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Earth and Planetary Sciences ETDs Electronic Theses and Dissertations
12-1-2014
Continental-scale isotope hydrologyScott Jasechko
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Scott Jasechko Candidate
Earth and Planetary Sciences
Department
This dissertation is approved, and it is acceptable in quality and form for publication:
Approved by the Dissertation Committee:
Dr. Zachary D. Sharp , Co-chairperson
Dr. Peter J. Fawcett , Co-chairperson
Dr. Joseph Galewsky
Dr. Juske Horita
CONTINENTAL-SCALE ISOTOPE HYDROLOGY
by
SCOTT ALLAN JASECHKO
B.Sc., University of Victoria, 2009 M.Sc. University of Waterloo, 2011
DISSERTATION
Submitted in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
Earth and Planetary Sciences
The University of New Mexico Albuquerque, New Mexico
December, 2014
iii
DEDICATION
To Jennifer, Gordon, Glenn and Edith – for your love and your encouragement.
iv
ACKNOWLEDGMENTS
I am grateful to Zachary Sharp, Peter Fawcett and Joseph Galewsky for supporting, challenging and
guiding me throughout my Ph.D. education. I am thankful for my friends and mentors who continue
to grant me the joy of belonging in a community.
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CONTINENTAL-SCALE ISOTOPE HYDROLOGY
By
Scott Jasechko
B.Sc., Physical Geography and Earth and Ocean Sciences, University of Victoria, 2009
M.Sc., Earth and Environmental Sciences, University of Waterloo, 2011
Ph.D., Earth and Planetary Sciences, University of New Mexico, 2014
ABSTRACT
Providing sustainable sources of fresh water for a growing population of 7 billion people is one of
the grand challenges of the 21st century. This dissertation outlines several applications of isotope
hydrology to address four previously unknown questions involving surface- and ground-water
resources at regional- to continental-spatial scales over contemporary- to millennial-temporal scales.
The four chapters in this dissertation investigate (1) the rate of plant transpiration, (2) the seasonality
of groundwater recharge, (3) the climate of the last ice age, and (4) the chemistry of Ugandan waters.
(1) Chapter one presents a new global compilation of lake water isotopic data, river isotopic data,
stand-level transpiration rates, and water use efficiency measurements, and analyzes the newly
synthesized data to show that plant transpiration is the largest water flux from Earth’s continents,
exceeding both physical evaporation and continental runoff. (2) Chapter two presents a new global
synthesis of rain, snow and groundwater isotopic compositions, and analyzes the paired
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precipitation-groundwater dataset to show that the percentage of precipitation that recharges aquifers
is at a maximum during the winter (extra-tropics) and wet (tropics) seasons. (3) Chapter three
presents a new global compilation of groundwater radiocarbon, tritium, and stable O and H isotopic
data, and maps the isotopic shift of meteoric waters since the last ice age. The analysis shows that the
majority (~90%) of precipitation during the last ice age had lower 18O/16O and 2H/1H ratios than the
modern day, except in some exclusively coastal locations. We also show that current isotope-enabled
general circulation models capture some, but not all, spatial variability in ice-age-to-late-Holocene
18O/16O and 2H/1H shifts, providing a new calibration tool that can be used to improve our
understanding of glacial climate dynamics. (4) Chapter four presents isotopic and chemical analyses
of Ugandan lake, river, rain, and ground water collected during a field expedition led in July of 2013.
Analysis of this new dataset reveals new estimates of lake water balances across Uganda.
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TABLE OF CONTENTS
DEDICATION .................................................................................................................................................. iii
ACKNOWLEDGMENTS ............................................................................................................................... iv
ABSTRACT .......................................................................................................................................................... v
TABLE OF CONTENTS ............................................................................................................................... vii
PREFACE............................................................................................................................................................. 1
CHAPTER 1 — GLOBAL PLANT TRANSPIRATION FLUXES ........................................................ 9
1.1 Abstract ...................................................................................................................................................... 9
1.2 Introduction ............................................................................................................................................... 9
1.3 Dataset and methods .............................................................................................................................. 32
1.4 Results ....................................................................................................................................................... 54
1.5 Discussion ................................................................................................................................................ 61
1.6 References ................................................................................................................................................ 63
CHAPTER 2 — THE SEASONALITY OF GLOBAL GROUNDWATER RECHARGE ............ 83
2.1 Abstract .................................................................................................................................................... 83
2.2 Introduction ............................................................................................................................................. 83
2.3 Dataset and methods .............................................................................................................................. 88
2.4 Results ..................................................................................................................................................... 101
2.5 Discussion .............................................................................................................................................. 106
2.6 References .............................................................................................................................................. 121
CHAPTER 3 — THE ISOTOPIC COMPOSITION OF ICE AGE GROUNDWATERS ........... 142
3.1 Abstract .................................................................................................................................................. 142
3.2 Introduction ........................................................................................................................................... 143
3.3 Dataset and Methods............................................................................................................................ 148
3.4 Results ..................................................................................................................................................... 151
3.5 Discussion .............................................................................................................................................. 164
3.6 References .............................................................................................................................................. 188
CHAPTER 4 — THE ISOTOPE HYDROLOGY OF UGANDA ..................................................... 212
4.1 Abstract .................................................................................................................................................. 212
4.2 Introduction ........................................................................................................................................... 212
4.3 Dataset and methods ............................................................................................................................ 214
4.4 Results ..................................................................................................................................................... 217
4.5 Discussion .............................................................................................................................................. 231
4.6 References .............................................................................................................................................. 241
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List of Figures
1-1. Schematic of fresh water fluxes ............................................................................................................... 10
1-2. Schematic of plant transpiration .............................................................................................................. 12
1-3. Water yields before and after the clearing of vegetation in experimental watersheds .................... 15
1-4. Compiled transpiration/evapotranspiration ratios ............................................................................... 17
1-5. Compiled transpiration/evapotranspiration ratios and precipitation rates ...................................... 28
1-6. Transpiration/evapotranspiration measurements sorted by technique............................................. 29
1-7. Compiled desert transpiration/evapotranspiration ratios and precipitation rates ........................... 30
1-8. Locations of transpiration study watersheds ......................................................................................... 34
1-9. Water use efficiency as a function of vapor pressure deficit ............................................................... 50
1-10. Spatial distribution of water use efficiency .......................................................................................... 50
1-11. The deuterium excess of 31 major rivers ............................................................................................. 52
1-12. The O and H isotopic composition of large lakes .............................................................................. 54
1-13. Heterogeneity of lake water O and H isotopes ................................................................................... 55
1-14. Temperature and O isotopic composition of Baikal and Tanganyika ............................................. 56
1-15. The isotopic composition of the North American Great Lakes at depth ...................................... 56
1-16. The transpiration rate calculated for 54 lake catchments grouped by biome................................. 58
1-17. The transpiration rate for 10% of Earth’s ice free land area ............................................................ 59
1-18. The transpiration rate for 73 catchments ............................................................................................. 60
1-19. Gross primary productivity for 10% of ice free land areas ............................................................... 61
2-1. An estimate of the global annual groundwater recharge ratio ............................................................ 86
2-2. Locations of paired precipitation-groundwater isotopic data ............................................................. 91
2-3. The change in meteoric 3H from 1930 to 2009..................................................................................... 96
2-4. The isotopic approach to quantifying recharge/precipitation seasonality ...................................... 100
2-5. Comparison of groundwater and precipitation O and H isotopic data .......................................... 101
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2-6. The seasonality of groundwater recharge ratios at 54 locations ....................................................... 103
2-7. Comparison of groundwater and precipitation isotopic compositions ........................................... 106
2-8. The seasonality of O and H isotopic data in precipitation ................................................................ 108
2-9. Seasonality of normalized difference vegetation indices across land surfaces ............................... 110
2-10. A comparison of modelled and isotope-based recharge ratio seasonality .................................... 113
2-11. Cross plot of modelled and isotope-based recharge ratio seasonalities ........................................ 115
3-1. Temperature changes from the last glacial maximum to the modern day ...................................... 145
3-2. Map of O and H isotopic change from the last ice age to the late-Holocene................................ 152
3-3. Ranges of isotopic change from the last ice age to the late-Holocene observed in records ........ 162
3-4. The difference between δ18Oice age and δ18Olate-Holocene with latitude ................................................. 163
3-5. The modelled (CCSM) precipitation δ18Olast glacial maximum – δ18Opre-industrial ........................................ 167
3-6. The modelled (ECHAM) precipitation δ18Olast glacial maximum – δ18Opre-industrial ................................... 168
3-7. The modelled (IsoGSM) precipitation δ18Olast glacial maximum – δ18Opre-industrial .................................... 169
3-8. The modelled (LMDZ) precipitation δ18Olast glacial maximum – δ18Opre-industrial ...................................... 170
3-9. Locations where all models agree on the sign of δ18Olast glacial maximum – δ18Opre-industrial ................... 171
3-10. Agreement for 3 of 4 models on the sign of δ18Olast glacial maximum – δ18Opre-industrial ........................ 172
4-1. The O and H isotopic composition of Ugandan waters ................................................................... 218
4-2. Sampling locations of Ugandan waters ................................................................................................. 221
4-3. A Piper diagram showing the hydrochemistry of Ugandan waters .................................................. 229
4-4. Stable-isotope-based evaporation/input ratios for 24 Ugandan Lakes ........................................... 230
4-5. O isotopic composition and conductivity of Ugandan lakes and groundwaters ........................... 232
4-6. Deuterium excess of Ugandan lakes and groundwaters with electrical conductivity .................... 233
4-7. The deuterium excess and sample elevation of Ugandan rivers and groundwaters ...................... 234
4-8. Stable-isotope-based evaporation/input ratios based on O and H isotopes ................................. 237
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List of Tables
1-1. Compiled transpiration/evapotranspiration ratios ......................................................................... 19–26
1-2. Compiled transpiration/evapotranspiration sorted by ecoregion ...................................................... 27
1-3. Modelled transpiration/evapotranspiration fractions at a global scale ............................................. 32
1-4. Study lake information ........................................................................................................................ 35–36
1-5. Study lake hydrography ....................................................................................................................... 37–38
1-6. Transpiration model input parameters (isotope) ............................................................................ 44–45
1-7. Transpiration model input parameters (isotope, temperature and humidity) ............................ 45–46
1-8. Compiled large lake isotopic investigations ..................................................................................... 48–49
1-9. Compiled plant water use efficiencies..................................................................................................... 51
1-10. Deuterium excess of major rivers.......................................................................................................... 53
1-11. The terrestrial sublimation flux estimated in previous studies ......................................................... 62
2-1. Locations of paired groundwater and precipitation data ............................................................... 89–90
2-2. Seasonal groundwater recharge ratio results ............................................................................... 104–105
3-1. Modern and ice age physical and isotopic data for the oceans and the cryosphere ...................... 147
3-2. Differences in the δ18O value of the last ice age and the late-Holocene................................ 153–154
3-3. Observed ranges of δ18Oice age and δ18Olate-Holocene values in groundwaters ............................. 155–158
3-4. Speleothem δ18O from the last ice age to the late-Holocene ............................................................ 159
3-5. Ice core δ18O from the last ice age to the late-Holocene .................................................................. 160
4-1. The isotopic composition of Ugandan waters .................................................................................... 220
4-2. The isotopic composition of Ugandan waters sampled and measured in this study ........... 222–227
4-3. Major ion chemistry of Ugandan waters ..................................................................................... 235–236
1
PREFACE
This dissertation uses stable O and H isotopic compositions of meteoric waters to quantify
the sources and processes that govern the storage and movement of water on continents. This
preface is divided into two parts: (Part A) an outline of the publication yields from this dissertation,
specifically addressing the publication authorship guidelines as stipulated by The University of New
Mexico’s Department of Earth and Planetary Sciences, and (Part B) an outline for this dissertation,
with brief terminology and background information relevant to all four chapters within this
dissertation.
(Part A) This dissertation is linked to six planned-or-published peer reviewed publications.
Three articles have already been published in top-tier peer-reviewed journals. One other publication
is currently in review. Two manuscripts are being prepared for submission to a peer reviewed journal
at the time this dissertation is submitted. Chapter 1 is linked to three publications in the journals
Nature (two publications) and Agricultural and Forest Meteorology. Chapter 2 is linked to a
manuscript that is currently undergoing peer review. Publications linked to chapters 3 and 4 are being
prepared for submission at the time that this dissertation is being submitted.
S. Jasechko is the lead author of three published articles, is the second author on the third
publication that has only two authors, in total. S. Jasechko is the lead author on the fourth
publication that is currently in review, and will also be the lead author for publication six that results
from dissertation chapter 4. To follow the requirements of the Department of Earth and Planetary
Sciences, the contributions and roles of each co-author within each publication are outlined below.
Chapter 1 uses a global dataset of isotopic data compiled for lakes and rivers to quantify the
rate that plants uptake water. Chapter 1 has yielded three publications in peer reviewed journals, the
first of which was published in April-2013 in Nature, the second was published in February-2014 in
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Nature, and the third was published in June-2014 in Agricultural and Forest Meteorology. The first
of three Chapter one publications included five analysis components, all of which were completed by
S. Jasechko in entirety: (i) compilation and synthesis of data, (ii) geospatial analyses, (iii) development
of methodology and equations, (iv) analysis of global remote sensing data, and (v) calculation of
transpiration fluxes. The manuscripts published in Nature were written by S. Jasechko with
comments and suggestions from Z. D. Sharp, J. J. Gibson, S. J. Birks, Y. Yi and P. J. Fawcett. The
publication in Agricultural and Forest Meteorology represents a collaboration between W.
Schlesinger and S. Jasechko, with W. Schlesinger and S. Jasechko sharing in all stages of manuscript
development (data compilation, figure development, statistical analyses and writing of the manuscript
text.).
Chapter 2 synthesizes global isotopic datasets of modern groundwater and precipitation and
analyzes the database to quantify the seasonal differences in groundwater recharge ratios at 54
globally-distributed locations, where the “groundwater recharge ratio” is defined as the fraction of
precipitation that recharges groundwater aquifers. Chapter 2 has yielded one manuscript that is in
press for publication in Water Resources Research at the time that this dissertation was submitted.
The Chapter 2 project included six parts: (i) compilation of a global groundwater isotopic dataset, (ii)
amalgamation of three continental-scale precipitation isotopic datasets, (iii) geospatial synthesis of
groundwater isotopic data with precipitation data, (iv) development of a new set of equations, (v)
calculation of groundwater recharge ratios, (vi) comparison of results with a state-of-the-art global
hydrological model. S. Jasechko led all components of this analysis, and worked together with
collaborators on two components: part (ii): “amalgamation of three continental-scale precipitation
isotopic datasets” (working with S. J. Birks and J. M. Welker), and part (vi): “comparison of results
with a state-of-the-art global hydrological model” (working with T. Gleeson and Y. Wada). The
manuscript was prepared by S. Jasechko (first author), and incorporates comments and suggestions
3
from all co-authors: S. J. Birks, T. Gleeson, Y. Wada, P. J. Fawcett, Z. D. Sharp, J. J. McDonnell and
J. M. Welker.
Chapter 3 uses a newly developed global groundwater dataset of radioactive carbon,
radioactive hydrogen, stable oxygen isotopes and stable hydrogen isotopes to delineate ice age
groundwaters and to map the distribution of 18O/16O and 2H/1H ratios of meteoric waters from the
last ice age. A manuscript linked to Chapter 3 — which will be submitted for publication — has been
prepared by S. Jasechko (first author) and is now circulating amongst co-authors at the time this
dissertation is submitted. The project included four components: (i) groundwater isotopic data
compilation, (ii) radiocarbon dating of groundwaters, (iii) statistical comparison of modern- and
paleo-groundwater isotopic compositions, (iv) geospatial comparison of groundwater isotopic
observations with the results of four isotope-enabled general circulation models run under last ice age
conditions. S. Jasechko led each component, and worked with leaders of four general circulation
models on component (iv): “geospatial comparison of groundwater isotopic observations with the
results of four isotope-enabled general circulation models run under last ice age conditions.” The
manuscript in preparation was written by S. Jasechko incorporates comments and suggestions from
all co-authors.
Chapter 4 uses a newly developed isotopic dataset of Ugandan groundwaters, lakes, rivers,
precipitation and springs to quantify hydrological processes controlling water availability throughout
the country. Chapter 4 presents a newly developed dataset resulting from fieldwork planned and led
by S. Jasechko, with on-the-ground collaborative support from M. Kizza (Makerere University) and
M. GebreEgziabher (Addis Ababa University). The cost of travel, lodging, transportation, sampling
equipment and geochemical analysis were supported in entirety by the combined graduate student
research funds awarded through four graduate student research grants to S. Jasechko: (i) the
Consortium of Universities for the Advancement of Hydrologic Science’s Pathfinder Fellowship, (ii)
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the American Geophysical Union’s Horton Hydrology Research Grant, (iii) the Geological Society of
America’s Graduate Student Research Grant, and (iv) the Caswell-Silver Foundation’s Kelley-Silver
Graduate Fellowship research allotment. Special sampling equipment was loaned to S. Jasechko by T.
Fischer, L. Crossey and K. Karlstrom. Sample preparation and chemical analyses were completed by
S. Jasechko with guidance from A. S. Ali. Oxygen, hydrogen and carbon isotopic analyses were
completed by S. Jasechko with laboratory support from V. Atudorei. The results of this project will
be submitted to an appropriate peer reviewed journal, with S. Jasechko listed as the lead author.
(Part B) This dissertation is divided into four chapters (in order): (1) Global plant
transpiration fluxes, (2) The seasonality of global groundwater recharge, (3) A global database of ice
age groundwaters, and (4) The isotope hydrology of Uganda. The four chapters examine a variety of
spatial scales, ranging from local (~101 km2) and regional scales (~104 km2; e.g., Chapter 4) to
continental scales (~106 to 107 km2). The four chapters explore different time periods, ranging from
the climate of the last ice age (104 years before today) to the climate of the present day. The cross-
cutting theme that binds there four chapters into one dissertation is that all four chapters investigate
distributions of 18O/16O and 2H/1H ratios in environmental waters. An introduction to the
application of oxygen and hydrogen isotopes in hydrology is presented next before delving into each
chapter.
The elements of oxygen and hydrogen were first discovered in the 18th century by Henry
Cavendish and Antoine Lavoisier. However, it was not for another 150 years that the isotopes of
oxygen and hydrogen were first discovered. The first isotopes to be identified were of thorium and
uranium (McCoy and Ross, 1907) and were first acknowledged by F. Soddy (Soddy, 1913), who
received the Nobel Prize in 1922 for this work. Soddy used the term isotopes to describe
radionuclides that had different decay rates but seemed at the time to be identical in all other
manners: "Put colloquially, their atoms have identical outsides but different insides... These elements
5
which are identical in their whole chemical character and are not separable by any method of
chemical analysis are now called isotopes" (F. Soddy’s Nobel Prize address in 1922; within reference:
Soddy, 1966).
Soddy first identified differences in the radioactive decay rates of isotopes (Soddy, 1913).
Since his work more than 100 years ago, we have learned that small chemical differences between
stable isotopes exist. The differences arise due to differences in mass between isotopes, which is
defined by the sum of protons (Z) and neutrons (N) within the atomic nucleus (minus a small
amount of “missing” mass that has been converted to nuclear binding energy). The discovery of the
existence of stable isotopes of oxygen can be credited to Blackett (1925) who used photography to
document the production of 17O from the capture of an alpha particle (i.e., He2+, a particle
comprised of two neutrons and two protons) by a common nitrogen atom (14N). Soon after this
laboratory experiment, different stable isotopes of oxygen (16O, 17O, 18O) were discovered to be
naturally occurring within Earth’s atmosphere (Giauque and Johnson, 1929a; 1929b). The discovery
of a stable isotope of hydrogen is credited to Urey (1932) who applied electrolysis to natural waters
to extract deuterium and confirm the existence of two stable hydrogen isotopes in nature (2H, 1H).
The discovery of naturally occurring stable isotopes of O and H have led to a vast array of
hydrological and paleo-climate investigations. Landmark work in the 1950s and 1960s identified
several features of the global isotopic data that have been reproduced many times over since their
foundation (i.e., Friedman, 1953, Craig, 1961, Dansgaard, 1964): (i) the ratios of 18O/16O and 2H/1H
covary in precipitation (Friedman 1953; Craig, 1961), (ii) the isotopic composition of precipitation is
controlled by temperature-dependent fractionation during rainout, leading to lower 18O/16O and
2H/1H ratios farther from moisture sources (Dansgaard, 1964), (iii) the process of evaporation
changes 18O/16O and 2H/1H ratios in different proportions than condensation due to additional
kinetic (i.e., disequilibrium) isotope effects (Craig, 1961), (iv) plant transpiration does not modify the
6
isotopic composition of water (Wershaw et al., 1966), (v) the isotopic composition of waters has
changed over Earth’s history and provides information about past climates (e.g., Urey et al., 1951).
Many other interesting discoveries have been made in the field of stable isotope hydrology over the
past 60 years; however, aforementioned discoveries are of particular importance to the discoveries
outlined in the forthcoming chapters.
Last, before I begin chapter 1, some isotopic terminology must be presented. Isotopic data
are presented in per mille notation on a scale that ranges from −1000 to +∞. Delta notation is
described mathematically as δ = (Rsample/Rstandard) × 1000 ‰, where R represented the ratio of
18O/16O or the ratio of 2H/1H, and the subscripts sample and standard refer to the ratio in the
measured sample or in an international standard, respectively. The international standard most
commonly applied to O and H isotopes is oceanic water: “standard mean ocean water” (or, SMOW),
that has an 18O/16O ratio of 0.00200520±0.00000043 and a 2H/1H ratio of 0.00015575±0.00000008
(Baertschi, 1976; de Wit et al., 1980).
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References (preface)
Baertschi, P. (1976), Absolute 18O content of standard mean ocean water, Earth and Planetary
Science Letters, 31, 341–344.
Blackett, P. M. S. (1927), The Ejection of Protons from Nitrogen Nuclei, Photographed by
the Wilson Method, Proceedings of the Royal Society of London, 107, 49–360.
Craig, H. (1961), Isotopic variations in meteoric waters, Science, 133, 1702–1703.
Dansgaard, W. (1964), Stable isotopes in precipitation, Tellus, 16, 436–468.
de Wit J.C., van der Straaten, C. M., Mook, W. G. (1980), Determination of the absolute
hydrogen isotopic ratio of V-SMOW and SLAP, Geostandards Newsletter 4, 33–36.
Friedman, I. (1953), Deuterium content of natural water and other substances. Geochimica
Cosmochimica Acta, 4, 89–103.
Giauque, W. F., and Johnson, H. L. (1929a), An isotope of oxygen of mass 17 in the earth’s
atmosphere, Nature, 123, 831.
Giauque, W. F., and Johnson, H. L. (1929b), An isotope of oxygen, mass 18, Nature, 123,
318.
McCoy, H. N., and Ross, W. H. (1907), The specific radioactivity of thorium and the
variation of the activity with chemical treatment and with time. Journal of the American Chemical Society,
29, 1709–1718.
Soddy, F. (1913), The Radio-elements and the Periodic Law, Chemical News, 107, 97–99.
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Soddy, F. (1966), The Origins of the Conceptions of Isotopes, Nobel Lecture, December 12,
1922, In Nobel Lectures Including Presentation Speeches and Laureates' Biographies: Chemistry
1901–1921, New York: Elsevier, pp. 367–401.
Urey H. C., Brickwedde, F. G., and Murphy, G. M. (1932), A Hydrogen Isotope of Mass 2,
Physical Review, 39, 164–165.
Urey, H. C., Lowenstam, H. A., Epstein, S., & McKinney, C. R. (1951), Measurement of
paleotemperatures and temperatures of the Upper Cretaceous of England, Denmark, and the
southeastern United States, Geological Society of America Bulletin, 62, 399–416.
9
CHAPTER 1 — GLOBAL PLANT TRANSPIRATION FLUXES
1.1 Abstract
Terrestrial water stores are balanced by inputs from rainfall and snowfall and losses via evaporation,
transpiration, river discharges and submarine groundwater discharges. Two-thirds of all precipitation
on land surfaces is vaporized by transpiration or by evaporation, but current general circulation and
land surface models span a wide range of predicted transpiration/evaporation ratios. Here I analyze a
global dataset of river and lake water isotopic data to show that gas exchange at plant stoma
represents both (i) the largest outgoing water flux from Earth’s continents, and (ii) the greatest
assimilation of CO2 in the global climate system. This result suggests that current land surface and
climate models can prioritize biological, rather than physical (evaporation), water fluxes to enhance
predictions of water availability under varying climate and land use futures.
1.2 Introduction
Chapter 1 describes a new approach to quantifying transpiration using isotopic data in lakes
and rivers. The approach and results of Chapter 1 were published in April of 2013 (Jasechko et al.,
2013). Three subsequent works have been published (or are in review) since April of 2013 as a result
of this initial publication (Schlesinger and Jasechko, 2014; Jasechko, 2014, Evaristo et al., in review).
This chapter presents a background to plant transpiration investigations in hydrology, discusses the
isotopic dataset and approach taken to quantify transpiration, and concludes by discussing the
ramifications of this work and presenting a vision for this field moving ahead.
Water transport on continents is replenished by precipitation on land surfaces that provides
about 110,000 km3 of fresh water each year (Oki and Kanae, 2006). The path that water takes after
falling on the land surface involves mixing, storage and transportation either as a liquid (i.e.,
advection-dispersion through porous media, or streamflow) or through vapourization via
evaporation or plant transpiration. It has long been recognized that evapotranspiration outweighs
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streamflow on continents by a factor of close to 2. Evapotranspiration consumes two-thirds of all
precipitation on continents, with an annual flux close to 70,000 km3/year (Jung et al., 2011).
Streamflow, on the other hand, has an annual flux of ~36,000 to ~38,000 km3/year (Dai and
Trenberth, 2002; Syed et al., 2010; Figure 1-1).
Figure 1-1. Schematic of current knowledge of fresh water fluxes on continents using rounded
numbers (note, submarine groundwater discharge not depicted, although this flux is expected to be
10 to 10,000 times less than continental runoff in rivers; Taniguchi et al., 2002).
Evapotranspiration is comprised of two components: plant transpiration (a biological
process) and evaporation (a physical process). Transpiration supports multiple life-sustaining roles
for plants. First, plants – like humans – require water for their cellular structures. Where humans are
about 70 % water, plants are ~80 % water. Transpiration supports cellular growth through the
provision of fresh water to plant cells. Second, plants move nutrients from the subsurface into
photosynthetically-active regions within the plant. For tall trees in forests this is often the canopy,
which can be in excess of 10s of meters above the ground surface. Third, plant transpiration requires
11
energy to convert liquid water into vapor (i.e., latent energy); plants capitalize upon this vaporization
energy requirement to cool off their leaf surfaces and, thus, moderate their growing leaf temperature
close to a cool, pan-biome temperature of 21°C (Helliker and Richter, 2008; Figure 1-2).
12
Figure 1-2. Schematic of gas exchange at plant stoma. Plants draw water from soil and groundwater
reservoirs, moving the water and entrained nutrients up the xylem by capitalizing on capillary action.
At stoma (upper right) plants passively release H2O (liquid to vapor conversion) via evaporation at leaf
surfaces (termed transpiration), which also cools leaf surfaces and maintains leaf temperatures that are
optimal for growth (Helliker and Richter, 2008).
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The separate fluxes of evaporation and transpiration have been considered as a single
component in hydrological investigations, lumped into the single term: evapotranspiration. However,
separating the fluxes of evaporation and transpiration is important for hydroclimatology because the
processes of evaporation and transpiration are different, and will respond differently to land use and
climate modifications. Where evaporation is a physical process, transpiration is a biological process
that is central to primary production on continents: the largest carbon flux in the climate system
(~120 Gt C/year; Beer et al., 2010). The response of evaporation and transpiration to a changing
climate will be different. Evaporation is a physical process, and the potential for evaporation can
broadly be expected to increase with future warming, although some evaporation pan data suggest
that other factors may exert a stronger control than temperature alone (e.g., Roderick and Farquhar,
2002). The response of plant transpiration to a warming climate is, on the other hand, complicated
by several sometimes conflicting responses. For example, warmer temperatures and fertilization of
the biosphere through enriched atmospheric CO2 is expected to increase plant productivity, and
consequentially increase transpiration. However, the enrichment of CO2 in the atmosphere has also
been predicted to increase the water use efficiency of plants (i.e., the ratio of H2O transpired to CO2
uptake), which is expected to decrease transpiration. Indeed a shift to more water-efficient
ecosystems has recently been observed across a variety of biomes in North America (Keenan et al.,
2013). Examining the supplementary data within this publication (Keenan et al., 2013) shows that
water use efficiency may be changing at a greater rate (i.e., as percentage of the total flux) than
streamflow (e.g., Labat et al., 2002; Peterson et al., 2002; McClelland et al., 2006), precipitation
(Zhang et al., 2007) or evapotranspiration (Jung et al., 2010; Miralles et al., 2014), highlighting that
knowledge of the exact flux of transpiration on continents is important to accurately predicting
change in the global hydrological cycle. These qualitative predictions are further complicated by
expected limitations to the “CO2 fertilization effect” imparted by fast approaching nitrogen
limitations upon the extent of CO2 fertilization. The broad implication of this analysis, is that models
14
of the Earth’s critical zone and the climate that neglect any one of physics, chemistry or biology are
at risk of missing important feedbacks, interactions and thresholds between the atmosphere,
biosphere, hydrosphere and lithosphere.
Changing land uses via deforestation and agriculture change transpiration fluxes and
influence downstream liquid water yields in rivers. Deforestation by humans has, globally, led to a
decrease in natural evapotranspiration of ~3000 km3 per year (about 4% of global
evapotranspiration; Gordon et al., 2005; Jung et al., 2010), and irrigation for agriculture – which
reactivated groundwater that is part of long flow paths, often removed from the “active”
hydrosphere, on human time scales – has increased terrestrial vapor fluxes by ~2600 km3 per year
(Gordon et al., 2005). This pumping of unsustainable groundwater sources has been investigated and
mapped at a global scale (Wada et al., 2012).
Other examples of land use modifications and impacts upon downstream hydrology date
back to experimental watershed work completed in the 1970s- and 1980s. Bosch and Hewlett (1983)
present a series of watershed studies where river flows were measured before and after the complete
deforestation of the upstream watershed. Follow deforestation, water yields downstream increased by
75% due to reduced evapotranspiration fluxes following forest clearing, highlighting the important
role of transpiration in total evapotranspiration fluxes (average of n = 25 experimental watersheds
that were completely deforested; 10th-90th percentile spans +10% increase to 194% increase in water
yields; Figure 1-3; Bosch and Hewlett, 1983).
15
Figure 1-3. Water yields before and after the clearing of vegetation within experimental watersheds
show an increase in runoff following deforestation. Each point represents a single watershed, which
had its outflow monitored before and after forest clearing. Data presented in this figure from Bosch
and Hewlett (1983).
Changing climate chemistry is expected to impact transpiration, and will produce an
important feedback to regional warming. For example, a general circulation model (Community Land
and Community Atmosphere Model) simulation using a transpiration/evapotranspiration ratio of
40% (pers. comm. L. Cao; Cao et al., 2010) showed that the physiological response to a CO2 enriched
atmosphere was a decrease in transpiration that reduced latent heat fluxes on continents and
ultimately accounted for ~15% of land surface warming, with the remainder largely attributed to CO2
radiative forcing and other feedback mechanisms. Similarly, more than half (8.4%) of the predicted
increase in future global river discharges (predicted runoff increase of 15% of present day) in this
model were predicted to be derived from reduced transpiration water fluxes. Given the potential of
changes to transpiration to warm land surfaces (Cao et al., 2010) and the relatively rapid increases in
water use efficiency reported by Keenan et al. (2013) there is a need to quantify the proportion of
evapotranspiration completed by vegetation through transpiration.
16
An extensive review of ~100 peer-reviewed publications was completed to uncover studies
that have decoupled evapotranspiration into its components: evaporation, and transpiration
(Schlesinger and Jasechko, 2014, building upon a compilation originally presented by Schlesinger and
Bernhardt, 2013). Three groups of studies exist: (i) forest- or cropland-scale field measurements using
a suite of techniques (e.g., sap flow meters, radial flow meters, isotope partitioning), (ii) general
circulation models, and (iii) land surface models. These three groups of approaches are reviewed in
the coming sections.
More than 80 studies have quantified transpiration fluxes at forest stand scales over the past
50 years. The results of these studies were recently compiled and reviewed by Schlesinger and
Jasechko (2014). The locations and transpiration fluxes (reported as a percentage of annual
evapotranspiration) of the compiled studies are presented in Figure 1-4. Existing transpiration flux
measurements have been completed on all continents and span a variety of biomes with different
plant life forms. The studies use on a variety of different approaches to estimate transpiration as a
proportion of evapotranspiration.
17
Figure 1-4. (top). Locations of stand-level measurements transpiration (T) as a proportion of
evapotranspiration (ET; i.e., T/ET). Colors mark the share of total evapotranspiration accounted for
by transpiration alone (Figure reproduced from Schlesinger and Jasechko, 2014). (bottom) Ranges of
transpiration/evapotranspiration ratios compiled from 81 studies sorted into major biomes. Bars
mark the 25th-75th percentile range of compiled studies for each biome; whiskers mark the 10th-90th
percentile range of compiled studies for each biome. Colors delineate the annual flux of
evapotranspiration from each biome as a proportion of total terrestrial evapotranspiration, which is
~70,000 km3/year (Jung et al., 2010). Evapotranspiration rates across each biome were obtained
from long term mean annual satellite-based evapotranspiration flux data (Mu et al., 2011).
Transpiration can be estimated using a variety of approaches. The most commonly applied
approaches broadly fall under the category of hydroclimatological models that utilize meteorological
measurements and are often coupled to sap flow measurements or transpiration fluxes (43 studies
18
compiled by Schlesinger and Jasechko, 2014). Other approaches that have been used to measure
transpiration fluxes include radial sap flow meters (e.g., Nizinski et al., 2011), energy balance models
(e.g., Tajchman, 1972; Liu et al., 2012), stand level O and H isotope based models (e.g, Hsieh et al.,
1998; Ferretti et al., 2003; Wang et al., 2013), catchment scale O and H isotope based models (e.g.,
Telmer and Veizer, 2000; Gibson and Edwards, 2002), satellite-based estimates (e.g., Tian et al.,
2013) and a water balance approaches comparing water fluxes from control and bare-soil plots
(Schlesinger et al., 1987). A comprehensive review of available stand level measurements is presented
in Table 1-1.
19
Table 1-1. Compiled transpiration/evapotranspiration studies.
Ecoregion Country Latitude Longitude Location
1 Tropical
Rainforest India 22.5 87.3 Arabari Range
2 Temperate Forest Germany 52.4 13.8 Berlin
3 Temperate Forest Germany 52.4 13.8 Berlin
4 Temperate Forest United
Kingdom - - Pinus sylvestris plantation
5 Temperate Deciduous
Forests Russia 50.8 42.5 Tellermanovsky
6 Boreal Forest - - - -
7 Boreal Forest Germany 51.8 10.5 Harz Mountains
8 Temperate Grassland
United States of America
41.1 -104.7 Wyoming: High Plains Grasslands
Research Station
9 Tropical
Rainforest Puerto Rico 18.3 -65.7 Luquillo Experimental Forest
10 Temperate Forest Germany 48.0 11.6 Near Munich
11 Desert China 44.3 87.9 Gubantonggut Desert: Fukang
Station of Desert Ecology
12 Boreal Forest Canada 63.4 -114.3 Northwest Territories and Nunavut
13 Boreal Forest Canada 45.7 -76.9 Ottawa River basin
14 Tundra Canada 64.5 -112.7 Northwest Territories and Nunavut
15 Tropical
Grassland United States of America
20.1 -155.8 Kohala, Hawaii
16 Tropical
Grassland United States of America
20.1 -155.8 Kohala, Hawaii
17 Tropical
Grassland United States of America
20.1 -155.8 Kohala, Hawaii
18 Tropical
Grassland United States of America
20.1 -155.8 Kohala, Hawaii
19 Temperate Grassland
United States of America
40.7 -104.8 Colorado: Central Plains
Experimental Range
20 Temperate Grassland
United States of America
35.0 -97.5 Oklahoma: Kessler Farm field
laboratory
21 Tropical
Rainforest Brazil -3.0 -60.0 Manaus
22 Tropical
Rainforest Brazil -3.0 -60.0 Manaus
23 Tropical
Rainforest Brazil -3.0 -60.0 Manaus
24 Tropical
Rainforest Brazil -3.1 -60.0 Ducke Forest Reserve
25 Tropical
Rainforest Indonesia -6.6 106.3 Janlappa Nature Reserve
20
Ecoregion Country Latitude Longitude Location
26 Temperate Forest Czech
Republic 49.1 13.7 National Park Sumava
27 Temperate Forest United
Kingdom 52.4 0.7 East Anglia
28 Temperate Forest United
Kingdom 52.0 -3.5 East of Aberystwyth
29 Temperate Deciduous
Forests
The Netherlands
52.5 4.6 North Holland
30 Temperate Deciduous
Forests Germany 51.1 10.5
Hainich National Park, Central Germany
31 Temperate Deciduous
Forests Denmark 56.4 9.3 Hald Ege
32 Mediterranean
Shrubland United States of America
32.8 -116.4 Echo Valley, California
33 Mediterranean
Shrubland United States of America
32.8 -116.4 Echo Valley, California
34 Mediterranean
Shrubland Chile -33.1 -71.0 Fundo Santa Laura
35 Temperate Grassland
United States of America
40.7 -104.8 Colorado: Central Plains
Experimental Range
36 Temperate Grassland
United States of America
40.5 -104.8 Colorado: Long term Ecological
Research Station
37 Temperate Grassland
China 37.6 101.7 Shidi
38 Temperate Grassland
China 37.7 101.7 Gancaitan
39 Temperate Grassland
China 30.9 91.1 Dangxiong
40 Temperate Grassland
China 43.6 116.7 Neimeng
41 Steppe Tunisia 35.8 9.2 Southern Tunisia
42 Steppe China 43.5 116.7 Inner Mongolia Grassland
Ecosystem Research Station
43 Desert United States of America
36.6 -115.7 Nevada: Mojave Global Change
Facility
44 Desert United States of America
36.9 -116.6 Nevada test site
45 Desert Israel 30.9 34.4 Negev Desert
46 Desert United States of America
32.0 -112.9 Arizona: Ajo Mountains
47 Desert United States of America
31.7 -110.1 Arizona: Walnut Gulch
Experimental Watershed (Sonoran and Chihuahuan Deserts)
21
Ecoregion Country Latitude Longitude Location
48 Desert United States of America
31.7 -110.1 Arizona: Walnut Gulch
Experimental Watershed (Sonoran and Chihuahuan Deserts)
49 Wetland United States of America
41.1 -97.9 Nebraska, near Central City (Platte
River)
50 Agricultural Australia -34.3 142.2 Red Cliffs
51 Agricultural France 43.9 1.2 Auradé
52 Agricultural France 43.8 1.4 Lamasquère
53 Temperate Forest Japan 33.1 130.7 Kahoku Expt. Watershed, Kyushu
Island
54 Temperate Deciduous
Forests
United States of America
36.0 -84.3 Oak Ridge, Tennessee
55 Mediterranean
Shrubland Israel 31.4 35.0 Yatir Forest
56 Desert United States of America
31.9 -110.8 Arizona: Sonoran Desert
57 Desert United States of America
31.7 -110.1 Arizona: Walnut Gulch
Experimental Watershed (Sonoran and Chihuahuan Deserts)
58 Desert United States of America
31.7 -110.1 Arizona: Walnut Gulch
Experimental Watershed (Sonoran and Chihuahuan Deserts)
59 Desert United States of America
31.4 -110.4 Huachuca Mountains
60 Desert United States of America
31.4 -110.4 Huachuca Mountains
61 Desert Israel 32.8 35.2 Alon ha’Galil
62 Agricultural Argentina -28.6 -66.8 Northwestern Argentina
63 Agricultural Vanuatu -15.4 167.2 Vanuatu Agricultural Research and
Technical Center
64 Temperate Forest United States of America
34.6 -111.8 Arizona, Beaver Creek
65 Temperate Forest United States of America
34.0 -85.8 Southeastern U.S.A.
66 Temperate Forest United States of America
35.1 -83.4 Cowetta (Pine)
67 Temperate Forest United States of America
44.2 -122.3 Oregon, Andrews watershed
68 Temperate Deciduous
Forests
United States of America
35.1 -83.4 Cowetta (Hardwood)
69 Steppe Argentina -45.0 -70.0 Southern Argentina
70 Desert United States of America
35.8 -116.1 Mojave Desert
22
Ecoregion Country Latitude Longitude Location
71 Tropical
Rainforest D.R. Congo -4.7 12.1 Pointe–Noire
72 Tropical
Grassland D.R. Congo -4.7 12.1 Pointe–Noire
73 Temperate Forest New Zealand -43.2 170.3 Okarito Forest, Westland
74 Temperate Deciduous
Forests Australia -32.3 117.9 Corrigin, Western Australia
75 Temperate Deciduous
Forests Portugal 38.5 -8.0 Herdade da Alfarrobeira
76 Temperate Deciduous
Forests France 48.7 7.1 Hesse
77 Temperate Deciduous
Forests
United States of America
46.2 -89.3 Ottawa National Forest
78 Boreal Forest Sweden 60.0 17.3 Uppsala
79 Boreal Forest Sweden 60.0 17.3 Uppsala
80 Desert China 39.8 99.5 Heihe River Basin
81 Desert United States of America
32.5 -106.8 Jornada Experimental Range
23
Table 1-1. (continued)
Method P* T (%
of P)
E (% of
P)
Q (% of
P)
T/ET (%)
Reference
1 - 1623 45 56 45 Banerjee in Galoux et al., 1981
2 - 626 50 49 50 Lutzke & Simon in Galoux et al.,
1981
3 - 627 40 48 41 Lutzke & Simon in Galoux et al.,
1981
4 - 710 47 39 55 Rutter cited in Galoux et al., 1981
5 - 513 49 36 5 58 Molchanov cited in Galoux et al.,
1981
6 - 502 39 35 53 Ten studies by Molchanov 1963, cited by Choudhury et al., 1998
7 - 1237 19 26 42 Two studies by Delfs (1967), cited
by Choudhury et al., 1998
8 Diffusion porometer
365 65a Trlica and Biondini, 1990
9 Diurnal water table
changes 3725 14 9 61 Frangi and Lugo, 1985
10 Energy balance
model 725 37 22 41 63 Tajchman, 1972
11 Energy balance
model 150 38 62 38 Liu et al., 2012
12 Isotope-based (catchment)
340 71 18 12 81 Gibson and Edwards, 2002
13 Isotope-based (catchment)
872 45 8 85 Telmer and Veizer, 2000
14 Isotope-based (catchment)
310 34 8 58 80 Gibson and Edwards, 2002
15 Isotope-based (stand level)
1410* 32b 68 32 Hsieh et al., 1998
16 Isotope-based (stand level)
1410* 59 41 59 Hsieh et al., 1998
17 Isotope-based (stand level)
1380 61 39 61 Hsieh et al., 1998
18 Isotope-based (stand level)
2500 72 28 72 Hsieh et al., 1998
19 Isotope-based (stand level)
329 93 Ferretti et al., 2003
20 Isotope-based (stand level)
911 65-77 Wang et al., 2013
21 Model (with met.
data) 2000 49 26 26 65 Salati and Vose, 1984
22 Model (with met.
data) 2000 62 19 19 77 Salati and Vose, 1984
23 Model (with met.
data) 2232* 40 10 50 80 Shuttleworth, 1988
24
Method P* T (%
of P)
E (% of
P)
Q (% of
P)
T/ET (%)
Reference
24 Model (with met.
data) 2209 56 11 32 84 Leopoldo et al., 1995
25 Model (with met.
data) 2851 31 21 60 Calder et al., 1986
26 Model (with met.
data) 366 52a 53 52 Prazak et al., 1994
27 Model (with met.
data) 595 59 36 55 Gash and Stewart, 1997
28 Model (with met.
data) 2620 7 23 23 Hudson, 1988
29 Model (with met.
data) 234 93 17 84 Dolman, 1988
30 Model (with met.
data) 590
28-47
Gebauer et al., 2012
31 Model (with met.
data) 549 54 9 86 Ladekari, 1998
32 Model (with met.
data) 475 60 40 60 Poole et al., 1981
33 Model (with met.
data) 475 32 51 4 39 Poole et al., 1981
34 Model (with met.
data) 590 35 55 10 39 Poole et al., 1981
35 Model (with met.
data) 335 46 54 51 Lauenroth and Bradford, 2006
36 Model (with met.
data) 379* 67 33 0 67 Massman, 1992
37 Model (with met.
data) 350 39 73 39 Hu et al., 2009
38 Model (with met.
data) 477 37 67 37 Hu et al., 2009
39 Model (with met.
data) 580 56 83 56 Hu et al., 2009
40 Model (with met.
data) 580 39 49 44 Hu et al., 2009
41 Model (with met.
data) 144 45 55 0 45 Floret et al., 1982
42 Model (with met.
data) 275 55a 34 62 Huang et al., 2010
43 Model (with met.
data) 74 40a 60 40 Young et al., 2009
44 Model (with met.
data) 150 35 65 35 Smith et al., 1995
45 Model (with met.
data) 170 41 Littman and Veste, 2006
46 Model (with met.
data) 200 80 20 80 Liu et al., 1995
25
Method P* T (%
of P)
E (% of
P)
Q (% of
P)
T/ET (%)
Reference
47 Model (with met.
data) 223 64 64 Moran et al., 2009
48 Model (with met.
data) 233 79 79 Moran et al., 2009
49 Model (with met.
data) 687* 63a Kabenge and Irmak, 2012
50 Model (with met.
data) 476 67 63 52 Yunusa et al., 1997
51 Model (with met.
data) 615 42 51 46 Beziat et al., 2013
52 Model (with met.
data) 684 23 53 33 Beziat et al., 2013
53 Model (with met.
data), sap flow 2128 23 20 53 Kumagai et al., (in press)
54 Model (with met.
data), sap flow 1333 19a 14 58 Wilson et al., 2001
55 Model (with met.
data), sap flow 285 45 46 48 Raz-Yaseef et al., 2012
56 Model (with met.
data), sap flow 212 21a 27 47 Cavanaugh et al., 2011
57 Model (with met.
data), sap flow 260 21a 36 42 Cavanaugh et al., 2011
58 Model (with met.
data), sap flow 322 37 63 58 Scott et al., 2006
59 Model (with met.
data), sap flow 400 >45 Ffolliott et al., 2003
60 Model (with met.
data), sap flow 477 >75 Ffolliott et al., 2003
61 Model (with met.
data), sap flow 515 >40 Ffolliott et al., 2003
62 Model (with met.
data), sap flow 455* 70-80 Rousseaux et al., 2009
63 Model (with met.
data), sap flow 2763 68 Roupsard et al., 2006
64 Modelled (no obs.) 1085 49 15 41 76 Waring et al., 1981
65 Modelled (no obs.) 1225 49 15 38 77 McNulty et al., 1996
66 Modelled (no obs.) 2175 35 15 46 70 Waring et al., 1981
67 Modelled (no obs.) 2355 16 11 72 59 Waring et al., 1981
68 Modelled (no obs.) 2175 28 12 55 70 Waring et al., 1981
69 Modelled (no obs.) 150 34 56 10 38 Paruelo and Sala, 1995
70 Modelled (no obs.) 165 27 73 27 Lane et al., 1984
71 Radial flow meter 1019 81 12 87 Nizinski et al., 2011
72 Radial flow meter 1019 58 11 84 Nizinski et al., 2011
73 Sap flow 1127 8a 12 80 39 Barbour et al., 2005
74 Sap flow 265 53 78 40 Mitchell et al., 2009
26
Method P* T (%
of P)
E (% of
P)
Q (% of
P)
T/ET (%)
Reference
75 Sap flow 669 73 27 73 Paco et al., 2009
76 Sap flow 763 33a 15 69 Granier et al., 2000
77 Sap flow 896 65a Tang et al., 2006
78 Sap flow 250 46a 25 65 Grelle et al., 1997
79 Sap flow 271 51a Cienciala et al., 1997
80 Satellite-based 285 38-73
Tian et al., 2013
81 Water-balance;
control and bare plots
210 72 28 72 Schlesinger et al., 1987
Compiled transpiration/evapotranspiration ratios range from minimums of 23% (United
Kingdom, East of Aberystwyth; Hudson, 1988) to 93% (Colorado: Central Plains Experimental
Range; Ferretti et al., 2003; Table 1-1). The average T/ET ratio for compiled studies is 60%.
