.6 0.33 0.280 26 14.5 0.15 0.252 101596 16.1 0.993 19 16.4 18 16.2 0.00 0.288 18 16.2 0.00 0.288 101597 16.1 0.993 49 14.3 48 14.2 0.00 0.288 48 14.2 0.00 0.288 101598 16.1 0.993 10 17.1 9 16.9 0.00 0.288 9 16.9 0.00 0.288 101600 16.1 0.993 13 18.1 12 17.8 0.00 0.288 12 17.8 0.00 0.288 101601 16.5 0.995 33 15.7 32 15.7 0.00 0.288 33 16.0 0.22 0.237 101602 16.5 0.995 40 15.1 40 15.1 0.04 0.287 40 15.3 0.16 0.250 101603 16.5 0.995 46 16.0 46 16.3 0.19 0.282 47 16.8 0.46 0.193 101604 16.1 0.993 10 17.3 10 17.1 0.00 0.288 10 17.1 0.00 0.288 101605 16.1 0.993 21 15.8 21 15.9 0.18 0.285 21 16.0 0.24 0.234 101606 16.1 0.993 11 13.9 10 13.9 0.00 0.288 10 13.9 0.00 0.288 101607 16.1 0.993 11 14.0 11 13.9 0.00 0.288 11 13.9 0.00 0.288 101608 16.1 0.993 15 14.4 14 14.3 0.00 0.288 14 14.3 0.00 0.288 101610 16.5 0.988 22 11.5 21 11.2 0.00 0.288 21 11.2 0.00 0.288 101620 18.4 0.986 - - 292 15.6 0.12 0.266 308 20.2 1.08 0.116 101621 18.4 0.986 84 16.7 85 18.1 0.23 0.274 86 18.8 0.64 0.167 101622 18.4 0.986 147 16.2 146 16.2 0.00 0.288 145 16.1 0.00 0.288

