ABSTRACT BAYASGALAN, GANTULGA. Late Cenozoic Landscape Evolution in the Khangay Mountains, Mongolia

ABSTRACT

BAYASGALAN, GANTULGA. Late Cenozoic Landscape Evolution in the Khangay Mountains, Mongolia. (Under the direction of Dr. Karl W. Wegmann).

Timing, rates, and systems responsible for uplift of intracontinental mountain ranges provide insight into the creation of high-elevation regions on Earth that are distant from active plate tectonic margins. The Khangay Mountains in central Mongolia is an intriguing research site that is suitable for investigating the timing of geologic processes responsible for topographic uplift and the development of continental scale drainage patterns, as well as the climatic-geomorphic responses to such. This dissertation focuses on defining shallow crustal and surficial processes contributing to the development of the Khangay Mountains at both short and long topographic wavelengths, as well as the topographic evolution of the range.

The first chapter of this dissertation focuses on cross-strike drainage development via the formation and capture of small tectonic basins (lakes) in front of an active thrust known as the Bayankhongor fault along the southern flank of the Khangay Mountains. The field research site contains ample geomorphological features that I will use to reconstruct the interplay between surface uplift by faulting, temporary formation of lakes in footwall basins and fluvial incision. Although ultimately these landforms (e.g. water gaps) reflect the defeat of rivers during sustained rock uplift, the role of climate oscillations in their formation is an underexplored topic. I used a coupled tectonics–landscape evolution-climate change model to test the hypothesis that a ~130 km2 late Quaternary lake in the Galuut Valley along the southern flank of the Khangay Mountains drained, perhaps catastrophically. I hypothesize that fault-controlled uplift of the original outlet exceeded the elevation of a low drainage divide between an adjacent range-front basin, possibly during a climatically-forced low stand in lake level.

The research of my second chapter focuses upon characterizing the relative importance of chemical and physical weathering to landscape development in the Khangay Mountains through an investigation of geochemical major and minor trace elemental analysis. The samples are collected along major stream valleys in the Khangay from well-developed saprolitic paleosols formed in metasediments (middle Orkhon), granite (upper Orkhon), and Miocene fluvial deposits (upper Chuluut) preserved beneath 40Ar/39Ar dated basaltic lava flows with ages of 11.2, 7.6 and

3.1 Ma, respectively. I used the Chemical Index of alteration (CIA), Plagioclase Index of Alteration (PIA) and Chemical Index of Weathering (CIW) derived from the fine-sediment fraction of these paleosols to reconstruct estimates of integrated paleo-mean annual temperature and precipitation in the millennia preceding lava burial. I used these data to compare estimates of geomorphic processes from the end of the middle Miocene and Pliocene with the cold-region, continental climate and physical weathering-dominant geomorphic processes that exist across the Khangay Mountains during the Quaternary.

The third chapter focuses on quantifying valley sedimentary fill (alluvium) thicknesses in the Khangay Mountains. My hypothesis is that a regional change from relatively stable, warmer and more humid Miocene and Pliocene climate to the oscillatory cold and dry-dominated climate of the Quaternary resulted in substantial geomorphic process change. Specifically, regional hillslopes transitioned from transport-limited to weathering-limited, which resulted in substantial valley aggradation. The presence of thick packages of aggraded clastic fluvial sediment in both glaciated and non-glaciated valleys of the Khangay Mountains is an indication that glaciers alone are not responsible for the backfilling of regional valleys with sediment. I employed GIS analyses to estimate the thickness (depth to bedrock) and volume of aggraded Quaternary sediments from both Pleistocene glaciated and non-glaciated drainages. Estimates of these parameters provided constraints for reconstructing landscape-scale erosion rates and the modeling of isostatic uplift of the Khangay Mountain due to erosional unloading.

Late Cenozoic landscape evolution in the Khangay Mountains, Mongolia

by Gantulga Bayasgalan

A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Marine, Earth, and Atmospheric Sciences

Raleigh, North Carolina

2018

APPROVED BY:

______Karl W. Wegmann Elana L. Leithold Committee Chair

______Helena Mitasova Ethan Hyland

DEDICATION

To my parents, L. Bayasgalan and L. Enkhtuya.

BIOGRAPHY

Gantulga Bayasgalan was born in Undurkhaan, Mongolia, and raised in Ulaanbaatar, Mongolia. He earned a Bachelor of Science degree in environmental science from National University of Mongolia in 2002 and a Master of Science degree in Physical Land Resources from the Free University of Brussels in 2007. He spent 5 years working as a lecturer in the Department of geology, Mongolian University of Science and Technology before enrolling in the PhD program at NC State in 2013.

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ACKNOWLEDGEMENTS

This work was completed with the help of many people to whom I owe a great debt. My first and foremost thanks go to Prof. Karl W. Wegmann for his support and valued advice throughout my graduate study and for giving me a great opportunity to work and study at North Carolina State University. His guidance and editing in order to make this thesis readable is especially appreciated.

I would like to thank the members of my graduate committee Dr. Lonnie Leithold, Dr. Helena Mitasova and Dr. Ethan Hyland. This dissertation has been significantly improved as a result of their valuable guidance and suggestions. I am also grateful to Dr. A. Bayasgalan for his suggestions and support during my Ph.D. program at NCSU.

A huge debt of gratitude is owed to Dr. Emanuele Giachetta at the ETH-Zurich for his guidance and help for manipulating SIGNUM landscape evolution model, which intensively used for chapter 1. I would also like to thank Dr. R. Fodor at the NCSU, for his advice for calculating Major Element analysis for chapter 2, Dr. E. Hestir and Dr. D. Bohnenstiehl for their help and encouragement of my research and valuable discussions. Doing research with this effective working group was a great help in finishing my Ph.D. program. I am also indebted to Dr. Kh. Tseedulam, at the Mongolian University of Science and Technology for her help in performing geophysical Vertical Electric Sounding field survey for my research chapter 1. I also would like to thank the professors of the Marine Earth and Atmospheric Science Department at NCSU for their enlightening lectures, especially Jim Hibbard and Gary Lackmann. I immensely thank to Mrs. Meredith Henry, Mrs. Laura Holland and Mrs. Beth Graf for their professional administrative assistance. For the fieldwork in Mongolia, I would like to thank Narangerel Mandakh for his excellent driving and navigation skills throughout the countryside of Mongolia, and Steve Smith, Nathan Lyons, and Matthew Morriss for their great companionship and support both in lab and in the field. Many warm and happy memories accumulated related to my friends. I thank to all of my friends for their longtime friendship. Mongolia research was supported by National Science Foundation Research Grants EAR-1009702 and EAR-1009680.

My deepest gratitude goes to my family for their unflagging love and support throughout my life. Without their help and encouragement from them, this thesis would not have been completed.

TABLE OF CONTENTS

LIST OF TABLES………………………………….……………………….……….…..…… viii LIST OF FIGURES……………………….………………………………....…...………….... xii APPENDICES………….………………………………………..…….…………...... xviii

Chapter 1: Paleoenvironmental Reconstruction of Late Quaternary Lacustrine

Sediments and their Tectonic Implications, Southern Khangay Mountains, Mongolia

1.1. Abstract...... 1 1.2. Introduction ...... 2 1 1.3. Background ...... 5 1.4. Materials and Methods ...... 2 9 1.4.1. SIGNUM modeling ...... 5 11

1.4.2. Stratigraphy and radiocarbon ages...... 9 12 1.4.3. Geophysical investigation ...... 13 11 1.4.3.1. Vertical Electric Sounding (VES) method ...... 13 1.4.3.2. Ground penetrating radar (GPR) survey ...... 14 1.4.4. Geographic Information System Modeling ...... 15

1.4.5. Geomorphological Mapping…………...... 15 1.4.6. SIGNUM modeling general testing scenarios...... 15

1.4.7. SIGNUM modeling specified testing scenarios...... 17

1.5. Results ...... 19 1.5.1. Stratigraphy and radiocarbon ages ...... 19

1.5.2. Geophysical survey results VES and GPR ...... 20 1.5.3. Geographic Information System Modeling ...... 20

1.5.4. Geomorphological Mapping…………...... 21

1.5.5. SIGNUM modeling general testing scenarios ...... 24 1.5.6. SIGNUM modeling specified testing scenarios ...... 25 1.6. Discussion ...... 26 1.6.1. Stratigraphy……………………………...... 27

1.6.2. Geophysical survey VES and GPR...... 28

1.6.3. Topographic development stages in SIGNUM ...... 29

1.7. Conclusions ...... 30 1.8. Acknowledgement ...... 33 1.9. References: ...... 34

Chapter 2: Contrasting Late Miocene to Present Landscape Evolution across Mongolia’s Khangay Mountains through the lens of Chemical and Physical Weathering Processes

2.1. Abstract ...... 95 2.2. Introduction ...... 96 2.3. Materials and Methods ...... 100 2.4. Results ...... 104 2.4.1. Middle Orkhon Section (>7.6 Ma) ...... 105 2.4.2. Upper Orkhon Section (>3 Ma) ...... 105 2.4.3. Upper Chuluut Section (>11.2 Ma) ...... 106 2.5. Discussion ...... 107 2.5.1. Middle Orkhon Section (>7.6 Ma) ...... 107 2.5.2. Upper Orkhon Section (>3 Ma) ...... 108 2.5.3. Upper Chuluut Section (>11.2 Ma) ...... 108 2.5.4. Principal Component Analysis of Geochemical Results...... 109

2.5.5. Al2O3 versus K2O ...... 111 2.5.6. Diagenesis and Sediment Recycling ...... 111 2.5.7. Paleoclimate Estimates ...... 112 2.5.7.1. Paleo Mean Annual Temperature reconstruction ...... 112 2.5.7.2. Paleo Mean Annual Precipitation reconstruction ...... 116

2.5.7.3. Paleoprecipitation indicator CIA (molar)/K2O/Na2O ratio ...... 117

2.5.8. CIA vs K2O/(Na2O+CaO*) ratio ...... 118 2.5.9. Comparison to other paleo-proxy records in the region ...... 119 2.6. Conclusions ...... 120 2.7. Acknowledgements ...... 122 2.8. References ...... 123

Chapter 3: Reconstructing Late Cenozoic Landscape Evolution Across the Khangay Mountains using Estimates of Alluvial Valley Fill Thickness

3.1. Abstract ...... 196 3.2. Background ...... 197 3.3. Materials and methods ...... 201 3.3.1. Digital topography...... 201 3.3.2. Estimating accommodation space and application of volume area scaling ...... 202 3.4. Results ...... 204 3.4.1. Volume estimates ...... 204 3.4.2. Depth estimates ...... 206 3.4.3. Cross checking ...... 206 3.5 Discussion ...... 206 3.5.1. Spatial pattern of Khangay valley fills ...... 206 3.5.2. Sediment transport patterns and erosion rate ...... 210 3.5.3. Residence times of valley fills ...... 211 3.6. Conclusion ...... 212 3.7. Acknowledgements ...... 213 3.8. References…………………………………………..……………………...………..…... 214

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LIST OF TABLES

CHAPTER 1

Table 1. Geophysical VES survey point locations……………….…………..…. .. 44 Table 2. Radiocarbon results for lacustrine sediments from the paleo Galuut Lake deposits………………………………..……………….….….….... 45 Table 3-A. Sediment deposition rate for paleo Galuut Lake deposits………...... 46 Table 3-B. Radiocarbon analyses for paleo Lake Galuut deposits………….…...… .. 47 Table 4-A. Reconstructed ages for relative lake water depth and climate cycles from stratigraphic section at the upper paleo lake………...... … 48 Table 4-B. Reconstructed volume for relative lake water depth and climate cycles from stratigraphic section at the upper paleo lake……...…....…. .. 49 Table 5-A. Reconstructed ages for relative lake water depth and climate cycles from stratigraphic section at the lower paleo lake………..………..……. 50 Table 5-B. Reconstructed volumes for relative lake water depth and climate cycles from stratigraphic section at the lower paleo lake…………….…. 50 Table 6. Paleolake level simulation table reconstructed from shoreline altitudes for the Upper side of the Galuut Canyon……………...... ….. . 51

CHAPTER 2

Table 1. Profile description for the Middle Orkhon section...... 131 Table 2. Profile description for Upper Orkhon section...... 132 Table 3. Profile description for Upper Chuluut section ...... 133 Table 4. General information for the all samples collected for weathering indices... …………………………………………………………..…… 134 Table 5. Major element concentrations of all samples, reported in weight percent .. …………………………………………………………..……135 Table 6. Major element oxide concentrations of all samples, reported in weight percent ...... 136 Table 7. Summary of weathering indices ...... 137 Table 8. Weathering indices for all samples reported as percentage values. …....138

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Table 9-A. Modern drainage basin metrics per catchment ...... 139 Table 9-B. Weathering indices for all modern stream flood plain samples collected in percent…………………………………..…………..….… 139 Table 9-C. Correlation coefficient for DEM’s mean and sampled elevation versus late Holocene floodplain (LHFP) sample weathering indices….. 139 Table 10-A. Principal Component Analysis: Summary statistics:...... 140 Table 10-B. Covariance matrix (Covariance (n)) ...... 140 Table 11-A. Principal Component Analysis, Eigenvalues: ...... 141 Table 11-B. Eigenvectors: ...... 141 Table 11-C. Factor loadings:...... 141 Table 11-D. Correlations between variables and factors: ...... 142 Table 11-E. Contribution of the variables (%): ...... 142 Table 12-A. Squared cosines of the variables:...... 143 Table 12-B. Factor scores: ...... 144 Table 12-C. Contribution of the observations (%): ...... 145 Table 12-D. Squared cosines of the observations: ...... 146

Table 13. K2O versus Al2O3 ratio...... 147 Table 14. Zr/Sc and Th/Sc ratio ...... 148 Table 15. Modern weather station climate data (MAT, MAP) for representing Late Holocene weather ...... 149 Table 16. The correlation matrix for modern flood plain weathering indices. ….... 149 Table 17. The correlation coefficient R for modern MAT vs floodplain sediments for direct correlation method ...... 149 Table 18-A-E. Reconstructed Mean Annual Temperature (MAT) values using direct correlation version for each sites ...... 150 Table 18-A. The reconstructed MAT values for modern floodplain samples ...... 150 Table 18-B. The reconstructed MAT values for Middle Orkhon section (>7.6 Ma)... 150 Table 18-C. The reconstructed MAT values for Upper Orkhon section (>3 Ma) - Section A …………………………………………………..…….…….. 151 Table 18-D. The reconstructed MAT values for Upper Orkhon section (>3 Ma) - Section B …………………………………………………….……….... 151 159 ix

Table 18-E. The reconstructed MAT values for Upper Chuluut section ...... 152 Table 19. The correlation coefficient R for modern MAT vs weathering indices of floodplain sediments for altitude corrected second version ...... 153 Table 20-A-E. Reconstructed Mean Annual Temperature (MAT) values using altitude corrected second method for each sites ...... 153 Table 20-A. The reconstructed MAT values for modern floodplain samples...... 153 Table 20-B. The reconstructed MAT values for Middle Orkhon section (>3 Ma)…. 163 Table 20-C. The reconstructed MAT values for Upper Orkhon section (>7.6 Ma) - Section A………………………………………………………….…… 154 Table 20-D. The reconstructed MAT values for Upper Orkhon section (>7.6 Ma) - Section B………………………….…………………………………..... 154 Table 20-E. The reconstructed MAT values for Upper Orkhon section (>11.2 Ma).. .155 Table 21-A-E. Reconstructed Mean Annual Temperature (MAT) values using DEM mean altitude corrected third method for each sites ...... 156 Table 21-A. The reconstructed MAT values for modern floodplain samples ...... 156 Table 21-B. The reconstructed MAT values for Middle Orkhon section (>7.6 Ma)... 156 Table 21-C. The reconstructed MAT values for Upper Orkhon section (>3 Ma) - Section A …………………………….…………….……….. 157 Table 21-D. The reconstructed MAT values for Upper Orkhon section (>3 Ma) - Section B …………………………………………………….….……... 157 Table 21-E. The reconstructed MAT values for Upper Chuluut section (>11.2 Ma) ………………………………………………………………...... 158 Table 22-A. The reconstructed MAP values for modern floodplain samples ...... 159 Table 22-B. The reconstructed MAP values for Middle Orkhon section (>7.6 Ma) …………………………………………………………….….………… 159 Table 22-C. The reconstructed MAP values for Upper Orkhon section (>3 Ma) - Section A ……...………………………………………………..……… 160 Table 22-D. The reconstructed MAP values for Upper Orkhon section (>3 Ma) - Section B ………………….…………………………….……….…….. 160 Table 22-E. The reconstructed MAP values for Upper Chuluut section (>11.2 Ma) ...... 161

Table 23. CIA vs K2O/(Na2O + CaO*) molar ratio ...... 162

CHAPTER 3

Table 1-A. Estimated area vs volume in selected fluvial valleys………...…....……. 222 Table 1-B. Estimated area vs volume in selected glacial valleys..……….….…….... 222 Table 2. Floor areas, volumes and their ratio for each of the watershed areas…... 223 Table 3. Hypsometric integral and Digital Elevation model based parameters….. 224 Table 4-A. Parameters for major mountain ranges and their estimated valley fill volume .…………………………………………….……….…..……… .225 Table 4-B. Comparison of parameters for major mountain ranges and their estimated valley fill volume …………..……………………...….…..…. 225 Table 5. Cross validation results for the area to volume power law scaling…...… 226

LIST OF FIGURES

CHAPTER 1

Figure 1. Location of Mongolia in central Asia……………………..….………… 52 Figure 2. Digital elevation model of the Khangay Mountains……….….….…….. 53 Figure 3. The aerial view of research area, courtesy of Google Earth……..….….. 54 Figure 4. The aerial view of our research area, courtesy of Google Earth…...…… 55 Figure 5 A, B. Location of Bayankhongor fault …………………………………...…… 56 Figure 6-A. The view of the Galuut paleo lake valley………………………...….….. 57 Figure 6-B. Thick lacustrine sediment section along the Ozyit River…..….…..……. 57 Figure 7. Olzyit River flowing through the Galuut Canyon……………………….. 58 Figure 8. Olzyit river longitudinal profile……………………………….….….…. 59 Figure 9. A cartoon depicting the main model inputs……………….…….…….... 60 Figure 10. The stages of stream drainage capturing due to tectonic uplift along thrust fault…………………………………………………….…….…... 61 Figure 11. The stages of stream drainage capturing due to tectonic uplift along thrust fault…………………………………………………….…….…... 62 Figure 12 A, B. Stratigraphic section at the upper and lower side of the Galuut Canyon……………………………………………………….…….…… 63 Figure 13 A, B, C & D. Stratigraphic sections at the upper paleo-lake…….….…….….. 64 Figure 14 A, B. Stratigraphic sections at lower side of the Galuut Canyon…….…...…. 65 Figure 14 C, D, E. Stratigraphic sections at upper side of the Galuut Canyon….…...…. 66 Figure 15 A, B. Age, depth correlation graph for sections at upper and lower side of the Galuut Canyon...... 67 Figure 16. Today’s paleo lakebed profile along geophysical VES line along A-A’. Lakefill at maximum extent of 2051 meters……………..…...... 68 Figure 17 A, B. The Pseudo section profile constructed from Geophysical VES survey…………………………………………………………..…….…. 69 Figure 17 C, D. The Pseudo section profile constructed from Geophysical VES survey……………………………………………………….…..………. 70

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Figure 17 E. The Pseudo section profile constructed from Geophysical VES survey…………………………………………………….…………..…71 Figure 17 F. The entire sketch of lithology constructed from Geophysical VES survey…………………………………………………………..….…....72 Figure 18 A-E.The ground penetration radar (GPR) survey profile line………..……...73 Figure 18 F. The ground penetration radar (GPR) survey profile line………..……...74 Figure 19 A, B. Lake fill simulation at maximum extent of 2050 meters……….....….. 75 Figure 20 A, B. SIGNUM Model general scenario for the wet (high precipitation) and dry (low precipitation) case………………………………….……..76 Figure 21 A, B, C. SIGNUM model simulation results. General scenario 1…….….….77 Figure 21 D, E, F. SIGNUM model simulation results. General scenario 1…….……...78 Figure 22 A, B, C. SIGNUM model simulation results. General scenario 2…….….….79 Figure 22 D, E, F. SIGNUM model simulation results. General scenario 2…………... 80 Figure 23 A, B. SIGNUM model simulation uplift profiles for general scenario 1….…81 Figure 24 A, B. SIGNUM model simulation uplift profiles for general scenario 2….…82 Figure 25 A, B. SIGNUM Model specified scenario 1 for the wet (high precipitation) and dry (low precipitation) case……………….….…… 83 Figure 25. C, D. SIGNUM Model specified scenario 1 for the fluctuating precipitation case, specified scenario 2 for the wet case……….…….... 84 Figure 25. E, F. SIGNUM Model specified scenario 2 for the dry (low precipitation) and fluctuating precipitation case…….……….…………85 Figure 26. Topographic development profile line A-A’…………………….…..… 86 Figure 27 A. Topographic development along A-A’ for 60K time intervals…....…... 87 Figure 27 B. Topographic development along A-A’ for 80K time intervals…..…..…88 Figure 27 C. Topographic development along A-A’ for 95K time intervals…..……..89 Figure 27 D. Topographic development along A-A’ for 135K time intervals…..……90 Figure 27 E. Topographic development along A-A’ for 150K time intervals…..……91 Figure 28 A, B. Stream defeat uplift stages after earthquakes for equal amount of uplift. Wet and dry case………………………………………….…..… 92 Figure 28 C, D. Stream defeat uplift stages after earthquakes for equal amount of uplift. Fluctuating and 200 mm precipitation case…………….…….… 93

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Figure 28 E. Stream defeat uplift stages after earthquakes for equal amount of uplift. 1500 mm precipitation case……………………….…………...... 94

CHAPTER 2

Figure 1. Location of the Khangay Mountains in Mongolia, Central Asia ...... 163 Figure 2. Location of preserved fossil soil locations from the northern Khangay Mountains. . ……………………………………………..…... 164 Figure 3. Field photograph of Middle Orkhon Section...... 165 Figure 4. Field photograph of Upper Orkhon Section...... 166 Figure 5. Field photograph of the 3.1 ± 0.1 Ma basalt flow-granite saprolite section from the upper Orkhon River valley...... 167 Figure 6. Field photograph of the 11.2 ± 0.2 Ma basalt flow from the upper Chuluut River section...... 168 Figure 7. Weathering profile and schematic weathering indices plot of the 7.6 ± 0.1 Ma basalt flow section from the middle Orkhon River ...... 169 Figure 8-A. Weathering profile and schematic weathering indices plot of the 3.1 ± 0.1 Ma basalt flow section from the Upper Orkhon River...... 171 Figure 8-B. Weathering profile and schematic weathering indices plot of the 3.1 ± 0.1 Ma basalt flow section from the Upper Orkhon River...... 172 Figure 9. Weathering profile and schematic weathering indices plot of the 11.2 ± 0.2 Ma basalt flow section from the Upper Chuluut River…...... 173 Figure 10. Eigenvalues and cumulative percentages for the Principal Component Analysis. ……………………………………………..….... 174 Figure 11. Principal components analysis PC1 and PC2 biplot for oxides...... 175 Figure 12-A. Principal Component Analysis, PC-1 and PC-2 bi-plot for oxides …... . 175 Figure 12-B. Principal components analysis, PC-1 and PC-2 biplot for oxides and distribution of observations...... 176 Figure 13. A-CN-K Ternary plot ...... 177

Figure 14. K2O and Al2O3 ratio...... 178 Figure 15. Reconstructed paleo temperature values, using chemical weathering indices for Middle Orkhon section...... 179

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Figure 16-A. Reconstructed paleo-temperature values using chemical weathering indices for Upper Orkhon section, Section A...... 180 Figure 16-B. Reconstructed paleo-temperature values using chemical weathering indices for Upper Orkhon section, Section B...... 181 Figure 17. Reconstructed paleo-temperature values using chemical weathering indices for Upper Chuluut section...... 182 Figure 18. Reconstructed paleo moisture values using chemical weathering indices for Middle Orkhon section...... 183 Figure 19-A. Reconstructed paleo-moisture values using chemical weathering indices for Upper Orkhon section, Section A...... 184 Figure 19-B. Reconstructed paleo-moisture values using chemical weathering indices for Upper Orkhon section, Section B...... 185 Figure 20. Reconstructed paleo-moisture values using chemical weathering indices for Upper Chuluut section...... 186 Figure 21-A. Correlation coefficient between modern MAT and Chemical Index of Alteration and its transfer function...... 187 Figure 21-B. Correlation coefficient between modern MAT and Chemical Index of Weathering and its transfer function...... 187 Figure 21-C. Correlation coefficient between modern MAT and Plagioclase Index of Alteration and its transfer function...... 188 Figure 21-D. Correlation coefficient between modern MAT and Vogt’s Residual index and its transfer function ...... 188 Figure 22-A. Weathering indices versus reconstructed paleo mean annual temperatures...... 190 Figure 22-B. Vogt’s Residual index versus reconstructed paleo mean annual temperatures…………………………………………………………... 190 Figure 23-A. Weathering indices versus reconstructed paleo mean annual precipitation ...... 191 Figure 23-B. Vogt’s residual index versus reconstructed paleo mean annual precipitation ...... 191

Figure 24-A. CIA molar versus Al2O3 ratio, Indicator for paleo mean annual precipitation……………………………………………….………...... 192

Figure 24-B. CIA molar versus K2O/Na2O ratio...... …...….…..… 193

Figure 25 A, B. CIA molar versus Al2O3 and K2O/Na2O ratio...... …….… 194

Figure 26. CIA vs K2O/(Na2O + CaO*) molar ratio...... 195

CHAPTER 3

Figure 1. Location of Khangay Mountains in Mongolia………………………....227 Figure 2. Aerial photographs illustrating the back-filled alluvial nature of rivers draining the Khangay Mountains…………………..…………....228 Figure 3. Study area distributed as a function of elevation using a 100 m elevation bin size………………………………………………. 229 Figure 4. Schematic procedure to obtain sediment area-volume scaling relationship for study area valleys…………………….………………. 230 Figure 5. Cartoon showing the method used to define (A) fluvial and (B) glacial valley floor sedimentary fill thickness………………………… 231 Figure 6-A. Estimated area vs volume for selected fluvial valleys……………….…232 Figure 6-B. Estimated area vs volume for selected glacial valleys……………….…232 Figure 7. Khangay Mountains Digital elevation model…………………………. 233 Figure 8. Khangay Mountains slope map………………………..……………….234 Figure 9. Khangay Mountains local relief map……….…………………………. 235 Figure 10. Khangay major watershed volumes normalized by drainage area….….236 Figure 11. Size and distribution of Khangay fluvial valley fill volumes…...…...... 237 Figure 12. Depth patterns of Khangay fluvial valley fills………….……………... 238 Figure 13. Size and distribution of Khangay glacial valley fill volumes……..…... 239 Figure 14. Depth patterns of Khangay glacial valley fills…………….……….…..240 Figure 15. Area frequency histogram…………………………………....…….….. 241 Figure 16. Cumulative area frequency histogram………………………..……...... 242 Figure 17. Volume frequency histogram…………………………………...…...... 243 Figure 18. Cumulative volume frequency histogram……………………………... 244 Figure 19. Valley fill depth histogram………………...………………………...... 245

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Figure 20. Linear correlation coefficient between power law equation-1 produced volumes vs cross-validated volumes………….….….....…… 246 Figure 21. Linear correlation coefficient between power law equation-2 produced volumes vs cross-validated volumes…………….…...…...… 246 Figure 22. Hypsometric integral graph for the northern watersheds of the Khangay…………………………………………………………..…… 247 Figure 23. Hypsometric integral graph for the southern watersheds of the Khangay……………………………………………………………...... 247 Figure 24-A-E. Assumed landscape (topography) development across Khangay from Late Pliocene (~3 Ma) through Late Quaternary (today) with 1 Ma yrs interval……………………………………………….…..….. 248

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APPENDICES

Appendix 1. Topographic map of the Galuut Valley study area………………..….... 251 Appendix 2. Digital elevation model and geomorphic map for the “Appendix lake” location…………………...…………………………………….... 252 Appendix 3. Digital elevation model and geomorphic map for the “Crow’s Head” location…...... 253 Appendix 4. Digital elevation model and geomorphic map for the “Wind Gap” location………………………………………..……………………….. 254 Appendix 5. Digital elevation model and geomorphic map for the “Water Gap” location…………………………………………………..…….………. 255 Appendix 6. Digital elevation model and geomorphic map for the “Galuut Canyon” location……………..……………………………..………… . 256 Appendix 7. Digital elevation model and geomorphic map for the “Bayankhongor fault” location……………….……………………………....……….… 258

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CHAPTER 1

PALEOENVIRONMENTAL RECONSTRUCTION OF LATE QUATERNARY

LACUSTRINE SEDIMENTS AND THEIR TECTONIC IMPLICATIONS, SOUTHERN

KHANGAY MOUNTAINS, MONGOLIA

1.1 Abstract

Wind gaps, passes through a mountain front not occupied by streams, may exist in mountain ranges bounded by faults and in regions of active shortening (Jackson et al., 1996; Keller et al., 1998; Burbank et al., 1999; Hetzel et al., 2004). Juvenile and growing mountain ranges are the surface demonstration of successive tectonic slip along active faults. Therefore, the overall shape of a young mountain range reflects the geometry and slip of the fault at depth, if the range has not experienced significant erosion (Hetzel et al., 2004; Ellis and Densmore, 2006; Hilley and Arrowsmith, 2008). In regions of active crustal shortening, the elevation of mountain peaks above rivers that cross the range (i.e. the local relief) is expected to equal the amount of rock uplift caused by the thrust fault underneath (Burbank et al., 1999; Hetzel et al., 2004). If a river that traverses the growing range cannot keep pace with rock uplift and abandons its valley a wind gap may form (Sobel et al., 2003), which subsequently rises with the growing range. Hence, the present-day elevation of wind gaps above the local base level is a proxy for tectonic uplift since river defeat (Hampel et al., 2016). In these circumstances, lakes can develop in local topographic minima (basins) formed in front of the propagating fault due to tectonic and isostatic (sediment loading) subsidence. At geologic timescales, most lakes are ephemeral systems that, once the forcing mechanism ceases, disappear by outlet erosion and/or sediment overfill (Ollier, 1981). Active tectonism in combination with wet/dry climate cycles (high or low precipitation, evaporation ratios), can activate or neutralize the lake outlet, closing or opening its drainage and presumably contributing to the longevity of the lacustrine system.

For this particular research, I hypothesize that a now dry ~130 km2 late Quaternary lake, herein named “paleo-Lake Galuut”, located along the southern flank of the Khangay Mountains drained when fault-controlled uplift of the original outlet occurred along the Bayankhongor fault. The lake drainage event may have occurred during a climatically-forced high stand in lake level that

1 resulted in the subsequent overtopping of a low topographic divide. Once the low divide was overtopped, run-away stream incision carved the Galuut Canyon gorge, which led to the lowering of base level and lake drainage. The main hypothesis focuses on the fact that active tectonics has a major impact on defeating streams and creating a lake in the adjacent basin, until the lake drains due to the run-away incision, regardless of any climatic interruption. The alternative (null) hypothesis is that the fault did not play a role in the draining of lake, which instead slowly decreased in size due to regional late Pleistocene – Holocene warming and aridification.

The preliminary results of my research fundamentally based on SIGNUM Landscape evolution model developed by Refice et al. (2012), suggests the draining of the Galuut-lake is generally independent from precipitation changes (dry-wet climate oscillation); however, tectonics (i.e., southern Bayankhongor fault) played a major role in both the formation and extinction of the Quaternary lake.

1.2 Introduction

Uplift of the Khangay Mountains in central Mongolia has impacted continental-scale drainage patterns, regional climate, erosion rates, and locally the pace of biological evolution (Lehmkuhl et al., 2004, Walker et al., 2007). Constraining the timing, rates, and processes responsible for uplift of this intracontinental mountain range is important for improving our understanding of the processes involved in the creation of high-elevation regions on Earth that are distant from active plate tectonic margins. Our overall aim is to define the surface and shallow crustal processes contributing to the topographic evolution of this mountain range at both long and short wavelengths.

The Khangay Mountains occupy more than 200,000 km2 in west-central Mongolia, with topography exceeding 3500 m that rises from surrounding plains at ~1500 m. The main NW-SE trend of elevated summits along the crest of the range defines the drainage divide between the Selenga River system, which flows northward into Lake Baikal and eventually to the Arctic Ocean, and many smaller streams that drain into the endorheic Gobi Desert. Strong climatic

2 gradients exist across the Khangay, with significantly higher precipitation in the north compared to the south and to a lesser extent in the east compared to the west, both as a consequence of the orographic barrier imposed by Khangay topography, which prevents south and eastward transport of moisture from Siberia. The onset of the rock uplift that generated the topography of the modern Khangay Mountains is not well known, but is thought to have begun ca. 25–30 Ma based on the increase in clastic sedimentation in surrounding basins at this time (Yanshin, 1975; Sjostrom et al., 2001). The basement geology of the Khangay Mountains consists of a regionally extensive Precambrian block, containing tonalitic gneisses, granitoids and various magmatic rocks as well as high grade schists (Fox et. al, 2008). These basement units crop out sporadically, but are assumed to underlie most of the region (Fox et.al, 2008). The southern side of the Khangay Mountains is dominated by outcroppings of Paleozoic sedimentary rocks that have undergone compressive deformation and have been intruded by Permian to Jurassic granitoids. The southern margin of the Khangay is defined by a major Paleozoic suture zone, the Bayan Khongor Ophiolite and associated subduction-related terrains, while to the north in the interior of the range the Precambrian block is exposed at the surface (Barsbold and Dorjnamjaa, 1993; Tomurtogoo, 1999; 2003). Late Cenozoic volcanics are scattered across the region, and may be associated with Khangay uplift (Harris et al., 2010; Ancuta et al., 2018), but these are relatively limited in terms of the areal extent of their surface exposure. Khangay mountain tops are characterized by low- relief summit plateaus and stepped side profiles of cryoplanation terraces formed on slopes, spurs and broad interfluves that attest to the periglacial climate conditions that dominate across the range at intermediate to high elevations.

Tectonically, the Khangay Mountains are located between two large, active E-W trending left- lateral shear zones, the Gobi-Altai to the south and the Bulnay to the north (Figure 1); both are associated with tectonic movement of Mongolia with respect to Eurasia (Baljinnyam et al., 1993; Ritz et al., 1995; Walker et al., 2007, 2008). In a broad sense, large-scale deformation is consistent with regional tectonic strain from collision of India and Eurasia, though the resulting northeast-southwest shortening neither explains the high topography of the Khangay Mountains, nor localized northeast-southwest extension along normal and strike slip faults embedded within the range (Baljinnyam et al., 1993; Walker et al., 2017). Other explanations for the underlying

3 causes and timing of Khangay uplift remain ambiguous. The north and south flanks of the Khangay express differing amounts of tectonic activity and seismicity. Recent historic earthquakes attest to tectonic activity along the northern and eastern side of the range, with the 1905 Bulnay (Mw 8.4) and Tsetserleg earthquakes (Mw 7.9) (Figure 1), being two of the strongest in the twentieth century, especially for continental interior regions. In addition, the 1967 Mogod (Mw 6.7) earthquake on the east side of the range confirmed that active seismicity is not confined just to the Bulnay-Tsetserleg fault zone. In contrast, along the southern flank of the range, many fault traces are visible in the topography and satellite imagery for the area including some that appear to offset Quaternary landforms (e.g. Walker et al., 2008); however, none of them have been associated with historic seismicity. Despite the lack of historic earthquakes in the southern Khangay, they should be expected given regional clockwise-oriented transpression recorded by a central Asian GPS network (e.g. Calais et al., 2003; 2006) and paleoseismology results from active faults to the south in the Gobi-Altay that record northward directed shortening of ca. 0.4 mm/yr (Bayasgalan et. al 2005).

The southern flank of the Khangay, along with the eastern edge of Altay Range comprises the catchment area for two large internally-drained endorheic hydrologic basins, the Great Lakes Depression and the Valley of Lakes, and the lowlands of the Gobi Desert (Figures 1 & 2). These two internally-drained basins contain a number of large and intermediate sized lakes, the area and volume of which is sensitive to climatic variations. These lakes serve as localized moisture sources for orographic precipitation in the western and southern Khangay. Improving our understanding of the long-term evolution of tectonic uplift and stream capture in mid-catchment, internally-drained basins like those that exist along the southern flank of the Khangay Mountains is important since these systems are climate-sensitive discontinuities in the sediment flow from mountains to depositional basins. In addition, their sedimentary infill constitutes a record of the climatic and tectonic evolution along this erosional pathway. In the southern Khangay, both the short and long-term landscape evolution integrates competing external forcing imposed on the topography and drainage network by regional and fault-localized tectonic uplift, and the ever- oscillating climate characteristic of the Late Quaternary.

1.3 Background

Wind gaps are found in mountain ranges bounded by propagating and non-propagating faults and in regions of active shortening (Jackson et al., 1996; Keller et al., 1998; Burbank et al., 1999; Hetzel et al., 2004). Juvenile and growing mountain ranges are the surface demonstration of active faults as they are uplifted above regional base level by successive accumulation of fault slip. The overall shape of a young mountain range reflects, to a first order, the geometry and slip of the fault at depth, if the range has not experienced significant erosion (Hetzel et al., 2004; Ellis and Densmore, 2006). In regions of active crustal shortening, the elevation of mountain peaks above rivers that cross the range (i.e. the local relief) should equal a minimum estimate of amount of rock uplift caused by the thrust fault underneath (Burbank et al., 1999; Hetzel et al., 2004), assuming that zero erosion has occurred off the top of the rising range. If a river that traverses the growing range cannot keep pace with rock uplift and abandons its valley (Sobel et al., 2003), a wind gap is formed, which subsequently rises with the growing range. Hence, the present-day elevation of wind gaps above the local base level may serve as a proxy for tectonic uplift since the time of river defeat (Hampel et al., 2016). In such circumstances, lakes can develop in local topographic minima formed by crustal tectonic deformation. These lakes are ephemeral systems that, once the forcing mechanism ceases, disappear by outlet erosion and/or sediment overfill (e.g., Ollier, 1981). Most long-lived lakes that exist and persist beyond glacial- interglacial cycles are originated by active tectonic processes, such as continental rifting (e.g., Lake Baikal, Lake Tahoe, and the east African Rift lakes), or compression (e.g., Lake Titicaca) interfering with an existing river network. Independent of whether extensional or compressional forces are responsible for generating the barrier to cross-range flow of water, the presence of lakes in such settings is often interpreted such that the formation of a tectonic lake is coeval with increasing vertical movements, while the demise of the lake coincides with decreasing or negligible tectonism and/or increased erosional power across the lake outlet. For our research, we hypothesize that a paleo-lake, Galuut, formed as a result of increased activity along the Bayankhongor fault (Figures 3, 4, 5 A-B), which produced a topographic blockage perpendicular to the regional northeast-to-southwest drainage. Consequently, the lake disappeared due to drainage capture by runaway incision across a low topographic divide in the footwall basin occupied by the lake, the elevation of which was decreased due to Holocene fault movement.

Paleo Lake Galuut, at its maximum extent, was a 130 km2 late Quaternary water body located on the southern flank of the Khangay Mountains in an intermediate position between the continental drainage divide and the higher elevations of the range to the northeast and the terminal basins of the Gobi further to the south-southwest (Figures 2-4). The water body consists of two individual (topographically upper and lower) sections that are divided by Galuut Canyon, possibly having different histories of formation and extinction. The upper lake is greater in size and assumed to have originated earlier. The lower elevation and smaller paleo lake is drained later than the upper lake. The formation and demise of paleo-lake Galuut may resulted from tectonic uplift along the fault outcompeting the rate of stream incision across the growing mountain range coupled with drainage capture of the lake by an adjacent stream (Figures 3, 4, 5). The Galuut Valley occupies a transition zone between mountainous forest ecosystems to the north from the semi-arid to arid steppe and desert regions of the Gobi Desert to the south. This transition zone is sensitive to climate change and an ideal place for the reconstruction of paleoclimate because it is situated on the southern flank of the topographic divide (Khangay Mountains) in central Mongolia that separates atmospheric flow associated with Western air masses to the north from Monsoonal-derived moisture originating from the south (Peck et al., 2002).

The tectonic geomorphology of the Paleo Lake Galuut area is previously unstudied. Active tectonism in combination with wet/dry climate cycles (high versus low precipitation and varying effective moisture conditions), can activate or neutralize the lake outlet, closing or opening its drainage and presumably contributing to the longevity or brevity of the lacustrine system. In an open lake, the amount of water egressing from the water body is reduced by evaporation from its surface. If evaporation becomes greater than the collected ingress from surface and groundwater inflow, and direct precipitation, the lake becomes the center of a closed (endorheic) drainage basin, under which circumstances the water level is climatically controlled (e.g., Lake Issyk-Kul, Kyrgyzstan; De Batist et al., 2002). In a different example, paleolake Minchin of the intramountain Altiplano basin in the Andes reached its late Pleistocene highstand level when the mean precipitation rate must have been higher than 300 mm/yr to compensate for a rate of evaporation similar to the present day (Fornari et al., 2001). In such cases, due to the absence of river incision across an outlet, the lake’s life expectancy is likely to increase. In closed basin lakes, either a climate change to wetter conditions, or sediment overfill of the lake (both inducing

6 lake level rise), or a basin capture from an adjacent river can trigger drainage opening (e.g., Garcia-Castellanos et al., 2003), abruptly activating basin-wide incision and lake extinction, as occurred for Pleistocene Lake Bonneville in the western U.S. (e.g., O’Connor, 1993). While these processes are often cited in the literature as governing the extinction of lacustrine basins, little is known about the relative importance and competition between these external and internal factors that contribute to the longevity of temporary lakes born from active tectonics in semi-arid to arid regions where the knowledge of the interplay between hydrology, erosion, and tectonics is needed (Garcia-Castellanos, 2003). Therefore, we were aware of the importance of climate variations for wind gap development and lake formation. However, many other geomorphological indicators of active faulting, such as river terraces, are known to form as a result of both tectonics and climate variations (Hancock and Anderson, 2002; Hetzel et al., 2002; Pan et al., 2003). As variations in precipitation can significantly alter the capacity of rivers to transport their sediment load and cut into bedrock (Sklar and Dietrich, 2001; Whipple and Tucker, 2002), it is expected that climatic change may also play an important role during wind gap formation (e.g., Keller et al., 1998; Amos et al., 2010).

Across Central Asia, internally drained basins, such as exists between the Khangay and Altay Mountains of west-central Mongolia, often present vivid examples of captured drainages, due to the interplay between active tectonics and climate with lakes acting as intermediaries. The development of wind gaps, drainage capture, and lake formation in actively growing mountain ranges is a testament to the competition between tectonics and fluvial incision. Although it is clear that these landforms reflect the defeat of rivers during sustained rock uplift, the role of climate changes in their formation has not been well explored. In this research, I employ the SIGNUM coupled tectonics–landscape evolution model to test the topographic development in the Galuut Valley (Figure 3, 4, 6-A & B) paleo-lake.

Following the tectonic uplift along the Bayankhongor fault which traverses the modern wind gap, the demise of paleo Lake Galuut may have occurred as a result of climatically-controlled lake-level rise that was followed by run-away incision, the cutting of a river gorge (Galuut Canyon) and lake drainage (Figure 7). Alternatively, the null hypothesis posits that the fault did not play a role in the disappearance of the lake, which instead slowly decreased in size due to

7 regional late Pleistocene to Holocene warming and aridification. This region is relatively arid today, as much of the southern part of the Khangay, receives between 200 to 250 mm of annual precipitation. We endeavor to distinguish between tectonic and climate signals in the surface- water elevation history and disappearance of the Galuut paleo-lake.

