Close
About
FAQ
Home
Collections
Login
USC Login
Register
0
Selected
Invert selection
Deselect all
Deselect all
Click here to refresh results
Click here to refresh results
USC
/
Digital Library
/
University of Southern California Dissertations and Theses
/
Ages, origins and biogeochemical role of water across a tropical mountain to floodplain transition
(USC Thesis Other)
Ages, origins and biogeochemical role of water across a tropical mountain to floodplain transition
PDF
Download
Share
Open document
Flip pages
Contact Us
Contact Us
Copy asset link
Request this asset
Transcript (if available)
Content
AGES, ORIGINS AND BIOGEOCHEMICAL ROLE OF WATER ACROSS A TROPICAL
MOUNTAIN TO FLOODPLAIN TRANSITION
By
Emily Irene Burt
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(GEOLOGICAL SCIENCES)
December 2022
Copyright (2022) Emily Irene Burt
ii
Dedication
To Janeane Burt-Flores, the best Bassett Hound who ever lived. The comfort and peace
you provided while I wrote my dissertation was immense. You are missed.
iii
Acknowledgements
None of the work presented here would be possible without the Peruvian scientists who
collaborated on this project. To Adan Julian Ccahuana Quispe and Daxs Herson Coayla Rimachi,
it’s been a true pleasure to do science together. Your expertise on all things related to plants,
forests and carbon contributed immensely to the success of this work. Erick Scott Vargas Laura
and Daniella Vizcarra Salazar, your support in the field is greatly appreciated. Each of you
taught me so much about the flora, fauna and landscapes of Peru. Un abrazo grande a todos
ustedes.
I am grateful for the Amazon Conservation Association and Explorer’s Inn Tambopata
for providing wonderful field stations to call home during my dissertation. Thank you to the field
staff for graciously sharing their knowledge of the forest, including where to find the best rivers.
Thank you to the kitchen staff for providing the fuel to carry out this work.
To my advisor, Josh West: I have grown so much from your thoughtful feedback,
suggestions and teaching over the years. Your guidance will always be appreciated. Thank you
for the extra support and understanding you provided during the unforeseen challenges of the
past two years. To Emily Cooperdock: thank you for the extra mentorship you provided during
the last year of my dissertation. Your enthusiasm helped keep me engaged and curious about
science during an otherwise difficult time. To my dissertation committee members: Naomi
Levine and Doug Hammond, thank you for the stimulating discussions and thoughtful feedback
provided along the way. To my qualifying exam committee members: Julien Emile-Geay, Sarah
Feakins, Felipe de Barros and Doug Hammond, thank you for dedicating your time and energy to
helping me grow as a scientist.
iv
To the USC Earth Sciences staff: Karen Young, Alex Aloia, Cindy Waite, Vardui Ter-
Simonian, John Yu, Steve Lin and Darlene Garza, thank you for your support as I navigated
graduate school and coordinated an international field project.
To my collaborators: John Christensen, Mark Conrad, Markus Bill, Robert Hilton,
Mathieu Dellinger, Nick Bouskill, Dana Chadwick and Greg Goldsmith, it’s been a pleasure to
share science with all of you. Each of you provided invaluable insight, motivation and
camaraderie at different points in my graduate career, in both the field and lab.
To those who funded this research: The National Science Foundation, the USC Research
Enhancement Fellowship, The Department of Energy Office of Science Graduate Student
Research Scholarship, Lawrence Berkeley National Lab and the Elizabeth and Jerol Sonosky
Fellowship, thank you.
To my lab mates, past and present: Abra Atwood, Isabel Smith, Chan-Mao Chen, Ronmel
Rugama-Montenegro, Adit Ghosh, Kilian Ashley, Katie Denniston, Jessica Stellmann, Joyce
Yager, Kirstin Washington, Max Dahlquist, Audra Bardsley, Nathan Kemnitz, Jotis Baronas,
Gen Li, Mark Torres, Mathieu Dellinger, Paulina Piñedo, Eric Kleinsasser. Thank you for your
support during the many different stages of my time at USC – from those who helped me find
my footing when I was brand new, to those who helped me as I was preparing to leave. I will
always appreciate your enduring friendships.
To the students who I was lucky enough to mentor in the lab: Jesse Fang, Francis Mel de
Fontenay, Yanet Ibarra and Jazmine Muñoz, thank you. Each of you contributed to my
dissertation work in significant ways, and helped me to learn how to be a better mentor and
teacher.
v
To my parents: Lyn and Bill Burt, who originally nurtured my love of learning. I never
would have made it to this point if it weren’t for the earlier education you helped me access. To
my siblings: Kate and James, thank you for the enduring love and consistent phone calls.
To my family: Ani, Janeane and Gus – I love you.
vi
Table of Contents
Dedication.......................................................................................................................... ii
Acknowledgements…………………………………………............………………........ iii
List of Tables.………………………………………………………………..................... ix
List of Figures……………………………………………………………….................... x
Abstract……………………………………………………………………...................... xi
Chapter 1: Introduction………………………………………………………...…........... 1
1. Water chemistry as a tool for unpacking Earth surface processes………………......... 1
2. Tropical watersheds across extreme environmental gradients…………………........... 1
2.1. Rationale for the Andes mountains and Amazon floodplain as a study location... 1
2.2. Long-term hydrochemical monitoring of the Andes mountains and Amazon
floodplain.......................................………………..........………………...............
3
2.3. Data availability……………………………………………....……….................. 4
3. Hydrochemical approaches to Earth surface problems and key results in this
dissertation.....................................................................................................................
7
3.1. Sulfur and oxygen isotopes in sulfate……………………………......................... 7
3.2. Oxygen isotopes in streams and precipitation constrain catchment water
transit………...………......………..........………………..........……………….....
8
3.3. Major element chemistry sheds light on riverine transport of rock-derived
nutrients…....………………..........………………..........….....…………….........
9
3.4. Oxygen isotopes in plant xylem waters constrain seasonal origins of water used
by plants………………………………………………………..............................
10
References……………………………………………………………………….......... 11
Chapter 2: Conservative transport of dissolved sulfate across the Rio Madre de Dios
floodplain in Peru…………………………………………………………………….......
15
Abstract………………………………………………………………………………...... 16
1. Introduction………………………………………………………………………........ 17
2. Sampling Campaign and Analysis Methods……………………………….................. 19
3. SO4
2-
Dual Isotopic Data (
34
SSO4 vs.
18
OSO4) ………………………………............. 20
4. Oxygen Isotope Gradient to Trace Cycling of SO4
2-
………………………………...... 23
5. Basin-wide mass balance of SO4
2-
and its isotopes………………………………........ 25
6. Conclusions……………………………………………………………….................... 27
Acknowledgements………………………………………………………………........ 28
References………………………………………………………………...................... 29
Supplemental material…………………………………………………………............ 32
Chapter 3: Hydroclimate and bedrock permeability determine young water fractions in
streamflow across the tropical Andes mountains and Amazon floodplain………............
35
Abstract………………………………………………………………………………...... 36
1. Introduction………………………………………………………………………........ 37
2. Data and methods………………………………………………………………........... 39
2.1. Study area and sampling design………………………………………………...... 39
2.2. Analytical techniques and data analysis………………………………………...... 43
vii
3. Results……………………………………………………………………………........ 44
3.1. Oxygen and hydrogen isotopes in streamflow and precipitation……………....… 44
3.2. Young water fractions……………………………………………………….....… 48
4. Discussion……………………………………………………….....………………..... 50
4.1. Hydroclimate and permeability controls on stream young water fractions……… 50
4.2. Implications for the role of mountains in modulating water, erosional, and
biogeochemical fluxes…………………………………………………………....
55
5. Conclusions…………………………………………………………………………… 59
Acknowledgements………………………………………………………………........ 60
References…………………………………………………………………………….. 61
Chapter 4: Stormflow-driven loss of nutrients from a tropical forest ecosystem……….. 65
Abstract…………………………………………………………………………….......... 66
1. Introduction………………………………………………………………………........ 67
2. Materials and methods………………………………………………………………... 69
2.1. Study site…………………………………………………………………………. 69
2.2. Sampling scheme……………………………………………………………….... 71
2.3 Discharge measurements………………………………………………………….. 72
2.4. Sample collection and analysis…………………………………………………... 72
2.5. Young water fractions and stream hydrograph separations……………………… 73
3. Results……………………………………………………………………………….... 74
3.1. Stream water major element fluxes and relation to discharge………………….... 74
3.2. Stable isotopes of rain and stream water………………………………................. 76
3.3. Young water fractions and event water fractions……………………………….... 77
4. Discussion…………………………………………………………………………….. 78
4.1. Los Amigos streamflow regimes: Rapid stormflow conveyance of water and
nutrients....…………………………………………………………......................
78
4.2. Leaky forests: stormflow and the nutrient balance of the Los Amigos ecosystem. 81
5. Conclusions…………………………………………………………………………… 84
Acknowledgements………………………………………………………………........ 85
References……………………………………………………………………….......... 86
Chapter 5: Trees use young water in an Andean tropical montane cloud forest………… 92
Abstract………………………………………………………………………………...... 93
1. Introduction…………………………………………………………………………… 93
2. Study site & sampling………………………………………………………………… 95
2.1. Plant sampling…………………………………………………………………..... 96
2.2. Hydrochemical sampling & monitoring…………………………………………. 97
3. Analytical methods…………………………………………………………………..... 99
4. Results……………………………………………………………………………….... 101
5. Discussion…………………………………………………………………………...... 108
5.1. Plants use recent rain in a tropical montane cloud forest……………………….... 108
5.2. Streamflow and water used by plants display different seasonal dynamics…....... 109
5.3. Common cloud forest trees do not utilize cloud water………....………………... 109
6. Conclusions and future work………………………………....………………………. 110
Acknowledgements………………………………………………………………........ 111
viii
References…………………………………………………………………………….. 112
Chapter 6: Conclusions………………………………………………………………..… 115
Appendix A: Stream concentration-discharge relationships across a tropical
geomorphic gradient…………………………………………………………...................
118
1. Site characteristics…………………………………………………………………...... 119
2. Relevant equations………………..………………………………………………....... 119
Appendix B: Sulfur and oxygen isotope data table…………………………………….... 127
Compiled references…………………………………………………………………...… 132
ix
List of Tables
Table 3.1. Study site characteristics……………………………………………………... 42
Table 3.2. Stream and precipitation stable water isotope data........................................... 47
Table A.1. Study site characteristics. …………………………………………………… 119
Table A.2. Catchment B-values for major element concentration-discharge
relationships……………………………………………………………………….......
121
Table A.3. Catchment A-values for major element concentration-discharge
relationships……………………………………………………………………….......
124
Table B.1. Sulfur and oxygen isotopes, sulfate concentrations in rivers and
groundwaters………………………………………………………………………......
131
x
List of Figures
Figure 1.1. Global distribution of Critical Zone Observatories......................................... 3
Figure 1.2. Photos of watershed monitoring…………………………………………...... 6
Figure 2.1. Map of watersheds………………………………………………………....... 19
Figure 2.2. Sulfur and oxygen isotope composition of SO4
2-
from Rio Madre de Dios
system………………………………………………………………………………....
22
Figure 2.3. Isotopic composition of SO4
2-
and H2O in watersheds…………………….... 24
Figure 2.4. Mass balance of sulfate and water in the Rio Madre de Dios system……..... 26
Figure 3.1. Map of watersheds…………………………………………………………... 41
Figure 3.2. Oxygen isotope composition in rivers and precipitation………………......... 45
Figure 3.3. Oxygen and hydrogen isotope composition in rivers and precipitation…...... 45
Figure 3.4. Oxygen isotope seasonal cycle in rivers and precipitation………………….. 46
Figure 3.5. Oxygen isotope seasonal cycle and sinusoidal fits in rivers and precipitation 49
Figure 3.6. Precipitation return intervals………………………………………………… 53
Figure 3.7. Young water fraction distributions………………………………………...... 54
Figure 3.8. Young water fractions compared to watershed characteristics..…………...... 58
Figure 4.1. Map of watershed and precipitation data…………………………………..... 70
Figure 4.2. Stream elemental fluxes…………………………………………………....... 75
Figure 4.3. Oxygen isotopes in river and precipitation………………………………….. 77
Figure 4.4. Rock-derived nutrients in the watershed……………………………………. 80
Figure 5.1. Map of study site……………………………………………………………. 96
Figure 5.2. Forest canopy sampling locations…………………………………………… 98
Figure 5.3. Plant stem preparation and xylem water extraction…………………………. 99
Figure 5.4. Oxygen and hydrogen isotope composition in waters………………………. 102
Figure 5.5. Oxygen isotope seasonal cycle…………………………………………….... 103
Figure 5.6. Deuterium-excess in waters………………………………………………..... 104
Figure 5.7. Seasonal origin indices of plant, river and soil water……………………….. 106
Figure 5.8. Seasonal origin indices of three plant species……………………………..... 107
Figure 5.9. Young water fraction distributions of plant, river and soil water……............ 108
Figure A.1. Major element concentrations in rivers…………………………………....... 120
Figure A.2. B-values of concentration-discharge relationships………………................. 122
Figure A.3. B-values of concentration-discharge relationships…………………............. 123
Figure A.4. A-values of concentration-discharge relationships…………………............. 125
Figure A.5. A-values of concentration-discharge relationships……………………......... 126
xi
Abstract
Earth’s “critical zone” – the living skin from the top of the tree canopy to the water table
– is essential for sustaining a habitable planet and is exceedingly vulnerable to climate change. In
the critical zone, water, rocks and life interact, leading to biogeochemical reactions, complex
networks of fluid flow, and significant amounts of water transpiration. Each of these processes
are important in their own right – for example, biogeochemistry mediates atmospheric carbon
dioxide levels and produces nutrients that allows life to grow in soils. Subterranean reservoirs of
water sustain society and ecosystem transpiration is a natural cooling mechanism for the planet.
Yet, a key feature of each of these critical zone processes is the complexity of interactions that
influence them: water, rocks and life in the critical zone cannot be studied in a vacuum.
The work presented here is an investigation of critical zone processes across the
transition from Andes mountains to Amazon foreland floodplain in southern Peru. I carried out
hydrochemical monitoring for approximately four years at small seven watersheds that span a
transition from mountain to floodplain to understand how changes in landscape structure (from
steep mountains to flat floodplain), affect critical zone processes. I use the chemistry of water
found in rivers, soils and plants as a tool to understand processes that occur inside of watersheds.
The vast majority of critical zone studies to date have been carried out in northern latitudes,
leaving tropical latitudes largely understudied despite their outsized role in the global water cycle
and high levels of primary productivity and biodiversity. The dramatic transition between the
steep slopes and high elevation of the Andes mountain to the flat Amazon floodplain provides
another layer of motivation for this study. Mountainous regions play key roles in global water
cycles, receiving high amounts of precipitation and providing water to drier downstream areas
across the globe. Given the role of mountains in storing and releasing water, I am particularly
xii
interested in exploring how the varied mountain environments targeted in this research transmit
water.
I begin my exploration of the linkages between landscapes, water, rocks and life in
Chapter two, where I study sulfide mineral oxidation from the Andes mountains to Amazon
floodplain. Work on sulfide mineral oxidation over the past two decades has expanded our view
of chemical weathering: rather than viewing chemical weathering as a reliable sink of carbon
dioxide, we now know that sulfide mineral oxidation can lead to carbon dioxide release from
rocks. In this chapter, I ask the question, “what happens to sulfate released from sulfide mineral
oxidation in the Andes mountains?” which has implications for how much carbon dioxide is
consumed or released from rock weathering. I use stable isotopes of sulfur and oxygen in
riverine sulfate combined with a mass-balance framework to show that there is not a significant
microbial recycling of sulfate along the mountain-to-floodplain transition. Some studies have
pointed to microbial sulfate reduction as a mechanism that lessens the potential for carbon
dioxide release; I suggest that this process is not as important as previously thought.
After investigating the importance of pyrite oxidation and weathering from sulfuric acid
in the Andes mountains, I examine water transit across the mountain to floodplain transition. In
Chapter three, I use the stable isotope composition of oxygen and hydrogen in precipitation and
streamflow to understand how quickly water moves from precipitation to streamflow. I employ a
widely-used metric of water transit, the “young water fraction” to quantitatively determine how
water transit varies from mountain to floodplain. I show that water moves through the subsurface
the fastest in watersheds with low-permeability bedrock and poorly developed soils. I also show
that the young water fraction depends on local hydroclimate. The results of this study highlight
the complex nature of water transit in mountainous regions.
xiii
Chapter four examines the role that storms play in driving nutrient loss from a nutrient-
poor, tropical, terra firme terrace. In this chapter, I combine stream stable isotope data with
major element chemistry collected during storms. Using this data, I show that storm water travels
through surface flowpaths and leaches rock-derived nutrients accumulated in the surface soil due
to biological cycling. I compare estimates of surface soil nutrient stocks and riverine nutrient
losses to show that storms represent a “leak” in ecosystem nutrient cycling – significant due to
the nutrient-poor nature of tropical fluvial terraces and the reality that climate change will lead to
more storms in the future. This work spans hydrology and ecology by combining hydrochemical
data with estimates of nutrient stocks in soil and vegetation to create an interpretive framework
applicable across disciplinary boundaries.
In Chapter five, I compare the sources of precipitation that sustain streamflow and plant
transpiration in a tropical montane cloud forest watershed. I measure the oxygen stable isotope
composition of plant xylem water, stream water, soil water and precipitation to determine the
fraction of recent precipitation that supplies stream, soil and plant xylem waters. I show that
plants take up about a quarter of their water from recent precipitation, while streamflow is
sourced from less than ten percent recent precipitation. The abundance of recent precipitation in
plant xylem waters suggests that in this tropical mountainous watershed, plants take up recent
rains using shallow root networks. This is in direct contrast to Mediterranean and temperate
climates where plants transpire water that has been stored within landscapes for months or
longer. The importance of recent precipitation in tropical plant water use suggests that these
environments may be particularly vulnerable to hydrologic changes associated with climate
change.
xiv
I also include two appendices with additional datasets I collected during my Ph.D.:
Appendix A provides an exploration of stream major element chemistry in the seven small
watersheds monitored in this study. Measuring major element chemistry across a range of
hydrologic conditions provides insight towards chemical weathering processes across the
geomorphic gradient of the Andes mountains and Amazon floodplain. Appendix B includes
additional sulfur and oxygen isotopes in riverine sulfate that were not published with Chapter 2.
1
Chapter 1: Introduction
1. Water chemistry as a tool for unpacking Earth surface processes
My motivation to pursue this dissertation was fueled by curiosity about water in Earth’s
subsurface: water exists from centimeters to kilometers beneath the ground, and is crucial for
sustaining life on Earth. Despite the fact that we cannot see most fresh water, we know that it is
dynamic, undergoing biogeochemical reactions that can sequester atmospheric carbon dioxide
(Ebelmen, 1845; Garrels & Mackenzie, 1967; Urey, 1952) or produce nutrients that sustain
ecosystems (Bormann & Likens, 1967). Amid a changing climate and demands on water to
support a growing population, the need to understand how water behaves in Earth’s subsurface
has never been greater. Water chemistry provides an invaluable tool to studying water behavior,
recording the processes and transformations that water experiences, starting when it falls from
the sky as precipitation. In this dissertation, watersheds are my unit of study. I analyzed
chemistry of river water, precipitation, soil water and plant xylem waters in order to understand
how long it takes water to move through watersheds, chemical reactions within watershed and
the origins of water that sustain streamflow and plant transpiration.
2. Tropical watersheds across extreme environmental gradients
2.1. Rationale for the Andes mountains and Amazon floodplain as a study location
The experimental watershed is a widely used model in hydrology for understanding how
precipitation makes its way through watersheds to become streamflow. The earliest models of
streamflow generation resulted from work carried out at intensely monitored watersheds, where
hydrometric (Hewlett & Hibbert, 1967) and hydrochemical data were regularly collected (Dunne
2
& Black, 1970; Hooper et al., 1990; J. Kirchner et al., 2000; Mcdonnell, 1990). Over the past
century of hydrologic research, the number of intensely monitored watersheds has grown
dramatically, along with the frequency of sampling and ability to make in situ measurements of
water chemistry (e.g., Gallart et al., 2020; von Freyberg et al., 2017). Despite a greater spatial
and temporal resolution of hydrochemical data than ever before, the global distribution of
experimental watersheds remains concentrated in the northern hemisphere (Fig. 1.1). Tropical
latitudes, and the Amazon river watershed in particular contain very few long-term hydrological
monitoring sites. It is well established, however, that tropical regions produced outsized fluxes of
water and solutes (Fekete et al., 2002; Meybeck, 1987) and also host high terrestrial primary
productivity (Malhi & Grace, 2000). The Andes mountains and Amazon foreland floodplain
represent a key gap in the global understanding of Earth surface processes.
The Andes mountains to Amazon foreland floodplain transition hosts a range in
catchment slopes from 3 − 37 ; a range that many other studies are unable to replicate. The
motivation for understanding water transit within mountainous regions is particularly high:
mountains are often dubbed “water towers” for humanity, because they store water that can be
used in drier downstream areas (Immerzeel et al., 2020; Viviroli et al., 2007). Water transit in
mountainous regions can also have significant impacts on chemical weathering fluxes (Ibarra et
al., 2016, 2017; Maher, 2010, 2011; Maher & Chamberlain, 2014). And, an evolving picture of
chemical weathering has shown that mountain weathering can lead to carbon dioxide release to
the atmosphere (Bufe et al., 2021; Hilton & West, 2020; Torres et al., 2016). Given the
importance of water transit and chemical weathering in mountainous environments and the
understudied nature of tropical regions, the Andes mountains to Amazon foreland floodplain
transition provides a unique opportunity to study critical zone science.
3
Figure 1.1. Global distribution of Critical Zone Observatories. Data courtesy of the Critical Zone
Collaborative Network.
2.2. Long-term hydrochemical monitoring of the Andes mountains and Amazon floodplain
Josh West and I first traveled to Peru in 2015, to identify streams that would be suitable
for hydrochemical monitoring. We selected seven small (< 3 km
2
) catchments to carry out
regular hydrochemical monitoring and began the campaign in 2016. Approximately every two
weeks, one of our Peruvian collaborators conducted a hydrochemical sampling trip:Adan Julian
Ccahuana Quispe was the primary coordinator of sampling in the Andes mountain sites and in
the Amazon foreland floodplain sites, Daxs Herson Coayla Rimachi and Erick Scott Vargas
Laura both carried out sampling.
For all seven sites, each routine trip consisted of collecting stream samples (separate
aliquots for stable isotopes of water, cations and anions) and stream discharge measurements.
For four of the seven sites we carried out more detailed sampling, including: precipitation
collection, in situ soil water collection from suction lysimeters (Fig. 1.2) and tree branches for
xylem water extraction. At the University of Southern California I used the Agilent 8900
4
Inductively Coupled Plasma-Optical Emission Spectrometer to measure cation concentrations
and the Metrohm 850 Ion Chromatograph to measure anion concentrations. I also measured the
stable isotope composition of water at three different laboratories over the course of the study:
Caltech (LGR WIA), Lawrence Berkeley National Lab (LGR WIA) and Chapman University
(Picaro CRDS). The data obtained from this hydrochemical monitoring campaign provide the
basis for the work presented in Chapters 3 through 5.
I carried out additional sampling campaigns for the work presented in Chapter 2, to
understand the fate of sulfate released from pyrite oxidation in the Andes Mountains. The nature
of this project involved measuring sulfate stable isotopes in a series of nested catchments from
mountain to floodplain: I targeted large rivers to capture broad spatial trends in sulfate
geochemistry. Ion-exchange columns were deployed in the field to trap sulfate for stable isotope
measurements. Stable isotopes of oxygen and sulfur in sulfate were then measured at Lawrence
Berkeley National Lab via Total Combustion Elemental Analysis Isotope Ratio Mass
Spectrometry for oxygen (TC-EA-IRMS) and EA-IRMS for sulfur.
2.3. Data availability
The data presented in this dissertation can be accessed through Hydroshare.org, an open-
source data repository that supports FAIR (findable, accessible, interoperable and reusable) data
principles. Given that the data collected in this study fill a significant gap in the global
distribution of studies, it is important that they are made available to other researchers. We
grouped the data into repositories based on data-type.
5
Water isotope (
18
O and D) data (Chapter 3 & associated work) are available via:
http://www.hydroshare.org/resource/c01ef51ca2b3495785d0f24c62142e23
Major element concentration data (Chapter 4, Appendix A) are available via:
http://www.hydroshare.org/resource/7f41a36f120f405eae582c27f0bd9024
Plant xylem water isotope (
18
O and D) data (Chapter 5) are available via:
http://www.hydroshare.org/resource/fdfdddbc35494e21ad2dda279f81832b.
Lastly, sulfur and oxygen isotopes (
18
O and
34
S) and aqueous sulfate concentrations can be
found in Appendix B.
6
Figure 1.2. Top photo: an intensively monitored watershed in the Andes mountains, with water
level logger. Bottom photo: suction lysimeter collecting in situ soil water in the Andes mountain
foothills.
7
3. Hydrochemical approaches to Earth surface problems and key results in this dissertation
Understanding the interplay between water transport, biogeochemical cycling and
chemical weathering is a complex problem in critical zone science. Throughout the course of my
dissertation, I employed several different geochemical approaches in order to tackle different
questions. A common thread throughout the course of the work presented here is the importance
of field work: all samples were collected in the field between 2016-2019. We achieved high
resolution geochemical sampling, both spatially and temporally.
3.1. Sulfur and oxygen isotopes in sulfate: partitioning sulfate sources and assessing bacterial
sulfate reduction
Chapter two (Burt et al., 2021) expands upon our growing understanding of pyrite
oxidation and its relation to the geologic carbon cycle. Although myriad recent papers have
shown pyrite oxidation and weathering from sulfuric acid to be important chemical weathering
processes, especially in mountainous systems, there is not an established consensus of what
happens to the sulfate liberated from pyrite oxidation. If sulfate is reduced via bacterial sulfate
reduction and exported from the watershed in the reduced phase, then the sulfate (and sulfuric
acid) cannot weather carbonate rocks and contribute to CO2 release. Bacterial sulfate reduction
(BSR) is known to occur in wetlands and other anoxic environments, but its extent in oxygenated
river systems is not fully understood. Some studies have suggested the presence of bacterial
sulfate reduction in the Himalaya (Hemingway et al., 2020; Turchyn et al., 2013), but we lack
observations of BSR in other systems.
In order to fully understand the potential for pyrite oxidation to lead to carbon dioxide
release, a more quantitative understanding of bacterial sulfate reduction in river systems is
8
needed. In the publication presented in Chapter two, I utilize stable isotopes of oxygen and sulfur
and a sulfate mass balance to understand the extent of bacterial sulfate reduction. I explore the
utility of different isotopic proxies for determining bacterial sulfate reduction. I combine
multiple lines of evidence to show that there is no significant bacterial sulfate reduction as
sulfate is liberated from pyrite oxidation in the Andes mountains and transported across several
hundred kilometers of Amazon floodplain. In effect, the sulfate released from pyrite oxidation
can still actively weather carbonate rocks and lead to a rock-derived release of carbon dioxide;
bacterial sulfate reduction does not dampen the carbon dioxide release in this region.
3.2. Oxygen isotopes in streams and precipitation constrain catchment water transit
Chapter three focuses on water transport – from rainfall to runoff – in the small
catchments I monitored across the mountain to floodplain transition of southern Peru. This work
highlights the importance of catchment permeability and structure in controlling water transit –
something that has not been fully explored in the literature, especially in tropical mountainous
regions. Using stable isotopes of oxygen in streamflow and precipitation, we are able to calculate
the fraction of streamflow that is comprised of recent precipitation (referred to as the “young
water fraction”, Kirchner, 2016a, 2016b). Changes in catchment lithology explain the observed
trends in young water fractions. The catchments with the highest young water fractions are those
in the mid-elevation range, that are either comprised of granite or colluvium. These catchments
have poorly developed soils and thus water is rapidly transmitted from rainfall to runoff. In the
high mountainous regions, we observe low young water fractions as a result of deeply fractured
and permeable shale that allows for greater infiltration of water and longer flow paths. We also
explore the role of hydroclimate in controlling water transit (Gallart et al., 2020; von Freyberg et
9
al., 2018), and found that sites with a high precipitation recurrence interval showed higher young
water fractions.
3.3. Major element chemistry sheds light on riverine transport of rock-derived nutrients
Chapter four of my dissertation explores samples collected from a small catchment on an
Amazon fluvial terrace. In this chapter, I explore the role of storms and water flow paths in
nutrient export. I was motivated to identify fluid flow paths and solute fluxes associated with
storms and baseflow (i.e., when it is not raining) because tropical fluvial terraces often have thin
soils limited in rock-derived nutrients (Cuevas & Medina, 1988; Vitousek & Farrington, 1997),
and hydrologic redistribution of key elements has previously been posited to play an important
role in this setting (Chadwick & Asner, 2016, 2018). Water samples were collected from 2016-
2019, with two high resolution sampling campaigns: one in 2017 for five days, and one in 2019
for two days. I measured the oxygen stable isotope composition of stream water and precipitation
as well as the concentration of rock-derived nutrients in the stream to determine dominant fluid
flow paths and solute exports under different hydrologic conditions. I coupled the stream
hydrochemical data with existing soil and vegetation chemistry datasets (Chadwick & Asner,
2016, 2018), to provide a view of stream solute fluxes in the context of ecosystem nutrient
availability. I show that there are significant losses of rock-derived nutrients from the surface soil
driven by storms. These data provide valuable insight into what we may expect to see in a
warmer, wetter future with more intense and frequent storms.
10
3.4. Oxygen isotopes in plant xylem waters constrain the seasonal origin of water used by plants
Chapter five focuses on a significant component of the water cycle not addressed in prior
chapters: transpiration. Given that transpiration contributes to approximately 40% of
precipitation recycling over land, understanding the sources of water that sustain transpiration is
crucial, especially in light of climate change. As part of the ongoing hydrochemical monitoring
during my dissertation, I collected plant stem samples and cryogenically extracted the water from
plant xylem. I compared plant xylem water oxygen isotope composition to precipitation oxygen
isotope composition to understand the seasonal origins of the waters taken up by plants (Allen et
al., 2019). Additionally, I applied the young water fraction framework from Chapter 3 to the
plant xylem waters, soil waters and stream waters. I found that compared to stream and soil
waters, plant xylem waters had the most seasonally dynamic reservoir of water. This suggests
that plants take up younger water from the surface soil, while streamflow and soil water are
sustained by deeper waters that are retained within the watershed for longer.
11
References
Allen, S. T., Kirchner, J. W., Braun, S., Siegwolf, R. T. W., & Goldsmith, G. R. (2019). Seasonal
origins of soil water used by trees. Hydrology and Earth System Sciences, 23(2), 1199–
1210. https://doi.org/10.5194/hess-23-1199-2019
Bormann, F. H., & Likens, G. E. (1967). Nutrient Cycling. Science, 155(3761), 424–429.
Bufe, A., Hovius, N., Emberson, R., Rugenstein, J. K. C., Galy, A., Hassenruck-Gudipati, H. J.,
& Chang, J.-M. (2021). Co-variation of silicate, carbonate and sulfide weathering drives
CO 2 release with erosion. Nature Geoscience, 14(4), 211–216.
https://doi.org/10.1038/s41561-021-00714-3
Burt, E. I., Bill, M., Conrad, M. E., Quispe, A. J. C., Christensen, J. N., Hilton, R. G., Dellinger,
M., & West, A. J. (2021). Conservative transport of dissolved sulfate across the Rio
Madre de Dios floodplain in Peru. Geology. https://doi.org/10.1130/G48997.1
Chadwick, K. D., & Asner, G. P. (2016). Tropical soil nutrient distributions determined by biotic
and hillslope processes. Biogeochemistry, 127(2–3), 273–289.
https://doi.org/10.1007/s10533-015-0179-z
Chadwick, K. D., & Asner, G. P. (2018). Landscape evolution and nutrient rejuvenation reflected
in Amazon forest canopy chemistry. Ecology Letters, 21(7), 978–988.
https://doi.org/10.1111/ele.12963
Cuevas, E., & Medina, E. (1988). Nutrient dynamics within amazonian forests: II. Fine root
growth, nutrient availability and leaf litter decomposition. Oecologia, 76(2), 222–235.
https://doi.org/10.1007/BF00379956
Dunne, T., & Black, R. D. (1970). Partial Area Contributions to Storm Runoff in a Small New
England Watershed. Water Resources Research, 6(5), 1296–1311.
https://doi.org/10.1029/WR006i005p01296
Ebelmen, J.-J. (1845). Sur les produits de la décomposition des espèces minérales de la famille
des silicates. Annales Des Mines, 7(3), 3–66.
Fekete, B. M., Vörösmarty, C. J., & Grabs, W. (2002). High-resolution fields of global runoff
combining observed river discharge and simulated water balances. Global
Biogeochemical Cycles, 16(3), 15-1-15–10. https://doi.org/10.1029/1999GB001254
Gallart, F., Valiente, M., Llorens, P., Cayuela, C., Sprenger, M., & Latron, J. (2020).
Investigating young water fractions in a small Mediterranean mountain catchment: Both
precipitation forcing and sampling frequency matter. Hydrological Processes, 34(17),
3618–3634. https://doi.org/10.1002/hyp.13806
Garrels, R. M., & Mackenzie, F. T. (1967). Origin of the Chemical Compositions of Some
Springs and Lakes. In Equilibrium Concepts in Natural Water Systems (Vol. 67, pp. 222–
242). American Chemical Society. https://doi.org/10.1021/ba-1967-0067.ch010
12
Hemingway, J. D., Olson, H., Turchyn, A. V., Tipper, E. T., Bickle, M. J., & Johnston, D. T.
(2020). Triple oxygen isotope insight into terrestrial pyrite oxidation. Proceedings of the
National Academy of Sciences, 117(14), 7650–7657.
https://doi.org/10.1073/pnas.1917518117
Hewlett, J. D., & Hibbert, A. R. (1967). Factors affecting the response of small watersheds to
precipitation in humid areas. Forest Hydrology, 1, 275–290.
