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Late Quaternary Tectonics, Incision, and Landscape Evolution of the Calchaquí River Catchment, Eastern Cordillera, NW
Argentina
by
James Anderson McCarthy
A thesis submitted in conformity with the requirements for the degree of Master of Applied Science
Graduate Department of Earth Sciences University of Toronto
© Copyright by James Anderson McCarthy 2014
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Late Quaternary Tectonics, Incision, and Landscape Evolution of the
Calchaquí River Catchment, Eastern Cordillera, NW Argentina
James Anderson McCarthy
Master of Applied Science
Graduate Department of Earth Sciences University of Toronto
2014
Abstract
In this study we use field investigations, analysis of longitudinal river profiles, and 10Be-derived
erosion rates and paleo-erosion rates to examine the Quaternary landscape evolution of the
Calchaquí River Catchment of the southernmost Eastern Cordillera, in NW Argentina. The
spatial distribution of erosion rates, normalized steepness indices, concavity indices, and
knickpoints reflect active tectonics and resistant lithologies exposed along preexisting structural
heterogeneities. Shortening is distributed across multiple structures and controls local base-
levels. Field studies document active faults and ~100m of channel incision in <300 kyr.
Catchment mean erosion rates and paleo-erosion rates are not markedly different, suggesting that
Quaternary climate changes have not significantly influenced erosion rates at cosmogenic
nuclide time scales. Collectively, our results demonstrate that the rate and style of landscape
evolution in the southern Eastern Cordillera is primarily controlled by Quaternary tectonics and
pre-orogenic structure, thus complicating regional investigations of tectonic and climatic
feedbacks.
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Acknowledgments
I would first like to thank my supervisor, Dr. Lindsay M. Schoenbohm, for giving me
opportunities to learn from her, to do field work in NW Argentina, to do research in the exciting
and dynamic field of Tectonic Geomorphology, to work with her other amazing students (Mark,
Neil, and Renjie), and to get to know her family. I especially appreciate Dr. Schoenbohm’s
ability to trust in me and to let me take this project in my own direction.
I would also like to thank Dr. Paul Bierman and Dr. Dylan Rood, of the University of Vermont
and the University of Glasgow, respectively, for their help with the attached manuscript. Thank
you to my supervisory committee members, Dr. Pierre Robin and Dr. Nick Eyles, for their
comments and support throughout my degree program. I would also like to thank Dr. Bodo
Bookhagen (UCSB) and Dr. John Gosse (Dalhousie) for useful comments. Thanks to Santiago
Uriburu Quintana for assistance in the field.
Financial support for this work was provided by Dr. Schoenbohm through NSF and NSERC
grants. I thank the Geological Society of America for awarding me a Graduate Student Research
Grant (specifically the John Montagne Quaternary Geomorphology Award), which enabled
additional analyses that are critical to my conclusions. Conference support was provided by the
University of Toronto Mississauga Department of Chemical & Physical Sciences, as well as the
University of Toronto School of Graduate Studies. Personal support was provided through a
Connaught International Student Scholarship and by the Department of Earth Sciences.
Lastly, thanks to all my friends in the Department of Earth Sciences for pulling me through.
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Table of Contents Acknowledgments ........................................................................................................................ iii
Table of Contents ......................................................................................................................... iv
List of Tables ............................................................................................................................... vii
List of Figures ............................................................................................................................. viii
List of Appendices ....................................................................................................................... xii
Chapter 1: Introduction ................................................................................................................1
1.1 Evolution of the Eastern Puna Plateau & Retroarc Foreland ...............................................1
1.1.1 Central Andean Plateau ............................................................................................1
1.1.2 Southern Eastern Cordillera .....................................................................................5
1.2 Tectonic-Climatic Interactions.............................................................................................7
1.3 Longitudinal River Profile Analysis ..................................................................................10
1.3.1 Background ............................................................................................................10
1.3.2 Theory ....................................................................................................................11
1.3.3 Interpretation ..........................................................................................................13
1.4 Terrestrial Cosmogenic Nuclide (TCN) Chronology ........................................................14
1.4.1 Theory ....................................................................................................................14
1.4.2 Application & Limitations .....................................................................................16
1.4.3 Dating Stable Landforms .......................................................................................17
1.4.4 Measuring Catchment Mean Erosion Rates ...........................................................18
2 Chapter 2: Late Quaternary Tectonics, Incision, and Landscape Evolution of the Calchaquí River Catchment, Eastern Cordillera, NW Argentina .....................................21
2.1 Abstract ..............................................................................................................................22
2.2 Introduction ........................................................................................................................22
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2.3 Landscape Analysis As A Tool For Evaluating Tectonics and Climate in Spatially Heterogeneous Regions .....................................................................................................24
2.4 Geologic Setting.................................................................................................................25
2.4.1 Structural Evolution ...............................................................................................25
2.4.2 Quaternary Climate & Geomorphology .................................................................28
2.5 Methods..............................................................................................................................28
2.5.1 Field Studies...........................................................................................................28
2.5.2 Longitudinal River Profile Analysis ......................................................................29
2.5.3 Terrestrial Cosmogenic Nuclide (10Be) Chronology .............................................30
2.5.4 Paleo-Erosion Rates ...............................................................................................32
2.6 Results ................................................................................................................................33
2.6.1 Field Studies...........................................................................................................33
2.6.2 River Profile Analysis ............................................................................................35
2.6.3 10Be Catchment Mean Erosion Rates .....................................................................38
2.6.4 10Be Depth Profiles ................................................................................................40
2.6.5 Paleo-Erosion Rates ...............................................................................................42
2.7 Discussion ..........................................................................................................................42
2.7.1 Controls on River Morphology ..............................................................................43
2.7.2 Controls on Landscape Evolution of the Pucará Valley ........................................50
2.7.3 Tectonic Implications.............................................................................................51
2.8 Conclusions ........................................................................................................................53
3 Chapter 3: Concluding Remarks ...........................................................................................55
3.1 Methodological Considerations .........................................................................................55
3.1.1 River Profile Analysis & Catchment-Mean Erosion Rates ....................................55
3.1.2 TCN Depth Profiles ...............................................................................................55
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3.2 Future Research .................................................................................................................56
References .....................................................................................................................................58
Appendices ....................................................................................................................................67
Appendix A: Supporting Information for Field Studies ................................................................67
Appendix B: Geologic Map of the Pucará Valley .........................................................................76
Appendix C: 10Be Analytical Results ............................................................................................77
Appendix D: Supporting Information for 10Be Depth Profiles ......................................................78
Appendix E: Supporting Information for River Profile Analysis ..................................................86
Appendix F: Explanation of Attached Digital Material .................................................................91
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List of Tables
Table 1. 10Be Concentrations, Catchment-mean production rates, Catchment mean erosion rates,
and corresponding topographic and climatic characteristics. ....................................................... 38
Table 2. Vertical incision rates and catchment mean paleo-erosion rates derived from 10Be depth
profile ages and inheritance, respectively. See section 2.5.4 for methodology. ........................... 42
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List of Figures
Figure 1. Characteristics of the Altiplano Plateau and Puna Plateau. Top panel shows (in km)
topography, crustal thickness, mantle lithosphere thickness, and depth of the subducting Nazca
Plate, along a coastline-parallel section (bottom panel). In the bottom panel, thick black line
denotes political boundaries, dark grey denotes areas above 3km, light gray zones are thin-
skinned fold-and-thrust belts and cross-hatched zones are thick-skinned foreland provinces. East-
west (vertical) rule shows area of high seismic-wave attenuation. From Allmendinger et al.
(1997). ............................................................................................................................................. 2
Figure 2. Shaded relief maps of the Central Andes, showing (a) the names and extent of
morphotectonic provinces (SBS = Santa Bárbara System), and (b) mean annual precipitation (m
yr-1). Dashed vertical black bars along the y-axes represent the latitudinal zone in which the
subducting plate transitions north to south from steep slab to shallow slab geometry (Cahill and
Isacks, 1992). Solid lines represent the latitudinal zone of flat-slab subduction (Barazangi and
Isacks, 1976). Red boxes indicate the approximate location of the study area. Base figure from
Strecker at al. (2007). ...................................................................................................................... 4
Figure 3. Structural and topographic comparison of A) the thin-skinned retroarc fold-and-thrust
belt of Bolivia and B) the thick-skinned retroarc foreland of NW Argentina, which is the focus of
this study. From Strecker et al. (2011) ............................................................................................ 6
Figure 4. Schematic model of an aridity-driven tectonic-climatic feedback system, proposed for
the eastern margin of the Puna-Altiplano Plateau and the northern border of the Tibetan Plateau.
In Stage 1 (top), continued crustal shortening drives uplift of the frontal range, creating an
orographic barrier to precipitation. In Stage 2 (middle), channels in Basin B are defeated due to
aridity-driven reductions in stream power, and cessation of sediment export leads to crustal
loading and forelandward propagation of deformation. In Stage 3 (bottom), Basins A and B
coalesce and an orographic barrier to precipitation begins to isolate Basin C. Figure from Sobel
et al. (2003) ..................................................................................................................................... 9
Figure 5. Oblique view of the frontal Himalaya and the abrupt physiographic transition which
corresponds with the Main Central Thrust (MCT). Longitudinal profile steepness indices (ksn,
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see section 1.3.2 for description) correspond with this physiographic transition, and have been
used to argue for out-of-sequence deformation along the MCT. MFT, Main Frontal Thrust;
MBT, Main Boundary Thrust. From Kirby and Whipple (2012). ................................................ 10
Figure 6. Longitudinal river profiles and slope-area scaling (inset) under A) differing concavity
indices (θ) and B) differing normalized steepness indices (ksn). From Kirby and Whipple (2012).
....................................................................................................................................................... 12
Figure 7. Vertical-step and slope-break knickpoint morphology in (a & c) profile form and (b &
d) slope-area space. From Kirby and Whipple (2012). ................................................................. 13
Figure 8. Idealized attenuation curves for production of 10Be in A) rock ( ρ = 2.7 g cm-3) and B)
soil (ρ = 1.2 g cm-3). Figure from Bierman and Nichols (2004). ................................................. 15
Figure 9. At isotopic steady-state, where the production of a given TCN in a catchment is equal
to the export of that TCN, the mass flux (dM/dt) can be calculated with the measured TCN
concentration (C, in atoms g-1) of river sediments at the catchment outlet. Catchment mean
erosion rate (mm ky-1) is determined by dividing the mass flux by the catchment area and
bedrock density. Erosion rates are spatially non-uniform across the catchment, but sediment is
well mixed in the channel network such that a sample represents a catchment-integrated TCN
concentration. Figure from von Blanckenburg (2005). ................................................................ 19
Figure 10. Composite digital elevation model and shaded relief map of the south central Andes
with major tectonomorphic provinces outlined in black. Thicker black line delineates internally
drained Puna Plateau from the externally drained Eastern Cordillera and Sierras Pampeanas.
Yellow line outlines the Calchaqui River catchment (CRC). Red box outlines the Pucará Valley,
where field studies were focused. SBS = Santa Barbara System. CG = Cerro Galán Caldera. ... 24
Figure 11. Quaternary strath terraces and pediment surfaces in the Pucará Valley. Depositional
ages derived from cosmogenic 10Be depth profiles. Numbered soil pits are described in TABLE
SOILS. JVT = Jasimaná-Vallecito Thrust. SQT = Sierra de Quilmes Thrust. PT = Pucará Thrust.
See Auxiliary Material for complete geologic map. Fault nomenclature and structure modified
from (Carrera and Munoz, 2008). Area shown by red box in Figure Regional. ........................... 27
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Figure 12. Photograph from the site of the Q5 depth profile in Figure 11, looking approximately
northeast. Foreground shows Q5 strath terrace beveled into sedimentary rocks of the Tertiary
Payogastilla Group, which rest in angular unconformity over Cretaceous Pirgua Group redbeds.
In the background Q2, Q3, Q4, Q6 and Q7 surfaces are beveled into both Tertiary and
Cretaceous sedimentary units. Rio Pucará flows from right (south) to left (north). Note
monoclinal structure within Cretaceous units beneath the Q7 surface. High ranges are composed
of the Neoproterozoic Puncoviscana Formation. .......................................................................... 34
Figure 13 (opposite). (a) Shaded relief map of the CRC, 10Be Catchment mean erosion rate
samples and corresponding subcatchments (labeled) from this study and Bookhagen and Strecker
(2012). Stream network derived from ASTER 30m DEM and a minimum accumulation of
35,000 pixels (1.05 km2). (b) Lithologic divisions, major faults, and knickpoints in the CRC.
Knickpoints according to legend in (d). Dashed lines are newly mapped faults. CNT = Cerro
Negro Thrust (Carrapa et al., 2011). (c) 10Be catchment mean erosion rates, in mm kyr-1.
Sample locations as per legend in (a). (d) Normalized channel steepness indices and knickpoints
in the CRC. See text for description of knickpoint typology and channel regression parameters.
Labeled streams are displayed in profile in Figure Streams. ........................................................ 36
Figure 14. Correlations between catchment mean erosion rates and catchment mean annual
precipitation, catchment area, catchment mean slope, and catchment mean elevation. See Table 1
for data. ......................................................................................................................................... 39
Figure 15. In situ 10Be depth profiles and monte carlo simulator results for age, inheritance, and
surface erosion rates when run for 100,000 solutions at 1 sigma uncertainty, according to
parameters described in the text and appendices. Black line is the best fit. Gray lines are 100,000
model solutions. Solid black dots are subsurface samples used in the model simulations. Hollow
dots are surface sediment samples that were analyzed, but not used in model simulations due to
evidence of bioturbation. Hollow square represents a quartz cobble amalgamation (n=85) sample
that was simularly excluded from model simulations. .................................................................. 40
Figure 16. Selected longitudinal river profiles and corresponding local slope/drainage area
regressions. Individual segments are bound by knickpoints or confluences with trunk streams and
were regressed with a reference concavity of 0.45. Resulting normalized steepness indices and
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raw concavity indices are displayed for each segment. Question marks identify faults with
unknown dip. In slope-area space light and dark blue lines represent forced and unforced
regressions, respectively. See Figure 13 for stream locations. CNT = Cerro Negro Thrust; PT =
Pucara Thrust; JVT = Jasimana-Vallecito Thrust. ........................................................................ 45
Figure 17. Concavity indices and mean annual rainfall in the CRC. See Figure 13 for knickpoint
classification. TRMM precipitation data from Bookhagen and Strecker (2008). Labeled streams
are displayed in profile in Figure Streams. ................................................................................... 47
Figure 18. Vertical distribution of knickpoints in the CRC. See Figures 13 and 17 for plan view.
....................................................................................................................................................... 49
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List of Appendices Appendix A: Supporting Information for Field Studies ............................................................... 67
Appendix B: Geologic Map of the Pucará Valley ........................................................................ 76
Appendix C: 10Be Analytical Results ........................................................................................... 77
Appendix D: Supporting Information for 10Be Depth Profiles ..................................................... 78
Appendix E: Supporting Information for River Profile Analysis ................................................. 86
Appendix F: Explanation of Attached Digital Material ................................................................ 91
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Chapter 1: Introduction
This study investigates the Quaternary landscape evolution of a large (12,860 km2) catchment in
the Eastern Cordillera of NW Argentina, immediately east of the Central Andean Puna Plateau.
Due to the position of the study area with respect to large scale (100s of km) tectonic, climatic,
and surface process transitions, investigation of the style and rates of both tectonic deformation
and erosion speak to various outstanding geologic questions, including the kinematics of Andean
deformation and plateau growth, the role of geodynamic processes in the topographic evolution
of the Puna Plateau, and the dynamic interactions between climate and tectonics. At smaller
scales (e.g. 10s of km), my results evaluate the capacity of arid landscapes to achieve erosional
steady state, as well as the relative importance of lithology, climate, and relief in controlling
local erosion rates. Many of these questions are addressed in a manuscript set for submission to
the Journal of Geophysical Research – Earth Surface, which comprises Chapter 2 of this thesis.
Chapter 1 provides important background for that work and for additional conclusions in Chapter
3. Some of the content in Chapter 1 is reiterated in Chapter 2, although I limited repetition as
much as possible while retaining clarity.
1.1 Evolution of the Eastern Puna Plateau & Retroarc Foreland
1.1.1 Central Andean Plateau
The Central Andean Puna-Altiplano Plateau is the highest and largest plateau in the world to in a
Cordilleran arc setting, and investigations into the temporal and spatial evolution of this orogen
have improved understanding of tectonic, geodynamic and climatic controls on topography at
multiple scales (Allmendinger et al., 1997). At the plate-tectonic scale, the geometry of the
subducting Nazca Plate exerts a first-order control on plateau extent. The Nazca plate dip angle
shallows to ~10° at either end of the plateau (15°S & 28°S), whereas the plate dips ~30° beneath
the plateau (Fig. 1) (Cahill and Isacks, 1992). These zones of flat subduction are spatially
coincident with young, buoyant oceanic plateaus and the absence of arc volcanism, suggesting
that the age and thickness of subducting oceanic crust provide strong controls on orogen
morphology (Barazangi and Isacks, 1976; Gutscher et al., 2000).
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Figure 1. Characteristics of the Altiplano Plateau and Puna Plateau. Top panel shows (in km) topography, crustal
thickness, mantle lithosphere thickness, and depth of the subducting Nazca Plate, along a coastline-parallel section
(bottom panel). In the bottom panel, thick black line denotes political boundaries, dark grey denotes areas above
3km, light gray zones are thin-skinned fold-and-thrust belts and cross-hatched zones are thick-skinned foreland
provinces. East-west (vertical) rule shows area of high seismic-wave attenuation. From Allmendinger et al. (1997).
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Within the plateau, differences in topography, magmatism, lithospheric structure and temporal
evolution define two distinct regions; the Altiplano to the North and the Puna to the south (Figs.
1 and 2) (Allmendinger et al., 1997). The Altiplano Plateau is characterized by ~3.2 km mean
elevation, extremely low relief, crustal shortening >500 km, crustal thicknesses >60 km and total
lithospheric thicknesses >80 km; in contrast, the Puna Plateau is characterized by ~4 km mean
elevations, rugged topography with multiple, isolated depocenters, 150 – 200 km crustal
shortening, crustal thicknesses ≤55 km and reduced lithospheric thickness, especially in the
central Puna (~60 km), where mantle lithosphere may be absent (McQuarrie, 2002; Tassara et
al., 2006; Zhou et al., 2013).