This study aims to characterize the lake level history and paleoenvironmental conditions based on lacustrine sediments that are preserved in late Pleistocene deposits of the Galuut valley. There are no published paleoclimate investigations from the southern flank of the Khangay Mountains; however, Quaternary sedimentary records have been described from the north and northwestern part of the Khangay (Peck et al., 2002). The continental climate dynamics of central Asia are in part controlled by the presence/absence of topography in central Mongolia. In addition, it is important to emphasize the late Quaternary climate cycles in the region relative to orogeny and topography, which are important components in regional climate dynamics of intracontinental settings. Only a few results have been published in western journals on the Quaternary geology of Mongolia. Relatively little detail compared to other regions (e.g. Tibetan Plateau) has been presented on the Pleistocene, Late Glacial and Holocene climatic fluctuations in this important transition zone between the Cenozoic orogenic belts to the south and Siberia to the north.

The lake level change in Galuut Valley may be due to tectonic uplift and/or climate change. Determining the ages of past fault rupture is important since it may denote the timing of significant lake-level drawdown. Through field and laboratory analyses we attempt to distinguish between climate and tectonic signals that caused catastrophic and/or slow draining of the lake based on Quaternary lacustrine sediments. We used organic materials (i.e. peat, shells) from well-exposed lacustrine sedimentary sections (Figure 6-B) from the now drained lake in order to determine age-depth relationships. The exposed lacustrine sediment section contains relative climate scenarios and its duration during the Quaternary through interlayered peat and silt sections.

The primary goal of this research focuses on using a coupled landscape evolution model to show that tectonic uplift and climate variations act together to create the characteristic age–elevation relationship of wind gaps observed in the southern Khangay Mountains. In our domain, a

8 consistently growing mountain range by slip accumulation on a thrust fault is subject to variations in precipitation. SIGNUM landscape evolution model is most appropriate to test this interface, which emphasizes topographic development embedded on coupled tectonic uplift and climate interaction scenarios. Using ArcGIS 10.3, we attempted to model lake formation due to stream defeat, lakeshore dynamics, and endeavor to evaluate the importance of tectonics, climate, and fluvial erosion in the disappearance of the Lake Galuut.

Many large lakes in and around western Mongolia are dammed by active faults and could be prone to outburst flooding in a fashion analogous to our hypothesized mechanism for the disappearance of Lake Galuut. Establishing that this lake drained catastrophically in response to active tectonism will be of regional importance for paleoenvironmental reconstruction and geologic hazard planning purposes. Our overall research goal is to investigate the sediments and topography associated with the Galuut valley lake to better understand the impact of global and regional climate change on late Pleistocene paleoenvironments of the southern Khangay Mountains as well as the role that local tectonics may have in the modification of lacustrine and fluvial environments.

1.4 Materials and Methods

There are many research papers focused on tectonic uplift, fluvial incision and sediment transport that have utilized quantitative numerical models to better understand landscape evolution. Some of these models and resulting publications have been helpful in understanding the dynamics of tectonic defeat of antecedent rivers (van der Beek et al., 2002; Sobel et al., 2003). For example, Hampel and colleagues (2016) explored how temporal changes in precipitation rate may exert an important control on wind gap formation across actively growing fault-cored anticlines. In models with a constant low precipitation rate, rivers flowing across a growing range are either defeated at an early stage or they quickly abandon their valleys after uplift commenced. Hampel et al. (2016) suggested that the Late Quaternary aridification of Central Asia may contributed to wind gap formation in active mountain ranges. These studies conclude that the main controlling factors are the rate of tectonic uplift across the path of a river, the mean annual precipitation of the catchment above the fault, and the rock strength of the

9 uplifting barrier. A relationship between wind gap elevation and age can be obtained from landscape evolution models, which demonstrate a periodic pattern of river deflection and wind gap occurrence across a fault-controlled, laterally propagating ridge (Tomkin and Braun, 1999). Range growth and propagation, however, were simulated by prescribing the surface displacement. While uplift and tilting due to fault activity (Amos et al., 2010) play a primary role in wind gap formation and river deflection, several studies have identified additional controlling factors. Burbank et al. (1996) showed that the rate of bedrock erosion across the growing fold is a key factor, because it leads to the exposure of more resistant lithologies and the potential reduction in fluvial incision rate across the water (wind) gap. Based on a theoretical model of an equilibrium river responding to local bedrock uplift, Humphrey and Konrad (2000) argued that sediment flux and uplift rate are the controlling parameters and that sediment aggradation upstream of the growing structure primarily controls whether rivers are diverted or continue to incise. A similar conclusion on the importance of upstream sedimentation and ponding was reached by Douglass and Schmeeckle (2007) using an analogue modeling approach to test the importance of sediment and accommodation space dynamics on the up-catchment side of the rising ridge.

However, these models do not explicitly account for the formation of a lake in front of the uplifted area and its subsequent effects on the water balance between collected runoff and evaporation. This is of key relevance for internally drained basins, because by definition the transition from an intermountain basin to an internally drained basin requires the evaporation at the lake surface to compensate for the collected water entering into the lake. Even before tectonic uplift forces drainage closure, evaporation from the lake surface reduces water discharge across the uplifting area, reducing erosion rates and presumably accelerating the drainage closure process. Different landscape evolution models developed by various research groups have their advantages and weaknesses. For example, the well-known CASCADE model commonly used for fluvial geomorphology considers that erosional processes generate scale-invariant, fractal topography. Another model, SIBERIA was designed to examine the relationships between hydrology, tectonics and catchment form over geomorphic timescales. It uses a grid of square cells and for every iteration the model determines a discharge for each cell according to a runoff

10 constant and the contributing catchment area (Coulthard, 2001). GOLEM was developed to look at long term landscape evolution (105 to 107 years) and linkages between erosion and tectonics. In the grid cell containing a channel, GOLEM allows two types, bedrock and alluvial. This importantly allowed the model to make the distinction between supply limited (bedrock channels) and transport limited catchments (alluvial). This is coupled to models of weathering and sediment production, so the effects of an arid environment (with low weathering rates) can be compared to that of a humid climate. Furthermore, GOLEM incorporates a tectonic model that allows the user to simulate uplift caused by the removal of sediment, and depression caused by sediment loading (Coulthard, 2001). An alternative model, CHILD uses an adaptable irregular mesh or TIN of nodes derived from Delaunay triangulation. Water is routed from node to node by the steepest slope using the CASCADE algorithm, but instead of using a fixed time step, every model iteration represents a storm event. This ‘event’ has a rainfall intensity and duration that are used to drive a hydrological model, which calculates how much water to add to each node. The fluvial erosion and deposition for each storm event are then determined and the elevation of the node updated accordingly. CHILD incorporates a more elaborate representation of fluvial processes, calculating a channel width and depth within each node (Coulthard 2001). Apart from all of above mentioned models, we picked SIGNUM as the most appropriate for testing our hypotheses. SIGNUM has significant advantages over other models since it is appropriately focused on long term landscape evolution with coupled tectonic processes, such as uplift and subsidence with external interruptions of wet (high precipitation) and dry (low precipitation) climate cycles.

1.4.1 SIGNUM modeling

For the numerical simulations conducted in this study, we used SIGNUM (Simple Integrated Geomorphological Numerical Model), an independent and self-contained Matlab, TIN-based landscape evolution model, built to simulate topographic development at various space and time scales. SIGNUM was developed by geomorphologists at the University of Bari, Italy (Refice and Giachetta et al., 2009). SIGNUM is presently capable of simulating hillslope processes such as linear and nonlinear diffusion; fluvial incision into bedrock; spatially varying surface uplift, which can be used to simulate changes in base level; thrust faulting; as well as the effects of

11 climatic changes as manifest through variations in the annual precipitation prescribed in the model. Although the model code is based on accepted and well-known processes and algorithms in its present version, it is built with a modular structure, which allows modification to simulate physical processes to suit the needs of virtually any user (Refice and Giachetta et al., 2009). Because of the above attributes, the SIGNUM model was chosen to test our hypothesis of tectonic uplift, drainage capture, and gorge incision as modulated by late Quaternary climate variation.

Coupling well-established quantitative approaches to both tectonic and surface processes, the SIGNUM model allows for the focusing of the research on the interplay between these two regimes in controlling the long-term landscape evolution resulting from tectonic uplift, stream capturing, lake formation and disappearance (Figures 9-11). A cross-sectional model of lake evolution was developed as input into SIGNUM to account for tectonic uplift of a structural barrier, precipitation patterns directly related to climate cycles, hydrological balance, and surface sediment transport (Figures 9-11). The model permits parameterization of the potential formation of lakes and closed (internally drained) basins, their sediment infill, and their extinction as a function of the timing of tectonic uplift versus climate-driven variations in precipitation. Using SIGNUM, we simulated differential uplift across the trace of the model fault with varying precipitation, which directly influences the fluvial incision, erosion, and sedimentation output rates of the different landscape evolution scenarios. Slope-Area relationships were tracked through each stage of development within the model as one metric for observing and quantifying differences in model output between different simulation runs.

1.4.2 Stratigraphy and radiocarbon ages

In order to investigate climate variability during the late Pleistocene-to-Holocene, we tested the research hypothesis by characterizing and AMS radiocarbon dating the lacustrine sediments of paleo-Lake Galuut. The modern stream that drains the former bed of paleo-Lake Galuut has incised through 10-15 meters of fine lacustrine siliciclastic sediment that contain many medium- to-thick interbeds of peat, especially closer to the margins of the paleo lake (Figures 12, 13 & 14 subfigures). Our main proxy materials are layered peats that accumulated in shallow water

12 conditions; whereas the non-organic, thinly bedded siliciclastic intervals represent deposition during lake-level high-stands. Additionally there are abundant gastropod shells in the lacustrine sediment sections. The radiocarbon ages were calibrated using Calib 7.1 and Oxcal online calibration source. The Before Present (BP) ages were calculated with the 95.4% (2σ) confidence interval. The accuracy of the 14C-based chronology was improved by dating modern shells from the same snail genus to determine potential water 14C reservoir affects in the paleo Lake Galuut basin. We correlated each stratigraphic section and developed an age–depth model of lithological units and their related lake stage based on layer’s thicknesses and assumed depositional rate (Figure 15 A-B).

1.4.3 Geophysical investigation

1.4.3.1 Vertical Electric Sounding (VES) method

In addition, in 2015 we conducted an 8.65 km-long vertical electric sounding (VES) geophysical survey in order to investigate the lacustrine sediment thickness and ascertain the location of the trace of the Bayankhongor thrust fault where it crosses the mouth of the wind gap. A Vertical Electrical Sounding (VES) resistivity survey was conducted along a single profile line aligned in a NE-SW direction beginning in the wind-gap valley and extending across the now-drained pale- Lake Galuut (Figures 16; 17-A-F). The VES resistivity survey used the Schlumberger configuration. The current and potential electrodes were maintained at the same relative spacing and the whole spread was progressively expanded about a fixed central point, following the protocol of Philip and Michael (1984). Four electrodes were placed along a straight line on the Earth surface in the same order, A M N B, as in the Wenner array but with AB ≥ 5 MN. For any linear symmetric array with electrodes configured A M N B, the apparent resistivity (ρa), applying the Schlumberger configuration, is determined where AM is the distance on the Earth surface between the positive current electrode A and the potential electrode M. When two current electrodes A and B are used and the potential difference (ΔV) is measured between the two receiving electrodes M and N, the apparent resistivity is calculated by:

ρa = π ΔV/I * [ ((AB/2)2 – (MN/2)2) / MN] or ρa = π K ΔV/I [1]

The apparent resistivity (ρ) value depends on the geometry of the electrode array used, as defined by the geometric factor (K) (Reynolds, 2000). The apparent resistivity applying a Wenner array configuration can be written in the form:

ρ = 2π a ΔV / I , [2] where ρ is the apparent resistivity, 2πa is the geometric factor (K), a is the electrode spacing, ΔV is the potential difference, and I is the electric current. Electrical resistivity data was collected with an ABEM Terrameter SAS 4000/SAS 1000 with appropriate electrodes, cables on reels, and other accessories. The Four electrode Schlumberger array was chosen in the VES survey in order to provide both fast data acquisition and enhanced signal-to-noise ratio for post-processing interpretations. The vertical electrical resistivity sounding was carried out along a single profile line crossing the wind gap valley, the range bounding and buried Bayankhongor fault, out across the now dry floor of the former Lake Galuut with the AB/2 and MN/2 spacing ranging from 1.5 to220 m and 0.5 to45 m respectively. Forty-one VES points were collected along the single profile line. The spacing between two successive VES points was initially 100 m for the first twenty points, then increased to 200 m for the next fifteen, and finally extended to 500 m for the last six collection points (Table 1). Collected VES field survey data were interpreted using the IPI2win software in order to estimate the thickness, nature and lateral variations of the geologic units beneath the survey line. The apparent resistivity and layer thicknesses were approximated by interpretation based upon direct field observations of geologic exposures along the profile line as well as our knowledge of the regional geology.

1.4.3.2 Ground penetrating radar (GPR) survey

We performed a GPR survey that crossed the mouth of the wind gap valley, the Bayankhongor fault zone and out onto the edge of the former lake basin (Figure 18 A-F). Both 100 and 500 MHz antennas were used in the survey, which allowed for penetration depths of up to 30 and 5 m, respectively. The total survey was 250 m long and was performed in five sections of 50 m each. Processing of the raw radar data was performed with the RADPro 3.41 software suite. The use of a DC filter reduced random noise in the resulting radargrams.

1.4.4 Geographic Information System Modeling

We also experimented with the paleo-lakeshore dynamics using the flood simulation tool in ArcGIS 10.3 to fill the paleo Lake Galuut valley to different altitudes (Figure 19 A, B). From this, we were able to estimate the lake shore dynamics throughout various wet-dry climate cycles according to radiocarbon ages obtained from peat and accumulated lacustrine silts as a function of depth beneath the elevation of lake-floor abandonment. Variations in the surface size, maximum depth, and water volume were obtained by subtracting simulated lake level elevations from a 30 m DEM. Simulations include lake level at its maximum extent, as well as its draining and disappearance in the Holocene. MapInfo 12.0 and ArcGIS 10.3 were used to generate color- coded shape files of lakeshore evolution model output at different times. Using the stratigraphic and geochronological results, shoreline dynamics were modeled following regional climate change through time, which is important for testing the hypothesis that lake level overtopping of a low divide caused the carving of Galuut Canyon and the abandonment of the wind gap valley as the egress location for water leaving the lake basin.

1.4.5 Geomorphological Mapping

We used a DJI Phantom 4 drone and two Trimble GeoXH differential GPS devices to collect high resolution (10 to 50 cm/pixel) aerial imagery and ground control points for six key geomorphic locations within the study area (Appendix-Figure 1). The Agisoft Photoscan software package was used to generate geo-rectified orthoimages and hillshaded digital elevation models to assist with the geomorphic mapping. Details for each of the six locations are provided in the sub-chapter 1.5.4.

1.4.6 SIGNUM modeling general testing scenarios

The SIGNUM model (Refice et al, 2009) was used to simulate the timing and rate of tectonic uplift, drainage reorganization, wind gap formation and evaluation of precipitation variations on lake levels and stream incision. We simulated two general scenarios that used differential uplift rates and varying precipitation.

The model surface is scaled down to a 2 x 3 km simulated surface, since our actual area is ten times bigger and requires more computing memory and faster CPU processing speeds than were readily available. The model is run with 1-year time steps. Internal tests with the SIGNUM model confirm that for both the uplift and precipitation component of the model, the scaled- down model size results in the same output values to an un-scaled model, except in the horizontal direction.

In the first scenario, the model contains a constantly uplifting surface with two streams. The uplift was set at 1 mm/yr uniformly across the initial surface. After 60 ky of simulation time, the south block (footwall of the Bayankhongor fault) begins to uplift at 5 mm/yr, which initiates tectonic uplift and a large drainage captured along the edge of the uplifting block. Following the capturing of the larger catchment, closed basins such as lakes form on the north side of the block with the constantly low uplift rate (footwall side of the fault), and wind gaps form in the uplifting block as a result of drainage defeat of older fluvial valleys that were not able to incise at the same rate as the prescribed uplift. In subsequent time steps, the smaller river along the other edge of the domain progressively captures the closed basins that are formed on the low uplift block. In this first model run, the precipitation is held constant at 1000 mm/yr throughout the entire duration of 150 ka, after which the simulation stops (Figure 20-A). A precipitation rate of 1000 mm/yr is much higher than actual for the Galuut region of the southern Khangay at present. Since the precipitation rate is high in this initial simulation, we treat this scenario as representing wet conditions, where both stream erosion and precipitation tend to be high, which directly influences resulting surface uplift.

In the second scenario, the main difference in the model is precipitation patterns, otherwise the rest of the domain remains unchanged. In this model, precipitation was reduced from 1000 mm/yr to 100 mm/yr to simulate a dry period after 20 ka of model run time (Figure 20-B). All of the remaining uplift patterns stay the same, when again at 60 ka of simulation time the south block begins uplifting at 5 mm/yr, and the larger drainage is captured along the edge of the uplifting block. The simulation stops at 150 ka. This dramatic change (lowering) of precipitation rate results in faster surface uplift rate because of reduced stream discharge and erosion.

1.4.7 SIGNUM modeling specified testing scenarios

After initial testing of generalized scenarios that demonstrate the overall behavior of the SIGNUM model, we tested various scenarios by modifying differential uplift and precipitation rates in order to test which model conditions result in as close to what is observed in the Galuut valley study area. The run time for the model is fixed at 150 ka since the late Quaternary lake, which came into existence by defeat of the wind gap stream, persisted for ~ 60 ka until its disappearance about 6 ka following overtopping of a low divide and the runaway incision that formed Galuut Canyon. We applied two different uplift patterns A) Equal uplift rate on both domains B) Faster uplift rate on the south domain, and three different precipitation patterns: A) Wet, B) Dry, and C) Fluctuating. See details below.

Uplift patterns - Equal uplift rate on both north and south domains

Uplift patterns for all scenarios are divided into two different major types, in order to test how different precipitation models respond to these types of uplift patterns.

Our first scenario has a uniform uplift of 1 mm/year for the entire domain until 60 ka, at which point the south block (analogue to wind gap) uplifts 5 meters instantaneously, as would be experienced during a single earthquake event of ~M 7, thus creating the Bayankhongor fault in the model domain and a small, shallow basin on the north side. After this uplift event, sediment accumulation continues at the rate of 1 mm/yr on the north block, while pre-uplift conditions (1 mm/yr) continue for the model domain south of the fault until 95 ka. At 95 ka, a second earthquake event takes place with a similar slip distribution. A third uplift event is prescribed at 135 ka, also having similar displacement patterns with the first two events. The 1 mm/year sediment accumulation rate on the north side of the fault (in the deepening lake) will continue until the completion of the model run at 150 ka (Figure 25A-C).

Faster uplift rate on the south domain

Our second scenario begins with uniform uplift of 1 mm/year for the entire domain until 60 ka, like the above-mentioned scenario. In addition, we tested 2 mm/year uplift for the wind gap domain (southern portion of the block model) between the earthquake events, in order to represent increased hillslope erosion due to uplift following the first earthquake at 60 ka. The

17 remaining parameters stay the same as the first scenario, but the only difference is after each earthquake event, the uplift rate is 2 mm/yr. This modulation is incorporated into all subsequent models (Figure 25-D-F).

Precipitation patterns - Wet case scenario

The wet case scenario contains a relatively high amount of rainfall for the entire time domain. We applied 20 ka precipitation cycles that varied between 1000 mm/yr and 800 mm/yr. This precipitation pattern is solely for experimenting cases and is unlikely to match the real pattern of precipitation over the Galuut valley for the past 150 ka. Because the study site is located in a relatively high and dry continental interior at 46° latitude, it is very unlikely that actual annual precipitation ever exceeded 1000 mm/yr during the late Quaternary (Figure 25-A & D).

Dry case scenario

Our dry case scenario contains relatively low amounts of rainfall for the entire time domain. We prescribed a 20 ka cycle varying between 500 and 300 mm/yr. Again, this precipitation pattern is solely for experimenting cases and is not thought to be the actual history of precipitation at the study site over the past 150 ka; however, most of the Khangay and much of Mongolia fall within the 300-500 mm/yr isopleths (Figure 25-B & E).

Fluctuating case scenario

The fluctuating model scenario varies between high (800 mm/yr) and low (300-400 mm/yr) annual precipitation along 20 ka cycles for the entire time domain. The case utilizes an average precipitation range of 500-600 mm/years together with highs and lows. This precipitation pattern is not proven to be true for real world and is solely for experimentation. This model contains a higher range between the maximum and minimum precipitation values than do the other two. These three different precipitation scenarios are picked for representing relatively dry, wet and fluctuating climate for the continental interiors. We use the model results to test how these patterns respond to uplift and assist, or not, in defeating streams and overall landscape development (Figure 25-C & F).

1.5 Results

1.5.1 Stratigraphy and radiocarbon ages

Paleo Lake Galuut existed on the southern side of the Khangay drainage divide. While we do not know when the basin that the lake occupied initially formed, it certainly existed in the late Pleistocene into the middle Holocene. This is confirmed by radiocarbon dated gastropod shells, and aquatic plant material collected from lacustrine peats (Table 2 A-E; Table 2, 3-B, Figures 12-A-B, 13-A-D, 14-A-E).

The age-depth models for selected stratigraphic sections are shown on Figures 15A & B. The ages from dated gastropods collected from the top-most lacustrine sediments from both the upper and lower lakes are 8,100 ± 100 cal ybp for the upper lake and 5,800 ± 70 cal ybp for the lower lake. The upper and lower lakes were separated by a low bedrock ridge, through which Galuut Canyon was carved and the upper lake was drained. Furthermore, radiocarbon dating confirms that both the upper and lower lakes coexisted at the same time and that the upper lake disappeared (drained) at least 2100 years prior to demise of the lower lake by our estimate.

The floor of the lower lake (1930 m) is 100 meters lower than the lakebed of the former upper lake (2030 m). Through the lake shore reconstruction on the edges of the upper lake, the maximum water level was determined to be 2051m above sea level (masl) (Figure 16). The average lacustrine section altitude ranges between 2040-2043 meters, indicating that the average water depth in the lake prior to its drainage was between 7 to 10 m. At present, at least 5 streams of varying sizes flow through the Galuut Valley, the biggest is the Olzyit River (Figures 6-8), which cuts a water gap through the footwall of the Bayankhongor fault at the western side of the former lower lake. Portions of these streams cut through the lacustrine sediments in both the upper and lower lakes, creating high banks of exposed sediments (Figure 6A). The current elevation of the Olzyit river as it flows through the lacustrine sediments of the lower lake is 2030 m asl.

1.5.2 Geophysical survey results VES and GPR

The Vertical Electric Sounding (VES) and Ground Penetrating radar (GPR) surveys support the existence and late Quaternary activity of the Bayankhongor fault, which is the structure responsible for uplift and interbasin drainage capture between the Galuut and Olzyit River (Figure 17-A-F, Figure 18-A-F). The thickness of lacustrine sediments reaches up to 15 meters along the banks of modern streams (Figure 6-B) that have incised into the lacustrine unit after drainage of the paleo lake. However, the geophysical surveys were useful in estimating the total thickness of the lacustrine sediments including unexposed parts on the footwall basin of the fault. The total thickness of lacustrine sediment, as determined by the VES geophysical survey is 25- 30 m (Figure 17-F). The VES survey resistivity data from the single profile line is presented and geologically interpreted graphically, that includes layer thickness and depth beneath the ground surface (Figure 17-A-F). In terms of resistivity, igneous rocks such as granite, diorite and gabbro have the highest resistivity values while sedimentary rocks such as shale and sandstone have a lower resistivity compared to igneous rocks; this is due to the high fluid content in them. Metamorphic rocks on the other hand have intermediate but overlapping resistivity values (Felix, 2008).

The GPR survey results on the wind gap effectively revealed the presence of the fault at depth (Figure 18 A-E, Figure 18-F). The high frequency antenna (100 MHz) having greater penetration depth (20-30 m) compared to the low frequency (500 MHz) one, disclosed the fault resembling features in the depth.

1.5.3 Geographic Information System Modeling

We simulated the lake depth, area, and volume using ArcGIS 10.3 Spatial Analyst and Surface tools (See Table 6). Maximum lake depth reaches 7-10 meter with total area of ~130 km2 and possibly contained ~1 km3 of water volume at its maximum extent. We started the maximum fill at 2060 meters, and gradually lowered it down by 5 meters interval and estimated the lake fill surface area, depth and volume for each of the intervals. However, with the help of Google Earth and D-GPS survey, we determined maximum lake shore to be at 2051meters. See Figure 19-A-B for ArcGIS simulated maximum lake fill for the upper lake. The simulation stopped at 2030

20 meters as this is considered the minimum depth of the lake; although 2030 meters may be lower than the elevation of the lake floor prior to stream erosion into lacustrine sediments following drainage of the paleo lake. See Table 6 for simulation results. This simulation helps us to understand lake level dynamics of the paleo lake. In addition, it can improve our understanding of the lake level dynamics on par with the wet/dry cycle period and gradual climate change. However we need to reconsider the sedimentation rate during history of lake level dynamics simultaneously, since it will slightly change the depth, floor area and volume depending on the timing of the simulation of lake depth and sediment thickness. We cannot take it as a constant lake depth, since it was always fluctuating depending on the climate cycles, sedimentation rate, and quite possibly vertical land level changes imposed by pre-historic earthquakes sourced along the Bayankhongor fault.

1.5.4 Geomorphological Mapping

The geomorphological mapping of selected sites and their corresponding interpretations are presented below. The mapping sites were selected based on their prominent geological and geomorphological features related to the Late Quaternary paleo-lake development and its history within the Galuut Valley.

A. Appendix Lake The Appendix Lake geomorphic mapping area lies on the east side of the upper lake (Appendix- 2). This north-south oriented depression occupies the last remaining portion of the much bigger upper paleo Lake Galuut, for which the modern streams have not yet captured and integrated. Several prominent shorelines are preserved in this area and they were the target for the drone mapping. The maximum elevation of the paleo lake, as recorded by the highest shoreline was 2051m. Mongolian hamsters (Allocricetulus curtatus) take advantage of the sandy soils associated with the paleo-shorelines for their burrows, which appear as small mounds in the orthophoto-based DEM.

B. Crow’s Head Island Crow’s Head Island (name given by local residents) is a promontory jutting out from the southeastern shore of the upper paleo lake (Appendix- 3). During maximum lake level (2051m),

21 to the east of the “island” would have been a beach-ridge tombolo, that then extended westward beyond the promontory as a bay-mouth bar, which partially enclosed a small basin that at lower water levels would have been a separate lake. Lacustrine geomorphic features observed in this area include the topographic high occupied by Crow’s Head hill that separates the edge of the main lake to the north from several much smaller depressions that once were filled with water. The beach ridge is relatively flat and there are no signs of water inundation for extended period observed on the ridge. A Few hundred meters north of Crow’s Head Island there is a bedrock erosional highstand feature at 2050 m created by impacting waves and near-shore sediment transport.

C. Wind Gap The Wind Gap geomorphic mapping area is located along the south-central shore of the upper paleo lake and crosses the trace of the Bayankhongor fault (Appendix- 4). The former egress channel for the lake was uplifted by an earthquake, perhaps when the water level in the lake was climatically reduced below the 2050 m highstand. Following abandonment of the former water gap, and its conversion into a wind gap, aggradation of alluvial fans along the head of the valley further increased the elevation of the wind gap, helping to insure that when water levels rose again in the lake, a new outlet formed where Galuut Canyon exists. Today, the Wind Gap elevation is 10 m higher than the highest paleo shoreline and some 20 meters above the top of the lacustrine sediments preserved in the basin center. Prominent geomorphologic landforms and deposits include the thrust fault scarp, alluvial fans, Quaternary lacustrine sediments, small active erosional channels, and gullies that support hillslope erosion above and deposition onto the floor of the Wind Gap valley.

D. Water Gap The Water Gap is on the far southwestern side of the lower paleo Lake Galuut (Appendix-5). This area includes the lowest elevations, where the Olzyit River flows across the hanging wall of the Bayankhangor fault. Several prominent landslide features exist in this section, with the southern one exhibiting signs of recent activity that include multiple ground fissures, bare internal scarps, and sheared rock and soil along lateral margins. The trace of the Bayankhongor fault is quite apparent in this section of the valley and is observable on the drone orthophoto and DEM as well as in a stream cut just north of where it crosses the Olzyit River. Alluvial fan,

22 active channel, floodplain, landslide and Quaternary lacustrine lakebeds are the prominent geomorphologic deposits and land forms in this mapping area. Where the Olzyit River crosses the fault, it has cut a gorge that is more than 300 m deep. At present, the landslides at the upper end of the water gap are not impeding the flow of the river; however, our working hypothesis is that it was these (or similar) landslides further downstream within the canyon that blocked stream flow for several thousands of years resulting in the creation of the lower paleo Lake Galuut.

E. Galuut Canyon Galuut Canyon is located in the central part of the valley (Appendix- 6). It separates the upper (southern) and lower (northern) paleo Lake Galuut. The canyon is about 1000 m long and up to 40 m deep. Prominent shoreline features were recorded at a D-GPS quantified elevation of 2050 m along the southern flank of the canyon signifying that this highstand existed for a long enough period to bevel shoreline features into bedrock. The side slopes of the canyon extend upwards to above 2100 m, which is at least 50 m higher than the elevation of the maximum lake level. Our preferred model is that overtopping of the Galuut divide and carving of the canyon occurred in response to earthquake uplift of the outlet for the upper lake. However, it is possible that headward retreat and groundwater sapping caused the formation of the Galuut Canyon and drainage of the upper paleo lake. At present, it is not known whether the upper lake drained catastrophically as an outburst flood, or if the drainage occurred more slowly. Both sides of the upper canyon are filled with lacustrine sediments, indicating that the lake abutted up against what would have been the northern shoreline. Quaternary deposits and landforms in this mapping area include alluvial deposits, floodplains, alluvial fans, landslides and paleo-lake shorelines.

F. Bayankhongor Fault Scarp (North) This investigation area is located along the trace of the Bayankhongor fault, approximately midway between the upper and lower paleo lakes (Appendix-7). A prominent fault scarp is observed in both the orthophoto and in the DEM that expresses mostly vertical motion (southwest side up) based upon the lack of observed horizontal shifts in the center line of crossing drainages. Many small temporarily active stream channels and gullies are present on the hanging wall side of the fall. Many of these small gullies and channels disappear across the trace

23 of the fault, as they transition from erosional features on the upthrown side of the fault scarp to depositional features on the downthrown side.

1.5.5 SIGNUM modeling general testing scenarios

In the first scenario, the model contains a constantly uplifting surface with two streams. Since the precipitation rate is high in this initial simulation, we treat this scenario as representing wet conditions, where both stream erosion and precipitation tend to be high, which directly influences resulting surface uplift. See Figures 21 A-F that depicts model uplift and Figures 23 A-B that illustrate surface uplift patterns at the west tip of the domain. The total tectonic uplift is slightly above 200 meters for a 150 kyr period.

In the second scenario, precipitation amounts are changed, otherwise the rest of the domain remains unaltered. The dramatic change (lowering) of precipitation rate results in faster surface uplift rate because of reduced stream discharge and erosion. See model summary for (Figures 22 A-F and Figures 24 A-B) uplift patterns at the west tip of the domain for comparison to the first model run that had consistently high precipitation for the duration of the model run time. The total surface uplift in the second model is greater than 400 m for the 150 ka period, which is at least 200 m higher than in the first “high precipitation” scenario.

From the model output surfaces, we determined the slope area relationship through time for each simulation (Figures 21-22-A'-F'). For the first scenario (Figure 21 A'-F' series) right when the uplift begins a single main stream collects all the water from a single drainage basin. The uplift forced drainage reorganization and gradually two drainages start separating from each other over the course of the 150 ka timing of the simulation. In this case erosion is faster due to the higher prescribed precipitation rate.

For the second scenario with relatively dry conditions (Figure 22 A'-F' series), the drainage separation process progresses at a much slower rate compared to the first. After 150 ka of model run time, the drainage separation has not entirely taken place yet, and it has a delayed response that probably after 100 ka of additional time will result in a full separation. Therefore, we can conclude from the modeling that precipitation rate can directly influence the pace and timing of drainage separation and capture where differential uplift occurs perpendicular to the regional

24 topographic and stream flow direction (e.g., southerly trend of stream flow direction off the south slope of the Khangay Mountains towards the Gobi).

1.5.6 SIGNUM modeling specified testing scenarios

The results for topographic development stages for all scenarios are depicted in Figures 27-A-E. For each given timing interval of the model, the total surface uplift pattern is depicted for each climate scenarios and uplift patterns. In the graphs, the scenarios noted as 1 stands for equal uplift of 1 mm/yr on both of the domains, while scenarios noted as 2 stands for faster uplift rate of 2 mm/yr for the south domain (wind gap) between the earthquakes (Figures 27-A-E). The total surface uplift patterns were distinguished from each other in different scenarios. See details below.

Precipitation patterns - Wet case scenario. The result for topographic development stages for wet case scenario is depicted in 27-A-E. As noted here, until the first earthquake event (60 ky), the total surface uplift were similar for both Wet-1 (equal) and Wet-2 (faster-wind gap) scenarios. However, after an earthquake event, the 2nd uplift cases were supported by additional hillslope erosion of 1 mm/yr, totaling uplift rate to 2 mm/yr. Subsequently, the total surface uplift for the 2nd uplift case is much higher than that of the first case, demonstrating that the river will be defeated and a wind gap will form. While in the slower uplift rate, even after an earthquake event, the streams were able to defeat the temporarily created wind gap on the south domain (Figure 27-A-E, 28-A-E). The total surface uplift for Wet-2 case was 71 meters at the south domain (wind gap) for the entire simulation span of 150 ky.

Dry case scenario. The result for topographic development stages for the dry case scenario is depicted in 27-A-E. As noted here, until the first earthquake event (60 ky), the total surface uplift was similar for both the Dry-1 (equal) and Dry-2 (faster-wind gap) scenarios. The total surface uplift for the 2nduplift case is higher than that of first case, supported by hillslope erosion, showing that the river will be defeated, a wind gap will form and the stream will not catch back up again. While in the slower uplift rate, even after an earthquake event, the streams were able to defeat the temporarily created wind gap on the south domain (Figure 27-A-E, 28A-E). The total

25 surface uplift for Dry-2 case was 112 meters at the south domain (wind gap) for the entire simulation span of 150 ky.

Fluctuating case scenario. The result for topographic development stages for the fluctuating precipitation case scenario is depicted in 27-A-E. As noted here, until the first earthquake event (60 ky), the total surface uplift was similar for both Fluctuating-1 (equal) and Fluctuating-2 (faster-wind gap) scenarios. The total surface uplift for 2nduplift case is higher than that of first case, demonstrating that the wind gap will permanently defeat rivers. While in the slower uplift rate, even after an earthquake event, the streams were able to defeat the temporarily created wind gap on the south domain (Figure 27-A-E, 28-A-E). The total surface uplift for Fluctuating-2 case was 84 meters at the south domain (wind gap) for entire simulation span of 150 ky.

1.6 Discussion

According to other research results regarding late Quaternary climate in the central Mongolian plains, a slight cooling and drying trend was dominant. Although many of those research results are not quantified, the trend is relatively reconstructed and we attempted to reflect this in our research. We tested two general scenarios in the SIGNUM model with only wet and dry climate spans for the entire period of 150 ky, with continuous uplift in order to observe the topographic development. The model results suggest that in the continuous uplift, a drier climate results in a greater amount of total surface uplift compared to that resulting from wetter climate. Our second set of specified scenarios are more specific as they include three sequential earthquake events after 60 ky, each having total surface uplift of ~5 meters (at 60K, 95K and 135K yrs),which is a replica of the modern wind gap coupled with three different climate scenarios (wet, dry and fluctuating case) of various amount of precipitation. According to topographic development plots, when earthquake event coupled with higher uplift rate combined with hillslope erosion on the south domain (wind gap), any form of precipitation patterns (low to high) will not catch up with the total surface uplift, signifying literally that tectonic uplift will defeat the streams. This supports our hypothesis that stream capture and wind gap formation is solely from tectonic uplift i.e Bayankhongor fault, independent of any climate forms.

1.6.1 Stratigraphy

The paleoake bed is filled with at least 15 meters thick lacustrine silicoclastic sediment. The paleolake maximum shoreline elevation stands at 2050meters and the current lake bed elevation is at 2030 meters. However, this cannot represent the paleo lake bed elevation, since the river has incised into the lacustrine section of the lower lake since it drained. Therefore, our estimate suggests that the maximum depth of the lake was between 10-12 m (2038-2040 m above sea level). The calculated sedimentation rate for the upper and lower lakes is different. For the upper Lake we estimate an average rate of 0.3 mm/yr, while for the lower lake it was 0.17 to 0.2 mm/yr. See the sedimentation rate from table 3-A. The 30% difference between the upper and lower paleo lakes is attributed to the fact that there are more streams flowing into the upper lake prior to their physical connection via the Galuut Canyon capture. The difference may also be in part due to the location of the dated sedimentary sections, which were near the center of the lower lake basin, but closer to the margins of the upper lake basin. Lacustrine sediment thickness and sedimentation rate helped us to determine the duration of the upper and lower paleo lakes of the Galuut Valley.

For the upper Lake Galuut, using a lacustrine sediment thickness of 15 m divided by the sedimentation rate of 0.3 mm/yr results in a minimum period of lake existence of 50,000 years. However, according to total thickness investigation using geophysical methods, the lake existence period could reach up to ~100,000 years. Radiocarbon results revealed that the upper lake drained 8100±100 years ago, while the lower lake drained 5,800 ± 70 years ago. Based on this information, we assume that tectonic uplift of the Bayankhongor range front commenced and the lake started forming about ~60,000 years ago and completely disappeared 5,800 ± 70 years ago through overspill and drainage capture through Galuut Canyon, then exited through the lower paleo lake.

In an attempt to determine the climate cycles from stratigraphic sections for the upper and lower lakes, we assume that variations between lacustrine peats, and siliciclastic deposits at a location are an indication of lake level variations through time. The lacustrine peat layers signify warmer period with shallow lake conditions that represent high evaporation and low precipitation, or at least when evaporation exceeds precipitation and the shorelines of the lake move basinward. In

27 contrast, the several meters-thick intervals of fine sandy silt sedimentation represent relatively deeper water conditions at a location and shoreline transgression (Tables 4, 5). The sedimentologic and chronologic data on the Galuut Valley lacustrine sediment section is an important location for understanding late Pleistocene and Holocene paleo environmental conditions and dynamics in the region.

1.6.2 Geophysical survey VES and GPR

The pseudo cross section along the profile line shows that the highest resistivity values are observed mainly below a depth of 70 m beneath the ground surface with resistivity values ranging from 1,500 to 10,000 Ohm-m, and in some instances even higher. We believe that the highest resistivity values likely correlate with the underlying bedrock that most likely consists of granite. The depth of the late Quaternary lacustrine sediments is interpreted at 25-30 m with lower resistivity values of 200-400 Ohm-m and some higher recordings where fine dry silt is preserved. The dry peat or gytta resistivity values are noted as very low, ranging between 30-60 Ohm-m. Peat and organic-poor silts are interlayered, denoting lake level variations, probably driven by variations in the effective moisture balance of the paleo lake basin, or alternative by fault-controlled lake-level change. The layers between ~20 to 70 meters exhibit highly variable resistivity values ranging between 300-2,500 Ohm-m, which is interpreted as valley backfill with a mixture of gravel, sand and even possibly a high resistivity granite intrusion. We note that points along the profile collected adjacent to modern streams had notably lower resistivity values, likely denoting wet conditions or, shallow groundwater. For the section of the VES profile situated across the mouth of the wind gap four very high resistivity values were recorded just beneath the ground surface and these points are identified as a marked distortion on the pseudo section (Figure 17-A). We note that this disturbed portion of the VES survey corresponds to the trace of the Bayankongor fault zone, which was later confirmed by the ground penetrating radar (GPR) survey. The GPR processed data effectively revealed the Bayankhongor fault zone in the shallow subsurface beneath the mouth of the wind gap valley (Figures 18-A-F).

1.6.3 Topographic development stages in SIGNUM

We tracked the development of topography for each scenario at selected time intervals. Doing so provides information about how the resulting model landscape and drainage patterns differ between each of the wet, dry and fluctuating type scenarios, and with for the cases that include enhanced uplift on the south (wind gap) relative to the north (lake) model domains. The 3- dimensional topography (DEMs) produced by successive iterations within each SIGNUM model run is converted to a 1-dimensional topographic profile line (A-A'; Figure 26) so that semi- quantitative and visual comparisons between model runs are easily observed. The topography along profile line A-A' (Figure 27-A-E) is recorded at 60 ky, just before the first earthquake (uplift) event, at 80 ky, after first earthquake, and at 95 ky right before the 2nd earthquake, and at 135kybefore the 3rd earthquake until the model run ends at 150 ky (Figure 27-A-E).

Equal uplift rate on both domains. For the case of an equal amount of uplift (1 mm/yr) applied to both the hanging wall (south-wind gap) and footwall (north) domains, with hanging wall side interrupted by series of abrupt and fast uplift such as an earthquake, streams tend to win back the uplift following an earthquake after a few thousand years. In model runs incorporating the “dry” precipitation scheme, streams will defeat the earthquake-generated uplift faster than during the “wet” model scenario (Figure 28-A-E). This perhaps non-intuitive outcome is interpreted through the lens of the impact of vegetation on hillslope sediment transport. During the “dry” precipitation model run, hillslope sediment erosion and transport occurs much faster than during the “wet” scenario. This increased hillslope erosion and sediment transport fills in the negative topography (lake) created by the earthquake. As the negative topography is filled in, the lake spills back out through the cross range valley, maintaining it as a water gap. Additionally, along the north face of the uplifted hanging wall (south) block, increased hillslope erosion transfers sediment to the footwall side of the fault (north block), therefore assisting in the sedimentary infilling of the negative topography created by the earthquake (Figure 28 A-E). Therefore, perhaps non-intuitively, the cross-range water gap is less likely to become a wind gap during the “dry” scenario model runs than it is during the “wet” scenarios due to more rapid sedimentary infilling and the reestablishment of a fluvial transport slope from the footwall to hanging wall sides of the fault when the background uplift (non-earthquake) rate is constant across the entire model at 1 mm/yr. In this case, there is minimal difference in the resulting topography (Figure 27

A-E). We assume this is the underlying cause for streams that are capable of defeating the uplift (a wind gap is not formed) when there is an equal amount of uplift (1 mm/yr) on both sides (Figure 27 A-E) after each of the earthquake event.

Faster uplift rate on south domain. Overall, an uplift rate of 2 mm/yr on the hanging wall (south) model domain, that incorporates hillslope erosion of the uplifting block, after an earthquake, will develop a wind gap for the stream that previously crossed the range with any of the precipitation scenarios (Figure 27A-E). In contrast, when uplift is only 1 mm/yr beneath the hanging wall block, there is minimal difference in the resulting topography (Figure 27A-E).

1.7 Conclusions

Our estimate suggests that the Galuut Valley paleo lakes formed due to the temporary defeat of cross-range streams by differential tectonic uplift along the Bayankhongor fault. We estimate that the fault-initiated formation of the lakes occurred during the early late Pleistocene. The Galuut Valley paleo lake was likely in existence from about 70 ka to 6 ka, according to our radiocarbon results and extrapolation of dated sediments backwards in time using sedimentation rate curves. At its maximum extent, just before capture by the lower lake basin through what is now Galuut Canyon, the upper paleo lake had a surface area extent of ~130 km2, with an average depth (~10 m) calculated for when the highest shoreline altitude was 2,050 m above sea level. Sedimentary exposures where modern streams have cut down into the lacustrine sediments show two facies associations. In the middle of the lake basin, where water levels were deepest, only siliciclastic sediments were deposited. Closer to the shorelines, alternating siliciclastic and shoreline peats were deposited, indicating that water levels in the lake fluctuated and the shoreline position was not stable through time. Based upon these stratigraphic observations and our interpretations of what they mean about the paleo-environmental record, we reconstructed wet/dry climate cycles for the duration of the lake.

We modeled various uplift and precipitation scenarios using SIGNUM, a TIN-based Matlab simulation software in order to test different hypotheses regarding the important factors controlling the development and extinction of the lake and defeat of cross-strike drainages, or wind gap formation. During “wet” conditions, high stream discharge combined with slow net

30 surface uplift due to erosional processes delays the defeat of cross-strike drainage and the formation of a wind gap (Figure 28A-E). Conversely, during “dry” scenario conditions, defeat of the cross-strike stream and the formation of a wind gap proceeds more quickly due to slower erosion of the cross-strike valley floor by the stream (less stream power) and faster net surface uplift. We also simulated the lake surface area, depth and volume starting from highest shoreline altitude at 2051 meters using ArcGIS 10.3.