Hilton, R. G., & West, A. J. (2020). Mountains, erosion and the carbon cycle. Nature Reviews
Earth & Environment, 1, 16. https://doi.org/10.1038/s43017-020-0058-6
Hooper, R. P., Christophersen, N., & Peters, N. E. (1990). Modelling streamwater chemistry as a
mixture of soilwater end-members—An application to the Panola Mountain catchment,
Georgia, U.S.A. Journal of Hydrology, 116(1–4), 321–343. https://doi.org/10.1016/0022-
1694(90)90131-G
Ibarra, D. E., Caves, J. K., Moon, S., Thomas, D. L., Hartmann, J., Chamberlain, C. P., & Maher,
K. (2016). Differential weathering of basaltic and granitic catchments from
concentration–discharge relationships. Geochimica et Cosmochimica Acta, 190, 265–293.
https://doi.org/10.1016/j.gca.2016.07.006
Ibarra, D. E., Moon, S., Caves, J. K., Chamberlain, C. P., & Maher, K. (2017). Concentration–
discharge patterns of weathering products from global rivers. Acta Geochimica, 36(3),
405–409. https://doi.org/10.1007/s11631-017-0177-z
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S.,
Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M., Fernández, A.,
Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink, P. D. A., Kulkarni, A. V.,
Mayewski, P. A., … Baillie, J. E. M. (2020). Importance and vulnerability of the world’s
water towers. Nature, 577(7790), 364–369. https://doi.org/10.1038/s41586-019-1822-y
Jasechko, S., Kirchner, J. W., Welker, J. M., & McDonnell, J. J. (2016). Substantial proportion
of global streamflow less than three months old. Nature Geoscience, 9(2), 126–129.
https://doi.org/10.1038/ngeo2636
Kirchner, J., Feng, X., & Neal, C. (2000). Fractal stream chemistry and its Implications for
Contaminant Transport in Catchments. Nature, 403, 524–527.
https://doi.org/10.1038/35000537
Kirchner, J. W. (2016a). Aggregation in environmental systems – Part 1: Seasonal tracer cycles
quantify young water fractions, but not mean transit times, in spatially heterogeneous
catchments. Hydrology and Earth System Sciences, 20(1), 279–297.
https://doi.org/10.5194/hess-20-279-2016
Kirchner, J. W. (2016b). Aggregation in environmental systems – Part 2: Catchment mean transit
times and young water fractions under hydrologic nonstationarity. Hydrology and Earth
System Sciences, 20(1), 299–328. https://doi.org/10.5194/hess-20-299-2016
13
Maher, K. (2010). The dependence of chemical weathering rates on fluid residence time. Earth
and Planetary Science Letters, 294(1–2), 101–110.
https://doi.org/10.1016/j.epsl.2010.03.010
Maher, K. (2011). The role of fluid residence time and topographic scales in determining
chemical fluxes from landscapes. Earth and Planetary Science Letters, 312(1–2), 48–58.
https://doi.org/10.1016/j.epsl.2011.09.040
Maher, K., & Chamberlain, C. P. (2014). Hydrologic Regulation of Chemical Weathering and
the Geologic Carbon Cycle. Science, 343(6178), 1502–1504.
https://doi.org/10.1126/science.1250770
Malhi, Y., & Grace, J. (2000). Tropical forests and atmospheric carbon dioxide. Trends in
Ecology & Evolution, 15(8), 332–337. https://doi.org/10.1016/S0169-5347(00)01906-6
Mcdonnell, J. (1990). A Rationale for Old Water Discharge Through Macropores in a Steep,
Humid Catchment. Water Resources Research, 26, 2821–2832.
https://doi.org/10.1029/WR026i011p02821
McGlynn, B., McDonnell, J., Stewart, M., & Seibert, J. (2003). On the relationships between
catchment scale and streamwater mean residence time. Hydrological Processes, 17(1),
175–181. https://doi.org/10.1002/hyp.5085
McGuire, K. J., & McDonnell, J. J. (2006). A review and evaluation of catchment transit time
modeling. Journal of Hydrology, 330(3–4), 543–563.
https://doi.org/10.1016/j.jhydrol.2006.04.020
McGuire, K. J., McDonnell, J. J., Weiler, M., Kendall, C., McGlynn, B. L., Welker, J. M., &
Seibert, J. (2005). The role of topography on catchment-scale water residence time.
Water Resources Research, 41(5). https://doi.org/10.1029/2004WR003657
Meybeck, M. (1987). Global chemical weathering of surficial rocks estimated from river
dissolved loads. American Journal of Science, 287, 401–428.
Tetzlaff, D., Seibert, J., McGuire, K. J., Laudon, H., Burns, D. A., Dunn, S. M., & Soulsby, C.
(2009). How does landscape structure influence catchment transit time across different
geomorphic provinces? Hydrological Processes, 23(6), 945–953.
https://doi.org/10.1002/hyp.7240
Tetzlaff, D., Seibert, J., & Soulsby, C. (2009). Inter-catchment comparison to assess the
influence of topography and soils on catchment transit times in a geomorphic province;
the Cairngorm mountains, Scotland. Hydrological Processes, 23(13), 1874–1886.
https://doi.org/10.1002/hyp.7318
14
Torres, M. A., West, A. J., Clark, K. E., Paris, G., Bouchez, J., Ponton, C., Feakins, S. J., Galy,
V., & Adkins, J. F. (2016). The acid and alkalinity budgets of weathering in the Andes–
Amazon system: Insights into the erosional control of global biogeochemical cycles.
Earth and Planetary Science Letters, 450, 381–391.
https://doi.org/10.1016/j.epsl.2016.06.012
Turchyn, A. V., Tipper, E. T., Galy, A., Lo, J.-K., & Bickle, M. J. (2013). Isotope evidence for
secondary sulfide precipitation along the Marsyandi River, Nepal, Himalayas. Earth and
Planetary Science Letters, 374, 36–46. https://doi.org/10.1016/j.epsl.2013.04.033
Urey, H. C. (1952). On the Early Chemical History of the Earth and the Origin of Life.
Proceedings of the National Academy of Sciences, 38(4), 351–363.
https://doi.org/10.1073/pnas.38.4.351
Vitousek, P. M., & Farrington, H. (1997). Nutrient limitation and soil development:
Experimental test of a biogeochemical theory. Biogeochemistry, 37, 13.
Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M., & Weingartner, R. (2007). Mountains of
the world, water towers for humanity: Typology, mapping, and global significance.
Water Resources Research, 43(7). https://doi.org/10.1029/2006WR005653
von Freyberg, J., Allen, S. T., Seeger, S., Weiler, M., & Kirchner, J. W. (2018). Sensitivity of
young water fractions to hydro-climatic forcing and landscape properties across 22 Swiss
catchments. Hydrology and Earth System Sciences, 22(7), 3841–3861.
https://doi.org/10.5194/hess-22-3841-2018
von Freyberg, J., Studer, B., & Kirchner, J. W. (2017). A lab in the field: High-frequency
analysis of water quality and stable isotopes in stream water and precipitation. Hydrology
and Earth System Sciences, 21(3), 1721–1739. https://doi.org/10.5194/hess-21-1721-
2017
15
Chapter 2: Conservative transport of dissolved sulfate across the
Rio Madre de Dios floodplain in Peru
Contributors: Markus Bill,
Mark Conrad, Adan Julian Ccahuana Quispe, John Christensen,
Robert Hilton, Mathieu Dellinger, A. Joshua West
Opening statement
In this chapter, I explore the fate of sulfate released from sulfide mineral oxidation in the
Andes mountains. Sulfide mineral oxidation is a significant geochemical process because it can
lead to carbon dioxide release from rocks into the atmosphere. However, if sulfate released from
sulfide mineral oxidation is reduced (and not reoxidized), the carbon release can be negated.
Previous work has suggested that sulfate reduction may be significant in riverine systems – in
this paper I show that there is not a significant reduction of sulfate from sulfide weathering in the
Andes mountains.
This work was conceptualized by myself and Joshua West. Adan Julian Ccahuana
Quispe, Robert Hilton, Mathieu Dellinger, Joshua West and I collected the samples. I completed
the column chemistry with guidance from John Christensen. Markus Bill and I analyzed the
samples. I analyzed the data and made the figures. I wrote the manuscript with input from all
authors. I carried out this work as part of the Department of Energy Office of Science Graduate
Student Research (SCGSR) program.
This paper was published as: Emily I. Burt, Markus Bill, Mark E. Conrad, Adan Julian Ccahuana
Quispe, John N. Christensen, Robert G. Hilton, Mathieu Dellinger, A. Joshua West;
Conservative transport of dissolved sulfate across the Rio Madre de Dios floodplain in Peru.
Geology 2021; 49 (9): 1064–1068. doi: https://doi.org/10.1130/G48997.1
16
Abstract
Mineral weathering plays a primary role in the geologic carbon cycle. Silicate weathering
by carbonic acid consumes CO2 and stabilizes Earth’s climate system. However, when sulfuric
acid drives weathering, CO2 can be released to the atmosphere. Recent work has established that
sulfuric acid weathering resulting from sulfide mineral oxidation is globally significant and
particularly important in rapidly eroding environments. However, if SO4
2-
produced by sulfide
oxidation is reduced during continental transit, then CO2 release may be negated. Yet, little is
known about how much SO4
2-
reduction takes place in terrestrial environments. We report
oxygen and sulfur stable isotopes of SO4
2-
in river waters and mass budget calculations, which
together suggest that SO4
2-
released from pyrite oxidation in the Peruvian Andes mountains is
conservatively exported across ~300 km of the Amazon floodplain. In this system, floodplain
SO4
2-
reduction does not counteract the large flux from Andean pyrite weathering or measurably
affect the stable isotope composition of riverine SO4
2-
. These findings support the hypothesis that
uplift and erosion of sedimentary rocks drives release of CO2 from the rock reservoir to the
atmosphere.
17
1. Introduction
Rock weathering can sequester or release CO2, depending on the acid source (carbonic
versus sulfuric), the type of mineral weathered (silicate versus carbonate), and the fate of the
weathering products. The canonical view of weathering as a sink for atmospheric CO2 (Ebelmen,
1845; Urey, 1952) is based on dissolution of silicate minerals with carbonic acid:
CaSiO3 + 3H2O + 2CO2 → 2HCO3
-
+ Ca
2+
+ H4SiO4 (Eq. 1)
followed by precipitation of carbonate minerals:
Ca
+2
+ 2HCO3
-
→ CaCO3 + H2O + CO2 (Eq. 2)
The net effect is to sequester atmospheric CO2 into the rock reservoir.
In contrast, weathering of carbonate minerals with sulfuric acid (as produced by oxidative
weathering of pyrite, OWP) generates SO4
2-
and can lead to CO2 release (Spence and Telmer,
2005):
4FeS2 + 15O2 + 8CaCO3 → 2Fe2O3 + 8 Ca
2+
+ 8SO4
2-
+ 8CO2 (Eq. 3)
Recent studies found the global flux of OWP-derived S to the oceans is 1.3 0.2 Tmol S/y
(Burke et al., 2018), ~2.0-2.7 times larger than earlier estimates (Francois and Walker, 1992;
Lerman et al., 2007). The SO4
2-
fluxes from OWP increase with erosion rate (Calmels et al.,
2007; Das et al., 2012; Torres et al., 2016), so mountainous terrains rich in sulfide minerals are
primed to release CO2 via weathering (Eq. 3). The close relationship between erosion rate and
OWP may determine how mountain building affects long-term climate (Torres et al., 2014).
Reduction of SO4
2-
can return carbon back to the rock reservoir. The typical pathway for
SO4
2-
reduction is anaerobic respiration of organic carbon (Berner, 1982), referred to here as
bacterial sulfate reduction (BSR):
SO4
2-
+ 2CH2O → H2S + 2HCO3
-
(Eq. 4)
18
The HCO3
-
produced in this reaction can combine with Ca
2+
to form CaCO3 (Eq. 2). Since
organic carbon originally derives from atmospheric CO2 (fixed via photosynthesis), the net effect
(Eqs. 4 + 2) is to reverse the release of CO2 from OWP. The oceanic residence time of SO4
2-
is
~10 Myrs, so delivery of SO4
2-
from OWP can sustain CO2 release to the ocean-atmosphere
system for several Myrs before SO4
2-
is consumed by Eq. 4 (Torres et al., 2014).
However, little is known about how much SO4
2-
is reduced before it reaches the oceans.
If terrestrial BSR is quantitatively significant, and reduced phases are not re-oxidized, loss of
SO4
2-
could counteract CO2 release associated with OWP and dampen the link between erosion,
pyrite weathering, and the carbon cycle. Moreover, significant riverine BSR could reset isotopic
signatures of SO4
2-
used for paleo-environmental reconstruction (Hemingway et al., 2020). Scale
and environment are important: localized S cycling in wetlands and lakes is widely documented
(Holmer and Storkholm, 2001; Pester et al., 2012; Ng et al., 2017) but large river systems are
less well-studied. Isotopic evidence suggests minimal redox transformation of SO4
2-
in the
Mississippi River and Llobregat River in Spain (Killingsworth et al., 2018; Otero et al., 2008),
reduction and loss of SO4
2-
in the Sleepers River in Vermont, USA and the Marsyandi River in
Nepal (Hemingway et al., 2020; Mayer et al., 2010; Turchyn et al., 2013), and active reduction
and re-oxidation, but with no significant net loss of SO4
2-
, in lower reaches of the Amazon River
(Longinelli and Edmond, 1983).
In this study, we evaluate the role of SO4
2-
reduction in the Andes mountains and
Amazon floodplain of southern Peru, where prior work in the Madre de Dios basin revealed high
rates of OWP (Torres et al., 2016). We expected this environment to provide optimal conditions
for in-catchment SO4
2-
reduction, because of high fluxes of dissolved SO4
2-
and reactive organic
matter, along with extensive floodplain transit in a tropical climate (Clark et al., 2017; Feakins et
19
al., 2018). To evaluate the fate of SO4
2-
released by OWP, we combine the mass budget of SO4
2-
with analyses of its oxygen and sulfur isotopes (
18
OSO4 and
34
SSO4), which fingerprint where
oxidation reactions take place.
Figure 2.1. The Madre de Dios watershed. A: Large tributaries used for mass balance: (1) Rio
Alto Madre de Dios (6,025 km
2
draining the Andes; site 1a = SO4
2-
isotope sample; 1b =
discharge and SO4
2-
flux measurement), (2) Hot spring, (3) Rio Manu (13,449 km
2
draining
floodplain and Andes foothills), (4) Rio Chilibe (1,277 km
2
draining floodplain), (5) Rio
Colorado (3,674 km
2
draining floodplain that includes artisanal mining operations), (6) Rio
Madre de Dios (27,830 km
2
). B: nested catchments trace
18
OSO4 released in the Andes through
floodplain transport (Fig. 2.3): (A) Wayqecha (50 km
2
), (B) Kosñipata @ San Pedro (161 km
2
),
(C) Kosñipata @ Pilcopata (2970 km
2
), (D) Rio Alto Madre de Dios, (E) Rio Madre de Dios.
2. Sampling Campaign and Analysis Methods
We sampled several rivers within the Madre de Dios basin (Fig. 2.1). We use samples
from the four main tributaries of the Rio Madre de Dios (Fig. 2.1A) to create a SO4
2-
budget,
while nested catchments (Fig. 2.1B) track the composition of SO4
2-
as it moves from mountain to
foreland floodplain. Since hydrology can influence OWP (Killingsworth et al., 2018; Winnick et
al., 2017) and change contributions of water and solutes from different tributaries (Torres et al.,
20
2017), we conducted four field campaigns: August 2018 (middle of the dry season); January
2019 (early wet season); March 2019 (late wet season); and May 2019 (early dry season). “Dry”
and “wet” seasons are relative, with wet season precipitation ~3-4 times that in the dry season
(Clark et al., 2014). Mean annual precipitation ranges from ~1 m/yr at the highest elevations to 5
m/yr in mid-elevation precipitation hotspots (Rapp and Silman, 2012). Tributaries drain an area
that spans from tropical montane cloud forest to tropical rainforest, across elevations from 3500
m to 200 m, and with mean annual temperature from 11 to 23 C. Bedrock consists of Paleozoic
sedimentary and meta-sedimentary units at high elevations, felsic plutons at mid elevations, and
Tertiary and Quaternary deposits in the foreland floodplain (Torres et al., 2016).
Water samples were filtered at the time of collection. Filtered samples were loaded onto
Dowex 1X8 ion exchange columns; SO4
2-
was quantitatively eluted with 0.4 N HCl and
precipitated as BaSO4 for isotope analysis (
34
S and
18
O) using 0.5 mg BaSO4 powder in silver
capsules (with V2O5 added to
34
S samples to aid combustion).
34
S was measured using an
Elemental Analyzer Isotope Ratio Mass Spectrometer (EA-IRMS) and
18
O via Total
Combustion-EA-IRMS (TC-EA-IRMS). Analytical reproducibility for BaSO4 standards was
±0.3 (1σ) for δ
34
SSO4 and ±0.5 (1σ) for δ
18
OSO4. Discharge was measured by Acoustic Doppler
Current Profiler (Sontek M9) at the time of sample collection during the March and May 2019
campaigns for sites in Fig. 2.1A. Data are reported in the Data Repository.
3. SO4
2-
Dual Isotopic Data (
34
SSO4 vs.
18
OSO4)
Dual isotope space (
34
SSO4 v.
18
OSO4) has been used to parse sources of SO4
2-
between
pyrite and evaporite weathering (e.g., Calmels et al., 2007; Karim and Veizer, 2000), and to
identify BSR in river systems based on departure from two end-member mixing (Turchyn et al.,
21
2013). The
34
SSO4 values for rivers draining Andean shale (Fig. 2.2A) fall in the range expected
from OWP, based on local
34
Spyrite (Torres et al., 2016). Andean
18
OSO4 values (Fig. 2.2B) are
consistent with ~75-80% of O derived from meteoric water vs. atmospheric O2 during OWP
(Killingsworth et al., 2018; Kohl and Bao, 2011) and effective fractionation factors of ca. 5 to
12‰ (Taylor and Wheeler, 1993; Van Stempvoort and Krouse, 1993).
The floodplain rivers and Madre de Dios mainstem have somewhat higher
34
SSO4 and
18
OSO4. It is difficult from the dual isotope data alone to ascribe this increase to BSR versus
mixing with evaporite-derived SO4
2-
, primarily because the end-members have wide ranges (Fig.
2.2A). Nonetheless, trends seem most consistent with mixing rather than reduction across the
floodplain. Hot springs at the foothills of the Andes Mountains show
34
SSO4 and
18
OSO4
consistent with BSR, but SO4
2-
concentrations in the springs are low (~6 mg/L) and mass balance
using Cl
-
concentration as a conservative tracer yields a maximum hot spring contribution to the
mainstem river of 0.01% of total discharge (see Supplement) — indicating negligible impact on
SO4
2-
flux.
22
Figure 2.2. A:
34
S and
18
O of SO4
2-
from Rio Madre de Dios system. Black arrow: trend
expected for BSR with 4:1 slope (Turchyn et al., 2013), originating at mean
34
SSO4 and
18
OSO4
for OWP. Evaporite endmember: global dataset (Claypool et al., 1980). OWP
34
S: local pyrite
samples (Torres et al., 2016). Lower limit of OWP
18
OSO4: source water -13‰ (lowest Andean
18
OH2O), 95% of O from H2O/5% from atm. O2 (min. estimated by Killingsworth et al., 2018),
effective fractionation factor 5‰ (lower end from Taylor and Wheeler, 1993). Upper limit:
source water -9‰ (highest Andean
18
OH2O), 75% of O from H2O (max. from Killingsworth et
al., 2018); fractionation factor 12‰ (upper est. of Taylor and Wheeler, 1993). B: Predicted OWP
18
OSO4 values as a function of fraction of oxygen from atmospheric O2 (y-axis) and effective
fractionation between sulfate and water (x-axis), for constant source water
18
OH2O ~ -12‰.
23
4. Oxygen Isotope Gradient to Trace Cycling of SO4
2-
Orographic effects impart an elevational gradient in
18
OH2O of ~2.3‰ per km across the
Madre de Dios basin (Ponton et al., 2014; Fig. 2.3A), so we expect differences in
18
OSO4
depending on where SO4
2-
is produced. The isotopic fractionation between H2O and SO4
2-
can
vary, as can the proportion of O from atmospheric O2 vs. H2O, adding complexity to
interpretation of
18
OSO4 (Fig. 2.2B).
We compared the
18
OSO4 of the nested sampling sites (Fig. 2.1B) to the elevational
gradient in
18
OH2O (Fig. 2.3). Sites A-D encompass the Andes Mountains, progressively from
headwaters to the mountain front, and represent the
18
OSO4 value of SO4
2-
released from Andean
OWP. Amongst these sites,
18
OH2O values increase downstream, but
18
OSO4 is variable, likely
because of hydrological complexity, i.e., the sources of solutes at each site and across the
seasons vary between tributaries with different elevation ranges and biogeochemical
characteristics. Similar variability was observed at these sites in the H-isotope composition of
biomarkers in river sediment (Ponton et al., 2014; Feakins et al., 2018), highlighting the complex
nature of both solute and sediment sourcing in mountainous environments.
Between Sites D and E (Fig. 2.1B), the river traverses the Madre de Dios floodplain, and
the
18
OSO4 value increases in each season, mirroring downstream increases in
18
OH2O (Fig. 2.3).
The increases in
18
OSO4 could be explained by: (1) BSR and net loss of SO4
2-
, driving residual
SO4
2-
to higher
18
OSO4 (Fig. 2.2A, 2.3D), (2) BSR
and re-oxidation with meteoric water from
lower elevations and/or with a greater proportion of O from atmospheric O2 (Fig. 2.3E), or (3)
addition of evaporite-derived SO4
2-
(Fig. 2.3C), either in the floodplain or associated with
tributaries entering between Sites D and E.
24
Figure 2.3.
18
O of SO4
2-
and H2O from nested catchments (Fig. 2.1B) vs. distance (A) and
elevation (B). Colored circles:
18
OSO4; blue diamonds:
18
OH2O; black line: elevational profile of
Rio Madre de Dios. Variable offset between
18
OSO4 and
18
OH2O may be due in part to changing
mixture of O sources in SO4
2-
(O2 vs. H2O; Fig. 2.2B), as well as transport of SO4
2-
from high to
low elevations. Black arrows in B show floodplain increase in
18
OSO4 (
18
OSO4). C: Addition of
evaporite SO4
2-
needed to explain observed
18
OSO4 (bars represent range of evaporite
18
OSO4
from 10-20‰; Claypool, 1980). D: Fraction of SO4
2-
pool to undergo reduction and removal in
the reduced phase (e.g., as in BSR) required to explain floodplain
18
OSO4 (bars represent a
range of separation factors for Rayleigh fractionation, see Supplement). E: Fraction of SO4
2-
pool
that would have to be reduced and re-oxidized with low-elevation H2O to explain observed
18
OSO4 (assuming 10-20% O from O2 and apparent fractionation factor of 9‰ ).
25
5. Basin-wide mass balance of SO4
2-
and its isotopes
Key additional information comes from the mass balance of SO4
2-
and its isotopes. We
test for net loss (or gain) of SO4
2-
by summing the flux J of SO4
2-
from each of the major
tributaries:
JSO4, Madre de Dios = JSO4, Alto MdD + JSO4, Manu + JSO4, Colorado + JSO4, Chilibe (Eq. 5)
and comparing this flux to that measured at the outlet of the Rio Madre de Dios (Fig. 2.1A). Loss
of SO4
2-
during floodplain transit would yield a calculated flux, JSO4, Madre de Dios, smaller than
observed. Instead, we find that the SO4
2-
flux balances within uncertainty in the wet season
(March 2019; Fig. 2.4A). In the dry season, the observed SO4
2-
flux at the Rio Madre de Dios is
slightly higher than calculated by mass balance, perhaps reflecting a small but measurable
floodplain addition of SO4
2-
. In any case, we do not find a SO4
2-
deficit, suggesting SO4
2-
is not
lost via BSR.
Similarly, we can calculate the mass balance of S isotopes in SO4
2-
as:
34
SSO4, Madre de Dios = fSO4, Alto MdD
34
SSO4, Alto MdD + fSO4, Manu
34
SSO4, Manu +
fSO4, Colorado
34
SSO4, Colorado + fSO4, Chilibe
34
SSO4, Chilibe (Eq. 6)
where f is the flux fraction of SO4
2-
from each tributary. This budget also balances within 0.6‰
in the wet season, similar to measurement errors of ~ ±0.3 ‰, while the analogous budget for
18
OSO4 closes within 0.9‰ in the wet season and 0.7‰ in the dry season (measurement error ~
±0.5‰). The consistency of the isotope mass balance for both O and S confirms that SO4
2-
isotopic changes we see across the floodplain (Fig. 2.3) can be attributed to contributions from
tributaries with different
34
SSO4 and
18
OSO4, rather than biogeochemical cycling within the
floodplain system (i.e., the increase in
18
OSO4 across the floodplain results from addition of
tributaries carrying high
18
OSO4). Thus we find no apparent role for floodplain BSR in
26
determining the flux or isotopic composition of SO4
2-
at the scale of the Madre de Dios basin.
Active BSR likely takes place in localized environments in the floodplain (e.g., riparian and
hyporheic zones), but these sites either do not actively exchange enough with the main river or
are too limited in extent to be detected at the regional scale.
Figure 2.4. Mass budget for SO4
2-
(A, C) and H2O (B, D) for the four major tributaries of the Rio
Madre de Dios. Circles scaled logarithmically to fluxes. Water isotopes (
18
OH2O) provide
conservative tracer of tributary inputs (Torres et al., 2017); closure of
18
OH2O budget (B, D;
using analog to Eq. 6) lends confidence to calculating the SO4
2-
budget.
27
6. Conclusions
34
S and
18
O of SO4
2-
and mass balance calculations reveal that SO4
2-
released from
pyrite oxidation in the Andes mountains is transported conservatively through the Madre de Dios
watershed (across >300 km of floodplain), at least within the resolution of our isotopic and mass
balance approaches. We do not find evidence for a quantitatively significant sink of reduced
sulfur in the regional sulfur budget. In contrast, organic carbon from the Andes is largely
replaced during transport across this floodplain (Feakins et al., 2018). Because the large flux of
SO4
2-
from Andean pyrite weathering survives the floodplain reactor, uplift and erosion of
Andean sedimentary rocks may drive release of CO2 from the rock reservoir to the atmosphere.
In addition, since most SO4
2-
reduction is thought to be microbially mediated, our results suggest
that microbial processes in floodplains (at least those similar to the Madre de Dios) may not reset
the isotopic composition of SO4
2-
used in environmental studies and paleo-environmental
reconstructions. Observations of significant riverine BSR elsewhere (Hemingway et al., 2020;
Mayer et al., 2010; Turchyn et al., 2013) suggest more work is needed to understand the scale
and circumstances under which this process is important.
28
Acknowledgements
Thanks to Daxs Coayla, Abra Atwood, Erick Vargas, Amazon Journeys and Explorer’s
Inn Tambopata for field support, Ved Bhoot, Greg Goldsmith, Fernando Silva and Amanda
Hayton for lab analyses, Jordon Hemingway for discussions and Bryan Killingsworth and two
anonymous reviewers for their feedback.. This work was funded by a U.S. DOE Office of
Science Graduate Student Research award to EIB and NSF award EAR-1455352 to AJW.
Isotopic analyses were supported as part of the Watershed Function Scientific Focus Area at
LBNL funded by DOE award DE-AC02-05CH11231.
29
References
Berner, R. A. (1982). Burial of organic carbon and pyrite sulfur in the modern ocean; its
geochemical and environmental significance. American Journal of Science, 282(4), 451–
473. https://doi.org/10.2475/ajs.282.4.451
Burke, A., Present, T. M., Paris, G., Rae, E. C. M., Sandilands, B. H., Gaillardet, J., Peucker-
Ehrenbrink, B., Fischer, W. W., McClelland, J. W., Spencer, R. G. M., Voss, B. M., &
Adkins, J. F. (2018). Sulfur isotopes in rivers: Insights into global weathering budgets,
pyrite oxidation, and the modern sulfur cycle. Earth and Planetary Science Letters, 496,
168–177. https://doi.org/10.1016/j.epsl.2018.05.022
Calmels, D., Gaillardet, J., Brenot, A., & France-Lanord, C. (2007). Sustained sulfide oxidation
by physical erosion processes in the Mackenzie River basin: Climatic perspectives.
Geology, 35(11), 1003–1006. https://doi.org/10.1130/G24132A.1
Clark, K. E., Hilton, R. G., West, A. J., Caceres, A. R., Gröcke, D. R., Marthews, T. R.,
Ferguson, R. I., Asner, G. P., New, M., & Malhi, Y. (2017). Erosion of organic carbon
from the Andes and its effects on ecosystem carbon dioxide balance. Journal of
Geophysical Research: Biogeosciences, 122(3), 449–469.
https://doi.org/10.1002/2016JG003615
Clark, K. E., Torres, M. A., West, A. J., Hilton, R. G., New, M., Horwath, A. B., Fisher, J. B.,
Rapp, J. M., Robles Caceres, A., & Malhi, Y. (2014). The hydrological regime of a
forested tropical Andean catchment. Hydrology and Earth System Sciences, 18(12),
5377–5397. https://doi.org/10.5194/hess-18-5377-2014
Claypool, G. E., Holser, W. T., Kaplan, I. R., Sakai, H., & Zak, I. (1980). The age curves of
sulfur and oxygen isotopes in marine sulfate and their mutual interpretation. Chemical
Geology, 28, 199–260. https://doi.org/10.1016/0009-2541(80)90047-9
Das, A., Chung, C.-H., & You, C.-F. (2012). Disproportionately high rates of sulfide oxidation
from mountainous river basins of Taiwan orogeny: Sulfur isotope evidence. Geophysical
Research Letters, 39(12). https://doi.org/10.1029/2012GL051549
Ebelmen, J.-J. (1845). Sur les produits de la décomposition des espèces minérales de la famille
des silicates. Annales Des Mines, 7(3), 3–66.
Feakins, S. J., Wu, M. S., Ponton, C., Galy, V., & West, A. J. (2018). Dual isotope evidence for
sedimentary integration of plant wax biomarkers across an Andes-Amazon elevation
transect. Geochimica et Cosmochimica Acta, 242, 64–81.
https://doi.org/10.1016/j.gca.2018.09.007
Francois, L. M., & Walker, J. C. G. (1992). Modelling the Phanerozoic carbon cycle and climate;
constraints from the 87 Sr/ 86 Sr isotopic ratio of seawater. American Journal of Science,
292(2), 81–135. https://doi.org/10.2475/ajs.292.2.81
Hemingway, J. D., Olson, H., Turchyn, A. V., Tipper, E. T., Bickle, M. J., & Johnston, D. T.
(2020). Triple oxygen isotope insight into terrestrial pyrite oxidation. Proceedings of the
National Academy of Sciences, 117(14), 7650–7657.
https://doi.org/10.1073/pnas.1917518117
30
Holmer, M., & Storkholm, P. (2001). Sulphate reduction and sulphur cycling in lake sediments:
A review. Freshwater Biology, 46(4), 431–451. https://doi.org/10.1046/j.1365-
2427.2001.00687.x
Karim, A., & Veizer, J. (2000). Weathering processes in the Indus River Basin: Implications
from riverine carbon, sulfur, oxygen, and strontium isotopes. Chemical Geology, 170(1–
4), 153–177. https://doi.org/10.1016/S0009-2541(99)00246-6
Killingsworth, B. A., Bao, H., & Kohl, I. E. (2018). Assessing Pyrite-Derived Sulfate in the
Mississippi River with Four Years of Sulfur and Triple-Oxygen Isotope Data.
Environmental Science & Technology, 52(11), 6126–6136.
https://doi.org/10.1021/acs.est.7b05792
Kohl, I., & Bao, H. (2011). Triple-oxygen-isotope determination of molecular oxygen
incorporation in sulfate produced during abiotic pyrite oxidation (pH=2–11). Geochimica
et Cosmochimica Acta, 75(7), 1785–1798. https://doi.org/10.1016/j.gca.2011.01.003
Lerman, A., Wu, L., & Mackenzie, F. T. (2007). CO2 and H2SO4 consumption in weathering and
material transport to the ocean, and their role in the global carbon balance. Marine
Chemistry, 106(1–2), 326–350. https://doi.org/10.1016/j.marchem.2006.04.004
Longinelli, A., & Edmond, J. M. (1983). Isotope geochemistry of the Amazon Basin: A
reconnaissance. Journal of Geophysical Research: Oceans, 88(C6), 3703–3717.
https://doi.org/10.1029/JC088iC06p03703
Mayer, B., Shanley, J. B., Bailey, S. W., & Mitchell, M. J. (2010). Identifying sources of stream
water sulfate after a summer drought in the Sleepers River watershed (Vermont, USA)
using hydrological, chemical, and isotopic techniques. Applied Geochemistry, 25(5),
747–754. https://doi.org/10.1016/j.apgeochem.2010.02.007
Ng, G.-H. C., Yourd, A. R., Johnson, N. W., & Myrbo, A. E. (2017). Modeling hydrologic
controls on sulfur processes in sulfate-impacted wetland and stream sediments. Journal of
Geophysical Research: Biogeosciences, 122(9), 2435–2457.
https://doi.org/10.1002/2017JG003822
Otero, N., Soler, A., & Canals, À. (2008). Controls of δ
34
S and δ
18
O in dissolved sulphate:
Learning from a detailed survey in the Llobregat River (Spain). Applied Geochemistry,
23(5), 1166–1185. https://doi.org/10.1016/j.apgeochem.2007.11.009
Pester, M., Knorr, K.-H., Friedrich, M. W., Wagner, M., & Loy, A. (2012). Sulfate-reducing
microorganisms in wetlands – fameless actors in carbon cycling and climate change.
Frontiers in Microbiology, 3. https://doi.org/10.3389/fmicb.2012.00072
Ponton, C., West, A. J., Feakins, S. J., & Galy, V. (2014). Leaf wax biomarkers in transit record
river catchment composition. Geophysical Research Letters, 41(18), 6420–6427.
https://doi.org/10.1002/2014GL061328
Rapp, J., & Silman, M. (2012). Diurnal, seasonal, and altitudinal trends in microclimate across a
tropical montane cloud forest. Climate Research, 55(1), 17–32.
https://doi.org/10.3354/cr01127
31
Spence, J., & Telmer, K. (2005). The role of sulfur in chemical weathering and atmospheric CO2
fluxes: Evidence from major ions, δ
13
CDIC, and δ
34
SSO4 in rivers of the Canadian
Cordillera. Geochimica et Cosmochimica Acta, 69(23), 5441–5458.
https://doi.org/10.1016/j.gca.2005.07.011
Taylor, B. E., & Wheeler, M. C. (1993). Sulfur- and Oxygen-Isotope Geochemistry of Acid
Mine Drainage in the Western United States: Field and Experimental Studies Revisited.
In C. N. Alpers & D. W. Blowes (Eds.), Environmental Geochemistry of Sulfide
Oxidation (Vol. 550, pp. 481–514). American Chemical Society.
https://doi.org/10.1021/bk-1994-0550.ch030
Torres, M. A., Baronas, J. J., Clark, K. E., Feakins, S. J., & West, A. J. (2017). Mixing as a
driver of temporal variations in river hydrochemistry: 1. Insights from conservative
tracers in the Andes-Amazon transition. Water Resources Research, 53(4), 3102–3119.
https://doi.org/10.1002/2016WR019733
Torres, M. A., West, A. J., Clark, K. E., Paris, G., Bouchez, J., Ponton, C., Feakins, S. J., Galy,
V., & Adkins, J. F. (2016). The acid and alkalinity budgets of weathering in the Andes–
Amazon system: Insights into the erosional control of global biogeochemical cycles.
Earth and Planetary Science Letters, 450, 381–391.
https://doi.org/10.1016/j.epsl.2016.06.012
Torres, M. A., West, A. J., & Li, G. (2014). Sulphide oxidation and carbonate dissolution as a
source of CO2 over geological timescales. Nature, 507(7492), 346–349.
https://doi.org/10.1038/nature13030
Turchyn, A. V., Tipper, E. T., Galy, A., Lo, J.-K., & Bickle, M. J. (2013). Isotope evidence for
secondary sulfide precipitation along the Marsyandi River, Nepal, Himalayas. Earth and
Planetary Science Letters, 374, 36–46. https://doi.org/10.1016/j.epsl.2013.04.033
Urey, H. C. (1952). On the Early Chemical History of the Earth and the Origin of Life.
Proceedings of the National Academy of Sciences, 38(4), 351–363.
https://doi.org/10.1073/pnas.38.4.351
Van Stempvoort, D. R., & Krouse, H. R. (1993). Controls of δ
18
O in Sulfate: Review of
Experimental Data and Application to Specific Environments. In C. N. Alpers & D. W.
Blowes (Eds.), Environmental Geochemistry of Sulfide Oxidation (Vol. 550, pp. 446–
480). American Chemical Society. https://doi.org/10.1021/bk-1994-0550.ch029
Winnick, M. J., Carroll, R. W. H., Williams, K. H., Maxwell, R. M., Dong, W., & Maher, K.
(2017). Snowmelt controls on concentration-discharge relationships and the balance of
oxidative and acid-base weathering fluxes in an alpine catchment, East River, Colorado.
