PREDICTORS OF SOIL ORGANIC CARBON IN AGRICULTURAL PASTURES

VEIKKO R KUNKEL

B Env Sci (Hons I) (University of New England) A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in Physical Geography

December 2017

This research was supported by an Australian Government Research Training Program (RTP) Scholarship Statement of Originality

I hereby certify that the work embodied in the thesis is my own work, conducted under normal supervision.

The thesis contains no material which has been accepted, or is being examined, for the award of any other degree or diploma in any university or other tertiary institution and, to the best of my knowledge and belief, contains no material previously published or written by another person, except where due reference has been made in the text. I give consent to the final version of my thesis being made available worldwide when deposited in the University’s

Digital Repository, subject to the provisions of the Copyright Act 1968 and any approved embargo.

Maps throughout this book were created using ArcGIS® software by Esri. ArcGIS® and

ArcMap™ are the intellectual property of Esri and are used herein under license. Copyright ©

Esri. All rights reserved. For more information about Esri® software, please visit www.esri.com.

…………………………… (December 2017)

ii ACKNOWLEDGEMENTS

I would like to thank my supervisors, A/P Greg Hancock and Dr. Tony Wells, for their support, guidance, patience and assistance throughout the course of my PhD.

Thank you to Chris Dever, Alisa Williamson and Matthew Braggins for their assistance in collecting field data, and thanks again to Chris Dever for laboratory support. Thanks also to

Cristina Martinez for provision of 2006 soil data.

Thanks goes to Olivier Rey-Lescure for providing data and suggestions in GIS processing. Thanks also goes to Graham Lancaster from the Environmental Analysis

Laboratory (EAL), Lismore, for the prompt analysis of soil samples for C and N using dry combustion (LECO). Thanks to the landholders and managers of the Merriwa district, upon whose properties the field work was conducted. I respectfully acknowledge the past and present traditional Indigenous custodians of the land where the study site is located.

To all my family, friends and fellow PhD students, thank you for your support and encouragement.

iii

ABBREVIATIONS

137Cs – Caesium-137

AGB – Above-Ground Biomass

APAR – Absorbed Photosynthetically Active Radiation

BD – Bulk Density

C – Carbon

C:N – Carbon:Nitrogen Ratio

CO2 – Carbon Dioxide

DEM – Digital Elevation Model

DEM-H – Hydrologically conditioned and drainage enforced Digital Elevation Model

DSA – Digital Soil Assessment

DSRA – Digital Soil Risk Assessment

DTM – Digital Terrain Model

EO-1 – Earth Observer 1

ETM+ - Enhanced Thematic Mapper

EVI – Enhanced Vegetation Index

GA – Geoscience Australia

GIS – Geographical Information Systems

GVF – Green Vegetation Fraction

HDF – Hierarchical Data Format

IR – Infra-Red

iv

ISSS – International Society of Soil Science kh – horizontal or plan curvature kv – vertical or profile curvature

LAI – Leaf Area Index

LECO – Laboratory Equipment Corporation

LIC – Lithogenic Organic Carbon

LPI – Land and Property Information

MIR – Mid Infra-Red

MODIS - Moderate Resolution Imaging Spectroradiometer

MSS – Multispectral Scanner System

NIR – Near Infra-Red

NDVI – Normalised Difference Vegetation Index

NOAA-AVHRR – National Oceanic and Atmospheric Administration-Advanced Very High

Resolution Radiometer

NPP – Net Primary Production

OLI – Operational Land Imager

OM – Organic matter

PIC – Pedogenic Organic Carbon

QA – Quality Assurance

RUSLE – Revised Universal Soil Loss Equation

SAC-C – Scientific Application Satellite-C

v

SLC – Scan Line Corrector

SIC – Soil Inorganic Carbon

SOC – Soil Organic Carbon

SRTM – Shuttle Radar Topography Mission

SWIR – Short Wave Infra-Red

TM – Thematic Mapper

USDA – United States Department of Agriculture

USLE – Universal Soil Loss Equation

VI – Vegetation Index

VIF – Variance Inflation Factor

WGS84 – World Geodetic System 84

Yr – Year

vi

CONTENTS

Statement of Originality ...... ii

Acknowledgements ...... iii

Abbreviations ...... iv

Contents ...... vii

Abstract ...... xii

List of Figures ...... xiv

List of Tables ...... xxii

Chapter 1. Introduction ...... 1-1 1.1. Introduction ...... 1-1 1.2. The global carbon cycle ...... 1-3 1.3. The soil carbon pool...... 1-4 1.3.1. SOC sequestration ...... 1-5 1.4. Quantifying SOC Spatiotemporal Distribution ...... 1-6 1.4.1. Plant carbon inputs ...... 1-8 1.4.2. Geomorphology: topography ...... 1-10 1.5. Rational for the Current Study ...... 1-11 1.5.1. Significance of the study site ...... 1-13 1.5.2. Aims and objectives ...... 1-15 1.6. Thesis Structure ...... 1-16

Chapter 2. Study Site Description ...... 2-1 2.1. Introduction ...... 2-1 2.2. Study Location ...... 2-1

vii 2.3. Climate ...... 2-4 2.3.1. Climate Data ...... 2-4 2.3.2. Rainfall ...... 2-5 2.3.3. Soil Temperature ...... 2-8 2.4. Geology ...... 2-10 2.5. Landforms, Land Systems and Soils ...... 2-13 2.6. Terrain ...... 2-20 2.7. Land-use and land-use history ...... 2-23 2.8. Summary Comparison of Krui River and Merriwa River Catchments ...... 2-26