Through our hypothesis, we assume that climate variations had a negligible effect on the draining of the paleo upper Lake Galuut. There are currently flowing streams through lacustrine sediment sections and the Galuut Canyon, within the valley. The process that led to lake formation is no longer active, or conditions are no longer met for lake formation today. It appears that the lake overtopped a low topographic divide between the upper and lower basins. The elevation of a low spot along this ridge may have decreased, relative to the elevation at the mouth of the cross-strike valley (today’s wind gap valley) due to differential uplift/subsidence between the hanging wall (wind gap valley) and footwall (Galuut Canyon) sides of the Bayankhongor fault. Today, streams flowing through the former upper lake basin floor drain through Galuut Canyon, situated between the upper and lower lake basins. The upper lake may have drained catastrophically when run-away incision across the low topographic ridge now occupied by Galuut Canyon proceeded, much like pluvial Lake Bonneville drained catastrophically when the topographic sill at Red Rock Pass, Idaho, was overtopped – leading to what is known as the Bonneville Flood (O’Conner, 1993). Holocene reconstructions for the Bayankhangor area of central Mongolia suggests that the climate was not intensely arid through the period when the Galuut paleo lakes disappeared (Peck et al., 2002). Thus, aridification, and evaporation of the lake is unlikely to have been the cause of the disappearance of the lakes. Based upon our reconstruction of geologic events, Galuut Canyon, a dramatic erosional feature that is one of the top tourist destinations within the Bayankhongor Aimag, formed about ~6,000 years ago in response to tectonically (earthquake) controlled changes in outlet elevations.

The upper lake underwent various stages of wet-dry cycles through its history that is confirmed by the sequence of silt and peat layers now exposed in post-drainage stream cuts; however even during drier periods it never fully disappeared. There may have been times when the lake did not flow out through the outlet that would become the wind gap, as evidenced by lower shorelines

31 visible in Google Earth and from our drone reconnaissance mapping, but the stratigraphy suggests that it did not dry up completely. Therefore there must be a different factor responsible for the draining of the upper lake. We believe that the evidence supports a tectonic, rather than climatic cause for the disappearance of the lake. In addition, if just prior to drainage the climate conditions were dominated by a dry, low precipitation phase, we would expect the shoreline to migrate basinward and for the uppermost lacustrine sediments to be dominated by a regressive peat layer, but this is not what is observed. Instead, the uppermost sediments at the sedimentary sections investigated are representative of deep-water deposition of fine-grained siliclastics. For the lower lake, the top of the lacustrine section is mostly covered by thick silt layers rather than peat, which indicates faster drainage rather than a slow recession and gradual draining. For the upper lake, since it had a higher surface water altitude than the lower lake, it may have slowly eroded the rocks and sediments once infilling the current Galuut Canyon, and when the natural barrier diminishes, it may have drained through the narrow gorge to the lower lake. The upper lake draining supports more abrupt event rather than slow since the lacustrine sediment in the central sections are covered by fine silts rather than peat. According to our SIGNUM model results, the overall faster uplift on the hanging wall (wind gap) side of the model, which includes hillslope erosion, could defeat streams regardless of wet or dry precipitation patterns. This faster total surface uplift on the hanging wall side of the Bayankhongor fault also ensures that the water gap will convert into a wind gap earlier in the model simulation. However, after an earthquake event, in the case of equal uplift continuing for a long enough periods; it is possible for a temporary wind gap to be converted back to a water gap by the streams from the north if earthquake events are rare. It is confirmed that the lower lake drained more recently than the upper lake by about 2,100 years. In order to more fully understand the lake formation history, sedimentary drilling in the central lakebed areas of both the upper and lower lakes would be beneficial.

We tested our hypothesis by ground observations, application of late Holocene climate trends and earth surface process simulation models. We assert that tectonic uplift is solely responsible for lake formation by the creation of a tectonic basin where subsidence outpaced sedimentation rate, likely for much of the late Quaternary, if not longer. The disappearance of the upper lake is due to overtopping of a low sill between the upper and lower lakes when water gap elevation of the upper lake was raised above the sill elevation, likely during an earthquake. We do not yet

32 know if the lake drained catastrophically through the creation of the Galuut Canyon, or if this was a gradual process. Although climate was responsible for expansion and shrinking of the upper paleo lake, it did not disappear merely from climate change at about ~6,000 years before present.

Our research seeks to establish that there is the potential to retrieve new high-resolution proxy data from the Mongolian Plateau to examine the temporal and spatial patterns of the late Quaternary climatic or/and environmental changes. The variability in lake level recorded in the sediments may represent water balance variations driven by climate, but the potentially catastrophic draining of the lake may have been triggered by tectonic forcing. The reconstructed patterns will contribute valuable data for testing controlling mechanisms that are important to regional and global paleoclimate modelling efforts in relation to localized tectonic activity in intracontinental settings.

1.8 Acknowledgement

We would like to thank Dr. Bayasgalan Amgalan for his professional advice. I also would like to thank Dr. Kh. Tseedulam for performing geophysical VES and GPR surveys for my research. I am also immensely thankful for Dr. Emanuele Giachetta at the ETH Zurich for his teaching of fundamental concepts and basic coding for SIGNUM model. This work is supported by U.S. National Science Foundation Research Grants EAR-1009702 and EAR-1009680.

1.9 References:

Alley R.B., P. A Mayewski, T. Sowers, M. Stuiver, K. C. Taylor and P. U. Clark,1997, Holocene climatic instability: A prominent, widespread event 8200 yr ago,Geology 25, p. 483-486.

Amos, C.B., Burbank, D.W. and Read, S.A.L., 2010. Along-strike growth of the Ostler fault, New Zealand: Consequences for drainage deflection above active thrusts. Tectonics, 29, TC4021.

Ancuta, L., D., Zeitler, P. K., Idleman, B. D., and Jordan, B. T., 2018, Whole-rock 40Ar/39Ar geochronology, geochemistry, and stratigraphy of intraplate Cenozoic volcanic rocks, central Mongolia: Geological Society of America Bulletin, v. 130, no. 7-8, p. 1397-1408.

Arkhipov S. A, L. L Isayeva, V. Bespaly, O. Glushkova., 1986, Glaciation of Siberia and North- East USSR, Quaternary Science reviews 5, p 463-474.

Bairu, A., Yirgale G/her, Gebrehiwot G/her., 2013, Application of vertical electrical sounding and horizontal profiling methods to decipher the existing subsurface stratification in River Segen dam site, Tigray, Northern Ethiopia, Journal of Environment and Earth Science ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online) Vol. 3, No.10

Baljinnyam, I., et al. 1993, Ruptures of Major Earthquakes and Active Deformation in Mongolia and Its Surroundings, Geol. Soc. of Am. Mem. 181, Geol. Soc. of Am., Boulder, Colo.

Barsbold, R., and Dorjnamjaa, D., 1993, Geolgic map of the Hangay Highland: State Geological Centre, Ulaan Baatar, Mongolia, 1:500,000.

Battarbee R. W., 2000, Palaeolimnological approaches to climate change, with special regard to the biological record. Quaternary Science Reviews 19, 107-124, Quaternary Science Reviews 19, p.107-124.

Bayasgalan A., J. Jackson, D. McKenzie., 2005, Lithosphere rheology and active tectonics in Mongolia: relations between earthquake source parameters, gravity and GPS measurements, Geophysical Journal International 163, p. 1151–1179.

Benson L.V.; Lund, S.P.; Smoot, J.P.; Rhode, D.E.; Spencer, R.J.; Verosub, K.L.; Louderback, L.A.; Johnson, C.A.; Rye, R.O.; and Negrini, R.M., 2011, The rise and fall of Lake Bonneville between 45 and 10.5 ka., USGS Staff, Published Research. Paper 726.

Bills B. G., K D. Adams, S. G. Wesnousky., 2007,Viscosity structure of the crust and upper mantle in western Nevada from isostatic rebound patterns of the late Pleistocene Lake Lahontan high shoreline, Journal of geophysical research, Vol. 112, B06405

Braun J, Sambridge M. 1997. Modeling landscape evolution on geological time scales: a new method based on irregular spatial discretization. Basin Research 9: p.27–52.

Burbank, D.W., Meigs, A. and Brozovic, N., 1996. Interactions of growing folds and depositional systems. Basin Res., 8, 199–223.

Burbank, D.W., McLean, J.K., Bullen, M., Abdrakhmatov, K.Y. and Miller, M.M., 1999. Partitioning of intermontane basins by thrust-related folding, Tien Shan, Kyrgyzstan. Basin Res., 11, 75–92.

Calais E., L. Dong, M. Wang, Z. Shen, M. Vergnolle., 2006, Continental deformation in Asia from a combined GPS solution, Geophysical Research Letters, Vol. 33, L24319

Calais E., M. Vergnolle, V San’kov, A. Lukhnev, A. Miroshnitchenko, S. Amarjargal, J. De´verche`re., 2003, GPS measurements of crustal deformation in the Baikal-Mongolia area (1994–2002): Implications for current kinematics of Asia, Journal of Geophysical Research, Vol. 108, No. B10, 2501

Calais, E., Dong, L., Wang, M., Shen, Z., and Vergnolle, M., 2006, Continental deformation in Asia from a combined GPS solution: Geophysical Research Letters, v. 33, L24319, doi:10.1029/2006GL028433.

Cheng-Bang A., F. Chen, L. Barton., 2008, Holocene environmental changes in Mongolia: A review, Global and Planetary Change 63, p. 283–289

Clemens S.C., Murray, D.W, Prell W, L., 1996, Nonstationary Phase of the Plio-Pleistocene Asian Monsoon Science 274, p.943-948.

Coulthard, T.J., 2001, Landscape evolution models: a software review, HYDROLOGICAL PROCESSES Hydrol. Process. 15, p.165–173.

De Batist, M., Klerkx, J., Imbo, Y., Giralt, S., Lignier, V., Beck, C., Delvaux, D., Vermeesch, P., Kalugin, I., and Abdrachmatov, K., 2002, Bathymetry and sedimentary environments of a large, high-altitude, tectonic lake: Lake Issyk-Kul, Kyrgyz Republic (Central Asia), in Klerkx, J., and Imanackunov, B., eds., The Issyk-Kul Lake: Evaluation of environmental state and its remediation: Dordrecht, Netherlands, Kluwer Academic Publishers, North Atlantic Treaty Organization Science Series IV, Earth and Environmental Series, v. 13, p. 101–123., p. 101–124.

Douglass, J. and Schmeeckle, M., 2007. Analogue modeling of transverse drainage mechanisms. Geomorphology, 84, 22–43.

Ellis, M.A. and Densmore, A.L., 2006. First-order topography over blind thrusts. In: Tectonics, climate, and landscape evolution (S.D. Willett, N. Hovius, M.T. Brandon and D. Fisher, eds). Geol. Soc. Am. Spec. Paper, 398, 251–266.

Fedotov A.P., E. Chebykin, M. Semenov et al., 2004, Changes in the Volume and Salinity of Lake Khubsugul (Mongolia) in Response to Global Climate Changes in the Upper Pleistocene and the Holocene, Palaeogeography, Palaeoclimatology, Palaeoecology 209, p. 245-257.

Felix, O., 2008, A Comparison of Resistivity and Electromagnetics as Geophysical Techniques. African Institute for Mathematical Sciences, University of Cape Town, South Africa.

Feng Z. D., W.G Wang, P. Khosbayar, T. Narantsetseg, A.T Jull et.al., 2005, Lacustrine and eolian records of Holocene climate changes in the Mongolian Plateau: preliminary results, Quaternary International 136, p.25-32.

Feng Z.D., 2001, Gobi Dynamics in the northern Mongolian Plateau during the past 20,000+ years: preliminary results, Quaternary International 76/77 p. 77-84.

Fornari, M., Risacher, F., and Feraud, G., 2001, Dating of paleolakes in the central Altiplano of Bolivia: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 172, p. 269–282, doi: 10.1016/S0031-0182(01)00301-7.

Fox M.R., 2008, Geomorphology and active faulting within the Hangay Mountains, Central Mongolia, Master’s research thesis, Department of Earth Sciences, University of Oxford, 2008

Fritz S. C., S. Juggins, R. W Battarbee, D. R Engstrom., 1991, Reconstruction of past changes in salinity and climate using a diatom-based transfer function, Nature 352, p. 706-708.

Fukumoto Yu., K. Kashima, A. Orkhonselenge, U. Ganzorig., 2012,Holocene environmental changes in northern Mongolia inferred from diatom and pollen records of peat sediment Quaternary International 254 p. 83-91.

Garcia-Castellanos D., 2006, Long-term evolution of tectonic lakes: Climatic controls on the development of internally drained basins, Geological Society of America, Special Paper 398, p.283-295

Garcia-Castellanos D., Vergés, J., Gaspar-Escribano, J.M., and Cloetingh, S., 2003, Interplay between tectonics, climate and fluvial transport during the Cenozoic evolution of the Ebro Basin (NE Iberia): Journal of Geophysical Research, v. 108, no. B7, p. 2347.

Garcia-Castellanos D., I. Jimenez-Munt., 2015, Topographic Evolution and Climate Aridification during Continental Collision: Insights from Computer Simulations, PLoS ONE 10(8): e0132252.

Garcia-Castellanos D., Fernàndez, M., and Torné, M., 1997, Numerical modeling of foreland basin formation: A program relating thrusting, flexure, sediment geometry and lithosphere rheology: Computers & Geosciences, v. 23, no. 9, p. 993–1003.

Grunert J., F. Lehmkuhl, M. Walther., 2000, Paleoclimatic evolution of the Uvs Nuur basin and adjacent areas (Western Mongolia) Quaternary International 65-66, p 171-192.

Gillespie A.R., R. M, Burke, G. Komatsu, A. Bayasgalan., 2008, Late Pleistocene glaciers in Darhad basin, northern Mongolia Quaternary Research, 69, p.169-187

Hampel, A., Hetzel, R., Maniatis, G. and Karow, T., 2009. Three-dimensional numerical modeling of slip rate variations on normal and thrust fault arrays during ice cap growth and melting. J. Geophys. Res., 115, B08406.

Hampel A.,R. Hetzel., 2016, Role of climate changes for wind gap formation in a young, actively growing mountain range, Terra Nova, 00: p. 1–8.

Hancock, G.S. and Anderson, R.S., 2002. Numerical modeling of fluvial strathterrace formation in response to oscillating climate. Geol. Soc. Am. Bull., 115, 1131–1142.

Harris, N., A. Hunt, I. Parkinson, A. Tindle, M. Yondon, and S. Hammond., 2010, Tectonic implications of garnet-bearing mantle xenoliths exhumed by Quaternary magmatism in the Hangay dome, central Mongolia, Contrib. Mineral. Petrol., 160(1), p 67–81

Harrison S. P., G. Yu, P. E Tarasov., 1996, Late Quaternary lake-level record from northern Eurasia Quaternary Research 45, p. 138-159.

Hetzel, R., Niedermann, S., Tao, M., Kubik, P.W., Ivy-Ochs, S., Gao, B. and Strecker, M.R., 2002. Low slip rates and long-term preservation of geomorphic features in Central Asia. Nature, 417, p. 428–432.

Hetzel, R., Tao, M., Niedermann, S., Strecker, M.R., Ivy-Ochs, S., Kubik, P.W. and Gao, B., 2004. Implications of the fault scaling law for the growth of topography: mountain ranges in the broken foreland of north-east Tibet. Terra Nova, 16, p. 157–162.

Humphrey, N.F. and Konrad, S.K., 2000. River incision or diversion in response to bedrock uplift. Geology, 28, 43–46.

Jackson, J., Norris, R. and Youngson, J., 1996. The structural evolution of active fault and fold systems in central Otago, New Zealand: evidence revealed by drainage patterns. J. Struct. Geol., 18, 217–234.

Kashiwaya K., S. Ochiai, H. Tsukahara, H. Sakai, T. Kawai., 2003, Long-Term Late Cenozoic Global Environmental Changes Inferred from Lake Baikal Sediments, Long Continental Records from Lake Baikal, Springer-Verlag, Tokyo.

Keller, E.A., Zepeda, R.L., Rockwell, T.K., Ku, T.L. and Dinklage, W.S., 1998. Active tectonics at Wheeler Ridge, southern San Joaquin Valley, California. Geol. Soc. Am. Bull., 110, 298–310.

Krivonogov S., S. Yi, K. Kashiwaya, J.C Kim, T. Narantsetseg., 2012,Solved and unsolved problems of sedimentation, glaciation and paleolakes of the Darhad Basin, Northern Mongolia, Quaternary Science reviews 56, p. 142-163.

Kurfeß D., O. Heidbach., 2009, CASQUS: A new simulation tool for coupled 3D finite element modeling of tectonic and surface processes based on ABAQUS and CASCADE, Computers & Geosciences 35, p. 1959–1967

Lehmkuhl F., 1998, Quaternary Glaciations in Central and Western Mongolia, Journal of Quaternary Sciences, p. 153-167

Lukhnev A.V., V.A. San’kov, A.I. Miroshnichenko, S.V. Ashurkov, E. Calais., 2010, GPS rotation and strain rates in the Baikal–Mongolia region, Russian Geology and Geophysics 51, p. 785–793

Meyers P.A., R. Ishiwatari., 1995, Organic Matter Accumulation Records in Lake Sediments, Physics and Chemistry of lakes, Springer, Verlag, Berlin, p. 279-328.

Narantsetseg Ts., S. Krivonogov, Ts. Oyunchimeg, G.S Burr, et. al., 2013,Late Glacial to Middle Holocene climate and environmental changes as recorded in Lake Dood sediments, Darhad Basin, northern Mongolia, Quaternary International 311, p. 12-24.

O’Connor, J.E., 1993, Hydrology, hydraulics, and geomorphology of the Bonneville Flood: Geological Society of America Special Paper 274, p. 90.

Ollier, C., 1981, Tectonics and landforms: Longman, UK, Harlow, v. 324, p. 1981.

Pan, B., Burbank, D., Wang, Y., Wu, G., Li, J. and Guan, Q., 2003. A 900 k.y. record of strath terrace formation during glacial-interglacial transitions in northwest China. Geology, 31, 957–960.

Patrickson S.J., D. Sack, A.R. Brunelle, K.A. Moser., 2010, Late Pleistocene to Early Holocene lake level and paleoclimate insights from Stansbury Island, Bonneville Basin, Utah, Quaternary Research 73, p.237-246

Peck J. A., 2000, Mongolian lake systems record past climate change, Maritimes 42, p.21-24.

Peck J.A., P. Khosbayar, S.J Fowell, R. B Pearce, S. Ariunbileg, B. Hansen., 2002, Mid to Late Holocene climate change in north central Mongolia as recorded in the sediments of Lake Telmen. Palaeogeography, Palaeoclimatology, Palaeoecology 183, p. 135-153.

Philip K., M. Brooks., 1984, An Introduction to Geophysical Exploration. Blackwell, London.

Prokopenko A., D. Williams, E. Karabanov, G. Khursevich., 1999, Response of Lake Baikal

ecosystem to climate forcing and pCO 2 change over the last glacial/interglacial transition Earth Planetary Science Letter 172, p.239-253.

Refice A., E. Giachetta, D. Capolongo., 2012, SIGNUM: A Matlab, TIN-based landscape evolution model, Computers & Geosciences 45, p 293–303.

Reynolds, J. M., 2000, An Introduction to Applied and Environmental Geophysics. John Wiley and Sons, Inc., New York, USA.

Ritz, J. F., E. T. Brown, D. L. Bourle`s, H. Philip, A. Schlupp, G. M. Raisbeck, F. Yiou, and B. Enkhtuvshin., 1995, Slip rates along active faults estimated with cosmic-ray-exposure dates: Application to the Bogd fault, Gobi-Altaı¨, Mongolia, Geology, 23(11), p.1019– 1022

Sjostrom, D.J., Mendrix, M.S., Graham, S.A., Badamgarav, D., and Nelson, B.K., 2001, Sedimentology, provenance, and tectonic setting of Mesozoic non-marine strata in western Mongolia: implications for intracontinental deformation, in Hendrix, M.S., and

Davis, G.A., eds., Paleozoic and Mesozoic tectonic evolution of cental Asia – from continental assembly to intracontinental deformation: Boulder, CO, Geological Society of America Memoir 194, p. 361-388.

Sklar, L.S. and Dietrich, W.E., 2001. Sediment and rock strength controls river incision into bedrock. Geology, 29, p. 1087–1090.

Stager J.C., P. A Mayewski., 1997, Abrupt early mid-Holocene climatic transition registered at the equator and the poles, Science 276, p. 1834-1836.

Sobel, E.R., Hilley, G.E. and Strecker, M.R., 2003. Formation of internallydrained contractional basins by aridity limited bedrock incision. J. Geophys. Res., 108, 2344.

Tarasov P.E., J. Guilot, R. Cheddadi, A. A. Andreev et. al., 1999, Climate in northern Eurasia 6000 years ago reconstructed from pollen data, Earth and Planetary Science Letter, 171, p. 635-645.

Thybo H., E. Stein, N. Noe-Nygaard., 1996, Resistivity and georadar mapping of lacustrine and glaciofluvial sediments in the late glacial to postglacial Store Amose basin, Denmark, Bulletin of the Geological Society of Denmark, Vol.43, p.87-98.

Tiercelin, J.L., 2002, Lake sediments; archives of active tectonics and climate changes: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 187, p. 209–212,

Tomkin, J.H. and Braun, J., 1999. Simple models of drainage reorganization on a tectonically active ridge system, New Zealand. J. Geol. Geophys., 42, p. 1–10.

Tomurtogoo, O., 1999, Geological Map of Mongolia: Ulaanbaatar, Mongolian Academy of Sciences, scale: 1:1,000,000.

Tomurtogoo, O., 2003, Tectonic map of Mongolia: Mongolian Academy of Sciences, scale 1:1,000,000.

Tucker, G.E. and Slingerland, R., 1997. Drainage basin response to climate change. Water Resour. Res., 33, 2013–2047.

Tucker G. E., G. R. Hancock., 2010, Earth Surface Processes and Landforms 35, p. 28–50.

41 van der Beek, P. and Braun, J., 1998. Numerical modeling of landscape evolution on geological time-scales: A parameter analysis and comparison with the south-eastern highlands of Australia. Basin Res., 10, p. 49–68.

Verschuren D., 1999, Sedimentation controls on the preservation and time resolution of climate- proxy records from shallow fluctuating lakes, Quaternary Science Reviews 18, p. 821- 837.

Walker R.T., E. Nissen, A. Bayasgalan., 2007, Reinterpretation of the active faulting in central Mongolia, Geology, v. 35; no. 8; p. 759–762.

Walker, R. T., E. Molor, M. Fox, and A. Bayasgalan., 2008, Active tectonics of an apparently aseismic region: Distributed active strike-slip faulting in the Hangay Mountains of central Mongolia, Geophys. J. Int., 174(3), p. 1121–1137

Walker, R.T., Wegmann, K.W., Bayasgalan, A., Carson, R.J., Elliott, J., Fox, M., Nissen, E., Sloan, R.A., Williams, J.M., and Wright, E., 2017, The Egiin Davaa prehistoric rupture, central Mongolia: a large magnitude normal faulting earthquake on a reactivated fault with little cumulative slip located in a slowly deforming intraplate setting: Geological Society, London, Special Publications, v. 432, p. 187-212.

Weninger B., O. Jöris, U. Danzeglocke, CalPal-2007., 2010, Cologne Radiocarbon Calibration & Paleoclimate Research Package: http://www.calpal.de/.

West, A.J., Fox, M., Walker, R.T., Carter, A., Harris, T., Watts, A.B., and Gantulga, B., 2013, Links between climate, erosion, uplift, and topography during intracontinental mountain building of the Hangay Dome, Mongolia: Geochemistry, Geophysics, Geosystems, v. 14, p. 5171-5193.

Whipple, K.X. and Tucker, G.E., 2002. Implications of sediment-flux-dependent river incision models for landscape evolution. J. Geophys. Res., 107, 1–20 Williams W. D., 1999, Chinese and Mongolian saline lakes: a limnological review, Hydrobiologia 210, p. 39-66.

Willett, S.D., 1999, Orogeny and orography: The effects of erosion on the structure of mountain belts: Journal of Geophysical Research, v. 104, no. B12, p. 28,957–28,981.

Williams, D.F., Peck, J., Karabanov, E.B., Prokopenko, A.A., Kravchinsky, V., King, J., and Kuzmin, M.L., 1997, Lake Baikal record of continental climate response to orbital insolation during the past 5 million years: Science, v. 278, p. 1114–1117.

Yan, J.P., Hinderer, M., and Einsele, G., 2002, Geochemical evolution of closed-basin lakes: General model and application to lakes Qinghai and Turkana: Sedimentary Geology, v. 148, p. 105–122.

Yanshin, A.L., 1975, Mesozoic and Cenozoic tectonics and the magmatism of Mongolia: Joint Soviet-Mongolian Scientific Research Geological Expedition, Volume 11 (in Russian): Moscow, Nauka, 309 p

Zhang C., M. Zheng, A. Prokopenko, S. Mischke., 2009, Carbon and oxygen isotopic composition of surface-sediment carbonate in Bosten Lake (Xinjiang, China) and its controlling factors, Journal of the Geological Society of China (English edition), Vol 83, No. 6 p. 1101-1115.

Table 1. Geophysical VES survey point locations in UTM-Zone 47 Northern Hemisphere projection and Geographic (Longitude/Latitude projections).

No x y Longitude Latitude 1 574065 5156255 99.96624 46.55578 2 574140 5156330 99.96723 46.55645 3 574210 5156400 99.96815 46.55707 4 574286 5156466 99.96915 46.55765 5 574375 5156535 99.97032 46.55827 6 574424 5156622 99.97098 46.55904 7 574485 5156694 99.97178 46.55968 8 574560 5156763 99.97277 46.5603 9 574625 5156838 99.97363 46.56097 10 574693 5156910 99.97453 46.56161 11 574762 5156987 99.97544 46.56229 12 574830 5157060 99.97634 46.56294 13 574898 5157133 99.97724 46.56359 14 574993 5157231 99.9785 46.56446 15 575093 5157333 99.97982 46.56537 16 575172 5157401 99.98086 46.56597 17 575250 5157466 99.98189 46.56655 18 575326 5157530 99.98289 46.56711 19 575854 5157984 99.98985 46.57114 20 575999 5158127 99.99177 46.57241 21 576143 5158263 99.99367 46.57362 22 576289 5158403 99.9956 46.57486 23 576434 5158543 99.99751 46.5761 24 576566 5158693 99.99926 46.57744 25 576743 5158899 100.0016 46.57927 26 576841 5158994 100.0029 46.58012 27 576978 5159124 100.00471 46.58127 28 577128 5159263 100.00669 46.5825 29 577274 5159396 100.00862 46.58368 30 575662 5157847 99.98733 46.56993 31 577340 5159633 100.00952 46.58581 32 577465 5159789 100.01117 46.5872 33 577595 5159932 100.0129 46.58847 34 577738 5160079 100.01479 46.58977 35 577870 5160226 100.01653 46.59108 36 578048 5160460 100.0189 46.59317 37 578431 5160884 100.02397 46.59694

Table 1 (continued).

38 578551 5161351 100.02561 46.60113 39 578661 5161850 100.02713 46.6056 40 578716 5162433 100.02795 46.61084 41 578775 5162789 100.02878 46.61404

Table 2. Radiocarbon results for lacustrine sediments from the paleo Galuut Lake deposits.

Section No. Actual depth Age BP Difference age Material Stratigraphic Location to (cm) section canyon

MN14-03A 100 13680±105 13680±105 Plant materials A Lower side

MN15-02A (0 cm) 0 7690±70 5850±70 Shells D Lower side

MN15-02A (55 cm) 55 7970±40 6130±40 Shells D Lower side

MN14-04C 25-35 9940±100 8100±100 Shells B Upper side

MN14-04A 460 20280±200 20280±200 Peat (Grass) B Upper side

MN14-04B 550 24105±215 24105±215 Peat (Grass) B Upper side

MN15-01B (20 cm) 680 22250±210 22250±210 Peat (Grass) C Upper side

070313-1A 600 (above) 14930±220 13100±220 Shells E Dry Valley

MN15-03 0 (Pond) 1840±60 Present Snail shells - Pond near canyon

Table 3. A. Sediment deposition rate for paleo Galuut Lake deposits from the Lower and Upper side of the Galuut Canyon.

Section No. Depth (cm) Thickness Span (years) Number Deposition rate Stratigraphic Location to (mm) of years (mm/year) section canyon

MN15-02A 0-55 550 5850-6130 280 1.96 D Lower side (0 cm)

MN15-02A 55-100 450 6130-13680 7550 0.08 (off place) D Lower side (55 cm)

MN14-03A 0-100 1000 5850-13680 7830 0.15 (combined) A Lower side

MN14-04C 25-460 4350 8100-20280 12180 0.35 B Upper side

MN14-04A 460-550 900 20280-24105 3825 0.24 B Upper side

MN14-04B 0-35 250 6950-8100 1150 0.3 (average) B Upper side

Table 3. B. Radiocarbon analyses for paleo Lake Galuut deposits.

DirectAMS code Submitter ID Sample type Radiocarbon age Calibrated age BP 1σ error BP 2σ error

D-AMS 024070 MN15-01A 0 cm Plant matter 18244 65 22111 ±215 D-AMS 024071 MN15-01A 20 cm Plant matter 18593 80 22459 ±198 D-AMS 024072 MN15-01A 40 cm Plant matter 19786 68 23821 ±232 D-AMS 024073 MN15-01B 0 cm Plant matter 17998 68 21802 ±224 D-AMS 024074 MN15-01B 40 cm Plant matter 18836 64 22689 ±226 D-AMS 024075 MN15-01B 58 cm Plant matter 19997 66 24058 ±218 D-AMS 024076 MN15-01B 120 cm Plant matter 21358 104 25697 ±213 D-AMS 024077 MN16-01 30 cm Plant matter 18717 83 22571 ±250

D-AMS 024078 MN16-01 40 cm Plant matter 19045 66 22931 ±267 D-AMS 024079 MN16-01 50 cm Plant matter 19595 65 23610 ±260 D-AMS 024080 MN16-01 60 cm Plant matter 19442 90 23416 ±275 D-AMS 024081 MN16-01 110 cm Plant matter 16404 55 19795 ±208 D-AMS 024082 MN16-01 115 cm Plant matter 19954 69 24012 ±233

D-AMS 024083 MN16-01 120 cm Plant matter 20243 70 24314 ±216 D-AMS 024084 MN16-01 125 cm Plant matter 20331 66 24408 ±251 D-AMS 024085 MN16-01 150 cm Plant matter 20411 77 24518 ±257 D-AMS 024086 MN17-01A Shells 10799 43 12712 ±43 D-AMS 024087 MN17-01B Shells 8387 37 9429 ±58 D-AMS 024088 MN17-01C Shells 9623 35 10941 ±41

Table 4. A. Reconstructed ages for relative lake water depth and climate cycles from stratigraphic section B (Profiles MN14-04 A, B & C), at the Upper side of the Galuut Canyon. Radiocarbon dated ages are shown in bold. At this side deposition rate is 0.3mm/year for silt and 0.2 mm/year for peat layers. This section is located near the shoreline at the southwest corner of the former lake.

No. Depth from To Thickness BP Age (Years) Material Water Period Age RC or (cm) (cm) depth reconstructed 1 0-35 35 6950-8100 Fine Silt Deep Cool Reconstructed 2 35-36 1 8100 Fine Silt Deep Cool Radiocarbon shells 3 36-460 420 8100-20280 Fine Silt Deep Cool Reconstructed 4 460-480 20 20280-21280 Peat Shallow Warm Radiocarbon peat 5 480-490 10 21280-21630 Fine Silt Deep Cool Reconstructed 6 490-491 1 21650 Peat Shallow Warm Reconstructed 7 491-500 10 21650-22000 Fine Silt Deep Cool Reconstructed 8 500-501 1 22050 Peat Shallow Warm Reconstructed 9 501-510 10 22050-22400 Fine Silt Deep Cool Reconstructed 10 510-520 10 22400-22900 Peat Shallow Warm Reconstructed 11 520-545 25 22900-23800 Fine Silt Deep Cool Reconstructed 12 545-550 5 23800-24100 Peat Shallow Warm Radiocarbon peat 13 550-570 20 24800 Fine Silt Deep Cool Reconstructed 14 570-571 1 24850 Peat Shallow Warm Reconstructed 15 571-575 5 25150 Fine Silt Deep Cool Reconstructed 16 575-576 1 25200 Peat Shallow Warm Reconstructed 17 576-580 5 25500 Fine Silt Deep Cool Reconstructed 18 580-581 1 25550 Peat Shallow Warm Reconstructed 19 581-585 5 25600 Fine Silt Deep Cool Reconstructed 20 585-586 1 25650 Peat Shallow Warm Reconstructed 21 585-610 25 26500 Fine Silt Deep Cool Reconstructed 22 610-611 1 26550 Peat Shallow Warm Reconstructed 23 611-640 30 27600 Fine Silt Deep Cool Reconstructed 24 640-1100 460 27600-43000 Fine Silt Deep Cool Reconstructed 25 1100-1280 180 43000-52000 Peat Shallow Warm Reconstructed 26 Below 1280 ?? Pre 52000 ?? ?? ?? Reconstructed

Table 4. B. Reconstructed volume for relative lake water depth and climate cycles from stratigraphic section B (Profiles MN14-04 A, B & C), at the Upper side of the Galuut Canyon. Radiocarbon dated ages are shown in bold. At this side deposition rate is 0.3mm/year for silt and 0.2 mm/year for peat layers. This section is located near along the shoreline at the southwest tip within the paleo-lake. Maximum Upper lake area is 97 km2.

Depth Thickness BP Age Cumulative volume Cumulative volume Water No. from To Material Period (cm) (Years) reversed (m3) reversed (km3) depth (cm) 1 0-35 35 6950-8100 Fine Silt 1244510000 1.245 Deep Cool 2 35-36 1 8100 Fine Silt 1210560000 1.211 Deep Cool 3 36-460 420 8100-20280 Fine Silt 1209590000 1.210 Deep Cool 4 460-480 20 20280-21280 Peat 802190000 0.802 Shallow Warm 5 480-490 10 21280-21630 Fine Silt 782790000 0.783 Deep Cool 6 490-491 1 21650 Peat 773090000 0.773 Shallow Warm 7 491-500 10 21650-22000 Fine Silt 772120000 0.772 Deep Cool 8 500-501 1 22050 Peat 762420000 0.762 Shallow Warm 9 501-510 10 22050-22400 Fine Silt 761450000 0.761 Deep Cool 10 510-520 10 22400-22900 Peat 751750000 0.752 Shallow Warm 11 520-545 25 22900-23800 Fine Silt 742050000 0.742 Deep Cool 12 545-550 5 23800-24100 Peat 717800000 0.718 Shallow Warm 13 550-570 20 24800 Fine Silt 712950000 0.713 Deep Cool 14 570-571 1 24850 Peat 693550000 0.694 Shallow Warm 15 571-575 5 25150 Fine Silt 692580000 0.693 Deep Cool 16 575-576 1 25200 Peat 687730000 0.688 Shallow Warm 17 576-580 5 25500 Fine Silt 686760000 0.687 Deep Cool 18 580-581 1 25550 Peat 681910000 0.682 Shallow Warm 19 581-585 5 25600 Fine Silt 680940000 0.681 Deep Cool 20 585-586 1 25650 Peat 676090000 0.676 Shallow Warm 21 585-610 25 26500 Fine Silt 675120000 0.675 Deep Cool 22 610-611 1 26550 Peat 650870000 0.651 Shallow Warm 23 611-640 30 27600 Fine Silt 649900000 0.650 Deep Cool 24 640-1100 460 27600-43000 Fine Silt 620800000 0.621 Deep Cool 25 1100-1280 180 43000-52000 Peat 174600000 0.175 Shallow Warm Below 26 ?? Pre 52000 ?? 1280

Table 5. A. Reconstructed ages for relative lake water depth and climate cycles from stratigraphic section A & D (Profiles MN14-03A & MN15-02A) at the Lower side of the Galuut Canyon. Radiocarbon ages are shown in bold. At this side of Galuut Canyon the sediment deposition rate is notably lower than for the Upper side at 0.15-0.2 mm/year for silt layers. This section is centrally located within the paleo-lake. If the deposition rate is 0.2mm/yr, the lake was in existence for at least 67 ky. If the deposition rate is 0.3 mm/yr, the lake was in existence for 55 ky. The total thickness of stratigraphic section is assumed 11 to 12 meters.

No. Depth from To Thickness BP Age Material Water depth Period Age RC or (cm) (cm) (Years) reconstructed 1 0-100 100 5850-13680 Fine Silt Deep Cool Radiocarbon shells (off place) 2 100-101 1 13680 Peat Shallow? Warm Radiocarbon peat 3 101-280 180 13680-25680 Fine Silt Deep Cool Reconstructed 4 280-281 1 25680 Peat Shallow? Warm Reconstructed 5 281-320 40 25680-28350 Fine Silt Deep Cool Reconstructed 6 320-1300 980 *28350-67000 Fine Silt Deep Cool Reconstructed

Table 5. B. Reconstructed volumes for relative lake water depth and climate cycles from stratigraphic section A & D (Profiles MN14-03A & MN15-02A), at the Lower side of the Galuut Canyon. Radiocarbon dated ages are shown in bold. For the lower lake, the rate of deposition is notably lower than for the upper lake, at 0.15-0.2 mm/year for silt layers. This section is centrally located within the paleo- lake. The maximum lower lake area was 51 km2.

Depth Thickness Cumulative volume Cumulative volume Water No. from To BP Age (Years) Material Period (cm) reversed (m3) reversed (km3) depth (cm) 1 0-100 100 5850-13680 Fine Silt 664020000 0.664 Deep Cool Shallow 2 100-101 1 13680 Peat 613020000 0.613 Warm ? 3 101-280 180 13680-25680 Fine Silt 612510000 0.613 Deep Cool Shallow 4 280-281 1 25680 Peat 520710000 0.521 Warm ? 5 281-320 40 25680-28350 Fine Silt 520200000 0.520 Deep Cool 6 320-1100 980 *28350-67000 Fine Silt 499800000 0.500 Deep Cool

Table 6. Paleolake level simulation table reconstructed from shoreline altitudes for the Upper side of the Galuut Canyon. Area, 3D area and volume are calculated for each of the given levels (altitudes). We used ArcGIS 10.3, Spatial Analyst & Surface Tools for the simulation. The maximum level of the lake was at 2051 meters. Maximum depth was 15 meters, average depth 8-10 meters. Currently lakebeds are strongly incised by flowing streams which lowered the level of paleolake floor by 10 to 15 meters.

Level (m) Depth Actual Area 3D Area Volume Area 3D Area Volume (m) depth (m) (m2) (m2) (m3) (km2) (km2) (km3) 2005 5 0 34330 36550 169575 0.034 0.037 0.00017 2010 10 0 167182 172928 599789 0.167 0.173 0.0006 2015 15 0 557065 573654 2269220 0.557 0.574 0.0023 2020 20 0 1515941 1560682 7068867 1.52 1.56 0.0071 2025 25 0 4078784 4191218 20190443 4.08 4.19 0.020 2030 30 0 10425884 10683081 54278975 10.43 10.68 0.054 2035 35 0 23394420 23911222 136130680 23.39 23.91 0.13 2040 40 1 42912943 43808155 299963758 42.91 43.80 0.30 2045 45 5 64979515 66317468 570391893 64.98 66.32 0.57 2050 50 10 84580052 86343836 946582575 84.58 86.34 0.95 2055 55 15 94212107 96197480 1215144309 94.21 96.20 1.21 2060 60 20 110271508 112507511 1620066596 110.28 112.51 1.62

Figure 1. Location of Mongolia in central Asia. Khangay Mountains highlighted by red oval. The study area on the southern flank of the Khangay Mountains is identified by the black circle.

Figure 2. Digital elevation model of the Khangay Mountains, which reach a maximum altitude of over ~4000 meters at its peak Otgon Tenger Uul (highlighted by green star). Our study area is located on one of the two most active incising rivers (Olzyit River)

Lower Lake Galuut Canyon

Bayankhongor

Figure 3. The aerial view of our research area, courtesy of Google Earth. Late Pleistocene, Holocene, dry lake bed in the Galuut Valley along Olzyit River. The configuration of Bayankhongor thrust fault (yellow line), and elevation profile across the fault. We assume stream is defeated and triggered drainage capturing and formation of lake in the Late Pleistocene. The location of wind gap (former drainage) and water gap (current stream is shown). At least 5 streams of varying size and discharge (Olzyit is biggest) passes through today’s dry lacustrine bed. Google Earth cross-section profile is shown in dashed red line.

Upper Lake

Figure 4. The aerial view of our research area, courtesy of Google Earth. Late Pleistocene, Holocene, dry lake bed in the Galuut Valley along Olzyit River. The configuration of Bayankhongor thrust fault (yellow line), and elevation profile (green line) across the dry lacustrine lake bed. The location of wind gap (former drainage) and water gap (current stream) is shown. The location of Galuut Canyon at Northeastern tip of the dry lake bed is shown. We assume lake drained through Galuut Canyon in the Mid Holocene once lake erodes away the narrow gorge and caused catastrophic flooding. From lakebed profile, we can define the depth of the Galuut paleo-lake. Geophysical VES research profile shown in red dots.

Figure 5. A, B. Location of Bayankhongor fault which triggered the drainage capturing and paleo-lake formation (Adapted from Walker et. al 2008). C. Bayankhongor thrust fault view in place.

Figure 6-A. The view of the Galuut paleo lake valley. Upper Lake. We can observe currently flowing stream (Olzyit River).

Figure 6-B. Thick lacustrine sediment section along the Ozyit River on the lower side of the Galuut Canyon.

Figure 7. Olzyit River flowing through the Galuut Canyon. The canyon is located in between the upper and lower paleo-lake sections.

NE SW

Figure 8. Olzyit River longitudinal profile from NE to SW. The flatter section depicts the Galuut Valley paleolake. The Bayankhongor fault is located along the southern margin of the Galuut Valley.

Reconstructed lake level

Figure 9. A cartoon depicting the main model inputs. Our goal is to model the tectonic uplift and stream capture, drainage reorganization, lake formation and its history with the essence of changing climate (rainfall data). Reconstructed lake level is shown by the dashed blue line.

Figure 10. The stages of stream drainage capturing due to tectonic uplift along thrust fault at a given time intervals: Map view. Time 1) Pre- existing condition before tectonic uplift Time 2) After the uplift of south block, wind gap (dry drainage) showed in dashed red line. At this stage lake forms on the north block from stream water accumulation in the valley. Time 3) Stream starts erodes away through the narrow gorge and starts draining lake. Time 4) Modern conditions. Lake is drained and stream is flowing through water gap (current drainage). We will include precipitation patterns into the model in order to calculate the climate influence.

Figure 11. The stages of stream capture due to tectonic uplift along thrust fault at a given time intervals: A. Three dimensional view. B. Cross sectional view. Time 1) Pre-existing condition before tectonic uplift Time 2) After the uplift of south block, wind gap (dry drainage) showed in dashed red line. At this stage lake forms on the north block from stream water accumulation in the valley. Stream starts erodes away through the narrow gorge and starts draining lake. Time 3) Modern conditions. Lake is drained and stream is flowing through water gap (current drainage). We will include precipitation patterns into the model in order to calculate the climate influence.

Figure 12 A. Stratigraphic section at the upper side of the Galuut Canyon (upper paleo-lake) B. Stratigraphic section at the lower side of the Galuut Canyon (lower paleo lake).