Water Resources Research, 53(3), 2507–2523. https://doi.org/10.1002/2016WR019724
32
Supplemental material
1. Cl-based mass balance for hot spring contribution to Rio Madre de Dios
To determine the role of hot springs in the regional SO4
2-
budget of the Madre de Dios
watershed, a chloride based mass balance was used:
[Cl
-
]downstream hot springs = fhot springs[Cl
-
]hotsprings + fupstream hotsprings[Cl
-
]upstream hotsprings
and fhot springs + fupstream hotsprings = 1,
where f is the fractional contribution to discharge. In May 2019 the fractional contribution of hot
springs to riverine discharge was 0.01% and in March 2019, the contribution was undetectable.
Given their low contribution to discharge and low SO4
2-
(~6-10 mg/L, compared to 8-12 mg/L in
the Rio Alto Madre de Dios), we conclude that the hot springs do not have a significant impact
on the regional SO4
2-
budget.
2. Explanations for evolution of
18
OSO4 across the Madre de Dios floodplain
Figure 2.3 illustrates the oxygen isotope composition of riverine SO4
2-
from mountain to
floodplain. In all sampling campaigns,
18
OSO4 increased across the floodplain, i.e., from the
foothills of the Andes (~2000 m median catchment elevation, site D in Fig. 2.1B) to Los Amigos
(~450 m median catchment elevation, site E in Fig. 2.1B). The magnitude of these floodplain
increases is annotated on Fig. 2.3B for each season.
To explore possible causes of the observed changes in
18
OSO4 across the floodplain, we
consider three scenarios (main text Fig. 2.3C-E): (1) mixing of SO4
2-
derived from oxidative
weathering of pyrite (OWP) and evaporite weathering, (2) reduction of SO4
2-
and removal via a
reduced phase (H2S or secondary sulfide mineral) and (3) reduction and re-oxidation of SO4
2-
at
33
lower elevation, similar to the mechanism invoked to explain SO4
2-
isotope composition in lower
reaches of the Amazon River by Longinelli and Edmond, 1983.
In the first case (Fig 23C), weathering source mixing calculations followed a two
endmember mixing model, with
18
OSO4 = 10-20‰ for the evaporite weathering endmember
(Claypool et. al, 1980):
18
OSO4, River =
18
OSO4, OWPFSO4,OWP +
18
OSO4, EvaporiteFSO4,Evaporite,
where FSO4 is the fraction of SO4
2-
from OWP or evaporite weathering.
In the second case (Fig. 2. 3D), Rayleigh fractionation is used to model the isotopic
impacts of sulfate reduction and removal as a reduced phase. A range of separation constants are
used from the literature ( = 0.990, Mandernack et al., 2003; = 0.995 and = 0.996, Turchyn
et al., 2013).
In the third case (Fig. 2.3E), calculations of the effect of reduction and re-oxidation were
carried out as follows: we calculated a theoretical
18
OSO4,Floodplain,predicted value using a floodplain
water isotope composition (i.e., assuming all SO4
2-
is reduced and reoxidized in the floodplain).
The fractionation factor between SO4
2-
and source water oxygen isotopes was assumed to be 9‰
(an average literature value from Taylor & Wheeler, 1993 and Van Stempvoort & Krouse, 1993;
e.g., within the range in Fig. 2.2B). The fraction of oxygen in sulfate from atmospheric O2 was
varied from 10-20%.
The fraction of reduction and re-oxidation needed to explain the observed change in
18
OSO4 from the foothills of the Andes to the floodplain was calculated as:
Fre-oxidation = (
18
OSO4, Floodplain, measured -
18
OSO4, Foothills, measured)/(
18
OSO4, Floodplain,predicted -
18
OSO4, Foothills, measured),
34
where Fre-oxidation is the fraction of the SO4
2-
pool that was reduced and re-oxidized,
18
OSO4,
Floodplain, measured is the measured oxygen isotope composition of the Rio Madre de Dios (site E,
Fig. 2.1B)
18
OSO4, Floodplain, predicted is a predicted oxygen isotope composition that would result
from total re-oxidation of SO4
2-
with a floodplain source water isotope composition, and
18
OSO4,
Foothills, measured is the measured oxygen isotope composition of SO4
2-
as it leaves the Andes
mountains and enters the floodplain (site D, Fig. 2.1B).
Supplemental material references
Longinelli, A., & Edmond, J. M. (1983). Isotope geochemistry of the Amazon Basin: A
reconnaissance. Journal of Geophysical Research: Oceans, 88(C6), 3703–3717.
https://doi.org/10.1029/JC088iC06p03703
Mandernack, K. W., Krouse, H. R., & Skei, J. M. (2003). A stable sulfur and oxygen isotopic
investigation of sulfur cycling in an anoxic marine basin, Framvaren Fjord, Norway.
Chemical Geology, 195(1), 181–200. https://doi.org/10.1016/S0009-2541(02)00394-7
Taylor, B. E., & Wheeler, M. C. (1993). Sulfur- and Oxygen-Isotope Geochemistry of Acid
Mine Drainage in the Western United States: Field and Experimental Studies Revisited.
In C. N. Alpers & D. W. Blowes (Eds.), Environmental Geochemistry of Sulfide
Oxidation (Vol. 550, pp. 481–514). American Chemical Society.
https://doi.org/10.1021/bk-1994-0550.ch030
Turchyn, A. V., Tipper, E. T., Galy, A., Lo, J.-K., & Bickle, M. J. (2013). Isotope evidence for
secondary sulfide precipitation along the Marsyandi River, Nepal, Himalayas. Earth and
Planetary Science Letters, 374, 36–46. https://doi.org/10.1016/j.epsl.2013.04.033
Van Stempvoort, D. R., & Krouse, H. R. (1993). Controls of δ
18
O in Sulfate: Review of
Experimental Data and Application to Specific Environments. In C. N. Alpers & D. W.
Blowes (Eds.), Environmental Geochemistry of Sulfide Oxidation (Vol. 550, pp. 446–
480). American Chemical Society. https://doi.org/10.1021/bk-1994-0550.ch029
35
Chapter 3: Hydroclimate and bedrock permeability determine
young water fractions in streamflow across the tropical Andes
mountains and Amazon floodplain
Contributors: Daxs Herson Coayla Rimachi, Adan Julian Ccahuana Quispe, A. Joshua West
Opening statement
In this chapter I use stable isotopes of water to determine the fraction of streamflow
comprised of recent precipitation in a series of small watersheds that span the transition from
Andes mountains to Amazon floodplain. I explore the factors influencing how precipitation
infiltrates watersheds, travels through the ground, and eventually becomes streamflow. Carrying
out these analyses across a mountain to floodplain transition provides a natural laboratory for
understanding basic factors influencing water transit, as we study a range of watershed slopes
(37 to 3 ) rarely seen in other studies.
This work was conceptualized by myself and Joshua West. Adan Julian Ccahuana
Quispe, Daxs Herson Coayla Rimachi and I collected the samples. I analyzed the samples,
analyzed the data, wrote the code for the Monte Carlo simulation and made the figures. I wrote
the paper with guidance and feedback from Joshua West.
This manuscript is currently in review in Hydrology and Earth System Science and can be
accessed as a preprint via: https://doi.org/10.5194/hess-2022-188.
36
Abstract
The role of topography on water transit times and pathways through catchments is
unclear, especially in mountainous environments — yet these environments play central roles in
global water, sediment, and biogeochemical fluxes. Moreover, the vast majority of intensively
monitored catchments are located in northern latitudes. As a result, the interplay between water
transit, topography and other landscape characteristics is particularly underexplored in tropical
environments. Here we present the results of a multi-year hydrologic sampling campaign (twice-
monthly and storm sampling) to quantify water transit in seven small catchments (< 3 km
2
)
across the transition from the Andes mountains to Amazon floodplain in southern Peru. We use
the stable isotope composition of water (δ
18
OH2O) to calculate the fraction of streamflow
comprised of recent precipitation (“young water fraction”) for each of the seven small
catchments. Mean unweighted young water fractions (Fyw) are 3−10 % in the Andes, 15−23 % at
mid-elevation and 3−4 % in the foreland floodplain. Weighting the Fyw calculation by volume of
streamflow and precipitation yield Fyw of 7 −47 %. Across these catchments, topography does not
exert a clear control on water transit; instead stream Fyw is controlled by a combination of
hydroclimate and bedrock permeability. Mid-elevation sites are posited to have the highest Fyw
due to less permeable bedrock, poorly developed soils and more frequent and intense rainfall.
The data presented here allow us to explore relationships between topography, bedrock
permeability, hydroclimate and stream baseflow Fyw — particularly highlighting the role of
bedrock permeability and hydroclimate in determining water transit times in a tropical mountain
setting.
37
1. Introduction
As water moves from rainfall to river runoff, it is stored in soil and rock for variable
amounts of time. The length of time it takes for rainfall to exit a catchment in streams and rivers,
known as the water transit time, exerts an important control on biogeochemical and
ecohydrologic processes. While water is within a catchment, it reacts with soil and rock,
acquiring solutes (Gibbs, 1970; Drever, 1988), and it interacts with ecosystems, sustaining
photosynthesis and transpiration (Allen et al., 2019; Rempe and Dietrich, 2018). Water transit
times also influence the availability of freshwater resources and the potential for environmental
hazards such as flooding.
Mountainous regions play particularly important roles in the global water cycle, receiving
outsized amounts of precipitation and acting as “water towers” that store and gradually release
water for drier downstream areas (Barnett et al., 2005; Immerzeel et al., 2020; Meybeck et al.,
2001; Viviroli et al., 2007). The impacts of climate change on the water cycle (Scanlon et al.,
2018; Wilusz et al., 2017), especially diminished snowpack and warming across altitudinal
gradients in mountainous regions, emphasize the importance of understanding water transit times
in mountainous systems. Beyond serving as water towers, mountains have high erosion rates,
exposing fresh mineral surfaces to chemical weathering processes that control the geological
carbon cycle (Gaillardet et al., 1999; Hilton and West, 2020). Mineral weathering reactions in
mountainous environments are modulated by a balance between water transit and mineral supply
and reactivity (Ameli et al., 2017; Berner, 1978; Maher, 2010, 2011; West et al., 2005).
Understanding the linkages between hydrology, erosion and the carbon cycle depends on
quantifying water transit in mountainous environments. Finally, mountainous regions control the
38
export of sediment and nutrients to rivers downstream, playing important roles in water quality
and regional biogeochemistry.
Despite their global hydrological importance, much is not understood about water transit
times in mountain systems. Global data suggest that streamflow in mountainous catchments
carries less young water than in more gently sloping catchments (Jasechko, 2016; Lutz et al.,
2018), potentially because of long water flow paths through fractured bedrock (e.g., Muñoz-
Villers et al., 2016). Yet the relationships between topography and young water fractions are
weak, and few studies have tested these ideas across the dramatic topographic gradients of major
mountain ranges. Moreover, other studies have suggested complex relationships between
topography and water transit times, with other factors including watershed organization and area,
as well as bedrock permeability and subsurface structure, also playing important roles (Asano et
al., 2002; McGlynn et al., 2003; McGuire et al., 2005; Tetzlaff, Seibert, McGuire, et al., 2009;
Tetzlaff, Seibert, & Soulsby, 2009; Asano & Uchida, 2012; Hale et al., 2016; Hale &
McDonnell, 2016; Xiao et al., 2021). Altogether, it remains unclear to what extent mountain
regions affect fluid transit times, and for what reasons.
To address this problem, we collected a four-year time series (2016−2019) of
approximately fortnightly stream and precipitation samples from seven small (< 3 km
2
)
catchments in southern Peru. The study catchments are within the Madre de Dios region in
southern Peru, which includes the transition from the eastern Andes mountains (3472 m) to
Amazon foreland floodplain (214 m; Fig. 3.1) and a gradient in catchment slopes from 37−3 °.
We present a systematic evaluation of the movement and retention of water within these varied
tropical landscapes, focusing on isotope-derived stream young water fractions. Because stable
isotopes of precipitation vary with time, and the stable isotope composition of water is
39
conservative during transport through catchments, a comparison of time series of stable O or H
isotopes in rainfall and stream water can be used to infer transit time (McGuire & McDonnell,
2006). The most general and robust interpretive framework uses isotope time series to calculate
the stream young water fraction, which is the fraction of streamflow that fell as precipitation
within the prior 2-3 months (Kirchner, 2016a, b). We build a stable isotope dataset and analyze
the young water fractions across a range in topography (3472−214 m) and slopes (37−3 °) rarely
seen in other studies, allowing us novel insight into the effect of mountains on water transit.
Moreover, we provide stable isotope constraints on water transit in tropical lowlands, where little
information of this kind has been reported previously.
2. Data and methods
2.1. Study area and sampling design
In this study, we carried out detailed hydrochemical monitoring at seven small (areas
ranging from 0.03−3.00 km
2
) catchments spanning the transition from the eastern flank of the
Andes Mountains to the Amazon foreland floodplain (Fig. 3.1; Table 3.1). The small catchments
(SC) in this study are referred to by their sampling point elevation in meters, followed by “-SC”.
Two small catchments (3472-SC and 3077-SC) are in the high Andes mountains, underlain by
fractured shale bedrock, with mean slopes ranging from ~25 −35 °. Two mid-elevation small
catchments (2432-SC and 1540-SC) are in the similarly steep mid-elevation Andes, with one
(1540-SC) underlain by a granitic intrusion. One small catchment is situated in the foreland fold
and thrust belt at the foothills of the Andes (609-SC), underlain by uplifted Andean sediments,
with a mean slope of 20.8 °. Two of the small catchments are situated on fluvial terraces in the
40
foreland floodplain (276-SC and 214-SC), with the bedrock at these sites comprised of
weathered sediments from the Andes. These catchments have much lower slopes, averaging 3 −4
°. We also consider stable isotope data from two nested mesoscale catchments studied in Clark et
al., 2014 (dashed white line in Fig. 3.1B-D). The catchments from Clark et al., 2014 are referred
to by their mean elevation in meters, followed by “-Clark”: 3195-Clark (mean slope 26 °; mean
area 49 km
2
) and 2805-Clark (mean slope 28 °; mean area 164 km
2
). Site 3195-Clark drains
Andean shales and site 2805-Clark drains Andean shales and the same granitic intrusion that
underlies 1540-SC (Fig. 3.1D).
The seven small streams were sampled approximately bi-weekly beginning in April 2016.
In addition to stream sampling, precipitation was collected at sites 3077-SC, 1540-SC , 609-SC,
276-SC and 214-SC. For sites 3472-SC and 2432-SC we calculated approximate precipitation
oxygen isotope values by linearly interpolating between nearby precipitation samples collected at
higher and lower elevations, supported by the observation that in this region precipitation
isotopes have a linear relationship with elevation (Ponton et al., 2014). Precipitation was
collected in a bucket left out between each sampling trip, with a layer of oil to prevent
evaporative loss. Point discharge was manually measured each time a sample was taken. For
sites 3077-SC and 609-SC, continuous discharge was measured in 2019 and 2020 with WL16
Global Water Level Loggers. Rainfall amount data are from tipping bucket and Vaisala rain
gauges maintained by the Andes Biodiversity and Ecosystem Research Group, a manual rain
gauge maintained by the Los Amigos Biological Station, and rain gauges operated by the
Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI).
41
Figure 3.1. (a) Digital Elevation Model (DEM, from ALOS 30m data) of the Andes mountains
and Amazon floodplain in southern Peru. White circles indicate sampling locations. (b−d) show
the area within the black rectangle in (a), with small catchments from this study delineated by
solid red lines, and catchments from Clark et al., 2014 by dashed white lines. (b) shows elevation
of Andean sites, (c) Landsat imagery, and (d) geology, using data from INGEMMET.
42
Sites,
this
study
Location
Latitude
(°)
Long-
itude (°)
Area
(km
2
)
Mean
slope (°)
Geology Vegetation
3472-SC
Carretera
Manu near
Ajanaco
-13.20617 -71.61168 0.395 24.7
Sandia Fm. -
shale
Puna
3077-SC
Wayqecha
Biological
Station
-13.19255 -71.58795 0.242 33.8
San José Group -
shale
TMCF
2432-SC
Carretera
Manu near
Pillahuata
-13.15969 -71.59378 0.0287 29.5
San José Group -
shale
TMCF
1540-SC
Carretera
Manu near
San Pedro
-13.06454 -71.56038 0.613 36.9 Granite Intrusion UPRF
609-SC
Villa Carmen
Biological
Station
-12.89614 -71.41826 0.145 20.8 Paucartambo Fm. Bamboo
276-SC
Los Amigos
Biological
Station
-12.55884 -70.09931 0.377 4.5
Fluvial terrace
(Quaternary)
TRF
214-SC
Explorer’s Inn
Tambopata
-12.82955 -69.27132 3.00 3.2
Fluvial terrace
(Quaternary)
TRF
Sites,
existing
dataset
Location
Latitude
(°)
Long-
itude (°)
Area
(km
2
)
Mean
slope (°)
Geology Vegetation
3195-
Clark
Kosñipata
River at
Wayqecha
-13.16278 -71.58917 49.8 27.5
Sandia Fm., San
José Group
Puna, TMCF,
UPRF
2805-
Clark
Kosñipata
River at San
Pedro
-13.06028 -71.54444 165.2 29.9
Sandia Fm., San
José Group,
Granite Intrusion
Puna, TMCF,
UPRF
Table 3.1. Characteristics of small catchments from this study and mesoscale catchments from
Clark et al., 2014. TMCF = tropical montane cloud forest, UPRF = upper rainforest, TRF =
tropical rainforest.
43
2.2. Analytical techniques and data analysis
Samples were analyzed for stable isotopes of water (δ
18
O and δ
2
H), with results reported
here using permille notation relative to the Vienna Standard Mean Ocean Water standard. The
stream oxygen or hydrogen isotope composition is referred to as δ
18
Ostream and δDstream and
precipitation oxygen and hydrogen isotope composition as δ
18
Oprecip and δDprecip. The analyses
were carried out via two Los Gatos Research Liquid Water Isotope Analyzers (LGR) (Caltech
and Lawrence Berkeley National Lab) and a Picarro L2130i Cavity Ring Down Spectrometer
(Chapman University). The internal error of isotope measurements on the Picarro was 0.1 ‰ or
better for δ
18
O and 2 ‰ or better for δD. On the LGR at Lawrence Berkeley National Lab the
internal error was 0.1 ‰ or better for
18
O and 1 ‰ or better for D. On the LGR at Caltech the
internal error was 0.3 ‰ or better for
18
O and 1 ‰ or better for D. Long-term accuracy on
certified isotope standards was within one standard deviation of the known isotopic values.
Young water fractions were calculated for each small catchment following Kirchner
(2016a, 2016b). Stream and precipitation oxygen isotope data were fit with Equation (1):
𝐶 (𝑡 ) = 𝑎 𝑠 × 𝑐𝑜𝑠 (2𝜋𝑓𝑡 ) + 𝑏 𝑠 × 𝑠𝑖𝑛 (2𝜋𝑓𝑡 ) + 𝑘 (1)
where C is the concentration of a tracer in stream or precipitation, t is time, f is the frequency of
the interval, a and b are the cosine and sine coefficients and k is the vertical shift. The fit to
stream and precipitation isotope data was performed with and without stream discharge and
rainfall amount weighting. The young water fraction was then calculated using Equations (2-4),
where:
𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 , 𝑂 18
𝑆𝑡𝑟𝑒𝑎𝑚 = √𝑎 𝑠 2
+ 𝑏 𝑠 2
(2)
𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 , 𝑂 18
𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛 = √𝑎 𝑝 2
+ 𝑏 𝑝 2
(3)
𝑌𝑜𝑢𝑛𝑔 𝑤𝑎𝑡𝑒𝑟 𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 (%) = 𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 , 𝑂 18
𝑆𝑡𝑟𝑒𝑎𝑚 /𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 , 𝑂 18
𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛 (4)
44
A bootstrap resampling regime was performed to assess the uncertainty associated with
the young water fraction calculations. We resampled the
18
Ostream and
18
Oprecip for each site to
generate 10,000 isotope datasets, and then applied equations (1 −4) to each dataset. In order to
assess the differences in young water fraction distributions between sites, a null dataset was
generated using all of the stream and precipitation isotope data across all of the sites, by
subtracting each individual isotope value from the site-specific mean isotope value. We then
applied the same bootstrap resampling routine and equations (1 −4) to the null dataset. Through
the bootstrap resampling regime, we obtain a distribution of young water fractions for each
watershed and the null dataset (Fig. 3.7). Stream baseflow indices were calculated for sites 3077-
SC and 609-SC using the Matlab HydRun hydrograph analysis package (Tang and Carey, 2017).
3. Results
3.1. Oxygen and hydrogen isotopes in streamflow and precipitation
Dual isotope space (δ
18
OH2O and δDH2O) reveals no significant deviation from the local
meteoric water line (Fig. 3.3a), indicating no significant evaporative signal in the stream waters.
The δ
18
Ostream and δ
18
Oprecip values follow an orographic trend across the transition from high
Andes mountains to foothills (3472-SC to 609-SC), with the highest elevation streams showing
the lightest isotopic values (Fig. 3.2, 3.3b). Along this same mountain-to-foothill transition,
δ
18
Oprecip and δDprecip display a marked seasonal cycle (amplitude δ
18
Oprecip ~4−5 ‰) that is
slightly greater in the Andes mountains than the foothills or foreland floodplain (Table 2; Figs.
2b, 4c −d).
45
Figure 3.2. Boxplots of unweighted δ
18
Ostream (a) and δ
18
Oprecip (b) from the duration of the study.
The box represents the interquartile range of δ
18
O, the whiskers represent the non-outlier
maximum and minimum and the diamonds are outliers (defined by more than 1.5 times the
interquartile range). Sites 3195 and 2805 are mesoscale catchments from Clark et al. (2014).
Figure 3.3. (a) δ
18
O and δD of stream and precipitation. (b) mean
18
Ostream as a function of
catchment elevation at sampling point for the small catchments, and mean catchment elevation
for the mesoscale catchments. Circles represent stream isotope data from this study, squares are
mesoscale catchments from Clark et al. (2014) and diamonds are precipitation.
46
Relative to the δ
18
Oprecip inputs, δ
18
Ostream values are damped (Fig. 3.4). The degree of
isotope dampening and therefore the amplitude of the δ
18
Ostream seasonal cycle varies between the
small catchments situated from mountain-to-foothill (Fig. 3.4a −b; Fig. 3.7). The seasonal
amplitude of δ
18
Ostream values is smallest within the Andes mountains (3472-SC, 3077-SC, 2432-
SC) and foreland floodplain sites (276-SC and 214-SC) and highest for the mid-elevation
mountain (1540-SC) and mountain foothills sites (609-SC) (Fig. 3.2a, 3.4a −b ). Of the two
mesoscale catchments, 3195-Clark has a smaller amplitude in δ
18
Ostream than 2805-Clark.
Figure 3.4. δ
18
Ostream (a and b) and δ
18
Oprecip (c and d) for the duration of the study period
(2016−2019), plotted by day of year. δ
18
Ostream from small catchments is denoted with circles,
and the mesoscale catchment data is denoted with squares. δ
18
Oprecip is denoted with diamonds.
Panels (a) and (c) show sites in the Andes and mountain foothills; panels (b) and (d) show the
foreland floodplain sites.
47
Sites, this
study
Location
n,
stream
samples
δ
18
O stream,
avg
Amplitude
δ
18
O stream
n,
precip
samples
δ
18
O precip,
avg.
Amplitude
δ
18
O precip
3472-SC Mountain 55 -13.8 0.53 - - 5.3
3077-SC Mountain 63 -13.4 0.17 65 -10.5 5.1
2432-SC Mountain 56 -12.0 0.57 - - 5.2
1540-SC
Mid-elevation
mountain
62 -9.3 1.13 60 -7.7 4.9
609-SC
Mountain
foothills
66 -8.0 0.59 58 -6.6 4.0
276-SC
Foreland
floodplain
95 -6.8 0.11 35 -5.3 4.2
214-SC
Foreland
floodplain
28 -6.6 0.17 15 -5.0 4.9
n samples
total
425 233
Sites,
published
dataset
Location
n,
stream
samples
δ
18
O stream,
avg
Amplitude
δ
18
O stream
n,
precip
samples
δ
18
O precip,
avg.
Amplitude
δ
18
O precip
3195-
Clark
Mountain 60 -13.7 0.42 - 5.29 -
2805-
Clark
Mountain/mid
-elevation
mountain
62 -12.1 0.96 - 5.21 -
n samples
total
122
Table 3.2. Stream and precipitation stable water isotope data from this study and Clark et. al,
2014. ‘ - ’ indicates where samples were not collected. For sites without precipitation collection,
δ
18
Oprecip was linearly interpolated by elevation from the nearest sites.
48
3.2 Young water fractions
Young water fractions (Fyw) vary between the catchments across the mountain-to-floodplain
transition. Figure 3.7 shows calculated Fyw values for each catchment, with violin plots reflecting
ranges generated using Monte Carlo simulation. 3472-SC, 3077-SC and 2432-SC have mean
unweighted Fyw between 3 and 11 %. Mesoscale catchment 3195-Clark, draining approximately
50 km
2
of Andean shales, has a mean Fyw of 8 %, roughly averaging the Fyw seen in the three
small Andean catchments. At mid-elevation, 1540-SC, which drains granitic intrusions, has a
mean unweighted Fyw of 23 %. The second mesoscale catchment, 2805-Clark, which drains a
165 km
2
area including Andean shales and granitic intrusions, has a mean unweighted Fyw of 18
%. 609-SC, in the foothills of the Andes and underlain by colluvium, has a mean unweighted Fyw
of 15 %. On the foreland floodplain, 276-SC and 214-SC located on fluvial terraces, have mean
unweighted Fyw of 3 and 4 %, respectively. For comparison, the null dataset, generated from a
compilation of isotope data from all sites, yields Fyw of 7 %. In addition to changes in the mean
values across the Andes-Amazon gradient, the Monte Carlo distributions change, with wider
distributions for the mid-elevation catchments and tighter distributions in the high Andes and
Amazon lowland catchments.
49
Figure 3.5. δ
18
Ostream (solid circles) and δ
18
Oprecip (open diamonds) from twice-monthly sampling
campaigns in each small catchment. The size of the solid circles corresponds to the flow quantile
that the δ
18
Ostream is from. Data in (a−c) are from small catchments in the mountains, (d) is from
the mid-elevation mountain small catchment, (e) is from the foothills small catchment and (f) is
from the foreland floodplain small catchment.
50
4. Discussion
4.1. Hydroclimate and permeability controls on stream young water fractions
All else being equal, catchment topography is expected to control water transit times;
steeper flow paths should produce shorter transit times (e.g., following Darcy’s Law), although
greater relief may generate longer flow paths and consequently longer transit times. Yet, despite
much effort to demonstrate such effects, past work has shown no systematic relationship between
catchment topography and isotope-based young water fractions, including in regional studies and
across global compilations (e.g., Tetzlaff et al., 2009b). Similarly, in our results, we find no
simple relationship between catchment topography and Fyw across the Amazon-Andes gradient
studied here (Fig. 3.8). While unweighted Fyw is low (mean values <5 %) in both of our lowland
catchments (276-SC and 214-SC), the other catchments from mountain to foothills show a wide
range of unweighted Fyw, from 3 −23 %, with no apparent relationship to either slope angle or
flow path length (Fig. 3.8a, b). There is, however, some coherent pattern in Fyw across these
catchments that may help to explain the decoupling of Fyw and topography at least across these
sites, and perhaps more generally.
Specifically, the small catchments in the high Andes Mountains (3472-SC, 3077-SC and
2432-SC) all have low Fyw, with unweighted means between 3 −10 % and relatively tight
distributions, while the mid-elevation small catchments show a much wider spread, tending
toward much higher Fyw values (Fig. 3.7). The Fyw values inferred from the mesoscale
catchments studied by Clark et al. (2014) are consistent with the patterns from the small
catchments. The mesoscale catchment in the high Andes, underlain entirely by shale bedrock,
has a similar Fyw to that of the high elevation small catchments (unweighted mean value <10%).
51
In contrast, the mesoscale catchment that spans across the high- to mid-elevations (2805-Clark)
has an unweighted mean Fyw of 19%, consistent with a mixture of older water from upstream,
high-permeability shale-dominated portions of the study region and younger water from low-
permeability granitic areas. Overall, our data point to low and tightly distributed Fyw in the high
mountains, but higher and more broadly distributed Fyw in the mid-elevations.
We attribute the low Fyw observed in the high mountain sites to high permeability of the
fractured shale bedrock. Fractures create conduits for fluid flow that can be magnified by
dissolution of reactive minerals, such as the sulfides that are relatively abundant in the Paleozoic
shale underlying our Andes Mountains catchments. Previous studies of stream hydrochemistry in
the region have emphasized the importance of sulfide mineral oxidation as a primary weathering
process (Burt et al., 2021; Torres et al., 2016), and pyrite oxidation is known to generate porosity
and permeability in shale bedrock (Gu et al., 2020). In our conceptual model of water transit, the
combination of pore-scale chemical weathering and regional stresses create a fractured
subsurface that is conducive to long fluid flow paths, leading to overall low young water
fractions in Andean streams.
The mid-elevation catchments differ in two respects that we think can explain the distinct
transit times inferred for these streams. The increased spread in estimated Fyw for the catchments
between 3000 and 500m coincides with a shift to a flashier hydroclimate, with more rainfall
events of higher magnitude at the mid-elevations compared to either the high Andes or the
Amazon lowlands (Fig. 3.6a; also see Clark et al., 2016). Correspondingly, the stream
hydrograph at 609-SC is flashier than at 3077-SC (Fig. 3.6b; these are the two catchments with a
semi-continuous discharge record). We quantify the flashiness of the two watersheds with semi-
continuous discharge records using baseflow indices: the ratio of streamflow that occurs during
52
baseflow (i.e., when it is not raining), to total streamflow. A comparison of stream baseflow
indices for sites 3077-SC and 609-SC shows a higher baseflow index for site 3077-SC (BFI =
0.77) and lower baseflow index for site 609-SC (BFI = 0.64). We interpret the first-order shift in
Fyw values from the high Andes (where baseflow indices are high) to the mid-elevations (where
baseflow indices are lower) as being related to this change towards a stormier climate,
suggesting a primary role for hydroclimate forcing in determining transit times in these
mountainous catchments. An important role for precipitation and discharge regimes has emerged
from other recent transit time studies focused on single catchments with higher temporal
resolution data collection (Gallart et al., 2020; von Freyberg et al., 2018; Stockinger et al., 2016).
Although we see some slight variability in the amplitude of δ
18
Ostream as a function of discharge
in our results (Fig. 3.5), we lack data across the range of discharge that would be needed for
robust quantitative analysis of this effect. Higher frequency sampling across gradients such as
those in the Andes, though daunting given the logistical challenges of this environment, would
be an interesting target for future work.
Superimposed on the overall differences that characterize the mid-elevation catchments,
the Fyw in 1540-SC stands out as especially high (Fig. 3.8; mean Fyw estimate >50% when
amount-weighted). Unlike the other catchments in our study that are characterized by
sedimentary bedrock, this catchment is underlain by a granitic intrusion (Clark et al., 2014). We
attribute the especially high Fyw in this part of the study region to the low permeability of this
granite bedrock, which prevents water from infiltrating deeply and leads to rapid, surficial flow
paths over the steep topography. Altogether, then, we interpret the highly variable transit times
across the Andean catchments as being related principally to a combination of hydroclimate and
bedrock permeability, with these factors outweighing the influence of catchment topography.
53
Figure 3.6. (a) Precipitation return interval for rain gauges near sites 3077-SC, 1540-SC, 609-SC
and 276-SC. (b) Stream runoff records for sites 3077-SC and 609-SC, showing baseflow indices
for both sites.
54
Figure 3.7. Unweighted (a) and weighted (b) stream young water fractions for all catchments and
a null dataset. 3195-Clark and 2805-Clark are the mesoscale catchments from Clark et al., 2014.
55
4.2. Implications for the role of mountains in modulating water, erosional, and biogeochemical
fluxes.
The role of mountains as water towers, and particularly the response of these freshwater
resources to climate change, depends in part on water transit times through mountain catchments.
In revealing the importance of hydroclimate for transit times, our results suggest that shifting
precipitation regimes may be important in determining not just how much precipitation falls over
mountain regions (or indeed the balance of snow and rain), but also the fate of precipitation as it
makes its way through mountain catchments. If our spatial comparison of catchments across the
Andes-Amazon region translates to temporal trends, then a flashier rainfall regime in the future
might be expected to produce a wider range of transit times including higher young water
fractions in streams draining mountainous terrain. In this sense, our results are consistent with
recent studies suggesting that catchments can amplify rainfall variability (Müller Schmied et al.,
2020). The implications for downstream flooding and the buffering of droughts may warrant
further consideration.
The hydrology of mountainous catchments may play important geological roles, too.
River discharge, and particularly discharge variability, exerts a primary control on erosion (e.g.,
Tucker and Bras, 2000). Longer transit times may dampen the relationship between precipitation
variability and the river incision that drives mountain erosion; systematic relationships between
topography and water transit times could therefore either dampen or amplify erosional efficiency
of a given precipitation regime. Catchment hydrology has also been invoked as central to the role
of mountain building in the global carbon cycle over geologic timescales (Maher and
Chamberlain, 2014). This argument depends on both the exposure of fresh minerals for chemical
weathering by rapid erosion, as well as systematic changes in hydrologic flow paths associated
56
with mountain building. However the mountainous sites within this study display a wide range of
values in Fyw (from ~3 −23 %; Fig. 3.8), with no systematic relationship between topography and
Fyw. Although a global compilation of stream Fyw shows a general negative correlation between
topographic relief and Fyw (Jasechko et al., 2016), that relationship is notably weak — and the
Fyw from the small catchments studied here emphasize how other environmental factors
(hydroclimate, catchment architecture) play important roles in determining the Fyw of
streamflow. Moreover, when comparing across the high Andes and Amazon lowlands, there is
remarkably little difference in Fyw despite dramatic differences in topography: catchments with
average slope angles of ~5° and ~35° have similar Fyw ~5 %. This result argues against a
systematic shift in water transit times associated with mountain building, but rather a variable
response modulated by climatic and geologic factors — although our results do point to a wider
range in Fyw associated with mountains than lowlands, at least for the tropical setting of the
Andes-Amazon system.
While our results, and especially the Fyw of lowland catchments, may be specific to the
Andes-Amazon setting, we expect the hydroclimatic and geological effects that we document
here to be more generally relevant in other mountainous regions, too. Orographic controls on
precipitation tend to force the highest precipitation, as well as the most intense rainfall, along
mountain fronts and at mid-elevations. In addition to the Andes, similar patterns have been
shown in the Himalaya (Bookhagen and Burbank, 2006) and the European Alps (Napoli et al.,
2019) and models predict complex spatial patterns of orographic precipitation that depend on
several factors including climatic variables (e.g., (Barros and Lettenmaier, 1994; Roe and Baker,
2006)The dependence of catchment transit times on hydroclimate, as we find in the Andes and as
reported in other recent work (von Freyberg et al., 2018; Gallart et al., 2020), suggests that
57
orographic effects on rainfall regime may be a primary determinant of hydrologic processes in
major mountain ranges. Similarly, we expect fractured bedrock, and associated high
permeability, to be generally characteristic of mountain systems as seen in our work and other
studies (e.g., Muñoz-Villers et al., 2016; Moon et al., 2017), though our results also highlight
how the geological complexity of mountains – such as the presence of a granitic intrusion in our
study area of the Andes – can introduce heterogeneity. Full understanding of the role of
mountainous regions in water, sediment, and geochemical cycles will depend on evaluating the
role of these multiple factors in determining hydrological behavior.
58
Figure 3.8. Circles represent small catchments from this study, triangles represent mesoscale
catchments from Clark et al. (2014). In panels (a-c), dashed circles and triangles indicate volume
weighted young water fractions; solid circles and triangles are unweighted young water fractions.