Chapter 3. Methods ...... 3-1 3.1. Introduction ...... 3-1 3.2. Field Sampling ...... 3-2 3.2.1. Sampling Periods ...... 3-2 3.2.2. Sampling Design ...... 3-2 3.2.3. Sampling Processes ...... 3-4 3.2.4. Sampling Density ...... 3-9 3.3. Laboratory Procedures ...... 3-10 3.3.1. Field Sample Preparation ...... 3-10 3.3.2. SOC Analysis ...... 3-10 3.3.3. 137Cs Analysis...... 3-11 3.3.4. Particle Size Analysis ...... 3-15 3.3.5. pH analysis ...... 3-15 3.4. DEM processing...... 3-16 3.4.1. D-infinity flow algorithm ...... 3-17 3.4.2. Deriving Terrain Attribute Models ...... 3-18 3.4.3. Local Terrain Attributes ...... 3-19 3.4.4. Upslope Terrain Attributes ...... 3-26 3.5. Summary of terrain attributes ...... 3-33 3.6. Satellite Remote Sensing Platforms ...... 3-34 3.6.1. Moderate Resolution Imaging Spectroradiometer (MODIS) ...... 3-34 3.6.2. Landsat Series Satellites ...... 3-35 3.7. Satellite Remote Sensing Imagery ...... 3-40 3.7.1. Normalised Difference Vegetation Index (NDVI) ...... 3-41 3.7.2. Enhanced Vegetation Index (EVI) ...... 3-42 3.7.3. Landsat VI processing ...... 3-45

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3.7.4. MODIS VI processing ...... 3-47 3.7.5. Temporal Range and Temporal Resolution of VIs ...... 3-48 3.8. Climate Data ...... 3-51 3.8.1. Acquisition of rainfall and soil moisture data ...... 3-51

Chapter 4. SOC Dynamics and Geomorphology ...... 4-1 4.1. Introduction ...... 4-1 4.1.1. The Link Between SOC and Geomorphology ...... 4-1 4.1.2. Field Quantification of SOC ...... 4-5 4.1.3. Digital mapping of SOC ...... 4-6 4.1.4. Soil Erosion Quantification 137Cs Technique ...... 4-8 4.1.5. Review of Past SOC Spatial Distribution Studies Based on Geomorphology ... 4-10 4.2. Soil Attributes ...... 4-14 4.2.1. Observed SOC data ...... 4-15 4.2.2. Relationships between soil attributes ...... 4-19 4.2.3. Linear regressions of SOC with soil attributes ...... 4-20 4.3. SOC vs Terrain Attributes ...... 4-23 4.3.1. A comparison of terrain attributes for each catchment...... 4-23 4.3.2. Relationships between SOC and terrain attributes ...... 4-24 4.4. SOC vs Climate Variables ...... 4-29 4.4.1. SOC vs Climate: Rainfall ...... 4-31 4.4.2. SOC vs Climate: Soil Moisture ...... 4-34 4.5. Large Catchment Scale Soil Redistribution Patterns ...... 4-36 4.5.1. ∆137Cs soil erosion ...... 4-38 4.6. Discussion ...... 4-43 4.6.1. The relationship of SOC with soil attributes ...... 4-43 4.6.2. SOC, terrain, and climate relationships ...... 4-46 4.6.3. Determining SOC redistribution and SOC erodibility using surrogates ...... 4-49 4.7. Conclusions ...... 4-49

Chapter 5. SOC Dynamics and Remote Sensing ...... 5-1 5.1. Introduction ...... 5-1 5.1.1. Methods of Remote Sensing Prediction of SOC in the Literature ...... 5-4 5.2. Justification of using NDVI and EVI for predicting carbon...... 5-8 5.3. Aims and layout of this chapter ...... 5-8 5.4. Results: Overview of Vegetation Indices ...... 5-10

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5.4.1. Land Use Classification ...... 5-10 5.4.2. MODIS VI map comparison between wet, dry and typical years...... 5-14 5.4.3. VI Seasonal Patterns ...... 5-17 5.4.4. Historical Variability of VIs ...... 5-21 5.5. Sample Site NDVI vs Catchment NDVI ...... 5-25 5.5.1. Aboveground Biomass vs VIs ...... 5-27 5.6. Results: Comparison of Above-Ground Biomass and SOC ...... 5-29 5.6.1. Spatial Distribution of SOC ...... 5-29 5.6.2. Above-ground Biomass vs SOC ...... 5-29 5.7. Results: Relationship between SOC and VIs ...... 5-30 5.7.1. SOC vs single image NDVI data ...... 5-31 5.7.2. SOC vs single image EVI data ...... 5-35 5.7.3. SOC vs aggregated VIs ...... 5-37 5.7.4. Single image vs cumulative average images ...... 5-46 5.8. Assessing SOC-VI correlation stabilisation...... 5-51 5.8.1. Testing the robustness of the retrospective cumulative averaging method ...... 5-53 5.9. Results: The Effect of Spatial Resolution on the SOC-VI Relationship ...... 5-59 5.10. Results: Relationship between remote sensing VIs and terrain ...... 5-63 5.11. Discussion ...... 5-66 5.11.1. Relationships between SOC and VIs ...... 5-66 5.11.2. The effect of remote sensing spatial resolution on SOC prediction ...... 5-68 5.12. Conclusions ...... 5-70