Additionally, the Altiplano and Puna plateaus exhibit significant differences in the timing of
uplift and deformation. Paleoaltimetry and sedimentology suggest that uplift of the Altiplano
began ~25 Ma, but was punctuated by brief, rapid periods of uplift (>1 km) between ~10 and 6
Ma in the northern Altiplano and between ~16 and 9 Ma in the southern Altiplano (Allmendinger
et al., 1997; Garzione et al., 2008; Garzione et al., 2014). The uplift and deformation history of
the Puna is less thoroughly resolved. Volcanic glass paleoaltimetry, clumped isotope
thermometry and detrital thermochronology of sedimentary rocks in the retroarc foreland
(southern Eastern Cordillera, Fig. 2) suggest that the Puna reached near-modern elevations
between 36 Ma and at least ~10 Ma, and deformation had reached the Eastern Cordillera by ~14
Ma (Carrapa et al., 2012; Canavan et al., 2014; Carrapa et al., 2014a)
In the Puna Plateau, shortening is insufficient to explain the crustal thicknesses observed,
suggesting that magmatic additions and/or geodynamic processes play important roles in the
evolution of the orogen (McQuarrie, 2002). For example, lower crustal flow from the Altiplano
and underplating of material from subduction erosion of the forearc have been proposed to
explain the discrepancy between crustal shortening and crustal thickness in the Puna (Kley and
Monaldi, 1998). A well-documented Late Miocene kinematic shift from contraction to extension
suggests that gravitational spreading processes, likely driven by reduced Nazca-South America
plate convergence rate, control the rate and style of deformation on the Puna Plateau
(Allmendinger et al., 1997; Schoenbohm and Strecker, 2009; Lanza et al., 2013). Additionally,
the combination of high mean elevations, volcanic rock geochemistry and partial or total absence
of mantle lithosphere beneath the central Puna Plateau suggests that small-scale lithospheric
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foundering events also play an important role in the evolution of the Puna Plateau (Schoenbohm
and Strecker, 2009; Lanza et al., 2013; Zhou et al., 2013). Recent interpretations of seismic
tomography indicate a descending and detached high-velocity block, supporting this conclusion
(Bianchi et al., 2013).
The degree to which gravitational spreading, extension, and lithospheric foundering of the
Central Puna Plateau influence deformation within the adjacent retroarc foreland, which includes
the southern Eastern Cordillera and northern Sierras Pampeanas (Fig. 2), is unresolved. Regional
kinematic analyses indicate Pliocene shifts from subvertical to subhorizontal extension in many
localities throughout the Eastern Cordillera and Sierras Pampeanas (Marrett et al., 1994;
Schoenbohm and Strecker, 2009; Pearson et al., 2012; Lanza et al., 2013). However, Pliocene -
Quaternary shortening has also been documented in the region (Strecker et al., 1989; Hilley and
Strecker, 2005; Carrera and Munoz, 2008; Hain et al., 2011; Santimano and Riller, 2012).
Figure 2. Shaded relief maps of the Central Andes, showing (a) the names and extent of morphotectonic provinces
(SBS = Santa Bárbara System), and (b) mean annual precipitation (m yr-1). Dashed vertical black bars along the y-
axes represent the latitudinal zone in which the subducting plate transitions north to south from steep slab to
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shallow slab geometry (Cahill and Isacks, 1992). Solid lines represent the latitudinal zone of flat-slab subduction
(Barazangi and Isacks, 1976). Red boxes indicate the approximate location of the study area. Base figure from
Strecker at al. (2007).
1.1.2 Southern Eastern Cordillera
The southern Eastern Cordillera is a bi-vergent fold and thrust belt, characterized by basement-
involved reverse faults that preferentially occur along preexisting structural heterogeneities,
including inverted Cretaceous rift structures and earlier metamorphic fabrics (Grier et al., 1991;
Strecker et al., 2007; Carrera and Munoz, 2008; Santimano and Riller, 2012). Uplifted basement
blocks are composed of Precambrian metasedimentary units, Paleozoic granitoids, and redbeds
from the Cretaceous Salta Rift (Grier et al., 1991; Coutand et al., 2006). Cenozoic sedimentary
rocks in intramontane basins within the region, which are derived from the Precambrian to
Cretaceous units, reflect a spatiotemporal transition from proximal foredeep to wedge-top to
intramontane basin as well as the transition to increasingly arid climate due to the uplift of
orographic barriers to precipitation (Bywater-Reyes et al., 2010; Carrapa et al., 2012). U-Pb
zircon ages and sedimentary provenance studies indicate that Andean shortening reached the
western edge of the study area in the Eocene, and roughly propagated from west to east until the
Pliocene, at which point deformation was primarily accommodated by the Santa Barbara System
to the east (Carrapa et al., 2012 and references therein). However, the inherited structural
heterogeneity of the southern Eastern Cordillera, northern Sierras Pampeanas, and Santa Barbara
System has led to significant differences in the timing of deformation over distances as small as
50km, therefore presenting a fundamental difference with the more uniformly deforming thin-
skinned Bolivian retroarc foreland basin system (Fig. 3) (Hongn et al., 2007; DeCelles et al.,
2011; Carrapa et al., 2012). A resulting debate exists as to whether the southern Cordillera
Oriental region formed as part of a continuous or broken foreland, the answer to which has
important implications for Andean fault kinematics and Andean paleoclimate (Strecker et al.,
2011; Carrapa et al., 2012).
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Figure 3. Structural and topographic comparison of A) the thin-skinned retroarc fold-and-thrust belt of Bolivia and
B) the thick-skinned retroarc foreland of NW Argentina, which is the focus of this study. From Strecker et al. (2011)
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For example, crustal heterogeneities in NW Argentina may have limited the crustal flexural
response of the advancing orogenic wedge, such that creation of accommodation space and
subsequent sedimentation are spatially limited relative to the wide foreland basin in Bolivia
(Strecker et al., 2011). Shortening along reactivated high angle faults in NW Argentina creates
relief that impedes penetration of moisture to wedge-top and intramontane basins, whereas
moisture transport in the Bolivian system is more uniformly distributed across the wedge-top
(Figure 2) (Strecker et al., 2007). The lack of internal moisture transport in NW Argentina,
except along structurally controlled lows in the landscape, may explain the well-documented
history of alternating sediment storage and excavation in intramontane basins in this region
(Hilley and Strecker, 2005; González Bonorino and Abascal, 2012). Structural control on climate
(through the uplift of orographic barriers to precipitation) may in turn influence the rate and style
of deformation by decreasing erosional efficiency within intramontane basins (see section 1.2)
(Sobel et al., 2003; Hilley et al., 2005; Hilley and Strecker, 2005; Bookhagen and Strecker,
2008).
1.2 Tectonic-Climatic Interactions
The surface of Earth represents a dynamic balance between spatially and temporally variant
forces that build (e.g. tectonics) and reduce (e.g. erosion) topography. Both tectonics and climate
are capable of creating relief and modifying the landscape, and the effects of different tectonic or
climatic regimes on sedimentary systems and topography can often be described through simple
physical arguments and confirmed through observation of the sedimentary record (Miall, 2006) .
However, the dynamic nature of earth’s sedimentary system necessitates an understanding of the
interaction between these various controls, as negative and positive feedbacks in complex
geologic environments may serve to diminish or amplify sedimentary processes (e.g., Sobel et
al., 2003).
Tectonic deformation influences global, regional, and local climate over various time scales.
Across >10 Myr timescales, migration of tectonic plates controls the distribution of continental
lithosphere across the Earth. Concentration of continental lithosphere in large landmasses at the
poles, such as with Gondwana during the late Paleozoic, limits solar radiation to landmasses and
thus promotes the formation of ice sheets and glacial climates (Caputo and Crowell, 1985). The
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motion of continental landmasses also controls atmospheric and oceanic circulation patterns. The
development of the Antarctic circumpolar current and the subsequent ice-growth on Antarctica
are attributed to the drift of South America and Australia in the Cenozoic (Barker and Burrell,
1977; Kennett, 1977; Raymo and Ruddiman, 1992). On timescales relevant to this study,
tectonics influences local climate by creating orographic barriers to precipitation (Sobel et al.,
2003). Orographic barriers cause aridity leeward of high ranges, for example north of the
Himalaya and west of the central Andes (Bookhagen and Burbank, 2006; Bookhagen and
Strecker, 2008).
The role of climate in driving tectonics is less thoroughly established. The capacity for climate to
influence the rates, magnitude, and styles of tectonic deformation is primarily suggested due to
spatial coincidence between intense precipitation and high crustal exhumation rates in the
Himalaya and in Taiwan (Beaumont et al., 2001). However, high uplift rates may be spatially
coincident with high precipitation rates and high erosion rates, but such coincidence may simply
reflect the creation of an orographic barrier through tectonic shortening (thus enhancing local
rainfall), while uplift occurs independently of surface erosion rates. Numerical modeling
indicates that climate drives tectonics by focused erosion that rapidly removes mass from an
advancing orogenic wedge (i.e. fold-and-thrust belt), lowering lithostatic pressure above faults
and thus enhancing rates of deformation within the orogenic wedge (Davis et al., 1983; Whipple,
2009). This erosion-driven deformation may also be achieved through ductile flow of the middle
crust (in contrast to brittle deformation by faulting), a condition that is proposed for the
Himalaya (Beaumont et al., 2001). Conversely, the lack of both erosion and sediment export due
to extreme aridity may affect tectonics; in active orogens, it is thought that the development of
internal drainage due to aridity can topographically load the lithosphere, increasing frictional
forces on faults within the orogenic wedge and thus drive deformation to the foreland (Sobel et
al., 2003). Furthermore, positive feedbacks between sediment trapping, basin elevation and
aridification allow for significant aggradation in basins that remain hyrdologically connected,
further increasing topographic load within the orogenic wedge (Hain et al., 2011). Modeling of
aridity-driven channel defeat, sediment trapping, and wedge-propagation suggests that these
feedbacks contribute to the large widths of the Puna Plateau and Tibetan Plateau (Fig. 4) (Sobel
et al., 2003; Hilley and Strecker, 2005). Attempts to explain tectonic (and associated
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topographic) evolution of complex landscapes through climate change, beyond simple isostatic
adjustment, are made more compelling if pronounced climate changes can be matched to
sustained (>1 Myr) changes in uplift rate or pattern (Burbank et al., 2003; Whipple, 2009).
Figure 4. Schematic model of an aridity-driven tectonic-climatic feedback system, proposed for the eastern margin
of the Puna-Altiplano Plateau and the northern border of the Tibetan Plateau. In Stage 1 (top), continued crustal
shortening drives uplift of the frontal range, creating an orographic barrier to precipitation. In Stage 2 (middle),
channels in Basin B are defeated due to aridity-driven reductions in stream power, and cessation of sediment
export leads to crustal loading and forelandward propagation of deformation. In Stage 3 (bottom), Basins A and B
coalesce and an orographic barrier to precipitation begins to isolate Basin C. Figure from Sobel et al. (2003)
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1.3 Longitudinal River Profile Analysis
1.3.1 Background
Given that the overall relief structure of a landscape is primarily set by the fluvial channel
network, analysis of longitudinal river profile morphology is now the standard method for
topographic interpretations of tectonics (Whipple and Tucker, 1999). Longitudinal river profiles
are adjusted to balance erosion and uplift and have readily predictable forms under different
climatic, lithologic, and transient conditions, making them particularly useful in tectonic
geomorphology. For example, longitudinal river profile analysis has been used to demonstrate
out-of-sequence deformation in the Himalaya (Fig. 5), perhaps influenced by climate (Seeber and
Gornitz, 1983; Wobus et al., 2006c; Kirby and Whipple, 2012), and also pulsed uplift along the
eastern margin of the Tibetan Plateau (Kirby et al., 2003; Schoenbohm et al., 2004).
Longitudinal river profiles are more useful than hillslope gradients in tectonic interpretations due
to higher erosion thresholds: beyond ~200 mm kyr-1, an erosion rate which is exceeded in many
active orogens, hillslopes reach threshold gradients and fail, whereas longitudinal river profiles
continue to steepen up to ~600 mm kyr-1 (Ouimet et al., 2009; Portenga and Bierman, 2011).
Figure 5. Oblique view of the frontal Himalaya and the abrupt physiographic transition which corresponds with the
Main Central Thrust (MCT). Longitudinal profile steepness indices (ksn, see section 1.3.2 for description)
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correspond with this physiographic transition, and have been used to argue for out-of-sequence deformation along
the MCT. MFT, Main Frontal Thrust; MBT, Main Boundary Thrust. From Kirby and Whipple (2012).
1.3.2 Theory
Along a bedrock channel reach of uniform lithology, uplift rate, and climate, river profiles are
expected to exhibit a power law relationship between slope and drainage area, such that
S = ks A -θ (1)
where S is the local channel gradient, ks is the channel steepness index, A is the contributing
drainage area, and θ is the concavity index. At steady state, this relationship reflects a dynamic
equilibrium between sediment supply and sediment export, such that uplift rate (U) and channel
erosion (E) are equal. Channel erosion is typically modeled as a function of substrate erodibility
(K) and bed shear stress, which is itself a function of contributing drainage area (A) and local
gradient (S) and modified by m and n, positive constants which reflect differences in erosion
process, channel geometry, and basin hydrology (Whipple and Tucker, 1999; Whipple, 2001):
E = K A m S n (2)
Solving for channel slope, and assuming that uplift and erosion are balanced (i.e. assuming
steady-state) yields the following relationship, which is similar in form to Equation 1:
S = (U/K)1/nA-m/n (3)
Comparing equations 1 and 3 reveals that steepness indices should vary due to differences in
uplift rate, lithology, and climate, while concavity indices should be influenced by factors
controlling the sediment transport ability of the profile such as drainage basin shape and
downstream changes in channel width vs. discharge (Kirby and Whipple, 2012). Concavity
indices typically fall into a narrow range (0.4 – 0.7) when channels have uniform climate, uplift
rate, and lithology along their length. In contrast, steepness indices vary widely with differing
uplift rates, matching theoretical predictions of incision models (Whipple, 2004 and references
therein). In order to accurately compare channels and channel segments of varying drainage area,
steepness indices are normalized to a user-specified reference concavity (Figure XX) (Kirby and
Whipple, 2012).
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Figure 6. Longitudinal river profiles and slope-area scaling (inset) under A) differing concavity indices (θ) and B)
differing normalized steepness indices (ksn). From Kirby and Whipple (2012).
When along-channel changes in lithology, climate, or uplift rate occur, sharp breaks
(knickpoints) in the profile separate segments with different steepness and concavity indices
(Fig. 6). Similarly, if a temporal change in uplift rate or climate occurs, a transient knickpoint
will develop at the basin outlet, and propagate upstream as an incisional wave, separating the
newly equilibrated lower reaches from upper reaches equilibrated with previous conditions
(Whipple and Tucker, 1999; Schoenbohm et al., 2004). Typically transient knickpoints exhibit
“slope-break” morphology while spatially-bound knickpoints exhibit “vertical-step” morphology
(Fig. 6), but distinguishing between spatially-bound or transient knickpoints is difficult based
only on analysis of channel form. However, the distribution of knickpoints in the landscape can
be diagnostic; a transient knickpoint will split at tributary junctions as it heads upstream, and its
descendants will be found at similar elevations, while spatially-bound knickpoints will typically
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occur along discrete shear zones or lithologic boundaries (Wobus et al., 2006a). The collective
analysis of channel steepness and concavity indices, knickpoint distribution and form, and
supporting landscape information (e.g. lithologic and climatic variability) provides the basis for
using river profile morphology to interpret spatial and temporal changes in uplift rates (e.g.
Schoenbohm et al., 2004; Harkins et al., 2007).
Figure 7. Vertical-step and slope-break knickpoint morphology in (a & c) profile form and (b & d) slope-area space.
From Kirby and Whipple (2012).
1.3.3 Interpretation
Tectonic interpretations of longitudinal river profile morphology require a priori knowledge of
along-channel changes in lithology and climate, as changes in substrate erodibility (through
climate or lithology) can be identical to changes in uplift rate (Cyr et al., 2014). For example, an
increase in uplift rate (U) or a decrease in substrate erodibility (K, which can be achieved
through drier climate or stronger lithology) will both result in the steepening of the channel
profile (see equation 3). However, in complex landscapes, the covariance of normalized
steepness indices (ksn) and catchment mean erosion rates can distinguish lithologic and tectonic
controls on profile form (Cyr et al., 2014). At steady state, channel gradients are adjusted to the
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competing influence of uplift rates (U) and channel erodibility (K) such that the change in
channel elevation over time is zero. It follows that steepness indices are high along channel
segments with either high uplift rates or resistant lithologies. In the case of high uplift rate, the
channel segment will erode at a high rate to keep pace with the uplifting block, while, in the case
of resistant lithology, the channel segment will erode at a low rate. Therefore, high and low
catchment mean erosion rates should indicate tectonic or lithologic control, respectively (Cyr et
al., 2014).
1.4 Terrestrial Cosmogenic Nuclide (TCN) Chronology
1.4.1 Theory
Cosmogenic nuclides (e.g. 10Be and 26Al) are rare isotopes produced by cosmic ray
bombardment of elements in the atmosphere and within rocks and soil in the near-surface
environment. Cosmogenic nuclides produced in the atmosphere (known as “meteoric”) are later
deposited on the Earth’s surface through precipitation and dustfall. Conversely, cosmogenic
nuclides produced in rocks and soils are known as in situ or “terrestrial” cosmogenic nuclides
(TCN). Due to increasing accuracy in estimates of cosmogenic nuclide production rates,
measurement of cosmogenic nuclide activities in rocks and soils allows for the dating of stable
surfaces or the calculation of erosion/deposition rates (Lal, 1991; Gosse and Stone, 2001). This
study measures in situ production of 10Be in sediments to date pediments and terraces, and
constrain catchment-mean erosion rates.