155

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

101623 18.4 0.986 94 14.2 96 16.5 0.27 0.268 98 17.6 0.92 0.130 101624 18.4 0.986 82 14.8 83 15.6 0.16 0.279 83 15.6 0.28 0.225 101625 18.4 0.986 34 17.2 35 19.0 0.60 0.261 35 19.5 1.33 0.093 101626 18.4 0.986 37 16.6 37 17.3 0.40 0.274 38 17.5 0.61 0.170 101627 18.4 0.986 61 14.3 63 16.6 0.42 0.263 65 18.5 1.42 0.085 101628 18.4 0.986 - - 148 17.3 0.24 0.263 153 19.5 1.21 0.103 101629 18.4 0.986 46 15.6 47 17.0 0.45 0.267 48 17.9 1.09 0.112 101630 18.4 0.986 21 16.8 21 17.4 0.63 0.268 21 17.5 0.75 0.151 101631 18.4 0.986 30 14.7 31 15.6 0.56 0.266 31 15.8 0.88 0.133 101632 18.4 0.986 35 15.5 34 15.3 0.00 0.288 34 15.4 0.00 0.288 101633 18.4 0.986 17 14.3 18 15.7 0.78 0.252 19 16.0 1.78 0.060 101634 18.4 0.986 - - 74 17.6 0.40 0.262 77 19.5 1.41 0.087 101635 18.4 0.986 11 15.0 11 15.4 0.80 0.263 11 15.5 1.04 0.115 101636 18.4 0.986 37 15.9 38 17.4 0.55 0.263 39 18.2 1.27 0.097 101637 18.4 0.986 31 15.3 31 16.5 0.58 0.264 32 17.4 1.38 0.088 101638 18.4 0.986 16 18.2 17 20.3 0.82 0.251 18 20.9 2.29 0.042 101639 18.4 0.986 71 16.5 72 17.8 0.27 0.273 73 18.0 0.55 0.180 101640 18.4 0.986 - - 432 19.4 0.06 0.275 445 22.3 0.61 0.175 101641 18.4 0.986 328 18.9 - - - - - - - - 101642 18.4 0.986 - - 232 18.3 0.15 0.267 242 21.7 1.05 0.121 101643 18.4 0.986 25 14.4 26 16.1 0.71 0.254 26 16.3 1.52 0.076 101644 18.4 0.986 95 18.2 96 19.1 0.13 0.280 96 19.5 0.36 0.212 101645 18.4 0.986 89 14.6 89 14.8 0.06 0.285 89 14.8 0.08 0.268 101646 18.4 0.986 - - 153 17.7 0.22 0.264 158 19.6 1.08 0.115 101647 18.4 0.986 85 16.5 85 17.3 0.15 0.280 86 17.5 0.33 0.216 101648 18.4 0.986 56 15.8 54 15.7 0.00 0.288 54 15.6 0.00 0.288 101649 18.4 0.986 47 14.2 48 16.3 0.51 0.260 50 17.7 1.50 0.079 101660 18.4 0.986 87 15.1 88 15.3 0.04 0.286 88 15.3 0.06 0.274 101661 18.4 0.986 305 17.2 307 18.9 0.03 0.282 309 19.5 0.24 0.235 101662 18.4 0.986 - - 157 18.5 0.27 0.258 169 24.2 1.73 0.071 101663 18.4 0.986 - - 408 17.0 0.06 0.273 421 20.1 0.66 0.165 101664 18.4 0.986 - - 46 17.7 0.59 0.253 49 20.1 2.11 0.048 101665 15.6 0.989 106 14.9 103 14.6 0.00 0.288 102 14.6 0.00 0.288 101666 15.6 0.991 157 14.5 155 14.4 0.00 0.288 155 14.3 0.00 0.288 101667 15.6 0.991 234 14.1 234 14.6 0.02 0.285 235 14.7 0.09 0.265 101668 15.6 0.989 205 14.4 205 14.7 0.02 0.286 206 15.0 0.11 0.262 101673 15.6 0.989 149 14.7 147 14.6 0.00 0.288 147 14.6 0.00 0.288 101677 18.4 0.986 318 12.4 319 14.0 0.04 0.281 323 14.9 0.29 0.222 101678 18.4 0.986 - - 186 15.5 0.16 0.267 192 17.4 0.91 0.131 101679 18.4 0.986 - - 69 15.6 0.42 0.260 72 18.1 1.60 0.073 101680 18.4 0.986 63 13.0 65 15.8 0.44 0.259 67 17.2 1.43 0.083 101681 18.4 0.986 23 14.3 23 14.4 0.26 0.283 23 14.3 0.00 0.288 101682 18.4 0.986 - - 165 14.5 0.16 0.269 168 15.7 0.74 0.150 101683 18.4 0.986 - - 157 17.2 0.23 0.262 164 20.5 1.32 0.095 101684 18.4 0.986 - - 218 15.9 0.12 0.270 224 18.1 0.80 0.144 101686 9.1 0.880 9 7.7 9 8.0 0.83 0.258 9 8.0 1.13 0.099 101687 9.1 0.880 14 7.6 13 7.5 0.00 0.288 13 7.5 0.00 0.288 101688 9.8 0.880 14 6.6 14 6.6 0.00 0.288 14 6.7 0.07 0.269 101690 9.8 0.877 9 6.2 10 6.8 0.87 0.245 14 10.6 3.83 0.008 101691 9.8 0.880 10 10.9 11 11.8 0.87 0.245 15 16.1 3.84 0.010 101692 10.3 0.877 19 10.4 19 10.4 0.00 0.288 19 10.5 0.11 0.259 101693 10.3 0.905 54 9.4 55 10.9 0.43 0.261 57 12.5 1.40 0.081 101694 10.3 0.905 37 10.3 37 10.4 0.12 0.285 38 10.7 0.33 0.212 101695 10.3 0.905 44 10.9 45 11.3 0.28 0.277 45 11.4 0.40 0.199 101696 8.1 0.848 13 12.3 14 12.6 0.70 0.269 14 13.0 1.24 0.094 101697 8.1 0.848 14 10.7 15 11.6 0.81 0.250 16 12.6 2.21 0.039 101698 8.1 0.848 13 11.2 12 11.2 0.00 0.288 13 11.2 0.00 0.288

156

Sample No.