Chapter 6. Modelling SOC Dynamics ...... 6-1 6.1. Introduction ...... 6-1 6.1.1. Statistical comparison of derived models ...... 6-3 6.1.2. Model validation and model selection ...... 6-5 6.1.3. Model application and comparison to other models ...... 6-5 6.2. Single variable linear SOC modelling using elevation ...... 6-7 6.2.1. SOC-elevation model assessment ...... 6-7 6.2.2. Validation of elevation-derived SOC distribution models ...... 6-10 6.2.3. Testing the general applicability of the elevation-derived SOC distribution model ...... 6-12 6.3. Single variable linear SOC modelling using Landsat NDVI ...... 6-16 6.3.1. Selection of a VI for single variable linear modelling ...... 6-16 6.3.2. SOC-NDVI model assessment ...... 6-18

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6.3.3. Validation of NDVI-derived SOC distribution model ...... 6-22 6.3.4. Testing the general applicability of the SOC-NDVI model ...... 6-24 6.4. Multiple linear regression SOC modelling ...... 6-28 6.4.1. Multivariable model derivation and validation ...... 6-30 6.4.2. Testing the general applicability of the multivariable SOC models ...... 6-33 6.4.3. Comparison of single variable and multivariable models ...... 6-36 6.4.4. Extrapolated SOC prediction map for the study site ...... 6-38 6.5. RothC modelling ...... 6-40 6.5.1. RothC model input data ...... 6-42 6.5.2. Landscape Scale RothC ...... 6-43 6.5.3. Space-for-time RothC ...... 6-44 6.5.4. RothC results: landscape scale RothC ...... 6-45 6.5.5. RothC results: space-for-time comparison ...... 6-46 6.5.6. RothC results: future predictions ...... 6-47 6.6. Comparing observed SOC to the baseline Australian SOC map ...... 6-49 6.7. Discussion ...... 6-54 6.7.1. Empirical modelling ...... 6-54 6.7.2. RothC modelling ...... 6-58 6.7.3. Comparison of observed SOC with CSIRO baseline SOC ...... 6-59 6.8. Conclusions ...... 6-60

Chapter 7. Discussion and Conclusions ...... 7-1 7.1. Major Findings of the Current Study ...... 7-1 7.2. Final Conclusions...... 7-9

References ...... 7-11

Appendix A. Soil Temperature Dynamics at the Catchment Scale . A-1

Appendix B. Appendix B. High Purity Germanium Gamma Ray Detector ...... B-1

Appendix C. Comparison of terrain attributes for the Krui and Merriwa catchments ...... C-1

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ABSTRACT

The interaction between atmospheric CO2 and the global pool of soil organic carbon

(SOC) is one of the largest and at the same time most uncertain feedbacks of the carbon

cycle. The current state, spatial distribution and temporal trends of SOC pools, including for

Australian soils, are largely unknown or uncertain, principally because there are too few data

available. Hence, ongoing field-sampling and mapping of SOC is necessary. This study

assesses the catchment scale climate and geomorphic controls on SOC. The study was

conducted in the Upper Hunter Valley, New South Wales, Australia, and focussed on a

562 km2 (Krui River) catchment sampled across 41 sites in 2006 and 2014, and a 606 km2

(Merriwa River) catchment sampled across 47 sites in 2015. Both catchments have similar

soils, topography and landuse. It was found that there was no significant difference in SOC %

between Krui 2006 and Krui 2014 data sets. SOC % was also shown to have no significant

difference between Krui catchment and Merriwa catchment, indicating that SOC is spatially

stable for catchments of similar land-use, climate and geomorphology. SOC % from all three

data sets were compared to a range of terrain attributes and cumulative average vegetation

indices (VIs). Similar with other studies, elevation, as a surrogate for orographic rainfall, and average Normalised Difference Vegetation Index (NDVI), as a surrogate for historical

vegetation SOC input, were found to have the strongest significant controls on SOC %.

Confirmation of the use of elevation as a surrogate for rainfall was made by comparing SOC

with rainfall obtained from a network of weather stations across the study sites. Krui ΔSOC

(change in SOC from 2006 to 2014) was compared to Krui Δ137Cs (change in 137Cs from

2006 to 2014). A moderate but significant relationship was observed between ΔSOC and

Δ137Cs, indicating that catchment-scale erosion and deposition processes may control catchment SOC distribution. The findings demonstrate that for catchments with similar soils,

topography and climate, SOC can be reliably predicted using linear models that incorporate

xii elevation and/or average NDVI variables. The methods here provide a robust tool which can be used for SOC assessment at other sites as well as assist in understanding SOC distribution and controls for longer term and regional scales.