Compiled transpiration/evapotranspiration ratios are found to be highest in the tropics (e.g., tropical
rainforest T/ET of 70%±14%, tropical grassland T/ET of 62%±19%) and lower in Mediterranean
climates (47%±10%; Table 1-2; Figure 1-4). The highest evapotranspiration fluxes off of the
continents are from tropical regions. Spatially-weighting transpiration/evapotranspiration to the
percent of terrestrial evapotranspiration accounted for by each biome yields a
transpiration/evapotranspiration ratio of ~61%. This ratio is equivalent to a
transpiration/evaporation ratio of ~1.5, or a 50% greater transpiration flux than evaporation flux on
continents.
27
Table 1-2. Compiled transpiration/evapotranspiration (Schlesinger and Jasechko, 2014)
Ecoregion T/ET percent average ±1 s.d.
Land area (%)
P (mm/yr)
Percent of land
precipitation
ET (mm/yr)
Percent of
terrestrial ET
Tropical Rainforest
70±14 (n = 8) 16 1830 35 1076 33.1
Tropical Grassland
62±19 (n = 5) 12 950 14 583 13.9
Temperate Deciduous
Forests 67±14 (n = 9) 9 850 10 549 10.1
Boreal Forest 65±18 (n = 5) 14 500 8 356 9.5
Temperate Grassland
57±19 (n = 8) 8 470 5 332 5.4
Desert 54±18 (n = 14) 18 180 4 209 7.3
Temperate Coniferous
Forest 55±15 (n = 13) 4 880 4 458 3.4
Steppe 48±12 (n = 3) 4 440 2 467 3.4
Mediterranean shrubland
47±10 (n = 4) 2 480 1 302 1.0
The stand level measurements were scaled up in Schlesinger and Jasechko (2014) to estimate
global fluxes. However, we note that transpiration/evapotranspiration ratios in some studies neglect
understory transpiration fluxes, suggesting that the reported terrestrial
transpiration/evapotranspiration flux is likely to be a low end member of the actual terrestrial
transpiration/evapotranspiration ratio (Schlesinger and Jasechko, 2014).
The compiled data showed little spatial coherence in transpiration/evapotranspiration ratios.
First, studies completed at the same research site produced very different estimates of
transpiration/evapotranspiration. For example, Cavanaugh et al. (2011) and Moran et al. (2009) both
investigated a research site in Arizona (U.S.A.) and produced transpiration/evapotranspiration
estimates of 42% and 79%, respectively. The difference in these two results highlights the difficulty
28
associated with measuring transpiration fluxes and the uncertainties coupled to the scaling of point
(often tree-size scale) observations up to regional scales.
No trend was observed between precipitation rates and transpiration/evapotranspiration
ratios within the compiled data (Figure 1-5), highlighting that climate is not the only control upon
ecosystem productivity and primary production. Indeed, satellite based investigations of climate
controls upon terrestrial primary production reveals a three tier set of controls that includes
temperature, sunlight and water. Water is limiting in arid and semi-arid regions, but is a less
important control in other regions (e.g., the Amazon basin; Running et al., 2004). Primary production
in cold regions — which cover half of Earth’s of ice-free land surfaces (Jasechko et al., in review)
under the definition of Bates and Bilello (1966) — is limited by temperature and sunlight, and
primary production in tropical forests is limited by sunlight (Running et al., 2004).
Figure 1-5. Transpiration/evapotranspiration ratios compared to site-specific precipitation rates.
Reproduced from Schlesinger and Jasechko (2014).
29
Figure 1-6. Compiled estimates of transpiration/evapotranspiration ratios sorted into study
approach. Whiskers mark the 10th-90th percentile range of the data, the shaded rectangles mark the
25th-75th percentile range, the black line marks the median of each dataset.
Reducing the dataset in Figure 1-5 to include only studies within desert and steppe biomes
— which are expected to broadly be water-limited ecosystems — improves the trend between
precipitation and transpiration/evapotranspiration ratios slightly (R2 of 0.07; Figure 1-7) over the
entire, global compilation (R2 of 0.01). This suggests that further site-specific studies in deserts could
help to enhance our understanding of how water-limited ecosystems might respond to changes in
precipitation amounts.
30
Figure 1-7. Arid region transpiration/evapotranspiration ratios compared to site-specific precipitation
rates. A regression through the data reveals a significant (p < 0.05) trend towards higher
transpiration/evapotranspiration ratios with increasing precipitation amount.
Different methodologies used to calculate transpiration fluxes are found to produce slightly
different transpiration/evapotranspiration ratios. Isotope-based studies have a higher average
transpiration/evapotranspiration ratio of ~70%, whereas sap flow and meteorological models
suggest an average transpiration/evapotranspiration ratio of ~55% (Figure 1-6).
Several general circulation model based estimates of transpiration fluxes have been reported
over the past decade. The general circulation model estimates of transpiration/evapotranspiration
ratios are shown in Figure 1-1All alongside the compiled stand level data.
Generally, the GCMs have lower transpiration/evapotranspiration ratios than those
suggested by stand level measurements. GCM transpiration/evapotranspiration ratios range from
25% to 65%, whereas stand level transpiration measurements indicate a global transpiration flux of
closer to 60%, although this is likely to be a low end-member because many transpiration studies do
not include understory transpiration fluxes. Lawrence et al. (2007) first pointed out that the
Community Land Model (version 3) was underpredicting transpiration fluxes. General circulation
31
model estimates of transpiration/evapotranspiration range from 13 % (Community Land Model 3,
without improvements made by Lawrence et al., 2007), to 65 % (Lund–Potsdam–Jena model; Gerten
et al., 2005). A global land surface model that integrates satellite data (Miralles et al., 2011) has a
transpiration/evapotranspiration ratio of 80 %. A biophysical model developed by Choudhury et al.
(1998) proposes a transpiration/evapotranspiration ratio of 52 %. The broad range of
transpiration/evapotranspiration ratios estimated by earlier works (Table 1-3) highlights the immense
challenge of estimating this ratio. Upscaling a compilation of stand level measurements
(transpiration/evapotranspiration ratio of 61 %; Schlesinger and Jasechko, 2014) and a continental-
scale isotope-based approach (transpiration/evapotranspiration of 80 to 90 %; Jasechko et al., 2013)
suggest that the majority of general circulation models underestimate the role of transpiration in the
global water cycle, and that transpiration is the largest water flux from Earth’s continents.
32
Table 1-3. Global transpiration estimates.
Climate model Transpiration / Evapotranspiration Reference
Community Land Model
(version 3)
13 % (prior to improvements by
Lawrence et al., 2007) Lawrence et al., 2007
Community Land Model
(version 3)
41 % (with improvements by
Lawrence et al., 2007) Lawrence et al., 2007
Community Land Model 3.5
coupled to Community
Atmosphere Model 3.5
40 % Cao et al., 2010
Joint UK Land Environment
Simulator 38 % to 48 % Alton et al., 2009
Lund–Potsdam–Jena model 65 % Gerten et al., 2005
Global Soil Wetness Project 48 % Dirmeyer et al., 2006
n/a 52 % Choudhury et al., 1998
Global Land-surface
Evaporation: the Amsterdam
Methodology
80 % Miralles et al., 2011
Vegetation Integrative Simulator
for Trace Gases 24 % Ito and Inatomi, 2012
1.3 Dataset and methods
The development of isotope-based transpiration calculations is divided into three sections:
(i) development of a global lake water O and H isotope database and geospatial analysis of lake
catchments, (iii) calculation setup, geospatial data extraction, and analysis.
1.3.1 Development of a global lake water O and H isotope database
To develop a continental scale estimate of transpiration fluxes we required a continental
scale isotopic dataset. A lake-by-lake compilation of isotopic data was completed over four months
(September 2011 to December 2011) and the resulting compilation was presented at the American
Geophysical Union Fall Meeting in December of 2011 (Jasechko et al., 2011).
33
The dataset spans lakes from all continents with the exception of Antarctica. The dataset
contains 2129 measurements of δ18O and 2098 measurements of lake water δ2H from 73 unique
lakes compiled from 61 published datasets. Only lakes with surface areas on the order of 102-104 km2
were included in the large lake isotopic database.
The location of each of the compiled lakes is presented in Tables 1-4 and 1-5 and Figure 1-8.
Large lakes are concentrated geographically into scoured basins at the margins of the Laurentide and
Fennoscandanavian ice sheets that resided over North America and Eurasia during the last ice age.
These lakes include Great Bear, Great Slave, Lake Winnipeg, Lake Superior, Lake Huron, Lake
Michigan, Lake Erie and Lake Ontario in North America, and Lake Ladoga and Lake Onega in
Eurasia. Other large lakes are concentrated in geological rift valleys and include Lake Baikal and Lake
Tanganyika, which combine to a total volume that comprises more than one-third of all fresh water
at Earth’s surface. The majority of compiled lakes are exorheic (i.e., externally drained), with a
minority of endorheic (i.e., closed basin) lakes that include the Aral and Caspian Seas, Great Salt
Lake, and Lake Chad.
Before analyzing hydroclimate and hydrological data for each lake, the potential contributing
area to each lake was quantified by delineating watersheds for each of the 73 lakes in our database.
Lake catchment areas were delineated using the Shuttle Radar Topography Mission
(www2.jpl.nasa.gov/srtm) Digital Elevation Model and global river spatial data (waterbase.org).
Catchments were delineated by hand in a geographic information system on the basis of topographic
highs from the Shuttle Radar Topography Mission data and drainage basin data.
34
Figure 1-8. Locations of lake catchments studied in chapter one. The entire set of catchments covers
~10% of Earth’s surface. Small catchments are delineated with diamonds for clarity. Insets are
shown for the western region of North America, eastern Africa and the Tibetan plateau.
35
Table 1-4. Lake information
Lake Basin type Outflow Lat. Lon. Elevation (m.a.s.l.)
Abhe Endorheic - 11.1 41.8 240
Abiyata Endorheic - 7.8 38.7 1573
Afdera Endorheic - 13.3 40.9 -100
Albert Chain White Nile 1.7 30.9 615
Aral Sea Endorheic - 45.1 58.3 53
Athabasca Headwater Slave River 59.0 -110.0 213
Awasa Endorheic - 7.1 38.5 1708
Baikal Headwater Angara River 53.1 107.7 450
Baringo Endorheic - 0.6 36.1 970
Beysehir Endorheic - 37.7 31.5 1116
Biwa Headwater Seta River 35.3 136.2 86
Caspian Endorheic - 42.0 51.0 -28
Chad Endorheic - 13.0 14.2 244
Chamo Endorheic - 5.9 37.6 1110
Dagze Co Endorheic - 31.9 87.6 4478
Dead Sea Endorheic - 31.3 35.5 -420
Edward Headwater Semliki River -0.4 29.6 912
Egridir Endorheic - 38 30.9 924
Elephant Butte Headwater Rio Grande 33.4 107.2 1312
Erie Chain Niagara River 42.5 -79.6 173
Garda Headwater Mincio 45.6 10.7 65
Geneva Headwater Rhone River 46.4 6.6 372
Great Bear Headwater Great Bear R. 66.0 -120.0 156
Great Salt Endorheic - 41.2 -112.6 1270
Great Slave Chain Mackenzie R. 61.8 -114 176
Huron Chain St. Clair River 43.5 -82 176
Issyk-Kul Endorheic - 42.5 77.3 1600
Jackson Headwater Snake River 43.9 -110.6 2067
Kainji Headwater Niger River 10.4 4.6 139
Kivu Headwater Ruzizi River -2.0 29.0 1460
Kluane Headwater Kluane River 61.1 -138.5 781
Ladoga Chain Neva River 60.8 31.4 11
Lucern Headwater Reuss River 47.0 8.4 433
Malawi Headwater Shire River -12.0 34.5 471
Manasarovar Endorheic - 30.7 81.5 4584
Mar Chiquita Endorheic - -30.5 -62.7 67
36
Table 1-4. Lake information (continued)
Lake Basin type Outflow Lat. Lon. Elevation (m.a.s.l.)
Mead Chain Colorado River 36.1 -114.7 367
Michigan Chain Mackinac 42.4 -87.0 176
Naivasha Headwater - -0.8 36.4 1884
Namco Endorheic - 30.7 90.6 4718
Nasser Chain Nile 22.3 31.7 179
Ngangla Ringco Endorheic - 31.4 83.4 4724
Nicaragua Headwater San Juan River 11.2 -85.5 31
Oahe Chain Missouri River 44.4 -100.4 490
Okanagan Headwater Okanagan River 50.2 -119.4 345
Onega Headwater Svir River 61.9 35.4 56
Ontario Chain St. Lawrence R. 43.5 -79.4 86
Powell Headwater Colorado River 36.9 -111.5 1130
Poyang Headwater Changjiang 29.1 116.3 10
Qarhan Salt Lake Endorheic - 37.0 95.1 2685
Qinghai Hu Endorheic - 36.9 100.1 3200
Rukwa Endorheic - -8.4 32.7 800
Sakakawea Chain Missouri River 47.5 -101.4 561
Salton Sea Endorheic - 33.2 -115.7 -71
Sambhar Salt Endorheic - 27.0 75.1 360
Shala Endorheic - 7.4 38.6 1559
Superior Headwater St. Marys River 47.0 -85.2 183
Tahoe Headwater Truckee River 39.1 -120.1 1900
Tana Headwater Blue Nile 11.6 37.4 1790
Tanganyika Chain Rukuga River -4.9 29.5 773
Taro Co Endorheic - 31.1 84.3 4579
Taupo Headwater Waikato River -38.8 175.9 395
Titicaca Endorheic - -15.5 -69.4 3827
Tonlé Sap Chain Tonlé Sap River 11.6 104.9 14
Turkana Endorheic - 4.0 36.0 360
Valencia Endorheic - 10.2 -68.1 410
Van Endorheic - -38.7 -43.4 1646
Victoria Headwater White Nile -1.0 33.0 1133
Winnipeg Headwater Nelson River 52.1 -97.8 217
Yamdruk-tso Endorheic - 28.8 90.6 4458
Yellowstone Headwater Yellowstone R. 44.5 -110.4 2357
Zhari Namco Endorheic - 31.1 85.4 4624
Zige Tangco Endorheic - 32.0 90.8 4575
37
Table 1-5. Physical hydrology of lakes
Lake Catchment area
(km2) Open water
(km2) Volume
(km3) τ *
(years)
Abhe 94200 1600 6 50
Abiyata 10400 830 1.4 5
Afdera 7100 110 6 90
Albert 58800 5900 132 3
Aral Sea 949500 77900 193 6
Athabasca 271100 26900 110 1.8
Awasa 1500 100 1 10
Baikal 583200 37900 23600 280
Baringo 6600 150 0.2 2.3
Beysehir 15400 880 2 2
Biwa 3700 680 28 6.5
Caspian 3024400 428800 78000 260
Chad 976300 26200 72 4
Chamo 1900 320 4 17
Dagze Co 12800 640 3 6
Dead Sea 43200 1200 136 160
Edward 26800 2800 77 6
Egridir 3300 480 10 25
Elephant Butte 89900 820 2.5 2
Erie 103700 27300 484 2.1
Garda 2200 370 50 28
Geneva 7900 700 90 14
Great Bear 148500 41100 2300 55
Great Salt 81900 7700 20 6
Great Slave 702200 73400 2090 11
Huron 192100 66000 3540 15
Issyk-Kul 22000 6300 1740 170
Jackson 2000 150 6 7
Kainji 1565300 12800 15 1
Kivu 7500 2400 350 60
Kluane 5500 400 12 9
Ladoga 225800 34400 850 9
Lucern 2200 120 12 3
Malawi 124900 29100 7775 250
Manasarovar 5100 490 20 20
Mar Chiquita 129700 3100 6 3
*τ: approximate residence time of each lake
38
Table 1-5. Physical hydrology of lakes (continued)
Lake Catchment area
(km2) Open water
(km2) Volume (km3)
τ * (years)
Mead 147200 1100 25 2
Michigan 174400 60100 4920 64
Naivasha 3200 110 1 2
Namco 10700 1900 63 25
Nasser 2330500 16000 132 2
Ngangla Ringco 12500 610 5 6
Nicaragua 27900 8900 108 6
Oahe 150400 2400 25 1
Okanagan 6000 390 25 23
Onega 54500 12200 280 8
Ontario 82000 21000 1640 7
Powell 278600 3300 33 1.5
Poyang 161500 4100 3 1
Qarhan Salt 109700 820 50 8
Qinghai Hu 29600 4700 70 20
Rukwa 79300 3000 40 12
Sakakawea 461900 6400 29 1.4
Salton Sea 20000 930 9 3
Sambhar Salt 5900 20 0.2 1
Shala 4100 310 40 43
Superior 226200 92700 12100 88
Tahoe 1300 500 160 130
Tana 15000 3100 28 4
Tanganyika 230800 34000 19000 400
Taro Co 16800 830 5 6
Taupo 3500 630 60 12
Titicaca 56900 8600 900 160
Tonlé Sap 58800 ~3200 ~160 ~1
Turkana 180400 8700 200 40
Valencia 3000 360 6 10
Van 17100 3700 607 62
Victoria 264100 68400 2750 26
Winnipeg 1048200 88100 284 3
Yamdruk-tso 10000 1100 20 41
Yellowstone 2700 370 15 8
Zhari Namco 20100 1400 30 40
Zige Tangco 3300 190 3 19
*τ: approximate residence time of each lake
39
1.3.2 Calculation approach
To calculate transpiration rates we first developed a set of equations that can be applied to
estimate transpiration/evaporation ratios. A hydrological catchment’s water balance can be described
by water fluxes and changes to water storages (Equation 1.1):
QTExPIdt
dV Equation 1.1
where dV/dt is the rate of change in water storage in the catchment, I represents the flux of
precipitation entering the catchment plus any upstream liquid inflows from chain lake systems, E
represents physical evaporation losses from a catchment, T represents transpiration water losses
from a catchment, Q represents liquid losses via stream discharges and via groundwater recharge and
advection out of the basin, x represents the fraction of precipitation (P) that is intercepted by
vegetation and returned to the atmosphere through evaporation. At steady state Equation 1.1 reduces
to (Equation 1.2):
QTExPI Equation 1.2
In addition to the physical water balance, a steady state stable isotope mass balance can be
described as (Equation 1.3):
QTExPI QTEPI Equation 1.3
where δI is the flux-weighted isotopic composition of inputs (precipitation and chain lake inflows), δP
is the isotopic composition of precipitation, δE is the isotopic composition of evaporating moisture
(isotope fractionation labelled), δT is the isotopic composition of water used by plants in transpiration
(not isotope fractionation labelled) and δi is the isotopic composition of intercepted rain and snow.
40
Combining equations 1 and 2 yields a single equation representing the transpiration flux exiting a
hydrological catchment under steady state conditions (Equation 1.4):
ET
EPEQEI xPQIT
Equation 1.4
1.3.3 Geospatial analysis
Next, each of the inputs into equation 1.4 were quantified using multiple global geospatial
datasets. The following paragraphs examine each of the input parameters in equation 1.4 one by one.
The precipitation input to each hydrological catchment (P) was calculated using global high
resolution precipitation data spanning the continents (New et al., 2002). The catchment area of each
lake was calculated, as was the mean annual precipitation rate for each catchment. The two
components were multiplied together to estimate the annual flux of precipitation inputs for each
basin. Annual water inputs to each catchment (I) were calculated as the sum of precipitation inputs
(P) plus contributions from upstream chain lake systems.
The liquid fluxes out of each catchment (Q) were compiled on a river-by-river basis using
data within the primary literature. These water fluxes were also used to quantify chain lake inflows
into downstream lake basins where appropriate. Groundwater fluxes out of lakes were also collected
on a lake-by-lake basin for endorheic basins with known connections with regional groundwater flow
systems (e.g., Isiorho et al., 1996; Ojiambo et al., 2003).
The proportion of precipitation intercepted and returned to the atmosphere (x) was
calculated using satellite-based grids developed by Miralles et al. (2010) coupled to annual
precipitation rates (New et al., 2002).
41
δP, the flux-weighted isotopic composition of precipitation inputs entering each hydrological
catchment. δP was estimated for each hydrological catchment considering seasonal and spatial
variability in precipitation amounts. Geospatial grids of monthly precipitation isotope compositions
are available for download from waterisotopes.org following methods of Bowen and Revenaugh
(2003), Bowen and Wilkinson (2002) and Bowen (2010). The seasonality of precipitation amount was
quantified by flux weighting each grid call at a monthly time step (where δP(j) is the isotopic
composition of precipitation for month j, and Pj represents the monthly precipitation rate (mm per
month) at each grid cell). Our calculation also accounts for the spatial distribution of precipitation
was included by weighting the individual grid cells to their respective precipitation amounts (i.e., grid
cell i) following equation 1.5:
i
n
1i
i
ij
12
1j
Pj
12
1jn
1i
PP
PP
Pj
Equation 1.5
The isotopic composition of water inputs (δI) to each catchment was calculated by flux
weighting the isotopic compositions of precipitation (δP) against contributions from upstream lakes
(i.e., river inflows from upstream lakes).
The isotopic composition of evaporate from each catchment (δE) was calculated using an
evaporation model (Craig and Gordon, 1965; Equation 1.6):
K
KALake
Eh1
h*/*
Equation 1.6
where δLake is the isotopic composition of lake water (compiled from primary literature), α* is an
isotopic equilibrium fractionation factor (temperature dependent; temperature data from New et al.,
2002)), ε* is an equilibrium isotopic separation factor (approximated as: α* − 1), h is the relative
humidity of the catchment (calculated for each catchment using geospatial data from New et al.,
42
2002), δA is the isotopic composition of atmospheric vapor (estimated in two ways: once by using
precipitation as a liquid signature of atmospheric vapor, and back calculating the vapor isotope
composition using temperature data (New et al., 2002), and second by compiling δA values from an
isotope-enabled general circulation model developed by Yoshimura et al., 2008) and εK is a kinetic
isotopic separation factor calculated by CK·[1 – h] (Gonfiantini, 1986).
The isotopic composition of transpired moisture (δT) was estimated across the continents
using isotopic data for precipitation and seasonality in primary productivity. We weighted the
isotopic composition of precipitation15 spatially (i) to long-term monthly mean normalized difference
vegetation indices (NDVI; proxy for chlorophyll abundance), with NDVI values below zero set to a
value of zero. A range of two temporal (j) weighting approaches is used for δT, one weighted to
growing season (representing shallow rooted end-member; Equation 1.7) and another to monthly
precipitation (representing a deep rooted end-member, i.e., a phreatophyte; Equation 1.8):
in
1i
i
ij
12
1j
Pj
12
1jn
1i
SHALLOWTNDVI
NDVINDVI
NDVIj
Equation 1.7
in
1i
i
ij
12
1j
Pj
12
1jn
1i
DEEPTNDVI
NDVIP
Pj
Equation 1.8
Water use efficiency functions were compiled from the primary literature to develop
catchment wide estimates of water use efficiency. A review of water use efficiency data as a function
of humidity reveals differences between C3 and C4 plants (Table 1-9; reproduced from Jasechko et al.,
2013), such that a global grid of C3/C4 photosynthesis types was also sought after. We assessed
spatial variability in C3/C4 species abundances using grids developed by Still et al. (2003),
downloaded from http://webmap.ornl.gov/wcsdown/dataset.jsp?ds_id=932. Power regressions of
43
C3 and C4 datasets were applied to develop water use efficiency/climate relationships for each
photosynthetic pathway: C3: Water use efficiency = 4.21×(Vapor pressure deficit)-0.67 and C4: Water
use efficiency = 6.91×(Vapor pressure deficit)-0.40. Daytime vapor pressure deficit grids were then
estimated at a monthly time step by averaging the maximum and average monthly mean temperatures
at each grid cell (data from Hijmans et al., 2005) and catchment-wide water use efficiencies for each
basin were calculated (Figure 1-9, 1-10). Resulting transpiration fluxes were then converted into gross
primary productivity using the catchment water use efficiency data. The inputs for each calculation
are presented in Tables 1-6 to 1-8.
44
Table 1-6. Model input parameters (± 1 s.d. uncertainty shown)
Lake δ18OL (‰)
δ2HL (‰)
δ18OT (‰)
δ2HT
(‰) δ18OI (‰)
δ2HI
(‰)
Abhe 3.7±0.5 -4±4 0.2±1.4 12±11 -0.3±1 8±9
Abiyata 8.5±1.3 59±11 -1.8±1.3 -3±10 -2.0±1 -4±9
Afdera 6.4±0.5 28±2 0.9±1.5 14±11 0.5±1 13±9
Albert 5.2±0.5 37±4 -2.8±1.3 -10±10 -0.8±0.7 -1±7
Aral Sea 2.8±1.8 0±11 -5.8±2.7 -32±22 -9.9±1 -65±9
Athabasca -16.8±1.3 -131±4 -16.6±1.7 -127±14 -17.9±1 -137±9
Awasa 7.5±0.7 51±4 -1.7±1.4 -3±11 -2.3±1 -6±9
Baikal -15.8±0.3 -123±1 -12.2±1.2 -91±10 -12.3±1 -92±9
Baringo 7.5±1.3 42±8 -3.8±1.5 -17±12 -4.4±1 -21±9
Beysehir -1.4±0.9 -18±4 -6.8±1.9 -40±14 -8.2±1 -50±9
Biwa -7.0±0.5 -44±5 -8.1±1.2 -53±10 -8.0±1 -52±9
Caspian -1.7±0.2 -20±3 -10.5±2 -76±16 -11.6±1 -85±9
Chad 8.2±3.6 45±19 -1.8±1.9 -8±14 -3.2±1 -17±9
Chamo 7.7±0.8 50±3 -1.4±1.2 0±9 -1.4±1 0±9
Dagze Co -6.4±0.5 -69±4 -16.1±1.4 -115±10 -16.5±1 -117±9
Dead Sea 1.4±2.3 4±2 -5.1±1.6 -25±11 -6.1±1 -28±9
Edward 4.2±0.2 30±1 -3.5±1.3 -15±11 -3.7±1 -17±9
Egridir -2.4±0.6 -21±2 -7.4±1.7 -48±13 -8.5±1 -53±9
Elephant Butte -7.8±1.2 -68±6 -12.9±1.2 -91±10 -12.8±1 -93±9
Erie -6.6±0.3 -48±9 -7.1±1.9 -46±15 -7.6±0.5 -55±9
Garda -7.3±0.2 -55±1 -8.0±1.2 -52±9 -7.8±1 -51±9
Geneva -12.3±0.1 -88±2 -9.1±1.9 -61±14 -10.9±1 -74±9
Great Bear -18.7±0.5 -155±4 -17.2±3.7 -130±30 -22.3±1 -171±9
Great Salt -4.8±1.0 -67±9 -12.5±2.4 -93±18 -14.8±1 -110±9
Great Slave -17.8±0.3 -141±3 -16.5±2.3 -127±18 -18.6±0.9 -143±9
Huron -7.1±0.1 -54±2 -8.1±2.2 -54±17 -9.1±0.6 -64±6
Issyk-Kul -0.7±0.1 -9±2 -10.2±1.5 -62±15 -10.6±1 -72±9
Jackson -17.9±0.1 -141±4 -13.3±3.2 -96±26 -17.4±1 -129±9
Kainji -17±11 -2.2±2.2 -12±16 -4.5±1 -27±9
Kivu 1.5±1.4 18±6 -4.3±1.4 -20±12 -4.7±1 -25±9
Kluane -22.6±0.5 -177±3 -18.5±2.3 -149±16 -21.8±1 -169±9
Ladoga -9.5±0.5 -10.8±1.8 -78±15 -11.9±0.9 -76±8
Lucern -12.7±0.5 -9.9±1.7 -67±13 -11.0±1 -75±9
Malawi 2.0±0.1 12±1 -3.3±2 -14±16 -4.6±1 -24±9
Manasarovar -5.5±3.5 -58±16 -17.1±1.5 -112±12 -16.4±1 -117±9
Mar Chiquita 3.1±0.2 18±1 -5.4±1.4 -32±10 -5.0±1 -31±9
45
Table 1-6. Model input parameters (± 1 s.d. uncertainty shown; continued)
Lake δ18OL (‰)
δ2HL (‰)
δ18OT (‰)
δ2HT
(‰) δ18OI (‰)
δ2HI
(‰)
Mead -12.9±0.7 -103±5 -11.4±1.5 -85±12 -12.9±0.9 -97±8
Michigan -5.8±0.1 -44±1 -7.9±1.8 -54±14 -8.6±0.8 -61±8
Naivasha 4.6±1.1 26±7 -5.2±1.3 -28±10 -5.5±1 -29±9
Namco -7.3±0.4 -70±3 -17.8±1.3 -130±11 -17.5±1 -126±9
Nasser 0.0±1.4 8±8 -0.9±1.5 1±12 -1.5±1 -3±9
Ngangla Ringco -4.2±0.5 -57±4 -16.6±1.2 -120±10 -16.6±1 -118±9
Nicaragua -2.0±0.5 -9±4 -4.6±1.8 -27±15 -5.7±1 -37±9
Oahe -14.2±0.2 -116±2 -11.8±1.4 -88±11 -13.0±0.8 -98±8
Okanagan -11.4±0.5 -103±3 -12.8±1.9 -99±15 -14.5±1 -111±9
Onega -10.4±0.7 -10.8±2.2 -78±18 -12.8±1 -95±9
Ontario -6.6±0.1 -49±1 -8.2±2.1 -51±17 -7.4±0.3 -54±3
Powell -15.0±0.2 -115±2 -12.6±2.1 -93±16 -14.6±1 -107±9
Poyang -6.8±1.1 -38±9 -7.0±1.4 -46±11 -6.5±1 -42±9
Qarhan Salt 6.6±0.5 -16±4 -12.6±1.3 -92±10 -13.3±1 -95±9
Qinghai Hu 2.4±0.7 12±5 -12.5±1.4 -86±10 -12.0±1 -86±9
Rukwa 4.3±0.2 26±2 -3.6±1.7 -17±14 -4.7±1 -26±9
Sakakawea -15.5±0.2 -124±1 -13.3±1.8 -99±14 -14.5±1 -109±9
Salton Sea -3.6±2.4 -52±12 -6.5±1.8 -52±14 -8.4±1 -66±9
Sambhar Salt 11.5±11.2 -4.9±1.2 -31±10 -5.0±1 -30±9
Shala 7.5±0.7 52±3 -1.3±1.2 1±10 -1.4±1 0±9
Superior -8.6±0.1 -66±1 -9.6±2.1 -67±17 -11.4±1 -81±9
Tahoe -5.5±0.3 -59±16 -11.2±2.5 -87±18 -13.8±1 -103±9
Tana 4.5±0.9 35±6 -2.4±1.4 -9±11 -2.7±1 -11±9
Tanganyika 3.8±0.4 26±2 -3.3±1.6 -15±13 -4.0±1 -20±9
Taro Co -5.6±0.5 -68±4 -16.3±1.3 -118±10 -16.6±1 -119±9
Taupo -5.3±0.4 -33±3 -7.1±1.3 -44±10 -6.9±1 -43±9
Titicaca -3.8±0.7 -50±3 -12.7±1.7 -86±14 -13.6±1 -94±9
Tonlé Sap -5.2±1.0 -5.5±1.5 -34±12 -7.0±1 -25±6
Turkana 5.6±0.4 38±4 -1.6±1.2 -1±10 -1.7±1 -2±9
Valencia 22±4 -4.1±1.4 -29±11 -4.6±1 -32±9
Van 1.0±0.1 -7±0 -7.4±2.9 -45±22 -10.7±1 -70±9
Victoria 3.5±0.5 -3.5±1.3 -15±11 -3.6±1 -16±9
Winnipeg -10.4±0.5 -79±8 -11.6±2.3 -92±15 -14.3±1 -107±9
Yamdruk-tso -5.5±0.5 -68±4 -18.0±1.8 -136±17 -16.7±1 -121±9
Yellowstone -16.5±0.2 -135±4 -15.1±2.6 -118±17 -17.8±1 -133±9
Zhari Namco -6.7±0.5 -75±4 -17.3±1.3 -122±10 -17.0±1 -122±9
Zige Tangco -6.1±0.5 -68±4 -16.1±1.6 -115±12 -17.0±1 -122±9
46
Table 1-7. Model input parameters (± 1 s.d. uncertainty shown)
Lake δ18OP (‰)
δ2HP (‰)
δ18OA (‰)
δ2HA
(‰) TL
(°C) hA (%)
Abhe -0.3±1 8±9 -7.7±2 -51±33 26.1±1 67±3
Abiyata -2.0±1 -4±9 -10.5±1 -73±12 20.0±1 59±3
Afdera 0.5±1 13±9 -7.1±3 -46±37 27.1±1 68±3
Albert -2.9±1 -11±9 -11.3±1 -77±9 24.4±1 69±3
Aral Sea -9.9±1 -65±9 -18.4±1 -142±9 14.2±1 55±3
Athabasca -17.9±1 -137±9 -35.5±4 -288±85 -9.3±1 74±3
Awasa -2.3±1 -6±9 -11.5±1 -81±9 18.0±1 59±3
Baikal -12.3±1 -92±9 -30.6±2 -251±53 -8.4±1 79±3
Baringo -4.4±1 -21±9 -12.4±1 -85±9 24.2±1 54±3
Beysehir -8.2±1 -50±9 -16.7±1 -123±21 14.0±1 56±3
Biwa -8.0±1 -52±9 -18.0±2 -134±30 14.9±1 75±3
Caspian -11.6±1 -85±9 -17.7±1 -136±27 16.0±1 68±3
Chad -3.2±1 -17±9 -9.3±3 -69±39 27.4±1 36±3
Chamo -1.4±1 0±9 -10.4±1 -72±18 21.9±1 57±3
Dagze Co -16.5±1 -117±9 -28.9±4 -225±66 0.1±1 58±3
Dead Sea -6.1±1 -28±9 -13.1±1 -91±9 23.3±1 56±3
Edward -3.7±1 -17±9 -12.2±1 -85±9 23.6±1 70±3
Egridir -8.5±1 -53±9 -17.1±2 -126±26 14.7±1 55±3
Elephant Butte -12.8±1 -93±9 -25.6±2 -200±47 5.4±1 49±3
Erie -8.7±1 -58±9 -19.4±2 -146±30 11.0±1 74±3
Garda -7.8±1 -51±9 -16.0±1 -118±15 18.0±1 75±3
Geneva -10.9±1 -74±9 -17.2±2 -129±28 13.8±1 71±3
Great Bear -22.3±1 -171±9 -39.3±5 -318±96 -14.2±1 73±3
Great Salt -14.8±1 -110±9 -21.7±1 -171±24 16.2±1 46±3
Great Slave -18.9±1 -145±9 -36.9±4 -300±87 -11.6±1 74±3
Huron -10.1±1 -69±9 -23.7±2 -183±38 1.6±1 79±3
Issyk-Kul -10.6±1 -72±9 -20.7±1 -158±31 9.7±1 52±3
Jackson -17.4±1 -129±9 -24.9±3 -197±51 7.0±1 51±3
Kainji -4.5±1 -27±9 -9.2±3 -68±42 28.2±1 41±3
Kivu -4.7±1 -25±9 -13.8±1 -98±10 19.5±1 74±3
Kluane -21.8±1 -169±9 -31.0±4 -251±77 3.8±1 75±3
Ladoga -12.1±1 -90±9 -23.7±3 -188±52 4.9±1 85±3
Lucern -11.0±1 -75±9 -18.9±2 -143±38 10.9±1 76±3
Malawi -4.6±1 -24±9 -12.1±1 -84±9 24.1±1 71±3
Manasarovar -16.4±1 -117±9 -27.1±6 -209±98 0.5±1 60±3
Mar Chiquita -5.0±1 -31±9 -14.6±1 -107±12 21.2±1 71±3
47
Table 1-7. Model input parameters (± 1 s.d. uncertainty shown; continued)
Lake δ18OP (‰)
δ2HP (‰)
δ18OA (‰)
δ2HA
(‰) TL
(°C) hA (%)
Mead -12.0±1 -89±9 -19.5±1 -152±12 19.9±1 34±3
Michigan -9.0±1 -62±9 -22.6±2 -175±38 4.3±1 76±3
Naivasha -5.5±1 -29±9 -15.4±1 -111±17 14.0±1 68±3
Namco -17.5±1 -126±9 -28.6±4 -223±77 1.3±1 57±3
Nasser -1.5±1 -3±9 -10.1±1 -68±20 27.7±1 44±3
Ngangla Ringco -16.6±1 -118±9 -28.0±4 -217±79 -0.4±1 58±3
Nicaragua -5.7±1 -37±9 -13.7±1 -99±9 26.6±1 80±3
Oahe -12.3±1 -91±9 -23.4±2 -183±43 11.1±1 62±3
Okanagan -14.5±1 -111±9 -22.8±2 -184±42 10.0±1 60±3
Onega -12.8±1 -95±9 -24.4±3 -194±53 4.1±1 86±3
Ontario -9.9±1 -66±9 -22.8±2 -174±37 4.4±1 76±3
Powell -14.6±1 -107±9 -23.2±1 -181±29 11.9±1 46±3
Poyang -6.5±1 -42±9 -16.4±1 -121±20 21.8±1 76±3
Qarhan Salt -13.3±1 -95±9 -25.9±2 -201±44 2.4±1 48±3
Qinghai Hu -12.0±1 -86±9 -23.9±2 -185±35 4.2±1 49±3
Rukwa -4.7±1 -26±9 -12.6±1 -88±9 23.9±1 67±3
Sakakawea -14.5±1 -108±9 -25.4±3 -200±50 9.1±1 59±3
Salton Sea -8.4±1 -66±9 -15.5±1 -126±9 25.1±1 51±3
Sambhar Salt -5.0±1 -30±9 -12.7±1 -92±9 27.2±1 43±3
Shala -1.4±1 0±9 -10.5±1 -73±11 19.8±1 59±3
Superior -11.4±1 -81±9 -27.3±3 -215±54 -3.1±1 75±3
Tahoe -13.8±1 -103±9 -22.5±2 -181±46 7.9±1 53±3
Tana -2.7±1 -11±9 -11.1±1 -78±9 20.1±1 53±3
Tanganyika -4.1±1 -21±9 -12.2±1 -85±9 23.8±1 71±3
Taro Co -16.6±1 -119±9 -27.0±4 -209±76 1.9±1 59±3
Taupo -6.9±1 -43±9 -16.4±2 -123±26 13.4±1 81±3
Titicaca -13.6±1 -94±9 -21.9±3 -165±57 8.4±1 57±3
Tonlé Sap -6.3±1 -40±9 -14.5±1 -104±9 27.6±1 80±3
Turkana -1.7±1 -2±9 -9.4±2 -62±26 29.0±1 52±3
Valencia -4.6±1 -32±9 -12.6±1 -98±14 23.4±1 74±3
Van -10.7±1 -70±9 -18.2±2 -136±34 12.0±1 57±3
Victoria -3.6±1 -16±9 -12.3±1 -86±9 22.2±1 72±3
Winnipeg -14.3±1 -107±9 -22.2±2 -174±36 12.9±1 69±3
Yamdruk-tso -16.7±1 -121±9 -26.9±4 -210±71 3.1±1 55±3
Yellowstone -17.8±1 -133±9 -25.3±3 -200±54 6.6±1 51±3
Zhari Namco -17±1 -122±9 -27.8±5 -216±91 1.7±1 59±3
Zige Tangco -17±1 -122±9 -28.9±2 -225±40 0.7±1 59±3
48
Table 1-8. Lake isotope investigations
Lake n Reference
Abhe 1 Kebede et al., 2009
Abiyata 7 Kebede et al., 2009; Craig et al., 1977
Afdera 11 Gonfiantini et al., 1973
Albert 1 Bahati et al., 2005
Aral Sea 36 Oberhansli et al., 2009
Athabasca 4 Hitchon and Karouse, 1972; Wolfe et al., 2007
Awasa 33 Kebede et al., 2009; Craig et al., 1977; Darling et al., 1996
Baikal 32 Seal and Shanks, 1998
Baringo 3 Cerling et al., 1988; Becht et al., 2005
Beysehir 11 Dincer et al., 1968
Biwa 15 Taniguichi et al., 2001
Caspian 25 Froehlich et al., 2000
Chad 95 Fontes et al., 1970
Chamo 13 Kebede et al., 2009
Dagze Co 1 Yuan et al., 2011
Dead Sea 27 Gat et al., 1984
Edward 4 Rossel et al., 2006
Egridir 10 Dincer et al., 1968
Elephant Butte 12 Phillips et al., 2003; This work
Erie 151 Yang et al., 1996; Karim et al., 2008; Jasechko et al., 2014
Garda 176 Longinelli et al., 2008
Geneva 9 Fontes et al., 1970
Great Bear 32 Hitchon et Krouse, 1972; This work
Great Salt 32 Nielson and Bowen, 2010
Great Slave 7 Hitchon and Krouse, 1972; Brock et al., 2009
Huron 142 Yang et al., 1996; Karim et al., 2008; Jasechko et al., 2014
Issyk-Kul 7 Ricketts et al., 2001
Jackson 2 This work
Kainji 18 Zimmerman et al., 1976
Kivu 19 Cohen et al., 1997
Kluane 15 Brahney, 2007
Ladoga 3 Luz and Barkan et al., 2010
Lucern 1 Luz and Barkan et al., 2010
Malawi 21 Gonfiantini et al., 1979
Manasarovar 7 Yao et al., 2009
Mar Chiquita 31 Dapena et al., 1997
49
Table 1-8. Lake isotope investigations
Lake n Reference
Mead 12 Craig, 1966; This work
Michigan 80 Jasechko et al., 2014
Naivasha 9 Darling et al., 1997; Cerling et al., 1988; Becht et al., 2005
Namco 2 Liu et al., 2009
Nasser 41 Aly et al., 1993
Ngangla Ringco 1 Yuan et al., 2011
Nicaragua 1 Lachniet et al., 2002
Oahe 11 Kendall and Coplen, 2001
Okanagan 36 Wassenaar et al., 2011
Onega 2 Luz and Barkan, 2010
Ontario 68 Yang et al., 1996; Karim et al., 2008; Jasechko et al., 2014
Powell 118 Kendall and Coplen, 2001; This work
Poyang 35 Wenbin et al., 2007
Qarhan Salt 1 Yuan et al., 2011
Qinghai Hu 10 Henderson et al., 2010
Rukwa 4 Bergonzini et al., 2001
Sakakawea 15 Kendall and Coplen, 2001
Salton Sea 3 Mazzini et al., 2011
Sambhar Salt 8 Yadav et al., 1997
Shala 19 Craig et al., 1977
Superior 161 Karim et al., 2008; Jasechko et al., 2014
Tahoe 4 McKenna, 1992
Tana 52 Gonfiantini et al., 1973
Tanganyika 48 Craig et al., 1975
Taro Co 1 Yuan et al., 2011
Taupo 1 Stewart et al., 1981
Titicaca 12 Fontes et al., 1979
Tonlé Sap 14 Kabeya et al., 2008
Turkana 9 Cerling et al., 1988
Valencia 1 Friedman et al., 1964
Van 2 Kwiecien, 2011
Victoria 1 Beuing et al., 1997
Winnipeg 3 Buhay et al., 1998; This work
Yamdruk-tso 1 Yuan et al., 2011
Yellowstone 1 This work
Zhari Namco 1 Yuan et al., 2011
50
Figure 1-9. Compiled water use efficiency and vapor pressure deficit relationshiops (compiled data
and original references presented in Jasechko et al., 2013). Black lines mark C4 pathways and grey
lines mark C3 pathways. Thick black and red lines mark the regressions through the entire C4 (red)
and C3 (black) datasets, respectively.
Figure 1-10. Estimated spatial distributions of water use efficiency, accounting for climate and
photosynthesis types (C3 and C4) Figure based upon work by Jasechko et al. (2013).