The production rate of cosmogenic nuclides in rock or sediments attenuates exponentially with
increasing depth and density in rock or soil, thereby restricting the measurable production of
cosmogenic nuclides to the near-surface environment (<5 m) (Fig. 8). The production rate (Px) of
a cosmogenic nuclide at a given depth (x) in soil or rock of known density (ρ) is moderated by a
characteristic attenuation factor (Λ), so that:
Px = P0e-xρ/ᴧ
The production rate at the surface (P0) varies as a function of latitude, altitude, and a shielding
factor, which takes into account the geometry of both the surface and surrounding topography
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(Lal, 1991; Bierman and Steig, 1996; Balco et al., 2008). The latitudinal and altitudinal controls
on cosmogenic production rate reflect variations in geomagnetic field strength and atmospheric
thickness, respectively. With a known surface production rate, the production rate at a given
depth can thus be calculated. A production rate at depth (x), coupled with a measured
cosmogenic nuclide activity at depth (x) (surface samples can be used), provides the basis for
using TCN as chronometers.
Figure 8. Idealized attenuation curves for production of 10Be in A) rock ( ρ = 2.7 g cm-3) and B) soil (ρ = 1.2 g cm-3).
Figure from Bierman and Nichols (2004).
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1.4.2 Application & Limitations
Research in the previous two decades demonstrates both the utility and the expanding application
of cosmogenic nuclides in determining rates of landscape evolution and in dating landforms.
Considering only studies that use in situ produced 10Be, applications include dating terraces and
alluvial fans, improving glacial chronologies, determining rates of bedrock erosion, and
constraining rates of soil formation (Bierman et al., 2002 and references therein). However,
correct interpretation of nuclide activities depends on the application of an appropriate
geomorphic model to a given study (Bierman and Nichols, 2004). Determining the validity of
assumptions inherent to a geomorphic model requires data obtained through traditional
geomorphic and sedimentologic analyses. Thus, cosmogenic nuclide analyses do not replace
older techniques, but rather work in conjunction to place more precise constraints on the
evolution of a landscape.
The two most basic applications of TCN model continuous surface exposure and steady-state
erosion, allowing for the calculation of either minimum exposure ages or steady-state erosion
rates. In the case of a sudden and continuous exposure (e.g. by a landslide or glacial retreat), and
assuming no erosion since initial exposure, a model exposure age (t) is determined by the
measured cosmogenic radionuclide activity (N) and the decay constant (λ) (Lal, 1991; Bierman et
al., 2002).
Nx = (Px/λ)(1 – e-λt)
In the case of steady-state erosion, assuming small, high-frequency erosion events, the erosion
rate (ε) is determined by the following equation (Lal, 1991):
Nx = e-xρ/ᴧ[(Px)/(λ + ρᴧ-1ε)]
The validity of modeled exposure ages and erosion rates depend on the satisfaction of
assumptions inherent to each model. However, surfaces commonly have complex exposure and
burial histories, and experience variable erosion rates through time (Anderson et al., 1996;
Bierman and Steig, 1996). As a result, most TCN studies employ refined versions of these
models, specific to the geomorphology and research objectives in a given study area.
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Furthermore, the history of a landform is not always made apparent through geomorphic and
sedimentologic observation, thereby encouraging the development of statistical analyses of
multi-run models to determine the most likely exposure age and/or erosion rate at a sample site
(e.g., Hidy et al., 2010).
1.4.3 Dating Stable Landforms
To date incised fluvial terraces and pediments in our study, we use the depth-profile technique
demonstrated by Anderson et al. (1996), and refined by Hancock et al. (1999), in which at least 2
sand samples, one from the surface and the other(s) at known depth(s) (extending greater than 1
m), are analyzed. This technique, by virtue of the difference between post-depositional nuclide
production rates at the surface and at depth (ΔP), allows for the quantification of the surface age
(T):
T = (1/λ)ln[ΔP/(ΔP – λΔN)]
This technique also permits the quantification of the average cosmogenic nuclide “inheritance,”
which is the nuclide concentration attained prior to deposition in the target surface.
Quantitatively, inheritance (Nin) is the difference between the nuclide concentration of a surface
or subsurface sample (Ns or Nss) and the product of the production rate (at the surface or at depth)
and the depositional age of the landform:
Nin = Nss – PssT = Ns – PsT
The inheritance thus represents the sum of nuclide production during a previous exposure-burial
cycle (if applicable), during the exhumation of the clast from the source area, and during
transport to the final depositional site (Anderson et al., 1996). Not accounting for inheritance can
lead to the overestimation of landform ages. Using sand or an amalgamation of clasts (>30),
rather than single clasts, is an important step as it accounts for the variance in exposure histories
among clasts; due to the stochastic nature of particle trajectories in a sedimentary system,
different clasts moving from a similar source area to a depositional site can experience vastly
different exposure histories (Anderson et al., 1996).
Despite the concerns regarding variable inheritance, the dating of alluvial fan and terrace
surfaces with TCN depth-profiles are powerful tools, and have been used to estimate fault slip
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rates, paleoclimatic evolution, and the relative importance of tectonics and climate in a given
landscape’s evolution (e.g., Hancock et al., 1999; Vassallo et al., 2005; Hetzel et al., 2006).
Indeed, the inheritance itself is useful, as it can indicate the rates at which sediments are
exhumed and transported (Anderson et al., 1996). Varying inheritance between cobbles and
sands has been used to indicate different sources; the variable inheritance reflects differing
transport rates due to landscape position (Schmidt et al., 2011).
1.4.4 Measuring Catchment Mean Erosion Rates
Of particular interest to tectonic geomorphology is the application of TCN in the determination
of catchment mean erosion rates. The ability to measure erosion rates of individual catchments at
103-105 year timescales has enabled a greater understanding of the spatiotemporal controls on
erosion, and provided a bridge between modern erosion rate estimates (e.g. sediment yield
measurements, dam records) and long-term (>105 years) estimates of exhumation (e.g.
thermochronology) (Bierman and Steig, 1996; Granger et al., 1996). For example, along the
eastern margin of the Puna Plateau in NW Argentina, where rainfall varies from >2 m yr-1 in the
foreland to 0.1 m yr-1 in the orogen interior (Fig. 2), Bookhagen and Strecker (2012) measured a
10 fold difference in 10Be-derived catchment mean erosion rates, suggesting that precipitation
was the primary control on erosion rates in the region. In the Appalachian Great Smoky
Mountains, Matmon et al. (2003) found that erosion rates derived from modern sediment yields, 10Be catchment mean erosion rates, and Mesozoic fission track exhumation rates were similar,
suggesting that erosion rates have been relatively constant in the Mesozoic and Cenozoic. In
contrast, Paleozoic exhumation rates were an order of magnitude higher. These results suggest
that the Appalachians have remained high due to deep crustal roots and isostatic adjustment to
erosion, rather than more recent tectonics (Matmon et al., 2003).
To measure catchment mean erosion rates, a sample of sediment from a modern river is
analyzed. This approach assumes that, although certain areas of a given catchment may erode at
different rates, sediments are well mixed in transport such that a sample contains volumes of a
target mineral from each subcatchment proportional to their relative erosion rates. In other
words, the catchment is in isotopic steady state, where the rate of isotope production (in the
target mineral) is equal to the rate of isotope export from the catchment (Fig. 9) (Bierman and
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Steig, 1996). This approach also assumes availability of the target mineral in similar quantities
and grain sizes throughout the catchment. When the target mineral is non-uniformly distributed
(e.g. in catchments of mixed lithology), corrections need to be made (e.g., Safran et al., 2005).
In practice, 10Be is most commonly used to determine catchment mean erosion rates due to its
favorably long half-life (1.4 Myr), its production in an abundant and chemically resistant target
mineral (quartz), and its high analytical precision (von Blanckenburg, 2005).
Figure 9. At isotopic steady-state, where the production of a given TCN in a catchment is equal to the export of
that TCN, the mass flux (dM/dt) can be calculated with the measured TCN concentration (C, in atoms g-1) of river
sediments at the catchment outlet. Catchment mean erosion rate (mm ky-1) is determined by dividing the mass
flux by the catchment area and bedrock density. Erosion rates are spatially non-uniform across the catchment, but
sediment is well mixed in the channel network such that a sample represents a catchment-integrated TCN
concentration. Figure from von Blanckenburg (2005).
10Be concentrations in river sediments are converted to erosion rates by estimating the mean 10Be
production rate of the contributing drainage area. Given the non-linear dependence of 10Be
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production rates on elevation, mean production rates are often calculated by quantifying basin
hypsometry or by computer codes which calculate the production rate of each pixel in a digital
elevation model of the catchment; using mean elevation will result in significant inaccuracies in
high relief environments (Bierman and Nichols, 2004; Bookhagen and Strecker, 2012).
Catchment mean erosion rate (ε) is related to catchment mean production rate (P) and a
measured TCN concentration in sediment (C) by the following equation:
ε = (P/C – λ)(Λ/ρ)
Catchment mean erosion rates integrate over a period dependent on the erosion rate itself,
because the erosion rate sets the residence time for a parcel of rock in the landscape. Averaging
timescales range from 102 to 105 year timescales from tectonically active orogens to stable
cratons (von Blanckenburg, 2005).
Comprehensive review of published 10Be catchment mean erosion rates (n = 1149) reveals that,
on a global scale, erosion rate correlates most strongly with basin slope (R2 = 0.33), and exhibits
no significant relationship with mean annual temperature, mean annual precipitation, basin area
and basin latitude (Portenga and Bierman, 2011). Correlations improve when considering basins
from similar climatic, tectonic or lithologic settings. The large scale meta-analyses by Portenga
and Bierman (2011) indicate that landscape complexity (due to mixed lithology, climatic
gradients, and non-steady state conditions) must be considered in interpretations of 10Be
catchment-mean erosion rates (Safran et al., 2005; Hilley and Coutand, 2010; Bookhagen and
Strecker, 2012).
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2 Chapter 2: Late Quaternary Tectonics, Incision, and Landscape Evolution of the Calchaquí River
Catchment, Eastern Cordillera, NW Argentina
James A. McCarthy, Department of Earth Sciences, University of Toronto, Toronto, Ontario,
Canada
Lindsay M. Schoenbohm, Chemical & Physical Sciences, University of Toronto Mississauga,
Mississauga, Ontario, Canada AND Department of Earth Sciences, University of Toronto,
Toronto, Ontario, Canada
Paul R. Bierman, Department of Geology and Rubenstein School of the Environment and
Natural Resources, University of Vermont, Burlington, VT, USA 05405
Dylan Rood, Scottish Universities Environmental Research Centre, University of Glasgow, East
Kilbride, Scotland, UK
NOTE ON JOURNAL: JGR-Earth Surface focuses on the physical, chemical, and biological
processes that affect the form and function of the surface of the solid Earth over all temporal and
spatial scales, including fluvial, eolian, and coastal sediment transport; hillslope mass
movements; glacial and periglacial activity; weathering and pedogenesis; and surface
manifestations of volcanism and tectonics (from the website).
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2.1 Abstract
In this study we use field investigations, systematic analysis of longitudinal river profiles, 10Be-
derived catchment mean erosion rates, and paleo-erosion rates and vertical incision rates from 10Be depth profiles to examine the late Quaternary landscape evolution of the Calchaquí River
Catchment (CRC) of the Eastern Cordillera, NW Argentina. We find that the spatial distribution
of erosion rates, normalized steepness indices, and concavity indices reflect active tectonics and
the exposure of resistant lithologies along preexisting structural heterogeneities in the study
region. Abundant knickpoints are spatially coincident with tectonic and/or lithologic
discontinuities, indicating local base-level control by thrust faulting that is distributed across
multiple structures. Field studies document active faults, corroborating our interpretations of
river profiles. Field studies also document the progressive abandonment of pediment and strath
terraces, resulting in ~100 m of channel incision in <300 kyr. Catchment mean erosion rates and
paleo-erosion rates are similar, suggesting Quaternary climate changes have not influenced
erosion rates at ~10 ka time scales. Collectively, our data demonstrate that the rate and style of
landscape evolution in the southern Eastern Cordillera is primarily driven by Quaternary tectonic
deformation and inherited structural heterogeneity, complicating interpretations of tectonic-
climatic feedbacks on the eastern margin of the Puna Plateau. We speculate that out-of-sequence
shortening in the Calchaquí River Catchment, and perhaps localized extension on the plateau
margin, reflect gravitational spreading processes on the Puna Plateau.
2.2 Introduction
The increasing availability of high-resolution topographic data and improvements in our ability
to measure basin-scale erosion permit the establishment of empirical relationships between
topographic metrics and erosion rates in modern landscapes (Portenga and Bierman, 2011;
Bookhagen and Strecker, 2012; Kirby and Whipple, 2012). These advances in our understanding
of surface processes and surface process thresholds elucidate tectonic and climatic controls on
erosion, setting the stage for field-based investigations of the coupling between climate and
tectonics (Wobus et al., 2006a; Ouimet et al., 2009; Whipple, 2009). However, establishing a
causal link between climatically-moderated erosion and tectonics is complicated by the pre-
existing geologic complexity commonly observed in natural landscapes, such that systematic
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analyses must be carried out to isolate lithologic, climatic, and tectonic controls on topography
and erosion (e.g., Hilley and Coutand, 2010). Furthermore, basin-scale erosion rates are typically
integrated over millennia, so the degree to which modern climate data reflect measured erosion
rates are dependent on the frequency and magnitude of past climate changes (Bierman and Steig,
1996).
With these challenges in mind, we investigate the Late Quaternary landscape evolution of the
southernmost Eastern Cordillera, a tectonomorphic province of the Central Andes (Figure
Regional). Bordering on the Puna Plateau to the west, the Sierras Pampeanas to the south, and
the Santa Bárbara System to the east, the study area lies within the west-east transition from high
plateau to complex retroarc foreland (Allmendinger et al., 1997). Pronounced climatic gradients
exist as well along the eastern margin of the central Andes due to orographic shielding of
easterly moisture-bearing winds, resulting in stark differences in surface processes and erosional
efficiency between the plateau and the foreland (Strecker et al., 2007; Bookhagen and Strecker,
2012). As a result, the southern Eastern Cordillera and nearby regions provide an ideal natural
laboratory in which to examine the control exerted by preexisting structural fabrics, variable
lithology, and geodynamic processes on topography, as well as the potential interactions between
climatically-moderated surface processes and tectonics. We employ field investigations,
longitudinal river profile analysis, 10Be catchment-mean erosion rates, and estimates of paleo-
erosion rates derived from 10Be depth-profiles to examine the various controls on erosion and
topography in the Calchaquí River Catchment (CRC). We focus our field studies in the lower
Pucará Valley, an intramontane basin within the CRC (Figure Regional).
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Figure 10. Composite digital elevation model and shaded relief map of the south central Andes with major
tectonomorphic provinces outlined in black. Thicker black line delineates internally drained Puna Plateau from the
externally drained Eastern Cordillera and Sierras Pampeanas. Yellow line outlines the Calchaqui River catchment
(CRC). Red box outlines the Pucará Valley, where field studies were focused. SBS = Santa Barbara System. CG =
Cerro Galán Caldera.
2.3 Landscape Analysis As A Tool For Evaluating Tectonics and Climate in Spatially Heterogeneous Regions
Fluvial channel network morphology and catchment mean denudatuion rate are sensitive
indicators of both tectonic and climatic forcing (Whipple and Tucker, 1999). For example, in a
region of uniform lithology and climate, the steepness of bedrock river channels should vary due
to differences in uplift rate, while channel concavity should be influenced by factors controlling
the sediment transport ability of the profile such as drainage basin shape and downstream
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changes in channel width vs. discharge (Kirby and Whipple, 2012). When along-channel
changes in lithology, climate, or uplift rate occur, sharp breaks (knickpoints) in the profile
separate segments with different steepness and concavity. Similarly, if a temporal change in
uplift rate or climate occurs, a transient knickpoint will develop at the basin outlet, and propagate
upstream as an incisional wave, separating the newly equilibrated lower reaches from upper
reaches equilibrated with previous conditions (Whipple and Tucker, 1999; Schoenbohm et al.,
2004).
In isolation, longitudinal river profile analysis cannot explicitly distinguish the relative effects of
tectonics, lithology, climate and transient perturbations on profile form, but the incorporation of
supporting information (e.g. lithologic and climatic data) can provide the keys to diagnosing
these influences in complex landscapes (Kirby and Whipple, 2012). In particular, the covariance
of normalized steepness indices and 10Be catchment mean erosion rates can distinguish lithologic
and tectonic controls on channel steepness (Cyr et al., 2014). High channel steepness and high
erosion rates along a discrete channel segment indicate higher uplift rate whereas high channel
steepness and (comparatively) low erosion rates indicate locally resistant substrate (Cyr et al.,
2014). The covariance of 10Be catchment mean erosion rates and channel steepness, together
with a priori knowledge of the distribution of lithology and precipitation, allow us to evaluate
the dominant controls on landscape evolution in the CRC.
2.4 Geologic Setting
2.4.1 Structural Evolution
The southern Eastern Cordillera is a bi-vergent fold and thrust belt, characterized by basement-
involved reverse faults that preferentially occur along preexisting structural heterogeneities,
including inverted Cretaceous rift structures and earlier metamorphic fabrics (Grier et al., 1991;
Strecker et al., 2007; Carrera and Munoz, 2008; Santimano and Riller, 2012). Basement uplifts
are composed of Precambrian metasedimentary units, Paleozoic granitoids, and sedimentary
rocks related to the Cretaceous Salta Rift (Grier et al., 1991; Coutand et al., 2006). Deposition of
Cenozoic sedimentary rocks in intramontane basins within the CRC reflects eastward
propagation of the orogenic front from late Eocene to Pliocene (Coutand et al., 2006; Carrapa et
al., 2012). Pliocene to Quaternary deformation was primarily accommodated by the Santa
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Barbara System to the east (Hilley and Strecker, 2005; Coutand et al., 2006; González Bonorino
and Abascal, 2012). Aridity similarly propagated eastward due to uplift of orographic barriers to
precipitation (Coutand et al., 2006; Bywater-Reyes et al., 2010; Carrapa et al., 2012).