xiii LIST OF FIGURES

Figure 1.1. The global carbon cycle showing major C fluxes. Reservoir sizes in GtC, fluxes and rates in GtC yr-1. Diagram adapted from Riebeek (2011)...... 1-4 Figure 1.2. Conceptual diagram showing abiotic and biotic controls on SOC, and their potential feedbacks...... 1-9 Figure 1.3. Thesis structure...... 1-18 Figure 2.1. Location map of Krui (orange outline) and Merriwa River catchments (green outline), Goulburn River catchment (white outline) and Hunter River catchment (red outline)...... 2-2 Figure 2.2. True-colour image derived from Landsat 8 remote sensing data showing the Krui River (orange boundary) and Merriwa River (green boundary) catchments...... 2-3 Figure 2.3. Distribution of climate monitoring sites and weather stations used in this study. Adapted from Kunkel et al. (2016)...... 2-5 Figure 2.4. Annual rainfall (mm) across the study site of the Krui and Merriwa catchments. The dashed line shows the average annual rainfall from 2003 to 2015...... 2-6 Figure 2.5. Annual rainfall (mm) from 2003 to 2015 for the Krui and Merriwa catchments...... 2-6 Figure 2.6. Average monthly rainfall (mm) between 2002 and 2015 for both Krui and Merriwa catchments ...... 2-7 Figure 2.7. Average annual rainfall vs elevation for the Krui weather stations K1 to K6 and Merriwa weather stations M1 to M7...... 2-7 Figure 2.8. Rainfall distribution across the Goulburn catchment...... 2-8 Figure 2.9. The spatial variation in 2003-2014 daily average soil temperatures (oC) for the Krui and Merriwa catchments as well as the Stanley sub-catchment (inset). Adapted from Kunkel et al. (2016)...... 2-9 Figure 2.10. Daily average soil temperature (to a depth of 150 mm) compared to elevation across both the Krui and Merriwa catchments...... 2-9 Figure 2.11. The daily average soil temperature for the Krui and Merriwa catchments over a year. The plot shows the similarity in daily average soil temperature between the two catchments. Adapted from Kunkel et al. (2016)...... 2-10 Figure 2.12. Geological map of the Hunter catchment. The Krui and Merriwa catchments are outlined in red. The Hunter-Mooki thrust fault runs from Merriwa to Maitland (black line). Modified from Mika et al. (2010) ...... 2-12 Figure 2.13. Goulburn River catchment hill-shade relief map, showing the 4 major landforms of the catchment. The Goulburn, Krui and Merriwa rivers are shown in blue. Krui and Merriwa River catchments are outlined in green...... 2-14 Figure 2.14. The spatial distribution and extent of the soil land systems of the Krui and Merriwa catchments. Sourced from the 1:100 000 Murrurundi and 1:250 000 Singleton Soil Landscape Sheets (Kovac and Lawrie, 1991; Story et al., 1963)...... 2-17 Figure 2.15. Elevation map of the Krui (left) and Merriwa (right) catchments...... 2-20 Figure 2.16. Aspect map of the Krui (left) and Merriwa (right) catchments...... 2-21 Figure 2.17. Slope angle of Krui (left) and Merriwa (right) catchments...... 2-22 Figure 2.18. Land use classification derived from supervised Maximum Likelihood Classification in ArcGIS 10.2. The red circle shows the location of the township of Merriwa...... 2-25

xiv

Figure 3.1. Location of soil sample sites for Krui 2006 and Krui 2014 (41 sites, circles), and Merriwa 2015 (47 sites, triangles) (Repeated from Chapter 2 Site Description for convenience)...... 3-5 Figure 3.2. Location of validation soil sample sites for Krui 2006 (17 sites, orange triangles), Krui 2014 (9 sites, blue circles) and Merriwa 2015 (20 sites, green crosses). (Repeated from Chapter 2 Site Description for convenience)...... 3-6 Figure 3.3. Example of 0.5 m x 0.5 m quadrat pre- and post-sampling of aboveground biomass and soil cores...... 3-8 Figure 3.4. Results from previous studies on digital mapping of SOC: the relationship between grid spacing (resolution) and extent of the studied areas (left), and the relationship between sample density and resolution of the digital soil maps (right). Krui catchment data (triangle) and Merriwa catchment data (cross) was included for comparison (Adapted from Minasny et al. (2013))...... 3-9 Figure 3.5. Location of the 137Cs reference site, in relation to Krui and Merriwa sample sites...... 3-14 Figure 3.6. Flow direction derived from DEM using D-8 (left) and D-infinity (right) algorithms...... 3-17 Figure 3.7. Coloured elevation grid across the Krui and Merriwa catchments, with an example of the spatial elevation data contained in each cell (inset)...... 3-19 Figure 3.8. Example of slope angle derived from the 25 m LPI DEM...... 3-20 Figure 3.9. Example of aspect gradient derived from elevation. The top colour wheel shows aspect categorised by cardinal direction. The bottom colour wheel shows aspect categorised by transformed cardinal direction from -1 to 1 along the N-S axis...... 3-21 Figure 3.10. Map of plan curvature derived from 25 m LPI DEM is shown for the Krui and Merriwa catchments. Three representations of plan curvature are also depicted, showing divergent (kh > 0), convergent (kh < 0), and linear flow (kh = 0)...... 3-22 Figure 3.11. Map of profile curvature derived from 25 m LPI DEM is shown for the Krui and Merriwa catchments. Three representations of profile curvature are also depicted, showing accelerating flow (kv < 0), decelerating flow (kv > 0), and linear flow (kv = 0)...... 3-23 Figure 3.12. Standard curvature derived from 25 m LPI DEM is shown for the Krui and Merriwa catchments (bottom). Nine examples of standard curvature, representing combinations of both profile and planform curvatures, are also depicted. The columns show the planform curvature and the rows show the profile curvature. The planform columns are kh > 0, kh < 0, kh = 0 going from left to right. The profiles curves are kv < 0, kv < 0, and kv = 0 going from top to bottom...... 3-24 Figure 3.13. Cumulative solar insolation (Wh y-1 m-2) derived from the 25 m LPI DEM for the Krui and Merriwa catchments. Solar radiation was generally uniform across the study site, except for south-facing slopes of the Liverpool Range in the northern reaches of each catchment...... 3-25 Figure 3.14. An example of upslope area, derived from the 25 m DEM using D-infinity contributing area (Tarboton, 1997) for six Krui soil sample sites...... 3-26 Figure 3.15. Example of upslope area, showing D-infinity flow accumulation, for Krui site 16...... 3-27 Figure 3.16. D-infinity TWI derived from the 25 m LPI DEM...... 3-28