51
Table 1-9. Plant water use efficiency (WUE) as a function of vapor pressure deficit (VPD)
Plant C3/C4 WUE
(mmol CO2/mol H2O) VPD range (kPa)
Acer saccharum, Betula alleghaniesis, Tsuga canadensis
C3 2.8×(VPD)−0.37 <0.1 to 1.7
Arachis C3 1.4 to 2.3 2.2
Deciduous C3 5.9×(VPD)−0.98 0.3 to 0.9
Encilia fariosa C3 3.5×(VPD)−1.02 1.1 to 3.8
Evergreen C3 7.1×(VPD)−0.93 0.3 to 0.9
Hordeum vulgare C3 3.0×(VPD)−0.39 0.6 to 1.9
Ipomoea vagans C3 8.0×(VPD)−0.77 0.6 to 3.0
Larrea tridentata C3 3.6×(VPD)−0.38 0.5 to 2.0
Nicotiana glauca C3 5.0×(VPD)−0.80 0.5 to 2.0
Olea europaea L. C3 5.2×(VPD)−0.94 0.2 to 6.8
Oryza sativa C3 4.8×(VPD)−0.48 0.5 to 2.0
Oryza sativa L. C3 1.8×(VPD)−0.28 0.3 to 1.6
Phalaris aquatica C3 3.2×(VPD)−0.44 0.5 to 2.0
Phaseolus vulgaris C3 4.8×(VPD)−0.76 0.5 to 2.0
Pinus sylvestris, Picea abies C3 5.8×(VPD)−0.39 0.1 to 1.3
Populus tremuloides C3 5.5×(VPD)−0.82 0.5 to 4.5
Prosopis juliflora C3 10.6×(VPD)−1.38 1.0 to 9.6
Pseudotsuga C3 8.9×(VPD)−0.49 0.3 to 3.2
Quercus C3 6.6×(VPD)−0.44 0.2 to 1.8
Salix viminalis C3 7.8×(VPD)−0.58 0.2 to 2.1
Triticum C3 3.0×(VPD)−0.67 0.5 to 1.5
Triticum C3 5.9×(VPD)−0.50 <0.1 to 2.8
Dactyloctenium aegyptium C4 14.3×(VPD)−0.73 1.2 to 3.8
Eragrostis tremula C4 17.9×(VPD)−1.12 2.1 to 5.0
Miscanthus giganteus, Spartina cynosuroides
C4 5.3×(VPD)−1.18 1.0 to 1.2
Paspalum plicatulum C4 5.7×(VPD)−0.35 0.5 to 2.0
Pleuraphis rigida C4 7.0×(VPD)−0.78 1.3 to 3.8
Schoenefeldia gracilis C4 10.1×(VPD)−1.05 0.9 to 3.0
Zea mays C4 8.9×(VPD)−0.35 <0.1 to 2.9
Zea mays C4 7.7×(VPD)−0.47 0.5 to 2.0
References for each of the above studies compiled within Jasechko et al. (2013)
52
A global in scale calculation was developed using a new global compilation of river isotopic
data (Table 1-10). A deuterium excess mass balance of the continents was used to estimate the global
transpiration/evapotranspiration ratio on land surfaces:
ET
EPEQEI
dd
ddxPddQddIT
Equation 1.9
where d represents the deuterium excess of each flux, I represents the flux of precipitation entering
the catchment, E represents physical evaporation losses from a catchment, T represents transpiration
water losses from a catchment, Q represents liquid losses via runoff and groundwater discharge out
of the basin, x represents the fraction of precipitation (P) that is intercepted by vegetation and
returned to the atmosphere through evaporation. The global water use efficiency was estimated by
spatially weighting our grid cell estimates of water use efficiency to mean annual mean normalized
difference vegetation indices (values less than zero assigned a value of zero), and was found to be
close to 3.2±0.9 mmol CO2 per mol H2O.
Figure 1-11. The deuterium excess of 31 major rivers (a) and associated annual streamflow (b).
Discharge data from Dai and Trenberth (2002).
53
Table 1-10. Deuterium excess of major rivers ranked by discharge
River Q (km3/y)** Rank** Deuterium excess* (‰)
Amazon 6642 1 9.8±1.8
Changjiang 944 4 8.8±2.5
Mississippi 610 6 8.5±1.8
Yenisey 599 7 8.2±2.6
Paraná 568 8 7.7±4.0
Lena 531 9 7.2±1.3
Mekong 525 10 5.9±4.5
Ob 412 13 5.8±2.1
St Lawrence 363 16 3.1±1.3
Amur 354 17 6.3±1.3
Mackenzie 290 19 -1.0±1.2
Columbia 252 21 6.6±1.6
Yukon 212 24 3.5±1.6
Danube 202 26 9.0±1.2
Fraser 144 30 2.9±3.8
Kolyma 118 35 5.7±1.9
Indus 104 38 13.5±5.9
Neva 79 45 6.4±1.0
Sacramento 69 50 8.6±0.9
Kuskokwim 57 54 5.7±1.2
Alabama 51 68 10.4±2.3
Stikine 51 69 8.3±1.1
Susquehanna 46 75 12.9±2.3
Susitna 45 78 4.7±1.1
Volta 37 86 1.3±6.9
Copper 34 96 6.5±1.4
Nushagak 31 109 5.7±1.3
Tombigbee 27 124 11.6±2.9
Colorado R. 12 165 -1.8±1.2
Brazos 7 180 2.9±5.4
Colorado (TX) 3 195 4.6±8.3
Rio Grande 2 196 -1.5±1.2
* Deuterium excess (d) defined as (Dansgaard, 1964): d = δ2H − 8∙δ18O (±1 s.d. shown), references
to original data sources presented in Jasechko et al. (2013).
** Runoff data from Dai and Trenberth (2002)
54
1.4 Results
Figure 1-12 presents the range of δ18O and δ2H values observed in Earth’s large lakes. The
lowest δ18O and δ2H values in large lakes are generally found at high altitudes and latitudes, whereas
the highest δ18O and δ2H values are found in lakes that are located at low latitudes and altitudes. The
entire dataset spans the range of −23‰ to +15‰ in δ18O and −180‰ to +80‰ in δ2H values. The
majority of lakes are found to plot below a regression of meteoric waters (i.e., the “global meteoric
water line;” Craig, 1961) because of kinetic isotope effect occurring during the process of
evaporation (Craig, 1961).
Figure 1-12. The stable O and H isotopic composition of Earth’s large lakes and inland/semi-
enclosed seas. The lowest δ18O and δ2H values are observed at high altitudes and latitudes (e.g.,
Kluane Lake) and the highest δ18O and δ2H values are observed at low latitudes and altitudes (e.g.,
Lake Turkana). Dots mark individual water samples, with shaded areas enclosing all points for a
single lake (“convex hull” – from Jasechko et al., 2013). References presented in Table 1-8.
55
Some lakes have greater internal variability in δ18O and δ2H values than others. Generally,
stratified and shallow lakes (e.g., Lake Chad, Great Salt Lake and the Aral Sea) have larger variations
in δ18O and δ2H values than well mixed, deep lakes (e.g., the North American Laurentian Great
Lakes or Lake Baikal; Figure 1-13). Stratified lakes (e.g., Tanganyika) have different δ18O values at the
lake surface compared to values at depth (Figure 1-14); whereas, well-mixed lakes such as Lake Baikal
(Figure 1-14) and each of the North American Great Lakes (Figure 1-15) have relatively homogenous
δ18O and δ2H values.
Figure 1-13. The internal variability in a selection of large lake δ18O and δ2H values. The upper pane
highlights two well-mixed, deep (>400 m maximum depth) lakes that have homogenous isotopic
compositions. The lower pane delineates heterogeneous lake δ18O and δ2H values found in shallow
large lakes and inland seas.
56
Figure 1-14. Temperature (top row) and δ18O (bottom row) profiles for the two largest (volumetric)
lakes on Earth: Tanganyika (left) and Baikal (right; data from Craig, 1975 and Seal and Shanks, 1998).
Baikal has a heterogenous temperature profile, but a homogenous isotopic composition. Tanganyika
has a heterogeneous isotopic profile and a heterogenous temperature profile.
Figure 1-15. The isotopic composition and temperature (April) of the North American Great Lakes
(profile shown here spans Superior, Huron, Erie and Ontario, but skips Michigan, which has the
highest δ18O and δ2H values of the Laurentian Great Lakes; from Jasechko et al., 2014).
57
The rate of transpiration spanning 10% of Earth’s ice free area based on stable isotopic data
is presented in Figure 1-17, both as a percentage of total evapotranspiration (Figure 1-17 a) and as a
transpiration rate (Figure 1-17 b). The rate of transpiration is greatest in the humid tropics where
primary production is rarely limited by water and temperature (e.g., east African Great Lakes; Figure
1-16; Running et al., 2004). The rate of transpiration – in our dataset – is smallest in the boreal forest,
where growth is limited to a short growing season due to the pronounced seasonality of the high
latitudes. Transpiration rates are also discovered to be small in arid climates, where growth is
expected to be limited by water supplies.
Global transpiration fluxes calculated by a deuterium excess mass balance of continental
waters support conclusions reached using our global lake dataset: transpiration is the largest H2O flux
from the continents. Our global analysis suggests that 80 to 90% of vapor flows from the continents
are funneled through transpiration, with smaller percentages left to evaporation or sublimation.
Volumetrically, isotopic data support a transpiration flux of ~60,000 km3 of water per year, which
has a corresponding latent energy requirement of 33 W/m2, suggesting that a substantial percentage
of radiation absorbed by Earth’s surfaces is appropriated to the vaporization of water at plant leaf
surfaces (“solar absorbed at the surface” reported as ranging between 147 W/m2 and 174 W/m2
(Trenberth et al., 2009).
58
Figure 1-16. The transpiration rate for seven ecozones (each bar is one lake catchment). The total
evapotranspiration flux for each catchment is marked by a black line, and the median result of Monte
Carlo calculation realizations is represented by a square. Bars extend to the 25th-75th percentiles of
Monte Carlo realizations.
59
Figure 1-17. Transpiration rates for 10% of ice free land areas. (a) Transpiration is shown both as a
proportion of total evapotranspiration (%; pane a) and as a rate (mm H2O year-1; b).
60
Figure 1-18. Catchment-by-catchment transpiration rates for 73 lakes. Colors mark ecoregions, with
grey bars marking lake catchments that fall into more than one ecoregion. Total evapotranspiration
rates are marked as a dash, whereas the median of calculation results is marked by a square.
Global primary production assimilates ~123±8 Gt of carbon each year (Beer et al., 2010).
The transpiration fluxes reported in this study can be used to calculate gross primary production by
applying the water use efficiency data calculated within each catchment to transpiration fluxes (i.e.,
converting mm H2O transpired per year into g C assimilated per year). Gross primary production rate
calculated using transpiration fluxes are presented in Figure 1-19.
61
Figure 1-19. Gross primary productivity (catchment averages) for 10% of Earth’s ice free land area
calculated by coupling isotope-based transpiration fluxes to water use efficiency data.
1.5 Discussion
Transpiration is found to account for more than two-thirds of evapotranspiration in more
than 80% of catchments studied. We find that even though potential evaporation rates likely exceed
transpiration on land surfaces, the rate of evaporation is limited by the small area of open water on
“land” surfaces (~3%, globally; Downing et al., 2006). Therefore our results suggest that biological
fluxes of water into the atmosphere is the greatest vapor flow from the continents, rather than
evaporation. Plant roots tap into ground- and soil-water reservoirs and effectively move these
underground water sources upward to Earth’s boundary layer for evaporation at leaf surfaces,
whereas evaporation is limited in its water supply to water that is at or near the surface.
Our analysis neglects snow sublimation, which has been proposed as a non-fractionating
process (although more recent work suggests that sublimation is indeed a fractionation labelled
process; Koeniger et al., 2006), is thought to be very small at pan-continental scales. A compilation of
62
three global climate and land surface model estimates places sublimation at less than 2% of terrestrial
evapotranspiration (not considered in one, ~1% in another, and ~2% in the third). Although locally
sublimation may be an important H2O vaporization process, current land surface and general
circulation models suggest that >98% of continental vapor flows are represented by transpiration,
interception and evaporation (i.e., the vapor flows considered in this study).
Table 1-11. Published estimates of sublimation relative to terrestrial evapotranspiration
Study Sublimation / total evapotranspiration
Model or methodology
Dirmeyer et al., 2006
<1%
Calculation: Sublimation of 0.35 mm/mo (i.e., 4.2 mm/year) is shown. Even including seasonally snow covered regions and ice-caps that cover 46,000,000 km2 (see *), this calculation yields a sublimation flux of ~200 km3/yr, or less than 0.5 % of terrestrial ET.
Lawrence et al., 2007
Not considered Community Land Model Version 3
Miralles et al., 2011
2% Global Land-surface Evaporation: the Amsterdam Methodology
* National Snow and Ice Data Center: Snow and Climate. nsidc.org/cryosphere/snow/climate.html
The connections between carbon and water fluxes made in this study highlight a novel
approach for quantifying water and carbon fluxes on continents. Continental water and carbon cycles
are connected by water use efficiency ratios – compiled in this study, for the first time – highlighting
an opportunity for atmospheric models to take advantage of this natural H2O-CO2 accounting
system on continents. Because of the higher proportion of transpiration/evapotranspiration
discovered here, this analysis suggests that biological changes due to climate and land use
modifications will exert a dominant impact upon fresh water fluxes, and associated transports of
nutrients, contaminants, sediment and other solutes. The results of this study also highlight that
changes to vegetation in the past, such as the evolution and spread of the C4 photosynthetic pathway
or the emergence of vascular plants onto continents, are likely to have profoundly modified the
global water and carbon cycles.
63
1.6 References
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evapotranspiration using semiempirical and mechanistic schemes of plant hydrology, Global
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Aly, A. I. M., Froehlich, K., Nada, A., Hamza, M. and Salem, W. M. (1993), Study of
environmental isotope distribution in the Aswan High Dam Lake (Egypt) for estimating the
evaporation of lake water and its recharge to adjacent groundwater. Environmental Geochemistry and
Health, 15, 37–49.
Bahati, G., Pang, Z., Armannsson, H., Isabirye, E.M. and Kato, V. (2005), Hydrology and
reservoir characteristics of three geothermal systems in Western Uganda, Geothermics, 34, 568–591.
Barbour, M. M., Hunt, J. E., Walcroft, A. S., Rogers, G. N. D., McSeveny, T. M. and
Whitehead, D. (2005), Components of ecosystem evaporation in a temperate coniferous rainforest,
with canopy transpiration scaled using sap–wood density, New Phytologist, 165, 549–558.
Bates, R. E. and Bilello, M. (1966), Defining the cold regions of the northern hemisphere,
U.S.A/ Cold Regions Research and Engineering Laboratory, Technical Report 178.
Becht, R., Mwango, F. and Muno, F. A. (2005), Groundwater links between Kenyan Rift
Valley lakes (eds. Odada et al.) 7 – 14 (Proceedings of 11th World Lakes Conference, Nairobi,
Kenya, 2005).
Beer, C. et al. (2010), Terrestrial gross carbon dioxide uptake: global distribution and
covariation with climate, Science, 329, 834–838.
64
Bergonzini, L., Gibert, E., Winckel, A. and Merdaci, O. (2001), Bilans hydrologique et
isotopique (18O et 2H) du Lac Massoko, Tanzanie. Quantification des échanges lac–eaux souterraines.
C. R. Acad. Sci. Paris, Sciences de la Terre et des Planètes 333, 617–623.
Beuning K. R. M., Kelts, K., Ito, E. and Johnson, T. C. (1997), Paleohydrology of Lake
Victoria, East Africa, inferred from 18O/16O ratios in sediment cellulose. Geology, 25, 1083–1086.
Beziat, P., Rivalland, V., Tallec, T., Jarosz, N., Boulet, G., Gentine, P. and Ceschia, E. (2013),
Evaluation of a simple approach for crop evapotranspiration partitioning and analysis of the water
budget distribution for several crop species, Agricultural and Forest Meteorology, 177, 46–56.
Bosch, J. M., and J. D. Hewlett (1983), A review of catchment experiments to determine the
effect of vegetation changes on water yield and evapotranspiration, Journal of Hydrology, 55, 3–23.
Bowen, G. J. (2010), Statistical and geostatistical mapping of precipitation water isotope
ratios. In Isoscapes, Springer, Netherlands, pp. 139–160.
Bowen, G. J., and Revenaugh, J. (2003), Interpolating the isotopic composition of modern
meteoric precipitation. Water Resources Research, 39, 1299.
Bowen, G. J., and Wilkinson, B. (2002), Spatial distribution of δ18O in meteoric precipitation,
Geology, 30, 315–318.
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83
CHAPTER 2 — THE SEASONALITY OF GLOBAL GROUNDWATER
RECHARGE
2.1 Abstract
Groundwater is recharged as rain and snowmelt infiltrate underground into aquifers.
Groundwater is a vital resource that sustains 40% of crop irrigation. Many studies report annual
groundwater recharge rates, yet few studies report seasonal differences in groundwater recharge rates,
particularly in light of differing precipitation fluxes between seasons. In this chapter I define the
“groundwater recharge ratio” as the proportion of rain and snow that recharges groundwater
aquifers. On the basis of a newly compiled set of 54 paired precipitation-groundwater isotopic data, I
show that groundwater recharge ratios are highest during the winter in most arid and temperate
climates, and are at a maximum during the wet season in the tropics. The isotope-based seasonal
assessment of groundwater recharge ratios are compared with the outputs of a global hydrological
model (PCR-GLOBWB), and the model is found to compare closely with the isotope observations in
most, but not all locations. The seasonal difference in the efficiency of groundwater recharge
suggests that changes to winter (temperate and arid regions) and wet season (tropics) hydrological
processes will be the most important to future changes in groundwater recharge fluxes.
2.2 Introduction
Groundwater supplies one third of modern-day human water uses (Wada et al., 2014) and
represents the lion’s share (~99%) of unfrozen terrestrial water (Aeschbach-Hertig and Gleeson,
2012). Groundwater is replenished by rain and snowmelt that infiltrates through the critical zone near
to Earth’s surface and into aquifers. Groundwater is depleted by natural discharges of groundwater
flow paths into the water at the surface – such as streams, lakes and seas – and also is depleted by
human extractions via wells. Humans need groundwater to sustain modern livelihoods. Groundwater
supplied drinking water for two billion people, and sustains about 40% of global cropland irrigation
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(Siebert et al., 2010; Foley et al., 2011). Although groundwater is a pivotal component of modern
human livelihoods, the extractions of groundwater by humans are unsustainable and are draining
aquifers at the global- (Konikow and Kendy, 2005; Wada et al., 2010; Konikow, 2011; Gleeson et al.,
2012) and regional scales (Rodell et al., 2009; Famiglietti et al., 2011; Scanlon et al., 2012; Feng et al.,
2013; Steward et al., 2013; Voss et al., 2013; Joodaki et al., 2014). Unsustainable groundwater
extractions have been spotlighted in multiple regional scale studies including the northern Gangetic
Plain (India, Rodell et al., 2009), the North China Plain (Feng et al., 2013), the Middle East (Voss et
al., 2013; Joodaki et al., 2014), the High Plains of the central United States of America (Scanlon et al.,
2012; Steward et al., 2013) and the Californian Central Valley (western U.S.A., Famiglietti et al., 2011;
Scanlon et al., 2012) and the Colorado River basin (Castle et al., 2014). To reverse these examples of
regional-scale, non-sustainable pumping, groundwater managers will need to set and achieve long
term pumping rate goals that will realize sustainable withdrawals (Gleeson et al., 2012; Aeschbach-
Hertig and Gleeson, 2012). However, these pumping rate goals must be established in the face of a
changing climate, and, therefore, a moving target. In order to predict future groundwater
replenishment rates, it is important that the best information possible be made available regarding
natural groundwater recharge fluxes and their controlling processes which include: the physical state,
amount and intensity of precipitation; topography; water table characteristics; geology; soil type;
vegetation characteristics; boundary layer climatology; irrigation return flows).
Some previous work has evaluated controls upon groundwater recharge fluxes. A
compilation of chloride mass balance recharge estimates suggests that plant life form distributions are
a leading determinant for groundwater recharge, falling second only to precipitation amounts in
terms of importance (Kim and Jackson, 2012). Work presented in Chapter one has indeed shown
that transpiration is a dominant process in the global hydrological cycle (Jasechko et al., 2013).
85
Knowledge of annual groundwater recharge fluxes are a common research target in regional
and continental scale scientific investigations (e.g., Scanlon et al., 2006; Döll and Fielder, 2007; Wada
et al., 2010). Fewer studies have explored seasonal differences in recharge fluxes as a proportion of
precipitation. Understanding the seasonal distribution of groundwater recharge is important because
climate change will impact the hydrology of each season in different ways.
Here we define the groundwater recharge ratio: “the groundwater recharge (R) flux as a
proportion of precipitation (P): R/P.” Previous modelling studies have estimated that the annual
groundwater recharge ratio is close to ~10% (Figure 2-1). The groundwater recharge is estimated to
be lowest in arid climates (average of 4%) and higher in boreal, temperate, and moist tropical forests
(averages recharge ratios of ~14%, ~15%, and ~16%, respectively; recharge estimates from Döll and
Fielder, 2007 and precipitation data from the Global Precipitation Climatology Project, accessed at
www.gewex.org). However, these estimates are highly uncertain as a result of land use and irrigation
return flows not embedded within most hydrological models, in addition to the immense challenges
associated with accurately representing complex interactions of plants, rocks, and climate at Earth’s
critical zone (where these interactions are at a maximum).
86
Figure 2-1. The global groundwater recharge ratio (recharge data from Döll and Fielder, 2007;
precipitation data from the Global Precipitation Climatology Project: www.gewex.org) in map form
(a) and presented as ecozone statistics (b; colored bars mark 25th-75th percentiles, lines mark 10th-90th
percentile distribution).
Previous fieldwork has revealed that winter groundwater recharge ratios are higher than
summer groundwater recharge ratios (Heppner et al., 2007; Jukić and Denić-Jukić, 2009; Yeh and
Famiglietti, 2009; Dripps and Bradbury, 2010; Dripps, 2012; Leterme et al., 2012). Seasonality of
groundwater recharge ratios have been assessed in Belgium (Leterme et al., 2012), Greenland
(Leterme et al., 2012), the northeastern U.S.A. (Heppner et al., 2007; Yeh and Famiglietti, 2009;
Dripps and Bradbury, 2010; Dripps, 2012) and Croatia (Jukić and Denić-Jukić, 2009). In some cases,
summer groundwater recharge has been shown to be restricted solely to high intensity
thundershowers (Wisconsin, U.S.A.; Dripps, 2012). Field based monitoring of groundwater recharge
in Tanzania has shown that groundwater recharge ratios are at their highest when rainfall is most
intense (Taylor et al., 2013), suggesting that an intensifying hydrosphere (Durack et al., 2012) could,
in fact, be beneficial from the sole standpoint of groundwater recharge fluxes. Temperate climate
groundwater recharge has been found to be extremely and rapid process during snowmelt (Gleeson
87
et al., 2009), with the ice content of the shallow subsurface being a controlling factor upon on the
proportion of snowmelt that recharges the subsurface aquifers (Granger et al., 1984). Groundwater
recharge investigations in the mid-western United States of America has found that snowmelt can
comprise two thirds of annual groundwater recharge (Delin et al., 2007; Dripps, 2012). Yet, in spite
of these examples of seasonal biases in the efficiency of groundwater recharge, different recharge
ratios between different seasons have not been observed in in all cases (e.g., Spain, Leterme et al.,
2012), opening an opportunity to calculate and assess the potential for seasonality in groundwater
recharge ratios across different biomes with different lithologies, plant life forms and hydroclimates.
In this chapter, I hypothesize that by coupling groundwater and precipitation isotopic data,
one may calculate seasonal differences in the groundwater recharge ratio. Several studies have
compared precipitation and groundwater isotopic compositions. These studies have found
differences in some cases, and no differences in other cases, between precipitation and groundwater
δ18O and δ2H values.
Studies finding similarities in the isotopic compositions of flux-weighted annual precipitation
and modern groundwater include locations such as China (Li et al., 2000), Finland (Kortelainen,
2004), France (Genty et al., 2014), Israel (Even et al., 1986), Italy (Madonia et al., 2013), Korea (Lee
et al., 1999; Lee and Kim, 2007), New Zealand (Williams and Fowler, 2002), Tasmania (Goede et al.,
1982), the United Kingdom (Darling and Bath, 1988; Darling et al., 2003) and the United States of
America (Yonge et al., 1985; van Beynen and Febbroriello, 2006). These finding suggest at first
glance – without statistical analysis, per se – that groundwater recharge ratios in these sites are similar
year round.
Studies finding differences in the isotopic compositions of flux-weighted annual
precipitation and modern groundwater include field sites in South Africa (Vogel et al., 1963), the
south-western United States (Arizona, Simpson et al., 1972, Kalin, 1994; Nevada, Winograd et al.,
88
1998), the north-eastern United States (Pennsylvania, O’driscoll et al., 2005; Vermont: Abbott et al.,
2000), central Canada (Alberta, Maulé et al., 1994; Grasby et al., 2010), southern Canada (Ontario;
Huddart et al., 1999), French Guyana (Negrel et al., 2010), St. Croix (Gill, 1994), Spain (Julian et al.,
1992), Barbados, Puerto Rico and Guam (Jones et al., 2000; 2003). These differences have been
interpreted as a reflection of higher groundwater recharge ratios during winter (Vogel et al., 1963;
Simpson et al., 1972; Maulé et al., 1994; Kalin, 1994; Winograd et al., 1998; Abbott et al., 2000;
O’driscoll et al., 2005) and wet seasons (Jones et al., 2000; 2003; Negrel; et al., 2010). These isotope-
based results have never been synthesized at a global scale, nor have all studies quantitatively assessed
the seasonal difference in groundwater recharge ratios, expressing these observations qualitatively
instead.
The objective of this chapter of my dissertation is to test for, and quantify, seasonal
differences in groundwater recharge ratios across a variety of field sites by analyzing a newly
compiled global dataset of precipitation and groundwater isotopic data.
2.3 Dataset and methods
Here I calculate the seasonality of groundwater recharge ratios (two seasons) for 54 globally-
distributed locations (Figure 2-2). I analyze global isotopic data for precipitation from regional and
global monitoring networks (Araguás-Araguás et al., 2000; Welker, 2000; Birks and Edwards, 2009;
Welker, 2012) and compare precipitation isotopic data to nearby groundwater isotopic data that have
been compiled from previous field reports. Precipitation data are available through the International
Atomic Energy Agency (e.g., Araguás-Araguás et al., 2000), the United States Network for Isotopes
in Precipitation (Welker, 2000; Welker, 2012) and the Canadian Network for Isotopes in
Precipitation (Birks and Edwards, 2009). Groundwater isotopic data were compiled from >40
published datasets within the primary literature. Original field studies have been properly credited
and are referenced within Table 2-1.
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Table 2-1. Locations of paired precipitation and groundwater isotopic data
Station Data Lon. Lat. Aquifer Reference
Cayenne IAEA -52.4 4.8 Guyana Shield Negrel and Petelet-Giraud,
2010
Taguac IAEA 144.8 13.6 Guam caves Jones and Banner, 2003
Seawell IAEA -59.5 13.1 Barbados aqfr. Jones et al., 2000
Jakarta IAEA 106.8 -6.2 Jakarta aqfr. Kagabu et al., 2011
New Dehli IAEA 77.2 28.6 Gangetic Plain Das et al., 1988; Lorenzen et
al., 2012
Dar es Salaam IAEA 39.2 -6.9 Coastal aqfr. Bakari et al., 2012
Addis Ababa IAEA 38.7 9.0 Akaki Volcanics Demlie et al., 2007; Kebede
et al., 2007; Rango et al., 2010; Bretzler et al., 2011
Santa Maria IAEA -120.5 34.9 CA Coast www.waterqualitydata.us
Beit Dagan IAEA 34.8 32.0 Israel coast aqfr. Yechieli et al., 2008
Pisa IAEA 10.4 43.7 Pisa Plain Grassi and Cortecci, 2005
Trout Lake USNIP -89.7 46.1 Surficial aqfr. www.waterqualitydata.us
Yellowstone USNIP -110.4 44.9 Alluvial aqfr. www.waterqualitydata.us
Smith's Ferry USNIP -116.1 44.3 Idaho Batholith Schlegel et al., 2009
Lake Geneva USNIP -88.5 42.6 Surficial aqfr. www.waterqualitydata.us
East MA USNIP -71.2 42.4 Surficial aqfr. www.waterqualitydata.us
Niwot Saddle USNIP -105.6 40.1 Surficial aqfr. www.waterqualitydata.us
Wye USNIP -76.2 38.9 Aquia aqfr. Aeschbach-Hertig et al. 2002
Purdue Agr. USNIP -87.5 38.7 Surficial aqfr. www.waterqualitydata.us
Clinton Stn. USNIP -78.3 35.0 Atlantic Plain www.waterqualitydata.us
Caddo Valley USNIP -93.1 34.2 MI River Valley www.waterqualitydata.us
Coffeeville USNIP -89.8 34.0 MI Embayment www.waterqualitydata.us
Saturna CNIP -123.2 48.8 Surficial aqfr. Allan, 2003
Ottawa CNIP -75.7 45.3 Surficial aqfr. Praamsma et al., 2009
Wallingford IAEA -1.1 51.6 London Chalk Elliot et al., 1999; Darling et
al., 1997
P. Douradas IAEA -7.6 40.4 Serra da Estrela Carreira et al., 2011
Krakow IAEA 19.9 50.1 Malm
Limestones Zuber et al. 2004;
Samborska et al., 2012
Cuxhave IAEA 8.7 53.9 N. German Bsn. Kloppman et al., 1998
Orleans IAEA 1.9 47.9 Paris Bsn. Kloppman et al., 1998
Melbourne IAEA 145.0 -37.8 Yarra Bsn. Tweed et al., 2004
Newcastle IAEA -104.2 43.9 Surficial aqfr. www.waterqualitydata.us
Little Bighorn USNIP -107.4 45.6 Surficial aqfr. www.waterqualitydata.us
Lamberton USNIP -95.3 44.2 Mt. Simon aqfr. Berg and Person, 2012;
www.waterqualitydata.us
N. Platte Agr. USNIP -100.8 41.1 N. High Plains McMahon et al., 2006
Mon Mouth USNIP -90.7 40.9 Surficial aqfr. www.waterqualitydata.us
Great Plains USNIP -97.5 35.0 Arbuckle aqfr. www.waterqualitydata.us
Edmonton CNIP -113.5 53.6 Surficial aqfr. Maule et al., 1994
Saskatoon CNIP -106.6 52.1 Dalmeny aqfrs. Fortin et al., 1991
Wynyard CNIP -104.2 51.8 Surficial aqfr. unpublished data
90
Station Data Lon. Lat. Aquifer Reference
Esther CNIP -110.2 51.7 Surficial aqfr. Wallick, 1981
Calgary CNIP -114.0 51.0 Surficial aqfr. Lanza, 2009; Cheung and Mayer, 2009; Rock and
Mayer, 2009
Icelandic Park / Gimli
USNIP / CNIP
-97.8 48.8 Winnipeg fm. Ferguson et al., 2007
Craters of the Moon
USNIP -113.6 43.5 Surficial aqfr. www.waterqualitydata.us
Pinedale USNIP -109.8 42.9 Colorado Plat. www.waterqualitydata.us
Sand Spring USNIP -107.7 40.5 Surficial aqfr. www.waterqualitydata.us
Smith Valley USNIP -119.3 38.8 Basin & Range www.waterqualitydata.us
Tuscon ** -110.8 32.2 Tucson Basin Cunningham et al., 1998
Chihuahua IAEA -106.1 28.6 Chihuahua Plain Wassenaar et al., 2009
Alice Springs IAEA 133.9 -23.8 Amadeus Bsn. Wischusen et al., 2000
Zhangye IAEA 100.4 38.9 Hexi Corridor Qin et al., 2011
Yinchuan IAEA 106.2 38.5 Yinchuan Plain Wang, L. et al., 2012
Yellowknife CNIP -114.3 62.3 Con Mine Douglas et al., 2000
Whitehorse CNIP -135.1 60.7 Surficial aqfr. Carey and Quinton, 2005
Chapais CNIP -75.0 49.8 Surficial aqfr. Boutin, 2009
91
Figure 2-2. Locations where precipitation and groundwater isotopic data are available. Circles mark
54 study sites where sufficient groundwater and precipitation were available to assess groundwater
recharge ratio seasonality. Diamonds mark locations where only a comparison of groundwater and
precipitation isotopic data could be made (i.e., no seasonal recharge ratio
Most studies have only grab samples of groundwater, and do not report long term
monitoring isotopic data. However, the multi-year isotopic monitoring studies of groundwater δ18O
and δ2H values show little seasonal variability, suggesting that grab samples are suitable archives of
multi-year recharge fluxes. Examples of long term groundwater monitoring for isotopic data include
records analyzed in Finland (Kortelainen et al., 2004), Italy (Iacumin et al., 2009), the United
Kingdom (Darling et al., 2003), New Zealand (Williams and Fowler, 2002), eastern Canada (Savard et
al., 2007) and France (Genty et al., 2014. The temporal homogeneity of groundwater δ18O and δ2H
values is interpreted to be the result of hydrodynamic dispersion and multi-year groundwater
residence times.
92
Precipitation isotopic data has been collected for more than 50 years by the International Atomic
Energy Agency (Araguás-Araguás et al., 2000) and other country-wide precipitation networks
(Welker, 2000; Kurita et al., 2004; Birks and Edwards, 2009; Welker 2012, Liu et al., 2013). The
analysis of precipitation data for this study involved to steps: (i) calculation of amount-weighted
annual precipitation isotopic compositions, and (ii) calculation of amount-weighted precipitation
isotopic compositions for two six-month seasons for each meteorology station. Seasons are defined
as winter and summer in the extra-tropics, and the wettest and driest consecutive six-month interval
in the tropics.
First, the amount-weighted isotopic composition of precipitation (δP(annual)) was calculated following
(Equation 2.1):
δP(annual) =∑ δP(i)Pi
12i=1
∑ Pi12i=1
Equation 2.1
where δP(i) is the monthly isotopic composition of precipitation during month i, and Pi is the amount
of precipitation (i.e., the rate) during month i.
Second, the amount-weighted isotopic composition of season 1 (δP(season 1); defined as October-March
in the northern hemisphere extra-tropics, and the wettest consecutive six month interval in the
tropics) and season 2 (δP(season 2); defined as April-September in the extra-tropics, and the driest
consecutive six month interval in the tropics) precipitation was calculated following (Equations 2.2
and 2.3):
δP(season 1) =δP(10)P10+δP(11)P11+δP(12)P12+δP(1)P1+δP(2)P2+δP(3)P3
P10+P11+P12+P1+P2+P3 Equation 2.2
δP(season 2) =δP(4)P4+δP(5)P5+δP(6)P6+δP(7)P7+δP(8)P8+δP(9)P9
P4+P5+P6+P7+P8+P9 Equation 2.3
93
Following the same symbology as outlined for Equation 2.1. Southern hemisphere (e.g., Melbourne,
Australia) sites had winter and summer months inverted.
Before completing an analysis of groundwater and precipitation data at similar locations,
paleo-groundwater were required to be delineated and removed from the analysis because these
groundwaters are not reflective of the modern climate where precipitation measurements have been
made. Indeed fossil groundwaters that recharged during the Pleistocene (i.e., fossil groundwaters)
have been shown to be different than modern groundwaters by −8 to +2 ‰ in δ18O due to different
Pleistocene hydroclimatology (e.g., Plummer, 1993; Edmunds, 2009) or due to subglacial recharge of
groundwaters beneath the Laurentide and Fennoscandanavian ice sheets that resided over the
northern portions of Eurasia and North America 20,000 years ago (e.g., Estonia, Karro et al., 2004;
central Canada, Grasby and Chen, 2005; reviews by Jiráková et al., 2011 and McIntosh et al., 2012).
Groundwater δ18O and δ2H values, well depths, and 3H and 14C radioactivity levels were
compiled from earlier works (Table 2-1). Considerations of (i) possible effects of evaporation during
groundwater recharge, and (ii) possible shifts in δ18O and δ2H values related to paleoclimates
recorded in fossil groundwaters were made before comparing precipitation and groundwater stable
isotopic data.
First, partially evaporated groundwater samples were removed from our analysis using the
deuterium excess parameter (Dansgaard, 1964). Partial evaporation leads to changes in isotopic
compositions along δ2H/δ18O slopes of less than eight because of differences in the vapor pressures
of the 1H1H16O, 1H1H18O and 2H1H16O isotopologues. The deuterium excess parameter (d = δ2H –
8×δ18O; Dansgaard, 1964) integrates information within both δ18O and δ2H values and is used here
to test for modifications to the isotopic composition of groundwaters due to partial evaporation.
Samples bearing an evaporative signature will have a lower deuterium excess than that of meteoric
waters, which have a global mean deuterium excess of close to +10 ‰. All groundwater samples with
94
a deuterium excess value of less than zero were removed from this analysis to ensure that the
calculation of seasonal groundwater recharge ratios was not biased to evaporative influences.
Second, groundwater ages in excess of ~10,000 years were removed from this analysis on
the basis of 3H, 14C and well depths because fossil groundwaters have different δ18O and δ2H values
from modern groundwaters (Plummer, 1993; Edmunds and Milne, 2001; Grasby and Chen, 2005;
Karro et al., 2004; Edmunds, 2009; Jiráková et al., 2011; McIntosh et al., 2012). Samples with a 14C
concentration of greater than 60 p.m.C. were included in this study, as the maximum groundwater
age of samples with 60 p.m.C. can be no more than 5,000 years. Paleo-groundwater shifts in δ18O
and δ2H values do not become apparent until ~12 ka (Edmunds and Milne, 2001; Edmunds, 2009;
Darling, 2011), such that groundwaters with 14C activities of exceeding 60 p.m.C. should be suitable
for comparison with modern precipitation.
A similar delineation of “modern” groundwater can be derived from tritium groundwater
data, a commonly applied age tracer in groundwater investigations. Here I a mixing model that
accounts for groundwater residence time and mixing of different groundwaters with different ages
(i.e., time elapsed since recharge). I apply a mixing model that defines modern and old groundwater
using the year 1950 as a threshold, where “post-1950 groundwater” is defined as groundwater having
recharged after the year 1950 and “pre-1950 groundwater” is defined as groundwater that recharged
prior to 1950.
To use the compiled 3H groundwater data to quantify the mixing components of post-1950
and pre-1950 groundwater an estimate of the activity of 3H in meteoric waters was required. I
downloaded and analyzed a global dataset of 3H in precipitation measurements made since ~1960 at
various locations by the International Atomic Energy Agency. Records of pre-1950 3H activities in
meteoric waters are available from wine and ice cores compiled by Kotzer et al. (2000).
95
The 3H mixing model used to calculate pre- and post-1950 age components of each
groundwater sample is presented next. The mass (m) fraction of groundwater having recharged after
the year 1950 for a water sample (mpost-1950/msample) is calculated as:
mpost−1950
msample=
Hsample− Hpre−195033
Hpost−19503 − Hpre−1950
3 Equation 2.4.
where 3Hsample represents the 3H concentration of a given sample, 3Hpre-1950 represents the range of
possible 3H values for groundwater that recharged prior to 1950, and or 3Hpost-1950 represents the
range of possible 3H values for groundwater that recharged after 1950.
The calculation includes spatio-temporal variations in meteoric tritium activities in addition
to the radioactive decay of 3H. Possible 3Hpre-1950 and 3Hpost-1950 concentrations were determined using
linear regressions of latitude against the annual average 3H concentration in precipitation at various
locations (Figure 2-3).
96
Figure 2-3. The variations of tritium in meteoric water over time. The left pane shows linear
regressions through International Atomic Energy Agency precipitation stations (mean annual 3H
values computed for every site, for every year). The color of each line marks the corresponding year
that the regression was completed. The right pane shows an example of changes to 3H in
precipitation over time for 45°N calculated using the regressions presented in the left pane. The
squares and diamonds mark wine and ice core data (Kotzer et al., 2000). The darker lines and points
show the 2009-equivilent 3H activity after considering radioactive decay, whereas the lighter (grey)
points and line mark the uncorrected (i.e., “real time”) 3H activity of precipitation.
Regressions of 3H and latitude were developed using all International Atomic Energy
Stations with data for any given year (the number of stations available ranged from 12 to 89 sites,
annually). Regressions for the southern and northern hemisphere were completed separately because
of known inter-hemispheric differences in the 3H activity of precipitation, imparted because the
atmosphere is not completely mixed (Rozanski et al., 1991). The latitude and sample date for every
groundwater well location was entered into the latitude-time regressions of 3H in precipitation to
develop a range of possible 3Hpost-1950 and 3Hpre-1950 activities, with considerations for radioactive decay
made by calculating an equivalent 3H concentration for the date that each groundwater sample was
collected (i.e., meteoric tritium decay corrected up to the date that the compiled groundwater sample
was collected; 3H half-life of 12.3 years).
97
3Hsample values (i.e., measured groundwater tritium activity) and corresponding ranges for
3Hpost-1950 and 3Hpre-1950 were input into equation 2.4 to quantify the mixing proportion of “modern”
(i.e., post-1950) groundwater within each groundwater sample. All samples being comprised of
>80% “post-1950 groundwater” (median value from calculation used) were included in this study, as
these were presumed to have recharged during the contemporary climate, where precipitation data is
also available. All samples that did not meet this threshold were removed from our calculation, as
mixing with paleo-waters could not be precluded. This dataset reduction step is likely to be
conservative as many “3H-dead” (i.e., below detection tritium activities) groundwater samples may
have recharged more recently than the mid-Holocene, and, therefore, could have been compared to
modern precipitation in principle.
Finally, 90 percent of samples obtained from depths shallower than 40 meters underground
were found to meet the aforementioned “modern groundwater” criteria set for 14C and 3H data.
Therefore, a depth threshold of 40 meters below ground level was set as a threshold for modern
groundwater for compiled datasets that present groundwater δ18O or δ2H measurements but do not
present 14C and 3H data. Additional care was taken on an aquifer-by-aquifer basis where paleo-waters
are known to occur (e.g., Ferguson et al., 2007) in order to ensure that paleo-water isotopic data did
not propagate into the calculation of groundwater recharge
Now that modern groundwaters have been delineated using the above 3H, 14C and well
depth based methods, a stable isotope based calculation of groundwater recharge ratio seasonality
can proceed. To calculate the seasonal difference in groundwater recharge ratios, we compare
modern groundwaters (delineated sing 14C, 3H and well depths as per the preceding paragraphs) with
modern precipitation data by combining a water budget (equation 2.5) and an isotopic (equation 2.6)
mass balance:
Pannual = Pseason 1 + Pseason 2 Equation 2.5
98
PannualδP(annual) = Pseason 1δP(season 1) + Pseason 2δP(season 2) Equation 2.6
where Pannual, Pseason 1 and Pseason 2 are the precipitation rates for the year (i.e., annual), for season 1 (i.e.,
winter in the extra-tropics, and the wet season in the tropics), and for season 2 (summer in the extra-
tropics, and the dry season in the tropics). Similarly, δP(annual), δP(season 1) and δP(season 2) are the amount-
weighted isotopic compositions for annual, season 1 or season 2 time intervals. Combining equations
2.5 and 2.6 yields an isotope-based solution for the contribution of season 2 (i.e., summer or dry
season) rainfall to total annual precipitation:
Pseason 2
Pannual=
δP(annual)−δP(season 1)
δP(season 2)−δP(season 1) Equation 2.7
A similar set of equations can be derived for groundwater recharge rates (R) rather than precipitation
rates.
Rannual = Rseason 1 + Rseason 2 Equation 2.8
Rannualδgroundwater = Rseason 1δP(season 1) + Rseason 1δP(season 2) Equation 2.9
where Rannual, Rseason 1 and Rseason 2 are annual, season 1 and season 2 recharge rates, and δgroundwater is
the isotopic composition of recently recharged groundwater. Combining equations 2.8 and 2.9 yields
the an equation representing theproportion of season 2 recharge as a ratio of annual recharge f
(equation 2.10).
Rseason 2
Rannual=
δgroundwater−δP(season 1)
δP(season 2)−δP(season 1) Equation 2.10
Combining equations 2.7 and 2.10 yields the isotope-based equation for the recharge ratio during the
summertime (extra-tropics) or during the dry season (tropics; Rseason 2/Pseason 2; equation 2.11).
99
Rseason 2
Pseason 2=
δgroundwater−δP(season 1)
δP(annual)−δP(season 1)(
Rannual
Pannual) Equation 2.11
A similar derivation (i.e., equations 4 – 10) can be made to calculate the recharge ratio during season
1 (Rseason 1/Pseason 1; equation 2.12):
Rseason 1
Pseason 1=
δgroundwater−δP(season 2)
δP(annual)−δP(season 2)(
Rannual
Pannual) Equation 2.12
Finally, the isotope-based equation representing the seasonal difference in the groundwater recharge
ratio (R/P) between season 1 and season 2 can be made – without knowledge of annual precipitation
and recharge fluxes – by combining equations 2.11 and 2.12, yielding (Equation 2.13):
(R/P)season 1
(R/P)season 2= (
δgroundwater−δP(season 2)
δP(annual)−δP(season 2)) / (
δgroundwater−δP(season 1)
δP(annual)−δP(season 1)) Equation 2.13
This isotopic derivation of seasonal differences in groundwater recharge ratios is presented
schematically in Figure 2-4 (lower axis).
100
Figure 2-4. A schematic representation of the isotope-based approach to estimating seasonal
differences in the groundwater recharge ratio, defined as the proportion of rain and snow that
infiltrates into groundwater aquifers. The four isotopic data shown are: the flux weighted isotopic
composition of season one precipitation (δP(season 1)), the flux weighted isotopic composition of season
two precipitation (δP(season 2)), the flux weighted isotopic composition of annual precipitation (δP(annual)),
and the isotopic composition of groundwater (δgroundwater).
Uncertainties were estimated by completing the calculation using every combination of input
data and subsequently computing percentile ranges from the various calculation results on a site-by-
site basis. The calculation of seasonality in groundwater recharge ratios was only made for locations
that had at least three groundwater δ18O or δ2H values and three annual amount-weighted δ18O and
δ2H values for precipitation. 16 stations were excluded in the analysis because no precipitation data
were available for the summer or the winter season (e.g., Damascus, Syria) or because the δ18O and
δ2H values of winter and summer precipitation were not consistently higher or lower than the
opposing season (e.g., Quincy and Kennedy Space Center in Florida, U.S.A.). Locations that not
101
included in this study of seasonal differences in the groundwater recharge ratio are marked as
diamonds in Figure 2-2.
2.4 Results
Paired measurements of the isotopic composition of precipitation and groundwater at 54
globally-distributed locations are shown in Figure 2-5. Results show that, for the majority of samples,
precipitation and groundwater isotopic compositions are similar, or that groundwater δ18O or δ2H
values are lower than annual precipitation δ18O or δ2H values.
Figure 2-5. Comparison of groundwater and mean annual precipitation isotopic data at 54 globally-
distributed locations. Vertical error bars mark one standard deviation of inter-annual variability in
amount-weighted isotopic compositions of precipitation. Horizontal error bars bracket one standard
deviation of groundwater isotopic data.
102
Isotope-based calculation results of seasonal differences of groundwater recharge ratios (i.e.,
(R/P)winter/(R/P)summer) are presented in Figure 2-6. Similarly, Table 2-2 presents 25th-75th percentile
ranges of our isotope-based calculations of (i) the ratio of the summer groundwater recharge fluxes
relative to winter groundwater recharge fluxes (Rsummer/Rwinter), (ii) summer recharge efficiencies
(Rsummer/Psummer), and (iii) winter recharge efficiencies (Rwinter/Pwinter) for each study site.