Figure 13-A. Stratigraphic section at the upper paleo-lake. B. Peat layers collected from upper paleo lake. C. Shells at lower paleo lake. D. Stratigraphic section at the upper side of the Galuut Canyon (Upper Lake)

Figure 14. A, B Stratigraphic sections at lower side of the Galuut Canyon at the lake bed. MN14-03A& MN15-02A

Figure 14. C, D, E. Stratigraphic sections at upper side of the Galuut Canyon at the lake bed. MN15-01A, B & MN14-04A, B

Figure 15-A. Age, depth correlation graph of MN14-04A, Section B at the upper side of the Galuut Canyon

Figure 15-B. Age, depth correlation graph of MN15-02A, Section C at the lower side of the Galuut Canyon

Figure 16. Today’s paleo lakebed profile along geophysical VES line along A-A'. The total length of VES survey is 8.65 km. The image also shows lakefill at maximum extent of 2051 meters. The black triangles are geophysical VES points. The blue markers are location of currently flowing modern streams along lakebed. We were able to determine lake depth.

Figure 17 A, B. The Pseudo section profile constructed from Geophysical VES survey at first two kilometers. The profile line crosses wind gap, thrust fault, lake shore and southern end of lakebed lacustrine sediment. VES ground point interval is 100 meters. The main goal is determining fault presence and lacustrine sediment thickness. The red circle probably determines fault presence. We believe lacustrine sediment section is 8 to 15 meters. The red arrow points out the lacustrine sediment thickness.

Figure 17 C, D. The Pseudo section profile constructed from Geophysical VES survey at four to six kilometers. The profile line entirely crosses lakebed lacustrine sediment with presence of modern streams. VES ground point interval is 200 meters. We believe lacustrine sediment thickness reaches 10 to 15 meters through this profile line. The red arrow points out the lacustrine sediment thickness.

Figure 17 E. The Pseudo section profile constructed from Geophysical VES survey at 6 to 8.65 kilometers. The profile line entirely crosses lakebed lacustrine sediment with presence of modern streams. The profile line ends outside paleo lake shore at the mountain ridge. VES ground point interval is 500 meters. We believe lacustrine sediment thickness reaches to 15 meters through this profile line with shallow parts by the lake shore. The red arrow points out the maximum lacustrine sediment thickness. This VES survey needs ground confirmation such as drilling.

Figure 17 F. The entire sketch of lithology constructed from Geophysical VES survey. The profile line entirely crosses lakebed lacustrine sediment with presence of modern streams. The profile line ends outside paleo lake shore at the mountain ridge. VES ground point interval is 500 meters. Lacustrine sediment thickness reaches to 15-20 meters. The VES survey needs ground confirmation such as drilling.

A. B. C.

D. E.

Figure 18. A, B, C, D, E. The ground penetration radar (GPR) survey profile line along wind gap, thrust fault, paleo-lakeshore line and edges of lacustrine sediment section. The GPR survey length is 250 meters. Thrust fault presence is revealed at 18B B. & C along black lines

Figure 18. F. The ground penetration radar (GPR) survey profile line along wind gap, thrust fault, paleo-lakeshore line and edges of lacustrine sediment section. The GPR survey length is 250 meters. Thrust fault presence is revealed in the red oval section.

5 km

Figure 19-A. Lake fill simulation at maximum extent of 2050 meters, Upper Lake draped over 3D raster image created with the ArcGIS 10.3 Flood simulation tool.

1 km

Figure 19-B. Lake fill simulation at maximum extent of 2050 meters, Upper Lake draped over 3D raster image created with the ArcGIS 10.3 Flood simulation tool

Figure 20-A. SIGNUM Model general scenario for the wet (high precipitation) case. See text for details

Figure 20-B. SIGNUM Model general scenario for the dry (low precipitation) case. The scenario is intended to test model’s uplift patterns for dry climate. See text for details

A A'

B B'

C' C

Figure 21 A, B, C. SIGNUM model simulation results. Scenario 1 (See text for details). At 60000 (A), 61000 (B) and 80000 (C) years. Figure 21 A', B', C'. Slope area relationship for given time interval

D D'

E E'

F Fˈ

Figure 21 D, E, F. SIGNUM model simulation results. Scenario 1 (See text for details). At 100,000 (A), 120,000 (B) and 150,000 (C) years. Figure 21 D', E', F'. Slope area relationship for given time intervals

A Aˈ

B Bˈ

C Cˈ

Figure 22 A, B, C. SIGNUM model simulation results. Scenario 2 (See text for details). At 60,000 (A), 61,000 (B) and 80,000 (C) years. Figure 22 A', B', C'. Slope area relationship for given time intervals

D Dˈ

E Eˈ

F Fˈ

Figure 22 D, E, F. SIGNUM model simulation results. Scenario 2. See text for details. At 100,000 (A), 120,000 (B) and 150,000 (C) years. Figure 22 D', E', F'. Slope area relationship for given time intervals.

Figure 23-A. SIGNUM model simulation uplift profiles for General Scenario 1. At 60 to 100 ky.

Figure 23-B. SIGNUM model simulation uplift profiles for General Scenario 1 at 110 to150 ky

Figure 24-A. SIGNUM model simulation uplift profiles for General Scenario 2 at 60 to 100 ky.

Figure 24-B. SIGNUM model simulation uplift profiles for Scenario 2 at 110 to 150 ky.

Figure 25-A. SIGNUM Model specified scenario for the wet (high precipitation) case. See text for details

Figure 25-B. SIGNUM Model specified scenario for the dry (low precipitation) case. See text for details

Figure 25-C. SIGNUM Model specified scenario for the fluctuating precipitation case. See text for details

Figure 25-D. SIGNUM Model specified scenario 2 for the wet (high precipitation) case. Note that higher overall uplift of 2 mm/year due to hillslope erosion on the wind gap domain. See text for details

Figure 25-E. SIGNUM Model specified scenario 2 for the dry (low precipitation) case. Note that higher overall uplift of 2 mm/year due to hillslope erosion on the wind gap domain. See text for details

Figure 25-F. SIGNUM Model specified scenario 2 for the fluctuating precipitation case. Note that higher overall uplift of 2 mm/year due to hillslope erosion on the wind gap domain. See text for details

Figure 26. Topographic development profile (red dash) line A-A'. The bold red line represents the earthquake fault. South domain has the elevated surface, which defeated the streams.

Junction of north and south block

Figure 27-A. Topographic development along A-A' for 60 ky time intervals (pre-1st earthquake) for all scenarios. The window block represents south domain with current wind gap before starts uplifting. Note that except the case differences, the second scenarios of 2 mm/year uplift on south domain is not started yet.

Figure 27-B. Topographic development along A-A' for 80 ky time intervals (after 1st earthquake) for all scenarios. The window block represents south domain with current wind gap after first uplift.

Topographic development along A-A' for 95K time intervals for all scenarios (Before 2nd earthquake) 90 Junction of north 80 and south block 70

Elevation Elevation (m) 30

0 0 500 1000 1500 2000 2500 3000 Horizontal Distance (m)

Figure 27-C. Topographic development along A-A' for 95 ky time intervals (Before 2nd earthquake) for all scenarios. The window block represents south domain with current wind gap.

Topographic development along A-A' for 135K time intervals for all scenarios (After 2nd earthquake) 120 Junction of north 100 and south block

Elevation Elevation (m) 40

0 0 500 1000 1500 2000 2500 3000 Horizontal distance (m)

Figure 27-D. Topographic development along A-A' for 135 ky time intervals (After 2nd earthquake) for all scenarios. The window block represents south domain with current wind gap. Note that wind gap starts uplifting for case 2 (2 mm/year uplift on south block), which representing faster uplift rate could defeat the stream.

Topographic development along A-A' for 150K time intervals for all scenarios (After 3rd earthquake) 120 Junction of north 100 and south block 80

Elevation Elevation (m) 40

0 0 500 1000 1500 2000 2500 3000 Horizontal distance (m)

Figure 27-E.Topographic development along A-A' for 150 ky time intervals (After 3rd earthquake) for all scenarios. The window block represents south domain with current wind gap. Note that wind gap uplifted high enough for case 2 (2 mm/year uplift on south block), Dry case uplifts faster than fluctuating and wet cases. Overall uplift coupled with hillslope erosion on wind gap could beat any precipitation (high to low) case scenarios

Figure 28-A. Stream defeat uplift stages after earthquakes (Red star) for equal amount of uplift (1 mm/year) on wind gap and north domain. This represents three streams in the model which topographically high, middle and low side of the domain. The bold line represents the water gap and dashed line represents the wind gap. Small numbers represent thousands of years for when streams start to defeat the uplift. Size-wise Middle river has the highest discharge (bigger), and other two are relatively short and smaller streams. For wet case, there is a delayed response of streams defeating uplift relative to the dry case.

Figure 28-B. Streams defeat uplift stages after earthquakes (Red star) for equal amount of uplift (1 mm/year) on wind gap and north domain. This represents three streams in the model across the topographically high, middle and low side of the domain. The bold line represents the water gap and dashed line represents the wind gap. Small numbers represent thousands of years before the streams start to defeat the uplift. Size-wise, the Middle river has the highest discharge (bigger) and other two are relatively short, small streams. The dry case has a faster response of streams defeating uplift than does the wet case.

Figure 28-C. Streams defeat uplift stages after earthquakes (Red star) for equal amount of uplift (1 mm/year) on wind gap and north domain. This represents three streams in the model across the topographically high, middle and low side of the domain. The bold line represents the water gap and dashed line represents the wind gap. Small numbers represent the thousands of years before streams start defeating uplift. Size-wise, the middle river has the highest discharge (bigger), and other two are relatively small, short streams. The fluctuating case has a delayed response of streams defeating uplift relative to the dry case, but faster than observed for the wet case.

Figure 28-D. Streams defeat uplift stages after earthquakes (Red star) for equal amount of uplift (1 mm/year) on wind gap and north domain during the relatively dry scenario of 200 mm/year precipitation. This represents three streams in the model across the topographically high, middle and low side of the domain. The bold line represents the water gap and dashed line represents the wind gap. Small numbers represent thousands of years before the streams start defeating uplift. Size-wise, the Middle river has the highest discharge (bigger), and other two are relatively small and short streams. For 200 mm/year precipitation case, it has the fastest response of streams defeating uplift than observed from any of the other higher precipitation scenarios.

Figure 28-E. Streams defeat uplift stages after earthquakes (Red star) for equal amount of uplift (1 mm/year) on wind gap and north domain in the relatively wet case scenario of 1500 mm/year of precipitation. The three streams in the model are from the topographically high, middle and low side of the domain. The bold line represents the water gap and dashed line represents the wind gap. Small numbers represent thousands of years before streams start defeating the uplift. Size-wise, the Middle river has the highest discharge (bigger), and other two are relatively small and short streams. For the 1500 mm/year precipitation scenario, a delayed response of stream defeat of uplift is observed relative to the dry case.

CHAPTER 2

CONTRASTING LATE MIOCENE TO PRESENT LANDSCAPE EVOLUTION ACROSS

MONGOLIA’S KHANGAY MOUNTAINS THROUGH THE LENS OF CHEMICAL AND

PHYSICAL WEATHERING PROCESSES.

2.1 Abstract

Our understanding of the impacts of climate change on the geomorphology of terrestrial landscapes is often derived from proxy sedimentary records preserved in depositional fluvial and lacustrine basins that integrate landscape responses at scales of 102 to 104 km2. At million-year time scales, most mountainous regions are characterized by net erosion and the export of chemically and physically weathered bedrock. Thus, the direct observation of near-surface weathering, sediment and soil production, and geomorphic character of the mountainous regions is often asserted through examination of physical sedimentation and bio-geochemical proxies preserved in depositional basins hundreds to thousands of meters below and tens-to-hundreds of kilometers distant from the bedrock and hillslopes from which they were originally liberated. In the Khangay Mountains of central Mongolia, late Cenozoic valley-conforming lava flows preserve “snap-shots” of hillslope weathering regimes in the headwaters of the Selenga-Baikal depositional system during the critical climate transition from the late Miocene (ca. 12 Ma) into the Quaternary.

Our research focuses on characterizing the relative importance of chemical and physical weathering to landscape development in this upland intracontinental setting through an investigation of geochemical major and minor trace elemental composition of well-developed saprolitic paleosols formed in metasediments (middle Orkhon), granite (upper Orkhon), and Miocene fluvial deposits (upper Chuluut) preserved beneath basaltic lavas at 11.9, 7.5 and 3.1 Ma, respectively. Today, the Khangay Mountains is dominated by a continental climate resulting in cold-region physical weathering processes. We used the Chemical Index of alteration (CIA), Plagioclase Index of Alteration (PIA) and Chemical Index of Weathering (CIW) to reconstruct integrated paleo-Mean Annual Temperature

(MAT) and Mean Annual Precipitation (MAP) from the saprolite locations. Preliminary results reveal that from the late Miocene into the Pliocene, climate was 5-10°C warmer, slightly more humid and less variable than today in the upland continental interior of west-central Mongolia. This result matches with both long-term paleo records from Lake Baikal and late Cenozoic global cooling trends from other proxies.

2.2 Introduction

The Khangay Mountains of central Mongolia (Figure 1) are an outstanding field laboratory for studying the impacts of late Cenozoic global cooling on the landscape evolution of intracontinental mountain ranges via investigations of hillslope and fluvial geomorphic processes. The onset of the rock uplift that generated the topography of the Khangay Mountains is believed to have occurred in the late Paleogene to early Neogene (c. 30 – 25 Ma) based on an increase in clastic sedimentation in surrounding basins (e.g., Höck et al., 1997; Caves et al., 2014; 2015), the initiation of significant regional basalt volcanism (Yarmolyuk, 1974; Yarmolyuk et al., 2008; Ancuta et al., 2018), and the existence of localized paleo-topographic relief similar to modern values across the range as constrained by late Miocene valley-filling lava flows (Smith et al., 2016). This Oligocene uplift has subsequently influenced regional climate, drainage patterns and even the evolution of aquatic species (e.g. Wegmann et al., 2015). The causes of the uplift may be related to dynamic forces associated with surface-directed mantle flow (Cunningham, 2001; West et al., 2013; Ancuta et al., 2018). More recent, low-temperature thermochronology research by McDannell et al. (2018) indicates that the exhumation of rocks now at the surface in the Khangay Mountains occurred in the late Cretaceous and that exhumation was essentially complete by 100 Ma, following which the region has experienced minimal additional exhumation. Numerical modeling performed for central Mongolia supports a scenario where higher topographic relief existed in the Mesozoic, was rapidly eroded, and over the past ca. 100 million years has undergone minimal erosion due to a predominantly arid, continental interior climatic regime with low tectonic activity. McDannell and colleagues (2018) advocate that Mesozoic topographic evolution and relative stability of the landscape persisted throughout the Cenozoic with very little subsequent exhumation.

The geology of the Khangay Mountains is complicated. Surface exposures are dominated by early Paleozoic metasediments intruded by late Paleozoic-to-early Mesozoic granitoids that record the collision of the Siberian and North China cratons and the pre-Cenozoic orogenic assembly of central Asia (Tomurtogoo, 1999; Cunningham, 2001). A large Precambrian crustal block is thought to underlie the range (Cunningham, 2001); although it is not exposed at the surface. Presently, the Khangay Mountains are characterized by low-gradient summit plateaus, often with benched side-slope profiles in the form of cryoplanation terraces (Lehmkhul et al., 2004; West et al., 2013; Lyons et al., 2013; Walker et al., 2015). Late Cenozoic volcanics are scattered across the region and may be associated with Khangay uplift, but they are relatively limited in terms of the areal extent of their surface exposure (Ancuta et al., 2018). These mid-to- late Cenozoic basalt flows help to preserve snapshots of the landscapes across which they flowed and in-filled (e.g., Smith et al., 2016).

The primary goal of this research is to characterize the relative importance of chemical and physical weathering to landscape development in this intracontinental setting from the late Miocene (ca. 12 Ma) to the present through an investigation of geochemical major and minor trace elemental analysis of saprolitic paleosols formed in metasedimentary rocks (Middle Orkhon), granitic parent materials (Upper Orkhon) and Miocene fluvial deposits (Upper Chuluut) that were preserved when they were buried by basaltic lavas on the north flank of the Khangay Mountains at 11.9, 7.5 and 3.05 Ma, respectively (Figure 2). Today, the Khangay are dominated by a continental climate resulting in cold-region physical weathering processes, but this was likely not the case in the late Miocene to early Pliocene, as is evidenced by the presence of well-developed saprolites locally preserved beneath lava flows. The research goal represented by this chapter is to improve upon our understanding of upland weathering and sediment production processes from the late Miocene into the Quaternary in central Mongolia, and to investigate linkages between these processes and the modern hillslope and fluvial geomorphology of the Khangay Mountains.

From the late Miocene (Tortonian) into the Quaternary the mean climate of central-northeast Asia is believed to have followed global patterns by experiencing gradual overall cooling, as indicated by a 12 Ma-long proxy record reconstructed from sediment cores recovered from Lake Baikal (Kashiwaya et al., 2000), located about 600 km north of the Khangay Mountains in a

Cenozoic extensional basin at the southern edge of the Siberian craton. The Khangay form the continental drainage divide separating the north-flowing Selenga River and its tributaries that drain to Lake Baikal from rivers that flow west and south off the range into the internally drained Mongolian Depression and Valley of Lakes, respectively (Fig. 1). The Selenga River is the largest tributary to Lake Baikal and is the single largest source of clastic sediment input to the lake (Dong et. al, 2013). Because the Khangay Mountains form the headwaters of the Selenga River, chemical and physical weathering processes in these upland reaches are likely to have an impact on the downstream proxy records of climate change preserved in the deep-time depositional basins of Lake Baikal.

Our understanding of the impacts of climate change on the geomorphology of terrestrial landscapes is derived often from proxy sedimentary records preserved in depositional basins (e.g. lakes) that integrate catchment response at the scale of hundreds-to-thousands of km2, as is the case for upland responses to climate change preserved in Baikal and other similar sedimentary archives (Kashiwaya et. al., 2000; Prokopenko et. al., 2009). At million-year time scales most mountainous regions are characterized by net erosion and the export of chemically and physically weathered bedrock (Riebe et. al 2015). Thus, the direct observation of near-surface weathering, sediment and soil production, and geomorphic character of the mountainous regions is often asserted through examination of physical sedimentation and bio-geochemical proxies preserved in depositional basins hundreds to thousands of meters below and tens-to-hundreds of kilometers distant from the bedrock and hillslopes from which they were originally liberated. In the Khangay Mountains, late Cenozoic valley-conforming lava flows preserve “snap-shots” of hillslope weathering regimes in the headwaters of the Selenga-Baikal depositional system during the critical climate transition into the Quaternary ice age.

Recent contributions by Caves et al. (2014; 2015) propose that aridity increased in central northeast Asia in the late Neogene, reflecting the effects of Northern Hemisphere glaciation and cooling. This widespread de-greening (browning) increased regional albedo and modified basin- scale water balances, resulting in greater dust fluxes due to reduced vegetative cover and precipitation. The atmospheric modeling of Caves and colleagues (2014), constrained by paleo soil-carbonate proxy records from Biger and Taatsiin basins to the west and south of the Khangay Mountains in Mongolia, demonstrates that moisture transport and balance patterns have

98 changed significantly since perhaps as far back as the Eocene, due to orographic development across the region. Caves et al. (2014) additionally argue that the topographic development of the Tibetan plateau to the south had little effect in shaping the dry and cold climate pattern in central northeast Asia, whereas uplift of the Altay, Tian Shan, and Khangay Mountain ranges beginning in the middle Cenozoic had a significant control on moisture transport pathways across western and central Mongolia. In relation to uplift of the Altay and Khangay Mountains, central Asia became more arid due to moisture imbalances. The research by Caves and colleagues suggests that uplift of the Khangay in the early Oligocene blocked Siberian moisture sources, which resulted in aridification of the northern Gobi. In contrast, Late Miocene surface uplift of the Altay, situated astride the China-Mongolia-Russia border, blocked western-derived moisture from penetrating eastward into Mongolia. Thus, the northern Gobi became increasingly arid from east-to-west since the Oligocene, likely driven by orographic development in the Khangay during the Oligocene and the Altay in the Late Miocene.

Several research results from clastic sedimentary proxy records in the region support the isotopic paleo-moisture reconstructions of Caves et al. (2014, 2015) for western Mongolia. Earlier research suggested that the Valley of Lakes in Mongolia and Tarim Basin in China experienced a significant rain shadow effects on the lee side of the growing mountains during late Oligocene to early Miocene (Hellwig et al., 2018). Specifically, a study of the mineralogy and geochemistry of Oligo-Miocene sediments from the Valley of the Lakes to the south of the Khangay Mountains by Ricoz et al. (2016) concluded that Late Cenozoic basin sediments are mostly of aeolian origin from increased dust, consistent with regional climate change dominated by gradual cooling and drying. In addition, Voigt and colleagues (2017) postulate that the continental settings of Central Asia witnessed increased desertification during the late Cenozoic as a result of regional mountain uplift combined with retreat of the remnants of the Paratethys Sea from the Aktau Basin of Kazakhstan as a source region for eastwardly transported moisture (Voigt, 2017). Proxy records from various basins provide evidence that Central Asia’s climate was warmer and wetter in the mid-Miocene in comparison to the long-term Oligocene to early Miocene mean state that is characterized by aridity and desertification (e.g., Guo et al., 2002; Sun & Windley, 2015). Today, climate in central Mongolia is generally cold and dry due to its high altitude, distance from oceans, and surrounding high mountain chains that block the wet, monsoonal air masses from the Pacific and the Indian Oceans. Central Mongolia is typified by an extreme continental

99 climate with long, cold winters and short summers, during which the majority of the annual precipitation falls.

The goal of this research is to reconstruct the mean climate state that was dominant in the late Miocene and Pliocene epochs along the north flank of the Khangay Mountains in order to better understand upland weathering processes and hillslope sediment production. The research utilizes chemical weathering indices derived from the analysis of both major and minor trace elements in samples of saprolites and weathered bedrock preserved beneath Ar39/Ar40 dated basalt flows, as a proxy for the integrated climate conditions present prior to landscape preservation beneath the outpourings of lava. This research helps to close the gap between our knowledge of the climate and environmental succession from the Oligocene to early Miocene concluded by other researchers, by adding additional upland data points for the middle Miocene to Pliocene. The research presented in this chapter aims to test the hypothesis that a warmer and more humid climate in the late Miocene resulted in chemical weathering of hillslopes and decreased hillslope sediment transport, relative to the cool, arid climate and physical weathering processes that have dominated central Mongolian landscapes since the onset of global cooling beginning in the mid Pliocene. In contrast, the null hypothesis posits that even in the late Miocene, chemical weathering was generally negligible and physical weathering of hillslopes was dominant in this region.

This research provides a first-of-its kind dataset linking late Cenozoic climate change to upland weathering and sediment production regimes in the headwaters of the Selenga River basin. The results of this research are compared with long-term proxy sedimentary records recovered from Lake Baikal (Kashiwaya et. al 1997), into which the Khangay Mountains drain.

2.3 Materials and Methods

Climate is the primary control on chemical and physical weathering processes affecting the upper continental crust (Bahlburg et al., 2011). Direct evidence of past weathering conditions and thus climate can be obtained from palaeosols through a combination of field observation, petrography (particularly diagenetic phases), isotope geochemistry, and whole rock X-ray diffractometry (Bahlburg et al., 2011).

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Chemical weathering indices are useful tools in characterizing soil weathering profiles and determining the extent of chemical weathering (Ohta, 2007). Our hypothesis is tested through major/minor element analyses of fossil soil samples collected from beneath 40Ar/39Ar dated basalt flows. From major element analysis, the hypothesis is evaluated by constructing different weathering indices such as the Chemical Index of Alteration (CIA), Chemical Index of Weathering (CIW) and Plagioclase Index of Alteration (PIA) for the upper Orkhon and Chuluut River valleys, where saprolitized bedrock and paleosols are preserved. The Miocene and Pliocene weathering indices are compared against nearby modern climate stations as well as those constructed from sediments collected from active floodplains adjacent to each paleosol section believed to represent late Holocene climate from the upstream contributing basin. The chemical index of alteration (CIA), first applied by Nesbitt and Young (1982), is one of the more commonly used approaches to evaluate the progressive alteration of plagioclase and potassium feldspars to clay minerals that occurs within the critical zone. The applicability of this index lies with the fact that feldspar is a dominant mineral in the upper crust (Nesbitt and Young, 1989). Past conditions of physical and chemical weathering may be inferred if application of the CIA is combined with feasible other approaches (Bahlburg et al., 2011). When applied to the reconstruction of climate conditions during late Miocene and Pliocene epochs, the CIA data may provide insights into the changes in the relative contributions of chemical and physical weathering in the form of sedimentary detritus and weathered granite saprolite. The CIA data are thus instrumental not only in documenting changes between long term icehouse and greenhouse climates, but also in recognizing shorter-term climate oscillations between glacial and warm– humid conditions (Behstadt, 2009). The CIA index is calculated as:

CIA = [Al2O3 / (Al2O3+CaO*+Na2O+K2O)] * 100, [1] where oxides are expressed in molar proportions and CaO* is the amount of CaO incorporated in the silicic fraction of the rock. CIA values obtained from modern and buried soils and sediments have been used as an indicator of the intensity of weathering (Nesbitt and Young, 1982). High CIA values (e.g. > 80) indicates the removal of labile elements like Ca, Na, K relative to the static residual constituent (Al+3) during weathering (Nesbitt and Young, 1982), while low CIA values (< 50) indicate minimal chemical weathering. Samples with low CIA values are

101 commonly interpreted as reflecting either or both cool and arid conditions. CIA values of unweathered igneous rocks containing unaltered plagioclase and K-feldspar grains range from 40-50, whereas intensely weathered igneous saprolites found in humid tropical environments can have CIA values approaching 100, indicating complete conversion of feldspars to clay minerals like gibbsite and kaolinite (Fedo et al., 1995). For the estimation of weathering conditions in the source area of sediments, many authors also use the Chemical Index of Weathering (CIW) and Plagioclase Index of Alteration (PIA) (Fedo et al., 1995; Harnois, 1988). Of the major indices proposed to monitor chemical weathering only the CIW index avoids the problems related to the remobilization of K during diagenesis or metamorphism (Harnois, 1988). The CIW is defined as:

CIW = [Al2O3/(Al2O3+CaO*+Na2O )*100], [2]

where, Al2O3, CaO* and Na2O are represented in molar proportion. This index does not incorporate potassium because it may be leached out of the mineral grains, or alternatively it may accumulate in the residue during weathering. The CIW index increases with the degree of depletion of Na and Ca in the sediment relative to Al, reflecting the amount of chemical weathering experienced by the source rock and sediment. In comparison to other weathering indices, it is often considered a superior method that involves only a restricted number of components, which have fairly well-known and consistent geochemical behavior during weathering. The PIA is the third weathering index considered in this study. The PIA is widely used to quantify the degree of source rock weathering for ancient sedimentary rocks (Fedo et al., 1995). The PIA is expressed as:

PIA=Al2O3-K2O/Al2O3+CaO+ Na2O-K2O*100, [3]

Where the maximum value of 100 is reserved for completely altered material (kaolinite, gibbsite), while fresh, unweathered plagioclase has a value of 50. The properties of the weathering indices calculated using molecular proportions of elements oxides evaluated in this study are summarized in Table 7 (Adapted from Price et al., 2003).

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The fourth weathering index considered, Vogt’s residual index (VRI), was first proposed by Stefanie Vogt in 1927. This is a geochemical method for assessing the maturity of residual sediments. Roaldset (1938) used this index to determine the weathering status of clays in Quaternary deposits of the Numedal Area in Norway. The index was used to compare the bulk chemistry of moraine and marine clay deposits. The Vogt’s Residual Index is defined by the following formula:

VRI = (Al2O3 + K2O) / (MgO +*CaO + Na2O) [4] where the maximum value tends to be infinite for completely altered materials, while fresh, unweathered plagioclase has a value of between 0 and 1.

A simple and useful way to evaluate the chemical weathering trend in sedimentary samples is through the construction of an A-CN-K ternary plot, where A is Al2O3, CN is CaO*+Na2O, and

K is K2O, in molecular proportions. CaO* represents CaO incorporated into silicate minerals (Nesbitt and Young, 1984; Nesbitt, 2003) and is expressed as:

Al2O3- (CaO*+Na2O-K2O). [5]

On these ternary diagrams, plagioclase and K-feldspar plot at 50% Al2O3 on the left and right boundaries to form the feldspar join. Biotite and K-feldspar, augite, and amphiboles plot near the CN apex and calcite plots at the Ca apex, while illite and smectite appear between 70 to 85%

Al2O3. The clay mineral group plots at the A apex. At the initial stage of weathering, sediment samples tend to be parallel to the A-CN line because Na2O and CaO are leached out from the earlier dissolved plagioclase and those samples, which have undergone less weathering, will plot parallel and close to the A-CN line. Continued weathering leads to the destruction of plagioclase through the removal of CaO and Na2O and such samples will plot closer to the A - K boundary. During advanced stages of weathering, K is removed in preference to Al from the K-feldspar, and as a result, the trend is redirected towards the Al2O3 apex (Fedo et al., 1995). K-enrichment involves addition of K2O to aluminous clays; it follows the path towards the K2O apex of the triangle. K-metasomatism of sediments can take two different paths representing either the

103 conversion of aluminous clay minerals (Kaolinite) to illite, or the conversion of plagioclase to K- feldspar. The major and minor trace elemental data used to derive the rock and sediment chemical weathering indices were analyzed for significance using a multivariate principal component analysis (PCA) statistical approach. The PCA is used to determine the dominant variables among major elements in paleosols, saprolite, and bedrock samples for investigating primary mineral transformations. In addition, PCA was applied to the geochemical datasets for the evaluation of the dominant chemical processes and likely trends that are responsible for the bedrock-to- saprolite-to-paleosol transformation. All of the samples were collected during the 2014 summer field season. Saprolite, granite, fresh basalt, paleosols, and latest Holocene floodplain samples were prepared for inorganic (major, minor, and trace) elemental analyses in the Department of Marine, Earth, and Atmospheric Sciences at NC State University. Samples were disaggregated and homogenized with a ceramic mortar and pestle, and passed through a 63 μm sieve prior to analysis. Pre-treated samples from vertical transects beginning at the overlying basalt – paleosol unconformity and progressing downward through the weathering zone into relatively chemically unaltered materials were sent to Bureau Veritas Mineral Laboratories in Vancouver, Canada for major and minor trace element geochemistry using ICP-MS. The lab-reported major and minor trace element results were converted from weight percent to oxide concentration in weight percentage. These values were then converted into molecular proportions in order to facilitate the calculation of the weathering indices through the above-mentioned formulas for each of the three study sites. All of the weathering indices are plotted on A-CN-K ternary plots in order to define the weathering trend and source definition. In addition, the K2O/Al2O3 ratio was calculated for defining the parent material source.

2.4 Results

In order to determine the paleo-weathering regimes, three stratigraphic sections were selected from within the upper basins of the Orkhon and Chuluut Rivers, which are on the northern flank of the Khangay Mountains. Both of these basins drain into Lake Baikal via the Selenga River. These three selected sites were the only ones with accessible, well-preserved paleosols exposed

104 beneath lava flows that we identified during three previous summer field seasons. The basalt flows at each site were dated with the whole-rock 40Ar/39Ar technique as a part of our larger Khangay Mountains research efforts by Ancuta and colleagues (2018) at 11.2±0.2 Ma, 7.6±0.1 Ma, and 3.1±0.1 Ma for the Chuluut, Upper, and Middle Orkhon sections, respectively (Figures. 2-6). These ages are from the Tortonian stage of the late Miocene (Middle Orkhon and Chuluut sections), and the Piacenzian stage of the late Pliocene (Upper Orkhon section). Profile descriptions and weathering indices for each of the sections are discussed below. Results are presented first for the Middle and Upper Orkhon River sections followed by the upper Chuluut River section (Figure 2).

2.4.1 Middle Orkhon Section (>7.6 Ma)

The Middle Orkhon section lies near to the northern mountain-foreland limits of the Khangay Mountains at an altitude of 1570 meters. Of the three sections, this is the lowest in elevation. In this section a basalt flow with an age of 7.6±0.06 Ma, is superposed on top of Paleozoic sedimentary rock. See the profile description from Table 1. The Middle Orkhon section (Figure 3) has a single argillite sample that returned results with a CIA value of 65.68, PIA of 72.52, CIW of 77.44 and Vogt’s residual index (VRI) of 2.26 respectively (Figure 7). The surficial part of this sedimentary succession represents pre-7.6 Ma, and may characterize the surficial weathering environment at this time. The samples collected from the floodplain of the Middle Orkhon section are analyzed to represent the late Holocene to modern chemical weathering regime for the catchment from which the sediments are derived. These samples from the floodplain showed a relatively low degree of chemical weathering, with a CIA value of only 55.19, 56.75 for PIA, 62.42 for CIW, and 1.32 for VRI (Table 8 and 9b, Figure 7).

2.4.2 Upper Orkhon Section (>3 Ma)

The Upper Orkhon section is 60 km upstream and ~500 m higher (2010 m) than the Middle Orkhon locale. At the upper Orkhon section, a ca. 10 m thick paleosol-to-saprolite-to unweathered granite bedrock section is preserved beneath a 3.06 Ma basalt flow. Two stratigraphic sections, MN14-02A and 02B, separated by about 100 m were described at this site,

105 and their data provides geochemical redundancy and an internal check on the results. Because of the thickness of the exposures at the Upper Orkhon section, we have the greatest amount of geochemical information for this site compared to the Middle Orkhon and Upper Chuluut sites. The weathering profile at this site is formed directly from the intrusive igneous bedrock, which is not the case for the Middle Orkhon and Upper Chuluut sections, which represent Paleozoic sedimentary and Miocene fluvial depositional parent materials, respectively. See profile description of Upper Orkhon section from Table 2. Weathering index values derived from the paleosol beneath the Upper Orkhon section basalt (Figure 4, 5-field photograph) have CIA values between 60.3-66.72, PIA values between 63.73 to 74.45, CIW values between 69.39.1 to 79.2, and VRI results between 1.24 to 1.81 (Table 8; Figures 8a, b). In contrast, however, the capping basalt flow, lower saprolite (MN14-02A-01, 02A-10 and 02A-11), and minimally weathered granite exhibit relatively low degrees of chemical weathering (Figure 8a). For these stratigraphic intervals, CIA values range from 46.38 for granite bedrock to 53.72 for saprolite, and 48.35 for the basalt cap. Similarly, PIA values are 45.18 for the basalt, 54.75 for the saprolite, and 48.03 for the granite bedrock. CIW and VRI values are also relatively low as well (Table 8; Figure 8a), indicating that these samples represent progressively less-weathered bedrock, either as basalt capping the paleosol, or as one moves to greater depth within the soil-saprolite profile. The modern floodplain sample from the Upper Orkhon site returned very low weathering indices, with a CIA of 53.99, PIA of 55.07, CIW of 60.40, and VRI of 1.01 (Table 8, Figure 8b), consistent with the integrated upstream weathering regime of the Upper Orkhon drainage basin during the Holocene. In comparison to the modern floodplain sediment collected from the Middle Orkhon section, the Upper Orkhon results are slightly lower, representing an overall decrease in chemical weathering as one moves up in altitude within the basin.

2.4.3 Upper Chuluut section (>11.2 Ma)

The Upper Chuluut section is located 200 km to the west-northwest of the Orkhon sites, and at 2200 m above sea level is 200 m above the Upper Orkhon and 600 m above the Middle Orkhon sites (Figs. 2, 6). Five samples were collected from a single 1 m thick exposed weathering profile capped by an 11.2 ± 0.02 Ma (late Miocene) basalt flow. Please see the profile description from

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Table 3. The Chuluut section consists of in-situ Miocene fluvial sediments varying between layers of fine silt, sand, and gravel. The materials here are classified as paleosols as in the other sections. CIA values for the Chuluut section range from 51.01 to 68.58 in the fine sand/silt lens. PIA ranges from 51.11 to 71.60 and CIW values are from 53.46 to 73.74, while the VRI varies from 0.91 to 1.97 (Figure 9, Table 8).

Modern-to-late Holocene floodplain sediments (LHFP) from all three locations returned relatively low weathering index results, with the Upper Chuluut River sample (MN14-09E) exhibiting very low values with a CIA of 50.36, PIA of 50.43, CIW of 54.66 and VRI of 0.84 (Table 8, 9B). The Chuluut location, at 2200 m above sea level is the highest in altitude of all three sites. Digital topographic metrics, including hypsometric integral statistics for all three drainage basins are presented in Tables 9-A, while Holocene floodplain weathering patterns, and correlation coefficients for catchment mean and sampled elevation vs. each of the weathering indices are reported on Tables 9-B and 9-C, respectively.

2.5 Discussion

2.5.1 Middle Orkhon Section (>7.6 Ma)

With respect to the weathering indices returned from the Middle Orkhon section, CIW values are higher than CIA values from the analyzed samples. I interpret this as a result of the exclusion of

K2O from the index, as is commonly observed when comparing CIW and CIA results from the same samples (Fedo et al., 1995; Harnois, 1988). The single CIW and PIA value from the Middle Orkhon section paleosol (MN-14-01B) suggests low-to-moderate weathering of the local bedrock prior to burial by the 7.6 Ma basalt flows, in contrast to the two samples that represent less-weathered sediment sources. Sample MN14-01A returned anomalously low weathering indices, possibly due to contamination, and is treated as an outlier and omitted from our analyses. MN14-01C is interpreted to represent an amalgamated snapshot of the late Holocene climate from the catchment above the collection site. The higher weathering indices for the weathered Miocene bedrock (saprolite) preserved beneath the 7.59 Ma basalt flow versus the much less- weathered alluvial fan deposits (also derived from the same sedimentary bedrock source as the

107 saprolite sample) suggests that chemical weathering of bedrock is reduced in the modern environment, consistent with the hypothesis that the late Miocene landscape along the north flank of the Khangay Mountains was likely somewhat warmer and more humid than during the late Quaternary. The degree to which this is true will be explored further in the following discussion of the two additional collection sites, for which I have more geochemical data.

2.5.2 Upper Orkhon section (>3 Ma)

At the Upper Orkhon section, sample results from the basalt cap, deep in the saprolite, and from the minimally weathered granite bedrock, all exhibit weathering indices with relatively low values, indicating that from a chemical weathering perspective they are relatively fresh. The values of weathering indices from the Upper Orkhon Section paleosol indicate low to moderate weathering of source rocks. The weathering results are homogeneous with no significant deviation in depth throughout either of the two paleosol sections, indicating that the paleosol underwent development and chemical weathering during a period of relatively stable climate. As observed from weathering indices from the Upper Orkhon section paleosols buried beneath 3.1 Ma basalt flow, versus the late Holocene floodplain sample (MN14-02B-06), it appears that the climate prior to ca. 3 Ma in the upper Orkhon basin was more favorable to chemical weathering than during the present, with likely somewhat more humid and warmer conditions.

2.5.3 Upper Chuluut section (>11.2 Ma)

Overall, the Upper Chuluut section is more variable than either of the two Orkhon sites, which may in part be related to the unweathered nature of the fine gravel (sample MN14-09). In contrast, the fine grained sediments from beneath the capping basalt flow at the Chuluut site exhibit nearly identical weathering patterns to the Upper and Middle section paleosol samples (Table 8). Based upon the weathering index values, the Upper Chuluut section paleosol indicates low to moderate weathering of Miocene fluvial sediments derived from weathering of Paleozoic and Mesozoic rocks eroded from further up in the drainage basin that were transported and deposited at the collection location some unknown amount of time prior to the emplacement of the 11.2 Ma capping lava flow.

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The different chemical indices, such as the CIA, CIW, PIA and K2O/Al2O3 ratio, suggest that the source rock of the Orkhon and Chuluut paleosol sections record a low to moderate degree of weathering, and extreme chemical weathering conditions likely did not exist in the source area for the time periods of interest, ca. 11 to 3 Ma, as constrained by the age of the capping lava flows. However, the geochemical weathering indexes do suggest that, relative to the late Holocene floodplain soil samples used as a proxy for the current chemical weathering regime of the northern Khangay Mountains, the late Miocene and mid Pliocene climate was slightly warmer and more humid than that of today. Overall, we can conclude that all three investigated sections exhibit consistent, moderate weathering patterns from the late Miocene to Pliocene.

Heavy rainfall, vegetation cover, relief, high surface temperature and high atmospheric CO2 are the major factors that control the intensity of chemical weathering of bedrock and sediments. Our overall synthesis of geochemical data of the upper middle Orkhon and upper Chuluut river paleosol sections suggests, low to moderate weathering in the source area, in the Miocene to

Pliocene. Low to moderate weathering in the source area does not support more CO2 enriched atmosphere and unusually high surface temperature in the absence of vegetation. Theoretically, atmospheric CO2 consumption has a direct correlation with the intensity of weathering (Hartmann et al., 2009). Therefore, the paleo-atmospheric condition in the north Khangay during the Miocene does not support a more carbon-enriched environment. However it is important to address issues such as origin of rock materials, sediment diagenesis, rock type and lithological classes.

2.5.4 Principal Component Analysis of Geochemical Results

Principal component analysis (PCA) is a statistical technique that combines a set of observations of possibly correlated variables into several independent or latent variables that underlie the multivariate data. Ideally, each component should be independent of other principal components. If this is the case, the output will provide a full account of the behaviors of multiple elements and may enable the extraction of a latent variable that indicates the degree of bedrock weathering independent of the geochemistry of the unweathered parent rock (Ohta et al., 2007). PCA loadings of rock and soil geochemical data are presented in Tables 10 through 12 with sub numbers and Figures 10 to 12. PCA analysis was performed on the complete sample dataset. The

109 first principal component (PC1) shows a high positive correlation with FeO components, as well as lower but positive relationship with MnO and high negative correlation with K2O oxides. The second principal component (PC2) correlates positively with CaO, P2O5, MgO, and TiO. PC-1 and PC-2 captures 80.55% and 10.61% of the total variability, respectively. Collectively, 91.16% of the total variability was explained by these two components, with a much smaller amount being explained by the subsequent principal component (5.39% for PC-3), which had high positive correlation with Al2O3.

PC-1 and PC-2 are illustrated in bi-plot form on Figures 10 through 12. Samples collected from weathering profiles plot parallel to the PC-1 coordinate. For our case, the higher positive PC-1 score indicates more oxidation due to higher FeO content (Figure 12a). Whereas PC-2 scores help to discriminate between higher or positive values for the relatively unweathered or fresh samples versus neutral to negative values for samples expected to have undergone more chemical weathering. All of the late Holocene flood plain (modern stream) samples, saprolite and granite, which are believed to be relatively unweathered, plot on the positive side of the PC- 2 axis with lower iron content, whereas the single basalt sample plots in positive territory for both the PC-1 and PC-2 regions, as it has a higher iron content (Figure 12a). Generally, samples that plot within the lower left corner of the axis are more weathered, since Al2O3 content directs that way (Figure 12b). Most of the paleosol samples display higher weathering indices, lower iron content, and less oxidation.

Results from the principal components analysis indicate that oxidation processes are a major factor defining the properties of the collected samples. It is especially noticeable for the Upper Chuluut study location. The trends of the weathering indices are also defined by the principal components, as PC-2 exhibits mostly higher-end negative correlation with more weathered samples, and positive correlations with relatively unweathered samples such as basalt, basal saprolite, granite, and late Holocene floodplain sediment samples. Data plotted on a A-CN-K ternary plot (Figure 15) indicates that the Middle and Upper Orkhon sections have similar trends with varying degrees of weathering rates dependent upon sample type (paleosol, granite, or late Holocene floodplain sediments). While data from the Upper Chuluut section concentrates to the left side of the triangle, indicating variable weathering.

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2.5.5 Al2O3 versus K2O

Results of an Al2O3 versus K2O analysis indicates that all the sediment samples of the Orkhon and Chuluut River sections plot close to the illite line (Figure 14). This indicates that the major

K2O and Al2O3 bearing minerals in all sediment samples are illite, suggesting decomposition of K-feldspars and muscovite during weathering under somewhat humid climate conditions as K remained fixed in clay minerals. K2O/Al2O3 ratios can be used to indicate how much alkali feldspar versus plagioclase and clay minerals were present in the original rock. The K2O/Al2O3 ratio of Orkhon samples range from 0.19-0.22 (avg. 0.21), while samples from the Chuluut locale vary from 0.08-0.13 (avg. 0.09) (Table 13). The sample values from the Orkhon section are typical for illite, because for K-feldspars this ratio has a typical range of 0.40-0.45 (Cox et al., 1995), indicating that alkali feldspar is present in source and illite has played a major role in the distribution of these elements in the sampled paleosol sediments. This also indicates that K- feldspar is present in the parent rock and may have altered to illite, which forms during weathering of source rocks (Nesbitt et al., 1980).