The Pucará Valley, like other intramontane basins in the CRC, is defined by N-S trending
contractional structures (Fig. 11). On the west, the Jasimaná–Vallecito Thrust, an inverted
Cretaceous normal fault, carries Cretaceous sedimentary rocks over Holocene sediments
(Coutand et al., 2006). On the east, the Sierra de Quilmes Thrust carries Precambrian basement
over Cretaceous rift strata (Carrera and Munoz, 2008). Cenozoic strata of the Pucará Valley
record the evolution from a distal to proximal foredeep from Late Eocene to Middle Miocene
(Carrapa et al., 2012). Eastward propagation of deformation led to the development of a wedge-
top basin from approximately 14-10 Ma, and further shortening of the wedge-top after 10 Ma led
to the development of the modern intramontane physiography (Coutand et al., 2006; Carrera and
Munoz, 2008; Carrapa et al., 2012).
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Figure 11. Quaternary strath terraces and pediment surfaces in the Pucará Valley. Depositional ages derived from
cosmogenic 10Be depth profiles. Numbered soil pits are described in TABLE SOILS. JVT = Jasimaná-Vallecito Thrust.
SQT = Sierra de Quilmes Thrust. PT = Pucará Thrust. See Auxiliary Material for complete geologic map. Fault
nomenclature and structure modified from (Carrera and Munoz, 2008). Area shown by red box in Figure Regional.
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2.4.2 Quaternary Climate & Geomorphology
The CRC is characterized by an arid, intramontane climate, reflecting the effects of significant
orographic barriers to precipitation and highly seasonal rainfall. Mean annual precipitation in the
CRC is <250 mm yr-1, but most rainfall occurs in the summer months, when a seasonal low-
pressure system brings humid northeasterly and easterly winds to the region (Trauth et al.,
2003b; Bookhagen and Strecker, 2008). Interannual variability in precipitation is significant
(±75%), and driven primarily by ENSO and the Tropical Atlantic Sea-surface Temperature
Variability (TAV) (Trauth et al., 2003b). Cooler and more humid periods occurred throughout
the Quaternary, increasing landslide-frequency, expanding glacial and periglacial zones, and
apparently increasing overall catchment erosional efficiency (Bobst et al., 2001; Haselton et al.,
2002; Trauth et al., 2003a; Fritz et al., 2004; May and Soler, 2010; Bookhagen and Strecker,
2012).
The geomorphology of the CRC and nearby regions reflect arid, highly seasonal climate and
relief >1000 m in intramontane basins. Aerial photography reveals abundant pediment surfaces
and alluvial fans throughout the CRC, most of which are incised by modern channels. For
example, in the Pucará Valley, incision and base-level lowering of ~100 m have abandoned a
sequence of pediments and strath terraces (this study). Similar evidence for Quaternary incision
is well documented in the Sierras Pampeanas and Santa Barbara System (Strecker et al., 1989;
Hilley and Strecker, 2005; González Bonorino and Abascal, 2012). Pedogenesis is weak, and
soils are dominated by soil carbonate (May and Soler, 2010, and this study). Periglacial
processes are restricted to areas over 4500 m elevation, but this limit may have been depressed as
much as 900 m during the Pleistocene, as evidenced by broad convex range crests and moraines
in the Sierra de Quilmes and northwestern CRC (Haselton et al., 2002).
2.5 Methods
2.5.1 Field Studies
Field studies were focused in the lower Pucará Valley with the goal of characterizing neotectonic
structures and Quaternary landscape evolution. We conducted structural and geomorphic
mapping of the valley on aerial photography and ASTER 30 m topography. Geology was
compiled from existing maps by Carrera and Muñoz (2008) and Coutand et al. (2006).
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Additionally, we described soils at 13 sites within the valley. We selected sites to ensure
investigation of soils at various pediment levels, and described soils according to USDA soil
taxonomy guidelines (Staff, 2010). Descriptions are solely morphological and geochemical
classification metrics (e.g. weight percent CaCO3) are inferred. Reported stages (Appendix A) of
pedogenic carbonate and gypsum accumulation follow the morphological classification scheme
of Gile et al. (1966). We determine desert pavement indices (PDI) according to methods
developed by Al-Farraj and Harvey (2000). See Appendix A for more thorough description of
PDI methodology.
2.5.2 Longitudinal River Profile Analysis We rely on digital topographic data and coupled ArcGIS and Matlab scripts to derive
normalized channel steepness indices (ksn) and concavity indices (θ) (www.geomorphtools.org).
Following methods outlined by Wobus et al. (2006a), we extracted channel topographic data
from 30 m ASTER topography (NASA) , removed data irregularities, smoothed channel data
along a 450 m moving average window, determined local slopes over a 10 m vertical interval,
and set a minimum drainage area of 3000 pixels. The above parameters balance our desires to
preserve channel topographic complexity, remove artifacts in digital topographic data associated
with high relief landscapes, and exclude channel headwaters that are dominated by debris-flow
processes (Wobus et al., 2006a). Channel steepness and concavity indices are determined by
linear regression of local channel slope and drainage area after log transformation. We normalize
steepness indices to a reference concavity of 0.45, following empirical and theoretical
predictions for detachment limited systems (Whipple and Tucker, 2002).
We identify individual segments along a profile by the occurrence of major knickpoints or
downstream confluences with larger trunk streams, and regress the data from each segment to
derive ksn and θ. Considering the large scale of our analysis, we selected knickpoints that are
conspicuous in log-slope/log-area plots and in topographic profiles. We classify knickpoints
according to morphology: slope-break knickpoints, vertical step knickpoints, the base of a
convex reach, and the top of a convex reach (see Kirby and Whipple, 2012). We also classify
knickpoints genetically, based on their spatial coincidence with significant tectonic (e.g faults)
and/or lithologic boundaries (e.g. transition from crystalline basement to Tertiary sedimentary
rock), giving rise to four knickpoint types: lithologic, tectonic, lithotectonic, and undefined. We
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specifically focus on slope-break and “undefined” knickpoints, because they may represent
transient channel responses to an external forcing (e.g. Harkins et al., 2007).
2.5.3 Terrestrial Cosmogenic Nuclide (10Be) Chronology
2.5.3.1 Analytical Procedures
In this study we isolate and analyze in situ produced cosmogenic 10Be in quartz to determine
catchment mean erosion rates and date stable geomorphic surfaces. Samples were processed at
the University of Vermont Cosmogenic Nuclide Laboratory using standard analytical methods
(see auxiliary materials and www.uvm.edu/cosmolab for detailed methodology). First, quartz
was purified for 10Be analysis using mineral separation procedures modified from Kohl and
Nishiizumi (1992). For Beryllium isolation, samples were prepared in batches that contained a
full-process blank and 11 unknowns. We used between 11.6 and 23.0 g of purified quartz for
analysis. We added ~250 µg of 9Be carrier made from beryl at the University of Vermont to each
sample. After isolation, Be was precipitated at pH 8 as hydroxide gel, dried, ignited to produce
BeO, ground, and packed into copper cathodes with Nb powder at 1:1 molar ratio for accelerator
mass spectrometry (AMS) measurements.
10Be/9Be ratios were measured at the Scottish University Environmental Research Center and
were normalized to NIST standard with an assumed ratio of 2.79 ·10-15 based on a half life of
1.36 My. The average measured sample ratio (10Be/9Be) was 947 x 10-15 and AMS measurement
precisions, including blank corrections propagated quadratically, averaged 1.9 %. The blank
correction is an inconsequential part of most measured isotopic ratios (<0.7% on average,
maximum 2.0%). The CRONUS N standard was run with these samples and returned a
concentration of 2.31±0.06 x 105 atoms g-1, consistent with values reported by other labs.
2.5.3.2 10Be Catchment Mean Erosion Rates
We contribute five new 10Be-derived catchment mean erosion rates from the Pucará River
catchment and its subcatchments (see Figure 13 for locations of samples BW1,2,3,5, and 6).
Samples were collected from bars within active streams. For each sample, we determined the
contributing drainage area using GIS software, and 10Be production rates were calculated for
each pixel of a DEM at 250 m resolution. Our Matlab code incorporates elevation, shielding, and
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muonogenic production for each pixel, but relies on mean latitude for each catchment. The code
follows the scaling scheme of Lal (1991) and a sea-level high-altitude surface production rate of
4.76 atoms g-1 yr-1 (Nishiizumi et al., 2007; Hidy et al., 2010). We calculate erosion rates using a
sample density of 2.6 g cm-3 and an attenuation length of 160 g cm-2 (von Blanckenburg, 2005).
The uncertainties which accompany our reported erosion rates reflect the uncertainties in both
AMS measurements and production rates. Major lithologies in the CRC are quartz rich, so we
make no corrections for variably distributed quartz (Sparks et al., 1985; Francis et al., 1989; Do
Campo and Guevara, 2005; Marquillas et al., 2005; Coutand et al., 2006).
In addition to our own samples, we re-analyze seven previously published catchment-mean
erosion rates in the CRC (Bookhagen and Strecker, 2012). Using reported sample locations and
nuclide concentrations, we recalculate production rates and erosion rates using the same methods
as for our own samples, and find that recalculated and reported values differ by <8%. Similarly,
inputting mean latitudes and elevations for each catchment into the CRONUS calculator (rather
than using a pixel-by-pixel code) produces erosion rates that differ from our results by <11%
(Balco et al., 2008). For sampled catchments which contain sampled subcatchments (BW5 and
M2), we calculate the differential erosion rate by area-weighting erosion rates from the
contributing subcatchments (Granger et al., 1996).
2.5.3.3 10Be Depth Profiles
To date pediment surfaces, we hand-excavated 2 m deep pits for the sampling of cosmogenic
nuclide (10Be) depth profiles at three locations (Fig. 11). Soil pits were dug at geomorphically
stable sites, with minimal evidence for erosion, bioturbation, and complex shielding histories.
However, the absence of bar & swale topography, the presence of Av horizons, and the heavily
dissected nature of the pediment surfaces throughout the valley collectively suggest some degree
of surface degradation at all sites. We sampled ~1 kg of sand-sized grains in ~2 cm thick
horizons at 0, 50, 100, 150, and 200 cm depths, across the width of the pit. All samples were
field-sieved to remove the < 250 µm fractions, which made up minor portions (<25%) of the
total soil mass.
To determine surface exposure age, inheritance and erosion rate for each depth profile, we
employ the Monte Carlo simulator developed by Hidy et al. (2010). Results reported are from
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100,000 model simulations at 1σ uncertainties, based on model parameters described in the
auxiliary materials. Although average AMS uncertainty for the data set was <2%, we assigned
nuclide concentration uncertainties of 5% for all depth-profile samples, to reflect errors in
sampling (e.g. sample depth and thickness), laboratory analysis (e.g. balance errors), geomorphic
variability (e.g. bioturbation/cryoturbation, shielding variations), and also systematic errors (e.g.
temporal variation in cosmic ray flux, and scaling uncertainty) (Gosse and Phillips, 2001). Model
inputs of density and associated uncertainties are based off of previous field determinations in
similar soil types with similar ranges of carbonate and gypsum development (Reheis, 1987;
Reheis et al., 1995; Hidy et al., 2010).
2.5.4 Paleo-Erosion Rates
We use inheritance values for each depth-profile to calculate catchment mean paleo-erosion
rates. Comparing paleo-erosion rates to modern erosion rates should evaluate whether
Quaternary climate changes significantly affected sediment transfer rates in the CRC. We
calculate catchment mean 10Be production rates via the methods described above, deriving paleo-
drainage basins from modern topography. For each depth profile, we calculate 10Be
concentrations of a “paleo-sample” with the maximum, minimum and modal solutions for
inheritance, corrected for radioactive decay of 10Be (using the appropriate minimum, maximum,
and modal depositional ages, respectively). We find no evidence for stream captures or major
drainage reorganization in the Pucará River catchment, suggesting that the use of modern
topography is valid, especially given much larger uncertainties in inheritance. We report
maximum, minimum, and modal erosion rates for each profile.
We also derive vertical incision rates for the Pucará River, using minimum, maximum and modal
ages for each depth-profile. We estimate vertical incision as the difference in elevation between
the modern Pucará River floodplain and each dated surface, projecting the surface to the modern
floodplain, using a reference slope of 3.5°. The reference slope reflects the results of differential
GPS surveys we conducted across pediments and strath terraces in the Pucará Valley. We use
Trimble differential GPS equipment with <10 cm vertical and ~1 m horizontal precision.
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2.6 Results
2.6.1 Field Studies
The semi-arid Pucará Valley contains seven abandoned and incised geomorphic surfaces (Q1 –
Q7, youngest to oldest) from 5 m to ~100 m above modern base-level, that record multiple
pulses of incision in the late Quaternary (Fig. 12). Abandoned pediments and strath terraces
dominate the landscape, we find no evidence for significant depositional intervals, and valley
incision continues currently. Structural mapping reveals a series of blind and emergent thrusts
east of the valley (syncline) axis, active in the Quaternary (Fig. 11). The Pucará Thrust visibly
offsets Q3 surfaces, although differential GPS transects across the fault measure vertical
displacement <1 m (see Appendix XX). In the southern end of the field area, we observe heavily
dissected surfaces, steep rivers, and deeply incised canyons spatially coincident with a N-S
striking monocline, suggesting Quaternary activity along a blind thrust. However, additional
differential GPS transects of pediment surfaces between these two areas do not reveal any clear
signal of deformation (e.g. tilting, oversteepening), suggesting that late Quaternary deformation
within the lower Pucará Valley is of relatively low magnitude.
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Figure 12. Photograph from the site of the Q5 depth profile in Figure 11, looking approximately northeast.
Foreground shows Q5 strath terrace beveled into sedimentary rocks of the Tertiary Payogastilla Group, which rest
in angular unconformity over Cretaceous Pirgua Group redbeds. In the background Q2, Q3, Q4, Q6 and Q7 surfaces
are beveled into both Tertiary and Cretaceous sedimentary units. Rio Pucará flows from right (south) to left
(north). Note monoclinal structure within Cretaceous units beneath the Q7 surface. High ranges are composed of
the Neoproterozoic Puncoviscana Formation.
Soils in the study area classify broadly as aridisols, and range from Ustic Haplocambids on
modern surfaces to Ustic Haplocalcids, Ustic Petrocalcids, Leptic Haplogypsids, and Ustic
Petrogypsids on the abandoned surfaces (see Appendix A). The differences between these soil
taxons reflect differing degrees of pedogenic accumulation of either carbonate or gypsum.
Carbonate and Gypsum reach stage III and incipient stage IV morphology on the highest (Q6 –
Q3) surfaces, do not exceed Stage II on lower (Q2 – Q1) surfaces, and exhibit minimal carbonate
accumulation on modern surfaces. Similarly, desert pavements exhibit greater development on
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the oldest surfaces, although the differences are minimal, likely because of destructive forces
acting on pavements such as vegetation, surface erosion, and human modification. Importantly,
the correlations between relative landform age and degrees of pedogenic salt and pavement
development indicate that arid or semi-arid conditions in the study area are long-lived and that
past humid phases, at least locally, were not significant enough (>750 mm MAP) to cause major
dissolution of soil carbonate or gypsum (Gile et al., 1966; Royer, 1999; Buck and Van Hoesen,
2002).
2.6.2 River Profile Analysis
We analyzed 77 streams in the Calchaquí River catchment, giving rise to 147 separately
regressed segments, and 75 knickpoints (Fig. 13). Normalized steepness indices range from 28 to
>1000, with a mean ksn of 175. Mean concavity index is 0.9, with a maximum of 28 and
minimum values <0 (convex) (Fig. 17). The highest steepness indices occur in a narrow band
within and between the high crystalline ranges in the western half of the study area. These steep
segments vary greatly in morphology; some are small tributaries to the Calchaqui River, running
perpendicular to the structural grain within crystalline bedrock (e.g. STR13 on Figure 13), some
are parallel to the structural grain, running within sedimentary rocks in valleys bound by thrust
faults (e.g. STR11 on Figure 13), and others represent a combination of those morphologies (e.g.
STR16 on Figure 13). A common feature to all steep segments (and the corresponding
catchments) is that they cross one or more N to NW striking thrust faults within the high Eastern
Cordillera.
The lowest steepness indices are generally observed in the eastern part of the catchment along
small tributaries to the Calchaquí River (Fig. 13). Many of these tributaries are segmented, with
knickpoints and convexities coincident with the Cerro Negro Thrust and other west-vergent
thrust faults. We also observe low normalized steepness indices in the southwestern CRC. These
segments are typically bound by prominent lithotectonic or lithologic kickpoints (Figures 13D
and Streams S67, S65, and S1) coincident with two prominent NW striking lineaments.
Morphologically, we classified 25 knickpoints as vertical-step knickpoints, 10 as slope-break
knickpoints, and the remaining 40 as high and low bounds on convex channel reaches (Kirby and
Whipple, 2012). From a genetic standpoint we classified 10 knickpoints as lithologic, 15 as
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tectonic, 26 as lithotectonic, and 24 as undefined. We find no clear correlation between
knickpoint morphology and knickpoint genesis, and note that only 1 undefined knickpoint also
has slope-break morphology. See auxiliary materials for stream profile figures, stream profile
regression data, and knickpoint data.
______________________________________________________________________________
Figure 13 (opposite). (a) Shaded relief map of the CRC, 10Be Catchment mean erosion rate samples and
corresponding subcatchments (labeled) from this study and Bookhagen and Strecker (2012). Stream network
derived from ASTER 30m DEM and a minimum accumulation of 35,000 pixels (1.05 km2). (b) Lithologic divisions,
major faults, and knickpoints in the CRC. Knickpoints according to legend in (d). Dashed lines are newly mapped
faults. CNT = Cerro Negro Thrust (Carrapa et al., 2011). (c) 10Be catchment mean erosion rates, in mm kyr-1.
Sample locations as per legend in (a). (d) Normalized channel steepness indices and knickpoints in the CRC. See
text for description of knickpoint typology and channel regression parameters. Labeled streams are displayed in
profile in Figure Streams.