Winter groundwater recharge ratios are higher than summer groundwater recharge ratios for
93% of desert (7 of a total of 9), temperate grassland (11 of a total of 13) or temperate forest
locations (16 of a total of 18; median of δ18O-based results of Monte-Carlo realizations). Winter
recharge is at least twice as effective (i.e., higher recharge/precipitation ratio) as summer recharge for
half of all temperate grasslands and temperate forests (15 of 31 locations) and for three-quarters of
deserts and xeric shrublands (7 of 9 locations). Also, one quarter of temperate or arid locations have
a winter groundwater recharge efficiency that is more than five times that of the summer.
Seasonal changes in the groundwater recharge ratios for tropical climates (n = 7) show that
all of the tropical sites tested here have higher groundwater recharge ratios during the wet season
relative to the dry season (i.e., (R/P)wet >> (R/P)dry; Figure 5). Only a few locations were available for
Mediterranean climates (n = 3) and boreal forests (n = 3). Mediterranean climates examined here
showed very little variability between summer and winter precipitation δ18O and δ2H values, resulting
in highly uncertain isotope-based calculations of groundwater recharge ratios for these coastal
locations (i.e., small change between δP(summer) and δP(winter); Figure 6). The few boreal sites (n = 3)
have a similar groundwater recharge ratio during the summer and winter seasons. Analysis of
groundwater recharge ratios in boreal forests are limited by the lack of groundwater isotopic data,
likely associated with the low boreal population density (~2.5 persons/km2) relative to the global
average (~50 persons/km2; population dataset from
www.sedac.ciesin.columbia.edu/data/collection/gpw-v3).
103
Figure 2-6. Seasonal differences in groundwater recharge ratios (recharge/precipitation: R/P)
between the (a) summer and winter seasons (extra-tropics), or between the (b) wet and dry seasons
(tropics). Colored bars mark the 25th-75th percentile ranges of calculation results and lines mark the
10th-90th percentile range of calculation results.
104
Table 2-2. Seasonal groundwater recharge ratios (isotope-based)
Sta
tio
n
δ18O
P(a
nn
ual
)
δ2H
P(a
nn
ual
)
δ18O
P(s
um
mer
) -
δ18O
P(w
inte
r)
δ2H
P(s
um
mer
) -
δ2H
P(w
inte
r)
Rsu
mm
er/
Rw
inte
r
(R/
P) s
easo
n 1
(%)
(R/
P) s
easo
n 2
(%)
Cayenne −2.2 −10 1.4 4 0 0 - 39 0
Taguac −5.3 −33 2.2 19 0 - 0.3 65 - 100 0 - 48
Seawell −1.9 −6 1.8 13 0 - 0.1 17 - 35 0 - 6
Jakarta −5.6 −35 1.1 9 0 - 0.2 48 - 100 0 - 26
New Dehli −5.8 −38 5.0 41 0 - 0.3 11 - 23 0 - 21
Dar es Salaam −2.6 −12 1.7 15 0 - 0.3 3.9 - 16 0
Addis Ababa −1.3 +3 0.9 9 0 29 - 96 0
Santa Maria −5.0 −35 2.0 12 0 - 0.1 0 - 30 0 - 100
Beit Dagan −5.1 −22 1.7 6 0 - 0.4 0 - 15 0 - 34
Pisa −5.5 −33 0.7 n/a 0 - 0.2 34 - 75 0 - 10
Trout Lake −11.1 −77 6.1 49 0 - 0.6 85 - 100 0 - 24
Yellowstone −16.2 −122 9.4 69 0.2 - 0.6 12 - 26 3.2 - 6
Smith's Ferry −15.6 −118 4.9 36 0 - 0.3 11 - 16 0 - 5
Lake Geneva −7.6 −53 4.5 32 0.4 - 1.1 33 - 39 21 - 24
East MA −7.5 −51 2.2 25 0 - 1.4 17 - 56 0 - 30
Niwot Saddle −17.6 −130 8.5 65 0.3 - 0.7 3.9 - 5 2.2 - 4.4
Wye −7.3 −44 2.8 17 0 - 1.7 1.2 - 16 16 - 26
Purdue Agr. −5.7 −33 3.4 24 0 - 1.5 22 - 40 0 - 18
Clinton Stn. −5.0 −29 1.8 16 0 - 0.8 0 - 27 3.6 - 18
Caddo Valley −4.9 −27 2.1 15 0 - 0.6 16 - 21 0.8 - 9
Coffeeville −5.0 −32 1.5 12 0 - 1.9 0 - 24 21 - 72
Saturna −10.9 −79 2.2 14 0.2 - 2.4 6 - 57 64 - 100
Ottawa −11.0 −75 5.5 38 0.6 - 1.7 46 - 85 40 - 71
Wallingford −7.2 −49 1.5 10 0 - 0.6 15 - 54 0 - 25
P. Douradas −7.6 −45 0.9 6 0 - 0.5 12 - 42 0 - 39
Krakow −9.1 −65 3.8 29 0.2 – 1.0 22 - 38 4.4 - 13
Cuxhave −7.0 −49 1.4 9 0 - 0.5 22 - 51 0 - 17
Orleans −6.9 −46 1.8 13 0.1 - 1.4 0 - 16 0 - 6
Melbourne −5.0 −28 1.4 15 0 - 0.1 8 - 25 0 - 1.4
Newcastle −11.2 −89 4.3 47 0 - 2.0 1.3 - 8 0 - 1.2
Little Bighorn −15.1 −115 5.9 44 0 - 0.5 1.9 - 3.1 0 - 0.4
Lamberton −7.6 −51 6.7 37 0.6 - 2.0 31 - 47 7 - 11
N. Platte Agr. −9.0 −61 5.6 53 1.3 - 4.6 13 - 36 5 - 8
105
Sta
tio
n
δ18O
P(a
nn
ual
)
δ2H
P(a
nn
ual
)
δ18O
P(s
um
mer
) -
δ18O
P(w
inte
r)
δ2H
P(s
um
mer
) -
δ2H
P(w
inte
r)
Rsu
mm
er/
Rw
inte
r
(R/
P) s
easo
n 1
(%)
(R/
P) s
easo
n 2
(%)
Mon Mouth −6.7 −41 3.5 24 0.5 - 2.1 14 - 25 8 - 15
Great Plains −5.8 −35 2.4 17 0.7 - 4.7 0 - 22 22 - 50
Edmonton −17.6 −131 10.6 84 1.1 - 2.7 68 - 100 43 - 56
Saskatoon −14.3 −111 9.0 76 0.1 - 0.5 up to 100 10 - 27
Wynyard −16.0 −124 7.8 62 0.6 - 1.4 78 - 100 33 - 52
Esther −15.7 −124 10.0 72 0.3 - 1.0 up to 100 17 - 37
Calgary −17.8 −138 8.6 49 0.2 - 3.7 48 - 100 36 - 63
Gimli −14.0 −102 11.3 72 1.8 - 2.8 4.1 - 6 4.4 - 5
Craters of Moon −16.9 −128 3.3 52 0.1 - 0.5 0 - 0.1 0 - 0.1
Pinedale −14.8 −110 9.9 38 0.1 - 0.7 6 - 14 2.1 - 6
Sand Spring −12.8 −96 6.5 61 0 - 0.1 21 - 31 0 - 1.6
Smith Valley −12.4 −94 3.8 24 0 - 0.4 33 - 100 0 - 55
Tuscon −7.1 −53 2.5 8 0 13 - 25 0
Chihuahua −4.1 −26 6.1 42 0 - 1.9 0 - 18 0 - 2.4
Alice Springs −5.2 −22 1.6 20 0 - 0.6 0 - 15 0 - 0
Zhangye −6.7 −46 9.4 61 0.6 - 2.9 1.2 - 2.2 0.3 - 0.5
Yinchuan −7.4 −48 8.9 50 0.8 - 1.9 4.6 - 14 0 - 0.7
Yellowknife −20.7 −158 2.5 21 0 - 1.9 0 - 64 56 - 100
Whitehorse −21.3 −164 4.9 31 0.2 - 2.9 48 - 100 25 - 66
Chapais −13.5 −97 5.2 45 1.2 - 1.5 54 - 100 44 - 64
** Seawell (Barbados) recharge data from Jones and Banner, 2000
* Taguac (Guam) recharge data from Jocson et al., 2002
106
2.5 Discussion
Recharge ratios were calculated for 54 aquifer-precipitation pairings. Further, an additional
16 sites were available for a comparison of precipitation and groundwater δ18O and δ2H values, but
were not suited for quantifying groundwater recharge ratios due to the lack of summer or winter
precipitation end-members. A comparison of δ18O and δ2H values for the amount-weighted isotopic
composition of precipitation and groundwater is shown in Figure 2-7 for these 70 locations (average
±1 s.d. uncertainty). Groundwater matched the amount-weighted precipitation from nearby
monitoring stations within 1 ‰ for δ18O and within 9 ‰ for δ2H for half of the locations in this
study or 2 ‰ for δ18O and within 16 ‰ for δ2H for four-fifths of study locations.
Figure 2-7. Differences in the stable oxygen and hydrogen isotopic compositions of amount-
weighted precipitation (δP(annual)) and local groundwaters (δGroundwater). Error bars mark one standard
deviation from the mean.
107
The difference between precipitation and groundwater isotopic compositions ranges from
+1.8 ‰ to −5.6 ‰ for δ18O and from +9 ‰ to −45 ‰ for δ2H. The closest match between the
isotopic composition of groundwater and precipitation were found in the tropics. All locations
having an average groundwater δ18O value of greater than −5 ‰ have an amount-weighted
precipitation value that matches groundwater is within 1.5 ‰. In contrast, regions with lower δ18O
groundwater values have a broader range of differences between groundwater and precipitation. At
locations where groundwater δ18O values are less than −10 ‰ (n=24) the range in δ18OGroundwater −
δ18OP(annual) was between −5.6 ‰ and +1.0 ‰.
More tightly constrained groundwater-precipitation isotopic data in regions with higher
δ18O values is reconciled by an examination of seasonal fluctuations in δ18O and δ2H values.
Regions having higher δ18O and δ2H values also have more subdued seasonal fluctuations in the
isotopic composition of precipitation. Conversely, regions with lower δ18Oprecipitation and δ2Hprecipitation
values tend to exhibit greater seasonal changes in the isotopic composition of precipitation (Figure 2-
8).
108
Figure 2-8. The absolute value of the difference between summer and winter δ18O values for 333
globally-distributed locations. The top pane shows the seasonality of δ18O on a global map, and this
spatial presentation reveals that locations having the greatest seasonality in precipitation δ18O are
located at high latitudes or far from continents. The bottom pane presents a cross plot of the
seasonality of δ18O in precipitation, with each point representing on precipitation monitoring station.
The most subdue seasonal fluctuations in δ18O are also locations that have high overall δ18O values,
and tend to be located in the humid tropics.
The difference between summer (April to September) and winter (October to March) δ18O
values is less than 2 ‰ for the overwhelming majority (95 %) of stations that have an amount-
weighted δ18Oprecipitation value greater than −3 ‰ (i.e., 18 of 19 stations). Conversely, the difference
between summer and winter δ18O values is greater than 5‰ for the majority (87 %) of stations
having an amount-weighted precipitation δ18O value below −15 ‰ (i.e., 27 of 31 stations).
109
Geographically, stations located within the tropics have an average difference between winter and
summer δ18O values of 2.3 ‰ (s.d. of 1.6 ‰, n = 46), whereas locations in the extra-tropics have an
average difference between winter and summer δ18O values of 5.0 ‰ (s.d. of 4.0 ‰, n = 176).
Offsets have also been reported for North America, where surface water is isotopically light
compared to rainfall, due in large part to snow fall and snow melt water inputs in the western North
American watersheds compared to the central and eastern regions of North America (Dutton et al.,
2005).
Overall, it appears that groundwater values may be of use as a proxy for the long-term
annual amount-weighted isotopic composition of precipitation in some cases, but that the application
of an offset may be appropriate because the majority of groundwaters have lower δ18O and δ2H
values than amount-weighted annual precipitation. There may be the potential to develop predictive
models of the isotopic composition of groundwater that can complement existing global maps of the
isotopic composition of precipitation (Bowen and Wilkinson, 2002; Bowen and Revenaugh, 2003).
Now this discussion will turn attention from raw isotopic datasets to groundwater recharge
ratios. As a reminder, this chapter shows that arid and temperate climates have higher winter
recharge ratios than summer recharge ratios. This suggests that a given unit change in winter
precipitation will be more important than the same unit change in summer precipitation, from a
groundwater recharge perspective.
The high groundwater recharge ratios found during the winter in arid and temperate climates
may be due to seasonal changes in evapotranspiration potential. Many arid and temperate climates
examined here have pronounced seasonal differences in surface temperatures and plant productivity.
Lower recharge ratios during summertime are explained in part by the higher potential for
evapotranspiration. Higher winter recharge ratios are explained in part by lower potentials for
evapotranspiration because of reduced atmospheric temperatures and dormant vegetation (Welker et
110
al., 1991, Blumenthal et al., 2008, Chimner and Welker, 2005; Anderson-Smith et al., 2014). A global
map of the seasonality in chlorophyll abundance, calculated using long-term monthly mean values of
the normalized difference vegetation index (NDVI), highlights the pronounced seasonality of plant
growth (Figure 2-9).
Figure 2-9. The ratio of summer (six-month) and winter (six-month) normalized difference
vegetation index spanning Earth’s continents (stippled areas highlight areas having <10% difference
between summer and winter normalized difference vegetation indices; southern hemisphere locations
have had summer/winter months reversed relative to the northern hemisphere).
One quarter of continental areas – mostly located in the tropics – have less than a 10%
difference between summer and winter plant productivity (stippled regions in Figure 2-9), suggesting
no a dominant growing season in these regions. The greatest intra-annual changes in plant activity is
found in cold regions (defined as having at least one month with a mean temperature <0°C, Bates
and Bilello, 1966), which cover one half of the continents (New et al., 2002). Cold regions have
normalized difference vegetation indices that are 14 times higher during the summer relative to
during the winter (global average), whereas non-cold regions have an average normalized difference
111
vegetation indices that have more subdued intra-annual variations (non-cold-region
NDVIsummer/NDVIwinter have a global average value of 1; Figure 2-9).
Some cold regions have seasonally frozen ground during the winter that inhibits winter
groundwater recharge (Hayashi et al., 2003; Cable et al., 2013). This seasonal blocking of recharge
may have an effect, but overall it does not appear to override the seasonality of groundwater recharge
ratios in temperate regions. The effect of a temporally variable “frozen ground aquitard” may be
offset by elevated groundwater recharge during the rapid melt of seasonal snowpack. Groundwater
recharge in the United States is more than double monthly precipitation during snowmelt, implying
that snowmelt constitutes an important component of annual recharge (Dripps and Bradbury, 2010;
Dripps, 2012; Allan et al., 2014).
There are four temperate locations that show summer groundwater recharge ratios that are
higher than winter recharge ratios: Coffeeville (Mississippi, in the southern U.S.A.), Great Plains
Apiaries (Oklahoma, in the south-central U.S.A.) Saturna Island (British Columbia, off the coast of
western Canada) and Wye (Maryland, in eastern U.S.A.). It is noteworthy that all of these locations
do not have a large winter snowpack (i.e., less than 5 mm of snow-water equivalent in February, as
obtained from long-term monthly mean snow water equivalent data analyzed from passive
microwave satellite products: www.globsnow.info). Large-scale mapping can provide better
knowledge of the importance of snowmelt to annual groundwater recharge. For some locations that
have a Mediterranean-type precipitation seasonal pattern (e.g., Saturna Island), wintertime storage
may fill and inhibit recharge, generating runoff instead (Sayama et al., 2011). This could in part help
to explain the isotope-based observation of higher summer recharge ratios, although more detailed
research in these locations is supported by the global perspective presented here.
Groundwater recharge ratios in the tropics are higher during the wet season than during the
dry season in all cases examined. This finding suggests that more intense rainfall leads to higher
112
recharge as a proportion of precipitation (i.e., more intense rain leads to higher groundwater recharge
ratios). This finding is consistent with previous isotope and water-level monitoring based work in
Namibia, Uganda, Ethiopia and Tanzania (Wanke et al., 2008; Owor et al., 2009; Walraevens et al.,
2009; Taylor et al., 2013). Each of these studies found that groundwater recharge is most efficient
during high intensity rainfall events in the tropics. This finding implies that possible increases in the
frequency of high-intensity rainfall events under in intensifying water cycle (Durack et al., 2012) may
enhance groundwater availability in some tropical locations. However, a more intense water cycle
may elevate geohazard risks to local communities (Belle et al., 2013).
Uncertainty in isotope-based calculations of the groundwater recharge ratio in tropical
settings are greater than uncertainties than regions with more pronounced seasonality because of the
small differences between summer and winter precipitation isotopic compositions (average of 1.9‰).
The intra-annual variability in δ18O values of precipitation is presented in Figure 2-8. Inland and
high-latitude locations are characterized by a greater intra-annual range in δ18O and δ2H values than
coastal sites and the tropics. Subdued seasonality of δ18O in the tropics results in higher uncertainties
in the seasonality of the groundwater recharge ratio, suggesting that isotope-based approaches to
calculating seasonal differences in groundwater recharge ratios will be better constrained outputs in
hydro-climates characterized by pronounced seasonality. In spite of the high uncertainties, there exist
more than 60 tropical locations with long-term precipitation isotopic data (International Atomic
Energy Agency database: www-naweb.iaea.org/napc/ih/IHS_resources_gnip.html), highlighting an
unfilled opportunity to calculate groundwater recharge ratios should groundwater investigations be
completed at these locations.
Next, I compare the isotope based observations of groundwater recharge ratio seasonality
with outputs from a global hydrological model (pers. comm. Y. Wada; e.g., Gleeson et al., 2012). The
113
spatial comparison of the isotope-based groundwater recharge ratios with a global hydrological
model is shown in Figure 2-10 (PCR-GLOBWB; Wada et al., 2010).
Figure 2-10. The seasonality of groundwater recharge ratios from isotope-based calculations (this
study; points) and a global hydrological model (PCR-GLOBWB; Wada et al., 2010).
114
PCR-GLOB (Wada et al., 2010) is a global hydrological model that simulates water balances
at a 0.5o×0.5o spatial resolution and a daily temporal resolution. The model is setup with two soil
layers and an underlying groundwater aquifer. The model simulates exchanges such as infiltration,
capillary action between the layers, and also simulates exchanges between the top soil horizon and
the atmosphere via rainfall, snowmelt, evaporation and transpiration, canopy interception and
snowpack storages. The groundwater aquifer in the model is representative of the deeper subsurface,
such that vegetation is not considered to play a critical role in these exchanges via hydraulic lift or
plant transpiration. Groundwater storage is parameterized using geospatial lithology and topography
datasets. A detailed description of the model is presented within works by Wada et al. (2012a; b).
A cross plot comparison of median groundwater recharge ratios from the isotope- and
model-based approaches show that PCR-GLOBWB outputs match isotopic outputs (within error) in
most, but not all locations in the extra-tropics (a range of PCR-GLOB modelled recharge ratios
falling within 100 km of each study location were used to bound possible model recharge ratio
values). The 10th-90th percentile range of isotope-based recharge ratios overlaps with modeled PCR-
GLOBWB recharge ratios in 85% of extra-tropical locations analyzed in this chapter (Figure 2-11).
115
Figure 2-11. Comparison of recharge ratios calculated using isotope-based and hydrological
modelling based approaches for (a) summer (April-September) and (b) winter (October-March).
Error bars (x-axis) mark the 10th-90th percentile ranges of isotope-based calculations (squares mark
the median). PCR-GLOB error bars mark the range of model results within 100 km of each study
location. Colors for each square correspond to ecoregions as shown in previous figures in this
chapter.
116
The extra-tropical locations where PCR-GLOBWB recharge ratios do not overlap within the
10th-90th percentiles of isotope-based groundwater recharge ratios are all located in regions that have
a February snowpack of between 18 and 81 mm (now water equivalent; Trout Lake and Craters of
the Moon in the U.S.A., and Edmonton, Saskatoon, Wynyard and Esther in Canada, long-term
monthly means of snow storages from www.globsnow.info). Part of the model-isotope differences
observed may be the result of the fact that the isotope-based calculation assesses the seasonal
distribution of recharge relative to the timing of precipitation, not necessarily the timing of recharge.
For example, recharge of snow falling during the winter (October to March) but not melting until the
spring season (e.g., April to June) is – from the isotopic standpoint – winter recharge. Whereas –
from the model standpoint – this snowpack-delayed recharge is considered as summer recharge.
Other sources of discrepancy between the isotope and model groundwater recharge ratio
estimates include cropland irrigation return flow that are not incorporated into PCR-GLOBWB, nor
the isotope based model, per se (as this flow would be evident in the groundwater isotopic data, but
will not be included in precipitation fluxes). Irrigation return flows can constitute an overwhelming
component of groundwater recharge in some regions, such as the California Central Valley, for
example (Faunt, 2009). Irrigation can also aid recharge indirectly by enhancing the proportion of
rainfall that infiltrates (e.g., Chiew and McMahon, 1991). Further, PCR-GLOBWB does not include
groundwater recharge from lakes, wetlands and rivers that may account for some component of
recharge in arid and semi-arid regions.
The broader implications of this study are three fold: (i) implications for climate change, (ii)
impliactions for paleoclimatology, and (iii) implications for ecosystem ecology.
Current climate model projections of groundwater recharge are highly uncertain because of
large differences between different general circulation models, different downscaling methods and
variable coupling with hydrological models (Crosbie et al., 2011). Previous works that have assessed
117
the potential for change to groundwater recharge have found that different climate models range
both in the direction and magnitude of predicted changes to groundwater recharge. The differences
range from ±20 to ±50% changes to future groundwater recharge (Allan et al., 2010; Stoll et al.,
2011; Dams et al., 2012). Very few models have quantified changes to the intra-annual distribution of
groundwater recharge (Dams et al., 2012). Therefore, current models are likely to be overlooking
potentially important changes to individual seasons and their associated impacts upon the annual
groundwater recharge flux. The isotope based recharge ratios presented here may be used to assess
the most important seasonal hydrological processes governing groundwater recharge under a future,
warmer and more energetic water cycle.
In temperate regions, our results suggest that a higher percentage of winter precipitation is
able to recharge aquifers. This finding suggests that a unit change to winter precipitation will be more
important, from a groundwater recharge perspective, than the same unit change to summer
precipitation. The bias towards winter recharge could also be altered if the factors limiting summer
recharge occur, such as summer evapotranspiration and storm intensities. The observed bias towards
winter precipitation recharge in the extra-tropics can been attributed to the seasonal filtering of
precipitation, whereby greater proportions of winter precipitation reach the water table relative to the
total summer precipitation amount. This result is interpreted to be due to the high evapotranspiration
rates that limit the amount of summer precipitation that recharges.
In tropical settings, we found that recharge ratios are highest during the rainy season. This
finding supports the integration of rainfall intensity and intra-annual distributions of rainfall amounts
as a central component of future forecasts of groundwater recharge in a warming climate. Existing
studies of site-specific modeling in Uganda have found that by including intra-annual variability in
precipitation amounts, and variable rainfall intensities, into projections of future groundwater
recharge fluxes substantially modifies the projection of future groundwater availability, changing the
118
prediction from a 55% decrease to, instead, a 53% increase in annual groundwater recharge (Mileham
et al., 2009). Given the large number of precipitation monitoring stations (e.g., 330 locations in
Figure 2-8) and the equations and approach derived in this chapter, a new opportunity now exists to
quantify groundwater recharge ratios across the continents using isotopic data for groundwaters. The
incorporation of these data as calibration and validation toolsets in groundwater-equipped general
circulation models may help to confirm the validity of projections from these models. Similarly, the
paired investigation of precipitation and groundwater isotopic compositions at the same field site can
be used to test isotope-enabled general circulation models’ conceptualization of
groundwater/surface-water interactions (ECHAM: Hoffman et al., 1998; CCSM: Noone et al., 2002;
IsoGSM: Yoshimura et al., 2003; GISS: Schmidt et al., 2007; LMDZ4: Risi et al., 2010; iLOVECLIM:
Roche et al., 2013).
Our finding that groundwater recharge fluxes do not match precipitation fluxes one-to-one
(Figure 2-7) has three partially-overlapping implications for the interpretation of isotope-based
paleoclimate proxies such as fossil groundwaters and speleothems.
First, changes to the seasonality of precipitation fluxes may not be recorded – isotopically –
on a one-to-one basis in paleoclimate records involving subsurface waters such as paleo-
groundwaters, smectite, tree rings, speleothems and vein calcite (e.g., Winograd et al., 1992; Plummer,
1993; McCarroll and Loader, 2004; Asmerom et al., 2010; Stevenson et al., 2010, Winnick et al., 2013;
Mix and Chamberlain, in press). Groundwater recharge is generally a more efficient process during
the winter relative to the summer as shown in this study. Paleoclimate records based on
groundwaters may be more tightly linked (i.e., biased) to changes in winter (or, wet season) climate,
relative to summer (or, dry season). This realization map help to explain some of the discrepancies
that have been observed in fossil groundwaters and lake sediment records located near to one
another. For example, paleo-limnologic records of Owens Lake, California show δ18O shifts of up to
119
10 ‰ during the past 500,000 years (Smith and Bischoff, 1997; Menking et al., 1997). Conversely,
groundwater-precipitated calcite from nearby Devils Hole, Nevada shows a much smaller range of
δ18O shifts (less than 3 ‰) over the past 500,000 years (Winograd et al., 1992).
Second, the dramatic shifts in climate and biomes from the last ice age to today — such as
the shift from deserts to forested climates in parts of in Europe and Alaska (Williams, 2003) — may
have modified the recharge ratios in these settings, and thereby created changes to groundwater δ18O
values. Our limited set of boreal observations in this study are of particular interest for further word
because of the apparent similarity between precipitation and groundwater isotopic compositions in
this zone. The boreal biome shifted to lower latitudes during the last glacial maximum (Williams,
2003), and could have modified the seasonality of groundwater recharge ratios, too. This research
calls for more work in boreal sites that have long-term precipitation δ18O and δ2H data in order to
further test this observation.
Third, seasonal changes in the isotopic composition of precipitation, shown in Figure 2-8,
provide some information for the possible range of δ18O shifts in paleoclimate records that can be
attributed to changes in the seasonality of precipitation. Seasonality is commonly discussed as a
potential source isotopic change amongst other factors such as differences in paleo-ocean δ18O,
atmospheric and sea surface temperatures and air mass trajectories. For example, a complete
shutdown of precipitation from a single six month (summer or winter) interval can account for a
shift no greater than ~9 ‰ in δ18O (much less in most regions), if the seasonality of precipitation
δ18O were similar in the past to today. Some lacustrine paleoclimatic records show more than 9 ‰
variation during the Pleistocene (e.g., Owens Lake, California; Smith and Bischoff, 1997), and this
analysis may help to put quantitative bounds on the magnitudes of δ18O and δ2H shifts that may be
attributed to seasonality when interpreting paleoclimate records.
120
Finally, ecosystem ecology is linked to groundwater recharge fluxes. The groundwater
recharge ratio patterns assessed here span a variety of biomes with different plant life forms, with
unique temporal and spatial partitioning of water sources by vegetation with different rooting and
growth patterns (Ehleringer and Cooper 1992; Dodd et al., 1998; Alstad et al., 1999; Welker 2000;
Dawson et al., 2002; Kulmatiski et al., 2010; Leffler and Welker, 2013). In deserts, temperate
grasslands and temperate forests, seasonal hydrological processes support the growth of a diversity of
life forms (grasses and shrubs, trees and understory plants) that utilize soil- and ground-water
resources from different depths and are thereby linked to water movements close to Earth’s surface.
Developing an improved understanding of the seasonal changes in vegetation and coupled feedbacks
to groundwater recharge – such as interception, transpiration and hydraulic redistribution – will help
to better predict how large-scale biome shifts may impact groundwater. For example, ongoing tree
death due to mountain pine beetle infestation has recently been shown to reduce transpiration fluxes,
resulting in a one-third increase in groundwater fluxes that becomes particularly apparent in late
summer (Bearup et al., 2014). Changing seasonality in groundwater recharge fluxes due to vegetation
shifts have important implications for aquatic species that depend upon groundwater refugia for
habitat (e.g., Power et al., 1999). Plant life forms are expected to shift in a warming climate, and yet
these shifts will likely contain surprises such as recent work that has shown that some plant species
move downhill (toward warmer temperatures) as climate warms, an unexpected response likely
related to plant’s selection of optimal water requirements over temperature trends (Crimmins et al.,
2011; Harsch and Janneke, 2014). Continuing to monitor groundwater and precipitation isotopic
compositions can help to quantify vegetation water sources and to assess eco-hydrological feedbacks
as transpiration fluxes are modified by changing human land uses (Gordon et al., 2005) and plant
water use efficiencies (Keenan et al., 2013).
121
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CHAPTER 3 — THE ISOTOPIC COMPOSITION OF ICE AGE
GROUNDWATERS
3.1 Abstract
In Chapter 3 I present a global compilation of the isotopic composition of groundwater
recharge from the late-Holocene (δ18Olate-Holocene) and the last ice age (δ18Oice age). Changes to meteoric
water δ18O values from the last ice age to the late-Holocene are described herein as Δδ18Oice age
(where, Δδ18Oice age = δ18Olast ice age − δ18Olate-Holocene). Groundwater Δδ18Oice age values range from −3.6
‰ (i.e., δ18Olast ice age < δ18Olate-Holocene) to +2.0 ‰ (i.e., δ18Olast ice age > δ18Olate-Holocene). More than 90%
of study aquifers have δ18Olast ice age < δ18Olate-Holocene, in spite of higher-than-modern seawater δ18O
values during the last ice age. The few study aquifers where δ18Olast ice age > δ18Olate-Holocene are found
exclusively within 300 km of coasts and generally confined to the subtropics. Groundwater Δδ18Oice
age values are closer to zero (average groundwater Δδ18Oice age of −0.6 ‰) than Greenland and
Antarctic ice cores (average polar ice core Δδ18Oice age of −5.5 ‰). Δδ18Oice age values from four
different isotope-enabled general circulation models are able to reproduce some but not all ice-age-
to-late-Holocene δ18O shifts (pre-industrial and last glacial maximum climate simulations). Each of
the four models do not reproduce the negative Δδ18Oice age values over tropical Africa and South
America, potentially reflecting imperfect parameterization of convective precipitation. The four
isotope enabled general circulation models have a similar sign of Δδ18Oice age for about half of Earth’s
areas, generally showing multi-model agreement upon positive Δδ18Oice age over the tropical oceans,
and negative Δδ18Oice age over extra-tropical land areas. However, simulated Δδ18Oice age values do not
reproduce the observed negative Δδ18Oice age values over Africa and Brazil, potentially due to different
or incomplete model parameterization of convective rainfall during the last glacial maximum and
present day.
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3.2 Introduction
The isotopic composition of groundwater recharge from the last ice age has been used to
improve the scientific community’s understanding of water availability under climates of the past for
more than a half century (e.g., Thatcher et al., 1961; Tamers, 1967; Gat et al., 1969; Salati et al., 1974).
The use of paleo-groundwaters to reconstruct past climates has both advantages and disadvantages in
comparison to other types of paleoclimate records. For example, shifts in climate on time scales of
100 to 103 years that can be distinguished in lacustrine (e.g., von Grafenstein et al., 1999) and
speleothem (e.g., Denniston et al., 2007) records often cannot be identified in groundwater records
because of hydrodynamic dispersion during multi-millennia groundwater residence times. However,
groundwaters have an advantage over other paleoclimate records because paleo-groundwaters are a
direct sample of paleo-precipitation and because paleo-groundwaters are found in many regions. As
such, the chemistry of groundwaters provide insights into hydrologic and climate changes since the
last ice age such as changes to air mass trajectories (Rozanski et al., 1985; Legrande and Schmidt,
2009) and land surface temperatures (Stute et al., 1989; 1995a; 1995b; Clark et al., 1998; Aeschbach-
Hertig et al., 2002). Groundwater paleoclimate archives are not subject to complicating effects that
must be accounted for in other paleoclimate archives before interpreting the isotopic composition of
paleowaters. For example, extracting quantitative paleoclimate information from the isotopic
compositions of lake sediments, tree rings or speleothems is challenging due to several factors,
including: (1) uncertainty in paleo-temperatures, which directly impact water-proxy isotopic
fractionation factors (e.g., lake sediment calcite, diatom silica and sediment cellulose; tree ring
cellulose; speleothem calcite), (2) uncertainty in the magnitude of evaporation-induced 18O-
enrichment of surface waters prior to preservation in proxy records (e.g., lake sediment calcite,
diatom silica and sediment cellulose; Leng and Marshall, 2004), (3) uncertainty and variability in the
timing and seasonality of mineral precipitation and bioform growth, which impacts both the isotopic
composition of the water being preserved in the proxy record, and the water-proxy fractionation
144
factor due to seasonality in temperatures (e.g., lake sediment calcite, diatom silica and sediment
cellulose), (4) uncertainty in the impact of diagenesis (e.g., travertine; O’Brien et al., 2006) and (5)
uncertainty in paleo-atmospheric humidity (e.g., tree ring cellulose; Roden and Ehleringer, 1999),
each of which impacts the isotopic offset between paleo-waters and the preserved proxy record. In
contrast, the relationship between paleo-precipitation groundwater isotope compositions is more
direct and reliably identifiable. A recent study of 70 paired precipitation-groundwater isotopic
datasets found systematic relationships between the isotopic composition of annual precipitation and
groundwater. Differences between modern annual precipitation and groundwater isotopic
compositions are related to the ratio of recharge as a proportion of precipitation (i.e.,
recharge/precipitation: Jasechko et al., 2014).
Ice-age-to-late-Holocene changes to isotopic compositions measured in proxy records have
been ascribed in earlier works to several partially overlapping influences. Perhaps the two best-
constrained and global-in-scale changes from the last ice age to the late-Holocene are (i) the change
to atmospheric and surface ocean temperatures (MARGO Members, 2009; Annan and Hargreaves,
2013), and (ii) the change to the isotopic composition of the ocean (Emiliani, 1955; Dansgaard and
Tauber, 1969; Schrag et al., 1996). Atmospheric temperatures have increased by a global average of
about 4°C since the last glacial maximum, as constrained by compilations of proxy-based
reconstructions (Shakun and Carlson, 2010, Annan and Hargreaves, 2013). Climate warming over the
past 20,000 years is thought to have been greatest in the extra-tropics (average increase of 6.3°C for
latitudes of greater than 25°; Annan and Hargreaves, 2013) and more subdued in the tropics (average
increase of 1.7°C for latitudes of less than 25°; Annan and Hargreaves, 2013; Figure 3-1), although
some terrestrial noble gas based temperature reconstructions suggest much greater tropical cooling
(e.g., eastern Brazil 5.4°C cooler than today during the last glacial maximum; Stute et al., 1995b).
145
Figure 3-1. The change in surface air temperatures from the last glacial maximum to the preindustrial
era (gridded data from Annan and Hargreaves, 2013). (a) Percentile ranges of temperature changes
since the last glacial maximum for 10 degree latitudinal bands. Blue shading mark 25th-75th percentile
ranges and the thin horizontal lines mark 10th-90th percentile ranges. The grey band shows the
globally-averaged estimate of temperature change since the last glacial maximum of −4.0±0.8 °C
(Annan and Hargreaves, 2013). (b) Gridded surface air temperature anomaly from the last glacial
maximum to the preindustrial era (Annan and Hargreaves, 2013).
The δ18O value of the last glacial period ocean was 1.0±0.1 ‰ higher than the modern
ocean, as constrained by paleo-ocean water samples collected from pore waters trapped within sea
floor sediments (Schrag et al., 2002; where δ18O = (18O/16Osample) / (18O/16Ostandard – 1)×1000). In
addition to differences in temperatures and ocean water isotope compositions, a variety of additional
explanations for observed changes to δ18O values found in paleoclimate records have been proposed,
including variations in hurricane and storm intensity (e.g., Plummer et al., 1993), changes to large-
scale atmospheric circulation patterns (e.g., Rozanski et al., 1985; Weyhenmeyer et al., 2000;
McDermott et al., 2001; Asmerom et al., 2010), shifts in monsoon strength (e.g., Denniston et al.,
2000; 2013; Lachniet et al., 2004; Liu et al., 2007; Pausata et al., 2011), fluctuations in the seasonality
of precipitation (e.g., Cruz et al., 2005), and modifications to El Niño-Southern Oscillation patterns
(e.g., Vuille and Werner, 2005).
146
In this study I present a global compilation of proxy isotope records of groundwaters (n =
65), speleothems (n = 15) and ice cores (n = 11) spanning both the last ice age and the late-
Holocene. Global compilations already exist for speleothems (Shah et al., 2013) and polar ice cores
(Pedro et al., 2011; Stenni et al., 2011; Clark et al., 2012); this compilation is the first global
compilation of isotopic data for groundwaters from the last ice age, building upon existing reviews of
paleowaters across Europe (Edmunds and Milne, 2001; Jiráková et al., 2011) and Africa (Edmunds,
2009).
The objective of this study is to develop and analyze a global database of ice age
groundwater chemistry and constrain the processes and mechanisms controlling meteoric water
isotopic changes since the last ice age. This new compilation provides a global scale perspective that
can be used to quantitatively interpret the magnitudes of δ18O and δ2H anomalies observed within
various Quaternary climate records and to validate outputs from isotope-enabled general circulation
models.
147
Table 3-1. Modern and ice age physical and isotopic data for the oceans and the cryosphere
Present day Ocean Ice:
Antarctica Ice:
Greenland Ice: Laurentide and Fennoscandinavian
Volume 13.7 × 109 km3 d 28 × 106 km3 d 3.1 × 106 km3 0 km3
Depth 3800 m - - -
δ18O value 0 ‰ e −20 to −60 ‰ f −30 to −45 ‰
-
Last glacial maximum
Ocean Ice:
Antarctica Ice:
Greenland Ice: Laurentide and Fennoscandinavian
Volume a 13.2 × 109 km3 d 38 × 106 km3 d 4.4 × 106 km3 g 20 to 60 × 106 km3
Depth b 3665 to 3680 m - - -
δ18O value c +1.0±0.1 ‰ e −20 to −60 ‰ f −30 to −45 ‰
h −22 to −25 ‰
Sea level change *
b change of −120 m to −135 m
d −19.2 m d −3.1 m g −40 to −115 m
a Lambeck et al., 2000
b Clark and Mix, 2002
c Schrag et al., 2002
d Huybrechts., 2002 (maximum values shown)
e Range of Antarctic ice cores: Byrd Glacier, (Blunier and Brook, 2001) Dome Fuji, (Kawamura et al., 2007) Dronning Maud, (EPICA Community, 2006) Law Dome, (Pedro et al., 2011) Siple Dome (Pedro et al., 2011) and TALD Ice (Buiron et al., 2011) f Range from NGRIP1 core
g Range of model predictions from Beghin et al., 2013
h Subglacial recharge from the Laurentide and Fennoscandinavian ice sheets (Karro et al., 2004; Grasby and Chen, 2005; Ferguson et al., 2007; Stotler et al., 2010) * Presented as relative to the modern ocean level
148
3.3 Dataset and Methods
I compiled 18O/16O ratios, 2H/1H ratios, 13C/12C ratios, 3H activities, 14C activities for 1640
groundwater samples collected from 65 aquifers as reported in 68 publications. Each compiled
aquifer dataset has between 4 and 182 groundwater samples (average of 27) that had previously been
analyzed for both stable oxygen and hydrogen isotopic compositions and for radioactive carbon
activities (14C). 14C data were required to ensure that compiled samples identifiably recharged during
the last ice age. A mean 14C-based groundwater age (t, the time elapsed since recharge) was calculated
for each sample by accounting for the radioactive decay of 14C and for the dissolution of 14C-dead
aquifer materials (Clark and Fritz, 1997):
𝑡 = −8267 × 𝑙𝑛 (𝐴
𝑞×𝐴𝑜) Equation 3.1
where t is the time elapsed since the groundwater sample recharged (i.e., groundwater age), A is the
measured 14C activity in a groundwater sample, Ao is the initial 14C activity (100 pmC) and q is a
correction factor applied to account for the dissolution of aquifer material with zero 14C (i.e., 14C-
dead). In cases where δ13C data was available q was calculated as:
𝑞 =𝛿13𝐶𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 − 𝛿13𝐶𝑎𝑞𝑢𝑖𝑓𝑒𝑟
𝛿13𝐶𝑟𝑒𝑐ℎ𝑎𝑟𝑔𝑒 − 𝛿13𝐶𝑎𝑞𝑢𝑖𝑓𝑒𝑟 Equation 3.2
where δ13Cmeasured, δ13Caquifer and δ13Crecharge represent the carbon isotope composition of a groundwater
sample, the aquifer and recharging groundwater. δ13Caquifer was set to 1.1±1.6 ‰ PDB as determined
by the 25th to 75th percentile range of δ13C values for 16359 rock and sediment samples compiled and
presented by Veizer et al. (1999). δ13Crecharge was set to −12.8±3.1 ‰ PDB as determined by the 25th to
75th percentiles of compiled δ13C values for 261 groundwater samples having a 14C activity of greater
than 90 p.m.C. (i.e., recently recharged water bearing near-atmospheric radioactive activities;
Burchuladze et al., 1989). q was set to 0.86±0.14 in cases where δ13Cmeasured data were unavailable, as
149
determined by the most common δ13C-based q values (q = 0.86±0.14 represents the average and one
standard deviation of δ13C-based q values).
Equivalent calendar year ages were estimated from 14C-ages by applying a polynomial fit of
compiled 14C-to-calender age corrections (Fairbanks et al., 2005). Samples were then divided into two
age categories: (i) the late-Holocene (14C-based age of less than 5,000 calendar years before present,
or a 3H activity of greater than 5 T.U.), or (ii) the last ice age (14C-based age of 19,500 to ~50,000
calendar years before present (Clark et al., 2009) and samples with 14C activities below analytical
detection). An upper ice age limit of ~50,000 years before present was set because of limitations with
14C age calculations, even though the most recent ice age extends to ~110,000 years before present
(Lisiecki and Raymo, 2005).
Groundwater δ18O and δ2H values for the late-Holocene and the last ice age were analyzed
on an aquifer-by-aquifer basis. Only aquifers with a minimum of two samples dated to both the late-
Holocene and the last glacial time periods were included in this analysis. Comparisons of isotopic
data for the last ice age and the late-Holocene were made by subtracting median δ18O and δ2H values
from each age group, with errors calculated by maximizing the 25th to 75th percentile distributions for
the two data groups (i.e., late-Holocene and last glacial period age groups). Samples were omitted
from our analysis if they exhibited an evaporative signature (δ2H – 8×δ18O of less than 0), contained
a mixture of modern and ice age groundwater (3H activity of greater than 1 tritium unit and a 14C-age
of more than 19,500 calendar years before present), were suspected to have mixed with intruding
seawater (e.g., Bouchaou et al., 2008; Morrissey et al., 2010) or were presumed to have been
recharged by subglacial meltwaters from the Fennoscandinavian (e.g., Karro et al., 2004) or the
Laurentide (e.g., Grasby and Chen, 2005; Ferguson et al., 2007; Stotler et al., 2010) ice sheets (review
by McIntosh et al., 2012).
150
A correction to speleothem δ18O values was applied because of the different H2O-CaCO3
isotopic fractionation factor for the last ice age and the late-Holocene imparted by the different
atmospheric temperatures during each time period (Shakun and Carlson, 2010). Temperature-based
H2O-CaCO3 fractionation factors were obtained from O’Neil et al. (1969) with temperatures
calculated under the assumption that atmospheric temperatures are indicative of temperatures in the
shallow subsurface. Temperatures for the late-Holocene were assumed to be equivalent to modern
mean annual near surface temperatures (New et al., 2002), potentially introducing <1°C of error
because of temperature change throughout the last 5,000 years (Marcott et al., 2013). Adding 1°C of
added uncertainty into late-Holocene temperature equates to an added ±0.4 ‰ (δ18O) of uncertainty
in the temperature-corrected difference between ice age and late-Holocene δ18O values (O’Neil et al.,
1969). Last glacial period temperatures were calculated by applying the temperature offset of the last
glacial maximum (Figure 3-1; Annan and Hargreaves, 2013) to gridded values of modern mean
annual air temperatures (New et al., 2002).
With the help of F. Pausata, M. Werner, C. Risi, K. Yoshimura I assembled modelled
precipitation Δδ18Oice age values from four isotope-enabled general circulation models: (i) CCSM3
(e.g., Pausata et al., 2011), (ii) ECHAM (e.g., Hoffman et al., 1998; Werner et al., 2011), (iii) IsoGSM
(e.g., Yoshimura et al., 2003) and (iv) LMDZ4 (e.g., Risi et al., 2010a). The models span a range of
spatio-temporal resolutions and isotopic/atmospheric parameterizations that are explained in detail
in the above references. Simulations run for the last glacial maximum and pre-industrial time periods
were assembled to analyze global grids of the amount-weighted isotopic composition of precipitation
for each time period. General circulation model outputs were compared to ice age groundwater data
by extracting model estimates of the annual isotopic composition of precipitation at the locations of
each of the 65 aquifers analyzed in this study. We acknowledge that the general circulation models
explicitly analyze the last glacial maximum and the pre-industrial climate scenarios, whereas the
151
groundwater aquifers integrate hydroclimatology over longer (103 year) time scales that will damp
abrupt δ18O changes because of storage and mixing.
3.4 Results
Groundwater, speleothem and ice core data sources, locations and isotopic changes since the
last glacial maximum are presented in Figure 3-2 and in Tables 3-2 and Table 3-4. Fossil groundwater
δ18O records are unevenly distributed amongst Europe (n = 13), Africa (n = 19), Asia (n = 13),
Oceania and the Malay Archipelago (n = 4), North America (n = 13) and South America (n = 3;
Table 3-2), with 30% of records located in the tropics and 70% of records located in the extra-tropics
(tropics defined as spanning 0° to 25° latitude). The magnitude of change in meteoric δ18O from the
last ice age is described herein as Δδ18Oice age (where Δδ18Oice age = δ18Olast ice age − δ18Olate-Holocene).
152
Figure 3-2. Meteoric water δ18O changes from the last ice age (19,500 to ~50,000 years ago) to the
late-Holocene (within past ~5,000 years): Δδ18Oice age = δ18Olast ice age − δ18Olate-Holocene.