xv

Figure 3.17. Example of upslope plan curvature (top); average of all plan curvature values within the upslope area above Krui site 16 (bottom). The mean upslope plan curvature was generally linear for Krui sample site 16...... 3-29 Figure 3.18. Example of upslope profile curvature (top); average of all profile curvature values within the upslope area above Krui site 16 (bottom). The mean upslope profile curvature was generally linear for Krui sample site 16...... 3-30 Figure 3.19. Example of upslope standard curvature (top); average of all standard curvature values within the upslope area above Krui site 16 (bottom). The mean upslope standard curvature was generally linear for Krui sample site 16...... 3-31 Figure 3.20. Example of upslope slope (top); average of all upslope slope values within the upslope area above Krui site 16 (bottom). The mean upslope slope angle was generally moderate for Krui sample site 16...... 3-32 Figure 3.21. A comparison of Red and Near-Infrared bandwidths used for calculation of NDVI for Landsat and Terra (MODIS) platforms...... 3-38 Figure 3.22. Spectral reflectance for vegetation ...... 3-44 Figure 3.23. Visual representation of the dates of scenes used in this study. Soil sampling dates are represented by the dashed vertical line (A = Krui 2006, B = Krui 2014, C = Merriwa 2015). Landsat 5 and 7 was collected up to 4 years prior to Krui 2006 sampling period. Landsat 8 was collected for 12 months and 24 months prior to Krui 2014 and Merriwa 2015 sampling periods respectively. MODIS data was collected for vegetation dynamics over 15 years...... 3-50 Figure 3.24. Map showing the location Krui 2006, Krui 2014 and Merriwa 2015 sample sites, as well as the location of Krui and Merriwa weather stations...... 3-52 Figure 4.1. Example of the importance of aspect and latitude on receiving incident solar radiation...... 4-4 Figure 4.2. Example of orographic precipitation. Adapted from Roe (2005)...... 4-4 Figure 4.3. Spatial distribution of observed SOC % for the Krui catchment in 2006. Circles represent sample sites, while squares represent validation sites...... 4-16 Figure 4.4. Spatial distribution of observed SOC % for the Krui catchment in 2014 and the Merriwa catchment in 2015. Circles represent sample sites, while squares represent validation sites...... 4-17 Figure 4.5. Comparison of SOC % between Krui 2006 (left), Krui 2014 (middle) and Merriwa 2015 (right) sampling periods. Boxplots show min, max, lower quartile, median, upper quartile, and average SOC % (red cross). Blue dots (if present) represent outliers outside 95% CIs...... 4-18 Figure 4.6. Krui 2006 SOC % vs Krui 2014 SOC %...... 4-19 Figure 4.7. A comparison of linear regression (blue dashed line) compared to Theil- Sen estimator (black line) for SOC-137CS relationships for the three sampling periods. The 95% confidence intervals for the Theil-Sen estimate are shown (orange dotted line)...... 4-21 Figure 4.8. A comparison of linear regression (blue dashed line) compared to Theil- Sen estimator (black line) for SOC-pH relationships for the three sampling periods. The 95% confidence intervals for the Theil-Sen estimate are shown (orange dotted line)...... 4-22 Figure 4.9. SOC % vs Elevation (m) (derived from the 25 m DEM) for the three catchments: Krui 2006 (n =41) (left), Krui 2014 (n = 41) (middle), Merriwa 2015 (n = 47) (right). Axes were set to the same scale for the three plots for ease of comparison...... 4-26

xvi

Figure 4.10. Plots showing slope angle varies significantly with elevation for Krui 2006 (top left), Krui 2014 (top middle), and Merriwa 2015 (top right); plots showing mean upslope slope angle varies with elevation for Krui 2006 (bottom left), Krui 2014 (bottom middle) and Merriwa 2015 (bottom right)...... 4-27 Figure 4.11. SOC vs elevation for combined Krui 2014 and Merriwa 2015 data...... 4-28 Figure 4.12. A comparison of SOC with rainfall for the weather stations, based on the strongest correlations, for each sampling period...... 4-33 Figure 4.13. Plots of the strongest relationships between SOC and average soil moisture for the weather stations across the study catchments...... 4-35 Figure 4.14. ∆137Cs compared to Krui 2006 SOC (left) and Krui 2014 SOC (right)...... 4-38 Figure 4.15. Comparison of ∆137Cs with ∆SOC...... 4-39 Figure 4.16. Annual rainfall (mm) for Krui weather stations K1 (top) through to K6 (bottom)...... 4-41 Figure 4.17. A comparison of daily rainfall for the years 2004, 2007 and 2010...... 4-42 Figure 5.1. True colour image of the Krui and Merriwa catchments acquired by Landsat 8 on 8 June 2014. The red circle shows the location of the township of Merriwa...... 5-12 Figure 5.2. Land-use classification derived from supervised maximum Likelihood Classification. The red circle shows the location of the township of Merriwa...... 5-13 Figure 5.3. Annual rainfall for the Terragong BOM site. Dashed line represents the average annual rainfall from 2003 to 2015...... 5-14 Figure 5.4. Seasonal MODIS NDVI for a dry year (2006, top), a typical year (2008, middle), and a wet year (2010, bottom), of rainfall. Seasons from left to right are summer, autumn, winter and spring. The NDVI scale applies to all 12 maps...... 5-15 Figure 5.5. Seasonal MODIS EVI for a dry year (2006, top), a typical year (2008, middle), and a wet year (2010, bottom) of rainfall. Seasons from left to right are summer, autumn, winter and spring...... 5-16 Figure 5.6. Krui and Merriwa Average Monthly MODIS NDVI from Feb 2000 to Dec 2015...... 5-18 Figure 5.7. Krui and Merriwa Average Monthly MODIS EVI from Feb 2000 to Dec 2015...... 5-19 Figure 5.8. Monthly Krui and Merriwa average MODIS EVI (left) and MODIS NDVI (right) for the period 2000-2015. Both plots have the same y-axis scale for comparison...... 5-21 Figure 5.9. Comparison of Krui and Merriwa time series of MODIS NDVI (top) and EVI (bottom). The x-axis shows years although the data used 16-day images...... 5-22 Figure 5.10. MODIS EVI maps (32-day intervals, 1 year per row) from 1 January 2011 to 1 November 2015. The scale is shown at the bottom right...... 5-23 Figure 5.11. MODIS NDVI maps (32-day intervals, 1 year per row) from 1 January 2011 to 1 November 2015 ...... 5-24 Figure 5.12. Comparison of catchment average MODIS NDVI and point-scale sample site average MODIS NDVI for the image acquired on 12 Jul 2005...... 5-26 Figure 5.13. Time series of sample site average NDVI and catchment average NDVI, with Krui 2006 sites (top) Krui 2014 sites (middle), and Merriwa 2015 sites (bottom)...... 5-26