MAAT (°C)a

Atmo. pres. (atm)b

UA ΔNe (%)c

UA NGT (°C)c

CE ΔNe (%)c

CE NGT (°C)c

CE Fc

value CE

Lexc,d

PR ΔNe (%)c

PR NGT (°C)c

PR Rc

value PR

Lexc,d

101699 8.1 0.848 12 11.1 12 11.4 0.73 0.267 13 12.3 1.86 0.053 101700 8.2 0.863 23 18.0 22 17.9 0.00 0.288 22 17.9 0.00 0.288 101701 8.2 0.863 18 17.3 16 17.2 0.00 0.288 16 17.1 0.00 0.288 101702 8.2 0.862 23 12.6 23 12.7 0.27 0.283 23 12.8 0.20 0.240 101703 8.2 0.863 16 17.9 13 17.8 0.00 0.288 13 17.7 0.00 0.288 101704 8.2 0.862 33 12.9 33 12.8 0.00 0.288 33 12.8 0.00 0.288 101705 8.2 0.862 26 16.0 25 15.9 0.00 0.288 24 15.9 0.00 0.288 101706 8.9 0.863 32 10.7 31 10.6 0.00 0.288 31 10.6 0.00 0.288 101707 6.8 0.856 21 8.5 21 8.9 0.59 0.267 22 9.0 0.72 0.147 101708 6.8 0.856 54 10.3 55 10.8 0.22 0.277 55 11.1 0.44 0.193 101709 8.8 0.887 6 6.1 6 6.0 0.00 0.288 6 6.0 0.00 0.288 101710 8.8 0.887 4 6.1 4 6.1 0.00 0.288 4 6.1 0.00 0.288 101711 8.8 0.887 6 6.4 6 6.4 0.00 0.288 6 6.4 0.00 0.288 101747 15.2 0.979 147 17.4 147 18.3 0.07 0.282 148 18.9 0.30 0.223 101754 15.2 0.979 112 13.1 113 15.4 0.22 0.269 116 16.8 0.89 0.133 101755 15.2 0.979 299 17.7 292 17.4 0.00 0.288 291 17.4 0.00 0.288 101756 15.2 0.979 94 13.1 95 14.2 0.18 0.275 96 14.4 0.42 0.199 101757 15.2 0.979 118 10.4 119 12.3 0.19 0.270 121 12.9 0.66 0.158 101758 15.2 0.979 91 13.2 90 13.1 0.00 0.288 90 13.1 0.00 0.288 101759 15.2 0.979 145 14.0 146 14.2 0.02 0.286 146 14.1 0.03 0.281 101760 15.2 0.979 121 12.3 122 13.6 0.14 0.275 124 14.1 0.48 0.188 101761 15.2 0.979 90 11.1 91 12.0 0.17 0.276 92 12.7 0.56 0.174 101763 15.2 0.979 75 15.8 75 16.2 0.12 0.282 76 16.9 0.42 0.199 101764 15.2 0.979 87 16.9 87 17.0 0.02 0.287 88 17.2 0.13 0.258 101765 15.2 0.979 19 15.8 22 20.4 0.80 0.245 23 20.7 2.84 0.026 103118 17.8 0.983 127 14.2 129 16.4 0.18 0.272 131 17.7 0.76 0.150 103119 18.0 0.964 38 16.5 39 18.6 0.58 0.260 41 20.0 1.65 0.071 103120 16.8 0.957 - - 160 13.9 0.21 0.263 166 16.0 1.10 0.110 103121 17.8 0.976 142 13.6 143 14.6 0.09 0.280 144 15.2 0.36 0.210 103122 18.3 0.986 28 17.7 29 19.2 0.65 0.261 30 20.2 1.58 0.076 103123 17.8 0.984 - - 121 20.0 0.33 0.259 129 24.1 1.72 0.071 103124 16.8 0.958 29 15.1 29 16.0 0.58 0.265 30 16.8 1.26 0.096 103125 18.2 0.989 106 14.7 107 15.8 0.15 0.277 108 16.4 0.48 0.189 103126 18.2 0.993 51 17.3 52 18.8 0.41 0.269 53 19.7 1.03 0.121 103127 18.2 0.973 45 16.2 46 17.6 0.47 0.266 47 19.3 1.37 0.089 103128 18.0 0.957 - - 408 15.3 0.06 0.273 420 17.7 0.61 0.170 103129 18.0 0.957 - - 308 14.7 0.08 0.272 317 16.9 0.68 0.160 103130 17.6 0.971 - - 250 15.0 0.10 0.272 256 17.0 0.69 0.159 103132 18.2 0.980 - - 247 15.1 0.13 0.266 257 18.1 0.98 0.124 103133 18.2 0.984 88 15.1 89 16.5 0.22 0.273 91 17.6 0.74 0.152 103134 18.2 0.986 75 14.7 76 17.1 0.35 0.265 79 18.8 1.22 0.102 103135 13.4 0.976 78 13.6 78 13.9 0.09 0.283 79 14.2 0.27 0.226 103136 16.8 0.973 - - 145 21.3 0.26 0.263 151 24.5 1.38 0.095 103137 18.0 0.973 66 16.0 66 16.4 0.15 0.281 66 16.7 0.30 0.223 103138 18.2 0.973 46 14.2 46 15.0 0.38 0.271 47 15.6 0.81 0.141 103139 18.2 0.973 51 14.9 52 15.7 0.32 0.274 52 16.1 0.65 0.163 103140 18.2 0.984 84 12.6 86 14.5 0.28 0.268 88 15.7 0.96 0.123 103141 18.2 0.984 145 13.4 146 14.5 0.09 0.279 147 15.2 0.39 0.204 103142 18.2 0.993 - - 217 19.1 0.21 0.258 - - - - 103143 18.2 0.985 244 12.8 245 13.7 0.03 0.283 246 14.3 0.22 0.237 103144 17.8 0.972 - - 126 16.2 0.25 0.264 131 18.5 1.16 0.106