(a) shows Fyw as a function of mean catchment flow path length, (b) shows Fyw as a function of
mean catchment slope. Both catchment flow path length and slope are calculated from ALOS 30
m DEM. (c) Shows Fyw as a function of catchment elevation at sampling point for the small
catchments, and mean catchment elevation for the mesoscale catchments. (d) Compares
weighted mean Fyw to unweighted mean Fyw.
59
5. Conclusions
We collected stream and precipitation samples for analysis of O and H stable isotope
ratios in rainfall and stream water at seven streams and four rainfall stations spanning the Andes-
Amazon gradient over a period of four years. Samples were collected approximately twice
monthly for most sites. The stream young water fraction varied significantly between sites.
Highest elevation sites 3472-SC, 3077-SC and 2432-SC displayed young water fractions
between 3 −10 %. Mid-elevation small catchments (1540-SC and 609-SC) displayed the higher
young water fractions of 15 −23 %. Catchments in the foreland floodplain had low young water
fractions, ranging from 3 −4 %.
We suggest that the low young water fractions observed in Andean catchments are a
result of long flow paths in fractured shale. High young water fractions observed at mid-
elevation sites result from a combination of a stormier climate, and in the case of 1540-SC,
granitic bedrock with poorly developed soils and low permeability, meaning that water moves
through the catchment faster. In the lowlands, low permeability clay terraces and low relief
together generate low young water fractions. Thus a combination of topography, climate, and
bedrock properties conspire to determine water transit in this setting. Our results emphasize the
complexity of the role of mountainous regions in the hydrological cycle and potentially help to
explain why it has been difficult to identify a simple topographic control on young water
fractions at the global scale. Accounting for the multiple factors that control water transit will be
important for fully understanding the role of mountain water towers in water, sediment, and
carbon fluxes.
60
Acknowledgements
This work was funded by NSF award EAR-1455352 to AJW. We thank the Andes
Biodiversity and Ecosystem Research Group (ABERG) for field support and access to rainfall
data. ABERG rainfall data were collected with support of NSF DEB LTREB 1754647 to Miles
Silman. We thank Alex Sessions and Fenfang Wu at Caltech, Markus Bill at Lawrence Berkeley
National Lab and Fernando Silva at Chapman University for support with the stable isotope
measurements. We thank Greg Goldsmith for support with stable isotope measurements and for
helpful discussions. We thank Abra Atwood and Julien Emile-Geay for helpful discussions with
respect to data analysis.
61
References
Allen, S. T., Kirchner, J. W., Braun, S., Siegwolf, R. T. W., & Goldsmith, G. R. (2019). Seasonal
origins of soil water used by trees. Hydrology and Earth System Sciences, 23(2), 1199–
1210. https://doi.org/10.5194/hess-23-1199-2019
Ameli, A. A., Beven, K., Erlandsson, M., Creed, I. F., McDonnell, J. J., & Bishop, K. (2017).
Primary weathering rates, water transit times, and concentration-discharge relations: A
theoretical analysis for the critical zone. Water Resources Research, 53(1), 942–960.
https://doi.org/10.1002/2016WR019448
Asano, Y., Uchida, T., & Ohte, N. (2002). Residence times and flow paths of water in steep
unchannelled catchments, Tanakami, Japan. Journal of Hydrology, 20.
Barnett, T. P., Adam, J. C., & Lettenmaier, D. P. (2005). Potential impacts of a warming climate
on water availability in snow-dominated regions. Nature, 438(7066), 303–309.
https://doi.org/10.1038/nature04141
Barros, A. P., & Lettenmaier, D. P. (1994). Dynamic modeling of orographically induced
precipitation. Reviews of Geophysics, 32(3), 265. https://doi.org/10.1029/94RG00625
Berner, R. A. (1978). Rate control of mineral dissolution under Earth surface conditions.
American Journal of Science, 278(9), 1235–1252. https://doi.org/10.2475/ajs.278.9.1235
Bookhagen, B., & Burbank, D. W. (2006). Topography, relief, and TRMM-derived rainfall
variations along the Himalaya. Geophysical Research Letters, 33(8), L08405.
https://doi.org/10.1029/2006GL026037
Burt, E. I., Bill, M., Conrad, M. E., Quispe, A. J. C., Christensen, J. N., Hilton, R. G., Dellinger,
M., & West, A. J. (2021). Conservative transport of dissolved sulfate across the Rio
Madre de Dios floodplain in Peru. Geology. https://doi.org/10.1130/G48997.1
Clark, K. E., Torres, M. A., West, A. J., Hilton, R. G., New, M., Horwath, A. B., Fisher, J. B.,
Rapp, J. M., Robles Caceres, A., & Malhi, Y. (2014). The hydrological regime of a
forested tropical Andean catchment. Hydrology and Earth System Sciences, 18(12),
5377–5397. https://doi.org/10.5194/hess-18-5377-2014
Clark, K. E., West, A. J., Hilton, R. G., Asner, G. P., Quesada, C. A., Silman, M. R., Saatchi, S.
S., Farfan-Rios, W., Martin, R. E., Horwath, A. B., Halladay, K., New, M., & Malhi, Y.
(2016). Storm-triggered landslides in the Peruvian Andes and implications for
topography, carbon cycles, and biodiversity. Earth Surface Dynamics, 4(1), 47–70.
https://doi.org/10.5194/esurf-4-47-2016
Drever, J. I. (1988). The geochemistry of natural waters (2nd ed). Prentice Hall.
Gaillardet, J., Dupré, B., Louvat, P., & Allègre, C. J. (1999). Global silicate weathering and CO2
consumption rates deduced from the chemistry of large rivers. Chemical Geology, 159(1),
3–30. https://doi.org/10.1016/S0009-2541(99)00031-5
62
Gallart, F., Valiente, M., Llorens, P., Cayuela, C., Sprenger, M., & Latron, J. (2020).
Investigating young water fractions in a small Mediterranean mountain catchment: Both
precipitation forcing and sampling frequency matter. Hydrological Processes, 34(17),
3618–3634. https://doi.org/10.1002/hyp.13806
Gibbs, R. J. (1970). Mechanisms Controlling World Water Chemistry. Science, 170(3962),
1088–1090. https://doi.org/10.1126/science.170.3962.1088
Gu, X., Rempe, D. M., Dietrich, W. E., West, A. J., Lin, T.-C., Jin, L., & Brantley, S. L. (2020).
Chemical reactions, porosity, and microfracturing in shale during weathering: The effect
of erosion rate. Geochimica et Cosmochimica Acta, 269, 63–100.
https://doi.org/10.1016/j.gca.2019.09.044
Hilton, R. G., & West, A. J. (2020). Mountains, erosion and the carbon cycle. Nature Reviews
Earth & Environment, 1, 16. https://doi.org/10.1038/s43017-020-0058-6
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S.,
Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M., Fernández, A.,
Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink, P. D. A., Kulkarni, A. V.,
Mayewski, P. A., … Baillie, J. E. M. (2020). Importance and vulnerability of the world’s
water towers. Nature, 577(7790), 364–369. https://doi.org/10.1038/s41586-019-1822-y
Jasechko, S. (2016). Partitioning young and old groundwater with geochemical tracers. Chemical
Geology, 427, 35–42. https://doi.org/10.1016/j.chemgeo.2016.02.012
Jasechko, S., Kirchner, J. W., Welker, J. M., & McDonnell, J. J. (2016). Substantial proportion
of global streamflow less than three months old. Nature Geoscience, 9(2), 126–129.
https://doi.org/10.1038/ngeo2636
Kirchner, J. W. (2016a). Aggregation in environmental systems – Part 1: Seasonal tracer cycles
quantify young water fractions, but not mean transit times, in spatially heterogeneous
catchments. Hydrology and Earth System Sciences, 20(1), 279–297.
https://doi.org/10.5194/hess-20-279-2016
Kirchner, J. W. (2016b). Aggregation in environmental systems – Part 2: Catchment mean transit
times and young water fractions under hydrologic nonstationarity. Hydrology and Earth
System Sciences, 20(1), 299–328. https://doi.org/10.5194/hess-20-299-2016
Lutz, S. R., Krieg, R., Müller, C., Zink, M., Knöller, K., Samaniego, L., & Merz, R. (2018).
Spatial Patterns of Water Age: Using Young Water Fractions to Improve the
Characterization of Transit Times in Contrasting Catchments. Water Resources Research,
54(7), 4767–4784. https://doi.org/10.1029/2017WR022216
Maher, K. (2010). The dependence of chemical weathering rates on fluid residence time. Earth
and Planetary Science Letters, 294(1–2), 101–110.
https://doi.org/10.1016/j.epsl.2010.03.010
Maher, K. (2011). The role of fluid residence time and topographic scales in determining
chemical fluxes from landscapes. Earth and Planetary Science Letters, 312(1–2), 48–58.
https://doi.org/10.1016/j.epsl.2011.09.040
63
Maher, K., & Chamberlain, C. P. (2014). Hydrologic Regulation of Chemical Weathering and
the Geologic Carbon Cycle. Science, 343(6178), 1502–1504.
https://doi.org/10.1126/science.1250770
McGlynn, B., McDonnell, J., Stewart, M., & Seibert, J. (2003). On the relationships between
catchment scale and streamwater mean residence time. Hydrological Processes, 17(1),
175–181. https://doi.org/10.1002/hyp.5085
McGuire, K. J., & McDonnell, J. J. (2006). A review and evaluation of catchment transit time
modeling. Journal of Hydrology, 330(3–4), 543–563.
https://doi.org/10.1016/j.jhydrol.2006.04.020
McGuire, K. J., McDonnell, J. J., Weiler, M., Kendall, C., McGlynn, B. L., Welker, J. M., &
Seibert, J. (2005). The role of topography on catchment-scale water residence time.
Water Resources Research, 41(5). https://doi.org/10.1029/2004WR003657
Meybeck, M., Green, P., & Vörösmarty, C. (2001). A New Typology for Mountains and Other
Relief Classes: An Application to Global Continental Water Resources and Population
Distribution. Mountain Research and Development, 21(1), 34–45.
https://doi.org/10.1659/0276-4741(2001)021[0034:ANTFMA]2.0.CO;2
Moon, S., Perron, J. T., Martel, S. J., Holbrook, W. S., & St. Clair, J. (2017). A model of three-
dimensional topographic stresses with implications for bedrock fractures, surface
processes, and landscape evolution: Three-Dimensional Topographic Stress. Journal of
Geophysical Research: Earth Surface, 122(4), 823–846.
https://doi.org/10.1002/2016JF004155
Müller Schmied, H., Cáceres, D., Eisner, S., Flörke, M., Herbert, C., Niemann, C., Peiris, T. A.,
Popat, E., Portmann, F. T., Reinecke, R., Schumacher, M., Shadkam, S., Telteu, C.-E.,
Trautmann, T., & Döll, P. (2020). The global water resources and use model WaterGAP
v2.2d: Model description and evaluation [Preprint]. Hydrology.
https://doi.org/10.5194/gmd-2020-225
Muñoz-Villers, L. E., Geissert, D. R., Holwerda, F., & McDonnell, J. J. (2016). Factors
influencing stream baseflow transit times in tropical montane watersheds. Hydrology and
Earth System Sciences, 20(4), 1621–1635. https://doi.org/10.5194/hess-20-1621-2016
Napoli, A., Crespi, A., Ragone, F., Maugeri, M., & Pasquero, C. (2019). Variability of
orographic enhancement of precipitation in the Alpine region. Scientific Reports, 9(1),
13352. https://doi.org/10.1038/s41598-019-49974-5
Ponton, C., West, A. J., Feakins, S. J., & Galy, V. (2014). Leaf wax biomarkers in transit record
river catchment composition. Geophysical Research Letters, 41(18), 6420–6427.
https://doi.org/10.1002/2014GL061328
Rempe, D. M., & Dietrich, W. E. (2018). Direct observations of rock moisture, a hidden
component of the hydrologic cycle. Proceedings of the National Academy of Sciences,
115(11), 2664–2669. https://doi.org/10.1073/pnas.1800141115
Roe, G. H., & Baker, M. B. (2006). Microphysical and Geometrical Controls on the Pattern of
Orographic Precipitation. Journal of the Atmospheric Sciences, 63(3), 861–880.
https://doi.org/10.1175/JAS3619.1
64
Scanlon, B. R., Zhang, Z., Save, H., Sun, A. Y., Müller Schmied, H., van Beek, L. P. H., Wiese,
D. N., Wada, Y., Long, D., Reedy, R. C., Longuevergne, L., Döll, P., & Bierkens, M. F.
P. (2018). Global models underestimate large decadal declining and rising water storage
trends relative to GRACE satellite data. Proceedings of the National Academy of
Sciences, 115(6). https://doi.org/10.1073/pnas.1704665115
Stockinger, M. P., Bogena, H. R., Lücke, A., Diekkrüger, B., Cornelissen, T., & Vereecken, H.
(2016). Tracer sampling frequency influences estimates of young water fraction and
streamwater transit time distribution. Journal of Hydrology, 541, 952–964.
https://doi.org/10.1016/j.jhydrol.2016.08.007
Tang, W., & Carey, S. K. (2017). HydRun: A MATLAB toolbox for rainfall-runoff analysis.
Hydrological Processes, 31(15), 2670–2682. https://doi.org/10.1002/hyp.11185
Tetzlaff, D., Seibert, J., McGuire, K. J., Laudon, H., Burns, D. A., Dunn, S. M., & Soulsby, C.
(2009). How does landscape structure influence catchment transit time across different
geomorphic provinces? Hydrological Processes, 23(6), 945–953.
https://doi.org/10.1002/hyp.7240
Tetzlaff, D., Seibert, J., & Soulsby, C. (2009). Inter-catchment comparison to assess the
influence of topography and soils on catchment transit times in a geomorphic province;
the Cairngorm mountains, Scotland. Hydrological Processes, 23(13), 1874–1886.
https://doi.org/10.1002/hyp.7318
Torres, M. A., West, A. J., Clark, K. E., Paris, G., Bouchez, J., Ponton, C., Feakins, S. J., Galy,
V., & Adkins, J. F. (2016). The acid and alkalinity budgets of weathering in the Andes–
Amazon system: Insights into the erosional control of global biogeochemical cycles.
Earth and Planetary Science Letters, 450, 381–391.
https://doi.org/10.1016/j.epsl.2016.06.012
Tucker, G. E., & Bras, R. L. (2000). A stochastic approach to modeling the role of rainfall
variability in drainage basin evolution. Water Resources Research, 36(7), 1953–1964.
https://doi.org/10.1029/2000WR900065
Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M., & Weingartner, R. (2007). Mountains of
the world, water towers for humanity: Typology, mapping, and global significance.
Water Resources Research, 43(7). https://doi.org/10.1029/2006WR005653
von Freyberg, J., Allen, S. T., Seeger, S., Weiler, M., & Kirchner, J. W. (2018). Sensitivity of
young water fractions to hydro-climatic forcing and landscape properties across 22 Swiss
catchments. Hydrology and Earth System Sciences, 22(7), 3841–3861.
https://doi.org/10.5194/hess-22-3841-2018
West, A., Galy, A., & Bickle, M. (2005). Tectonic and climatic controls on silicate weathering.
Earth and Planetary Science Letters, 235(1–2), 211–228.
https://doi.org/10.1016/j.epsl.2005.03.020
Wilusz, D. C., Harman, C. J., & Ball, W. P. (2017). Sensitivity of Catchment Transit Times to
Rainfall Variability Under Present and Future Climates. Water Resources Research,
53(12), 10231–10256. https://doi.org/10.1002/2017WR020894
65
Chapter 4: Stormflow-driven loss of nutrients from a tropical forest
ecosystem
Contributors: K. Dana Chadwick, Nicholas J. Bouskill, Daxs Herson Coayla Rimachi, A.
Joshua West
Opening statement
In this chapter, I explore the role of storms in tropical ecosystem nutrient budgets. Our
motivation for this work was founded in the decades-long understanding that despite being
highly biodiverse and productive, many tropical ecosystems are limited by one or more rock-
derived nutrients (such as calcium, magnesium or potassium). Through sampling river water
chemistry before, during and after storms, I am able to show that storms represent a significant
“leak” in the forest nutrient cycle, by leaching rock derived nutrients from surface soil. I posit
that in a warmer and wetter future, with more significant storms, tropical ecosystems face the
possibility of intensified nutrient loss.
This work was conceptualized by myself and Joshua West. Daxs Herson Coayla Rimachi
and I carried out the water sampling. Dana Chadwick provided the soil chemistry data and
helped with the soil and vegetation nutrient stock estimates. Nicholas Bouksill provided input on
soil nutrient cycling. I analyzed the water samples and geochemical data and made the figures. I
wrote the manuscript with input from all authors.
66
Abstract
Nutrient availability is a limiting factor for biomass accumulation and carbon fixation
within many terrestrial ecosystems. Although ecosystems typically recycle nutrients via
throughfall, litterfall, decomposition, and root uptake, this is a leaky cycle that involves some
continuous loss, typically via stream and ground waters. Yet the impact of key hydrological
processes such as stormflow generation on nutrient loss have received little attention. Storm-
driven nutrient loss may be particularly important in tropical forests because of ubiquitous
surface flow paths and bioaccumulation of scarce nutrients in the near-surface zone characterized
by intensely weathered soils. Here we present observations of storm-flow driven loss of rock-
derived nutrients from a tropical forest developed on terra firme fluvial terraces typical of
Amazonia. Results from our three-year hydrochemical study suggest that storms play a key role
in ecosystem nutrient loss, particularly for rock-derived nutrients such as calcium, magnesium,
potassium and phosphate. Stable isotopes of water reveal two dominant flow regimes: stream
baseflow with relatively old water (~4% young water fraction) and storms comprised of 30-70%
recent precipitation. Concentrations of nutrients – especially calcium and magnesium – are
elevated during storms, consistent with water taking rapid, surficial flow paths where key rock-
derived nutrients have bioaccumulated. The rapid conveyance of these storm flowpaths short-
circuits the nutrient cycle in this system, driving nutrient loss from the near-surface reservoir that
maintains the ecosystem. With the potential for more intense hydrologic events in a warming
climate, storm-driven nutrient loss from tropical ecosystems such as that studied here may play
an important but little-recognized role in the response of the terrestrial biosphere to climate
change.
67
1. Introduction
Nutrient availability is critical for the productivity of terrestrial ecosystems, and for
associated carbon sequestration (Elser et al., 2007; Fisher et al., 2012; Malhi et al., 2004). Many
terrestrial ecosystems are thought to be limited by nitrogen, but phosphorous and other rock-
derived nutrients are limiting or co-limiting across much of the global land area (Du et al., 2020),
especially in older landscapes (Quesada et al., 2011; Vitousek & Farrington, 1997; Walker &
Syers, 1976). In addition to P, other rock-derived elements including calcium, magnesium and
potassium may be crucial, particularly in the tropics (Asner et al., 2015; Baillie et al., 1987; K.
D. Chadwick & Asner, 2018; Cuevas & Medina, 1988; Wright et al., 2011). Limiting nutrients
are often tightly cycled by ecosystems (Bol et al., 2016; Odum, 1969; Vitousek & Sanford,
1986), but some loss occurs via leaching and export in flowing waters (Bormann & Likens,
1967; Leys et al., 2016; Uhlig & von Blanckenburg, 2019). Increased leaching in wet climates
can deplete rock-derived nutrients from soils over timescales of thousands of years and longer
(O. A. Chadwick et al., 2003; Porder & Chadwick, 2009). While this long-term role of climate is
well understood, less is known about whether and how hydrologic processes over shorter
timescales, such as during individual storms, determine nutrient losses.
Storm events are disproportionately important for the export of many constituents in
streams and rivers, including sediment (Meybeck et al., 2003), organic carbon (Clark et al., 2013;
Raymond & Saiers, 2010), and N and P (Frazar et al., 2019; Marinos et al., 2018; Vaughan et al.,
2017). Yet, globally, the concentrations of key nutrient elements in most streams are close to
“chemostatic,” changing little in response to changes in discharge (Godsey et al., 2019). This
observation informs a general assumption that short-term hydrological variability plays a muted
role in the loss of rock-derived nutrients from forested ecosystems (Bol et al., 2016; Sohrt et al.,
68
2019). However, most related data come from temperate catchments. A dearth of hydrochemical
studies in the tropics means that the role of hydrology in determining nutrient fate and fluxes in
these environments is poorly understood — even though tropical ecosystems are amongst the
most productive and biodiverse on Earth (Malhi, 2012), contribute outsized fluxes of water and
solutes to the oceans (Meybeck, 1987; Fekete et al., 2002), and are thought to play critical but
still poorly understood roles in the global carbon cycle and its response to climate change (Cox et
al., 2013; Fisher et al., 2020). Storm frequency and intensity are expected to increase in a
warming global climate (e.g., Trenberth, 2011), so storm-dependent losses of limiting nutrients
from tropical forests could play important but largely unrecognized roles in climate-carbon cycle
feedbacks.
Specific features of tropical environments conspire to prime them for nutrient loss that
depends on streamflow regime. Shallow fluid flow paths are particularly important in many
tropical watersheds, due to high rates of bioturbation and extensive plant root development
(Birch et al., 2021; Gardner et al., 2017; Ogden et al., 2013), as well as clay-rich, low-
permeability soils (Elsenbeer, 2001). Meanwhile, the tight nutrient cycling between canopy,
litterfall and surface soil in tropical environments concentrates nutrients in surface soils
(Edwards, 1982; Wilcke et al., 2002, 2017), producing high vertical stratification that may
interact with preferential shallow flow paths to facilitate nutrient loss during storm events as seen
in agricultural catchments (Heathwaite & Dils, 2000). Additionally, tropical forests may be
limited by multiple rock-derived nutrients (Asner et al., 2015; Baillie et al., 1987; K. D.
Chadwick & Asner, 2018; Cuevas & Medina, 1988; Wright et al., 2011), and thus particularly
susceptible to loss of elements like Ca and Mg that may not be as easily bound to particles as P
69
(Walker & Syers, 1976). Despite these multiple factors, it remains to be understood if and how
hydrologic flowpaths and streamflow regimes drive nutrient loss from tropical forests.
Here, we present the results from three years of hydrochemical monitoring of a small
catchment developed on a terra firme fluvial terrace at the Los Amigos Preserve in the Peruvian
Amazon floodplain (Fig. 4.1), where prior work has demonstrated the importance of rock derived
nutrients, and particularly calcium, in sustaining the tropical forest ecosystem (K. D. Chadwick
& Asner, 2018). Our goal was to understand how the interplay between climate, hydrologic flow
paths and soil nutrient distributions controls the export of nutrients from this system. Stream and
rainwater samples were collected approximately twice-monthly for the duration of the study with
two additional high-resolution sampling campaigns that captured storm events. Samples were
analyzed for water stable isotope ratios, which were used as conservative (i.e., non-reactive)
geochemical tracers to infer fluid flow paths and distinguish the fraction of river water comprised
of recent rainfall (Burns et al., 2001; Kirchner, 2016a, 2016b). We combine these data with
analyses of dissolved major element concentrations to calculate rock-derived nutrient export
associated with annual flows and individual storm events — and specifically to identify the role
of high flow during storm events in determining nutrient losses.
2. Materials and methods
2.1. Study site
Samples were collected from a small catchment (0.377 km
2
; S 12.55884, W 70.099311,
275 masl) on a tropical floodplain terra firme terrace of approximately Quaternary age
(Latrubesse et al., 2010; Rigsby et al., 2009). The catchment is located in the southwestern
70
Amazon basin (Fig. 4.1), in the Madre de Dios watershed, a major tributary of the Madeira
River. The study site falls within the Los Amigos Conservation Concession and consists of
mostly old growth tropical rainforest, but has experienced selective logging in the past (K. D.
Chadwick & Asner, 2016). The studied watershed avoids seasonal flooding from the mainstem
Madre de Dios River and is therefore cut off from a supply of sediment containing un-weathered
minerals from the Andes.
Figure 4.1. (A) Map of South America; the black box shows the approximate location of (B).
(B) Digital elevation model of southwestern Peru (DEM, from SRTM 30 m data), showing major
rivers on the western flank of the Andes mountains and foreland floodplain. The red circle
indicates the location of the study watershed. (C) DEM of the studied catchment, derived from
ALOS 30m data. (D) Average monthly rainfall from 2010-2017.
71
Between years 2010-2017, precipitation averaged 2500 mm/year, with a marked
seasonality in precipitation amount: wet season (January-March) consists of nearly four times the
dry season precipitation (July-September) (Fig. 4.1D; Amazon Conservation Association,
unpublished data). For the same period, average yearly temperature was 24 C, with a range in
average minimum-maximum from 21-28 C (Amazon Conservation Association, unpublished
data).
2.2. Sampling scheme
Stream waters were sampled at two temporal scales: an approximately twice-monthly to
monthly sample collection from mid 2016-early 2019 (referred to as “twice-monthly sampling”),
and high-frequency sampling campaigns over the course of a week in 2017 and a few days 2019
(referred to as “high-frequency sampling”). Due to the remote location of the study site and
difficulty of access, twice-monthly sampling was occasionally less frequent, though we retain the
terminology of referring to these samples as being collected twice per month for simplicity.
During the 2017 high frequency sampling campaign, samples were collected at least every four
hours, including at night. During 2019, a storm occurred during a routine twice-monthly
sampling trip; samples were taken before, during and after the storm.
Integrated precipitation samples for water isotope analysis were collected over
approximately two-week intervals in a bucket with a layer of oil to prevent evaporation (referred
to as integrated precipitation). These samples were collected throughout the period of 2016-2019.
During the 2017 and 2019 high frequency sampling campaigns, individual rainstorms were
sampled in buckets and collected once it stopped raining (referred to as instantaneous
precipitation).
72
2.3. Discharge measurements
During both the twice-monthly and high frequency sampling, point discharge
measurements were made each time a sample was collected. Water level was measured manually
with a ruler and water velocity was measured with a Global Water FP311 Flow Probe.
Additionally, in January 2019, a Global Water WL16U-003-025 Water Level Logger was
installed in the small catchment to monitor water level (measurement taken every 15 minutes).
Stream cross sections were measured manually several times throughout the course of the study
to calculate water fluxes.
2.4. Sample collection and analysis
Each sample collection followed the same protocol. Stream water samples were filtered
in the field using 0.2 m using polyethersulfone (PES) filters. Separate aliquots of river water
were put into HDPE bottles for cations, anions, and stable isotopes of nitrate and glass vials with
a PolyCone seal lid (Fisher Brand) for stable isotopes of water. Cations (Ca, Mg, Na, K, Si,)
were measured using an Agilent 5110 ICP-OES at the University of Southern California, with
reproducibility of 3% RSD or better for each analyte. Anions (Cl
-
, SO4
2
) were measured via
Metrohm 850 IC at the University of Southern California and Lawrence Berkeley National
Laboratory. PO4
3-
was measured via flow injection analysis (Lachat Instruments QuikChem 8500
Series 2) at the University of California at Santa Barbara, with reproducibility of 1% RSD or
better for both analytes. Stable isotopes of water (
18
O and D) were measured via two Los
Gatos Research Liquid Water Isotope Analyzers (LGR) (Caltech and Lawrence Berkeley
National Lab) and a Picarro L2130i Cavity Ring Down Spectrometer (Chapman University). The
internal error of isotope measurements on the Picarro was 0.1 ‰ or better for δ
18
O and 2 ‰ or
73
better for δD. On the LGR at Lawrence Berkeley National Lab the internal error was 0.1 ‰ or
better for
18
O and 1 ‰ or better for D. On the LGR at Caltech the internal error was 0.3 ‰ or
better for
18
O and 1 ‰ or better for D. Long-term accuracy on certified isotope standards was
within one standard deviation of the known isotopic values.
2.5. Young water fractions and stream hydrograph separations
We calculated the young water fraction from the ratio of the amplitude of the seasonal
isotope cycle in precipitation to the amplitude of the seasonal cycle in streamflow (Kirchner,
2016). A stream discharge and rainfall amount-weighted fit was applied to stable isotope data
using Equation 2:
𝐶 (𝑡 ) = 𝑎 𝑠 × 𝑐𝑜𝑠 (2𝜋𝑓𝑡 ) + 𝑏 𝑠 × 𝑠𝑖𝑛 (2𝜋𝑓𝑡 ) + 𝑘 (2)
where C is the concentration of stable isotope tracer in stream or precipitation, t is time, f is the
frequency of the seasonal cycle (specified as 1 year), a and b are the cosine and sine coefficients
and k is the vertical shift between rain and streamflow seasonal cycles.
The young water fraction was then calculated using Equations (3-5), where:
𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 , 𝑂 18
𝑆𝑡𝑟𝑒𝑎𝑚 = √𝑎 𝑠 2
+ 𝑏 𝑠 2
(3)
𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 , 𝑂 18
𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛 = √𝑎 𝑝 2
+ 𝑏 𝑝 2
(4)
𝑌𝑜𝑢𝑛𝑔 𝑤𝑎𝑡𝑒𝑟 𝑓 𝑟𝑎𝑐𝑡𝑖𝑜𝑛 (%) = 𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 , 𝑂 18
𝑆𝑡𝑟𝑒𝑎𝑚 /𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 , 𝑂 18
𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛 (5)
The event water fraction was calculated using two endmember mixing between precipitation and
pre-event water (streamflow prior to the start of rain).
𝐶 (𝑡 )
𝑠𝑡𝑟𝑒𝑎𝑚 = 𝐶 (𝑡 )
𝑒𝑣𝑒𝑛𝑡 𝐹 𝑒𝑣𝑒𝑛𝑡 + 𝐶 (𝑡 )
𝑝𝑟𝑒 −𝑒𝑣𝑒𝑛𝑡 𝐹 𝑝𝑟𝑒 −𝑒𝑣𝑒𝑛𝑡 (6)
74
3. Results
3.1. Stream water major element fluxes and relation to discharge
Rock-derived nutrients calcium, magnesium, potassium and phosphate fluxes in stream
water from the Los Amigos catchment showed similar trends across the range of discharge (Fig.
4.2). Fluxes for Ca, Mg, K and PO4
3-
scaled supralinearly to discharge during periods of lowest
flow, with a gradual transition to sublinear scaling at mid-range of discharge, and then a return to
supralinearly scaling at highest discharge. Phosphate displayed a slightly weaker increase in flux
at the highest discharge compared to calcium, magnesium and potassium.
Silica displays markedly different behavior compared to Ca, Mg, K and PO4
3-
(Fig. 4.2).
Silica fluxes scaled linearly with discharge, indicating that concentrations were nearly constant,
regardless of discharge (i.e, essentially chemostatic). Silica displayed only a slightly sub-linear
scaling of flux at the highest discharge, indicating some weak dilution of the solute. These
patterns are similar to well-documented concentration-discharge trends for this element at other
locations (e.g., Kennedy, 1971; Maher, 2011). Sodium fluxes also scale proportionately to
discharge across the range of hydrologic conditions. Neither sodium nor silica shows the increase
in elemental flux at high discharge that is observed for to Ca, Mg, K and PO4
3-
.
75
Figure 4.2. Panel A: fluxes of bioactive rock-derived nutrients Ca, Mg, K and PO4
3-
and bio-
inactive major solutes Na and Si, across the full range of hydrologic conditions captured in this
study. Solid lines represent the best-fit power laws for each set of solute data. Panel B: Ratios of
Ca, Mg and Na concentrations, normalized to Si concentrations. Panel C: Normalized flux of Ca
and Mg, calculated as the ratio of the actual flux to the expected flux based on a power law
relationship between flux and discharge (see the lines in Panel A for the power law relationship
for each element). Panel D: Normalized flux of Na and Si.
76
3.2. Stable isotopes of rain and stream water
Stable isotope samples were collected over two scales of temporal resolution. One sample
set was collected approximately twice a month (referred to as twice-monthly stream samples and
twice-monthly integrated rain samples), while the other sample set was collected on sub-daily to
hourly basis for two periods of several days (referred to as high-frequency stream samples and
instantaneous rain samples). The volume-weighted average oxygen isotope value (
18
O value) of
the twice-monthly stream water samples was -6.6‰ and ranged from -6.0‰ to -8.1‰. For the
February 2017 high-frequency sampling campaign, the average
18
O value was -7.2‰, with a
range from -6.0‰ to -9.7‰. Similarly, during the January 2019 high-frequency campaign, the
average oxygen isotope composition was -7.8‰, and ranged from -6.6‰ to -9.4‰. Thus, both
high-frequency campaigns, despite their short duration, captured the entire range of
18
O
observed in the twice-monthly samples of stream water, which showed relatively little change
with time.
Two-week average precipitation
18
O values ranged from -11.2 to 1.1‰, with a volume-
weighted average of -6.7‰. Strong seasonal differences were observed in the integrated
precipitation
18
O values, in contrast to relatively small seasonal differences in stream water
values. This difference is broadly consistent with observations from many other sites around the
world (e.g., Jasechko et al., 2016)). Instantaneous precipitation
18
O values for February 2017
ranged from -7.5 to -17.8‰, averaging -13.1‰. In January 2019, the instantaneous precipitation
18
O ranged from -8.3 to -12.1‰ and had an average value of -10.2‰.
77
Figure 4.3. Panel A:
18
O of average precipitation (diamonds), twice-monthly stream samples
(circles) and the range of values for stream samples collected during the high frequency sampling
campaigns (shaded boxes). The sinusoidal lines represent the fit of the seasonal cycle with the
equation from Kirchner, 2016a. Panels B and C:
18
O of instantaneous precipitation (triangles)
and stream samples (circles) collected during the two high-frequency sampling campaigns.
Lower panels of B and C: daily rainfall (shown with bars) and stream discharge (shown with
circles in B and a line in C).
3.3. Young water fractions and event water fractions
The water isotope twice-monthly data were used to determine the proportion of stream
water derived from recent precipitation (see Methods). The seasonally integrated “young water
fraction” represents the proportion of stream water that fell as precipitation within the prior ~2-3
78
months, based on the seasonal patterns in stream vs. rain water
18
O values (Kirchner, 2016).
The average young water fraction at Los Amigos was 3%, and increased to 7% when volume-
weighted for stream discharge and rainfall amount. This young water fraction is amongst the
lowest of the values observed in recent global compilation and is considerably lower than the
~34% global flow-weighted average value (Jasechko et al., 2016).
In contrast to the muted seasonal variability in stream water
18
O values, during high-
discharge events at Los Amigos,
18
O values of stream water departed significantly from the
relatively constant baseflow value, by 3.2‰ during the storm sampled in February 2017 and
2.7‰ in January 2019. As a result, the event water fraction (the fraction of storm-event
precipitation in streamflow; Genereux & Hooper, 1998) was approximately 30% in 2017 and
70% in 2019. These values are consistent with the commonly observed contribution of pre-event
water to stormflow (Genereux & Hooper, 1998). However, they also reveal a much higher
contribution of recent rainfall to streamflow during storms than is reflected in the seasonally
integrated young water fraction at Los Amigos.
4. Discussion
4.1. Los Amigos streamflow regimes: Rapid stormflow conveyance of water and nutrients
Our data indicate two distinct flow regimes at Los Amigos. The high major element
concentrations at low flow are best explained by streamflow sourced from groundwater with
significant contact time between water and rocks (e.g., Kennedy, 1971; Maher, 2011). The lack
of a seasonal cycle in
18
O and associated low young water fraction are consistent with baseflow
being sustained by “old” groundwater. Dilution of major elements (Ca and Mg, and to a lesser
79
extent Na and Si) at mid-range discharge values could be explained by a rising water table during
the transition from dry to wet season. Such patterns have been widely observed in other
catchment systems (Moatar et al., 2017).