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Figure 5.14. Comparison of 2006 AGB with MODIS NDVI (left), MODIS EVI (middle), and Landsat NDVI (right). The scale range for the VIs on the y- axis are kept the same for comparison...... 5-28 Figure 5.15. Comparison of 2014 AGB with MODIS NDVI (left), MODIS MODIS EVI (middle), and Landsat NDVI (right). The scale range for the VIs on the y-axis are kept the same for comparison...... 5-28 Figure 5.16. Aboveground biomass compared to Krui 2006 SOC % (left) and Krui 2014 SOC % (right)...... 5-30 Figure 5.17. MODIS NDVI acquisition images immediately prior to each sampling period. Average NDVI and standard deviation across sample sites are given for each image...... 5-32 Figure 5.18. Correlation r-values for SOC vs single image MODIS NDVI for Krui 2006 (left), Krui 2014 (middle), and Merriwa 2015 (right)...... 5-33 Figure 5.19. Correlation r-values for SOC vs single image Landsat NDVI for Krui 2006 (left), Krui 2014 (middle), and Merriwa 2015 (right)...... 5-34 Figure 5.20. MODIS EVI acquisition images immediately prior to each sampling period. Average NDVI and standard deviation across sample sites are given for each image...... 5-35 Figure 5.21. Correlaiton r-values for SOC % vs single image MODIS EVI for Krui 2006 (left), Krui 2014 (middle) and Merriwa 2015 (right)...... 5-36 Figure 5.22. The MODIS NDVI and MODIS EVI maps (top) show the effect of cumulative averaging images for the Krui 2006 sampling period. The plot below the maps shows the time-series of correlation values between Krui 2006 SOC and MODIS NDVI and MODIS EVI. The arrows on the plot show the date corresponding to the cumulative average VI maps, moving from left to right...... 5-38 Figure 5.23. The MODIS NDVI and MODIS EVI maps (top) show the effect of cumulative averaging VI images for the Krui 2014 sampling period. The plot below the maps shows the time-series of correlation values between Krui 2014 SOC and MODIS NDVI and MODIS EVI. The arrows on the plot correspond to the cumulative average VI maps, moving from left to right...... 5-39 Figure 5.24. The MODIS NDVI and MODIS EVI maps (top) show the effect of cumulative averaging VI images prior to the Merriwa 2015 sampling period. The plot below the maps shows the time-series of correlation values between Merriwa 2015 SOC and MODIS NDVI and MODIS EVI. The arrows on the plot correspond to the cumulative average VI maps, moving from left to right...... 5-40 Figure 5.25. The Landsat NDVI maps (top) show the effect of cumulative averaging Landsat NDVI images prior to the Krui 2006 sampling period. The plot below the maps shows the time-series of correlation values between Krui 2006 SOC and Landsat NDVI. The arrows on the plot correspond to the cumulative average NDVI maps moving from left to right...... 5-41 Figure 5.26. The Landsat NDVI maps (top) show the effect of cumulative averaging Landsat NDVI images prior to the Krui 2014 sampling period. The plot below the maps shows the time-series of correlation values between Krui 2014 SOC and Landsat NDVI. The arrows on the plot correspond to the cumulative average NDVI maps moving from left to right...... 5-42 Figure 5.27. The Landsat NDVI maps (top) show the effect of cumulative averaging Landsat NDVI images prior to the Merriwa 2015 sampling period. The plot