2.5.6 Diagenesis and Sediment Recycling

Investigations of geochemical weathering indices from soil and sediment samples needs to consider processes such as sediment recycling, diagenesis, illitization, and sample impurity or heterogeneity. Individually or in combination, these processes could result in an overestimation of the actual degree of chemical weathering at the study sites. Since the time of lava emplacement at each of the three sites, we envision that the paleosols, sediments, and weathered bedrock beneath the capping lava exist in an isolated, pseudo-closed system in which mixing with other materials and the addition of new material (e.g. loess) to these soil profiles ceased. During sample collection, we dug at least one meter deep horizontal pits into the hillslope in order to collect samples that were as pristine as possible. For the late Holocene-to-modern floodplain samples, we collected fine-grained (silt + clay) sediments that are fine enough (< 4 μm) that they should represent weathering indices correctly and they are an amalgamation of sources from the catchment above the collection site that are presently undergoing mostly physical weathering processes that predominate in the modern cold and dry climate. We have

111 minimal loess content in our samples and no significant presence of calcium carbonate that might result in erroneous interpretations of the results of the weathering indices. As all of these sites are near-surface, and we know their history thanks to the age of the capping lava flows; metasomatism and secondary illitization are very unlikely to have occurred at any of the three study sites. Finally, the CIA and CIW weathering indices are typically considered generally independent of sediment recycling (Garzanti et. al 2013).

One test of the primary versus recycled nature of sediments analyzed in a geochemical weathering proxy examination is to evaluate the Zr/Sc and Th/Sc ratios (Mahshar et al., 2011). Values for these ratios below 10 indicate that the sediments are minimally recycled (Table 14), as is our interpretation of the paleosol and late Holocene floodplain sediment samples from the three study sites, which return values between 4 and 8. In general, researchers have found that either the CIW or CIA-K indices are more reliable for the reconstruction of paleo-temperatures and moisture since they do not include potassium metasomatism and recycling from secondary sources (Yang et al., 2016).

2.5.7 Paleoclimate Estimates

2.5.7. 1 Paleo Mean Annual Temperature Reconstruction

Paleo-temperature reconstruction was attempted based on transfer functions derived from linear relationships between the weathering indices of our sample locations and the historic climate record from the closest meteorological stations to the three study locations (Table 15, Institute of Meteorology and Hydrology, Mongolia, Archive). For example, the Kharkorin meteorological station is paired with the late Holocene floodplain sediment sample from the Middle Orkhon section, which is 35 km to the south. The Upper Orkhon section floodplain samples are taken as an analogue to the weather station located in the town of Chuluut 120 km west of the sampled location. Despite the greater distance between the sample site and climate station of this pair, they are at similar elevations and environments. For the Upper Chuluut section, we utilize the Jargalant weather station, which is 50 km southwest of the sampled location and also at a similar elevation and environmental setting. We make the assumption that each of the chemical weathering indices from the late Holocene floodplain sediments are confined to relatively small 112 source areas and are altitude dependent. Therefore, they can be related to the mean annual temperature (MAT) and precipitation (MAP) as recorded at the above mentioned climate stations (Table 15). As the late Holocene floodplain sediments represent chemical weathering conditions occurring within the catchment during the current Ice Age (albeit that includes interglacials like the Holocene) where physical weathering dominates over chemical weathering and the development of thick soils, their resulting chemical weathering indices are comparatively lower than recorded in the paleosol samples at each site. Applying a linear regression between the Holocene floodplain soil chemical weathering indices and the MAT or MAP data from the chosen meteorological stations allows for an approximation of paleo-temperatures and moistures derived from the chemical weathering indices from the paleosol samples. A correlation matrix between each of the weathering indices using modern floodplain sediment samples is shown on Table 16. The different weathering indices of late Holocene floodplain samples are strongly correlated with each other.

We reconstructed the Mean Annual Temperature (MAT) using simple linear relations with a few different adjustments in order to test the variability, range difference, and standard deviation for each of the methods. Temperature is reconstructed using three different approaches as follows: 1) direct correlation, 2) a meteorological station – sample site altitude corrected version, and 3) a meteorological station – mean catchment altitude corrected version. All three approaches are described in the next section.

MAT Reconstruction via Direct Correlation. We reconstructed the paleo MAT based on the correlation coefficient between the historic meteorological station MAT and our chemical weathering index results from late Holocene floodplain soils (Table 17).The reconstructed paleo- MAT values for each study location using the direct meteorological station-to-sample site correlation method is presented in Tables 18-A-E. At the Middle Orkhon section, an argillite sample beneath Ar40/Ar39 dated basalt flow indicates MAT of 11.6±1.7 °C during the chemical weathering of the Paleozoic argillite as it was exposed in a near-surface environment prior to burial by the lava flow at 7.6 Ma (Table 18-B). This result is ~ 10°C warmer than the MAT today at the middle Orkhon collection site. The VRI-based reconstruction resulted in the highest MAT estimate for the Middle Orkhon site, while the CIW-based reconstruction was the lowest.

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In general, reconstructed MAT from the Middle Orkhon fall within a relatively small range (Table 18).

Our reconstruction of MAT for the upper Orkhon section as recorded in the granite paleosol and saprolite preserved beneath the 3 Ma lava flow show temperatures 6 to 15° warmer than at present, depending upon which weathering index is utilized for the reconstruction (Table 18. C, D). In contrast, the late Holocene floodplain sample returns a relatively low MAT that is essentially the same as the MAT from the nearby meteorological station. Individual sub-sampled sections of the paleosol between the overlying basalt flow and underlying saprolite return generally consistent warmer-than-present results. Stratigraphic Section A of the Upper Orkhon location (Table 18-C) exhibits an integrated MAT of 8.4±2.1°C, while Stratigraphic Section B, only several hundred meters distant, (Table 18-D) returned an MAT value of 7.1±2.5 °C. This indicates that for some amount of time prior to the 3 Ma age of the lava flow and that spans a long enough time to develop a substantial soil-to-saprolite section onto of granite that the regional climate was warmer along the north flank of the Khangay Mountains than it is a present. The standard errors of the average MAT is calculated by combining (averaging) each of the individual samples from the profiles and calculating the resulting standard deviation of these values.

The Upper Chuluut section (> 11.2 Ma) (Table 18-E) exhibits consistent reconstructed MAT values that are similar to our other two study locations. The paleo MAT values here indicates results of 6.9±3.0 °C. Reconstructed MAT values for the late Holocene floodplain sample from the Upper Chuluut section range from -4.5±0.1 °C, close to the -4.1 ° C MAT recorded from our comparative climate station in the town of Jargalant. The Upper Chuluut study site is the highest of the three, and the mean elevation of the catchment above the study site is also the highest, thus it should not be surprising that the late Holocene floodplain sample collected at Upper Chuluut returned the coldest reconstructed late Holocene MAT of the three floodplain samples in the study.

MAT Reconstruction via an Altitude-Corrected Approach. The altitude corrected version of the MAT reconstructions requires the method of finding the temperatures at the sampled location based on the elevation-dependent atmospheric lapse rate for dry air of 9.8 °C/km, using the elevation difference between the sample location and that of the meteorology stations used as a

114 proxy for the climate conditions at each study site. Rather than using the direct correlation of modern temperatures with the MAT values from the meteorological stations, the station MAT values are adjusted in line with the elevation difference between the meteorological site and study site. By using this method, correlation coefficients increased over those calculated using the direct correlation method, and from this, we assume that it improves the accuracy of the reconstructed paleo-MAT values (Table 19). Table 20-A illustrates the reconstructed MATs for the late Holocene floodplain sediments and their deviation from modern MAT for each of the chemical weathering indices. Note that VRI has the highest correlation and accuracy. The reconstructed paleo-MAT values for each sites using the altitude corrected method are reported in Tables 20-A-E. These were 12.2±1.5°C for the Middle Orkhon, 8.6±2.3°C for the Upper Orkhon and 7.4±1.6°C for the Upper Chuluut. The standard deviation increased for some of the reconstructed paleo-MAT values in comparison to the initial direct comparison approach presented in the previous section. The paleo-MAT standard errors were calculated using all of the samples averaged as a continuous single profile from which the standard deviations were calculated. If we consider that the relative modern day elevation difference between the sample locations and meteorological stations has not changed significantly since the Miocene due to relatively dormant tectonic activity, than the altitude dependent lapse rate of the paleo-MAT is not dramatically different than that of modern elevations. The correlation error is deemed to be ±20 percent for CIA, PIA and CWI, as the coefficients range between 0.77-0.79 respectively, while VRI has relatively low correlation error that is within ±10 percent.

MAT Reconstruction via a DEM-based Mean Altitude Corrected Approach The digital elevation model (DEM)-based mean altitude corrected version of the MAT reconstruction assumes that the late Holocene floodplain sediments were transported from higher altitudes within the catchment than the elevations of the sampling locations. The mean elevation of each of the catchments above each sampling location was identified from a 90 m SRTM-based DEM utilizing the hydrologic modeling routines within ArcGIS. First, the modern floodplain sampled altitude was correlated with each of the chemical indices. Then, the MAT values based upon the chemical indices were re-calculated based upon the DEM-derived mean catchment elevations, which then allowed for the development of a simple linear regression equation for reconstructing paleo-temperatures based on each of the chemical weathering indices. This approach is based upon the assumption that, the fine-grained sediment collected at the floodplain

115 sites was transported from source regions higher up in the catchments. The uncertainties for this method increased slightly in comparison to the other two reconstruction techniques; therefore, it is deemed as being slightly less accurate than the other two. The results are 15.0±2.0°C for Middle Orkhon, 10.6±2.6°C for Upper Orkhon, and 9.8±3.5°C for Upper Chuluut. Results for the DEM-based MAT reconstructions are reported on Table 21-A-E.

2.5.7.2 Paleo Mean Annual Precipitation Reconstruction

The reconstruction of paleo-MAP utilizes linear relationships between the weathering indices of the late Holocene floodplain samples and the historic precipitation data using the same study site–meteorological station pairs as were utilized for the paleo-MAT restrictions (Table 15). For estimating the paleo-precipitation, we calculated the correlation coefficient based transfer function using a similar methodology as was applied to the temperature reconstructions. The Middle Orkhon study location resulted in an estimated MAP from the pre-7.6 Ma weathering horizon of 507±26.1 mm. For the Upper Orkhon (>3 Ma) section sites A and B, paleo-MAP estimates are between 451±50.1 mm (Table 22 C, D). This range is nearly double the modern MAP from the Chuluut meteorological station. The paleo-MAP reconstruction for the Upper Chuluut (>11.2 Ma) section (Table 22-E), returned consistent results ranging between 430±65 mm, very similar to the other two study locations.

Our paleoclimate reconstruction using transfer functions is based on modern MAT and MAP from the closest climate stations to our sample collection sites and modern weathering rates inferred from the Late Holocene floodplain sediments from the Middle and Upper Orkhon and Upper Chuluut research sites. The details of the transfer function results are presented above, but in summary our analysis indicates that MAT was at least 5 to 10° C warmer during most of late Miocene through Pliocene on the northern flank of the Khangay Mountains at elevations today that range between 1,560 to 2,222 m. As inferred from paleogeographic maps, the Khangay occupied a temperate zone in the Eurasian continental interior during late Miocene (Ronov et al., 1984; 1989). Several researchers (An et al., 2001; Molnar et al., 2005) suggested that the Himalayas were not as high as they are today during this period, which would allow increased moisture transport via monsoonal air masses into the continental interiors. In addition, global average sea surface temperature was warmer during the period of interest than today, which

116 influenced global atmospheric circulation and moisture advection into continental Asia (Herbert et al., 2016). During this period, the northern flank of the Khangay Mountains, and by inference central Mongolia, probably had less extreme annual temperature differences than today. With respect to regional precipitation estimates based upon the chemical weathering proxies from the three study sites, the results allow for an approximate doubling of MAP along the northern side of the Khangay Mountains that is consistent with a longer warm season. Increased moisture during the late Miocene to middle Pliocene may have derived both from enhanced monsoonal penetration into central Mongolia both from the Indian Ocean and East China Sea as well as an enhanced rain shadow effect that developed post 5 Ma with the uplift the Altai and Sayan ranges to the west and north of the Khangay, respectively (Caves et al., 2014; Arzhannikova et al., 2011). A study of the mineralogy and geochemistry of Oligo-Miocene sediments from the Valley of the Lakes to the south of the Khangay Mountains by Ricoz et al. (2016) concluded that Late Cenozoic basin sediments are mostly of aeolian origin from increased dust, consistent with regional climate change dominated by gradual cooling and drying. Voigt and colleagues (2017) concluded that the continental settings of Central Asia experienced increased desertification during the late Cenozoic as a result of regional mountain uplift. Proxy records from various basins in Central Asia provide evidence that climate was warmer and more humid during the mid-Miocene compared to the late Pliocene (e.g., Guo et al., 2002; Sun & Windley, 2015). While our paleo-precipitation proxy based upon chemical weathering indices from soil profiles and weathered bedrock and sediments preserved beneath basalt flows suggests that the northern Khangay would have represented the dry end of modern sub-tropical climate regime (UN FAO Sustainable Development Department, 2002). The paleo-MAP during the late Miocene probably was not as intense as modern sub-tropics because of distance from moisture sources and proximity to moisture-blocking elevated topography; albeit that was likely somewhat less elevated than it is today.

2.5.7.3 Paleoprecipitation indicator CIA (molar)/K2O/Na2O ratio

Goldberg et al. (2010) used an approach to approximate precipitation patterns from various sources of paleo and modern weathering profile samples from various climatic regions. Tropical regions exhibited high ratios of K2O/Na2O vs CIA molar ratio, while the subtropics, temperate

117 and drier regions exhibit relatively low ratios (Figure 24-A-B). Following the approach of

Goldberg et al. (2010), we plotted our data on the same CIA (molar)/K2O/Na2O plots and found that the late Holocene floodplain samples fall into what is typical for dry or arid regions. In contrast, our paleosol samples plot as being geochemically similar to modern samples from around the world collected from the cool end of the subtropics (Figure 24-A-B, 25-A-B). Overall, the modern and paleo ratio results generally match with our transfer-function reconstructions of MAT and MAP.

The molar ratio results are not separated too far apart on the discrimination plots from each other, signifying that during the late Miocene and Pliocene, climate was somewhat more humid and a few degrees warmer than experienced across the northern Khangay today, consistent with the study area location centered at relatively high elevations (> 1500 m) and in the interior of the world’s largest continental land mass. We assume tectonic uplift is negligible since late Miocene for the Khangay (Caves et al., 2014).

Comparison of various other proxy techniques for paleoenvironmental reconstruction could be evaluated against our results from the north flank of the Khangay Mountains. Sheldon et al. (2009) compiled the major paleo-proxy approaches that are applicable to paleo-environmental reconstruction. Some of the paleosol and major/minor element based approaches were used in order to test the validity, for example of paleo-precipitation approaches. However, most of these methods are either soil specific, condition specific, or site specific; therefore, the results did not match well with modern data. Stable isotope (δ18O, δ13C,) based approaches are likely feasible; however, we did not investigate the stable isotopic composition of our samples.

2.5.8 CIA vs K2O/(Na2O+CaO*) ratio

For the late Holocene floodplain sediments of the Orkhon and Chuluut rivers, the CIA values are 2 well correlated (r =0.99) with the K2O/(Na2O + CaO*) molar ratio as a function of the mean elevation of the catchment above the sample collection point (Table 13, Figure 26). This trend suggests that changes of the mineralogy between an end-member rich in plagioclase (high content of Na and Ca) and another rich in K-feldspar, micas, and illite as a function of either sample collection elevation or mean catchment elevation (Liu et. al 2007). Increase in the

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K2O/(Na2O + CaO) molar ratio is associated with CIA increase, suggesting a preferential hydrolysis of plagioclase (enrichment of Na) relative to K-feldspar and micas (enrichment in K) during the silicate weathering process of river sediments. A linear correlation of CIA with the

K2O/(Na2O + *CaO) ratio also demonstrates that the degree of chemical weathering is strongly dependent on altitude for modern floodplain sediments (Liu et. al 2007).

Illite appears to be the most abundant clay mineral in the Khangay drainage basins (Figure 14). Illite is considered as a primary mineral, which reflects decreased hydrolytic processes in continental weathering and increased direct rock erosion under cold and arid climatic conditions. In our environment, illite could have been derived from physical erosion of metamorphic and granitic parent rocks, mainly located at high elevations in the Khangay. The history of tectonic activity and river incision from the late Miocene to present in the Khangay suggests that primarily global cooling combined with a dominant dry and cold climate prevailed in the basin headwaters, which may have significantly increased physical erosion. These weathering processes likely resulted in the high content of illite (primary mineral) as found in samples analyzed. These statements suggest that modern elevation was relatively indifferent since the late Miocene in the Khangay and the weathering regime has changed from more chemical to physical under relatively stable tectonic conditions, perhaps due to other external factors such as transformations of the global climate system manifest at the regional scale.

2.5.9 Comparison to other paleo-proxy records in the region

The results presented in this chapter generally correspond with proxy data sets in numerous locations indicating substantial global cooling at the Pliocene-Quaternary boundary. Perhaps one of the best records for continental central Asia comes from the 12 million years of sediment records recovered from Lake Baikal, located ~600 km north of our study area and that serves as the receiving basin for the Selenga River. The Baikal core and resulting proxy-based studies revealed that since 12 Ma the climate in southern Siberia and the Selenga catchment underwent cooling with minor short-term fluctuations (Kashiwaya et al., 2001). According to reports from Baikal Drilling project group (2000), the climate change resulted from changes in astronomic forcing that occurred during the deposition of the section, i.e., from the early Miocene (10.2 Ma ago). However, the amplitudes of these fluctuations in the Miocene were lower, possibly because 119 of a smaller annual difference between the cold and warm periods. The report suggests that conifer tree species were broadly distributed across the region during the warmer subtropical climate of the Miocene.

In addition, a high-resolution multi-proxy climatic record from the precise magnetostratigraphic dated Hongbaishan section in the central Taklimakan Desert in northwest China is interpreted as demonstrating that a fundamental climate change, characterized by significant cooling, enhanced aridity, and intensified atmospheric circulation, occurred at 2.8 Ma (Wang et al. 2013). This region is located in central Asian orogenic belt that includes the Tian Shan, Altay and Khangay Mountains. Good correlations between paleo-environmental records in the dust sources and downwind areas suggest a broadly consistent climate evolution of northwestern China during the late Cenozoic, which is probably driven by the uplift of the Tibet Plateau and glaciation of the Northern Hemisphere.

Caves et al. (2014) provided evidence that the late Miocene interaction of the mid latitude atmospheric jets with actively uplifting northern Central Asian (Tian Shan and Altay) ranges resulted in a reorganization of Central Asia climate. This reorganization gave rise to starkly different seasonal precipitation regimes, further drying interior China and southern Mongolia, and thus increasing the incidence of lee cyclones that deposit dust across the Chinese Loess Plateau. Caves et al. (2014) conclude that paleoclimatic changes in Central Asia in the Neogene are more tightly controlled by the interaction of the mid-latitude westerlies with the bounding ranges of northern Central Asia than by changes in the height or extent of the Tibetan Plateau. These results of Central Asian paleo-proxy records from surrounding areas of the Khangay suggest that Khangay likely also experienced comparable climatic trend during late Cenozoic, consistent with the cooling and drying observed in weathered sediments and soils preserved beneath basalt flows from the three study sites versus the modern climatology at these locations.

2.6 Conclusions

Our goal is to understand the landscape evolution, surface hillslope processes, and paleo- weathering regimes during the Late Cenozoic, particularly the late Miocene and Pliocene epochs in the Khangay Mountains of central Mongolia. In order to reflect this, integrated mean paleo-

120 climate conditions in the region were reconstructed using chemical weathering indices from paleosols preserved beneath basalt flows previously dated by Ar39/Ar40 geochronology. The reconstructed paleo-weathering regime estimates from this study assist not only in constraining the climate conditions once dominant in this continental interior as far back as ~12 Ma, but also as insight into the likely timing of the regional transition from transport-limited to weathering- limited hillslopes, which is a necessary constraint for estimating Quaternary erosion and the volume of sediment now stored in valleys draining the Khangay Mountains. Our late Holocene floodplain sediment samples returned very low chemical weathering indices, as would be expected given the cold, dry climate typical along the northern flank of the Khangay Mountains where physical weathering processes and landforms dominate the environment. Importantly, we did recognize relative differences in terms of the altitude at which the late Holocene floodplain samples were collected, with lower-elevation samples with slightly warmer mean annual temperatures returning higher chemical weathering indices compared to higher- elevation samples. The correlation coefficient of altitude dependent chemical weathering indices ranged from r= -0.86 to r=-0.99, signifying the high negative correlation between the two variables (Table 9-C). This observation implies that chemical weathering indices may be suitable for determining altitude-dependent climate conditions in the northern Khangay Mountains in a tectonically dormant environment.

The patterns of chemical weathering indices from the Middle Orkhon (7.6 Ma yrs), Upper Orkhon (3.1 Ma yrs) and Upper Chuluut River (11.2 Ma) study locations indicates at least 15 to 20% higher weathering indices than in comparison to the modern day analogue. These results were consistent and nearly identical for all three sections, and indicate that for the north flank of the Khangay Mountains, the mean climate was somewhat warmer and more humid during the late Miocene and Pliocene than today. The reconstructed paleoclimate results indicate that it was at least 5 to 10° C warmer MAT than today with nearly double the amount of MAP (550-600 mm), in relation to modern (late Holocene) climate. We believe that during the late Cenozoic, this slightly warmer and more humid climate spans for at least a few million years. The climate during these periods was likely less variable between seasons, with a slight cooling trend towards the Quaternary. However, additional proxy investigations are needed to in order to verify and expand our research results.

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Generally, a chemical weathering based analysis for reconstructing the integrated mean state of the climate during the period when the bedrock was weathering and a soil was forming is deemed a practical approach. Based upon our analysis of paleosols and weathered bedrock (saprolite) preserved beneath capping lava flows at select sites in the Khangay Mountains, we concluded that throughout the span of ~12 Ma years covering late Miocene and Pliocene, the climate was slightly warmer and more humid than that of today. Following the global trend, throughout the late Miocene and Pliocene epochs, this warm climate was generally stable with negligible fluctuations. Sometime after the 3 Ma age indicated by the Upper Orkhon study site, regional climate transitioned to a cooler and drier state with greater annual variability. The very general outline of climate conditions recovered from the geochemical analysis of these sparsely preserved Khangay hillslope paleo-weathering profiles are similar in outline with both the long- term sedimentary record from Lake Baikal and the well-established latest Cenozoic global cooling. We believe that our research delivers a first-of-its kind dataset linking late Cenozoic climate change to upland weathering and sediment production regimes in the Khangay Mountains of central Mongolia.

2.7 Acknowledgements

We would like to thank Narangerel Mandah for his excellent driving and navigation throughout central Mongolia, and Stephen Smith and Nathan Lyons for their camaraderie and support in the field. I also would like to thank Ron Fodor at NCSU for his advice for calculating Major Element analysis. This work was supported by U.S. National Science Foundation Research Grants EAR- 1009702 and EAR-1009680.

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2.8 References

An, Z. S., Kutzbach, J. E., Prell, W. L. and Porter, S. C., 2001, Evolution of Asian monsoons and phased uplift of the Himalaya-Tibetan plateau since Late Miocene times. Nature 411, p. 62–66.

Ancuta, L.D., Zeitler, P.K., Idleman, B.D., and Jordan, B.T., 2018. Whole-rock 40Ar/39Ar geochronology, geochemistry, and stratigraphy of intraplate Cenozoic volcanic rocks, Central Mongolia: Geological Society of America Bulletin. v. 130, p. 1397-1408

Arzhannikova, A., Arzhannikov, S., Jolivet, M., Vassallo, R., and Chauvet, A., 2011, Pliocene to Quaternary deformation in South East Sayan (Siberia): Initiation of the Tertiary compressive phase in the southern termination of the Baikal Rift System: Journal of Asian Earth Sciences, v. 40, p. 581-594.

Baikal Drilling Project Group 2000, Late Cenozoic Paleoclimate record in bottom sediments of Lake Baikal, (600 m deep drilling data). Russian Geology and Geophysics, Vol. 41, No. 1, pp. 1-29

Bechstädt, T., H. Jäger, J., G. Spence, G. Werner, G., 2009. Late Cryogenian (Neoproterozoic) glacial and post-glacial successions at the southern margin of the Congo Craton, northern Namibia: facies, palaeogeography and hydrocarbon perspective., Global Neoproterozoic Petroleum Systems: The Emerging Potential in North Africa, Geol. Soc. Lond., Spec. Publ, 326, p. 255-287

Bhalburg, H., N. Dobrzinski, 2011. A review of the Chemical Index of Alteration (CIA) and its application to the study of Neoproterozoic glacial deposits and climate transitions, The Geological Record of Neoproterozoic Glaciations. Geological Society, London, Memoir, p. 81-93

Buggle, B., B. Glaser, U. Hambach, N. Gerasimenko, S. Markovic., 2011. An evaluation of geochemical weathering indices in loess-paleosol studies, Quaternary International 240, p.12-21

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Caves, J.K., Sjostrom, D.J., Mix, H.T., Winnick, M.J., and Chamberlain, C.P., 2014. Aridification of Central Asia and uplift of the Altai and Hangay Mountains, Mongolia: Stable isotope evidence: American Journal of Science, v. 314, p. 1171-1201.

Caves, J.K., Winnick, M.J., Graham, S.A., Sjostrom, D.J., Mulch, A., and Chamberlain, C.P., 2015. Role of the westerlies in Central Asia climate over the Cenozoic: Earth and Planetary Science Letters, v. 428, p. 33-43.

Caves, J. K., B. U. Bayshashov, A.Zhamangara, A. J. Ritch, D. E. Ibarra, D.J.,Sjostrom, H. T. Mix, M. J., Winnick, Chamberlain,C. P., 2017, Late Miocene Uplift of the Tian Shan and Altai and Reorganization of Central Asia Climate GSA Today,Volume 27 Issue 2, p. 19- 26

Ceryan, S., 2012. Weathering Indices for Assessment of Weathering Effect and Classification of Weathered Rocks: A Case Study from NE Turkey, Earth Sciences, p.19-45.

Cox, R., D.R. Lowe and R.L. Cullers. 1995. The influence of sediment recycling and basement composition on evolution of mudrock chemistry in the southwestern United States, Geochim. Cosmochim. Acta, 59/14, p. 2919-2940

Cunningham, W.D., 2001. Cenozoic normal faulting and regional doming in the southern Hangay region, Central Mongolia: implications for the origin of the Baikal rift province: Tectonophysics, v. 331, p. 389-411

Dessert, C., B. Dupre, J. Gaillardet, L. M. Francois, C. J. Allegre., 2003. Basalt weathering laws and the impact of basalt weathering on the global carbon cycle, Chemical Geology 202, p.257-273

Dong, T. Y.; Il'icheva, L.; Nittrouer, J. A.; Pavlov, M. 2013. Morphology and Sediment Transport Dynamics of the Selenga River Delta, Lake Baikal, Russia, American Geophysical Union, Fall Meeting 2013, abstract #EP31A-0844

Drysdale, J., 2008. Element mobility as a result of chemical weathering in a granitoid near Valparaiso, Chile, Unpublished Master’s Thesis, Faculty of Science, Dalhousie University, Halifax, Nova Scotia

124

Fedo, C.M., Nesbitt, H.W. and Young, G.M., 1995. Unravelling the Effects of Potassium Metasomatism in Sedimentary Rocks and Paleosols, with Implications for Palaeo- Weathering Conditions and Provenance. Geology, 23, 921-924

Fox, M., 2008. Geomorphology and active faulting within the Khangay Mountains, Central Mongolia, MSc. Thesis, Department of Earth Sciences, University of Oxford

Garzanti, E., Padoan, M., Setti, M., Najman, Y., Peruta, L., and Villa, I.M., 2013, Weathering geochemistry and Sr-Nd fingerprints of equatorial upper Nile and Congo muds: Geochemistry Geophysics Geosystems, v15. p.295-316

Goldberg K., M. Humayun., 2010. The applicability of the Chemical Index of Alteration as a paleoclimatic indicator: An example from the Permian of the Parana Basin, Brazil, Palaeogeography, Paaleoclimatology, Palaeoecology 293, p. 175-184

Guo, Z.T., Ruddiman, W.F., Hao, Q.Z., Wu, H.B., Qiao, Y.S., Zhu, R.X., Peng, S.Z., Wei, J.J., Yuan, B.Y. and Liu, T.S., 2002. Onset of Asian desertification by 22 My ago inferred from loess deposits in China. Nature, 416, p. 159–163

Harnois, L., 1988. The CIW Index: A New Chemical Index of Weathering. Sedimentary Geology, 55, p.319-322, http://dx.doi.org/10.1016/0037-0738(88)90137-6

Hartmann, J., N. Jansen., H. Durr., S. Kempe., P. Köhler., 2009. Global CO2-consumption by chemical weathering: What is the contribution of highly active weathering regions?, Global and Planetary Change, Vol. 69, Issue 4, p.185-194

Hellwig, A., Voigt, S., Mulch, A., Frisch, K., Bartenstein, A., Pross, J., Gerdes, A. and Voigt, T., 2018. Late Oligocene to early Miocene humidity change recorded in terrestrial sequences in the Ili Basin (south-eastern Kazakhstan, Central Asia). Sedimentology, 65: 517–539. doi:10.1111/sed.12390

Herbert T.D., K.T. Lawrence, A. Tzanova, L.C. Peterson, R. Caballero-Gill, C.S. Kelly, Late Miocene global cooling and the rise of modern ecosystems. Nature Geoscience. 2016; 9: 843±847. https://doi.org/10.1038/NGGO2813

125

Höck, V., Daxner-Höck, G., Schmid, H.P., Badamgarav, D., Frank, W., Furtmüller, G., Montag, O., Barsbold, R., Khand, Y., and Sodov, J., 1997. Oliogocene-Miocene sediments, fossils and basalts from the Valley of Lakes (Central Mongolia) – An integrated study: Mitteilungen der Österreichischen Geologischen Gesellschaft, v. 90, p. 83-125

Hyland, E., Sheldon N. D., and M. Fan., 2013, Terrestrial paleoenvironmental reconstructions indicate transient peak warming during the early Eocene climatic optimum GSA Bulletin; v.125; no. 7/8; p.1338–1348; doi: 10.1130/B30761.1

Hyland, E., Sheldon, N.D., Van der Voo, R., Badgley, C.E., Abrajevitch, A., 2015, A new paleoprecipitation proxy based on soil magnetic properties: implications for expanding paleoclimate reconstructions: GSA Bulletin 127, p.975–981. doi: 10.1130/B31207.1

Hyland, E., Sheldon, N.D., 2016, Examining the spatial consistency of paleosol proxies: implications for paleoclimatic and paleoenvironmental reconstructions in terrestrial sedimentary basins: Sedimentology, doi: 10.1111/sed.12245

Kashiwaya, K., S. Ochiai., H. Sakai., T. Kawai., 2001. Orbit-related long-term climate cycles revealed in a 12-Myr continental record from Lake Baikal, Nature 410, p.71-74

Li, C., S. Yang., 2010. Is chemical index of alteration (CIA) a reliable proxy for chemical weathering in global drainage basins? American Journal of Science, Vol 310, p.111-127

Liu, Z., C. Colin, W. Huang, K. P. Le, S. Tong, Z. Chen, and A. Trentesaux, 2007. Climatic and tectonic controls on weathering in south China and Indochina Peninsula: Clay mineralogical and geochemical investigations from the Pearl, Red, and Mekong drainage basins, Geochem. Geophys. Geosyst., 8, Q05005, doi:10.1029/2006GC001490

Lyons, N.J., Wegmann, K.W., Gosse, J.C., and Bayasgalan, G., 2013. Generation of summit plateaus by periglacial processes: Implications for tectonic interpretations: Geological Society of America Abstracts with Programs, v. 45, p. 121

Mahshar, R , Ahmad, A. H. M. , S. Khan, M. and F. Khan, 2011. Geochemistry and detrital modes of Proterozoic sedimentary rocks, Bayana Basin, north Delhi fold belt: implications for provenance and source-area weathering', International Geology Review.

126

McDannell, K. T., L.D. Ancuta., S. G. Smith., P. K. Zeitler., 2013. Long-term landscape evolution in the Khangay Dome, Mongolia, AGU fall meeting, Conference paper

McDannell, K.T., Zeitler, P.K., and Idleman, B.D., 2018, Relict topography within the Hangay Mountains in central Mongolia: Quantifying long-term exhumation and relief change in an old landscape: Tectonics, doi:10.1029/2017TC004682.

Mohammadi, N., S. Rais, T. Habib, 2014. Provenance, tectonic setting and paleoclimate of Upper Bhander sandstone in parts of Raisen district, Madhya Pradesh (India), International Journal of Advancement in Earth and Environmental Science, Vol 2, No.2 p.39-45

Molnar, P. Mio-Pliocene growth of the Tibetan Plateau and evolution of East Asian climate., 2005, Palaeontol. Electron. 8, p.1–23.

Nesbitt, H.W., 1979. Mobility and fractionation of rare earth elements during weathering of a granidiorite. Nature, 279, p.206-210

Nesbitt, H.W. &Young G.M., 1982. Early Proterozoic climates and plate motions inferred from major element chemistry of lutites. Nature. 299. p.715-718

Nesbitt, H. W., G. M. Young, S. M. McLennan, and R. R. Keays., 1996. Effects of Chemical Weathering and Sorting on the Petrogenesis of Siliciclastic Sediments, with Implications for Provenance Studies, The Journal of Geology 104, no. 5, p.525-42. http://www.jstor.org/stable/30065140.

Nesbitt, H.W., Fedo, C.M., Young, G.M., 1997. Quartz and feldspar stability, steady and nonsteady-state weathering and petrogenesis of siliciclastic sands and muds. Journal of Geology, 105, 173-191

Nesbitt, H.W., Young, G.M., 1989. Formation and diagenesis of weathering profiles. Journal of Geology, 97:2, p.129-146

Ohta, T., H. Arai., 2007. Statistical empirical index of chemical weathering in igneous rocks : A new tool for evaluating the degree of weathering, Chemical Geology 240, p.280-297

127

Oliva, P., J. Viers, B. Dupre., 2003. Chemical weathering in granitic environments, Chemical Geology 202, p.225-256

Price, J. R., M. A. Velbel., 2003. Chemical weathering indices applied to weathering profiles developed on heterogeneous felsic metamorphic parent rock, Chemical Geology 202, p.397-416

Prokopenko, A. A., E. B. Karabanov., D. F. Williams., 2002. Geomorphology (Communication arising): Age of long sediment cores from Lake Baikal, Nature 415, 976

Rea, D. K., Snoeckx, H. & Joseph, L. H. Late Cenozoic eolian deposition in the North Pacific: Asian drying, Tibetan uplift, and cooling of the Northern Hemisphere., 1998 Paleoceanography 13, p.215–224.

Richoz, S., A. Baldermann, A. Frauwallner, M. Harzhauser, G. Daxner-Höck, D. Klammer, W. E. Piller., 2017. Geochemistry and mineralogy of the Oligo-Miocene sediments of the Valley of Lakes, Mongolia, Palaeobiology Palaeoenvironment, i.97, p.233–258

Ronov, A., Khain, V., Balukhovsky, 1989. Atlas of Lithological Paleogeographical Maps of the World, Mesozoic and Cenozoic of Continents and Oceans. USSR Academy of Sciences, Leningrad, 79p.

Sahagian., D., A. Proussevitch, L. D. Ancuta, B. D. Idleman, and P. K. Zeitler., 2016. Uplift of Central Mongolia Recorded in Vesicular Basalts., The Journal of Geology, 124:4, p. 435- 445

Savatenkov., V. M., V. V. Yarmolyuk, E.A. Kudryashova, A. M. Kozlovsky, 2010. Sources and geodynamics of the Late Cenozoic volcanism of Central Mongolia: Evidence from Isotope-Geochemical studies, Petrology, Vol 18, No.3, p.278-307

Shao, J., S. Yang, C. Li., 2012. Chemical indices (CIA and WIP) as proxies for integrated chemical weathering in China: Inferences from analysis of fluvial sediments, Sedimentary Geology 265-266, p.110-120

Sheldon, N D., Tabor, N. J., 2009. Quantitative paleoenvironmental and paleoclimatic reconstruction using paleosols., Earth-Science Reviews 95, p.1–52

128

Sheldon, N.D., 2003. Pedogenesis and geochemical alteration of the picture gorge subgroup, Columbia River Basalt, Oregon. Geological Society of America Bulletin 115, p.1377– 1387.

Shinpaugh, J. E., 2013. Geochemical signatures of potential sediment sources to Lake Quinault, Washington, MSc. Thesis, Department of Marine Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina

Smith, S.G., K. W. Wegmann., G. Bayasgalan., L. Ancuta., J. Gosse., 2013. Using Landsat Imagery and GIS to constrain the evolution of the Khangay dome, Mongolia, AGU poster

Smith, S.G., Wegmann, K.W., Ancuta, L.D., Gosse, J.C., and Hopkins, C.E., 2016. Paleotopography and erosion rates in the central Hangay Dome, Mongolia: Landscape evolution since the mid-Miocene: Journal of Asian Earth Sciences, v. 125, p. 37-57

Sun, J., Ye, J., Wu, W., Ni, X., Bi, S., Zhang, Z., Liu, W. and Meng, J., 2010. Late Oligocene- Miocene mid-latitude aridification and wind patterns in the Asian interior. Geol. Soc. Am., 38, 515–518

The Baikal Drilling Project Group, 1998, A continuous record of climate changes for the last five million years from the bottom sediments of Lake Baikal, Geologiya i Geofizika (Russian Geology and Geophysics), vol. 39, no. 2, p. 139(135).

The National Institute of Meteorology and Hydrology Archive, Mean Annual Temperature and Precipitation data for selected stations

Tomurtogoo, O., 1999. Geological Map of Mongolia: Ulaanbaatar, Mongolian Academy of Sciences, scale: 1:1,000,000

United Nations, Food and Agricultural Organization, Sustainable Development Department, Global climate maps report, 2002, https://www.climate-charts.com/World-Climate- Maps.html

Voigt, S., Y. Weber, K. Frisch, A. Bartenstein, A. Hellwig, R. Petschick, A. Bahr, J. Pross, A. Koutsodendris, T. Voigt, V. Verestek and E. Appel., 2017. Climatically forced moisture

129

supply, sediment flux and pedogenesis in Miocene mudflat deposits of south-east Kazakhstan, Central Asia, The Depositional Record; 3(2): 209–232 doi: 10.1002/dep2.34

Walker, R.T., Wegmann, K.W., Bayasgalan, A., Carson, R.J., Elliott, J., Fox, M., Nissen, E., Sloan, R.A., Williams, J.M., and Wright, E., 2015. The Egiin Davaa prehistoric rupture, central Mongolia: a large magnitude normal faulting earthquake on a reactivated fault with little cumulative slip located in a slowly deforming intraplate setting: Geological Society, London, Special Publications, v. 432, p. 187-212

Wang, X., H. Sun, F. Wang, B. F. Li, S. Wu, F. Guo, Z. J. Li, Y. B. Zhang, and F. H. Chen., 2013, A high-resolution multi-proxy record of late Cenozoic environment change from central Taklimakan Desert, China, Climate of the past, 9, p.2731–2739,

Wegmann, K.W., Mendelson, T., Sabaj-Pérez, M.H., Smith, S.G., Lopresti, M., Gosse, J., Gallen, S.F., and Bayasgalan, G., 2015. Constraining the Late Cenozoic topographic evolution of central Mongolia (Hangay Mtns) using landscape metrics and sequence divergence of freshwater fishes: Geological Society of America Abstracts with Programs, v. 47, p. 35

West, A. J., M. Fox, R.T Walker, G. Bayasgalan., A. Carter., 2013. Links between climate, erosion, uplift and topography during intracontinental mountain building of the Khangay Dome, Mongolia, Geochemistry Geophysics Geosystems, Vol 14, No. 12, p. 5171-5193

Yang, J., P.A. Cawood, D, Yuansheng, L. Wenqian Y. Jiaxin, 2016. Reconstructing Early Permian tropical climates from chemical weathering indices, GSA Bulletin; May/June 2016; v. 128; no. 5/6; p. 739–751

Yarmolyuk, V. V., E.A. Kudryashova, A.M. Kozlovsky, V. M. Savatenkov, 2007. Late Cretacesous-Early Cenozoic Volcanism of Southern Mongolia: A trace of the south Khangay mantle hotspot, Journal of Volcanology and Seismology, Vol. 1, No. 1, p.1-27

Yarmolyuk, V.V., Kudryashova, E.A., Kozlovsky, A.M., and Lebedev, V.A., 2008. Late Cenozoic Volcanism of Khangai (Central Mongolia): Evidence for Recent Orogeny in Central Asia: Doklady Earth Sciences, v. 422, p. 1032-1036

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Table 1. Profile description for the Middle Orkhon section.

