Late Quaternary Tectonics, Incision, and Landscape …...Late Quaternary Tectonics, Incision, and...

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).

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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




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


Averages 2.56 2 1.11 0 4 1.78 2 1.92

Pit Number

Grid Intersection




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


Averages 1.89 1.78 1.22 2 4 2.44 1 2.05

Pit Number

Grid Intersection




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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69

5 2 2 2 2

6 3 3 2 2

7 3 3 2 1

8 3 2 2 1


Averages 2.44 2.44 2.11 0 4 2 3 2.29

Pit Number

Grid Intersection




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


Averages 1.89 1.89 1.22 3 4 2.11 1 2.16

Pit Number

Grid Intersection




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


Averages 2.56 2.11 2 1 4 2 2 2.24

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Pit Number

Grid Intersection




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


Averages 2.44 2.44 1.89 3 4 2 3 2.68

Pit Number

Grid Intersection




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


Averages 2.44 2.22 2.67 1 4 2.44 2 2.40

Pit Number

Grid Intersection




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

71

71

6 0 2 2 1

7 3 3 3 3

8 2 0 2 2


Averages 1.78 1.44 2.22 4 4 2.33 1 2.40

Pit Number

Grid Intersection




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


Averages 2.56 2.33 1.78 2 4 3.11 3 2.68

Pit Number

Grid Intersection




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


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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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.

http://www.uvm.edu/cosmolab

79

79

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)



Uncertainty (%)

Density Depth (cm)

Density (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)



Uncertainty (%)

Density (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

86

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

88

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








42608 71363 2306 3E+08 803653 7162823 1 lithotectonic S58






2532 138246 2894 6447600 793259 7191344 4 lithotectonic S51










90

90



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

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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.

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