The increases in Ca, Mg, K and PO4
3-
fluxes at high discharge (Fig. 4.2), as well as the
high event-water contributions to streamflow (Fig. 4.3), suggest a major change in flow paths as
discharge increases to the highest observed values during storms. Increased atmospheric
deposition cannot explain the rise rock-derived nutrients at high discharge, as calculations using
chloride concentrations show a relatively constant 10-20% sourcing of these elements from the
atmosphere. Previous work on the Los Amigos terra firme terraces has shown that Ca, Mg, K
and P are elevated in the near-surface soils, due to tight ecosystem cycling (K. D. Chadwick &
Asner, 2016). The elevated fluxes of stream water rock-derived nutrients during stormflow are
consistent with water that has taken near-surface flow paths to the stream, traveling through the
nutrient-rich litterfall and shallow soil zone and moving quickly enough (due to storm
conditions) that soil infiltration and root zone uptake is too slow to re-acquire these nutrients.
The importance of rapid flow paths that convey rainfall quickly to streams is supported by the
high event water fractions inferred from the water isotope values. In this conceptual model of
storm waters rapidly moving through the near surface zone, there is too little contact time
between water and rock for Na and Si to reach equilibrium concentrations, explaining why these
elements show dilution at highest flow.
In summary, the combination of water isotope and major element concentration data from
Los Amigos suggest that the stream concentration-discharge relationships at highest flows can be
explained by mixing of high Ca-Mg/low Na-Si water (taking rapid surface flow paths) and low
Ca-Mg/high Na-Si waters (wet season groundwater, where Na-Si remain relatively high and Ca-
80
Mg are diluted relative to high-concentration dry-season groundwater). Preferential flow paths
are expected in tropical catchments, in part because of intensive plant root networks and
bioturbation (Cheng et al., 2017, 2018), Additionally, tropical fluvial soils are often rich in clay
minerals (Quesada et al., 2011), and clay-rich acrisols in Amazonia have been observed to drive
rapid lateral flow with limited infiltration during periods of high rainfall (Elsenbeer, 2001;
Elsenbeer & Lack, 1996). During large storms in Panama and Ecuador, activation of preferential
flow paths in near surface soils has been implicated as the cause of changes in stream chemistry
at high flow (Boy et al., 2008; Gardner et al., 2017). At Los Amigos, we have combined water
isotope and solute concentration data to document this link between hydrology and stream
chemistry — and, importantly, to evaluate its effect on nutrient loss from the forested ecosystem.
Figure 4.4. Schematic showing major sources of Ca in the Los Amigos terra firme system.
Squares are scaled to the size of the Ca reservoir (in kg). The boxplot shows the distribution of
exchangeable Ca concentrations in soil using data from Chadwick and Asner, 2016. The depth
on the boxplot is not to scale.
81
4.2. Leaky forests: stormflow and the nutrient balance of the Los Amigos ecosystem
The elevated Ca, Mg, K and PO4
3-
fluxes at high discharge imply substantial storm-driven
losses of these otherwise tightly-cycled elements from the Los Amigos ecosystem. Most of the
Los Amigos stream flow was derived from baseflow, even during the wet season: for the period
of January 19, 2019 – April 31, 2019, when continuous discharge data is available, the ratio of
stream baseflow to stormflow was approximately 73% for water flux (following the method of
Tang & Carey, 2017). The Ca flux associated with baseflow was 64% of the total stream Ca flux
during the January-April period, meaning that 36% of Ca was lost during storms, despite only
27% of water discharge occurring during storms. However, unlike baseflow fluxes, which we
infer to be derived primarily from groundwater and presumably from mineral weathering sources
at depth, we interpret a significant portion of the stormflow flux to be derived from shallow soils
(Fig. 4.4). We estimated the total riverine Ca flux per year to be 576 mg Ca m
-2
year
-1
, which is
consistent with other riverine Ca fluxes in Ecuador (Wilcke et al., 2017). Using the range of
calculated event water fractions (30 and 70% event water in the observed storms in 2017 and
2019, respectively), we estimate that ~11-25% of total stream Ca fluxes were leached from the
near surface. Applying these proportions to the estimated yearly Ca stream flux results in a total
surface soil-derived stream export flux of 610-1430 mg Ca m
-3
year
-1
. This loss compares to a
total inventory of surface soil exchangeable Ca of 7426 mg m
-3
in the top 10 cm of Los Amigos
soils, calculated across the catchment area based on previously published data from this site
(Chadwick and Asner, 2016). Surface soil-derived export fluxes of Ca therefore account for a
loss of 8-19% of the surface soil exchangeable Ca pool. For Mg, surface soil-derived export
fluxes also represent 8-19% of the surface soil exchangeable pool, and for K, 3-8%. Thus the
activation of shallow flow paths during high flow events, while not dominating the total stream
82
flux of rock-derived nutrients, may represents a key pathway for leakage of these nutrients from
this ecosystem.
The nutrient losses that we observe during storms at Los Amigos result from the marked
vertical and lateral stratification of nutrients in this intensely weathered system (Chadwick and
Asner, 2016, 2018). While globally-averaged concentration-discharge relationships for nutrient
elements are near chemostatic (Godsey et al., 2019), some storm events in other settings are also
characterized by increased stream water nutrient concentrations, similar to trends we observe at
Los Amigos, especially for biologically active elements (e.g., Knapp et al., 2020; Moatar et al.,
2017). Yet tropical forests on deeply weathered soils such as those at Los Amigos are
particularly characterized by the scarcity of rock-derived nutrients and their concentration in the
near surface, with the ecosystem largely separated from deeper mineral sources (Chadwick and
Asner, 2016). When storms drive the loss of these nutrients from the near surface in this setting,
they deplete a reservoir that is not as easily replenished as it may be in other systems that may
utilize a ready supply from bedrock weathering (e.g., Uhlig and von Blanckenburg, 2019)
— making the storm-driven loss particularly relevant for ecosystems in this setting. In this
regard, one interesting feature of our data is that the mobilization of P during storms is less
pronounced than that of Ca and Mg (Fig. 4.2). It is possible that this difference may be due to the
tighter binding of P in soils, e.g., associated with organics, occluded by mineral-association
(Walker and Syers, 1976) or biogeochemical cycling via P-acquisition strategies (Lambers et al.,
2006), making it less readily leached during stormflow.
Nutrients are also horizontally stratified across the landscape at Los Amigos, with greater
foliar concentrations in the incised valley bottoms than on the less-disturbed terrace surfaces
(Chadwick and Asner, 2018). These patterns could reflect the downslope transport of nutrient
83
elements from the terrace surfaces, though our results suggest that this transport is relatively
rapid and leads to streamwater export. The higher nutrient contents in valley bottoms could
alternatively result from more rock-derived nutrient availability due to erosion exposing fresher
material (as in Porder et al., 2006). Our hydrochemical data suggest another possibility, which is
that baseflow could deliver nutrients from rock weathering along groundwater flowpaths,
supplying these to the valley bottom ecosystems but not those on the terrace surfaces.
The loss of nutrients from the Los Amigos surface reservoir during storms may have
implications for the response of this forested ecosystem to climate change. Warming
temperatures are projected to intensify the water cycle, with extreme events becoming more
common. If increased storminess leads to greater loss of key limiting nutrients, it will reduce the
ecosystem net primary productivity and carbon sequestration capability of soils — pointing to
the importance of further work to understand the link between hydrology events and nutrient
availability in tropical environments.
Future related work might consider the role of antecedent conditions on event-scale
hydrochemistry and nutrient loss. Antecedent moisture can play an important role in determining
hydrological flow paths in tropical catchments (Muñoz-Villers & McDonnell, 2012), and a study
of 30 precipitation events across two years in a Swiss catchment showed that biologically-active
elements can exhibit considerable variation in hydrochemical behavior between storm events
(Knapp et al., 2020), perhaps related in part to varying pre-event conditions. Similarly extensive
high-frequency sampling across multiple storms was not possible as part of the present study, but
our results point to the need for research of this scale to understand the hydrological role in
nutrient cycling in tropical forests, and by extension, the response of these ecosystems to climate
change.
84
5. Conclusions
A small stream on a terra firme floodplain terrace in the southwestern Amazon basin was
sampled approximately twice-weekly from a period of May 2016-January 2019, with a five-day
high frequency sampling campaign carried out in February 2017, and two days in January 2019.
Samples were analyzed for major element concentrations and stable isotopes of water. Fluxes of
rock-derived nutrients Ca, Mg, K and PO4
3-
scale supralinearly to their fitted power-law
relationships at high discharge. Bio-inactive major solutes Na and Si scale according their power
law distribution at high discharge, meaning they do not have the same increased fluxes
associated with storms. Stable isotopes of water in streamflow show strong attenuation of the
seasonal cycle in
18
OH2O, consistent with baseflow that is sustained by older groundwater — but
during storm events, stream
18
OH2O deviated from the baseline by up to ~3‰ in response to
precipitation, reflecting event water fractions of 30-70%. These major element and stable isotope
data suggest a system where shallow flow paths dominate during storms, flushing key nutrients
such as calcium and magnesium from the surface soil and exporting these via streams. This
hydrological role in nutrient loss may be a fundamental link between ecosystems and the climate
conditions that determine rainfall amounts and intensity, as well as the surface and subsurface
properties that control fluid flow, such as permeability, porosity, and hydraulic gradient (Hale et
al., 2016; Hale & McDonnell, 2016; Muñoz-Villers & McDonnell, 2012). These links merit
greater attention in future work, with an emphasis on high frequency hydrochemical sampling to
capture dynamics of nutrient losses not revealed by routine (e.g., weekly or less frequent)
datasets. Short-term hydrological events may play more important roles in linking climate,
hydrology, and ecosystems than is widely recognized.
85
Acknowledgements
This work was funded by NSF award EAR-1455352 to AJW. We thank Alex Sessions
and Fenfang Wu at Caltech, Markus Bill at Lawrence Berkeley National Lab and Fernando Silva
and Greg Goldsmith at Chapman University for support with the stable isotope measurements.
We thank the Amazon Conservation Association and Erick Vargas for support in the field. We
thank the staff at the Los Amigos Biological Station for field support and precipitation data
collection.
86
References
Asner, G. P., Anderson, C. B., Martin, R. E., Tupayachi, R., Knapp, D. E., & Sinca, F. (2015).
Landscape biogeochemistry reflected in shifting distributions of chemical traits in the
Amazon forest canopy. Nature Geoscience, 8(7), 567–573.
https://doi.org/10.1038/ngeo2443
Baillie, I. C., Ashton, P. S., Court, M. N., Anderson, J. A. R., Fitzpatrick, E. A., & Tinsley, J.
(1987). Site Characteristics and the Distribution of Tree Species in Mixed Dipterocarp
Forest on Tertiary Sediments in Central Sarawak, Malaysia. Journal of Tropical Ecology,
3(3), 201–220.
Birch, A., Stallard, R., Bush, S., & Barnard, H. (2021). The influence of land cover and storm
magnitude on hydrologic flowpath activation and runoff generation in steep tropical
catchments of central Panama. Journal of Hydrology, 596, 126138.
https://doi.org/10.1016/j.jhydrol.2021.126138
Bol, R., Julich, D., Brödlin, D., Siemens, J., Kaiser, K., Dippold, M. A., Spielvogel, S., Zilla, T.,
Mewes, D., Blanckenburg, F. von, Puhlmann, H., Holzmann, S., Weiler, M., Amelung,
W., Lang, F., Kuzyakov, Y., Feger, K.-H., Gottselig, N., Klumpp, E., … Hagedorn, F.
(2016). Dissolved and colloidal phosphorus fluxes in forest ecosystems—An almost blind
spot in ecosystem research. Journal of Plant Nutrition and Soil Science, 179(4), 425–438.
https://doi.org/10.1002/jpln.201600079
Bormann, F. H., & Likens, G. E. (1967). Nutrient Cycling. Science, 155(3761), 424–429.
Boy, J., Valarezo, C., & Wilcke, W. (2008). Water flow paths in soil control element exports in
an Andean tropical montane forest. European Journal of Soil Science, 59(6), 1209–1227.
https://doi.org/10.1111/j.1365-2389.2008.01063.x
Burns, D. A., McDonnell, J. J., Hooper, R. P., Peters, N. E., Freer, J. E., Kendall, C., & Beven,
K. (2001). Quantifying contributions to storm runoff through end-member mixing
analysis and hydrologic measurements at the Panola Mountain Research Watershed
(Georgia, USA). Hydrological Processes, 15(10), 1903–1924.
https://doi.org/10.1002/hyp.246
Calmels, D., Galy, A., Hovius, N., Bickle, M., West, A. J., Chen, M.-C., & Chapman, H. (2011).
Contribution of deep groundwater to the weathering budget in a rapidly eroding mountain
belt, Taiwan. Earth and Planetary Science Letters, 303(1–2), 48–58.
https://doi.org/10.1016/j.epsl.2010.12.032
Chadwick, K. D., & Asner, G. P. (2016). Tropical soil nutrient distributions determined by biotic
and hillslope processes. Biogeochemistry, 127(2–3), 273–289.
https://doi.org/10.1007/s10533-015-0179-z
Chadwick, K. D., & Asner, G. P. (2018). Landscape evolution and nutrient rejuvenation reflected
in Amazon forest canopy chemistry. Ecology Letters, 21(7), 978–988.
https://doi.org/10.1111/ele.12963
87
Chadwick, O. A., Gavenda, R. T., Kelly, E. F., Ziegler, K., Olson, C. G., Elliott, W. C., &
Hendricks, D. M. (2003). The impact of climate on the biogeochemical functioning of
volcanic soils. Chemical Geology, 202(3–4), 195–223.
https://doi.org/10.1016/j.chemgeo.2002.09.001
Cheng, Y., Ogden, F. L., & Zhu, J. (2017). Earthworms and tree roots: A model study of the
effect of preferential flow paths on runoff generation and groundwater recharge in steep,
saprolitic, tropical lowland catchments. Water Resources Research, 53(7), 5400–5419.
https://doi.org/10.1002/2016WR020258
Cheng, Y., Ogden, F. L., Zhu, J., & Bretfeld, M. (2018). Land Use-Dependent Preferential Flow
Paths Affect Hydrological Response of Steep Tropical Lowland Catchments With
Saprolitic Soils. Water Resources Research, 54(8), 5551–5566.
https://doi.org/10.1029/2017WR021875
Clark, K. E., Hilton, R. G., West, A. J., Malhi, Y., Gröcke, D. R., Bryant, C. L., Ascough, P. L.,
Caceres, A. R., & New, M. (2013). New views on “old” carbon in the Amazon River:
Insight from the source of organic carbon eroded from the Peruvian Andes.
Geochemistry, Geophysics, Geosystems, 14(5), 1644–1659.
https://doi.org/10.1002/ggge.20122
Cox, P. M., Pearson, D., Booth, B. B., Friedlingstein, P., Huntingford, C., Jones, C. D., & Luke,
C. M. (2013). Sensitivity of tropical carbon to climate change constrained by carbon
dioxide variability. Nature, 494(7437), 341–344. https://doi.org/10.1038/nature11882
Cuevas, E., & Medina, E. (1988). Nutrient dynamics within amazonian forests: II. Fine root
growth, nutrient availability and leaf litter decomposition. Oecologia, 76(2), 222–235.
https://doi.org/10.1007/BF00379956
Du, E., Terrer, C., Pellegrini, A. F. A., Ahlström, A., van Lissa, C. J., Zhao, X., Xia, N., Wu, X.,
& Jackson, R. B. (2020). Global patterns of terrestrial nitrogen and phosphorus
limitation. Nature Geoscience, 13(3), 221–226. https://doi.org/10.1038/s41561-019-
0530-4
Edwards, P. J. (1982). Studies of Mineral Cycling in a Montane Rain Forest in New Guinea: V.
Rates of Cycling in Throughfall and Litter Fall. The Journal of Ecology, 70(3), 807.
https://doi.org/10.2307/2260106
Elsenbeer, H. (2001). Hydrologic flowpaths in tropical rainforest soilscapes—A review.
Hydrological Processes, 15(10), 1751–1759. https://doi.org/10.1002/hyp.237
Elsenbeer, H., & Lack, A. (1996). Hydrometric and hydrochemical evidence for fast flowpaths at
La Cuenca, Western Amazonia. Journal of Hydrology, 14.
Elser, J. J., Bracken, M. E. S., Cleland, E. E., Gruner, D. S., Harpole, W. S., Hillebrand, H.,
Ngai, J. T., Seabloom, E. W., Shurin, J. B., & Smith, J. E. (2007). Global analysis of
nitrogen and phosphorus limitation of primary producers in freshwater, marine and
terrestrial ecosystems. Ecology Letters, 10(12), 1135–1142.
https://doi.org/10.1111/j.1461-0248.2007.01113.x
Fekete, B. M., Vörösmarty, C. J., & Grabs, W. (2002). High-resolution fields of global runoff
combining observed river discharge and simulated water balances. Global
Biogeochemical Cycles, 16(3), 15-1-15–10. https://doi.org/10.1029/1999GB001254
88
Fisher, J. B., Badgley, G., & Blyth, E. (2012). Global nutrient limitation in terrestrial vegetation.
Global Biogeochemical Cycles, 26(3). https://doi.org/10.1029/2011GB004252
Fisher, J. B., Perakalapudi, N. V., Turner, B. L., Schimel, D. S., & Cusack, D. F. (2020).
Competing effects of soil fertility and toxicity on tropical greening. Scientific Reports,
10(1), 6725. https://doi.org/10.1038/s41598-020-63589-1
Frazar, S., Gold, A. J., Addy, K., Moatar, F., Birgand, F., Schroth, A. W., Kellogg, D. Q., &
Pradhanang, S. M. (2019). Contrasting behavior of nitrate and phosphate flux from high
flow events on small agricultural and urban watersheds. Biogeochemistry, 145(1–2), 141–
160. https://doi.org/10.1007/s10533-019-00596-z
Gardner, C. B., Litt, G. F., Lyons, W. B., & Ogden, F. L. (2017). Evidence for the Activation of
Shallow Preferential Flow Paths in a Tropical Panama Watershed Using Germanium and
Silicon. Water Resources Research, 53(10), 8533–8553.
https://doi.org/10.1002/2017WR020429
Genereux, D. P., & Hooper, R. P. (1998). Chapter 10—Oxygen and Hydrogen Isotopes in
Rainfall-Runoff Studies. In C. Kendall & J. J. McDONNELL (Eds.), Isotope Tracers in
Catchment Hydrology (pp. 319–346). Elsevier. https://doi.org/10.1016/B978-0-444-
81546-0.50017-3
Godsey, S. E., Hartmann, J., & Kirchner, J. W. (2019). Catchment chemostasis revisited: Water
quality responds differently to variations in weather and climate. Hydrological Processes,
33(24), 3056–3069. https://doi.org/10.1002/hyp.13554
Hale, V. C., & McDonnell, J. J. (2016). Effect of bedrock permeability on stream base flow
mean transit time scaling relations: 1. A multiscale catchment intercomparison. Water
Resources Research, 52(2), 1358–1374. https://doi.org/10.1002/2014WR016124
Hale, V. C., McDonnell, J. J., Stewart, M. K., Solomon, D. K., Doolitte, J., Ice, G. G., & Pack,
R. T. (2016). Effect of bedrock permeability on stream base flow mean transit time
scaling relationships: 2. Process study of storage and release. Water Resources Research,
52(2), 1375–1397. https://doi.org/10.1002/2015WR017660
Heartsill Scalley, T., Scatena, F. N., Moya, S., & Lugo, A. E. (2012). Long-term dynamics of
organic matter and elements exported as coarse particulates from two Caribbean montane
watersheds. Journal of Tropical Ecology, 28(2), 127–139.
https://doi.org/10.1017/S0266467411000733
Heathwaite, A. L., & Dils, R. M. (2000). Characterising phosphorus loss in surface and
subsurface hydrological pathways. Science of The Total Environment, 251 –252, 523–538.
https://doi.org/10.1016/S0048-9697(00)00393-4
Jasechko, S., Kirchner, J. W., Welker, J. M., & McDonnell, J. J. (2016). Substantial proportion
of global streamflow less than three months old. Nature Geoscience, 9(2), 126–129.
https://doi.org/10.1038/ngeo2636
Kennedy, V. C. (1971). Silica Variation in Stream Water with Time and Discharge. In
Nonequilibrium Systems in Natural Water Chemistry (Vol. 106, pp. 94–130). American
Chemical Society. https://doi.org/10.1021/ba-1971-0106.ch004
89
Kirchner, J. W. (2016a). Aggregation in environmental systems – Part 1: Seasonal tracer cycles
quantify young water fractions, but not mean transit times, in spatially heterogeneous
catchments. Hydrology and Earth System Sciences, 20(1), 279–297.
https://doi.org/10.5194/hess-20-279-2016
Kirchner, J. W. (2016b). Aggregation in environmental systems – Part 2: Catchment mean transit
times and young water fractions under hydrologic nonstationarity. Hydrology and Earth
System Sciences, 20(1), 299–328. https://doi.org/10.5194/hess-20-299-2016
Knapp, J. L. A., von Freyberg, J., Studer, B., Kiewiet, L., & Kirchner, J. W. (2020).
Concentration–discharge relationships vary among hydrological events, reflecting
differences in event characteristics. Hydrology and Earth System Sciences, 24(5), 2561–
2576. https://doi.org/10.5194/hess-24-2561-2020
Lambers, H., Shane, M. W., Cramer, M. D., Pearse, S. J., & Veneklaas, E. J. (2006). Root
Structure and Functioning for Efficient Acquisition of Phosphorus: Matching
Morphological and Physiological Traits. Annals of Botany, 98(4), 693–713.
https://doi.org/10.1093/aob/mcl114
Latrubesse, E. M., Cozzuol, M., da Silva-Caminha, S. A. F., Rigsby, C. A., Absy, M. L., &
Jaramillo, C. (2010). The Late Miocene paleogeography of the Amazon Basin and the
evolution of the Amazon River system. Earth-Science Reviews, 99(3–4), 99–124.
https://doi.org/10.1016/j.earscirev.2010.02.005
Leys, B. A., Likens, G. E., Johnson, C. E., Craine, J. M., Lacroix, B., & McLauchlan, K. K.
(2016). Natural and anthropogenic drivers of calcium depletion in a northern forest
during the last millennium. Proceedings of the National Academy of Sciences, 113(25),
6934–6938. https://doi.org/10.1073/pnas.1604909113
Maher, K. (2011). The role of fluid residence time and topographic scales in determining
chemical fluxes from landscapes. Earth and Planetary Science Letters, 312(1–2), 48–58.
https://doi.org/10.1016/j.epsl.2011.09.040
Malhi, Y. (2012). The productivity, metabolism and carbon cycle of tropical forest vegetation.
Journal of Ecology, 100(1), 65–75. https://doi.org/10.1111/j.1365-2745.2011.01916.x
Malhi, Y., Baker, T. R., Phillips, O. L., Almeida, S., Alvarez, E., Arroyo, L., Chave, J.,
Czimczik, C. I., Fiore, A. D., Higuchi, N., Killeen, T. J., Laurance, S. G., Laurance, W.
F., Lewis, S. L., Montoya, L. M. M., Monteagudo, A., Neill, D. A., Vargas, P. N., Patiño,
S., … Lloyd, J. (2004). The above-ground coarse wood productivity of 104 Neotropical
forest plots. Global Change Biology, 10(5), 563–591. https://doi.org/10.1111/j.1529-
8817.2003.00778.x
Marinos, R. E., Campbell, J. L., Driscoll, C. T., Likens, G. E., McDowell, W. H., Rosi, E. J.,
Rustad, L. E., & Bernhardt, E. S. (2018). Give and Take: A Watershed Acid Rain
Mitigation Experiment Increases Baseflow Nitrogen Retention but Increases Stormflow
Nitrogen Export. Environmental Science & Technology, 52(22), 13155–13165.
https://doi.org/10.1021/acs.est.8b03553
Meybeck, M. (1987). Global chemical weathering of surficial rocks estimated from river
dissolved loads. American Journal of Science, 287, 401–428.
90
Meybeck, M., Laroche, L., Dürr, H. H., & Syvitski, J. P. M. (2003). Global variability of daily
total suspended solids and their fluxes in rivers. Global and Planetary Change, 39(1),
65–93. https://doi.org/10.1016/S0921-8181(03)00018-3
Moatar, F., Abbott, B. W., Minaudo, C., Curie, F., & Pinay, G. (2017). Elemental properties,
hydrology, and biology interact to shape concentration-discharge curves for carbon,
nutrients, sediment, and major ions. Water Resources Research, 53(2), 1270–1287.
https://doi.org/10.1002/2016WR019635
Muñoz-Villers, L. E., & McDonnell, J. J. (2012). Runoff generation in a steep, tropical montane
cloud forest catchment on permeable volcanic substrate. Water Resources Research,
48(9). https://doi.org/10.1029/2011WR011316
Odum, E. P. (1969). The Strategy of Ecosystem Development. Science, 164(3877), 262–270.
https://doi.org/10.1126/science.164.3877.262
Ogden, F. L., Crouch, T. D., Stallard, R. F., & Hall, J. S. (2013). Effect of land cover and use on
dry season river runoff, runoff efficiency, and peak storm runoff in the seasonal tropics of
Central Panama. Water Resources Research, 49(12), 8443–8462.
https://doi.org/10.1002/2013WR013956
Okin, G. S., Mahowald, N., Chadwick, O. A., & Artaxo, P. (2004). Impact of desert dust on the
biogeochemistry of phosphorus in terrestrial ecosystems. Global Biogeochemical Cycles,
18(2). https://doi.org/10.1029/2003GB002145
Porder, S., & Chadwick, O. A. (2009). Climate and soil-age constraints on nutrient uplift and
retention by plants. Ecology, 90(3), 623–636. https://doi.org/10.1890/07-1739.1
Quesada, C. A., Lloyd, J., Anderson, L. O., Fyllas, N. M., Schwarz, M., & Czimczik, C. I.
(2011). Soils of Amazonia with particular reference to the RAINFOR sites.
Biogeosciences, 8(6), 1415–1440. https://doi.org/10.5194/bg-8-1415-2011
Raymond, P. A., & Saiers, J. E. (2010). Event controlled DOC export from forested watersheds.
Biogeochemistry, 100(1), 197–209. https://doi.org/10.1007/s10533-010-9416-7
Rigsby, C. A., Hemric, E. M., & Baker, P. A. (2009). Late Quaternary Paleohydrology of the
Madre de Dios River, southwestern Amazon Basin, Peru. Geomorphology, 113(3–4),
158–172. https://doi.org/10.1016/j.geomorph.2008.11.017
Sohrt, J., Uhlig, D., Kaiser, K., von Blanckenburg, F., Siemens, J., Seeger, S., Frick, D. A.,
Krüger, J., Lang, F., & Weiler, M. (2019). Phosphorus Fluxes in a Temperate Forested
Watershed: Canopy Leaching, Runoff Sources, and In-Stream Transformation. Frontiers
in Forests and Global Change, 0. https://doi.org/10.3389/ffgc.2019.00085
Swap, R., Garstang, M., Greco, S., Talbot, R., & Kållberg, P. (1992). Saharan dust in the
Amazon Basin. Tellus B, 44(2), 133–149. https://doi.org/10.1034/j.1600-0889.1992.t01-
1-00005.x
Tang, W., & Carey, S. K. (2017). HydRun: A MATLAB toolbox for rainfall-runoff analysis.
Hydrological Processes, 31(15), 2670–2682. https://doi.org/10.1002/hyp.11185
Trenberth, K. (2011). Changes in precipitation with climate change. Climate Research, 47(1),
123–138. https://doi.org/10.3354/cr00953
91
Uhlig, D., & von Blanckenburg, F. (2019). How Slow Rock Weathering Balances Nutrient Loss
During Fast Forest Floor Turnover in Montane, Temperate Forest Ecosystems. Frontiers
in Earth Science, 0. https://doi.org/10.3389/feart.2019.00159
Vaughan, M. C. H., Bowden, W. B., Shanley, J. B., Vermilyea, A., Sleeper, R., Gold, A. J.,
Pradhanang, S. M., Inamdar, S. P., Levia, D. F., Andres, A. S., Birgand, F., & Schroth, A.
W. (2017). High-frequency dissolved organic carbon and nitrate measurements reveal
differences in storm hysteresis and loading in relation to land cover and seasonality.
Water Resources Research, 53(7), 5345–5363. https://doi.org/10.1002/2017WR020491
Vitousek, P. M., & Farrington, H. (1997). Nutrient limitation and soil development:
Experimental test of a biogeochemical theory. Biogeochemistry, 37, 13.
Vitousek, P. M., & Sanford, R. L. (1986). Nutrient Cycling in Moist Tropical Forest. Annual
Review of Ecology and Systematics, 17, 137–167.
Walker, T. W., & Syers, J. K. (1976). The fate of phosphorus during pedogenesis. Geoderma,
15(1), 1–19. https://doi.org/10.1016/0016-7061(76)90066-5
Wilcke, W., Velescu, A., Leimer, S., Bigalke, M., Boy, J., & Valarezo, C. (2017). Biological
versus geochemical control and environmental change drivers of the base metal budgets
of a tropical montane forest in Ecuador during 15 years. Biogeochemistry, 136(2), 167–
189. https://doi.org/10.1007/s10533-017-0386-x
Wilcke, W., Yasin, S., Abramowski, U., Valarezo, C., & Zech, W. (2002). Nutrient storage and
turnover in organic layers under tropical montane rain forest in Ecuador: Nutrient storage
and turnover in montane forest. European Journal of Soil Science, 53(1), 15–27.
https://doi.org/10.1046/j.1365-2389.2002.00411.x
Wright, S. J., Yavitt, J. B., Wurzburger, N., Turner, B. L., Tanner, E. V. J., Sayer, E. J., Santiago,
L. S., Kaspari, M., Hedin, L. O., Harms, K. E., Garcia, M. N., & Corre, M. D. (2011).
Potassium, phosphorus, or nitrogen limit root allocation, tree growth, or litter production
in a lowland tropical forest. Ecology, 92(8), 1616–1625. https://doi.org/10.1890/10-
1558.1
92
Chapter 5: Trees use young water in an Andean tropical montane
cloud forest
Contributors: Roxanne M. Cruz-de Hoyos, Adan Julian Ccahuana Quispe, Gregory R.
Goldsmith, A. Joshua West
Opening statement
Chapters 2-4 utilized river chemistry to understand streamflow generation and
(bio)geochemical processes. In this chapter, I explore the movement of water through landscapes
from a different perspective: by studying plant xylem waters. Extracting the water within plant
xylem provides an opportunity to explore the origins of water that supplies transpiration, an
important component of the water cycle.
This work was conceptualized by myself, Joshua West, Roxanne Cruz-de Hoyos and
Adan Julian Ccahuana Quispe. Adan Julian Ccahuana Quispe identified the plants sampled in
this study. Adan Julian Ccahuana Quispe and myself collected all of the samples in this study. I
extracted and analyzed all of the plant xylem samples with support from the staff at the Berkeley
Center for Stable Isotope Biogeochemistry. I performed the water isotope analyses with help
from Gregory Goldsmith. Gregory Goldsmith and myself reduced the water isotope data. I
carried out the data analysis, wrote the code for the young water fraction Monte Carlo
simulation, made the figures and wrote the manuscript. Gregory Goldsmith provided helpful
guidance on some of the figures.
93
Abstract
Transpiration is responsible for a significant portion of precipitation recycling over land.
Efforts to understand the origins of water that sustains plant transpiration show that plants often
transpire water stored within landscapes for months or more. Determining the age and origin of
water that sustains plant transpiration is important for assessing ecosystem resiliency to drought
and understanding how water moves within landscapes. We collected plant xylem samples twice
a month from February 2018-August 2018, and stream and soil water samples twice a month
from April 2016-May 2019 in a tropical montane cloud forest watershed. All samples were
analyzed for
18
OH2O and young water fractions (Fyw) calculated. We show that the water
sustaining plant transpiration is much younger (Fyw = 27%) than the water sustaining streamflow
(Fyw = 9%). We do not observe a significant seasonal carryover in the sources of water sustaining
transpiration: during the dry season, plants transpire mostly water from dry season precipitation.
During the wet season, plants transpire mostly water from wet season precipitation. This is in
direct contrast to observations in temperate and Mediterranean climates, where plants transpire
water that has been in the landscape for months or longer. The results presented here advance our
understanding of water that sustains transpiration in humid tropical environments.
1. Introduction
Transpiration comprises a majority of global evapotranspiration fluxes over land (Fatichi
& Pappas, 2017; Jasechko et al., 2013) and leads to recycling of nearly 40% of precipitation over
land (Schlesinger & Jasechko, 2014). Despite the important role of transpiration in the global
hydrologic cycle, there are open questions about the sources of water that sustain transpiration.
Previous studies have demonstrated that transpiration can be sustained by water stored within
94
landscapes for months or longer (Allen et al., 2019), and that seasonally dry landscapes rely
more heavily upon stored winter precipitation to feed summer transpiration (Goldsmith et al.,
2022). Transpiration and streamflow can be sustained by different reservoirs of water (Brooks et
al., 2010; Dawson & Ehleringer, 1991; Evaristo et al., 2015), raising questions about how
precipitation entering a watershed will be partitioned between streamflow and evapotranspiration
(Kirchner & Allen, 2020). In a changing climate with more frequent storms and droughts,
understanding how much recent precipitation plants rely on for transpiration is important for
assessing ecosystem resilience.
Previous work has shed light on the multiple factors that influence plant water use,
including plant water acquisition strategies and climate. Studies in Mediterranean climates have
shown that plants can use deep rooting strategies to access groundwater for transpiration
(McCormick et al., 2021; Rempe & Dietrich, 2018). Other research has observed niche
hydrologic partitioning to minimize inter-species competition for water (Brum et al., 2017,
2019), and that wetter ecosystems have more hydrologic niche overlap than drier ecosystems
(Guo et al., 2018). In wet tropical ecosystems, shallow root networks are common, and plants
have been shown to use shallow soil water (Goldsmith et al., 2012). Despite the understanding
that plants often transpire water that has been stored within the landscape for months or longer,
we lack a systematic exploration of plant water use across temporal scales, especially in tropical
environments where shallow root networks prevail.
In this study, we explore seasonal variability in sources of water that sustain transpiration
and streamflow in a tropical montane cloud forest. We use stable isotopes of water to estimate
how much transpiration and streamflow is comprised of recent precipitation (Kirchner, 2016a,
2016b). We explore the potential for seasonal retention of water within landscapes, and address
95
if streamflow and transpiration are supplied by the same source of water. Many studies of plant-
water relations focus on one point in time: we measure the plant water isotope composition
across the transition from wet to dry seasons, allowing a more detailed understanding of how
transpiration water sources can change over time.
2. Study site & sampling
This study was carried out in a small watershed near the Amazon Conservation
Association Wayqecha Biological Station (S 13.175130, W 71.587239) in the southeastern
Peruvian Andes mountains (Fig. 5.1). The watershed is located within tropical montane cloud
forest, at an elevation of 3077 m (-13.192550, -71.587950). The watershed has an area of 0.242
km
2
and mean slope of 33.8 . The dominant lithology is the San José Group shale (c/o data from
INGEMMET). Stream, soil water and precipitation samples were collected approximately every
two weeks starting in April 2016 and continuing until March 2020. Plants were sampled
approximately every two weeks between February 2018 and March 2020.
96
Figure 5.1. (A) Map of South America, red circle shows approximate location of study site. (B)
Google Earth imagery of the study watershed, outlined in red. The red star shows the location of
the plant sampling, the red triangle shows stream sampling location and the red squares show the
location of the two soil water lysimeters.