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2.6.3 10Be Catchment Mean Erosion Rates
Catchment mean erosion rates range from 145 ± 19 mm kyr-1 to 27 ± 3 mm kyr-1 (Table 1),
indicating that the averaging time scales of the sampled catchments range from 4 – 23 kyr (von
Blanckenburg, 2005). Erosion rates do not correlate significantly with catchment mean annual
precipitation, catchment area, or catchment mean elevation, but show a modest correlation with
catchment mean slope (Fig. 14). Comparing catchment mean erosion rates with lithology (Fig.
13) reveals that catchments dominated by resistant lithologies (e.g. crystalline bedrock) exhibit
some of the highest (e.g. STR13) and lowest (e.g. BW3) erosion rates in the study, suggesting
that lithologic resistance alone cannot explain the spatial variation in erosion rates in the CRC.
Table 1. 10Be Concentrations, Catchment-mean production rates, Catchment mean erosion rates, and
corresponding topographic and climatic characteristics.
Sample Name
Sample Latitude
Sample Longitude
Sample Elevation,
m
10Be Concnetration,
atoms g-1
10Be Concentration 1σ, atoms g-1
Mean Production Rate, atoms
g-1 yr-1
Mean Production
rate 1σ , atoms g-1
yr-1
Erosion Rate, mm
kyr-1
Erosion Rate 1σ, mm kyr-1
BW1 -25.8137 -66.28566 2266 1.83E+05 4.79E+03 27.2 3.5 91.4 12.1
BW2 -25.9744 -66.28309 2860 1.11E+06 1.47E+04 56.6 7.3 31.2 4.1
BW3 -25.9364 -66.30455 2730 9.39E+05 1.45E+04 41.4 5.4 26.8 3.5
BW5 -25.7725 -66.24303 2206 4.83E+05 1.13E+04 45.8 5.9 58.1 7.6
BW5 * -25.7725 -66.24303 2206 4.83E+05 1.13E+04 N/A N/A 86.4 11.3
BW6 -25.8467 -66.35731 2472 3.67E+05 7.09E+03 28.8 3.7 48.1 6.3
M2 -25.999 -65.855 1548 2.42E+05 5.56E+03 38.0 4.9 96.6 12.7
M2* -25.999 -65.855 1548 2.42E+05 5.56E+03 N/A N/A 118.2 15.5
STR2 -25.8314 -65.9677 1692 1.64E+05 2.21E+03 15.7 2.0 58.8 7.6
STR3 -25.0105 -66.09571 2496 5.14E+05 1.52E+04 34.6 4.5 41.1 5.4
STR11 -25.4359 -66.30796 2048 3.29E+05 7.54E+03 52.2 6.7 97.1 12.8
STR13 -24.9342 -66.1408 2566 2.33E+05 3.91E+03 53.8 6.9 141.8 18.5
STR16 -25.4359 -66.3101 2045 5.85E+05 1.42E+04 41.9 5.4 43.7 5.8
STR19 -25.7949 -65.97427 1726 1.17E+05 2.40E+03 27.5 3.6 144.8 19.0
Sample Name
Apparent Age, kyr
Centroid Latitude
Centroid Longitude
Mean elevation (m)
Mean Precipitation
(mm yr-1)
Drainage Area (km-1)
Mean Slope
(degrees)
Mean 1km radius
relief, m
Mean 5km radius
relief, m
BW1 6.7 -25.8403 -66.2404 2846 332 8.83 17.2 304 N/A
BW2 19.7 -26.1920 -66.4224 4204 239 1006 14.1 329 897
BW3 22.9 -26.0134 -66.4273 3597 209 323 14.9 330 951
BW5 10.6 -25.9209 -66.5193 3745 262 2701 16.4 392 1124
BW5 * 7.1 -25.8506 -66.5137 3462 285 1319.98 18.4 423 1190
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BW6 12.8 -25.8170 -66.3863 2967 483 43.2 17.5 448 N/A
M2 6.4 -25.4333 -66.2565 3339 236 12858.4 16.7 400 1157
M2* 5.2 -25.3834 -66.0718 2727 241 5335.7 13.9 313 934
STR2 10.5 -25.8434 -66.0323 2004 695 19.06 10.9 142 N/A
STR3 15.0 -24.9908 -65.9768 3273 196 326.65 14.2 317 1009
STR11 6.3 -25.1507 -66.4897 4000 203 1392.32 20.3 491 1403
STR13 4.3 -24.7254 -66.2536 4124 193 1451.89 23 575 1591
STR16 14.1 -25.5606 -66.5376 3565 230 1359.39 18.1 415 1128
STR19 4.2 -25.8494 -66.1221 2801 353 271.985 18.3 368 1027
Figure 14. Correlations between catchment mean erosion rates and catchment mean annual precipitation,
catchment area, catchment mean slope, and catchment mean elevation. See Table 1 for data.
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2.6.4 10Be Depth Profiles
At all three sites, surface samples exhibit low 10Be concentrations compared to depth-profile
attenuation curves (Fig. 15) (Anderson et al., 1996). Previous work suggests that low surface
concentrations reflect bioturbation, so we exclude the surface samples from our depth-profile
simulations (Hidy et al., 2010). Although such exclusion significantly increases the range of
solutions (the uncertainty), the modal ages produced (hereafter referred to as best-fit ages) more
accurately reflect depositional ages. The depth-profile simulator yields best fit ages of 42.9 ka,
96.3 ka, and 157.6 ka for our Q2, Q5, and Q6 surfaces, respectively, therefore agreeing with
geomorphic relative-age constraints. Additionally, simple calculations using the formulations of
Anderson et al. (1996), which assume no surface erosion, yield ages of 41.8 ka, 85.7 ka, and 141
ka (see auxiliary materials for methods and inputs), suggesting that we have robust age signals.
Here we report uncertainty as the maximum and minimum solutions to acknowledge the full
range of model solutions, but refer the reader to appendix C to view frequency histograms for
age, inheritance, and surface erosion rate.
Figure 15. In situ 10Be depth profiles and monte carlo simulator results for age, inheritance, and surface erosion
rates when run for 100,000 solutions at 1 sigma uncertainty, according to parameters described in the text and
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appendices. Black line is the best fit. Gray lines are 100,000 model solutions. Solid black dots are subsurface
samples used in the model simulations. Hollow dots are surface sediment samples that were analyzed, but not
used in model simulations due to evidence of bioturbation. Hollow square represents a quartz cobble
amalgamation (n=85) sample that was simularly excluded from model simulations.
AR13-01 is located on a Q2 strath terrace, consisting of ~4 m of channel sands and lag deposits
which lie in angular unconformity over Miocene age sedimentary rock (Angastaco Fm.). The
soil consists of a coarse desert pavement, underlain by a vesicular A horizon (Av), which is
underlain by a Bk horizon that diffusely transitions to a C horizon. We find only minor field
evidence for bioturbation at this site, but the weak stratification and uniformity of the soil
framework grains makes identification of vertical mixing difficult. Model simulations yield a
modal age of 42.9 ka and a modal surface erosion rate of 2.4 mm kyr-1, thereby indicating that
~10 cm of erosion has occurred at this site.
AR13-02 is located on a Q5 fluvial strath terrace sourced dominantly from Paleozoic granitoids
and Tertiary volcanics southwest of the Pucará valley. This deposit consists of couplets of fine
and coarse grained layers, similar to AR13-01, but the sedimentology is partially obscured by
significant carbonate accumulation. The soil consists of a pavement layer over a shallow, weakly
developed Av horizon over a massive and root-limiting Bkk horizon over a Bk horizon which
diffusely transitions to a C horizon. The shallow depth to the Bkk horizon (10 cm) suggests that
significant erosion of the surface has occurred, prompting us to input a wide range (10 – 90 cm)
for the “total erosion threshold” parameter in the depth profile simulator (Royer, 1999). We
report a modal age of 96.3 ka and a modal surface erosion rate of 3.7 mm kyr-1, which yield an
erosion estimate of ~36 cm, confirming our suspicion of surface degradation at this site.
AR13-03 is located on a Q6 pediment surface and is notable for its coarse sedimentology and its
pedogenic gypsum content. The lower portion of the alluvial deposit is a clast-supported pebble
to cobble conglomerate, with moderate internal stratification and moderate sorting within
individual strata, indicating that it was deposited by sheetflood processes (Blair and McPherson,
1994). The upper part of the pit appears to be a storm deposit, likely of similar age to the
underlying material, given similar degrees of soil development. This event scoured a channel
into the existing alluvial surface, and deposited a poorly sorted, matrix-rich conglomerate with
no noticeable stratification. The soil consists of an Av over a Byy horizon over a Byk horizon.
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Similar to AR13-02, we place large bounds on the “total erosion threshold” parameter for AR13-
03. Depth profile simulations yield a modal age of 157.6 ka and a modal erosion rate of 1.2 mm
kyr-1, suggesting that ~20 cm of erosion has occurred on this surface. See auxiliary materials for
annotated pit photos and model input parameters for each depth profile.
2.6.5 Paleo-Erosion Rates
Vertical incision estimates for our Q2, Q5, and Q6 depth profiles are 11, 70, and 76 m,
respectively, yielding vertical incision rates of 270, 730, and 480 mm kyr-1 when modal ages are
used (Table 2). Catchment mean paleo-erosion rates derived from modal inheritances of the three
profiles are 98, 52, and 50 mm kyr-1 respecticely. The depth profile AR13-01 (Q2) paleo-
drainage reaches the edge of the Puna Plateau. At this site modal values suggest that the incision
rate is ~2.5 times higher than the catchment mean paleo-erosion rate, although the rates overlap
given their large uncertainty. Depth profile AR13-02 (Q5) has a paleo-drainage nearly identical
in extent to catchment BW3, and yields a vertical incision rate at minimum 3.5 times the
catchment mean paleo-erosion rate at this site. The paleo-drainage for depth profile AR13-03
(Q6) is a local tributary for the Pucará River, similar to the modern BW1 catchment. The modal
vertical incision rate (480 mm kyr-1) is nearly an order of magnitude higher than the catchment
mean paleo-erosion rate (50 mm kyr-1) at this site.
Table 2. Vertical incision rates and catchment mean paleo-erosion rates derived from 10Be depth profile ages and
inheritance, respectively. See section 2.5.4 for methodology.
Vertical Incision Rates Inherited Catchment Mean Erosion Rates
Depth Profile Total
Incision (m)
Mode (mm kyr-1)
Maximum (mm kyr-1)
Minimum (mm kyr-1)
Mode (mm kyr-1)
Maximum (mm kyr-1)
Minimum (mm kyr-1)
JM-AR13-01 11 270 430 90 98 147 90
JM-AR13-02 70 730 1250 310 52 86 45
JM-AR13-03 76 480 840 250 50 N/A 36
2.7 Discussion
Here we interpret our results with respect to the distribution of lithology, faults, and precipitation
in the CRC. We find that the spatial distribution of steepness indices and catchment mean
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erosion rates indicate strong lithologic control on erosion and topography throughout the
catchment. We also find evidence for active tectonics, particularly in the western CRC. The
distribution of anomalously high concavity indices also supports active faulting, as does our
analysis of knickpoints. Paleo-erosion rates indicate different uplift rates across major faults in
the CRC, and are similar to modern rates, while vertical incision rates may reflect transient
landscape adjustment or variable lithologic resistance. Lastly we consider the tectonic
implications of out-of-sequence deformation in the retroarc foreland.
2.7.1 Controls on River Morphology
2.7.1.1 Normalized Channel Steepness Indices
In the eastern part of the catchment, normalized steepness indices are controlled, at least in part,
by lithology (Figure 13). For example, in catchment M2, where less resistant sedimentary rocks
and Quaternary alluvium dominate, we observe low steepness indices (<200), but a high
catchment erosion rate (118 ± 16 mm kyr-1), indicating that low steepness indices reflect weaker
lithologies rather than low uplift rates in the eastern CRC. STR3, a small eastern catchment that
has its headwaters in more resistant crystalline rock, has similarly low steepness indices but a
significantly lower erosion rate than M2 (41 ± 5 mm kyr-1), further supporting the notion that
erosion rates in the eastern CRC are predominantly controlled by lithologic resistance.
In the western CRC, high ksn values are measured in crystalline rocks within the high ranges
bordering the plateau (Figure 13). To some extent this may reflect greater lithologic resistance to
erosion, but we note that erosion rates vary widely across the western catchments, suggesting
that spatially variable uplift rates may also control steepness indices. For example, in catchment
STR13, which is principally composed of crystalline bedrock, tributaries to the Calchaquí River
exhibit high (>200) ksn values, and the catchment erodes at a high rate (142 ± 18 mm kyr-1),
suggesting that uplift rates are higher here than in other parts of the CRC (e.g. catchment STR3).
Catchment STR11 (Luracatao River) similarly exhibits high erosion rate and steepness indices.
In contrast, we measure low erosion rates (<40 mm kyr-1) in catchments BW2 and BW3. In these
two catchments low steepness indices occur above prominent knickpoints that coincide with a
major NW striking lineament, upstream of which we also observe small Quaternary basins.
Below these lithotectonic knickpoints we observe steep segments with anomalously high
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concavity indices (see below for discussion of channel concavities). This suggests that BW2 and
BW3 exhibit such low erosion rates because the majority of their drainage areas lie above a
major fault which separates areas of low and high uplift rates (Willenbring et al., 2013).
In the SW catchments (BW2, BW3, BW5, and STR16) we observe evidence for a major NW-SE
striking fault. It is mapped in its southern end (within BW2, BW3, and part of BW5) but we use
our data to infer its extension to the north (Figure 13 B). For example, we suspect that the upper
reaches of catchment BW5 (e.g. S67; Figure 16), would exhibit similarly low catchment mean
erosion rates if sampled at or above the prominent lithotectonic knickpoints along the fault.
Catchment STR16 also records a low catchment mean erosion rate, which suggests tectonic
isolation similar to catchments BW2 and BW3. Although direct evidence for a fault is obscured
by Tertiary ignimbrites, knickpoint distribution and form suggest that a previously unidentified
fault (parallel to the dominant structural grain) divides two zones of differing uplift rate (Figure
13).
In addition to the dominant tectonic controls, climate also exerts some control on channel
steepness in the CRC. Bookhagen and Strecker (2012) demonstrated that correcting for the effect
of spatially variable precipitation on discharge significantly influences the distribution of
normalized steepness indices in this region. However, we find that the steepest channel reaches
in our analysis closely match those identified in the precipitation-corrected analysis by
Bookhagen and Strecker (2012), indicating that climatic corrections would not significantly
affect our interpretations (see Figure DR8 in Bookhagen and Strecker, 2012). This is expected
given the relatively uniform precipitation of the CRC compared to the steep precipitation
gradient that was the subject of the previous work. We find that our systematic investigation of
river profiles, which uses a significantly shorter channel-smoothing window (450 m vs. 5 km
used by Bookhagen and Strecker, 2012), allows for analysis of more spatially discrete (e.g.
lithologic and tectonic) controls on channel morphology.
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Figure 16. Selected longitudinal river profiles and corresponding local slope/drainage area regressions. Individual
segments are bound by knickpoints or confluences with trunk streams and were regressed with a reference
concavity of 0.45. Resulting normalized steepness indices and raw concavity indices are displayed for each
segment. Question marks identify faults with unknown dip. In slope-area space light and dark blue lines represent
forced and unforced regressions, respectively. See Figure 13 for stream locations. CNT = Cerro Negro Thrust; PT =
Pucara Thrust; JVT = Jasimana-Vallecito Thrust.
2.7.1.2 Concavity Indices and Non-Uniform River Profile Morphology
A key assumption in tectonic interpretations of normalized steepness indices in bedrock channels
is that lithology, climate, and uplift rate are uniform along a given channel reach, and that abrupt
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changes are marked by knickpoints. When this is true, concavity indices typically fall into a
relatively narrow range (0.4 – 0.7) (Kirby and Whipple, 2012). However, when uplift or climate
gradients exist, or when transitions from detachment-limited to transport-limited conditions
occur, concavity indices can vary widely (Whipple, 2004). Our results for all streams (n = 147)
yield an anomalously high mean concavity of 0.9, with respect to theoretical expectations, which
rises further to >2 when we exclude convex segments (e.g. θ < 0) (Figure 17). Here we address
the spatial distribution of concavity indices in the CRC, and the factors promoting such high
channel concavities.
The Calchaquí River itself is a well-graded profile (Figure 16). The lower segment exhibits a
concavity index within the expected range for river profiles in tectonically active orogens (0.53),
while the upper segment has a slightly low concavity index (0.34), likely reflecting the influence
of debris-flows and/or high sediment flux in the upper most part of the catchment (Whipple,
2004). The Calchaquí River flows through (and actively incises) sedimentary rock and
Quaternary alluvium, and also crosses the Cerro Negro Thrust, but we note no major breaks
across lithologic or tectonic boundaries. The narrow range of concavity and the well graded
profile suggests that the Calchaquí River is equilibrated to the prevailing climatic and tectonic
conditions and thus is in steady-state (Whipple et al., 2013).
Small tributaries to the Calchaquí River typically have concavities between 0.3 and 1, within the
normal range of incising rivers (Figure 17). Low concavities (<0.4) likely reflect the effects of
debris-flow processes and incision thresholds, especially for smaller catchments which undergo
periglacial processes in their headwaters. Higher concavities (0.7<θ<2) likely reflect
downstream reductions in both lithologic resistance (thus, an increase in K) and uplift rate, as
well as transitions to alluvial conditions at the range front (Whipple, 2004 and references
therein). In the CRC all three of these conditions are common, as rivers typically originate in
fault-bounded crystalline ranges that are bordered by Tertiary-Quaternary intramontane basins.
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Figure 17. Concavity indices and mean annual rainfall in the CRC. See Figure 13 for knickpoint classification. TRMM
precipitation data from Bookhagen and Strecker (2008). Labeled streams are displayed in profile in Figure Streams.