153
Table 3-2. Groundwater datasets compiled in this study
Country Aquifer Citation(s)
Algeria Great Oriental Erg: CI Edmunds et al., 2003
Botswana Kalahari: Ntane Kulongoski et al., 2004
Botswana Lokalane-Nakojane Rahube, 2003
Burkina Faso Taoudeni basin Huneau et al., 2011
Chad Chad aquifer Edmunds, 2009
Egypt Nubian aquifer Patterson et al., 2005
Mali Mali aquifer Edmunds, 2009
Morocco N. Morocco aquifer Winckel et al., 2002
Morocco Tadla basin Bouchaou et al., 2009
Morocco Nappe des sables Castany et al., 1974
Namibia Omatako basin Külls, 2000
Niger Djardo-Bilma Dodo and Zuppi, 1997; 1999
Niger Irhazer: CI Andrews et al., 1994
Niger Illumeden: CT Beyerle et al. 2003
Nigeria Chad basin Maduabuchi et al., 2006
Senegal Senegalese CT Castany et al., 1974
South Africa Uitenhage aquifer Heaton et al., 1986
Tunisia Kairouan Plain Derwich et al., 2012
Zimbabwe Zimbabwe aquifer Larsen et al., 2002
Australia Canning basin Harrington et al., 2011
Australia Ngalia and Amadeus Leaney and Allison, 1986
Australia Murray aquifer Cresswell et al., 1999
Bangladesh Bengal basin Aggarwal et al., 2000
China Songnen plain Chen et al. 2011
China Hexi Corridor: east Gates et al., 2008
China North China Plain Kreuzer et al. 2009
China Yuncheng basin Currell et al., 2010
India Cuddalore sandstone Sukhija et al., 1998
India Tiruvadanai aquifer Kumar et al., 2009
Indonesia Jakarta basin Geyh and Sofner, 1989
Israel Israel coastal aquifer Yechieli et al., 2008
Israel Dead Sea rift valley Gat and Galai, 1982
Kuwait Kuwait aquifer Robinson and Gunatilaka, 1991; Al-Ruwaih et al., 2004
Oman Batinah coastal plain Weyhenmeyer et al., 2002
Oman Najd aquifer Al-Mashaikhi et al., 2012
Syria Aleppo basin Stadler et al., 2012
Belgium Ledo-Paniselian Blaser et al., 2010
Czech Rep. Sokolov aquifer Noseck et al., 2009
France Bathonian coast Barbecot et al., 2000
France Lorraine sandstone Celle-Jeanton et al., 2009
France Aquitaine basin Jiráková et al., 2009
Hungary Great Hungarian Plain Stute and Deak, 1989
154
Country Aquifer Citation(s)
Hungary Pannonian basin Varsanyi et al., 2011
Poland Mazovian basin Zuber et al., 2000
Poland S. Poland carbonates Samborska et al., 2012
Poland Malm limestone Zuber et al. 2004
Portugal Sado basin Fernandes and Carreira, 2008
United Kingdom Lincolnshire limestone Darling et al., 1997
United Kingdom Chalk aquifer Darling and Bath, 1988; Dennis et al., 1997; Elliot et al., 1999; Gooddy et al., 2006
U.S.A. Columbia Flood Bslts. Douglas et al., 2007
U.S.A. Black Hills: Pahasapa Back et al., 1983
U.S.A. Idaho Batholith Schlegel et al., 2009
U.S.A. Cambrian-Ordovician Siegel, 1991
U.S.A. High Plains: North Gosselin et al., 2001
U.S.A. Mahomet aquifer Hackley et al., 2010
U.S.A. Aquia aquifer Aeschbach-Hertig et al. 2002
U.S.A. High Plains: Central Clark et al. 1998
U.S.A. San Juan Basin Stute et al., 1995a
U.S.A. Middle Rio Grande Plummer et al., 2011
U.S.A. Los Angeles Basin Swarzenski et al., 2013
U.S.A. Floridan aquifer Clark et al., 1997
U.S.A. Floridan surficial aqfr. Morrissey et al., 2010
Brazil Portigar basin: Acu Salati et al., 1974
Brazil Botacatu: central Gouvea da Silva, 1983
Brazil Botucatu: south Roboucas and Santiago, 1989
155
Table 3-2 (continued). Observed Δδ18Oice age values in groundwaters
Country Aquifer Lon. Lat. Δδ18Oice age
(‰ V−SMOW)
Africa
Algeria Great Oriental Erg: CI 5.9 32.4 −0.5 (−1.6 to −0.3)
Botswana Kalahari: Ntane 25.2 −24.0 −0.5 (−0.7 to −0.3)
Botswana Lokalane-Nakojane 22.0 −22.3 −1.1 (−1.2 to −0.9)
Burkina Faso Taoudeni basin −4.7 12.8 −0.5 (−1.0 to −0.3)
Chad Chad aquifer 18.3 11.2 −0.9 (−2.4 to −0.5)
Egypt Nubian aquifer 28.9 25.7 −1.6 (−3.6 to −0.3)
Mali Mali aquifer −7.2 15.2 −0.5 (−1.3 to +0.1)
Morocco N. Morocco aquifer −4.9 34.0 −0.6 (−2.2 to 1.4)
Morocco Tadla basin −6.7 32.6 −0.9 (−1.5 to −0.1)
Morocco Nappe des sables −14.5 15.4 +0.2 (−0.5 to +0.8)
Namibia Omatako basin 17.9 −20.1 −0.9 (−1.3 to −0.1)
Niger Djardo-Bilma 12.9 18.9 +2.0 (−0.4 to +2.5)
Niger Irhazer: CI 7.5 17.3 −0.9 (−2.3 to +0.2)
Niger Illumeden: CT 2.8 13.6 −3.0 (−3.7 to −2.1)
Nigeria Chad basin 13.0 12.0 −0.3 (−2.2 to +0.5)
Senegal Senegalese CT −16.4 15.2 +0.3 (−0.6 to +1.0)
South Africa Uitenhage aquifer 25.5 −33.7 −0.5 (−1.0 to −0.4)
Tunisia Kairouan Plain 10.0 35.5 +0.2 (−0.9 to +0.4)
Zimbabwe Zimbabwe aquifer 28.1 −19.5 −0.9 (−1.3 to −0.5)
Asia and western Pacific
Australia Canning basin 125.1 −17.5 −1.0 (−2.3 to +0.9)
Australia Ngalia and Amadeus 131.9 −23.4 −0.3 (−0.6 to +1.1)
Australia Murray aquifer 140.2 −34.2 −0.3 (−1.1 to +0.1)
Bangladesh Bengal basin 90.0 23.6 +1.6 (+0.9 to +2.3)
China Songnen plain 124.5 45.9 −0.2 (−0.8 to 0.3)
China Hexi Corridor: east 102.1 38.7 −1.4 (−2.4 to −0.3)
China North China Plain 114.9 38.0 −2.3 (−2.6 to −1.7)
China Yuncheng basin 110.6 35.0 −1.1 (−2.1 to −0.1)
India Cuddalore sandstone 79.5 11.4 +0.8 (0.1 to +1.5)
India Tiruvadanai aquifer 78.7 10.0 −0.9 (−1.5 to −0.5)
Indonesia Jakarta basin 106.8 −6.3 +0.1 (−0.1 to +0.5)
Israel Israel coastal aquifer 34.8 32.0 +0.2 (−0.1 to +0.5)
Israel Dead Sea rift valley 35.2 30.7 −1.4 (−2.0 to −0.8)
Kuwait Kuwait aquifer 47.7 29.8 −1.6 (−2.1 to −1.3)
Oman Batinah coastal plain 57.7 23.6 +1.1 (−0.2 to +2.0)
Oman Najd aquifer 53.9 18.1 −0.6 (−3.4 to +2.3)
Syria Aleppo basin 37.3 35.9 −1.4 (−2.5 to −0.1)
Europe
Belgium Ledo-Paniselian 3.5 51.2 −0.5 (−1.0 to +0.1)
Czech Rep. Sokolov aquifer 12.7 50.2 −0.8 (−0.8 to −0.3)
France Bathonian coast −0.2 49.2 −0.4 (−0.6 to −0.3)
France Lorraine sandstone 6.6 48.9 −1.0 (−1.5 to −0.8)
156
Country Aquifer Lon. Lat. Δδ18Oice age
(‰ V−SMOW)
France Aquitaine basin −0.4 45.9 −1.5 (−2.5 to −0.1)
Hungary Great Hungarian Plain 20.8 47.6 −1.8 (−2.3 to −1.3)
Hungary Pannonian basin 20.1 46.3 −3.6 (−4.1 to −2.7)
Poland Mazovian basin 21.0 52.2 0.0 (−0.4 to +0.2)
Poland S. Poland carbonates 19.2 50.6 −2.0 (−2.5 to −1.1)
Poland Malm limestone 19.8 50.0 −1.0 (−1.9 to +0.4)
Portugal Sado basin −8.5 38.3 +0.1 (−0.3 to +0.2)
United Kingdom Lincolnshire limestone −0.4 52.7 −0.4 (−0.4 to −0.2)
United Kingdom Chalk aquifer −1.4 51.5 −0.4 (−0.4 to −0.3)
The Americas
U.S.A. Columbia Flood Basalts −119.0 46.6 −2.8 (−3.6 to −1.2)
U.S.A. Black Hills: Pahasapa −103.5 44.3 −0.4 (−0.9 to +1.6)
U.S.A. Idaho Batholith −116.1 43.7 −0.7 (−1.1 to +0.1)
U.S.A. Cambrian-Ordovician −93.2 42.9 0.0 (−0.6 to +0.5)
U.S.A. High Plains: North −101.3 40.9 +0.3 (−1.3 to +2.2)
U.S.A. Mahomet aquifer −88.8 39.9 −0.2 (−0.5 to +0.1)
U.S.A. Aquia aquifer −76.6 38.7 0.0 (−0.4 to +0.3)
U.S.A. High Plains: Central −101.0 37.5 −2.6 (−4.3 to −1.4)
U.S.A. San Juan Basin −107.8 36.5 −3.1 (−3.4 to −2.5)
U.S.A. Middle Rio Grande −106.4 35.1 −0.6 (−2.8 to +0.8)
U.S.A. Los Angeles Basin −118.2 33.8 −1.4 (−2.0 to −0.8)
U.S.A. Floridan aquifer −82.1 32.0 +1.0 (+0.6 to +1.3)
U.S.A. Floridan surficial aqfr. −81.0 26.7 +1.8 (+0.6 to +2.1)
Brazil Portigar basin: Acu −38.0 −5.6 −2.2 (−3.3 to −1.9)
Brazil Botacatu: central −48.7 −22.3 0.0 (−1.4 to +1.8)
Brazil Botucatu: south −52.9 −28.1 −1.5 (−1.9 to −1.0)
157
Table 3-3. Observed ranges of groundwater δ18Oice age and δ18Olate-Holocene (Shown as δ18OHolo.) values
Aquifer δ18OHolo. HighHolo. LowHolo. δ18Oice age Highice age Lowice age
Great Oriental Erg −7.9 −7.0 −8.0 −8.4 −8.3 −8.6
Kalahari: Ntane −5.6 −5.2 −5.8 −6.1 −5.9 −6.1
Lokalane-Nakojane −6.1 −6.1 −6.3 −7.2 −7.2 −7.3
Taoudeni basin −5.2 −5.0 −5.4 −5.8 −5.7 −5.9
Chad aquifer −4.3 −3.5 −4.6 −5.3 −5.1 −5.9
Nubian aquifer −8.9 −7.0 −9.9 −10.5 −10.2 −10.6
Mali aquifer −6.0 −5.4 −6.5 −6.5 −6.4 −6.7
N. Morocco aquifer −6.4 −6.0 −6.6 −7.0 −5.2 −8.2
Tadla basin −5.6 −5.1 −6.4 −6.6 −6.5 −6.6
Nappe des sables −6.1 −5.6 −6.6 −5.9 −5.8 −6.1
Omatako basin −8.4 −8.1 −9.1 −9.3 −9.2 −9.3
Djardo-Bilma −10.1 −7.9 −10.4 −8.1 −7.9 −8.2
Irhazer: CI −6.4 −5.5 −7.0 −7.3 −6.8 −7.7
Illumeden: CT −4.4 −3.8 −5.2 −7.4 −7.3 −7.6
Chad basin −5.9 −4.4 −6.4 −6.2 −5.9 −6.6
Senegalese CT −6.2 −5.6 −6.5 −5.9 −5.5 −6.2
Uitenhage aquifer −4.9 −4.5 −5.0 −5.4 −5.4 −5.5
Kairouan Plain −5.8 −5.4 −5.9 −5.6 −5.5 −6.2
Zimbabwe aquifer −6.0 −5.7 −6.3 −6.9 −6.8 −7.0
Canning basin −6.6 −5.5 −8.4 −7.6 −7.5 −7.8
Ngalia/Amadeus −4.3 −4.0 −4.5 −4.5 −4.4 −5.1
Murray aquifer −7.0 −6.8 −8.4 −7.3 −7.2 −7.4
Bengal basin −4.7 −4.2 −5.3 −3.1 −3.0 −3.3
Songnen plain −10.0 −9.5 −10.3 −10.2 −10.0 −10.3
Hexi Corridor: east −9.1 −8.4 −9.8 −10.5 −10.1 −10.8
North China Plain −8.6 −8.3 −8.9 −10.8 −10.6 −10.9
Yuncheng basin −9.2 −8.4 −9.4 −10.3 −9.5 −10.5
Cuddalore sst. −5.6 −5.5 −5.8 −4.9 −4.4 −5.4
Tiruvadanai aquifer −4.1 −3.7 −4.4 −5.0 −4.9 −5.2
Jakarta basin −6.1 −5.7 −6.2 −6.0 −5.6 −6.2
Israel coastal −4.7 −4.5 −5.0 −4.5 −4.5 −4.6
Dead Sea rift valley −4.8 −4.8 −5.4 −6.2 −6.2 −6.8
Kuwait aquifer −2.9 −2.6 −3.0 −4.5 −4.4 −4.7
Batinah coast −2.7 −1.6 −3.4 −1.6 −1.4 −1.8
Najd aquifer −3.1 −0.7 −5.4 −3.6 −3.2 −4.1
Aleppo basin −6.0 −5.4 −6.9 −7.4 −6.9 −7.9
Ledo-Paniselian −6.5 −6.1 −6.9 −7.0 −6.8 −7.1
158
Aquifer δ18OHolo. HighHolo. LowHolo. δ18Oice age Highice age Lowice age
Sokolov aquifer −9.0 −8.9 −9.1 −9.8 −9.4 −9.8
Bathonian coast −6.6 −6.5 −6.7 −7.0 −7.0 −7.1
Lorraine sandstone −8.9 −8.7 −9.0 −10.0 −9.8 −10.2
Aquitaine basin −5.8 −5.6 −6.2 −7.3 −6.3 −8.1
Grt Hungarian Plain −9.5 −9.3 −9.6 −11.3 −10.9 −11.6
Pannonian basin −9.4 −9.1 −9.6 −13.0 −12.3 −13.2
Mazovian basin −10.2 −10.0 −10.2 −10.1 −10.0 −10.3
S. Poland −9.9 −9.6 −10.1 −11.8 −11.2 −12.1
Malm limestone −10.1 −10.0 −11.1 −11.1 −10.7 −11.9
Sado basin −4.8 −4.4 −4.8 −4.7 −4.7 −4.7
Lincolnshire limest. −7.8 −7.8 −7.9 −8.2 −8.1 −8.2
Chalk aquifer −7.4 −7.4 −7.4 −7.8 −7.7 −7.8
Columbia Floods −15.3 −14.8 −16.4 −18.1 −17.6 −18.5
Black Hills −17.1 −16.7 −17.4 −17.5 −15.8 −17.6
Idaho Batholith −16.8 −16.5 −17.4 −17.5 −17.2 −17.6
Cambrian-Ordo. −8.8 −8.2 −9.2 −8.8 −8.7 −8.8
High Plains: North −9.9 −9.3 −10.8 −9.6 −8.5 −10.6
Mahomet aquifer −6.8 −6.7 −7.0 −7.0 −6.9 −7.2
Aquia aquifer −7.1 −7.0 −7.1 −7.1 −6.8 −7.4
High Plains: Cent. −9.5 −8.1 −10.5 −12.1 −11.9 −12.4
San Juan Basin −11.4 −11.2 −11.7 −14.5 −14.2 −14.6
Middle Rio Grande −11.8 −10.2 −12.9 −12.5 −12.1 −13.0
Los Angeles Basin −7.3 −7.2 −7.3 −8.6 −8.1 −9.2
Floridan aquifer −4.7 −4.6 −4.7 −3.7 −3.4 −4.0
Floridan surf. aqfr. −3.4 −2.4 −3.5 −1.6 −1.4 −1.8
Portigar basin: Acu −2.3 −1.4 −2.5 −4.5 −4.4 −4.6
Botacatu: central −8.6 −7.4 −9.3 −8.6 −7.5 −8.8
Botucatu: south −6.2 −5.9 −6.4 −7.6 −7.4 −7.8
* High and Low refer to 25th and 75th percentile ranges of modern (mod. i.e., late-Holocene) and
glacial (i.e., last ice age) data groups.
159
Table 3-4. Speleothem δ18O from the last ice age to the late-Holocene
Cave Country Reference Lon. Lat. Δδ18Oice age
(‰ V−SMOW)
Corr.x
Speleothems
Gunung Buda Borneo Partin et al., 2007 114.8 4.0 +1.9 (+1.8 to +2.4)
0.9±0.2
Botuverá Cave Brazil Cruz et al., 2005; Wang et al., 2007
−49.2 −27.2 −0.7 (−1.1 to −0.3)
0.9±0.2
Dongge t China Dykoski et al., 2005; Yuan et al., 2004
108.1 25.3 +2.5 (+2.1 to +3.2)
0.9±0.2
Hulu * China Wang et al., 2001 119.2 32.5 +1.6 (+1.4 to +1.9)
1.0±0.2
Jiuxian t China Cai et al., 2010 109.1 33.6 +1.0 (−0.8 to +3.7)
1.0±0.2
Yaman Cave t China Yang et al., 2010 107.9 24.5 +2.9 (+2.3 to +3.3)
0.9±0.2
Soreq Israel Bar-Matthews et al., 2003
35.0 31.5 +2.3 (+2.1 to +2.5)
1.0±0.1
Peqin* Israel Bar-Matthews et al., 2003
35.2 32.6 +2.1 (+1.8 to +2.3)
0.9±0.2
Jerusalem W. Israel Frumkin et al., 1999 35.2 31.7 +1.6 (+1.4 to +2.3)
1.0±0.2
NW South Island
New Zealand
Williams et al., 2010 172.0 −42.0 +0.4 (+0.2 to +0.6)
1.0±0.3
Cold Air Cave South Africa
Holmgren et al., 2003 29.1 −24.0 +1.2 (+0.7 to +1.7)
1.0±0.1
Sofular Turkey Fleitmann et al., 2009 31.9 41.4 −4.7 (−4.8 to −4.5)
1.0±0.2
Fort Stanton* U.S.A. Asmerom et al., 2010 −105.3 33.3 −2.0 (−2.8 to −1.2)
1.0±0.2
Cave of the bells*
U.S.A. Wagner et al., 2010 −110.8 31.8 −2.4 (−2.6 to −2.4)
1.0±0.2
Moomi* Yemen Shakun et al., 2007 54.0 12.5 +2.3 (+1.7 to +2.9)
0.9±0.2
* early Holocene value used (i.e., shift likely larger than shown)
t values from 15.0 ka used
x Calcite-water fractionation correction subtracted from raw observed Δδ18Oice age to correct for the
4.0±0.8 °C colder climate (Annan and Hargreaves, 2013) at the last glacial stage (from O’Neil et al.,
1969; modern temperatures from New et al., 2002; Δδ18Oice age values in preceding column are shown
in raw (i.e, uncorrected) form.
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Table 3-5. Ice core δ18O from the last ice age to the late-Holocene
Ice core Country Reference Lon. Lat. Δδ18Oice age
(‰ V−SMOW)
Ice cores
Sajama Bolivia Thompson et al., 1998 −63.9 −18.1 −4.9 (−3.7 to −5.6)
Huascaran Peru Thompson et al., 1995 −77.6 −9.1 −6.5 (−5.9 to −7.4)
Qinghai-Tibetan
Tibet Thompson et al., 1997 81.5 35.3 −0.9 (+2.0 to −3.2)
TALD Ice Antarctica Buiron et al., 2011 159.2 −72.8 −3.9 (−3.3 to −4.3)
Byrd Glacier Antarctica Blunier and Brook, 2001
−119.5 −80.0 −6.0 (−5.0 to −6.9)
Dome Fuji Antarctica Kawamura et al. 2007 39.7 −77.3 −3.6 (−2.7 to −4.5)
Dronning Maud
Antarctica EPICA Community Members, 2006
2.0 −75.0 −5.2 (−4.5 to −6.0)
Law Dome Antarctica Pedro et al. 2011 112.8 −66.8 −6.9 (−6.0 to −7.5)
Siple Dome Antarctica Pedro et al. 2011 −148.8 −81.7 −7.1 (−6.2 to −7.9)
Renland ice core
Greenland Vinther et al., 2008 −27.0 71.0 −4.1 (−2.8 to −4.9)
NGRIP1 Greenland Vinther et al., 2006 −42.3 75.1 −7.3 (−5.5 to −8.7)
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The magnitude of change in δ18O values from the last ice age (19,500 to 50,000 years before
present) to the late-Holocene (<5,000 years before present; i.e., Δδ18Oice age) is shown in Figures 3-3
and 3-5. The Δδ18Oice age value of groundwater ranges from −3.6 ‰ (i.e., δ18Olast ice age < δ18Olate-
Holocene) to +2.0 (i.e., δ18Olast ice age > δ18Olate-Holocene), with more than 90 percent of aquifers having
negative Δδ18Oice age values (Figure 3-4). No systematic latitudinal trend in Δδ18Oice age values can be
observed for either the fossil groundwater or speleothem records (Figure 3-4), unlike temperature
(Figure 3-1). However, cases where δ18Oice age values exceed δ18Olate-Holocene values are constrained to
coastal aquifers in the subtropics (e.g., Bangladesh: +1.6 ‰, less than 300 km from the coast; Florida:
+1.0 and +1.8 ‰, less than 100 km from the coast; southern India: +0.9 ‰, less than 100 km from
the coast). In comparison, aquifers characterized by lower δ18Oice age values than δ18Olate-Holocene values
are found in both coastal regions and farther inland. Aquifers located farthest from coastlines exhibit
the lowest Δδ18Oice age values (e.g., Hungary: −3.6 ‰, ~500 km inland; New Mexico: −3.1 ‰, ~1000
km inland; Niger: −3.0 ‰, ~800 km inland). Greenland and Antarctic ice cores have consistently
negative Δδ18Oice age values that are of a greater magnitude (average of −5.5 ‰, range from −3.6 ‰
to −7.3 ‰) than groundwater Δδ18Oice age values (average of −0.6 ‰, range from −3.6 ‰ to +2.0 ‰;
Figure 3-3).
162
Figure 3-3. The Δδ18Oice age value of
groundwaters, speleothems and ice
cores. Colored bars mark the 25th-
75th percentile ranges of late-
Holocene and last glacial stage
datasets for each aquifer (blue
shades, unique to geographic
regions), speleothem (red), and ice
core (light brown marks non-polar,
dark brown marks ice cores for
Antarctica and Greenland).
Speleothem data are corrected for
the different isotope effects during
precipitation due to different ice age
and modem temperatures. An early
Holocene δ18O range was used for
the Byrd, Dronning Maud, Law
Dome ice cores and Dongge, Fort
Stanton, Hulu, Jiuxian and Moomi
speleothems due to lacking late-
Holocene data in these records. See
Tables 3-2 through 3-5 for
descriptions.
163
Figure 3-4. (top pane) Latitudinal variations of Δδ18Oice age values of groundwater (circles, each circle
is one aquifer), ice cores (squares) and caves (i.e., speleothems; triangles). Dashed lines mark 10°
zonal means of terrestrial precipitation δ18O values predicted by four different general circulation
models (CCSM, ECHAM, LMDZ and IsoGSM). (bottom pane) Histogram of observed Δδ18Oice age
values for in speleothems, ice cores and groundwaters (n = 92 records, in total). Red bars mark
records where δ18Oice age > δ18Olate-Holocene, blue bars mark records where δ18Oice age < δ18Olate-Holocene.
164
3.5 Discussion
3.5.1 Ice age groundwaters as a paleoclimate proxy
The isotopic composition of groundwater from the last ice age provides a valuable tracer of
the isotopic composition of past meteoric waters. The meteoric nature of ice-age-to-late-Holocene
δ18O and δ2H shifts found in the compiled groundwater data demonstrates that paleo-groundwaters
with minimal evaporative influence can be readily identified, making them valuable archives of
paleoclimate information. Such identification of evaporative influence is often difficult to decouple
for other records based solely upon δ18O or δ2H values (e.g., lake sediments), highlighting the value
of groundwater archives for paleoclimate investigations. The ability to measure both δ18O and δ2H
values of groundwater and ice core paleoclimate records is another primary advantage of these “dual
isotope” (i.e., both δ18O and δ2H) paleoclimate records over “single isotope” paleoclimate records
that can only provide either δ18O (e.g., speleothem, lake sediment carbonate, diatom or cellulose
records) or δ2H (e.g., lake sediment leaf wax records) values, but not both. Because of the ability to
examine deuterium excess values, ice age groundwater records may be better suited than speleothems
for determining changes to moisture sources as recently evidenced by isotope enabled general
circulation model reanalysis of ice core isotopic data (Lewis et al., 2013).
Where groundwater data may be advantageous over other records in its availability of “dual
isotope” data, these records suffer in temporal resolution. Lake sediment, ice core and speleothem
records can often be resolved at time scales of 100 to 103 years, whereas groundwater paleoclimate
records can be resolved at time scales of >103 years because of uncertainties in corrected 14C-based
groundwater ages and because of hydrodynamic dispersion that “smears” the groundwater isotopic
record. The impact of hydrodynamic dispersion and long groundwater residence times may help to
explain a portion of the discrepancies in the magnitude of δ18O shifts observed in lake sediment and
groundwater δ18O records at similar locations, just as comparing the standard deviation of daily
precipitation will differ from the standard deviation of monthly precipitation at the same location.
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Each type of paleoclimate record has advantages and disadvantages, and all records are
useful to advancing our understanding of the climate during the last ice age. Groundwater records of
last glacial climate are globally-distributed and are able to be analyzed for “dual isotopes” to confirm
the meteoric nature of the paleoclimate record as completed in this study. However, paleo-
groundwater records of past climates have a poor temporal resolution (>103 years) that negates the
detection of rapid and dramatic shifts in climate. Speleothem isotopic records of last glacial climate
have high temporal resolution (100 to 102 years) but only have a single isotope available (δ18O in
carbonate) and are not as common (n = 15) as groundwater records (n = 65). Ice core records of last
glacial climate can be analyzed for both oxygen and hydrogen isotopic data and have a high temporal
resolution, but are very uncommon on land masses other than Antarctica and Greenland. Lake
sediment isotopic records of last glacial climate can have a high temporal resolution and are available
for a multitude of globally-distributed locations, however, lake sediment records have large
uncertainties in reconstructing past changes to meteoric δ18O because of the need to (i) quantify the
temperature of the water that the paleoclimate archive (e.g., lake sediment diatom, cellulose and
carbonate; speleothem carbonate) precipitated from in the past, and (ii) know the impact of
evaporation upon isotopic composition of water in the past, both of which are highly difficult to
reconstruct considering that most lake sediment archives are “single isotope” records (i.e., one of
δ18O or δ2H analyzed).
3.5.2 Isotope-enabled general circulation models
The δ18O value of annual precipitation from four isotope-enabled general circulation models
analyzed for the pre-industrial era and the last glacial maximum scenarios are shown in Figures 3-5,
3-6, 3-7 and 3-8. Points in each of the figures mark the observed ice-age-to-modern differences in
δ18O observed in groundwater, speleothems and ice cores. Locations where three or four models
agree on the sign of Δδ18Oice age are shown in Figures 3-9 and 3-10.
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Generally, modelled Δδ18Oice age values are lowest over the Fennoscandanavian and
Laurentide ice sheets (less than 3 per mille), and highest over the tropical oceans. Positive modelled
Δδ18Oice age values occur near to coasts in the tropics and subtropics. Extra-tropical land surfaces
generally have negative Δδ18Oice age values, whereas tropical land surface Δδ18Oice age values are more
variable in their sign amongst the models (Figure 3-9 and 3-10). The disagreement amongst the four
models on the sign of Δδ18Oice age values is potentially related to different parameterizations of
convective rainfall along air mass trajectories, which is a leading control upon precipitation δ18O
values in tropical regions (Risi et al., 2008; Risi et al., 2010b; Lee et al., 2009; 2012; Lekshmy et al.,
2014; Samuels-Crow et al., 2014).
Simulated Δδ18Oice age values reproduce the sign of observed Δδ18Oice age values across North
America and Europe (extratropics) better than over Africa and South America (Figure 3-9 and 3-10).
Simulated isotopic compositions of rain over tropical Africa and South America have both
disagreement amongst different models on Δδ18Oice age values, and also Δδ18Oice age disagreement
between simulated (generally positive) and observed (generally negative) values. The negative
Δδ18Oice age values across Africa are consistent with enhanced air mass distillation during transport
due to higher-than-modern upstream rainout during the last ice age. Assuming that convection is a
leading control upon the isotopic composition of tropical precipitation, the models perhaps
overestimate the change in convection from the last glacial maximum to the present day. The
stronger agreement between simulated and observed Δδ18Oice age values in the extratropics relative to
the tropics suggests that models may simulate isotopic distillation via frontal advective hydroclimates
better than via convective rainout. The poorer simulation of Δδ18Oice age values in the tropics than in
the extratropics is consistent with other works that show that simulating the isotopic composition of
convective rains is highly sensitive to model parameterization (Lee et al., 2009).
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Figure 3-5. The modelled difference in the δ18O value of precipitation from the last glacial maximum
to the pre-industrial time period (CCSM, pers. comm. F. Pausata): δ18Olast glacial maximum – δ18Olate-Holocene.
Circles show the observed Δδ18Oice age values (median) from groundwater, speleothem and ice core
records compiled and analyzed in this study.
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Figure 3-6. The modelled difference in the δ18O value of precipitation from the last glacial maximum
to the pre-industrial time period (ECHAM, pers. comm. M. Werner): δ18Olast glacial maximum – δ18Olate-
Holocene. Circles show the observed Δδ18Oice age values (median) from groundwater, speleothem and ice
core records compiled and analyzed in this study.
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Figure 3-7. The modelled difference in the δ18O value of precipitation from the last glacial maximum
to the pre-industrial time period (IsoGSM, pers. comm. K. Yoshimura): δ18Olast glacial maximum – δ18Olate-
Holocene. Circles show the observed Δδ18Oice age values (median) from groundwater, speleothem and ice
core records compiled and analyzed in this study.
170
Figure 3-8. The modelled difference in the δ18O value of precipitation from the last glacial maximum
to the pre-industrial time period (LMDZ, pers. comm. C. Risi): δ18Olast glacial maximum – δ18Olate-Holocene.
Circles show the observed Δδ18Oice age values (median) from groundwater, speleothem and ice core
records compiled and analyzed in this study.
171
Figure 3-9. Locations where all four models agree on the sign of Δδ18Oice age values (i.e., positive or
negative). Red colors mark regions where there is unanimous prediction of higher-than-modern δ18O
values at the last ice age amongst the four models, whereas blues colors mark regions where there is
unanimous prediction of lower-than-modern δ18O values at the last ice age amongst the four models.
The shades of red and blue are the multi-model average of modelled ice-age-to-late-Holocene
changes in the δ18O value of meteoric water. White regions show areas where at least one of the four
models predicts a different sign of Δδ18Oice age values (i.e., some models predict negative glacial to
modern shifts, other models predict positive glacial to modern shifts).
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Figure 3-10. Locations where at least three of four models agree on the sign of Δδ18Oice age values (i.e.,
positive or negative). Red colors mark regions where there is higher-than-modern δ18O values at the
last ice age amongst the models, whereas blues colors mark regions where there is lower-than-
modern δ18O values at the last ice age amongst the models. The shades of red and blue are the multi-
model average of modelled ice-age-to-late-Holocene changes in the δ18O value of meteoric water.
White regions show areas where at least one of the four models predicts a different sign of Δδ18Oice
age values (i.e., some models predict negative glacial to modern shifts, other models predict positive
glacial to modern shifts).
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Models predict similar Δδ18Oice age values in some regions (e.g., precipitation over the
tropical oceans) and different Δδ18Oice age values in other regions (e.g., Africa). Figure 3-9 delineates
locations where all four models agree on the sign of Δδ18Oice age values (i.e., all models are positive, or
all models are negative), and shows that all models agree on the sign of Δδ18Oice age over ~40% of
Earth’s surface. All four models agree on the sign of Δδ18Oice age values for half of continental areas
and for one-third ocean areas. For continental precipitation, 80% of locations where all models agree
on the sign of Δδ18Oice age values have a unanimously negative simulated Δδ18Oice age value. Conversely,
75% of cases where all models agree on the sign of over-ocean precipitation Δδ18Oice age values have a
unanimously positive simulated Δδ18Oice age value.
All four models have positive Δδ18Oice age values over the western and southern Pacific
Ocean, the tropical and mid-latitude Atlantic Ocean, the southeastern United States of America,
northeast Brazil, western Africa, eastern China and southwestern Australia. All four models predict
negative Δδ18Oice age values over the western United States of America, and northern and western
Canada, the southern margins of Argentina, northern Europe, the Norwegian Sea, throughout
Russia, Kazakhstan, Uzbekistan, and Turkmenistan, and over northern Mongolia and the Tibetan
plateau.
The general circulation model ice-age-to-modern δ18O changes agree with the observations
for some, but not all, locations. In general, simulated Δδ18Oice age values match observed Δδ18Oice age
values more closely in the extratropics than in the tropics. For example, general observed Δδ18Oice age
patterns over North America and Europe are reproduced by most general circulation models. In
contract, observed Δδ18Oice age values over Africa and South America are not reproduced by most
models. The modelled Δδ18Oice age reproduces some of the observed positive and observed negative
Δδ18Oice age values in groundwaters, speleothems and ice cores (Figure 3-9 and 3-10), highlighting the
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much greater potential of these models for reconstruction of Δδ18Oice age values than temperature-
δ18O regressions, alone (e.g., Dansgaard, 1964).
3.5.3 Regional Δδ18Oice age values
3.5.3.1 Australia and Oceania
Australian records of Δδ18Oice age (n=3) range from -1.0‰ (Canning Basin) to -0.3‰ (Ngalia
and Amadeus, Murray aquifers; Table 3-2). General circulation models predict positive Δδ18Oice age
values across Australia; whereas the three observed groundwater records have negative Δδ18Oice age
values. Observed Δδ18Oice age values are similar for all three Australian records despite different
climates amongst the records that range from humid northern regions (Canning Basin) to more arid
interior settings (Ngalia and Amadeus basins).
Spatial differences in climate change across the Australia continent are evidenced by higher-
than-modern lake levels during the last glacial maximum in southeastern Australia (Galloway, 1965;
Williams, 2001), but lower-than-modern ice age lake levels in central Australia (Hope, 2005). The
climate at the last ice age in parts of Australia was more arid (Nanson et al., 1992), dustier (Chen et
al., 1993) and ~10°C cooler (Miller et al., 1997) than present day. Observations of higher-than-
modern ice age lake levels are attributed to lower evaporative potential at the last glacial maximum
(Hope, 2005). Observed negative Δδ18Oice age values are consistent with cooler-than-modern
condensation temperatures (i.e., enhanced air mass distillation) supported by 10°C cooler land
surface temperatures (Miller et al., 1997). Alternatively, atmospheric models suggest that precipitation
was more seasonal during the last glacial maximum than today due to cooler-than-modern sea surface
temperatures (Hope, 2005) able to produce negative Δδ18Oice age values.
Oceania records of paleoclimate include groundwater data for the Jakarta basin (Geyh and
Sofner, 1989), and speleothem data in Borneo (Partin et al., 2007) and New Zealand (Williams et al.,
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2010). Oceania isotopic records of speleothems (Partin et al., 2007) and groundwaters (Aggarwal et
al., 2004) from Vietnam, Thailand, The Philippines and Borneo each have near-zero or positive
Δδ18Oice age values that have been attributed to ice-age-to-modern changes in monsoonal strength
and atmospheric convection (Aggarwal et al., 2004; Partin et al., 2007). Simulated Δδ18Oice age values
are generally positive or near-zero over Bangladesh, Vietnam, Thailand, The Philippines and Borneo
(Figure 3-9 and 3-10) consistent with the sign of observed Δδ18Oice age values. Simulated precipitation
δ18O values overlying Borneo are controlled by changes to precipitation amount caused by spatial
shifts in the position of the intertropical convergence zone (Lewis et al., 2010; 2011), suggesting that
the +1‰ higher-than-modern ice age seawater value was offset in part by drier-than-modern climate
during the last glacial maximum(applying interpretation of Lewis et al., 2011).
3.5.3.2 Southeast Asia
Southeast Asian Δδ18Oice age values range from −2.3 ‰ to +1.9 ‰ (n = 13). The highest
regional Δδ18Oice age values are found in Bangladesh (Δδ18Oice age of +1.6 ‰; Aggarwal et al., 2000) and
in central and south-eastern China (Δδ18Oice age of 0.0 ‰ to +1.9 ‰; Wang et al., 2001; Yuan et al.,
2004; Dykoski et al., 2005; Cai et al., 2010; Yang et al., 2010). The high Bangladeshi Δδ18Oice age value
of +1.6 ‰ cannot be explained solely by ice-age-to-modern changes in seawater δ18O (δ18Oice age
seawater > δ18Omodern seawater by +1 ‰), suggesting that changes to temperature and humidity of the over-
ocean moisture sources, air mass rainout history, precipitation seasonality, or seasonal filtering of
groundwater recharge must have occurred in Bangladesh between the last ice age and the late-
Holocene.
Chinese speleothem records located between latitudes 25°N to 35°N have near-zero or
positive Δδ18Oice age values. The Chinese speleothem records have been interpreted to reflect the
strength of the East Asian (Wang et al., 2001; Dykoski et al., 2005; Cosford et al., 2008) or Indian
monsoons (Pausata et al., 2011). Recent work proposes that interpreting Chinese speleothem isotopic
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data as records of the summer monsoon strength is incorrect (Caley et al., 2014). Proxy evidence
suggest a weaker-than-modern summer monsoon at the last glacial maximum (Wang et al., 2001) and
stronger-than-modern winter precipitation (Sagawa et al., 2011; Clark et al., 2012). The North China
Plain (northeastern China; Zongyu et al., 2005) and the eastern Hexi Corridor (northern China; Gates
et al., 2008) aquifers have the lowest Δδ18Oice age values observed across east Asia (Δδ18Oice age of −1.4
‰ and −2.3 ‰). Combining northern Chinese groundwater Δδ18Oice age observations (Zongyu et al.,
2005; Gates et al., 2008) with the observed positive Δδ18Oice age values of across central China (Wang
et al., 2001; Yuan et al., 2004; Dykoski et al., 2005; Cai et al., 2010; Yang et al., 2010) reveals a south-
to-north decrease in Δδ18Oice age (Figure 3-2).
The observed north-to-south Δδ18Oice age decrease is spatially consistent with intra-annual
precipitation δ18O seasonality across southeastern Asia. Previous studies have identified increasing
precipitation δ18O values from the coast (i.e., Hong Kong) to inland China (e.g., Zhangye) during the
wet season, sharply contrasting spatial patterns expected from Rayleigh distillation (Aragúas-Aragúas
et al., 1998). This pattern has been interpreted as the maximum northward extent of the intertropical
convergence zone (i.e., broad scale Hadley circulation; Aragúas-Aragúas et al., 1998). However, more
recent work suggests that low wet-season precipitation δ18O values over southern Chinese are
controlled by the deflection of westerlies from the Tibetan Plateau, whereas precipitation δ18O over
northern China is controlled by local-scale precipitation fluxes and subsequent evaporation of falling
raindrops (Lee et al., 2012). Therefore observed Δδ18Oice age values in southern China may be
reflective of broader scale atmospheric circulation pattern changes, whereas Δδ18Oice age over northern
China could reflect ice-age-to-modern changes to local meteorology. The source of precipitation over
China varies on intra-annual time scales, and about half of all rainfall is sourced from continental
moisture recycling (Lewis et al., 2013). Generally, Chinese atmospheric vapor sourced from the
Indian Ocean is at a maximum during the summer, whereas Pacific-sourced moisture is greatest
during the winter (Lewis et al., 2013). The strong modelled intra-annual variation in moisture sources
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over China (Lewis et al., 2013) suggests that observed Δδ18Oice age values may represent broad-scale
changes to moisture sources and associated air mass trajectories that have resulted in a strengthening
of monsoon rains from the last ice age to the present day.
General circulation models predict positive Δδ18Oice age values near to the Chinese coastlines,
and negative Δδ18Oice age values in western and northern China (Figures 3-9 and 3-10), consistent with
observed south-to-north decrease in Δδ18Oice age values. Generally, spatial patterns of the sign of the
multi-model average Δδ18Oice age agrees with the sign of the observed Δδ18Oice age (Figures 3-9 and 3-
10) in these monsoonal regions. A single hydrological process that explains all observed Δδ18Oice age
values is not identifiable nor expected given the variety of processes controlling modern precipitation
in southeastern Asia (Aragúas-Aragúas et al., 1998; Lee et al., 2012). However, the strong inter-model
agreement on Δδ18Oice age values and model capture of the south-to-north decrease in Δδ18Oice age
implies that general circulation models reproduce the broad atmospheric boundary defining the
different hydrological processes governing southern vs. northern Chinese precipitation regimes.
3.5.3.3 The Middle East
The Middle East has four Δδ18Oice age records ranging from −1.6 ‰ (Kuwait) to +1.4 ‰
(Yemen); all four sites in the Middle East are located within 100 km of a coast. Records collected in
Kuwait and Yemen have both been interpreted to reflect an ice age climate that was wetter than
today’s (Al-Ruwaih and Shehata, 2004; Shakun et al., 2007), although the Δδ18Oice age value is of a
different sign (i.e., Kuwait being negative, and Yemen positive). Groundwater noble gas records in
Oman reveal a 6.5°C temperature increase from the last ice age to the late-Holocene (Weyhenmeyer
et al., 2000). Groundwaters have an ice-age-to-late-Holocene deuterium excess increase (i.e., dice age <
dmodern) interpreted to be the result of a switch in the moisture source to Oman: from the Indian
Ocean during the last ice age, to the Mediterranean Ocean today (Weyhenmeyer et al., 2000) that may
be a leading control upon other records in the region.
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3.5.3.4 Africa
African Δδ18Oice age values range from −3.0 ‰ to near-zero (Figure 3-2). 80% of African
Δδ18Oice age values are negative. Near-zero Δδ18Oice age values are generally found near to coasts (e.g.,
Senegal Δδ18Oice age of +0.3 ‰) whereas the lowest African Δδ18Oice age value is located in Niger
(Δδ18Oice age of −3.0 ‰) 800 kilometers from the coast. Records located north and south of the
equator have negative Δδ18Oice age values.
Northern African changes to hydrological processes are complicated by multiple interlinked
controls such as the strength of Atlantic meridional overturning circulation (Jullien et al., 2007) and
meridional shifts in the position of the intertropical convergence zone (Arbuszewski et al., 2013).
Paleowater isotopic records indicate that northern Africa was 2-3°C cooler than today (Guendouz et
al., 1998) and that westerly winds transporting moisture to northern Africa were stronger than
present day (Sultan et al., 1997; Abouelmagd et al., 2014). North African Δδ18Oice age observations
were also likely impacted by higher-than-modern sea surface humidity as evidenced by lower ice age
deuterium excess values in paleowaters (Rozanski, 1985). Potentially cooler-than-modern final air
mass condensation temperatures during the last ice age coupled to changes in moisture source and
sea surface temperature and humidity have each been suggested and result in unanimously negative
Δδ18Oice age values across northern Africa.
Southern Africa lacustrine sediment records recovered at Lake Tanganyika and Malawi show
that the eastern Africa was 2°C to 4°C cooler than modern, and that the isotopic composition of leaf
waxes was highly variable between the early and late-Holocene (Powers et al., 2005; Tiereny, 2008;
2013). These records are interpreted as indicative of precipitation variations imparted by changes to
Indian Ocean temperatures (Tiereny, 2008; 2013); although this interpretation is not supported by all
(Schefuß et al., 2011). Pollen records suggest that the African tropics were both cooler and more arid
at the last glacial maximum (Gasse, 2000). General circulation model simulate lower-than-modern ice
179
age precipitation fluxes over tropical Africa (Otto-Bliesner et al., 2006). Pollen records and climate
model simulation models both suggest a more arid region, indicative of lower-than-modern moisture
recycling during the last ice age. Rainfall originates from both Indian and Atlantic Oceanic sources,
with Atlantic-sourced moisture travelling across the Congo rainforest (Levin et al., 2009). A
reduction in continental moisture recycling is consistent with the observed negative Δδ18Oice age values
across southern tropical Africa. Model simulations of Heinrich event precipitation δ18O confirm
changes to moisture recycling and transport distance can change Δδ18Oice age values over southern
Africa. Higher-than-modern upwind convection during the last ice age may have produced negative
Δδ18Oice age values (e.g., Lekshmy et al., 2014) consistent with observed negative Δδ18Oice age values.
However, stronger-than-modern convective rainout at the last ice age is contrary to the cooler-than-
modern land surface temperatures, suggesting that increases to transport distance and vapor origin
changes are more likely sources of the observed negative Δδ18Oice age values (Lewis et al., 2010).
Isotope enabled general circulation model Δδ18Oice age values and observed groundwater
Δδ18Oice age values are shown in Figures 3-9 and 3-10. The four general circulation models do not
agree with each other nor with multiple compiled Δδ18Oice age values over the majority of Africa
(Figure 3-9 and Figure 3-10). While the source of this discrepancy remains unclear, different
parameterizations of convective rainfall amongst the models may help to explain disagreements
between models (e.g., inter-model differences in the timescale for consumption of convective
available potential energy; Lee et al., 2009). Indeed, recent work has shown that convection, not
precipitation amount (the “amount effect”), drives tropical variations in meteoric water δ18O values
(Lekshmy et al., 2014). However, the observed negative Δδ18Oice age values are consistent with higher-
than-modern upwind convection during the last ice age. Higher-than-modern convection during the
last ice age is difficult to reconcile given the cooler-than-modern land surface temperatures at the last
ice age (Figure 3-1). However, the observed negative Δδ18Oice age values must reflect rainout
180
processes, since ice-age-to-modern changes to seawater δ18O induce an opposing effect (i.e., positive
Δδ18Oice age) to observations.