2.1. Plant sampling
We sampled trees from two of the most common genera in the region: Weinmannia
(which comprises 16% of forest woody stems) and Clusia (13% of woody stems) (Rapp et al.,
2012). On each sampling trip, we collected one branch from one individual tree of three different
species: Weinmannia bangii, Weinmannia reticulata and Clusia alata. Although we did not
sample the same three trees every trip (to preserve the integrity of the crown of the tree), we
sampled trees of roughly the same stature in close proximity to each other. Each sample was
taken from the crown of the tree via a canopy bridge, located along the side of valley and within
~100 m of a stream (Fig. 5.1, 5.2). A mature branch was sampled at least 10 cm away from
leaves (Fig. 5.3A), bark removed, and the pieces placed into a tightly sealed vial immediately
wrapped with parafilm. The samples were frozen upon return from the field (<24 hrs) and kept
frozen until ready for analysis.
97
2.2. Hydrochemical sampling & monitoring
Soil water samples were collected from two suction lysimeters, each approximately 1 m
deep. One lysimeter was placed near the stream, in the valley bottom (referred to as soil water A)
and one lysimeter was placed near the plant sampling location, on the hillslope (referred to as
soil water B). Stream and soil water samples were filtered with a 0.2 m diameter
polyethersulfone filter in the field and stored in glass vials with a PolyCone seal lid (Fisher
Brand). Precipitation samples were collected over a two-week period, in a bucket with a layer of
vegetable oil to prevent evaporation loss. During each sampling trip, precipitation samples were
collected in glass vials with a PolyCone seal lid, the collection bucket was emptied, and the oil
layer replaced. Precipitation samples were filtered in the lab before analysis to remove any
particulates. Cloud water isotope data are from Clark et al., 2014. Precipitation amount data were
collected using a tipping bucket gauge maintained by the Andes Biodiversity and Ecosystem
Research Group. Stream water level was logged every 15 min using a WL16U Water Level
Logger from Global Water starting in January 2019.
98
Figure 5.2. Top photo: the study watershed, as seen from the canopy bridge, overlooking the
valley bottom where the stream is located. Bottom photo: perspective from the canopy tower,
looking at one side of the valley. The other canopy tower on the left side of the photo is where
the plants were sampled for this study.
99
3. Analytical methods
Plant xylem waters were extracted via cryogenic vacuum distillation (Fig. 5.3B, C) and
analyzed for
18
O and D via Isotope Ratio Mass Spectrometry at the Berkeley Center for Stable
Isotope Biogeochemistry. Stream, soil water, and precipitation were analyzed for
18
O and D
via two Los Gatos Research Liquid Water Isotope Analyzers (LGR) (Caltech and Lawrence
Berkeley National Lab) and a Picarro L2130i Cavity Ring Down Spectrometer (Chapman
University). On the Picarro, the long-term standard deviation of an independent quality control
sample is 0.11‰ for
18
O and 0.51‰ for δD.. On the LGR at Lawrence Berkeley National Lab
the internal error was 0.1 ‰ or better for
18
O and 1 ‰ or better for D. On the LGR at Caltech
the internal error was 0.3 ‰ or better for
18
O and 1 ‰ or better for D. Long-term accuracy on
certified isotope standards was within one standard deviation of the known isotopic values. All
values are represented as ‰ relative to V-SMOW.
Figure 5.3. (A) Plant stem sample from a mature tree (Clusia alata). (B) Plant xylem sample in
the lab, bark removed and prepared for cryogenic extraction. (C) Cryogenic extraction set up: the
plant sample is in boiling water on the right and a cold trap is on the left, where the xylem water
accumulates.
100
To account for the isotopic effects of evaporation, plants xylem water isotopes (
18
O and
D) were corrected back to their source waters using the method described in Benettin et al.,
2018. Water isotope evaporation lines were calculated for each month using long-term average
relative humidity and temperature.
Deuterium-excess (Dxs) was calculated for each sample as:
Dxs = D − 8 × O
18
(1)
We calculated young water fractions following the method of Kirchner (2016a, 2016b) for each
of the sampled reservoirs of water in the system: plant, soil, and stream. Data from each
reservoir, as well as precipitation oxygen isotope data, were fit as:
𝐶 (𝑡 ) = 𝑎 𝑟 × 𝑐𝑜𝑠 (2𝜋𝑓𝑡 ) + 𝑏 𝑟 × 𝑠𝑖𝑛 (2𝜋𝑓𝑡 ) + 𝑘 (2)
where C is the concentration of a tracer in the reservoir (r), t is time, f is the frequency of the
interval, a and b are the cosine and sine coefficients and k is the vertical shift. The fit to reservoir
and precipitation isotope data was performed with and without stream discharge and rainfall
amount weighting. The young water fraction was then calculated as:
𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 , 𝑂 18
𝑅𝑒𝑠𝑒𝑟𝑣𝑜𝑖𝑟 = √𝑎 𝑠 2
+ 𝑏 𝑠 2
(3)
𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 , 𝑂 18
𝑃𝑟𝑒𝑐𝑖𝑝 .
= √𝑎 𝑝 2
+ 𝑏 𝑝 2
(4)
𝑌𝑜𝑢𝑛𝑔 𝑤𝑎𝑡𝑒𝑟 𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 (%) = 𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 , 𝑂 18
𝑅𝑒𝑠𝑒𝑟𝑣𝑜𝑖𝑟 /𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 , 𝑂 18
𝑃𝑟𝑒𝑐𝑖𝑝 .
(5)
A bootstrap resampling regime was performed to assess the uncertainty associated with
the young water fraction calculations, using random resampling with replacement to generate
10,000 isotope datasets for stream, soil, plant and precipitation, and then applying equations
(2−5) to each dataset.
101
We also calculated the seasonal origin index for each sampled reservoir in the system,
following Allen et al., (2019) as:
SOI =
( q − P)
( Ps − P)
if q > P (6)
SOI =
( q − P)
( P − PW)
if q < P (7)
Where q is the oxygen isotope composition of individual streamflow samples, P is the volume-
weighted average precipitation oxygen isotope composition, Ps is the average summer
precipitation oxygen isotope composition and PW is the average winter precipitation oxygen
isotope composition. SOI near -1.0 indicates the water is mostly comprised of wet-season
precipitation; a value of +1.0 indicates the water is mostly comprised of dry season precipitation.
4. Results
Precipitation isotope ratios ranged from -20.1 to -0.4 ‰ for
18
O and -153.1 to 17.7 ‰
for D and were used to calculate the local meteoric water line, which had a slope of 8.518 and
an intercept of 20.7351 (Fig. 5.4). Cloud waters from Clark et. al, (2014) are isotopically
depleted compared to the local meteoric water line. Stream water isotope ratios ranged from -
14.5 to -12.3 ‰ for
18
O and -97.2 to -84.5 ‰ for D. Most stream water samples plot along the
local meteoric water line, with only a few to the left of the local meteoric water line, indicating a
minimal contribution of cloud water to the stream. Soil water isotope ratios ranged from -18.0 to
-7.9 for
18
O and -129.2 to -47.4 ‰ for D. Plant water isotope ratios ranged from -14.8 to -4.6
for
18
O and –105.9 to -37.3 ‰ for D. Plant water isotope ratios were consistent with
precipitation or soil water isotopes, or demonstrated some evaporative enrichment. Given the
evaporative signal for some plant samples, we applied a local evaporation correction to the plant
102
water isotope values following the method of Benettin et al. (2018), to obtain the isotope values
of the source water that the plants took up. Once the plant waters were corrected back to their
source waters, the isotope ratios ranged from -15.5 to -8.4 for
18
O and –112.2 to -51.3 ‰ for
D. We use the local evaporation corrected plant water isotope values for the seasonal origin
index calculation and the young water fraction calculation.
Figure 5.4. Precipitation, stream, soil and plant water isotope ratios collected between 2016 and
2019 from the Wayqecha Biological Station in the Department of Cusco, Peru. Additional data
on cloud water isotope ratios are from Clark et al., 2014.
103
Figure 5.5. Timeseries of
18
OH2O for plant xylem waters (A), stream water (B), soil waters (C
and D) and a local regression of precipitation
18
O (all panels).
Precipitation
18
O displays a strong seasonal cycle (Fig. 5.5), with heavier
18
O values
during dry seasons (July-October) and lighter
18
O values during wet seasons (December-April).
The plant xylem waters (Fig. 5.5A) follow the precipitation isotope seasonal cycle, though are
slightly attenuated. Stream water
18
O (Fig. 5.5B) has a strongly attenuated signal with minimal
seasonal variability. Soil water A (Fig. 5.5C) shows little seasonal variability, while soil water B
shows slightly more seasonal variability (Fig. 5.5D).
104
Figure 5.6. Timeseries of deuterium-excess for plant xylem waters (A), stream water (B), soil
waters (C and D), and precipitation (all panels).
Precipitation has a weak seasonal signal in deuterium-excess (Fig. 5.6). Stream and soil
waters fall almost completely within the same range of deuterium-excess values as precipitation.
Plant xylem waters have deuterium-excess values up to ~20‰ more depleted than precipitation
(Fig. 5.6A). This is consistent with plants taking up water that has undergone some evaporation –
for this reason we have accounted for the effects of evaporation on the plant water isotope
values.
The seasonal origin index (SOI) was calculated for plant xylem waters, stream waters,
and soil waters to infer the seasonal origins of the water found in streamflow and taken up by
plants (Fig. 5.7). During the wet season, the SOI of plant xylem water was as low as -1.0, which
105
is consistent with plants taking up water largely comprised of wet season precipitation (Fig.
5.7A). In the wet to dry season transition, plant water has a SOI closer to zero, consistent with
water from a roughly equal mixture of wet and dry season precipitation. In the dry season, plant
waters have higher SOI, between 0 and 0.5, indicating that plants are taking up more dry season
precipitation than wet season precipitation. The significant changes in plant xylem water SOI are
consistent with a shift in the water supplying plant transpiration depending on the season and
environmental conditions.
Stream water SOI does not exceed 0.1, and typically is less than zero throughout the
course of the year, consistent with wet season precipitation supplying water found in the stream,
even during the dry season (Fig. 5.7B). Stream water SOI does not appear to vary systematically
between wet and dry seasons, which supports notion that stream water mostly consists of wet
season precipitation at any time of the year. Though the soil water SOI displays slightly more
variability than stream water SOI, it does not vary nearly as much as plant xylem water SOI (Fig.
5.7C).
106
Figure 5.7. Seasonal origin indices for plant xylem waters, stream waters, and soil waters. The
soil waters are from two different lysimeters, distinguished by upwards-facing and sideways-
facing triangles in different shades of green. Data are plotted versus the day of year they were
collected. SOI near +1 indicate the sample is predominantly comprised of water from dry season
precipitation; SOI near -1 indicate the sample is predominantly comprised of water from wet
season precipitation. Blue shading indicate the transition from wet season to dry season.
107
To assess if different plant species use waters from different seasonal origins, we plotted
the SOI of different plant species collected on the same dates (Fig. 5.8). Although data do not fall
exactly along the 1:1 line, there does not appear to be a systematic variation between plants of
different species. This supports the notion that these particular plant species do not access
different reservoirs of water, but rather that there is some heterogeneity within the water isotope
composition of the reservoirs that plants access.
Figure 5.8. Seasonal origin indices for the different plant species sampled on the same dates;
each point represents one sampling date. The dashed line shows 1:1 behavior.
Young water fractions (Fyw) were calculated for plant xylem, stream and soil water (Fig.
5.9). Plant xylem mean Fyw was 27%, median Fyw was 27% and the interquartile range (IQR) was
9%. Stream mean Fyw was 9%, median Fyw was 9% and the IQR was 5%. Soil water A (located
in the valley bottom riparian zone, see Fig. 5.2), had a mean Fyw of 7%, median Fyw of 6% and an
IQR of 6%. Soil water B (located on the hillslope near the plant sampling location, see Fig. 5.2)
had a mean Fyw of 16%, median Fyw of 15% and an IQR of 13%. The distribution of Fyw (Fig. 5.9)
shows that plants take up more recent precipitation than is found in streamflow and the pool of
water sampled with suction lysimeters.
108
Figure 5.9. Distribution of young water fractions for plant xylem water, stream water, and soil
water from two suction lysimeters, determined from a Monte Carlo simulation.
5. Discussion
5.1. Plants use recent rain in a tropical montane cloud forest
Plant xylem water displayed high mean Fyw (27%), consistent with taking up water from
a reservoir regularly replenished by recent rainfall. Given that water from 1 m deep lysimeters
had lower mean Fyw (7-16%) than plants, we infer that the sampled plants have a rooting depth of
less than 1 m. This is consistent with other work in tropical environments that showed tropical
trees relying on shallowly sourced soil water (Goldsmith et al., 2012).
18
Oxylem shows a slight
evaporative enrichment (Fig. 5.4), consistent with taking up water from a soil reservoir that has
undergone some evaporation. One potential mechanism that could explain the abundance of
shallow, frequently replenished water is the significant epiphyte biomass in the cloud forest.
Recent work has shown that epiphytes in this region may store up to 20 mm of water (Horwath et
al., 2019).
109
5.2. Streamflow and water used by plants display different seasonal dynamics
18
Ostream varies little with time, in direct contrast to
18
Oxylem which is highly dynamic
over the six month sampling period (Fig. 5.5). Stream water has a low Fyw (9%), consistent with
water taking long flow paths to the stream. Global studies suggest, somewhat counterintuitively,
that steep, mountainous catchments often have low Fyw due to long flow paths along deep rock
fractures (Jasechko et al., 2016). Additionally, this portion of the Andes mountains is underlain
by fractured shale rich in sulfide minerals (Torres et al., 2016), which react quickly to generate
porosity, enhancing the ability of rock to store water (Gu et al., 2020). Taken together, the low
Fyw and consistently negative seasonal origin index point to a streamflow regime dominated by
wet-season precipitation that is stored within the watershed over seasonal to interannual
timescales. This suggests a streamflow regime more resilient to drought and changes in
precipitation, while the surficial origins of water used by plants may leave trees more vulnerable
to hydrologic changes.
5.3. Common cloud forest trees do not utilize cloud water
18
Oxylem of plants sampled in this study fall to the right of the meteoric water line, and
are isotopically distinct from cloud water (Fig. 5.4). Other
18
Oxylem datasets from studies in the
same region do indicate that some species of trees utilize cloud water (Feakins et al., 2016).
However, without more detailed investigation it is unclear if the cloud water isotope signal
observed by Feakins et al. (2016) in
18
Oxylem resulted from direct foliar water uptake by plants,
or if the cloud water first entered the soil and was then taken up by plant roots.
110
Though foliar water uptake has been widely documented across the globe (Berry et al., 2019;
Eller et al., 2013), and may in some instances be beneficial to plants on the individual and
ecosystem scale (Dawson & Goldsmith, 2018), a thorough assessment of the role of foliar water
uptake in this TMCF ecosystem is not within the scope of this study.
6. Conclusions and future work
We measured the
18
O of plant xylem water, stream water, soil water and precipitation in
a tropical montane cloud forest and calculated the young water fractions and seasonal origin
indices for the different reservoirs of water. Plant xylem water displayed significantly higher Fyw
(27%) than stream water (9%) and soil water (7-16%). Plant xylem seasonal origin indices
changed significantly over the six month period for which xylem water data was available.
Stream and soil water indices did not vary significantly over the three year period for which data
was available. The high young water fraction and significantly changing seasonal origin indices
displayed by plants is indicative of transpiration supplied by water within shallow soils that is
consistently replenished with soil from recent rains. Streamflow, on the other hand, appears to be
more significantly sustained by water that fell as wet season precipitation, perhaps pointing to a
deeper groundwater source that is replenished during heavy wet-season rains. Given that plants
in this setting do not appear to access deeper sources of groundwater, this ecosystem may be
even more vulnerable to dry-season droughts than previously thought. Additionally, the common
genera of plants analyzed in this study do not appear to use cloud water to sustain transpiration,
though we are unable to say with certainty that cloud water is not an important component of
plant-water relations in this watershed. Exploring the role of cloud water in sustaining
transpiration and streamflow is a potential avenue for future work.
111
Acknowledgements
This work was funded by NSF award EAR-1455352 to AJW. We thank the Andes
Biodiversity and Ecosystem Research Group (ABERG) for access to rainfall data, collected with
support of NSF DEB LTREB 1754647 to Miles Silman. We thank Alex Sessions and Fenfang
Wu at Caltech, Markus Bill at Lawrence Berkeley National Lab and Fernando Silva at Chapman
University for support with the stable isotope measurements. We thank the Berkeley Center for
Stable Isotope Biogeochemistry for support with the plant xylem water extractions and isotopic
analysis of waters extracted from plants.
112
References
Allen, S. T., Kirchner, J. W., Braun, S., Siegwolf, R. T. W., & Goldsmith, G. R. (2019). Seasonal
origins of soil water used by trees. Hydrology and Earth System Sciences, 23(2), 1199–
1210. https://doi.org/10.5194/hess-23-1199-2019
Benettin, P., Volkmann, T. H. M., & Kirchner, J. W. (2018). Effects of climatic seasonality on
the isotopic composition of evaporating soil waters. Hydrol. Earth Syst. Sci., 11.
Berry, Z. C., Emery, N. C., Gotsch, S. G., & Goldsmith, G. R. (2019). Foliar water uptake:
Processes, pathways, and integration into plant water budgets. Plant, Cell &
Environment, 42(2), 410–423. https://doi.org/10.1111/pce.13439
Brooks, J. R., Barnard, H. R., Coulombe, R., & McDonnell, J. J. (2010). Ecohydrologic
separation of water between trees and streams in a Mediterranean climate. Nature
Geoscience, 3, 5.
Brum, M., Teodoro, G. S., Abrahão, A., & Oliveira, R. S. (2017). Coordination of rooting depth
and leaf hydraulic traits defines drought-related strategies in the campos rupestres, a
tropical montane biodiversity hotspot. Plant and Soil, 420(1–2), 467–480.
https://doi.org/10.1007/s11104-017-3330-x
Brum, M., Vadeboncoeur, M. A., Ivanov, V., Asbjornsen, H., Saleska, S., Alves, L. F., Penha,
D., Dias, J. D., Aragão, L. E. O. C., Barros, F., Bittencourt, P., Pereira, L., & Oliveira, R.
S. (2019). Hydrological niche segregation defines forest structure and drought tolerance
strategies in a seasonal Amazon forest. Journal of Ecology, 107(1), 318–333.
https://doi.org/10.1111/1365-2745.13022
Clark, K. E., Torres, M. A., West, A. J., Hilton, R. G., New, M., Horwath, A. B., Fisher, J. B.,
Rapp, J. M., Robles Caceres, A., & Malhi, Y. (2014). The hydrological regime of a
forested tropical Andean catchment. Hydrology and Earth System Sciences, 18(12),
5377–5397. https://doi.org/10.5194/hess-18-5377-2014
Dawson, T. E., & Ehleringer, J. R. (1991). Streamside trees that do not use stream water. Nature,
350.
Dawson, T. E., & Goldsmith, G. R. (2018). The value of wet leaves. New Phytologist, 219(4),
1156–1169. https://doi.org/10.1111/nph.15307
Eller, C. B., Lima, A. L., & Oliveira, R. S. (2013). Foliar uptake of fog water and transport
belowground alleviates drought effects in the cloud forest tree species, D. rimys
brasiliensis (W. interaceae). New Phytologist, 199(1), 151–162.
https://doi.org/10.1111/nph.12248
Evaristo, J., Jasechko, S., & McDonnell, J. J. (2015). Global separation of plant transpiration
from groundwater and streamflow. Nature, 525(7567), 91–94.
https://doi.org/10.1038/nature14983
113
Fatichi, S., & Pappas, C. (2017). Constrained variability of modeled T: ET ratio across biomes:
Transpiration: Evapotranspiration Ratio. Geophysical Research Letters, 44(13), 6795–
6803. https://doi.org/10.1002/2017GL074041
Feakins, S. J., Bentley, L. P., Salinas, N., Shenkin, A., Blonder, B., Goldsmith, G. R., Ponton, C.,
Arvin, L. J., Wu, M. S., Peters, T., West, A. J., Martin, R. E., Enquist, B. J., Asner, G. P.,
& Malhi, Y. (2016). Plant leaf wax biomarkers capture gradients in hydrogen isotopes of
precipitation from the Andes and Amazon. Geochimica et Cosmochimica Acta, 182, 155–
172. https://doi.org/10.1016/j.gca.2016.03.018
Goldsmith, G. R., Allen, S. T., Braun, S., Siegwolf, R. T. W., & Kirchner, J. W. (2022). Climatic
Influences on Summer Use of Winter Precipitation by Trees. Geophysical Research
Letters, 49(10). https://doi.org/10.1029/2022GL098323
Goldsmith, G. R., Muñoz‐Villers, L. E., Holwerda, F., McDonnell, J. J., Asbjornsen, H., &
Dawson, T. E. (2012). Stable isotopes reveal linkages among ecohydrological processes
in a seasonally dry tropical montane cloud forest. Ecohydrology, 5(6), 779–790.
https://doi.org/10.1002/eco.268
Gu, X., Rempe, D. M., Dietrich, W. E., West, A. J., Lin, T.-C., Jin, L., & Brantley, S. L. (2020).
Chemical reactions, porosity, and microfracturing in shale during weathering: The effect
of erosion rate. Geochimica et Cosmochimica Acta, 269, 63–100.
https://doi.org/10.1016/j.gca.2019.09.044
Guo, J. S., Hungate, B. A., Kolb, T. E., & Koch, G. W. (2018). Water source niche overlap
increases with site moisture availability in woody perennials. Plant Ecology, 219(6),
719–735. https://doi.org/10.1007/s11258-018-0829-z
Horwath, A. B., Royles, J., Tito, R., Gudiño, J. A., Salazar Allen, N., Farfan-Rios, W., Rapp, J.
M., Silman, M. R., Malhi, Y., Swamy, V., Latorre Farfan, J. P., & Griffiths, H. (2019).
Bryophyte stable isotope composition, diversity and biomass define tropical montane
cloud forest extent. Proceedings of the Royal Society B: Biological Sciences, 286(1895),
20182284. https://doi.org/10.1098/rspb.2018.2284
Jasechko, S., Kirchner, J. W., Welker, J. M., & McDonnell, J. J. (2016). Substantial proportion
of global streamflow less than three months old. Nature Geoscience, 9(2), 126–129.
https://doi.org/10.1038/ngeo2636
Jasechko, S., Sharp, Z. D., Gibson, J. J., Birks, S. J., Yi, Y., & Fawcett, P. J. (2013). Terrestrial
water fluxes dominated by transpiration. Nature, 496(7445), 347–350.
https://doi.org/10.1038/nature11983
Kirchner, J. W. (2016a). Aggregation in environmental systems – Part 1: Seasonal tracer cycles
quantify young water fractions, but not mean transit times, in spatially heterogeneous
catchments. Hydrology and Earth System Sciences, 20(1), 279–297.
https://doi.org/10.5194/hess-20-279-2016
114
Kirchner, J. W. (2016b). Aggregation in environmental systems – Part 2: Catchment mean transit
times and young water fractions under hydrologic nonstationarity. Hydrology and Earth
System Sciences, 20(1), 299–328. https://doi.org/10.5194/hess-20-299-2016
Kirchner, J. W., & Allen, S. T. (2020). Seasonal partitioning of precipitation between streamflow
and evapotranspiration, inferred from end-member splitting analysis. Hydrology and
Earth System Sciences, 24(1), 17–39. https://doi.org/10.5194/hess-24-17-2020
McCormick, E. L., Dralle, D. N., Hahm, W. J., Tune, A. K., Schmidt, L. M., Chadwick, K. D., &
Rempe, D. M. (2021). Widespread woody plant use of water stored in bedrock. Nature,
597(7875), 225–229. https://doi.org/10.1038/s41586-021-03761-3
Rapp, J. M., Silman, M. R., Clark, J. S., Girardin, C. A. J., Galiano, D., & Tito, R. (2012). Intra-
and interspecific tree growth across a long altitudinal gradient in the Peruvian Andes.
Ecology, 93(9), 2061–2072. https://doi.org/10.1890/11-1725.1
Rempe, D. M., & Dietrich, W. E. (2018). Direct observations of rock moisture, a hidden
component of the hydrologic cycle. Proceedings of the National Academy of Sciences,
115(11), 2664–2669. https://doi.org/10.1073/pnas.1800141115
Schlesinger, W. H., & Jasechko, S. (2014). Transpiration in the global water cycle. Agricultural
and Forest Meteorology, 189–190, 115–117.
https://doi.org/10.1016/j.agrformet.2014.01.011
Torres, M. A., West, A. J., Clark, K. E., Paris, G., Bouchez, J., Ponton, C., Feakins, S. J., Galy,
V., & Adkins, J. F. (2016). The acid and alkalinity budgets of weathering in the Andes–
Amazon system: Insights into the erosional control of global biogeochemical cycles.
Earth and Planetary Science Letters, 450, 381–391.
https://doi.org/10.1016/j.epsl.2016.06.012
115
Chapter 6: Conclusions
In this dissertation, I have analyzed water chemistry to study chemical weathering and the
geologic carbon cycle (Chapter 2, Appendix A), the factors that control water transit across a
mountain to floodplain transition (Chapter 3), the role of storms in nutrient cycling (Chapter 4),
and the sources of water taken up by plants (Chapter 5). Taken together, the insights gained from
each chapter help establish a picture of Earth surface processes across the Andes mountains and
Amazon floodplain. In mountainous regions, bedrock type is a key control on the dominant
chemical weathering processes and water transit regime. High elevation mountainous sites are
underlain by shale rich in sulfide minerals. Shale bedrock lent itself to slow water transit
regimes, indicative of water taking long, flow paths, presumably due to flow through fractures.
Water from shale catchments had higher solute concentrations than any other type of bedrock
(Appendix A), and stable isotope work revealed that sulfate released from pyrite oxidation was
transported to the foreland floodplain without significant microbial cycling. My work highlights
the importance of the Andes mountains as a source of water (through the storage and gradual
release of precipitation) and solutes.
The comparison of hydrochemical dynamics between mountainous watersheds underlain
by shale versus granite proved to be another particularly interesting aspect of this dissertation. I
found that water transit in a granitic watershed was significantly faster than water transit in the
shale-dominated watersheds (Chapter 3). These findings highlight the complex nature of water
transit in mountainous regions. The observation of fast water transit in a steep granite-dominated
(low permeability) catchment and slow water transit in steep shale-dominated catchments (high
permeability) helps to underscore the importance of bedrock permeability in controlling water
transit.
116
In Chapter 5, I created a more detailed view of water movement through landscapes in
one of the mountainous, shale-dominated catchments, addressing the sources of water that
sustain not only streamflow, but water taken up by plants. I showed that plants take up water
with dynamic seasonal origins, while streamflow is sustained by water from the wet season
during the entire year. This work has stimulated exciting new avenues for thinking: if streamflow
is more seasonally buffered than water taken up by plants, is it less vulnerable to climate-change-
associated changes in hydrology?
Chapter 4 moves away from the mountains, to the fluvial terraces of the foreland
floodplain. This work also raises questions about how climate change (and a changing water
cycle) may influence watershed functioning. I found that storms leach already scarce rock-
derived nutrients from the surface soil on fluvial terraces. Again, catchment structure and
bedrock play a key role, as fluvial terraces are comprised of low-permeability clay minerals.
Surface flowpaths abound in this setting, and the combination of surface flow during storms and
nutrients bioaccumulating in the surface soil make storms a key mechanism for nutrient loss.
As evidenced in this dissertation, temporally rich datasets provide opportunity for
scientific growth. Capturing seasonal (Chapters 3 and 5) and storm (Chapter 4) dynamics in
hydrochemistry was essential to fully understand the movement of water and solutes through
landscapes in this tropical setting. As the field of hydrology grows, automated sampling devices,
ion-specific electrodes and in situ laboratories are increasingly commonplace. Yet, most
intensely monitored watersheds are located in northern latitudes. It is essential to ask how we, as
a scientific community, can bridge the gap between the most and least intensively studied areas?
Work in remote tropical environments does not currently lend itself well to automated devices,
which cannot be easily maintained. I expect that in the coming decades as western methods of
117
science expand across the globe, more hydrologic monitoring will be established in tropical
regions. As scientists we must proceed with caution, ensuring that research conducted in areas
where we are not indigenous is done ethically, with local scientists as leaders and collaborators.
For this reason I would like end with thanking again my collaborators, Adan Julian Ccahuana
Quispe and Daxs Herson Coayla Rimachi for sharing their expertise over the past seven years.
118
Appendix A: Stream concentration-discharge relationships across a
tropical geomorphic gradient
Contributors: A. Joshua West, Adan Julian Ccahuana Quispe, Daxs Herson Coayla Rimachi
Opening statement
This appendix synthesizes stream major element concentrations measured across a range
of hydrologic conditions in seven small watersheds that span the transition from Andes mountain
to Amazon floodplain. This project was conceptualized by myself and Joshua West. Adan Julian
Ccahuana Quispe, Daxs Herson Coayla and myself collected the samples. I analyzed all of the
samples, carried out the data analysis and made the figures with input from Joshua West.
119
1. Site characteristics
Sites,
this
study
Location
Latitude
(°)
Long-
itude (°)
Area
(km
2
)
Mean
slope
(°)
Geology Vegetation
3472-SC
Carretera Manu
near Ajanaco
-13.20617 -71.61168 0.395 24.7 Sandia Fm. - shale Puna
3077-SC
Wayqecha
Biological
Station
-13.19255 -71.58795 0.242 33.8
San José Group -
shale
TMCF
2432-SC
Carretera Manu
near Pillahuata
-13.15969 -71.59378 0.0287 29.5
San José Group -
shale
TMCF
1540-SC
Carretera Manu
near San Pedro
-13.06454 -71.56038 0.613 36.9 Granite Intrusion UPRF
609-SC
Villa Carmen
Biological
Station
-12.89614 -71.41826 0.145 20.8 Paucartambo Fm. Bamboo
276-SC
Los Amigos
Biological
Station
-12.55884 -70.09931 0.377 4.5
Fluvial terrace
(Quaternary)
TRF
214-SC
Explorer’s Inn
Tambopata
-12.82955 -69.27132 3.00 3.2
Fluvial terrace
(Quaternary)
TRF
Table A.1. Characteristics of small catchments from this study. TMCF = tropical montane cloud
forest, UPRF = upper rainforest, TRF = tropical rainforest. Table adapted from Burt et al., 2022
in review
2. Relevant equations
Stream concentration-discharge relationships are defined by measuring major element
concentrations in river water across a range of hydrologic conditions. In order to quantitatively
compare concentration-discharge relationships between different watersheds, I applied the
permeability-porosity-aperture model from Godsey et al. (2009):
C = a × Q
b
(1)
Where C is the concentration of solute in river water, Q is the river discharge and a and b are
fitted parameters. In the tables below, I report a-values and b-values for each of the small
watersheds in this study.
120
Figure A.1. Major element concentrations in small rivers spanning a mountain to floodplain transition in
southern Peru. “Hi Res” signifies samples collected during a high-resolution sampling campaign in 2017,
where samples were collected every four hours for approximately seven days at sites 3077, 609, 276 and
214. “2x month” signifies samples collected twice a month.
121
C-Q relationship B-values (slope)
Watershed
Elevation
Sample type Ca K Mg Na Si Sr
214
High-
resolution
-0.086 0.0191 -0.091 -0.123 -0.044 -0.3
214
Twice-
monthly
-0.762 -0.408 -0.566 -0.662 -0.277 -0.745
276
High-
resolution
0.3009 0.4808 0.1955 -0.138 -0.211 0.4821
276
Twice-
monthly
-0.333 -0.416 -0.259 -0.068 -0.03 -0.56
609
High-
resolution
-0.106 0.3288 -0.137 -0.298 -0.334 -0.103
609
Twice-
monthly
-0.165 -0.035 -0.154 -0.18 -0.118 -0.164
1540
Twice-
monthly
-0.121 0.0676 -0.127 -0.148 -0.134 -0.125
2432
Twice-
monthly
-0.156 -0.036 -0.093 -0.169 -0.089 -0.125
3077
High-
resolution
-0.188 0.114 -0.222 -0.251 -0.215 -0.186
3077
Twice-
monthly
-0.361 -0.268 -0.435 -0.359 -0.276 -0.377
3472
Twice-
monthly
-0.148 -0.164 -0.144 -0.106 -0.034 -0.15
Table A.2. Summary of catchment B-values for major element concentration-discharge
relationships.
122
Figure A.2. B-values of concentration-discharge relationships versus young water fractions for
small rivers spanning a mountain to floodplain transition in southern Peru.
123
Figure A.3. B-values of concentration-discharge relationships versus watershed slope for small
rivers spanning a mountain to floodplain transition in southern Peru.
124
C-Q relationship A-values (intercept; see Eqn. 1)
Watershed
Elevation
Sample type Ca K Mg Na Si Sr
214
High-
resolution
0.092 0.3598 0.3049 0.2104 4.4169 0.0017
214
Twice-
monthly
0.1324 0.3493 0.3186 0.2529 4.7058 0.0017
276
High-
resolution
0.0013 0.5397 0.1656 0.0668 4.8116 0.0012
276
Twice-
monthly
0.2557 0.5388 0.1617 0.0712 4.6301 0.0013
609
High-
resolution
1.8557 0.3319 1.2117 2.9517 13.445 0.0209
609
Twice-
monthly
2.1981 0.7141 1.3183 2.8356 7.8184 0.0134
1540
Twice-
monthly
2.5333 0.2762 0.4818 2.8356 7.8184 0.0134
2432
Twice-
monthly
6.1803 0.3551 4.7621 8.1327 11.561 0.0144
3077
High-
resolution
4.2333 0.1923 3.1575 3.6259 10.356 0.0268
3077
Twice-
monthly
7.0968 0.2882 6.4408 6.5468 15.446 0.0458
3472
Twice-
monthly
16.787 0.1778 6.7463 3.0133 5.4668 0.1059
Table A.3. Summary of catchment A-values for major element concentration-discharge
relationships.
125
Figure A.4. A-values of concentration-discharge relationships versus young water fractions for
small rivers spanning a mountain to floodplain transition in southern Peru.
126
Figure A.5. A-values of concentration-discharge relationships versus watershed slope for small
rivers spanning a mountain to floodplain transition in southern Peru.
References
Godsey, S. E., Kirchner, J. W., & Clow, D. W. (2009). Concentration-discharge relationships
reflect chemostatic characteristics of US catchments. Hydrol. Process., 21.
127
Appendix B: Sulfur and oxygen isotope data table
Contributors: Markus Bill,
Mark Conrad, Adan Julian Ccahuana Quispe, John Christensen,
Robert Hilton, Mathieu Dellinger, A. Joshua West
Opening statement
This appendix includes all of the sulfur and oxygen isotope data from Chapter 2, and
additional data collected during my dissertation. I collected river waters, ground waters and soil
waters across the altitudinal transect from Andes mountains to Amazon foreland floodplain. For
details on sample collection and processing, please refer to Chapter 2.
Sample name Sample type Latitude Longitude
Eleva-
tion (m)
Date 𝛅 18
O (‰) 𝛅 34
S (‰)
[SO 4
2-
]
(mg/L)
Rio Tambopata River -12.83393 -69.29762 190
August
2018
-0.4 ± 0.5 N.D. N.D.
Rio Madre de Dios at
Los Amigos
Biological Station
River -12.56977 -70.10076 219
August
2018
4.3 ± 0.5 4.8 ± 0.3 7.4
Rio Los Amigos River -12.57892 -70.07388 229
August
2018
7.8 ± 0.5 N.D. N.D.