Extreme concavities (>2) occur along segments that are in the hanging walls of major thrust
faults, just downstream of lithotectonic knickpoints. Downstream lithologic changes commonly
occur along these segments, but in some cases we observe no such change (e.g. S65; Figure 16),
suggesting that the faults which bound these segments are active, and gradual downstream
reductions in uplift rates drive the high concavities (Whipple, 2004). We also observe
downstream increases in precipitation (increasing K) along many high-concavity reaches, but we
find the magnitude of increase to be too small (<500 mm yr-1) to have a significant effect (Figure
17) (Schlunegger et al., 2011; Bookhagen and Strecker, 2012). Channel convexities (θ<0) are
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also closely associated with tectonic features in the CRC. In most cases channel convexities are
short (<10 km) and occur across faults. However, some convex reaches can be as long as 50 km,
and tend to run sub-parallel, or at low angles, to faults in the study area (e.g. S1; Fig. 16).
Steepness indices are usually low above convex reaches and higher below, providing evidence
that these convexities represent transitions from zones of low to high uplift rate (Whipple et al.,
2013).
Overall we find that deviations from the expected range of concavity indices in erosive
landscapes (0.4<θ<0.7) can be reasonably well explained by the structurally controlled
distribution of lithology in the CRC; resistant crystalline ranges – bound by faults – are the
headwaters for streams, and lower reaches flow through less resistant (and potentially more
slowly uplifting) sedimentary rocks and alluvium, leading to high concavities. In some cases,
increasing downstream precipitation may contribute to this affect. Channel convexities are
associated with discrete tectonic features, and may separate regions of low and high uplift rate.
In particular, we argue that deformation is most active in the western half of the CRC, along a
narrow band of NNW-SSE striking reverse faults.
2.7.1.3 Knickpoint Genesis, Form, and Distribution
The majority of knickpoints (51 of 75) in the study area are spatially coincident with tectonic
and/or lithologic discontinuities along channels, providing further evidence that channel
morphology in the CRC primarily reflects structurally-controlled, lithologic heterogeneity.
However, we identify 24 knickpoints of undefined genesis, which can be employed to evaluate
the passage of transient channel responses in a landscape (e.g. Schoenbohm et al., 2004; Crosby
and Whipple, 2006; Harkins et al., 2007). Such an approach relies on the prediction that a drop in
base-level or change in uplift-rate/climate will create a slope-break knickpoint that migrates up
the channel network at a horizontal rate dependent on contributing drainage area (that is, stream
power), and at a fixed vertical rate. Transient knickpoints can therefore be identified in the
landscape by uniform elevations and map-view positions in the channel network (Wobus et al.,
2006b). Importantly, this approach assumes that concavity indices do not respond to rock uplift
rates. Our analysis suggests that concavity indices in the CRC do indeed reflect changes in uplift
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rates, so the migration of transient knickpoints in our study area may not produce uniform
elevations.
This approach also assumes detachment-limited conditions throughout channel reaches; in
transport-limited erosional systems, transient responses are characterized by a gradual change in
channel gradient along the entire reach, making transient and steady-state morphologies
indistinguishable (Whipple and Tucker, 2002). Although our field observations support
detachment-limited conditions along steep channel reaches in the CRC, we note that transport-
limited conditions are dominant in the intramontane Pucará Valley (evidenced by mixed
bedrock-alluvial channel morphology and >3 m thick sedimentary cover on abandoned strath
terraces) (Whipple and Tucker, 2002). Similar transport-limited segments likely occur in other
intramontane basins in the CRC.
Figure 18. Vertical distribution of knickpoints in the CRC. See Figures 13 and 17 for plan view.
Given these complexities, we find little evidence for transience in our analysis of knickpoint
distribution and form. Undefined knickpoints (those which are not associated with discrete
lithologic and/or tectonic discontinuities) are observed across a wide range of elevations (Fig.
18), and do not exhibit physical relationships that predict transient knickpoint behavior, such as
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the power law relationship between knickpoint contributing drainage area and knickpoint
distance upstream of tributary mouths (i.e horizontal celerity) (Harkins et al., 2007). We find
nine knickpoints clustered at approximately 4000 m elevation, but they are linearly aligned and
are parallel to previously mapped thrusts, suggesting structural rather than temporal control
(Wobus et al., 2006a).
2.7.2 Controls on Landscape Evolution of the Pucará Valley
2.7.2.1 Spatial Controls
In the Pucará Valley, vertical incision rates are local measurements, integrated over long
timescales (as much as 300 kyr in the case of the Q6 pit), while catchment mean paleo-erosion
rates integrate over much larger areas, but short (<20 kyr) periods. As a result, discrepancies
between vertical incision rates and paleo-erosion rates may reflect variations in tectonic,
climatic, and lithologic controls on erosion in different areas of the catchment. Our analyses of
the Q2 and Q5 depth profiles reveal that the lower Pucará Valley has best-fit vertical incision
rates 2.5 to 13 times higher than catchment mean paleo-erosion rates, suggesting that the lower
Pucará Valley has eroded at a higher rate than its headwaters for the last ~100 ka (the
approximate age of the Q5 surface). We acknowledge the difficulty in comparing vertical
incision and catchment mean denudation, but this explanation is supported by our analysis of
modern denudation rates and channel steepness indices, which are lower in the headwaters and
higher in the Pucará Valley (Harkins et al., 2007). Differential rates of denudation can most
easily be explained by differing uplift rates across major faults (Figure 13).
The Q6 depth profile, excavated on a pediment surface derived from a smaller catchment area,
provides a more local estimate of catchment-mean paleo-erosion rate than do the Q2 and Q5
depth profiles. Vertical incision and paleo-erosion rates may therefore be expected to agree,
assuming long-term topographic steady-state (Dortch et al., 2011). However, at this site, as at our
other sites, best-fit vertical incision rate is nearly an order of magnitude higher than catchment
mean paleo-erosion rates. This discrepancy may reflect a low erosion rate at the time of surface
deposition (~160 ka), and a subsequent sustained increase in erosion rate. Alternatively, this
discrepancy in erosion rate may reflect substantially lower erosion rate in the sediment source
area (Sierra de Quilmes) than in the Cretaceous and Tertiary sedimentary rocks where pediment
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gravels are deposited (Figure 11). Previous reconstructions of a landslide-dammed paleo-lake
suggest that, during cyclic short-term (annual to decadal) humid phases (e.g. La Niña), erosion
rates increase significantly (10-15%) in Cretaceous – Quaternary sedimentary units while erosion
rates in the high crystalline ranges remain relatively constant (Trauth et al., 2003b). Over 103-105
year timescales, this effect may intensify the overall relief of the Eastern Cordillera and explain
the discrepancy between vertical incision rate and catchment mean paleo-erosion rate.
2.7.2.2 Temporal Controls
Given the lack of transient signals in modern streams, we assume that rates of tectonic uplift
have been relatively constant across the <300 ka timescale of our paleo-erosion rate analyses.
Differences between modern and paleo-erosion rates should therefore reflect climatic changes,
which have occurred frequently throughout the Quaternary in this region (Bobst et al., 2001;
Trauth et al., 2003a; Fritz et al., 2004). Our catchment mean paleo-erosion rates (36 – 147 mm
kyr-1) are not markedly different from modern catchment mean erosion rates (27 – 145 mm kyr-
1), suggesting that climate has not significantly influenced erosion rates over this period (Table
2), or that short term climate changes in the Quaternary were not significant enough to affect
erosion rates in the CRC on cosmogenic nuclide timescales. This finding contrasts with recent
reconstructions of a ~6700 year sedimentary record from a landslide-dammed paleo-lake that
existed during the humid Minchin Phase (25 to 40 ka), which yields catchment mean erosion
rates an order of magnitude higher than modern rates in the CRC (Bookhagen and Strecker,
2012). Given the large uncertainties associated with our depth-profile surface ages, we cannot
associate paleo-erosion rates with discrete climate intervals. Further, 10Be catchment mean
erosion rates and paleo-erosion rates are averaged over 4 – 23 kyr timescales in this study, and
thus may integrate across multiple climate phases (Bierman and Steig, 1996).
2.7.3 Tectonic Implications
Our analysis of longitudinal river profiles, catchment mean erosion rates, and paleo-erosion rates
provide strong evidence that Quaternary tectonic deformation influences the rate and style of
landscape evolution in the Eastern Cordillera. Coupled steepness indices and catchment mean
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erosion rates, high concavity indices, linearly-aligned knickpoints, and ponded Quaternary
sediment above knickpoints all point to differential uplift across a band of N-S to NW-SE
trending reverse faults in the western CRC. The orientation of faults within this band reflects
preexisting structural anisotropies within the crystalline bedrock, and reactivated Cretaceous rift
structures (Grier et al., 1991; Hongn et al., 2007; Santimano and Riller, 2012; Carrapa et al.,
2014b). Further, field investigations in the lower Pucará valley reveal an active reverse fault, an
active blind thrust, and locally deeply incised (~100 m) pediment surfaces, supporting our
interpretations of active shortening in the western CRC. In the southeastern CRC, lithotectonic
knickpoints, high channel concavities, and channel convexities suggest that the Cerro Negro
Thrust and other west-vergent thrusts are also active (Figure 13). Therefore, we argue that
Quaternary shortening is active throughout the CRC, along most major faults in the study area.
This assertion is supported by field evidence for shortening in subcatchments within the CRC
and in adjacent areas (Strecker et al., 1989; Hilley and Strecker, 2005; Carrera and Munoz, 2008;
Hain et al., 2011; Santimano and Riller, 2012).
Quaternary shortening in the CRC has implications for tectonic and kinematic models of the
Eastern Cordillera. Active shortening in the interior of the thick-skinned orogenic wedge across
the southern Puna (DeCelles et al., 2011) could reflect reduced surface slopes, possibly due to
increased erosional efficiency (Davis et al., 1983; Whipple, 2009). However, erosion rates in the
Eastern Cordillera have likely decreased or not changed in the Late Quaternary due to increased
aridity since the Minchin Phase (Bookhagen et al., 2001), which would favor eastward
propagation of deformation rather than internal deformation (Bookhagen and Strecker, 2012).
This suggests that localized shortening in the Eastern Cordillera is driven by kinematic (e.g.
changing slab geometry) or geodynamic (e.g. gravitational spreading) processes (Schoenbohm
and Strecker, 2009) rather than climatic changes.
Irrespective of the causes for shortening in the CRC, the style of deformation we observe
suggests a localized coupling between tectonics and climatically-moderated erosion. Numerical
modeling of mountain belts bound by preexisting high-angle faults (as is the case for the western
CRC) shows that, as shortening occurs, dry (less erosive) conditions favor the build-up of surface
slopes, which in turn drives deformation to adjacent structures, while humid conditions focus
erosion and deformation along a discrete orographic front (Willett, 1999; Hilley et al., 2005).
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Morphologically, dry conditions are reflected by wide mountain ranges with intramontane basins
and distributed deformation along many inherited structures (Hilley and Coutand, 2010), such as
can be observed in the CRC (this study) and in the northernmost Sierras Pampeanas, which
border the study area to the south (Fig. 10) (Sobel and Strecker, 2003; Hilley et al., 2005). We
argue that, in these regions, preexisting structural heterogeneities are the dominant control on
mountain range structure, and the current dry climate favors accommodation of shortening across
multiple structures (Hilley and Coutand, 2010).
We also present speculative evidence for active extension in the southwestern CRC. In the upper
reaches of the Pucará River catchment, we identify a previously unmapped NNW-striking fault
(Figs. 13 and 17). We did not observe this fault in the field, and so cannot constrain its dip.
However, this fault is parallel to strike-slip and extensional faults on the Puna Plateau mapped by
Schoenbohm and Strecker (2009), to minor Quaternary strike-slip faults (not shown) in the Cachi
Range that are coincident with knickpoints in our analysis (Pearson et al., 2012), and to a major
fault zone immediately north of the study area, which records Quaternary strike-slip faulting and
extension on the Puna Plateau (Lanza et al., 2013). Plio-Quaternary strike-slip and extensional
tectonics in NW Argentina have been attributed to gravitational spreading on the Puna Plateau,
potentially in response to lithospheric foundering (Schoenbohm and Strecker, 2009; Zhou et al.,
2013). Regardless of the morphology of this newly mapped fault, continued displacement in the
current dry climate could lead to upstream channel defeat and basin isolation, and ultimately
morphologic incorporation into the Puna Plateau (Humphrey and Konrad, 2000; Sobel et al.,
2003)
2.8 Conclusions
In this study we use field investigations, systematic analysis of longitudinal river profiles, 10Be-
derived catchment mean erosion rates, and paleo-erosion rates and vertical incision rates from 10Be depth profiles to examine the late Quaternary landscape evolution of the Calchaquí River
Catchment. The distribution of high normalized steepness indices, abrupt lithotectonic
knickpoints, and variable catchment mean erosion rates demonstrate that incision and sediment
routing in this landscape are largely controlled by active tectonics and the structural juxtaposition
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of variably resistant lithologies. Anomalously high channel concavities, typically observed in the
hanging walls of thrust faults, reflect some combination of downstream decreases in uplift rate,
decreases in bedrock resistance (through lithologic and/or climatic changes), and transitions from
bedrock to alluvial channel reaches. Lithologic and tectonic controls, including preexisting
structural heterogeneity, obscure the effects of spatially variable climate on erosion and river
profile morphology, but aridity in the CRC may contribute to the distributed pattern of
deformation (Hilley et al., 2005). We find no clear evidence for transience in the landscape, but
along-channel fluctuations between detachment-limited and transport-limited conditions restrict
our ability to evaluate whether erosion and uplift are balanced. Knickpoints reveal that
previously unidentified faults – subparallel to the dominant structural grain – provide important
base-level controls on the uppermost reaches of the western CRC. Aggradation behind these
uplifting blocks occurs to keep pace with deformation, but continued tectonic isolation of base-
level and low precipitation rates could lead to channel defeat, internal drainage, and
incorporation into the Puna Plateau. We speculate that pervasive shortening in the CRC – and
perhaps localized extension – reflect gravitational spreading on the Puna Plateau. Future
kinematic analyses may elucidate the controls on active shortening in the CRC, and Quaternary
paleoclimatic analyses may better evaluate the coupling of climate and tectonics in the Central
Andean retroarc foreland, but our findings suggest that a catchment scale understanding of the
controls on erosion is a prerequisite to regional analyses of tectonic and climatic interactions.
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3 Chapter 3: Concluding Remarks
3.1 Methodological Considerations
3.1.1 River Profile Analysis & Catchment-Mean Erosion Rates
The results of this study demonstrate that successful application of longitudinal river profile
analysis in complex landscapes can be achieved through systematic analysis of river profiles in
multiple orientations with regards to structural trends (Kirby and Whipple, 2012). Given the
variable lithology, climate, and surface process domains (e.g. transport-limited and detachment-
limited channels) that we observed in the study area, the incorporation of catchment-mean
erosion rates further improved our ability to evaluate the spatial distribution of deformation in
the study area (Cyr et al., 2014). That being said, our arguments may have been more strongly
supported had we sampled river sediment directly at knickpoints, thereby measuring erosion
rates along discrete channel segments identified in our river profile analysis. This speaks to the
need for carrying out longitudinal river profile analysis before field work. We selected catchment
mean erosion rate sample sites based on qualitative assessment of landscape relief (e.g. aerial
photography, Google Earth), but longitudinal river profile analysis provides a quantitative
measure of relief and thus a higher likelihood of separating regions of different erosion rates (and
by extension, uplift rates). For future investigations we suggest that researchers analyze
longitudinal river profiles first, draft testable hypotheses (see Cyr et al., 2014), and then sample
for catchment-mean erosion rates in locations that will allow for the evaluation of such
hypotheses.
3.1.2 TCN Depth Profiles
The large uncertainties associated with our TCN depth-profile ages limit our ability to
investigate discrete climate intervals in the past, so we cannot definitively evaluate the effect of
Quaternary climate changes on catchment erosion rates. To improve depth-profile precision we
suggest that the surface sample (0 cm) be replaced with a subsurface sample that is definitively
deeper than the zone of bioturbation. In this study a sample at 20 cm would be of sufficient depth
for all three profiles. Communication with J. Gosse suggests that this sampling methodology is
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of increasing favor among researchers who frequently work with TCN depth profiles (Hidy et al.,
2010).
3.2 Future Research
This work advances the application of longitudinal river profile analysis by using catchment-
mean erosion rates to explore the relative effects of variable lithology and uplift rate on profile
form. This technique was recently proved by Cyr et al. (2014), where uplift rates and lithologic
resistance were previously known, thereby setting the stage for the application of longitudinal
river profile analysis into environments of increasing complexity. This technique should advance
our understanding of the ways that landscape relief, tectonics, and climate control erosion rates.
When paired with hillslope erosion rates, thermochronologic estimates of erosion, and paleo-
erosion rates from 10Be depth profiles, our understanding of the controls on erosion and sediment
routing in active orogens should further improve (Portenga and Bierman, 2011; Kirby and
Whipple, 2012).
I believe that continued work in the Calchaquí River Catchment could advance this technique,
and also improve our knowledge of the Quaternary landscape evolution of the southern Eastern
Cordillera. For example, erosion rate sampling of additional perched low-relief subcatchments in
the western CRC (similar to BW2 and BW3) would further test our hypothesis that the low relief
landscapes reflect markedly lower uplift rates. Field investigations at the transitions to those
landscapes (i.e. at lithotectonic knickpoints) would allow one to assess the dip of faults and
therefore assess whether extension or shortening is occurring, which has implications for
gravitational spreading processes on the Puna (Schoenbohm and Strecker, 2009). Dating more
abandoned surfaces with TCN depth profiles (in the Pucará and beyond), using the revised
sampling methodology from section 3.1.2, should help evaluate the controls on base-level in the
CRC: if terraces in other subcatchments within the CRC have similar ages that suggests a
synchronous base-level control, most likely climate, whereas a diachronous response would most
likely reflect tectonic control (Hilley and Strecker, 2005). Numerical modeling of channel
incision processes, constrained by field analyses, would permit the derivation of bedrock
erodibility (K) and therefore allow us to estimate uplift rate (U) (Sobel et al., 2003; Hilley and
Strecker, 2005). Numerical modeling would also be able to evaluate whether the climatically-
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moderated changes in erosional efficiency that have occurred in the Quaternary are significant
enough to influence tectonics (Beaumont et al., 2001; Sobel et al., 2003; Hilley et al., 2005).