Sample name Depth beneath Thickness (m) Material capping basalt flow (m) MN14-01A 1 0.5 Unweathered Miocene alluvial fan deposits from beneath capping lava flow. Source rock is Paleozoic argillites and, coarse cobble conglomerates from surrounding hillslopes to north of exposure. Sample represents minimally chemically-weathered regional bedrock. MN14-01B 2 0.05 Saprolite developed on chemically weathered Paleozoic bedrock. Weathering likely took place before it was buried beneath basalt flow, as overlying Miocene alluvial fan sediments are minimally weathered chemically. MN14-01C -- 0.05 Modern floodplain sediment from the Orkhon River

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Table 2. Profile description for Upper Orkhon section

Sample name Depth beneath Thickness (m) Material capping basalt flow (m) MN14-02A-01 0 0.5 Chip of unweathered 3.06 Ma basalt MN14-02A-02 0.05 0.05 Yellow brown (7.5 YR 6/6) soil at the base of basalt flow, with silty matrix, basalt clasts, and smeary texture. MN14-02A-03 0.1 0.05 Top of Pliocene granite-derived soil, just beneath lava flow. Bt1, color = 5YR 5/8 (brick red), massive to weak granular texture, with poor clay films. MN14-02A-04 0.6 0.5 Bottom of top interval –Bt2, granular to weak, blocky texture, weak to medium clay films. MN14-02A-05 1.2 0.6 Middle of BC1 horizon – 7.5 YR 4/5 (brown) soil horizon with granular to weak peds, and weak clay films. MN14-02A-06 2.7 1.5 BC2 horizon – 10 YR 6/6 (light yellow brown) with granular structure and very weak clay films. MN14-02A-07 3.0 0.3 BC3 or Btb – 7.5 YR 4/5 (medium brown), granular texture with weak to moderate peds, moderate clay films, and a 10-15 cm thick clay rich zone MN14-02A-08 3.3 0.3 BC2b – 7.5 YR 6/6 (yellow brown), granular structure to very weak peds; similar to samples 05 and 06 MN14-02A-09 4.5 1.1 BC3b – 7.5 YR 6/6 (yellow brown), granular structure to very weak peds MN14-02A-10 5.0 0.6 Top of granite saprolite. Obvious petrofabric and joints visible. MN14-02A-11 15.5 10.5 Weathered saprolite, corestones beginning to emerge from this interval. MN14-02B-01 0.15 0.15 Top of Pliocene granite-derived soil, just beneath lava flow. Bt1, color=5YR 5/8 (brick red), massive to weak granular texture, with poor clay films. MN14-02B-02 0.5 0.35 Yellow brown (7.5 YR 6/6) soil at the base of basalt flow, with silty matrix, basalt clasts, and smeary texture. MN14-02B-03 1.2 0.7 BC2 horizon – 10 YR 6/6 (light yellow brown) with granular structure and very weak clay films. MN14-02B-04 1.6 0.4 Bottom of top interval –Bt2, granular to weak, blocky texture, weak to medium clay films. MN14-02B-05 2.0 0.4 BC2 horizon – 7.5 YR 4/5 (brown) soil horizon with granular to weak peds, and weak clay films. MN14-02B-06 -- Modern Late Holocene flood plain sample for weathering floodplain comparison

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Table 3. Profile description for Upper Chuluut section

Sample name Depth beneath Thickness Material capping basalt (m) flow (m) MN14-09A 0 0.5 Top sample from just beneath basalt flow, Fine grained silt lens ~30 cm beneath flow bottom MN14-09B 1.5 1 Sandy lens, 1.5 m beneath basalt flow MN14-09C 2.5 1 Sandy lens, 2.5 m beneath basalt flow MN14-09D 3.5 1 3.5 meter beneath base of basalt flow, clay lens in coarser granules MN14-09E -- Modern Modern Stream flood plain, fine sand and silt floodplain

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Table 4. General information for the all samples collected for weathering indices. Depth represented from beneath capping basalt flow. LHFP – Late Holocene Floodplain Sediments

No. Sample ID Depth (m) X Y Elevation Material Age (Ma) Place (m)

1 MN14-01A 1.0 102.54882 46.96197 1567 Argillite >7.59±0.11 Middle Orkhon 2 MN14-01B 2.0 102.55182 46.96244 1565 Argillite >7.59±0.11 Middle Orkhon 3 MN14-01C LHFP 102.55476 46.96336 1560 Alluvium >7.59±0.11 Middle Orkhon 4 MN14-02A-01 0 101.53859 46.84562 2040 Basalt >3.05±0.06 Upper Orkhon 5 MN14-02A-02 0.05 same same 2040 Paleosol >3.05±0.06 Upper Orkhon 6 MN14-02A-03 0.1 same same 2040 Paleosol >3.05±0.06 Upper Orkhon 7 MN14-02A-04 0.6 same same 2040 Paleosol >3.05±0.06 Upper Orkhon 8 MN14-02A-05 1.2 same same 2040 Paleosol >3.05±0.06 Upper Orkhon 9 MN14-02A-06 2.7 same same 2040 Paleosol >3.05±0.06 Upper Orkhon 10 MN14-02A-07 3.0 same same 2040 Paleosol >3.05±0.06 Upper Orkhon 11 MN14-02A-08 3.3 same same 2040 Paleosol >3.05±0.06 Upper Orkhon 12 MN14-02A-09 4.5 same same 2040 Paleosol >3.05±0.06 Upper Orkhon 13 MN14-02A-10 5.0 same same 2040 Saprolite Mesozoic Upper Orkhon 14 MN14-02A-11 15.5 same same 2040 Granite Mesozoic Upper Orkhon 15 MN14-02B-01 0.15 101.54442 46.84445 2046 Paleosol >3.05±0.06 Upper Orkhon 16 MN14-02B-02 0.5 same same 2046 Paleosol >3.05±0.06 Upper Orkhon 17 MN14-02B-03 1.2 same same 2046 Paleosol >3.05±0.06 Upper Orkhon 18 MN14-02B-04 1.60 same same 2046 Paleosol >3.05±0.06 Upper Orkhon 19 MN14-02B-05 2.00 same same 2046 Paleosol >3.05±0.06 Upper Orkhon 20 MN14-02B-06 LHFP same same 2010 Alluvium >3.05±0.06 Upper Orkhon 21 MN14-09A 0 100.10606 47.33363 2317 Fluvial gravel >11.21±0.18 Upper Chuluut 22 MN14-09B 1.5 same same 2317 Fluvial >11.21±0.18 Upper Chuluut Sand/silt 23 MN14-09C 2.5 same same 2317 Fluvial >11.21±0.18 Upper Chuluut Sand/silt 24 MN14-09D 3.5 same same 2317 Fluvial >11.21±0.18 Upper Chuluut Sand/silt 25 MN14-09E LHFP 100.09887 47.33372 2222 Alluvium >11.21±0.18 Upper Chuluut

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Table 5. Major element concentrations of all samples, reported in weight percent

Samples Ti Al Fe Mn Mg Ca Na K P MN14-01A 0.19 4.26 2.03 0.0341 1.62 19.38 0.735 0.99 0.068 MN14-01B 0.432 7.43 6.56 0.1016 0.85 0.49 1.548 2.49 0.108 MN14-01C 0.524 7.46 3.49 0.0765 1.05 1.56 2.305 2.27 0.11 MN14-02A-01 1.481 7.96 8.08 0.0928 2.89 3.77 2.28 2.53 0.358 MN14-02A-02 1.304 8.28 8.55 0.1371 1.6 1.27 1.309 2.65 0.227 MN14-02A-03 0.63 8.45 5.87 0.1101 1.54 1.3 1.399 2.61 0.084 MN14-02A-04 0.661 8.2 6.2 0.1118 1.46 1.02 1.033 2.62 0.06 MN14-02A-05 0.685 7.67 6.26 0.1123 1.52 0.75 1.014 2.63 0.066 MN14-02A-06 0.754 7.47 6.5 0.1408 1.75 1.27 1.193 2.3 0.133 MN14-02A-07 0.658 7.65 6.03 0.0763 1.31 0.81 1.164 2.34 0.088 MN14-02A-08 0.709 8.06 6.28 0.1918 1.67 1.49 1.394 2.33 0.125 MN14-02A-09 0.724 7.82 6.62 0.1259 1.78 1.58 1.223 2.24 0.131 MN14-02A-010 0.527 7.5 5.52 0.1182 1.54 2.45 1.843 2.2 0.181 MN14-02A-011 0.41 6.56 5.03 0.1288 2.2 3.42 2.04 2.55 0.408 MN14-02B-01 0.63 7.47 6.09 0.0999 1.46 0.68 1.021 2.77 0.043 MN14-02B-02 0.794 7.47 6.84 0.1628 2.09 1.51 1.227 2.25 0.129 MN14-02B-03 0.732 7.45 6.49 0.1236 1.68 1.34 1.217 2.47 0.119 MN14-02B-04 0.759 7.25 6.56 0.1776 1.81 1.48 1.375 2.36 0.142 MN14-02B-05 0.663 6.8 5.76 0.0713 1.48 0.73 1.101 2.45 0.09 MN14-02B-06 0.613 6.5 4.67 0.1131 1.55 2.19 1.472 1.85 0.145 MN14-09A 0.678 6.27 14.88 0.1916 0.89 1.74 1.218 1.16 0.131 MN14-09B 0.864 6.97 18.07 0.2625 0.64 1.31 1.113 1.03 0.204 MN14-09C 1.259 7.6 11.05 0.1804 1.13 3.96 1.673 0.99 0.238 MN14-09D 1.328 8.15 6.76 0.0543 1.03 2.18 1.516 1.17 0.165 MN14-09E 0.87 7.21 5.75 0.1214 1.75 3.16 1.862 1.63 0.161

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Table 6. Major element oxide concentrations of all samples, reported in weight percent

TiO Al2O3 FeO Fe2O3 MnO MgO CaO Na2O K2O P2O5 Sample % % % % % % % % % % MN14-01A 0.317 8.049 2.612 2.902 0.044 2.686 27.170 0.991 1.193 0.156 MN14-01B 0.721 14.039 8.439 9.379 0.131 1.410 0.687 2.087 2.999 0.247 MN14-01C 0.874 14.095 4.490 4.990 0.099 1.741 2.187 3.107 2.734 0.252 MN14-02A-01 2.471 15.040 10.395 11.552 0.120 4.792 5.285 3.073 3.048 0.820 MN14-02A-02 2.176 15.645 10.999 12.224 0.177 2.653 1.780 1.764 3.192 0.520 MN14-02A-03 1.051 15.966 7.552 8.393 0.142 2.554 1.823 1.886 3.144 0.192 MN14-02A-04 1.103 15.494 7.976 8.864 0.144 2.421 1.430 1.392 3.156 0.137 MN14-02A-05 1.143 14.492 8.053 8.950 0.145 2.521 1.051 1.367 3.168 0.151 MN14-02A-06 1.258 14.114 8.362 9.293 0.182 2.902 1.780 1.608 2.771 0.305 MN14-02A-07 1.098 14.454 7.758 8.621 0.099 2.172 1.136 1.569 2.819 0.202 MN14-02A-08 1.183 15.229 8.079 8.979 0.248 2.769 2.089 1.879 2.807 0.286 MN14-02A-09 1.208 14.776 8.517 9.465 0.163 2.952 2.215 1.649 2.698 0.300 MN14-02A-010 0.879 14.171 7.101 7.892 0.153 2.554 3.435 2.484 2.650 0.415 MN14-02A-011 0.684 12.395 6.471 7.192 0.166 3.648 4.795 2.750 3.072 0.934 MN14-02B-01 1.051 14.114 7.835 8.707 0.129 2.421 0.953 1.376 3.337 0.098 MN14-02B-02 1.325 14.114 8.800 9.779 0.210 3.466 2.117 1.654 2.710 0.295 MN14-02B-03 1.221 14.077 8.349 9.279 0.160 2.786 1.879 1.640 2.975 0.273 MN14-02B-04 1.266 13.699 8.439 9.379 0.229 3.001 2.075 1.853 2.843 0.325 MN14-02B-05 1.106 12.848 7.410 8.235 0.092 2.454 1.023 1.484 2.951 0.206 MN14-02B-06 1.023 12.282 6.008 6.677 0.146 2.570 3.070 1.984 2.229 0.332 MN14-09A 1.131 11.847 19.143 21.275 0.247 1.476 2.439 1.642 1.397 0.300 MN14-09B 1.442 13.170 23.247 25.835 0.339 1.061 1.837 1.500 1.241 0.467 MN14-09C 2.101 14.360 14.216 15.799 0.233 1.874 5.552 2.255 1.193 0.545 MN14-09D 2.216 15.399 8.697 9.665 0.070 1.708 3.056 2.044 1.409 0.378 MN14-09E 1.452 13.623 7.397 8.221 0.157 2.902 4.430 2.510 1.964 0.369

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Table 7. Summary of weathering indices (As calculated using molecular proportions of elemental oxides) evaluated in this study (Adapted from Price et. al, 2003).

Index Formula Optimu Optimum Ideal trend of Allows Al Reference m fresh weathered index up-profile mobility value value (increase in weathering)

(100)[Al2O3/(Al2O3 + Nesbitt and CIA ≤ 50 100 Positive No *CaO + Na2O+ K2O)] Young (1982)

(100)[(Al2O3 –K2O)/ Fedo et al. PIA ≤ 50 100 Positive No (Al2O3 + *CaO + Na2O–K2O)] (1995)

(100)[Al2O3/(Al2O3 + CIW ≤ 50 100 Positive No Harnois (1988) *CaO + Na2O)]

(Al2O3 + K2O)/(MgO + VRI < 1 Infinite Positive No Vogt (1927) *CaO + Na2O)

(100)[(2Na2O/0.35)+ WIP (MgO/0.9)+(2K2O/0.25)+ > 100 0 Negative Yes Parker (1970) (*CaO/0.7)] For the weathering of silicate rocks, the CaO is restricted to that derived from silicate minerals and is expressed by *CaO.

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Table 8. Weathering indices for all samples reported as percentage values. LHFP – Late Holocene Floodplain samples

Sample CIA PIA CIW Vogt RI Location Sample type MN14-01A 13.42 11.77 13.71 0.16 Middle Orkhon Argillite MN14-01B 65.68 72.52 77.44 2.26 Middle Orkhon Argillite MN14-01C 55.19 56.75 62.42 1.32 Middle Orkhon LHFP MN14-02A-01 48.45 48.03 54.21 0.74 Upper Orkhon Basalt MN14-02A-02 65.20 71.34 76.16 1.65 Upper Orkhon Paleosol MN14-02A-03 63.04 67.84 72.83 1.56 Upper Orkhon Paleosol MN14-02A-04 66.01 72.58 77.25 1.77 Upper Orkhon Paleosol MN14-02A-05 66.72 74.44 79.23 1.76 Upper Orkhon Paleosol MN14-02A-06 63.39 68.32 73.25 1.37 Upper Orkhon Paleosol MN14-02A-07 66.70 73.25 77.64 1.81 Upper Orkhon Paleosol MN14-02A-08 62.23 66.27 71.05 1.38 Upper Orkhon Paleosol MN14-02A-09 62.30 66.31 71.04 1.31 Upper Orkhon Paleosol MN14-02A-010 53.72 54.75 60.27 1.08 Upper Orkhon Saprolite MN14-02A-011 46.38 45.18 52.97 0.78 Upper Orkhon Granite MN14-02B-01 65.69 73.63 78.96 1.79 Upper Orkhon Paleosol MN14-02B-02 61.60 65.60 70.65 1.17 Upper Orkhon Paleosol MN14-02B-03 61.85 66.53 72.04 1.38 Upper Orkhon Paleosol MN14-02B-04 60.03 63.73 69.39 1.23 Upper Orkhon Paleosol MN14-02B-05 64.72 71.71 77.13 1.60 Upper Orkhon Paleosol MN14-02B-06 53.99 55.07 60.40 1.01 Upper Orkhon LHFP MN14-09A 59.90 61.69 64.86 1.32 Upper Chuluut Gravel MN14-09B 68.58 71.60 73.74 1.97 Upper Chuluut Sand/silt MN14-09C 51.01 51.11 53.46 0.91 Upper Chuluut Sand/silt MN14-09D 61.75 63.38 65.77 1.37 Upper Chuluut Sand/silt MN14-09E 50.36 50.43 54.66 0.84 Upper Chuluut LHFP

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Table 9-A. Modern drainage basin metrics per catchment

Digital Elevation Model (90 m resolution) derived min, max and mean elevation (m) for each of the watershed areas. Hypsometric integral is calculated for the smaller study watersheds and for the entire length of Orkhon and Chuluut Rivers.

Places Min Max Mean Sample Hypsometric Stream length Watershed elevation elevation elevation elevation Integral (km) area (km2) Middle Orkhon 1560 3415 2269 1560 0.38 170 4831 Upper Orkhon 1997 3185 2507 2010 0.43 25 115 Upper Chuluut 2223 3493 2742 2222 0.41 38.4 403 All Orkhon 601 3539 1423 Not sampled 0.28 989 139,885 All Chuluut 1195 3495 2276 Not sampled 0.47 363 18,523

Table 9-B. Weathering indices for all modern stream flood plain samples collected in percent

Stream sample CIA PIA CIW Vogt RI Sample type Middle Orkhon 55.19 56.75 62.42 1.32 Modern St. Upper Orkhon 53.99 55.07 60.40 1.01 Modern St Upper Chuluut 50.36 50.43 54.66 0.84 Modern St.

Table 9-C. Correlation coefficient for DEM’s mean and sampled elevation versus late Holocene floodplain (LHFP) sample weathering indices for Middle Orkhon, Upper Orkhon and Upper Chuluut sampling sites. Note that the weathering indices show strong negative correlation with increasing altitude.

Correlation With DEM With sample coefficient (R) mean elevation elevation With CIA -0.959 -0.863 With PIA -0.964 -0.873 With CIW -0.963 -0.870 With VRI -0.987 -0.997

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Table 10 (A) Principal components analysis, summary statistics and (B) covariance matrix for the entire sample dataset.

Table 10-A. Summary statistics:

Variable Observations Minimum Maximum Mean Std. deviation TiO 24 0.684 2.471 1.299 0.473 Al2O3 24 11.847 15.966 14.143 1.074 FeO 24 4.490 23.247 9.322 4.121 MnO 24 0.070 0.339 0.166 0.061 MgO 24 1.061 4.792 2.534 0.793 CaO 24 0.687 5.552 2.422 1.373 Na2O 24 1.367 3.107 1.940 0.513 K2O 24 1.193 3.337 2.604 0.663 P2O5 24 0.098 0.934 0.348 0.198

Table 10-B. Covariance matrix (Covariance (n)):

Variables TiO Al2O3 FeO MnO MgO CaO Na2O K2O P2O5 TiO 0.2140 0.199 0.608 0.001 0.076 0.280 0.036 -0.103 0.037 Al2O3 0.199 1.10568 -1.031 -0.016 0.124 -0.179 -0.021 0.225 -0.034 FeO 0.608 -1.031 16.271 0.177 -1.305 0.407 -0.508 -1.712 0.151 MnO 0.001 -0.016 0.177 0.0035 -0.008 0.008 -0.007 -0.017 0.002 MgO 0.076 0.124 -1.305 -0.008 0.6029 0.420 0.121 0.259 0.069 CaO 0.280 -0.179 0.407 0.008 0.420 1.807 0.486 -0.358 0.206 Na2O 0.036 -0.021 -0.508 -0.007 0.121 0.486 0.25176 -0.023 0.062 K2O -0.103 0.225 -1.712 -0.017 0.259 -0.358 -0.023 0.4218 -0.022 P2O5 0.037 -0.034 0.151 0.002 0.069 0.206 0.062 -0.022 0.037

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Table 11 (A) Eigenvalues, (B) Eigenvectors, (C) Factor Loadings, (D) Correlations between variables and factors, (E) Contribution of the element oxides to the factor loads in the Principal Component Analysis for all 24 samples. Table 11-A. Principal Component Analysis, Eigenvalues:

F1 F2 F3 F4 F5 F6 F7 F8 F9

Eigenvalue 16.688 2.198 1.117 0.476 0.133 0.067 0.029 0.007 0.001 Variability (%) 80.552 10.612 5.393 2.300 0.643 0.323 0.141 0.032 0.005 Cumulative % 80.552 91.164 96.557 98.856 99.499 99.822 99.963 99.995 100.000

Table 11-B. Eigenvectors:

F1 F2 F3 F4 F5 F6 F7 F8 F9

TiO 0.036 0.132 0.275 -0.019 -0.458 0.753 0.335 -0.097 0.082 Al2O3 -0.067 -0.108 0.937 -0.197 0.113 -0.199 -0.111 0.046 -0.017 FeO 0.987 -0.026 0.077 0.109 0.079 -0.018 -0.009 -0.022 -0.013 MnO 0.011 0.001 -0.003 0.015 0.006 -0.070 -0.060 0.055 0.994 MgO -0.082 0.256 0.162 0.802 -0.214 0.002 -0.459 0.022 -0.039 CaO 0.028 0.899 0.048 -0.135 0.015 -0.301 0.263 -0.105 0.001 Na2O -0.030 0.255 -0.010 -0.124 0.684 0.535 -0.395 -0.092 0.017 K2O -0.107 -0.145 0.108 0.517 0.493 -0.009 0.637 -0.196 0.039 P2O5 0.009 0.105 0.008 0.069 0.122 0.106 0.174 0.963 -0.037

Table 11-C. Factor loadings:

F1 F2 F3 F4 F5 F6 F7 F8 F9

TiO 0.148 0.196 0.290 -0.013 -0.167 0.195 0.057 -0.008 0.003 Al2O3 -0.275 -0.161 0.990 -0.136 0.041 -0.051 -0.019 0.004 -0.001 FeO 4.032 -0.038 0.082 0.075 0.029 -0.005 -0.002 -0.002 0.000 MnO 0.044 0.002 -0.004 0.011 0.002 -0.018 -0.010 0.004 0.032 MgO -0.333 0.379 0.171 0.554 -0.078 0.001 -0.078 0.002 -0.001 CaO 0.114 1.332 0.051 -0.093 0.006 -0.078 0.045 -0.009 0.000 Na2O -0.122 0.379 -0.010 -0.086 0.249 0.139 -0.067 -0.008 0.001 K2O -0.437 -0.215 0.114 0.357 0.180 -0.002 0.109 -0.016 0.001 P2O5 0.038 0.155 0.008 0.048 0.044 0.027 0.030 0.079 -0.001

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Table 11-D. Correlations between variables and factors:

F1 F2 F3 F4 F5 F6 F7 F8 F9 TiO 0.321 0.423 0.628 -0.029 -0.362 0.421 0.124 -0.017 0.006 Al2O3 -0.262 -0.153 0.942 -0.129 0.039 -0.049 -0.018 0.004 -0.001 FeO 1.000 -0.009 0.020 0.019 0.007 -0.001 0.000 0.000 0.000 MnO 0.736 0.036 -0.059 0.178 0.038 -0.307 -0.171 0.076 0.539 MgO -0.429 0.488 0.221 0.713 -0.100 0.001 -0.101 0.002 -0.002 CaO 0.085 0.991 0.038 -0.069 0.004 -0.058 0.033 -0.006 0.000 Na2O -0.244 0.755 -0.020 -0.171 0.497 0.276 -0.134 -0.015 0.001 K2O -0.673 -0.331 0.176 0.549 0.277 -0.004 0.167 -0.025 0.002 P2O5 0.194 0.800 0.042 0.246 0.229 0.141 0.153 0.406 -0.006

Table 11-E. Contribution of the variables (%):

F1 F2 F3 F4 F5 F6 F7 F8 F9

TiO 0.132 1.741 7.546 0.038 21.017 56.668 11.231 0.950 0.677 Al2O3 0.454 1.176 87.808 3.866 1.278 3.950 1.229 0.211 0.029 FeO 97.417 0.065 0.598 1.185 0.630 0.031 0.008 0.049 0.017 MnO 0.011 0.000 0.001 0.023 0.004 0.495 0.356 0.301 98.808 MgO 0.666 6.540 2.629 64.332 4.567 0.000 21.067 0.050 0.149 CaO 0.078 80.753 0.231 1.832 0.023 9.079 6.901 1.102 0.000 Na2O 0.090 6.522 0.009 1.544 46.728 28.650 15.585 0.844 0.028 K2O 1.144 2.106 1.173 26.701 24.266 0.008 40.599 3.851 0.153 P2O5 0.009 1.097 0.006 0.479 1.487 1.117 3.024 92.643 0.139

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Table 12. (A) Squared cosines of the variables (B) Factor scores (C) Contribution of the observations and (D) Squared cosines of the observations for each observation (sample) of the Principal Component Analysis.

Table 12-A. Squared cosines of the variables:

F1 F2 F3 F4 F5 F6 F7 F8 F9 TiO 0.103 0.179 0.394 0.001 0.131 0.177 0.015 0.000 0.000 Al2O3 0.068 0.023 0.887 0.017 0.002 0.002 0.000 0.000 0.000 FeO 0.999 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 MnO 0.542 0.001 0.003 0.032 0.001 0.094 0.029 0.006 0.291 MgO 0.184 0.238 0.049 0.508 0.010 0.000 0.010 0.000 0.000 CaO 0.007 0.982 0.001 0.005 0.000 0.003 0.001 0.000 0.000 Na2O 0.059 0.570 0.000 0.029 0.247 0.076 0.018 0.000 0.000 K2O 0.452 0.110 0.031 0.302 0.077 0.000 0.028 0.001 0.000 P2O5 0.038 0.639 0.002 0.060 0.052 0.020 0.023 0.165 0.000 Values in bold correspond for each variable to the factor for which the squared cosine is the largest

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Table 12-B. Factor scores:

Observation F1 F2 F3 F4 F5 F6 F7 F8 F9 MN14-01B -0.890 -1.919 -0.550 -0.553 0.680 0.188 0.064 0.038 -0.005 MN14-01C -4.774 -0.072 -0.673 -1.198 0.821 0.462 -0.181 -0.076 0.021 MN14-02A-01 0.859 3.455 1.790 1.462 0.260 0.482 -0.081 -0.086 -0.045 MN14-02A-02 1.503 -0.748 1.832 0.391 0.056 0.444 0.362 0.083 0.044 MN14-02A-03 -1.955 -0.827 1.535 -0.175 0.376 -0.386 -0.096 -0.042 -0.027 MN14-02A-04 -1.490 -1.300 1.105 -0.026 0.017 -0.413 0.120 -0.049 -0.019 MN14-02A-05 -1.364 -1.510 0.183 0.320 -0.146 -0.083 0.119 -0.046 -0.004 MN14-02A-06 -1.003 -0.573 -0.063 0.409 -0.299 0.001 -0.111 0.056 0.013 MN14-02A-07 -1.593 -1.410 0.021 -0.198 -0.105 -0.010 0.002 0.043 -0.048 MN14-02A-08 -1.352 -0.392 0.933 -0.003 0.073 -0.227 -0.206 0.040 0.069 MN14-02A-09 -0.883 -0.232 0.575 0.234 -0.202 -0.279 -0.173 0.049 -0.027 MN14-02A-010 -2.203 1.053 -0.209 -0.400 0.433 -0.289 -0.040 -0.010 -0.011 MN14-02A-011 -2.812 2.799 -1.686 0.800 0.511 -0.287 0.206 0.217 -0.014 MN14-02B-01 -1.571 -1.617 -0.216 0.388 -0.061 -0.045 0.248 -0.127 -0.005 MN14-02B-02 -0.600 -0.110 0.089 0.825 -0.410 -0.035 -0.323 0.018 0.019 MN14-02B-03 -1.026 -0.540 -0.102 0.408 -0.142 -0.035 0.074 -0.029 0.003 MN14-02B-04 -0.913 -0.185 -0.409 0.547 -0.155 0.129 -0.081 -0.008 0.070 MN14-02B-05 -1.865 -1.295 -1.453 0.401 -0.372 0.310 0.146 -0.020 -0.029 MN14-02B-06 -3.102 0.906 -2.081 -0.249 -0.502 0.052 0.039 0.001 0.018 MN14-09A 10.067 -0.184 -1.738 0.084 0.020 -0.009 -0.067 -0.109 -0.032 MN14-09B 14.080 -1.035 -0.207 -0.036 0.279 0.007 -0.135 0.094 0.009 MN14-09C 5.130 2.907 0.690 -1.232 -0.222 -0.273 0.331 -0.078 0.036 MN14-09D -0.460 0.563 1.147 -1.711 -0.657 0.335 -0.072 0.146 -0.047 MN14-09E -1.782 2.265 -0.513 -0.487 -0.253 -0.039 -0.144 -0.106 0.009

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Table 12-C. Contribution of the observations (%):

F1 F2 F3 F4 F5 F6 F7 F8 F9 MN14-01B 0.198 6.983 1.129 2.672 14.463 2.204 0.594 0.918 0.103 MN14-01C 5.691 0.010 1.687 12.546 21.119 13.265 4.697 3.562 1.784 MN14-02A-01 0.184 22.628 11.950 18.692 2.119 14.478 0.945 4.628 7.995 MN14-02A-02 0.564 1.060 12.523 1.335 0.100 12.244 18.690 4.305 7.910 MN14-02A-03 0.955 1.297 8.789 0.267 4.416 9.264 1.319 1.074 2.909 MN14-02A-04 0.554 3.203 4.551 0.006 0.009 10.593 2.069 1.508 1.450 MN14-02A-05 0.464 4.320 0.125 0.895 0.665 0.428 2.027 1.294 0.056 MN14-02A-06 0.251 0.623 0.015 1.460 2.801 0.000 1.759 1.982 0.716 MN14-02A-07 0.634 3.768 0.002 0.342 0.346 0.007 0.001 1.142 9.153 MN14-02A-08 0.457 0.291 3.248 0.000 0.167 3.217 6.080 0.988 19.370 MN14-02A-09 0.195 0.102 1.235 0.481 1.276 4.834 4.268 1.479 2.855 MN14-02A-010 1.212 2.101 0.163 1.397 5.870 5.192 0.228 0.064 0.455 MN14-02A-011 1.975 14.847 10.597 5.591 8.173 5.126 6.047 29.236 0.829 MN14-02B-01 0.616 4.953 0.175 1.315 0.116 0.124 8.767 9.961 0.119 MN14-02B-02 0.090 0.023 0.030 5.946 5.255 0.076 14.894 0.199 1.393 MN14-02B-03 0.263 0.553 0.039 1.457 0.635 0.077 0.775 0.540 0.045 MN14-02B-04 0.208 0.065 0.623 2.622 0.748 1.040 0.941 0.040 19.599 MN14-02B-05 0.869 3.178 7.879 1.404 4.339 5.977 3.058 0.249 3.404 MN14-02B-06 2.403 1.556 16.150 0.542 7.885 0.165 0.219 0.001 1.328 MN14-09A 25.304 0.064 11.268 0.062 0.012 0.005 0.632 7.370 4.101 MN14-09B 49.500 2.030 0.160 0.012 2.438 0.003 2.614 5.539 0.346 MN14-09C 6.570 16.021 1.777 13.285 1.540 4.621 15.656 3.783 5.089 MN14-09D 0.053 0.600 4.905 25.596 13.497 6.964 0.744 13.185 8.694 MN14-09E 0.793 9.725 0.981 2.077 2.011 0.094 2.977 6.951 0.298

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Table 12-D. Squared cosines of the observations:

F1 F2 F3 F4 F5 F6 F7 F8 F9 MN14-01B 0.142 0.659 0.054 0.055 0.083 0.006 0.001 0.000 0.000 MN14-01C 0.890 0.000 0.018 0.056 0.026 0.008 0.001 0.000 0.000 MN14-02A-01 0.040 0.651 0.175 0.117 0.004 0.013 0.000 0.000 0.000 MN14-02A-02 0.339 0.084 0.504 0.023 0.000 0.030 0.020 0.001 0.000 MN14-02A-03 0.531 0.095 0.327 0.004 0.020 0.021 0.001 0.000 0.000 MN14-02A-04 0.417 0.318 0.229 0.000 0.000 0.032 0.003 0.000 0.000 MN14-02A-05 0.431 0.528 0.008 0.024 0.005 0.002 0.003 0.000 0.000 MN14-02A-06 0.625 0.204 0.002 0.104 0.056 0.000 0.008 0.002 0.000 MN14-02A-07 0.554 0.434 0.000 0.009 0.002 0.000 0.000 0.000 0.000 MN14-02A-08 0.618 0.052 0.294 0.000 0.002 0.017 0.014 0.001 0.002 MN14-02A-09 0.569 0.039 0.242 0.040 0.030 0.057 0.022 0.002 0.001 MN14-02A-010 0.754 0.172 0.007 0.025 0.029 0.013 0.000 0.000 0.000 MN14-02A-011 0.402 0.399 0.145 0.033 0.013 0.004 0.002 0.002 0.000 MN14-02B-01 0.460 0.487 0.009 0.028 0.001 0.000 0.011 0.003 0.000 MN14-02B-02 0.270 0.009 0.006 0.510 0.126 0.001 0.078 0.000 0.000 MN14-02B-03 0.679 0.188 0.007 0.108 0.013 0.001 0.003 0.001 0.000 MN14-02B-04 0.601 0.025 0.120 0.216 0.017 0.012 0.005 0.000 0.004 MN14-02B-05 0.453 0.218 0.275 0.021 0.018 0.013 0.003 0.000 0.000 MN14-02B-06 0.638 0.054 0.287 0.004 0.017 0.000 0.000 0.000 0.000 MN14-09A 0.971 0.000 0.029 0.000 0.000 0.000 0.000 0.000 0.000 MN14-09B 0.994 0.005 0.000 0.000 0.000 0.000 0.000 0.000 0.000 MN14-09C 0.711 0.228 0.013 0.041 0.001 0.002 0.003 0.000 0.000 MN14-09D 0.040 0.059 0.246 0.548 0.081 0.021 0.001 0.004 0.000 MN14-09E 0.357 0.576 0.030 0.027 0.007 0.000 0.002 0.001 0.000 Values in bold correspond for each observation to the factor for which the squared cosine is the largest

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Table 13. K2O versus Al2O3 ratio, which defines source rock. Here our result indicates they are mostly from an illite-rich source.

Sample K2O Al2O3 K2O/Al2O3 Material MN14-01A 1.19 8.05 0.15 Argillite MN14-01B 3.00 14.04 0.21 Argillite MN14-01C 2.73 14.10 0.19 LHFP MN14-02A-01 3.05 15.04 0.20 Basalt MN14-02A-02 3.19 15.64 0.20 Paleosol MN14-02A-03 3.14 15.97 0.20 Paleosol MN14-02A-04 3.16 15.49 0.20 Paleosol MN14-02A-05 3.17 14.49 0.22 Paleosol MN14-02A-06 2.77 14.11 0.20 Paleosol MN14-02A-07 2.82 14.45 0.20 Paleosol MN14-02A-08 2.81 15.23 0.18 Paleosol MN14-02A-09 2.70 14.78 0.18 Paleosol MN14-02A-010 2.65 14.17 0.19 Saprolite MN14-02A-011 3.07 12.39 0.25 Granite MN14-02B-01 3.34 14.11 0.24 Paleosol MN14-02B-02 2.71 14.11 0.19 Paleosol MN14-02B-03 2.98 14.08 0.21 Paleosol MN14-02B-04 2.84 13.70 0.21 Paleosol MN14-02B-05 2.95 12.85 0.23 Paleosol MN14-02B-06 2.23 12.28 0.18 LHFP MN14-09A 1.40 11.85 0.12 Gravel MN14-09B 1.24 13.17 0.09 Sand/silt MN14-09C 1.19 14.36 0.08 Sand/silt MN14-09D 1.41 15.40 0.09 Sand/silt MN14-09E 1.96 13.62 0.14 LHFP

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Table 14. Zr/Sc and Th/Sc ratio which generally defines the degree of re-working and recycling. Zr/Sc values near or lower than 10 indicates the materials have generally homogeneous source.

Sample Th Zr Sc Zr/Sc Th/Sc Material MN14-01A 6.1 49.4 4 12.35 1.53 Argillite MN14-01B 11.8 112 11 10.18 1.07 Argillite MN14-01C 15.1 102.8 10 10.28 1.51 LHFP MN14-02A-01 3.5 224.6 13 17.28 0.27 Basalt MN14-02A-02 15.7 213.8 16 13.36 0.98 Paleosol MN14-02A-03 25.4 95.4 17 5.61 1.49 Paleosol MN14-02A-04 20.8 96.9 17 5.70 1.22 Paleosol MN14-02A-05 20.8 90.7 15 6.05 1.39 Paleosol MN14-02A-06 37.6 108.8 19 5.73 1.98 Paleosol MN14-02A-07 25.4 100.9 16 6.31 1.59 Paleosol MN14-02A-08 41.6 103.6 19 5.45 2.19 Paleosol MN14-02A-09 44.8 110.9 20 5.55 2.24 Paleosol MN14-02A-010 70.5 138.3 21 6.59 3.36 Saprolite MN14-02A-011 162.9 233.1 26 8.97 6.27 Granite MN14-02B-01 16.8 53.8 16 3.36 1.05 Paleosol MN14-02B-02 28.4 93.3 18 5.18 1.58 Paleosol MN14-02B-03 31.1 99.4 18 5.52 1.73 Paleosol MN14-02B-04 38.1 101.5 18 5.64 2.12 Paleosol MN14-02B-05 23.2 93.2 16 5.83 1.45 Paleosol MN14-02B-06 21 104.9 16 6.56 1.31 LHFP MN14-09A 10.8 127.4 16 7.96 0.68 Gravel MN14-09B 13.9 175.6 22 7.98 0.63 Sand/silt MN14-09C 6.9 231.8 22 10.54 0.31 Sand/silt MN14-09D 12.2 209.1 19 11.01 0.64 Sand/silt MN14-09E 8.4 128.8 15 8.59 0.56 LHFP

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Table 15. Modern weather station climate data (MAT, MAP) for representing Late Holocene weather and taken as analogue to modern weathering regime for selected weathering indices

Weather Elevation Average T° Annual Analogue to Sample CIA PIA CIW Vogt RI stations (m) (°C) precip (mm) elevation (m) Kharkhorin 1470 +1.8±0.99 287.1±57.2 Middle Orkhon 1560 55.19 56.75 62.42 1.32 Chuluut 2026 -3.0±0.91 266.5±55.9 Upper Orkhon 2010 53.99 55.07 60.40 1.01 Jargalant 2050 -4.1±1.01 185.9±50.0 Upper Chuluut 2222 50.36 50.43 54.66 0.84 Bat-Olzyi 1680 -0.2±0.72 270.8±53.9 Upper Orkhon 2010

Table 16. The correlation matrix for modern floodplain weathering indices. As seen from this table, the results of modern stream floodplain samples are keeping good correlations with each other

Variables CIA PIA CIW VRI CIA 1 0.990 0.966 0.902 PIA 0.990 1 0.990 0.904 CIW 0.966 0.990 1 0.888 VRI 0.902 0.904 0.888 1

Table 17. The correlation coefficient R for modern MAT vs floodplain sediments for direct correlation method

Parameter Correlation coefficient (R) with: CIA PIA CIW Vogt RI Current 0.80 0.82 0.82 0.98 average annual T°

149

Table 18-A-E. Reconstructed Mean Annual Temperature (MAT) values using direct correlation version for each sites Table 18-A. The reconstructed MAT values for modern floodplain samples

Stream CIA PIA CIW Vogt Weather Reconstruc Reconstruc Reconstructe Reconstru Period samples RI station ted t° (CIA) ted T° (PIA) d T° (CIW) cted T° indicating T° (°C) (VRI) Lower 55.19 56.75 62.42 1.32 +1.8 0.26 0.33 0.30 1.57 Cool Orkhon Middle 53.99 55.07 60.40 1.01 -3.0 -0.95 -0.99 -0.98 -2.36 Cold Orkhon Upper 50.36 50.43 54.66 0.84 -4.1 -4.61 -4.63 -4.63 -4.51 Cold Chuluut

Table 18-B. The reconstructed MAT values for Middle Orkhon section (>7.6 Ma)

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Std. name reconstruct reconstruct reconstruct reconstruct deviation ed T° (°C) ed T° (°C) ed T° (°C) ed T° (°C) Middle Argillite 65.68 72.52 77.44 2.26 10.83 12.70 9.85 13.49 11.72±1.67 Orkhon Middle Floodplain 55.19 56.75 62.42 1.32 0.26 0.33 0.30 1.57 0.62±0.64 Orkhon sediment

150

Table 18-C. The reconstructed MAT values for Upper Orkhon section (>3 Ma) - Section A

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Std. name reconstruct reconstruct reconstruct reconstruct deviation ed T° (°C) ed T° (°C) ed T° (°C) ed T° (°C) Upper Paleosol 65.2 71.34 76.16 1.65 10.35 11.78 9.03 5.76 9.23±2.57 Orkhon Upper Paleosol 63.04 67.84 72.83 1.56 8.17 9.03 6.92 4.62 7.18±1.91 Orkhon Upper Paleosol 66.01 72.58 77.25 1.77 11.17 12.75 9.73 7.28 10.23±2.32 Orkhon Upper Paleosol 66.72 74.44 79.23 1.76 11.88 14.21 10.98 7.15 11.06±2.95 Orkhon Upper Paleosol 63.39 68.32 73.25 1.37 8.52 9.41 7.18 2.21 6.84±3.22 Orkhon Upper Paleosol 66.7 73.25 77.64 1.81 11.86 13.28 9.97 7.79 10.72±2.38 Orkhon Upper Paleosol 62.23 66.27 71.05 1.38 7.35 7.80 5.79 2.33 5.82±2.48 Orkhon Upper Paleosol 62.3 66.31 71.04 1.31 7.43 7.83 5.78 1.45 5.62±2.93 Orkhon

Table 18-D. The reconstructed MAT values for Upper Orkhon section (>3 Ma) - Section B

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Std. name reconstruct reconstruct reconstruct reconstruct deviation ed T° (°C) ed T° (°C) ed T° (°C) ed T° (°C) Upper Paleosol 65.69 73.63 78.96 1.79 10.84 13.58 10.81 7.53 10.69±2.4 Orkhon 7 Upper Paleosol 61.6 65.6 70.65 1.17 6.72 7.27 5.53 -0.33 4.80±3.50 Orkhon Upper Paleosol 61.85 66.53 72.04 1.38 6.97 8.00 6.42 2.33 5.93±2.50 Orkhon Upper Paleosol 60.03 63.73 69.39 1.23 5.14 5.81 4.73 0.43 4.03±2.45 Orkhon Upper Paleosol 64.72 71.71 77.13 1.6 9.86 12.07 9.65 5.12 9.18±2.93 Orkhon Upper Modern 53.99 55.07 60.4 1.01 -0.95 -0.99 -0.98 -2.36 - Orkhon St 1.32±0.69

151

Table 18-E. The reconstructed MAT values for Upper Chuluut section

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Std. name reconstruct reconstruct reconstruct reconstruct deviation ed T° (°C) ed T° (°C) ed T° (°C) ed T° (°C) Upper Sand/silt 68.58 71.6 73.74 1.97 13.76 11.98 7.50 9.81 10.76±2.71 Chuluut Upper Sand/silt 61.75 63.38 65.77 1.37 6.87 5.53 2.43 2.21 4.26±2.32 Chuluut Upper Modern 50.36 50.43 54.66 0.84 -4.61 -4.63 -4.63 -4.51 -4.60±0.05 Chuluut St.

152

Table 19. The correlation coefficient R for modern MAT vs weathering indices of floodplain sediments for altitude corrected second version

Parameter Correlation coefficient (R) with: CIA PIA CIW Vogt RI Current 0.88 0.89 0.89 0.99 average annual T°

Table 20-A-E. Reconstructed Mean Annual Temperature (MAT) values using altitude corrected second method for each sites

Table 20-A. The reconstructed MAT values for modern floodplain samples

Analogue CIA PIA CIW Vogt Weather Reconstruc Reconstruc Reconstruct Reconstruc Diff to RI station T° ted t° (CIA) ted T° (PIA) ed T° (CIW) ted T° (VRI) VRI (°C) Lower 55.19 56.75 62.42 1.32 +1.8 -0.34 -0.27 -0.29 0.87 0.05 Orkhon Middle 53.99 55.07 60.40 1.01 -3.0 -1.69 -1.74 -1.73 -3.22 -0.14 Orkhon Upper 50.36 50.43 54.66 0.84 -4.1 -5.79 -5.81 -5.80 -5.46 0.09 Chuluut

Table 20-B. The reconstructed MAT values for Middle Orkhon section (>3 Ma)

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Std. name reconstruct reconstruct reconstruct reconstruct deviation ed T° (°C) ed T° (°C) ed T° (°C) ed T° (°C) Middle Argillite 65.68 72.52 77.44 2.26 11.50 13.53 10.36 13.26 12.16 ±1.50 Orkhon Middle Floodplain 55.19 56.75 62.42 1.32 -0.34 -0.27 -0.29 0.87 -0.01±0.58 Orkhon sediment

153

Table 20-C. The reconstructed MAT values for Upper Orkhon section (>7.6 Ma) - Section A

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Std. name reconstruct reconstruct reconstruct reconstruct deviation ed T° (°C) ed T° (°C) ed T° (°C) ed T° (°C) Upper Paleosol 65.2 71.34 76.16 1.65 10.96 12.49 9.45 5.22 9.53±3.13 Orkhon Upper Paleosol 63.04 67.84 72.83 1.56 8.52 9.43 7.09 4.03 7.27±2.36 Orkhon Upper Paleosol 66.01 72.58 77.25 1.77 11.87 13.58 10.23 6.80 10.62±2.89 Orkhon Upper Paleosol 66.72 74.44 79.23 1.76 12.67 15.21 11.63 6.67 11.55±3.58 Orkhon Upper Paleosol 63.39 68.32 73.25 1.37 8.92 9.85 7.39 1.53 6.93±3.74 Orkhon Upper Paleosol 66.7 73.25 77.64 1.81 12.65 14.17 10.50 7.33 11.15±2.97 Orkhon Upper Paleosol 62.23 66.27 71.05 1.38 7.61 8.06 5.83 1.66 5.79±2.93 Orkhon Upper Paleosol 62.3 66.31 71.04 1.31 7.69 8.09 5.82 0.73 5.58±3.38 Orkhon

Table 20-D. The reconstructed MAT values for Upper Orkhon section (>7.6 Ma) - Section B

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Std. name reconstruct reconstruct reconstruct reconstruct deviation ed T° (°C) ed T° (°C) ed T° (°C) ed T° (°C) Upper Paleosol 65.69 73.63 78.96 1.79 11.51 14.50 11.44 7.06 11.13±3.06 Orkhon Upper Paleosol 61.6 65.6 70.65 1.17 6.90 7.47 5.55 -1.11 4.70±3.96 Orkhon Upper Paleosol 61.85 66.53 72.04 1.38 7.18 8.29 6.53 1.66 5.91±2.93 Orkhon Upper Paleosol 60.03 63.73 69.39 1.23 5.13 5.83 4.65 -0.32 3.82±2.80 Orkhon Upper Paleosol 64.72 71.71 77.13 1.6 10.42 12.82 10.14 4.56 9.48±3.50 Orkhon Upper Modern 53.99 55.07 60.4 1.01 -1.69 -1.74 -1.73 -3.22 -2.10±0.75 Orkhon St

154

Table 20-E.The reconstructed MAT values for Upper Orkhon section (>11.2 Ma)

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Std. name reconstructe reconstruct reconstruct reconstruct deviation d T° (°C) ed T° (°C) ed T° (°C) ed T° (°C) Upper Sand/silt 68.58 71.6 73.74 1.97 14.77 12.72 7.74 9.44 11.17±3.17 Chuluut Upper Sand/silt 61.75 63.38 65.77 1.37 7.07 5.53 2.08 1.53 5.36±0.90 Chuluut Upper Modern 50.36 50.43 54.66 0.84 -5.79 -5.81 -5.80 -5.46 -5.71±0.17 Chuluut St.