276-Small catchment River -12.55884 -70.09931 276
August
2018
5.0 ± 0.5 N.D. 0.4
Alto Madre de Dios River -12.65557 -71.24129 489
August
2018
2.5 ± 0.5 0.1 ± 0.3 12.3
544-Small catchment River -12.81379 -71.39796 544
August
2018
8.0 ± 0.5 6.7 ± 0.3 9.5
1175-Small
catchment
River -13.03355 -71.52541 1175
August
2018
6.6 ± 0.5 0.5 ± 0.3 2.3
Rio Kosñipata @ San
Pedro
River -13.05832 -71.54556 1360
August
2018
3.6 ± 0.5 0.7 ± 0.3 27.8
1616-Small
catchment
River -13.07095 -71.56963 1616
August
2018
-1.6 ± 0.5 1.7 ± 0.3 7.2
2032-Small
catchment
River -13.10948 -71.57490 2032
August
2018
5.9 ± 0.5 3.1 ± 0.3 15.9
2203-Small
catchment
River -13.12937 -71.57815 2203
August
2018
2.1 ± 0.5 2.1 ± 0.3 12.0
2280-Small
catchment
River -13.14581 -71.58760 2280
August
2018
6.4 ± 0.5 0.2 ± 0.3 28.7
128
2380-Small
catchment
River -13.15772 -71.59926 2380
August
2018
-0.5 ± 0.5 N.D. 79.6
2432-Small
catchment
River -13.15948 -71.59393 2432
August
2018
7.3 ± 0.5 3.2 ± 0.3 30.8
2783-Small
catchment
River -13.17932 -71.60978 2783
August
2018
-1.8 ± 0.5 -3.8 ± 0.3 53.5
3077-Groundwater Groundwater -13.19255 -71.58795 3077
August
2018
11.4 ± 0.5 0.8 ± 0.3 N.D.
3077-Small
catchment
River -13.19255 -71.58795 3077
August
2018
4 ± 0.5 N.D. 53.7
3472-Small
catchment
River -13.20608 -71.61183 3472
August
2018
-1.2 ± 0.5 1.9 ± 0.3 N.D.
3475-Small
catchment
River -13.20524 -71.61356 3475
August
2018
7.6 ± 0.5 0.9 ± 0.3 15.0
3489-Small
catchment
River -13.20460 -71.61610 3489
August
2018
-4.1 ± 0.5 9.8 ± 0.3 9.6
Rio Tambopata River -12.83393 -69.29762 190 Jan 2019 1.8 ± 0.1 3.7 ± 0.6 5.8
214-Small catchment River -12.82955 -69.27132 214 Jan 2019 N.D. 3.1 ± 0.3 N.D.
Rio Madre de Dios at
Los Amigos
Biological Station
River -12.56977 -70.10076 219 Jan 2019 2.1 ± 0.5 0.3 ± 0.1 6.3
Rio Los Amigos River -12.57892 -70.07388 229 Jan 2019 4.6 ± 0.5 N.D. 0.6
276-Small catchment River -12.55884 -70.09931 276 Jan 2019 3 ± 0.5 8.6 ± 0.3 N.D.
Alto Madre de Dios River -12.65557 -71.24129 489 Jan 2019 1.3 ± 1.3 -1.5 ± 0.1 8.4
1175-Small
catchment
River -13.03355 -71.52541 1175 Jan 2019 5.6 ± 0.4 -1.1 ± 0.3 1.3
Rio Kosñipata @ San
Pedro
River -13.05832 -71.54556 1360 Jan 2019 0.2 ± 0.5 N.D. 16.6
1540-Small
catchment
River -13.06507 -71.56221 1540 Jan 2019 -1.8 ± 0.5 5.7 ± 0.3 0.4
1616-Small
catchment
River -13.07095 -71.56963 1616 Jan 2019 -1.0 ± 0.5 2.4 ± 1.4 5.1
2032-Small
catchment
River -13.10948 -71.57490 2032 Jan 2019 3.4 ± 0.5 N.D. 12.1
2203-Small
catchment
River -13.12937 -71.57815 2203 Jan 2019 4.6 ± 0.5 N.D. 8.2
2280-Small
catchment
River -13.14581 -71.58760 2280 Jan 2019 5.0 ± 1.6 -1.0 ± 0.8 18.4
2380-Small
catchment
River -13.15772 -71.59926 2380 Jan 2019 -1.1 ± 0.5 -8.6 ± 0.3 58.1
2432-Small
catchment
River -13.15948 -71.59393 2432 Jan 2019 6.6 ± 1.5 1.7 ± 0.3 22.7
2783-Small
catchment
River -13.17932 -71.60978 2783 Jan 2019 -2.0 ± 0.5 -5.1 ± 0.3 33.5
2815-Small
catchment
River -13.18296 -71.60825 2815 Jan 2019 -2.9 ± 1.0 -2.7 ± 0.3 60.4
3077-Small
catchment
River -13.19255 -71.58795 3077 Jan 2019 7.1 ± 1.6 -0.5 ± 0.3 21.7
3077-Groundwater Groundwater -13.19255 -71.58795 3077 Jan 2019 9.4 ± 0.9 0.5 ± 0.4 52.3
3077-Lysimeter Soil water -13.19255 -71.58795 3077 Jan 2019 11.4 ± 0.5 3.5 ± 1.4 N.D.
129
3472-Small
catchment
River -13.20608 -71.61183 3472 Jan 2019 -1.3 ± 2.1 1.3 ± 0.3 7.5
3475-Small
catchment
River -13.20524 -71.61356 3475 Jan 2019 -2.4 ± 0.5 2.2 ± 0.3 6.7
3489-Small
catchment
River -13.20460 -71.61610 3489 Jan 2019 -4.2 ± 0.5 9.2 ± 0.3 4.4
Rio Madre de Dios at
Los Amigos
Biological Station
River -12.56977 -70.10076 219
March
2019
1.1 ± 0.5 1.3 ± 0.3 4.4
Rio Los Amigos River -12.57892 -70.07388 229
March
2019
3.1 ± 0.5 0.4 ± 0.3 0.5
Rio Colorado River -12.60930 -70.41171 234
March
2019
6.7 ± 0.5 6.2 ± 0.3 1.5
Rio Chilibe River -12.49570 -70.59979 248
March
2019
3.7 ± 0.5 -2.8 ± 0.3 1.0
Rio Blanco River -12.38314 -70.70824 272
March
2019
5.6 ± 0.5 3.1 ± 0.3 0.7
276-Small catchment River -12.55884 -70.09931 276
March
2019
N.D. 8.4 ± 0.3 0.5
Rio Manu River -12.27125 -70.93437 279
March
2019
5.6 ± 0.5 5.8 ± 0.3 2.9
Aguas Calientes Hot spring -12.66858 -71.26978 415
March
2019
-0.4 ± 0.5 7.6 ± 0.3 6.0
440-Small catchment River -12.74578 -71.37193 440
March
2019
1.8 ± 2.3 -5.3 ± 0.3 2.4
Alto Madre de Dios River -12.65557 -71.24129 489
March
2019
-1.7 ± 0.5 -1.6 ± 0.3 7.8
Rio Carbon River -12.89150 -71.35048 504
March
2019
N.D. 4.2 ± 0.3 5.3
Rio Pini Pini River -12.88863 -71.40383 516
March
2019
N.D. N.D. 13.7
Rio Tono River -12.90643 -71.40372 520
March
2019
2.5 ± 0.5 -0.9 ± 0.3 3.1
Rio Sabaluyo River -12.92422 -71.39297 532
March
2019
-0.6 ± 0.5 -0.4 ± 0.3 2.0
Rio Kosñipata @
Pilcopata
River -12.92527 -71.39507 535
March
2019
-1.1 ± 0.5 2.2 ± 0.3 8.6
1175-Small
catchment
River -13.03355 -71.52541 1175
March
2019
6.1 ± 0.5 -1.2 ± 0.3 1.5
Rio Kosñipata @ San
Pedro
River -13.05832 -71.54556 1360
March
2019
-0.3 ± 0.5 N.D. 15.3
1540-Small
catchment
River -13.06507 -71.56221 1540
March
2019
3.7 ± 0.5 4.1 ± 0.3 0.5
1616-Small
catchment
River -13.07095 -71.56963 1616
March
2019
0.6 ± 0.4 N.D. 3.8
2032-Small
catchment
River -13.10948 -71.57490 2032
March
2019
4.3 ± 1.5 2.5 ± 0.7 11.4
2203-Small
catchment
River -13.12937 -71.57815 2203
March
2019
4.8 ± 0.3 1.7 ± 0.3 7.7
2250-River River -13.16179 -71.58877 2250
March
2019
0.8 ± 0.7 N.D. 38.8
130
2280-Small
catchment
River -13.14581 -71.58760 2280
March
2019
5.0 ± 0.5 N.D. 20.1
2380-Small
catchment
River -13.15772 -71.59926 2380
March
2019
0.4 ± 0.5 -2.6 ± 0.3 59.3
2432-Small
catchment
River -13.15948 -71.59393 2432
March
2019
7.1 ± 0.5 1.3 ± 0.3 21.1
2783-Small
catchment
River -13.17932 -71.60978 2783
March
2019
0.0 ± 0.9 -5.2 ± 0.1 30.5
3077-Small
catchment
River -13.19255 -71.58795 3077
March
2019
6.1 ± 0.5 -3.1 ± 0.3 26.5
3077-Lysimeter Soil water -13.19255 -71.58795 3077
March
2019
11.9 ± 0.5 2.9 ± 0.3 53.3
3077-Groundwater Groundwater -13.19255 -71.58795 3077
March
2019
9.2 ± 0.5 2.9 ± 0.3 48.6
3472-Small
catchment
River -13.20608 -71.61183 3472
March
2019
-1.4 ± 0.9 0.9 ± 0.3 7.7
3475-Small
catchment
River -13.20524 -71.61356 3475
March
2019
-3.2 ± 0.5 2.4 ± 0.2 7.6
3489-Small
catchment
River -13.20460 -71.61610 3489
March
2019
-3.6 ± 0.5 N.D. 4.9
Rio Tambopata River -12.83393 -69.29762 190 May 2019 1.6 ± 0.5 3.9 ± 0.3 6.4
214-Small catchment River -12.82955 -69.27132 214 May 2019 N.D. N.D. 0.1
Rio Madre de Dios at
Los Amigos
Biological Station
River -12.56977 -70.10076 219 May 2019 4.2 ± 0.5 N.D. 5.9
Rio Los Amigos River -12.57892 -70.07388 229 May 2019 4.4 ± 0.5 -1.3 ± 0.3 N.D.
Rio Colorado River -12.60930 -70.41171 234 May 2019 6.8 ± 0.5 5.0 ± 0.3 1.1
Rio Chilibe River -12.49570 -70.59979 248 May 2019 3.3 ± 0.5 0.2 ± 0.3 0.6
Rio Blanco River -12.38314 -70.70824 272 May 2019 5.7 ± 0.5 3.6 ± 0.3 0.4
276-Small catchment River -12.55884 -70.09931 276 May 2019 3.8 ± 0.5 N.D. 0.0
Rio Manu River -12.27125 -70.93437 279 May 2019 10.5 ± 0.5 6.8 ± 0.3 9.6
Aguas Calientes Hot spring -12.66858 -71.26978 415 May 2019 3.3 ± 0.5 8.8 ± 0.3 6.3
Alto Madre de Dios River -12.65557 -71.24129 489 May 2019 1.5 ± 0.5 -0.6 ± 0.3 10.4
Rio Carbon River -12.89150 -71.35048 504 May 2019 N.D. 1.6 ± 0.3 6.6
Rio Pini Pini River -12.88863 -71.40383 516 May 2019 -0.5 ± 0.5 -0.2 ± 0.3 N.D.
Rio Tono River -12.90643 -71.40372 520 May 2019 3.5 ± 0.5 -0.4 ± 0.3 3.1
Rio Sabaluyo River -12.92422 -71.39297 532 May 2019 N.D. -0.4 ± 0.3 2.6
Rio Kosñipata @
Pilcopata
River -12.92527 -71.39507 535 May 2019 -0.5 ± 0.5 -0.2 ± 0.3 11.5
1175-Small
catchment
River -13.03355 -71.52541 1175 May 2019 6.3 ± 0.5 -0.6 ± 0.3 N.D.
Rio Kosñipata @ San
Pedro
River -13.05832 -71.54556 1360 May 2019 3.5 ± 0.5 -0.8 ± 0.3 21.2
1540-Small
catchment
River -13.06507 -71.56221 1540 May 2019 -0.5 ± 0.5 2.6 ± 0.3 0.2
1616-Small
catchment
River -13.07095 -71.56963 1616 May 2019 -1.1 ± 0.5 2.8 ± 0.3 N.D.
131
2032-Small
catchment
River -13.10948 -71.57490 2032 May 2019 4.1 ± 0.5 2.7 ± 0.3 N.D.
2203-Small
catchment
River -13.12937 -71.57815 2203 May 2019 N.D. 1.9 ± 0.3 9.3
2250-Groundwater Groundwater -13.16179 -71.58877 2250 May 2019 4.8 ± 0.5 5.7 ± 0.3 N.D.
2250-River River -13.16179 -71.58877 2250 May 2019 -0.3 ± 0.5 N.D. N.D.
2280-Small
catchment
River -13.14581 -71.58760 2280 May 2019 N.D. N.D. 23.6
2380-Small
catchment
River -13.15772 -71.59926 2380 May 2019 1.0 ± 0.5 -2.7 ± 0.3 64.2
2432-Small
catchment
River -13.15948 -71.59393 2432 May 2019 6.8 ± 0.5 1.9 ± 0.3 25.8
2783-Small
catchment
River -13.17932 -71.60978 2783 May 2019 -2.1 ± 0.5 N.D. 39.9
2815-Small
catchment
River -13.18296 -71.60825 2815 May 2019 -3.1 ± 0.5 -2.7 ± 0.3 54.3
3077-Small
catchment
River -13.19255 -71.58795 3077 May 2019 4.3 ± 0.5 N.D. 38.7
3077-Lysimeter Soil water -13.19255 -71.58795 3077 May 2019 N.D. N.D. 53.1
3472-Small
catchment
River -13.20608 -71.61183 3472 May 2019 -0.7 ± 0.5 1.9 ± 0.3 9.7
3475-Small
catchment
River -13.20524 -71.61356 3475 May 2019 3.9 ± 0.5 2.4 ± 0.3 N.D.
3489-Small
catchment
River -13.20460 -71.61610 3489 May 2019 N.D. 10.4 ± 0.3 N.D.
Table B.1. Oxygen and sulfur isotope composition and concentrations of aqueous sulfate.
Samples collected in August, January and May were analyzed for sulfate concentrations via
Metrohm 850 Ion Chromatography at the University of Southern California (Analytical precision
7% or better from SUPER 05 Certified Reference Material from Environment Canada (lot
0815)). Samples collected in March were analyzed for sulfate concentrations at Durham
University via Dionex ICS3000 Ion Chromatography system (Analytical precision 2% or better
from replicate analyses of reference materials). N.D. = no data.
132
Compiled References
Allen, S. T., Kirchner, J. W., Braun, S., Siegwolf, R. T. W., & Goldsmith, G. R. (2019). Seasonal
origins of soil water used by trees. Hydrology and Earth System Sciences, 23(2), 1199–
1210. https://doi.org/10.5194/hess-23-1199-2019
Ameli, A. A., Beven, K., Erlandsson, M., Creed, I. F., McDonnell, J. J., & Bishop, K. (2017).
Primary weathering rates, water transit times, and concentration-discharge relations: A
theoretical analysis for the critical zone. Water Resources Research, 53(1), 942–960.
https://doi.org/10.1002/2016WR019448
Asano, Y., Uchida, T., & Ohte, N. (2002). Residence times and flow paths of water in steep
unchannelled catchments, Tanakami, Japan. Journal of Hydrology, 20.
Asner, G. P., Anderson, C. B., Martin, R. E., Tupayachi, R., Knapp, D. E., & Sinca, F. (2015).
Landscape biogeochemistry reflected in shifting distributions of chemical traits in the
Amazon forest canopy. Nature Geoscience, 8(7), 567–573.
https://doi.org/10.1038/ngeo2443
Baillie, I. C., Ashton, P. S., Court, M. N., Anderson, J. A. R., Fitzpatrick, E. A., & Tinsley, J.
(1987). Site Characteristics and the Distribution of Tree Species in Mixed Dipterocarp
Forest on Tertiary Sediments in Central Sarawak, Malaysia. Journal of Tropical Ecology,
3(3), 201–220.
Barnett, T. P., Adam, J. C., & Lettenmaier, D. P. (2005). Potential impacts of a warming climate
on water availability in snow-dominated regions. Nature, 438(7066), 303–309.
https://doi.org/10.1038/nature04141
Barros, A. P., & Lettenmaier, D. P. (1994). Dynamic modeling of orographically induced
precipitation. Reviews of Geophysics, 32(3), 265. https://doi.org/10.1029/94RG00625
Benettin, P., Volkmann, T. H. M., & Kirchner, J. W. (2018). Effects of climatic seasonality on
the isotopic composition of evaporating soil waters. Hydrol. Earth Syst. Sci., 11.
Berner, R. A. (1978). Rate control of mineral dissolution under Earth surface conditions.
American Journal of Science, 278(9), 1235–1252. https://doi.org/10.2475/ajs.278.9.1235
Berner, R. A. (1982). Burial of organic carbon and pyrite sulfur in the modern ocean; its
geochemical and environmental significance. American Journal of Science, 282(4), 451–
473. https://doi.org/10.2475/ajs.282.4.451
Berry, Z. C., Emery, N. C., Gotsch, S. G., & Goldsmith, G. R. (2019). Foliar water uptake:
Processes, pathways, and integration into plant water budgets. Plant, Cell &
Environment, 42(2), 410–423. https://doi.org/10.1111/pce.13439
Birch, A., Stallard, R., Bush, S., & Barnard, H. (2021). The influence of land cover and storm
magnitude on hydrologic flowpath activation and runoff generation in steep tropical
catchments of central Panama. Journal of Hydrology, 596, 126138.
https://doi.org/10.1016/j.jhydrol.2021.126138
133
Bol, R., Julich, D., Brödlin, D., Siemens, J., Kaiser, K., Dippold, M. A., Spielvogel, S., Zilla, T.,
Mewes, D., Blanckenburg, F. von, Puhlmann, H., Holzmann, S., Weiler, M., Amelung,
W., Lang, F., Kuzyakov, Y., Feger, K.-H., Gottselig, N., Klumpp, E., … Hagedorn, F.
(2016). Dissolved and colloidal phosphorus fluxes in forest ecosystems—An almost blind
spot in ecosystem research. Journal of Plant Nutrition and Soil Science, 179(4), 425–438.
https://doi.org/10.1002/jpln.201600079
Bookhagen, B., & Burbank, D. W. (2006). Topography, relief, and TRMM-derived rainfall
variations along the Himalaya. Geophysical Research Letters, 33(8), L08405.
https://doi.org/10.1029/2006GL026037
Bormann, F. H., & Likens, G. E. (1967). Nutrient Cycling. Science, 155(3761), 424–429.
Boy, J., Valarezo, C., & Wilcke, W. (2008). Water flow paths in soil control element exports in
an Andean tropical montane forest. European Journal of Soil Science, 59(6), 1209–1227.
https://doi.org/10.1111/j.1365-2389.2008.01063.x
Brooks, J. R., Barnard, H. R., Coulombe, R., & McDonnell, J. J. (2010). Ecohydrologic
separation of water between trees and streams in a Mediterranean climate. Nature
Geoscience, 3, 5.
Brum, M., Teodoro, G. S., Abrahão, A., & Oliveira, R. S. (2017). Coordination of rooting depth
and leaf hydraulic traits defines drought-related strategies in the campos rupestres, a
tropical montane biodiversity hotspot. Plant and Soil, 420(1–2), 467–480.
https://doi.org/10.1007/s11104-017-3330-x
Brum, M., Vadeboncoeur, M. A., Ivanov, V., Asbjornsen, H., Saleska, S., Alves, L. F., Penha,
D., Dias, J. D., Aragão, L. E. O. C., Barros, F., Bittencourt, P., Pereira, L., & Oliveira, R.
S. (2019). Hydrological niche segregation defines forest structure and drought tolerance
strategies in a seasonal Amazon forest. Journal of Ecology, 107(1), 318–333.
https://doi.org/10.1111/1365-2745.13022
Bufe, A., Hovius, N., Emberson, R., Rugenstein, J. K. C., Galy, A., Hassenruck-Gudipati, H. J.,
& Chang, J.-M. (2021). Co-variation of silicate, carbonate and sulfide weathering drives
CO 2 release with erosion. Nature Geoscience, 14(4), 211–216.
https://doi.org/10.1038/s41561-021-00714-3
Burke, A., Present, T. M., Paris, G., Rae, E. C. M., Sandilands, B. H., Gaillardet, J., Peucker-
Ehrenbrink, B., Fischer, W. W., McClelland, J. W., Spencer, R. G. M., Voss, B. M., &
Adkins, J. F. (2018). Sulfur isotopes in rivers: Insights into global weathering budgets,
pyrite oxidation, and the modern sulfur cycle. Earth and Planetary Science Letters, 496,
168–177. https://doi.org/10.1016/j.epsl.2018.05.022
Burns, D. A., McDonnell, J. J., Hooper, R. P., Peters, N. E., Freer, J. E., Kendall, C., & Beven,
K. (2001). Quantifying contributions to storm runoff through end-member mixing
analysis and hydrologic measurements at the Panola Mountain Research Watershed
(Georgia, USA). Hydrological Processes, 15(10), 1903–1924.
https://doi.org/10.1002/hyp.246
Burt, E. I., Bill, M., Conrad, M. E., Quispe, A. J. C., Christensen, J. N., Hilton, R. G., Dellinger,
M., & West, A. J. (2021). Conservative transport of dissolved sulfate across the Rio
Madre de Dios floodplain in Peru. Geology. https://doi.org/10.1130/G48997.1
134
Calmels, D., Gaillardet, J., Brenot, A., & France-Lanord, C. (2007). Sustained sulfide oxidation
by physical erosion processes in the Mackenzie River basin: Climatic perspectives.
Geology, 35(11), 1003–1006. https://doi.org/10.1130/G24132A.1
Calmels, D., Galy, A., Hovius, N., Bickle, M., West, A. J., Chen, M.-C., & Chapman, H. (2011).
Contribution of deep groundwater to the weathering budget in a rapidly eroding mountain
belt, Taiwan. Earth and Planetary Science Letters, 303(1–2), 48–58.
https://doi.org/10.1016/j.epsl.2010.12.032
Chadwick, K. D., & Asner, G. P. (2016). Tropical soil nutrient distributions determined by biotic
and hillslope processes. Biogeochemistry, 127(2–3), 273–289.
https://doi.org/10.1007/s10533-015-0179-z
Chadwick, K. D., & Asner, G. P. (2018). Landscape evolution and nutrient rejuvenation reflected
in Amazon forest canopy chemistry. Ecology Letters, 21(7), 978–988.
https://doi.org/10.1111/ele.12963
Chadwick, O. A., Gavenda, R. T., Kelly, E. F., Ziegler, K., Olson, C. G., Elliott, W. C., &
Hendricks, D. M. (2003). The impact of climate on the biogeochemical functioning of
volcanic soils. Chemical Geology, 202(3–4), 195–223.
https://doi.org/10.1016/j.chemgeo.2002.09.001
Cheng, Y., Ogden, F. L., & Zhu, J. (2017). Earthworms and tree roots: A model study of the
effect of preferential flow paths on runoff generation and groundwater recharge in steep,
saprolitic, tropical lowland catchments. Water Resources Research, 53(7), 5400–5419.
https://doi.org/10.1002/2016WR020258
Cheng, Y., Ogden, F. L., Zhu, J., & Bretfeld, M. (2018). Land Use-Dependent Preferential Flow
Paths Affect Hydrological Response of Steep Tropical Lowland Catchments With
Saprolitic Soils. Water Resources Research, 54(8), 5551–5566.
https://doi.org/10.1029/2017WR021875
Clark, K. E., Hilton, R. G., West, A. J., Caceres, A. R., Gröcke, D. R., Marthews, T. R.,
Ferguson, R. I., Asner, G. P., New, M., & Malhi, Y. (2017). Erosion of organic carbon
from the Andes and its effects on ecosystem carbon dioxide balance. Journal of
Geophysical Research: Biogeosciences, 122(3), 449–469.
https://doi.org/10.1002/2016JG003615
Clark, K. E., Hilton, R. G., West, A. J., Malhi, Y., Gröcke, D. R., Bryant, C. L., Ascough, P. L.,
Caceres, A. R., & New, M. (2013). New views on “old” carbon in the Amazon River:
Insight from the source of organic carbon eroded from the Peruvian Andes.
Geochemistry, Geophysics, Geosystems, 14(5), 1644–1659.
https://doi.org/10.1002/ggge.20122
Clark, K. E., Torres, M. A., West, A. J., Hilton, R. G., New, M., Horwath, A. B., Fisher, J. B.,
Rapp, J. M., Robles Caceres, A., & Malhi, Y. (2014). The hydrological regime of a
forested tropical Andean catchment. Hydrology and Earth System Sciences, 18(12),
5377–5397. https://doi.org/10.5194/hess-18-5377-2014
135
Clark, K. E., West, A. J., Hilton, R. G., Asner, G. P., Quesada, C. A., Silman, M. R., Saatchi, S.
S., Farfan-Rios, W., Martin, R. E., Horwath, A. B., Halladay, K., New, M., & Malhi, Y.
(2016). Storm-triggered landslides in the Peruvian Andes and implications for
topography, carbon cycles, and biodiversity. Earth Surface Dynamics, 4(1), 47–70.
https://doi.org/10.5194/esurf-4-47-2016
Claypool, G. E., Holser, W. T., Kaplan, I. R., Sakai, H., & Zak, I. (1980). The age curves of
sulfur and oxygen isotopes in marine sulfate and their mutual interpretation. Chemical
Geology, 28, 199–260. https://doi.org/10.1016/0009-2541(80)90047-9
Cox, P. M., Pearson, D., Booth, B. B., Friedlingstein, P., Huntingford, C., Jones, C. D., & Luke,
C. M. (2013). Sensitivity of tropical carbon to climate change constrained by carbon
dioxide variability. Nature, 494(7437), 341–344. https://doi.org/10.1038/nature11882
Cuevas, E., & Medina, E. (1988). Nutrient dynamics within amazonian forests: II. Fine root
growth, nutrient availability and leaf litter decomposition. Oecologia, 76(2), 222–235.
https://doi.org/10.1007/BF00379956
Das, A., Chung, C.-H., & You, C.-F. (2012). Disproportionately high rates of sulfide oxidation
from mountainous river basins of Taiwan orogeny: Sulfur isotope evidence. Geophysical
Research Letters, 39(12). https://doi.org/10.1029/2012GL051549
Dawson, T. E., & Ehleringer, J. R. (1991). Streamside trees that do not use stream water. Nature,
350.
Dawson, T. E., & Goldsmith, G. R. (2018). The value of wet leaves. New Phytologist, 219(4),
1156–1169. https://doi.org/10.1111/nph.15307
Drever, J. I. (1988). The geochemistry of natural waters (2nd ed). Prentice Hall.
Du, E., Terrer, C., Pellegrini, A. F. A., Ahlström, A., van Lissa, C. J., Zhao, X., Xia, N., Wu, X.,
& Jackson, R. B. (2020). Global patterns of terrestrial nitrogen and phosphorus
limitation. Nature Geoscience, 13(3), 221–226. https://doi.org/10.1038/s41561-019-
0530-4
Dunne, T., & Black, R. D. (1970). Partial Area Contributions to Storm Runoff in a Small New
England Watershed. Water Resources Research, 6(5), 1296–1311.
https://doi.org/10.1029/WR006i005p01296
Ebelmen, J.-J. (1845). Sur les produits de la décomposition des espèces minérales de la famille
des silicates. Annales Des Mines, 7(3), 3–66.
Edwards, P. J. (1982). Studies of Mineral Cycling in a Montane Rain Forest in New Guinea: V.
Rates of Cycling in Throughfall and Litter Fall. The Journal of Ecology, 70(3), 807.
https://doi.org/10.2307/2260106
Eller, C. B., Lima, A. L., & Oliveira, R. S. (2013). Foliar uptake of fog water and transport
belowground alleviates drought effects in the cloud forest tree species, D. rimys
brasiliensis (W. interaceae). New Phytologist, 199(1), 151–162.
https://doi.org/10.1111/nph.12248
Elsenbeer, H. (2001). Hydrologic flowpaths in tropical rainforest soilscapes—A review.
Hydrological Processes, 15(10), 1751–1759. https://doi.org/10.1002/hyp.237
136
Elsenbeer, H., & Lack, A. (1996). Hydrometric and hydrochemical evidence for fast flowpaths at
La Cuenca, Western Amazonia. Journal of Hydrology, 14.
Elser, J. J., Bracken, M. E. S., Cleland, E. E., Gruner, D. S., Harpole, W. S., Hillebrand, H.,
Ngai, J. T., Seabloom, E. W., Shurin, J. B., & Smith, J. E. (2007). Global analysis of
nitrogen and phosphorus limitation of primary producers in freshwater, marine and
terrestrial ecosystems. Ecology Letters, 10(12), 1135–1142.
https://doi.org/10.1111/j.1461-0248.2007.01113.x
Evaristo, J., Jasechko, S., & McDonnell, J. J. (2015). Global separation of plant transpiration
from groundwater and streamflow. Nature, 525(7567), 91–94.
https://doi.org/10.1038/nature14983
Fatichi, S., & Pappas, C. (2017). Constrained variability of modeled T: ET ratio across biomes:
Transpiration: Evapotranspiration Ratio. Geophysical Research Letters, 44(13), 6795–
6803. https://doi.org/10.1002/2017GL074041
Feakins, S. J., Bentley, L. P., Salinas, N., Shenkin, A., Blonder, B., Goldsmith, G. R., Ponton, C.,
Arvin, L. J., Wu, M. S., Peters, T., West, A. J., Martin, R. E., Enquist, B. J., Asner, G. P.,
& Malhi, Y. (2016). Plant leaf wax biomarkers capture gradients in hydrogen isotopes of
precipitation from the Andes and Amazon. Geochimica et Cosmochimica Acta, 182, 155–
172. https://doi.org/10.1016/j.gca.2016.03.018
Feakins, S. J., Wu, M. S., Ponton, C., Galy, V., & West, A. J. (2018). Dual isotope evidence for
sedimentary integration of plant wax biomarkers across an Andes-Amazon elevation
transect. Geochimica et Cosmochimica Acta, 242, 64–81.
https://doi.org/10.1016/j.gca.2018.09.007
Fekete, B. M., Vörösmarty, C. J., & Grabs, W. (2002). High-resolution fields of global runoff
combining observed river discharge and simulated water balances. Global
Biogeochemical Cycles, 16(3), 15-1-15–10. https://doi.org/10.1029/1999GB001254
Fisher, J. B., Badgley, G., & Blyth, E. (2012). Global nutrient limitation in terrestrial vegetation.
Global Biogeochemical Cycles, 26(3). https://doi.org/10.1029/2011GB004252
Fisher, J. B., Perakalapudi, N. V., Turner, B. L., Schimel, D. S., & Cusack, D. F. (2020).
Competing effects of soil fertility and toxicity on tropical greening. Scientific Reports,
10(1), 6725. https://doi.org/10.1038/s41598-020-63589-1
Francois, L. M., & Walker, J. C. G. (1992). Modelling the Phanerozoic carbon cycle and climate;
constraints from the 87 Sr/ 86 Sr isotopic ratio of seawater. American Journal of Science,
292(2), 81–135. https://doi.org/10.2475/ajs.292.2.81
Frazar, S., Gold, A. J., Addy, K., Moatar, F., Birgand, F., Schroth, A. W., Kellogg, D. Q., &
Pradhanang, S. M. (2019). Contrasting behavior of nitrate and phosphate flux from high
flow events on small agricultural and urban watersheds. Biogeochemistry, 145(1–2), 141–
160. https://doi.org/10.1007/s10533-019-00596-z
Gaillardet, J., Dupré, B., Louvat, P., & Allègre, C. J. (1999). Global silicate weathering and CO2
consumption rates deduced from the chemistry of large rivers. Chemical Geology, 159(1),
3–30. https://doi.org/10.1016/S0009-2541(99)00031-5
137
Gallart, F., Valiente, M., Llorens, P., Cayuela, C., Sprenger, M., & Latron, J. (2020).
Investigating young water fractions in a small Mediterranean mountain catchment: Both
precipitation forcing and sampling frequency matter. Hydrological Processes, 34(17),
3618–3634. https://doi.org/10.1002/hyp.13806
Gardner, C. B., Litt, G. F., Lyons, W. B., & Ogden, F. L. (2017). Evidence for the Activation of
Shallow Preferential Flow Paths in a Tropical Panama Watershed Using Germanium and
Silicon. Water Resources Research, 53(10), 8533–8553.
https://doi.org/10.1002/2017WR020429
Garrels, R. M., & Mackenzie, F. T. (1967). Origin of the Chemical Compositions of Some
Springs and Lakes. In Equilibrium Concepts in Natural Water Systems (Vol. 67, pp. 222–
242). American Chemical Society. https://doi.org/10.1021/ba-1967-0067.ch010
Genereux, D. P., & Hooper, R. P. (1998). Chapter 10—Oxygen and Hydrogen Isotopes in
Rainfall-Runoff Studies. In C. Kendall & J. J. McDONNELL (Eds.), Isotope Tracers in
Catchment Hydrology (pp. 319–346). Elsevier. https://doi.org/10.1016/B978-0-444-
81546-0.50017-3
Gibbs, R. J. (1970). Mechanisms Controlling World Water Chemistry. Science, 170(3962),
1088–1090. https://doi.org/10.1126/science.170.3962.1088
Godsey, S. E., Hartmann, J., & Kirchner, J. W. (2019). Catchment chemostasis revisited: Water
quality responds differently to variations in weather and climate. Hydrological Processes,
33(24), 3056–3069. https://doi.org/10.1002/hyp.13554
Godsey, S. E., Kirchner, J. W., & Clow, D. W. (2009). Concentration-discharge relationships
reflect chemostatic characteristics of US catchments. Hydrol. Process., 21.
Goldsmith, G. R., Allen, S. T., Braun, S., Siegwolf, R. T. W., & Kirchner, J. W. (2022). Climatic
Influences on Summer Use of Winter Precipitation by Trees. Geophysical Research
Letters, 49(10). https://doi.org/10.1029/2022GL098323
Goldsmith, G. R., Muñoz‐Villers, L. E., Holwerda, F., McDonnell, J. J., Asbjornsen, H., &
Dawson, T. E. (2012). Stable isotopes reveal linkages among ecohydrological processes
in a seasonally dry tropical montane cloud forest. Ecohydrology, 5(6), 779–790.
https://doi.org/10.1002/eco.268
Gu, X., Rempe, D. M., Dietrich, W. E., West, A. J., Lin, T.-C., Jin, L., & Brantley, S. L. (2020).
Chemical reactions, porosity, and microfracturing in shale during weathering: The effect
of erosion rate. Geochimica et Cosmochimica Acta, 269, 63–100.
https://doi.org/10.1016/j.gca.2019.09.044
Guo, J. S., Hungate, B. A., Kolb, T. E., & Koch, G. W. (2018). Water source niche overlap
increases with site moisture availability in woody perennials. Plant Ecology, 219(6),
719–735. https://doi.org/10.1007/s11258-018-0829-z
Hale, V. C., & McDonnell, J. J. (2016). Effect of bedrock permeability on stream base flow
mean transit time scaling relations: 1. A multiscale catchment intercomparison. Water
Resources Research, 52(2), 1358–1374. https://doi.org/10.1002/2014WR016124
138
Hale, V. C., McDonnell, J. J., Stewart, M. K., Solomon, D. K., Doolitte, J., Ice, G. G., & Pack,
R. T. (2016). Effect of bedrock permeability on stream base flow mean transit time
scaling relationships: 2. Process study of storage and release. Water Resources Research,
52(2), 1375–1397. https://doi.org/10.1002/2015WR017660
Heathwaite, A. L., & Dils, R. M. (2000). Characterising phosphorus loss in surface and
subsurface hydrological pathways. Science of The Total Environment, 251 –252, 523–538.
https://doi.org/10.1016/S0048-9697(00)00393-4
Hemingway, J. D., Olson, H., Turchyn, A. V., Tipper, E. T., Bickle, M. J., & Johnston, D. T.