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Appendices
Appendix A: Supporting Information for Field Studies
Appendix A includes a description of desert pavement development index (PDI) methodology,
PDI data, annotated photographs of depth profiles, and photographs of differing degrees of soil
carbonate development.
1. Desert Pavement Methodology and Results
1.1 Methods
We determined desert pavement indices according to the method developed by Al-Farraj
and Harvey (2000). At each site of desert pavement analysis, we took nine photographs in grid-
fashion looking vertically down on the surface from a height of approximately 1m. Later these
photographs were digitally merged using Adobe Photoshop and a 3X3 grid of arbitrary
dimensions was placed atop the merged photo. At each grid intersection, we measured the
multiple criteria of Al-Farraj and Harvey (2000). Thus each individual criterion is a mean of nine
values, with exception of those criteria that describe the overall surface, in which case only one
value is assigned. The mean values of the seven individual criteria are then averaged to yield a
pavement development index (PDI).
1.2 PDI Results. See Figure 11 for locations.
Pit Number
Grid Intersection
Relative clast size Sorting Angularity Clast
Fracturing Depositional
Fabrics Interlocking Clast relief
2
1 1 1 2
0 4
3
3
2 1 2 2 1
3 2 3 2 2
4 2 3 1 4
5 1 3 2 2
6 2 2 1 2
7 2 2 1 2
8 1 3 1 3
9 2 4 2 3 Final Score
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Averages 1.56 2.56 1.56 0 4 2.44 3 2.16
Pit Number
Grid Intersection
Relative clast size Sorting Angularity Clast
Fracturing Depositional
Fabrics Interlocking Clast relief
3
1 2 2 1
0 4
2
2
2 3 1 2 2
3 2 2 1 2
4 1 1 2 1
5 3 2 1 1
6 3 3 0 2
7 3 2 1 2
8 2 3 1 3
9 4 2 1 1 Final Score
Averages 2.56 2 1.11 0 4 1.78 2 1.92
Pit Number
Grid Intersection
Relative clast size Sorting Angularity Clast
Fracturing Depositional
Fabrics Interlocking Clast relief
4
1 2 1 1
2 4
3
1
2 2 2 1 3
3 2 2 2 3
4 2 2 1 2
5 1 1 1 1
6 2 2 1 3
7 3 2 1 2
8 1 2 1 2
9 2 2 2 3 Final Score
Averages 1.89 1.78 1.22 2 4 2.44 1 2.05
Pit Number
Grid Intersection
Relative clast size Sorting Angularity Clast
Fracturing Depositional
Fabrics Interlocking Clast relief
5
1 2 3 3
0 4
2
3
2 3 3 2 3
3 3 3 2 2
4 1 0 2 2
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5 2 2 2 2
6 3 3 2 2
7 3 3 2 1
8 3 2 2 1
9 2 3 2 3 Final Score
Averages 2.44 2.44 2.11 0 4 2 3 2.29
Pit Number
Grid Intersection
Relative clast size Sorting Angularity Clast
Fracturing Depositional
Fabrics Interlocking Clast relief
6
1 0 0 1
3 4
1
1
2 1 1 1 1
3 3 3 1 3
4 3 3 2 2
5 3 3 1 3
6 2 2 1 1
7 2 2 2 3
8 2 2 1 3
9 1 1 1 2 Final Score
Averages 1.89 1.89 1.22 3 4 2.11 1 2.16
Pit Number
Grid Intersection
Relative clast size Sorting Angularity Clast
Fracturing Depositional
Fabrics Interlocking Clast relief
7
1 3 2 3
1 4
3
2
2 2 2 2 1
3 3 2 2 2
4 2 2 1 2
5 3 3 3 3
6 2 1 1 1
7 2 2 2 2
8 3 2 2 2
9 3 3 2 2 Final Score
Averages 2.56 2.11 2 1 4 2 2 2.24
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Pit Number
Grid Intersection
Relative clast size Sorting Angularity Clast
Fracturing Depositional
Fabrics Interlocking Clast relief
8
1 3 2 2
3 4
2
3
2 3 3 2 2
3 1 3 1 2
4 2 1 2 3
5 3 2 3 2
6 2 3 2 2
7 3 2 1 1
8 3 3 2 2
9 2 3 2 2 Final Score
Averages 2.44 2.44 1.89 3 4 2 3 2.68
Pit Number
Grid Intersection
Relative clast size Sorting Angularity Clast
Fracturing Depositional
Fabrics Interlocking Clast relief
9
1 0 1 3
1 4
3
2
2 2 3 3 3
3 2 2 3 2
4 2 2 3 3
5 2 2 3 1
6 3 4 3 4
7 4 2 2 1
8 4 2 2 2
9 3 2 2 3 Final Score
Averages 2.44 2.22 2.67 1 4 2.44 2 2.40
Pit Number
Grid Intersection
Relative clast size Sorting Angularity Clast
Fracturing Depositional
Fabrics Interlocking Clast relief
10
1 1 1 1
4 4
3
1
2 2 1 3 3
3 3 3 3 3
4 2 1 1 2
5 3 2 2 3
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71
6 0 2 2 1
7 3 3 3 3
8 2 0 2 2
9 0 0 3 1 Final Score
Averages 1.78 1.44 2.22 4 4 2.33 1 2.40
Pit Number
Grid Intersection
Relative clast size Sorting Angularity Clast
Fracturing Depositional
Fabrics Interlocking Clast relief
11
1 3 4 1
2 4
3
3
2 3 3 2 4
3 3 3 2 4
4 2 2 2 2
5 3 2 2 3
6 2 1 2 2
7 2 1 2 3
8 3 3 2 4
9 2 2 1 3 Final Score
Averages 2.56 2.33 1.78 2 4 3.11 3 2.68
Pit Number
Grid Intersection
Relative clast size Sorting Angularity Clast
Fracturing Depositional
Fabrics Interlocking Clast relief
12
1 2 2 2
1 4
1
3
2 2 3 1 2
3 2 3 1 2
4 3 3 2 2
5 3 4 2 3
6 2 2 2 2
7 3 3 2 3
8 1 2 1 3
9 2 3 1 2 Final Score
Averages 2.22 2.78 1.56 1 4 2.22 3 2.40
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2. Annotated Depth Profile Photographs
Here we include annotated photographs of each depth profile referred to in the text. For
AR13-02 and AR13-03, we merged two photographs using Adobe Photoshop software, so
images are distorted and the scale increases towards the bottom of each pit. We sampled every
50 cm, marked by the red lines in each photo. We also include two photographs which detail the
differing degree of soil carbonate development on Q2 (AR13-01) and Q5 (AR13-02) surfaces.
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Soil pit descriptions from the Pucará Valley. Locations shown in Figure 11.
Pit # Pit Type Pit
Depth, cm
Surface level Soil Type Pedogenic
Salt Stage PDI
1 Soil 100 Q0 Ustic Haplocambid 0 n/a
2 Soil 50 Q1 Ustic Haplocambid 1.5 2.2
3 Cosmo 200 Q2 Ustic Haplocalcid 2 1.9
4 Soil 20 Q3 Ustic Haplocalcid 1.5 2.0
5 Soil 50 Q3 Ustic Petrocalcid 3.5 2.3
6 Soil 50 Q3 Ustic Haplocalcid 2.5 2.2
7 Pavement 0 Q4 n/a n/a 2.2
8 Soil 25 Q4 Ustic Haplocambid 1 2.7
9 Cosmo 200 Q5 Ustic Petrocalcid 3.5 2.4
10 Cosmo 195 Q6 Leptic Haplogypsid 3 2.4
11 Pavement 0 Q6 n/a n/a 2.7
12 Soil 25 Q6 Ustic Petrocalcid 3.5 2.4
13 Soil 80 Q6 Ustic Petrogypsid 3.5 n/a
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Appendix B: Geologic Map of the Pucará Valley
Lithology and structure compiled from (2008) and Coutand et al. (2006). Topographic base map
derived from ASTER 30 m digital elevation model. Strike and dips and updated structure are all
from this study.
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Appendix C: 10Be Analytical Results
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78
Appendix D: Supporting Information for 10Be Depth Profiles
1. 10Be Analytical Procedures
Samples were processed at the University of Vermont Cosmogenic Nuclide Laboratory using
standard analytical methods. Quartz was purified for 10Be analysis using mineral separation
procedures modified from Kohl and Nishiizumi (1992). Material was sieved to isolate the 250 to
850 µm grainsize and magnetically separated. Samples were ultrasonically etched in hot 6N HCl,
washed, and then etched at least three more times in hot dilute (1%) HF-HNO3 to preferentially
dissolve all grains except quartz. Samples were then etched in (0.5%) HF-HNO3 for 7 to 10 days.
Quartz was tested for purity by inductively coupled plasma - optical emission spectroscopy, and
additional HF-HNO3 etches were performed until desired purity levels were reached.
For Beryllium isolation, samples were prepared in batches that contained a full-process blank
and 11 unknowns. See www.uvm.edu/cosmolab for detailed methods. We used between 11.6
and 23.0 g of purified quartz for analysis. We added ~250 µg of 9Be carrier made from beryl at
the University of Vermont to each sample and dissolved samples in 120 g of hot, concentrated
HF. After dissolution and HF evaporation, samples were treated with HClO4, and then HCl. We
removed Fe in anion exchange columns and removed Ti, Be, Al, and B in cation exchange
columns. Be was precipitated at pH 8 as hydroxide gel, dried, ignited to produce BeO, ground,
and packed into copper cathodes with Nb powder at 1:1 molar ratio for accelerator mass
spectrometry (AMS) measurements.
10Be/9Be ratios were measured at the Scottish University Environmental Research Center and
were normalized to NIST standard with an assumed ratio of 2.79 ·10-15 based on a half life of
1.36 My. The average measured sample ratio (10Be/9Be) was 947 x 10-15 and AMS measurement
precisions, including blank corrections propagated quadratically, averaged 1.9 %. The full
process blank associated with the three batches in which these samples were run was 4.24±1.54 x
10-16. Because all samples received similar amounts of carrier, the blank ratio was subtracted
from the measured ratio for the sample. The blank correction is an inconsequential part of most
measured isotopic ratios (<0.7% on average, maximum 2.0%). The CRONUS N standard was
run with these samples and returned a concentration of 2.31±0.06 x 105 atoms g-1, consistent with
values reported by other labs.
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2. Depth-profile Input Parameters 2.1. JM-AR13-01 Sample Depth (cm)
Sample Thickness (cm)
10Be Concentration (atoms/g)
Uncertainty (%)
Density (g/cm^3)
Density Uncertainty (g/cm^3)
0 5 836327.03 0.05 1.7 0.2 50 5 644368.71 0.05 1.7 0.2 100 5 448475.77 0.05 1.8 0.2 150 5 344355.82 0.05 1.8 0.2 200 5 320317.85 0.05 1.8 0.2
2.2. JM-AR13-02 Sample Depth (cm)
Sample Thickness (cm)
10Be Concentration (atoms/g)
Uncertainty (%)
Density Depth (cm)
Density (g/cm^3)
Density Uncertainty (g/cm^3)
0 5 1408386.15 0.05 0 1.6 0.3 50 5 1254493.51 0.05 25 1.6 0.3 100 5 813334.52 0.05 45 1.8 0.2 150 5 604903.5 0.05 95 1.8 0.2 200 5 514874.8 0.05 145 1.8 0.2
195 1.8 0.2
2.3. JM-AR13-03 Sample Depth (cm)
Sample Thickness (cm)
10Be Concentration (atoms/g)
Uncertainty (%)
Density (g/cm^3)
Density Uncertainty (g/cm^3)
0 5 2667116.505 0.05 1.7 0.3 50 8 1528144.475 0.05 1.8 0.3 100 8 872348.5282 0.05 1.9 0.2 150 8 538749.3251 0.05 1.9 0.2 195 8 402625.1976 0.05 1.9 0.2
80
80
3. Depth Profile User Interfaces
3.1. JM-AR13-01
81
81
3.2. JM-AR13-02
82
82
3.3. JM-AR13-03
83
83
4. Depth Profile Solutions: Frequency Histograms
4.1. JM-AR13-01
84
84
4.2. JM-AR13-02
85
85
4.3. JM-AR13-03
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86
Appendix E: Supporting Information for River Profile Analysis
1. River Profile Analysis Results
“FID_” refers to the identifier in the georeferenced ArcMap file associated with this data-set.
“FigureFile” refers to an image file for each stream analyzed. These files are contained in a .zip
folder “streamfigures.zip”.
FID_ Reference Concavity ksn ksn 2σ high
ksn 2σ low
Minimum Drainage Area (m2)
Maximum Drainage Area (m2) Concavity
Concavity 2σ ks
Figure File
0 0.45 95.49 99.07 91.91 4591800 67502700 0.64 0.13 2590.29 S77.jpg 1 0.45 191.81 195.86 187.77 9341790 72032234 0.81 0.09 91641.6 S76.jpg 2 0.45 182.61 186.91 178.31 2277859 9341790 -0.47 0.28 0.00011 S76.jpg 3 0.45 106.75 109.52 103.99 2881890 15327943 0.96 0.11 336866 S75.jpg 4 0.45 157.96 161.23 154.68 4025675 18918283 1.15 0.13 1.1E+07 S74.jpg 5 0.45 84.98 86.93 83.03 2846434 9695253 0.98 0.13 300229 S73.jpg 6 0.45 217.40 223.94 210.86 17960400 75676500 1.04 0.13 7353307 S72.jpg 7 0.45 145.02 147.51 142.52 5176800 17985600 0.44 0.21 115.459 S72.jpg 8 0.45 228.38 231.40 225.37 41265907 208877891 0.56 0.19 1871.69 S71.jpg 9 0.45 106.97 111.23 102.71 4230036 40758205 0.34 0.19 20.4369 S71.jpg
10 0.45 345.24 378.04 312.45 473804100 548167500 9.60 4.55 1.1E+82 S69.jpg 11 0.45 174.48 188.83 160.14 148043700 474120000 2.77 0.65 3.9E+21 S69.jpg 12 0.45 116.65 130.27 103.02 52660800 148046400 1.30 0.79 9.1E+08 S69.jpg 13 0.45 141.35 143.23 139.47 8253900 52791300 0.54 0.14 702.202 S69.jpg 14 0.45 122.02 126.46 117.58 58481100 123404400 0.86 0.60 256093 S68.jpg 15 0.45 238.99 251.57 226.41 38452500 58481100 -1.98 0.95 0 S68.jpg 16 0.45 191.42 225.88 156.97 27373500 40864500 5.43 1.99 4E+39 S68.jpg 17 0.45 174.44 180.26 168.62 9053100 27373500 1.11 0.26 9810186 S68.jpg 18 0.45 344.67 367.46 321.88 233874900 400167000 3.91 0.57 6.1E+31 S67.jpg 19 0.45 108.41 120.28 96.55 34847100 233989200 1.37 0.24 1.8E+09 S67.jpg 20 0.45 112.73 114.66 110.79 11104200 34893900 0.20 0.21 1.68589 S67.jpg 21 0.45 133.67 136.44 130.91 44041500 121350600 -0.27 0.42 0.00026 S66.jpg 22 0.45 28.12 30.33 25.90 11018700 47338200 0.71 0.81 2593.17 S66.jpg 23 0.45 288.76 308.29 269.24 326590200 341906400 22.79 9.34 8E+192 S65.jpg 24 0.45 234.39 258.78 210.00 170769600 326705400 3.43 0.94 1.5E+27 S65.jpg 25 0.45 188.60 220.08 157.12 155179800 170769600 15.46 11.27 3E+125 S65.jpg 26 0.45 106.31 117.23 95.39 71800200 155249100 2.86 0.84 1.7E+21 S65.jpg 27 0.45 122.14 133.44 110.85 6705000 71800200 1.26 0.10 8E+07 S65.jpg 28 0.45 242.69 247.23 238.15 18489600 141094800 0.75 0.07 53036 S64.jpg 29 0.45 133.53 138.95 128.12 4688100 18489600 -0.38 0.17 0.00017 S64.jpg 30 0.45 178.99 180.88 177.09 10701900 274561200 0.53 0.04 756.939 S63.jpg 31 0.45 97.77 101.18 94.36 12115277 61324555 0.94 0.08 393861 S62.jpg 32 0.45 104.85 108.28 101.43 4075821 11966220 -0.34 0.16 0.00037 S62.jpg 33 0.45 98.00 100.47 95.54 6057007 111096128 0.64 0.18 2730.46 S61.jpg 34 0.45 159.20 168.15 150.25 614626200 705426300 3.69 3.95 6.7E+30 S60.jpg 35 0.45 144.97 157.43 132.51 478957500 615087000 -5.90 2.86 0 S60.jpg 36 0.45 56.82 58.37 55.26 14628600 526234500 0.18 0.14 0.41548 S60.jpg 37 0.45 98.33 106.58 90.08 42338700 44655300 28.12 12.92 3E+213 S59.jpg 38 0.45 142.90 153.49 132.32 24055200 42340500 -3.17 0.55 0 S59.jpg 39 0.45 33.38 34.87 31.90 4556193 24534937 0.62 0.47 603.161 S59.jpg 40 0.45 183.40 197.13 169.68 310409843 503052027 11.76 3.45 9.3E+98 S58.jpg