a mean annual air temp. for station nearest the well (from Western Regional Climate Center) b calculated from wellhead elevation using P=Po[(288.15/(288.15-(0.0065*H)))-5.25588] where Po is pressure at sea level (101 325 Pa) and H is the height above sea level in meters c "-" indicates sample not fit by model d Lex is the He to Ne ratio of the excess air component (= Heex/Neex)

157

Appendix D:

SHAW Model Input Files

158

Table D1. File containing list of input and output files (bc.inp). Line File contents 1 0 1 0 2 BC.SIT 3 BC.WEA 4 BC.MOI 5 BC.TMP 6 24 0 24 24 24 24 24 24 0 24 0 0 24 7 OUT.OUT 8 PROFIL.OUT 9 TEMP.OUT 10 MOIST.OUT 11 MATRIC.OUT 12 ENERGY.OUT 13 WATER.OUT 14 WFLOW.OUT 15 ROOTXT.OUT 16 FROST.OUT 17 SALTS.OUT 18 SOLUT.OUT 19 0 1 0

159

Table D2. File containing moisture profile data for loam Base Case scenario (bc.moi). Line File contents 1 338 12 49 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9

-0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.6 -0.3 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.8 5.5 7.0 9.0 11.0 13.0 14.9 16.4 16.92

2 1 12 50 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.6 -0.3 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.8 5.5 7.0 9.0 11.0 13.0 14.9 16.4 16.92

3 357 12 91 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.6 -0.3 0.1 0.6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.8 5.5 7.0 9.0 11.0 13.0 14.9 16.4 16.92

160

Table D3. File containing temperature profile data for loam Base Case scenario (bc.tmp). Line File contents 1 338 12 49 14.66 14.66 14.66 14.66 14.66 14.66 14.66