(2020). Triple oxygen isotope insight into terrestrial pyrite oxidation. Proceedings of the
National Academy of Sciences, 117(14), 7650–7657.
https://doi.org/10.1073/pnas.1917518117
Hewlett, J. D., & Hibbert, A. R. (1967). Factors affecting the response of small watersheds to
precipitation in humid areas. Forest Hydrology, 1, 275–290.
Hilton, R. G., & West, A. J. (2020). Mountains, erosion and the carbon cycle. Nature Reviews
Earth & Environment, 1, 16. https://doi.org/10.1038/s43017-020-0058-6
Holmer, M., & Storkholm, P. (2001). Sulphate reduction and sulphur cycling in lake sediments:
A review. Freshwater Biology, 46(4), 431–451. https://doi.org/10.1046/j.1365-
2427.2001.00687.x
Hooper, R. P., Christophersen, N., & Peters, N. E. (1990). Modelling streamwater chemistry as a
mixture of soilwater end-members—An application to the Panola Mountain catchment,
Georgia, U.S.A. Journal of Hydrology, 116(1–4), 321–343. https://doi.org/10.1016/0022-
1694(90)90131-G
Horwath, A. B., Royles, J., Tito, R., Gudiño, J. A., Salazar Allen, N., Farfan-Rios, W., Rapp, J.
M., Silman, M. R., Malhi, Y., Swamy, V., Latorre Farfan, J. P., & Griffiths, H. (2019).
Bryophyte stable isotope composition, diversity and biomass define tropical montane
cloud forest extent. Proceedings of the Royal Society B: Biological Sciences, 286(1895),
20182284. https://doi.org/10.1098/rspb.2018.2284
Ibarra, D. E., Caves, J. K., Moon, S., Thomas, D. L., Hartmann, J., Chamberlain, C. P., & Maher,
K. (2016). Differential weathering of basaltic and granitic catchments from
concentration–discharge relationships. Geochimica et Cosmochimica Acta, 190, 265–293.
https://doi.org/10.1016/j.gca.2016.07.006
Ibarra, D. E., Moon, S., Caves, J. K., Chamberlain, C. P., & Maher, K. (2017). Concentration–
discharge patterns of weathering products from global rivers. Acta Geochimica, 36(3),
405–409. https://doi.org/10.1007/s11631-017-0177-z
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S.,
Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M., Fernández, A.,
Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink, P. D. A., Kulkarni, A. V.,
Mayewski, P. A., … Baillie, J. E. M. (2020). Importance and vulnerability of the world’s
water towers. Nature, 577(7790), 364–369. https://doi.org/10.1038/s41586-019-1822-y
Jasechko, S. (2016). Partitioning young and old groundwater with geochemical tracers. Chemical
Geology, 427, 35–42. https://doi.org/10.1016/j.chemgeo.2016.02.012
139
Jasechko, S., Kirchner, J. W., Welker, J. M., & McDonnell, J. J. (2016). Substantial proportion
of global streamflow less than three months old. Nature Geoscience, 9(2), 126–129.
https://doi.org/10.1038/ngeo2636
Jasechko, S., Sharp, Z. D., Gibson, J. J., Birks, S. J., Yi, Y., & Fawcett, P. J. (2013). Terrestrial
water fluxes dominated by transpiration. Nature, 496(7445), 347–350.
https://doi.org/10.1038/nature11983
Karim, A., & Veizer, J. (2000). Weathering processes in the Indus River Basin: Implications
from riverine carbon, sulfur, oxygen, and strontium isotopes. Chemical Geology, 170(1–
4), 153–177. https://doi.org/10.1016/S0009-2541(99)00246-6
Kennedy, V. C. (1971). Silica Variation in Stream Water with Time and Discharge. In
Nonequilibrium Systems in Natural Water Chemistry (Vol. 106, pp. 94–130). American
Chemical Society. https://doi.org/10.1021/ba-1971-0106.ch004
Killingsworth, B. A., Bao, H., & Kohl, I. E. (2018). Assessing Pyrite-Derived Sulfate in the
Mississippi River with Four Years of Sulfur and Triple-Oxygen Isotope Data.
Environmental Science & Technology, 52(11), 6126–6136.
https://doi.org/10.1021/acs.est.7b05792
Kirchner, J., Feng, X., & Neal, C. (2000). Fractal stream chemistry and its Implications for
Contaminant Transport in Catchments. Nature, 403, 524–527.
https://doi.org/10.1038/35000537
Kirchner, J. W. (2016a). Aggregation in environmental systems – Part 1: Seasonal tracer cycles
quantify young water fractions, but not mean transit times, in spatially heterogeneous
catchments. Hydrology and Earth System Sciences, 20(1), 279–297.
https://doi.org/10.5194/hess-20-279-2016
Kirchner, J. W. (2016b). Aggregation in environmental systems – Part 2: Catchment mean transit
times and young water fractions under hydrologic nonstationarity. Hydrology and Earth
System Sciences, 20(1), 299–328. https://doi.org/10.5194/hess-20-299-2016
Kirchner, J. W., & Allen, S. T. (2020). Seasonal partitioning of precipitation between streamflow
and evapotranspiration, inferred from end-member splitting analysis. Hydrology and
Earth System Sciences, 24(1), 17–39. https://doi.org/10.5194/hess-24-17-2020
Knapp, J. L. A., von Freyberg, J., Studer, B., Kiewiet, L., & Kirchner, J. W. (2020).
Concentration–discharge relationships vary among hydrological events, reflecting
differences in event characteristics. Hydrology and Earth System Sciences, 24(5), 2561–
2576. https://doi.org/10.5194/hess-24-2561-2020
Kohl, I., & Bao, H. (2011). Triple-oxygen-isotope determination of molecular oxygen
incorporation in sulfate produced during abiotic pyrite oxidation (pH=2–11). Geochimica
et Cosmochimica Acta, 75(7), 1785–1798. https://doi.org/10.1016/j.gca.2011.01.003
Lambers, H., Shane, M. W., Cramer, M. D., Pearse, S. J., & Veneklaas, E. J. (2006). Root
Structure and Functioning for Efficient Acquisition of Phosphorus: Matching
Morphological and Physiological Traits. Annals of Botany, 98(4), 693–713.
https://doi.org/10.1093/aob/mcl114
140
Latrubesse, E. M., Cozzuol, M., da Silva-Caminha, S. A. F., Rigsby, C. A., Absy, M. L., &
Jaramillo, C. (2010). The Late Miocene paleogeography of the Amazon Basin and the
evolution of the Amazon River system. Earth-Science Reviews, 99(3–4), 99–124.
https://doi.org/10.1016/j.earscirev.2010.02.005
Lerman, A., Wu, L., & Mackenzie, F. T. (2007). CO2 and H2SO4 consumption in weathering and
material transport to the ocean, and their role in the global carbon balance. Marine
Chemistry, 106(1–2), 326–350. https://doi.org/10.1016/j.marchem.2006.04.004
Leys, B. A., Likens, G. E., Johnson, C. E., Craine, J. M., Lacroix, B., & McLauchlan, K. K.
(2016). Natural and anthropogenic drivers of calcium depletion in a northern forest
during the last millennium. Proceedings of the National Academy of Sciences, 113(25),
6934–6938. https://doi.org/10.1073/pnas.1604909113
Longinelli, A., & Edmond, J. M. (1983). Isotope geochemistry of the Amazon Basin: A
reconnaissance. Journal of Geophysical Research: Oceans, 88(C6), 3703–3717.
https://doi.org/10.1029/JC088iC06p03703
Lutz, S. R., Krieg, R., Müller, C., Zink, M., Knöller, K., Samaniego, L., & Merz, R. (2018).
Spatial Patterns of Water Age: Using Young Water Fractions to Improve the
Characterization of Transit Times in Contrasting Catchments. Water Resources Research,
54(7), 4767–4784. https://doi.org/10.1029/2017WR022216
Maher, K. (2010). The dependence of chemical weathering rates on fluid residence time. Earth
and Planetary Science Letters, 294(1–2), 101–110.
https://doi.org/10.1016/j.epsl.2010.03.010
Maher, K. (2011). The role of fluid residence time and topographic scales in determining
chemical fluxes from landscapes. Earth and Planetary Science Letters, 312(1–2), 48–58.
https://doi.org/10.1016/j.epsl.2011.09.040
Maher, K., & Chamberlain, C. P. (2014). Hydrologic Regulation of Chemical Weathering and
the Geologic Carbon Cycle. Science, 343(6178), 1502–1504.
https://doi.org/10.1126/science.1250770
Malhi, Y. (2012). The productivity, metabolism and carbon cycle of tropical forest vegetation.
Journal of Ecology, 100(1), 65–75. https://doi.org/10.1111/j.1365-2745.2011.01916.x
Malhi, Y., Baker, T. R., Phillips, O. L., Almeida, S., Alvarez, E., Arroyo, L., Chave, J.,
Czimczik, C. I., Fiore, A. D., Higuchi, N., Killeen, T. J., Laurance, S. G., Laurance, W.
F., Lewis, S. L., Montoya, L. M. M., Monteagudo, A., Neill, D. A., Vargas, P. N., Patiño,
S., … Lloyd, J. (2004). The above-ground coarse wood productivity of 104 Neotropical
forest plots. Global Change Biology, 10(5), 563–591. https://doi.org/10.1111/j.1529-
8817.2003.00778.x
Malhi, Y., & Grace, J. (2000). Tropical forests and atmospheric carbon dioxide. Trends in
Ecology & Evolution, 15(8), 332–337. https://doi.org/10.1016/S0169-5347(00)01906-6
Mandernack, K. W., Krouse, H. R., & Skei, J. M. (2003). A stable sulfur and oxygen isotopic
investigation of sulfur cycling in an anoxic marine basin, Framvaren Fjord, Norway.
Chemical Geology, 195(1), 181–200. https://doi.org/10.1016/S0009-2541(02)00394-7
141
Marinos, R. E., Campbell, J. L., Driscoll, C. T., Likens, G. E., McDowell, W. H., Rosi, E. J.,
Rustad, L. E., & Bernhardt, E. S. (2018). Give and Take: A Watershed Acid Rain
Mitigation Experiment Increases Baseflow Nitrogen Retention but Increases Stormflow
Nitrogen Export. Environmental Science & Technology, 52(22), 13155–13165.
https://doi.org/10.1021/acs.est.8b03553
Mayer, B., Shanley, J. B., Bailey, S. W., & Mitchell, M. J. (2010). Identifying sources of stream
water sulfate after a summer drought in the Sleepers River watershed (Vermont, USA)
using hydrological, chemical, and isotopic techniques. Applied Geochemistry, 25(5),
747–754. https://doi.org/10.1016/j.apgeochem.2010.02.007
McCormick, E. L., Dralle, D. N., Hahm, W. J., Tune, A. K., Schmidt, L. M., Chadwick, K. D., &
Rempe, D. M. (2021). Widespread woody plant use of water stored in bedrock. Nature,
597(7875), 225–229. https://doi.org/10.1038/s41586-021-03761-3
Mcdonnell, J. (1990). A Rationale for Old Water Discharge Through Macropores in a Steep,
Humid Catchment. Water Resources Research, 26, 2821–2832.
https://doi.org/10.1029/WR026i011p02821
McGlynn, B., McDonnell, J., Stewart, M., & Seibert, J. (2003). On the relationships between
catchment scale and streamwater mean residence time. Hydrological Processes, 17(1),
175–181. https://doi.org/10.1002/hyp.5085
McGuire, K. J., & McDonnell, J. J. (2006). A review and evaluation of catchment transit time
modeling. Journal of Hydrology, 330(3–4), 543–563.
https://doi.org/10.1016/j.jhydrol.2006.04.020
McGuire, K. J., McDonnell, J. J., Weiler, M., Kendall, C., McGlynn, B. L., Welker, J. M., &
Seibert, J. (2005). The role of topography on catchment-scale water residence time.
Water Resources Research, 41(5). https://doi.org/10.1029/2004WR003657
Meybeck, M. (1987). Global chemical weathering of surficial rocks estimated from river
dissolved loads. American Journal of Science, 287, 401–428.
Meybeck, M., Green, P., & Vörösmarty, C. (2001). A New Typology for Mountains and Other
Relief Classes: An Application to Global Continental Water Resources and Population
Distribution. Mountain Research and Development, 21(1), 34–45.
https://doi.org/10.1659/0276-4741(2001)021[0034:ANTFMA]2.0.CO;2
Meybeck, M., Laroche, L., Dürr, H. H., & Syvitski, J. P. M. (2003). Global variability of daily
total suspended solids and their fluxes in rivers. Global and Planetary Change, 39(1),
65–93. https://doi.org/10.1016/S0921-8181(03)00018-3
Moatar, F., Abbott, B. W., Minaudo, C., Curie, F., & Pinay, G. (2017). Elemental properties,
hydrology, and biology interact to shape concentration-discharge curves for carbon,
nutrients, sediment, and major ions. Water Resources Research, 53(2), 1270–1287.
https://doi.org/10.1002/2016WR019635
Moon, S., Perron, J. T., Martel, S. J., Holbrook, W. S., & St. Clair, J. (2017). A model of three-
dimensional topographic stresses with implications for bedrock fractures, surface
processes, and landscape evolution: Three-Dimensional Topographic Stress. Journal of
Geophysical Research: Earth Surface, 122(4), 823–846.
https://doi.org/10.1002/2016JF004155
142
Müller Schmied, H., Cáceres, D., Eisner, S., Flörke, M., Herbert, C., Niemann, C., Peiris, T. A.,
Popat, E., Portmann, F. T., Reinecke, R., Schumacher, M., Shadkam, S., Telteu, C.-E.,
Trautmann, T., & Döll, P. (2020). The global water resources and use model WaterGAP
v2.2d: Model description and evaluation [Preprint]. Hydrology.
https://doi.org/10.5194/gmd-2020-225
Muñoz-Villers, L. E., Geissert, D. R., Holwerda, F., & McDonnell, J. J. (2016). Factors
influencing stream baseflow transit times in tropical montane watersheds. Hydrology and
Earth System Sciences, 20(4), 1621–1635. https://doi.org/10.5194/hess-20-1621-2016
Muñoz-Villers, L. E., & McDonnell, J. J. (2012). Runoff generation in a steep, tropical montane
cloud forest catchment on permeable volcanic substrate. Water Resources Research,
48(9). https://doi.org/10.1029/2011WR011316
Napoli, A., Crespi, A., Ragone, F., Maugeri, M., & Pasquero, C. (2019). Variability of
orographic enhancement of precipitation in the Alpine region. Scientific Reports, 9(1),
13352. https://doi.org/10.1038/s41598-019-49974-5
Ng, G.-H. C., Yourd, A. R., Johnson, N. W., & Myrbo, A. E. (2017). Modeling hydrologic
controls on sulfur processes in sulfate-impacted wetland and stream sediments. Journal of
Geophysical Research: Biogeosciences, 122(9), 2435–2457.
https://doi.org/10.1002/2017JG003822
Odum, E. P. (1969). The Strategy of Ecosystem Development. Science, 164(3877), 262–270.
https://doi.org/10.1126/science.164.3877.262
Ogden, F. L., Crouch, T. D., Stallard, R. F., & Hall, J. S. (2013). Effect of land cover and use on
dry season river runoff, runoff efficiency, and peak storm runoff in the seasonal tropics of
Central Panama. Water Resources Research, 49(12), 8443–8462.
https://doi.org/10.1002/2013WR013956
Okin, G. S., Mahowald, N., Chadwick, O. A., & Artaxo, P. (2004). Impact of desert dust on the
biogeochemistry of phosphorus in terrestrial ecosystems. Global Biogeochemical Cycles,
18(2). https://doi.org/10.1029/2003GB002145
Otero, N., Soler, A., & Canals, À. (2008). Controls of δ
34
S and δ
18
O in dissolved sulphate:
Learning from a detailed survey in the Llobregat River (Spain). Applied Geochemistry,
23(5), 1166–1185. https://doi.org/10.1016/j.apgeochem.2007.11.009
Pester, M., Knorr, K.-H., Friedrich, M. W., Wagner, M., & Loy, A. (2012). Sulfate-reducing
microorganisms in wetlands – fameless actors in carbon cycling and climate change.
Frontiers in Microbiology, 3. https://doi.org/10.3389/fmicb.2012.00072
Ponton, C., West, A. J., Feakins, S. J., & Galy, V. (2014). Leaf wax biomarkers in transit record
river catchment composition. Geophysical Research Letters, 41(18), 6420–6427.
https://doi.org/10.1002/2014GL061328
Porder, S., & Chadwick, O. A. (2009). Climate and soil-age constraints on nutrient uplift and
retention by plants. Ecology, 90(3), 623–636. https://doi.org/10.1890/07-1739.1
Quesada, C. A., Lloyd, J., Anderson, L. O., Fyllas, N. M., Schwarz, M., & Czimczik, C. I.
(2011). Soils of Amazonia with particular reference to the RAINFOR sites.
Biogeosciences, 8(6), 1415–1440. https://doi.org/10.5194/bg-8-1415-2011
143
Rapp, J. M., Silman, M. R., Clark, J. S., Girardin, C. A. J., Galiano, D., & Tito, R. (2012). Intra-
and interspecific tree growth across a long altitudinal gradient in the Peruvian Andes.
Ecology, 93(9), 2061–2072. https://doi.org/10.1890/11-1725.1
Rapp, J., & Silman, M. (2012). Diurnal, seasonal, and altitudinal trends in microclimate across a
tropical montane cloud forest. Climate Research, 55(1), 17–32.
https://doi.org/10.3354/cr01127
Raymond, P. A., & Saiers, J. E. (2010). Event controlled DOC export from forested watersheds.
Biogeochemistry, 100(1), 197–209. https://doi.org/10.1007/s10533-010-9416-7
Rempe, D. M., & Dietrich, W. E. (2018). Direct observations of rock moisture, a hidden
component of the hydrologic cycle. Proceedings of the National Academy of Sciences,
115(11), 2664–2669. https://doi.org/10.1073/pnas.1800141115
Rigsby, C. A., Hemric, E. M., & Baker, P. A. (2009). Late Quaternary Paleohydrology of the
Madre de Dios River, southwestern Amazon Basin, Peru. Geomorphology, 113(3–4),
158–172. https://doi.org/10.1016/j.geomorph.2008.11.017
Roe, G. H., & Baker, M. B. (2006). Microphysical and Geometrical Controls on the Pattern of
Orographic Precipitation. Journal of the Atmospheric Sciences, 63(3), 861–880.
https://doi.org/10.1175/JAS3619.1
Scanlon, B. R., Zhang, Z., Save, H., Sun, A. Y., Müller Schmied, H., van Beek, L. P. H., Wiese,
D. N., Wada, Y., Long, D., Reedy, R. C., Longuevergne, L., Döll, P., & Bierkens, M. F.
P. (2018). Global models underestimate large decadal declining and rising water storage
trends relative to GRACE satellite data. Proceedings of the National Academy of
Sciences, 115(6). https://doi.org/10.1073/pnas.1704665115
Schlesinger, W. H., & Jasechko, S. (2014). Transpiration in the global water cycle. Agricultural
and Forest Meteorology, 189–190, 115–117.
https://doi.org/10.1016/j.agrformet.2014.01.011
Sohrt, J., Uhlig, D., Kaiser, K., von Blanckenburg, F., Siemens, J., Seeger, S., Frick, D. A.,
Krüger, J., Lang, F., & Weiler, M. (2019). Phosphorus Fluxes in a Temperate Forested
Watershed: Canopy Leaching, Runoff Sources, and In-Stream Transformation. Frontiers
in Forests and Global Change, 0. https://doi.org/10.3389/ffgc.2019.00085
Spence, J., & Telmer, K. (2005). The role of sulfur in chemical weathering and atmospheric CO2
fluxes: Evidence from major ions, δ
13
CDIC, and δ
34
SSO4 in rivers of the Canadian
Cordillera. Geochimica et Cosmochimica Acta, 69(23), 5441–5458.
https://doi.org/10.1016/j.gca.2005.07.011
Stockinger, M. P., Bogena, H. R., Lücke, A., Diekkrüger, B., Cornelissen, T., & Vereecken, H.
(2016). Tracer sampling frequency influences estimates of young water fraction and
streamwater transit time distribution. Journal of Hydrology, 541, 952–964.
https://doi.org/10.1016/j.jhydrol.2016.08.007
Swap, R., Garstang, M., Greco, S., Talbot, R., & Kållberg, P. (1992). Saharan dust in the
Amazon Basin. Tellus B, 44(2), 133–149. https://doi.org/10.1034/j.1600-0889.1992.t01-
1-00005.x
144
Tang, W., & Carey, S. K. (2017). HydRun: A MATLAB toolbox for rainfall-runoff analysis.
Hydrological Processes, 31(15), 2670–2682. https://doi.org/10.1002/hyp.11185
Taylor, B. E., & Wheeler, M. C. (1993). Sulfur- and Oxygen-Isotope Geochemistry of Acid
Mine Drainage in the Western United States: Field and Experimental Studies Revisited.
In C. N. Alpers & D. W. Blowes (Eds.), Environmental Geochemistry of Sulfide
Oxidation (Vol. 550, pp. 481–514). American Chemical Society.
https://doi.org/10.1021/bk-1994-0550.ch030
Tetzlaff, D., Seibert, J., McGuire, K. J., Laudon, H., Burns, D. A., Dunn, S. M., & Soulsby, C.
(2009). How does landscape structure influence catchment transit time across different
geomorphic provinces? Hydrological Processes, 23(6), 945–953.
https://doi.org/10.1002/hyp.7240
Tetzlaff, D., Seibert, J., & Soulsby, C. (2009). Inter-catchment comparison to assess the
influence of topography and soils on catchment transit times in a geomorphic province;
the Cairngorm mountains, Scotland. Hydrological Processes, 23(13), 1874–1886.
https://doi.org/10.1002/hyp.7318
Torres, M. A., Baronas, J. J., Clark, K. E., Feakins, S. J., & West, A. J. (2017). Mixing as a
driver of temporal variations in river hydrochemistry: 1. Insights from conservative
tracers in the Andes-Amazon transition. Water Resources Research, 53(4), 3102–3119.
https://doi.org/10.1002/2016WR019733
Torres, M. A., West, A. J., Clark, K. E., Paris, G., Bouchez, J., Ponton, C., Feakins, S. J., Galy,
V., & Adkins, J. F. (2016). The acid and alkalinity budgets of weathering in the Andes–
Amazon system: Insights into the erosional control of global biogeochemical cycles.
Earth and Planetary Science Letters, 450, 381–391.
https://doi.org/10.1016/j.epsl.2016.06.012
Torres, M. A., West, A. J., & Li, G. (2014). Sulphide oxidation and carbonate dissolution as a
source of CO2 over geological timescales. Nature, 507(7492), 346–349.
https://doi.org/10.1038/nature13030
Trenberth, K. (2011). Changes in precipitation with climate change. Climate Research, 47(1),
123–138. https://doi.org/10.3354/cr00953
Tucker, G. E., & Bras, R. L. (2000). A stochastic approach to modeling the role of rainfall
variability in drainage basin evolution. Water Resources Research, 36(7), 1953–1964.
https://doi.org/10.1029/2000WR900065
Turchyn, A. V., Tipper, E. T., Galy, A., Lo, J.-K., & Bickle, M. J. (2013). Isotope evidence for
secondary sulfide precipitation along the Marsyandi River, Nepal, Himalayas. Earth and
Planetary Science Letters, 374, 36–46. https://doi.org/10.1016/j.epsl.2013.04.033
Uhlig, D., & von Blanckenburg, F. (2019). How Slow Rock Weathering Balances Nutrient Loss
During Fast Forest Floor Turnover in Montane, Temperate Forest Ecosystems. Frontiers
in Earth Science, 0. https://doi.org/10.3389/feart.2019.00159
Urey, H. C. (1952). On the Early Chemical History of the Earth and the Origin of Life.
Proceedings of the National Academy of Sciences, 38(4), 351–363.
https://doi.org/10.1073/pnas.38.4.351
145
Van Stempvoort, D. R., & Krouse, H. R. (1993). Controls of δ
18
O in Sulfate: Review of
Experimental Data and Application to Specific Environments. In C. N. Alpers & D. W.
Blowes (Eds.), Environmental Geochemistry of Sulfide Oxidation (Vol. 550, pp. 446–
480). American Chemical Society. https://doi.org/10.1021/bk-1994-0550.ch029
Vaughan, M. C. H., Bowden, W. B., Shanley, J. B., Vermilyea, A., Sleeper, R., Gold, A. J.,
Pradhanang, S. M., Inamdar, S. P., Levia, D. F., Andres, A. S., Birgand, F., & Schroth, A.
W. (2017). High-frequency dissolved organic carbon and nitrate measurements reveal
differences in storm hysteresis and loading in relation to land cover and seasonality.
Water Resources Research, 53(7), 5345–5363. https://doi.org/10.1002/2017WR020491
Vitousek, P. M., & Farrington, H. (1997). Nutrient limitation and soil development:
Experimental test of a biogeochemical theory. Biogeochemistry, 37, 13.
Vitousek, P. M., & Sanford, R. L. (1986). Nutrient Cycling in Moist Tropical Forest. Annual
Review of Ecology and Systematics, 17, 137–167.
Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M., & Weingartner, R. (2007). Mountains of
the world, water towers for humanity: Typology, mapping, and global significance.
Water Resources Research, 43(7). https://doi.org/10.1029/2006WR005653
von Freyberg, J., Allen, S. T., Seeger, S., Weiler, M., & Kirchner, J. W. (2018). Sensitivity of
young water fractions to hydro-climatic forcing and landscape properties across 22 Swiss
catchments. Hydrology and Earth System Sciences, 22(7), 3841–3861.
https://doi.org/10.5194/hess-22-3841-2018
von Freyberg, J., Studer, B., & Kirchner, J. W. (2017). A lab in the field: High-frequency
analysis of water quality and stable isotopes in stream water and precipitation. Hydrology
and Earth System Sciences, 21(3), 1721–1739. https://doi.org/10.5194/hess-21-1721-
2017
Walker, T. W., & Syers, J. K. (1976). The fate of phosphorus during pedogenesis. Geoderma,
15(1), 1–19. https://doi.org/10.1016/0016-7061(76)90066-5
West, A., Galy, A., & Bickle, M. (2005). Tectonic and climatic controls on silicate weathering.
Earth and Planetary Science Letters, 235(1–2), 211–228.
https://doi.org/10.1016/j.epsl.2005.03.020
Wilcke, W., Velescu, A., Leimer, S., Bigalke, M., Boy, J., & Valarezo, C. (2017). Biological
versus geochemical control and environmental change drivers of the base metal budgets
of a tropical montane forest in Ecuador during 15 years. Biogeochemistry, 136(2), 167–
189. https://doi.org/10.1007/s10533-017-0386-x
Wilcke, W., Yasin, S., Abramowski, U., Valarezo, C., & Zech, W. (2002). Nutrient storage and
turnover in organic layers under tropical montane rain forest in Ecuador: Nutrient storage
and turnover in montane forest. European Journal of Soil Science, 53(1), 15–27.
https://doi.org/10.1046/j.1365-2389.2002.00411.x
Wilusz, D. C., Harman, C. J., & Ball, W. P. (2017). Sensitivity of Catchment Transit Times to
Rainfall Variability Under Present and Future Climates. Water Resources Research,
53(12), 10231–10256. https://doi.org/10.1002/2017WR020894
146
Winnick, M. J., Carroll, R. W. H., Williams, K. H., Maxwell, R. M., Dong, W., & Maher, K.
(2017). Snowmelt controls on concentration-discharge relationships and the balance of
oxidative and acid-base weathering fluxes in an alpine catchment, East River, Colorado.
Water Resources Research, 53(3), 2507–2523. https://doi.org/10.1002/2016WR019724
Wright, S. J., Yavitt, J. B., Wurzburger, N., Turner, B. L., Tanner, E. V. J., Sayer, E. J., Santiago,
L. S., Kaspari, M., Hedin, L. O., Harms, K. E., Garcia, M. N., & Corre, M. D. (2011).
Potassium, phosphorus, or nitrogen limit root allocation, tree growth, or litter production
in a lowland tropical forest. Ecology, 92(8), 1616–1625. https://doi.org/10.1890/10-
1558.1
Abstract (if available)
Abstract
Earth’s “critical zone” – the living skin from the top of the tree canopy to the water table – is essential for sustaining a habitable planet and is exceedingly vulnerable to climate change. In the critical zone, water, rocks and life interact, leading to biogeochemical reactions, complex networks of fluid flow, and significant amounts of water transpiration. Each of these processes are important in their own right – for example, biogeochemistry mediates atmospheric carbon dioxide levels and produces nutrients that allows life to grow in soils. Subterranean reservoirs of water sustain society and ecosystem transpiration is a natural cooling mechanism for the planet. Yet, a key feature of each of these critical zone processes is the complexity of interactions that influence them: water, rocks and life in the critical zone cannot be studied in a vacuum.
The work presented here is an investigation of critical zone processes across the transition from Andes mountains to Amazon foreland floodplain in southern Peru. I carried out hydrochemical monitoring for approximately four years at small seven watersheds that span a transition from mountain to floodplain to understand how changes in landscape structure (from steep mountains to flat floodplain), affect critical zone processes. I use the chemistry of water found in rivers, soils and plants as a tool to understand processes that occur inside of watersheds. The vast majority of critical zone studies to date have been carried out in northern latitudes, leaving tropical latitudes largely understudied despite their outsized role in the global water cycle and high levels of primary productivity and biodiversity. The dramatic transition between the steep slopes and high elevation of the Andes mountain to the flat Amazon floodplain provides another layer of motivation for this study. Mountainous regions play key roles in global water cycles, receiving high amounts of precipitation and providing water to drier downstream areas across the globe. Given the role of mountains in storing and releasing water, I am particularly interested in exploring how the varied mountain environments targeted in this research transmit water.
I begin my exploration of the linkages between landscapes, water, rocks and life in Chapter two, where I study sulfide mineral oxidation from the Andes mountains to Amazon floodplain. Work on sulfide mineral oxidation over the past two decades has expanded our view of chemical weathering: rather than viewing chemical weathering as a reliable sink of carbon dioxide, we now know that sulfide mineral oxidation can lead to carbon dioxide release from rocks. In this chapter, I ask the question, “what happens to sulfate released from sulfide mineral oxidation in the Andes mountains?” which has implications for how much carbon dioxide is consumed or released from rock weathering. I use stable isotopes of sulfur and oxygen in riverine sulfate combined with a mass-balance framework to show that there is not a significant microbial recycling of sulfate along the mountain-to-floodplain transition. Some studies have pointed to microbial sulfate reduction as a mechanism that lessens the potential for carbon dioxide release; I suggest that this process is not as important as previously thought.
After investigating the importance of pyrite oxidation and weathering from sulfuric acid in the Andes mountains, I examine water transit across the mountain to floodplain transition. In Chapter three, I use the stable isotope composition of oxygen and hydrogen in precipitation and streamflow to understand how quickly water moves from precipitation to streamflow. I employ a widely-used metric of water transit, the “young water fraction” to quantitatively determine how water transit varies from mountain to floodplain. I show that water moves through the subsurface the fastest in watersheds with low-permeability bedrock and poorly developed soils. I also show that the young water fraction depends on local hydroclimate. The results of this study highlight the complex nature of water transit in mountainous regions.
Chapter four examines the role that storms play in driving nutrient loss from a nutrient-poor, tropical, terra firme terrace. In this chapter, I combine stream stable isotope data with major element chemistry collected during storms. Using this data, I show that storm water travels through surface flowpaths and leaches rock-derived nutrients accumulated in the surface soil due to biological cycling. I compare estimates of surface soil nutrient stocks and riverine nutrient losses to show that storms represent a “leak” in ecosystem nutrient cycling – significant due to the nutrient-poor nature of tropical fluvial terraces and the reality that climate change will lead to more storms in the future. This work spans hydrology and ecology by combining hydrochemical data with estimates of nutrient stocks in soil and vegetation to create an interpretive framework applicable across disciplinary boundaries.
In Chapter five, I compare the sources of precipitation that sustain streamflow and plant transpiration in a tropical montane cloud forest watershed. I measure the oxygen stable isotope composition of plant xylem water, stream water, soil water and precipitation to determine the fraction of recent precipitation that supplies stream, soil and plant xylem waters. I show that plants take up about a quarter of their water from recent precipitation, while streamflow is sourced from less than ten percent recent precipitation. The abundance of recent precipitation in plant xylem waters suggests that in this tropical mountainous watershed, plants take up recent rains using shallow root networks. This is in direct contrast to Mediterranean and temperate climates where plants transpire water that has been stored within landscapes for months or longer. The importance of recent precipitation in tropical plant water use suggests that these environments may be particularly vulnerable to hydrologic changes associated with climate change.
I also include two appendices with additional datasets I collected during my Ph.D.: Appendix A provides an exploration of stream major element chemistry in the seven small watersheds monitored in this study. Measuring major element chemistry across a range of hydrologic conditions provides insight towards chemical weathering processes across the geomorphic gradient of the Andes mountains and Amazon floodplain. Appendix B includes additional sulfur and oxygen isotopes in riverine sulfate that were not published with Chapter 2.
Linked assets
University of Southern California Dissertations and Theses
Conceptually similar
PDF
Germanium and silicon isotope geochemistry in terrestrial and marine low-temperature environments
PDF
Isotopic insights to human-impacted hydrologic systems: from urban watersheds to hydraulically fractured basins
PDF
Critical zone response to perturbation: from mountain building to wildfire
PDF
Chemical weathering across spatial and temporal scales: from laboratory experiments to global models
PDF
From tree tops to river runoff: tracing plant wax biomarkers across the Peruvian Andes and Amazon
PDF
Diagenesis of C, N, and Si in marine sediments from the Western Tropical North Atlantic and Eastern Subtropical North Pacific: pore water models and sedimentary studies
PDF
Flowstone ideograms: deciphering the climate messages of Asian speleothems
PDF
Concentration and size partitioning of trace metals in surface waters of the global ocean and storm runoff
PDF
Discerning local and long-range causes of deoxygenation and their impact on the accumulation of trace, reduced compounds
PDF
Antarctic climate variability from greenhouse to icehouse world
PDF
Tracking fluctuations in the eastern tropical north Pacific oxygen minimum zone: a high-resolution geochemical evaluation of laminated sediments along western North America
PDF
The distributions and geochemistry of iodine and copper in the Pacific Ocean
Asset Metadata
Creator
Burt, Emily Irene (author)
Core Title
Ages, origins and biogeochemical role of water across a tropical mountain to floodplain transition
School
College of Letters, Arts and Sciences
Degree
Doctor of Philosophy
Degree Program
Geological Sciences
Degree Conferral Date
2022-12
Publication Date
09/04/2022
Defense Date
08/10/2022
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
Catchment hydrology,chemical weathering,OAI-PMH Harvest,plant water use,pyrite oxidation,water transit times
Format
application/pdf
(imt)
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
West, Joshua (
committee chair
), Hammond, Douglas (
committee member
), Levine, Naomi (
committee member
)
Creator Email
burt.emily1@gmail.com,eburt@usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-oUC111637625
Unique identifier
UC111637625
Legacy Identifier
etd-BurtEmilyI-11174
Document Type
Dissertation
Format
application/pdf (imt)
Rights
Burt, Emily Irene
Type
texts
Source
20220906-usctheses-batch-977
(batch),
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Access Conditions
The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.
Repository Name
University of Southern California Digital Library
Repository Location
USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Repository Email
cisadmin@lib.usc.edu
Tags
Catchment hydrology
chemical weathering
plant water use
pyrite oxidation
water transit times