3.5.3.5 Europe and Mediterranean nations
Europe and nations bordering the eastern Mediterranean Sea have Δδ18Oice age values ranging
from −5.7 ‰ to near-zero (n = 20; Figure 3-2). 80% of European Δδ18Oice age values are negative.
Δδ18Oice age values are generally higher in western Europe (+0.1 ‰ to −1.5 ‰ in Portugal and the
United Kingdom and France) than in eastern Europe (−1.8 ‰ to −3.6 ‰ in Poland and Hungary).
The lowest ice Δδ18Oice age value is observed in a speleothem in Turkey near to the Black Sea (−5.7
‰), which is interpreted to be the dominant source of moisture over the region (Fleitmann et al.,
2009). The interpretation of a change in moisture source is consistent with recent reporting of lower-
than-modern deuterium excess values during the last ice age (Arslan et al., 2013) and pollen records
indicative of a drier ice age climate in eastern Turkey (Kaplan, 2013). Indeed there is large potential
for an ice-age-to-modern change in the moisture sources and air mass trajectories over Turkey given
the large number of potential moisture sources (e.g., Black Sea, Mediterranean Sea, Atlantic Ocean)
and the cave’s location near to the margin of the Fennoscandanavian ice sheet at the last glacial
maximum. The highest European Δδ18Oice age value (near-zero) is found along the Portugal coast
(Galego Fernandes and Carreira, 2008). The near-zero Δδ18Oice age value in the Portugal aquifer
suggests that the effect of the higher ice age δ18Oseawater value is cancelled out by a combination of ice-
age-to-modern changes in sea surface temperature and humidity, cooler condensation temperatures
at the last ice age and/or greater fluxes of winter precipitation entering aquifers at the last glacial
maximum.
Positive Δδ18Oice age values are observed in the eastern Mediterranean speleothems found in
Israel (Frumkin et al., 1999; Bar-Matthews et al., 2003; Ayalon et al., 2013), although groundwater
aquifers in eastern Israel and in Syria have negative Δδ18Oice age values. These records are near to one
181
another, such that the opposing sign of Δδ18Oice age observed in each record is surprising. The
groundwater record may record information from higher recharge-zone elevations located ~102 km
from the measurement location due to advection along regional-scale flowpaths. This compilation
and spatial analysis advocated for further comparative research into speleothem and groundwater
isotopic compositions to ensure each record indeed reflects paleo-meteoric water δ18O values
unaltered by subsequent effects (e.g., partial evaporation, etc.).
Some European aquifers have a prolonged gap in 14C-based groundwater ages interpreted to
be the result of the inhibition of recharge due to permafrost aggradation (Darling, 2004). Changes to
freeze-thaw conditions of the ground surface between the last ice age the modern climate may have
also impacted the seasonality of groundwater recharge ratios (Darling, 2011; Jasechko et al., 2014),
suggesting that recharge dynamics may represent a process that has not yet been applied to reconcile
observed Δδ18Oice age values. Indeed, pollen records indicate that northern Europe was tundra-like at
the last glacial maximum and that southern Europe was semi-arid, receiving ~300 mm less
precipitation than modern day (Clark et al., 2012 and references therein). The glacial-to-modern
transition from semi-arid deserts to temperate forests may have modified the seasonality of the
groundwater recharge ratio as evidenced by modern day recharge being much more efficient during
the winter (Jasechko et al., 2014). This potential ice-age-to-modern change in recharge/precipitation
ratios would have likely enhanced winter recharge fluxes resulting in negative shifts in Δδ18Oice age
values consistent with observations.
General circulation model outputs of Δδ18Oice age values over Europe are unanimously
negative (i.e., all four models agree on the sign of Δδ18Oice age Figure 3-9; 3-10), with the exception of
southern Portugal and Spain. The model predictions across Europe closely match the observations of
Δδ18Oice age values in groundwaters and speleothems. Earlier works have suggested the European
moisture sources and air mass trajectories have not changed considerably since the last ice age
182
(Rozanski et al., 1985; Loosli et al., 2001). The match between simulated and modelled negative
Δδ18Oice age values implies that the last ice age had cooler final air mass condensation temperatures,
higher winter groundwater recharge ratios or higher proportions of winter precipitation as a
proportion of annual totals. However, modelled Δδ18Oice age values agree less frequently over Israel
and Syria, where aquifer and speleothem observations also show both positive and negative Δδ18Oice
age values. The conflicting model outputs and observations of Δδ18Oice age values in the eastern
Mediterranean suggest that moisture sources, air mass trajectories and meteorology is highly sensitive
to change in the eastern Mediterranean, and that this sensitivity varies over distances of ~102
kilometers.
3.5.3.6 South America
South American Δδ18Oice age values range from −6.5 ‰ to 0.0 ‰ (Figure 3-2), with the
lowest values found in ice cores in the Andes (Bolivia: δ18O anomaly of −4.9 ‰; Thompson et al.,
1998; Bolivia: δ18O anomaly of −6.5 ‰; Thompson et al., 1998). The Δδ18Oice age values found in the
ice cores have been interpreted to have been coupled to substantial cooling of the tropics (quoted as
8°C to 12°C; Thompson et al., 1995), and may also be related to changes in moisture recycling over
the Amazon, which is the dominant moisture source to the Andes (Thompson et al., 1998).
Paleowater Δδ18Oice age data is available in the semi-arid eastern portion of Brazil. The interpretation
of this record was that rainfall was higher-than-modern during the Pleistocene (Salati et al., 1974),
consistent with greater upwind convective rainfall leading to negative Δδ18Oice age values. However,
recent work proposes that precipitation was lower-than-modern in eastern Brazil at the last glacial
maximum (Cruz et al., 2009; Clark et al., 2012). Eastern Brazilian precipitation is anti-phased with
precipitation fluxes in the South American monsoon region (Cruz et al., 2009) where ice age
precipitation fluxes are thought to be higher-than-modern (Wang et al., 2007). This aquifer is located
~100 km from the Atlantic Ocean at a latitude of 5°S, and the region was 5.4°C cooler than today
during the last glacial maximum (Stute et al., 1995b). The intra-annual variability in the isotopic
183
composition of precipitation is subdued, with the summer/wet-season (April to September) having a
nearly identical isotopic composition (−2.2‰) to that of the winter/dry-season (−2.0‰; data from
Ceara Mirim, Brazil; data accessed from www.iaea.org/water), suggesting that changes to the
seasonality of precipitation amounts or the seasonality of the groundwater recharge ratio are not the
source of observed negative Δδ18Oice age value in eastern Brazil. Possible processes that may explain
the negative Δδ18Oice age values in eastern Brazil include higher-than-modern upwind convection
during the last ice age (Salati et al., 1974), supported as a leading control on Δδ18Oice age by general
circulation model simulations that suggest local rainfall amounts govern precipitation δ18O (Lewis et
al., 2010). Observed Δδ18Oice age values in eastern Brazil support the interpretation of Salati et al.
(1974) that eastern Brazil was wetter than today during the last glacial maximum.
General circulation models have unanimously positive Δδ18Oice age values over semi-arid
eastern Brazil (Figures 3-9 and 3-10). Interestingly, the Δδ18Oice age value observed in this region is
negative (Salati et al., 1974). It is clear that the models have not captured all processes in this region,
as the predicted Δδ18Oice age values are of a different sign than the observed Δδ18Oice age value. I have
ruled out seasonality of precipitation fluxes as the sole process controlling Δδ18Oice age values in this
region. However, changes to moisture sources, air mass recycling, rainout history and moisture
recycling (i.e., processes ii through v) may each be an important control upon Δδ18Oice age values in
eastern Brazil. Similarly, upstream convective rainstorms (potentially not accurately parameterized
within all general circulation models) stronger-than-modern during the last ice age could have
contributed to observed negative Δδ18Oice age values.
3.5.3.7 North America
North American Δδ18Oice age records are all located in the United States of America and range
from −3.1 ‰ to +1.8 ‰ (n = 14). Easternmost USA has Δδ18Oice age values that are positive or near-
zero. The positive Δδ18Oice age values are highest in Florida (latitude: 27°N; Δδ18Oice age of +1.8 ‰)
184
and decrease northward through Georgia (latitude: 32°N; Δδ18Oice age of +1.0 ‰) to coastal Maryland
(latitude 39°N; Δδ18Oice age of 0.0 ‰). The decreasing Δδ18Oice age values observed with increasing
latitude along the USA eastern seaboard that may be partially explained by the isotopic distillation of
air masses advecting northward from the tropics. The effect of the higher ice age δ18Oseawater values
has been shown to be offset by the lowering of the sea level during the last ice age which increases
the distance-to-the-coast because of sea level regression (Clark et al., 1997; Aeschbach-Hertig et al.,
2002). Other potentially important processes that may reconcile the observed Δδ18Oice age values
include changes to seasonal precipitation rates, changes to moisture recycling due to differing
Pleistocene vegetation in the region (Harrison et al., 2003) or changes to hurricane frequency and
intensity (i.e., precipitation seasonality; Plummer, 1993) could have impacted Δδ18Oice age values.
Further, recent research show that seawater δ18O values changed over time in the Gulf of Mexico
(i.e., one of the moisture sources to central and southeastern USA; Feng et al., 2014). δ18Oseawater
changes from the last ice age to the present day due to fluctuations in Mississippi River discharge
may have impacted terrestrial Δδ18Oice age values, with higher Mississippi discharges leading to lower
seawater δ18O and lower terrestrial precipitation δ18O values.
Westernmost USA has negative Δδ18Oice age values (e.g., Los Angeles basin Δδ18Oice age of
−1.4 ‰), contrasting Δδ18Oice age values observed along the eastern coast at similar latitudes.
Although the reason for this east-coast/west-coast difference may have multiple explanations, higher
than modern winter precipitation fluxes during the last ice age could invoke a negative Δδ18Oice age
value consistent with observations.
Central USA has the lowest Δδ18Oice age values that range from −0.6‰ to −3.1‰. The
southwestern USA was 5°C cooler than today during the last glacial maximum (Stute et al., 1995a).
The low inland Δδ18Oice age values observed in central North America are consistent with the
enhanced isotopic distillation of moisture advecting overland due to cooler final condensation
185
temperatures, or higher proportions of annual precipitation falling during the winter season. The
observed Δδ18Oice age values in these records have been attributed to lower-than-modern summer
precipitation fluxes during the late Pleistocene (New Mexico, Phillips et al., 1986), latitudinal shifts in
the positions of the polar jet stream and the intertropical convergence zone (New Mexico, Asmerom
et al., 2010) and changes to over-ocean humidity, temperature or moisture sources (Idaho, Schlegel et
al., 2009). Pollen records indicate widespread forests throughout the present day deserts of the
American southwest, indicative of wetter-than-modern conditions at the last glacial maximum
(Williams, 2003). An isotopic record at Cave of the Bells is interpreted to reflect southwestern aridity,
with reductions in paleo-δ18O interpreted to reflect a cooler and a wetter climate (Arizona; Wagner et
al., 2010). Extending this interpretation to the observed negative Δδ18Oice age values throughout the
southwest USA, the groundwater Δδ18Oice age values suggest that the American southwest was both
cooler and more humid during the last ice age compared to present day. The source of higher-than-
modern ice age humidity may be linked to changes in air mass trajectories and moisture sources to
the southwestern USA (Asmerom et al., 2010; Wagner et al., 2010). Further, the strong intra-annual
variability in the isotopic composition of modern day precipitation (more than a 7 ‰ difference
between the summer and winter δ18O values) suggests that increases to winter precipitation or higher
recharge/precipitation ratios could also contribute to the observed negative Δδ18Oice age values in
southwestern USA groundwaters.
General circulation model results Δδ18Oice age values are generally positive along the eastern
seaboard, the Gulf States and the central plains of the USA (Figures 3-9; 3-10). The modelled
Δδ18Oice age results are consistent with the sign of Δδ18Oice age values observed in aquifers across
Florida, Georgia and Maryland (Plummer, 1993; Clark et al., 1997; Aeschbach-Hertig et al., 2002;
Morrissey et al., 2010; Figures 3-9 and 3-10), although no isotopic record of the last glacial maximum
is available for aquifers across the Gulf States (e.g., Edwards Aquifer, Texas). The general circulation
models Δδ18Oice age values are generally negative west of the Rocky Mountains, consistent with the
186
sign of observed Δδ18Oice age values in Colorado, New Mexico and Idaho (Clark et al., 1998; Stute et
al., 1995a; Schlegel et al., 2009; Asmerom et al., 2010).
Conclusions
The Δδ18Oice age of groundwater aquifers compiled in this study ranges from −3.6 ‰ (i.e.,
δ18Oice age < δ18Olate-Holocene) to +2.0 ‰ (i.e., δ18Oice age > δ18Olate-Holocene). ~90% of aquifers have
negative Δδ18Oice age values. Aquifers with positive Δδ18Oice age values are found exclusively near to
coasts. Future research may capitalize upon the broad availability of groundwater isotopic records of
last ice age and late-Holocene climate in order to isolate and constrain hydrological processes
responsible for observed Δδ18Oice age values using general circulation models (e.g., Lewis et al., 2010).
Further, observed Δδ18Oice age values are compared to general circulation model outputs of Δδ18Oice age
values. The general circulation models agree in the sign and magnitude of Δδ18Oice age values for
some, but not all locations. This synthesis and sensitivity analysis advocates for the use of
quantitative models when interpreting precipitation δ18O paleoclimate records.
Regional paleoclimate signals show that during the last ice age:
- Australia was more arid, ~10°C cooler, and had either greater contributions of winter
precipitation or increased rainout and isotopic fractionation of air masses potentially due to
cooler-than-modern atmospheric condensation temperatures.
- Southern Chinese summer monsoons were weaker-than-modern, winter rainfall was higher-
than-modern. Northern China was 5°C cooler and more humid than present climate
conditions.
- The Middle East was 6.5°C cooler than today and received greater vapour fluxes from the
Indian Ocean than present day, producing a wetter overall climate during the last ice age.
- Northern Africa was 2°C to 3°C cooler than modern climate and had greater vapor influxes
from westerly moisture sources, creating a more humid climate than present day.
187
- Southern African was 2°C to 4°C cooler than modern, had higher-than-modern vapor inputs
from westerly moisture sources, potentially lower-than-modern moisture recycling over the
Congo rainforest and potentially greater-than-modern upwind convective rainfall.
- European climate was 3°C to 9°C cooler than present day, had broadly similar-to-present
vapor inflows from westerly moisture sources, and may have had substantially higher-than-
modern winter groundwater recharge ratios or cooler final condensation temperatures (i.e.,
greater upwind air mass distillation) leading to observed unanimously negative Δδ18Oice age
values.
- Eastern Brazil was 5°C cooler and was more humid than present day climate due to greater-
than-modern monsoonal rainstorms.
- The southwestern USA was 5°C cooler than today (Stute et al., 1995a), was more humid
than modern climate, and potentially received greater-than-modern vapor fluxes from
westerly moisture sources or higher-than-modern winter precipitation fluxes.
Acknowledgements
I thank C. Risi, F. Pausata, K. Yoshimura and M. Werner for their time and help compiling
results from the isotope enabled general circulation models. I also thank A. Lechler, F. Pausata and
T. Gleeson for the valuable insights that have improved this chapter.
188
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CHAPTER 4 — THE ISOTOPE HYDROLOGY OF UGANDA
4.1 Abstract
In the final chapter of this dissertation I present the results of a comprehensive sampling
campaign and stable O and H isotopic investigation of Ugandan rivers, lakes, wetlands and
groundwaters. Surface- and ground-waters in Uganda have δ18O values ranging between −4.0 ‰ and
+8.7 ‰, δ2H values ranging between −13.2 ‰ and +55.5 ‰, and deuterium excess values ranging
between −17 ‰ and +22‰. The highest δ18O and δ2H values and lowest deuterium excess values
are found in Ugandan lakes; whereas, the lowest δ18O and δ2H values and highest deuterium excess
values are found in springs and river waters sourced from the Rwenzori mountains of southwest
Uganda. I analyze the isotopic composition of lake waters using a stable-isotope-mass-balance to
calculate the fraction of evaporation as a proportion of water inputs to 24 lakes. I show that a sample
of lake water analyzed for O and H isotopes, coupled to the application of a stable-isotope-mass-
balance, can rapidly delineate well flushed (low evaporation/input ratio) and terminal
(evaporation/input ratio of close to 1) lake systems.
4.2 Introduction
In Uganda, 70% of the 35 million people living there have access to an improved water
source, ranking Uganda 148 out of 179 nations reporting in 2010 (Millennium Development Goals
Indicators). Groundwater is the primary drinking water source for 80% of Ugandans and cultivated
lands cover one third of the country, highlighting the importance of agriculture (usually rain-fed) to
the Ugandan economy. Lake Victoria, located in southeastern Uganda, sustains a commercial fishery,
supports over-lake transportation, feeds municipal water supplies and generates hydroelectric power
at the Kiira and Nalubaale power stations near Jinja.
Uganda is a landlocked nation. It is situated in the humid tropics of east Africa, bordered by
Sudan (north), Kenya (east), Tanzania (south), Rwanda (southwest) and the Democratic Republic of
213
the Congo (west). The country is constrained between 1°S and 4°N latitude and 29°E to 35°E
longitude and covers a total of 240,000 km2. Land surface elevations range between 600 to 5,200
meters above sea level, with 80% of the country resting between 900 and 1,500 meters above sea
level. The majority of land surfaces have been converted to rain-fed croplands. Natural Ugandan
vegetation that remains intact includes closed canopy forests of the relatively-wet southwest and
open shrublands of the relatively-arid northwest. Regolith depths can extend to depths of 30m below
groundwater surfaces, and the bedrock geology includes endogenous granulites in central Uganda
and metasedimentary rocks along the western arm of the east African rift system, located in western
Uganda (Taylor and Howard, 1998a; 1998b; 2000).
The hydrography of Uganda ranges from flashy systems in the steep mountainous systems
of western Uganda, to slow-flowing and vast wetlands in the subdued relief in central Uganda, to
semi-arid ephemeral flow systems on the westward slopes of Mt. Kenya and other mountains in
northeastern Uganda. Annual rainfall ranges from minimums of ~700 mm per year in the northeast
to maximums of ~1,500 mm per year in the southwest. Lake Victoria – the second largest area of
fresh surface water on Earth – borders Uganda’s southeastern margins and its outflow generates the
headwaters of the White Nile. Uganda’s drainage system is dominated by two flow systems: (i)
drainage into Lake Victoria and Lake Kyoga, and (ii) drainage into the rift lakes of western Uganda
(i.e., Lakes George, Edward, and Albert). The two drainage systems converge at the northern margin
of Lake Albert before flowing northward into Sudan.
Stable isotope investigations of Ugandan waters have targeted improved knowledge of
groundwaters (Taylor and Howard, 1996; 1998a; Tindimugaya et al., 2007), surface waters (Russell
and Johnson, 2006) and geothermal systems (Kato, 2000; Bahati et al., 2005). Groundwater stable
isotopes have revealed that recharge occurs almost exclusively during the rainy season (April to
October) and that recharge is at a maximum during high intensity rainfall events (Taylor and Howard
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1996; 1998)). Coupled hydrology-geomorphology investigations have shown that the regolith and
subsurface bedrock are hydraulically-linked, that the regolith is more permeable than bedrock
aquifers, and that this weathered mantle hosts a more active hydrosphere than underlying fractured
bedrock systems (Taylor and Howard, 1996; 1998a; 2000). Annual groundwater recharge rates are
spatially variable. Paired watershed studies show that recharge at each catchment varies ten-fold with
recharge rates of 20 to 200 mm/year in each respective catchment (Taylor and Howard, 1998a).
Groundwater ages calculated using chlorofluorocarbons and tritium show that modern groundwater
with a mean age of less than 50 years exists at depths of more than 60 meters below ground level,
suggesting that certain shallow aquifers are well-flushed (Tindimugaya et al., 2007). Other isotope
based investigations have quantified over-lake evaporation from Lake Edward using a stable isotope
based approach (Russell and Johnson, 2006) and assessed geothermal activity (Kato, 2000; Bahati et
al., 2005).
The primary objective of this study is to quantify water fluxes into and out of Ugandan
surface waters via a stable isotope mass balance.
4.3 Dataset and methods
4.3.1 Sample collection and analysis
Samples of water were collected in high-density polyethylene bottles over a three week
sampling campaign in July of 2013. Water samples were collected from rivers, lakes, wetlands, springs
and groundwater wells throughout Uganda. Two tiers of samples were collected: tier 1, where waters
were sampled for analysis of 18O/16O and 2H/1H ratios, and tier 2, where waters were sampled for
18O/16O and 2H/1H ratios, concentrations of Ca2+, Mg2+, K+, Na+, Cl-, SO42- and 43 other solutes.
Tier 2 water samples (n = 45) were filtered through a 0.45 micron filter in the field. One sample was
preserved with ultrapure nitric acid (major cations, trace metals and uranium series elements) and
another was left without acidification (major anions). Chemical analyses were completed at the
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University of New Mexico’s Analytical Chemistry Laboratory. Stable oxygen and hydrogen isotopic
compositions of water samples were analyzed at the University of New Mexico using a Picarro
L1102-i liquid water analyzer.
The three week field campaign led to the collection of 225 water samples. Because of the
short duration of our field trip we specifically targeted water bodies expected to integrate prolonged
residence times such as lakes (n = 36), groundwater wells (n = 75) and springs (n = 13). We also
sampled streams (n = 67), tap water (n = 22) and swamps (n = 12) throughout Uganda where
sampling opportunities arose.
4.3.2 Surface waters – evaporation modelling
We use the stable O and H isotopic data of lake waters to calculate evaporation losses from
each system. The approach taken has been described in numerous studies and readers are directed to
these earlier works for additional descriptions (Zuber, 1983; Gonfiantini, 1986; Gat et al., 1996;
Gibson, 2002; Gibson and Edwards, 2002; Gibson et al., 1996; 1998; 2002; Froehlich, 2000; Russell
and Johnson, 2006; Horita et al., 2008; Yi et al., 2008; Brock et al., 2009; Turner et al., 2010; 2014).
The calculation of lake water balances via a stable-isotope-based approach couples a hydrologic
(Equation 4.1) and isotopic (Equation 4.2) mass balance under an assumption of steady state:
𝐼 = 𝐸 + 𝑄 Equation 4.1
𝐼𝛿𝐼 = 𝐸𝛿𝐸 + 𝑄𝛿𝑄 Equation 4.2
where I, E and Q are the fluxes of water entering the lake (I), evaporation from the lake (E) and
liquid outflow from the lake via surface or groundwater discharges (Q), and δ denotes the isotopic
composition of each flux. Combining equations 4.1 and 4.2 yields an estimate of the evaporation flux
as a proportion of water inputs to each lake (i.e., evaporation/input ratio: E/I; Equation 4.3)
216
𝐸
𝐼=
𝛿𝐼−𝛿𝐿𝑎𝑘𝑒
𝛿𝐸−𝛿𝐿𝑎𝑘𝑒 Equation 4.3
assuming liquid outflows from the lake have the same isotopic composition as the bulk lake (i.e., well
mixed assumption: δQ = δLake). The isotopic composition of water inputs to lakes is estimated as the
intercept of a regression of lake O and H isotopic compositions (“local evaporation line: LEL;”
Figure 4-1) and a regression of Ugandan rainfall (“meteoric water line: MWL;” Figure 4-1). We find
that the isotopic composition of input waters to each lake is between δ18O values of −2.1 ‰ to +0.0
‰ and δ2H input values between −3.5 ‰ and +9.8 ‰ (maximum and minimum intercepts of 95th
percent confidence interval regressions of meteoric waters (MWL) and lake water (LEL)). Annual
evaporation rates (i.e., mm/year, rather than E/I ratios) can be calculated by rearranging equations
4.1 and 4.2 in cases where the liquid outflow from the lake is gauged (Jasechko et al., 2014):
𝐸 = 𝑄 ×𝛿𝐼−𝛿𝐿𝑎𝑘𝑒
𝛿𝐸−𝛿𝐼 Equation 4.4
The isotopic composition of evaporate is estimated applying an evaporation model (Craig
and Gordon, 1965):
𝛿𝐸 =(𝛿𝐿𝑎𝑘𝑒−[𝛼𝑙∙𝑣
∗−1])/𝛼𝑙∙𝑣∗−ℎ𝛿𝐴−(𝐶𝑘[1−ℎ])
1−ℎ+(𝐶𝑘[1−ℎ]) Equation 4.5
where 𝛼𝑙∙𝑣∗ represents a temperature-dependent equilibrium isotope fractionation factor (Horita and
Wesolowski, 1994), h represents the relative humidity near to the lake surface (derived from New et
al., 2002), δA represents the isotopic composition of the atmosphere (calculated as δA = δP – (𝛼𝑙∙𝑣∗−
1; Gibson et al., 2002) and CK is a constant that describes kinetic isotope effect during evaporation
(CK is 13.7 to 20.7 for δ18O-based calculations and 7.5 to 16.1 for δ2H based calculations; Jasechko et
al., 2014).
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4.4 Results
4.4.1 Stable O and H isotopic composition of Ugandan waters
The isotopic composition of Ugandan surface- and ground-waters sampled in this study
range from −4.0 ‰ to +8.7 ‰ in δ18O, −13.2 ‰ to +55.5 ‰ in δ2H, and −16.7 ‰ to +21.9 ‰ in
deuterium excess (Figure 4-1; Table 4-1; Table 4-2). Monthly precipitation samples (n = 267)
collected at Entebbe (Uganda, n = 182), Soroti (Uganda, n = 11), Jinja, (Uganda, n = 27), Masaka
(Uganda, n = 20), Wobulenzi (Uganda, n = 8) and Kericho (western Kenya, n = 19) between 1960
and 2010 by the International Atomic Energy Agency (e.g., Araguás-Araguás et al., 2002) range from
−11.6 ‰ to +11.4 ‰ in δ18O, −81.2 ‰ to +69.0 ‰ in δ2H, and −22.1 ‰ to +27.1 ‰ in deuterium
excess, and plot along a regression – herein the “Ugandan meteoric water line” – of δ2H =
7.21(±0.13)×δ18O + 10.76(±0.40) (uncertainties are standard error of regression; Figure 4-1; Table
1). Water sampling locations are shown in Figure 4-2.
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Figure 4-1. The O and H isotopic composition of Ugandan waters. Different symbols mark unique
water types, including groundwaters (squares), rivers (circles) and lakes (triangles). Black lines mark
linear regressions of meteoric waters (MWL) and lakes (LEL; grey funnel plot marks the 95th percent
confidence interval of regressions).
Lakes have the highest δ18O and δ2H values and the lowest deuterium excess values of each
of the sample groups. Lakes plot near to groundwater in some cases, but also plot along a trajectory
“beneath” meteoric waters in δ18O-δ2H space (Figure 4-1). A regression of the lake data gives a
δ2H/δ18O slope of 5.13±0.13, significantly shallower than a regression of meteoric waters (δ2H/δ18O
slope of 7.21±0.13).
River and wetlands have oxygen and hydrogen isotopic compositions that are similar to
Ugandan rainfall in most cases. A subset of river samples have deuterium excess values of less than
zero and plot close to lakes sampled in this study (10 of 67 river water samples, 15%). Some river
219
samples having deuterium excess values of less than zero were sampled downstream of large lakes
(e.g., outflows of Lakes Victoria and Lake Kachera). Perennial wetlands are common in central
Uganda near to Lake Kyoga. Isotopic analysis of wetland samples shows that most samples plot
along the Ugandan meteoric water line near to groundwater samples.
Groundwater samples collected from wells (n = 75) plot near to the Ugandan rainfall in
most cases in δ2H-δ18O space. Groundwater samples have deuterium excess values that are similar to
Ugandan rainfall (average deuterium excess values of 10.2 ‰ for groundwater and 12.3 ‰ for
rainfall). Six of our 75 groundwater samples (8 % of all groundwater samples) have a deuterium
excess value of less than zero, similar to Ugandan lakes (average deuterium excess of −1.3 ‰; Table
4-1).
Springs and tap waters have δ18O and δ2H values that fall within the range of precipitation in
Uganda. Springs have a deuterium excess value that is similar to rainfall (average of 15.5 ‰). The
lowest δ18O value observed in our dataset (−4.0 ‰) is a hot spring sample from the foothills of the
Rwenzori Mountains collected at an elevation of 1600 meters above sea level. The sources of tap
water were unknown in most cases. Tap waters are found to have stable O and H isotopic
compositions that fall within the range of groundwaters and surface waters collected in this study.
220
Table 4-1. The isotopic composition of Ugandan waters
Sample type n δ18O (‰) δ2H (‰) d-excess (‰)
Avg. s.d. Avg. s.d. Avg. s.d.
Groundwater 75 −0.5 1.9 +6.1 10.2 +10.2 6.0
Lakes 36 +3.4 2.6 +25.9 13.3 −1.3 7.6
Rivers 67 −0.5 2.4 +5.8 12.7 +10.0 7.6
Springs 13 −2.0 1.7 −0.3 9.3 +15.5 4.7
Swamp water 12 −0.3 2.0 +8.2 12.6 +10.5 6.5
Tap water 22 +0.1 2.5 +8.8 12.8 +7.9 7.5
Rainfall * 267 −2.0 2.4 −3.6 17.7 +12.3 5.3
* rainfall statistics from the combined precipitation datasets collected at Entebbe, Soroti, Jinja,
Masaka, Wobulenzi and Kericho; data obtained from the International Atomic Energy Agency’s
Water Resources Programme: www.iaea.org/water.
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Figure 4-2. Locations of water samples collected in Uganda: groundwater (yellow squares), crater
lakes (red circles), other lakes (blue circles), rivers (blue triangles), springs (black triangles), wetlands
(green circles), tap waters (small dots).
222
Table 4-2. The isotopic composition and electrical conductivity of Ugandan water samples
ID Type Lat. [°]
Lon. [°]
Alt. [m]
EC [μs/cm]
T
[⁰C]
δ18O [‰ SMOW]
δ2H [‰ SMOW]
d−excess
T01 Tap 0.1 32.5 399 105 25.6 −0.24 5.9 7.9
T02 Tap 0.3 32.6 1187 132 26.3 1.88 16.6 1.6
T03 Tap 0.0 32.0 1166 −3.15 −9.9 15.4
T04 Tap −0.3 31.8 1250 −2.93 −10.2 13.2
T05 Tap 0.1 32.5 1188 3.53 26.7 −1.6
T06 Tap −0.6 31.0 1279 0.96 10.1 2.5
T07 Tap −0.5 30.7 1466 144 26.4 −1.64 0.6 13.7
T08 Tap −1.3 30.0 1888 265 17.5 3.94 27.9 −3.7
T09 Tap −0.2 30.0 980 −2.64 −3.5 17.6
T10 Tap 0.6 31.4 1286 173 29.4 0.05 6.9 6.5
T11 Tap 0.4 32.7 1196 129 24.9 3.16 25.3 0.1
T12 Tap 0.5 33.3 1195 103 26.3 3.54 26.9 −1.4
T13 Tap 0.8 33.7 1122 132 27.1 −1.83 −3.7 10.9
T14 Tap 1.1 34.2 1126 133 29.4 −1.57 2.5 15.1
T15 Tap 1.1 34.2 1126 130 26.3 −1.62 2.6 15.5
T16 Tap 0.4 32.7 1196 129 24.9 3.38 25.2 −1.9
T17 Tap 0.4 33.1 1245 106 25.0 4.06 28.8 −3.7
T18 Tap 2.3 31.6 636 943 32.1 −0.63 9.6 14.6
T19 Tap 1.4 32.3 1088 −2.17 −0.4 17.0
T20 Tap 1.6 31.7 1191 −1.42 2.1 13.5
T21 Tap 1.2 32.4 1084 53 26.3 −1.04 2.1 10.5
T22 Tap 0.2 30.1 964 −1.17 1.3 10.7
G01 Grdwtr. −0.4 31.5 1263 350 25.0 −0.64 4.7 9.8
G02 Grdwtr. −0.5 31.0 1285 258 25.1 3.44 28.1 0.6
G03 Grdwtr. −0.7 30.2 1504 71 18.0 −2.00 0.5 16.5
G04 Grdwtr. −1.0 30.2 1433 126 21.8 −1.64 −0.8 12.4
G05 Grdwtr. −0.6 29.8 1031 102 29.0 −0.98 3.6 11.4
G06 Grdwtr. −0.4 29.9 995 88 22.8 0.35 11.6 8.9
G07 Grdwtr. 0.5 30.1 1650 −2.50 −3.3 16.7
G08 Grdwtr. 1.2 34.2 1166 255 27.2 −1.32 2.0 12.5
G09 Grdwtr. 1.9 34.6 1146 726 28.4 0.21 13.8 12.1
G10 Grdwtr. 2.5 34.6 1259 652 25.5 0.08 9.5 8.8
G11 Grdwtr. 0.5 33.4 1148 239 26.4 −2.35 −5.1 13.6
G12 Grdwtr. 0.6 33.5 1144 116 27.6 3.95 29.1 −2.5
G13 Grdwtr. 0.8 33.6 1085 171 29.0 −1.79 −2.8 11.5
G14 Grdwtr. 1.2 34.3 1117 198 22.7 −1.45 2.1 13.8
223
ID Type Lat. [°]
Lon. [°]
Alt. [m]
EC [μs/cm]
T
[⁰C]
δ18O [‰ SMOW]
δ2H [‰ SMOW]
d−excess
G15 Grdwtr. 1.7 34.6 1144 723 28.1 −1.44 −0.4 11.1
G16 Grdwtr. 2.5 34.7 1407 718 23.6 −1.08 4.7 13.3
G17 Grdwtr. 2.4 34.5 1205 491 27.1 0.27 5.5 3.4
G18 Grdwtr. 2.1 34.2 1180 1060 26.4 −0.40 5.4 8.5
G19 Grdwtr. 2.0 34.1 1085 394 27.5 −0.23 7.0 8.9
G20 Grdwtr. 1.9 34.0 1060 619 27.5 −0.83 4.0 10.6
G21 Grdwtr. 1.9 34.0 1099 624 26.2 −0.68 6.3 11.7
G22 Grdwtr. 1.9 33.8 1054 187 27.9 −1.79 −2.3 11.9
G23 Grdwtr. 1.9 33.8 1078 129 27.3 3.87 23.0 −8.0
G24 Grdwtr. 1.8 33.5 1067 179 27.9 −1.15 4.0 13.2
G25 Grdwtr. 1.9 33.2 1098 233 26.8 −1.29 3.6 13.9
G26 Grdwtr. 2.0 33.1 1047 371 26.8 −0.11 7.6 8.4
G27 Grdwtr. 2.1 32.9 1066 147 27.3 0.12 10.1 9.2
G28 Grdwtr. 2.2 32.3 1041 100 27.2 1.02 24.1 15.9
G29 Grdwtr. 2.2 32.3 1045 189 28.3 −1.41 2.6 13.9
G30 Grdwtr. 2.3 34.3 1175 1390 26.1 −1.24 2.8 12.7
G31 Grdwtr. 1.9 34.0 1099 178 25.4 −1.47 −3.2 8.6
G32 Grdwtr. 1.8 33.6 1075 184 28.1 −1.51 −0.9 11.2
G33 Grdwtr. 1.9 33.3 1129 152 27.6 −1.71 −4.2 9.5
G34 Grdwtr. 1.9 33.1 1055 146 27.9 −3.09 −5.8 18.9
G35 Grdwtr. 2.0 33.0 1065 460 22.9 −0.94 5.0 12.5
G36 Grdwtr. 2.3 32.4 1055 101 27.2 −0.74 5.6 11.5
G37 Grdwtr. 2.5 32.4 1067 133 27.2 −0.96 3.0 10.6
G38 Grdwtr. 2.6 32.4 1073 188 26.5 −0.68 7.3 12.7
G39 Grdwtr. 2.7 32.3 1090 161 25.8 −0.75 5.3 11.4
G40 Grdwtr. 2.8 32.2 1080 293 25.1 1.12 2.5 −6.5
G41 Grdwtr. 2.6 31.8 959 160 27.2 −0.77 3.3 9.4
G42 Grdwtr. 2.8 32.2 1096 153 25.0 0.11 5.1 4.2
G43 Grdwtr. 2.7 32.2 1084 119 26.4 −1.26 −0.2 9.9
G44 Grdwtr. 2.6 32.0 985 162 26.7 −1.25 0.5 10.6
G45 Grdwtr. 2.6 31.9 962 144 24.0 −0.96 3.5 11.2
G46 Grdwtr. 2.6 31.6 854 232 28.5 −0.74 5.5 11.4
G47 Grdwtr. 1.7 31.3 1056 118 26.1 7.80 50.4 −12.1
G48 Grdwtr. 1.5 31.3 1128 62 24.4 5.03 32.7 −7.5
G49 Grdwtr. 2.2 31.5 719 372 27.2 3.35 25.8 −1.0
G50 Grdwtr. 1.6 31.3 1079 217 27.6 0.48 11.1 7.3
G51 Grdwtr. 1.6 31.3 1090 235 25.6 −0.27 8.9 11.1
G52 Grdwtr. 1.5 31.3 1231 34 24.7 −1.39 2.0 13.1
G53 Grdwtr. 1.6 31.8 1191 194 23.0 −1.42 2.1 13.5
224
ID Type Lat. [°]
Lon. [°]
Alt. [m]
EC [μs/cm]
T
[⁰C]
δ18O [‰ SMOW]
δ2H [‰ SMOW]
d−excess
G54 Grdwtr. 0.8 32.5 1117 277 26.2 2.67 25.9 4.6
G55 Grdwtr. 1.6 32.0 1053 327 25.3 −1.72 −1.9 11.8
G56 Grdwtr. 1.4 32.3 1088 82 26.2 −1.17 1.1 10.5
G57 Grdwtr. 0.1 32.5 1149 104 25.6 3.98 32.6 0.8
G58 Grdwtr. −0.6 30.6 1417 128 28.1 −1.42 3.2 14.5
G59 Grdwtr. −0.6 30.4 1469 454 25.1 −1.77 1.5 15.7
G60 Grdwtr. −1.1 29.9 1960 212 18.5 −2.17 0.7 18.0
G61 Grdwtr. −1.0 29.9 1715 305 25.7 −2.62 −2.7 18.3
G62 Grdwtr. 0.3 30.1 1202 1530 28.8 −2.03 −0.5 15.8
G63 Grdwtr. 0.5 31.1 1369 75 24.0 −1.07 4.9 13.4
G64 Grdwtr. 1.0 33.8 1079 911 28.5 −1.97 −0.7 15.0
G65 Grdwtr. 0.8 33.7 1132 −0.69 8.1 13.6
G66 Grdwtr. 1.4 34.3 1109 922 27.9 −0.78 4.8 11.0
G67 Grdwtr. 1.6 34.5 1100 1100 28.5 −2.15 −6.6 10.6
G68 Grdwtr. 2.4 34.4 1194 1000 26.3 −1.76 −1.0 13.1
G69 Grdwtr. 2.8 32.1 1068 587 25.0 −0.68 6.0 11.4
G70 Grdwtr. 2.1 31.5 664 485 31.2 −1.71 −2.9 10.8
G71 Grdwtr. 1.5 31.5 1146 74 25.7 −1.11 4.7 13.5
G72 Grdwtr. 1.4 32.3 1095 408 25.6 −1.45 0.7 12.3
G73 Grdwtr. 0.8 32.5 1113 206 25.7 −1.43 1.9 13.4
G74 Grdwtr. 0.2 30.1 1417 −1.27 4.4 14.5
G75 Grdwtr. 0.2 30.1 1417 −1.27 4.4 14.5
S01 Spring 0.2 32.5 1196 3.27 26.9 0.8
S02 Spring 0.5 30.1 1650 93 18.8 −2.11 −0.3 16.6
S03 Spring 0.5 30.1 1740 59 20.0 −2.82 −4.0 18.6
S04 Spring 0.4 30.2 1126 4100 24.8 −2.41 −1.7 17.6
S05 Spring 0.4 30.2 1126 4750 24.9 −2.41 −1.7 17.6
S06 Spring 0.4 30.2 1126 4750 24.9 −2.19 0.3 17.9
S07 Hot Spring −0.7 30.2 1632 760 54.0 −2.13 0.6 17.6
S08 Hot Spring −0.7 30.2 1632 761 54.0 −1.95 0.2 15.8
S09 Hot Spring −0.9 30.0 1382 1710 54.0 −3.15 −8.1 17.1
S10 Hot Spring 0.5 30.1 1650 1710 66.8 −4.03 −13.2 19.1
S11 Hot spring 2.8 31.9 1019 −2.23 −3.5 14.4
S12 Hot spring 2.8 31.9 1019 −2.23 −3.5 14.4
S13 Hot spring 2.8 31.9 1019 −2.12 −1.1 15.9
L01 Crater Lk. 0.4 30.2 1166 6750 28.1 7.51 46.6 −13.5
L02 Crater Lk. −0.4 30.3 1549 947 28.0 6.64 39.7 −13.4
L03 Crater Lk. 0.4 30.2 1169 1110 29.5 8.44 50.8 −16.7
L04 Crater Lk. −0.4 30.3 1533 376 26.8 −0.29 7.4 9.8
225
ID Type Lat. [°]
Lon. [°]
Alt. [m]
EC [μs/cm]
T
[⁰C]
δ18O [‰ SMOW]
δ2H [‰ SMOW]
d−excess
L05 Crater Lk. −0.4 30.3 1491 413 27.5 5.05 34.1 −6.3
L06 Crater Lk. −0.4 30.3 1574 451 23.6 1.17 15.0 5.7
L07 Crater Lk. −0.5 30.3 1645 320 29.6 4.68 30.3 −7.1
L08 Crater Lk. 0.5 30.3 1471 338 28.1 4.20 30.6 −3.0
L09 Crater Lk. −0.5 30.3 1471 4.20 30.6 −3.0
L10 Crater Lk. 0.5 30.3 1346 508 3.55 27.9 −0.4
L11 Crater Lk. 0.5 30.3 1281 434 3.81 29.4 −1.1
L12 Crater Lk. −0.3 30.1 1337 119 26.2 3.18 25.3 −0.1
L13 Crater Lk. −0.3 30.1 1335 152 26.4 2.74 22.4 0.5
L14 Crater Lk. −0.3 30.1 1344 119 24.9 3.59 26.8 −2.0
L15 Crater Lk. −0.3 30.1 1304 −1.57 −0.9 11.7
L16 Crater Lk. −0.3 30.1 1304 167 28.1 2.45 18.3 −1.2
L17 Crater Lk. −0.3 30.8 1317 343 28.2 5.61 36.1 −8.8
L18 Crater Lk. −0.3 30.1 1268 469 28.2 2.17 20.1 2.7
L19 Crater Lk. −0.3 30.1 1318 745 29.7 7.17 44.7 −12.7
L20 Crater Lk. −0.3 30.1 1318 7.17 44.7 −12.7
L21 Crater Lk. −0.2 30.1 1038 728 25.1 −1.08 4.8 13.4
L22 Crater Lk. −0.3 30.1 1387 298 4.56 28.8 −7.7
L23 Crater Lk. −0.3 30.1 1387 315 27.0 5.48 34.7 −9.1
L24 Lake −0.7 30.9 1259 0.08 8.8 8.2
L25 Lake −0.7 30.9 1259 0.08 8.8 8.2
L26 Lake −0.7 30.9 1259 0.08 8.8 8.2
L27 Lake −0.6 31.0 1281 126 0.57 9.2 4.6
L28 Lake 0.1 32.5 1149 122 25.7 3.62 31.4 2.4
L29 Lake −0.5 31.2 1268 374 26.2 2.91 22.4 −0.9
L30 Lake −1.3 29.9 2122 264 20.0 4.45 32.6 −2.9
L31 Lake −0.3 29.9 914 857 29.0 4.22 34.7 0.9
L32 Lake 0.4 32.0 1168 341 28.5 5.40 36.8 −6.3
L33 Lake −1.3 29.8 1905 2.11 17.4 0.5
L34 Lake −1.2 29.7 1907 1.23 13.8 3.9
L35 Lake −1.3 29.7 1815 1.29 15.0 4.7
L36 Lake 1.8 31.3 618 591 30.0 6.22 45.8 −4.0
Q01 Swamp 0.2 32.3 1178 105 19.7 −1.44 −1.6 9.9
Q02 Swamp 0.9 33.7 1056 139 23.0 −1.41 −1.2 10.1
Q03 Swamp 1.9 33.8 1067 352 25.7 −1.39 −0.3 10.8
Q04 Swamp 1.9 33.2 1090 379 27.7 0.46 11.1 7.4
Q05 Swamp 2.0 34.1 1066 139 21.9 −2.44 −3.5 16.0
Q06 Swamp 2.0 33.0 1036 160 27.9 1.51 31.2 19.1
Q07 Swamp 2.8 31.9 1019 −2.32 −5.1 13.5
226
ID Type Lat. [°]
Lon. [°]
Alt. [m]
EC [μs/cm]
T
[⁰C]
δ18O [‰ SMOW]
δ2H [‰ SMOW]
d−excess
Q08 Pond −0.6 30.3 1539 32 25.7 1.39 13.3 2.1
Q09 Pond −0.6 29.7 953 590 20.0 −1.16 0.4 9.7
Q10 Pond 2.2 34.3 1185 75 21.3 −0.24 11.7 13.6
Q11 Pond 2.3 34.4 1168 375 19.1 −1.89 1.0 16.1
Q12 Pond 0.2 32.4 1205 109 18.3 −1.13 1.2 10.2
R01 Major river 2.3 31.6 634 4.64 32.7 −4.5
R02 Major river 0.0 32.0 1197 117 21.1 −0.91 4.5 11.8
R03 Major river −1.4 30.0 1958 142 −1.41 −2.5 8.7
R04 Major river 2.5 31.5 624 2410 −2.56 −9.2 11.3
R05 Major river 1.7 32.1 1044 127 3.36 26.0 −0.9
R06 River −0.5 31.2 1265 3.00 14.7 −9.3
R07 River −0.5 30.9 1338 101 24.1 1.37 5.3 −5.7
R08 River −0.6 30.6 1417 620 26.1 −0.59 −0.6 4.1
R09 River −0.6 30.3 1524 690 24.3 −1.79 −0.4 13.9
R10 River −0.6 30.2 1515 540 25.2 −1.45 1.8 13.3
R11 River −1.3 30.1 1858 101 20.7 0.26 8.0 5.9
R12 River −1.1 29.9 1969 161 15.0 −2.56 −3.9 16.7
R13 River −1.0 29.9 1932 87 16.3 −2.75 −4.7 17.3
R14 River −0.9 30.0 1444 104 23.4 −1.21 2.1 11.8
R15 River −0.8 29.8 1372 185 24.5 −1.82 −0.7 13.8
R16 River −0.8 29.8 1449 91 24.4 −1.51 1.3 13.4
R17 River −0.7 29.9 1239 92 25.7 −1.61 0.2 13.1
R18 River −1.3 30.0 1907 168 16.2 −2.47 −5.7 14.1
R19 River −1.0 30.0 1722 385 20.9 −2.94 −7.4 16.1
R20 River −0.8 29.8 1372 286 22.5 −0.93 3.9 11.4
R21 River −0.8 29.8 1449 154 24.9 −1.49 1.8 13.7
R22 River −0.7 29.9 1170 243 25.1 −0.58 5.7 10.4
R23 River 0.0 29.8 1042 335 25.7 −1.31 −0.8 9.7
R24 River 0.0 29.9 1088 84 23.5 0.86 10.1 3.2
R25 River −0.6 29.7 969 216 23.6 0.92 14.0 6.6
R26 River −0.5 29.7 918 59 23.4 −2.04 −2.0 14.3
R27 River −0.3 29.9 986 151 23.4 −1.19 0.9 10.4
R28 River −0.2 30.0 963 334 19.1 −2.34 −2.5 16.2
R29 River −0.3 30.1 1304 85 20.9 −3.18 −6.8 18.6
R30 River 0.3 30.1 1107 86 20.0 −2.94 −4.7 18.8
R31 River 0.4 30.2 1067 −2.85 −4.9 17.9
R32 River 0.7 30.3 1520 761 20.6 −1.24 2.6 12.5
R33 River 0.6 30.4 1462 426 20.7 −1.70 0.6 14.2
R34 River 0.6 30.6 1364 −0.55 5.1 9.5
227
ID Type Lat. [°]
Lon. [°]
Alt. [m]
EC [μs/cm]
T
[⁰C]
δ18O [‰ SMOW]
δ2H [‰ SMOW]
d−excess
R35 River 0.4 32.0 1178 41 21.0 −1.64 −1.1 12.0
R36 River 2.4 34.6 1257 316 30.5 4.49 30.3 −5.6
R37 River 0.4 32.8 1120 42 20.0 −1.49 −1.6 10.3
R38 River 1.1 34.2 1126 322 24.9 1.35 16.4 5.6
R39 River 1.8 34.6 1228 294 33.6 −1.35 1.4 12.2
R40 River 2.4 34.6 1268 106 25.6 1.46 32.4 20.7
R41 River 1.9 33.3 1093 132 29.5 −0.90 6.7 13.9
R42 River 2.0 33.0 1065 233 29.1 −2.81 −3.3 19.2
R43 River 2.4 32.4 1063 170 26.8 −1.12 1.4 10.3
R44 River 2.5 34.7 1407 272 18.3 −1.27 1.3 11.5
R45 River 2.4 34.5 1198 180 18.3 3.69 28.4 −1.1
R46 River 1.8 33.5 1058 208 30.1 −2.33 −4.5 14.1
R47 River 1.9 33.4 1043 195 26.4 −1.61 1.7 14.5
R48 River 1.9 33.3 1118 172 27.7 3.23 26.2 0.4
R49 River 1.9 33.2 1084 173 28.3 3.56 25.1 −3.4
R50 River 2.2 32.9 1136 149 24.2 −0.91 4.3 11.6
R51 River 2.8 32.0 1043 63 21.0 −2.30 −6.0 12.4
R52 River 2.6 32.1 990 134 25.1 8.62 55.5 −13.5
R53 River 2.8 32.0 1042 64 20.7 −0.80 5.2 11.6
R54 River 2.3 31.7 716 4.63 32.8 −4.2
R55 River 1.9 31.4 634 108 25.7 −0.55 6.7 11.1
R56 River 1.9 31.5 634 151 25.9 −0.75 5.0 11.0
R57 River 1.7 31.4 991 324 25.5 −0.86 3.3 10.1
R58 River 1.6 31.6 1111 32.3 −0.60 7.0 11.9
R59 River 1.5 32.0 1045 −1.14 2.3 11.4
R60 River 1.6 31.8 1123 151 22.5 −0.39 4.8 7.9
R61 River 1.7 32.1 1044 127 25.1 −1.18 1.9 11.3
R62 River 1.0 32.5 1076 121 22.4 −3.20 −12.2 13.4
R63 River −0.6 30.9 1280 754 21.8 −2.32 −5.2 13.4
R64 River 0.2 30.0 1420 59 17.2 −2.94 −1.7 21.9
R65 River 0.5 30.1 1650 121 15.7 −3.04 −2.7 21.6
R66 River 2.2 32.2 1047 110 27.0 4.87 36.4 −2.6
R67 River −1.2 29.7 1949 −1.05 5.3 13.7
228
4.4.2 Ugandan hydrochemistry
The salinity of Ugandan waters sampled in this study ranges from total dissolved solids
values of 25 ppm to 3900 ppm. The highest salinities are observed in hot springs that have total
dissolved solids ranging between 248 mg/L and 3851 mg/L. The lowest salinities are observed in
rivers (n = 9) that have total dissolved solids of less than 1,000 mg/L. Crater lakes have the largest
variability in total dissolved solids, with a minimum of 80 mg/L and a maximum of 2100 mg/L.