14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66

2 1 12 50 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66

3 357 12 91 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66 14.66

161

Table D4. File containing site characteristics data for loam Base Case scenario (bc.sit). Line File contents 1 BDC site - 38.5° N, 97° W (in Kansas) (SITE CHARACTERTICS) LINE A 2 1 12 50 365 89 LINE B 3 38 30 5. 180.0 12.0 400. LINE C 4 0 0 1 47 0 00.001 1 0 0 0 1 0 0 LINE D 5 0.6 2.0 1.00 LINE E 6 1.0 .15 ****** SNOW LINE G 7 0.90 0.25 6000. 2.0 0.0 2000. ****** RESIDUE LINE H 8 0 1 0.15 1.0 ****** SOIL LINE J 9 0.00 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-1 10 0.01 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-2 11 0.02 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-3 12 0.03 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-4 13 0.04 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-5 14 0.05 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-6 15 0.06 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-7 16 0.08 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-8 17 0.10 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-9 18 0.12 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-10 19 0.15 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-11 20 0.18 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-12 21 0.22 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-13 22 0.26 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-14 23 0.31 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-15 24 0.36 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-16 25 0.43 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-17 26 0.50 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-18 27 0.60 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-19 28 0.70 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-20 29 0.83 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-21 30 1.00 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-22 31 1.20 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-23 32 1.40 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-24 33 1.60 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-25 34 1.80 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-26 35 2.00 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-27 36 2.30 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-28 37 2.60 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-29 38 3.00 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-30 39 3.50 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-31 40 4.00 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-32 41 4.50 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-33 42 5.00 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-34 43 5.50 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-35 44 6.00 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-36 45 6.50 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-37 46 7.00 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-38 47 7.75 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-39 48 8.50 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-40 49 10.0 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-41 50 12.0 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-42

162

Line File contents 51 14.0 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-43 52 16.0 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-44 53 18.0 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-45 54 19.5 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-46 55 20.0 4.5 -0.11 0.684 1500. 0.43 40. 40. 20. 0.0 2.8 0.005 LINE J-47

163

Table D5. File containing site characteristics data for sand Base Case scenario (bc.sit). Line File contents 1 BDC site - 38.5° N, 97° W (in Kansas) (SITE CHARACTERTICS) LINE A 2 1 12 50 365 89 LINE B 3 38 30 5. 180.0 12.0 400. LINE C 4 0 0 1 47 0 00.001 1 0 0 0 1 0 0 LINE D 5 0.6 2.0 1.00 LINE E 6 1.0 .15 ****** SNOW LINE G 7 0.90 0.25 6000. 2.0 0.0 2000. ****** RESIDUE LINE H 8 0 1 0.15 1.0 ****** SOIL LINE J 9 0.00 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-1 10 0.01 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-2 11 0.02 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-3 12 0.03 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-4 13 0.04 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-5 14 0.05 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-6 15 0.06 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-7 16 0.08 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-8 17 0.10 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-9 18 0.12 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-10 19 0.15 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-11 20 0.18 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-12 21 0.22 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-13 22 0.26 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-14 23 0.31 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-15 24 0.36 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-16 25 0.43 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-17 26 0.50 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-18 27 0.60 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-19 28 0.70 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-20 29 0.83 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-21 30 1.00 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-22 31 1.20 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-23 32 1.40 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-24 33 1.60 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-25 34 1.80 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-26 35 2.00 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-27 36 2.30 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-28 37 2.60 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-29 38 3.00 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-30 39 3.50 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-31 40 4.00 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-32 41 4.50 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-33 42 5.00 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-34 43 5.50 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-35 44 6.00 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-36 45 6.50 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-37 46 7.00 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-38 47 7.75 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-39 48 8.50 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-40 49 10.0 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-41 50 12.0 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-42

164

Line File contents 51 14.0 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-43 52 16.0 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-44 53 18.0 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-45 54 19.5 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-46 55 20.0 1.7 -0.07 20.88 1500. 0.42 90. 5. 5. 0.0 2.8 0.005 LINE J-47

165

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Vita

Bradley Donald Cey was born in Saskatoon, SK, Canada on June 23, 1974, the

son of Bernard and Magdalene Cey. After graduating from Landis High School, Landis,

SK, Canada, in 1992, he entered the University of Saskatchewan in Saskatoon. He

received the degrees of Bachelor of Science in 1996 and Master of Science in 1999 from

the University of Saskatchewan. He was employed as an engineer by MDH Engineered

Solutions Corp. in Saskatoon from 1999 until 2003. In August 2003, he began graduate

studies at the University of Texas at Austin.

Permanent address: Landis, SK, Canada S0K 2K0

This dissertation was typed by the author.

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