155

Table 21-A-E. Reconstructed Mean Annual Temperature (MAT) values using DEM mean altitude corrected third method for each sites Table 21-A. The reconstructed MAT values for modern floodplain samples

Analogue CIA PIA CIW Vogt Weather Reconstruc Reconstruc Reconstructe Reconstruc to RI station T° ted t° (CIA) ted T° (PIA) d T° (CIW) ted T° (VRI) (°C) Middle 55.19 56.75 62.42 1.32 +1.8 0.36 0.26 0.33 0.46 Orkhon Upper 53.99 55.07 60.40 1.01 -3.0 -1.36 -1.60 -1.48 -3.75 Orkhon Upper 50.36 50.43 54.66 0.84 -4.1 -6.59 -6.72 -6.63 -6.05 Chuluut

Table 21-B. The reconstructed MAT values for Middle Orkhon section (>7.6 Ma)

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Std. name reconstruct reconstruct reconstruct reconstruct deviation ed T° (°C) ed T° (°C) ed T° (°C) ed T° (°C) Middle Argillite 65.68 72.52 77.44 2.26 15.46 17.68 13.79 13.22 15.04±2.00 Orkhon Middle Floodplain 55.19 56.75 62.42 1.32 0.36 0.26 0.33 0.46 0.35±0.08 Orkhon sediment

156

Table 21-C. The reconstructed MAT values for Upper Orkhon section (>3 Ma) - Section A

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Std. name reconstruct reconstruct reconstruct reconstruct deviation ed T° (°C) ed T° (°C) ed T° (°C) ed T° (°C) Upper Paleosol 65.2 71.34 76.16 1.65 14.77 16.38 12.64 4.94 12.18±5.07 Orkhon Upper Paleosol 63.04 67.84 72.83 1.56 11.66 12.51 9.66 3.72 9.39±3.97 Orkhon Upper Paleosol 66.01 72.58 77.25 1.77 15.94 17.75 13.62 6.57 13.47±4.90 Orkhon Upper Paleosol 66.72 74.44 79.23 1.76 16.96 19.80 15.39 6.43 14.65±5.77 Orkhon Upper Paleosol 63.39 68.32 73.25 1.37 12.17 13.04 10.03 1.15 9.10±5.45 Orkhon Upper Paleosol 66.7 73.25 77.64 1.81 16.93 18.49 13.97 7.11 14.12±5.04 Orkhon Upper Paleosol 62.23 66.27 71.05 1.38 10.50 10.78 8.06 1.27 7.65±4.42 Orkhon Upper Paleosol 62.3 66.31 71.04 1.31 10.60 10.82 8.05 0.32 7.4.±4.2 Orkhon

Table 21-D. The reconstructed MAT values for Upper Orkhon section (>3 Ma) - Section B

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Std. name reconstruct reconstruct reconstruct reconstruct deviation ed T° (°C) ed T° (°C) ed T° (°C) ed T° (°C) Upper Paleosol 65.69 73.63 78.96 1.79 15.48 18.91 15.15 6.84 14.09±5.13 Orkhon Upper Paleosol 61.6 65.6 70.65 1.17 9.59 10.04 7.70 -1.58 6.45±5.45 Orkhon Upper Paleosol 61.85 66.53 72.04 1.38 9.95 11.06 8.95 1.27 7.81±4.45 Orkhon Upper Paleosol 60.03 63.73 69.39 1.23 7.33 7.97 6.57 -0.76 5.28±4.07 Orkhon Upper Paleosol 64.72 71.71 77.13 1.6 14.08 16.79 13.51 4.26 12.16±5.46 Orkhon Upper Modern 53.99 55.07 60.4 1.01 -1.36 -1.60 -1.48 -3.75 -2.05±1.15 Orkhon St

157

Table 21-E. The reconstructed MAT values for Upper Chuluut section (>11.2 Ma)

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Std. name reconstruct reconstruct reconstruct reconstruct deviation ed T° (°C) ed T° (°C) ed T° (°C) ed T° (°C) Upper Sand/silt 68.58 71.6 73.74 1.97 19.64 16.67 10.47 9.28 14.01±4.95 Chuluut Upper Sand/silt 61.75 63.38 65.77 1.37 9.81 7.58 3.33 1.14 5.46±3.95 Chuluut Upper Modern 50.36 50.43 54.66 0.84 -6.59 -6.72 -6.63 -6.05 -6.50±0.30 Chuluut St.

158

Table 22-A-E. Reconstructed Mean Annual Precipitation (MAP) values for each sites

Table 22-A. The reconstructed MAP values for modern floodplain samples

Analogue CIA PIA CIW Vogt Moisture Reconstruc Reconstruc Reconstructe Reconstruc to RI today (mm) ted P (CIA) ted P (PIA) d P (CIW) ted P (VRI) Middle 55.19 56.75 62.42 1.32 287.1±57.1 289.18 289.99 289.73 297.37 Orkhon Upper 53.99 55.07 60.40 1.01 265.1±55.1 263.69 262.60 262.94 237.48 Orkhon Upper 50.36 50.43 54.66 0.84 186.1±53.2 186.58 186.95 186.83 195.34 Chuluut

Table 22-B. The reconstructed MAP values for Middle Orkhon section (>7.6 Ma)

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Standard name reconstruct reconstruct reconstruct reconstruct deviation ed P (mm) ed P(mm) ed P (mm) ed P (mm) Middle Argillite 65.68 72.52 77.44 2.26 512.02 547.08 488.91 478.98 506.7±26.1 Orkhon Middle Floodplain 55.19 56.75 62.42 1.32 289.18 289.99 289.73 297.37 293.5±3.36 Orkhon sediment

159

Table 22-C. The reconstructed MAP values for Upper Orkhon section (>3 Ma) - Section A

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Standard name reconstruc reconstruc reconstruc reconstruc deviation ted P (mm) ted P(mm) ted P (mm) ted P (mm) Upper Paleosol 65.2 71.34 76.16 1.65 501.82 527.85 471.94 361.13 465.7±63.5 Orkhon Upper Paleosol 63.04 67.84 72.83 1.56 455.94 470.79 427.78 343.74 424.6±49.2 Orkhon Upper Paleosol 66.01 72.58 77.25 1.77 519.03 548.06 486.39 384.31 484.4±61.8 Orkhon Upper Paleosol 66.72 74.44 79.23 1.76 534.11 578.39 512.65 382.38 501.9±73 Orkhon Upper Paleosol 63.39 68.32 73.25 1.37 463.37 478.61 433.35 307.03 420.6±67.6 Orkhon Upper Paleosol 66.7 73.25 77.64 1.81 533.69 558.98 491.56 392.04 494.1±63.6 Orkhon Upper Paleosol 62.23 66.27 71.05 1.38 438.73 445.19 404.17 308.97 399.3±54.4 Orkhon Upper Paleosol 62.3 66.31 71.04 1.31 440.22 445.84 404.04 295.44 396.4±60.4 Orkhon

Table 22-D. The reconstructed MAP values for Upper Orkhon section (>3 Ma) - Section B

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Standard name reconstruct reconstruct reconstruct reconstruct deviation ed P (mm) ed P(mm) ed P (mm) ed P (mm) Upper Paleosol 65.69 73.63 78.96 1.79 512.23 565.18 509.07 388.18 493.7±64.9 Orkhon Upper Paleosol 61.6 65.6 70.65 1.17 425.35 434.27 398.87 268.39 381.7±66.7 Orkhon Upper Paleosol 61.85 66.53 72.04 1.38 430.66 449.43 417.30 308.97 401.6±54.7 Orkhon Upper Paleosol 60.03 63.73 69.39 1.23 392.00 403.78 382.16 279.99 364.5±49.4 Orkhon Upper Paleosol 64.72 71.71 77.13 1.6 491.63 533.88 484.80 351.47 465.4±68.4 Orkhon Upper Modern 53.99 55.07 60.4 1.01 263.69 262.60 262.94 237.48 256.7±11.1 Orkhon St

160

Table 22-E. The reconstructed MAP values for Upper Chuluut section (>11.2 Ma)

Place Material CIA PIA CIW VRI CIA PIA CIW VRI Standard name reconstruc reconstruc reconstruc reconstruc deviation ted P (mm) ted P(mm) ted P (mm) ted P (mm) Upper Sand silt 59.9 61.69 64.86 1.32 389.24 370.52 322.09 297.37 344.8±36.7 Chuluut Upper Sand/silt 68.58 71.6 73.74 1.97 573.62 532.08 439.85 422.95 492.1±62.8 Chuluut Upper Sand/silt 61.75 63.38 65.77 1.37 428.54 398.07 334.16 307.03 367±48.5 Chuluut Upper Modern 50.36 50.43 54.66 0.84 186.58 186.95 186.83 195.35 188.9±3.7 Chuluut St.

161

Table 23. CIA vs K2O/(Na2O + CaO*) molar ratio. It suggests the degree of chemical weathering is strongly dependent of altitude for modern floodplain sediments. Increase in the K2O/(Na2O + CaO*) molar ratio is associated with CIA increase, suggesting a preferential hydrolysis of plagioclase (enrichment of Na) relative to K-feldspar and micas (enrichment in K) during the silicate weathering process of river sediments. See correlation from Figure 26.

Holocene Floodplain K2O/(Na2O+CaO*) CIA Middle Orkhon 0.35 55.19 Upper Orkhon 0.30 53.99 Upper Chuluut 0.19 50.36

162

Figure 1. Location of the Khangay Mountains in Mongolia, Central Asia.

163

Figure 2. Location of preserved fossil soil locations (1 – 3) beneath 40Ar/39Ar-dated basalt flows from the northern Khangay Mountains. Basalt flow ages: 11.2±0.2 Ma (1); 7.6±0.1 Ma (2); 3.1±0.1 Ma (3) are from Ancuta et al. (2018).

164

7.6±0.1 Ma basalt flow

2 m

Figure 3. Field photograph of Middle Orkhon Section. Weathered sedimentary rocks (argillite & conglomerate) overlain by 7.6±0.1 Ma basalt flow.

165

3.1 ± 0.1 Ma basalt flow

Mio-Pliocene soil + saprolite

Granite e

3 m

Figure 4. Field photograph of Upper Orkhon Section. Granite, saprolite and paleosol sections beneath 3.1±0.1 Ma basalt flow.

166

3.1 ± 0.1 Ma basalt flow

Paleosols

1 m

Figure 5. Field photograph of the 3.1 ± 0.1 Ma basalt flow-granite saprolite section from the upper Orkhon River valley. Note change in color towards the top of the saprolite, indicating the accumulation of pedogenic clays and primary mineral alteration associated with more intense chemical weathering of the landscape (or possibly from diagenesis) during the early to mid-Pliocene in comparison to today's environment.

167

Fluvial gravels

Figure 6. Field photograph of the 11.2 ± 0.2 Ma basalt flow from the upper Chuluut River section.

168

Weathering indices (%)

Figure 7. Weathering profile and schematic weathering indices plot of the 7.6 ± 0.1 Ma basalt flow section from the middle Orkhon River. Weathering indices expressed in percentage.CIA – Chemical Index of Alteration, PIA- Plagioclase index of Alteration, CIW – Chemical Index of Weathering. Tabular results from the whole rock geochemical analysis are presented in table 8.

169

Figure 8-A. Weathering profile and schematic weathering indices plot of the 3.1 ± 0.1 Ma basalt flow section from the Upper Orkhon River. Weathering indices expressed in percentage. CIA – Chemical Index of Alteration, PIA- Plagioclase index of Alteration, CIW – Chemical Index of Weathering. Tabular results from the whole rock geochemical analysis are presented in table 8.

170

Weathering indices (%)

171

Weathering indices (%)

Figure 8-B. Weathering profile and schematic weathering indices plot of the 3.1 ± 0.1 Ma basalt flow section from the Upper Orkhon River. Weathering indices expressed in percentage. CIA – Chemical Index of Alteration, PIA- Plagioclase index of Alteration, CIW – Chemical Index of Weathering. Tabular results from the whole rock geochemical analysis are presented in table 8.

172

Weathering indices (%)

Figure 9. Weathering profile and schematic weathering indices plot of the 11.2 ± 0.2 Ma basalt flow section from the Upper Chuluut River. Weathering indices expressed in percentage. CIA – Chemical Index of Alteration, PIA- Plagioclase index of Alteration, CIW – Chemical Index of Weathering. Tabular results from the whole rock geochemical analysis are presented in table 8.

173

Figure 10. Eigenvalues and cumulative percentages for the Principal Component Analysis of all 24 samples.

174

Figure 11. Principal components analysis PC1 and PC2 biplot for oxides.

Observations (axes F1 and F2: 91.16 %)

10 Unweathered late Holocene flood plain samples, saprolite and fresh granite Unweathered basalt

5 F2 (10.61 %) (10.61 F2 0

More Oxidized condition weathered paleosols -5 -5 0 5 10 15 F1 (80.55 %)

Figure 12-A. Principal Component Analysis, PC-1 and PC-2 bi-plot for oxides mapping the distribution of sample types. Note that, more stable samples (low chemical weathering) have positive correlation with PC-2, while PC-1 has strong correlation with oxidized condition.

175

Figure 12-B. Principal components analysis, PC-1 and PC-2 biplot for oxides and distribution of observations.

176

Al2O3

A B B B

Relatively fresh materials

K2O Na2O+*CaO

Figure 13. A-CN-K Ternary plot, indicating weathering intensity values at the higher end, closer to the

Al2O3, suggesting more weathering intensity. Note that, Upper and Middle Orkhon section showed nearly identical results. In the lower end towards Na2O+CaO* corner, there are saprolite, granite and modern stream samples (within black circle) results which showed negligible chemical weathering. Generally, paleosol sections exhibit low to medium weathering intensity within the PIA range of 61-68%.

177

Illite line Upper and Middle

Orkhon section

Modern Stream Upper Chuluut section

Figure 14. K2O and Al2O3 ratio. This indicates how much of alkali feldspar versus plagioclase and clay minerals were present in the original rock. Note that, Upper and middle Orkhon section showed very close to illite line, which says parent material is generally from the illite source.

178

Temperature °C

Figure 15. Reconstructed paleo temperature values, using chemical weathering indices for Middle Orkhon section. Note we have only two samples from here, modern floodplain and Miocene paleosols. The method used is a direct correlation method. The temperature ranges between 1.5-2.5°C for late Holocene floodplain sample, which generally indifferent from the average annual temperature for the closest weather station, Kharkhorin (+1.8°C), that is applied as analogue. The reconstructed paleo temperature range from the single sample is 11.6±2.7°C, and may represent the MAT for the Middle Orkhon local prior to 7.6 Ma. The paleo-temperature estimate is from chemically stable B horizon.

179

Temperature °

Figure 16-A. Reconstructed paleo-temperature values using chemical weathering indices for Upper Orkhon section, Section A. This section is overlain by 3.1±0.1 Ma Ar39/Ar40 dated basalt flows that represents from late Pliocene-Miocene shift to Early Pleistocene epoch. The reconstructed paleo temperature range is 8.4±2.1°C using direct correlation method. The paleo-temperature is calculated from average and standard deviation of all samples within this profile section. The paleo-temperature estimate is from chemically stable B horizon.

180

Temperature °C

Figure 16-B. Reconstructed paleo-temperature values using chemical weathering indices for Upper Orkhon section, Section B. This section is overlain by 3.1±0.1 Ma Ar39/Ar40 dated basalt flows that represents from Late Pliocene epoch. It positions only a kilometer distance upward stream from Section A. The reconstructed paleo temperature range is 7.1±2.5°C using direct correlation. The paleo- temperature is calculated from average and standard deviation of all samples within this profile section. The paleo-temperature estimate is from chemically stable B horizon.

181

Temperature °C

Figure 17. Reconstructed paleo-temperature values using chemical weathering indices for Upper Chuluut section. This section is overlain by 11.2±0.2 Ma Ar39/Ar40 dated basalt flows that represents Mid to Late Miocene possibly Langhian to Tortonian, Late Messinian stage. We collected five samples from this section. The reconstructed paleo temperature range is 6.9±3.0°C using direct correlation. The paleo- temperature is calculated from average and standard deviation of all samples within this profile section. The paleo-temperature estimate is from chemically stable B horizon.

182

Precipitation (mm)

Figure 18. Reconstructed paleo moisture values using chemical weathering indices for Middle Orkhon section. Note we have only two samples from here, modern stream and paleo-argillite. We used average precipitation values of all three analogue weather stations. According to reconstruction, the moisture ranges between 450 to 550 mm (507±26.1 mm) for paleosol section of >7.6 Ma. For modern stream sample MAP value returned 295±5.7 mm, which doesn’t deviate much from average annual precipitation for Kharkhorin station (287±57.1 mm), that taken as analogue. The paleo-precipitation is calculated from average and standard deviation of all samples within this profile section. The paleo-precipitation estimate is from chemically stable B horizon.

183

Precipitation (mm)

Figure 19-A. Reconstructed paleo-moisture values using chemical weathering indices for Upper Orkhon section, Section A. This section is overlain by 3.1±0.1 Ma Ar39/Ar40 dated basalt flows that represents from late Pliocene-Miocene shift to Early Pleistocene epoch. The paleosol sections represent relatively wet period of 451±50.1 mm, (See table 22-C-D). The paleo-precipitation is calculated from average and standard deviation of all sample within this profile section. The paleo-precipitation estimate is from chemically stable B horizon.

184

Precipitation (mm)

Figure 19-B. Reconstructed paleo-moisture values using chemical weathering indices for Upper Orkhon section, Section B. It positions only a kilometer distance upward stream from Section A. The paleosol sections represent relatively wet period of 451±50.1 mm, (See table 22-C-D). The modern stream sample reconstructed moisture is about 257 ±11.5 mm and slightly lower but close to annual precipitation for Chuluut station (265±55.9 mm), that taken as analogue. The paleo-precipitation is calculated from average and standard deviation of all samples within this profile section. The paleo-precipitation estimate is from chemically stable B horizon.

185

Precipitation (mm)

Figure 20. Reconstructed paleo-moisture values using chemical weathering indices for Upper Chuluut section. This section is overlain by 11.2±0.2 Ma Ar39/Ar40 dated basalt flows that represents mid-to-late Miocene (Langhian and Tortonian to Late Messinian stage). The paleosol sections represent relatively wet period of 430±65 mm (See Table 22-E). The modern stream sample reconstructed precipitation is 189±3.7 mm which is identical to precipitation for Jargalant station (186±50.1 mm), that taken as analogue. This moisture reconstruction pattern represents similar Paleo-MAP results with the other sections. The paleo-precipitation is calculated from average and standard deviation of all samples within this profile section. The paleo-precipitation estimate is from chemically stable B horizon.

186

Figure 21-A. Correlation coefficient between modern MAT and Chemical Index of Alteration and its transfer function.

Figure 21-B. Correlation coefficient between modern MAT and Chemical Index of Weathering and its transfer function.

187

Figure 21-C. Correlation coefficient between modern MAT and Plagioclase Index of Alteration and its transfer function.

Figure 21-D. Correlation coefficient between modern MAT and Vogt’s Residual index and its transfer function.

188

Figure 22-A-B. Legend.

189

Figure 22-A. Weathering indices versus reconstructed paleo mean annual temperatures.

Vogt’s Residual Index

Figure 22-B. Vogt’s Residual index versus reconstructed paleo mean annual temperatures.

190

Late Miocene to Pliocene

Modern Floodplain

sediment

Late Miocene to Pliocene

Figure 23-A. Weathering indices versus reconstructed paleo mean annual precipitation.

Late Miocene to Pliocene

Modern Floodplain sediment

Figure 23-B Vogt’s residual index versus reconstructed paleo mean annual precipitation.

191

Figure 24-A. Paleoprecipitation indicators (Adapted from Goldberg et al. 2010). Modern vs Paleo- precipitation patterns (Red star - Modern, Green star – Paleo precipitation). Paleo moisture regime classified into arid yet more humid regime compared to modern.

192

Figure 24-B. Paleoprecipitation indicators (Adapted from Goldberg et al. 2010). Modern vs Paleo- precipitation patterns (Red star - Modern, Green star – Paleo precipitation). Paleo moisture regime classified into arid yet more humid regime compared to modern.

193

Paleosols

Unweathered conditions

Figure 25-A. CIA molar versus K2O/Na2O ratio, Indicator for paleo mean annual precipitation, Modern stream and unweathered samples cluster around 1 indicating dry and cold climate, Paleosols concentrated between values of 1.5 to 2 meaning cool end of subtropical region.

Paleosols Unweathered conditions

Figure 25-B. CIA molar versus Al2O3 ratio, Indicator for paleo mean annual precipitation Modern stream and unweathered samples cluster around 1 indicating dry and cold climate, Paleosols concentrated between value of 1.5 to 2 meaning cool end of subtropical region.

194

Figure 26. CIA vs K2O/(Na2O + CaO*) molar ratio suggests the degree of chemical weathering is strongly dependent of altitude for modern floodplain sediments. Increase in the K2O/(Na2O + CaO*) molar ratio is associated with CIA increase, suggesting a preferential hydrolysis of plagioclase (enrichment of Na) relative to K-feldspar and micas (enrichment in K) during the silicate weathering process of river sediments. See Table 23.

195

CHAPTER 3:

RECONSTRUCTING LATE CENOZOIC LANDSCAPE EVOLUTION ACROSS THE

KHANGAY MOUNTAINS USING ESTIMATES OF ALLUVIAL VALLEY FILL THICKNESS

3.1 Abstract

The sedimentary (alluvial) fills of mountain valleys are underutilized archives with the potential to yield information on the interaction between tectonic, erosional, and depositional processes (DeCelles et al., 2012). Sediment storage is an often-neglected term in sediment budgets, despite being the crucial link between rates of erosion and sediment yield (Straumann et al., 2009). Mountain belts in particular may host large valley fills that modulate fluxes of water and sediment, buffer geomorphic coupling between hillslopes and river channels, reduce local valley relief, and protect bedrock from fluvial incision (e.g., Straumann et al., 2009). Here we propose a method to approximate valley fill thicknesses in glacial and fluvial valleys draining the Khangay Mountains of central Mongolia by employing a simple geometry and GIS-based empirical volume-area scaling relationship to valley fill outlines modified from the methods of Blothe et al. (2013). We sub-divide valleys into two populations, glacial and non-glacial, in order to assess if this characteristic influences valley geometry and fill volume patterns. Our produced power law equation is based on slope-gradient criterion to automatically extract areas of glacial, postglacial fluvial, and lacustrine valley fills from digital topography at the mountain-belt scale. Applying this method to the Khangay Mountains, we find that the size-frequency relationship of ~113,000 individual sediment storage units of various sizes, expressed by either area or volume, follows a power law over four and five orders of magnitude with a scaling exponent of –1.31 ± 0.06. Khangay valley-fills occupy 15.1% of the total study area, including both fluvial and glacial.

We estimate that the total combined volume of Khangay fluvial valley fills are ~3800 ± 110 km3. The volume of glacial valley sediment is estimated at 23.2 ± 1.6 km3, preserved in 3584 spatial units above 2500 m in elevation, mostly surrounding major peaks. We show that the majority of the area covered by sediment storage is in the lower quartile of the mountain belt’s elevation and below the median local relief. On average, low-gradient valley fill occupies 22% of the studied

196 drainage basins. Our approach concluded that the north side of the Khangay Mountains is eroding slightly faster than the south due to increased precipitation, glacial activity, and perhaps active uplift. This fraction increases with basin size, likely reflecting the larger accommodation space of trunk valleys in the lower quartile of the mountain front. Repositioning the sedimentary fills preserved in flat valleys back across the Khangay Mountain landscape results in ~45 m increase in overall elevation of the range since the late Pliocene period of 3 Ma years. In contrast, many valley floor segments were at least 100 m lower than their current elevation, marking the depth to bedrock. The rate of erosion required to remove ~ 45 m from the Khangay upland landscape for the past ca. 3 Ma is 15±0.4 m/Ma (0.015±0.0004 mm/yr) is similar to results obtained by different methods and research groups. Comparison with valley sediment storage mapped in the Himalaya (Blothe et al., 2013) and Swiss Alps (Straumann et al., 2009) indicates that rates of uplift, precipitation and erosion patterns control the extent and spatial distribution of sediment storage at the mountain-belt scale for the Khangay.

3.2 Background

The Khangay Mountains in central Mongolia (Figure 1) is an important mountain belt in the continental interior of northeast Asian whose drainage basins flow to the Arctic Ocean through the Selenga River and its tributaries, and to endorheic basins, such as the Great Lakes Depression, and Valley of the Lakes. The Selenga River is the largest contributing drainage basin to Lake Baikal, the largest by volume and deepest lake on Earth. The average altitude of the Khangay Mountains varies between 2000-2500 m, with most summits between 3200-3500 m. The highest peak, Otgontenger, stands at 4008 m above sea level. High mountain meadows above the tree line occur across the range beginning at 2350-2800 m. The Khangay Mountains are characterized by high elevation, low relief upland surfaces that are often assumed to represent the remnants of recently uplifted Mesozoic-to-early Cenozoic regional erosional surface (peneplain) (Devyatkin, 1975; Cunningham, 2001; Jolivet et al., 2007; West et al., 2013). The mountain range is contained by active strike slip faults to the north (Bulnay fault system), south (Gobi Altai fault system), and west (Mongolian Altay fault system) while minor extensional faulting characterizes the central Khangay Mountains (Walker et al., 2007; 2017). These active intracontinental faults are the result of far-field compressional forces resulting from

197 the India-Asia collision, ~ 2500 km to the south (e.g., Molnar and Tapponnier, 1975; Bayasgalan et al., 2005; Calais et al., 2006). Thrust faults accommodate far-field compression from the India-Asia collision to the south of the Khangay and transition into extensional deformation north of the Khangay, as exemplified by the Khovsgol and Baikal continental rift zones (Molnar and Tapponnier, 1975; Cunningham, 2001; Bayasgalan et al., 2005). The landscape of the Khangay Mountains is dominated by upwardly convex hillslopes, which suggest that the active sediment transport processes are diffusional (e.g., Fox et al., 2008). It is believed that at present, Khangay hillslopes are dominated by mostly gravitational hillslope transport (creep and solifluction) due to prominence of physical weathering. Khangay hillslopes are also mostly devoid of large mass-wasting events such as landslides and rock avalanches (Lehmkuhl et al., 2004). In the highest elevations of the Khangay Mountains, there are many signs of Quaternary glaciation. Field evidence includes erosional landforms such as cirques and U-shaped valleys, and depositional landforms like moraines. Identification of regions that were glaciated during the Last Glacial Maximum (LGM) is based upon field observations of the presence of glacial landforms that correspond to a mean glacial equilibrium line altitude (ELA) of 2800 m (Lehmkuhl et al., 2004). The predominant rock types exposed in the Khangay are either granites or metasedimentary rocks. In the central and eastern portions of the range, Miocene and younger basalt flows unconformably overlie both the high topography and infill some of the valleys (e.g., Yarmolyuk et al., 2008; Smith et al., 2016; Ancuta et al., 2017). The most active stream channels contain a mix of granitic, metasedimentary, and basaltic clasts (Hopkins, 2012). Streams in the Khangay are eroding into alluvium as opposed to bedrock, and are located in a dominantly diffusional landscape with no observed evidence of recent or old mass wasting events. Modern erosion is limited to the amount of sediment that can be transported by the streams. Much of the terrain to the east, south, and west surrounding of the Khangay is occupied by a plateau at an altitude between 1,000 and 1,500 meters. These plateaus are dominated by strong winds throughout much of the year. Due to the aridity of the climate, these winds can bring dust storms, which are more frequent in the south, less frequent in the center, and rare in the north. In winter, the region is dominated by cold air masses from Siberia. The climate of the Khangay is generally warmer and less variable than the surrounding areas.

This relatively arid regional climate has been in place from the late Pliocene through the Quaternary, concomitant with global cooling. Caves et al. (2014) suggest that the uplift of the

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Tibetan Plateau, along with the Khangay Mountains themselves in the late Eocene through early Oligocene, shielded the warm monsoonal air masses and contributed to regional drought. The results from chemical weathering indices derived from paleosols preserved beneath 40Ar/39Ar- dated late Miocene and Pliocene basalts (11 to 3 Ma) in several of the major river valleys revealed that mean annual temperatures along the north flank of the Khangay region were 5-10 °C warmer and mean annual precipitation was likely twice as great as at present during this period. Unlike today, which is more dominated by physical weathering due to the cold climate, during the late Miocene to early Pliocene, soil production was more robust, signaling that hillslopes were transport limited, consistent with enhanced near-surface chemical weathering relative to that of today. At present, the Khangay Mountains exhibit negligible amounts of erosion due to low precipitation. Most of the river valleys are backfilled with sediments derived from either hillslope or glacial erosion. These sediment fills are hypothesized to be relatively thick due to the observation that streams lack the transport power to efficiently remove sediments added from hillslope and glacial erosion, and therefore the valleys have backfilled over time, possibly beginning in the mid-to-late Pliocene with the transition from transport-limited to weathering-limited hillslopes. The observation of sediment-choked valleys would not normally be that unusual for currently or formerly glaciated terrain, where glacio-fluvial outwash overwhelms the transport capacity of rivers, forcing them to aggrade. However, in the Khangay, even valleys that never harbored glaciers appear to have thick accumulations of fluvial sediments, and the streams are entirely alluvial with no exposures of bedrock along the channels (Figure 2).

The sedimentary (alluvial) fills of mountain valleys are underutilized archives that have the potential to yield information on the interaction between tectonic, erosion and depositional processes (DeCelles et al., 2012). At the mountain-belt scale, for example, volumetric estimates of dated molasse deposits allow inference of gross deposition and erosion over geological time scales (e.g., Kuhlemann et al., 2002). However, most studies that have attempted to quantify sediment storage in steep upland terrain have focused on much smaller areas (Schrott et al., 2003; Kasai et al., 2004; Lancaster and Casebeer, 2007), and these studies are not all that informative about the distribution and relevance of sediment storage at the mountain-belt scale. This is a major shortcoming, especially as large intramontane valley fills modulate significant fluxes of water and sediment, and help buffer the geomorphic coupling between hillslopes and

199 river channels, thus delaying delivery of hillslope debris to the drainage network, and ultimately to depositional basins, such as Lake Baikal for the northern flank of the Khangay Mountains. On interglacial time scales large valley fills further contribute to reducing local topographic relief, and aid in the protection of bedrock from erosion by fluvial incision and mass wasting (e.g., Sklar and Dietrich, 2001; Korup and Tweed, 2007). During glacial-interglacial cycles, the gradual replacement of mountain ice caps and valley glaciers by large bodies of postglacial sediment and vice versa affects glacio-isostatic and erosion-induced uplift (Champagnac et al., 2007), while deglaciation in particular may boost sediment yields through the rapid evacuation of large storage volumes (Koppes and Hallet, 2006).

I hypothesize that regional cooling and aridification, following the global trend, from the late Miocene into the Quaternary resulted in significant changes in hillslope and river processes across the Khangay Mountains. Specifically, a decrease in the effectiveness of chemical weathering and soil production rates with the onset of global cooling in the mid-to-late Pliocene resulted in a shift from transport-limited to weathering-limited hillslopes and the concomitant infilling of Khangay valleys as the streams lacked the capacity to transport the volume of in- coming sediment delivered from adjacent hillslopes. Based on the assumption that valley fill volumes began aggrading in earn est at ca. ~3 Ma, on par with cooling climate trends, I aim to determine the sedimentary fill volumes and equivalent long-term erosion rate for the Khangay for the past 3 Ma. Here, the main age control factor is the global climate cooling since the late Pliocene (as recorded in numerous accounts of sedimentary proxies) that ushered in a transformation near-surface sediment production, transport, and storage processes.

My hypothesis attempts to test the idea that Khangay valleys began filling in the late Pliocene due to the geomorphic regime shift to primarily physical weathering dominated hillslope processes. Streams in the Khangay today do not have sufficient discharge needed to transport these sediments away from the mountain front. I also predict that even in formerly glaciated valleys of the Khangay, alluviation is at least in part the result of non-glacial hillslope processes resulting in the transformation of the Khangay landscape over the last several millions of years.

The spatial patterns of erosion and valley fill may be useful in conjunction with other studies focused on regional seismology, thermochronology, fish genetics, petrology and geochemistry, to decipher the rate and timing of crustal and mantle processes responsible for the uplift of the

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Khangay Mountains (e.g., Meltzer et al., 2015). The present ambiguity with respect to the geologic history and topographic development across Khangay will be disentangled step by step through the combination of results from these various research endeavors, of which this study represents a substantial contribution towards understanding the spatio-temporal distribution and volumes of sediment in the near-surface environment.

3.3 Materials and methods

3.3.1 Digital topography

We utilized digital topographic data from the SRTM30 DEM (NASA JPL, 2013) with gaps filled by topographic map data (www.earthexplorer.org). Hydrologic correction for the DEM was carried out using a fill algorithm within ArcMap Spatial Analyst. Individual DEM tiles were merged to provide entire coverage across the Khangay drainage networks, excluding areas below a smoothed 1900-m contour line in order to restrict our analyses to the mountain range. However, deeply incised low topographic valleys are included within the 1900-meter altitude limit (Figure 3). Valley floors areas were defined through the utilization of components of the ArcGIS Hydrology toolset and various other algorithms (Figure 4). On the gap-filled SRTM30 m DEM, we performed flow direction followed by flow accumulation algorithms in order to define the major watersheds. After clipping out the 12 major watersheds that drain the Khangay Mountains, we delineated streams and stream links in order to outline the sub-watersheds within each of the major watersheds. The area-threshold limit for sub-watersheds was set at 16,650 cells resulting in a maximum limiting size for most sub-watersheds between 15-30 km2. We defined valley fills as either flat or gently sloping valley bottoms, elevated terrace surfaces, or alluvial fans covering a maximum area of 30,000 m2 (i.e. 16,650 SRTM pixels) in order to avoid volume exaggeration when using volume-area scaling relationships (e.g., Bahr, 1997). However, we excluded sediment storage on hillslopes (colluvium), flat-topped plateaus, and lava flows, which are concentrated in the southwest and central part of the range. In order to define flat surfaces, we ran the ArcGIS slope algorithm with a maximum threshold of 5°. In order to define the local relief, we utilized the ArcGIS focal statistics algorithm with the function of range with a circular radius of three pixels (90 m) that was set to a local relief threshold greater than 15 m. Areas

201 exceeding the maximum slope and local relief thresholds were excluded. Local relief results highlight more generalized flat surfaces containing fewer hummocks compared to surfaces with slopes < 5°. Therefore, we used local relief data as a more reliable indicator for identifying valley bottom areas likely to contain accumulations of alluvial fill. Then, using the Raster Calculator function in ArcGIS, we overlapped the flat valley bottom surface areas with sub- watersheds, in order to isolate all of the flat surfaces at the sub-watershed scale.

Most major fluvial valleys lie below 2500 m. Valleys above 2500 m are considered as formerly glaciated for the purposes of this analysis. The regional Khangay last glacial maximum ELA was at 2800 m (Lehmkuhl, 2004). Therefore, from the gap-filled digital elevation models, we clipped out the areas above 2500 meter in order to isolate the fluvial component of the valley networks. We again used the Raster Calculator function to isolate flat-surfaced regions below 2500 m. Within the resulting dataset, some areas still existed that are less than 2500 m and 5° surface slopes, but represent flat-topped hills or plateaus. In order to exclude these areas, we used the ArcGIS Raster to Polygon function in order to convert all raster areas into vector polygons. The flat-topped low-lying hills were then manually removed from the digital dataset. We also excluded isolated individual polygons that retained all of the characteristics of flat valley bottoms, but for which their area is less than 1 km2. By systematically following the routines and procedures outlined on Figure 4, we delineated the portions of the Khangay landscape that are suitable for use in an evaluation of area-to-volume scaling relationship based upon a power law equation.

3.3.2 Estimating accommodation space and application of volume area scaling

In order to quantify the maximum accommodation volume for sediment from a given valley-ﬁll planform area and valley topography, we generated a simple geometry approximation for fluvial and glacial valley cross-sections based upon downward projection of the hillslope gradient at the intersection with the valley floor, as defined from the DEM (Figure 5). This approach works on the assumption that the bedrock topography beneath existing valley ﬁlls have simple geometries based on a direct slope continuation from adjacent hillslopes. In developing the mathematical Area-to-Volume scaling relationship, we choose 15 semi-randomly distributed areas along the major drainage basins for fluvial valleys (<2500 m) and an additional 16 areas for glacial valleys

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(>2500 m) of the Khangay. These basins were corrected for the skewed size distribution of contributing catchment area. The hypothetical hillslope continuation into the subsurface for calculating valley cross-sectional areas was accomplished using a combination of ArcGIS elevation profile routine across selected valley bottoms with MapInfo GIS software using the Non-earth meters projection in order to calculate the cross sectional area for the selected section (Figure 5). The selected cross sections include one from each of the major watersheds and were selected at variable elevations from both narrow to wide valley planform width types. The resulting cross sectional areas [m2] were multiplied by a 500 m length, except where tributaries joined the main valley causing it to widen, in order to calculate a volume (m3) for every 0.5 km of valley length. Then, the representing flat surface area of the given section is compared to the volume for the same section. The results of this component constitute our base of estimation for the power-law scaling relationship between valley flat surface areas and the volume of alluvial fill contained within valleys of the Khangay Mountains (Table 1). For this, we used the following power-law equation relating volume from calculated area:

V = bAs, [1] where V is the volume of sediment fill, A is the map view area, b is the y-axis intercept, and s is the scaling exponent. We assume that for the high-to-mid altitude valleys with narrow-to medium widths, the resulting volume is accurately calculated given minimal(±10%) uncertainties; however our volume estimation for broad, lower elevation valleys may tend to overestimate the true volume due to power-law exaggeration in relation to increased individual area sizes.

The geophysical Vertical Electric Sounding (VES) survey performed in the Galuut Valley along the southern flank of the Khangay, reported in Chapter 1 of this thesis, provides evidence that at least in the narrow-to medium-width valleys, our simple geometry based estimate is in agreement with VES survey revealed valley depth results. Unfortunately, geotechnical and water well drilling data from Khangay valley bottoms that penetrated through the complete depth of fluvial sediments into the underlying bedrock are very rare, except in the administrative centers, for which there are a few well depth values from hydrogeological investigations (Batuskh et al., 2018). These wells indicated that sedimentary fill depths range between 60 to 120 m (Batsukh et. al 2018), but most of these wells did not reach to bedrock, so these are minimum values. There

203 are not enough reliable estimates of the valley-fill thicknesses for streams draining the Khangay Mountains for us to utilize existing geotechnical data, thus necessitating the approach we have undertaken in this chapter. Our estimate for the volume-area scaling from our test case of 15 fluvial (Table 1-A, Figure 6-A) and 16 glacial (Table 1-B, Figure 6-B) valleys have coefficient of determination (r2) value of 0.81 (fluvial) and 0.91 (glacial) respectively, when low-lying gentle sloped flat areas are excluded. In order to avoid volume exaggeration we tend to delineate individual sub-watershed areas not to exceed 30 km2 (16,650 cells), because overestimates can be large if individual areas are too big (e.g., Bahr et. al 2015).

3.4 Results

We delineated 12 major watersheds for use in the volume area scaling, and also created sub- watersheds with a maximum individual area limit of 30 km2 (16,650 cells) to minimize exaggeration in the area-volume scaling relationship. The total estimated volume of stored valley bottom sediment across the Khangay range is 3,806 ± 109 km3 for fluvial valleys and 23.2 ± 1.6 km3 for glacial valleys (Table 2). Of all the major catchments, The Ider River Basin, the largest tributary to the Selenga River, contains the largest volume of aggraded sediment, which at 941 ± 26 km3 accounts for 24% of the total estimated volume across the study area. The Ider has a volume to watershed area ratio of 0.036 and a volume to floor area ratio of 0.155 (Table 2).

3.4.1 Volume estimates

The use of volume-area scaling relationships is popular because its application is relatively easy and because area data are readily available while volume data are not (Bahr, 1997). We estimate that the total volume of aggraded sediment contained within Khangay valley is 3806 ± 109 km3 for fluvial valleys and 23.2 ± 1.6 km3 for glacial valleys. Power-law scaling exponents are somewhat different for fluvial (1.31 ± 0.06) and glacial (1.24 ± 0.15) valleys. Sediment-filled valleys occupy 15.1% of the entire Khangay Mountain study area (Table 2). Our volumetric scaling as a function of valley floor area also varies slightly between most of the major litho- tectonic units, likely reflecting a rock mass-dependent susceptibility to erosion and sediment storage. Quantile regression yields higher scaling exponents for upper percentiles, indicating that

204 valleys with larger plan-view areas store disproportionately more volume per unit area than do those characterized by smaller surface areas. Our volume-area scaling exponents from a global median regression, where s = 1.31 ± 0.06, are similar in magnitude to reported values from a similar study conducted in valleys of the European Alps (Straumann and Korup, 2009). Our volumetric estimates are clearly conservative, as we excluded from our calculations most of the outside basins along the margins, together with those in the Khangay foreland (Selenga basin) that may host substantial amounts of pre-Quaternary sediments at depth as well (Table 2; Figures 7, 11).

The spatial pattern of alluvial sediment volume stored per unit study area shows a distinct regional partitioning, with most sediment sequestered in the major river basins, such as the Ider (941 ± 26 km3) and Chuluut (513.8 ± 14.7 km3) on the north flank of the range, and the Baidrag (856.9 ± 32.1 km3) that drains the southern flank (Figure 7, 10, 11; Table 2). The total sediment volume derived from the Khangay Range is stored in the 12 major stream basins, especially in the lower reaches (Fig. 11). A number of large (> 1 km3) areas of alluvial fill also cluster in the Baidrag watershed, especially downstream, where a major part of the total volume is contained in the flat low-lying lands (Figure 7, 10, 11; Table 2). As observed from slope and local relief maps (Figure 8, 9) most flat low-lying areas match with this. Figure 10 shows the major watershed volumes normalized by drainage area. The highest ratio occurs with the Baidrag on the south flank due to its lower gradient and southeastern watersheds that have more flat areas. More steep sections tend to have lower ratios such as upper reaches of Orkhon and Zavkhan rivers (Figure 10).

For glacial valleys, volume estimates are comparably low due to their relative size, cross sectional area and assumed depth. See Figure 13 for details of these calculations. The maximum volume of fill contained in glacial valleys can reach up to 0.35 km3, but such valleys are very rare. See area, volume, and depth frequency histograms for the entire Khangay Range for both fluvial and glacial valleys on Figures 15 to 19.

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3.4.2 Depth estimates

We estimated the depth using a volume versus area ratio approach, similar to the common method applied to define the average depth of lakes. Using this approach, the maximum derived (estimated) depth for the larger flat valley-floor areas in the lower parts of many of the drainage basins was between 242 to 260 m, depending on the size of the area, while minimum depths ranged between 23 to 32 m (Figure 12; Table 2). For glacial valleys, the depth ranges from a minimum of ~10 m to > 80 m (Figure 14).

3.4.3 Cross checking

After estimating the individual and aggregate volumes for each of the major watersheds, we cross-validated our derived values using ten selected valleys. Using the power-law equation for volume-area we compared results to the side slope projection-continuation method. The direct comparison resulted in r2 values of 0.91 to 0.93 (Figures 20, 21, Table 5).