Most samples are usually Ca-HCO3 water types (Figure 4-3), with the exception of hot springs that
have salinities dominated by Na+-K+ and Cl- and SO42- (Table 4-3).
Contaminants measured in this study include arsenic, fluoride and nitrate. The maximum
contaminant levels set by the Environmental Protection Agency are 10 ppb (arsenic), 4 mg/L
(fluoride) and 10 mg/L (nitrate as NO3-N). Most groundwater and river water samples meet the
drinking water standards. However, nitrate concentrations exceeding the maximum contaminant level
concentration were found in a subset of groundwater samples (e.g., 44 mg/L NO3-N). The origin of
the observed high nitrate concentrations is unknown.
229
Figure 4-3. A Piper diagram showing the major cation (lower left ternary) and major anion (lower
right ternary) projected onto a combined cation-anion diamond (top-most). Bicarbonate
concentrations were calculated using a charge balance because measurements in the field were not
possible.
230
4.4.3 Stable isotope-based evaporation fluxes
Our stable isotope based evaporation/inflow ratios range from near-zero (i.e., well-flushed
lakes) to near 100% (terminal lakes, where evaporation is the only water loss). The Bunyaruguru and
Kasenda crater lake systems (i.e., lakes with “B” and “K” in title, respectively) have a mixture of both
well-flushed lakes and lakes that are terminal (Figure 4-4). This approach, in spite of large
uncertainties, shows that terminal and well-flushed lakes can be distinguished on the basis of δ18O
and δ2H values.
Figure 4-4. Stable-isotope-based evaporation/input ratios for 24 Ugandan Lakes. Grey bars mark the
degree of flushing, with light grey being well-flushed lakes, and dark grey representing lakes having
the majority of water losses via evaporation. Lakes entitled with “B” are the Bunyaruguru crater lakes
system (south of Lake George) and lakes entitled with “K” are the Kasenda crater lakes system
(north of Lake George). The 18O/16O-based model results are shown here.
231
4.5 Discussion
Stable oxygen and hydrogen isotopic data reveal distinct hydrogeochemical processes across
Uganda. For example, stable oxygen isotopic data and electrical conductivity (a proxy for salinity)
show that the processes of mineral dissolution and evapo-concentration can be distinguished.
Mineral dissolution does not alter the oxygen isotopic composition of water and produces an
increase in the electrical conductivity of the water. Evapo-concentration, on the other hand, increases
both the electrical conductivity and the δ18O value of water (Figure 4-5). The process of
evapoconcentration also emerges when examining deuterium excess values and electrical
conductivity. The lake having the highest electrical conductivity also has the lowest deuterium excess
value, consistent with evapoconcentration. Groundwaters generally have a near-meteoric isotopic
composition and a large range of electrical conductivity values, implying that the source of salinity in
nearly all groundwaters is likely to be low-temperature mineral dissolution. Lakes, on the other hand,
have electrical conductivities that rise with increasing δ18O, highlighting that evapoconcentration of
input waters is an important control upon the salinity of certain lakes in Uganda. The process of
evapoconcentration is also evidenced by the relationship between deuterium excess and electrical
conductivity (Figure 4-6); the lake with the lowest deuterium excess (greatest evaporation/input ratio)
also has the highest electrical conductivity (Figure 4-6). The highest
232
Figure 4-5. Oxygen isotopic composition and electrical conductivity of Ugandan lakes (triangles) and
groundwaters (squares). Dashed lines mark schematic trajectories for mineral dissolution under low
temperatures and evapoconcentration of waters.
The deuterium excess of Ugandan river- and ground-waters is elevated for high latitude samples
(Figure 4-7), primarily collected in southwestern Uganda near to the Rwenzori Mountains. Although
the sampling site is different from source water elevations (due to streamflow and groundwater
advection), this finding suggests that precipitation in the Rwenzori mountains has a higher deuterium
excess than other Ugandan waters, perhaps due to moisture recycling in the Congo basin to the west
(Ndembo et al., 2007), or implicating that snowmelt comprises a portion of these samples because of
the known build-up of deuterium excess in snow (Gat et al., 1994).
233
Figure 4-6. Deuterium excess and electrical conductivity of Ugandan lakes (triangles) and
groundwaters (squares).
234
Figure 4-7. The deuterium excess and sample elevation of Ugandan Rivers (circles) and
groundwaters. High altitude samples (i.e., altitudes above 1,600 meters above sea level) have high
deuterium excess values, potentially related to kinetic isotope effects during snow formation or
moisture recycling from the Congo basin to the west.
235
Table 4-3. Major ion chemistry of Ugandan waters (units of ppm)
Sample Type Ca2+ K+ Mg2+ Na+ Si Cl- SO42-
L-19 Crater lake 11.0 71.6 21.4 83.1 18.2 14.6 0.7
R-63 River 58.5 4.5 23.7 43.8 21.0 19.8 321.1
GW-62 Groundwater 125.2 30.9 41.4 108.3 32.1 74.3 208.8
L-03 Crater lake 9.6 81.5 58.2 95.4 4.7 51.1 0.6
L-23 Crater lake 20.4 11.9 18.2 8.5 15.2 5.7 0.6
L-08 Crater lake 19.6 5.7 25.0 8.7 14.1 3.6 1.3
L-10 Crater lake 34.3 9.7 30.8 11.7 14.8 3.7 0.5
L-11 Crater lake 31.4 6.8 25.9 9.3 12.0 3.4 0.7
GW-64 Groundwater 49.9 10.1 12.7 97.5 29.0 199.3 22.0
GW-65 Groundwater 100.8 2.3 20.5 23.9 17.9 37.8 2.2
GW-66 Groundwater 104.8 4.6 18.7 54.3 17.0 3.9 1.1
GW-67 Groundwater bdl 13.1 bdl 244.0 8.4 11.7 5.4
GW-68 Groundwater 81.6 4.8 19.8 100.1 30.4 16.5 71.6
R-53 River 40.5 5.4 18.5 26.1 28.5 136.4 2.3
GW-46 Groundwater 8.3 34.3 8.4 35.2 31.2 2.5 1.1
R-66 River 6.0 3.0 3.3 9.8 2.4 4.3 0.9
GW-70 Groundwater 26.8 8.1 7.5 57.9 38.3 17.4 9.0
L-36 Lake 9.7 37.3 23.1 62.0 0.5 22.7 19.5
GW-71 Groundwater 3.1 0.6 1.8 6.1 6.3 4.9 1.0
GW-72 Groundwater 27.2 4.0 10.5 30.4 37.7 39.7 33.0
GW-73 Groundwater 13.0 3.0 3.7 18.7 34.9 2.2 1.8
R-04 Major river 32.7 12.4 9.8 340.6 16.5 585.0 n.a.
R-05 Major river 5.8 33.6 3.4 20.0 3.1 4.9 0.7
R-03 Major river 7.1 3.5 5.2 11.6 6.4 6.3 4.0
SP-10 Hot Spring 26.7 63.3 4.5 1439.0 24.5 786.8 1434.0
L-32 Lake 17.6 11.3 6.7 32.3 8.3 24.9 0.7
L-28 Lake 8.3 3.3 2.6 10.3 0.8 6.1 3.9
GW-57 Groundwater 5.0 2.6 2.4 9.1 0.8 10.6 11.1
SW-12 Pond 6.7 1.2 3.0 4.3 12.8 1.0 0.5
R-02 Major river 4.9 2.2 2.8 12.4 7.5 6.4 n.a.
L-29 Lake 22.1 9.9 11.3 33.5 10.2 49.3 12.7
GW-58 Groundwater 6.7 1.0 3.3 9.8 6.8 4.8 37.2
GW-59 Groundwater 17.6 12.4 9.2 39.1 16.5 69.2 15.9
SP-07 Hot Spring 33.1 11.0 0.3 185.1 32.2 83.8 344.7
SP-08 Hot Spring 33.2 11.1 0.2 185.7 32.4 115.9 342.4
L-30 Lake 18.2 4.3 8.6 16.3 0.4 26.2 1.6
236
Sample Type Ca2+ K+ Mg2+ Na+ Si Cl- SO42-
GW-60 Groundwater 15.7 2.0 10.6 6.6 14.2 6.4 12.6
GW-61 Groundwater 26.8 3.3 15.6 5.8 7.5 12.0 15.1
SP-09 Hot Spring 66.9 26.3 18.5 434.1 34.0 218.5 469.0
L-31 Lake 13.9 57.0 33.6 77.9 5.1 22.5 25.6
R-64 River 4.6 1.3 1.6 3.5 7.2 1.1 5.9
SP-04 Spring 323.4 70.5 86.7 410.3 37.9 227.8 634.4
R-65 River 9.1 1.5 2.1 13.2 8.1 6.0 12.9
GW-74 Groundwater 42.1 14.1 19.1 9.7 11.9 4.4 13.7
L-01 Crater lake 2.8 161.1 42.8 1229.0 3.4 483.3 178.6
SP-11 Hot spring 9.1 3.9 1.6 89.0 28.4 43.9 64.6
GW-63 Groundwater 2.4 2.1 1.3 5.5 15.6 3.0 2.6
Lake evaporation to input ratios can be derived from both isotopic tracers (i.e., 18O/16O and
2H/1H). Both isotopic tracers are conservative and should yield the same evaporation/inflow ratio if
all model parameters adequately represent reality. However, our results show that (i) the 2H/1H-
based model is more sensitive than the 18O/16O-based model, and (ii) that the 2H/1H-based model
yields higher evaporation/inflow ratios compared to results from the 18O/16O-based model (Figure
4-8).
237
Figure 4-8. Stable-isotope-based evaporation/input ratios computed using a 2H/1H-based model (y-
axis) and an 18O/16O-based model (x-axis).
Previous studies recognizing a mismatch between 2H/1H- and 18O/16O-based
evaporation/input ratios have multiplied α*l-v values by a constant (e.g. Bennett et al., 2008; Gibson
and Reid, 2014) or have only reported 18O-based results (e.g., Zuber, 1983) as 18O/16O-based
evaporation/input ratios are generally more reasonable (i.e., between 0 and 100 percent) than 2H/1H-
based evaporation/input ratios. In this study I report output from each tracer and acknowledge that
2H/1H- and 18O/16O-based results do not match. Next, I reanalyze modelled 2H/1H- and 18O/16O-
based evaporation/input ratios to test for the reasoning behind this discrepancy by modifying
multiple model input parameters: (i) relative humidity, (ii) kinetic fractionation coefficient, (iii)
atmospheric isotopic composition under changing deuterium excess, (iv) atmospheric isotopic
composition under constant deuterium excess.
First, the model input relative humidity of the atmosphere near to the lake surface was
modified to test if relative humidity could lead to convergence of 2H/1H- and 18O/16O-based
evaporation/input ratios. This analysis showed that modifying modelled relative humidity cannot
238
explain the observed mismatch of 2H/1H- and 18O/16O-based evaporation/input ratios. This finding
is consistent with expectations because changes to model input relative humidity simultaneously
impacts both 2H/1H- and 18O/16O-based evaporation/input ratios. Changing relative humidity does
not allow 2H/1H- and 18O/16O-based evaporation/input ratios to converge because the ratio of
CK(δ18O model) / CK(δ2H model) is close to one (see Equation 4.5).
Second, modifying the constant describing the kinetic evaporative isotope effect of an open
water body (CK) was able to converge 18O and 2H-based evaporation/input ratios when the ratio of
CK(δ18O model) / CK(δ2H model) was set to 0.20 to 0.45. To test to see if these CK(δ18O model) /
CK(δ2H model) ratios are reasonable I examined a compilation of empirically-based CK values for
δ18O and δ2H (Jasechko et al., 2014) that show CK(δ18O model) / CK(δ2H model) ratios to be
between 0.8 to 2.8. The CK(δ18O model) / CK(δ2H model) ratio required for convergence of 2H/1H-
and 18O/16O-based evaporation/input ratios (0.20 to 0.45) is lower than empirical CK(δ18O model) /
CK(δ2H model) ratios (0.8 to 2.8), suggesting that the constant describing the kinetic evaporative
isotope effect of an open water body (CK) is not the primary source of the observed difference
between 2H/1H- and 18O/16O-based evaporation/input ratios.
Third, modifying the modelled deuterium excess of the atmosphere converged the 2H/1H-
and 18O/16O-based evaporation/input ratios if the deuterium excess of δA is increased. However, the
modelled deuterium excess of atmospheric vapor must be increased to between +15 ‰ and +60 ‰
before the two evaporation/input ratios match. Although not conclusive, I propose that a build-up
of evaporated moisture over the lake surfaces may impact the isotopic composition of the
atmosphere during evaporation, thereby increasing the deuterium excess of δA and providing a
feedback into future evaporate. This build-up of deuterium excess has been discovered over large
lakes and semi-constrained seas (e.g., deuterium excess values of up to +85‰ observed downwind of
the North American Great Lakes: Machavaram and Krishnamurthy, 1995 other examples: Gat et al.,
239
1994; Bowen et al., 2012; Jasechko et al., 2014). An increase in the deuterium excess of near-surface
atmospheric vapor may explain part of the observed difference in 18O and 2H-based
evaporation/input ratios. However, the deuterium excess values required for convergence of 18O and
2H-based evaporation/input ratios (+15 ‰ and +60 ‰) are unreasonably high for some lakes in this
region, given the deuterium excess of regional precipitation (+12.3; Table 4.1), suggesting that
another model parameter must also be causing observed differences in 18O and 2H-based
evaporation/input ratios.
Fourth, modifying the modelled isotopic composition of the atmosphere under fixed
deuterium excess conditions (i.e., modifying δA under fixed deuterium excess) resulted in a
convergence of 2H/1H- and 18O/16O-based evaporation/input ratios if the offset between vapour-
precipitation was reduced to ~3.8‰ for δ18O and ~30‰ for δ2H. Model predictions of the isotopic
composition of the near-surface atmosphere using an equilibrium offset (Horita and Wesolowski,
1994) suggest higher offsets between atmospheric vapour and precipitation of ~9‰ for δ18O and
~70‰ for δ2H. This finding suggests that the near-surface atmospheric vapor δ18O and δ2H value is
higher than that of condensing vapor, conceptually consistent with known decreases in vapor δ18O
values with increasing height above the land surface (e.g., Strong, 2012). Indeed, Strong (2012) show
that atmospheric vapor δ2H values at >500 metres above the land surface are ~30‰ to ~150‰
lower than at the near surface. This finding shows that precipitation isotopic compositions are a poor
determinant of near-surface atmospheric vapor and that treating δA values determined by an
assumption of equilibrium with precipitation isotopic compositions provides a minimum value for
near surface atmospheric vapor δ18O and δ2H values.
The above discussion rules out changes to the modelled (i) relative humidity, (ii) kinetic
fractionation coefficient and (iii) atmospheric isotopic composition deuterium excess values as the
cause of the discrepancy between 2H/1H- and 18O/16O-based evaporation/input ratios. I show that
240
increasing model input δA under fixed deuterium excess conditions is able to constrain 2H/1H- and
18O/16O-based evaporation/input ratios. This increase is conceptually consistent with a decrease in
atmospheric vapor from the near surface to condensation altitudes. The magnitude of the increase in
model input δA values under fixed deuterium excess required to converge 2H/1H- and 18O/16O-based
evaporation/input ratios is consistent with observations (Strong, 2012). This research suggests that
stable isotope mass balances should use atmospheric vapor isotopic compositions derived from
equilibrium offset with precipitation (i.e., δA = δP – (𝛼𝑙∙𝑣∗− 1)) only as a minimum value.
Conclusions
The primary objective of this study were to quantify Ugandan lake water balances using a stable
isotope mass balance. The water balance of lakes throughout Uganda was indeed quantified using a
stable isotope mass balance and showed that well flushed (evaporation/inflow ratio approaching
zero) and terminal lakes (evaporation/input ratio approaching one) can be determined using a stable
isotope mass balance. Results from δ18O and δ2H mass balances were discovered to produce
inconsistent results with 2H-based evaporation/inflow ratios generally exceeding 18O-based
evaporation/inflow ratios. Further analysis of input parameters to the Craig-Gordon evaporation
model showed that the two tracers are synchronized when the modelled isotope composition of
atmospheric vapour is increased from original predictions that were based upon precipitation
isotopic compositions as a proxy for atmospheric vapor. This finding is conceptually consistent with
a decrease in atmospheric vapor δ18O and δ2H from the near surface to condensation altitudes. This
research suggests that stable isotope mass balances should use atmospheric vapor isotopic
compositions derived from equilibrium offset with precipitation (i.e., δA = δP – (𝛼𝑙∙𝑣∗− 1)) as a
minimum value.
241
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245
APPENDICIES
246
The stable isotopic composition of Earth’s large lakes
247
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Abhe 3.73 -3.5 Ladoga -9.53
Abiyata 10.00 64.2 Ladoga -10.45 -77.2
Abiyata 7.09 46.9 Ladoga -10.31 -75.6
Abiyata 8.36 Lucern -12.68
Abiyata 7.56 Malawi 1.70 10.6
Abiyata 7.52 Malawi 1.66 11.0
Abiyata 8.37 51.9 Malawi 1.60 11.7
Abiyata 7.99 56.1 Malawi 1.60 12.3
Afdera 6.61 29.0 Malawi 1.90 12.3
Afdera 6.62 27.9 Malawi 1.85 12.4
Afdera 5.90 23.9 Malawi 1.89 13.0
Afdera 6.28 29.0 Malawi 1.94 12.5
Afdera 6.49 29.4 Malawi 1.94 13.0
Afdera 6.66 29.2 Malawi 2.08 13.2
Afdera 6.71 28.5 Malawi 2.16 13.2
Afdera 6.87 28.8 Malawi 2.08 13.2
Afdera 6.79 28.3 Malawi 2.14 13.2
Afdera 6.57 27.3 Malawi 2.14 13.5
Afdera 5.28 25.3 Malawi 2.08 13.6
Albert 5.20 37.0 Malawi 2.02 14.0
Aral Sea 3.77 7.0 Malawi 2.09 13.4
Aral Sea 3.57 7.9 Malawi 2.07 13.4
Aral Sea 3.79 9.0 Malawi 2.13 13.5
Aral Sea 3.97 9.2 Malawi 2.06 13.6
Aral Sea 3.98 10.3 Malawi 2.00 13.2
Aral Sea 3.90 -0.1 Manasarovar -11.34 -83.7
Aral Sea 3.84 8.9 Manasarovar -9.36 -75.3
Aral Sea 4.07 9.4 Manasarovar -4.96 -56.2
Aral Sea 3.88 7.5 Manasarovar -3.81 -49.2
Aral Sea 3.89 10.6 Manasarovar -3.30 -44.9
Aral Sea 3.89 9.3 Manasarovar -3.34 -51.6
Aral Sea 3.56 8.0 Manasarovar -2.22 -43.6
Aral Sea 3.25 3.9 Mar Chiquita 3.20 16.0
Aral Sea 3.40 2.2 Mar Chiquita 3.00 16.0
Aral Sea 1.80 -8.4 Mar Chiquita 3.20 17.0
Aral Sea 4.30 7.0 Mar Chiquita 3.30 19.0
Aral Sea 3.60 4.0 Mar Chiquita 3.20 19.0
Aral Sea 3.00 3.2 Mar Chiquita 3.10 16.0
Aral Sea 0.80 -10.4 Mar Chiquita 3.00 18.0
Aral Sea 0.80 -8.8 Mar Chiquita 3.30 20.0
Aral Sea 1.50 -10.6 Mar Chiquita 3.20 18.0
Aral Sea 1.79 -5.9 Mar Chiquita 3.20 18.0
Aral Sea 2.40 -3.9 Mar Chiquita 3.10 18.0
Aral Sea 2.20 -7.6 Mar Chiquita 3.20 16.0
Aral Sea 2.89 0.4 Mar Chiquita 3.10 17.0
Aral Sea 1.60 -7.7 Mar Chiquita 3.00 18.0
248
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Aral Sea -5.30 -47.9 Mar Chiquita 2.90 18.0
Aral Sea 3.60 5.2 Mar Chiquita 3.20 19.0
Aral Sea 1.80 2.5 Mar Chiquita 3.20 18.0
Aral Sea 4.60 4.9 Mar Chiquita 3.20 18.0
Aral Sea -0.50 -19.4 Mar Chiquita 3.20 17.0
Aral Sea 3.20 1.7 Mar Chiquita 3.20 18.0
Aral Sea 3.90 3.6 Mar Chiquita 3.00 18.0
Aral Sea 3.70 4.2 Mar Chiquita 3.20 18.0
Aral Sea 3.80 4.1 Mar Chiquita 3.30 20.0
Aral Sea 3.70 4.6 Mar Chiquita 3.30 18.0
Athabasca -16.40 Mar Chiquita 3.10 18.0
Athabasca -18.30 Mar Chiquita 3.10 17.0
Athabasca -17.00 Mar Chiquita 3.20 17.0
Athabasca -15.30 -131.0 Mar Chiquita 2.90 18.0
Awasa 8.25 54.5 Mar Chiquita 3.30 18.0
Awasa 7.80 53.0 Mar Chiquita 3.30 20.0
Awasa 7.80 53.0 Mar Chiquita 2.10 13.0
Awasa 7.91 54.5 Mead -14.50 -113.5
Awasa 7.85 53.8 Mead -12.80 -100.6
Awasa 7.92 55.3 Mead -12.73 -101.7
Awasa 8.10 57.6 Mead -12.57 -99.3
Awasa 8.18 54.7 Mead -13.54 -107.3
Awasa 8.14 55.9 Mead -13.36 -106.9
Awasa 8.20 55.6 Mead -13.53 -107.5
Awasa 8.27 54.7 Mead -13.82 -108.5
Awasa 8.20 54.3 Mead -14.34 -112.4
Awasa 8.21 56.0 Mead -13.63 -107.8
Awasa 8.22 56.0 Mead -13.67 -106.9
Awasa 8.24 56.3 Mead -13.50 -107.7
Awasa 8.22 56.1 Mediterranean 2.19 8.4
Awasa 8.26 56.3 Mediterranean 1.76 7.4
Awasa 8.26 57.2 Mediterranean 1.73 8.0
Awasa 8.21 57.1 Mediterranean 1.74 8.3
Awasa 8.21 56.8 Mediterranean 1.75 7.5
Awasa 8.25 55.4 Mediterranean 2.20 8.1
Awasa 7.68 46.6 Mediterranean 2.38 8.2
Awasa 7.58 48.4 Mediterranean 1.84 7.8
Awasa 7.48 51.4 Mediterranean 2.04 10.4
Awasa 6.60 44.8 Mediterranean 1.84 9.1
Awasa 5.36 38.3 Mediterranean 10.3
Awasa 6.77 45.2 Mediterranean 2.00 8.6
Awasa 6.65 46.1 Mediterranean 2.13 9.7
Awasa 6.74 45.9 Mediterranean 2.42 5.6
Awasa 6.92 46.2 Mediterranean 2.16 8.2
Awasa 5.46 39.2 Mediterranean 1.53 8.7
Awasa 6.74 43.4 Mediterranean 1.48 8.7
249
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Awasa 7.31 45.6 Mediterranean 1.50 8.1
Baikal -15.90 -123.7 Mediterranean 1.51 8.7
Baikal -15.90 -125.5 Mediterranean 1.48 8.0
Baikal -15.80 -121.6 Mediterranean 1.54 7.4
Baikal -15.90 -123.0 Mediterranean 1.39 6.4
Baikal -16.00 -123.4 Mediterranean 1.55 9.1
Baikal -16.00 -122.5 Mediterranean 1.41 7.8
Baikal -15.80 -122.5 Mediterranean 1.52 6.6
Baikal -15.90 -123.1 Mediterranean 1.46 8.4
Baikal -15.90 -122.2 Mediterranean 1.46 8.1
Baikal -15.80 -122.6 Mediterranean 1.90 7.6
Baikal -15.90 -123.3 Mediterranean 1.43 7.8
Baikal -14.40 -118.1 Mediterranean 1.63 8.4
Baikal -15.80 -123.7 Mediterranean 2.11 8.2
Baikal -15.90 -124.4 Mediterranean 1.74 8.1
Baikal -15.80 -123.5 Mediterranean 1.64 7.8
Baikal -15.90 -121.6 Mediterranean 1.61 6.9
Baikal -15.80 -122.9 Mediterranean 1.55 7.8
Baikal -15.90 -123.4 Mediterranean 1.68 7.5
Baikal -15.90 -123.0 Mediterranean 1.63 7.9
Baikal -15.90 -122.7 Mediterranean 2.19 8.4
Baikal -15.80 -124.2 Mediterranean 1.76 7.4
Baikal -15.90 -123.8 Mediterranean 1.73 8.0
Baikal -15.80 -124.0 Mediterranean 1.74 8.3
Baikal -15.90 -123.2 Mediterranean 1.95 7.5
Baikal -15.90 -124.2 Mediterranean 2.20 8.1
Baikal -15.80 -123.4 Mediterranean 2.38 8.2
Baikal -15.80 -123.7 Mediterranean 1.84 7.8
Baikal -15.80 -122.1 Mediterranean 2.04 10.4
Baikal -15.70 -123.0 Mediterranean 1.84 9.1
Baikal -15.80 -121.3 Mediterranean 10.3
Baikal -15.80 -123.2 Mediterranean 2.00 8.6
Baikal -15.70 -123.4 Mediterranean 2.13 9.7
Baltic Sea -8.20 -61.0 Mediterranean 2.42 8.6
Baltic Sea -7.70 -60.0 Mediterranean 2.16 8.2
Baltic Sea -7.30 -57.0 Mediterranean 1.53 8.7
Baltic Sea -7.40 -57.0 Mediterranean 1.48 8.7
Baltic Sea -7.30 -56.0 Mediterranean 1.50 8.1
Baltic Sea -6.90 -56.0 Mediterranean 1.51 8.7
Baltic Sea -6.90 -55.0 Mediterranean 1.48 8.0
Baltic Sea -7.00 -55.0 Mediterranean 1.54 7.4
Baltic Sea -7.20 -54.0 Mediterranean 1.39 6.4
Baltic Sea -6.80 -54.0 Mediterranean 1.55 9.1
Baltic Sea -6.70 -54.0 Mediterranean 1.41 7.8
Baltic Sea -6.90 -54.0 Mediterranean 1.52 6.6
Baltic Sea -6.90 -54.0 Mediterranean 1.46 8.4
250
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Baltic Sea -7.00 -53.0 Mediterranean 1.46 8.1
Baltic Sea -6.50 -52.0 Mediterranean 1.90 7.6
Baltic Sea -6.70 -51.0 Mediterranean 1.43 7.8
Baltic Sea -6.40 -51.0 Mediterranean 1.63 8.4
Baltic Sea -6.50 -51.0 Mediterranean 2.11 8.2
Baltic Sea -6.70 -51.0 Mediterranean 1.74 8.1
Baltic Sea -6.30 -51.0 Mediterranean 1.64 7.8
Baltic Sea -6.30 -50.0 Mediterranean 1.61 7.0
Baltic Sea -6.40 -49.0 Mediterranean 1.55 7.8
Baltic Sea -6.60 -49.0 Mediterranean 1.68 7.5
Baltic Sea -6.10 -49.0 Mediterranean 1.63 7.9
Baltic Sea -6.40 -47.0 Mediterranean 1.51 8.9
Baltic Sea -5.80 -47.0 Mediterranean 1.71 7.6
Baltic Sea -5.80 -46.0 Mediterranean 1.51 8.9
Baltic Sea -5.80 -46.0 Mediterranean 1.54 8.0
Baltic Sea -5.60 -45.0 Mediterranean 1.19 7.5
Baltic Sea -5.30 -42.0 Mediterranean 1.21 7.9
Baltic Sea -5.30 -41.0 Mediterranean 1.20 8.2
Baltic Sea -5.30 -40.0 Mediterranean 1.33 7.5
Baltic Sea -4.10 -36.0 Mediterranean 1.13 7.8
Baltic Sea -4.30 -35.0 Mediterranean 0.99 7.5
Baltic Sea -1.70 -15.0 Mediterranean 1.38 7.8
Baltic Sea -5.0 Mediterranean 1.39 7.7
Baringo 8.70 47.8 Mediterranean 1.62 7.6
Baringo 8.40 48.0 Mediterranean 1.55 8.5
Baringo 6.60 36.0 Mediterranean 1.50 7.7
Beysehir -1.60 -16.0 Mediterranean 1.43 8.4
Beysehir -1.40 -23.0 Mediterranean 1.82 8.4
Beysehir -1.60 -21.0 Mediterranean 1.54 8.3
Beysehir -1.50 -22.0 Mediterranean 1.41 6.9
Beysehir -1.70 -20.0 Mediterranean 1.80 7.5
Beysehir -1.30 -19.0 Mediterranean 1.63 7.5
Beysehir -3.70 Mediterranean 1.61 8.6
Beysehir -0.60 -13.0 Mediterranean 1.55 7.5
Beysehir -0.70 -13.0 Mediterranean 1.57 7.1
Beysehir -0.70 -16.0 Mediterranean 1.30 7.8
Beysehir -0.60 -14.0 Mediterranean 1.45 7.9
Biwa -6.79 -42.2 Mediterranean 1.49 6.5
Biwa -6.49 -40.2 Mediterranean 1.51 7.8
Biwa -6.84 -42.8 Mediterranean 1.38 7.4
Biwa -6.50 -40.3 Mediterranean 1.84 8.0
Biwa -7.36 -41.7 Mediterranean 1.83 7.3
Biwa -6.81 -41.1 Mediterranean 1.99 7.5
Biwa -6.70 -41.2 Mediterranean 1.99 8.3
Biwa -7.83 -54.4 Mediterranean 1.68 7.2
Biwa -6.94 -46.2 Mediterranean 1.61 7.1
251
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Biwa -7.24 -47.2 Mediterranean 1.60 8.0
Biwa -6.30 -37.9 Mediterranean 1.53 8.1
Biwa -6.89 -44.6 Mediterranean 1.94 8.2
Biwa -7.79 -51.3 Mediterranean 1.77 7.3
Biwa -7.37 -50.8 Mediterranean 1.76 8.4
Biwa -6.29 -41.0 Mediterranean 1.80 7.7
Black Sea -3.59 -27.7 Mediterranean 2.03 7.0
Black Sea -3.49 -26.7 Mediterranean 2.01 8.0
Black Sea -3.42 -27.2 Mediterranean 2.17 7.5
Black Sea -3.37 -26.9 Mediterranean 2.08 7.0
Black Sea -3.38 -26.6 Mediterranean 1.58 7.6
Black Sea -3.29 -25.7 Mediterranean 1.49 8.0
Black Sea -3.27 -25.3 Mediterranean 1.72 7.1
Black Sea -2.98 -23.6 Mediterranean 1.47 7.9
Black Sea -2.97 -23.9 Mediterranean 1.95 8.4
Black Sea -2.97 -24.2 Mediterranean 1.69 7.9
Black Sea -2.93 -24.6 Mediterranean 2.17 7.8
Black Sea -2.84 -23.2 Mediterranean 1.63 7.0
Black Sea -2.63 -23.0 Mediterranean 1.95 7.1
Black Sea -2.69 -22.8 Mediterranean 1.74 7.3
Black Sea -2.71 -22.4 Mediterranean 1.37 7.2
Black Sea -2.67 -22.5 Mediterranean 1.82 8.1
Black Sea -2.63 -22.5 Mediterranean 1.69 8.4
Black Sea -2.65 -22.3 Mediterranean 1.97 8.2
Black Sea -2.69 -22.0 Mediterranean 2.05 7.4
Black Sea -2.68 -21.9 Mediterranean 1.69 8.3
Black Sea -2.63 -22.2 Mediterranean 1.61 7.9
Black Sea -2.63 -21.9 Mediterranean 2.37 7.4
Black Sea -2.64 -21.8 Mediterranean 1.83 7.8
Black Sea -2.61 -21.7 Mediterranean 1.56 7.5
Black Sea -2.61 -22.4 Mediterranean 1.80 7.3
Black Sea -2.58 -22.1 Mediterranean 1.74 7.8
Black Sea -2.57 -21.8 Mediterranean 1.49 8.4
Black Sea -2.59 -21.9 Mediterranean 1.64 8.3
Black Sea -2.57 -21.6 Mediterranean 1.54 8.7
Black Sea -2.56 -21.2 Mediterranean 1.49 8.1
Black Sea -2.60 -21.0 Mediterranean 1.48 8.5
Black Sea -2.62 -21.5 Mediterranean 1.50 7.9
Black Sea -2.65 -21.6 Mediterranean 1.27 7.9
Black Sea -2.67 -21.9 Mediterranean 1.31 8.1
Black Sea -2.60 -21.3 Michigan -5.70 -43.9
Black Sea -2.58 -21.0 Michigan -5.81 -43.3
Black Sea -2.56 -20.9 Michigan -5.79 -43.8
Black Sea -2.57 -20.5 Michigan -5.90 -43.4
Black Sea -2.63 -20.7 Michigan -5.84 -43.6
Black Sea -2.68 -21.6 Michigan -5.77 -43.9
252
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Black Sea -2.65 -23.0 Michigan -5.78 -43.2
Black Sea -2.49 -21.6 Michigan -5.86 -43.3
Black Sea -2.49 -20.8 Michigan -5.78 -44.0
Black Sea -2.50 -20.4 Michigan -5.74 -43.9
Black Sea -2.50 -20.2 Michigan -5.76 -43.6
Black Sea -2.48 -19.7 Michigan -5.87 -43.9
Black Sea -2.42 -20.7 Michigan -5.76 -43.3
Black Sea -2.39 -21.1 Michigan -5.86 -44.2
Black Sea -2.41 -20.3 Michigan -5.75 -43.3
Black Sea -2.35 -20.4 Michigan -5.79 -43.6
Black Sea -2.27 -20.4 Michigan -5.73 -43.9
Black Sea -2.31 -20.1 Michigan -5.76 -43.6
Black Sea -2.31 -19.9 Michigan -5.82 -43.8
Black Sea -2.35 -19.9 Michigan -5.82 -43.8
Black Sea -2.41 -19.3 Michigan -5.82 -44.1
Black Sea -2.34 -19.0 Michigan -5.79 -43.9
Black Sea -2.29 -18.8 Michigan -5.79 -44.4
Black Sea -2.30 -19.1 Michigan -5.83 -44.5
Black Sea -2.25 -18.7 Michigan -5.78 -44.2
Black Sea -2.23 -19.3 Michigan -5.83 -43.9
Black Sea -2.20 -19.0 Michigan -5.87 -44.0
Black Sea -2.21 -18.7 Michigan -5.90 -43.7
Black Sea -2.16 -18.2 Michigan -5.88 -43.5
Black Sea -2.17 -17.8 Michigan -5.83 -44.1
Black Sea -2.16 -17.5 Michigan -5.84 -44.0
Black Sea -2.14 -17.8 Michigan -5.89 -44.4
Black Sea -2.08 -17.8 Michigan -5.90 -44.0
Black Sea -2.08 -17.4 Michigan -5.78 -44.6
Black Sea -2.08 -17.2 Michigan -5.81 -44.3
Black Sea -2.05 -17.2 Michigan -5.94 -44.9
Black Sea -2.05 -17.6 Michigan -5.85 -44.4
Black Sea -2.06 -17.5 Michigan -5.87 -44.4
Black Sea -2.06 -17.6 Michigan -5.78 -43.9
Black Sea -2.03 -17.5 Michigan -5.78 -44.3
Black Sea -2.01 -17.3 Michigan -5.84 -44.3
Black Sea -2.02 -17.2 Michigan -5.78 -43.8
Black Sea -1.95 -17.3 Michigan -5.81 -44.7
Black Sea -2.00 -17.1 Michigan -5.87 -44.3
Black Sea -2.03 -16.8 Michigan -5.84 -44.5
Black Sea -1.89 -16.9 Michigan -5.77 -44.7
Black Sea -1.90 -16.8 Michigan -5.90 -43.9
Black Sea -1.85 -15.7 Michigan -5.74 -44.1
Black Sea -1.79 -15.5 Michigan -5.83 -44.7
Black Sea -1.75 -15.5 Michigan -5.78 -44.8
Black Sea -1.75 -16.0 Michigan -5.81 -44.7
Black Sea -1.78 -16.2 Michigan -5.85 -44.8
253
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Black Sea -1.79 -16.4 Michigan -5.85 -44.4
Black Sea -2.63 -21.6 Michigan -5.81 -44.2
Black Sea -2.62 -22.1 Michigan -5.84 -44.2
Caspian Sea -2.00 -25.0 Michigan -5.85 -44.1
Caspian Sea -2.30 -25.2 Michigan -6.02 -44.5
Caspian Sea -1.87 -26.2 Michigan -5.78 -44.4
Caspian Sea -1.78 -23.8 Michigan -5.92 -44.4
Caspian Sea -1.75 -23.1 Michigan -5.89 -44.0
Caspian Sea -1.83 -22.3 Michigan -5.95 -45.3
Caspian Sea -1.86 -20.8 Michigan -5.87 -44.5
Caspian Sea -1.87 -19.4 Michigan -5.86 -44.6
Caspian Sea -1.73 -21.9 Michigan -5.89 -44.4
Caspian Sea -1.73 -21.2 Michigan -5.90 -44.0
Caspian Sea -1.70 -19.8 Michigan -5.81 -44.3
Caspian Sea -1.74 -18.7 Michigan -5.79 -44.2
Caspian Sea -1.54 -22.5 Michigan -5.79 -44.2
Caspian Sea -1.57 -21.3 Michigan -5.86 -44.2
Caspian Sea -1.61 -20.2 Michigan -5.74 -44.4
Caspian Sea -1.56 -19.6 Michigan -5.94 -45.7
Caspian Sea -1.48 -19.6 Michigan -5.91 -44.7
Caspian Sea -1.40 -18.7 Michigan -5.91 -44.4
Caspian Sea -1.44 -19.4 Michigan -5.83 -45.0
Caspian Sea -1.46 -18.5 Michigan -5.82 -43.7
Caspian Sea -1.44 -16.9 Michigan -5.94 -44.1
Caspian Sea -1.57 -18.3 Michigan -5.95 -44.1
Caspian Sea -1.65 -17.3 Michigan -5.85 -44.8
Caspian Sea -1.73 -16.2 Michigan -5.80 -44.4
Caspian Sea -1.62 -14.8 Michigan -5.92 -44.6
Chad -0.89 -2.8 Naivasha 6.56 36.3
Chad 0.35 2.4 Naivasha 6.30 40.4
Chad 0.30 3.8 Naivasha 6.60 36.0
Chad 0.85 10.5 Naivasha 3.60 23.0
Chad 1.69 8.0 Naivasha 4.10 24.0
Chad 2.54 12.9 Naivasha 4.40 18.0
Chad 4.67 25.2 Naivasha 4.20 20.0
Chad 5.01 25.7 Naivasha 4.90 33.0
Chad 5.14 23.7 Naivasha 6.60 36.0
Chad 5.51 29.8 Nam Co -7.57 -73.0
Chad 6.40 31.8 Nam Co -7.03 -66.7
Chad 6.80 34.2 Nasser -1.17 1.2
Chad 6.97 41.9 Nasser -1.15 0.7
Chad 7.21 42.6 Nasser -1.11 -0.1
Chad 7.88 45.0 Nasser -0.99 1.1
Chad 8.06 43.8 Nasser -0.57 4.1
Chad 8.06 41.1 Nasser 0.06 10.5
Chad 8.60 54.1 Nasser 0.17 8.3
254