87
87
41 0.45 98.03 98.98 97.08 44205300 296146800 0.20 0.24 1.0247 S58.jpg 42 0.45 147.59 151.83 143.35 77586283 334344011 -0.38 0.24 2.3E-05 S57.jpg 43 0.45 84.80 85.83 83.77 9938294 74757697 0.49 0.16 179.583 S57.jpg 44 0.45 105.14 108.79 101.50 4126591 109729293 0.17 0.03 0.81765 S56.jpg 45 0.45 90.13 93.22 87.04 18455637 59824864 1.65 0.36 8.4E+10 S55.jpg 46 0.45 62.47 65.71 59.23 4075821 19392527 -0.56 0.11 6.1E-06 S55.jpg 47 0.45 75.25 78.20 72.30 10572886 72929498 0.93 0.16 271568 S54.jpg 48 0.45 84.76 85.70 83.82 4230036 87810892 0.36 0.04 18.7774 S53.jpg 49 0.45 137.34 138.78 135.89 56934609 119662202 1.40 0.49 4.1E+09 S52.jpg 50 0.45 153.65 158.19 149.12 22334400 59553900 -1.01 0.48 1.2E-09 S52.jpg 51 0.45 48.01 50.51 45.51 4847121 25149979 -0.03 0.37 0.02289 S52.jpg 52 0.45 84.62 85.72 83.53 23815800 48883500 0.23 0.31 2.08927 S51.jpg 53 0.45 201.01 223.55 178.47 6390900 23831100 -0.31 0.17 0.0009 S51.jpg 54 0.45 48.48 54.61 42.36 2959200 6390900 -0.63 0.75 3.2E-06 S51.jpg 55 0.45 81.68 85.42 77.94 31040984 66052989 0.39 1.21 27.0206 S50.jpg 56 0.45 87.29 89.03 85.56 2216267 105448759 0.21 0.03 1.71736 S49.jpg 57 0.45 156.89 160.54 153.24 95759704 756889112 0.12 0.16 0.29437 S47.jpg 58 0.45 56.11 56.59 55.62 4025675 80521893 0.53 0.07 225.941 S46.jpg 59 0.45 85.31 86.61 84.01 3302305 135431731 0.51 0.06 261.451 S45.jpg 60 0.45 93.46 95.39 91.53 25149979 66052989 0.10 0.41 0.20389 S44.jpg 61 0.45 87.45 89.67 85.22 8890470 22778592 1.98 0.33 7.4E+12 S44.jpg 62 0.45 95.61 96.75 94.47 4907499 79531218 0.34 0.08 13.8473 S43.jpg 63 0.45 114.16 119.48 108.84 30659081 981602111 0.61 0.14 2461.95 S42.jpg 64 0.45 238.55 251.57 225.53 19153937 25149979 -4.29 4.34 0 S42.jpg 65 0.45 117.25 119.93 114.57 4177993 18918283 0.37 0.14 31.8666 S42.jpg 66 0.45 62.78 66.40 59.16 79531218 738379397 0.11 0.46 0.13731 S41.jpg 67 0.45 169.24 175.97 162.51 30281877 62088441 2.35 0.40 6.1E+16 S41.jpg 68 0.45 264.25 291.49 237.01 21948146 27426604 -19.35 3.29 0 S41.jpg 69 0.45 142.04 144.84 139.23 3221547 23349607 0.11 0.12 0.68951 S41.jpg 70 0.45 52.71 55.83 49.58 9575970 738379397 0.02 0.20 0.02377 S40.jpg 71 0.45 69.37 74.48 64.25 2222154 6132456 3.01 0.36 3.1E+18 S40.jpg 72 0.45 110.75 111.64 109.86 4387500 75973500 0.52 0.07 363.727 S39.jpg 73 0.45 78.33 80.59 76.07 7240500 625302000 0.67 0.07 5194.44 S38.jpg 74 0.45 120.72 126.87 114.58 21411405 41779933 3.51 0.85 8.3E+24 S37.jpg 75 0.45 102.85 108.15 97.56 7203223 23062332 -0.19 0.33 0.00284 S37.jpg 76 0.45 206.28 234.17 178.38 15139360 24233079 6.65 0.84 2E+47 S36.jpg 77 0.45 212.91 225.00 200.82 9816021 14953098 -2.80 0.57 0 S36.jpg 78 0.45 205.60 224.40 186.80 13543173 19153937 6.36 1.18 7E+44 S35.jpg 79 0.45 346.24 364.39 328.08 9001214 13711873 -2.20 1.13 0 S35.jpg 80 0.45 218.99 229.82 208.16 6364488 15327943 2.06 0.18 4.2E+13 S34.jpg 81 0.45 154.89 158.91 150.88 4230036 21411405 0.84 0.23 82364 S33.jpg 82 0.45 147.12 149.35 144.89 3737495 10704586 0.57 0.19 1053.94 S32.jpg 83 0.45 141.78 146.79 136.77 5418399 33434401 1.03 0.15 2101857 S31.jpg 84 0.45 159.75 163.95 155.54 13376549 31040984 1.16 0.52 3.2E+07 S30.jpg 85 0.45 255.45 274.09 236.82 26426705 43360749 -12.54 17.62 0 S29.jpg 86 0.45 128.08 133.18 122.99 10497600 24319800 1.50 0.40 5.1E+09 S29.jpg 87 0.45 134.64 137.92 131.36 7001100 23336100 0.92 0.15 299410 S28.jpg 88 0.45 339.46 359.28 319.65 29541334 38311868 20.73 7.70 1E+155 S27.jpg 89 0.45 236.63 252.56 220.70 7383794 28818901 2.00 0.29 2E+13 S27.jpg 90 0.45 107.85 146.49 69.20 19682100 44820000 -2.86 3.19 0 S26.jpg 91 0.45 70.19 75.79 64.60 4728585 33850874 1.56 0.29 5.4E+09 S26.jpg 92 0.45 295.50 307.55 283.45 29422800 65511000 -1.55 0.36 1E-13 S25.jpg 93 0.45 200.95 210.97 190.93 18509400 29575800 0.97 0.61 1345364 S25.jpg 94 0.45 334.30 352.61 315.99 31409100 126907200 1.21 0.20 3E+08 S24.jpg 95 0.45 383.52 391.89 375.15 20388600 31409100 -0.38 0.43 0.00025 S24.jpg 96 0.45 227.36 238.92 215.80 14055602 20376978 3.12 0.96 4.1E+21 S24.jpg 97 0.45 199.52 205.61 193.42 6160500 30856500 1.18 0.11 3E+07 S23.jpg
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88
98 0.45 214.71 260.48 168.94 104723100 1205768700 0.34 0.49 48.3406 S22.jpg 99 0.45 508.51 517.97 499.06 57069900 104824800 0.86 0.37 801318 S22.jpg
100 0.45 250.54 283.79 217.30 41712300 57556800 9.02 3.59 7.4E+67 S22.jpg 101 0.45 39.07 42.13 36.02 3282300 41712300 0.54 0.30 230.16 S22.jpg 102 0.45 275.75 286.78 264.73 144079486 196340889 5.68 2.09 3.4E+45 S21.jpg 103 0.45 1241.14 1309.20 1173.08 124189827 142306850 -41.70 19.42 0 S21.jpg 104 0.45 284.52 293.02 276.02 75688911 127303021 7.23 5.71 1.6E+57 S21.jpg 105 0.45 186.08 198.51 173.65 65240327 78552731 -12.62 8.12 0 S21.jpg 106 0.45 123.50 124.93 122.06 3927227 66052989 0.51 0.10 352.596 S21.jpg 107 0.45 149.47 151.74 147.20 6024600 207945900 0.56 0.07 1096.64 S20.jpg 108 0.45 125.21 127.75 122.67 6431400 41218200 0.80 0.08 50570.2 S19.jpg 109 0.45 183.17 193.22 173.11 17989200 59067000 2.11 0.22 4.7E+14 S18.jpg 110 0.45 268.54 280.68 256.41 127303021 478748611 1.71 0.31 8.2E+12 S17.jpg 111 0.45 272.45 281.97 262.93 32094000 143267400 1.09 0.18 2.9E+07 S17.jpg 112 0.45 161.46 163.03 159.89 5886000 32265000 0.49 0.12 306.138 S17.jpg 113 0.45 230.03 231.64 228.42 3427254 467040818 0.52 0.02 753.426 S16.jpg 114 0.45 243.74 246.54 240.93 63797400 223772400 0.84 0.17 400818 S15.jpg 115 0.45 177.07 179.09 175.06 3466800 63813600 0.55 0.05 878.415 S15.jpg 116 0.45 215.27 232.86 197.68 103348800 120878100 5.97 3.79 6.1E+46 S14.jpg 117 0.45 210.09 215.88 204.30 63440100 103668300 1.15 0.87 8.5E+07 S14.jpg 118 0.45 185.53 191.00 180.05 8117100 63440100 0.79 0.13 69262.8 S14.jpg 119 0.45 251.23 255.03 247.43 33478200 156691800 -0.01 0.13 0.06195 S13.jpg 120 0.45 214.29 216.54 212.04 18700200 704192400 0.39 0.12 73.9766 S12.jpg 121 0.45 293.20 299.55 286.86 39991500 103978800 1.01 0.20 8226147 S11.jpg 122 0.45 219.24 221.83 216.65 21699900 361544400 0.25 0.24 6.54904 S10.jpg 123 0.45 197.62 201.06 194.18 17820900 159719400 0.76 0.25 44626.8 S9.jpg 124 0.45 181.86 185.44 178.27 51938100 542167200 0.13 0.32 0.52415 S8.jpg 125 0.45 139.67 150.91 128.42 45627300 290161800 0.43 1.04 107.946 S7.jpg 126 0.45 148.66 161.96 135.36 21380400 46314000 -1.42 1.27 9.1E-13 S7.jpg 127 0.45 112.03 119.07 104.99 9113337 36460948 1.57 0.23 1.3E+10 S7.jpg 128 0.45 255.30 271.07 239.53 591354900 2724800400 2.66 0.34 1.6E+22 S6.jpg 129 0.45 463.48 492.38 434.58 543605400 591588900 -3.61 8.09 0 S6.jpg 130 0.45 133.01 147.52 118.50 156368700 543765600 0.78 0.97 111552 S6.jpg 131 0.45 228.47 264.94 192.00 91425600 156368700 3.94 1.66 2.7E+30 S6.jpg 132 0.45 87.02 91.19 82.85 26021700 92223000 0.12 0.59 0.33534 S6.jpg 133 0.45 178.42 197.71 159.13 1267044300 7049611800 0.96 0.50 1.3E+07 S5.jpg 134 0.45 291.15 300.72 281.59 716949900 1267044300 2.22 0.77 1.9E+18 S5.jpg 135 0.45 220.53 223.01 218.05 108681300 722467800 0.28 0.25 9.75188 S5.jpg 136 0.45 109.29 111.70 106.88 4872600 113628600 0.21 0.12 2.01169 S5.jpg 137 0.45 134.30 135.40 133.20 521378100 12677505300 0.53 0.10 959.214 S4.jpg 138 0.45 151.39 153.55 149.22 2578044 677088069 0.34 0.04 21.1363 S4.jpg 139 0.45 128.34 130.46 126.21 2286900 67183200 0.43 0.08 82.2679 S3.jpg 140 0.45 70.49 76.46 64.51 98374500 355807800 1.69 0.39 2.1E+12 S2.jpg 141 0.45 190.70 211.07 170.34 66576600 100162800 -6.25 1.76 0 S2.jpg 142 0.45 46.77 51.36 42.18 2456100 67038300 0.92 0.13 103414 S2.jpg 143 0.45 383.56 429.44 337.69 2678003100 10445895000 1.21 1.37 7.3E+09 S1.jpg 144 0.45 146.08 159.48 132.67 1206870300 2678319900 0.82 1.16 477783 S1.jpg 145 0.45 248.83 258.72 238.95 186293700 1209246300 -0.42 0.16 7.3E-06 S1.jpg 146 0.45 35.57 38.74 32.40 2685600 186729300 0.61 0.19 575.954 S1.jpg
2. Knickpoint Data
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89
“Knickpoint Morphology” refers to the knickpoint forms described in the text. 1,2,3, and 4 refer
to vertical-step knickpoints, slope-break knickpoints, the downstream end of a convex reach, an
the upstream end of a convex reach, respectively. “Stream segment” indicates from which stream
the following knickpoints were identified. See previous table. Distance from Divide (m)
Distance from mouth (m)
Elevation (m)
Drainage Area (m2)
UTM 19S Easting
UTM 19S Northing
Knickpoint Morphology
Knickpoint Type
Stream Segment
17824 189935 3974 1.1E+08 752532 7240772 1 litholofic S5
15115 141786 3735 5.3E+07 745523 7144997 1 lithologic S69
25988 130913 3412 1.5E+08 747880 7153517 1 lithologic S69
31064 122204 3129 1.7E+08 763802 7125963 1 lithologic S65
9681 29778 1884 3.9E+07 814741 7136809 1 lithologic S61
13471 184630 3004 2.9E+07 803864 7218445 2 lithologic S44
12059 172668 3686 4.6E+07 734918 7170980 3 lithologic S7
18520 166207 3429 1.1E+08 738876 7174424 1 lithologic S6
30472 160404 3344 5.4E+08 743136 7171675 4 lithologic S6
22946 167930 3491 1.5E+08 739390 7176147 1 Lithologic S6
55460 101440 2652 4.7E+08 765977 7148864 1 lithotectonic S69
15136 115104 2889 5.9E+07 756309 7146085 3 lithotectonic S68
27980 119993 3366 2.3E+08 757548 7132640 2 lithotectonic S67
8878 131907 3306 4.4E+07 761777 7118289 1 lithotectonic S66
25781 127488 3295 1.6E+08 760418 7123908 1 lithotectonic S65
42564 110704 2658 3.3E+08 769240 7131129 1 lithotectonic S65
5586 79914 2561 1.2E+07 784890 7146024 3 lithotectonic S62
8485 53150 2290 2.4E+07 808366 7152309 4 lithotectonic S59
42608 71363 2306 3E+08 803653 7162823 1 lithotectonic S58
18877 85772 2601 7.2E+07 802565 7174062 4 lithotectonic S57
9201 91565 2316 2E+07 790208 7168503 3 lithotectonic S55
10627 116231 2622 6E+07 791416 7181525 3 lithotectonic S52
5622 121236 2917 2.2E+07 793622 7185120 4 lithotectonic S52
3941 136838 2674 2.4E+07 792897 7192129 3 lithotectonic S51
2532 138246 2894 6447600 793259 7191344 4 lithotectonic S51
7447 194987 3486 1.9E+07 808608 7229684 4 lithotectonic S42
9806 192628 3231 2.2E+07 807429 7228687 3 lithotectonic S42
5769 201684 3631 2.5E+07 810179 7238476 4 lithotectonic S41
5407 199829 3120 1.5E+07 789724 7251619 3 lithotectonic S36
5024 203994 3251 1.3E+07 788365 7253975 3 lithotectonic S35
14591 175214 3488 6.6E+07 753620 7228324 3 lithotectonic S25
22584 133283 2455 1E+08 757880 7190468 3 lithotectonic S22
8490 186559 4275 3.2E+07 765554 7229805 2 lithotectonic S17
19860 175189 3306 1.4E+08 770509 7222101 2 lithotectonic S17
90
90
10205 210108 3934 6.4E+07 770479 7245062 2 lithotectonic S15
92586 86794 2201 2.7E+09 777307 7146749 1 lithotectonic S1
4568 156608 3598 1.8E+07 750568 7178231 2 tectonic S72
10743 137230 3779 3.5E+07 745553 7126477 1 tectonic S67
15361 46274 1853 4.2E+07 805103 7149348 3 tectonic S59
5848 186298 2933 2.4E+07 783803 7233793 2 tectonic S37
7609 199844 3395 3.3E+07 809242 7237237 3 tectonic S41
1877 203359 3609 1E+07 791839 7253280 4 tectonic S36
3129 205889 3646 1E+07 789090 7255335 4 tectonic S35
12244 143623 3841 5.7E+07 750478 7194335 4 tectonic S22
21144 149571 3659 1.3E+08 749269 7200740 1 tectonic S21
13330 214482 3575 6.7E+07 773984 7250954 2 tectonic S14
20908 206904 3093 1E+08 779090 7250863 1 tectonic S14
51089 156670 3006 7.2E+08 757699 7213671 1 tectonic S5
81510 126249 2269 1.3E+09 762412 7189471 1 tectonic S5
12008 176285 4295 6.7E+07 750871 7099829 4 tectonic S2
20204 168089 3908 9.8E+07 750417 7106566 3 tectonic S2
3495 120386 3445 9474300 779603 7125600 3 undefined S76
9146 165771 3662 4.1E+07 743740 7154303 1 undefined S71
30068 126832 3199 1.8E+08 750448 7154846 1 undefined S69
6759 123481 3516 2.7E+07 750629 7142580 1 undefined S68
10158 120082 3294 4.1E+07 752593 7144997 4 undefined S68
12988 140281 3614 7.2E+07 751988 7118168 1 undefined S65
6768 132501 4234 1.9E+07 778244 7112095 2 undefined S64
76989 45496 1922 6.1E+08 814862 7145632 3 undefined S60
62044 60442 2163 4.8E+08 815466 7156508 4 undefined S60
5395 190778 4050 3.4E+07 749632 7239715 4 undefined S26
10532 188361 4045 2.5E+07 753983 7239201 1 undefined S29
8764 182723 3960 3.2E+07 751686 7232826 1 undefined S27
4788 185017 4369 2.9E+07 745674 7228596 4 undefined S25
8303 174140 4479 2E+07 747577 7221103 4 undefined S24
10460 171983 4108 3.1E+07 749058 7220016 3 undefined S24
8082 147785 4151 4.2E+07 748151 7195181 1 undefined S22
13535 157180 4136 6.7E+07 745704 7206088 1 undefined S21
6568 178160 3961 2.1E+07 730386 7170557 4 undefined S7
35708 155168 3067 5.9E+08 747094 7169923 3 undefined S6
15534 175342 3876 9.2E+07 743166 7181464 1 undefined S6
42968 210870 2966 6.9E+08 783984 7262435 1 undefined S4
49663 138629 3501 3.6E+08 766702 7109829 4 undefined S2
14184 165196 4097 1.9E+08 758696 7089586 4 undefined S1
70932 108448 2438 1.2E+09 772684 7131280 3 undefined S1
91
91
Appendix F: Explanation of Attached Digital Material
Attached to this thesis are digital materials generated during longitudinal river profile analysis.
The first is an ArcMap shapefile, titled Ksn_all.shp, which is georeferenced in WGS1984, UTM
zone 19S. It contains all stream segments identified in our analysis, including the data contained
in Appendix E.
The second file is a compressed folder, titled streamfigures.zip, which contains Matlab figure
files and JPEG images generated by the GeomorphTools StreamProfiler code
(geomorphtools.org) that we used in our analysis. These figures permit the analysis of each
segment in profile form.