3.5 Discussion

3.5.1 Spatial pattern of Khangay valley fills

The Khangay Mountains are generally tectonically inactive except in the northwest and northeastern sections. Therefore, valley-bottom sediment accumulation and storage due to direct tectonic forcing is believed to be minimal. The Khangay region endures a cold climate for three quarters of the year, with average annual temperatures below 0°C in most parts. Annually, the Khangay receives 350-400 mm/yr of rainfall, which falls mostly during the summer months. This suggests that the Khangay experiences mostly hillslope-based physical weathering today, rather than chemical-dominated rock disintegration. This trend may well have continued since the late Pliocene through the Quaternary, likely beginning in the Pliocene with globally-induced cooling and aridification of the Khangay region (see Chapter 2 for additional details). The negligible amount of erosion and minimal stream power due to low precipitation and high alluvial infiltration rates may be one of the main reasons for such large volumes of sedimentary

206 backfill sequestered in the valleys. Chemical weathering indicators suggest that paleosol production and chemical weathering was more intense, due to warmer temperature and higher amount of precipitation, during the late Miocene into the middle (> 3 Ma) Pliocene.

When comparing the ratio between flat/floor area versus volume across individual basins from the north and south sides of the Khangay Mountains, it is confirmed that southern watersheds contain a larger percentage of valley bottom flat area, meaning that there is a correspondingly higher ratio of area to volume on this side of the range. The broader south-side valleys have lower gradients that could be the result of either slower or earlier tectonic uplift, and/or the fact that this side receives about half the MAP as received by north-flank basins, which translates to less stream power for sediment transport out away from the range. Therefore, it is assumed that basins on the south side of the range have experienced slower rates of total sediment export (net erosion) compared to sister basins across the continental divide on the north side of the range (Table 2).

Hypsometric analysis is the correlation of normalized horizontal cross-sectional drainage basin area to elevation. Hypsometric curves and hypsometric integrals are important indicators of watershed conditions, based on differences in the shape of the curve (Pike et al. 1971). Hypsometric integral values are related to the degree of disequilibrium in the balance of erosive and tectonic forces. The value ranges between 0 and 1 with lower being tectonically dormant, flatter area with lesser erosion rate, while higher values express higher erosion rate somewhat in relation to increased tectonic activity and steeper topography. The Hypsometric integral is most useful in studies of active tectonics. A useful attribute of the hypsometric curve is that drainage basins of different sizes can be compared with each other, because an area elevation is plotted as functions of normalized total area and total elevation. That is, the hypsometric curve is independent of differences in basin size and relief (Pike et al. 1971). The calculation of the Hypsometric integral (distribution of area as a function of normalized elevation within a catchment) from the north and south side drainage basins of the Khangay reveals that north- flowing basins have slightly more high-standing topography and are more steep (0.377) relative to south-flowing ones (0.369) (Table 3; Figures 22, 23). Overall, the southern flank of the Khangay is slightly less affected by tectonic uplift and less prone to erosion due to lower amount of precipitation compared to the north. Increased amounts of wind-transported sediment from

207 desert regions to the south into surrounding basins could be an additional factor for southern watersheds (Table 3; Figures 22, 23). The average hypsometric integral values calculated for north and south Khangay watersheds reveal that the mountain range is in the mature stage (Singh et al., 2008) of the topographic development.

Regions containing large volumes of fill occur where valleys are broader, mostly in the downstream part of the major watersheds, such as the Ider, Chuluut, Orkhon and Baidrag (Figure 11). However, in the upper parts of the Ider River in the Telmen Lake valley, for example, a significant amount sedimentary fill is likely preserved (Figure 7, 11). Today there are no active glaciers in the Khangay Range, but there is ample evidence for extensive valley glaciation during cold phases of the Pleistocene that formed areas of high local relief and scoured, U-shaped valleys that in part provide accommodation space for postglacial alluvial sedimentation, mostly above the 2500 m altitude threshold. In downstream reaches of Khangay rivers, where channel steepness decreases substantially, major valley floors are as wide as 10 km. The volume of large valley fills increases with wider fluvial valleys downstream, such that we infer that these sediment fills are in large part derived from transport of sediments off adjacent hillslopes and delivered to valley bottoms via tributary channels and alluvial fans. Glacial widening and overdeepening of valleys has contributed to some accommodation space along structurally controlled valleys above the 2500 m threshold. In general, valleys in the Khangay experience negligible fluvial incision today, and most exhibit signs of equilibrium-to-aggradation.

Previously, other researchers estimated valley fill volumes in the mountain front of the Himalayas (Blothe, 2013), and in the Swiss Alps (Straumann, 2009), through similar application of the power-law equation based upon volume-area scaling relationship. However, we used a slightly different approach for the accommodation space estimation in the Khangay Mountains. Blothe and colleagues’ (2013) volume estimates for Himalayan valleys are notably lower than ours for the Khangay Mountains (Table 4-A, B). This is due to intense tectonic activity in the Himalaya resulting in steeper valley slope profiles that is coupled with significantly higher amount of precipitation in the region. The combination of these factors results in faster sediment transport out away from the core of the Himalayan range by streams with greater amounts of flow velocity, discharge, and channel slope. As a result of these factors Himalaya valleys keep thin layers of sediment compared to the Khangay. Similarly, the Swiss Alps are more recently

208 tectonically active in comparison to the Khangay, were more intensely glaciated, and receive at least 3 to 5 times (1100-1600 mm) (Table 4-A, B) more MAP, all leading to a faster overall erosion rate, thus preserving lesser amount of sediment volume in the valleys in comparison to the Khangay (Table 4-A, B).

Korup and Blothe (2013) originally proposed that superimposed on the first-order topographic constraint on glacier-induced sediment storage is a significant decline of individual valley-fill volumes with increased mean annual precipitation, supporting the intuitive notion of lower sediment storage potential in areas of higher erosion, which is facilitated by larger MAP. This is further augmented by their finding of a strong decline in total volume with increasing mean local relief and precipitation. The climate-local topographic relief-sediment storage hypothesis and findings of Korup and Blothe (2013) are supported by our results, as the Khangay receives meters less MAP and is orders of magnitude less tectonically active in comparison to the Himalayas, and yet have a substantially larger (8x) total aggraded sediment volume-to-area ratio than do the Himalayas (Table 4).

Our method has captured mostly low-gradient alluvial valley ﬁlls. This volume is stored in 12 major watersheds containing > 100 km3 each, especially in the Ider and Chuluut basin in the north and the Baidrag basin in the south. The overall observation that 45% of the alluvial sediments associated with the range are trapped in downstream reaches of Khangay rivers supports the notion that slow tectonic uplift and low MAP for the Khangay results in a spatial disequilibrium with respect to the sediment budget for the range that favors lower-water reaches of its major rivers. Here, large volumes of sediments are retained given the relatively shallow river gradients in concert with persistent aridity and streams that appear to lose discharge in the downstream direction, as is typical of semi-arid to arid streams that drain somewhat wetter highlands.

Increased Quaternary aridity in the Khangay region may also be an additional factor that influences the volume of sediment in lower valley bottoms through the addition of aeolian transported sand and silt from the endorheic basins of the Valley of Lakes and Great Depression of Lakes to the south and west of the Khangay, respectively. In several instances, eastward migrating sand dune fields have blocked streams flowing out of the western Khangay, forming for example the spectacular dune-dammed Ulaagchiin Khar Lake (48.35° N, 96.08° E).

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3.5.2 Sediment transport patterns and erosion rate

As noted above, 3,806 ± 109 km3 of sediment is believed to be temporarily stored in the valley bottoms of the Khangay, and the transition from incision to aggradation likely initiated in the mid Pliocene and continues today. Assuming streams were flowing on or near to bedrock prior to 3 Ma due to increased chemical weathering (transport-limited hillslopes) and higher amounts of precipitation, then Pliocene bedrock floors of the valleys were likely ~150 meters below the elevation of modern streams today. If we drape the current valley preserved sediments back on the hillsides and mountain tops of the range, accounting for the volume conversion factor for sediment to rock, the mean elevation of the mountain range would be increased by approximately 45 ± 1.3 m (Figure 24 A-E). We used a method of dividing total sediment volume by hillslope area excluding current valley floors. It is based on the assumption that weathering- limited erosion is dominant since ~3 Ma on par with global cooling and tectonic inference is negligible during this period.

Geomorphic observations coupled with 40Ar/39Ar dated whole-rock basalt stratigraphy (Ancuta, 2017) allowed us to understand minimum incision rates in the Khangay region during mid Miocene through Late Pliocene. The Orkhon River headwaters show rates of ~20 m/My through the late Pliocene to Holocene. The northern ﬂank of the Khangay shows rates of ~10 m/My in 13–14 Ma basalts exposed along the Ikh Tamir River, a tributary of the Orkhon. The Chuluut River valley, to the west, shows variable incision rates of 10–23 m/My over the past ca. 8–10 Ma. As concluded by cosmogenic studies by Hopkins, (2012), and Smith et al., (2016), the mean 104 to 105 yr 10Be-derived basin-average erosion rate for the Khangay ranges between 12 to 20 m/Ma. In accordance with these earlier whole rock basalt stratigraphy (Ancuta et al 2017) and cosmogenic rate estimates (Smith et al. 2016), our derived estimate for basin average erosion rate is 15 m/Ma, or 0.015±0.0004 mm/yr (Figure 24 A-E). In addition, a thermochronology based study by McDannell et al. (2018) concluded that in the Khangay, in the absence of strong tectonic or climate forcing, erosion is limited and remnant landscapes can persist over tens of millions of years in a state of disequilibrium.

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3.5.3 Residence times of valley ﬁlls

It is presumed that under a high precipitation and chemical weathering dominated environment triggered by warm temperatures, the transport of sediment from the upper reaches of the watershed should be intense. In addition, steep slopes due to increased tectonic activities will further accelerate the discharge of sediments away from the mountain fronts. This is particularly true for Himalaya (Meetei et al. 2006). The geologic history, paleo and modern environmental conditions have been opposite for the Khangay in comparison with the Himalayas probably at least for last few million years. This arises further questions of how long the sediment volume has been kept in the valleys under such dry, immobile and tectonically dormant conditions for the Khangay. Therefore, our volumetric estimates provide not only the spatial distribution of Khangay valley ﬁlls, but also allow for the possible assessment of sediment residence times within intra-montane catchments. We estimate the regional mean residence time of Khangay valley ﬁlls in a hypothetical framework. First, we assume that Khangay valley fills represent the net accumulation over the past ca. 3 Ma. Concomitant with global cooling and increased aridity in the continental interiors, regional sediment transportation efficiency is significantly reduced. The main erosion pattern throughout the Khangay since the middle Pliocene was most likely dominated by physical weathering processes (e.g. frost shattering of bedrock) weathering-limited hillslopes, and valley-bottom aggradation. We believe that, for the majority of valleys across the Khangay, there has been net sediment aggradation through at least the last 3 million years and this pattern will continue into the future unless significant tectonic activity, or a regional state- change in climate occurs. In terms of modern tectonic activity, the northern flank of the range is slightly more active compared to the south. The south flank contains more sediment storage due to decreased precipitation and increased aeolian input derived from the terminal, endorheic basins in the Gobi.

These hypotheses highlight the substantial lag times introduced by intermediate sediment storage in valleys along the Khangay that may complicate interpretations and correlations of high- resolution intramontane sedimentary archives with those in the surrounding foreland basins. Assuming that build-up and full removal of sediment storage occurs at a time-averaged rate across the Khangay, we can estimate median residence times for Khangay valley fills. The size distribution and spatial pattern of valley fills predicts that several dozen of the observed large 211 valley fills in the lower reaches of major streams along the northern and southern margin may have started accumulating since 3 Myr. This orogen-scale assessment of sediment storage supports contributions from Late Pliocene and the entire span of Quaternary sediments stored in large watershed basins: The Ider watershed alone contains one fourth of the sediment volume of the entire Khangay, i.e. 941 ± 26.1 km3. Larger basin fills such as the Ider, Chuluut, Orkhon and Baidrag have accumulated > 2000 km3 of sediment since Pliocene times, or an amount equivalent to ~10% of the total water volume of Lake Baikal (Table 2). The volumes of such long-lived alluvial basin fills rival volumetric estimates for many Quaternary terminal basin fill deposits (Blothe et. al, 2013), and highlight the importance of recognizing the substantial lag times that may potentially buffer signals of climatic variability in the Khangay and similar sediment routing system for the last few million years.

3.6 Conclusion

In conclusion, we present the first comprehensive estimate of million year-scale sediment storage in large valley fills for the entire Khangay Range in central Mongolia. Future work on the alluvial sediment budget of the Khangay may refine the volumetric accuracy, though the implications of the spatial distribution of valley fills remain. However, we need to consider a few important steps in order to refine or improve our research. First, we need robust valley fill thickness data collected from geotechnical, water well, or other geophysical measurements. Second, we recommend increasing the number of valley cross sectional area determinations using the ArcGIS-MapInfo method developed for this research, to refine and improve the scaling relationship based on the area-volume power-law equation. Third, our simple geometry-based downward projection of hillslope profiles for estimating valley cross-sectional area could be improved by attempting the technique in additional locations than initially attempted. Finally, the long residence time and volume of sediment stored in Khangay valleys may complicate interpretation of sedimentary archives from terrestrial and lacustrine basins, and resulting attribution of shorter-term seismic or climatic disturbance signals associated with the Khangay Mountains source region. The results presented in this chapter help us to better understand the history of the carving of valleys into bedrock and their apparent partial sedimentary in-filling.

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3.7 Acknowledgements

I would like to thank Dr. Oliver Korup at the University of Potsdam for his guidance of combined GIS and power law equation method for calculating sediment fill volumes. This work is supported by U.S. National Science Foundation Research Grants EAR-1009702 and EAR1009680.

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3.8 References

Adhikari, S., and S. J. Marshall., 2012. Glacier volume-area relation for high-order mechanics and transient glacier states, Geophys. Res. Lett., L16505, doi: 10.1029/2012GL052712

Ancuta, L. D., 2017, Toward an Improved Understanding of Intraplate Uplift and Volcanism: Geochronology and Geochemistry of Intraplate Volcanic Rocks and Lower-Crustal Xenoliths (p. 207). Bethlehem, PA: Lehigh University.

Ahnert, F., 1970. Functional relationships between denudation, relief, and uplift in large mid- latitude drainage basins: American Journal of Science, v. 268, p. 243-263.

Amerson, B.E. D.R Montgomery, G. Meyer., 2007. Relative size of fluvial and glaciated valleys in central Idaho. Geomorphology, doi:10.1016/j.geomorph.2007.04.001

Arendt, A. 2006., Volume changes of Alaska Glaciers: Contributions to rising sea level and links to changing climate, PhD thesis, Univ. of Alaska, Fairbanks.

Arendt, A., K. Echelmeyer, W. Harrison, C. Lingle, S. Zirnheld, V. Valentine, B. Ritchie, and M. Druckenmiller., 2006. Updated estimates of glacier volume changes in the western Chugach Mountains, Alaska, and a comparison of regional extrapolation methods, J. Geophys. Res., 111, F03019, doi: 10.1029/2005JF000436.

Bahr, D. B., 1997a. Global distributions of glacier properties: A stochastic scaling paradigm, Water Resour. Res., 33(7), p.1669–1679, doi: 10.1029/ 97WR00824.

Bahr, D. B., 1997b. Width and length scaling of glaciers, J. Glaciol., 43(145), 557–562.

Bahr, D. B., 2009. On fundamental limits to glacier flow models: Computational theory and implications, J. Glaciol., 55(190), p.229–238.

Bahr, D. B., 2011. Estimation of Glacier Volume and Volume Change by Scaling Methods, in Encyclopedia of Snow, Ice and Glaciers, Encycl. Earth Sci. Ser., pp. 278–280, Springer, Netherlands, doi:10.1007/978-90-481-2642-2_690.

214

Bahr, D. B., and M. B. Dyurgerov., 1999. Characteristic mass balance scaling with valley glacier size, J. Glaciol., 45(149), p.17–21.

Bahr, D. B., and V. Radić., 2012. Significant contribution to total mass from very small glaciers, Cryosphere, 6(4), p.763–70, doi: 10.5194/ tc-6-763-2012.

Bahr, D. B., and J. B. Rundle., 1995. Theory of lattice Boltzmann simulations of glacier flow, J. Glaciol., 41(139), p.634–40.

Bahr, D. B., W. T. Pfeffer, and M. F. Meier., 1994. Theoretical limitations to englacial stress calculations, J. Glaciol., 40 (136), p.509–518.

Bahr, D. B., M. F. Meier, and S. D. Peckham., 1997. The physical basis of glacier volume-area scaling, J. Geophys. Res., 102(B9), p.20355–20362, doi: 10.1029/97JB01696.

Bahr, D. B., W. T. Pfeffer, C. Sassolas, and M. F. Meier., 1998. Response time of glaciers as a function of size and mass balance: 1. Theory, J. Geophys. Res., 103(B5), p.9777–9782, doi: 10.1029/98JB00507.

Bahr, D. B., M. Dyurgerov, and M. F. Meier (2009), Sea-level rise from glaciers and ice caps: A lower bound, Geophys. Res. Let., 36, L03501, doi: 10.1029/2008GL036309.

Bahr, D. B., W. T. Pfeffer, and G. Kaser., 2014. Glacier volume estimation as an ill-posed inversion, J. Glaciol., 60(223), p.922–924, doi: 10.3189/ 2014 JoG14J062.

Bahr, D. B., W. T. Pfeffer, and G. Kaser, 2015. A review of volume-area scaling of glaciers, Rev. Geophys., 53, p.95–140, doi: 10.1002/2014RG000470.

Balco, G. 2001. Cosmogenic isotope production rates over large areas: http://depts.washington.edu/cosmolab/P_by_GIS.html.

Balco, G. 2006. Converting Al and Be isotope ratio measurements to nuclide concentrations in quartz. http://hess.ess.washington.edu/math/docs/common/ams_data_reduction.pdf.

Balco, G., Stone, J.O., Lifton, N.A., and Dunai, T.J., 2008. A complete and easily accessible means of calculating surface exposure ages or erosion rates from 10Be and 26Al measurements: Quaternary Geochronology, v. 3, p. 174—195.

215

Batsukh, N., Natural water resources and its study of geological environment. 2018, Publication from the Mongolian University of Science and Technology, p. 42-69

Blothe, J.H., O. Korup., 2013. Millennial lag times in the Himalayan sediment routing system, Earth and Planetary Science Letters 382 (2013) p. 38–46

Burbank, D.W., Leland, J., Fielding, E., Anderson, R.S., Brozovic, N., Reid, M.R., and Duncan, C., 1996. Bedrock incision, rock uplift and threshold hillslopes in the northwestern Himalayas: Nature, v. 379, p.505-510.

Burbank, D.W. and Anderson, R.S., 2012. Tectonic Geomorphology, 2nd edition: Blackwell Publishing Ltd.

Calais, E., Dong, L., Wang, M., Shen, Z., and Vergnolle, M., 2006, Continental deformation in Asia from a combined GPS solution: Geophysical Research Letters, v. 33, L24319, doi:10.1029/2006GL028433.

Caves, J.K., Sjostrom, D.J., Mix, H.T., Winnick, M.J., and Chamberlain, C.P., 2014, Aridification of Central Asia and uplift of the Altai and Hangay Mountains, Mongolia: Stable isotope evidence: American Journal of Science, v. 314, p. 1171-1201.

Caves, J.K., Winnick, M.J., Graham, S.A., Sjostrom, D.J., Mulch, A., and Chamberlain, C.P., 2015, Role of the westerlies in Central Asia climate over the Cenozoic: Earth and Planetary Science Letters, v. 428, p. 33-43.

Champagnac, J.D., Molnar, P., Anderson, R.S., Sue, C., and Delacou, B., 2007, Quaternary erosion induced isostatic rebound in the western Alps: Geology, v. 35, p. 195–198, doi: 10.1130/G23053A.1.

DeCelles, P. (2012), Foreland basin systems revisited: Variations in response to tectonic settings, in Recent Advances in Tectonics of Sedimentary Basins, edited by C. Busby and A. Azor, p. 405–426, Wiley-Blackwell, Oxford, Calif.

216

Fox M.R., 2008, Geomorphology and active faulting within the Hangay Mountains, Central Mongolia, Master’s research thesis, Department of Earth Sciences, University of Oxford, 2008

Frey, H., H. Machguth, M. Huss, C. Huggel, S. Bajracharya, T. Bolch, A. Kulkarni, A. Linsbauer, N. Salzmann, and M. Stoffel., 2014. Estimating the volume of glaciers in the Himalayan–Karakoram region using different methods The Cryosphere, 8, p.2313–2333, doi:10.5194/tc-8-2313-2014

Hinderer, M., 2001. Late Quaternary denudation of the Alps, valley and lake fillings and modern river loads. Geodin. Acta 14, 231–263.

Hinderer, M., 2012. From gullies to mountain belts: Are view of sediment budgets at various scales. Sediment. Geol.280, p.21–59.

Hopkins, C., Beryllium-10 derived erosion rates from the Hangay Mountains, Mongolia: landscape evolution in a periglacially-dominated continental interior.,2012., Master’s Thesis, Georgia Institue of Technology

Jaboyedoff M., and M. H, Derron., 2005, A new method to estimate the infilling of alluvial sediment of glacial valleys using a sloping local base level. (IT ISSN 1724-4757) Geogr. Fis. Dinam. Quat. 28, p.37-46, 10 fig.

Jolivet, M., et al. (2007), Mongolian summits: An uplifted, ﬂat, old but still preserved erosion surface, Geology, 35(10), p. 871–874.

Kasai, M., Marutani, T., and Brierley, G.J., 2004, Patterns of sediment slug translation and dispersion following typhoon-induced disturbance, Oyabu Creek, Kyushu, Japan: Earth Surface Processes and Landforms, v. 29, p. 59–76, doi: 10.1002/esp.1013.

Koppes, M., and Hallet, B., 2006, Erosion rates during deglaciation in Icy Bay, Alaska: Journal of Geophysical Research, v.111, F02023, doi: 10.1029/2005JF000349.

217

Korup, O., Clague, J.J., Hermanns, R.L., Hewitt, K., Strom, A.L., Weidinger, J.T., 2007. Giant landslides, topography, and erosion. Earth Planet. Sci. Lett.261, p.578–589.

Korup, O., and Schlunegger, F., 2009, Rock type control on erosion-induced uplift in the eastern Swiss Alps: Earth and Planetary Science Letters, v. 278, p.278–285, doi: 10.1016/j.epsl.2008.12.012.

Korup, O., and Tweed, F., 2007, Ice, moraine, and landslide dams in mountainous terrain: Quaternary Science Reviews, v. 26, p.3406–3422, doi: 10.1016/j.quascirev.2007.10.012.

Kuhlemann, J., Frisch, W., Székely, B., Dunkl, I., and Kázmér, M., 2002. Post-collisional sediment budget history of the Alps: Tectonic versus climatic control: International Journal of Earth Sciences, v. 91, p. 818–837, doi: 10.1007/s00531-002-0266-y.

Lancaster, S.T., and Casebeer, N.E., 2007, Sediment storage and evacuation in headwater valleys at the transition between debris-flow and fluvial processes: Geology, v. 35, p. 1027– 1030, doi: 10.1130/G239365A.1.

Landman, R.L., 2007. Petrologic constraints on the sources of granites from the Hangay Mountains, central Mongolia: Dept. of Geology, Amherst College. 47

Lehmkuhl, F.; M. Klinge & G. Stauch, 2004: The extent of Late Pleistocene glaciations in the Altai and Khangai Mountains, 243-254 ff in Ehlers J. and Gibbard, P. L. (ed.), Quaternary Glaciations - Extent and Chronology, Part III. Amsterdam.

Linsbauer, A., F. Paul, M. Hoelzle, H. Frey, and W. Haeberli., 2009. The Swiss Alps Without Glaciers – A GIS-based Modeling Approach for Reconstruction of Glacier Beds, Proceedings of Geomorphometry 2009. Zurich, Switzerland, 31 August - 2 September, 2009

McDannell, K. T., Zeitler, P. K., & Idleman, B. D., 2018, Relict topography within the Hangay Mountains in central Mongolia: Quantifying long-term exhumation and relief change in an old landscape. Tectonics, 37, p.2531–2558

218

Meetei L. I., S. K. Pattanayak, A. Bhaskar, M. K. Pandit, S. K. Tandon.,2007, Climatic imprints in Quaternary valley fill deposits of the middle Teesta valley, Sikkim Himalaya, Quaternary International.Vol.159. p.32-46

Meltzer, A., Ancuta, L., Carlson, R., Caves, J., Chamberlain, C.P., Gosse, J., Idleman, B., Ionov, D., McDannell, K., Mendelson, T., Mix, H., Munkhuu, U., Proussevitch, A., Russo, R., Sabaj-Perez, M., Sahagian, D., Sjostrom, D., Smith, S., Stachnik, J., Tsagaan, B., Wegmann, K., Winnick, M., and Zeitler, P., 2015, Betwixt and between: structure and evolution of Central Mongolia: Eos Trans. AGU, Fall Meet. Suppl., Abstract T22A-05.

Mey, J., D. Scherler, G. Zeilinger, and M. R. Strecker., 2015. Estimating the fill thickness and bedrock topography in intermontane valleys using artificial neural networks, J. Geophys. Res. Earth Surf., 120, p.1301–1320, doi:10.1002/ 2014JF003270.

Molnar, P., and Tapponnier, P., 1975, Tectonics of Asia: Consequences and implications of a continental collision: Science, v. 189, p. 419-426.

Montgomery D. R., Valley formation by fluvial and glacial erosion Geology; November 2002; v. 30; no. 11; p. 1047–1050; 4 figures.

NASA JPL, 2013, NASA Shuttle Radar Topography Mission Global 1 arc second: NASA EOSDIS Land Processes DAAC, doi: 10.5067/MEaSUREs/SRTM/SRTMGL1.003

Otto, J. C., J. Goetz and L. Schrott., 2008. Sediment storage in Alpine sedimentary systems- quantification and scaling issues, Sediment Dynamics in Changing Environments (Proceedings of a symposium held in Christchurch, New Zealand, December 2008). IAHS Publ. 325.

Phillips, J., 2003. Alluvial storage and the long-term stability of sediment yields. Basin Res. 15, p.153–165.

Pike R. J., S. E. Wilson., 1971, Elevation-Relief Ratio, Hypsometric Integral, and Geomorphic Area-Altitude Analysis. GSA Bulletin ; 82 (4): p.1079–1084.

Price, M., 2002, Deriving volumes with ArcGIS, ESRI publications, Arc User, October- December 2002, p.52-56

219

Schrott, L., Hufschmidt, G., Hankammer, M., Hofmann, T., and Dikau, R., 2003, Spatial distribution of sediment storage types and quantification of valley fill deposits in an alpine basin, Reintal, Bavarian Alps, Germany: Geomorphology, v. 55, p. 45–63, doi: 10.1016/S0169-555X(03)00131-4.

Singh O., Sarangi A., Sharma M.C., 2008, Hypsometric integral estimation methods and its relevance on erosion status of north-western lesser Himalayan watersheds. Water.Resour.Manag. 22(11): p.1545–1560

Sklar, L.S., and Dietrich, W.E., 2001, Sediment and rock strength controls on river incision into bedrock: Geology, v. 29, p. 1087–1090, doi: 10.1130/0091-7613(2001)029<1087: SARSCO>2.0.CO;2.

Smith, S.G., K. W. Wegmann., G. Bayasgalan., L. Ancuta., J. Gosse., Using Landsat Imagery and GIS to constrain the evolution of the Khangay dome, Mongolia, AGU poster, 2013

Smith, S.G., Wegmann, K.W., Ancuta, L.D., Gosse, J.C., and Hopkins, C.E., 2016, Paleotopography and erosion rates in the central Hangay Dome, Mongolia: Landscape evolution since the mid-Miocene: Journal of Asian Earth Sciences, v. 125, p. 37-57.

Straumann, R.K., Korup, O., 2009. Quantifying post-glacial sediment storage at the mountain- belt scale. Geology 37, p.1079–1082.

Straumann, R.K., and Purves, R.S., 2007, Resolution sensitivity of a compound terrain derivative as computed from LiDAR-based elevation data, in Fabrikant, S.I., and Wachowicz, M., eds., The European Information Society: Leading the way with geo-information: Lecture Notes in Geoinformation and Cartography, Proceedings of the 2007 AGILE International Conference, v. 18: Berlin, Springer, p.87–109.

Straumann, R.K., and Purves, R.S., 2008, Delineation of valleys and valley floors, in Cova, T.J., et al., eds., Proceedings of the 5th international conference on Geographic Information Science: Lecture Notes In Computer Science, v. 5266: Berlin, Springer, p.320–336.

Tomurtogoo, O., 1999, Geological Map of Mongolia: Ulaanbaatar, Mongolian Academy of Sciences, scale: 1:1,000,000.

220

Wegmann, K.W., Mendelson, T., Sabaj-Pérez, M.H., Smith, S.G., Lopresti, M., Gosse, J., Gallen, S.F., and Bayasgalan, G., 2015, Constraining the Late Cenozoic topographic evolution of central Mongolia (Hangay Mtns) using landscape metrics and sequence divergence of freshwater fishes: Geological Society of America Abstracts with Programs, v. 47, p. 35.

Walker R.T., E. Nissen, A. Bayasgalan., 2007, Reinterpretation of the active faulting in central Mongolia, Geology, v. 35; no. 8; p. 759–762.

Yarmolyuk, V.V., Kudryashova, E.A., Kozlovsky, A.M., and Lebedev, V.A., 2008, Late Cenozoic Volcanism of Khangai (Central Mongolia): Evidence for Recent Orogeny in Central Asia: Doklady Earth Sciences, v. 422, p. 1032-1036.

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Table 1-A. Estimated area vs volume in selected fluvial valleys. We picked 15 representative valleys in order to obtain robust volume-area scaling relationship. Correlation coefficient R2=0.81.

Number Area (km2) Volume (km3)

1 4.01 0.748 2 7.1 0.778 3 17.2 7.458 4 12.1 4.347 5 9.968 2.774 6 4.2 0.163 7 10 0.878 8 22.5 5.5 9 45 7.3 10 69 11.5 11 9.3 1.24 12 23.2 7.24 13 18.4 6.2 14 43 8.9 15 136 27.78

Table 1-B. Estimated area vs volume for selected glacial valleys. We picked 16 representative valleys in order to obtain robust volume-area scaling relationship. Correlation coefficient R2=0.91.

Number Area (km2) Volume (km3)

1 1.38 0.071 2 2.17 0.138 3 5.79 0.277 4 0.95 0.059 5 2.56 0.18 6 2.17 0.069 7 4.2 0.297 8 3.1 0.19 9 2 0.11 10 0.57 0.016 11 0.169 0.0052 12 3.7 0.25 13 3.02 0.24 14 1.6 0.068 15 2.1 0.21 16 1.78 0.16

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Table 2. Floor areas, volumes and their ratio each of the watershed areas along major streams.

Watershed Total Floor Total Volume Floor area/ Volume/ Volume/ Number name watershed area (km3) Watershed Watershed area Floor area of floor area (km2) (km2) area ratio ratio ratio area units Ider 26791 6047.6 941 ± 26 0.226 0.036 0.155 28163 Chuluut 19867 3671 513.8 ± 14.7 0.185 0.027 0.139 17959 Orkhon 4804 654.7 89 ± 2.8 0.136 0.020 0.135 3564 Tamir 4010 770.8 105 ± 3.5 0.192 0.028 0.13.6 4252 Tsenkher 2672 420 61.8 ± 1.6 0.157 0.024 0.147 1374 Khanui 4469 1412.2 216.2 ± 5.4 0.316 0.050 0.153 5341 North total 62613 12976.3 1926.8 ± 53.9 0.207 0.031 0.143 60653 Baidrag 16635 5031 856.9 ± 32.1 0.302 0.051 0.170 18780 Tui 5620 1570.2 270.8 ± 10.8 0.279 0.048 0.171 5493 Taats 3164 933.6 155.2 ± 5.4 0.295 0.049 0.166 4368 Zavkhan 10447 1525.2 198.9 ± 7.9 0.146 0.020 0.131 8097 Chigestey 8501 1781.6 267.3 ± 7.1 0.210 0.032 0.15 10875 Ongi 2699 853.3 131.8 ± 3.5 0.316 0.050 0.154 4771 South total 47066 11694.9 1880.9 ± 66.7 0.248 0.042 0.157 52384 Glacial 19890 428.4 23.2 ± 1.6 0.021 0.0011 0.053 3584 Total 109679 24671.2 3806 ± 109.9 0.225 0.036 0.151 113037 Outliers 32950 11649 2121 ± 109.1 0.354 0.063 0.178 32882 Total with 142629 36320.2 5926 ± 218.1 0.255 0.038 0.164 145919 outliers

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Table 3. Hypsometric integral and Digital Elevation model based parameters for each of the watersheds.

North watersheds Hypsometric Max elevation Min elevation Mean elevation Watershed area integral (m) (m) (m) (km2) Orkhon 0.397 3424 1571 2312 6593 Chuluut 0.467 3518 1191 2278 27953 Ider 0.397 3450 1191 2089 38086 Tamir 0.332 3540 1511 2180 5573 Tsenkher 0.393 3475 1607 2339 3695 Khanui 0.271 3198 1538 1980 6273 Average/Sum 0.376 3434 1435 2196 88173

South watersheds Hypsometric Max elevation Min elevation Mean elevation Watershed area integral (m) (m) (m) (km2) Baidrag 0.424 3540 1528 2381 22876 Zavkhan 0.387 3974 1826 2658 14497 Tui 0.409 3456 1645 2389 7656 Taats 0.381 3146 1679 2242 4289 Ongi 0.286 3226 1743 2168 3684 Chigestey 0.326 3970 1536 2330 11880 Average/Sum 0.369 3552 1660 2361 64882 Total Average/Sum 0.373 3493 1547 2279 153055

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Table 4-A. Parameters for major mountain ranges and their estimated valley fill volume based on power- law scaling relationships. (Blothe, Korup et al. 2013, Straumann, 2009)

Mountain range Tectonic activity Annual precipitation patterns Erosion patterns Khangay Least active 300-400 mm Negligible Himalaya Very active 2000-10000 mm Fast Swiss Alps Somewhat active 1200-1600 mm Somewhat fast

Table 4-B. Comparison of parameters for major mountain ranges and their estimated valley fill volume based on power-law scaling relationships. (Blothe, Korup et al. 2013, Straumann 2009). Khangay contains a higher amount of sediment fill volume compared to the Himalaya and Swiss Alps.

Mountain Method Total Floor Volume Floor/ Volume/ Volume/ Correlation Exponent range area area Total Total Area Floor coefficient area area Khangay Projectile 153000 24671 3806 ± 0.161 0.025 0.151 0.78-0.81 1.31±0.06 continuation 109.1 of slopes Himalaya Natural dam 438780 18248 690 0.042 0.003 0.065 0.79 1.29±0.01 heights (+452; 242) Swiss Alps Formerly 65950 5093 411 ±12 0.077 0.006 0.081 0.78 1.12±0.15 defined volume/area

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Table 5. Cross validation results for the area-to-volume power law scaling. Column 2 and 3 represents different power-law equations used in the study. Both were approximated to cross-validated volumes.

Volumes (km3) No of Areas (km2) area Power law 1 Power Law 2 Cross-check 1 7.97 1.01 0.97 0.79 2 15.78 2.81 2.89 2.1 3 15.45 2.72 2.79 2.3 4 17.10 3.17 3.29 3.5 5 4.22 0.39 0.35 0.38 6 3.62 0.31 0.28 0.31 7 4.46 0.42 0.38 0.32 8 4.65 0.45 0.41 0.46 9 17.03 3.15 3.26 3.9 10 16.35 2.96 3.06 3.5

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Figure 1. Location of Khangay Mountains in Mongolia, Central Asia

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Figure 2. Oblique aerial photographs illustrating the back-filled alluvial nature of rivers draining the northern (A) and southern (B) Khangay Mountains. The views in both photos are oriented up stream. The top photo (A) is of the Terkh River and was acquired at 48.073° N, 98.956° E, while the bottom photo (B) is of the Tuin River from a vantage point at 46.228° N, 100.734° E.

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Fluvial valleys

Glacial valleys

Foreland basin

Peaks

Figure 3. Study area distributed as a function of elevation using a 100 m elevation bin size. Note that our research area is limited between 1900 and (lower altitude considered as foreland basin) 2500 m. Dashed rectangle denotes primarily fluvial valleys while the solid line denotes formerly glaciated valleys.

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Figure 4. Schematic procedure to obtain sediment area-volume scaling relationship for study area valleys.

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Figure 5. Cartoon showing the method used to define (A) fluvial and (B) glacial valley floor sedimentary fill thickness. We combined ArcGIS and MapInfo GIS softwares to accomplish this task. For fluvial valleys, a downward projecting triangle is used to approximate assumed bedrock geometry, while an arc is used for glacial valleys.

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Figure 6-A. Estimated area vs volume for selected fluvial valleys (n=15). R2=0.81

Figure 6-B. Estimated area vs volume for selected glacial valleys (n=16). R2=0.91

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Lake Telmen Ider

Chuluut Khanui Chigestey Tamir

Zavkhan Tsenkher Baidrag Orkhon Ongi Tui

Taats

Figure 7. Khangay Mountains Digital elevation model (color). We delineated 12 major watershed areas with major streams.

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Figure 8. Khangay Mountains slope map. As seen from map, steeper parts are in the southwest and southeastern sections and along the drainage divides.

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Figure 9. Khangay Mountains Local relief map. The light blue sections represent the valley floor areas that match slope below 5°. These areas are used for our research for finding volume-area scaling relationship.

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Ider

Chuluut Khanui

Chigestey Tamir

Zavkhan Tsenkher Baidrag Orkhon Ongi Tui

Taats

Figure 10. Khangay major watershed volumes normalized by drainage area. Outlying parts are excluded due to consideration as foreland basin.

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Figure 11. Size and distribution of Khangay fluvial valley fill volumes. Major volume units are distributed along lower (more flat) parts of the range.

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Figure 12. Depth patterns of Khangay fluvial valley fills. Depth estimation is based on the assumption that volume/area ratio. The deeper sedimentary fills are along lower (more flat) parts of the range.

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Figure 13. Size and distribution of Khangay glacial valley fill volumes. Major volume units are distributed along the edges of major peaks.

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Figure 14. Depth patterns of Khangay fluvial valley fills. Major depth units are distributed along the edges of major peaks.

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Figure 15. Area frequency histogram. As seen from histogram, most number of area units fall into 0.003- 0.1 km2 area bins.

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Figure 16. Cumulative area frequency histogram. As seen from histogram, major part of area fall between 10-23 km2 bins.

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Figure 17. Volume frequency histogram. As seen from histogram, most number of volume units fall into 0.00003-0.32 km3 volume bins.

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3 Binned volumes (km )

Figure 18. Cumulative volume frequency histogram. As seen from histogram, major part of volumes fall between 0.32-2.1 km3 bins.

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Figure 19. Valley fill depth histogram. Majority of fill depths fall into category of 23-32 meters.

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4.5

) 3 3.5 y = 1.0851x - 0.1314 R² = 0.9197 3

2.5

validated validated volume (km 1.5 -

1 Cross 0.5

0 0 0.5 1 1.5 2 2.5 3 3.5 Power-law equation 1 produced volume (km3)

Figure 20. Linear correlation coefficient between power law equation-1 produced volumes vs cross- validated volumes. R2=0.9197

4.5

4 )

3 3.5 y = 1.031x - 0.0671 R² = 0.9224 3 2.5 2

1.5

validated validated volume(km -

1 Cross 0.5 0 0 0.5 1 1.5 2 2.5 3 3.5

Power-law equation 2 produced volume (km3)

Figure 21. Linear correlation coefficient between power law equation-2 produced volumes vs cross- validated volumes. R2=0.9224

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Figure 22. Hypsometric integral curves for the northern watersheds of the Khangay.

Figure 23. Hypsometric integral curves for the southern watersheds of the Khangay.

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Figure 24 A, B, C, D, E. Assumed landscape (topography) development across Khangay from Late Pliocene (~3 Ma) through Late Quaternary (today) with 1 Ma yrs interval. Erosion rate of 15m/My (0.015 mm/yr-1±0.0004) matches with former researchers results. See the assumed sedimentary thickness for the given period. The valley fill sediment volume is draped back over the hillslope surfaces based on the assumption that weathering-limited erosion is dominant since ~3 Ma on par with global cooling. The modern valley fill topography is increased by ~150 meters, while the average hillslope surface is decreased by ~45±1.3 meters for last 3 million years. It is assumed that tectonic activity is negligible during these periods.

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APPENDICES

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Appendix 1. Topographic map of the Galuut Valley study area outlining the maximum shoreline positions of the former “upper” and “lower” late Quaternary lakes and the trace of the Bayankhongor fault. The locations where detailed aerial drone imagery was collected for the production of sub-meter digital elevation models, and derivative geomorphic maps are shown by the lettered rectangles.

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Appendix 2. Shorelines from the Appendix Lake area of the upper paleo Lake Galuut. The extant Appendix Lake is a remnant of the much larger Pleistocene to Holocene lake (see Appendix- 1). Note that the uppermost shoreline is at 2051 m above sea level, indicating the maximum lake level. Small mounds visible in the DEM were created by Mongolian hamsters (Allocricetulus curtatus) after the lake drained.

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2050 Bedrock erosional high stand

2050 2050

2050

Appendix 3. Digital elevation model and geomorphic map for the “Crow’s Head” location (see Appendix- 1). Note the prominent beach ridge-bay mouth bar at 2050 m that separates lacustrine (Ql) deposits from the main upper lake basin to the north from a small embayment to the south. See text for details.

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Maximum Lake shoreline – 2051m

Appendix 4. Digital elevation model for the beginning of the “wind gap”, where the former outlet of upper paleo Lake Galuut cuts across the hanging wall of the Bayankhongor fault (Appendix- 1). Prominent geomorphologic features at this location include the scarp of the Bayankhongor fault, alluvial fans (Qaf), Quaternary lakebed lacustrine sediment (Ql), small active channels (Qac), and recent hillslope gullying. The old wind gap uplifted by Bayankhongor fault, thus defeating drainage and further supported by hillslope erosion. The floor of the wind gap is at least 10 meters higher than the highest lake level high stand and some 20 meters above the low-lying lacustrine section. The double dashed line is a dirt road. See text for details.

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Appendix 5. Digital elevation model of the modern water gap, located at the western end of the former lower paleo Lake Galuut, where the Olzyit River has cut a gorge through the hanging wall uplift of the Bayankhongor fault (Appendix- 1). Water in the river flows from east to west. An observable fault scarp and the co-location of many erosional gullies formed on the hanging wall side of the fault are clearly visible in the field and on the DEM. This geomorphic mapping area includes alluvial fan (Qaf), floodplain (Qfp), landslide (Ql) and lacustrine lakebed deposits (Ql). Ridges on either side of the water gap rise to more than 300 m above the modern stream. Landslide deposits are present on both sides, and while they do not impede stream flow at present, they may have been responsible for the formation of the late Pleistocene-to-Holocene lower Lake Galuut.

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Appendix 6. Digital elevation model and geomorphic map for the head of Galuut Canyon and the modern outlet of the upper Lake Galuut basin (Appendix- 1). The Galuut Canyon separates the upper and lower paleo lake basins. The elevation of the highest shoreline at 2051 m is just slightly below the lowest elevation (2054 m) preserved along the sides of the canyon walls today. It is likely that when the upper paleo Lake Galuut reached a water surface elevation of 2051 m, it crested the low divide and began the incision process that would form Galuut Canyon – now a popular tourist destination. At present, we do not know if the incision process proceeded rapidly or slowly likely that this canyon cut through incising erosion from upper lake and flow through, causing outburst or slow flooding towards the lower lake. Alluvium and floodplains, alluvial fans, shorelines, and lake-bottom deposits are main geomorphological features observed in the Galuut Canyon section.

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1970

2000

2030

2050

Appendix 7. Hillshaded digital elevation model at the Bayankhangor fault “north” data collection location (Appendix-1). A prominent fault scarp observed along this section of the mountain front appears to express mostly vertical displacement. Numerous erosional gullies are developed on the hanging wall (upthrown) side of the fault, with alluvial fan deposition on the footwall side. Larger ephemeral stream valleys that cross the fault do not show evidence for horizontal (strike-slip) displacement.

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