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below the maps shows the time-series of correlation values between Merriwa SOC and Landsat NDVI. The arrows on the plot correspond to the cumulative average NDVI maps moving from left to right...... 5-43 Figure 5.28. Illustrating correlation stabilization over 10 years of historical cumulative average MODIS EVI and MODIS NDVI vs Merriwa 2015 SOC. Correlations stabilised after approximately 12 months...... 5-44 Figure 5.29. Plots (columns) show the comparison of SOC with MODIS NDVI single image, 12-month average image, and 24-month average image. Plots (rows) show each SOC-MODIS NDVI relationship by sampling period...... 5-48 Figure 5.30. Plots (columns) show the comparison of SOC with MODIS EVI single image, 12-month average image, and 24-month average image. Plots (rows) show each SOC-MODIS EVI relationship by sampling period. Each plot includes the correlation r-value...... 5-49 Figure 5.31. Plots (columns) show the comparison of SOC with Landsat NDVI single image, 12-month average image, and 24-month average image. Plots (rows) show each SOC- Landsat NDVI relationship by sampling period...... 5-50 Figure 5.32. Comparison in average monthly rainfall for the Merriwa catchment for 2014 and 2015 for the weather stations M1 to M7. The blue line on the left of the plot shows the average 12-month rainfall from November to October 2014. The blue line on the right shows the average 12-month rainfall from November to October 2015...... 5-51 Figure 5.33. Time-series of standard deviations of cumulative average NDVI for Krui 2006 sites (top), Krui 2014 sites (middle) and Merriwa 2015 (bottom). Time series standard deviations decreased and stabilised as NDVI images were cumulatively averaged across each catchment’s respective sample sites...... 5-52 Figure 5.34.Time-series plot of correlations between Krui 2014 SOC and MODIS NDVI (red line). Monthly rainfall (columns) is also included to help determine where dry periods or clouds may be influenceing the SOC-NDVI relationship...... 5-54 Figure 5.35. Example of cloud cover (left) and drought (right), across the Krui catchment...... 5-56 Figure 5.36. Demonstrating the effect of initial image cloud contamination on the SOC- cumulative average MODIS NDVI relationship...... 5-57 Figure 5.37. Demonstrating the effect of the first three images containing cloud cover on the SOC- cumulative average MODIS NDVI relationship...... 5-57 Figure 5.38. Demonstrating the effect of a range of drought durations on correlation stabilization between cumulative average MODIS NDVI and Krui 2014 SOC...... 5-59 Figure 5.39. A comparison of cumulative average NDVI maps (top) between MODIs and Landsat for equivalent dates. The plot shows the time-series correlation values between Merriwa 2015 SOC and NDVI for both MODIS and Landsat, using equivalent image dates (bottom) The arrow on the plot correspond to the cumulative average NDVI maps...... 5-61 Figure 5.40. The plot shows that the higher temporal resolution of retrospective cumulative average MODIS NDVI reached peak correlation with Merriwa 2015 SOC faster than the temporally filtered MODIS NDVI or temporally coarse Landsat NDVI...... 5-62

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Figure 5.41. The plot compares the effect of increasing the number of temporal images, by combining Landsat and MODIS NDVI, on the SOC-NDVI relationship...... 5-62 Figure 6.1. Illustrating the simplified physical links between elevation, rainfall, soil moisture, vegetation, aggregation of SOC, and mineralisation of C to atmosphere...... 6-2 Figure 6.2. Plots of predicted SOC vs the square of the residuals. No heteroscedasticity was evident between any SOC-elevation model...... 6-8 Figure 6.3. A comparison of observed SOC and predicted SOC, based on elevation, for Krui 2006 (left), Krui 2014 (middle), and Merriwa 2015 (right). Each plot contained both the sample site data (black circles) and validation data (red crosses) for comparison. The dotted line shows the regression between predicted and observed SOC. The solid grey lines show the 95 % confidence intervals between predicted and observed SOC for the sample sites...... 6-11 Figure 6.4. Application of the SOC-elevation model over the range of spatiotemporal scales of the study sites. The dashed line is the 1:1 line representing perfect agreement between predicted and observed values. The solid line is the linear regression line...... 6-15 Figure 6.5. Plots of predicted SOC vs the square of the residuals. Krui 2014 SOC- Landsat NDVI regression had the highest tendency towards heteroscedasticity...... 6-19 Figure 6.6. Krui 2014 SOC-MODIS NDVI ordinary least squares and Theil-Sen estimator regressions...... 6-20 Figure 6.7. A comparison of observed SOC and predicted SOC, based on 24-month cumulative average Landsat NDVI, for Krui 2006 (left), Krui 2014 (middle), and Merriwa 2015 (right). Each plot contained both the training data set and validation data set for comparison. The dotted line shows the regression between predicted and observed SOC, while the solid grey lines show the 95% confidence interval of the data...... 6-23 Figure 6.8. Plots showing the observed vs predicted SOC for testing the general applicability of the three SOC-NDVI models. The dashed diagonal line represents perfect agreement between observed and predicted SOC...... 6-26 Figure 6.9. Plots showing the observed vs predicted SOC for testing the general applicability of the three multivariable models...... 6-35 Figure 6.10. Predictive map of SOC %, based on the Merriwa multivariable elevation and NDVI model (Equation 6.1)...... 6-39 Figure 6.11. Structure of the Rothamsted Carbon Model. Adapted from Coleman and Jenkinson (1996) ...... 6-41 Figure 6.12. RothC predicted SOC stocks and observed SOC stocks for the Krui catchment between April 2006 and June 2014. Markers indicate the yearly predicted SOC stocks. The standard deviations of observed SOC stocks for Krui 2006 and Krui 2014 are shown as error bars...... 6-46 Figure 6.13. RothC predicted SOC stocks and observed SOC stocks for K1 weather station between April 2006 and June 2014 (top)...... 6-47 Figure 6.14. RothC predicted SOC stocks, calibrated RothC predicted SOC stocks, and observed SOC stocks for weather station K6 between April 2006 and June 2014...... 6-47 Figure 6.15. RothC modelling of SOC stocks for three climate scenarios...... 6-48