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Chad 10.61 57.5 Nasser -1.35 -0.1
Chad 12.37 64.9 Nasser -1.30 1.1
Chad 11.33 57.3 Nasser -1.20 2.1
Chad 9.52 47.7 Nasser -0.62 5.7
Chad 11.60 59.2 Nasser -1.22 0.6
Chad 12.69 69.8 Nasser -1.23 1.1
Chad 13.01 70.0 Nasser -1.27 -0.1
Chad 12.99 67.6 Nasser -1.24 0.3
Chad 13.11 73.7 Nasser -1.25 1.0
Chad 13.51 72.7 Nasser -1.41 1.1
Chad 13.88 69.8 Nasser -0.90 5.1
Chad 14.05 74.7 Nasser -0.76 4.4
Chad 14.97 76.7 Nasser -0.64 3.8
Chad 0.11 -1.3 Nasser -0.84 3.0
Chad 3.57 22.5 Nasser -0.85 3.5
Chad 4.09 25.7 Nasser -0.80 3.0
Chad 3.95 22.0 Nasser -0.78 3.2
Chad 4.09 22.7 Nasser -0.73 2.4
Chad 4.07 20.0 Nasser 0.69 11.9
Chad 4.59 23.9 Nasser 0.46 10.1
Chad 4.94 24.9 Nasser 0.27 9.5
Chad 5.46 26.6 Nasser 0.19 9.2
Chad 5.21 27.9 Nasser 0.34 9.4
Chad 5.63 31.1 Nasser 2.16 19.6
Chad 5.91 30.1 Nasser 2.11 18.8
Chad 5.93 31.5 Nasser 2.01 20.0
Chad 5.98 33.8 Nasser 2.12 21.0
Chad 5.85 33.8 Nasser 2.12 20.2
Chad 6.27 37.7 Nasser 2.11 20.1
Chad 6.35 36.7 Nasser 2.10 18.9
Chad 6.45 36.7 Nasser 2.11 20.8
Chad 6.65 36.7 Nasser 2.18 21.2
Chad 6.72 35.5 Nasser 1.56 15.8
Chad 6.82 38.7 Nasser 2.41 21.9
Chad 7.04 38.9 Ngangla Ringco -4.22 -56.6
Chad 7.32 36.2 Nicaragua -2.00 -9.0
Chad 7.72 35.7 Oahe -14.17 -112.6
Chad 7.67 37.7 Oahe -14.06 -113.5
Chad 7.84 39.1 Oahe -14.02 -113.1
Chad 7.09 40.6 Oahe -14.39 -116.7
Chad 7.29 44.6 Oahe -14.45 -117.4
Chad 7.39 43.8 Oahe -14.24 -116.8
Chad 7.54 44.3 Oahe -14.28 -116.0
Chad 7.96 43.1 Oahe -14.23 -115.8
Chad 8.11 42.6 Oahe -14.57 -117.4
Chad 8.36 42.1 Oahe -14.03 -115.5
255
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Chad 8.71 43.1 Oahe -14.21 -116.0
Chad 8.21 45.8 Okanagan -11.1 -104.0
Chad 7.98 49.5 Okanagan -12.0 -105.0
Chad 8.53 49.5 Okanagan -11.6 -105.0
Chad 8.90 52.9 Okanagan -10.6 -105.0
Chad 9.07 51.4 Okanagan -11.4 -103.0
Chad 9.12 50.9 Okanagan -10.7 -102.0
Chad 8.95 47.2 Okanagan -9.9 -101.0
Chad 9.10 47.7 Okanagan -11.5 -102.0
Chad 9.42 49.9 Okanagan -11.3 -101.0
Chad 9.22 55.8 Okanagan -11.6 -101.0
Chad 9.62 57.3 Okanagan -11.6 -102.0
Chad 9.54 56.1 Okanagan -11.8 -102.0
Chad 10.11 57.5 Okanagan -11.7 -101.0
Chad 10.24 50.7 Okanagan -11.2 -99.0
Chad 10.61 56.6 Okanagan -11.7 -108.0
Chad 10.74 54.8 Okanagan -10.7 -108.0
Chad 11.04 53.4 Okanagan -11.4 -108.0
Chad 10.91 57.0 Okanagan -11.9 -109.0
Chad 11.21 59.5 Okanagan -11.8 -105.0
Chad 11.55 62.7 Okanagan -10.7 -106.0
Chad 11.80 64.7 Okanagan -11.2 -104.0
Chad 12.02 70.6 Okanagan -10.7 -102.0
Chad 12.14 70.8 Okanagan -10.7 -98.0
Chad 12.29 68.6 Okanagan -11.8 -101.0
Chad 12.47 68.8 Okanagan -11.5 -101.0
Chad 12.08 58.7 Okanagan -11.8 -103.0
Chad 13.36 71.5 Okanagan -11.5 -101.0
Chad 13.88 75.4 Okanagan -11.9 -102.0
Chad 14.28 77.6 Okanagan -12.1 -104.0
Chad 14.95 79.9 Okanagan -11.9 -102.0
Chamo 8.54 50.4 Okanagan -11.8 -102.0
Chamo 6.55 45.1 Okanagan -11.7 -103.0
Chamo 6.63 45.2 Okanagan -11.3 -100.0
Chamo 6.60 45.6 Okanagan -11.9 -107.0
Chamo 7.59 49.5 Okanagan -11.7 -109.0
Chamo 7.46 50.1 Okanagan -11.6 -109.0
Chamo 8.12 50.9 Okavango Delta -4.72 -34.5
Chamo 8.23 53.0 Okavango Delta -4.54 -30.2
Chamo 8.31 49.5 Okavango Delta -4.06 -32.2
Chamo 9.33 55.0 Okavango Delta -4.11 -29.1
Chamo 8.13 53.7 Okavango Delta -3.91 -29.1
Chamo 7.12 47.9 Okavango Delta -3.77 -28.2
Chamo 7.80 51.2 Okavango Delta -1.48 -16.5
Dabusun -0.61 -45.9 Okavango Delta -2.31 -28.0
Dabusun -0.21 -43.5 Okavango Delta -0.85 -14.8
256
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Dabusun 0.17 -40.7 Okavango Delta -0.81 -11.9
Dabusun 0.54 -39.6 Okavango Delta -0.15 -3.3
Dabusun 0.56 -45.6 Okavango Delta 0.11 0.4
Dabusun 0.37 -47.8 Okavango Delta 0.56 -2.7
Dabusun 1.57 -40.8 Okavango Delta 0.65 -5.1
Dabusun 2.34 -38.6 Okavango Delta 0.71 -4.2
Dagze Co -6.38 -69.5 Okavango Delta 0.79 -4.2
Dead Sea 4.30 4.0 Okavango Delta 0.99 -4.2
Dead Sea 3.74 2.2 Okavango Delta 0.79 -2.2
Dead Sea 4.15 5.7 Okavango Delta 1.22 -0.5
Dead Sea 4.31 -1.7 Okavango Delta 1.36 -3.1
Dead Sea 4.35 1.9 Okavango Delta 1.57 -1.1
Dead Sea 4.46 -0.7 Okavango Delta 1.43 2.1
Dead Sea 5.00 2.0 Okavango Delta 2.06 4.4
Dead Sea 5.10 5.0 Okavango Delta 2.11 3.2
Dead Sea 4.90 3.0 Okavango Delta 2.28 3.5
Dead Sea 0.10 4.6 Okavango Delta 2.43 4.4
Dead Sea 0.80 4.8 Okavango Delta 2.51 6.1
Dead Sea 0.20 4.8 Okavango Delta 2.66 5.2
Dead Sea -0.10 4.4 Okavango Delta 2.34 1.2
Dead Sea 0.10 4.4 Onega -10.94
Dead Sea -0.40 Onega -9.95
Dead Sea 0.10 4.7 Ontario -6.61 -49.2
Dead Sea -0.50 4.9 Ontario -6.37 -48.6
Dead Sea 0.70 4.5 Ontario -6.58 -48.9
Dead Sea -1.20 4.4 Ontario -6.42 -49.0
Dead Sea 0.10 4.5 Ontario -6.57 -48.8
Dead Sea 0.50 3.9 Ontario -6.50 -49.2
Dead Sea 0.20 4.5 Ontario -6.58 -49.4
Dead Sea 1.10 4.7 Ontario -6.67 -48.7
Dead Sea -0.50 4.5 Ontario -6.48 -49.1
Dead Sea -0.40 4.4 Ontario -6.53 -49.1
Dead Sea -1.90 4.5 Ontario -6.45 -49.2
Dead Sea -0.80 4.5 Ontario -6.58 -49.2
Edward 4.30 29.0 Ontario -6.62 -48.6
Edward 4.50 31.0 Ontario -6.56 -49.2
Edward 4.20 29.0 Ontario -6.68 -49.2
Edward 4.20 30.0 Ontario -6.48 -48.6
Egridir -1.90 -18.0 Ontario -6.59 -49.2
Egridir -1.60 -22.0 Ontario -6.57 -49.2
Egridir -2.30 -20.0 Ontario -6.65 -49.0
Egridir -1.30 -19.0 Ontario -6.68 -48.7
Egridir -2.30 -19.0 Ontario -6.58 -49.0
Egridir -2.70 -22.0 Ontario -6.64 -48.9
Egridir -2.90 -23.0 Ontario -6.50 -48.5
Egridir -3.20 -23.0 Ontario -6.63 -49.1
257
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Egridir -3.00 -25.0 Ontario -6.58 -49.1
Egridir -2.30 -22.0 Ontario -6.61 -49.4
Elephant Butte
-8.8 -73.0 Ontario -6.62 -49.3
Elephant Butte
-7.6 -67.0 Ontario -6.47 -48.8
Elephant Butte
-7.4 -66.0 Ontario -6.68 -49.0
Elephant Butte
-7.2 -65.0 Ontario -6.47 -49.2
Elephant Butte
-7.6 -68.0 Ontario -6.57 -49.0
Elephant Butte
-7.8 -68.0 Ontario -6.54 -49.6
Elephant Butte
-7.0 -65.0 Ontario -6.60 -51.6
Elephant Butte
-7.1 -64.0 Ontario -6.63 -49.7
Elephant Butte
-7.0 -65.0 Ontario -6.62 -49.7
Elephant Butte
-6.6 -63.0 Ontario -6.32 -47.9
Elephant Butte
-7.8 -64.0 Ontario -6.67 -49.5
Elephant Butte
-7.8 -65.7 Ontario -6.58 -49.1
Erie -6.47 -47.2 Ontario -6.62 -49.3
Erie -6.54 -47.6 Ontario -6.56 -48.8
Erie -6.42 -47.0 Ontario -6.67 -49.4
Erie -6.57 -47.8 Ontario -6.48 -49.1
Erie -6.47 -47.5 Ontario -6.59 -49.4
Erie -6.56 -47.0 Ontario -6.45 -48.6
Erie -6.70 -48.6 Ontario -6.57 -49.2
Erie -6.44 -47.2 Ontario -6.56 -49.2
Erie -6.71 -49.2 Ontario -6.59 -48.8
Erie -6.53 -47.5 Ontario -6.57 -49.1
Erie -6.75 -48.8 Ontario -6.62 -49.1
Erie -6.53 -47.4 Ontario -6.64 -49.2
Erie -6.43 -48.5 Ontario -6.48 -48.5
Erie -6.48 -47.8 Ontario -6.58 -49.2
Erie -6.53 -47.2 Ontario -6.65 -49.5
Erie -6.41 -47.4 Ontario -6.60 -49.1
Erie -6.55 -47.3 Ontario -6.61 -49.1
Erie -6.50 -47.3 Ontario -6.68 -49.4
Erie -6.37 -47.4 Ontario -6.45 -48.2
Erie -6.80 -44.6 Ontario -6.56 -49.1
258
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Erie -6.75 -46.6 Ontario -6.63 -49.0
Erie -6.38 -47.6 Ontario -6.53 -48.9
Erie -6.57 -49.2 Ontario -6.70 -49.3
Erie -6.43 -47.7 Ontario -6.55 -49.0
Erie -6.41 -47.3 Ontario -6.60 -49.3
Erie -6.63 -47.7 Ontario -6.70 -53.0
Erie -6.61 -47.9 Ontario -6.70 -51.0
Erie -6.85 -47.1 Ontario -6.60 -50.0
Erie -6.45 -47.6 Ontario -6.60 -51.0
Erie -6.48 -47.7 Ontario -6.60 -52.0
Erie -6.44 -47.9 Powell -14.61 -113.0
Erie -6.56 -48.0 Powell -14.82 -116.4
Erie -6.57 -48.0 Powell -15.46 -119.1
Erie -6.43 -47.5 Powell -15.02 -114.3
Erie -6.45 -47.7 Powell -15.12 -114.8
Erie -6.64 -47.4 Powell -14.94 -113.1
Erie -6.40 -47.8 Powell -14.92 -114.3
Erie -6.60 -47.1 Powell -15.14 -115.5
Erie -6.61 -47.8 Powell -14.87 -113.4
Erie -6.46 -47.8 Powell -14.72 -113.6
Erie -6.36 -47.8 Powell -15.35 -119.4
Erie -6.56 -47.5 Powell -15.44 -120.2
Erie -6.45 -48.0 Powell -15.36 -121.1
Erie -6.67 -48.3 Powell -15.37 -121.4
Erie -6.72 -47.9 Powell -15.37 -121.8
Erie -6.44 -47.7 Powell -15.44 -121.8
Erie -6.44 -48.2 Powell -15.29 -121.7
Erie -6.61 -48.0 Powell -15.33 -121.3
Erie -6.38 -47.5 Powell -15.47 -122.9
Erie -6.77 -48.2 Powell -15.23 -121.3
Erie -6.35 -47.5 Powell -15.61 -122.8
Erie -6.52 -47.6 Powell -15.64 -122.7
Erie -6.33 -47.5 Powell -15.58 -122.9
Erie -6.51 -47.3 Powell -15.63 -123.2
Erie -6.79 -50.8 Powell -15.52 -123.3
Erie -6.47 -48.0 Powell -15.45 -122.6
Erie -6.84 -50.6 Powell -15.46 -122.6
Erie -6.53 -47.6 Powell -15.51 -122.4
Erie -6.55 -47.5 Powell -15.45 -122.3
Erie -6.76 -51.3 Powell -15.60 -122.6
Erie -6.53 -46.6 Powell -15.57 -122.7
Erie -6.39 -48.4 Powell -15.54 -123.3
Erie -6.55 -47.9 Powell -15.29 -119.8
Erie -6.37 -48.5 Powell -14.76 -118.4
Erie -6.49 -46.7 Powell -14.82 -118.9
Erie -6.42 -48.4 Powell -14.92 -119.3
259
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Erie -6.61 -48.8 Powell -15.38 -120.9
Erie -7.48 -54.9 Powell -15.12 -120.1
Erie -6.57 -47.8 Powell -15.47 -121.3
Erie -7.37 -55.2 Powell -15.41 -121.3
Erie -6.55 -48.7 Powell -15.41 -121.6
Erie -7.37 -55.1 Powell -15.15 -121.2
Erie -7.11 -53.7 Powell -15.31 -121.6
Erie -6.51 -48.3 Powell -15.25 -120.3
Erie -6.54 -48.5 Powell -15.33 -121.1
Erie -7.22 -54.2 Powell -15.43 -121.1
Erie -6.63 -48.6 Powell -15.44 -121.2
Erie -7.18 -53.9 Powell -15.12 -120.0
Erie -7.24 -53.5 Powell -15.25 -120.2
Erie -6.88 -50.5 Powell -15.32 -120.8
Erie -6.92 -50.5 Powell -14.87 -119.4
Erie -7.18 -52.3 Powell -14.82 -116.9
Erie -7.20 -53.4 Powell -15.14 -117.7
Erie -6.91 -51.0 Powell -15.07 -117.7
Erie -7.06 -54.0 Powell -15.52 -120.2
Erie -6.90 -50.9 Powell -15.45 -120.9
Erie -7.07 -53.4 Powell -15.58 -121.1
Erie -6.96 -51.1 Powell -15.62 -121.7
Erie -7.15 -53.8 Powell -15.58 -121.9
Erie -6.86 -50.8 Powell -15.59 -121.4
Erie -6.37 -47.7 Powell -15.59 -121.5
Erie -6.31 -48.3 Powell -15.55 -121.0
Erie -6.31 -48.1 Powell -15.54 -120.7
Erie -6.48 -47.0 Powell -15.52 -120.7
Erie -6.45 -47.6 Powell -15.57 -121.3
Erie -6.41 -47.8 Powell -15.56 -121.0
Erie -6.35 -48.4 Powell -15.45 -120.7
Erie -6.54 -49.4 Powell -15.52 -120.5
Erie -6.50 -49.3 Powell -15.48 -120.8
Erie -6.33 -48.3 Powell -15.43 -120.5
Erie -6.46 -48.3 Powell -15.53 -120.9
Erie -6.52 -49.3 Powell -15.53 -121.0
Erie -6.41 -48.1 Powell -15.56 -120.9
Erie -6.56 -49.6 Powell -15.56 -120.9
Erie -6.47 -49.2 Powell -15.47 -120.5
Erie -6.42 -49.3 Powell -15.48 -120.9
Erie -6.42 -48.6 Powell -15.54 -120.9
Erie -6.43 -49.1 Powell -15.60 -120.7
Erie -6.65 -51.6 Powell -15.56 -121.3
Erie -7.21 -53.5 Powell -15.59 -120.5
Erie -6.72 -52.3 Powell -15.49 -121.3
Erie -7.23 -54.0 Powell -15.46 -120.9
260
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Erie -6.75 -51.8 Powell -15.48 -120.7
Erie -7.30 -54.7 Powell -15.35 -120.4
Erie -6.85 -52.0 Powell -15.43 -120.8
Erie -7.32 -54.1 Powell -15.37 -120.3
Erie -6.83 -52.5 Powell -15.35 -120.8
Erie -7.18 -54.4 Powell -15.06 -118.6
Erie -6.90 -52.2 Powell -15.31 -121.3
Erie -7.25 -53.6 Powell -15.37 -120.8
Erie -6.40 -48.8 Powell -15.42 -121.3
Erie -6.37 -49.2 Powell -15.28 -120.3
Erie -6.45 -49.0 Powell -15.35 -120.3
Erie -6.58 -49.4 Powell -15.28 -121.3
Erie -6.48 -49.3 Powell -15.43 -120.5
Erie -6.63 -50.3 Powell -15.25 -120.7
Erie -6.30 -48.0 Powell -15.37 -120.6
Erie -6.40 -52.0 Powell -15.37 -120.4
Erie -6.40 -47.0 Powell -15.19 -120.9
Erie -6.40 -46.0 Powell -15.33 -120.5
Erie -6.50 -50.0 Powell -15.23 -120.9
Erie -6.50 -46.0 Powell -15.34 -120.4
Erie -6.50 -48.0 Powell -15.21 -119.9
Erie -6.50 -50.0 Powell -14.99 -118.3
Erie -6.70 -52.0 Powell -14.88 -117.3
Erie -6.70 -49.0 Powell -15.17 -117.9
Erie -6.50 -50.0 Powell -14.95 -116.2
Erie -6.50 -51.0 Powell -14.93 -116.2
Erie -6.50 -48.0 Powell -15.12 -117.2
Erie -6.70 -49.0 Powell -15.16 -118.1
Erie -6.60 -45.0 Powell -14.86 -117.0
Erie -6.70 -51.0 Powell -14.89 -117.5
Erie -6.70 -52.0 Powell -15.20 -118.3
Erie -6.80 -53.0 Powell -15.32 -118.5
Erie -6.60 -49.0 Powell -14.86 -117.3
Erie -6.50 -48.0 Powell -15.24 -117.9
Erie -6.70 Powell -15.18 -118.2
Erie -6.60 -49.0 Powell -14.88 -117.1
Erie -6.80 -51.0 Poyang -10.61 -68.4
Erie -6.80 -56.0 Poyang -9.64 -57.4
Erie -6.10 -46.5 Poyang -9.28 -55.5
Garda -7.30 -55.1 Poyang -8.35 -49.2
Garda -7.20 -54.0 Poyang -7.85 -51.5
Garda -7.00 -53.4 Poyang -7.74 -43.9
Garda -7.40 -55.7 Poyang -6.83 -41.3
Garda -7.20 -55.0 Poyang -6.24 -42.2
Garda -9.20 -69.9 Poyang -6.42 -41.6
Garda -7.80 -59.0 Poyang -6.51 -38.5
261
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Garda -7.40 -56.2 Poyang -6.39 -38.1
Garda -7.30 -54.1 Poyang -6.28 -38.4
Garda -7.30 -55.0 Poyang -6.09 -34.1
Garda -7.20 -54.4 Poyang -6.16 -33.0
Garda -7.30 -55.5 Poyang -6.37 -29.9
Garda -7.20 -56.2 Poyang -6.48 -30.9
Garda -7.40 -56.9 Poyang -6.54 -32.3
Garda -7.30 -54.3 Poyang -6.53 -33.3
Garda -7.40 -54.8 Poyang -6.46 -33.7
Garda -7.40 -55.4 Poyang -6.30 -34.6
Garda -7.60 -56.4 Poyang -6.33 -33.3
Garda -7.30 -54.8 Poyang -6.48 -38.9
Garda -7.30 -54.3 Poyang -6.33 -39.7
Garda -7.40 -55.3 Poyang -6.30 -38.1
Garda -7.30 -54.2 Poyang -6.20 -33.0
Garda -7.00 -53.0 Poyang -6.37 -32.3
Garda -7.30 -54.3 Poyang -6.37 -33.6
Garda -7.30 -54.8 Poyang -6.45 -32.8
Garda -7.10 -53.0 Poyang -6.29 -32.6
Garda -7.40 -54.1 Poyang -6.46 -31.3
Garda -7.30 -54.4 Poyang -6.45 -33.9
Garda -7.30 -54.2 Poyang -6.27 -34.7
Garda -7.50 -54.7 Poyang -6.30 -32.0
Garda -7.30 -54.5 Poyang -6.50 -32.9
Garda -7.40 -55.3 Poyang -6.11 -33.6
Garda -7.20 -53.9 Qarhan Salt 6.63 -15.6
Garda -7.40 -55.1 Qianhai Hu 0.97 4.4
Garda -7.10 -53.8 Qianhai Hu 1.26 3.1
Garda -7.48 -55.0 Qianhai Hu 2.48 11.9
Garda -7.46 -55.0 Qianhai Hu 2.69 11.9
Garda -7.19 -54.8 Qianhai Hu 2.80 12.5
Garda -7.23 -54.3 Qianhai Hu 2.60 14.0
Garda -7.15 -53.9 Qianhai Hu 2.78 14.8
Garda -7.24 -53.5 Qianhai Hu 2.85 15.6
Garda -7.31 -54.2 Qianhai Hu 2.69 15.6
Garda -7.35 -55.3 Qianhai Hu 2.76 16.9
Garda -7.36 -54.2 Red Sea 0.98 5.4
Garda -7.30 -54.7 Red Sea 1.13 6.2
Garda -7.23 -53.9 Red Sea 1.33 8.2
Garda -7.26 -54.3 Red Sea 1.85 11.3
Garda -7.36 -55.0 Red Sea 1.95 11.5
Garda -7.34 -54.5 Red Sea 1.14 7.0
Garda -7.30 -55.1 Red Sea 1.16 7.1
Garda -7.32 -55.9 Red Sea 1.16 7.2
Garda -7.34 -54.9 Red Sea 1.19 7.7
Garda -7.33 -55.9 Red Sea 1.22 7.2
262
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Garda -7.26 -55.0 Red Sea 1.36 8.1
Garda -7.35 -55.3 Red Sea 1.38 7.7
Garda -7.27 -55.5 Red Sea 1.55 9.3
Garda -7.31 -55.1 Red Sea 1.57 9.0
Garda -7.38 -55.9 Red Sea 1.59 9.3
Garda -7.41 -55.5 Red Sea 1.62 9.5
Garda -7.38 -55.5 Rukwa 4.50 27.0
Garda -7.23 -54.7 Rukwa 4.30 27.0
Garda -7.38 -54.7 Rukwa 4.40 27.0
Garda -7.33 -54.4 Rukwa 4.10 23.0
Garda -7.21 -54.7 Sakakawea -15.35 -122.0
Garda -7.27 -55.6 Sakakawea -15.35 -122.5
Garda -7.45 -56.2 Sakakawea -15.60 -123.7
Garda -7.26 -55.9 Sakakawea -15.70 -123.3
Garda -7.43 -56.4 Sakakawea -15.75 -126.2
Garda -7.37 -55.4 Sakakawea -15.33 -123.1
Garda -7.47 -55.8 Sakakawea -15.22 -122.7
Garda -7.17 -54.6 Sakakawea -15.34 -125.6
Garda -7.39 -55.9 Sakakawea -15.37 -125.7
Garda -7.40 -56.1 Sakakawea -15.39 -124.1
Garda -7.42 -56.5 Sakakawea -15.46 -125.4
Garda -7.32 -54.7 Sakakawea -15.37 -122.6
Garda -7.12 -54.5 Sakakawea -15.63 -124.7
Garda -7.39 -55.1 Sakakawea -15.77 -126.0
Garda -7.29 -55.3 Sakakawea -15.53 -122.6
Garda -7.35 -55.8 Salton Sea -1.95 -43.0
Garda -7.35 -55.8 Salton Sea -5.30 -60.0
Garda -7.33 -55.9 Salton Sea -8.83 -82.7
Garda -7.32 -55.0 Sambhar Salt -5.50
Garda -7.29 -55.7 Sambhar Salt -1.00
Garda -7.31 -55.5 Sambhar Salt 4.50
Garda -7.34 -55.7 Sambhar Salt 9.60
Garda -7.29 -55.4 Sambhar Salt 19.10
Garda -7.22 -54.3 Sambhar Salt 19.70
Garda -7.24 -55.1 Sambhar Salt 21.40
Garda -7.29 -55.3 Sambhar Salt 24.00
Garda -7.18 -54.9 Shala 7.29 53.2
Garda -7.28 -55.4 Shala 7.92 55.1
Garda -7.33 -55.1 Shala 7.40 51.9
Garda -7.46 -56.2 Shala 6.22 45.7
Garda -7.46 -55.7 Shala 7.36 50.9
Garda -7.45 -55.8 Shala 7.52 48.4
Garda -7.10 -55.1 Shala 7.66
Garda -7.12 -54.3 Shala 7.49
Garda -7.13 -55.0 Shala 7.73
Garda -7.10 -54.9 Shala 7.77
263
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Garda -7.16 -54.5 Shala 5.36
Garda -6.99 -53.8 Shala 7.82
Garda -7.15 -54.0 Shala 7.23
Garda -7.08 -55.1 Shala 6.96
Garda -7.14 -54.6 Shala 8.30 54.4
Garda -7.36 -55.7 Shala 8.28 54.3
Garda -7.28 -55.2 Shala 8.49 54.5
Garda -7.29 -55.7 Shala 7.78 51.6
Garda -7.17 -55.0 Shala 7.05 52.5
Garda -7.33 -54.3 Superior -8.58 -65.6
Garda -7.27 -55.2 Superior -8.61 -66.8
Garda -7.21 -55.1 Superior -8.66 -66.7
Garda -7.30 -54.8 Superior -8.60 -65.9
Garda -7.31 -54.8 Superior -8.66 -66.9
Garda -7.37 -54.7 Superior -8.70 -66.8
Garda -7.34 -54.3 Superior -8.64 -65.6
Garda -7.34 -54.3 Superior -8.60 -66.7
Garda -7.43 -55.7 Superior -8.63 -65.1
Garda -7.47 -54.6 Superior -8.66 -66.8
Garda -7.30 -54.7 Superior -8.59 -65.9
Garda -7.38 -55.3 Superior -8.65 -67.2
Garda -7.46 -56.0 Superior -8.66 -65.6
Garda -7.42 -55.1 Superior -8.63 -66.8
Garda -7.29 -54.0 Superior -8.61 -66.6
Garda -7.24 -54.1 Superior -8.72 -65.0
Garda -7.19 -55.6 Superior -8.74 -66.8
Garda -7.39 -56.3 Superior -8.56 -65.3
Garda -7.30 -54.8 Superior -8.64 -66.8
Garda -7.30 -55.4 Superior -8.58 -66.8
Garda -7.32 -55.7 Superior -8.61 -64.6
Garda -7.44 -55.1 Superior -8.61 -67.4
Garda -7.41 -55.5 Superior -8.65 -64.6
Garda -7.54 -55.6 Superior -8.53 -67.4
Garda -7.51 -55.8 Superior -8.62 -65.3
Garda -7.58 -55.9 Superior -8.52 -67.2
Garda -7.44 -56.0 Superior -8.61 -65.4
Garda -7.41 -55.0 Superior -8.47 -67.0
Garda -7.35 -55.6 Superior -8.65 -64.4
Garda -7.39 -55.1 Superior -8.64 -66.7
Garda -7.43 -56.0 Superior -8.58 -66.5
Garda -7.40 -55.5 Superior -8.55 -64.7
Garda -7.25 -54.8 Superior -8.67 -64.8
Garda -7.26 -54.1 Superior -8.49 -67.4
Garda -7.37 -54.8 Superior -8.49 -67.0
Garda -7.51 -56.2 Superior -8.64 -64.9
Garda -7.47 -55.4 Superior -8.63 -67.0
264
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Garda -7.42 -56.1 Superior -8.65 -67.3
Garda -7.37 -54.4 Superior -8.69 -64.9
Garda -7.41 -55.7 Superior -8.55 -67.1
Garda -7.42 -54.5 Superior -8.60 -66.9
Garda -7.25 -54.5 Superior -8.74 -64.9
Garda -7.21 -53.5 Superior -8.60 -67.0
Garda -7.23 -54.4 Superior -8.71 -65.1
Garda -7.27 -55.0 Superior -8.52 -67.1
Garda -7.44 -56.1 Superior -8.65 -65.0
Garda -7.46 -55.8 Superior -8.55 -67.1
Garda -7.43 -54.8 Superior -8.55 -67.1
Garda -7.42 -55.5 Superior -8.59 -64.8
Garda -7.47 -55.5 Superior -8.62 -66.9
Garda -7.17 -54.6 Superior -8.61 -65.1
Garda -7.23 -55.2 Superior -8.53 -66.6
Garda -7.34 -55.1 Superior -8.60 -64.8
Garda -6.88 -54.5 Superior -8.51 -66.8
Garda -7.27 -55.6 Superior -8.67 -66.6
Garda -7.02 -54.0 Superior -8.57 -64.7
Garda -7.21 -55.3 Superior -8.74 -65.1
Garda -7.39 -55.3 Superior -8.56 -67.0
Garda -7.16 -55.5 Superior -8.56 -67.2
Garda -7.33 -55.9 Superior -8.61 -65.0
Garda -7.26 -55.6 Superior -8.57 -67.1
Garda -7.36 -54.8 Superior -8.63 -64.8
Garda -7.38 -56.0 Superior -8.69 -67.2
Garda -7.29 -55.2 Superior -8.55 -66.8
Garda -7.38 -54.9 Superior -8.68 -65.0
Garda -7.21 -55.5 Superior -8.60 -66.8
Garda -7.23 -55.9 Superior -8.62 -64.8
Garda -7.40 -56.2 Superior -8.54 -67.5
Geneva -12.38 -87.5 Superior -8.65 -64.9
Geneva -12.43 -88.5 Superior -8.58 -67.4
Geneva -12.23 -88.2 Superior -8.75 -64.9
Geneva -12.38 -85.2 Superior -8.62 -65.7
Geneva -12.17 -85.6 Superior -8.66 -67.2
Geneva -12.32 -88.5 Superior -8.65 -64.8
Geneva -12.41 -89.5 Superior -8.62 -66.8
Geneva -12.29 -86.2 Superior -8.50 -66.9
Geneva -12.21 -85.9 Superior -8.63 -65.1
Great Bear -17.90 Superior -8.73 -64.7
Great Bear -18.55 -157.6 Superior -8.72 -65.3
Great Bear -18.88 -155.7 Superior -8.57 -64.6
Great Bear -18.53 -153.9 Superior -8.64 -64.9
Great Bear -18.44 -155.7 Superior -8.63 -65.4
Great Bear -18.40 -152.5 Superior -8.70 -64.8
265
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Great Bear -18.37 -155.7 Superior -8.60 -65.4
Great Bear -18.45 -155.7 Superior -8.77 -64.9
Great Bear -18.83 -154.8 Superior -8.49 -64.9
Great Bear -18.40 -152.3 Superior -8.59 -65.4
Great Bear -18.72 -153.6 Superior -8.72 -65.1
Great Bear -18.75 -151.4 Superior -8.69 -65.6
Great Bear -18.92 -155.4 Superior -8.68 -65.8
Great Bear -18.48 -153.4 Superior -8.62 -64.8
Great Bear -18.52 -153.6 Superior -8.71 -64.9
Great Bear -19.18 -155.7 Superior -8.63 -65.7
Great Bear -18.80 -155.5 Superior -8.55 -65.0
Great Bear -18.94 -155.6 Superior -8.68 -65.2
Great Bear -18.92 -154.4 Superior -8.57 -65.5
Great Bear -18.46 -153.4 Superior -8.73 -65.3
Great Bear -18.57 -153.8 Superior -8.67 -65.5
Great Bear -18.61 -155.0 Superior -8.82 -65.5
Great Bear -18.65 -154.7 Superior -8.64 -66.3
Great Bear -18.40 -151.5 Superior -8.79 -66.0
Great Bear -18.49 -153.8 Superior -8.70 -65.1
Great Bear -18.62 -152.6 Superior -8.54 -66.0
Great Bear -18.59 -154.2 Superior -8.68 -65.3
Great Bear -18.73 -155.5 Superior -8.68 -64.7
Great Bear -18.86 -155.7 Superior -8.71 -65.2
Great Bear -18.92 -157.3 Superior -8.68 -65.1
Great Bear -18.83 -157.3 Superior -8.60 -65.4
Great Bear -18.90 -153.2 Superior -8.64 -64.9
Great Salt -6.28 -72.8 Superior -8.65 -65.6
Great Salt -5.69 -69.8 Superior -8.54 -66.4
Great Salt -4.88 -67.8 Superior -8.66 -65.1
Great Salt -4.93 -64.8 Superior -8.70 -65.2
Great Salt -4.06 -60.9 Superior -8.54 -65.4
Great Salt -4.15 -60.9 Superior -8.63 -65.5
Great Salt -3.89 -60.6 Superior -8.59 -64.9
Great Salt -4.40 -61.3 Superior -8.64 -65.2
Great Salt -5.48 -70.9 Superior -8.66 -65.3
Great Salt -5.45 -71.1 Superior -8.67 -65.4
Great Salt -4.96 -65.9 Superior -8.55 -65.5
Great Salt -3.94 -64.4 Superior -8.71 -64.9
Great Salt -3.68 -61.4 Superior -8.53 -65.8
Great Salt -3.51 -61.4 Superior -8.71 -65.0
Great Salt -3.70 -60.9 Superior -8.59 -65.7
Great Salt -4.00 -63.1 Superior -8.62 -65.1
Great Salt -5.53 -72.8 Superior -8.59 -65.0
Great Salt -5.53 -69.8 Superior -8.56 -66.2
Great Salt -4.72 -66.4 Superior -8.72 -65.2
Great Salt -4.69 -64.5 Superior -8.56 -65.2
266
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Great Salt -4.20 -60.9 Superior -8.55 -64.8
Great Salt -4.08 -60.6 Superior -8.62 -65.8
Great Salt -4.57 -60.6 Superior -8.49 -65.9
Great Salt -4.14 -62.5 Superior -8.62 -65.0
Great Salt -8.44 -83.4 Superior -8.69 -65.1
Great Salt -7.03 -78.9 Superior -8.54 -65.4
Great Salt -5.05 -65.8 Superior -8.56 -65.9
Great Salt -4.93 -64.1 Superior -8.51 -65.8
Great Salt -4.15 -60.3 Superior -8.59 -65.2
Great Salt -3.94 -60.5 Superior -8.53 -66.0
Great Salt -4.43 -60.2 Superior -8.59 -65.3
Great Salt -4.42 -62.2 Superior -8.70 -64.0
Great Slave -17.89 -140.1 Superior -8.80 -66.0
Great Slave -17.58 -141.7 Superior -8.70 -66.0
Great Slave -17.85 -144.2 Superior -8.70 -64.0
Great Slave -18.10 -145.2 Superior -8.80 -66.0
Great Slave -18.17 -138.3 Superior -8.70 -66.0
Great Slave -17.44 -136.5 Superior -8.80 -64.0
Great Slave -17.90 Superior -8.80
Huron -7.18 -52.9 Superior -8.80 -67.0
Huron -6.90 -54.3 Superior -8.80 -66.0
Huron -6.86 -54.1 Superior -8.80 -66.0
Huron -7.02 -52.1 Superior -8.70 -66.0
Huron -6.96 -54.0 Superior -8.70
Huron -7.00 -53.8 Superior -8.70 -66.0
Huron -7.04 -53.4 Superior -8.70 -65.0
Huron -6.97 -53.0 Superior -8.70 -65.0
Huron -7.04 -54.3 Superior -8.80 -65.0
Huron -7.07 -54.3 Superior -8.80 -67.0
Huron -7.03 -53.1 Superior -8.80 -68.0
Huron -6.92 -54.4 Superior -8.80 -67.0
Huron -7.08 -53.4 Superior -8.70 -68.0
Huron -7.01 -54.2 Tahoe -5.20 -59.0
Huron -7.00 -52.9 Tahoe -5.80 -59.0
Huron -7.05 -54.0 Tahoe -5.20 -56.0
Huron -7.09 -54.1 Tahoe -5.80 -56.0
Huron -7.06 -54.0 Tana 3.33 30.3
Huron -7.15 -53.9 Tana 4.15 33.5
Huron -7.12 -51.5 Tana 4.35 36.3
Huron -7.04 -54.0 Tana 4.57 36.6
Huron -7.06 -54.1 Tana 4.95 37.8
Huron -7.04 -52.4 Tana 5.11 38.4
Huron -6.97 -54.2 Tana 5.98 44.8
Huron -7.19 -53.4 Tana 5.32 42.8
Huron -6.94 -53.9 Tana 4.87 37.9
Huron -7.00 -53.5 Tana 4.88 38.0
267
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Huron -6.91 -54.1 Tana 4.87 37.8
Huron -7.07 -54.8 Tana 4.90 37.7
Huron -7.04 -53.2 Tana 4.11 35.8
Huron -7.10 -54.9 Tana 3.77 32.9
Huron -7.08 -53.2 Tana 3.93 34.5
Huron -6.90 -54.1 Tana 4.26 36.7
Huron -7.12 -52.8 Tana 6.30 46.0
Huron -6.93 -54.2 Tana 6.70 45.0
Huron -7.09 -53.7 Tana 6.80 50.0
Huron -7.01 -54.2 Tana 3.10 27.5
Huron -7.08 -54.0 Tana 3.20 29.0
Huron -7.05 -52.9 Tana 3.50 33.4
Huron -7.15 -53.9 Tana 3.85 32.5
Huron -7.06 -53.4 Tana 5.48 41.3
Huron -7.16 -53.7 Tana 5.72 43.1
Huron -7.07 -53.7 Tana 6.17 44.1
Huron -7.05 -53.8 Tana 6.52 48.7
Huron -7.10 -54.6 Tana 4.30 34.6
Huron -7.09 -53.4 Tana 3.74 29.5
Huron -7.02 -54.7 Tana 3.60 28.8
Huron -7.01 -54.4 Tana 3.46 28.8
Huron -7.01 -53.6 Tana 3.57 29.7
Huron -7.11 -53.3 Tana 3.55 29.8
Huron -7.06 -54.7 Tana 3.70 29.6
Huron -7.09 -54.0 Tana 3.68 30.7
Huron -7.05 -53.7 Tana 3.56 30.5
Huron -6.94 -54.7 Tana 3.67 30.8
Huron -7.00 -54.0 Tana 4.11 32.6
Huron -6.93 -53.9 Tana 3.67 32.1
Huron -7.18 -54.9 Tana 4.24 34.3
Huron -7.01 -54.4 Tana 4.27 35.1
Huron -7.08 -53.6 Tana 3.88 31.4
Huron -7.13 -54.7 Tana 4.30 34.0
Huron -7.10 -54.3 Tana 4.03 33.5
Huron -7.15 -54.3 Tana 4.05 34.2
Huron -7.03 -53.8 Tana 4.41 35.4
Huron -6.99 -55.2 Tana 4.31 36.2
Huron -7.08 -54.6 Tana 4.38 37.0
Huron -7.03 -54.3 Tana 5.16 37.8
Huron -7.06 -53.0 Tana 4.41 37.6
Huron -7.04 -53.9 Tana 3.91 35.4
Huron -6.96 -54.6 Tana 3.98 31.5
Huron -7.13 -53.5 Tanganyika 3.50 23.8
Huron -7.16 -53.8 Tanganyika 3.51 23.8
Huron -7.15 -54.6 Tanganyika 3.57 23.9
Huron -7.13 -55.0 Tanganyika 3.71 24.7
268
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Huron -7.09 -53.2 Tanganyika 3.82 25.3
Huron -7.00 -54.1 Tanganyika 3.95 26.2
Huron -6.93 -54.6 Tanganyika 4.01 26.6
Huron -7.09 -53.6 Tanganyika 4.08 26.9
Huron -7.18 -54.0 Tanganyika 4.14 27.5
Huron -7.21 -54.5 Tanganyika 4.15 27.5
Huron -7.18 -55.1 Tanganyika 4.18 27.5
Huron -7.29 -53.5 Tanganyika 4.16 27.8
Huron -7.16 -54.9 Tanganyika 4.18 27.9
Huron -7.15 -53.6 Tanganyika 4.19 28.0
Huron -7.16 -54.8 Tanganyika 4.18 27.9
Huron -6.94 -53.9 Tanganyika 4.19 28.0
Huron -7.10 -53.1 Tanganyika 4.21 28.0
Huron -6.94 -54.0 Tanganyika 4.21 28.0
Huron -7.18 -53.5 Tanganyika 4.21 27.9
Huron -6.99 -53.8 Tanganyika 3.52 23.5
Huron -6.89 -53.7 Tanganyika 3.80 26.1
Huron -7.11 -53.6 Tanganyika 4.14 27.6
Huron -7.06 -52.6 Tanganyika 4.16 27.8
Huron -7.11 -54.9 Tanganyika 4.20 27.9
Huron -7.11 -55.4 Tanganyika 3.26 23.7
Huron -7.12 -53.2 Tanganyika 3.96 26.2
Huron -7.07 -54.2 Tanganyika 4.08 27.1
Huron -7.12 -53.5 Tanganyika 4.17 27.2
Huron -7.20 -54.5 Tanganyika 4.18 27.9
Huron -7.20 -54.5 Tanganyika 4.17 23.7
Huron -7.14 -53.2 Tanganyika 4.12 24.7
Huron -7.20 -54.6 Tanganyika 4.02 26.6
Huron -7.25 -54.8 Tanganyika 3.48 27.7
Huron -7.11 -53.9 Tanganyika 3.45 23.8
Huron -7.35 -55.6 Tanganyika 3.62 24.5
Huron -7.06 -53.3 Tanganyika 3.88 25.7
Huron -7.20 -53.0 Tanganyika 3.96 26.5
Huron -7.20 -54.0 Tanganyika 4.17 27.6
Huron -7.00 -55.0 Tanganyika 3.45 23.8
Huron -7.20 -59.0 Tanganyika 3.50 23.8
Huron -7.30 -54.0 Tanganyika 3.51 23.9
Huron -7.30 -54.0 Tanganyika 3.57 24.6
Huron -7.20 -54.0 Tanganyika 3.56 24.6
Huron -7.20 -54.0 Tanganyika 3.62 24.7
Huron -7.20 -52.0 Tanganyika 3.66 24.6
Huron -7.30 -53.0 Tanganyika 3.64 24.7
Huron -7.20 Tanganyika 3.68 24.8
Huron -7.30 -56.0 Tanganyika 3.54 24.6
Huron -7.30 -54.0 Taro Co -5.63 -68.5
Huron -7.30 -58.0 Taupo -5.31 -33.0
269
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Huron -7.40 -55.0 Titicaca -4.15 -52.0
Huron -7.40 -55.0 Titicaca -4.25 -53.0
Huron -7.20 -55.0 Titicaca -4.15 -50.4
Huron -7.20 -53.0 Titicaca -2.90 -47.3
Huron -7.10 -53.0 Titicaca -2.92 -47.3
Huron -7.10 -51.0 Titicaca -2.85 -47.1
Huron -7.30 -56.0 Titicaca -3.30 -46.3
Huron -7.40 -55.0 Titicaca -3.40 -48.5
Huron -6.40 -47.0 Titicaca -3.35 -46.4
Huron -7.50 -53.0 Titicaca -4.70 -54.8
Huron -8.60 Titicaca -4.60 -51.5
Huron -8.50 -62.0 Titicaca -4.70 -51.8
Huron -7.70 -58.0 Tonlé Sap -6.09
Huron -7.40 -53.0 Tonlé Sap -4.32
Huron -7.40 -58.0 Tonlé Sap -5.95
Huron -7.50 -52.0 Tonlé Sap -8.60
Huron -7.40 -53.0 Tonlé Sap -7.78
Huron -7.40 -53.0 Tonlé Sap -8.60
Huron -7.30 -54.0 Tonlé Sap -6.22
Huron -7.40 Tonlé Sap -3.61
Huron -7.40 Tonlé Sap -5.82
Huron -7.40 -55.0 Tonlé Sap -8.60
Huron -6.60 -53.6 Tonlé Sap -7.76
Issyk-Kul -0.97 -10.9 Tonlé Sap -8.47
Issyk-Kul -0.69 -10.9 Tonlé Sap -6.09
Issyk-Kul -0.58 -10.7 Tonlé Sap -4.32
Issyk-Kul -0.56 -8.9 Turkana 5.80 37.0
Issyk-Kul -0.73 -9.5 Turkana 6.10 40.0
Issyk-Kul -0.68 -7.9 Turkana 5.60 39.0
Issyk-Kul -0.60 -6.7 Turkana 4.80 30.0
Jackson -17.97 -138.3 Turkana 5.80 40.0
Jackson -17.89 -137.5 Turkana 5.50 37.0
Kainji -17.5 Turkana 5.80 38.0
Kainji -3.7 Turkana 6.10 42.0
Kainji -2.5 Turkana 5.30 35.0
Kainji -2.2 Valencia 22.0
Kainji -2.9 Van 1.00 -6.6
Kainji -30.4 Van 0.90 -6.8
Kainji -31.9 Victoria 3.50
Kainji -29.9 Winnipeg -11.00
Kainji -28.8 Winnipeg -10.52 -79.8
Kainji -25.9 Winnipeg -10.40 -79.0
Kainji -23.8 Yamdruk-tso -5.48 -68.0
Kainji -20.0 Yellowstone -16.64 -134.5
Kainji -11.6 Zhari Namco -6.67 -75.2
Kainji -9.8 Ziway 6.70 49.0
270
Lake δ18O
(SMOW) δ2H
(SMOW) Lake
δ18O (SMOW)
δ2H (SMOW)
Kainji -8.6 Ziway 8.16 57.4
Kainji -12.8 Ziway 6.50 47.3
Kainji -12.8 Ziway 7.09 52.2
Kainji -25.8 Ziway 8.40 58.9
Kivu 0.11 10.0 Ziway 6.49 46.3
Kivu -0.29 10.8 Ziway 6.90 49.3
Kivu -0.19 12.3 Ziway 6.64 47.4
Kivu -0.15 12.1 Ziway 6.64 48.6
Kivu 0.00 12.2 Ziway 6.53 48.9
Kivu 0.47 10.8 Ziway 6.29 47.3
Kivu 0.68 11.5 Ziway 6.37 47.6
Kivu 0.70 13.6 Ziway 5.43 41.6
Kivu 0.59 16.4 Ziway 5.78 42.4
Kivu 1.36 19.9 Ziway 5.06 37.3
Kivu 1.56 20.3 Ziway 6.74 49.3
Kivu 1.85 21.5 Ziway 6.78 50.2
Kivu 2.50 24.9 Ziway 7.00 46.8
Kivu 2.51 22.0 Ziway 6.69 49.1
Kivu 3.05 24.3 Ziway 5.15 40.5
Kivu 3.15 25.4 Ziway 4.38 32.4
Kivu 3.27 24.8 Ziway 6.70 49.0
Kivu 3.47 25.7
Kivu 3.24 27.4
Kluane -21.13 -168.2
Kluane -22.51 -176.7
Kluane -22.94 -180.6
Kluane -22.86 -179.3
Kluane -22.95 -177.5
Kluane -22.86 -178.1
Kluane -22.58 -177.5
Kluane -22.81 -177.2
Kluane -22.79 -178.9
Kluane -22.84 -178.1
Kluane -22.52 -179.0
Kluane -22.47 -176.0
Kluane -22.49 -176.6
Kluane -22.54 -175.0
Kluane -22.16 -175.9