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Figure 6.16. Extract of baseline SOC stocks map of the Krui and Merriwa catchments. Adapted from Rossel et al. (2014)...... 6-51 Figure 6.17. Each of the plots above contains a linear regression of predicted 30cm CSIRO baseline SOC stocks with elevation, and observed 20cm SOC stocks vs elevation, for Krui 2006 (top), Krui 2014 (middle), and Merriwa 2015 (bottom). The predicted 30cm baseline SOC stocks includes error bars of 95% confidence intervals, calculated from the summation of the variance in the Cubst-kriging estimates and the average kriging variances of the residuals (Rossel et al., 2014) ...... 6-52

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LIST OF TABLES

Table 2.1. Characteristics of the major land systems of the Krui and Merriwa catchments ...... 2-18 Table 2.2. A comparison of the climate, geology, geomorphology and other attributes of the Krui and Merriwa catchments...... 2-27 Table 3.1. Brief description of each terrain attribute and its physical representation...... 3-33 Table 3.2. The inverse band-specific linear coefficients for converting Landsat 5 DN to Landsat 7 DN. Adapted from Vogelmann et al. (2001)...... 3-45 Table 3.3. Band-specific gain and band rescaling factors...... 3-46 Table 3.4. Details of platforms and corresponding VI used to estimate SOC distribution over the three sampling periods...... 3-50 Table 3.5. Distance between nearest sample site and weather station...... 3-51 Table 4.1 Studies of SOC concentration and carbon density maps that have been produced using digital soil mapping technology (Adapted from Minasny et al. (2013))...... 4-13 Table 4.3. Student t-tests for SOC concentrations between the three sampling periods. No sampling period had significantly different SOC concentrations from the other periods at the P = 0.05 level...... 4-18 Table 4.4. Correlation r values and their corresponding P-values between SOC and other soil attributes...... 4-19 Table 4.5. Descriptive statistics of terrain attributes for Krui and Merriwa catchments across sample sites...... 4-23 Table 4.6. Correlation r-values for the relationship between SOC and terrain attributes for the three sample periods. Relationships for both 25 m and 30 m DEM terrain attributes are shown. Significant relationships are indicated with an asterix (*), as well as shaded...... 4-24 Table 4.7. Correlation r-values of SOC compared to rainfall over a range of temporal averages. Significant relationships at the P = 0.05 level are marked with an asterix...... 4-32 Table 4.8. Correlation r-values of SOC compared to soil moisture over a range of temporal averages. Significant relationships at the P = 0.05 level are marked with an asterix...... 4-34 Table 5.1. Studies arranged alphabetically by author, showing study region, scale of study site, platform details, spatial resolution, index used, method of index processing, and method of SOC sampling ...... 5-5 Table 5.2. Simplified land-use classification system for use with Landsat data (adapted from Anderson (1976))...... 5-11 Table 5.3. Land-use area of Krui and Merriwa catchments ...... 5-13 Table 5.4. Comparison of sample dates with the immediately prior MODIS and Landsat image acquisition...... 5-31 Table 5.5. A comparison of the correlation r-values between 12 and 24-month cumulative average VIs with SOC across the three sampling periods...... 5-46 Table 5.6. Comparison of VIs with terrain attributes, categorized by satellite platform VI and sampling period. Significant (P = 0.05) correlations are in bold...... 5-65 Table 6.1. Results for Breusch-Pagan and White’s heteroscedasticity tests of SOC- elevation models. No model was significantly heteroscedastic...... 6-8 Table 6.2. Linear regression R2, mean square error, and model parameters of the three SOC-elevation models, modelling only their associated catchment and sample period...... 6-9

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Table 6.3. Comparison of slopes between the three SOC-elevation models...... 6-10 Table 6.4. Comparison of MSE of validation sites based on sample site SOC-elevation linear regression...... 6-10 Table 6.5. Assessment and comparison of the general spatiotemporal applicability of the three SOC-elevation models ...... 6-14 Table 6.6. Comparison of correlations between VI and SOC, categorised by sampling period and either 12-month or 24-month average VI...... 6-17 Table 6.7. Results for both Breusch-Pagan and White’s heteroscedasticity test of SOC- Landsat NDVI models...... 6-19 Table 6.8. Linear regression R2, root mean square error, and model parameters of the three SOC-NDVI models...... 6-20 Table 6.9. NDVI values determined from SOC-NDVI regression models when SOC = 0 % ...... 6-21 Table 6.10. Comparison of slopes between the three predictive SOC models based on Landsat NDVI...... 6-21 Table 6.11. Comparison of MSE of validation sites based on sample site SOC-NDVI linear regression...... 6-22 Table 6.12. Assessment and comparison of the general spatiotemporal applicability of the three SOC-Landsat NDVI models ...... 6-25 Table 6.13. Assessment and comparison of the general spatiotemporal applicability of the SOC-elevation and SOC-NDVI training models outside their associated catchment/sampling period...... 6-27 Table 6.14. Multivariable linear regression results for the elevation and Landsat NDVI parameters in predicting SOC for each sampling period...... 6-30 Table 6.15. Step-wise linear regression results in predicting SOC for each sampling period...... 6-31 Table 6.16. Comparison of best stepwise multivariable regression models for each sampling period. In the case of Merriwa 2015, the “best” stepwise multivariable regression model only consisted of elevation. The multivariable elevation and Landsat NDVI models for each sampling period are included for comparison...... 6-33 Table 6.17. Assessment and comparison of the general spatiotemporal applicability of the three multivariable models...... 6-34 Table 6.18. Comparison of the general applicability of single variable and multivariable models, using average MSE, as applied to data outside each model’s associated catchment/sample period...... 6-37 Table 6.19. Variable data used in the RothC modelling...... 6-43 Table 6.20. Comparison of slopes between the three SOC-elevation models...... 6-53

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